From 4973ac70dc8fd10f59e3e8822f1090499cb83e41 Mon Sep 17 00:00:00 2001
From: Marco Rovere <marco.rovere@cern.ch>
Date: Mon, 3 Sep 2018 12:26:35 +0200
Subject: [PATCH 01/95] Add back standalone GPU fit test

---
 .../PixelTrackFitting/test/BuildFile.xml      |   5 +
 .../PixelTrackFitting/test/testEigenGPU.cu    | 262 ++++++++++++++++++
 2 files changed, 267 insertions(+)
 create mode 100644 RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu

diff --git a/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml b/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
index 21e227ea3e7e7..d6beb57b862b8 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
@@ -11,6 +11,11 @@
 <use   name="RecoTracker/TkTrackingRegions"/>
 <use   name="RecoPixelVertexing/PixelTriplets"/>
 <use   name="RecoPixelVertexing/PixelTrackFitting"/>
+<bin file="testEigenGPU.cu" name="testEigenGPU_t">
+  <use name="cuda"/>
+  <use name="cuda-api-wrappers"/>
+  <flags CXXFLAGS="-g"/>
+</bin>
 <bin file="testEigenGPUNoFit.cu" name="testEigenGPUNoFit_t">
   <use name="cuda"/>
   <use name="cuda-api-wrappers"/>
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
new file mode 100644
index 0000000000000..9b8e870f5c4b7
--- /dev/null
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
@@ -0,0 +1,262 @@
+#include <iostream>
+
+#include <Eigen/Core>
+#include <Eigen/Eigenvalues>
+
+#include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
+
+#include "test_common.h"
+
+using namespace Eigen;
+
+__global__
+void kernelFullFit(Rfit::Matrix3xNd * hits,
+    Rfit::Matrix3Nd * hits_cov,
+    double B,
+    bool errors,
+    Rfit::circle_fit * circle_fit_resultsGPU,
+    Rfit::line_fit * line_fit_resultsGPU) {
+  Vector4d fast_fit = Rfit::Fast_fit(*hits);
+
+  u_int n = hits->cols();
+  Rfit::VectorNd rad = (hits->block(0, 0, 2, n).colwise().norm());
+
+  Rfit::Matrix2xNd hits2D_local = (hits->block(0,0,2,n)).eval();
+  Rfit::Matrix2Nd hits_cov2D_local = (hits_cov->block(0, 0, 2 * n, 2 * n)).eval();
+  Rfit::printIt(&hits2D_local, "kernelFullFit - hits2D_local: ");
+  Rfit::printIt(&hits_cov2D_local, "kernelFullFit - hits_cov2D_local: ");
+  /*
+  printf("kernelFullFit - hits address: %p\n", hits);
+  printf("kernelFullFit - hits_cov address: %p\n", hits_cov);
+  printf("kernelFullFit - hits_cov2D address: %p\n", &hits2D_local);
+  printf("kernelFullFit - hits_cov2D_local address: %p\n", &hits_cov2D_local);
+  */
+  /* At some point I gave up and locally construct block on the stack, so that
+     the next invocation to Rfit::Circle_fit works properly. Failing to do so
+     implied basically an empty collection of hits and covariances. That could
+     have been partially fixed if values of the passed in matrices would have
+     been printed on screen since that, maybe, triggered internally the real
+     creations of the blocks. To be understood and compared against the myriad
+     of compilation warnings we have.
+     */
+  (*circle_fit_resultsGPU) =
+    Rfit::Circle_fit(hits->block(0,0,2,n), hits_cov->block(0, 0, 2 * n, 2 * n),
+      fast_fit, rad, B, errors);
+  /*
+  (*circle_fit_resultsGPU) =
+    Rfit::Circle_fit(hits2D_local, hits_cov2D_local,
+      fast_fit, rad, B, errors, scattering);
+   */
+  (*line_fit_resultsGPU) = Rfit::Line_fit(*hits, *hits_cov, *circle_fit_resultsGPU, fast_fit, errors);
+
+  return;
+}
+
+__global__
+void kernelFastFit(Rfit::Matrix3xNd * hits, Vector4d * results) {
+  (*results) = Rfit::Fast_fit(*hits);
+}
+
+__global__
+void kernelCircleFit(Rfit::Matrix3xNd * hits,
+    Rfit::Matrix3Nd * hits_cov, Vector4d * fast_fit_input, double B,
+    Rfit::circle_fit * circle_fit_resultsGPU) {
+  u_int n = hits->cols();
+  Rfit::VectorNd rad = (hits->block(0, 0, 2, n).colwise().norm());
+
+#if TEST_DEBUG
+  printf("fast_fit_input(0): %f\n", (*fast_fit_input)(0));
+  printf("fast_fit_input(1): %f\n", (*fast_fit_input)(1));
+  printf("fast_fit_input(2): %f\n", (*fast_fit_input)(2));
+  printf("fast_fit_input(3): %f\n", (*fast_fit_input)(3));
+  printf("rad(0,0): %f\n", rad(0,0));
+  printf("rad(1,1): %f\n", rad(1,1));
+  printf("rad(2,2): %f\n", rad(2,2));
+  printf("hits_cov(0,0): %f\n", (*hits_cov)(0,0));
+  printf("hits_cov(1,1): %f\n", (*hits_cov)(1,1));
+  printf("hits_cov(2,2): %f\n", (*hits_cov)(2,2));
+  printf("hits_cov(11,11): %f\n", (*hits_cov)(11,11));
+  printf("B: %f\n", B);
+#endif
+  (*circle_fit_resultsGPU) =
+    Rfit::Circle_fit(hits->block(0,0,2,n), hits_cov->block(0, 0, 2 * n, 2 * n),
+      *fast_fit_input, rad, B, false);
+}
+
+__global__
+void kernelLineFit(Rfit::Matrix3xNd * hits,
+                   Rfit::Matrix3Nd * hits_cov,
+                   Rfit::circle_fit * circle_fit,
+                   Vector4d * fast_fit,
+                   Rfit::line_fit * line_fit)
+{
+  (*line_fit) = Rfit::Line_fit(*hits, *hits_cov, *circle_fit, *fast_fit, true);
+}
+
+void fillHitsAndHitsCov(Rfit::Matrix3xNd & hits, Rfit::Matrix3Nd & hits_cov) {
+  hits << 1.98645, 4.72598, 7.65632, 11.3151,
+          2.18002, 4.88864, 7.75845, 11.3134,
+          2.46338, 6.99838,  11.808,  17.793;
+  hits_cov(0,0) = 7.14652e-06;
+  hits_cov(1,1) = 2.15789e-06;
+  hits_cov(2,2) = 1.63328e-06;
+  hits_cov(3,3) = 6.27919e-06;
+  hits_cov(4,4) = 6.10348e-06;
+  hits_cov(5,5) = 2.08211e-06;
+  hits_cov(6,6) = 1.61672e-06;
+  hits_cov(7,7) = 6.28081e-06;
+  hits_cov(8,8) = 5.184e-05;
+  hits_cov(9,9) = 1.444e-05;
+  hits_cov(10,10) = 6.25e-06;
+  hits_cov(11,11) = 3.136e-05;
+  hits_cov(0,4) = hits_cov(4,0) = -5.60077e-06;
+  hits_cov(1,5) = hits_cov(5,1) = -1.11936e-06;
+  hits_cov(2,6) = hits_cov(6,2) = -6.24945e-07;
+  hits_cov(3,7) = hits_cov(7,3) = -5.28e-06;
+}
+
+void testFit() {
+  constexpr double B = 0.0113921;
+  Rfit::Matrix3xNd hits(3,4);
+  Rfit::Matrix3Nd hits_cov = MatrixXd::Zero(12,12);
+  Rfit::Matrix3xNd * hitsGPU = new Rfit::Matrix3xNd(3,4);
+  Rfit::Matrix3Nd * hits_covGPU = nullptr;
+  Vector4d * fast_fit_resultsGPU = new Vector4d();
+  Vector4d * fast_fit_resultsGPUret = new Vector4d();
+  Rfit::circle_fit * circle_fit_resultsGPU = new Rfit::circle_fit();
+  Rfit::circle_fit * circle_fit_resultsGPUret = new Rfit::circle_fit();
+
+  fillHitsAndHitsCov(hits, hits_cov);
+
+  // FAST_FIT_CPU
+  Vector4d fast_fit_results = Rfit::Fast_fit(hits);
+#if TEST_DEBUG
+  std::cout << "Generated hits:\n" << hits << std::endl;
+#endif
+  std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]):\n" << fast_fit_results << std::endl;
+
+  // FAST_FIT GPU
+  cudaMalloc((void**)&hitsGPU, sizeof(Rfit::Matrix3xNd(3,4)));
+  cudaMalloc((void**)&fast_fit_resultsGPU, sizeof(Vector4d));
+  cudaMemcpy(hitsGPU, &hits, sizeof(Rfit::Matrix3xNd(3,4)), cudaMemcpyHostToDevice);
+
+  kernelFastFit<<<1, 1>>>(hitsGPU, fast_fit_resultsGPU);
+  cudaDeviceSynchronize();
+  
+  cudaMemcpy(fast_fit_resultsGPUret, fast_fit_resultsGPU, sizeof(Vector4d), cudaMemcpyDeviceToHost);
+  std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]): GPU\n" << *fast_fit_resultsGPUret << std::endl;
+  assert(isEqualFuzzy(fast_fit_results, (*fast_fit_resultsGPUret)));
+
+  // CIRCLE_FIT CPU
+  u_int n = hits.cols();
+  Rfit::VectorNd rad = (hits.block(0, 0, 2, n).colwise().norm());
+
+  Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n),
+      hits_cov.block(0, 0, 2 * n, 2 * n),
+      fast_fit_results, rad, B, false);
+  std::cout << "Fitted values (CircleFit):\n" << circle_fit_results.par << std::endl;
+
+  // CIRCLE_FIT GPU
+  cudaMalloc((void **)&hits_covGPU, sizeof(Rfit::Matrix3Nd(12,12)));
+  cudaMalloc((void **)&circle_fit_resultsGPU, sizeof(Rfit::circle_fit));
+  cudaMemcpy(hits_covGPU, &hits_cov, sizeof(Rfit::Matrix3Nd(12,12)), cudaMemcpyHostToDevice);
+
+  kernelCircleFit<<<1,1>>>(hitsGPU, hits_covGPU,
+      fast_fit_resultsGPU, B, circle_fit_resultsGPU);
+  cudaDeviceSynchronize();
+
+  cudaMemcpy(circle_fit_resultsGPUret, circle_fit_resultsGPU,
+      sizeof(Rfit::circle_fit), cudaMemcpyDeviceToHost);
+  std::cout << "Fitted values (CircleFit) GPU:\n" << circle_fit_resultsGPUret->par << std::endl;
+  assert(isEqualFuzzy(circle_fit_results.par, circle_fit_resultsGPUret->par));
+
+  // LINE_FIT CPU
+  Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_cov, circle_fit_results, fast_fit_results, true);
+  std::cout << "Fitted values (LineFit):\n" << line_fit_results.par << std::endl;
+
+  // LINE_FIT GPU
+  Rfit::line_fit * line_fit_resultsGPU = nullptr;
+  Rfit::line_fit * line_fit_resultsGPUret = new Rfit::line_fit();
+
+  cudaMalloc((void **)&line_fit_resultsGPU, sizeof(Rfit::line_fit));
+
+  kernelLineFit<<<1,1>>>(hitsGPU, hits_covGPU, circle_fit_resultsGPU, fast_fit_resultsGPU, line_fit_resultsGPU);
+  cudaDeviceSynchronize();
+
+  cudaMemcpy(line_fit_resultsGPUret, line_fit_resultsGPU, sizeof(Rfit::line_fit), cudaMemcpyDeviceToHost);
+  std::cout << "Fitted values (LineFit) GPU:\n" << line_fit_resultsGPUret->par << std::endl;
+  assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par));
+}
+
+void testFitOneGo(bool errors, double epsilon=1e-6) {
+  constexpr double B = 0.0113921;
+  Rfit::Matrix3xNd hits(3,4);
+  Rfit::Matrix3Nd hits_cov = MatrixXd::Zero(12,12);
+
+  fillHitsAndHitsCov(hits, hits_cov);
+
+  // FAST_FIT_CPU
+  Vector4d fast_fit_results = Rfit::Fast_fit(hits);
+  // CIRCLE_FIT CPU
+  u_int n = hits.cols();
+  Rfit::VectorNd rad = (hits.block(0, 0, 2, n).colwise().norm());
+
+  Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n), 
+      hits_cov.block(0, 0, 2 * n, 2 * n),
+      fast_fit_results, rad, B, errors);
+  // LINE_FIT CPU
+  Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_cov, circle_fit_results,
+      fast_fit_results, errors);
+
+  // FIT GPU
+  std::cout << "GPU FIT" << std::endl;
+  Rfit::Matrix3xNd * hitsGPU = nullptr; // new Rfit::Matrix3xNd(3,4);
+  Rfit::Matrix3Nd * hits_covGPU = nullptr;
+  Rfit::line_fit * line_fit_resultsGPU = nullptr;
+  Rfit::line_fit * line_fit_resultsGPUret = new Rfit::line_fit();
+  Rfit::circle_fit * circle_fit_resultsGPU = nullptr; // new Rfit::circle_fit();
+  Rfit::circle_fit * circle_fit_resultsGPUret = new Rfit::circle_fit();
+
+  cudaCheck(cudaMalloc((void **)&hitsGPU, sizeof(Rfit::Matrix3xNd(3,4))));
+  cudaCheck(cudaMalloc((void **)&hits_covGPU, sizeof(Rfit::Matrix3Nd(12,12))));
+  cudaCheck(cudaMalloc((void **)&line_fit_resultsGPU, sizeof(Rfit::line_fit)));
+  cudaCheck(cudaMalloc((void **)&circle_fit_resultsGPU, sizeof(Rfit::circle_fit)));
+  cudaCheck(cudaMemcpy(hitsGPU, &hits, sizeof(Rfit::Matrix3xNd(3,4)), cudaMemcpyHostToDevice));
+  cudaCheck(cudaMemcpy(hits_covGPU, &hits_cov, sizeof(Rfit::Matrix3Nd(12,12)), cudaMemcpyHostToDevice));
+
+  kernelFullFit<<<1, 1>>>(hitsGPU, hits_covGPU, B, errors,
+      circle_fit_resultsGPU, line_fit_resultsGPU);
+  cudaCheck(cudaDeviceSynchronize());
+
+  cudaCheck(cudaMemcpy(circle_fit_resultsGPUret, circle_fit_resultsGPU, sizeof(Rfit::circle_fit), cudaMemcpyDeviceToHost));
+  cudaCheck(cudaMemcpy(line_fit_resultsGPUret, line_fit_resultsGPU, sizeof(Rfit::line_fit), cudaMemcpyDeviceToHost));
+
+  std::cout << "Fitted values (CircleFit) CPU:\n" << circle_fit_results.par << std::endl;
+  std::cout << "Fitted values (LineFit): CPU\n" << line_fit_results.par << std::endl;
+  std::cout << "Fitted values (CircleFit) GPU:\n" << circle_fit_resultsGPUret->par << std::endl;
+  std::cout << "Fitted values (LineFit): GPU\n" << line_fit_resultsGPUret->par << std::endl;
+  assert(isEqualFuzzy(circle_fit_results.par, circle_fit_resultsGPUret->par, epsilon));
+  assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par, epsilon));
+
+  cudaCheck(cudaFree(hitsGPU));
+  cudaCheck(cudaFree(hits_covGPU));
+  cudaCheck(cudaFree(line_fit_resultsGPU));
+  cudaCheck(cudaFree(circle_fit_resultsGPU));
+  delete line_fit_resultsGPUret;
+  delete circle_fit_resultsGPUret;
+
+  cudaDeviceReset();
+}
+
+int main (int argc, char * argv[]) {
+//  testFit();
+  std::cout << "TEST FIT, NO ERRORS" << std::endl;
+  testFitOneGo(false);
+
+  std::cout << "TEST FIT, ERRORS AND SCATTER" << std::endl;
+  testFitOneGo(true, 1e-5);
+
+  return 0;
+}
+

From 5e21c6b67678d58aba289b69c47050871e395e35 Mon Sep 17 00:00:00 2001
From: Giovanni Maria Tomaselli <gimmytomas@gmail.com>
Date: Mon, 3 Sep 2018 12:49:25 +0200
Subject: [PATCH 02/95] First Broken Line import

---
 .../PixelTrackFitting/interface/BrokenLine.h  | 633 ++++++++++++++++++
 .../interface/PixelFitterByBrokenLine.h       |  26 +
 .../PixelFitterByBrokenLineProducer.cc        |  47 ++
 .../src/PixelFitterByBrokenLine.cc            | 119 ++++
 4 files changed, 825 insertions(+)
 create mode 100644 RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
 create mode 100644 RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h
 create mode 100644 RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
 create mode 100644 RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
new file mode 100644
index 0000000000000..35de601059791
--- /dev/null
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -0,0 +1,633 @@
+#ifndef RECOPIXELVERTEXING_PIXELTRACKFITTING_BROKENLINE_H
+#define RECOPIXELVERTEXING_PIXELTRACKFITTING_BROKENLINE_H
+
+#include <Eigen/Core>
+#include <Eigen/Cholesky>
+#include <Eigen/Eigenvalues>
+#include <cuda.h>
+
+#ifdef __CUDACC__
+#define CUDA_HOSTDEV __host__ __device__
+#else
+#define CUDA_HOSTDEV
+#endif
+
+namespace BrokenLine {
+	
+	using namespace Eigen;
+	
+	constexpr unsigned int max_nop = 5;  //!< In order to avoid use of dynamic memory
+	
+	using MatrixNd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop, max_nop>;
+	using Matrix3Nd = Eigen::Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop>;
+	using Matrix2xNd = Eigen::Matrix<double, 2, Dynamic, 0, 2, max_nop>;
+	using Matrix3xNd = Eigen::Matrix<double, 3, Dynamic, 0, 3, max_nop>;
+	using VectorNd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop, 1>;
+	using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
+	using Matrix5d = Eigen::Matrix<double, 5, 5>;
+	using Vector5d = Eigen::Matrix<double, 5, 1>;
+	using u_int    = unsigned int;
+	
+	struct karimaki_circle_fit {
+		Vector3d par;  //!< Karimäki's parameters: (phi, d, k=1/R)
+		Matrix3d cov;
+		/*!< covariance matrix: \n
+		 |cov(phi,phi)|cov( d ,phi)|cov( k ,phi)| \n
+		 |cov(phi, d )|cov( d , d )|cov( k , d )| \n
+		 |cov(phi, k )|cov( d , k )|cov( k , k )|
+		 */
+		int q;  //!< particle charge
+		double chi2;
+	};
+	
+	struct line_fit {
+		Vector2d par;  //!< parameters: (cotan(theta),Zip)
+		Matrix2d cov;
+		/*!< covariance matrix: \n
+		 |cov(c_t,c_t)|cov(Zip,c_t)| \n
+		 |cov(c_t,Zip)|cov(Zip,Zip)|
+		 */
+		double chi2;
+	};
+	
+	struct helix_fit {
+		Vector5d par;  //!< parameters: (phi,Tip,p_t,cotan(theta)),Zip)
+		Matrix5d cov;
+		/*!< covariance matrix: \n
+		 |(phi,phi)|(Tip,phi)|(p_t,phi)|(c_t,phi)|(Zip,phi)| \n
+		 |(phi,Tip)|(Tip,Tip)|(p_t,Tip)|(c_t,Tip)|(Zip,Tip)| \n
+		 |(phi,p_t)|(Tip,p_t)|(p_t,p_t)|(c_t,p_t)|(Zip,p_t)| \n
+		 |(phi,c_t)|(Tip,c_t)|(p_t,c_t)|(c_t,c_t)|(Zip,c_t)| \n
+		 |(phi,Zip)|(Tip,Zip)|(p_t,Zip)|(c_t,Zip)|(Zip,Zip)|
+		 */
+		double chi2_circle;
+		double chi2_line;
+		Vector4d fast_fit;
+		int q;  //!< particle charge
+	} __attribute__ ((aligned(16)) );
+	
+	/*!
+	 \brief data needed for the Broken Line fit procedure.
+	 */
+	struct PreparedBrokenLineData {
+		int q; //!< particle charge
+		Matrix2xNd radii; //!< xy data in the system in which the pre-fitted center is the origin
+		VectorNd s; //!< total distance traveled in the transverse plane starting from the pre-fitted closest approach
+		VectorNd S; //!< total distance traveled (three-dimensional)
+		VectorNd Z; //!< orthogonal coordinate to the pre-fitted line in the sz plane
+		VectorNd VarBeta; //!< kink angles in the SZ plane
+	};
+	
+	/*!
+	 \brief raise to square.
+	 */
+	CUDA_HOSTDEV inline double sqr(const double a) {
+		return a*a;
+	}
+	
+	/*!
+	 \brief Computes the Coulomb multiple scattering variance of the planar angle.
+	 
+	 \param length length of the track in the material.
+	 \param B magnetic field in Gev/cm/c.
+	 \param R radius of curvature (needed to evaluate p).
+	 \param Layer denotes which of the four layers of the detector is the endpoint of the multiple scattered track. For example, if Layer=3, then the particle has just gone through the material between the second and the third layer.
+	 
+	 \todo add another Layer variable to identify also the start point of the track, so if there are missing hits or multiple hits, the part of the detector that the particle has traversed can be exactly identified.
+	 
+	 \warning the formula used here assumes beta=1, and so neglects the dependence of theta_0 on the mass of the particle at fixed momentum.
+	 
+	 \return the variance of the planar angle ((theta_0)^2 /3).
+	 */
+	CUDA_HOSTDEV inline double MultScatt(const double& length, const double B, const double& R, int Layer, double slope) {
+		double XX_0; //!< radiation length of the material in cm
+		if(Layer==1) XX_0=16/0.06;
+		else XX_0=16/0.06;
+		XX_0*=1; //1.55 correction made by looking at the pulls
+		return (sqr((13.6/1000)/(1*B*R*sqrt(1+sqr(slope))))*(abs(length)/XX_0)*sqr(1+0.038*log(abs(length)/XX_0)))/3;
+	}
+	
+	/*!
+	 \brief Computes the 2D rotation matrix that transforms the line y=slope*x into the line y=0.
+	 
+	 \param slope tangent of the angle of rotation.
+	 
+	 \return 2D rotation matrix.
+	 */
+	CUDA_HOSTDEV inline Matrix2d RotationMatrix(const double& slope) {
+		Matrix2d Rot;
+		Rot(0,0)=1/sqrt(1+sqr(slope));
+		Rot(0,1)=slope*Rot(0,0);
+		Rot(1,0)=-Rot(0,1);
+		Rot(1,1)=Rot(0,0);
+		return Rot;
+	}
+	
+	/*!
+	 \brief Changes the Karimäki parameters (and consequently their covariance matrix) under a translation of the coordinate system, such that the old origin has coordinates (x0,y0) in the new coordinate system. The formulas are taken from Karimäki V., 1990, Effective circle fitting for particle trajectories, Nucl. Instr. and Meth. A305 (1991) 187.
+	 
+	 \param circle circle fit in the old coordinate system.
+	 \param x0 x coordinate of the translation vector.
+	 \param y0 y coordinate of the translation vector.
+	 */
+	CUDA_HOSTDEV inline void TranslateKarimaki(karimaki_circle_fit& circle, const double& x0, const double& y0) {
+		double A,U,BB,C,DO,DP,uu,xi,v,mu,lambda,zeta;
+		DP=x0*cos(circle.par(0))+y0*sin(circle.par(0));
+		DO=x0*sin(circle.par(0))-y0*cos(circle.par(0))+circle.par(1);
+		uu=1+circle.par(2)*circle.par(1);
+		C=-circle.par(2)*y0+uu*cos(circle.par(0));
+		BB=circle.par(2)*x0+uu*sin(circle.par(0));
+		A=2*DO+circle.par(2)*(sqr(DO)+sqr(DP));
+		U=sqrt(1+circle.par(2)*A);
+		xi=1/(sqr(BB)+sqr(C));
+		v=1+circle.par(2)*DO;
+		lambda=(A/2)/(U*sqr(1+U));
+		mu=1/(U*(1+U))+circle.par(2)*lambda;
+		zeta=sqr(DO)+sqr(DP);
+		
+		Matrix3d Jacob;
+		Jacob << xi*uu*v, -xi*sqr(circle.par(2))*DP, xi*DP,
+		2*mu*uu*DP, 2*mu*v, mu*zeta-lambda*A,
+		0, 0, 1;
+		
+		circle.par(0)=atan2(BB,C);
+		circle.par(1)=A/(1+U);
+		// circle.par(2)=circle.par(2);
+		
+		circle.cov=Jacob*circle.cov*Jacob.transpose();
+	}
+	
+	/*!
+	 \brief Compute cross product of two 2D vector (assuming z component 0), returning the z component of the result.
+	 
+	 \param a first 2D vector in the product.
+	 \param b second 2D vector in the product.
+	 
+	 \return z component of the cross product.
+	 */
+	
+	CUDA_HOSTDEV inline double cross2D(const Vector2d& a, const Vector2d& b) {
+		return a.x()*b.y()-a.y()*b.x();
+	}
+	
+	/*!
+	 \brief Computes the data needed for the Broken Line fit procedure that are mainly common for the circle and the line fit.
+	 
+	 \param hits hits coordinates.
+	 \param hits_cov hits covariance matrix.
+	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
+	 \param B magnetic field in Gev/cm/c.
+	 
+	 \return
+	 */
+	CUDA_HOSTDEV inline PreparedBrokenLineData PrepareBrokenLineData(const Matrix3xNd& hits,
+																	 const Matrix3Nd& hits_cov,
+																	 const Vector4d& fast_fit,
+																	 const double B) {
+		u_int n=hits.cols();
+		u_int i;
+		Vector2d d;
+		Vector2d e;
+		PreparedBrokenLineData results;
+		results.radii=Matrix2xNd::Zero(2,n);
+		results.s=VectorNd::Zero(n);
+		results.S=VectorNd::Zero(n);
+		results.Z=VectorNd::Zero(n);
+		results.VarBeta=VectorNd::Zero(n);
+		
+		results.q=1;
+		d=hits.block(0,1,2,1)-hits.block(0,0,2,1);
+		e=hits.block(0,n-1,2,1)-hits.block(0,n-2,2,1);
+		if(cross2D(d,e)>0) results.q=-1;
+		
+		const double slope=-results.q/fast_fit(3);
+		
+		Matrix2d R=RotationMatrix(slope);
+		
+		results.radii=hits.block(0,0,2,n)-fast_fit.head(2)*MatrixXd::Constant(1,n,1);
+		e=-fast_fit(2)*fast_fit.head(2)/fast_fit.head(2).norm();
+		for(i=0;i<n;i++) {
+			d=results.radii.block(0,i,2,1);
+			results.s(i)=results.q*fast_fit(2)*atan2(cross2D(d,e),d.dot(e)); // calculates the arc length
+			//if(results.s(i)<=0);
+		}
+		VectorNd z=hits.block(2,0,1,n).transpose();
+		
+		Matrix2xNd pointsSZ=Matrix2xNd::Zero(2,n);
+		for(i=0;i<n;i++) {
+			pointsSZ(0,i)=results.s(i);
+			pointsSZ(1,i)=z(i);
+			pointsSZ.block(0,i,2,1)=R*pointsSZ.block(0,i,2,1);
+		}
+		results.S=pointsSZ.block(0,0,1,n).transpose();
+		results.Z=pointsSZ.block(1,0,1,n).transpose();
+		
+		for(i=1;i<n-1;i++) {
+			results.VarBeta(i)=MultScatt(results.S(i+1)-results.S(i),B,fast_fit(2),i+2,slope)+MultScatt(results.S(i)-results.S(i-1),B,fast_fit(2),i+1,slope);
+		}
+		
+		return results;
+	}
+	
+	/*!
+	 \brief Computes the n-by-n band matrix obtained minimizing the Broken Line's cost function w.r.t u. This is the whole matrix in the case of the line fit and the main n-by-n block in the case of the circle fit.
+	 
+	 \param w weights of the first part of the cost function, the one with the measurements and not the angles (\sum_{i=1}^n w*(y_i-u_i)^2).
+	 \param S total distance traveled by the particle from the pre-fitted closest approach.
+	 \param VarBeta kink angles' variance.
+	 
+	 \return
+	 */
+	CUDA_HOSTDEV inline MatrixNd MatrixC_u(const VectorNd& w, const VectorNd& S, const VectorNd& VarBeta) {
+		u_int n=S.rows();
+		u_int i;
+		
+		MatrixNd C_U=MatrixNd::Zero(n,n);
+		for(i=0;i<n;i++) {
+			C_U(i,i)=w(i);
+			if(i>1) C_U(i,i)+=1/(VarBeta(i-1)*sqr(S(i)-S(i-1)));
+			if(i>0 && i<n-1) C_U(i,i)+=(1/VarBeta(i))*sqr((S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
+			if(i<n-2) C_U(i,i)+=1/(VarBeta(i+1)*sqr(S(i+1)-S(i)));
+			
+			if(i>0 && i<n-1) C_U(i,i+1)=1/(VarBeta(i)*(S(i+1)-S(i)))*(-(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
+			if(i<n-2) C_U(i,i+1)+=1/(VarBeta(i+1)*(S(i+1)-S(i)))*(-(S(i+2)-S(i))/((S(i+2)-S(i+1))*(S(i+1)-S(i))));
+			
+			if(i<n-2) C_U(i,i+2)=1/(VarBeta(i+1)*(S(i+2)-S(i+1))*(S(i+1)-S(i)));
+			
+			C_U(i,i)=C_U(i,i)/2;
+		}
+		MatrixNd C_u;
+		C_u=C_U+C_U.transpose();
+		
+		return C_u;
+	}
+	
+	/*!
+	 \brief A very fast helix fit.
+	 
+	 \return (X0,Y0,R,tan(theta)).
+	 
+	 \warning sign of theta is (intentionally, for now) mistaken for negative charges.
+	 */
+	
+	CUDA_HOSTDEV inline Vector4d BL_Fast_fit(const Matrix3xNd& hits) {
+		Vector4d result;
+		u_int n=hits.cols();
+		
+		const Vector2d a=hits.block(0,n/2,2,1)-hits.block(0,0,2,1);
+		const Vector2d b=hits.block(0,n-1,2,1)-hits.block(0,n/2,2,1);
+		const Vector2d c=hits.block(0,0,2,1)-hits.block(0,n-1,2,1);
+		
+		result(0)=hits(0,0)-(a(1)*c.squaredNorm()+c(1)*a.squaredNorm())/(2*cross2D(c,a));
+		result(1)=hits(1,0)+(a(0)*c.squaredNorm()+c(0)*a.squaredNorm())/(2*cross2D(c,a));
+		// check on Wikipedia for these formulas
+		
+		result(2)=(a.norm()*b.norm()*c.norm())/(2*abs(cross2D(b,a)));
+		// Using Math Olympiad's formula R=abc/(4A)
+		
+		const Vector2d d=hits.block(0,0,2,1)-result.head(2);
+		const Vector2d e=hits.block(0,n-1,2,1)-result.head(2);
+		
+		result(3)=result(2)*atan2(cross2D(d, e), d.dot(e))/(hits(2,n-1)-hits(2,0));
+		// ds/dz slope between last and first point
+		
+		return result;
+	}
+	
+	/*!
+	 \brief A very fast helix fit: it fits a circle by three points (first, middle
+	 and last point) and a line by two points (first and last). [Recycled for the Broken Line fit!]
+	 
+	 \param hits points to be fitted
+	 
+	 \return result in this form: (X0,Y0,R,tan(theta)).
+	 
+	 \warning points must be passed ordered (from internal layer to external) in
+	 order to maximize accuracy and do not mistake tan(theta) sign.
+	 
+	 \details This fast fit is used as pre-fit which is needed for:\n
+	 - momentum estimation for the computation of the multiple scattering variances;\n
+	 - properly change the coordinate system (rotate the line or define the curvilinear coordinates for the circle) in order to apply the Broken Line fit procedure.
+	 */
+	
+	CUDA_HOSTDEV inline Vector4d Fast_fit(const Matrix3xNd& hits) {
+		Vector4d result;
+		u_int n = hits.cols(); // get the number of hits
+		
+		// CIRCLE FIT
+		// Make segments between middle-to-first(b) and last-to-first(c) hits
+		const Vector2d b = hits.block(0, n / 2, 2, 1) - hits.block(0, 0, 2, 1);
+		const Vector2d c = hits.block(0, n - 1, 2, 1) - hits.block(0, 0, 2, 1);
+
+		// Compute their lengths
+		const double b2 = b.squaredNorm();
+		const double c2 = c.squaredNorm();
+		double X0;
+		double Y0;
+		// The algebra has been verified (MR). The usual approach has been followed:
+		// * use an orthogonal reference frame passing from the first point.
+		// * build the segments (chords)
+		// * build orthogonal lines through mid points
+		// * make a system and solve for X0 and Y0.
+		// * add the initial point
+		if (abs(b.x()) > abs(b.y())) {  //!< in case b.x is 0 (2 hits with same x)
+			const double k = c.x() / b.x();
+			const double div = 2. * (k * b.y() - c.y());
+			// if aligned TO FIX
+			Y0 = (k * b2 - c2) / div;
+			X0 = b2 / (2 * b.x()) - b.y() / b.x() * Y0;
+		} else {
+			const double k = c.y() / b.y();
+			const double div = 2. * (k * b.x() - c.x());
+			// if aligned TO FIX
+			X0 = (k * b2 - c2) / div;
+			Y0 = b2 / (2 * b.y()) - b.x() / b.y() * X0;
+		}
+		
+		result(0) = X0 + hits(0, 0);
+		result(1) = Y0 + hits(1, 0);
+		result(2) = sqrt(sqr(X0) + sqr(Y0));
+		
+		// LINE FIT
+		const Vector2d d = hits.block(0, 0, 2, 1) - result.head(2);
+		const Vector2d e = hits.block(0, n - 1, 2, 1) - result.head(2);
+		// Compute the arc-length between first and last point: L = R * theta = R *  atan (tan (Theta) )
+		const double dr = result(2) * atan2(cross2D(d, e), d.dot(e));
+		// Simple difference in Z between last and first hit
+		const double dz = hits(2, n - 1) - hits(2, 0);
+		
+		result(3) = (dr / dz);
+		
+		return result;
+	}
+	
+	/*!
+	 \brief Performs the Broken Line fit in the curved track case (that is, the fit parameters are the interceptions u and the curvature correction \Delta\kappa).
+	 
+	 \param hits hits coordinates.
+	 \param hits_cov hits covariance matrix.
+	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
+	 \param B magnetic field in Gev/cm/c.
+	 
+	 \details The function implements the steps 2 and 3 of the Broken Line fit with the curvature correction.\n
+	 The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and \Delta\kappa and their covariance matrix.
+	 The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
+	 
+	 \return circle_results karimaki_circle_fit:
+	 -par parameter of the line in this form: (phi, d, k); \n
+	 -cov covariance matrix of the fitted parameter; \n
+	 -chi2 value of the cost function in the minimum.
+	 */
+	
+	CUDA_HOSTDEV inline karimaki_circle_fit BL_Circle_fit(const Matrix3xNd& hits,
+														  const Matrix3Nd& hits_cov,
+														  const Vector4d& fast_fit,
+														  const double B,
+														  const PreparedBrokenLineData& data) {
+		u_int n=hits.cols();
+		u_int i;
+		karimaki_circle_fit circle_results;
+		
+		circle_results.q=data.q;
+		Matrix2xNd radii=data.radii;
+		VectorNd s=data.s;
+		VectorNd S=data.S;
+		VectorNd VarBeta=data.VarBeta;
+		const double slope=-circle_results.q/fast_fit(3);
+		VarBeta*=1+sqr(slope); // the kink angles are projected!
+		
+		VectorNd Z=VectorNd::Zero(n);
+		for(i=0;i<n;i++) {
+			Z(i)=radii.block(0,i,2,1).norm()-fast_fit(2);
+		}
+		
+		Matrix2d V; // covariance matrix
+		VectorNd w=VectorNd::Zero(n); // weights
+		Matrix2d RR; // rotation matrix point by point
+		double Slope; // slope of the circle point by point
+		for(i=0;i<n;i++) {
+			V(0,0)=hits_cov(i,i); // I could not find an easy access to sub-matrices in Eigen...
+			V(0,1)=hits_cov(i,i+n);
+			V(1,0)=hits_cov(i+n,i);
+			V(1,1)=hits_cov(i+n,i+n);
+			Slope=-radii(0,i)/radii(1,i);
+			RR=RotationMatrix(Slope);
+			w(i)=1/((RR*V*RR.transpose())(1,1)); // compute the orthogonal weight point by point
+		}
+		
+		VectorNd r_u=VectorNd::Zero(n+1);
+		for(i=0;i<n;i++) {
+			r_u(i)=w(i)*Z(i);
+		} r_u(n)=0;
+		
+		MatrixNd C_U=MatrixNd::Zero(n+1,n+1);
+		for(i=0;i<n;i++) {
+			if(i>0 && i<n-1) C_U(i,n)+=-(s(i+1)-s(i-1))/(2*VarBeta(i))*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)));
+			if(i>1) C_U(i,n)+=(s(i)-s(i-2))/(2*VarBeta(i-1)*(s(i)-s(i-1)));
+			if(i<n-2) C_U(i,n)+=(s(i+2)-s(i))/(2*VarBeta(i+1)*(s(i+1)-s(i)));
+			
+			if(i>0 && i<n-1) C_U(n,n)+=sqr(s(i+1)-s(i-1))/(4*VarBeta(i));
+		} C_U(n,n)=C_U(n,n)/2;
+		MatrixNd C_u;
+		C_u=C_U+C_U.transpose();
+		C_u.block(0,0,n,n)=MatrixC_u(w,s,VarBeta);
+		MatrixNd I=C_u.inverse();
+		
+		VectorNd u=I*r_u; // obtain the fitted parameters by solving the linear system
+		
+		// (phi, d_ca, k) in the system in which the first exp. data is the origin
+		double alpha=(s(1)-s(0))/fast_fit(2);
+		circle_results.par << atan2(-radii(0,0),radii(1,0))+circle_results.q*(u(1)-u(0)/cos(alpha))/(2*fast_fit(2)*tan(alpha/2)),
+		-circle_results.q*u(0), circle_results.q*(1/fast_fit(2)+u(n));
+		
+		if(circle_results.q==-1) {
+			if(circle_results.par(0)<0) circle_results.par(0)+=3.14159265358979323;
+			else circle_results.par(0)+=-3.14159265358979323;
+		}
+		
+		circle_results.cov << (I(1,1)-2*I(0,1)/cos(alpha)+I(0,0)/sqr(cos(alpha)))/sqr(2*fast_fit(2)*tan(alpha/2))+(1+sqr(slope))*MultScatt(S(1)-S(0),B,fast_fit(2),2,slope), (I(0,0)/cos(alpha)-I(0,1))/(2*fast_fit(2)*tan(alpha/2)), (I(1,n)-I(0,n)/cos(alpha))/(2*fast_fit(2)*tan(alpha/2)),
+		(I(0,0)/cos(alpha)-I(0,1))/(2*fast_fit(2)*tan(alpha/2)), I(0,0), -I(0,n),
+		(I(1,n)-I(0,n)/cos(alpha))/(2*fast_fit(2)*tan(alpha/2)), -I(0,n), I(n,n);
+		
+		// translate to the original xy system
+		TranslateKarimaki(circle_results,hits(0,0),hits(1,0));
+		
+		// compute chi2
+		circle_results.chi2=0;
+		for(i=0;i<n;i++) {
+			circle_results.chi2+=w(i)*sqr(Z(i)-u(i));
+			if(i>0 && i<n-1) circle_results.chi2+=sqr(u(i-1)/(s(i)-s(i-1))-u(i)*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)))+u(i+1)/(s(i+1)-s(i))+(s(i+1)-s(i-1))*u(n)/2)/VarBeta(i);
+		}
+		
+		assert(circle_results.chi2>=0);
+		
+		return circle_results;
+	}
+	
+	/*!
+	 \brief Performs the Broken Line fit in the straight track case (that is, the fit parameters are only the interceptions u).
+	 
+	 \param hits hits coordinates.
+	 \param hits_cov hits covariance matrix.
+	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
+	 \param B magnetic field in Gev/cm/c.
+	 
+	 \details The function implements the steps 2 and 3 of the Broken Line fit without the curvature correction.\n
+	 The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and their covariance matrix.
+	 The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
+	 
+	 \return circle_results karimaki_circle_fit:
+	 -par parameter of the line in this form: (phi, d, k); \n
+	 -cov covariance matrix of the fitted parameter; \n
+	 -chi2 value of the cost function in the minimum.
+	 */
+	
+	CUDA_HOSTDEV inline line_fit BL_Line_fit(const Matrix3xNd& hits,
+											 const Matrix3Nd& hits_cov,
+											 const Vector4d& fast_fit,
+											 const double B,
+											 const PreparedBrokenLineData& data) {
+		u_int n=hits.cols();
+		u_int i;
+		line_fit line_results;
+		
+		Matrix2xNd radii=data.radii;
+		VectorNd S=data.S;
+		VectorNd Z=data.Z;
+		VectorNd VarBeta=data.VarBeta;
+		
+		const double slope=-data.q/fast_fit(3);
+		Matrix2d R=RotationMatrix(slope);
+		
+		Matrix3d V=Matrix3d::Zero(); // covariance matrix XYZ
+		Matrix2x3d JacobXYZtosZ=Matrix2x3d::Zero(); // jacobian for the computation of the error on s (xyz -> sz)
+		VectorNd w=VectorNd::Zero(n);
+		for(i=0;i<n;i++) {
+			V(0,0)=hits_cov(i,i); // I could not find an easy way to access the sub-matrices in Eigen...
+			V(0,1)=hits_cov(i,i+n);
+			V(0,2)=hits_cov(i,i+2*n);
+			V(1,0)=hits_cov(i+n,i);
+			V(1,1)=hits_cov(i+n,i+n);
+			V(1,2)=hits_cov(i+n,i+2*n);
+			V(2,0)=hits_cov(i+2*n,i);
+			V(2,1)=hits_cov(i+2*n,i+n);
+			V(2,2)=hits_cov(i+2*n,i+2*n);
+			JacobXYZtosZ(0,0)=radii(1,i)/radii.block(0,i,2,1).norm();
+			JacobXYZtosZ(0,1)=-radii(0,i)/radii.block(0,i,2,1).norm();
+			JacobXYZtosZ(1,2)=1;
+			w(i)=1/((R*JacobXYZtosZ*V*JacobXYZtosZ.transpose()*R.transpose())(1,1)); // compute the orthogonal weight point by point
+		}
+		
+		VectorNd r_u=VectorNd::Zero(n);
+		for(i=0;i<n;i++) {
+			r_u(i)=w(i)*Z(i);
+		}
+		
+		MatrixNd C_u=MatrixC_u(w,S,VarBeta);
+		MatrixNd I=C_u.inverse();
+		VectorNd u=I*r_u; // obtain the fitted parameters by solving the linear system
+		
+		// line parameters in the system in which the first hit is the origin and with axis along SZ
+		line_results.par << (u(1)-u(0))/(S(1)-S(0)), u(0);
+		line_results.cov << (I(0,0)-2*I(0,1)+I(1,1))/sqr(S(1)-S(0))+MultScatt(S(1)-S(0),B,fast_fit(2),2,slope), (I(0,1)-I(0,0))/(S(1)-S(0)),
+		(I(0,1)-I(0,0))/(S(1)-S(0)), I(0,0);
+		
+		// translate to the original SZ system
+		Matrix2d Jacob;
+		Jacob(0,0)=1;
+		Jacob(0,1)=0;
+		Jacob(1,0)=-S(0);
+		Jacob(1,1)=1;
+		line_results.par(1)+=-line_results.par(0)*S(0);
+		line_results.cov=Jacob*line_results.cov*Jacob.transpose();
+		
+		// rotate to the original sz system
+		Jacob(0,0)=1/sqr(R(0,0)-line_results.par(0)*R(0,1));
+		Jacob(0,1)=0;
+		Jacob(1,0)=line_results.par(1)*R(0,1)/sqr(R(0,0)-line_results.par(0)*R(0,1));
+		Jacob(1,1)=1/(R(0,0)-line_results.par(0)*R(0,1));
+		line_results.par(1)=line_results.par(1)/(R(0,0)-line_results.par(0)*R(0,1));
+		line_results.par(0)=(R(0,1)+line_results.par(0)*R(0,0))/(R(0,0)-line_results.par(0)*R(0,1));
+		line_results.cov=Jacob*line_results.cov*Jacob.transpose();
+		
+		// compute chi2
+		line_results.chi2=0;
+		for(i=0;i<n;i++) {
+			line_results.chi2+=w(i)*sqr(Z(i)-u(i));
+			if(i>0 && i<n-1) line_results.chi2+=sqr(u(i-1)/(S(i)-S(i-1))-u(i)*(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1)))+u(i+1)/(S(i+1)-S(i)))/VarBeta(i);
+		}
+		
+		assert(line_results.chi2>=0);
+		
+		return line_results;
+	}
+	
+	/*!
+	 \brief Helix fit by three step:
+	 -fast pre-fit (see Fast_fit() for further info); \n
+	 -circle fit of the hits projected in the transverse plane by Broken Line algorithm (see BL_Circle_fit() for further info); \n
+	 -line fit of the hits projected on the (pre-fitted) cilinder surface by Broken Line algorithm (see BL_Line_fit() for further info); \n
+	 Points must be passed ordered (from inner to outer layer).
+	 
+	 \param hits Matrix3xNd hits coordinates in this form: \n
+	 |x1|x2|x3|...|xn| \n
+	 |y1|y2|y3|...|yn| \n
+	 |z1|z2|z3|...|zn|
+	 \param hits_cov Matrix3Nd covariance matrix in this form (()->cov()): \n
+	 |(x1,x1)|(x2,x1)|(x3,x1)|(x4,x1)|.|(y1,x1)|(y2,x1)|(y3,x1)|(y4,x1)|.|(z1,x1)|(z2,x1)|(z3,x1)|(z4,x1)| \n
+	 |(x1,x2)|(x2,x2)|(x3,x2)|(x4,x2)|.|(y1,x2)|(y2,x2)|(y3,x2)|(y4,x2)|.|(z1,x2)|(z2,x2)|(z3,x2)|(z4,x2)| \n
+	 |(x1,x3)|(x2,x3)|(x3,x3)|(x4,x3)|.|(y1,x3)|(y2,x3)|(y3,x3)|(y4,x3)|.|(z1,x3)|(z2,x3)|(z3,x3)|(z4,x3)| \n
+	 |(x1,x4)|(x2,x4)|(x3,x4)|(x4,x4)|.|(y1,x4)|(y2,x4)|(y3,x4)|(y4,x4)|.|(z1,x4)|(z2,x4)|(z3,x4)|(z4,x4)| \n
+	 .       .       .       .       . .       .       .       .       . .       .       .       .       . \n
+	 |(x1,y1)|(x2,y1)|(x3,y1)|(x4,y1)|.|(y1,y1)|(y2,y1)|(y3,x1)|(y4,y1)|.|(z1,y1)|(z2,y1)|(z3,y1)|(z4,y1)| \n
+	 |(x1,y2)|(x2,y2)|(x3,y2)|(x4,y2)|.|(y1,y2)|(y2,y2)|(y3,x2)|(y4,y2)|.|(z1,y2)|(z2,y2)|(z3,y2)|(z4,y2)| \n
+	 |(x1,y3)|(x2,y3)|(x3,y3)|(x4,y3)|.|(y1,y3)|(y2,y3)|(y3,x3)|(y4,y3)|.|(z1,y3)|(z2,y3)|(z3,y3)|(z4,y3)| \n
+	 |(x1,y4)|(x2,y4)|(x3,y4)|(x4,y4)|.|(y1,y4)|(y2,y4)|(y3,x4)|(y4,y4)|.|(z1,y4)|(z2,y4)|(z3,y4)|(z4,y4)| \n
+	 .       .       .    .          . .       .       .       .       . .       .       .       .       . \n
+	 |(x1,z1)|(x2,z1)|(x3,z1)|(x4,z1)|.|(y1,z1)|(y2,z1)|(y3,z1)|(y4,z1)|.|(z1,z1)|(z2,z1)|(z3,z1)|(z4,z1)| \n
+	 |(x1,z2)|(x2,z2)|(x3,z2)|(x4,z2)|.|(y1,z2)|(y2,z2)|(y3,z2)|(y4,z2)|.|(z1,z2)|(z2,z2)|(z3,z2)|(z4,z2)| \n
+	 |(x1,z3)|(x2,z3)|(x3,z3)|(x4,z3)|.|(y1,z3)|(y2,z3)|(y3,z3)|(y4,z3)|.|(z1,z3)|(z2,z3)|(z3,z3)|(z4,z3)| \n
+	 |(x1,z4)|(x2,z4)|(x3,z4)|(x4,z4)|.|(y1,z4)|(y2,z4)|(y3,z4)|(y4,z4)|.|(z1,z4)|(z2,z4)|(z3,z4)|(z4,z4)|
+	 \param B magnetic field in the center of the detector in Gev/cm/c, in order to perform the p_t calculation.
+	 
+	 \warning see BL_Circle_fit(), BL_Line_fit() and Fast_fit() warnings.
+	 
+	 \bug see BL_Circle_fit(), BL_Line_fit() and Fast_fit() bugs.
+	 
+	 \return (phi,Tip,p_t,cotan(theta)),Zip), their covariance matrix and the chi2's of the circle and line fits.
+	 */
+	
+	CUDA_HOSTDEV inline helix_fit Helix_fit(const Matrix3xNd& hits,
+											const Matrix3Nd& hits_cov,
+											const double B) {
+		helix_fit helix;
+		
+		helix.fast_fit=BL_Fast_fit(hits);
+		
+		const PreparedBrokenLineData data=PrepareBrokenLineData(hits,hits_cov,helix.fast_fit,B);
+		
+		karimaki_circle_fit circle=BL_Circle_fit(hits,hits_cov,helix.fast_fit,B,data);
+		line_fit line=BL_Line_fit(hits,hits_cov,helix.fast_fit,B,data);
+		
+		// the circle fit gives k, but here we want p_t, so let's change the parameter and the covariance matrix
+		Matrix3d Jacob;
+		Jacob << 1,0,0,
+		0,1,0,
+		0,0,-abs(circle.par(2))*B/(sqr(circle.par(2))*circle.par(2));
+		circle.par(2)=B/abs(circle.par(2));
+		circle.cov=Jacob*circle.cov*Jacob.transpose();
+		
+		helix.par << circle.par, line.par;
+		helix.cov=MatrixXd::Zero(5, 5);
+		helix.cov.block(0,0,3,3)=circle.cov;
+		helix.cov.block(3,3,2,2)=line.cov;
+		helix.q=circle.q;
+		helix.chi2_circle=circle.chi2;
+		helix.chi2_line=line.chi2;
+		
+		return helix;
+	}
+	
+}  // namespace BrokenLine
+
+#endif
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h
new file mode 100644
index 0000000000000..44be969822e84
--- /dev/null
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h
@@ -0,0 +1,26 @@
+#ifndef RecoPixelVertexing_PixelTrackFitting_PixelFitterByBrokenLine_H
+#define RecoPixelVertexing_PixelTrackFitting_PixelFitterByBrokenLine_H
+
+#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterBase.h"
+#include "DataFormats/TrackingRecHit/interface/TrackingRecHit.h"
+#include "RecoTracker/TkTrackingRegions/interface/TrackingRegion.h"
+#include "FWCore/Framework/interface/EventSetup.h"
+#include "DataFormats/TrackReco/interface/Track.h"
+#include "FWCore/ParameterSet/interface/ParameterSet.h"
+
+#include <vector>
+
+
+
+class PixelFitterByBrokenLine final : public PixelFitterBase {
+public:
+  explicit PixelFitterByBrokenLine(const edm::EventSetup *es, const MagneticField *field);
+  virtual ~PixelFitterByBrokenLine() = default;
+  virtual std::unique_ptr<reco::Track> run(const std::vector<const TrackingRecHit *>& hits,
+                                           const TrackingRegion& region) const override;
+
+private:
+  const edm::EventSetup *es_;
+  const MagneticField *field_;
+};
+#endif
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
new file mode 100644
index 0000000000000..977e242c7ee10
--- /dev/null
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
@@ -0,0 +1,47 @@
+#include "FWCore/Framework/interface/Frameworkfwd.h"
+#include "FWCore/Framework/interface/global/EDProducer.h"
+
+#include "FWCore/Framework/interface/Event.h"
+#include "FWCore/Framework/interface/MakerMacros.h"
+#include "FWCore/Framework/interface/EventSetup.h"
+#include "FWCore/Framework/interface/ESHandle.h"
+
+#include "FWCore/ParameterSet/interface/ParameterSet.h"
+#include "FWCore/ParameterSet/interface/ConfigurationDescriptions.h"
+#include "FWCore/ParameterSet/interface/ParameterSetDescription.h"
+
+#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitter.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h"
+
+#include "MagneticField/Engine/interface/MagneticField.h"
+#include "MagneticField/Records/interface/IdealMagneticFieldRecord.h"
+
+class PixelFitterByBrokenLineProducer: public edm::global::EDProducer<> {
+public:
+  explicit PixelFitterByBrokenLineProducer(const edm::ParameterSet& iConfig)
+  {
+    produces<PixelFitter>();
+  }
+  ~PixelFitterByBrokenLineProducer() {}
+
+  static void fillDescriptions(edm::ConfigurationDescriptions& descriptions) {
+    edm::ParameterSetDescription desc;
+    descriptions.add("pixelFitterByBrokenLineDefault", desc);
+  }
+
+private:
+  virtual void produce(edm::StreamID, edm::Event& iEvent, const edm::EventSetup& iSetup) const override;
+};
+
+
+void PixelFitterByBrokenLineProducer::produce(edm::StreamID, edm::Event& iEvent, const edm::EventSetup& iSetup) const {
+  edm::ESHandle<MagneticField> fieldESH;
+  iSetup.get<IdealMagneticFieldRecord>().get(fieldESH);
+
+  auto impl = std::make_unique<PixelFitterByBrokenLine>(&iSetup,
+      fieldESH.product());
+  auto prod = std::make_unique<PixelFitter>(std::move(impl));
+  iEvent.put(std::move(prod));
+}
+
+DEFINE_FWK_MODULE(PixelFitterByBrokenLineProducer);
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
new file mode 100644
index 0000000000000..794088ac40641
--- /dev/null
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
@@ -0,0 +1,119 @@
+#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
+
+#include "FWCore/ParameterSet/interface/ParameterSet.h"
+
+#include "FWCore/Framework/interface/EventSetup.h"
+
+#include "DataFormats/GeometryCommonDetAlgo/interface/GlobalError.h"
+#include "DataFormats/GeometryVector/interface/GlobalPoint.h"
+#include "DataFormats/GeometryVector/interface/LocalPoint.h"
+
+#include "DataFormats/TrackingRecHit/interface/TrackingRecHit.h"
+#include "Geometry/CommonDetUnit/interface/GeomDet.h"
+#include "RecoTracker/TkMSParametrization/interface/PixelRecoUtilities.h"
+
+#include "DataFormats/GeometryCommonDetAlgo/interface/Measurement1D.h"
+
+#include "Geometry/CommonDetUnit/interface/GeomDetType.h"
+
+#include "MagneticField/Engine/interface/MagneticField.h"
+
+#include "FWCore/MessageLogger/interface/MessageLogger.h"
+
+#include "DataFormats/GeometryVector/interface/Pi.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackBuilder.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackErrorParam.h"
+
+#include "CommonTools/Utils/interface/DynArray.h"
+
+using namespace std;
+
+
+PixelFitterByBrokenLine::PixelFitterByBrokenLine(const edm::EventSetup* es,
+                                                               const MagneticField* field)
+    : es_(es), field_(field) {}
+
+std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
+    const std::vector<const TrackingRecHit*>& hits, const TrackingRegion& region) const {
+
+  using namespace BrokenLine;
+
+  std::unique_ptr<reco::Track> ret;
+
+  unsigned int nhits = hits.size();
+
+  if (nhits < 2) return ret;
+
+  declareDynArray(GlobalPoint, nhits, points);
+  declareDynArray(GlobalError, nhits, errors);
+  declareDynArray(bool, nhits, isBarrel);
+
+  for (unsigned int i = 0; i != nhits; ++i) {
+    auto const& recHit = hits[i];
+    points[i] = GlobalPoint(recHit->globalPosition().basicVector() - region.origin().basicVector());
+    errors[i] = recHit->globalPositionError();
+    isBarrel[i] = recHit->detUnit()->type().isBarrel();
+  }
+
+  Matrix<double, 3, Dynamic, 0, 3, max_nop> brokenLineHits(3, nhits);
+
+  Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop> brokenLineHits_cov =
+      MatrixXd::Zero(3 * nhits, 3 * nhits);
+
+  for (unsigned int i = 0; i < nhits; ++i) {
+    brokenLineHits.col(i) << points[i].x(), points[i].y(), points[i].z();
+
+    const auto& errorMatrix = errors[i].matrix4D();
+
+    for (auto j = 0; j < 3; ++j) {
+      for (auto l = 0; l < 3; ++l) {
+        brokenLineHits_cov(i + j * nhits, i + l * nhits) = errorMatrix(j, l);
+      }
+    }
+  }
+
+  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
+  helix_fit fittedTrack = BrokenLine::Helix_fit(brokenLineHits, brokenLineHits_cov, bField);
+  int iCharge = fittedTrack.q;
+
+  // parameters are:
+  // 0: phi
+  // 1: tip
+  // 2: curvature
+  // 3: cottheta
+  // 4: zip
+  float valPhi = fittedTrack.par(0);
+
+  float valTip = fittedTrack.par(1);
+
+  float valCotTheta = fittedTrack.par(3);
+
+  float valZip = fittedTrack.par(4);
+  float valPt = fittedTrack.par(2);
+  //
+  //  PixelTrackErrorParam param(valEta, valPt);
+  float errValPhi = std::sqrt(fittedTrack.cov(0, 0));
+  float errValTip = std::sqrt(fittedTrack.cov(1, 1));
+
+  float errValPt = std::sqrt(fittedTrack.cov(2, 2));
+
+  float errValCotTheta = std::sqrt(fittedTrack.cov(3, 3));
+  float errValZip = std::sqrt(fittedTrack.cov(4, 4));
+
+  float chi2 = (fittedTrack.chi2_line + fittedTrack.chi2_circle);
+
+  //std::cout << valPt << " " << fittedTrack.chi2_line <<	" " << fittedTrack.chi2_circle << " " << chi2 << std::endl;
+
+  PixelTrackBuilder builder;
+  Measurement1D phi(valPhi, errValPhi);
+  Measurement1D tip(valTip, errValTip);
+
+  Measurement1D pt(valPt, errValPt);
+  Measurement1D cotTheta(valCotTheta, errValCotTheta);
+  Measurement1D zip(valZip, errValZip);
+
+  ret.reset(
+      builder.build(pt, phi, cotTheta, tip, zip, chi2, iCharge, hits, field_, region.origin()));
+  return ret;
+}

From d447fbbac1e94f18760760e2a1baa8bb0daf2773 Mon Sep 17 00:00:00 2001
From: Giovanni Maria Tomaselli <gimmytomas@gmail.com>
Date: Tue, 4 Sep 2018 18:41:20 +0200
Subject: [PATCH 03/95] BrokenLineGPU: work in progress

---
 .../PixelTrackFitting/interface/BrokenLine.h  |  28 +++-
 .../PixelTrackFitting/interface/RiemannFit.h  |   2 +-
 .../PixelTrackFitting/test/BuildFile.xml      |   2 +-
 .../PixelTrackFitting/test/testEigenGPU.cu    | 126 +++++++++++++++++-
 4 files changed, 147 insertions(+), 11 deletions(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
index 35de601059791..ac2ce2183588a 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -16,18 +16,32 @@ namespace BrokenLine {
 	
 	using namespace Eigen;
 	
-	constexpr unsigned int max_nop = 5;  //!< In order to avoid use of dynamic memory
+	constexpr unsigned int max_nop = 4;  //!< In order to avoid use of dynamic memory
 	
 	using MatrixNd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop, max_nop>;
+	using MatrixNplusONEd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop + 1, max_nop + 1>;
 	using Matrix3Nd = Eigen::Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop>;
 	using Matrix2xNd = Eigen::Matrix<double, 2, Dynamic, 0, 2, max_nop>;
 	using Matrix3xNd = Eigen::Matrix<double, 3, Dynamic, 0, 3, max_nop>;
 	using VectorNd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop, 1>;
+	using VectorNplusONEd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop + 1, 1>;
 	using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
 	using Matrix5d = Eigen::Matrix<double, 5, 5>;
 	using Vector5d = Eigen::Matrix<double, 5, 1>;
 	using u_int    = unsigned int;
 	
+	/*using MatrixNd = Eigen::Matrix<double, max_nop, max_nop, 0, max_nop, max_nop>;
+	using MatrixNplusONEd = Eigen::Matrix<double, max_nop + 1, max_nop + 1, 0, max_nop + 1, max_nop + 1>;
+	using Matrix3Nd = Eigen::Matrix<double, 3 * max_nop, 3 * max_nop, 0, 3 * max_nop, 3 * max_nop>;
+	using Matrix2xNd = Eigen::Matrix<double, 2, max_nop, 0, 2, max_nop>;
+	using Matrix3xNd = Eigen::Matrix<double, 3, max_nop, 0, 3, max_nop>;
+	using VectorNd = Eigen::Matrix<double, max_nop, 1, 0, max_nop, 1>;
+	using VectorNplusONEd = Eigen::Matrix<double, max_nop + 1, 1, 0, max_nop + 1, 1>;
+	using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
+	using Matrix5d = Eigen::Matrix<double, 5, 5>;
+	using Vector5d = Eigen::Matrix<double, 5, 1>;
+	using u_int    = unsigned int;*/
+	
 	struct karimaki_circle_fit {
 		Vector3d par;  //!< Karimäki's parameters: (phi, d, k=1/R)
 		Matrix3d cov;
@@ -280,7 +294,7 @@ namespace BrokenLine {
 		
 		result(0)=hits(0,0)-(a(1)*c.squaredNorm()+c(1)*a.squaredNorm())/(2*cross2D(c,a));
 		result(1)=hits(1,0)+(a(0)*c.squaredNorm()+c(0)*a.squaredNorm())/(2*cross2D(c,a));
-		// check on Wikipedia for these formulas
+		// check Wikipedia for these formulas
 		
 		result(2)=(a.norm()*b.norm()*c.norm())/(2*abs(cross2D(b,a)));
 		// Using Math Olympiad's formula R=abc/(4A)
@@ -415,12 +429,12 @@ namespace BrokenLine {
 			w(i)=1/((RR*V*RR.transpose())(1,1)); // compute the orthogonal weight point by point
 		}
 		
-		VectorNd r_u=VectorNd::Zero(n+1);
+		VectorNplusONEd r_u=VectorNplusONEd::Zero(n+1);
 		for(i=0;i<n;i++) {
 			r_u(i)=w(i)*Z(i);
 		} r_u(n)=0;
 		
-		MatrixNd C_U=MatrixNd::Zero(n+1,n+1);
+		MatrixNplusONEd C_U=MatrixNplusONEd::Zero(n+1,n+1);
 		for(i=0;i<n;i++) {
 			if(i>0 && i<n-1) C_U(i,n)+=-(s(i+1)-s(i-1))/(2*VarBeta(i))*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)));
 			if(i>1) C_U(i,n)+=(s(i)-s(i-2))/(2*VarBeta(i-1)*(s(i)-s(i-1)));
@@ -428,12 +442,12 @@ namespace BrokenLine {
 			
 			if(i>0 && i<n-1) C_U(n,n)+=sqr(s(i+1)-s(i-1))/(4*VarBeta(i));
 		} C_U(n,n)=C_U(n,n)/2;
-		MatrixNd C_u;
+		MatrixNplusONEd C_u;
 		C_u=C_U+C_U.transpose();
 		C_u.block(0,0,n,n)=MatrixC_u(w,s,VarBeta);
-		MatrixNd I=C_u.inverse();
+		MatrixNplusONEd I=C_u.inverse();
 		
-		VectorNd u=I*r_u; // obtain the fitted parameters by solving the linear system
+		VectorNplusONEd u=I*r_u; // obtain the fitted parameters by solving the linear system
 		
 		// (phi, d_ca, k) in the system in which the first exp. data is the origin
 		double alpha=(s(1)-s(0))/fast_fit(2);
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
index 0eed4d7a12faf..2764d7d641ce1 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
@@ -6,7 +6,7 @@
 #include <Eigen/Eigenvalues>
 
 #ifndef RFIT_DEBUG
-#define RFIT_DEBUG 0
+#define RFIT_DEBUG 1
 #endif  // RFIT_DEBUG
 
 namespace Rfit
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml b/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
index d6beb57b862b8..5c8a3b26a08a9 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
@@ -14,7 +14,7 @@
 <bin file="testEigenGPU.cu" name="testEigenGPU_t">
   <use name="cuda"/>
   <use name="cuda-api-wrappers"/>
-  <flags CXXFLAGS="-g"/>
+  <flags CXXFLAGS="-g" CUDA_FLAGS="-g -lineinfo"/>
 </bin>
 <bin file="testEigenGPUNoFit.cu" name="testEigenGPUNoFit_t">
   <use name="cuda"/>
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
index 9b8e870f5c4b7..f4287a6eb55f2 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
@@ -4,6 +4,7 @@
 #include <Eigen/Eigenvalues>
 
 #include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
 
 #include "test_common.h"
@@ -53,6 +54,52 @@ void kernelFullFit(Rfit::Matrix3xNd * hits,
   return;
 }
 
+__global__
+void kernelFullBrokenLine(BrokenLine::Matrix3xNd * hits,
+BrokenLine::Matrix3Nd * hits_cov,
+BrokenLine::PreparedBrokenLineData * data,
+Vector4d * fast_fit,
+double B,
+BrokenLine::helix_fit * helix_fit_resultsGPU,
+BrokenLine::karimaki_circle_fit * circleGPU,
+BrokenLine::line_fit * lineGPU) {
+
+	/*hits->resize(3,4);
+	hits_cov->resize(12, 12);*/
+
+	BrokenLine::helix_fit& helix = (*helix_fit_resultsGPU);
+	BrokenLine::karimaki_circle_fit& circle = (*circleGPU);
+	BrokenLine::line_fit& line = (*lineGPU);
+
+	helix.fast_fit=BrokenLine::BL_Fast_fit(*hits);
+
+	//const BrokenLine::PreparedBrokenLineData data=BrokenLine::PrepareBrokenLineData(*hits,*hits_cov,helix.fast_fit,B);
+	(*data) = BrokenLine::PrepareBrokenLineData(*hits,*hits_cov,helix.fast_fit,B);
+
+	circle=BrokenLine::BL_Circle_fit(*hits,*hits_cov,helix.fast_fit,B,*data);
+	line=BrokenLine::BL_Line_fit(*hits,*hits_cov,helix.fast_fit,B,*data);
+
+	// the circle fit gives k, but here we want p_t, so let's change the parameter and the covariance matrix
+	Matrix3d Jacob;
+	Jacob << 1,0,0,
+	0,1,0,
+	0,0,-abs(circle.par(2))*B/(BrokenLine::sqr(circle.par(2))*circle.par(2));
+	circle.par(2)=B/abs(circle.par(2));
+	circle.cov=Jacob*circle.cov*Jacob.transpose();
+
+	helix.par << circle.par, line.par;
+	helix.cov=MatrixXd::Zero(5, 5);
+	helix.cov.block(0,0,3,3)=circle.cov;
+	helix.cov.block(3,3,2,2)=line.cov;
+	helix.q=circle.q;
+	helix.chi2_circle=circle.chi2;
+	helix.chi2_line=line.chi2;
+
+	//(*helix_fit_resultsGPU) = BrokenLine::Helix_fit(*hits, *hits_cov, B);
+
+	return;
+}
+
 __global__
 void kernelFastFit(Rfit::Matrix3xNd * hits, Vector4d * results) {
   (*results) = Rfit::Fast_fit(*hits);
@@ -116,6 +163,28 @@ void fillHitsAndHitsCov(Rfit::Matrix3xNd & hits, Rfit::Matrix3Nd & hits_cov) {
   hits_cov(3,7) = hits_cov(7,3) = -5.28e-06;
 }
 
+void fillHitsAndHitsCovBrokenLine(BrokenLine::Matrix3xNd & hits, BrokenLine::Matrix3Nd & hits_cov) {
+hits << 1.98645, 4.72598, 7.65632, 11.3151,
+2.18002, 4.88864, 7.75845, 11.3134,
+2.46338, 6.99838,  11.808,  17.793;
+hits_cov(0,0) = 7.14652e-06;
+hits_cov(1,1) = 2.15789e-06;
+hits_cov(2,2) = 1.63328e-06;
+hits_cov(3,3) = 6.27919e-06;
+hits_cov(4,4) = 6.10348e-06;
+hits_cov(5,5) = 2.08211e-06;
+hits_cov(6,6) = 1.61672e-06;
+hits_cov(7,7) = 6.28081e-06;
+hits_cov(8,8) = 5.184e-05;
+hits_cov(9,9) = 1.444e-05;
+hits_cov(10,10) = 6.25e-06;
+hits_cov(11,11) = 3.136e-05;
+hits_cov(0,4) = hits_cov(4,0) = -5.60077e-06;
+hits_cov(1,5) = hits_cov(5,1) = -1.11936e-06;
+hits_cov(2,6) = hits_cov(6,2) = -6.24945e-07;
+hits_cov(3,7) = hits_cov(7,3) = -5.28e-06;
+}
+
 void testFit() {
   constexpr double B = 0.0113921;
   Rfit::Matrix3xNd hits(3,4);
@@ -249,13 +318,66 @@ void testFitOneGo(bool errors, double epsilon=1e-6) {
   cudaDeviceReset();
 }
 
+void testBrokenLineOneGo(double epsilon=1e-6) {
+	constexpr double B = 0.0113921;
+	BrokenLine::Matrix3xNd hits(3,4);
+	BrokenLine::Matrix3Nd hits_cov = MatrixXd::Zero(12,12);
+
+	fillHitsAndHitsCovBrokenLine(hits, hits_cov);
+
+	// HELIX_FIT CPU
+	BrokenLine::helix_fit helix_fit_results = BrokenLine::Helix_fit(hits,hits_cov,B);
+
+	std::cout << "Fitted values (HelixFit) CPU:\n" << helix_fit_results.par << std::endl;
+
+	// FIT GPU
+	std::cout << "GPU FIT" << std::endl;
+	BrokenLine::Matrix3xNd * hitsGPU = nullptr;
+	BrokenLine::Matrix3Nd * hits_covGPU = nullptr;
+BrokenLine::PreparedBrokenLineData * dataGPU = nullptr;
+Vector4d * fast_fitGPU = nullptr;
+BrokenLine::karimaki_circle_fit * circleGPU = nullptr;
+BrokenLine::line_fit * lineGPU = nullptr;
+	BrokenLine::helix_fit * helix_fit_resultsGPU = nullptr;
+	BrokenLine::helix_fit * helix_fit_resultsGPUret = new BrokenLine::helix_fit();
+
+	cudaCheck(cudaMalloc((void **)&hitsGPU, sizeof(BrokenLine::Matrix3xNd(3,4))));
+    cudaCheck(cudaMalloc((void **)&hits_covGPU, sizeof(BrokenLine::Matrix3Nd(12,12))));
+cudaCheck(cudaMalloc((void **)&dataGPU, sizeof(BrokenLine::PreparedBrokenLineData)));
+cudaCheck(cudaMalloc((void **)&fast_fitGPU, sizeof(Vector4d)));
+cudaCheck(cudaMalloc((void **)&circleGPU, sizeof(BrokenLine::karimaki_circle_fit)));
+cudaCheck(cudaMalloc((void **)&lineGPU, sizeof(BrokenLine::line_fit)));
+	cudaCheck(cudaMalloc((void **)&helix_fit_resultsGPU, sizeof(BrokenLine::helix_fit)));
+
+	cudaCheck(cudaMemcpy(hitsGPU, &hits, sizeof(BrokenLine::Matrix3xNd(3,4)), cudaMemcpyHostToDevice));
+	cudaCheck(cudaMemcpy(hits_covGPU, &hits_cov, sizeof(BrokenLine::Matrix3Nd(12,12)), cudaMemcpyHostToDevice));
+
+	kernelFullBrokenLine<<<1, 1>>>(hitsGPU, hits_covGPU, dataGPU, fast_fitGPU, B, helix_fit_resultsGPU, circleGPU, lineGPU);
+	cudaCheck(cudaDeviceSynchronize());
+
+	cudaCheck(cudaMemcpy(helix_fit_resultsGPUret, helix_fit_resultsGPU, sizeof(BrokenLine::helix_fit), cudaMemcpyDeviceToHost));
+
+	std::cout << "Fitted values (HelixFit) GPU:\n" << helix_fit_resultsGPUret->par << std::endl;
+	assert(isEqualFuzzy(helix_fit_results.par, helix_fit_resultsGPUret->par, epsilon));
+
+	cudaCheck(cudaFree(hitsGPU));
+	cudaCheck(cudaFree(hits_covGPU));
+	cudaCheck(cudaFree(helix_fit_resultsGPU));
+	delete helix_fit_resultsGPUret;
+
+	cudaDeviceReset();
+}
+
 int main (int argc, char * argv[]) {
 //  testFit();
-  std::cout << "TEST FIT, NO ERRORS" << std::endl;
+  /*std::cout << "TEST FIT, NO ERRORS" << std::endl;
   testFitOneGo(false);
 
   std::cout << "TEST FIT, ERRORS AND SCATTER" << std::endl;
-  testFitOneGo(true, 1e-5);
+  testFitOneGo(true, 1e-5);*/
+
+	std::cout << "TEST BROKEN LINE" << std::endl;
+	testBrokenLineOneGo(1e-5);
 
   return 0;
 }

From 55a86bc193b6aee57811e5ceebfc680d8a1f279d Mon Sep 17 00:00:00 2001
From: Giovanni Maria Tomaselli <gimmytomas@gmail.com>
Date: Wed, 19 Sep 2018 14:49:12 +0200
Subject: [PATCH 04/95] Full implementation of the broken line fit. For the
 moment it needs matrices of static dimensions to run on the GPUs

---
 .../PixelTrackFitting/interface/BrokenLine.h  | 281 ++++----
 .../PixelTrackFitting/interface/RiemannFit.h  |   2 +-
 .../PixelTrackFilterByKinematicsProducer.cc   |   2 +-
 .../plugins/PixelTrackProducer.cc             |   7 +
 .../python/PixelTracks_cff.py                 |  13 +-
 .../src/PixelFitterByBrokenLine.cc            |  23 +-
 .../src/PixelFitterByRiemannParaboloid.cc     |  14 +-
 .../src/PixelTrackFilterByKinematics.cc       |   2 +-
 .../PixelTrackFitting/test/BuildFile.xml      |   2 +-
 .../PixelTrackFitting/test/testEigenGPU.cu    | 628 ++++++++++--------
 .../test/testEigenGPUNoFit.cu                 |  37 ++
 11 files changed, 564 insertions(+), 447 deletions(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
index ac2ce2183588a..ebdb13437ee0f 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -18,19 +18,21 @@ namespace BrokenLine {
 	
 	constexpr unsigned int max_nop = 4;  //!< In order to avoid use of dynamic memory
 	
-	using MatrixNd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop, max_nop>;
-	using MatrixNplusONEd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop + 1, max_nop + 1>;
-	using Matrix3Nd = Eigen::Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop>;
-	using Matrix2xNd = Eigen::Matrix<double, 2, Dynamic, 0, 2, max_nop>;
-	using Matrix3xNd = Eigen::Matrix<double, 3, Dynamic, 0, 3, max_nop>;
-	using VectorNd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop, 1>;
-	using VectorNplusONEd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop + 1, 1>;
-	using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
-	using Matrix5d = Eigen::Matrix<double, 5, 5>;
-	using Vector5d = Eigen::Matrix<double, 5, 1>;
-	using u_int    = unsigned int;
+	// WARNING: USE STATIC DIMENSIONS ON GPUs. To do so, comment these definitions and uncomment the others following
+	
+	/*using MatrixNd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop, max_nop>;
+	 using MatrixNplusONEd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop + 1, max_nop + 1>;
+	 using Matrix3Nd = Eigen::Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop>;
+	 using Matrix2xNd = Eigen::Matrix<double, 2, Dynamic, 0, 2, max_nop>;
+	 using Matrix3xNd = Eigen::Matrix<double, 3, Dynamic, 0, 3, max_nop>;
+	 using VectorNd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop, 1>;
+	 using VectorNplusONEd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop + 1, 1>;
+	 using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
+	 using Matrix5d = Eigen::Matrix<double, 5, 5>;
+	 using Vector5d = Eigen::Matrix<double, 5, 1>;
+	 using u_int    = unsigned int;*/
 	
-	/*using MatrixNd = Eigen::Matrix<double, max_nop, max_nop, 0, max_nop, max_nop>;
+	using MatrixNd = Eigen::Matrix<double, max_nop, max_nop, 0, max_nop, max_nop>;
 	using MatrixNplusONEd = Eigen::Matrix<double, max_nop + 1, max_nop + 1, 0, max_nop + 1, max_nop + 1>;
 	using Matrix3Nd = Eigen::Matrix<double, 3 * max_nop, 3 * max_nop, 0, 3 * max_nop, 3 * max_nop>;
 	using Matrix2xNd = Eigen::Matrix<double, 2, max_nop, 0, 2, max_nop>;
@@ -40,7 +42,7 @@ namespace BrokenLine {
 	using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
 	using Matrix5d = Eigen::Matrix<double, 5, 5>;
 	using Vector5d = Eigen::Matrix<double, 5, 1>;
-	using u_int    = unsigned int;*/
+	using u_int    = unsigned int;
 	
 	struct karimaki_circle_fit {
 		Vector3d par;  //!< Karimäki's parameters: (phi, d, k=1/R)
@@ -117,8 +119,9 @@ namespace BrokenLine {
 		double XX_0; //!< radiation length of the material in cm
 		if(Layer==1) XX_0=16/0.06;
 		else XX_0=16/0.06;
-		XX_0*=1; //1.55 correction made by looking at the pulls
-		return (sqr((13.6/1000)/(1*B*R*sqrt(1+sqr(slope))))*(abs(length)/XX_0)*sqr(1+0.038*log(abs(length)/XX_0)))/3;
+		XX_0*=1;
+		double geometry_factor=0.7; //!< number between 1/3 (uniform material) and 1 (thin scatterer) to be manually tuned
+		return geometry_factor*sqr((13.6/1000)/(1*B*R*sqrt(1+sqr(slope))))*(abs(length)/XX_0)*sqr(1+0.038*log(abs(length)/XX_0));
 	}
 	
 	/*!
@@ -144,7 +147,7 @@ namespace BrokenLine {
 	 \param x0 x coordinate of the translation vector.
 	 \param y0 y coordinate of the translation vector.
 	 */
-	CUDA_HOSTDEV inline void TranslateKarimaki(karimaki_circle_fit& circle, const double& x0, const double& y0) {
+	CUDA_HOSTDEV inline void TranslateKarimaki(karimaki_circle_fit& circle, const double& x0, const double& y0, Matrix3d& Jacob) {
 		double A,U,BB,C,DO,DP,uu,xi,v,mu,lambda,zeta;
 		DP=x0*cos(circle.par(0))+y0*sin(circle.par(0));
 		DO=x0*sin(circle.par(0))-y0*cos(circle.par(0))+circle.par(1);
@@ -159,7 +162,6 @@ namespace BrokenLine {
 		mu=1/(U*(1+U))+circle.par(2)*lambda;
 		zeta=sqr(DO)+sqr(DP);
 		
-		Matrix3d Jacob;
 		Jacob << xi*uu*v, -xi*sqr(circle.par(2))*DP, xi*DP,
 		2*mu*uu*DP, 2*mu*v, mu*zeta-lambda*A,
 		0, 0, 1;
@@ -192,17 +194,17 @@ namespace BrokenLine {
 	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
 	 \param B magnetic field in Gev/cm/c.
 	 
-	 \return
+	 \return see description of PreparedBrokenLineData.
 	 */
-	CUDA_HOSTDEV inline PreparedBrokenLineData PrepareBrokenLineData(const Matrix3xNd& hits,
-																	 const Matrix3Nd& hits_cov,
-																	 const Vector4d& fast_fit,
-																	 const double B) {
+	CUDA_HOSTDEV inline void PrepareBrokenLineData(const Matrix3xNd& hits,
+												   const Matrix3Nd& hits_cov,
+												   const Vector4d& fast_fit,
+												   const double B,
+												   PreparedBrokenLineData & results) {
 		u_int n=hits.cols();
 		u_int i;
 		Vector2d d;
 		Vector2d e;
-		PreparedBrokenLineData results;
 		results.radii=Matrix2xNd::Zero(2,n);
 		results.s=VectorNd::Zero(n);
 		results.S=VectorNd::Zero(n);
@@ -218,6 +220,7 @@ namespace BrokenLine {
 		
 		Matrix2d R=RotationMatrix(slope);
 		
+		// calculate radii and s
 		results.radii=hits.block(0,0,2,n)-fast_fit.head(2)*MatrixXd::Constant(1,n,1);
 		e=-fast_fit(2)*fast_fit.head(2)/fast_fit.head(2).norm();
 		for(i=0;i<n;i++) {
@@ -227,6 +230,7 @@ namespace BrokenLine {
 		}
 		VectorNd z=hits.block(2,0,1,n).transpose();
 		
+		//calculate S and Z
 		Matrix2xNd pointsSZ=Matrix2xNd::Zero(2,n);
 		for(i=0;i<n;i++) {
 			pointsSZ(0,i)=results.s(i);
@@ -236,11 +240,10 @@ namespace BrokenLine {
 		results.S=pointsSZ.block(0,0,1,n).transpose();
 		results.Z=pointsSZ.block(1,0,1,n).transpose();
 		
+		//calculate VarBeta
 		for(i=1;i<n-1;i++) {
 			results.VarBeta(i)=MultScatt(results.S(i+1)-results.S(i),B,fast_fit(2),i+2,slope)+MultScatt(results.S(i)-results.S(i-1),B,fast_fit(2),i+1,slope);
 		}
-		
-		return results;
 	}
 	
 	/*!
@@ -308,73 +311,6 @@ namespace BrokenLine {
 		return result;
 	}
 	
-	/*!
-	 \brief A very fast helix fit: it fits a circle by three points (first, middle
-	 and last point) and a line by two points (first and last). [Recycled for the Broken Line fit!]
-	 
-	 \param hits points to be fitted
-	 
-	 \return result in this form: (X0,Y0,R,tan(theta)).
-	 
-	 \warning points must be passed ordered (from internal layer to external) in
-	 order to maximize accuracy and do not mistake tan(theta) sign.
-	 
-	 \details This fast fit is used as pre-fit which is needed for:\n
-	 - momentum estimation for the computation of the multiple scattering variances;\n
-	 - properly change the coordinate system (rotate the line or define the curvilinear coordinates for the circle) in order to apply the Broken Line fit procedure.
-	 */
-	
-	CUDA_HOSTDEV inline Vector4d Fast_fit(const Matrix3xNd& hits) {
-		Vector4d result;
-		u_int n = hits.cols(); // get the number of hits
-		
-		// CIRCLE FIT
-		// Make segments between middle-to-first(b) and last-to-first(c) hits
-		const Vector2d b = hits.block(0, n / 2, 2, 1) - hits.block(0, 0, 2, 1);
-		const Vector2d c = hits.block(0, n - 1, 2, 1) - hits.block(0, 0, 2, 1);
-
-		// Compute their lengths
-		const double b2 = b.squaredNorm();
-		const double c2 = c.squaredNorm();
-		double X0;
-		double Y0;
-		// The algebra has been verified (MR). The usual approach has been followed:
-		// * use an orthogonal reference frame passing from the first point.
-		// * build the segments (chords)
-		// * build orthogonal lines through mid points
-		// * make a system and solve for X0 and Y0.
-		// * add the initial point
-		if (abs(b.x()) > abs(b.y())) {  //!< in case b.x is 0 (2 hits with same x)
-			const double k = c.x() / b.x();
-			const double div = 2. * (k * b.y() - c.y());
-			// if aligned TO FIX
-			Y0 = (k * b2 - c2) / div;
-			X0 = b2 / (2 * b.x()) - b.y() / b.x() * Y0;
-		} else {
-			const double k = c.y() / b.y();
-			const double div = 2. * (k * b.x() - c.x());
-			// if aligned TO FIX
-			X0 = (k * b2 - c2) / div;
-			Y0 = b2 / (2 * b.y()) - b.x() / b.y() * X0;
-		}
-		
-		result(0) = X0 + hits(0, 0);
-		result(1) = Y0 + hits(1, 0);
-		result(2) = sqrt(sqr(X0) + sqr(Y0));
-		
-		// LINE FIT
-		const Vector2d d = hits.block(0, 0, 2, 1) - result.head(2);
-		const Vector2d e = hits.block(0, n - 1, 2, 1) - result.head(2);
-		// Compute the arc-length between first and last point: L = R * theta = R *  atan (tan (Theta) )
-		const double dr = result(2) * atan2(cross2D(d, e), d.dot(e));
-		// Simple difference in Z between last and first hit
-		const double dz = hits(2, n - 1) - hits(2, 0);
-		
-		result(3) = (dr / dz);
-		
-		return result;
-	}
-	
 	/*!
 	 \brief Performs the Broken Line fit in the curved track case (that is, the fit parameters are the interceptions u and the curvature correction \Delta\kappa).
 	 
@@ -393,24 +329,26 @@ namespace BrokenLine {
 	 -chi2 value of the cost function in the minimum.
 	 */
 	
-	CUDA_HOSTDEV inline karimaki_circle_fit BL_Circle_fit(const Matrix3xNd& hits,
-														  const Matrix3Nd& hits_cov,
-														  const Vector4d& fast_fit,
-														  const double B,
-														  const PreparedBrokenLineData& data) {
+	CUDA_HOSTDEV inline void BL_Circle_fit(const Matrix3xNd& hits,
+										   const Matrix3Nd& hits_cov,
+										   const Vector4d& fast_fit,
+										   const double B,
+										   PreparedBrokenLineData& data,
+										   karimaki_circle_fit & circle_results,
+										   Matrix3d& Jacob,
+										   MatrixNplusONEd& C_U) {
 		u_int n=hits.cols();
 		u_int i;
-		karimaki_circle_fit circle_results;
 		
 		circle_results.q=data.q;
-		Matrix2xNd radii=data.radii;
-		VectorNd s=data.s;
-		VectorNd S=data.S;
-		VectorNd VarBeta=data.VarBeta;
+		Matrix2xNd& radii=data.radii;
+		const VectorNd& s=data.s;
+		const VectorNd& S=data.S;
+		VectorNd& Z=data.Z;
+		VectorNd& VarBeta=data.VarBeta;
 		const double slope=-circle_results.q/fast_fit(3);
 		VarBeta*=1+sqr(slope); // the kink angles are projected!
 		
-		VectorNd Z=VectorNd::Zero(n);
 		for(i=0;i<n;i++) {
 			Z(i)=radii.block(0,i,2,1).norm()-fast_fit(2);
 		}
@@ -418,14 +356,14 @@ namespace BrokenLine {
 		Matrix2d V; // covariance matrix
 		VectorNd w=VectorNd::Zero(n); // weights
 		Matrix2d RR; // rotation matrix point by point
-		double Slope; // slope of the circle point by point
+		//double Slope; // slope of the circle point by point
 		for(i=0;i<n;i++) {
 			V(0,0)=hits_cov(i,i); // I could not find an easy access to sub-matrices in Eigen...
 			V(0,1)=hits_cov(i,i+n);
 			V(1,0)=hits_cov(i+n,i);
 			V(1,1)=hits_cov(i+n,i+n);
-			Slope=-radii(0,i)/radii(1,i);
-			RR=RotationMatrix(Slope);
+			//Slope=-radii(0,i)/radii(1,i);
+			RR=RotationMatrix(-radii(0,i)/radii(1,i));
 			w(i)=1/((RR*V*RR.transpose())(1,1)); // compute the orthogonal weight point by point
 		}
 		
@@ -434,37 +372,66 @@ namespace BrokenLine {
 			r_u(i)=w(i)*Z(i);
 		} r_u(n)=0;
 		
-		MatrixNplusONEd C_U=MatrixNplusONEd::Zero(n+1,n+1);
+		C_U=MatrixNplusONEd::Zero(n+1,n+1);
+		//add the border to the C_u matrix
 		for(i=0;i<n;i++) {
-			if(i>0 && i<n-1) C_U(i,n)+=-(s(i+1)-s(i-1))/(2*VarBeta(i))*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)));
-			if(i>1) C_U(i,n)+=(s(i)-s(i-2))/(2*VarBeta(i-1)*(s(i)-s(i-1)));
-			if(i<n-2) C_U(i,n)+=(s(i+2)-s(i))/(2*VarBeta(i+1)*(s(i+1)-s(i)));
+			if(i>0 && i<n-1) {
+				C_U(i,n)+=-(s(i+1)-s(i-1))/(2*VarBeta(i))*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)));
+				C_U(n,i)=C_U(i,n);
+			}
+			if(i>1) {
+				C_U(i,n)+=(s(i)-s(i-2))/(2*VarBeta(i-1)*(s(i)-s(i-1)));
+				C_U(n,i)=C_U(i,n);
+			}
+			if(i<n-2) {
+				C_U(i,n)+=(s(i+2)-s(i))/(2*VarBeta(i+1)*(s(i+1)-s(i)));
+				C_U(n,i)=C_U(i,n);
+			}
 			
 			if(i>0 && i<n-1) C_U(n,n)+=sqr(s(i+1)-s(i-1))/(4*VarBeta(i));
-		} C_U(n,n)=C_U(n,n)/2;
-		MatrixNplusONEd C_u;
-		C_u=C_U+C_U.transpose();
-		C_u.block(0,0,n,n)=MatrixC_u(w,s,VarBeta);
-		MatrixNplusONEd I=C_u.inverse();
+		}
+		C_U.block(0,0,n,n)=MatrixC_u(w,s,VarBeta);
+		MatrixNplusONEd& I=C_U;
+		I=C_U.inverse();//MatrixNplusONEd I=C_U.inverse();
 		
-		VectorNplusONEd u=I*r_u; // obtain the fitted parameters by solving the linear system
+		VectorNplusONEd& u=r_u;
+		u=I*r_u; // obtain the fitted parameters by solving the linear system
 		
-		// (phi, d_ca, k) in the system in which the first exp. data is the origin
-		double alpha=(s(1)-s(0))/fast_fit(2);
-		circle_results.par << atan2(-radii(0,0),radii(1,0))+circle_results.q*(u(1)-u(0)/cos(alpha))/(2*fast_fit(2)*tan(alpha/2)),
-		-circle_results.q*u(0), circle_results.q*(1/fast_fit(2)+u(n));
+		// compute (phi, d_ca, k) in the system in which the midpoint of the first two corrected hits is the origin...
 		
-		if(circle_results.q==-1) {
-			if(circle_results.par(0)<0) circle_results.par(0)+=3.14159265358979323;
-			else circle_results.par(0)+=-3.14159265358979323;
-		}
+		radii.block(0,0,2,1)/=radii.block(0,0,2,1).norm();
+		radii.block(0,1,2,1)/=radii.block(0,1,2,1).norm();
+		
+		Vector2d d=hits.block(0,0,2,1)+(-Z(0)+u(0))*radii.block(0,0,2,1);
+		Vector2d e=hits.block(0,1,2,1)+(-Z(1)+u(1))*radii.block(0,1,2,1);
+		
+		circle_results.par << atan2((e-d)(1),(e-d)(0)),
+		-circle_results.q*(fast_fit(2)-sqrt(sqr(fast_fit(2))-(e-d).squaredNorm()/4)),
+		circle_results.q*(1/fast_fit(2)+u(n));
+		
+		assert(circle_results.q*circle_results.par(1)<=0);
 		
-		circle_results.cov << (I(1,1)-2*I(0,1)/cos(alpha)+I(0,0)/sqr(cos(alpha)))/sqr(2*fast_fit(2)*tan(alpha/2))+(1+sqr(slope))*MultScatt(S(1)-S(0),B,fast_fit(2),2,slope), (I(0,0)/cos(alpha)-I(0,1))/(2*fast_fit(2)*tan(alpha/2)), (I(1,n)-I(0,n)/cos(alpha))/(2*fast_fit(2)*tan(alpha/2)),
-		(I(0,0)/cos(alpha)-I(0,1))/(2*fast_fit(2)*tan(alpha/2)), I(0,0), -I(0,n),
-		(I(1,n)-I(0,n)/cos(alpha))/(2*fast_fit(2)*tan(alpha/2)), -I(0,n), I(n,n);
+		Vector2d eMinusd=e-d;
+		double tmp1=eMinusd.squaredNorm();
 		
-		// translate to the original xy system
-		TranslateKarimaki(circle_results,hits(0,0),hits(1,0));
+		Jacob << (radii(1,0)*eMinusd(0)-eMinusd(1)*radii(0,0))/tmp1,(radii(1,1)*eMinusd(0)-eMinusd(1)*radii(0,1))/tmp1,0,
+		(circle_results.q/2)*(eMinusd(0)*radii(0,0)+eMinusd(1)*radii(1,0))/sqrt(sqr(2*fast_fit(2))-tmp1),(circle_results.q/2)*(eMinusd(0)*radii(0,1)+eMinusd(1)*radii(1,1))/sqrt(sqr(2*fast_fit(2))-tmp1),0,
+		0,0,circle_results.q;
+		
+		circle_results.cov << I(0,0), I(0,1), I(0,n),
+		I(1,0), I(1,1), I(1,n),
+		I(n,0), I(n,1), I(n,n);
+		
+		circle_results.cov=Jacob*circle_results.cov*Jacob.transpose();
+		
+		//...Translate in the system in which the first corrected hit is the origin, adding the m.s. correction...
+		
+		TranslateKarimaki(circle_results,(e-d)(0)/2,(e-d)(1)/2,Jacob);
+		circle_results.cov(0,0)+=(1+sqr(slope))*MultScatt(S(1)-S(0),B,fast_fit(2),2,slope);
+		
+		//...And translate back to the original system
+		
+		TranslateKarimaki(circle_results,d(0),d(1),Jacob);
 		
 		// compute chi2
 		circle_results.chi2=0;
@@ -474,8 +441,6 @@ namespace BrokenLine {
 		}
 		
 		assert(circle_results.chi2>=0);
-		
-		return circle_results;
 	}
 	
 	/*!
@@ -491,30 +456,30 @@ namespace BrokenLine {
 	 The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
 	 
 	 \return circle_results karimaki_circle_fit:
-	 -par parameter of the line in this form: (phi, d, k); \n
+	 -par parameter of the line in this form: (cot(theta), Zip); \n
 	 -cov covariance matrix of the fitted parameter; \n
 	 -chi2 value of the cost function in the minimum.
 	 */
 	
-	CUDA_HOSTDEV inline line_fit BL_Line_fit(const Matrix3xNd& hits,
-											 const Matrix3Nd& hits_cov,
-											 const Vector4d& fast_fit,
-											 const double B,
-											 const PreparedBrokenLineData& data) {
+	CUDA_HOSTDEV inline void BL_Line_fit(const Matrix3xNd& hits,
+										 const Matrix3Nd& hits_cov,
+										 const Vector4d& fast_fit,
+										 const double B,
+										 const PreparedBrokenLineData& data,
+										 line_fit & line_results) {
 		u_int n=hits.cols();
 		u_int i;
-		line_fit line_results;
 		
-		Matrix2xNd radii=data.radii;
-		VectorNd S=data.S;
-		VectorNd Z=data.Z;
-		VectorNd VarBeta=data.VarBeta;
+		const Matrix2xNd& radii=data.radii;
+		const VectorNd& S=data.S;
+		const VectorNd& Z=data.Z;
+		const VectorNd& VarBeta=data.VarBeta;
 		
 		const double slope=-data.q/fast_fit(3);
 		Matrix2d R=RotationMatrix(slope);
 		
 		Matrix3d V=Matrix3d::Zero(); // covariance matrix XYZ
-		Matrix2x3d JacobXYZtosZ=Matrix2x3d::Zero(); // jacobian for the computation of the error on s (xyz -> sz)
+		Matrix2x3d JacobXYZtosZ=Matrix2x3d::Zero(); // jacobian for computation of the error on s (xyz -> sz)
 		VectorNd w=VectorNd::Zero(n);
 		for(i=0;i<n;i++) {
 			V(0,0)=hits_cov(i,i); // I could not find an easy way to access the sub-matrices in Eigen...
@@ -537,8 +502,7 @@ namespace BrokenLine {
 			r_u(i)=w(i)*Z(i);
 		}
 		
-		MatrixNd C_u=MatrixC_u(w,S,VarBeta);
-		MatrixNd I=C_u.inverse();
+		MatrixNd I=MatrixC_u(w,S,VarBeta).inverse();
 		VectorNd u=I*r_u; // obtain the fitted parameters by solving the linear system
 		
 		// line parameters in the system in which the first hit is the origin and with axis along SZ
@@ -556,12 +520,13 @@ namespace BrokenLine {
 		line_results.cov=Jacob*line_results.cov*Jacob.transpose();
 		
 		// rotate to the original sz system
-		Jacob(0,0)=1/sqr(R(0,0)-line_results.par(0)*R(0,1));
+		double tmp=R(0,0)-line_results.par(0)*R(0,1);
+		Jacob(0,0)=1/sqr(tmp);
 		Jacob(0,1)=0;
-		Jacob(1,0)=line_results.par(1)*R(0,1)/sqr(R(0,0)-line_results.par(0)*R(0,1));
-		Jacob(1,1)=1/(R(0,0)-line_results.par(0)*R(0,1));
-		line_results.par(1)=line_results.par(1)/(R(0,0)-line_results.par(0)*R(0,1));
-		line_results.par(0)=(R(0,1)+line_results.par(0)*R(0,0))/(R(0,0)-line_results.par(0)*R(0,1));
+		Jacob(1,0)=line_results.par(1)*R(0,1)/sqr(tmp);
+		Jacob(1,1)=1/tmp;
+		line_results.par(1)=line_results.par(1)/tmp;
+		line_results.par(0)=(R(0,1)+line_results.par(0)*R(0,0))/tmp;
 		line_results.cov=Jacob*line_results.cov*Jacob.transpose();
 		
 		// compute chi2
@@ -572,8 +537,6 @@ namespace BrokenLine {
 		}
 		
 		assert(line_results.chi2>=0);
-		
-		return line_results;
 	}
 	
 	/*!
@@ -608,7 +571,7 @@ namespace BrokenLine {
 	 
 	 \bug see BL_Circle_fit(), BL_Line_fit() and Fast_fit() bugs.
 	 
-	 \return (phi,Tip,p_t,cotan(theta)),Zip), their covariance matrix and the chi2's of the circle and line fits.
+	 \return (phi,Tip,p_t,cot(theta)),Zip), their covariance matrix and the chi2's of the circle and line fits.
 	 */
 	
 	CUDA_HOSTDEV inline helix_fit Helix_fit(const Matrix3xNd& hits,
@@ -618,13 +581,17 @@ namespace BrokenLine {
 		
 		helix.fast_fit=BL_Fast_fit(hits);
 		
-		const PreparedBrokenLineData data=PrepareBrokenLineData(hits,hits_cov,helix.fast_fit,B);
+		PreparedBrokenLineData data;
+		karimaki_circle_fit circle;
+		line_fit line;
+		Matrix3d Jacob;
+		MatrixNplusONEd C_U;
 		
-		karimaki_circle_fit circle=BL_Circle_fit(hits,hits_cov,helix.fast_fit,B,data);
-		line_fit line=BL_Line_fit(hits,hits_cov,helix.fast_fit,B,data);
+		PrepareBrokenLineData(hits,hits_cov,helix.fast_fit,B,data);
+		BL_Line_fit(hits,hits_cov,helix.fast_fit,B,data,line);
+		BL_Circle_fit(hits,hits_cov,helix.fast_fit,B,data,circle,Jacob,C_U);
 		
 		// the circle fit gives k, but here we want p_t, so let's change the parameter and the covariance matrix
-		Matrix3d Jacob;
 		Jacob << 1,0,0,
 		0,1,0,
 		0,0,-abs(circle.par(2))*B/(sqr(circle.par(2))*circle.par(2));
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
index 2764d7d641ce1..0eed4d7a12faf 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
@@ -6,7 +6,7 @@
 #include <Eigen/Eigenvalues>
 
 #ifndef RFIT_DEBUG
-#define RFIT_DEBUG 1
+#define RFIT_DEBUG 0
 #endif  // RFIT_DEBUG
 
 namespace Rfit
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackFilterByKinematicsProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackFilterByKinematicsProducer.cc
index a226cc6ce71b7..e2bab92b9dbf7 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackFilterByKinematicsProducer.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackFilterByKinematicsProducer.cc
@@ -43,7 +43,7 @@ void PixelTrackFilterByKinematicsProducer::fillDescriptions(edm::ConfigurationDe
 
   desc.add<double>("ptMin", 0.1);
   desc.add<double>("nSigmaInvPtTolerance", 0.0);
-  desc.add<double>("tipMax", 1.0);
+  desc.add<double>("tipMax", 0.1);
   desc.add<double>("nSigmaTipMaxTolerance", 0.0);
   desc.add<double>("chi2", 1000.0);
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
index eadfda8cb6a26..4101dd756ae91 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
@@ -50,11 +50,18 @@ void PixelTrackProducer::produce(edm::Event& ev, const edm::EventSetup& es)
   LogDebug("PixelTrackProducer, produce")<<"event# :"<<ev.id();
 
   TracksWithTTRHs tracks;
+  //std::chrono::time_point<std::chrono::system_clock> start, end;
+  //start = std::chrono::system_clock::now();
+  //std::cout << "qui comincia il tempo" << std::endl;
   if (!runOnGPU_)
     theReconstruction.run(tracks, ev, es);
   else {
     theGPUReconstruction.run(tracks, ev, es);
   }
+  //end = std::chrono::system_clock::now();
+  //std::cout << "qui finisce il tempo" << std::endl;
+  //int elapsed_microseconds = std::chrono::duration_cast<std::chrono::microseconds>(end-start).count();
+  //std::cout << " " << elapsed_microseconds << std::endl;
   edm::ESHandle<TrackerTopology> httopo;
   es.get<TrackerTopologyRcd>().get(httopo);
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py b/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
index 16803a957c928..6ff92b8675127 100644
--- a/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
+++ b/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
@@ -13,6 +13,7 @@
 from RecoTracker.TkSeedingLayers.TTRHBuilderWithoutAngle4PixelTriplets_cfi import *
 from RecoPixelVertexing.PixelTrackFitting.pixelFitterByHelixProjections_cfi import pixelFitterByHelixProjections
 from RecoPixelVertexing.PixelTrackFitting.pixelFitterByRiemannParaboloid_cfi import pixelFitterByRiemannParaboloid
+from RecoPixelVertexing.PixelTrackFitting.pixelFitterByBrokenLine_cfi import pixelFitterByBrokenLine
 from RecoPixelVertexing.PixelTrackFitting.pixelTrackFilterByKinematics_cfi import pixelTrackFilterByKinematics
 from RecoPixelVertexing.PixelTrackFitting.pixelTrackCleanerBySharedHits_cfi import pixelTrackCleanerBySharedHits
 from RecoPixelVertexing.PixelTrackFitting.pixelTracks_cfi import pixelTracks as _pixelTracks
@@ -83,8 +84,16 @@
 # Use Riemann fit and substitute previous Fitter producer with the Riemann one
 from Configuration.ProcessModifiers.riemannFit_cff import riemannFit
 from Configuration.ProcessModifiers.riemannFitGPU_cff import riemannFitGPU
-riemannFit.toModify(pixelTracks, Fitter = "pixelFitterByRiemannParaboloid")
+
+# Comment/uncomment to switch between broken line and Riemann fit (and do the same below)
+riemannFit.toModify(pixelTracks, Fitter = "pixelFitterByBrokenLine")
+#riemannFit.toModify(pixelTracks, Fitter = "pixelFitterByRiemannParaboloid")
+
 riemannFitGPU.toModify(pixelTracks, runOnGPU = True)
 _pixelTracksSequence_riemannFit = pixelTracksSequence.copy()
-_pixelTracksSequence_riemannFit.replace(pixelFitterByHelixProjections, pixelFitterByRiemannParaboloid)
+
+# Comment/uncomment to switch between broken line and Riemann fit (and do the same above)
+_pixelTracksSequence_riemannFit.replace(pixelFitterByHelixProjections, pixelFitterByBrokenLine)
+#_pixelTracksSequence_riemannFit.replace(pixelFitterByHelixProjections, pixelFitterByRiemannParaboloid)
+
 riemannFit.toReplaceWith(pixelTracksSequence, _pixelTracksSequence_riemannFit)
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
index 794088ac40641..2832cfba4e95d 100644
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
@@ -1,3 +1,4 @@
+
 #include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h"
 #include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
 
@@ -26,6 +27,7 @@
 #include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackErrorParam.h"
 
 #include "CommonTools/Utils/interface/DynArray.h"
+#include <chrono>
 
 using namespace std;
 
@@ -56,6 +58,12 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
     isBarrel[i] = recHit->detUnit()->type().isBarrel();
   }
 
+  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
+
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  //std::cout << "qui comincia il tempo" << std::endl;
+
   Matrix<double, 3, Dynamic, 0, 3, max_nop> brokenLineHits(3, nhits);
 
   Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop> brokenLineHits_cov =
@@ -73,8 +81,12 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
     }
   }
 
-  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
   helix_fit fittedTrack = BrokenLine::Helix_fit(brokenLineHits, brokenLineHits_cov, bField);
+  
+  end = std::chrono::system_clock::now();
+  //std::cout << "qui finisce il tempo" << std::endl;
+  int elapsed_nanoseconds = std::chrono::duration_cast<std::chrono::nanoseconds>(end-start).count();
+
   int iCharge = fittedTrack.q;
 
   // parameters are:
@@ -103,8 +115,15 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
 
   float chi2 = (fittedTrack.chi2_line + fittedTrack.chi2_circle);
 
-  //std::cout << valPt << " " << fittedTrack.chi2_line <<	" " << fittedTrack.chi2_circle << " " << chi2 << std::endl;
+  std::cout << valPt << " " << std::asinh(valCotTheta) << " " << fittedTrack.chi2_line << " " << fittedTrack.chi2_circle
+  << " " << elapsed_nanoseconds << std::endl;
 
+  //std::cout << valPt << " " << fittedTrack.chi2_line <<	" " << fittedTrack.chi2_circle << " " << chi2 << std::endl;
+  if (valPt > 4 && valPt < 5 && valCotTheta > 0.4 && valCotTheta < 2 && fittedTrack.chi2_line > 1.2 && fittedTrack.chi2_line < 
+3 && fittedTrack.chi2_circle > 0.6 && fittedTrack.chi2_circle < 2) {
+  // std::cout << brokenLineHits << std::endl;
+  // std::cout << brokenLineHits_cov << std::endl;
+  }
   PixelTrackBuilder builder;
   Measurement1D phi(valPhi, errValPhi);
   Measurement1D tip(valTip, errValTip);
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
index dd1460adaf05a..680acc884d4f6 100644
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
@@ -26,6 +26,7 @@
 #include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackErrorParam.h"
 
 #include "CommonTools/Utils/interface/DynArray.h"
+#include <chrono>
 
 using namespace std;
 
@@ -59,6 +60,12 @@ std::unique_ptr<reco::Track> PixelFitterByRiemannParaboloid::run(
     isBarrel[i] = recHit->detUnit()->type().isBarrel();
   }
 
+  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
+
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  //std::cout << "qui comincia il tempo" << std::endl;
+
   Matrix<double, 3, Dynamic, 0, 3, max_nop> riemannHits(3, nhits);
 
   Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop> riemannHits_cov =
@@ -76,8 +83,13 @@ std::unique_ptr<reco::Track> PixelFitterByRiemannParaboloid::run(
     }
   }
 
-  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
   helix_fit fittedTrack = Rfit::Helix_fit(riemannHits, riemannHits_cov, bField, useErrors_);
+
+  end = std::chrono::system_clock::now();
+  //std::cout << "qui finisce il tempo" << std::endl;
+  int elapsed_nanoseconds = std::chrono::duration_cast<std::chrono::nanoseconds>(end-start).count();
+  std::cout << elapsed_nanoseconds << std::endl;
+
   int iCharge = fittedTrack.q;
 
   // parameters are:
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelTrackFilterByKinematics.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelTrackFilterByKinematics.cc
index d61c284a3e6cf..6cbfefe437140 100644
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelTrackFilterByKinematics.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelTrackFilterByKinematics.cc
@@ -27,8 +27,8 @@ bool PixelTrackFilterByKinematics::operator()(const reco::Track* ptrack, const P
 {
   if (!ptrack) return false;
   auto const & track = *ptrack;
-  if (track.chi2() > theChi2Max) return false;
   if ( (std::abs(track.d0())-theTIPMax) > theNSigmaTipMaxTolerance*track.d0Error()) return false;
+  if (track.chi2() > theChi2Max) return false;
   
   float pt_v = float(track.pt());
   float opt_v = 1.f/pt_v;
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml b/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
index 5c8a3b26a08a9..d6beb57b862b8 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
@@ -14,7 +14,7 @@
 <bin file="testEigenGPU.cu" name="testEigenGPU_t">
   <use name="cuda"/>
   <use name="cuda-api-wrappers"/>
-  <flags CXXFLAGS="-g" CUDA_FLAGS="-g -lineinfo"/>
+  <flags CXXFLAGS="-g"/>
 </bin>
 <bin file="testEigenGPUNoFit.cu" name="testEigenGPUNoFit_t">
   <use name="cuda"/>
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
index f4287a6eb55f2..ee3f6e3c601e9 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
@@ -13,80 +13,126 @@ using namespace Eigen;
 
 __global__
 void kernelFullFit(Rfit::Matrix3xNd * hits,
-    Rfit::Matrix3Nd * hits_cov,
-    double B,
-    bool errors,
-    Rfit::circle_fit * circle_fit_resultsGPU,
-    Rfit::line_fit * line_fit_resultsGPU) {
-  Vector4d fast_fit = Rfit::Fast_fit(*hits);
-
-  u_int n = hits->cols();
-  Rfit::VectorNd rad = (hits->block(0, 0, 2, n).colwise().norm());
-
-  Rfit::Matrix2xNd hits2D_local = (hits->block(0,0,2,n)).eval();
-  Rfit::Matrix2Nd hits_cov2D_local = (hits_cov->block(0, 0, 2 * n, 2 * n)).eval();
-  Rfit::printIt(&hits2D_local, "kernelFullFit - hits2D_local: ");
-  Rfit::printIt(&hits_cov2D_local, "kernelFullFit - hits_cov2D_local: ");
-  /*
-  printf("kernelFullFit - hits address: %p\n", hits);
-  printf("kernelFullFit - hits_cov address: %p\n", hits_cov);
-  printf("kernelFullFit - hits_cov2D address: %p\n", &hits2D_local);
-  printf("kernelFullFit - hits_cov2D_local address: %p\n", &hits_cov2D_local);
-  */
-  /* At some point I gave up and locally construct block on the stack, so that
-     the next invocation to Rfit::Circle_fit works properly. Failing to do so
-     implied basically an empty collection of hits and covariances. That could
-     have been partially fixed if values of the passed in matrices would have
-     been printed on screen since that, maybe, triggered internally the real
-     creations of the blocks. To be understood and compared against the myriad
-     of compilation warnings we have.
-     */
-  (*circle_fit_resultsGPU) =
-    Rfit::Circle_fit(hits->block(0,0,2,n), hits_cov->block(0, 0, 2 * n, 2 * n),
-      fast_fit, rad, B, errors);
-  /*
-  (*circle_fit_resultsGPU) =
-    Rfit::Circle_fit(hits2D_local, hits_cov2D_local,
-      fast_fit, rad, B, errors, scattering);
-   */
-  (*line_fit_resultsGPU) = Rfit::Line_fit(*hits, *hits_cov, *circle_fit_resultsGPU, fast_fit, errors);
+				   Rfit::Matrix3Nd * hits_cov,
+				   double B,
+				   bool errors,
+				   Rfit::circle_fit * circle_fit_resultsGPU,
+				   Rfit::line_fit * line_fit_resultsGPU) {
+	Vector4d fast_fit = Rfit::Fast_fit(*hits);
+	
+	u_int n = hits->cols();
+	Rfit::VectorNd rad = (hits->block(0, 0, 2, n).colwise().norm());
+	
+	Rfit::Matrix2xNd hits2D_local = (hits->block(0,0,2,n)).eval();
+	Rfit::Matrix2Nd hits_cov2D_local = (hits_cov->block(0, 0, 2 * n, 2 * n)).eval();
+	Rfit::printIt(&hits2D_local, "kernelFullFit - hits2D_local: ");
+	Rfit::printIt(&hits_cov2D_local, "kernelFullFit - hits_cov2D_local: ");
+	/*
+	 printf("kernelFullFit - hits address: %p\n", hits);
+	 printf("kernelFullFit - hits_cov address: %p\n", hits_cov);
+	 printf("kernelFullFit - hits_cov2D address: %p\n", &hits2D_local);
+	 printf("kernelFullFit - hits_cov2D_local address: %p\n", &hits_cov2D_local);
+	 */
+	/* At some point I gave up and locally construct block on the stack, so that
+	 the next invocation to Rfit::Circle_fit works properly. Failing to do so
+	 implied basically an empty collection of hits and covariances. That could
+	 have been partially fixed if values of the passed in matrices would have
+	 been printed on screen since that, maybe, triggered internally the real
+	 creations of the blocks. To be understood and compared against the myriad
+	 of compilation warnings we have.
+	 */
+	(*circle_fit_resultsGPU) =
+	Rfit::Circle_fit(hits->block(0,0,2,n), hits_cov->block(0, 0, 2 * n, 2 * n),
+					 fast_fit, rad, B, errors);
+	/*
+	 (*circle_fit_resultsGPU) =
+	 Rfit::Circle_fit(hits2D_local, hits_cov2D_local,
+	 fast_fit, rad, B, errors, scattering);
+	 */
+	(*line_fit_resultsGPU) = Rfit::Line_fit(*hits, *hits_cov, *circle_fit_resultsGPU, fast_fit, errors);
+	
+	return;
+}
 
-  return;
+__global__
+void kernelFullBrokenLineFastFitAndData(BrokenLine::Matrix3xNd * hits,
+										BrokenLine::Matrix3Nd * hits_cov,
+										BrokenLine::PreparedBrokenLineData * data,
+										Vector4d * fast_fit,
+										double B,
+										BrokenLine::helix_fit * helix_fit_resultsGPU,
+										BrokenLine::karimaki_circle_fit * circleGPU,
+										BrokenLine::line_fit * lineGPU,
+										Matrix3d * JacobGPU,
+										BrokenLine::MatrixNplusONEd * C_UGPU) {
+	
+	BrokenLine::helix_fit& helix = (*helix_fit_resultsGPU);
+	
+	helix.fast_fit=BrokenLine::BL_Fast_fit(*hits);
+	
+	BrokenLine::PrepareBrokenLineData(*hits,*hits_cov,helix.fast_fit,B,*data);
 }
 
 __global__
-void kernelFullBrokenLine(BrokenLine::Matrix3xNd * hits,
-BrokenLine::Matrix3Nd * hits_cov,
-BrokenLine::PreparedBrokenLineData * data,
-Vector4d * fast_fit,
-double B,
-BrokenLine::helix_fit * helix_fit_resultsGPU,
-BrokenLine::karimaki_circle_fit * circleGPU,
-BrokenLine::line_fit * lineGPU) {
+void kernelFullBrokenLineLine(BrokenLine::Matrix3xNd * hits,
+							  BrokenLine::Matrix3Nd * hits_cov,
+							  BrokenLine::PreparedBrokenLineData * data,
+							  Vector4d * fast_fit,
+							  double B,
+							  BrokenLine::helix_fit * helix_fit_resultsGPU,
+							  BrokenLine::karimaki_circle_fit * circleGPU,
+							  BrokenLine::line_fit * lineGPU,
+							  Matrix3d * JacobGPU,
+							  BrokenLine::MatrixNplusONEd * C_UGPU) {
+	
+	BrokenLine::helix_fit& helix = (*helix_fit_resultsGPU);
+	BrokenLine::line_fit& line = (*lineGPU);
+	
+	BrokenLine::BL_Line_fit(*hits,*hits_cov,helix.fast_fit,B,*data,line);
+}
 
-	/*hits->resize(3,4);
-	hits_cov->resize(12, 12);*/
+__global__
+void kernelFullBrokenLineCircle(BrokenLine::Matrix3xNd * hits,
+								BrokenLine::Matrix3Nd * hits_cov,
+								BrokenLine::PreparedBrokenLineData * data,
+								Vector4d * fast_fit,
+								double B,
+								BrokenLine::helix_fit * helix_fit_resultsGPU,
+								BrokenLine::karimaki_circle_fit * circleGPU,
+								BrokenLine::line_fit * lineGPU,
+								Matrix3d * JacobGPU,
+								BrokenLine::MatrixNplusONEd * C_UGPU) {
+	
+	BrokenLine::helix_fit& helix = (*helix_fit_resultsGPU);
+	BrokenLine::karimaki_circle_fit& circle = (*circleGPU);
+	
+	BrokenLine::BL_Circle_fit(*hits,*hits_cov,helix.fast_fit,B,*data,circle,*JacobGPU,*C_UGPU);
+}
 
+__global__
+void kernelFullBrokenLineHelix(BrokenLine::Matrix3xNd * hits,
+							   BrokenLine::Matrix3Nd * hits_cov,
+							   BrokenLine::PreparedBrokenLineData * data,
+							   Vector4d * fast_fit,
+							   double B,
+							   BrokenLine::helix_fit * helix_fit_resultsGPU,
+							   BrokenLine::karimaki_circle_fit * circleGPU,
+							   BrokenLine::line_fit * lineGPU,
+							   Matrix3d * JacobGPU,
+							   BrokenLine::MatrixNplusONEd * C_UGPU) {
+	
 	BrokenLine::helix_fit& helix = (*helix_fit_resultsGPU);
 	BrokenLine::karimaki_circle_fit& circle = (*circleGPU);
 	BrokenLine::line_fit& line = (*lineGPU);
-
-	helix.fast_fit=BrokenLine::BL_Fast_fit(*hits);
-
-	//const BrokenLine::PreparedBrokenLineData data=BrokenLine::PrepareBrokenLineData(*hits,*hits_cov,helix.fast_fit,B);
-	(*data) = BrokenLine::PrepareBrokenLineData(*hits,*hits_cov,helix.fast_fit,B);
-
-	circle=BrokenLine::BL_Circle_fit(*hits,*hits_cov,helix.fast_fit,B,*data);
-	line=BrokenLine::BL_Line_fit(*hits,*hits_cov,helix.fast_fit,B,*data);
-
+	
 	// the circle fit gives k, but here we want p_t, so let's change the parameter and the covariance matrix
-	Matrix3d Jacob;
+	Matrix3d& Jacob=(*JacobGPU);
 	Jacob << 1,0,0,
 	0,1,0,
 	0,0,-abs(circle.par(2))*B/(BrokenLine::sqr(circle.par(2))*circle.par(2));
 	circle.par(2)=B/abs(circle.par(2));
 	circle.cov=Jacob*circle.cov*Jacob.transpose();
-
+	
 	helix.par << circle.par, line.par;
 	helix.cov=MatrixXd::Zero(5, 5);
 	helix.cov.block(0,0,3,3)=circle.cov;
@@ -94,291 +140,311 @@ BrokenLine::line_fit * lineGPU) {
 	helix.q=circle.q;
 	helix.chi2_circle=circle.chi2;
 	helix.chi2_line=line.chi2;
-
+	
 	//(*helix_fit_resultsGPU) = BrokenLine::Helix_fit(*hits, *hits_cov, B);
-
-	return;
 }
 
 __global__
 void kernelFastFit(Rfit::Matrix3xNd * hits, Vector4d * results) {
-  (*results) = Rfit::Fast_fit(*hits);
+	(*results) = Rfit::Fast_fit(*hits);
 }
 
 __global__
 void kernelCircleFit(Rfit::Matrix3xNd * hits,
-    Rfit::Matrix3Nd * hits_cov, Vector4d * fast_fit_input, double B,
-    Rfit::circle_fit * circle_fit_resultsGPU) {
-  u_int n = hits->cols();
-  Rfit::VectorNd rad = (hits->block(0, 0, 2, n).colwise().norm());
-
+					 Rfit::Matrix3Nd * hits_cov, Vector4d * fast_fit_input, double B,
+					 Rfit::circle_fit * circle_fit_resultsGPU) {
+	u_int n = hits->cols();
+	Rfit::VectorNd rad = (hits->block(0, 0, 2, n).colwise().norm());
+	
 #if TEST_DEBUG
-  printf("fast_fit_input(0): %f\n", (*fast_fit_input)(0));
-  printf("fast_fit_input(1): %f\n", (*fast_fit_input)(1));
-  printf("fast_fit_input(2): %f\n", (*fast_fit_input)(2));
-  printf("fast_fit_input(3): %f\n", (*fast_fit_input)(3));
-  printf("rad(0,0): %f\n", rad(0,0));
-  printf("rad(1,1): %f\n", rad(1,1));
-  printf("rad(2,2): %f\n", rad(2,2));
-  printf("hits_cov(0,0): %f\n", (*hits_cov)(0,0));
-  printf("hits_cov(1,1): %f\n", (*hits_cov)(1,1));
-  printf("hits_cov(2,2): %f\n", (*hits_cov)(2,2));
-  printf("hits_cov(11,11): %f\n", (*hits_cov)(11,11));
-  printf("B: %f\n", B);
+	printf("fast_fit_input(0): %f\n", (*fast_fit_input)(0));
+	printf("fast_fit_input(1): %f\n", (*fast_fit_input)(1));
+	printf("fast_fit_input(2): %f\n", (*fast_fit_input)(2));
+	printf("fast_fit_input(3): %f\n", (*fast_fit_input)(3));
+	printf("rad(0,0): %f\n", rad(0,0));
+	printf("rad(1,1): %f\n", rad(1,1));
+	printf("rad(2,2): %f\n", rad(2,2));
+	printf("hits_cov(0,0): %f\n", (*hits_cov)(0,0));
+	printf("hits_cov(1,1): %f\n", (*hits_cov)(1,1));
+	printf("hits_cov(2,2): %f\n", (*hits_cov)(2,2));
+	printf("hits_cov(11,11): %f\n", (*hits_cov)(11,11));
+	printf("B: %f\n", B);
 #endif
-  (*circle_fit_resultsGPU) =
-    Rfit::Circle_fit(hits->block(0,0,2,n), hits_cov->block(0, 0, 2 * n, 2 * n),
-      *fast_fit_input, rad, B, false);
+	(*circle_fit_resultsGPU) =
+	Rfit::Circle_fit(hits->block(0,0,2,n), hits_cov->block(0, 0, 2 * n, 2 * n),
+					 *fast_fit_input, rad, B, false);
 }
 
 __global__
 void kernelLineFit(Rfit::Matrix3xNd * hits,
-                   Rfit::Matrix3Nd * hits_cov,
-                   Rfit::circle_fit * circle_fit,
-                   Vector4d * fast_fit,
-                   Rfit::line_fit * line_fit)
+				   Rfit::Matrix3Nd * hits_cov,
+				   Rfit::circle_fit * circle_fit,
+				   Vector4d * fast_fit,
+				   Rfit::line_fit * line_fit)
 {
-  (*line_fit) = Rfit::Line_fit(*hits, *hits_cov, *circle_fit, *fast_fit, true);
+	(*line_fit) = Rfit::Line_fit(*hits, *hits_cov, *circle_fit, *fast_fit, true);
 }
 
 void fillHitsAndHitsCov(Rfit::Matrix3xNd & hits, Rfit::Matrix3Nd & hits_cov) {
-  hits << 1.98645, 4.72598, 7.65632, 11.3151,
-          2.18002, 4.88864, 7.75845, 11.3134,
-          2.46338, 6.99838,  11.808,  17.793;
-  hits_cov(0,0) = 7.14652e-06;
-  hits_cov(1,1) = 2.15789e-06;
-  hits_cov(2,2) = 1.63328e-06;
-  hits_cov(3,3) = 6.27919e-06;
-  hits_cov(4,4) = 6.10348e-06;
-  hits_cov(5,5) = 2.08211e-06;
-  hits_cov(6,6) = 1.61672e-06;
-  hits_cov(7,7) = 6.28081e-06;
-  hits_cov(8,8) = 5.184e-05;
-  hits_cov(9,9) = 1.444e-05;
-  hits_cov(10,10) = 6.25e-06;
-  hits_cov(11,11) = 3.136e-05;
-  hits_cov(0,4) = hits_cov(4,0) = -5.60077e-06;
-  hits_cov(1,5) = hits_cov(5,1) = -1.11936e-06;
-  hits_cov(2,6) = hits_cov(6,2) = -6.24945e-07;
-  hits_cov(3,7) = hits_cov(7,3) = -5.28e-06;
+	hits << 1.98645, 4.72598, 7.65632, 11.3151,
+	2.18002, 4.88864, 7.75845, 11.3134,
+	2.46338, 6.99838,  11.808,  17.793;
+	hits_cov(0,0) = 7.14652e-06;
+	hits_cov(1,1) = 2.15789e-06;
+	hits_cov(2,2) = 1.63328e-06;
+	hits_cov(3,3) = 6.27919e-06;
+	hits_cov(4,4) = 6.10348e-06;
+	hits_cov(5,5) = 2.08211e-06;
+	hits_cov(6,6) = 1.61672e-06;
+	hits_cov(7,7) = 6.28081e-06;
+	hits_cov(8,8) = 5.184e-05;
+	hits_cov(9,9) = 1.444e-05;
+	hits_cov(10,10) = 6.25e-06;
+	hits_cov(11,11) = 3.136e-05;
+	hits_cov(0,4) = hits_cov(4,0) = -5.60077e-06;
+	hits_cov(1,5) = hits_cov(5,1) = -1.11936e-06;
+	hits_cov(2,6) = hits_cov(6,2) = -6.24945e-07;
+	hits_cov(3,7) = hits_cov(7,3) = -5.28e-06;
 }
 
 void fillHitsAndHitsCovBrokenLine(BrokenLine::Matrix3xNd & hits, BrokenLine::Matrix3Nd & hits_cov) {
-hits << 1.98645, 4.72598, 7.65632, 11.3151,
-2.18002, 4.88864, 7.75845, 11.3134,
-2.46338, 6.99838,  11.808,  17.793;
-hits_cov(0,0) = 7.14652e-06;
-hits_cov(1,1) = 2.15789e-06;
-hits_cov(2,2) = 1.63328e-06;
-hits_cov(3,3) = 6.27919e-06;
-hits_cov(4,4) = 6.10348e-06;
-hits_cov(5,5) = 2.08211e-06;
-hits_cov(6,6) = 1.61672e-06;
-hits_cov(7,7) = 6.28081e-06;
-hits_cov(8,8) = 5.184e-05;
-hits_cov(9,9) = 1.444e-05;
-hits_cov(10,10) = 6.25e-06;
-hits_cov(11,11) = 3.136e-05;
-hits_cov(0,4) = hits_cov(4,0) = -5.60077e-06;
-hits_cov(1,5) = hits_cov(5,1) = -1.11936e-06;
-hits_cov(2,6) = hits_cov(6,2) = -6.24945e-07;
-hits_cov(3,7) = hits_cov(7,3) = -5.28e-06;
+	hits << 1.98645, 4.72598, 7.65632, 11.3151,
+	2.18002, 4.88864, 7.75845, 11.3134,
+	2.46338, 6.99838,  11.808,  17.793;
+	hits_cov(0,0) = 7.14652e-06;
+	hits_cov(1,1) = 2.15789e-06;
+	hits_cov(2,2) = 1.63328e-06;
+	hits_cov(3,3) = 6.27919e-06;
+	hits_cov(4,4) = 6.10348e-06;
+	hits_cov(5,5) = 2.08211e-06;
+	hits_cov(6,6) = 1.61672e-06;
+	hits_cov(7,7) = 6.28081e-06;
+	hits_cov(8,8) = 5.184e-05;
+	hits_cov(9,9) = 1.444e-05;
+	hits_cov(10,10) = 6.25e-06;
+	hits_cov(11,11) = 3.136e-05;
+	hits_cov(0,4) = hits_cov(4,0) = -5.60077e-06;
+	hits_cov(1,5) = hits_cov(5,1) = -1.11936e-06;
+	hits_cov(2,6) = hits_cov(6,2) = -6.24945e-07;
+	hits_cov(3,7) = hits_cov(7,3) = -5.28e-06;
 }
 
 void testFit() {
-  constexpr double B = 0.0113921;
-  Rfit::Matrix3xNd hits(3,4);
-  Rfit::Matrix3Nd hits_cov = MatrixXd::Zero(12,12);
-  Rfit::Matrix3xNd * hitsGPU = new Rfit::Matrix3xNd(3,4);
-  Rfit::Matrix3Nd * hits_covGPU = nullptr;
-  Vector4d * fast_fit_resultsGPU = new Vector4d();
-  Vector4d * fast_fit_resultsGPUret = new Vector4d();
-  Rfit::circle_fit * circle_fit_resultsGPU = new Rfit::circle_fit();
-  Rfit::circle_fit * circle_fit_resultsGPUret = new Rfit::circle_fit();
-
-  fillHitsAndHitsCov(hits, hits_cov);
-
-  // FAST_FIT_CPU
-  Vector4d fast_fit_results = Rfit::Fast_fit(hits);
+	constexpr double B = 0.0113921;
+	Rfit::Matrix3xNd hits(3,4);
+	Rfit::Matrix3Nd hits_cov = MatrixXd::Zero(12,12);
+	Rfit::Matrix3xNd * hitsGPU = new Rfit::Matrix3xNd(3,4);
+	Rfit::Matrix3Nd * hits_covGPU = nullptr;
+	Vector4d * fast_fit_resultsGPU = new Vector4d();
+	Vector4d * fast_fit_resultsGPUret = new Vector4d();
+	Rfit::circle_fit * circle_fit_resultsGPU = new Rfit::circle_fit();
+	Rfit::circle_fit * circle_fit_resultsGPUret = new Rfit::circle_fit();
+	
+	fillHitsAndHitsCov(hits, hits_cov);
+	
+	// FAST_FIT_CPU
+	Vector4d fast_fit_results = Rfit::Fast_fit(hits);
 #if TEST_DEBUG
-  std::cout << "Generated hits:\n" << hits << std::endl;
+	std::cout << "Generated hits:\n" << hits << std::endl;
 #endif
-  std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]):\n" << fast_fit_results << std::endl;
-
-  // FAST_FIT GPU
-  cudaMalloc((void**)&hitsGPU, sizeof(Rfit::Matrix3xNd(3,4)));
-  cudaMalloc((void**)&fast_fit_resultsGPU, sizeof(Vector4d));
-  cudaMemcpy(hitsGPU, &hits, sizeof(Rfit::Matrix3xNd(3,4)), cudaMemcpyHostToDevice);
-
-  kernelFastFit<<<1, 1>>>(hitsGPU, fast_fit_resultsGPU);
-  cudaDeviceSynchronize();
-  
-  cudaMemcpy(fast_fit_resultsGPUret, fast_fit_resultsGPU, sizeof(Vector4d), cudaMemcpyDeviceToHost);
-  std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]): GPU\n" << *fast_fit_resultsGPUret << std::endl;
-  assert(isEqualFuzzy(fast_fit_results, (*fast_fit_resultsGPUret)));
-
-  // CIRCLE_FIT CPU
-  u_int n = hits.cols();
-  Rfit::VectorNd rad = (hits.block(0, 0, 2, n).colwise().norm());
-
-  Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n),
-      hits_cov.block(0, 0, 2 * n, 2 * n),
-      fast_fit_results, rad, B, false);
-  std::cout << "Fitted values (CircleFit):\n" << circle_fit_results.par << std::endl;
-
-  // CIRCLE_FIT GPU
-  cudaMalloc((void **)&hits_covGPU, sizeof(Rfit::Matrix3Nd(12,12)));
-  cudaMalloc((void **)&circle_fit_resultsGPU, sizeof(Rfit::circle_fit));
-  cudaMemcpy(hits_covGPU, &hits_cov, sizeof(Rfit::Matrix3Nd(12,12)), cudaMemcpyHostToDevice);
-
-  kernelCircleFit<<<1,1>>>(hitsGPU, hits_covGPU,
-      fast_fit_resultsGPU, B, circle_fit_resultsGPU);
-  cudaDeviceSynchronize();
-
-  cudaMemcpy(circle_fit_resultsGPUret, circle_fit_resultsGPU,
-      sizeof(Rfit::circle_fit), cudaMemcpyDeviceToHost);
-  std::cout << "Fitted values (CircleFit) GPU:\n" << circle_fit_resultsGPUret->par << std::endl;
-  assert(isEqualFuzzy(circle_fit_results.par, circle_fit_resultsGPUret->par));
-
-  // LINE_FIT CPU
-  Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_cov, circle_fit_results, fast_fit_results, true);
-  std::cout << "Fitted values (LineFit):\n" << line_fit_results.par << std::endl;
-
-  // LINE_FIT GPU
-  Rfit::line_fit * line_fit_resultsGPU = nullptr;
-  Rfit::line_fit * line_fit_resultsGPUret = new Rfit::line_fit();
-
-  cudaMalloc((void **)&line_fit_resultsGPU, sizeof(Rfit::line_fit));
-
-  kernelLineFit<<<1,1>>>(hitsGPU, hits_covGPU, circle_fit_resultsGPU, fast_fit_resultsGPU, line_fit_resultsGPU);
-  cudaDeviceSynchronize();
-
-  cudaMemcpy(line_fit_resultsGPUret, line_fit_resultsGPU, sizeof(Rfit::line_fit), cudaMemcpyDeviceToHost);
-  std::cout << "Fitted values (LineFit) GPU:\n" << line_fit_resultsGPUret->par << std::endl;
-  assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par));
+	std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]):\n" << fast_fit_results << std::endl;
+	
+	// FAST_FIT GPU
+	cudaMalloc((void**)&hitsGPU, sizeof(Rfit::Matrix3xNd(3,4)));
+	cudaMalloc((void**)&fast_fit_resultsGPU, sizeof(Vector4d));
+	cudaMemcpy(hitsGPU, &hits, sizeof(Rfit::Matrix3xNd(3,4)), cudaMemcpyHostToDevice);
+	
+	kernelFastFit<<<1, 1>>>(hitsGPU, fast_fit_resultsGPU);
+	cudaDeviceSynchronize();
+	
+	cudaMemcpy(fast_fit_resultsGPUret, fast_fit_resultsGPU, sizeof(Vector4d), cudaMemcpyDeviceToHost);
+	std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]): GPU\n" << *fast_fit_resultsGPUret << std::endl;
+	assert(isEqualFuzzy(fast_fit_results, (*fast_fit_resultsGPUret)));
+	
+	// CIRCLE_FIT CPU
+	u_int n = hits.cols();
+	Rfit::VectorNd rad = (hits.block(0, 0, 2, n).colwise().norm());
+	
+	Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n),
+														   hits_cov.block(0, 0, 2 * n, 2 * n),
+														   fast_fit_results, rad, B, false);
+	std::cout << "Fitted values (CircleFit):\n" << circle_fit_results.par << std::endl;
+	
+	// CIRCLE_FIT GPU
+	cudaMalloc((void **)&hits_covGPU, sizeof(Rfit::Matrix3Nd(12,12)));
+	cudaMalloc((void **)&circle_fit_resultsGPU, sizeof(Rfit::circle_fit));
+	cudaMemcpy(hits_covGPU, &hits_cov, sizeof(Rfit::Matrix3Nd(12,12)), cudaMemcpyHostToDevice);
+	
+	kernelCircleFit<<<1,1>>>(hitsGPU, hits_covGPU,
+							 fast_fit_resultsGPU, B, circle_fit_resultsGPU);
+	cudaDeviceSynchronize();
+	
+	cudaMemcpy(circle_fit_resultsGPUret, circle_fit_resultsGPU,
+			   sizeof(Rfit::circle_fit), cudaMemcpyDeviceToHost);
+	std::cout << "Fitted values (CircleFit) GPU:\n" << circle_fit_resultsGPUret->par << std::endl;
+	assert(isEqualFuzzy(circle_fit_results.par, circle_fit_resultsGPUret->par));
+	
+	// LINE_FIT CPU
+	Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_cov, circle_fit_results, fast_fit_results, true);
+	std::cout << "Fitted values (LineFit):\n" << line_fit_results.par << std::endl;
+	
+	// LINE_FIT GPU
+	Rfit::line_fit * line_fit_resultsGPU = nullptr;
+	Rfit::line_fit * line_fit_resultsGPUret = new Rfit::line_fit();
+	
+	cudaMalloc((void **)&line_fit_resultsGPU, sizeof(Rfit::line_fit));
+	
+	kernelLineFit<<<1,1>>>(hitsGPU, hits_covGPU, circle_fit_resultsGPU, fast_fit_resultsGPU, line_fit_resultsGPU);
+	cudaDeviceSynchronize();
+	
+	cudaMemcpy(line_fit_resultsGPUret, line_fit_resultsGPU, sizeof(Rfit::line_fit), cudaMemcpyDeviceToHost);
+	std::cout << "Fitted values (LineFit) GPU:\n" << line_fit_resultsGPUret->par << std::endl;
+	assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par));
 }
 
 void testFitOneGo(bool errors, double epsilon=1e-6) {
-  constexpr double B = 0.0113921;
-  Rfit::Matrix3xNd hits(3,4);
-  Rfit::Matrix3Nd hits_cov = MatrixXd::Zero(12,12);
-
-  fillHitsAndHitsCov(hits, hits_cov);
-
-  // FAST_FIT_CPU
-  Vector4d fast_fit_results = Rfit::Fast_fit(hits);
-  // CIRCLE_FIT CPU
-  u_int n = hits.cols();
-  Rfit::VectorNd rad = (hits.block(0, 0, 2, n).colwise().norm());
-
-  Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n), 
-      hits_cov.block(0, 0, 2 * n, 2 * n),
-      fast_fit_results, rad, B, errors);
-  // LINE_FIT CPU
-  Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_cov, circle_fit_results,
-      fast_fit_results, errors);
-
-  // FIT GPU
-  std::cout << "GPU FIT" << std::endl;
-  Rfit::Matrix3xNd * hitsGPU = nullptr; // new Rfit::Matrix3xNd(3,4);
-  Rfit::Matrix3Nd * hits_covGPU = nullptr;
-  Rfit::line_fit * line_fit_resultsGPU = nullptr;
-  Rfit::line_fit * line_fit_resultsGPUret = new Rfit::line_fit();
-  Rfit::circle_fit * circle_fit_resultsGPU = nullptr; // new Rfit::circle_fit();
-  Rfit::circle_fit * circle_fit_resultsGPUret = new Rfit::circle_fit();
-
-  cudaCheck(cudaMalloc((void **)&hitsGPU, sizeof(Rfit::Matrix3xNd(3,4))));
-  cudaCheck(cudaMalloc((void **)&hits_covGPU, sizeof(Rfit::Matrix3Nd(12,12))));
-  cudaCheck(cudaMalloc((void **)&line_fit_resultsGPU, sizeof(Rfit::line_fit)));
-  cudaCheck(cudaMalloc((void **)&circle_fit_resultsGPU, sizeof(Rfit::circle_fit)));
-  cudaCheck(cudaMemcpy(hitsGPU, &hits, sizeof(Rfit::Matrix3xNd(3,4)), cudaMemcpyHostToDevice));
-  cudaCheck(cudaMemcpy(hits_covGPU, &hits_cov, sizeof(Rfit::Matrix3Nd(12,12)), cudaMemcpyHostToDevice));
-
-  kernelFullFit<<<1, 1>>>(hitsGPU, hits_covGPU, B, errors,
-      circle_fit_resultsGPU, line_fit_resultsGPU);
-  cudaCheck(cudaDeviceSynchronize());
-
-  cudaCheck(cudaMemcpy(circle_fit_resultsGPUret, circle_fit_resultsGPU, sizeof(Rfit::circle_fit), cudaMemcpyDeviceToHost));
-  cudaCheck(cudaMemcpy(line_fit_resultsGPUret, line_fit_resultsGPU, sizeof(Rfit::line_fit), cudaMemcpyDeviceToHost));
-
-  std::cout << "Fitted values (CircleFit) CPU:\n" << circle_fit_results.par << std::endl;
-  std::cout << "Fitted values (LineFit): CPU\n" << line_fit_results.par << std::endl;
-  std::cout << "Fitted values (CircleFit) GPU:\n" << circle_fit_resultsGPUret->par << std::endl;
-  std::cout << "Fitted values (LineFit): GPU\n" << line_fit_resultsGPUret->par << std::endl;
-  assert(isEqualFuzzy(circle_fit_results.par, circle_fit_resultsGPUret->par, epsilon));
-  assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par, epsilon));
-
-  cudaCheck(cudaFree(hitsGPU));
-  cudaCheck(cudaFree(hits_covGPU));
-  cudaCheck(cudaFree(line_fit_resultsGPU));
-  cudaCheck(cudaFree(circle_fit_resultsGPU));
-  delete line_fit_resultsGPUret;
-  delete circle_fit_resultsGPUret;
-
-  cudaDeviceReset();
+	constexpr double B = 0.0113921;
+	Rfit::Matrix3xNd hits(3,4);
+	Rfit::Matrix3Nd hits_cov = MatrixXd::Zero(12,12);
+	
+	fillHitsAndHitsCov(hits, hits_cov);
+	
+	// FAST_FIT_CPU
+	Vector4d fast_fit_results = Rfit::Fast_fit(hits);
+	// CIRCLE_FIT CPU
+	u_int n = hits.cols();
+	Rfit::VectorNd rad = (hits.block(0, 0, 2, n).colwise().norm());
+	
+	Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n),
+														   hits_cov.block(0, 0, 2 * n, 2 * n),
+														   fast_fit_results, rad, B, errors);
+	// LINE_FIT CPU
+	Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_cov, circle_fit_results,
+													 fast_fit_results, errors);
+	
+	// FIT GPU
+	std::cout << "GPU FIT" << std::endl;
+	Rfit::Matrix3xNd * hitsGPU = nullptr; // new Rfit::Matrix3xNd(3,4);
+	Rfit::Matrix3Nd * hits_covGPU = nullptr;
+	Rfit::line_fit * line_fit_resultsGPU = nullptr;
+	Rfit::line_fit * line_fit_resultsGPUret = new Rfit::line_fit();
+	Rfit::circle_fit * circle_fit_resultsGPU = nullptr; // new Rfit::circle_fit();
+	Rfit::circle_fit * circle_fit_resultsGPUret = new Rfit::circle_fit();
+	
+	cudaCheck(cudaMalloc((void **)&hitsGPU, sizeof(Rfit::Matrix3xNd(3,4))));
+	cudaCheck(cudaMalloc((void **)&hits_covGPU, sizeof(Rfit::Matrix3Nd(12,12))));
+	cudaCheck(cudaMalloc((void **)&line_fit_resultsGPU, sizeof(Rfit::line_fit)));
+	cudaCheck(cudaMalloc((void **)&circle_fit_resultsGPU, sizeof(Rfit::circle_fit)));
+	cudaCheck(cudaMemcpy(hitsGPU, &hits, sizeof(Rfit::Matrix3xNd(3,4)), cudaMemcpyHostToDevice));
+	cudaCheck(cudaMemcpy(hits_covGPU, &hits_cov, sizeof(Rfit::Matrix3Nd(12,12)), cudaMemcpyHostToDevice));
+	
+	kernelFullFit<<<1, 1>>>(hitsGPU, hits_covGPU, B, errors,
+							circle_fit_resultsGPU, line_fit_resultsGPU);
+	cudaCheck(cudaDeviceSynchronize());
+	
+	cudaCheck(cudaMemcpy(circle_fit_resultsGPUret, circle_fit_resultsGPU, sizeof(Rfit::circle_fit), cudaMemcpyDeviceToHost));
+	cudaCheck(cudaMemcpy(line_fit_resultsGPUret, line_fit_resultsGPU, sizeof(Rfit::line_fit), cudaMemcpyDeviceToHost));
+	
+	std::cout << "Fitted values (CircleFit) CPU:\n" << circle_fit_results.par << std::endl;
+	std::cout << "Fitted values (LineFit): CPU\n" << line_fit_results.par << std::endl;
+	std::cout << "Fitted values (CircleFit) GPU:\n" << circle_fit_resultsGPUret->par << std::endl;
+	std::cout << "Fitted values (LineFit): GPU\n" << line_fit_resultsGPUret->par << std::endl;
+	assert(isEqualFuzzy(circle_fit_results.par, circle_fit_resultsGPUret->par, epsilon));
+	assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par, epsilon));
+	
+	cudaCheck(cudaFree(hitsGPU));
+	cudaCheck(cudaFree(hits_covGPU));
+	cudaCheck(cudaFree(line_fit_resultsGPU));
+	cudaCheck(cudaFree(circle_fit_resultsGPU));
+	delete line_fit_resultsGPUret;
+	delete circle_fit_resultsGPUret;
+	
+	cudaDeviceReset();
 }
 
 void testBrokenLineOneGo(double epsilon=1e-6) {
 	constexpr double B = 0.0113921;
 	BrokenLine::Matrix3xNd hits(3,4);
 	BrokenLine::Matrix3Nd hits_cov = MatrixXd::Zero(12,12);
-
+	
 	fillHitsAndHitsCovBrokenLine(hits, hits_cov);
-
+	
 	// HELIX_FIT CPU
 	BrokenLine::helix_fit helix_fit_results = BrokenLine::Helix_fit(hits,hits_cov,B);
-
+	
 	std::cout << "Fitted values (HelixFit) CPU:\n" << helix_fit_results.par << std::endl;
-
+	
 	// FIT GPU
 	std::cout << "GPU FIT" << std::endl;
 	BrokenLine::Matrix3xNd * hitsGPU = nullptr;
 	BrokenLine::Matrix3Nd * hits_covGPU = nullptr;
-BrokenLine::PreparedBrokenLineData * dataGPU = nullptr;
-Vector4d * fast_fitGPU = nullptr;
-BrokenLine::karimaki_circle_fit * circleGPU = nullptr;
-BrokenLine::line_fit * lineGPU = nullptr;
+	BrokenLine::PreparedBrokenLineData * dataGPU = nullptr;
+	Vector4d * fast_fitGPU = nullptr;
+	BrokenLine::karimaki_circle_fit * circleGPU = nullptr;
+	BrokenLine::line_fit * lineGPU = nullptr;
+	Matrix3d * JacobGPU = nullptr;
+	BrokenLine::MatrixNplusONEd * C_UGPU = nullptr;
 	BrokenLine::helix_fit * helix_fit_resultsGPU = nullptr;
 	BrokenLine::helix_fit * helix_fit_resultsGPUret = new BrokenLine::helix_fit();
-
+	
 	cudaCheck(cudaMalloc((void **)&hitsGPU, sizeof(BrokenLine::Matrix3xNd(3,4))));
-    cudaCheck(cudaMalloc((void **)&hits_covGPU, sizeof(BrokenLine::Matrix3Nd(12,12))));
-cudaCheck(cudaMalloc((void **)&dataGPU, sizeof(BrokenLine::PreparedBrokenLineData)));
-cudaCheck(cudaMalloc((void **)&fast_fitGPU, sizeof(Vector4d)));
-cudaCheck(cudaMalloc((void **)&circleGPU, sizeof(BrokenLine::karimaki_circle_fit)));
-cudaCheck(cudaMalloc((void **)&lineGPU, sizeof(BrokenLine::line_fit)));
+	cudaCheck(cudaMalloc((void **)&hits_covGPU, sizeof(BrokenLine::Matrix3Nd(12,12))));
+	cudaCheck(cudaMalloc((void **)&dataGPU, sizeof(BrokenLine::PreparedBrokenLineData)));
+	cudaCheck(cudaMalloc((void **)&fast_fitGPU, sizeof(Vector4d)));
+	cudaCheck(cudaMalloc((void **)&circleGPU, sizeof(BrokenLine::karimaki_circle_fit)));
+	cudaCheck(cudaMalloc((void **)&lineGPU, sizeof(BrokenLine::line_fit)));
+	cudaCheck(cudaMalloc((void **)&JacobGPU, sizeof(Matrix3d)));
+	cudaCheck(cudaMalloc((void **)&C_UGPU, sizeof(BrokenLine::MatrixNplusONEd)));
 	cudaCheck(cudaMalloc((void **)&helix_fit_resultsGPU, sizeof(BrokenLine::helix_fit)));
-
+	
 	cudaCheck(cudaMemcpy(hitsGPU, &hits, sizeof(BrokenLine::Matrix3xNd(3,4)), cudaMemcpyHostToDevice));
 	cudaCheck(cudaMemcpy(hits_covGPU, &hits_cov, sizeof(BrokenLine::Matrix3Nd(12,12)), cudaMemcpyHostToDevice));
-
-	kernelFullBrokenLine<<<1, 1>>>(hitsGPU, hits_covGPU, dataGPU, fast_fitGPU, B, helix_fit_resultsGPU, circleGPU, lineGPU);
+	
+	/*
+	 IMPORTANT: if compiling with "-g -G" you need to increase the stack size (uncomment the following lines)
+	 */
+	//cudaDeviceSetLimit(cudaLimitStackSize, 9080);
+	//cudaCheck(cudaDeviceSynchronize());
+	
+	kernelFullBrokenLineFastFitAndData<<<1, 1>>>(hitsGPU, hits_covGPU, dataGPU, fast_fitGPU, B, helix_fit_resultsGPU, circleGPU, lineGPU, JacobGPU, C_UGPU);
 	cudaCheck(cudaDeviceSynchronize());
-
+	kernelFullBrokenLineLine<<<1, 1>>>(hitsGPU, hits_covGPU, dataGPU, fast_fitGPU, B, helix_fit_resultsGPU, circleGPU, lineGPU, JacobGPU, C_UGPU);
+	cudaCheck(cudaDeviceSynchronize());
+	kernelFullBrokenLineCircle<<<1, 1>>>(hitsGPU, hits_covGPU, dataGPU, fast_fitGPU, B, helix_fit_resultsGPU, circleGPU, lineGPU, JacobGPU, C_UGPU);
+	cudaCheck(cudaDeviceSynchronize());
+	kernelFullBrokenLineHelix<<<1, 1>>>(hitsGPU, hits_covGPU, dataGPU, fast_fitGPU, B, helix_fit_resultsGPU, circleGPU, lineGPU, JacobGPU, C_UGPU);
+	cudaCheck(cudaDeviceSynchronize());
+	
 	cudaCheck(cudaMemcpy(helix_fit_resultsGPUret, helix_fit_resultsGPU, sizeof(BrokenLine::helix_fit), cudaMemcpyDeviceToHost));
-
+	
 	std::cout << "Fitted values (HelixFit) GPU:\n" << helix_fit_resultsGPUret->par << std::endl;
 	assert(isEqualFuzzy(helix_fit_results.par, helix_fit_resultsGPUret->par, epsilon));
-
+	
 	cudaCheck(cudaFree(hitsGPU));
 	cudaCheck(cudaFree(hits_covGPU));
 	cudaCheck(cudaFree(helix_fit_resultsGPU));
+	cudaCheck(cudaFree(dataGPU));
+	cudaCheck(cudaFree(fast_fitGPU));
+	cudaCheck(cudaFree(circleGPU));
+	cudaCheck(cudaFree(lineGPU));
+	cudaCheck(cudaFree(JacobGPU));
+	cudaCheck(cudaFree(C_UGPU));
 	delete helix_fit_resultsGPUret;
-
+	
 	cudaDeviceReset();
 }
 
 int main (int argc, char * argv[]) {
-//  testFit();
-  /*std::cout << "TEST FIT, NO ERRORS" << std::endl;
-  testFitOneGo(false);
-
-  std::cout << "TEST FIT, ERRORS AND SCATTER" << std::endl;
-  testFitOneGo(true, 1e-5);*/
-
+	//  testFit();
+	/*std::cout << "TEST FIT, NO ERRORS" << std::endl;
+	 testFitOneGo(false);
+	 
+	 std::cout << "TEST FIT, ERRORS AND SCATTER" << std::endl;
+	 testFitOneGo(true, 1e-5);*/
+	
 	std::cout << "TEST BROKEN LINE" << std::endl;
 	testBrokenLineOneGo(1e-5);
-
-  return 0;
+	
+	return 0;
 }
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu
index 2112f5f6027a5..9d18d2a3e493f 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu
@@ -4,9 +4,12 @@
 #include <Eigen/Eigenvalues>
 
 #include "test_common.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
 
 using namespace Eigen;
 
+using Matrix5d = Matrix<double, 5, 5>;
+
 __host__ __device__ void eigenValues(Matrix3d * m, Eigen::SelfAdjointEigenSolver<Matrix3d>::RealVectorType * ret) {
 #if TEST_DEBUG
   printf("Matrix(0,0): %f\n", (*m)(0,0));
@@ -31,6 +34,10 @@ __global__ void kernelInverse4x4(Matrix4d * in, Matrix4d * out) {
   (*out) = in->inverse();
 }
 
+__global__ void kernelInverse5x5(Matrix5d * in, Matrix5d * out) {
+	(*out) = in->inverse();
+}
+
 
 template<typename M1, typename M2, typename M3>
 __global__ void kernelMultiply(M1 * J,
@@ -141,6 +148,32 @@ void testInverse4x4() {
   assert(isEqualFuzzy(m_inv, *mCPUret));
 }
 
+void testInverse5x5() {
+	std::cout << "TEST INVERSE 5x5" << std::endl;
+	Matrix5d m = Matrix5d::Random();
+	Matrix5d m_inv = m.inverse();
+	Matrix5d *mGPU = nullptr;
+	Matrix5d *mGPUret = nullptr;
+	Matrix5d *mCPUret = new Matrix5d();
+	
+#if TEST_DEBUG
+	std::cout << "Here is the matrix m:" << std::endl << m << std::endl;
+	std::cout << "Its inverse is:" << std::endl << m.inverse() << std::endl;
+#endif
+	cudaMalloc((void **)&mGPU, sizeof(Matrix5d));
+	cudaMalloc((void **)&mGPUret, sizeof(Matrix5d));
+	cudaMemcpy(mGPU, &m, sizeof(Matrix5d), cudaMemcpyHostToDevice);
+	
+	kernelInverse5x5<<<1,1>>>(mGPU, mGPUret);
+	cudaDeviceSynchronize();
+	
+	cudaMemcpy(mCPUret, mGPUret, sizeof(Matrix5d), cudaMemcpyDeviceToHost);
+#if TEST_DEBUG
+	std::cout << "Its GPU inverse is:" << std::endl << (*mCPUret) << std::endl;
+#endif
+	assert(isEqualFuzzy(m_inv, *mCPUret));
+}
+
 void testEigenvalues() {
   std::cout << "TEST EIGENVALUES" << std::endl;
   Matrix3d m = Matrix3d::Random();
@@ -176,10 +209,14 @@ std::cout << "*************************\n\n" << std::endl;
 
 
 int main (int argc, char * argv[]) {
+	
+	//cudaDeviceSetLimit(cudaLimitStackSize, 8500);
+	//cudaCheck(cudaDeviceSynchronize());
 
   testEigenvalues();
   testInverse3x3();
   testInverse4x4();
+	testInverse5x5();
   testMultiply<1, 2, 2, 1>();
   testMultiply<1, 2, 2, 2>();
   testMultiply<1, 2, 2, 3>();

From d864079004ec56072236028d630a4b6c74e6d0b1 Mon Sep 17 00:00:00 2001
From: Giovanni Maria Tomaselli <gimmytomas@gmail.com>
Date: Wed, 19 Sep 2018 15:08:37 +0200
Subject: [PATCH 05/95] Full implementation of the broken line fit. For the
 moment it needs to use matrices of static dimensions in order to run on the
 GPUs.

---
 .../PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py

diff --git a/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py b/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py
new file mode 100644
index 0000000000000..0f296c0c45f8b
--- /dev/null
+++ b/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py
@@ -0,0 +1,6 @@
+import FWCore.ParameterSet.Config as cms
+
+from RecoPixelVertexing.PixelTrackFitting.pixelFitterByBrokenLineDefault_cfi import pixelFitterByBrokenLineDefault
+
+pixelFitterByBrokenLine = pixelFitterByBrokenLineDefault.clone()
+

From e29cb32509afa5250c98ba102b4a689b556892ed Mon Sep 17 00:00:00 2001
From: Giovanni Maria Tomaselli <gimmytomas@gmail.com>
Date: Thu, 20 Sep 2018 14:03:09 +0200
Subject: [PATCH 06/95] Fixed the requested changes: - deleted the forgotten
 prints and time measurements; - created a new modifier for the broken line
 fit; - switched back from tipMax=1 to tipMax=0.1 (the change will maybe be
 done in another PR); - restored the original order of the cuts on chi2 and
 tip; - deleted the default label to pixelFitterByBrokenLine; - switched from
 CUDA_HOSTDEV to __host__ __device__; - BrokenLine.h now uses
 dinamically-sized-matrices (the advantage in using them over the
 statically-sized ones is that the code would also work with n>4) and, as
 before, the switch can be easily done at the start of the file; - hence, the
 test on GPUs now needs an increase in the stack size (at least 1761 bytes); -
 some doxygen comments in BrokenLine.h have been updated.

---
 .../PixelTrackFitting/interface/BrokenLine.h  | 136 +++++++++---------
 .../PixelFitterByBrokenLineProducer.cc        |   2 +-
 .../PixelTrackFilterByKinematicsProducer.cc   |   2 +-
 .../plugins/PixelTrackProducer.cc             |   7 -
 .../python/PixelTracks_cff.py                 |  17 ++-
 .../src/PixelFitterByBrokenLine.cc            |  17 ---
 .../src/PixelFitterByRiemannParaboloid.cc     |   9 --
 .../src/PixelTrackFilterByKinematics.cc       |   2 +-
 .../PixelTrackFitting/test/testEigenGPU.cu    |   8 +-
 9 files changed, 86 insertions(+), 114 deletions(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
index ebdb13437ee0f..382ff5dec3fec 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -4,13 +4,7 @@
 #include <Eigen/Core>
 #include <Eigen/Cholesky>
 #include <Eigen/Eigenvalues>
-#include <cuda.h>
-
-#ifdef __CUDACC__
-#define CUDA_HOSTDEV __host__ __device__
-#else
-#define CUDA_HOSTDEV
-#endif
+#include <cuda_runtime.h>
 
 namespace BrokenLine {
 	
@@ -20,30 +14,32 @@ namespace BrokenLine {
 	
 	// WARNING: USE STATIC DIMENSIONS ON GPUs. To do so, comment these definitions and uncomment the others following
 	
-	/*using MatrixNd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop, max_nop>;
-	 using MatrixNplusONEd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop + 1, max_nop + 1>;
-	 using Matrix3Nd = Eigen::Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop>;
-	 using Matrix2xNd = Eigen::Matrix<double, 2, Dynamic, 0, 2, max_nop>;
-	 using Matrix3xNd = Eigen::Matrix<double, 3, Dynamic, 0, 3, max_nop>;
-	 using VectorNd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop, 1>;
-	 using VectorNplusONEd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop + 1, 1>;
-	 using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
-	 using Matrix5d = Eigen::Matrix<double, 5, 5>;
-	 using Vector5d = Eigen::Matrix<double, 5, 1>;
-	 using u_int    = unsigned int;*/
-	
-	using MatrixNd = Eigen::Matrix<double, max_nop, max_nop, 0, max_nop, max_nop>;
-	using MatrixNplusONEd = Eigen::Matrix<double, max_nop + 1, max_nop + 1, 0, max_nop + 1, max_nop + 1>;
-	using Matrix3Nd = Eigen::Matrix<double, 3 * max_nop, 3 * max_nop, 0, 3 * max_nop, 3 * max_nop>;
-	using Matrix2xNd = Eigen::Matrix<double, 2, max_nop, 0, 2, max_nop>;
-	using Matrix3xNd = Eigen::Matrix<double, 3, max_nop, 0, 3, max_nop>;
-	using VectorNd = Eigen::Matrix<double, max_nop, 1, 0, max_nop, 1>;
-	using VectorNplusONEd = Eigen::Matrix<double, max_nop + 1, 1, 0, max_nop + 1, 1>;
+	using MatrixNd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop, max_nop>;
+	using MatrixNplusONEd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop + 1, max_nop + 1>;
+	using Matrix3Nd = Eigen::Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop>;
+	using Matrix2xNd = Eigen::Matrix<double, 2, Dynamic, 0, 2, max_nop>;
+	using Matrix3xNd = Eigen::Matrix<double, 3, Dynamic, 0, 3, max_nop>;
+	using VectorNd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop, 1>;
+	using VectorNplusONEd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop + 1, 1>;
 	using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
 	using Matrix5d = Eigen::Matrix<double, 5, 5>;
 	using Vector5d = Eigen::Matrix<double, 5, 1>;
 	using u_int    = unsigned int;
 	
+	/*
+	 using MatrixNd = Eigen::Matrix<double, max_nop, max_nop, 0, max_nop, max_nop>;
+	 using MatrixNplusONEd = Eigen::Matrix<double, max_nop + 1, max_nop + 1, 0, max_nop + 1, max_nop + 1>;
+	 using Matrix3Nd = Eigen::Matrix<double, 3 * max_nop, 3 * max_nop, 0, 3 * max_nop, 3 * max_nop>;
+	 using Matrix2xNd = Eigen::Matrix<double, 2, max_nop, 0, 2, max_nop>;
+	 using Matrix3xNd = Eigen::Matrix<double, 3, max_nop, 0, 3, max_nop>;
+	 using VectorNd = Eigen::Matrix<double, max_nop, 1, 0, max_nop, 1>;
+	 using VectorNplusONEd = Eigen::Matrix<double, max_nop + 1, 1, 0, max_nop + 1, 1>;
+	 using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
+	 using Matrix5d = Eigen::Matrix<double, 5, 5>;
+	 using Vector5d = Eigen::Matrix<double, 5, 1>;
+	 using u_int    = unsigned int;
+	 */
+	
 	struct karimaki_circle_fit {
 		Vector3d par;  //!< Karimäki's parameters: (phi, d, k=1/R)
 		Matrix3d cov;
@@ -97,7 +93,7 @@ namespace BrokenLine {
 	/*!
 	 \brief raise to square.
 	 */
-	CUDA_HOSTDEV inline double sqr(const double a) {
+	__host__ __device__ inline double sqr(const double a) {
 		return a*a;
 	}
 	
@@ -115,7 +111,7 @@ namespace BrokenLine {
 	 
 	 \return the variance of the planar angle ((theta_0)^2 /3).
 	 */
-	CUDA_HOSTDEV inline double MultScatt(const double& length, const double B, const double& R, int Layer, double slope) {
+	__host__ __device__ inline double MultScatt(const double& length, const double B, const double& R, int Layer, double slope) {
 		double XX_0; //!< radiation length of the material in cm
 		if(Layer==1) XX_0=16/0.06;
 		else XX_0=16/0.06;
@@ -131,7 +127,7 @@ namespace BrokenLine {
 	 
 	 \return 2D rotation matrix.
 	 */
-	CUDA_HOSTDEV inline Matrix2d RotationMatrix(const double& slope) {
+	__host__ __device__ inline Matrix2d RotationMatrix(const double& slope) {
 		Matrix2d Rot;
 		Rot(0,0)=1/sqrt(1+sqr(slope));
 		Rot(0,1)=slope*Rot(0,0);
@@ -146,8 +142,9 @@ namespace BrokenLine {
 	 \param circle circle fit in the old coordinate system.
 	 \param x0 x coordinate of the translation vector.
 	 \param y0 y coordinate of the translation vector.
+	 \param Jacob passed by reference in order to save stack.
 	 */
-	CUDA_HOSTDEV inline void TranslateKarimaki(karimaki_circle_fit& circle, const double& x0, const double& y0, Matrix3d& Jacob) {
+	__host__ __device__ inline void TranslateKarimaki(karimaki_circle_fit& circle, const double& x0, const double& y0, Matrix3d& Jacob) {
 		double A,U,BB,C,DO,DP,uu,xi,v,mu,lambda,zeta;
 		DP=x0*cos(circle.par(0))+y0*sin(circle.par(0));
 		DO=x0*sin(circle.par(0))-y0*cos(circle.par(0))+circle.par(1);
@@ -182,7 +179,7 @@ namespace BrokenLine {
 	 \return z component of the cross product.
 	 */
 	
-	CUDA_HOSTDEV inline double cross2D(const Vector2d& a, const Vector2d& b) {
+	__host__ __device__ inline double cross2D(const Vector2d& a, const Vector2d& b) {
 		return a.x()*b.y()-a.y()*b.x();
 	}
 	
@@ -193,14 +190,13 @@ namespace BrokenLine {
 	 \param hits_cov hits covariance matrix.
 	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
 	 \param B magnetic field in Gev/cm/c.
-	 
-	 \return see description of PreparedBrokenLineData.
+	 \param results PreparedBrokenLineData to be filled (see description of PreparedBrokenLineData).
 	 */
-	CUDA_HOSTDEV inline void PrepareBrokenLineData(const Matrix3xNd& hits,
-												   const Matrix3Nd& hits_cov,
-												   const Vector4d& fast_fit,
-												   const double B,
-												   PreparedBrokenLineData & results) {
+	__host__ __device__ inline void PrepareBrokenLineData(const Matrix3xNd& hits,
+														  const Matrix3Nd& hits_cov,
+														  const Vector4d& fast_fit,
+														  const double B,
+														  PreparedBrokenLineData & results) {
 		u_int n=hits.cols();
 		u_int i;
 		Vector2d d;
@@ -253,9 +249,9 @@ namespace BrokenLine {
 	 \param S total distance traveled by the particle from the pre-fitted closest approach.
 	 \param VarBeta kink angles' variance.
 	 
-	 \return
+	 \return the n-by-n matrix of the linear system
 	 */
-	CUDA_HOSTDEV inline MatrixNd MatrixC_u(const VectorNd& w, const VectorNd& S, const VectorNd& VarBeta) {
+	__host__ __device__ inline MatrixNd MatrixC_u(const VectorNd& w, const VectorNd& S, const VectorNd& VarBeta) {
 		u_int n=S.rows();
 		u_int i;
 		
@@ -282,12 +278,14 @@ namespace BrokenLine {
 	/*!
 	 \brief A very fast helix fit.
 	 
+	 \param hits the measured hits.
+	 
 	 \return (X0,Y0,R,tan(theta)).
 	 
 	 \warning sign of theta is (intentionally, for now) mistaken for negative charges.
 	 */
 	
-	CUDA_HOSTDEV inline Vector4d BL_Fast_fit(const Matrix3xNd& hits) {
+	__host__ __device__ inline Vector4d BL_Fast_fit(const Matrix3xNd& hits) {
 		Vector4d result;
 		u_int n=hits.cols();
 		
@@ -318,25 +316,27 @@ namespace BrokenLine {
 	 \param hits_cov hits covariance matrix.
 	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
 	 \param B magnetic field in Gev/cm/c.
+	 \param data PreparedBrokenLineData.
+	 \param circle_results struct to be filled with the results in this form:
+	 -par parameter of the line in this form: (phi, d, k); \n
+	 -cov covariance matrix of the fitted parameter; \n
+	 -chi2 value of the cost function in the minimum.
+	 \param Jacob passed by reference in order to save stack.
+	 \param C_U passed by reference in order to sake stack.
 	 
 	 \details The function implements the steps 2 and 3 of the Broken Line fit with the curvature correction.\n
 	 The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and \Delta\kappa and their covariance matrix.
 	 The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
-	 
-	 \return circle_results karimaki_circle_fit:
-	 -par parameter of the line in this form: (phi, d, k); \n
-	 -cov covariance matrix of the fitted parameter; \n
-	 -chi2 value of the cost function in the minimum.
 	 */
 	
-	CUDA_HOSTDEV inline void BL_Circle_fit(const Matrix3xNd& hits,
-										   const Matrix3Nd& hits_cov,
-										   const Vector4d& fast_fit,
-										   const double B,
-										   PreparedBrokenLineData& data,
-										   karimaki_circle_fit & circle_results,
-										   Matrix3d& Jacob,
-										   MatrixNplusONEd& C_U) {
+	__host__ __device__ inline void BL_Circle_fit(const Matrix3xNd& hits,
+												  const Matrix3Nd& hits_cov,
+												  const Vector4d& fast_fit,
+												  const double B,
+												  PreparedBrokenLineData& data,
+												  karimaki_circle_fit & circle_results,
+												  Matrix3d& Jacob,
+												  MatrixNplusONEd& C_U) {
 		u_int n=hits.cols();
 		u_int i;
 		
@@ -450,23 +450,23 @@ namespace BrokenLine {
 	 \param hits_cov hits covariance matrix.
 	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
 	 \param B magnetic field in Gev/cm/c.
+	 \param data PreparedBrokenLineData.
+	 \param line_results struct to be filled with the results in this form:
+	 -par parameter of the line in this form: (cot(theta), Zip); \n
+	 -cov covariance matrix of the fitted parameter; \n
+	 -chi2 value of the cost function in the minimum.
 	 
 	 \details The function implements the steps 2 and 3 of the Broken Line fit without the curvature correction.\n
 	 The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and their covariance matrix.
 	 The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
-	 
-	 \return circle_results karimaki_circle_fit:
-	 -par parameter of the line in this form: (cot(theta), Zip); \n
-	 -cov covariance matrix of the fitted parameter; \n
-	 -chi2 value of the cost function in the minimum.
 	 */
 	
-	CUDA_HOSTDEV inline void BL_Line_fit(const Matrix3xNd& hits,
-										 const Matrix3Nd& hits_cov,
-										 const Vector4d& fast_fit,
-										 const double B,
-										 const PreparedBrokenLineData& data,
-										 line_fit & line_results) {
+	__host__ __device__ inline void BL_Line_fit(const Matrix3xNd& hits,
+												const Matrix3Nd& hits_cov,
+												const Vector4d& fast_fit,
+												const double B,
+												const PreparedBrokenLineData& data,
+												line_fit & line_results) {
 		u_int n=hits.cols();
 		u_int i;
 		
@@ -574,9 +574,9 @@ namespace BrokenLine {
 	 \return (phi,Tip,p_t,cot(theta)),Zip), their covariance matrix and the chi2's of the circle and line fits.
 	 */
 	
-	CUDA_HOSTDEV inline helix_fit Helix_fit(const Matrix3xNd& hits,
-											const Matrix3Nd& hits_cov,
-											const double B) {
+	__host__ __device__ inline helix_fit Helix_fit(const Matrix3xNd& hits,
+												   const Matrix3Nd& hits_cov,
+												   const double B) {
 		helix_fit helix;
 		
 		helix.fast_fit=BL_Fast_fit(hits);
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
index 977e242c7ee10..6e3df6746be2b 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
@@ -26,7 +26,7 @@ class PixelFitterByBrokenLineProducer: public edm::global::EDProducer<> {
 
   static void fillDescriptions(edm::ConfigurationDescriptions& descriptions) {
     edm::ParameterSetDescription desc;
-    descriptions.add("pixelFitterByBrokenLineDefault", desc);
+    descriptions.add("pixelFitterByBrokenLine", desc);
   }
 
 private:
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackFilterByKinematicsProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackFilterByKinematicsProducer.cc
index e2bab92b9dbf7..a226cc6ce71b7 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackFilterByKinematicsProducer.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackFilterByKinematicsProducer.cc
@@ -43,7 +43,7 @@ void PixelTrackFilterByKinematicsProducer::fillDescriptions(edm::ConfigurationDe
 
   desc.add<double>("ptMin", 0.1);
   desc.add<double>("nSigmaInvPtTolerance", 0.0);
-  desc.add<double>("tipMax", 0.1);
+  desc.add<double>("tipMax", 1.0);
   desc.add<double>("nSigmaTipMaxTolerance", 0.0);
   desc.add<double>("chi2", 1000.0);
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
index 4101dd756ae91..eadfda8cb6a26 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
@@ -50,18 +50,11 @@ void PixelTrackProducer::produce(edm::Event& ev, const edm::EventSetup& es)
   LogDebug("PixelTrackProducer, produce")<<"event# :"<<ev.id();
 
   TracksWithTTRHs tracks;
-  //std::chrono::time_point<std::chrono::system_clock> start, end;
-  //start = std::chrono::system_clock::now();
-  //std::cout << "qui comincia il tempo" << std::endl;
   if (!runOnGPU_)
     theReconstruction.run(tracks, ev, es);
   else {
     theGPUReconstruction.run(tracks, ev, es);
   }
-  //end = std::chrono::system_clock::now();
-  //std::cout << "qui finisce il tempo" << std::endl;
-  //int elapsed_microseconds = std::chrono::duration_cast<std::chrono::microseconds>(end-start).count();
-  //std::cout << " " << elapsed_microseconds << std::endl;
   edm::ESHandle<TrackerTopology> httopo;
   es.get<TrackerTopologyRcd>().get(httopo);
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py b/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
index 6ff92b8675127..c5f0a724ffbff 100644
--- a/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
+++ b/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
@@ -81,19 +81,22 @@
 _pixelTracksSequence_lowPU.replace(pixelTracksHitQuadruplets, pixelTracksHitTriplets)
 trackingLowPU.toReplaceWith(pixelTracksSequence, _pixelTracksSequence_lowPU)
 
-# Use Riemann fit and substitute previous Fitter producer with the Riemann one
+# Use Riemann/BrokenLine fit and substitute previous Fitter producer with the Riemann/BrokenLine one
 from Configuration.ProcessModifiers.riemannFit_cff import riemannFit
 from Configuration.ProcessModifiers.riemannFitGPU_cff import riemannFitGPU
+from Configuration.ProcessModifiers.brokenLine_cff import brokenLine
+from Configuration.ProcessModifiers.brokenLineGPU_cff import brokenLineGPU
 
-# Comment/uncomment to switch between broken line and Riemann fit (and do the same below)
-riemannFit.toModify(pixelTracks, Fitter = "pixelFitterByBrokenLine")
-#riemannFit.toModify(pixelTracks, Fitter = "pixelFitterByRiemannParaboloid")
+riemannFit.toModify(pixelTracks, Fitter = "pixelFitterByRiemannParaboloid")
+brokenLine.toModify(pixelTracks, Fitter = "pixelFitterByBrokenLine")
 
 riemannFitGPU.toModify(pixelTracks, runOnGPU = True)
 _pixelTracksSequence_riemannFit = pixelTracksSequence.copy()
+brokenLineGPU.toModify(pixelTracks, runOnGPU = True)
+_pixelTracksSequence_brokenLine = pixelTracksSequence.copy()
 
-# Comment/uncomment to switch between broken line and Riemann fit (and do the same above)
-_pixelTracksSequence_riemannFit.replace(pixelFitterByHelixProjections, pixelFitterByBrokenLine)
-#_pixelTracksSequence_riemannFit.replace(pixelFitterByHelixProjections, pixelFitterByRiemannParaboloid)
+_pixelTracksSequence_riemannFit.replace(pixelFitterByHelixProjections, pixelFitterByRiemannParaboloid)
+_pixelTracksSequence_brokenLine.replace(pixelFitterByHelixProjections, pixelFitterByBrokenLine)
 
 riemannFit.toReplaceWith(pixelTracksSequence, _pixelTracksSequence_riemannFit)
+brokenLine.toReplaceWith(pixelTracksSequence, _pixelTracksSequence_brokenLine)
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
index 2832cfba4e95d..f6e3a2601ad97 100644
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
@@ -60,10 +60,6 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
 
   float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
 
-  std::chrono::time_point<std::chrono::system_clock> start, end;
-  start = std::chrono::system_clock::now();
-  //std::cout << "qui comincia il tempo" << std::endl;
-
   Matrix<double, 3, Dynamic, 0, 3, max_nop> brokenLineHits(3, nhits);
 
   Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop> brokenLineHits_cov =
@@ -83,10 +79,6 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
 
   helix_fit fittedTrack = BrokenLine::Helix_fit(brokenLineHits, brokenLineHits_cov, bField);
   
-  end = std::chrono::system_clock::now();
-  //std::cout << "qui finisce il tempo" << std::endl;
-  int elapsed_nanoseconds = std::chrono::duration_cast<std::chrono::nanoseconds>(end-start).count();
-
   int iCharge = fittedTrack.q;
 
   // parameters are:
@@ -115,15 +107,6 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
 
   float chi2 = (fittedTrack.chi2_line + fittedTrack.chi2_circle);
 
-  std::cout << valPt << " " << std::asinh(valCotTheta) << " " << fittedTrack.chi2_line << " " << fittedTrack.chi2_circle
-  << " " << elapsed_nanoseconds << std::endl;
-
-  //std::cout << valPt << " " << fittedTrack.chi2_line <<	" " << fittedTrack.chi2_circle << " " << chi2 << std::endl;
-  if (valPt > 4 && valPt < 5 && valCotTheta > 0.4 && valCotTheta < 2 && fittedTrack.chi2_line > 1.2 && fittedTrack.chi2_line < 
-3 && fittedTrack.chi2_circle > 0.6 && fittedTrack.chi2_circle < 2) {
-  // std::cout << brokenLineHits << std::endl;
-  // std::cout << brokenLineHits_cov << std::endl;
-  }
   PixelTrackBuilder builder;
   Measurement1D phi(valPhi, errValPhi);
   Measurement1D tip(valTip, errValTip);
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
index 680acc884d4f6..18cd87ed84635 100644
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
@@ -62,10 +62,6 @@ std::unique_ptr<reco::Track> PixelFitterByRiemannParaboloid::run(
 
   float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
 
-  std::chrono::time_point<std::chrono::system_clock> start, end;
-  start = std::chrono::system_clock::now();
-  //std::cout << "qui comincia il tempo" << std::endl;
-
   Matrix<double, 3, Dynamic, 0, 3, max_nop> riemannHits(3, nhits);
 
   Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop> riemannHits_cov =
@@ -85,11 +81,6 @@ std::unique_ptr<reco::Track> PixelFitterByRiemannParaboloid::run(
 
   helix_fit fittedTrack = Rfit::Helix_fit(riemannHits, riemannHits_cov, bField, useErrors_);
 
-  end = std::chrono::system_clock::now();
-  //std::cout << "qui finisce il tempo" << std::endl;
-  int elapsed_nanoseconds = std::chrono::duration_cast<std::chrono::nanoseconds>(end-start).count();
-  std::cout << elapsed_nanoseconds << std::endl;
-
   int iCharge = fittedTrack.q;
 
   // parameters are:
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelTrackFilterByKinematics.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelTrackFilterByKinematics.cc
index 6cbfefe437140..d61c284a3e6cf 100644
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelTrackFilterByKinematics.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelTrackFilterByKinematics.cc
@@ -27,8 +27,8 @@ bool PixelTrackFilterByKinematics::operator()(const reco::Track* ptrack, const P
 {
   if (!ptrack) return false;
   auto const & track = *ptrack;
-  if ( (std::abs(track.d0())-theTIPMax) > theNSigmaTipMaxTolerance*track.d0Error()) return false;
   if (track.chi2() > theChi2Max) return false;
+  if ( (std::abs(track.d0())-theTIPMax) > theNSigmaTipMaxTolerance*track.d0Error()) return false;
   
   float pt_v = float(track.pt());
   float opt_v = 1.f/pt_v;
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
index ee3f6e3c601e9..7a4f5ba43e6bf 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
@@ -401,10 +401,12 @@ void testBrokenLineOneGo(double epsilon=1e-6) {
 	cudaCheck(cudaMemcpy(hits_covGPU, &hits_cov, sizeof(BrokenLine::Matrix3Nd(12,12)), cudaMemcpyHostToDevice));
 	
 	/*
-	 IMPORTANT: if compiling with "-g -G" you need to increase the stack size (uncomment the following lines)
+	 IMPORTANT: if compiling with "-g -G" or using dinamically-sized matrices, you need to increase the stack size.
+	 You can comment the following two lines when using static-sized-matrices.
+	 1761 bytes is the minimum value that makes it work with dinamically-sized matrices.
 	 */
-	//cudaDeviceSetLimit(cudaLimitStackSize, 9080);
-	//cudaCheck(cudaDeviceSynchronize());
+	cudaDeviceSetLimit(cudaLimitStackSize, 1761);
+	cudaCheck(cudaDeviceSynchronize());
 	
 	kernelFullBrokenLineFastFitAndData<<<1, 1>>>(hitsGPU, hits_covGPU, dataGPU, fast_fitGPU, B, helix_fit_resultsGPU, circleGPU, lineGPU, JacobGPU, C_UGPU);
 	cudaCheck(cudaDeviceSynchronize());

From 7557d8d988931780dc85f247a97bfa5d60f81e22 Mon Sep 17 00:00:00 2001
From: Giovanni Maria Tomaselli <gimmytomas@gmail.com>
Date: Thu, 20 Sep 2018 14:14:59 +0200
Subject: [PATCH 07/95] Just forgot to add the files for the broken line
 modifier.

