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h2D.py
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h2D.py
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#!/usr/bin/env python3
from collections import defaultdict
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import os, sys
import traceback
import platform
import colorcet as cc
from scipy import stats
from boututils.datafile import DataFile
from boutdata.collect import collect
from boutdata.data import BoutData
import xbout
class Load:
def __init__(self):
pass
def case_1D(casepath):
datapath = os.path.join(casepath, "BOUT.dmp.*.nc")
inputfilepath = os.path.join(casepath, "BOUT.inp")
ds = xbout.load.open_boutdataset(
datapath = datapath,
inputfilepath = inputfilepath,
info = False,
keep_yboundaries=True,
)
ds = ds.squeeze(drop = True)
return Case(ds, casepath)
def case_2D(casepath, gridfilepath = None,
verbose = False, keep_boundaries = True, squeeze = True,
unnormalise_geom = True):
datapath = os.path.join(casepath, "BOUT.dmp.*.nc")
inputfilepath = os.path.join(casepath, "BOUT.inp")
print(gridfilepath)
print(casepath)
ds = xbout.load.open_boutdataset(
datapath = datapath,
inputfilepath = inputfilepath,
gridfilepath = gridfilepath,
info = False,
geometry = "toroidal",
keep_xboundaries=keep_boundaries,
keep_yboundaries=keep_boundaries,
)
if squeeze:
ds = ds.squeeze(drop = True)
return Case(ds, casepath, unnormalise_geom)
class Case:
def __init__(self, ds, casepath, unnormalise_geom):
self.ds = ds
self.name = os.path.split(casepath)[-1]
self.datapath = os.path.join(casepath, "BOUT.dmp.*.nc")
self.inputfilepath = os.path.join(casepath, "BOUT.inp")
self.normalised_vars = []
if "x" in self.ds.dims:
self.is_2d = True
else:
self.is_2d = False
self.colors = ["cyan", "lime", "crimson", "magenta", "black", "red"]
self.guard_replaced = False
self.unnormalise(unnormalise_geom = unnormalise_geom)
self.derive_vars()
if self.is_2d:
self.extract_2d_tokamak_geometry()
else:
self.extract_1d_tokamak_geometry()
self.guard_replace()
print(f"CHECK: Total domain volume is {self.ds.dv.values.sum():.3E} [m3]")
def unnormalise(self, unnormalise_geom):
self.calc_norms()
if unnormalise_geom == False:
list_skip = ["dx", "dy", "J"]
print("--> dx, dy and J will not be unnormalised")
else:
list_skip = []
for data_var in self.norms.keys():
# Unnormalise variables and coordinates
if data_var in self.ds.variables or data_var in self.ds.coords:
if data_var not in self.normalised_vars and data_var not in list_skip:
self.ds[data_var] = self.ds[data_var] * self.norms[data_var]["conversion"]
self.normalised_vars.append(data_var)
self.ds[data_var].attrs.update(self.norms[data_var])
def derive_vars(self):
ds = self.ds
q_e = constants("q_e")
if "Ph+" in ds.data_vars:
ds["Th+"] = ds["Ph+"] / (ds["Nh+"] * q_e)
ds["Th+"].attrs.update({
"units": "eV",
"standard_name": "ion temperature (h+)",
"long_name": "Ion temperature (h+)",
})
if "Ph" in ds.data_vars:
ds["Th"] = ds["Ph"] / (ds["Nh"] * q_e)
ds["Th"].attrs.update({
"units": "eV",
"standard_name": "neutral temperature (h)",
"long_name": "Neutral temperature (h)",
})
if "Pd" in ds.data_vars:
ds["Td"] = ds["Pd"] / (ds["Nd"] * q_e)
ds["Td"].attrs.update({
"units": "eV",
"standard_name": "neutral temperature (d)",
"long_name": "Neutral temperature (d)",
})
if "Pd+" in ds.data_vars:
ds["Td+"] = ds["Pd+"] / (ds["Nd+"] * q_e)
ds["Td+"].attrs.update({
"units": "eV",
"standard_name": "ion temperature (d+)",
"long_name": "Ion temperature (d+)",
})
def guard_replace(self):
if self.is_2d == False:
if self.ds.metadata["keep_yboundaries"] == 1:
# Replace inner guard cells with values at cell boundaries
# Hardcoded dimension order: t, y
# Cell order at target:
# ... | last | guard | second guard
# ^target ^not used
# | -3 | -2 | -1
if self.guard_replaced == False:
for var_name in self.ds.data_vars:
var = self.ds[var_name]
if "y" in var.dims:
if "t" in var.dims:
var[:, -2] = (var[:,-3] + var[:,-2])/2
var[:, 1] = (var[:, 1] + var[:, 2])/2
else:
var[-2] = (var[-3] + var[-2])/2
var[1] = (var[1] + var[2])/2
else:
raise Exception("Guards already replaced!")
self.guard_replaced = True
else:
raise Exception("Y guards are missing from file!")
else:
raise Exception("2D guard replacement not done yet!")
def calc_norms(self):
m = self.ds.metadata
q_e = constants("q_e")
d = {
# Is the geometry normalised or not???
# Results seem to suggest that these come in SI already!!
"dx": {
"conversion": m["rho_s0"]**2 * m["Bnorm"],
"units": "Wb",
"standard_name": "radial cell width",
"long_name": "Radial cell width in flux space",
},
"dy": {
"conversion": 1,
"units": "radian",
"standard_name": "poloidal cell angular width",
"long_name": "Poloidal cell angular width",
},
"J": {
"conversion": m["rho_s0"] / m["Bnorm"],
"units": "m/radianT",
"standard_name": "Jacobian",
"long_name": "Jacobian to translate from flux to cylindrical coordinates in real space",
},
"Th+": {
"conversion": m["Tnorm"],
"units": "eV",
"standard_name": "ion temperature (h+)",
"long_name": "Ion temperature (h+)",
},
"Td+": {
"conversion": m["Tnorm"],
"units": "eV",
"standard_name": "ion temperature (d+)",
"long_name": "Ion temperature (d+)",
},
"Te": {
"conversion": m["Tnorm"],
"units": "eV",
"standard_name": "electron temperature",
"long_name": "Electron temperature",
},
"Ne": {
"conversion": m["Nnorm"],
"units": "m-3",
"standard_name": "density",
"long_name": "Electron density"
},
"Nh+": {
"conversion": m["Nnorm"],
"units": "Pa",
"standard_name": "ion density (h+)",
"long_name": "Ion density (h+)"
},
"Nd+": {
"conversion": m["Nnorm"],
"units": "Pa",
"standard_name": "ion density (d+)",
"long_name": "Ion density (d+)"
},
"Nd": {
"conversion": m["Nnorm"],
"units": "Pa",
"standard_name": "neutral density (d)",
"long_name": "Neutral density (d)"
},
"Pe": {
"conversion": m["Nnorm"] * m["Tnorm"] * q_e,
"units": "Pa",
"standard_name": "electron pressure",
"long_name": "Electron pressure"
},
"Ph+": {
"conversion": m["Nnorm"] * m["Tnorm"] * q_e,
"units": "Pa",
"standard_name": "ion pressure (h+)",
"long_name": "Ion pressure (h+)"
},
"Pd+": {
"conversion": m["Nnorm"] * m["Tnorm"] * q_e,
"units": "Pa",
"standard_name": "ion pressure (d+)",
"long_name": "Ion pressure (d+)"
},
"Pd+_src": {
"conversion": q_e * m["Nnorm"] * m["Tnorm"] * m["Omega_ci"],
"units": "Wm-3",
"standard_name": "ion energy source (d+)",
"long_name": "Ion energy source (d+)"
},
"Pe_src": {
"conversion": q_e * m["Nnorm"] * m["Tnorm"] * m["Omega_ci"],
"units": "Wm-3",
"standard_name": "electron energy source (d+)",
"long_name": "Electron energy source (d+)"
},
"Sd+_src": {
"conversion": m["Nnorm"] * m["Omega_ci"],
"units": "m-3s-1",
"standard_name": "ion density source (d+)",
"long_name": "Ion density source (d+)"
},
"Rd+_ex": {
"conversion": q_e * m["Nnorm"] * m["Tnorm"] * m["Omega_ci"],
"units": "Wm-3",
"standard_name": "exciation radiation (d+)",
"long_name": "Multi-step ionisation radiation (d+)"
},
"Sd+_iz": {
"conversion": m["Nnorm"] * m["Omega_ci"],
"units": "m-3s-1",
"standard_name": "ionisation",
"long_name": "Ionisation ion source (d+)"
},
"NVd+": {
"conversion": constants("mass_p") * m["Nnorm"] * m["Cs0"],
"units": "kgms-1",
"standard_name": "ion momentum",
"long_name": "Ion momentum (d+)"
},
"NVd": {
"conversion": constants("mass_p") * m["Nnorm"] * m["Cs0"],
"units": "kgms-1",
"standard_name": "neutral momentum",
"long_name": "Neutral momentum (d+)"
},
}
self.norms = d
def slices(self, name):
"""
DOUBLE NULL ONLY
Pass this touple to a field of any parameter spanning the grid
to select points of the appropriate region.