---
 Configuration/ProcessModifiers/python/brokenLineGPU_cff.py | 5 +++++
 Configuration/ProcessModifiers/python/brokenLine_cff.py    | 5 +++++
 2 files changed, 10 insertions(+)
 create mode 100644 Configuration/ProcessModifiers/python/brokenLineGPU_cff.py
 create mode 100644 Configuration/ProcessModifiers/python/brokenLine_cff.py

diff --git a/Configuration/ProcessModifiers/python/brokenLineGPU_cff.py b/Configuration/ProcessModifiers/python/brokenLineGPU_cff.py
new file mode 100644
index 0000000000000..4c3ef3f111e60
--- /dev/null
+++ b/Configuration/ProcessModifiers/python/brokenLineGPU_cff.py
@@ -0,0 +1,5 @@
+import FWCore.ParameterSet.Config as cms
+
+# This modifier is for replacing the default pixel track "fitting" with the broken line fit on GPU
+
+brokenLineGPU =  cms.Modifier()
diff --git a/Configuration/ProcessModifiers/python/brokenLine_cff.py b/Configuration/ProcessModifiers/python/brokenLine_cff.py
new file mode 100644
index 0000000000000..57f1e4793733e
--- /dev/null
+++ b/Configuration/ProcessModifiers/python/brokenLine_cff.py
@@ -0,0 +1,5 @@
+import FWCore.ParameterSet.Config as cms
+
+# This modifier is for replacing the default pixel track "fitting" with broken line fit
+
+brokenLine =  cms.Modifier()

From 9b81f01e128bad51a65e2598a6c3eac6e3381ebc Mon Sep 17 00:00:00 2001
From: Andrea Bocci <andrea.bocci@cern.ch>
Date: Thu, 20 Sep 2018 14:49:32 +0200
Subject: [PATCH 08/95] DO NOT MERGE - reuse the 10824.7 and .9 workflows to
 test the broken line fit

---
 Configuration/PyReleaseValidation/python/relval_steps.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Configuration/PyReleaseValidation/python/relval_steps.py b/Configuration/PyReleaseValidation/python/relval_steps.py
index 8488b5b0ad952..824d67d96aef3 100644
--- a/Configuration/PyReleaseValidation/python/relval_steps.py
+++ b/Configuration/PyReleaseValidation/python/relval_steps.py
@@ -1737,10 +1737,10 @@ def gen2018HiMix(fragment,howMuch):
     '--eventcontent': 'RECOSIM,DQM',
 }
 step3_riemannFit = {
-    '--procModifiers': 'riemannFit',
+    '--procModifiers': 'brokenLine',
 }
 step3_riemannFitGPU = {
-    '--procModifiers': 'riemannFitGPU',
+    '--procModifiers': 'brokenLineGPU',
 }
 step3_gpu = {
     '--procModifiers': 'gpu',

From 174a6cc38768f41f5479fae0ea0a4c0593f0fca7 Mon Sep 17 00:00:00 2001
From: Andrea Bocci <andrea.bocci@cern.ch>
Date: Thu, 20 Sep 2018 15:49:01 +0200
Subject: [PATCH 09/95] Replaced by the autogenerated cfi file

---
 .../PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py | 6 ------
 1 file changed, 6 deletions(-)
 delete mode 100644 RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py

diff --git a/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py b/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py
deleted file mode 100644
index 0f296c0c45f8b..0000000000000
--- a/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByBrokenLine_cfi.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import FWCore.ParameterSet.Config as cms
-
-from RecoPixelVertexing.PixelTrackFitting.pixelFitterByBrokenLineDefault_cfi import pixelFitterByBrokenLineDefault
-
-pixelFitterByBrokenLine = pixelFitterByBrokenLineDefault.clone()
-

From 7f731ade46459c48487c831fed615ef3c260b36d Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 29 Jan 2019 15:52:25 +0100
Subject: [PATCH 10/95] fix matrix init

---
 .../PixelTrackFitting/test/testEigenGPUNoFit.cu               | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu
index 44c242b1042d0..da1fafd4e9215 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu
@@ -158,7 +158,9 @@ void testInverse4x4() {
 
 void testInverse5x5() {
 	std::cout << "TEST INVERSE 5x5" << std::endl;
-	Matrix5d m = Matrix5d::Random();
+        Matrix5d m;
+        fillMatrix(m);
+        m += m.transpose().eval();
 	Matrix5d m_inv = m.inverse();
 	Matrix5d *mGPU = nullptr;
 	Matrix5d *mGPUret = nullptr;

From e8207e6aeef5bfb41b9c0f191ddd4af3ad7cc883 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 30 Jan 2019 18:06:30 +0100
Subject: [PATCH 11/95] make it working

---
 .../PixelTrackFitting/interface/BrokenLine.h  | 1130 ++++++++---------
 .../PixelTrackFitting/interface/FitResult.h   |    5 +
 .../PixelTrackFitting/interface/RiemannFit.h  |  125 +-
 .../src/PixelFitterByBrokenLine.cc            |   22 +-
 .../PixelTrackFitting/test/testEigenGPU.cu    |  117 +-
 .../PixelTrackFitting/test/testRiemannFit.cpp |   40 +-
 6 files changed, 690 insertions(+), 749 deletions(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
index 382ff5dec3fec..3a8eb31396a89 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -4,611 +4,535 @@
 #include <Eigen/Core>
 #include <Eigen/Cholesky>
 #include <Eigen/Eigenvalues>
-#include <cuda_runtime.h>
+
+#include "FitUtils.h"
+
+
 
 namespace BrokenLine {
-	
-	using namespace Eigen;
-	
-	constexpr unsigned int max_nop = 4;  //!< In order to avoid use of dynamic memory
-	
-	// WARNING: USE STATIC DIMENSIONS ON GPUs. To do so, comment these definitions and uncomment the others following
-	
-	using MatrixNd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop, max_nop>;
-	using MatrixNplusONEd = Eigen::Matrix<double, Dynamic, Dynamic, 0, max_nop + 1, max_nop + 1>;
-	using Matrix3Nd = Eigen::Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop>;
-	using Matrix2xNd = Eigen::Matrix<double, 2, Dynamic, 0, 2, max_nop>;
-	using Matrix3xNd = Eigen::Matrix<double, 3, Dynamic, 0, 3, max_nop>;
-	using VectorNd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop, 1>;
-	using VectorNplusONEd = Eigen::Matrix<double, Dynamic, 1, 0, max_nop + 1, 1>;
-	using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
-	using Matrix5d = Eigen::Matrix<double, 5, 5>;
-	using Vector5d = Eigen::Matrix<double, 5, 1>;
-	using u_int    = unsigned int;
-	
-	/*
-	 using MatrixNd = Eigen::Matrix<double, max_nop, max_nop, 0, max_nop, max_nop>;
-	 using MatrixNplusONEd = Eigen::Matrix<double, max_nop + 1, max_nop + 1, 0, max_nop + 1, max_nop + 1>;
-	 using Matrix3Nd = Eigen::Matrix<double, 3 * max_nop, 3 * max_nop, 0, 3 * max_nop, 3 * max_nop>;
-	 using Matrix2xNd = Eigen::Matrix<double, 2, max_nop, 0, 2, max_nop>;
-	 using Matrix3xNd = Eigen::Matrix<double, 3, max_nop, 0, 3, max_nop>;
-	 using VectorNd = Eigen::Matrix<double, max_nop, 1, 0, max_nop, 1>;
-	 using VectorNplusONEd = Eigen::Matrix<double, max_nop + 1, 1, 0, max_nop + 1, 1>;
-	 using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
-	 using Matrix5d = Eigen::Matrix<double, 5, 5>;
-	 using Vector5d = Eigen::Matrix<double, 5, 1>;
-	 using u_int    = unsigned int;
-	 */
-	
-	struct karimaki_circle_fit {
-		Vector3d par;  //!< Karimäki's parameters: (phi, d, k=1/R)
-		Matrix3d cov;
-		/*!< covariance matrix: \n
-		 |cov(phi,phi)|cov( d ,phi)|cov( k ,phi)| \n
-		 |cov(phi, d )|cov( d , d )|cov( k , d )| \n
-		 |cov(phi, k )|cov( d , k )|cov( k , k )|
-		 */
-		int q;  //!< particle charge
-		double chi2;
-	};
-	
-	struct line_fit {
-		Vector2d par;  //!< parameters: (cotan(theta),Zip)
-		Matrix2d cov;
-		/*!< covariance matrix: \n
-		 |cov(c_t,c_t)|cov(Zip,c_t)| \n
-		 |cov(c_t,Zip)|cov(Zip,Zip)|
-		 */
-		double chi2;
-	};
-	
-	struct helix_fit {
-		Vector5d par;  //!< parameters: (phi,Tip,p_t,cotan(theta)),Zip)
-		Matrix5d cov;
-		/*!< covariance matrix: \n
-		 |(phi,phi)|(Tip,phi)|(p_t,phi)|(c_t,phi)|(Zip,phi)| \n
-		 |(phi,Tip)|(Tip,Tip)|(p_t,Tip)|(c_t,Tip)|(Zip,Tip)| \n
-		 |(phi,p_t)|(Tip,p_t)|(p_t,p_t)|(c_t,p_t)|(Zip,p_t)| \n
-		 |(phi,c_t)|(Tip,c_t)|(p_t,c_t)|(c_t,c_t)|(Zip,c_t)| \n
-		 |(phi,Zip)|(Tip,Zip)|(p_t,Zip)|(c_t,Zip)|(Zip,Zip)|
-		 */
-		double chi2_circle;
-		double chi2_line;
-		Vector4d fast_fit;
-		int q;  //!< particle charge
-	} __attribute__ ((aligned(16)) );
-	
-	/*!
-	 \brief data needed for the Broken Line fit procedure.
-	 */
-	struct PreparedBrokenLineData {
-		int q; //!< particle charge
-		Matrix2xNd radii; //!< xy data in the system in which the pre-fitted center is the origin
-		VectorNd s; //!< total distance traveled in the transverse plane starting from the pre-fitted closest approach
-		VectorNd S; //!< total distance traveled (three-dimensional)
-		VectorNd Z; //!< orthogonal coordinate to the pre-fitted line in the sz plane
-		VectorNd VarBeta; //!< kink angles in the SZ plane
-	};
-	
-	/*!
-	 \brief raise to square.
-	 */
-	__host__ __device__ inline double sqr(const double a) {
-		return a*a;
-	}
-	
-	/*!
-	 \brief Computes the Coulomb multiple scattering variance of the planar angle.
-	 
-	 \param length length of the track in the material.
-	 \param B magnetic field in Gev/cm/c.
-	 \param R radius of curvature (needed to evaluate p).
-	 \param Layer denotes which of the four layers of the detector is the endpoint of the multiple scattered track. For example, if Layer=3, then the particle has just gone through the material between the second and the third layer.
-	 
-	 \todo add another Layer variable to identify also the start point of the track, so if there are missing hits or multiple hits, the part of the detector that the particle has traversed can be exactly identified.
-	 
-	 \warning the formula used here assumes beta=1, and so neglects the dependence of theta_0 on the mass of the particle at fixed momentum.
-	 
-	 \return the variance of the planar angle ((theta_0)^2 /3).
-	 */
-	__host__ __device__ inline double MultScatt(const double& length, const double B, const double& R, int Layer, double slope) {
-		double XX_0; //!< radiation length of the material in cm
-		if(Layer==1) XX_0=16/0.06;
-		else XX_0=16/0.06;
-		XX_0*=1;
-		double geometry_factor=0.7; //!< number between 1/3 (uniform material) and 1 (thin scatterer) to be manually tuned
-		return geometry_factor*sqr((13.6/1000)/(1*B*R*sqrt(1+sqr(slope))))*(abs(length)/XX_0)*sqr(1+0.038*log(abs(length)/XX_0));
-	}
-	
-	/*!
-	 \brief Computes the 2D rotation matrix that transforms the line y=slope*x into the line y=0.
-	 
-	 \param slope tangent of the angle of rotation.
-	 
-	 \return 2D rotation matrix.
-	 */
-	__host__ __device__ inline Matrix2d RotationMatrix(const double& slope) {
-		Matrix2d Rot;
-		Rot(0,0)=1/sqrt(1+sqr(slope));
-		Rot(0,1)=slope*Rot(0,0);
-		Rot(1,0)=-Rot(0,1);
-		Rot(1,1)=Rot(0,0);
-		return Rot;
-	}
-	
-	/*!
-	 \brief Changes the Karimäki parameters (and consequently their covariance matrix) under a translation of the coordinate system, such that the old origin has coordinates (x0,y0) in the new coordinate system. The formulas are taken from Karimäki V., 1990, Effective circle fitting for particle trajectories, Nucl. Instr. and Meth. A305 (1991) 187.
-	 
-	 \param circle circle fit in the old coordinate system.
-	 \param x0 x coordinate of the translation vector.
-	 \param y0 y coordinate of the translation vector.
-	 \param Jacob passed by reference in order to save stack.
-	 */
-	__host__ __device__ inline void TranslateKarimaki(karimaki_circle_fit& circle, const double& x0, const double& y0, Matrix3d& Jacob) {
-		double A,U,BB,C,DO,DP,uu,xi,v,mu,lambda,zeta;
-		DP=x0*cos(circle.par(0))+y0*sin(circle.par(0));
-		DO=x0*sin(circle.par(0))-y0*cos(circle.par(0))+circle.par(1);
-		uu=1+circle.par(2)*circle.par(1);
-		C=-circle.par(2)*y0+uu*cos(circle.par(0));
-		BB=circle.par(2)*x0+uu*sin(circle.par(0));
-		A=2*DO+circle.par(2)*(sqr(DO)+sqr(DP));
-		U=sqrt(1+circle.par(2)*A);
-		xi=1/(sqr(BB)+sqr(C));
-		v=1+circle.par(2)*DO;
-		lambda=(A/2)/(U*sqr(1+U));
-		mu=1/(U*(1+U))+circle.par(2)*lambda;
-		zeta=sqr(DO)+sqr(DP);
-		
-		Jacob << xi*uu*v, -xi*sqr(circle.par(2))*DP, xi*DP,
-		2*mu*uu*DP, 2*mu*v, mu*zeta-lambda*A,
-		0, 0, 1;
-		
-		circle.par(0)=atan2(BB,C);
-		circle.par(1)=A/(1+U);
-		// circle.par(2)=circle.par(2);
-		
-		circle.cov=Jacob*circle.cov*Jacob.transpose();
-	}
-	
-	/*!
-	 \brief Compute cross product of two 2D vector (assuming z component 0), returning the z component of the result.
-	 
-	 \param a first 2D vector in the product.
-	 \param b second 2D vector in the product.
-	 
-	 \return z component of the cross product.
-	 */
-	
-	__host__ __device__ inline double cross2D(const Vector2d& a, const Vector2d& b) {
-		return a.x()*b.y()-a.y()*b.x();
-	}
-	
-	/*!
-	 \brief Computes the data needed for the Broken Line fit procedure that are mainly common for the circle and the line fit.
-	 
-	 \param hits hits coordinates.
-	 \param hits_cov hits covariance matrix.
-	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
-	 \param B magnetic field in Gev/cm/c.
-	 \param results PreparedBrokenLineData to be filled (see description of PreparedBrokenLineData).
-	 */
-	__host__ __device__ inline void PrepareBrokenLineData(const Matrix3xNd& hits,
-														  const Matrix3Nd& hits_cov,
-														  const Vector4d& fast_fit,
-														  const double B,
-														  PreparedBrokenLineData & results) {
-		u_int n=hits.cols();
-		u_int i;
-		Vector2d d;
-		Vector2d e;
-		results.radii=Matrix2xNd::Zero(2,n);
-		results.s=VectorNd::Zero(n);
-		results.S=VectorNd::Zero(n);
-		results.Z=VectorNd::Zero(n);
-		results.VarBeta=VectorNd::Zero(n);
-		
-		results.q=1;
-		d=hits.block(0,1,2,1)-hits.block(0,0,2,1);
-		e=hits.block(0,n-1,2,1)-hits.block(0,n-2,2,1);
-		if(cross2D(d,e)>0) results.q=-1;
-		
-		const double slope=-results.q/fast_fit(3);
-		
-		Matrix2d R=RotationMatrix(slope);
-		
-		// calculate radii and s
-		results.radii=hits.block(0,0,2,n)-fast_fit.head(2)*MatrixXd::Constant(1,n,1);
-		e=-fast_fit(2)*fast_fit.head(2)/fast_fit.head(2).norm();
-		for(i=0;i<n;i++) {
-			d=results.radii.block(0,i,2,1);
-			results.s(i)=results.q*fast_fit(2)*atan2(cross2D(d,e),d.dot(e)); // calculates the arc length
-			//if(results.s(i)<=0);
-		}
-		VectorNd z=hits.block(2,0,1,n).transpose();
-		
-		//calculate S and Z
-		Matrix2xNd pointsSZ=Matrix2xNd::Zero(2,n);
-		for(i=0;i<n;i++) {
-			pointsSZ(0,i)=results.s(i);
-			pointsSZ(1,i)=z(i);
-			pointsSZ.block(0,i,2,1)=R*pointsSZ.block(0,i,2,1);
-		}
-		results.S=pointsSZ.block(0,0,1,n).transpose();
-		results.Z=pointsSZ.block(1,0,1,n).transpose();
-		
-		//calculate VarBeta
-		for(i=1;i<n-1;i++) {
-			results.VarBeta(i)=MultScatt(results.S(i+1)-results.S(i),B,fast_fit(2),i+2,slope)+MultScatt(results.S(i)-results.S(i-1),B,fast_fit(2),i+1,slope);
-		}
-	}
-	
-	/*!
-	 \brief Computes the n-by-n band matrix obtained minimizing the Broken Line's cost function w.r.t u. This is the whole matrix in the case of the line fit and the main n-by-n block in the case of the circle fit.
-	 
-	 \param w weights of the first part of the cost function, the one with the measurements and not the angles (\sum_{i=1}^n w*(y_i-u_i)^2).
-	 \param S total distance traveled by the particle from the pre-fitted closest approach.
-	 \param VarBeta kink angles' variance.
-	 
-	 \return the n-by-n matrix of the linear system
-	 */
-	__host__ __device__ inline MatrixNd MatrixC_u(const VectorNd& w, const VectorNd& S, const VectorNd& VarBeta) {
-		u_int n=S.rows();
-		u_int i;
-		
-		MatrixNd C_U=MatrixNd::Zero(n,n);
-		for(i=0;i<n;i++) {
-			C_U(i,i)=w(i);
-			if(i>1) C_U(i,i)+=1/(VarBeta(i-1)*sqr(S(i)-S(i-1)));
-			if(i>0 && i<n-1) C_U(i,i)+=(1/VarBeta(i))*sqr((S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
-			if(i<n-2) C_U(i,i)+=1/(VarBeta(i+1)*sqr(S(i+1)-S(i)));
-			
-			if(i>0 && i<n-1) C_U(i,i+1)=1/(VarBeta(i)*(S(i+1)-S(i)))*(-(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
-			if(i<n-2) C_U(i,i+1)+=1/(VarBeta(i+1)*(S(i+1)-S(i)))*(-(S(i+2)-S(i))/((S(i+2)-S(i+1))*(S(i+1)-S(i))));
-			
-			if(i<n-2) C_U(i,i+2)=1/(VarBeta(i+1)*(S(i+2)-S(i+1))*(S(i+1)-S(i)));
-			
-			C_U(i,i)=C_U(i,i)/2;
-		}
-		MatrixNd C_u;
-		C_u=C_U+C_U.transpose();
-		
-		return C_u;
-	}
-	
-	/*!
-	 \brief A very fast helix fit.
-	 
-	 \param hits the measured hits.
-	 
-	 \return (X0,Y0,R,tan(theta)).
-	 
-	 \warning sign of theta is (intentionally, for now) mistaken for negative charges.
-	 */
-	
-	__host__ __device__ inline Vector4d BL_Fast_fit(const Matrix3xNd& hits) {
-		Vector4d result;
-		u_int n=hits.cols();
-		
-		const Vector2d a=hits.block(0,n/2,2,1)-hits.block(0,0,2,1);
-		const Vector2d b=hits.block(0,n-1,2,1)-hits.block(0,n/2,2,1);
-		const Vector2d c=hits.block(0,0,2,1)-hits.block(0,n-1,2,1);
-		
-		result(0)=hits(0,0)-(a(1)*c.squaredNorm()+c(1)*a.squaredNorm())/(2*cross2D(c,a));
-		result(1)=hits(1,0)+(a(0)*c.squaredNorm()+c(0)*a.squaredNorm())/(2*cross2D(c,a));
-		// check Wikipedia for these formulas
-		
-		result(2)=(a.norm()*b.norm()*c.norm())/(2*abs(cross2D(b,a)));
-		// Using Math Olympiad's formula R=abc/(4A)
-		
-		const Vector2d d=hits.block(0,0,2,1)-result.head(2);
-		const Vector2d e=hits.block(0,n-1,2,1)-result.head(2);
-		
-		result(3)=result(2)*atan2(cross2D(d, e), d.dot(e))/(hits(2,n-1)-hits(2,0));
-		// ds/dz slope between last and first point
-		
-		return result;
-	}
-	
-	/*!
-	 \brief Performs the Broken Line fit in the curved track case (that is, the fit parameters are the interceptions u and the curvature correction \Delta\kappa).
-	 
-	 \param hits hits coordinates.
-	 \param hits_cov hits covariance matrix.
-	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
-	 \param B magnetic field in Gev/cm/c.
-	 \param data PreparedBrokenLineData.
-	 \param circle_results struct to be filled with the results in this form:
-	 -par parameter of the line in this form: (phi, d, k); \n
-	 -cov covariance matrix of the fitted parameter; \n
-	 -chi2 value of the cost function in the minimum.
-	 \param Jacob passed by reference in order to save stack.
-	 \param C_U passed by reference in order to sake stack.
-	 
-	 \details The function implements the steps 2 and 3 of the Broken Line fit with the curvature correction.\n
-	 The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and \Delta\kappa and their covariance matrix.
-	 The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
-	 */
-	
-	__host__ __device__ inline void BL_Circle_fit(const Matrix3xNd& hits,
-												  const Matrix3Nd& hits_cov,
-												  const Vector4d& fast_fit,
-												  const double B,
-												  PreparedBrokenLineData& data,
-												  karimaki_circle_fit & circle_results,
-												  Matrix3d& Jacob,
-												  MatrixNplusONEd& C_U) {
-		u_int n=hits.cols();
-		u_int i;
-		
-		circle_results.q=data.q;
-		Matrix2xNd& radii=data.radii;
-		const VectorNd& s=data.s;
-		const VectorNd& S=data.S;
-		VectorNd& Z=data.Z;
-		VectorNd& VarBeta=data.VarBeta;
-		const double slope=-circle_results.q/fast_fit(3);
-		VarBeta*=1+sqr(slope); // the kink angles are projected!
-		
-		for(i=0;i<n;i++) {
-			Z(i)=radii.block(0,i,2,1).norm()-fast_fit(2);
-		}
-		
-		Matrix2d V; // covariance matrix
-		VectorNd w=VectorNd::Zero(n); // weights
-		Matrix2d RR; // rotation matrix point by point
-		//double Slope; // slope of the circle point by point
-		for(i=0;i<n;i++) {
-			V(0,0)=hits_cov(i,i); // I could not find an easy access to sub-matrices in Eigen...
-			V(0,1)=hits_cov(i,i+n);
-			V(1,0)=hits_cov(i+n,i);
-			V(1,1)=hits_cov(i+n,i+n);
-			//Slope=-radii(0,i)/radii(1,i);
-			RR=RotationMatrix(-radii(0,i)/radii(1,i));
-			w(i)=1/((RR*V*RR.transpose())(1,1)); // compute the orthogonal weight point by point
-		}
-		
-		VectorNplusONEd r_u=VectorNplusONEd::Zero(n+1);
-		for(i=0;i<n;i++) {
-			r_u(i)=w(i)*Z(i);
-		} r_u(n)=0;
-		
-		C_U=MatrixNplusONEd::Zero(n+1,n+1);
-		//add the border to the C_u matrix
-		for(i=0;i<n;i++) {
-			if(i>0 && i<n-1) {
-				C_U(i,n)+=-(s(i+1)-s(i-1))/(2*VarBeta(i))*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)));
-				C_U(n,i)=C_U(i,n);
-			}
-			if(i>1) {
-				C_U(i,n)+=(s(i)-s(i-2))/(2*VarBeta(i-1)*(s(i)-s(i-1)));
-				C_U(n,i)=C_U(i,n);
-			}
-			if(i<n-2) {
-				C_U(i,n)+=(s(i+2)-s(i))/(2*VarBeta(i+1)*(s(i+1)-s(i)));
-				C_U(n,i)=C_U(i,n);
-			}
-			
-			if(i>0 && i<n-1) C_U(n,n)+=sqr(s(i+1)-s(i-1))/(4*VarBeta(i));
-		}
-		C_U.block(0,0,n,n)=MatrixC_u(w,s,VarBeta);
-		MatrixNplusONEd& I=C_U;
-		I=C_U.inverse();//MatrixNplusONEd I=C_U.inverse();
-		
-		VectorNplusONEd& u=r_u;
-		u=I*r_u; // obtain the fitted parameters by solving the linear system
-		
-		// compute (phi, d_ca, k) in the system in which the midpoint of the first two corrected hits is the origin...
-		
-		radii.block(0,0,2,1)/=radii.block(0,0,2,1).norm();
-		radii.block(0,1,2,1)/=radii.block(0,1,2,1).norm();
-		
-		Vector2d d=hits.block(0,0,2,1)+(-Z(0)+u(0))*radii.block(0,0,2,1);
-		Vector2d e=hits.block(0,1,2,1)+(-Z(1)+u(1))*radii.block(0,1,2,1);
-		
-		circle_results.par << atan2((e-d)(1),(e-d)(0)),
-		-circle_results.q*(fast_fit(2)-sqrt(sqr(fast_fit(2))-(e-d).squaredNorm()/4)),
-		circle_results.q*(1/fast_fit(2)+u(n));
-		
-		assert(circle_results.q*circle_results.par(1)<=0);
-		
-		Vector2d eMinusd=e-d;
-		double tmp1=eMinusd.squaredNorm();
-		
-		Jacob << (radii(1,0)*eMinusd(0)-eMinusd(1)*radii(0,0))/tmp1,(radii(1,1)*eMinusd(0)-eMinusd(1)*radii(0,1))/tmp1,0,
-		(circle_results.q/2)*(eMinusd(0)*radii(0,0)+eMinusd(1)*radii(1,0))/sqrt(sqr(2*fast_fit(2))-tmp1),(circle_results.q/2)*(eMinusd(0)*radii(0,1)+eMinusd(1)*radii(1,1))/sqrt(sqr(2*fast_fit(2))-tmp1),0,
-		0,0,circle_results.q;
-		
-		circle_results.cov << I(0,0), I(0,1), I(0,n),
-		I(1,0), I(1,1), I(1,n),
-		I(n,0), I(n,1), I(n,n);
-		
-		circle_results.cov=Jacob*circle_results.cov*Jacob.transpose();
-		
-		//...Translate in the system in which the first corrected hit is the origin, adding the m.s. correction...
-		
-		TranslateKarimaki(circle_results,(e-d)(0)/2,(e-d)(1)/2,Jacob);
-		circle_results.cov(0,0)+=(1+sqr(slope))*MultScatt(S(1)-S(0),B,fast_fit(2),2,slope);
-		
-		//...And translate back to the original system
-		
-		TranslateKarimaki(circle_results,d(0),d(1),Jacob);
-		
-		// compute chi2
-		circle_results.chi2=0;
-		for(i=0;i<n;i++) {
-			circle_results.chi2+=w(i)*sqr(Z(i)-u(i));
-			if(i>0 && i<n-1) circle_results.chi2+=sqr(u(i-1)/(s(i)-s(i-1))-u(i)*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)))+u(i+1)/(s(i+1)-s(i))+(s(i+1)-s(i-1))*u(n)/2)/VarBeta(i);
-		}
-		
-		assert(circle_results.chi2>=0);
-	}
-	
-	/*!
-	 \brief Performs the Broken Line fit in the straight track case (that is, the fit parameters are only the interceptions u).
-	 
-	 \param hits hits coordinates.
-	 \param hits_cov hits covariance matrix.
-	 \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
-	 \param B magnetic field in Gev/cm/c.
-	 \param data PreparedBrokenLineData.
-	 \param line_results struct to be filled with the results in this form:
-	 -par parameter of the line in this form: (cot(theta), Zip); \n
-	 -cov covariance matrix of the fitted parameter; \n
-	 -chi2 value of the cost function in the minimum.
-	 
-	 \details The function implements the steps 2 and 3 of the Broken Line fit without the curvature correction.\n
-	 The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and their covariance matrix.
-	 The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
-	 */
-	
-	__host__ __device__ inline void BL_Line_fit(const Matrix3xNd& hits,
-												const Matrix3Nd& hits_cov,
-												const Vector4d& fast_fit,
-												const double B,
-												const PreparedBrokenLineData& data,
-												line_fit & line_results) {
-		u_int n=hits.cols();
-		u_int i;
-		
-		const Matrix2xNd& radii=data.radii;
-		const VectorNd& S=data.S;
-		const VectorNd& Z=data.Z;
-		const VectorNd& VarBeta=data.VarBeta;
-		
-		const double slope=-data.q/fast_fit(3);
-		Matrix2d R=RotationMatrix(slope);
-		
-		Matrix3d V=Matrix3d::Zero(); // covariance matrix XYZ
-		Matrix2x3d JacobXYZtosZ=Matrix2x3d::Zero(); // jacobian for computation of the error on s (xyz -> sz)
-		VectorNd w=VectorNd::Zero(n);
-		for(i=0;i<n;i++) {
-			V(0,0)=hits_cov(i,i); // I could not find an easy way to access the sub-matrices in Eigen...
-			V(0,1)=hits_cov(i,i+n);
-			V(0,2)=hits_cov(i,i+2*n);
-			V(1,0)=hits_cov(i+n,i);
-			V(1,1)=hits_cov(i+n,i+n);
-			V(1,2)=hits_cov(i+n,i+2*n);
-			V(2,0)=hits_cov(i+2*n,i);
-			V(2,1)=hits_cov(i+2*n,i+n);
-			V(2,2)=hits_cov(i+2*n,i+2*n);
-			JacobXYZtosZ(0,0)=radii(1,i)/radii.block(0,i,2,1).norm();
-			JacobXYZtosZ(0,1)=-radii(0,i)/radii.block(0,i,2,1).norm();
-			JacobXYZtosZ(1,2)=1;
-			w(i)=1/((R*JacobXYZtosZ*V*JacobXYZtosZ.transpose()*R.transpose())(1,1)); // compute the orthogonal weight point by point
-		}
-		
-		VectorNd r_u=VectorNd::Zero(n);
-		for(i=0;i<n;i++) {
-			r_u(i)=w(i)*Z(i);
-		}
-		
-		MatrixNd I=MatrixC_u(w,S,VarBeta).inverse();
-		VectorNd u=I*r_u; // obtain the fitted parameters by solving the linear system
-		
-		// line parameters in the system in which the first hit is the origin and with axis along SZ
-		line_results.par << (u(1)-u(0))/(S(1)-S(0)), u(0);
-		line_results.cov << (I(0,0)-2*I(0,1)+I(1,1))/sqr(S(1)-S(0))+MultScatt(S(1)-S(0),B,fast_fit(2),2,slope), (I(0,1)-I(0,0))/(S(1)-S(0)),
-		(I(0,1)-I(0,0))/(S(1)-S(0)), I(0,0);
-		
-		// translate to the original SZ system
-		Matrix2d Jacob;
-		Jacob(0,0)=1;
-		Jacob(0,1)=0;
-		Jacob(1,0)=-S(0);
-		Jacob(1,1)=1;
-		line_results.par(1)+=-line_results.par(0)*S(0);
-		line_results.cov=Jacob*line_results.cov*Jacob.transpose();
-		
-		// rotate to the original sz system
-		double tmp=R(0,0)-line_results.par(0)*R(0,1);
-		Jacob(0,0)=1/sqr(tmp);
-		Jacob(0,1)=0;
-		Jacob(1,0)=line_results.par(1)*R(0,1)/sqr(tmp);
-		Jacob(1,1)=1/tmp;
-		line_results.par(1)=line_results.par(1)/tmp;
-		line_results.par(0)=(R(0,1)+line_results.par(0)*R(0,0))/tmp;
-		line_results.cov=Jacob*line_results.cov*Jacob.transpose();
-		
-		// compute chi2
-		line_results.chi2=0;
-		for(i=0;i<n;i++) {
-			line_results.chi2+=w(i)*sqr(Z(i)-u(i));
-			if(i>0 && i<n-1) line_results.chi2+=sqr(u(i-1)/(S(i)-S(i-1))-u(i)*(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1)))+u(i+1)/(S(i+1)-S(i)))/VarBeta(i);
-		}
-		
-		assert(line_results.chi2>=0);
-	}
-	
-	/*!
-	 \brief Helix fit by three step:
-	 -fast pre-fit (see Fast_fit() for further info); \n
-	 -circle fit of the hits projected in the transverse plane by Broken Line algorithm (see BL_Circle_fit() for further info); \n
-	 -line fit of the hits projected on the (pre-fitted) cilinder surface by Broken Line algorithm (see BL_Line_fit() for further info); \n
-	 Points must be passed ordered (from inner to outer layer).
-	 
-	 \param hits Matrix3xNd hits coordinates in this form: \n
-	 |x1|x2|x3|...|xn| \n
-	 |y1|y2|y3|...|yn| \n
-	 |z1|z2|z3|...|zn|
-	 \param hits_cov Matrix3Nd covariance matrix in this form (()->cov()): \n
-	 |(x1,x1)|(x2,x1)|(x3,x1)|(x4,x1)|.|(y1,x1)|(y2,x1)|(y3,x1)|(y4,x1)|.|(z1,x1)|(z2,x1)|(z3,x1)|(z4,x1)| \n
-	 |(x1,x2)|(x2,x2)|(x3,x2)|(x4,x2)|.|(y1,x2)|(y2,x2)|(y3,x2)|(y4,x2)|.|(z1,x2)|(z2,x2)|(z3,x2)|(z4,x2)| \n
-	 |(x1,x3)|(x2,x3)|(x3,x3)|(x4,x3)|.|(y1,x3)|(y2,x3)|(y3,x3)|(y4,x3)|.|(z1,x3)|(z2,x3)|(z3,x3)|(z4,x3)| \n
-	 |(x1,x4)|(x2,x4)|(x3,x4)|(x4,x4)|.|(y1,x4)|(y2,x4)|(y3,x4)|(y4,x4)|.|(z1,x4)|(z2,x4)|(z3,x4)|(z4,x4)| \n
-	 .       .       .       .       . .       .       .       .       . .       .       .       .       . \n
-	 |(x1,y1)|(x2,y1)|(x3,y1)|(x4,y1)|.|(y1,y1)|(y2,y1)|(y3,x1)|(y4,y1)|.|(z1,y1)|(z2,y1)|(z3,y1)|(z4,y1)| \n
-	 |(x1,y2)|(x2,y2)|(x3,y2)|(x4,y2)|.|(y1,y2)|(y2,y2)|(y3,x2)|(y4,y2)|.|(z1,y2)|(z2,y2)|(z3,y2)|(z4,y2)| \n
-	 |(x1,y3)|(x2,y3)|(x3,y3)|(x4,y3)|.|(y1,y3)|(y2,y3)|(y3,x3)|(y4,y3)|.|(z1,y3)|(z2,y3)|(z3,y3)|(z4,y3)| \n
-	 |(x1,y4)|(x2,y4)|(x3,y4)|(x4,y4)|.|(y1,y4)|(y2,y4)|(y3,x4)|(y4,y4)|.|(z1,y4)|(z2,y4)|(z3,y4)|(z4,y4)| \n
-	 .       .       .    .          . .       .       .       .       . .       .       .       .       . \n
-	 |(x1,z1)|(x2,z1)|(x3,z1)|(x4,z1)|.|(y1,z1)|(y2,z1)|(y3,z1)|(y4,z1)|.|(z1,z1)|(z2,z1)|(z3,z1)|(z4,z1)| \n
-	 |(x1,z2)|(x2,z2)|(x3,z2)|(x4,z2)|.|(y1,z2)|(y2,z2)|(y3,z2)|(y4,z2)|.|(z1,z2)|(z2,z2)|(z3,z2)|(z4,z2)| \n
-	 |(x1,z3)|(x2,z3)|(x3,z3)|(x4,z3)|.|(y1,z3)|(y2,z3)|(y3,z3)|(y4,z3)|.|(z1,z3)|(z2,z3)|(z3,z3)|(z4,z3)| \n
-	 |(x1,z4)|(x2,z4)|(x3,z4)|(x4,z4)|.|(y1,z4)|(y2,z4)|(y3,z4)|(y4,z4)|.|(z1,z4)|(z2,z4)|(z3,z4)|(z4,z4)|
-	 \param B magnetic field in the center of the detector in Gev/cm/c, in order to perform the p_t calculation.
-	 
-	 \warning see BL_Circle_fit(), BL_Line_fit() and Fast_fit() warnings.
-	 
-	 \bug see BL_Circle_fit(), BL_Line_fit() and Fast_fit() bugs.
-	 
-	 \return (phi,Tip,p_t,cot(theta)),Zip), their covariance matrix and the chi2's of the circle and line fits.
-	 */
-	
-	__host__ __device__ inline helix_fit Helix_fit(const Matrix3xNd& hits,
-												   const Matrix3Nd& hits_cov,
-												   const double B) {
-		helix_fit helix;
-		
-		helix.fast_fit=BL_Fast_fit(hits);
-		
-		PreparedBrokenLineData data;
-		karimaki_circle_fit circle;
-		line_fit line;
-		Matrix3d Jacob;
-		MatrixNplusONEd C_U;
-		
-		PrepareBrokenLineData(hits,hits_cov,helix.fast_fit,B,data);
-		BL_Line_fit(hits,hits_cov,helix.fast_fit,B,data,line);
-		BL_Circle_fit(hits,hits_cov,helix.fast_fit,B,data,circle,Jacob,C_U);
-		
-		// the circle fit gives k, but here we want p_t, so let's change the parameter and the covariance matrix
-		Jacob << 1,0,0,
-		0,1,0,
-		0,0,-abs(circle.par(2))*B/(sqr(circle.par(2))*circle.par(2));
-		circle.par(2)=B/abs(circle.par(2));
-		circle.cov=Jacob*circle.cov*Jacob.transpose();
-		
-		helix.par << circle.par, line.par;
-		helix.cov=MatrixXd::Zero(5, 5);
-		helix.cov.block(0,0,3,3)=circle.cov;
-		helix.cov.block(3,3,2,2)=line.cov;
-		helix.q=circle.q;
-		helix.chi2_circle=circle.chi2;
-		helix.chi2_line=line.chi2;
-		
-		return helix;
-	}
-	
+  
+  using namespace Rfit;
+
+  //!< Karimäki's parameters: (phi, d, k=1/R)
+  /*!< covariance matrix: \n
+    |cov(phi,phi)|cov( d ,phi)|cov( k ,phi)| \n
+    |cov(phi, d )|cov( d , d )|cov( k , d )| \n
+    |cov(phi, k )|cov( d , k )|cov( k , k )|
+  */
+  using karimaki_circle_fit = Rfit::circle_fit;
+  
+  
+  /*!
+    \brief data needed for the Broken Line fit procedure.
+  */
+  template<int N>
+  struct PreparedBrokenLineData {
+    int q; //!< particle charge
+    Matrix2xNd<N> radii; //!< xy data in the system in which the pre-fitted center is the origin
+    VectorNd<N> s; //!< total distance traveled in the transverse plane starting from the pre-fitted closest approach
+    VectorNd<N> S; //!< total distance traveled (three-dimensional)
+    VectorNd<N> Z; //!< orthogonal coordinate to the pre-fitted line in the sz plane
+    VectorNd<N> VarBeta; //!< kink angles in the SZ plane
+  };
+  
+   /*!
+    \brief Computes the Coulomb multiple scattering variance of the planar angle.
+    
+    \param length length of the track in the material.
+    \param B magnetic field in Gev/cm/c.
+    \param R radius of curvature (needed to evaluate p).
+    \param Layer denotes which of the four layers of the detector is the endpoint of the multiple scattered track. For example, if Layer=3, then the particle has just gone through the material between the second and the third layer.
+    
+    \todo add another Layer variable to identify also the start point of the track, so if there are missing hits or multiple hits, the part of the detector that the particle has traversed can be exactly identified.
+    
+    \warning the formula used here assumes beta=1, and so neglects the dependence of theta_0 on the mass of the particle at fixed momentum.
+    
+    \return the variance of the planar angle ((theta_0)^2 /3).
+  */
+  __host__ __device__ inline double MultScatt(const double& length, const double B, const double& R, int Layer, double slope) {
+    double XX_0; //!< radiation length of the material in cm
+    if(Layer==1) XX_0=16/0.06;
+    else XX_0=16/0.06;
+    XX_0*=1;
+    double geometry_factor=0.7; //!< number between 1/3 (uniform material) and 1 (thin scatterer) to be manually tuned
+    return geometry_factor*sqr((13.6/1000.)/(1.*B*R*sqrt(1.+sqr(slope))))*(abs(length)/XX_0)*sqr(1.+0.038*log(abs(length)/XX_0));
+  }
+  
+  /*!
+    \brief Computes the 2D rotation matrix that transforms the line y=slope*x into the line y=0.
+    
+    \param slope tangent of the angle of rotation.
+    
+    \return 2D rotation matrix.
+  */
+  __host__ __device__ inline Matrix2d RotationMatrix(const double& slope) {
+    Matrix2d Rot;
+    Rot(0,0)=1./sqrt(1.+sqr(slope));
+    Rot(0,1)=slope*Rot(0,0);
+    Rot(1,0)=-Rot(0,1);
+    Rot(1,1)=Rot(0,0);
+    return Rot;
+  }
+  
+  /*!
+    \brief Changes the Karimäki parameters (and consequently their covariance matrix) under a translation of the coordinate system, such that the old origin has coordinates (x0,y0) in the new coordinate system. The formulas are taken from Karimäki V., 1990, Effective circle fitting for particle trajectories, Nucl. Instr. and Meth. A305 (1991) 187.
+    
+    \param circle circle fit in the old coordinate system.
+    \param x0 x coordinate of the translation vector.
+    \param y0 y coordinate of the translation vector.
+    \param Jacob passed by reference in order to save stack.
+  */
+  __host__ __device__ inline void TranslateKarimaki(karimaki_circle_fit& circle, const double& x0, const double& y0, Matrix3d& Jacob) {
+    double A,U,BB,C,DO,DP,uu,xi,v,mu,lambda,zeta;
+    DP=x0*cos(circle.par(0))+y0*sin(circle.par(0));
+    DO=x0*sin(circle.par(0))-y0*cos(circle.par(0))+circle.par(1);
+    uu=1+circle.par(2)*circle.par(1);
+    C=-circle.par(2)*y0+uu*cos(circle.par(0));
+    BB=circle.par(2)*x0+uu*sin(circle.par(0));
+    A=2*DO+circle.par(2)*(sqr(DO)+sqr(DP));
+    U=sqrt(1+circle.par(2)*A);
+    xi=1/(sqr(BB)+sqr(C));
+    v=1+circle.par(2)*DO;
+    lambda=(A/2)/(U*sqr(1+U));
+    mu=1/(U*(1+U))+circle.par(2)*lambda;
+    zeta=sqr(DO)+sqr(DP);
+    
+    Jacob << xi*uu*v, -xi*sqr(circle.par(2))*DP, xi*DP,
+      2*mu*uu*DP, 2*mu*v, mu*zeta-lambda*A,
+      0, 0, 1;
+    
+    circle.par(0)=atan2(BB,C);
+    circle.par(1)=A/(1+U);
+    // circle.par(2)=circle.par(2);
+    
+    circle.cov=Jacob*circle.cov*Jacob.transpose();
+  }
+  
+ 
+  /*!
+    \brief Computes the data needed for the Broken Line fit procedure that are mainly common for the circle and the line fit.
+    
+    \param hits hits coordinates.
+    \param hits_cov hits covariance matrix.
+    \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
+    \param B magnetic field in Gev/cm/c.
+    \param results PreparedBrokenLineData to be filled (see description of PreparedBrokenLineData).
+  */
+  template<typename M3xN, typename V4, int N>
+  __host__ __device__ inline void prepareBrokenLineData(const M3xN& hits,
+							const V4& fast_fit,
+							const double B,
+							PreparedBrokenLineData<N> & results) {
+    constexpr auto n = N;
+    u_int i;
+    Vector2d d;
+    Vector2d e;
+    results.radii=Matrix2xNd<N>::Zero();
+    results.s=VectorNd<N>::Zero();
+    results.S=VectorNd<N>::Zero();
+    results.Z=VectorNd<N>::Zero();
+    results.VarBeta=VectorNd<N>::Zero();
+    
+    results.q=1;
+    d=hits.block(0,1,2,1)-hits.block(0,0,2,1);
+    e=hits.block(0,n-1,2,1)-hits.block(0,n-2,2,1);
+    if(cross2D(d,e)>0) results.q=-1;
+    
+    const double slope=-results.q/fast_fit(3);
+    
+    Matrix2d R=RotationMatrix(slope);
+    
+    // calculate radii and s
+    results.radii=hits.block(0,0,2,n)-fast_fit.head(2)*MatrixXd::Constant(1,n,1);
+    e=-fast_fit(2)*fast_fit.head(2)/fast_fit.head(2).norm();
+    for(i=0;i<n;i++) {
+      d=results.radii.block(0,i,2,1);
+      results.s(i)=results.q*fast_fit(2)*atan2(cross2D(d,e),d.dot(e)); // calculates the arc length
+      //if(results.s(i)<=0);
+    }
+    VectorNd<N> z=hits.block(2,0,1,n).transpose();
+    
+    //calculate S and Z
+    Matrix2xNd<N> pointsSZ=Matrix2xNd<N>::Zero();
+    for(i=0;i<n;i++) {
+      pointsSZ(0,i)=results.s(i);
+      pointsSZ(1,i)=z(i);
+      pointsSZ.block(0,i,2,1)=R*pointsSZ.block(0,i,2,1);
+    }
+    results.S=pointsSZ.block(0,0,1,n).transpose();
+    results.Z=pointsSZ.block(1,0,1,n).transpose();
+    
+    //calculate VarBeta
+    for(i=1;i<n-1;i++) {
+      results.VarBeta(i)=MultScatt(results.S(i+1)-results.S(i),B,fast_fit(2),i+2,slope)+MultScatt(results.S(i)-results.S(i-1),B,fast_fit(2),i+1,slope);
+    }
+  }
+  
+  /*!
+    \brief Computes the n-by-n band matrix obtained minimizing the Broken Line's cost function w.r.t u. This is the whole matrix in the case of the line fit and the main n-by-n block in the case of the circle fit.
+    
+    \param w weights of the first part of the cost function, the one with the measurements and not the angles (\sum_{i=1}^n w*(y_i-u_i)^2).
+    \param S total distance traveled by the particle from the pre-fitted closest approach.
+    \param VarBeta kink angles' variance.
+    
+    \return the n-by-n matrix of the linear system
+  */
+  template<int N>
+  __host__ __device__ inline MatrixNd<N> MatrixC_u(const VectorNd<N>& w, const VectorNd<N>& S, const VectorNd<N>& VarBeta) {
+    constexpr u_int n=N;
+    u_int i;
+    
+    MatrixNd<N> C_U=MatrixNd<N>::Zero();
+    for(i=0;i<n;i++) {
+      C_U(i,i)=w(i);
+      if(i>1) C_U(i,i)+=1/(VarBeta(i-1)*sqr(S(i)-S(i-1)));
+      if(i>0 && i<n-1) C_U(i,i)+=(1/VarBeta(i))*sqr((S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
+      if(i<n-2) C_U(i,i)+=1/(VarBeta(i+1)*sqr(S(i+1)-S(i)));
+      
+      if(i>0 && i<n-1) C_U(i,i+1)=1/(VarBeta(i)*(S(i+1)-S(i)))*(-(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
+      if(i<n-2) C_U(i,i+1)+=1/(VarBeta(i+1)*(S(i+1)-S(i)))*(-(S(i+2)-S(i))/((S(i+2)-S(i+1))*(S(i+1)-S(i))));
+      
+      if(i<n-2) C_U(i,i+2)=1/(VarBeta(i+1)*(S(i+2)-S(i+1))*(S(i+1)-S(i)));
+      
+      C_U(i,i)=C_U(i,i)/2;
+    }
+    MatrixNd<N> C_u;
+    C_u=C_U+C_U.transpose();
+    
+    return C_u;
+  }
+  
+  /*!
+    \brief A very fast helix fit.
+    
+    \param hits the measured hits.
+    
+    \return (X0,Y0,R,tan(theta)).
+    
+    \warning sign of theta is (intentionally, for now) mistaken for negative charges.
+  */
+  
+  template<typename M3xN, typename V4>
+  __host__ __device__ inline void BL_Fast_fit(const M3xN& hits, V4 & result)
+  {
+    
+    constexpr uint32_t N = M3xN::ColsAtCompileTime;
+    constexpr auto n = N; // get the number of hits
+    
+    const Vector2d a=hits.block(0,n/2,2,1)-hits.block(0,0,2,1);
+    const Vector2d b=hits.block(0,n-1,2,1)-hits.block(0,n/2,2,1);
+    const Vector2d c=hits.block(0,0,2,1)-hits.block(0,n-1,2,1);
+    
+    result(0)=hits(0,0)-(a(1)*c.squaredNorm()+c(1)*a.squaredNorm())/(2*cross2D(c,a));
+    result(1)=hits(1,0)+(a(0)*c.squaredNorm()+c(0)*a.squaredNorm())/(2*cross2D(c,a));
+    // check Wikipedia for these formulas
+    
+    result(2)=(a.norm()*b.norm()*c.norm())/(2*abs(cross2D(b,a)));
+    // Using Math Olympiad's formula R=abc/(4A)
+    
+    const Vector2d d=hits.block(0,0,2,1)-result.head(2);
+    const Vector2d e=hits.block(0,n-1,2,1)-result.head(2);
+    
+    result(3)=result(2)*atan2(cross2D(d, e), d.dot(e))/(hits(2,n-1)-hits(2,0));
+    // ds/dz slope between last and first point
+    
+  }
+  
+  /*!
+    \brief Performs the Broken Line fit in the curved track case (that is, the fit parameters are the interceptions u and the curvature correction \Delta\kappa).
+    
+    \param hits hits coordinates.
+    \param hits_cov hits covariance matrix.
+    \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
+    \param B magnetic field in Gev/cm/c.
+    \param data PreparedBrokenLineData.
+    \param circle_results struct to be filled with the results in this form:
+    -par parameter of the line in this form: (phi, d, k); \n
+    -cov covariance matrix of the fitted parameter; \n
+    -chi2 value of the cost function in the minimum.
+    \param Jacob passed by reference in order to save stack.
+    \param C_U passed by reference in order to sake stack.
+    
+    \details The function implements the steps 2 and 3 of the Broken Line fit with the curvature correction.\n
+    The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and \Delta\kappa and their covariance matrix.
+    The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
+  */
+  template<typename M3xN, typename M6xN, typename V4, int N>
+  __host__ __device__ inline void BL_Circle_fit(const M3xN& hits,
+						const  M6xN & hits_ge,
+						const V4& fast_fit,
+						const double B,
+						PreparedBrokenLineData<N>& data,
+						karimaki_circle_fit & circle_results,
+						Matrix3d& Jacob,
+						MatrixNplusONEd<N>& C_U) {
+
+    constexpr u_int n = N;
+    u_int i;
+    
+    circle_results.q=data.q;
+    auto & radii=data.radii;
+    const auto & s=data.s;
+    const auto & S=data.S;
+    auto & Z=data.Z;
+    auto & VarBeta=data.VarBeta;
+    const double slope=-circle_results.q/fast_fit(3);
+    VarBeta*=1.+sqr(slope); // the kink angles are projected!
+    
+    for(i=0;i<n;i++) {
+      Z(i)=radii.block(0,i,2,1).norm()-fast_fit(2);
+    }
+    
+    Matrix2d V; // covariance matrix
+    VectorNd<N> w=VectorNd<N>::Zero(); // weights
+    Matrix2d RR; // rotation matrix point by point
+    //double Slope; // slope of the circle point by point
+    for(i=0;i<n;i++) {
+      V(0,0)=hits_ge.col(i)[0];                // x errors
+      V(0,1)=V(1,0)=hits_ge.col(i)[1];   // cov_xy
+      V(1,1)=hits_ge.col(i)[2];                // y errors
+      //Slope=-radii(0,i)/radii(1,i);
+      RR=RotationMatrix(-radii(0,i)/radii(1,i));
+      w(i)=1/((RR*V*RR.transpose())(1,1)); // compute the orthogonal weight point by point
+    }
+    
+    VectorNplusONEd<N> r_u=VectorNplusONEd<N>::Zero();
+    for(i=0;i<n;i++) {
+      r_u(i)=w(i)*Z(i);
+    } r_u(n)=0;
+    
+    C_U=MatrixNplusONEd<N>::Zero();
+    //add the border to the C_u matrix
+    for(i=0;i<n;i++) {
+      if(i>0 && i<n-1) {
+	C_U(i,n)+=-(s(i+1)-s(i-1))/(2*VarBeta(i))*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)));
+	C_U(n,i)=C_U(i,n);
+      }
+      if(i>1) {
+	C_U(i,n)+=(s(i)-s(i-2))/(2*VarBeta(i-1)*(s(i)-s(i-1)));
+	C_U(n,i)=C_U(i,n);
+      }
+      if(i<n-2) {
+	C_U(i,n)+=(s(i+2)-s(i))/(2*VarBeta(i+1)*(s(i+1)-s(i)));
+	C_U(n,i)=C_U(i,n);
+      }
+      
+      if(i>0 && i<n-1) C_U(n,n)+=sqr(s(i+1)-s(i-1))/(4*VarBeta(i));
+    }
+    C_U.block(0,0,n,n)=MatrixC_u(w,s,VarBeta);
+    MatrixNplusONEd<N>& I=C_U;
+    I=C_U.inverse();//MatrixNplusONEd I=C_U.inverse();
+    
+    VectorNplusONEd<N>& u=r_u;
+    u=I*r_u; // obtain the fitted parameters by solving the linear system
+    
+    // compute (phi, d_ca, k) in the system in which the midpoint of the first two corrected hits is the origin...
+    
+    radii.block(0,0,2,1)/=radii.block(0,0,2,1).norm();
+    radii.block(0,1,2,1)/=radii.block(0,1,2,1).norm();
+    
+    Vector2d d=hits.block(0,0,2,1)+(-Z(0)+u(0))*radii.block(0,0,2,1);
+    Vector2d e=hits.block(0,1,2,1)+(-Z(1)+u(1))*radii.block(0,1,2,1);
+    
+    circle_results.par << atan2((e-d)(1),(e-d)(0)),
+      -circle_results.q*(fast_fit(2)-sqrt(sqr(fast_fit(2))-(e-d).squaredNorm()/4)),
+      circle_results.q*(1/fast_fit(2)+u(n));
+    
+    assert(circle_results.q*circle_results.par(1)<=0);
+    
+    Vector2d eMinusd=e-d;
+    double tmp1=eMinusd.squaredNorm();
+    
+    Jacob << (radii(1,0)*eMinusd(0)-eMinusd(1)*radii(0,0))/tmp1,(radii(1,1)*eMinusd(0)-eMinusd(1)*radii(0,1))/tmp1,0,
+      (circle_results.q/2)*(eMinusd(0)*radii(0,0)+eMinusd(1)*radii(1,0))/sqrt(sqr(2*fast_fit(2))-tmp1),(circle_results.q/2)*(eMinusd(0)*radii(0,1)+eMinusd(1)*radii(1,1))/sqrt(sqr(2*fast_fit(2))-tmp1),0,
+      0,0,circle_results.q;
+    
+    circle_results.cov << I(0,0), I(0,1), I(0,n),
+      I(1,0), I(1,1), I(1,n),
+      I(n,0), I(n,1), I(n,n);
+    
+    circle_results.cov=Jacob*circle_results.cov*Jacob.transpose();
+    
+    //...Translate in the system in which the first corrected hit is the origin, adding the m.s. correction...
+    
+    TranslateKarimaki(circle_results,(e-d)(0)/2,(e-d)(1)/2,Jacob);
+    circle_results.cov(0,0)+=(1+sqr(slope))*MultScatt(S(1)-S(0),B,fast_fit(2),2,slope);
+    
+    //...And translate back to the original system
+    
+    TranslateKarimaki(circle_results,d(0),d(1),Jacob);
+    
+    // compute chi2
+    circle_results.chi2=0;
+    for(i=0;i<n;i++) {
+      circle_results.chi2+=w(i)*sqr(Z(i)-u(i));
+      if(i>0 && i<n-1) circle_results.chi2+=sqr(u(i-1)/(s(i)-s(i-1))-u(i)*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)))+u(i+1)/(s(i+1)-s(i))+(s(i+1)-s(i-1))*u(n)/2)/VarBeta(i);
+    }
+    
+    assert(circle_results.chi2>=0);
+  }
+  
+  /*!
+    \brief Performs the Broken Line fit in the straight track case (that is, the fit parameters are only the interceptions u).
+    
+    \param hits hits coordinates.
+    \param hits_cov hits covariance matrix.
+    \param fast_fit pre-fit result in the form (X0,Y0,R,tan(theta)).
+    \param B magnetic field in Gev/cm/c.
+    \param data PreparedBrokenLineData.
+    \param line_results struct to be filled with the results in this form:
+    -par parameter of the line in this form: (cot(theta), Zip); \n
+    -cov covariance matrix of the fitted parameter; \n
+    -chi2 value of the cost function in the minimum.
+    
+    \details The function implements the steps 2 and 3 of the Broken Line fit without the curvature correction.\n
+    The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and their covariance matrix.
+    The step 3 is the correction of the fast pre-fitted parameters for the innermost part of the track. It is first done in a comfortable coordinate system (the one in which the first hit is the origin) and then the parameters and their covariance matrix are transformed to the original coordinate system.
+  */
+  template<typename V4, typename M6xN, int N>
+  __host__ __device__ inline void BL_Line_fit(const  M6xN & hits_ge,
+					      const V4& fast_fit,
+					      const double B,
+					      const PreparedBrokenLineData<N>& data,
+					      line_fit & line_results) {
+    constexpr u_int n = N;
+    u_int i;
+    
+    const auto & radii=data.radii;
+    const auto & S=data.S;
+    const auto & Z=data.Z;
+    const auto& VarBeta=data.VarBeta;
+    
+    const double slope=-data.q/fast_fit(3);
+    Matrix2d R=RotationMatrix(slope);
+    
+    Matrix3d V=Matrix3d::Zero(); // covariance matrix XYZ
+    Matrix2x3d JacobXYZtosZ=Matrix2x3d::Zero(); // jacobian for computation of the error on s (xyz -> sz)
+    VectorNd<N> w=VectorNd<N>::Zero();
+    for(i=0;i<n;i++) {
+      V(0,0)=hits_ge.col(i)[0];                // x errors
+      V(0,1)=V(1,0)=hits_ge.col(i)[1];   // cov_xy
+      V(0,2)=V(2,0)= hits_ge.col(i)[3];   // cov_xz
+      V(1,1)=hits_ge.col(i)[2];                // y errors
+      V(2,1)=V(1,2)=hits_ge.col(i)[4];   // cov_yz
+      V(2,2)=hits_ge.col(i)[5];                // z errors
+      JacobXYZtosZ(0,0)=radii(1,i)/radii.block(0,i,2,1).norm();
+      JacobXYZtosZ(0,1)=-radii(0,i)/radii.block(0,i,2,1).norm();
+      JacobXYZtosZ(1,2)=1;
+      w(i)=1/((R*JacobXYZtosZ*V*JacobXYZtosZ.transpose()*R.transpose())(1,1)); // compute the orthogonal weight point by point
+    }
+    
+    VectorNd<N> r_u=VectorNd<N>::Zero();
+    for(i=0;i<n;i++) {
+      r_u(i)=w(i)*Z(i);
+    }
+    
+    MatrixNd<N> I=MatrixC_u(w,S,VarBeta).inverse();
+    VectorNd<N> u=I*r_u; // obtain the fitted parameters by solving the linear system
+    
+    // line parameters in the system in which the first hit is the origin and with axis along SZ
+    line_results.par << (u(1)-u(0))/(S(1)-S(0)), u(0);
+    line_results.cov << (I(0,0)-2*I(0,1)+I(1,1))/sqr(S(1)-S(0))+MultScatt(S(1)-S(0),B,fast_fit(2),2,slope), (I(0,1)-I(0,0))/(S(1)-S(0)),
+      (I(0,1)-I(0,0))/(S(1)-S(0)), I(0,0);
+    
+    // translate to the original SZ system
+    Matrix2d Jacob;
+    Jacob(0,0)=1;
+    Jacob(0,1)=0;
+    Jacob(1,0)=-S(0);
+    Jacob(1,1)=1;
+    line_results.par(1)+=-line_results.par(0)*S(0);
+    line_results.cov=Jacob*line_results.cov*Jacob.transpose();
+    
+    // rotate to the original sz system
+    double tmp=R(0,0)-line_results.par(0)*R(0,1);
+    Jacob(0,0)=1/sqr(tmp);
+    Jacob(0,1)=0;
+    Jacob(1,0)=line_results.par(1)*R(0,1)/sqr(tmp);
+    Jacob(1,1)=1/tmp;
+    line_results.par(1)=line_results.par(1)/tmp;
+    line_results.par(0)=(R(0,1)+line_results.par(0)*R(0,0))/tmp;
+    line_results.cov=Jacob*line_results.cov*Jacob.transpose();
+    
+    // compute chi2
+    line_results.chi2=0;
+    for(i=0;i<n;i++) {
+      line_results.chi2+=w(i)*sqr(Z(i)-u(i));
+      if(i>0 && i<n-1) line_results.chi2+=sqr(u(i-1)/(S(i)-S(i-1))-u(i)*(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1)))+u(i+1)/(S(i+1)-S(i)))/VarBeta(i);
+    }
+    
+    assert(line_results.chi2>=0);
+  }
+  
+  /*!
+    \brief Helix fit by three step:
+    -fast pre-fit (see Fast_fit() for further info); \n
+    -circle fit of the hits projected in the transverse plane by Broken Line algorithm (see BL_Circle_fit() for further info); \n
+    -line fit of the hits projected on the (pre-fitted) cilinder surface by Broken Line algorithm (see BL_Line_fit() for further info); \n
+    Points must be passed ordered (from inner to outer layer).
+    
+    \param hits Matrix3xNd hits coordinates in this form: \n
+    |x1|x2|x3|...|xn| \n
+    |y1|y2|y3|...|yn| \n
+    |z1|z2|z3|...|zn|
+    \param hits_cov Matrix3Nd covariance matrix in this form (()->cov()): \n
+    |(x1,x1)|(x2,x1)|(x3,x1)|(x4,x1)|.|(y1,x1)|(y2,x1)|(y3,x1)|(y4,x1)|.|(z1,x1)|(z2,x1)|(z3,x1)|(z4,x1)| \n
+    |(x1,x2)|(x2,x2)|(x3,x2)|(x4,x2)|.|(y1,x2)|(y2,x2)|(y3,x2)|(y4,x2)|.|(z1,x2)|(z2,x2)|(z3,x2)|(z4,x2)| \n
+    |(x1,x3)|(x2,x3)|(x3,x3)|(x4,x3)|.|(y1,x3)|(y2,x3)|(y3,x3)|(y4,x3)|.|(z1,x3)|(z2,x3)|(z3,x3)|(z4,x3)| \n
+    |(x1,x4)|(x2,x4)|(x3,x4)|(x4,x4)|.|(y1,x4)|(y2,x4)|(y3,x4)|(y4,x4)|.|(z1,x4)|(z2,x4)|(z3,x4)|(z4,x4)| \n
+    .       .       .       .       . .       .       .       .       . .       .       .       .       . \n
+    |(x1,y1)|(x2,y1)|(x3,y1)|(x4,y1)|.|(y1,y1)|(y2,y1)|(y3,x1)|(y4,y1)|.|(z1,y1)|(z2,y1)|(z3,y1)|(z4,y1)| \n
+    |(x1,y2)|(x2,y2)|(x3,y2)|(x4,y2)|.|(y1,y2)|(y2,y2)|(y3,x2)|(y4,y2)|.|(z1,y2)|(z2,y2)|(z3,y2)|(z4,y2)| \n
+    |(x1,y3)|(x2,y3)|(x3,y3)|(x4,y3)|.|(y1,y3)|(y2,y3)|(y3,x3)|(y4,y3)|.|(z1,y3)|(z2,y3)|(z3,y3)|(z4,y3)| \n
+    |(x1,y4)|(x2,y4)|(x3,y4)|(x4,y4)|.|(y1,y4)|(y2,y4)|(y3,x4)|(y4,y4)|.|(z1,y4)|(z2,y4)|(z3,y4)|(z4,y4)| \n
+    .       .       .    .          . .       .       .       .       . .       .       .       .       . \n
+    |(x1,z1)|(x2,z1)|(x3,z1)|(x4,z1)|.|(y1,z1)|(y2,z1)|(y3,z1)|(y4,z1)|.|(z1,z1)|(z2,z1)|(z3,z1)|(z4,z1)| \n
+    |(x1,z2)|(x2,z2)|(x3,z2)|(x4,z2)|.|(y1,z2)|(y2,z2)|(y3,z2)|(y4,z2)|.|(z1,z2)|(z2,z2)|(z3,z2)|(z4,z2)| \n
+    |(x1,z3)|(x2,z3)|(x3,z3)|(x4,z3)|.|(y1,z3)|(y2,z3)|(y3,z3)|(y4,z3)|.|(z1,z3)|(z2,z3)|(z3,z3)|(z4,z3)| \n
+    |(x1,z4)|(x2,z4)|(x3,z4)|(x4,z4)|.|(y1,z4)|(y2,z4)|(y3,z4)|(y4,z4)|.|(z1,z4)|(z2,z4)|(z3,z4)|(z4,z4)|
+    \param B magnetic field in the center of the detector in Gev/cm/c, in order to perform the p_t calculation.
+    
+    \warning see BL_Circle_fit(), BL_Line_fit() and Fast_fit() warnings.
+    
+    \bug see BL_Circle_fit(), BL_Line_fit() and Fast_fit() bugs.
+    
+    \return (phi,Tip,p_t,cot(theta)),Zip), their covariance matrix and the chi2's of the circle and line fits.
+  */
+  template<int N>
+  inline helix_fit BL_Helix_fit(const Matrix3xNd<N>& hits, const Eigen::Matrix<float,6,4>& hits_ge, const double B)
+{
+    helix_fit helix;
+    Vector4d fast_fit;
+    BL_Fast_fit(hits,fast_fit);
+
+    PreparedBrokenLineData<N> data;
+    karimaki_circle_fit circle;
+    line_fit line;
+    Matrix3d Jacob;
+    MatrixNplusONEd<N> C_U;
+    
+    prepareBrokenLineData(hits,fast_fit,B,data);
+    BL_Line_fit(hits_ge,fast_fit,B,data,line);
+    BL_Circle_fit(hits,hits_ge,fast_fit,B,data,circle,Jacob,C_U);
+    
+    // the circle fit gives k, but here we want p_t, so let's change the parameter and the covariance matrix
+    Jacob << 1,0,0,
+      0,1,0,
+      0,0,-abs(circle.par(2))*B/(sqr(circle.par(2))*circle.par(2));
+    circle.par(2)=B/abs(circle.par(2));
+    circle.cov=Jacob*circle.cov*Jacob.transpose();
+    
+    helix.par << circle.par, line.par;
+    helix.cov=MatrixXd::Zero(5, 5);
+    helix.cov.block(0,0,3,3)=circle.cov;
+    helix.cov.block(3,3,2,2)=line.cov;
+    helix.q=circle.q;
+    helix.chi2_circle=circle.chi2;
+    helix.chi2_line=line.chi2;
+    
+    return helix;
+  }
+  
 }  // namespace BrokenLine
 
 #endif
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h b/RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h
index ba0f0aa13e1a6..3b9f142e1d983 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h
@@ -21,6 +21,8 @@ using MatrixXd = Eigen::MatrixXd;
 template<int N>
 using MatrixNd = Eigen::Matrix<double, N, N>;
 template<int N>
+using MatrixNplusONEd = Eigen::Matrix<double, N+1, N+1>; 
+template<int N>
 using ArrayNd = Eigen::Array<double, N, N>;
 template<int N>
 using Matrix2Nd = Eigen::Matrix<double, 2 * N, 2 * N>;
@@ -39,6 +41,8 @@ using MatrixNx5d = Eigen::Matrix<double, N, 5>;
 template<int N>
 using VectorNd = Eigen::Matrix<double, N, 1>;
 template<int N>
+using VectorNplusONEd  = Eigen::Matrix<double, N+1, 1>;
+template<int N>
 using Vector2Nd = Eigen::Matrix<double, 2 * N, 1>;
 template<int N>
 using Vector3Nd = Eigen::Matrix<double, 3 * N, 1>;
@@ -58,6 +62,7 @@ using Matrix4d = Eigen::Matrix4d;
 using Matrix5d = Eigen::Matrix<double, 5, 5>;
 using Matrix6d = Eigen::Matrix<double, 6, 6>;
 using Vector5d = Eigen::Matrix<double, 5, 1>;
+using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
 
 using Matrix3f = Eigen::Matrix3f;
 using Vector3f = Eigen::Vector3f;
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
index 35cf3d3256b6b..6980941bfd0fc 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
@@ -1,113 +1,12 @@
 #ifndef RecoPixelVertexing_PixelTrackFitting_interface_RiemannFit_h
 #define RecoPixelVertexing_PixelTrackFitting_interface_RiemannFit_h
 
-#include "FitResult.h"
+#include "FitUtils.h"
 
 
 namespace Rfit
 {
 
-template <class C>
-__host__ __device__ void printIt(C* m, const char* prefix = "")
-{
-#ifdef RFIT_DEBUG
-    for (u_int r = 0; r < m->rows(); ++r)
-    {
-        for (u_int c = 0; c < m->cols(); ++c)
-        {
-            printf("%s Matrix(%d,%d) = %g\n", prefix, r, c, (*m)(r, c));
-        }
-    }
-#endif
-}
-
-/*!
-    \brief raise to square.
-*/
-template <typename T>
-__host__ __device__ inline T sqr(const T a)
-{
-    return a * a;
-}
-
-/*!
-    \brief Compute cross product of two 2D vector (assuming z component 0),
-    returning z component of the result.
-    \param a first 2D vector in the product.
-    \param b second 2D vector in the product.
-    \return z component of the cross product.
-*/
-
-__host__ __device__ inline double cross2D(const Vector2d& a, const Vector2d& b)
-{
-    return a.x() * b.y() - a.y() * b.x();
-}
-
-/*!
- *  load error in CMSSW format to our formalism
- *  
- */
-  template<typename M6x4f, typename M2Nd>
-  __host__ __device__ void loadCovariance2D(M6x4f const & ge,  M2Nd & hits_cov) {
-    // Index numerology:
-    // i: index of the hits/point (0,..,3)
-    // j: index of space component (x,y,z)
-    // l: index of space components (x,y,z)
-    // ge is always in sync with the index i and is formatted as:
-    // ge[] ==> [xx, xy, yy, xz, yz, zz]
-    // in (j,l) notation, we have:
-    // ge[] ==> [(0,0), (0,1), (1,1), (0,2), (1,2), (2,2)]
-    // so the index ge_idx corresponds to the matrix elements:
-    // | 0  1  3 |
-    // | 1  2  4 |
-    // | 3  4  5 |
-    constexpr uint32_t  hits_in_fit = 4; // Fixme
-    for (uint32_t i=0; i< hits_in_fit; ++i) {
-      auto ge_idx = 0; auto j=0; auto l=0;
-      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-      ge_idx = 2; j=1; l=1;
-      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-      ge_idx = 1; j=1; l=0;
-      hits_cov(i + l * hits_in_fit, i + j * hits_in_fit) =
-      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-    }
-  }
-  
-  template<typename M6x4f, int N>
-  __host__ __device__ void loadCovariance(M6x4f const & ge,  Matrix3Nd<N> & hits_cov) {
-
-    // Index numerology:
-    // i: index of the hits/point (0,..,3)
-    // j: index of space component (x,y,z)
-    // l: index of space components (x,y,z)
-    // ge is always in sync with the index i and is formatted as:
-    // ge[] ==> [xx, xy, yy, xz, yz, zz]
-    // in (j,l) notation, we have:
-    // ge[] ==> [(0,0), (0,1), (1,1), (0,2), (1,2), (2,2)]
-    // so the index ge_idx corresponds to the matrix elements:
-    // | 0  1  3 |
-    // | 1  2  4 |
-    // | 3  4  5 |
-    constexpr uint32_t  hits_in_fit = 4; // Fixme
-    for (uint32_t i=0; i<hits_in_fit; ++i) {
-      auto ge_idx = 0; auto j=0; auto l=0;
-      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-      ge_idx = 2; j=1; l=1;
-      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-      ge_idx = 5; j=2; l=2;
-      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-      ge_idx = 1; j=1; l=0;
-      hits_cov(i + l * hits_in_fit, i + j * hits_in_fit) =
-	hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-      ge_idx = 3; j=2; l=0;
-      hits_cov(i + l * hits_in_fit, i + j * hits_in_fit) =
-	hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-      ge_idx = 4; j=2; l=1;
-      hits_cov(i + l * hits_in_fit, i + j * hits_in_fit) =
-	hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
-    }
-  }
-
   
 /*!  Compute the Radiation length in the uniform hypothesis
  *
@@ -139,7 +38,6 @@ void computeRadLenUniformMaterial(const VNd1 &length_values,
   // Radiation length of the pixel detector in the uniform assumption, with
   // 0.06 rad_len at 16 cm
   constexpr double XX_0_inv = 0.06/16.;
-//  const double XX_0 = 1000.*16.f/(0.06);
   u_int n = length_values.rows();
   rad_lengths(0) = length_values(0)*XX_0_inv;
   for (u_int j = 1; j < n; ++j) {
@@ -178,7 +76,7 @@ __host__ __device__ inline auto Scatter_cov_line(Matrix2d const * cov_sz,
 #ifdef RFIT_DEBUG
     Rfit::printIt(&s_arcs, "Scatter_cov_line - s_arcs: ");
 #endif
-    constexpr auto n = N;
+    constexpr u_int n = N;
     double p_t = std::min(20.,fast_fit(2) * B);   // limit pt to avoid too small error!!!
     double p_2 = p_t * p_t * (1. + 1. / (fast_fit(3) * fast_fit(3)));
     VectorNd<N> rad_lengths_S;
@@ -237,7 +135,7 @@ __host__ __device__ inline auto Scatter_cov_line(Matrix2d const * cov_sz,
 							 VectorNd<N> const& rad,
 							 double B)
 {
-    u_int n = N;
+    constexpr u_int n = N;
     double p_t = std::min(20.,fast_fit(2) * B);   // limit pt to avoid too small error!!!
     double p_2 = p_t * p_t * (1. + 1. / (fast_fit(3) * fast_fit(3)));
     double theta = atan(fast_fit(3));
@@ -293,7 +191,7 @@ __host__ __device__ inline auto Scatter_cov_line(Matrix2d const * cov_sz,
     printf("Address of p2D: %p\n", &p2D);
 #endif
     printIt(&p2D, "cov_radtocart - p2D:");
-    u_int n = p2D.cols();
+    constexpr u_int n = N;
     Matrix2Nd<N> cov_cart = Matrix2Nd<N>::Zero();
     VectorNd<N> rad_inv = rad.cwiseInverse();
     printIt(&rad_inv, "cov_radtocart - rad_inv:");
@@ -329,7 +227,7 @@ __host__ __device__ inline auto Scatter_cov_line(Matrix2d const * cov_sz,
 							 const Matrix2Nd<N>& cov_cart,
 							 const VectorNd<N>& rad)
 {
-    u_int n = p2D.cols();
+    constexpr u_int n = N;
     VectorNd<N> cov_rad;
     const VectorNd<N> rad_inv2 = rad.cwiseInverse().array().square();
     for (u_int i = 0; i < n; ++i)
@@ -362,7 +260,7 @@ template<typename M2xN, typename V4, int N>
 							      V4& fast_fit,
 							      const VectorNd<N>& rad)
 {
-    u_int n = p2D.cols();
+    constexpr u_int n = N;
     VectorNd<N> cov_rad;
     for (u_int i = 0; i < n; ++i)
     {
@@ -528,7 +426,8 @@ __host__ __device__ inline Vector2d min_eigen2D(const Matrix2d& A, double& chi2)
 template<typename M3xN, typename V4>
 __host__ __device__ inline void Fast_fit(const M3xN& hits, V4 & result)
 {
-    u_int n = hits.cols();  // get the number of hits
+    constexpr uint32_t N = M3xN::ColsAtCompileTime;
+    constexpr auto n = N; // get the number of hits
     printIt(&hits, "Fast_fit - hits: ");
 
     // CIRCLE FIT
@@ -618,7 +517,7 @@ __host__ __device__ inline circle_fit Circle_fit(const  M2xN& hits2D,
 #endif
     // INITIALIZATION
     Matrix2Nd<N> V = hits_cov2D;
-    u_int n = hits2D.cols();
+    constexpr u_int n = N;
     printIt(&hits2D, "circle_fit - hits2D:");
     printIt(&hits_cov2D, "circle_fit - hits_cov2D:");
 
@@ -804,7 +703,7 @@ __host__ __device__ inline circle_fit Circle_fit(const  M2xN& hits2D,
         printIt(&C0, "circle_fit - C0:");
 
         const MatrixNd<N> W = weight * weight.transpose();
-        const MatrixNd<N> H = MatrixXd::Identity(n, n).rowwise() - weight.transpose();
+        const MatrixNd<N> H = MatrixNd<N>::Identity().rowwise() - weight.transpose();
         const MatrixNx3d<N> s_v = H * p3D.transpose();
         printIt(&W, "circle_fit - W:");
         printIt(&H, "circle_fit - H:");
@@ -953,7 +852,7 @@ inline line_fit Line_fit(const M3xN& hits,
 			 const bool error) {
     
   constexpr uint32_t N = M3xN::ColsAtCompileTime;
-  auto n = hits.cols();
+  constexpr auto n = N;
   double theta = -circle.q*atan(fast_fit(3));
   theta = theta < 0. ? theta + M_PI : theta;
     
@@ -1143,7 +1042,7 @@ template<int N>
 inline helix_fit Helix_fit(const Matrix3xNd<N>& hits, const Eigen::Matrix<float,6,4>& hits_ge, const double B,
                            const bool error)
 {
-    u_int n = hits.cols();
+    constexpr u_int n = N;
     VectorNd<4> rad = (hits.block(0, 0, 2, n).colwise().norm());
 
     // Fast_fit gives back (X0, Y0, R, theta) w/o errors, using only 3 points.
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
index f6e3a2601ad97..e4dfeec50572f 100644
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
@@ -58,26 +58,20 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
     isBarrel[i] = recHit->detUnit()->type().isBarrel();
   }
 
-  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
-
-  Matrix<double, 3, Dynamic, 0, 3, max_nop> brokenLineHits(3, nhits);
+  assert(nhits==4);
+  Rfit::Matrix3xNd<4> riemannHits;
 
-  Matrix<double, Dynamic, Dynamic, 0, 3 * max_nop, 3 * max_nop> brokenLineHits_cov =
-      MatrixXd::Zero(3 * nhits, 3 * nhits);
+  Eigen::Matrix<float,6,4> riemannHits_ge = Eigen::Matrix<float,6,4>::Zero();
 
   for (unsigned int i = 0; i < nhits; ++i) {
-    brokenLineHits.col(i) << points[i].x(), points[i].y(), points[i].z();
+    riemannHits.col(i) << points[i].x(), points[i].y(), points[i].z();
 
-    const auto& errorMatrix = errors[i].matrix4D();
-
-    for (auto j = 0; j < 3; ++j) {
-      for (auto l = 0; l < 3; ++l) {
-        brokenLineHits_cov(i + j * nhits, i + l * nhits) = errorMatrix(j, l);
-      }
-    }
+    riemannHits_ge.col(i) <<  errors[i].cxx(), errors[i].cyx(), errors[i].cyy(),
+                              errors[i].czx(), errors[i].czy(), errors[i].czz();
   }
+  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
 
-  helix_fit fittedTrack = BrokenLine::Helix_fit(brokenLineHits, brokenLineHits_cov, bField);
+  Rfit::helix_fit fittedTrack = BrokenLine::BL_Helix_fit(riemannHits, riemannHits_ge, bField);
   
   int iCharge = fittedTrack.q;
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
index 3917de89a8185..ab2090719122a 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
@@ -4,9 +4,15 @@
 #include <Eigen/Eigenvalues>
 
 #include "test_common.h"
+
 #include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
+
+#ifdef USE_BL
+#include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
+#else
 #include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
+#endif
 
 using namespace Eigen;
 
@@ -26,9 +32,56 @@ void kernelFastFit(double * __restrict__ phits, double * __restrict__ presults)
   auto i = blockIdx.x*blockDim.x + threadIdx.x;
   Rfit::Map3x4d hits(phits+i,3,4);
   Rfit::Map4d result(presults+i,4);
+#ifdef USE_BL
+  BrokenLine::BL_Fast_fit(hits, result);
+#else
   Rfit::Fast_fit(hits,  result);
+#endif
+}
+
+#ifdef USE_BL
+
+__global__
+void kernelBrokenLineFit(double * __restrict__ phits,
+			 float * __restrict__ phits_ge, 
+			 double * __restrict__ pfast_fit_input, 
+			 double B,
+			 Rfit::circle_fit * circle_fit,
+			 Rfit::line_fit * line_fit
+			 ) {
+  auto i = blockIdx.x*blockDim.x + threadIdx.x;
+  Rfit::Map3x4d hits(phits+i,3,4);
+  Rfit::Map4d   fast_fit_input(pfast_fit_input+i,4);
+  Rfit::Map6x4f hits_ge(phits_ge+i,6,4);
+
+  constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
+  constexpr auto n = N;
+  
+  BrokenLine::PreparedBrokenLineData<N> data;
+  Rfit::Matrix3d Jacob;
+  Rfit::MatrixNplusONEd<N> C_U;
+
+  auto & line_fit_results = line_fit[i];
+  auto & circle_fit_results = circle_fit[i];
+  
+  BrokenLine::prepareBrokenLineData(hits,fast_fit_input,B,data);
+  BrokenLine::BL_Line_fit(hits_ge,fast_fit_input,B,data,line_fit_results);
+  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_input,B,data,circle_fit_results,Jacob,C_U);
+  Jacob << 1,0,0,
+    0,1,0,
+    0,0,-B/std::copysign(Rfit::sqr(circle_fit_results.par(2)),circle_fit_results.par(2));
+  circle_fit_results.par(2)=B/std::abs(circle_fit_results.par(2));
+  circle_fit_results.cov=Jacob*circle_fit_results.cov*Jacob.transpose();
+
+#ifdef TEST_DEBUG
+if (0==i) {
+  printf("Circle param %f,%f,%f\n",circle_fit[i].par(0),circle_fit[i].par(1),circle_fit[i].par(2));
+ }
+#endif
 }
 
+#else
+
 __global__
 void kernelCircleFit(double * __restrict__ phits,
     float * __restrict__ phits_ge, 
@@ -36,16 +89,15 @@ void kernelCircleFit(double * __restrict__ phits,
     double B,
     Rfit::circle_fit * circle_fit_resultsGPU) {
 
-auto i = blockIdx.x*blockDim.x + threadIdx.x;
+  auto i = blockIdx.x*blockDim.x + threadIdx.x;
   Rfit::Map3x4d hits(phits+i,3,4);
   Rfit::Map4d   fast_fit_input(pfast_fit_input+i,4);
   Rfit::Map6x4f hits_ge(phits_ge+i,6,4);
 
   constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
   constexpr auto n = N;
-  
-  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
 
+  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
   Rfit::Matrix2Nd<N> hits_cov =  MatrixXd::Zero(2 * n, 2 * n);
   Rfit::loadCovariance2D(hits_ge,hits_cov);
   
@@ -91,6 +143,7 @@ void kernelLineFit(double * __restrict__ phits,
   Rfit::Map6x4f hits_ge(phits_ge+i,6,4);
   line_fit[i] = Rfit::Line_fit(hits, hits_ge, circle_fit[i], fast_fit, B, true);
 }
+#endif
 
 template<typename M3x4, typename M6x4>
 __device__ __host__
@@ -137,6 +190,7 @@ void testFit() {
   Rfit::circle_fit * circle_fit_resultsGPU = nullptr;
   Rfit::circle_fit * circle_fit_resultsGPUret = new Rfit::circle_fit();
   Rfit::line_fit * line_fit_resultsGPU = nullptr;
+  Rfit::line_fit * line_fit_resultsGPUret = new Rfit::line_fit();
 
   fillHitsAndHitsCov(hits, hits_ge);
 
@@ -147,7 +201,11 @@ void testFit() {
   std::cout << "Generated cov:\n" << hits_ge << std::endl;
 
   // FAST_FIT_CPU
+#ifdef USE_BL
+  Vector4d fast_fit_results; BrokenLine::BL_Fast_fit(hits, fast_fit_results);
+#else
   Vector4d fast_fit_results; Rfit::Fast_fit(hits, fast_fit_results);
+#endif
   std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]):\n" << fast_fit_results << std::endl;
 
   // for timing    purposes we fit    4096 tracks
@@ -170,9 +228,35 @@ void testFit() {
   std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]): GPU\n" << fast_fit << std::endl;
   assert(isEqualFuzzy(fast_fit_results, fast_fit));
 
-  // CIRCLE_FIT CPU
   constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
   constexpr auto n = N;
+
+#ifdef USE_BL
+  // CIRCLE AND LINE FIT CPU
+  BrokenLine::PreparedBrokenLineData<N> data;
+  BrokenLine::karimaki_circle_fit circle_fit_results;
+  Rfit::line_fit line_fit_results;
+  Rfit::Matrix3d Jacob;
+  Rfit::MatrixNplusONEd<N> C_U;
+  BrokenLine::prepareBrokenLineData(hits,fast_fit_results,B,data);
+  BrokenLine::BL_Line_fit(hits_ge,fast_fit_results,B,data,line_fit_results);
+  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_results,B,data,circle_fit_results,Jacob,C_U);
+  Jacob << 1,0,0,
+    0,1,0,
+    0,0,-B/std::copysign(Rfit::sqr(circle_fit_results.par(2)),circle_fit_results.par(2));
+  circle_fit_results.par(2)=B/std::abs(circle_fit_results.par(2));
+  circle_fit_results.cov=Jacob*circle_fit_results.cov*Jacob.transpose();
+
+  // fit on GPU
+  kernelBrokenLineFit<<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU,
+					  fast_fit_resultsGPU, B,
+					  circle_fit_resultsGPU,
+					  line_fit_resultsGPU);
+  cudaDeviceSynchronize();
+
+  
+#else
+  // CIRCLE_FIT CPU
   Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
 
   Rfit::Matrix2Nd<N> hits_cov =  MatrixXd::Zero(2 * n, 2 * n);
@@ -180,33 +264,36 @@ void testFit() {
   Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n),
       hits_cov,
       fast_fit_results, rad, B, true);
-  std::cout << "Fitted values (CircleFit):\n" << circle_fit_results.par << std::endl;
 
   // CIRCLE_FIT GPU
-
   kernelCircleFit<<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU,
       fast_fit_resultsGPU, B, circle_fit_resultsGPU);
   cudaDeviceSynchronize();
-
-  cudaMemcpy(circle_fit_resultsGPUret, circle_fit_resultsGPU,
-      sizeof(Rfit::circle_fit), cudaMemcpyDeviceToHost);
-  std::cout << "Fitted values (CircleFit) GPU:\n" << circle_fit_resultsGPUret->par << std::endl;
-  assert(isEqualFuzzy(circle_fit_results.par, circle_fit_resultsGPUret->par));
-
+ 
   // LINE_FIT CPU
   Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_ge, circle_fit_results, fast_fit_results, B, true);
-  std::cout << "Fitted values (LineFit):\n" << line_fit_results.par << std::endl;
 
-  // LINE_FIT GPU
-  Rfit::line_fit * line_fit_resultsGPUret = new Rfit::line_fit();
 
   kernelLineFit<<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU, B, circle_fit_resultsGPU, fast_fit_resultsGPU, line_fit_resultsGPU);
   cudaDeviceSynchronize();
+#endif
 
+  std::cout << "Fitted values (CircleFit):\n" << circle_fit_results.par << std::endl;
+
+  
+  cudaMemcpy(circle_fit_resultsGPUret, circle_fit_resultsGPU,
+	     sizeof(Rfit::circle_fit), cudaMemcpyDeviceToHost);
+  std::cout << "Fitted values (CircleFit) GPU:\n" << circle_fit_resultsGPUret->par << std::endl;
+  assert(isEqualFuzzy(circle_fit_results.par, circle_fit_resultsGPUret->par));
+  
+  
+  std::cout << "Fitted values (LineFit):\n" << line_fit_results.par << std::endl;
+    // LINE_FIT GPU
   cudaMemcpy(line_fit_resultsGPUret, line_fit_resultsGPU, sizeof(Rfit::line_fit), cudaMemcpyDeviceToHost);
   std::cout << "Fitted values (LineFit) GPU:\n" << line_fit_resultsGPUret->par << std::endl;
   assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par));
 
+  
   std::cout << "Fitted cov (CircleFit) CPU:\n" << circle_fit_results.cov << std::endl;
   std::cout << "Fitted cov (LineFit): CPU\n" << line_fit_results.cov << std::endl;
   std::cout << "Fitted cov (CircleFit) GPU:\n" << circle_fit_resultsGPUret->cov << std::endl;
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp b/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
index af4a3e52f46fa..c4b791eecbe4d 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
@@ -1,9 +1,16 @@
 #include <iostream>
 
+// #define __host__
+// #define __device__
+
 #include <Eigen/Core>
 #include <Eigen/Eigenvalues>
 
+#ifdef USE_BL
+#include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
+#else
 #include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
+#endif
 
 #include "test_common.h"
 
@@ -57,25 +64,50 @@ void testFit() {
   std::cout << "Generated cov:\n" << hits_ge << std::endl;
 
   // FAST_FIT_CPU
+#ifdef USE_BL
+  Vector4d fast_fit_results; BrokenLine::BL_Fast_fit(hits, fast_fit_results);
+#else
   Vector4d fast_fit_results; Rfit::Fast_fit(hits, fast_fit_results);
+#endif
   std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]):\n" << fast_fit_results << std::endl;
 
 
   // CIRCLE_FIT CPU
   constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
   constexpr auto n = N;
-  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
 
+#ifdef USE_BL
+  BrokenLine::PreparedBrokenLineData<N> data;
+  BrokenLine::karimaki_circle_fit circle_fit_results;
+  Rfit::Matrix3d Jacob;
+  Rfit::MatrixNplusONEd<N> C_U;
+    
+  BrokenLine::prepareBrokenLineData(hits,fast_fit_results,B,data);
+  Rfit::line_fit line_fit_results;
+  BrokenLine::BL_Line_fit(hits_ge,fast_fit_results,B,data,line_fit_results);
+  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_results,B,data,circle_fit_results,Jacob,C_U);
+  Jacob << 1,0,0,
+    0,1,0,
+    0,0,-B/std::copysign(Rfit::sqr(circle_fit_results.par(2)),circle_fit_results.par(2));
+  circle_fit_results.par(2)=B/std::abs(circle_fit_results.par(2));
+  circle_fit_results.cov=Jacob*circle_fit_results.cov*Jacob.transpose();
+#else
+  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
   Rfit::Matrix2Nd<N> hits_cov =  MatrixXd::Zero(2 * n, 2 * n);
   Rfit::loadCovariance2D(hits_ge,hits_cov);
   Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n),
       hits_cov,
       fast_fit_results, rad, B, true);
-  std::cout << "Fitted values (CircleFit):\n" << circle_fit_results.par << std::endl;
-
   // LINE_FIT CPU
   Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_ge, circle_fit_results, fast_fit_results, B, true);
-  std::cout << "Fitted values (LineFit):\n" << line_fit_results.par << std::endl;
+  Rfit::par_uvrtopak(circle_fit_results, B, true);
+
+#endif
+  
+  std::cout << "Fitted values (CircleFit):\n" << circle_fit_results.par
+	    << "\nchi2 " << circle_fit_results.chi2 << std::endl;
+  std::cout << "Fitted values (LineFit):\n" << line_fit_results.par
+	    << "\nchi2 " << line_fit_results.chi2 << std::endl;
 
   std::cout << "Fitted cov (CircleFit) CPU:\n" << circle_fit_results.cov << std::endl;
   std::cout << "Fitted cov (LineFit): CPU\n" << line_fit_results.cov << std::endl;

From a3950ea16ffd8605400542a21f3516542c54eb2a Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 30 Jan 2019 18:06:48 +0100
Subject: [PATCH 12/95] make it working

---
 .../PixelTrackFitting/interface/FitUtils.h    | 115 ++++++++++++++++++
 1 file changed, 115 insertions(+)
 create mode 100644 RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h b/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
new file mode 100644
index 0000000000000..e647098374959
--- /dev/null
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
@@ -0,0 +1,115 @@
+#ifndef RecoPixelVertexing_PixelTrackFitting_interface_FitUtils_h
+#define RecoPixelVertexing_PixelTrackFitting_interface_FitUtils_h
+
+
+#include "FitResult.h"
+
+
+namespace Rfit
+{
+  
+  template <class C>
+  __host__ __device__ void printIt(C* m, const char* prefix = "")
+  {
+#ifdef RFIT_DEBUG
+    for (u_int r = 0; r < m->rows(); ++r)
+      {
+        for (u_int c = 0; c < m->cols(); ++c)
+	  {
+            printf("%s Matrix(%d,%d) = %g\n", prefix, r, c, (*m)(r, c));
+	  }
+      }
+#endif
+  }
+  
+  /*!
+    \brief raise to square.
+  */
+  template <typename T>
+  __host__ __device__ inline T sqr(const T a)
+  {
+    return a * a;
+  }
+  
+  /*!
+    \brief Compute cross product of two 2D vector (assuming z component 0),
+    returning z component of the result.
+    \param a first 2D vector in the product.
+    \param b second 2D vector in the product.
+    \return z component of the cross product.
+  */
+  
+  __host__ __device__ inline double cross2D(const Vector2d& a, const Vector2d& b)
+  {
+    return a.x() * b.y() - a.y() * b.x();
+  }
+  
+  /*!
+   *  load error in CMSSW format to our formalism
+   *  
+   */
+  template<typename M6x4f, typename M2Nd>
+  __host__ __device__ void loadCovariance2D(M6x4f const & ge,  M2Nd & hits_cov) {
+    // Index numerology:
+    // i: index of the hits/point (0,..,3)
+    // j: index of space component (x,y,z)
+    // l: index of space components (x,y,z)
+    // ge is always in sync with the index i and is formatted as:
+    // ge[] ==> [xx, xy, yy, xz, yz, zz]
+    // in (j,l) notation, we have:
+    // ge[] ==> [(0,0), (0,1), (1,1), (0,2), (1,2), (2,2)]
+    // so the index ge_idx corresponds to the matrix elements:
+    // | 0  1  3 |
+    // | 1  2  4 |
+    // | 3  4  5 |
+    constexpr uint32_t  hits_in_fit = 4; // Fixme
+    for (uint32_t i=0; i< hits_in_fit; ++i) {
+      auto ge_idx = 0; auto j=0; auto l=0;
+      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+      ge_idx = 2; j=1; l=1;
+      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+      ge_idx = 1; j=1; l=0;
+      hits_cov(i + l * hits_in_fit, i + j * hits_in_fit) =
+      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+    }
+  }
+  
+  template<typename M6x4f, typename M3xNd>
+  __host__ __device__ void loadCovariance(M6x4f const & ge,  M3xNd & hits_cov) {
+    
+    // Index numerology:
+    // i: index of the hits/point (0,..,3)
+    // j: index of space component (x,y,z)
+    // l: index of space components (x,y,z)
+    // ge is always in sync with the index i and is formatted as:
+    // ge[] ==> [xx, xy, yy, xz, yz, zz]
+    // in (j,l) notation, we have:
+    // ge[] ==> [(0,0), (0,1), (1,1), (0,2), (1,2), (2,2)]
+    // so the index ge_idx corresponds to the matrix elements:
+    // | 0  1  3 |
+    // | 1  2  4 |
+    // | 3  4  5 |
+    constexpr uint32_t  hits_in_fit = 4; // Fixme
+    for (uint32_t i=0; i<hits_in_fit; ++i) {
+      auto ge_idx = 0; auto j=0; auto l=0;
+      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+      ge_idx = 2; j=1; l=1;
+      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+      ge_idx = 5; j=2; l=2;
+      hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+      ge_idx = 1; j=1; l=0;
+      hits_cov(i + l * hits_in_fit, i + j * hits_in_fit) =
+	hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+      ge_idx = 3; j=2; l=0;
+      hits_cov(i + l * hits_in_fit, i + j * hits_in_fit) =
+	hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+      ge_idx = 4; j=2; l=1;
+      hits_cov(i + l * hits_in_fit, i + j * hits_in_fit) =
+	hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
+    }
+  }
+  
+}
+
+
+#endif

From 8578b61c8fe2040d184f42f8815d2b0d51cfcdfb Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 30 Jan 2019 18:34:20 +0100
Subject: [PATCH 13/95] tests runs

---
 .../PixelTrackFitting/interface/FitUtils.h    | 29 +++++++++++++++++++
 .../PixelTrackFitting/interface/RiemannFit.h  | 29 -------------------
 .../PixelTrackFitting/test/BuildFile.xml      | 27 ++++++++++++++++-
 .../test/PixelTrackRiemannFit.cc              | 21 +++++++-------
 .../PixelTrackFitting/test/testRiemannFit.cpp |  7 ++---
 5 files changed, 69 insertions(+), 44 deletions(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h b/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
index e647098374959..5ac3740406bd7 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
@@ -108,6 +108,35 @@ namespace Rfit
 	hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
     }
   }
+
+
+/*!
+    \brief Transform circle parameter from (X0,Y0,R) to (phi,Tip,p_t) and
+    consequently covariance matrix.
+    \param circle_uvr parameter (X0,Y0,R), covariance matrix to
+    be transformed and particle charge.
+    \param B magnetic field in Gev/cm/c unit.
+    \param error flag for errors computation.
+*/
+__host__ __device__ inline void par_uvrtopak(circle_fit& circle, const double B, const bool error)
+{
+    Vector3d par_pak;
+    const double temp0 = circle.par.head(2).squaredNorm();
+    const double temp1 = sqrt(temp0);
+    par_pak << atan2(circle.q * circle.par(0), -circle.q * circle.par(1)),
+        circle.q * (temp1 - circle.par(2)), circle.par(2) * B;
+    if (error)
+    {
+        const double temp2 = sqr(circle.par(0)) * 1. / temp0;
+        const double temp3 = 1. / temp1 * circle.q;
+        Matrix3d J4;
+        J4 << -circle.par(1) * temp2 * 1. / sqr(circle.par(0)), temp2 * 1. / circle.par(0), 0., 
+               circle.par(0) * temp3, circle.par(1) * temp3, -circle.q,
+               0., 0., B;
+        circle.cov = J4 * circle.cov * J4.transpose();
+    }
+    circle.par = par_pak;
+}
   
 }
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
index 6980941bfd0fc..f5f098999a0c0 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
@@ -312,35 +312,6 @@ template<typename M2xN>
     return ((p2D(0, 1) - p2D(0, 0)) * (par_uvr.y() - p2D(1, 0)) - (p2D(1, 1) - p2D(1, 0)) * (par_uvr.x() - p2D(0, 0)) > 0)? -1 : 1;
 }
 
-/*!
-    \brief Transform circle parameter from (X0,Y0,R) to (phi,Tip,p_t) and
-    consequently covariance matrix.
-    \param circle_uvr parameter (X0,Y0,R), covariance matrix to
-    be transformed and particle charge.
-    \param B magnetic field in Gev/cm/c unit.
-    \param error flag for errors computation.
-*/
-
-__host__ __device__ inline void par_uvrtopak(circle_fit& circle, const double B, const bool error)
-{
-    Vector3d par_pak;
-    const double temp0 = circle.par.head(2).squaredNorm();
-    const double temp1 = sqrt(temp0);
-    par_pak << atan2(circle.q * circle.par(0), -circle.q * circle.par(1)),
-        circle.q * (temp1 - circle.par(2)), circle.par(2) * B;
-    if (error)
-    {
-        const double temp2 = sqr(circle.par(0)) * 1. / temp0;
-        const double temp3 = 1. / temp1 * circle.q;
-        Matrix3d J4;
-        J4 << -circle.par(1) * temp2 * 1. / sqr(circle.par(0)), temp2 * 1. / circle.par(0), 0., 
-               circle.par(0) * temp3, circle.par(1) * temp3, -circle.q,
-               0., 0., B;
-        circle.cov = J4 * circle.cov * J4.transpose();
-    }
-    circle.par = par_pak;
-}
-
 /*!
     \brief Compute the eigenvector associated to the minimum eigenvalue.
     \param A the Matrix you want to know eigenvector and eigenvalue.
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml b/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
index e6c67be031342..f824e60dcf471 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTrackFitting/test/BuildFile.xml
@@ -21,13 +21,22 @@
   <flags CXXFLAGS="-g"/>
 </bin>
 
+
+<bin file="testRiemannFit.cpp" name="testBrokenLineFit">
+  <use name="eigen"/>
+  <use name="cuda"/>
+  <flags CXXFLAGS="-g -DUSE_BL"/>
+</bin>
+
+
 <bin file="testRiemannFit.cpp" name="testRiemannFitDump">
   <use name="eigen"/>
   <use name="cuda"/>
   <flags CXXFLAGS="-g -DRFIT_DEBUG"/>
 </bin>
 
-<bin file="testEigenGPU.cu" name="testEigenGPU_t">
+
+<bin file="testEigenGPU.cu" name="testRiemannFitGPU_t">
   <use name="eigen"/>
   <use name="cuda"/>
   <use name="cuda-api-wrappers"/>
@@ -35,6 +44,15 @@
   <flags CXXFLAGS="-g"/>
 </bin>
 
+<bin file="testEigenGPU.cu" name="testBrokenLineFitGPU_t">
+  <use name="eigen"/>
+  <use name="cuda"/>
+  <use name="cuda-api-wrappers"/>
+  <use name="HeterogeneousCore/CUDAUtilities"/>
+  <flags CXXFLAGS="-g -DUSE_BL"/>
+</bin>
+
+
 <bin file="testEigenGPUNoFit.cu" name="testEigenGPUNoFit_t">
   <use name="eigen"/>
   <use name="cuda"/>
@@ -50,6 +68,13 @@
   <flags CXXFLAGS="-DEIGEN_NO_DEBUG"/>
 </bin>
 
+<bin file="PixelTrackRiemannFit.cc" name = "PixelTrackBrokenLineFit">
+  <use name="eigen"/>
+  <use name="cuda"/>
+  <use name="root"/>
+  <flags CXXFLAGS="-DEIGEN_NO_DEBUG -DUSE_BL"/>
+</bin>
+
 <bin file="PixelTrackRiemannFit.cc" name = "PixelTrackRiemannFit_Debug">
   <use name="eigen"/>
   <use name="cuda"/>
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/PixelTrackRiemannFit.cc b/RecoPixelVertexing/PixelTrackFitting/test/PixelTrackRiemannFit.cc
index adcabd7dde508..f19618e23d252 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/PixelTrackRiemannFit.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/test/PixelTrackRiemannFit.cc
@@ -10,8 +10,11 @@
 #include <TFile.h>
 #include <TH1F.h>
 
+#ifdef USE_BL
+#include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
+#else
 #include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
-//#include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
+#endif
 
 using namespace std;
 using namespace Eigen;
@@ -335,7 +338,6 @@ void computePull(std::array<Fit, N> & fit, const char * label,
 
 void test_helix_fit(bool getcin) {
   int n_;
-  bool return_err;
   const double B_field = 3.8 * c_speed / pow(10, 9) / 100;
   Matrix<double, 6, 1> gen_par;
   Vector5d true_par;
@@ -343,7 +345,7 @@ void test_helix_fit(bool getcin) {
   generator.seed(1);
   std::cout << std::setprecision(6);
   cout << "_________________________________________________________________________\n";
-  cout << "n x(cm) y(cm) z(cm) phi(grad) R(Gev/c) eta iteration return_err debug" << endl;
+  cout << "n x(cm) y(cm) z(cm) phi(grad) R(Gev/c) eta iteration debug" << endl;
   if (getcin) {
     cout << "hits: ";
     cin  >> n_;
@@ -368,14 +370,11 @@ void test_helix_fit(bool getcin) {
      gen_par(4) = 10.;   // R (p_t)
      gen_par(5) = 1.;   // eta
   }
-  return_err = true;
 
   const int iteration = 5000;
   gen_par = New_par(gen_par, 1, B_field);
   true_par = True_par(gen_par, 1, B_field);
-  // Matrix3xNd<4> hits;
   std::array<helix_fit, iteration> helixRiemann_fit;
-//  std::array<BrokenLine::helix_fit, iteration> helixBrokenLine_fit;
 
   std::cout << "\nTrue parameters: "
     << "phi: " << true_par(0) << " "
@@ -396,11 +395,14 @@ void test_helix_fit(bool getcin) {
     //      gen.hits.col(2) << 7.25991010666, 7.74653434753, 30.6931324005;
     //      gen.hits.col(3) << 8.99161434174, 9.54262828827, 38.1338043213;
     delta -= std::chrono::high_resolution_clock::now()-start;
-    helixRiemann_fit[i%iteration] = Rfit::Helix_fit(gen.hits, gen.hits_ge, B_field, return_err);
+    helixRiemann_fit[i%iteration] =
+#ifdef USE_BL
+      BrokenLine::BL_Helix_fit(gen.hits, gen.hits_ge, B_field);
+#else
+      Rfit::Helix_fit(gen.hits, gen.hits_ge, B_field, true);
+#endif
     delta += std::chrono::high_resolution_clock::now()-start;
 
-//    helixBrokenLine_fit[i] = BrokenLine::Helix_fit(gen.hits, gen.hits_cov, B_field);
-
     if (helixRiemann_fit[i%iteration].par(0)>10.) std::cout << "error" << std::endl;
     if (0==i)
       cout << std::setprecision(6)
@@ -423,7 +425,6 @@ void test_helix_fit(bool getcin) {
   }
   std::cout << "elapsted time " << double(std::chrono::duration_cast<std::chrono::nanoseconds>(delta).count())/1.e6 << std::endl;
   computePull(helixRiemann_fit, "Riemann", n_, iteration, true_par);
-//  computePull(helixBrokenLine_fit, "BrokenLine", n_, iteration, true_par);
 }
 
 int main(int nargs, char**) {
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp b/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
index c4b791eecbe4d..77f1f39ee29e6 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
@@ -74,7 +74,6 @@ void testFit() {
 
   // CIRCLE_FIT CPU
   constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
-  constexpr auto n = N;
 
 #ifdef USE_BL
   BrokenLine::PreparedBrokenLineData<N> data;
@@ -92,10 +91,10 @@ void testFit() {
   circle_fit_results.par(2)=B/std::abs(circle_fit_results.par(2));
   circle_fit_results.cov=Jacob*circle_fit_results.cov*Jacob.transpose();
 #else
-  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
-  Rfit::Matrix2Nd<N> hits_cov =  MatrixXd::Zero(2 * n, 2 * n);
+  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, N).colwise().norm());
+  Rfit::Matrix2Nd<N> hits_cov =  Rfit::Matrix2Nd<N>::Zero();
   Rfit::loadCovariance2D(hits_ge,hits_cov);
-  Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n),
+  Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, N),
       hits_cov,
       fast_fit_results, rad, B, true);
   // LINE_FIT CPU

From 36550f229cebbd28259f0b4bc25937f8235a1acd Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 30 Jan 2019 19:15:23 +0100
Subject: [PATCH 14/95] some previous clenup

---
 .../plugins/CAHitQuadrupletGeneratorKernels.cu        |  8 ++++----
 RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h  |  2 +-
 .../PixelTriplets/plugins/gpuFishbone.h               |  2 +-
 .../PixelTriplets/plugins/gpuPixelDoublets.h          | 11 +++++++++--
 4 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index ec9c4960062ef..22fa16e604a80 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -75,7 +75,7 @@ kernel_fishboneCleaner(GPUCACell const * cells, uint32_t const * __restrict__ nC
                             pixelTuplesHeterogeneousProduct::Quality * quality
                            ) {
 
-   constexpr auto bad = pixelTuplesHeterogeneousProduct::bad;
+  constexpr auto bad = pixelTuplesHeterogeneousProduct::bad;
 
   auto cellIndex = threadIdx.x + blockIdx.x * blockDim.x;
 
@@ -95,9 +95,9 @@ kernel_fastDuplicateRemover(GPUCACell const * cells, uint32_t const * __restrict
                             pixelTuplesHeterogeneousProduct::Quality * quality
                            ) {
 
-   constexpr auto bad = pixelTuplesHeterogeneousProduct::bad;
-   constexpr auto dup = pixelTuplesHeterogeneousProduct::dup;
-   // constexpr auto loose = pixelTuplesHeterogeneousProduct::loose;
+  constexpr auto bad = pixelTuplesHeterogeneousProduct::bad;
+  constexpr auto dup = pixelTuplesHeterogeneousProduct::dup;
+  // constexpr auto loose = pixelTuplesHeterogeneousProduct::loose;
 
   auto cellIndex = threadIdx.x + blockIdx.x * blockDim.x;
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index dbd4eecbaab3c..0858167190301 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -159,7 +159,7 @@ class GPUCACell {
     tmpNtuplet.push_back_unsafe(theDoubletId);
     assert(tmpNtuplet.size()<=4);
 
-    if(theOuterNeighbors.size()>0) { // continue
+    if(theOuterNeighbors.size()>0) {
       for (int j = 0; j < theOuterNeighbors.size(); ++j) {
         auto otherCell = theOuterNeighbors[j];
         cells[otherCell].find_ntuplets(cells, foundNtuplets, apc, tmpNtuplet,
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuFishbone.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuFishbone.h
index 796241eaf50ff..4368bc12ab5f8 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuFishbone.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuFishbone.h
@@ -54,7 +54,7 @@ namespace gpuPixelDoublets {
     auto sg=0;
     for (uint32_t ic=0; ic<s; ++ic) {
       auto & ci = cells[vc[ic]];
-      if (checkTrack && 0==ci.theTracks.size()) continue;
+      if (checkTrack && ci.theTracks.empty()) continue;
       cc[sg] = vc[ic];
       d[sg] = ci.get_inner_detId(hh);
 //      l[sg] = layer(d[sg]);
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 192efd0d8919f..0e67c4cc3e28b 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -107,15 +107,20 @@ namespace gpuPixelDoublets {
       auto kl = Hist::bin(int16_t(mep-iphicut));
       auto kh = Hist::bin(int16_t(mep+iphicut));
       auto incr = [](auto & k) { return k = (k+1) % Hist::nbins();};
+
+#ifdef GPU_DEBUG
       int  tot  = 0;
       int  nmin = 0;
+      int tooMany=0;
+#endif
+
       auto khh = kh;
       incr(khh);
-
-      int tooMany=0;
       for (auto kk = kl; kk != khh; incr(kk)) {
+#ifdef GPU_DEBUG
         if (kk != kl && kk != kh)
           nmin += hist.size(kk+hoff);
+#endif
         auto const * __restrict__ p = hist.begin(kk+hoff);
         auto const * __restrict__ e = hist.end(kk+hoff);
         p+=first;
@@ -132,8 +137,10 @@ namespace gpuPixelDoublets {
           // int layerPairId, int doubletId, int innerHitId, int outerHitId)
           cells[ind].init(hh, pairLayerId, ind, i, oi);
           isOuterHitOfCell[oi].push_back(ind);
+#ifdef GPU_DEBUG
           if (isOuterHitOfCell[oi].full()) ++tooMany;
           ++tot;
+#endif
         }
       }
 #ifdef GPU_DEBUG

From 790ccfa5c6a080e82593cbba2d1808a763cc8dba Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 30 Jan 2019 19:33:40 +0100
Subject: [PATCH 15/95] change name of fit driver

---
 .../plugins/CAHitQuadrupletGeneratorGPU.cc           |  2 +-
 .../plugins/CAHitQuadrupletGeneratorGPU.h            |  4 ++--
 .../plugins/{RiemannFitOnGPU.cc => HelixFitOnGPU.cc} |  6 +++---
 .../plugins/{RiemannFitOnGPU.h => HelixFitOnGPU.h}   | 12 ++++++------
 .../PixelTriplets/plugins/RiemannFitOnGPU.cu         |  4 ++--
 5 files changed, 14 insertions(+), 14 deletions(-)
 rename RecoPixelVertexing/PixelTriplets/plugins/{RiemannFitOnGPU.cc => HelixFitOnGPU.cc} (85%)
 rename RecoPixelVertexing/PixelTriplets/plugins/{RiemannFitOnGPU.h => HelixFitOnGPU.h} (81%)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index 4963bd68c712c..00cda5029fd5c 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -172,7 +172,7 @@ void CAHitQuadrupletGeneratorGPU::launchKernels(HitsOnCPU const & hh,
 
   kernels.launchKernels(hh, gpu_, cudaStream); 
   if (doRiemannFit) {
-    fitter.launchKernels(hh, hh.nHits, CAConstants::maxNumberOfQuadruplets(), cudaStream);
+    fitter.launchRiemannKernels(hh, hh.nHits, CAConstants::maxNumberOfQuadruplets(), cudaStream);
     kernels.classifyTuples(hh, gpu_, cudaStream);
   }
   if (transferToCPU) {
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.h
index d2bc4e347bf9a..5f0b29a94b376 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.h
@@ -23,7 +23,7 @@
 #include "RecoPixelVertexing/PixelTriplets/plugins/RecHitsMap.h"
 
 #include "CAHitQuadrupletGeneratorKernels.h"
-#include "RiemannFitOnGPU.h"
+#include "HelixFitOnGPU.h"
 
 #include "RecoPixelVertexing/PixelTriplets/plugins/pixelTuplesHeterogeneousProduct.h"
 
@@ -94,7 +94,7 @@ class CAHitQuadrupletGeneratorGPU {
 
 
     CAHitQuadrupletGeneratorKernels kernels;
-    RiemannFitOnGPU fitter;
+    HelixFitOnGPU fitter;
 
     // not really used at the moment
     const float caThetaCut = 0.00125f;
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.cc
similarity index 85%
rename from RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cc
rename to RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.cc
index fe95e10a48b5a..fa45918f9acec 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.cc
@@ -1,7 +1,7 @@
-#include "RiemannFitOnGPU.h"
+#include "HelixFitOnGPU.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
 
-void RiemannFitOnGPU::allocateOnGPU(TuplesOnGPU::Container const * tuples, Rfit::helix_fit * helix_fit_results) {
+void HelixFitOnGPU::allocateOnGPU(TuplesOnGPU::Container const * tuples, Rfit::helix_fit * helix_fit_results) {
 
   tuples_d = tuples;
   helix_fit_results_d = helix_fit_results;
@@ -22,7 +22,7 @@ void RiemannFitOnGPU::allocateOnGPU(TuplesOnGPU::Container const * tuples, Rfit:
 
 }
 
-void RiemannFitOnGPU::deallocateOnGPU() {
+void HelixFitOnGPU::deallocateOnGPU() {
 
   cudaFree(hitsGPU_);
   cudaFree(hits_geGPU_);
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.h b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
similarity index 81%
rename from RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.h
rename to RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
index fac88ac2c2bd4..46c1ee4174719 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
@@ -1,5 +1,5 @@
-#ifndef RecoPixelVertexing_PixelTrackFitting_plugins_RiemannFitOnGPU_h
-#define RecoPixelVertexing_PixelTrackFitting_plugins_RiemannFitOnGPU_h
+#ifndef RecoPixelVertexing_PixelTrackFitting_plugins_HelixFitOnGPU_h
+#define RecoPixelVertexing_PixelTrackFitting_plugins_HelixFitOnGPU_h
 
 #include "RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h"
 #include "RecoPixelVertexing/PixelTriplets/plugins/pixelTuplesHeterogeneousProduct.h"
@@ -20,7 +20,7 @@ namespace Rfit {
 }
 
 
-class RiemannFitOnGPU {
+class HelixFitOnGPU {
 public:
 
    using HitsOnGPU = siPixelRecHitsHeterogeneousProduct::HitsOnGPU;
@@ -28,11 +28,11 @@ class RiemannFitOnGPU {
 
    using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
 
-   RiemannFitOnGPU() = default;
-   ~RiemannFitOnGPU() { deallocateOnGPU();}
+   HelixFitOnGPU() = default;
+   ~HelixFitOnGPU() { deallocateOnGPU();}
 
    void setBField(double bField) { bField_ = bField;}
-   void launchKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream);
+   void launchRiemannKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream);
 
    void allocateOnGPU(TuplesOnGPU::Container const * tuples, Rfit::helix_fit * helix_fit_results);
    void deallocateOnGPU();
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
index 1bcfb847d2ae8..86cc4928f736c 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
@@ -2,7 +2,7 @@
 // Author: Felice Pantaleo, CERN
 //
 
-#include "RiemannFitOnGPU.h"
+#include "HelixFitOnGPU.h"
 #include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
 
 #include <cstdint>
@@ -167,7 +167,7 @@ void kernelLineFitAllHits(TuplesOnGPU::Container const * __restrict__ foundNtupl
 }
 
 
-void RiemannFitOnGPU::launchKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream)
+void HelixFitOnGPU::launchRiemannKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream)
 {
     assert(tuples_d); assert(fast_fit_resultsGPU_);
 

From 11482bf98df3f135a9d7885ce3961d9b746560dd Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 31 Jan 2019 10:57:52 +0100
Subject: [PATCH 16/95] BL driver ok

---
 .../plugins/BrokenLineFitOnGPU.cu             | 157 ++++++++++++++++++
 .../PixelTriplets/plugins/HelixFitOnGPU.h     |   1 +
 2 files changed, 158 insertions(+)
 create mode 100644 RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
new file mode 100644
index 0000000000000..4eda9569cc511
--- /dev/null
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
@@ -0,0 +1,157 @@
+//
+// Author: Felice Pantaleo, CERN
+//
+
+#include "HelixFitOnGPU.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
+
+#include <cstdint>
+#include <cuda_runtime.h>
+
+#include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
+#include "RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h"
+#include "RecoLocalTracker/SiPixelRecHits/plugins/siPixelRecHitsHeterogeneousProduct.h"
+
+
+using HitsOnCPU = siPixelRecHitsHeterogeneousProduct::HitsOnCPU;
+
+using HitsOnGPU = siPixelRecHitsHeterogeneousProduct::HitsOnGPU;
+using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
+
+using namespace Eigen;
+
+__global__
+void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
+    HitsOnGPU const * __restrict__ hhp,
+    int hits_in_fit,
+    double * __restrict__ phits,
+    float * __restrict__ phits_ge,
+    double * __restrict__ pfast_fit,
+    uint32_t offset)
+{
+
+  assert(hits_in_fit==4); // FixMe later template
+
+  assert(pfast_fit); assert(foundNtuplets);
+
+  auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
+  auto helix_start = local_start + offset;
+
+  if (helix_start>=foundNtuplets->nbins()) return;
+  if (foundNtuplets->size(helix_start)<hits_in_fit) {
+    return;
+  }
+
+  Rfit::Map3x4d hits(phits+local_start);
+  Rfit::Map4d   fast_fit(pfast_fit+local_start);
+  Rfit::Map6x4f hits_ge(phits_ge+local_start);
+
+  // Prepare data structure
+  auto const * hitId = foundNtuplets->begin(helix_start);
+  for (unsigned int i = 0; i < hits_in_fit; ++i) {
+    auto hit = hitId[i];
+    // printf("Hit global: %f,%f,%f\n", hhp->xg_d[hit],hhp->yg_d[hit],hhp->zg_d[hit]);
+    float ge[6];
+    hhp->cpeParams->detParams(hhp->detInd_d[hit]).frame.toGlobal(hhp->xerr_d[hit], 0, hhp->yerr_d[hit], ge);
+    // printf("Error: %d: %f,%f,%f,%f,%f,%f\n",hhp->detInd_d[hit],ge[0],ge[1],ge[2],ge[3],ge[4],ge[5]);
+
+    hits.col(i) << hhp->xg_d[hit], hhp->yg_d[hit], hhp->zg_d[hit];
+    hits_ge.col(i) << ge[0],ge[1],ge[2],ge[3],ge[4],ge[5];
+  }
+  BrokenLine::BL_Fast_fit(hits,fast_fit);
+
+  // no NaN here....
+  assert(fast_fit(0)==fast_fit(0));
+  assert(fast_fit(1)==fast_fit(1));
+  assert(fast_fit(2)==fast_fit(2));
+  assert(fast_fit(3)==fast_fit(3));
+
+}
+
+__global__
+void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
+    int hits_in_fit,
+    double B,
+    Rfit::helix_fit *results,
+    double * __restrict__ phits,
+    float * __restrict__ phits_ge,
+    double * __restrict__ pfast_fit,
+    uint32_t offset)
+{
+
+  assert(results); assert(pfast_fit);
+
+  auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
+  auto helix_start = local_start + offset;
+
+  if (helix_start>=foundNtuplets->nbins()) return;
+  if (foundNtuplets->size(helix_start)<hits_in_fit) {
+    return;
+  }
+
+  Rfit::Map3x4d hits(phits+local_start);
+  Rfit::Map4d   fast_fit(pfast_fit+local_start);
+  Rfit::Map6x4f hits_ge(phits_ge+local_start);
+
+  constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
+  
+  BrokenLine::PreparedBrokenLineData<N> data;
+  Rfit::Matrix3d Jacob;
+  Rfit::MatrixNplusONEd<N> C_U;
+
+  BrokenLine::karimaki_circle_fit circle;
+  Rfit::line_fit line;
+ 
+  BrokenLine::prepareBrokenLineData(hits,fast_fit,B,data);
+  BrokenLine::BL_Line_fit(hits_ge,fast_fit,B,data,line);
+  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit,B,data,circle,Jacob,C_U);
+  Jacob << 1,0,0,
+    0,1,0,
+    0,0,-B/std::copysign(Rfit::sqr(circle.par(2)),circle.par(2));
+  circle.par(2)=B/std::abs(circle.par(2));
+  circle.cov=Jacob*circle.cov*Jacob.transpose();
+
+
+  // Grab helix_fit from the proper location in the output vector
+  auto & helix = results[helix_start];
+  helix.par << circle.par, line.par;
+
+  helix.cov = Rfit::Matrix5d::Zero();
+  helix.cov.block(0, 0, 3, 3) = circle.cov;
+  helix.cov.block(3, 3, 2, 2) = line.cov;
+
+  helix.q = circle.q;
+  helix.chi2_circle = circle.chi2;
+  helix.chi2_line = line.chi2;
+
+#ifdef GPU_DEBUG
+  printf("kernelBLFit circle.par(0,1,2): %d %f,%f,%f\n", helix_start,
+         circle.par(0), circle.par(1), circle.par(2));
+  printf("kernelLineFitAllHits line.par(0,1): %d %f,%f\n", helix_start, line.par(0),line.par(1));
+  printf("kernelLineFitAllHits chi2 cov %f/%f %f,%f,%f,%f,%f\n",helix.chi2_circle,helix.chi2_line, 
+         helix.cov(0,0),helix.cov(1,1),helix.cov(2,2),helix.cov(3,3),helix.cov(4,4));
+#endif
+}
+
+
+void HelixFitOnGPU::launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream)
+{
+    assert(tuples_d); assert(fast_fit_resultsGPU_);
+
+    auto blockSize = 128;
+    auto numberOfBlocks = (maxNumberOfConcurrentFits_ + blockSize - 1) / blockSize;
+
+    for (uint32_t offset=0; offset<maxNumberOfTuples; offset+=maxNumberOfConcurrentFits_) {
+      kernelBLFastFit<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, hh.gpu_d, 4,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+      cudaCheck(cudaGetLastError());
+
+      kernelBLFit<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tuples_d, 4,  bField_, helix_fit_results_d,
+             hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
+             offset);
+      cudaCheck(cudaGetLastError());
+    }
+}
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
index 46c1ee4174719..034ca88368f7f 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
@@ -33,6 +33,7 @@ class HelixFitOnGPU {
 
    void setBField(double bField) { bField_ = bField;}
    void launchRiemannKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream);
+   void launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream);
 
    void allocateOnGPU(TuplesOnGPU::Container const * tuples, Rfit::helix_fit * helix_fit_results);
    void deallocateOnGPU();

From 91eeafd5a4d7554e2d28abb313944abb75a224fa Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 31 Jan 2019 11:00:09 +0100
Subject: [PATCH 17/95] trivial if

---
 .../PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc      | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index 00cda5029fd5c..47aa6a5b34cc3 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -174,7 +174,11 @@ void CAHitQuadrupletGeneratorGPU::launchKernels(HitsOnCPU const & hh,
   if (doRiemannFit) {
     fitter.launchRiemannKernels(hh, hh.nHits, CAConstants::maxNumberOfQuadruplets(), cudaStream);
     kernels.classifyTuples(hh, gpu_, cudaStream);
+  } else {
+    fitter.launchBrokenLineKernels(hh, hh.nHits, CAConstants::maxNumberOfQuadruplets(), cudaStream);
+    kernels.classifyTuples(hh, gpu_, cudaStream);
   }
+
   if (transferToCPU) {
     cudaCheck(cudaMemcpyAsync(tuples_,gpu_.tuples_d,
                               sizeof(TuplesOnGPU::Container),

From 7f0f80ca99bf77f3263ae18dca1fd584fb1389f8 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 31 Jan 2019 12:35:11 +0100
Subject: [PATCH 18/95] make BL default

---
 .../plugins/CAHitNtupletHeterogeneousEDProducer.cc              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitNtupletHeterogeneousEDProducer.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitNtupletHeterogeneousEDProducer.cc
index 141b960b993f0..6a531a6d4e3df 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitNtupletHeterogeneousEDProducer.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitNtupletHeterogeneousEDProducer.cc
@@ -94,7 +94,7 @@ void CAHitNtupletHeterogeneousEDProducer::fillDescriptions(
 
   desc.add<edm::InputTag>("trackingRegions", edm::InputTag("globalTrackingRegionFromBeamSpot"));
   desc.add<edm::InputTag>("heterogeneousPixelRecHitSrc", edm::InputTag("siPixelRecHitsPreSplitting"));
-  desc.add<bool>("doRiemannFit", true);  // mandatory!
+  desc.add<bool>("doRiemannFit", false);  // false == BrokenLine
   desc.add<bool>("gpuEnableTransfer", true);
   desc.add<bool>("gpuEnableConversion", true);
 

From 2013a29931a892977fa35bd351b1a5f4e7f3c5c3 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 3 Feb 2019 15:40:11 +0100
Subject: [PATCH 19/95] allow dump of hits

---
 .../plugins/BrokenLineFitOnGPU.cu             | 23 +++++++++++++++----
 1 file changed, 19 insertions(+), 4 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
index 4eda9569cc511..20337093ac79c 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
@@ -47,15 +47,30 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
   Rfit::Map4d   fast_fit(pfast_fit+local_start);
   Rfit::Map6x4f hits_ge(phits_ge+local_start);
 
+#ifdef BL_DUMP_HITS
+  __shared__ int done;
+  done = 0;
+  __syncthreads(); 
+  bool dump =  (foundNtuplets->size(helix_start)==5 &&
+                0 == atomicAdd(&done,1));
+  auto imax = foundNtuplets->size(helix_start);
+#else
+  auto imax = hits_in_fit;
+#endif
+
   // Prepare data structure
   auto const * hitId = foundNtuplets->begin(helix_start);
-  for (unsigned int i = 0; i < hits_in_fit; ++i) {
+  for (unsigned int i = 0; i < imax; ++i) {
     auto hit = hitId[i];
-    // printf("Hit global: %f,%f,%f\n", hhp->xg_d[hit],hhp->yg_d[hit],hhp->zg_d[hit]);
     float ge[6];
     hhp->cpeParams->detParams(hhp->detInd_d[hit]).frame.toGlobal(hhp->xerr_d[hit], 0, hhp->yerr_d[hit], ge);
-    // printf("Error: %d: %f,%f,%f,%f,%f,%f\n",hhp->detInd_d[hit],ge[0],ge[1],ge[2],ge[3],ge[4],ge[5]);
-
+#ifdef BL_DUMP_HITS
+    if (dump){
+      printf("Hit global: %d: %d hits.col(%d) << %f,%f,%f\n", helix_start, hhp->detInd_d[hit],i,hhp->xg_d[hit],hhp->yg_d[hit],hhp->zg_d[hit]);
+      printf("Error: %d: %d  hits_ge.col(%d) << %e,%e,%e,%e,%e,%e\n",helix_start,hhp->detInd_d[hit],i,ge[0],ge[1],ge[2],ge[3],ge[4],ge[5]);
+    }
+    if (i>=hits_in_fit) continue;
+#endif
     hits.col(i) << hhp->xg_d[hit], hhp->yg_d[hit], hhp->zg_d[hit];
     hits_ge.col(i) << ge[0],ge[1],ge[2],ge[3],ge[4],ge[5];
   }

From cc6893035915bc934f2d89d5aaa7a76ab0453322 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 3 Feb 2019 19:46:34 +0100
Subject: [PATCH 20/95] inversion by Cholesky Decomposition

---
 DataFormats/Math/BuildFile.xml                |   1 +
 .../Math/interface/choleskyInversion.h        | 353 ++++++++++++++++++
 DataFormats/Math/test/BuildFile.xml           |  13 +
 DataFormats/Math/test/CholeskyInvert_t.cpp    | 147 ++++++++
 DataFormats/Math/test/CholeskyInvert_t.cu     | 242 ++++++++++++
 5 files changed, 756 insertions(+)
 create mode 100644 DataFormats/Math/interface/choleskyInversion.h
 create mode 100644 DataFormats/Math/test/CholeskyInvert_t.cpp
 create mode 100644 DataFormats/Math/test/CholeskyInvert_t.cu

diff --git a/DataFormats/Math/BuildFile.xml b/DataFormats/Math/BuildFile.xml
index 6aa1d86287860..f628573593eb0 100644
--- a/DataFormats/Math/BuildFile.xml
+++ b/DataFormats/Math/BuildFile.xml
@@ -1,5 +1,6 @@
 <use   name="DataFormats/Common"/>
 <use   name="rootmath"/>
+<use   name="eigen">
 
 <export>
   <lib   name="1"/>
diff --git a/DataFormats/Math/interface/choleskyInversion.h b/DataFormats/Math/interface/choleskyInversion.h
new file mode 100644
index 0000000000000..6670d4c382fc1
--- /dev/null
+++ b/DataFormats/Math/interface/choleskyInversion.h
@@ -0,0 +1,353 @@
+#ifndef DataFormat_Math_choleskyInversion_H
+#define DataFormat_Math_choleskyInversion_H
+
+#include<cmath>
+#include <Eigen/Core>
+
+
+/**
+ * fully inlined specialized code to perform the inversion of a
+ * positive defined matrix of rank up to 6.
+ *
+ * originally by
+ * @author Manuel Schiller
+ * @date Aug 29 2008
+ *
+ *
+ */
+namespace choleskyInversion {
+
+  template<typename M1, typename M2> 
+  inline constexpr
+  void invert11(M1 const & src, M2 & dst) {
+    using F = decltype(src(0,0));
+    dst(0,0) = F(1.0) / src(0,0);
+  }
+  
+  template<typename M1, typename M2> 
+  inline constexpr
+  void invert22(M1 const & src, M2 & dst) {
+    using F = decltype(src(0,0));
+    auto luc0 = F(1.0) / src(0,0);
+    auto luc1 = src(1,0)*src(1,0) * luc0;
+    auto luc2 = F(1.0) / (src(1,1)-luc1);
+
+    auto li21 = luc1 * luc0 * luc2;
+
+    dst(0,0) = li21 + luc0;
+    dst(1,0) = - src(1,0)*luc0*luc2;
+    dst(1,1) = luc2;
+  }
+  
+  template<typename M1, typename M2> 
+  inline constexpr
+  void invert33(M1 const & src, M2 & dst) {
+    using F = decltype(src(0,0));
+    auto luc0 = F(1.0) / std::sqrt(src(0,0));
+    auto luc1 = src(1,0) * luc0;
+    auto luc2 = src(1,1) - luc1 * luc1;
+    luc2 = F(1.0) / std::sqrt(luc2);
+    auto luc3 = src(2,0) * luc0;
+    auto luc4 = (src(2,1) - luc1 * luc3) * luc2;
+    auto luc5 = src(2,2) - (luc3 * luc3 + luc4 * luc4);
+    luc5 = F(1.0) / std::sqrt(luc5);
+    
+    auto li21 = -luc1 * luc0 * luc2;
+    auto li32 = -luc4 * luc2 * luc5;
+    auto li31 = (luc1 * luc4 * luc2 - luc3) * luc0 * luc5;
+
+    dst(0,0) = li31*li31 + li21*li21 + luc0*luc0;
+    dst(1,0) = li31*li32 + li21*luc2;
+    dst(1,1) = li32*li32 + luc2*luc2;
+    dst(2,0) = li31*luc5;
+    dst(2,1) = li32*luc5;
+    dst(2,2) = luc5*luc5;
+    
+  }
+
+  template<typename M1, typename M2> 
+  inline constexpr
+  void invert44(M1 const & src, M2 & dst) {
+    using F = decltype(src(0,0));
+    auto luc0 = F(1.0) / std::sqrt(src(0,0));
+    auto luc1 = src(1,0) * luc0;
+    auto luc2 = src(1,1) - luc1 * luc1;
+    luc2 = F(1.0) / std::sqrt(luc2);
+    auto luc3 = src(2,0) * luc0;
+    auto luc4 = (src(2,1) - luc1 * luc3) * luc2;
+    auto luc5 = src(2,2) - (luc3 * luc3 + luc4 * luc4);
+    luc5 = F(1.0) / std::sqrt(luc5);
+    auto luc6 = src(3,0) * luc0;
+    auto luc7 = (src(3,1) - luc1 * luc6) * luc2;
+    auto luc8 = (src(3,2) - luc3 * luc6 - luc4 * luc7) * luc5;
+    auto luc9 = src(3,3) - (luc6 * luc6 + luc7 * luc7 + luc8 * luc8);
+    luc9 = F(1.0) / std::sqrt(luc9);
+    
+    auto li21 = -luc1 * luc0 * luc2;
+    auto li32 = -luc4 * luc2 * luc5;
+    auto li31 = (luc1 * luc4 * luc2 - luc3) * luc0 * luc5;
+    auto li43 = -luc8 * luc9 * luc5;
+    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2 * luc9;
+    auto li41 = (-luc1 * luc4 * luc8 * luc2 * luc5 +
+		 luc1 * luc7 * luc2 + luc3 * luc8 * luc5 - luc6) * luc0 * luc9;
+
+    dst(0,0) = li41*li41 + li31*li31 + li21*li21 + luc0*luc0;
+    dst(1,0) = li41*li42 + li31*li32 + li21*luc2;
+    dst(1,1) = li42*li42 + li32*li32 + luc2*luc2;
+    dst(2,0) = li41*li43 + li31*luc5;
+    dst(2,1) = li42*li43 + li32*luc5;
+    dst(2,2) = li43*li43 + luc5*luc5;
+    dst(3,0) = li41*luc9;
+    dst(3,1) = li42*luc9;
+    dst(3,2) = li43*luc9;
+    dst(3,3) = luc9*luc9;  
+    
+  }
+
+  
+  template<typename M1, typename M2> 
+  inline constexpr
+  void invert55(M1 const & src, M2 & dst) {
+    using F = decltype(src(0,0));
+    auto luc0 = F(1.0) / std::sqrt(src(0,0));
+    auto luc1 = src(1,0) * luc0;
+    auto luc2 = src(1,1) - luc1 * luc1;
+    luc2 = F(1.0) / std::sqrt(luc2);
+    auto luc3 = src(2,0) * luc0;
+    auto luc4 = (src(2,1) - luc1 * luc3) * luc2;
+    auto luc5 = src(2,2) - (luc3 * luc3 + luc4 * luc4);
+    luc5 = F(1.0) / std::sqrt(luc5);
+    auto luc6 = src(3,0) * luc0;
+    auto luc7 = (src(3,1) - luc1 * luc6) * luc2;
+    auto luc8 = (src(3,2) - luc3 * luc6 - luc4 * luc7) * luc5;
+    auto luc9 = src(3,3) - (luc6 * luc6 + luc7 * luc7 + luc8 * luc8);
+    luc9 = F(1.0) / std::sqrt(luc9);
+    auto luc10 = src(4,0) * luc0;
+    auto luc11 = (src(4,1) - luc1 * luc10) * luc2;
+    auto luc12 = (src(4,2) - luc3 * luc10 - luc4 * luc11) * luc5;
+    auto luc13 = (src(4,3) - luc6 * luc10 - luc7 * luc11 - luc8 * luc12) * luc9;
+    auto luc14 = src(4,4) - (luc10*luc10+luc11*luc11+luc12*luc12+luc13*luc13);
+    luc14 = F(1.0) / std::sqrt(luc14);
+    
+    
+    auto li21 = -luc1 * luc0 * luc2;
+    auto li32 = -luc4 * luc2 * luc5;
+    auto li31 = (luc1 * luc4 * luc2 - luc3) * luc0 * luc5;
+    auto li43 = -luc8 * luc9 * luc5;
+    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2 * luc9;
+    auto li41 = (-luc1 * luc4 * luc8 * luc2 * luc5 +
+		 luc1 * luc7 * luc2 + luc3 * luc8 * luc5 - luc6) * luc0 * luc9;
+    auto li54 = -luc13 * luc14 * luc9;
+    auto li53 = (luc13 * luc8 * luc9 - luc12) * luc5 * luc14;
+    auto li52 = (-luc4 * luc8 * luc13 * luc5 * luc9 +
+		 luc4 * luc12 * luc5 + luc7 * luc13 * luc9 - luc11) * luc2 * luc14;
+    auto li51 = (luc1*luc4*luc8*luc13*luc2*luc5*luc9 -
+		 luc13*luc8*luc3*luc9*luc5 - luc12*luc4*luc1*luc2*luc5 - luc13*luc7*luc1*luc9*luc2 +
+		 luc11*luc1*luc2 + luc12*luc3*luc5 + luc13*luc6*luc9 -luc10) * luc0 * luc14;
+    
+    dst(0,0) = li51*li51 + li41*li41 + li31*li31 + li21*li21 + luc0*luc0;
+    dst(1,0) = li51*li52 + li41*li42 + li31*li32 + li21*luc2;
+    dst(1,1) = li52*li52 + li42*li42 + li32*li32 + luc2*luc2;
+    dst(2,0) = li51*li53 + li41*li43 + li31*luc5;
+    dst(2,1) = li52*li53 + li42*li43 + li32*luc5;
+    dst(2,2) = li53*li53 + li43*li43 + luc5*luc5;
+    dst(3,0) = li51*li54 + li41*luc9;
+    dst(3,1) = li52*li54 + li42*luc9;
+    dst(3,2) = li53*li54 + li43*luc9;
+    dst(3,3) = li54*li54 + luc9*luc9;
+    dst(4,0) = li51*luc14;
+    dst(4,1) = li52*luc14;
+    dst(4,2) = li53*luc14;
+    dst(4,3) = li54*luc14;
+    dst(4,4) = luc14*luc14;
+  }
+
+  template<typename M1, typename M2> 
+  inline __attribute__((always_inline)) constexpr
+  void invert66(M1 const & src, M2 & dst) {
+    using F = decltype(src(0,0));
+    auto luc0 = F(1.0) / std::sqrt(src(0,0));
+    auto luc1 = src(1,0) * luc0;
+    auto luc2 = src(1,1) - luc1 * luc1;
+    luc2 = F(1.0) / std::sqrt(luc2);
+    auto luc3 = src(2,0) * luc0;
+    auto luc4 = (src(2,1) - luc1 * luc3) * luc2;
+    auto luc5 = src(2,2) - (luc3 * luc3 + luc4 * luc4);
+    luc5 = F(1.0) / std::sqrt(luc5);
+    auto luc6 = src(3,0) * luc0;
+    auto luc7 = (src(3,1) - luc1 * luc6) * luc2;
+    auto luc8 = (src(3,2) - luc3 * luc6 - luc4 * luc7) * luc5;
+    auto luc9 = src(3,3) - (luc6 * luc6 + luc7 * luc7 + luc8 * luc8);
+    luc9 = F(1.0) / std::sqrt(luc9);
+    auto luc10 = src(4,0) * luc0;
+    auto luc11 = (src(4,1) - luc1 * luc10) * luc2;
+    auto luc12 = (src(4,2) - luc3 * luc10 - luc4 * luc11) * luc5;
+    auto luc13 = (src(4,3) - luc6 * luc10 - luc7 * luc11 - luc8 * luc12) * luc9;
+    auto luc14 = src(4,4) - (luc10*luc10+luc11*luc11+luc12*luc12+luc13*luc13);
+    luc14 = F(1.0) / std::sqrt(luc14);
+    auto luc15 = src(5,0) * luc0;
+    auto luc16 = (src(5,1) - luc1 * luc15) * luc2;
+    auto luc17 = (src(5,2) - luc3 * luc15 - luc4 * luc16) * luc5;
+    auto luc18 = (src(5,3) - luc6 * luc15 - luc7 * luc16 - luc8 * luc17) * luc9;
+    auto luc19 = (src(5,4) - luc10 * luc15 - luc11 * luc16 - luc12 * luc17 - luc13 * luc18) * luc14;
+    auto luc20 = src(5,5) - (luc15*luc15+luc16*luc16+luc17*luc17+luc18*luc18+luc19*luc19);
+    luc20 = F(1.0) / std::sqrt(luc20);
+
+    auto li21 = -luc1 * luc0 * luc2;
+    auto li32 = -luc4 * luc2 * luc5;
+    auto li31 = (luc1 * luc4 * luc2 - luc3) * luc0 * luc5;
+    auto li43 = -luc8 * luc9 * luc5;
+    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2 * luc9;
+    auto li41 = (-luc1 * luc4 * luc8 * luc2 * luc5 +
+		 luc1 * luc7 * luc2 + luc3 * luc8 * luc5 - luc6) * luc0 * luc9;
+    auto li54 = -luc13 * luc14 * luc9;
+    auto li53 = (luc13 * luc8 * luc9 - luc12) * luc5 * luc14;
+    auto li52 = (-luc4 * luc8 * luc13 * luc5 * luc9 +
+		 luc4 * luc12 * luc5 + luc7 * luc13 * luc9 - luc11) * luc2 * luc14;
+    auto li51 = (luc1*luc4*luc8*luc13*luc2*luc5*luc9 -
+		 luc13*luc8*luc3*luc9*luc5 - luc12*luc4*luc1*luc2*luc5 - luc13*luc7*luc1*luc9*luc2 +
+		 luc11*luc1*luc2 + luc12*luc3*luc5 + luc13*luc6*luc9 -luc10) * luc0 * luc14;
+    auto li65 = -luc19 * luc20 * luc14;
+    auto li64 = (luc19 * luc13 * luc14 - luc18) * luc9 * luc20;
+    auto li63 = (-luc8 * luc13 * luc19 * luc9 * luc14 +
+		 luc8 * luc18 * luc9 + luc12 * luc19 * luc14 - luc17) * luc5 * luc20;
+        auto li62 = (luc4*luc8*luc13*luc19*luc5*luc9*luc14 -
+		 luc18*luc8*luc4*luc9*luc5 - luc19*luc12*luc4*luc14*luc5 -luc19*luc13*luc7*luc14*luc9 +
+		 luc17*luc4*luc5 + luc18*luc7*luc9 + luc19*luc11*luc14 - luc16) * luc2 * luc20;
+    auto li61 = (-luc19*luc13*luc8*luc4*luc1*luc2*luc5*luc9*luc14 +
+		 luc18*luc8*luc4*luc1*luc2*luc5*luc9 + luc19*luc12*luc4*luc1*luc2*luc5*luc14 +
+		 luc19*luc13*luc7*luc1*luc2*luc9*luc14 + luc19*luc13*luc8*luc3*luc5*luc9*luc14 -
+		 luc17*luc4*luc1*luc2*luc5 - luc18*luc7*luc1*luc2*luc9 - luc19*luc11*luc1*luc2*luc14 -
+		 luc18*luc8*luc3*luc5*luc9 - luc19*luc12*luc3*luc5*luc14 - luc19*luc13*luc6*luc9*luc14 +
+		 luc16*luc1*luc2 + luc17*luc3*luc5 + luc18*luc6*luc9 + luc19*luc10*luc14 - luc15) * luc0 * luc20;    
+
+    dst(0,0) = li61*li61 + li51*li51 + li41*li41 + li31*li31 + li21*li21 + luc0*luc0;
+    dst(1,0) = li61*li62 + li51*li52 + li41*li42 + li31*li32 + li21*luc2;
+    dst(1,1) = li62*li62 + li52*li52 + li42*li42 + li32*li32 + luc2*luc2;
+    dst(2,0) = li61*li63 + li51*li53 + li41*li43 + li31*luc5;
+    dst(2,1) = li62*li63 + li52*li53 + li42*li43 + li32*luc5;
+    dst(2,2) = li63*li63 + li53*li53 + li43*li43 + luc5*luc5;
+    dst(3,0) = li61*li64 + li51*li54 + li41*luc9;
+    dst(3,1) = li62*li64 + li52*li54 + li42*luc9;
+    dst(3,2) = li63*li64 + li53*li54 + li43*luc9;
+    dst(3,3) = li64*li64 + li54*li54 + luc9*luc9;
+    dst(4,0) = li61*li65 + li51*luc14;
+    dst(4,1) = li62*li65 + li52*luc14;
+    dst(4,2) = li63*li65 + li53*luc14;
+    dst(4,3) = li64*li65 + li54*luc14;
+    dst(4,4) = li65*li65 + luc14*luc14;
+    dst(5,0) = li61*luc20;
+    dst(5,1) = li62*luc20;
+    dst(5,2) = li63*luc20;
+    dst(5,3) = li64*luc20;
+    dst(5,4) = li65*luc20;
+    dst(5,5) = luc20*luc20;
+      
+  }
+
+  
+
+  template<typename M>
+  inline constexpr
+  void symmetrize11(M & dst) {}
+  template<typename M>
+  inline constexpr
+  void symmetrize22(M & dst) {
+    dst(0,1) = dst(1,0);
+  }
+  template<typename M>
+  inline constexpr
+  void symmetrize33(M & dst) {
+    symmetrize22(dst);
+    dst(0,2) = dst(2,0);
+    dst(1,2) = dst(2,1);
+  }
+  template<typename M>
+  inline constexpr
+  void symmetrize44(M & dst) {
+    symmetrize33(dst);
+    dst(0,3) = dst(3,0);
+    dst(1,3) = dst(3,1);
+    dst(2,3) = dst(3,2);
+  }
+  template<typename M>
+  inline constexpr
+  void symmetrize55(M & dst) {
+    symmetrize44(dst);
+    dst(0,4) = dst(4,0);
+    dst(1,4) = dst(4,1);
+    dst(2,4) = dst(4,2);
+    dst(3,4) = dst(4,3);
+  }
+  template<typename M>
+  inline constexpr
+  void symmetrize66(M & dst) {
+    symmetrize55(dst);
+    dst(0,5) = dst(5,0);
+    dst(1,5) = dst(5,1);
+    dst(2,5) = dst(5,2);
+    dst(3,5) = dst(5,3);
+    dst(4,5) = dst(5,4);
+  }
+
+  
+  template<typename M1, typename M2, int N>
+  struct Inverter {
+    static constexpr void eval(M1 const & src, M2 & dst){ dst=src.inverse();}
+  };
+  template<typename M1, typename M2>
+  struct Inverter<M1,M2,1> {
+    static constexpr void eval(M1 const & src, M2 & dst) {
+      invert11(src,dst);
+    }
+  };
+  template<typename M1, typename M2>
+  struct Inverter<M1,M2,2> {
+    static constexpr void eval(M1 const & src, M2 & dst) {
+      invert22(src,dst);
+      symmetrize22(dst);
+    }
+  };
+  template<typename M1, typename M2>
+  struct Inverter<M1,M2,3> {
+    static constexpr void eval(M1 const & src, M2 & dst) {
+      invert33(src,dst);
+      symmetrize33(dst);
+    }
+  };
+  template<typename M1, typename M2>
+  struct Inverter<M1,M2,4> {
+    static constexpr void eval(M1 const & src, M2 & dst) {
+      invert44(src,dst);
+      symmetrize44(dst);
+    }
+  };
+  template<typename M1, typename M2>
+  struct Inverter<M1,M2,5> {
+    static constexpr void eval(M1 const & src, M2 & dst) {
+      invert55(src,dst);
+      symmetrize55(dst);
+    }
+  };
+  template<typename M1, typename M2>
+  struct Inverter<M1,M2,6> {
+    static constexpr void eval(M1 const & src, M2 & dst) {
+      invert66(src,dst);
+      symmetrize66(dst);
+    }
+  };
+
+  // Eigen interface
+  template<typename D1, typename D2> 
+  inline constexpr
+  void invert(Eigen::DenseBase<D1> const & src, Eigen::DenseBase<D2> & dst) {
+    using M1 = Eigen::DenseBase<D1>;
+    using M2 = Eigen::DenseBase<D2>;
+    Inverter<M1,M2,M2::ColsAtCompileTime>::eval(src,dst);
+  }
+
+  
+}
+
+
+#endif
diff --git a/DataFormats/Math/test/BuildFile.xml b/DataFormats/Math/test/BuildFile.xml
index 7d3f50abe7c91..1597c00cb7520 100644
--- a/DataFormats/Math/test/BuildFile.xml
+++ b/DataFormats/Math/test/BuildFile.xml
@@ -66,6 +66,10 @@
 <bin file="MulSymMatrix_t.cpp" name="DataFormatsMulSymMatrix_t">
 </bin>
 
+<bin file="CholeskyInvert_t.cpp" name="CholeskyInvert_t"/>
+
+
+
 <bin file="testGraph.cpp">
 </bin>
 
@@ -100,4 +104,13 @@
   <flags CUDA_FLAGS="--expt-relaxed-constexpr -fmad=false -ftz=false -prec-div=true -prec-sqrt=true"/>
   <flags CXXFLAGS="-ffp-contract=off"/>
 </bin>
+
+<bin file="CholeskyInvert_t.cu" name="CholeskyInvertOnGPU_t">
+  <use name="cuda"/>
+  <use name="cuda-api-wrappers"/>
+  <use name="HeterogeneousCore/CUDAUtilities"/>
+</bin>
+
 </iftool>
+
+
diff --git a/DataFormats/Math/test/CholeskyInvert_t.cpp b/DataFormats/Math/test/CholeskyInvert_t.cpp
new file mode 100644
index 0000000000000..8c28b63d96820
--- /dev/null
+++ b/DataFormats/Math/test/CholeskyInvert_t.cpp
@@ -0,0 +1,147 @@
+// nvcc -O3 CholeskyDecomp_t.cu -Icuda-api-wrappers/src/ --expt-relaxed-constexpr -gencode arch=compute_61,code=sm_61 --compiler-options="-Ofast -march=native"
+// add -DDOPROF to run  nvprof --metrics all
+
+#include "DataFormats/Math/interface/choleskyInversion.h"
+#include <Eigen/Core>
+#include <Eigen/Eigenvalues>
+
+#include <iomanip>
+#include <memory>
+#include <algorithm>
+#include <chrono>
+#include<random>
+
+
+#include<cassert>
+#include<iostream>
+#include<limits>
+
+constexpr int stride() { return 5*1024;}
+template<int DIM>
+using MXN = Eigen::Matrix<double,DIM,DIM>;
+template<int DIM>
+using MapMX = Eigen::Map<MXN<DIM>, 0, Eigen::Stride<DIM*stride(),stride()> >;
+
+// generate matrices
+template<class M>
+void genMatrix(M  & m ) {
+  using T = typename std::remove_reference<decltype(m(0,0))>::type;
+  int n = M::ColsAtCompileTime;
+  std::mt19937 eng;
+  // std::mt19937 eng2;
+  std::uniform_real_distribution<T> rgen(0.,1.);
+  
+  // generate first diagonal elemets
+  for (int i = 0; i < n; ++i) {
+    double maxVal = i*10000/(n-1) + 1;  // max condition is 10^4
+    m(i,i) = maxVal*rgen(eng);
+  }
+  for (int i = 0; i < n; ++i) {
+    for (int j = 0; j < i; ++j) {
+      double v = 0.3*std::sqrt( m(i,i) * m(j,j) ); // this makes the matrix pos defined
+      m(i,j) = v*rgen(eng);
+      m(j,i) = m(i,j);
+    }
+  }
+}
+
+
+template<int N>
+void go(bool soa) {
+
+  constexpr unsigned int DIM = N;
+  using MX =  MXN<DIM>;
+  std::cout << "testing Matrix of dimension " << DIM << " size " << sizeof(MX) 
+            << " in " << (soa ? "SOA" : "AOS") << " mode"<< std::endl;
+
+
+  auto start = std::chrono::high_resolution_clock::now();
+  auto delta = start - start;
+  auto delta1 = delta;
+  auto delta2 = delta;
+
+  
+  constexpr unsigned int SIZE=4*1024;
+  
+  alignas(128) MX mm[stride()];  // just storage in case of SOA
+  double * __restrict__ p = (double*)__builtin_assume_aligned(mm, 128);
+
+  if (soa) {
+    for (unsigned int i=0; i<SIZE; ++i) {
+      MapMX<N> m(p+i);
+      genMatrix(m);
+    }
+  }else{  
+    for ( auto & m : mm) 
+      genMatrix(m);
+  }
+
+  std::cout << mm[SIZE/2](1,1) << std::endl;
+
+  if (soa)
+    for (unsigned int i=0; i<SIZE; ++i) {
+      MapMX<N> m(p+i);
+      choleskyInversion::invert(m,m);
+      choleskyInversion::invert(m,m);
+    }
+  else
+    for ( auto & m : mm) {
+      choleskyInversion::invert(m,m);
+      choleskyInversion::invert(m,m);
+    }
+
+  std::cout << mm[SIZE/2](1,1) << std::endl;
+
+
+  constexpr int NKK = 
+#ifdef DOPROF
+    2;
+#else
+    1000;
+#endif
+  for (int kk=0; kk<NKK; ++kk) {
+  
+    delta2 -= (std::chrono::high_resolution_clock::now()-start);
+    if (soa)
+      #pragma GCC ivdep
+      #pragma clang loop vectorize(enable) interleave(enable)
+      for (unsigned int i=0; i<SIZE; ++i) {
+	MapMX<N> m(p+i);
+	choleskyInversion::invert(m,m);
+      }
+    else
+      #pragma GCC ivdep
+      for ( auto & m : mm) {
+	choleskyInversion::invert(m,m);
+      }
+    
+    delta2 += (std::chrono::high_resolution_clock::now()-start);
+
+  }
+  
+  std::cout << mm[SIZE/2](1,1) << std::endl; 
+  
+  double DNNK = NKK;
+  std::cout <<"x86 computation took "
+	    << std::chrono::duration_cast<std::chrono::milliseconds>(delta2).count()/DNNK  << ' '
+	    << " ms" << std::endl;
+
+}
+
+int main() { 
+
+  go<2>(false);
+  go<3>(false);
+  go<4>(false);
+  go<5>(false);
+  go<6>(false);
+
+  go<2>(true);
+  go<3>(true);
+  go<4>(true);
+  go<5>(true);
+  go<6>(true);
+
+  // go<10>();
+  return 0;
+}
diff --git a/DataFormats/Math/test/CholeskyInvert_t.cu b/DataFormats/Math/test/CholeskyInvert_t.cu
new file mode 100644
index 0000000000000..f62edb01bd859
--- /dev/null
+++ b/DataFormats/Math/test/CholeskyInvert_t.cu
@@ -0,0 +1,242 @@
+// nvcc -O3 CholeskyDecomp_t.cu -Icuda-api-wrappers/src/ --expt-relaxed-constexpr -gencode arch=compute_61,code=sm_61 --compiler-options="-Ofast -march=native"
+// add -DDOPROF to run  nvprof --metrics all
+
+#include "DataFormats/Math/interface/choleskyInversion.h"
+#include <Eigen/Core>
+#include <Eigen/Eigenvalues>
+
+#include <iomanip>
+#include <memory>
+#include <algorithm>
+#include <chrono>
+#include<random>
+
+
+#include<cassert>
+#include<iostream>
+#include<limits>
+
+#include "cuda/api_wrappers.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
+
+constexpr int stride() { return 5*1024;}
+template<int DIM>
+using MXN = Eigen::Matrix<double,DIM,DIM>;
+template<int DIM>
+using MapMX = Eigen::Map<MXN<DIM>, 0, Eigen::Stride<DIM*stride(),stride()> >;
+
+
+template<int N>
+__global__
+void invertSOA(double * __restrict__ p, unsigned int n) {
+
+  auto i = blockIdx.x*blockDim.x + threadIdx.x;
+  if (i>=n) return;
+
+  MapMX<N> m(p+i);
+  choleskyInversion::invert(m,m);
+ 
+}
+
+template<typename M, int N>
+__global__
+void invert (M * mm, unsigned int n) {
+
+  auto i = blockIdx.x*blockDim.x + threadIdx.x;
+  if (i>=n) return;
+
+  auto & m = mm[i];
+  choleskyInversion::invert(m,m);
+ 
+}
+
+template<typename M, int N>
+__global__
+void invertSeq (M * mm, unsigned int n) {
+
+  if (threadIdx.x!=0) return;
+  auto first = blockIdx.x*blockDim.x;
+  auto last = std::min(first+blockDim.x,n);
+  
+  for (auto i=first; i<last; ++i) {
+    auto & m = mm[i];
+    choleskyInversion::invert(m,m);
+  }
+}
+
+
+
+// generate matrices
+template<class M>
+void genMatrix(M  & m ) {
+  using T = typename std::remove_reference<decltype(m(0,0))>::type;
+  int n = M::ColsAtCompileTime;
+  std::mt19937 eng;
+  // std::mt19937 eng2;
+  std::uniform_real_distribution<T> rgen(0.,1.);
+  
+  // generate first diagonal elemets
+  for (int i = 0; i < n; ++i) {
+    double maxVal = i*10000/(n-1) + 1;  // max condition is 10^4
+    m(i,i) = maxVal*rgen(eng);
+  }
+  for (int i = 0; i < n; ++i) {
+    for (int j = 0; j < i; ++j) {
+      double v = 0.3*std::sqrt( m(i,i) * m(j,j) ); // this makes the matrix pos defined
+      m(i,j) = v*rgen(eng);
+      m(j,i) = m(i,j);
+    }
+  }
+}
+
+
+template<int N>
+void go(bool soa) {
+
+  constexpr unsigned int DIM = N;
+  using MX =  MXN<DIM>;
+  std::cout << "testing Matrix of dimension " << DIM << " size " << sizeof(MX) << std::endl;
+
+
+  auto start = std::chrono::high_resolution_clock::now();
+  auto delta = start - start;
+  auto delta1 = delta;
+  auto delta2 = delta;
+
+  
+  if (cuda::device::count() == 0) {
+	std::cerr << "No CUDA devices on this system" << "\n";
+	exit(EXIT_FAILURE);
+  }
+
+  auto current_device = cuda::device::current::get(); 
+
+  constexpr unsigned int SIZE=4*1024;
+  
+  MX mm[stride()];  // just storage in case of SOA
+  double * __restrict__ p = (double *)(mm);
+
+  if (soa) {
+    for (unsigned int i=0; i<SIZE; ++i) {
+      MapMX<N> m(p+i);
+      genMatrix(m);
+    }
+  }else{  
+    for ( auto & m : mm) 
+      genMatrix(m);
+  }
+
+  std::cout << mm[SIZE/2](1,1) << std::endl;
+
+  if (soa)
+    for (unsigned int i=0; i<SIZE; ++i) {
+      MapMX<N> m(p+i);
+      choleskyInversion::invert(m,m);
+      choleskyInversion::invert(m,m);
+    }
+  else
+    for ( auto & m : mm) {
+      choleskyInversion::invert(m,m);
+      choleskyInversion::invert(m,m);
+    }
+
+  std::cout << mm[SIZE/2](1,1) << std::endl;
+
+  auto m_d = cuda::memory::device::make_unique<double[]>(current_device, DIM*DIM*stride());
+  cuda::memory::copy(m_d.get(), (double const*)(mm), stride()*sizeof(MX));
+
+
+  constexpr int NKK = 
+#ifdef DOPROF
+    2;
+#else
+    1000;
+#endif
+  for (int kk=0; kk<NKK; ++kk) {
+  
+    int threadsPerBlock =128;
+    int blocksPerGrid = SIZE/threadsPerBlock;
+    
+    delta -= (std::chrono::high_resolution_clock::now()-start);
+    
+    if (soa)
+      cuda::launch(
+		   invertSOA<DIM>,
+		   { blocksPerGrid, threadsPerBlock },
+		   m_d.get(),SIZE
+		   );
+    else    
+      cuda::launch(
+		   invert<MX,DIM>,
+		   { blocksPerGrid, threadsPerBlock },
+		   (MX*)(m_d.get()),SIZE
+		   );
+    
+    cuda::memory::copy(&mm, m_d.get(),stride()*sizeof(MX));
+    delta += (std::chrono::high_resolution_clock::now()-start);
+    
+    if (0==kk) std::cout << mm[SIZE/2](1,1) << std::endl;
+    
+    if (!soa) {
+      
+      delta1 -= (std::chrono::high_resolution_clock::now()-start);
+      
+#ifndef DOPROF
+      cuda::launch(
+		   invertSeq<MX,DIM>,
+		   { blocksPerGrid, threadsPerBlock },
+		   (MX*)(m_d.get()),SIZE
+		   );
+      
+      cuda::memory::copy(&mm, m_d.get(),stride()*sizeof(MX));
+#endif
+      delta1 += (std::chrono::high_resolution_clock::now()-start);
+      
+      if (0==kk) std::cout << mm[SIZE/2](1,1) << std::endl;
+    }
+  
+    delta2 -= (std::chrono::high_resolution_clock::now()-start);
+    if (soa)
+      #pragma GCC ivdep
+      for (unsigned int i=0; i<SIZE; ++i) {
+	MapMX<N> m(p+i);
+	choleskyInversion::invert(m,m);
+      }
+    else
+      #pragma GCC ivdep
+      for ( auto & m : mm) {
+	choleskyInversion::invert(m,m);
+      }
+    
+    delta2 += (std::chrono::high_resolution_clock::now()-start);
+
+  }
+  
+  std::cout << mm[SIZE/2](1,1) << std::endl; 
+  
+  double DNNK = NKK;
+  std::cout <<"cuda/cudaSeq/x86 computation took "
+	    << std::chrono::duration_cast<std::chrono::milliseconds>(delta).count()/DNNK << ' '
+	    << std::chrono::duration_cast<std::chrono::milliseconds>(delta1).count()/DNNK  << ' '
+	    << std::chrono::duration_cast<std::chrono::milliseconds>(delta2).count()/DNNK  << ' '
+	    << " ms" << std::endl;
+
+}
+
+int main() { 
+
+  exitSansCUDADevices();
+
+  go<2>(false);
+  go<4>(false);
+  go<5>(false);
+  go<6>(false);
+
+  go<2>(true);
+  go<4>(true);
+  go<5>(true);
+  go<6>(true);
+
+  // go<10>();
+  return 0;
+}

From 9dbd97cbe3277e7c1131d324c6e18e15a69dfc10 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 4 Feb 2019 10:59:54 +0100
Subject: [PATCH 21/95] use cholesky invert, enable 3,4,5 hit fit

---
 .../PixelTrackFitting/interface/BrokenLine.h  | 199 +++++++++---------
 .../PixelTrackFitting/interface/FitResult.h   |  98 +++------
 .../PixelTrackFitting/interface/FitUtils.h    |  95 +++++++--
 .../PixelTrackFitting/interface/RiemannFit.h  |  20 +-
 .../PixelTrackFitting/test/testEigenGPU.cu    | 185 ++++++++++------
 .../PixelTrackFitting/test/testRiemannFit.cpp |  94 ++++++---
 .../plugins/BrokenLineFitOnGPU.cu             |   3 +-
 7 files changed, 409 insertions(+), 285 deletions(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
index 3a8eb31396a89..0740fd36f0952 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -1,13 +1,9 @@
 #ifndef RECOPIXELVERTEXING_PIXELTRACKFITTING_BROKENLINE_H
 #define RECOPIXELVERTEXING_PIXELTRACKFITTING_BROKENLINE_H
 
-#include <Eigen/Core>
-#include <Eigen/Cholesky>
-#include <Eigen/Eigenvalues>
-
 #include "FitUtils.h"
 
-
+#include <Eigen/Eigenvalues>
 
 namespace BrokenLine {
   
@@ -49,13 +45,15 @@ namespace BrokenLine {
     
     \return the variance of the planar angle ((theta_0)^2 /3).
   */
-  __host__ __device__ inline double MultScatt(const double& length, const double B, const double& R, int Layer, double slope) {
-    double XX_0; //!< radiation length of the material in cm
-    if(Layer==1) XX_0=16/0.06;
-    else XX_0=16/0.06;
-    XX_0*=1;
-    double geometry_factor=0.7; //!< number between 1/3 (uniform material) and 1 (thin scatterer) to be manually tuned
-    return geometry_factor*sqr((13.6/1000.)/(1.*B*R*sqrt(1.+sqr(slope))))*(abs(length)/XX_0)*sqr(1.+0.038*log(abs(length)/XX_0));
+  __host__ __device__ inline double MultScatt(const double& length, const double B, const double R, int Layer, double slope) {
+    constexpr double XXI_0 = 0.06/16.; //!< inverse of radiation length of the material in cm
+    //if(Layer==1) XXI_0=0.06/16.;
+    // else XXI_0=0.06/16.;
+    //XX_0*=1;
+    constexpr double geometry_factor=0.7; //!< number between 1/3 (uniform material) and 1 (thin scatterer) to be manually tuned
+    constexpr double fact = geometry_factor*sqr(13.6/1000.);
+    return fact/sqr(1.*B*R*sqrt(1.+sqr(slope)))
+      *(std::abs(length)*XXI_0)*sqr(1.+0.038*log(std::abs(length)*XXI_0));
   }
   
   /*!
@@ -65,7 +63,7 @@ namespace BrokenLine {
     
     \return 2D rotation matrix.
   */
-  __host__ __device__ inline Matrix2d RotationMatrix(const double& slope) {
+  __host__ __device__ inline Matrix2d RotationMatrix(double slope) {
     Matrix2d Rot;
     Rot(0,0)=1./sqrt(1.+sqr(slope));
     Rot(0,1)=slope*Rot(0,0);
@@ -82,24 +80,24 @@ namespace BrokenLine {
     \param y0 y coordinate of the translation vector.
     \param Jacob passed by reference in order to save stack.
   */
-  __host__ __device__ inline void TranslateKarimaki(karimaki_circle_fit& circle, const double& x0, const double& y0, Matrix3d& Jacob) {
+  __host__ __device__ inline void TranslateKarimaki(karimaki_circle_fit& circle, double x0, double y0, Matrix3d& Jacob) {
     double A,U,BB,C,DO,DP,uu,xi,v,mu,lambda,zeta;
     DP=x0*cos(circle.par(0))+y0*sin(circle.par(0));
     DO=x0*sin(circle.par(0))-y0*cos(circle.par(0))+circle.par(1);
     uu=1+circle.par(2)*circle.par(1);
     C=-circle.par(2)*y0+uu*cos(circle.par(0));
     BB=circle.par(2)*x0+uu*sin(circle.par(0));
-    A=2*DO+circle.par(2)*(sqr(DO)+sqr(DP));
-    U=sqrt(1+circle.par(2)*A);
-    xi=1/(sqr(BB)+sqr(C));
-    v=1+circle.par(2)*DO;
-    lambda=(A/2)/(U*sqr(1+U));
-    mu=1/(U*(1+U))+circle.par(2)*lambda;
+    A=2.*DO+circle.par(2)*(sqr(DO)+sqr(DP));
+    U=sqrt(1.+circle.par(2)*A);
+    xi=1./(sqr(BB)+sqr(C));
+    v=1.+circle.par(2)*DO;
+    lambda=(0.5*A)/(U*sqr(1.+U));
+    mu=1./(U*(1.+U))+circle.par(2)*lambda;
     zeta=sqr(DO)+sqr(DP);
     
     Jacob << xi*uu*v, -xi*sqr(circle.par(2))*DP, xi*DP,
-      2*mu*uu*DP, 2*mu*v, mu*zeta-lambda*A,
-      0, 0, 1;
+      2.*mu*uu*DP, 2.*mu*v, mu*zeta-lambda*A,
+      0, 0, 1.;
     
     circle.par(0)=atan2(BB,C);
     circle.par(1)=A/(1+U);
@@ -127,16 +125,10 @@ namespace BrokenLine {
     u_int i;
     Vector2d d;
     Vector2d e;
-    results.radii=Matrix2xNd<N>::Zero();
-    results.s=VectorNd<N>::Zero();
-    results.S=VectorNd<N>::Zero();
-    results.Z=VectorNd<N>::Zero();
-    results.VarBeta=VectorNd<N>::Zero();
     
-    results.q=1;
     d=hits.block(0,1,2,1)-hits.block(0,0,2,1);
     e=hits.block(0,n-1,2,1)-hits.block(0,n-2,2,1);
-    if(cross2D(d,e)>0) results.q=-1;
+    results.q = cross2D(d,e)>0 ? -1 : 1;
     
     const double slope=-results.q/fast_fit(3);
     
@@ -148,7 +140,6 @@ namespace BrokenLine {
     for(i=0;i<n;i++) {
       d=results.radii.block(0,i,2,1);
       results.s(i)=results.q*fast_fit(2)*atan2(cross2D(d,e),d.dot(e)); // calculates the arc length
-      //if(results.s(i)<=0);
     }
     VectorNd<N> z=hits.block(2,0,1,n).transpose();
     
@@ -163,6 +154,7 @@ namespace BrokenLine {
     results.Z=pointsSZ.block(1,0,1,n).transpose();
     
     //calculate VarBeta
+    results.VarBeta(0)=results.VarBeta(n-1)=0;
     for(i=1;i<n-1;i++) {
       results.VarBeta(i)=MultScatt(results.S(i+1)-results.S(i),B,fast_fit(2),i+2,slope)+MultScatt(results.S(i)-results.S(i-1),B,fast_fit(2),i+1,slope);
     }
@@ -185,21 +177,18 @@ namespace BrokenLine {
     MatrixNd<N> C_U=MatrixNd<N>::Zero();
     for(i=0;i<n;i++) {
       C_U(i,i)=w(i);
-      if(i>1) C_U(i,i)+=1/(VarBeta(i-1)*sqr(S(i)-S(i-1)));
-      if(i>0 && i<n-1) C_U(i,i)+=(1/VarBeta(i))*sqr((S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
-      if(i<n-2) C_U(i,i)+=1/(VarBeta(i+1)*sqr(S(i+1)-S(i)));
+      if(i>1) C_U(i,i)+=1./(VarBeta(i-1)*sqr(S(i)-S(i-1)));
+      if(i>0 && i<n-1) C_U(i,i)+=(1./VarBeta(i))*sqr((S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
+      if(i<n-2) C_U(i,i)+=1./(VarBeta(i+1)*sqr(S(i+1)-S(i)));
       
-      if(i>0 && i<n-1) C_U(i,i+1)=1/(VarBeta(i)*(S(i+1)-S(i)))*(-(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
-      if(i<n-2) C_U(i,i+1)+=1/(VarBeta(i+1)*(S(i+1)-S(i)))*(-(S(i+2)-S(i))/((S(i+2)-S(i+1))*(S(i+1)-S(i))));
+      if(i>0 && i<n-1) C_U(i,i+1)=1./(VarBeta(i)*(S(i+1)-S(i)))*(-(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1))));
+      if(i<n-2) C_U(i,i+1)+=1./(VarBeta(i+1)*(S(i+1)-S(i)))*(-(S(i+2)-S(i))/((S(i+2)-S(i+1))*(S(i+1)-S(i))));
       
-      if(i<n-2) C_U(i,i+2)=1/(VarBeta(i+1)*(S(i+2)-S(i+1))*(S(i+1)-S(i)));
-      
-      C_U(i,i)=C_U(i,i)/2;
+      if(i<n-2) C_U(i,i+2)=1./(VarBeta(i+1)*(S(i+2)-S(i+1))*(S(i+1)-S(i)));
+
+      C_U(i,i)*=0.5;
     }
-    MatrixNd<N> C_u;
-    C_u=C_U+C_U.transpose();
-    
-    return C_u;
+    return C_U+C_U.transpose();
   }
   
   /*!
@@ -222,12 +211,13 @@ namespace BrokenLine {
     const Vector2d a=hits.block(0,n/2,2,1)-hits.block(0,0,2,1);
     const Vector2d b=hits.block(0,n-1,2,1)-hits.block(0,n/2,2,1);
     const Vector2d c=hits.block(0,0,2,1)-hits.block(0,n-1,2,1);
-    
-    result(0)=hits(0,0)-(a(1)*c.squaredNorm()+c(1)*a.squaredNorm())/(2*cross2D(c,a));
-    result(1)=hits(1,0)+(a(0)*c.squaredNorm()+c(0)*a.squaredNorm())/(2*cross2D(c,a));
+
+    auto tmp = 0.5/cross2D(c,a);
+    result(0)=hits(0,0)-(a(1)*c.squaredNorm()+c(1)*a.squaredNorm())*tmp;
+    result(1)=hits(1,0)+(a(0)*c.squaredNorm()+c(0)*a.squaredNorm())*tmp;
     // check Wikipedia for these formulas
     
-    result(2)=(a.norm()*b.norm()*c.norm())/(2*abs(cross2D(b,a)));
+    result(2)=sqrt(a.squaredNorm()*b.squaredNorm()*c.squaredNorm())/(2.*std::abs(cross2D(b,a)));
     // Using Math Olympiad's formula R=abc/(4A)
     
     const Vector2d d=hits.block(0,0,2,1)-result.head(2);
@@ -250,8 +240,6 @@ namespace BrokenLine {
     -par parameter of the line in this form: (phi, d, k); \n
     -cov covariance matrix of the fitted parameter; \n
     -chi2 value of the cost function in the minimum.
-    \param Jacob passed by reference in order to save stack.
-    \param C_U passed by reference in order to sake stack.
     
     \details The function implements the steps 2 and 3 of the Broken Line fit with the curvature correction.\n
     The step 2 is the least square fit, done by imposing the minimum constraint on the cost function and solving the consequent linear system. It determines the fitted parameters u and \Delta\kappa and their covariance matrix.
@@ -263,9 +251,8 @@ namespace BrokenLine {
 						const V4& fast_fit,
 						const double B,
 						PreparedBrokenLineData<N>& data,
-						karimaki_circle_fit & circle_results,
-						Matrix3d& Jacob,
-						MatrixNplusONEd<N>& C_U) {
+						karimaki_circle_fit & circle_results
+                                               ) {
 
     constexpr u_int n = N;
     u_int i;
@@ -284,7 +271,7 @@ namespace BrokenLine {
     }
     
     Matrix2d V; // covariance matrix
-    VectorNd<N> w=VectorNd<N>::Zero(); // weights
+    VectorNd<N> w; // weights
     Matrix2d RR; // rotation matrix point by point
     //double Slope; // slope of the circle point by point
     for(i=0;i<n;i++) {
@@ -293,38 +280,46 @@ namespace BrokenLine {
       V(1,1)=hits_ge.col(i)[2];                // y errors
       //Slope=-radii(0,i)/radii(1,i);
       RR=RotationMatrix(-radii(0,i)/radii(1,i));
-      w(i)=1/((RR*V*RR.transpose())(1,1)); // compute the orthogonal weight point by point
+      w(i)=1./((RR*V*RR.transpose())(1,1)); // compute the orthogonal weight point by point
     }
     
-    VectorNplusONEd<N> r_u=VectorNplusONEd<N>::Zero();
+    VectorNplusONEd<N> r_u;
+    r_u(n)=0;
     for(i=0;i<n;i++) {
       r_u(i)=w(i)*Z(i);
-    } r_u(n)=0;
+    }
     
-    C_U=MatrixNplusONEd<N>::Zero();
+    MatrixNplusONEd<N> C_U;
+    C_U.block(0,0,n,n)=MatrixC_u(w,s,VarBeta);
+    C_U(n,n) =0;
     //add the border to the C_u matrix
     for(i=0;i<n;i++) {
+      C_U(i,n) =0;
       if(i>0 && i<n-1) {
-	C_U(i,n)+=-(s(i+1)-s(i-1))/(2*VarBeta(i))*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)));
-	C_U(n,i)=C_U(i,n);
+	C_U(i,n)+=-(s(i+1)-s(i-1))*(s(i+1)-s(i-1))/(2.*VarBeta(i)*(s(i+1)-s(i))*(s(i)-s(i-1)));
       }
       if(i>1) {
-	C_U(i,n)+=(s(i)-s(i-2))/(2*VarBeta(i-1)*(s(i)-s(i-1)));
-	C_U(n,i)=C_U(i,n);
+	C_U(i,n)+=(s(i)-s(i-2))/(2.*VarBeta(i-1)*(s(i)-s(i-1)));
       }
       if(i<n-2) {
-	C_U(i,n)+=(s(i+2)-s(i))/(2*VarBeta(i+1)*(s(i+1)-s(i)));
-	C_U(n,i)=C_U(i,n);
+	C_U(i,n)+=(s(i+2)-s(i))/(2.*VarBeta(i+1)*(s(i+1)-s(i)));
       }
-      
-      if(i>0 && i<n-1) C_U(n,n)+=sqr(s(i+1)-s(i-1))/(4*VarBeta(i));
+      C_U(n,i) = C_U(i,n);
+      if(i>0 && i<n-1) C_U(n,n)+=sqr(s(i+1)-s(i-1))/(4.*VarBeta(i));
     }
-    C_U.block(0,0,n,n)=MatrixC_u(w,s,VarBeta);
-    MatrixNplusONEd<N>& I=C_U;
-    I=C_U.inverse();//MatrixNplusONEd I=C_U.inverse();
+
+#ifdef CPP_DUMP
+    std::cout << "CU5\n" << C_U << std::endl;
+#endif
+    MatrixNplusONEd<N> I;
+    choleskyInversion::invert(C_U,I);
+    // MatrixNplusONEd<N> I = C_U.inverse();
+#ifdef CPP_DUMP
+    std::cout << "I5\n" << I << std::endl;
+#endif
+
     
-    VectorNplusONEd<N>& u=r_u;
-    u=I*r_u; // obtain the fitted parameters by solving the linear system
+    VectorNplusONEd<N> u = I*r_u; // obtain the fitted parameters by solving the linear system
     
     // compute (phi, d_ca, k) in the system in which the midpoint of the first two corrected hits is the origin...
     
@@ -335,14 +330,15 @@ namespace BrokenLine {
     Vector2d e=hits.block(0,1,2,1)+(-Z(1)+u(1))*radii.block(0,1,2,1);
     
     circle_results.par << atan2((e-d)(1),(e-d)(0)),
-      -circle_results.q*(fast_fit(2)-sqrt(sqr(fast_fit(2))-(e-d).squaredNorm()/4)),
-      circle_results.q*(1/fast_fit(2)+u(n));
+      -circle_results.q*(fast_fit(2)-sqrt(sqr(fast_fit(2))- 0.25*(e-d).squaredNorm())),
+      circle_results.q*(1./fast_fit(2)+u(n));
     
     assert(circle_results.q*circle_results.par(1)<=0);
     
     Vector2d eMinusd=e-d;
     double tmp1=eMinusd.squaredNorm();
     
+    Matrix3d Jacob;
     Jacob << (radii(1,0)*eMinusd(0)-eMinusd(1)*radii(0,0))/tmp1,(radii(1,1)*eMinusd(0)-eMinusd(1)*radii(0,1))/tmp1,0,
       (circle_results.q/2)*(eMinusd(0)*radii(0,0)+eMinusd(1)*radii(1,0))/sqrt(sqr(2*fast_fit(2))-tmp1),(circle_results.q/2)*(eMinusd(0)*radii(0,1)+eMinusd(1)*radii(1,1))/sqrt(sqr(2*fast_fit(2))-tmp1),0,
       0,0,circle_results.q;
@@ -355,7 +351,7 @@ namespace BrokenLine {
     
     //...Translate in the system in which the first corrected hit is the origin, adding the m.s. correction...
     
-    TranslateKarimaki(circle_results,(e-d)(0)/2,(e-d)(1)/2,Jacob);
+    TranslateKarimaki(circle_results,0.5*(e-d)(0),0.5*(e-d)(1),Jacob);
     circle_results.cov(0,0)+=(1+sqr(slope))*MultScatt(S(1)-S(0),B,fast_fit(2),2,slope);
     
     //...And translate back to the original system
@@ -416,49 +412,61 @@ namespace BrokenLine {
       V(1,1)=hits_ge.col(i)[2];                // y errors
       V(2,1)=V(1,2)=hits_ge.col(i)[4];   // cov_yz
       V(2,2)=hits_ge.col(i)[5];                // z errors
-      JacobXYZtosZ(0,0)=radii(1,i)/radii.block(0,i,2,1).norm();
-      JacobXYZtosZ(0,1)=-radii(0,i)/radii.block(0,i,2,1).norm();
-      JacobXYZtosZ(1,2)=1;
-      w(i)=1/((R*JacobXYZtosZ*V*JacobXYZtosZ.transpose()*R.transpose())(1,1)); // compute the orthogonal weight point by point
+      auto tmp = 1./radii.block(0,i,2,1).norm();
+      JacobXYZtosZ(0,0)=radii(1,i)*tmp;
+      JacobXYZtosZ(0,1)=-radii(0,i)*tmp;
+      JacobXYZtosZ(1,2)=1.;
+      w(i)=1./((R*JacobXYZtosZ*V*JacobXYZtosZ.transpose()*R.transpose())(1,1)); // compute the orthogonal weight point by point
     }
     
-    VectorNd<N> r_u=VectorNd<N>::Zero();
+    VectorNd<N> r_u;
     for(i=0;i<n;i++) {
       r_u(i)=w(i)*Z(i);
     }
-    
-    MatrixNd<N> I=MatrixC_u(w,S,VarBeta).inverse();
+#ifdef CPP_DUMP
+    std::cout << "CU4\n" << MatrixC_u(w,S,VarBeta) << std::endl;
+#endif
+    MatrixNd<N> I; choleskyInversion::invert(MatrixC_u(w,S,VarBeta),I);
+    //    MatrixNd<N> I=MatrixC_u(w,S,VarBeta).inverse();
+#ifdef CPP_DUMP
+    std::cout << "I4\n" << I << std::endl;
+#endif
+
     VectorNd<N> u=I*r_u; // obtain the fitted parameters by solving the linear system
     
     // line parameters in the system in which the first hit is the origin and with axis along SZ
     line_results.par << (u(1)-u(0))/(S(1)-S(0)), u(0);
-    line_results.cov << (I(0,0)-2*I(0,1)+I(1,1))/sqr(S(1)-S(0))+MultScatt(S(1)-S(0),B,fast_fit(2),2,slope), (I(0,1)-I(0,0))/(S(1)-S(0)),
-      (I(0,1)-I(0,0))/(S(1)-S(0)), I(0,0);
+    auto idiff = 1./(S(1)-S(0));
+    line_results.cov << (I(0,0)-2*I(0,1)+I(1,1))*sqr(idiff)+MultScatt(S(1)-S(0),B,fast_fit(2),2,slope),
+      (I(0,1)-I(0,0))*idiff,
+      (I(0,1)-I(0,0))*idiff, I(0,0);
     
     // translate to the original SZ system
     Matrix2d Jacob;
-    Jacob(0,0)=1;
+    Jacob(0,0)=1.;
     Jacob(0,1)=0;
     Jacob(1,0)=-S(0);
-    Jacob(1,1)=1;
+    Jacob(1,1)=1.;
     line_results.par(1)+=-line_results.par(0)*S(0);
     line_results.cov=Jacob*line_results.cov*Jacob.transpose();
     
     // rotate to the original sz system
-    double tmp=R(0,0)-line_results.par(0)*R(0,1);
-    Jacob(0,0)=1/sqr(tmp);
+    auto tmp=R(0,0)-line_results.par(0)*R(0,1);
+    Jacob(1,1)=1./tmp;
+    Jacob(0,0)=Jacob(1,1)*Jacob(1,1);
     Jacob(0,1)=0;
-    Jacob(1,0)=line_results.par(1)*R(0,1)/sqr(tmp);
-    Jacob(1,1)=1/tmp;
-    line_results.par(1)=line_results.par(1)/tmp;
-    line_results.par(0)=(R(0,1)+line_results.par(0)*R(0,0))/tmp;
+    Jacob(1,0)=line_results.par(1)*R(0,1)*Jacob(0,0);
+    line_results.par(1)=line_results.par(1)*Jacob(1,1);
+    line_results.par(0)=(R(0,1)+line_results.par(0)*R(0,0))*Jacob(1,1);
     line_results.cov=Jacob*line_results.cov*Jacob.transpose();
     
     // compute chi2
     line_results.chi2=0;
     for(i=0;i<n;i++) {
       line_results.chi2+=w(i)*sqr(Z(i)-u(i));
-      if(i>0 && i<n-1) line_results.chi2+=sqr(u(i-1)/(S(i)-S(i-1))-u(i)*(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1)))+u(i+1)/(S(i+1)-S(i)))/VarBeta(i);
+      if(i>0 && i<n-1) line_results.chi2+=sqr(u(i-1)/(S(i)-S(i-1))
+					      -u(i)*(S(i+1)-S(i-1))/((S(i+1)-S(i))*(S(i)-S(i-1)))
+					      +u(i+1)/(S(i+1)-S(i)))/VarBeta(i);
     }
     
     assert(line_results.chi2>=0);
@@ -509,17 +517,16 @@ namespace BrokenLine {
     karimaki_circle_fit circle;
     line_fit line;
     Matrix3d Jacob;
-    MatrixNplusONEd<N> C_U;
     
     prepareBrokenLineData(hits,fast_fit,B,data);
     BL_Line_fit(hits_ge,fast_fit,B,data,line);
-    BL_Circle_fit(hits,hits_ge,fast_fit,B,data,circle,Jacob,C_U);
+    BL_Circle_fit(hits,hits_ge,fast_fit,B,data,circle);
     
     // the circle fit gives k, but here we want p_t, so let's change the parameter and the covariance matrix
-    Jacob << 1,0,0,
-      0,1,0,
-      0,0,-abs(circle.par(2))*B/(sqr(circle.par(2))*circle.par(2));
-    circle.par(2)=B/abs(circle.par(2));
+    Jacob << 1.,0,0,
+      0,1.,0,
+      0,0,-std::abs(circle.par(2))*B/(sqr(circle.par(2))*circle.par(2));
+    circle.par(2)=B/std::abs(circle.par(2));
     circle.cov=Jacob*circle.cov*Jacob.transpose();
     
     helix.par << circle.par, line.par;
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h b/RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h
index 3b9f142e1d983..0d9be5a346d0a 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h
@@ -2,102 +2,58 @@
 #define RecoPixelVertexing_PixelTrackFitting_interface_FitResult_h
 
 #include <cmath>
+#include <cstdint>
 
 #include <cuda_runtime.h>
 #include <Eigen/Core>
 #include <Eigen/Eigenvalues>
 
-#include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
 
 namespace Rfit
 {
 
-constexpr double d = 1.e-4;          //!< used in numerical derivative (J2 in Circle_fit())
-constexpr unsigned int max_nop = 4;  //!< In order to avoid use of dynamic memory
 
+  using Vector2d = Eigen::Vector2d;
+  using Vector3d = Eigen::Vector3d;
+  using Vector4d = Eigen::Vector4d;
+  using Vector5d = Eigen::Matrix<double, 5, 1>;
+  using Matrix2d = Eigen::Matrix2d;
+  using Matrix3d = Eigen::Matrix3d;
+  using Matrix4d = Eigen::Matrix4d;
+  using Matrix5d = Eigen::Matrix<double, 5, 5>;
+  using Matrix6d = Eigen::Matrix<double, 6, 6>;
 
-using VectorXd = Eigen::VectorXd;
-using MatrixXd = Eigen::MatrixXd;
-template<int N>
-using MatrixNd = Eigen::Matrix<double, N, N>;
-template<int N>
-using MatrixNplusONEd = Eigen::Matrix<double, N+1, N+1>; 
-template<int N>
-using ArrayNd = Eigen::Array<double, N, N>;
-template<int N>
-using Matrix2Nd = Eigen::Matrix<double, 2 * N, 2 * N>;
-template<int N>
-using Matrix3Nd = Eigen::Matrix<double, 3 * N, 3 * N>;
-template<int N>
-using Matrix2xNd = Eigen::Matrix<double, 2, N>;
-template<int N>
-using Array2xNd = Eigen::Array<double, 2, N>;
-template<int N>
-using Matrix3xNd = Eigen::Matrix<double, 3, N>;
-template<int N>
-using MatrixNx3d = Eigen::Matrix<double, N, 3>;
-template<int N>
-using MatrixNx5d = Eigen::Matrix<double, N, 5>;
-template<int N>
-using VectorNd = Eigen::Matrix<double, N, 1>;
-template<int N>
-using VectorNplusONEd  = Eigen::Matrix<double, N+1, 1>;
-template<int N>
-using Vector2Nd = Eigen::Matrix<double, 2 * N, 1>;
-template<int N>
-using Vector3Nd = Eigen::Matrix<double, 3 * N, 1>;
-template<int N>
-using RowVectorNd = Eigen::Matrix<double, 1, 1, N>;
-template<int N>
-using RowVector2Nd = Eigen::Matrix<double, 1, 2 * N>;
 
+ template<int N>
+ using Matrix3xNd = Eigen::Matrix<double, 3, N>; // used for inputs hits
 
 
-using Vector2d = Eigen::Vector2d;
-using Vector3d = Eigen::Vector3d;
-using Vector4d = Eigen::Vector4d;
-using Matrix2d = Eigen::Matrix2d;
-using Matrix3d = Eigen::Matrix3d;
-using Matrix4d = Eigen::Matrix4d;
-using Matrix5d = Eigen::Matrix<double, 5, 5>;
-using Matrix6d = Eigen::Matrix<double, 6, 6>;
-using Vector5d = Eigen::Matrix<double, 5, 1>;
-using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
-
-using Matrix3f = Eigen::Matrix3f;
-using Vector3f = Eigen::Vector3f;
-using Vector4f = Eigen::Vector4f;
-using Vector6f = Eigen::Matrix<double, 6, 1>;
-
-using u_int = unsigned int;
-
-
-struct circle_fit
-{
+  struct circle_fit
+  {
     Vector3d par;  //!< parameter: (X0,Y0,R)
     Matrix3d cov;
     /*!< covariance matrix: \n
       |cov(X0,X0)|cov(Y0,X0)|cov( R,X0)| \n
       |cov(X0,Y0)|cov(Y0,Y0)|cov( R,Y0)| \n
       |cov(X0, R)|cov(Y0, R)|cov( R, R)|
-  */
+    */
     int32_t q;  //!< particle charge
     float chi2 = 0.0;
-};
-
-struct line_fit
-{
+  };
+  
+  struct line_fit
+  {
     Vector2d par;  //!<(cotan(theta),Zip)
     Matrix2d cov;
     /*!<
       |cov(c_t,c_t)|cov(Zip,c_t)| \n
       |cov(c_t,Zip)|cov(Zip,Zip)|
-  */
+    */
     double chi2 = 0.0;
-};
-
-struct helix_fit
-{
+  };
+  
+  struct helix_fit
+  {
     Vector5d par;  //!<(phi,Tip,pt,cotan(theta)),Zip)
     Matrix5d cov;
     /*!< ()->cov() \n
@@ -106,12 +62,12 @@ struct helix_fit
       |(phi,p_t)|(Tip,p_t)|(p_t,p_t)|(c_t,p_t)|(Zip,p_t)| \n
       |(phi,c_t)|(Tip,c_t)|(p_t,c_t)|(c_t,c_t)|(Zip,c_t)| \n
       |(phi,Zip)|(Tip,Zip)|(p_t,Zip)|(c_t,Zip)|(Zip,Zip)|
-  */
+    */
     float chi2_circle;
     float chi2_line;
-//    Vector4d fast_fit;
+    //    Vector4d fast_fit;
     int32_t q;  //!< particle charge
-} __attribute__((aligned(16)));
+  }; // __attribute__((aligned(16)));
 
 } // namespace RFit
 #endif
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h b/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
index 5ac3740406bd7..fa9d101b4a001 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
@@ -4,9 +4,68 @@
 
 #include "FitResult.h"
 
+#include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
+
+#include "DataFormats/Math/interface/choleskyInversion.h"
+
 
 namespace Rfit
 {
+
+
+  constexpr double d = 1.e-4;          //!< used in numerical derivative (J2 in Circle_fit())
+
+
+
+  using VectorXd = Eigen::VectorXd;
+  using MatrixXd = Eigen::MatrixXd;
+  template<int N>
+  using MatrixNd = Eigen::Matrix<double, N, N>;
+  template<int N>
+  using MatrixNplusONEd = Eigen::Matrix<double, N+1, N+1>; 
+  template<int N>
+  using ArrayNd = Eigen::Array<double, N, N>;
+  template<int N>
+  using Matrix2Nd = Eigen::Matrix<double, 2 * N, 2 * N>;
+  template<int N>
+  using Matrix3Nd = Eigen::Matrix<double, 3 * N, 3 * N>;
+  template<int N>
+  using Matrix2xNd = Eigen::Matrix<double, 2, N>;
+  template<int N>
+  using Array2xNd = Eigen::Array<double, 2, N>;
+  template<int N>
+  using MatrixNx3d = Eigen::Matrix<double, N, 3>;
+  template<int N>
+  using MatrixNx5d = Eigen::Matrix<double, N, 5>;
+  template<int N>
+  using VectorNd = Eigen::Matrix<double, N, 1>;
+  template<int N>
+  using VectorNplusONEd  = Eigen::Matrix<double, N+1, 1>;
+  template<int N>
+  using Vector2Nd = Eigen::Matrix<double, 2 * N, 1>;
+  template<int N>
+  using Vector3Nd = Eigen::Matrix<double, 3 * N, 1>;
+  template<int N>
+  using RowVectorNd = Eigen::Matrix<double, 1, 1, N>;
+  template<int N>
+  using RowVector2Nd = Eigen::Matrix<double, 1, 2 * N>;
+
+
+  using Matrix2x3d = Eigen::Matrix<double, 2, 3>;
+
+  
+  using Matrix3f = Eigen::Matrix3f;
+  using Vector3f = Eigen::Vector3f;
+  using Vector4f = Eigen::Vector4f;
+  using Vector6f = Eigen::Matrix<double, 6, 1>;
+
+
+
+  
+  using u_int = unsigned int;
+
+
+
   
   template <class C>
   __host__ __device__ void printIt(C* m, const char* prefix = "")
@@ -26,7 +85,7 @@ namespace Rfit
     \brief raise to square.
   */
   template <typename T>
-  __host__ __device__ inline T sqr(const T a)
+  constexpr T sqr(const T a)
   {
     return a * a;
   }
@@ -48,8 +107,8 @@ namespace Rfit
    *  load error in CMSSW format to our formalism
    *  
    */
-  template<typename M6x4f, typename M2Nd>
-  __host__ __device__ void loadCovariance2D(M6x4f const & ge,  M2Nd & hits_cov) {
+  template<typename M6xNf, typename M2Nd>
+  __host__ __device__ void loadCovariance2D(M6xNf const & ge,  M2Nd & hits_cov) {
     // Index numerology:
     // i: index of the hits/point (0,..,3)
     // j: index of space component (x,y,z)
@@ -62,7 +121,7 @@ namespace Rfit
     // | 0  1  3 |
     // | 1  2  4 |
     // | 3  4  5 |
-    constexpr uint32_t  hits_in_fit = 4; // Fixme
+    constexpr uint32_t hits_in_fit = M6xNf::ColsAtCompileTime;
     for (uint32_t i=0; i< hits_in_fit; ++i) {
       auto ge_idx = 0; auto j=0; auto l=0;
       hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
@@ -74,8 +133,8 @@ namespace Rfit
     }
   }
   
-  template<typename M6x4f, typename M3xNd>
-  __host__ __device__ void loadCovariance(M6x4f const & ge,  M3xNd & hits_cov) {
+  template<typename M6xNf, typename M3xNd>
+  __host__ __device__ void loadCovariance(M6xNf const & ge,  M3xNd & hits_cov) {
     
     // Index numerology:
     // i: index of the hits/point (0,..,3)
@@ -89,7 +148,7 @@ namespace Rfit
     // | 0  1  3 |
     // | 1  2  4 |
     // | 3  4  5 |
-    constexpr uint32_t  hits_in_fit = 4; // Fixme
+    constexpr uint32_t hits_in_fit = M6xNf::ColsAtCompileTime;
     for (uint32_t i=0; i<hits_in_fit; ++i) {
       auto ge_idx = 0; auto j=0; auto l=0;
       hits_cov(i + j * hits_in_fit, i + l * hits_in_fit) = ge.col(i)[ge_idx];
@@ -109,34 +168,34 @@ namespace Rfit
     }
   }
 
-
-/*!
+  /*!
     \brief Transform circle parameter from (X0,Y0,R) to (phi,Tip,p_t) and
     consequently covariance matrix.
     \param circle_uvr parameter (X0,Y0,R), covariance matrix to
     be transformed and particle charge.
     \param B magnetic field in Gev/cm/c unit.
     \param error flag for errors computation.
-*/
-__host__ __device__ inline void par_uvrtopak(circle_fit& circle, const double B, const bool error)
-{
+  */
+  __host__ __device__
+  inline void par_uvrtopak(circle_fit& circle, const double B, const bool error)
+  {
     Vector3d par_pak;
     const double temp0 = circle.par.head(2).squaredNorm();
     const double temp1 = sqrt(temp0);
     par_pak << atan2(circle.q * circle.par(0), -circle.q * circle.par(1)),
-        circle.q * (temp1 - circle.par(2)), circle.par(2) * B;
+      circle.q * (temp1 - circle.par(2)), circle.par(2) * B;
     if (error)
-    {
+      {
         const double temp2 = sqr(circle.par(0)) * 1. / temp0;
         const double temp3 = 1. / temp1 * circle.q;
         Matrix3d J4;
         J4 << -circle.par(1) * temp2 * 1. / sqr(circle.par(0)), temp2 * 1. / circle.par(0), 0., 
-               circle.par(0) * temp3, circle.par(1) * temp3, -circle.q,
-               0., 0., B;
+	  circle.par(0) * temp3, circle.par(1) * temp3, -circle.q,
+	  0., 0., B;
         circle.cov = J4 * circle.cov * J4.transpose();
-    }
+      }
     circle.par = par_pak;
-}
+  }
   
 }
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
index f5f098999a0c0..fdec04981770c 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
@@ -3,7 +3,6 @@
 
 #include "FitUtils.h"
 
-
 namespace Rfit
 {
 
@@ -312,6 +311,7 @@ template<typename M2xN>
     return ((p2D(0, 1) - p2D(0, 0)) * (par_uvr.y() - p2D(1, 0)) - (p2D(1, 1) - p2D(1, 0)) * (par_uvr.x() - p2D(0, 0)) > 0)? -1 : 1;
 }
 
+
 /*!
     \brief Compute the eigenvector associated to the minimum eigenvalue.
     \param A the Matrix you want to know eigenvector and eigenvalue.
@@ -511,7 +511,8 @@ __host__ __device__ inline circle_fit Circle_fit(const  M2xN& hits2D,
         printIt(&V, "circle_fit - V:");
         cov_rad += scatter_cov_rad;
         printIt(&cov_rad, "circle_fit - cov_rad:");
-        G = cov_rad.inverse();
+        choleskyInversion::invert(cov_rad,G);
+        // G = cov_rad.inverse();
 	renorm = G.sum();
         G *= 1. / renorm;
         weight = Weight_circle(G);
@@ -858,7 +859,7 @@ inline line_fit Line_fit(const M3xN& hits,
 
   // associated Jacobian, used in weights and errors computation
   Matrix6d Cov = Matrix6d::Zero();
-  Matrix2d cov_sz[4];  // FIXME: should be "N"
+  Matrix2d cov_sz[N];
   for (u_int i = 0; i < n; ++i)
   {
     Vector2d p = hits.block(0, i, 2, 1) - o;
@@ -928,11 +929,12 @@ inline line_fit Line_fit(const M3xN& hits,
 #endif
 
   // Build A^T V-1 A, where V-1 is the covariance of only the Y components.
-  MatrixNd<N> Vy_inv = cov_with_ms.inverse();
-  Eigen::Matrix<double, 2, 2> Inv_Cov = A*Vy_inv*A.transpose();
-
+  MatrixNd<N> Vy_inv; choleskyInversion::invert(cov_with_ms,Vy_inv);
+  // MatrixNd<N> Vy_inv = cov_with_ms.inverse();
+  Eigen::Matrix<double, 2, 2> Cov_params = A*Vy_inv*A.transpose();
   // Compute the Covariance Matrix of the fit parameters
-  Eigen::Matrix<double, 2, 2> Cov_params = Inv_Cov.inverse();
+  choleskyInversion::invert(Cov_params,Cov_params);
+
 
   // Now Compute the Parameters in the form [2,1]
   // The first component is q.
@@ -1010,7 +1012,7 @@ inline line_fit Line_fit(const M3xN& hits,
 */
 
 template<int N>
-inline helix_fit Helix_fit(const Matrix3xNd<N>& hits, const Eigen::Matrix<float,6,4>& hits_ge, const double B,
+inline helix_fit Helix_fit(const Matrix3xNd<N>& hits, const Eigen::Matrix<float,6,N>& hits_ge, const double B,
                            const bool error)
 {
     constexpr u_int n = N;
@@ -1019,7 +1021,7 @@ inline helix_fit Helix_fit(const Matrix3xNd<N>& hits, const Eigen::Matrix<float,
     // Fast_fit gives back (X0, Y0, R, theta) w/o errors, using only 3 points.
     Vector4d fast_fit; 
     Fast_fit(hits,fast_fit);
-    Rfit::Matrix2Nd<4> hits_cov =  MatrixXd::Zero(2 * n, 2 * n);
+    Rfit::Matrix2Nd<N> hits_cov =  MatrixXd::Zero(2 * n, 2 * n);
     Rfit::loadCovariance2D(hits_ge,hits_cov);
     circle_fit circle = Circle_fit(hits.block(0, 0, 2, n),
                                    hits_cov,
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
index ab2090719122a..88ba8139f01ae 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testEigenGPU.cu
@@ -3,34 +3,59 @@
 #include <Eigen/Core>
 #include <Eigen/Eigenvalues>
 
-#include "test_common.h"
-
-#include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
-
 #ifdef USE_BL
 #include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
 #else
 #include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
 #endif
 
+
+#include "test_common.h"
+
+#include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
+
 using namespace Eigen;
 
 namespace Rfit {
   constexpr uint32_t maxNumberOfTracks() { return 5*1024; }
   constexpr uint32_t stride() { return maxNumberOfTracks();}
-  using Matrix3x4d = Eigen::Matrix<double,3,4>;
-  using Map3x4d = Eigen::Map<Matrix3x4d,0,Eigen::Stride<3*stride(),stride()> >;
-  using Matrix6x4f = Eigen::Matrix<float,6,4>;
-  using Map6x4f = Eigen::Map<Matrix6x4f,0,Eigen::Stride<6*stride(),stride()> >;
+  // hits
+  template<int N>
+  using Matrix3xNd = Eigen::Matrix<double,3,N>;
+  template<int N>
+  using Map3xNd = Eigen::Map<Matrix3xNd<N>,0,Eigen::Stride<3*stride(),stride()> >;
+  // errors
+  template<int N>
+  using Matrix6xNf = Eigen::Matrix<float,6,N>;
+  template<int N>
+  using Map6xNf = Eigen::Map<Matrix6xNf<N>,0,Eigen::Stride<6*stride(),stride()> >;
+  // fast fit
   using Map4d = Eigen::Map<Vector4d,0,Eigen::InnerStride<stride()> >;
 
 }
 
+
+
+template<int N>
+__global__
+void kernelPrintSizes(double * __restrict__ phits,
+                      float * __restrict__ phits_ge
+		      ) {
+  auto i = blockIdx.x*blockDim.x + threadIdx.x;
+  Rfit::Map3xNd<N> hits(phits+i,3,4);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+i,6,4);
+  if (i!=0) return;
+  printf("GPU sizes %lu %lu %lu %lu %lu\n",sizeof(hits[i]),sizeof(hits_ge[i]),
+	 sizeof(Vector4d),sizeof(Rfit::line_fit),sizeof(Rfit::circle_fit));
+}
+
+
+template<int N>
 __global__
 void kernelFastFit(double * __restrict__ phits, double * __restrict__ presults) {
   auto i = blockIdx.x*blockDim.x + threadIdx.x;
-  Rfit::Map3x4d hits(phits+i,3,4);
+  Rfit::Map3xNd<N> hits(phits+i,3,N);
   Rfit::Map4d result(presults+i,4);
 #ifdef USE_BL
   BrokenLine::BL_Fast_fit(hits, result);
@@ -41,6 +66,7 @@ void kernelFastFit(double * __restrict__ phits, double * __restrict__ presults)
 
 #ifdef USE_BL
 
+template<int N>
 __global__
 void kernelBrokenLineFit(double * __restrict__ phits,
 			 float * __restrict__ phits_ge, 
@@ -50,25 +76,21 @@ void kernelBrokenLineFit(double * __restrict__ phits,
 			 Rfit::line_fit * line_fit
 			 ) {
   auto i = blockIdx.x*blockDim.x + threadIdx.x;
-  Rfit::Map3x4d hits(phits+i,3,4);
+  Rfit::Map3xNd<N> hits(phits+i,3,N);
   Rfit::Map4d   fast_fit_input(pfast_fit_input+i,4);
-  Rfit::Map6x4f hits_ge(phits_ge+i,6,4);
-
-  constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
-  constexpr auto n = N;
+  Rfit::Map6xNf<N> hits_ge(phits_ge+i,6,N);
   
   BrokenLine::PreparedBrokenLineData<N> data;
   Rfit::Matrix3d Jacob;
-  Rfit::MatrixNplusONEd<N> C_U;
-
+  
   auto & line_fit_results = line_fit[i];
   auto & circle_fit_results = circle_fit[i];
   
   BrokenLine::prepareBrokenLineData(hits,fast_fit_input,B,data);
   BrokenLine::BL_Line_fit(hits_ge,fast_fit_input,B,data,line_fit_results);
-  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_input,B,data,circle_fit_results,Jacob,C_U);
-  Jacob << 1,0,0,
-    0,1,0,
+  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_input,B,data,circle_fit_results);
+  Jacob << 1.,0,0,
+    0,1.,0,
     0,0,-B/std::copysign(Rfit::sqr(circle_fit_results.par(2)),circle_fit_results.par(2));
   circle_fit_results.par(2)=B/std::abs(circle_fit_results.par(2));
   circle_fit_results.cov=Jacob*circle_fit_results.cov*Jacob.transpose();
@@ -82,6 +104,7 @@ if (0==i) {
 
 #else
 
+template<int N>
 __global__
 void kernelCircleFit(double * __restrict__ phits,
     float * __restrict__ phits_ge, 
@@ -90,11 +113,10 @@ void kernelCircleFit(double * __restrict__ phits,
     Rfit::circle_fit * circle_fit_resultsGPU) {
 
   auto i = blockIdx.x*blockDim.x + threadIdx.x;
-  Rfit::Map3x4d hits(phits+i,3,4);
+  Rfit::Map3xNd<N> hits(phits+i,3,N);
   Rfit::Map4d   fast_fit_input(pfast_fit_input+i,4);
-  Rfit::Map6x4f hits_ge(phits_ge+i,6,4);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+i,6,N);
 
-  constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
   constexpr auto n = N;
 
   Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
@@ -129,60 +151,87 @@ if (0==i) {
 #endif
 }
 
+template<int N>
 __global__
 void kernelLineFit(double * __restrict__ phits,
 		   float * __restrict__ phits_ge,
                    double B,
                    Rfit::circle_fit * circle_fit,
-                   double * __restrict__ pfast_fit,
+                   double * __restrict__ pfast_fit_input,
                    Rfit::line_fit * line_fit)
 {
   auto i = blockIdx.x*blockDim.x + threadIdx.x;
-  Rfit::Map3x4d hits(phits+i,3,4);
-  Rfit::Map4d   fast_fit(pfast_fit+i,4);
-  Rfit::Map6x4f hits_ge(phits_ge+i,6,4);
-  line_fit[i] = Rfit::Line_fit(hits, hits_ge, circle_fit[i], fast_fit, B, true);
+  Rfit::Map3xNd<N> hits(phits+i,3,N);
+  Rfit::Map4d   fast_fit_input(pfast_fit_input+i,4);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+i,6,N);
+  line_fit[i] = Rfit::Line_fit(hits, hits_ge, circle_fit[i], fast_fit_input, B, true);
 }
 #endif
 
-template<typename M3x4, typename M6x4>
+template<typename M3xN, typename M6xN>
 __device__ __host__
-void fillHitsAndHitsCov(M3x4 & hits, M6x4 & hits_ge) {
-  hits << 1.98645, 4.72598, 7.65632, 11.3151,
-          2.18002, 4.88864, 7.75845, 11.3134,
-          2.46338, 6.99838,  11.808,  17.793;
+void fillHitsAndHitsCov(M3xN & hits, M6xN & hits_ge) {
+
+  constexpr uint32_t N = M3xN::ColsAtCompileTime;
+
+  if (N==5) {
+    hits << 2.934787,  6.314229, 8.936963, 10.360559,  12.856387,
+      0.773211, 1.816356, 2.765734, 3.330824, 4.422212,
+      -10.980247, -23.162731, -32.759060, -38.061260, -47.518867;
+    hits_ge.col(0) << 1.424715e-07,-4.996975e-07,1.752614e-06,3.660689e-11,1.644638e-09,7.346080e-05;
+    hits_ge.col(1) << 6.899177e-08,-1.873414e-07,5.087101e-07,-2.078806e-10,-2.210498e-11,4.346079e-06;
+    hits_ge.col(2) << 1.406273e-06,4.042467e-07,6.391180e-07,-3.141497e-07,6.513821e-08,1.163863e-07;
+    hits_ge.col(3) << 1.176358e-06,2.154100e-07,5.072816e-07,-8.161219e-08,1.437878e-07,5.951832e-08;
+    hits_ge.col(4) << 2.852843e-05,7.956492e-06,3.117701e-06,-1.060541e-06,8.777413e-09,1.426417e-07;
+    return;
+  }
+
+ 
+  
+  if (N>3) 
+    hits << 1.98645, 4.72598, 7.65632, 11.3151,
+      2.18002, 4.88864, 7.75845, 11.3134,
+      2.46338, 6.99838,  11.808,  17.793;
+  else
+    hits << 1.98645, 4.72598, 7.65632,
+      2.18002, 4.88864, 7.75845,
+      2.46338, 6.99838,  11.808;
+  
   hits_ge.col(0)[0] = 7.14652e-06;
   hits_ge.col(1)[0] = 2.15789e-06;
   hits_ge.col(2)[0] = 1.63328e-06;
-  hits_ge.col(3)[0] = 6.27919e-06;
+  if (N>3) hits_ge.col(3)[0] = 6.27919e-06;
   hits_ge.col(0)[2] = 6.10348e-06;
   hits_ge.col(1)[2] = 2.08211e-06;
   hits_ge.col(2)[2] = 1.61672e-06;
-  hits_ge.col(3)[2] = 6.28081e-06;
+  if (N>3) hits_ge.col(3)[2] = 6.28081e-06;
   hits_ge.col(0)[5] = 5.184e-05;
   hits_ge.col(1)[5] = 1.444e-05;
   hits_ge.col(2)[5] = 6.25e-06;
-  hits_ge.col(3)[5] = 3.136e-05;
+  if (N>3) hits_ge.col(3)[5] = 3.136e-05;
   hits_ge.col(0)[1] = -5.60077e-06;
   hits_ge.col(1)[1] = -1.11936e-06;
   hits_ge.col(2)[1] = -6.24945e-07;
-  hits_ge.col(3)[1] = -5.28e-06;
+  if (N>3) hits_ge.col(3)[1] = -5.28e-06;
 }
 
+
+template<int N>
 __global__
 void kernelFillHitsAndHitsCov(double * __restrict__ phits,
   float * phits_ge) {
   auto i = blockIdx.x*blockDim.x + threadIdx.x;
-  Rfit::Map3x4d hits(phits+i,3,4);
-  Rfit::Map6x4f hits_ge(phits_ge+i,6,4);
-  hits_ge = MatrixXf::Zero(6,4);
+  Rfit::Map3xNd<N> hits(phits+i,3,N);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+i,6,N);
+  hits_ge = MatrixXf::Zero(6,N);
   fillHitsAndHitsCov(hits,hits_ge);
 }
 
+template<int N>
 void testFit() {
   constexpr double B = 0.0113921;
-  Rfit::Matrix3xNd<4> hits;
-  Rfit::Matrix6x4f hits_ge = MatrixXf::Zero(6,4);
+  Rfit::Matrix3xNd<N> hits;
+  Rfit::Matrix6xNf<N> hits_ge = MatrixXf::Zero(6,N);
   double * hitsGPU = nullptr;;
   float * hits_geGPU = nullptr;
   double * fast_fit_resultsGPU = nullptr;
@@ -194,8 +243,12 @@ void testFit() {
 
   fillHitsAndHitsCov(hits, hits_ge);
 
-  std::cout << "sizes " << sizeof(hits) << ' ' << sizeof(hits_ge)
-	    << ' ' << sizeof(Vector4d)<< std::endl;
+  std::cout << "sizes " << N << ' '
+	    << sizeof(hits) << ' ' << sizeof(hits_ge)
+	    << ' ' << sizeof(Vector4d) 
+	    << ' ' << sizeof(Rfit::line_fit) 
+            << ' ' << sizeof(Rfit::circle_fit)
+            << std::endl;
   
   std::cout << "Generated hits:\n" << hits << std::endl;
   std::cout << "Generated cov:\n" << hits_ge << std::endl;
@@ -210,17 +263,21 @@ void testFit() {
 
   // for timing    purposes we fit    4096 tracks
   constexpr uint32_t Ntracks = 4096;
-  cudaCheck(cudaMalloc(&hitsGPU, Rfit::maxNumberOfTracks()*sizeof(Rfit::Matrix3xNd<4>)));
-  cudaCheck(cudaMalloc(&hits_geGPU, Rfit::maxNumberOfTracks()*sizeof(Rfit::Matrix6x4f)));
+  cudaCheck(cudaMalloc(&hitsGPU, Rfit::maxNumberOfTracks()*sizeof(Rfit::Matrix3xNd<N>)));
+  cudaCheck(cudaMalloc(&hits_geGPU, Rfit::maxNumberOfTracks()*sizeof(Rfit::Matrix6xNf<N>)));
   cudaCheck(cudaMalloc(&fast_fit_resultsGPU, Rfit::maxNumberOfTracks()*sizeof(Vector4d)));
-  cudaCheck(cudaMalloc((void **)&line_fit_resultsGPU, Rfit::maxNumberOfTracks()*sizeof(Rfit::line_fit)));
-  cudaCheck(cudaMalloc((void **)&circle_fit_resultsGPU, Rfit::maxNumberOfTracks()*sizeof(Rfit::circle_fit)));
+  cudaCheck(cudaMalloc(&line_fit_resultsGPU, Rfit::maxNumberOfTracks()*sizeof(Rfit::line_fit)));
+  cudaCheck(cudaMalloc(&circle_fit_resultsGPU, Rfit::maxNumberOfTracks()*sizeof(Rfit::circle_fit)));
+
+  cudaCheck(cudaMemset(fast_fit_resultsGPU, 0, Rfit::maxNumberOfTracks()*sizeof(Vector4d)));
+  cudaCheck(cudaMemset(line_fit_resultsGPU, 0, Rfit::maxNumberOfTracks()*sizeof(Rfit::line_fit)));
 
 
-  kernelFillHitsAndHitsCov<<<Ntracks/64, 64>>>(hitsGPU,hits_geGPU);
+  kernelPrintSizes<N><<<Ntracks/64, 64>>>(hitsGPU,hits_geGPU);
+  kernelFillHitsAndHitsCov<N><<<Ntracks/64, 64>>>(hitsGPU,hits_geGPU);
 
   // FAST_FIT GPU
-  kernelFastFit<<<Ntracks/64, 64>>>(hitsGPU, fast_fit_resultsGPU);
+  kernelFastFit<N><<<Ntracks/64, 64>>>(hitsGPU, fast_fit_resultsGPU);
   cudaDeviceSynchronize();
   
   cudaMemcpy(fast_fit_resultsGPUret, fast_fit_resultsGPU, Rfit::maxNumberOfTracks()*sizeof(Vector4d), cudaMemcpyDeviceToHost);
@@ -228,8 +285,6 @@ void testFit() {
   std::cout << "Fitted values (FastFit, [X0, Y0, R, tan(theta)]): GPU\n" << fast_fit << std::endl;
   assert(isEqualFuzzy(fast_fit_results, fast_fit));
 
-  constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
-  constexpr auto n = N;
 
 #ifdef USE_BL
   // CIRCLE AND LINE FIT CPU
@@ -237,18 +292,17 @@ void testFit() {
   BrokenLine::karimaki_circle_fit circle_fit_results;
   Rfit::line_fit line_fit_results;
   Rfit::Matrix3d Jacob;
-  Rfit::MatrixNplusONEd<N> C_U;
   BrokenLine::prepareBrokenLineData(hits,fast_fit_results,B,data);
   BrokenLine::BL_Line_fit(hits_ge,fast_fit_results,B,data,line_fit_results);
-  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_results,B,data,circle_fit_results,Jacob,C_U);
-  Jacob << 1,0,0,
-    0,1,0,
+  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_results,B,data,circle_fit_results);
+  Jacob << 1.,0,0,
+    0,1.,0,
     0,0,-B/std::copysign(Rfit::sqr(circle_fit_results.par(2)),circle_fit_results.par(2));
   circle_fit_results.par(2)=B/std::abs(circle_fit_results.par(2));
   circle_fit_results.cov=Jacob*circle_fit_results.cov*Jacob.transpose();
 
   // fit on GPU
-  kernelBrokenLineFit<<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU,
+  kernelBrokenLineFit<N><<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU,
 					  fast_fit_resultsGPU, B,
 					  circle_fit_resultsGPU,
 					  line_fit_resultsGPU);
@@ -257,16 +311,16 @@ void testFit() {
   
 #else
   // CIRCLE_FIT CPU
-  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
+  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, N).colwise().norm());
 
-  Rfit::Matrix2Nd<N> hits_cov =  MatrixXd::Zero(2 * n, 2 * n);
+  Rfit::Matrix2Nd<N> hits_cov =  Rfit::Matrix2Nd<N>::Zero();
   Rfit::loadCovariance2D(hits_ge,hits_cov);
-  Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, n),
+  Rfit::circle_fit circle_fit_results = Rfit::Circle_fit(hits.block(0, 0, 2, N),
       hits_cov,
       fast_fit_results, rad, B, true);
 
   // CIRCLE_FIT GPU
-  kernelCircleFit<<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU,
+  kernelCircleFit<N><<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU,
       fast_fit_resultsGPU, B, circle_fit_resultsGPU);
   cudaDeviceSynchronize();
  
@@ -274,7 +328,7 @@ void testFit() {
   Rfit::line_fit line_fit_results = Rfit::Line_fit(hits, hits_ge, circle_fit_results, fast_fit_results, B, true);
 
 
-  kernelLineFit<<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU, B, circle_fit_resultsGPU, fast_fit_resultsGPU, line_fit_resultsGPU);
+  kernelLineFit<N><<<Ntracks/64, 64>>>(hitsGPU, hits_geGPU, B, circle_fit_resultsGPU, fast_fit_resultsGPU, line_fit_resultsGPU);
   cudaDeviceSynchronize();
 #endif
 
@@ -291,7 +345,7 @@ void testFit() {
     // LINE_FIT GPU
   cudaMemcpy(line_fit_resultsGPUret, line_fit_resultsGPU, sizeof(Rfit::line_fit), cudaMemcpyDeviceToHost);
   std::cout << "Fitted values (LineFit) GPU:\n" << line_fit_resultsGPUret->par << std::endl;
-  assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par));
+  assert(isEqualFuzzy(line_fit_results.par, line_fit_resultsGPUret->par, N==5 ? 1e-4 : 1e-6)); // requires fma on CPU
 
   
   std::cout << "Fitted cov (CircleFit) CPU:\n" << circle_fit_results.cov << std::endl;
@@ -304,7 +358,10 @@ void testFit() {
 int main (int argc, char * argv[]) {
   exitSansCUDADevices();
 
-  testFit();
+  testFit<4>();
+  testFit<3>();
+  testFit<5>();
+
   std::cout << "TEST FIT, NO ERRORS" << std::endl;
 
   return 0;
diff --git a/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp b/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
index 77f1f39ee29e6..a1e1049392ad0 100644
--- a/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
+++ b/RecoPixelVertexing/PixelTrackFitting/test/testRiemannFit.cpp
@@ -1,8 +1,5 @@
 #include <iostream>
 
-// #define __host__
-// #define __device__
-
 #include <Eigen/Core>
 #include <Eigen/Eigenvalues>
 
@@ -19,45 +16,90 @@ using namespace Eigen;
 namespace Rfit {
   constexpr uint32_t maxNumberOfTracks() { return 5*1024; }
   constexpr uint32_t stride() { return maxNumberOfTracks();}
-  using Matrix3x4d = Eigen::Matrix<double,3,4>;
-  using Map3x4d = Eigen::Map<Matrix3x4d,0,Eigen::Stride<3*stride(),stride()> >;
-  using Matrix6x4f = Eigen::Matrix<float,6,4>;
-  using Map6x4f = Eigen::Map<Matrix6x4f,0,Eigen::Stride<6*stride(),stride()> >;
+  // hits
+  template<int N>
+  using Matrix3xNd = Eigen::Matrix<double,3,N>;
+  template<int N>
+  using Map3xNd = Eigen::Map<Matrix3xNd<N>,0,Eigen::Stride<3*stride(),stride()> >;
+  // errors
+  template<int N>
+  using Matrix6xNf = Eigen::Matrix<float,6,N>;
+  template<int N>
+  using Map6xNf = Eigen::Map<Matrix6xNf<N>,0,Eigen::Stride<6*stride(),stride()> >;
+  // fast fit
   using Map4d = Eigen::Map<Vector4d,0,Eigen::InnerStride<stride()> >;
 
 }
 
-template<typename M3x4, typename M6x4>
-void fillHitsAndHitsCov(M3x4 & hits, M6x4 & hits_ge) {
-  hits << 1.98645, 4.72598, 7.65632, 11.3151,
-          2.18002, 4.88864, 7.75845, 11.3134,
-          2.46338, 6.99838,  11.808,  17.793;
+/*
+Hit global: 641,0 2: 2.934787,0.773211,-10.980247
+Error: 641,0 2: 1.424715e-07,-4.996975e-07,1.752614e-06,3.660689e-11,1.644638e-09,7.346080e-05
+Hit global: 641,1 104: 6.314229,1.816356,-23.162731
+Error: 641,1 104: 6.899177e-08,-1.873414e-07,5.087101e-07,-2.078806e-10,-2.210498e-11,4.346079e-06
+Hit global: 641,2 1521: 8.936963,2.765734,-32.759060
+Error: 641,2 1521: 1.406273e-06,4.042467e-07,6.391180e-07,-3.141497e-07,6.513821e-08,1.163863e-07
+Hit global: 641,3 1712: 10.360559,3.330824,-38.061260
+Error: 641,3 1712: 1.176358e-06,2.154100e-07,5.072816e-07,-8.161219e-08,1.437878e-07,5.951832e-08
+Hit global: 641,4 1824: 12.856387,4.422212,-47.518867
+Error: 641,4 1824: 2.852843e-05,7.956492e-06,3.117701e-06,-1.060541e-06,8.777413e-09,1.426417e-07
+*/
+
+template<typename M3xN, typename M6xN>
+void fillHitsAndHitsCov(M3xN & hits, M6xN & hits_ge) {
+
+  constexpr uint32_t N = M3xN::ColsAtCompileTime;
+
+  if (N==5) {
+    hits << 2.934787,  6.314229, 8.936963, 10.360559,  12.856387,
+      0.773211, 1.816356, 2.765734, 3.330824, 4.422212,
+      -10.980247, -23.162731, -32.759060, -38.061260, -47.518867;
+    hits_ge.col(0) << 1.424715e-07,-4.996975e-07,1.752614e-06,3.660689e-11,1.644638e-09,7.346080e-05;
+    hits_ge.col(1) << 6.899177e-08,-1.873414e-07,5.087101e-07,-2.078806e-10,-2.210498e-11,4.346079e-06;
+    hits_ge.col(2) << 1.406273e-06,4.042467e-07,6.391180e-07,-3.141497e-07,6.513821e-08,1.163863e-07;
+    hits_ge.col(3) << 1.176358e-06,2.154100e-07,5.072816e-07,-8.161219e-08,1.437878e-07,5.951832e-08;
+    hits_ge.col(4) << 2.852843e-05,7.956492e-06,3.117701e-06,-1.060541e-06,8.777413e-09,1.426417e-07;
+    return;
+  }
+
+  
+  if (N>3) 
+    hits << 1.98645, 4.72598, 7.65632, 11.3151,
+      2.18002, 4.88864, 7.75845, 11.3134,
+      2.46338, 6.99838,  11.808,  17.793;
+  else
+    hits << 1.98645, 4.72598, 7.65632,
+      2.18002, 4.88864, 7.75845,
+      2.46338, 6.99838,  11.808;
+  
   hits_ge.col(0)[0] = 7.14652e-06;
   hits_ge.col(1)[0] = 2.15789e-06;
   hits_ge.col(2)[0] = 1.63328e-06;
-  hits_ge.col(3)[0] = 6.27919e-06;
+  if (N>3) hits_ge.col(3)[0] = 6.27919e-06;
   hits_ge.col(0)[2] = 6.10348e-06;
   hits_ge.col(1)[2] = 2.08211e-06;
   hits_ge.col(2)[2] = 1.61672e-06;
-  hits_ge.col(3)[2] = 6.28081e-06;
+  if (N>3) hits_ge.col(3)[2] = 6.28081e-06;
   hits_ge.col(0)[5] = 5.184e-05;
   hits_ge.col(1)[5] = 1.444e-05;
   hits_ge.col(2)[5] = 6.25e-06;
-  hits_ge.col(3)[5] = 3.136e-05;
+  if (N>3) hits_ge.col(3)[5] = 3.136e-05;
   hits_ge.col(0)[1] = -5.60077e-06;
   hits_ge.col(1)[1] = -1.11936e-06;
   hits_ge.col(2)[1] = -6.24945e-07;
-  hits_ge.col(3)[1] = -5.28e-06;
+  if (N>3) hits_ge.col(3)[1] = -5.28e-06;
 }
 
+
+template<int N>
 void testFit() {
   constexpr double B = 0.0113921;
-  Rfit::Matrix3xNd<4> hits;
-  Rfit::Matrix6x4f hits_ge = MatrixXf::Zero(6,4);
+  Rfit::Matrix3xNd<N> hits;
+  Rfit::Matrix6xNf<N> hits_ge = MatrixXf::Zero(6,N);
 
   fillHitsAndHitsCov(hits, hits_ge);
 
-  std::cout << "sizes " << sizeof(hits) << ' ' << sizeof(hits_ge)
+  std::cout << "sizes " << N << ' '
+	    <<sizeof(hits) << ' ' << sizeof(hits_ge)
 	    << ' ' << sizeof(Vector4d)<< std::endl;
   
   std::cout << "Generated hits:\n" << hits << std::endl;
@@ -73,20 +115,19 @@ void testFit() {
 
 
   // CIRCLE_FIT CPU
-  constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
+
 
 #ifdef USE_BL
   BrokenLine::PreparedBrokenLineData<N> data;
   BrokenLine::karimaki_circle_fit circle_fit_results;
   Rfit::Matrix3d Jacob;
-  Rfit::MatrixNplusONEd<N> C_U;
     
   BrokenLine::prepareBrokenLineData(hits,fast_fit_results,B,data);
   Rfit::line_fit line_fit_results;
   BrokenLine::BL_Line_fit(hits_ge,fast_fit_results,B,data,line_fit_results);
-  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_results,B,data,circle_fit_results,Jacob,C_U);
-  Jacob << 1,0,0,
-    0,1,0,
+  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit_results,B,data,circle_fit_results);
+  Jacob << 1.,0,0,
+    0,1.,0,
     0,0,-B/std::copysign(Rfit::sqr(circle_fit_results.par(2)),circle_fit_results.par(2));
   circle_fit_results.par(2)=B/std::abs(circle_fit_results.par(2));
   circle_fit_results.cov=Jacob*circle_fit_results.cov*Jacob.transpose();
@@ -113,7 +154,10 @@ void testFit() {
 }
 
 int main (int argc, char * argv[]) {
-  testFit();
+  testFit<4>();
+  testFit<3>();
+  testFit<5>();
+
   return 0;
 }
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
index 20337093ac79c..36adae90c6eb9 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
@@ -113,14 +113,13 @@ void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
   
   BrokenLine::PreparedBrokenLineData<N> data;
   Rfit::Matrix3d Jacob;
-  Rfit::MatrixNplusONEd<N> C_U;
 
   BrokenLine::karimaki_circle_fit circle;
   Rfit::line_fit line;
  
   BrokenLine::prepareBrokenLineData(hits,fast_fit,B,data);
   BrokenLine::BL_Line_fit(hits_ge,fast_fit,B,data,line);
-  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit,B,data,circle,Jacob,C_U);
+  BrokenLine::BL_Circle_fit(hits,hits_ge,fast_fit,B,data,circle);
   Jacob << 1,0,0,
     0,1,0,
     0,0,-B/std::copysign(Rfit::sqr(circle.par(2)),circle.par(2));

From ad060c95b4e773214bcdfa4f7a235d9bbb0f09b6 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 5 Feb 2019 12:19:06 +0100
Subject: [PATCH 22/95] remove sqrt from choleshy

---
 .../Math/interface/choleskyInversion.h        | 321 +++++++++---------
 1 file changed, 161 insertions(+), 160 deletions(-)

diff --git a/DataFormats/Math/interface/choleskyInversion.h b/DataFormats/Math/interface/choleskyInversion.h
index 6670d4c382fc1..fa591e06cc811 100644
--- a/DataFormats/Math/interface/choleskyInversion.h
+++ b/DataFormats/Math/interface/choleskyInversion.h
@@ -43,25 +43,25 @@ namespace choleskyInversion {
   inline constexpr
   void invert33(M1 const & src, M2 & dst) {
     using F = decltype(src(0,0));
-    auto luc0 = F(1.0) / std::sqrt(src(0,0));
-    auto luc1 = src(1,0) * luc0;
-    auto luc2 = src(1,1) - luc1 * luc1;
-    luc2 = F(1.0) / std::sqrt(luc2);
-    auto luc3 = src(2,0) * luc0;
-    auto luc4 = (src(2,1) - luc1 * luc3) * luc2;
-    auto luc5 = src(2,2) - (luc3 * luc3 + luc4 * luc4);
-    luc5 = F(1.0) / std::sqrt(luc5);
+    auto luc0 = F(1.0) / src(0,0);
+    auto luc1 = src(1,0);
+    auto luc2 = src(1,1) - luc0 * luc1 * luc1;
+    luc2 = F(1.0) / luc2;
+    auto luc3 = src(2,0);
+    auto luc4 = (src(2,1) - luc0 * luc1 * luc3);
+    auto luc5 = src(2,2) - (luc0 * luc3 * luc3 + (luc2 * luc4) * luc4);
+    luc5 = F(1.0) / luc5;
     
-    auto li21 = -luc1 * luc0 * luc2;
-    auto li32 = -luc4 * luc2 * luc5;
-    auto li31 = (luc1 * luc4 * luc2 - luc3) * luc0 * luc5;
+    auto li21 = -luc0 * luc1;
+    auto li32 = -(luc2 * luc4);
+    auto li31 = (luc1 * (luc2 * luc4)  - luc3) * luc0;
 
-    dst(0,0) = li31*li31 + li21*li21 + luc0*luc0;
-    dst(1,0) = li31*li32 + li21*luc2;
-    dst(1,1) = li32*li32 + luc2*luc2;
-    dst(2,0) = li31*luc5;
-    dst(2,1) = li32*luc5;
-    dst(2,2) = luc5*luc5;
+    dst(0,0) = luc5*li31*li31 + li21*li21*luc2 + luc0;
+    dst(1,0) = luc5*li31*li32 + li21*luc2;
+    dst(1,1) = luc5*li32*li32 + luc2;
+    dst(2,0) = luc5*li31;
+    dst(2,1) = luc5*li32;
+    dst(2,2) = luc5;
     
   }
 
@@ -69,38 +69,38 @@ namespace choleskyInversion {
   inline constexpr
   void invert44(M1 const & src, M2 & dst) {
     using F = decltype(src(0,0));
-    auto luc0 = F(1.0) / std::sqrt(src(0,0));
-    auto luc1 = src(1,0) * luc0;
-    auto luc2 = src(1,1) - luc1 * luc1;
-    luc2 = F(1.0) / std::sqrt(luc2);
-    auto luc3 = src(2,0) * luc0;
-    auto luc4 = (src(2,1) - luc1 * luc3) * luc2;
-    auto luc5 = src(2,2) - (luc3 * luc3 + luc4 * luc4);
-    luc5 = F(1.0) / std::sqrt(luc5);
-    auto luc6 = src(3,0) * luc0;
-    auto luc7 = (src(3,1) - luc1 * luc6) * luc2;
-    auto luc8 = (src(3,2) - luc3 * luc6 - luc4 * luc7) * luc5;
-    auto luc9 = src(3,3) - (luc6 * luc6 + luc7 * luc7 + luc8 * luc8);
-    luc9 = F(1.0) / std::sqrt(luc9);
+    auto luc0 = F(1.0) / src(0,0);
+    auto luc1 = src(1,0);
+    auto luc2 = src(1,1) - luc0 * luc1 * luc1;
+    luc2 = F(1.0) / luc2;
+    auto luc3 = src(2,0);
+    auto luc4 = (src(2,1) - luc0 * luc1 * luc3);
+    auto luc5 = src(2,2) - (luc0 * luc3 * luc3 + luc2 * luc4 * luc4);
+    luc5 = F(1.0) / luc5;
+    auto luc6 = src(3,0);
+    auto luc7 = (src(3,1) - luc0 * luc1 * luc6);
+    auto luc8 = (src(3,2) - luc0 * luc3 * luc6 - luc2 * luc4 * luc7);
+    auto luc9 = src(3,3) - (luc0 * luc6 * luc6 + luc2 * luc7 * luc7 + luc8 * (luc8 *luc5) );
+    luc9 = F(1.0) / luc9;
     
-    auto li21 = -luc1 * luc0 * luc2;
-    auto li32 = -luc4 * luc2 * luc5;
-    auto li31 = (luc1 * luc4 * luc2 - luc3) * luc0 * luc5;
-    auto li43 = -luc8 * luc9 * luc5;
-    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2 * luc9;
-    auto li41 = (-luc1 * luc4 * luc8 * luc2 * luc5 +
-		 luc1 * luc7 * luc2 + luc3 * luc8 * luc5 - luc6) * luc0 * luc9;
+    auto li21 = -luc1 * luc0;
+    auto li32 = -luc2 * luc4;
+    auto li31 = (luc1 * (luc2 * luc4) - luc3) * luc0;
+    auto li43 = -(luc8 * luc5);
+    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2;
+    auto li41 = (-luc1 * (luc2*luc4) * (luc8 * luc5) +
+		 luc1 * (luc2*luc7) + luc3 * (luc8 * luc5) - luc6) * luc0;
 
-    dst(0,0) = li41*li41 + li31*li31 + li21*li21 + luc0*luc0;
-    dst(1,0) = li41*li42 + li31*li32 + li21*luc2;
-    dst(1,1) = li42*li42 + li32*li32 + luc2*luc2;
-    dst(2,0) = li41*li43 + li31*luc5;
-    dst(2,1) = li42*li43 + li32*luc5;
-    dst(2,2) = li43*li43 + luc5*luc5;
-    dst(3,0) = li41*luc9;
-    dst(3,1) = li42*luc9;
-    dst(3,2) = li43*luc9;
-    dst(3,3) = luc9*luc9;  
+    dst(0,0) = luc9*li41*li41 + luc5*li31*li31 + luc2*li21*li21 + luc0;
+    dst(1,0) = luc9*li41*li42 + luc5*li31*li32 + luc2*li21;
+    dst(1,1) = luc9*li42*li42 + luc5*li32*li32 + luc2;
+    dst(2,0) = luc9*li41*li43 + luc5*li31;
+    dst(2,1) = luc9*li42*li43 + luc5*li32;
+    dst(2,2) = luc9*li43*li43 + luc5;
+    dst(3,0) = luc9*li41;
+    dst(3,1) = luc9*li42;
+    dst(3,2) = luc9*li43;
+    dst(3,3) = luc9;  
     
   }
 
@@ -109,140 +109,142 @@ namespace choleskyInversion {
   inline constexpr
   void invert55(M1 const & src, M2 & dst) {
     using F = decltype(src(0,0));
-    auto luc0 = F(1.0) / std::sqrt(src(0,0));
-    auto luc1 = src(1,0) * luc0;
-    auto luc2 = src(1,1) - luc1 * luc1;
-    luc2 = F(1.0) / std::sqrt(luc2);
-    auto luc3 = src(2,0) * luc0;
-    auto luc4 = (src(2,1) - luc1 * luc3) * luc2;
-    auto luc5 = src(2,2) - (luc3 * luc3 + luc4 * luc4);
-    luc5 = F(1.0) / std::sqrt(luc5);
-    auto luc6 = src(3,0) * luc0;
-    auto luc7 = (src(3,1) - luc1 * luc6) * luc2;
-    auto luc8 = (src(3,2) - luc3 * luc6 - luc4 * luc7) * luc5;
-    auto luc9 = src(3,3) - (luc6 * luc6 + luc7 * luc7 + luc8 * luc8);
-    luc9 = F(1.0) / std::sqrt(luc9);
-    auto luc10 = src(4,0) * luc0;
-    auto luc11 = (src(4,1) - luc1 * luc10) * luc2;
-    auto luc12 = (src(4,2) - luc3 * luc10 - luc4 * luc11) * luc5;
-    auto luc13 = (src(4,3) - luc6 * luc10 - luc7 * luc11 - luc8 * luc12) * luc9;
-    auto luc14 = src(4,4) - (luc10*luc10+luc11*luc11+luc12*luc12+luc13*luc13);
-    luc14 = F(1.0) / std::sqrt(luc14);
+    auto luc0 = F(1.0) / src(0,0);
+    auto luc1 = src(1,0);
+    auto luc2 = src(1,1) - luc0 * luc1 * luc1;
+    luc2 = F(1.0) / luc2;
+    auto luc3 = src(2,0);
+    auto luc4 = (src(2,1) - luc0 * luc1 * luc3);
+    auto luc5 = src(2,2) - (luc0 * luc3 * luc3 + luc2 * luc4 * luc4);
+    luc5 = F(1.0) / luc5;
+    auto luc6 = src(3,0);
+    auto luc7 = (src(3,1) - luc0 * luc1 * luc6);
+    auto luc8 = (src(3,2) - luc0 * luc3 * luc6 - luc2 * luc4 * luc7);
+    auto luc9 = src(3,3) - (luc0 * luc6 * luc6 + luc2 * luc7 * luc7 + luc8 * (luc8 *luc5) );
+    luc9 = F(1.0) / luc9;
+    auto luc10 = src(4,0);
+    auto luc11 = (src(4,1) - luc0 * luc1 * luc10);
+    auto luc12 = (src(4,2) - luc0 * luc3 * luc10 - luc2 * luc4 * luc11);
+    auto luc13 = (src(4,3) - luc0 * luc6 * luc10 - luc2 * luc7 * luc11 - luc5 * luc8 * luc12);
+    auto luc14 = src(4,4) - (luc0*luc10*luc10 + luc2*luc11*luc11 + luc5*luc12*luc12 + luc9*luc13*luc13);
+    luc14 = F(1.0) / luc14;
     
     
-    auto li21 = -luc1 * luc0 * luc2;
-    auto li32 = -luc4 * luc2 * luc5;
-    auto li31 = (luc1 * luc4 * luc2 - luc3) * luc0 * luc5;
-    auto li43 = -luc8 * luc9 * luc5;
-    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2 * luc9;
-    auto li41 = (-luc1 * luc4 * luc8 * luc2 * luc5 +
-		 luc1 * luc7 * luc2 + luc3 * luc8 * luc5 - luc6) * luc0 * luc9;
-    auto li54 = -luc13 * luc14 * luc9;
-    auto li53 = (luc13 * luc8 * luc9 - luc12) * luc5 * luc14;
+    auto li21 = -luc1 * luc0;
+    auto li32 = -luc2 * luc4;
+    auto li31 = (luc1 * (luc2 * luc4) - luc3) * luc0;
+    auto li43 = -(luc8 * luc5);
+    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2;
+    auto li41 = (-luc1 * (luc2*luc4) * (luc8 * luc5) +
+		 luc1 * (luc2*luc7) + luc3 * (luc8 * luc5) - luc6) * luc0;
+    auto li54 = -luc13 * luc9;
+    auto li53 = (luc13 * luc8 * luc9 - luc12) * luc5;
     auto li52 = (-luc4 * luc8 * luc13 * luc5 * luc9 +
-		 luc4 * luc12 * luc5 + luc7 * luc13 * luc9 - luc11) * luc2 * luc14;
+		 luc4 * luc12 * luc5 + luc7 * luc13 * luc9 - luc11) * luc2;
     auto li51 = (luc1*luc4*luc8*luc13*luc2*luc5*luc9 -
 		 luc13*luc8*luc3*luc9*luc5 - luc12*luc4*luc1*luc2*luc5 - luc13*luc7*luc1*luc9*luc2 +
-		 luc11*luc1*luc2 + luc12*luc3*luc5 + luc13*luc6*luc9 -luc10) * luc0 * luc14;
+		 luc11*luc1*luc2 + luc12*luc3*luc5 + luc13*luc6*luc9 -luc10) * luc0;
     
-    dst(0,0) = li51*li51 + li41*li41 + li31*li31 + li21*li21 + luc0*luc0;
-    dst(1,0) = li51*li52 + li41*li42 + li31*li32 + li21*luc2;
-    dst(1,1) = li52*li52 + li42*li42 + li32*li32 + luc2*luc2;
-    dst(2,0) = li51*li53 + li41*li43 + li31*luc5;
-    dst(2,1) = li52*li53 + li42*li43 + li32*luc5;
-    dst(2,2) = li53*li53 + li43*li43 + luc5*luc5;
-    dst(3,0) = li51*li54 + li41*luc9;
-    dst(3,1) = li52*li54 + li42*luc9;
-    dst(3,2) = li53*li54 + li43*luc9;
-    dst(3,3) = li54*li54 + luc9*luc9;
-    dst(4,0) = li51*luc14;
-    dst(4,1) = li52*luc14;
-    dst(4,2) = li53*luc14;
-    dst(4,3) = li54*luc14;
-    dst(4,4) = luc14*luc14;
+    dst(0,0) = luc14*li51*li51 + luc9*li41*li41 + luc5*li31*li31 + luc2*li21*li21 + luc0;
+    dst(1,0) = luc14*li51*li52 + luc9*li41*li42 + luc5*li31*li32 + luc2*li21;
+    dst(1,1) = luc14*li52*li52 + luc9*li42*li42 + luc5*li32*li32 + luc2;
+    dst(2,0) = luc14*li51*li53 + luc9*li41*li43 + luc5*li31;
+    dst(2,1) = luc14*li52*li53 + luc9*li42*li43 + luc5*li32;
+    dst(2,2) = luc14*li53*li53 + luc9*li43*li43 + luc5;
+    dst(3,0) = luc14*li51*li54 + luc9*li41;
+    dst(3,1) = luc14*li52*li54 + luc9*li42;
+    dst(3,2) = luc14*li53*li54 + luc9*li43;
+    dst(3,3) = luc14*li54*li54 + luc9;  
+    dst(4,0) = luc14*li51;
+    dst(4,1) = luc14*li52;
+    dst(4,2) = luc14*li53;
+    dst(4,3) = luc14*li54;
+    dst(4,4) = luc14;
   }
 
   template<typename M1, typename M2> 
   inline __attribute__((always_inline)) constexpr
   void invert66(M1 const & src, M2 & dst) {
     using F = decltype(src(0,0));
-    auto luc0 = F(1.0) / std::sqrt(src(0,0));
-    auto luc1 = src(1,0) * luc0;
-    auto luc2 = src(1,1) - luc1 * luc1;
-    luc2 = F(1.0) / std::sqrt(luc2);
-    auto luc3 = src(2,0) * luc0;
-    auto luc4 = (src(2,1) - luc1 * luc3) * luc2;
-    auto luc5 = src(2,2) - (luc3 * luc3 + luc4 * luc4);
-    luc5 = F(1.0) / std::sqrt(luc5);
-    auto luc6 = src(3,0) * luc0;
-    auto luc7 = (src(3,1) - luc1 * luc6) * luc2;
-    auto luc8 = (src(3,2) - luc3 * luc6 - luc4 * luc7) * luc5;
-    auto luc9 = src(3,3) - (luc6 * luc6 + luc7 * luc7 + luc8 * luc8);
-    luc9 = F(1.0) / std::sqrt(luc9);
-    auto luc10 = src(4,0) * luc0;
-    auto luc11 = (src(4,1) - luc1 * luc10) * luc2;
-    auto luc12 = (src(4,2) - luc3 * luc10 - luc4 * luc11) * luc5;
-    auto luc13 = (src(4,3) - luc6 * luc10 - luc7 * luc11 - luc8 * luc12) * luc9;
-    auto luc14 = src(4,4) - (luc10*luc10+luc11*luc11+luc12*luc12+luc13*luc13);
-    luc14 = F(1.0) / std::sqrt(luc14);
-    auto luc15 = src(5,0) * luc0;
-    auto luc16 = (src(5,1) - luc1 * luc15) * luc2;
-    auto luc17 = (src(5,2) - luc3 * luc15 - luc4 * luc16) * luc5;
-    auto luc18 = (src(5,3) - luc6 * luc15 - luc7 * luc16 - luc8 * luc17) * luc9;
-    auto luc19 = (src(5,4) - luc10 * luc15 - luc11 * luc16 - luc12 * luc17 - luc13 * luc18) * luc14;
-    auto luc20 = src(5,5) - (luc15*luc15+luc16*luc16+luc17*luc17+luc18*luc18+luc19*luc19);
-    luc20 = F(1.0) / std::sqrt(luc20);
+    auto luc0 = F(1.0) / src(0,0);
+    auto luc1 = src(1,0);
+    auto luc2 = src(1,1) - luc0 * luc1 * luc1;
+    luc2 = F(1.0) / luc2;
+    auto luc3 = src(2,0);
+    auto luc4 = (src(2,1) - luc0 * luc1 * luc3);
+    auto luc5 = src(2,2) - (luc0 * luc3 * luc3 + luc2 * luc4 * luc4);
+    luc5 = F(1.0) / luc5;
+    auto luc6 = src(3,0);
+    auto luc7 = (src(3,1) - luc0 * luc1 * luc6);
+    auto luc8 = (src(3,2) - luc0 * luc3 * luc6 - luc2 * luc4 * luc7);
+    auto luc9 = src(3,3) - (luc0 * luc6 * luc6 + luc2 * luc7 * luc7 + luc8 * (luc8 *luc5) );
+    luc9 = F(1.0) / luc9;
+    auto luc10 = src(4,0);
+    auto luc11 = (src(4,1) - luc0 * luc1 * luc10);
+    auto luc12 = (src(4,2) - luc0 * luc3 * luc10 - luc2 * luc4 * luc11);
+    auto luc13 = (src(4,3) - luc0 * luc6 * luc10 - luc2 * luc7 * luc11 - luc5 * luc8 * luc12);
+    auto luc14 = src(4,4) - (luc0*luc10*luc10 + luc2*luc11*luc11 + luc5*luc12*luc12 + luc9*luc13*luc13);
+    luc14 = F(1.0) / luc14;
+    auto luc15 = src(5,0);
+    auto luc16 = (src(5,1) - luc0 * luc1 * luc15);
+    auto luc17 = (src(5,2) - luc0 * luc3 * luc15 - luc2 * luc4 * luc16);
+    auto luc18 = (src(5,3) - luc0 * luc6 * luc15 - luc2 * luc7 * luc16 - luc5 * luc8 * luc17);
+    auto luc19 = (src(5,4) - luc0 * luc10 * luc15 - luc2 * luc11 * luc16 - luc5 * luc12 * luc17 - luc9 * luc13 * luc18);
+    auto luc20 = src(5,5) - (luc0 * luc15*luc15 + luc2*luc16*luc16 + luc5*luc17*luc17 +luc9*luc18*luc18 + luc14*luc19*luc19);
+    luc20 = F(1.0) / luc20;
 
-    auto li21 = -luc1 * luc0 * luc2;
-    auto li32 = -luc4 * luc2 * luc5;
-    auto li31 = (luc1 * luc4 * luc2 - luc3) * luc0 * luc5;
-    auto li43 = -luc8 * luc9 * luc5;
-    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2 * luc9;
-    auto li41 = (-luc1 * luc4 * luc8 * luc2 * luc5 +
-		 luc1 * luc7 * luc2 + luc3 * luc8 * luc5 - luc6) * luc0 * luc9;
-    auto li54 = -luc13 * luc14 * luc9;
-    auto li53 = (luc13 * luc8 * luc9 - luc12) * luc5 * luc14;
+
+    auto li21 = -luc1 * luc0;
+    auto li32 = -luc2 * luc4;
+    auto li31 = (luc1 * (luc2 * luc4) - luc3) * luc0;
+    auto li43 = -(luc8 * luc5);
+    auto li42 = (luc4 * luc8 * luc5 - luc7) * luc2;
+    auto li41 = (-luc1 * (luc2*luc4) * (luc8 * luc5) +
+		 luc1 * (luc2*luc7) + luc3 * (luc8 * luc5) - luc6) * luc0;
+    auto li54 = -luc13 * luc9;
+    auto li53 = (luc13 * luc8 * luc9 - luc12) * luc5;
     auto li52 = (-luc4 * luc8 * luc13 * luc5 * luc9 +
-		 luc4 * luc12 * luc5 + luc7 * luc13 * luc9 - luc11) * luc2 * luc14;
+		 luc4 * luc12 * luc5 + luc7 * luc13 * luc9 - luc11) * luc2;
     auto li51 = (luc1*luc4*luc8*luc13*luc2*luc5*luc9 -
 		 luc13*luc8*luc3*luc9*luc5 - luc12*luc4*luc1*luc2*luc5 - luc13*luc7*luc1*luc9*luc2 +
-		 luc11*luc1*luc2 + luc12*luc3*luc5 + luc13*luc6*luc9 -luc10) * luc0 * luc14;
-    auto li65 = -luc19 * luc20 * luc14;
-    auto li64 = (luc19 * luc13 * luc14 - luc18) * luc9 * luc20;
-    auto li63 = (-luc8 * luc13 * luc19 * luc9 * luc14 +
-		 luc8 * luc18 * luc9 + luc12 * luc19 * luc14 - luc17) * luc5 * luc20;
-        auto li62 = (luc4*luc8*luc13*luc19*luc5*luc9*luc14 -
-		 luc18*luc8*luc4*luc9*luc5 - luc19*luc12*luc4*luc14*luc5 -luc19*luc13*luc7*luc14*luc9 +
-		 luc17*luc4*luc5 + luc18*luc7*luc9 + luc19*luc11*luc14 - luc16) * luc2 * luc20;
+		 luc11*luc1*luc2 + luc12*luc3*luc5 + luc13*luc6*luc9 -luc10) * luc0;
+
+    auto li65 = -luc19 * luc14;
+    auto li64 = (luc19 * luc14 * luc13 - luc18) * luc9;
+    auto li63 = (-luc8 * luc13 * (luc19 * luc14) * luc9 +
+		 luc8 * luc9 * luc18 + luc12 * (luc19 * luc14) - luc17) * luc5;
+    auto li62 = (luc4*(luc8*luc9)*luc13*luc5*(luc19*luc14) -
+		 luc18*luc4*(luc8*luc9)*luc5 - luc19*luc12*luc4*luc14*luc5 -luc19*luc13*luc7*luc14*luc9 +
+		 luc17*luc4*luc5 + luc18*luc7*luc9 + luc19*luc11*luc14 - luc16) * luc2;
     auto li61 = (-luc19*luc13*luc8*luc4*luc1*luc2*luc5*luc9*luc14 +
 		 luc18*luc8*luc4*luc1*luc2*luc5*luc9 + luc19*luc12*luc4*luc1*luc2*luc5*luc14 +
 		 luc19*luc13*luc7*luc1*luc2*luc9*luc14 + luc19*luc13*luc8*luc3*luc5*luc9*luc14 -
 		 luc17*luc4*luc1*luc2*luc5 - luc18*luc7*luc1*luc2*luc9 - luc19*luc11*luc1*luc2*luc14 -
 		 luc18*luc8*luc3*luc5*luc9 - luc19*luc12*luc3*luc5*luc14 - luc19*luc13*luc6*luc9*luc14 +
-		 luc16*luc1*luc2 + luc17*luc3*luc5 + luc18*luc6*luc9 + luc19*luc10*luc14 - luc15) * luc0 * luc20;    
+		 luc16*luc1*luc2 + luc17*luc3*luc5 + luc18*luc6*luc9 + luc19*luc10*luc14 - luc15) * luc0;    
 
-    dst(0,0) = li61*li61 + li51*li51 + li41*li41 + li31*li31 + li21*li21 + luc0*luc0;
-    dst(1,0) = li61*li62 + li51*li52 + li41*li42 + li31*li32 + li21*luc2;
-    dst(1,1) = li62*li62 + li52*li52 + li42*li42 + li32*li32 + luc2*luc2;
-    dst(2,0) = li61*li63 + li51*li53 + li41*li43 + li31*luc5;
-    dst(2,1) = li62*li63 + li52*li53 + li42*li43 + li32*luc5;
-    dst(2,2) = li63*li63 + li53*li53 + li43*li43 + luc5*luc5;
-    dst(3,0) = li61*li64 + li51*li54 + li41*luc9;
-    dst(3,1) = li62*li64 + li52*li54 + li42*luc9;
-    dst(3,2) = li63*li64 + li53*li54 + li43*luc9;
-    dst(3,3) = li64*li64 + li54*li54 + luc9*luc9;
-    dst(4,0) = li61*li65 + li51*luc14;
-    dst(4,1) = li62*li65 + li52*luc14;
-    dst(4,2) = li63*li65 + li53*luc14;
-    dst(4,3) = li64*li65 + li54*luc14;
-    dst(4,4) = li65*li65 + luc14*luc14;
-    dst(5,0) = li61*luc20;
-    dst(5,1) = li62*luc20;
-    dst(5,2) = li63*luc20;
-    dst(5,3) = li64*luc20;
-    dst(5,4) = li65*luc20;
-    dst(5,5) = luc20*luc20;
-      
+    dst(0,0) = luc20*li61*li61 + luc14*li51*li51 + luc9*li41*li41 + luc5*li31*li31 + luc2*li21*li21 + luc0;
+    dst(1,0) = luc20*li61*li62 + luc14*li51*li52 + luc9*li41*li42 + luc5*li31*li32 + luc2*li21;
+    dst(1,1) = luc20*li62*li62 + luc14*li52*li52 + luc9*li42*li42 + luc5*li32*li32 + luc2;
+    dst(2,0) = luc20*li61*li63 + luc14*li51*li53 + luc9*li41*li43 + luc5*li31;
+    dst(2,1) = luc20*li62*li63 + luc14*li52*li53 + luc9*li42*li43 + luc5*li32;
+    dst(2,2) = luc20*li63*li63 + luc14*li53*li53 + luc9*li43*li43 + luc5;
+    dst(3,0) = luc20*li61*li64 + luc14*li51*li54 + luc9*li41;
+    dst(3,1) = luc20*li62*li64 + luc14*li52*li54 + luc9*li42;
+    dst(3,2) = luc20*li63*li64 + luc14*li53*li54 + luc9*li43;
+    dst(3,3) = luc20*li64*li64 + luc14*li54*li54 + luc9;  
+    dst(4,0) = luc20*li61*li65 + luc14*li51;
+    dst(4,1) = luc20*li62*li65 + luc14*li52;
+    dst(4,2) = luc20*li63*li65 + luc14*li53;
+    dst(4,3) = luc20*li64*li65 + luc14*li54;
+    dst(4,4) = luc20*li65*li65 + luc14;
+    dst(5,0) = luc20*li61;
+    dst(5,1) = luc20*li62;
+    dst(5,2) = luc20*li63;
+    dst(5,3) = luc20*li64;
+    dst(5,4) = luc20*li65;
+    dst(5,5) = luc20;
+          
   }
 
   
@@ -349,5 +351,4 @@ namespace choleskyInversion {
   
 }
 
-
 #endif

From e7fc479387771521abfca5430d36215244372902 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 5 Feb 2019 15:41:38 +0100
Subject: [PATCH 23/95] cleanup cpu workflows with Rimann or BrokenLine

---
 .../ProcessModifiers/python/brokenLine_cff.py |   5 -
 ...enLineGPU_cff.py => pixelNtupleFit_cff.py} |   4 +-
 .../python/riemannFitGPU_cff.py               |   5 -
 .../ProcessModifiers/python/riemannFit_cff.py |   5 -
 .../python/relval_steps.py                    |  14 +-
 DataFormats/Math/BuildFile.xml                |   2 +-
 .../interface/PixelFitterByBrokenLine.h       |  26 ---
 ...mannParaboloid.h => PixelNtupletsFitter.h} |  18 +-
 .../PixelTrackFitting/plugins/BuildFile.xml   |   2 +-
 .../PixelFitterByBrokenLineProducer.cc        |  47 ----
 ...ucer.cc => PixelNtupletsFitterProducer.cc} |  30 ++-
 .../plugins/PixelTrackProducer.cc             |   9 +-
 .../plugins/PixelTrackProducer.h              |   4 -
 .../plugins/PixelTrackReconstructionGPU.cc    | 195 ----------------
 .../plugins/PixelTrackReconstructionGPU.cu    | 211 ------------------
 .../plugins/PixelTrackReconstructionGPU.h     |  43 ----
 .../python/PixelTracks_cff.py                 |  16 +-
 .../pixelFitterByRiemannParaboloid_cfi.py     |   6 -
 .../src/PixelFitterByRiemannParaboloid.cc     | 115 ----------
 ...ByBrokenLine.cc => PixelNtupletsFitter.cc} |  36 +--
 20 files changed, 56 insertions(+), 737 deletions(-)
 delete mode 100644 Configuration/ProcessModifiers/python/brokenLine_cff.py
 rename Configuration/ProcessModifiers/python/{brokenLineGPU_cff.py => pixelNtupleFit_cff.py} (59%)
 delete mode 100644 Configuration/ProcessModifiers/python/riemannFitGPU_cff.py
 delete mode 100644 Configuration/ProcessModifiers/python/riemannFit_cff.py
 delete mode 100644 RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h
 rename RecoPixelVertexing/PixelTrackFitting/interface/{PixelFitterByRiemannParaboloid.h => PixelNtupletsFitter.h} (50%)
 delete mode 100644 RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
 rename RecoPixelVertexing/PixelTrackFitting/plugins/{PixelFitterByRiemannParaboloidProducer.cc => PixelNtupletsFitterProducer.cc} (54%)
 delete mode 100644 RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.cc
 delete mode 100644 RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.cu
 delete mode 100644 RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.h
 delete mode 100644 RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByRiemannParaboloid_cfi.py
 delete mode 100644 RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
 rename RecoPixelVertexing/PixelTrackFitting/src/{PixelFitterByBrokenLine.cc => PixelNtupletsFitter.cc} (75%)

diff --git a/Configuration/ProcessModifiers/python/brokenLine_cff.py b/Configuration/ProcessModifiers/python/brokenLine_cff.py
deleted file mode 100644
index 57f1e4793733e..0000000000000
--- a/Configuration/ProcessModifiers/python/brokenLine_cff.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import FWCore.ParameterSet.Config as cms
-
-# This modifier is for replacing the default pixel track "fitting" with broken line fit
-
-brokenLine =  cms.Modifier()
diff --git a/Configuration/ProcessModifiers/python/brokenLineGPU_cff.py b/Configuration/ProcessModifiers/python/pixelNtupleFit_cff.py
similarity index 59%
rename from Configuration/ProcessModifiers/python/brokenLineGPU_cff.py
rename to Configuration/ProcessModifiers/python/pixelNtupleFit_cff.py
index 4c3ef3f111e60..db8a2ac229a02 100644
--- a/Configuration/ProcessModifiers/python/brokenLineGPU_cff.py
+++ b/Configuration/ProcessModifiers/python/pixelNtupleFit_cff.py
@@ -1,5 +1,5 @@
 import FWCore.ParameterSet.Config as cms
 
-# This modifier is for replacing the default pixel track "fitting" with the broken line fit on GPU
+# This modifier is for replacing the default pixel track "fitting" with eihter Riemann or BrokenLine fit
 
-brokenLineGPU =  cms.Modifier()
+pixelNtupleFit =  cms.Modifier()
diff --git a/Configuration/ProcessModifiers/python/riemannFitGPU_cff.py b/Configuration/ProcessModifiers/python/riemannFitGPU_cff.py
deleted file mode 100644
index ef622f26b2da0..0000000000000
--- a/Configuration/ProcessModifiers/python/riemannFitGPU_cff.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import FWCore.ParameterSet.Config as cms
-
-# This modifier is for replacing the default pixel track "fitting" with Riemann fit on GPU
-
-riemannFitGPU =  cms.Modifier()
diff --git a/Configuration/ProcessModifiers/python/riemannFit_cff.py b/Configuration/ProcessModifiers/python/riemannFit_cff.py
deleted file mode 100644
index f97f50df63fb6..0000000000000
--- a/Configuration/ProcessModifiers/python/riemannFit_cff.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import FWCore.ParameterSet.Config as cms
-
-# This modifier is for replacing the default pixel track "fitting" with Riemann fit
-
-riemannFit =  cms.Modifier()
diff --git a/Configuration/PyReleaseValidation/python/relval_steps.py b/Configuration/PyReleaseValidation/python/relval_steps.py
index 3f0a38e2783e2..2e8483697e855 100644
--- a/Configuration/PyReleaseValidation/python/relval_steps.py
+++ b/Configuration/PyReleaseValidation/python/relval_steps.py
@@ -2023,11 +2023,8 @@ def gen2018HiMix(fragment,howMuch):
     '--datatier': 'GEN-SIM-RECO,DQMIO',
     '--eventcontent': 'RECOSIM,DQM',
 }
-step3_riemannFit = {
-    '--procModifiers': 'brokenLine',
-}
-step3_riemannFitGPU = {
-    '--procModifiers': 'brokenLineGPU',
+step3_pixelNtupleFit = {
+    '--procModifiers': 'pixelNtupleFit',
 }
 step3_gpu = {
     '--procModifiers': 'gpu',
@@ -3100,12 +3097,7 @@ def gen2018HiMix(fragment,howMuch):
 
     for step in upgradeSteps['pixelTrackingOnlyRiemannFit']['steps']:
         stepName = step + upgradeSteps['pixelTrackingOnlyRiemannFit']['suffix']
-        if 'Reco' in step: upgradeStepDict[stepName][k] = merge([step3_riemannFit, step3_pixelTrackingOnly, upgradeStepDict[step][k]])
-        elif 'HARVEST' in step: upgradeStepDict[stepName][k] = merge([{'-s': 'HARVESTING:@trackingOnlyValidation+@pixelTrackingOnlyDQM'}, upgradeStepDict[step][k]])
-
-    for step in upgradeSteps['pixelTrackingOnlyRiemannFitGPU']['steps']:
-        stepName = step + upgradeSteps['pixelTrackingOnlyRiemannFitGPU']['suffix']
-        if 'Reco' in step: upgradeStepDict[stepName][k] = merge([step3_riemannFitGPU, step3_pixelTrackingOnly, upgradeStepDict[step][k]])
+        if 'Reco' in step: upgradeStepDict[stepName][k] = merge([step3_pixelNtupleFit, step3_pixelTrackingOnly, upgradeStepDict[step][k]])
         elif 'HARVEST' in step: upgradeStepDict[stepName][k] = merge([{'-s': 'HARVESTING:@trackingOnlyValidation+@pixelTrackingOnlyDQM'}, upgradeStepDict[step][k]])
 
     for step in upgradeSteps['pixelTrackingOnlyGPU']['steps']:
diff --git a/DataFormats/Math/BuildFile.xml b/DataFormats/Math/BuildFile.xml
index f628573593eb0..42749635cbbe1 100644
--- a/DataFormats/Math/BuildFile.xml
+++ b/DataFormats/Math/BuildFile.xml
@@ -1,6 +1,6 @@
 <use   name="DataFormats/Common"/>
 <use   name="rootmath"/>
-<use   name="eigen">
+<use   name="eigen"/>
 
 <export>
   <lib   name="1"/>
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h
deleted file mode 100644
index 44be969822e84..0000000000000
--- a/RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h
+++ /dev/null
@@ -1,26 +0,0 @@
-#ifndef RecoPixelVertexing_PixelTrackFitting_PixelFitterByBrokenLine_H
-#define RecoPixelVertexing_PixelTrackFitting_PixelFitterByBrokenLine_H
-
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterBase.h"
-#include "DataFormats/TrackingRecHit/interface/TrackingRecHit.h"
-#include "RecoTracker/TkTrackingRegions/interface/TrackingRegion.h"
-#include "FWCore/Framework/interface/EventSetup.h"
-#include "DataFormats/TrackReco/interface/Track.h"
-#include "FWCore/ParameterSet/interface/ParameterSet.h"
-
-#include <vector>
-
-
-
-class PixelFitterByBrokenLine final : public PixelFitterBase {
-public:
-  explicit PixelFitterByBrokenLine(const edm::EventSetup *es, const MagneticField *field);
-  virtual ~PixelFitterByBrokenLine() = default;
-  virtual std::unique_ptr<reco::Track> run(const std::vector<const TrackingRecHit *>& hits,
-                                           const TrackingRegion& region) const override;
-
-private:
-  const edm::EventSetup *es_;
-  const MagneticField *field_;
-};
-#endif
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByRiemannParaboloid.h b/RecoPixelVertexing/PixelTrackFitting/interface/PixelNtupletsFitter.h
similarity index 50%
rename from RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByRiemannParaboloid.h
rename to RecoPixelVertexing/PixelTrackFitting/interface/PixelNtupletsFitter.h
index f9a0f12ecb500..22aef3c7d9359 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByRiemannParaboloid.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/PixelNtupletsFitter.h
@@ -1,5 +1,5 @@
-#ifndef RecoPixelVertexing_PixelTrackFitting_PixelFitterByRiemannParaboloid_H
-#define RecoPixelVertexing_PixelTrackFitting_PixelFitterByRiemannParaboloid_H
+#ifndef RecoPixelVertexing_PixelTrackFitting_PixelNtupletsFitter_H
+#define RecoPixelVertexing_PixelTrackFitting_PixelNtupletsFitter_H
 
 #include <vector>
 
@@ -10,19 +10,17 @@
 #include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterBase.h"
 #include "RecoTracker/TkTrackingRegions/interface/TrackingRegion.h"
 
-class PixelFitterByRiemannParaboloid final : public PixelFitterBase {
+class PixelNtupletsFitter final : public PixelFitterBase {
 public:
-  explicit PixelFitterByRiemannParaboloid(const edm::EventSetup *es, const MagneticField *field,
-      bool useErrors, bool useMultipleScattering);
-  ~PixelFitterByRiemannParaboloid() override = default;
+  explicit PixelNtupletsFitter(float nominalB, const MagneticField *field, bool useRiemannFit);
+  ~PixelNtupletsFitter() override = default;
   std::unique_ptr<reco::Track> run(const std::vector<const TrackingRecHit *>& hits,
                                    const TrackingRegion& region) const override;
 
 private:
-  const edm::EventSetup *es_;
+  float nominalB_;
   const MagneticField *field_;
-  bool useErrors_;
-  bool useMultipleScattering_;
+  bool useRiemannFit_;
 };
 
-#endif // RecoPixelVertexing_PixelTrackFitting_PixelFitterByRiemannParaboloid_H
+#endif // RecoPixelVertexing_PixelTrackFitting_PixelNtupletsFitter_H
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/BuildFile.xml b/RecoPixelVertexing/PixelTrackFitting/plugins/BuildFile.xml
index d8177a0e9447c..62a8e8541aa64 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/BuildFile.xml
@@ -3,6 +3,6 @@
 <use name="HeterogeneousCore/Producer"/>
 <use name="HeterogeneousCore/Product"/>
 <use name="RecoPixelVertexing/PixelTrackFitting"/>
-<library file="*.cc *.cu" name="RecoPixelVertexingPixelTrackFittingPlugins">
+<library file="*.cc" name="RecoPixelVertexingPixelTrackFittingPlugins">
   <flags EDM_PLUGIN="1"/>
 </library>
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
deleted file mode 100644
index 6e3df6746be2b..0000000000000
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByBrokenLineProducer.cc
+++ /dev/null
@@ -1,47 +0,0 @@
-#include "FWCore/Framework/interface/Frameworkfwd.h"
-#include "FWCore/Framework/interface/global/EDProducer.h"
-
-#include "FWCore/Framework/interface/Event.h"
-#include "FWCore/Framework/interface/MakerMacros.h"
-#include "FWCore/Framework/interface/EventSetup.h"
-#include "FWCore/Framework/interface/ESHandle.h"
-
-#include "FWCore/ParameterSet/interface/ParameterSet.h"
-#include "FWCore/ParameterSet/interface/ConfigurationDescriptions.h"
-#include "FWCore/ParameterSet/interface/ParameterSetDescription.h"
-
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitter.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h"
-
-#include "MagneticField/Engine/interface/MagneticField.h"
-#include "MagneticField/Records/interface/IdealMagneticFieldRecord.h"
-
-class PixelFitterByBrokenLineProducer: public edm::global::EDProducer<> {
-public:
-  explicit PixelFitterByBrokenLineProducer(const edm::ParameterSet& iConfig)
-  {
-    produces<PixelFitter>();
-  }
-  ~PixelFitterByBrokenLineProducer() {}
-
-  static void fillDescriptions(edm::ConfigurationDescriptions& descriptions) {
-    edm::ParameterSetDescription desc;
-    descriptions.add("pixelFitterByBrokenLine", desc);
-  }
-
-private:
-  virtual void produce(edm::StreamID, edm::Event& iEvent, const edm::EventSetup& iSetup) const override;
-};
-
-
-void PixelFitterByBrokenLineProducer::produce(edm::StreamID, edm::Event& iEvent, const edm::EventSetup& iSetup) const {
-  edm::ESHandle<MagneticField> fieldESH;
-  iSetup.get<IdealMagneticFieldRecord>().get(fieldESH);
-
-  auto impl = std::make_unique<PixelFitterByBrokenLine>(&iSetup,
-      fieldESH.product());
-  auto prod = std::make_unique<PixelFitter>(std::move(impl));
-  iEvent.put(std::move(prod));
-}
-
-DEFINE_FWK_MODULE(PixelFitterByBrokenLineProducer);
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByRiemannParaboloidProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelNtupletsFitterProducer.cc
similarity index 54%
rename from RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByRiemannParaboloidProducer.cc
rename to RecoPixelVertexing/PixelTrackFitting/plugins/PixelNtupletsFitterProducer.cc
index 1b471510774aa..0e7774ddf0de7 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelFitterByRiemannParaboloidProducer.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelNtupletsFitterProducer.cc
@@ -11,43 +11,41 @@
 #include "FWCore/ParameterSet/interface/ParameterSetDescription.h"
 
 #include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitter.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByRiemannParaboloid.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelNtupletsFitter.h"
 
 #include "MagneticField/Engine/interface/MagneticField.h"
 #include "MagneticField/Records/interface/IdealMagneticFieldRecord.h"
+#include "RecoTracker/TkMSParametrization/interface/PixelRecoUtilities.h"
 
-class PixelFitterByRiemannParaboloidProducer: public edm::global::EDProducer<> {
+class PixelNtupletsFitterProducer: public edm::global::EDProducer<> {
 public:
-  explicit PixelFitterByRiemannParaboloidProducer(const edm::ParameterSet& iConfig)
-    : useErrors_(iConfig.getParameter<bool>("useErrors")),
-    useMultipleScattering_(iConfig.getParameter<bool>("useMultipleScattering"))
+  explicit PixelNtupletsFitterProducer(const edm::ParameterSet& iConfig)
+    : useRiemannFit_(iConfig.getParameter<bool>("useRiemannFit"))
   {
     produces<PixelFitter>();
   }
-  ~PixelFitterByRiemannParaboloidProducer() override {}
+  ~PixelNtupletsFitterProducer() override {}
 
   static void fillDescriptions(edm::ConfigurationDescriptions& descriptions) {
     edm::ParameterSetDescription desc;
-    desc.add<bool>("useErrors", true);
-    desc.add<bool>("useMultipleScattering", true);
-    descriptions.add("pixelFitterByRiemannParaboloidDefault", desc);
+    desc.add<bool>("useRiemannFit", false);
+    descriptions.add("pixelNtupletsFitterDefault", desc);
   }
 
 private:
-  bool useErrors_;
-  bool useMultipleScattering_;
+  bool useRiemannFit_;
   void produce(edm::StreamID, edm::Event& iEvent, const edm::EventSetup& iSetup) const override;
 };
 
 
-void PixelFitterByRiemannParaboloidProducer::produce(edm::StreamID, edm::Event& iEvent, const edm::EventSetup& iSetup) const {
+void PixelNtupletsFitterProducer::produce(edm::StreamID, edm::Event& iEvent, const edm::EventSetup& iSetup) const {
   edm::ESHandle<MagneticField> fieldESH;
   iSetup.get<IdealMagneticFieldRecord>().get(fieldESH);
-
-  auto impl = std::make_unique<PixelFitterByRiemannParaboloid>(&iSetup,
-      fieldESH.product(), useErrors_, useMultipleScattering_);
+  float bField = 1 / PixelRecoUtilities::fieldInInvGev(iSetup);
+  auto impl = std::make_unique<PixelNtupletsFitter>(bField,
+      fieldESH.product(), useRiemannFit_);
   auto prod = std::make_unique<PixelFitter>(std::move(impl));
   iEvent.put(std::move(prod));
 }
 
-DEFINE_FWK_MODULE(PixelFitterByRiemannParaboloidProducer);
+DEFINE_FWK_MODULE(PixelNtupletsFitterProducer);
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
index 7f13c7218eafa..57c6d6ec0e806 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.cc
@@ -24,9 +24,7 @@ using namespace pixeltrackfitting;
 using edm::ParameterSet;
 
 PixelTrackProducer::PixelTrackProducer(const ParameterSet& cfg)
-  : runOnGPU_(cfg.getParameter<bool>("runOnGPU")),
-  theReconstruction(cfg, consumesCollector()),
-  theGPUReconstruction(cfg, consumesCollector())
+  :   theReconstruction(cfg, consumesCollector())
 {
   edm::LogInfo("PixelTrackProducer")<<" construction...";
   produces<reco::TrackCollection>();
@@ -40,7 +38,6 @@ void PixelTrackProducer::fillDescriptions(edm::ConfigurationDescriptions& descri
   edm::ParameterSetDescription desc;
 
   desc.add<std::string>("passLabel", "pixelTracks"); // What is this? It is not used anywhere in this code.
-  desc.add<bool>("runOnGPU", false);
   PixelTrackReconstruction::fillDescriptions(desc);
 
   descriptions.add("pixelTracks", desc);
@@ -51,11 +48,7 @@ void PixelTrackProducer::produce(edm::Event& ev, const edm::EventSetup& es)
   LogDebug("PixelTrackProducer, produce")<<"event# :"<<ev.id();
 
   TracksWithTTRHs tracks;
-  if (!runOnGPU_)
     theReconstruction.run(tracks, ev, es);
-  else {
-    theGPUReconstruction.run(tracks, ev, es);
-  }
   edm::ESHandle<TrackerTopology> httopo;
   es.get<TrackerTopologyRcd>().get(httopo);
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.h b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.h
index 7e0d5d73b03fc..8852c884c7cc5 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.h
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackProducer.h
@@ -4,8 +4,6 @@
 #include "FWCore/Framework/interface/stream/EDProducer.h"
 #include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackReconstruction.h"
 
-#include "PixelTrackReconstructionGPU.h"
-
 namespace edm { class Event; class EventSetup; class ParameterSet; class ConfigurationDescriptions; }
 class TrackerTopology;
 
@@ -21,9 +19,7 @@ class PixelTrackProducer :  public edm::stream::EDProducer<> {
   void produce(edm::Event& ev, const edm::EventSetup& es) override;
 
 private:
-  bool runOnGPU_;
   PixelTrackReconstruction theReconstruction;
-  PixelTrackReconstructionGPU theGPUReconstruction;
 };
 
 #endif // PixelTrackProducer_h
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.cc
deleted file mode 100644
index 2a360b1da58a6..0000000000000
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.cc
+++ /dev/null
@@ -1,195 +0,0 @@
-#include <vector>
-
-#include <cuda_runtime.h>
-
-#include "DataFormats/TrackReco/interface/Track.h"
-#include "DataFormats/TrackReco/interface/TrackExtra.h"
-#include "DataFormats/TrackReco/interface/TrackFwd.h"
-#include "FWCore/Framework/interface/ESHandle.h"
-#include "FWCore/Framework/interface/Event.h"
-#include "FWCore/Framework/interface/EventSetup.h"
-#include "FWCore/MessageLogger/interface/MessageLogger.h"
-#include "FWCore/ParameterSet/interface/ConfigurationDescriptions.h"
-#include "FWCore/ParameterSet/interface/ParameterSetDescription.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitter.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackBuilder.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackCleaner.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackCleanerWrapper.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackFilter.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h" // for helix_fit
-#include "RecoTracker/TkHitPairs/interface/RegionsSeedingHitSets.h"
-#include "RecoTracker/TkMSParametrization/interface/PixelRecoUtilities.h"
-#include "RecoTracker/TkTrackingRegions/interface/TrackingRegion.h"
-
-#include "PixelTrackReconstructionGPU.h"
-
-using namespace pixeltrackfitting;
-using edm::ParameterSet;
-
-PixelTrackReconstructionGPU::PixelTrackReconstructionGPU(const ParameterSet& cfg,
-	   edm::ConsumesCollector && iC)
-  : theHitSetsToken(iC.consumes<RegionsSeedingHitSets>(cfg.getParameter<edm::InputTag>("SeedingHitSets"))),
-    theFitterToken(iC.consumes<PixelFitter>(cfg.getParameter<edm::InputTag>("Fitter"))),
-    theCleanerName(cfg.getParameter<std::string>("Cleaner"))
-{
-  edm::InputTag filterTag = cfg.getParameter<edm::InputTag>("Filter");
-  if(!filterTag.label().empty()) {
-    theFilterToken = iC.consumes<PixelTrackFilter>(filterTag);
-  }
-}
-
-PixelTrackReconstructionGPU::~PixelTrackReconstructionGPU()
-{
-}
-
-void PixelTrackReconstructionGPU::fillDescriptions(edm::ParameterSetDescription& desc) {
-  desc.add<edm::InputTag>("SeedingHitSets", edm::InputTag("pixelTracksHitTriplets"));
-  desc.add<edm::InputTag>("Fitter", edm::InputTag("pixelFitterByHelixProjections"));
-  desc.add<edm::InputTag>("Filter", edm::InputTag("pixelTrackFilterByKinematics"));
-  desc.add<std::string>("Cleaner", "pixelTrackCleanerBySharedHits");
-}
-
-void PixelTrackReconstructionGPU::run(TracksWithTTRHs& tracks,
-    edm::Event& ev, const edm::EventSetup& es)
-{
-  edm::ESHandle<MagneticField> fieldESH;
-  es.get<IdealMagneticFieldRecord>().get(fieldESH);
-
- edm::Handle<RegionsSeedingHitSets> hhitSets;
-  ev.getByToken(theHitSetsToken, hhitSets);
-  const auto& hitSets = *hhitSets;
-
-  const PixelTrackFilter *filter = nullptr;
-  if(!theFilterToken.isUninitialized()) {
-    edm::Handle<PixelTrackFilter> hfilter;
-    ev.getByToken(theFilterToken, hfilter);
-    filter = hfilter.product();
-  }
-
-  float bField = 1 / PixelRecoUtilities::fieldInInvGev(es);
-
-  std::vector<float> hits_and_covariances;
-  float * hits_and_covariancesGPU = nullptr;
-  Rfit::helix_fit * helix_fit_results = nullptr;
-  Rfit::helix_fit * helix_fit_resultsGPU = nullptr;
-
-  const int points_in_seed = 4;
-  // We use 3 floats for GlobalPosition and 6 floats for GlobalError (that's what is used by the Riemann fit).
-  // Assume a safe maximum of 3K seeds: it will dynamically grow, if needed.
-  int total_seeds = 0;
-  hits_and_covariances.reserve(sizeof(float)*3000*(points_in_seed*12));
-  for (auto const & regionHitSets : hitSets) {
-    const TrackingRegion& region = regionHitSets.region();
-    for (auto const & tuplet : regionHitSets) {
-      for (unsigned int iHit = 0; iHit < tuplet.size(); ++iHit) {
-        auto const& recHit = tuplet[iHit];
-        auto point = GlobalPoint(recHit->globalPosition().basicVector() - region.origin().basicVector());
-        auto errors = recHit->globalPositionError();
-        hits_and_covariances.push_back(point.x());
-        hits_and_covariances.push_back(point.y());
-        hits_and_covariances.push_back(point.z());
-        hits_and_covariances.push_back(errors.cxx());
-        hits_and_covariances.push_back(errors.cyx());
-        hits_and_covariances.push_back(errors.cyy());
-        hits_and_covariances.push_back(errors.czx());
-        hits_and_covariances.push_back(errors.czy());
-        hits_and_covariances.push_back(errors.czz());
-      }
-      total_seeds++;
-    }
-  }
-
-  // We pretend to have one fit for every seed
-  cudaCheck(cudaMallocHost(&helix_fit_results, sizeof(Rfit::helix_fit)*total_seeds));
-  cudaCheck(cudaMalloc(&hits_and_covariancesGPU, sizeof(float)*hits_and_covariances.size()));
-  cudaCheck(cudaMalloc(&helix_fit_resultsGPU, sizeof(Rfit::helix_fit)*total_seeds));
-  cudaCheck(cudaMemset(helix_fit_resultsGPU, 0x00, sizeof(Rfit::helix_fit)*total_seeds ));
-  // CUDA MALLOC OF HITS AND COV AND HELIX_FIT RESULTS
-
-  // CUDA MEMCOPY HOST2DEVICE OF HITS AND COVS AND HELIX_FIT RESULTS
-  cudaCheck(cudaMemcpy(hits_and_covariancesGPU, hits_and_covariances.data(),
-      sizeof(float)*hits_and_covariances.size(), cudaMemcpyDefault));
-
-  // LAUNCH THE KERNEL FIT
-  launchKernelFit(hits_and_covariancesGPU, 12*4*total_seeds, 4,
-      bField, helix_fit_resultsGPU);
-  // CUDA MEMCOPY DEVICE2HOST OF HELIX_FIT
-  cudaCheck(cudaDeviceSynchronize());
-  cudaCheck(cudaGetLastError());
-  cudaCheck(cudaMemcpy(helix_fit_results, helix_fit_resultsGPU,
-      sizeof(Rfit::helix_fit)*total_seeds, cudaMemcpyDefault));
-
-  cudaCheck(cudaFree(hits_and_covariancesGPU));
-  cudaCheck(cudaFree(helix_fit_resultsGPU));
-  // Loop on results, create tracks, filter them and pass them down the chain.
-  // In order to avoid huge mess with indexing and remembering who did what,
-  // simply iterate again over the main containers in the same order, since we
-  // are guaranteed that the results have been packed following the very same
-  // order. If not, we are doomed.
-  PixelTrackBuilder builder;
-  int counter = 0;
-  std::vector<const TrackingRecHit *> hits;
-  hits.reserve(4);
-  for (auto const & regionHitSets : hitSets) {
-    const TrackingRegion& region = regionHitSets.region();
-    for(const SeedingHitSet& tuplet: regionHitSets) {
-      auto nHits = tuplet.size(); hits.resize(nHits);
-
-      for (unsigned int iHit = 0; iHit < nHits; ++iHit)
-      {
-          hits[iHit] = tuplet[iHit];
-      }
-      auto const &fittedTrack = helix_fit_results[counter];
-      counter++;
-      int iCharge       = fittedTrack.q;
-      float valPhi      = fittedTrack.par(0);
-      float valTip      = fittedTrack.par(1);
-      float valPt       = fittedTrack.par(2);
-      float valCotTheta = fittedTrack.par(3);
-      float valZip      = fittedTrack.par(4);
-
-      //  PixelTrackErrorParam param(valEta, valPt);
-      float errValPhi = std::sqrt(fittedTrack.cov(0, 0));
-      float errValTip = std::sqrt(fittedTrack.cov(1, 1));
-      float errValPt = std::sqrt(fittedTrack.cov(2, 2));
-      float errValCotTheta = std::sqrt(fittedTrack.cov(3, 3));
-      float errValZip = std::sqrt(fittedTrack.cov(4, 4));
-
-      float chi2 = fittedTrack.chi2_line;
-
-      Measurement1D phi(valPhi, errValPhi);
-      Measurement1D tip(valTip, errValTip);
-
-      Measurement1D pt(valPt, errValPt);
-      Measurement1D cotTheta(valCotTheta, errValCotTheta);
-      Measurement1D zip(valZip, errValZip);
-
-      std::unique_ptr<reco::Track> track(
-          builder.build(pt, phi, cotTheta, tip, zip, chi2, iCharge, hits,
-            fieldESH.product(), region.origin()));
-      if (!track) continue;
-
-      if (filter) {
-        if (!(*filter)(track.get(), hits)) {
-          continue;
-        }
-      }
-      // add tracks
-      tracks.emplace_back(track.release(), tuplet);
-    }
-  }
-
-  cudaCheck(cudaFreeHost(helix_fit_results));
-
-  // skip overlapped tracks
-  if(!theCleanerName.empty()) {
-    edm::ESHandle<PixelTrackCleaner> hcleaner;
-    es.get<PixelTrackCleaner::Record>().get(theCleanerName, hcleaner);
-    const auto& cleaner = *hcleaner;
-    if(cleaner.fast())
-      cleaner.cleanTracks(tracks);
-    else
-      tracks = PixelTrackCleanerWrapper(&cleaner).clean(tracks);
-  }
-}
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.cu b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.cu
deleted file mode 100644
index 19a91ba8c83b4..0000000000000
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.cu
+++ /dev/null
@@ -1,211 +0,0 @@
-#include <Eigen/Core>
-#include <Eigen/Eigenvalues>
-
-#include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
-#include "PixelTrackReconstructionGPU.h"
-
-using namespace Eigen;
-
-__global__ void
-KernelFastFitAllHits(float *hits_and_covariances,
-    int hits_in_fit,
-    int cumulative_size,
-    float B,
-    Rfit::helix_fit *results,
-    Rfit::Matrix3xNd<4> *hits,
-    Eigen::Matrix<float,6,4> *hits_ge,
-    Rfit::circle_fit *circle_fit,
-    Vector4d *fast_fit,
-    Rfit::line_fit *line_fit)
-{
-  // Reshape Eigen components from hits_and_covariances, using proper thread and block indices
-  // Perform the fit
-  // Store the results in the proper vector, using again correct indices
-
-  // Loop for hits_in_fit times:
-  //   first 3 are the points
-  //   the rest is the covariance matrix, 3x3
-  int start = (blockIdx.x * blockDim.x + threadIdx.x) * hits_in_fit * 12;
-  int helix_start = (blockIdx.x * blockDim.x + threadIdx.x);
-  if (start >= cumulative_size) {
-    return;
-  }
-
-#ifdef GPU_DEBUG
-  printf("BlockDim.x: %d, BlockIdx.x: %d, threadIdx.x: %d, start: %d, cumulative_size: %d\n",
-      blockDim.x, blockIdx.x, threadIdx.x, start, cumulative_size);
-#endif
-
-
-  // Prepare data structure (stack)
-  for (unsigned int i = 0; i < hits_in_fit; ++i) {
-    hits[helix_start].col(i) << hits_and_covariances[start],
-        hits_and_covariances[start + 1], hits_and_covariances[start + 2];
-    start += 3;
-
-    hits_ge[helix_start].col(i) << hits_and_covariances[start],
-        hits_and_covariances[start + 1], hits_and_covariances[start + 2],
-        hits_and_covariances[start + 3], hits_and_covariances[start + 4],
-        hits_and_covariances[start + 5];
-    start += 6;
-  }
-
-  Rfit::Fast_fit(hits[helix_start],fast_fit[helix_start]);
-}
-
-__global__ void
-KernelCircleFitAllHits(float *hits_and_covariances, int hits_in_fit,
-                       int cumulative_size, float B, Rfit::helix_fit *results,
-                       Rfit::Matrix3xNd<4> *hits, Eigen::Matrix<float,6,4> *hits_ge,
-                       Rfit::circle_fit *circle_fit, Vector4d *fast_fit,
-                       Rfit::line_fit *line_fit)
-{
-  // Reshape Eigen components from hits_and_covariances, using proper thread and block indices
-  // Perform the fit
-  // Store the results in the proper vector, using again correct indices
-
-  // Loop for hits_in_fit times:
-  //   first 3 are the points
-  //   the rest is the covariance matrix, 3x3
-  int start = (blockIdx.x * blockDim.x + threadIdx.x) * hits_in_fit * 12;
-  int helix_start = (blockIdx.x * blockDim.x + threadIdx.x);
-  if (start >= cumulative_size) {
-    return;
-  }
-
-#ifdef GPU_DEBUG
-    printf("BlockDim.x: %d, BlockIdx.x: %d, threadIdx.x: %d, start: %d, "
-           "cumulative_size: %d\n",
-           blockDim.x, blockIdx.x, threadIdx.x, start, cumulative_size);
-#endif
-  u_int n = hits[helix_start].cols();
-
-  constexpr uint32_t N = 4;
-
-  Rfit::VectorNd<N> rad = (hits[helix_start].block(0, 0, 2, n).colwise().norm());
-  Rfit::Matrix2Nd<N> hits_cov =  MatrixXd::Zero(2 * n, 2 * n);
-  Rfit::loadCovariance2D(hits_ge[helix_start],hits_cov);
-  circle_fit[helix_start] =
-      Rfit::Circle_fit(hits[helix_start].block(0, 0, 2, n),
-                       hits_cov,
-                       fast_fit[helix_start], rad, B, true);
-
-#ifdef GPU_DEBUG
-    printf("KernelCircleFitAllHits circle.par(0): %d %f\n", helix_start,
-           circle_fit[helix_start].par(0));
-    printf("KernelCircleFitAllHits circle.par(1): %d %f\n", helix_start,
-           circle_fit[helix_start].par(1));
-    printf("KernelCircleFitAllHits circle.par(2): %d %f\n", helix_start,
-           circle_fit[helix_start].par(2));
-#endif
-
-}
-
-__global__ void
-KernelLineFitAllHits(float *hits_and_covariances, int hits_in_fit,
-                     int cumulative_size, float B, Rfit::helix_fit *results,
-                      Rfit::Matrix3xNd<4> *hits, Eigen::Matrix<float,6,4> *hits_ge,
-                     Rfit::circle_fit *circle_fit, Vector4d *fast_fit,
-                     Rfit::line_fit *line_fit)
-{
-  // Reshape Eigen components from hits_and_covariances, using proper thread and block indices
-  // Perform the fit
-  // Store the results in the proper vector, using again correct indices
-
-  // Loop for hits_in_fit times:
-  //   first 3 are the points
-  //   the rest is the covariance matrix, 3x3
-  int start = (blockIdx.x * blockDim.x + threadIdx.x) * hits_in_fit * 12;
-  int helix_start = (blockIdx.x * blockDim.x + threadIdx.x);
-  if (start >= cumulative_size) {
-    return;
-  }
-
-#ifdef GPU_DEBUG
-
-    printf("BlockDim.x: %d, BlockIdx.x: %d, threadIdx.x: %d, start: %d, "
-           "cumulative_size: %d\n",
-           blockDim.x, blockIdx.x, threadIdx.x, start, cumulative_size);
-#endif
-
-  line_fit[helix_start] =
-      Rfit::Line_fit(hits[helix_start], hits_ge[helix_start],
-                     circle_fit[helix_start], fast_fit[helix_start], B, true);
-
-  par_uvrtopak(circle_fit[helix_start], B, true);
-
-  // Grab helix_fit from the proper location in the output vector
-  Rfit::helix_fit &helix = results[helix_start];
-  helix.par << circle_fit[helix_start].par, line_fit[helix_start].par;
-
-  // TODO: pass properly error booleans
-
-  helix.cov = MatrixXd::Zero(5, 5);
-  helix.cov.block(0, 0, 3, 3) = circle_fit[helix_start].cov;
-  helix.cov.block(3, 3, 2, 2) = line_fit[helix_start].cov;
-
-  helix.q = circle_fit[helix_start].q;
-  helix.chi2_circle = circle_fit[helix_start].chi2;
-  helix.chi2_line = line_fit[helix_start].chi2;
-
-#ifdef GPU_DEBUG
-
-    printf("KernelLineFitAllHits line.par(0): %d %f\n", helix_start,
-           circle_fit[helix_start].par(0));
-    printf("KernelLineFitAllHits line.par(1): %d %f\n", helix_start,
-           line_fit[helix_start].par(1));
-#endif
-}
-
-void PixelTrackReconstructionGPU::launchKernelFit(
-    float *hits_and_covariancesGPU, int cumulative_size, int hits_in_fit,
-    float B, Rfit::helix_fit *results)
-{
-  const dim3 threads_per_block(32, 1);
-  int num_blocks = cumulative_size / (hits_in_fit * 12) / threads_per_block.x + 1;
-  auto numberOfSeeds = cumulative_size / (hits_in_fit * 12);
-
-  Rfit::Matrix3xNd<4> *hitsGPU;
-  cudaCheck(cudaMalloc(&hitsGPU, 48 * numberOfSeeds * sizeof(Rfit::Matrix3xNd<4>)));
-  cudaCheck(cudaMemset(hitsGPU, 0x00, 48 * numberOfSeeds * sizeof(Rfit::Matrix3xNd<4>)));
-
-  Eigen::Matrix<float,6,4> *hits_geGPU = nullptr;
-  cudaCheck(cudaMalloc(&hits_geGPU, 48 * numberOfSeeds * sizeof(Eigen::Matrix<float,6,4>)));
-  cudaCheck(cudaMemset(hits_geGPU, 0x00, 48 * numberOfSeeds * sizeof(Eigen::Matrix<float,6,4>)));
-
-  Vector4d *fast_fit_resultsGPU = nullptr;
-  cudaCheck(cudaMalloc(&fast_fit_resultsGPU, 48 * numberOfSeeds * sizeof(Vector4d)));
-  cudaCheck(cudaMemset(fast_fit_resultsGPU, 0x00, 48 * numberOfSeeds * sizeof(Vector4d)));
-
-  Rfit::circle_fit *circle_fit_resultsGPU = nullptr;
-  cudaCheck(cudaMalloc(&circle_fit_resultsGPU, 48 * numberOfSeeds * sizeof(Rfit::circle_fit)));
-  cudaCheck(cudaMemset(circle_fit_resultsGPU, 0x00, 48 * numberOfSeeds * sizeof(Rfit::circle_fit)));
-
-  Rfit::line_fit *line_fit_resultsGPU = nullptr;
-  cudaCheck(cudaMalloc(&line_fit_resultsGPU, numberOfSeeds * sizeof(Rfit::line_fit)));
-  cudaCheck(cudaMemset(line_fit_resultsGPU, 0x00, numberOfSeeds * sizeof(Rfit::line_fit)));
-
-  KernelFastFitAllHits<<<num_blocks, threads_per_block>>>(
-      hits_and_covariancesGPU, hits_in_fit, cumulative_size, B, results,
-      hitsGPU, hits_geGPU, circle_fit_resultsGPU, fast_fit_resultsGPU,
-      line_fit_resultsGPU);
-  cudaCheck(cudaGetLastError());
-
-  KernelCircleFitAllHits<<<num_blocks, threads_per_block>>>(
-      hits_and_covariancesGPU, hits_in_fit, cumulative_size, B, results,
-      hitsGPU, hits_geGPU, circle_fit_resultsGPU, fast_fit_resultsGPU,
-      line_fit_resultsGPU);
-  cudaCheck(cudaGetLastError());
-
-  KernelLineFitAllHits<<<num_blocks, threads_per_block>>>(
-      hits_and_covariancesGPU, hits_in_fit, cumulative_size, B, results,
-      hitsGPU, hits_geGPU, circle_fit_resultsGPU, fast_fit_resultsGPU,
-      line_fit_resultsGPU);
-  cudaCheck(cudaGetLastError());
-
-  cudaFree(hitsGPU);
-  cudaFree(hits_geGPU);
-  cudaFree(fast_fit_resultsGPU);
-  cudaFree(circle_fit_resultsGPU);
-  cudaFree(line_fit_resultsGPU);
-}
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.h b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.h
deleted file mode 100644
index 7488ba43b6f93..0000000000000
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelTrackReconstructionGPU.h
+++ /dev/null
@@ -1,43 +0,0 @@
-#ifndef RecoPixelVertexing_PixelTrackFitting_plugins_PixelTrackReconstructionGPU_h
-#define RecoPixelVertexing_PixelTrackFitting_plugins_PixelTrackReconstructionGPU_h
-
-#include <memory>
-
-#include "FWCore/Framework/interface/ConsumesCollector.h"
-#include "FWCore/ParameterSet/interface/ParameterSet.h"
-#include "FWCore/Utilities/interface/EDGetToken.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/TracksWithHits.h"
-
-class PixelFitter;
-class PixelTrackCleaner;
-class PixelTrackFilter;
-class RegionsSeedingHitSets;
-
-namespace edm {
-  class Event;
-  class EventSetup;
-  class Run;
-  class ParameterSetDescription;
-}
-
-class PixelTrackReconstructionGPU {
-public:
-
-  PixelTrackReconstructionGPU( const edm::ParameterSet& conf, edm::ConsumesCollector && iC);
-  ~PixelTrackReconstructionGPU();
-
-  static void fillDescriptions(edm::ParameterSetDescription& desc);
-
-  void run(pixeltrackfitting::TracksWithTTRHs& tah, edm::Event& ev, const edm::EventSetup& es);
-  void launchKernelFit(float * hits_and_covariances, int cumulative_size, int hits_in_fit, float B,
-      Rfit::helix_fit * results);
-
-private:
-  edm::EDGetTokenT<RegionsSeedingHitSets> theHitSetsToken;
-  edm::EDGetTokenT<PixelFitter> theFitterToken;
-  edm::EDGetTokenT<PixelTrackFilter> theFilterToken;
-  std::string theCleanerName;
-};
-
-#endif // RecoPixelVertexing_PixelTrackFitting_plugins_PixelTrackReconstructionGPU_h
diff --git a/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py b/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
index e868ff1921965..a89c48ce7a2ec 100644
--- a/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
+++ b/RecoPixelVertexing/PixelTrackFitting/python/PixelTracks_cff.py
@@ -12,7 +12,7 @@
 from RecoTracker.TkSeedingLayers.PixelLayerTriplets_cfi import *
 from RecoTracker.TkSeedingLayers.TTRHBuilderWithoutAngle4PixelTriplets_cfi import *
 from RecoPixelVertexing.PixelTrackFitting.pixelFitterByHelixProjections_cfi import pixelFitterByHelixProjections
-from RecoPixelVertexing.PixelTrackFitting.pixelFitterByRiemannParaboloid_cfi import pixelFitterByRiemannParaboloid
+from RecoPixelVertexing.PixelTrackFitting.pixelNtupletsFitter_cfi import pixelNtupletsFitter
 from RecoPixelVertexing.PixelTrackFitting.pixelTrackFilterByKinematics_cfi import pixelTrackFilterByKinematics
 from RecoPixelVertexing.PixelTrackFitting.pixelTrackCleanerBySharedHits_cfi import pixelTrackCleanerBySharedHits
 from RecoPixelVertexing.PixelTrackFitting.pixelTracks_cfi import pixelTracks as _pixelTracks
@@ -87,13 +87,11 @@
 _pixelTracksTask_lowPU.replace(pixelTracksHitQuadruplets, pixelTracksHitTriplets)
 trackingLowPU.toReplaceWith(pixelTracksTask, _pixelTracksTask_lowPU)
 
-# Use Riemann fit and substitute previous Fitter producer with the Riemann one
-from Configuration.ProcessModifiers.riemannFit_cff import riemannFit
-from Configuration.ProcessModifiers.riemannFitGPU_cff import riemannFitGPU
-riemannFit.toModify(pixelTracks, Fitter = "pixelFitterByRiemannParaboloid")
-riemannFitGPU.toModify(pixelTracks, runOnGPU = True)
-_pixelTracksTask_riemannFit = pixelTracksTask.copy()
-_pixelTracksTask_riemannFit.replace(pixelFitterByHelixProjections, pixelFitterByRiemannParaboloid)
-riemannFit.toReplaceWith(pixelTracksTask, _pixelTracksTask_riemannFit)
+# Use ntuple fit and substitute previous Fitter producer with the Riemann one
+from Configuration.ProcessModifiers.pixelNtupleFit_cff import pixelNtupleFit as ntupleFit
+ntupleFit.toModify(pixelTracks, Fitter = "pixelNtupletsFitter")
+_pixelTracksTask_ntupleFit = pixelTracksTask.copy()
+_pixelTracksTask_ntupleFit.replace(pixelFitterByHelixProjections, pixelNtupletsFitter)
+ntupleFit.toReplaceWith(pixelTracksTask, _pixelTracksTask_ntupleFit)
 
 pixelTracksSequence = cms.Sequence(pixelTracksTask)
diff --git a/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByRiemannParaboloid_cfi.py b/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByRiemannParaboloid_cfi.py
deleted file mode 100644
index 0e0ab13b01bd6..0000000000000
--- a/RecoPixelVertexing/PixelTrackFitting/python/pixelFitterByRiemannParaboloid_cfi.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import FWCore.ParameterSet.Config as cms
-
-from RecoPixelVertexing.PixelTrackFitting.pixelFitterByRiemannParaboloidDefault_cfi import pixelFitterByRiemannParaboloidDefault
-
-pixelFitterByRiemannParaboloid = pixelFitterByRiemannParaboloidDefault.clone()
-
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
deleted file mode 100644
index 45ee6266bcc31..0000000000000
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByRiemannParaboloid.cc
+++ /dev/null
@@ -1,115 +0,0 @@
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByRiemannParaboloid.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
-
-#include "FWCore/ParameterSet/interface/ParameterSet.h"
-
-#include "FWCore/Framework/interface/EventSetup.h"
-
-#include "DataFormats/GeometryCommonDetAlgo/interface/GlobalError.h"
-#include "DataFormats/GeometryVector/interface/GlobalPoint.h"
-#include "DataFormats/GeometryVector/interface/LocalPoint.h"
-
-#include "DataFormats/TrackingRecHit/interface/TrackingRecHit.h"
-#include "Geometry/CommonDetUnit/interface/GeomDet.h"
-#include "RecoTracker/TkMSParametrization/interface/PixelRecoUtilities.h"
-
-#include "DataFormats/GeometryCommonDetAlgo/interface/Measurement1D.h"
-
-#include "Geometry/CommonDetUnit/interface/GeomDetType.h"
-
-#include "MagneticField/Engine/interface/MagneticField.h"
-
-#include "FWCore/MessageLogger/interface/MessageLogger.h"
-
-#include "DataFormats/GeometryVector/interface/Pi.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackBuilder.h"
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackErrorParam.h"
-
-#include "CommonTools/Utils/interface/DynArray.h"
-
-using namespace std;
-
-
-PixelFitterByRiemannParaboloid::PixelFitterByRiemannParaboloid(const edm::EventSetup* es,
-                                                               const MagneticField* field,
-                                                               bool useErrors,
-                                                               bool useMultipleScattering)
-    : es_(es), field_(field),
-    useErrors_(useErrors), useMultipleScattering_(useMultipleScattering) {}
-
-std::unique_ptr<reco::Track> PixelFitterByRiemannParaboloid::run(
-    const std::vector<const TrackingRecHit*>& hits, const TrackingRegion& region) const {
-
-  using namespace Rfit;
-
-  std::unique_ptr<reco::Track> ret;
-
-  unsigned int nhits = hits.size();
-
-  if (nhits < 2) return ret;
-
-  declareDynArray(GlobalPoint, nhits, points);
-  declareDynArray(GlobalError, nhits, errors);
-  declareDynArray(bool, nhits, isBarrel);
-
-  for (unsigned int i = 0; i != nhits; ++i) {
-    auto const& recHit = hits[i];
-    points[i] = GlobalPoint(recHit->globalPosition().basicVector() - region.origin().basicVector());
-    errors[i] = recHit->globalPositionError();
-    isBarrel[i] = recHit->detUnit()->type().isBarrel();
-  }
-
-   assert(nhits==4);
-   Rfit::Matrix3xNd<4> riemannHits;
-
-  Eigen::Matrix<float,6,4> riemannHits_ge = Eigen::Matrix<float,6,4>::Zero();
-
-  for (unsigned int i = 0; i < nhits; ++i) {
-    riemannHits.col(i) << points[i].x(), points[i].y(), points[i].z();
-
-    riemannHits_ge.col(i) <<  errors[i].cxx(), errors[i].cyx(), errors[i].cyy(),
-                              errors[i].czx(), errors[i].czy(), errors[i].czz();
-  }
-
-  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
-  helix_fit fittedTrack = Rfit::Helix_fit(riemannHits, riemannHits_ge, bField, useErrors_);
-  int iCharge = fittedTrack.q;
-
-  // parameters are:
-  // 0: phi
-  // 1: tip
-  // 2: curvature
-  // 3: cottheta
-  // 4: zip
-  float valPhi = fittedTrack.par(0);
-
-  float valTip = fittedTrack.par(1);
-
-  float valCotTheta = fittedTrack.par(3);
-
-  float valZip = fittedTrack.par(4);
-  float valPt = fittedTrack.par(2);
-  //
-  //  PixelTrackErrorParam param(valEta, valPt);
-  float errValPhi = std::sqrt(fittedTrack.cov(0, 0));
-  float errValTip = std::sqrt(fittedTrack.cov(1, 1));
-
-  float errValPt = std::sqrt(fittedTrack.cov(2, 2));
-
-  float errValCotTheta = std::sqrt(fittedTrack.cov(3, 3));
-  float errValZip = std::sqrt(fittedTrack.cov(4, 4));
-
-  float chi2 = fittedTrack.chi2_line + fittedTrack.chi2_circle;
-
-  PixelTrackBuilder builder;
-  Measurement1D phi(valPhi, errValPhi);
-  Measurement1D tip(valTip, errValTip);
-
-  Measurement1D pt(valPt, errValPt);
-  Measurement1D cotTheta(valCotTheta, errValCotTheta);
-  Measurement1D zip(valZip, errValZip);
-
-  ret.reset(
-      builder.build(pt, phi, cotTheta, tip, zip, chi2, iCharge, hits, field_, region.origin()));
-  return ret;
-}
diff --git a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc b/RecoPixelVertexing/PixelTrackFitting/src/PixelNtupletsFitter.cc
similarity index 75%
rename from RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
rename to RecoPixelVertexing/PixelTrackFitting/src/PixelNtupletsFitter.cc
index e4dfeec50572f..51836c8fdf546 100644
--- a/RecoPixelVertexing/PixelTrackFitting/src/PixelFitterByBrokenLine.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/src/PixelNtupletsFitter.cc
@@ -1,5 +1,5 @@
-
-#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelFitterByBrokenLine.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/PixelNtupletsFitter.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h"
 #include "RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h"
 
 #include "FWCore/ParameterSet/interface/ParameterSet.h"
@@ -27,19 +27,19 @@
 #include "RecoPixelVertexing/PixelTrackFitting/interface/PixelTrackErrorParam.h"
 
 #include "CommonTools/Utils/interface/DynArray.h"
-#include <chrono>
 
 using namespace std;
 
 
-PixelFitterByBrokenLine::PixelFitterByBrokenLine(const edm::EventSetup* es,
-                                                               const MagneticField* field)
-    : es_(es), field_(field) {}
+PixelNtupletsFitter::PixelNtupletsFitter(float nominalB, const MagneticField* field,
+                                         bool useRiemannFit)
+    : nominalB_(nominalB), field_(field),
+    useRiemannFit_(useRiemannFit) {}
 
-std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
+std::unique_ptr<reco::Track> PixelNtupletsFitter::run(
     const std::vector<const TrackingRecHit*>& hits, const TrackingRegion& region) const {
 
-  using namespace BrokenLine;
+  using namespace Rfit;
 
   std::unique_ptr<reco::Track> ret;
 
@@ -58,21 +58,23 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
     isBarrel[i] = recHit->detUnit()->type().isBarrel();
   }
 
-  assert(nhits==4);
-  Rfit::Matrix3xNd<4> riemannHits;
+   assert(nhits==4);
+   Rfit::Matrix3xNd<4> hits_gp;
 
-  Eigen::Matrix<float,6,4> riemannHits_ge = Eigen::Matrix<float,6,4>::Zero();
+   Eigen::Matrix<float,6,4> hits_ge = Eigen::Matrix<float,6,4>::Zero();
 
   for (unsigned int i = 0; i < nhits; ++i) {
-    riemannHits.col(i) << points[i].x(), points[i].y(), points[i].z();
+    hits_gp.col(i) << points[i].x(), points[i].y(), points[i].z();
 
-    riemannHits_ge.col(i) <<  errors[i].cxx(), errors[i].cyx(), errors[i].cyy(),
+    hits_ge.col(i) <<  errors[i].cxx(), errors[i].cyx(), errors[i].cyy(),
                               errors[i].czx(), errors[i].czy(), errors[i].czz();
   }
-  float bField = 1 / PixelRecoUtilities::fieldInInvGev(*es_);
 
-  Rfit::helix_fit fittedTrack = BrokenLine::BL_Helix_fit(riemannHits, riemannHits_ge, bField);
-  
+
+  helix_fit fittedTrack = useRiemannFit_ ?
+	Rfit::Helix_fit(hits_gp, hits_ge, nominalB_, true)
+        : BrokenLine::BL_Helix_fit(hits_gp, hits_ge, nominalB_);
+
   int iCharge = fittedTrack.q;
 
   // parameters are:
@@ -99,7 +101,7 @@ std::unique_ptr<reco::Track> PixelFitterByBrokenLine::run(
   float errValCotTheta = std::sqrt(fittedTrack.cov(3, 3));
   float errValZip = std::sqrt(fittedTrack.cov(4, 4));
 
-  float chi2 = (fittedTrack.chi2_line + fittedTrack.chi2_circle);
+  float chi2 = fittedTrack.chi2_line + fittedTrack.chi2_circle;
 
   PixelTrackBuilder builder;
   Measurement1D phi(valPhi, errValPhi);

From 7b7dd7b2a356ae582037d842ae5e163d8192360b Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 5 Feb 2019 15:41:54 +0100
Subject: [PATCH 24/95] cleanup cpu workflows with Rimann or BrokenLine

---
 .../PixelTrackFitting/python/pixelNtupletsFitter_cfi.py     | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 RecoPixelVertexing/PixelTrackFitting/python/pixelNtupletsFitter_cfi.py

diff --git a/RecoPixelVertexing/PixelTrackFitting/python/pixelNtupletsFitter_cfi.py b/RecoPixelVertexing/PixelTrackFitting/python/pixelNtupletsFitter_cfi.py
new file mode 100644
index 0000000000000..10e1e3852e9c4
--- /dev/null
+++ b/RecoPixelVertexing/PixelTrackFitting/python/pixelNtupletsFitter_cfi.py
@@ -0,0 +1,6 @@
+import FWCore.ParameterSet.Config as cms
+
+from RecoPixelVertexing.PixelTrackFitting.pixelNtupletsFitterDefault_cfi import pixelNtupletsFitterDefault
+
+pixelNtupletsFitter = pixelNtupletsFitterDefault.clone()
+

From 5b9e9e66a3d9daab7f124966d6cbd867f17f24a6 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 5 Feb 2019 16:30:52 +0100
Subject: [PATCH 25/95] change name to workflows as well

---
 .../PyReleaseValidation/python/relval_steps.py    |  4 ++--
 .../PyReleaseValidation/python/relval_upgrade.py  |  2 +-
 .../python/upgradeWorkflowComponents.py           | 15 ++-------------
 3 files changed, 5 insertions(+), 16 deletions(-)

diff --git a/Configuration/PyReleaseValidation/python/relval_steps.py b/Configuration/PyReleaseValidation/python/relval_steps.py
index 2e8483697e855..9dfccc38dd442 100644
--- a/Configuration/PyReleaseValidation/python/relval_steps.py
+++ b/Configuration/PyReleaseValidation/python/relval_steps.py
@@ -3095,8 +3095,8 @@ def gen2018HiMix(fragment,howMuch):
         if 'Reco' in step: upgradeStepDict[stepName][k] = merge([step3_pixelTrackingOnly, upgradeStepDict[step][k]])
         elif 'HARVEST' in step: upgradeStepDict[stepName][k] = merge([{'-s': 'HARVESTING:@trackingOnlyValidation+@pixelTrackingOnlyDQM'}, upgradeStepDict[step][k]])
 
-    for step in upgradeSteps['pixelTrackingOnlyRiemannFit']['steps']:
-        stepName = step + upgradeSteps['pixelTrackingOnlyRiemannFit']['suffix']
+    for step in upgradeSteps['pixelTrackingOnlyNtupleFit']['steps']:
+        stepName = step + upgradeSteps['pixelTrackingOnlyNtupleFit']['suffix']
         if 'Reco' in step: upgradeStepDict[stepName][k] = merge([step3_pixelNtupleFit, step3_pixelTrackingOnly, upgradeStepDict[step][k]])
         elif 'HARVEST' in step: upgradeStepDict[stepName][k] = merge([{'-s': 'HARVESTING:@trackingOnlyValidation+@pixelTrackingOnlyDQM'}, upgradeStepDict[step][k]])
 
diff --git a/Configuration/PyReleaseValidation/python/relval_upgrade.py b/Configuration/PyReleaseValidation/python/relval_upgrade.py
index 99bd8f643407b..48e16afe001b0 100644
--- a/Configuration/PyReleaseValidation/python/relval_upgrade.py
+++ b/Configuration/PyReleaseValidation/python/relval_upgrade.py
@@ -76,7 +76,7 @@ def makeStepName(key,frag,step,suffix):
             # special workflows for tracker
             if (upgradeDatasetFromFragment[frag]=="TTbar_13" or upgradeDatasetFromFragment[frag]=="TTbar_14TeV") and not 'PU' in key and hasHarvest:
                 # skip ALCA and Nano
-                trackingVariations = ['trackingOnly','trackingRun2','trackingOnlyRun2','trackingLowPU','pixelTrackingOnly','pixelTrackingOnlyRiemannFit','pixelTrackingOnlyRiemannFitGPU','pixelTrackingOnlyGPU']
+                trackingVariations = ['trackingOnly','trackingRun2','trackingOnlyRun2','trackingLowPU','pixelTrackingOnly','pixelTrackingOnlyNtupleFit','pixelTrackingOnlyGPU']
                 for tv in trackingVariations:
                     stepList[tv] = [s for s in stepList[tv] if (("ALCA" not in s) and ("Nano" not in s))]
                 workflows[numWF+upgradeSteps['trackingOnly']['offset']] = [ upgradeDatasetFromFragment[frag], stepList['trackingOnly']]
diff --git a/Configuration/PyReleaseValidation/python/upgradeWorkflowComponents.py b/Configuration/PyReleaseValidation/python/upgradeWorkflowComponents.py
index 6895d52f12a8f..5651f081d0ea3 100644
--- a/Configuration/PyReleaseValidation/python/upgradeWorkflowComponents.py
+++ b/Configuration/PyReleaseValidation/python/upgradeWorkflowComponents.py
@@ -158,7 +158,7 @@
     'suffix' : '_pixelTrackingOnly',
     'offset' : 0.5,
 }
-upgradeSteps['pixelTrackingOnlyRiemannFit'] = {
+upgradeSteps['pixelTrackingOnlyNtupleFit'] = {
     'steps' : [
         'RecoFull',
         'HARVESTFull',
@@ -166,7 +166,7 @@
         'HARVESTFullGlobal',
     ],
     'PU' : [],
-    'suffix' : '_pixelTrackingOnlyRiemannFit',
+    'suffix' : '_pixelTrackingOnlyNtupleFit',
     'offset' : 0.51,
 }
 upgradeSteps['pixelTrackingOnlyGPU'] = {
@@ -180,17 +180,6 @@
     'suffix' : '_pixelTrackingOnlyGPU',
     'offset' : 0.52,
 }
-upgradeSteps['pixelTrackingOnlyRiemannFitGPU'] = {
-    'steps' : [
-        'RecoFull',
-        'HARVESTFull',
-        'RecoFullGlobal',
-        'HARVESTFullGlobal',
-    ],
-    'PU' : [],
-    'suffix' : '_pixelTrackingOnlyRiemannFitGPU',
-    'offset' : 0.53,
-}
 upgradeSteps['Timing'] = {
     'steps' : upgradeSteps['baseline']['steps'],
     'PU' : upgradeSteps['baseline']['PU'],

From e0ebdfe41ff2a93aaffbc112741910d3651e84f4 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 6 Feb 2019 09:26:21 +0100
Subject: [PATCH 26/95] apply Matti's suggestions

---
 .../PixelTrackFitting/interface/BrokenLine.h          |  2 +-
 .../PixelTrackFitting/interface/FitUtils.h            |  2 +-
 .../PixelTrackFitting/interface/RiemannFit.h          |  2 +-
 .../plugins/PixelNtupletsFitterProducer.cc            |  2 +-
 .../plugins/CAHitNtupletHeterogeneousEDProducer.cc    |  8 ++++----
 .../plugins/CAHitQuadrupletGeneratorGPU.cc            | 11 +++++------
 .../plugins/CAHitQuadrupletGeneratorGPU.h             |  4 ++--
 7 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
index 0740fd36f0952..8d7ff4d830df7 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -1,7 +1,7 @@
 #ifndef RECOPIXELVERTEXING_PIXELTRACKFITTING_BROKENLINE_H
 #define RECOPIXELVERTEXING_PIXELTRACKFITTING_BROKENLINE_H
 
-#include "FitUtils.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h"
 
 #include <Eigen/Eigenvalues>
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h b/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
index fa9d101b4a001..03ccb011645ec 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h
@@ -2,7 +2,7 @@
 #define RecoPixelVertexing_PixelTrackFitting_interface_FitUtils_h
 
 
-#include "FitResult.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/FitResult.h"
 
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
 
diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
index fdec04981770c..3b3fd75223d95 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
@@ -1,7 +1,7 @@
 #ifndef RecoPixelVertexing_PixelTrackFitting_interface_RiemannFit_h
 #define RecoPixelVertexing_PixelTrackFitting_interface_RiemannFit_h
 
-#include "FitUtils.h"
+#include "RecoPixelVertexing/PixelTrackFitting/interface/FitUtils.h"
 
 namespace Rfit
 {
diff --git a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelNtupletsFitterProducer.cc b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelNtupletsFitterProducer.cc
index 0e7774ddf0de7..eeca145ab93e3 100644
--- a/RecoPixelVertexing/PixelTrackFitting/plugins/PixelNtupletsFitterProducer.cc
+++ b/RecoPixelVertexing/PixelTrackFitting/plugins/PixelNtupletsFitterProducer.cc
@@ -28,7 +28,7 @@ class PixelNtupletsFitterProducer: public edm::global::EDProducer<> {
 
   static void fillDescriptions(edm::ConfigurationDescriptions& descriptions) {
     edm::ParameterSetDescription desc;
-    desc.add<bool>("useRiemannFit", false);
+    desc.add<bool>("useRiemannFit", false)->setComment("true for Riemann, false for BrokenLine");
     descriptions.add("pixelNtupletsFitterDefault", desc);
   }
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitNtupletHeterogeneousEDProducer.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitNtupletHeterogeneousEDProducer.cc
index 6a531a6d4e3df..8007e9a67df3f 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitNtupletHeterogeneousEDProducer.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitNtupletHeterogeneousEDProducer.cc
@@ -66,7 +66,7 @@ class CAHitNtupletHeterogeneousEDProducer
   bool emptyRegions = false;
   std::unique_ptr<RegionsSeedingHitSets> seedingHitSets_;
 
-  const bool doRiemannFit_;
+  const bool useRiemannFit_;
   const bool enableConversion_;
   const bool enableTransfer_;
 };
@@ -77,7 +77,7 @@ CAHitNtupletHeterogeneousEDProducer::CAHitNtupletHeterogeneousEDProducer(
       gpuHits_(consumesHeterogeneous(iConfig.getParameter<edm::InputTag>("heterogeneousPixelRecHitSrc"))),
       cpuHits_(consumes<SiPixelRecHitCollectionNew>(iConfig.getParameter<edm::InputTag>("heterogeneousPixelRecHitSrc"))),
       GPUGenerator_(iConfig, consumesCollector()),
-      doRiemannFit_(iConfig.getParameter<bool>("doRiemannFit")),
+      useRiemannFit_(iConfig.getParameter<bool>("useRiemannFit")),
       enableConversion_(iConfig.getParameter<bool>("gpuEnableConversion")),
       enableTransfer_(enableConversion_ || iConfig.getParameter<bool>("gpuEnableTransfer"))
 {
@@ -94,7 +94,7 @@ void CAHitNtupletHeterogeneousEDProducer::fillDescriptions(
 
   desc.add<edm::InputTag>("trackingRegions", edm::InputTag("globalTrackingRegionFromBeamSpot"));
   desc.add<edm::InputTag>("heterogeneousPixelRecHitSrc", edm::InputTag("siPixelRecHitsPreSplitting"));
-  desc.add<bool>("doRiemannFit", false);  // false == BrokenLine
+  desc.add<bool>("useRiemannFit", false)->setComment("true for Riemann, false for BrokenLine");
   desc.add<bool>("gpuEnableTransfer", true);
   desc.add<bool>("gpuEnableConversion", true);
 
@@ -124,7 +124,7 @@ void CAHitNtupletHeterogeneousEDProducer::acquireGPUCuda(
   LogDebug("CAHitNtupletHeterogeneousEDProducer")
         << "Creating ntuplets on GPU";
 
-  GPUGenerator_.hitNtuplets(gHits, iSetup, doRiemannFit_, enableTransfer_, cudaStream.id());
+  GPUGenerator_.hitNtuplets(gHits, iSetup, useRiemannFit_, enableTransfer_, cudaStream.id());
 
   
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index 47aa6a5b34cc3..20952a3bfd458 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -56,12 +56,12 @@ CAHitQuadrupletGeneratorGPU::~CAHitQuadrupletGeneratorGPU() {
 void CAHitQuadrupletGeneratorGPU::hitNtuplets(
     HitsOnCPU const& hh,
     edm::EventSetup const& es,
-    bool doRiemannFit,
+    bool useRiemannFit,
     bool transferToCPU,
     cudaStream_t cudaStream)
 {
   hitsOnCPU = &hh;
-  launchKernels(hh, doRiemannFit, transferToCPU, cudaStream);
+  launchKernels(hh, useRiemannFit, transferToCPU, cudaStream);
 }
 
 void CAHitQuadrupletGeneratorGPU::fillResults(
@@ -165,19 +165,18 @@ void CAHitQuadrupletGeneratorGPU::allocateOnGPU()
 }
 
 void CAHitQuadrupletGeneratorGPU::launchKernels(HitsOnCPU const & hh,
-                                                bool doRiemannFit,
+                                                bool useRiemannFit,
                                                 bool transferToCPU,
                                                 cudaStream_t cudaStream)
 {
 
   kernels.launchKernels(hh, gpu_, cudaStream); 
-  if (doRiemannFit) {
+  if (useRiemannFit) {
     fitter.launchRiemannKernels(hh, hh.nHits, CAConstants::maxNumberOfQuadruplets(), cudaStream);
-    kernels.classifyTuples(hh, gpu_, cudaStream);
   } else {
     fitter.launchBrokenLineKernels(hh, hh.nHits, CAConstants::maxNumberOfQuadruplets(), cudaStream);
-    kernels.classifyTuples(hh, gpu_, cudaStream);
   }
+  kernels.classifyTuples(hh, gpu_, cudaStream);
 
   if (transferToCPU) {
     cudaCheck(cudaMemcpyAsync(tuples_,gpu_.tuples_d,
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.h
index 5f0b29a94b376..5bf4964ab3488 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.h
@@ -69,7 +69,7 @@ class CAHitQuadrupletGeneratorGPU {
 
     void hitNtuplets(HitsOnCPU const & hh,
                      const edm::EventSetup& es,
-                     bool doRiemannFit,
+                     bool useRiemannFit,
                      bool transferToCPU,
                      cudaStream_t stream);
 
@@ -87,7 +87,7 @@ class CAHitQuadrupletGeneratorGPU {
 
 private:
 
-    void launchKernels(HitsOnCPU const & hh, bool doRiemannFit, bool transferToCPU, cudaStream_t);
+    void launchKernels(HitsOnCPU const & hh, bool useRiemannFit, bool transferToCPU, cudaStream_t);
 
 
     std::vector<std::array<int,4>> fetchKernelResult(int);

From ef1387a6e1c3ea7aeca36b0b4e26904c66d8c4cb Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 6 Feb 2019 17:24:03 +0100
Subject: [PATCH 27/95] try to use triplets in hole

---
 .../plugins/BrokenLineFitOnGPU.cu             | 46 +++++++++++++------
 .../plugins/CAHitQuadrupletGeneratorGPU.cc    | 19 +++-----
 .../CAHitQuadrupletGeneratorKernels.cu        |  7 ++-
 .../PixelTriplets/plugins/GPUCACell.h         | 26 ++++++++---
 .../PixelTriplets/plugins/HelixFitOnGPU.h     | 12 +++++
 5 files changed, 75 insertions(+), 35 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
index 36adae90c6eb9..15953477bf1f7 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
@@ -21,17 +21,17 @@ using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
 
 using namespace Eigen;
 
+template<int N>
 __global__
 void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
     HitsOnGPU const * __restrict__ hhp,
-    int hits_in_fit,
     double * __restrict__ phits,
     float * __restrict__ phits_ge,
     double * __restrict__ pfast_fit,
     uint32_t offset)
 {
 
-  assert(hits_in_fit==4); // FixMe later template
+  constexpr uint32_t hits_in_fit = N;
 
   assert(pfast_fit); assert(foundNtuplets);
 
@@ -39,13 +39,14 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
   auto helix_start = local_start + offset;
 
   if (helix_start>=foundNtuplets->nbins()) return;
+
   if (foundNtuplets->size(helix_start)<hits_in_fit) {
     return;
   }
 
-  Rfit::Map3x4d hits(phits+local_start);
+  Rfit::Map3xNd<N> hits(phits+local_start);
   Rfit::Map4d   fast_fit(pfast_fit+local_start);
-  Rfit::Map6x4f hits_ge(phits_ge+local_start);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+local_start);
 
 #ifdef BL_DUMP_HITS
   __shared__ int done;
@@ -82,11 +83,11 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
   assert(fast_fit(2)==fast_fit(2));
   assert(fast_fit(3)==fast_fit(3));
 
-}
+} 
 
+template<int N>
 __global__
 void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
-    int hits_in_fit,
     double B,
     Rfit::helix_fit *results,
     double * __restrict__ phits,
@@ -95,6 +96,8 @@ void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
     uint32_t offset)
 {
 
+  constexpr uint32_t hits_in_fit = N;
+
   assert(results); assert(pfast_fit);
 
   auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
@@ -105,12 +108,11 @@ void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
     return;
   }
 
-  Rfit::Map3x4d hits(phits+local_start);
+
+  Rfit::Map3xNd<N> hits(phits+local_start);
   Rfit::Map4d   fast_fit(pfast_fit+local_start);
-  Rfit::Map6x4f hits_ge(phits_ge+local_start);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+local_start);
 
-  constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
-  
   BrokenLine::PreparedBrokenLineData<N> data;
   Rfit::Matrix3d Jacob;
 
@@ -157,15 +159,31 @@ void HelixFitOnGPU::launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t nhits
     auto numberOfBlocks = (maxNumberOfConcurrentFits_ + blockSize - 1) / blockSize;
 
     for (uint32_t offset=0; offset<maxNumberOfTuples; offset+=maxNumberOfConcurrentFits_) {
-      kernelBLFastFit<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-          tuples_d, hh.gpu_d, 4,
+
+      // fit triplets
+      kernelBLFastFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, hh.gpu_d,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+      cudaCheck(cudaGetLastError());
+
+      kernelBLFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tuples_d, bField_, helix_fit_results_d,
+             hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
+             offset);
+      cudaCheck(cudaGetLastError());
+
+      // fit quads
+      kernelBLFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, hh.gpu_d,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
       cudaCheck(cudaGetLastError());
 
-      kernelBLFit<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-             tuples_d, 4,  bField_, helix_fit_results_d,
+      kernelBLFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tuples_d, bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
              offset);
       cudaCheck(cudaGetLastError());
     }
+
+
 }
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index 20952a3bfd458..570a7c7f4e8b4 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -16,6 +16,9 @@
 
 #include "CAHitQuadrupletGeneratorGPU.h"
 
+
+#define GPU_DEBUG
+
 namespace {
 
   template <typename T> T sqr(T x) { return x * x; }
@@ -79,9 +82,6 @@ void CAHitQuadrupletGeneratorGPU::fillResults(
   auto const & foundQuads = fetchKernelResult(index);
   unsigned int numberOfFoundQuadruplets = foundQuads.size();
 
-  std::array<GlobalPoint, 4> gps;
-  std::array<GlobalError, 4> ges;
-  std::array<bool, 4> barrels;
   std::array<BaseTrackerRecHit const*, 4> phits;
 
   indToEdm.clear();
@@ -90,12 +90,10 @@ void CAHitQuadrupletGeneratorGPU::fillResults(
   int nbad=0;
   // loop over quadruplets
   for (unsigned int quadId = 0; quadId < numberOfFoundQuadruplets; ++quadId) {
-    auto isBarrel = [](const unsigned id) -> bool {
-      return id == PixelSubdetector::PixelBarrel;
-    };
     bool bad = pixelTuplesHeterogeneousProduct::bad == quality_[quadId];
     for (unsigned int i = 0; i < 4; ++i) {
       auto k = foundQuads[quadId][i];
+      if (k<0) { phits[i] = nullptr;continue; } // (actually break...)
       assert(k<int(nhits));
       auto hp = hitmap_.get((*hitsOnCPU).detInd[k],(*hitsOnCPU).mr[k], (*hitsOnCPU).mc[k]);
       if (hp==nullptr) {
@@ -103,11 +101,6 @@ void CAHitQuadrupletGeneratorGPU::fillResults(
         break;
       }
       phits[i] = static_cast<BaseTrackerRecHit const *>(hp);
-      auto const &ahit = *phits[i];
-      gps[i] = ahit.globalPosition();
-      ges[i] = ahit.globalPositionError();
-      barrels[i] = isBarrel(ahit.geographicalId().subdetId());
-
     }
     if (bad) { nbad++; quality_[quadId] = pixelTuplesHeterogeneousProduct::bad; continue;}
     if (quality_[quadId] != pixelTuplesHeterogeneousProduct::loose) continue; // FIXME remove dup
@@ -220,12 +213,12 @@ CAHitQuadrupletGeneratorGPU::fetchKernelResult(int)
     ++nTuples_;
     ++sizes[sz];
     for (auto j=tuples.begin(i); j!=tuples.end(i); ++j) add(*j);
-    if (sz<4) continue;
+    // if (sz<4) continue;
     quadsInterface.emplace_back(std::array<int, 4>());
     quadsInterface.back()[0] = tuples.begin(i)[0];
     quadsInterface.back()[1] = tuples.begin(i)[1];
     quadsInterface.back()[2] = tuples.begin(i)[2];   // [sz-2];
-    quadsInterface.back()[3] = tuples.begin(i)[3];   // [sz-1];
+    quadsInterface.back()[3] = sz>3 ? tuples.begin(i)[3] : -1;   // [sz-1];
   }
 
 #ifdef GPU_DEBUG
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 22fa16e604a80..a13107ce25b24 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -169,12 +169,14 @@ kernel_connect(AtomicPairCounter * apc1, AtomicPairCounter * apc2,  // just to z
 
 __global__ 
 void kernel_find_ntuplets(
+    GPUCACell::Hits const *  __restrict__ hhp,
     GPUCACell * __restrict__ cells, uint32_t const * nCells,
     TuplesOnGPU::Container * foundNtuplets, AtomicPairCounter * apc,
     unsigned int minHitsPerNtuplet)
 {
 
   // recursive: not obvious to widen
+  auto const & hh = *hhp;
 
   auto cellIndex = threadIdx.x + blockIdx.x * blockDim.x;
   if (cellIndex >= (*nCells) ) return;
@@ -182,7 +184,7 @@ void kernel_find_ntuplets(
   if (thisCell.theLayerPairId!=0 && thisCell.theLayerPairId!=3 && thisCell.theLayerPairId!=8) return; // inner layer is 0 FIXME
   GPUCACell::TmpTuple stack;
   stack.reset();
-  thisCell.find_ntuplets(cells, *foundNtuplets, *apc, stack, minHitsPerNtuplet);
+  thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, stack, minHitsPerNtuplet);
   assert(stack.size()==0);
   // printf("in %d found quadruplets: %d\n", cellIndex, apc->get());
 }
@@ -281,10 +283,11 @@ void CAHitQuadrupletGeneratorKernels::launchKernels( // here goes algoparms....
   cudaCheck(cudaGetLastError());
 
   kernel_find_ntuplets<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+      hh.gpu_d,
       device_theCells_, device_nCells_,
       gpu_.tuples_d,
       gpu_.apc_d,
-      4
+      3
   );
   cudaCheck(cudaGetLastError());
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index 0858167190301..7ace85eafe965 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -144,6 +144,7 @@ class GPUCACell {
 
   __device__
   inline void find_ntuplets(
+      Hits const & hh,
       GPUCACell * __restrict__ cells,
       TuplesOnGPU::Container & foundNtuplets, 
       AtomicPairCounter & apc,
@@ -162,16 +163,29 @@ class GPUCACell {
     if(theOuterNeighbors.size()>0) {
       for (int j = 0; j < theOuterNeighbors.size(); ++j) {
         auto otherCell = theOuterNeighbors[j];
-        cells[otherCell].find_ntuplets(cells, foundNtuplets, apc, tmpNtuplet,
+        cells[otherCell].find_ntuplets(hh, cells, foundNtuplets, apc, tmpNtuplet,
                                        minHitsPerNtuplet);
       }
     } else {  // if long enough save...
       if ((unsigned int)(tmpNtuplet.size()) >= minHitsPerNtuplet-1) {
-        hindex_type hits[6]; auto nh=0U;
-        for (auto c : tmpNtuplet) hits[nh++] = cells[c].theInnerHitId;
-        hits[nh] = theOuterHitId; 
-        uint16_t it = foundNtuplets.bulkFill(apc,hits,tmpNtuplet.size()+1);
-        for (auto c : tmpNtuplet) cells[c].theTracks.push_back(it);
+        // triplets accepted only pointing to the hole
+        auto hole = [&]()->bool {
+          constexpr float r4 = 16.f;
+          auto const & c0 = cells[tmpNtuplet[0]];
+          auto ri = c0.get_inner_r(hh);
+          auto zi = c0.get_inner_z(hh);
+          auto ro = get_outer_r(hh);
+          auto zo = get_outer_z(hh);
+          auto z4 = std::abs(zi + (r4-ri)*(zo-zi)/(ro-ri));
+          return z4>25.f && z4<33.f;
+        };
+        if (tmpNtuplet.size()>=3 || hole()) {
+          hindex_type hits[6]; auto nh=0U;
+          for (auto c : tmpNtuplet) hits[nh++] = cells[c].theInnerHitId;
+          hits[nh] = theOuterHitId; 
+          uint16_t it = foundNtuplets.bulkFill(apc,hits,tmpNtuplet.size()+1);
+          for (auto c : tmpNtuplet) cells[c].theTracks.push_back(it);
+        };
       }
     }
     tmpNtuplet.pop_back();
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
index 034ca88368f7f..1756fdd8ff92c 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
@@ -15,6 +15,18 @@ namespace Rfit {
   using Map3x4d = Eigen::Map<Matrix3x4d,0,Eigen::Stride<3*stride(),stride()> >;
   using Matrix6x4f = Eigen::Matrix<float,6,4>;
   using Map6x4f = Eigen::Map<Matrix6x4f,0,Eigen::Stride<6*stride(),stride()> >;
+
+  // hits
+  template<int N>
+  using Matrix3xNd = Eigen::Matrix<double,3,N>;
+  template<int N>
+  using Map3xNd = Eigen::Map<Matrix3xNd<N>,0,Eigen::Stride<3*stride(),stride()> >;
+  // errors
+  template<int N>
+  using Matrix6xNf = Eigen::Matrix<float,6,N>;
+  template<int N>
+  using Map6xNf = Eigen::Map<Matrix6xNf<N>,0,Eigen::Stride<6*stride(),stride()> >;
+  // fast fit
   using Map4d = Eigen::Map<Vector4d,0,Eigen::InnerStride<stride()> >;
 
 }

From bf9e9eee9a707753221ac73b776a5456b4ebdf0e Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 6 Feb 2019 17:58:40 +0100
Subject: [PATCH 28/95] pass triplets to verfication

---
 .../PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu   | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index a13107ce25b24..e8c468795ff1f 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -203,8 +203,7 @@ void kernel_VerifyFit(TuplesOnGPU::Container const * __restrict__ tuples,
 
   quality[idx] = pixelTuplesHeterogeneousProduct::bad;
 
-  // only quadruplets
-  if (tuples->size(idx)<4) { 
+  if (tuples->size(idx)<3) { 
     return;
   }
 

From c1f7483fa5f8b24a64c00e9549333cb247f93a7b Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 7 Feb 2019 11:26:54 +0100
Subject: [PATCH 29/95] get nutple multiplicity

---
 .../PixelTriplets/plugins/CAConstants.h       |  1 +
 .../CAHitQuadrupletGeneratorKernels.cu        | 28 ++++++++++++++++++-
 .../plugins/CAHitQuadrupletGeneratorKernels.h |  3 ++
 ...> CAHitQuadrupletGeneratorKernelsAlloc.cu} |  6 ++++
 .../PixelTriplets/plugins/GPUCACell.h         |  8 ++++--
 5 files changed, 43 insertions(+), 3 deletions(-)
 rename RecoPixelVertexing/PixelTriplets/plugins/{CAHitQuadrupletGeneratorKernels.cc => CAHitQuadrupletGeneratorKernelsAlloc.cu} (84%)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAConstants.h b/RecoPixelVertexing/PixelTriplets/plugins/CAConstants.h
index 942404a9313e3..1c7777ee37b3e 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAConstants.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAConstants.h
@@ -25,6 +25,7 @@ namespace CAConstants {
    using OuterHitOfCell = GPU::VecArray< uint32_t, maxCellsPerHit()>;
    using TuplesContainer = OneToManyAssoc<hindex_type, maxTuples(), 5*maxTuples()>;
    using HitToTuple = OneToManyAssoc<tindex_type, PixelGPUConstants::maxNumberOfHits, 4*maxTuples()>; // 3.5 should be enough
+   using TupleMultiplicity = OneToManyAssoc<tindex_type,8,maxTuples()>;
 
 }
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index e8c468795ff1f..6e516a1be9f35 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -172,6 +172,7 @@ void kernel_find_ntuplets(
     GPUCACell::Hits const *  __restrict__ hhp,
     GPUCACell * __restrict__ cells, uint32_t const * nCells,
     TuplesOnGPU::Container * foundNtuplets, AtomicPairCounter * apc,
+    GPUCACell::TupleMultiplicity * tupleMultiplicity,
     unsigned int minHitsPerNtuplet)
 {
 
@@ -184,12 +185,28 @@ void kernel_find_ntuplets(
   if (thisCell.theLayerPairId!=0 && thisCell.theLayerPairId!=3 && thisCell.theLayerPairId!=8) return; // inner layer is 0 FIXME
   GPUCACell::TmpTuple stack;
   stack.reset();
-  thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, stack, minHitsPerNtuplet);
+  thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, *tupleMultiplicity, stack, minHitsPerNtuplet);
   assert(stack.size()==0);
   // printf("in %d found quadruplets: %d\n", cellIndex, apc->get());
 }
 
 
+__global__
+void kernel_fillMultiplicity(
+      TuplesOnGPU::Container const * __restrict__ foundNtuplets, 
+      GPUCACell::TupleMultiplicity * tupleMultiplicity
+     )
+{
+  auto it = blockIdx.x * blockDim.x + threadIdx.x;
+
+  if (it>=foundNtuplets->nbins()) return;
+
+  auto nhits = foundNtuplets->size(it);
+  if (nhits<3) return;
+  tupleMultiplicity->fillDirect(nhits,it);
+}
+
+
 __global__
 void kernel_VerifyFit(TuplesOnGPU::Container const * __restrict__ tuples,
                  Rfit::helix_fit const *  __restrict__ fit_results,
@@ -286,6 +303,7 @@ void CAHitQuadrupletGeneratorKernels::launchKernels( // here goes algoparms....
       device_theCells_, device_nCells_,
       gpu_.tuples_d,
       gpu_.apc_d,
+      device_tupleMultiplicity,
       3
   );
   cudaCheck(cudaGetLastError());
@@ -293,6 +311,14 @@ void CAHitQuadrupletGeneratorKernels::launchKernels( // here goes algoparms....
   numberOfBlocks = (TuplesOnGPU::Container::totbins() + blockSize - 1)/blockSize;
   cudautils::finalizeBulk<<<numberOfBlocks, blockSize, 0, cudaStream>>>(gpu_.apc_d,gpu_.tuples_d);
 
+  cudautils::launchFinalize(device_tupleMultiplicity,device_tmws,cudaStream);
+
+
+  blockSize = 128;
+  numberOfBlocks = (CAConstants::maxTuples() + blockSize - 1) / blockSize;
+  kernel_fillMultiplicity<<<numberOfBlocks, blockSize, 0, cudaStream>>>(gpu_.tuples_d,device_tupleMultiplicity);
+  cudaCheck(cudaGetLastError());
+
   if (lateFishbone_) {
     auto nthTot = 128;
     auto stride = 16;
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
index 97b5617a54f89..1614ffdc23132 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
@@ -17,6 +17,7 @@ class CAHitQuadrupletGeneratorKernels {
    using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
 
    using HitToTuple = CAConstants::HitToTuple;
+   using TupleMultiplicity = CAConstants::TupleMultiplicity;
 
    CAHitQuadrupletGeneratorKernels(bool earlyFishbone, bool lateFishbone) :
     earlyFishbone_(earlyFishbone),
@@ -42,6 +43,8 @@ class CAHitQuadrupletGeneratorKernels {
     HitToTuple * device_hitToTuple_ = nullptr;
     AtomicPairCounter * device_hitToTuple_apc_ = nullptr;
 
+    TupleMultiplicity * device_tupleMultiplicity = nullptr;
+    uint8_t * device_tmws;    
 
     const bool earlyFishbone_;
     const bool lateFishbone_;
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
similarity index 84%
rename from RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cc
rename to RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
index d00a5db18610c..369d484ac0b06 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
@@ -32,6 +32,9 @@ void CAHitQuadrupletGeneratorKernels::allocateOnGPU()
 //   cudaCheck(cudaMalloc(&device_hitToTuple_, sizeof(HitToTuple)));
    cudaCheck(cudaMalloc(&device_hitToTuple_apc_, sizeof(AtomicPairCounter)));
 
+   cudaCheck(cudaMalloc(&device_tupleMultiplicity,sizeof(TupleMultiplicity)));
+   cudaCheck(cudaMemset(device_tupleMultiplicity,0,sizeof(TupleMultiplicity))); // overkill
+   cudaCheck(cudaMalloc(&device_tmws, TupleMultiplicity::wsSize()));
 }
 
 void CAHitQuadrupletGeneratorKernels::cleanup(cudaStream_t cudaStream) {
@@ -40,5 +43,8 @@ void CAHitQuadrupletGeneratorKernels::cleanup(cudaStream_t cudaStream) {
                             PixelGPUConstants::maxNumberOfHits * sizeof(CAConstants::OuterHitOfCell),
                             cudaStream));
   cudaCheck(cudaMemsetAsync(device_nCells_, 0, sizeof(uint32_t), cudaStream));
+
+  cudautils::launchZero(device_tupleMultiplicity,cudaStream);
+
 }
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index 7ace85eafe965..5c26e4facb37a 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -29,6 +29,8 @@ class GPUCACell {
 
   using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
 
+  using TupleMultiplicity = CAConstants::TupleMultiplicity;
+
   GPUCACell() = default;
 #ifdef __CUDACC__
 
@@ -148,6 +150,7 @@ class GPUCACell {
       GPUCACell * __restrict__ cells,
       TuplesOnGPU::Container & foundNtuplets, 
       AtomicPairCounter & apc,
+      TupleMultiplicity & tupleMultiplicity,
       TmpTuple & tmpNtuplet,
       const unsigned int minHitsPerNtuplet) const
   {
@@ -163,8 +166,8 @@ class GPUCACell {
     if(theOuterNeighbors.size()>0) {
       for (int j = 0; j < theOuterNeighbors.size(); ++j) {
         auto otherCell = theOuterNeighbors[j];
-        cells[otherCell].find_ntuplets(hh, cells, foundNtuplets, apc, tmpNtuplet,
-                                       minHitsPerNtuplet);
+        cells[otherCell].find_ntuplets(hh, cells, foundNtuplets, apc, tupleMultiplicity, 
+                                       tmpNtuplet, minHitsPerNtuplet);
       }
     } else {  // if long enough save...
       if ((unsigned int)(tmpNtuplet.size()) >= minHitsPerNtuplet-1) {
@@ -185,6 +188,7 @@ class GPUCACell {
           hits[nh] = theOuterHitId; 
           uint16_t it = foundNtuplets.bulkFill(apc,hits,tmpNtuplet.size()+1);
           for (auto c : tmpNtuplet) cells[c].theTracks.push_back(it);
+          tupleMultiplicity.countDirect(tmpNtuplet.size()+1);
         };
       }
     }

From d0f0ff2f44b5365e73c216abae457871b4649879 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 7 Feb 2019 12:42:43 +0100
Subject: [PATCH 30/95] fits3,4 and 5

---
 .../PixelTrackFitting/interface/BrokenLine.h  |  4 +-
 .../plugins/BrokenLineFitOnGPU.cu             | 73 +++++++++++++------
 .../PixelTriplets/plugins/BuildFile.xml       |  2 +-
 .../plugins/CAHitQuadrupletGeneratorGPU.cc    |  2 +-
 .../plugins/CAHitQuadrupletGeneratorKernels.h |  3 +
 .../PixelTriplets/plugins/HelixFitOnGPU.cc    |  5 +-
 .../PixelTriplets/plugins/HelixFitOnGPU.h     |  4 +-
 7 files changed, 64 insertions(+), 29 deletions(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
index 8d7ff4d830df7..048769d181936 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -365,7 +365,7 @@ namespace BrokenLine {
       if(i>0 && i<n-1) circle_results.chi2+=sqr(u(i-1)/(s(i)-s(i-1))-u(i)*(s(i+1)-s(i-1))/((s(i+1)-s(i))*(s(i)-s(i-1)))+u(i+1)/(s(i+1)-s(i))+(s(i+1)-s(i-1))*u(n)/2)/VarBeta(i);
     }
     
-    assert(circle_results.chi2>=0);
+    // assert(circle_results.chi2>=0);
   }
   
   /*!
@@ -469,7 +469,7 @@ namespace BrokenLine {
 					      +u(i+1)/(S(i+1)-S(i)))/VarBeta(i);
     }
     
-    assert(line_results.chi2>=0);
+    // assert(line_results.chi2>=0);
   }
   
   /*!
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
index 15953477bf1f7..a7e2edfef8c8e 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
@@ -21,9 +21,13 @@ using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
 
 using namespace Eigen;
 
+
+#define BL_DUMP_HITS
+
 template<int N>
 __global__
 void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
+    CAConstants::TupleMultiplicity const * __restrict__ tupleMultiplicity,
     HitsOnGPU const * __restrict__ hhp,
     double * __restrict__ phits,
     float * __restrict__ phits_ge,
@@ -35,14 +39,21 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
 
   assert(pfast_fit); assert(foundNtuplets);
 
+  // look in bin for this hit multiplicity
   auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
-  auto helix_start = local_start + offset;
 
-  if (helix_start>=foundNtuplets->nbins()) return;
+#ifdef GPU_DEBUG
+  if (0==local_start) printf("%d Ntuple for %d hits to fit\n",tupleMultiplicity->size(hits_in_fit), hits_in_fit);
+#endif
 
-  if (foundNtuplets->size(helix_start)<hits_in_fit) {
-    return;
-  }
+  auto tuple_start = local_start + offset;
+  if (tuple_start >= tupleMultiplicity->size(hits_in_fit)) return;
+
+  // get it for the ntuple container (one to one to helix)
+  auto helix_start = *(tupleMultiplicity->begin(hits_in_fit)+tuple_start);
+  assert (helix_start < foundNtuplets->nbins());
+
+  assert (foundNtuplets->size(helix_start)==hits_in_fit);
 
   Rfit::Map3xNd<N> hits(phits+local_start);
   Rfit::Map4d   fast_fit(pfast_fit+local_start);
@@ -54,14 +65,11 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
   __syncthreads(); 
   bool dump =  (foundNtuplets->size(helix_start)==5 &&
                 0 == atomicAdd(&done,1));
-  auto imax = foundNtuplets->size(helix_start);
-#else
-  auto imax = hits_in_fit;
 #endif
 
   // Prepare data structure
   auto const * hitId = foundNtuplets->begin(helix_start);
-  for (unsigned int i = 0; i < imax; ++i) {
+  for (unsigned int i = 0; i < hits_in_fit; ++i) {
     auto hit = hitId[i];
     float ge[6];
     hhp->cpeParams->detParams(hhp->detInd_d[hit]).frame.toGlobal(hhp->xerr_d[hit], 0, hhp->yerr_d[hit], ge);
@@ -70,7 +78,6 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
       printf("Hit global: %d: %d hits.col(%d) << %f,%f,%f\n", helix_start, hhp->detInd_d[hit],i,hhp->xg_d[hit],hhp->yg_d[hit],hhp->zg_d[hit]);
       printf("Error: %d: %d  hits_ge.col(%d) << %e,%e,%e,%e,%e,%e\n",helix_start,hhp->detInd_d[hit],i,ge[0],ge[1],ge[2],ge[3],ge[4],ge[5]);
     }
-    if (i>=hits_in_fit) continue;
 #endif
     hits.col(i) << hhp->xg_d[hit], hhp->yg_d[hit], hhp->zg_d[hit];
     hits_ge.col(i) << ge[0],ge[1],ge[2],ge[3],ge[4],ge[5];
@@ -88,6 +95,7 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
 template<int N>
 __global__
 void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
+    CAConstants::TupleMultiplicity const * __restrict__ tupleMultiplicity,
     double B,
     Rfit::helix_fit *results,
     double * __restrict__ phits,
@@ -100,13 +108,19 @@ void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
 
   assert(results); assert(pfast_fit);
 
+
+  // same as above...
+
+  // look in bin for this hit multiplicity
   auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
-  auto helix_start = local_start + offset;
+  auto tuple_start = local_start + offset;
+  if (tuple_start >= tupleMultiplicity->size(hits_in_fit)) return;
 
-  if (helix_start>=foundNtuplets->nbins()) return;
-  if (foundNtuplets->size(helix_start)<hits_in_fit) {
-    return;
-  }
+  // get it for the ntuple container (one to one to helix)
+  auto helix_start = *(tupleMultiplicity->begin(hits_in_fit)+tuple_start);
+  assert (helix_start<foundNtuplets->nbins());
+
+  assert (foundNtuplets->size(helix_start)==hits_in_fit);
 
 
   Rfit::Map3xNd<N> hits(phits+local_start);
@@ -142,10 +156,11 @@ void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
   helix.chi2_line = line.chi2;
 
 #ifdef GPU_DEBUG
-  printf("kernelBLFit circle.par(0,1,2): %d %f,%f,%f\n", helix_start,
+  if ( !(circle.chi2>=0) || !(line.chi2>=0) ) printf("kernelBLFit failed! %f/%f\n", helix.chi2_circle,helix.chi2_line);
+  printf("kernelBLFit %d circle.par(0,1,2): %d %f,%f,%f\n", hits_in_fit, helix_start,
          circle.par(0), circle.par(1), circle.par(2));
-  printf("kernelLineFitAllHits line.par(0,1): %d %f,%f\n", helix_start, line.par(0),line.par(1));
-  printf("kernelLineFitAllHits chi2 cov %f/%f %f,%f,%f,%f,%f\n",helix.chi2_circle,helix.chi2_line, 
+  printf("kernelBLHits line.par(0,1): %d %f,%f\n", helix_start, line.par(0),line.par(1));
+  printf("kernelBLHits chi2 cov %f/%f  %f,%f,%f,%f,%f\n",helix.chi2_circle,helix.chi2_line, 
          helix.cov(0,0),helix.cov(1,1),helix.cov(2,2),helix.cov(3,3),helix.cov(4,4));
 #endif
 }
@@ -162,27 +177,41 @@ void HelixFitOnGPU::launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t nhits
 
       // fit triplets
       kernelBLFastFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-          tuples_d, hh.gpu_d,
+          tuples_d, tupleMultiplicity_d, hh.gpu_d,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
       cudaCheck(cudaGetLastError());
 
       kernelBLFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-             tuples_d, bField_, helix_fit_results_d,
+             tuples_d, tupleMultiplicity_d, bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
              offset);
       cudaCheck(cudaGetLastError());
 
       // fit quads
       kernelBLFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-          tuples_d, hh.gpu_d,
+          tuples_d, tupleMultiplicity_d, hh.gpu_d,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
       cudaCheck(cudaGetLastError());
 
       kernelBLFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-             tuples_d, bField_, helix_fit_results_d,
+             tuples_d, tupleMultiplicity_d, bField_, helix_fit_results_d,
+             hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
+             offset);
+      cudaCheck(cudaGetLastError());
+
+      // fit penta
+      kernelBLFastFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, tupleMultiplicity_d, hh.gpu_d,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+      cudaCheck(cudaGetLastError());
+
+      kernelBLFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tuples_d, tupleMultiplicity_d, bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
              offset);
       cudaCheck(cudaGetLastError());
+
+
     }
 
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml b/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
index 3c8397cf572f6..9c4de13165232 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
@@ -12,7 +12,7 @@
 <use name="RecoTracker/TkSeedingLayers"/>
 <use name="RecoTracker/TkTrackingRegions"/>
 <flags CXXFLAGS="-g -fno-math-errno"/>
-<flags CUDA_FLAGS="-g"/>
+<flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
 <library file="*.cu *.cc" name="RecoPixelVertexingPixelTripletsPlugins">
   <flags EDM_PLUGIN="1"/>
 </library>
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index 570a7c7f4e8b4..4ada99488ad8a 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -152,7 +152,7 @@ void CAHitQuadrupletGeneratorGPU::allocateOnGPU()
   cudaCheck(cudaMallocHost(&quality_, sizeof(Quality)*maxNumberOfQuadruplets_));
 
   kernels.allocateOnGPU();
-  fitter.allocateOnGPU(gpu_.tuples_d, gpu_.helix_fit_results_d);
+  fitter.allocateOnGPU(gpu_.tuples_d, kernels.tupleMultiplicity(), gpu_.helix_fit_results_d);
 
 
 }
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
index 1614ffdc23132..b0805c850ff98 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
@@ -24,6 +24,9 @@ class CAHitQuadrupletGeneratorKernels {
     lateFishbone_(lateFishbone){}
    ~CAHitQuadrupletGeneratorKernels() { deallocateOnGPU();}
 
+
+   TupleMultiplicity const * tupleMultiplicity() const { return device_tupleMultiplicity;}
+
    void launchKernels(HitsOnCPU const & hh, TuplesOnGPU & tuples_d, cudaStream_t cudaStream);
 
    void classifyTuples(HitsOnCPU const & hh, TuplesOnGPU & tuples_d, cudaStream_t cudaStream);
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.cc
index fa45918f9acec..393eb9b020c3d 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.cc
@@ -1,12 +1,13 @@
 #include "HelixFitOnGPU.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
 
-void HelixFitOnGPU::allocateOnGPU(TuplesOnGPU::Container const * tuples, Rfit::helix_fit * helix_fit_results) {
+void HelixFitOnGPU::allocateOnGPU(TuplesOnGPU::Container const * tuples, TupleMultiplicity const * tupleMultiplicity, Rfit::helix_fit * helix_fit_results) {
 
   tuples_d = tuples;
+  tupleMultiplicity_d = tupleMultiplicity;
   helix_fit_results_d = helix_fit_results;
 
-  assert(tuples_d); assert(helix_fit_results_d);
+  assert(tuples_d); assert(tupleMultiplicity_d); assert(helix_fit_results_d);
 
   cudaCheck(cudaMalloc(&hitsGPU_, maxNumberOfConcurrentFits_ * sizeof(Rfit::Matrix3xNd<4>)));
   cudaCheck(cudaMemset(hitsGPU_, 0x00, maxNumberOfConcurrentFits_ * sizeof(Rfit::Matrix3xNd<4>)));
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
index 1756fdd8ff92c..5cc4be08269b9 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
@@ -39,6 +39,7 @@ class HelixFitOnGPU {
    using HitsOnCPU = siPixelRecHitsHeterogeneousProduct::HitsOnCPU;
 
    using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
+   using TupleMultiplicity = CAConstants::TupleMultiplicity;
 
    HelixFitOnGPU() = default;
    ~HelixFitOnGPU() { deallocateOnGPU();}
@@ -47,7 +48,7 @@ class HelixFitOnGPU {
    void launchRiemannKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream);
    void launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream);
 
-   void allocateOnGPU(TuplesOnGPU::Container const * tuples, Rfit::helix_fit * helix_fit_results);
+   void allocateOnGPU(TuplesOnGPU::Container const * tuples, TupleMultiplicity const * tupleMultiplicity, Rfit::helix_fit * helix_fit_results);
    void deallocateOnGPU();
 
 
@@ -57,6 +58,7 @@ class HelixFitOnGPU {
 
     // fowarded
     TuplesOnGPU::Container const * tuples_d = nullptr;
+    TupleMultiplicity const * tupleMultiplicity_d = nullptr;
     double bField_;
     Rfit::helix_fit * helix_fit_results_d = nullptr;
 

From acef0305f8ad5d64fdabf44000290382d2c59749 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 7 Feb 2019 16:25:07 +0100
Subject: [PATCH 31/95] fit 5 as 4

---
 .../plugins/BrokenLineFitOnGPU.cu             | 66 ++++++++++---------
 .../PixelTriplets/plugins/BuildFile.xml       |  2 +-
 .../CAHitQuadrupletGeneratorKernels.cu        |  7 +-
 3 files changed, 41 insertions(+), 34 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
index a7e2edfef8c8e..1d85af1de7928 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
@@ -22,7 +22,7 @@ using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
 using namespace Eigen;
 
 
-#define BL_DUMP_HITS
+// #define BL_DUMP_HITS
 
 template<int N>
 __global__
@@ -32,10 +32,13 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
     double * __restrict__ phits,
     float * __restrict__ phits_ge,
     double * __restrict__ pfast_fit,
+    uint32_t nHits,
     uint32_t offset)
 {
 
-  constexpr uint32_t hits_in_fit = N;
+  constexpr uint32_t hitsInFit = N;
+
+  assert(hitsInFit<=nHits);
 
   assert(pfast_fit); assert(foundNtuplets);
 
@@ -43,17 +46,17 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
   auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
 
 #ifdef GPU_DEBUG
-  if (0==local_start) printf("%d Ntuple for %d hits to fit\n",tupleMultiplicity->size(hits_in_fit), hits_in_fit);
+  if (0==local_start) printf("%d Ntuple of size %d for %d hits to fit\n",tupleMultiplicity->size(nHits), nHits, hitsInFit);
 #endif
 
   auto tuple_start = local_start + offset;
-  if (tuple_start >= tupleMultiplicity->size(hits_in_fit)) return;
+  if (tuple_start >= tupleMultiplicity->size(nHits)) return;
 
-  // get it for the ntuple container (one to one to helix)
-  auto helix_start = *(tupleMultiplicity->begin(hits_in_fit)+tuple_start);
+  // get it from the ntuple container (one to one to helix)
+  auto helix_start = *(tupleMultiplicity->begin(nHits)+tuple_start);
   assert (helix_start < foundNtuplets->nbins());
 
-  assert (foundNtuplets->size(helix_start)==hits_in_fit);
+  assert (foundNtuplets->size(helix_start)==nHits);
 
   Rfit::Map3xNd<N> hits(phits+local_start);
   Rfit::Map4d   fast_fit(pfast_fit+local_start);
@@ -69,7 +72,7 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
 
   // Prepare data structure
   auto const * hitId = foundNtuplets->begin(helix_start);
-  for (unsigned int i = 0; i < hits_in_fit; ++i) {
+  for (unsigned int i = 0; i < hitsInFit; ++i) {
     auto hit = hitId[i];
     float ge[6];
     hhp->cpeParams->detParams(hhp->detInd_d[hit]).frame.toGlobal(hhp->xerr_d[hit], 0, hhp->yerr_d[hit], ge);
@@ -94,17 +97,20 @@ void kernelBLFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
 
 template<int N>
 __global__
-void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
+void kernelBLFit(
     CAConstants::TupleMultiplicity const * __restrict__ tupleMultiplicity,
     double B,
     Rfit::helix_fit *results,
     double * __restrict__ phits,
     float * __restrict__ phits_ge,
     double * __restrict__ pfast_fit,
+    uint32_t nHits,
     uint32_t offset)
 {
 
-  constexpr uint32_t hits_in_fit = N;
+  constexpr uint32_t hitsInFit = N;
+
+  assert(hitsInFit<=nHits);
 
   assert(results); assert(pfast_fit);
 
@@ -114,13 +120,10 @@ void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
   // look in bin for this hit multiplicity
   auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
   auto tuple_start = local_start + offset;
-  if (tuple_start >= tupleMultiplicity->size(hits_in_fit)) return;
+  if (tuple_start >= tupleMultiplicity->size(nHits)) return;
 
   // get it for the ntuple container (one to one to helix)
-  auto helix_start = *(tupleMultiplicity->begin(hits_in_fit)+tuple_start);
-  assert (helix_start<foundNtuplets->nbins());
-
-  assert (foundNtuplets->size(helix_start)==hits_in_fit);
+  auto helix_start = *(tupleMultiplicity->begin(nHits)+tuple_start);
 
 
   Rfit::Map3xNd<N> hits(phits+local_start);
@@ -157,7 +160,7 @@ void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
 
 #ifdef GPU_DEBUG
   if ( !(circle.chi2>=0) || !(line.chi2>=0) ) printf("kernelBLFit failed! %f/%f\n", helix.chi2_circle,helix.chi2_line);
-  printf("kernelBLFit %d circle.par(0,1,2): %d %f,%f,%f\n", hits_in_fit, helix_start,
+  printf("kernelBLFit %d circle.par(0,1,2): %d %f,%f,%f\n", hitsInFit, helix_start,
          circle.par(0), circle.par(1), circle.par(2));
   printf("kernelBLHits line.par(0,1): %d %f,%f\n", helix_start, line.par(0),line.par(1));
   printf("kernelBLHits chi2 cov %f/%f  %f,%f,%f,%f,%f\n",helix.chi2_circle,helix.chi2_line, 
@@ -166,11 +169,11 @@ void kernelBLFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
 }
 
 
-void HelixFitOnGPU::launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t nhits, uint32_t maxNumberOfTuples, cudaStream_t cudaStream)
+void HelixFitOnGPU::launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t hitsInFit, uint32_t maxNumberOfTuples, cudaStream_t cudaStream)
 {
     assert(tuples_d); assert(fast_fit_resultsGPU_);
 
-    auto blockSize = 128;
+    auto blockSize = 64;
     auto numberOfBlocks = (maxNumberOfConcurrentFits_ + blockSize - 1) / blockSize;
 
     for (uint32_t offset=0; offset<maxNumberOfTuples; offset+=maxNumberOfConcurrentFits_) {
@@ -178,37 +181,40 @@ void HelixFitOnGPU::launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t nhits
       // fit triplets
       kernelBLFastFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
           tuples_d, tupleMultiplicity_d, hh.gpu_d,
-          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
+          3, offset);
       cudaCheck(cudaGetLastError());
 
       kernelBLFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-             tuples_d, tupleMultiplicity_d, bField_, helix_fit_results_d,
+             tupleMultiplicity_d, bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
-             offset);
+             3, offset);
       cudaCheck(cudaGetLastError());
 
       // fit quads
       kernelBLFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
           tuples_d, tupleMultiplicity_d, hh.gpu_d,
-          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
+          4, offset);
       cudaCheck(cudaGetLastError());
 
       kernelBLFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-             tuples_d, tupleMultiplicity_d, bField_, helix_fit_results_d,
+             tupleMultiplicity_d, bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
-             offset);
+             4, offset);
       cudaCheck(cudaGetLastError());
 
-      // fit penta
-      kernelBLFastFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+      // fit penta (only first 4)
+      kernelBLFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
           tuples_d, tupleMultiplicity_d, hh.gpu_d,
-          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
+          5, offset);
       cudaCheck(cudaGetLastError());
 
-      kernelBLFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-             tuples_d, tupleMultiplicity_d, bField_, helix_fit_results_d,
+      kernelBLFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tupleMultiplicity_d, bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
-             offset);
+             5, offset);
       cudaCheck(cudaGetLastError());
 
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml b/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
index 9c4de13165232..3c8397cf572f6 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
@@ -12,7 +12,7 @@
 <use name="RecoTracker/TkSeedingLayers"/>
 <use name="RecoTracker/TkTrackingRegions"/>
 <flags CXXFLAGS="-g -fno-math-errno"/>
-<flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
+<flags CUDA_FLAGS="-g"/>
 <library file="*.cu *.cc" name="RecoPixelVertexingPixelTripletsPlugins">
   <flags EDM_PLUGIN="1"/>
 </library>
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 6e516a1be9f35..03ebe08852945 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -231,14 +231,15 @@ void kernel_VerifyFit(TuplesOnGPU::Container const * __restrict__ tuples,
   isNaN |=  !(fit_results[idx].chi2_line+fit_results[idx].chi2_circle < 100.f);  // catch NaN as well
 
 #ifdef GPU_DEBUG
- if (isNaN) printf("NaN or Bad Fit %d %f/%f\n",idx,fit_results[idx].chi2_line,fit_results[idx].chi2_circle);
+ if (isNaN) printf("NaN or Bad Fit %d size %d chi2 %f/%f\n",idx,tuples->size(idx), fit_results[idx].chi2_line,fit_results[idx].chi2_circle);
 #endif
 
   // impose "region cuts" (NaN safe)
   // phi,Tip,pt,cotan(theta)),Zip
-  bool ok = std::abs(fit_results[idx].par(1)) < 0.1f 
-         && fit_results[idx].par(2) > 0.3f
+  bool ok = std::abs(fit_results[idx].par(1)) < ( tuples->size(idx)>3 ? 0.1f : 0.05f) 
+         && fit_results[idx].par(2) > ( tuples->size(idx)>3 ? 0.3f : 0.5f)
          && std::abs(fit_results[idx].par(4)) < 12.f;
+  
   ok &= (!isNaN);
   quality[idx] = ok ? pixelTuplesHeterogeneousProduct::loose : pixelTuplesHeterogeneousProduct::bad; 
 }

From 28e3e65d62fefe3371e9dbc27f9f0b44acb4f2ac Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 7 Feb 2019 18:55:54 +0100
Subject: [PATCH 32/95] also rienmann 3,4&5

---
 .../PixelTriplets/plugins/RiemannFitOnGPU.cu  | 170 ++++++++++++------
 1 file changed, 119 insertions(+), 51 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
index 86cc4928f736c..77f77fc9dbdc2 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
@@ -21,35 +21,48 @@ using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
 
 using namespace Eigen;
 
+template<int N>
 __global__
-void kernelFastFitAllHits(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
+void kernelFastFit(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
+    CAConstants::TupleMultiplicity const * __restrict__ tupleMultiplicity,
+    uint32_t nHits,
     HitsOnGPU const * __restrict__ hhp,
-    int hits_in_fit,
     double * __restrict__ phits,
     float * __restrict__ phits_ge,
     double * __restrict__ pfast_fit,
     uint32_t offset)
 {
 
-  assert(hits_in_fit==4); // FixMe later template
+  constexpr uint32_t hitsInFit = N;
 
-  assert(pfast_fit); assert(foundNtuplets);
+  assert(hitsInFit<=nHits);
 
+  assert(pfast_fit); assert(foundNtuplets); assert(tupleMultiplicity);
+
+  // look in bin for this hit multiplicity
   auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
-  auto helix_start = local_start + offset;
 
-  if (helix_start>=foundNtuplets->nbins()) return;
-  if (foundNtuplets->size(helix_start)<hits_in_fit) {
-    return;
-  }
+#ifdef GPU_DEBUG
+  if (0==local_start) printf("%d Ntuple of size %d for %d hits to fit\n",tupleMultiplicity->size(nHits), nHits, hitsInFit);
+#endif
+
+  auto tuple_start = local_start + offset;
+  if (tuple_start >= tupleMultiplicity->size(nHits)) return;
+
+  // get it from the ntuple container (one to one to helix)
+  auto helix_start = *(tupleMultiplicity->begin(nHits)+tuple_start);
+  assert (helix_start < foundNtuplets->nbins());
 
-  Rfit::Map3x4d hits(phits+local_start);
+  assert (foundNtuplets->size(helix_start)==nHits);
+
+  Rfit::Map3xNd<N> hits(phits+local_start);
   Rfit::Map4d   fast_fit(pfast_fit+local_start);
-  Rfit::Map6x4f hits_ge(phits_ge+local_start);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+local_start);
+
 
   // Prepare data structure
   auto const * hitId = foundNtuplets->begin(helix_start);
-  for (unsigned int i = 0; i < hits_in_fit; ++i) {
+  for (unsigned int i = 0; i < hitsInFit; ++i) {
     auto hit = hitId[i];
     // printf("Hit global: %f,%f,%f\n", hhp->xg_d[hit],hhp->yg_d[hit],hhp->zg_d[hit]);
     float ge[6];
@@ -69,9 +82,11 @@ void kernelFastFitAllHits(TuplesOnGPU::Container const * __restrict__ foundNtupl
 
 }
 
+template<int N>
 __global__
-void kernelCircleFitAllHits(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
-    int hits_in_fit,
+void kernelCircleFit(
+    CAConstants::TupleMultiplicity const * __restrict__ tupleMultiplicity,
+    uint32_t nHits,
     double B,
     double * __restrict__ phits,
     float * __restrict__ phits_ge,
@@ -79,66 +94,78 @@ void kernelCircleFitAllHits(TuplesOnGPU::Container const * __restrict__ foundNtu
     Rfit::circle_fit *circle_fit,
     uint32_t offset)
 {
+
   assert(circle_fit); 
 
-  auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
-  auto helix_start = local_start + offset;
+  constexpr uint32_t hitsInFit = N;
+  assert(hitsInFit<=nHits);
 
-  if (helix_start>=foundNtuplets->nbins()) return;
-  if (foundNtuplets->size(helix_start)<hits_in_fit) {
-    return;
-  }
+  // same as above...
 
-  Rfit::Map3x4d hits(phits+local_start);
-  Rfit::Map4d   fast_fit(pfast_fit_input+local_start);
-  Rfit::Map6x4f hits_ge(phits_ge+local_start);
+  // look in bin for this hit multiplicity
+  auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
+  auto tuple_start = local_start + offset;
+  if (tuple_start >= tupleMultiplicity->size(nHits)) return;
+
+  // get it for the ntuple container (one to one to helix)
+  auto helix_start = *(tupleMultiplicity->begin(nHits)+tuple_start);
 
-  constexpr uint32_t N = Rfit::Map3x4d::ColsAtCompileTime;
-  constexpr auto n = N;
 
-  assert(4==n); // later will be templated...
+  Rfit::Map3xNd<N> hits(phits+local_start);
+  Rfit::Map4d   fast_fit(pfast_fit_input+local_start);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+local_start);
 
-  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, n).colwise().norm());
+  Rfit::VectorNd<N> rad = (hits.block(0, 0, 2, N).colwise().norm());
 
-  Rfit::Matrix2Nd<N> hits_cov =  Rfit::Matrix2Nd<4>::Zero();
+  Rfit::Matrix2Nd<N> hits_cov =  Rfit::Matrix2Nd<N>::Zero();
   Rfit::loadCovariance2D(hits_ge,hits_cov);
 
   circle_fit[local_start] =
-      Rfit::Circle_fit(hits.block(0, 0, 2, n),
+      Rfit::Circle_fit(hits.block(0, 0, 2, N),
                        hits_cov,
                        fast_fit, rad, B, true);
 
 #ifdef GPU_DEBUG
-//  printf("kernelCircleFitAllHits circle.par(0,1,2): %d %f,%f,%f\n", helix_start, 
+//  printf("kernelCircleFit circle.par(0,1,2): %d %f,%f,%f\n", helix_start, 
 //         circle_fit[local_start].par(0), circle_fit[local_start].par(1), circle_fit[local_start].par(2));
 #endif
 }
 
+
+template<int N>
 __global__
-void kernelLineFitAllHits(TuplesOnGPU::Container const * __restrict__ foundNtuplets,
-    int hits_in_fit,
+void kernelLineFit(
+    CAConstants::TupleMultiplicity const * __restrict__ tupleMultiplicity,
+    uint32_t nHits,
     double B,
     Rfit::helix_fit *results,
     double * __restrict__ phits,
     float * __restrict__ phits_ge,
-    double * __restrict__ pfast_fit,
+    double * __restrict__ pfast_fit_input,
     Rfit::circle_fit * __restrict__ circle_fit,
     uint32_t offset)
 {
 
   assert(results); assert(circle_fit);
 
+  constexpr uint32_t hitsInFit = N;
+  assert(hitsInFit<=nHits);
+
+  // same as above...
+
+  // look in bin for this hit multiplicity
   auto local_start = (blockIdx.x * blockDim.x + threadIdx.x);
-  auto helix_start = local_start + offset;
+  auto tuple_start = local_start + offset;
+  if (tuple_start >= tupleMultiplicity->size(nHits)) return;
 
-  if (helix_start>=foundNtuplets->nbins()) return;
-  if (foundNtuplets->size(helix_start)<hits_in_fit) {
-    return;
-  }
+  // get it for the ntuple container (one to one to helix)
+  auto helix_start = *(tupleMultiplicity->begin(nHits)+tuple_start);
+
+
+  Rfit::Map3xNd<N> hits(phits+local_start);
+  Rfit::Map4d   fast_fit(pfast_fit_input+local_start);
+  Rfit::Map6xNf<N> hits_ge(phits_ge+local_start);
 
-  Rfit::Map3x4d hits(phits+local_start);
-  Rfit::Map4d   fast_fit(pfast_fit+local_start);
-  Rfit::Map6x4f hits_ge(phits_ge+local_start);
   auto const & line_fit = Rfit::Line_fit(hits, hits_ge, circle_fit[local_start], fast_fit, B, true);
 
   par_uvrtopak(circle_fit[local_start], B, true);
@@ -158,10 +185,10 @@ void kernelLineFitAllHits(TuplesOnGPU::Container const * __restrict__ foundNtupl
   helix.chi2_line = line_fit.chi2;
 
 #ifdef GPU_DEBUG
-  printf("kernelLineFitAllHits circle.par(0,1,2): %d %f,%f,%f\n", helix_start,
+  printf("kernelLineFit size %d for %d hits circle.par(0,1,2): N,nHits, %d %f,%f,%f\n", helix_start,
          circle_fit[local_start].par(0), circle_fit[local_start].par(1), circle_fit[local_start].par(2));
-  printf("kernelLineFitAllHits line.par(0,1): %d %f,%f\n", helix_start, line_fit.par(0),line_fit.par(1));
-  printf("kernelLineFitAllHits chi2 cov %f/%f %f,%f,%f,%f,%f\n",helix.chi2_circle,helix.chi2_line, 
+  printf("kernelLineFit line.par(0,1): %d %f,%f\n", helix_start, line_fit.par(0),line_fit.par(1));
+  printf("kernelLineFit chi2 cov %f/%f %f,%f,%f,%f,%f\n",helix.chi2_circle,helix.chi2_line, 
          helix.cov(0,0),helix.cov(1,1),helix.cov(2,2),helix.cov(3,3),helix.cov(4,4));
 #endif
 }
@@ -171,25 +198,66 @@ void HelixFitOnGPU::launchRiemannKernels(HitsOnCPU const & hh, uint32_t nhits, u
 {
     assert(tuples_d); assert(fast_fit_resultsGPU_);
 
-    auto blockSize = 128;
+    auto blockSize = 64;
     auto numberOfBlocks = (maxNumberOfConcurrentFits_ + blockSize - 1) / blockSize;
 
     for (uint32_t offset=0; offset<maxNumberOfTuples; offset+=maxNumberOfConcurrentFits_) {
-      kernelFastFitAllHits<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-          tuples_d, hh.gpu_d, 4,
+
+      // triplets
+      kernelFastFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, tupleMultiplicity_d, 3, 
+          hh.gpu_d,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+      cudaCheck(cudaGetLastError());
+
+      kernelCircleFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tupleMultiplicity_d, 3, bField_,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_, offset);
+      cudaCheck(cudaGetLastError());
+
+      kernelLineFit<3><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tupleMultiplicity_d, 3,  bField_, helix_fit_results_d,
+             hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_,
+             offset);
+      cudaCheck(cudaGetLastError());
+
+      // quads
+      kernelFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, tupleMultiplicity_d, 4,
+          hh.gpu_d,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
       cudaCheck(cudaGetLastError());
 
-      kernelCircleFitAllHits<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-          tuples_d, 4, bField_,
+      kernelCircleFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tupleMultiplicity_d, 4, bField_,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_, offset);
       cudaCheck(cudaGetLastError());
 
+      kernelLineFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tupleMultiplicity_d, 4,  bField_, helix_fit_results_d,
+             hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_,
+             offset);
+      cudaCheck(cudaGetLastError());
+
+
+      // penta
+      kernelFastFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, tupleMultiplicity_d, 5,
+          hh.gpu_d,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+      cudaCheck(cudaGetLastError());
+
+      kernelCircleFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tupleMultiplicity_d, 5, bField_,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_, offset);
+      cudaCheck(cudaGetLastError());
 
-      kernelLineFitAllHits<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
-             tuples_d, 4,  bField_, helix_fit_results_d,
+      kernelLineFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tupleMultiplicity_d, 5,  bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_,
              offset);
       cudaCheck(cudaGetLastError());
+
     }
+
 }

From 05c35275d45a83dfb4b485e20a7447199d606e17 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 7 Feb 2019 19:45:13 +0100
Subject: [PATCH 33/95] riemann 5 as 4 as well

---
 RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
index 77f77fc9dbdc2..3afa0f58326f4 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
@@ -241,18 +241,18 @@ void HelixFitOnGPU::launchRiemannKernels(HitsOnCPU const & hh, uint32_t nhits, u
 
 
       // penta
-      kernelFastFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+      kernelFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
           tuples_d, tupleMultiplicity_d, 5,
           hh.gpu_d,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
       cudaCheck(cudaGetLastError());
 
-      kernelCircleFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+      kernelCircleFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
           tupleMultiplicity_d, 5, bField_,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_, offset);
       cudaCheck(cudaGetLastError());
 
-      kernelLineFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+      kernelLineFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
              tupleMultiplicity_d, 5,  bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_,
              offset);

From b8c7529bf28f6279bd0684c2b42267bfddd7332a Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 8 Feb 2019 10:34:59 +0100
Subject: [PATCH 34/95] add configurables

---
 .../plugins/BrokenLineFitOnGPU.cu             | 30 +++++++++----
 .../plugins/CAHitQuadrupletGeneratorGPU.cc    |  6 ++-
 .../CAHitQuadrupletGeneratorKernels.cu        |  2 +-
 .../plugins/CAHitQuadrupletGeneratorKernels.h |  5 ++-
 .../PixelTriplets/plugins/GPUCACell.h         | 30 ++++++-------
 .../PixelTriplets/plugins/HelixFitOnGPU.h     |  7 ++--
 .../PixelTriplets/plugins/RiemannFitOnGPU.cu  | 42 +++++++++++++------
 7 files changed, 80 insertions(+), 42 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
index 1d85af1de7928..6a87a66dc85aa 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
@@ -108,9 +108,7 @@ void kernelBLFit(
     uint32_t offset)
 {
 
-  constexpr uint32_t hitsInFit = N;
-
-  assert(hitsInFit<=nHits);
+  assert(N<=nHits);
 
   assert(results); assert(pfast_fit);
 
@@ -204,21 +202,35 @@ void HelixFitOnGPU::launchBrokenLineKernels(HitsOnCPU const & hh, uint32_t hitsI
              4, offset);
       cudaCheck(cudaGetLastError());
 
-      // fit penta (only first 4)
-      kernelBLFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+      if (fit5as4_) {
+        // fit penta (only first 4)
+        kernelBLFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
           tuples_d, tupleMultiplicity_d, hh.gpu_d,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
           5, offset);
-      cudaCheck(cudaGetLastError());
+        cudaCheck(cudaGetLastError());
 
-      kernelBLFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+        kernelBLFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
              tupleMultiplicity_d, bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
              5, offset);
-      cudaCheck(cudaGetLastError());
+        cudaCheck(cudaGetLastError());
+      } else {
+        // fit penta (all 5)
+        kernelBLFastFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, tupleMultiplicity_d, hh.gpu_d,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
+          5, offset);
+        cudaCheck(cudaGetLastError());
 
+        kernelBLFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tupleMultiplicity_d, bField_, helix_fit_results_d,
+             hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,
+             5, offset);
+        cudaCheck(cudaGetLastError());
+      }
 
-    }
+    } // loop on concurrent fits
 
 
 }
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index 4ada99488ad8a..6b9f24ecfcf0e 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -32,7 +32,9 @@ constexpr unsigned int CAHitQuadrupletGeneratorGPU::minLayers;
 CAHitQuadrupletGeneratorGPU::CAHitQuadrupletGeneratorGPU(
     const edm::ParameterSet &cfg,
     edm::ConsumesCollector &iC) : 
-    kernels(cfg.getParameter<bool>("earlyFishbone"),cfg.getParameter<bool>("lateFishbone")),
+    kernels(cfg.getParameter<unsigned int>("minHitsPerNtuplet"), 
+            cfg.getParameter<bool>("earlyFishbone"),cfg.getParameter<bool>("lateFishbone")),
+    fitter(cfg.getParameter<bool>("fit5as4")),
     caThetaCut(cfg.getParameter<double>("CAThetaCut")),
     caPhiCut(cfg.getParameter<double>("CAPhiCut")),
     caHardPtCut(cfg.getParameter<double>("CAHardPtCut"))
@@ -45,6 +47,8 @@ void CAHitQuadrupletGeneratorGPU::fillDescriptions(edm::ParameterSetDescription
   desc.add<double>("CAHardPtCut", 0);
   desc.add<bool>("earlyFishbone",false);
   desc.add<bool>("lateFishbone",true);
+  desc.add<unsigned int>("minHitsPerNtuplet",4);
+  desc.add<bool>("fit5as4",true);
 }
 
 void CAHitQuadrupletGeneratorGPU::initEvent(edm::Event const& ev, edm::EventSetup const& es) {
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 03ebe08852945..46861dcaaea2c 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -305,7 +305,7 @@ void CAHitQuadrupletGeneratorKernels::launchKernels( // here goes algoparms....
       gpu_.tuples_d,
       gpu_.apc_d,
       device_tupleMultiplicity,
-      3
+      minHitsPerNtuplet_      
   );
   cudaCheck(cudaGetLastError());
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
index b0805c850ff98..91688b1e5dd3f 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
@@ -19,7 +19,9 @@ class CAHitQuadrupletGeneratorKernels {
    using HitToTuple = CAConstants::HitToTuple;
    using TupleMultiplicity = CAConstants::TupleMultiplicity;
 
-   CAHitQuadrupletGeneratorKernels(bool earlyFishbone, bool lateFishbone) :
+   CAHitQuadrupletGeneratorKernels(uint32_t minHitsPerNtuplet,
+    bool earlyFishbone, bool lateFishbone) :
+    minHitsPerNtuplet_(minHitsPerNtuplet),
     earlyFishbone_(earlyFishbone),
     lateFishbone_(lateFishbone){}
    ~CAHitQuadrupletGeneratorKernels() { deallocateOnGPU();}
@@ -49,6 +51,7 @@ class CAHitQuadrupletGeneratorKernels {
     TupleMultiplicity * device_tupleMultiplicity = nullptr;
     uint8_t * device_tmws;    
 
+    const uint32_t minHitsPerNtuplet_;
     const bool earlyFishbone_;
     const bool lateFishbone_;
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index 5c26e4facb37a..2d0819a776a5b 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -117,7 +117,7 @@ class GPUCACell {
 
   
   __device__
-  bool
+  inline bool
   dcaCut(Hits const & hh, GPUCACell const & otherCell,
                        const float region_origin_radius_plus_tolerance,
                        const float maxCurv) const {
@@ -139,10 +139,22 @@ class GPUCACell {
 
   }
 
+  __device__
+  inline bool 
+  hole(Hits const & hh, GPUCACell const & innerCell) const {
+    constexpr float r4 = 16.f;
+    auto ri = innerCell.get_inner_r(hh);
+    auto zi = innerCell.get_inner_z(hh);
+    auto ro = get_outer_r(hh);
+    auto zo = get_outer_z(hh);
+    auto z4 = std::abs(zi + (r4-ri)*(zo-zi)/(ro-ri));
+    return z4>25.f && z4<33.f;
+  }
+
+
   // trying to free the track building process from hardcoded layers, leaving
   // the visit of the graph based on the neighborhood connections between cells.
 
-// #ifdef __CUDACC__
 
   __device__
   inline void find_ntuplets(
@@ -172,24 +184,14 @@ class GPUCACell {
     } else {  // if long enough save...
       if ((unsigned int)(tmpNtuplet.size()) >= minHitsPerNtuplet-1) {
         // triplets accepted only pointing to the hole
-        auto hole = [&]()->bool {
-          constexpr float r4 = 16.f;
-          auto const & c0 = cells[tmpNtuplet[0]];
-          auto ri = c0.get_inner_r(hh);
-          auto zi = c0.get_inner_z(hh);
-          auto ro = get_outer_r(hh);
-          auto zo = get_outer_z(hh);
-          auto z4 = std::abs(zi + (r4-ri)*(zo-zi)/(ro-ri));
-          return z4>25.f && z4<33.f;
-        };
-        if (tmpNtuplet.size()>=3 || hole()) {
+        if (tmpNtuplet.size()>=3 || hole(hh, cells[tmpNtuplet[0]])) {
           hindex_type hits[6]; auto nh=0U;
           for (auto c : tmpNtuplet) hits[nh++] = cells[c].theInnerHitId;
           hits[nh] = theOuterHitId; 
           uint16_t it = foundNtuplets.bulkFill(apc,hits,tmpNtuplet.size()+1);
           for (auto c : tmpNtuplet) cells[c].theTracks.push_back(it);
           tupleMultiplicity.countDirect(tmpNtuplet.size()+1);
-        };
+        }
       }
     }
     tmpNtuplet.pop_back();
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
index 5cc4be08269b9..62e7e2a5fddd7 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/HelixFitOnGPU.h
@@ -41,7 +41,7 @@ class HelixFitOnGPU {
    using TuplesOnGPU = pixelTuplesHeterogeneousProduct::TuplesOnGPU;
    using TupleMultiplicity = CAConstants::TupleMultiplicity;
 
-   HelixFitOnGPU() = default;
+   explicit HelixFitOnGPU(bool fit5as4) : fit5as4_(fit5as4) {}
    ~HelixFitOnGPU() { deallocateOnGPU();}
 
    void setBField(double bField) { bField_ = bField;}
@@ -62,14 +62,15 @@ class HelixFitOnGPU {
     double bField_;
     Rfit::helix_fit * helix_fit_results_d = nullptr;
 
-
-
    // Riemann Fit internals
    double *hitsGPU_ = nullptr;
    float *hits_geGPU_ = nullptr;
    double *fast_fit_resultsGPU_ = nullptr;
    Rfit::circle_fit *circle_fit_resultsGPU_ = nullptr;
 
+    const bool fit5as4_;
+
+
 };
 
 #endif
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
index 3afa0f58326f4..77f2935af7ac7 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
@@ -96,9 +96,7 @@ void kernelCircleFit(
 {
 
   assert(circle_fit); 
-
-  constexpr uint32_t hitsInFit = N;
-  assert(hitsInFit<=nHits);
+  assert(N<=nHits);
 
   // same as above...
 
@@ -147,9 +145,7 @@ void kernelLineFit(
 {
 
   assert(results); assert(circle_fit);
-
-  constexpr uint32_t hitsInFit = N;
-  assert(hitsInFit<=nHits);
+  assert(N<=nHits);
 
   // same as above...
 
@@ -239,24 +235,44 @@ void HelixFitOnGPU::launchRiemannKernels(HitsOnCPU const & hh, uint32_t nhits, u
              offset);
       cudaCheck(cudaGetLastError());
 
+      if (fit5as4_) {
+        // penta
+        kernelFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tuples_d, tupleMultiplicity_d, 5,
+          hh.gpu_d,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
+        cudaCheck(cudaGetLastError());
 
-      // penta
-      kernelFastFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+        kernelCircleFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+          tupleMultiplicity_d, 5, bField_,
+          hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_, offset);
+        cudaCheck(cudaGetLastError());
+
+        kernelLineFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+             tupleMultiplicity_d, 5,  bField_, helix_fit_results_d,
+             hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_,
+             offset);
+        cudaCheck(cudaGetLastError());
+      } else {
+        // penta all 5
+        kernelFastFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
           tuples_d, tupleMultiplicity_d, 5,
           hh.gpu_d,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_,offset);
-      cudaCheck(cudaGetLastError());
+        cudaCheck(cudaGetLastError());
 
-      kernelCircleFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+        kernelCircleFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
           tupleMultiplicity_d, 5, bField_,
           hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_, offset);
-      cudaCheck(cudaGetLastError());
+        cudaCheck(cudaGetLastError());
 
-      kernelLineFit<4><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
+        kernelLineFit<5><<<numberOfBlocks, blockSize, 0, cudaStream>>>(
              tupleMultiplicity_d, 5,  bField_, helix_fit_results_d,
              hitsGPU_, hits_geGPU_, fast_fit_resultsGPU_, circle_fit_resultsGPU_,
              offset);
-      cudaCheck(cudaGetLastError());
+        cudaCheck(cudaGetLastError());
+
+      }
 
     }
 

From c40436c1d4bc0d60044d88bc56fe8a4893016b13 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 8 Feb 2019 15:20:50 +0100
Subject: [PATCH 35/95] fix printout

---
 .../PixelTriplets/plugins/BrokenLineFitOnGPU.cu               | 4 ++--
 RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml        | 2 +-
 .../PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc      | 1 -
 RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu   | 4 ++--
 4 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
index 6a87a66dc85aa..ed4ae56ae11a2 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BrokenLineFitOnGPU.cu
@@ -158,10 +158,10 @@ void kernelBLFit(
 
 #ifdef GPU_DEBUG
   if ( !(circle.chi2>=0) || !(line.chi2>=0) ) printf("kernelBLFit failed! %f/%f\n", helix.chi2_circle,helix.chi2_line);
-  printf("kernelBLFit %d circle.par(0,1,2): %d %f,%f,%f\n", hitsInFit, helix_start,
+  printf("kernelBLFit size %d for %d hits circle.par(0,1,2): %d %f,%f,%f\n", N,nHits, helix_start,
          circle.par(0), circle.par(1), circle.par(2));
   printf("kernelBLHits line.par(0,1): %d %f,%f\n", helix_start, line.par(0),line.par(1));
-  printf("kernelBLHits chi2 cov %f/%f  %f,%f,%f,%f,%f\n",helix.chi2_circle,helix.chi2_line, 
+  printf("kernelBLHits chi2 cov %f/%f  %e,%e,%e,%e,%e\n",helix.chi2_circle,helix.chi2_line, 
          helix.cov(0,0),helix.cov(1,1),helix.cov(2,2),helix.cov(3,3),helix.cov(4,4));
 #endif
 }
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml b/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
index 3c8397cf572f6..9c4de13165232 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
@@ -12,7 +12,7 @@
 <use name="RecoTracker/TkSeedingLayers"/>
 <use name="RecoTracker/TkTrackingRegions"/>
 <flags CXXFLAGS="-g -fno-math-errno"/>
-<flags CUDA_FLAGS="-g"/>
+<flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
 <library file="*.cu *.cc" name="RecoPixelVertexingPixelTripletsPlugins">
   <flags EDM_PLUGIN="1"/>
 </library>
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index 6b9f24ecfcf0e..202f95cb2a8a0 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -217,7 +217,6 @@ CAHitQuadrupletGeneratorGPU::fetchKernelResult(int)
     ++nTuples_;
     ++sizes[sz];
     for (auto j=tuples.begin(i); j!=tuples.end(i); ++j) add(*j);
-    // if (sz<4) continue;
     quadsInterface.emplace_back(std::array<int, 4>());
     quadsInterface.back()[0] = tuples.begin(i)[0];
     quadsInterface.back()[1] = tuples.begin(i)[1];
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
index 77f2935af7ac7..afb6d201b6ca4 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/RiemannFitOnGPU.cu
@@ -181,10 +181,10 @@ void kernelLineFit(
   helix.chi2_line = line_fit.chi2;
 
 #ifdef GPU_DEBUG
-  printf("kernelLineFit size %d for %d hits circle.par(0,1,2): N,nHits, %d %f,%f,%f\n", helix_start,
+  printf("kernelLineFit size %d for %d hits circle.par(0,1,2): %d %f,%f,%f\n", N,nHits, helix_start,
          circle_fit[local_start].par(0), circle_fit[local_start].par(1), circle_fit[local_start].par(2));
   printf("kernelLineFit line.par(0,1): %d %f,%f\n", helix_start, line_fit.par(0),line_fit.par(1));
-  printf("kernelLineFit chi2 cov %f/%f %f,%f,%f,%f,%f\n",helix.chi2_circle,helix.chi2_line, 
+  printf("kernelLineFit chi2 cov %f/%f %e,%e,%e,%e,%e\n",helix.chi2_circle,helix.chi2_line, 
          helix.cov(0,0),helix.cov(1,1),helix.cov(2,2),helix.cov(3,3),helix.cov(4,4));
 #endif
 }

From cb2ffbd7dd1cb8337dcc7516f0f7c0b0c36d6dfe Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sat, 9 Feb 2019 18:35:30 +0100
Subject: [PATCH 36/95] remove debug

---
 RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml         | 2 +-
 .../PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc       | 3 ---
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml b/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
index 9c4de13165232..3c8397cf572f6 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
+++ b/RecoPixelVertexing/PixelTriplets/plugins/BuildFile.xml
@@ -12,7 +12,7 @@
 <use name="RecoTracker/TkSeedingLayers"/>
 <use name="RecoTracker/TkTrackingRegions"/>
 <flags CXXFLAGS="-g -fno-math-errno"/>
-<flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
+<flags CUDA_FLAGS="-g"/>
 <library file="*.cu *.cc" name="RecoPixelVertexingPixelTripletsPlugins">
   <flags EDM_PLUGIN="1"/>
 </library>
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index 202f95cb2a8a0..f498dc8b674b4 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -16,9 +16,6 @@
 
 #include "CAHitQuadrupletGeneratorGPU.h"
 
-
-#define GPU_DEBUG
-
 namespace {
 
   template <typename T> T sqr(T x) { return x * x; }

From 7ea5f285724ea9d59e784e5571cf35a296ea2db4 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Feb 2019 15:41:00 +0100
Subject: [PATCH 37/95] add ability to count locally

---
 .../CUDAUtilities/interface/HistoContainer.h  | 14 ++++++
 .../CUDAUtilities/test/OneToManyAssoc_t.cu    | 48 +++++++++++++++++++
 2 files changed, 62 insertions(+)

diff --git a/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h b/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
index 61ccc7484d68b..a721e440a475d 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
@@ -134,6 +134,8 @@ class HistoContainer {
   using Counter = std::atomic<uint32_t>;
 #endif
 
+  using CountersOnly = HistoContainer<T,NBINS,0,S,I,NHISTS>;
+
   using index_type = I;
   using UT = typename std::make_unsigned<T>::type;
 
@@ -178,11 +180,23 @@ class HistoContainer {
     return (t >> shift) & mask;
   }
 
+  __host__ __device__
   void zero() {
     for (auto & i : off)
       i = 0;
   }
 
+  __host__ __device__
+  void add(CountersOnly const & co) {
+    for (uint32_t i = 0; i<totbins(); ++i) {
+      #ifdef __CUDA_ARCH__
+      atomicAdd(off+i,co.off[i]);
+      #else
+      off[i]+=co.off[i]; 
+      #endif
+    }
+  }
+
   static __host__ __device__
   __forceinline__
   uint32_t atomicIncrement(Counter & x) {
diff --git a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
index 6d9cfe69b9489..f391889ee12db 100644
--- a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
+++ b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
@@ -15,8 +15,39 @@ constexpr uint32_t MaxTk=8000;
 constexpr uint32_t MaxAssocs = 4*MaxTk;
 using Assoc = OneToManyAssoc<uint16_t,MaxElem,MaxAssocs>;
 
+using Multiplicity = OneToManyAssoc<uint16_t,8,MaxTk>;
+
 using TK = std::array<uint16_t,4>;
 
+__global__
+void countMultiLocal(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, uint32_t n) {
+   auto i = blockIdx.x * blockDim.x + threadIdx.x;
+   if (i>=n) return;
+
+   __shared__ Multiplicity::CountersOnly local;
+   if (threadIdx.x==0) local.zero();
+   __syncthreads();
+   local.countDirect(4);
+   __syncthreads();
+   if (threadIdx.x==0) assoc->add(local);
+}
+
+
+__global__
+void countMulti(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, uint32_t n) {
+   auto i = blockIdx.x * blockDim.x + threadIdx.x;
+   if (i>=n) return;
+   assoc->countDirect(4);
+}
+
+
+__global__
+void verifyMulti(Multiplicity * __restrict__ m1, Multiplicity * __restrict__ m2) {
+   auto i = blockIdx.x * blockDim.x + threadIdx.x;
+   if (i>=Multiplicity::totbins() ) return;
+   assert(m1->off[i]==m2->off[i]);
+}
+
 __global__ 
 void count(TK const * __restrict__ tk, Assoc * __restrict__ assoc, uint32_t n) {
    auto i = blockIdx.x * blockDim.x + threadIdx.x;
@@ -155,6 +186,23 @@ int main() {
   assert(0==la.size(N));
   std::cout << "found with ave occupancy " << double(ave)/N <<' '<< imax << std::endl;
 
+
+  // here verify use of block local counters
+  auto m1_d = cuda::memory::device::make_unique<Multiplicity[]>(current_device,1);
+  auto m2_d = cuda::memory::device::make_unique<Multiplicity[]>(current_device,1);
+  cudautils::launchZero(m1_d.get(),0);
+  cudautils::launchZero(m2_d.get(),0);
+
+  nBlocks = (4*N + nThreads - 1) / nThreads;
+  countMulti<<<nBlocks,nThreads>>>(v_d.get(),m1_d.get(),N);
+  countMultiLocal<<<nBlocks,nThreads>>>(v_d.get(),m2_d.get(),N);
+  verifyMulti<<<1,Multiplicity::totbins()>>>(m1_d.get(),m2_d.get());
+
+  cudautils::launchFinalize(m1_d.get(),ws_d.get(),0);
+  cudautils::launchFinalize(m2_d.get(),ws_d.get(),0);
+  verifyMulti<<<1,Multiplicity::totbins()>>>(m1_d.get(),m2_d.get());
+
+
   cudaCheck(cudaGetLastError());
   cudaCheck(cudaDeviceSynchronize());
   return 0;

From 294a4476c31df38a7c53779c49d19ba6c38b58d3 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Feb 2019 16:19:02 +0100
Subject: [PATCH 38/95] use local counters

---
 .../plugins/CAHitQuadrupletGeneratorKernels.cu           | 9 +++++++++
 RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h     | 4 ++--
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 46861dcaaea2c..fb31e22d072c4 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -185,9 +185,18 @@ void kernel_find_ntuplets(
   if (thisCell.theLayerPairId!=0 && thisCell.theLayerPairId!=3 && thisCell.theLayerPairId!=8) return; // inner layer is 0 FIXME
   GPUCACell::TmpTuple stack;
   stack.reset();
+
+  __shared__ GPUCACell::TupleMultiplicity::CountersOnly local;
+  if (0==threadIdx.x) local.zero();
+  __syncthreads();
+
   thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, *tupleMultiplicity, stack, minHitsPerNtuplet);
   assert(stack.size()==0);
   // printf("in %d found quadruplets: %d\n", cellIndex, apc->get());
+  __syncthreads();
+ 
+  if (0==threadIdx.x) tupleMultiplicity->add(local);
+  
 }
 
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index 2d0819a776a5b..c9abaae27ab8b 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -155,14 +155,14 @@ class GPUCACell {
   // trying to free the track building process from hardcoded layers, leaving
   // the visit of the graph based on the neighborhood connections between cells.
 
-
+  template<typename CM>
   __device__
   inline void find_ntuplets(
       Hits const & hh,
       GPUCACell * __restrict__ cells,
       TuplesOnGPU::Container & foundNtuplets, 
       AtomicPairCounter & apc,
-      TupleMultiplicity & tupleMultiplicity,
+      CM & tupleMultiplicity,
       TmpTuple & tmpNtuplet,
       const unsigned int minHitsPerNtuplet) const
   {

From e9dee7bcd21e981ae97ef93a9f5ef76524d733a3 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Feb 2019 16:43:40 +0100
Subject: [PATCH 39/95] update local for real

---
 .../PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index fb31e22d072c4..65108fd6d6bf7 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -190,7 +190,7 @@ void kernel_find_ntuplets(
   if (0==threadIdx.x) local.zero();
   __syncthreads();
 
-  thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, *tupleMultiplicity, stack, minHitsPerNtuplet);
+  thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, local, stack, minHitsPerNtuplet);
   assert(stack.size()==0);
   // printf("in %d found quadruplets: %d\n", cellIndex, apc->get());
   __syncthreads();

From 72743280d0638596b8ff5f1faa47c95e569654a1 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Feb 2019 16:57:23 +0100
Subject: [PATCH 40/95] avoid early return

---
 .../CUDAUtilities/test/OneToManyAssoc_t.cu        |  4 ++--
 .../plugins/CAHitQuadrupletGeneratorKernels.cu    | 15 ++++++++-------
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
index f391889ee12db..c175acacb2ccc 100644
--- a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
+++ b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
@@ -27,7 +27,7 @@ void countMultiLocal(TK const * __restrict__ tk, Multiplicity * __restrict__ ass
    __shared__ Multiplicity::CountersOnly local;
    if (threadIdx.x==0) local.zero();
    __syncthreads();
-   local.countDirect(4);
+   local.countDirect(2+i%4);
    __syncthreads();
    if (threadIdx.x==0) assoc->add(local);
 }
@@ -37,7 +37,7 @@ __global__
 void countMulti(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, uint32_t n) {
    auto i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i>=n) return;
-   assoc->countDirect(4);
+   assoc->countDirect(2+i%4);
 }
 
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 65108fd6d6bf7..5c646c8d358cb 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -182,21 +182,22 @@ void kernel_find_ntuplets(
   auto cellIndex = threadIdx.x + blockIdx.x * blockDim.x;
   if (cellIndex >= (*nCells) ) return;
   auto &thisCell = cells[cellIndex];
-  if (thisCell.theLayerPairId!=0 && thisCell.theLayerPairId!=3 && thisCell.theLayerPairId!=8) return; // inner layer is 0 FIXME
-  GPUCACell::TmpTuple stack;
-  stack.reset();
 
   __shared__ GPUCACell::TupleMultiplicity::CountersOnly local;
   if (0==threadIdx.x) local.zero();
   __syncthreads();
 
-  thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, local, stack, minHitsPerNtuplet);
-  assert(stack.size()==0);
-  // printf("in %d found quadruplets: %d\n", cellIndex, apc->get());
+  if (thisCell.theLayerPairId==0 || thisCell.theLayerPairId==3 || thisCell.theLayerPairId==8) { // inner layer is 0 FIXME
+    GPUCACell::TmpTuple stack;
+    stack.reset();
+    thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, local, stack, minHitsPerNtuplet);
+    assert(stack.size()==0);
+    // printf("in %d found quadruplets: %d\n", cellIndex, apc->get());
+  }
   __syncthreads();
  
   if (0==threadIdx.x) tupleMultiplicity->add(local);
-  
+
 }
 
 

From 5dd17ccf04248f5509a90d716b1a1d8f966759b5 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 11 Feb 2019 11:46:54 +0100
Subject: [PATCH 41/95] back to global

---
 .../plugins/CAHitQuadrupletGeneratorKernels.cu  | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 5c646c8d358cb..7ab74b3656054 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -183,21 +183,30 @@ void kernel_find_ntuplets(
   if (cellIndex >= (*nCells) ) return;
   auto &thisCell = cells[cellIndex];
 
+#ifdef CA_USE_LOCAL_COUNTERS
   __shared__ GPUCACell::TupleMultiplicity::CountersOnly local;
   if (0==threadIdx.x) local.zero();
   __syncthreads();
+#endif
 
   if (thisCell.theLayerPairId==0 || thisCell.theLayerPairId==3 || thisCell.theLayerPairId==8) { // inner layer is 0 FIXME
     GPUCACell::TmpTuple stack;
     stack.reset();
-    thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, local, stack, minHitsPerNtuplet);
+    thisCell.find_ntuplets(hh, cells, *foundNtuplets, *apc, 
+                           #ifdef CA_USE_LOCAL_COUNTERS
+                           local,
+                           #else
+                           *tupleMultiplicity,
+                           #endif 
+                           stack, minHitsPerNtuplet);
     assert(stack.size()==0);
     // printf("in %d found quadruplets: %d\n", cellIndex, apc->get());
   }
-  __syncthreads();
- 
-  if (0==threadIdx.x) tupleMultiplicity->add(local);
 
+#ifdef CA_USE_LOCAL_COUNTERS
+  __syncthreads(); 
+  if (0==threadIdx.x) tupleMultiplicity->add(local);
+#endif
 }
 
 

From 9aafe7c48f1673da78b591c21eac21f1ef6debca Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 14 Feb 2019 11:06:00 +0100
Subject: [PATCH 42/95] on gpu works

---
 .../interface/AtomicPairCounter.h             | 12 +--
 .../CUDAUtilities/interface/HistoContainer.h  | 88 ++++++++++---------
 .../CUDAUtilities/interface/cudaCompat.h      | 76 ++++++++++++++++
 .../CUDAUtilities/interface/prefixScan.h      | 14 ++-
 .../CUDAUtilities/test/OneToManyAssoc_t.cu    | 70 ++++++++-------
 5 files changed, 176 insertions(+), 84 deletions(-)
 create mode 100644 HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h

diff --git a/HeterogeneousCore/CUDAUtilities/interface/AtomicPairCounter.h b/HeterogeneousCore/CUDAUtilities/interface/AtomicPairCounter.h
index 425bc1ef8a701..63fc3872c8140 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/AtomicPairCounter.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/AtomicPairCounter.h
@@ -1,7 +1,7 @@
 #ifndef HeterogeneousCoreCUDAUtilitiesAtomicPairCounter_H
 #define HeterogeneousCoreCUDAUtilitiesAtomicPairCounter_H
 
-#include <cuda_runtime.h>
+#include "HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h"
 #include <cstdint>
 
 class AtomicPairCounter {
@@ -25,24 +25,26 @@ class AtomicPairCounter {
     c_type ac;
   };
 
-#ifdef __CUDACC__
-
   static constexpr c_type incr = 1UL<<32;
 
   __device__ __host__
   Counters get() const { return counter.counters;}
 
   // increment n by 1 and m by i.  return previous value
-  __device__
+  __host__ __device__
+  __forceinline__
   Counters add(uint32_t i) {
     c_type c = i; 
     c+=incr;
     Atomic2 ret;
+    #ifdef __CUDA_ARCH__
     ret.ac = atomicAdd(&counter.ac,c);
+    #else
+    ret.ac=counter.ac; counter.ac+=c;
+    #endif
     return ret.counters;
   }
 
-#endif
 
 private:
 
diff --git a/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h b/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
index a721e440a475d..7073210d8745e 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
@@ -16,52 +16,59 @@
 #include "HeterogeneousCore/CUDAUtilities/interface/cudastdAlgorithm.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
-#ifdef __CUDACC__
 #include "HeterogeneousCore/CUDAUtilities/interface/prefixScan.h"
-#endif
 #include "HeterogeneousCore/CUDAUtilities/interface/AtomicPairCounter.h"
 
 
-#ifdef __CUDACC__
 namespace cudautils {
 
   template<typename Histo, typename T>
   __global__
   void countFromVector(Histo * __restrict__ h,  uint32_t nh, T const * __restrict__ v, uint32_t const * __restrict__ offsets) {
-     auto i = blockIdx.x * blockDim.x + threadIdx.x;
-     if(i >= offsets[nh]) return;
-     auto off = cuda_std::upper_bound(offsets, offsets + nh + 1, i);
-     assert((*off) > 0);
-     int32_t ih = off - offsets - 1;
-     assert(ih >= 0);
-     assert(ih < nh);
-     (*h).count(v[i], ih);
+     int first = blockDim.x * blockIdx.x + threadIdx.x;
+     for (int i = first; i < offsets[nh]; i += gridDim.x*blockDim.x) {
+       auto off = cuda_std::upper_bound(offsets, offsets + nh + 1, i);
+       assert((*off) > 0);
+       int32_t ih = off - offsets - 1;
+       assert(ih >= 0);
+       assert(ih < nh);
+       (*h).count(v[i], ih);
+     }
   }
 
   template<typename Histo, typename T>
   __global__
   void fillFromVector(Histo * __restrict__ h,  uint32_t nh, T const * __restrict__ v, uint32_t const * __restrict__ offsets) {
-     auto i = blockIdx.x * blockDim.x + threadIdx.x;
-     if(i >= offsets[nh]) return;
-     auto off = cuda_std::upper_bound(offsets, offsets + nh + 1, i);
-     assert((*off) > 0);
-     int32_t ih = off - offsets - 1;
-     assert(ih >= 0);
-     assert(ih < nh);
-     (*h).fill(v[i], i, ih);
+     int first = blockDim.x * blockIdx.x + threadIdx.x;
+     for (int i = first; i < offsets[nh]; i += gridDim.x*blockDim.x) {
+       auto off = cuda_std::upper_bound(offsets, offsets + nh + 1, i);
+       assert((*off) > 0);
+       int32_t ih = off - offsets - 1;
+       assert(ih >= 0);
+       assert(ih < nh);
+       (*h).fill(v[i], i, ih);
+     }
   }
 
   template<typename Histo>
-  void launchZero(Histo * __restrict__ h, cudaStream_t stream) {
+  void launchZero(Histo * __restrict__ h, cudaStream_t stream = 0) {
     uint32_t * off = (uint32_t *)( (char*)(h) +offsetof(Histo,off));
+#ifdef __CUDACC__
     cudaMemsetAsync(off,0, 4*Histo::totbins(),stream);
+#else
+    ::memset(off,0, 4*Histo::totbins());
+#endif
   }
 
   template<typename Histo>
-  void launchFinalize(Histo * __restrict__ h, uint8_t *  __restrict__ ws, cudaStream_t stream) {
+  void launchFinalize(Histo * __restrict__ h, uint8_t *  __restrict__ ws=nullptr, cudaStream_t stream=0) {
     uint32_t * off = (uint32_t *)( (char*)(h) +offsetof(Histo,off));
+#ifdef __CUDACC__
     size_t wss = Histo::wsSize();
     CubDebugExit(cub::DeviceScan::InclusiveSum(ws, wss, off, off, Histo::totbins(), stream));
+#else
+    h.finalize();
+#endif
   }
 
 
@@ -69,13 +76,19 @@ namespace cudautils {
   void fillManyFromVector(Histo * __restrict__ h, uint8_t *  __restrict__ ws,  
                           uint32_t nh, T const * __restrict__ v, uint32_t const * __restrict__ offsets, uint32_t totSize, 
                           int nthreads, cudaStream_t stream) {
-    launchZero(h,stream); 
+    launchZero(h,stream);
+#ifdef __CUDACC__
     auto nblocks = (totSize + nthreads - 1) / nthreads;
     countFromVector<<<nblocks, nthreads, 0, stream>>>(h, nh, v, offsets);
     cudaCheck(cudaGetLastError());
     launchFinalize(h,ws,stream);
     fillFromVector<<<nblocks, nthreads, 0, stream>>>(h, nh, v, offsets);
     cudaCheck(cudaGetLastError());
+#else
+    countFromVector(h, nh, v, offsets);
+    h.finalize();
+    fillFromVector(h, nh, v, offsets);
+#endif
   }
 
 
@@ -86,7 +99,7 @@ namespace cudautils {
   }
 
 } // namespace cudautils
-#endif
+
 
 
 // iteratate over N bins left and right of the one containing "v"
@@ -234,8 +247,7 @@ class HistoContainer {
   }
 
 
-#ifdef __CUDACC__
-  __device__
+  __device__ __host__
   __forceinline__
   uint32_t bulkFill(AtomicPairCounter & apc, index_type const * v, uint32_t n) {
     auto c = apc.add(n);
@@ -244,26 +256,24 @@ class HistoContainer {
     return c.m;
   }
 
-  __device__
+  __device__ __host__
   __forceinline__
   void bulkFinalize(AtomicPairCounter const & apc) {
      off[apc.get().m]=apc.get().n;
   }
 
-  __device__
+  __device__ __host__
   __forceinline__
   void bulkFinalizeFill(AtomicPairCounter const & apc) {
      auto m = apc.get().m;
      auto n = apc.get().n;
-     auto i = m + blockIdx.x * blockDim.x + threadIdx.x;
-     if (i>=totbins()) return;
-     off[i]=n;
+     int first = m + blockDim.x * blockIdx.x + threadIdx.x;
+     for (int i = first; i < totbins(); i += gridDim.x*blockDim.x) {
+       off[i]=n;
+     }
   }
 
 
-#endif
-
-
   __host__ __device__
   __forceinline__
   void count(T t) {
@@ -305,21 +315,13 @@ class HistoContainer {
     bins[w-1] = j;
   }
 
-#ifdef __CUDACC__
-  __device__
+  __device__ __host__
   __forceinline__
-  void finalize(Counter * ws) {
+  void finalize(Counter * ws=nullptr) {
     assert(off[totbins()-1]==0);
     blockPrefixScan(off,totbins(),ws);
     assert(off[totbins()-1]==off[totbins()-2]);
   }
-  __host__
-#endif
-  void finalize() {
-    assert(off[totbins()-1]==0);
-    for(uint32_t i=1; i<totbins(); ++i) off[i]+=off[i-1];
-    assert(off[totbins()-1]==off[totbins()-2]);
-  }
 
   constexpr auto size() const { return uint32_t(off[totbins()-1]);}
   constexpr auto size(uint32_t b) const { return off[b+1]-off[b];}
diff --git a/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h b/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h
new file mode 100644
index 0000000000000..be693980f021c
--- /dev/null
+++ b/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h
@@ -0,0 +1,76 @@
+#ifndef HeterogeneousCore_CUDAUtilities_cudaCompat_H
+#define HeterogeneousCore_CUDAUtilities_cudaCompat_H
+
+/*
+ * Everything you need to run cuda code in plain sequential c++ code
+ */
+
+
+
+
+// #ifndef __CUDA_ARCH__
+#ifndef __CUDACC__
+
+#include<cstdint>
+#include<algorithm>
+#include<cstring>
+
+#include "cuda_runtime.h"
+
+namespace cudaCompat {
+
+#ifndef __CUDA_RUNTIME_H__
+  struct dim3{uint32_t x,y,z;};
+#endif
+  const dim3 threadIdx = {0,0,0};
+  const dim3 blockDim = {1,1,1};
+  thread_local dim3 blockIdx = {0,0,0};
+  thread_local dim3 gridDim = {1,1,1};
+
+
+
+ template<typename T1, typename T2>
+ T1  atomicInc(T1* a, T2 b) {auto ret=*a; if ((*a)<b) (*a)++; return ret;}
+
+  
+  template<typename T1, typename T2>
+  T1  atomicAdd(T1* a, T2 b) {auto ret=*a; (*a) +=b; return ret;}
+
+  template<typename T1, typename T2>
+  T1  atomicSub(T1* a, T2 b) {auto ret=*a; (*a) -=b; return ret;}
+
+
+  template<typename T1, typename T2>
+  T1  atomicMin(T1* a, T2 b) {auto ret=*a; *a = std::min(*a,b);return ret;}
+  template<typename T1, typename T2>
+  T1  atomicMax(T1* a, T2 b) {auto ret=*a; a = std::max(*a,b);return ret;}
+
+  
+  void __syncthreads(){}
+  bool __syncthreads_or(bool x) { return x;}
+  bool __syncthreads_and(bool x) { return x;}
+
+  void resetGrid() {
+    blockIdx = {0,0,0};
+    gridDim = {1,1,1};
+  }
+  
+}
+
+#ifndef __CUDA_RUNTIME_H__
+#define __host__
+#define __device__
+#define __global__
+#define __shared__
+#define __forceinline__
+#endif
+
+
+#ifndef __CUDA_ARCH__
+using namespace cudaCompat;
+#endif
+
+#endif
+
+
+#endif
diff --git a/HeterogeneousCore/CUDAUtilities/interface/prefixScan.h b/HeterogeneousCore/CUDAUtilities/interface/prefixScan.h
index acad1e2cabdc1..46291d5c6796a 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/prefixScan.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/prefixScan.h
@@ -3,8 +3,12 @@
 
 #include <cstdint>
 
+
+#include "HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
 
+
+#ifdef __CUDA_ARCH__
 template<typename T>
 __device__
 void 
@@ -19,16 +23,17 @@ warpPrefixScan(T * c, uint32_t i, uint32_t mask) {
    }
    c[i] = x;
 }
+#endif
 
 // limited to 32*32 elements....
 template<typename T>
-__device__
+__device__ __host__
 void
 __forceinline__
-blockPrefixScan(T * c, uint32_t size, T* ws) {
+blockPrefixScan(T * c, uint32_t size, T* ws=nullptr) {
+#ifdef __CUDA_ARCH__
   assert(size<=1024);
   assert(0==blockDim.x%32);
-
   auto first = threadIdx.x;
   auto mask = __ballot_sync(0xffffffff,first<size);
 
@@ -49,6 +54,9 @@ blockPrefixScan(T * c, uint32_t size, T* ws) {
     c[i]+=ws[warpId-1];
   }
   __syncthreads();
+#else
+  for(uint32_t i=1; i<size; ++i) c[i]+=c[i-1];
+#endif
 }
 
 
diff --git a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
index c175acacb2ccc..f1ff9edc896e7 100644
--- a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
+++ b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
@@ -21,54 +21,57 @@ using TK = std::array<uint16_t,4>;
 
 __global__
 void countMultiLocal(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, uint32_t n) {
-   auto i = blockIdx.x * blockDim.x + threadIdx.x;
-   if (i>=n) return;
-
-   __shared__ Multiplicity::CountersOnly local;
-   if (threadIdx.x==0) local.zero();
-   __syncthreads();
-   local.countDirect(2+i%4);
-   __syncthreads();
-   if (threadIdx.x==0) assoc->add(local);
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < n; i += gridDim.x*blockDim.x) {
+
+     __shared__ Multiplicity::CountersOnly local;
+     if (threadIdx.x==0) local.zero();
+     __syncthreads();
+     local.countDirect(2+i%4);
+     __syncthreads();
+     if (threadIdx.x==0) assoc->add(local);
+  }
 }
 
-
 __global__
 void countMulti(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, uint32_t n) {
-   auto i = blockIdx.x * blockDim.x + threadIdx.x;
-   if (i>=n) return;
-   assoc->countDirect(2+i%4);
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < n; i += gridDim.x*blockDim.x)
+     assoc->countDirect(2+i%4);
 }
 
 
 __global__
 void verifyMulti(Multiplicity * __restrict__ m1, Multiplicity * __restrict__ m2) {
-   auto i = blockIdx.x * blockDim.x + threadIdx.x;
-   if (i>=Multiplicity::totbins() ) return;
-   assert(m1->off[i]==m2->off[i]);
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < Multiplicity::totbins(); i += gridDim.x*blockDim.x)
+     assert(m1->off[i]==m2->off[i]);
 }
 
 __global__ 
 void count(TK const * __restrict__ tk, Assoc * __restrict__ assoc, uint32_t n) {
-   auto i = blockIdx.x * blockDim.x + threadIdx.x;
-   auto k = i/4;
-   auto j = i - 4*k;
-   assert(j<4);
-   if (k>=n) return;
-   if (tk[k][j]<MaxElem)
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < 4*n; i += gridDim.x*blockDim.x) {
+     auto k = i/4;
+     auto j = i - 4*k;
+     assert(j<4);
+     if (k>=n) return;
+     if (tk[k][j]<MaxElem)
      assoc->countDirect(tk[k][j]);
+   }
 }
 
 __global__
 void fill(TK const * __restrict__ tk, Assoc * __restrict__ assoc, uint32_t n) {
-   auto i = blockIdx.x * blockDim.x + threadIdx.x;
-   auto    k = i/4;
-   auto    j = i - 4*k;
-   assert(j<4);
-   if (k>=n) return;
-   if (tk[k][j]<MaxElem)
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < 4*n; i += gridDim.x*blockDim.x) {
+     auto    k = i/4;
+     auto    j = i - 4*k;
+     assert(j<4);
+     if (k>=n) return;
+     if (tk[k][j]<MaxElem)
      assoc->fillDirect(tk[k][j],k);
-
+   }
 }
 
 __global__
@@ -78,10 +81,11 @@ void verify(Assoc * __restrict__ assoc) {
 
 __global__
 void fillBulk(AtomicPairCounter * apc, TK const * __restrict__ tk, Assoc * __restrict__ assoc, uint32_t n) {
-   auto k = blockIdx.x * blockDim.x + threadIdx.x;
-   if (k>=n) return;
-   auto m = tk[k][3]<MaxElem ? 4 : 3;
-   assoc->bulkFill(*apc,&tk[k][0],m);
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int k = first; k < n; k += gridDim.x*blockDim.x) {
+     auto m = tk[k][3]<MaxElem ? 4 : 3;
+     assoc->bulkFill(*apc,&tk[k][0],m);
+   }
 }
 
 int main() {

From 61361ec41a5026f639b5fc68df2660ce035c6821 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 14 Feb 2019 11:34:17 +0100
Subject: [PATCH 43/95] compiles

---
 .../CUDAUtilities/interface/HistoContainer.h  |  16 +-
 .../CUDAUtilities/test/BuildFile.xml          |   4 +
 .../CUDAUtilities/test/OneToManyAssoc_t.cu    | 213 +-----------------
 3 files changed, 14 insertions(+), 219 deletions(-)

diff --git a/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h b/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
index 7073210d8745e..ac2f8d3b8013b 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
@@ -62,12 +62,12 @@ namespace cudautils {
 
   template<typename Histo>
   void launchFinalize(Histo * __restrict__ h, uint8_t *  __restrict__ ws=nullptr, cudaStream_t stream=0) {
-    uint32_t * off = (uint32_t *)( (char*)(h) +offsetof(Histo,off));
 #ifdef __CUDACC__
+    uint32_t * off = (uint32_t *)( (char*)(h) +offsetof(Histo,off));
     size_t wss = Histo::wsSize();
     CubDebugExit(cub::DeviceScan::InclusiveSum(ws, wss, off, off, Histo::totbins(), stream));
 #else
-    h.finalize();
+    h->finalize();
 #endif
   }
 
@@ -175,16 +175,18 @@ class HistoContainer {
 
   static constexpr auto histOff(uint32_t nh) { return NBINS*nh; }
 
-#ifdef __CUDACC__
   __host__
   static size_t wsSize() {
+#ifdef __CUDACC__
     uint32_t * v =nullptr;
     void * d_temp_storage = nullptr;
     size_t  temp_storage_bytes = 0;
     cub::DeviceScan::InclusiveSum(d_temp_storage, temp_storage_bytes, v, v, totbins());
     return temp_storage_bytes;
-  }
+#else
+    return 0;
 #endif
+  }
 
 
   static constexpr UT bin(T t) {
@@ -252,7 +254,7 @@ class HistoContainer {
   uint32_t bulkFill(AtomicPairCounter & apc, index_type const * v, uint32_t n) {
     auto c = apc.add(n);
     off[c.m] = c.n;
-    for(int j=0; j<n; ++j) bins[c.n+j]=v[j];
+    for(uint32_t j=0; j<n; ++j) bins[c.n+j]=v[j];
     return c.m;
   }
 
@@ -267,8 +269,8 @@ class HistoContainer {
   void bulkFinalizeFill(AtomicPairCounter const & apc) {
      auto m = apc.get().m;
      auto n = apc.get().n;
-     int first = m + blockDim.x * blockIdx.x + threadIdx.x;
-     for (int i = first; i < totbins(); i += gridDim.x*blockDim.x) {
+     auto first = m + blockDim.x * blockIdx.x + threadIdx.x;
+     for (auto i = first; i < totbins(); i += gridDim.x*blockDim.x) {
        off[i]=n;
      }
   }
diff --git a/HeterogeneousCore/CUDAUtilities/test/BuildFile.xml b/HeterogeneousCore/CUDAUtilities/test/BuildFile.xml
index d3f8d77a35d32..c9862eb55b263 100644
--- a/HeterogeneousCore/CUDAUtilities/test/BuildFile.xml
+++ b/HeterogeneousCore/CUDAUtilities/test/BuildFile.xml
@@ -61,6 +61,10 @@
   <flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
 </bin>
 
+<bin file="OneToManyAssoc_t.cpp" name="cpuOneToManyAssoc_t">
+  <flags CXXFLAGS="-g -DGPU_DEBUG"/>
+</bin>
+
 <bin file="prefixScan_t.cu" name="gpuPrefixScan_t">
   <use name="cub"/>
   <flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
diff --git a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
index f1ff9edc896e7..3a1ee5b1fb240 100644
--- a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
+++ b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cu
@@ -1,213 +1,2 @@
-#include <algorithm>
-#include <cassert>
-#include <iostream>
-#include <random>
-#include <limits>
-#include <array>
+#include "OneToManyAssoc_t.h"
 
-#include <cuda/api_wrappers.h>
-
-#include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
-
-constexpr uint32_t MaxElem=64000;
-constexpr uint32_t MaxTk=8000;
-constexpr uint32_t MaxAssocs = 4*MaxTk;
-using Assoc = OneToManyAssoc<uint16_t,MaxElem,MaxAssocs>;
-
-using Multiplicity = OneToManyAssoc<uint16_t,8,MaxTk>;
-
-using TK = std::array<uint16_t,4>;
-
-__global__
-void countMultiLocal(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, uint32_t n) {
-   int first = blockDim.x * blockIdx.x + threadIdx.x;
-   for (int i = first; i < n; i += gridDim.x*blockDim.x) {
-
-     __shared__ Multiplicity::CountersOnly local;
-     if (threadIdx.x==0) local.zero();
-     __syncthreads();
-     local.countDirect(2+i%4);
-     __syncthreads();
-     if (threadIdx.x==0) assoc->add(local);
-  }
-}
-
-__global__
-void countMulti(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, uint32_t n) {
-   int first = blockDim.x * blockIdx.x + threadIdx.x;
-   for (int i = first; i < n; i += gridDim.x*blockDim.x)
-     assoc->countDirect(2+i%4);
-}
-
-
-__global__
-void verifyMulti(Multiplicity * __restrict__ m1, Multiplicity * __restrict__ m2) {
-   int first = blockDim.x * blockIdx.x + threadIdx.x;
-   for (int i = first; i < Multiplicity::totbins(); i += gridDim.x*blockDim.x)
-     assert(m1->off[i]==m2->off[i]);
-}
-
-__global__ 
-void count(TK const * __restrict__ tk, Assoc * __restrict__ assoc, uint32_t n) {
-   int first = blockDim.x * blockIdx.x + threadIdx.x;
-   for (int i = first; i < 4*n; i += gridDim.x*blockDim.x) {
-     auto k = i/4;
-     auto j = i - 4*k;
-     assert(j<4);
-     if (k>=n) return;
-     if (tk[k][j]<MaxElem)
-     assoc->countDirect(tk[k][j]);
-   }
-}
-
-__global__
-void fill(TK const * __restrict__ tk, Assoc * __restrict__ assoc, uint32_t n) {
-   int first = blockDim.x * blockIdx.x + threadIdx.x;
-   for (int i = first; i < 4*n; i += gridDim.x*blockDim.x) {
-     auto    k = i/4;
-     auto    j = i - 4*k;
-     assert(j<4);
-     if (k>=n) return;
-     if (tk[k][j]<MaxElem)
-     assoc->fillDirect(tk[k][j],k);
-   }
-}
-
-__global__
-void verify(Assoc * __restrict__ assoc) {
-   assert(assoc->size()<Assoc::capacity());
-}
-
-__global__
-void fillBulk(AtomicPairCounter * apc, TK const * __restrict__ tk, Assoc * __restrict__ assoc, uint32_t n) {
-   int first = blockDim.x * blockIdx.x + threadIdx.x;
-   for (int k = first; k < n; k += gridDim.x*blockDim.x) {
-     auto m = tk[k][3]<MaxElem ? 4 : 3;
-     assoc->bulkFill(*apc,&tk[k][0],m);
-   }
-}
-
-int main() {
-  exitSansCUDADevices();
-
-  std::cout << "OneToManyAssoc " << Assoc::nbins() << ' ' << Assoc::capacity() << ' '<< Assoc::wsSize() << std::endl;
-
-  auto current_device = cuda::device::current::get();
-
-  std::mt19937 eng;
-
-  std::geometric_distribution<int> rdm(0.8);
-
-  constexpr uint32_t N = 4000;
-
-  std::vector<std::array<uint16_t,4>> tr(N);
-
-  // fill with "index" to element
-  long long ave=0;
-  int imax=0;
-  auto n=0U;
-  auto z=0U;
-  auto nz=0U;
-  for (auto i=0U; i<4U; ++i) {
-    auto j=0U;
-    while(j<N && n<MaxElem) {
-      if (z==11) { ++n; z=0; ++nz; continue;} // a bit of not assoc 
-      auto x = rdm(eng);
-      auto k = std::min(j+x+1,N);
-      if (i==3 && z==3) { // some triplets time to time
-        for (;j<k; ++j) tr[j][i] = MaxElem+1;
-      } else {
-        ave+=x+1;
-        imax = std::max(imax,x);
-        for (;j<k; ++j) tr[j][i] = n;
-        ++n; 
-      }
-      ++z;
-    }
-    assert(n<=MaxElem);
-    assert(j<=N);
-  }
-  std::cout << "filled with "<< n << " elements " << double(ave)/n <<' '<< imax << ' ' << nz << std::endl;
-
-  auto v_d = cuda::memory::device::make_unique<std::array<uint16_t,4>[]>(current_device, N);
-  assert(v_d.get());
-  auto a_d = cuda::memory::device::make_unique<Assoc[]>(current_device,1);
-  auto ws_d = cuda::memory::device::make_unique<uint8_t[]>(current_device, Assoc::wsSize());
-
-  cuda::memory::copy(v_d.get(), tr.data(), N*sizeof(std::array<uint16_t,4>));
-
-  cudautils::launchZero(a_d.get(),0);
-
-  auto nThreads = 256;
-  auto nBlocks = (4*N + nThreads - 1) / nThreads;
-
-  count<<<nBlocks,nThreads>>>(v_d.get(),a_d.get(),N);
-  
-  cudautils::launchFinalize(a_d.get(),ws_d.get(),0);
-  verify<<<1,1>>>(a_d.get());
-  fill<<<nBlocks,nThreads>>>(v_d.get(),a_d.get(),N);
-
-  Assoc la;
-  cuda::memory::copy(&la,a_d.get(),sizeof(Assoc));
-  std::cout << la.size() << std::endl;
-  imax = 0;
-  ave=0;
-  z=0;
-  for (auto i=0U; i<n; ++i) {
-    auto x = la.size(i);
-    if (x==0) { z++; continue;}
-    ave+=x;
-    imax = std::max(imax,int(x));
-  }
-  assert(0==la.size(n));
-  std::cout << "found with "<< n << " elements " << double(ave)/n <<' '<< imax << ' ' << z << std::endl;
-
-  // now the inverse map (actually this is the direct....)
-  AtomicPairCounter * dc_d;
-  cudaMalloc(&dc_d, sizeof(AtomicPairCounter));
-  cudaMemset(dc_d, 0, sizeof(AtomicPairCounter));
-  nBlocks = (N + nThreads - 1) / nThreads;
-  fillBulk<<<nBlocks,nThreads>>>(dc_d,v_d.get(),a_d.get(),N);
-  cudautils::finalizeBulk<<<nBlocks,nThreads>>>(dc_d,a_d.get());
-
-  AtomicPairCounter dc;
-  cudaMemcpy(&dc, dc_d, sizeof(AtomicPairCounter), cudaMemcpyDeviceToHost);
-
-  std::cout << "final counter value " << dc.get().n << ' ' << dc.get().m << std::endl;
-
-  cuda::memory::copy(&la,a_d.get(),sizeof(Assoc));
-  std::cout << la.size() << std::endl;
-  imax = 0;
-  ave=0;
-  for (auto i=0U; i<N; ++i) {
-    auto x = la.size(i);
-    if (!(x==4 || x==3)) std::cout << i << ' ' << x << std::endl;
-    assert(x==4 || x==3);
-    ave+=x;
-    imax = std::max(imax,int(x));
-  }
-  assert(0==la.size(N));
-  std::cout << "found with ave occupancy " << double(ave)/N <<' '<< imax << std::endl;
-
-
-  // here verify use of block local counters
-  auto m1_d = cuda::memory::device::make_unique<Multiplicity[]>(current_device,1);
-  auto m2_d = cuda::memory::device::make_unique<Multiplicity[]>(current_device,1);
-  cudautils::launchZero(m1_d.get(),0);
-  cudautils::launchZero(m2_d.get(),0);
-
-  nBlocks = (4*N + nThreads - 1) / nThreads;
-  countMulti<<<nBlocks,nThreads>>>(v_d.get(),m1_d.get(),N);
-  countMultiLocal<<<nBlocks,nThreads>>>(v_d.get(),m2_d.get(),N);
-  verifyMulti<<<1,Multiplicity::totbins()>>>(m1_d.get(),m2_d.get());
-
-  cudautils::launchFinalize(m1_d.get(),ws_d.get(),0);
-  cudautils::launchFinalize(m2_d.get(),ws_d.get(),0);
-  verifyMulti<<<1,Multiplicity::totbins()>>>(m1_d.get(),m2_d.get());
-
-
-  cudaCheck(cudaGetLastError());
-  cudaCheck(cudaDeviceSynchronize());
-  return 0;
-}

From 10253aaf35278d719dfc0f5d540203168c23385f Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 14 Feb 2019 11:34:30 +0100
Subject: [PATCH 44/95] compiles

---
 .../CUDAUtilities/test/OneToManyAssoc_t.cpp   |   2 +
 .../CUDAUtilities/test/OneToManyAssoc_t.h     | 260 ++++++++++++++++++
 2 files changed, 262 insertions(+)
 create mode 100644 HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cpp
 create mode 100644 HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h

diff --git a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cpp b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cpp
new file mode 100644
index 0000000000000..3a1ee5b1fb240
--- /dev/null
+++ b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.cpp
@@ -0,0 +1,2 @@
+#include "OneToManyAssoc_t.h"
+
diff --git a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h
new file mode 100644
index 0000000000000..36a79a34bbc0e
--- /dev/null
+++ b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h
@@ -0,0 +1,260 @@
+#include <algorithm>
+#include <cassert>
+#include <iostream>
+#include <random>
+#include <limits>
+#include <array>
+#include <memory>
+
+#ifdef __CUDACC__
+#include <cuda/api_wrappers.h>
+#include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
+#endif
+
+#include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
+
+constexpr uint32_t MaxElem=64000;
+constexpr uint32_t MaxTk=8000;
+constexpr uint32_t MaxAssocs = 4*MaxTk;
+using Assoc = OneToManyAssoc<uint16_t,MaxElem,MaxAssocs>;
+
+using Multiplicity = OneToManyAssoc<uint16_t,8,MaxTk>;
+
+using TK = std::array<uint16_t,4>;
+
+__global__
+void countMultiLocal(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, int32_t n) {
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < n; i += gridDim.x*blockDim.x) {
+
+     __shared__ Multiplicity::CountersOnly local;
+     if (threadIdx.x==0) local.zero();
+     __syncthreads();
+     local.countDirect(2+i%4);
+     __syncthreads();
+     if (threadIdx.x==0) assoc->add(local);
+  }
+}
+
+__global__
+void countMulti(TK const * __restrict__ tk, Multiplicity * __restrict__ assoc, int32_t n) {
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < n; i += gridDim.x*blockDim.x)
+     assoc->countDirect(2+i%4);
+}
+
+
+__global__
+void verifyMulti(Multiplicity * __restrict__ m1, Multiplicity * __restrict__ m2) {
+   auto first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (auto i = first; i < Multiplicity::totbins(); i += gridDim.x*blockDim.x)
+     assert(m1->off[i]==m2->off[i]);
+}
+
+__global__ 
+void count(TK const * __restrict__ tk, Assoc * __restrict__ assoc, int32_t n) {
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < 4*n; i += gridDim.x*blockDim.x) {
+     auto k = i/4;
+     auto j = i - 4*k;
+     assert(j<4);
+     if (k>=n) return;
+     if (tk[k][j]<MaxElem)
+     assoc->countDirect(tk[k][j]);
+   }
+}
+
+__global__
+void fill(TK const * __restrict__ tk, Assoc * __restrict__ assoc, int32_t n) {
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int i = first; i < 4*n; i += gridDim.x*blockDim.x) {
+     auto    k = i/4;
+     auto    j = i - 4*k;
+     assert(j<4);
+     if (k>=n) return;
+     if (tk[k][j]<MaxElem)
+     assoc->fillDirect(tk[k][j],k);
+   }
+}
+
+__global__
+void verify(Assoc * __restrict__ assoc) {
+   assert(assoc->size()<Assoc::capacity());
+}
+
+__global__
+void fillBulk(AtomicPairCounter * apc, TK const * __restrict__ tk, Assoc * __restrict__ assoc, int32_t n) {
+   int first = blockDim.x * blockIdx.x + threadIdx.x;
+   for (int k = first; k < n; k += gridDim.x*blockDim.x) {
+     auto m = tk[k][3]<MaxElem ? 4 : 3;
+     assoc->bulkFill(*apc,&tk[k][0],m);
+   }
+}
+
+int main() {
+#ifdef __CUDACC__
+  exitSansCUDADevices();
+  auto current_device = cuda::device::current::get();
+#endif
+
+
+  std::cout << "OneToManyAssoc " << Assoc::nbins() << ' ' << Assoc::capacity() << ' '<< Assoc::wsSize() << std::endl;
+
+
+  std::mt19937 eng;
+
+  std::geometric_distribution<int> rdm(0.8);
+
+  constexpr uint32_t N = 4000;
+
+  std::vector<std::array<uint16_t,4>> tr(N);
+
+  // fill with "index" to element
+  long long ave=0;
+  int imax=0;
+  auto n=0U;
+  auto z=0U;
+  auto nz=0U;
+  for (auto i=0U; i<4U; ++i) {
+    auto j=0U;
+    while(j<N && n<MaxElem) {
+      if (z==11) { ++n; z=0; ++nz; continue;} // a bit of not assoc 
+      auto x = rdm(eng);
+      auto k = std::min(j+x+1,N);
+      if (i==3 && z==3) { // some triplets time to time
+        for (;j<k; ++j) tr[j][i] = MaxElem+1;
+      } else {
+        ave+=x+1;
+        imax = std::max(imax,x);
+        for (;j<k; ++j) tr[j][i] = n;
+        ++n; 
+      }
+      ++z;
+    }
+    assert(n<=MaxElem);
+    assert(j<=N);
+  }
+  std::cout << "filled with "<< n << " elements " << double(ave)/n <<' '<< imax << ' ' << nz << std::endl;
+
+#ifdef __CUDACC__
+  auto v_d = cuda::memory::device::make_unique<std::array<uint16_t,4>[]>(current_device, N);
+  assert(v_d.get());
+  auto a_d = cuda::memory::device::make_unique<Assoc[]>(current_device,1);
+  auto ws_d = cuda::memory::device::make_unique<uint8_t[]>(current_device, Assoc::wsSize());
+
+  cuda::memory::copy(v_d.get(), tr.data(), N*sizeof(std::array<uint16_t,4>));
+#else
+  auto a_d = std::make_unique<Assoc>();
+  auto v_d = tr.data();
+#endif
+
+  cudautils::launchZero(a_d.get(),0);
+
+
+#ifdef __CUDACC__
+  auto nThreads = 256;
+  auto nBlocks = (4*N + nThreads - 1) / nThreads;
+
+  count<<<nBlocks,nThreads>>>(v_d.get(),a_d.get(),N);
+  
+  cudautils::launchFinalize(a_d.get(),ws_d.get(),0);
+  verify<<<1,1>>>(a_d.get());
+  fill<<<nBlocks,nThreads>>>(v_d.get(),a_d.get(),N);
+#else
+  count(v_d,a_d.get(),N);
+  cudautils::launchFinalize(a_d.get());
+  verify(a_d.get());
+  fill(v_d,a_d.get(),N);
+#endif
+
+  Assoc la;
+
+#ifdef __CUDACC__
+  cuda::memory::copy(&la,a_d.get(),sizeof(Assoc));
+#else
+  memcpy(&la,a_d.get(),sizeof(Assoc)); // not required, easier
+#endif
+
+  std::cout << la.size() << std::endl;
+  imax = 0;
+  ave=0;
+  z=0;
+  for (auto i=0U; i<n; ++i) {
+    auto x = la.size(i);
+    if (x==0) { z++; continue;}
+    ave+=x;
+    imax = std::max(imax,int(x));
+  }
+  assert(0==la.size(n));
+  std::cout << "found with "<< n << " elements " << double(ave)/n <<' '<< imax << ' ' << z << std::endl;
+
+  // now the inverse map (actually this is the direct....)
+  AtomicPairCounter * dc_d;
+  AtomicPairCounter dc;
+
+#ifdef __CUDACC__
+  cudaMalloc(&dc_d, sizeof(AtomicPairCounter));
+  cudaMemset(dc_d, 0, sizeof(AtomicPairCounter));
+  nBlocks = (N + nThreads - 1) / nThreads;
+  fillBulk<<<nBlocks,nThreads>>>(dc_d,v_d.get(),a_d.get(),N);
+  cudautils::finalizeBulk<<<nBlocks,nThreads>>>(dc_d,a_d.get());
+
+  cuda::memory::copy(&la,a_d.get(),sizeof(Assoc));
+  cudaMemcpy(&dc, dc_d, sizeof(AtomicPairCounter), cudaMemcpyDeviceToHost);
+#else
+  dc_d = &dc;
+  fillBulk(dc_d,v_d,a_d.get(),N);
+  cudautils::finalizeBulk(dc_d,a_d.get());
+  memcpy(&la,a_d.get(),sizeof(Assoc));
+#endif
+
+  std::cout << "final counter value " << dc.get().n << ' ' << dc.get().m << std::endl;
+
+  std::cout << la.size() << std::endl;
+  imax = 0;
+  ave=0;
+  for (auto i=0U; i<N; ++i) {
+    auto x = la.size(i);
+    if (!(x==4 || x==3)) std::cout << i << ' ' << x << std::endl;
+    assert(x==4 || x==3);
+    ave+=x;
+    imax = std::max(imax,int(x));
+  }
+  assert(0==la.size(N));
+  std::cout << "found with ave occupancy " << double(ave)/N <<' '<< imax << std::endl;
+
+
+  // here verify use of block local counters
+#ifdef __CUDACC__
+  auto m1_d = cuda::memory::device::make_unique<Multiplicity[]>(current_device,1);
+  auto m2_d = cuda::memory::device::make_unique<Multiplicity[]>(current_device,1);
+#else
+  auto m1_d = std::make_unique<Multiplicity>();
+  auto m2_d = std::make_unique<Multiplicity>();
+#endif
+  cudautils::launchZero(m1_d.get(),0);
+  cudautils::launchZero(m2_d.get(),0);
+
+#ifdef __CUDACC__
+  nBlocks = (4*N + nThreads - 1) / nThreads;
+  countMulti<<<nBlocks,nThreads>>>(v_d.get(),m1_d.get(),N);
+  countMultiLocal<<<nBlocks,nThreads>>>(v_d.get(),m2_d.get(),N);
+  verifyMulti<<<1,Multiplicity::totbins()>>>(m1_d.get(),m2_d.get());
+
+  cudautils::launchFinalize(m1_d.get(),ws_d.get(),0);
+  cudautils::launchFinalize(m2_d.get(),ws_d.get(),0);
+  verifyMulti<<<1,Multiplicity::totbins()>>>(m1_d.get(),m2_d.get());
+
+  cudaCheck(cudaGetLastError());
+  cudaCheck(cudaDeviceSynchronize());
+#else
+  countMulti(v_d,m1_d.get(),N);
+  countMultiLocal(v_d,m2_d.get(),N);
+  verifyMulti(m1_d.get(),m2_d.get());
+
+  cudautils::launchFinalize(m1_d.get());
+  cudautils::launchFinalize(m2_d.get());
+  verifyMulti(m1_d.get(),m2_d.get());
+#endif
+  return 0;
+}

From 24065d7127ed70fe6bfe57caa68d27b6a7f21105 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 14 Feb 2019 12:25:17 +0100
Subject: [PATCH 45/95] fix thread local init

---
 .../CUDAUtilities/interface/cudaCompat.h          | 15 +++++++--------
 HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc | 12 ++++++++++++
 2 files changed, 19 insertions(+), 8 deletions(-)
 create mode 100644 HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc

diff --git a/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h b/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h
index be693980f021c..56d454be5fb8f 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h
@@ -24,10 +24,9 @@ namespace cudaCompat {
 #endif
   const dim3 threadIdx = {0,0,0};
   const dim3 blockDim = {1,1,1};
-  thread_local dim3 blockIdx = {0,0,0};
-  thread_local dim3 gridDim = {1,1,1};
-
 
+  extern thread_local dim3 blockIdx;
+  extern thread_local dim3 gridDim;
 
  template<typename T1, typename T2>
  T1  atomicInc(T1* a, T2 b) {auto ret=*a; if ((*a)<b) (*a)++; return ret;}
@@ -46,15 +45,15 @@ namespace cudaCompat {
   T1  atomicMax(T1* a, T2 b) {auto ret=*a; a = std::max(*a,b);return ret;}
 
   
-  void __syncthreads(){}
-  bool __syncthreads_or(bool x) { return x;}
-  bool __syncthreads_and(bool x) { return x;}
+  inline void __syncthreads(){}
+  inline bool __syncthreads_or(bool x) { return x;}
+  inline bool __syncthreads_and(bool x) { return x;}
 
-  void resetGrid() {
+  inline void resetGrid() {
     blockIdx = {0,0,0};
     gridDim = {1,1,1};
   }
-  
+
 }
 
 #ifndef __CUDA_RUNTIME_H__
diff --git a/HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc b/HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc
new file mode 100644
index 0000000000000..3771e265b70fd
--- /dev/null
+++ b/HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc
@@ -0,0 +1,12 @@
+#include "HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h"
+namespace cudaCompat {
+  thread_local dim3 blockIdx;
+  thread_local dim3 gridDim;
+
+ struct InitGrid {
+   InitGrid() { resetGrid();}
+ };
+ 
+ InitGrid initGrid;
+
+}

From b2f779ada08ab16244ecf3785e7c28e52004886c Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 14 Feb 2019 12:29:43 +0100
Subject: [PATCH 46/95] san tommaso

---
 HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h
index 36a79a34bbc0e..4888a661bc8e4 100644
--- a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h
+++ b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h
@@ -95,6 +95,11 @@ int main() {
 #ifdef __CUDACC__
   exitSansCUDADevices();
   auto current_device = cuda::device::current::get();
+#else
+  // make sure cuda emulation is working
+  std::cout << "cuda x's " << threadIdx.x << ' ' << blockIdx.x << ' ' << blockDim.x << ' ' << gridDim.x << std::endl;
+  std::cout << "cuda y's " << threadIdx.y << ' ' << blockIdx.y << ' ' << blockDim.y << ' ' << gridDim.y << std::endl;
+  std::cout << "cuda z's " << threadIdx.z << ' ' << blockIdx.z << ' ' << blockDim.z << ' ' << gridDim.z << std::endl;
 #endif
 
 

From 583f1e6d940081c14e88407d64ddda92c92cd76d Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 14 Feb 2019 17:29:35 +0100
Subject: [PATCH 47/95] run on gpu & cpu

---
 .../plugins/gpuClusterChargeCut.h             | 12 ++--
 .../plugins/gpuClustering.h                   | 40 +++++++------
 .../SiPixelClusterizer/test/BuildFile.xml     | 15 ++++-
 .../test/cpuClustering_t.cpp                  |  1 +
 .../test/gpuClustering_t.cu                   |  1 +
 .../{gpuClustering.cu => gpuClustering_t.h}   | 59 +++++++++++++++++--
 6 files changed, 98 insertions(+), 30 deletions(-)
 create mode 100644 RecoLocalTracker/SiPixelClusterizer/test/cpuClustering_t.cpp
 create mode 100644 RecoLocalTracker/SiPixelClusterizer/test/gpuClustering_t.cu
 rename RecoLocalTracker/SiPixelClusterizer/test/{gpuClustering.cu => gpuClustering_t.h} (83%)

diff --git a/RecoLocalTracker/SiPixelClusterizer/plugins/gpuClusterChargeCut.h b/RecoLocalTracker/SiPixelClusterizer/plugins/gpuClusterChargeCut.h
index 855216960d659..cf94b1f43094b 100644
--- a/RecoLocalTracker/SiPixelClusterizer/plugins/gpuClusterChargeCut.h
+++ b/RecoLocalTracker/SiPixelClusterizer/plugins/gpuClusterChargeCut.h
@@ -18,7 +18,7 @@ namespace gpuClustering {
                            uint32_t * __restrict__ nClustersInModule,    // modified: number of clusters found in each module
                            uint32_t const * __restrict__ moduleId,             // module id of each module
                            int32_t * __restrict__  clusterId,            // modified: cluster id of each pixel
-                           int numElements)
+                           uint32_t numElements)
   {
 
     if (blockIdx.x >= moduleStart[0])
@@ -43,12 +43,12 @@ namespace gpuClustering {
     auto first = firstPixel + threadIdx.x;
 
     __shared__ int32_t charge[MaxNumClustersPerModules];
-    for (int i=threadIdx.x; i<nclus; i += blockDim.x) {
+    for (auto i=threadIdx.x; i<nclus; i += blockDim.x) {
       charge[i]=0;
     }
     __syncthreads();
 
-    for (int i = first; i < numElements; i += blockDim.x) {
+    for (auto i = first; i < numElements; i += blockDim.x) {
       if (id[i] == InvId) continue;     // not valid
       if (id[i] != thisModuleId) break;           // end of module
       atomicAdd(&charge[clusterId[i]], adc[i]);
@@ -58,7 +58,7 @@ namespace gpuClustering {
     auto chargeCut = thisModuleId<96 ? 2000 : 4000; // move in constants (calib?)
     __shared__ uint8_t ok[MaxNumClustersPerModules];
     __shared__ uint16_t newclusId[MaxNumClustersPerModules];
-    for (int i=threadIdx.x; i<nclus; i += blockDim.x) {
+    for (auto i=threadIdx.x; i<nclus; i += blockDim.x) {
        newclusId[i] = ok[i] =  charge[i]>chargeCut ? 1 : 0;
     }
 
@@ -76,13 +76,13 @@ namespace gpuClustering {
     __syncthreads();
 
     // mark bad cluster again
-    for (int i=threadIdx.x; i<nclus; i += blockDim.x) {
+    for (auto i=threadIdx.x; i<nclus; i += blockDim.x) {
       if (0==ok[i]) newclusId[i]=InvId+1;
     }
     __syncthreads();
 
     // reassign id
-    for (int i = first; i < numElements; i += blockDim.x) {
+    for (auto i = first; i < numElements; i += blockDim.x) {
       if (id[i] == InvId) continue;     // not valid
       if (id[i] != thisModuleId) break;           // end of module
       clusterId[i] = newclusId[clusterId[i]]-1;
diff --git a/RecoLocalTracker/SiPixelClusterizer/plugins/gpuClustering.h b/RecoLocalTracker/SiPixelClusterizer/plugins/gpuClustering.h
index 5c21a39302d70..d7f6af65b61a6 100644
--- a/RecoLocalTracker/SiPixelClusterizer/plugins/gpuClustering.h
+++ b/RecoLocalTracker/SiPixelClusterizer/plugins/gpuClustering.h
@@ -17,19 +17,19 @@ namespace gpuClustering {
                                int32_t * __restrict__ clusterId,
                                int numElements)
   {
-    int i = blockDim.x * blockIdx.x + threadIdx.x;
-    if (i >= numElements)
-      return;
-    clusterId[i] = i;
-    if (InvId == id[i])
-      return;
-    auto j = i - 1;
-    while (j >= 0 and id[j] == InvId)
-      --j;
-    if (j < 0 or id[j] != id[i]) {
-      // boundary...
-      auto loc = atomicInc(moduleStart, MaxNumModules);
-      moduleStart[loc + 1] = i;
+    int first = blockDim.x * blockIdx.x + threadIdx.x;
+    for (int i = first; i < numElements; i += gridDim.x*blockDim.x) {
+      clusterId[i] = i;
+      if (InvId == id[i])
+        continue;
+      auto j = i - 1;
+      while (j >= 0 and id[j] == InvId)
+        --j;
+      if (j < 0 or id[j] != id[i]) {
+        // boundary...
+        auto loc = atomicInc(moduleStart, MaxNumModules);
+        moduleStart[loc + 1] = i;
+      }
     }
   }
 
@@ -120,15 +120,19 @@ namespace gpuClustering {
         hist.fill(y[i],i-firstPixel);
       }
 
+#ifdef __CUDA_ARCH__
     // assume that we can cover the whole module with up to 10 blockDim.x-wide iterations
     constexpr int maxiter = 10;
+#else
+    auto maxiter = hist.size();
+#endif
     if (threadIdx.x==0) {
       assert((hist.size()/ blockDim.x) <= maxiter);
     }
     // nearest neighbour
     uint16_t nn[maxiter][5];
     uint8_t nnn[maxiter]; // number of nn
-    for (int k = 0; k < maxiter; ++k)
+    for (uint32_t k = 0; k < maxiter; ++k)
       nnn[k] = 0;
 
     __syncthreads();  // for hit filling!
@@ -151,7 +155,7 @@ namespace gpuClustering {
 #endif
 
     // fill NN
-    for (int j=threadIdx.x, k = 0; j<hist.size(); j+=blockDim.x, ++k) {
+    for (auto j=threadIdx.x, k = 0U; j<hist.size(); j+=blockDim.x, ++k) {
         auto p = hist.begin()+j;
         auto i = *p + firstPixel;
         assert (id[i] != InvId);
@@ -167,7 +171,7 @@ namespace gpuClustering {
           assert(l<5);
           nn[k][l]=*p;
         }
-     }
+    }
 
     // for each pixel, look at all the pixels until the end of the module;
     // when two valid pixels within +/- 1 in x or y are found, set their id to the minimum;
@@ -177,7 +181,7 @@ namespace gpuClustering {
     int nloops=0;
     while (__syncthreads_or(more)) {
       if (1==nloops%2) {
-        for (int j=threadIdx.x, k = 0; j<hist.size(); j+=blockDim.x, ++k) {
+        for (auto j=threadIdx.x, k = 0U; j<hist.size(); j+=blockDim.x, ++k) {
              auto p = hist.begin()+j;
              auto i = *p + firstPixel;
              auto m = clusterId[i];
@@ -186,7 +190,7 @@ namespace gpuClustering {
         }
       } else {
         more = false;
-        for (int j=threadIdx.x, k = 0; j<hist.size(); j+=blockDim.x, ++k) {
+        for (auto j=threadIdx.x, k = 0U; j<hist.size(); j+=blockDim.x, ++k) {
           auto p = hist.begin()+j;
           auto i = *p + firstPixel;
           for (int kk=0; kk<nnn[k]; ++kk) {
diff --git a/RecoLocalTracker/SiPixelClusterizer/test/BuildFile.xml b/RecoLocalTracker/SiPixelClusterizer/test/BuildFile.xml
index 66a87291221f2..46f83f663faab 100644
--- a/RecoLocalTracker/SiPixelClusterizer/test/BuildFile.xml
+++ b/RecoLocalTracker/SiPixelClusterizer/test/BuildFile.xml
@@ -32,7 +32,7 @@
   <flags EDM_PLUGIN="1"/>
 </library>
 
-<bin file="gpuClustering.cu" name="gpuClustering_t">
+<bin file="gpuClustering_t.cu" name="gpuClustering_t">
   <use name="cuda"/>
   <use name="cub"/>
   <use name="cuda-api-wrappers"/>
@@ -40,7 +40,7 @@
   <flags CXXFLAGS="-g"/>
 </bin>
 
-<bin file="gpuClustering.cu" name="gpuClustering_debug">
+<bin file="gpuClustering_t.cu" name="gpuClustering_debug">
   <use name="cuda"/>
   <use name="cub"/>
   <use name="cuda-api-wrappers"/>
@@ -48,3 +48,14 @@
   <flags CXXFLAGS="-g -DGPU_DEBUG"/>
   <flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
 </bin>
+
+<bin file="cpuClustering_t.cpp" name="cpuClustering_t">
+  <use name="HeterogeneousCore/CUDAUtilities"/>
+  <flags CXXFLAGS="-g"/>
+</bin>
+
+<bin file="cpuClustering_t.cpp" name="cpuClustering_debug">
+  <use name="HeterogeneousCore/CUDAUtilities"/>
+  <flags CXXFLAGS="-g -DGPU_DEBUG"/>
+</bin>
+
diff --git a/RecoLocalTracker/SiPixelClusterizer/test/cpuClustering_t.cpp b/RecoLocalTracker/SiPixelClusterizer/test/cpuClustering_t.cpp
new file mode 100644
index 0000000000000..19a3b8d014c9c
--- /dev/null
+++ b/RecoLocalTracker/SiPixelClusterizer/test/cpuClustering_t.cpp
@@ -0,0 +1 @@
+#include "gpuClustering_t.h"
diff --git a/RecoLocalTracker/SiPixelClusterizer/test/gpuClustering_t.cu b/RecoLocalTracker/SiPixelClusterizer/test/gpuClustering_t.cu
new file mode 100644
index 0000000000000..19a3b8d014c9c
--- /dev/null
+++ b/RecoLocalTracker/SiPixelClusterizer/test/gpuClustering_t.cu
@@ -0,0 +1 @@
+#include "gpuClustering_t.h"
diff --git a/RecoLocalTracker/SiPixelClusterizer/test/gpuClustering.cu b/RecoLocalTracker/SiPixelClusterizer/test/gpuClustering_t.h
similarity index 83%
rename from RecoLocalTracker/SiPixelClusterizer/test/gpuClustering.cu
rename to RecoLocalTracker/SiPixelClusterizer/test/gpuClustering_t.h
index 27c2e1d6fd110..add45a98088c3 100644
--- a/RecoLocalTracker/SiPixelClusterizer/test/gpuClustering.cu
+++ b/RecoLocalTracker/SiPixelClusterizer/test/gpuClustering_t.h
@@ -9,20 +9,24 @@
 #include <set>
 #include <vector>
 
+#ifdef __CUDACC__
 #include <cuda/api_wrappers.h>
-
 #include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
+#endif
 #include "RecoLocalTracker/SiPixelClusterizer/plugins/gpuClustering.h"
 #include "RecoLocalTracker/SiPixelClusterizer/plugins/gpuClusterChargeCut.h"
 
 int main(void)
 {
+#ifdef __CUDACC__
   exitSansCUDADevices();
 
   if (cuda::device::count() == 0) {
     std::cerr << "No CUDA devices on this system" << "\n";
     exit(EXIT_FAILURE);
   }
+#endif
+
   using namespace gpuClustering;
 
   int numElements = MaxNumPixels;
@@ -34,6 +38,7 @@ int main(void)
 
   auto h_clus = std::make_unique<int[]>(numElements);
 
+#ifdef __CUDACC__
   auto current_device = cuda::device::current::get();
   auto d_id = cuda::memory::device::make_unique<uint16_t[]>(current_device, numElements);
   auto d_x = cuda::memory::device::make_unique<uint16_t[]>(current_device, numElements);
@@ -46,6 +51,13 @@ int main(void)
 
   auto d_clusInModule = cuda::memory::device::make_unique<uint32_t[]>(current_device, MaxNumModules);
   auto d_moduleId = cuda::memory::device::make_unique<uint32_t[]>(current_device, MaxNumModules);
+#else
+
+  auto h_moduleStart = std::make_unique<uint32_t[]>(MaxNumModules+1);
+  auto h_clusInModule = std::make_unique<uint32_t[]>(MaxNumModules);
+  auto h_moduleId = std::make_unique<uint32_t[]>(MaxNumModules);
+
+#endif
 
   // later random number
   int n=0;
@@ -223,11 +235,12 @@ int main(void)
   assert(n<=numElements);
 
 
+  uint32_t nModules=0;
+#ifdef __CUDACC__
   size_t size32 = n * sizeof(unsigned int);
   size_t size16 = n * sizeof(unsigned short);
   // size_t size8 = n * sizeof(uint8_t);
 
-  uint32_t nModules=0;
   cuda::memory::copy(d_moduleStart.get(),&nModules,sizeof(uint32_t));
 
   cuda::memory::copy(d_id.get(), h_id.get(), size16);
@@ -271,6 +284,7 @@ int main(void)
     uint32_t nclus[MaxNumModules], moduleId[nModules];
 
     cuda::memory::copy(&nclus,d_clusInModule.get(),MaxNumModules*sizeof(uint32_t));
+
     std::cout << "before charge cut found " << std::accumulate(nclus,nclus+MaxNumModules,0) << " clusters" << std::endl;
     for (auto i=MaxNumModules; i>0; i--) if (nclus[i-1]>0) {std::cout << "last module is " << i-1 << ' ' << nclus[i-1] << std::endl; break;}
     if (ncl!=std::accumulate(nclus,nclus+MaxNumModules,0)) std::cout << "ERROR!!!!! wrong number of cluster found" << std::endl;
@@ -287,21 +301,58 @@ int main(void)
 
 
     cudaDeviceSynchronize();
+#else
+    h_moduleStart[0]= nModules;
+    countModules(h_id.get(), h_moduleStart.get() ,h_clus.get(),n);
+    memset(h_clusInModule.get(),0,MaxNumModules*sizeof(uint32_t));
+    gridDim.x = MaxNumModules; //not needed in the kernel for this specific case;
+    assert(blockIdx.x==0);
+    for (;blockIdx.x<gridDim.x; ++blockIdx.x)
+    findClus(
+               h_id.get(), h_x.get(), h_y.get(),
+               h_moduleStart.get(),
+               h_clusInModule.get(), h_moduleId.get(),
+               h_clus.get(),
+               n
+               );
+    resetGrid();
+
+    nModules = h_moduleStart[0];
+    auto nclus = h_clusInModule.get();    
+
+    std::cout << "before charge cut found " << std::accumulate(nclus,nclus+MaxNumModules,0) << " clusters" << std::endl;
+    for (auto i=MaxNumModules; i>0; i--) if (nclus[i-1]>0) {std::cout << "last module is " << i-1 << ' ' << nclus[i-1] << std::endl; break;}
+    if (ncl!=std::accumulate(nclus,nclus+MaxNumModules,0)) std::cout << "ERROR!!!!! wrong number of cluster found" << std::endl;
+
+  gridDim.x = MaxNumModules; // no needed in the kernel for in this specific case
+  assert(blockIdx.x==0);
+  for (;blockIdx.x<gridDim.x; ++blockIdx.x)
+    clusterChargeCut(
+                     h_id.get(), h_adc.get(),
+                     h_moduleStart.get(),
+                     h_clusInModule.get(), h_moduleId.get(),
+                     h_clus.get(),
+                     n
+                     );
+  resetGrid();
+
+#endif
 
   std::cout << "found " << nModules << " Modules active" << std::endl;
 
+#ifdef __CUDACC__
   cuda::memory::copy(h_id.get(), d_id.get(), size16);
   cuda::memory::copy(h_clus.get(), d_clus.get(), size32);
   cuda::memory::copy(&nclus,d_clusInModule.get(),MaxNumModules*sizeof(uint32_t));
   cuda::memory::copy(&moduleId,d_moduleId.get(),nModules*sizeof(uint32_t));
-
+#endif
 
   std::set<unsigned int> clids;
   for (int i=0; i<n; ++i) {
     assert(h_id[i]!=666);  // only noise
     if (h_id[i]==InvId) continue;
     assert(h_clus[i]>=0);
-    assert(h_clus[i]<nclus[h_id[i]]);
+    assert(h_clus[i]<int(nclus[h_id[i]]));
     clids.insert(h_id[i]*1000+h_clus[i]);
                  // clids.insert(h_clus[i]);
   }

From c0a3d225165411f4c44c987d943f5cad7ee2d0b2 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 15 Feb 2019 13:29:17 +0100
Subject: [PATCH 48/95] seems working

---
 .../pixelVertexHeterogeneousProduct.h         |  10 --
 .../PixelVertexFinding/src/gpuClusterTracks.h |   6 +-
 .../PixelVertexFinding/src/gpuFitVertices.h   |   6 +-
 .../PixelVertexFinding/src/gpuSortByPt2.h     |   4 +-
 .../PixelVertexFinding/src/gpuSplitVertices.h |   6 +-
 .../PixelVertexFinding/src/gpuVertexFinder.cu |  72 +++-----
 .../PixelVertexFinding/src/gpuVertexFinder.h  |  49 +++---
 .../test/gpuVertexFinder_t.cu                 | 157 ++++++++----------
 8 files changed, 122 insertions(+), 188 deletions(-)

diff --git a/RecoPixelVertexing/PixelVertexFinding/interface/pixelVertexHeterogeneousProduct.h b/RecoPixelVertexing/PixelVertexFinding/interface/pixelVertexHeterogeneousProduct.h
index 691c3e9ef429c..df9aec8ad4f60 100644
--- a/RecoPixelVertexing/PixelVertexFinding/interface/pixelVertexHeterogeneousProduct.h
+++ b/RecoPixelVertexing/PixelVertexFinding/interface/pixelVertexHeterogeneousProduct.h
@@ -12,15 +12,6 @@ namespace pixelVertexHeterogeneousProduct {
     reco::VertexCollection collection;
   };
 
-  struct VerticesOnGPU{
-    float * z_d;
-    float * zerr_d;
-    float * chi2_d;
-    uint16_t * sortInd_d;
-    int32_t * ivtx_d; // this should be indexed with the original tracks, not the reduced set (oops)
-  };
-
-
   struct VerticesOnCPU {
     VerticesOnCPU() = default;
 
@@ -31,7 +22,6 @@ namespace pixelVertexHeterogeneousProduct {
 
     uint32_t nVertices=0;
     uint32_t nTracks=0;
-    VerticesOnGPU const * gpu_d = nullptr;
   };
 
 
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
index dfc9efe28a81d..c57fe123d3ca9 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
@@ -32,12 +32,12 @@ namespace gpuVertexFinder {
     auto er2mx = errmax*errmax;
     
     auto & __restrict__ data = *pdata;
-    auto nt = *data.ntrks;
+    auto nt = data.ntrks;
     float const * __restrict__ zt = data.zt;
     float const * __restrict__ ezt2 = data.ezt2;
 
-    uint32_t & nvFinal = *data.nvFinal;
-    uint32_t & nvIntermediate = *data.nvIntermediate;
+    uint32_t & nvFinal = data.nvFinal;
+    uint32_t & nvIntermediate = data.nvIntermediate;
     
     uint8_t  * __restrict__ izt = data.izt;
     int32_t * __restrict__ nn = data.nn;
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
index e9124816ab37e..bc2ec2f629f4a 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
@@ -24,14 +24,14 @@ namespace gpuVertexFinder {
     constexpr bool verbose = false; // in principle the compiler should optmize out if false
 
     auto & __restrict__ data = *pdata;
-    auto nt = *data.ntrks;
+    auto nt = data.ntrks;
     float const * __restrict__ zt = data.zt;
     float const * __restrict__ ezt2 = data.ezt2;
     float * __restrict__ zv = data.zv;
     float * __restrict__ wv = data.wv;
     float * __restrict__ chi2 = data.chi2;
-    uint32_t & nvFinal  = *data.nvFinal;
-    uint32_t & nvIntermediate = *data.nvIntermediate;
+    uint32_t & nvFinal  = data.nvFinal;
+    uint32_t & nvIntermediate = data.nvIntermediate;
 
     int32_t * __restrict__ nn = data.nn;
     int32_t * __restrict__ iv = data.iv;
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
index d6f2c20a4420d..0cfb6b4137f8e 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
@@ -20,9 +20,9 @@ namespace gpuVertexFinder {
                  OnGPU * pdata
                 )  {
     auto & __restrict__ data = *pdata;
-    auto nt = *data.ntrks;
+    auto nt = data.ntrks;
     float const * __restrict__ ptt2 = data.ptt2;
-    uint32_t const & nvFinal = *data.nvFinal;
+    uint32_t const & nvFinal = data.nvFinal;
 
     int32_t const * __restrict__ iv = data.iv;
     float * __restrict__ ptv2 = data.ptv2;
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
index 56dc6ed41a00f..ac719622140f8 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
@@ -25,13 +25,13 @@ namespace gpuVertexFinder {
 
 
     auto & __restrict__ data = *pdata;
-    auto nt = *data.ntrks;
+    auto nt = data.ntrks;
     float const * __restrict__ zt = data.zt;
     float const * __restrict__ ezt2 = data.ezt2;
     float * __restrict__ zv = data.zv;
     float * __restrict__ wv = data.wv;
     float const * __restrict__ chi2 = data.chi2;
-    uint32_t & nvFinal  = *data.nvFinal;
+    uint32_t & nvFinal  = data.nvFinal;
 
     int32_t const * __restrict__ nn = data.nn;
     int32_t * __restrict__ iv = data.iv;
@@ -116,7 +116,7 @@ namespace gpuVertexFinder {
     
     // get a new global vertex
     __shared__ uint32_t igv;
-    if (0==threadIdx.x) igv = atomicInc(data.nvIntermediate,1024);
+    if (0==threadIdx.x) igv = atomicInc(&data.nvIntermediate,1024);
     __syncthreads();
     for (auto k = threadIdx.x; k<nq; k+=blockDim.x) {
       if(1==newV[k]) iv[it[k]]=igv;
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
index 6df3687ca6e37..d969a8c092ed4 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
@@ -3,59 +3,23 @@
 #include "gpuSortByPt2.h"
 #include "gpuSplitVertices.h"
 
+
+// a macro SORRY
+#define FROM(M) ((char*)(onGPU_d) +offsetof(OnGPU,M))
+
+
 namespace gpuVertexFinder {
 
 
   void Producer::allocateOnGPU() {
-    cudaCheck(cudaMalloc(&onGPU.ntrks, sizeof(uint32_t)));
-    cudaCheck(cudaMemset(onGPU.ntrks, 0, sizeof(uint32_t)));
-    cudaCheck(cudaMalloc(&onGPU.itrk, OnGPU::MAXTRACKS*sizeof(uint16_t)));
-    cudaCheck(cudaMalloc(&onGPU.zt, OnGPU::MAXTRACKS*sizeof(float)));
-    cudaCheck(cudaMalloc(&onGPU.ezt2, OnGPU::MAXTRACKS*sizeof(float)));
-    cudaCheck(cudaMalloc(&onGPU.ptt2, OnGPU::MAXTRACKS*sizeof(float)));
-    cudaCheck(cudaMalloc(&onGPU.iv, OnGPU::MAXTRACKS*sizeof(int32_t)));
-
-    cudaCheck(cudaMalloc(&onGPU.nvFinal, sizeof(uint32_t)));
-    cudaCheck(cudaMalloc(&onGPU.nvIntermediate, sizeof(uint32_t)));
-    cudaCheck(cudaMalloc(&onGPU.zv, OnGPU::MAXVTX*sizeof(float)));
-    cudaCheck(cudaMalloc(&onGPU.wv, OnGPU::MAXVTX*sizeof(float)));
-    cudaCheck(cudaMalloc(&onGPU.chi2, OnGPU::MAXVTX*sizeof(float)));
-    cudaCheck(cudaMalloc(&onGPU.ptv2, OnGPU::MAXVTX*sizeof(float)));
-    cudaCheck(cudaMalloc(&onGPU.sortInd, OnGPU::MAXVTX*sizeof(uint16_t)));
-
-
-    cudaCheck(cudaMalloc(&onGPU.izt, OnGPU::MAXTRACKS*sizeof(uint8_t)));
-    cudaCheck(cudaMalloc(&onGPU.nn, OnGPU::MAXTRACKS*sizeof(int32_t)));
-
     cudaCheck(cudaMalloc(&onGPU_d,sizeof(OnGPU)));
-    cudaCheck(cudaMemcpy(onGPU_d,&onGPU,sizeof(OnGPU),cudaMemcpyHostToDevice));
-
   }
 	      
   void Producer::deallocateOnGPU() {
-    cudaCheck(cudaFree(onGPU.ntrks));
-    cudaCheck(cudaFree(onGPU.itrk));
-    cudaCheck(cudaFree(onGPU.zt));
-    cudaCheck(cudaFree(onGPU.ezt2));
-    cudaCheck(cudaFree(onGPU.ptt2));
-    cudaCheck(cudaFree(onGPU.iv));
-
-    cudaCheck(cudaFree(onGPU.nvFinal));
-    cudaCheck(cudaFree(onGPU.nvIntermediate));
-    cudaCheck(cudaFree(onGPU.zv));
-    cudaCheck(cudaFree(onGPU.wv));
-    cudaCheck(cudaFree(onGPU.chi2));
-    cudaCheck(cudaFree(onGPU.ptv2));
-    cudaCheck(cudaFree(onGPU.sortInd));
-
-    cudaCheck(cudaFree(onGPU.izt));
-    cudaCheck(cudaFree(onGPU.nn));
-
     cudaCheck(cudaFree(onGPU_d));
-
   }
 
-  
+
   __global__
   void loadTracks(pixelTuplesHeterogeneousProduct::TuplesOnGPU const * tracks,
                   OnGPU * pdata,
@@ -79,7 +43,7 @@ namespace gpuVertexFinder {
     if (fittedTrack.par(2)<ptMin) return; 
 
     auto & data = *pdata;   
-    auto it = atomicAdd(data.ntrks,1);
+    auto it = atomicAdd(&data.ntrks,1);
     data.itrk[it] = idx;
     data.zt[it] = fittedTrack.par(4);
     data.ezt2[it] = fittedTrack.cov(4, 4);
@@ -90,7 +54,7 @@ namespace gpuVertexFinder {
   void Producer::produce(cudaStream_t stream, TuplesOnCPU const & tracks, float ptMin) {
     
     assert(onGPU_d);assert(tracks.gpu_d);
-    cudaCheck(cudaMemsetAsync(onGPU.ntrks, 0, sizeof(uint32_t),stream));
+    init<<<1,1,0,stream>>>(onGPU_d);
     auto blockSize = 128;
     auto numberOfBlocks = (CAConstants::maxTuples() + blockSize - 1) / blockSize;
     loadTracks<<<numberOfBlocks,blockSize,0,stream>>>(tracks.gpu_d,onGPU_d, ptMin);
@@ -110,9 +74,11 @@ namespace gpuVertexFinder {
     cudaCheck(cudaGetLastError());
 
     if(enableTransfer) {
-      cudaCheck(cudaMemcpyAsync(&gpuProduct.nVertices, onGPU.nvFinal, sizeof(uint32_t),
+      auto from = (char*)(onGPU_d) +offsetof(OnGPU,nvFinal);
+      cudaCheck(cudaMemcpyAsync(&gpuProduct.nVertices, from, sizeof(uint32_t),
                                 cudaMemcpyDeviceToHost, stream));
-      cudaCheck(cudaMemcpyAsync(&gpuProduct.nTracks, onGPU.ntrks, sizeof(uint32_t),
+      from = (char*)(onGPU_d) +offsetof(OnGPU,ntrks);
+      cudaCheck(cudaMemcpyAsync(&gpuProduct.nTracks, from, sizeof(uint32_t),
                               cudaMemcpyDeviceToHost, stream));
     }
   }
@@ -123,24 +89,24 @@ namespace gpuVertexFinder {
 
     // finish copy
     gpuProduct.ivtx.resize(gpuProduct.nTracks);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.ivtx.data(),onGPU.iv,sizeof(int32_t)*gpuProduct.nTracks,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.ivtx.data(),FROM(iv),sizeof(int32_t)*gpuProduct.nTracks,
                               cudaMemcpyDeviceToHost, stream));
     gpuProduct.itrk.resize(gpuProduct.nTracks);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.itrk.data(),onGPU.itrk,sizeof(int16_t)*gpuProduct.nTracks,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.itrk.data(),FROM(itrk),sizeof(int16_t)*gpuProduct.nTracks,
                               cudaMemcpyDeviceToHost, stream));
 
     gpuProduct.z.resize(gpuProduct.nVertices);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.z.data(),onGPU.zv,sizeof(float)*gpuProduct.nVertices,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.z.data(),FROM(zv),sizeof(float)*gpuProduct.nVertices,
 			      cudaMemcpyDeviceToHost, stream));
     gpuProduct.zerr.resize(gpuProduct.nVertices);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.zerr.data(),onGPU.wv,sizeof(float)*gpuProduct.nVertices,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.zerr.data(),FROM(wv),sizeof(float)*gpuProduct.nVertices,
 			      cudaMemcpyDeviceToHost, stream));
     gpuProduct.chi2.resize(gpuProduct.nVertices);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.chi2.data(),onGPU.chi2,sizeof(float)*gpuProduct.nVertices,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.chi2.data(),FROM(chi2),sizeof(float)*gpuProduct.nVertices,
 			      cudaMemcpyDeviceToHost, stream));
         
     gpuProduct.sortInd.resize(gpuProduct.nVertices);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.sortInd.data(),onGPU.sortInd,sizeof(uint16_t)*gpuProduct.nVertices,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.sortInd.data(),FROM(sortInd),sizeof(uint16_t)*gpuProduct.nVertices,
                               cudaMemcpyDeviceToHost, stream));
 
     cudaStreamSynchronize(stream);
@@ -149,3 +115,5 @@ namespace gpuVertexFinder {
   }
 	
 } // end namespace
+
+#undef FROM
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.h
index 572460f7b837b..57b626feaadf7 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.h
@@ -16,33 +16,34 @@ namespace gpuVertexFinder {
 
     OnGPU() = default;
 
-    OnGPU(std::nullptr_t) noexcept:
-      ntrks{nullptr}, itrk{nullptr}, zt{nullptr}, ezt2{nullptr}, ptt2{nullptr},
-      zv{nullptr}, wv{nullptr}, chi2{nullptr}, ptv2{nullptr}, nvFinal{nullptr},
-      nvIntermediate{nullptr}, iv{nullptr}, sortInd{nullptr},
-      izt{nullptr}, nn{nullptr}
-    {}
-
-    uint32_t * ntrks; // number of "selected tracks"
-    uint16_t * itrk; // index of original track    
-    float * zt;   // input track z at bs
-    float * ezt2; // input error^2 on the above
-    float * ptt2; // input pt^2 on the above
-
-    float * zv;  // output z-posistion of found vertices
-    float * wv;  //  output weight (1/error^2) on the above
-    float * chi2;  // vertices chi2
-    float * ptv2;  // vertices pt^2
-    uint32_t * nvFinal;  // the number of vertices
-    uint32_t * nvIntermediate;  // the number of vertices after splitting pruning etc.
-    int32_t * iv;  // vertex index for each associated track
-    uint16_t * sortInd; // sorted index (by pt2)
+    uint32_t ntrks; // number of "selected tracks"
+    uint16_t itrk[MAXTRACKS]; // index of original track    
+    float zt[MAXTRACKS];   // input track z at bs
+    float ezt2[MAXTRACKS]; // input error^2 on the above
+    float ptt2[MAXTRACKS]; // input pt^2 on the above
+
+    float zv[MAXVTX];  // output z-posistion of found vertices
+    float wv[MAXVTX];  //  output weight (1/error^2) on the above
+    float chi2[MAXVTX];  // vertices chi2
+    float ptv2[MAXVTX];  // vertices pt^2
+    uint32_t nvFinal;  // the number of vertices
+    uint32_t nvIntermediate;  // the number of vertices after splitting pruning etc.
+    int32_t iv[MAXTRACKS];  // vertex index for each associated track
+    uint16_t sortInd[MAXVTX]; // sorted index (by pt2)
 
     // workspace  
-    uint8_t * izt;  // interized z-position of input tracks
-    int32_t * nn; // number of nearest neighbours (reused as number of dof for output vertices)
+    uint8_t izt[MAXTRACKS];  // interized z-position of input tracks
+    int32_t nn[MAXVTX]; // number of nearest neighbours (reused as number of dof for output vertices)
+
+    __host__ __device__
+    void init() { ntrks=0;nvFinal=0;nvIntermediate=0;}
+ 
   };
   
+#ifdef __CUDACC__
+  __global__
+  void init(OnGPU * pdata) {pdata->init();}
+#endif
 
   struct OnCPU {
     OnCPU() = default;
@@ -73,7 +74,6 @@ namespace gpuVertexFinder {
 	     float ichi2max,   // max normalized distance to cluster
              bool ienableTransfer
 	     ) :
-      onGPU(nullptr),
       minT(iminT),
       eps(ieps),
       errmax(ierrmax),
@@ -93,7 +93,6 @@ namespace gpuVertexFinder {
 
   private:
     OnCPU gpuProduct;
-    OnGPU onGPU;
     OnGPU * onGPU_d=nullptr;
 
     int minT;  // min number of neighbours to be "core"
diff --git a/RecoPixelVertexing/PixelVertexFinding/test/gpuVertexFinder_t.cu b/RecoPixelVertexing/PixelVertexFinding/test/gpuVertexFinder_t.cu
index 84334917052ed..56c8ad95b1cea 100644
--- a/RecoPixelVertexing/PixelVertexFinding/test/gpuVertexFinder_t.cu
+++ b/RecoPixelVertexing/PixelVertexFinding/test/gpuVertexFinder_t.cu
@@ -7,7 +7,14 @@
 #include <cuda/api_wrappers.h>
 
 #include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
+#ifdef USE_DBSCAN
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h"
+#elif USE_ITERATIVE
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h"
+#else
 #include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h"
+#endif
+
 #include "RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h"
 #include "RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h"
 #include "RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h"
@@ -76,54 +83,28 @@ struct ClusterGenerator {
 
 };
 
+// a macro SORRY
+#define LOC_ONGPU(M) ((char*)(onGPU_d.get()) +offsetof(OnGPU,M))
+
+
+__global__
+void print(OnGPU const * pdata) {
+  auto const & __restrict__ data = *pdata;
+  printf("nt,nv %d %d,%d\n",data.ntrks,data.nvFinal,data.nvIntermediate); 
+
+}
 
 int main() {
   exitSansCUDADevices();
 
   auto current_device = cuda::device::current::get();
   
-  auto ntrks_d = cuda::memory::device::make_unique<uint32_t[]>(current_device, 1);
-  auto zt_d = cuda::memory::device::make_unique<float[]>(current_device, 64000);
-  auto ezt2_d = cuda::memory::device::make_unique<float[]>(current_device, 64000);
-  auto ptt2_d = cuda::memory::device::make_unique<float[]>(current_device, 64000);
-  auto zv_d = cuda::memory::device::make_unique<float[]>(current_device, 256);
-  auto wv_d = cuda::memory::device::make_unique<float[]>(current_device, 256);
-  auto chi2_d = cuda::memory::device::make_unique<float[]>(current_device, 256);
-  auto ptv2_d = cuda::memory::device::make_unique<float[]>(current_device, 256);
-  auto ind_d = cuda::memory::device::make_unique<uint16_t[]>(current_device, 256);
-
-  auto izt_d = cuda::memory::device::make_unique<uint8_t[]>(current_device, 64000);
-  auto nn_d = cuda::memory::device::make_unique<int32_t[]>(current_device, 64000);
-  auto iv_d = cuda::memory::device::make_unique<int32_t[]>(current_device, 64000);
-
-  auto nv_d = cuda::memory::device::make_unique<uint32_t[]>(current_device, 1);
-  auto nv2_d = cuda::memory::device::make_unique<uint32_t[]>(current_device, 1);
- 
   auto onGPU_d = cuda::memory::device::make_unique<OnGPU[]>(current_device, 1);
 
-  OnGPU onGPU;
-
-  onGPU.ntrks = ntrks_d.get();
-  onGPU.zt = zt_d.get();
-  onGPU.ezt2 = ezt2_d.get();
-  onGPU.ptt2 = ptt2_d.get();
-  onGPU.zv = zv_d.get();
-  onGPU.wv = wv_d.get();
-  onGPU.chi2 = chi2_d.get();
-  onGPU.ptv2 = ptv2_d.get();
-  onGPU.sortInd = ind_d.get();
-  onGPU.nvFinal = nv_d.get();
-  onGPU.nvIntermediate = nv2_d.get();
-  onGPU.izt = izt_d.get();
-  onGPU.nn = nn_d.get();
-  onGPU.iv = iv_d.get();
-
-
-  cuda::memory::copy(onGPU_d.get(), &onGPU, sizeof(OnGPU));
-
-
   Event  ev;
 
+  float eps = 0.1f;
+  std::array<float,3> par{{eps, 0.01f,9.0f}};
   for (int nav=30;nav<80;nav+=20){ 
 
   ClusterGenerator gen(nav,10);
@@ -134,55 +115,49 @@ int main() {
 
   gen(ev);
   
-  std::cout << ev.zvert.size() << ' ' << ev.ztrack.size() << std::endl;
-  auto nt = ev.ztrack.size();
-  cuda::memory::copy(onGPU.ntrks,&nt,sizeof(uint32_t));
-  cuda::memory::copy(onGPU.zt,ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
-  cuda::memory::copy(onGPU.ezt2,ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
-  cuda::memory::copy(onGPU.ptt2,ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
+  init<<<1,1,0,0>>>(onGPU_d.get());
 
-  float eps = 0.1f;
-  
-  std::cout << "M eps " << kk << ' ' << eps << std::endl;
-  
-  if ( (i%4) == 0 )
-    cuda::launch(clusterTracks,
-		 { 1, 512+256 },
-		 onGPU_d.get(),kk,eps,
-		 0.02f,12.0f
-		 );
-  
-  if ( (i%4) == 1 )
-    cuda::launch(clusterTracks,
-		 { 1, 512+256 },
-		 onGPU_d.get(),kk,eps,
-		 0.02f,9.0f
-		 );
+  std::cout << "v,t size " << ev.zvert.size() << ' ' << ev.ztrack.size() << std::endl;
+  auto nt = ev.ztrack.size();
+  cuda::memory::copy(LOC_ONGPU(ntrks),&nt,sizeof(uint32_t));
+  cuda::memory::copy(LOC_ONGPU(zt),ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
+  cuda::memory::copy(LOC_ONGPU(ezt2),ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
+  cuda::memory::copy(LOC_ONGPU(ptt2),ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
   
-  if ( (i%4) == 2 )
-    cuda::launch(clusterTracks,
-		 { 1, 512+256 },
-		 onGPU_d.get(),kk,eps,
-		 0.01f,9.0f
-		 );
+  std::cout << "M eps, pset " << kk << ' ' << eps << ' ' << (i%4) << std::endl;
   
-  if ( (i%4) == 3 )
-    cuda::launch(clusterTracks,
+#define CLUSTERIZE clusterTracks
+
+  if ( (i%4) == 0 ) par = {{eps, 0.02f,12.0f}};
+  if ( (i%4) == 1 ) par = {{eps, 0.02f,9.0f}};
+  if ( (i%4) == 2 ) par = {{eps, 0.01f,9.0f}};
+  if ( (i%4) == 3 ) par = {{0.7f*eps, 0.01f,9.0f}};
+
+  print<<<1,1,0,0>>>(onGPU_d.get());
+  cudaCheck(cudaGetLastError());
+  cudaDeviceSynchronize();
+
+  cuda::launch(CLUSTERIZE,
 		 { 1, 512+256 },
-		 onGPU_d.get(),kk,0.7f*eps,
-		 0.01f,9.0f
+		 onGPU_d.get(),kk,par[0],
+		 par[1],par[2]
 		 );
+  print<<<1,1,0,0>>>(onGPU_d.get());
+
   cudaCheck(cudaGetLastError());
   cudaDeviceSynchronize();
 
+
   cuda::launch(fitVertices, 
                { 1,1024-256 },
                onGPU_d.get(),50.f
               );
   cudaCheck(cudaGetLastError());
 
+
+
   uint32_t nv;
-  cuda::memory::copy(&nv, onGPU.nvFinal, sizeof(uint32_t));
+  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
   if (nv==0) {
     std::cout << "NO VERTICES???" << std::endl;
     continue;
@@ -194,25 +169,27 @@ int main() {
   int32_t nn[2*nv];
   uint16_t ind[2*nv];
 
-  cuda::memory::copy(&nn, onGPU.nn, nv*sizeof(int32_t));
-  cuda::memory::copy(&chi2, onGPU.chi2, nv*sizeof(float));
+  cuda::memory::copy(&nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(&chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+
   for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]);
   {
     auto mx = std::minmax_element(chi2,chi2+nv);
-    std::cout << "after fit min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
+    std::cout << "after fit nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
   }
 
   cuda::launch(fitVertices,
                { 1,1024-256 },
                onGPU_d.get(), 50.f
               );
-  cuda::memory::copy(&nv, onGPU.nvFinal, sizeof(uint32_t));
-  cuda::memory::copy(&nn, onGPU.nn, nv*sizeof(int32_t));
-  cuda::memory::copy(&chi2, onGPU.chi2, nv*sizeof(float));
+  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
+  cuda::memory::copy(&nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(&chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+
   for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]);
   {
     auto mx = std::minmax_element(chi2,chi2+nv);
-    std::cout << "before splitting min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
+    std::cout << "before splitting nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
   }
 
   cuda::launch(splitVertices,
@@ -220,7 +197,8 @@ int main() {
                onGPU_d.get(),
                9.f
               );
- cuda::memory::copy(&nv, onGPU.nvIntermediate, sizeof(uint32_t));
+ cuda::memory::copy(&nv, LOC_ONGPU(nvIntermediate), sizeof(uint32_t));
+
  std::cout << "after split " << nv << std::endl; 
 
   cuda::launch(fitVertices,
@@ -229,13 +207,12 @@ int main() {
               );
   cudaCheck(cudaGetLastError());
 
-
   cuda::launch(sortByPt2,
                { 1, 256 },
                onGPU_d.get()
               );
-
-  cuda::memory::copy(&nv, onGPU.nvFinal, sizeof(uint32_t));
+  cudaCheck(cudaGetLastError());
+  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
 
   if (nv==0) {
     std::cout << "NO VERTICES???" << std::endl;
@@ -243,16 +220,16 @@ int main() {
   }
 
 
-  cuda::memory::copy(&zv, onGPU.zv, nv*sizeof(float));
-  cuda::memory::copy(&wv, onGPU.wv, nv*sizeof(float));
-  cuda::memory::copy(&chi2, onGPU.chi2, nv*sizeof(float));
-  cuda::memory::copy(&ptv2, onGPU.ptv2, nv*sizeof(float));
-  cuda::memory::copy(&nn, onGPU.nn, nv*sizeof(int32_t));
-  cuda::memory::copy(&ind, onGPU.sortInd, nv*sizeof(uint16_t));
+  cuda::memory::copy(&zv, LOC_ONGPU(zv), nv*sizeof(float));
+  cuda::memory::copy(&wv, LOC_ONGPU(wv), nv*sizeof(float));
+  cuda::memory::copy(&chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+  cuda::memory::copy(&ptv2, LOC_ONGPU(ptv2), nv*sizeof(float));
+  cuda::memory::copy(&nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(&ind, LOC_ONGPU(sortInd), nv*sizeof(uint16_t));
   for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]); 
   {
     auto mx = std::minmax_element(chi2,chi2+nv);
-    std::cout << "min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
+    std::cout << "nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
   }
 
   {

From dafcac82a7caf85849fc6abc9554412f20b0ea31 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 15 Feb 2019 14:58:57 +0100
Subject: [PATCH 49/95] cpu and gpu same

---
 .../CUDAUtilities/interface/cudaCompat.h      |   2 +-
 .../PixelVertexFinding/src/gpuClusterTracks.h |  25 +-
 .../PixelVertexFinding/src/gpuFitVertices.h   |  14 +-
 .../PixelVertexFinding/src/gpuSortByPt2.h     |  13 +-
 .../PixelVertexFinding/src/gpuSplitVertices.h |   7 +-
 .../PixelVertexFinding/test/BuildFile.xml     |   6 +
 .../test/gpuVertexFinder_t.cu                 | 271 +-----------------
 7 files changed, 38 insertions(+), 300 deletions(-)

diff --git a/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h b/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h
index 56d454be5fb8f..da1a3dc538347 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/cudaCompat.h
@@ -29,7 +29,7 @@ namespace cudaCompat {
   extern thread_local dim3 gridDim;
 
  template<typename T1, typename T2>
- T1  atomicInc(T1* a, T2 b) {auto ret=*a; if ((*a)<b) (*a)++; return ret;}
+ T1  atomicInc(T1* a, T2 b) {auto ret=*a; if ((*a)<T1(b)) (*a)++; return ret;}
 
   
   template<typename T1, typename T2>
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
index c57fe123d3ca9..3828ab7c3c756 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
@@ -7,7 +7,6 @@
 
 #include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/radixSort.h"
 
 #include "gpuVertexFinder.h"
 
@@ -57,7 +56,7 @@ namespace gpuVertexFinder {
     assert(nt<=hist.capacity());
     
     // fill hist  (bin shall be wider than "eps")
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       assert(i<OnGPU::MAXTRACKS);
       int iz =  int(zt[i]*10.); // valid if eps<=0.1
       // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
@@ -75,16 +74,16 @@ namespace gpuVertexFinder {
     hist.finalize(ws);
     __syncthreads();
     assert(hist.size()==nt);
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       hist.fill(izt[i],uint16_t(i));
     }
     __syncthreads();    
 
 
     // count neighbours
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (ezt2[i]>er2mx) continue;
-      auto loop = [&](int j) {
+      auto loop = [&](uint32_t j) {
         if (i==j) return;
         auto dist = std::abs(zt[i]-zt[j]);
         if (dist>eps) return;
@@ -107,19 +106,19 @@ namespace gpuVertexFinder {
     bool more = true;
     while (__syncthreads_or(more)) {
      if (1==nloops%2) {
-      for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
         auto m = iv[i];
         while (m!=iv[m]) m=iv[m];
         iv[i]=m;
       }
       }  else {
        more=false;
-       for (int  k = threadIdx.x; k < hist.size(); k += blockDim.x) {
+       for (auto  k = threadIdx.x; k < hist.size(); k += blockDim.x) {
         auto p = hist.begin()+k;
         auto i = (*p);
         auto be = std::min(Hist::bin(izt[i])+1,int(hist.nbins()-1));
 	if (nn[i]<minT) continue; // DBSCAN core rule
-	auto loop = [&](int j) {
+	auto loop = [&](uint32_t j) {
 	  assert (i!=j);
 	  if (nn[j]<minT) return;  // DBSCAN core rule
           auto dist = std::abs(zt[i]-zt[j]);
@@ -142,7 +141,7 @@ namespace gpuVertexFinder {
     
     
     // collect edges (assign to closest cluster of closest point??? here to closest point)
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       //    if (nn[i]==0 || nn[i]>=minT) continue;    // DBSCAN edge rule
       if (nn[i]>=minT) continue;    // DBSCAN edge rule
       float mdist=eps;
@@ -164,8 +163,8 @@ namespace gpuVertexFinder {
     
     // find the number of different clusters, identified by a tracks with clus[i] == i;
     // mark these tracks with a negative id.
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
-      if (iv[i] == i) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] == int(i)) {
 	if  (nn[i]>=minT) {
 	  auto old = atomicInc(&foundClusters, 0xffffffff);
 	  iv[i] = -(old + 1);
@@ -179,7 +178,7 @@ namespace gpuVertexFinder {
     assert(foundClusters<OnGPU::MAXVTX);
     
     // propagate the negative id to all the tracks in the cluster.
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (iv[i] >= 0) {
 	// mark each track in a cluster with the same id as the first one
 	iv[i] = iv[iv[i]];
@@ -188,7 +187,7 @@ namespace gpuVertexFinder {
     __syncthreads();
     
     // adjust the cluster id to be a positive value starting from 0
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       iv[i] = - iv[i] - 1;
     }
     
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
index bc2ec2f629f4a..483b3b9dede70 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
@@ -7,8 +7,6 @@
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
 
 #include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/radixSort.h"
-
 
 #include "gpuVertexFinder.h"
 
@@ -44,7 +42,7 @@ namespace gpuVertexFinder {
     auto foundClusters = nvFinal;
  
     // zero
-    for (int i = threadIdx.x; i < foundClusters; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < foundClusters; i += blockDim.x) {
       zv[i]=0;
       wv[i]=0;
       chi2[i]=0;
@@ -57,13 +55,13 @@ namespace gpuVertexFinder {
     __syncthreads();
 
     // compute cluster location
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (iv[i]>9990) {
         if (verbose) atomicAdd(&noise, 1);
         continue;
       }
       assert(iv[i]>=0);
-      assert(iv[i]<foundClusters);
+      assert(iv[i]<int(foundClusters));
       auto w = 1.f/ezt2[i];
       atomicAdd(&zv[iv[i]],zt[i]*w);
       atomicAdd(&wv[iv[i]],w);
@@ -71,7 +69,7 @@ namespace gpuVertexFinder {
 
     __syncthreads();
     // reuse nn
-    for (int i = threadIdx.x; i < foundClusters; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < foundClusters; i += blockDim.x) {
       assert(wv[i]>0.f);
       zv[i]/=wv[i];
       nn[i]=-1;  // ndof
@@ -80,7 +78,7 @@ namespace gpuVertexFinder {
 
 
     // compute chi2
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (iv[i]>9990) continue;
 
       auto c2 = zv[iv[i]]-zt[i]; c2 *=c2/ezt2[i];
@@ -89,7 +87,7 @@ namespace gpuVertexFinder {
       atomicAdd(&nn[iv[i]],1);
     }
     __syncthreads();
-    for (int i = threadIdx.x; i < foundClusters; i += blockDim.x) if(nn[i]>0) wv[i] *= float(nn[i])/chi2[i];
+    for (auto i = threadIdx.x; i < foundClusters; i += blockDim.x) if(nn[i]>0) wv[i] *= float(nn[i])/chi2[i];
 
     if(verbose && 0==threadIdx.x) printf("found %d proto clusters ",foundClusters);
     if(verbose && 0==threadIdx.x) printf("and %d noise\n",noise);
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
index 0cfb6b4137f8e..750923b4fafc4 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
@@ -8,8 +8,9 @@
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
 
 #include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
+#ifdef __CUDA_ARCH__
 #include "HeterogeneousCore/CUDAUtilities/interface/radixSort.h"
-
+#endif
 
 #include "gpuVertexFinder.h"
 
@@ -31,13 +32,13 @@ namespace gpuVertexFinder {
     if (nvFinal<1) return;
 
     // can be done asynchronoisly at the end of previous event
-    for (int i = threadIdx.x; i < nvFinal; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nvFinal; i += blockDim.x) {
       ptv2[i]=0;
     }
     __syncthreads();
 
 
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (iv[i]>9990) continue;
       atomicAdd(&ptv2[iv[i]], ptt2[i]);
     }
@@ -47,9 +48,13 @@ namespace gpuVertexFinder {
       if (threadIdx.x==0) sortInd[0]=0;
       return;
     }
+#ifdef __CUDA_ARCH__
     __shared__ uint16_t ws[1024];
     radixSort(ptv2,sortInd,ws,nvFinal);
-
+#else
+    for(uint16_t i=0; i<nvFinal; ++i) sortInd[i]=i;
+    std::sort(sortInd,sortInd+nvFinal,[&](auto i, auto j){return ptv2[i]<ptv2[j];});
+#endif
     assert(ptv2[sortInd[nvFinal-1]]>=ptv2[sortInd[nvFinal-2]]);
     assert(ptv2[sortInd[1]]>=ptv2[sortInd[0]]);
   }
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
index ac719622140f8..d4d8f4e1a0192 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
@@ -7,7 +7,6 @@
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
 
 #include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/radixSort.h"
 
 
 #include "gpuVertexFinder.h"
@@ -58,7 +57,7 @@ namespace gpuVertexFinder {
 
     // copy to local
     for (auto k = threadIdx.x; k<nt; k+=blockDim.x) {
-      if (iv[k]==kv) {
+      if (iv[k]==int(kv)) {
         auto old = atomicInc(&nq,1024);
         zz[old] = zt[k]-zv[kv];
         newV[old] = zz[old]<0 ? 0 : 1;
@@ -70,7 +69,7 @@ namespace gpuVertexFinder {
     __shared__ float znew[2], wnew[2];  // the new vertices
     
     __syncthreads();
-    assert(nq==nn[kv]+1);
+    assert(int(nq)==nn[kv]+1);
     
 
     int  maxiter=20;
@@ -116,7 +115,7 @@ namespace gpuVertexFinder {
     
     // get a new global vertex
     __shared__ uint32_t igv;
-    if (0==threadIdx.x) igv = atomicInc(&data.nvIntermediate,1024);
+    if (0==threadIdx.x) igv = atomicAdd(&data.nvIntermediate,1);
     __syncthreads();
     for (auto k = threadIdx.x; k<nq; k+=blockDim.x) {
       if(1==newV[k]) iv[it[k]]=igv;
diff --git a/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml b/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
index 6ab97d3171d8e..763b2403a255d 100644
--- a/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
+++ b/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
@@ -25,3 +25,9 @@
   <flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
   <flags CXXFLAGS="-g"/>
 </bin>
+
+
+<bin file="cpuVertexFinder_t.cpp" name="cpuVertexFinder_t">
+  <flags CXXFLAGS="-g -DGPU_DEBUG"/>
+</bin>
+
diff --git a/RecoPixelVertexing/PixelVertexFinding/test/gpuVertexFinder_t.cu b/RecoPixelVertexing/PixelVertexFinding/test/gpuVertexFinder_t.cu
index 56c8ad95b1cea..a7906fe0d03f5 100644
--- a/RecoPixelVertexing/PixelVertexFinding/test/gpuVertexFinder_t.cu
+++ b/RecoPixelVertexing/PixelVertexFinding/test/gpuVertexFinder_t.cu
@@ -1,270 +1 @@
-#include <cmath>
-#include <cstdint>
-#include <iostream>
-#include <random>
-#include <vector>
-
-#include <cuda/api_wrappers.h>
-
-#include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
-#ifdef USE_DBSCAN
-#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h"
-#elif USE_ITERATIVE
-#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h"
-#else
-#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h"
-#endif
-
-#include "RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h"
-#include "RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h"
-#include "RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h"
-
-using namespace gpuVertexFinder;
-
-struct Event {
-  std::vector<float> zvert;
-  std::vector<uint16_t>  itrack;
-  std::vector<float> ztrack;
-  std::vector<float> eztrack;
-  std::vector<float> pttrack;
-  std::vector<uint16_t> ivert;
-};
-
-struct ClusterGenerator {
-
-  explicit ClusterGenerator(float nvert, float ntrack) :
-    rgen(-13.,13), errgen(0.005,0.025), clusGen(nvert), trackGen(ntrack), gauss(0.,1.), ptGen(1.)
-  {}
-
-  void operator()(Event & ev) {
-
-    int nclus = clusGen(reng);
-    ev.zvert.resize(nclus);
-    ev.itrack.resize(nclus);
-    for (auto & z : ev.zvert) { 
-       z = 3.5f*gauss(reng);
-    }
-
-    ev.ztrack.clear(); 
-    ev.eztrack.clear();
-    ev.ivert.clear();
-    for (int iv=0; iv<nclus; ++iv) {
-      auto nt = trackGen(reng);
-      ev.itrack[nclus] = nt;
-      for (int it=0; it<nt; ++it) {
-       auto err = errgen(reng); // reality is not flat....
-       ev.ztrack.push_back(ev.zvert[iv]+err*gauss(reng));
-       ev.eztrack.push_back(err*err);
-       ev.ivert.push_back(iv);
-       ev.pttrack.push_back( (iv==5? 1.f:0.5f) + ptGen(reng) );
-       ev.pttrack.back()*=ev.pttrack.back();
-      }
-    }
-    // add noise
-    auto nt = 2*trackGen(reng);
-    for (int it=0; it<nt; ++it) {
-      auto err = 0.03f;
-      ev.ztrack.push_back(rgen(reng));
-      ev.eztrack.push_back(err*err);
-      ev.ivert.push_back(9999);
-      ev.pttrack.push_back( 0.5f + ptGen(reng) );
-      ev.pttrack.back()*=ev.pttrack.back();
-    }
-
-  }
-
-  std::mt19937 reng;
-  std::uniform_real_distribution<float> rgen;
-  std::uniform_real_distribution<float> errgen;
-  std::poisson_distribution<int> clusGen;
-  std::poisson_distribution<int> trackGen;
-  std::normal_distribution<float> gauss;
-  std::exponential_distribution<float> ptGen;
-
-};
-
-// a macro SORRY
-#define LOC_ONGPU(M) ((char*)(onGPU_d.get()) +offsetof(OnGPU,M))
-
-
-__global__
-void print(OnGPU const * pdata) {
-  auto const & __restrict__ data = *pdata;
-  printf("nt,nv %d %d,%d\n",data.ntrks,data.nvFinal,data.nvIntermediate); 
-
-}
-
-int main() {
-  exitSansCUDADevices();
-
-  auto current_device = cuda::device::current::get();
-  
-  auto onGPU_d = cuda::memory::device::make_unique<OnGPU[]>(current_device, 1);
-
-  Event  ev;
-
-  float eps = 0.1f;
-  std::array<float,3> par{{eps, 0.01f,9.0f}};
-  for (int nav=30;nav<80;nav+=20){ 
-
-  ClusterGenerator gen(nav,10);
-
-  for (int i=8; i<20; ++i) {
-
-  auto  kk=i/4;  // M param
-
-  gen(ev);
-  
-  init<<<1,1,0,0>>>(onGPU_d.get());
-
-  std::cout << "v,t size " << ev.zvert.size() << ' ' << ev.ztrack.size() << std::endl;
-  auto nt = ev.ztrack.size();
-  cuda::memory::copy(LOC_ONGPU(ntrks),&nt,sizeof(uint32_t));
-  cuda::memory::copy(LOC_ONGPU(zt),ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
-  cuda::memory::copy(LOC_ONGPU(ezt2),ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
-  cuda::memory::copy(LOC_ONGPU(ptt2),ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
-  
-  std::cout << "M eps, pset " << kk << ' ' << eps << ' ' << (i%4) << std::endl;
-  
-#define CLUSTERIZE clusterTracks
-
-  if ( (i%4) == 0 ) par = {{eps, 0.02f,12.0f}};
-  if ( (i%4) == 1 ) par = {{eps, 0.02f,9.0f}};
-  if ( (i%4) == 2 ) par = {{eps, 0.01f,9.0f}};
-  if ( (i%4) == 3 ) par = {{0.7f*eps, 0.01f,9.0f}};
-
-  print<<<1,1,0,0>>>(onGPU_d.get());
-  cudaCheck(cudaGetLastError());
-  cudaDeviceSynchronize();
-
-  cuda::launch(CLUSTERIZE,
-		 { 1, 512+256 },
-		 onGPU_d.get(),kk,par[0],
-		 par[1],par[2]
-		 );
-  print<<<1,1,0,0>>>(onGPU_d.get());
-
-  cudaCheck(cudaGetLastError());
-  cudaDeviceSynchronize();
-
-
-  cuda::launch(fitVertices, 
-               { 1,1024-256 },
-               onGPU_d.get(),50.f
-              );
-  cudaCheck(cudaGetLastError());
-
-
-
-  uint32_t nv;
-  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
-  if (nv==0) {
-    std::cout << "NO VERTICES???" << std::endl;
-    continue;
-  }
-  float chi2[2*nv];  // make space for splitting...
-  float zv[2*nv];
-  float wv[2*nv];
-  float ptv2[2*nv];
-  int32_t nn[2*nv];
-  uint16_t ind[2*nv];
-
-  cuda::memory::copy(&nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
-  cuda::memory::copy(&chi2, LOC_ONGPU(chi2), nv*sizeof(float));
-
-  for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]);
-  {
-    auto mx = std::minmax_element(chi2,chi2+nv);
-    std::cout << "after fit nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
-  }
-
-  cuda::launch(fitVertices,
-               { 1,1024-256 },
-               onGPU_d.get(), 50.f
-              );
-  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
-  cuda::memory::copy(&nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
-  cuda::memory::copy(&chi2, LOC_ONGPU(chi2), nv*sizeof(float));
-
-  for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]);
-  {
-    auto mx = std::minmax_element(chi2,chi2+nv);
-    std::cout << "before splitting nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
-  }
-
-  cuda::launch(splitVertices,
-               { 1024, 64 },
-               onGPU_d.get(),
-               9.f
-              );
- cuda::memory::copy(&nv, LOC_ONGPU(nvIntermediate), sizeof(uint32_t));
-
- std::cout << "after split " << nv << std::endl; 
-
-  cuda::launch(fitVertices,
-               { 1,1024-256 },
-               onGPU_d.get(),5000.f
-              );
-  cudaCheck(cudaGetLastError());
-
-  cuda::launch(sortByPt2,
-               { 1, 256 },
-               onGPU_d.get()
-              );
-  cudaCheck(cudaGetLastError());
-  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
-
-  if (nv==0) {
-    std::cout << "NO VERTICES???" << std::endl;
-    continue;
-  }
-
-
-  cuda::memory::copy(&zv, LOC_ONGPU(zv), nv*sizeof(float));
-  cuda::memory::copy(&wv, LOC_ONGPU(wv), nv*sizeof(float));
-  cuda::memory::copy(&chi2, LOC_ONGPU(chi2), nv*sizeof(float));
-  cuda::memory::copy(&ptv2, LOC_ONGPU(ptv2), nv*sizeof(float));
-  cuda::memory::copy(&nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
-  cuda::memory::copy(&ind, LOC_ONGPU(sortInd), nv*sizeof(uint16_t));
-  for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]); 
-  {
-    auto mx = std::minmax_element(chi2,chi2+nv);
-    std::cout << "nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
-  }
-
-  {
-    auto mx = std::minmax_element(wv,wv+nv);
-    std::cout << "min max error " << 1./std::sqrt(*mx.first) << ' ' <<  1./std::sqrt(*mx.second) << std::endl;
-  }
-
-  {
-    auto mx = std::minmax_element(ptv2,ptv2+nv);
-    std::cout << "min max ptv2 " << *mx.first << ' ' <<  *mx.second << std::endl;
-    std::cout << "min max ptv2 " << ptv2[ind[0]] << ' ' <<  ptv2[ind[nv-1]] << " at "  << ind[0] << ' ' << ind[nv-1] << std::endl;
-
-  }  
-
-  float dd[nv];
-  for (auto kv=0U; kv<nv; ++kv) {
-   auto zr = zv[kv];
-   auto md=500.0f;
-   for (auto zs : ev.ztrack) { 
-     auto d = std::abs(zr-zs);
-     md = std::min(d,md);
-   }
-   dd[kv] = md;
-  }
-  if (i==6) {
-    for (auto d:dd) std::cout << d << ' ';
-    std::cout << std::endl;
-  }
-  auto mx = std::minmax_element(dd,dd+nv);
-  float rms=0;
-  for (auto d:dd) rms+=d*d; rms = std::sqrt(rms)/(nv-1);
-  std::cout << "min max rms " << *mx.first << ' ' << *mx.second << ' ' << rms << std::endl;
-
-  } // loop on events
-  } // lopp on ave vert
-  
-  return 0;
-}
+#include "VertexFinder_t.h"

From fb702ccd1deb1631fa9561b1f570ff9f90f7c3fb Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 15 Feb 2019 14:59:09 +0100
Subject: [PATCH 50/95] cpu and gpu same

---
 .../PixelVertexFinding/test/VertexFinder_t.h  | 356 ++++++++++++++++++
 .../test/cpuVertexFinder_t.cpp                |   1 +
 2 files changed, 357 insertions(+)
 create mode 100644 RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
 create mode 100644 RecoPixelVertexing/PixelVertexFinding/test/cpuVertexFinder_t.cpp

diff --git a/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h b/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
new file mode 100644
index 0000000000000..292bdb29a4ebe
--- /dev/null
+++ b/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
@@ -0,0 +1,356 @@
+#include <cmath>
+#include <cstdint>
+#include <iostream>
+#include <random>
+#include <vector>
+
+#include <cuda/api_wrappers.h>
+
+#include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
+#ifdef USE_DBSCAN
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h"
+#elif USE_ITERATIVE
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h"
+#else
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h"
+#endif
+
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h"
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h"
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h"
+
+using namespace gpuVertexFinder;
+
+struct Event {
+  std::vector<float> zvert;
+  std::vector<uint16_t>  itrack;
+  std::vector<float> ztrack;
+  std::vector<float> eztrack;
+  std::vector<float> pttrack;
+  std::vector<uint16_t> ivert;
+};
+
+struct ClusterGenerator {
+
+  explicit ClusterGenerator(float nvert, float ntrack) :
+    rgen(-13.,13), errgen(0.005,0.025), clusGen(nvert), trackGen(ntrack), gauss(0.,1.), ptGen(1.)
+  {}
+
+  void operator()(Event & ev) {
+
+    int nclus = clusGen(reng);
+    ev.zvert.resize(nclus);
+    ev.itrack.resize(nclus);
+    for (auto & z : ev.zvert) { 
+       z = 3.5f*gauss(reng);
+    }
+
+    ev.ztrack.clear(); 
+    ev.eztrack.clear();
+    ev.ivert.clear();
+    for (int iv=0; iv<nclus; ++iv) {
+      auto nt = trackGen(reng);
+      ev.itrack[nclus] = nt;
+      for (int it=0; it<nt; ++it) {
+       auto err = errgen(reng); // reality is not flat....
+       ev.ztrack.push_back(ev.zvert[iv]+err*gauss(reng));
+       ev.eztrack.push_back(err*err);
+       ev.ivert.push_back(iv);
+       ev.pttrack.push_back( (iv==5? 1.f:0.5f) + ptGen(reng) );
+       ev.pttrack.back()*=ev.pttrack.back();
+      }
+    }
+    // add noise
+    auto nt = 2*trackGen(reng);
+    for (int it=0; it<nt; ++it) {
+      auto err = 0.03f;
+      ev.ztrack.push_back(rgen(reng));
+      ev.eztrack.push_back(err*err);
+      ev.ivert.push_back(9999);
+      ev.pttrack.push_back( 0.5f + ptGen(reng) );
+      ev.pttrack.back()*=ev.pttrack.back();
+    }
+
+  }
+
+  std::mt19937 reng;
+  std::uniform_real_distribution<float> rgen;
+  std::uniform_real_distribution<float> errgen;
+  std::poisson_distribution<int> clusGen;
+  std::poisson_distribution<int> trackGen;
+  std::normal_distribution<float> gauss;
+  std::exponential_distribution<float> ptGen;
+
+};
+
+// a macro SORRY
+#define LOC_ONGPU(M) ((char*)(onGPU_d.get()) +offsetof(OnGPU,M))
+
+
+__global__
+void print(OnGPU const * pdata) {
+  auto const & __restrict__ data = *pdata;
+  printf("nt,nv %d %d,%d\n",data.ntrks,data.nvFinal,data.nvIntermediate); 
+
+}
+
+int main() {
+
+#ifdef __CUDACC__
+  exitSansCUDADevices();
+
+  auto current_device = cuda::device::current::get();
+
+  auto onGPU_d = cuda::memory::device::make_unique<OnGPU[]>(current_device, 1);
+#else
+  auto onGPU_d = std::make_unique<OnGPU>();
+#endif
+
+  Event  ev;
+
+  float eps = 0.1f;
+  std::array<float,3> par{{eps, 0.01f,9.0f}};
+  for (int nav=30;nav<80;nav+=20){ 
+
+  ClusterGenerator gen(nav,10);
+
+  for (int i=8; i<20; ++i) {
+
+  auto  kk=i/4;  // M param
+
+  gen(ev);
+  
+#ifdef __CUDACC__
+  init<<<1,1,0,0>>>(onGPU_d.get());
+#else
+  onGPU_d->init();
+#endif
+
+  std::cout << "v,t size " << ev.zvert.size() << ' ' << ev.ztrack.size() << std::endl;
+  auto nt = ev.ztrack.size();
+#ifdef __CUDACC__
+  cuda::memory::copy(LOC_ONGPU(ntrks),&nt,sizeof(uint32_t));
+  cuda::memory::copy(LOC_ONGPU(zt),ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
+  cuda::memory::copy(LOC_ONGPU(ezt2),ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
+  cuda::memory::copy(LOC_ONGPU(ptt2),ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
+#else
+  ::memcpy(LOC_ONGPU(ntrks),&nt,sizeof(uint32_t));  
+  ::memcpy(LOC_ONGPU(zt),ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
+  ::memcpy(LOC_ONGPU(ezt2),ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
+  ::memcpy(LOC_ONGPU(ptt2),ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
+#endif
+
+  std::cout << "M eps, pset " << kk << ' ' << eps << ' ' << (i%4) << std::endl;
+  
+#define CLUSTERIZE clusterTracks
+
+  if ( (i%4) == 0 ) par = {{eps, 0.02f,12.0f}};
+  if ( (i%4) == 1 ) par = {{eps, 0.02f,9.0f}};
+  if ( (i%4) == 2 ) par = {{eps, 0.01f,9.0f}};
+  if ( (i%4) == 3 ) par = {{0.7f*eps, 0.01f,9.0f}};
+
+  uint32_t nv=0;
+#ifdef __CUDACC__
+  print<<<1,1,0,0>>>(onGPU_d.get());
+  cudaCheck(cudaGetLastError());
+  cudaDeviceSynchronize();
+
+  cuda::launch(CLUSTERIZE,
+		 { 1, 512+256 },
+		 onGPU_d.get(),kk,par[0],
+		 par[1],par[2]
+		 );
+  print<<<1,1,0,0>>>(onGPU_d.get());
+
+  cudaCheck(cudaGetLastError());
+  cudaDeviceSynchronize();
+
+
+  cuda::launch(fitVertices, 
+               { 1,1024-256 },
+               onGPU_d.get(),50.f
+              );
+  cudaCheck(cudaGetLastError());
+  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
+
+#else
+  print(onGPU_d.get());
+  CLUSTERIZE(
+                 onGPU_d.get(),kk,par[0],
+                 par[1],par[2]
+                 );
+  print(onGPU_d.get());
+  fitVertices(
+               onGPU_d.get(),50.f
+              );
+  nv = onGPU_d->nvFinal;
+#endif
+
+  if (nv==0) {
+    std::cout << "NO VERTICES???" << std::endl;
+    continue;
+  }
+
+  float * zv = nullptr;
+  float * wv = nullptr;
+  float * ptv2 = nullptr;
+  int32_t * nn = nullptr;
+  uint16_t * ind = nullptr;
+
+  // keep chi2 separated...
+  float chi2[2*nv];  // make space for splitting...
+
+#ifdef __CUDACC__
+  float hzv[2*nv];
+  float hwv[2*nv];
+  float hptv2[2*nv];
+  int32_t hnn[2*nv];
+  uint16_t hind[2*nv];
+
+  zv = hzv;
+  wv = hwv;
+  ptv2 = hptv2;
+  nn = hnn;
+  ind = hind;
+#else
+  zv = onGPU_d->zv;
+  wv = onGPU_d->wv;
+  ptv2 = onGPU_d->ptv2;
+  nn = onGPU_d->nn;
+  ind = onGPU_d->sortInd;
+#endif
+
+#ifdef __CUDACC__
+  cuda::memory::copy(nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+#else
+  memcpy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+#endif
+
+  for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]);
+  {
+    auto mx = std::minmax_element(chi2,chi2+nv);
+    std::cout << "after fit nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
+  }
+
+#ifdef __CUDACC__
+  cuda::launch(fitVertices,
+               { 1,1024-256 },
+               onGPU_d.get(), 50.f
+              );
+  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
+  cuda::memory::copy(nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+#else
+  fitVertices(
+               onGPU_d.get(),50.f
+              );
+  nv = onGPU_d->nvFinal;
+  memcpy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+#endif
+
+  for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]);
+  {
+    auto mx = std::minmax_element(chi2,chi2+nv);
+    std::cout << "before splitting nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
+  }
+
+#ifdef __CUDACC__
+  // one vertex per block!!!
+  cuda::launch(splitVertices,
+               { 1024, 64 },
+               onGPU_d.get(),
+               9.f
+              );
+  cuda::memory::copy(&nv, LOC_ONGPU(nvIntermediate), sizeof(uint32_t));
+#else
+  gridDim.x = 1024; // nv ????
+  assert(blockIdx.x==0);
+  for(;blockIdx.x<gridDim.x; ++blockIdx.x)
+    splitVertices(
+               onGPU_d.get(),
+               9.f
+              );
+   resetGrid();
+   nv = onGPU_d->nvIntermediate;
+#endif
+ std::cout << "after split " << nv << std::endl; 
+
+
+#ifdef __CUDACC__
+  cuda::launch(fitVertices,
+               { 1,1024-256 },
+               onGPU_d.get(),5000.f
+              );
+  cudaCheck(cudaGetLastError());
+
+  cuda::launch(sortByPt2,
+               { 1, 256 },
+               onGPU_d.get()
+              );
+  cudaCheck(cudaGetLastError());
+  cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
+#else
+  fitVertices(onGPU_d.get(),5000.f);
+  sortByPt2(onGPU_d.get());
+  nv = onGPU_d->nvFinal;
+  memcpy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+#endif
+
+
+  if (nv==0) {
+    std::cout << "NO VERTICES???" << std::endl;
+    continue;
+  }
+
+#ifdef __CUDACC__
+  cuda::memory::copy(zv, LOC_ONGPU(zv), nv*sizeof(float));
+  cuda::memory::copy(wv, LOC_ONGPU(wv), nv*sizeof(float));
+  cuda::memory::copy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
+  cuda::memory::copy(ptv2, LOC_ONGPU(ptv2), nv*sizeof(float));
+  cuda::memory::copy(nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(ind, LOC_ONGPU(sortInd), nv*sizeof(uint16_t));
+#endif
+  for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]); 
+  {
+    auto mx = std::minmax_element(chi2,chi2+nv);
+    std::cout << "nv, min max chi2 " << nv << " " << *mx.first << ' ' <<  *mx.second << std::endl;
+  }
+
+  {
+    auto mx = std::minmax_element(wv,wv+nv);
+    std::cout << "min max error " << 1./std::sqrt(*mx.first) << ' ' <<  1./std::sqrt(*mx.second) << std::endl;
+  }
+
+  {
+    auto mx = std::minmax_element(ptv2,ptv2+nv);
+    std::cout << "min max ptv2 " << *mx.first << ' ' <<  *mx.second << std::endl;
+    std::cout << "min max ptv2 " << ptv2[ind[0]] << ' ' <<  ptv2[ind[nv-1]] << " at "  << ind[0] << ' ' << ind[nv-1] << std::endl;
+
+  }  
+
+  float dd[nv];
+  for (auto kv=0U; kv<nv; ++kv) {
+   auto zr = zv[kv];
+   auto md=500.0f;
+   for (auto zs : ev.ztrack) { 
+     auto d = std::abs(zr-zs);
+     md = std::min(d,md);
+   }
+   dd[kv] = md;
+  }
+  if (i==6) {
+    for (auto d:dd) std::cout << d << ' ';
+    std::cout << std::endl;
+  }
+  auto mx = std::minmax_element(dd,dd+nv);
+  float rms=0;
+  for (auto d:dd) rms+=d*d; 
+  rms = std::sqrt(rms)/(nv-1);
+  std::cout << "min max rms " << *mx.first << ' ' << *mx.second << ' ' << rms << std::endl;
+
+  } // loop on events
+  } // lopp on ave vert
+  
+  return 0;
+}
diff --git a/RecoPixelVertexing/PixelVertexFinding/test/cpuVertexFinder_t.cpp b/RecoPixelVertexing/PixelVertexFinding/test/cpuVertexFinder_t.cpp
new file mode 100644
index 0000000000000..a7906fe0d03f5
--- /dev/null
+++ b/RecoPixelVertexing/PixelVertexFinding/test/cpuVertexFinder_t.cpp
@@ -0,0 +1 @@
+#include "VertexFinder_t.h"

From d1e8e800c0a535fec252cb6e91805b00308e3845 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 15 Feb 2019 16:45:00 +0100
Subject: [PATCH 51/95] add two alternative (faster) track clusterizers

---
 .../PixelVertexFinding/src/gpuClusterTracks.h | 203 +---------------
 .../src/gpuClusterTracksByDensity.h           | 206 ++++++++++++++++
 .../src/gpuClusterTracksDBSCAN.h              | 224 ++++++++++++++++++
 .../src/gpuClusterTracksIterative.h           | 201 ++++++++++++++++
 .../PixelVertexFinding/src/gpuVertexFinder.cu |   2 +-
 .../PixelVertexFinding/test/BuildFile.xml     |  21 +-
 .../PixelVertexFinding/test/VertexFinder_t.h  |   7 +-
 7 files changed, 656 insertions(+), 208 deletions(-)
 create mode 100644 RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
 create mode 100644 RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
 create mode 100644 RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h

diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
index 3828ab7c3c756..ebefbce18bf2a 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h
@@ -1,201 +1,2 @@
-#ifndef RecoPixelVertexing_PixelVertexFinding_clusterTracks_H
-#define RecoPixelVertexing_PixelVertexFinding_clusterTracks_H
-
-#include <algorithm>
-#include <cmath>
-#include <cstdint>
-
-#include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
-
-#include "gpuVertexFinder.h"
-
-namespace gpuVertexFinder {
-
-  // this algo does not really scale as it works in a single block...
-  // enough for <10K tracks we have
-  __global__ 
-  void clusterTracks(
-		     OnGPU * pdata,
-		     int minT,  // min number of neighbours to be "core"
-		     float eps, // max absolute distance to cluster
-		     float errmax, // max error to be "seed"
-		     float chi2max   // max normalized distance to cluster
-		     )  {
-
-    constexpr bool verbose = false; // in principle the compiler should optmize out if false
-
-
-    if(verbose && 0==threadIdx.x) printf("params %d %f %f %f\n",minT,eps,errmax,chi2max);
-    
-    auto er2mx = errmax*errmax;
-    
-    auto & __restrict__ data = *pdata;
-    auto nt = data.ntrks;
-    float const * __restrict__ zt = data.zt;
-    float const * __restrict__ ezt2 = data.ezt2;
-
-    uint32_t & nvFinal = data.nvFinal;
-    uint32_t & nvIntermediate = data.nvIntermediate;
-    
-    uint8_t  * __restrict__ izt = data.izt;
-    int32_t * __restrict__ nn = data.nn;
-    int32_t * __restrict__ iv = data.iv;
-    
-    assert(pdata);
-    assert(zt);
-    
-    using Hist=HistoContainer<uint8_t,256,16000,8,uint16_t>;
-    __shared__ Hist hist;
-    __shared__ typename Hist::Counter ws[32];
-    for (auto j=threadIdx.x; j<Hist::totbins(); j+=blockDim.x) { hist.off[j]=0;}
-    __syncthreads();
-    
-    if(verbose && 0==threadIdx.x) printf("booked hist with %d bins, size %d for %d tracks\n",hist.nbins(),hist.capacity(),nt);
-    
-    assert(nt<=hist.capacity());
-    
-    // fill hist  (bin shall be wider than "eps")
-    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      assert(i<OnGPU::MAXTRACKS);
-      int iz =  int(zt[i]*10.); // valid if eps<=0.1
-      // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
-      iz = std::min(std::max(iz, INT8_MIN),INT8_MAX);
-      izt[i]=iz-INT8_MIN;
-      assert(iz-INT8_MIN >= 0);
-      assert(iz-INT8_MIN < 256);
-      hist.count(izt[i]);
-      iv[i]=i;
-      nn[i]=0;
-    }
-    __syncthreads();
-    if (threadIdx.x<32) ws[threadIdx.x]=0;  // used by prefix scan...
-    __syncthreads();
-    hist.finalize(ws);
-    __syncthreads();
-    assert(hist.size()==nt);
-    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      hist.fill(izt[i],uint16_t(i));
-    }
-    __syncthreads();    
-
-
-    // count neighbours
-    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      if (ezt2[i]>er2mx) continue;
-      auto loop = [&](uint32_t j) {
-        if (i==j) return;
-        auto dist = std::abs(zt[i]-zt[j]);
-        if (dist>eps) return;
-        if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
-        nn[i]++;
-      };
-
-      forEachInBins(hist,izt[i],1,loop);
-    }
-
-
-    __shared__ int nloops;
-    nloops=0;
-      
-    __syncthreads();
-
-
-    
-    // cluster seeds only
-    bool more = true;
-    while (__syncthreads_or(more)) {
-     if (1==nloops%2) {
-      for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-        auto m = iv[i];
-        while (m!=iv[m]) m=iv[m];
-        iv[i]=m;
-      }
-      }  else {
-       more=false;
-       for (auto  k = threadIdx.x; k < hist.size(); k += blockDim.x) {
-        auto p = hist.begin()+k;
-        auto i = (*p);
-        auto be = std::min(Hist::bin(izt[i])+1,int(hist.nbins()-1));
-	if (nn[i]<minT) continue; // DBSCAN core rule
-	auto loop = [&](uint32_t j) {
-	  assert (i!=j);
-	  if (nn[j]<minT) return;  // DBSCAN core rule
-          auto dist = std::abs(zt[i]-zt[j]);
-          if (dist>eps) return;
-	  if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
-	  auto old = atomicMin(&iv[j], iv[i]);
-	  if (old != iv[i]) {
-	    // end the loop only if no changes were applied
-	    more = true;
-	  }
-	  atomicMin(&iv[i], old);
-	};
-        ++p;
-        for (;p<hist.end(be);++p) loop(*p);
-       } // for i
-      }
-      if (threadIdx.x==0) ++nloops;
-    } // while
-    
-    
-    
-    // collect edges (assign to closest cluster of closest point??? here to closest point)
-    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      //    if (nn[i]==0 || nn[i]>=minT) continue;    // DBSCAN edge rule
-      if (nn[i]>=minT) continue;    // DBSCAN edge rule
-      float mdist=eps;
-      auto loop = [&](int j) {
-	if (nn[j]<minT) return;  // DBSCAN core rule
-	auto dist = std::abs(zt[i]-zt[j]);
-	if (dist>mdist) return;
-	if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return; // needed?
-	mdist=dist;
-	iv[i] = iv[j]; // assign to cluster (better be unique??)
-      };
-      forEachInBins(hist,izt[i],1,loop);
-    }
-    
-    
-    __shared__ unsigned int foundClusters;
-    foundClusters = 0;
-    __syncthreads();
-    
-    // find the number of different clusters, identified by a tracks with clus[i] == i;
-    // mark these tracks with a negative id.
-    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      if (iv[i] == int(i)) {
-	if  (nn[i]>=minT) {
-	  auto old = atomicInc(&foundClusters, 0xffffffff);
-	  iv[i] = -(old + 1);
-	} else { // noise
-	  iv[i] = -9998;
-	}
-      }
-    }
-    __syncthreads();
-
-    assert(foundClusters<OnGPU::MAXVTX);
-    
-    // propagate the negative id to all the tracks in the cluster.
-    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      if (iv[i] >= 0) {
-	// mark each track in a cluster with the same id as the first one
-	iv[i] = iv[iv[i]];
-      }
-    }
-    __syncthreads();
-    
-    // adjust the cluster id to be a positive value starting from 0
-    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      iv[i] = - iv[i] - 1;
-    }
-    
-    nvIntermediate = nvFinal = foundClusters;
-
-    if(verbose && 0==threadIdx.x) printf("found %d proto vertices\n",foundClusters);
-
-  }
-
-}
-#endif
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h"
+#define CLUSTERIZE clusterTracksIterative
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
new file mode 100644
index 0000000000000..25e6efd730bf9
--- /dev/null
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
@@ -0,0 +1,206 @@
+#ifndef RecoPixelVertexing_PixelVertexFinding_clusterTracksByDensity_H
+#define RecoPixelVertexing_PixelVertexFinding_clusterTracksByDensity_H
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+
+#include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/radixSort.h"
+
+#include "gpuVertexFinder.h"
+
+namespace gpuVertexFinder {
+
+  // this algo does not really scale as it works in a single block...
+  // enough for <10K tracks we have
+  // 
+  // based on Rodrighez&Laio algo
+  //
+  __global__ 
+  void clusterTracksByDensity(
+		     OnGPU * pdata,
+		     int minT,  // min number of neighbours to be "seed"
+		     float eps, // max absolute distance to cluster
+		     float errmax, // max error to be "seed"
+		     float chi2max   // max normalized distance to cluster
+		     )  {
+
+    constexpr bool verbose = false; // in principle the compiler should optmize out if false
+
+
+    if(verbose && 0==threadIdx.x) printf("params %d %f %f %f\n",minT,eps,errmax,chi2max);
+    
+    auto er2mx = errmax*errmax;
+    
+    auto & __restrict__ data = *pdata;
+    auto nt = data.ntrks;
+    float const * __restrict__ zt = data.zt;
+    float const * __restrict__ ezt2 = data.ezt2;
+
+    uint32_t & nvFinal = data.nvFinal;
+    uint32_t & nvIntermediate = data.nvIntermediate;
+    
+    uint8_t  * __restrict__ izt = data.izt;
+    int32_t * __restrict__ nn = data.nn;
+    int32_t * __restrict__ iv = data.iv;
+    
+    assert(pdata);
+    assert(zt);
+    
+    using Hist=HistoContainer<uint8_t,256,16000,8,uint16_t>;
+    __shared__ Hist hist;
+    __shared__ typename Hist::Counter ws[32];
+    for (auto j=threadIdx.x; j<Hist::totbins(); j+=blockDim.x) { hist.off[j]=0;}
+    __syncthreads();
+    
+    if(verbose && 0==threadIdx.x) printf("booked hist with %d bins, size %d for %d tracks\n",hist.nbins(),hist.capacity(),nt);
+    
+    assert(nt<=hist.capacity());
+    
+    // fill hist  (bin shall be wider than "eps")
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      assert(i<OnGPU::MAXTRACKS);
+      int iz =  int(zt[i]*10.); // valid if eps<=0.1
+      // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
+      iz = std::min(std::max(iz, INT8_MIN),INT8_MAX);
+      izt[i]=iz-INT8_MIN;
+      assert(iz-INT8_MIN >= 0);
+      assert(iz-INT8_MIN < 256);
+      hist.count(izt[i]);
+      iv[i]=i;
+      nn[i]=0;
+    }
+    __syncthreads();
+    if (threadIdx.x<32) ws[threadIdx.x]=0;  // used by prefix scan...
+    __syncthreads();
+    hist.finalize(ws);
+    __syncthreads();
+    assert(hist.size()==nt);
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      hist.fill(izt[i],uint16_t(i));
+    }
+    __syncthreads();    
+
+
+    // count neighbours
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (ezt2[i]>er2mx) continue;
+      auto loop = [&](int j) {
+        if (i==j) return;
+        auto dist = std::abs(zt[i]-zt[j]);
+        if (dist>eps) return;
+        if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
+        nn[i]++;
+      };
+
+      forEachInBins(hist,izt[i],1,loop);
+    }
+
+    __syncthreads();
+
+    // find closest above me .... (we ignore the possibility of two j at same distance from i)
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      float mdist=eps;
+      auto loop = [&](int j) {
+        if (nn[j]<nn[i]) return;
+        if (nn[j]==nn[i] && zt[j]>=zt[i]) return; // if equal use natural order...
+        auto dist = std::abs(zt[i]-zt[j]);
+        if (dist>mdist) return;
+        if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;  // (break natural order???)
+        mdist=dist;
+        iv[i] = j; // assign to cluster (better be unique??)
+      };
+      forEachInBins(hist,izt[i],1,loop);
+    }
+
+   __syncthreads();
+
+#ifdef GPU_DEBUG
+   //  mini verification
+   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    if (iv[i]!=i) assert(iv[iv[i]]!=i);  
+   }
+   __syncthreads();
+#endif
+
+   // consolidate graph (percolate index of seed)
+   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+       auto m = iv[i];
+       while (m!=iv[m]) m=iv[m];
+       iv[i]=m;
+   }
+
+#ifdef GPU_DEBUG
+   __syncthreads();
+   //  mini verification
+   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    if (iv[i]!=i) assert(iv[iv[i]]!=i);
+   }
+#endif
+
+#ifdef GPU_DEBUG
+  // and verify that we did not spit any cluster...
+  for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      auto minJ=i;
+      auto mdist=eps;
+      auto loop = [&](int j) {
+        if (nn[j]<nn[i]) return;
+        if (nn[j]==nn[i] && zt[j]>=zt[i]) return; // if equal use natural order...
+        auto dist = std::abs(zt[i]-zt[j]);
+        if (dist>mdist) return;
+        if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
+        mdist = dist;
+        minJ=j;
+      };
+      forEachInBins(hist,izt[i],1,loop);
+      // should belong to the same cluster...
+      assert(iv[i]==iv[minJ]);
+      assert(nn[i]<=nn[iv[i]]);
+  }
+  __syncthreads();
+#endif
+    
+    __shared__ unsigned int foundClusters;
+    foundClusters = 0;
+    __syncthreads();
+    
+    // find the number of different clusters, identified by a tracks with clus[i] == i and density larger than threshold;
+    // mark these tracks with a negative id.
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] == i) {
+	if  (nn[i]>=minT) {
+	  auto old = atomicInc(&foundClusters, 0xffffffff);
+	  iv[i] = -(old + 1);
+	} else { // noise
+	  iv[i] = -9998;
+	}
+      }
+    }
+    __syncthreads();
+
+    assert(foundClusters<OnGPU::MAXVTX);
+    
+    // propagate the negative id to all the tracks in the cluster.
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] >= 0) {
+	// mark each track in a cluster with the same id as the first one
+	iv[i] = iv[iv[i]];
+      }
+    }
+    __syncthreads();
+    
+    // adjust the cluster id to be a positive value starting from 0
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      iv[i] = - iv[i] - 1;
+    }
+    
+    nvIntermediate = nvFinal = foundClusters;
+
+    if(verbose && 0==threadIdx.x) printf("found %d proto vertices\n",foundClusters);
+
+  }
+
+}
+#endif
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
new file mode 100644
index 0000000000000..365546a5b0db8
--- /dev/null
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
@@ -0,0 +1,224 @@
+#ifndef RecoPixelVertexing_PixelVertexFinding_clusterTracksDBSCAN_H
+#define RecoPixelVertexing_PixelVertexFinding_clusterTracksDBSCAN_H
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+
+#include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/radixSort.h"
+
+#include "gpuVertexFinder.h"
+
+namespace gpuVertexFinder {
+
+  // this algo does not really scale as it works in a single block...
+  // enough for <10K tracks we have
+  __global__ 
+  void clusterTracksDBSCAN(
+		     OnGPU * pdata,
+		     int minT,  // min number of neighbours to be "core"
+		     float eps, // max absolute distance to cluster
+		     float errmax, // max error to be "seed"
+		     float chi2max   // max normalized distance to cluster
+		     )  {
+
+    constexpr bool verbose = false; // in principle the compiler should optmize out if false
+
+
+    if(verbose && 0==threadIdx.x) printf("params %d %f %f %f\n",minT,eps,errmax,chi2max);
+    
+    auto er2mx = errmax*errmax;
+    
+    auto & __restrict__ data = *pdata;
+    auto nt = data.ntrks;
+    float const * __restrict__ zt = data.zt;
+    float const * __restrict__ ezt2 = data.ezt2;
+
+    uint32_t & nvFinal = data.nvFinal;
+    uint32_t & nvIntermediate = data.nvIntermediate;
+    
+    uint8_t  * __restrict__ izt = data.izt;
+    int32_t * __restrict__ nn = data.nn;
+    int32_t * __restrict__ iv = data.iv;
+    
+    assert(pdata);
+    assert(zt);
+    
+    using Hist=HistoContainer<uint8_t,256,16000,8,uint16_t>;
+    __shared__ Hist hist;
+    __shared__ typename Hist::Counter ws[32];
+    for (auto j=threadIdx.x; j<Hist::totbins(); j+=blockDim.x) { hist.off[j]=0;}
+    __syncthreads();
+    
+    if(verbose && 0==threadIdx.x) printf("booked hist with %d bins, size %d for %d tracks\n",hist.nbins(),hist.capacity(),nt);
+    
+    assert(nt<=hist.capacity());
+    
+    // fill hist  (bin shall be wider than "eps")
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      assert(i<OnGPU::MAXTRACKS);
+      int iz =  int(zt[i]*10.); // valid if eps<=0.1
+      // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
+      iz = std::min(std::max(iz, INT8_MIN),INT8_MAX);
+      izt[i]=iz-INT8_MIN;
+      assert(iz-INT8_MIN >= 0);
+      assert(iz-INT8_MIN < 256);
+      hist.count(izt[i]);
+      iv[i]=i;
+      nn[i]=0;
+    }
+    __syncthreads();
+    if (threadIdx.x<32) ws[threadIdx.x]=0;  // used by prefix scan...
+    __syncthreads();
+    hist.finalize(ws);
+    __syncthreads();
+    assert(hist.size()==nt);
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      hist.fill(izt[i],uint16_t(i));
+    }
+    __syncthreads();    
+
+
+    // count neighbours
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (ezt2[i]>er2mx) continue;
+      auto loop = [&](int j) {
+        if (i==j) return;
+        auto dist = std::abs(zt[i]-zt[j]);
+        if (dist>eps) return;
+//        if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
+        nn[i]++;
+      };
+
+      forEachInBins(hist,izt[i],1,loop);
+    }
+
+    __syncthreads();
+
+    // find NN with smaller z...
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (nn[i]<minT) continue;    // DBSCAN core rule
+      float mz=zt[i];
+      auto loop = [&](int j) {
+        if (zt[j]>=mz) return;
+        if (nn[j]<minT) return;    // DBSCAN core rule
+        auto dist = std::abs(zt[i]-zt[j]);
+        if (dist>eps) return;
+//        if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
+        mz = zt[j];
+        iv[i] = j; // assign to cluster (better be unique??)
+      };
+      forEachInBins(hist,izt[i],1,loop);
+    }
+
+   __syncthreads();
+
+#ifdef GPU_DEBUG
+   //  mini verification
+   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    if (iv[i]!=i) assert(iv[iv[i]]!=i);
+   }
+   __syncthreads();
+#endif
+
+
+   // consolidate graph (percolate index of seed)
+   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+       auto m = iv[i];
+       while (m!=iv[m]) m=iv[m];
+       iv[i]=m;
+   }
+
+   __syncthreads();
+
+#ifdef GPU_DEBUG
+   //  mini verification
+   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    if (iv[i]!=i) assert(iv[iv[i]]!=i);
+   }
+   __syncthreads();
+#endif
+
+#ifdef GPU_DEBUG
+  // and verify that we did not spit any cluster...
+  for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (nn[i]<minT) continue;    // DBSCAN core rule
+      assert(zt[iv[i]]<=zt[i]);
+      auto loop = [&](int j) {
+        if (nn[j]<minT) return;    // DBSCAN core rule
+        auto dist = std::abs(zt[i]-zt[j]);
+        if (dist>eps) return;
+      //  if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
+        // they should belong to the same cluster, isn't it?
+        if(iv[i]!=iv[j]) {
+          printf("ERROR %d %d %f %f %d\n",i,iv[i],zt[i],zt[iv[i]],iv[iv[i]]);
+          printf("      %d %d %f %f %d\n",j,iv[j],zt[j],zt[iv[j]],iv[iv[j]]);;
+        }
+        assert(iv[i]==iv[j]);
+      };
+      forEachInBins(hist,izt[i],1,loop);
+  }
+  __syncthreads();
+#endif
+    
+    // collect edges (assign to closest cluster of closest point??? here to closest point)
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      //    if (nn[i]==0 || nn[i]>=minT) continue;    // DBSCAN edge rule
+      if (nn[i]>=minT) continue;    // DBSCAN edge rule
+      float mdist=eps;
+      auto loop = [&](int j) {
+	if (nn[j]<minT) return;  // DBSCAN core rule
+	auto dist = std::abs(zt[i]-zt[j]);
+	if (dist>mdist) return;
+	if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return; // needed?
+	mdist=dist;
+	iv[i] = iv[j]; // assign to cluster (better be unique??)
+      };
+      forEachInBins(hist,izt[i],1,loop);
+    }
+    
+    
+    __shared__ unsigned int foundClusters;
+    foundClusters = 0;
+    __syncthreads();
+    
+    // find the number of different clusters, identified by a tracks with clus[i] == i;
+    // mark these tracks with a negative id.
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] == i) {
+	if  (nn[i]>=minT) {
+	  auto old = atomicInc(&foundClusters, 0xffffffff);
+	  iv[i] = -(old + 1);
+	} else { // noise
+	  iv[i] = -9998;
+	}
+      }
+    }
+    __syncthreads();
+
+    assert(foundClusters<OnGPU::MAXVTX);
+    
+    // propagate the negative id to all the tracks in the cluster.
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] >= 0) {
+	// mark each track in a cluster with the same id as the first one
+	iv[i] = iv[iv[i]];
+      }
+    }
+    __syncthreads();
+    
+    // adjust the cluster id to be a positive value starting from 0
+    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+      iv[i] = - iv[i] - 1;
+    }
+    
+    nvIntermediate = nvFinal = foundClusters;
+
+    if(verbose && 0==threadIdx.x) printf("found %d proto vertices\n",foundClusters);
+
+  }
+
+}
+#endif
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h
new file mode 100644
index 0000000000000..ded93b030e6ae
--- /dev/null
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h
@@ -0,0 +1,201 @@
+#ifndef RecoPixelVertexing_PixelVertexFinding_clusterTracksIterative_H
+#define RecoPixelVertexing_PixelVertexFinding_clusterTracksIterative_H
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+
+#include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
+#include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
+
+#include "gpuVertexFinder.h"
+
+namespace gpuVertexFinder {
+
+  // this algo does not really scale as it works in a single block...
+  // enough for <10K tracks we have
+  __global__ 
+  void clusterTracksIterative(
+		     OnGPU * pdata,
+		     int minT,  // min number of neighbours to be "core"
+		     float eps, // max absolute distance to cluster
+		     float errmax, // max error to be "seed"
+		     float chi2max   // max normalized distance to cluster
+		     )  {
+
+    constexpr bool verbose = false; // in principle the compiler should optmize out if false
+
+
+    if(verbose && 0==threadIdx.x) printf("params %d %f %f %f\n",minT,eps,errmax,chi2max);
+    
+    auto er2mx = errmax*errmax;
+    
+    auto & __restrict__ data = *pdata;
+    auto nt = data.ntrks;
+    float const * __restrict__ zt = data.zt;
+    float const * __restrict__ ezt2 = data.ezt2;
+
+    uint32_t & nvFinal = data.nvFinal;
+    uint32_t & nvIntermediate = data.nvIntermediate;
+    
+    uint8_t  * __restrict__ izt = data.izt;
+    int32_t * __restrict__ nn = data.nn;
+    int32_t * __restrict__ iv = data.iv;
+    
+    assert(pdata);
+    assert(zt);
+    
+    using Hist=HistoContainer<uint8_t,256,16000,8,uint16_t>;
+    __shared__ Hist hist;
+    __shared__ typename Hist::Counter ws[32];
+    for (auto j=threadIdx.x; j<Hist::totbins(); j+=blockDim.x) { hist.off[j]=0;}
+    __syncthreads();
+    
+    if(verbose && 0==threadIdx.x) printf("booked hist with %d bins, size %d for %d tracks\n",hist.nbins(),hist.capacity(),nt);
+    
+    assert(nt<=hist.capacity());
+    
+    // fill hist  (bin shall be wider than "eps")
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      assert(i<OnGPU::MAXTRACKS);
+      int iz =  int(zt[i]*10.); // valid if eps<=0.1
+      // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
+      iz = std::min(std::max(iz, INT8_MIN),INT8_MAX);
+      izt[i]=iz-INT8_MIN;
+      assert(iz-INT8_MIN >= 0);
+      assert(iz-INT8_MIN < 256);
+      hist.count(izt[i]);
+      iv[i]=i;
+      nn[i]=0;
+    }
+    __syncthreads();
+    if (threadIdx.x<32) ws[threadIdx.x]=0;  // used by prefix scan...
+    __syncthreads();
+    hist.finalize(ws);
+    __syncthreads();
+    assert(hist.size()==nt);
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      hist.fill(izt[i],uint16_t(i));
+    }
+    __syncthreads();    
+
+
+    // count neighbours
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (ezt2[i]>er2mx) continue;
+      auto loop = [&](uint32_t j) {
+        if (i==j) return;
+        auto dist = std::abs(zt[i]-zt[j]);
+        if (dist>eps) return;
+        if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
+        nn[i]++;
+      };
+
+      forEachInBins(hist,izt[i],1,loop);
+    }
+
+
+    __shared__ int nloops;
+    nloops=0;
+      
+    __syncthreads();
+
+
+    
+    // cluster seeds only
+    bool more = true;
+    while (__syncthreads_or(more)) {
+     if (1==nloops%2) {
+      for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+        auto m = iv[i];
+        while (m!=iv[m]) m=iv[m];
+        iv[i]=m;
+      }
+      }  else {
+       more=false;
+       for (auto  k = threadIdx.x; k < hist.size(); k += blockDim.x) {
+        auto p = hist.begin()+k;
+        auto i = (*p);
+        auto be = std::min(Hist::bin(izt[i])+1,int(hist.nbins()-1));
+	if (nn[i]<minT) continue; // DBSCAN core rule
+	auto loop = [&](uint32_t j) {
+	  assert (i!=j);
+	  if (nn[j]<minT) return;  // DBSCAN core rule
+          auto dist = std::abs(zt[i]-zt[j]);
+          if (dist>eps) return;
+	  if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return;
+	  auto old = atomicMin(&iv[j], iv[i]);
+	  if (old != iv[i]) {
+	    // end the loop only if no changes were applied
+	    more = true;
+	  }
+	  atomicMin(&iv[i], old);
+	};
+        ++p;
+        for (;p<hist.end(be);++p) loop(*p);
+       } // for i
+      }
+      if (threadIdx.x==0) ++nloops;
+    } // while
+    
+    
+    
+    // collect edges (assign to closest cluster of closest point??? here to closest point)
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      //    if (nn[i]==0 || nn[i]>=minT) continue;    // DBSCAN edge rule
+      if (nn[i]>=minT) continue;    // DBSCAN edge rule
+      float mdist=eps;
+      auto loop = [&](int j) {
+	if (nn[j]<minT) return;  // DBSCAN core rule
+	auto dist = std::abs(zt[i]-zt[j]);
+	if (dist>mdist) return;
+	if (dist*dist>chi2max*(ezt2[i]+ezt2[j])) return; // needed?
+	mdist=dist;
+	iv[i] = iv[j]; // assign to cluster (better be unique??)
+      };
+      forEachInBins(hist,izt[i],1,loop);
+    }
+    
+    
+    __shared__ unsigned int foundClusters;
+    foundClusters = 0;
+    __syncthreads();
+    
+    // find the number of different clusters, identified by a tracks with clus[i] == i;
+    // mark these tracks with a negative id.
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] == int(i)) {
+	if  (nn[i]>=minT) {
+	  auto old = atomicInc(&foundClusters, 0xffffffff);
+	  iv[i] = -(old + 1);
+	} else { // noise
+	  iv[i] = -9998;
+	}
+      }
+    }
+    __syncthreads();
+
+    assert(foundClusters<OnGPU::MAXVTX);
+    
+    // propagate the negative id to all the tracks in the cluster.
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] >= 0) {
+	// mark each track in a cluster with the same id as the first one
+	iv[i] = iv[iv[i]];
+      }
+    }
+    __syncthreads();
+    
+    // adjust the cluster id to be a positive value starting from 0
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      iv[i] = - iv[i] - 1;
+    }
+    
+    nvIntermediate = nvFinal = foundClusters;
+
+    if(verbose && 0==threadIdx.x) printf("found %d proto vertices\n",foundClusters);
+
+  }
+
+}
+#endif
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
index d969a8c092ed4..c8c9319bfce9e 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
@@ -60,7 +60,7 @@ namespace gpuVertexFinder {
     loadTracks<<<numberOfBlocks,blockSize,0,stream>>>(tracks.gpu_d,onGPU_d, ptMin);
     cudaCheck(cudaGetLastError());
 
-    clusterTracks<<<1,1024-256,0,stream>>>(onGPU_d,minT,eps,errmax,chi2max);
+    CLUSTERIZE<<<1,1024-256,0,stream>>>(onGPU_d,minT,eps,errmax,chi2max);
     cudaCheck(cudaGetLastError());
     fitVertices<<<1,1024-256,0,stream>>>(onGPU_d,50.);
     cudaCheck(cudaGetLastError());
diff --git a/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml b/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
index 763b2403a255d..48d48951b45ae 100644
--- a/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
+++ b/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
@@ -19,15 +19,30 @@
 <use name="SimDataFormats/Track"/>
 <use name="TrackingTools/TransientTrack"/>
 
-<bin file="gpuVertexFinder_t.cu" name="gpuVertexFinder_t">
+<bin file="gpuVertexFinder_t.cu" name="gpuVertexFinderByDensity_t">
   <use name="cuda"/>
   <use name="cuda-api-wrappers"/>
   <flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
   <flags CXXFLAGS="-g"/>
 </bin>
 
+<bin file="gpuVertexFinder_t.cu" name="gpuVertexFinderDBSCAN_t">
+  <use name="cuda"/>
+  <use name="cuda-api-wrappers"/>
+  <flags CUDA_FLAGS="-g -DGPU_DEBUG -DUSE_DBSCAN"/>
+  <flags CXXFLAGS="-g"/>
+</bin>
+
+<bin file="gpuVertexFinder_t.cu" name="gpuVertexFinderIterative_t">
+  <use name="cuda"/>
+  <use name="cuda-api-wrappers"/>
+  <flags CUDA_FLAGS="-g -DGPU_DEBUG -DUSE_ITERATIVE"/>
+  <flags CXXFLAGS="-g"/>
+</bin>
+
+
 
-<bin file="cpuVertexFinder_t.cpp" name="cpuVertexFinder_t">
-  <flags CXXFLAGS="-g -DGPU_DEBUG"/>
+<bin file="cpuVertexFinder_t.cpp" name="cpuVertexFinderIterative_t">
+  <flags CXXFLAGS="-g -DGPU_DEBUG -DUSE_ITERATIVE"/>
 </bin>
 
diff --git a/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h b/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
index 292bdb29a4ebe..290e35596ace9 100644
--- a/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
+++ b/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
@@ -9,10 +9,13 @@
 #include "HeterogeneousCore/CUDAUtilities/interface/exitSansCUDADevices.h"
 #ifdef USE_DBSCAN
 #include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h"
+#define CLUSTERIZE clusterTracksDBSCAN
 #elif USE_ITERATIVE
 #include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h"
+#define CLUSTERIZE clusterTracksIterative
 #else
-#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracks.h"
+#include "RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h"
+#define CLUSTERIZE clusterTracksByDensity
 #endif
 
 #include "RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h"
@@ -142,8 +145,6 @@ int main() {
 
   std::cout << "M eps, pset " << kk << ' ' << eps << ' ' << (i%4) << std::endl;
   
-#define CLUSTERIZE clusterTracks
-
   if ( (i%4) == 0 ) par = {{eps, 0.02f,12.0f}};
   if ( (i%4) == 1 ) par = {{eps, 0.02f,9.0f}};
   if ( (i%4) == 2 ) par = {{eps, 0.01f,9.0f}};

From ee732826c76021b063de9f084d7b25e473d003a2 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 15 Feb 2019 16:57:06 +0100
Subject: [PATCH 52/95] make it working on cpu as well

---
 .../src/gpuClusterTracksByDensity.h           | 35 ++++++++---------
 .../src/gpuClusterTracksDBSCAN.h              | 39 +++++++++----------
 .../PixelVertexFinding/test/BuildFile.xml     |  6 ++-
 3 files changed, 40 insertions(+), 40 deletions(-)

diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
index 25e6efd730bf9..6da54c6be84f8 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
@@ -7,7 +7,6 @@
 
 #include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/radixSort.h"
 
 #include "gpuVertexFinder.h"
 
@@ -60,7 +59,7 @@ namespace gpuVertexFinder {
     assert(nt<=hist.capacity());
     
     // fill hist  (bin shall be wider than "eps")
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       assert(i<OnGPU::MAXTRACKS);
       int iz =  int(zt[i]*10.); // valid if eps<=0.1
       // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
@@ -78,16 +77,16 @@ namespace gpuVertexFinder {
     hist.finalize(ws);
     __syncthreads();
     assert(hist.size()==nt);
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       hist.fill(izt[i],uint16_t(i));
     }
     __syncthreads();    
 
 
     // count neighbours
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (ezt2[i]>er2mx) continue;
-      auto loop = [&](int j) {
+      auto loop = [&](uint32_t j) {
         if (i==j) return;
         auto dist = std::abs(zt[i]-zt[j]);
         if (dist>eps) return;
@@ -101,9 +100,9 @@ namespace gpuVertexFinder {
     __syncthreads();
 
     // find closest above me .... (we ignore the possibility of two j at same distance from i)
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       float mdist=eps;
-      auto loop = [&](int j) {
+      auto loop = [&](uint32_t j) {
         if (nn[j]<nn[i]) return;
         if (nn[j]==nn[i] && zt[j]>=zt[i]) return; // if equal use natural order...
         auto dist = std::abs(zt[i]-zt[j]);
@@ -119,14 +118,14 @@ namespace gpuVertexFinder {
 
 #ifdef GPU_DEBUG
    //  mini verification
-   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
-    if (iv[i]!=i) assert(iv[iv[i]]!=i);  
+   for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+    if (iv[i]!=int(i)) assert(iv[iv[i]]!=int(i));  
    }
    __syncthreads();
 #endif
 
    // consolidate graph (percolate index of seed)
-   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+   for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
        auto m = iv[i];
        while (m!=iv[m]) m=iv[m];
        iv[i]=m;
@@ -135,17 +134,17 @@ namespace gpuVertexFinder {
 #ifdef GPU_DEBUG
    __syncthreads();
    //  mini verification
-   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
-    if (iv[i]!=i) assert(iv[iv[i]]!=i);
+   for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+    if (iv[i]!=int(i)) assert(iv[iv[i]]!=int(i));
    }
 #endif
 
 #ifdef GPU_DEBUG
   // and verify that we did not spit any cluster...
-  for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+  for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       auto minJ=i;
       auto mdist=eps;
-      auto loop = [&](int j) {
+      auto loop = [&](uint32_t j) {
         if (nn[j]<nn[i]) return;
         if (nn[j]==nn[i] && zt[j]>=zt[i]) return; // if equal use natural order...
         auto dist = std::abs(zt[i]-zt[j]);
@@ -168,8 +167,8 @@ namespace gpuVertexFinder {
     
     // find the number of different clusters, identified by a tracks with clus[i] == i and density larger than threshold;
     // mark these tracks with a negative id.
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
-      if (iv[i] == i) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] == int(i)) {
 	if  (nn[i]>=minT) {
 	  auto old = atomicInc(&foundClusters, 0xffffffff);
 	  iv[i] = -(old + 1);
@@ -183,7 +182,7 @@ namespace gpuVertexFinder {
     assert(foundClusters<OnGPU::MAXVTX);
     
     // propagate the negative id to all the tracks in the cluster.
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (iv[i] >= 0) {
 	// mark each track in a cluster with the same id as the first one
 	iv[i] = iv[iv[i]];
@@ -192,7 +191,7 @@ namespace gpuVertexFinder {
     __syncthreads();
     
     // adjust the cluster id to be a positive value starting from 0
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       iv[i] = - iv[i] - 1;
     }
     
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
index 365546a5b0db8..9f784d6308e09 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
@@ -7,7 +7,6 @@
 
 #include "HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h"
 #include "HeterogeneousCore/CUDAUtilities/interface/cuda_assert.h"
-#include "HeterogeneousCore/CUDAUtilities/interface/radixSort.h"
 
 #include "gpuVertexFinder.h"
 
@@ -57,7 +56,7 @@ namespace gpuVertexFinder {
     assert(nt<=hist.capacity());
     
     // fill hist  (bin shall be wider than "eps")
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       assert(i<OnGPU::MAXTRACKS);
       int iz =  int(zt[i]*10.); // valid if eps<=0.1
       // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
@@ -75,16 +74,16 @@ namespace gpuVertexFinder {
     hist.finalize(ws);
     __syncthreads();
     assert(hist.size()==nt);
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       hist.fill(izt[i],uint16_t(i));
     }
     __syncthreads();    
 
 
     // count neighbours
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (ezt2[i]>er2mx) continue;
-      auto loop = [&](int j) {
+      auto loop = [&](uint32_t j) {
         if (i==j) return;
         auto dist = std::abs(zt[i]-zt[j]);
         if (dist>eps) return;
@@ -98,10 +97,10 @@ namespace gpuVertexFinder {
     __syncthreads();
 
     // find NN with smaller z...
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (nn[i]<minT) continue;    // DBSCAN core rule
       float mz=zt[i];
-      auto loop = [&](int j) {
+      auto loop = [&](uint32_t j) {
         if (zt[j]>=mz) return;
         if (nn[j]<minT) return;    // DBSCAN core rule
         auto dist = std::abs(zt[i]-zt[j]);
@@ -117,15 +116,15 @@ namespace gpuVertexFinder {
 
 #ifdef GPU_DEBUG
    //  mini verification
-   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
-    if (iv[i]!=i) assert(iv[iv[i]]!=i);
+   for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+    if (iv[i]!=int(i)) assert(iv[iv[i]]!=int(i));
    }
    __syncthreads();
 #endif
 
 
    // consolidate graph (percolate index of seed)
-   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+   for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
        auto m = iv[i];
        while (m!=iv[m]) m=iv[m];
        iv[i]=m;
@@ -135,18 +134,18 @@ namespace gpuVertexFinder {
 
 #ifdef GPU_DEBUG
    //  mini verification
-   for (int i = threadIdx.x; i < nt; i += blockDim.x) {
-    if (iv[i]!=i) assert(iv[iv[i]]!=i);
+   for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+    if (iv[i]!=int(i)) assert(iv[iv[i]]!=int(i));
    }
    __syncthreads();
 #endif
 
 #ifdef GPU_DEBUG
   // and verify that we did not spit any cluster...
-  for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+  for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (nn[i]<minT) continue;    // DBSCAN core rule
       assert(zt[iv[i]]<=zt[i]);
-      auto loop = [&](int j) {
+      auto loop = [&](uint32_t j) {
         if (nn[j]<minT) return;    // DBSCAN core rule
         auto dist = std::abs(zt[i]-zt[j]);
         if (dist>eps) return;
@@ -164,11 +163,11 @@ namespace gpuVertexFinder {
 #endif
     
     // collect edges (assign to closest cluster of closest point??? here to closest point)
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       //    if (nn[i]==0 || nn[i]>=minT) continue;    // DBSCAN edge rule
       if (nn[i]>=minT) continue;    // DBSCAN edge rule
       float mdist=eps;
-      auto loop = [&](int j) {
+      auto loop = [&](uint32_t j) {
 	if (nn[j]<minT) return;  // DBSCAN core rule
 	auto dist = std::abs(zt[i]-zt[j]);
 	if (dist>mdist) return;
@@ -186,8 +185,8 @@ namespace gpuVertexFinder {
     
     // find the number of different clusters, identified by a tracks with clus[i] == i;
     // mark these tracks with a negative id.
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
-      if (iv[i] == i) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
+      if (iv[i] == int(i)) {
 	if  (nn[i]>=minT) {
 	  auto old = atomicInc(&foundClusters, 0xffffffff);
 	  iv[i] = -(old + 1);
@@ -201,7 +200,7 @@ namespace gpuVertexFinder {
     assert(foundClusters<OnGPU::MAXVTX);
     
     // propagate the negative id to all the tracks in the cluster.
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       if (iv[i] >= 0) {
 	// mark each track in a cluster with the same id as the first one
 	iv[i] = iv[iv[i]];
@@ -210,7 +209,7 @@ namespace gpuVertexFinder {
     __syncthreads();
     
     // adjust the cluster id to be a positive value starting from 0
-    for (int i = threadIdx.x; i < nt; i += blockDim.x) {
+    for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
       iv[i] = - iv[i] - 1;
     }
     
diff --git a/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml b/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
index 48d48951b45ae..dbb25529e884e 100644
--- a/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
+++ b/RecoPixelVertexing/PixelVertexFinding/test/BuildFile.xml
@@ -26,6 +26,10 @@
   <flags CXXFLAGS="-g"/>
 </bin>
 
+<bin file="cpuVertexFinder_t.cpp" name="cpuVertexFinderByDensity_t">
+  <flags CXXFLAGS="-g  -DGPU_DEBUG"/>
+</bin>
+
 <bin file="gpuVertexFinder_t.cu" name="gpuVertexFinderDBSCAN_t">
   <use name="cuda"/>
   <use name="cuda-api-wrappers"/>
@@ -40,8 +44,6 @@
   <flags CXXFLAGS="-g"/>
 </bin>
 
-
-
 <bin file="cpuVertexFinder_t.cpp" name="cpuVertexFinderIterative_t">
   <flags CXXFLAGS="-g -DGPU_DEBUG -DUSE_ITERATIVE"/>
 </bin>

From 4097c6de8cbb0ea2fcb1a754ae302181bef069e7 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 17 Feb 2019 11:14:42 +0100
Subject: [PATCH 53/95] spit workspace

---
 .../src/PixelVertexHeterogeneousProducer.cc   |  2 +-
 .../src/gpuClusterTracksByDensity.h           | 27 ++++---
 .../src/gpuClusterTracksDBSCAN.h              | 27 ++++---
 .../src/gpuClusterTracksIterative.h           | 27 ++++---
 .../PixelVertexFinding/src/gpuFitVertices.h   | 15 ++--
 .../PixelVertexFinding/src/gpuSortByPt2.h     | 13 +--
 .../PixelVertexFinding/src/gpuSplitVertices.h | 15 ++--
 .../PixelVertexFinding/src/gpuVertexFinder.cu | 50 ++++++------
 .../PixelVertexFinding/src/gpuVertexFinder.h  | 63 +++++++++------
 .../PixelVertexFinding/test/VertexFinder_t.h  | 79 ++++++++++---------
 10 files changed, 173 insertions(+), 145 deletions(-)

diff --git a/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc b/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
index 3bd1e522cb776..bd75b678e0051 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
+++ b/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
@@ -44,7 +44,7 @@ class PixelVertexHeterogeneousProducer : public HeterogeneousEDProducer<heteroge
   static void fillDescriptions(edm::ConfigurationDescriptions &descriptions);
   void beginStreamGPUCuda(edm::StreamID streamId,
                           cuda::stream_t<> &cudaStream) override {
-    m_gpuAlgo.allocateOnGPU();
+    m_gpuAlgo.allocate();
   }
   void acquireGPUCuda(const edm::HeterogeneousEvent &iEvent,
                       const edm::EventSetup &iSetup,
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
index 6da54c6be84f8..6d2eb22f7dbdf 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksByDensity.h
@@ -19,7 +19,7 @@ namespace gpuVertexFinder {
   //
   __global__ 
   void clusterTracksByDensity(
-		     OnGPU * pdata,
+                     ZVertices * pdata, WorkSpace * pws,
 		     int minT,  // min number of neighbours to be "seed"
 		     float eps, // max absolute distance to cluster
 		     float errmax, // max error to be "seed"
@@ -34,23 +34,24 @@ namespace gpuVertexFinder {
     auto er2mx = errmax*errmax;
     
     auto & __restrict__ data = *pdata;
-    auto nt = data.ntrks;
-    float const * __restrict__ zt = data.zt;
-    float const * __restrict__ ezt2 = data.ezt2;
+    auto & __restrict__ ws = *pws;
+    auto nt = ws.ntrks;
+    float const * __restrict__ zt = ws.zt;
+    float const * __restrict__ ezt2 = ws.ezt2;
 
     uint32_t & nvFinal = data.nvFinal;
-    uint32_t & nvIntermediate = data.nvIntermediate;
+    uint32_t & nvIntermediate = ws.nvIntermediate;
     
-    uint8_t  * __restrict__ izt = data.izt;
-    int32_t * __restrict__ nn = data.nn;
-    int32_t * __restrict__ iv = data.iv;
+    uint8_t  * __restrict__ izt = ws.izt;
+    int32_t * __restrict__ nn = data.ndof;
+    int32_t * __restrict__ iv = ws.iv;
     
     assert(pdata);
     assert(zt);
     
     using Hist=HistoContainer<uint8_t,256,16000,8,uint16_t>;
     __shared__ Hist hist;
-    __shared__ typename Hist::Counter ws[32];
+    __shared__ typename Hist::Counter hws[32];
     for (auto j=threadIdx.x; j<Hist::totbins(); j+=blockDim.x) { hist.off[j]=0;}
     __syncthreads();
     
@@ -60,7 +61,7 @@ namespace gpuVertexFinder {
     
     // fill hist  (bin shall be wider than "eps")
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      assert(i<OnGPU::MAXTRACKS);
+      assert(i<ZVertices::MAXTRACKS);
       int iz =  int(zt[i]*10.); // valid if eps<=0.1
       // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
       iz = std::min(std::max(iz, INT8_MIN),INT8_MAX);
@@ -72,9 +73,9 @@ namespace gpuVertexFinder {
       nn[i]=0;
     }
     __syncthreads();
-    if (threadIdx.x<32) ws[threadIdx.x]=0;  // used by prefix scan...
+    if (threadIdx.x<32) hws[threadIdx.x]=0;  // used by prefix scan...
     __syncthreads();
-    hist.finalize(ws);
+    hist.finalize(hws);
     __syncthreads();
     assert(hist.size()==nt);
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
@@ -179,7 +180,7 @@ namespace gpuVertexFinder {
     }
     __syncthreads();
 
-    assert(foundClusters<OnGPU::MAXVTX);
+    assert(foundClusters<ZVertices::MAXVTX);
     
     // propagate the negative id to all the tracks in the cluster.
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
index 9f784d6308e09..a29fa64ad7135 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksDBSCAN.h
@@ -16,7 +16,7 @@ namespace gpuVertexFinder {
   // enough for <10K tracks we have
   __global__ 
   void clusterTracksDBSCAN(
-		     OnGPU * pdata,
+                     ZVertices * pdata, WorkSpace * pws,
 		     int minT,  // min number of neighbours to be "core"
 		     float eps, // max absolute distance to cluster
 		     float errmax, // max error to be "seed"
@@ -31,23 +31,24 @@ namespace gpuVertexFinder {
     auto er2mx = errmax*errmax;
     
     auto & __restrict__ data = *pdata;
-    auto nt = data.ntrks;
-    float const * __restrict__ zt = data.zt;
-    float const * __restrict__ ezt2 = data.ezt2;
+    auto & __restrict__ ws = *pws;
+    auto nt = ws.ntrks;
+    float const * __restrict__ zt = ws.zt;
+    float const * __restrict__ ezt2 = ws.ezt2;
 
     uint32_t & nvFinal = data.nvFinal;
-    uint32_t & nvIntermediate = data.nvIntermediate;
+    uint32_t & nvIntermediate = ws.nvIntermediate;
     
-    uint8_t  * __restrict__ izt = data.izt;
-    int32_t * __restrict__ nn = data.nn;
-    int32_t * __restrict__ iv = data.iv;
+    uint8_t  * __restrict__ izt = ws.izt;
+    int32_t * __restrict__ nn = data.ndof;
+    int32_t * __restrict__ iv = ws.iv;
     
     assert(pdata);
     assert(zt);
     
     using Hist=HistoContainer<uint8_t,256,16000,8,uint16_t>;
     __shared__ Hist hist;
-    __shared__ typename Hist::Counter ws[32];
+    __shared__ typename Hist::Counter hws[32];
     for (auto j=threadIdx.x; j<Hist::totbins(); j+=blockDim.x) { hist.off[j]=0;}
     __syncthreads();
     
@@ -57,7 +58,7 @@ namespace gpuVertexFinder {
     
     // fill hist  (bin shall be wider than "eps")
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      assert(i<OnGPU::MAXTRACKS);
+      assert(i<ZVertices::MAXTRACKS);
       int iz =  int(zt[i]*10.); // valid if eps<=0.1
       // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
       iz = std::min(std::max(iz, INT8_MIN),INT8_MAX);
@@ -69,9 +70,9 @@ namespace gpuVertexFinder {
       nn[i]=0;
     }
     __syncthreads();
-    if (threadIdx.x<32) ws[threadIdx.x]=0;  // used by prefix scan...
+    if (threadIdx.x<32) hws[threadIdx.x]=0;  // used by prefix scan...
     __syncthreads();
-    hist.finalize(ws);
+    hist.finalize(hws);
     __syncthreads();
     assert(hist.size()==nt);
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
@@ -197,7 +198,7 @@ namespace gpuVertexFinder {
     }
     __syncthreads();
 
-    assert(foundClusters<OnGPU::MAXVTX);
+    assert(foundClusters<ZVertices::MAXVTX);
     
     // propagate the negative id to all the tracks in the cluster.
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h
index ded93b030e6ae..4d47cc15f7c6d 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuClusterTracksIterative.h
@@ -16,7 +16,7 @@ namespace gpuVertexFinder {
   // enough for <10K tracks we have
   __global__ 
   void clusterTracksIterative(
-		     OnGPU * pdata,
+		     ZVertices * pdata, WorkSpace * pws,
 		     int minT,  // min number of neighbours to be "core"
 		     float eps, // max absolute distance to cluster
 		     float errmax, // max error to be "seed"
@@ -31,23 +31,24 @@ namespace gpuVertexFinder {
     auto er2mx = errmax*errmax;
     
     auto & __restrict__ data = *pdata;
-    auto nt = data.ntrks;
-    float const * __restrict__ zt = data.zt;
-    float const * __restrict__ ezt2 = data.ezt2;
+    auto & __restrict__ ws = *pws;
+    auto nt = ws.ntrks;
+    float const * __restrict__ zt = ws.zt;
+    float const * __restrict__ ezt2 = ws.ezt2;
 
     uint32_t & nvFinal = data.nvFinal;
-    uint32_t & nvIntermediate = data.nvIntermediate;
+    uint32_t & nvIntermediate = ws.nvIntermediate;
     
-    uint8_t  * __restrict__ izt = data.izt;
-    int32_t * __restrict__ nn = data.nn;
-    int32_t * __restrict__ iv = data.iv;
+    uint8_t  * __restrict__ izt = ws.izt;
+    int32_t * __restrict__ nn = data.ndof;
+    int32_t * __restrict__ iv = ws.iv;
     
     assert(pdata);
     assert(zt);
     
     using Hist=HistoContainer<uint8_t,256,16000,8,uint16_t>;
     __shared__ Hist hist;
-    __shared__ typename Hist::Counter ws[32];
+    __shared__ typename Hist::Counter hws[32];
     for (auto j=threadIdx.x; j<Hist::totbins(); j+=blockDim.x) { hist.off[j]=0;}
     __syncthreads();
     
@@ -57,7 +58,7 @@ namespace gpuVertexFinder {
     
     // fill hist  (bin shall be wider than "eps")
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
-      assert(i<OnGPU::MAXTRACKS);
+      assert(i<ZVertices::MAXTRACKS);
       int iz =  int(zt[i]*10.); // valid if eps<=0.1
       // iz = std::clamp(iz, INT8_MIN, INT8_MAX);  // sorry c++17 only
       iz = std::min(std::max(iz, INT8_MIN),INT8_MAX);
@@ -69,9 +70,9 @@ namespace gpuVertexFinder {
       nn[i]=0;
     }
     __syncthreads();
-    if (threadIdx.x<32) ws[threadIdx.x]=0;  // used by prefix scan...
+    if (threadIdx.x<32) hws[threadIdx.x]=0;  // used by prefix scan...
     __syncthreads();
-    hist.finalize(ws);
+    hist.finalize(hws);
     __syncthreads();
     assert(hist.size()==nt);
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
@@ -175,7 +176,7 @@ namespace gpuVertexFinder {
     }
     __syncthreads();
 
-    assert(foundClusters<OnGPU::MAXVTX);
+    assert(foundClusters<ZVertices::MAXVTX);
     
     // propagate the negative id to all the tracks in the cluster.
     for (auto i = threadIdx.x; i < nt; i += blockDim.x) {
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
index 483b3b9dede70..1645416cc9edb 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuFitVertices.h
@@ -15,24 +15,25 @@ namespace gpuVertexFinder {
 
   __global__
   void fitVertices(
-                   OnGPU * pdata,
+                   ZVertices * pdata, WorkSpace * pws,
                    float chi2Max // for outlier rejection
                   )  {
 
     constexpr bool verbose = false; // in principle the compiler should optmize out if false
 
     auto & __restrict__ data = *pdata;
-    auto nt = data.ntrks;
-    float const * __restrict__ zt = data.zt;
-    float const * __restrict__ ezt2 = data.ezt2;
+    auto & __restrict__ ws = *pws;
+    auto nt = ws.ntrks;
+    float const * __restrict__ zt = ws.zt;
+    float const * __restrict__ ezt2 = ws.ezt2;
     float * __restrict__ zv = data.zv;
     float * __restrict__ wv = data.wv;
     float * __restrict__ chi2 = data.chi2;
     uint32_t & nvFinal  = data.nvFinal;
-    uint32_t & nvIntermediate = data.nvIntermediate;
+    uint32_t & nvIntermediate = ws.nvIntermediate;
 
-    int32_t * __restrict__ nn = data.nn;
-    int32_t * __restrict__ iv = data.iv;
+    int32_t * __restrict__ nn = data.ndof;
+    int32_t * __restrict__ iv = ws.iv;
 
     assert(pdata);
     assert(zt);
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
index 750923b4fafc4..099cb476efd3f 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuSortByPt2.h
@@ -18,14 +18,15 @@ namespace gpuVertexFinder {
 
   __global__
   void sortByPt2(
-                 OnGPU * pdata
+                 ZVertices * pdata, WorkSpace * pws
                 )  {
     auto & __restrict__ data = *pdata;
-    auto nt = data.ntrks;
-    float const * __restrict__ ptt2 = data.ptt2;
+    auto & __restrict__ ws = *pws;
+    auto nt = ws.ntrks;
+    float const * __restrict__ ptt2 = ws.ptt2;
     uint32_t const & nvFinal = data.nvFinal;
 
-    int32_t const * __restrict__ iv = data.iv;
+    int32_t const * __restrict__ iv = ws.iv;
     float * __restrict__ ptv2 = data.ptv2;
     uint16_t * __restrict__ sortInd = data.sortInd;
 
@@ -49,8 +50,8 @@ namespace gpuVertexFinder {
       return;
     }
 #ifdef __CUDA_ARCH__
-    __shared__ uint16_t ws[1024];
-    radixSort(ptv2,sortInd,ws,nvFinal);
+    __shared__ uint16_t sws[1024];
+    radixSort(ptv2,sortInd,sws,nvFinal);
 #else
     for(uint16_t i=0; i<nvFinal; ++i) sortInd[i]=i;
     std::sort(sortInd,sortInd+nvFinal,[&](auto i, auto j){return ptv2[i]<ptv2[j];});
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
index d4d8f4e1a0192..02ecb4d7bfb83 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuSplitVertices.h
@@ -16,7 +16,7 @@ namespace gpuVertexFinder {
 
   __global__
   void splitVertices(
-                   OnGPU * pdata,
+                     ZVertices * pdata, WorkSpace * pws,
                    float maxChi2
                   )  {
 
@@ -24,16 +24,17 @@ namespace gpuVertexFinder {
 
 
     auto & __restrict__ data = *pdata;
-    auto nt = data.ntrks;
-    float const * __restrict__ zt = data.zt;
-    float const * __restrict__ ezt2 = data.ezt2;
+    auto & __restrict__ ws = *pws;
+    auto nt = ws.ntrks;
+    float const * __restrict__ zt = ws.zt;
+    float const * __restrict__ ezt2 = ws.ezt2;
     float * __restrict__ zv = data.zv;
     float * __restrict__ wv = data.wv;
     float const * __restrict__ chi2 = data.chi2;
     uint32_t & nvFinal  = data.nvFinal;
 
-    int32_t const * __restrict__ nn = data.nn;
-    int32_t * __restrict__ iv = data.iv;
+    int32_t const * __restrict__ nn = data.ndof;
+    int32_t * __restrict__ iv = ws.iv;
 
     assert(pdata);
     assert(zt);
@@ -115,7 +116,7 @@ namespace gpuVertexFinder {
     
     // get a new global vertex
     __shared__ uint32_t igv;
-    if (0==threadIdx.x) igv = atomicAdd(&data.nvIntermediate,1);
+    if (0==threadIdx.x) igv = atomicAdd(&ws.nvIntermediate,1);
     __syncthreads();
     for (auto k = threadIdx.x; k<nq; k+=blockDim.x) {
       if(1==newV[k]) iv[it[k]]=igv;
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
index c8c9319bfce9e..8393afefc998a 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
@@ -5,24 +5,27 @@
 
 
 // a macro SORRY
-#define FROM(M) ((char*)(onGPU_d) +offsetof(OnGPU,M))
+#define LOC_ZV(M) ((char*)(gpu_d) +offsetof(ZVertices,M))
+#define LOC_WS(M) ((char*)(ws_d) +offsetof(WorkSpace,M))
 
 
 namespace gpuVertexFinder {
 
 
-  void Producer::allocateOnGPU() {
-    cudaCheck(cudaMalloc(&onGPU_d,sizeof(OnGPU)));
+  void Producer::allocate() {
+    cudaCheck(cudaMalloc(&gpu_d,sizeof(ZVertices)));
+    cudaCheck(cudaMalloc(&ws_d,sizeof(WorkSpace)));
   }
 	      
-  void Producer::deallocateOnGPU() {
-    cudaCheck(cudaFree(onGPU_d));
+  void Producer::deallocate() {
+    cudaCheck(cudaFree(gpu_d));
+   cudaCheck(cudaFree(ws_d));
   }
 
 
   __global__
   void loadTracks(pixelTuplesHeterogeneousProduct::TuplesOnGPU const * tracks,
-                  OnGPU * pdata,
+                  WorkSpace * pws,
                   float ptMin
                  ){
 
@@ -42,7 +45,7 @@ namespace gpuVertexFinder {
 
     if (fittedTrack.par(2)<ptMin) return; 
 
-    auto & data = *pdata;   
+    auto & data = *pws;   
     auto it = atomicAdd(&data.ntrks,1);
     data.itrk[it] = idx;
     data.zt[it] = fittedTrack.par(4);
@@ -53,31 +56,31 @@ namespace gpuVertexFinder {
 
   void Producer::produce(cudaStream_t stream, TuplesOnCPU const & tracks, float ptMin) {
     
-    assert(onGPU_d);assert(tracks.gpu_d);
-    init<<<1,1,0,stream>>>(onGPU_d);
+    assert(gpu_d);assert(ws_d);assert(tracks.gpu_d);
+    init<<<1,1,0,stream>>>(gpu_d,ws_d);
     auto blockSize = 128;
     auto numberOfBlocks = (CAConstants::maxTuples() + blockSize - 1) / blockSize;
-    loadTracks<<<numberOfBlocks,blockSize,0,stream>>>(tracks.gpu_d,onGPU_d, ptMin);
+    loadTracks<<<numberOfBlocks,blockSize,0,stream>>>(tracks.gpu_d,ws_d, ptMin);
     cudaCheck(cudaGetLastError());
 
-    CLUSTERIZE<<<1,1024-256,0,stream>>>(onGPU_d,minT,eps,errmax,chi2max);
+    CLUSTERIZE<<<1,1024-256,0,stream>>>(gpu_d,ws_d,minT,eps,errmax,chi2max);
     cudaCheck(cudaGetLastError());
-    fitVertices<<<1,1024-256,0,stream>>>(onGPU_d,50.);
+    fitVertices<<<1,1024-256,0,stream>>>(gpu_d,ws_d,50.);
     cudaCheck(cudaGetLastError());
 
-    splitVertices<<<1024,128,0,stream>>>(onGPU_d,9.f);
+    splitVertices<<<1024,128,0,stream>>>(gpu_d,ws_d,9.f);
     cudaCheck(cudaGetLastError());
-    fitVertices<<<1,1024-256,0,stream>>>(onGPU_d,5000.);
+    fitVertices<<<1,1024-256,0,stream>>>(gpu_d,ws_d,5000.);
     cudaCheck(cudaGetLastError());
 
-    sortByPt2<<<1,256,0,stream>>>(onGPU_d);
+    sortByPt2<<<1,256,0,stream>>>(gpu_d,ws_d);
     cudaCheck(cudaGetLastError());
 
     if(enableTransfer) {
-      auto from = (char*)(onGPU_d) +offsetof(OnGPU,nvFinal);
+      auto from = (char*)(gpu_d) +offsetof(ZVertices,nvFinal);
       cudaCheck(cudaMemcpyAsync(&gpuProduct.nVertices, from, sizeof(uint32_t),
                                 cudaMemcpyDeviceToHost, stream));
-      from = (char*)(onGPU_d) +offsetof(OnGPU,ntrks);
+      from = (char*)(ws_d) +offsetof(WorkSpace,ntrks);
       cudaCheck(cudaMemcpyAsync(&gpuProduct.nTracks, from, sizeof(uint32_t),
                               cudaMemcpyDeviceToHost, stream));
     }
@@ -88,25 +91,26 @@ namespace gpuVertexFinder {
     if(!enableTransfer) return gpuProduct;
 
     // finish copy
+    // FIXME copy to zv idv
     gpuProduct.ivtx.resize(gpuProduct.nTracks);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.ivtx.data(),FROM(iv),sizeof(int32_t)*gpuProduct.nTracks,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.ivtx.data(),LOC_WS(iv),sizeof(int32_t)*gpuProduct.nTracks,
                               cudaMemcpyDeviceToHost, stream));
     gpuProduct.itrk.resize(gpuProduct.nTracks);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.itrk.data(),FROM(itrk),sizeof(int16_t)*gpuProduct.nTracks,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.itrk.data(),LOC_WS(itrk),sizeof(int16_t)*gpuProduct.nTracks,
                               cudaMemcpyDeviceToHost, stream));
 
     gpuProduct.z.resize(gpuProduct.nVertices);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.z.data(),FROM(zv),sizeof(float)*gpuProduct.nVertices,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.z.data(),LOC_ZV(zv),sizeof(float)*gpuProduct.nVertices,
 			      cudaMemcpyDeviceToHost, stream));
     gpuProduct.zerr.resize(gpuProduct.nVertices);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.zerr.data(),FROM(wv),sizeof(float)*gpuProduct.nVertices,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.zerr.data(),LOC_ZV(wv),sizeof(float)*gpuProduct.nVertices,
 			      cudaMemcpyDeviceToHost, stream));
     gpuProduct.chi2.resize(gpuProduct.nVertices);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.chi2.data(),FROM(chi2),sizeof(float)*gpuProduct.nVertices,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.chi2.data(),LOC_ZV(chi2),sizeof(float)*gpuProduct.nVertices,
 			      cudaMemcpyDeviceToHost, stream));
         
     gpuProduct.sortInd.resize(gpuProduct.nVertices);
-    cudaCheck(cudaMemcpyAsync(gpuProduct.sortInd.data(),FROM(sortInd),sizeof(uint16_t)*gpuProduct.nVertices,
+    cudaCheck(cudaMemcpyAsync(gpuProduct.sortInd.data(),LOC_ZV(sortInd),sizeof(uint16_t)*gpuProduct.nVertices,
                               cudaMemcpyDeviceToHost, stream));
 
     cudaStreamSynchronize(stream);
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.h b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.h
index 57b626feaadf7..7fc69878fa846 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.h
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.h
@@ -9,42 +9,55 @@
 
 namespace gpuVertexFinder {
 
-  struct OnGPU {
-
+  // SOA for vertices 
+  // These vertices are clusterized and fitted only along the beam line (z)
+  // to obtain their global coordinate the beam spot position shall be added (eventually correcting for the beam angle as well) 
+  // FIXME move to DataFormats
+  struct ZVertices {
     static constexpr uint32_t MAXTRACKS = 16000;
     static constexpr uint32_t MAXVTX    = 1024;
 
-    OnGPU() = default;
-
-    uint32_t ntrks; // number of "selected tracks"
-    uint16_t itrk[MAXTRACKS]; // index of original track    
-    float zt[MAXTRACKS];   // input track z at bs
-    float ezt2[MAXTRACKS]; // input error^2 on the above
-    float ptt2[MAXTRACKS]; // input pt^2 on the above
-
+    int32_t idv[MAXTRACKS];  // vertex index for each associated (original) track
     float zv[MAXVTX];  // output z-posistion of found vertices
     float wv[MAXVTX];  //  output weight (1/error^2) on the above
     float chi2[MAXVTX];  // vertices chi2
     float ptv2[MAXVTX];  // vertices pt^2
-    uint32_t nvFinal;  // the number of vertices
-    uint32_t nvIntermediate;  // the number of vertices after splitting pruning etc.
-    int32_t iv[MAXTRACKS];  // vertex index for each associated track
+    int32_t ndof[MAXVTX]; // vertices number of dof (resued as workspace for the number of nearest neighbours)
     uint16_t sortInd[MAXVTX]; // sorted index (by pt2)
+    uint32_t nvFinal;  // the number of vertices
+
+    __host__ __device__
+    void init() { nvFinal=0;}
+
 
-    // workspace  
+  };
+
+  // workspace used in the vertex reco algos 
+  struct WorkSpace {
+    static constexpr uint32_t MAXTRACKS = 16000;
+    static constexpr uint32_t MAXVTX    = 1024;
+
+    uint32_t ntrks; // number of "selected tracks"
+    uint16_t itrk[MAXTRACKS]; // index of original track
+    float zt[MAXTRACKS];   // input track z at bs
+    float ezt2[MAXTRACKS]; // input error^2 on the above
+    float ptt2[MAXTRACKS]; // input pt^2 on the above
     uint8_t izt[MAXTRACKS];  // interized z-position of input tracks
-    int32_t nn[MAXVTX]; // number of nearest neighbours (reused as number of dof for output vertices)
+    int32_t iv[MAXTRACKS];  // vertex index for each associated track
+
+    uint32_t nvIntermediate;  // the number of vertices after splitting pruning etc.
 
     __host__ __device__
-    void init() { ntrks=0;nvFinal=0;nvIntermediate=0;}
- 
+    void init() { ntrks=0; nvIntermediate=0;}
+
   };
   
 #ifdef __CUDACC__
   __global__
-  void init(OnGPU * pdata) {pdata->init();}
+  void init(ZVertices * pdata, WorkSpace * pws) {pdata->init(); pws->init();}
 #endif
 
+  // Data Format on cpu???
   struct OnCPU {
     OnCPU() = default;
 
@@ -55,7 +68,7 @@ namespace gpuVertexFinder {
 
     uint32_t nVertices=0;
     uint32_t nTracks=0;
-    OnGPU const * gpu_d = nullptr;
+    ZVertices const * gpu_d = nullptr;
   };
 
   class Producer {
@@ -64,7 +77,8 @@ namespace gpuVertexFinder {
     using TuplesOnCPU = pixelTuplesHeterogeneousProduct::TuplesOnCPU;
 
     using OnCPU = gpuVertexFinder::OnCPU;
-    using OnGPU = gpuVertexFinder::OnGPU;
+    using ZVertices = gpuVertexFinder::ZVertices;
+    using WorkSpace = gpuVertexFinder::WorkSpace;
 
 
     Producer(
@@ -81,19 +95,20 @@ namespace gpuVertexFinder {
       enableTransfer(ienableTransfer)
     {}
     
-    ~Producer() { deallocateOnGPU();}
+    ~Producer() { deallocate();}
 
     void produce(cudaStream_t stream, TuplesOnCPU const & tuples, float ptMin);
 
     OnCPU const & fillResults(cudaStream_t stream);
     
 
-    void allocateOnGPU();
-    void deallocateOnGPU();
+    void allocate();
+    void deallocate();
 
   private:
     OnCPU gpuProduct;
-    OnGPU * onGPU_d=nullptr;
+    ZVertices * gpu_d=nullptr;
+    WorkSpace * ws_d=nullptr;
 
     int minT;  // min number of neighbours to be "core"
     float eps; // max absolute distance to cluster
diff --git a/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h b/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
index 290e35596ace9..130af51e9b83c 100644
--- a/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
+++ b/RecoPixelVertexing/PixelVertexFinding/test/VertexFinder_t.h
@@ -87,13 +87,14 @@ struct ClusterGenerator {
 };
 
 // a macro SORRY
-#define LOC_ONGPU(M) ((char*)(onGPU_d.get()) +offsetof(OnGPU,M))
-
+#define LOC_ONGPU(M) ((char*)(onGPU_d.get()) +offsetof(ZVertices,M))
+#define LOC_WS(M) ((char*)(ws_d.get()) +offsetof(WorkSpace,M))
 
 __global__
-void print(OnGPU const * pdata) {
+void print(ZVertices const * pdata, WorkSpace const * pws) {
   auto const & __restrict__ data = *pdata;
-  printf("nt,nv %d %d,%d\n",data.ntrks,data.nvFinal,data.nvIntermediate); 
+  auto const & __restrict__ ws = *pws;
+  printf("nt,nv %d %d,%d\n",ws.ntrks,data.nvFinal,ws.nvIntermediate); 
 
 }
 
@@ -104,9 +105,11 @@ int main() {
 
   auto current_device = cuda::device::current::get();
 
-  auto onGPU_d = cuda::memory::device::make_unique<OnGPU[]>(current_device, 1);
+  auto onGPU_d = cuda::memory::device::make_unique<ZVertices[]>(current_device, 1);
+  auto ws_d = cuda::memory::device::make_unique<WorkSpace[]>(current_device, 1);
 #else
-  auto onGPU_d = std::make_unique<OnGPU>();
+  auto onGPU_d = std::make_unique<ZVertices>();
+  auto ws_d = std::make_unique<WorkSpace>();
 #endif
 
   Event  ev;
@@ -124,23 +127,23 @@ int main() {
   gen(ev);
   
 #ifdef __CUDACC__
-  init<<<1,1,0,0>>>(onGPU_d.get());
+  init<<<1,1,0,0>>>(onGPU_d.get(),ws_d.get());
 #else
-  onGPU_d->init();
+  onGPU_d->init();ws_d->init();
 #endif
 
   std::cout << "v,t size " << ev.zvert.size() << ' ' << ev.ztrack.size() << std::endl;
   auto nt = ev.ztrack.size();
 #ifdef __CUDACC__
-  cuda::memory::copy(LOC_ONGPU(ntrks),&nt,sizeof(uint32_t));
-  cuda::memory::copy(LOC_ONGPU(zt),ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
-  cuda::memory::copy(LOC_ONGPU(ezt2),ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
-  cuda::memory::copy(LOC_ONGPU(ptt2),ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
+  cuda::memory::copy(LOC_WS(ntrks),&nt,sizeof(uint32_t));
+  cuda::memory::copy(LOC_WS(zt),ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
+  cuda::memory::copy(LOC_WS(ezt2),ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
+  cuda::memory::copy(LOC_WS(ptt2),ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
 #else
-  ::memcpy(LOC_ONGPU(ntrks),&nt,sizeof(uint32_t));  
-  ::memcpy(LOC_ONGPU(zt),ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
-  ::memcpy(LOC_ONGPU(ezt2),ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
-  ::memcpy(LOC_ONGPU(ptt2),ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
+  ::memcpy(LOC_WS(ntrks),&nt,sizeof(uint32_t));  
+  ::memcpy(LOC_WS(zt),ev.ztrack.data(),sizeof(float)*ev.ztrack.size());
+  ::memcpy(LOC_WS(ezt2),ev.eztrack.data(),sizeof(float)*ev.eztrack.size());
+  ::memcpy(LOC_WS(ptt2),ev.pttrack.data(),sizeof(float)*ev.eztrack.size());
 #endif
 
   std::cout << "M eps, pset " << kk << ' ' << eps << ' ' << (i%4) << std::endl;
@@ -152,16 +155,16 @@ int main() {
 
   uint32_t nv=0;
 #ifdef __CUDACC__
-  print<<<1,1,0,0>>>(onGPU_d.get());
+  print<<<1,1,0,0>>>(onGPU_d.get(),ws_d.get());
   cudaCheck(cudaGetLastError());
   cudaDeviceSynchronize();
 
   cuda::launch(CLUSTERIZE,
 		 { 1, 512+256 },
-		 onGPU_d.get(),kk,par[0],
+		 onGPU_d.get(),ws_d.get(),kk,par[0],
 		 par[1],par[2]
 		 );
-  print<<<1,1,0,0>>>(onGPU_d.get());
+  print<<<1,1,0,0>>>(onGPU_d.get(),ws_d.get());
 
   cudaCheck(cudaGetLastError());
   cudaDeviceSynchronize();
@@ -169,20 +172,20 @@ int main() {
 
   cuda::launch(fitVertices, 
                { 1,1024-256 },
-               onGPU_d.get(),50.f
+               onGPU_d.get(),ws_d.get(),50.f
               );
   cudaCheck(cudaGetLastError());
   cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
 
 #else
-  print(onGPU_d.get());
+  print(onGPU_d.get(),ws_d.get());
   CLUSTERIZE(
-                 onGPU_d.get(),kk,par[0],
+                 onGPU_d.get(),ws_d.get(),kk,par[0],
                  par[1],par[2]
                  );
-  print(onGPU_d.get());
+  print(onGPU_d.get(),ws_d.get());
   fitVertices(
-               onGPU_d.get(),50.f
+               onGPU_d.get(),ws_d.get(),50.f
               );
   nv = onGPU_d->nvFinal;
 #endif
@@ -217,12 +220,12 @@ int main() {
   zv = onGPU_d->zv;
   wv = onGPU_d->wv;
   ptv2 = onGPU_d->ptv2;
-  nn = onGPU_d->nn;
+  nn = onGPU_d->ndof;
   ind = onGPU_d->sortInd;
 #endif
 
 #ifdef __CUDACC__
-  cuda::memory::copy(nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(nn, LOC_ONGPU(ndof), nv*sizeof(int32_t));
   cuda::memory::copy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
 #else
   memcpy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
@@ -237,14 +240,14 @@ int main() {
 #ifdef __CUDACC__
   cuda::launch(fitVertices,
                { 1,1024-256 },
-               onGPU_d.get(), 50.f
+               onGPU_d.get(),ws_d.get(), 50.f
               );
   cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
-  cuda::memory::copy(nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(nn, LOC_ONGPU(ndof), nv*sizeof(int32_t));
   cuda::memory::copy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
 #else
   fitVertices(
-               onGPU_d.get(),50.f
+               onGPU_d.get(),ws_d.get(),50.f
               );
   nv = onGPU_d->nvFinal;
   memcpy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
@@ -260,20 +263,20 @@ int main() {
   // one vertex per block!!!
   cuda::launch(splitVertices,
                { 1024, 64 },
-               onGPU_d.get(),
+               onGPU_d.get(),ws_d.get(),
                9.f
               );
-  cuda::memory::copy(&nv, LOC_ONGPU(nvIntermediate), sizeof(uint32_t));
+  cuda::memory::copy(&nv, LOC_WS(nvIntermediate), sizeof(uint32_t));
 #else
   gridDim.x = 1024; // nv ????
   assert(blockIdx.x==0);
   for(;blockIdx.x<gridDim.x; ++blockIdx.x)
     splitVertices(
-               onGPU_d.get(),
+               onGPU_d.get(),ws_d.get(),
                9.f
               );
    resetGrid();
-   nv = onGPU_d->nvIntermediate;
+   nv = ws_d->nvIntermediate;
 #endif
  std::cout << "after split " << nv << std::endl; 
 
@@ -281,19 +284,19 @@ int main() {
 #ifdef __CUDACC__
   cuda::launch(fitVertices,
                { 1,1024-256 },
-               onGPU_d.get(),5000.f
+               onGPU_d.get(),ws_d.get(),5000.f
               );
   cudaCheck(cudaGetLastError());
 
   cuda::launch(sortByPt2,
                { 1, 256 },
-               onGPU_d.get()
+               onGPU_d.get(),ws_d.get()
               );
   cudaCheck(cudaGetLastError());
   cuda::memory::copy(&nv, LOC_ONGPU(nvFinal), sizeof(uint32_t));
 #else
-  fitVertices(onGPU_d.get(),5000.f);
-  sortByPt2(onGPU_d.get());
+  fitVertices(onGPU_d.get(),ws_d.get(),5000.f);
+  sortByPt2(onGPU_d.get(),ws_d.get());
   nv = onGPU_d->nvFinal;
   memcpy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
 #endif
@@ -309,7 +312,7 @@ int main() {
   cuda::memory::copy(wv, LOC_ONGPU(wv), nv*sizeof(float));
   cuda::memory::copy(chi2, LOC_ONGPU(chi2), nv*sizeof(float));
   cuda::memory::copy(ptv2, LOC_ONGPU(ptv2), nv*sizeof(float));
-  cuda::memory::copy(nn, LOC_ONGPU(nn), nv*sizeof(int32_t));
+  cuda::memory::copy(nn, LOC_ONGPU(ndof), nv*sizeof(int32_t));
   cuda::memory::copy(ind, LOC_ONGPU(sortInd), nv*sizeof(uint16_t));
 #endif
   for (auto j=0U; j<nv; ++j) if (nn[j]>0) chi2[j]/=float(nn[j]); 

From 1b3c093834e3e118dfb6f384f61ef9af8bd6770c Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 19 Feb 2019 10:12:54 +0100
Subject: [PATCH 54/95] implement Matti's suggestions

---
 .../CUDAUtilities/interface/HistoContainer.h  | 41 +++++++++++++------
 .../CUDAUtilities/interface/prefixScan.h      |  7 +++-
 .../CUDAUtilities/src/cudaCompat.cc           |  6 ++-
 3 files changed, 39 insertions(+), 15 deletions(-)

diff --git a/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h b/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
index ac2f8d3b8013b..a232cc7c53e47 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/HistoContainer.h
@@ -51,7 +51,11 @@ namespace cudautils {
   }
 
   template<typename Histo>
-  void launchZero(Histo * __restrict__ h, cudaStream_t stream = 0) {
+  void launchZero(Histo * __restrict__ h, cudaStream_t stream 
+#ifndef __CUDACC__
+                  = 0
+#endif
+                 ) {
     uint32_t * off = (uint32_t *)( (char*)(h) +offsetof(Histo,off));
 #ifdef __CUDACC__
     cudaMemsetAsync(off,0, 4*Histo::totbins(),stream);
@@ -61,8 +65,17 @@ namespace cudautils {
   }
 
   template<typename Histo>
-  void launchFinalize(Histo * __restrict__ h, uint8_t *  __restrict__ ws=nullptr, cudaStream_t stream=0) {
+  void launchFinalize(Histo * __restrict__ h, uint8_t *  __restrict__ ws
+#ifndef __CUDACC__
+                      =nullptr
+#endif
+                      , cudaStream_t stream
+#ifndef __CUDACC__
+                  = 0
+#endif
+                 ) {
 #ifdef __CUDACC__
+    assert(ws);
     uint32_t * off = (uint32_t *)( (char*)(h) +offsetof(Histo,off));
     size_t wss = Histo::wsSize();
     CubDebugExit(cub::DeviceScan::InclusiveSum(ws, wss, off, off, Histo::totbins(), stream));
@@ -75,7 +88,11 @@ namespace cudautils {
   template<typename Histo, typename T>
   void fillManyFromVector(Histo * __restrict__ h, uint8_t *  __restrict__ ws,  
                           uint32_t nh, T const * __restrict__ v, uint32_t const * __restrict__ offsets, uint32_t totSize, 
-                          int nthreads, cudaStream_t stream) {
+                          int nthreads, cudaStream_t stream
+#ifndef __CUDACC__
+                  = 0
+#endif
+                 ) {
     launchZero(h,stream);
 #ifdef __CUDACC__
     auto nblocks = (totSize + nthreads - 1) / nthreads;
@@ -204,32 +221,32 @@ class HistoContainer {
   __host__ __device__
   void add(CountersOnly const & co) {
     for (uint32_t i = 0; i<totbins(); ++i) {
-      #ifdef __CUDA_ARCH__
+#ifdef __CUDA_ARCH__
       atomicAdd(off+i,co.off[i]);
-      #else
+#else
       off[i]+=co.off[i]; 
-      #endif
+#endif
     }
   }
 
   static __host__ __device__
   __forceinline__
   uint32_t atomicIncrement(Counter & x) {
-    #ifdef __CUDA_ARCH__
+#ifdef __CUDA_ARCH__
     return atomicAdd(&x, 1);
-    #else
+#else
     return x++;
-    #endif
+#endif
   }
 
   static __host__ __device__
   __forceinline__
   uint32_t atomicDecrement(Counter & x) {
-    #ifdef __CUDA_ARCH__
+#ifdef __CUDA_ARCH__
     return atomicSub(&x, 1);
-    #else
+#else
     return x--;
-    #endif
+#endif
   }
 
  __host__ __device__
diff --git a/HeterogeneousCore/CUDAUtilities/interface/prefixScan.h b/HeterogeneousCore/CUDAUtilities/interface/prefixScan.h
index 46291d5c6796a..e11d3e8d59f5e 100644
--- a/HeterogeneousCore/CUDAUtilities/interface/prefixScan.h
+++ b/HeterogeneousCore/CUDAUtilities/interface/prefixScan.h
@@ -30,8 +30,13 @@ template<typename T>
 __device__ __host__
 void
 __forceinline__
-blockPrefixScan(T * c, uint32_t size, T* ws=nullptr) {
+blockPrefixScan(T * c, uint32_t size, T* ws
+#ifndef __CUDA_ARCH__
+                =nullptr
+#endif
+               ) {
 #ifdef __CUDA_ARCH__
+  assert(ws);
   assert(size<=1024);
   assert(0==blockDim.x%32);
   auto first = threadIdx.x;
diff --git a/HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc b/HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc
index 3771e265b70fd..3dd00ec986696 100644
--- a/HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc
+++ b/HeterogeneousCore/CUDAUtilities/src/cudaCompat.cc
@@ -2,11 +2,13 @@
 namespace cudaCompat {
   thread_local dim3 blockIdx;
   thread_local dim3 gridDim;
+}
 
+namespace {
  struct InitGrid {
-   InitGrid() { resetGrid();}
+   InitGrid() { cudaCompat::resetGrid();}
  };
  
- InitGrid initGrid;
+ const InitGrid initGrid;
 
 }

From 36834dfe2653ab779983488c0630a6f3291df536 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 19 Feb 2019 10:17:58 +0100
Subject: [PATCH 55/95] assert cuda emulation

---
 .../CUDAUtilities/test/OneToManyAssoc_t.h            | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h
index 4888a661bc8e4..5521e46dd9dbb 100644
--- a/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h
+++ b/HeterogeneousCore/CUDAUtilities/test/OneToManyAssoc_t.h
@@ -100,6 +100,18 @@ int main() {
   std::cout << "cuda x's " << threadIdx.x << ' ' << blockIdx.x << ' ' << blockDim.x << ' ' << gridDim.x << std::endl;
   std::cout << "cuda y's " << threadIdx.y << ' ' << blockIdx.y << ' ' << blockDim.y << ' ' << gridDim.y << std::endl;
   std::cout << "cuda z's " << threadIdx.z << ' ' << blockIdx.z << ' ' << blockDim.z << ' ' << gridDim.z << std::endl;
+  assert(threadIdx.x==0);
+  assert(threadIdx.y==0);
+  assert(threadIdx.z==0);
+  assert(blockIdx.x==0);
+  assert(blockIdx.y==0);
+  assert(blockIdx.z==0);
+  assert(blockDim.x==1);
+  assert(blockDim.y==1);
+  assert(blockDim.z==1);
+  assert(gridDim.x==1);
+  assert(gridDim.y==1);
+  assert(gridDim.z==1);
 #endif
 
 

From ddb641987c50ad0ef35cc74e9153f83835e2f311 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 19 Feb 2019 17:13:06 +0100
Subject: [PATCH 56/95] fix missing free and add _

---
 .../plugins/CAHitQuadrupletGeneratorKernels.cu        |  6 +++---
 .../plugins/CAHitQuadrupletGeneratorKernels.h         |  6 +++---
 .../plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu   | 11 ++++++-----
 3 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 7ab74b3656054..965e7be70dac8 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -323,7 +323,7 @@ void CAHitQuadrupletGeneratorKernels::launchKernels( // here goes algoparms....
       device_theCells_, device_nCells_,
       gpu_.tuples_d,
       gpu_.apc_d,
-      device_tupleMultiplicity,
+      device_tupleMultiplicity_,
       minHitsPerNtuplet_      
   );
   cudaCheck(cudaGetLastError());
@@ -331,12 +331,12 @@ void CAHitQuadrupletGeneratorKernels::launchKernels( // here goes algoparms....
   numberOfBlocks = (TuplesOnGPU::Container::totbins() + blockSize - 1)/blockSize;
   cudautils::finalizeBulk<<<numberOfBlocks, blockSize, 0, cudaStream>>>(gpu_.apc_d,gpu_.tuples_d);
 
-  cudautils::launchFinalize(device_tupleMultiplicity,device_tmws,cudaStream);
+  cudautils::launchFinalize(device_tupleMultiplicity_,device_tmws_,cudaStream);
 
 
   blockSize = 128;
   numberOfBlocks = (CAConstants::maxTuples() + blockSize - 1) / blockSize;
-  kernel_fillMultiplicity<<<numberOfBlocks, blockSize, 0, cudaStream>>>(gpu_.tuples_d,device_tupleMultiplicity);
+  kernel_fillMultiplicity<<<numberOfBlocks, blockSize, 0, cudaStream>>>(gpu_.tuples_d,device_tupleMultiplicity_);
   cudaCheck(cudaGetLastError());
 
   if (lateFishbone_) {
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
index 91688b1e5dd3f..f062a207ce056 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
@@ -27,7 +27,7 @@ class CAHitQuadrupletGeneratorKernels {
    ~CAHitQuadrupletGeneratorKernels() { deallocateOnGPU();}
 
 
-   TupleMultiplicity const * tupleMultiplicity() const { return device_tupleMultiplicity;}
+   TupleMultiplicity const * tupleMultiplicity() const { return device_tupleMultiplicity_;}
 
    void launchKernels(HitsOnCPU const & hh, TuplesOnGPU & tuples_d, cudaStream_t cudaStream);
 
@@ -48,8 +48,8 @@ class CAHitQuadrupletGeneratorKernels {
     HitToTuple * device_hitToTuple_ = nullptr;
     AtomicPairCounter * device_hitToTuple_apc_ = nullptr;
 
-    TupleMultiplicity * device_tupleMultiplicity = nullptr;
-    uint8_t * device_tmws;    
+    TupleMultiplicity * device_tupleMultiplicity_ = nullptr;
+    uint8_t * device_tmws_ = nullptr;    
 
     const uint32_t minHitsPerNtuplet_;
     const bool earlyFishbone_;
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
index 369d484ac0b06..e95093103a817 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
@@ -10,7 +10,8 @@ CAHitQuadrupletGeneratorKernels::deallocateOnGPU()
   cudaFree(device_nCells_);
 //  cudaFree(device_hitToTuple_);
   cudaFree(device_hitToTuple_apc_);
-
+  cudaFree(device_tupleMultiplicity_);
+  cudaFree(device_tmws_);
 }
 
 void CAHitQuadrupletGeneratorKernels::allocateOnGPU()
@@ -32,9 +33,9 @@ void CAHitQuadrupletGeneratorKernels::allocateOnGPU()
 //   cudaCheck(cudaMalloc(&device_hitToTuple_, sizeof(HitToTuple)));
    cudaCheck(cudaMalloc(&device_hitToTuple_apc_, sizeof(AtomicPairCounter)));
 
-   cudaCheck(cudaMalloc(&device_tupleMultiplicity,sizeof(TupleMultiplicity)));
-   cudaCheck(cudaMemset(device_tupleMultiplicity,0,sizeof(TupleMultiplicity))); // overkill
-   cudaCheck(cudaMalloc(&device_tmws, TupleMultiplicity::wsSize()));
+   cudaCheck(cudaMalloc(&device_tupleMultiplicity_,sizeof(TupleMultiplicity)));
+   cudaCheck(cudaMemset(device_tupleMultiplicity_,0,sizeof(TupleMultiplicity))); // overkill
+   cudaCheck(cudaMalloc(&device_tmws_, TupleMultiplicity::wsSize()));
 }
 
 void CAHitQuadrupletGeneratorKernels::cleanup(cudaStream_t cudaStream) {
@@ -44,7 +45,7 @@ void CAHitQuadrupletGeneratorKernels::cleanup(cudaStream_t cudaStream) {
                             cudaStream));
   cudaCheck(cudaMemsetAsync(device_nCells_, 0, sizeof(uint32_t), cudaStream));
 
-  cudautils::launchZero(device_tupleMultiplicity,cudaStream);
+  cudautils::launchZero(device_tupleMultiplicity_,cudaStream);
 
 }
 

From 9f06db84f8cecb150acdb4958b2c564e20525769 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 25 Feb 2019 11:46:16 +0100
Subject: [PATCH 57/95] add clus size

---
 .../SiPixelRecHits/interface/pixelCPEforGPU.h | 21 +++++++++++++++++++
 .../SiPixelRecHits/plugins/PixelRecHits.cu    |  9 +++++---
 .../SiPixelRecHits/plugins/gpuPixelRecHits.h  |  9 +++++++-
 .../siPixelRecHitsHeterogeneousProduct.h      |  3 +++
 4 files changed, 38 insertions(+), 4 deletions(-)

diff --git a/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h b/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
index fa326865ced73..1172d657a16bc 100644
--- a/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
+++ b/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
@@ -79,6 +79,10 @@ namespace pixelCPEforGPU {
 
     float xerr[N];
     float yerr[N];
+
+    int16_t xsize[N]; // clipped at 127 if negative is edge....
+    int16_t ysize[N];
+
   };
 
 
@@ -173,6 +177,23 @@ namespace pixelCPEforGPU {
     auto mx = llxl+urxl;
     auto my = llyl+uryl;
 
+    auto xsize = int(urxl+2)-int(llyl);
+    auto ysize = int(urxl+2)-int(llyl);
+    assert(xsize>0);
+    assert(ysize>0);
+
+    if(phase1PixelTopology::isBigPixX(cp.minRow[ic])) ++xsize;
+    if(phase1PixelTopology::isBigPixX(cp.maxRow[ic])) ++xsize;
+    if(phase1PixelTopology::isBigPixY(cp.minCol[ic])) ++ysize;
+    if(phase1PixelTopology::isBigPixY(cp.maxCol[ic])) ++ysize;
+
+    cp.xsize[ic] = std::min(xsize,127);
+    cp.ysize[ic] = std::min(ysize,127);
+
+    if(cp.minRow[ic]==0 || cp.maxRow[ic]==phase1PixelTopology::lastRowInModule) cp.xsize[ic] = -cp.xsize[ic];
+    if(cp.minCol[ic]==0 || cp.maxCol[ic]==phase1PixelTopology::lastColInModule) cp.ysize[ic] = -cp.ysize[ic];
+
+
     // apply the lorentz offset correction
     auto xPos = detParams.shiftX + comParams.thePitchX*(0.5f*float(mx)+float(phase1PixelTopology::xOffset));
     auto yPos = detParams.shiftY + comParams.thePitchY*(0.5f*float(my)+float(phase1PixelTopology::yOffset));
diff --git a/RecoLocalTracker/SiPixelRecHits/plugins/PixelRecHits.cu b/RecoLocalTracker/SiPixelRecHits/plugins/PixelRecHits.cu
index 947cd20d97919..c915753f5fb38 100644
--- a/RecoLocalTracker/SiPixelRecHits/plugins/PixelRecHits.cu
+++ b/RecoLocalTracker/SiPixelRecHits/plugins/PixelRecHits.cu
@@ -61,14 +61,16 @@ namespace pixelgpudetails {
     gpu_.rg_d = slicePitch<float>(gpu_.owner_32bit_, gpu_.owner_32bit_pitch_, 8);
 
     // Order such that the first ones are the ones transferred to CPU
-    cudaCheck(cudaMallocPitch(&gpu_.owner_16bit_, &gpu_.owner_16bit_pitch_, MAX_HITS*sizeof(uint16_t), 5));
-    cudaCheck(cudaMemsetAsync(gpu_.owner_16bit_, 0x0, gpu_.owner_16bit_pitch_*5, cudaStream.id()));
+    cudaCheck(cudaMallocPitch(&gpu_.owner_16bit_, &gpu_.owner_16bit_pitch_, MAX_HITS*sizeof(uint16_t), 7));
+    cudaCheck(cudaMemsetAsync(gpu_.owner_16bit_, 0x0, gpu_.owner_16bit_pitch_*7, cudaStream.id()));
     //edm::LogPrint("Foo") << "Allocate 16bit with pitch " << gpu_.owner_16bit_pitch_;
     gpu_.detInd_d = slicePitch<uint16_t>(gpu_.owner_16bit_, gpu_.owner_16bit_pitch_, 0);
     gpu_.mr_d = slicePitch<uint16_t>(gpu_.owner_16bit_, gpu_.owner_16bit_pitch_, 1);
     gpu_.mc_d = slicePitch<uint16_t>(gpu_.owner_16bit_, gpu_.owner_16bit_pitch_, 2);
     gpu_.iphi_d = slicePitch<int16_t>(gpu_.owner_16bit_, gpu_.owner_16bit_pitch_, 3);
     gpu_.sortIndex_d = slicePitch<uint16_t>(gpu_.owner_16bit_, gpu_.owner_16bit_pitch_, 4);
+    gpu_.xsize_d = slicePitch<int16_t>(gpu_.owner_16bit_, gpu_.owner_16bit_pitch_, 5);
+    gpu_.ysize_d = slicePitch<int16_t>(gpu_.owner_16bit_, gpu_.owner_16bit_pitch_, 6);
 
     cudaCheck(cudaMalloc((void **) & gpu_.hist_d, sizeof(HitsOnGPU::Hist)));
     cudaCheck(cudaMalloc((void **) & gpu_.hws_d, HitsOnGPU::Hist::wsSize()));
@@ -161,7 +163,8 @@ namespace pixelgpudetails {
       gpu_.iphi_d,
       gpu_.xl_d, gpu_.yl_d,
       gpu_.xerr_d, gpu_.yerr_d,
-      gpu_.mr_d, gpu_.mc_d
+      gpu_.mr_d, gpu_.mc_d,
+      gpu_.xsize_d, gpu_.ysize_d
     );
     cudaCheck(cudaGetLastError());
 
diff --git a/RecoLocalTracker/SiPixelRecHits/plugins/gpuPixelRecHits.h b/RecoLocalTracker/SiPixelRecHits/plugins/gpuPixelRecHits.h
index 6864a046bf1dc..74446f9468d22 100644
--- a/RecoLocalTracker/SiPixelRecHits/plugins/gpuPixelRecHits.h
+++ b/RecoLocalTracker/SiPixelRecHits/plugins/gpuPixelRecHits.h
@@ -31,7 +31,9 @@ namespace gpuPixelRecHits {
 			  float * xg, float * yg, float * zg, float * rg, int16_t * iph,
                           float * xl, float * yl,
                           float * xe, float * ye, 
-                          uint16_t * mr, uint16_t * mc)
+                          uint16_t * mr, uint16_t * mc,
+                          int16_t * xs, int16_t * ys
+                          )
   {
 
     // to be moved in common namespace...
@@ -135,10 +137,15 @@ namespace gpuPixelRecHits {
     xl[h]= clusParams.xpos[ic];   
     yl[h]= clusParams.ypos[ic]; 
 
+    xs[h]= clusParams.xsize[ic];
+    ys[h]= clusParams.ysize[ic];
+
+
     xe[h]= clusParams.xerr[ic]*clusParams.xerr[ic];
     ye[h]= clusParams.yerr[ic]*clusParams.yerr[ic];
     mr[h]= clusParams.minRow[ic];
     mc[h]= clusParams.minCol[ic];
+
   
     // to global and compute phi... 
     cpeParams->detParams(me).frame.toGlobal(xl[h],yl[h], xg[h],yg[h],zg[h]);
diff --git a/RecoLocalTracker/SiPixelRecHits/plugins/siPixelRecHitsHeterogeneousProduct.h b/RecoLocalTracker/SiPixelRecHits/plugins/siPixelRecHitsHeterogeneousProduct.h
index ea6eaf8458dde..e9d1b922c155b 100644
--- a/RecoLocalTracker/SiPixelRecHits/plugins/siPixelRecHitsHeterogeneousProduct.h
+++ b/RecoLocalTracker/SiPixelRecHits/plugins/siPixelRecHitsHeterogeneousProduct.h
@@ -34,6 +34,9 @@ namespace siPixelRecHitsHeterogeneousProduct {
      uint16_t * sortIndex_d;
      uint16_t * mr_d;
      uint16_t * mc_d;
+     int16_t * xsize_d;
+     int16_t * ysize_d;
+
 
      using Hist = HistoContainer<int16_t,128,maxHits(),8*sizeof(int16_t),uint16_t,10>;
      Hist * hist_d;

From f6452bdb756dd207ff94b6158be4ba05a05b3bf3 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 25 Feb 2019 14:27:24 +0100
Subject: [PATCH 58/95] apply zsize cut at doublets

---
 .../SiPixelRecHits/interface/pixelCPEforGPU.h         |  8 ++++----
 .../PixelTriplets/plugins/gpuPixelDoublets.h          | 11 ++++++++++-
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h b/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
index 1172d657a16bc..4ac8e9a1d7da6 100644
--- a/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
+++ b/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
@@ -177,10 +177,10 @@ namespace pixelCPEforGPU {
     auto mx = llxl+urxl;
     auto my = llyl+uryl;
 
-    auto xsize = int(urxl+2)-int(llyl);
-    auto ysize = int(urxl+2)-int(llyl);
-    assert(xsize>0);
-    assert(ysize>0);
+    auto xsize = int(urxl)+2-int(llxl);
+    auto ysize = int(uryl)+2-int(llyl);
+    assert(xsize>=0);  // 0 if bixpix...
+    assert(ysize>=0);
 
     if(phase1PixelTopology::isBigPixX(cp.minRow[ic])) ++xsize;
     if(phase1PixelTopology::isBigPixX(cp.maxRow[ic])) ++xsize;
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 0e67c4cc3e28b..be0fdae16ba78 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -82,7 +82,8 @@ namespace gpuPixelDoublets {
       auto mep = iphi[i];
       auto mez = __ldg(hh.zg_d+i);
       auto mer = __ldg(hh.rg_d+i);
-
+      auto mes = __ldg(hh.ysize_d+i);
+ 
       constexpr float z0cut = 12.f;                     // cm
       constexpr float hardPtCut = 0.5f;                 // GeV
       constexpr float minRadius = hardPtCut * 87.78f;   // cm (1 GeV track has 1 GeV/c / (e * 3.8T) ~ 87 cm radius in a 3.8T field)
@@ -102,6 +103,13 @@ namespace gpuPixelDoublets {
           dr<0 || std::abs((mez*ro - mer*zo)) > z0cut*dr;
       };
 
+      auto zsizeCut = [&](int j) {
+        auto onlyBarrel = outer<4;
+//        auto onlyB234 = outer==2 || outer==3;
+        auto so = __ldg(hh.ysize_d+j);
+        return onlyBarrel && mes>0 && so>0  && std::abs(so-mes)>2;
+      };
+
       auto iphicut = phicuts[pairLayerId];
 
       auto kl = Hist::bin(int16_t(mep-iphicut));
@@ -131,6 +139,7 @@ namespace gpuPixelDoublets {
 
           if (std::min(std::abs(int16_t(iphi[oi]-mep)), std::abs(int16_t(mep-iphi[oi]))) > iphicut)
             continue;
+          if (zsizeCut(oi)) continue;
           if (z0cutoff(oi) || ptcut(oi)) continue;
           auto ind = atomicAdd(nCells, 1); 
           if (ind>=MaxNumOfDoublets) {atomicSub(nCells, 1); break; } // move to SimpleVector??

From a12d3aec92e80e10e7594efc2bea2a0b8f651327 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 25 Feb 2019 14:57:54 +0100
Subject: [PATCH 59/95] mitigate broken clus

---
 RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index be0fdae16ba78..a783960839bfc 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -107,7 +107,8 @@ namespace gpuPixelDoublets {
         auto onlyBarrel = outer<4;
 //        auto onlyB234 = outer==2 || outer==3;
         auto so = __ldg(hh.ysize_d+j);
-        return onlyBarrel && mes>0 && so>0  && std::abs(so-mes)>2;
+        auto dz = inner==0 ? mes-so : std::abs(mes-so); // avoid broken clus on B1
+        return onlyBarrel && mes>0 && so>0  && dz>2;
       };
 
       auto iphicut = phicuts[pairLayerId];

From b345b9d8dfc87bc157a8f3c9169860317e3d10de Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 25 Feb 2019 15:32:58 +0100
Subject: [PATCH 60/95] try larger cut on b1

---
 RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index a783960839bfc..7204568cdd56e 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -107,8 +107,8 @@ namespace gpuPixelDoublets {
         auto onlyBarrel = outer<4;
 //        auto onlyB234 = outer==2 || outer==3;
         auto so = __ldg(hh.ysize_d+j);
-        auto dz = inner==0 ? mes-so : std::abs(mes-so); // avoid broken clus on B1
-        return onlyBarrel && mes>0 && so>0  && dz>2;
+        auto dy = inner==0 ? 3 : 2;
+        return onlyBarrel && mes>0 && so>0  && std::abs(so-mes)<dy;
       };
 
       auto iphicut = phicuts[pairLayerId];

From 97b6a46304776a92072a2cff7c0478e15f4431f9 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 25 Feb 2019 15:47:51 +0100
Subject: [PATCH 61/95] try larger cut on b1

---
 RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 7204568cdd56e..6b14f5585467c 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -108,7 +108,7 @@ namespace gpuPixelDoublets {
 //        auto onlyB234 = outer==2 || outer==3;
         auto so = __ldg(hh.ysize_d+j);
         auto dy = inner==0 ? 3 : 2;
-        return onlyBarrel && mes>0 && so>0  && std::abs(so-mes)<dy;
+        return onlyBarrel && mes>0 && so>0  && std::abs(so-mes)>dy;
       };
 
       auto iphicut = phicuts[pairLayerId];

From 9cddb3b03c0676d560b4ea3ea70f699aa45c9ba5 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 26 Feb 2019 04:44:29 -0500
Subject: [PATCH 62/95] introduce z cut

---
 .../CAHitQuadrupletGeneratorKernels.cu        |  2 +-
 .../PixelTriplets/plugins/gpuPixelDoublets.h  | 24 +++++++++++--------
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 965e7be70dac8..9b1e9a1520eab 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -375,7 +375,7 @@ void CAHitQuadrupletGeneratorKernels::buildDoublets(HitsOnCPU const & hh, cudaSt
 
   int stride=1;
   int threadsPerBlock = gpuPixelDoublets::getDoubletsFromHistoMaxBlockSize/stride;
-  int blocks = (3 * nhits + threadsPerBlock - 1) / threadsPerBlock;
+  int blocks = (2 * nhits + threadsPerBlock - 1) / threadsPerBlock;
   dim3 blks(1,blocks,1);
   dim3 thrs(stride,threadsPerBlock,1);
   gpuPixelDoublets::getDoubletsFromHisto<<<blks, thrs, 0, stream>>>(
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 6b14f5585467c..ca37f9d23ca81 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -81,6 +81,10 @@ namespace gpuPixelDoublets {
       // found hit corresponding to our cuda thread, now do the job
       auto mep = iphi[i];
       auto mez = __ldg(hh.zg_d+i);
+
+      // if (outer==4 || outer==7)
+      // if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
+
       auto mer = __ldg(hh.rg_d+i);
       auto mes = __ldg(hh.ysize_d+i);
  
@@ -140,7 +144,7 @@ namespace gpuPixelDoublets {
 
           if (std::min(std::abs(int16_t(iphi[oi]-mep)), std::abs(int16_t(mep-iphi[oi]))) > iphicut)
             continue;
-          if (zsizeCut(oi)) continue;
+          // if (zsizeCut(oi)) continue;
           if (z0cutoff(oi) || ptcut(oi)) continue;
           auto ind = atomicAdd(nCells, 1); 
           if (ind>=MaxNumOfDoublets) {atomicSub(nCells, 1); break; } // move to SimpleVector??
@@ -173,9 +177,9 @@ namespace gpuPixelDoublets {
     constexpr int nPairs = 13;
     constexpr const uint8_t layerPairs[2*nPairs] = {
       0, 1,  1, 2,  2, 3,
-   // 0, 4,  1, 4,  2, 4,  4, 5,  5, 6,
-      0, 7,  1, 7,  2, 7,  7, 8,  8, 9,
-      0, 4,  1, 4,  2, 4,  4, 5,  5, 6
+      // 0, 4,  1, 4,  2, 4,  4, 5,  5, 6,
+      0, 7,  1, 7,  2, 7,  7, 8,  8, 9, // neg
+      0, 4,  1, 4,  2, 4,  4, 5,  5, 6,  // pos
     };
 
     constexpr int16_t phi0p05 = 522;    // round(521.52189...) = phi2short(0.05);
@@ -189,15 +193,15 @@ namespace gpuPixelDoublets {
     };
 
     float const minz[nPairs] = {
-      0., 0., 0.,
-      0., 0., 0., 0., 0.,
-      0., 0., 0., 0., 0.
+      -20., -22., -22.,
+      -30., -30.,-30., -70., -70.,
+        0., 10., 15., -70., -70.
     };
 
     float const maxz[nPairs] = {
-      20., 15., 12.,
-      30., 20., 20., 50., 50.,
-      30., 20., 20., 50., 50.
+      20., 22., 22.,
+       0., -10., -15., 70., 70.,
+      30., 30., 30., 70., 70.
     };
 
     float const maxr[nPairs] = {

From e4aa589cb5c5f57bedd6dbb5c43e0d66c43440fd Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 26 Feb 2019 17:04:22 +0100
Subject: [PATCH 63/95] debugging tp assoc on gpu

---
 .../SiPixelRecHits/interface/pixelCPEforGPU.h            | 9 +++++++--
 .../PixelTriplets/plugins/gpuPixelDoublets.h             | 2 +-
 SimTracker/TrackerHitAssociation/plugins/BuildFile.xml   | 1 +
 .../TrackerHitAssociation/plugins/ClusterSLOnGPU.cu      | 6 ++++--
 .../plugins/ClusterTPAssociationHeterogeneous.cc         | 4 ++--
 5 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h b/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
index 4ac8e9a1d7da6..5776e054fd330 100644
--- a/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
+++ b/RecoLocalTracker/SiPixelRecHits/interface/pixelCPEforGPU.h
@@ -187,8 +187,13 @@ namespace pixelCPEforGPU {
     if(phase1PixelTopology::isBigPixY(cp.minCol[ic])) ++ysize;
     if(phase1PixelTopology::isBigPixY(cp.maxCol[ic])) ++ysize;
 
-    cp.xsize[ic] = std::min(xsize,127);
-    cp.ysize[ic] = std::min(ysize,127);
+     int unbalanceX = 8.*std::abs(float(cp.Q_f_X[ic]-cp.Q_l_X[ic]))/float(cp.Q_f_X[ic]+cp.Q_l_X[ic]);
+     int unbalanceY = 8.*std::abs(float(cp.Q_f_Y[ic]-cp.Q_l_Y[ic]))/float(cp.Q_f_Y[ic]+cp.Q_l_Y[ic]);
+     xsize = 8*xsize - unbalanceX;
+     ysize = 8*ysize - unbalanceY;
+
+    cp.xsize[ic] = std::min(xsize,1023);
+    cp.ysize[ic] = std::min(ysize,1023);
 
     if(cp.minRow[ic]==0 || cp.maxRow[ic]==phase1PixelTopology::lastRowInModule) cp.xsize[ic] = -cp.xsize[ic];
     if(cp.minCol[ic]==0 || cp.maxCol[ic]==phase1PixelTopology::lastColInModule) cp.ysize[ic] = -cp.ysize[ic];
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index ca37f9d23ca81..6d083a2c073f9 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -83,7 +83,7 @@ namespace gpuPixelDoublets {
       auto mez = __ldg(hh.zg_d+i);
 
       // if (outer==4 || outer==7)
-      // if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
+      if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
 
       auto mer = __ldg(hh.rg_d+i);
       auto mes = __ldg(hh.ysize_d+i);
diff --git a/SimTracker/TrackerHitAssociation/plugins/BuildFile.xml b/SimTracker/TrackerHitAssociation/plugins/BuildFile.xml
index a63c38264c20d..03d455d20c29a 100644
--- a/SimTracker/TrackerHitAssociation/plugins/BuildFile.xml
+++ b/SimTracker/TrackerHitAssociation/plugins/BuildFile.xml
@@ -11,4 +11,5 @@
   <use name="cuda"/>
   <use name="cuda-api-wrappers"/>
   <flags EDM_PLUGIN="1"/>
+  <flags CUDA_FLAGS="-g -DGPU_DEBUG"/>
 </library>
diff --git a/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu b/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
index b402daef07a05..fed7dcc087ca0 100644
--- a/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
+++ b/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
@@ -107,9 +107,10 @@ void dumpLink(int first, int ev, clusterSLOnGPU::HitsOnGPU const * hhp, uint32_t
   auto const & tk1 = sl.links_d[sl.tkId_d[i]];
   auto const & tk2 = sl.links_d[sl.tkId2_d[i]];
 
-  printf("HIT: %d %d %d %d %f %f %f %f %d %d %d %d %d %d %d\n", ev, i,
+  printf("HIT: %d %d %d %d %f %f %f %f %d %d %d %d %d %d %d %d %d\n", ev, i,
          hh.detInd_d[i], hh.charge_d[i],
          hh.xg_d[i], hh.yg_d[i], hh.zg_d[i], hh.rg_d[i], hh.iphi_d[i],
+         hh.xsize_d[i],hh.ysize_d[i],
          tk1[2], tk1[3], sl.n1_d[i],
          tk2[2], tk2[3], sl.n2_d[i]
         );
@@ -121,9 +122,10 @@ namespace clusterSLOnGPU {
   constexpr uint32_t invTK = 0; // std::numeric_limits<int32_t>::max();
 
   void printCSVHeader() {
-    printf("HIT: %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s\n", "ev", "ind",
+    printf("HIT: %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s\n", "ev", "ind",
         "det", "charge",
         "xg","yg","zg","rg","iphi",
+        "xsize","ysize",
         "tkId","pt","n1","tkId2","pt2","n2"
         );
   }
diff --git a/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc b/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
index a5e0f403adcad..c0ae79e1211fa 100644
--- a/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
+++ b/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
@@ -158,7 +158,7 @@ void ClusterTPAssociationHeterogeneous::makeMap(const edm::HeterogeneousEvent &i
       for (auto itrk  = trackingParticle->g4Track_begin();
                 itrk != trackingParticle->g4Track_end(); ++itrk) {
         std::pair<uint32_t, EncodedEventId> trkid(itrk->trackId(), eid);
-        //std::cout << "creating map for id: " << trkid.first << " with tp: " << trackingParticle.key() << std::endl;
+        // std::cout << "creating map for id: " << trkid.first << " with tp: " << trackingParticle.key() << std::endl;
         mapping.insert(std::make_pair(trkid, trackingParticle));
       }
     }
@@ -296,7 +296,7 @@ ClusterTPAssociationHeterogeneous::produceLegacy(edm::HeterogeneousEvent &iEvent
 	     iset != simTkIds.end(); iset++) {
 	  auto ipos = mapping.find(*iset);
 	  if (ipos != mapping.end()) {
-	    //std::cout << "cluster in detid: " << detid << " from tp: " << ipos->second.key() << " " << iset->first << std::endl;
+	    std::cout << "cluster in detid: " << detid << " from tp: " << ipos->second.key() << " " << iset->first << std::endl;
 	    clusterTPList.emplace_back(OmniClusterRef(c_ref), ipos->second);
 	  }
 	}

From c06d5656821f3cefa91602f771c9b696cd7dc8fb Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 27 Feb 2019 10:14:36 +0100
Subject: [PATCH 64/95] fix parentesis after not

---
 SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu      | 2 +-
 .../plugins/ClusterTPAssociationHeterogeneous.cc                | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu b/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
index fed7dcc087ca0..83683284f98b9 100644
--- a/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
+++ b/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
@@ -42,7 +42,7 @@ void simLink(const SiPixelDigisCUDA::DeviceConstView *dd, uint32_t ndigis, const
 
   auto less = [] __host__ __device__ (std::array<uint32_t, 4> const & a, std::array<uint32_t, 4> const & b)->bool {
      // in this context we do not care of [2]
-     return a[0] < b[0] or (not b[0] < a[0] and a[1] < b[1]);
+     return a[0] < b[0] or ( (not (b[0]<a[0]) ) and (a[1]<b[1]) );
   };
 
   auto equal = [] __host__ __device__ (std::array<uint32_t, 4> const & a, std::array<uint32_t, 4> const & b)->bool {
diff --git a/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc b/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
index c0ae79e1211fa..03d2d8d1e5809 100644
--- a/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
+++ b/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
@@ -296,7 +296,7 @@ ClusterTPAssociationHeterogeneous::produceLegacy(edm::HeterogeneousEvent &iEvent
 	     iset != simTkIds.end(); iset++) {
 	  auto ipos = mapping.find(*iset);
 	  if (ipos != mapping.end()) {
-	    std::cout << "cluster in detid: " << detid << " from tp: " << ipos->second.key() << " " << iset->first << std::endl;
+	    // std::cout << "cluster in detid: " << detid << " from tp: " << ipos->second.key() << " " << iset->first << std::endl;
 	    clusterTPList.emplace_back(OmniClusterRef(c_ref), ipos->second);
 	  }
 	}

From aebdf9a3d48fd5035e7aba0e158c97dba43bd9c9 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 27 Feb 2019 13:35:07 +0100
Subject: [PATCH 65/95] enable clus size cut

---
 .../plugins/CAHitQuadrupletGeneratorKernels.cu              | 4 ++--
 RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 9b1e9a1520eab..5318993f51de3 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -33,9 +33,9 @@ void kernel_checkOverflows(TuplesOnGPU::Container * foundNtuplets, AtomicPairCou
  __syncthreads();
   
  auto idx = threadIdx.x + blockIdx.x * blockDim.x;
- #ifdef GPU_DEBUG
+ #ifdef GPU_STAT
  if (0==idx) {
-   printf("number of found cells %d, found tuples %d with total hits %d,%d\n",*nCells, apc->get().m, foundNtuplets->size(), apc->get().n);
+   printf("number of found cells %d, found tuples %d with total hits %d out of %d\n",*nCells, apc->get().m, apc->get().n, nHits);
    assert(foundNtuplets->size(apc->get().m)==0);
    assert(foundNtuplets->size()==apc->get().n);
  }
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 6d083a2c073f9..d3185abf1e6f3 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -79,12 +79,12 @@ namespace gpuPixelDoublets {
       assert(i < offsets[inner+1]);
 
       // found hit corresponding to our cuda thread, now do the job
-      auto mep = iphi[i];
       auto mez = __ldg(hh.zg_d+i);
 
       // if (outer==4 || outer==7)
       if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
 
+      auto mep = iphi[i];
       auto mer = __ldg(hh.rg_d+i);
       auto mes = __ldg(hh.ysize_d+i);
  
@@ -111,7 +111,7 @@ namespace gpuPixelDoublets {
         auto onlyBarrel = outer<4;
 //        auto onlyB234 = outer==2 || outer==3;
         auto so = __ldg(hh.ysize_d+j);
-        auto dy = inner==0 ? 3 : 2;
+        auto dy = inner==0 ? 24 : 16;  // now size is *8....
         return onlyBarrel && mes>0 && so>0  && std::abs(so-mes)>dy;
       };
 
@@ -144,7 +144,7 @@ namespace gpuPixelDoublets {
 
           if (std::min(std::abs(int16_t(iphi[oi]-mep)), std::abs(int16_t(mep-iphi[oi]))) > iphicut)
             continue;
-          // if (zsizeCut(oi)) continue;
+          if (zsizeCut(oi)) continue;
           if (z0cutoff(oi) || ptcut(oi)) continue;
           auto ind = atomicAdd(nCells, 1); 
           if (ind>=MaxNumOfDoublets) {atomicSub(nCells, 1); break; } // move to SimpleVector??

From b8168da52af75e71ac8a8349834d2076a8a8c192 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 27 Feb 2019 14:20:01 +0100
Subject: [PATCH 66/95] add conters

---
 .../CAHitQuadrupletGeneratorKernels.cu        | 38 ++++++++++++++++---
 .../plugins/CAHitQuadrupletGeneratorKernels.h | 16 ++++++++
 .../CAHitQuadrupletGeneratorKernelsAlloc.cu   |  6 +++
 3 files changed, 55 insertions(+), 5 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 5318993f51de3..3373f7c108625 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -26,14 +26,25 @@ __global__
 void kernel_checkOverflows(TuplesOnGPU::Container * foundNtuplets, AtomicPairCounter * apc,
                GPUCACell const * __restrict__ cells, uint32_t const * __restrict__ nCells,
                GPUCACell::OuterHitOfCell const * __restrict__ isOuterHitOfCell,
-               uint32_t nHits) {
+               uint32_t nHits, CAHitQuadrupletGeneratorKernels::Counters * counters) {
 
  __shared__ uint32_t killedCell;
  killedCell=0;
  __syncthreads();
-  
+
  auto idx = threadIdx.x + blockIdx.x * blockDim.x;
- #ifdef GPU_STAT
+
+ auto    & c = *counters;
+ // counters once per event
+ if(0==idx) {
+   atomicAdd(&c.nEvents,1);
+   atomicAdd(&c.nHits,nHits);
+   atomicAdd(&c.nCells,*nCells);   
+   atomicAdd(&c.nTuples,apc->get().m);
+   atomicAdd(&c.nUsedHits,apc->get().n);
+ }
+  
+ #ifdef GPU_DEBUG
  if (0==idx) {
    printf("number of found cells %d, found tuples %d with total hits %d out of %d\n",*nCells, apc->get().m, apc->get().n, nHits);
    assert(foundNtuplets->size(apc->get().m)==0);
@@ -65,7 +76,8 @@ void kernel_checkOverflows(TuplesOnGPU::Container * foundNtuplets, AtomicPairCou
  }
 
  __syncthreads();
-// if (threadIdx.x==0) printf("number of killed cells %d\n",killedCell);
+ if (threadIdx.x==0) atomicAdd(&c.nKilledCells,killedCell);
+ // if (threadIdx.x==0) printf("number of killed cells %d\n",killedCell);
 }
 
 
@@ -360,7 +372,8 @@ void CAHitQuadrupletGeneratorKernels::launchKernels( // here goes algoparms....
   kernel_checkOverflows<<<numberOfBlocks, blockSize, 0, cudaStream>>>(
                         gpu_.tuples_d, gpu_.apc_d,
                         device_theCells_, device_nCells_,
-                        device_isOuterHitOfCell_, nhits
+                        device_isOuterHitOfCell_, nhits,
+                        counters_
                        );
   cudaCheck(cudaGetLastError());
 #endif
@@ -395,3 +408,18 @@ void CAHitQuadrupletGeneratorKernels::classifyTuples(HitsOnCPU const & hh, Tuple
     kernel_fastDuplicateRemover<<<numberOfBlocks, blockSize, 0, cudaStream>>>(device_theCells_, device_nCells_,tuples.tuples_d,tuples.helix_fit_results_d, tuples.quality_d);
 
 }
+
+
+__global__
+void kernel_printCounters(CAHitQuadrupletGeneratorKernels::Counters const * counters) {
+   
+   auto const & c = *counters;
+   printf("Counters Raw %lld %lld %lld %lld %lld %lld\n",c.nEvents,c.nHits,c.nCells,c.nTuples,c.nUsedHits,c.nKilledCells);
+   printf("Counters Norm %lld %f %f %f %f %f\n",c.nEvents,c.nHits/double(c.nEvents),c.nCells/double(c.nEvents),
+                                                c.nTuples/double(c.nEvents),c.nUsedHits/double(c.nEvents),c.nKilledCells/double(c.nEvents));
+
+}
+
+void CAHitQuadrupletGeneratorKernels::printCounters() const {
+  kernel_printCounters<<<1,1>>>(counters_);
+}
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
index f062a207ce056..b59d1f851a2fb 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
@@ -8,9 +8,22 @@
 
 #include "GPUCACell.h"
 
+
+
+
 class CAHitQuadrupletGeneratorKernels {
 public:
 
+    // counters
+    struct Counters {
+      unsigned long long nEvents;
+      unsigned long long nHits;
+      unsigned long long nCells;
+      unsigned long long nTuples;
+      unsigned long long nUsedHits;
+      unsigned long long nKilledCells;
+    };
+
    using HitsOnGPU = siPixelRecHitsHeterogeneousProduct::HitsOnGPU;
    using HitsOnCPU = siPixelRecHitsHeterogeneousProduct::HitsOnCPU;
 
@@ -37,9 +50,12 @@ class CAHitQuadrupletGeneratorKernels {
    void allocateOnGPU();
    void deallocateOnGPU();
    void cleanup(cudaStream_t cudaStream);
+   void printCounters() const;
 
 private:
 
+    Counters * counters_ = nullptr;
+
     // workspace
     GPUCACell* device_theCells_ = nullptr;
     GPUCACell::OuterHitOfCell* device_isOuterHitOfCell_ = nullptr;
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
index e95093103a817..bd5e17e0e4136 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
@@ -5,6 +5,9 @@ void
 CAHitQuadrupletGeneratorKernels::deallocateOnGPU()
 {
 
+  printCounters();
+  cudaFree(counters_);
+
   cudaFree(device_theCells_);
   cudaFree(device_isOuterHitOfCell_);
   cudaFree(device_nCells_);
@@ -20,6 +23,9 @@ void CAHitQuadrupletGeneratorKernels::allocateOnGPU()
   // ALLOCATIONS FOR THE INTERMEDIATE RESULTS (STAYS ON WORKER)
   //////////////////////////////////////////////////////////
 
+  cudaCheck(cudaMalloc(&counters_, sizeof(Counters)));
+  cudaCheck(cudaMemset(counters_,0,sizeof(Counters)));
+
   cudaCheck(cudaMalloc(&device_theCells_,
              CAConstants::maxNumberOfLayerPairs() * CAConstants::maxNumberOfDoublets() * sizeof(GPUCACell)));
   cudaCheck(cudaMalloc(&device_nCells_, sizeof(uint32_t)));

From 07d8001acf8a822247035e35b6293cb2a7ea5811 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 27 Feb 2019 16:18:04 +0100
Subject: [PATCH 67/95] add clus size cut on B1

---
 .../plugins/CAHitQuadrupletGeneratorKernels.cu        |  2 +-
 .../PixelTriplets/plugins/gpuPixelDoublets.h          | 11 +++++++++--
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 3373f7c108625..c5e87af4e768a 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -415,7 +415,7 @@ void kernel_printCounters(CAHitQuadrupletGeneratorKernels::Counters const * coun
    
    auto const & c = *counters;
    printf("Counters Raw %lld %lld %lld %lld %lld %lld\n",c.nEvents,c.nHits,c.nCells,c.nTuples,c.nUsedHits,c.nKilledCells);
-   printf("Counters Norm %lld %f %f %f %f %f\n",c.nEvents,c.nHits/double(c.nEvents),c.nCells/double(c.nEvents),
+   printf("Counters Norm %lld %.1f %.1f %.1f %.1f %.1f\n",c.nEvents,c.nHits/double(c.nEvents),c.nCells/double(c.nEvents),
                                                 c.nTuples/double(c.nEvents),c.nUsedHits/double(c.nEvents),c.nKilledCells/double(c.nEvents));
 
 }
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index d3185abf1e6f3..10a9b030b2f9f 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -15,6 +15,8 @@
 #include "GPUCACell.h"
 #include "CAConstants.h"
 
+// #define NO_ZCUT
+
 namespace gpuPixelDoublets {
 
   constexpr uint32_t MaxNumOfDoublets = CAConstants::maxNumberOfDoublets();  // not really relevant
@@ -80,13 +82,16 @@ namespace gpuPixelDoublets {
 
       // found hit corresponding to our cuda thread, now do the job
       auto mez = __ldg(hh.zg_d+i);
+      auto mes = __ldg(hh.ysize_d+i);
 
-      // if (outer==4 || outer==7)
+#ifndef NO_ZCUT
+      if (inner==0 && outer>3 )
+         if (mes>0 && mes<5*8) continue; // only long cluster
       if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
+#endif
 
       auto mep = iphi[i];
       auto mer = __ldg(hh.rg_d+i);
-      auto mes = __ldg(hh.ysize_d+i);
  
       constexpr float z0cut = 12.f;                     // cm
       constexpr float hardPtCut = 0.5f;                 // GeV
@@ -144,7 +149,9 @@ namespace gpuPixelDoublets {
 
           if (std::min(std::abs(int16_t(iphi[oi]-mep)), std::abs(int16_t(mep-iphi[oi]))) > iphicut)
             continue;
+#ifndef  NO_ZCUT
           if (zsizeCut(oi)) continue;
+#endif
           if (z0cutoff(oi) || ptcut(oi)) continue;
           auto ind = atomicAdd(nCells, 1); 
           if (ind>=MaxNumOfDoublets) {atomicSub(nCells, 1); break; } // move to SimpleVector??

From 43110c7105d9d1c0743376ad74cdaafe2998b891 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 27 Feb 2019 13:27:47 -0500
Subject: [PATCH 68/95] add good track counter

---
 .../CAHitQuadrupletGeneratorKernels.cu        | 26 ++++++++++++++++---
 .../plugins/CAHitQuadrupletGeneratorKernels.h |  1 +
 .../PixelTriplets/plugins/gpuPixelDoublets.h  |  2 +-
 3 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index c5e87af4e768a..bd66d4256fabf 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -275,6 +275,23 @@ void kernel_VerifyFit(TuplesOnGPU::Container const * __restrict__ tuples,
   quality[idx] = ok ? pixelTuplesHeterogeneousProduct::loose : pixelTuplesHeterogeneousProduct::bad; 
 }
 
+__global__
+void kernel_countTracks(TuplesOnGPU::Container const * __restrict__ tuples,
+                        Quality *  __restrict__ quality,
+                        CAHitQuadrupletGeneratorKernels::Counters * counters) {
+
+  auto idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (idx>= tuples->nbins()) return;
+  if (tuples->size(idx)==0) {
+    return;
+  }
+
+  if(quality[idx] != pixelTuplesHeterogeneousProduct::loose ) return;
+
+  atomicAdd(&(counters->nGoodTracks),1);
+
+}
+
 __global__
 void kernel_print_found_ntuplets(TuplesOnGPU::Container * foundNtuplets, uint32_t maxPrint) {
   for (int i = 0; i < std::min(maxPrint, foundNtuplets->size()); ++i) {
@@ -407,6 +424,8 @@ void CAHitQuadrupletGeneratorKernels::classifyTuples(HitsOnCPU const & hh, Tuple
     numberOfBlocks = (CAConstants::maxNumberOfDoublets() + blockSize - 1)/blockSize;
     kernel_fastDuplicateRemover<<<numberOfBlocks, blockSize, 0, cudaStream>>>(device_theCells_, device_nCells_,tuples.tuples_d,tuples.helix_fit_results_d, tuples.quality_d);
 
+    kernel_countTracks<<<numberOfBlocks, blockSize, 0, cudaStream>>>(tuples.tuples_d,tuples.quality_d,counters_);
+
 }
 
 
@@ -414,9 +433,10 @@ __global__
 void kernel_printCounters(CAHitQuadrupletGeneratorKernels::Counters const * counters) {
    
    auto const & c = *counters;
-   printf("Counters Raw %lld %lld %lld %lld %lld %lld\n",c.nEvents,c.nHits,c.nCells,c.nTuples,c.nUsedHits,c.nKilledCells);
-   printf("Counters Norm %lld %.1f %.1f %.1f %.1f %.1f\n",c.nEvents,c.nHits/double(c.nEvents),c.nCells/double(c.nEvents),
-                                                c.nTuples/double(c.nEvents),c.nUsedHits/double(c.nEvents),c.nKilledCells/double(c.nEvents));
+   printf("Counters Raw %lld %lld %lld %lld %lld %lld %lld\n",c.nEvents,c.nHits,c.nCells,c.nTuples,c.nGoodTracks,c.nUsedHits,c.nKilledCells);
+   printf("Counters Norm %lld %.1f %.1f %.1f %.1f %.1f %.1f\n",c.nEvents,c.nHits/double(c.nEvents),c.nCells/double(c.nEvents),
+                                                c.nTuples/double(c.nEvents),c.nGoodTracks/double(c.nEvents),
+                                                c.nUsedHits/double(c.nEvents),c.nKilledCells/double(c.nEvents));
 
 }
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
index b59d1f851a2fb..8ecad0c28282d 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
@@ -20,6 +20,7 @@ class CAHitQuadrupletGeneratorKernels {
       unsigned long long nHits;
       unsigned long long nCells;
       unsigned long long nTuples;
+      unsigned long long nGoodTracks;
       unsigned long long nUsedHits;
       unsigned long long nKilledCells;
     };
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 10a9b030b2f9f..ab082a5507429 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -85,7 +85,7 @@ namespace gpuPixelDoublets {
       auto mes = __ldg(hh.ysize_d+i);
 
 #ifndef NO_ZCUT
-      if (inner==0 && outer>3 )
+      if (inner==0 && outer>3 )  // B1 and F1
          if (mes>0 && mes<5*8) continue; // only long cluster
       if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
 #endif

From db230806e3e421f3e2ad8b5dea94034eeace9fe2 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 28 Feb 2019 11:22:17 +0100
Subject: [PATCH 69/95] add possibility to test full doublet combinatorics

---
 .../plugins/CAHitQuadrupletGeneratorKernels.cu             | 5 +++++
 .../PixelTriplets/plugins/gpuPixelDoublets.h               | 7 +++++--
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index bd66d4256fabf..8cfa4751c5d99 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -336,6 +336,11 @@ void CAHitQuadrupletGeneratorKernels::launchKernels( // here goes algoparms....
   auto stride = 4;
   auto blockSize = nthTot/stride;
   auto numberOfBlocks = (maxNumberOfDoublets_ + blockSize - 1)/blockSize;
+  auto rescale = numberOfBlocks/65536;
+  blockSize*=(rescale+1);
+  numberOfBlocks = (maxNumberOfDoublets_ + blockSize - 1)/blockSize;
+  assert(numberOfBlocks<65536);
+  assert(blockSize>0 && 0==blockSize%16);
   dim3 blks(1,numberOfBlocks,1);
   dim3 thrs(stride,blockSize,1);
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index ab082a5507429..df7b63b939826 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -15,7 +15,8 @@
 #include "GPUCACell.h"
 #include "CAConstants.h"
 
-// #define NO_ZCUT
+// #define ONLY_PHICUT
+#define NO_ZCUT
 
 namespace gpuPixelDoublets {
 
@@ -149,10 +150,12 @@ namespace gpuPixelDoublets {
 
           if (std::min(std::abs(int16_t(iphi[oi]-mep)), std::abs(int16_t(mep-iphi[oi]))) > iphicut)
             continue;
-#ifndef  NO_ZCUT
+#ifndef ONLY_PHICUT
+#ifndef NO_ZCUT
           if (zsizeCut(oi)) continue;
 #endif
           if (z0cutoff(oi) || ptcut(oi)) continue;
+#endif
           auto ind = atomicAdd(nCells, 1); 
           if (ind>=MaxNumOfDoublets) {atomicSub(nCells, 1); break; } // move to SimpleVector??
           // int layerPairId, int doubletId, int innerHitId, int outerHitId)

From ed027379cfda8eff8ce0072d882a9862408d45be Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 28 Feb 2019 17:35:31 +0100
Subject: [PATCH 70/95] new default

---
 .../PixelTriplets/plugins/gpuPixelDoublets.h  | 29 ++++++++++++++++---
 1 file changed, 25 insertions(+), 4 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index df7b63b939826..a1837c1932e66 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -16,7 +16,9 @@
 #include "CAConstants.h"
 
 // #define ONLY_PHICUT
+
 #define NO_ZCUT
+#define HARD_ZCUTS
 
 namespace gpuPixelDoublets {
 
@@ -39,6 +41,20 @@ namespace gpuPixelDoublets {
                          float const * __restrict__ maxz,
                          float const * __restrict__ maxr)
   {
+
+#ifndef NO_ZCUT 
+    // ysize cuts (z in the barrel)
+#ifdef HARD_ZCUTS
+    constexpr int minYsize=40;
+    constexpr int maxDYsize12=24;
+    constexpr int maxDYsize=16;
+#else
+    constexpr int minYsize=36;
+    constexpr int maxDYsize12=28;
+    constexpr int maxDYsize=20;
+#endif
+#endif
+
     auto layerSize = [=](uint8_t li) { return offsets[li+1]-offsets[li]; };
 
     // nPairsMax to be optimized later (originally was 64).
@@ -85,9 +101,12 @@ namespace gpuPixelDoublets {
       auto mez = __ldg(hh.zg_d+i);
       auto mes = __ldg(hh.ysize_d+i);
 
+      // auto mesx = __ldg(hh.xsize_d+i);
+      // if (mesx<0) continue; // remove edges in x as overlap will take care
+     
 #ifndef NO_ZCUT
       if (inner==0 && outer>3 )  // B1 and F1
-         if (mes>0 && mes<5*8) continue; // only long cluster
+         if (mes>0 && mes<minYsize) continue; // only long cluster  (5*8)
       if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
 #endif
 
@@ -113,13 +132,15 @@ namespace gpuPixelDoublets {
           dr<0 || std::abs((mez*ro - mer*zo)) > z0cut*dr;
       };
 
+#ifndef NO_ZCUT
       auto zsizeCut = [&](int j) {
         auto onlyBarrel = outer<4;
-//        auto onlyB234 = outer==2 || outer==3;
         auto so = __ldg(hh.ysize_d+j);
-        auto dy = inner==0 ? 24 : 16;  // now size is *8....
-        return onlyBarrel && mes>0 && so>0  && std::abs(so-mes)>dy;
+        //auto sox = __ldg(hh.xsize_d+j);
+        auto dy = inner==0 ? maxDYsize12 : maxDYsize;  // now size is *8....
+        return onlyBarrel && mes>0 && so>0 && std::abs(so-mes)>dy;
       };
+#endif
 
       auto iphicut = phicuts[pairLayerId];
 

From cb7496a064a3ded25233b1e94d7bcd248d4a968a Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 1 Mar 2019 13:59:02 +0100
Subject: [PATCH 71/95] soft b1-only-Outer

---
 RecoLocalTracker/SiPixelRecHits/src/PixelCPEFast.cc    |  4 ++++
 .../PixelTriplets/plugins/gpuPixelDoublets.h           | 10 +++++++---
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/RecoLocalTracker/SiPixelRecHits/src/PixelCPEFast.cc b/RecoLocalTracker/SiPixelRecHits/src/PixelCPEFast.cc
index eb51dd5a2eaeb..df6a27a857606 100644
--- a/RecoLocalTracker/SiPixelRecHits/src/PixelCPEFast.cc
+++ b/RecoLocalTracker/SiPixelRecHits/src/PixelCPEFast.cc
@@ -86,6 +86,10 @@ void PixelCPEFast::fillParamsForGpu() {
   m_commonParamsGPU.thePitchX = m_DetParams[0].thePitchX;
   m_commonParamsGPU.thePitchY = m_DetParams[0].thePitchY;
 
+  std::cout << "th, pitch " << m_commonParamsGPU.theThicknessB << ' ' << m_commonParamsGPU.theThicknessE
+            << m_commonParamsGPU.thePitchX << ' ' << m_commonParamsGPU.thePitchY << std::endl;
+
+
   //uint32_t oldLayer = 0;
   m_detParamsGPU.resize(m_DetParams.size());
   for (auto i=0U; i<m_DetParams.size(); ++i) {
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index a1837c1932e66..f24c6d313df9d 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -17,8 +17,8 @@
 
 // #define ONLY_PHICUT
 
-#define NO_ZCUT
-#define HARD_ZCUTS
+// #define NO_ZCUT
+// #define HARD_ZCUTS
 
 namespace gpuPixelDoublets {
 
@@ -101,11 +101,15 @@ namespace gpuPixelDoublets {
       auto mez = __ldg(hh.zg_d+i);
       auto mes = __ldg(hh.ysize_d+i);
 
+      auto mi = __ldg(hh.detInd_d+i);
+      if (inner==0) assert(mi<96);     
+      bool isOuterLadder = 0 == (mi/8)%2; // only for B1/B2/B3 B4 is opposite, FPIX:noclue...
+
       // auto mesx = __ldg(hh.xsize_d+i);
       // if (mesx<0) continue; // remove edges in x as overlap will take care
      
 #ifndef NO_ZCUT
-      if (inner==0 && outer>3 )  // B1 and F1
+      if (inner==0 && outer>3 && isOuterLadder)  // B1 and F1
          if (mes>0 && mes<minYsize) continue; // only long cluster  (5*8)
       if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
 #endif

From 429b43453f7638a9136418dbcad6614ec5a97375 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 1 Mar 2019 14:46:05 +0100
Subject: [PATCH 72/95] no size cut in B1inner at all

---
 RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index f24c6d313df9d..d8ecea51673cd 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -141,7 +141,7 @@ namespace gpuPixelDoublets {
         auto onlyBarrel = outer<4;
         auto so = __ldg(hh.ysize_d+j);
         //auto sox = __ldg(hh.xsize_d+j);
-        auto dy = inner==0 ? maxDYsize12 : maxDYsize;  // now size is *8....
+        auto dy = inner==0 ? ( isOuterLadder ? maxDYsize12: 100 ) : maxDYsize;  // now size is *8....
         return onlyBarrel && mes>0 && so>0 && std::abs(so-mes)>dy;
       };
 #endif

From 631b6b129f42c5b6177e8c9e976a83a42a981196 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Fri, 1 Mar 2019 15:07:15 +0100
Subject: [PATCH 73/95] no size cut in B1inner at all

---
 RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index d8ecea51673cd..69dbea633a6b6 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -101,14 +101,14 @@ namespace gpuPixelDoublets {
       auto mez = __ldg(hh.zg_d+i);
       auto mes = __ldg(hh.ysize_d+i);
 
+#ifndef NO_ZCUT
       auto mi = __ldg(hh.detInd_d+i);
       if (inner==0) assert(mi<96);     
       bool isOuterLadder = 0 == (mi/8)%2; // only for B1/B2/B3 B4 is opposite, FPIX:noclue...
 
       // auto mesx = __ldg(hh.xsize_d+i);
       // if (mesx<0) continue; // remove edges in x as overlap will take care
-     
-#ifndef NO_ZCUT
+
       if (inner==0 && outer>3 && isOuterLadder)  // B1 and F1
          if (mes>0 && mes<minYsize) continue; // only long cluster  (5*8)
       if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;

From c0a44bbac96bfc87a22f04bde7a8cef7029df840 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 4 Mar 2019 14:49:26 +0100
Subject: [PATCH 74/95] cut on B2 clean

---
 .../PixelTriplets/plugins/CAConstants.h            |  9 ++++++++-
 .../plugins/CAHitQuadrupletGeneratorKernels.cu     |  2 +-
 .../PixelTriplets/plugins/GPUCACell.h              |  4 ++--
 .../PixelTriplets/plugins/gpuPixelDoublets.h       | 14 ++++++++++----
 4 files changed, 21 insertions(+), 8 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAConstants.h b/RecoPixelVertexing/PixelTriplets/plugins/CAConstants.h
index 1c7777ee37b3e..71215026e55c4 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAConstants.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAConstants.h
@@ -8,15 +8,22 @@
 #include "HeterogeneousCore/CUDAUtilities/interface/GPUVecArray.h"
 #include "RecoLocalTracker/SiPixelClusterizer/interface/PixelTrackingGPUConstants.h"
 
+// #define ONLY_PHICUT
 
 namespace CAConstants {
 
    // constants
+
    constexpr uint32_t maxNumberOfQuadruplets() { return 10000; }
+#ifndef ONLY_PHICUT
+   constexpr uint32_t maxNumberOfDoublets() { return 262144; }
    constexpr uint32_t maxCellsPerHit() { return 128; }
+#else
+   constexpr uint32_t maxNumberOfDoublets() { return 6*262144; }
+   constexpr uint32_t maxCellsPerHit() { return 4*128; }
+#endif
    constexpr uint32_t maxNumberOfLayerPairs() { return 13; }
    constexpr uint32_t maxNumberOfLayers() { return 10; }
-   constexpr uint32_t maxNumberOfDoublets() { return 262144; }
    constexpr uint32_t maxTuples() { return 10000;}
 
    // types
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 8cfa4751c5d99..2a1b7b1021c32 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -439,7 +439,7 @@ void kernel_printCounters(CAHitQuadrupletGeneratorKernels::Counters const * coun
    
    auto const & c = *counters;
    printf("Counters Raw %lld %lld %lld %lld %lld %lld %lld\n",c.nEvents,c.nHits,c.nCells,c.nTuples,c.nGoodTracks,c.nUsedHits,c.nKilledCells);
-   printf("Counters Norm %lld %.1f %.1f %.1f %.1f %.1f %.1f\n",c.nEvents,c.nHits/double(c.nEvents),c.nCells/double(c.nEvents),
+   printf("Counters Norm %lld ||  %.1f|  %.1f|  %.1f|  %.1f|  %.1f|  %.1f||\n",c.nEvents,c.nHits/double(c.nEvents),c.nCells/double(c.nEvents),
                                                 c.nTuples/double(c.nEvents),c.nGoodTracks/double(c.nEvents),
                                                 c.nUsedHits/double(c.nEvents),c.nKilledCells/double(c.nEvents));
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index c9abaae27ab8b..5dd28656ec7f3 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -18,8 +18,8 @@
 class GPUCACell {
 public:
 
-  static constexpr int maxCellsPerHit = 128; // was 256
-  using OuterHitOfCell = GPU::VecArray< unsigned int, maxCellsPerHit>;
+  static constexpr int maxCellsPerHit = CAConstants::maxCellsPerHit();
+  using OuterHitOfCell = CAConstants::OuterHitOfCell;
 
 
   using Hits = siPixelRecHitsHeterogeneousProduct::HitsOnGPU;
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 69dbea633a6b6..b5450d850aacc 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -17,6 +17,8 @@
 
 // #define ONLY_PHICUT
 
+
+// default is soft cuts
 // #define NO_ZCUT
 // #define HARD_ZCUTS
 
@@ -45,11 +47,13 @@ namespace gpuPixelDoublets {
 #ifndef NO_ZCUT 
     // ysize cuts (z in the barrel)
 #ifdef HARD_ZCUTS
-    constexpr int minYsize=40;
+    constexpr int minYsizeB1=40;
+    constexpr int minYsizeB2=32;
     constexpr int maxDYsize12=24;
     constexpr int maxDYsize=16;
 #else
-    constexpr int minYsize=36;
+    constexpr int minYsizeB1=36;
+    constexpr int minYsizeB2=28;
     constexpr int maxDYsize12=28;
     constexpr int maxDYsize=20;
 #endif
@@ -99,9 +103,9 @@ namespace gpuPixelDoublets {
 
       // found hit corresponding to our cuda thread, now do the job
       auto mez = __ldg(hh.zg_d+i);
-      auto mes = __ldg(hh.ysize_d+i);
 
 #ifndef NO_ZCUT
+      auto mes = __ldg(hh.ysize_d+i);
       auto mi = __ldg(hh.detInd_d+i);
       if (inner==0) assert(mi<96);     
       bool isOuterLadder = 0 == (mi/8)%2; // only for B1/B2/B3 B4 is opposite, FPIX:noclue...
@@ -110,7 +114,9 @@ namespace gpuPixelDoublets {
       // if (mesx<0) continue; // remove edges in x as overlap will take care
 
       if (inner==0 && outer>3 && isOuterLadder)  // B1 and F1
-         if (mes>0 && mes<minYsize) continue; // only long cluster  (5*8)
+         if (mes>0 && mes<minYsizeB1) continue; // only long cluster  (5*8)
+      if (inner==1 && outer>3)  // B2 and F1
+         if (mes>0 && mes<minYsizeB2) continue;
       if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
 #endif
 

From f3cdcebe7fe0c48afa91735d3a64f46531ae3f56 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sat, 9 Mar 2019 12:39:08 +0100
Subject: [PATCH 75/95] ideal setting

---
 .../PixelTriplets/plugins/gpuPixelDoublets.h         | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index b5450d850aacc..76c95d15747d6 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -17,6 +17,7 @@
 
 // #define ONLY_PHICUT
 
+#define IDEAL_COND
 
 // default is soft cuts
 // #define NO_ZCUT
@@ -106,10 +107,13 @@ namespace gpuPixelDoublets {
 
 #ifndef NO_ZCUT
       auto mes = __ldg(hh.ysize_d+i);
+#ifndef IDEAL_COND
       auto mi = __ldg(hh.detInd_d+i);
-      if (inner==0) assert(mi<96);     
-      bool isOuterLadder = 0 == (mi/8)%2; // only for B1/B2/B3 B4 is opposite, FPIX:noclue...
-
+      if (inner==0) assert(mi<96);    
+      const bool isOuterLadder =  0 == (mi/8)%2; // only for B1/B2/B3 B4 is opposite, FPIX:noclue...
+#else
+      constexpr bool isOuterLadder =  true;  // if ideal no need to restrict to outer
+#endif
       // auto mesx = __ldg(hh.xsize_d+i);
       // if (mesx<0) continue; // remove edges in x as overlap will take care
 
@@ -118,7 +122,7 @@ namespace gpuPixelDoublets {
       if (inner==1 && outer>3)  // B2 and F1
          if (mes>0 && mes<minYsizeB2) continue;
       if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
-#endif
+#endif  // ZCUT
 
       auto mep = iphi[i];
       auto mer = __ldg(hh.rg_d+i);

From 29b3e94bcbced82ce536f6372e7c9833be2f1710 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sat, 9 Mar 2019 15:04:24 +0100
Subject: [PATCH 76/95] allow all triplets, protect vertex from triplets

---
 RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h       | 7 ++++++-
 .../src/PixelVertexHeterogeneousProducer.cc                | 5 +++--
 .../PixelVertexFinding/src/gpuVertexFinder.cu              | 1 +
 3 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index 5dd28656ec7f3..8bd4808894d37 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -15,6 +15,8 @@
 
 #include "RecoPixelVertexing/PixelTriplets/plugins/pixelTuplesHeterogeneousProduct.h"
 
+// #define ALL_TRIPLETS
+
 class GPUCACell {
 public:
 
@@ -183,8 +185,11 @@ class GPUCACell {
       }
     } else {  // if long enough save...
       if ((unsigned int)(tmpNtuplet.size()) >= minHitsPerNtuplet-1) {
+#ifndef ALL_TRIPLETS
         // triplets accepted only pointing to the hole
-        if (tmpNtuplet.size()>=3 || hole(hh, cells[tmpNtuplet[0]])) {
+        if (tmpNtuplet.size()>=3 || hole(hh, cells[tmpNtuplet[0]]))
+#endif
+       {
           hindex_type hits[6]; auto nh=0U;
           for (auto c : tmpNtuplet) hits[nh++] = cells[c].theInnerHitId;
           hits[nh] = theOuterHitId; 
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc b/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
index bd75b678e0051..7ef7eb80609f8 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
+++ b/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
@@ -199,6 +199,7 @@ void PixelVertexHeterogeneousProducer::produceGPUCuda(
     }
     auto nt = itrk.size();
     if (nt==0) { std::cout << "vertex " << i << " with no tracks..." << std::endl; continue;}
+    if (nt<2) { itrk.clear(); continue;}  // remove outliers
     (*vertexes).emplace_back(reco::Vertex::Point(x,y,z), err, gpuProduct.chi2[i], nt-1, nt );
     auto & v = (*vertexes).back();
     for (auto it: itrk) {
@@ -210,13 +211,13 @@ void PixelVertexHeterogeneousProducer::produceGPUCuda(
     }
     itrk.clear();
   }
-  
+  /*
   assert(uind.size()==(*vertexes).size());
   if (!uind.empty()) {
     assert(0 == *uind.begin());
     assert(uind.size()-1 == *uind.rbegin());  
   }
-  
+  */
 
   if (verbose_) {
     edm::LogInfo("PixelVertexHeterogeneousProducer") << ": Found " << vertexes->size() << " vertexes\n";
diff --git a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
index 8393afefc998a..2cebb5d969fa3 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
+++ b/RecoPixelVertexing/PixelVertexFinding/src/gpuVertexFinder.cu
@@ -39,6 +39,7 @@ namespace gpuVertexFinder {
       return;
     }
 
+    if (tuples.size(idx)<4) return; // no triplets
     if(quality[idx] != pixelTuplesHeterogeneousProduct::loose ) return;
 
     auto const & fittedTrack = fit[idx];

From 73e1644856dc22680881abc6516dddf8ad619d9c Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Mar 2019 12:38:33 +0100
Subject: [PATCH 77/95] separate zcut form clsize

---
 .../PixelTriplets/plugins/gpuPixelDoublets.h    | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 76c95d15747d6..6a7833d008c04 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -20,7 +20,8 @@
 #define IDEAL_COND
 
 // default is soft cuts
-// #define NO_ZCUT
+#define NO_ZCUT
+// #define NO_CLSCUT
 // #define HARD_ZCUTS
 
 namespace gpuPixelDoublets {
@@ -45,7 +46,8 @@ namespace gpuPixelDoublets {
                          float const * __restrict__ maxr)
   {
 
-#ifndef NO_ZCUT 
+
+#ifndef NO_CLSCUT 
     // ysize cuts (z in the barrel)
 #ifdef HARD_ZCUTS
     constexpr int minYsizeB1=40;
@@ -106,6 +108,10 @@ namespace gpuPixelDoublets {
       auto mez = __ldg(hh.zg_d+i);
 
 #ifndef NO_ZCUT
+     if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
+#endif
+
+#ifndef NO_CLSCUT
       auto mes = __ldg(hh.ysize_d+i);
 #ifndef IDEAL_COND
       auto mi = __ldg(hh.detInd_d+i);
@@ -121,8 +127,7 @@ namespace gpuPixelDoublets {
          if (mes>0 && mes<minYsizeB1) continue; // only long cluster  (5*8)
       if (inner==1 && outer>3)  // B2 and F1
          if (mes>0 && mes<minYsizeB2) continue;
-      if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
-#endif  // ZCUT
+#endif // NO_CLSCUT
 
       auto mep = iphi[i];
       auto mer = __ldg(hh.rg_d+i);
@@ -146,7 +151,7 @@ namespace gpuPixelDoublets {
           dr<0 || std::abs((mez*ro - mer*zo)) > z0cut*dr;
       };
 
-#ifndef NO_ZCUT
+#ifndef NO_CLSCUT
       auto zsizeCut = [&](int j) {
         auto onlyBarrel = outer<4;
         auto so = __ldg(hh.ysize_d+j);
@@ -186,7 +191,7 @@ namespace gpuPixelDoublets {
           if (std::min(std::abs(int16_t(iphi[oi]-mep)), std::abs(int16_t(mep-iphi[oi]))) > iphicut)
             continue;
 #ifndef ONLY_PHICUT
-#ifndef NO_ZCUT
+#ifndef NO_CLSCUT
           if (zsizeCut(oi)) continue;
 #endif
           if (z0cutoff(oi) || ptcut(oi)) continue;

From df9ab83fee81ec135c0c300b0ebd0fadb70599f3 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Mar 2019 14:35:11 +0100
Subject: [PATCH 78/95] cleanup

---
 .../PixelTriplets/plugins/gpuPixelDoublets.h  | 22 +++++++++----------
 1 file changed, 10 insertions(+), 12 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 6a7833d008c04..aefef19fdc4f6 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -19,10 +19,8 @@
 
 #define IDEAL_COND
 
-// default is soft cuts
 #define NO_ZCUT
 // #define NO_CLSCUT
-// #define HARD_ZCUTS
 
 namespace gpuPixelDoublets {
 
@@ -41,25 +39,19 @@ namespace gpuPixelDoublets {
                          siPixelRecHitsHeterogeneousProduct::HitsOnGPU const &  __restrict__ hh,
                          GPUCACell::OuterHitOfCell * isOuterHitOfCell,
                          int16_t const * __restrict__ phicuts,
+#ifndef NO_ZCUT
                          float const * __restrict__ minz,
                          float const * __restrict__ maxz,
+#endif
                          float const * __restrict__ maxr)
   {
 
-
 #ifndef NO_CLSCUT 
-    // ysize cuts (z in the barrel)
-#ifdef HARD_ZCUTS
-    constexpr int minYsizeB1=40;
-    constexpr int minYsizeB2=32;
-    constexpr int maxDYsize12=24;
-    constexpr int maxDYsize=16;
-#else
+    // ysize cuts (z in the barrel)  times 8
     constexpr int minYsizeB1=36;
     constexpr int minYsizeB2=28;
     constexpr int maxDYsize12=28;
     constexpr int maxDYsize=20;
-#endif
 #endif
 
     auto layerSize = [=](uint8_t li) { return offsets[li+1]-offsets[li]; };
@@ -242,6 +234,7 @@ namespace gpuPixelDoublets {
       phi0p07, phi0p06, phi0p06, phi0p05, phi0p05
     };
 
+#ifndef NO_ZCUT
     float const minz[nPairs] = {
       -20., -22., -22.,
       -30., -30.,-30., -70., -70.,
@@ -253,6 +246,7 @@ namespace gpuPixelDoublets {
        0., -10., -15., 70., 70.,
       30., 30., 30., 70., 70.
     };
+#endif
 
     float const maxr[nPairs] = {
       20., 20., 20.,
@@ -264,7 +258,11 @@ namespace gpuPixelDoublets {
     doubletsFromHisto(layerPairs, nPairs, cells, nCells,
                       hh.iphi_d, *hh.hist_d, hh.hitsLayerStart_d,
                       hh, isOuterHitOfCell,
-                      phicuts, minz, maxz, maxr);
+                      phicuts, 
+#ifndef NO_ZCUT
+                      minz, maxz, 
+#endif
+                      maxr);
   }
 
 

From 9c4e8556b2b63ccaa775394ba2680497304a88af Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Mar 2019 15:00:24 +0100
Subject: [PATCH 79/95] make cuts configurables

---
 .../plugins/CAHitQuadrupletGeneratorGPU.cc     |  4 +++-
 .../plugins/CAHitQuadrupletGeneratorKernels.cu |  2 +-
 .../plugins/CAHitQuadrupletGeneratorKernels.h  |  7 ++++---
 .../PixelTriplets/plugins/gpuPixelDoublets.h   | 18 +++++++++---------
 4 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
index f498dc8b674b4..1769f395b4445 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorGPU.cc
@@ -30,7 +30,8 @@ CAHitQuadrupletGeneratorGPU::CAHitQuadrupletGeneratorGPU(
     const edm::ParameterSet &cfg,
     edm::ConsumesCollector &iC) : 
     kernels(cfg.getParameter<unsigned int>("minHitsPerNtuplet"), 
-            cfg.getParameter<bool>("earlyFishbone"),cfg.getParameter<bool>("lateFishbone")),
+            cfg.getParameter<bool>("earlyFishbone"),cfg.getParameter<bool>("lateFishbone"),
+            cfg.getParameter<bool>("idealConditions")),
     fitter(cfg.getParameter<bool>("fit5as4")),
     caThetaCut(cfg.getParameter<double>("CAThetaCut")),
     caPhiCut(cfg.getParameter<double>("CAPhiCut")),
@@ -44,6 +45,7 @@ void CAHitQuadrupletGeneratorGPU::fillDescriptions(edm::ParameterSetDescription
   desc.add<double>("CAHardPtCut", 0);
   desc.add<bool>("earlyFishbone",false);
   desc.add<bool>("lateFishbone",true);
+  desc.add<bool>("idealConditions",true),
   desc.add<unsigned int>("minHitsPerNtuplet",4);
   desc.add<bool>("fit5as4",true);
 }
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 2a1b7b1021c32..34625043f511f 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -414,7 +414,7 @@ void CAHitQuadrupletGeneratorKernels::buildDoublets(HitsOnCPU const & hh, cudaSt
   dim3 blks(1,blocks,1);
   dim3 thrs(stride,threadsPerBlock,1);
   gpuPixelDoublets::getDoubletsFromHisto<<<blks, thrs, 0, stream>>>(
-            device_theCells_, device_nCells_, hh.gpu_d, device_isOuterHitOfCell_);
+            device_theCells_, device_nCells_, hh.gpu_d, device_isOuterHitOfCell_, idealConditions_);
   cudaCheck(cudaGetLastError());
 }
 
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
index 8ecad0c28282d..1d17687c9f750 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.h
@@ -34,10 +34,11 @@ class CAHitQuadrupletGeneratorKernels {
    using TupleMultiplicity = CAConstants::TupleMultiplicity;
 
    CAHitQuadrupletGeneratorKernels(uint32_t minHitsPerNtuplet,
-    bool earlyFishbone, bool lateFishbone) :
+    bool earlyFishbone, bool lateFishbone, bool idealConditions) :
     minHitsPerNtuplet_(minHitsPerNtuplet),
     earlyFishbone_(earlyFishbone),
-    lateFishbone_(lateFishbone){}
+    lateFishbone_(lateFishbone),
+    idealConditions_(idealConditions){}
    ~CAHitQuadrupletGeneratorKernels() { deallocateOnGPU();}
 
 
@@ -71,7 +72,7 @@ class CAHitQuadrupletGeneratorKernels {
     const uint32_t minHitsPerNtuplet_;
     const bool earlyFishbone_;
     const bool lateFishbone_;
-
+    const bool idealConditions_;
 };
 
 #endif // RecoPixelVertexing_PixelTriplets_plugins_CAHitQuadrupletGeneratorKernels_h
diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index aefef19fdc4f6..571736d4691f0 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -43,7 +43,7 @@ namespace gpuPixelDoublets {
                          float const * __restrict__ minz,
                          float const * __restrict__ maxz,
 #endif
-                         float const * __restrict__ maxr)
+                         float const * __restrict__ maxr, bool ideal_cond)
   {
 
 #ifndef NO_CLSCUT 
@@ -105,13 +105,12 @@ namespace gpuPixelDoublets {
 
 #ifndef NO_CLSCUT
       auto mes = __ldg(hh.ysize_d+i);
-#ifndef IDEAL_COND
+
+      // if ideal treat inner ladder as outer
       auto mi = __ldg(hh.detInd_d+i);
       if (inner==0) assert(mi<96);    
-      const bool isOuterLadder =  0 == (mi/8)%2; // only for B1/B2/B3 B4 is opposite, FPIX:noclue...
-#else
-      constexpr bool isOuterLadder =  true;  // if ideal no need to restrict to outer
-#endif
+      const bool isOuterLadder = ideal_cond ? true : 0 == (mi/8)%2; // only for B1/B2/B3 B4 is opposite, FPIX:noclue...
+
       // auto mesx = __ldg(hh.xsize_d+i);
       // if (mesx<0) continue; // remove edges in x as overlap will take care
 
@@ -148,7 +147,7 @@ namespace gpuPixelDoublets {
         auto onlyBarrel = outer<4;
         auto so = __ldg(hh.ysize_d+j);
         //auto sox = __ldg(hh.xsize_d+j);
-        auto dy = inner==0 ? ( isOuterLadder ? maxDYsize12: 100 ) : maxDYsize;  // now size is *8....
+        auto dy = inner==0 ? ( isOuterLadder ? maxDYsize12: 100 ) : maxDYsize;
         return onlyBarrel && mes>0 && so>0 && std::abs(so-mes)>dy;
       };
 #endif
@@ -214,7 +213,8 @@ namespace gpuPixelDoublets {
   void getDoubletsFromHisto(GPUCACell * cells,
                             uint32_t * nCells,
                             siPixelRecHitsHeterogeneousProduct::HitsOnGPU const *  __restrict__ hhp,
-                            GPUCACell::OuterHitOfCell * isOuterHitOfCell)
+                            GPUCACell::OuterHitOfCell * isOuterHitOfCell,
+                            bool ideal_cond)
   {
     constexpr int nPairs = 13;
     constexpr const uint8_t layerPairs[2*nPairs] = {
@@ -262,7 +262,7 @@ namespace gpuPixelDoublets {
 #ifndef NO_ZCUT
                       minz, maxz, 
 #endif
-                      maxr);
+                      maxr , ideal_cond);
   }
 
 

From 33f4e12d72fbec6a721c2cc04cb6f67f372101ba Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Mar 2019 16:01:45 +0100
Subject: [PATCH 80/95] make default with no ifdef, new notebook

---
 .../PixelTriplets/plugins/gpuPixelDoublets.h  |   16 +-
 .../PixelTriplets/test/pixHits.ipynb          | 3891 +++++++++++++++--
 2 files changed, 3446 insertions(+), 461 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 571736d4691f0..7007554fce9e6 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -15,11 +15,10 @@
 #include "GPUCACell.h"
 #include "CAConstants.h"
 
-// #define ONLY_PHICUT
-
-#define IDEAL_COND
 
-#define NO_ZCUT
+// useful for benchmark
+// #define ONLY_PHICUT
+// #define USE_ZCUT
 // #define NO_CLSCUT
 
 namespace gpuPixelDoublets {
@@ -39,7 +38,7 @@ namespace gpuPixelDoublets {
                          siPixelRecHitsHeterogeneousProduct::HitsOnGPU const &  __restrict__ hh,
                          GPUCACell::OuterHitOfCell * isOuterHitOfCell,
                          int16_t const * __restrict__ phicuts,
-#ifndef NO_ZCUT
+#ifdef USE_ZCUT
                          float const * __restrict__ minz,
                          float const * __restrict__ maxz,
 #endif
@@ -99,7 +98,8 @@ namespace gpuPixelDoublets {
       // found hit corresponding to our cuda thread, now do the job
       auto mez = __ldg(hh.zg_d+i);
 
-#ifndef NO_ZCUT
+#ifdef USE_ZCUT
+     // this statement is responsible for a 10% slow down of the kernel once all following cuts are optimized...
      if (mez<minz[pairLayerId] || mez>maxz[pairLayerId]) continue;
 #endif
 
@@ -234,7 +234,7 @@ namespace gpuPixelDoublets {
       phi0p07, phi0p06, phi0p06, phi0p05, phi0p05
     };
 
-#ifndef NO_ZCUT
+#ifdef USE_ZCUT
     float const minz[nPairs] = {
       -20., -22., -22.,
       -30., -30.,-30., -70., -70.,
@@ -259,7 +259,7 @@ namespace gpuPixelDoublets {
                       hh.iphi_d, *hh.hist_d, hh.hitsLayerStart_d,
                       hh, isOuterHitOfCell,
                       phicuts, 
-#ifndef NO_ZCUT
+#ifdef USE_ZCUT
                       minz, maxz, 
 #endif
                       maxr , ideal_cond);
diff --git a/RecoPixelVertexing/PixelTriplets/test/pixHits.ipynb b/RecoPixelVertexing/PixelTriplets/test/pixHits.ipynb
index 12027fe01cdd8..6860c25666e6c 100644
--- a/RecoPixelVertexing/PixelTriplets/test/pixHits.ipynb
+++ b/RecoPixelVertexing/PixelTriplets/test/pixHits.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,12 +39,13 @@
     "import math\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from bisect import *"
+    "from bisect import *\n",
+    "from decimal import Decimal"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
@@ -68,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,19 +312,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#rawhits 6842643\n"
+      "#rawhits 13982672\n"
      ]
     }
    ],
    "source": [
-    "file = '/Users/innocent/data/ttbarPU50Hits.csv'\n",
+    "file = '/Users/innocent/data/ttbarIdealPU50HitsC.csv'\n",
+    "# file = '/Users/innocent/data/ttbarPU50Hits.csv'\n",
     "# file = '/Users/innocent/data/ttbarPU0Hits.csv'\n",
     "rawHits = pd.read_csv(file, delimiter=\" \")\n",
     "print '#rawhits', len(rawHits)"
@@ -331,47 +333,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#hits 2865257\n",
-      "       ev   ind   det  charge        xg         yg         zg         rg  \\\n",
-      "2068    1  3380   261   39382 -0.719951  -7.235875  12.478751   7.271603   \n",
-      "5523    1  4947   582   61290 -0.775613 -11.347950  19.134975  11.374425   \n",
-      "109     1  1549    66   75059 -0.153022  -3.402119  -9.927705   3.405559   \n",
-      "2865    1  3345   256  114378 -0.533277  -7.253811 -22.095730   7.273387   \n",
-      "10198   1  9090  1567   25476 -1.101961 -10.707680 -33.038609  10.764234   \n",
+      "#hits 5833191\n",
+      "      ev   ind  det  charge        xg         yg         zg         rg   iphi  \\\n",
+      "1014   1   406   19   45617  0.327468   3.295420  -4.949985   3.311650  15350   \n",
+      "2373   1  2245  146   32991  0.701991   7.170877  -9.392261   7.205155  15365   \n",
+      "1805   1  3789  401   35961  1.082605  11.257833 -14.069117  11.309768  15383   \n",
+      "7400   1  5096  793   71921  1.532926  16.360044 -19.902737  16.431705  15409   \n",
+      "1015   1   407   19   39949  0.265110   3.311063  -4.962011   3.321660  15550   \n",
       "\n",
-      "        iphi  tkId   pt  n1  tkId2  pt2  n2       phi       eta  seq   trackID  \n",
-      "2068  -17419     0  769   3      0    0   0 -1.669967  1.308952    2  10000000  \n",
-      "5523  -17096     0  769   3      0    0   0 -1.639038  1.291801    3  10000000  \n",
-      "109   -16853     2  740   7      0    0   0 -1.615744 -1.791266    1  10000002  \n",
-      "2865  -17150     2  740   8      0    0   0 -1.644181 -1.830360    2  10000002  \n",
-      "10198 -17455     2  740   3      0    0   0 -1.673348 -1.840135    8  10000002  \n",
-      "          ev    ind   det  charge         xg        yg         zg         rg  \\\n",
-      "6839298  500   9339  1420   34842  -9.546642 -3.838250  48.859261  10.289341   \n",
-      "6841294  500   9575  1449   27589  -9.140972 -3.633035  46.708469   9.836479   \n",
-      "6837952  500  10073  1505   23716  -9.103679 -3.615468  46.529385   9.795334   \n",
-      "6834639  500   7848  1239   24579  14.649280 -2.504236  30.849955  14.861783   \n",
-      "6838867  500   9420  1428   27807   3.696498 -3.074924  48.992340   4.808249   \n",
+      "      xsize  ...       pt  n1  tkId2  pt2  n2       phi       eta  seq  \\\n",
+      "1014     13  ...    12402   3      0    0   0  1.471751 -1.191830    1   \n",
+      "2373     20  ...    12402   4      0    0   0  1.473213 -1.080612    2   \n",
+      "1805     14  ...    12402   4      0    0   0  1.474927 -1.043826    3   \n",
+      "7400     14  ...    12402   4      0    0   0  1.477370 -1.023149    4   \n",
+      "1015      8  ...    11881   2      0    0   0  1.490899 -1.191338    1   \n",
       "\n",
-      "          iphi    tkId   pt  n1  tkId2  pt2  n2       phi       eta  seq  \\\n",
-      "6839298 -28781  114033  685   3      0    0   0 -2.759318  2.261890    7   \n",
-      "6841294 -28822  114033  685   2      0    0   0 -2.763291  2.261882    7   \n",
-      "6837952 -28825  114033  685   3      0    0   0 -2.763551  2.262225    7   \n",
-      "6834639  -1767  114052  414   3      0    0   0 -0.169309  1.477025    5   \n",
-      "6838867  -7238  114059  556   2      0    0   0 -0.693861  3.016878    7   \n",
+      "       trackID   norm  \n",
+      "1014  10000000  0.001  \n",
+      "2373  10000000  0.001  \n",
+      "1805  10000000  0.001  \n",
+      "7400  10000000  0.001  \n",
+      "1015  10000001  0.001  \n",
       "\n",
-      "            trackID  \n",
-      "6839298  5000114033  \n",
-      "6841294  5000114033  \n",
-      "6837952  5000114033  \n",
-      "6834639  5000114052  \n",
-      "6838867  5000114059  \n"
+      "[5 rows x 22 columns]\n",
+      "            ev   ind   det  charge         xg        yg         zg         rg  \\\n",
+      "13971041  1000  1855    79  139699   0.908132 -2.588966  23.074427   2.743620   \n",
+      "13976222  1000  7324  1237   81765   9.910853 -5.013310  30.899717  11.106678   \n",
+      "13976754  1000  7728  1294   94676  10.830434 -5.359623  31.711411  12.084033   \n",
+      "13972961  1000  1503    63   37790  -2.004861 -1.870963  24.916054   2.742257   \n",
+      "13971607  1000  3509   247   29787  -3.807022 -5.802213  26.619453   6.939675   \n",
+      "\n",
+      "           iphi  xsize  ...     pt  n1  tkId2  pt2  n2       phi       eta  \\\n",
+      "13971041 -12866     43  ...    569   8      0    0   0 -1.233436  2.826110   \n",
+      "13976222  -4885     10  ...    569   4      0    0   0 -0.468309  1.747186   \n",
+      "13976754  -4793     14  ...    569   4      0    0   0 -0.459533  1.692410   \n",
+      "13972961 -24935      8  ...    427   2      0    0   0 -2.390728  2.902893   \n",
+      "13971607 -22442      8  ...    427   2      0    0   0 -2.151471  2.054108   \n",
+      "\n",
+      "          seq      trackID   norm  \n",
+      "13971041    1  10000114092  0.001  \n",
+      "13976222    5  10000114092  0.001  \n",
+      "13976754    5  10000114092  0.001  \n",
+      "13972961    1  10000114179  0.001  \n",
+      "13971607    2  10000114179  0.001  \n",
+      "\n",
+      "[5 rows x 22 columns]\n"
      ]
     }
    ],
@@ -380,6 +393,7 @@
     "rawHits['eta'] =  eta(rawHits['rg'],rawHits['zg'])\n",
     "rawHits['seq'] = rawHits['det'].apply(layer)\n",
     "rawHits['trackID'] = rawHits['tkId']+10000000*rawHits['ev']\n",
+    "rawHits['norm'] = 1./1000.\n",
     "rawHits.sort_values(by=['ev','tkId','det'],inplace=True)\n",
     "hits =  rawHits[rawHits['pt']>400]\n",
     "print '#hits', len(hits)\n",
@@ -389,28 +403,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 221,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qBh/+91TVA33306OrgT9KcpTBX4D9gyR/329LvToOHK+q00eEX2IQCK36Q+B7VTVTVf8DPAD8Ts89LQfPJ7kUoHs+NeoVGABLJEkYnOM9XFWf6rufPlXVh6tqXVVNMLi4909V1exveFX1HHAsyZu70jXAUz221LfvA1cleW33c3MNDV8UH7IH2NpNbwUeHPUKDIClczVwM4Pfdh/rHtf33ZSWjT8B7knyOLAJ+Mue++lNdyT0JeBR4AkGn0tNfSs4yb3AvwBvTnI8yS3ADuAdSY4wOEraMfL1+k1gSWqTRwCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRv0vFWOmzYgehJ8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2561.399 2087.692 1748.84  1478.249  944.839 1022.755 1078.439  950.139\n",
+      " 1029.349 1080.971]\n",
+      "All Hits 1.3983E+04\n",
+      "[5347412.201107463, 3651039.2772803963, 2585220.981160521, 2420109.6697606524, 1972532.821587918, 1652372.236760108, 966338.8114449463, 1102978.8794449903, 2433685.0844606548, 1983597.589187921, 1661641.0887601115, 978024.6295109489, 1112696.417878993, 0]\n",
+      "All Doublets 2.7868E+07\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All triplets [9351768353.785784, 5397145160.600975, 5052443596.68196, 3449644299.706186, 2475179265.2960234, 1042137461.475881, 2017417805.9431248, 5080784881.347837, 3468994807.8757772, 2505111308.004462, 1057216261.7870855, 2041814194.8329737, 0]\n",
+      "4.2940E+10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "#print hits[hits['tkId']==3].head(50)"
+    "#print hits[hits['tkId']==3].head(50)\n",
+    "n,b,p = plt.hist(rawHits['seq'],weights=rawHits['norm'])\n",
+    "plt.show()\n",
+    "print n \n",
+    "print 'All Hits', '{:.4E}'.format(np.sum(n))\n",
+    "doublets = [n[0]*n[1],n[1]*n[2],n[2]*n[3],\n",
+    "            n[0]*n[4],n[1]*n[4],n[2]*n[4],n[4]*n[5],n[5]*n[6],\n",
+    "            n[0]*n[7],n[1]*n[7],n[2]*n[7],n[7]*n[8],n[8]*n[9],0\n",
+    "           ]\n",
+    "print doublets\n",
+    "print 'All Doublets', '{:.4E}'.format(np.sum(doublets))\n",
+    "xy = np.arange(0.,14.,1.)\n",
+    "plt.step(xy,doublets,where='post')\n",
+    "plt.show()\n",
+    "triplets = [n[0]*n[1]*n[2],n[1]*n[2]*n[3],\n",
+    "            n[0]*n[1]*n[4],n[1]*n[2]*n[4],\n",
+    "            n[0]*n[4]*n[5],n[4]*n[5]*n[6],\n",
+    "            n[1]*n[4]*n[5],\n",
+    "            n[0]*n[1]*n[7],n[1]*n[2]*n[7],\n",
+    "            n[0]*n[7]*n[8],n[7]*n[8]*n[9],\n",
+    "            n[1]*n[7]*n[8],0\n",
+    "           ]\n",
+    "print 'All triplets', triplets\n",
+    "print '{:.4E}'.format(np.sum(triplets))\n",
+    "xy = np.arange(0.,len(triplets),1)\n",
+    "plt.step(xy,triplets,where='post')\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "plt.hist(hits['seq'],weights=hits['norm'])\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "648743 455518 369878 305588 355791 363545 366194\n"
+      "1204739 932585 751813 619508 763058 779521 781967\n"
      ]
     },
     {
      "data": {
-      "image/png": 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JkycXo+mSpFkMHA5Jfhb4HPAbVfX9/mNVVUAN+h59r7enqqaqamrNmjWL9bKSpBkGCockP0EvGO6oqs+34mfa5SLa52db+XFgfd/p61rZXOWSpCEZZLVSgNuAI1X1yb5DB4AzK462A/f0lV/fVi1dATzfLj/dB1yd5MI2EX11K5MkDcmqAc59E/DvgG8n+VYr+y3gY8BdSW4AngLe0Y7dC1wHTAMvAO8FqKpTST4CfL3V+3BVnRqgXZKkAS04HKrqL4HMcfiqWeoXcOMcr7UX2LvQtkiSFpd3SEuSOgwHSVKH4SBJ6jAcJEkdhoMkqcNwkCR1GA6SpA7DQZLUYThIkjoMB0lSh+EgSeowHCRJHYaDJKnDcJAkdRgOkqQOw0GS1GE4SJI6DAdJUofhIEnqMBwkSR2GgySpw3CQJHUYDpKkDsNBktQxMuGQZEuSx5JMJ9k57PZI0ko2EuGQ5Dzg94Frgc3AO5NsHm6rJGnlGolwAC4Hpqvqiar6AbAf2DrkNknSijUq4bAWONq3f6yVSZKGYNWwG3AukuwAdrTdF5M8PMz2LJOLgb8ddiOWmH2cDPZxGeTjA7/EPz2bSqMSDseB9X3761rZj6mqPcAegCSHq2pqeZo3PCuhn/ZxMtjHyTIql5W+DmxKsjHJ+cA24MCQ2yRJK9ZIjByq6nSS9wP3AecBe6vqkSE3S5JWrJEIB4Cquhe49xxO2bNUbRkxK6Gf9nEy2McJkqoadhskSSNmVOYcJEkjZOzCIclHkjyU5FtJvpTkVa08SW5pj994KMllw27rQiX5RJLvtH78cZLVfcd2tT4+luSaYbZzEEnenuSRJP+QZGrGsYnoI0zmY2GS7E3ybP9S8iQXJTmY5PH2+cJhtnFQSdYn+XKSR9vX6Qda+UT18+WMXTgAn6iq11fVPwP+FPhvrfxaYFP72AHcOqT2LYaDwOuq6vXAXwO7ANojRbYBrwW2AJ9ujx4ZRw8D/xb4an/hJPVxgh8L8xl6/zf9dgL3V9Um4P62P85OA79ZVZuBK4Ab2//dpPVzTmMXDlX1/b7dnwHOTJpsBW6vnkPA6iSXLnsDF0FVfamqTrfdQ/Tu+4BeH/dX1YtV9SQwTe/RI2Onqo5U1WOzHJqYPjKhj4Wpqq8Cp2YUbwX2te19wFuXtVGLrKpOVNU32vbfAUfoPbVhovr5csYuHACS3JzkKPBuXho5TOojOH4d+GLbntQ+9pukPk5SX+ZzSVWdaNtPA5cMszGLKckG4A3AA0xwP2camaWs/ZL8GfBPZjl0U1XdU1U3ATcl2QW8H9i9rA1cBPP1sdW5id7w9o7lbNtiOZs+avJUVSWZiGWQSX4W+BzwG1X1/SQ/OjZJ/ZzNSIZDVf3yWVa9g969Ebs5y0dwjIr5+pjkPcBbgKvqpfXGE9XHOYxVH+cxSX2ZzzNJLq2qE+1y7rPDbtCgkvwEvWC4o6o+34onrp9zGbvLSkk29e1uBb7Ttg8A17dVS1cAz/cN/8ZKki3AB4FfraoX+g4dALYluSDJRnqT718bRhuX0CT1cSU9FuYAsL1tbwfGemSY3hDhNuBIVX2y79BE9fPljN1NcEk+B/wC8A/AU8D7qup4+8/8PXqrKF4A3ltVh4fX0oVLMg1cAHy3FR2qqve1YzfRm4c4TW+o+8XZX2W0Jfk3wP8A1gDfA75VVde0YxPRR4Ak1wG/w0uPhbl5yE0aWJI7gSvpPaH0GXoj9z8B7gJeTe/78h1VNXPSemwk+ZfAXwDfpvezBuC36M07TEw/X87YhYMkaemN3WUlSdLSMxwkSR2GgySpw3CQJHUYDpKkDsNBktRhOEiSOgwHSVLH/weCYe0QV/Ih0gAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -420,7 +526,7 @@
     },
     {
      "data": {
-      "image/png": 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FnA68Kcnpk22VJM2vqQgH4Axgb1V9tap+BFwLbJ5wmyRpbk1LOKwFHliwv6+VSZIm4LhJN+BoJNkKbG2730/ylQGnOwn47+Gtmgpey/RZLdcBXstUyfsf2xznWn7uSCtOSzjsB9Yv2F/Xyh6nqrYD25fjBZPsrqpNy3GuSfNaps9quQ7wWqbVSl/LtAwr3QGcluTUJE8HLgB2TrhNkjS3pqLnUFUHk7wduBl4KrCjqu6ecLMkaW5NRTgAVNVNwE3H8CWXZXhqSngt02e1XAd4LdNqRa8lVbWS55ckzaBpmXOQJE2RuQ6HJO9N8oUkdyX5VJIXTLpN40ryl0m+3K7nn5McP+k2jSPJ65PcneQnSWZyVclquRVMkh1JHkrypUm3Zagk65PckuSe9vt18aTbNI4kP5Pk80n+s13HX6zYa83zsFKS51bVd9v2O4DTq+r3J9yssSQ5B/hsm9x/P0BVXTLhZh21JL8E/AT4W+BPq2r3hJt0VNqtYP4L+C1GX+a8A3hTVd0z0YaNIclLge8DV1fVCyfdniGSnAKcUlV3JnkOsAc4f9b+vyQJ8Oyq+n6SpwGfAy6uqtuW+7XmuudwKBiaZwMzm5RV9amqOth2b2P0XZGZU1X3VtWQLzdO2qq5FUxV/RtwYNLtWA5V9WBV3dm2vwfcywzehaFGvt92n9YeK/K+NdfhAJDksiQPAG8G/nzS7Vkmvwv8y6QbMae8FcyUS7IReBFw+2RbMp4kT01yF/AQsKuqVuQ6Vn04JPl0ki8t8tgMUFXvrqr1wDXA2yfb2if2ZNfS6rwbOMjoeqbSkVyHtBKS/CzwCeCPDhs5mBlV9eOq+jVGowNnJFmRIb+p+Z7DSqmqVxxh1WsYfc/i0hVsziBPdi1J3gq8Bji7pngy6Sj+n8yiI7oVjI69Nkb/CeCaqvrkpNszVFU9kuQW4Fxg2RcNrPqewxNJctqC3c3AlyfVlqGSnAv8GfDaqvrBpNszx7wVzBRqE7lXAvdW1Qcm3Z5xJVlzaCVikmcyWviwIu9b875a6RPALzJaHXM/8PtVNZOf8pLsBZ4BfLsV3TaLK6+S/Dbw18Aa4BHgrqp65WRbdXSSnAd8iJ/eCuayCTdpLEk+DryM0d0/vwVcWlVXTrRRY0rym8C/A19k9PcO8K52Z4aZkeRXgKsY/W49Bbiuqt6zIq81z+EgSVrcXA8rSZIWZzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjr/D+W0M1nbbnciAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -430,7 +536,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -440,7 +546,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -450,7 +556,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -460,7 +566,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -470,7 +576,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -480,7 +586,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -490,7 +596,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -500,7 +606,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -510,7 +616,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -520,7 +626,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -530,7 +636,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -540,7 +646,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -550,7 +656,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -560,7 +666,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -570,7 +676,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -580,7 +686,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -590,7 +696,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -600,7 +706,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -666,7 +772,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(hb1['ysize']/8.,log=False, bins=30,range=[0,30])\n",
+    "plt.show()\n",
+    "plt.hist(hb2['ysize']/8.,log=False, bins=30,range=[0,30])\n",
+    "plt.show()\n",
+    "plt.hist(hb3['ysize']/8.,log=False, bins=30,range=[0,30])\n",
+    "plt.show()\n",
+    "plt.hist(hb4['ysize']/8.,log=False, bins=30,range=[0,30])\n",
+    "plt.show()\n",
+    "plt.hist(hf1['ysize']/8.,log=False, bins=30,range=[0,30])\n",
+    "plt.show()\n",
+    "plt.hist(hf2['ysize']/8.,log=False, bins=30,range=[0,30])\n",
+    "plt.show()\n",
+    "plt.hist(hf3['ysize']/8.,log=False, bins=30,range=[0,30])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
@@ -674,83 +873,97 @@
      "output_type": "stream",
      "text": [
       "min/max\n",
-      "      ev   ind  det   charge        xg        yg         zg        rg   iphi  \\\n",
-      "min    1     0    0      696 -3.133961 -3.443828 -27.465214  2.664958 -32766   \n",
-      "max  500  4931   95  6379805  3.184488  3.305549  25.885403  3.443848  32766   \n",
+      "       ev   ind  det   charge        xg        yg         zg         rg  \\\n",
+      "min     1     0    0     2019 -3.158685 -3.374328 -47.909229   2.738755   \n",
+      "max  1000  4379   95  8107997  3.158685  9.439112  26.674952  14.278748   \n",
+      "\n",
+      "      iphi  xsize     ...         tkId      pt   n1   tkId2     pt2   n2  \\\n",
+      "min -32766  -1023     ...            0     401    1       0       0    0   \n",
+      "max  32765   1023     ...       142904  292338  355  142143  196301  313   \n",
+      "\n",
+      "          phi       eta  seq      trackID  \n",
+      "min -3.141570 -2.971995    1     10000000  \n",
+      "max  3.141403  2.971991    1  10000114179  \n",
       "\n",
-      "       tkId      pt   n1   tkId2     pt2  n2       phi       eta  seq  \\\n",
-      "min       0     401    1       0       0   0 -3.141559 -3.027653    1   \n",
-      "max  145940  236569  173  144781  155324  70  3.141505  2.968422    1   \n",
+      "[2 rows x 21 columns]\n",
+      "       ev   ind  det   charge        xg         yg         zg         rg  \\\n",
+      "min     1  1305   96     4021 -6.980564 -14.810458 -31.160593   2.738874   \n",
+      "max  1000  8045  319  5558487  9.779302  12.969457  39.338615  15.979434   \n",
       "\n",
-      "        trackID  \n",
-      "min    10000002  \n",
-      "max  5000114033  \n",
-      "      ev   ind  det   charge        xg        yg         zg        rg   iphi  \\\n",
-      "min    1  1349   96      627 -6.956163 -7.319355 -27.465387  6.502839 -32767   \n",
-      "max  500  8491  319  6790313  7.005610  7.180817  25.886454  7.320724  32766   \n",
+      "      iphi  xsize     ...         tkId      pt   n1   tkId2     pt2  n2  \\\n",
+      "min -32767  -1023     ...            0     401    0       0       0   0   \n",
+      "max  32767   1023     ...       142904  292338  240  140239  124077  97   \n",
       "\n",
-      "       tkId      pt   n1   tkId2    pt2   n2       phi       eta  seq  \\\n",
-      "min       0     401    1       0      0    0 -3.141582 -2.147105    2   \n",
-      "max  145940  255145  241  145934  81469  164  3.141493  2.089339    2   \n",
+      "          phi       eta  seq      trackID  \n",
+      "min -3.141586 -2.907090    2     10000000  \n",
+      "max  3.141580  2.833558    2  10000114179  \n",
       "\n",
-      "        trackID  \n",
-      "min    10000000  \n",
-      "max  5000114028  \n",
-      "      ev    ind  det   charge         xg         yg         zg         rg  \\\n",
-      "min    1   2404  320      641 -11.041545 -11.406182 -27.465218  10.633793   \n",
-      "max  500  11579  671  4936108  11.091197  11.267007  25.886602  11.406182   \n",
+      "[2 rows x 21 columns]\n",
+      "       ev    ind  det   charge         xg         yg         zg         rg  \\\n",
+      "min     1   2326  320     4022 -11.065842 -11.335875 -26.675051   4.987466   \n",
+      "max  1000  14260  671  6401557  11.065842  11.335875  50.631355  11.335896   \n",
       "\n",
-      "      iphi    tkId      pt   n1   tkId2    pt2  n2       phi       eta  seq  \\\n",
-      "min -32766       0     401    1       0      0   0 -3.141551 -1.677385    3   \n",
-      "max  32766  145940  255145  174  143437  60151  19  3.141545  1.622253    3   \n",
+      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2  n2  \\\n",
+      "min -32767  -1023     ...            0     401    1       0      0   0   \n",
+      "max  32767    908     ...       142826  292338  225  139205  58626  94   \n",
       "\n",
-      "        trackID  \n",
-      "min    10000000  \n",
-      "max  5000114028  \n",
-      "      ev    ind   det   charge         xg         yg         zg         rg  \\\n",
-      "min    1   3271   672      350 -16.142319 -16.506441 -27.465492  15.738359   \n",
-      "max  500  14303  1183  5762376  16.191597  16.367704  25.885468  16.526356   \n",
+      "          phi       eta  seq      trackID  \n",
+      "min -3.141591 -1.643974    3     10000000  \n",
+      "max  3.141590  3.010971    3  10000113896  \n",
       "\n",
-      "      iphi    tkId      pt   n1   tkId2    pt2  n2       phi       eta  seq  \\\n",
-      "min -32766       0     401    1       0      0   0 -3.141524 -1.323260    4   \n",
-      "max  32767  145940  255145  175  140301  20956  54  3.141583  1.272212    4   \n",
+      "[2 rows x 21 columns]\n",
+      "       ev    ind   det   charge         xg         yg         zg         rg  \\\n",
+      "min     1   3277   672     4017 -16.166553 -16.436216 -26.675051   6.577823   \n",
+      "max  1000  13712  1183  5935571  16.166553  16.436216  26.674952  16.436220   \n",
       "\n",
-      "        trackID  \n",
-      "min    10000005  \n",
-      "max  5000114028  \n",
-      "      ev    ind   det   charge         xg         yg         zg         rg  \\\n",
-      "min    1   4041  1184      797 -16.040541 -16.070353 -35.943035   4.541135   \n",
-      "max  500  20919  1631  7674224  16.086744  15.934120  34.369064  16.135990   \n",
+      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2  n2  \\\n",
+      "min -32767  -1023     ...            0     401    1       0      0   0   \n",
+      "max  32767    918     ...       142826  292338  176  142824  70357  53   \n",
       "\n",
-      "      iphi    tkId      pt   n1   tkId2    pt2  n2       phi       eta  seq  \\\n",
-      "min -32767       0     401    1       0      0   0 -3.141586 -2.718763    5   \n",
-      "max  32767  146066  121703  324  140871  40124  65  3.141585  2.671790    8   \n",
+      "          phi       eta  seq      trackID  \n",
+      "min -3.141579 -1.819175    4     10000000  \n",
+      "max  3.141589  1.294038    4  10000113896  \n",
       "\n",
-      "        trackID  \n",
-      "min    10000002  \n",
-      "max  5000114052  \n",
-      "      ev    ind   det   charge        xg         yg         zg         rg  \\\n",
-      "min    1   4487  1296      805 -16.04524 -16.070236 -43.468384   4.539285   \n",
-      "max  500  22665  1743  3752645  16.08004  15.932957  41.899700  16.137133   \n",
+      "[2 rows x 21 columns]\n",
+      "       ev    ind   det   charge         xg         yg         zg         rg  \\\n",
+      "min     1   4148  1184     4006 -16.067684 -16.001060 -35.182049   4.614048   \n",
+      "max  1000  20609  1631  4746542  16.065769  16.002853  35.186981  16.070286   \n",
       "\n",
-      "      iphi    tkId      pt   n1   tkId2    pt2  n2       phi       eta  seq  \\\n",
-      "min -32766       0     401    1       0      0   0 -3.141546 -2.914465    6   \n",
-      "max  32767  146066  115736  233  143908  60793  59  3.141591  2.876371    9   \n",
+      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2  n2  \\\n",
+      "min -32767  -1023     ...            0     401    0       0      0   0   \n",
+      "max  32766    892     ...       142904  232351  229  139156  51334  61   \n",
       "\n",
-      "        trackID  \n",
-      "min    10000002  \n",
-      "max  5000114033  \n",
-      "      ev    ind   det   charge         xg         yg         zg         rg  \\\n",
-      "min    1   4974  1408      650 -16.038914 -16.070156 -52.979748   4.543934   \n",
-      "max  500  24450  1855  8573611  16.078123  15.933924  51.400307  16.140593   \n",
+      "          phi       eta  seq      trackID  \n",
+      "min -3.141578 -2.683826    5     10000007  \n",
+      "max  3.141570  2.683821    8  10000114092  \n",
       "\n",
-      "      iphi    tkId      pt   n1   tkId2    pt2  n2       phi       eta  seq  \\\n",
-      "min -32766       0     401    1       0      0   0 -3.141555 -3.118078    7   \n",
-      "max  32766  146066  146147  417  141195  60793  50  3.141576  3.087029   10   \n",
+      "[2 rows x 21 columns]\n",
+      "       ev    ind   det   charge         xg         yg         zg         rg  \\\n",
+      "min     1   4622  1296     4018 -16.066727 -16.001966 -49.761089   2.739524   \n",
+      "max  1000  22376  1743  6625049  16.054962  16.001966  42.686981  16.070562   \n",
       "\n",
-      "        trackID  \n",
-      "min    10000010  \n",
-      "max  5000114059  \n"
+      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2   n2  \\\n",
+      "min -32767  -1023     ...            0     401    0       0      0    0   \n",
+      "max  32767   1013     ...       142812  232351  303  142276  31140  106   \n",
+      "\n",
+      "          phi       eta  seq      trackID  \n",
+      "min -3.141582 -3.373930    6     10000015  \n",
+      "max  3.141592  2.966494    9  10000113902  \n",
+      "\n",
+      "[2 rows x 21 columns]\n",
+      "       ev    ind   det   charge         xg         yg         zg         rg  \\\n",
+      "min     1   5146  1408     4006 -16.070560 -15.997278 -52.180595   4.614048   \n",
+      "max  1000  24119  1855  6750257  16.067684  16.000870  52.186104  16.070562   \n",
+      "\n",
+      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2   n2  \\\n",
+      "min -32767  -1023     ...            0     401    0       0      0    0   \n",
+      "max  32767   1023     ...       142812  232351  268  140451  55949  194   \n",
+      "\n",
+      "          phi       eta  seq      trackID  \n",
+      "min -3.141588 -3.090228    7     10000008  \n",
+      "max  3.141587  3.090218   10  10000114029  \n",
+      "\n",
+      "[2 rows x 21 columns]\n"
      ]
     }
    ],
@@ -767,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -775,6 +988,7 @@
     " return pd.DataFrame({ 'z'+n : hh['zg'],\n",
     "                    'r'+n : hh['rg'],\n",
     "                    'phi'+n : hh['phi'],\n",
+    "                    'ys'+n : hh['ysize'],\n",
     "                    'pt'+n : hh['pt'],\n",
     "                    'det'+n : hh['det'],\n",
     "                    'trackID' : hh['trackID']\n",
@@ -793,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -822,7 +1036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 171,
    "metadata": {
     "scrolled": true
    },
@@ -842,12 +1056,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -857,7 +1071,7 @@
     },
     {
      "data": {
-      "image/png": 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s96Fh+cDPhd35VwBHgXO7yy/pvgb4Q+CWoWtdg5pvAd7enX8dcGToWvuuuRu7EfjQ0DWuRc3d6Um+fWByD/DhoWtdg3/bL+6+ng98Anj90LWepvatrH4w9lq+82DsZ7rxFwFfZvlA7Au78y/qrlt5MPaaiec09A9lxQ/hGpZ/Q38JeE839l7gDd3564Evdrf5Y+D8bvx7gUe606eAV4085ieBh7p/LH8OPH/oOteg5hewvKp9iOU/e185dJ1919xdfz+wc+j61vB5flP3HH++q/1lQ9e5BjX/PnAMeBT4raFrPE3NdwJfB/6P5b+s97C8ALmxuz7Abd3P5CFgaeS+bweOd6e3jYwvdfn1JeADdC90neTkK2MlqXGL1KOXJPXAoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXH/D5EFVIGJvGleAAAAAElFTkSuQmCC\n",
+      "image/png": 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MXWvP9R4A3t5dfw1w39C19l1z13Yd8IGha9yImrvLU3x3x+Re4CND17oB/7df2P17NvBJ4LVD13qS2i9i7Z2xV/G9O2M/27U/D/gyox2x53XXn9dtW70z9sqp+zT0i3KSF+JKRp/SXwLe3bW9B3h9d/3NwBe7+/wZcHbX/gPAF7rLp4FXjD3np4AHu/8wHwKeM3SdPdf7XEaj2gcZ/dn78qHr7LvmbvvdwK6h69vA9/mN3Xv8QFf7S4aucwNq/kNG56h+GPjtoWs8Sc23AF8D/o/RX9Z7GQ1Aruu2B7ipe00eBFbGHvt24Hh3edtY+0qXXV8C/oTui67TXPxmrCQ1btHm6CVJc2bQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuP8HCtkgUJ83vYAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -867,7 +1081,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -877,7 +1091,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -887,7 +1101,7 @@
     },
     {
      "data": {
-      "image/png": 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CH2/OfxA4OOlau665GdsBfGnSNY6j5ub0Aq9tmNwGfHXStY7h3/bbmp9nAA8Al0y61pPUvp75N8Zezus3xj7cjJ8DfIfehtizm/PnNNfN3Rh72cBzmvSLMudFuIzeb+hngM80YzcCH27OX03vKJdPA7cBZzTjbwaebE4PARf3PeY3gMebfyx/Abxl0nWOoea30lvVPk7vz973TLrOrmturn8Q2Dzp+sb4Pn+0eY8fbWq/YNJ1jqHmPwaOAE8Bvz/pGk9S8x7ge8D/0fvLehu9BciO5voAbm1ek8eBmb77fhw42pw+1jc+0+TXM8AtNB90HeTkJ2Mlqbhp6tFLkjpg0EtScQa9JBVn0EtScQa9JBVn0EtScQa9JBVn0EtScf8PVOFV+/xMlsoAAAAASUVORK5CYII=\n",
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f7+5gvJFBHxoGO37O666/AjgEnN3dfkn3b4A/Bm6ad60zqPkm4J3d9dcDD8y71r5r7sauBz487xpnUXN3eYLv7pjcBXx03rXO4P/2i7t/zwE+Bbxh3rWepvYtrL0z9mqeuTP2c934i4CvMtgR+8Lu+ou6bat3xl419pzm/aKsehGuYvAT+ivA+7qx9wNv7K5fC3y5u8+fAed0498HPNxdPgO8aug5Pw082P1n+Uvg+fOucwY1v4DBqvZBBr/2vnLedfZdc7f9PmD7vOub4fv85u49/mJX+8vmXecMav5D4DDwCPDb867xNDXfDnwT+D8Gv1nvYrAAub7bHuCW7jV5EFgZeuw7gSPd5R1D4ytdfn0F+CDdH7qOc/EvYyWpcYvUo5ck9cCgl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcf8P3/YwAyMzvKUAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -897,7 +1111,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -907,7 +1121,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEONJREFUeJzt3XuMXGd5x/Hvr44cVBAmIRGlSYwdkYZaqgRolKAilUu5OE1zEY3AVlEDdeMmbfinqoRRKrVCQkD/QYqSilqQGmjlkLqitYtRCoQo/wQap+KSi0JMAMUpxYYUS70lBJ7+MWfJsNn1zu6c2dl9/f1Iq515z+3Zd1bPvvuc95yTqkKS1K5fmHUAkqTpMtFLUuNM9JLUOBO9JDXORC9JjTPRS1LjTPSS1DgTvSQ1zkQvSY07Y9YBAJxzzjm1ZcuWWYchSevK/fff/4OqOnep9dZEot+yZQtHjhyZdRiStK4k+e446820dJPkiiR7T548OcswJKlpM030VXWoqnZv2rRplmFIUtM8GStJjTPRS1LjTPSS1DgTvSQ1zkQvSY0z0UtS49bEBVOSdDrZsuezP3v9nQ9dPvXjOaKXpMaZ6CWpcVNJ9Emen+RIkt+exv4lSeMbK9EnuS3J8SQPzGvfnuSRJEeT7BlZ9F7gjj4DlSStzLgj+n3A9tGGJBuAW4HLgG3AziTbkrwZeAg43mOckqQVGmvWTVXdk2TLvOZLgKNV9RhAktuBq4AXAM9nmPz/N8nhqvppbxFLkpZlkumV5wGPj7w/BlxaVTcCJHkX8IPFknyS3cBugM2bN08QhiTpVKY266aq9lXVP59i+d6qGlTV4Nxzl3xAiiRphSZJ9E8AF4y8P79rG5sPHpGk6Zsk0d8HXJRka5KNwA7g4HJ24INHJGn6xp1euR+4F7g4ybEku6rqGeBG4E7gYeCOqnpwOQd3RC9J0zfurJudi7QfBg6v9OBVdQg4NBgMrlvpPiRJp+YtECSpcTNN9JZuJGn6ZproPRkrSdNn6UaSGmfpRpIaZ+lGkhpn6UaSGmeil6TGWaOXpMZZo5ekxlm6kaTGmeglqXEmeklqnCdjJalxnoyVpMZZupGkxpnoJalxJnpJapyJXpIa56wbSWqcs24kqXGWbiSpcSZ6SWqciV6SGmeil6TGmeglqXEmeklqnPPoJalxzqOXpMZZupGkxpnoJalxJnpJapyJXpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGmeil6TG9Z7ok/xqko8mOZDkhr73L0lanrESfZLbkhxP8sC89u1JHklyNMkegKp6uKquB94OvLb/kCVJy3HGmOvtA24BPjnXkGQDcCvwZuAYcF+Sg1X1UJIrgRuAT/UbriStT1v2fHZmxx5rRF9V9wBPzmu+BDhaVY9V1dPA7cBV3foHq+oy4HcX22eS3UmOJDly4sSJlUUvSVrSuCP6hZwHPD7y/hhwaZLXA28DzgQOL7ZxVe0F9gIMBoOaIA5J0ilMkugXVFV3A3f3vV9J0spMMuvmCeCCkffnd21j88EjkjR9kyT6+4CLkmxNshHYARxczg588IgkTd+40yv3A/cCFyc5lmRXVT0D3AjcCTwM3FFVDy7n4I7oJWn6xqrRV9XORdoPc4oTrmPs9xBwaDAYXLfSfUiSTs1bIEhS42aa6C3dSNL0zTTRezJWkqav93n0kqTZ3vJgPks3ktQ4SzeS1Dhn3UhS40z0ktQ4a/SS1Dhr9JLUOEs3ktQ4E70kNc4avSQ1bqZXxnr3SkktWUtXw46ydCNJjTPRS1LjTPSS1DgTvSQ1zlk3ktQ4Z91I0gTW6kybUZZuJKlxJnpJapyJXpIa5zNjJWmZ1kNdfpQjeklqnIlekhpnopekxnnBlCQ1zgumJGkM6+0E7Chn3UjSItZzch9ljV6SGmeil6TGmeglqXEmeklqnIlekhrnrBtJGtHKTJtRJnpJp70Wk/soSzeS1LipjOiTXA1cDrwQ+HhV/cs0jiNJWtrYI/oktyU5nuSBee3bkzyS5GiSPQBV9Y9VdR1wPfCOfkOWJC3Hcko3+4Dtow1JNgC3ApcB24CdSbaNrPJn3XJJ0oyMneir6h7gyXnNlwBHq+qxqnoauB24KkMfBj5XVf+20P6S7E5yJMmREydOrDR+SdISJj0Zex7w+Mj7Y13be4A3AdckuX6hDatqb1UNqmpw7rnnThiGJGkxUzkZW1U3AzcvtV6SK4ArXv7yl08jDEkSk4/onwAuGHl/ftc2lqo6VFW7N23aNGEYkqTFTJro7wMuSrI1yUZgB3Bw8rAkSX0Zu3STZD/weuCcJMeAP6+qjye5EbgT2ADcVlUPLmOflm4krZrRK2C/86HLZxjJ6kpVzToGBoNBHTlyZNZhSGrcWrzVwSR/cJLcX1WDpdbzXjeSmrYWk/tqm+m9bpJckWTvyZMnZxmGJDVtpiP6qjoEHBoMBtfNMg5JbXEU//O8e6UkNc7SjSQ1bqaJ3gumJGn6nHUjqQnW5RdnjV6SGmeNXpIaZ41ekhpn6UaSGmeil6TGzXTWjXevlDQOZ9RMxhq9JDXO0o0kNc5EL0mN88pYSWuSdfn+OKKXpMY560ZSr07X57KuZc66kaTGWaOXNBFr6WufNXpJapwjekmrbrE6vv8dTIeJXtKqMInPjole0lhWMptmnOTuH4Dps0YvSY1zRC/1wLnjWsu8YEpaB6b9h8Q/VG3zgilJapylG2mFpn0S0ZOU6ouJXjpN+Yfk9OGsG0lqnIlekhpnopekxlmjl2ZgNaczTnKsxer41vfXF0f0ktQ4E70kNa730k2SC4GbgE1VdU3f+5fWE69o1Vow1og+yW1Jjid5YF779iSPJDmaZA9AVT1WVbumEawkafnGHdHvA24BPjnXkGQDcCvwZuAYcF+Sg1X1UN9BSlqeSU6WeqK1PWON6KvqHuDJec2XAEe7EfzTwO3AVT3HJ0ma0CQ1+vOAx0feHwMuTfJi4APAq5K8r6o+uNDGSXYDuwE2b948QRjS+tbXCNqRuBbT+8nYqvohcP0Y6+0F9gIMBoPqOw5J0tAkif4J4IKR9+d3bWPzfvRaCWeaSMszyTz6+4CLkmxNshHYARxczg68H70kTd+40yv3A/cCFyc5lmRXVT0D3AjcCTwM3FFVD04vVEnSSoxVuqmqnYu0HwYOr/Tglm7UorV8f5i1EINWn48SlKTGea8bSWrcTG9T3HfpZi3OxlgLMa2FGFZivcY9bZZftFyWbiSpcZZuJKlxTZVudHqz1CMtzNKNJDXO0o0kNW7dl27W0wyEtVxa6DO2xfa12Gc1yfHW0+cvzYqlG0lqnKUbSWqciV6SGmeil6TGrfuTsdMyycnJlk8QTuNnG6ev+3rY9TifZcufn05PnoyVpMZZupGkxpnoJalxJnpJapyJXpIa1+ysm5XMmulrZsc09DVzxBklz1rLt6SQ+uSsG0lqnKUbSWqciV6SGmeil6TGmeglqXEmeklqnIlekhpnopekxjV7wdQ4xr14aLnPQJ3k2H3tv0+rGUdfx/KCMelZXjAlSY2zdCNJjTPRS1LjTPSS1DgTvSQ1zkQvSY0z0UtS40z0ktQ4E70kNa73K2OTPB/4K+Bp4O6q+ru+jyFJGt9YI/oktyU5nuSBee3bkzyS5GiSPV3z24ADVXUdcGXP8UqSlmnc0s0+YPtoQ5INwK3AZcA2YGeSbcD5wOPdaj/pJ0xJ0kqNleir6h7gyXnNlwBHq+qxqnoauB24CjjGMNmPvX9J0vRMUqM/j2dH7jBM8JcCNwO3JLkcOLTYxkl2A7sBNm/ePEEYbRnn7oregbF/9qla1vvJ2Kr6b+DdY6y3F9gLMBgMqu84JElDk5RWngAuGHl/ftc2tiRXJNl78uTJCcKQJJ3KJIn+PuCiJFuTbAR2AAeXswPvRy9J0zfu9Mr9wL3AxUmOJdlVVc8ANwJ3Ag8Dd1TVg9MLVZK0EmPV6Ktq5yLth4HDKz34rB8lKEmnAx8lKEmNm2mi92SsJE2fI3pJapxXrkpS41I1+2uVkpwAvrtKhzsH+MEqHWsSxtmv9RDneogRjLNvk8T5sqo6d6mV1kSiX01JjlTVYNZxLMU4+7Ue4lwPMYJx9m014rR0I0mNM9FLUuNOx0S/d9YBjMk4+7Ue4lwPMYJx9m3qcZ52NXpJOt2cjiN6STqtNJnok5yd5PNJHu2+n7XAOm9I8tWRr/9LcnW3bF+Sb48se+Ws4uzW+8lILAdH2rcm+Ur3zN5Pd3cRnUmcSV6Z5N4kDyb5epJ3jCybWn8u8tzi0eVndn1ztOurLSPL3te1P5LkrX3FtMI4/yTJQ13ffTHJy0aWLfj5zyjOdyU5MRLPH4wsu7b7HXk0ybUzjPEjI/F9M8mPRpatZl8u+KztkeVJcnP3c3w9yatHlvXbl1XV3Bfwl8Ce7vUe4MNLrH82w0cl/mL3fh9wzVqJE/ivRdrvAHZ0rz8K3DCrOIFfAS7qXv8y8D3gRdPsT2AD8C3gQmAj8DVg27x1/gj4aPd6B/Dp7vW2bv0zga3dfjZMqf/GifMNI79/N8zFearPf0Zxvgu4ZYFtzwYe676f1b0+axYxzlv/PcBtq92X3bF+A3g18MAiy38L+BwQ4DXAV6bVl02O6Bk+u/YT3etPAFcvsf41wOeq6n+mGtVzLTfOn0kS4I3AgZVsv0xLxllV36yqR7vX/w4cB5a8kGNCiz23eNRo7AeA3+z67irg9qp6qqq+DRzt9jeTOKvqSyO/f1/m2ecur6Zx+nMxbwU+X1VPVtV/Ap8Htq+BGHcC+6cQx5Jq4Wdtj7oK+GQNfRl4UZKXMoW+bDXRv6Sqvte9/g/gJUusv4Pn/jJ8oPt36iNJzuw9wqFx43xekiNJvjxXXgJeDPyohs8FgOEze8+bcZwAJLmE4WjrWyPN0+jPhZ5bPL8PfrZO11cnGfbdONv2ZbnH2sVwpDdnoc9/GsaN83e6z/JAkrmnzK1Wf459nK78tRW4a6R5tfpyHIv9LL33Ze/PjF0tSb4A/NICi24afVNVlWTRqUXdX9BfY/gAlTnvY5jQNjKc+vRe4P0zjPNlVfVEkguBu5J8g2HC6k3P/fkp4Nqq+mnX3Ft/ti7JO4EB8LqR5ud8/lX1rYX3MHWHgP1V9VSSP2T439IbZxTLUnYAB6rqJyNta6kvV826TfRV9abFliX5fpKXVtX3usRz/BS7ejvwmar68ci+50avTyX5G+BPZxlnVT3RfX8syd3Aq4B/YPiv3hndSHXZz+ztO84kLwQ+C9zU/Ss6t+/e+nOecZ5bPLfOsSRnAJuAH465bV/GOlaSNzH8w/q6qnpqrn2Rz38ayWnJOKvqhyNvP8bw/M3ctq+ft+3dvUe4vM9tB/DHow2r2JfjWOxn6b0vWy3dHATmzlRfC/zTKdZ9Tg2vS2ZzdfCrgQXPmvdgyTiTnDVX6khyDvBa4KEanrX5EsPzC4tuv4pxbgQ+w7DmeGDesmn15zjPLR6N/Rrgrq7vDgI7MpyVsxW4CPjXnuJadpxJXgX8NXBlVR0faV/w859hnC8deXslw8eIwvA/4rd08Z4FvIWf/y951WLs4nwFwxOZ9460rWZfjuMg8Hvd7JvXACe7QVH/fblaZ6BX84thDfaLwKPAF4Czu/YB8LGR9bYw/Ov5C/O2vwv4BsOE9LfAC2YVJ/DrXSxf677vGtn+QobJ6Sjw98CZM4zzncCPga+OfL1y2v3JcObCNxmOym7q2t7PMGECPK/rm6NdX104su1N3XaPAJdN+XdyqTi/AHx/pO8OLvX5zyjODwIPdvF8CXjFyLa/3/XzUeDds4qxe/8XwIfmbbfafbmf4eyzHzOss+8Crgeu75YHuLX7Ob4BDKbVl14ZK0mNa7V0I0nqmOglqXEmeklqnIlekhpnopekxpnoJalxJnpJapyJXpIa9/93PocXi3VKTwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -917,7 +1131,7 @@
     },
     {
      "data": {
-      "image/png": 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v8LGnuT+AW5vX4wlgse+xHwaONZcP9bUvNtn1DPAnNAe5DnvxyFhJKm6W5uglSS0w6CWpOINekooz6CWpOINekooz6CWpOINekooz6CWpuP8DYjvjibMm+e8AAAAASUVORK5CYII=\n",
+      "image/png": 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vfmzFPufz5AHG6xnMQ8PgwM953fUXAUeAs7vbz+t+BvgT4IZZ17oBNd8AvLW7/grgnlnX2nfNXds1wAdnXeNG1NxdHuHJA5N7gY/OutYN+Lf93O7nOcCngVfOutbT1L6D1Q/GXsn3H4z9fNf+HODrDA7EPru7/pxu28qDsVeM3adZPykrnoQrGLxDfw14V9f2buA13fU3AF/t9nk/cE7X/gPAV7rLZ4GXDD3mZ4B7u38sfwk8c9Z1bkDNz2Iwqr2XwcfeF8+6zr5r7rbfBSzPur4NfJ1f173GX+pqf8Gs69yAmv8IOArcD/zurGs8Tc23AN8G/o/BJ+u9DAYg13TbA9zUPSf3AktD930rcKy7vGWofanLr68Bf0b3RddxLn4zVpIaN09z9JKkHhj0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ17v8BwtU4J46u9VMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -927,7 +1141,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -937,7 +1151,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -947,7 +1161,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -957,7 +1171,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -967,7 +1181,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -977,7 +1191,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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eWN7wcgy4gB9svPnRFcuczQ82MN7Ich8aljf8vLi7/irgCHBad/ul3c8AfwbcNO9aZ1DzTcA7uuuXAIfnXWvfNXdj1wEfnHeNs6i5uzzODzZMXgN8dN61zuDf9ku6n2cAnwFeP+9aT1D7+ay+MfYKnrkx9ovd+IuAr7O8IfaF3fUXdfet3Bh7+dhzmvcvZcUv4XKWP6G/Bry7G3sv8Mbu+luAr3bL/AVwRjf+XODB7vJ54DVDz/lZ4P7uH8uHgefPu84Z1PwCltdq72f5z95Xz7vOvmvu7r8b2Dnv+mb4Pr+5e4+/3NX+8nnXOYOa/wR4CHgY+N1513iCmm8HvgX8H8t/WV/D8grIdd39AW7tfif3A4Ohx74DONpd3j40Pujy62vALXQHuo5z8chYSWrcIvXoJUk9MOglqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrc/wN0Y0D8lDnN6AAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -987,7 +1201,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -997,7 +1211,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1007,7 +1221,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1017,7 +1231,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1037,152 +1251,159 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 255,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "548570\n",
-      "493149\n",
-      "434057\n",
-      "792527\n",
-      "2274850\n",
-      "540108\n",
-      "483260\n",
-      "931967\n",
+      "doublets\n",
+      "1119054\n",
+      "1023158\n",
+      "879245\n",
+      "861764\n",
+      "482689\n",
+      "187506\n",
+      "1058518\n",
+      "1079161\n",
+      "triplets and quads\n",
+      "1686712\n",
+      "4892329\n",
+      "1127517\n",
+      "933900\n",
+      "1796774\n",
       "pentuplets\n",
-      "612902\n",
+      "1272044\n",
       "triplets\n",
-      "111601\n",
-      "47552\n",
-      "4230185\n",
-      "   det1      phi1   pt1        r1   trackID       z1  det2      phi2   pt2  \\\n",
-      "0    83 -0.534839  1698  3.209876  10000005 -2.03853   299 -0.522814  1698   \n",
-      "1    83 -0.534839  1698  3.209876  10000005 -2.03853   299 -0.522814  1698   \n",
-      "2    83 -0.534839  1698  3.209876  10000005 -2.03853   299 -0.522814  1698   \n",
-      "3    83 -0.534839  1698  3.209876  10000005 -2.03853   299 -0.522814  1698   \n",
-      "4    83 -0.534839  1698  3.209876  10000005 -2.03853   299 -0.508068  1698   \n",
+      "212084\n",
+      "90346\n",
+      "8750520\n",
+      "   det1      phi1    pt1        r1   trackID  ys1        z1  det2      phi2  \\\n",
+      "0    19  1.471751  12402  3.311650  10000000   17 -4.949985   146  1.473213   \n",
+      "1    19  1.490899  11881  3.321660  10000001   28 -4.962011   146  1.492998   \n",
+      "2    19  1.508659   1116  3.332094  10000002   15 -3.228904   147  1.529033   \n",
+      "3    11  0.957661   1220  2.772534  10000003   31 -5.223804   130  0.937362   \n",
+      "4    43  2.933400    480  2.740693  10000005   14 -2.197470   195  2.887583   \n",
       "\n",
-      "         r2    ...      det3      phi3   pt3         r3        z3  det4  \\\n",
-      "0  6.636066    ...       642 -0.506715  1698  11.091832 -8.901971  1146   \n",
-      "1  6.636066    ...       642 -0.506715  1698  11.091832 -8.901971  1146   \n",
-      "2  6.636066    ...       642 -0.506715  1698  11.091832 -8.901971  1146   \n",
-      "3  6.636066    ...       642 -0.506715  1698  11.091832 -8.901971  1146   \n",
-      "4  6.638935    ...       642 -0.506715  1698  11.091832 -8.901971  1146   \n",
+      "     pt2    ...        pt3         r3  ys3         z3  det4      phi4    pt4  \\\n",
+      "0  12402    ...      12402  11.309768   21 -14.069117   793  1.477370  12402   \n",
+      "1  11881    ...      11881  11.306963   18 -14.051542   793  1.497169  11881   \n",
+      "2   1116    ...       1116  11.323120   15  -8.101905   802  1.574145   1116   \n",
+      "3   1220    ...       1220  11.037246   23 -17.280849   744  0.894308   1220   \n",
+      "4    480    ...        480  10.726357    8  -5.142550   898  2.780511    480   \n",
       "\n",
-      "       phi4   pt4         r4         z4  \n",
-      "0 -0.469588  1698  16.203526 -13.062537  \n",
-      "1 -0.464408  1698  16.201468 -13.123804  \n",
-      "2 -0.488653  1698  16.214857 -13.366333  \n",
-      "3 -0.471780  1698  16.204531 -13.442224  \n",
-      "4 -0.469588  1698  16.203526 -13.062537  \n",
+      "          r4  ys4         z4  \n",
+      "0  16.431705   19 -19.902737  \n",
+      "1  16.420803   21 -19.880468  \n",
+      "2  15.831116   16 -10.845050  \n",
+      "3  16.157537   31 -24.723768  \n",
+      "4  15.817390   14  -7.052351  \n",
       "\n",
-      "[5 rows x 21 columns]\n",
-      "   det1      phi1  pt1        r1   trackID        z1  det2      phi2  pt2  \\\n",
-      "0    19  1.342908  791  3.215434  10000094 -6.674059   137  1.316544  791   \n",
-      "1     3  0.042201  662  3.147929  10000100 -6.337495    97  0.071146  662   \n",
-      "2     3  0.038396  662  3.150830  10000100 -6.388875    97  0.071146  662   \n",
-      "3    92 -0.362996  487  2.798809  10000109  0.902371   308 -0.413166  487   \n",
-      "4    92 -0.362996  487  2.798809  10000109  0.902371   308 -0.413166  487   \n",
+      "[5 rows x 25 columns]\n",
+      "   det1      phi1   pt1        r1   trackID  ys1        z1  det2      phi2  \\\n",
+      "0    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
+      "1    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
+      "2    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
+      "3    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
+      "4    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
       "\n",
-      "         r2    ...      det3      phi3  pt3         r3         z3  det4  \\\n",
-      "0  6.533934    ...       392  1.287120  791  10.669562 -23.916361  1605   \n",
-      "1  6.962423    ...       320  0.107237  662  11.064787 -24.246792  1599   \n",
-      "2  6.962423    ...       320  0.107237  662  11.064787 -24.246792  1599   \n",
-      "3  7.010263    ...       644 -0.463537  487  11.100529   1.352261  1195   \n",
-      "4  7.010263    ...       644 -0.463537  487  11.100529   1.352261  1196   \n",
+      "    pt2    ...       pt3         r3  ys3        z3  det4      phi4   pt4  \\\n",
+      "0  1100    ...      1100  11.037732    8 -5.367550  1283 -2.449416  1100   \n",
+      "1  1100    ...      1100  11.037732    8 -5.367550  1530  2.865597  1100   \n",
+      "2  1100    ...      1100  11.066208   21  0.744545  1283 -2.449416  1100   \n",
+      "3  1100    ...      1100  11.066208   21  0.744545  1530  2.865597  1100   \n",
+      "4  1100    ...      1100  10.719485   22  0.355225  1283 -2.449416  1100   \n",
       "\n",
-      "       phi4  pt4         r4         z4  \n",
-      "0  1.260540  791  14.376982 -32.514194  \n",
-      "1  0.133849  662  14.090476 -31.091267  \n",
-      "2  0.133849  662  14.090476 -31.091267  \n",
-      "3 -2.994291  487   8.460827  30.964153  \n",
-      "4 -2.864020  487   5.959154  31.328598  \n",
+      "          r4  ys4         z4  \n",
+      "0  12.628432   27  31.032444  \n",
+      "1   7.301930    8 -33.531631  \n",
+      "2  12.628432   27  31.032444  \n",
+      "3   7.301930    8 -33.531631  \n",
+      "4  12.628432   27  31.032444  \n",
       "\n",
-      "[5 rows x 21 columns]\n",
-      "   det1      phi1   pt1        r1   trackID        z1  det2      phi2   pt2  \\\n",
-      "0    66 -1.615744   740  3.405559  10000002 -9.927705   256 -1.644181   740   \n",
-      "1    66 -1.615744   740  3.405559  10000002 -9.927705   256 -1.644181   740   \n",
-      "2    26  1.914170  2464  2.671153  10000010 -9.608046   160  1.903031  2464   \n",
-      "3    26  1.910948  2464  2.670602  10000010 -9.678793   160  1.903031  2464   \n",
-      "4    26  1.922151  2464  2.672636  10000010 -9.662353   160  1.903031  2464   \n",
+      "[5 rows x 25 columns]\n",
+      "   det1      phi1   pt1        r1   trackID  ys1         z1  det2      phi2  \\\n",
+      "0    50 -2.850126  3818  3.056340  10000067   47 -10.186829   216 -2.855479   \n",
+      "1    50 -2.850126  3818  3.056340  10000067   47 -10.186829   216 -2.855479   \n",
+      "2    58 -2.570249  2031  2.812866  10000096   53  -9.784139   224 -2.582103   \n",
+      "3    13  0.878464   413  2.746907  10000543   39   7.014045   126  0.923011   \n",
+      "4    13  0.878464   413  2.746907  10000543   39   7.014045   134  0.927235   \n",
       "\n",
-      "         r2    ...      det3      phi3   pt3         r3         z3  det4  \\\n",
-      "0  7.273387    ...      1567 -1.673348   740  10.764234 -33.038609  1735   \n",
-      "1  7.273387    ...      1623 -1.672782   740  10.705758 -32.855053  1735   \n",
-      "2  6.863056    ...      1583  1.898713  2464   8.804473 -33.619747  1664   \n",
-      "3  6.863056    ...      1583  1.898713  2464   8.804473 -33.619747  1664   \n",
-      "4  6.863056    ...      1583  1.898713  2464   8.804473 -33.619747  1664   \n",
+      "    pt2    ...       pt3         r3  ys3         z3  det4      phi4   pt4  \\\n",
+      "0  3818    ...      3818  10.791581   15 -32.936413  1672 -2.863041  3818   \n",
+      "1  3818    ...      3818  10.791581   15 -32.936413  1729 -2.863443  3818   \n",
+      "2  2031    ...      2031  10.528687   16 -33.342438  1674 -2.596598  2031   \n",
+      "3   413    ...       413  12.144506   11  30.916100  1380  1.044963   413   \n",
+      "4   413    ...       413  12.144506   11  30.916100  1380  1.044963   413   \n",
       "\n",
-      "       phi4   pt4         r4         z4  \n",
-      "0 -1.692311   740  13.137235 -40.457005  \n",
-      "1 -1.692311   740  13.137235 -40.457005  \n",
-      "2  1.895945  2464  10.021165 -38.362865  \n",
-      "3  1.895945  2464  10.021165 -38.362865  \n",
-      "4  1.895945  2464  10.021165 -38.362865  \n",
+      "          r4  ys4         z4  \n",
+      "0  12.287411    8 -37.328735  \n",
+      "1  12.594734   10 -38.235123  \n",
+      "2  12.152595    8 -38.289001  \n",
+      "3  15.323860   16  39.172184  \n",
+      "4  15.323860   16  39.172184  \n",
       "\n",
-      "[5 rows x 21 columns]\n",
-      "   det1      phi1   pt1        r1   trackID         z1  det2      phi2   pt2  \\\n",
-      "0    26  1.914170  2464  2.671153  10000010  -9.608046  1583  1.898713  2464   \n",
-      "1    26  1.910948  2464  2.670602  10000010  -9.678793  1583  1.898713  2464   \n",
-      "2    26  1.922151  2464  2.672636  10000010  -9.662353  1583  1.898713  2464   \n",
-      "3    42  2.955944  1148  2.702642  10000032 -10.496306  1530  2.928084  1148   \n",
-      "4    42  2.955944  1148  2.702642  10000032 -10.496306  1530  2.928084  1148   \n",
+      "[5 rows x 25 columns]\n",
+      "   det1      phi1   pt1        r1   trackID  ys1         z1  det2      phi2  \\\n",
+      "0    89 -0.019187  3565  2.810611  10000023  -68 -13.579367  1520 -0.011948   \n",
+      "1    22  1.262401   733  3.282485  10000044   87  16.099026  1188  1.233651   \n",
+      "2    49 -2.766413   854  3.070601  10000047   96 -19.082594  1532 -2.749733   \n",
+      "3    49 -2.766413   854  3.070601  10000047   96 -19.082594  1532 -2.749733   \n",
+      "4    49 -2.766413   854  3.070601  10000047   96 -19.082594  1589 -2.748735   \n",
       "\n",
-      "         r2    ...      det3      phi3   pt3         r3         z3  det4  \\\n",
-      "0  8.804473    ...      1664  1.895945  2464  10.021165 -38.362865  1776   \n",
-      "1  8.804473    ...      1664  1.895945  2464  10.021165 -38.362865  1776   \n",
-      "2  8.804473    ...      1664  1.895945  2464  10.021165 -38.362865  1776   \n",
-      "3  8.442572    ...      1642  2.919404  1148  10.328422 -42.279213  1838   \n",
-      "4  8.442572    ...      1670  2.921753  1148   9.830442 -40.189011  1838   \n",
+      "    pt2    ...       pt3        r3  ys3         z3  det4      phi4   pt4  \\\n",
+      "0  3565    ...      3565  8.741679   15 -39.608997  1766 -0.006668  3565   \n",
+      "1   733    ...       733  7.990392    8  41.015194  1412  1.205148   733   \n",
+      "2   854    ...       854  6.703705   16 -40.343700  1756 -2.728390   854   \n",
+      "3   854    ...       854  6.703705   16 -40.343700  1813 -2.727099   854   \n",
+      "4   854    ...       854  6.703705   16 -40.343700  1756 -2.728390   854   \n",
       "\n",
-      "       phi4   pt4         r4         z4  \n",
-      "0  1.890452  2464  12.449927 -47.847328  \n",
-      "1  1.890452  2464  12.449927 -47.847328  \n",
-      "2  1.890452  2464  12.449927 -47.847328  \n",
-      "3  2.910286  1148  12.075643 -49.587833  \n",
-      "4  2.910286  1148  12.075643 -49.587833  \n",
+      "          r4  ys4         z4  \n",
+      "0  10.429797    8 -47.022720  \n",
+      "1   9.857387    8  50.917034  \n",
+      "2   8.207004   15 -49.147671  \n",
+      "3   8.398738   10 -50.271732  \n",
+      "4   8.207004   15 -49.147671  \n",
       "\n",
-      "[5 rows x 21 columns]\n",
-      "   det1      phi1   pt1        r1   trackID        z1  det2      phi2   pt2  \\\n",
-      "0    35  2.637022  2080  3.098070  10000034 -4.770354   186  2.646844  2080   \n",
-      "1    43  2.640189  2080  2.788378  10000034 -4.232756   186  2.646844  2080   \n",
-      "2    27  1.656182   419  2.715456  10000037 -2.543939   155  1.604027   419   \n",
-      "3    20  1.074587   775  3.318337  10000053  4.562479   133  1.103368   775   \n",
-      "4    19  1.363694  1260  3.217331  10000077 -5.626595   146  1.380944  1260   \n",
+      "[5 rows x 25 columns]\n",
+      "   det1      phi1  pt1        r1   trackID  ys1         z1  det2      phi2  \\\n",
+      "0    50 -2.658289  891  3.121739  10000026   40 -10.357058   217 -2.740865   \n",
+      "1    50 -2.721266  891  3.087358  10000026   29  -7.173188   217 -2.740865   \n",
+      "2    58 -2.550391  891  2.800395  10000026   16 -11.385898   217 -2.740865   \n",
+      "3    58 -2.620771  891  2.850214  10000026    8 -11.932549   217 -2.740865   \n",
+      "4    66 -2.044674  712  3.363156  10000065   32  -7.485303   241 -2.070432   \n",
       "\n",
-      "         r2 ...  det4  phi4  pt4  r4  z4  det5  phi5  pt5  r5  z5  \n",
-      "0  6.528466 ...   NaN   NaN  NaN NaN NaN   NaN   NaN  NaN NaN NaN  \n",
-      "1  6.528466 ...   NaN   NaN  NaN NaN NaN   NaN   NaN  NaN NaN NaN  \n",
-      "2  6.535251 ...   NaN   NaN  NaN NaN NaN   NaN   NaN  NaN NaN NaN  \n",
-      "3  6.909348 ...   NaN   NaN  NaN NaN NaN   NaN   NaN  NaN NaN NaN  \n",
-      "4  7.170426 ...   NaN   NaN  NaN NaN NaN   NaN   NaN  NaN NaN NaN  \n",
+      "   pt2 ...  pt4  r4  ys4  z4  det5  phi5  pt5  r5  ys5  z5  \n",
+      "0  891 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
+      "1  891 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
+      "2  891 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
+      "3  891 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
+      "4  712 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
       "\n",
-      "[5 rows x 26 columns]\n",
-      "   det1      phi1   pt1        r1   trackID         z1  det2      phi2   pt2  \\\n",
-      "0    26  1.914170  2464  2.671153  10000010  -9.608046   160  1.903031  2464   \n",
-      "1    26  1.910948  2464  2.670602  10000010  -9.678793   160  1.903031  2464   \n",
-      "2    26  1.922151  2464  2.672636  10000010  -9.662353   160  1.903031  2464   \n",
-      "3    42  2.955944  1148  2.702642  10000032 -10.496306   200  2.936726  1148   \n",
-      "4    42  2.955944  1148  2.702642  10000032 -10.496306   200  2.936726  1148   \n",
+      "[5 rows x 31 columns]\n",
+      "   det1      phi1   pt1        r1   trackID  ys1         z1  det2      phi2  \\\n",
+      "0    50 -2.850126  3818  3.056340  10000067   47 -10.186829   216 -2.855479   \n",
+      "1    50 -2.850126  3818  3.056340  10000067   47 -10.186829   216 -2.855479   \n",
+      "2    85 -0.899789  1021  3.082222  10099486   56  13.192469   287 -0.920796   \n",
+      "3    85 -0.899789  1021  3.082222  10099486   56  13.192469   287 -0.920796   \n",
+      "4    85 -0.899789  1021  3.082222  10099486   56  13.192469   287 -0.920796   \n",
       "\n",
-      "         r2    ...      det4      phi4   pt4         r4         z4  det5  \\\n",
-      "0  6.863056    ...      1664  1.895945  2464  10.021165 -38.362865  1776   \n",
-      "1  6.863056    ...      1664  1.895945  2464  10.021165 -38.362865  1776   \n",
-      "2  6.863056    ...      1664  1.895945  2464  10.021165 -38.362865  1776   \n",
-      "3  6.584924    ...      1642  2.919404  1148  10.328422 -42.279213  1838   \n",
-      "4  6.584924    ...      1670  2.921753  1148   9.830442 -40.189011  1838   \n",
+      "    pt2    ...       pt4         r4  ys4         z4  det5      phi5   pt5  \\\n",
+      "0  3818    ...      3818  12.287411    8 -37.328735  1784 -2.867168  3818   \n",
+      "1  3818    ...      3818  12.594734   10 -38.235123  1784 -2.867168  3818   \n",
+      "2  1021    ...      1021   9.937539   14  38.306942  1515 -0.954581  1021   \n",
+      "3  1021    ...      1021  10.711860   12  41.138309  1515 -0.954581  1021   \n",
+      "4  1021    ...      1021   9.888903   11  38.124836  1515 -0.954581  1021   \n",
       "\n",
-      "       phi5   pt5         r5         z5  \n",
-      "0  1.890452  2464  12.449927 -47.847328  \n",
-      "1  1.890452  2464  12.449927 -47.847328  \n",
-      "2  1.890452  2464  12.449927 -47.847328  \n",
-      "3  2.910286  1148  12.075643 -49.587833  \n",
-      "4  2.910286  1148  12.075643 -49.587833  \n",
+      "          r5  ys5         z5  \n",
+      "0  15.496239    8 -46.742580  \n",
+      "1  15.496239    8 -46.742580  \n",
+      "2  12.504428    8  47.705566  \n",
+      "3  12.504428    8  47.705566  \n",
+      "4  12.504428    8  47.705566  \n",
       "\n",
-      "[5 rows x 26 columns]\n"
+      "[5 rows x 31 columns]\n"
      ]
     }
    ],
@@ -1190,10 +1411,28 @@
     "t12 = pd.merge(build(hb1,'1'),build(hb2,'2'),on='trackID')\n",
     "t23 = pd.merge(build(hb2,'1'),build(hb3,'2'),on='trackID')\n",
     "t34 = pd.merge(build(hb3,'1'),build(hb4,'2'),on='trackID')\n",
-    "t123 = pd.merge(t12,build(hb3,'3'),on='trackID')\n",
+    "\n",
+    "print 'doublets'\n",
     "print len(t12)\n",
     "print len(t23)\n",
     "print len(t34)\n",
+    "t11 = pd.merge(build(hb1,'1'),build(hf1,'2'),on='trackID')\n",
+    "print len(t11)\n",
+    "\n",
+    "t21 = pd.merge(build(hb2,'1'),build(hf1,'2'),on='trackID')\n",
+    "print len(t21)\n",
+    "t31 = pd.merge(build(hb3,'1'),build(hf1,'2'),on='trackID')\n",
+    "print len(t31)\n",
+    "\n",
+    "t12f = pd.merge(build(hf1,'1'),build(hf2,'2'),on='trackID')\n",
+    "print len(t12f)\n",
+    "t23f = pd.merge(build(hf2,'1'),build(hf3,'2'),on='trackID')\n",
+    "print len(t23f)\n",
+    "\n",
+    "lpairs = [t12,t23,t34,t11,t21,t31,t12f,t23f]\n",
+    "\n",
+    "print 'triplets and quads'\n",
+    "t123 = pd.merge(t12,build(hb3,'3'),on='trackID')\n",
     "print len(t123)\n",
     "t1234 = pd.merge(t123,build(hb4,'4'),on='trackID')\n",
     "print len(t1234)\n",
@@ -1231,12 +1470,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 256,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1246,7 +1485,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1256,7 +1495,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1278,7 +1517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 257,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1369,15 +1608,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 275,
    "metadata": {},
    "outputs": [],
    "source": [
+    "kq=0\n",
     "def plotDoublets(quad,mpt) :\n",
     "  quadc = quad[quad['pt1']>mpt]\n",
     "\n",
-    "  maxc = 1000./(mpt*87.)\n",
+    "  maxc = 1000./(mpt*87.78)\n",
     "\n",
+    "  print \" \"\n",
+    "  print \"New QUAD\",kq\n",
+    "  kq+=1\n",
+    "  print \" \"\n",
+    "    \n",
     "  print 'dphi'\n",
     "  d1 = quadc['phi2']-quadc['phi1']\n",
     "  plt.hist(d1[abs(d1)<.1], bins=100,log=True)\n",
@@ -1408,6 +1653,17 @@
     "  plt.show()\n",
     "\n",
     "\n",
+    "  print 'zinner'\n",
+    "  d1 = quadc['z1']\n",
+    "  plt.hist(d1[np.logical_and(pcut,abs(d1)<35)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "  d2 = quadc['z2']\n",
+    "  plt.hist(d2[np.logical_and(pcut,abs(d2)<35)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "  d3 = quadc['z3']\n",
+    "  plt.hist(d3[np.logical_and(pcut,abs(d3)<35)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "\n",
     "  print 'dz'\n",
     "  d1 = quadc['z2']-quadc['z1']\n",
     "  plt.hist(d1[np.logical_and(pcut,abs(d1)<35)], bins=100,log=True)\n",
@@ -1456,27 +1712,95 @@
     "  plt.hist(d3[np.logical_and(pcut,abs(d3)<.1)], bins=100,log=True)\n",
     "  plt.show()\n",
     "\n",
+    "  print 'zinner zcut'\n",
+    "  d1 = quadc['z1']\n",
+    "  plt.hist(d1[np.logical_and(pcut,abs(d1)<35)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "  d2 = quadc['z2']\n",
+    "  plt.hist(d2[np.logical_and(pcut,abs(d2)<35)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "  d3 = quadc['z3']\n",
+    "  plt.hist(d3[np.logical_and(pcut,abs(d3)<35)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "\n",
+    "  print 'module'\n",
+    "  d1 = quadc['det1']\n",
+    "  plt.hist(d1[pcut], range=[0,96], bins=96, log=False)\n",
+    "  plt.show()\n",
+    "  d2 = quadc['det2']\n",
+    "  plt.hist(d2[pcut], range=[0,2000], bins=2000, log=False)\n",
+    "  plt.show()\n",
+    "  d3 = quadc['det3']\n",
+    "  plt.hist(d3[pcut], range=[0,2000], bins=2000, log=False)\n",
+    "  plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "  norm = 1./8.\n",
+    "  print 'dysize'\n",
+    "  d1 = (quadc['ys2']-quadc['ys1'])*norm\n",
+    "  c = np.logical_and(quadc['ys2']>0,quadc['ys1']>0)\n",
+    "  plt.hist(d1[np.logical_and(pcut,c)], range=[-8,8],bins=32,log=True)\n",
+    "  plt.show()\n",
+    "  c = np.logical_and(quadc['ys2']>0,quadc['ys3']>0)\n",
+    "  d2 = (quadc['ys3']-quadc['ys2'])*norm\n",
+    "  plt.hist(d2[np.logical_and(pcut,c)], range=[-6,6],bins=26,log=True)\n",
+    "  plt.show()\n",
+    "  c = np.logical_and(quadc['ys4']>0,quadc['ys3']>0)\n",
+    "  d3 = (quadc['ys4']-quadc['ys3'])*norm\n",
+    "  plt.hist(d3[np.logical_and(pcut,c)], range=[-6,6],bins=26,log=True)\n",
+    "  plt.show()\n",
     "\n",
     "    \n",
+    "  print 'ysize'\n",
+    "  d1 = quadc['ys1']*norm\n",
+    "  plt.hist(d1[pcut], bins=30,range=[0,30],log=True)\n",
+    "  plt.show()\n",
+    "  d2 = quadc['ys2']*norm\n",
+    "  plt.hist(d2[pcut], bins=30,range=[0,30],log=True)\n",
+    "  plt.show()\n",
+    "  d3 = quadc['ys3']*norm\n",
+    "  plt.hist(d3[pcut], bins=30,range=[0,30],log=True)\n",
+    "  plt.show()\n",
+    " \n",
+    "  print 'ys-pred'\n",
+    "  th = 0.0285\n",
+    "  ptc = 0.015\n",
+    "  fac = th/ptc\n",
+    "  d1 = fac*(quadc['z1']-quadc['z2'])/(quadc['r1']-quadc['r2'])\n",
+    "  d1 = quadc['ys1']*norm - abs(d1)\n",
+    "  plt.hist(d1[np.logical_and(pcut,quadc['ys1']>0)], range=[-8,8],bins=32,log=True)\n",
+    "  plt.show()\n",
+    "  d2 = fac*(quadc['z3']-quadc['z2'])/(quadc['r3']-quadc['r2'])\n",
+    "  d2 = quadc['ys2']*norm - abs(d2)\n",
+    "  plt.hist(d2[np.logical_and(pcut,quadc['ys2']>0)], range=[-8,8],bins=32,log=True)\n",
+    "  plt.show()\n",
+    "  d3 = fac*(quadc['z3']-quadc['z4'])/(quadc['r3']-quadc['r4'])\n",
+    "  d3 = quadc['ys3']*norm - abs(d3)\n",
+    "  plt.hist(d3[np.logical_and(pcut,quadc['ys3']>0)], range=[-8,8],bins=32,log=True)\n",
+    "  plt.show()\n",
+    "\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
+   "execution_count": 259,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "delta123 227975\n",
+      "delta123 480048\n",
       "field\n"
      ]
     },
     {
      "data": {
-      "image/png": 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89cIDkmxO8uO9x28DfgF4atUqHEyX8a0H/gq4q6om4pdXR0uObQp1WaG1f9zXAX9X0/HhlIFXn51wS44vyXuBPwc+WFUtTEwWV1UT84f53uZDwDPAg8Dbe/tngc/0Hl8NPAb8S++/+8Zd95DH91Hg/4Bv9v25dNy1D2Nsve2vAaeB7zPfN33/uGtfYlwfAJ5m/n2Sm3v7fp/5cAB4E/CXwHHgn4B3jbvmIY7tZ3s/o/9h/lXKsXHXPOTxPcj8aryv/zs7OO6aR/XHT8ZKUuMmrXUjSRoyg16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9P2e28Kc0JOI2AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1488,12 +1812,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 227975\n"
+      "thcut,pzcut,dcacut 480048\n"
      ]
     },
     {
      "data": {
-      "image/png": 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0/0O/93dPs/0a4G/pLxf8G+B1HanrY814PQEcYyDIpl0X8CP0WzJPsGS5YpvjtUZdbY7XD9BfJvgE/X/fv9aF5+MadbX6fBy4/VYWB/3Q4zV48ZOxklRcF3r0kqQpMuglqTiDXpKKM+glqTiDXpKKM+glqTiDXpKKM+glqbj/A7U8xT07Yao9AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1503,7 +1827,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1513,7 +1837,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1525,12 +1849,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 227975\n"
+      "thcut2,pzcut2 480048\n"
      ]
     },
     {
      "data": {
-      "image/png": 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yNmdwS//Yb7MWHACvA/4YOAb8NfCOrmveRFseBr418B4c7LrmjbbllHMfYQ5X3Yz4noS1Yaij/czaM8nX98pYSWrcIg7dSJLGYNBLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wFnK7YC5IyS0QAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADWZJREFUeJzt3X2opOdZx/Hv1Sxb6dvpy65tTbI9KbspXRRaHVJFSqs2sFE3kVbajS0kELJoCAgiuJD/9B+rKAgN6qIlVWiTWLTumpW2iS4Bydbd0DY2CUm2qzUnxq5Re6D40oZe/jGTMl33nHlm5jnzPHPN9wMh8/LsOdfNnPmde67nfu4TmYkkqa6XdV2AJGlnGfSSVJxBL0nFGfSSVJxBL0nFGfSSVJxBL0nFGfSSVJxBL0nF7eq6AIA9e/bk+vp612VI0lJ59NFHX8jMvZOO60XQr6+vc+7cua7LkKSlEhFfa3KcrRtJKs6gl6TiDHpJKq7ToI+IwxFxfHNzs8syJKm0ToM+M09m5tG1tbUuy5Ck0mzdSFJxBr0kFWfQS1JxvbhgSuqT9WMPfPf2P/3mz3RYidQOg17ahqGvCmzdSFJxBr0kFWfQS1JxBr0kFdfpydiIOAwc3r9/f5dlSN9z0rXpMZ6c1bJwCwRJKs7WjSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnFuaibNyA3PtCwMeq2sJhdJSRXYupGk4gx6SSrOoJek4gx6SSrOoJek4joN+og4HBHHNzc3uyxDkkpzm2JJKs7WjSQVZ9BLUnFeGauVslNXw7odgvrMGb0kFWfQS1JxBr0kFWfQS1JxBr0kFWfQS1JxBr0kFWfQS1JxBr0kFWfQS1JxboGg8vwj4Fp1Br3UMve9Ud/YupGk4gx6SSrOoJek4gx6SSrOoJek4nYk6CPilRFxLiJ+die+viSpuUZBHxEfj4iLEfGVSx4/FBFPRcT5iDg29tSvAfe3WagkaTZNZ/T3AIfGH4iIK4C7gRuAg8DNEXEwIq4HngAutlinJGlGjS6YysyHI2L9koevA85n5gWAiLgXuAl4FfBKhuH/3xFxKjO/01rFkqSpzHNl7JXAs2P3N4B3ZeadABFxK/DCViEfEUeBowD79u2bowxJ0nZ2bNVNZt6TmX+1zfPHM3OQmYO9e/fuVBmStPLmmdE/B1w9dv+q0WNS5/qykZn73qgP5pnRnwUORMQ1EbEbOAKcaKcsSVJbmi6v/BTwCPC2iNiIiNsy80XgTuCzwJPA/Zn5+DTfPCIOR8Txzc3NaeuWJDXUdNXNzVs8fgo4Nes3z8yTwMnBYHD7rF9DkrQ9t0CQpOIMekkqrtOgt0cvSTuv06DPzJOZeXRtba3LMiSpNFs3klScQS9JxRn0klScJ2MlqThPxkpScfNsaib1Sl82MtuKG5ypK/boJak4g16SijPoJak4V91IUnGuupGk4mzdSFJxBr0kFWfQS1JxBr0kFWfQS1JxLq+UpOIiM7uugcFgkOfOneu6DC2hvu9v04T73mhWEfFoZg4mHWfrRpKKM+glqTiDXpKKM+glqTiDXpKKM+glqTiDXpKK84IpSSrO/eglqThbN5JU3K6uC5CmVWHbA2mRDHqpY+O/uNz3RjvB1o0kFWfQS1JxBr0kFWfQS1JxBr0kFeeVsZJUnFfGSlJxrqPXUvAiKWl2Br3UI148pZ3gyVhJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6TivGBKvbXqV8N68ZTa4oxekooz6CWpOINekopzP3pJKs796CWpOFs3klScQS9JxbmOXloCrqnXPAx69cqqXyQl7QRbN5JUnEEvScUZ9JJUnEEvScUZ9JJUnKtupCXjUktNyxm9JBVn0EtScbZu1DkvkpJ2ljN6SSrOoJek4gx6SSrOoJek4gx6SSrOVTfSEvPiKTXhjF6Simt9Rh8Rbwd+GdgDPJSZv9/299Dyc+28tDiNZvQR8fGIuBgRX7nk8UMR8VREnI+IYwCZ+WRm/iLwQeDH2y9ZkjSNpq2be4BD4w9ExBXA3cANwEHg5og4OHruRuAB4FRrlUqSZtIo6DPzYeA/Lnn4OuB8Zl7IzG8B9wI3jY4/kZk3AB9us1hJ0vTm6dFfCTw7dn8DeFdEvBd4P/BytpnRR8RR4CjAvn375ihDy8K+vNSN1k/GZuZp4HSD444DxwEGg0G2XYckaWieoH8OuHrs/lWjxyR1wDX12so8QX8WOBAR1zAM+CPAL7RSlcqwXdMNQ1/jmi6v/BTwCPC2iNiIiNsy80XgTuCzwJPA/Zn5+DTfPCIOR8Txzc3NaeuWJDXUaEafmTdv8fgp5lhCmZkngZODweD2Wb+GJGl7boEgScUZ9JJUXKdBb49eknZep9sU26OvyZU2Ur/YupGk4gx6SSrOoJek4jwZK0nFdRr0mXkyM4+ura11WYYkleYfB5eKc98b2aOXpOKc0WtmzhSl5WDQqxVeJCX1l6tuJKk4V91IUnG2bjQVWzTLbavXz3MstbnqRpKKc0Yv6Xu4mqoeZ/SSVJxBL0nFubxSkopzeaUkFefJWE3kkkppudmjl6TiDHpJKs7Wjf4fWzWrp8lr7vr65WXQLznffNpJW/0C8OduuRj0ApzFS5W5jl6SinMdvSQV56obSSrOHr2kuVx6fseTs/1j0K8AV0hIq82gX2GutJFWg0EvqVV+guwfg34JzXMRi7N4afW46kaSijPoJak4Wzc9Y39TVc3TWvS9MJ9Ogz4iDgOH9+/f32UZC7PIELcXrz7ow8+hk6eOgz4zTwInB4PB7V3WIWmxDN/FsnWzw6bd53ueYyTpcjwZK0nFOaPvMWfxktqw0kHvZkySVsFKB/12tjpZ1NZJJGfr0lBb7wVP8G7NHr0kFeeMvgFn35KWWamg96ObVJNXzM6nVNB3yV8ykvrKoJ+DLR2pn6a9CLH65MyTsZJUXNkZfZe/rZ3pS4vRt6WZff2UUDboJWlefQ3uaa3cNsXbzQCciUvayjKHvtsUS1oZq7qb7Eq0bpbtRZG0/LbLnUV/IliJoJekPll0G2jpg97ZuiRtz3X0klScQS9JxRn0klScQS9JxRn0klScQS9JxRn0klScQS9JxRn0klRcZGbXNRAR/wZ8bcZ/vgd4ocVyuuRY+qfKOMCx9NU8Y3lLZu6ddFAvgn4eEXEuMwdd19EGx9I/VcYBjqWvFjEWWzeSVJxBL0nFVQj6410X0CLH0j9VxgGOpa92fCxL36OXJG2vwoxekrSNpQv6iHh9RHw+Ip4Z/f91Wxy3LyI+FxFPRsQTEbG+2EonazqW0bGviYiNiPjYImtsqslYIuIdEfFIRDweEY9FxIe6qPVyIuJQRDwVEecj4thlnn95RNw3ev4Lffx5ekmDsfzK6D3xWEQ8FBFv6aLOJiaNZey4D0RERkQvV+I0GUdEfHD0ujweEZ9stYDMXKr/gN8Cjo1uHwM+usVxp4HrR7dfBbyi69pnHcvo+d8DPgl8rOu6Zx0LcC1wYHT7B4Dngdf2oPYrgK8CbwV2A18GDl5yzB3AH4xuHwHu67ruOcbyEy+9H4BfWuaxjI57NfAwcAYYdF33jK/JAeCLwOtG97+/zRqWbkYP3AR8YnT7E8DPXXpARBwEdmXm5wEy85uZ+V+LK7GxiWMBiIgfAd4IfG5Bdc1i4lgy8+nMfGZ0+1+Ai8DEiz0W4DrgfGZeyMxvAfcyHM+48fF9GvipiIgF1tjUxLFk5t+OvR/OAFctuMammrwuAL8BfBT4n0UWN4Um47gduDsz/xMgMy+2WcAyBv0bM/P50e1/ZRiAl7oW+EZE/HlEfDEifjsirlhciY1NHEtEvAz4HeBXF1nYDJq8Lt8VEdcxnN18dacLa+BK4Nmx+xujxy57TGa+CGwCb1hIddNpMpZxtwF/vaMVzW7iWCLih4GrM7PPfzy6yWtyLXBtRPxdRJyJiENtFtDLPw4eEQ8Cb7rMU3eN38nMjIjLLRvaBbwbeCfwz8B9wK3AH7db6WQtjOUO4FRmbnQ9gWxhLC99nTcDfwrckpnfabdKNRURHwEGwHu6rmUWo0nQ7zJ8by+7XQzbN+9l+Anr4Yj4ocz8RltfvHcy831bPRcRX4+IN2fm86PAuNxHnA3gS5l5YfRvPgP8KB0EfQtj+THg3RFxB8NzDbsj4puZueWJqZ3SwliIiNcADwB3ZeaZHSp1Ws8BV4/dv2r02OWO2YiIXcAa8O+LKW8qTcZCRLyP4S/o92Tm/y6otmlNGsurgR8ETo8mQW8CTkTEjZl5bmFVTtbkNdkAvpCZ3wb+MSKeZhj8Z9soYBlbNyeAW0a3bwH+8jLHnAVeGxEv9X9/EnhiAbVNa+JYMvPDmbkvM9cZtm/+pIuQb2DiWCJiN/AXDMfw6QXWNslZ4EBEXDOq8QjD8YwbH9/PA3+To7NmPTNxLBHxTuAPgRvb7gW3bNuxZOZmZu7JzPXR++MMwzH1KeSh2c/XZxjO5omIPQxbORdaq6DrM9IznMF+A/AQ8AzwIPD60eMD4I/GjrseeAz4B+AeYHfXtc86lrHjb6W/q24mjgX4CPBt4Etj/72j69pHtf008DTDcwZ3jR77dYbBAfB9wJ8B54G/B97adc1zjOVB4Otjr8GJrmuedSyXHHuaHq66afiaBMM21BOjzDrS5vf3ylhJKm4ZWzeSpCkY9JJUnEEvScUZ9JJUnEEvScUZ9JJUnEEvScUZ9JJU3P8BhjNTviu6OHAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1540,7 +1864,7 @@
     },
     {
      "data": {
-      "image/png": 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XAvfOq64kLwf+DHhDVQ03eqa/vWaxx3naPwz6Hz8JPAB8AnhhN30V+PPu9luA/wU+P/Tzsm7eeQz+EY8BHwbOnFdd3f1/Ap4AvsOgv+113fRPAV9iEFh/BTxvQerqe3v9dvfcx4Df6qY9F7gL+CJwD/CnTDjSBfgV4MsMWnDXd9PeyeAfD+A53d9/rNse5w2te3233v3ApVN+v2+pLuDXum3zeeBzwN451/Wz3fvovxl887lns9e077qAn+v+/77Q/b5mznV9Avga38urg7PaXh4ZK0mNW5auG0nSFhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ17v8BZM1XtbjjJ/4AAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1558,7 +1882,7 @@
     },
     {
      "data": {
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+      "image/png": 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f662sPQ/jxp+XdwF/V218QKXL3Fu25fyTvBH4E+DtVbV9Fz5V5dcEX6z1Xs8CXwUeAl41Gl8C/nT0/XuB/wX+eezrDUPXPo+5j7a/AHwD+B5rfc23DV37lPP+eeArrL3P8qHR2O+w9ssN8DLgz4EV4B+B1w9d8xzn/tOjv+P/Zu1VzIWha57z/B8Cvj72e35q6Jo3+vKTsZLUOFs3ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9H0a13bw7kaieAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1575,7 +1899,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1587,12 +1911,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 227975\n"
+      "thcut,pzcut,dcacut 480048\n"
      ]
     },
     {
      "data": {
-      "image/png": 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wd/SXC/4t8KqO1PVnzXg9ATzEQJBNuy7gR+i3ZJ5g1XLFNsdrnbraHK8foL9M8An6/79/rQvvx3XqavX9OPD4zawM+pHHa/DmT8ZKUnFd6NFLkqbIoJek4gx6SSrOoJek4gx6SSrOoJek4gx6SSrOoJek4v4fSNitZLlSgWIAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1602,7 +1926,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1612,7 +1936,7 @@
     },
     {
      "data": {
-      "image/png": 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+AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1624,12 +1948,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 227975\n"
+      "thcut2,pzcut2 480048\n"
      ]
     },
     {
      "data": {
-      "image/png": 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t4MsD/97Xde392n4GeI7VYwY39Lf9JqvBAfAG4E+B48DfAe/puuYtjOUB4GsDr8Hhrmve7FjW7PsQM7jqZsjXJKy2oZ7uZ9a+cT6/Z8ZKUuPmsXUjSRqBQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+D2GXrzgAXn6YAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1639,7 +1963,7 @@
     },
     {
      "data": {
-      "image/png": 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X1dVzbnf5fOClwL2LqivJi4E/A15TVaONntkfr3mccZ71D8P+x48CDwAfAZ7dLR8Af95dfj3wv8BnRn5e1K27iOEL8QTwfuDcRdXVXf9H4HHgmwz7217VLf8Y8HmGgfVXwDOWpK6+j9dvd/d9AvitbtnTgePA54B7gD9lypEuwC8BX2DYgruxW/YWhi88gKd1f/+J7nhcNLLvjd1+9wNXzPj5vqW6gF/pjs1ngE8D+xZc1093z6P/YvjO557NHtO+6wJ+pnv9fbb7fd2C6/oI8BW+k1eH53W8/GSsJDVuu3TdSJK2yKCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/w8eW0XhBtdajwAAAABJRU5ErkJggg==\n",
+      "image/png": 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dEeoBBifyNquu1VzwPu27rq6eXd3l3cALgXs2q64kzwfeBryqqoYPeqa/v2ZxxnnaPwz6jx8G7gM+BDyzm74I/GV3+bXA/wCfHvp5XjfvCgZPxGXgb4Bdm1VXd/3jwKPAtxn0217WTf8I8DkGgfXXwNPnpK6+99dvdbe9DPxmN+1pwBngs8DdwJ8z4UgX4OXAFxgcwd3YTXsTgycewFO7v3+52x9XDK17Y7fevcBVU368b6gu4Je6ffNp4FPAkU2u66e7x9F/MXjnc/da92nfdQE/0z3/PtP9vmGT6/oQ8GW+m1cnZrW//GSsJDVuq7RuJEkbZNBLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wP3tV5j3yyGXgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1651,13 +1975,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "delta123 40709\n",
+      "delta123 76745\n",
       "field\n"
      ]
     },
     {
      "data": {
-      "image/png": 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fj/4d3AD8XS3eh1Amvs7zAtlyrEneD/wx8JGqam3y8mZV5dcYX5zrzz4MPAs8BLxjuH0AfGp4++PA/wL/MvL1vr5rn8VYh/f/ATgDfJtzPdEf77v2bYzxJ4CvcO4Yym3Dbb/OuRAA+G7gL4CTwD8B7+275hmN84eH/3bf5NxvLCf6rnmGY30I+PrIe/NI3zXP6stPxkpS42zdSFLjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhr3/8Lgy/dFqf6HAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1669,12 +1993,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 40709\n"
+      "thcut,pzcut,dcacut 76745\n"
      ]
     },
     {
      "data": {
-      "image/png": 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/f6ML78dN4mr1/dj3+J2sTvRDz1f/zTNjJam4LvToJUnbyEQvScWZ6CWpOBO9JBVnopek4kz0klSciV6SijPRS1Jx/w9PoKgoyPcY5QAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1684,7 +2008,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1694,7 +2018,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1706,12 +2030,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 40709\n"
+      "thcut2,pzcut2 76745\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1721,7 +2045,7 @@
     },
     {
      "data": {
-      "image/png": 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n6V5JSxOu6y/L++hXan3zOdjrNa27LkmvLZ+/H5TfV0y4rtsl/VR/yKu949peHBkLAMlNS+sGADAkgh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4Akvsd953xe9B6ZmsAAAAASUVORK5CYII=\n",
+      "image/png": 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g83wzrw6Pa3t5ZqwkFTctXTeSpE0y6CWpOINekooz6CWpOINekooz6CWpOINekooz6CWpuP8FIRQNhErzqhEAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1739,7 +2063,7 @@
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1756,7 +2080,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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m/Hvg+T30Iz8WUzbMWO4HXpfkim5WzuuA+5NcluRKgCQ/CLyB+T4vjwD7u1lMO1nfQblxdsPg+N4EPNA9ByeBw91Mlr3AfuBf5lT3xYw9liRXJdkB0G097md9J2ZfhhnLZi76WptRnZcy9ji6+i/vTl8JvAr43ETV9LFHeow92C8BPgF8Hvg48OJu+SrwvoH1/hF4Fvgm6z2x13fLHwA+y3qQ/AXwoiUey7Wsh8oa8FfA5Uswlt/p6l0Dfrtb9kLgUeAzwOPAe5jzzBXgV4B/Y31L6V3dsncDb+xOv6B7jNe6x/zageu+q7vek8ANfT0Hk44F+LXu8f8U8Eng0BKM5ee698T/sP4J6/GtXmvLNg7g57u8+nT395ZJa/GbsZLUuGVp3UiSxmTQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuP8DzWb1biiM3LgAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1768,12 +2092,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 40709\n"
+      "thcut,pzcut,dcacut 76745\n"
      ]
     },
     {
      "data": {
-      "image/png": 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X9zJYJvgUg//fv9qH9+M2cXX6fhx6fD8bE33r8Rq+eWasJBXXhx69JGmGTPSSVJyJXpKKM9FLUnEmekkqzkQvScWZ6CWpOBO9JBX3f7ohknwSsz76AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1783,7 +2107,7 @@
     },
     {
      "data": {
-      "image/png": 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KLG1yXT/JSn/ff7PyzefJoWN/fVDvCeDX+lAX8NPA44P/9I8D+za5rr9hZbXWxwY/R3pyvs5YVw/O15eG/n9/k6Fg6/j9eMa6un4/rtr3GIOgn8X58spYSWpcn/voJUlTYNBLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wc+ZJI98Ol/3gAAAABJRU5ErkJggg==\n",
+      "image/png": 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ORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1793,7 +2117,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1805,12 +2129,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 40709\n"
+      "thcut2,pzcut2 76745\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1820,7 +2144,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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fo8A13eiXvcB+4J9nqGUudSW5KMkugK6Fup/Ribytqms9Z/2f9l1XV88F3fRu4LnAp7eqriTPBv4MeGlVjTd65r+/FnHGed4/jPofPwDcD7wfeGo3fwV4Uzf9CuB/gI+P/TyrW3YZoyfiKvBXwAVbVVd3+x+BLwJfZ9Tf9qJu/geBTzEKrL8AnjSQuvreX7/RPfYq8OvdvCcCdwGfBO4BXs+MI12Anwc+w6gFd1M377WMnngAT+j+/tVuf1w2tu1N3Xb3AVfO+XjfVF3AL3b75uPAx4BDW1zXT3TH0X8xeudzz0b/077rAn6ye/59ovt9/RbX9X7gC3wrr44uan/5yVhJatx26bqRJG2SQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+D+ewYjUTuU5AAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1832,13 +2156,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "delta123 152561\n",
+      "delta123 274344\n",
       "field\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1850,12 +2174,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 152561\n"
+      "thcut,pzcut,dcacut 274344\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1865,7 +2189,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1875,7 +2199,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1887,12 +2211,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 152561\n"
+      "thcut2,pzcut2 274344\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1902,7 +2226,7 @@
     },
     {
      "data": {
-      "image/png": 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fC7ycyfbX3cDBDGcY7WZ4UHP1rIvReq8GPtLtn2PANd3sl/3AQeCfJ6hlKnUluTDJLoCuQz3I8EDedtW1lnO+p33X1dVzfnd7L/B84L7tqivJc4E/B15RVaNNz/T31yyOOE/7D8Pxxw8DDwIfAp7eLR8Ab+1uvwb4H+BTI3+e0z12KcP/iCvAu4Hzt6uu7v4/Al8GvslwvO2l3fKPAJ9lGFh/CTxlTurqe3/9RvfaK8Cvd8ueDJwEPgPcC7yJCWe6AL8AfI5hB3dTt+wNDP/jATyp+/evdPvj0pFtb+q2ewC4csqf9y3VBfxSt28+BXwSOLzNdf1U9zn6L4bffO5d7z3tuy7gp7v/f5/u/r5um+v6EPAlvpNXx2a1vzwzVpIatyhDN5KkLTLoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8BL85NqVRsWW4AAAAASUVORK5CYII=\n",
+      "image/png": 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v8a28OjKt7eWRsZJU3Ly0biRJW2TQS1JxBr0kFWfQS1JxBr0kFWfQS1JxBr0kFWfQS1Jx/w+B6MwdpvNpmAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1920,7 +2244,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1937,7 +2261,7 @@
     },
     {
      "data": {
-      "image/png": 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LoZM17rcAj57+HnM1cV+6D+XfASeP0I/9XMzYKH25D3hjknO7WTlvBO5Lsj3JToAkPwhcw+a+LqOs7B7u31uBz3SvwUHgum4my27gEuCfN6ndpzJxX5Kcn7VTjtONHi9h7SBmX6ZZcX/K99qc2nkmE/eja//Z3eWdwGuAx6dqTR9HpCc4gv0y4NPAl4FPAed12wfAB4f2+wfgP4DvsFYTe1O3/TPAF1kLkj8HXrrEfbmYtVA5CvwlcPYS9OW3uvYeBX6z23YO8AjwBeAx4H1s8swV4JeAL7E2Urql2/Zu4M3d5Zd0z/HR7jm/eOi+t3T3exK4qq/XYNq+AL/SPf+fAz4L7FmCvvxs95n4b9a+YT12uvfasvUD+Lkurz7f/X/DtG1xZawkNW5ZSjeSpAkZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe5/AV3XBojqQLcuAAAAAElFTkSuQmCC\n",
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3IXSux/0a4JGJVjeaTY+le1P+I3BuD/3Ij8WEDTOW+4FXJbm4W5XzKuD+JBcl2QWQ5AeBG5jt83IC2NetYtrO6g7KtasbBsd3E/Cx7jk4BhzsVrLsAfYB/zqjus9n02NJckmSbQDd7HEfqzsy+zDMONZz3tfZlOocxqbH0o1hR3d6F/Ay4LGxqulrr/SIe7BfCHwU+BzwEeAF3fZl4J0D1/s48AzwLVZ7Ytd22z8GfJbVIPkr4HkLPJYrWA2VFeCvgR0LMJbf6updAX6z2/Zc4GHgM8CjwNuY8coV4JeBf2d1pvTmbttbgFd3p5/TPcYr3WN+xcBt39zd7gngur6eg3HHAvxK9/h/CvgkcGDOx/Fz3fvhv1n96+rRjV5nizgW4Oe7vPp09/OWcWvxk7GS1LhFad1IkjbJoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXH/C8I+AoKYMCAnAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1949,12 +2273,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 152561\n"
+      "thcut,pzcut,dcacut 274344\n"
      ]
     },
     {
      "data": {
-      "image/png": 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39P9Nt/d3e7P9cuCv6J4u+JfA61tS1x804/U48BA9QTbpuoAfotuSeZwtpyuWHK8d6io5Xt9L9zTBx+n+fP9yG16PO9RV9PXYc/+NbA76gcer98tPxkpS5drQo5ckTZBBL0mVM+glqXIGvSRVzqCXpMoZ9JJUOYNekipn0EtS5f4XQ/rAOorkC+cAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1964,7 +2288,7 @@
     },
     {
      "data": {
-      "image/png": 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FTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1974,7 +2298,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1986,12 +2310,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 152561\n"
+      "thcut2,pzcut2 274344\n"
      ]
     },
     {
      "data": {
-      "image/png": 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acAD8EPBnwEngb4F3dV3zFsbyIPDNgdfg3q5r3uxY1p37EHO462bE1ySstaGe6mfWwUl+fz8ZK0mNW8TWjSRpDAa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+1+SsZAVNVVDpAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2001,7 +2325,7 @@
     },
     {
      "data": {
-      "image/png": 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vA+5Mcl6SPQBJvhd4JZPtr7uAAxmcYXQ+g4Oaa8+6GK73auCj3f45ClzTnf2yHzgA/NMEtUylriR7k+wC6EaoBxgcyNuqutZz1ue077q6ei7obu8BXgjct1V1JXk+8CfAq6pqeNAz/f01iyPO0/5h0H/8CPAg8GHgmd30JeBPu9uvB/4b+PTQz/O6eZcy+EVcAf4SuGCr6uru/wPwFeBbDPptL++mfxT4HIPA+nPgaXNSV9/769e6x14BfrWb9lTgbuCzwL3AHzHhmS7AzwOfZzCCu6mb9hYGv3gAT+n+/yvd/rh0aN2buvUeAK6c8ut9U3UBv9jtm08DnwKWt7iun+xeR//J4J3PvRs9p33XBfxU9/v3me7f67e4rg8DX+a7eXV0VvvLT8ZKUuO2S+tGkrRJBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY37XzGYYfRQASKlAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2013,13 +2337,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "delta123 400905\n",
+      "delta123 747101\n",
       "field\n"
      ]
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2031,12 +2355,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 400905\n"
+      "thcut,pzcut,dcacut 747101\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2046,7 +2370,7 @@
     },
     {
      "data": {
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+      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2056,7 +2380,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2068,12 +2392,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 400905\n"
+      "thcut2,pzcut2 747101\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2083,7 +2407,7 @@
     },
     {
      "data": {
-      "image/png": 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sjs3DwGeB1W2u68eb99F/Mfjm89hmr2nXdQE/0fz9fa75fe0213UP8BW+nVeHZnW8vDJWkoqbl64bSdIWGfSSVJxBL0nFGfSSVJxBL0nFGfSSVJxBL0nFGfSSVNz/Aala2/6PgVtuAAAAAElFTkSuQmCC\n",
+      "image/png": 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u4Keaz99nm+/Xz7iu+4Cv8O28OjSt4+WVsZJU3KJ03UiStsigl6TiDHpJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6Ti/g8ZyAV1FGSsNwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2101,7 +2425,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2118,7 +2442,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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TurXb9hHg3d3p87rHeLl7zC8buO2t3e2eAq7r6zkYdSzAb3WP/5eBLwH7Z3wcv9S9H/6HlU9Xj6/3OpvHsQC/3OXVV7qfN49ai9+MlaTGzUvrRpK0SQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z8xXWc7rL3UfAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2130,12 +2454,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 400905\n"
+      "thcut,pzcut,dcacut 747101\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2145,7 +2469,7 @@
     },
     {
      "data": {
-      "image/png": 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+m/6Hf9eK5Z1urzXq6np77Rq6vRc4Mrjd9edxtbrm4vM4WP8wbwzGTn17TfwXmuUP8HPA1wb/qW8bLPtD+nsxAGcDf0l/sOKfgXcNPfe2wfOeBq6eh7qADwJHgceAR4G9G1zXj9Hv9/03/d98jg4991cH9R4DfmUe6gJ+Anh88J/+ceDGDa7r74D/HPx7PQbcOyfb65R1zcH2+uTQ/+8vMxRsHX8eT1lX15/HFeseZhD0s9heHhkrSY2b5x69JGkKDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhr3/0VWnHTOeYC3AAAAAElFTkSuQmCC\n",
+      "image/png": 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CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2155,7 +2479,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2167,12 +2491,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 400905\n"
+      "thcut2,pzcut2 747101\n"
      ]
     },
     {
      "data": {
-      "image/png": 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d17zZsazZ9wF6uOpmzPckrLahnhhm1pWz/P6eGStJjVvE1o0kaQIGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjft/x5ul8uciO5UAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2182,7 +2506,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2198,7 +2522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 260,
    "metadata": {},
    "outputs": [
     {
@@ -2210,7 +2534,7 @@
     },
     {
      "data": {
-      "image/png": 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gOYIeAJL7H1ewN1NLFFeGAAAAAElFTkSuQmCC\n",
+      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2220,7 +2544,7 @@
     },
     {
      "data": {
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yLCRJTYZFz40rXcACrGs81jUe6xrfrNY29bo8ZyFJarJnIUlqWtVhkeQVSe5N8rXu/5cv0O53knwnyecH5m9K8uUke5PcluTIbv5R3fTebvnGKdX13q7N15K8t5t3TJKH+/59K8lHu2XvS/LNvmU/v1x1dfN3Jnm8b//Hd/NX8ngdnWRHkq8m2Z3kI33tF3W8kmzp7ufeJFcNWb7g/U3yi938x5O8fdRtTrOuJOck2ZXkT7r/z+5bZ+hjukx1bUzy/b5939C3zk919e5N8vEkWca6Lhp4Dr6Q5A3dsuU4Xm9O8lCS55O8Z2DZQs/NJR8vqmrV/gOuBa7qbl8F/MoC7d4KbAU+PzD/s8AF3e0bgA91t/85cEN3+wLgtknXBbwC2N/9//Lu9suHtNsFvLm7/T7gE9M8XoerC9gJnDZknRU7XsDRwD/q2hwJ/D5w7mKPF7AO2Ae8utveV4CTR7m/wMld+6OATd121o2yzSnXdSrwN7rbpwAH+9YZ+pguU10bgUcX2O4fAW8CAvy3Q4/pctQ10ObvAvuW+XhtBP4ecDPwnhGfm0s6XlW1unsWwHnAb3a3fxP4x8MaVdUXgO/2z+uS92zgjiHr92/3DuCtYyb1KHW9Hbi3qr5dVf8buBfYMlDjTwLH03sBnISJ1NXY7rIer6r6y6q6D6CqngMeAjaMse9BpwN7q2p/t71bu/oWqrf//p4H3FpVz1bVnwJ7u+2Nss2p1VVVf1xV/7Obvxv48SRHjbn/ide10AaTvAr4a1X1h9V7JbyZBZ7by1DXhd26k9Ksq6q+XlWPAC8MrDv0OTCh47Xqw+Inquob3e0/A35ijHWPBb5TVc930weAE7rbJwBPAnTLn+7aT7KuF/cxZP+HHHq303+VwruTPJLkjiQnjlHTpOr6ja77/W/6nlgzcbySvIxeD/ILfbPHPV6jPC4L3d+F1h1lm9Osq9+7gYeq6tm+ecMe0+Wqa1OSP07y35Oc2df+QGOb067rkH8K3DIwb9rHa9x1J3G8OGLcFWZNkt8F/vqQRb/UP1FVlWTZLv1aprouAH6ub/ou4JaqejbJB+m9Kzq7f4Up13VRVR1Mcgzw211tN4+y4rSPV5Ij6D2pP15V+7vZzeO1liT5O8CvAG/rm73ox3QCvgH8zar68yQ/BXyuq3EmJHkj8JdV9Wjf7JU8XlM192FRVT+90LIk/yvJq6rqG11X7KkxNv3nwMuSHNG9q9gAHOyWHQROBA50L0Lru/aTrOsgcFbf9AZ646GHtvF64Iiq2tW3z/4aPkVvrP9HTLOuqjrY/f/dJJ+h16W+mRk4XvSuQ/9aVX20b5/N47XAfvp7IP1/F4NtBu/v4dZtbXOadZFkA3AncHFV7Tu0wmEe06nX1fWYn+32vyvJPuAnu/b9Q4nLfrw6FzDQq1im43W4dc8aWHcnkzleq34Yajtw6IqA9wL/ddQVuz/U+4BDVxv0r9+/3fcAvzcwFDSJuu4B3pbk5eld/fO2bt4hFzLwh9q9kB6yDdgzRk1LqivJEUmO6+p4KfBO4NA7rhU9XkmuofdE/4X+FRZ5vB4ATkrvSrkj6b1gbD9Mvf33dztwQXpX2WwCTqJ34nGUbU6trm54bge9iwjuP9S48ZguR12vTLKu2/+r6R2v/d2Q5F8keVM3zHMxYzy3l1pXV89LgPPpO1+xjMdrIUOfAxM6Xqv+aqhj6Y1Pfw34XeAV3fzTgE/1tft94JvA9+mN5729m/9qek/mvcDtwFHd/B/rpvd2y189pbou6faxF3j/wDb2A68dmPcf6J2g/Aq9oHvtctUF/BV6V2Y90tXwMWDdSh8veu+iil4QPNz9+/mlHC/gHcD/oHfVyi918/4dsK11f+kNq+0DHqfvipRh21zE3/ui6gJ+Gfhe3/F5mN6FEws+pstU17u7/T5M78KErX3bPI3eC/E+4BN0HzBejrq6ZWcBfziwveU6Xv+A3uvU9+j1dHa3XjMmcbz8BLckqWm1D0NJkibAsJAkNRkWkqQmw0KS1GRYSJKaDAtJUpNhIUlqMiwkSU3/F9Kc0e/Ju8HDAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2230,7 +2554,7 @@
     },
     {
      "data": {
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JRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2247,7 +2571,44 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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CrzLfkvw48PvAVcC/An9dVe8dHmtijABJfgz4PWAPcLyqPjrjLvViuAf0TWzezfEbwEeAzwMPA6ts3kn2p6pq6wnbhZHkR4C/AP4WeGPY/Ots1umbGWdXBr0kNc7SjSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/wuA0ArMshzpuAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2257,7 +2618,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADblJREFUeJzt3X+o3fddx/Hny2j7x4ZXXcoc+WEiCbIowuDQCfpHwZWl27LMoTPRPzYcDRUrCoKmVuhfA0WQMVcZFxKywWgoU2euS+l+4Ih/OE06RNPW6KU6mlKbdMWroFhK3/5xT+fpXe6959xzTr7nfM7z8U/v+ZyTc9+f3OZ1Puf9/ZzPTVUhSWrX93RdgCRpugx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuO+t+sCAHbv3l0HDhzougxJmitPPfXUy1V113aPm4mgP3DgAFeuXOm6DEmaK0m+NczjbN1IUuMMeklq3FSCPslbklxJ8oFpPL8kaXhDBX2Ss0luJLm6YfxokmtJVpOcHrjrd4DHJ1moJGlnhl3RnwOODg4k2QU8CtwHHAFOJjmS5F7gGeDGBOuUJO3QULtuqupSkgMbhu8GVqvqOYAk54HjwFuBt7Ae/v+T5GJVvT6xiiVJIxlne+Ue4PmB29eBd1fVgwBJPga8vFnIJzkFnALYv3//GGVIkrYytV03VXWuqv5yi/uXq6pXVb277tp2v78kaYfGWdG/AOwbuL23PybNrAOnv/Sdr//t99/fYSXS7TNO0F8GDic5yHrAnwB+aZQnSHIMOHbo0KExypC2Nhjum40PE/qbPc+wfGFRV1JV2z8oeQy4B9gNvAQ8UlVnkrwP+CSwCzhbVZ/YSRG9Xq88AkHjGjeI37BZIE/q+bf6HtIokjxVVb3tHjfsrpuTm4xfBC6OWJs0lkkGblc2m8MwLzK+SGhUnR5qZutGW+kq0LsM1RZexDR7Og36qloBVnq93v1d1qHZYdBJk+ehZpLUuJk4j16Lx57zZPj3qGHYo5caYehrM/boddvYf5e6YetGUzXv4T6v9bu61yAvxkpS4+zRa+LmdRXcKlf3skcvLRBDfzHZupGkxnkxVhNhu0aaXQa9tKBs4ywOL8Zqx1zFd8O/d42q0x59Va1U1amlpaUuy5Ckptm6UedcoXbPNk7bDHqNxFCW5o/bKyWpca7oJb2JbZz2uOtG27JdI803d91IUuPs0UtS4+zRS9qU/fo2uKKXpMa5otd38eKr1BZX9JLUuE6DPsmxJMtra2tdliFJTXN7pSQ1ztaNJDXOi7ECvAArtcyglzQU99TPL1s3ktQ4g16SGmfQS1LjDHpJapwXYyWNzAuz88UVvSQ1zt8wtcDcOy8tBo9AkKTG2aOXNJaN7wzt2c8ee/SS1DiDXpIaZ9BLUuPs0S8Yd9pIi8cVvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxEw/6JO9M8pkkX0jyq5N+fknSaIYK+iRnk9xIcnXD+NEk15KsJjkNUFXPVtUDwEeAn558yZKkUQy7oj8HHB0cSLILeBS4DzgCnExypH/fB4EvARcnVqkkaUeGCvqqugS8smH4bmC1qp6rqleB88Dx/uMvVNV9wC9PslhJ0ujGOdRsD/D8wO3rwLuT3AN8GLiTLVb0SU4BpwD2798/RhmSZpW/RHw2TPz0yqr6OvD1IR63DCwD9Hq9mnQdkqR14wT9C8C+gdt7+2OaMR5NLC22cbZXXgYOJzmY5A7gBHBhlCdIcizJ8tra2hhlSJK2MtSKPsljwD3A7iTXgUeq6kySB4EngV3A2ap6epRvXlUrwEqv17t/tLIlzSrfQc6eoYK+qk5uMn4Rt1BK0kzr9AgEWzeSNH2d/s5YWzfT49tnSW/wUDNJapxBL0mNs0cvSY3rNOiraqWqTi0tLXVZhiQ1rdOLsZIWh+fedMcevSQ1zh69JDXOHr0kNc7WjSQ1zouxDfHTsJJuxRW9JDXOi7GS1DgvxkpS4+zRS7rt/PDU7WWPXpIaZ9BLUuMMeklqnLtuJKlx7rqRpMbZupGkxhn0ktQ499HPOc+3kbQdV/SS1DiDXpIa5/ZKSWqc2yslqXG2biSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN81CzOeRBZmqJvyh8+jwCQZIa1+mKvqpWgJVer3d/l3VImg2u7qfDHr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnB+YmhN+SErSTrmil6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY2byvbKJB8C3g98P3Cmqr48je8jqV2eZDk5Q6/ok5xNciPJ1Q3jR5NcS7Ka5DRAVX2xqu4HHgB+cbIlS5JGMcqK/hzwaeBzbwwk2QU8CtwLXAcuJ7lQVc/0H/J7/fslacdc3Y9n6BV9VV0CXtkwfDewWlXPVdWrwHngeNb9AfBEVX1zcuVKkkY17sXYPcDzA7ev98d+HXgP8PNJHrjVH0xyKsmVJFdu3rw5ZhmSpM1M5WJsVX0K+NQ2j1kGlgF6vV5Now5J0vgr+heAfQO39/bHJEkzYtygvwwcTnIwyR3ACeDCsH84ybEky2tra2OWIUnazNCtmySPAfcAu5NcBx6pqjNJHgSeBHYBZ6vq6WGfs6pWgJVer3f/aGUvBo8mljQJQwd9VZ3cZPwicHFiFUmSJqrTIxBs3UjS9HUa9FW1UlWnlpaWuixDkprmoWaS1DiDXpIaZ49ekhpnj16SGmfrRpIaZ9BLUuPs0UtS46ZyeuWwPALhu3nsgaRJs3UjSY0z6CWpcZ22bhaNv/dSUhc6Dfokx4Bjhw4d6rKMThj6km4XPzAlSY2zRy9JjTPoJalxBr0kNc6gl6TGuetG0lxxx9ro3HUjSY3zA1OS5tZmZ0O50n8zg16SNtFKm8igH1ErP3hJi8OgnwEeTSxpmtxeKUmNM+glqXHuo5ekKdjYku3ymp776CWpcbZuJKlx7rqRtPBa3zZt0E9I6/+jSJpftm4kqXEGvSQ1ztaNJI1hHtq2Bv0m5uGHJ0nDMOgHeOaMpGnpcvHoJ2MlaUCLCz4/GStJjbN1I6k5XmN7M4N+ylp8GyhpvriPXpIaZ9BLUuNs3UhaGIvau3dFL0mNW7gV/U5e0b2gKrWny3/Xt/udhSt6SWrcwq3oJ8mVvjT7/Hfqil6SmueKXpJGNG/vElzRS1LjmlrRb/Yqu9lV7Xl7VZaknZj4ij7JjyY5k+QLk35uSdLohgr6JGeT3EhydcP40STXkqwmOQ1QVc9V1cenUawkaXTDtm7OAZ8GPvfGQJJdwKPAvcB14HKSC1X1zKSLHNftbtHYEpIW06z+2x9qRV9Vl4BXNgzfDaz2V/CvAueB4xOuT5I0pnF69HuA5wduXwf2JHlbks8A70ry0GZ/OMmpJFeSXLl58+YYZUiStjLxXTdV9W3ggSEetwwsA/R6vZp0HZKkdeOs6F8A9g3c3tsfkyTNkHGC/jJwOMnBJHcAJ4ALozxBkmNJltfW1sYoQ5K0lWG3Vz4G/A3wY0muJ/l4Vb0GPAg8CTwLPF5VT4/yzatqpapOLS0tjVq3JGlIQ/Xoq+rkJuMXgYsTrUiSNFGdnnVj60aSpq/ToLd1I0nT5+mVktQ4g16SGmePXpIaZ49ekhpn60aSGmfQS1LjOv1VgkmOAccOHTq04+eY1fOfJbVlnrPGHr0kNc7WjSQ1zqCXpMYZ9JLUOD8wJUmN82KsJDXO1o0kNc6gl6TGGfSS1LhUVdc1kOQm8K2u65iw3cDLXRdxGyzCPJ1jG1qc449U1V3bPWgmgr5FSa5UVa/rOqZtEebpHNuwCHPcjK0bSWqcQS9JjTPop2e56wJuk0WYp3NswyLM8Zbs0UtS41zRS1LjDPoJS/KHSf4pyT8k+fMkPzBw30NJVpNcS/LeLuscR5JfSPJ0kteT9Dbc18QcAZIc7c9jNcnpruuZlCRnk9xIcnVg7IeSfCXJv/T/+4Nd1jiuJPuS/FWSZ/r/r/5Gf7ypeQ7LoJ+8rwA/UVU/Cfwz8BBAkiPACeDHgaPAnyTZ1VmV47kKfBi4NDjY0hz7dT8K3AccAU7259eCc6z/fAadBr5WVYeBr/Vvz7PXgN+qqiPATwG/1v/5tTbPoRj0E1ZVX66q1/o3vwHs7X99HDhfVf9bVf8KrAJ3d1HjuKrq2aq6dou7mpkj63WvVtVzVfUqcJ71+c29qroEvLJh+Djw2f7XnwU+dFuLmrCqerGqvtn/+r+AZ4E9NDbPYRn00/UrwBP9r/cAzw/cd70/1pKW5tjSXIbx9qp6sf/1vwNv77KYSUpyAHgX8Lc0PM+tdPrLwedVkq8CP3yLux6uqr/oP+Zh1t8+fv521jYpw8xRbaqqStLEdrwkbwX+FPjNqvrPJN+5r6V5bseg34Gqes9W9yf5GPAB4Gfr//evvgDsG3jY3v7YTNpujpuYqzluo6W5DOOlJO+oqheTvAO40XVB40ryfayH/Oer6s/6w83Ncxi2biYsyVHgt4EPVtV/D9x1ATiR5M4kB4HDwN91UeMUtTTHy8DhJAeT3MH6ReYLHdc0TReAj/a//igw1+/asr50PwM8W1V/NHBXU/Mclh+YmrAkq8CdwLf7Q9+oqgf69z3Met/+NdbfSj5x62eZbUl+Dvhj4C7gP4C/r6r39u9rYo4ASd4HfBLYBZytqk90XNJEJHkMuIf10xxfAh4Bvgg8Duxn/STZj1TVxgu2cyPJzwB/Dfwj8Hp/+HdZ79M3M89hGfSS1DhbN5LUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG/R+8KWhzUv2CKAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2267,7 +2628,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2284,7 +2645,7 @@
     },
     {
      "data": {
-      "image/png": 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g7QyvP3wWeO/oYYsdV1X5tcEvhhcinwK+NPq6a+y+2xnW384A+7vu6xRj+zmG9cJvAd8ATjY0tncwXCX1VYZlqs77NOU47gOeAf539P/qMPAqhitSvgJ8Gnhl1/2cYlw/zfAi5ZfHfrfe0fexAa8H/n40rseA3x+1v5bhZOks8JfA1Yvqg5+MlaTGWbqRpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe7/APGZuuhLhQeZAAAAAElFTkSuQmCC\n",
+      "image/png": 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FvNY1rq8neWVVPd21HM/1XdCoJC9kGPJ3V9Wfd8NzXfN5VfWtJA8AP8awpfuCbla/qT4jzc3oV5Nk98jD/cBXuvtHgV/sVt+8Dlge+VrZqyR7gd8A3lpV/zny1FHgQJLLk+wCdgOf76PGNZjXmife/7hnR4F3d/ffDfxVj7X8H0kC3Ak8UVV/MPLUPNd81fnVbUm+F3gjw3MLDwDv6A6bq5ovqe+zwRt5YzirOAk8BhwDttXzZ9lvZ9iH+ztGVor0fWN4wvIM8MXudsfIc7d2NZ8Cbui71pG6fo5hD/PbwNeB+zdBzW9iuCLka8CtfdezSp33AE8D/939O74ReDnwNwyvAPsZ4GV91zlS708wbMs8NvIZftOc1/wjwN92NZ8E3t+Nv4rhxOQ08KfA5X3XOu7Nv4yVpMYtVOtGkhaRQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+B1VowwgZxoj2AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2294,7 +2655,7 @@
     },
     {
      "data": {
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JZSMPdwNf7e4fAH6jW33zNuD0yJ+TvUqyE/h94Nqq+u+Rpw4Ae5JckGQbcBnwpT5qXIF5rnniPZJ7dgD4QHf/A8Df91jL90kS4JPA0ar685Gn5rbuJBe/ssotyQ8B72J4bOEB4P3dy+aj5r6PBs/TjeFs4gjwBHAQ2FSvHl2/lWH/7Z8ZWSnS943hAcsTwOPd7baR527qaj4GXN13rSN1/RLD3uV3gG8B9897zV1t72a4GuQbwE1913OGOu8CngP+t/v/fB3wRuAfga8DXwDe0Hedi2r+WYZtmSdGvsvvnue6gZ8E/qmr+QjwkW78UoYTlOPAXwMX9F2rZ8ZKUuNs3UhS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa9387EHRRm944HQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2304,7 +2665,7 @@
     },
     {
      "data": {
-      "image/png": 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jvz72Lsle4FeAm6rqP0eeOgYcSHJpkp3AbuBTfdS4SvNe98R7IPfsGPDO7v47gb/usZYlJQnwAeBUVf3eyFNzXXuS17y46i3JtwFvZnh84ePAO7qXzV3d/0ffR4NnfWM4ezgJPAkcB7bWS0fS72TYa/sHRlaIzMON4cHKs8AT3e2ukedu6+o+DdzQd62L6v4Jhv3KrwFfAh7cCHV39b2F4UqQzwO39V3PReq8D3gO+O/u//Uh4NXA3wKfAz4GvKrvOpeo+wcZtmWeHPlev2Xeawe+F/j7ru6TwHu78WsYTlbOAH8GXNp3rcvdPDNWkhrXfOtGkjY7g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9Dzr/joDv4jcOAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2321,7 +2682,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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/Phj+lyQvrapvDkqXF7uLcOJeBdyc5PXA84AXAn9Ev2z7nMGsvpnPxNyUbqrqq1X1Y1W1v6r20/+z7BVV9S36a93+yqD75pXA6po/YZuT5BD9P1lvrqrvrnnoDHAkyXOTHKB/cvpvu4hxG4y85vGsGtSiPwI8UlV/sOahM8DbBl+/DfjEdse2Xarq9qraM8gFR4C/qaq3Ap8D3jTYrZlj0NyM/gqdBV5P/+Tjd4F3dBvOxN0JPBf4zOCvmy9U1a1VdT7JaeBh+iWdd1bV9zqMc2Kq6ukkl9Y83gGc3OqaxzPsVcAvA19N8neDsfcC7wdOJzkKfAN4c0fxdek9wKkkvwN8mf4vxJnnlbGS1Li5Kd1I0rwy0UtS40z0ktQ4E70kNc5EL0mNM9FLUuNM9JLUOBO9JDXufwEQNUWzPE+eZAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2331,7 +2692,7 @@
     },
     {
      "data": {
-      "image/png": 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7G4PumzcB54Y+0jYjyS76H1+vq6rvDr10DNiX5NIk2+lflP67LmJcBRM/L3neDOrRtwOPVdWfDr10DHj34Ot3A59f7dhWQ1XdVFWbB3/v9wF/U1XvAr4KvGOw25o9f2f003MCeCv9i5DfBd7bbTgz8zHgUuCewaeaB6rqhqo6leQo8Cj9ks77qup7HcY5M1X1QpIXn5e8ATg87vOS59CbgV8Hvpnk7wdjHwJuAY4mOQB8G/jVjuLrygeBI0n+EPg6/X8M1xzvjJWkxlm6kaTGmeglqXEmeklqnIlekhpnopekxpnoJalxJnpJapyJXpIa9/+Civ9GfpGBBAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2341,7 +2702,7 @@
     },
     {
      "data": {
-      "image/png": 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2VdUu+n/evbmqvkN/r9xfG6y+eQuwvOJP37mWZB/9P3OvrarvrnjqOHAgycVJdtO/EP23XdQ4BWPvhzwPBr3p24HHquoPVjx1HHjf4Ov3AZ/b6NqmpapurKrtg5/hA8BfV9V7ga8AvzI4rKlzBmf0k3ISeDv9C5LfBT7QbTkTdQtwMXDv4C+ZB6rq+qo6neQu4FH6LZ0PVtX3OqxzYqrqhSQv7oe8BTg66n7Ic+JK4FeBbyb5u8HYR4GPA3cluQ74NvDujurbSB8BjiX5PeDr9H8BNsNPxkpS42zdSFLjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhr3fwON9xaIAgQWAAAAAElFTkSuQmCC\n",
+      "image/png": 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dU1XnkpwEnmexpfP+qvqfDuscm6q6muQI8BiwATheVec6LmsSbgN+Efhqkr/pb/sQcD9wMskhFq8s++6O6ltLHwROJPlt4MssfgA2wTNjJalxtm4kqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjftfKpPCMK2qZHwAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2358,7 +2719,7 @@
     },
     {
      "data": {
-      "image/png": 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bZ27jv8N7Nvexv9q/vmb0PwQ8nORbLN9k/CtV9UCS305yy2if48BVSc6zfFT7roHU9WngSuAvknwjyUQ3RJ9hXX3pUttDwMtJnmZ5Zv3LVTXrGWqXuj4BHE3yTeBelkO/lzMIBzD2u9TVx9hf00DG/+sMYOy/vqaexrUkaU5679FLkmbLoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXH/C+4ViryBxesyAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2368,7 +2729,7 @@
     },
     {
      "data": {
-      "image/png": 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HbL2Pgy4frc/ofxG4L8mjwAlWe813J/njJFfPQLZ7gReSPMHqrPr3qmoaM9Mu2T4JXJfku8DtrP6x9/buu3XZbgHOS3IK+ARwQ1+54DXZPge8Gfj7JN9JcqzHaOuzDcZAxkGXbIMaBxvxnbGS1LjWZ/SSNPcs9JLUOAu9JDXOQi9JjbPQS1LjLPSS1DgLvSQ1zkIvSY37f5DCLBtcsnhNAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2378,7 +2739,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2395,7 +2756,7 @@
     },
     {
      "data": {
-      "image/png": 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Vel4i6Z2qjM+Psb36WfM10FB7TfSsm1eqOx77uKTPSzqvLO9IurWy3pckLUv6rrrjV1eU5Rep+0JckvQ3kraX5T9c7i+Vxy8aUV2/XY6xJOm3evZxRtLre5b9mboX076m7knq9eOqS9KPqjsD6OFSw4clbWm7vdTtvYS6If5Q+ffeYdpL0q9J+nd1Z0d8oCz7E0lXb/TzqjsUdVrSY6rMfFhrn5v4fd9UXZI+KOnblfZ5SN2L/H2f0zHVdW057kPqXkRfqOyzo26Inpb0EZU/3BxHXeWxSyV9pWd/42qvX1Q3p76t7juMUxtlRhPtxV/GAkBykzx0AwBoAEEPAMkR9ACQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMn9H0gmxHtpwsLcAAAAAElFTkSuQmCC\n",
+      "image/png": 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S9HeStpblP1zuL5XHL2mort8tx1iS9DsD+zgj6Q0Dy/5S/ZNpX1H/TeoNs6pL0nnqzwB6tNTw15I2td1e6vdeQv0Qf6T8e9c07SXp1yT9u/qzI95blv25pOvW+3nVH4o6LemUKjMfVtvnBL/vE9Ul6U8kfbvSPo+of5J/6HM6o7puKMd9RP2T6IuVffbUD9HTkj6o8sXNWdRVHrtC0hcG9jer9vpF9XPq2+p/wji5XmbU0V58MxYAkuvy0A0AoAYEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAk938tJLf/VuucHQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2405,7 +2766,7 @@
     },
     {
      "data": {
-      "image/png": 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CHgCSI+gBIDmCHgCS+y298kVc/m/4GgAAAABJRU5ErkJggg==\n",
+      "image/png": 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l6eKyfF7SXZX1PifpeUkvqjd+dU1Zfpl6b8QlSX8uaVtZ/upyfancfllDdf1quY8lSb/St4+zkt7St+yP1DuY9kX1PqTeMq66JF2o3gygx0oNH5G0pe32Uq/3EuqF+KPl3/s2016Sfk7Sv6s3O+KDZdkfSLp+vcer3lDUGUlPqjLzYbV9buD1vqG6JP2upK9X2udR9Q7yD3xOx1TXjeV+H1XvIPpCZZ/z6oXoGUl3qPxwcxx1ldv2Svp83/7G1V4/qV5OfV29bxin18uMOtqLX8YCQHJdHroBANSAoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5P4PnPWrle9Wd0QAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2415,7 +2776,7 @@
     },
     {
      "data": {
-      "image/png": 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vTs+6eaWq8djHJX1O0vll+byk22rrfUnSOUnfUTV+dWVZfomqN+KypL+UtKMs/8Fyf7k8fsmI6vqt8hzLkn6zZx+nJb2uZ9mfqjqZ9lVVv6ReN666JP2wqhlAD5caPixpW9vtpar3EqpC/KHy791baS9JvyLp31XNjvhAWfbHkq7Z6PWqGoo6Jekx1WY+rLXPTfy8b6ouSR+U9K1a+zyk6iR/32M6prquK8/7kKqT6Au1fc6rCtFTkj6i8sXNcdRVHtsn6cs9+xtXe/28qpz6lqpPGCc3yowm2otvxgJAcl0eugEANICgB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4Dk/g+axaPsZKDlwwAAAABJRU5ErkJggg==\n",
+      "image/png": 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/VB6/eEx1/UZ5jiVJv96zj9OS3tyz7E9VHUz7kqoPqTdPqi5J56qaAfRYqeHPJW1qu71U9V5CVYg/Wv69ZyPtJemXJP27qtkR7yvL/ljSdWv9vKqGok5JOqnazId++1zH7/u66pL0B5K+WWufR1Ud5B/4mk6orhvK8z6q6iD6Ym2fC6pC9JSkO1RO3JxEXeWxXZK+0LO/SbXXz6rKqW+q+gvjxFqZ0UR7cWYsACTX5aEbAEADCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASO7/AMd8p829TqlsAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2427,12 +2788,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dphi\n"
+      "zinner zcut\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2442,7 +2803,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2452,7 +2813,7 @@
     },
     {
      "data": {
-      "image/png": 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6/diHJX1O0nlleUfSRyvrfVHS45J+qm7/6vKy/EJ1X4jLkv5R0ray/FfK9eVy+4Vjquud5T6WJf1xzz5OS3phz7K/Vvdk2lfVfZN64aTqkvRr6s4AeqDU8LeStjR9vNQdvYS6IX5/+feuUY6XpN+T9N/qzo54f1n2l5Ku2OjxqtuKOiXppCozH9ba5yZ+3jdVl6QPSPpx5fjcr+5J/r7P6YTquqrc7/3qnkRfrOyzo26InpJ0g8ovbk6irnLbHklf6tnfpI7Xb6ubUz9W9xPGiY0yo47jxW/GAkBybW7dAABqQNADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHL/D9vOrjY7IlfxAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2464,12 +2825,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dphiNor\n"
+      "module\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2479,7 +2840,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2489,7 +2850,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADbpJREFUeJzt3X+M5PVdx/Hnu4fQROuW9mqLwHUhS2qx/0g2tNbGEFvNSVmorSbwjyUiKzUY/cuQ1GjiP4Iak5Ki5AKkralQpRZv7RFobQn/CHIQ4ArX2oPQcBfstTZZbUxa0bd/zPfqdG9nb3Z3vvP9znuej2Rz8+N7O+/5zOxrPvP+fuY7kZlIkup6TdcFSJLaZdBLUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQVd1bXBQDs3bs3FxcXuy5DkmbKk08++e3MfNOZtutF0C8uLnL48OGuy5CkmRIR3xhnu05bNxGxEhEH1tfXuyxDkkrrNOgzcy0zVxcWFrosQ5JKc2esJBVn0EtScQa9JBVn0EtScQa9JBVn0EtScb34wJTmw+Itn//B6ZdufX+HlUjzxRm9JBVn0EtScQa9JBVn0EtScQa9JBXnqhvtmKtoZsNuHicf4xqc0UtScc7otaU+zuj6WFPfDI+R5IxekopzRq9OOCufDPvvGodBL6Cff/R9rEmaRQa9JsJQ7p59eY1i0Os0uw0MA2cy2njxHOex8fGrx6CXNBbftc0ug16dcwZ5Zm2HrI9BbS6vlKTinNFrbM762ucYqw3O6CWpOGf00oxx1q/tMug1Eww3aecMeknb5lLL2WLQSx0wKDVNBr00Jbaf1JWJr7qJiLdHxJ0RcX9EfGTSv1+StD1jzegj4h7gKuBkZr5j6PL9wMeAPcBdmXlrZh4FboqI1wCfAv5q8mVLdTjTV9vGbd18Avg4g+AGICL2AHcAvwgcB56IiIOZ+XxEXA18BPjryZarSTJgNAnub+i/sYI+Mx+NiMUNF18OHMvMFwEi4j7gGuD5zDwIHIyIzwN/M7lypR9myEhntpudsecDLw+dPw68MyKuAD4InAMcGvWfI2IVWAXYt2/fLsqQJG1l4qtuMvMR4JExtjsAHABYXl7OSdchSRrYTdCfAC4cOn9Bc5nUCds40uZ2s7zyCeCSiLgoIs4GrgUOTqYsSdKkjBX0EXEv8M/A2yLieETckJmvAjcDDwFHgb/NzOfaK1WStBPjrrq5bsTlh9hih6skqXudHo8+IlYi4sD6+nqXZUhSaZ0e6yYz14C15eXlG7usQ2qLH0pTH/gNU5JUnEEvScV5mOI5YytBmj/O6CWpuE5n9BGxAqwsLS11WYYK8lOy0v9z1Y3KM/Q172zdSFJxBr0kFWfQS1JxBr0kFWfQS1JxLq+UJswPpalvOp3RZ+ZaZq4uLCx0WYYklWbrRpKK81g30i7ZqlHfOaOXpOIMekkqzqCXpOIMekkqzi8Hl6TiXEcvScW5vFJzxWPTax7Zo5ek4gx6SSrOoJek4gx6SSrOoJek4gx6SSrOoJek4vxkrCQV5ydjJak4WzeSVJxBL0nFeawbaUweJ0ezyhm9JBVn0EtScbZuirLNIOkUg17SxDjB6CdbN5JUnEEvScV5CARJKq7THn1mrgFry8vLN3ZZh+aT/WTNC1s3klScq24knN2rNoO+kOGwUrsca80Sg34Msz7bM5Sk+WaPXpKKM+glqTiDXpKKM+glqTiDXpKKK7XqZhqrY2Z9BY6k+VMq6CvzBUbSTtm6kaTiDHpJKs6gl6TiPB69JBXXadBn5lpmri4sLHRZhiSVZutGkooz6CWpOINekooz6CWpOINekooz6CWpOINekorzoGaSWrHxu4o9GF93nNFLUnEGvSQVZ9BLUnH26CWV4JfzjOaMXpKKc0YvbWHjyhFpFhn0Dd/2SarKoJekFvRp8miPXpKKM+glqThbN5KmbrttjT61QWZRp0EfESvAytLSUpdlnKYvKy36Uoek2eaXg0tScbZupA18JzVdu2nL+FiNx52xklScQS9JxRn0klScQS9JxRn0klScQS9JxRn0klSc6+gl9UbfDnXQt3p2yhm9JBVn0EtScQa9JBVn0EtScQa9JBVn0EtScS6vlDRTPDTx9jmjl6TiDHpJKs7WTQuqfJpOmiRbLt0x6CVpDLM8gZu7oJ/lB0uaJ74DmBx79JJUnEEvScUZ9JJUnEEvScXN3c7YCtyhLGk7nNFLUnEGvSQVZ9BLUnEGvSQV585YSZqQvn6ad+JBHxEfAN4P/Dhwd2Y+POnbkCSNb6ygj4h7gKuAk5n5jqHL9wMfA/YAd2XmrZn5APBARJwL/Dlg0Esqq6+z+GHj9ug/AewfviAi9gB3AL8MXApcFxGXDm3yB831kqQOjRX0mfko8J0NF18OHMvMFzPz+8B9wDUxcBvwYGY+NdlyJUnbtZse/fnAy0PnjwPvBH4HeB+wEBFLmXnnZv85IlaBVYB9+/btoox+mIW3b5Lm08R3xmbm7cDtY2x3ADgAsLy8nJOuQ5I0sJt19CeAC4fOX9BcJknqkd0E/RPAJRFxUUScDVwLHJxMWZKkSRl3eeW9wBXA3og4DvxRZt4dETcDDzFYXnlPZj7XWqWS1JLqR4QdK+gz87oRlx8CDu30xiNiBVhZWlra6a+QpKmbtcUXnR4CITPXgLXl5eUbu6xD0nyoPnMfZa6PdTONV+V5fWJJ6o+5Dvo+mrW3hJL6z8MUS1JxBr0kFddp0EfESkQcWF9f77IMSSrNVTdT5I5ZSV2wdSNJxbnqpgdcaSOpTc7oJak4Z/QzzncDks7EGb0kFefySkkqrtOgz8y1zFxdWFjosgxJKs0evaTS3I9lj16SyjPoJak4WzeS1LKuD39i0O+CvT9Js8DWjSQVZ9BLUnGdtm4iYgVYWVpa6rIMSXNonlqvfmBKkoqbi52x8/TKLUkb2aOXpOLmYkYvab74Lv6HOaOXpOIMekkqzqCXpOIMekkqzm+YkqTi/MCUJBXn8kpJmqKNSz+ncdhig74jrvOVNC3ujJWk4srO6J0xS9KAM3pJKm7mZ/TO3CVpa87oJak4g16SijPoJak4g16SivPLwSVpSMUFHh7rRpKKs3UjScUZ9JJUnEEvScUZ9JJUnEEvScUZ9JJUnEEvScUZ9JJUXGRm1zUQEd8CvrHD/74X+PYEy5kU69qevtYF/a3NuranYl1vzcw3nWmjXgT9bkTE4cxc7rqOjaxre/paF/S3Nuvannmuy9aNJBVn0EtScRWC/kDXBYxgXdvT17qgv7VZ1/bMbV0z36OXJG2twoxekrSFmQv6iPiziPhqRDwbEZ+LiNeP2G5/RHwtIo5FxC1TqOvXIuK5iPjfiBi5Bz0iXoqIIxHxdEQc7lFd0x6vN0TEFyLi682/547Y7n+asXo6Ig62WM+W9z8izomIzzTXPx4Ri23Vss26ro+Ibw2N0W9Oqa57IuJkRHxlxPUREbc3dT8bEZf1pK4rImJ9aLz+cEp1XRgRX46I55u/x9/dZJv2xiwzZ+oH+CXgrOb0bcBtm2yzB3gBuBg4G3gGuLTlut4OvA14BFjeYruXgL1THK8z1tXReP0pcEtz+pbNHsfmuu9OYYzOeP+B3wbubE5fC3ymJ3VdD3x8Ws+nodv9eeAy4Csjrr8SeBAI4F3A4z2p6wrgHzsYr/OAy5rTrwP+dZPHsrUxm7kZfWY+nJmvNmcfAy7YZLPLgWOZ+WJmfh+4D7im5bqOZubX2ryNnRizrqmPV/P7P9mc/iTwgZZvbyvj3P/heu8H3hsR0YO6OpGZjwLf2WKTa4BP5cBjwOsj4rwe1NWJzHwlM59qTv8ncBQ4f8NmrY3ZzAX9Br/B4BVwo/OBl4fOH+f0Qe1KAg9HxJMRsdp1MY0uxuvNmflKc/rfgDeP2O61EXE4Ih6LiLZeDMa5/z/YpplorANvbKme7dQF8KHmrf79EXFhyzWNq89/gz8bEc9ExIMR8dPTvvGm7fczwOMbrmptzDr9cvBRIuKLwFs2ueqjmfkPzTYfBV4FPt2nusbwnsw8ERE/AXwhIr7azEK6rmvitqpr+ExmZkSMWv711ma8Lga+FBFHMvOFSdc6w9aAezPzexHxWwzedfxCxzX12VMMnlPfjYgrgQeAS6Z14xHxY8Bngd/LzP+Y1u32Mugz831bXR8R1wNXAe/Nprm1wQlgeGZzQXNZq3WN+TtONP+ejIjPMXh7vqugn0BdUx+viPhmRJyXma80b09Pjvgdp8brxYh4hMFMaNJBP879P7XN8Yg4C1gA/n3CdWy7rswcruEuBvs++qCV59RuDYdrZh6KiL+MiL2Z2foxcCLiRxiE/Kcz8+832aS1MZu51k1E7Ad+H7g6M/9rxGZPAJdExEURcTaDnWetrdgYV0T8aES87tRpBjuWN10dMGVdjNdB4MPN6Q8Dp73ziIhzI+Kc5vRe4OeA51uoZZz7P1zvrwJfGjHJmGpdG3q4VzPo/fbBQeDXm5Uk7wLWh1p1nYmIt5zatxIRlzPIwLZfsGlu827gaGb+xYjN2huzae993u0PcIxBH+vp5ufUSoifBA4NbXclgz3bLzBoYbRd168w6Kl9D/gm8NDGuhisnnim+XmuL3V1NF5vBP4J+DrwReANzeXLwF3N6XcDR5rxOgLc0GI9p91/4I8ZTCgAXgv8XfP8+xfg4rbHaMy6/qR5Lj0DfBn4qSnVdS/wCvDfzfPrBuAm4Kbm+gDuaOo+whYr0aZc181D4/UY8O4p1fUeBvvnnh3KriunNWZ+MlaSipu51o0kaXsMekkqzqCXpOIMekkqzqCXpOIMekkqzqCXpOIMekkq7v8AzOvy8FxwrjQAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2501,12 +2862,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dz\n"
+      "dysize\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2516,7 +2877,7 @@
     },
     {
      "data": {
-      "image/png": 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3navM4ccZtGWeG/r78O45nMePAH/XzeMo8JFu/BoGBzongL8ALu+71qryylhJat1Mt24kSeMz6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJatz/AIoF96c76AzBAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2526,7 +2887,7 @@
     },
     {
      "data": {
-      "image/png": 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pbh4ZK0mNa6p1I0n6dga9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+1927Aald66VwgAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2538,12 +2899,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z0\n"
+      "ysize\n"
      ]
     },
     {
      "data": {
-      "image/png": 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GVpu377PKUzpT/UngIPAssBfYXK+feb+LQW/unxhaMdJz3iMMesjPdF93Dz12W5f3MHBV31m7TD/HoD/4beAbwCOznLfLdTWDlSFfA27rO88p8j0AvAz8b/f/9kYGPdl/AL4KfA54W985h/L+BIO2zLNDr9urZzUz8KPAl7q8B4GPduMXMZiMHAH+Gji776ynyP5eXl91s6q8fjJWkhrXZOtGkvQ6C70kNc5CL0mNs9BLUuMs9JLUOAu9JDXOQi9JjbPQS1Lj/g8g4yJebxxhCgAAAABJRU5ErkJggg==\n",
+      "image/png": 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gl6TGGfSS1DiDXpIa93+1hg7c/hFsgAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2553,7 +2914,7 @@
     },
     {
      "data": {
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+      "image/png": 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ngbOX/9Sm9yDwwe7nDwJ/PcVYNuzbSbHzM8zIfeke/H0SOF9Vf7jqrZm6L+v1YxbvS5KtSd7U/fydrEwkOc9Kwn9/d9hE7snMzroB6KZU/RGwBTheVb8z5ZA2JMnbWRnFw8r2jn8xS33p9hS+mZVV+J4Dfgv4LHAK2MnKyqS3VdWmftC5Tj9uZqU8UMDTwC+uqnFvWkneDTwKfBV4pWv+DVbq2zNzXy7Tj3lm7L4k+SFWHrZuYWWQfaqqfrv7/T8JvAX4B+Dnq+p/xnrtWU70kqQrm+XSjSSpBxO9JDXORC9JjTPRS1LjTPSS1DgTvSQ1zkQvSY0z0UtS4/4XYvTAnKh2PYgAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2563,7 +2924,7 @@
     },
     {
      "data": {
-      "image/png": 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1Z9BLUnP/B0R9pRypO5dvAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2575,12 +2936,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dr\n"
+      "ys-pred\n"
      ]
     },
     {
      "data": {
-      "image/png": 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h78I1nH1i79c4fXL3wa4ytsja+TnVNuvYPp2dVy2P68TnVdUR/48Dj0bEtxl9aPzXMvOhiPi9iLih2edzwFuBv4mIb0bERB8kP8ecfdEm6yPAyxHxDKNR9m9kZhej0zZZPwXcHBHfAu5n9EegF1c7juW8B7goIo4zWvlxW3fJztTDc2pDPT2vzmra88ordyWpmKojfkkqy8IvScVY+CWpGAu/JBVj4ZekYiz8klSMhV+SirHwS1Ix/w+ip7lYpgNDtgAAAABJRU5ErkJggg==\n",
+      "image/png": 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63lSpqr5YVd9TVXNVNUf/P8JLL0bIryTJNvpv/V9bVf/VcTkPA9cm2ZzkSvo3yg51XBPp/3a+HThRVb/XdT3nVdU7qmrj4GdqF/DXayDkGfxcn05y3WDoBuCxDks67yvAK5I8a/BvegMzvEl8yczoV7AHeN/gpsb/AHs7rue8g8DBJMeAJ4Ff6niWutZ9AHgG8MnBu43PVNXNXRRSVeeS7AMeoL8i4mBVHe+iliVeCfwi8MUknx+MvbOqjnRY01p3C3Dn4Bf2KeCXO66HQRvpXuBR+m3KzzHDT8j6yVhJalwrrRtJ0gUY9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe7/AKuo8V1uQpjdAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2590,7 +2951,7 @@
     },
     {
      "data": {
-      "image/png": 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8mOQphrPp36qqWc1Gu2b8NHA4yXeAOxn+Mvd6Nt6qfF8BLkxyEvgUcHN/yYZW5fsi8FPA3yX5dpKjPUZ7zZyNlXOas7HyY+ZhnHhmrCQ1rvUZvSRtexZ6SWqchV6SGmehl6TGWeglqXEWeklqnIVekhpnoZekxv0/sh0SU+kooDwAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2600,7 +2961,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2612,12 +2973,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dphi zcut\n"
+      "dphi\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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9ACT3G5aRDUDx7RP2AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2627,7 +2988,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2637,7 +2998,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2649,12 +3010,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dphi\n"
+      "dphiNor\n"
      ]
     },
     {
      "data": {
-      "image/png": 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ASI6gB4DkCHoASI6gB4DkCHoASI6gB4Dk/g/qSlbDuxX4IAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2664,7 +3025,7 @@
     },
     {
      "data": {
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ASI5EDwDJkegBILn/A1+faVKu7M6RAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2674,7 +3035,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2686,12 +3047,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dphiNor\n"
+      "zinner\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2701,7 +3062,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2711,7 +3072,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2728,7 +3089,7 @@
     },
     {
      "data": {
-      "image/png": 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4D+YFdhp4/+Dx+4Gl+USW/tD9PuB8Vf3l0EvL1aeq8mvoi/5f7nPAk8A6sG/QHvqzIr4G/DPQm3esI/TpIv3675cHX/cOvXbXoE8XgCPzjrVjf36Ffg37O8A3gEeWuT9Dsb+d/qyor9EvUc09pjH68CDwPPC/g5/RbcDr6M9M+SrwWeC1845zhP78Iv0JGU8O/f68fdn65J2xktQ4SzeS1DgTvSQ1zkQvSY0z0UtS40z0ktQ4E70kNc5EL0mNM9FLUuP+H5fryiYDDDwiAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADWZJREFUeJzt3V+I5eddx/H3x9XmQuP4J6GWTdZGdhNcRSgMiaAXASvdGDdbi6279aJqyBIwUkGQjRF6VVQEkdhUGciSCiEh+Kdm6Za0BkO8SDWbIpp0XV2ikg2xSRtcRcEQ+/ViTpvJdGf3zJxz5nfO97xfN5nznDNnnn2GfOY539/z/J5UFZKkvr5l6A5IkmbLoJek5gx6SWrOoJek5gx6SWrOoJek5gx6SWrOoJek5gx6SWruW4f84UkOA4evvvrqu2688cYhuyJJC+e55577SlVde6XXZR5ugbC6ulpnzpwZuhuStFCSPFdVq1d6naUbSWpu0KBPcjjJ2sWLF4fshiS1NmjQV9Wpqjq+srIyZDckqTVLN5LUnEEvSc1Zo5ek5qzRS1Jzlm4kqblBd8Zqsb37xGe+8fW//vbtA/ZE4/J3tpzm4hYI+/fvH7Ib2oaNQaH55e9JGw0a9FV1Cji1urp615D90OUZGtJis3SjqbAkIM0vL8ZKUnPO6KUmtlti81PY8nDDlCQ154YpSWrO0o2+yaSrbCwJSPPFi7GS1JxBL0nNWbqRZLmtOYNeWmDuWtY4XF4pSc15rxsBs5sZWhKQhufFWElqzhq9pLfxU1g/zuglqTmDXpKaM+glqTmDXpKa82KstGDcJKXtcsOUJDXnhqkl5sxQWg6WbiRtyTX1PRj02jWGhjQMV91IUnMGvSQ1Z9BLUnMGvSQ1Z9BLUnMGvSQ15/LKJeMmKe2Uy2MXlzN6SWrOoJek5qZeuknyg8BHgWuAJ6vqD6f9M6RlY8lNkxhrRp/kZJJXkzy/qf1QknNJzic5AVBVZ6vqbuBDwI9Nv8uSpO0Yt3TzEHBoY0OSPcADwG3AQeBYkoOj5+4APgOcnlpPJUk7MlbQV9XTwOubmm8GzlfVi1X1BvAocGT0+ser6jbg57d6zyTHk5xJcua1117bWe8lSVc0SY1+L/DShscXgFuS3Ap8ALiKy8zoq2oNWANYXV2tCfqhBeRSPWn3TP1ibFU9BTw17feVJO3MJMsrXwau3/D4ulHb2DxKUJJmb5IZ/bPAgSQ3sB7wR4EPb+cNPEpwd7g0T1pu4y6vfAR4BrgpyYUkd1bVm8A9wBPAWeCxqnphdl2VJO3EWDP6qjq2RftpJlhCmeQwcHj//v07fQtJ0hUMeguEqjpVVcdXVlaG7IYktebdKyVtm8tjF8ugM3pX3UjS7Fm6kaTmLN005ZJKSV9n6UaSmht0Ru+GKWlrfirTtHjClCQ1Z9BLUnPW6CWpOWv0kiay+VqCG6jmj6UbSWrOoJek5gx6SWrOoJek5lx1I0nNuepGg/OWt9JsWbqRpOYMeklqzqCXpOYMeklqzlU3ktScq24a8f7lki7FowQlTZXLZeePNXpJas6gl6TmDHpJas6gl6TmDHpJas6gl6TmDHpJam7QdfRJDgOH9+/fP2Q3NEeWfQ22m940C4PO6KvqVFUdX1lZGbIbktSaO2MXnDNASVdijV6SmjPoJak5g16SmrNGLw3M6yyaNWf0ktScQS9JzRn0ktScQS9JzRn0ktTcTFbdJHk/cDvwncCDVfW5WfwcSfNt2e9dNC/GntEnOZnk1STPb2o/lORckvNJTgBU1aer6i7gbuDnpttlSdJ2bKd08xBwaGNDkj3AA8BtwEHgWJKDG17ym6PnJUkDGTvoq+pp4PVNzTcD56vqxap6A3gUOJJ1vwN8tqq+OL3uSpK2a9Ia/V7gpQ2PLwC3AL8CvBdYSbK/qv5o8zcmOQ4cB9i3b9+E3Vgu7qSUtB0zuRhbVfcD91/hNWvAGsDq6mrNoh+SpMmD/mXg+g2Prxu1jcUTpnQ5rtiQpmPSoH8WOJDkBtYD/ijw4XG/uapOAadWV1fvmrAf0kKx/KbdNHbQJ3kEuBW4JskF4GNV9WCSe4AngD3Ayap6YSY9lbTQ/IQ2nLGDvqqObdF+Gji9kx9u6UaSZs/DwSWpOe91I0nNDXrClKUbjcv6rrRzlm4kqTlLN5LU3KBBn+RwkrWLFy8O2Q1Jas3SjSQ1Z+lGkpoz6CWpOYNekprzYqwkNTfohinvXql55Oas2XOMd9egQa/xeVtbSTtl0M8xw30xbTVb9fepoRj00gwZ7poHXoyVpObcGStJzVm6kabAEo3mmRumJKk5g16SmrN0MwfcPLI9jpe0PR4lKO2QdXktCm+BoPb8BKBlZ41ekpqzRq+l5Uxfy8KgnzPWfSVNm6UbSWrOGb3asBSzmPy9zZ4zeklqzhm9WvJah/QWZ/SS1Jw7YwfijHPx+DvbXdbup8edsVpo0wpfQ1ydWaOXLsM/AItjnLN6x/lk0PGThEEvaW5s9w+rf4jH48VYSWrOGb2kudexnLKbDHotFT/qLwd/z29n6UaSmjPoJak5g16SmjPoJak5g16Smpv6qpskPwDcB6xU1c9O+/0lLTdX1GzfWDP6JCeTvJrk+U3th5KcS3I+yQmAqnqxqu6cRWclSds37oz+IeATwB9/vSHJHuAB4CeBC8CzSR6vqi9Nu5OLwA0dkubVWDP6qnoaeH1T883A+dEM/g3gUeDIlPsnSZrQJDX6vcBLGx5fAG5J8r3Ax4H3JLm3qn7rUt+c5DhwHGDfvn0TdGN3OXOXtGimfjG2qr4K3D3G69aANYDV1dWadj8kSesmWV75MnD9hsfXjdrGluRwkrWLFy9O0A1J0uVMEvTPAgeS3JDkHcBR4PHtvEFVnaqq4ysrKxN0Q5J0OeMur3wEeAa4KcmFJHdW1ZvAPcATwFngsap6YXZdlSTtxFg1+qo6tkX7aeD0Tn9418PBxznSTJJ2y6C3QLB0I0mz571uJKm5QU+Y6lq6kbSc5nWfjaUbSWrO0o0kNWfQS1Jz1uglLb3uS5+t0UtSc5ZuJKk5g16SmrNGv4Vx1sOOU9frXvuTNP+s0UtSc5ZuJKk5g16SmjPoJak5L8ZKWhrbXRwxyaKMze1D3uTMi7GS1JylG0lqzqCXpOYMeklqzqCXpOYMeklqbuGXV261tGmezmuUpI12+2xZl1dKUnOWbiSpOYNekpoz6CWpOYNekpoz6CWpOYNekpoz6CWpuYXfMLUVD+WWNE27vclpmtwwJUnNWbqRpOYMeklqzqCXpOYMeklqzqCXpOYMeklqzqCXpOYMeklqzqCXpOYMeklqbur3ukny7cAngTeAp6rq4Wn/DEnS+Maa0Sc5meTVJM9vaj+U5FyS80lOjJo/APxJVd0F3DHl/kqStmnc0s1DwKGNDUn2AA8AtwEHgWNJDgLXAS+NXvZ/0+mmJGmnxgr6qnoaeH1T883A+ap6sareAB4FjgAXWA/7sd9fkjQ7k9To9/LWzB3WA/4W4H7gE0luB05t9c1JjgPHAfbt2zdBN2bPe9tLWmRTvxhbVf8N/OIYr1sD1gBWV1dr2v2QJK2bpLTyMnD9hsfXjdrGluRwkrWLFy9O0A1J0uVMEvTPAgeS3JDkHcBR4PHtvIEnTEnS7I27vPIR4BngpiQXktxZVW8C9wBPAGeBx6rqhdl1VZK0E2PV6Kvq2Bbtp4HTO/3hszwcXJK0zsPBJak517lLUnODBr2rbiRp9izdSFJzqRp+r1KS14B/G7ofwDXAV4buxMAcA8cAHANYjDH4/qq69kovmougnxdJzlTV6tD9GJJj4BiAYwC9xsCLsZLUnEEvSc0Z9G+3NnQH5oBj4BiAYwCNxsAavSQ154xekpoz6IEkv5vkH5P8fZI/T/JdG567d3Qm7rkk7xuyn7OU5INJXkjytSSrm55bljG41BnI7V3qTOgk35Pk80n+efTf7x6yj7OU5Pokf5XkS6P/Bz46am8zBgb9us8DP1xVPwL8E3AvwOgM3KPAD7F+Zu4nR2fldvQ86we7P72xcVnG4DJnIC+Dh9h0JjRwAniyqg4AT44ed/Um8GtVdRD4UeCXR7/7NmNg0ANV9bnRbZcBvsBbZ94eAR6tqv+tqn8BzrN+Vm47VXW2qs5d4qllGYOtzkBub4szoY8Anxp9/Sng/bvaqV1UVa9U1RdHX/8X67dd30ujMTDov9kvAZ8dfX2pc3H37nqPhrUsY7As/85xvbOqXhl9/e/AO4fszG5J8m7gPcDf0GgMpn5m7LxK8pfA913iqfuq6i9Gr7mP9Y9xD+9m33bLOGMgbVZVlaT98rwk3wH8KfCrVfWfSb7x3KKPwdIEfVW993LPJ/kF4KeBn6i31pxOfC7uPLnSGGyh1RhcxrL8O8f15STvqqpXkrwLeHXoDs1Skm9jPeQfrqo/GzW3GQNLN6yvtgB+Hbijqv5nw1OPA0eTXJXkBuAA8LdD9HFAyzIGE5+B3MzjwEdGX38EaPuJL+tT9weBs1X1exueajMGbpgCkpwHrgK+Omr6QlXdPXruPtbr9m+y/pHus5d+l8WW5GeAPwCuBf4D+Luqet/ouWUZg58Cfh/YA5ysqo8P3KVdMToT+lbW79b4ZeBjwKeBx4B9rN9Z9kNVtfmCbQtJfhz4a+AfgK+Nmn+D9Tp9izEw6CWpOUs3ktScQS9JzRn0ktScQS9JzRn0ktScQS9JzRn0ktScQS9Jzf0/LXmKvH+qDZEAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2738,7 +3099,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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JPuB3gTdW1X+NPHUMOJjk0iS7gD3A5/uocUKLXvfU+yH37Bjwtu7+24C/7bGWH5AkwIeAU1X15yNPLXrdVzyz4i3JDwOvZ3h84TPAW7qXLVzdwJZbdfNR4CTwCHAc2F7PHk2/lWG/7R8YWSGyCDeGByvPAF/sbreNPHdTV/djwLV917qi7l9m2LP8LvBN4N7NUHdX3xsYrgb5GnBT3/VcpM67gCeB/+n+rQ8DLwX+Dvgq8GngJX3XuaLmn2HYlnlk5Dv9hk1Q908Af9/VfRJ4Tzf+CoYTldPAXwGX9l3ryptnxkpS47ZU60aStiKDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0vBTZ481aZ9EYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2748,7 +3109,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2765,7 +3126,7 @@
     },
     {
      "data": {
-      "image/png": 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7Rj+/D/jcpe7bSlJVN1fV+lEm7Qb+uqp+Afgy8O7RblM5T02N6JfpOPB2hhcXvw18oN/u9O5W4HLg3tGnnwer6qaqOpnkCPAYw5LOB6vqOz32s1dV9WKSl9ZMXgMcHHfN5IZdA/wi8PUkfzdq+yjwceBIkr3AN4H39NS/le4jwOEkvw18leEfzYn4zVhJatxclG4kaZ4Z9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe7/AP6b8FRr+EHUAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2775,7 +3136,7 @@
     },
     {
      "data": {
-      "image/png": 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8R6rqzwbD/5LkxVX1tUEJ8mJ3EU7cK4CbkrwG+AHgecD76JdbnzWY1Tfz87Z0s05V9eWq+qGq2lNVe+h/vHtZVX2d/pq5vzjovnk5sDL00XeuJTlA/2PuTVX17aGHTgCHkjw7yV76J6L/posYp2DsdZHnwaA2/UHgXFX9/tBDJ4A3D75+M/CxzY5tWqrq9qraOXgPHwL+qqreBHwGeMNgt2aO2Rn9ZJ0CXkP/hOS3gbd2G85E3QU8G/jU4JPM56rqlqo6m+Q48Aj9ks7bq+o7HcY5MVX1VJKn10XeBhxd77rIc+IVwC8AX07yt4OxdwN3AMeTHAG+Cryxo/g207uAY0l+E/gi/T+Ac88rYyWpcZZuJKlxJnpJapyJXpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGmeil6TG/S+/Ny2KD/bNCwAAAABJRU5ErkJggg==\n",
+      "image/png": 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1B4EvrHffpq2qbquqLYPf9QPAX1TVB4CvAu8ZHDbz5z63I/o1OgG8g/6FyO8AH+62O1PxGeBy4MHBJ5rHqurmqjqV5BjwNP2Szkeq6rsd9nMqqupCklf2Qt4E3DUHeyFfD/wi8M0kfz1o+zjwSeBYkkP0V5d9b0f968LHgKNJfhP4Ov0/hDPLO2MlqXGWbiSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+x/IIfOJwV+OmQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2785,7 +3146,7 @@
     },
     {
      "data": {
-      "image/png": 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4CfgV4BtJ/qY39jHgXuBIkn3At4H3d1TfWvkocDjJ7wJfY+nNrxl+M1aSGmfrRpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wWNWZ/J8aC8BgAAAABJRU5ErkJggg==\n",
+      "image/png": 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u34eBO4EHk+wHvgm8q6f61tKHgMNJfgf4Ckv/ADbBO2MlqXG2biSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z8rGZ8QpQmjFgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2802,7 +3163,7 @@
     },
     {
      "data": {
-      "image/png": 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X9uXAQeBq4CjwJ0leN9mUL+iQ9yhwoqr2Ae8C/mzwuk9UkuuAc1X1yKSfe1jDZJ2GfWy7vNO4n+3E3BX651TVfwD3AS8ZVSZ5B/BR4Pqq+t6ks220SdbXAG8C7k/yDeCtwMlpOFC0xWu7Cpysqv+rqn8F/pm1wt+rLfIeA+4arPMg8ErWrn8yaW8Drh/8nu8E3p7kzzes8/w9nJO8nLVW07cnGXKgS9Zp2se2yzu1+9lQqmpu/gF7gdcNvv5h4O+B6zas8xbgX4CD0551w/r3s3aAa2rzslZIPzf4+hLWWg0XT3HeLwDvH3z9RtZ69On5fXE1cM8Flv8S8OnB1zeydhC5t5zbZJ2Kfaxr3g3r9Lqf7fTfvI3ofwS4L8nXWbu5+Rer6p4kv5Xk+sE6HwdeDfx1kq8mGeqG5xPOOk265L0X+HaSx1gbQf9KVfUx6uya98PA8SRfA+5grehPzRmGG7J+Frg4yVngQ8At/SV7qSndxzY1xfvZjnhmrCQ1bt5G9JI0dyz0ktQ4C70kNc5CL0mNs9BLUuMs9JLUOAu9JDXOQi9Jjft/jpFjSZy2xJIAAAAASUVORK5CYII=\n",
+      "image/png": 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BB1gJ/YW9cnBN7fcCFyc5DXwCuLO/ysZbkmN1XUtyrHbilbGS1LhFH9FLkiZk0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Lj/A1f36wkc+7O0AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2812,7 +3173,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2822,7 +3183,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2839,7 +3200,7 @@
     },
     {
      "data": {
-      "image/png": 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bvgZqaq9Wz7p5ubrjsY9L+ryk88vyjqSPVtb7sqRzkr6v7vjV1WX5Jeq+EFclfUrSzrL8R8v11XL7JROq67fLfaxK+q2efZyW9NqeZX+i7sG0r6v7JvXaadUl6cfVnQH0SKnhzyRta7q91O29hLoh/nD5995x2kvSr0v6N3VnR3ygLPsjSddu9feqOxR1StJJVWY+bLTPEZ7vI9Ul6YOSvltpn4fVPcjf9zGdUl3Xl/t9WN2D6MuVfXbUDdFTkm5X+eLmNOoqt10h6as9+5tWe/2yujn1XXU/YZzYKjPqaC++GQsAybV56AYAUAOCHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCS+18dgq3gZdewyQAAAABJRU5ErkJggg==\n",
+      "image/png": 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jnwI+A5zcLO8At7XW+wLwPPAduuNXFzXLz6D7RlwF/hw4sVn+/c391ebxMyZU1681x1gFfrVnH4eAt/Qs+wO6J9O+QveX1FumVRfwg3RnAD3W1HATsGXW7UW395J0Q/zR5t/7R2kv4OeBf6U7O+KDzbLfAy7b6OelOxR1EHiS1syH9fa5idf7puoCPgR8q9U+j9I9yd/3OZ1SXVc0x32U7kn0Xa19duiG6EHgFpo/3JxGXc1j5wNf6tnftNrrZ+jm1LfofsJ4fKPMGEd7+ZexklTcPA/dSJLGwKCXpOIMekkqzqCXpOIMekkqzqCXpOIMekkqzqCXpOL+B+7T4/S9DXE4AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2849,7 +3210,7 @@
     },
     {
      "data": {
-      "image/png": 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4TacR19kRsaM5/nn02+tYU8b734h4U1MaeQcjvLe3GlcTzynAL9Gqz0+xvdaz5ntgTO0107NuXkW/Hvs08EngzGZ5D7ittd5ngOeBb9GvX13WLD+P/hvxCPBXwOnN8u9v7h9pHj9vQnH9enOMI8CvDezjGPCGgWV/TH8w7Qv0P6TeMK24gB+kPwPo8SaGm4EdXbcX/d5L0k/ijzU/795KewG/APw7/dkR722W/SFw5UbPl34p6ijwFK2ZD2vtcxN/75uKC3gf8I1W+zxGf5B/3dd0SnFd3Rz3MfqD6MutffboJ9GjwAdpTtycRlzNYxcDnxvY37Ta62fo56lv0P+G8cRGOWMc7eWZsZJU3CyXbiRJY2Cil6TiTPSSVJyJXpKKM9FLUnEmekkqzkQvScWZ6CWpuP8DfVD9pSuCHKIAAAAASUVORK5CYII=\n",
+      "image/png": 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9uirj8xNsr0H6vgYaaq+pnnXzEnXHY5+Q9GlJF5flHUl3VNb7gqTzkr6j7vjVNWX55eq+EFclfVzSjrL8h8v91fL45WOq67fLMVYl/VbPPs5IemXPsj9T92Tal9V9k3rlpOqS9KPqzgB6pNTwfknb2m4vdXsvoW6IP1z+vWOU9pL0a5L+Xd3ZEe8uy/5E0nWb/bzqDkWdlvS4KjMf+u1zC7/vW6pL0nskfavSPg+re5J/4HM6obpuKMd9WN2T6MuVfXbUDdHTkj6g8sXNSdRVHtsr6Ys9+5tUe/2Sujn1LXU/YZzaLDOaaC++GQsAyU3z0A0AoAEEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAk939EYr16dzE4hQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2859,7 +3220,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2871,12 +3232,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dphi\n"
+      "zinner zcut\n"
      ]
     },
     {
      "data": {
-      "image/png": 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oyzuS7qis92VJ5yS9oO741VVl+SXqvhCXJP2lpK1l+Q+W60vl9kvGVNdvlPtYkvTrPfs4LektPcv+RN2DaV9T903qLZOqS9J56s4AeqzU8DFJm5puL3V7L6FuiD9afj4wSntJ+mVJ/6bu7IgPlWV/JOna9R6vukNRpyQ9qcrMh9X2uYG/9w3VJekPJH2n0j6PqnuQv+9zOqG6ri/3+6i6B9EXKvvsqBuipyR9QuWLm5Ooq9y2R9JXevY3qfZ6u7o59R11P2GcXC8z6mgvvhkLAMm1eegGAFADgh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4Akvtf8TSj78Wg5RwAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2886,7 +3247,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2896,7 +3257,7 @@
     },
     {
      "data": {
-      "image/png": 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kd6rSPz/G/dXPusdATftrokfdvErd/tgnJH1W0sVl+qKkj1Xm+4KkZyQ9p27/1bVl+mXqHoirkv5K0o4y/fvL89Xy+mUN1fUbZRurkn69Zx1nJb2xZ9qfqnsy7avqfki9cVx1SbpQ3RFAj5Ya/lzStrb3l7qtl1A3xB8p/943yv6S9EuS/lXd0RHvL9P+WNL1m/286nZFnZF0WpWRD+utcwu/71uqS9IfSvpOZf88ou5J/r7v6ZjqurFs9xF1T6IvVda5qG6InpH0EZULN8dRV3ntKklf7lnfuPbXz6qbU99R9xvGqc0yo479xZWxAJDcJHfdAABqQNADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHL/B/XjyUI5gIS7AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2908,12 +3269,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dphiNor\n"
+      "module\n"
      ]
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2923,7 +3284,7 @@
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2933,7 +3294,7 @@
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2945,12 +3306,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dz\n"
+      "dysize\n"
      ]
     },
     {
      "data": {
-      "image/png": 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276yLcv8cC33LbwHfAB6YkdzXsbAb5GvAzX3nOUvO+4AXgP/p/j/fALwV+DzwVeBzwFv6zrko80+w0JZ5auj9fN0M5P5R4B+63MeAj3Xjl7MwSTkB/AVwcd9ZPTJWkho3960bSWqdhV6SGmehl6TGWeglqXEWeklqnIVekhpnoZekxlnoJalx/wcC2D5z7QoWTAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2960,7 +3321,7 @@
     },
     {
      "data": {
-      "image/png": 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pcbZuJKlxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY37X+w3nw2lLz3cAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2970,7 +3331,7 @@
     },
     {
      "data": {
-      "image/png": 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jw68/AjT7v/IrnNFPKMmHgD8GbgW+nOTrVfX+qjqb5DjwEnAZ+I2q+laftU7Z7yV5L8utm1eBX+u3nOmpqstJruyHvAk4Mu5+yBvIbcAXksByHvx5Vf1VvyVNz3C/63uBzUmWgI8Bh4HjSQ6wfNXcX+yvwrXhJ2MlqXG2biSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z/V5yMD4wuYJQAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2982,12 +3343,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z0\n"
+      "ysize\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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XJD/G0sPWDSzdZB+rqt/rfv+PAu8E/hn4par637Eee5aDXpJ0bbNcupEk9WDQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuP8DdKok/MHNF7EAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2997,7 +3358,7 @@
     },
     {
      "data": {
-      "image/png": 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dljNS9wPXA4/1P8k8WVV3VdXpJMeAMyy1dD5QVf/dYZ0jU1WXkxwCHgU2AUeq6nTHZY3DzcDPAl9J8tf9fR8B7gWOJTnI0pVl391RfRvlw8DRJL8GfImlP37N8MxYSWqcrRtJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4/4HKLjMkCwAsx0AAAAASUVORK5CYII=\n",
+      "image/png": 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V/U739/8o8E7gn4BfqKr/Hem+pznoJUnXNs2tG0lSDwa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z9fRC5GZuZhOQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3007,7 +3368,7 @@
     },
     {
      "data": {
-      "image/png": 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1Ockx4CFgC3Ciqs51XNY43Az8IvD1JH/TH/sA8CHgwSRH6V1Z9u0d1bdR3g+cTPLbwFfp/fFrhmfGSlLjbN1IUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGve/HAa6+sir3uoAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3019,12 +3380,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dr\n"
+      "ys-pred\n"
      ]
     },
     {
      "data": {
-      "image/png": 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4Fb0kabQseklqnEUvSY2z6CWpcRa9JDXOopekxln0ktQ4i16SGvf/7haSWlwTqb8AAAAASUVORK5CYII=\n",
+      "image/png": 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zMBgjPQA8S39M+VUmeIWsV8ZKUuNaGd1Iks7Dopekxln0ktQ4i16SGmfRS1LjLHpJapxFL0mNs+glqXH/D+21fEiMO6cMAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3034,7 +3395,7 @@
     },
     {
      "data": {
-      "image/png": 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7gPWq+lrfQc1Kku9/pX6a5AjDf1cL8x9rFPsDwLNV9SdbvG1hj2GX9i3yMUyyN8lbRo+/F7ge+OdNbzsD/PLo8c3A39RopHSn69K+TeNPNzEc13lNnW7jqNeW4T2OjwJ7kqwBdzEclKGq7md4q8v3AheA/wY+2E+kk+nQvpuBDyd5GfgWcHJR/mON/CTwi8A/jeqqAL8DHIClOIZd2rfIx/BtwMeS7GJ40nqkqh5L8kfAalWdYXjy+0SSCwwnL5zsL9yxdWnfrye5CXiZYftuvdJO/YWvJDXIso8kNcjkL0kNMvlLUoNM/pLUIJO/JDXI5C9JDTL5S1KDTP6S1KD/A701VFclGvftAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3044,7 +3405,7 @@
     },
     {
      "data": {
-      "image/png": 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v2arOqvpCVf3f8O17gB+acWlvBG5N8lngQeBHk/zlyD7zMJbb1jmtsVzkoD8F/PxwpcgbgEtV9bm+ixqV5Du/1ktMcoT1v7OZ/88+rOG9wLmq+tMtdut9TLvUOQ9jmuQ1SV41fP1twJuAfxnZ7RTwC8PXbwU+XsMzdrPSpc6R8zC3sn5eZGaq6l1VdaCqDrF+ovXjVfWzI7v1PpZd6pzWWHZ6lOBelPXn3N4EXJPkAvC7rJ9IoqruZf0RiG8GzgP/C7x9Tut8K/DOJJeBLwPHZ/0f6NAbgZ8D/nnYrwX4LWBpQ63zMKZd6pyHMX0t8L4k+1j/h+ZkVT2c5A+As1V1ivV/sD6Q5DzrJ+yPz7jGrnX+SpJbgcvDOm/roc5vModjualZjKVXxkpS4xa5dSNJC8Ggl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcf8PRFaR4CRdaPwAAAAASUVORK5CYII=\n",
+      "image/png": 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z6CWpcRa9JDXOopekxln0ktS4/wOhfphM2v3dWAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3056,12 +3417,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dphi zcut\n"
+      "dphi\n"
      ]
     },
     {
      "data": {
-      "image/png": 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bv7qkWX42vSfiCvBnwCnN8lc211ea28+eUF2/1hxjBfjVvn0cBd7St+yP6J1M+wq9F6m3TKsu4FR6M4AebWr4CLCt6/ai13tJeiH+SPPvA1tpL+DngX+mNzvig82yPwAu2+j+0huKOgI8RWvmw1r73MTf+6bqAn4X+FarfR6hd5J/4GM6pbquaI77CL2T6Htb+1ymF6JHgJtoPrg5jbqa2y4AvtS3v2m110/Sy6lv0XuH8fhGmTGO9vKTsZJU3CwP3UiSxsCgl6TiDHpJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6Ti/hf4DJ+iQ7KMxQAAAABJRU5ErkJggg==\n",
+      "image/png": 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+P8H26mfd10BN7dXqWTdvUHc89mlJn5J0XlnekXRnZb3PS3pB0kvqjl9dWZZfqO4LcUXSX0naUZa/plxfKbdfOKa6fq3cx4qkX+3ZxylJb+1Z9ifqHkz7krpvUm+dVF2SzlF3BtDjpYaPSNrWdHup23sJdUP8sfLvA6O0l6Sfl/Qf6s6O+GBZ9keSrt7s71V3KOqkpKdUmfmw3j638HzfUl2Sfk/SNyvt85i6B/n7PqYTquvacr+PqXsQfbGyz466IXpS0u0qX9ycRF3ltn2SvtCzv0m110+rm1PfVPcTxonNMqOO9uKbsQCQXJuHbgAANSDoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASC5/wdJzJrOukiq+AAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3071,7 +3432,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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J71dlfH6M7dXPqq+Bmtqr1bNu3qjueOxTkj4j6aKyvCPprsp6X5D0nKQX1B2/urosv1TdF+KSpL+WtL0sf225vlRuv3REdf1muY8lSb/Rs4+zkt7es+zP1D2Y9jV136TePq66JF2g7gygx0oNH5O0pen2Urf3EuqG+KPl54ObaS9JvyzpP9SdHfHhsuxPJF233uNVdyjqjKQnVZn5sNo+N/D3vqG6JP2hpO9W2udRdQ/y931Ox1TXDeV+H1X3IPp8ZZ8ddUP0jKQ7VL64OY66ym37JH25Z3/jaq+fUzenvqvuJ4zT62VGHe3FN2MBILk2D90AAGpA0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcv8PqQCrGmvr71oAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3081,18 +3442,2642 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for quad in [t1234,t1231,t1212,t1123] :\n",
-    "  plotDoublets(quad,500)"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADZlJREFUeJzt3WuMXHUZx/HfzyKQAFkui4CFspAlCvpGMikIxDSiplwWvAc0ESKyommir7QJiSS+ETQx0YAhG2iAhACKol0p4SIQXoEUApSrlAZCm0pBklWiAZHHF3NKJsvOdqYz5zLPfD/Jhrkcdp45O/3N/zznP/9xRAgAkNeH6i4AAFAugh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASC5feouQJImJydjamqq7jIAYKQ89thjb0TE4XvarhFBPzU1pc2bN9ddBgCMFNuv9LIdrRsASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkGvGBKYyHqfV3vn/55SvPqbESYLyUMqK3fYDtzbbPLeP3AwB619OI3vYGSedK2hURn+y4fa2kX0laIem6iLiyuOvHkn475FoBDKjzqKoTR1i59dq6uUHS1ZJu2n2D7RWSrpH0eUnbJT1qe6OklZKelbT/UCtFKrRxgOr0FPQR8ZDtqUU3r5a0NSK2SZLtWyWdL+lASQdIOknSf2xvioj3hlYxgKHjjTe3QU7GrpT0asf17ZJOiYh1kmT7YklvdAt527OSZiVp1apVA5SBJuvWKkB1+BugtFk3EXHDHu6fkzQnSa1WK8qqAxgXwxqVM7rPZ5Cg3yHpmI7rRxe3YQwRDs3CKB6dBgn6RyWdYPs4tQP+AknfGEpVaJR+Z2oQMkCz9Dq98hZJayRN2t4u6YqIuN72Okl3qz29ckNEPFNapWgcAr0e7Hf0q9dZNxd2uX2TpE17++C2ZyTNTE9P7+2vQAkIkmZowt+BllwOtS6BEBHzkuZbrdalddaBevUSaIQMsPdY6waSmjF6BFAOgh5okCa/4dLGGV0sUwwAyTGix0hgNNks/D1GS60jetsztucWFhbqLAMAUmPWDUZOhtFkk3vxyIfWzRjLEDYZQh8oGydjASA5gh4AkiPoASA5gh4Akqv1ZCyLmmGYmn5iNsPJb4ymWkf0ETEfEbMTExN1lgEAqdG6AYDkCHoASI6gB4DkCHoASI4lEMbMuMz8aPoMHKBKjOgBIDnm0QMYyOKjRI6gmod59ACQHK0bAEiOk7FAicbl5DeajRE9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRXa9DbnrE9t7CwUGcZAJBardMrI2Je0nyr1bq0zjqQW9Xr3jClEk1D6wYAkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA51roBgORY6wbAUFW9thD2jO+MHQMssgWMN3r0AJAcQQ8AydG6wVihf4xxxIgeAJIj6AEgOVo3wBAwswlNxogeAJIj6AEgOYIeAJIj6AEgOYIeAJJj9UoASK7WoI+I+YiYnZiYqLMMAEiN1g0AJEfQA0ByBD0AJEfQA0ByrHUD7CXWt8GoIOgxtlibHuOC1g0AJEfQA0ByBD0AJEfQA0ByBD0AJMesG0DMwEFujOgBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSY3plUqysCGC3WoPe9oykmenp6TrLAHrCm2f/+HxCM/Dl4ACQHD16AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5FgCAVgGn4ZFBgQ9sAjhjmxo3QBAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACTHEggjqPMj+i9feU6NlQC943VbH0b0AJAcQQ8AyRH0AJAcPfoRR98TwJ4wogeA5Ah6AEiOoAeA5OjRJ8JX4AFYytBH9LZPtH2t7dttf2/Yvx8A0J+egt72Btu7bD+96Pa1tl+wvdX2ekmKiOci4jJJX5d0+vBLBgD0o9cR/Q2S1nbeYHuFpGsknSXpJEkX2j6puO88SXdK2jS0SgEAe6WnoI+IhyS9uejm1ZK2RsS2iHhH0q2Szi+23xgRZ0n65jCLBQD0b5CTsSslvdpxfbukU2yvkfRlSftpmRG97VlJs5K0atWqAcoAACxn6LNuIuJBSQ/2sN2cpDlJarVaMew6AABtg8y62SHpmI7rRxe3AQAaZJCgf1TSCbaPs72vpAskbRxOWQCAYempdWP7FklrJE3a3i7pioi43vY6SXdLWiFpQ0Q8U1qlDcEiYkD1+Hc3mJ6CPiIu7HL7JjGFEgAarda1bmzP2J5bWFioswwASK3WoI+I+YiYnZiYqLMMAEiN1SsBIDmCHgCSI+gBIDmCHgCSq/WLR2zPSJqZnp6uswwADcF8+XLUGvQRMS9pvtVqXVpnHXXjxQ2gTHyVIIDKMbipFkEPoFZ813H5OBkLAMkxoh8Ah58ARgFr3QBAcsy66cAIHUBG9OgBIDl69ADQIeORfdqgz/jHAsYJ0y6HJ23QjyreoAAMG0E/JP0GNKMVoBoMngj6RuMFCmAYxmL1ym6j57LCk4AG0CTMoweAHozyAI7WTYUG6cvT0wewt/jAFAAkx4i+B4ymgWYa5XZKlRjRA0ByjOhLxtEAgLoxogeA5Ah6AEhuLD4wBSAP2qH9q3VEHxHzETE7MTFRZxkAkBonYwGMvexHCQQ9AJRg8ZtHnfP8ORkLAMkR9ACQ3Fi3brL35QA0U9VLNzCiB4DkxnpEDyCPKo/QR20xNYIeAAYwCi1gWjcAkBxBDwDJ1Rr0tmdszy0sLNRZBgCkxpeDAxhLo9BbHxZaNwCQHEEPAMkR9ACQHPPoAaCLLH18RvQAkFyqEX2Wd18A+dSZT4zoASA5gh4AkiPoASC5VD16AFjOuJ7HY0QPAMkxou9iXN/5AeTD6pUAkFytQR8R8xExOzExUWcZAJAaPXoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASG7k16Nn3XgAWB4jegBIjqAHgOQIegBIjqAHgOQIegBIji8HB4Dk+HJwAEiO1g0AJEfQA0Byjoi6a5Dt1yW9spf/+6SkN4ZYzrBQV3+oqz9NrUtqbm0Z6zo2Ig7f00aNCPpB2N4cEa2661iMuvpDXf1pal1Sc2sb57po3QBAcgQ9ACSXIejn6i6gC+rqD3X1p6l1Sc2tbWzrGvkePQBgeRlG9ACAZYxc0Nv+he3nbT9l+w7bB3fZbq3tF2xvtb2+grq+ZvsZ2+/Z7noG3fbLtrfYfsL25gbVVfX+OtT2vbZfLP57SJft/lfsqydsbyyxnmWfv+39bN9W3P+I7amyaumzrottv96xj75TUV0bbO+y/XSX+23710XdT9k+uSF1rbG90LG/flJBTcfYfsD2s8W/xR8ssU25+ysiRupH0hck7VNcvkrSVUtss0LSS5KOl7SvpCclnVRyXSdK+pikByW1ltnuZUmTFe6vPdZV0/76uaT1xeX1S/0di/veqmAf7fH5S/q+pGuLyxdIuq0hdV0s6eqqXk8dj/sZSSdLerrL/WdLukuSJZ0q6ZGG1LVG0p8r3ldHSTq5uHyQpL8t8XcsdX+N3Ig+Iu6JiHeLqw9LOnqJzVZL2hoR2yLiHUm3Sjq/5Lqei4gXynyMvdFjXZXvr+L331hcvlHSF0t+vOX08vw7671d0pm23YC6ahERD0l6c5lNzpd0U7Q9LOlg20c1oK7KRcTOiHi8uPwvSc9JWrlos1L318gF/SLfVvtdcLGVkl7tuL5dH9yxdQlJ99h+zPZs3cUU6thfR0TEzuLy3yUd0WW7/W1vtv2w7bLeDHp5/u9vUww0FiQdVlI9/dQlSV8pDvdvt31MyTX1qsn/Bj9t+0nbd9n+RJUPXLT8PiXpkUV3lbq/Gvnl4Lbvk3TkEnddHhF/Kra5XNK7km5uUl09OCMidtj+iKR7bT9fjELqrmvolqur80pEhO1u07+OLfbX8ZLut70lIl4adq0jbF7SLRHxtu3vqn3U8dmaa2qyx9V+Tb1l+2xJf5R0QhUPbPtASb+X9MOI+GcVj7lbI4M+Ij633P22L5Z0rqQzo2hwLbJDUufI5ujitlLr6vF37Cj+u8v2HWofng8U9EOoq/L9Zfs120dFxM7iEHVXl9+xe39ts/2g2qOhYQd9L89/9zbbbe8jaULSP4ZcR991RURnDdepfe6jCUp5TQ2qM2AjYpPt39iejIhS18Cx/WG1Q/7miPjDEpuUur9GrnVje62kH0k6LyL+3WWzRyWdYPs42/uqffKstBkbvbJ9gO2Ddl9W+8TykrMDKlbH/too6aLi8kWSPnDkYfsQ2/sVlyclnS7p2RJq6eX5d9b7VUn3dxlkVFrXoj7ueWr3f5tgo6RvFbNJTpW00NGqq43tI3efW7G9Wu0MLPUNu3i86yU9FxG/7LJZufuryrPPw/iRtFXtXtYTxc/umRAflbSpY7uz1T67/ZLaLYyy6/qS2n21tyW9JunuxXWpPXviyeLnmabUVdP+OkzSXyS9KOk+SYcWt7ckXVdcPk3SlmJ/bZF0SYn1fOD5S/qp2gMKSdpf0u+K199fJR1f9j7qsa6fFa+lJyU9IOnjFdV1i6Sdkv5bvL4ukXSZpMuK+y3pmqLuLVpmJlrFda3r2F8PSzqtgprOUPvc3FMduXV2lfuLT8YCQHIj17oBAPSHoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5P4PLiLZrtggLFYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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lqXGWbiSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z/sr9jtjXYEGAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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sJElNloUkqcmykCQ1WRaSpKb/B+cNbc7RwvdgAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9B7SSJC9gmOQfqKq/7oYXJn6Aqvp6ko8DP8KwFHxFN6tfmM/OXM/oLyXJjpHNPcDnu/tHgF/uum9eB5wf+Zk4F5LsAn4LuKWq/nvkoSPA3iRXJdkG7AA+1UeMa7AosU98reM5cAR4W3f/bcDf9RjLipIEeB9wsqr+eOShuY8/ycsvdMMl+W7gJobHGD4OvLXbbS5jv6i+jwav9cZwlnACeAI4Cmyu54+W382wnvaPjHSGzMuN4YHKM8Bnu9s9I4/d3sV+CnhD37FeJPafZVib/AbwFeDhRYl9JM43MuwA+SJwe9/xXCbWB4Fngf/t/t/3Ay8D/h74AvAx4KV9x7lC7D/GsCzzxMhn/Y2LED/wQ8A/dLGfAN7djb+K4QTmNPCXwFV9xzrOzTNjJalxC1u6kSSNx0QvSY0z0UtS40z0ktQ4E70kNc5EL0mNM9FLUuNM9JLUuP8D5q+onkde7kkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/Mek1VfVd+nvT/vpg9c1rgStDHzeXSpKD9D9eXl9V3x966jRwJMkLkuylf+H6b7qosWMT723cokG/+Q7gkar6g6GnTgPvGvz7XcBnN7u2eVBVt1TVzkH2HAH+uqreCXwZeOvgsIU5P53P6DfoLPBG+hcYvw+8p9tyOnUb8ALg3sEnn/ur6qaqupDkbuBh+i2d91bVDzqssxNV9UySZ/c23gKcHHdv40a9Dvg14JtJ/nYw9kHgI8DdSW4Avg28raP65tUHgFNJfg/4Ov0/lnPPb8ZKUuPmtnUjSZoOg16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9L/KkELrTv3gRAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/AGyCV8ZKUuNs3UhS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa9391rY0kyui9NAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LSR7oK+wmXbIPUZfcjwDfSPIV1lfNv1ZVQ1hddsn+QeC2JF8C7mW99Af5ysFNue8CrkhyHvgAcKq/ZDtbkmN0S0twjE7EV8ZKUuOWYUUvSZqCRS9JjbPoJalxFr0kNc6il6TGWfSS1DiLXpIaZ9FLUuP+D8C04j6qQ8JBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Jz6szfnV1WX6xOi/EVUl/JWlLWf6D5fpquf3iCdX16+U+ViX9Wtc+Tkp6fdeyP1HnYNqX1HmTev206pJ0rjozgJ4oNfyZpE1Nt5c6vZdQJ8QfL//eN057SXqbpH9XZ3bE+8uyP5J0bb+/V52hqBOSjqky82G9fY7wfB+pLkm/L+nblfZ5XJ2D/D0f0ynVdX2538fVOYi+UtnnsjohekLSHSpf3JxGXeW2nZI+37W/abXXz6uTU99W5xPG0X6ZUUd78c1YAEiuzUM3AIAaEPQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkNz/AtQeuTd+GiTSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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q+p+xfvcsB70k6cpmuXUjSerBoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXH/B3z08UKjQ2dyAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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GktSBQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+D223QnqDMf0bAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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uQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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K/pjyi0zwE7J+MlaSGtfK6EaStACLXpIaZ9FLUuMseklqnEUvSY2z6CWpcRa9JDXOopekxv0/IjW24MCQbVkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1etbNa9Udj31C0mckXVyWdyR9vLLe5yU9I+k5dcevrinLL1P3jbgs6S8lbSvLX1nuL5fHLxtTXb9SnmNZ0i/37OOspLf0LPtjdQ+mfUXdD6m3TKouSReqOwPo0VLDn0ra0nR7qdt7CXVD/JHy9f5R2kvSuyT9q7qzIz5Qlv2hpOs2+nnVHYo6I+m0KjMf1trnJn7fN1WXpN+T9O1K+zyi7kH+vq/phOq6oTzvI+oeRF+s7LOjboiekfRRlRM3J1FXeWyvpC/27G9S7fUT6ubUt9X9C+PURplRR3txZiwAJNfmoRsAQA0IegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBI7v8A1XPaen4R2JAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADPxJREFUeJzt3V+opPddx/H3x63JRYtHJaGWJOsGzlJciyAMqaAXASvdmJ6mFq1ZvWixZIkYURA0NUK9ESKCaCUii1nSi5IQ/FP32IS0Fmu8aDWbIpo0RpfYkg2xmxg8Cooh5OvFGdrTk92cmZ2ZfWa+837d7JnnmZ3z/bEzn/093+f3PJOqQpLU17cNXYAkabEMeklqzqCXpOYMeklqzqCXpOYMeklqzqCXpOYMeklqzqCXpObeMnQBANdcc00dOXJk6DIkaaU8+eSTL1fVtQc9bymC/siRI5w9e3boMiRppST52iTPs3UjSc0Z9JLUnEEvSc0Z9JLUnEEvSc0Z9JLUnEEvSc0Z9JLU3FJcMKWLO3L3Z77x81fvvXXASiStMmf0ktTcoDP6JFvA1ubm5pBlLJW9s/hJnuNMX135Pp+fQYO+qraB7dFodMeQdawyPwxaFZd6r04yudFs7NEvgWln8dKqm/b97IRmNgb9QBYR3H4YtA58n0/PoG/KD4OWgUeiy8GgXzP+B6BOfD9PxqC/gpzdqKP972sDd/m4jl6SmnNGL6kF2ziXZtAv2DK3a/xg6HIt8/tab2TrRpKac0a/Bpx9aR4mfR8tw/vNo9VvZdAL8IMhdWbQS7qkZZida3b26CWpOWf0egMvgJF6cUYvSc05o5f0LezL92PQ60CuyFEX6/peNug1lXX9oEirzKCfEwNQ0rLyZKwkNbeQGX2StwJ/A/xmVf3lIn6HpPnpfAK289gmNVHQJzkNvA+4UFXv2rP9OPD7wCHgj6vq3vGuXwMennOtK8M3lqRlMmnr5gHg+N4NSQ4B9wG3AMeAE0mOJfkx4CvAhTnWKUm6TBPN6Kvq8SRH9m2+CThXVc8BJHkIuA14G/BWdsP/f5M8UlWv73/NJCeBkwCHDx++3PolXSaPPNfHLD3664Dn9zw+D7y7qu4CSPIR4OWLhTxAVZ0CTgGMRqOaoQ4NxJVG0mpY2PLKqnpgUa8tSZrcLEH/AnDDnsfXj7dJ0tJbpyPSWYL+CeBokhvZDfjbgZ+Z5gWSbAFbm5ubM5QhaVL25dfTpMsrHwRuBq5Jch74eFXdn+Qu4DF2l1eerqqnp/nlVbUNbI9GozumK3s5+KH5pnWaHUmrZtJVNycusf0R4JG5ViRJmitvgSBJzQ0a9Em2kpza2dkZsgxJam3Qu1eueo9eWgWeS5K3KZ6SHxpJq8ag19y5AkdaLga9pLXXfXLiyVhJam7QoK+q7ao6ubGxMWQZktSarRstVPdDYmkVGPQTcKWNpFXmlbGS1Jwzeqkhj0K116BB722KJS2bjueVXHUjSc3Zo5ek5gx6SWrOk7G6Yjr2PqVVYNBLTbjSRpfivW4kqTm/eESDsI0jXTm2bi7Bw2BJXbjqRpKac0YvSZfQpcVo0EsrzBajJmHrRpKaM+glqTnX0UtSc969UpKas3UjSc0Z9JLUnEEvSc0Z9JLUnBdM7eHFJ5I6MuglaQKrfDsEWzeS1Jwzei2VVZ41ScvKoJdWgP8BahaDBn2SLWBrc3NzyDI0ME+CS4vlLRAkqTlPxkpSc/bopSVlS0vzYtBLK8b/ADQtWzeS1JxBL0nNGfSS1Jw9ekma0qpdwOaMXpKaM+glqbm1bt24TE3SOljroNdyW7U+qLSsbN1IUnODBn2SrSSndnZ2hixDklrz7pWS1JytG0lqzqCXpOYMeklqzqCXpOYMeklqzgumtBK8eEq6fAa9tES8LYcWwdaNJDVn0EtScwa9JDVn0EtSc56M1UpzNY50MINeK8eVKdJ0DHpJmsEqHFUa9NLAPELRonkyVpKaW7sZvbMnSevGGb0kNWfQS1Jzcw/6JN+X5I+S/EmSn5/360uSpjNR0Cc5neRCkqf2bT+e5Nkk55LcDVBVz1TVncCHgB+ef8mSpGlMOqN/ADi+d0OSQ8B9wC3AMeBEkmPjfe8HPgM8MrdKJUmXZaKgr6rHgVf2bb4JOFdVz1XVq8BDwG3j55+pqluAn51nsZKk6c2yvPI64Pk9j88D705yM/BB4GreZEaf5CRwEuDw4cMzlCHtWoUrFKUhzH0dfVV9AfjCBM87BZwCGI1GNe86pGXm9Ry6kmZZdfMCcMOex9ePt0mSlsgsQf8EcDTJjUmuAm4HzsynLEnSvEy6vPJB4IvAO5OcT/LRqnoNuAt4DHgGeLiqnp7mlyfZSnJqZ2dn2rolSROaqEdfVScusf0RZlhCWVXbwPZoNLrjcl9jEvZDJa0zb4EgSc2t3d0rpaF4ZNnfsi7xHXRGb49ekhZv0KCvqu2qOrmxsTFkGZLUWtvWjYfJkrTLk7GS1JxBL0nNeTJWkprzZKwkNdf2ZKzW27KuZ5aGYI9ekpoz6CWpOYNekppz1Y0kNeeqG0lqzlU3krQA+2/DMuTqL3v0ktScQS9JzRn0ktScq24kqTlX3UhSc7ZuJKk5g16SmjPoJak5L5iSFsjvLtYycEYvSc0Z9JLU3KCtmyRbwNbm5uaQZag5v21Ky2DI9+GgQV9V28D2aDS6Y8g6pHmyL69lY+tGkpprterGmZQkvZEzeklqrtWMXpqGJ2m1LpzRS1JzBr0kNWfQS1JzBr0kNec3TElSc14ZK0lX2JVe8WXrRpKaM+glqTkvmNJa8TYZWkfO6CWpOYNekppb+daNh+KS9Oac0UtScwa9JDVn0EtScwa9JDVn0EtScyu/6kZaBq7+0jJzRi9JzXmbYklqbtCgr6rtqjq5sbExZBmS1JqtG0lqzqCXpOYMeklqzqCXpOZcRy9dJtfOa1U4o5ek5gx6SWrOoJek5gx6SWrOk7HShDz5qlVl0Ev77A30r95764CVSPNh60aSmjPoJak5WzcS9t/VmzN6SWrOoJek5gx6SWrOoJek5gx6SWpuIatuknwAuBX4DuD+qvrsIn6PJOlgE8/ok5xOciHJU/u2H0/ybJJzSe4GqKpPV9UdwJ3AT8+3ZEnSNKZp3TwAHN+7Ickh4D7gFuAYcCLJsT1P+Y3xfknSQCYO+qp6HHhl3+abgHNV9VxVvQo8BNyWXb8NPFpVX55fuZKkac16MvY64Pk9j8+Pt/0i8B7gJ5PcebG/mORkkrNJzr700kszliFJupSFnIytqk8AnzjgOaeAUwCj0agWUYckafYZ/QvADXseXz/eJklaErMG/RPA0SQ3JrkKuB04M3tZkqR5mWZ55YPAF4F3Jjmf5KNV9RpwF/AY8AzwcFU9PcVrbiU5tbOzM23dkqQJpWr49vhoNKqzZ89e1t/19rKSVtks32KW5MmqGh30PG+BIEnNGfSS1NygQW+PXpIWb9Cgr6rtqjq5sbExZBmS1JqtG0lqzqCXpOYMeklqzpOxktScJ2MlqbmluDI2yUvA14auY86uAV4euogFc4w9rMMYoec4v7eqrj3oSUsR9B0lOTvJpcmrzDH2sA5jhPUZ58V4MlaSmjPoJak5g35xTg1dwBXgGHtYhzHC+ozzDezRS1JzzuglqTmDfs6S/E6Sf07yj0n+PMl37tn3sSTnkjyb5L1D1jmLJD+V5OkkrycZ7dvXYowASY6Px3Euyd1D1zMPSU4nuZDkqT3bvjvJ55L86/jP7xqyxlkluSHJXyf5yvh9+kvj7a3GOQ2Dfv4+B7yrqn4A+BfgYwBJjrH7nbrfDxwH/jDJocGqnM1TwAeBx/du7DTGcd33AbcAx4AT4/GtugfY/bfZ627g81V1FPj8+PEqew34lao6BvwQ8Avjf7tu45yYQT9nVfXZ8XfpAnwJuH78823AQ1X1f1X1b8A54KYhapxVVT1TVc9eZFebMbJb97mqeq6qXgUeYnd8K62qHgde2bf5NuCT458/CXzgihY1Z1X1YlV9efzzf7P7fdbX0Wyc0zDoF+vngEfHP18HPL9n3/nxtk46jbHTWA7y9qp6cfzzvwNvH7KYeUpyBPhB4O9oPM6DvGXoAlZRkr8Cvuciu+6pqr8YP+cedg8hP3Ula5uXScaofqqqkrRYipfkbcCfAr9cVf+V5Bv7Oo1zEgb9Zaiq97zZ/iQfAd4H/Gh9c/3qC8ANe552/XjbUjpojJewUmM8QKexHOTrSd5RVS8meQdwYeiCZpXk29kN+U9V1Z+NN7cb56Rs3cxZkuPArwLvr6r/2bPrDHB7kquT3AgcBf5+iBoXqNMYnwCOJrkxyVXsnmQ+M3BNi3IG+PD45w8DK33Elt2p+/3AM1X1u3t2tRrnNLxgas6SnAOuBv5jvOlLVXXneN897PbtX2P3cPLRi7/KckvyE8AfANcC/wn8Q1W9d7yvxRgBkvw48HvAIeB0Vf3WwCXNLMmDwM3s3snx68DHgU8DDwOH2b2L7Ieqav8J25WR5EeAvwX+CXh9vPnX2e3TtxnnNAx6SWrO1o0kNWfQS1JzBr0kNWfQS1JzBr0kNWfQS1JzBr0kNWfQS1Jz/w/TVNWWBgGS2QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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CA8A7u90Gk3dZfZ8NHtKDxVnGKeARYA7YXt87834bi725f2JsxUjPeedZ7CF/qXvcPvbazV3eJ4Dr+s7aZfpFFvuZ3wG+Cdw35LxdrrewuDLka8DNfed5kXx3Ac8A/9P9294AvBr4DPBV4NPAq/rOOZb3p1lsyzwydty+ZaiZgR8H/rHLewr4QDd+FYuTkXngr4CL+8660sNvxkpS42zdSFLjLPSS1DgLvSQ1zkIvSY2z0EtS4yz0ktQ4C70kNc5CL0mN+z/2Z2EHL9iaiAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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vSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Lj/g/wCK9q1iPGQAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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3Pmkwqm/q923pZgxV9fWq+qmq2l9V++n/efeCqvoO/b34f22w+uaFwGNDf/outSSH6f+Z+4qq+v7QQ3cBx5Jck+QA/Ynov+6ij3Mw9f0WlsGgNn0b8EBV/eHQQ3cBrx98/Xrg49vdt3mpqluqas/gPXwM+Kuqeh3wOeDVg6c1dc6O6GfnLPAy+hOS3wd+vdvuzNT7gWuAzwz+kvlSVb2pqs4nuRP4Bv2Szpur6r877OfMVNUTSX54v4UdwKlG77fwIuBXga8n+ZtB29uBdwF3JrmR/s6yr+mof9vpbcDpJL8HfJX+B2ATvDJWkhpn6UaSGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuP8BN5P6P4BA0/gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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tm1Woqi9X1Q9X1UJVLdD/ePfKqvoq/ZU7f2kw++bVwMWhj75zLck++h9z31hV3xp66CRwMMmVSXbRPxH9t13UOAVjr846Dwa96VuBx6rqD4YeOgm8bfD124BPr3dt01JVN1fV9sHv8EHgr6vqF4DPAW8ZPK2pY3ZEPzmngdfTPyH5LeCXuy1noj4MXAncM/gk80BV3VBVZ5LcATxKv6Xzjqr63w7rnJiqeibJEeBuvrc665mOy5qG1wC/CHw5yd8N9r0LeC9wR5Lr6K8s+9aO6ltPNwEnkvwO8EX6fwCb4JWxktQ4WzeS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0fjyH1n5DQcBoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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OWcn3bbtxrfJ79oqq+nfg08CxLd/61nuW5FXAXuBri61u58aNaxbZuKig33Z/2S39zzsY9hdbcA74pdEqjrcDV6vqK30XNQtJvveVHmiSowz/PS39f6xRzQ8Cz1TVH485beXety7jWuH37KYkrxt9/V3AO4F/2nLaOeCXR1+/B/ibGl3NXFZdxjWLbOy0leC0qurlJK/sL7sHOFtVF5L8IbBRVeeAX09yB/AywwuAdy6itmlluJ/ubcC+JJeA9zO8oEJVPcBwq8V3AReB/wbe20+lk+swtvcA70vyMvAN4OSy/8caeQfDO6b+46g3CvA7wEFY6fety7hW9T17A/DhJHsY/nB6tKo+viVDHgT+IslFhhlysr9yO+syrqmz0U/GSlLjlulirCRpDgx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa938FHYUffxHu3gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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rgI8n2cTyL5ijVfVEkj8ElqrqGMu/qP4qyWmWJ+UPrFNsXeP79SS3AxeG8d25jvFd1ByN31qxzd3YXYzfjJWkxi1S60aSFpKJXpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGmeil6TGmeglqXH/D/pLXKY2WZLkAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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VvRAXJf2NpC1l+Q+W64vl9ovHVNdvlftYlPSbffs4LekNfcv+XNXJtC+p+iP1hknVJek8VTOAHis1fEDSpraPl6rRS6gK8UfLv3dv5HhJeruk/1A1O+K9ZdmfSrpmrcerqhV1StKTqs18WGmf6/h9X1ddkv5I0jdrx+dRVSf5Bz6nE6rrunK/j6o6ib5Q22dPVYiekvQhlQ9uTqKuctseSV/o29+kjtcvqMqpb6p6h3Fyrcxo4njxyVgASK7LrRsAQAMIegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBI7v8BkA6cEMhsZ1MAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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NwL8Bf1dVH+kfa+IcAZL8FPCHwDrgaFX9XsddGov+/s93srSS4zeAh4DPA08DW4GvAz9XVStv2M6MJD8B/DXwD8A7/ebfZqlO38x5DsOgl6TGWbqRpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe7/AEy9Dn0Pg0ZJAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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fOD3mmFYlyf39G00kuR/4OeDi8p9a804D7+y/fifwN2OMZdW+mxT7fpEJuS79G38fBS5X1Z8ueGuirstS/ZjE65LkNUke7L/+PuYnklxmPuH/Un+3kVyTiZ11A9CfUvVnwDrgWFX9wZhDWpUkr2d+FA9wH/CJSepLkhPAI8yvwvd14HeBTwOngM3Mr0z6jqpa0zc6l+jHI8yXBwq4Bvzaghr3mpXkTcAXgK8CL/abf5v5+vbEXJdl+nGACbsuSX6M+Zut65gfZJ+qqt/r//yfBF4N/CPwq1X1P0M99yQneknS3U1y6UaS1IGJXpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGmeil6TGmeglqXH/B1AyrZ6wxG0rAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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JC4HVdLyoUlV9sap+oKo2VtVG+v8jvPRChPxikmyj/9H/tVX1Xx2Xcxi4IsmmJKvp3yg70HFNpP/ufBdwrKre03U951TVrVW1bvA7tRP4m2UQ8gx+r08muXIwdC3weIclnfM14OVJnj34N72WGd4kvmiu6BdxE3D74KbG/wC7O67nnP3A/iSPAU8Bv9rxVepy9z7gUuBTg08bn6mqm7sopKrOJtkDPEh/RsT+qjraRS3zvAL4FeCLST4/GHtHVR3qsKbl7hbgQ4M37BPAr3VcD4M20v3Ao/TblJ9jht+Q9ZuxktS4Vlo3kqTzMOglqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrc/wH6NSTn880uRgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for quad in [t1234,t1231,t1212,t1123] :\n",
+    "  plotDoublets(quad,500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotPairs(quad,mpt,kp) :\n",
+    "  quadc = quad[quad['pt1']>mpt]\n",
+    "\n",
+    "  maxc = 1000./(mpt*87.78)\n",
+    "\n",
+    "  print \" \"\n",
+    "  print \"NEW PAIR\",kp\n",
+    "  print \" \" \n",
+    "    \n",
+    "  print 'dphi'\n",
+    "  d1 = quadc['phi2']-quadc['phi1']\n",
+    "  plt.hist(d1[abs(d1)<.1], bins=100,log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "\n",
+    "  pcut = abs(d1)<0.06\n",
+    "\n",
+    "\n",
+    "  print 'dphiNor'\n",
+    "    \n",
+    "  pc = phicut(quadc['r1'],quadc['r2'],maxc)\n",
+    "  d1 = (quadc['phi2']-quadc['phi1'])/pc\n",
+    "  plt.hist(d1[abs(d1)<2.5], bins=100,log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "\n",
+    "\n",
+    "  print 'zinner'\n",
+    "  d1 = quadc['z1']\n",
+    "  plt.hist(d1[np.logical_and(pcut,abs(d1)<35)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "\n",
+    "  print 'dz'\n",
+    "  d1 = quadc['z2']-quadc['z1']\n",
+    "  plt.hist(d1[np.logical_and(pcut,abs(d1)<35)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "\n",
+    "  print 'z0'\n",
+    "  d1 = (quadc['r1']*quadc['z2']-quadc['z1']*quadc['r2'])/(quadc['r1']-quadc['r2'])\n",
+    "  plt.hist(d1[np.logical_and(pcut,abs(d1)<50)], bins=100,log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "  \n",
+    "\n",
+    "  pcut = abs(d1)<12.\n",
+    "\n",
+    "\n",
+    "  print 'dr'\n",
+    "  d1 = quadc['r2']-quadc['r1']\n",
+    "  plt.hist(d1[np.logical_and(pcut,abs(d1)<20)], bins=100,log=True)\n",
+    "  plt.show()\n",
+    "\n",
+    "  print 'dphi zcut'\n",
+    "  d1 = quadc['phi2']-quadc['phi1']\n",
+    "  plt.hist(d1[np.logical_and(pcut,abs(d1)<.1)], bins=100,log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "\n",
+    "  print 'zinner zcut'\n",
+    "  d1 = quadc['z1']\n",
+    "  plt.hist(d1[np.logical_and(pcut,abs(d1)<35)], bins=100,log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "\n",
+    "    \n",
+    "  print 'module'\n",
+    "  d1 = quadc['det1']\n",
+    "  plt.hist(d1[pcut], range=[0,96], bins=96, log=False)\n",
+    "  plt.show()\n",
+    "\n",
+    "\n",
+    "  norm = 1./8.\n",
+    "  print 'dysize'\n",
+    "  d1 = (quadc['ys2']-quadc['ys1'])*norm\n",
+    "  c = np.logical_and(quadc['ys2']>0,quadc['ys1']>0)\n",
+    "  plt.hist(d1[np.logical_and(pcut,c)], range=[-8,8],bins=32,log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "    \n",
+    "  print 'ysize'\n",
+    "  d1 = quadc['ys1']*norm\n",
+    "  plt.hist(d1[pcut], bins=30,range=[0,20],log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    " \n",
+    "  print 'ys-pred'\n",
+    "  th = 0.0285\n",
+    "  ptc = 0.015\n",
+    "  fac = th/ptc\n",
+    "  d1 = fac*(quadc['z1']-quadc['z2'])/(quadc['r1']-quadc['r2'])\n",
+    "  d1 = quadc['ys1']*norm - abs(d1)\n",
+    "  plt.hist(d1[np.logical_and(pcut,quadc['ys1']>0)], range=[-8,8],bins=32,log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "NEW PAIR 0\n",
+      " \n",
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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8m2+RtFjctijpAknPFwEyVcxfZV2LkmZK17eo3f+3vI4fl7Q+Io6W7rNcw2fU7tt+k2HWFRGLxf9v2v5ztT+G3q0GtJfa44yfjYhPlu5z1fbqcj/lPf/y86Jzns7He6ZlV1vnMOuS7S2S/krSRyLi+PICZ9imQ6+r+KT6enH/R20fl/SeYv5y99vI26uwRx178yNqrzMtO9Ox7Lyqaa9Gd90clrR85PlGSZ/vdcHiSfawpOWj2uXly+u9TtLfdXSfVFHXg5Iut32O26NMLi+mLdurjidZEYLLrpb0dB81raku2+ttbyrq+B5JH5K0vKdTa3vZ/n21X6S/UV5gwPZ6RNJFbo/IOkvtF/vhM9RbfryHJe1xezTHVkkXqX2QrJd1Dq2uokvriNoHvP9heeZVtuko6jrP9rri/i9Uu71OFN14/2v7fUXXyEfUx2t7rXUV9bxF0odV6p8fYXt1s+JroKL2avSom3eq3R/7rKQvSDq3mN6S9JnSfF+W9LKk19Tuv7qimH6h2i/EBUl/IensYvr3FtcXitsvHFJdv1zcx4KkX+pYxwlJ7+2Y9gdqH0z7mtpvUu8dVV2SNqg9AuiJooY/krSu7vZSe+8l1A7xx4u/m9bSXpJ+TtK/qT064mPFtN+TdPVqj1ftrqjjkp5RaeTDSusc4Pk+UF2SflvSqVL7PK72Qf6u23REdV1b3O/jah9Eny2ts6V2iB6X9GkVX9wcRV3FbTOSvtKxvlG110+pnVOn1P6E8dRqmVFFe/HNWABIrsldNwCAChD0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJDc/wHNgiHwMG/KOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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SryYZ6iblE9Sl9lnUpe6HgZeSPMP6qPlXq2oWRpddav8IcHOSrwH3sh76M3nl4Ka67wIuSHIG+DBwW3+V7W5OjtFXmYPjc2heGStJjZuHEb0kaQQGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/x2KkyKlrs6oAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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84IkGIx32LPDuiHhT92t6HmM8SXzCHNH3cCVwY/ekxn/TvUVhC2wDtkXEY8CrwIcbPkptu88CbwC+0f208UBmXtVEkMw8FBGHl+BeAmwbZAnuMToXuBzYExGPdrd9IjPvaTBT210L3NL9hf008JsN56E7jHQ78AidYcpvMcYrZL0yVpKKqzJ0I0k6Botekoqz6CWpOItekoqz6CWpOItekoqz6CWpOItekor7X2vxcNaV8zOMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "NEW PAIR 1\n",
+      " \n",
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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jX8+XAz8xcEmrMvWLg8+jqnpDz03vYHQhlfcyhWviTuJ8NSd5J3A18AvdlwLmvOYVDFrzOcxrXX18I8krq+qprt14ZuiClkryQkYhf0dV/XU3PPd1V9W3knwa+GlG7dwXdLP6uf58bIoZ/bkk2TP28ADw5e7+UeDXu9U3rwPOjv2sHFSSfcDvAW+uqv8ee+oocDDJhUl2A3uAzw1R4yrMa80TXxN5QEeBd3T33wH83YC1/IAkAT4MnKyqPxt7ai7rTrLt+ZVtSX4YeCOj/QqfBt7WbTY39S5r6L3BQ98YzSoeAR4GjgHb63t72m9h1Iv7J8ZWigx9Y7TD8gngC93t1rHnbuxqfgy4cuhax+r6ZUZ9zG8D3wDu3QA1v4nRipCvAjcOXc8KNd4JPAX8b/f/9xrg5cA/AF8BPgW8bOg6l9T8s4zaMg+PfYbfNK91Az8J/GNX7yPAe7rxSxlNSk4BfwlcOHStK908MlaSGrfpWzeS1DqDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0fdAzLhgooV/QAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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b4Bxd1nqcp14ZK0mN632yTZI0WRZ6SWqchV6SGmehl6TGWeglqXEWeklqnIVekhpnoZekxv0Poc3NQWXQnNsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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PxAS9JE2qiem6kaRJZdBLUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnH/B5V0p9PMakkxAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAD8CAYAAACB3pQWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADSdJREFUeJzt3W+MZXV9x/H3p7sgfzSCckMsC12aEhpCIpAJwWJIC9rwx0Af+ACirW1I5omtYEwMpg+Mz2pirDZpSDaA0kqxZYWWUEuliDEmunYWKC4sVECEpeBeYvn7oIB+++Cebbbrzt5zlzkz85v7fiWTuX/O3P2eObPvvXM4h5OqQpLUpl9b6wEkSYfPiEtSw4y4JDXMiEtSw4y4JDXMiEtSw4y4JDXMiEtSw4y4JDVs8xAvesIJJ9TWrVuHeGlJ2pB27tz5QlWNZv26QSK+detWlpaWhnhpSdqQkvz0cL7O3SmS1DAjLkkNM+KS1DAjLkkNM+KS1LBeEU/yySQPJ9mV5NYkRw09mCRpuqkRT3IS8AlgoarOBDYBVw49mCRpur67UzYDRyfZDBwD/NdwI0mS+poa8ap6FvgC8DTwHPBSVX1r6MEkSdNNPWMzyfHAFcCpwIvAbUk+WlVfO2C5RWAR4JRTThlgVElaf7Ze98//d/upv7hs1f/8PrtTPgD8pKrGVfUGcDvwOwcuVFXbqmqhqhZGo5lP/5ckHYY+EX8aOC/JMUkCXATsHnYsSVIfffaJ7wC2A/cDP+q+ZtvAc0mSeuj1fzGsqs8Cnx14FknSjDxjU5IaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaNjXiSU5P8uB+Hy8nuXY1hpMkHdrUy7NV1WPAWQBJNgHPAncMPJckqYdZd6dcBDxRVT8dYhhJ0mxmjfiVwK1DDCJJml3viCc5ErgcuG2Z5xeTLCVZGo/HKzWfJOkQZnknfglwf1X97GBPVtW2qlqoqoXRaLQy00mSDmmWiF+Fu1IkaV3pFfEkxwIfBG4fdhxJ0iymHmIIUFWvAe8eeBZJ0ow8Y1OSGmbEJalhRlySGmbEJalhRlySGmbEJalhRlySGmbEJalhRlySGmbEJalhRlySGmbEJalhRlySGmbEJalhRlySGmbEJalhRlySGtb38mzHJdme5NEku5O8b+jBJEnT9bo8G/Bl4O6q+nCSI4FjBpxJktTT1IgneSdwAfDHAFX1OvD6sGNJkvroszvlVGAMfCXJA0luSHLsgQslWUyylGRpPB6v+KCSpF/VJ+KbgXOA66vqbOA14LoDF6qqbVW1UFULo9FohceUJB1Mn4jvAfZU1Y7u/nYmUZckrbGpEa+q54FnkpzePXQR8MigU0mSeul7dMqfAbd0R6Y8CfzJcCNJkvrqFfGqehBYGHgWSdKMPGNTkhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhrW68o+SZ4CXgF+AbxZVV7lR5LWgb7X2AT4vap6YbBJJEkzc3eKJDWsb8QL+FaSnUkWD7ZAksUkS0mWxuPxyk0oSVpW34i/v6rOAS4BPp7kggMXqKptVbVQVQuj0WhFh5QkHVyviFfVs93nvcAdwLlDDiVJ6mdqxJMcm+Qd+24Dvw/sGnowSdJ0fY5OORG4I8m+5f+uqu4edCpJUi9TI15VTwLvXYVZJEkz8hBDSWqYEZekhhlxSWqYEZekhhlxSWqYEZekhhlxSWqYEZekhhlxSWqYEZekhhlxSWqYEZekhhlxSWqYEZekhhlxSWqYEZekhhlxSWpY74gn2ZTkgSR3DTmQJKm/Wd6JXwPsHmoQSdLsekU8yRbgMuCGYceRJM2i7zvxLwGfBn653AJJFpMsJVkaj8crMpwk6dCmRjzJh4C9VbXzUMtV1baqWqiqhdFotGIDSpKW1+ed+PnA5UmeAr4OXJjka4NOJUnqZWrEq+ozVbWlqrYCVwLfrqqPDj6ZJGkqjxOXpIZtnmXhqvoO8J1BJpEkzcx34pLUMCMuSQ0z4pLUMCMuSQ0z4pLUMCMuSQ0z4pLUMCMuSQ0z4pLUMCMuSQ0z4pLUMCMuSQ0z4pLUMCMuSQ0z4pLUMCMuSQ3rc6Hko5L8MMl/JHk4yedWYzBJ0nR9ruzzP8CFVfVqkiOA7yX5l6r6wcCzSZKmmBrxqirg1e7uEd1HDTmUJKmfXvvEk2xK8iCwF7inqnYMO5YkqY9eEa+qX1TVWcAW4NwkZx64TJLFJEtJlsbj8UrPKUk6iJmOTqmqF4H7gIsP8ty2qlqoqoXRaLRS80mSDqHP0SmjJMd1t48GPgg8OvRgkqTp+hyd8h7g5iSbmET/H6rqrmHHkiT10efolIeAs1dhFknSjDxjU5IaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIaZsQlqWFGXJIa1ucamycnuS/JI0keTnLNagwmSZquzzU23wQ+VVX3J3kHsDPJPVX1yMCzSZKmmPpOvKqeq6r7u9uvALuBk4YeTJI03Uz7xJNsZXLR5B1DDCNJmk3viCd5O/AN4Nqqevkgzy8mWUqyNB6PV3JGSdIyekU8yRFMAn5LVd1+sGWqaltVLVTVwmg0WskZJUnL6HN0SoAbgd1V9cXhR5Ik9dXnnfj5wB8CFyZ5sPu4dOC5JEk9TD3EsKq+B2QVZpEkzcgzNiWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYX2usXlTkr1Jdq3GQJKk/vq8E/8qcPHAc0iSDsPUiFfVd4Gfr8IskqQZuU9ckhq2YhFPsphkKcnSeDxeqZeVJB3CikW8qrZV1UJVLYxGo5V6WUnSIbg7RZIa1ucQw1uB7wOnJ9mT5Orhx5Ik9bF52gJVddVqDCJJmp27UySpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYUZckhpmxCWpYb0inuTiJI8leTzJdUMPJUnqp881NjcBfw1cApwBXJXkjKEHkyRN1+ed+LnA41X1ZFW9DnwduGLYsSRJffSJ+EnAM/vd39M9JklaY1Ovdt9XkkVgsbv7apLHDvOlTgBeWJmpmjPP6w7zvf6u+waQzx/Wl+1b/984nC/uE/FngZP3u7+le+z/qaptwLbDGWJ/SZaqauGtvk6L5nndYb7X33Wfz3WHt77+fXan/DtwWpJTkxwJXAncebh/oCRp5Ux9J15Vbyb5U+BfgU3ATVX18OCTSZKm6rVPvKq+CXxz4Fn2ecu7ZBo2z+sO873+rvv8ekvrn6paqUEkSavM0+4lqWHrJuLzdmp/kpOT3JfkkSQPJ7mme/xdSe5J8uPu8/FrPetQkmxK8kCSu7r7pybZ0f0M/H33H9I3nCTHJdme5NEku5O8b862+ye7n/ldSW5NctRG3vZJbkqyN8mu/R476PbOxF9134eHkpwz7fXXRcTn9NT+N4FPVdUZwHnAx7t1vg64t6pOA+7t7m9U1wC797v/eeAvq+q3gP8Grl6TqYb3ZeDuqvpt4L1Mvgdzsd2TnAR8AlioqjOZHCxxJRt7238VuPiAx5bb3pcAp3Ufi8D10158XUScOTy1v6qeq6r7u9uvMPmLfBKT9b65W+xm4A/WZsJhJdkCXAbc0N0PcCGwvVtkQ657kncCFwA3AlTV61X1InOy3TubgaOTbAaOAZ5jA2/7qvou8PMDHl5ue18B/E1N/AA4Lsl7DvX66yXic31qf5KtwNnADuDEqnque+p54MQ1GmtoXwI+Dfyyu/9u4MWqerO7v1F/Bk4FxsBXul1JNyQ5ljnZ7lX1LPAF4Gkm8X4J2Ml8bPv9Lbe9Z27heon43EryduAbwLVV9fL+z9Xk0KENd/hQkg8Be6tq51rPsgY2A+cA11fV2cBrHLDrZKNud4Bu3+8VTP4x+3XgWH51V8Nceavbe71EvNep/RtNkiOYBPyWqrq9e/hn+3596j7vXav5BnQ+cHmSp5jsOruQyX7i47pfsWHj/gzsAfZU1Y7u/nYmUZ+H7Q7wAeAnVTWuqjeA25n8PMzDtt/fctt75haul4jP3an93T7gG4HdVfXF/Z66E/hYd/tjwD+t9mxDq6rPVNWWqtrKZFt/u6o+AtwHfLhbbKOu+/PAM0lO7x66CHiEOdjunaeB85Ic0/0d2Lf+G37bH2C57X0n8EfdUSrnAS/tt9vl4KpqXXwAlwL/CTwB/Plaz7MK6/t+Jr9CPQQ82H1cymTf8L3Aj4F/A9611rMO/H34XeCu7vZvAj8EHgduA9621vMNtM5nAUvdtv9H4Ph52u7A54BHgV3A3wJv28jbHriVyf7/N5j8Jnb1ctsbCJMj9Z4AfsTkKJ5Dvr5nbEpSw9bL7hRJ0mEw4pLUMCMuSQ0z4pLUMCMuSQ0z4pLUMCMuSQ0z4pLUsP8F2CLtj3RYaJ0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+BPxbZr4AEBF3AT8PDKToq4xuvgv8Uvf/fxn41xazzPQVYDNARLwbOJ2WT6qUmZOZ+aOZuTozV9P5h/C+YZT8bCJinM5b/wsz879bjrMfODMi1kTE6XQ+KLu75UxE57fzLcDBzPyztvMcl5mfysyV3Z+pS4B/HIGSp/tz/VxEnNXddC7wZIuRjnsW+EBEvL37PT2XAX5IPG/26GdxJXBj90ON/wG2t5znuF3Aroh4HHgV+FjLe6mj7gvAW4FvdN9tPJSZV7URJDOPRcTVwL10VkTsyswn2shygg8ClwGTEfFYd9unM3Nvi5lG3TXAbd1f2E8Dv9VyHrpjpDuBR+mMKb/JAI+Q9chYSSquyuhGknQSFr0kFWfRS1JxFr0kFWfRS1JxFr0kFWfRS1JxFr0kFfd/AmD2kKY1EvAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3DOp4NWPqlhtGcn6tlUS/CXiuY/kUL02oP21TvSnOAa8ZSnRAVSp6PfDIEpuvj4gnIuKrEfErQwopga9FxNGI2L3E9jp92qQddH/zjaK/zpvMzOerx98HJpdoM8q++xDtv8SWstJr3pQ9VVnpQJdSxCj76zeBM5n5bJftjffZotwwkvNrrST6sRYRE8A/AB/JzB8v2vxN2uWJXwX+CvjykMJ6U2ZeA9wI/F5EvHlIx11RRFwEvBv4+yU2j6q/XiLbf0ePzbC0iPgosAB8oUuTUbzmfwP8EvBrwPO0yyTjZCfLX8032mfL5YZhnl9rJdGfBi7rWL60Wrdkm4h4GbAB+GHTgUXEy2m/kF/IzH9cvD0zf5yZ89XjI8DLI2Jj03Fl5unq+1ngS7T/fO5Up0+bciPwzcw8s3jDqPqrw5nzJazq+9kl2gy97yJiF/Au4OYqQbxEjdd84DLzTGb+X2b+BPjbLsccyblW5YH3Afd2a9Nkn3XJDSM5v9ZKon8UuDIiLq+uBncAhxa1OQSc/3T6/cC/dHtDDEpV/7sbeDoz/7JLm58//1lBRLyBdp83+gsoItZHxCvPP6b9Yd7xRc0OAb8dbW8EznX8Sdm0rldZo+ivRTrPow8C/7REmweAt0fEJVWp4u3VukZExDbgj4B3Z+Z/d2lT5zVvIrbOz3Xe2+WYdd6/TXgr8O3MPLXUxib7bJncMJrza9CfNjf1RXuUyHdof3r/0Wrdn9I++QF+lnYp4ATwb8AVQ4jpTbT/9HoSeLz6egdwG3Bb1WYP8BTtkQYPA78+hLiuqI73RHXs8/3VGVcAd1X9eQxoDel1XE87cW/oWDeS/qL9y+Z54H9p10Fvof25zj8DzwJfB15dtW0Bf9ex74eqc+0E8DsNx3SCds32/Dl2fnTZLwBHlnvNh9Bfn6/OnydpJ7HXLY6tWn7J+7fJuKr1B8+fVx1th9Jny+SGkZxf/mesJBVurZRuJEl9MtFLUuFM9JJUOBO9JBXORC9JhTPRS1LhTPSSVDgTvSQV7v8BaZyrFhRs04oAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "NEW PAIR 2\n",
+      " \n",
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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1XCTpl1UZn6+xvfpZdRsYUXu1+qib16g7HvuMpI9LuqxMn5d0f2W+T0l6XtJL6o5f3VimX6Xuhrgk6a8lbSvTv7/8vVTuv2pMdf1aeYwlSb/as46zkt7QM+1P1N2Z9kV136TeUFddkrarewTQE6WG90na0nR7qdt7CXVD/PHyc89m2kvSL0j6N3WPjnh3mfZHkm5Z7/mqOxR1RtLTqhz5sNo6N/D/vqG6JP2BpPOV9nlc3Z38fV/Tmuq6rTzu4+ruRF+orHNe3RA9I+kDKidu1lFXuW+vpM/2rK+u9voZdXPqvLqfMJ5cLzNG0V6cGQsAybV56AYAMAIEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAk939sD3mjWTNOhAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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BQ8ApzFg/x2FYdN9FwO29x6cAj/YdO9LbN0tmqY+z1JeWl1XVY73H/wW8bJLFLKUk64EzgG8yw/1sed6kC1itkvwz8DtDDn2oqv6x1+ZDzA2Hb1jJ2pbKKH3U7KmqSjITp1kmeSHwReD9VfWTJM8em6V+jsKwmJCqOuu5jid5F3Au8Cf1/+c3HwVO7Wu2rrevk1p9nMdU9bFhlvrS8oMkL6+qx5K8HHh80gUtVpLnMxcUN1TVl3q7Z66fo3IaqoOSbAE+CJxXVf/Td+g24MIkxyc5DdgI/NskalxGs9THe4CNSU5LchxzC/e3Tbim5XIb8M7e43cCUz1yzNwQ4jrgUFV9ou/QTPVzHF6U10FJDgPHA//d2/WNqtrZO/Yh5tYxjjE3NL59+LN0W5I/BT4NnAz8GPhWVb25d2wm+giQ5Bzgk8AaYHdVfWTCJS1akhuBNzB3F9YfAFcBe4Gbgd9l7u7PF1TV4CL41Ejyx8C/AP8OPNPb/TfMrVvMTD/HYVhIkpqchpIkNRkWkqQmw0KS1GRYSJKaDAtJUpNhIUlqMiwkSU2GhSSp6f8AXx6TAnwksHUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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XpOL+ByBgAKvYHGaKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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S9COS3iTpY2q3Wi+qZvXpft60bvoQEUsR8RMRMRERE2q/xXtbRHxb0lFJv1Gtvnm7pJWOt7+NZnta7be6N0bE9zoeOippv+1LbO9U+0L0P5SosWbHJe2qVmJcrPYF56OFa6pd1Zu+T9KpiPiTjoeOSrq1+vpWSZ/d7NoGJSLujIjt1et3v6S/j4ibJX1R0k3VYanGLDGjr9MxSe9S+4Lk9yT9ZtlyavVxSZdIerx6J/NkRNweESdtPyTpGbVbOu+PiP8tWGctIuKc7TskPSZpi6T7I+Jk4bIG4VpJt0hasv3Vat8HJd0l6SHbh9T+VNn3FKpvM/2upHnbfyjpK2r/AkyDO2MBIDlaNwCQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMn9P8jpylQufWYAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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DXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0fAO+4isY/ZZwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6AXhz91NOrwSeGLotNdLM/GW6JD/H4D71GuDWqvrdKY+0rJK8Bvgb4Mv8333532bwvsSngQ0M/iXdX6qqxW96rRpJLgbeXVVvSPJjDF5ZnA08APxaVX1vmvMtlyQvZfCm/WnAw8BbGPyhcNVf+yTvB36ZwU/4PQC8lcF991V57ZN8CriYwb/2+ihwHfDnjLjWXTg/zuAW3HeBt1RV/2k//6xEQpK0dLNyu0mS9AwYCUlSk5GQJDUZCUlSk5GQJDUZCUlSk5GQJDUZCUlS0/8ARSXmFuwInocAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ngZ8HJlL0rYxuvgn80uDPvwz8W4dZZvsMsA4gyXnAmXR8UaWq2lNVP1ZVK6pqBf1vhNcsRMnPJcl6+r/6v6Wq/qfjOPcD5yZZmeRM+m+U3d1xJtL/6XwrsLeq/qLrPEdV1Q1VtXzwmroS+IcpKHkGr+unkpw/2HUJ8GiHkY56EnhdkpcM/k0vYYJvEp8yR/Rz2ATcPHhT43+BzR3nOeo24LYkDwPPA+/o+Ch12n0EeBHwxcFvG1+pqmu6CFJVR5JcB9xLf0XEbVX1SBdZjvEG4GpgT5KHBvveV1U7O8w07a4Hbh/8wH4c+K2O8zAYI90FPEh/TPk1JniGrGfGSlLjWhndSJKOw6KXpMZZ9JLUOItekhpn0UtS4yx6SWqcRS9JjbPoJalx/w+hNMLjvqn+gwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "NEW PAIR 3\n",
+      " \n",
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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0XW6+uyU9Iel5ZeNXq9L0U5TtiBOSfixpQZr+0nR9It1+Skl1fSTdx4SkDzesY4+k0xumXansYNrvlL1Ind6ruiQtVHYG0IOphq9Jmld1eynrvbiyEN+Z/j46l/aS9G5Jf1Z2dsQVadqXJF0w2+NVNhS1W9LDyp350GydXTzfu6pL0uck7c+1z05lB/lbbtMe1XVRut+dyg6ij+bWOawsRHdLukbpg5u9qCvdNiLpnob19aq93qwsp/Yre4fx0GyZUUR78clYAAiuzkM3AIACEPQAEBxBDwDBEfQAEBxBDwDBEfQAEBxBDwDBEfQAENx/AYejaVpYEezSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADBpJREFUeJzt3W+oZPdZB/Dv09Sq7Ep8kbJCEtzAhmBIBOmltfhm4x/c2mxLi0JCKQRjFsWAQkBT+saXAVEoNFiWGkKhZglVcbcbiRVc8iZKklKaJrESSkp3EWMQVhMLEvrzRW6Sm7A3O3fvzJyZZz6fV3fmTmafX+bM9/7Oc37nnBpjBIC+3jd1AQAslqAHaE7QAzQn6AGaE/QAzQl6gOYEPUBzgh6gOUEP0Nz7py4gSa655ppx+PDhqcvYs9deey0HDhyYuoyl2rQxb9p4E2NeJ88888wrY4wPXu51KxH0hw8fztNPPz11GXt27ty5HD16dOoylmrTxrxp402MeZ1U1fdneZ3WDUBzgh6gOUEP0JygB2hO0AM0J+gBmhP0AM0tJOir6kBVPV1Vty/i/QGY3UwnTFXVQ0luT/LyGOOWHc8fS/KFJFcl+fIY44HtX/1xkkfnXCswocP3n33r55ce+PiElbBXs54Z+3CSLyb5yptPVNVVSR5M8mtJzid5qqpOJ7k2yfNJfmKulQJLtzPcWV81xpjthVWHk3z9zRl9VX00yZ+MMX59+/Hntl96MMmBJDcn+WGST40xfnSJ9zuR5ESSHDp06EOnTp3a10Cm8Oqrr+bgwYNTlzEXz164uOvvbr326rd+7jTmWWzaeJN3jnm37WLnNtHBun7Ot9122zNjjK3LvW4/17q5NskPdjw+n+QjY4x7k6Sq7kryyqVCPknGGCeTnEySra2tsY7XmVjX62Ncyl3vMXN76TNH3/q505hnsWnjTd455t22i53bRAfdP+eFXdRsjPHwot4bgNntJ+gvJLl+x+Prtp9jTei/cqUcmF0v+1le+VSSG6vqhqr6QJI7kpyeT1kAzMtMQV9VjyR5MslNVXW+qu4eY7ye5N4kjyd5IcmjY4zn9vKPV9Xxqjp58eLuBwIB2J+ZWjdjjDt3ef6xJI9d6T8+xjiT5MzW1tY9V/oeALw3l0AAaE7QAzQn6AGaW4mbg7Padi6le/jYgQkrYRmevXDxPU+gY/1MOqO36gZg8SYN+jHGmTHGiauv7nXdDIBVokcP0Jwe/YZx2QPYPIIe2Jd3Tx5c+2b1aN0ANGfVDUBzVt0ANKd1A9CcoAdoTtADNCfoAZoT9ADNWV4J0JzllQDNad0ANCfoAZoT9ADNCXqA5gQ9QHOCHqA56+gBmpv0DlNjjDNJzmxtbd0zZR3A/Oy845S7Ta0GtxIE3hHO9906YSEshB49e/LshYs5fP9ZNxmHNWJGvwGEMmw2M3qA5gQ9QHOCHqA5QQ/QnKAHaM6ZsQDNucMUQHNaNwDNCXqA5gQ9QHOCHqA5QQ/QnKAHaE7QAzQn6AGaE/QAzQl6gOYEPUBzLmoG0JyLmgE0p3UD0JygB2hO0AM09/6pCwD6Onz/2bd+fumBj09YyWYT9LChdoYwvQn6ppbxJTZbg/WgRw/QnKAHaE7QAzQn6AGaE/QAzQl6gOYEPUBzgh6gOUEP0JygB2jOHaYAmnOHKYDmtG4AmhP0AM0JeoDmBD1Ac4IeoDlBD9CcoAdozj1jYYO4IfhmEvTAUriZ/HS0bgCaM6NvxG45cClm9ADNCXqA5rRumAsH2mB1mdEDNCfoAZoT9ADNCXqA5gQ9QHOCHqA5QQ/QnKAHaE7QAzTnzFhozsXuMKMHaE7QAzQ396Cvqp+rqi9V1deq6vfm/f4A7M1MPfqqeijJ7UleHmPcsuP5Y0m+kOSqJF8eYzwwxnghye9W1fuSfCXJX8y/bN60iv1XV7KE1TLrjP7hJMd2PlFVVyV5MMnHktyc5M6qunn7d59IcjbJY3OrFIArMtOMfozxRFUdftfTH07y4hjje0lSVaeSfDLJ82OM00lOV9XZJH81v3KBDuz1LVeNMWZ74RtB//U3WzdV9ZtJjo0xfmf78WeTfCTJ15J8OsmPJ/n2GOPBXd7vRJITSXLo0KEPnTp1al8DmcKrr76agwcPTlrDsxcuLvXfO/STyX/8cPbX33rt1YsrZglW4TPer71uI3v9jPdrFbaRdf2cb7vttmfGGFuXe93c19GPMc4lOTfD604mOZkkW1tb4+jRo/MuZeHOnTuXqeu+a8k9+vtufT1/9uzsm81Lnzm6uGKWYBU+4/3a6zay1894v1ZhG+nwOb+X/ay6uZDk+h2Pr9t+DoAVsp+gfyrJjVV1Q1V9IMkdSU7PpywA5mWmoK+qR5I8meSmqjpfVXePMV5Pcm+Sx5O8kOTRMcZziysVgCsx66qbO3d5/rHsYwllVR1PcvzIkSNX+hYAXMakl0AYY5wZY5y4+urpj7oDdOVaNwDNCXqA5gQ9QHOTBn1VHa+qkxcvLvfsToBNMukdpsYYZ5Kc2draumfKOqCbVbyqKdNxK8E15EsM7IUePUBzgh6gOa0bYFKuTb94kwa9SyD050sM03MJBIDm9OgBmtOjhyYsu2U3ZvQAzZnRszQOzMI0zOgBmrO8ElgZ9voWw/JKgOa0bgCaczCWSdhFh+UR9LDGrJ1nFoJ+TfhCA1dK0K8w4Q7Mg6Bncvr1sFhuDg7QnJuDAyvJnt78aN2wUny5Yf4EPawZB+nZK2fGAjRnRr9izNa4FNsF+yHoWVn69TAfWjcAzZnRrwC75VyK7eJt9u72x4weoDlnxgI058xYWCHaNSyCHj1rR78W9kbQsxbMdOHKCXqYgL0SlknQw8TsrbBogh5YK/aG9s46eoDmBD1Ac1o3rDW78XB5gn6JHHRbLKEPlyboYUbv/kPtj8n0/HGfjaBnYy0jJAQRq2DSoK+q40mOHzlyZMoy2CB7bZ89e+Fi7trlv9lriGvdMRUXNaMlM2l4m9bNgpnFAVOzjh6gOTN62ptlr2qerR57cawaQQ9zINyn57jM7gT9AvjSr7edn999t05YCMyJoAfaMbt/J0E/J2bxwKqy6gagOUEP0JzWzQz0+4B1ZkYP0JwZ/Q5m7tCP77UZPUB7Gz2jtyQSNstu3/mHjx1YciXLtdFBfyV2bijdNw6gB0G/C7N9oAtBvw/vdfchgFUx6cHYqjpeVScvXrw4ZRkArbW6leAsy6i0ZIBNsxGtG+EOzNO6rc23jh6gOUEP0JygB2huI3r0AMuwqr17M3qA5trO6K20AWa18+THjkuz1z7o1/l/PrD+1iGD1j7oAdbBlP17QQ+ww6oeUN0PB2MBmhP0AM0JeoDmBD1Acw7GAuxiUUsnl33A14weoDkzeoAFWKUTqczoAZoT9ADNCXqA5gQ9QHOCHqA5QQ/QnKAHaE7QAzQn6AGaqzHG1DWkqv4zyfenruMKXJPklamLWLJNG/OmjTcx5nXys2OMD17uRSsR9Ouqqp4eY2xNXccybdqYN228iTF3pHUD0JygB2hO0O/PyakLmMCmjXnTxpsYczt69ADNmdEDNCfo96Gq/rSq/rWqvl1Vf1tVPz11TYtWVb9VVc9V1Y+qqu0qhSSpqmNV9d2qerGq7p+6nkWrqoeq6uWq+s7UtSxLVV1fVf9UVc9vb9d/MHVNiyDo9+cbSW4ZY/x8kn9L8rmJ61mG7yT5dJInpi5kkarqqiQPJvlYkpuT3FlVN09b1cI9nOTY1EUs2etJ7htj3JzkF5P8fsfPWdDvwxjjH8YYr28//Ock101ZzzKMMV4YY3x36jqW4MNJXhxjfG+M8X9JTiX55MQ1LdQY44kk/zV1Hcs0xvj3McY3t3/+nyQvJLl22qrmT9DPz28n+fupi2Burk3ygx2Pz6dhAPC2qjqc5BeS/Mu0lcyfm4NfRlX9Y5KfucSvPj/G+Lvt13w+b+wCfnWZtS3KLGOGTqrqYJK/TvKHY4z/nrqeeRP0lzHG+NX3+n1V3ZXk9iS/MpqsVb3cmDfEhSTX73h83fZzNFNVP5Y3Qv6rY4y/mbqeRdC62YeqOpbkj5J8Yozxv1PXw1w9leTGqrqhqj6Q5I4kpyeuiTmrqkryl0leGGP8+dT1LIqg358vJvmpJN+oqm9V1ZemLmjRqupTVXU+yUeTnK2qx6euaRG2D7Lfm+TxvHGA7tExxnPTVrVYVfVIkieT3FRV56vq7qlrWoJfSvLZJL+8/R3+VlX9xtRFzZszYwGaM6MHaE7QAzQn6AGaE/QAzQl6gOYEPUBzgh6gOUEP0Nz/A0/2cHaObRTQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ktys3Pl9he3XTcR8oqb0afdbNM5WNx94t6WeSjkvTpyRdnpvvBkn3SXpY2fjVGWn6Scp2xHlJ35W0Kk1/aro+n24/aUh1vSfdx7ykd7et44CkF7ZN+4yyg2m/V/Yi9cKq6pK0WtkZQLemGr4oaUXd7aWs9+LKQvyW9HfxIO0l6S2S/qjs7IiPpWmfknT2co9X2VDUfkl3KXfmQ6d19vF876suSR+XdDjXPrcoO8jfdZtWVNe56X5vUXYQfXNunVPKQnS/pEuVPrhZRV3ptmlJN7atr6r2epWynDqs7B3G7ctlRhntxSdjASC4Jg/dAABKQNADQHAEPQAER9ADQHAEPQAER9ADQHAEPQAER9ADQHD/Aw3Ffj00R+BxAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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0fXaDoAeA5Bi6AYDkCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASO7/AYI5ilAwO9VmAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6s67zHFNVN1XV2v7P1OXA309AydP/uX46yfn9XRcD3+ow0jFPAW9N8ur+9/RixniReMmc0S/gGuDj/Ysa/w1s6TjPMbcCtyZ5DHgR+M2Oz1In3V8CrwAe6D/b+GpVXdtFkKo6muR64D56MyJurarHu8hynLcDVwJ7k3y9v+9DVbWrw0yT7gbgtv4f7CeB3+o4D/1hpDuBR+kNU36NMb5C1lfGSlLjWhm6kSSdgEUvSY2z6CWpcRa9JDXOopekxln0ktQ4i16SGmfRS1Lj/hcXi3q2pbEsCQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MPJaZP8zMHwF/02adQ9nXqhz4LPBguzZ1brM22TCU/WupBP1e4LKIuLg6GtwA7JzTZidw6uz0TcC/tPtA9EvV//dV4GBm/kWbNj996lxBRFxFY5vX+h9QRCyLiA+eekzjZN6BOc12Ar8VDR8BZpq+Utat7VHWMLbXHM370S3AP7Zo8zhwTUScV3VVXFNNq0VETAF/CNyQmf/Tpk0n73kdtTWf1/lMm3V28vmtw68D383MI61m1rnN5smG4exf/T7bXNcPjVEir9E4e//latqf0tj5AT5AoyvgEPBvwCUDqOljNL56vQy8WP1cB9wO3F612QK8QmOkwbPArwygrkuq9b1UrfvU9mquK4B7q+25H5gY0Pu4jEZwjzVNG8r2ovGfzZvA/9LoB72VxnmdfwZeB/4JOL9qOwH8bdOyX6j2tUPAb9dc0yEafban9rFTo8t+Btg933s+gO31jWr/eZlGiF0wt7bq+Wmf3zrrqqbvOLVfNbUdyDabJxuGsn95ZawkFW6pdN1Iknpk0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVLj/A9naf6nxwEleAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AJxuMdNSzwLsj4k3d7+lFjPBD4tPmjL6Hq4Gbux9q/DfdG5q0wHZge/c+u68CH2z4LLXtPgu8Afha993Gg5l5TRNBMvPIfCzYNwIXAlcBeyPise6+j3fXnNLxXQ/c1v2F/Qzwmw3noTuMdCfwKJ1hym8ywitkvTJWkoqrMnQjSToBi16SirPoJak4i16SirPoJak4i16SirPoJak4i16SivtfajdrfIHTsy8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "NEW PAIR 4\n",
+      " \n",
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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K8FngLX3XOVHvjzBuyzw18Zm9aog1Az8A/F1X6xHgY934xYwnIseATwFn913rtDfPjJWkxtm6kaTGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXufwG9nw1OhLg9BwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3SPqKpKmIeKF46FuSpmoKqyp/rPbJ2v8W22+U9B8dJzSp/t75FoUsge0vSPqxizz0cUkfU7tsk8ql+hwRnyv2+bjaX/UvuuA7xpftayT9haTfiIj/bJ/gtkVE2E4zPM/2uyWdj4hTtpt1x7MeSPQXERHvuFi77VlJ2yR9vfggbJH0VdvXq4Q1dOvUrc8X2N4v6d2Sfi5eHZM71n2+jMx9ew3bP6h2kv9sRPxl0fyi7TdHxAtFCfJ8fRGW7kZJt9h+l6QfkvR6SX+idrn1dcVZfaq/N6WbPkTEUkT8aERMR8S02l/v3hoR31J7vdxfLkbfvF3ScsdX37Fme5faX3NviYjvdDx0TNIe21fa3qb2hei/qyPGCgy9JvI4KGrT90h6OiL+qOOhY5I+WPz+QUmfW+/YqhIRt0fEluIzvEfS30bEPklfkvS+YrdUfeaMvjwnJb1L7QuS35H0oXrDKdVdkq6U9HDxTeaxiLg1Ik7bvl/SU2qXdD4cEd+rMc7SRMSq7QtrIm+QdDTpmsg3SvqApCXbf1+0fUzSHZLut31A7Zll319TfOvpo5IWbf+upK+p/Q9gCtwZCwDJUboBgORI9ACQHIkeAJIj0QNAciR6AEiORA8AyZHoASA5Ej0AJPd/NFKK3/fMbQQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4C+A3MvM/IuI771Vq5zAM+g3IzJ97t/cj4k7gg8DP5nfnr07UGrtrtXEVE9XGNVRqy1pei4j3ZearEfE+4PW2KzSqiPg+lkP+s5n5l/3icu0clkM3DYuI3cBvAjdl5n8NvHUY2BsRF0bEFcCVwD+2UccxqtTGkddEniCHgTv6P98BTPQ3tljuuj8CnMzMPxh4q1Q718MbphoWEaeAC4Fv9Yu+lJn39t+7n+Vx+yWWv04+df69dFtE/ALwx8ClwL8DX8nM9/ffK9FGgIj4eeAPgS3Ao5n58ZarNLL++s9zLD/J8TXgY8DngceBH2H5KbK/lJkrL9hOjIj4GeDvgBPAt/vFv83yOH2Zdq6HQS9JxTl0I0nFGfSSVJxBL0nFGfSSVJxBL0nFGfSSVJxBL0nFGfSSVNz/ATE5Yc+CQtMwAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bKlXV4ar6saraUFUb6P1HeM1SlPxCkkzT+9H/zVX13x3HuQc4P8l5Sc6kd6Hs9o4zkd6r803Aw1X1513nOaGq3ldV6/vfU5cD/zABJU//+/rxJK/s77oYeKjDSCd8E3h9kpf0/00vZowXiZfNiH4BVwEf7l/U+B9gV8d5TtgL7E3yAPAc8I6OR6mT7q+AFwFf6P+08eWqurqLIFV1PMk1wF30VkTsraoHu8hykjcAVwCHk9zf3/f+qjrQYaZJdy1wc/8F+1HgtzrOQ38a6TbgPnrTlF9ljO+Q9Z2xktS4VqZuJEmnYNFLUuMseklqnEUvSY2z6CWpcRa9JDXOopekxln0ktS4/wOkH4w5UNXuhQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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z3QlsAIiIdwFn0PBJlTJzf2b+WGaOZeYYnf8I712Mkp9PREzSeet/cWb+d8NxHgbOiYjVEXEGnQNldzWciej8dr4ZOJCZf9Z0nlmZ+anMXNl9TV0G/EMLSp7u6/qZiDi3u+kC4MkGI816Gnh/RLy1+zO9gCEeJD5lRvTzuAq4oXtQ43+ArQ3nmbUT2BkRjwMvAx9peJTadn8FvBn4RvfdxoOZ+bEmgmTmsYi4BriXzoqInZn5RBNZjvMB4Apgf0Q81t326czc02CmtrsWuKX7C/sp4LcazkN3Gul24FE605TfYoifkPWTsZJUXJWpG0nSCVj0klScRS9JxVn0klScRS9JxVn0klScRS9JxVn0klTc/wF8Qag3UHgxfQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "NEW PAIR 5\n",
+      " \n",
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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eHnvfXj3UmoEfAf6+q/cw8Mlu/GJGk5FjwF8C5/dd62o3vxkrSY2zdSNJjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8BtUoLYMs+v3YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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jQET8HPAHwDbgcGZ+cshd6ltr/ecZ1u7k+DpwP/AF4FHgh4GvA7+YmedfsB0bEfEzwN8CJ4Bvt5p/i7U6fZlxdsOgl6TiLN1IUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQV938aqd60S4TF0gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6AXhz91NOrwSeGLotNdLM/GW6JD/H4D71GuDWqvrdKY+0rJK8Bvgb4Mv8333532bwvsSngQ0M/iXdX6qqxW96rRpJLgbeXVVvSPJjDF5ZnA08APxaVX1vmvMtlyQvZfCm/WnAw8BbGPyhcNVf+yTvB36ZwU/4PQC8lcF991V57ZN8CriYwb/2+ihwHfDnjLjWXTg/zuAW3HeBt1RV/2k//6xEQpK0dLNyu0mS9AwYCUlSk5GQJDUZCUlSk5GQJDUZCUlSk5GQJDUZCUlS0/8ARSXmFuwInocAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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k8DPAWIq+laGbrwM/3//9LwD/0mGWxT4FbAVI8mLgbDpeWKmqDlbVj1TVxqraSO8/wstXo+SXkmSO3lv/11fVf3cc5wHggiSbkpxN74OyOzvORHqvzjcDj1bVn3Wd57iqendVbeg/p64E/n4CSp7+8/rJJC/pb7oE+EqHkY77GvCqJM/r/5tewhg/JD5tzuiXcDVwY/9Djf8Btnec57hdwK4kDwPPAL/R8VnqpPtL4DnAff13G5+rqmu6CFJVx5JcC9xDb0bErqp6pIssJ3gN8BbgYJIv9re9p6r2dZhp0l0H3NJ/wX4c+M2O89AfRrodeIjeMOUXGOM3ZP1mrCQ1rpWhG0nSSVj0ktQ4i16SGmfRS1LjLHpJapxFL0mNs+glqXEWvSQ17v8AajON5GzoNO4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "NEW PAIR 6\n",
+      " \n",
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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5Xg4cysxnMvM/gOPV/rrZ59DiyswvZuZ/Ve0PAc+NiHN6PP7A4+q0w4h4GfCCzPx8trPY7XR4b48grr3VtoOyalyZ+XhmPgh8f9m2K74HBjReY53oN2fm16vb/w1s7mHbFwPfyczT1f2TwHnV7fOArwFUjy9W/QcZ17PHWOH4S5ZmGfWz4W+JiAcj4s6IOL+HmAYV14eqX1n/uPamGIvxiogX0v7N7TO15l7Hq5vXpdPz7bRtN/scZlx1bwHuz8xnam0rvaajimtrRHwxIv4pIn651v/kKvscdlxL3grcsaxt2OPV67aDGK/G/zj4p4EfW+Ghd9XvZGZGxMiWB40orj3A22r354A7MvOZiPhd2rOR19Q3GHJcV2bmQkQ8H/jbKrbbu9lw2OMVEWfRfkPekJknquZVx2uaRMTPAO8FXl9r7vs1HYCvAz+Rmd+KiB3Ax6sYx0JEXAJ8LzO/XGtucryGqtFEn5m/0umxiPhGRLwsM79e/fryRA+7/hbwwog4q/o03wIsVI8tAOcDJ6sEsrHqP8i4FoDZ2v0ttOt/S/v4OeCszDxaO2Y9hptp17Z/yDDjysyF6v/vRsRHaP8aejtjMF601xk/mpnvrx1z1fHqcJz6zL/+c7G8z/Lne6ZtV9vnMOMiIrYAfwe8PTMfW9rgDK/p0OOqflN9pjr+0Yh4DHh51b9efhv5eFX2sGw2P6LxOtO2s8u2nWcw4zXWpZvDwNKZ56uAT3S7YfVDdi+wdFa7vn19v1cA/7isfDKIuO4BXh8R50Z7lcnrq7Yle1n2Q1YlwSVvAh7uIaY1xRURZ0XEpiqOHwEuA5ZmOo2OV0T8Ge036R/WN+hzvO4DLoz2iqyzab/ZD58h3vrzPQzsifZqjq3AhbRPknWzz6HFVZW0jtA+4f3PS51XeU1HEddLImJddfwLaI/XiaqM978R8aqqNPJ2enhvrzWuKp7nAL9BrT4/wvHqZMX3wIDGa6xX3byYdj32UeDTwIuq9hZwc63f54BvAk/Trl9dWrVfQPuNeBz4G+Ccqv1Hq/vHq8cvGFJcv10d4zjwW8v2cQJ4xbK2P6d9Mu1LtD+kXjGquID1tFcAPVjF8AFgXdPjRXv2krST+APVv6vXMl7ArwJfpb064l1V258Cb1rt+dIuRT0GPEJt5cNK++zj572vuIB3A0/VxucB2if5O76mI4rrLdVxH6B9En13bZ8t2kn0MeCDVF/cHEVc1WOzwOeX7W9U4/WLtPPUU7R/w3hotZwxiPHym7GSVLhxLt1IkgbARC9JhTPRS1LhTPSSVDgTvSQVzkQvSYUz0UtS4Uz0klS4/wfV4eiTZlHqrAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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GvSQ17v8A8SWQry4g6DwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6AXhz91NOrwSeGLotNdLM/GW6JD/H4D71GuDWqvrdKY+0rJK8Bvgb4Mv8333532bwvsSngQ0M/iXdX6qqxW96rRpJLgbeXVVvSPJjDF5ZnA08APxaVX1vmvMtlyQvZfCm/WnAw8BbGPyhcNVf+yTvB36ZwU/4PQC8lcF991V57ZN8CriYwb/2+ihwHfDnjLjWXTg/zuAW3HeBt1RV/2k//6xEQpK0dLNyu0mS9AwYCUlSk5GQJDUZCUlSk5GQJDUZCUlSk5GQJDUZCUlS0/8ARSXmFuwInocAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "NEW PAIR 7\n",
+      " \n",
+      "dphi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphiNor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAC/9JREFUeJzt3f+r5XVeB/DnK/vCMsb0g8sEKo2gSIMG0cUt9pexLzCmrrRU6MqCZA5FQoFQxv4DQmwQJSxDyfwiimzF7qRhG3TxFws1lh3dyRBx2Rkik4UpbWEZ9t0PXre77lznfjnnfM55ncfjp3vOPXPv6zOfc5/ndV6f9/l8aowRAPr6oakLAGC+BD1Ac4IeoDlBD9CcoAdoTtADNCfoAZoT9ADNCXqA5n546gKS5JprrhlHjx6duow9e++993Lo0KGpy1ioddvmddvexDavkldeeeWdMcbHr/S4pQj6o0eP5uWXX566jD3b3NzM8ePHpy5jodZtm9dtexPbvEqq6hu7eZzRDUBzgh6gOUEP0JygB2hO0AM0J+gBmhP0AM0JeoDmluIDU8DyO/ros9/7+q3H7pywEvZKRw/QnI6eJLo1Lm/784LVJej5AR/+4xb8sNoEPbBn3gGuFjN6gOYEPUBzRjdrzIE2WA86eoDmdPTA9zl74WIe8G6vlbl09FV1qKperqq75vHzAdi9XXX0VfVEkruSvD3GuGXb/SeS/FmSq5L85Rjjsa1v/VGSZ2ZcKxPZPss/fWL1LqAM6263Hf3pJCe231FVVyV5PMkdSY4lua+qjlXVryT5epK3Z1gnAPu0q45+jPFCVR390N23JXljjPFmklTV00nuSXJ1kkN5P/y/XVXPjTG+O7OKAdiTgxyMvTbJN7fdPp/kE2OMh5Okqh5I8s5OIV9VJ5OcTJIjR45kc3PzAKVM4913313Juj/wyK2X9vxvVn2b92rdtjdJjnxsb8+NDv8/3ffz3FbdjDFOX+H7p5KcSpKNjY1x/PjxeZUyN5ubm1nFuj+wn5UVp08cWult3qtV38f78edPfimfP7v7aHjr/uPzK2ZBuu/ng6y6uZDk+m23r9u6D4AlcpCO/qUkN1XVDXk/4O9N8pmZVMXc+DQss+Zsp8tvVx19VT2V5MUkN1fV+ap6cIxxKcnDSZ5Pci7JM2OM1/byy6vq7qo6dfHixb3WDcAu7XbVzX073P9ckuf2+8vHGGeSnNnY2Hhovz8DgI/mXDcAzQl6gOYEPUBzkwa9g7EA8zdp0I8xzowxTh4+fHjKMgBaM7oBaE7QAzQn6AGaE/QAzVl1A9CcVTcAzc3tfPT0dPbCxe+dx95ZCvvYfgbKR26dsBDmwoweoDkdPTBT298deNe3HHT0AM0JeoDmLK8EaM7ySoDmHIxdAy4IDuvNjB6gOUEP0JygB2hO0AM0J+gBmrOOHqA56+gBmjO6AWhO0AM0J+gBmhP0AM0JeoDmBD1Ac4IeoDlBD9CcT8YCNOeTsQDNGd0ANCfoAZoT9ADNuTh4Uy4IDnxA0MOaWkQzsP13vPXYnXP/fVye0Q1Aczp69k23BqtBRw/QnKAHaE7QAzQn6AGac1IzgOac1AygOaMbgOYEPUBzgh6gOUEP0JygB2hO0AM0J+gBmhP0AM0JeoDmBD1Acy480ojrxAKXo6MHaE7QAzRndANrxHhvPenoAZrT0TMT2zvFtx67c8JKWFaeI9NxhSmA5lxhCqA5M3qA5gQ9QHOCHqA5QQ/QnKAHaE7QAzQn6AGaE/QAzQl6gOYEPUBzgh6gOUEP0JygB2hO0AM0J+gBmnOFqRXnGqDAlejoAZrT0TNzrg3KlXiOLJagh+aM9zC6AWhO0AM0J+gBmhP0AM05GAsNOQDLdjp6gOZmHvRV9dNV9YWq+mJV/e6sfz4Ae7OroK+qJ6rq7ap69UP3n6iq16vqjap6NEnGGOfGGL+T5DeTfHL2JQOwF7vt6E8nObH9jqq6KsnjSe5IcizJfVV1bOt7n0rybJLnZlYpAPuyq6AfY7yQ5Fsfuvu2JG+MMd4cY3wnydNJ7tl6/JfHGHckuX+WxQKwdzXG2N0Dq44m+bsxxi1bt389yYkxxm9v3f5skk8k+WKSTyf5sSRfG2M8vsPPO5nkZJIcOXLk555++ukDbcgU3n333Vx99dWT1nD2wsWF/r4jH0v+89v7+7e3Xnt4tsUswDLs4/04yPPiIPt4P5bhebGq+/n2229/ZYyxcaXHzXx55RhjM8nmLh53KsmpJNnY2BjHjx+fdSlzt7m5manrfmDBy+geufVSPn92f0+bt+4/PttiFmAZ9vF+HOR5cZB9vB/L8LxY1f28WwdZdXMhyfXbbl+3dR8AS+QgL9svJbmpqm7I+wF/b5LPzKQqPpIPwwB7sdvllU8leTHJzVV1vqoeHGNcSvJwkueTnEvyzBjjtfmVCsB+7KqjH2Pct8P9z+UASyir6u4kd9944437/REAXMGk57oZY5xJcmZjY+OhKeuADoz02Ilz3QA05+yVwKRcP3b+dPQAzU0a9FV1d1WdunhxsZ/uBFgnkwb9GOPMGOPk4cPTfwQaoCszehbGLHb2rLRhN8zoAZoT9ADNCXqA5qy6AWjOKRCYhAOzsDhW3ayIzqsrhD7Mlxk9QHOCHqA5oxtYMZ3HeMyHoIcl5dgFszJp0LvCFLCdF7f5sLwSVoBxDQdhdLPE/HGzznT3syPoWSo7/XGvyx+9F3fmQdDDxIQ782YdPUBzgh6gOaMbmIBxDYtkHT1LSxjCbLg4OEBzRjfA0luX5bXzIuhZOTuNdJYxAAQUy0DQLxlzaWDWLK8EaE5HD6wU47C909EDNKejhxlznIVlI+hpw1t6uDyfjKW9ZXkB0OkzFVeYWgICYHGWJfRhkRyMhY9w9sLFHH30WS/GrDRBD9Ccg7G0pANfP8ZyOxP0rC3BsPq8oO+OoIcZEDgsM0EP+yTcWRWCfiJCAubHWO77WXUD0JyOHqIDpDdBPyM7BYUAWT3b99kjt05YCDPhb9DoBqA9JzWDXXIAnVU1aUc/xjgzxjh5+PDhKcsAaM3oBqA5B2MXyFt/YAqCHlhL2xuv0ycOTVjJ/BndADSnoz+Asxcu5oHLjGOMaIBloqMHaE5HD6yNdX23LegBdtDl9AmCHmAXVjn0Bf0eOeEV9LPTwoqdrFroOxgL0JyOHuAAVqG719EDNCfoAZozutnBKrwdA9iNtQh6oQ0swrJmzcpfYWqv/7Ef9cm4ZdoxALMyadCPMc4kObOxsfHQlHVcybp+bBroYS1GNwDLZNEjHkG/jc4dmJcp88XySoDmBD1Ac4IeoDkzeoA5WKZjfjp6gObadvTL9GoKMKVWQS/cAX6Q0Q1Ac4IeoDlBD9CcoAdoTtADNCfoAZoT9ADNCXqA5gQ9QHM1xpi6hlTVfyX5xtR17MM1Sd6ZuogFW7dtXrftTWzzKvmpMcbHr/SgpQj6VVVVL48xNqauY5HWbZvXbXsT29yR0Q1Ac4IeoDlBfzCnpi5gAuu2zeu2vYltbseMHqA5HT1Ac4L+AKrqT6rq36rqa1X1t1X1E1PXNG9V9RtV9VpVfbeq2q5SSJKqOlFVr1fVG1X16NT1zFtVPVFVb1fVq1PXsihVdX1V/VNVfX3ref37U9c0D4L+YL6S5JYxxs8k+fckfzxxPYvwapJPJ3lh6kLmqaquSvJ4kjuSHEtyX1Udm7aquTud5MTURSzYpSSPjDGOJfn5JL/XcT8L+gMYY/zDGOPS1s1/TnLdlPUswhjj3Bjj9anrWIDbkrwxxnhzjPGdJE8nuWfimuZqjPFCkm9NXccijTH+Y4zxr1tf/0+Sc0munbaq2RP0s/NbSf5+6iKYmWuTfHPb7fNpGAD8v6o6muRnk/zLtJXMXquLg89DVf1jkp+8zLc+N8b40tZjPpf33wI+ucja5mU32wydVNXVSf46yR+MMf576npmTdBfwRjjlz/q+1X1QJK7kvzSaLJW9UrbvCYuJLl+2+3rtu6jmar6kbwf8k+OMf5m6nrmwejmAKrqRJI/TPKpMcb/Tl0PM/VSkpuq6oaq+tEk9yb58sQ1MWNVVUn+Ksm5McafTl3PvAj6g/mLJD+e5CtV9dWq+sLUBc1bVf1aVZ1P8gtJnq2q56euaR62DrI/nOT5vH+A7pkxxmvTVjVfVfVUkheT3FxV56vqwalrWoBPJvlskl/c+hv+alX96tRFzZpPxgI0p6MHaE7QAzQn6AGaE/QAzQl6gOYEPUBzgh6gOUEP0Nz/AVpSbS0YJQgfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dz\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dr\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dphi zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zinner zcut\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "module\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6AXhz91NOrwSeGLotNdLM/GW6JD/H4D71GuDWqvrdKY+0rJK8Bvgb4Mv8333532bwvsSngQ0M/iXdX6qqxW96rRpJLgbeXVVvSPJjDF5ZnA08APxaVX1vmvMtlyQvZfCm/WnAw8BbGPyhcNVf+yTvB36ZwU/4PQC8lcF991V57ZN8CriYwb/2+ihwHfDnjLjWXTg/zuAW3HeBt1RV/2k//6xEQpK0dLNyu0mS9AwYCUlSk5GQJDUZCUlSk5GQJDUZCUlSk5GQJDUZCUlS0/8ARSXmFuwInocAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ysize\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ys-pred\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEWZJREFUeJzt3X9sXWd9x/H3d+kKJRZhW5knJdEclNIta8aPXFoY2mavMNy1aSdUsUaloqw0KloZoEijhf1gf0xksG4rbSdktVmEiGqVwGgbwgrTahBSYSEFlpYMVnUZjWFNWYU3d2VVxnd/3Gt2FyXxudf3+Nw8fr+kSDnH557zseN8fPyc554TmYkkqVw/1nQASVK9LHpJKpxFL0mFs+glqXAWvSQVzqKXpMJZ9JJUOItekgpn0UtS4c5qOgDAueeem2NjY3299plnnmH16tWDDTQA5uqNuXozrLlgeLOVmOvgwYPfy8wXL7phZjb+Z8uWLdmvBx98sO/X1slcvTFXb4Y1V+bwZisxF/CVrNCxDt1IUuEsekkqnEUvSYWz6CWpcBa9JBXOopekwln0klQ4i16SClfLO2MjYjXweeD9mbmvjmM0aeymTy+6ze7J4XsHnqSVqVLRR8Qu4DLgWGZe0LV+ErgVWAXcmZk7Ox96D3DPgLPWrkqBV3Vodo5rK+zvyM5LB3ZMSTqZqmf0u4HbgY8urIiIVcAdwOuBo8CBiLgPWAt8A3j+QJMu0SBLXJLOJJWKPjO/EBFjJ6y+EHgsMx8HiIhp4ApgBFgNbAKejYj9mfnDgSUuTJUfQJ71S1qKaN8Xp8KG7aLftzB0ExFXApOZ+bbO8jXARZl5Y2f5WuB7pxqjj4jtwHaA0dHRLdPT0319AvPz84yMjCy63aHZub7236/Rc+DJZwezr81r1wxmR1T/ei03c/VmWHPB8GYrMdfExMTBzGwttl1ttynOzN2LfHwKmAJotVo5Pj7e13Fu23Mvt3zxmQpbLu8dmXdsPs4thwZzzCNXjw9kPwAzMzP0+7Wuk7l6M6y5YHizreRcS2miWWB91/K6zrrKImIrsHXjxo1LiFG+qtcXHOKRdDJLmUd/ADgvIjZExNnAVcB9vewgM+/PzO1r1gxuaEKS9P9VKvqIuBt4CDg/Io5GxHWZeRy4EXgAOAzck5mP1hdVktSPqrNutp1i/X5gf78Hd+hGkurX6C0QHLqRpPo1WvQRsTUipubmlnfqoyStJJ7RS1LhvHulJBVued9FpFp5V01JJ+MYvSQVzjF6SSqcY/SSVDiLXpIK5xi9JBXOMXpJKpxDN5JUOItekgrnGL0kFc4xekkqnEM3klQ4i16SCmfRS1LhvHvlCnNodo5rK9zl8sjOS5chjaTl4KwbSSqcs24kqXCO0UtS4Sx6SSqcRS9JhbPoJalwFr0kFc6il6TCWfSSVDjfMCVJhfMNU5JUOIduJKlwFr0kFc6il6TCWfSSVDiLXpIK54NHdFJjPpxEKoZn9JJUOItekgpn0UtS4QZe9BHx8xHxkYjYGxFvH/T+JUm9qVT0EbErIo5FxCMnrJ+MiG9GxGMRcRNAZh7OzBuANwGvHXxkSVIvqp7R7wYmu1dExCrgDuASYBOwLSI2dT52OfBpYP/AkkqS+hKZWW3DiDFgX2Ze0Fl+DfD+zHxDZ/lmgMz8QNdrPp2ZJ52DFxHbge0Ao6OjW6anp/v6BI49PceTz/b10lqNnkPxuTavHdzN6Obn5xkZGRnY/gbFXL0b1mwl5pqYmDiYma3FtlvKPPq1wBNdy0eBiyJiHHgj8DxOc0afmVPAFECr1crx8fG+Qty2515uOTR8bwfYsfl48bmOXD0+kP0AzMzM0O/3QJ3M1bthzbaScw28iTJzBpgZ9H4lSf1ZyqybWWB91/K6zrrKfPCIJNVvKUV/ADgvIjZExNnAVcB9vezAB49IUv2qTq+8G3gIOD8ijkbEdZl5HLgReAA4DNyTmY/2cnDP6CWpfpXG6DNz2ynW72cJUygz837g/lardX2/+5AknZ63QJCkwjVa9A7dSFL9Gi16L8ZKUv0cupGkwjl0I0mFc+hGkgo3fDdj0RmjynNlwWfLSk1zjF6SCucYvSQVzjF6SSqcQzeSVDiLXpIKZ9FLUuG8GCtJhfNirCQVzqEbSSqcRS9JhbPoJalwFr0kFc5ZN5JUOGfdSFLhHLqRpMJZ9JJUOB88otpVeUDJ7snVy5BEWpk8o5ekwln0klQ4i16SCuc8ekkqnPPoJalwDt1IUuEsekkqnEUvSYWz6CWpcBa9JBXOopekwln0klQ4b2qmoXBodo5rK9z87MjOS5chjVQWz+glqXC1nNFHxG8ClwIvBO7KzM/WcRxJ0uIqn9FHxK6IOBYRj5ywfjIivhkRj0XETQCZ+anMvB64AfitwUaWJPWil6Gb3cBk94qIWAXcAVwCbAK2RcSmrk1+v/NxSVJDKhd9Zn4BePqE1RcCj2Xm45n5HDANXBFtfwp8JjMfHlxcSVKvIjOrbxwxBuzLzAs6y1cCk5n5ts7yNcBFwLeAtwAHgK9l5kdOsq/twHaA0dHRLdPT0319AseenuPJZ/t6aa1Gz8FcPaiaa/Pa5b3T6fz8PCMjI8t6zCqGNRcMb7YSc01MTBzMzNZi29VyMTYzPwx8eJFtpoApgFarlePj430d67Y993LLoeGbJbpj83Fz9aBqriNXj9cfpsvMzAz9fm/WaVhzwfBmW8m5ljq9chZY37W8rrOuEh88Ikn1W2rRHwDOi4gNEXE2cBVwX9UX++ARSapfL9Mr7wYeAs6PiKMRcV1mHgduBB4ADgP3ZOaj9USVJPWj8mBtZm47xfr9wP5+Dh4RW4GtGzdu7OflkqQKfGasJBWu0aL3Yqwk1c8zekkqnHevlKTCWfSSVLhG3yLprBv1asyHk0g9c4xekgrn0I0kFc6il6TCOY9ekgrnGL0kFW74bkwuLVGVmTng7BytHI7RS1LhHKOXpMI5Ri9JhXPoRpIKZ9FLUuEsekkqnNMrtWJVmYa5e3L1MiSR6uWsG0kqnLNuJKlwjtFLUuEsekkqnEUvSYWz6CWpcBa9JBXOopekwln0klQ43zAlSYXzDVOSVDiHbiSpcBa9JBXOopekwln0klQ470cvDRnvk69B84xekgpn0UtS4Sx6SSqcRS9JhRv4xdiIeAnwPmBNZl456P1LZ6oqF1mlOlQq+ojYBVwGHMvMC7rWTwK3AquAOzNzZ2Y+DlwXEXvrCCwtp0Ozc1xboaCP7Lx0GdJI/ak6dLMbmOxeERGrgDuAS4BNwLaI2DTQdJKkJYvMrLZhxBiwb+GMPiJeA7w/M9/QWb4ZIDM/0Fnee7qhm4jYDmwHGB0d3TI9Pd3XJ3Ds6TmefLavl9Zq9BzM1YMzPdfmtYvfmO/Q7ODu0rphzSpGRkYGtr9Bmp+fH8psJeaamJg4mJmtxbZbyhj9WuCJruWjwEUR8VPAnwCviIibF4r/RJk5BUwBtFqtHB8f7yvEbXvu5ZZDw/e+rx2bj5urB2d6riNXjy+6TZUhoKp2T66m3/8zdZuZmRnKbCs518D/Z2XmvwM3DHq/kqT+LGV65Sywvmt5XWddZT54RJLqt5SiPwCcFxEbIuJs4Crgvl524INHJKl+VadX3g2MA+dGxFHgjzLzroi4EXiA9vTKXZn5aC8Hj4itwNaNGzf2lloaMs6R1zCrVPSZue0U6/cD+/s9eGbeD9zfarWu73cfkqTT8xYIklS4Rovei7GSVL9Gi96LsZJUP4duJKlwjb4V0Vk3Un+q3mytqkHelG2Q2bxZ3GA4dCNJhXPoRpIKZ9FLUuEco5c0tKq849hx/MU5Ri9JhXPoRpIKZ9FLUuEsekkqnBdjJXnRs3BejJWkwjl0I0mFs+glqXAWvSQVzqKXpMI560bSilDl9smlzixy1o0kFc6hG0kqnEUvSYWz6CWpcBa9JBXOopekwln0klQ459FLOqNVufMmwI7NNQcZYs6jl6TCOXQjSYWz6CWpcBa9JBXOopekwln0klQ4i16SCmfRS1LhLHpJKpxFL0mFG/gtECJiNfBXwHPATGbuGfQxJEnVVTqjj4hdEXEsIh45Yf1kRHwzIh6LiJs6q98I7M3M64HLB5xXktSjqkM3u4HJ7hURsQq4A7gE2ARsi4hNwDrgic5m/zOYmJKkflUq+sz8AvD0CasvBB7LzMcz8zlgGrgCOEq77CvvX5JUn8jMahtGjAH7MvOCzvKVwGRmvq2zfA1wEfAe4HbgB8AXTzVGHxHbge0Ao6OjW6anp/v6BI49PceTz/b10lqNnoO5emCu3gxrLhjebFVybV67/HfSnZ+fZ2RkpK/XTkxMHMzM1mLbDfxibGY+A7y1wnZTwBRAq9XK8fHxvo532557ueVQo7fVP6kdm4+bqwfm6s2w5oLhzVYl15Grx5cnTJeZmRn67b+qljK0Mgus71pe11lXWURsjYipubm5JcSQJJ3OUor+AHBeRGyIiLOBq4D7etmBDx6RpPpVnV55N/AQcH5EHI2I6zLzOHAj8ABwGLgnMx+tL6okqR+VBtIyc9sp1u8H9vd7cJ8ZK0n185mxklS4Rovei7GSVD/P6CWpcL5zVZIKV/mdsbWGiHgK+Nc+X34u8L0BxhkUc/XGXL0Z1lwwvNlKzPWzmfnixTYaiqJfioj4SpW3AC83c/XGXL0Z1lwwvNlWci6HbiSpcBa9JBWuhKKfajrAKZirN+bqzbDmguHNtmJznfFj9JKk0yvhjF6SdBpFFH1EvDwivhQRX4uIr0TEhU1nWhAR74iIf4qIRyPig03n6RYROyIiI+LcprMARMSHOl+rf4yIv4mIFzWc52TPRG5URKyPiAcj4hud76l3Np2pW0SsioivRsS+prMsiIgXRcTezvfW4Yh4TdOZACLi3Z1/w0ci4u6IeH5dxyqi6IEPAn+cmS8H/rCz3LiImKD9eMWXZeYvAH/WcKQfiYj1wK8D3246S5fPARdk5i8C3wJubirIaZ6J3LTjwI7M3AS8GvidIcm14J2072Y7TG4F/jYzfw54GUOQLyLWAr8LtDpP7VtF+1bvtSil6BN4Yefva4DvNJil29uBnZn53wCZeazhPN3+Avg92l+7oZCZn+3c/hrgS/zfs4ebcKpnIjcqM7+bmQ93/v6ftEtrbbOp2iJiHXApcGfTWRZExBrgV4C7ADLzucz8frOpfuQs4JyIOAt4ATX2VilF/y7gQxHxBO2z5sbOBE/wUuCXI+LLEfH5iHhV04EAIuIKYDYzv950ltP4beAzDR5/LfBE1/JRhqRQF3Se4/wK4MvNJvmRv6R98vDDpoN02QA8Bfx1Z0jpzohY3XSozJyl3VXfBr4LzGXmZ+s63vA92PEUIuLvgJ85yYfeB1wMvDszPxERb6L90/t1Q5DrLOAnaf+K/Srgnoh4SS7DVKdFcr2X9rDNsjtdrsy8t7PN+2gPUZz0wfKCiBgBPgG8KzP/YwjyXAYcy8yDETHedJ4uZwGvBN6RmV+OiFuBm4A/aDJURPwE7d8QNwDfBz4eEW/OzI/Vcbwzpugz85TFHREfpT02CPBxlvFXx0VyvR34ZKfY/yEifkj7vhZPNZUrIjbT/ub6ekRAe3jk4Yi4MDP/ralcXfmuBS4DLl6OH4inseRnItclIn6cdsnvycxPNp2n47XA5RHxG8DzgRdGxMcy880N5zoKHM3Mhd969tIu+qa9DviXzHwKICI+CfwSUEvRlzJ08x3gVzt//zXgnxvM0u1TwARARLwUOJuGb6qUmYcy86czcywzx2j/R3jlcpT8YiJikvav/pdn5n81HGfJz0SuQ7R/Ot8FHM7MP286z4LMvDkz13W+p64C/n4ISp7O9/UTEXF+Z9XFwDcajLTg28CrI+IFnX/Ti6nxIvEZc0a/iOuBWzsXNX4AbG84z4JdwK6IeAR4DnhLw2epw+524HnA5zq/bXwpM29oIkhmHo+IhWcirwJ2DckzkV8LXAMcioivdda9t/NYT53cO4A9nR/YjwNvbTgPnWGkvcDDtIcpv0qN75D1nbGSVLhShm4kSadg0UtS4Sx6SSqcRS9JhbPoJalwFr0kFc6il6TCWfSSVLj/BcxGE4Z3RTxfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kp=0\n",
+    "for quad in lpairs :\n",
+    "  plotPairs(quad,500,kp)\n",
+    "  kp+=1"
    ]
   }
  ],

From de8a395e6de1f35303d116880bb9fefd8e6c9e75 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Sun, 10 Mar 2019 16:05:21 +0100
Subject: [PATCH 81/95] for release remove counter output

---
 .../plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu           | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
index bd5e17e0e4136..5ed692f4b3d14 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernelsAlloc.cu
@@ -5,7 +5,9 @@ void
 CAHitQuadrupletGeneratorKernels::deallocateOnGPU()
 {
 
-  printCounters();
+  // crash on multi-gpu processes
+  //printCounters();
+
   cudaFree(counters_);
 
   cudaFree(device_theCells_);

From e1f6196e9d6ca7ec1e302255d6009460d835b110 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 12 Mar 2019 14:39:43 +0100
Subject: [PATCH 82/95] add dump of zo,ro

---
 .../plugins/ClusterSLOnGPU.cu                 | 42 ++++++++++++++-----
 .../plugins/ClusterSLOnGPU.h                  |  2 +
 .../ClusterTPAssociationHeterogeneous.cc      | 14 +++++--
 ...rackerHitAssociationHeterogeneousProduct.h |  4 +-
 4 files changed, 46 insertions(+), 16 deletions(-)

diff --git a/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu b/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
index 83683284f98b9..e9ac2c9d3b957 100644
--- a/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
+++ b/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.cu
@@ -12,6 +12,10 @@
 #include "ClusterSLOnGPU.h"
 
 using ClusterSLGPU = trackerHitAssociationHeterogeneousProduct::ClusterSLGPU;
+using Clus2TP    = ClusterSLGPU::Clus2TP;
+
+
+// #define DUMP_TK2
 
 __global__
 void simLink(const SiPixelDigisCUDA::DeviceConstView *dd, uint32_t ndigis, const SiPixelClustersCUDA::DeviceConstView *cc, clusterSLOnGPU::HitsOnGPU const * hhp, ClusterSLGPU const * slp, uint32_t n)
@@ -38,14 +42,14 @@ void simLink(const SiPixelDigisCUDA::DeviceConstView *dd, uint32_t ndigis, const
   auto cl = first + cc->clus(i);
   assert(cl < 2000 * blockDim.x);
 
-  const std::array<uint32_t, 4> me{{id, ch, 0, 0}};
+  const Clus2TP me{{id, ch, 0, 0, 0,0}};
 
-  auto less = [] __host__ __device__ (std::array<uint32_t, 4> const & a, std::array<uint32_t, 4> const & b)->bool {
+  auto less = [] __host__ __device__ (Clus2TP const & a, Clus2TP const & b)->bool {
      // in this context we do not care of [2]
      return a[0] < b[0] or ( (not (b[0]<a[0]) ) and (a[1]<b[1]) );
   };
 
-  auto equal = [] __host__ __device__ (std::array<uint32_t, 4> const & a, std::array<uint32_t, 4> const & b)->bool {
+  auto equal = [] __host__ __device__ (Clus2TP const & a, Clus2TP const & b)->bool {
      // in this context we do not care of [2]
      return a[0] == b[0] and a[1] == b[1];
   };
@@ -101,19 +105,27 @@ void dumpLink(int first, int ev, clusterSLOnGPU::HitsOnGPU const * hhp, uint32_t
   assert(hh.cpeParams);
   float ge[6];
   hh.cpeParams->detParams(hh.detInd_d[i]).frame.toGlobal(hh.xerr_d[i], 0, hh.yerr_d[i],ge);
-  printf("Error: %d: %f,%f,%f,%f,%f,%f\n",hh.detInd_d[i],ge[0],ge[1],ge[2],ge[3],ge[4],ge[5]);
+  printf("Error: %d: %.4f,%.4f,%.4f,%.4f,%.4f,%.4f\n",hh.detInd_d[i],ge[0],ge[1],ge[2],ge[3],ge[4],ge[5]);
   */
 
   auto const & tk1 = sl.links_d[sl.tkId_d[i]];
+
+#ifdef DUMP_TK2
   auto const & tk2 = sl.links_d[sl.tkId2_d[i]];
 
-  printf("HIT: %d %d %d %d %f %f %f %f %d %d %d %d %d %d %d %d %d\n", ev, i,
+  printf("HIT: %d %d %d %d %.4f %.4f %.4f %.4f %d %d %d %d %d %d %d %d %d %d %d %d %d\n",
+#else
+  printf("HIT: %d %d %d %d %.4f %.4f %.4f %.4f %d %d %d %d %d %d %d %d\n",
+#endif
+         ev, i,
          hh.detInd_d[i], hh.charge_d[i],
          hh.xg_d[i], hh.yg_d[i], hh.zg_d[i], hh.rg_d[i], hh.iphi_d[i],
          hh.xsize_d[i],hh.ysize_d[i],
-         tk1[2], tk1[3], sl.n1_d[i],
-         tk2[2], tk2[3], sl.n2_d[i]
-        );
+         tk1[2], tk1[3], tk1[4], tk1[5], sl.n1_d[i]
+#ifdef DUMP_TK2
+        ,tk2[2], tk2[3], tk2[4], tk2[5], sl.n2_d[i]
+#endif
+           );
 
 }
 
@@ -122,11 +134,19 @@ namespace clusterSLOnGPU {
   constexpr uint32_t invTK = 0; // std::numeric_limits<int32_t>::max();
 
   void printCSVHeader() {
-    printf("HIT: %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s\n", "ev", "ind",
+#ifdef DUMP_TK2
+    printf("HIT: %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s\n", 
+#else
+    printf("HIT: %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s\n",
+#endif
+        "ev", "ind",
         "det", "charge",
         "xg","yg","zg","rg","iphi",
         "xsize","ysize",
-        "tkId","pt","n1","tkId2","pt2","n2"
+        "tkId","pt","z0","r0","n1"
+#ifdef DUMP_TK2
+        ,"tkId2","pt2", "z02","r02","n2"
+#endif
         );
   }
 
@@ -139,7 +159,7 @@ namespace clusterSLOnGPU {
   }
 
   void Kernel::alloc(cuda::stream_t<>& stream) {
-    cudaCheck(cudaMalloc((void**) & slgpu.links_d, (ClusterSLGPU::MAX_DIGIS)*sizeof(std::array<uint32_t, 4>)));
+    cudaCheck(cudaMalloc((void**) & slgpu.links_d, (ClusterSLGPU::MAX_DIGIS)*sizeof(Clus2TP)));
     cudaCheck(cudaMalloc((void**) & slgpu.tkId_d, (ClusterSLGPU::MaxNumModules*256)*sizeof(uint32_t)));
     cudaCheck(cudaMalloc((void**) & slgpu.tkId2_d, (ClusterSLGPU::MaxNumModules*256)*sizeof(uint32_t)));
     cudaCheck(cudaMalloc((void**) & slgpu.n1_d, (ClusterSLGPU::MaxNumModules*256)*sizeof(uint32_t)));
diff --git a/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.h b/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.h
index 00b0e34b301c8..4fdea7a8cd8f5 100644
--- a/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.h
+++ b/SimTracker/TrackerHitAssociation/plugins/ClusterSLOnGPU.h
@@ -19,6 +19,8 @@ namespace clusterSLOnGPU {
   using HitsOnGPU    = siPixelRecHitsHeterogeneousProduct::HitsOnGPU;
   using HitsOnCPU    = siPixelRecHitsHeterogeneousProduct::HitsOnCPU;
 
+  using Clus2TP = ClusterSLGPU::Clus2TP;
+
   class Kernel {
   public:
     Kernel(cuda::stream_t<>& stream, bool dump);
diff --git a/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc b/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
index 03d2d8d1e5809..657aa1541e7a0 100644
--- a/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
+++ b/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
@@ -52,6 +52,8 @@ class ClusterTPAssociationHeterogeneous : public HeterogeneousEDProducer<heterog
   using CPUProduct = trackerHitAssociationHeterogeneousProduct::CPUProduct;
   using Output = trackerHitAssociationHeterogeneousProduct::ClusterTPAHeterogeneousProduct;
 
+  using Clus2TP    = ClusterSLGPU::Clus2TP;
+
   using PixelDigiClustersH = siPixelRawToClusterHeterogeneousProduct::HeterogeneousDigiCluster;
   using PixelRecHitsH = siPixelRecHitsHeterogeneousProduct::HeterogeneousPixelRecHit;
 
@@ -96,7 +98,7 @@ class ClusterTPAssociationHeterogeneous : public HeterogeneousEDProducer<heterog
 
   std::map<std::pair<size_t, EncodedEventId>, TrackingParticleRef> mapping;
 
-  std::vector<std::array<uint32_t, 4>> digi2tp;
+   std::vector<Clus2TP> digi2tp;
 
   bool doDump;
 
@@ -194,7 +196,7 @@ void ClusterTPAssociationHeterogeneous::acquireGPUCuda(const edm::HeterogeneousE
     auto nhits = gHits.nHits;
 
     digi2tp.clear();
-    digi2tp.push_back({{0, 0, 0, 0}});  // put at 0 0
+    digi2tp.push_back({{0, 0, 0, 0,0,0}});  // put at 0 0
     for (auto const & links : *sipixelSimLinks) {
       DetId detId(links.detId());
       const GeomDetUnit * genericDet = geom->idToDetUnit(detId);
@@ -207,12 +209,16 @@ void ClusterTPAssociationHeterogeneous::acquireGPUCuda(const edm::HeterogeneousE
         auto ipos = mapping.find(tkid);
         if (ipos != mapping.end()) {
             uint32_t pt = 1000*(*ipos).second->pt();
-            digi2tp.push_back({{gind, uint32_t(link.channel()),(*ipos).second.key(), pt}});
+            uint32_t z0 = 10000*(*ipos).second->vz();  // in um
+            uint32_t r0 = 10000*std::sqrt((*ipos).second->vx()*(*ipos).second->vx()
+                                         +(*ipos).second->vy()*(*ipos).second->vy()
+                                         );  // in um
+            digi2tp.push_back({{gind, uint32_t(link.channel()),(*ipos).second.key(), pt,z0,r0}});
         }
       }
     }
     std::sort(digi2tp.begin(), digi2tp.end());
-    cudaCheck(cudaMemcpyAsync(gpuAlgo->slgpu.links_d, digi2tp.data(), sizeof(std::array<uint32_t, 4>)*digi2tp.size(), cudaMemcpyDefault, cudaStream.id()));
+    cudaCheck(cudaMemcpyAsync(gpuAlgo->slgpu.links_d, digi2tp.data(), sizeof(std::array<uint32_t, 5>)*digi2tp.size(), cudaMemcpyDefault, cudaStream.id()));
     gpuAlgo->algo(gDigis, ndigis, gHits, nhits, digi2tp.size(), cudaStream);
 
   //  end gpu stuff ---------------------
diff --git a/SimTracker/TrackerHitAssociation/plugins/trackerHitAssociationHeterogeneousProduct.h b/SimTracker/TrackerHitAssociation/plugins/trackerHitAssociationHeterogeneousProduct.h
index c3f86ddf855c8..8aedac8bdb650 100644
--- a/SimTracker/TrackerHitAssociation/plugins/trackerHitAssociationHeterogeneousProduct.h
+++ b/SimTracker/TrackerHitAssociation/plugins/trackerHitAssociationHeterogeneousProduct.h
@@ -21,8 +21,10 @@ namespace trackerHitAssociationHeterogeneousProduct {
 
   struct ClusterSLGPU {
 
+   using Clus2TP = std::array<uint32_t,6>;
+
    ClusterSLGPU * me_d=nullptr;
-   std::array<uint32_t,4> * links_d;
+   Clus2TP * links_d;
    uint32_t * tkId_d;
    uint32_t * tkId2_d;
    uint32_t * n1_d;

From 27f8bb46a4b4a79386c162a43b2ff8c843d64dd9 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 12 Mar 2019 15:08:38 +0100
Subject: [PATCH 83/95] add dump of zo,ro

---
 .../plugins/ClusterTPAssociationHeterogeneous.cc                | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc b/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
index 657aa1541e7a0..fd657d2519d3b 100644
--- a/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
+++ b/SimTracker/TrackerHitAssociation/plugins/ClusterTPAssociationHeterogeneous.cc
@@ -218,7 +218,7 @@ void ClusterTPAssociationHeterogeneous::acquireGPUCuda(const edm::HeterogeneousE
       }
     }
     std::sort(digi2tp.begin(), digi2tp.end());
-    cudaCheck(cudaMemcpyAsync(gpuAlgo->slgpu.links_d, digi2tp.data(), sizeof(std::array<uint32_t, 5>)*digi2tp.size(), cudaMemcpyDefault, cudaStream.id()));
+    cudaCheck(cudaMemcpyAsync(gpuAlgo->slgpu.links_d, digi2tp.data(), sizeof(Clus2TP)*digi2tp.size(), cudaMemcpyDefault, cudaStream.id()));
     gpuAlgo->algo(gDigis, ndigis, gHits, nhits, digi2tp.size(), cudaStream);
 
   //  end gpu stuff ---------------------

From cacd3a56c5774cbe66c7d63ba67ae3fe6be24a7a Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 12 Mar 2019 16:49:51 +0100
Subject: [PATCH 84/95] new notebook with z0 resolution

---
 .../PixelTriplets/test/pixHits.ipynb          | 1525 ++++++++++-------
 1 file changed, 895 insertions(+), 630 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/test/pixHits.ipynb b/RecoPixelVertexing/PixelTriplets/test/pixHits.ipynb
index 6860c25666e6c..ec33b344a9759 100644
--- a/RecoPixelVertexing/PixelTriplets/test/pixHits.ipynb
+++ b/RecoPixelVertexing/PixelTriplets/test/pixHits.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -295,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,19 +312,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#rawhits 13982672\n"
+      "#rawhits 14013319\n"
      ]
     }
    ],
    "source": [
-    "file = '/Users/innocent/data/ttbarIdealPU50HitsC.csv'\n",
+    "file = '/Users/innocent/data/ttbarIdealPU50HitsC2.csv'\n",
     "# file = '/Users/innocent/data/ttbarPU50Hits.csv'\n",
     "# file = '/Users/innocent/data/ttbarPU0Hits.csv'\n",
     "rawHits = pd.read_csv(file, delimiter=\" \")\n",
@@ -333,58 +333,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#hits 5833191\n",
-      "      ev   ind  det  charge        xg         yg         zg         rg   iphi  \\\n",
-      "1014   1   406   19   45617  0.327468   3.295420  -4.949985   3.311650  15350   \n",
-      "2373   1  2245  146   32991  0.701991   7.170877  -9.392261   7.205155  15365   \n",
-      "1805   1  3789  401   35961  1.082605  11.257833 -14.069117  11.309768  15383   \n",
-      "7400   1  5096  793   71921  1.532926  16.360044 -19.902737  16.431705  15409   \n",
-      "1015   1   407   19   39949  0.265110   3.311063  -4.962011   3.321660  15550   \n",
+      "#hits 5148484\n",
+      "      ev   ind  det  charge      xg      yg       zg      rg   iphi  xsize  \\\n",
+      "2233   1  1497   63  203782 -2.0126 -1.8634  23.3378  2.7428 -24977      9   \n",
+      "2217   1  1481   63  142319 -1.7204 -2.1465  21.7691  2.7508 -23432     13   \n",
+      "106    1  1258   54  116813 -3.0205 -0.5520  15.8207  3.0705 -30882     15   \n",
+      "1245   1  1085   46  107050 -2.7395  0.3352  16.0573  2.7600  31497      8   \n",
+      "2083   1  2179   93   81797  2.7517 -0.2867  11.9489  2.7666  -1084      8   \n",
       "\n",
-      "      xsize  ...       pt  n1  tkId2  pt2  n2       phi       eta  seq  \\\n",
-      "1014     13  ...    12402   3      0    0   0  1.471751 -1.191830    1   \n",
-      "2373     20  ...    12402   4      0    0   0  1.473213 -1.080612    2   \n",
-      "1805     14  ...    12402   4      0    0   0  1.474927 -1.043826    3   \n",
-      "7400     14  ...    12402   4      0    0   0  1.477370 -1.023149    4   \n",
-      "1015      8  ...    11881   2      0    0   0  1.490899 -1.191338    1   \n",
+      "      ...    tkId    pt     z0  r0  n1       phi       eta  seq   trackID  \\\n",
+      "2233  ...       2  1817  25188  19  15 -2.394669  2.837678    1  10000002   \n",
+      "2217  ...       5  2274  25188  19  13 -2.246446  2.765715    1  10000005   \n",
+      "106   ...       6  4734  25188  19   8 -2.960836  2.341913    1  10000006   \n",
+      "1245  ...       7   967  25188  19  10  3.019840  2.461386    1  10000007   \n",
+      "2083  ...       9   823  25188  19   7 -0.103816  2.169308    1  10000009   \n",
       "\n",
-      "       trackID   norm  \n",
-      "1014  10000000  0.001  \n",
-      "2373  10000000  0.001  \n",
-      "1805  10000000  0.001  \n",
-      "7400  10000000  0.001  \n",
-      "1015  10000001  0.001  \n",
+      "       norm  \n",
+      "2233  0.001  \n",
+      "2217  0.001  \n",
+      "106   0.001  \n",
+      "1245  0.001  \n",
+      "2083  0.001  \n",
       "\n",
-      "[5 rows x 22 columns]\n",
-      "            ev   ind   det  charge         xg        yg         zg         rg  \\\n",
-      "13971041  1000  1855    79  139699   0.908132 -2.588966  23.074427   2.743620   \n",
-      "13976222  1000  7324  1237   81765   9.910853 -5.013310  30.899717  11.106678   \n",
-      "13976754  1000  7728  1294   94676  10.830434 -5.359623  31.711411  12.084033   \n",
-      "13972961  1000  1503    63   37790  -2.004861 -1.870963  24.916054   2.742257   \n",
-      "13971607  1000  3509   247   29787  -3.807022 -5.802213  26.619453   6.939675   \n",
+      "[5 rows x 21 columns]\n",
+      "            ev    ind   det  charge      xg       yg       zg       rg   iphi  \\\n",
+      "14003543  1000   3710   265   65338  0.9296  -6.5121 -17.3848   6.5781 -14904   \n",
+      "14010785  1000   9896  1568   19832  1.8691 -10.1563 -29.6611  10.3268 -14485   \n",
+      "14012001  1000  10344  1625   30507  1.9370 -10.3811 -30.4224  10.5603 -14459   \n",
+      "14010409  1000  11280  1737   29523  2.6579 -12.5591 -37.8870  12.8373 -14208   \n",
+      "14012416  1000  12263  1849   21723  3.7319 -15.2245 -47.2765  15.6753 -13876   \n",
       "\n",
-      "           iphi  xsize  ...     pt  n1  tkId2  pt2  n2       phi       eta  \\\n",
-      "13971041 -12866     43  ...    569   8      0    0   0 -1.233436  2.826110   \n",
-      "13976222  -4885     10  ...    569   4      0    0   0 -0.468309  1.747186   \n",
-      "13976754  -4793     14  ...    569   4      0    0   0 -0.459533  1.692410   \n",
-      "13972961 -24935      8  ...    427   2      0    0   0 -2.390728  2.902893   \n",
-      "13971607 -22442      8  ...    427   2      0    0   0 -2.151471  2.054108   \n",
+      "          xsize  ...      tkId   pt     z0  r0  n1       phi       eta  seq  \\\n",
+      "14003543     15  ...    113902  530  42061  12   8 -1.429005 -1.699009    2   \n",
+      "14010785     13  ...    113902  530  42061  12   3 -1.388799 -1.777253    8   \n",
+      "14012001     11  ...    113902  530  42061  12   3 -1.386328 -1.780072    8   \n",
+      "14010409     16  ...    113902  530  42061  12   3 -1.362242 -1.802940    9   \n",
+      "14012416      8  ...    113902  530  42061  12   2 -1.330411 -1.823490   10   \n",
       "\n",
-      "          seq      trackID   norm  \n",
-      "13971041    1  10000114092  0.001  \n",
-      "13976222    5  10000114092  0.001  \n",
-      "13976754    5  10000114092  0.001  \n",
-      "13972961    1  10000114179  0.001  \n",
-      "13971607    2  10000114179  0.001  \n",
+      "              trackID   norm  \n",
+      "14003543  10000113902  0.001  \n",
+      "14010785  10000113902  0.001  \n",
+      "14012001  10000113902  0.001  \n",
+      "14010409  10000113902  0.001  \n",
+      "14012416  10000113902  0.001  \n",
       "\n",
-      "[5 rows x 22 columns]\n"
+      "[5 rows x 21 columns]\n"
      ]
     }
    ],
@@ -395,7 +395,7 @@
     "rawHits['trackID'] = rawHits['tkId']+10000000*rawHits['ev']\n",
     "rawHits['norm'] = 1./1000.\n",
     "rawHits.sort_values(by=['ev','tkId','det'],inplace=True)\n",
-    "hits =  rawHits[rawHits['pt']>400]\n",
+    "hits =  rawHits[np.logical_and(np.logical_and(rawHits['pt']>400, abs(rawHits['z0'])<150000),rawHits['r0']<1000)]\n",
     "print '#hits', len(hits)\n",
     "print hits.head()\n",
     "print hits.tail()"
@@ -403,14 +403,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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qBh/+91TVA33306OrgT9KcpTBX4D9gyR/329LvToOHK+q00eEX2IQCK36Q+B7VTVTVf8DPAD8Ts89LQfPJ7kUoHs+NeoVGABLJEkYnOM9XFWf6rufPlXVh6tqXVVNMLi4909V1exveFX1HHAsyZu70jXAUz221LfvA1cleW33c3MNDV8UH7IH2NpNbwUeHPUKDIClczVwM4Pfdh/rHtf33ZSWjT8B7knyOLAJ+Mue++lNdyT0JeBR4AkGn0tNfSs4yb3AvwBvTnI8yS3ADuAdSY4wOEraMfL1+k1gSWqTRwCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRv0vFWOmzYgehJ8AAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -422,16 +422,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2561.399 2087.692 1748.84  1478.249  944.839 1022.755 1078.439  950.139\n",
-      " 1029.349 1080.971]\n",
-      "All Hits 1.3983E+04\n",
-      "[5347412.201107463, 3651039.2772803963, 2585220.981160521, 2420109.6697606524, 1972532.821587918, 1652372.236760108, 966338.8114449463, 1102978.8794449903, 2433685.0844606548, 1983597.589187921, 1661641.0887601115, 978024.6295109489, 1112696.417878993, 0]\n",
-      "All Doublets 2.7868E+07\n"
+      "[2573.608 2095.2   1750.034 1479.679  945.452 1024.445 1079.237  951.194\n",
+      " 1030.265 1084.205]\n",
+      "All Hits 1.4013E+04\n",
+      "[5392223.481599452, 3666671.236800405, 2589488.559086526, 2433222.8308156473, 1980911.030399918, 1654573.145368111, 968563.574139946, 1105618.9484649901, 2448000.487951649, 1992941.6687999205, 1664621.840596114, 979981.8864099487, 1117018.4643249928, 0]\n",
+      "All Doublets 2.7994E+07\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -443,13 +443,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "All triplets [9351768353.785784, 5397145160.600975, 5052443596.68196, 3449644299.706186, 2475179265.2960234, 1042137461.475881, 2017417805.9431248, 5080784881.347837, 3468994807.8757772, 2505111308.004462, 1057216261.7870855, 2041814194.8329737, 0]\n",
-      "4.2940E+10\n"
+      "All triplets [9436574428.398449, 5425496428.998095, 5098088475.124949, 3466661654.17527, 2492702962.914902, 1045309646.0640776, 2029334400.538017, 5129050622.356299, 3487715680.4169817, 2522089222.7194767, 1062501261.1551037, 2053258048.4061267, 0]\n",
+      "4.3249E+10\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -459,7 +459,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADmNJREFUeJzt3W+IXXedx/H3ZztWbWWb/hlCTcJOwaAUwW0ZunELshjXbRsxfaBS2dUggTzparWCxn1S2IUlglgVlkJoqpEtXSUWGmxRS1qRhbU4rVLbRmmobZNs2oz2j64iWvzug/urncSmaefMzEn9vV8w3HN+59x7fnMh855z7r2TVBWSpP78xdgTkCSNwwBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1amrsCbyU8847r2ZmZsaehiS9qtx7770/r6rpk+13SgdgZmaGubm5sachSa8qSR57Oft5CUiSOmUAJKlTBkCSOmUAJKlTBkCSOmUAJKlTBkCSOmUAJKlTBkCSOnVKfxJ4qJntt49y3Ed3bBrluJL0SngGIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdOmkAktyU5GiSBxaMnZPkziQPt9uz23iSfCnJgST3J7l4wX22tP0fTrJleb4dSdLL9XLOAL4CXHbc2HZgX1WtB/a1dYDLgfXtaxtwA0yCAVwH/A1wCXDd89GQJI3jpAGoqu8BTx03vBnY3ZZ3A1cuGP9qTXwfWJXkfOAfgDur6qmqehq4kz+NiiRpBS32NYDVVXWkLT8BrG7La4CDC/Y71MZONC5JGsngF4GrqoBagrkAkGRbkrkkc/Pz80v1sJKk4yw2AE+2Szu026Nt/DCwbsF+a9vYicb/RFXtrKrZqpqdnp5e5PQkSSez2ADsBZ5/J88W4LYF4x9u7wbaADzbLhV9G3h3krPbi7/vbmOSpJFMnWyHJLcAfwecl+QQk3fz7AC+nmQr8Bjwgbb7HcAVwAHgN8BHAKrqqST/Bvyg7fevVXX8C8uSpBV00gBU1QdPsGnji+xbwNUneJybgJte0ewkScvGTwJLUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1amrsCfw5mtl++yjHfXTHplGOK+nVyTMASeqUAZCkThkASerUoAAk+USSB5M8kOSWJK9LckGSe5IcSPK1JKe3fV/b1g+07TNL8Q1IkhZn0QFIsgb4GDBbVW8FTgOuAj4LXF9VbwKeBra2u2wFnm7j17f9JEkjGXoJaAp4fZIp4AzgCPBOYE/bvhu4si1vbuu07RuTZODxJUmLtOgAVNVh4HPA40x+8D8L3As8U1XPtd0OAWva8hrgYLvvc23/c49/3CTbkswlmZufn1/s9CRJJzHkEtDZTH6rvwB4I3AmcNnQCVXVzqqararZ6enpoQ8nSTqBIZeA3gX8rKrmq+r3wK3ApcCqdkkIYC1wuC0fBtYBtO1nAb8YcHxJ0gBDAvA4sCHJGe1a/kbgIeBu4H1tny3AbW15b1unbb+rqmrA8SVJAwx5DeAeJi/m3gf8uD3WTuDTwLVJDjC5xr+r3WUXcG4bvxbYPmDekqSBBv0toKq6DrjuuOFHgEteZN/fAu8fcjxJ0tLxk8CS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdGvSfwuvUMrP99tGO/eiOTaMdW9LieAYgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUqUEBSLIqyZ4kP0myP8nbk5yT5M4kD7fbs9u+SfKlJAeS3J/k4qX5FiRJizH0DOCLwLeq6i3A24D9wHZgX1WtB/a1dYDLgfXtaxtww8BjS5IGWHQAkpwFvAPYBVBVv6uqZ4DNwO62227gyra8GfhqTXwfWJXk/EXPXJI0yJAzgAuAeeDLSX6Y5MYkZwKrq+pI2+cJYHVbXgMcXHD/Q21MkjSCIQGYAi4Gbqiqi4Bf88LlHgCqqoB6JQ+aZFuSuSRz8/PzA6YnSXopQwJwCDhUVfe09T1MgvDk85d22u3Rtv0wsG7B/de2sWNU1c6qmq2q2enp6QHTkyS9lEUHoKqeAA4meXMb2gg8BOwFtrSxLcBtbXkv8OH2bqANwLMLLhVJklbY0L8G+lHg5iSnA48AH2ESla8n2Qo8Bnyg7XsHcAVwAPhN21eSNJJBAaiqHwGzL7Jp44vsW8DVQ44nSVo6fhJYkjplACSpUwZAkjplACSpUwZAkjplACSpUwZAkjo19INgEgAz228f5biP7tg0ynGlPweeAUhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHVqcACSnJbkh0m+2dYvSHJPkgNJvpbk9Db+2rZ+oG2fGXpsSdLiLcUZwDXA/gXrnwWur6o3AU8DW9v4VuDpNn5920+SNJJBAUiyFtgE3NjWA7wT2NN22Q1c2ZY3t3Xa9o1tf0nSCIaeAXwB+BTwh7Z+LvBMVT3X1g8Ba9ryGuAgQNv+bNv/GEm2JZlLMjc/Pz9wepKkE1l0AJK8BzhaVfcu4Xyoqp1VNVtVs9PT00v50JKkBaYG3PdS4L1JrgBeB/wl8EVgVZKp9lv+WuBw2/8wsA44lGQKOAv4xYDjS5IGWPQZQFV9pqrWVtUMcBVwV1X9I3A38L622xbgtra8t63Ttt9VVbXY40uShlmOzwF8Grg2yQEm1/h3tfFdwLlt/Fpg+zIcW5L0Mg25BPRHVfVd4Ltt+RHgkhfZ57fA+5fieJKk4fwksCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1akk+ByCNZWb77WNPQVoWj+7YtOzH8AxAkjplACSpUwZAkjplACSpUwZAkjplACSpUwZAkjplACSpUwZAkjplACSpUwZAkjplACSpUwZAkjplACSpUwZAkjplACSpUwZAkjplACSpUwZAkjq16AAkWZfk7iQPJXkwyTVt/JwkdyZ5uN2e3caT5EtJDiS5P8nFS/VNSJJeuSFnAM8Bn6yqC4ENwNVJLgS2A/uqaj2wr60DXA6sb1/bgBsGHFuSNNCiA1BVR6rqvrb8K2A/sAbYDOxuu+0GrmzLm4Gv1sT3gVVJzl/0zCVJgyzJawBJZoCLgHuA1VV1pG16AljdltcABxfc7VAbkySNYHAAkrwB+Abw8ar65cJtVVVAvcLH25ZkLsnc/Pz80OlJkk5gUACSvIbJD/+bq+rWNvzk85d22u3RNn4YWLfg7mvb2DGqamdVzVbV7PT09JDpSZJewpB3AQXYBeyvqs8v2LQX2NKWtwC3LRj/cHs30Abg2QWXiiRJK2xqwH0vBT4E/DjJj9rYvwA7gK8n2Qo8BnygbbsDuAI4APwG+MiAY0uSBlp0AKrqv4GcYPPGF9m/gKsXezxJ0tLyk8CS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdMgCS1CkDIEmdWvEAJLksyU+THEiyfaWPL0maWNEAJDkN+A/gcuBC4INJLlzJOUiSJlb6DOAS4EBVPVJVvwP+C9i8wnOQJLHyAVgDHFywfqiNSZJW2NTYEzhekm3Atrb6f0l+OuZ8lsB5wM/HnsQpxOfjWD4fL/C5WCCfHfR8/NXL2WmlA3AYWLdgfW0b+6Oq2gnsXMlJLackc1U1O/Y8ThU+H8fy+XiBz8WxVuL5WOlLQD8A1ie5IMnpwFXA3hWegySJFT4DqKrnkvwz8G3gNOCmqnpwJecgSZpY8dcAquoO4I6VPu6I/mwuZy0Rn49j+Xy8wOfiWMv+fKSqlvsYkqRTkH8KQpI6ZQCWSZJ1Se5O8lCSB5NcM/acxpbktCQ/TPLNsecytiSrkuxJ8pMk+5O8few5jSnJJ9q/kweS3JLkdWPPaSUluSnJ0SQPLBg7J8mdSR5ut2cv9XENwPJ5DvhkVV0IbACu9s9ecA2wf+xJnCK+CHyrqt4CvI2On5cka4CPAbNV9VYmbxC5atxZrbivAJcdN7Yd2FdV64F9bX1JGYBlUlVHquq+tvwrJv/Au/3Uc5K1wCbgxrHnMrYkZwHvAHYBVNXvquqZcWc1uing9UmmgDOA/x15Piuqqr4HPHXc8GZgd1veDVy51Mc1ACsgyQxwEXDPuDMZ1ReATwF/GHsip4ALgHngy+2S2I1Jzhx7UmOpqsPA54DHgSPAs1X1nXFndUpYXVVH2vITwOqlPoABWGZJ3gB8A/h4Vf1y7PmMIcl7gKNVde/YczlFTAEXAzdU1UXAr1mG0/tXi3ZtezOTML4RODPJP407q1NLTd6uueRv2TQAyyjJa5j88L+5qm4dez4juhR4b5JHmfwF2Hcm+c9xpzSqQ8Chqnr+jHAPkyD06l3Az6pqvqp+D9wK/O3IczoVPJnkfIB2e3SpD2AAlkmSMLnGu7+qPj/2fMZUVZ+pqrVVNcPkxb27qqrb3/Cq6gngYJI3t6GNwEMjTmlsjwMbkpzR/t1spOMXxRfYC2xpy1uA25b6AAZg+VwKfIjJb7s/al9XjD0pnTI+Ctyc5H7gr4F/H3k+o2lnQnuA+4AfM/m51NWngpPcAvwP8OYkh5JsBXYAf5/kYSZnSTuW/Lh+EliS+uQZgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqcMgCR1ygBIUqf+HwvpXbx+MbiKAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -504,19 +504,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1204739 932585 751813 619508 763058 779521 781967\n"
+      "1130010 841967 660647 531391 660653 663638 660178\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -526,7 +526,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -536,7 +536,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -546,7 +546,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -556,7 +556,7 @@
     },
     {
      "data": {
-      "image/png": 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j7oI0NM8cJEkdhoMkqcNwkCR1GA6SpA7DQZLUYThIkjoMB0lSx9DhkOSYJA8m+R9tfUWSe5JMJvlie0ocSY5v65Nt+/K+17is1Z9McvawfZIkDWcUZw6fBB7vW78auKaqfh54Abio1S8CXmj1a1o7kpwGnA+8E1gN/FmSY0bQL0nSgIYKhyRLgQ8Cf9HWA7wfuKU12QSc15bXtHXa9rNa+zXA5qp6paq+Re8Z02cM0y9J0nCGPXP4Y+BTwI/a+tuAF6tqf1vfBSxpy0uAnQBt+0ut/Y/rU+wjSRqDgcMhyYeAPVV1/wj7M9N7rkuyPcn2vXv3Hq23laQFZ5gzh/cCv57k28BmesNJfwIsSnLgA/2WArvb8m5gGUDb/lbg+f76FPu8RlVtqKqJqppYvHjxEF2XJL2egcOhqi6rqqVVtZzehPLXq+o3gbuAD7dma4Fb2/KWtk7b/vWqqlY/v13NtAJYCdw7aL8kScM7Eh/ZfQmwOclngQeB61v9euAvk0wC++gFClW1I8nNwGPAfuDiqnr1CPRLknSIRhIOVfUN4Btt+WmmuNqoqv4O+Mg0+18JXDmKvkiShucd0pKkDsNBktRhOEiSOgwHSVKH4SBJ6jAcJEkdhoMkqeNI3AQnSXPa8ktv+/Hyt6/64Bh7Mj6eOUiSOgwHSVKH4SBJ6jAcJEkdhoMkqcNwkCR1GA6SpA7DQZLUMXA4JFmW5K4kjyXZkeSTrX5ikq1JnmrfT2j1JLk2yWSSh5Oc3vdaa1v7p5Ksne49JUlHxzBnDvuBP6yq04AzgYuTnAZcCtxZVSuBO9s6wDn0ng+9ElgHXAe9MAHWA++h9wS59QcCRZI0HgOHQ1U9W1UPtOW/BR4HlgBrgE2t2SbgvLa8BriherYBi5KcApwNbK2qfVX1ArAVWD1ovyRJwxvJnEOS5cC7gXuAk6vq2bbpOeDktrwE2Nm3265Wm64uSRqTocMhyc8AXwL+oKq+37+tqgqoYd+j773WJdmeZPvevXtH9bKSpIMMFQ5J3kAvGG6sqi+38nfbcBHt+55W3w0s69t9aatNV++oqg1VNVFVE4sXLx6m65Kk1zHM1UoBrgcer6rP9W3aAhy44mgtcGtf/cJ21dKZwEtt+OkOYFWSE9pE9KpWkySNyTDPc3gv8K+BR5I81GqfBq4Cbk5yEfAM8NG27XbgXGASeBn4GEBV7UtyBXBfa3d5Ve0bol+SpCENHA5V9TdAptl81hTtC7h4mtfaCGwctC+SpNHyDmlJUofhIEnqMBwkSR2GgySpw3CQJHUYDpKkDsNBktRhOEiSOgwHSVKH4SBJ6jAcJEkdhoMkqcNwkCR1DPOR3ToCll9627i7IEmeOUiSugwHSVLHrAmHJKuTPJlkMsml4+6PJC1ksyIckhwDfB44BzgNuCDJaePtlSQtXLMiHIAzgMmqerqqfghsBtaMuU+StGDNlnBYAuzsW9/VapKkMZhTl7ImWQesa6s/SPLkIex2EvC9I9ersfCY5gaPaW543WPK1UexJ6Pzesf09w/lBWZLOOwGlvWtL22116iqDcCGw3nhJNuramK47s0uHtPc4DHNDR7T1GbLsNJ9wMokK5IcB5wPbBlznyRpwZoVZw5VtT/JJ4A7gGOAjVW1Y8zdkqQFa1aEA0BV3Q7cfgRe+rCGoeYIj2lu8JjmBo9pCqmqUXREkjSPzJY5B0nSLLIgwiHJFUkeTvJQkq8mefu4+zSsJH+U5Il2XH+dZNG4+zSsJB9JsiPJj5LM6atH5tvHwSTZmGRPkkfH3ZdRSbIsyV1JHms/d58cd5+GleSnk9yb5JvtmP7jwK+1EIaVkrylqr7fln8fOK2qPj7mbg0lySrg620y/2qAqrpkzN0aSpJ/BPwI+K/Av6uq7WPu0kDax8H8H+AD9G7ovA+4oKoeG2vHhpDkV4AfADdU1bvG3Z9RSHIKcEpVPZDk7wH3A+fN8X+nAG+uqh8keQPwN8Anq2rb4b7WgjhzOBAMzZuBOZ+IVfXVqtrfVrfRuzdkTquqx6vqUG5snO3m3cfBVNXdwL5x92OUqurZqnqgLf8t8Dhz/JMZqucHbfUN7Wug33cLIhwAklyZZCfwm8C/H3d/Ruy3ga+MuxP6MT8OZo5Jshx4N3DPeHsyvCTHJHkI2ANsraqBjmnehEOSryV5dIqvNQBV9ZmqWgbcCHxivL09NDMdU2vzGWA/veOa9Q7lmKSjKcnPAF8C/uCgUYY5qaperap/Qm804YwkAw0Dzpr7HIZVVb92iE1vpHc/xfoj2J2RmOmYkvwW8CHgrJojk0eH8e80lx3Sx8Fo/Nq4/JeAG6vqy+PuzyhV1YtJ7gJWA4d9IcG8OXN4PUlW9q2uAZ4YV19GJclq4FPAr1fVy+Puj17Dj4OZA9rk7fXA41X1uXH3ZxSSLD5w5WKSN9K7KGKg33cL5WqlLwG/QO9KmGeAj1fVnP5LLskkcDzwfCttmwdXYP1L4L8Ai4EXgYeq6uzx9mowSc4F/piffBzMlWPu0lCS3AS8j96nfX4XWF9V14+1U0NK8s+A/w08Qu93A8Cn26c1zElJ/jGwid7P3U8BN1fV5QO91kIIB0nS4VkQw0qSpMNjOEiSOgwHSVKH4SBJ6jAcJEkdhoMkqcNwkCR1GA6SpI7/D7nSwYAuS/S4AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -566,7 +566,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -576,7 +576,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -586,7 +586,7 @@
     },
     {
      "data": {
-      "image/png": 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2wr7vAH4Z+JckD7baHwAfBW5MsgN4Cnhv23Yr8E5gBvgG8H6AqjqS5MPAfa3dh6rqyAj9kiSNaMHhUFX/CGSezRfN0b6AK+d5rL3A3oX2RZK0uPyGtCSpYzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjqGgySpYzhIkjorJhySbE3yWJKZJLvG3R9JmmQrIhySrAH+HLgU2AJckWTLeHslSZNrRYQDcB4wU1VPVNW3gH3AtjH3SZIm1koJh/XA00Prh1pNkjQGp427A69Gkp3Azrb6n0keG2d/lsFZwL+PuxNj5mswsGpeh1yzZA+9al6DJTbq6/CDJ9NopYTDYWDj0PqGVnuZqtoD7FmuTo1bkumqmhp3P8bJ12DA18HX4Jjleh1WyrDSfcDmJOckOR24HNg/5j5J0sRaEWcOVXU0yW8CtwFrgL1V9ciYuyVJE2tFhANAVd0K3DrufqwwEzOE9gp8DQZ8HXwNjlmW1yFVtRzHkSStIitlzkGStIIYDitckvckeSTJ/yWZqCs1vKUKJNmb5LkkD4+7L+OSZGOSO5I82v5f+MC4+7Tcknx3knuT/HN7Df5oqY9pOKx8DwO/ANw17o4sJ2+p8pJPAlvH3YkxOwr8XlVtAS4ArpzA/xa+CVxYVT8C/CiwNckFS3lAw2GFq6qDVXWqf9lvLt5SBaiqu4Aj4+7HOFXVM1X1pbb8H8BBJuwOCjXwn231Ne1nSSeMDQetVN5SRZ0km4C3A/eMtyfLL8maJA8CzwEHqmpJX4MVcynrJEvyD8D3z7HpD6vq5uXuj7QSJXk98NfA71TV18fdn+VWVd8GfjTJWuBvkrytqpZsLspwWAGq6qfH3YcV6KRuqaLJkOQ1DILhM1X1+XH3Z5yq6oUkdzCYi1qycHBYSSuVt1QRAEkCXA8crKprx92fcUiyrp0xkOR1wM8AX17KYxoOK1ySn09yCPhx4JYkt427T8uhqo4Cx26pchC4cRJvqZLks8A/AW9JcijJjnH3aQzeAfwycGGSB9vPO8fdqWX2JuCOJA8x+OB0oKq+sJQH9BvSkqSOZw6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnq/D+XTf9zt/bJMgAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -596,7 +596,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -606,7 +606,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -616,7 +616,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -626,7 +626,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -636,7 +636,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -646,7 +646,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -656,7 +656,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -666,7 +666,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -676,7 +676,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -686,7 +686,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -696,7 +696,7 @@
     },
     {
      "data": {
-      "image/png": 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ZPRVgB/BEVf3ZUNMe4LUZUJuBe4fq17dZVOuBl9ttrH3A5UnOaQ/ALwf2tbZXkqxvx7p+aF+SpDFYOsK2lwC/BzyW5Dut9ingZuDuJFuAZ4EPtLa9wFXAFPAq8CGAqjqS5LPAQ63fZ6rqSFv+CPAV4C3AN9tHkjQmcw6Nqvp74HjvTVw2S/8CbjjOvnYCO2epTwIXznWMkqT55RvhkqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6jbKy30asnrbN15f/v7NV49xJJJOJ8O/O04HXmlIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG5OuZWk09TM6bqnYrq/oTHkdJsvLUmnmrenJEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktRtwYdGkg1JnkwylWTbuMcjSWeyBR0aSZYAXwSuBNYB1yVZN95RSdKZa0GHBnAxMFVVT1fVT4HdwMYxj0mSzlgLPTRWAM8NrR9qNUnSGCyK/59Gkq3A1rb6z0meHOt4bpm3XZ0H/Gje9rZweZ6Lz5lyrgvqPEf83fMfezot9NA4DKwaWl/Zar+gqrYD20/VoE6VJJNVNTHucbzZPM/F50w51zPlPIct9NtTDwFrk6xJchawCdgz5jFJ0hlrQV9pVNWxJB8F9gFLgJ1VdXDMw5KkM9aCDg2AqtoL7B33OMZk0d1yOw7Pc/E5U871TDnP16Wqxj0GSdJpYqE/05AkLSCGxgKV5ONJKsl5bT1Jbmtfp/JokovGPcZRJfnTJN9r5/NXSZYNtd3YzvXJJFeMc5zzYbF+HU6SVUkeSPJ4koNJPtbq5ybZn+Sp9vOccY91PiRZkuSRJH/d1tckebD9u97VJuwsaobGApRkFXA58IOh8pXA2vbZCtw+hqHNt/3AhVX128A/AjcCtK+K2QS8E9gAfKl9pcxpaZF/Hc4x4ONVtQ5YD9zQzm0bcF9VrQXua+uLwceAJ4bWbwFurap3AEeBLWMZ1SlkaCxMtwKfAIYfOG0E7qiBA8CyJBeMZXTzpKr+pqqOtdUDDN7DgcG57q6qn1TVM8AUg6+UOV0t2q/Dqarnq+rbbfmfGPxCXcHg/Ha1bruAa8YzwvmTZCVwNfDlth7gUuCe1mVRnOfJGBoLTJKNwOGq+ocZTYv9K1X+APhmW15s57rYzmdWSVYD7wIeBM6vqudb0wvA+WMa1nz6cwZ/zP1bW3878NLQHz6L8t91pgU/5XYxSvJ/gX8/S9OngU8xuDW1KJzoXKvq3tbn0wxuc9x5Ksem+ZPkN4C/BP6oql4Z/BE+UFWV5LSeppnkfcCLVfVwkveOezzjZGiMQVX9zmz1JP8ZWAP8Q/uPbiXw7SQX0/mVKgvN8c71NUl+H3gfcFn9fP73aXmuJ7DYzucXJPkVBoFxZ1V9vZV/mOSCqnq+3UZ9cXwjnBeXAO9PchXwq8BbgS8wuE28tF1tLKp/1+Px9tQCUlWPVdVvVtXqqlrN4HL3oqp6gcHXp1zfZlGtB14euvw/LSXZwOBy//1V9epQ0x5gU5Kzk6xh8PD/W+MY4zxZtF+H0+7r7wCeqKo/G2raA2xuy5uBe0/12OZTVd1YVSvbf5ebgPur6oPAA8C1rdtpf549vNI4fewFrmLwUPhV4EPjHc68+AvgbGB/u7I6UFV/WFUHk9wNPM7gttUNVfWzMY5zJIv863AuAX4PeCzJd1rtU8DNwN1JtgDPAh8Y0/jebJ8Edif5HPAIgwBd1HwjXJLUzdtTkqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6/X+jscPeT/xPnAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -706,7 +706,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -772,12 +772,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -787,7 +787,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -797,7 +797,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -807,7 +807,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -817,7 +817,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAD8CAYAAACLrvgBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEaJJREFUeJzt3X3MXnV9x/H3xxaUoI6new1py8pmE4NmQ2wAo1kcRCiwrCxRAtlGZ4hdIiQYl8zqPyiOBJdNHImysNFYFrU2PoxGq7VBjPMPoK0ij2PcYgltgFbKg8SIAb/74/pVL+/dD78+cd33zfuVXLnO+Z7fOb/fySn3x/NwHVNVSJLU4zWjHoAkae4wNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdVs46gEcbieddFItW7Zs1MOQpDllx44dP6uqsZnazbvQWLZsGdu3bx/1MCRpTknyWE87L09Jkrp1hUaSnUnuS3JPku2tdkKSrUkead/Ht3qS3JhkPMm9Sc4Y2s7q1v6RJKuH6m9v2x9v62a6PiRJo3EgZxp/VlWnV9WKNr8WuL2qlgO3t3mAC4Dl7bMGuAkGAQBcA5wFnAlcMxQCNwEfGFpv5Qx9SJJG4FAuT60C1rfp9cDFQ/Vba+BO4LgkJwPnA1ural9VPQNsBVa2ZW+sqjtr8J72Wydsa7I+JEkj0BsaBXwnyY4ka1ptUVU90aafBBa16cXA40Pr7mq16eq7JqlP14ckaQR6n556V1XtTvL7wNYk/zO8sKoqyRH9f3Oaro8WZGsATjnllCM5DEl6Ves606iq3e17D/B1BvcknmqXlmjfe1rz3cDSodWXtNp09SWT1Jmmj4nju7mqVlTVirGxGR8zliQdpBlDI8mxSd6wfxo4D7gf2ATsfwJqNXBbm94EXN6eojobeK5dYtoCnJfk+HYD/DxgS1v2fJKz21NTl0/Y1mR9SJJGoOfy1CLg6+0p2IXAF6vq20m2ARuTXAE8BlzS2m8GLgTGgV8A7weoqn1JPglsa+2urap9bfqDwOeBY4BvtQ/A9VP0IUkagQweWJo/VqxYUUf6F+HL1n6zq93O6y86ouOQpMMlyY6hn1RMyV+ES5K6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkrp1h0aSBUl+lOQbbf7UJHclGU/y5SRHt/pr2/x4W75saBsfbfWHk5w/VF/ZauNJ1g7VJ+1DkjQaB3KmcTXw0ND8p4AbqupNwDPAFa1+BfBMq9/Q2pHkNOBS4C3ASuBzLYgWAJ8FLgBOAy5rbafrQ5I0Al2hkWQJcBHwH20+wDnAV1qT9cDFbXpVm6ctP7e1XwVsqKoXq+qnwDhwZvuMV9WjVfUrYAOwaoY+JEkj0Hum8RngH4Bft/kTgWer6qU2vwtY3KYXA48DtOXPtfa/qU9YZ6r6dH1IkkZgxtBI8ufAnqra8QqM56AkWZNke5Lte/fuHfVwJGne6jnTeCfwF0l2Mrh0dA7wr8BxSRa2NkuA3W16N7AUoC3/PeDp4fqEdaaqPz1NH7+jqm6uqhVVtWJsbKxjlyRJB2PG0Kiqj1bVkqpaxuBG9ner6q+AO4D3tmargdva9KY2T1v+3aqqVr+0PV11KrAcuBvYBixvT0od3frY1NaZqg9J0ggcyu80PgJ8OMk4g/sPt7T6LcCJrf5hYC1AVT0AbAQeBL4NXFlVL7d7FlcBWxg8nbWxtZ2uD0nSCCycuclvVdX3gO+16UcZPPk0sc0vgfdNsf51wHWT1DcDmyepT9qHJGk0/EW4JKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqduMoZHkdUnuTvLjJA8k+USrn5rkriTjSb6c5OhWf22bH2/Llw1t66Ot/nCS84fqK1ttPMnaofqkfUiSRqPnTONF4Jyq+hPgdGBlkrOBTwE3VNWbgGeAK1r7K4BnWv2G1o4kpwGXAm8BVgKfS7IgyQLgs8AFwGnAZa0t0/QhSRqBGUOjBl5os0e1TwHnAF9p9fXAxW16VZunLT83SVp9Q1W9WFU/BcaBM9tnvKoerapfARuAVW2dqfqQJI1A1z2NdkZwD7AH2Ar8BHi2ql5qTXYBi9v0YuBxgLb8OeDE4fqEdaaqnzhNH5KkEegKjap6uapOB5YwODN48xEd1QFKsibJ9iTb9+7dO+rhSNK8dUBPT1XVs8AdwDuA45IsbIuWALvb9G5gKUBb/nvA08P1CetMVX96mj4mjuvmqlpRVSvGxsYOZJckSQeg5+mpsSTHteljgPcADzEIj/e2ZquB29r0pjZPW/7dqqpWv7Q9XXUqsBy4G9gGLG9PSh3N4Gb5prbOVH1IkkZg4cxNOBlY355yeg2wsaq+keRBYEOSfwR+BNzS2t8C/GeScWAfgxCgqh5IshF4EHgJuLKqXgZIchWwBVgArKuqB9q2PjJFH5KkEZgxNKrqXuBtk9QfZXB/Y2L9l8D7ptjWdcB1k9Q3A5t7+5AkjYa/CJckdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd1mDI0kS5PckeTBJA8kubrVT0iyNckj7fv4Vk+SG5OMJ7k3yRlD21rd2j+SZPVQ/e1J7mvr3Jgk0/UhSRqNnjONl4C/r6rTgLOBK5OcBqwFbq+q5cDtbR7gAmB5+6wBboJBAADXAGcBZwLXDIXATcAHhtZb2epT9SFJGoEZQ6OqnqiqH7bpnwMPAYuBVcD61mw9cHGbXgXcWgN3AsclORk4H9haVfuq6hlgK7CyLXtjVd1ZVQXcOmFbk/UhSRqBA7qnkWQZ8DbgLmBRVT3RFj0JLGrTi4HHh1bb1WrT1XdNUmeaPiaOa02S7Um2792790B2SZJ0ALpDI8nrga8CH6qq54eXtTOEOsxj+x3T9VFVN1fViqpaMTY2diSHIUmval2hkeQoBoHxhar6Wis/1S4t0b73tPpuYOnQ6ktabbr6kknq0/UhSRqBnqenAtwCPFRVnx5atAnY/wTUauC2ofrl7Smqs4Hn2iWmLcB5SY5vN8DPA7a0Zc8nObv1dfmEbU3WhyRpBBZ2tHkn8DfAfUnuabWPAdcDG5NcATwGXNKWbQYuBMaBXwDvB6iqfUk+CWxr7a6tqn1t+oPA54FjgG+1D9P0IUkagRlDo6p+AGSKxedO0r6AK6fY1jpg3ST17cBbJ6k/PVkfkqTR8BfhkqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqtnDUA5jPlq39Zle7nddfdIRHIkmHh2cakqRuhoYkqZuhIUnqZmhIkroZGpKkbjOGRpJ1SfYkuX+odkKSrUkead/Ht3qS3JhkPMm9Sc4YWmd1a/9IktVD9bcnua+tc2OSTNeHJGl0es40Pg+snFBbC9xeVcuB29s8wAXA8vZZA9wEgwAArgHOAs4ErhkKgZuADwytt3KGPiRJIzJjaFTV94F9E8qrgPVtej1w8VD91hq4EzguycnA+cDWqtpXVc8AW4GVbdkbq+rOqirg1gnbmqwPSdKIHOw9jUVV9USbfhJY1KYXA48PtdvVatPVd01Sn64PSdKIHPKN8HaGUIdhLAfdR5I1SbYn2b53794jORRJelU72NB4ql1aon3vafXdwNKhdktabbr6kknq0/Xx/1TVzVW1oqpWjI2NHeQuSZJmcrChsQnY/wTUauC2ofrl7Smqs4Hn2iWmLcB5SY5vN8DPA7a0Zc8nObs9NXX5hG1N1ockaURmfGFhki8B7wZOSrKLwVNQ1wMbk1wBPAZc0ppvBi4ExoFfAO8HqKp9ST4JbGvtrq2q/TfXP8jgCa1jgG+1D9P0IUkakRlDo6oum2LRuZO0LeDKKbazDlg3SX078NZJ6k9P1ockaXT8RbgkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkrotHPUABMvWfrO77c7rLzqCI5Gk6XmmIUnqZmhIkroZGpKkboaGJKmboSFJ6jbrQyPJyiQPJxlPsnbU45GkV7NZ/chtkgXAZ4H3ALuAbUk2VdWDox3Z6PQ+nuujuZKOhNl+pnEmMF5Vj1bVr4ANwKoRj0mSXrVm9ZkGsBh4fGh+F3DWiMYypxzIDwYPJ89wpPlttodGlyRrgDVt9oUkDx/kpk4CfnZ4RjVyI9mXfOqwb9JjMju5L7PPoe7HH/Q0mu2hsRtYOjS/pNV+R1XdDNx8qJ0l2V5VKw51O7PBfNmX+bIf4L7MVvNlX16p/Zjt9zS2AcuTnJrkaOBSYNOIxyRJr1qz+kyjql5KchWwBVgArKuqB0Y8LEl61ZrVoQFQVZuBza9Qd4d8iWsWmS/7Ml/2A9yX2Wq+7Msrsh+pqleiH0nSPDDb72lIkmYRQ6OZL68rSbIzyX1J7kmyfdTjORBJ1iXZk+T+odoJSbYmeaR9Hz/KMfaaYl8+nmR3Ozb3JLlwlGPskWRpkjuSPJjkgSRXt/qcOy7T7MtcPC6vS3J3kh+3fflEq5+a5K72d+zL7QGiw9u3l6d+87qS/2XodSXAZXPxdSVJdgIrqmrOPXee5E+BF4Bbq+qtrfZPwL6qur6F+fFV9ZFRjrPHFPvyceCFqvrnUY7tQCQ5GTi5qn6Y5A3ADuBi4G+ZY8dlmn25hLl3XAIcW1UvJDkK+AFwNfBh4GtVtSHJvwE/rqqbDmffnmkM+LqSWaCqvg/sm1BeBaxv0+sZ/Ec+602xL3NOVT1RVT9s0z8HHmLwpoY5d1ym2Zc5pwZeaLNHtU8B5wBfafUjclwMjYHJXlcyJ/8xMfiH850kO9ov5ee6RVX1RJt+Elg0ysEcBlclubddvpr1l3SGJVkGvA24izl+XCbsC8zB45JkQZJ7gD3AVuAnwLNV9VJrckT+jhka88+7quoM4ALgynaZZF6owbXUuXw99Sbgj4DTgSeAfxntcPoleT3wVeBDVfX88LK5dlwm2Zc5eVyq6uWqOp3BmzLOBN78SvRraAx0va5kLqiq3e17D/B1Bv+Y5rKn2rXo/dek94x4PAetqp5q/6H/Gvh35sixadfMvwp8oaq+1spz8rhMti9z9bjsV1XPAncA7wCOS7L/93dH5O+YoTEwL15XkuTYdoOPJMcC5wH3T7/WrLcJWN2mVwO3jXAsh2T/H9nmL5kDx6bdcL0FeKiqPj20aM4dl6n2ZY4el7Ekx7XpYxg8xPMQg/B4b2t2RI6LT0817TG7z/Db15VcN+IhHbAkf8jg7AIGv/b/4lzajyRfAt7N4G2dTwHXAP8FbAROAR4DLqmqWX+DeYp9eTeDSyAF7AT+bui+wKyU5F3AfwP3Ab9u5Y8xuBcwp47LNPtyGXPvuPwxgxvdCxj8j/+NVXVt+xuwATgB+BHw11X14mHt29CQJPXy8pQkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG7/B7/G4TPm5vzDAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -827,7 +827,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -837,7 +837,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -865,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -873,95 +873,95 @@
      "output_type": "stream",
      "text": [
       "min/max\n",
-      "       ev   ind  det   charge        xg        yg         zg         rg  \\\n",
-      "min     1     0    0     2019 -3.158685 -3.374328 -47.909229   2.738755   \n",
-      "max  1000  4379   95  8107997  3.158685  9.439112  26.674952  14.278748   \n",
+      "       ev   ind  det   charge      xg      yg       zg       rg   iphi  xsize  \\\n",
+      "min     1     0    0     2019 -3.1587 -3.3743 -26.6751   2.7388 -32766  -1023   \n",
+      "max  1000  4382   95  7570745  6.6091  3.3743  26.6750  11.0548  32765   1023   \n",
       "\n",
-      "      iphi  xsize     ...         tkId      pt   n1   tkId2     pt2   n2  \\\n",
-      "min -32766  -1023     ...            0     401    1       0       0    0   \n",
-      "max  32765   1023     ...       142904  292338  355  142143  196301  313   \n",
+      "     ...      tkId      pt      z0   r0   n1       phi       eta  seq  \\\n",
+      "min  ...         0     401 -147338    0    0 -3.141557 -2.971983    1   \n",
+      "max  ...    142826  292338  149881  999  206  3.141416  2.971979    1   \n",
       "\n",
-      "          phi       eta  seq      trackID  \n",
-      "min -3.141570 -2.971995    1     10000000  \n",
-      "max  3.141403  2.971991    1  10000114179  \n",
+      "         trackID   norm  \n",
+      "min     10000002  0.001  \n",
+      "max  10000113902  0.001  \n",
       "\n",
       "[2 rows x 21 columns]\n",
-      "       ev   ind  det   charge        xg         yg         zg         rg  \\\n",
-      "min     1  1305   96     4021 -6.980564 -14.810458 -31.160593   2.738874   \n",
-      "max  1000  8045  319  5558487  9.779302  12.969457  39.338615  15.979434   \n",
+      "       ev   ind  det   charge      xg      yg       zg       rg   iphi  xsize  \\\n",
+      "min     1   855   96     4021 -6.9806 -7.2495 -26.6751   6.5775 -32767  -1023   \n",
+      "max  1000  8049  319  5558487  6.9806  7.2495  26.6750  15.8274  32767   1023   \n",
       "\n",
-      "      iphi  xsize     ...         tkId      pt   n1   tkId2     pt2  n2  \\\n",
-      "min -32767  -1023     ...            0     401    0       0       0   0   \n",
-      "max  32767   1023     ...       142904  292338  240  140239  124077  97   \n",
+      "     ...      tkId      pt      z0   r0   n1       phi       eta  seq  \\\n",
+      "min  ...         0     401 -147949    0    1 -3.141593 -2.108088    2   \n",
+      "max  ...    142826  292338  149881  999  196  3.141578  2.108084    2   \n",
       "\n",
-      "          phi       eta  seq      trackID  \n",
-      "min -3.141586 -2.907090    2     10000000  \n",
-      "max  3.141580  2.833558    2  10000114179  \n",
+      "         trackID   norm  \n",
+      "min     10000009  0.001  \n",
+      "max  10000113902  0.001  \n",
       "\n",
       "[2 rows x 21 columns]\n",
-      "       ev    ind  det   charge         xg         yg         zg         rg  \\\n",
-      "min     1   2326  320     4022 -11.065842 -11.335875 -26.675051   4.987466   \n",
-      "max  1000  14260  671  6401557  11.065842  11.335875  50.631355  11.335896   \n",
+      "       ev    ind  det   charge       xg       yg       zg       rg   iphi  \\\n",
+      "min     1   2326  320     4022 -11.0658 -11.3359 -26.6751  10.0193 -32767   \n",
+      "max  1000  11211  671  6401557  11.0658  11.3359  38.0934  15.9663  32767   \n",
       "\n",
-      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2  n2  \\\n",
-      "min -32767  -1023     ...            0     401    1       0      0   0   \n",
-      "max  32767    908     ...       142826  292338  225  139205  58626  94   \n",
+      "     xsize  ...      tkId      pt      z0   r0   n1       phi       eta  seq  \\\n",
+      "min  -1023  ...         0     401 -147949    0    1 -3.141593 -1.643978    3   \n",
+      "max    908  ...    142826  292338  149881  998  179  3.141593  1.643975    3   \n",
       "\n",
-      "          phi       eta  seq      trackID  \n",
-      "min -3.141591 -1.643974    3     10000000  \n",
-      "max  3.141590  3.010971    3  10000113896  \n",
+      "         trackID   norm  \n",
+      "min     10000015  0.001  \n",
+      "max  10000113896  0.001  \n",
       "\n",
       "[2 rows x 21 columns]\n",
-      "       ev    ind   det   charge         xg         yg         zg         rg  \\\n",
-      "min     1   3277   672     4017 -16.166553 -16.436216 -26.675051   6.577823   \n",
-      "max  1000  13712  1183  5935571  16.166553  16.436216  26.674952  16.436220   \n",
+      "       ev    ind   det   charge       xg       yg       zg       rg   iphi  \\\n",
+      "min     1   3277   672     4017 -16.1666 -16.4362 -26.6751  15.8152 -32767   \n",
+      "max  1000  13712  1183  5935571  16.1666  16.4362  26.6750  16.4362  32767   \n",
       "\n",
-      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2  n2  \\\n",
-      "min -32767  -1023     ...            0     401    1       0      0   0   \n",
-      "max  32767    918     ...       142826  292338  176  142824  70357  53   \n",
+      "     xsize  ...      tkId      pt      z0   r0   n1       phi       eta  seq  \\\n",
+      "min  -1023  ...         0     401 -147949    0    1 -3.141580 -1.294041    4   \n",
+      "max    918  ...    142826  292338  149881  998  170  3.141586  1.294038    4   \n",
       "\n",
-      "          phi       eta  seq      trackID  \n",
-      "min -3.141579 -1.819175    4     10000000  \n",
-      "max  3.141589  1.294038    4  10000113896  \n",
+      "         trackID   norm  \n",
+      "min     10000016  0.001  \n",
+      "max  10000113896  0.001  \n",
       "\n",
       "[2 rows x 21 columns]\n",
-      "       ev    ind   det   charge         xg         yg         zg         rg  \\\n",
-      "min     1   4148  1184     4006 -16.067684 -16.001060 -35.182049   4.614048   \n",
-      "max  1000  20609  1631  4746542  16.065769  16.002853  35.186981  16.070286   \n",
+      "       ev    ind   det   charge       xg       yg      zg       rg   iphi  \\\n",
+      "min     1   4148  1184     4014 -16.0677 -16.0011 -35.182   4.6140 -32767   \n",
+      "max  1000  20608  1631  4746542  16.0588  16.0029  35.187  16.0703  32766   \n",
       "\n",
-      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2  n2  \\\n",
-      "min -32767  -1023     ...            0     401    0       0      0   0   \n",
-      "max  32766    892     ...       142904  232351  229  139156  51334  61   \n",
+      "     xsize  ...      tkId      pt      z0   r0   n1       phi       eta  seq  \\\n",
+      "min  -1023  ...         0     401 -147338    0    1 -3.141583 -2.683823    5   \n",
+      "max    892  ...    142812  232351  149881  999  229  3.141567  2.683831    8   \n",
       "\n",
-      "          phi       eta  seq      trackID  \n",
-      "min -3.141578 -2.683826    5     10000007  \n",
-      "max  3.141570  2.683821    8  10000114092  \n",
+      "         trackID   norm  \n",
+      "min     10000095  0.001  \n",
+      "max  10000113902  0.001  \n",
       "\n",
       "[2 rows x 21 columns]\n",
-      "       ev    ind   det   charge         xg         yg         zg         rg  \\\n",
-      "min     1   4622  1296     4018 -16.066727 -16.001966 -49.761089   2.739524   \n",
-      "max  1000  22376  1743  6625049  16.054962  16.001966  42.686981  16.070562   \n",
+      "       ev    ind   det   charge       xg       yg       zg       rg   iphi  \\\n",
+      "min     1   4625  1296     4018 -16.0667 -16.0011 -42.6813   4.6140 -32767   \n",
+      "max  1000  22376  1743  6625049  16.0550  16.0020  42.6870  16.0706  32767   \n",
       "\n",
-      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2   n2  \\\n",
-      "min -32767  -1023     ...            0     401    0       0      0    0   \n",
-      "max  32767   1013     ...       142812  232351  303  142276  31140  106   \n",
+      "     xsize  ...      tkId      pt      z0   r0   n1       phi       eta  seq  \\\n",
+      "min  -1023  ...         0     401 -147949    0    1 -3.141581 -2.883513    6   \n",
+      "max   1013  ...    142812  232351  149881  999  303  3.141593  2.883501    9   \n",
       "\n",
-      "          phi       eta  seq      trackID  \n",
-      "min -3.141582 -3.373930    6     10000015  \n",
-      "max  3.141592  2.966494    9  10000113902  \n",
+      "         trackID   norm  \n",
+      "min     10108662  0.001  \n",
+      "max  10000113902  0.001  \n",
       "\n",
       "[2 rows x 21 columns]\n",
-      "       ev    ind   det   charge         xg         yg         zg         rg  \\\n",
-      "min     1   5146  1408     4006 -16.070560 -15.997278 -52.180595   4.614048   \n",
-      "max  1000  24119  1855  6750257  16.067684  16.000870  52.186104  16.070562   \n",
+      "       ev    ind   det   charge       xg       yg       zg       rg   iphi  \\\n",
+      "min     1   5146  1408     4006 -16.0706 -15.9973 -52.1806   4.6140 -32767   \n",
+      "max  1000  24119  1855  5782400  16.0677  16.0009  52.1861  16.0706  32767   \n",
       "\n",
-      "      iphi  xsize     ...         tkId      pt   n1   tkId2    pt2   n2  \\\n",
-      "min -32767  -1023     ...            0     401    0       0      0    0   \n",
-      "max  32767   1023     ...       142812  232351  268  140451  55949  194   \n",
+      "     xsize  ...      tkId      pt      z0   r0   n1       phi       eta  seq  \\\n",
+      "min  -1023  ...         0     401 -147338    0    1 -3.141585 -3.090231    7   \n",
+      "max    821  ...    142812  232351  149881  997  207  3.141585  3.090221   10   \n",
       "\n",
-      "          phi       eta  seq      trackID  \n",
-      "min -3.141588 -3.090228    7     10000008  \n",
-      "max  3.141587  3.090218   10  10000114029  \n",
+      "         trackID   norm  \n",
+      "min     10108661  0.001  \n",
+      "max  10000113902  0.001  \n",
       "\n",
       "[2 rows x 21 columns]\n"
      ]
@@ -980,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -991,7 +991,8 @@
     "                    'ys'+n : hh['ysize'],\n",
     "                    'pt'+n : hh['pt'],\n",
     "                    'det'+n : hh['det'],\n",
-    "                    'trackID' : hh['trackID']\n",
+    "                    'trackID' : hh['trackID'],\n",
+    "                    'tkz'+n : hh['z0']/10000.,\n",
     "                       })\n",
     "\n",
     "def buildXYZ(hh,n) :\n",
@@ -1007,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1036,7 +1037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -1056,12 +1057,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1071,7 +1072,7 @@
     },
     {
      "data": {
-      "image/png": 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MXWvP9R4A3t5dfw1w39C19l1z13Yd8IGha9yImrvLU3x3x+Re4CND17oB/7df2P17NvBJ4LVD13qS2i9i7Z2xV/G9O2M/27U/D/gyox2x53XXn9dtW70z9sqp+zT0i3KSF+JKRp/SXwLe3bW9B3h9d/3NwBe7+/wZcHbX/gPAF7rLp4FXjD3np4AHu/8wHwKeM3SdPdf7XEaj2gcZ/dn78qHr7LvmbvvdwK6h69vA9/mN3Xv8QFf7S4aucwNq/kNG56h+GPjtoWs8Sc23AF8D/o/RX9Z7GQ1Aruu2B7ipe00eBFbGHvt24Hh3edtY+0qXXV8C/oTui67TXPxmrCQ1btHm6CVJc2bQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuP8HCtkgUJ83vYAAAAAASUVORK5CYII=\n",
+      "image/png": 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EOL27/JLu3wB/AlwzdK0913sN8M7u/OuAe4aute+au21XAh8dusZF1NydnuDZDyb3Ap8cutYF/N9+cffvmcAdwOuHrnWN2rex/oexl/G9H8Z+odv+QuBrjD6IfUF3/oXddas/jL106jEN/aSs8URcyuhd+qvA+7pt7wfe2J1/K/CV7jZ/BpzZbf8B4Mvd6XPAq8Ye87PA/d1/mI8Dzxu6zp7rfT6jWe39jP7sfeXQdfZdc3f9ncCuoetb4Ov85u41vq+r/WVD17mAmv+I0TGqHwJ+d+ga16j5BuBbwP8x+st6L6MJyJXd9QGu656T+4GVsfu+Ezjand4xtn2ly66vAh+i+6LrNCe/GStJjVu2Hr0kac4MeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGvf/xDw0R6reRNQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1081,7 +1082,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1091,7 +1092,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1101,7 +1102,7 @@
     },
     {
      "data": {
-      "image/png": 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f7+5gvJFBHxoGO37O666/AjgEnN3dfkn3b4A/Bm6ad60zqPkm4J3d9dcDD8y71r5r7sauBz487xpnUXN3eYLv7pjcBXx03rXO4P/2i7t/zwE+Bbxh3rWepvYtrL0z9mqeuTP2c934i4CvMtgR+8Lu+ou6bat3xl419pzm/aKsehGuYvAT+ivA+7qx9wNv7K5fC3y5u8+fAed0498HPNxdPgO8aug5Pw082P1n+Uvg+fOucwY1v4DBqvZBBr/2vnLedfZdc7f9PmD7vOub4fv85u49/mJX+8vmXecMav5D4DDwCPDb867xNDXfDnwT+D8Gv1nvYrAAub7bHuCW7jV5EFgZeuw7gSPd5R1D4ytdfn0F+CDdH7qOc/EvYyWpcYvUo5ck9cCgl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcf8P3/YwAyMzvKUAAAAASUVORK5CYII=\n",
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bz+v+DfAnwA1D17qAmm8A3tpdfwVw39C19l1zN3YN8MGha1xEzd3lMb6zY3IP8OGha13A/+3ndv+eA3wCeOXQtZ6k9q2cemfslXz3ztjPdOPPBr7CaEfsed31Z3f3rd4Ze8XUcxr6m7Lqm3AFo9/QXwbe2Y29C3hNd/0NwJe6bd4PnNONfx/wxe7yKeAlY8/5SeCB7j/LXwLPHLrOBdT8LEar2gcY/dn74qHr7Lvm7v57gJ1D17fA1/l13Wv8+a72Fwxd5wJq/kPgMPAQ8DtD13iSmm8FvgH8H6O/rPcwWoBc090f4Kbue/IAsDL22LcCR7rLW8bGV7r8+jLwXro3uk5z8Z2xktS4ZerRS5J6YNBLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wcbWSFZ6q360gAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1111,7 +1112,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1121,7 +1122,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEOdJREFUeJzt3X2MpWV5x/Hvr0sWU19WUGItsLJkALtJEzUTMDWpL7W6lC4QS3Q3NUW7ZYst/tM0cQ1N2pgYtf+YEGnoRunalyzSbWyXsoaqSPgHLUvjC0iQFW1Yal2UuknfQPTqH+dZfTrM7Jwz55w5M/d8P8lkzrmft2vuM7nmnuu5n+dJVSFJatfPzDoASdJ0meglqXEmeklqnIlekhpnopekxpnoJalxJnpJapyJXpIaZ6KXpMadMcuDJ9kJ7HzhC1943cUXXzzLUCRp3XnggQe+V1XnLLde1sItEObn5+vo0aOzDkOS1pUkD1TV/HLrWbqRpMaZ6CWpcSZ6SWqciV6SGmeil6TGmeglqXEmeklqnIlekho30ytjJWkjumDfnT95/e0PXzH1401lRJ/k+UmOJvn1aexfkjS8oRJ9kluTnEjy4IL2HUkeSXIsyb7eovcBt08yUEnSygw7oj8A7Og3JNkE3AxcDmwHdifZnuRXga8DJyYYpyRphYaq0VfVvUkuWNB8KXCsqh4DSHIbcBXwAuD5DJL//yQ5UlU/nljEkqSRjHMy9lzg8d7748BlVXUDQJJ3Ad9bKskn2QvsBdi6desYYUiSTmdq0yur6kBV/eNplu+vqvmqmj/nnGVvpyxJWqFxEv0TwPm99+d1bUNLsjPJ/pMnT44RhiTpdMZJ9PcDFyXZlmQzsAs4PMoOquqOqtq7ZcuWMcKQJJ3OsNMrDwL3AZckOZ5kT1U9C9wA3AU8DNxeVQ9NL1RJ0koMO+tm9xLtR4AjKz34qWfGzs3NrXQXkqRlzPReN5ZuJGn6ZproPRkrSdPniF6SGudtiiWpcSZ6SWqcNXpJapw1eklqnKUbSWqcpRtJapylG0lqnKUbSWqciV6SGmeil6TGeTJWkhrnyVhJapylG0lqnIlekhpnopekxpnoJalxzrqRpMY560aSGmfpRpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGmeil6TGmeglqXFeMCVJjfOCKUlqnKUbSWqciV6SGmeil6TGmeglqXEmeklqnIlekhpnopekxpnoJalxE0/0SX4hyS1JDiV5z6T3L0kazVCJPsmtSU4keXBB+44kjyQ5lmQfQFU9XFXXA28HXjf5kCVJoxh2RH8A2NFvSLIJuBm4HNgO7E6yvVt2JXAncGRikUqSVmSoRF9V9wJPLWi+FDhWVY9V1TPAbcBV3fqHq+py4DcnGawkaXRnjLHtucDjvffHgcuSvAF4G3AmpxnRJ9kL7AXYunXrGGFIkk5nnES/qKq6B7hniPX2A/sB5ufna9JxSNJacsG+O2d27HFm3TwBnN97f17XNjTvRy9J0zdOor8fuCjJtiSbgV3A4VF24P3oJWn6hp1eeRC4D7gkyfEke6rqWeAG4C7gYeD2qnpoeqFKklZiqBp9Ve1eov0IY0yhTLIT2Dk3N7fSXUiSluGjBCWpcd7rRpIaN9NE76wbSZo+SzeS1DhLN5LUOEs3ktQ4SzeS1LiJ3+tGkjTbe9ssZI1ekhpnjV6SGmeNXpIaZ+lGkhpnopekxlmjl6TGWaOXpMZZupGkxnnBlCRNyFq6SKrPEb0kNc5EL0mNc9aNJDXOWTeS1DhLN5LUOBO9JDXORC9JjXMevSSNYa3One9zRC9JjTPRS1LjTPSS1LiZ1uiT7AR2zs3NzTIMSRrJeqjL93nBlCQ1ztKNJDXORC9JjXMevSQNYb3V5ftM9JK0hPWc3Pss3UhS40z0ktQ4E70kNc5EL0mNM9FLUuOmMusmydXAFcCLgE9U1T9N4ziSNGmtzLTpG3pEn+TWJCeSPLigfUeSR5IcS7IPoKr+vqquA64H3jHZkCVJoxildHMA2NFvSLIJuBm4HNgO7E6yvbfKH3XLJUkzMnSir6p7gacWNF8KHKuqx6rqGeA24KoMfAT4TFX9y2L7S7I3ydEkR5988smVxi9JWsa4Nfpzgcd7748DlwHvBd4MbEkyV1W3LNywqvYD+wHm5+drzDgkacVarMv3TeVkbFXdBNw0jX1LkkYz7vTKJ4Dze+/P69qGkmRnkv0nT54cMwxJ0lLGTfT3Axcl2ZZkM7ALODzsxj54RJKmb5TplQeB+4BLkhxPsqeqngVuAO4CHgZur6qHRtinI3pJmrKha/RVtXuJ9iPAkZUcvKruAO6Yn5+/biXbS5KW5y0QJKlxM030lm4kafpmmug9GStJ0+ejBCVtGP0Lo7794StmGMnqmmmiT7IT2Dk3NzfLMCQ1bKmrXlu/GrZvponeWTeSpmEjJfFhOOtGkhpnopekxjm9UpIaZ41eUhOsyy/N0o0kNc5EL0mNM9FLUuM8GStJjfNkrKR1yxOww/FeN5LWvI16j5pJsUYvSY1zRC9pXbFcMzpH9JLUOGfdSFLjfMKUJDXO0o0kNc6TsZLWJE+6To4jeklqnCN6SRPlxU1rjyN6SWqciV6SGuc8eklqnHevlDSUcWvvS82isY4/fZ6MldaBlk9wtvyzrRXW6CWpcY7opQlwVLq4US968iKp6TDRSxrrD5XJee0z0UsblAl647BGL0mNM9FLUuNM9JLUOBO9JDVu4idjk1wI3AhsqaprJr1/aa3wZKbWi6FG9EluTXIiyYML2nckeSTJsST7AKrqsaraM41gJUmjG7Z0cwDY0W9Isgm4Gbgc2A7sTrJ9otFJksY2VKKvqnuBpxY0Xwoc60bwzwC3AVdNOD5J0pjGqdGfCzzee38cuCzJS4APAq9O8v6q+tBiGyfZC+wF2Lp16xhhSILhzhl41evGNPGTsVX1feD6IdbbD+wHmJ+fr0nHIUkaGCfRPwGc33t/Xtc2tCQ7gZ1zc3NjhKGNpoUbiC31M4z6s01jxD3MPh3pry/jzKO/H7goybYkm4FdwOFRdlBVd1TV3i1btowRhiTpdIYa0Sc5CLwBeGmS48AfV9UnktwA3AVsAm6tqodGObgjem0kjoI1K0Ml+qravUT7EeDISg/uowQlafq8BYIkNW6m96OfdOlmLZ6kWwsxrYUYVmK9xt03TrlmVida1Z6Zjug9GStJ02fpRpIa11TpRhtbC6WeYVh+0ags3UhS4yzdSFLjTPSS1Dhr9Ktoo9SQl7JUbXmcvliL9eq1GJM2Nmv0ktQ4SzeS1DgTvSQ1zkQvSY1b9ydj1+KJr7UY03KGPVE8qQdmjHrs1ezTaZw0XsnxpEnxZKwkNc7SjSQ1zkQvSY0z0UtS40z0ktS4dT/rZimt3W5g1J9nqZkcG3GGxzRmCknribNuJKlxlm4kqXEmeklqnIlekhpnopekxpnoJalxJnpJalyz8+iHsXBO+VJzrJdaZ5LHHiWG1TbqnPxJ3clyHGul76S1wHn0ktQ4SzeS1DgTvSQ1zkQvSY0z0UtS40z0ktQ4E70kNc5EL0mNM9FLUuNM9JLUuInfAiHJ84E/A54B7qmqv5n0MSRJwxtqRJ/k1iQnkjy4oH1HkkeSHEuyr2t+G3Coqq4DrpxwvJKkEQ1bujkA7Og3JNkE3AxcDmwHdifZDpwHPN6t9qPJhClJWqmhEn1V3Qs8taD5UuBYVT1WVc8AtwFXAccZJPuh9y9Jmp5xavTn8tOROwwS/GXATcDHklwB3LHUxkn2AnsBtm7dOkYYkzPqrW2ncSvcYfbpLXhXbtRbLo+6jrQWTfxkbFX9F/DuIdbbD+wHmJ+fr0nHIUkaGKe08gRwfu/9eV3b0JLsTLL/5MmTY4QhSTqdcRL9/cBFSbYl2QzsAg6PsgMfPCJJ0zfs9MqDwH3AJUmOJ9lTVc8CNwB3AQ8Dt1fVQ9MLVZK0EkPV6Ktq9xLtR4AjKz34rJ8ZK0kbgc+MlaTGzTTRezJWkqbPEb0kNc4rVyWpcama/bVKSZ4E/nWVDvdS4HurdKxxGOdkrYc410OMYJyTNk6cr6iqc5ZbaU0k+tWU5GhVzc86juUY52SthzjXQ4xgnJO2GnFaupGkxpnoJalxGzHR7591AEMyzslaD3GuhxjBOCdt6nFuuBq9JG00G3FEL0kbSpOJPsnZST6b5NHu+1mLrPPGJF/uff1vkqu7ZQeSfKu37FWzirNb70e9WA732rcl+VL3zN5PdXcRnUmcSV6V5L4kDyX5apJ39JZNrT+XeG5xf/mZXd8c6/rqgt6y93ftjyR566RiWmGcf5Dk613ffT7JK3rLFv38ZxTnu5I82Yvnd3rLru1+Rx5Ncu0MY/xoL75vJPlBb9lq9uWiz9ruLU+Sm7qf46tJXtNbNtm+rKrmvoA/BfZ1r/cBH1lm/bMZPCrxZ7v3B4Br1kqcwH8u0X47sKt7fQvwnlnFCVwMXNS9/nngO8CLp9mfwCbgm8CFwGbgK8D2Bev8HnBL93oX8Knu9fZu/TOBbd1+Nk2p/4aJ842937/3nIrzdJ//jOJ8F/CxRbY9G3is+35W9/qsWcS4YP33Areudl92x/pl4DXAg0ss/zXgM0CA1wJfmlZfNjmiZ/Ds2k92rz8JXL3M+tcAn6mq/55qVM81apw/kSTAm4BDK9l+RMvGWVXfqKpHu9f/BpwAlr2QY0xLPbe4rx/7IeBXur67Critqp6uqm8Bx7r9zSTOqvpC7/fvi/z0ucuraZj+XMpbgc9W1VNV9R/AZ4EdayDG3cDBKcSxrFr8Wdt9VwF/WQNfBF6c5OVMoS9bTfQvq6rvdK//HXjZMuvv4rm/DB/s/p36aJIzJx7hwLBxPi/J0SRfPFVeAl4C/KAGzwWAwTN7z51xnAAkuZTBaOubveZp9Odizy1e2Ac/Wafrq5MM+m6YbSdl1GPtYTDSO2Wxz38aho3zN7rP8lCSU0+ZW63+HPo4XflrG3B3r3m1+nIYS/0sE+/LiT8zdrUk+Rzwc4ssurH/pqoqyZJTi7q/oL/I4AEqp7yfQULbzGDq0/uAD8wwzldU1RNJLgTuTvI1BglrYibcn38FXFtVP+6aJ9afrUvyTmAeeH2v+Tmff1V9c/E9TN0dwMGqejrJ7zL4b+lNM4plObuAQ1X1o17bWurLVbNuE31VvXmpZUm+m+TlVfWdLvGcOM2u3g58uqp+2Nv3qdHr00n+AvjDWcZZVU903x9Lcg/wauDvGPyrd0Y3Uh35mb2TjjPJi4A7gRu7f0VP7Xti/bnAMM8tPrXO8SRnAFuA7w+57aQMdawkb2bwh/X1VfX0qfYlPv9pJKdl46yq7/fefpzB+ZtT275hwbb3TDzC0T63XcDv9xtWsS+HsdTPMvG+bLV0cxg4dab6WuAfTrPuc2p4XTI7VQe/Glj0rPkELBtnkrNOlTqSvBR4HfD1Gpy1+QKD8wtLbr+KcW4GPs2g5nhowbJp9ecwzy3ux34NcHfXd4eBXRnMytkGXAT884TiGjnOJK8G/hy4sqpO9NoX/fxnGOfLe2+vZPAYURj8R/yWLt6zgLfw//9LXrUYuzhfyeBE5n29ttXsy2EcBn6rm33zWuBkNyiafF+u1hno1fxiUIP9PPAo8Dng7K59Hvh4b70LGPz1/JkF298NfI1BQvpr4AWzihP4pS6Wr3Tf9/S2v5BBcjoG/C1w5gzjfCfwQ+DLva9XTbs/Gcxc+AaDUdmNXdsHGCRMgOd1fXOs66sLe9ve2G33CHD5lH8nl4vzc8B3e313eLnPf0Zxfgh4qIvnC8Are9v+dtfPx4B3zyrG7v2fAB9esN1q9+VBBrPPfsigzr4HuB64vlse4Obu5/gaMD+tvvTKWElqXKulG0lSx0QvSY0z0UtS40z0ktQ4E70kNc5EL0mNM9FLUuNM9JLUuP8DnbF/HVVaGowAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1131,7 +1132,7 @@
     },
     {
      "data": {
-      "image/png": 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vfmzFPufz5AHG6xnMQ8PgwM953fUXAUeAs7vbz+t+BvgT4IZZ17oBNd8AvLW7/grgnlnX2nfNXds1wAdnXeNG1NxdHuHJA5N7gY/OutYN+Lf93O7nOcCngVfOutbT1L6D1Q/GXsn3H4z9fNf+HODrDA7EPru7/pxu28qDsVeM3adZPykrnoQrGLxDfw14V9f2buA13fU3AF/t9nk/cE7X/gPAV7rLZ4GXDD3mZ4B7u38sfwk8c9Z1bkDNz2Iwqr2XwcfeF8+6zr5r7rbfBSzPur4NfJ1f173GX+pqf8Gs69yAmv8IOArcD/zurGs8Tc23AN8G/o/BJ+u9DAYg13TbA9zUPSf3AktD930rcKy7vGWofanLr68Bf0b3RddxLn4zVpIaN09z9JKkHhj0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ17v8BwtU4J46u9VMAAAAASUVORK5CYII=\n",
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q628DjgLnVbffVP0bwB8Du5qudQQ17wI+VF1/N3C46Vrrrrka2wF8pukaR1FzdXmBV3dMbgc+33StI/i//cbq37XAg8B1Tde6RO2TLL8z9ga+d2fsI9X4xcA36OyIvai6fnF13+Kdsdf3PaemvymLvgnX03mHfgb4eDV2O/De6vpNwNerbT4FrK3Gvw/4WnV5GLim6zm/DDxR/Wf5S+D1Tdc5gprfQGdV+wSdX3vf3nSddddc3f8QMNN0fSN8nd9fvcaPVbVf2XSdI6j5D4FjwFPAbzdd4xI17wO+Dfwfnd+st9NZgOyo7g9gd/U9eQKY7nrsh4Dj1eWDXePTVX49A/wp1Qdd+7n4yVhJKlybevSSpBoY9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFe7/Ac5rNdRVkAtZAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1141,7 +1142,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1151,7 +1152,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1161,7 +1162,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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AAZYBLzbU/340EfOFwKuZ+Vy13peAX2owhp7l6e+v3WkjcF+2PQa8JSLeBrwf+FJmvpKZ36Ed22S17LzMfCzbWf8+4AO99mucEv3p7kG7ctY6TwI3Vs8/CPxoRFxYtU9GxI9ExHLg5/nBu1/dVf2Z9ImIOKeZ7velqZh/DPhQRByMiC9ExLrGIuhdk98ztP8TfDkz/33oPe9fUzH/OjAdETPArwDbG+p/P5qI+WXg7Ih4/eSim/jh73/cdftc5mqfOU17T8Yp0dfxu8D7IuKfgffRvnXhdzPzi7Svi/9PwOs3Sflu9Z6PAj8B/BTtP4tun+9OD6ifmM8B/ifbZ9v9BbBz3ns9mH5ift3matlC00/MvwNck5mrgM8AfzLvvR5MTzFXI9pNwCci4nHgP/jh71+nMU6J/oz3oM3MFzPzxsx8N3Bn1fZv1c+7MvPyzLyadi3ruar9perPpP+l/Z9hffOh1NZIzLR/6z9YPf874J3NhdCzpmKmGv2tBx5qNoSeDT3miFgBvCszv1Zt4m+An2k4jl409f/50cz82cxcD+yn4/tfILp9LnO1rzpNe2+GPRnR74P2xMsxYC1vTN785Kx1lvPGBONdtOvQ0J74ubB6/k7gEHB29fpt1c8A/hTYPupY5yHm7cCHq+dXAgdGHWvTMVdttwGfHXWM8xFz9XiZNyYmtwCfG3Ws8/Bv+63Vz3OALwNXjTrW08S+hu6Tsdfyg5Oxj1ftFwDfpD0Re371/IJq2ezJ2Gt67tOoP5RZH8I1tH9DPw/cWbV9DLihen4T8I1qnXuBc6r2NwHPVI/HgMs7tvkV4OnqH8tfAW8edZzzEPNbaI9qn6b9Z++7Rh1n0zFXyx8BJkcd3zx+zx+svuMnq9gvHnWc8xDzH9G+R/WzwG+POsbTxLwbeAn4P9p/WW+hPQC5rVoewI7qM3kaaHW898PA0epxS0d7q8pfzwP3UJ3o2svDM2MlqXDjVKOXJDXARC9JhTPRS1LhTPSSVDgTvSQVzkQvSYUz0UtS4Uz0klS4/wdf/Fd50bhONAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1171,7 +1172,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1181,7 +1182,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAELNJREFUeJzt3V2MXVd5h/HnjyMHFURISEQhibEjpwGrlfgYBdRKJVA+HMBxStNiq6ihdeOGNtxUlTBKL9pKVaE3qIioqUVTl1ZKSFNo7cbIBUIULhIaU/GRxDIxBhS7KTYELKG2CSFvL8422kxmxmfmnDNnZvn5SaM5e+2v1+scv7Pm3Wv2TlUhSWrXc6YdgCRpskz0ktQ4E70kNc5EL0mNM9FLUuNM9JLUOBO9JDXORC9JjTPRS1Ljzpl2AAAXXnhhrV+/ftphSNKq8qUvfem7VXXRmbZbEYl+/fr1HDx4cNphSNKqkuTbw2xn6UaSGjfVRJ9kS5Ldp06dmmYYktS0qSb6qtpXVTvPO++8aYYhSU2zdCNJjTPRS1LjTPSS1DgTvSQ1zkQvSY2b6h9MJdkCbNm4ceM0w5CkZbV+190/ef2tD7594udzeqUkNc7SjSQ1zkQvSY0z0UtS40z0ktQ4E70kNc5EL0mNG3uiT3JVki8kuTXJVeM+viRpcYZK9EluS3IiyUOz2jcnOZzkSJJdXXMBPwSeCxwbb7iSpMUadkS/B9jcb0iyBrgFuBrYBGxPsgn4QlVdDbwf+NPxhSpJWoqhEn1V3Qc8Mav5SuBIVR2tqqeAO4CtVfVMt/77wLlji1SStCSj3OvmYuCx3vIx4LVJ3gm8FXgh8NH5dk6yE9gJsG7duhHCkCQtZOw3NauqTwKfHGK73UkeB7asXbv2NeOOQ5I0MMqsm+PApb3lS7q2oXlTM0mavFES/YPA5Uk2JFkLbAP2LuYASbYk2X3q1KkRwpAkLWTY6ZW3A/cDVyQ5lmRHVT0N3AQcAA4Bd1bVw4s5uSN6SZq8oWr0VbV9nvb9wP6xRiRJGqup3gLB0o0kTZ5PmJKkxjmil6TGOaKXpMZ5m2JJapylG0lqnKUbSWqcpRtJapyJXpIaZ41ekhpnjV6SGmfpRpIaZ6KXpMaZ6CWpcV6MlaTGeTFWkhpn6UaSGmeil6TGmeglqXEmeklqnIlekhrn9EpJapzTKyWpcZZuJKlxJnpJapyJXpIaZ6KXpMaZ6CWpcSZ6SWrcRBJ9kuclOZjkHZM4viRpeEMl+iS3JTmR5KFZ7ZuTHE5yJMmu3qr3A3eOM1BJ0tIMO6LfA2zuNyRZA9wCXA1sArYn2ZTkzcAjwIkxxilJWqJzhtmoqu5Lsn5W85XAkao6CpDkDmAr8HzgeQyS//8m2V9Vz4wtYknSogyV6OdxMfBYb/kY8NqqugkgyXuA786X5JPsBHYCrFu3boQwJEkLmdism6raU1X/tsD63VU1U1UzF1100aTCkKSz3iiJ/jhwaW/5kq5taN69UpImb5RE/yBweZINSdYC24C9izmAd6+UpMkbdnrl7cD9wBVJjiXZUVVPAzcBB4BDwJ1V9fBiTu6IXpImb9hZN9vnad8P7F/qyatqH7BvZmbmhqUeQ5K0MJ8wJUmN8wlTktQ4b2omSY2zdCNJjbN0I0mNs3QjSY2zdCNJjbN0I0mNs3QjSY0z0UtS40a5H/3IkmwBtmzcuHGaYUjSxK3fdffUzm2NXpIaZ+lGkhpnopekxpnoJalx/sGUJDXOi7GS1DhLN5LUOBO9JDXORC9JjTPRS1LjTPSS1DjvdSNJEzDNe9vM5vRKSWqcpRtJapyJXpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGjf2RJ/kFUluTXJXkveO+/iSpMUZ6g+mktwGvAM4UVU/32vfDPwVsAb4WFV9sKoOATcmeQ7wceCvxx+2JK08K+mPpPqGHdHvATb3G5KsAW4BrgY2AduTbOrWXQPcDewfW6SSpCUZKtFX1X3AE7OarwSOVNXRqnoKuAPY2m2/t6quBn5znMFKkhZvlHvdXAw81ls+Brw2yVXAO4FzWWBEn2QnsBNg3bp1I4QhSdOzUss1fWO/qVlV3QvcO8R2u4HdADMzMzXuOCRJA6PMujkOXNpbvqRrG5oPB5ekyRsl0T8IXJ5kQ5K1wDZg72IO4N0rJWnyhkr0SW4H7geuSHIsyY6qehq4CTgAHALurKqHF3NyR/SSNHlD1eiravs87fsZYQplVe0D9s3MzNyw1GNIkhY21VsgOKKXpMnzCVOS1LipPjNWklaj1TB3vs/SjSQ1ztKNJDXO0o0kDWG1lWv6LN1IUuOmOqJ3Hr2klWw1j+L7fJSgJDXORC9JjbNGL0mNc3qlJDXO6ZWS1NPKBdg+a/SS1DgTvSQ1zouxktQ4L8ZKUuMs3UhS45x1I+ms1+JMmz5H9JLUOBO9JDXO0o2ks0brJZr5OL1Skhrn9EpJapw1eklqnIlekhpnopekxpnoJalxJnpJapzz6CU17WydO983kUSf5Frg7cALgL+tqn+fxHkkaS4m9582dOkmyW1JTiR5aFb75iSHkxxJsgugqv6lqm4AbgTeNd6QJUmLsZgR/R7go8DHTzckWQPcArwZOAY8mGRvVT3SbfLH3XpJmihH8fMbekRfVfcBT8xqvhI4UlVHq+op4A5gawY+BHy6qv5zfOFKkhZr1Fk3FwOP9ZaPdW3vA94EXJfkxrl2TLIzycEkB0+ePDliGJKk+UzkYmxVfQT4yBm22Z3kcWDL2rVrXzOJOCRJoyf648ClveVLurahVNU+YN/MzMwNI8Yh6SxkXX44o5ZuHgQuT7IhyVpgG7B32J29TbEkTd5iplfeDtwPXJHkWJIdVfU0cBNwADgE3FlVDw97TG9TLEmTN3Tppqq2z9O+H9g/togkaQGWaxbPJ0xJUuN8wpQkNW6qNzVLsgXYsnHjxmmGIWkF6pdovvXBt08xktXPEb0kNc7bFEta8bwAOxovxkpS4yzdSFLjfJSgJDVu1dfovTIvtcNa/GRYo5ekxlmjl6TGWaOXpMat+hq9pNXNuvzkOaKXpMY1da8bZ+BI0rN5MVaSGmfpRpIaZ6KXpMY560bSsnOmzfJyRC9JjTPRS1LjvNeNJDXO6ZWS1DhLN5LUOGfdSJoY/1p9ZXBEL0mNM9FLUuNM9JLUOBO9JDVu7Bdjk1wG3AycV1XXjfv4S+EFIUlns6FG9EluS3IiyUOz2jcnOZzkSJJdAFV1tKp2TCJYSdLiDVu62QNs7jckWQPcAlwNbAK2J9k01ugkSSMbKtFX1X3AE7OarwSOdCP4p4A7gK1jjk+SNKJRLsZeDDzWWz4GXJzkRUluBV6V5APz7ZxkZ5KDSQ6ePHlyhDAkSQsZ+8XYqvoecOMQ2+0GdgPMzMzUuOOQJA2MkuiPA5f2li/p2oY27oeDS1p+sx8iMt/MNh82Mj2jlG4eBC5PsiHJWmAbsHcxB/DulZI0ecNOr7wduB+4IsmxJDuq6mngJuAAcAi4s6oeXszJvR+9JE3eUKWbqto+T/t+YP9ST15V+4B9MzMzNyz1GJKkhfmEKUlqnE+YkqTGeVMzSWrcVJ8w5fRKqT1Oo1x5LN1IUuMs3UhS45x1I0mNs3QjSY2zdCNJjTPRS1Ljmp1eOd8UL58fK43OKZSrizV6SWqcpRtJapyJXpIaZ6KXpMY1ezF2nOa78OTFXEmrgRdjJalxlm4kqXEmeklqnIlekhpnopekxpnoJalxTq/sGdd9cLyfjlrk/W1WL6dXSlLjLN1IUuNM9JLUOBO9JDXORC9JjTPRS1LjTPSS1DgTvSQ1zkQvSY1LVU07BpKcBL69xN0vBL47xnDGxbgWx7gWZ6XGBSs3thbjellVXXSmjVZEoh9FkoNVNTPtOGYzrsUxrsVZqXHByo3tbI7L0o0kNc5EL0mNayHR7552APMwrsUxrsVZqXHByo3trI1r1dfoJUkLa2FEL0lawKpI9El+PcnDSZ5JMu/V6SSbkxxOciTJrl77hiRf7No/kWTtmOK6IMlnkjzafT9/jm3ekOTLva//S3Jtt25Pkm/21r1yueLqtvtx79x7e+3T7K9XJrm/e7+/muRdvXVj7a/5Pi+99ed2//4jXX+s7637QNd+OMlbR4ljCXH9YZJHuv75XJKX9dbN+Z4uU1zvSXKyd/7f7a27vnvfH01y/TLH9eFeTF9P8oPeukn2121JTiR5aJ71SfKRLu6vJnl1b914+6uqVvwX8ArgCuBeYGaebdYA3wAuA9YCXwE2devuBLZ1r28F3jumuP4S2NW93gV86AzbXwA8AfxMt7wHuG4C/TVUXMAP52mfWn8BPwdc3r1+KfA48MJx99dCn5feNr8P3Nq93gZ8onu9qdv+XGBDd5w1yxjXG3qfofeejmuh93SZ4noP8NE59r0AONp9P797ff5yxTVr+/cBt026v7pj/zLwauCheda/Dfg0EOB1wBcn1V+rYkRfVYeq6vAZNrsSOFJVR6vqKeAOYGuSAG8E7uq2+3vg2jGFtrU73rDHvQ74dFX9z5jOP5/FxvUT0+6vqvp6VT3avf4v4ARwxj8IWYI5Py8LxHsX8Ctd/2wF7qiqJ6vqm8CR7njLEldVfb73GXoAuGRM5x4prgW8FfhMVT1RVd8HPgNsnlJc24Hbx3TuBVXVfQwGdvPZCny8Bh4AXpjkJUygv1ZFoh/SxcBjveVjXduLgB9U1dOz2sfhxVX1ePf6v4EXn2H7bTz7Q/bn3a9tH05y7jLH9dwkB5M8cLqcxArqryRXMhilfaPXPK7+mu/zMuc2XX+cYtA/w+w7ybj6djAYFZ4213u6nHH9Wvf+3JXk0kXuO8m46EpcG4B7es2T6q9hzBf72Ptrqg8H70vyWeBn51h1c1X963LHc9pCcfUXqqqSzDuFqftJ/QvAgV7zBxgkvLUMpli9H/izZYzrZVV1PMllwD1JvsYgmS3ZmPvrH4Drq+qZrnnJ/dWiJO8GZoDX95qf9Z5W1TfmPsLY7QNur6onk/weg9+G3rhM5x7GNuCuqvpxr22a/bVsVkyir6o3jXiI48ClveVLurbvMfiV6JxuVHa6feS4knwnyUuq6vEuMZ1Y4FC/AXyqqn7UO/bp0e2TSf4O+KPljKuqjnffjya5F3gV8M9Mub+SvAC4m8EP+Qd6x15yf81hvs/LXNscS3IOcB6Dz9Mw+04yLpK8icEPz9dX1ZOn2+d5T8eRuM4YV1V9r7f4MQbXZE7ve9Wsfe8dQ0xDxdWzDfiDfsME+2sY88U+9v5qqXTzIHB5BjNG1jJ4U/fW4OrG5xnUxwGuB8b1G8Le7njDHPdZtcEu2Z2ui18LzHl1fhJxJTn/dOkjyYXALwGPTLu/uvfuUwxql3fNWjfO/prz87JAvNcB93T9sxfYlsGsnA3A5cB/jBDLouJK8irgb4BrqupEr33O93QZ43pJb/Ea4FD3+gDwli6+84G38NO/2U40ri62lzO4sHl/r22S/TWMvcBvdbNvXgec6gYz4++vcV9pnsQX8KsM6lRPAt8BDnTtLwX297Z7G/B1Bj+Rb+61X8bgP+IR4J+Ac8cU14uAzwGPAp8FLujaZ4CP9bZbz+Cn9HNm7X8P8DUGCesfgecvV1zAL3bn/kr3fcdK6C/g3cCPgC/3vl45if6a6/PCoBR0Tff6ud2//0jXH5f19r252+8wcPWYP+9niuuz3f+D0/2z90zv6TLF9RfAw935Pw+8vLfv73T9eAT47eWMq1v+E+CDs/abdH/dzmDW2I8Y5K8dwI3Ajd36ALd0cX+N3ozCcfeXfxkrSY1rqXQjSZqDiV6SGmeil6TGmeglqXEmeklqnIlekhpnopekxpnoJalx/w/S8V0zlWwZaAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1191,7 +1192,7 @@
     },
     {
      "data": {
-      "image/png": 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eWN7wcgy4gB9svPnRFcuczQ82MN7Ich8aljf8vLi7/irgCHBad/ul3c8AfwbcNO9aZ1DzTcA7uuuXAIfnXWvfNXdj1wEfnHeNs6i5uzzODzZMXgN8dN61zuDf9ku6n2cAnwFeP+9aT1D7+ay+MfYKnrkx9ovd+IuAr7O8IfaF3fUXdfet3Bh7+dhzmvcvZcUv4XKWP6G/Bry7G3sv8Mbu+luAr3bL/AVwRjf+XODB7vJ54DVDz/lZ4P7uH8uHgefPu84Z1PwCltdq72f5z95Xz7vOvmvu7r8b2Dnv+mb4Pr+5e4+/3NX+8nnXOYOa/wR4CHgY+N1513iCmm8HvgX8H8t/WV/D8grIdd39AW7tfif3A4Ohx74DONpd3j40Pujy62vALXQHuo5z8chYSWrcIvXoJUk9MOglqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrc/wN0Y0D8lDnN6AAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1201,7 +1202,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1211,7 +1212,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1221,7 +1222,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1231,7 +1232,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1251,7 +1252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1259,151 +1260,151 @@
      "output_type": "stream",
      "text": [
       "doublets\n",
-      "1119054\n",
-      "1023158\n",
-      "879245\n",
-      "861764\n",
-      "482689\n",
-      "187506\n",
-      "1058518\n",
-      "1079161\n",
+      "1046526\n",
+      "916430\n",
+      "760030\n",
+      "808590\n",
+      "436318\n",
+      "165447\n",
+      "934476\n",
+      "942520\n",
       "triplets and quads\n",
-      "1686712\n",
-      "4892329\n",
-      "1127517\n",
-      "933900\n",
-      "1796774\n",
+      "1567123\n",
+      "4597455\n",
+      "1070839\n",
+      "888560\n",
+      "1707720\n",
       "pentuplets\n",
-      "1272044\n",
+      "1229586\n",
       "triplets\n",
-      "212084\n",
-      "90346\n",
-      "8750520\n",
-      "   det1      phi1    pt1        r1   trackID  ys1        z1  det2      phi2  \\\n",
-      "0    19  1.471751  12402  3.311650  10000000   17 -4.949985   146  1.473213   \n",
-      "1    19  1.490899  11881  3.321660  10000001   28 -4.962011   146  1.492998   \n",
-      "2    19  1.508659   1116  3.332094  10000002   15 -3.228904   147  1.529033   \n",
-      "3    11  0.957661   1220  2.772534  10000003   31 -5.223804   130  0.937362   \n",
-      "4    43  2.933400    480  2.740693  10000005   14 -2.197470   195  2.887583   \n",
+      "197639\n",
+      "82324\n",
+      "8264574\n",
+      "   det1      phi1  pt1      r1     tkz1   trackID  ys1      z1  det2  \\\n",
+      "0    51 -2.743778  428  3.0782 -10.6632  10108743   38 -3.1901   149   \n",
+      "1    51 -2.743778  428  3.0782 -10.6632  10108743   38 -3.1901   149   \n",
+      "2    51 -2.743778  428  3.0782 -10.6632  10108743   38 -3.1901   220   \n",
+      "3    51 -2.743778  428  3.0782 -10.6632  10108743   38 -3.1901   220   \n",
+      "4    51 -2.743778  428  3.0782 -10.6632  10108743   38 -3.1901   276   \n",
       "\n",
-      "     pt2    ...        pt3         r3  ys3         z3  det4      phi4    pt4  \\\n",
-      "0  12402    ...      12402  11.309768   21 -14.069117   793  1.477370  12402   \n",
-      "1  11881    ...      11881  11.306963   18 -14.051542   793  1.497169  11881   \n",
-      "2   1116    ...       1116  11.323120   15  -8.101905   802  1.574145   1116   \n",
-      "3   1220    ...       1220  11.037246   23 -17.280849   744  0.894308   1220   \n",
-      "4    480    ...        480  10.726357    8  -5.142550   898  2.780511    480   \n",
+      "       phi2   ...        tkz3  ys3       z3  det4      phi4  pt4       r4  \\\n",
+      "0  1.556257   ...    -10.6632   15  16.2390   896  2.790262  428  15.8156   \n",
+      "1  1.556257   ...    -10.6632   15 -26.1485   896  2.790262  428  15.8156   \n",
+      "2 -2.697440   ...    -10.6632   15  16.2390   896  2.790262  428  15.8156   \n",
+      "3 -2.697440   ...    -10.6632   15 -26.1485   896  2.790262  428  15.8156   \n",
+      "4 -1.312738   ...    -10.6632   15  16.2390   896  2.790262  428  15.8156   \n",
       "\n",
-      "          r4  ys4         z4  \n",
-      "0  16.431705   19 -19.902737  \n",
-      "1  16.420803   21 -19.880468  \n",
-      "2  15.831116   16 -10.845050  \n",
-      "3  16.157537   31 -24.723768  \n",
-      "4  15.817390   14  -7.052351  \n",
+      "      tkz4  ys4       z4  \n",
+      "0 -10.6632   29 -20.3185  \n",
+      "1 -10.6632   29 -20.3185  \n",
+      "2 -10.6632   29 -20.3185  \n",
+      "3 -10.6632   29 -20.3185  \n",
+      "4 -10.6632   29 -20.3185  \n",
       "\n",
-      "[5 rows x 25 columns]\n",
-      "   det1      phi1   pt1        r1   trackID  ys1        z1  det2      phi2  \\\n",
-      "0    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
-      "1    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
-      "2    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
-      "3    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
-      "4    50 -2.823835  1100  3.058496  10099282  491 -9.514791    99  0.080578   \n",
+      "[5 rows x 29 columns]\n",
+      "   det1      phi1  pt1      r1    tkz1   trackID  ys1       z1  det2  \\\n",
+      "0    16  1.563797  520  3.3717 -0.5042  10109870    8 -24.5975   120   \n",
+      "1    16  1.563797  520  3.3717 -0.5042  10109870    8 -24.5975   120   \n",
+      "2    16  1.563797  520  3.3717 -0.5042  10109870    8 -24.5975   176   \n",
+      "3    16  1.563797  520  3.3717 -0.5042  10109870    8 -24.5975   176   \n",
+      "4    16  1.563797  520  3.3717 -0.5042  10109870    8 -24.5975   288   \n",
       "\n",
-      "    pt2    ...       pt3         r3  ys3        z3  det4      phi4   pt4  \\\n",
-      "0  1100    ...      1100  11.037732    8 -5.367550  1283 -2.449416  1100   \n",
-      "1  1100    ...      1100  11.037732    8 -5.367550  1530  2.865597  1100   \n",
-      "2  1100    ...      1100  11.066208   21  0.744545  1283 -2.449416  1100   \n",
-      "3  1100    ...      1100  11.066208   21  0.744545  1530  2.865597  1100   \n",
-      "4  1100    ...      1100  10.719485   22  0.355225  1283 -2.449416  1100   \n",
+      "       phi2   ...       tkz3  ys3       z3  det4      phi4  pt4      r4  \\\n",
+      "0  0.844785   ...    -0.5042    8 -23.8326  1584  2.232684  520  6.2797   \n",
+      "1  0.844785   ...    -0.5042   22 -16.4548  1584  2.232684  520  6.2797   \n",
+      "2  2.428455   ...    -0.5042    8 -23.8326  1584  2.232684  520  6.2797   \n",
+      "3  2.428455   ...    -0.5042   22 -16.4548  1584  2.232684  520  6.2797   \n",
+      "4 -0.699882   ...    -0.5042    8 -23.8326  1584  2.232684  520  6.2797   \n",
       "\n",
-      "          r4  ys4         z4  \n",
-      "0  12.628432   27  31.032444  \n",
-      "1   7.301930    8 -33.531631  \n",
-      "2  12.628432   27  31.032444  \n",
-      "3   7.301930    8 -33.531631  \n",
-      "4  12.628432   27  31.032444  \n",
+      "     tkz4  ys4       z4  \n",
+      "0 -0.5042   24 -32.4748  \n",
+      "1 -0.5042   24 -32.4748  \n",
+      "2 -0.5042   24 -32.4748  \n",
+      "3 -0.5042   24 -32.4748  \n",
+      "4 -0.5042   24 -32.4748  \n",
       "\n",
-      "[5 rows x 25 columns]\n",
-      "   det1      phi1   pt1        r1   trackID  ys1         z1  det2      phi2  \\\n",
-      "0    50 -2.850126  3818  3.056340  10000067   47 -10.186829   216 -2.855479   \n",
-      "1    50 -2.850126  3818  3.056340  10000067   47 -10.186829   216 -2.855479   \n",
-      "2    58 -2.570249  2031  2.812866  10000096   53  -9.784139   224 -2.582103   \n",
-      "3    13  0.878464   413  2.746907  10000543   39   7.014045   126  0.923011   \n",
-      "4    13  0.878464   413  2.746907  10000543   39   7.014045   134  0.927235   \n",
+      "[5 rows x 29 columns]\n",
+      "   det1      phi1  pt1      r1     tkz1   trackID  ys1       z1  det2  \\\n",
+      "0     9  0.607251  527  2.7912 -10.6632  10108665   38 -16.9096   112   \n",
+      "1     9  0.607251  527  2.7912 -10.6632  10108665   38 -16.9096   112   \n",
+      "2     1  0.407491  817  3.0819 -10.6632  10108708   39 -17.8389   104   \n",
+      "3     1  0.407491  817  3.0819 -10.6632  10108708   39 -17.8389   104   \n",
+      "4    18  1.477864  636  3.3147  -2.6296  10109242   61 -12.4370   144   \n",
       "\n",
-      "    pt2    ...       pt3         r3  ys3         z3  det4      phi4   pt4  \\\n",
-      "0  3818    ...      3818  10.791581   15 -32.936413  1672 -2.863041  3818   \n",
-      "1  3818    ...      3818  10.791581   15 -32.936413  1729 -2.863443  3818   \n",
-      "2  2031    ...      2031  10.528687   16 -33.342438  1674 -2.596598  2031   \n",
-      "3   413    ...       413  12.144506   11  30.916100  1380  1.044963   413   \n",
-      "4   413    ...       413  12.144506   11  30.916100  1380  1.044963   413   \n",
+      "       phi2   ...        tkz3  ys3       z3  det4      phi4  pt4       r4  \\\n",
+      "0  0.583124   ...    -10.6632   23 -33.3398  1713  0.503327  527  12.3503   \n",
+      "1  0.563076   ...    -10.6632   23 -33.3398  1713  0.503327  527  12.3503   \n",
+      "2  0.392422   ...    -10.6632   16 -32.6356  1712  0.346636  817  11.6805   \n",
+      "3  0.383833   ...    -10.6632   16 -32.6356  1712  0.346636  817  11.6805   \n",
+      "4  1.421299   ...     -2.6296   15 -34.3925  1718  1.436485  636  11.2936   \n",
       "\n",
-      "          r4  ys4         z4  \n",
-      "0  12.287411    8 -37.328735  \n",
-      "1  12.594734   10 -38.235123  \n",
-      "2  12.152595    8 -38.289001  \n",
-      "3  15.323860   16  39.172184  \n",
-      "4  15.323860   16  39.172184  \n",
+      "      tkz4  ys4       z4  \n",
+      "0 -10.6632   12 -38.2960  \n",
+      "1 -10.6632   12 -38.2960  \n",
+      "2 -10.6632   12 -37.9141  \n",
+      "3 -10.6632   12 -37.9141  \n",
+      "4  -2.6296    8 -39.6887  \n",
       "\n",
-      "[5 rows x 25 columns]\n",
-      "   det1      phi1   pt1        r1   trackID  ys1         z1  det2      phi2  \\\n",
-      "0    89 -0.019187  3565  2.810611  10000023  -68 -13.579367  1520 -0.011948   \n",
-      "1    22  1.262401   733  3.282485  10000044   87  16.099026  1188  1.233651   \n",
-      "2    49 -2.766413   854  3.070601  10000047   96 -19.082594  1532 -2.749733   \n",
-      "3    49 -2.766413   854  3.070601  10000047   96 -19.082594  1532 -2.749733   \n",
-      "4    49 -2.766413   854  3.070601  10000047   96 -19.082594  1589 -2.748735   \n",
+      "[5 rows x 29 columns]\n",
+      "   det1      phi1   pt1      r1     tkz1   trackID  ys1       z1  det2  \\\n",
+      "0     1  0.407491   817  3.0819 -10.6632  10108708   39 -17.8389  1578   \n",
+      "1    17  1.449064  1170  3.3014  -0.7713  30000000   96 -17.4924  1525   \n",
+      "2    17  1.449064  1170  3.3014  -0.7713  30000000   96 -17.4924  1581   \n",
+      "3    49 -2.780616  2358  3.0667  -0.7713  30000020   69 -13.9557  1532   \n",
+      "4    49 -2.780616  2358  3.0667  -0.7713  30000020   69 -13.9557  1532   \n",
       "\n",
-      "    pt2    ...       pt3        r3  ys3         z3  det4      phi4   pt4  \\\n",
-      "0  3565    ...      3565  8.741679   15 -39.608997  1766 -0.006668  3565   \n",
-      "1   733    ...       733  7.990392    8  41.015194  1412  1.205148   733   \n",
-      "2   854    ...       854  6.703705   16 -40.343700  1756 -2.728390   854   \n",
-      "3   854    ...       854  6.703705   16 -40.343700  1813 -2.727099   854   \n",
-      "4   854    ...       854  6.703705   16 -40.343700  1756 -2.728390   854   \n",
+      "       phi2   ...        tkz3  ys3       z3  det4      phi4   pt4       r4  \\\n",
+      "0  0.431484   ...    -10.6632   12 -37.9141  1745  0.429261   817   6.2581   \n",
+      "1  1.465457   ...     -0.7713    8 -41.5832  1749  1.481807  1170   9.9986   \n",
+      "2  1.465131   ...     -0.7713    8 -41.5832  1749  1.481807  1170   9.9986   \n",
+      "3 -2.770849   ...     -0.7713   10 -39.9314  1785 -2.762848  2358  10.8542   \n",
+      "4 -2.770849   ...     -0.7713   10 -39.9314  1841 -2.762935  2358  10.8153   \n",
       "\n",
-      "          r4  ys4         z4  \n",
-      "0  10.429797    8 -47.022720  \n",
-      "1   9.857387    8  50.917034  \n",
-      "2   8.207004   15 -49.147671  \n",
-      "3   8.398738   10 -50.271732  \n",
-      "4   8.207004   15 -49.147671  \n",
+      "      tkz4  ys4       z4  \n",
+      "0 -10.6632   27 -48.7891  \n",
+      "1  -0.7713   12 -51.4282  \n",
+      "2  -0.7713   12 -51.4282  \n",
+      "3  -0.7713   11 -47.4613  \n",
+      "4  -0.7713    8 -47.2809  \n",
       "\n",
-      "[5 rows x 25 columns]\n",
-      "   det1      phi1  pt1        r1   trackID  ys1         z1  det2      phi2  \\\n",
-      "0    50 -2.658289  891  3.121739  10000026   40 -10.357058   217 -2.740865   \n",
-      "1    50 -2.721266  891  3.087358  10000026   29  -7.173188   217 -2.740865   \n",
-      "2    58 -2.550391  891  2.800395  10000026   16 -11.385898   217 -2.740865   \n",
-      "3    58 -2.620771  891  2.850214  10000026    8 -11.932549   217 -2.740865   \n",
-      "4    66 -2.044674  712  3.363156  10000065   32  -7.485303   241 -2.070432   \n",
+      "[5 rows x 29 columns]\n",
+      "   det1      phi1   pt1      r1     tkz1   trackID  ys1       z1  det2  \\\n",
+      "0    90 -0.121753  1503  2.7600 -10.6632  10108654   15  -9.3011   314   \n",
+      "1    26  2.061310  1979  2.8019 -10.6632  10108681   16  -8.3855   171   \n",
+      "2    11  0.549674  1200  2.8279 -10.6632  10108723   38  -4.6503   116   \n",
+      "3     2  0.185950   692  3.0690 -10.6632  10108725    8 -10.2526    98   \n",
+      "4     2  0.474354   992  3.1161 -10.6632  10108762   12 -11.6205   114   \n",
       "\n",
-      "   pt2 ...  pt4  r4  ys4  z4  det5  phi5  pt5  r5  ys5  z5  \n",
-      "0  891 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
-      "1  891 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
-      "2  891 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
-      "3  891 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
-      "4  712 ...  NaN NaN  NaN NaN   NaN   NaN  NaN NaN  NaN NaN  \n",
+      "       phi2 ...  tkz4  ys4  z4  det5  phi5  pt5  r5  tkz5  ys5  z5  \n",
+      "0 -0.136638 ...   NaN  NaN NaN   NaN   NaN  NaN NaN   NaN  NaN NaN  \n",
+      "1  2.052115 ...   NaN  NaN NaN   NaN   NaN  NaN NaN   NaN  NaN NaN  \n",
+      "2  0.529316 ...   NaN  NaN NaN   NaN   NaN  NaN NaN   NaN  NaN NaN  \n",
+      "3  0.217089 ...   NaN  NaN NaN   NaN   NaN  NaN NaN   NaN  NaN NaN  \n",
+      "4  0.495698 ...   NaN  NaN NaN   NaN   NaN  NaN NaN   NaN  NaN NaN  \n",
       "\n",
-      "[5 rows x 31 columns]\n",
-      "   det1      phi1   pt1        r1   trackID  ys1         z1  det2      phi2  \\\n",
-      "0    50 -2.850126  3818  3.056340  10000067   47 -10.186829   216 -2.855479   \n",
-      "1    50 -2.850126  3818  3.056340  10000067   47 -10.186829   216 -2.855479   \n",
-      "2    85 -0.899789  1021  3.082222  10099486   56  13.192469   287 -0.920796   \n",
-      "3    85 -0.899789  1021  3.082222  10099486   56  13.192469   287 -0.920796   \n",
-      "4    85 -0.899789  1021  3.082222  10099486   56  13.192469   287 -0.920796   \n",
+      "[5 rows x 36 columns]\n",
+      "   det1      phi1   pt1      r1     tkz1   trackID  ys1       z1  det2  \\\n",
+      "0     1  0.407491   817  3.0819 -10.6632  10108708   39 -17.8389   104   \n",
+      "1     1  0.407491   817  3.0819 -10.6632  10108708   39 -17.8389   104   \n",
+      "2    49 -2.780616  2358  3.0667  -0.7713  30000020   69 -13.9557   229   \n",
+      "3    49 -2.780616  2358  3.0667  -0.7713  30000020   69 -13.9557   229   \n",
+      "4    52 -2.967134   638  3.0724   3.4827  30113381   47   3.4712   177   \n",
       "\n",
-      "    pt2    ...       pt4         r4  ys4         z4  det5      phi5   pt5  \\\n",
-      "0  3818    ...      3818  12.287411    8 -37.328735  1784 -2.867168  3818   \n",
-      "1  3818    ...      3818  12.594734   10 -38.235123  1784 -2.867168  3818   \n",
-      "2  1021    ...      1021   9.937539   14  38.306942  1515 -0.954581  1021   \n",
-      "3  1021    ...      1021  10.711860   12  41.138309  1515 -0.954581  1021   \n",
-      "4  1021    ...      1021   9.888903   11  38.124836  1515 -0.954581  1021   \n",
+      "       phi2   ...        tkz4  ys4       z4  det5      phi5   pt5       r5  \\\n",
+      "0  0.392422   ...    -10.6632   12 -37.9141  1745  0.429261   817   6.2581   \n",
+      "1  0.383833   ...    -10.6632   12 -37.9141  1745  0.429261   817   6.2581   \n",
+      "2 -2.469347   ...     -0.7713   10 -39.9314  1785 -2.762848  2358  10.8542   \n",
+      "3 -2.469347   ...     -0.7713   10 -39.9314  1841 -2.762935  2358  10.8153   \n",
+      "4  2.312844   ...      3.4827    8 -39.7081  1756 -2.802110   638   7.7169   \n",
       "\n",
-      "          r5  ys5         z5  \n",
-      "0  15.496239    8 -46.742580  \n",
-      "1  15.496239    8 -46.742580  \n",
-      "2  12.504428    8  47.705566  \n",
-      "3  12.504428    8  47.705566  \n",
-      "4  12.504428    8  47.705566  \n",
+      "      tkz5  ys5       z5  \n",
+      "0 -10.6632   27 -48.7891  \n",
+      "1 -10.6632   27 -48.7891  \n",
+      "2  -0.7713   11 -47.4613  \n",
+      "3  -0.7713    8 -47.2809  \n",
+      "4   3.4827    8 -49.2893  \n",
       "\n",
-      "[5 rows x 31 columns]\n"
+      "[5 rows x 36 columns]\n"
      ]
     }
    ],
@@ -1470,12 +1471,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1485,7 +1486,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1495,7 +1496,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1517,11 +1518,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plotTriplets(quad,mpt) :\n",
+    "def plotTriplets(quad,mpt,kq) :\n",
+    "    \n",
+    "  print \" \"\n",
+    "  print \"New QUAD\",kq\n",
+    "  print \" \"\n",
+    "    \n",
     "    \n",
     "  d1 = (quad['r1']*quad['z2']-quad['z1']*quad['r2'])/(quad['r1']-quad['r2'])\n",
     "  d2 = (quad['r2']*quad['z3']-quad['z2']*quad['r3'])/(quad['r2']-quad['r3'])\n",
@@ -1608,19 +1614,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 275,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "kq=0\n",
-    "def plotDoublets(quad,mpt) :\n",
+    "def plotDoublets(quad,mpt,kq) :\n",
     "  quadc = quad[quad['pt1']>mpt]\n",
     "\n",
     "  maxc = 1000./(mpt*87.78)\n",
     "\n",
     "  print \" \"\n",
     "  print \"New QUAD\",kq\n",
-    "  kq+=1\n",
     "  print \" \"\n",
     "    \n",
     "  print 'dphi'\n",
@@ -1785,7 +1790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 25,
    "metadata": {
     "scrolled": true
    },
@@ -1794,13 +1799,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "delta123 480048\n",
+      " \n",
+      "New QUAD 0\n",
+      " \n",
+      "delta123 453415\n",
       "field\n"
      ]
     },
     {
      "data": {
-      "image/png": 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3euAfanluTml5GZMtx5bk7cCfA++uqmWekGytqhbug7Ue5wPAN4D7gdcNtw+Avxg+fifwKPCvw/8e7rvuKY/v/cD/AV8b+Xhb37VPY2zD518GzgI/ZK1/+q6+a99gPL8CfJ218yMfGW77Q9bCAeCVwF8Bp4F/Bt7cd81THt/PDr8//8PaXyqn+q55imO7H3hu5PfreN81z+rDO2MlqXGL2rqRJE2JQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+HwG3qwf92XrfAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1812,12 +1820,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 480048\n"
+      "thcut,pzcut,dcacut 453415\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1827,7 +1835,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1837,7 +1845,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1849,12 +1857,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 480048\n"
+      "thcut2,pzcut2 453415\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1864,7 +1872,7 @@
     },
     {
      "data": {
-      "image/png": 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ywt9p33V19WztLp8J/Dzw6KzqSvI64C+Bt1XV8KBn8vtrGkecJ/3DoP94N/AEcBdwRrd8EfhAd/ldwH8Dnx36ubBbdx6DF+IR4G+ArbOqq7t+H/BV4DsM+m2XdMvvAb7AILD+GnjFnNTV9/767e6xjwC/1S07DXgI+DzwCPA+xpzpAvwK8EUGI7jru2XvZfDCA3h59/8/0u2P84Zue313u8eBt074+b6uuoBf7fbNZ4HPAHtmXNfPds+j/2TwyeeR1X6nfdcF/Fz3+vtc9+81M67rLuArfC+vDkxrf/nNWElq3EZp3UiS1smgl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcf8LemdbXr8KJkkAAAAASUVORK5CYII=\n",
+      "image/png": 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3VXWt57Sv6dB1dfXs6m6fB7wYuH+r6kryQuCvgFdX1Xijp//jNY8R576/GPU/fhh4CPgQ8Kxu+Qrw193tNwD/A3x67OsF3bqLGP0irgJ/B+zaqrq6+58Avgx8m1F/22Xd8o8An2MUWH8DPGNB6hr6eP1e99yrwO92y54O3AN8FrgP+EtmnOkC/BrweUYtuBu6ZW9h9IsH8LTu51/tjsdFY/ve0O33IPDKns/3TdUF/Hp3bD4NfArYv8V1/UJ3Hv0Xo3c+9230mg5dF/CL3e/fZ7rv125xXR8CvsT38+rwvI6X/xkrSY3bLl03kqRNMuglqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrc/wERWWD73Do5sAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1882,7 +1890,7 @@
     },
     {
      "data": {
-      "image/png": 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f662sPQ/jxp+XdwF/V218QKXL3Fu25fyTvBH4E+DtVbV9Fz5V5dcEX6z1Xs8CXwUeAl41Gl8C/nT0/XuB/wX+eezrDUPXPo+5j7a/AHwD+B5rfc23DV37lPP+eeArrL3P8qHR2O+w9ssN8DLgz4EV4B+B1w9d8xzn/tOjv+P/Zu1VzIWha57z/B8Cvj72e35q6Jo3+vKTsZLUOFs3ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9H0a13bw7kaieAAAAAElFTkSuQmCC\n",
+      "image/png": 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qav1MgKrKtyneWOm5PgJ8FXgYePV4fAR8fPz+e4D/Bf554u2NQ9e+yGMw3n4MOA98j5V+5tuGrn3G1/0rwFdYOd/yofHYH7DyTQ1wNfCXwFngH4E3DF3zgl//L4z/n/+bld9kzgxd8wDH4GHgGxPf98eHrrmq/MtYSWqdrRtJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4/4P7xfp7A5ICvMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1899,7 +1907,7 @@
     },
     {
      "data": {
-      "image/png": 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lOR/HL3S/D//DyqerJzd6ny3iWIBf7PLqq93PWyatxW/GSlLjFqV1I0naJINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG/R/JE0eCpNj3CQAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1911,12 +1919,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 480048\n"
+      "thcut,pzcut,dcacut 453415\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1926,7 +1934,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1936,7 +1944,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADalJREFUeJzt3X2oZHUdx/HPpxWtFG8+YT6tV7mbtIEYTOsfPWiotCpXJaR2RTAQL1rWH/3TgkERBG1/lbBklxQ1yIeEbK+7ammaBVquYpurrK5iuGruWnSNkkz89sccdbjdhzMzZ+ac+c77BcvOnDl35vu7d+Yzv/md3/mNI0IAgLzeV3cBAIDBIugBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSO6DuAiTpyCOPjMnJybrLAICR8thjj70WEUettF8jgn5yclI7duyouwwAGCm2/1JmP4ZuACA5gh4AkiPoASA5gh4AkiPoASC5WoPe9rTt2fn5+TrLAIDUag36iJiLiJmJiYk6ywCA1Bi6AYDkGnHCFNAkk5u2Lbr9he+dP+RKgGrQoweA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiu1umVtqclTU9NTdVZBrDklMrl9mG6JUYFZ8YCQHIM3QBAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACTHF49gbJU5G7bsz3OWLJqMHj0AJEfQA0ByBD0AJFdr0Nuetj07Pz9fZxkAkBqrVwJAcgzdAEByBD0AJEfQA0ByBD0AJEfQA0ByBD0AJMdaNxgr/a5vU+Z+WfcGTUOPHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSY60boGKse4OmGUjQ2z5Y0m8lfTsi7hrEYwBlDWohM2BUlBq6sX2D7X22n1ywfb3t3bb32N7UcdM3JN1eZaEAgN6UHaO/UdL6zg22V0naIulcSWslbbS91vY5kp6StK/COgEAPSo1dBMRD9meXLB5naQ9EfG8JNm+VdKFkg6RdLDa4f+G7e0R8fbC+7Q9I2lGklavXt1r/QCAFfQzRn+cpBc7ru+VdHpEXC1Jtr8k6bXFQl6SImJW0qwktVqt6KMOAMAyBjbrJiJuHNR9AwDK62ce/UuSTui4fnyxDQDQIP0E/aOS1tg+yfaBkjZI2trNHdietj07Pz/fRxkAgOWUnV55i6SHJZ1ie6/tyyPiLUlXS7pX0tOSbo+IXd08eETMRcTMxMREt3UDAEoqO+tm4xLbt0vaXmlFAIBKsdYNACRX61o3tqclTU9NTdVZBhJi2QPgPbUGfUTMSZprtVpX1FkHMCgscIYmYOgGAJIj6AEgOYIeAJKrNeg5YQoABq/WoOeEKQAYPIZuACA5gh4AkiPoASA5DsYCQHKcGYs0WPYAWBxDNwCQXK09emCcsO4N6kKPHgCSI+gBIDlm3QBAciyBAADJMXQDAMkR9ACQHEEPAMkR9ACQHEEPAMlxZixGGuvbACtjHj0AJMfqlUANWPcGw8QYPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHKcGQsAyXFmLEYO69sA3WFRM6BmLIeAQWOMHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCS48xYjASWPQB6R9ADDcJyCBgEVq8EgORqDfqImIuImYmJiTrLAIDUOBgLAMkR9ACQHEEPAMkx6waNxZRKoBr06AEgOYIeAJIj6AEgOcbogYbiLFlUhR49ACRH0ANAcgQ9ACRH0ANAcgQ9ACTHrBs0CmfDAtWjRw8AyRH0AJAcQQ8AyRH0AJBc5UFv+6O2r7N9h+2rqr5/AEB3SgW97Rts77P95ILt623vtr3H9iZJioinI+JKSV+Q9MnqSwbGz+Smbe/+A7pVtkd/o6T1nRtsr5K0RdK5ktZK2mh7bXHbBZK2SdpeWaUAgJ6UmkcfEQ/ZnlyweZ2kPRHxvCTZvlXShZKeioitkrba3ibpZ9WVi4zopQKD1c8JU8dJerHj+l5Jp9s+U9LnJR2kZXr0tmckzUjS6tWr+ygDALCcys+MjYgHJT1YYr9ZSbOS1Gq1ouo6AABt/cy6eUnSCR3Xjy+2AQAapJ8e/aOS1tg+Se2A3yDpkkqqArAkvnkK3So7vfIWSQ9LOsX2XtuXR8Rbkq6WdK+kpyXdHhG7unlw29O2Z+fn57utGwBQUtlZNxuX2L5dfUyhjIg5SXOtVuuKXu8DALA8lilGLZhSCQwPa90AQHK1Bj1j9AAweLUO3TBGD/SHGTgogzF6DA3j8kA9GKMHgOQYoweA5GoN+oiYi4iZiYmJOssAgNQYugGA5Ah6AEiOWTcYKGbaAPWjRw8AyTHrBgCSY9YNACTHGD0qx7h8PVgOAUthjB4AkiPoASA5gh4Akqt1jN72tKTpqampOssA0mG8Hp2YdQMAyTHrBpVgpg3QXIzRA0ByBD0AJEfQA0ByBD0AJEfQA0ByzKNHz5hpMxqYUw/m0QNAcgzdAEBynDCFFTFEA4w2evQAkBw9+kQ46AZgMQT9mCnzZsBQDZALQQ9JhDuQGUEPjCmG+sYHQZ9UmR46vfjxw998PHFm7IjjhQtgJbUGfUTMSZprtVpX1FkHMO4YxsmNefQAkBxj9ANA7wjjjOd/8xD0DcOLBHXjOZhPqqBv4hN0qZr6qZUDsBiWJr6m0L1UQd8pwxOUQEdTZXh9jZO0QT8oS4VvmSf7Uj9LoGOU8fxtPoK+A70UABkR9EvoNvTp1QD/j85TM4xd0PPEA3pDZ2Z0jXzQ8+QDgOWNfND3gzcJAOOAJRAAIDlWryyBnj/QP46P1WcsVq8kqAGMs7EeowfQXFUtE8KnB4IeANK/MRD0AIZuEMFa1RBtxtAn6AHUipVcB4+gBzA2MvbWyyDoAaRGr5+gB4CBWPgGU+cnCIIewEgZ1R56ncNGLIEAAMnRowcwlsZpOiZBD6DxmjBcMwqBvhSCHkBjNCHQy+j3+5+H/abBGD0AJEfQA0ByBD0AJMcYPQAsYVSOGayEHj0AJEfQA0ByAxm6sX2RpPMlHSrp+oj41SAeBwCwstI9ets32N5n+8kF29fb3m17j+1NkhQRd0bEFZKulPTFaksGAHSjm6GbGyWt79xge5WkLZLOlbRW0kbbazt2+WZxOwCgJqWDPiIekvT3BZvXSdoTEc9HxJuSbpV0ods2S7o7Ih6vrlwAQLf6PRh7nKQXO67vLbZ9VdLZki62feViP2h7xvYO2zv279/fZxkAgKUM5GBsRFwr6doV9pmVNCtJrVYrBlEHAKD/Hv1Lkk7ouH58sQ0A0BD99ugflbTG9klqB/wGSZeU/WHb05KmJb1u+9keazhS0ms9/mzT0JbmydIOibY0kjf31ZYTSz1GRLlRE9u3SDpT7V/wq5K+FRHX2z5P0g8krZJ0Q0R8t6dye2R7R0S0hvmYg0JbmidLOyTa0lTDaEvpHn1EbFxi+3ZJ2yurCABQKZZAAIDkMgT9bN0FVIi2NE+Wdki0pakG3pbSY/QAgNGUoUcPAFjGyAW97cNt/9r2s8X/hy2yz4m2H7f9hO1dS52dW7eSbTnN9sNFO3babuQicWXaUux3j+1/2L5r2DUuZ7HF+RbcfpDt24rb/2B7cvhVllOiLZ8pXh9v2b64jhrLKtGWr9t+qnht3G+71HTDYSvRjitt/7nIrN8vWDOsfxExUv8kfV/SpuLyJkmbF9nnQEkHFZcPkfSCpGPrrr3HtnxE0pri8rGSXpH0obpr76UtxW1nqX3uxF1119xR0ypJz0k6uXju/EnS2gX7fFnSdcXlDZJuq7vuPtoyKelUSTdLurjumvtsy2clfbC4fFUT/y4l23Fox+ULJN1TZQ0j16OXdKGkm4rLN0m6aOEOEfFmRPynuHqQmvvJpUxbnomIZ4vLL0vaJ+mooVVY3optkaSIuF/SP4dVVEmLLs63YJ/O9t0h6SzbHmKNZa3Yloh4ISJ2Snq7jgK7UKYtD0TEv4urj6h9dn7TlGnH6x1XD5ZU6cHTpgbgco6OiFeKy3+VdPRiO9k+wfZOtRdd21yEZNOUass7bK9Tu0fw3KAL60FXbWmYpRbnW3SfiHhL0rykI4ZSXXfKtGVUdNuWyyXdPdCKelOqHba/Yvs5tT8df63KAhr55eC275P04UVuuqbzSkSE7UXf+SLiRUmn2j5W0p2274iIV6uvdnlVtKW4n2Mk/VTSZRFRS0+sqrYAVbN9qaSWpDPqrqVXEbFF0hbbl6j9XR6XVXXfjQz6iDh7qdtsv2r7mIh4pQi/fSvc18vFt2J9Wu2P3ENVRVtsHyppm6RrIuKRAZW6oir/Lg1TZnG+d/bZa/sASROS/jac8rqSaaHBUm2xfbbanY0zOoZsm6Tbv8mtkn5UZQGjOHSzVe+9010m6ZcLd7B9vO0PFJcPk/QpSbuHVmF5ZdpyoKRfSLo5Iob+RtWFFdvSYO8uzlf8vjeo3Z5One27WNJvojhy1jBl2jIqVmyL7Y9L+rGkCyKiqZ2LMu1Y03H1fEm9LvK4uLqPSPdwBPsISfcXv4j7JB1ebG9J+klx+RxJO9U+ur1T0kzddffRlksl/VfSEx3/Tqu79l7aUlz/naT9kt5Qe6zyc3XXXtR1nqRn1D7+cU2x7TtqB4gkvV/SzyXtkfRHSSfXXXMfbflE8bv/l9qfSnbVXXMfbblP7UUW33ltbK275h7b8UNJu4o2PCDpY1U+PmfGAkByozh0AwDoAkEPAMkR9ACQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMn9Dz9CRzCxY0IpAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1948,12 +1956,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 480048\n"
+      "thcut2,pzcut2 453415\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1963,7 +1971,7 @@
     },
     {
      "data": {
-      "image/png": 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dEeoBBifyNquu1VzwPu27rq6eXd3l3cALgXs2q64kzwfeBryqqoYPeqa/v2ZxxnnaPwz6jx8G7gM+BDyzm74I/GV3+bXA/wCfHvp5XjfvCgZPxGXgb4Bdm1VXd/3jwKPAtxn0217WTf8I8DkGgfXXwNPnpK6+99dvdbe9DPxmN+1pwBngs8DdwJ8z4UgX4OXAFxgcwd3YTXsTgycewFO7v3+52x9XDK17Y7fevcBVU368b6gu4Je6ffNp4FPAkU2u66e7x9F/MXjnc/da92nfdQE/0z3/PtP9vmGT6/oQ8GW+m1cnZrW//GSsJDVuq7RuJEkbZNBLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wP3tV5j3yyGXgAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1975,13 +1983,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "delta123 76745\n",
+      " \n",
+      "New QUAD 1\n",
+      " \n",
+      "delta123 73463\n",
       "field\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1993,12 +2004,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 76745\n"
+      "thcut,pzcut,dcacut 73463\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2008,7 +2019,7 @@
     },
     {
      "data": {
-      "image/png": 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=\n",
+      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2018,7 +2029,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2030,12 +2041,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 76745\n"
+      "thcut2,pzcut2 73463\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2045,7 +2056,7 @@
     },
     {
      "data": {
-      "image/png": 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g83wzrw6Pa3t5ZqwkFTctXTeSpE0y6CWpOINekooz6CWpOINekooz6CWpOINekooz6CWpuP8FIRQNhErzqhEAAAAASUVORK5CYII=\n",
+      "image/png": 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jwCPA0ibX9VPN6+g/GXzzObbec9p1XcBPN++/zzS/b97kuu4Hvsy38+rgtPaXZ8ZKUnGz0nUjSdogg16SijPoJak4g16SijPoJak4g16SijPoJak4g16SivtfZNIUSyUhn7oAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2063,7 +2074,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2080,7 +2091,7 @@
     },
     {
      "data": {
-      "image/png": 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m/Hvg+T30Iz8WUzbMWO4HXpfkim5WzuuA+5NcluRKgCQ/CLyB+T4vjwD7u1lMO1nfQblxdsPg+N4EPNA9ByeBw91Mlr3AfuBf5lT3xYw9liRXJdkB0G097md9J2ZfhhnLZi76WptRnZcy9ji6+i/vTl8JvAr43ETV9LFHeow92C8BPgF8Hvg48OJu+SrwvoH1/hF4Fvgm6z2x13fLHwA+y3qQ/AXwoiUey7Wsh8oa8FfA5Uswlt/p6l0Dfrtb9kLgUeAzwOPAe5jzzBXgV4B/Y31L6V3dsncDb+xOv6B7jNe6x/zageu+q7vek8ANfT0Hk44F+LXu8f8U8Eng0BKM5ee698T/sP4J6/GtXmvLNg7g57u8+nT395ZJa/GbsZLUuGVp3UiSxmTQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuP8DzWb1biiM3LgAAAAASUVORK5CYII=\n",
+      "image/png": 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4fg/9yI/FlA0zlvuB1ye5sluV83rg/iSXJdkNkOT7gRuZ7/PyCHCgW8V0ORs7KDevbhgc35uBB7vn4BRwpFvJsg84APzLnOq+mLHHkuSqJLsAutnjATZ2YvZlmLFs5aKvtRnVeSljj6Or/4ru9G7g1cDnJqqmjz3SY+zBfgnwceDzwMeAF3fb14D3DVzvH4FngW+y0RN7Q7f9QeCzbATJXwAvWuKxXMtGqKwDfwlcsQRj+a2u3nXgN7ttLwQeBT4DPA68hzmvXAF+Cfg3NmZK7+q2vRt4Y3f6Bd1jvN495tcO3PZd3e2eBK7v6zmYdCzAr3SP/6eATwKHl2AsP9O9J/6HjU9Yj2/3Wlu2cQA/2+XVp7t/b560Fr8ZK0mNW5bWjSRpTAa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z+xG+/V0ekd+gAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2092,12 +2103,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 76745\n"
+      "thcut,pzcut,dcacut 73463\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADaJJREFUeJzt3X+MpHddwPH3x2s5TNCltA259Fq3dQlYiKlkPEwwpkGNV9ptiTGmxT+bXgRr/BGjJRiDJiYFNSKR2KxYT0RbKhrSlRrAH6T8QaBXxNrSVK8HpNdUWmhY9R9q5eMf8xzO7u3szuz8eJ75zPuVTG72mWee+ez39vnMZz/f7zwbmYkkqa7vaDsASdJsmeglqTgTvSQVZ6KXpOJM9JJUnIlekooz0UtScSZ6SSrORC9JxV3QdgAAl1xySa6urrYdhiQtlIcffvhrmXnpfvt1ItGvrq5y6tSptsOQpIUSEV8ZZT9bN5JUXKuJPiLWI2Jja2urzTAkqbRWE31mbmbmiZWVlTbDkKTSbN1IUnEmekkqzkQvScWZ6CWpOBO9JBXX6gemImIdWF9bW5vK8Vbv+Ni373/5zuunckxJWnStJvrM3AQ2e73ebQc9xmBylySdz9aNJBXXiWvdzIJtHEnqs6KXpOJM9JJUnIlekoor26MfZL9e0jKzopek4kp9YGoUVveSlo3Xo5ek4mzdSFJxJnpJKm4pVt0MY79e0jKwopek4kz0klSciV6SilvqHv0g+/WSqrKil6TiTPSSVNzSXQJhFDv/PKGtHEmLzEsgSFJxtm4kqThX3YzAFTmSFpkVvSQVZ6KXpOJM9JJUnD36Mdmvl7RorOglqTgTvSQVZ6KXpOJM9JJUnJOxE3BiVtIisKKXpOKs6KfE6l5SV7Va0UfEekRsbG1ttRmGJJXmZYolqThbNzNgG0dSlzgZK0nFWdHPmNW9pLZZ0UtScSZ6SSrORC9JxZnoJak4E70kFWeil6TiXF45Ry61lNQGK3pJKs5EL0nF2bppiW0cSfNiRS9JxZnoJak4E70kFWeil6TinIztACdmJc2SFb0kFWeil6TibN10jG0cSdM29Yo+Ir4vIu6KiI9ExNumfXxJ0nhGSvQRcXdEPBsRj+7YfjwinoiI0xFxB0BmPp6ZPwv8NPDG6YcsSRrHqBX9SeD44IaIOAS8H7gOuBq4JSKubh67EfgY8MDUIpUkHchIiT4zHwSe37H5GHA6M89k5gvAvcBNzf73Z+Z1wM9MM1hJ0vgmmYy9DHhq4OuzwBsi4lrgJ4HD7FHRR8QJ4ATAFVdcMUEYdTkxK2kapr7qJjM/BXxqhP02gA2AXq+X045DktQ3SaJ/Grh84OujzTbNgNW9pIOaJNE/BLwqIq6kn+BvBt46lai0J5O+pHGMurzyHuAzwKsj4mxE3JqZLwK3Ax8HHgfuy8zHxnnxiFiPiI2tra1x45YkjWikij4zbxmy/QEmWEKZmZvAZq/Xu+2gx5Ak7c1LICw42ziS9uNFzSSpuFYTvT16SZq9VhN9Zm5m5omVlZU2w5Ck0mzdSFJxTsYW4sSspN1Y0UtSca1W9BGxDqyvra21GUZJVveSznEyVpKKs3UjScWZ6CWpOFfdLAH79dJys6KXpOK8BIIkFeeqG0kqzh79krFfLy0fe/SSVJyJXpKKM9FLUnH26JeY/XppObi8UpKKc3mlJBVn60aAbRypMidjJak4E70kFWfrRnuypSMtPit6SSrOil7nGaziJS0+K3pJKs4PTElScX5gSpKKs3UjScU5GauR7ZykdbmltBis6CWpOBO9JBVn60YH5qdmpcVgRS9JxZnoJak4E70kFWePXlNn717qFi+BIEnFtVrRZ+YmsNnr9W5rMw5NziteSt1lj16SijPRS1JxJnpJKs5EL0nFmeglqTjX0Wumhq3GcX29ND9W9JJUnIlekoqzdaNWeJkEaX6s6CWpOBO9JBVnopek4uzRq3X266XZ8jLFklSclylWZ1npS9Nhj16SirNHr07xD5hI02dFL0nFmeglqThbN1oITsxKB2dFL0nFWdFroVnpS/sz0WvhuDJHGo+JXmVY3Uu7s0cvScWZ6CWpOBO9JBVnopek4kz0klScq25UkitwpP9nRS9JxZnoJak4WzcqzzaOlp0VvSQVZ6KXpOJs3UjY3lFtU0/0EfEW4Hrgu4E/ycxPTPs1JEmjGynRR8TdwA3As5n5uoHtx4E/AA4BH8jMOzPzo8BHI+Ii4HcBE70WitW9qhm1oj8J/CHwwXMbIuIQ8H7gx4GzwEMRcX9mfrHZ5debx6XSdl4f3zcHdc1Ik7GZ+SDw/I7Nx4DTmXkmM18A7gVuir53A3+XmZ8fdsyIOBERpyLi1HPPPXfQ+CVJ+5ikR38Z8NTA12eBNwA/D/wYsBIRa5l5125PzswNYAOg1+vlBHFIIxv3r1MN29+qXYtk6pOxmfk+4H3TPq4k6WAmWUf/NHD5wNdHm22SpA6ZpKJ/CHhVRFxJP8HfDLx1nANExDqwvra2NkEY0mIYtprHVT6atZEq+oi4B/gM8OqIOBsRt2bmi8DtwMeBx4H7MvOxcV48Mzcz88TKysq4cUuSRjRSRZ+ZtwzZ/gDwwFQjkiRNlde6kaTiTPSSVFyrFzVzMlbazglbzUKriT4zN4HNXq93W5txSNNkUlbXeJliqQjfYDSMiV6SbxLFORkrScU5GSsdwKgXRxv3ImrSLDgZKxVkK0aD7NFLHTWt3wZM+jLRS9rGN4Z6TPSShjLp12Cil1owSVvG5Ktxtbq8MiLWI2Jja2urzTAkqTRX3UhLxN8GlpOtG2mBuU5fo/CTsZJUnBW9pIl0pR00izi68r1NykQvSVPS1TcGWzeSVJwXNZM0EzsnirtU4bapjXFxeaW0pNpcsTPstX0zmA179JJGMkr/2eWe3WSil7SwRnljmfUE6SK8uZnoJc3FIiREWJw4x+GqG0kqzkQvScWZ6CWpONfRS9KYFq2P32pFn5mbmXliZWWlzTAkqTRX3UhaGsv6QS179JJUnBW9pLEtWo962ZnoJS29WbxxdemSxbZuJKk4K3pJnTduxW1raTsrekkqzkQvScWZ6CWpOC+BIKmTKvXZ2/5evASCJBXnqhtJGkHbVfkkTPSSOmORk2mXORkrScWZ6CWpOBO9JBVnopek4kz0klSciV6SijPRS1JxJnpJKs5EL0nFRWa2HQMR8RzwlQM+/RLga1MMZ1qMazzGNR7jGl9XY5skru/JzEv326kTiX4SEXEqM3ttx7GTcY3HuMZjXOPramzziMvWjSQVZ6KXpOIqJPqNtgMYwrjGY1zjMa7xdTW2mce18D16SdLeKlT0kqS9ZGbrN+A48ARwGrhjl8cPAx9uHv8ssDrw2Dua7U8AP7HfMYErm2Ocbo75ko7EdRL4EvCF5nbNnOO6G3gWeHTHsV4BfBL49+bfizoS17uApwfG683zigu4HPgn4IvAY8AvdGG89omrzfF6KfA54F+auH6zC+fjPnGdpMXzsXnsEPDPwN8eZLy2HWuUnWZ5a76ZJ4GrgJc0g371jn3eDtzV3L8Z+HBz/+pm/8PNADzZHG/oMYH7gJub+3cBb+tIXCeBn2pjvJrHfgR4Pecn1Pec++EF7gDe3ZG43gX8Sks/X0eA1zf7fBfwbwP/j62N1z5xtTleAbys2edC+onqhzpwPu4V10laPB+bx38Z+Eu2J/qRxmvnrQutm2PA6cw8k5kvAPcCN+3Y5ybgz5r7HwF+NCKi2X5vZn4zM79E/13u2LBjNs95U3MMmmO+pe24RhynWcZFZj4IPL/L6w0ea97jtVdco5p6XJn5TGZ+vonvv4DHgct2OdZcx2ufuEY1i7gyM/+72f/C5pZtn4/D4tp3hGYcF0BEHAWuBz5w7iBjjtc2XUj0lwFPDXx9lvN/OL+9T2a+CGwBF+/x3GHbLwa+0Rxj2Gu1Edc5vx0Rj0TE70fE4TnGtZdXZuYzzf3/AF7ZkbgAbm/G6+6IuKiNuCJiFfgB+tUgdGS8dokLWhyviDgUEV+g34b7ZGZ+lvbPx2FxndPm+fhe4FeBbw08Ps54bdOFRK++dwCvAX6Qfp/319oN53zZ/32xK8u0/gj4XuAa4Bng9+YdQES8DPhr4Bcz8z93Pt7WeA2Jq9Xxysz/zcxrgKPAsYh43Txff5g94mrtfIyIG4BnM/PhaR2zC4n+afqTSOccbbbtuk9EXACsAF/f47nDtn8deHlzjGGv1UZcNL92Z2Z+E/hTml/h5hTXXr4aEUeaYx2hX/m0HldmfrU5Sb8F/DFzHq+IuJB+Mv2LzPybgX1aHa9hcbU9XgNxfIP+hPFx2j8fh8XV9vn4RuDGiPgy/VbQmyLiQ4w3XtuN0sif5Q24ADhDfzLi3GTGa3fs83Nsn8y4r7n/WrZPZpyhPzky9JjAX7F9MuPtHYnrSPNv0P+17c55xTXwvFXOn/T8HbZPLr6nI3EdGbj/S/R7nfP6fwzgg8B7d3m91sZrn7jaHK9LgZc3+3wn8Gnghg6cj3vF1fr52OxzLdsnY0car/PiHGWnWd+AN9NfIfAk8M5m228BNzb3X9p8g6fpL4e6auC572ye9wRw3V7HbLZf1RzjdHPMwx2J6x+BfwUeBT5EsxpgjnHdQ/9X+v+h3/u7tdl+MfAP9JcL/j3wio7E9efNeD0C3M9AIpt1XMAP02/JPMKO5Yptjtc+cbU5Xt9Pf5ngI/R/vn+jC+fjPnG1ej4OPH4t2xP9yOM1ePOTsZJUXBd69JKkGTLRS1JxJnpJKs5EL0nFmeglqTgTvSQVZ6KXpOJM9JJU3P8BV2NnXmm2E+cAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2107,7 +2118,7 @@
     },
     {
      "data": {
-      "image/png": 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ORK5CYII=\n",
+      "image/png": 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FTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2117,7 +2128,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2129,12 +2140,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 76745\n"
+      "thcut2,pzcut2 73463\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2144,7 +2155,7 @@
     },
     {
      "data": {
-      "image/png": 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fo8A13eiXvcB+4J9nqGUudSW5KMkugK6Fup/Ribytqms9Z/2f9l1XV88F3fRu4LnAp7eqriTPBv4MeGlVjTd65r+/FnHGed4/jPofPwDcD7wfeGo3fwV4Uzf9CuB/gI+P/TyrW3YZoyfiKvBXwAVbVVd3+x+BLwJfZ9Tf9qJu/geBTzEKrL8AnjSQuvreX7/RPfYq8OvdvCcCdwGfBO4BXs+MI12Anwc+w6gFd1M377WMnngAT+j+/tVuf1w2tu1N3Xb3AVfO+XjfVF3AL3b75uPAx4BDW1zXT3TH0X8xeudzz0b/077rAn6ye/59ovt9/RbX9X7gC3wrr44uan/5yVhJatx26bqRJG2SQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+D+ewYjUTuU5AAAAAAElFTkSuQmCC\n",
+      "image/png": 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GTvs/XXRdXT3ndbd3AC8DHppXXUleAvwp8PqqGm70TH97zeKI87R/GPQ/fhx4FPgYcEE3fRX4s+72m4D/Ae4b+nlxN+9SBi/ENeCvgPPmVVd3/x+BrwDfYtDf9ppu+ieAzzEIrL8AntOTuha9vX69e+414Ne6ac8G7gXuBx4E/pgJR7oAPw98nkEL7qZu2jsYvPAAntX9/Wvd9rh0aN2buvUeAa6c8v6+pbqAX+y2zX3AZ4B9c67rJ7v96L8YfPJ5cLP/6aLrAn6qe/19tvt9w5zr+hjwZb6bV4dmtb38ZqwkNW5Zum4kSVtk0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Lj/A41dYBBwsInxAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2156,13 +2167,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "delta123 274344\n",
+      " \n",
+      "New QUAD 2\n",
+      " \n",
+      "delta123 262521\n",
       "field\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2174,12 +2188,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 274344\n"
+      "thcut,pzcut,dcacut 262521\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2189,7 +2203,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2199,7 +2213,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2211,12 +2225,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 274344\n"
+      "thcut2,pzcut2 262521\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2226,7 +2240,7 @@
     },
     {
      "data": {
-      "image/png": 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v8a28OjKt7eWRsZJU3Ly0biRJW2TQS1JxBr0kFWfQS1JxBr0kFWfQS1JxBr0kFWfQS1Jx/w+B6MwdpvNpmAAAAABJRU5ErkJggg==\n",
+      "image/png": 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t1M30DuStVF2LOe572nVdTT1rm9vrgJ8DnlypuiLi3cCfAx/KzP5GT/v7axxHnNv+odf/eB+9M1zeC5zWLJ8DbmpufxT4H+Cxvp9zmsfOpveLeAj4PLB2pepq7v8D8CLwCr3+tgua5fcDX6UXWH8JvGVC6up6f/1m89qHgN9olp0CPAI8DhwErmfEkS7ALwNfo9eCu7ZZ9gl6v3gAb27+/4ea/XF237bXNts9DVzY8ud9WXUBv9rsm8eAR4FtK1zXzzSfo/+i983n4FLvadd1AT/b/P59pfn38hWu617gm7yRV3vGtb+cGStJxU1L140kaZkMekkqzqCXpOIMekkqzqCXpOIMekkqzqCXpOIMekkq7v8AWY8+pbxMlFIAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2244,7 +2258,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2261,7 +2275,7 @@
     },
     {
      "data": {
-      "image/png": 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3IXSux/0a4JGJVjeaTY+le1P+I3BuD/3Ij8WEDTOW+4FXJbm4W5XzKuD+JBcl2QWQ5AeBG5jt83IC2NetYtrO6g7KtasbBsd3E/Cx7jk4BhzsVrLsAfYB/zqjus9n02NJckmSbQDd7HEfqzsy+zDMONZz3tfZlOocxqbH0o1hR3d6F/Ay4LGxqulrr/SIe7BfCHwU+BzwEeAF3fZl4J0D1/s48AzwLVZ7Ytd22z8GfJbVIPkr4HkLPJYrWA2VFeCvgR0LMJbf6updAX6z2/Zc4GHgM8CjwNuY8coV4JeBf2d1pvTmbttbgFd3p5/TPcYr3WN+xcBt39zd7gngur6eg3HHAvxK9/h/CvgkcGDOx/Fz3fvhv1n96+rRjV5nizgW4Oe7vPp09/OWcWvxk7GS1LhFad1IkjbJoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXH/C8I+AoKYMCAnAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2273,12 +2287,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 274344\n"
+      "thcut,pzcut,dcacut 262521\n"
      ]
     },
     {
      "data": {
-      "image/png": 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E1eZ4/SC9ZYJP0Pv//WtdOB+3iKvV87Hv8VtZm+iHHq/+m5+MlaTiutCjlyRNkIlekooz0UtScSZ6SSrORC9JxZnoJak4E70kFWeil6Ti/g+orrP+83E18AAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2288,7 +2302,7 @@
     },
     {
      "data": {
-      "image/png": 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GvSQ1zqCXpMYZ9JLUuP8HugFPsb+iXSoAAAAASUVORK5CYII=\n",
+      "image/png": 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Z9JLUOINekhpn0EtS4/4fs5NgVwy/hA0AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2298,7 +2312,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADcFJREFUeJzt3V+MZGWZx/HfTwysYmj/YFT+DA3pWeKYGDcp8cL/EeMgNpgNWRk0wYRMB5X1wpudBK/2atk7TSZqRwnqBYgkKi2jKKyG3QRXGqMjI0EGgmEQmWHN9m5cIxIfL+qotW3/OVV1Tr3nPPX9JJOpOnWm+nmnu3/11nPeOscRIQBAXi8oXQAAoF0EPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIvLF2AJJ199tmxuLhYugwA6JUHH3zw2Yh45W77dSLoFxcXtb6+XroMAOgV27+osx+tGwBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQ68YEpoEsWD9215fYn/uXyGVcCNIMZPQAkR9ADQHK0bgBt367ZaR9aOegLZvQAkBxBDwDJ0boBJjTayqGNgy4j6DG36vTlgQxaad3YPtP2uu33tfH8AID6agW97Zttn7T90Kbt+20/Yvu47UMjD/2TpNubLBQAMJm6M/pbJO0f3WD7NEmHJV0maZ+kA7b32X63pJ9JOtlgnQCACdXq0UfEfbYXN22+RNLxiHhckmzfJulKSS+RdKaG4f9b20ci4g+bn9P2iqQVSdqzZ8+k9QNjaasvz4FZdNk0B2PPlfTkyP0Tkt4UETdIku0PS3p2q5CXpIhYlbQqSYPBIKaoAwCwg9ZW3UTELW09NwCgvmlW3Twl6fyR++dV2wAAHTLNjP4BSXttX6hhwF8t6ZpGqgJ6jH49uqbu8spbJd0v6WLbJ2xfFxHPS7pB0t2SHpZ0e0QcG+eL2162vbqxsTFu3QCAmhxR/jjoYDCI9fX10mUgqZKfgGVGjzbZfjAiBrvtx0nNACA5gh4AkiPoASC5omevtL0saXlpaalkGUBrWIGDLig6o4+ItYhYWVhYKFkGAKTG+eiREueaB/6CHj0AJEfQA0ByRYOeT8YCQPs4GAsAyXEwFpgRllqiFHr0AJAcM3qkwZJKYGvM6AEgOVbdAEByrLoBgOTo0QMFsAIHs0TQo9c4AAvsjoOxAJAcQQ8AyRH0AJAcQQ8AybGOHgCSK7rqJiLWJK0NBoODJesASmKpJdrG8kr0DksqgfHQoweA5Ah6AEiOoAeA5Ah6AEiOg7HoBQ7AApNjRg8AyRWd0dtelrS8tLRUsgygM1hTjzZw4REASI7WDQAkR9ADQHIEPQAkx/JKdNa8L6nkwCyawoweAJIj6AEgOYIeAJIj6AEgOYIeAJJj1Q06Zd5X2gBt4OLgAJAcFwcHeoA19ZgGPXoASI6gB4DkCHoASI5VNyiOlTZAu5jRA0ByzOiBnmEFDsbFjB4AkiPoASA5gh4AkiPoASA5DsaiCJZUArPDjB4AkmNGD/QYSy1RBzN6AEiOoAeA5LjwCAAkVzToI2ItIlYWFhZKlgEAqXEwFjPDksp2cWAW26FHDwDJEfQAkBxBDwDJEfQAkBwHY4GEODCLUQQ9WsVKG6A8WjcAkBxBDwDJEfQAkBw9ejSOvjzQLczoASA5gh4AkiPoASA5gh4AkuNgLJAcn5IFQY9GsNIG6C5aNwCQHEEPAMnRugHmCP36+cSMHgCSY0aPiXEAFugHZvQAkFzjQW/7tbY/a/sO2x9p+vkBAOOpFfS2b7Z90vZDm7bvt/2I7eO2D0lSRDwcEddL+gdJb26+ZABNWDx015//ILe6M/pbJO0f3WD7NEmHJV0maZ+kA7b3VY9dIekuSUcaqxQAMJFaQR8R90n69abNl0g6HhGPR8Rzkm6TdGW1/50RcZmkDzZZLABgfNOsujlX0pMj909IepPtd0j6e0lnaIcZve0VSSuStGfPninKADAt1tfn1vjyyoj4vqTv19hvVdKqJA0Gg2i6DrSDfi7QP9OsunlK0vkj98+rtgEAOmSaGf0DkvbavlDDgL9a0jWNVIVOYRYP9Fvd5ZW3Srpf0sW2T9i+LiKel3SDpLslPSzp9og4Ns4Xt71se3VjY2PcugEANdWa0UfEgW22H9EUSygjYk3S2mAwODjpcwAAdsYpEAAgOU5qBuD/YallPszoASC5okHPwVgAaF/RoI+ItYhYWVhYKFkGAKRGjx7AtujX50CPHgCSY0aPv8InYYFcOBgLAMkVndHzyVigP+jX9xc9egBIjqAHgOQIegBIjqAHgORYXglJLKkEMmN5JQAkx7luACA5evQAkBw9+jlGXx6YDwT9nCHc0QQ+JdsvtG4AIDmCHgCSK9q6sb0saXlpaalkGQCmsLkdSCune1heCQDJ0boBgORYdZMUqyJQCj973cOMHgCSI+gBIDlaN3OAD0kB840ZPQAkR9ADQHJ8YApAa1iB0w1Fgz4i1iStDQaDgyXryIJePICt0LoBgOQIegBIjqAHgORYR99z9OUB7IagBzATrMAph9YNACTHjL4nmA0BmBRB32Hb9d/pyyOT7SYxTG6aQ+sGAJJjRg9g5nhXOltFZ/S2l22vbmxslCwDAFLjXDcAOoOZfjvo0QNAcgQ9ACRH0ANAcgQ9ACRH0ANAcqyj7wBWGgBoEzN6AEiOoAeA5Ah6AEiOHj2AzuNMltMh6AHMvewvJAQ9gF7JHsptIOhbwA8igC4h6AGkwJWqtkfQA5gb8xr6XHgEAJLjwiOFcNoDALNC6wYARmRs7xD0M8QsHuivPr8AcAoEAEiOGX1DmK0Ds7fd713bv499m90T9ADmUhsvBl19ASDox9TVbyQAbIegbxktHQClEfQ1lOoDAuivzflQsgPAqhsASI4ZPQBMoQ/v7Al6AJixWS/qSBv0k/xHsqIGQFtKzvzp0QNAcr2f0Y87C2fWDmDeMKMHgOR6P6OfRh+OlgPAtOY66HfCiwCALGjdAEByzOgBYBtZ3tmnCvos3xQAaBKtGwBIjqAHgOQIegBIrpUeve33S7pc0lmSvhAR32nj69RF7x7APKs9o7d9s+2Tth/atH2/7UdsH7d9SJIi4usRcVDS9ZI+0GzJAIBxjNO6uUXS/tENtk+TdFjSZZL2STpge9/ILp+sHgcAFFI76CPiPkm/3rT5EknHI+LxiHhO0m2SrvTQTZK+FRE/aq5cAMC4pj0Ye66kJ0fun6i2/aOkSyVdZfv6rf6h7RXb67bXT506NWUZAIDttHIwNiI+LenTu+yzKmlVkgaDQbRRBwBg+hn9U5LOH7l/XrUNANAR0wb9A5L22r7Q9umSrpZ05/RlAQCaMs7yylsl3S/pYtsnbF8XEc9LukHS3ZIelnR7RBwb4zmXba9ubGyMWzcAoKbaPfqIOLDN9iOSjkzyxSNiTdLaYDA4OMm/BwDszhHlj4PaPiXpFxP+87MlPdtgOSUxlu7JMg6JsXTVNGO5ICJeudtOnQj6adhej4hB6TqawFi6J8s4JMbSVbMYCyc1A4DkCHoASC5D0K+WLqBBjKV7soxDYixd1fpYet+jBwDsLMOMHgCwg94Fve2X2/6u7Uerv1+2xT4X2P6R7R/bPrbdidVKqzmWN9i+vxrHUdudPL9/nbFU+33b9n/b/uasa9zJVtdV2PT4Gba/Uj3+n7YXZ19lPTXG8rbq9+N521eVqLGuGmP5hO2fVb8b99q+oESdu6kxjutt/7TKrP/YdLr36UVEr/5I+ldJh6rbhyTdtMU+p0s6o7r9EklPSDqndO0TjuVvJe2tbp8j6WlJLy1d+yRjqR57l6RlSd8sXfNITadJekzSRdXPzk8k7du0z0clfba6fbWkr5Sue4qxLEp6vaQvSbqqdM1TjuWdkl5c3f5IF78vNcdx1sjtKyR9u8kaejejl3SlpC9Wt78o6f2bd4iI5yLid9XdM9Tddy51xvLziHi0uv1LSScl7foBiQJ2HYskRcS9kv53VkXVtOV1FTbtMzq+OyS9y7ZnWGNdu44lIp6IiKOS/lCiwDHUGcv3IuL/qrs/0PDEil1TZxz/M3L3TEmNHjztagDu5FUR8XR1+1eSXrXVTrbPt31Uw/Pl31SFZNfUGsuf2L5EwxnBY20XNoGxxtIx211XYct9YniOpw1Jr5hJdeOpM5a+GHcs10n6VqsVTabWOGx/zPZjGr47/niTBbRyPvpp2b5H0qu3eOjG0TsREba3fOWLiCclvd72OZK+bvuOiHim+Wp31sRYqud5jaQvS7o2IorMxJoaC9A02x+SNJD09tK1TCoiDks6bPsaDS/Dem1Tz93JoI+IS7d7zPYztl8TEU9X4Xdyl+f6ZXVB87dq+JZ7ppoYi+2zJN0l6caI+EFLpe6qye9Lx9S5rsKf9jlh+4WSFiT912zKG0uma0TUGovtSzWcbLx9pGXbJeN+T26T9JkmC+hj6+ZO/eWV7lpJ39i8g+3zbL+ouv0ySW+R9MjMKqyvzlhOl/Q1SV+KiJm/UI1h17F0WJ3rKoyO7ypJ/xbVkbOOyXSNiF3HYvvvJH1O0hUR0dXJRZ1x7B25e7mkRxutoPQR6QmOYL9C0r3Vf8Q9kl5ebR9I+nx1+92Sjmp4dPuopJXSdU8xlg9J+r2kH4/8eUPp2icZS3X/3yWdkvRbDXuV7ylde1XXeyX9XMPjHzdW2/5ZwwCRpL+R9FVJxyX9UNJFpWueYixvrP7vf6Phu5JjpWueYiz3SHpm5HfjztI1TziOT0k6Vo3he5Je1+TX55OxAJBcH1s3AIAxEPQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkNwfARSoQtzE6bRHAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2310,12 +2324,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 274344\n"
+      "thcut2,pzcut2 262521\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2325,7 +2339,7 @@
     },
     {
      "data": {
-      "image/png": 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DHXkbVddqTvqa9l1XV88Z3e3twKuBBzeqriSvBP4MeGNVDTd6pr+9ZrHHedp/DPof72RwhstPAmd105eA93a33wr8D/C5ob9XdPPOZfBBPAZ8BDhjo+rq7v8D8AzwTQb9bW/opt8FfIFBYP0F8II5qavv7fVb3XMfA36zm/Z84F7gPuAB4N1MONIF+GXgiwxacNd1097J4IMH8Lzu/3+s2x7nDq17Xbfew8BFU36/r6su4Fe7bfM54LPA3g2u62e699F/Mfjl88Bar2nfdQE/233+Pt/9e9UG1/VJ4Mt8J68Ozmp7eWSsJDVus3TdSJLWyaCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/we4yVw27CBe2QAAAABJRU5ErkJggg==\n",
+      "image/png": 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PY7Ajb1Z1reekj2nfdXX1nNGdPgt4NfDArOpK8krgT4A3VdXwoGfy22sae5wn/ceg//hJBke4/ATwwm75CvD+7vRbgP8BPjv094rusnMYvBCPAX8BnDGrurrzfw88BXyLQb/tjd3yTwFfYBBYfw48b07q6nt7/UZ338eAX++WPRe4B/g8cD/wHsac6QL8AvBFBiO4a7tl72LwwgN4Tvf/P9Ztj3OGrnttd72HgIsm/HzfUl3AL3Xb5rPAvcDeGdf1U93z6D8ZfPK5f6PHtO+6gJ/uXn+f6/69esZ1fQL4Ct/Oq4PT2l5+M1aSGrcorRtJ0hYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe7/AFrBck7SbAyPAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2337,13 +2351,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "delta123 747101\n",
+      " \n",
+      "New QUAD 3\n",
+      " \n",
+      "delta123 716071\n",
       "field\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2355,12 +2372,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 747101\n"
+      "thcut,pzcut,dcacut 716071\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2370,7 +2387,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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ASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2380,7 +2397,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2392,12 +2409,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 747101\n"
+      "thcut2,pzcut2 716071\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2407,7 +2424,7 @@
     },
     {
      "data": {
-      "image/png": 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u4Keaz99nm+/Xz7iu+4Cv8O28OjSt4+WVsZJU3KJ03UiStsigl6TiDHpJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6Ti/g8ZyAV1FGSsNwAAAABJRU5ErkJggg==\n",
+      "image/png": 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bY/MI8DngwIzr+tHmefQfDD/5PL7ZY9p1XcCPNa+/zze/r51xXfcDX+P/8+rwtI6X34yVpOLmpetGkrRNBr0kFWfQS1JxBr0kFWfQS1JxBr0kFWfQS1JxBr0kFfe/H8X0N+ongaAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2425,7 +2442,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2442,7 +2459,7 @@
     },
     {
      "data": {
-      "image/png": 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TurXb9hHg3d3p87rHeLl7zC8buO2t3e2eAq7r6zkYdSzAb3WP/5eBLwH7Z3wcv9S9H/6HlU9Xj6/3OpvHsQC/3OXVV7qfN49ai9+MlaTGzUvrRpK0SQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z8xXWc7rL3UfAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2454,12 +2471,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut,pzcut,dcacut 747101\n"
+      "thcut,pzcut,dcacut 716071\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2469,7 +2486,7 @@
     },
     {
      "data": {
-      "image/png": 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CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2479,7 +2496,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2491,12 +2508,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "thcut2,pzcut2 747101\n"
+      "thcut2,pzcut2 716071\n"
      ]
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADZ9JREFUeJzt3X2oZHUdx/HPJ5ct0m3KNC11W+PuRkuB1qBFSI/CWlyNilpRUJC9pPhXBC34T9Q/WRQECXapsIJSkx72plFqbUK4trtk5gPquj14zdysHJAeVPz2x5y16XbvnTMzZ+ac+c77BZedOXN27vfHmfnc3/x+v3PGESEAQF4vqrsAAMB4EfQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJbajzl9uelzS/adOmXdu2bauzFACYOgcPHnwyIk7st5+bcAmEdrsdBw4cqLsMAJgqtg9GRLvffgzdAEByBD0AJFdr0Nuet73Y6XTqLAMAUqs16CNiKSIWWq1WnWUAQGoM3QBAcgQ9ACRH0ANAckzGAkBytZ4ZGxFLkpba7fauOusAem3ZffOq23//2fdPuBKgGgzdAEByBD0AJEfQA0Byjbh65dzcXJ1lAGuOy6+3D2P2mBacGQsAyTF0AwDJEfQAkBxBDwDJEfQAkByrbjCzyqy0ATLgEgjAkHr/ULDUEk3G0A0AJEfQA0ByBD0AJEfQA0ByBD0AJEfQA0ByrKPHTGHtPGYR6+iBCrCmHk3G0A0AJEfQA0ByBD0AJEfQA0ByBD0AJEfQA0ByBD0AJFdr0Nuet73Y6XTqLAMAUuOEKaBinDyFpqk16IFJ4LIHmHWM0QNAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcpwZC4wRl0NAExD0SInLHgD/xdUrASC5WoM+IpYiYqHVatVZBgCkxmQsACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcpwwhTSafpIUZ8miLvToASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASC5sVzrxvaxkn4h6VMR8aNx/A5Aav71bdbCdW8wSaV69La/bvuI7XtXbN9h+0Hbh2zv7nnok5JurLJQAMBwyg7dXCdpR+8G28dIukbSeZK2S7rQ9nbb50q6X9KRCusEAAyp1NBNRNxhe8uKzWdJOhQRhyXJ9vWSLpB0nKRj1Q3/f9q+JSKeX/mcthckLUjS5s2bh60fANDHKGP0p0h6tOf+sqSzI+JKSbJ9qaQnVwt5SYqIRUmLktRut2OEOgAA6xjbF49ExHXjem4AQHmjLK98TNJpPfdPLbYBABpklKDfL2mr7dNtb5S0U9KeQZ7A9rztxU6nM0IZAID1lF1e+R1Jd0p6ve1l25dFxHOSrpT0E0kPSLoxIu4b5JdHxFJELLRarUHrBgCU5Ij650Hb7XYcOHCg7jIwJab1JKkyOHkKg7B9MCLa/fbjEggAkFytQc8YPQCMX61Bzxg9AIwfQzcAkBxBDwDJMUYPAMkxRg8AyTF0AwDJje2iZgAGxzdPYRwIekyFzGfDAuPGZCwAJMdkLAAkx2QsACRH0ANAcgQ9ACRH0ANAcgQ9ACRX6zp62/OS5ufm5uosA2gkTp5CVWoN+ohYkrTUbrd31VkHmomTpIBqMHQDAMkR9ACQHEEPAMkR9ACQHEEPAMlx9UoASI7llcAUYE09RsEXj6BRWDsPVI8xegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBIji8eQe1YOw+MF2fGAlOGs2QxKIZuACA5gh4AkiPoASA5gh4AkiPoASA5gh4AkuN69MAUY6klyiDoUQtOkgImh6EbAEiOoAeA5Ah6AEiu1qC3PW97sdPp1FkGAKRWa9BHxFJELLRarTrLAIDUWHWDiWGlzXix1BJrYYweAJIj6AEgOYIeAJIj6AEgOSZjMVZMwAL1o0cPAMnRowcSYqkletGjB4DkCHoASI6gB4DkCHoASI7JWFSOJZXNwsQs6NEDQHIEPQAkR9ADQHIEPQAkx2QsKsEELNBclffobb/B9rW2b7J9edXPDwAYTKmgt/1120ds37ti+w7bD9o+ZHu3JEXEAxHxMUkfkfT26ksGMKwtu29+4Qezo2yP/jpJO3o32D5G0jWSzpO0XdKFtrcXj50v6WZJt1RWKQBgKKWCPiLukPS3FZvPknQoIg5HxDOSrpd0QbH/nog4T9JFVRYLABjcKJOxp0h6tOf+sqSzbb9T0gclvVjr9OhtL0hakKTNmzePUAYAYD2Vr7qJiL2S9pbYb1HSoiS12+2oug4A5XGZhNxGCfrHJJ3Wc//UYhtmBBN6043jNztGWV65X9JW26fb3ihpp6Q9gzyB7Xnbi51OZ4QyAADrKbu88juS7pT0etvLti+LiOckXSnpJ5IekHRjRNw3yC+PiKWIWGi1WoPWDQAoqdTQTURcuMb2W8QSypnCx31g+nCtGwBIrtZr3dielzQ/NzdXZxkAerACJ59ae/SM0QPA+DF0AwDJEfQAkBzXo0dfrLQBphuTsfg/BDuQS61BHxFLkpba7fauOusA4Y7+1nqNsDKn+RijB4DkGKMHsKYyn/RW7kMPv3kI+hnDyTDA7GEyFsDY0LFoBiZjZxgTsBgHXlfNw2QsACRH0ANAckzGJsXHZwBHEfQAJoKJ2frUOnTDd8YCwPix6iYRhmswjejpjx9DN1OOcAfQD0EPoDHo3Y8HQQ9g4vgkOlmsoweA5OjRNxgfYwFUgaAH0EhlOjp0hsrh6pVTiPFNAINgHT2AxqNzMxqGbhqAj58AxomgH7NBQ3ytngs9GmB0s9qpYnklACRHj74m9NCBas1qb70Mgn6CCHdg8njfEfQAEiLc/9dMB/3KFwMf9wBkxAlTFWF8EJhe2d+/nDAFAGPQpBGDmR66GQZjf0AOs/ReJujHYJZeQMCsmObhnbRBP80HBcB0amonL23QD2Otg8QfCmB2NDWsR8ElEAAguZnr0Q/z1zrjX3gAsyNV0BPIACZh2uYApz7oCXcAWN/UBz0A1KlsZ7POTwFMxgJAcgQ9ACRH0ANAcly9EgAmbNLj9bX26CNiKSIWWq1WnWUAQGozseqGJZgAZhlj9ACQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMkR9ACQnCOi7hpk+y+S/jDkfz9B0pMVllMn2tI8Wdoh0ZamGqUtr42IE/vt1IigH4XtAxHRrruOKtCW5snSDom2NNUk2sLQDQAkR9ADQHIZgn6x7gIqRFuaJ0s7JNrSVGNvy9SP0QMA1pehRw8AWMfUBb3t423favvh4t9XrLHfZts/tf2A7fttb5lspf2VbUux78tsL9v+8iRrLKtMW2yfYftO2/fZvsf2R+uodTW2d9h+0PYh27tXefzFtm8oHr+ria+no0q05ePFe+Ie27fbfm0ddZbRry09+33Idthu5EqcMu2w/ZHiuNxn+9uVFhARU/Uj6XOSdhe3d0u6eo399ko6t7h9nKSX1l37sG0pHv+SpG9L+nLddQ/bFknbJG0tbr9G0uOSXt6A2o+R9Iik10naKOk3krav2OcKSdcWt3dKuqHuukdoy7uOvh8kXT7NbSn22yTpDkn7JLXrrnvIY7JV0q8lvaK4/6oqa5i6Hr2kCyR9o7j9DUkfWLmD7e2SNkTErZIUEU9HxD8mV2JpfdsiSbbfIukkST+dUF3D6NuWiHgoIh4ubv9J0hFJfU/2mICzJB2KiMMR8Yyk69VtT6/e9t0k6T22PcEay+rbloj4ec/7YZ+kUydcY1lljoskfUbS1ZL+NcniBlCmHbskXRMRf5ekiDhSZQHTGPQnRcTjxe0/qxuAK22T9JTt79n+te3P2z5mciWW1rcttl8k6QuSPjHJwoZQ5ri8wPZZ6vZuHhl3YSWcIunRnvvLxbZV94mI5yR1JL1yItUNpkxbel0m6cdjrWh4fdti+82STouIJn8xdJljsk3SNtu/tL3P9o4qC2jkl4Pbvk3Syas8dFXvnYgI26stG9og6RxJZ0r6o6QbJF0q6WvVVtpfBW25QtItEbFcdweygrYcfZ5XS/qWpEsi4vlqq0RZti+W1Jb0jrprGUbRCfqiuu/tabdB3eGbd6r7CesO22+KiKeqevLGiYj3rvWY7SdsvzoiHi8CY7WPOMuS7o6Iw8X/+YGkt6qGoK+gLW+TdI7tK9Sda9ho++mIWHNialwqaItsv0zSzZKuioh9Yyp1UI9JOq3n/qnFttX2Wba9QVJL0l8nU95AyrRFtt+r7h/od0TEvydU26D6tWWTpDdK2lt0gk6WtMf2+RFxYGJV9lfmmCxLuisinpX0O9sPqRv8+6soYBqHbvZIuqS4fYmkH66yz35JL7d9dPz33ZLun0Btg+rbloi4KCI2R8QWdYdvvllHyJfQty22N0r6vrptuGmCtfWzX9JW26cXNe5Utz29etv3YUk/i2LWrGH6tsX2mZK+Iun8qseCK7ZuWyKiExEnRMSW4v2xT902NSnkpXKvrx+o25uX7RPUHco5XFkFdc9IDzGD/UpJt0t6WNJtko4vtrclfbVnv3Ml3SPpt5Kuk7Sx7tqHbUvP/pequatu+rZF0sWSnpV0d8/PGXXXXtT2PkkPqTtncFWx7dPqBockvUTSdyUdkvQrSa+ru+YR2nKbpCd6jsGeumseti0r9t2rBq66KXlMrO4w1P1FZu2s8vdzZiwAJDeNQzcAgAEQ9ACQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMkR9ACQ3H8AAKZRyA1g6xQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2506,7 +2523,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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wVVV9ulv8NeDZSU7oRj8bumb+JOpaY9/HRrdPJnk38CdzUNesj9djwHkDz8+g35unqh7r/v5WkvfR//F41KAf5l4Kx9Y5nOQE4BT6x2es+zBMq67qN3ifBKiqg0keon/uanmT6lpr2/NWbHvHBGo6tu+R34uBz9TDSe4AXky/E7ApdSV5Of1B0Eur6smBbc9bse0d4xSzVVo3B4BjZ54vA/5x5Qrpzwz5B+CGqjrWX6b78H8SuHit7adV11q6sDvWF381cM+s65qD43U78Iokp6Y/K+cVwO1JTkhyGkCSHwUuZLzjNcy9FAbrvRj4RHd8DgCXdLNfdgHnAP82Ri0TqSvJziQ7ALoR6jn0T+RtVl2rOe57Ouu6unpO6h6fBvwycN9m1ZXkxcA7gFdV1eCgZ/LHaxpnnCf9h37/8ePAg8DHgOd0y3vAO7vHrwP+F/jswJ8Xda+dTf8b8RDwd8BJm1VX9/xTwBHgO/T7ba/sln8C+AL9wPpb4JlzUtesj9cfdF/7EPD73bJnAAeBzwP3Am9lzJkuwG8CX6Q/gruqW/Zm+t94ACd3//5D3fE4e2Dbq7rtHgDOn/DnfaS6gN/qjs1ngc8AS5tc1y92n6P/pv+Tz71rvaezrgv4pe7773Pd35dvcl0fA77C9/PqwLSOl78ZK0mN2yqtG0nSiAx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa9//mG8qh85U6iAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2516,25 +2533,30 @@
     }
    ],
    "source": [
+    "kq=0\n",
     "for quad in [t1234,t1231,t1212,t1123] :\n",
-    "    plotTriplets(quad,600)"
+    "    plotTriplets(quad,600, kq)\n",
+    "    kq+=1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      " \n",
+      "New QUAD 0\n",
+      " \n",
       "dphi\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2544,7 +2566,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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NzBv12lzx/qKq1u0P8D5gX3d7H/DeEe1eBuwG7hyYfiuwp7t9LfCO7va/Bq7tbu8Bbpl2XcDTgaPd76d1t582pN0h4MXd7TcCH5zl/jpZXcBBYOeQZVZtfwGnAT/ftTkV+HPg0uXuL2ATcAR4dre+LwHnjfN4gfO69puB7d16No2zzhnXdQHwD7rb5wPH+5YZ+pzOqa5twP0j1vsXwM8BAf7oxHM6j7oG2vwj4Mic99c24B8DNwKvG/O1uaL9VVXru2cBXA78bnf7d4FfHNaoqj4BfKd/Wpe8LwVuH7J8/3pvB142YVKPU9crgI9X1beq6n8DHwd2DdT4M8BZ9N4Ap2EqdTXWO9f9VVV/W1WfBKiq7wNfALZOsO1BFwJLVXW0W9/NXX2j6u1/vJcDN1fVY1X1V8BSt75x1jmzuqrqi1X1P7vpDwA/nmTzhNufel2jVpjkmcBPVNVnq/dOeCMjXttzqGtvt+y0NOuqqq9W1X3AEwPLDn0NTGl/rfuw+Mmq+np3+6+Bn5xg2WcAj1TV4939Y8DZ3e2zgYcAuvnf7tpPs64fbmPI9k848Wmn/yyF1ya5L8ntSc6ZoKZp1fU7Xff7N/peWAuxv5I8lV4P8hN9kyfdX+M8L6Me76hlx1nnLOvq91rgC1X1WN+0Yc/pvOranuSLSf4syYv62h9rrHPWdZ3wz4GbBqbNen9Nuuw09henTLrAoknyJ8DfHzLr1/rvVFUlmdupX3Oqaw/wr/ru3wHcVFWPJXkbvU9FL+1fYMZ1vb6qjic5HfiDrrYbx1lw1vsrySn0XtS/WVVHu8nN/bWRJPmHwHuBl/dNXvZzOgVfB55VVX+T5GeBj3U1LoQkFwF/W1X3901ezf01U2s+LKrqF0bNS/K/kjyzqr7edcUenmDVfwM8Nckp3aeKrcDxbt5x4BzgWPcmtKVrP826jgMX993fSm889MQ6ng+cUlWH+rbZX8NH6I31/4hZ1lVVx7vf30ny+/S61DeyAPuL3nnoX6mq9/dts7m/RmynvwfS/3cx2Gbw8Z5s2dY6Z1kXSbYCHwWurKojJxY4yXM687q6HvNj3fYPJTkC/EzXvn8oce77q7OHgV7FnPbXyZa9eGDZg0xnf637Yaj9wIkzAt4A/OG4C3Z/qJ8ETpxt0L98/3pfB/zpwFDQNOq6G3h5kqeld/bPy7tpJ+xl4A+1eyM94TLg8AQ1raiuJKckOaOr40nAq4ETn7hWdX8leTe9F/q/6V9gmfvrHuDc9M6UO5XeG8b+k9Tb/3j3A3vSO8tmO3AuvQOP46xzZnV1w3N30TuJ4NMnGjee03nUdWaSTd32n01vfx3thiT/T5Kf64Z5rmSC1/ZK6+rq+THgCvqOV8xxf40y9DUwpf217s+Gega98emvAH8CPL2bvhP4SF+7Pwe+AXyP3njeK7rpz6b3Yl4CbgM2d9Of3N1f6uY/e0Z1vbnbxhLwpoF1HAWeOzDtP9M7QPklekH33HnVBTyF3plZ93U1fADYtNr7i96nqKIXBPd2P7+0kv0FvBL4H/TOWvm1btp/BC5rPV56w2pHgAfpOyNl2DqX8fe+rLqAXwe+27d/7qV34sTI53ROdb222+699E5M2N23zp303oiPAB+ku8B4HnV18y4GPjuwvnntr39K733qu/R6Og+03jOmsb+8gluS1LTeh6EkSVNgWEiSmgwLSVKTYSFJajIsJElNhoUkqcmwkCQ1GRaSpKb/B8mafZ0FHZs+AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2554,7 +2576,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2571,7 +2593,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2581,7 +2603,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2591,7 +2613,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2608,7 +2630,7 @@
     },
     {
      "data": {
-      "image/png": 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CrzLfkvw48PvAVcC/An9dVe8dHmtijABJfgz4PWAPcLyqPjrjLvViuAf0TWzezfEbwEeAzwMPA6ts3kn2p6pq6wnbhZHkR4C/AP4WeGPY/Ots1umbGWdXBr0kNc7SjSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/wuA0ArMshzpuAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2618,7 +2640,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2628,7 +2650,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2645,7 +2667,7 @@
     },
     {
      "data": {
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FvNY1rq8neWVVPd21HM/1XdCoJC9kGPJ3V9Wfd8NzXfN5VfWtJA8AP8awpfuCbla/qT4jzc3oV5Nk98jD/cBXuvtHgV/sVt+8Dlge+VrZqyR7gd8A3lpV/zny1FHgQJLLk+wCdgOf76PGNZjXmife/7hnR4F3d/ffDfxVj7X8H0kC3Ak8UVV/MPLUPNd81fnVbUm+F3gjw3MLDwDv6A6bq5ovqe+zwRt5YzirOAk8BhwDttXzZ9lvZ9iH+ztGVor0fWN4wvIM8MXudsfIc7d2NZ8Cbui71pG6fo5hD/PbwNeB+zdBzW9iuCLka8CtfdezSp33AE8D/939O74ReDnwNwyvAPsZ4GV91zlS708wbMs8NvIZftOc1/wjwN92NZ8E3t+Nv4rhxOQ08KfA5X3XOu7Nv4yVpMYtVOtGkhaRQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+B1VowwgZxoj2AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2655,7 +2677,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2665,7 +2687,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2682,7 +2704,7 @@
     },
     {
      "data": {
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Gw/+a5BVV9c1B6/JSdxVO3WuBG5O8EXgB8GLgj+i3bZ83OKpv5j2xbVo3VfXVqvqJqtpXVfvo/1n2qqr6Fv173f76YPbNa4DloT9hm5PkIP0/WW+squ8MfeoMcDjJ85Psp39y+u+6qHETjH3P461q0Iv+GPBoVf3B0KfOAO8YfPwO4FObXdtmqapbq2r3IAsOA39bVW8HvgDcNNismX3Q3BH9Bp0F3kj/5ON3gHd1W87U3QE8H/jc4K+bB6vq5qq6kOQ08Aj9ls57quq7HdY5NVX1bJLL9zzeAZxc7z2Pt7DXAr8GfDXJ3w/GPgDcBpxOchT4BvDWjurr0vuBU0l+D/gy/V+IW55XxkpS47ZN60aStiuDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0fZ0UjYoYsw2IAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2692,7 +2714,7 @@
     },
     {
      "data": {
-      "image/png": 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q4LokbwReDLwM+GP67dcXDKr6mf5/t3Uzgqr6RlX9RFXtq6p99N/OvbaqvkN/v9xfG6y+eR2wtOzt7sxLcoj+W9vrquq7yx46CRxJ8qIk++lPRv9tFzFOydh7I8+KQV/6DuCRqvrDZQ+dBN41+PpdwKc3OrZpqqqbq2r34Gf6CPA3VfVO4EvAWwenzfR1W9FPzmngjfQnI78LvKfbcCbuNuBFwBcG72bur6obq+pckruBh+m3dN5bVd/rMM6Jqqpnkzy3N/I24PioeyPPkKuAXwW+keTvBmMfBD4M3J3keuBbwNs6im+jfQA4keR3ga/R/yU4k/xkrCQ1ztaNJDXORC9JjTPRS1LjTPSS1DgTvSQ1zkQvSY0z0UtS40z0ktS4/wO3VCLLuL0WRAAAAABJRU5ErkJggg==\n",
+      "image/png": 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Bfgh4AfBH9Muvzxn06uf6927pZgxV9bWq+tGq2ltVe+l/nXt5VX2L/nq5vzKYffMKYHXo6+7cS3KI/lfb66vqO0MPnQSOJHlukn30B6P/tosYZ2TitZHnxaAufTvwUFX9wdBDJ4G3DH5+C/DxzY5tlqrqlqraNXhPHwH+uqreDHwOeMNgt7k+b3v003MaeA39wcjvAG/rNpyp+yDwXOAzg28zX6yqm6rqbJK7gAfpl3TeXlXf7TDOqaqqp5M8szbyNuD4uGsjz5FXAr8MfC3J3w3a3g28D7gryQ3AN4E3dhTfZnsXcCLJ7wBfof8hOJe8MlaSGmfpRpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGmeil6TGmeglqXEmeklq3P8CZgUdmVbFl4EAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2702,7 +2724,7 @@
     },
     {
      "data": {
-      "image/png": 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dU1XnkpwEnmexpfP+qvqfDuscm6q6muQI8BiwATheVec6LmsSbgN+Efhqkr/pb/sQcD9wMskhFq8s++6O6ltLHwROJPlt4MssfgA2wTNjJalxtm4kqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjftfKpPCMK2qZHwAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2719,7 +2741,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2729,7 +2751,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2739,7 +2761,7 @@
     },
     {
      "data": {
-      "image/png": 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CYII=\n",
+      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2756,7 +2778,7 @@
     },
     {
      "data": {
-      "image/png": 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S9HeStpblP1zuL5XHL2mort8tx1iS9DsD+zgj6Q0Dy/5S/ZNpX1H/TeoNs6pL0nnqzwB6tNTw15I2td1e6vdeQv0Qf6T8e9c07SXp1yT9u/qzI95blv25pOvW+3nVH4o6LemUKjMfVtvnBL/vE9Ul6U8kfbvSPo+of5J/6HM6o7puKMd9RP2T6IuVffbUD9HTkj6o8sXNWdRVHrtC0hcG9jer9vpF9XPq2+p/wji5XmbU0V58MxYAkuvy0A0AoAYEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAk938tJLf/VuucHQAAAABJRU5ErkJggg==\n",
+      "image/png": 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vSHpJ3fGra8ryy9R9Ia5I+jNJW8ry15brK+X2yyZU1y+X+1iR9Es9+zgj6W09y/5Q3YNpX1T3Tept06pL0oXqzgB6otTwx5I2Nd1e6vZeQt0Qf7z8e9847SXppyX9q7qzIz5Qlv2+pOsHPV51h6JOSzqlysyHtfa5gb/3DdUl6bclfb3SPo+re5C/73M6pbpuLPf7uLoH0Zcq++yoG6KnJd2p8sXNadRVbtsj6fM9+5tWe/2Yujn1dXU/YZwclBl1tBffjAWA5No8dAMAqAFBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJ/Q9BZKrLcH2IFAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2766,7 +2788,7 @@
     },
     {
      "data": {
-      "image/png": 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l6eKyfF7SXZX1PifpeUkvqjd+dU1Zfpl6b8QlSX8uaVtZ/upyfancfllDdf1quY8lSb/St4+zkt7St+yP1DuY9kX1PqTeMq66JF2o3gygx0oNH5G0pe32Uq/3EuqF+KPl3/s2016Sfk7Sv6s3O+KDZdkfSLp+vcer3lDUGUlPqjLzYbV9buD1vqG6JP2upK9X2udR9Q7yD3xOx1TXjeV+H1XvIPpCZZ/z6oXoGUl3qPxwcxx1ldv2Svp83/7G1V4/qV5OfV29bxin18uMOtqLX8YCQHJdHroBANSAoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5P4PnPWrle9Wd0QAAAAASUVORK5CYII=\n",
+      "image/png": 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3fnVZs/xcei/EI8BfAKc1y1/TXD/S3H7uhOr6teY+jgC/2rePY8Bb+5b9Mb2DaV+m9yb11mnVBZxObwbQY00NNwNbum4ver2XpBfijzb/3r+R9gJ+Hvg3erMjPtgs+0PgyrX+XnpDUUeBJ2nNfFhtn+t4vq+rLuD3gW+12udRegf5Bz6mU6rr6uZ+H6V3EH2ptc9FeiF6FLiF5oub06irue1i4It9+5tWe/00vZz6Fr1PGI+vlRnjaC+/GStJxc3y0I0kaQwMekkqzqCXpOIMekkqzqCXpOIMekkqzqCXpOIMekkq7v8BExyxGfSPyI8AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2776,7 +2798,7 @@
     },
     {
      "data": {
-      "image/png": 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/VB6/eEx1/UZ5jiVJv96zj9OS3tyz7E9VHUz7kqoPqTdPqi5J56qaAfRYqeHPJW1qu71U9V5CVYg/Wv69ZyPtJemXJP27qtkR7yvL/ljSdWv9vKqGok5JOqnazId++1zH7/u66pL0B5K+WWufR1Ud5B/4mk6orhvK8z6q6iD6Ym2fC6pC9JSkO1RO3JxEXeWxXZK+0LO/SbXXz6rKqW+q+gvjxFqZ0UR7cWYsACTX5aEbAEADCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASO7/AMd8p829TqlsAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2793,7 +2815,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2803,7 +2825,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2813,7 +2835,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2830,7 +2852,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2840,7 +2862,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2850,7 +2872,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2867,7 +2889,7 @@
     },
     {
      "data": {
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+      "image/png": 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3nDtxAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2877,7 +2899,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2887,7 +2909,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2904,7 +2926,7 @@
     },
     {
      "data": {
-      "image/png": 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gl6TGGfSS1DiDXpIa93+1hg7c/hFsgAAAAABJRU5ErkJggg==\n",
+      "image/png": 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uS+zHbjp2XJL8GHMXW1cxd5J9tKp+v//7fwR4L/CPwK9V1f+M9bO7HPSSpBvrcutGkjQAg16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9H1YYOmpuB5jNAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2914,7 +2936,7 @@
     },
     {
      "data": {
-      "image/png": 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ngbOX/9Sm9yDwwe7nDwJ/PcVYNuzbSbHzM8zIfeke/H0SOF9Vf7jqrZm6L+v1YxbvS5KtSd7U/fydrEwkOc9Kwn9/d9hE7snMzroB6KZU/RGwBTheVb8z5ZA2JMnbWRnFw8r2jn8xS33p9hS+mZVV+J4Dfgv4LHAK2MnKyqS3VdWmftC5Tj9uZqU8UMDTwC+uqnFvWkneDTwKfBV4pWv+DVbq2zNzXy7Tj3lm7L4k+SFWHrZuYWWQfaqqfrv7/T8JvAX4B+Dnq+p/xnrtWU70kqQrm+XSjSSpBxO9JDXORC9JjTPRS1LjTPSS1DgTvSQ1zkQvSY0z0UtS4/4XYvTAnKh2PYgAAAAASUVORK5CYII=\n",
+      "image/png": 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o+/lDwF9PMZY1ezUpdn6aGbkv3YO/B4AzVfWHy16aqfuyUj9m8b4kuSHJdd3P38lgIskZBgn/g91pE7knMzvrBqCbUvVHwAbgcFX9zpRDWpMkb2cwiofB9o5/MUt96fYUvoXBKnzfBH4L+AxwDJhjsDLp7VW1rh90rtCPWxiUBwp4GvjFZTXudSvJe4AvAl8DXumaf4NBfXtm7ssq/Vhgxu5Lkh9k8LB1A4NB9rGq+u3u3/9R4C3APwA/X1X/M9Zrz3KilyRd3iyXbiRJPZjoJalxJnpJapyJXpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGve/U4q34iVclBQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2924,7 +2946,7 @@
     },
     {
      "data": {
-      "image/png": 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qInlslhN9M3vWJrmue9BEkuuAnwEeW/tTG94J4L3dz+8F/nqKsazbi0mx83PMyH3pHvzdC5ytqj9a8dZM3ZfV+jGL9yXJliSv7n7+XpYnkpxlOeH/fHfYRO7JzM66AeimVP0xsAk4UlW/N+WQ1iXJG1gexcPy9o5/OUt96fYUvpXlVfi+CfwO8FngOLCD5ZVJ31NVG/pB5yr9uJXl8kABTwC/vKLGvWEleRvwJeDrwAtd82+xXN+emfuyRj8WmLH7kuRHWX7YuonlQfbxqvrd7vf/GPBa4O+BX6yq/xnrtWc50UuSrmyWSzeSpB5M9JLUOBO9JDXORC9JjTPRS1LjTPSS1DgTvSQ1zkQvSY37Xy1Z3IkRny0SAAAAAElFTkSuQmCC\n",
+      "image/png": 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APw08vvanNrwTwPu7n98P/PUUY1m3byfFzs8yI/ele/B3H3C2qn5/xVszdV9W68cs3pckW5K8rvv5u1meSHKW5YT/3u6widyTmZ11A9BNqfoDYBNwpKp+Z8ohrUuSN7M8iofl7R3/Ypb60u0pfBvLq/B9A/gt4DPAcWCO5ZVJ31dVG/pB5yr9uI3l8kABTwG/uKLGvWEleTvwReBrwMtd82+wXN+emfuyRj8WmLH7kuSHWX7YuonlQfbxqvrt7vf/GPAG4B+An6+q/xnrtWc50UuSrm6WSzeSpB5M9JLUOBO9JDXORC9JjTPRS1LjTPSS1DgTvSQ1zkQvSY37X+SSxWZ/A3zbAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2941,7 +2963,7 @@
     },
     {
      "data": {
-      "image/png": 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63lSpqr5YVd9TVXNVNUf/P8JLL0bIryTJNvpv/V9bVf/VcTkPA9cm2ZzkSvo3yg51XBPp/3a+HThRVb/XdT3nVdU7qmrj4GdqF/DXayDkGfxcn05y3WDoBuCxDks67yvAK5I8a/BvegMzvEl8yczoV7AHeN/gpsb/AHs7rue8g8DBJMeAJ4Ff6niWutZ9AHgG8MnBu43PVNXNXRRSVeeS7AMeoL8i4mBVHe+iliVeCfwi8MUknx+MvbOqjnRY01p3C3Dn4Bf2KeCXO66HQRvpXuBR+m3KzzHDT8j6yVhJalwrrRtJ0gUY9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe7/AKuo8V1uQpjdAAAAAElFTkSuQmCC\n",
+      "image/png": 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kV5JkB/2X/q+pqv/qOM7DwDVJtiW5kv4bZfd1nIn0fzvfDhyvqj/oOs8FVfWOqto8+JnaDfz1Gih5Bj/Xp5JcOzh0PfBYh5Eu+Crw8iTPGPybXs8M3yReN2f0K9gLvG/wpsb/APs6znPBQeBgkqPAE8CvdHyWutZ9AHga8KnBq43PVtXNXQSpqvNJ9gMP0F8RcbCqjnWRZYlXAL8MfCnJFwbH3llVhzvMtNbdAnxk8Av7JPCrHedhMEa6B3iU/pjy88zwE7J+MlaSGtfK6EaSdBEWvSQ1zqKXpMZZ9JLUOItekhpn0UtS4yx6SWqcRS9Jjfs/1ojwVhuurLcAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2951,7 +2973,7 @@
     },
     {
      "data": {
-      "image/png": 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JnNHP4wbgtv5Fjf/h/1fF7NouYFf/gS3HgF/q+Cx12n0COA/4fP/TxqNVNe8CeZNQVceTnLjzexmwq6M7v+e6HPhF4KtJvtwf+2BV7e2wpml3E3BX/w37GeCXO66HfhvpXuBJem3KLzHBO2S9M1aSGtdK60aSdAoGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/eqq2MVXrJHoAAAAASUVORK5CYII=\n",
+      "image/png": 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oT+M64Nb+RY3/4f9XxezaDmBH/4EtR4Ff7vgsddZ9AjgX+Hz/08bDVXXaBfKmoaqOJTl+5/cSYEdHd36f6K3ALwFfTfLl/tgHq2pvhzXNuhuBO/tv2AeBX+m4HvptpLuBx+m1Kb/EFO+Q9c5YSWpcK60bSdJJGPSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXu/wAHnrgkupKZnQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2961,7 +2983,7 @@
     },
     {
      "data": {
-      "image/png": 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c/wHGvn0nMnPncAAAAABJRU5ErkJggg==\n",
+      "image/png": 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AAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2973,12 +2995,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      " \n",
+      "New QUAD 1\n",
+      " \n",
       "dphi\n"
      ]
     },
     {
      "data": {
-      "image/png": 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9ACT3G5aRDUDx7RP2AAAAAElFTkSuQmCC\n",
+      "image/png": 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br0tjt08AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2988,7 +3013,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2998,7 +3023,7 @@
     },
     {
      "data": {
-      "image/png": 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JEfQAkBxBDwDJEfQAkBxBDwDJEfQAkNz/A9U9NMByo41kAAAAAElFTkSuQmCC\n",
+      "image/png": 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AkiPoASA5gh4AkiPoASC5fwOCETZwa2JzpwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3015,7 +3040,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3025,7 +3050,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAD8CAYAAACGsIhGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEcZJREFUeJzt3XuspHV9x/H3pyBYK12VFaWA7uoSZEui0A1Y8UK8NAu6YG1LoKaKUOi2xWDSpNnEpqSNadU2JtpSDCrFJhQQFbsra/HGhtQgcgn3lboQDUtQtKYoNgWRb/+YWTqOc/Y3s+fM7Zz3K5nszPP8zjzfec7sfOZ3mTmpKiRJ2ptfmnYBkqTZZ1hIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJAkNRkWkqQmw0KS1LT/tAtYKqtXr641a9ZMuwxJmiu33nrrD6rq+a12yyYs1qxZwy233DLtMiRpriT5zjDtHIaSJDUZFpKkJsNCktRkWEiSmgwLSVKTYSFJajIsJElNhoUkqWnZfChP0nSt2XLt09e//f43T7ESjYNhIWmf9QbEqO0NlPliWEgr3LhfwEcNFM0mw0Izz3ej0+fvQIaFJs4XnunwvGsxDAtJT1uqQHHoafkxLDSTfLGRZothoama5OSqQy/SvjMstChL+WJsb0KaXYaFlswk3sXbU5Cmw6/7kCQ12bPQ2Dm8JM0/exaSpCZ7FpIGskeoXoaFxmISLzROdkuTM9NhkeRo4AJgNfCVqrp4yiVJy4K9Bo2qOWeR5JlJvpHkjiT3JPmrfT1YkkuTPJLk7gH7Nia5L8muJFsAqmpnVW0GTgdO3Nfjavlbs+Xapy+Slt4wE9yPA6+vqpcDrwA2Jnllb4MkhyQ5qG/bugH3dRmwsX9jkv2Ai4CTgfXAmUnWd/edClwLbB+iVknSGDSHoaqqgMe6N5/RvVRfs9cBm5OcUlWPJzkXeBudF//e+7ohyZoBhzke2FVVDwAkuRI4Dbi3qrYCW5NcC/zrsA9M6uccx9Kw97YyDTVn0X3nfyuwDrioqm7q3V9VVydZC1yV5GrgbOBNI9RxGPBgz+3dwAlJTqITOgeyQM8iySZg07p1gzoy0srmC7uWylBhUVU/A16R5DnANUmOqaq7+9p8sNsjuBh4aVU9Nui+RlFVO4AdjTbbgG0bNmw4d7HH0/KwEl8g7TVp3Eb6UF5V/TdwPYPnHV4DHANcA1w4Yh0PAUf03D68u02SNAOGWQ31/G6PgiS/TGd46Zt9bY4FLqEzz/Au4OAk7xuhjpuBI5OsTXIAcAawdYSflySN0TA9i0OB65PcSedF/UtV9fm+Ns8CTq+q+6vqKeAdwHf67yjJFcCNwFFJdic5B6CqngTOB64DdgKfqqp79vVBSZKW1jCroe4Ejm20+Vrf7Z8CHxvQ7sy93Md2XB4rSTPJLxKUJDXN9Nd9SFq+XME1XwwLrXi+aElthoVWpJX4WQxpMZyzkCQ1GRaSpCbDQpLUZFhIkpoMC0lSk6uhpGXGlV4aB3sWkqQmw0KS1OQwlDSnHG7SJNmzkCQ1GRaSpCaHoaQefqngdHjeZ589C0lSk2EhSWoyLCRJTc5ZSDPOJbKaBfYsJElNhoUkqcmwkCQ1GRaSpCbDQpLUZFhIkppcOquRuZRTWnnsWUiSmgwLSVKTYSFJajIsJElNTnBLM8hFBJo19iwkSU0z3bNIcjRwAbAa+EpVXTzlkrSC+NfbpP/X7FkkOSLJ9UnuTXJPkgv29WBJLk3ySJK7B+zbmOS+JLuSbAGoqp1VtRk4HThxX48rLdaaLdc+fZFWomGGoZ4E/qyq1gOvBP40yfreBkkOSXJQ37Z1A+7rMmBj/8Yk+wEXAScD64Ez9xwjyanAtcD2IWqVJI1BMyyq6uGquq17/cfATuCwvmavAz6X5ECAJOcC/zDgvm4AfjjgMMcDu6rqgap6ArgSOK37M1ur6mTg7UM/KknSkhppziLJGuBY4Kbe7VV1dZK1wFVJrgbOBt40wl0fBjzYc3s3cEKSk4C3AQeyQM8iySZg07p1gzoykqSlMHRYJHk28BngPVX1o/79VfXBJFcCFwMvrarHFltcVe0AdjTabAO2bdiw4dzFHk+SNNhQS2eTPINOUFxeVZ9doM1rgGOAa4ALR6zjIeCIntuHd7dJkmbAMKuhAnwC2FlVH1qgzbHAJXTmGd4FHJzkfSPUcTNwZJK1SQ4AzgC2jvDzkqQxGqZncSLwB8Drk9zevZzS1+ZZwOlVdX9VPQW8A/hO/x0luQK4ETgqye4k5wBU1ZPA+cB1dCbQP1VV9+zzo5IkLanmnEVV/QeQRpuv9d3+KfCxAe3O3Mt9bMflsZI0k/y6D0lSk2EhSWoyLCRJTYaFJKnJsJAkNRkWkqQmw0KS1GRYSJKaDAtJUpNhIUlqMiwkSU2GhSSpybCQJDUZFpKkppH+BrckWLPl2qevf/v9b55iJdLk2LOQJDUZFpKkJsNCktRkWEiSmpzglmZE78T5StZ/HlxEMBvsWUiSmuxZSFNiT0LzxLCQxsDPYmi5cRhKktRkWEiSmgwLSVKTYSFJanKCW0Nx5Y60stmzkCQ12bPQz3HJp6RB7FlIkprsWUhjZm9Ny4E9C0lSk2EhSWpyGEpaBIeYtFIYFtIS8bMoWs4MC2mCDBTNK+csJElNhoUkqcmwkCQ1GRaSpCbDQpLUZFhIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJAkNRkWkqQmw0KS1GRYSJKa/Ipy+bXZkpoMC0kzbSX/NcJZeuwOQ0mSmuxZaEEOT0naw7CQtGzN0jDOvHMYSpLUZFhIkpoMC0lSk2EhSWoyLCRJTa6G2gtXUkhShz0LSVKTYSFJaprJsEhydJKPJvl0kj+edj2StNJNLCySXJrkkSR3923fmOS+JLuSbAGoqp1VtRk4HThxUjVKkgabZM/iMmBj74Yk+wEXAScD64Ezk6zv7jsVuBbYPsEaJUkDTCwsquoG4Id9m48HdlXVA1X1BHAlcFq3/daqOhl4+6RqlCQNNu2ls4cBD/bc3g2ckOQk4G3AgeylZ5HkPOA8gBe96EXjq1KSVrhph8VAVbUD2DFEu0uASwA2bNhQ461KklauaYfFQ8ARPbcP726TpCXlh2wXZ9phcTNwZJK1dELiDOD3p1uSpHmw0Iv/Uv3RLsPl500sLJJcAZwErE6yG7iwqj6R5HzgOmA/4NKqumdSNUnSYqykQJlYWFTVmQts347LYydiJT2xJS2tmfwEtyRptkx7zkKSNIT+uZhJjw7Ys5AkNRkWkqQmw0KS1DT3cxZJNgGb1q1bN9bjTHMlkauYJE3b3PcsqmpbVZ23atWqaZciScvW3IeFJGn85n4YStLKsVRf5bGY467UoWDDYglNex20JI2Lw1CSpCbDQpLUZFhIkpqcs5CkEUxrkn3aDIs546oMSdPgMJQkqcmwkCQ1zX1YJNmU5JJHH3102qVI0rI193MWVbUN2LZhw4Zzp13LvpjWHMRKnaSTtG/mvmchSRo/w0KS1GRYSJKa5n7OYh45XyBp3hgWc8wP6EmaFIehJElNhoUkqcmwkCQ1GRaSpCbDQpLU5GqoMXK1kqTlwp6FJKnJnoUkLYHlPpIw92GRZBOwad26ddMuRdIcGveL/HIJkbkPi2l8Rfks/vJnsSZp3vhVPAtzzkKS1GRYSJKa5n4Yarlaqu6w3WqtBD7Px8+ehSSpyZ5Fn+XwDmU5PAZJs8WwkKQ5NOkVkA5DSZKa7FlMiENDkub581D2LCRJTYaFJKlppoehkrwVeDPwq8AnquqLUy5JklakocIiyXOAjwPHAAWcXVU3jnqwJJcCbwEeqapj+vZtBD4M7Ad8vKreX1WfAz6X5LnA3wOGhaRlZx7mNIcdhvow8O9V9TLg5cDO3p1JDklyUN+2QV8DexmwsX9jkv2Ai4CTgfXAmUnW9zT5i+5+SdIUNHsWSVYBrwXOAqiqJ4An+pq9Dtic5JSqejzJucDb6Lz4P62qbkiyZsBhjgd2VdUD3WNeCZyWZCfwfuALVXXbCI9rYubhHYEkLdYww1Brge8D/5zk5cCtwAVV9ZM9Darq6iRrgauSXA2cDbxphDoOAx7sub0bOAF4N/BGYFWSdVX10f4f9O9ZSJpH8/ZGc5hhqP2B44CLq+pY4CfAlv5GVfVB4H+Bi4FTq+qxxRZXVR+pqt+oqs2DgqLbZltVnbdq1arFHk6StIBhwmI3sLuqbure/jSd8Pg5SV5DZwL8GuDCEet4CDii5/bh3W2SpBnQDIuq+i7wYJKjupveANzb2ybJscAlwGnAu4CDk7xvhDpuBo5MsjbJAcAZwNYRfl6SNEbDroZ6N3B5kjuBVwB/07f/WcDpVXV/VT0FvAP4Tv+dJLkCuBE4KsnuJOcAVNWTwPnAdXRWWn2qqu7ZlwckSVp6Q33OoqpuBzbsZf/X+m7/FPjYgHZn7uU+tgPbh6lnqc3bRJMkTZpf9yFJaprpr/uQpHFwNGF0hsUM8QksLQ/L8f+yw1CSpCbDQpLUZFhIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJAkNRkWkqSmVNW0a1gSSb7PgG+6HdJq4AdLWM5Ssa7RWNdorGt0s1rbYup6cVU9v9Vo2YTFYiS5paoW/FbdabGu0VjXaKxrdLNa2yTqchhKktRkWEiSmgyLjkumXcACrGs01jUa6xrdrNY29rqcs5AkNdmzkCQ1rciwSPJ3Sb6Z5M4k1yR5zgLtNia5L8muJFsmUNfvJbknyVNJFlzZkOTbSe5KcnuSW2aorkmfr+cl+VKSb3X/fe4C7X7WPVe3J9k6xnr2+viTHJjkqu7+m5KsGVctI9Z1VpLv95yjP5xQXZcmeSTJ3QvsT5KPdOu+M8lxM1LXSUke7TlffzmBmo5Icn2Se7v/Fy8Y0Ga856uqVtwF+C1g/+71DwAfGNBmP+B+4CXAAcAdwPox13U0cBSwA9iwl3bfBlZP8Hw165rS+fogsKV7fcug32N332MTOEfNxw/8CfDR7vUzgKtmpK6zgH+c1POp57ivBY4D7l5g/ynAF4AArwRumpG6TgI+P+FzdShwXPf6QcB/Dvg9jvV8rcieRVV9saqe7N78OnD4gGbHA7uq6oGqegK4EjhtzHXtrKr7xnmMfTFkXRM/X937/2T3+ieBt475eHszzOPvrffTwBuSZAbqmoqqugH44V6anAb8S3V8HXhOkkNnoK6Jq6qHq+q27vUfAzuBw/qajfV8rciw6HM2nTTudxjwYM/t3fziL2daCvhikluTnDftYrqmcb5eUFUPd69/F3jBAu2emeSWJF9PMq5AGebxP92m+2blUeDgMdUzSl0Av9Mduvh0kiPGXNOwZvn/4G8muSPJF5L8+iQP3B2+PBa4qW/XWM/X/kt1R7MmyZeBFw7Y9d6q+rdum/cCTwKXz1JdQ3h1VT2U5BDgS0m+2X03NO26ltze6uq9UVWVZKGlfS/unq+XAF9NcldV3b/Utc6xbcAVVfV4kj+i0/t5/ZRrmmW30XlOPZbkFOBzwJGTOHCSZwOfAd5TVT+axDH3WLZhUVVv3Nv+JGcBbwHeUN0Bvz4PAb3vsA7vbhtrXUPex0Pdfx9Jcg2doYZFhcUS1DXx85Xke0kOraqHu93tRxa4jz3n64EkO+i8K1vqsBjm8e9pszvJ/sAq4L+WuI6R66qq3ho+TmcuaBaM5Tm1WL0v0lW1Pck/JVldVWP9zqgkz6ATFJdX1WcHNBnr+VqRw1BJNgJ/DpxaVf+zQLObgSOTrE1yAJ0JybGtpBlWkl9JctCe63Qm6weu2piwaZyvrcA7u9ffCfxCDyjJc5Mc2L2+GjgRuHcMtQzz+Hvr/V3gqwu8UZloXX3j2qfSGQ+fBVuBd3RX+bwSeLRn2HFqkrxwz1xTkuPpvI6ONfS7x/sEsLOqPrRAs/Ger0nO6M/KBdhFZ2zv9u5lzwqVXwO297Q7hc6qg/vpDMeMu67fpjPO+DjwPeC6/rrorGq5o3u5Z1bqmtL5Ohj4CvAt4MvA87rbNwAf715/FXBX93zdBZwzxnp+4fEDf03nTQnAM4Gru8+/bwAvGfc5GrKuv+0+l+4ArgdeNqG6rgAeBn7afX6dA2wGNnf3B7ioW/dd7GWF4ITrOr/nfH0deNUEano1nbnKO3tet06Z5PnyE9ySpKYVOQwlSRqNYSFJajIsJElNhoUkqcmwkCQ1GRaSpCbDQpLUZFhIkpr+D63iltdkNMDiAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3035,7 +3060,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3052,7 +3077,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADV1JREFUeJzt3V+Ipfddx/H3p6vNheL6Z5dakowbmaW4FqEwJF54EbDSjelka6m625uqIUuKEQVBEiPkqhgQRGNTZTBLqsSEULXu0i1pLYZ40WqSIppkjS7Rkg2xsS2OomCI/Xoxp804zuyc2XPOPud85/26yZzfOXPm+2x2P/M73+f3PL9UFZKkvt4ydAGSpNky6CWpOYNekpoz6CWpOYNekpoz6CWpOYNekpoz6CWpOYNekpr7lqELADh06FAdOXJk6DIkaaE8++yzX6mqw7u9bi6C/siRIzzzzDNDlyFJCyXJl8Z5na0bSWpu0KBPsppkbX19fcgyJKm1QYO+qs5V1emDBw8OWYYktWbrRpKaM+glqTmDXpKaM+glqTmDXpKam4sLpqRvOHL3p7759T/ff+uAlUh9uI5ekpobdEZfVeeAcysrK3cMWYc0jzZ/utmJn3o0Dls3GsQ4LZqdgs5w255tL+3EoNdVM84MVdL0GfTSHPGXoWbBoNfCsUWxO/+MtJlBr5lyhioNz6DXQuswc/WXoWbNoJcGcDXDvcMvQ03GWyBIUnODzuiTrAKry8vLQ5YhLSzbPhqHV8aqDS+w2p1tnP3JHr2mbq+zTGel0mwZ9NJV4i80DcWgl/Yp2zj7h6tuJKk5Z/Rqb8iZq+0azQNn9JLUnDN6XTFnq33Yr+/NoJemzF+AmjcGvfYVZ67aj+zRS1JzBr0kNTf1oE/yA0l+L8knknx42u8vSdqbsXr0Sc4A7wVeq6p3bho/Dvw2cAD4/aq6v6ouAHcmeQvwB8DvTr9saXL267fnn0s/456MfRj4KBvBDUCSA8CDwI8Bl4Cnk5ytqheS3AZ8GPjD6ZYrzSdX2miejRX0VfVUkiNbhm8ELlbVSwBJHgNOAC9U1VngbJJPAX80vXI1NANNWjyTLK+8Fnh50+NLwE1JbgbeD1wDnN/pm5OcBk4DLC0tTVCGJOlypr6OvqqeBJ4c43VrwBrAyspKTbsOadb8dKNFMUnQvwJcv+nxdaOxsbmVoBaJwa5FNUnQPw0cTXIDGwF/EvjgXt7ArQQ1L9yGUJ2Nu7zyUeBm4FCSS8B9VfVQkruAJ9hYXnmmqp6fWaUajDPZ/cullj2Mu+rm1A7j57nMCdfd2LqRpNkb9BYIVXWuqk4fPHhwyDIkqTXvdSNJzQ0a9ElWk6ytr68PWYYktWbrRpKac+MR/T+ustF2XIGzuOzRS1Jz9uglqTl79JLUnD166TI8X6EO7NFLUnMGvSQ158lYSWrOk7GS1JwnYyXtmRdPLRaDXoCrS6TOPBkrSc15MlaSmvNkrCQ1Z+tGkpoz6CWpOVfd7GOutJH2B4Ne0kS2ThhcVz9/bN1IUnMur5Sk5lxeKUnN2bqRpOYMeklqzqCXpOYMeklqzqCXpOYMeklqzqCXpOYMeklqbtB73SRZBVaXl5eHLEPSFLmf7PzxylhJas7WjSQ1Z9BLUnMGvSQ1Z9BLUnMGvSQ151aC+4z7xEr7jzN6SWrOoJek5gx6SWrOoJek5gx6SWrOVTf7gCttNBRvcDYfZhL0Sd4H3Ap8B/BQVX1mFj9HkrS7sVs3Sc4keS3Jc1vGjyd5McnFJHcDVNUnq+oO4E7gp6dbsiRpL/bSo38YOL55IMkB4EHgFuAYcCrJsU0v+bXR85KkgYzduqmqp5Ic2TJ8I3Cxql4CSPIYcCLJBeB+4NNV9cXt3i/JaeA0wNLS0t4rl7RQ7NcPZ9JVN9cCL296fGk09gvAu4EPJLlzu2+sqrWqWqmqlcOHD09YhiRpJzM5GVtVDwAPzOK9JUl7M+mM/hXg+k2PrxuNjSXJapK19fX1CcuQJO1k0qB/Gjia5IYkbwVOAmfH/Wb3jJWk2Ru7dZPkUeBm4FCSS8B9VfVQkruAJ4ADwJmqen4mlWpPvEhK0jfsZdXNqR3GzwPnr+SHJ1kFVpeXl6/k27WF4S5pO4Pe68bWjSTNnjc1k6TmBg16V91I0uzZupGk5mzdSFJzBr0kNWePXpKas0cvSc25laCkq85bFl9d9uglqTl79JLUnD16SWrO1o0kNWfQS1JzBr0kNWfQS1JzrrqRpOZcdSNJzdm6kaTmvAXCgnOfWC06b4cwe87oJak5g16SmjPoJam5QXv0SVaB1eXl5SHLWDj25SXthcsrJak5WzeS1JxBL0nNGfSS1JxBL0nNGfSS1Jy3QJgDXgIuaZac0UtSc87oJc0NP93OhlfGzjH/0kuahkGDvqrOAedWVlbuGLIOSYvDCdDe2aOXpOYMeklqzpOxC8I7Vkq6Us7oJak5g16SmrN1I2ku2a6cHmf0ktScQS9JzRn0ktScQS9JzRn0ktTc1FfdJPl+4F7gYFV9YNrvv9U83vdiHmuSuvPf3c7GmtEnOZPktSTPbRk/nuTFJBeT3A1QVS9V1e2zKFaStHfjtm4eBo5vHkhyAHgQuAU4BpxKcmyq1UmSJjZW0FfVU8DXtgzfCFwczeBfBx4DTky5PknShCbp0V8LvLzp8SXgpiTfA3wEeFeSe6rq17f75iSngdMAS0tLE5QxH7yKT9K8mvrJ2Kr6KnDnGK9bA9YAVlZWatp1SJI2TLK88hXg+k2PrxuNSZLmyCQz+qeBo0luYCPgTwIf3MsbLMqesZMs23LJlzQ7tkzHM+7yykeBzwPvSHIpye1V9QZwF/AEcAF4vKqe38sPr6pzVXX64MGDe61bkjSmsWb0VXVqh/HzwPmpViRJmqpB70e/KK0bSYvL9unA97qxdSNJs+dNzSSpOVs3m4zzEW/WHwNdRSANq2Orx9aNJDVn60aSmjPoJam5fdGj79hzk6Rx2aOXpOZs3UhScwa9JDW3L3r0kvYXr0f5v+zRS1Jztm4kqTmDXpKaM+glqTmDXpKac9WNpH1pP10x76obSWrO1o0kNWfQS1JzBr0kNWfQS1JzBr0kNefyyoF40yVpfkzr3+PllmwOuZzT5ZWS1JytG0lqzqCXpOYMeklqzqCXpOYMeklqzqCXpOYMeklqzqCXpOa8MnYHXrkq9bPXf9c7vX6cK1sv97Ou9lWyXhkrSc3ZupGk5gx6SWrOoJek5gx6SWrOoJek5gx6SWrOoJek5gx6SWrOoJek5gx6SWrOoJek5qZ+U7Mk3wZ8DHgdeLKqHpn2z5AkjW+sGX2SM0leS/LclvHjSV5McjHJ3aPh9wOfqKo7gNumXK8kaY/Gbd08DBzfPJDkAPAgcAtwDDiV5BhwHfDy6GX/M50yJUlXaqygr6qngK9tGb4RuFhVL1XV68BjwAngEhthP/b7S5JmZ5Ie/bW8OXOHjYC/CXgA+GiSW4FzO31zktPAaYClpaUJytjeTjf9H3JDETczkXq42huHTGrqJ2Or6j+Bnx3jdWvAGsDKykpNuw5J0oZJWiuvANdvenzdaEySNEcmCfqngaNJbkjyVuAkcHYvb5BkNcna+vr6BGVIki5n3OWVjwKfB96R5FKS26vqDeAu4AngAvB4VT2/lx/unrGSNHtj9eir6tQO4+eB81f6w5OsAqvLy8tX+haSpF0MuvzRGb0kzZ7r3CWpOYNekpobNOhddSNJs5eq4a9VSvKvwJeGrmPKDgFfGbqIq8Dj7MXjXCzfV1WHd3vRXAR9R0meqaqVoeuYNY+zF4+zJ3v0ktScQS9JzRn0s7M2dAFXicfZi8fZkD16SWrOGb0kNWfQT1mS30jy90n+NsmfJvnOTc/dM9pf98Uk7xmyzkkl+ckkzyf5epKVLc+1OU7YcW/khbfdXtBJvjvJZ5P84+i/3zVkjZNKcn2Sv0jywujv6y+Oxlsd524M+un7LPDOqvoh4B+AewBG++meBH6Qjf13Pzbad3dRPcfGRvBPbR7sdpyX2Ru5g4fZshc0cDfwuao6Cnxu9HiRvQH8clUdA34Y+PnR/79ux3lZBv2UVdVnRrdwBvgCb+6fewJ4rKr+u6r+CbjIxr67C6mqLlTVi9s81eo42Xlv5IW3w17QJ4CPj77+OPC+q1rUlFXVq1X1xdHX/8HGLdWvpdlx7sagn62fAz49+nq7PXavveoVzV634+x2PLt5W1W9Ovr6X4C3DVnMNCU5ArwL+CsaH+d2pr5n7H6Q5M+B793mqXur6s9Gr7mXjY+Nj1zN2qZpnONUX1VVSVosy0vy7cAfA79UVf+e5JvPdTrOnRj0V6Cq3n2555P8DPBe4EfrzfWrC7fH7m7HuYOFO85ddDue3Xw5ydur6tUkbwdeG7qgSSX5VjZC/pGq+pPRcLvjvBxbN1OW5DjwK8BtVfVfm546C5xMck2SG4CjwF8PUeOMdTvOifdGXjBngQ+Nvv4QsNCf3LIxdX8IuFBVv7npqVbHuRsvmJqyJBeBa4Cvjoa+UFV3jp67l42+/RtsfIT89PbvMv+S/ATwO8Bh4N+Av6mq94yea3OcAEl+HPgt4ABwpqo+MnBJUzHaC/pmNu7k+GXgPuCTwOPAEht3lP2pqtp6wnZhJPkR4C+BvwO+Phr+VTb69G2OczcGvSQ1Z+tGkpoz6CWpOYNekpoz6CWpOYNekpoz6CWpOYNekpoz6CWpuf8FxGtbTRA+NtIAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3062,7 +3087,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3072,7 +3097,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3089,7 +3114,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3099,7 +3124,7 @@
     },
     {
      "data": {
-      "image/png": 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JPuB3gTdW1X+NPHUMOJjk0iS7gD3A5/uocUKLXvfU+yH37Bjwtu7+24C/7bGWH5AkwIeAU1X15yNPLXrdVzyz4i3JDwOvZ3h84TPAW7qXLVzdwJZbdfNR4CTwCHAc2F7PHk2/lWG/7R8YWSGyCDeGByvPAF/sbreNPHdTV/djwLV917qi7l9m2LP8LvBN4N7NUHdX3xsYrgb5GnBT3/VcpM67gCeB/+n+rQ8DLwX+Dvgq8GngJX3XuaLmn2HYlnlk5Dv9hk1Q908Af9/VfRJ4Tzf+CoYTldPAXwGX9l3ryptnxkpS47ZU60aStiKDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0vBTZ481aZ9EYAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3109,7 +3134,7 @@
     },
     {
      "data": {
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79D6+fpbrBn4I+Meu5qPAR7rxKxlMTk4AnwQu7rvWxW6eGStJjVu3rRtJWi8MeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGvf/A0g/7dBlq6QAAAAASUVORK5CYII=\n",
+      "image/png": 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fB97ad51Lav4Jhm2ZJ0d+j69e5LqBHwH+vqv5GPDRbvwShpOTE8BfAuf2XetyN8+MlaTGbdjWjSRtFAa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z/PLUZ/em/mogAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3126,7 +3151,7 @@
     },
     {
      "data": {
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\n",
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G1+n95W6WzGuB5ZGvjzOT5OKRzX3A57vb85JvD8OvildV1f+O3HU3sD/J+Ul2AhcDn5x1vmcxL/kmvkb0jNwNvK27/Tagl29QXf/5vcDxqvrTkbvmJd/Lnp7BluR7gZ9meNzhY8Bb+s5XVddX1fau3u0H/rWq3rqufH0eiZ7WP+BLPDN7J8BNDPtxn2Nk5seMM70POAZ8FjgMbJuzfCcZ9qQ/0/27eeS+G7p8J4DLe8r3cwz/mH8T+Cpwzzzl63JcwXAWyhcZtqR6yTGS53bgceD/uv93VzPs+/4L8AXgo8BLesr24wwPjH525DN3xRzl+xHg012+Y8A7uv2vZDioOAn8A3D+HLzPr+eZ2TtrzucZuZLUkIVs70iS1seiL0kNsehLUkMs+pLUEIu+JDXEoi9JDbHoS1JDLPqS1JD/B1nucFb8lpokAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3136,7 +3161,7 @@
     },
     {
      "data": {
-      "image/png": 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1B4EvrHffpq2qbquqLYPf9QPAX1TVB4CvAu8ZHDbz5z63I/o1OgG8g/6FyO8AH+62O1PxGeBy4MHBJ5rHqurmqjqV5BjwNP2Szkeq6rsd9nMqqupCklf2Qt4E3DUHeyFfD/wi8M0kfz1o+zjwSeBYkkP0V5d9b0f968LHgKNJfhP4Ov0/hDPLO2MlqXGWbiSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+x/IIfOJwV+OmQAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3146,7 +3171,7 @@
     },
     {
      "data": {
-      "image/png": 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u34eBO4EHk+wHvgm8q6f61tKHgMNJfgf4Ckv/ADbBO2MlqXG2biSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z8rGZ8QpQmjFgAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3163,7 +3188,7 @@
     },
     {
      "data": {
-      "image/png": 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BB1gJ/YW9cnBN7fcCFyc5DXwCuLO/ysZbkmN1XUtyrHbilbGS1LhFH9FLkiZk0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Lj/A1f36wkc+7O0AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3173,7 +3198,7 @@
     },
     {
      "data": {
-      "image/png": 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+/BPAPwLXb7D/A6yeqFpozkmzs1o8vzD8+TJW2wmXbpHs9wIfHP78FlZ7f+k7+5qM7wTuucj23wA+O/z5IKsnq3rP2zH724B/B/b2nXGz2dfsszTHaofvfCmP047ZJzpOFz2i/2ng/iTfYPXm4l+uqnuS/F6SGxacZbO6ZL8P+E6Sb7I6av7NqlqGkWWX7B8DbkrydeBOVn+ZlvZqujXZPw9cmuQM8FHg1v6Sjbcm+6eB1wF/l+RrSU70GG2sLXKsvswWOU4vahbHqVfGSlLjej/ZJkmaLwu9JDXOQi9JjbPQS1LjLPSS1DgLvSQ1zkIvSY2z0EtS4/4f6qwgcs3/WxMAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3183,7 +3208,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3200,7 +3225,7 @@
     },
     {
      "data": {
-      "image/png": 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jnwI+A5zcLO8At7XW+wLwPPAduuNXFzXLz6D7RlwF/hw4sVn+/c391ebxMyZU1681x1gFfrVnH4eAt/Qs+wO6J9O+QveX1FumVRfwg3RnAD3W1HATsGXW7UW395J0Q/zR5t/7R2kv4OeBf6U7O+KDzbLfAy7b6OelOxR1EHiS1syH9fa5idf7puoCPgR8q9U+j9I9yd/3OZ1SXVc0x32U7kn0Xa19duiG6EHgFpo/3JxGXc1j5wNf6tnftNrrZ+jm1LfofsJ4fKPMGEd7+ZexklTcPA/dSJLGwKCXpOIMekkqzqCXpOIMekkqzqCXpOIMekkqzqCXpOL+B+7T4/S9DXE4AAAAAElFTkSuQmCC\n",
+      "image/png": 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BhtprqmfdvFzVeOwTkj4t6eKyvCPpg7X1Pi/pWUnfUjV+dW1ZfrmqF+KqpD+XtLMs//5yfbXcfvmY6vqNch+rkn69ax/nJL2ma9kfqzqY9iVVb1KvmVRdkn5Q1QygR0sNfyppW9vtpar3EqpC/JHy7x2jtJekX5b0b6pmR7ynLPtDSTds9XhVDUWdlXRGtZkPG+1ziL/3oeqS9F5J36i1zyOqDvL3fE4nVNdN5X4fUXUQfbm2z46qED0r6f0qX9ycRF3ltv2SvtC1v0m118+pyqlvqPqEcXqrzGiivfhmLAAkN81DNwCABhD0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJDc/wK+sbZxXOCeVgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3210,7 +3235,7 @@
     },
     {
      "data": {
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9uirj8xNsr0H6vgYaaq+pnnXzEnXHY5+Q9GlJF5flHUl3VNb7gqTzkr6j7vjVNWX55eq+EFclfVzSjrL8h8v91fL45WOq67fLMVYl/VbPPs5IemXPsj9T92Tal9V9k3rlpOqS9KPqzgB6pNTwfknb2m4vdXsvoW6IP1z+vWOU9pL0a5L+Xd3ZEe8uy/5E0nWb/bzqDkWdlvS4KjMf+u1zC7/vW6pL0nskfavSPg+re5J/4HM6obpuKMd9WN2T6MuVfXbUDdHTkj6g8sXNSdRVHtsr6Ys9+5tUe/2Sujn1LXU/YZzaLDOaaC++GQsAyU3z0A0AoAEEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAk939EYr16dzE4hQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3220,7 +3245,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3237,7 +3262,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3247,7 +3272,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3257,7 +3282,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3274,7 +3299,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3284,7 +3309,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3294,7 +3319,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3311,7 +3336,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3321,7 +3346,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3331,7 +3356,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3348,7 +3373,7 @@
     },
     {
      "data": {
-      "image/png": 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XJD/G0sPWDSzdZB+rqt/rfv+PAu8E/hn4par637Eee5aDXpJ0bbNcupEk9WDQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuP8DdKok/MHNF7EAAAAASUVORK5CYII=\n",
+      "image/png": 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/PTXnZZXjmGPKzkuSH2bpZusGli6yj1bV7/R//o8Abwf+AfiFqvrvse57moNeknRt09y6kSQNwKCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/wOZnyCeXus9mwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3358,7 +3383,7 @@
     },
     {
      "data": {
-      "image/png": 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V/U739/8o8E7gn4BfqKr/Hem+pznoJUnXNs2tG0lSDwa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z9fRC5GZuZhOQAAAABJRU5ErkJggg==\n",
+      "image/png": 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Pso9W1e8N/v4fAd4F/BPwS1X1P2N971kOeknS9c1y60aS1IFBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4/4XhmchTzrc+rUAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3368,7 +3393,7 @@
     },
     {
      "data": {
-      "image/png": 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AfwR+qar+Z6znnueglyRd3zyXbiRJAzDoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8CWl4gkoHFUt4AAAAASUVORK5CYII=\n",
+      "image/png": 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LQS9Jur5ZLt1IkgZg0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Lj/BY7zKYsuSrqIAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3385,7 +3410,7 @@
     },
     {
      "data": {
-      "image/png": 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zMBgjPQA8S39M+VUmeIWsV8ZKUuNaGd1Iks7Dopekxln0ktQ4i16SGmfRS1LjLHpJapxFL0mNs+glqXH/D+21fEiMO6cMAAAAAElFTkSuQmCC\n",
+      "image/png": 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RS1LjLHpJapxFL0mNs+glqXEWvSQ1zqKXpMb9L20xeZSrDtZdAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3395,7 +3420,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3405,7 +3430,7 @@
     },
     {
      "data": {
-      "image/png": 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z6CWpcRa9JDXOopekxln0ktS4/wOhfphM2v3dWAAAAABJRU5ErkJggg==\n",
+      "image/png": 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NWfSS1JxFL0nNWfSS1JxFL0nN/R9uQpfzH995SQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3417,12 +3442,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      " \n",
+      "New QUAD 2\n",
+      " \n",
       "dphi\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+P8H26mfd10BN7dXqWTdvUHc89mlJn5J0XlnekXRnZb3PS3pB0kvqjl9dWZZfqO4LcUXSX0naUZa/plxfKbdfOKa6fq3cx4qkX+3ZxylJb+1Z9ifqHkz7krpvUm+dVF2SzlF3BtDjpYaPSNrWdHup23sJdUP8sfLvA6O0l6Sfl/Qf6s6O+GBZ9keSrt7s71V3KOqkpKdUmfmw3j638HzfUl2Sfk/SNyvt85i6B/n7PqYTquvacr+PqXsQfbGyz466IXpS0u0qX9ycRF3ltn2SvtCzv0m110+rm1PfVPcTxonNMqOO9uKbsQCQXJuHbgAANSDoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASC5/wdJzJrOukiq+AAAAABJRU5ErkJggg==\n",
+      "image/png": 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3l5vHzxpTXb/YPMcy8At9+zgKvKVv2R/SO5j2BXofUm+ZVF3AyfRmAD3a1PARYNu024te7yXphfgjzb/3b6W9gJ8C/o3e7IgPNst+H7h0o5+X3lDUEeBJWjMf1trnJn7fN1UX8NvA11rt8wi9g/wDX9MJ1XV587yP0DuIvre1zyV6IXoEuIHmxM1J1NU8dj7wub79Taq9fpReTn2N3jeMxzfKjC7ayzNjJam4WR66kSR1wKCXpOIMekkqzqCXpOIMekkqzqCXpOIMekkqzqCXpOL+F53HmyozLMhKAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3432,7 +3460,7 @@
     },
     {
      "data": {
-      "image/png": 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J71dlfH6M7dXPqq+Bmtqr1bNu3qjueOxTkj4j6aKyvCPprsp6X5D0nKQX1B2/urosv1TdF+KSpL+WtL0sf225vlRuv3REdf1muY8lSb/Rs4+zkt7es+zP1D2Y9jV136TePq66JF2g7gygx0oNH5O0pen2Urf3EuqG+KPl54ObaS9JvyzpP9SdHfHhsuxPJF233uNVdyjqjKQnVZn5sNo+N/D3vqG6JP2hpO9W2udRdQ/y931Ox1TXDeV+H1X3IPp8ZZ8ddUP0jKQ7VL64OY66ym37JH25Z3/jaq+fUzenvqvuJ4zT62VGHe3FN2MBILk2D90AAGpA0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcv8PqQCrGmvr71oAAAAASUVORK5CYII=\n",
+      "image/png": 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+P8H26mfd58CI2muqZ928gd547DPAp4AzmuVLwO2t9T4HHAdepDd+dVmz/Fx6T8RDwF8CpzbLX9NcP9Tcfu6Y6vq15hiHgF9ds48jwNvWLPsTeifTvkTvReptk6oLOI3eDKAnmhpuAbZ13V70ei9JL8Qfb/69fyvtBfwc8O/0Zkd8qFn2R8BVG91fekNRh4Gnac18WG+fm/h731RdwO8D32q1z+P0TvL3fUwnVNc1zXEfp3cSfbm1zyV6IXoYuJXmg5uTqKu57WLgC2v2N6n2+il6OfUteu8wntwoM0bRXn4yVpKKm+ahG0nSCBj0klScQS9JxRn0klScQS9JxRn0klScQS9JxRn0klTc/wHK/bP2V4d5aQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3442,7 +3470,7 @@
     },
     {
      "data": {
-      "image/png": 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AGc3yFeDm1nqfA54DXqAzfnVps/wcOi/ENeDPgVOb5d/d3F9rHj9nTHX9WnOMNeBXu/ZxFHhj17I/pnMy7Ut03qTeOKm6gNPozAD6clPDh4Ad024vOr2XpBPijzT/3jNMewE/C/wLndkR72+W/SFw+VY/L52hqCPAk7RmPmy2z238vm+rLuD3gG+22ucROif5ez6nE6rryua4j9A5ib7a2ucKnRA9AtxI88XNSdTVPHYh8IWu/U2qvX6STk59k84njMe2yoxRtJffjJWk4mZ56EaSNAIGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQV978sLbTa+JYlYAAAAABJRU5ErkJggg==\n",
+      "image/png": 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W6yr1vEzS21UZn59gew2z5jFQU3t1etbNq9Ufj31K0qclXVyW9yR9uLLeFyQ9J+kF9cevri3LL1X/QFyW9FeSdpTlLy/Xl8vtlzZU12+W+1iW9BsD+zgr6Q0Dy/5U/ZNpX1b/ReoNk6pL0oXqzwB6vNTw55K2td1e6vdeQv0Qf6z8e/dW2kvSL0v6V/VnR7ynLPtjSddv9HjVH4o6I+m0KjMf1trnJv7eN1WXpD+Q9K1K+zym/kn+oc/phOq6sdzvY+qfRF+s7LOnfoiekXSnygc3J1FXuW2/pC8O7G9S7fWz6ufUt9R/h3Fqo8yoo734ZCwAJNfloRsAQA0IegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBI7v8ATRyyD9cCF7gAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3459,7 +3487,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3469,7 +3497,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3479,7 +3507,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADXtJREFUeJzt3W2MXGUZxvHrEgQSIMtLERAoCylBql8km4JADBE1BSn1PVQTISIVDYl+0iYkmvhF0MREI4ZsgIAJ4UUUbaWEF4HwRZCWAAUKUgiENkhBklWiAZHbD3Nah2VnO7tzXu/5/5JNz8ycztzzzMw1zzznOec4IgQAyOt9TRcAAKgWQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJDc3k0XIElLliyJycnJpssAgE7ZvHnzaxFx2J7Wa0XQT05OatOmTU2XAQCdYvvFYdZj6AYAkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4Akqsk6G3vb3uT7XOruH8AwPCG2mHK9rWSzpW0MyI+0nf9Skk/l7SXpKsj4vLipu9LuqXkWtFxk+tu3738wuWfabASYLwM26O/TtLK/its7yXpSklnS1ouaY3t5bY/JekpSTtLrBMAsEhD9egj4gHbk7OuXiFpW0Q8L0m2b5K0WtIBkvZXL/z/bXtjRLxTWsUAFq3/V1U/fmHlNsqxbo6S9FLf5e2STomISyXJ9oWSXhsU8rbXSlorSUuXLh2hDHQRwzhAfSo7qFlEXLeH26clTUvS1NRUVFUHgD3jize3UYJ+h6Rj+i4fXVwH7DZoqADlGxTWvAYYJegflnSC7ePUC/jzJX2llKoAjGSUcKd3n8+w0ytvlHSmpCW2t0v6YURcY/tSSXeqN73y2oh4srJK0ZhhNuDRawTaa9hZN2sGXL9R0sbFPrjtVZJWLVu2bLF3gQYR7s3jNcAwGj3DVERskLRhamrq4ibrwLsRHu3AmDvK0opTCWK8DRNo4z5W3FS48xrkQNBDEr1EIDOCHq3CF0570bvvLoIeaBG+6FCFRoOeWTdAN9G77xZm3aATCBZg8TjDFAAkxxj9GGM8uB14HVA1gh6d1tUhHcIddSLo0TmEJLAwjNEDQHKNBr3tVbanZ2ZmmiwDAFJjeiXS6Op4PVA1xuiBmrBtAU1hjB4AkiPoASA5gh4AkiPoASA5gh4AkuMwxWNmXGZ+MNUS+D/m0QMYyezOA1+s7cPQDQAkR9ADQHIEPQAkxyEQgAqNy8ZvtBs9egBIjqAHgOQIegBIjhOPAEByjQZ9RGyIiLUTExNNlgEAqTF0AwDJEfQAkBzz6JEeBzjDuCPogZKxkxTahqEbAEiOoAeA5Ah6AEiOoAeA5Ah6AEiOQyAAQHIcAgEAkmPoBgCSY4cpAKViT+T2oUcPAMnRox8D7JIPjDd69ACQHEEPAMkR9ACQHGP0GCvMCME4okcPAMkR9ACQHEM3QAmYwoo2o0cPAMkR9ACQHIcpBoDkOEwxACTH0A0AJEfQA0ByBD0AJEfQA0ByBD0AJEfQA0ByBD0AJEfQA0ByBD0AJEfQA0ByBD0AJMfx6DG2Rj2tIMegR1fQoweA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOc8YCQHKcMxYAkmOHKUCj7zwFtBlBnxR7bQLYhY2xAJAcPXoAlWFIrB3o0QNAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcsyjB4bE3sboKnr0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAce8YC82BvWGRAjx4AkiPoASA5hm46iBMuV4vhmmrwvm0OPXoASI6gB4DkCHoASI6gB4DkSg962yfZvsr2rba/Vfb9AwAWZqigt32t7Z22n5h1/Urbz9jeZnudJEXE1oi4RNKXJZ1efsnoN7nu9t1/ADCXYXv010la2X+F7b0kXSnpbEnLJa2xvby47TxJt0vaWFqlAIBFGSroI+IBSa/PunqFpG0R8XxEvCXpJkmri/XXR8TZkr5aZrEAgIUbZYepoyS91Hd5u6RTbJ8p6fOS9tU8PXrbayWtlaSlS5eOUAYAYD6l7xkbEfdLun+I9aYlTUvS1NRUlF3HOGKcHsBcRpl1s0PSMX2Xjy6uAwC0yChB/7CkE2wfZ3sfSedLWl9OWQCAsgw7vfJGSX+WdKLt7bYvioi3JV0q6U5JWyXdEhFPVlcqAGAxhhqjj4g1A67fqBGmUNpeJWnVsmXLFnsXteMIfAC6ptFDIETEhohYOzEx0WQZAJAax7oBgOQIegBIjjNMAWg9to2Nhh49ACTXaNDbXmV7emZmpskyACA1Zt0AQHIM3QBAcgQ9ACRH0ANAcgQ9ACRH0ANAco3uMNXFg5pVgZ1BAFSJ6ZUAkByHQOhDzxqoH5+76jFGDwDJ0aMH0Bpt6N23oYay0aMHgOTS9ugzfisD46T/M4zRpA16AO1VZ4jT6WMefevwpgRQtkaDPiI2SNowNTV1cZN1lIGABnLr8md87IZuuvxiAcBijF3Q12GYLxM2NAGoC0FfI8IdQBOYRw8AyY1Fj37UnjQ9cQBdNhZBnwFfNgAWi6BvMcIdQBkaHaO3vcr29MzMTJNlAEBqnHgEAJJj1g0AJMcYPQBUYPY2tib3xKdHDwDJ0aMfAbNigPpVcbyq7J9lgh5AZ3GQwuEQ9BXL3lMA0H4EPQDUrO5fIgQ9AJSkrb/gxzro2/qiAECZOGcsgHToxL0b54wFgBF04UuFHaYAIDmCHgCSG+uNsQDyqHMIpQvDNf3o0QNAcgQ9ACRH0ANAcgQ9ACRH0ANAcsy6ATCWujZzZhSpgn6cXjgA1SszU5rMJ4ZuACA5gh4AkiPoASC5RoPe9irb0zMzM02WAQCpNRr0EbEhItZOTEw0WQYApJZq1k2ZmMEDIAvG6AEgOYIeAJIj6AEgOcboAYyNcd32Ro8eAJIj6AEgOYIeAJIj6AEgOYIeAJIj6AEgOYIeAJIj6AEgOYIeAJIj6AEgOYIeAJLr/LFuxvXYFQAwLHr0AJAc54wFgOQ4ZywAJMfQDQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHKOiKZrkO1XJb24yP++RNJrJZZTFupaGOpauLbWRl0LM0pdx0bEYXtaqRVBPwrbmyJiquk6ZqOuhaGuhWtrbdS1MHXUxdANACRH0ANAchmCfrrpAgagroWhroVra23UtTCV19X5MXoAwPwy9OgBAPPoXNDb/qntp20/bvs22wcNWG+l7Wdsb7O9roa6vmT7Sdvv2B64Bd32C7a32H7U9qYW1VV3ex1i+27bzxb/Hjxgvf8WbfWo7fUV1jPv87e9r+2bi9sfsj1ZVS0LrOtC26/2tdE3aqrrWts7bT8x4Hbb/kVR9+O2T25JXWfanulrrx/UUNMxtu+z/VTxWfzOHOtU214R0ak/SZ+WtHexfIWkK+ZYZy9Jz0k6XtI+kh6TtLziuk6SdKKk+yVNzbPeC5KW1Nhee6yrofb6iaR1xfK6uV7H4rY3amijPT5/Sd+WdFWxfL6km1tS14WSflnX+6nvcT8u6WRJTwy4/RxJd0iypFMlPdSSus6U9Mea2+pISScXywdK+uscr2Ol7dW5Hn1E3BURbxcXH5R09ByrrZC0LSKej4i3JN0kaXXFdW2NiGeqfIzFGLKu2turuP/ri+XrJX224sebzzDPv7/eWyWdZdstqKsREfGApNfnWWW1pF9Hz4OSDrJ9ZAvqql1EvBwRjxTL/5S0VdJRs1artL06F/SzfF29b8HZjpL0Ut/l7XpvwzYlJN1le7PttU0XU2iivQ6PiJeL5b9JOnzAevvZ3mT7QdtVfRkM8/x3r1N0NGYkHVpRPQupS5K+UPzcv9X2MRXXNKw2fwY/Zvsx23fY/nCdD1wM+X1U0kOzbqq0vVp5cnDb90g6Yo6bLouIPxTrXCbpbUk3tKmuIZwRETtsf0DS3bafLnohTddVuvnq6r8QEWF70PSvY4v2Ol7Svba3RMRzZdfaYRsk3RgRb9r+pnq/Oj7RcE1t9oh676k3bJ8j6feSTqjjgW0fIOm3kr4bEf+o4zF3aWXQR8Qn57vd9oWSzpV0VhQDXLPskNTfszm6uK7Suoa8jx3Fvztt36bez/ORgr6EumpvL9uv2D4yIl4ufqLuHHAfu9rredv3q9cbKjvoh3n+u9bZbntvSROS/l5yHQuuKyL6a7havW0fbVDJe2pU/QEbERtt/8r2koio9Bg4tt+vXsjfEBG/m2OVSturc0M3tldK+p6k8yLiXwNWe1jSCbaPs72PehvPKpuxMSzb+9s+cNeyehuW55wdULMm2mu9pAuK5QskveeXh+2Dbe9bLC+RdLqkpyqoZZjn31/vFyXdO6CTUWtds8Zxz1Nv/LcN1kv6WjGb5FRJM31DdY2xfcSubSu2V6iXgZV+YRePd42krRHxswGrVdtedW59LuNP0jb1xrIeLf52zYT4oKSNfeudo97W7efUG8Kouq7PqTeu9qakVyTdObsu9WZPPFb8PdmWuhpqr0Ml/UnSs5LukXRIcf2UpKuL5dMkbSnaa4ukiyqs5z3PX9KP1OtQSNJ+kn5TvP/+Iun4qttoyLp+XLyXHpN0n6QP1VTXjZJelvSf4v11kaRLJF1S3G5JVxZ1b9E8M9FqruvSvvZ6UNJpNdR0hnrb5h7vy61z6mwv9owFgOQ6N3QDAFgYgh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkvsfxsbkMmc6TpAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3496,7 +3524,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3506,7 +3534,7 @@
     },
     {
      "data": {
-      "image/png": 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lqXGWbiSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z/sr9jtjXYEGAAAAABJRU5ErkJggg==\n",
+      "image/png": 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GGfSS1DiDXpIaZ9BLUuP+HzH3yEQxNMF9AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3516,7 +3544,7 @@
     },
     {
      "data": {
-      "image/png": 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sJElNloUkqcmykCQ1WRaSpKb/B+cNbc7RwvdgAAAAAElFTkSuQmCC\n",
+      "image/png": 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lIUlqsiwkSU3/D+cyXrs7B0SxAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3533,7 +3561,7 @@
     },
     {
      "data": {
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9B7SSJC9gmOQfqKq/7oYXJn6Aqvp6ko8DP8KwFHxFN6tfmM/OXM/oLyXJjpHNPcDnu/tHgF/uum9eB5wf+Zk4F5LsAn4LuKWq/nvkoSPA3iRXJdkG7AA+1UeMa7AosU98reM5cAR4W3f/bcDf9RjLipIEeB9wsqr+eOShuY8/ycsvdMMl+W7gJobHGD4OvLXbbS5jv6i+jwav9cZwlnACeAI4Cmyu54+W382wnvaPjHSGzMuN4YHKM8Bnu9s9I4/d3sV+CnhD37FeJPafZVib/AbwFeDhRYl9JM43MuwA+SJwe9/xXCbWB4Fngf/t/t/3Ay8D/h74AvAx4KV9x7lC7D/GsCzzxMhn/Y2LED/wQ8A/dLGfAN7djb+K4QTmNPCXwFV9xzrOzTNjJalxC1u6kSSNx0QvSY0z0UtS40z0ktQ4E70kNc5EL0mNM9FLUuNM9JLUuP8D5q+onkde7kkAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3543,7 +3571,7 @@
     },
     {
      "data": {
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ReS8zuB7uLFyu9iTvBN4E/GT3IYcFqR0m+n8/amHqv4xlqXM1X03ysqp6smtNnuu7oNUkeS7DkL+7qv6iG16qn6GqvpHkk8CPMGwJP6eb1c/189P8jH41SXaO3N0LfLG7fRj4xW73zWuACyN/Hi6EJLuB3wDeXFX/NfLQYWBfkiuTbAd2Ap/po8Y1Wpb6p75m8gI4DLyju/0O4K97rGVVSQJ8EDhZVX848tDC/wxJXvLMrrgk3w28nuEawyeBt3VPm2/tfa9I9/nFcHZwAngUOAJsrmdXyW9n2Ef7B0Z2hSzKF8NFyjPA57qvO0Yeu7Wr/RRwQ9+1rlD/zzDsS34T+CrwwDLV39X5Boa7P74M3Np3PZep9R7gSeB/uv/vNwIvBv4W+BLwCeBFfde5Sv0/xrAt8+jIZ/4Ny/AzAD8E/H1X+wngPd34yxlOYk4DfwZcOa8aPDJWkhq3oVs3krQRGPSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXufwGCMKhKUDjcTwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3553,7 +3581,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3570,7 +3598,7 @@
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3580,7 +3608,7 @@
     },
     {
      "data": {
-      "image/png": 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3mj4GHE7y+8A36L8ANsFPxkpS42zdSFLjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhr3/0HO0DY4x52wAAAAAElFTkSuQmCC\n",
+      "image/png": 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1Pz3WOTVV9UqSg8DDwDrgcFWd7rmsLtwI/BLwrSR/PRz7CPBx4KEkt7O4s+x7e6rvSvowcCTJbwHfYPEPYBN8Z6wkNc7WjSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/wswHc8bqjYP6gAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3590,7 +3618,7 @@
     },
     {
      "data": {
-      "image/png": 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/AGyCV8ZKUuNs3UhS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa9391rY0kyui9NAAAAABJRU5ErkJggg==\n",
+      "image/png": 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XpMYZ9JLUOINekhpn0EtS4/4PQABzFv8FQE4AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3607,7 +3635,7 @@
     },
     {
      "data": {
-      "image/png": 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LSR7oK+wmXbIPUZfcjwDfSPIV1lfNv1ZVQ1hddsn+QeC2JF8C7mW99Af5ysFNue8CrkhyHvgAcKq/ZDtbkmN0S0twjE7EV8ZKUuOWYUUvSZqCRS9JjbPoJalxFr0kNc6il6TGWfSS1DiLXpIaZ9FLUuP+D8C04j6qQ8JBAAAAAElFTkSuQmCC\n",
+      "image/png": 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53pfkt5PcONrnC8Cbgb9K8u0kJ/oqdpEudQ9Vl9ofBF5M8iQLo+Zfr6ohjC671P5J4JYk3wHuZiH0B3nm4JK67wIuTXIW+ARwpL/K1jYDx+iKZuQ47cwzYyWpcbMwopckbYJBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4/4Pp/3zWUk8v+oAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3617,7 +3645,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3627,7 +3655,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3644,7 +3672,7 @@
     },
     {
      "data": {
-      "image/png": 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Jz6szfnV1WX6xOi/EVUl/JWlLWf6D5fpquf3iCdX16+U+ViX9Wtc+Tkp6fdeyP1HnYNqX1HmTev206pJ0rjozgJ4oNfyZpE1Nt5c6vZdQJ8QfL//eN057SXqbpH9XZ3bE+8uyP5J0bb+/V52hqBOSjqky82G9fY7wfB+pLkm/L+nblfZ5XJ2D/D0f0ynVdX2538fVOYi+UtnnsjohekLSHSpf3JxGXeW2nZI+37W/abXXz6uTU99W5xPG0X6ZUUd78c1YAEiuzUM3AIAaEPQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkNz/AtQeuTd+GiTSAAAAAElFTkSuQmCC\n",
+      "image/png": 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lum/ENUl/JWl7Wf6D5fpauf3KKdX1W+U+1iT9Zs8+zkl6fc+yP1X3YNqX1P0j9fpZ1SXpEnVnAD1eavhzSVuabi91ey+hbog/Vn7eNUl7SfpVSf+u7uyI95ZlfyzpxkGPV92hqLOSzqgy82GjfY7xeh+rLkl/KOnblfZ5TN2D/H2f0xnVdXO538fUPYi+UtlnR90QPSvpwypf3JxFXeW2/ZI+37O/WbXXL6ibU99W9xPG6UGZUUd78c1YAEiuzUM3AIAaEPQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkNz/AY4ltDJXF93oAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3654,7 +3682,7 @@
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3664,7 +3692,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3681,7 +3709,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3691,7 +3719,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3701,7 +3729,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3718,7 +3746,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3728,7 +3756,7 @@
     },
     {
      "data": {
-      "image/png": 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vAx+sqqeAzwC/DLwO2MXgbei4vbGqTmbwbaznJ3nz8ML2l85ELgvL4IOBbwf+tTUthfF6jkmOz2ySfBjYB3yhNe0CfqmqTgL+BPiXJC8bY0lL7nk7wLt57h8ZYx+vGfYNzxr3a+xIDIA5v25isSV5IYMn+AtV9RWAqnqsqp6pqp8B/8T/H7YYW71VtbP93A18tdXw2P5DO+3n7nHX1ZwJ3FZVj7UaJz5ezajjM7b6kvwB8DbgPW3HQTvE8nibvpXB8fVfaTUMHyZalLoO4Xkb53gtA34PuHKo3rGO10z7Bib4GjsSA2CiXzfRjjFeBtxbVZ8cah8+fv67wP4rFK4BzklydJLVwBoGJ58Wuq4XJ3np/mkGJxHvao+//yqCjcDVQ3Wd265EOBV4cuht6mJ4zl9mkx6vIaOOz/XA6UmWt8Mfp7e2BZVkPfAh4O1V9ZOh9hUZ/N8NkryKwfg80Gp7Ksmp7TV67tC2LGRdoz5v4/x9/S3ge1X17KGdcY7XbPsGJvkaO5yz2kv1xuDs+fcZpPmHx/zYb2TwFu5O4I52Owv4PPDd1n4NcMLQfT7car2Pw7zS4CB1vYrBFRbfAe7ePy7AK4AbgB3AvwPHtvYw+Oc997e61y3imL0YeBx4+VDb2MeLQQDtAv6XwXHV8w5lfBgck59ut/ctUl3TDI4D73+Nfbb1/f32/N4B3Ab8ztB61jHYId8P/APtg6ALXNfIz9tC/77OVFdr/xzwxwf0Hed4zbZvmNhrzE8CS1KnjsRDQJKkeTAAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnq1P8BhM6DVV3UMl0AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3738,7 +3766,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3755,7 +3783,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADGxJREFUeJzt3W+MZfVdx/H3R1ZaS9OpZteYsOBgoOhabW2m2ypRW6lmkX+JDxo21lht2LQRpE0T3bbRxGekNSqJxGTTIjElEEqpMmWV1j+1Twqy0NYCK7pBCktrdonpaOIfsuHrg3sHJws7e2d27pw7332/Hs09c7n3w+7dz/nd3/mdc1JVSJL6+q6hA0iSpsuil6TmLHpJas6il6TmLHpJas6il6TmLHpJas6il6TmLHpJam7b0AEAtm/fXvPz80PHkKQt5ZFHHnm+qnac7nkzUfTz8/McOnRo6BiStKUk+eYkzxt06ibJ1UkOLC0tDRlDklobtOirarGq9s3NzQ0ZQ5Ja82CsJDVn0UtScxa9JDXnwVhJas6DsZLUnFM3ktTcTJwwJc2q+f33T/S8p2++cspJpPVzRC9JzXkwVpKa82CsJDXn1I0kNWfRS1JzFr0kNWfRS1JzFr0kNefySklqbtAzY6tqEVhcWFi4fsgcOjtNetartNU5dSNJzVn0ktScRS9JzVn0ktScRS9Jzbm8UpKa8+qVktScUzeS1JxFL0nNWfSS1JxFL0nNWfSS1JxFL0nNWfSS1JxFL0nNeWasJDXnmbGS1JxTN5LUnEUvSc1Z9JLUnEUvSc1Z9JLU3LahA0gbbX7//UNHkGaKRS9tgEl2Lk/ffOUmJJFezqkbSWrOopek5rwEgiQ15yUQJKk5p24kqTmLXpKas+glqTmLXpKas+glqTnPjJU2yaSXZvAMWm00R/SS1JxFL0nNWfSS1JxFL0nNWfSS1JxFL0nNWfSS1JxFL0nNWfSS1Jw3HpGk5rzxiCQ159SNJDVn0UtScxa9JDXnZYq1pUx6qV9J/88RvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1N5WiT3JekkNJrprG60uSJjdR0Se5LcmxJI+dtH1PkieTHEmyf8Wvfhu4eyODSpLWZ9IR/e3AnpUbkpwD3ApcAewC9ibZleTngSeAYxuYU5K0TtsmeVJVfTnJ/EmbdwNHquopgCR3AdcCrwXOY1T+/53kYFW9uGGJJUlrMlHRn8L5wLMrHh8F3lZVNwAkeS/w/KlKPsk+YB/AhRdeeAYxJEmrmdqqm6q6vao+v8rvD1TVQlUt7NixY1oxJOmsdyZF/xxwwYrHO8fbJEkz5EyK/mHgkiQXJTkXuA64b2NiSZI2yqTLK+8EvgJcmuRokvdV1QngBuAB4DBwd1U9vpY3T3J1kgNLS0trzS1JmtCkq272nmL7QeDget+8qhaBxYWFhevX+xqSpNV5CQRJas6il6TmLHpJam7QovdgrCRN36BFX1WLVbVvbm5uyBiS1JpTN5LUnEUvSc1Z9JLUnEUvSc256kaSmnPVjSQ159SNJDVn0UtScxa9JDVn0UtSc666kaTmJrrxyLR44xHp5eb333/a5zx985WbkERdOHUjSc1Z9JLUnEUvSc1Z9JLU3KAHY6VlkxyAlLQ+juglqTnX0UtSc169UpKac+pGkpqz6CWpOYtekppzeeUWMOnSQ69/IumVWPTSFuTOX2th0UvadO6oNpdFf5bxH5jWy7OXty5PmJKk5jxhSpKac3mlJDVn0UtScx6MlTSzvH/uxnBEL0nNOaKX5NLJ5hzRS1JzjuglbWmeBHh6Fv2UnA0fvrPh/1HqwKLX1Dn/Kw3LOXpJam7QEX2Sq4GrL7744iFjtOHIWdIrGbToq2oRWFxYWLh+yBx6OXcaUh/O0UuNucMWOEcvSe1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc25jl7SWeFsvluVI3pJas6il6TmnLoZmKeoS5o2R/SS1Jwjekka63rXNEf0ktScNx5ZoeveXNLZbdARfVUtVtW+ubm5IWNIUmtO3UhScxa9JDVn0UtScxa9JDVn0UtScxa9JDVn0UtScxa9JDVn0UtSc17UbB28tLCkrcQRvSQ1Z9FLUnMWvSQ15xy9JK3RJMfpZuly5o7oJak5i16SmrPoJak5i16SmrPoJak5i16SmrPoJak5i16SmrPoJam5DT8zNsmPADcB24G/qao/2ej3kKRZN+lVbjfjDNqJRvRJbktyLMljJ23fk+TJJEeS7AeoqsNV9X7g3cBlGx9ZkrQWk07d3A7sWbkhyTnArcAVwC5gb5Jd499dA9wPHNywpJKkdZmo6Kvqy8C/n7R5N3Ckqp6qqheAu4Brx8+/r6quAH75VK+ZZF+SQ0kOHT9+fH3pJUmndSZz9OcDz654fBR4W5J3AL8EvIpVRvRVdQA4ALCwsFBnkEOStIoNPxhbVV8CvrTRrytJWp8zWV75HHDBisc7x9skSTPkTIr+YeCSJBclORe4DrhvY2JJkjbKpMsr7wS+Alya5GiS91XVCeAG4AHgMHB3VT2+ljdPcnWSA0tLS2vNLUma0ERz9FW19xTbD3IGSyirahFYXFhYuH69ryFJWp2XQJCk5rb8zcFn6TRjSZpFg47onaOXpOkbtOirarGq9s3NzQ0ZQ5Jac45ekpqz6CWpOYtekpqz6CWpOVfdSFJzrrqRpOacupGk5ix6SWrOopek5rb8tW4mNek1cSSpG1fdSFJzrrqRpOaco5ek5ix6SWrOopek5ix6SWrOopek5ix6SWrOdfSS1FyqaugMJDkOfHOd//l24PkNjLNRzLU25lqbWc0Fs5utY64frKodp3vSTBT9mUhyqKoWhs5xMnOtjbnWZlZzwexmO5tzOUcvSc1Z9JLUXIeiPzB0gFMw19qYa21mNRfMbrazNteWn6OXJK2uw4hekrSKFkWf5M1JHkzytSSHkuweOtOyJDcm+ackjyf5+NB5Vkry4SSVZPvQWQCSfGL8Z/WPST6X5PUD59mT5MkkR5LsHzLLsiQXJPm7JE+MP1M3DZ1ppSTnJPlqks8PnWVZktcnuWf82Tqc5CeHzgSQ5EPjv8PHktyZ5NXTeq8WRQ98HPi9qnoz8Lvjx4NL8k7gWuBNVfWjwO8PHOklSS4AfgF4ZugsK3wReGNV/Tjwz8BHhgqS5BzgVuAKYBewN8muofKscAL4cFXtAt4O/MaM5Fp2E3B46BAnuQX4q6r6YeBNzEC+JOcDvwksVNUbgXOA66b1fl2KvoDXjX+eA741YJaVPgDcXFX/C1BVxwbOs9IfAr/F6M9uJlTVF6rqxPjhg8DOAePsBo5U1VNV9QJwF6Od9qCq6ttV9ej45/9kVFrnD5tqJMlO4Ergk0NnWZZkDvgZ4FMAVfVCVX1n2FQv2QZ8T5JtwGuYYm91KfoPAp9I8iyjUfNgI8GTvAH46SQPJfn7JG8dOhBAkmuB56rq60NnWcWvA3854PufDzy74vFRZqRQlyWZB34CeGjYJC/5I0aDhxeHDrLCRcBx4E/HU0qfTHLe0KGq6jlGXfUM8G1gqaq+MK332zI3B0/y18APvMKvPgZcDnyoqj6b5N2M9t7vmoFc24DvY/QV+63A3Ul+qDZhqdNpcn2U0bTNplstV1X9xfg5H2M0RXHHZmbbSpK8Fvgs8MGq+o8ZyHMVcKyqHknyjqHzrLANeAtwY1U9lOQWYD/wO0OGSvK9jL4hXgR8B/hMkvdU1aen8X5bpuir6pTFneTPGM0NAnyGTfzqeJpcHwDuHRf7PyR5kdF1LY4PlSvJjzH6cH09CYymRx5Nsruq/m2oXCvyvRe4Crh8M3aIq3gOuGDF453jbYNL8t2MSv6Oqrp36DxjlwHXJPlF4NXA65J8uqreM3Cuo8DRqlr+1nMPo6If2ruAf62q4wBJ7gV+CphK0XeZuvkW8LPjn38O+JcBs6z058A7AZK8ATiXgS+qVFXfqKrvr6r5qppn9A/hLZtR8qeTZA+jr/7XVNV/DRznYeCSJBclOZfRgbL7Bs5ERnvnTwGHq+oPhs6zrKo+UlU7x5+p64C/nYGSZ/y5fjbJpeNNlwNPDBhp2TPA25O8Zvx3ejlTPEi8ZUb0p3E9cMv4oMb/APsGzrPsNuC2JI8BLwC/OvAoddb9MfAq4IvjbxsPVtX7hwhSVSeS3AA8wGhFxG1V9fgQWU5yGfArwDeSfG287aNVdXDATLPuRuCO8Q77KeDXBs7DeBrpHuBRRtOUX2WKZ8h6ZqwkNddl6kaSdAoWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ193/T7W8txQ3EOAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3765,7 +3793,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3775,7 +3803,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADX9JREFUeJzt3W+snvVdx/H3x05YwrKiAWNCi60pQyu6uJwVDdGwbGprKZjFaBs1TgkNRMhMlji26QOfVV1UlpEsDVQeSCDNZEi3Tjajkz1gSEHngIo2BKVE0xK0ajSShq8Pzm08wZ3T+y/XOV/er0e9fvd9rut75fR8znW+1+/+XakqJEl9fcvQBUiSFsugl6TmDHpJas6gl6TmDHpJas6gl6TmDHpJas6gl6TmDHpJau5tQxcAcNlll9W2bduGLkOSNpSnnnrqlaq6/ELvWxdBv23bNk6cODF0GZK0oST5h3HeZ+tGkpoz6CWpOYNekpoz6CWpubkHfZLrk3w1yWeSXD/v/UuSJjNW0Cc5kuRMkmfeML47yfNJTiW5czRcwH8AbwdOz7dcSdKkxr2ivw/YvXIgySbgbmAPsBM4kGQn8NWq2gN8FPjN+ZUqSZrGWEFfVY8Br75heBdwqqpeqKrXgAeBm6rq9dHr/wJcvNo+kxxMciLJibNnz05RuiRpHLN8YOoK4KUV26eBa5N8EPgJ4FLg06t9cVUdBg4DLC0t+eBarUvb7vzCRO9/8dDeBVUiTW/un4ytqoeAh+a9X0nSdGaZdfMysHXF9pbR2NiS7Ety+Ny5czOUIUlayyxB/yRwVZLtSS4C9gOPTLKDqjpWVQc3b948QxmSpLWMO73yAeBx4Ookp5PcXFXngduBR4GTwNGqenZxpUqSpjFWj76qDqwyfhw4Pu3Bk+wD9u3YsWPaXUiSLmDQJRBs3UjS4rnWjSQ1Z9BLUnODPmHKHr2GMOmHoKSNzh69JDVn60aSmlsXDweXZmErRlrboFf0LoEgSYtnj16SmrNHL0nNGfSS1JxBL0nNeTNWkprzZqwkNWfrRpKaM+glqTmDXpKacwkEaY4mXY7hxUN7F1SJ9H+cdSNJzTnrRpKas0cvSc0Z9JLUnEEvSc0Z9JLUnEEvSc05vVKSmnN6pSQ1Z+tGkpoz6CWpOYNekpoz6CWpOYNekpoz6CWpOYNekpoz6CWpOYNekppzCQRJam7QZ8ZW1THg2NLS0i1D1qH1ZdLnrkpam60bSWrOoJek5gx6SWrOoJek5gx6SWrOoJek5gx6SWrOoJek5gx6SWrOoJek5gx6SWrOoJek5gx6SWpuIUGf5JIkJ5LcsIj9S5LGN9YyxUmOADcAZ6rqmhXju4G7gE3APVV1aPTSR4Gjc65VamfSJZlfPLR3QZWos3Gv6O8Ddq8cSLIJuBvYA+wEDiTZmeTHgOeAM3OsU5I0pbGu6KvqsSTb3jC8CzhVVS8AJHkQuAl4B3AJy+H/X0mOV9Xrc6tYkjSRWZ4wdQXw0ort08C1VXU7QJIPAa+sFvJJDgIHAa688soZypAkrWVhs26q6r6q+vwarx+uqqWqWrr88ssXVYYkveXNEvQvA1tXbG8ZjUmS1pFZgv5J4Kok25NcBOwHHplkB0n2JTl87ty5GcqQJK1lrKBP8gDwOHB1ktNJbq6q88DtwKPASeBoVT07ycGr6lhVHdy8efOkdUuSxjTurJsDq4wfB47PtSJJ0lwNugSCrRtJWrxBg97WjSQtnouaSVJztm4kqTlbN5LUnK0bSWrOoJek5uzRS1Jz9uglqblZlimWxjbpk5QkzY89eklqzqCXpOa8GStJzQ3ao6+qY8CxpaWlW4asQ5Oz5y5tHLZuJKk5g16SmjPoJak5g16SmnPWjSQ15xIIktScrRtJas6gl6TmDHpJas6gl6TmDHpJas6gl6TmnEcvSc05j16SmvNRgtIGMsny0C8e2rvASrSRGPRSU5M+M8BfDH15M1aSmjPoJak5g16SmjPoJak5g16SmjPoJak5p1cKmHwqnqSNwyUQJKk5l0CQpObs0UtScwa9JDVn0EtScwa9JDVn0EtScwa9JDXnB6YkAa5f35lX9JLUnEEvSc0Z9JLUnEEvSc0Z9JLUnEEvSc3NPeiTfG+SzyT5bJLb5r1/SdJkxgr6JEeSnEnyzBvGdyd5PsmpJHcCVNXJqroV+BnguvmXLEmaxLhX9PcBu1cOJNkE3A3sAXYCB5LsHL12I/AF4PjcKpUkTWWsoK+qx4BX3zC8CzhVVS9U1WvAg8BNo/c/UlV7gJ9bbZ9JDiY5keTE2bNnp6teknRBsyyBcAXw0ort08C1Sa4HPghczBpX9FV1GDgMsLS0VDPUIUlaw9zXuqmqrwBfmfd+JUnTmWXWzcvA1hXbW0ZjY/Ph4JK0eLME/ZPAVUm2J7kI2A88MskOfDi4JC3euNMrHwAeB65OcjrJzVV1HrgdeBQ4CRytqmcXV6okaRpj9eir6sAq48eZYQplkn3Avh07dky7C0nSBQy6BIKtG0laPNe6kaTmDHpJam7QoHd6pSQt3qAPB6+qY8CxpaWlW4aso6tJH/YsqSdbN5LUnEEvSc3Zo5ek5pxHL0nNDXozVtLGNcnN/hcP7V1gJboQe/SS1JxBL0nNeTNWkprzZqwkNWfrRpKaM+glqTmDXpKaM+glqTln3UhSc866kaTmbN1IUnMGvSQ1Z9BLUnOuXilp4SZ9rKWrXc6XV/SS1JzTKyWpOadXSlJztm4kqTlvxg7IG1SS3gxe0UtScwa9JDVn62YDmbTVI0ngFb0ktWfQS1Jztm4krTvOSJsvr+glqTmXQJCk5lwCQZKas3UjSc0Z9JLUnEEvSc0Z9JLUnEEvSc0Z9JLUnEEvSc25BMIFTPJRbD+GLWk9MuglveW81S7gbN1IUnMGvSQ1Z+tGkuZoPS6xbNBL2vB8zObaFhL0SX4K2Au8E7i3qr60iONIki5s7KBPcgS4AThTVdesGN8N3AVsAu6pqkNV9TDwcJJvAz4JvCWC3qsKSevRJDdj7wN2rxxIsgm4G9gD7AQOJNm54i2/PnpdkjSQsYO+qh4DXn3D8C7gVFW9UFWvAQ8CN2XZbwFfrKqn51euJGlSs06vvAJ4acX26dHYHcAHgJ9Ocus3+8IkB5OcSHLi7NmzM5YhSVrNQm7GVtWngE9d4D2HgcMAS0tLtYg6JEmzB/3LwNYV21tGY5LUQodJFrO2bp4ErkqyPclFwH7gkXG/OMm+JIfPnTs3YxmSpNWMHfRJHgAeB65OcjrJzVV1HrgdeBQ4CRytqmfH3WdVHauqg5s3b560bknSmMZu3VTVgVXGjwPH51aRJGmuBl3UzNaNJC3eoEFv60aSFs9liiWpOVs3ktScrRtJas7WjSQ1Z9BLUnODPmEqyT5g344dO6bex3p8bJckrSeDBn1VHQOOLS0t3fJmHbPDuhWSNAlbN5LUnEEvSc0Z9JLUnB+YkqTm/MCUJDVn60aSmjPoJak5g16SmjPoJam5VNVwBx8tgQD8LPD3U+7mMuCVuRU1LM9l/elyHuC5rFeznMt3VdXlF3rToEE/D0lOVNXS0HXMg+ey/nQ5D/Bc1qs341xs3UhScwa9JDXXIegPD13AHHku60+X8wDPZb1a+Lls+B69JGltHa7oJUlraBP0Se5I8rdJnk3y20PXM6skH0lSSS4bupZpJPmd0ffjb5J8LsmlQ9c0qSS7kzyf5FSSO4euZ1pJtib58yTPjX4+Pjx0TbNIsinJXyX5/NC1zCLJpUk+O/o5OZnkhxd1rBZBn+R9wE3Au6vq+4BPDlzSTJJsBX4c+Meha5nBl4FrquoHgL8DPjZwPRNJsgm4G9gD7AQOJNk5bFVTOw98pKp2Aj8E/MoGPheADwMnhy5iDu4C/qSqvgd4Nws8pxZBD9wGHKqq/waoqjMD1zOr3wN+DdiwN1Cq6ktVdX60+TVgy5D1TGEXcKqqXqiq14AHWb6Y2HCq6p+q6unRv/+d5UC5YtiqppNkC7AXuGfoWmaRZDPwo8C9AFX1WlX966KO1yXo3wX8SJInkvxFkvcOXdC0ktwEvFxVXx+6ljn6ZeCLQxcxoSuAl1Zsn2aDhuNKSbYBPwg8MWwlU/t9li+CXh+6kBltB84CfzBqQ92T5JJFHWzQh4NPIsmfAt/5TV76BMvn8e0s/1n6XuBoku+udTql6ALn8nGW2zbr3lrnUVV/PHrPJ1huHdz/Ztam/y/JO4A/An61qv5t6HomleQG4ExVPZXk+qHrmdHbgPcAd1TVE0nuAu4EfmNRB9sQquoDq72W5DbgoVGw/2WS11leP+Lsm1XfJFY7lyTfz/Jv+q8ngeV2x9NJdlXVP7+JJY5lre8JQJIPATcA71+vv3TX8DKwdcX2ltHYhpTkW1kO+fur6qGh65nSdcCNSX4SeDvwziR/WFU/P3Bd0zgNnK6q//3L6rMsB/1CdGndPAy8DyDJu4CL2IALHlXVN6rqO6pqW1VtY/k/w3vWY8hfSJLdLP+JfWNV/efQ9UzhSeCqJNuTXATsBx4ZuKapZPmq4V7gZFX97tD1TKuqPlZVW0Y/G/uBP9ugIc/oZ/qlJFePht4PPLeo422YK/oLOAIcSfIM8BrwixvwCrKbTwMXA18e/XXytaq6ddiSxldV55PcDjwKbAKOVNWzA5c1reuAXwC+keSvR2Mfr6rjA9YkuAO4f3Qh8QLwS4s6kJ+MlaTmurRuJEmrMOglqTmDXpKaM+glqTmDXpKaM+glqTmDXpKaM+glqbn/ARfKFAZkQXJTAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3792,7 +3820,7 @@
     },
     {
      "data": {
-      "image/png": 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q+p+xfvcsB70k6cpmuXUjSerBoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXH/B3z08UKjQ2dyAAAAAElFTkSuQmCC\n",
+      "image/png": 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J0hAMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGvd/74b6a5cTZm4AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3802,7 +3830,7 @@
     },
     {
      "data": {
-      "image/png": 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3qpXkg8AzwDeBN7rh32Ghvz0112WZ89jDlF2XJD/GwsPWNSzcZD9RVb/f/f0/Arwb+CfgV6rqf0Z67GkOeknSzU1z60aS1AeDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0f1EgLNg+Q1H0AAAAASUVORK5CYII=\n",
+      "image/png": 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2sdAeKOAy8MuLetxrVpIPAs8A3wDe6A7/Fgv97Ym5LiucxwwTdl2S/BALi63rWJhkH6+q3+3++T8GvBv4B+CXquq/h/rekxz0kqRbm+TWjSSpBwa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+1/PbxM5Z9juMgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3812,7 +3840,7 @@
     },
     {
      "data": {
-      "image/png": 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GktSBQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+D223QnqDMf0bAAAAAElFTkSuQmCC\n",
+      "image/png": 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CVf3Jspem6r6s1o9pvC9Jbkvyhu7n72RpIskFlhL++7rTxnJPpnbWDUA3pepPgU3Asar6/QmHtC5J3srSKB6Wtnf8y2nqS7en8F0srcL3deB3gc8CJ4EZllYmvbuqNvSDzlX6cRdL5YECLgG/vKzGvWEleSfwReCrwCtd82+xVN+emvuyRj/mmbL7kuSHWHrYuomlQfbJqvq97vf/BPAm4B+AX6yq/xnptac50UuSrm+aSzeSpB5M9JLUOBO9JDXORC9JjTPRS1LjTPSS1DgTvSQ1zkQvSY37X7OQmLPBhS0QAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3829,7 +3857,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3839,7 +3867,7 @@
     },
     {
      "data": {
-      "image/png": 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uQmCC\n",
+      "image/png": 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Z9JLUOItekhpn0UtS4yx6SWqcRS9Jjft/b2VDySd4E6cAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3849,7 +3877,7 @@
     },
     {
      "data": {
-      "image/png": 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K/pjyi0zwE7J+MlaSGtfK6EaStACLXpIaZ9FLUuMseklqnEUvSY2z6CWpcRa9JDXOopekxv0/IjW24MCQbVkAAAAASUVORK5CYII=\n",
+      "image/png": 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w7P1Vta/DTNPuduDewQ/sI8CvdZyHwRjpQeBJ+mPKLzHBT8j6yVhJalwroxtJ0llY9JLUOItekhpn0UtS4yx6SWqcRS9JjbPoJalxFr0kNe7/ALb2vB7VSoV6AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3861,12 +3889,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      " \n",
+      "New QUAD 3\n",
+      " \n",
       "dphi\n"
      ]
     },
     {
      "data": {
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+      "image/png": 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JZ5blLy1fr5f7L5xSXb9VHmNd0m/27eO4pDf0LfszVSfTHlH1Q+oNs6pL0lmqZgA9Wmr4C0k72j5eqkYvoSrEHy7/3jPJ8ZL0dkn/rmp2xPvKsj+RdNV2z1dVK+qYpCdUm/mw1T7H+H4fqy5JfyjpO7Xj87Cqk/wDX9MZ1XVNedyHVZ1EX6nts6cqRI9J+pDKBzdnUVe57xJJX+zb36yO18+pyqnvqPoN4+h2mdHE8eKTsQCQXJdbNwCABhD0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJDc/wOBIGodWflypgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3876,7 +3907,7 @@
     },
     {
      "data": {
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\n",
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+g6+LtcusfbwbrbvZNqdZF0kuBP4SeFVVPXh2hQ2e06nX1X1TfbS7/5NJHgSe1S0/OPw28/3VOcya3vyM9tdG6x5as+6dTGZ/LfTQzTHg7JHnVwN/PeqK3YvsI8DZo9qD6w9u9xXA360ZPplEXXcAL0pyfvqzTF7UtZ11DWteZF0InvVS4DNj1LStupKck2RfV8d3AS8BzvZ05rq/kvwB/Tfpbw2usMX9dRdwSfozss6l/2Y/tkG9g4/3GHA4/dkcB4BL6B8kG2WbU6urG9K6jf4B7384u/Amz+ks6npakj3d/V9Mf3+d6obx/ivJ87qhkVcxxnt7u3V19TwO+AUGxudnuL+GWfc9MKH9tdCzbp5Kfzz2AeBDwFO69h7wjoHlPgZ8Cfgm/fGrK7v2i+m/EVeAPwfO69of311f6W6/eEp1/Wp3HyvAr6zZxing2Wva/pD+wbRP0f+Qevas6gKeSH8G0Ke7Gt4K7Jn3/qLfeyn6IX539++129lfwM8Cn6M/O+INXdvvAy/d7PHSH4p6ELifgZkP621zC6/3LdUF/C7w9YH9czf9g/xDn9MZ1fXy7n7vpn8QfXlgmz36IfogcD3dDzdnUVd32yHg42u2N6v99ZP0c+rr9L9h3LtZZkxif/nLWElq3CIP3UiSJsCgl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcf8Le2gsu8ZlCQ4AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3886,7 +3917,7 @@
     },
     {
      "data": {
-      "image/png": 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1etbNa9Udj31C0mckXVyWdyR9vLLe5yU9I+k5dcevrinLL1P3jbgs6S8lbSvLX1nuL5fHLxtTXb9SnmNZ0i/37OOspLf0LPtjdQ+mfUXdD6m3TKouSReqOwPo0VLDn0ra0nR7qdt7CXVD/JHy9f5R2kvSuyT9q7qzIz5Qlv2hpOs2+nnVHYo6I+m0KjMf1trnJn7fN1WXpN+T9O1K+zyi7kH+vq/phOq6oTzvI+oeRF+s7LOjboiekfRRlRM3J1FXeWyvpC/27G9S7fUT6ubUt9X9C+PURplRR3txZiwAJNfmoRsAQA0IegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBI7v8A1XPaen4R2JAAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3903,7 +3934,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3913,7 +3944,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3923,7 +3954,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADYdJREFUeJzt3V2MXHUZx/HfTxBIgCxCKyBQFrINUq8kE0AghoiaUlzqe+iNEJFKTBO80iYkxngjaOKFETUNEjBBXkTRLpTwXrkBpCVAgVIppIQ2SEGTVaJBkceLOcVxu7Od2ZnzMs98P8mm83K688x/Z3/z3+f85xxHhAAAeb2v7gIAAOUi6AEgOYIeAJIj6AEgOYIeAJIj6AEgOYIeAJIj6AEgOYIeAJI7uO4CJGnJkiUxOTlZdxkAMFK2bt36ZkQsPdB2jQj6yclJbdmype4yAGCk2H6ll+1o3QBAcgQ9ACRH0ANAcgQ9ACRH0ANAcrUGve1p2xtmZ2frLAMAUqs16CNiJiLWTkxM1FkGAKRG6wYAkmvEB6YAVGNy/d3vXd51zUU1VoIqEfRAQp2BDhD0ALq+MTDrz4Ggx1D02xKghVA/Zv3jg6DHonULim4hTrAA9SDocUCDzL4J9+ow1uiGoMd+FgoMwqRZ+HmgFwQ9gK7Yl5IDQY9GIVgOjFk8+kXQo3YE14ExRhgEQQ9JBEkTNe1nwl9bo4ugR2ONY7A0LdyRAwc1A4DkCHoASI6gB4DkCHoASI6dsRgJ47hjFhiWWoPe9rSk6ampqTrLwIjJFvqstEHZag36iJiRNNNqta6osw4A/cn2ZpsdPXoASI4e/RijZQCMB4IeI21UWwi8yaJKtG4AIDmCHgCSo3UDVIR2DerCjB4AkiPoASA5WjdIY1RX4ABlY0YPAMkR9ACQHEEPAMnRox8zLPHDsM19TbF/pHmY0QNAcgQ9ACRH6wYoEa0yNAFBj5RYUw/8D60bAEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5Ah6AEiOoAeA5Eo5qJntwyX9QdJ3I+KuMh4DaCqOWImm6WlGb/sG23ttPzvn9pW2d9jeaXt9x13flnT7MAsFACxOr62bGyWt7LzB9kGSrpN0oaQVktbYXmH7U5Kel7R3iHUCABapp9ZNRDxie3LOzWdK2hkRL0uS7VslrZZ0hKTD1Q7/f9reFBHvDq1i9I1WAjDeBunRnyDp1Y7ruyWdFRHrJMn2ZZLe7BbyttdKWitJy5YtG6AMYGGchKRajHfzlLbqJiJuXGhHbERsiIhWRLSWLl1aVhkAMPYGCfo9kk7quH5icRsAoEEGCfonJC23fYrtQyRdImnjcMoCAAxLr8srb5H0qKTTbO+2fXlEvCNpnaR7JW2XdHtEPFdeqQCAxeh11c2aLrdvkrRpqBUBAIaq1kMg2J62vWF2drbOMgAgtVqDPiJmImLtxMREnWUAQGoc1AwAkiPoASA5gh4AkivlMMVAU/HxfIwjVt0AQHK1zugjYkbSTKvVuqLOOoBBcYRQNBk9egBIjqAHgOQIegBIjqAHgORYdQMAyXGsGwBIjtYNACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcqyjB4DkOHolgNJw/P9moHUDAMlxhimMLWabGBcEfVKcCKN8jDFGBa0bAEiOoAeA5Ah6AEiOoAeA5PjAFAAkx4lHACA5WjcAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJ1XriEdvTkqanpqbqLAPgbFNIjWPdAEBynEoQ6BGnDsSookcPAMkR9ACQHEEPAMnRowdQCVY21YcZPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIsrwQWwGEPkAFHrwTmINyRDUevBIDkaN0kwkwUwHzYGQsAyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyRH0AJAcQQ8AyXGsmxHH8W0AHAgzegBIjqAHgORo3QCoXGfLcdc1F9VYyXhgRg8AyRH0AJAcQQ8AydUa9LanbW+YnZ2tswwASI2TgwNAcrRuACA5gh4AkmMd/QjisAcA+sGMHgCSI+gBIDlaNyOCdg2AxWJGDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJsY4eQGNwisFyEPQAasWHActH6wYAkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkmMdPYBG4sNTw0PQNxgfJAEwDLRuACA5gh4AkqN1U5Nu/UfaNcD+6NcPhqAfEl6IAJqKoAcwUphU9W/oQW/7dElXSVoi6cGI+NmwH6MpaLMAGAU97Yy1fYPtvbafnXP7Sts7bO+0vV6SImJ7RFwp6cuSzh1+yQCAfvS66uZGSSs7b7B9kKTrJF0oaYWkNbZXFPddLOluSZuGVikAYFF6at1ExCO2J+fcfKaknRHxsiTZvlXSaknPR8RGSRtt3y3pV8Mrd7TR6gHKQ+++u0F69CdIerXj+m5JZ9k+X9LnJR2qBWb0ttdKWitJy5YtG6CM5uk30HkDAFCmoe+MjYjNkjb3sN0GSRskqdVqxbDrADC+mN3/v0E+GbtH0kkd108sbgMANMggQf+EpOW2T7F9iKRLJG0cTlkAgGHpdXnlLZIelXSa7d22L4+IdyStk3SvpO2Sbo+I58orFQCwGL2uulnT5fZNGmAJpe1pSdNTU1OL/RaVoN8HNBMLGXpT6yEQImJG0kyr1bqizjoA5MVEjWPddMVMAUAWqYKed24Ai5E9OzjxCAAkl2pGXwVaOkAO4/S7XGvQj8qqGwDjKUtLJ+2qmyw/IAAYFK0bAOhBL+d5buqkciyCvtcfxDj17ADUp+o3h5EPesIZQK/GNS9YXgkAyY38jB4AmqKp/fpaZ/S2p21vmJ2drbMMAEgt7fLKXoxrvw7AeBm71g3hDmAhGTOCnbEAkBxBDwDJEfQAkBxBDwDJcfRKAKhAnTt5x3p5JQAsxqitzKF1AwDJEfQAkNzYfWAKAKrQpPYOM3oASI6gB4DkCHoASI6gB4DkCHoASI4TjwBAcrUGfUTMRMTaiYmJOssAgNRo3QBAcgQ9ACTniKi7Btl+Q9Iri/zvSyS9OcRyhoW6+kNd/WlqXVJza8tY18kRsfRAGzUi6Adhe0tEtOquYy7q6g919aepdUnNrW2c66J1AwDJEfQAkFyGoN9QdwFdUFd/qKs/Ta1Lam5tY1vXyPfoAQALyzCjBwAsYOSC3vYPbb9g+xnbd9o+qst2K23vsL3T9voK6vqS7edsv2u76x5027tsb7P9lO0tDaqr6vE62vb9tl8s/v1Al+3+U4zVU7Y3lljPgs/f9qG2byvuf9z2ZFm19FnXZbbf6Bijr1VU1w2299p+tsv9tv3jou5nbJ/RkLrOtz3bMV7fqaCmk2w/bPv54nfxqnm2KXe8ImKkviR9WtLBxeVrJV07zzYHSXpJ0qmSDpH0tKQVJdd1uqTTJG2W1Fpgu12SllQ4Xgesq6bx+oGk9cXl9fP9HIv73qpgjA74/CV9Q9LPi8uXSLqtIXVdJuknVb2eOh7345LOkPRsl/tXSbpHkiWdLenxhtR1vqS7Kh6r4yWdUVw+UtKf5vk5ljpeIzejj4j7IuKd4upjkk6cZ7MzJe2MiJcj4l+SbpW0uuS6tkfEjjIfYzF6rKvy8Sq+/03F5Zskfbbkx1tIL8+/s947JF1g2w2oqxYR8Yikvy6wyWpJv4y2xyQdZfv4BtRVuYh4LSKeLC7/XdJ2SSfM2azU8Rq5oJ/jq2q/C851gqRXO67v1v4DW5eQdJ/trbbX1l1MoY7xOjYiXisu/1nSsV22O8z2FtuP2S7rzaCX5//eNsVEY1bSMSXV009dkvSF4s/9O2yfVHJNvWry7+DHbD9t+x7bH6nygYuW30clPT7nrlLHq5EnB7f9gKTj5rnr6oj4fbHN1ZLekXRzk+rqwXkRscf2ByXdb/uFYhZSd11Dt1BdnVciImx3W/51cjFep0p6yPa2iHhp2LWOsBlJt0TE27a/rvZfHZ+ouaYme1Lt19RbtldJ+p2k5VU8sO0jJP1G0jcj4m9VPOY+jQz6iPjkQvfbvkzSZyRdEEWDa449kjpnNicWt5VaV4/fY0/x717bd6r95/lAQT+EuiofL9uv2z4+Il4r/kTd2+V77Buvl21vVns2NOyg7+X579tmt+2DJU1I+suQ6+i7rojorOF6tfd9NEEpr6lBdQZsRGyy/VPbSyKi1GPg2H6/2iF/c0T8dp5NSh2vkWvd2F4p6VuSLo6If3TZ7AlJy22fYvsQtXeelbZio1e2D7d95L7Lau9Ynnd1QMXqGK+Nki4tLl8qab+/PGx/wPahxeUlks6V9HwJtfTy/Dvr/aKkh7pMMiqta04f92K1+79NsFHSV4rVJGdLmu1o1dXG9nH79q3YPlPtDCz1Dbt4vF9I2h4RP+qyWbnjVeXe52F8Sdqpdi/rqeJr30qID0na1LHdKrX3br+kdguj7Lo+p3Zf7W1Jr0u6d25daq+eeLr4eq4pddU0XsdIelDSi5IekHR0cXtL0vXF5XMkbSvGa5uky0usZ7/nL+l7ak8oJOkwSb8uXn9/lHRq2WPUY13fL15LT0t6WNKHK6rrFkmvSfp38fq6XNKVkq4s7rek64q6t2mBlWgV17WuY7wek3ROBTWdp/a+uWc6cmtVlePFJ2MBILmRa90AAPpD0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcv8FqfXdJzCLJ7cAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3940,7 +3971,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3950,7 +3981,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3958,6 +3989,14 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/ticker.py:2198: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
+      "  \"Data has no positive values, and therefore cannot be \"\n"
+     ]
+    },
     {
      "data": {
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@@ -3977,7 +4016,7 @@
     },
     {
      "data": {
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CA8A7u90Gk3dZfZ8NHtKDxVnGKeARYA7YXt87834bi725f2JsxUjPeedZ7CF/qXvcPvbazV3eJ4Dr+s7aZfpFFvuZ3wG+Cdw35LxdrrewuDLka8DNfed5kXx3Ac8A/9P9294AvBr4DPBV4NPAq/rOOZb3p1lsyzwydty+ZaiZgR8H/rHLewr4QDd+FYuTkXngr4CL+8660sNvxkpS42zdSFLjLPSS1DgLvSQ1zkIvSY2z0EtS4yz0ktQ4C70kNc5CL0mN+z/2Z2EHL9iaiAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3987,7 +4026,7 @@
     },
     {
      "data": {
-      "image/png": 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vSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Lj/g/wCK9q1iPGQAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3997,7 +4036,7 @@
     },
     {
      "data": {
-      "image/png": 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ggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4014,7 +4053,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4024,7 +4063,7 @@
     },
     {
      "data": {
-      "image/png": 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g/ZD/cFV9bLD7X5I8v6q+NmhBXuiuwol7KfDqJK8Eng48C3gv/Xbr0wZn9U39vG3dbEBVfaWqfriq9lbVXvof715cVV+nv17uLwxG37wEWB366DvXkhyk/zH31VX17aGHTgJHklydZB/9C9F/3UWNUzD2msjzYNCbvgN4pKp+f+ihk8AbB/9+I/CJra5tWqrq1qraNfgbPgL8ZVW9Afgc8LrB05o6Zs/oJ+c08Er6FyS/Dfxit+VM1PuBq4HPDD7JfL6qbqqqs0nuBh6m39J5a1X9T4d1TkxVPZnkqTWRtwHHG10T+aXAzwNfSfI3g33vBG4D7k5yI/2ZZV/fUX1b6R3AiSS/CXyJ/htgE7wzVpIaZ+tGkhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Lj/BYek/ch4f+TtAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4034,7 +4073,7 @@
     },
     {
      "data": {
-      "image/png": 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tm1Woqi9X1Q9X1UJVLdD/ePfKqvoq/ZU7f2kw++bVwMWhj75zLck++h9z31hV3xp66CRwMMmVSXbRPxH9t13UOAVjr846Dwa96VuBx6rqD4YeOgm8bfD124BPr3dt01JVN1fV9sHv8EHgr6vqF4DPAW8ZPK2pY3ZEPzmngdfTPyH5LeCXuy1noj4MXAncM/gk80BV3VBVZ5LcATxKv6Xzjqr63w7rnJiqeibJEeBuvrc665mOy5qG1wC/CHw5yd8N9r0LeC9wR5Lr6K8s+9aO6ltPNwEnkvwO8EX6fwCb4JWxktQ4WzeS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0fjyH1n5DQcBoAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4051,7 +4090,7 @@
     },
     {
      "data": {
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v23we+E6Xxxr0KzTse38M+EhVfbzvei5m+Fb+08C2vmtZ4NXAzcMe+N3Azyf5y35LOt9wpEdVnQI+wXx/dJrMAXML3q3dy3zwT6PtwGNV9Z99F3IBrwf+rapOV9W3gY8DP9NzTeepqn1V9ZNV9Rrgv4F/6fI4g34FhhOd+4DjVfUnfddzIUlemuQlw+9fCLwB+FK/VX1fVb2rqjZU1Sbm387/XVVN1QgqydrhZDvDdsgvMP/2eWpU1X8ATye5YXjodcDULApYZCdT2LYZ+grwU0muGv59v475ubepkuSHh//dyHx//qNdHtdpK8HLabg/7WuBa5LMAe+uqn39VnWeVwO/Dnxh2AMH+P3h1orT4mXAh4arHJ7H/J6+U7mEcYr9CPCJ+b97rgA+WlWf6rekC3o78JFha+RJ4K0913Oe4QvlG4Df7LuWC6mqR5LcCzwGPAt8jun8lOzHklwNfBt4W9eJ96lbXilJGi9bN5LUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG/T8wM3RPybgx4QAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4061,7 +4100,7 @@
     },
     {
      "data": {
-      "image/png": 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OWcn3bbtxrfJ79oqq+nfg08CxLd/61nuW5FXAXuBri61u58aNaxbZuKig33Z/2S39zzsY9hdbcA74pdEqjrcDV6vqK30XNQtJvveVHmiSowz/PS39f6xRzQ8Cz1TVH485beXety7jWuH37KYkrxt9/V3AO4F/2nLaOeCXR1+/B/ibGl3NXFZdxjWLbOy0leC0qurlJK/sL7sHOFtVF5L8IbBRVeeAX09yB/AywwuAdy6itmlluJ/ubcC+JJeA9zO8oEJVPcBwq8V3AReB/wbe20+lk+swtvcA70vyMvAN4OSy/8caeQfDO6b+46g3CvA7wEFY6fety7hW9T17A/DhJHsY/nB6tKo+viVDHgT+IslFhhlysr9yO+syrqmz0U/GSlLjlulirCRpDgx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa938FHYUffxHu3gAAAABJRU5ErkJggg==\n",
+      "image/png": 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UtCT5wVfrp0kOs/3vamn+Yw1jvxt4uqr+dI+3Le0x7NK+ZT6GSa5I8ubh4+8H3gv88663nQZ+Zfj4Q8Df1nCkdNF1ad+u8aeb2B7XeU2dlnHUa8v2Gsc3AJcn2QQ+xvagDFX1CbaXunwfcB74L+Aj/UQ6ng7t+xDw0SSvAN8Gji/Lf6yhdwO/BPzTsK4K8LvAGqzEMezSvmU+hm8FPpVkH9snrQeq6qEkfwicq6rTbJ/8PpPkPNuTF473F+7IurTvN5LcBLzCdvtuvthO/YWvJDXIso8kNcjkL0kNMvlLUoNM/pLUIJO/JDXI5C9JDTL5S1KDTP6S1KD/BfGNd8K7LxPCAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4071,7 +4110,7 @@
     },
     {
      "data": {
-      "image/png": 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rgI8n2cTyL5ijVfVEkj8ElqrqGMu/qP4qyWmWJ+UPrFNsXeP79SS3AxeG8d25jvFd1ByN31qxzd3YXYzfjJWkxi1S60aSFpKJXpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGmeil6TGmeglqXH/D/pLXKY2WZLkAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4088,7 +4127,7 @@
     },
     {
      "data": {
-      "image/png": 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VvRAXJf2NpC1l+Q+W64vl9ovHVNdvlftYlPSbffs4LekNfcv+XNXJtC+p+iP1hknVJek8VTOAHis1fEDSpraPl6rRS6gK8UfLv3dv5HhJeruk/1A1O+K9ZdmfSrpmrcerqhV1StKTqs18WGmf6/h9X1ddkv5I0jdrx+dRVSf5Bz6nE6rrunK/j6o6ib5Q22dPVYiekvQhlQ9uTqKuctseSV/o29+kjtcvqMqpb6p6h3Fyrcxo4njxyVgASK7LrRsAQAMIegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBI7v8BkA6cEMhsZ1MAAAAASUVORK5CYII=\n",
+      "image/png": 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vrirLL1H/jbgs6a8kbS/Lv7/cXy6PX9JQXb9RnmNZ0q8P7OO0pDcOLPsz9U+mfVn9H1JvnFRdks5TfwbQo6WGD0ra0vbxUn/0EuqH+CPl33s2c7wk/aKkf1V/dsT7yrI/kXTNel+v+q2oU5KeUGXmw2r73MD3+4bqkvQHkr5VOT6PqH+Sf+hrOqG6rivP+4j6J9EXK/vsqR+ipyR9WOWDm5Ooqzy2V9IXBvY3qeP1M+rn1LfU/w3j5HqZUcfx4pOxAJBcl1s3AIAaEPQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkNz/AU6HmJWbMe2UAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4098,7 +4137,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4108,7 +4147,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4125,7 +4164,7 @@
     },
     {
      "data": {
-      "image/png": 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NwL8Bf1dVH+kfa+IcAZL8FPCHwDrgaFX9XsddGov+/s93srSS4zeAh4DPA08DW4GvAz9XVStv2M6MJD8B/DXwD8A7/ebfZqlO38x5DsOgl6TGWbqRpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe7/AEy9Dn0Pg0ZJAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4135,7 +4174,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAC9VJREFUeJzt3X+o3fddx/Hny6btpNXU2TJqmng7Eqr5Q7Rcugkios6l7WJUFFIENywGhKL+5SKFyRhCp+AfYqEELJ1QGqtWTGik+0FH/WPrmmpX02VxWe1oSl0sc9GhbNa+/eN8Ew+X/Di9ufd+z3nn+YBDvufzPTn3xc25r3zP53y+35uqQpLU13eNHUCStL4seklqzqKXpOYseklqzqKXpOYseklqzqKXpOYseklqzqKXpOY2jR0A4MYbb6ylpaWxY0jSQnn++effqKqbLvW4uSj6paUljh49OnYMSVooSb42y+OcupGk5ix6SWrOopek5ix6SWrOopek5ix6SWrOopek5ix6SWpuLk6YWm9L+588t/3KA3ePmESSNp5H9JLU3BVxRC9JG2FeZw88opek5ix6SWrOopek5ix6SWrOopek5ix6SWrOopek5lxHL0nrYHpNPYy7rr5t0a/8JkvSlapt0V/IvJ65JknrZc2LPslPAR8DXgIOVtVn1/prSNK8WITZg5k+jE3ycJLTSY6tGN+V5ESSk0n2D8MFfAt4B3BqbeNKkt6uWVfdPALsmh5IchXwIHAnsBO4J8lO4O+r6k7gw8BH1y6qJGk1Zir6qnoG+MaK4TuAk1X1clV9BzgI7Kmqt4b9/w5cu2ZJJUmrcjlz9FuAV6funwLek+SXgPcDNwB/eqG/nGQfsA9g27ZtlxFDknQxa/5hbFU9ATwxw+MOAAcAlpeXa61zSJImLufM2NeArVP3bxnGJElz5HKK/jlgR5Jbk1wD7AUOrU0sSdJamXV55WPA54DbkpxKcm9VvQncBzwFHAcer6qX1i+qJGk1Zpqjr6p7LjB+BDiy2i+eZDewe/v27at9CknSJYx69cqqOlxV+zZv3jxmDElqzcsUS1JzFr0kNWfRS1JzFr0kNTdq0SfZneTAmTNnxowhSa256kaSmnPqRpKas+glqTmLXpKas+glqTmLXpKas+glqTnX0UtSc66jl6TmnLqRpOYseklqzqKXpOYseklqzqKXpOYseklqznX0ktSc6+glqTmnbiSpOYtekpqz6CWpOYtekpqz6CWpOYtekpqz6CWpOU+YkqTmPGFKkprbNHYASboSLO1/8tz2Kw/cvaFf2zl6SWqu1RH99P+YkqQJj+glqTmLXpKas+glqTmLXpKas+glqTmLXpKas+glqTmvdSNJzXmtG0lqzqkbSWrOopek5ix6SWrOopek5ix6SWrOopek5ix6SWrOopek5lr9hqm3a+VvpNro3+MoSRvhii56SVqNRfu1pU7dSFJzFr0kNWfRS1JzXqZYkprzMsWS1JxTN5LUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLUnEUvSc2NWvRJdic5cObMmTFjSFJroxZ9VR2uqn2bN28eM4YktebUjSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1Z9FLUnMWvSQ1ty5Fn+S6JEeTfGA9nl+SNLuZij7Jw0lOJzm2YnxXkhNJTibZP7Xrw8DjaxlUkrQ6sx7RPwLsmh5IchXwIHAnsBO4J8nOJO8DvgScXsOckqRV2jTLg6rqmSRLK4bvAE5W1csASQ4Ce4DrgeuYlP9/JzlSVW+tfM4k+4B9ANu2bVttfknSJcxU9BewBXh16v4p4D1VdR9Akg8Bb5yv5AGq6gBwAGB5ebkuI4ck6SIup+gvqqoeWa/nliTN7nJW3bwGbJ26f8swJkmaI5dT9M8BO5LcmuQaYC9waG1iSZLWyqzLKx8DPgfcluRUknur6k3gPuAp4DjweFW9tH5RJUmrMeuqm3suMH4EOLLaL55kN7B7+/btq30KSdIlrNuHsbOoqsPA4eXl5d8YM8dZS/ufPLf9ygN3j5hEktaO17qRpOYseklqbtSpG0laFNNTu4vGI3pJam7Uok+yO8mBM2fOjBlDklobteir6nBV7du8efOYMSSpNaduJKk5i16SmrPoJak5i16SmrPoJam5UU+YWouLmi3ySQyStBG8qJkkbbCNvoCiUzeS1JxFL0nNWfSS1JxFL0nNWfSS1JxFL0nNeZliSWrOyxRLUnNO3UhScxa9JDVn0UtScxa9JDVn0UtScxa9JDVn0UtScxa9JDXnmbGS1JxnxkpSc07dSFJzFr0kNWfRS1JzFr0kNWfRS1JzFr0kNWfRS1JzFr0kNWfRS1JzFr0kNee1biSpOa91I0nNOXUjSc1Z9JLUnEUvSc1Z9JLUnEUvSc1Z9JLU3KaxA8yrpf1Pntt+5YG7R0wiSZfHI3pJas6il6TmnLqRpAuYnsJdZB7RS1JzFr0kNWfRS1JzXqZYkprzMsWS1JxTN5LUnEUvSc1Z9JLUnEUvSc2lqsbOQJJ/A742do4VbgTeGDvEjBYpKyxW3kXKCouVd5Gywnzm/cGquulSD5qLop9HSY5W1fLYOWaxSFlhsfIuUlZYrLyLlBUWL+80p24kqTmLXpKas+gv7MDYAd6GRcoKi5V3kbLCYuVdpKyweHnPcY5ekprziF6SmrPoV0jysSQvJnkhySeT/MAwniR/kuTksP/2Ocj6R0m+POT5myQ3TO37vSHriSTvHzPnWUl+JclLSd5Ksrxi3zzm3TXkOZlk/9h5VkrycJLTSY5Njb0zyaeSfGX48/vGzHhWkq1Jnk7ypeE18NvD+NzlTfKOJF9I8sUh60eH8VuTPDu8Hv4iyTVjZ51ZVXmbugHfO7X9W8BDw/ZdwN8BAd4LPDsHWX8O2DRsfxz4+LC9E/gicC1wK/BV4Ko5yPvDwG3AZ4HlqfG5ywtcNeR4N3DNkG/n2N/DFRl/ErgdODY19ofA/mF7/9nXxNg34Gbg9mH7e4B/Hv7d5y7v8DN+/bB9NfDs8DP/OLB3GH8I+M2xs85684h+har6j6m71wFnP8TYA/x5TXweuCHJzRsecEpVfbKq3hzufh64ZdjeAxysqm9X1b8AJ4E7xsg4raqOV9WJ8+yax7x3ACer6uWq+g5wkEnOuVFVzwDfWDG8B/jEsP0J4Bc2NNQFVNXrVfUPw/Z/AseBLcxh3uFn/FvD3auHWwE/DfzVMD4XWWdl0Z9Hkj9I8irwq8BHhuEtwKtTDzs1jM2LX2fyjgPmP+tK85h3HjPN4l1V9fqw/a/Au8YMcz5JloAfY3KkPJd5k1yV5AXgNPApJu/uvjl1YLUorwfgCi36JJ9Ocuw8tz0AVXV/VW0FHgXum+esw2PuB95kkndUs+TVxqjJHMNcLatLcj3w18DvrHj3PFd5q+p/q+pHmbxLvgP4oZEjXZZNYwcYQ1X97IwPfRQ4Avw+8BqwdWrfLcPYurpU1iQfAj4A/MzwgwIjZYW39b2dNlrei5jHTLP4epKbq+r1YWrx9NiBzkpyNZOSf7SqnhiG5zYvQFV9M8nTwI8zma7dNBzVL8rrAbhCj+gvJsmOqbt7gC8P24eAXxtW37wXODP1lnMUSXYBvwv8fFX919SuQ8DeJNcmuRXYAXxhjIwzmse8zwE7hpUW1wB7meScd4eADw7bHwT+dsQs5yQJ8GfA8ar646ldc5c3yU1nV7Al+W7gfUw+U3ga+OXhYXORdWZjfxo8bzcmRxzHgBeBw8CW+v9P4h9kMlf3T0ytGhkx60km88gvDLeHpvbdP2Q9Adw5dtYh0y8ymdv8NvB14Kk5z3sXk9UhXwXuHzvPefI9BrwO/M/wfb0X+H7gM8BXgE8D7xw755D1J5hMy7w49Xq9ax7zAj8C/OOQ9RjwkWH83UwOQE4CfwlcO3bWWW+eGStJzTl1I0nNWfSS1JxFL0nNWfSS1JxFL0nNWfSS1JxFL0nNWfSS1Nz/AU02ZZQR2BZNAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4162,7 +4201,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4172,7 +4211,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4182,7 +4221,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4199,7 +4238,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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S4wx6SWqcQS9JjTPoJalxBr0kNe7/ABYllDMXfihLAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4209,7 +4248,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4219,7 +4258,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4236,7 +4275,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4246,7 +4285,7 @@
     },
     {
      "data": {
-      "image/png": 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fOD3mmFYlyf39G00kuR/4OeDi8p9a804D7+y/fifwN2OMZdW+mxT7fpEJuS79G38fBS5X1Z8ueGuirstS/ZjE65LkNUke7L/+PuYnklxmPuH/Un+3kVyTiZ11A9CfUvVnwDrgWFX9wZhDWpUkr2d+FA9wH/CJSepLkhPAI8yvwvd14HeBTwOngM3Mr0z6jqpa0zc6l+jHI8yXBwq4Bvzaghr3mpXkTcAXgK8CL/abf5v5+vbEXJdl+nGACbsuSX6M+Zut65gfZJ+qqt/r//yfBF4N/CPwq1X1P0M99yQneknS3U1y6UaS1IGJXpIaZ6KXpMaZ6CWpcSZ6SWqciV6SGmeil6TGmeglqXH/B1AyrZ6wxG0rAAAAAElFTkSuQmCC\n",
+      "image/png": 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R+8R6I3AQODvmmtYkyf29D5pIcj/wE8Cl1Z+17p0F3tH7+R3AX4+xljX7Vij2/CwTcl96H/x9GLhSVX+w5KGJui8rXcck3pckm5K8svfzd7C4kOQKi4H/tt5pI7knE7vqBqC3pOoPgQ3Aiar67TGXtCZJvofFWTzAfcBHJ+lakpwCHmRxF76vAL8BfAI4A2xjcWfSt1fVuv6gc4XreJDF9kAB14FfXNLjXreSvAn4LPAl4Pne8K+x2N+emPuyynXMMmH3JckPsvhh6wYWJ9lnquo3e7//TwOvBv4R+IWq+p+hvvckB70k6e4muXUjSerAoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXH/B/lftNmWuVgOAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4256,7 +4295,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4273,7 +4312,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4283,7 +4322,7 @@
     },
     {
      "data": {
-      "image/png": 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JC4HVdLyoUlV9sap+oKo2VtVG+v8jvPRChPxikmyj/9H/tVX1Xx2Xcxi4IsmmJKvp3yg70HFNpP/ufBdwrKre03U951TVrVW1bvA7tRP4m2UQ8gx+r08muXIwdC3weIclnfM14OVJnj34N72WGd4kvmiu6BdxE3D74KbG/wC7O67nnP3A/iSPAU8Bv9rxVepy9z7gUuBTg08bn6mqm7sopKrOJtkDPEh/RsT+qjraRS3zvAL4FeCLST4/GHtHVR3qsKbl7hbgQ4M37BPAr3VcD4M20v3Ao/TblJ9jht+Q9ZuxktS4Vlo3kqTzMOglqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrc/wH6NSTn880uRgAAAABJRU5ErkJggg==\n",
+      "image/png": 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8+ueBf+qxllGfAF4DkORFwEp63j2vqr5cVT9UVRuqagOD/xFediFCfjFJtjD46H9TVf1Xz+UcAq5KsjHJSgY3yvb3XBMZvDvfBRytqvf1Xc9ZVfXOqlo7/J3aDvzNFIQ8w9/rE0muHg5dBzzeY0lnfQN4ZZLnDv9Or2OCN4kvmhn9Im4B7hje1PgfYGfP9Zy1F9ib5DHgaeDXep6lTrsPAJcDnxl+2vhcVd3aRyFVdSbJLuBBBisi9lbVkT5qmedVwK8CX07yxeHYu6rqYI81TbvbgHuGb9jHgV/vuR6GbaT7gUcZtCm/wAS3QnALBElqXCutG0nSAgx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Lj/A48UHLNvZzQ1AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4293,7 +4332,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4303,13 +4342,15 @@
     }
    ],
    "source": [
+    "kq=0\n",
     "for quad in [t1234,t1231,t1212,t1123] :\n",
-    "  plotDoublets(quad,500)"
+    "  plotDoublets(quad,500,kq)\n",
+    "  kq+=1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 284,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4357,9 +4398,17 @@
     "  plt.show()\n",
     "  \n",
     "\n",
-    "  pcut = abs(d1)<12.\n",
-    "\n",
+    "  pcut = np.logical_and(pcut,abs(d1)<12.)\n",
     "\n",
+    "  print 'z0 res'  \n",
+    "  dz = d1 - quadc['tkz1']\n",
+    "  plt.hist(dz[pcut], bins=100,range=[-2.,2.],log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "  plt.hist(dz[pcut], bins=100,range=[-0.4,0.4],log=True)\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    " \n",
     "  print 'dr'\n",
     "  d1 = quadc['r2']-quadc['r1']\n",
     "  plt.hist(d1[np.logical_and(pcut,abs(d1)<20)], bins=100,log=True)\n",
@@ -4413,7 +4462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -4428,7 +4477,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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VdS1Imi3d36jO+N/SOn5G0tqIOFzaZrmGT6sztv06ddYVEQvF/9+1/dfq/Ay9Sy1oL3WOM34mIj5Z2uaK7dVjO+Wef/l90T1P9/M93bIrrbPOumR7o6TPS/pQRBxdWuA0r2ntdRW/VF8ptn/Y9lFJby/mLw+/jby9CjvV1ZsfUXudbtnZrmXnVE17tXro5oCkpT3PN0j6u34XLN5kD0la2qtdXr683usk/WPX8EkVdT0g6QrbZ7tzlMkVxbQl16vrTVaE4JKrJT01QE2rqsv2WtvnFnX8kKT3S1rq6TTaXrb/VJ0P6e+XFxiyvR6WdJE7R2Sdoc6H/cBp6i0/3wOSdrpzNMcmSReps5Osn3XWVlcxpHVInR3e/7I08wqv6SjqOs/2mmL7F6rTXseKYbz/s/3uYmjkQxrgs73auop63iDpAyqNz4+wvXpZ9jNQUXu1+qibt6gzHvuMpC9KOqeYPiPp06X5vizpRUkn1Rm/urKYfqE6H8R5SX8j6cxi+g8X9+eLxy+sqa7fLrYxL+m3utZxTNI7u6b9mTo7076uzpfUO0dVl6R16hwB9HhRw19IWtN0e6nTewl1Qvyx4t+Nq2kvSb8i6T/UOTriY8W0P5F09UrPV52hqKOSnlbpyIfl1jnE+32ouiT9oaSXS+3zmDo7+Xu+piOq69piu4+psxN9R2mdM+qE6FFJt6k4cXMUdRWPzUr6atf6RtVeP69OTr2szi+MJ1fKjCraizNjASC5Ng/dAAAqQNADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHL/D5ZIIeD6+nPyAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4445,7 +4494,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAC9xJREFUeJzt3e+rpOdZB/DvZWqkbGV9kbJCEtzAhmAwgnBIlb45qIWN6TZYUBKKsBiyWAwoLGik/0BA6hsbKAuGRQkJwR80SyKxgoe8iZJEiptkTVlCSnYRYigc3SiUpbcvcmJPtz05v2bOc+aaz+fVzjNzZq975pnv3HM/93M/NcYIAH39xNQFADBfgh6gOUEP0JygB2hO0AM0J+gBmhP0AM0JeoDmBD1Ac5+YuoAkueWWW8bx48enLmPXPvjggxw5cmTqMg7UsrV52dqbaPMiee21194fY3x6u8cdiqA/fvx4Xn311anL2LW1tbWsrq5OXcaBWrY2L1t7E21eJFX1nZ08ztANQHOCHqA5QQ/QnKAHaE7QAzQn6AGaE/QAzU0a9FV1qqrOra+vT1kGQGuTnjA1xriQ5MLKysojU9bBwTj+2PM/dvs7j99/wJX0sfk19TqylUNxZizLbasvgM2E2A9s9Xr5ImUrgp6Z20lws71ZvY56/Qh6OER8STIPgp6FoFc6G17H5STomQk9UTi8BD0Lp1uv1Jck8+aEKYDm9OhZaIvau9eL5yAJevZMWMFiEPSwpBb11xC7Z4weoDk9eto47D1UQ11MRY8eoDlBD9CcoAdoTtADNOdgLC0dlgOzDsByGOjRAzQn6AGaE/QAzQl6gOYEPUBzZt2wKxevrue0mSTtbJ4ddP7kkQkrYR706AGaE/QAzRm6ob3DcvIUTEXQw4w5G5bDxtANQHNzCfqqOlJVr1bV5+fx/ADs3I6CvqqerKr3qur1G7afrKq3qupyVT226a4/TvLsLAsFYG922qM/n+Tk5g1VdVOSJ5Lcl+TuJA9V1d1V9bkkbyZ5b4Z1ArBHOzoYO8Z4qaqO37D53iSXxxhvJ0lVPZPkgSSfSnIkH4b//1bVC2OM78+sYgB2ZT+zbm5N8u6m21eSfGaM8WiSVNXpJO9vFfJVdSbJmSQ5duxY1tbW9lHKNK5du7aQde/HsU8mZ++5PnUZe7bb92sv7/Eivz7Jcu7X3ds8t+mVY4zz29x/Lsm5JFlZWRmrq6vzKmVu1tbWsoh178efP/WNfPXi4s7KfedLq7t6/F7e40VfIuL8ySNLt193/yzvZ9bN1SS3b7p928Y2AA6R/QT9K0nurKo7qurmJA8meW42ZQEwKzudXvl0kpeT3FVVV6rq4THG9SSPJnkxyaUkz44x3phfqQDsxU5n3Ty0xfYXkryw1/+8qk4lOXXixIm9PgUA25j0qNoY40KSCysrK49MWQfLwwJnLKPFnT4Bh4iFzDjMLGoG0JygB2hO0AM0N2nQV9Wpqjq3vr4+ZRkArU0a9GOMC2OMM0ePHp2yDIDWDN0ANCfoAZoT9ADNCXqA5gQ9QHOmVwI0Z3olQHOGbgCaE/QAzQl6gOasRw971HUN+otX13N6o20uztKDoGdpudoUy2LSoHfN2MWwORDP3jNhIcCemF4J0JyDsQDNCXqA5gQ9QHOCHqA5QQ/QnKAHaE7QAzRnPXqA5pwwBdCcoRuA5gQ9QHOCHqA5QQ/QnPXoYYe6XmiE/vToAZrTo4e42hS96dEDNCfoAZqzBAJAc5ZAAGjO0A1Ac4IeoDlBD9CcoAdoTtADNCfoAZoT9ADNCXqA5gQ9QHOCHqA5QQ/QnPXo4Qab16Y/f/LIhJXAbEwa9FV1KsmpEydOTFkGbOni1fWcdglBFpzVKwGaM0YP0JygB2hO0AM0J+gBmjO9EtjS5qmm7zx+/4SVsB969ADNCXqA5gQ9QHPG6PkRx50JCq3o0QM0J+gBmhP0AM0JeoDmBD1Ac4IeoDlBD9CcoAdoTtADNDdp0FfVqao6t76+PmUZAK25ZixAc4ZuAJoT9ADNCXqA5gQ9QHOCHqA5QQ/QnKAHaE7QAzQn6AGaE/QAzQl6gOYEPUBzgh6gOUEP0Nwnpi4AWAzHH3v+///9zuP3T1gJu6VHD9CcHj1Jfri3BvSiRw/QnKAHaE7QAzQn6AGaE/QAzQl6gOZMrwR2zclTi0WPHqA5QQ/QnKAHaE7QAzQ386Cvqp+vqq9X1V9X1Zdn/fwA7M6Ogr6qnqyq96rq9Ru2n6yqt6rqclU9liRjjEtjjN9L8ttJPjv7kgHYjZ1Orzyf5GtJ/vKjDVV1U5InknwuyZUkr1TVc2OMN6vqC0m+nOSvZlsus2TFSlgOO+rRjzFeSvLdGzbfm+TyGOPtMcb3kjyT5IGNxz83xrgvyZdmWSwAu7efE6ZuTfLupttXknymqlaTfDHJTyV5Yas/rqozSc4kybFjx7K2traPUqZx7dq1haz7I2fvub7rvzn2yb393aJatvYmu2/zIn8GPrLon+XtzPzM2DHGWpK1HTzuXJJzSbKysjJWV1dnXcrcra2tZRHr/sjpPQzdnL3ner56cXlOqF629iZ7aPPFD37o5iKeKbvon+Xt7GfWzdUkt2+6fdvGNgAOkf0E/StJ7qyqO6rq5iQPJnluNmUBMCs7nV75dJKXk9xVVVeq6uExxvUkjyZ5McmlJM+OMd6YX6kA7MWOBuLGGA9tsf2FfMwB1+1U1akkp06cOLHXpwBgG5MeZRpjXEhyYWVl5ZEp6wDmw3LGh8NyTSfASVLM3Vb7mNCfjqBfAsIdlpvVKwGaE/QAzU0a9FV1qqrOra+vT1kGQGtm3QCTcpB2/gzdADQn6AGaM70SOHCm/B4sQd+IsU4WnX14PiYNemvdzI8eE/ARs24WnEAHtmPoBjiUDOPMjqA/QPvZcfXcgb0yvRKgOT36Q8BPVPh4W/2i9XnZGUEPLCydpJ0R9BPZycUZAGbB6pUAzZlHPyN+QgKHlVk3AM0JeoDmBD1Ac2bdzIGZM8BhIuh3aXOInz95ZMJKAHZG0AMtOHt2a0sd9DfuGHYI6GcnU58vXl3P6Y3HdcwBFx7Zh807B7BYNn8BnL1nwkIOwNKdMOVAKSyvZf38L/XQzY22+om3rDsH0EOroLcMAcCPahX0W9EjB5bZUgQ9wE51HBkQ9FvwKwDoom3QC2qAD1nUDKC5he/R67kD89JlvH7hgx7gICxy6LtmLEBzS7cEAsBBOEyLJhq6AdiHRRjSEfQAM7LTySEH/eVgeiVAc3r0AAdgyqngevQAzenRA+zSop2oqUcP0JygB2hO0AM0J+gBmhP0AM0JeoDmrF4J0NykQT/GuDDGOHP06NEpywBozdANQHM1xpi6hlTVfyb5ztR17MEtSd6fuogDtmxtXrb2Jtq8SH5ujPHp7R50KIJ+UVXVq2OMlanrOEjL1uZla2+izR0ZugFoTtADNCfo9+fc1AVMYNnavGztTbS5HWP0AM3p0QM0J+j3oar+tKr+var+rar+rqp+Zuqa5q2qfquq3qiq71dV21kKSVJVJ6vqraq6XFWPTV3PvFXVk1X1XlW9PnUtB6Wqbq+qf6qqNzf26z+YuqZ5EPT7880kvzDG+MUk307yJxPXcxBeT/LFJC9NXcg8VdVNSZ5Icl+Su5M8VFV3T1vV3J1PcnLqIg7Y9SRnxxh3J/nlJL/f8X0W9PswxviHMcb1jZv/nOS2Kes5CGOMS2OMt6au4wDcm+TyGOPtMcb3kjyT5IGJa5qrMcZLSb47dR0HaYzxH2OMf934938nuZTk1mmrmj1BPzu/m+Tvpy6Cmbk1ybubbl9JwwDgB6rqeJJfSvIv01Yyey4Ovo2q+sckP/tj7vrKGOMbG4/5Sj78CfjUQdY2LztpM3RSVZ9K8jdJ/nCM8V9T1zNrgn4bY4xf/7j7q+p0ks8n+bXRZK7qdm1eEleT3L7p9m0b22imqn4yH4b8U2OMv526nnkwdLMPVXUyyR8l+cIY43+mroeZeiXJnVV1R1XdnOTBJM9NXBMzVlWV5C+SXBpj/NnU9cyLoN+fryX56STfrKpvVdXXpy5o3qrqN6vqSpJfSfJ8Vb04dU3zsHGQ/dEkL+bDA3TPjjHemLaq+aqqp5O8nOSuqrpSVQ9PXdMB+GyS30nyqxuf4W9V1W9MXdSsOTMWoDk9eoDmBD1Ac4IeoDlBD9CcoAdoTtADNCfoAZoT9ADN/R+9/kYj++Fd1QAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4462,7 +4511,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4479,7 +4528,7 @@
     },
     {
      "data": {
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\n",
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vkuwHfg94U1X999hdR4HrklyW5CrgGuCzfWScwFCzHgeu6XZaXApcx2rWoTsKvK27/jbg73rM8j1JAtwBPFZVfz521+DyJrni3A62JD8MvI7VNYUHgLd0DxtE1on1vRo8pAurs42TwBeBFWBHfX8V/jZW+3T/xNiukZ7znmK1j/z57nL72H23dHkfB64dQNZfYrWv+W3g68D9Q806luv1rO4O+SpwS995zpPvHuBp4H+73+0NwAuBvwe+AnwSeEHfObusP8tqW+aLY3+vrx9iXuAngX/ssp4E3t2NX83qJOQU8JfAZX1nnfTikbGS1DhbN5LUOAu9JDXOQi9JjbPQS1LjLPSS1DgLvSQ1zkIvSY2z0EtS4/4fzpN46Vyh0bAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4496,7 +4545,34 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0 res\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4513,7 +4589,7 @@
     },
     {
      "data": {
-      "image/png": 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SryYZ6iblE9Sl9lnUpe6HgZeSPMP6qPlXq2oWRpddav8IcHOSrwH3sh76M3nl4Ka67wIuSHIG+DBwW3+V7W5OjtFXmYPjc2heGStJjZuHEb0kaQQGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/x2KkyKlrs6oAAAAASUVORK5CYII=\n",
+      "image/png": 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dW1frjOHVwBuBR5J8HXgLcKoPO23Ws8F7sQycqqr/rap/Bf6ZleDvnQ3GcAS4b7DOY8ArWLlmSV+8Fbhl8LtyL/C2JH+xZp0X7+Gc5ApWWlDfmmaRHXQZR9+37c3G0M9tu6p69Q+4Gnjt4OsfB/4eeOeada4H/gXYP+t6tzqGNes/wsoOqJnXvoX34iDwmcHXO1lpH1w169qHHMPngPcOvn4DKz36zLr2dcZzI/DAZZb/GvDpwde3sbJzeeb1bmEcvd62u4xhzTq92Lb7OKP/CeDhJF9j5Qbkn6+qB5L8bpJbBut8HHgV8NdJvpJkqJuST0GXMcyDLuN4CPhWkqdYmS3/RlX1aSbZZQwfAI4m+SpwDyuh3/szCdeM4W7gqiTngfcDx2dX2XDmbNu+rL5v254ZK0mN6+OMXpI0Rga9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z8Mv3S8tJsXbAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4530,7 +4606,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4547,7 +4623,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4564,7 +4640,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4581,7 +4657,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4598,7 +4674,7 @@
     },
     {
      "data": {
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tl6AfaRGxEvh74AuZ+c6s1c/S6Z74KeAvgG8MqaxPZOZFdCad++2I+OSQtrug6Fx3cRXwd3Osbmt/nSA7/0ePzLC0iPgicBz4Wo8mbbzmfwn8OPDTwJt0uklGySbmP5tvdJ/Nlw3DPL6WS9DXuQr3gzYRcSqwCvhu04VFxIfovJBfy8x/mL0+M9/JzGPV7d3AhyJiddN1Zeah6vsR4Ot0/n3u1uaVzZcDz2bm4dkr2tpfXQ7PdGFV34/M0Wbo+y4ibgR+GbiuCogT1HjNBy4zD2fm9zLz+8Bf9dhmK8dalQOfBe7r1abJfdYjG1o5vpZL0Ne5CncnMPPu9DXAv/T6hRiUqv/vK8DLmfnnPdr8yMx7BRFxCZ193ugfoIhYEREfmblN5828F2c12wn8enR8HDja9S9l03qeZbWxv2bpPo5uAP5xjjYPA5dFxBlVV8Vl1bJGRMQU8HvAVZn5Pz3a1HnNm6it+32dX+mxzbauov9F4NuZeXCulU3us3myoZ3ja9DvNjf1RWeUyKt03r3/YrXsj+gc/AA/SKcrYD/w78D5Q6jpE3T+9XoBeK76uoLOpG63Vm22AC/RGWnwJPBzQ6jr/Gp7z1fbntlf3XUFnc8TeB3YB0wM6XVcQSe4V3Uta2V/0flj8ybwv3T6QW+i877OPwOvAf8EnFm1naAzG+vMYz9fHWv7gd9ouKb9dPpsZ46xmdFlPwrsnu81H8L++tvq+HmBToidNbu26v4Jv79N1lUt3zFzXHW1Hco+mycbWjm+vDJWkgq3XLpuJEmLZNBLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klS4/wPhpy9CuFxSVQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4615,7 +4691,7 @@
     },
     {
      "data": {
-      "image/png": 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84IkGIx32LPDuiHhT92t6HmM8SXzCHNH3cCVwY/ekxn/TvUVhC2wDtkXEY8CrwIcbPkptu88CbwC+0f208UBmXtVEkMw8FBGHl+BeAmwbZAnuMToXuBzYExGPdrd9IjPvaTBT210L3NL9hf008JsN56E7jHQ78AidYcpvMcYrZL0yVpKKqzJ0I0k6Botekoqz6CWpOItekoqz6CWpOItekoqz6CWpOItekor7X2vxcNaV8zOMAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4635,7 +4711,7 @@
     },
     {
      "data": {
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UW/7V90X7PO3P93zLLrTOOuuS7XWS/lLSRyPi6NwC53lNa6+rfFJ9s2z/kO2jkt5X5q92vw19fxXb1NaaH9L+Ot+ym9qWPajB7K9Gd93slTR35vlOSX/d7YLlTfakpLmz2tXlq+vdKunv2rpPBlHX45Kut32BW6NMri/T5tymtjdZCcE5N0t6sYeaFlWX7eW2Lyp1/ICkmyTNtXRGur9s/6FaB+lvVxfoc389Jelyt0ZkrVDrYN97nnqrz3evpG1ujeZYL+lytU6SdbPO2uoqXVr71Trh/Q9zMy/wmg6jrottLyvbv0yt/XWsdOP9j+0Plq6Rj6qHY3uxdZV63ibpI6r0zw9xf3Uy7zEwoP3V6FE371arP/ZlSV+SdGGZPiXpwcp8X5X0mqQ31Oq/uqFMv0ytA3FG0p9LWlmm/2C5P1Mev6ymun69bGNG0q+1reOYpA+0TfsjtU6mPafWH6kPDKsuSavUGgH0fKnh05KWjXp/qdV6CbVC/Nnyc9di9pekX5L0r2qNjvhEmfYHkm5e6Pmq1RV1VNJLqox8mG+dfbzf+6pL0u9JOl3ZP8+qdZK/42s6pLpuLdt9Vq2T6NOVdU6pFaJHJd2n8sXNYdRVHtsk6ett6xvW/vo5tXLqtFqfMI4slBmD2F98MxYAkmty1w0AYAAIegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBI7n8BnAaDOGcjBiQAAAAASUVORK5CYII=\n",
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AY1WAzFTzN1nXPLCldn8jnfG/xXX8LLA2Mw/VnrNew+10xrbfYJR1ZeZ89f/3IuIv6XwNvZMJaC86xxk/nZmfrj3niu3V5XnqPf/652LpPEtf78mWXWmdo6yLiNgI/A3w0cx8ZnGBk7ynI6+r+qb6avX8hyLiGeBd1fz14bext1dlB0t682Nqr5Mtu2XJsgdppr0meujmXmBxz/P1wN/1umD1IXsIWNyrXV++vt6rgX9cMnzSRF0PAJdGxOnROcrk0mraomtY8iGrQnDRFcCTfdQ0VF0RsTYizqzq+BHgw8BiT6fV9oqIP6Wzkf5efYEB2+th4ILoHJF1Cp2N/d6T1Ft/vfcCO6JzNMe5wAV0dpL1ss6R1VUNaR2gs8P7nxZnXuE9HUddZ0XEmur5z6PTXkerYbz/i4j3VkMjH6WPbXvYuqp63gR8hNr4/Bjbq5tlt4GG2muij7p5O53x2KeBLwJnVNPngNtr830VeBF4hc741WXV9PPobIhHgL8CTq2mv7m6f6R6/LwR1fVb1XMcAX5zyTqOAu9ZMu3P6OxM+xadP1LvGVddwDo6RwA9VtXwGWBN2+1Fp/eSdEL80erfDcO0F/ArwL/TOTriE9W0PwGuWOn10hmKegZ4itqRD8utc4DP+0B1AX8IvFxrn0fp7OTv+p6Oqa6rqud9lM5O9O21dc7RCdFngFuoTtwcR13VY1uAry9Z37ja6xfo5NTLdL5hPLFSZjTRXp4ZK0mFm+ShG0lSAwx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIK9//RXRaq6Gr7nQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4652,7 +4728,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4669,7 +4745,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4686,7 +4762,7 @@
     },
     {
      "data": {
-      "image/png": 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jX8+XAz8xcEmrMvWLg8+jqnpDz03vYHQhlfcyhWviTuJ8NSd5J3A18AvdlwLmvOYVDFrzOcxrXX18I8krq+qprt14ZuiClkryQkYhf0dV/XU3PPd1V9W3knwa+GlG7dwXdLP6uf58bIoZ/bkk2TP28ADw5e7+UeDXu9U3rwPOjv2sHFSSfcDvAW+uqv8ee+oocDDJhUl2A3uAzw1R4yrMa80TXxN5QEeBd3T33wH83YC1/IAkAT4MnKyqPxt7ai7rTrLt+ZVtSX4YeCOj/QqfBt7WbTY39S5r6L3BQ98YzSoeAR4GjgHb63t72m9h1Iv7J8ZWigx9Y7TD8gngC93t1rHnbuxqfgy4cuhax+r6ZUZ9zG8D3wDu3QA1v4nRipCvAjcOXc8KNd4JPAX8b/f/9xrg5cA/AF8BPgW8bOg6l9T8s4zaMg+PfYbfNK91Az8J/GNX7yPAe7rxSxlNSk4BfwlcOHStK908MlaSGrfpWzeS1DqDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0fdAzLhgooV/QAAAAASUVORK5CYII=\n",
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CfAQ4W1V/PvTQ3OVNsvnSDrYkPwy8meVrCg8Dd3RPm4usI+v7avA8fbDcbZwBvgqcALbU/1+Fv5flOd0/MrRrpOe8SyzPkb/cfdw39Ng9Xd5zwO1zkPWXWZ5rfhf4NvDQvGYdyvUWlneHfBO4p+88l8n3APAM8D/dv+2dwKuBvwe+AXwOeFXfObusP8vyWOarQz+vb5nHvMBPAv/QZT0DvK9bfy3LTcgS8JfA9X1nHfXDV8ZKUuMc3UhS4yz0ktQ4C70kNc5CL0mNs9BLUuMs9JLUOAu9JDXOQi9Jjfs/TPNrqEvOtaQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4703,7 +4779,34 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0 res\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RvwZ8ge7bnjamGpj+MbB5WM/RyqLPzE8u9LmIOBIRH8zM16piOjrPZh8FPhYRN9OdC18dEccyc8GdICPK1ftYr0bEM8DH6E4FNJorIk4DdgG3ZuYTy8kzyFwjUued0k5scygi3g1MAP/WglxNqJUrIj5J95f6x3umLBvP1WMW+NOhJupaLNd7gYuBPdXA9APAjoi4IjOfHESAcZy62cH//wa+DvjGyRtk5rWZ+eOZOUV3+uae5Zb8IHJFxNkRcWp1+XTg54HnW5BrNfDXdL9Oy/qlM8hcI1TnndJ6814F/F1We9AaztWERXNFxE8DfwZckZmj+iVeJ9cFPVcvB15sOldmzmXmGZk5VXXWE3S/bgMp+RNPMlYfdOdFH6H7DXoYeF91ewf48jzbb2Y0q24WzQVcBjxNd6/708BMS3J9Fvhf4Kmej480nau6/vfAd4E36c5tfmpIeX4ReIHuvolbq9t+j+4PHMB7gL8CDgL/BHxo2N+7mrl+tvq6vEH3L4wDLcn1MHCk5/W0oyW57gAOVJkeBX6qDblO2nYPA15145GxklS4cZy6kST1waKXpMJZ9JJUOItekgpn0UtS4Sx6SSqcRS9JhbPoJalw/wcstWQDae/iAAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4720,7 +4823,7 @@
     },
     {
      "data": {
-      "image/png": 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b4Bxd1nqcp14ZK0mN632yTZI0WRZ6SWqchV6SGmehl6TGWeglqXEWeklqnIVekhpnoZekxv0Poc3NQWXQnNsAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4737,7 +4840,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4754,7 +4857,7 @@
     },
     {
      "data": {
-      "image/png": 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PxAS9JE2qiem6kaRJZdBLUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnH/B5V0p9PMakkxAAAAAElFTkSuQmCC\n",
+      "image/png": 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gvYOv3wv07q/DrJ+6Pwqcq6o/HNo0N7XpzRumkpwHXgt8bbDqC1V1/2Db+1nv219m/c+uT238KG1K8k7gT4DXA/8O/ENVvXWwre+1+Tngj4BdwLGq+mDHQ+pMkseAZdbvyvgy8AHgE8DjwA+xfgfad1fVlRdsm5bkp4G/Ac4C3x6s/h3W+/RzUZveBL0k9VVvWjeS1FcGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjfsfvRMaSLqUQI8AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4771,7 +4874,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4788,7 +4891,7 @@
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4805,7 +4908,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4822,7 +4925,7 @@
     },
     {
      "data": {
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4fLdJvu8vWLh4+F7XQRbZADwL/FVvSOm2JKu7DlVV8yx01deAbwJHquoz4zpfpzcHH0SSvwd+7Ad86f3AJcB7quoTSd7Cwm/vN05ArjOAH2HhJfbrgDuTvLJWYKrTErnex8KwzYp7oVxV9be9Y97PwhDFx1Yy26kkyRrgE8C7q+o/JyDP5cAzVXUgyUzXeRY5A3gt8M6q+nySW4CdwO93GSrJy1l4hbgB+A7w8SRvraqPjuN8p0zRV9VJizvJX7MwNgjwcVbwpeMSud4BfLJX7P+U5HssrGvxbFe5kmxm4YfrS0lgYXjkkSQXVdW/d5VrUb5rgcuBS1biF+ILmAfOXbS9rrevc0l+mIWS/1hVfbLrPD1vAN6c5FeAFwMvTfLRqnprx7meAp6qquOveu5ioei79kbgX6vqWYAknwR+DhhL0bcydPMN4Bd7f/4l4F86zLLYp4AtAEleBZxJx4sqVdXBqvrRqlpfVetZ+B/htStR8ktJMsvCS/83V9V/dxznIeC8JBuSnMnCG2V3d5yJLPx2vh04VFV/1nWe46rqvVW1rvczdRXwDxNQ8vR+rr+e5PzerkuAr3QY6bivAa9P8pLe9/QSxvgm8SlzRb+EG4Bbem9q/A+wveM8x+0Gdid5FHgeeFvHV6mT7i+BFwGf7b3a+FxVvb2LIFV1LMmNwH0szIjYXVWPdZHlBG8ArgEOJvlib9/7qmpfh5km3TuBj/V+YT8B/EbHeegNI90FPMLCMOUXGOMnZP1krCQ1rpWhG0nSSVj0ktQ4i16SGmfRS1LjLHpJapxFL0mNs+glqXEWvSQ17v8A4jC4XyETlKoAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4842,7 +4945,7 @@
     },
     {
      "data": {
-      "image/png": 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1XCTpl1UZn6+xvfpZdRsYUXu1+qib16g7HvuMpI9LuqxMn5d0f2W+T0l6XtJL6o5f3VimX6Xuhrgk6a8lbSvTv7/8vVTuv2pMdf1aeYwlSb/as46zkt7QM+1P1N2Z9kV136TeUFddkrarewTQE6WG90na0nR7qdt7CXVD/PHyc89m2kvSL0j6N3WPjnh3mfZHkm5Z7/mqOxR1RtLTqhz5sNo6N/D/vqG6JP2BpPOV9nlc3Z38fV/Tmuq6rTzu4+ruRF+orHNe3RA9I+kDKidu1lFXuW+vpM/2rK+u9voZdXPqvLqfMJ5cLzNG0V6cGQsAybV56AYAMAIEPQAkR9ADQHIEPQAkR9ADQHIEPQAkR9ADQHIEPQAk939sD3mjWTNOhAAAAABJRU5ErkJggg==\n",
+      "image/png": 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/ekx1/Vq5j1VJv9qzj/OSXt+z7I/UPZn2JXWfpF4/qbok7VR3BtCjpYY/lbSt6fZSt/cS6ob4I+Xnrq20l6RfkPSv6s6OeE9Z9geSbt7s71V3KOqcpCdUmfmw0T5H+H8fqS5JvyvpQqV9HlH3JH/fYzqhum4t9/uIuifRlyr7XFQ3RM9J+oDKGzcnUVe57YCkz/Xsb1Lt9dPq5tQFdV9hPL5ZZtTRXrwzFgCSa/PQDQCgBgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACT3v5M7dh7aDIKcAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4859,7 +4962,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAC7BJREFUeJzt3V+o5OdZB/DvY2pENrJepKyQBE9gQ3BpBPGQKr3Z+Ac2tttgUUkohaXRRTGgENBIb7wMiIJgoCwa9qZkCf7BpInECq65iZJEipt0jYSQ0l2EGApHNwpl6evFntiTNZvzb+b8Zp75fK52fjPMPu85c77zzvN75/3VGCMA9PV9UxcAwHwJeoDmBD1Ac4IeoDlBD9CcoAdoTtADNCfoAZoT9ADNfWzqApLk1ltvHWtra1OXsWvvvfdeDh06NHUZB2rVxrxq402MeZm8+uqr744xPr7d4xYi6NfW1vLKK69MXcaunT9/PsePH5+6jAO1amNetfEmxrxMquqbO3mc1g1Ac4IeoDlBD9CcoAdoTtADNCfoAZoT9ADNCXqA5hbiC1OshrXHnvvQ428//ukDrgRWi6Bnrm4U7rt9jDeD7/HzYrcEPTO3kyBie36OzIqghwUyq3DXJmMrQc9MzHv2ufX5hdXe+TmuJkHP0ukWVlo0zJvllQDNzWVGX1WHkvxDkt8fY3x1Hv8HJMs7uzeL5yDtKOir6skkn0nyzhjjE1uOn0jyx0luSvKnY4zHN+/63SRPz7hWFoywWm7L+ibJ7u20dXM2yYmtB6rqpiRPJLk/ybEkD1XVsar6+STfSPLODOsEYI92NKMfY7xYVWvXHb43yZtjjLeSpKrOJXkgyS1JDuVa+P9PVT0/xvjuzCoGYFdqjLGzB14L+q++37qpql9KcmKM8aubt7+Q5JNjjEc2b59K8u6NevRVdTrJ6SQ5cuTIT547d25fA5nClStXcsstt0xdxoHaOuYLlzcmrubG7rnt8EyeZ5a/42X5ea3663qZ3Hfffa+OMda3e9zclleOMc5uc/+ZJGeSZH19fSzjhXmX9YLC+7F1zKcWuEf/9uePz+R5Zvk7Xpaf16q/rjvaz/LKy0nu2HL79s1jACyQ/QT9y0nuqqo7q+rmJA8meWY2ZQEwKzsK+qp6KslLSe6uqktV9fAY42qSR5K8kORikqfHGK/Pr1QA9mKnq24eusHx55M8v9f/vKpOJjl59OjRvT4FB+zC5Y2F7jUvGt81YBFMugXCGOPZMcbpw4dns0ICgP/Ppma05Fufu7P153X2xKEJK2EebGoG0JygB2hO0AM0J+gBmps06KvqZFWd2dhY3D1AAJad5ZUAzVleCTPmS1IsGkFPe9bUs+qcjAVoTtADNCfoAZqzvBKgOcsrAZrTugFoTtADNCfoAZoT9ADNCXqA5gQ9QHOT7nVTVSeTnDx69OiUZbBC7HvDKrKOHqA5u1fCDNiamEWmRw/QnKAHaE7QAzQn6AGaE/QAzQl6gOZceASgOV+YAmhO6wagOUEP0JygB2hO0AM0J+gBmhP0AM3Zphj2yNbELAtBz8pytSlWhdYNQHOCHqA5e90ANGevG4DmtG4AmhP0AM0JeoDmBD1Ac4IeoDlBD9CcoAdoTtADNGdTM9ghu1WyrMzoAZozo4fYspjeBD3b2hqCj94zYSHAnmjdAB9w4fJG1h57zjmJRmxTDNCcbYoBmtO6AWhO0AM0J+gBmhP0AM0JeoDmBD1Ac4IeoDlBD9CcoAdoTtADNGf3SrjOB3frvJpTNvdiyZnRAzQn6AGaE/QAzQl6gOYEPUBzgh6gOUEP0JygB2jOxcEBmnNxcIDmtG4AmhP0AM0JeoDmBD1Ac4IeoDlBD9CcoAdoTtADNCfoAZoT9ADNCXqA5gQ9QHOCHqA5QQ/QnKAHaE7QAzQn6AGaE/QAzQl6gOYEPUBzgh6gOUEP0JygB2hO0AM0J+gBmhP0AM0JeoDmBD1Ac4IeoDlBD9Dcx6YugMWz9thzU5cAzNDMZ/RV9WNV9eWq+vOq+o1ZPz8Au7OjoK+qJ6vqnap67brjJ6rqjap6s6oeS5IxxsUxxq8n+ZUkn5p9yQDsxk5n9GeTnNh6oKpuSvJEkvuTHEvyUFUd27zvs0meS/L8zCoFYE92FPRjjBeTfPu6w/cmeXOM8dYY4ztJziV5YPPxz4wx7k/y+VkWC8Du7edk7G1JvrXl9qUkn6yq40k+l+QH8hEz+qo6neR0khw5ciTnz5/fRynTuHLlylLWvZ1H77l6w/uO/OBH39/Nqo03+eCYO76+P0zXv+X3zXzVzRjjfJLzO3jcmSRnkmR9fX0cP3581qXM3fnz57OMdW/n1Eesunn0nqv5wwurs1hr1cabXDfmC+/93/G3H//0RBXNX9e/5fft5xV8OckdW27fvnmMJWRJJfS1n+WVLye5q6rurKqbkzyY5JnZlAXArOx0eeVTSV5KcndVXaqqh8cYV5M8kuSFJBeTPD3GeH1+pQKwFztq3YwxHrrB8eezjyWUVXUyycmjR4/u9SkA2Make92MMZ4dY5w+fPjwlGUAtLZaywmAPdt6wr7zCpyO7F4J0JygB2hO62aFWTsPq2HSGX1VnayqMxsbG1OWAdCaVTcAzenRAzSnR79i9OVh9Qh6YNesqV8uWjcAzZnRN2XGBbzP8kqA5iad0Y8xnk3y7Pr6+q9NWUd3TsDCatOjB2hOj37B6K0Dsybol9CN3gy0aJjC9a87E5TFo3UD0JwZ/QLbSRvHLB7Yjhk9QHOTzuhX+eLgTrrSldf24rFNMUBzWjcAzTkZOwc+ugKLRNAvACtn6MqkZzFo3QA0Z0Y/I/OelZv1s+zM7qcj6PfhwuWNnBLAwIIT9AfIrByYwsoFvY+PwKrxzVhgUjf6pGsiNjuuMLXFbjcRe/Se3T0ncI2/i4NleSVAc4IeoLmVOxm7Fz5mAstM0N+AcIdpWSE3O1o3AM0t/YzehbKhv1nN7lf1U8LSB/1+eDOA5bOqYb0frYJecMNqEfo7o0cP0JygB2hu0qCvqpNVdWZjY2PKMgBamzToxxjPjjFOHz58eMoyAFprdTIWWF27PTG79fFnTxyaS02LQtADzMH1qwCnXBUk6IF2LLv8IEEPsEvL9kYi6AFmZFG/tGkdPUBzgh6gOa0boLWdtFMuXN7Iqc3H7eR60fut46D7+oIeYItF7bPvh6AH2IdleGPQowdoTtADNCfoAZqzTTFAc7YpBmhO6wagOcsrAQ7YQX95yoweoDlBD9CcoAdoTtADNCfoAZoT9ADNCXqA5gQ9QHOCHqC5GmNMXUOq6j+SfHPqOvbg1iTvTl3EAVu1Ma/aeBNjXiY/Osb4+HYPWoigX1ZV9coYY33qOg7Sqo151cabGHNHWjcAzQl6gOYE/f6cmbqACazamFdtvIkxt6NHD9CcGT1Ac4J+H6rqD6rqX6vqX6rqr6rqh6euad6q6per6vWq+m5VtV2lkCRVdaKq3qiqN6vqsanrmbeqerKq3qmq16au5aBU1R1V9fdV9Y3N1/VvTV3TPAj6/flakk+MMX48yb8l+b2J6zkIryX5XJIXpy5knqrqpiRPJLk/ybEkD1XVsWmrmruzSU5MXcQBu5rk0THGsSQ/leQ3O/6eBf0+jDH+doxxdfPmPya5fcp6DsIY4+IY442p6zgA9yZ5c4zx1hjjO0nOJXlg4prmaozxYpJvT13HQRpj/PsY4583//1fSS4muW3aqmZP0M/OF5P8zdRFMDO3JfnWltuX0jAA+J6qWkvyE0n+adpKZs/FwbdRVX+X5Ec+5K4vjTH+evMxX8q1j4BfOcja5mUnY4ZOquqWJH+R5LfHGP85dT2zJui3Mcb4uY+6v6pOJflMkp8dTdaqbjfmFXE5yR1bbt++eYxmqur7cy3kvzLG+Mup65kHrZt9qKoTSX4nyWfHGP89dT3M1MtJ7qqqO6vq5iQPJnlm4pqYsaqqJH+W5OIY44+mrmdeBP3+/EmSH0rytar6elV9eeqC5q2qfrGqLiX56STPVdULU9c0D5sn2R9J8kKunaB7eozx+rRVzVdVPZXkpSR3V9Wlqnp46poOwKeSfCHJz2z+DX+9qn5h6qJmzTdjAZozowdoTtADNCfoAZoT9ADNCXqA5gQ9QHOCHqA5QQ/Q3P8C5mVUsbyUzc4AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4876,7 +4979,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4893,7 +4996,7 @@
     },
     {
      "data": {
-      "image/png": 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XpOL+ByBgAKvYHGaKAAAAAElFTkSuQmCC\n",
+      "image/png": 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WB4F93UqL7cARVmoduxPAW7rttwB/M2Atz0kS4APA6ar6s4mHRldvkqsvrGBL8gPA61g5pvBp4Je6p42i1t6GPho8phsrs41HgC8DJ4Gd9X9H4W9npU/3D0ysGhm43jOs9JG/2N3umHjs1q7ex4AbRlDrL7DS1/wO8E3gvrHWOlHX61lZHfJ14Nah67lIfXcDTwH/3f3d3gS8FPg7Vq4G+yngJUPX2dX6k6y0Zb488X59/RjrBX4U+Puu1keAd3Xj17IyCTkD/CVw5dC19r15ZqwkNc7WjSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/wufLFArt3IzAAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4910,7 +5013,34 @@
     },
     {
      "data": {
-      "image/png": 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S9COS3iTpY2q3Wi+qZvXpft60bvoQEUsR8RMRMRERE2q/xXtbRHxb0lFJv1Gtvnm7pJWOt7+NZnta7be6N0bE9zoeOippv+1LbO9U+0L0P5SosWbHJe2qVmJcrPYF56OFa6pd1Zu+T9KpiPiTjoeOSrq1+vpWSZ/d7NoGJSLujIjt1et3v6S/j4ibJX1R0k3VYanGLDGjr9MxSe9S+4Lk9yT9ZtlyavVxSZdIerx6J/NkRNweESdtPyTpGbVbOu+PiP8tWGctIuKc7TskPSZpi6T7I+Jk4bIG4VpJt0hasv3Vat8HJd0l6SHbh9T+VNn3FKpvM/2upHnbfyjpK2r/AkyDO2MBIDlaNwCQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMkR9ACQHEEPAMn9P8jpylQufWYAAAAAAElFTkSuQmCC\n",
+      "image/png": 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tm21vV/tA9D/WUWMFTkvaUazEuEntg84na66pdEVv+lOSLkTEn3fcdFLSvcXP90r6wqBrq0pE3B8RW4vX8H5J/xARByV9RdLdxd1SjZkZfXlOSXqP2gckvy/p9+stp1R/KelmSU8Wn2SeiYj7IuK87ROSnlO7pfP+iPjfGussTUSs2D4q6QlJGyQ9EhHnay6rCu+WdI+kc7a/Xuz7kKQHJJ2wfVjtK8v+dk31DdIHJS3Y/lNJX1P7DTAFzowFgORo3QBAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACRH0ANAcgQ9ACT3f2Ak5cWwogo/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0 res\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kSUm1RZY7IWltD7fXkrlK2l5/JWksuzx2rucxu262B9toyccv6Q8kPZBdvkPS1yuSa7uk+3v1emq431+RdLWkV5pcf5Okb0qypA9Ler4iuYYlHejxtrpU0tXZ5fdI+rdzPI+Fbq9l16KPiCciYi6bfE7S+nMsdo2k4xHxRkT8SNKEpNsKznUsIl4r8j7akTNXz7dXtv6vZpe/KumTBd/fYvI8/sa8j0j6uG1XIFcpIuIZST9YZJHbJD0Udc9Jutj2pRXI1XMR8VZEfCe7/N+Sjklat2CxQrfXsiv0C/yu6p+CC62T9GbD9EmdvWHLEpKesH3E9mjZYTJlbK/+iHgru/wfkvqbLHeh7RdsP2e7qA+DPI//zDJZQ2NG0vsKytNKLkn6jezr/iO2NxScKa8qvwevs/2S7W/a/qVe3nHW5ffLkp5fcFWh26uSJwe3/ZSkD5zjqs9HxD9ly3xe0pykr1UpVw4fi4hp2z8v6Unb381aIWXn6rrFcjVORETYbjb864PZ9rpc0rdsT0XE693Ouoztl7Q3In5o+/dU/9bxqyVnqrLvqP6amrV9k6THJG3sxR3b7pP0j5L+OCL+qxf3Oa+ShT4iPrHY9ba3S7pF0scj6+BaYFpSY8tmfTav0Fw51zGd/T9t+xuqfz3vqNB3IVfPt5ftU7YvjYi3sq+op5usY357vWF7UvXWULcLfZ7HP7/MSdvnSVoj6T+7nKPlXBHRmOFB1fd9VEEhr6lONRbYiDhk++9sr42IQo+BY/tnVS/yX4uIR8+xSKHba9l13dgekfQnkm6NiP9psthhSRttX2b7fNV3nhU2YiMv26ttv2f+suo7ls85OqDHythe+yR9Jrv8GUlnffOwfYntC7LLayV9VNKrBWTJ8/gb894u6VtNGhk9zbWgH/dW1ft/q2CfpN/JRpN8WNJMQ1ddaWx/YH7fiu1rVK+BhX5gZ/f3FUnHIuLLTRYrdnv1cu9zN/4kHVe9L+vF7G9+JMQvSDrUsNxNqu/dfl31Loyic/266v1qP5R0StLjC3OpPnripezvaFVylbS93ifpnyV9T9JTkt6bza9JejC7/BFJU9n2mpJ0Z4F5znr8kv5S9QaFJF0o6R+y19+/Srq86G2UM9cXs9fSS5KelvSLPcq1V9Jbkv43e33dKeluSXdn11vSriz3lBYZidbjXDsattdzkj7Sg0wfU33f3MsNdeumXm4vfhkLAIlbdl03AIDWUOgBIHEUegBIHIUeABJHoQeAxFHoASBxFHoASByFHgAS9/+PWhCoUXVrKwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4927,7 +5057,7 @@
     },
     {
      "data": {
-      "image/png": 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DXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0fAO+4isY/ZZwAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4944,7 +5074,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4961,7 +5091,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4995,7 +5125,7 @@
     },
     {
      "data": {
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ngZ8HJlL0rYxuvgn80uDPvwz8W4dZZvsMsA4gyXnAmXR8UaWq2lNVP1ZVK6pqBf1vhNcsRMnPJcl6+r/6v6Wq/qfjOPcD5yZZmeRM+m+U3d1xJtL/6XwrsLeq/qLrPEdV1Q1VtXzwmroS+IcpKHkGr+unkpw/2HUJ8GiHkY56EnhdkpcM/k0vYYJvEp8yR/Rz2ATcPHhT43+BzR3nOeo24LYkDwPPA+/o+Ch12n0EeBHwxcFvG1+pqmu6CFJVR5JcB9xLf0XEbVX1SBdZjvEG4GpgT5KHBvveV1U7O8w07a4Hbh/8wH4c+K2O8zAYI90FPEh/TPk1JniGrGfGSlLjWhndSJKOw6KXpMZZ9JLUOItekhpn0UtS4yx6SWqcRS9JjbPoJalx/w+hNMLjvqn+gwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5012,7 +5142,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5029,7 +5159,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5049,7 +5179,7 @@
     },
     {
      "data": {
-      "image/png": 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0XW6+uyU9Iel5ZeNXq9L0U5TtiBOSfixpQZr+0nR9It1+Skl1fSTdx4SkDzesY4+k0xumXansYNrvlL1Ind6ruiQtVHYG0IOphq9Jmld1eynrvbiyEN+Z/j46l/aS9G5Jf1Z2dsQVadqXJF0w2+NVNhS1W9LDyp350GydXTzfu6pL0uck7c+1z05lB/lbbtMe1XVRut+dyg6ij+bWOawsRHdLukbpg5u9qCvdNiLpnob19aq93qwsp/Yre4fx0GyZUUR78clYAAiuzkM3AIACEPQAEBxBDwDBEfQAEBxBDwDBEfQAEBxBDwDBEfQAENx/AYejaVpYEezSAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5066,7 +5196,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5083,7 +5213,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5100,7 +5230,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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Olnp0/8jQipGe8y6y1Ed+qrvdOfTcrV3e54Dr+s7aZfoFlvqa3wa+CTw05XmvZ2l1yNeBW/vOc4Z89wMvAf/T/bveCLwN+ALwNeDzwFv7ztll/SmW2jJPDx2v109x3h8D/qHLexT4WDd+JUuTkEXgr4AL+846ys1fxkpS42zdSFLjLPSS1DgLvSQ1zkIvSY2z0EtS4yz0ktQ4C70kNc5CL0mN+z+J7QufEiViFwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5117,7 +5247,34 @@
     },
     {
      "data": {
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\n",
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0 res\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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9o+vfu5rOVV3/W0nfkfSKOrPNnxlSnp+V9C11zk3cW932W+r8wEnSGyT9maQzkv5e0tuH/bXrMdePV5+Xl9X5C+N0IbkelnS+6/vpSCG5PiPpdJXpuKQfKSHXkmMXVfOuGx4ZCwDJjePoBgDQB4oeAJKj6AEgOYoeAJKj6AEgOYoeAJKj6AEgOYoeAJL7XwdHYpyYra3nAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5134,7 +5291,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5151,7 +5308,7 @@
     },
     {
      "data": {
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ktys3Pl9he3XTcR8oqb0afdbNM5WNx94t6WeSjkvTpyRdnpvvBkn3SXpY2fjVGWn6Scp2xHlJ35W0Kk1/aro+n24/aUh1vSfdx7ykd7et44CkF7ZN+4yyg2m/V/Yi9cKq6pK0WtkZQLemGr4oaUXd7aWs9+LKQvyW9HfxIO0l6S2S/qjs7IiPpWmfknT2co9X2VDUfkl3KXfmQ6d19vF876suSR+XdDjXPrcoO8jfdZtWVNe56X5vUXYQfXNunVPKQnS/pEuVPrhZRV3ptmlJN7atr6r2epWynDqs7B3G7ctlRhntxSdjASC4Jg/dAABKQNADQHAEPQAER9ADQHAEPQAER9ADQHAEPQAER9ADQHD/Aw3Ffj00R+BxAAAAAElFTkSuQmCC\n",
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8leRgkp0AVXWgqq4DLgfOX64aJUmDLefI4g5gW++KJGuAW4ELgbOAHUnO6m67BNgL7FvGGiVJAyxbWFTVg8A3+1afCxysqmeq6kXgLuDSbvs9VXUh8J7lqlGSNNikP5R3GvBsz/Ih4LwkW4HLgHUcY2SR5FrgWoBTTjmF/fv3j1TEKT8IN77l5ZH2nTb2ZfqslH6AfZlWR44cGfn1b1iTDouBqmo/sH+IdruAXQBJ/vJd73rX10Y85AbgGyPuO23sy/RZKf0A+zKtxunLDw/TaNJhcRg4vWd5Y3fdglXVG0YtIskXq2rLqPtPE/syfVZKP8C+TKvl6MukL519GDgjyaYkxwFXAHsmXJMkqc9yXjp7J/AF4E1JDiV5X1W9DFwP3A8cAD5TVU8sV02SpOEs2zRUVe2YZ/0+Jn957K4JH38x2Zfps1L6AfZlWi15X1JVS30MSdKMm/Q5C0nSDFg1YZHk9Uk+m+Qr3X9PnKfdT3fbfCXJT/esPy7JriR/luRPk/z48lX//9U4Vl96tu/p//qV5TZOX5Icn2Rv9/F4IsmHl7f6wV9X07d9XZK7u9sfSjLXs+0D3fVPJflHy1n3IKP2JckFSR5J8lj333cvd+19dY78mHS3/1CSI0l+Yblqns+Yz6+/leQL3f8bjyV59VjFVNWq+AFuAXZ2b+8EPjKgzeuBZ7r/nti9fWJ32y8DN3dvvwrYMKt96W6/DPg08PisPi7A8cC7um2OA/4rcOEy1r4GeBr4ke7x/wQ4q6/NPwM+3r19BXB39/ZZ3fbrgE3d+1kzwcdhnL6cA/yN7u2zgcOz2I+e7fcAu4FfmFQ/FuExWQt8GXhrd/mkcZ9fE/tFTOAX/xRwavf2qcBTA9rsAH6jZ/k3gB3d288CJ0y6H4vUl9cAf9R9wZp0WIzVl752HwWuWcba3w7c37P8AeADfW3uB97evb2Wzgen0t+2t92EHoeR+9LXJnS+1mfdLPYD+MfArwIfmoKwGOf5dRHwO4tZz6qZhgJOqaqvd2//BXDKgDaDvn7ktCSv6y7/SpJHk+xOMmj/5TJyX7q3fwX4d8D/WbIKhzduXwDoPkbbgQeWosh5NOvqbVOdS8Wfp/Mub5h9l9M4fen148CjVfWdJaqzZeR+JHkN8K/ozCJMg3Eek78JVJL7u69ZvzhuMZP+BPeiSvKHwF8fsOmDvQtVVUkWchnYWjqfLv9vVfXzSX4e+LfAT41cbMNS9SXJ24A3VtW/7J+rXSpL+Lgcvf+1wJ3Af6yqZ0arUuNK8mbgI8A/nHQtI/oQ8B+q6kiSSdcyrrXA3wX+Np03hQ8keaSqRn4ztaLCoqr+wXzbkvyPJKdW1deTnAo8N6DZYWBrz/JGOt9R9T/p/MJ/r7t+N/C+xah5PkvYl7cDW5J8lc7jf3KS/VW1lSWyhH05ahfwlar6tUUodyGG+bqao20OdUNtPZ3n06J91c0iGacvJNkI3AtcWVVPL3258xqnH+cBP5HkFuB1wHeTfLuqPrb0ZQ80Tl8OAQ9W1TcAkuwDfpRxRt6TnJNb5vm/X+WVJ1JvGdDm9cCf0zl5emL39uu72+4C3t29fRWwe1b70tNmjsmfsxj3cbkZ+F3gVROofS2dk+2b+P4JyDf3tfnnvPIE5Ge6t9/MK09wP8NkT3CP05fXddtfNsnn0rj96GvzISZ/zmKcx+RE4FE6F4GsBf4QuHiseib94C7jL/4kOqn6le4v7uiLzRbgN3vaXQ0c7P68t2f9DwMP0rnC4AHgh2a1Lz3b55h8WIzcFzrvtIrOV8V8qfvzT5e5/ouAP6Nz1coHu+v+DXBJ9/ar6YxEDwJ/DPxIz74f7O73FMt4Fddi9wX4JeCFnsfgS8DJs9aPvvv4EBMOi0V4fv0k8ATwOAPehC30x09wS5KaVtPVUJKkERkWkqQmw0KS1GRYSJKaDAtJUpNhIUlqMiwkSU2GhSSp6f8BCSgjEO3ZrlMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5168,7 +5325,7 @@
     },
     {
      "data": {
-      "image/png": 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0fXaDoAeA5Bi6AYDkCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASO7/AYI5ilAwO9VmAAAAAElFTkSuQmCC\n",
+      "image/png": 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n0EtScQa9JBVn0EtScQa9JBVn0EtScf8HBkyiQ790w2sAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5185,7 +5342,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5202,7 +5359,7 @@
     },
     {
      "data": {
-      "image/png": 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6s67zHFNVN1XV2v7P1OXA309AydP/uX46yfn9XRcD3+ow0jFPAW9N8ur+9/RixniReMmc0S/gGuDj/Ysa/w1s6TjPMbcCtyZ5DHgR+M2Oz1In3V8CrwAe6D/b+GpVXdtFkKo6muR64D56MyJurarHu8hynLcDVwJ7k3y9v+9DVbWrw0yT7gbgtv4f7CeB3+o4D/1hpDuBR+kNU36NMb5C1lfGSlLjWhm6kSSdgEUvSY2z6CWpcRa9JDXOopekxln0ktQ4i16SGmfRS1Lj/hcXi3q2pbEsCQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5219,7 +5376,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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PwKlN2x7wVwPv/WSzr+0HfqflmvbTH7M9uo8dvbrsJ4A9q23zGfTX3zb7zzP0Q+y05bU1r9/x+W2zrmb+3Uf3q4G2M+mzVbKhk/3LO2Mlqbi1MnQjSZqQQS9JxRn0klScQS9JxRn0klScQS9JxRn0klScQS9Jxf0/hh/iFcDtEyAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5236,7 +5393,7 @@
     },
     {
      "data": {
-      "image/png": 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AJxuMdNSzwLsj4k3d7+lFjPBD4tPmjL6Hq4Gbux9q/DfdG5q0wHZge/c+u68CH2z4LLXtPgu8Afha993Gg5l5TRNBMvPIfCzYNwIXAlcBeyPise6+j3fXnNLxXQ/c1v2F/Qzwmw3noTuMdCfwKJ1hym8ywitkvTJWkoqrMnQjSToBi16SirPoJak4i16SirPoJak4i16SirPoJak4i16SivtfajdrfIHTsy8AAAAASUVORK5CYII=\n",
+      "image/png": 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PV9WfdJ3nmKp6T1Vt7D+mrgE+NwElT/9x/c0kr+qvugR4rMNIx3wDeEOSl/R/p5cwxjeJT5sj+kXcAHy4/6bG/wDbOs5zzE5gZ5JHgBeA3+r4KHXS/TlwJvDZ/quNL1XV27oIUlVHk9wI3E9vRsTOqnq0iyzHuRi4Ftif5Kv9de+tqj0dZpp07wBu6z9hPwn8dsd56A8j3Qk8TG+Y8iuM8ROyfjJWkhrXytCNJOkELHpJapxFL0mNs+glqXEWvSQ1zqKXpMZZ9JLUOItekhr3f+delnJpg9I5AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5256,7 +5413,7 @@
     },
     {
      "data": {
-      "image/png": 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WPzn9vpjuP6emut6RjrEo6e19+zgu6YV9y/5T2cW0Hyp7k3phU3VJ2qhsBtDdqYZPSFrXdnsp6724shC/K/1cM0p7SXq9pJ8omx3x4bTs3yVdutbjVTYUdUzSfcrNfFhpn0O83oeqS9K/SjqRa5+7lF3kH/icNlTX5em4dym7iD6b2+eMshA9JulTSh/cbKKudN9OSd/u219T7fWPynLqhLK/MO5ZKzOqaC8+GQsAwXV56AYAUAGCHgCCI+gBIDiCHgCCI+gBIDiCHgCCI+gBIDiCHgCC+yvp70WnA/R+1QAAAABJRU5ErkJggg==\n",
+      "image/png": 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vVedk2pfU+SX12knVJWm3OjOAHi41/KmkHU0fL3VGL6FOiD9U/r1jlOMl6S2S/k2d2RHvKcv+UNKN2z1edVpR5yQ9psrMh832OcTP+1B1SfpdSRcrx+chdU7y93xOJ1TXTeV+H1LnJPrRyj4X1AnRc5I+pPLBzUnUVW47LOlzXfub1PH6cXVy6qI67zAe3S4z6jhefDIWAJJrc+sGAFADgh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4Akvt/L8BsZDe0ej0AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5273,7 +5430,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADUFJREFUeJzt3V+IXGcZx/Hfz2ilRF2FyApJcAtbiosRxKFVvNn6BzbatVpUGkuxqF0EAwoBjSh4W9EKFouy2JAbaSj4r2tSUhWX3KgkLcUkxpZQWppFjKUwmloowceLbOp0m8memTkz78xzvp+rnTOTmefd7P72ned9zxlHhAAAeb2udAEAgOEi6AEgOYIeAJIj6AEgOYIeAJIj6AEgOYIeAJIj6AEgOYIeAJJ7fekCJGnbtm0xMzNTuoyevfjii9q6dWvpMkaqaWNu2nglxjxJHnvssecj4u2bPW4sgn5mZkYnTpwoXUbPVldXNT8/X7qMkWramJs2XokxTxLbz1Z53FBaN7a32j5h+5ZhPD8AoLpKQW/7gO3ztk9tOL5g+0nbZ23v77jrG5IeqrNQAEB/qs7oD0pa6Dxge4uk+yXtljQnaY/tOdsflfRXSedrrBMA0KdKPfqIOGZ7ZsPhGyWdjYinJcn2IUm3SnqTpK26FP4v2T4SEf+trWIAQE8GWYzdLum5jtvnJN0UEXslyfZdkp7vFvK2lyQtSdL09LRWV1cHKKWMCxcuTGTdg2jamJs2XokxZzS0XTcRcXCT+5clLUtSq9WKSVzxntSV+kE0bcxNG6/EmDMaZNfNmqSdHbd3rB8DAIyRQYL+uKTrbV9n+xpJt0t6uJ6yAAB1qdS6sf2gpHlJ22yfk/SdiHjA9l5JRyVtkXQgIk738uK2FyUtzs7O9lY1gNeY2X/4la+fuefjBSvBuKm662ZPl+NHJB3p98UjYkXSSqvVurvf58DkI6D61/m9A7oZi0sgIC+CqB58HzEIgh5jpVugNXGmP0i48y4JnQh61I7ZZ/+G8b0j9FE06FmMBUaL0G+mokHPYmweo5zFZwsr3gFh2GjdYCIQhvXL9gcT3fFRggCQHDN69I1ZNjAZCHpMtEltP/BHEqNUtHVje9H2crvdLlkGAKRWNOgjYiUilqampkqWAQCp0bpBGuPexqFdg1LYdQMAyRH0AJAcQQ8AyRH0AJAcFzVDSuOyMMsCLMYB2yvRk5Nrbc3sP0yAAROE1g0AJMc+egCveod2cGFrwUowDMzoASA5gh4AkiPoASA5tlcivXHZagmUwvZKAEiOXTdAzTjHAOOGHj0AJEfQA0ByBD0AJEfQA0ByBD0AJEfQA0ByBD0AJFc06G0v2l5ut9slywCA1DgzFgCS48xYNArXvUETEfRADbjsAcYZi7EAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJEfQAkBxBDwDJFT1hyvaipMXZ2dmSZWATnScD7dtVsBAAfeFaNwBe5eRaWzP7D3O2byK0bgAgOYIeAJIj6AEgOYIeAJIj6AEgOa5HD/SJXSmYFAQ9GotPm0JT0LoBgOQIegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOQIegBIjqAHgOSKBr3tRdvL7Xa7ZBkAkBqfMAUAydG6AYDkCHoASI6gB4DkuEwxUBHXn8ekIugBcW165EbrBgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDmCHgCSI+gBIDkuaobX4CqNQC4EPXAVJ9fauos/fJhwBD2wQec7mn27ChYC1IQePQAkR9ADQHIEPQAkR9ADQHK1L8bafpekr0raJun3EfHjul8DwGjwWbo5VJrR2z5g+7ztUxuOL9h+0vZZ2/slKSLORMSXJX1W0gfrLxkA0IuqrZuDkhY6D9jeIul+SbslzUnaY3tu/b5PSDos6UhtlQIA+lIp6CPimKQXNhy+UdLZiHg6Il6WdEjSreuPfzgidku6o85iAQC9G6RHv13Scx23z0m6yfa8pNskvVFXmdHbXpK0JEnT09NaXV0doJQyLly4MJF1b2bfrotd75u+9ur3Z9O08Urdx5zxZ/2yrL/Ll9W+GBsRq5JWKzxuWdKyJLVarZifn6+7lKFbXV3VJNa9maud8r9v10Xde7I5J1Q3bbxS9zE/c8f86IsZkay/y5cNsr1yTdLOjts71o8BAMbIIEF/XNL1tq+zfY2k2yU9XE9ZAIC6VN1e+aCkP0q6wfY521+MiIuS9ko6KumMpIci4nQvL2570fZyu93utW4AQEWVmo8RsafL8SMaYAtlRKxIWmm1Wnf3+xwAgKvjEggAkBxBDwDJEfQAkFzRoGcxFgCGr2jQR8RKRCxNTU2VLAMAUqN1AwDJEfQAkFyzLuKBrmaucn0bAJONGT0AJFd0Rm97UdLi7OxsyTIAVMDHCk4udt0AQHK0bgAgOYIeAJIj6AEgOYIeAJIj6AEgOS5qBgDJsb0SAJKjdQMAyRH0AJAcFzVrMC5kBjQDQd8whDvqwHVvJgutGwBIjqAHgOTYRw8AybGPHgCSYzG2AViABZqNHj0AJEfQA0ByBD0AJEfQA0ByLMYmxQIsgMuY0QNAcpwwBQDJFW3dRMSKpJVWq3V3yToA9G9jm5CLnI0fevQTjqsIAtgMPXoASI6gB4DkCHoASI6gB4DkWIxNhJOkAFwJQQ+gVlUmHOwQGy1aNwCQHEEPAMnRupkQnBgFoF/M6AEguaIzetuLkhZnZ2dLllEcs3UAw8RFzQqpsjOB7ZIA6kDrBgCSYzF2AjHTx6SjXTlaBP2Q8QMNoDRaNwCQHEEPAMnRuhkz9N8B1I2gB1AU61jDR+sGAJJjRg9gbDC7Hw5m9ACQHEEPAMnRuqkJbzkBjCuCHsBY6rbVmIlU7wj6EWKPPIAS6NEDQHJFg972ou3ldrtdsgwASK1o0EfESkQsTU1NlSwDAFKjdQMAybEY2wXbJQFkQdBXQOgD44/f0+4I+h51/jAdXNhasBIAqIagHwL2ywMYJyzGAkByzOgBTBR68b1jRg8AyTGjBzCxWA+rhhk9ACTHjH4AJ9fauosZBYAxR9ADSIcF21ejdQMAyTViRt/trzsLOQCaoBFBD6C5qkz0sl/OpHFBzywewEadGysy9vTp0QNAco2b0QNorl7f0WfZvcOMHgCSI+gBILm0rRsWXQH0I2N2MKMHgOQIegBIjqAHgOSG0qO3/UlJH5f0FkkPRMSjw3gdAChh0rZdVg562wck3SLpfES8u+P4gqQfStoi6acRcU9E/ErSr2y/TdL3JRH0ALBu1H8oemndHJS00HnA9hZJ90vaLWlO0h7bcx0P+fb6/QCAQioHfUQck/TChsM3SjobEU9HxMuSDkm61Zd8V9IjEfF4feUCAHrliKj+YHtG0m8ut25sf1rSQkR8af32nZJukvSUpM9LOi7piYj4yRWea0nSkiRNT0+/79ChQwMNZKOTa+1an+9Kpq+V/vHS0F9mrDRtzE0br8SYu9m1feqVrzvzpcrxjao+bjM333zzYxHR2uxxQ1mMjYj7JN23yWOWJS1LUqvVivn5+VprGMVH/O3bdVH3nkx7ztkVNW3MTRuvxJi7eeaO+Ve+7syXKsc3qvq4ugz6v7kmaWfH7R3rxwAglW5nzE7CmbSDBv1xSdfbvk6XAv52SZ8buKoeTMI3GUDzbMymktswe9le+aCkeUnbbJ+T9J2IeMD2XklHdWl75YGION3Dcy5KWpydne2tagCYMCUnpZWDPiL2dDl+RNKRfl48IlYkrbRarbv7+fcAgM1xCQQASI6gB4DkCHoASK5o0NtetL3cbg//5CYAaKqiQR8RKxGxNDXV/5lhAICro3UDAMkR9ACQHEEPAMn1dPXKoRVh/1PSs6Xr6MM2Sc+XLmLEmjbmpo1XYsyT5J0R8fbNHjQWQT+pbJ+oconQTJo25qaNV2LMGdG6AYDkCHoASI6gH8xy6QIKaNqYmzZeiTGnQ48eAJJjRg8AyRH0A7D9Pdt/s/0X27+0/dbSNQ2b7c/YPm37v7bT7lKQJNsLtp+0fdb2/tL1DJvtA7bP2z5VupZRsb3T9h9s/3X95/qrpWsaBoJ+ML+V9O6IeI+kpyR9s3A9o3BK0m2SjpUuZJhsb5F0v6TdkuYk7bE9V7aqoTsoaaF0ESN2UdK+iJiT9H5JX8n4/0zQDyAiHo2Ii+s3/6RLH46eWkSciYgnS9cxAjdKOhsRT0fEy5IOSbq1cE1DFRHHJL1Quo5Rioi/R8Tj61//W9IZSdvLVlU/gr4+X5D0SOkiUJvtkp7ruH1OCQMA/2d7RtJ7Jf25bCX1q/yZsU1l+3eS3nGFu74VEb9ef8y3dOkt4M9GWduwVBkzkIntN0n6uaSvRcS/StdTN4J+ExHxkavdb/suSbdI+nAk2au62ZgbYk3Szo7bO9aPIRnbb9ClkP9ZRPyidD3DQOtmALYXJH1d0ici4j+l60Gtjku63vZ1tq+RdLukhwvXhJrZtqQHJJ2JiB+UrmdYCPrB/EjSmyX91vYTtn9SuqBhs/0p2+ckfUDSYdtHS9c0DOuL7HslHdWlBbqHIuJ02aqGy/aDkv4o6Qbb52x/sXRNI/BBSXdK+tD67/ATtj9Wuqi6cWYsACTHjB4AkiPoASA5gh4AkiPoASA5gh4AkiPoASA5gh4AkiPoASC5/wF2rew5wflICwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5290,7 +5447,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5307,7 +5464,7 @@
     },
     {
      "data": {
-      "image/png": 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K8FngLX3XOVHvjzBuyzw18Zm9aog1Az8A/F1X6xHgY934xYwnIseATwFn913rtDfPjJWkxtm6kaTGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXufwG9nw1OhLg9BwAAAABJRU5ErkJggg==\n",
+      "image/png": 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wBNha3zn6fidL/bl/YGTFSM95F1jqIz/T3e4aeey2Lu8p4AN9Z+0y/RxLPc1vAd8AHhl43utZWhnyNeC2vvO8Rb4HgJeB/+5+rjcC7wL+Dvgq8HngnX3nHMn74yy1ZZ4d+Z29fqiZgR8G/r7LewL4ZDd+BUsTkQXgz4EL+856rpufjJWkxtm6kaTGWeglqXEWeklqnIVekhpnoZekxlnoJalxFnpJapyFXpIa97+FmDZRYZTE9wAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5324,7 +5481,34 @@
     },
     {
      "data": {
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\n",
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0 res\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5341,7 +5525,7 @@
     },
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAC6tJREFUeJzt3WGo3eddB/Dvz9Sqm3gVkzemrYmkVIMglUudDmS4CRk1q4hoK+6FlIYOO6cIkongWwciOiyOsNaizpZRhzQ22r2Yo2/GaNINbBsLoW5r6qSpwyi+qcWfL+6tu8t203Nzzsn/3Od+Pq9ynpxz7o8/ud/z5Pc8/+dUdweAcX3b1AUAsFyCHmBwgh5gcIIeYHCCHmBwgh5gcIIeYHCCHmBwgh5gcDdMXUCS7N+/vw8dOjR1GQC7yrlz517r7gNv9bxJg76qjic5fuTIkZw9e3bKUgB2nar68izPm7R1092nu/vE2tralGUADE2PHmBwgh5gcJMGfVUdr6pTly9fnrIMgKHp0QMMTusGYHCCHmBwevQAg5v0hqnuPp3k9Pr6+n1T1rGXHDr55P//+Ut/cOeElQDXy0ocgcA0tob+Vj4AYCyCnm9i1g9jEfR7wHYzd2BvWJlDzVhNs35ImPnD6rIYy0Jo98Dq0rph4YQ+rBZBz1IJfZieoOe6EfowDUcgAAzOrptdbrtZsi2VwJvsumES2jhw/VR3T11D1tfX25eDz27k2brQh9lV1bnuXn+r5+nRAwzOrhtWioPWYPHM6AEGJ+gBBucbpgAGN2nQd/fp7j6xtrY2ZRkAQ7MYy65g3z1cOz16gMEJeoDBCXqAwenR7xIjH3sALJcZPcDgzOhXjJn7W5vlGtmZA1/nPPqJ2C64XM7Mga9zTPFEzNxXiw8AdqNZjynWurmOhDswBUG/ZMJ9d9DqYWSCfkEEOrCqBD1chR0+jEDQw5yu/DAQ/KwaQQ9LZBstq0DQ75BePLDbCHpYMJMBVo2gh+vEFk6m4lAzgMEJeoDBOdQMJrbdzhw7dlgUh5rNwOIaq0To86ZZDzXTugEYnKAHGJztlbDL2KbJTgl6GITFW7Yj6GFAZv1sJei/BbtsgJEIethDtHf2JrtuAAZnRg97lG/P2jvM6AEGZ0YPbMs5PGMQ9MBM7EbbvQT9Jv+I4dqY3a8+QQ8sjNBfTRZjAQZnRg8sxaztUDP/5Vv4jL6qfqSqPlZVj1fVBxb9/gDszExBX1UPV9WrVfXcFePHqurFqrpQVSeTpLvPd/f9SX4pyTsXXzIAOzFr6+aRJH+a5C/eHKiqfUkeTPKzSS4meaaqnujuF6rqfUk+kOQvF1suMBonbS7fTEHf3U9X1aErhu9IcqG7X0qSqnosyV1JXujuJ5I8UVVPJvnrb/WeVXUiyYkkueWWW66p+HnZUgmryw6exZlnMfZgkpe3PL6Y5Ceq6l1JfiHJdyQ5s92Lu/tUklPJxpeDz1EHMDihP5+F77rp7s8m+eyi3xfgWviQmC/oX0ly85bHN22OAVwX+vuzmWd75TNJbq2qw1V1Y5K7kzyxkzeoquNVdery5ctzlAHA1cw0o6+qR5O8K8n+qrqY5Pe7+6GqeiDJU0n2JXm4u5/fyQ/v7tNJTq+vr9+3s7KBvWqWTRQ2WnyjWXfd3LPN+JlcZcEVgOk5AgHYM/bq+fqTBn1VHU9y/MiRI1OWAexBe6m9M2nQ69EDq2bE2b1jigEGJ+gBBjdp0NtHD7B8evQA2xjlzlvbKwF2aLct2OrRAwxO0AMMzmIswOAmDfruPt3dJ9bW1qYsA2Boe24xdi/d9gyQ6NEDDE/QAwzOYizA4NwZCzCH3XDzlNYNwOAEPcDgBD3A4AQ9wOAEPcDgbK8EGJyzbgAGp3UDMLg9d6gZwLKs6lcPmtEDDE7QAwxO0AMMTtADDM5iLMCSTX3CpRk9wODcGQswOHfGAgxuT/Tot7uJAWAv0KMHGJygBxicoAcYnKAHGJygBxicoAcYnKAHGJygBxicoAcYnLNuAAbnrBuAwWndAAxO0AMMbk+cXgmwKq48Tfd6fOOUGT3A4AQ9wOAEPcDgBD3A4AQ9wOAEPcDgBD3A4AQ9wOCGvWHqypsSAPYqM3qAwQl6gMEJeoDBCXqAwQl6gMEtfNdNVf18kjuTfE+Sh7r704v+GQDMbqYZfVU9XFWvVtVzV4wfq6oXq+pCVZ1Mku7+2+6+L8n9SX558SUDsBOztm4eSXJs60BV7UvyYJL3Jjma5J6qOrrlKb+3+fcATGimoO/up5N87YrhO5Jc6O6Xuvv1JI8luas2fCTJ33f3s9u9Z1WdqKqzVXX20qVL11o/AG9hnsXYg0le3vL44ubYB5O8J8kvVtX92724u09193p3rx84cGCOMgC4moUvxnb3R5N8dNHvC8C1mWdG/0qSm7c8vmlzDIAVMk/QP5Pk1qo6XFU3Jrk7yRM7eYOqOl5Vpy5fvjxHGQBczazbKx9N8rkkt1XVxaq6t7vfSPJAkqeSnE/yye5+fic/vLtPd/eJtbW1ndYNwIxm6tF39z3bjJ9JcmahFQGwUJMegaB1A7B8kwa91g3A8jnUDGBwgh5gcIIeYHAWYwEGZzEWYHBaNwCDE/QAgxP0AIOzGAswOIuxAIPTugEYnKAHGNzCv0pwSodOPjl1CQArx2IswOAsxgIMTo8eYHCCHmBwgh5gcIIeYHCCHmBwtlcCDM72SoDBad0ADE7QAwxO0AMMTtADDE7QAwxO0AMMTtADDM4NUwCDc8MUwOC0bgAGJ+gBBifoAQYn6AEGJ+gBBifoAQYn6AEGJ+gBBifoAQYn6AEGJ+gBBudQM4DBOdQMYHBaNwCDE/QAgxP0AIMT9ACDE/QAgxP0AIMT9ACDE/QAgxP0AIMT9ACDE/QAgxP0AIO7YeoC5nXo5JNTlwCw0szoAQYn6AEGJ+gBBrfwoK+qH6qqh6rq8UW/NwA7N1PQV9XDVfVqVT13xfixqnqxqi5U1ckk6e6XuvveZRQLwM7NOqN/JMmxrQNVtS/Jg0nem+Roknuq6uhCqwNgbjMFfXc/neRrVwzfkeTC5gz+9SSPJblrwfUBMKd5evQHk7y85fHFJAer6vur6mNJbq+qD2/34qo6UVVnq+rspUuX5igDgKtZ+A1T3f3vSe6f4XmnkpxKkvX19V50HQBsmCfoX0ly85bHN22O7di5c+deq6ovz1HLqtqf5LWpi1hRrs32XJvtDXdt6iNzvfwHZ3nSPEH/TJJbq+pwNgL+7iS/ci1v1N0H5qhjZVXV2e5en7qOVeTabM+12Z5rc21m3V75aJLPJbmtqi5W1b3d/UaSB5I8leR8kk929/PLKxWAazHTjL6779lm/EySMwutCICFcgTCcp2auoAV5tpsz7XZnmtzDarbhheAkZnRAwxO0C9BVd1cVf9YVS9U1fNV9aGpa1olVbWvqr5QVX83dS2rpqq+t6oer6p/rqrzVfWTU9e0CqrqtzZ/l56rqker6junrmk3EfTL8UaS3+7uo0nekeTXnQP0DT6UjZ1afLM/SfIP3f3DSX4srlOq6mCS30iy3t0/mmRfNrZzMyNBvwTd/dXufnbzz/+VjV/Wg9NWtRqq6qYkdyb5+NS1rJqqWkvy00keSpLufr27/2PaqlbGDUm+q6puSPK2JP86cT27iqBfsqo6lOT2JJ+ftpKV8cdJfifJ/05dyAo6nORSkj/fbG19vKrePnVRU+vuV5L8YZKvJPlqksvd/elpq9pdBP0SVdV3J/mbJL/Z3f85dT1Tq6qfS/Jqd5+bupYVdUOSH0/yZ919e5L/TnJy2pKmV1Xfl42TcQ8n+YEkb6+qX522qt1F0C9JVX17NkL+E939qanrWRHvTPK+qvpSNo61/pmq+qtpS1opF5Nc7O43//f3eDaCf697T5J/6e5L3f0/ST6V5KcmrmlXEfRLUFWVjT7r+e7+o6nrWRXd/eHuvqm7D2VjMe0z3W1mtqm7/y3Jy1V12+bQu5O8MGFJq+IrSd5RVW/b/N16dyxS78jCjykmycbM9f1J/qmqvrg59rubR0bA1XwwySeq6sYkLyX5tYnrmVx3f37zO6ifzcaOti/EHbI74s5YgMFp3QAMTtADDE7QAwxO0AMMTtADDE7QAwxO0AMMTtADDO7/AI+ua3Q1RbwGAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5358,7 +5542,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5375,7 +5559,7 @@
     },
     {
      "data": {
-      "image/png": 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4C+A3MvM/IuI771Vq5zAM+g3IzJ97t/cj4k7gg8DP5nfnr07UGrtrtXEVE9XGNVRqy1pei4j3ZearEfE+4PW2KzSqiPg+lkP+s5n5l/3icu0clkM3DYuI3cBvAjdl5n8NvHUY2BsRF0bEFcCVwD+2UccxqtTGkddEniCHgTv6P98BTPQ3tljuuj8CnMzMPxh4q1Q718MbphoWEaeAC4Fv9Yu+lJn39t+7n+Vx+yWWv04+df69dFtE/ALwx8ClwL8DX8nM9/ffK9FGgIj4eeAPgS3Ao5n58ZarNLL++s9zLD/J8TXgY8DngceBH2H5KbK/lJkrL9hOjIj4GeDvgBPAt/vFv83yOH2Zdq6HQS9JxTl0I0nFGfSSVJxBL0nFGfSSVJxBL0nFGfSSVJxBL0nFGfSSVNz/ATE5Yc+CQtMwAAAAAElFTkSuQmCC\n",
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HxNsR0Vr1WhE1wrprIo+0tdZ/jogfjoivRsQ/t//7Q022sV8RcWNE/HVE/GP7PP2t9v6i6uyGQT94XwVuycyfAl4CHgZor6e7G/hJVtbf/Xx73d1RdIqVReCf79xZUo1XWBN51B1m1frPwEHga5m5Hfhae3uULQMHMnMH8NPAb7b/7UqrszKDfsAy8yvtKZwBvs676+feAyxk5v9k5r8A51hZd3fkZOaZzDy7xkvF1Mj6ayKPtHXWf74H+GL75y8CH97URg1YZr6amd9o//yfrEyjvo3C6uyGQV+vXweebf+8DXil47UL7X0lKanGkmrZyGRmvtr++V+BySYbM0jtta4/APwdBde5keFbQHEERMRfAT+yxkuPZOZftt/zCCt/Qq65OPqwq1KjypOZGRFFDMWLiAngz4Dfzsz/iIjvvlZSnVUY9D3IzF+40usRsRf4EPDz+e741b7X2N1MG9W4jpGqcQMl1bKRSxHx/sx8NSLeD7zWdIP6FRHfx0rIfykz/7y9u7g6q7LrZsAiYhb4HeDuzPyvjpeOALsj4uqIuAnYDvx9E22sUUk19r0m8gg5Atzf/vl+YKT/YouVW/fHgTOZ+YcdLxVVZzd8YGrAIuIccDXw7faur2fmQ+3XHmGl336ZlT8nn137KMMtIn4Z+GPgeuDfgW9m5h3t14qoESAifgn4I2ALcCgzP91wk/rWXv95hpWZHC8BnwS+DDwJ/Cgrs8h+NDNXf2E7MiLiZ4G/AU4Cb7d3/y4r/fTF1NkNg16SCmfXjSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalw/weeQfJYYsiyfAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5392,7 +5576,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5409,7 +5593,7 @@
     },
     {
      "data": {
-      "image/png": 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bKlXV4ar6saraUFUb6P1HeM1SlPxCkkzT+9H/zVX13x3HuQc4P8l5Sc6kd6Hs9o4zkd6r803Aw1X1513nOaGq3ldV6/vfU5cD/zABJU//+/rxJK/s77oYeKjDSCd8E3h9kpf0/00vZowXiZfNiH4BVwEf7l/U+B9gV8d5TtgL7E3yAPAc8I6OR6mT7q+AFwFf6P+08eWqurqLIFV1PMk1wF30VkTsraoHu8hykjcAVwCHk9zf3/f+qjrQYaZJdy1wc/8F+1HgtzrOQ38a6TbgPnrTlF9ljO+Q9Z2xktS4VqZuJEmnYNFLUuMseklqnEUvSY2z6CWpcRa9JDXOopekxln0ktS4/wOkH4w5UNXuhQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5426,7 +5610,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAD85JREFUeJzt3XHMXfVdx/H31zImaWcHdnlEIBYsIVYalT6BTefyNFP2ACvMhcw2ZFLHaFCbuKRGa2Ym8R9Bg39giKRK080sFJxutlACqDT8MxBKgJYxoCM1a8NaEdNZJJl1X/+4p+bm6XOf59zbe+65z+95v5Invfec33nOt+ee+3nu/Z3fOScyE0lSuX6k7QIkSc0y6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFO6ftAgBWrFiRK1euHGjZd999l6VLlw63oCGwrv5YV3/GtS4Y39pKrGv//v1vZ+aH5m2Yma3/rF27Ngf11FNPDbxsk6yrP9bVn3GtK3N8ayuxLuD5rJGxdt1IUuFaDfqIWB8R20+cONFmGZJUtFaDPjP3ZObm5cuXt1mGJBXNrhtJKpxBL0mFM+glqXAejJWkwnkwVpIKNxZnxqpdK7c9Wrvt4btuaLASSU2wj16SCmfQS1LhDHpJKpyjbiSpcI66kaTC2XUjSYUz6CWpcI6jL9hs4+O3rjnFpj7GzUta+Ax69aXuyVWeWCWND4NejfAPgjQ+HF4pSYVzeKUkFc5RN5JUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwnjC1APVz6z9J8hO9JBXOM2MlqXCeGStJhbOPXq06fbxhvssne/EzaXD20UtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDjH0Y8Rr2EjqQl+opekwhn0klQ4g16SCtdI0EfE0oh4PiI+2cTvlyTVVyvoI2JHRByPiIMzpk9HxGsRcSgitnXN+gPg4WEWKkkaTN1P9DuB6e4JEbEEuA+4DlgNbIyI1RHxq8C3gONDrFOSNKBawysz8+mIWDlj8tXAocx8EyAidgE3AcuApXTC/72I2JuZPxxaxZKkvkRm1mvYCfpHMvPK6vnNwHRmfr56/lngmszcUj3fBLydmY/0+H2bgc0AExMTa3ft2jXQf+DkyZMsW7ZsoGWbNEhdB442f6etifPg2HuNr6Zv89W15qJ2bk5T0v41KuNaW4l1rVu3bn9mTs7XrrETpjJz5zzztwPbASYnJ3Nqamqg9ezbt49Bl23SIHXNdeONYdm65hT3HBi/8+Tmq+vwLVOjK6ZLSfvXqIxrbYu5rrN5xx8FLul6fnE1TRq6umcNeycq6UxnM7zyOeDyiLg0Is4FNgC7+/kF3hxckppXd3jlg8A3gSsi4khE3JaZp4AtwOPAq8DDmflKPyv35uCS1Ly6o2429pi+F9g71IokSUPV6iUQ7LqRpOa1GvR23UhS87yomSQVzq4bSSqcXTeSVDi7biSpcAa9JBWu1YueRMR6YP2qVavaLEMF8VIJ0pnso5ekwtl1I0mFG7/r1Tak7ld68Gu9pLI4jl6SCmcfvSQVzj56SSrcoumj74dD9CSVxE/0klQ4g16SCueoG0kqXKt99Jm5B9gzOTl5e5t1DGquvvyta06xqZpvX76kNnkwdgT6OVlLkobNPnpJKpxBL0mFM+glqXAGvSQVzuGVklQ4L2omSYWz60aSCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOC9TrEWp7qWjd04vbbgSqXleAkGSCuclECSpcPbRS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwQw/6iPiZiLg/Ir4WEb817N8vSepPraCPiB0RcTwiDs6YPh0Rr0XEoYjYBpCZr2bmHcBngF8afsmSpH7U/US/E5junhARS4D7gOuA1cDGiFhdzbsReBTYO7RKJUkDicys1zBiJfBIZl5ZPf8IcGdmfqJ6/ocAmfmnXcs8mpk39Ph9m4HNABMTE2t37do10H/g5MmTLFu2bN52B46O9uYmE+fBsfdGusparKs/ly5fUmv/GrW6+30bxrW2Eutat27d/sycnK/d2dxK8CLgu13PjwDXRMQU8Gng/czxiT4ztwPbASYnJ3NqamqgIvbt20edZTfVvHXcsGxdc4p7DozfnRqtqz87p5fW2r9Gre5+34ZxrW0x1zX0d1Zm7gP2Dfv3SpIGczZBfxS4pOv5xdW02iJiPbB+1apVZ1GG1JwDR0/U/jZ4+K5Zeyml1p3N8MrngMsj4tKIOBfYAOzu5xd4z1hJal7d4ZUPAt8EroiIIxFxW2aeArYAjwOvAg9n5ivNlSpJGkStrpvM3Nhj+l7OYgilXTeS1LxWL4Fg140kNc9r3UhS4VoN+ohYHxHbT5wY7clMkrSY2HUjSYWz60aSCmfQS1Lh7KOXpMLZRy9JhbPrRpIKZ9BLUuEMekkqnAdjJalwHoyVpMKN373bpAVqpTco0Ziyj16SCmfQS1LhDHpJKpyjbiSpcI66kaTC2XUjSYUz6CWpcAa9JBXOoJekwhn0klQ4h1dKUuEcXilJhbPrRpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4z4yVpMKd0+bKM3MPsGdycvL2NuuQRmnltkdrtTt81w0NV6LFwq4bSSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIK1+o4ekm9zTXefuuaU2yq5jveXvPxE70kFc6gl6TCGfSSVDiDXpIK18jB2Ij4FHAD8GPAA5n5RBPrkSTNr/Yn+ojYERHHI+LgjOnTEfFaRByKiG0AmfmNzLwduAP49eGWLEnqRz9dNzuB6e4JEbEEuA+4DlgNbIyI1V1N/qiaL0lqSe2gz8yngXdmTL4aOJSZb2bmD4BdwE3RcTfwWGa+MLxyJUn9isys3zhiJfBIZl5ZPb8ZmM7Mz1fPPwtcA7wO3Ao8B7yYmffP8rs2A5sBJiYm1u7atWug/8DJkydZtmzZvO0OHB3tXawmzoNj7410lbVYV39KqmvNRcubKWaGuu/JUSuxrnXr1u3PzMn52jVyMDYz7wXunafNdmA7wOTkZE5NTQ20rn379lFn2U017+ozLFvXnOKeA+N34rF19aekug7fMtVMMTPUfU+O2mKu62yHVx4FLul6fnE1rRbvGStJzTvboH8OuDwiLo2Ic4ENwO66C2fmnszcvHz5aL5SStJiVPu7X0Q8CEwBKyLiCPDHmflARGwBHgeWADsy85VGKpV0Vrwp+eJVO+gzc2OP6XuBvYOsPCLWA+tXrVo1yOKSpBpavQSCXTeS1DyvdSNJhRu/cWOSWmVffnla/UTv8EpJap599JJUOPvoJalwBr0kFc4+ekkqXKujbjJzD7BncnLy9jbrkNS/XqNztq45dcZFBB2h0y67biSpcAa9JBWu1a4br3UjqZsnazXDcfSSVDi7biSpcAa9JBXOoJekwhn0klQ4R91IWvRKH+3jmbGSGlc3SNUMbzwiqVjdf2BmuzTDYmEfvSQVzqCXpMIZ9JJUOPvoJS04HtztjzcekaTCeVEzSSqcffSSVDiDXpIK58FYSappoV4qwU/0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXBeAkGSCucdpiRpyPq56NrO6aUNVtJh140kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBUuMrPtGoiIfwf+bcDFVwBvD7GcYbGu/lhXf8a1Lhjf2kqs66cy80PzNRqLoD8bEfF8Zk62XcdM1tUf6+rPuNYF41vbYq7LrhtJKpxBL0mFKyHot7ddQA/W1R/r6s+41gXjW9uirWvB99FLkuZWwid6SdIcFkzQR8R0RLwWEYciYtss898fEQ9V85+NiJUjqOmSiHgqIr4VEa9ExO/O0mYqIk5ExIvVz5earqta7+GIOFCt8/lZ5kdE3Fttr5cj4qoR1HRF13Z4MSK+HxFfmNFmZNsrInZExPGIONg17YKIeDIi3qj+Pb/HsrdWbd6IiFsbrunPI+Lb1ev09Yj4YI9l53zNG6rtzog42vV6Xd9j2Tnfvw3U9VBXTYcj4sUeyzayzXplQ2v7V2aO/Q+wBPgOcBlwLvASsHpGm98G7q8ebwAeGkFdFwJXVY8/ALw+S11TwCMtbLPDwIo55l8PPAYE8GHg2RZe0+/RGQfcyvYCPgZcBRzsmvZnwLbq8Tbg7lmWuwB4s/r3/Orx+Q3WdC1wTvX47tlqqvOaN1TbncDv1Xit53z/DruuGfPvAb40ym3WKxva2r8Wyif6q4FDmflmZv4A2AXcNKPNTcCXq8dfAz4eEdFkUZn5Vma+UD3+L+BV4KIm1zlENwFfyY5ngA9GxIUjXP/Hge9k5qAnyp21zHwaeGfG5O796MvAp2ZZ9BPAk5n5Tmb+J/AkMN1UTZn5RGaeqp4+A1w8jHX1q8f2qqPO+7eRuqoM+Azw4LDWV7OmXtnQyv61UIL+IuC7Xc+PcGag/n+b6k1xAvjxkVQHVF1FvwA8O8vsj0TESxHxWET87IhKSuCJiNgfEZtnmV9nmzZpA73ffG1sr9MmMvOt6vH3gIlZ2rS57T5H55vYbOZ7zZuypepW2tGjK6LN7fXLwLHMfKPH/Ma32YxsaGX/WihBP9YiYhnw98AXMvP7M2a/QKd74ueAvwS+MaKyPpqZVwHXAb8TER8b0XrnFRHnAjcCfzfL7La21xmy8z16bIalRcQXgVPAV3s0aeM1/yvgp4GfB96i000yTjYy96f5RrfZXNkwyv1roQT9UeCSrucXV9NmbRMR5wDLgf9ourCIeB+dF/KrmfkPM+dn5vcz82T1eC/wvohY0XRdmXm0+vc48HU6X5+71dmmTbkOeCEzj82c0db26nLsdBdW9e/xWdqMfNtFxCbgk8AtVUCcocZrPnSZeSwz/zczfwj8dY91trKvVTnwaeChXm2a3GY9sqGV/WuhBP1zwOURcWn1aXADsHtGm93A6aPTNwP/0usNMSxV/98DwKuZ+Rc92vzE6WMFEXE1nW3e6B+giFgaER84/ZjOwbyDM5rtBn4jOj4MnOj6Stm0np+y2theM3TvR7cC/zhLm8eBayPi/Kqr4tpqWiMiYhr4feDGzPzvHm3qvOZN1NZ9XOfXeqyzzvu3Cb8CfDszj8w2s8ltNkc2tLN/Dftoc1M/dEaJvE7n6P0Xq2l/QmfnB/hROl0Bh4B/BS4bQU0fpfPV62XgxerneuAO4I6qzRbgFTojDZ4BfnEEdV1Wre+lat2nt1d3XQHcV23PA8DkiF7HpXSCe3nXtFa2F50/Nm8B/0OnH/Q2Osd1/hl4A/gn4IKq7STwN13Lfq7a1w4Bv9lwTYfo9Nme3sdOjy77SWDvXK/5CLbX31b7z8t0QuzCmbVVz894/zZZVzV95+n9qqvtSLbZHNnQyv7lmbGSVLiF0nUjSRqQQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuH+D97I0Dggfr8/AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5443,7 +5627,7 @@
     },
     {
      "data": {
-      "image/png": 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z3QlsAIiIdwFn0PBJlTJzf2b+WGaOZeYYnf8I712Mkp9PREzSeet/cWb+d8NxHgbOiYjVEXEGnQNldzWciej8dr4ZOJCZf9Z0nlmZ+anMXNl9TV0G/EMLSp7u6/qZiDi3u+kC4MkGI816Gnh/RLy1+zO9gCEeJD5lRvTzuAq4oXtQ43+ArQ3nmbUT2BkRjwMvAx9peJTadn8FvBn4RvfdxoOZ+bEmgmTmsYi4BriXzoqInZn5RBNZjvMB4Apgf0Q81t326czc02CmtrsWuKX7C/sp4LcazkN3Gul24FE605TfYoifkPWTsZJUXJWpG0nSCVj0klScRS9JxVn0klScRS9JxVn0klScRS9JxVn0klTc/wF8Qag3UHgxfQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5463,7 +5647,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5480,7 +5664,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5497,7 +5681,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5514,7 +5698,7 @@
     },
     {
      "data": {
-      "image/png": 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eHnvfXj3UmoEfAf6+q/cw8Mlu/GJGk5FjwF8C5/dd62o3vxkrSY2zdSNJjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8BtUoLYMs+v3YAAAAASUVORK5CYII=\n",
+      "image/png": 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/393umHju1q7Wp4Br+q61q+lnGfc0vwl8DXhwyPV2dV3LeHXIV4Bb+67nLPXdCzwH/E/37/ZG4A3AJ4EvA58AXt93nV2tP8q4LfPExGf22gHX+4PA33f1Hgfe341fzngichL4K+Civmud5uaRsZLUOFs3ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9HxmGSlS8zxZmAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5531,7 +5715,34 @@
     },
     {
      "data": {
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\n",
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0 res\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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dtc33DSnPLwDfpntu4r7qtt+n+wMH8EPAnwOngL8D3jbsr12fuX6m+ry8QvcvjJMtyfUEcLbn++lQS3I9CJysMh0FfrINuS459hg177rxnrGSVLhxXLqRJA3Aopekwln0klQ4i16SCmfRS1LhLHpJKpxFL0mFs+glqXD/BybJP7RHpIUTAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5548,7 +5759,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5565,7 +5776,7 @@
     },
     {
      "data": {
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\n",
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tzfTezZ5tij4VFrPAn/S1+xXgWPPzy33b3w48Qu9I9sPA26a1lr7bZ+g26Ieug97oJoGngSeanxs7qOG9wDP0Vkfc0mz7XeB9zeU30PsEeAz4R+Adffe9pbnfUVZ5xVCbtQCfBF7p2w9PAG+dtjoWPcatdBz0Lfx+/SLwFPAkSwyiVvrjN2MlqbhpWXUjSRqSQS9JxRn0klScQS9JxRn0klScQS9JxRn0klScQS9Jxf0/q0XkEP4xHhAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5582,7 +5793,7 @@
     },
     {
      "data": {
-      "image/png": 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jQET8HPAHwDbgcGZ+cshd6ltr/ecZ1u7k+DpwP/AF4FHgh4GvA7+YmedfsB0bEfEzwN8CJ4Bvt5p/i7U6fZlxdsOgl6TiLN1IUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQV938aqd60S4TF0gAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5616,7 +5827,7 @@
     },
     {
      "data": {
-      "image/png": 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k8DPAWIq+laGbrwM/3//9LwD/0mGWxT4FbAVI8mLgbDpeWKmqDlbVj1TVxqraSO8/wstXo+SXkmSO3lv/11fVf3cc5wHggiSbkpxN74OyOzvORHqvzjcDj1bVn3Wd57iqendVbeg/p64E/n4CSp7+8/rJJC/pb7oE+EqHkY77GvCqJM/r/5tewhg/JD5tzuiXcDVwY/9Djf8Btnec57hdwK4kDwPPAL/R8VnqpPtL4DnAff13G5+rqmu6CFJVx5JcC9xDb0bErqp6pIssJ3gN8BbgYJIv9re9p6r2dZhp0l0H3NJ/wX4c+M2O89AfRrodeIjeMOUXGOM3ZP1mrCQ1rpWhG0nSSVj0ktQ4i16SGmfRS1LjLHpJapxFL0mNs+glqXEWvSQ17v8AajON5GzoNO4AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5633,7 +5844,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5650,7 +5861,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5670,7 +5881,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5687,7 +5898,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5704,7 +5915,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5721,7 +5932,7 @@
     },
     {
      "data": {
-      "image/png": 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GvSQ17v8A8SWQry4g6DwAAAAASUVORK5CYII=\n",
+      "image/png": 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JW4Cb0+7QMCzdvNoF4GiS1yfZBxwA/n7Kfdoy/R/gl72b1ZvW82LkPZBbkeTuJD/08tfALzJfPwt3cgF4b//r9wIzVRVodkS/kSTvBv4c2AU8nuQbVfX2qrqS5DzwLHAb+O2q+u40+7rF/jjJW1kt3TwP/NZ0u7N1qup2kpf3QN4BnBl2D+SGvBn4QhJYzYjPVtXfTLdLW6e/R/aDwM4kK8AHgdPA+STHWV1p91en18Ph+clYSWqcpRtJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4/4PXkEdraHUaZUAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5738,7 +5949,34 @@
     },
     {
      "data": {
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\n",
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76rumRcQMC191b8rMb/c9dBTYGxGXR8SVLByI/vsuamzBU8COZjbGZSwcdD7acU1D1/SnHwCezcw/7nvoKPDe5uf3Ap8edW1tyMy7M3NL8/ndC/xdZt4KfB64pdmszHgvcI9+eI4D72LhgOS3gV/vtpyh+lPgcuCJ5pvMk5l5Z2aeioiHgWdYaOm8PzP/t8M6hyYzz0fEXcDjwDrgwcw81XFZbbgOuA04GRH/0Kz7EHAP8HBE3M7ClWXf01F9o/JBYDYi/gD4Cgt//MrwzFhJKs7WjSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnEGvSQVZ9BLUnH/B162CGXquO+DAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0 res\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5755,7 +5993,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5772,7 +6010,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5789,7 +6027,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5823,7 +6061,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5840,7 +6078,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5857,7 +6095,7 @@
     },
     {
      "data": {
-      "image/png": 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p4OvAbU0FOcuayE07AWzKzLXAK4Bfb0muk95JdzbbNvkw8NeZ+ePAS2hBvohYAfwG0Omt2reM7lTvtSil0Cfw/N7fLwa+2WCW+d4O3J6Z/w2QmUcbzjPfHwO/SfffrhUy87O96a8Bvsj/rT3chDOtidyozPxWZj7c+/t/0i1aK5pN1RURK4GrgY80neWkiLgY+DngHoDMfDYzv9Nsqu+7ALgoIi4AnkeNdauUQv8u4EMR8QTdu+bG7gRP8WLgZyPiSxHx+Yh4edOBACLiWuBIZn6t6Sxn8WvAZxq8/grgiXnbs7SkoJ7UW8f5p4EvNZvk+/6E7s3D95oOMs8a4CngL3stpY9ExPKmQ2XmEbq16hvAt4DjmfnZuq7XvoUdzyAi/gb40dN86H3AVcC7M/MTEfEmuj+9X92CXBcAP0z3V+yXAzsi4kW5BEOdFsj1XrptmyV3tlyZeX/vmPfRbVGcdmF5QUSMAJ8A3pWZ/9GCPK8HjmbmvoiYaDrPPBcALwPekZlfiogPA5uB324yVET8EN3fENcA3wE+HhFvzsyP1XG9c6bQZ+YZC3dEfJRubxDg4yzhr44L5Ho78MleYf+HiPge3XktnmoqV0SM0/3m+lpEQLc98nBEXJGZ/9ZUrnn5bgReD1y1FD8Qz2LRayLXJSJ+kG6R356Zn2w6T8+rgGsi4peA5wLPj4iPZeabG841C8xm5snfeu6jW+ib9mrgXzLzKYCI+CTwM0Athb6U1s03gZ/v/f0XgH9uMMt8nwImASLixcCFNDypUmbuz8wfyczVmbma7n+Ely1FkV9IREzR/dX/msz8r4bjLHpN5DpE96fzPcDBzPyjpvOclJm3ZebK3vfUdcDftaDI0/u+fiIiLuvtugp4tMFIJ30DeEVEPK/3Nb2KGt8kPmfu6BdwM/Dh3psa3wU2NpznpK3A1og4ADwLvKXhu9S2uwt4DvC53m8bX8zMW5oIkpknIuLkmsjLgK0tWRP5VcANwP6I+Gpv33t7y3rq9N4BbO/9wH4ceGvDeei1ke4DHqbbpvwKNT4h65OxklS4Ulo3kqQzsNBLUuEs9JJUOAu9JBXOQi9JhbPQS1LhLPSSVDgLvSQV7n8Bd9ob7nf60cUAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5877,7 +6115,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5894,7 +6132,7 @@
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5928,7 +6166,7 @@
     },
     {
      "data": {
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+      "image/png": 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ey3Lp5mXg16cbznhU1VtJ3lkbeRtwcti1kTeB+4DPJ4HlnPAXVfU30w1pdP21rvcDO5IsAY8DTwBnkxxlecbcD00vwvXjN2MlqXGWbiSpcSZ6SWqciV6SGmeil6TGmeglqXEmeklqnIlekhpnopekxv0fQXQNG6a/lkEAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5945,7 +6183,34 @@
     },
     {
      "data": {
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\n",
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itrH4NffazPxh30MHgJ0RcX5EXMLigeh/6KLGFhwCNjUzMc5j8aDzgY5rGrqmN/054Ghm/lnfQweAm5qfbwK+POra2pKZt2fm+uY9vBP4+8y8Afg6cF2zWakxu0c/PAeB97N4QPKHwO91W85Q3Q2cDzzafJN5IjNvycynI+JB4BkWWzofycz/7bDOocnM0xFxK/AIsAa4NzOf7risNlwJ3AgciYhvNes+DtwBPBgRN7N4ZdkPdVTfKH0M2B8RfwJ8k8UPwBI8M1aSirN1I0nFGfSSVJxBL0nFGfSSVJxBL0nFGfSSVJxBL0nFGfSSVNz/AUhDGQ5McLQ5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z0 res\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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xtt+2sW/F9rXqZWCtH9jF+j4n6VhE/OWAxerdXuPc+zyKH0nH1ZvLeqL42eiE+FlJh/qWu069vdvPqzeFUXddH1ZvXu0Hkl6S9PDmutTrnniy+HmmLXU1tL3eIumfJH1X0jclvbm4vSPpnuLyeyWtFdtrTdJtNdbzmtcv6U/VG1BI0uskfaV4//2LpCvr3kYl6/qz4r30pKTDkn5uTHXdL+lFSf9bvL9uk3SnpDuL+y3p7qLuNZ2jE23Mdd3Vt70elfTeMdT0fvX2zT3Vl1vXjXN7cWQsACQ3cVM3AIBqCHoASI6gB4DkCHoASI6gB4DkCHoASI6gB4DkCHoASO7/AbgUhNsGT80dAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5962,7 +6227,7 @@
     },
     {
      "data": {
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bY5fkJcBe4CuLEFtVfaWq/mf09H3Aj84ppDcC1yf5AnA/8BNJ/mrTOX2O27bx9TF2JvrtnQB+YbR65A3AelV9qe+gzkvyvef7j0kOMfw3nct/6tHnvh94qqr+dMxpvYxfl9h6HrtXJ3nl6PF3AdcC/7rptBPAL44e3wB8skazeX3Htmme5XqGcyAzV1Xvqqr9VXWQ4UTrJ6vq5zad1su4dY2vj7HrtJVgyzLcD/caYF+SNeDdDCefqKr3Mtwq8c3AWeC/gXcsWHw3AO9M8iLwdeDYvP5TM6xefh74l1E/F+C3gZUN8fU1fl1i63PsXgN8IMkehl8wD1TVQ0n+EDhTVScYflF9MMlZhhPyxxYotl9Ncj3w4ii2m+YU25YWZNzG6nvsvDJWkhpn60aSGmeil6TGmeglqXEmeklqnIlekhpnopekxpnoJalxJnpJatz/ArXesV3HL5diAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5979,7 +6244,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -6030,7 +6295,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -6047,7 +6312,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -6064,7 +6329,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]

From 4ed7314dfc9ac506cee3abf5d5221a96d3697e48 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 13 Mar 2019 09:47:20 +0100
Subject: [PATCH 85/95] no cluster shape cut

---
 RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 7007554fce9e6..54d85a6dd9874 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -19,7 +19,7 @@
 // useful for benchmark
 // #define ONLY_PHICUT
 // #define USE_ZCUT
-// #define NO_CLSCUT
+#define NO_CLSCUT
 
 namespace gpuPixelDoublets {
 

From dd7853a965f76dd58bdb6549ba65ceab6a332667 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 13 Mar 2019 10:26:13 +0100
Subject: [PATCH 86/95] fix long standing issue with tracks not on cpu

---
 .../src/PixelVertexHeterogeneousProducer.cc                  | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc b/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
index 7ef7eb80609f8..cde3e386a8b4b 100644
--- a/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
+++ b/RecoPixelVertexing/PixelVertexFinding/src/PixelVertexHeterogeneousProducer.cc
@@ -205,7 +205,10 @@ void PixelVertexHeterogeneousProducer::produceGPUCuda(
     for (auto it: itrk) {
       assert(it< (*tuples_).indToEdm.size());
       auto k = (*tuples_).indToEdm[it];
-      assert(k<tracks.size());
+      if (k>tracks.size()) {
+        std::cout << "oops track " << it << " does not exists on CPU " << k << std::endl;
+        continue;
+      }
       auto tk = reco::TrackRef(trackCollection, k);
       v.add(reco::TrackBaseRef(tk));
     }

From 578089a6623626cb1dce4ff71e99ef6028a4b34d Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 13 Mar 2019 10:26:56 +0100
Subject: [PATCH 87/95] back clus size cut

---
 RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
index 54d85a6dd9874..7007554fce9e6 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/gpuPixelDoublets.h
@@ -19,7 +19,7 @@
 // useful for benchmark
 // #define ONLY_PHICUT
 // #define USE_ZCUT
-#define NO_CLSCUT
+// #define NO_CLSCUT
 
 namespace gpuPixelDoublets {
 

From fee89bf48dc8208cb0362c1c1d6f12640a53e252 Mon Sep 17 00:00:00 2001
From: Felice <felice.pantaleo@cern.ch>
Date: Mon, 18 Mar 2019 14:23:05 +0100
Subject: [PATCH 88/95] loosening of the TIP cut from 1mm to 5mm

---
 .../PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 34625043f511f..2cf1fd3ee8677 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -267,7 +267,7 @@ void kernel_VerifyFit(TuplesOnGPU::Container const * __restrict__ tuples,
 
   // impose "region cuts" (NaN safe)
   // phi,Tip,pt,cotan(theta)),Zip
-  bool ok = std::abs(fit_results[idx].par(1)) < ( tuples->size(idx)>3 ? 0.1f : 0.05f) 
+  bool ok = std::abs(fit_results[idx].par(1)) < ( tuples->size(idx)>3 ? 0.5f : 0.05f) 
          && fit_results[idx].par(2) > ( tuples->size(idx)>3 ? 0.3f : 0.5f)
          && std::abs(fit_results[idx].par(4)) < 12.f;
   

From ce28b5771ad24ffc894843b9005999e8154de618 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Mon, 18 Mar 2019 12:10:36 +0100
Subject: [PATCH 89/95] limit pt in MS and remove a sqrt

---
 RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
index 048769d181936..32894c8aa432a 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/BrokenLine.h
@@ -46,13 +46,16 @@ namespace BrokenLine {
     \return the variance of the planar angle ((theta_0)^2 /3).
   */
   __host__ __device__ inline double MultScatt(const double& length, const double B, const double R, int Layer, double slope) {
+    // limit R to 20GeV...
+    auto pt2 = std::min(20.,B*R);
+    pt2 *=pt2;
     constexpr double XXI_0 = 0.06/16.; //!< inverse of radiation length of the material in cm
     //if(Layer==1) XXI_0=0.06/16.;
     // else XXI_0=0.06/16.;
     //XX_0*=1;
     constexpr double geometry_factor=0.7; //!< number between 1/3 (uniform material) and 1 (thin scatterer) to be manually tuned
     constexpr double fact = geometry_factor*sqr(13.6/1000.);
-    return fact/sqr(1.*B*R*sqrt(1.+sqr(slope)))
+    return fact/(pt2*(1.+sqr(slope)))
       *(std::abs(length)*XXI_0)*sqr(1.+0.038*log(std::abs(length)*XXI_0));
   }
   

From 0fb853025afa2d2c05c2c1d35d2de2808dda1b97 Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Wed, 20 Mar 2019 12:45:15 +0100
Subject: [PATCH 90/95] fix chi2 in riemann

---
 RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
index 3b3fd75223d95..3064bfe86ce1f 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
@@ -957,7 +957,7 @@ inline line_fit Line_fit(const M3xN& hits,
 
   VectorNd<N> res = p2D_rot.row(1).transpose() - A.transpose() * sol;
   double chi2 = res.transpose()*Vy_inv*res;
-  chi2 = chi2 / float(n);
+  // chi2 = chi2 / float(n);  // wrong????
 
   line_fit line;
   line.par << m, q;

From 29bdf795c032ab99a87dd730c0ab9c3c7d59981c Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 21 Mar 2019 15:21:59 +0100
Subject: [PATCH 91/95] remove comment

---
 RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h | 1 -
 1 file changed, 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
index 3064bfe86ce1f..656047aababf9 100644
--- a/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
+++ b/RecoPixelVertexing/PixelTrackFitting/interface/RiemannFit.h
@@ -957,7 +957,6 @@ inline line_fit Line_fit(const M3xN& hits,
 
   VectorNd<N> res = p2D_rot.row(1).transpose() - A.transpose() * sol;
   double chi2 = res.transpose()*Vy_inv*res;
-  // chi2 = chi2 / float(n);  // wrong????
 
   line_fit line;
   line.par << m, q;

From d02b6b9c014c4acfd7540972d073e24ce0c8083f Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 19 Mar 2019 14:47:52 +0100
Subject: [PATCH 92/95] apply chi2 cut as function of pt

---
 .../plugins/CAHitQuadrupletGeneratorKernels.cu           | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 34625043f511f..3210c69b4ae47 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -255,11 +255,18 @@ void kernel_VerifyFit(TuplesOnGPU::Container const * __restrict__ tuples,
     return;
   }
 
+  const float par[4] = {0.68177776,  0.74609577, -0.08035491,  0.00315399};
+  constexpr float chi2CutFact = 30;  // *0.7 if Riemann....
+  auto chi2Cut = [&](float pt) {
+     pt = std::min(pt,10.f);
+     return chi2CutFact*(par[0]+pt*(par[1]+pt*(par[2]+pt*par[3]))); 
+  };
+
   bool isNaN = false;
   for (int i=0; i<5; ++i) {
     isNaN |=  fit_results[idx].par(i)!=fit_results[idx].par(i);
   }
-  isNaN |=  !(fit_results[idx].chi2_line+fit_results[idx].chi2_circle < 100.f);  // catch NaN as well
+  isNaN |=  !(fit_results[idx].chi2_line+fit_results[idx].chi2_circle < chi2Cut(fit_results[idx].par(2)));  // catch NaN as well
 
 #ifdef GPU_DEBUG
  if (isNaN) printf("NaN or Bad Fit %d size %d chi2 %f/%f\n",idx,tuples->size(idx), fit_results[idx].chi2_line,fit_results[idx].chi2_circle);

From 46523babcacf825c4a3084571f8be42c09dbad5a Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 21 Mar 2019 15:59:01 +0100
Subject: [PATCH 93/95] move tip cut to 5mm

---
 .../PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
index 3210c69b4ae47..166893ed9f532 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
+++ b/RecoPixelVertexing/PixelTriplets/plugins/CAHitQuadrupletGeneratorKernels.cu
@@ -274,7 +274,7 @@ void kernel_VerifyFit(TuplesOnGPU::Container const * __restrict__ tuples,
 
   // impose "region cuts" (NaN safe)
   // phi,Tip,pt,cotan(theta)),Zip
-  bool ok = std::abs(fit_results[idx].par(1)) < ( tuples->size(idx)>3 ? 0.1f : 0.05f) 
+  bool ok = std::abs(fit_results[idx].par(1)) < ( tuples->size(idx)>3 ? 0.5f : 0.3f) 
          && fit_results[idx].par(2) > ( tuples->size(idx)>3 ? 0.3f : 0.5f)
          && std::abs(fit_results[idx].par(4)) < 12.f;
   

From 0ce22da7943323b0412f9c1fe3a103466451593a Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Tue, 19 Mar 2019 18:52:05 +0100
Subject: [PATCH 94/95] tune AlignedRZ in barrel

---
 RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index 8bd4808894d37..c7c5fdadeb1a9 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -95,7 +95,8 @@ class GPUCACell {
 
     auto r1 = otherCell.get_inner_r(hh);
     auto z1 = otherCell.get_inner_z(hh);
-    bool aligned = areAlignedRZ(r1, z1, ri, zi, ro, zo, ptmin, 0.003f); // 2.f*thetaCut); // FIXME tune cuts
+    auto br = otherCell.get_outer_detId(hh)<1184;
+    bool aligned = areAlignedRZ(r1, z1, ri, zi, ro, zo, ptmin, br ? 0.002f : 0.003f); // 2.f*thetaCut); // FIXME tune cuts
     return (aligned &&  dcaCut(hh, otherCell, otherCell.get_inner_detId(hh)<96 ? 0.15f : 0.25f, hardCurvCut));  // FIXME tune cuts
                             // region_origin_radius_plus_tolerance,  hardCurvCut));
   }

From 8ca8b5d4a58483fd3b494f6cb5592c2f8d58499a Mon Sep 17 00:00:00 2001
From: Vincenzo Innocente <vincenzo.innocente@cern.ch>
Date: Thu, 21 Mar 2019 16:01:35 +0100
Subject: [PATCH 95/95] make theta cut in the barrel stricter

---
 RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
index c7c5fdadeb1a9..d4da7a8b7713f 100644
--- a/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
+++ b/RecoPixelVertexing/PixelTriplets/plugins/GPUCACell.h
@@ -95,8 +95,8 @@ class GPUCACell {
 
     auto r1 = otherCell.get_inner_r(hh);
     auto z1 = otherCell.get_inner_z(hh);
-    auto br = otherCell.get_outer_detId(hh)<1184;
-    bool aligned = areAlignedRZ(r1, z1, ri, zi, ro, zo, ptmin, br ? 0.002f : 0.003f); // 2.f*thetaCut); // FIXME tune cuts
+    auto isBarrel = otherCell.get_outer_detId(hh)<1184;
+    bool aligned = areAlignedRZ(r1, z1, ri, zi, ro, zo, ptmin, isBarrel ? 0.002f : 0.003f); // 2.f*thetaCut); // FIXME tune cuts
     return (aligned &&  dcaCut(hh, otherCell, otherCell.get_inner_detId(hh)<96 ? 0.15f : 0.25f, hardCurvCut));  // FIXME tune cuts
                             // region_origin_radius_plus_tolerance,  hardCurvCut));
   }