Each slice is a tuple: (x slice, y slice)
Use it as: selected_array = array[slice] where slice = (x selection, y selection) = output from this method.
"""
def custom_core_ring(i):
"""
Creates custom SOL ring slice within the core.
i = 0 is at first domain cell.
i = -2 is at first inner guard cell.
i = ixseps - MXG is the separatrix.
"""
if i > self.ixseps1 - self.MXG:
raise Exception("i is too large!")
return (slice(0+self.MXG+i,1+self.MXG+i), np.r_[slice(self.j1_2g + 1, self.j2_2g + 1), slice(self.j1_1g + 1, self.j2_1g + 1)])
def custom_sol_ring(i, region = "all"):
"""
Creates custom SOL ring slice beyond the separatrix.
i = index of SOL ring (0 is separatrix, 1 is first SOL ring)
region = all, inner, inner_lower, inner_upper, outer, outer_lower, outer_upper
"""
i = i + self.ixseps1 - 1
if i > self.nx - self.MXG*2 :
raise Exception("i is too large!")
if region == "all":
return (slice(i+1,i+2), np.r_[slice(0+self.MYG, self.j2_2g + 1), slice(self.j1_1g + 1, self.nyg - self.MYG)])
if region == "inner":
return (slice(i+1,i+2), slice(0+self.MYG, self.ny_inner + self.MYG))
if region == "inner_lower":
return (slice(i+1,i+2), slice(0+self.MYG, int((self.j2_1g - self.j1_1g) / 2) + self.j1_1g +2))
if region == "inner_upper":
return (slice(i+1,i+2), slice(int((self.j2_1g - self.j1_1g) / 2) + self.j1_1g, self.ny_inner + self.MYG))
if region == "outer":
return (slice(i+1,i+2), slice(self.ny_inner + self.MYG*3, self.nyg - self.MYG))
if region == "outer_lower":
return (slice(i+1,i+2), slice(int((self.j2_2g - self.j1_2g) / 2) + self.j1_2g, self.nyg - self.MYG))
if region == "outer_upper":
return (slice(i+1,i+2), slice(self.ny_inner + self.MYG*3, int((self.j2_2g - self.j1_2g) / 2) + self.j1_2g + 2))
slices = dict()
slices["all"] = (slice(self.MXG,-self.MXG), np.r_[slice(self.MYG, self.ny_inner+self.MYG), slice(self.ny_inner+self.MYG*3, self.nyg - self.MYG)])
# slices["all"] = (slice(None,None), slice(self.MYG,-self.MYG))
slices["inner_core"] = (slice(0,self.ixseps1), np.r_[slice(self.j1_1g + 1, self.j2_1g+1), slice(self.j1_2g + 1, self.j2_2g + 1)])
slices["outer_core"] = (slice(self.ixseps1, None), slice(0, self.nyg))
slices["outer_core_edge"] = (slice(0+self.MXG,1+self.MXG), slice(self.j1_2g + 1, self.j2_2g + 1))
slices["inner_core_edge"] = (slice(0+self.MXG,1+self.MXG), slice(self.j1_1g + 1, self.j2_1g + 1))
slices["core_edge"] = (slice(0+self.MXG,1+self.MXG), np.r_[slice(self.j1_2g + 1, self.j2_2g + 1), slice(self.j1_1g + 1, self.j2_1g + 1)])
slices["outer_sol_edge"] = (slice(-1 - self.MXG,- self.MXG), slice(self.ny_inner+self.MYG*3, self.nyg - self.MYG))
slices["inner_sol_edge"] = (slice(-1 - self.MXG,- self.MXG), slice(self.MYG, self.ny_inner+self.MYG))
slices["sol_edge"] = (slice(-1 - self.MXG,- self.MXG), np.r_[slice(self.j1_1g + 1, self.j2_1g + 1), slice(self.ny_inner+self.MYG*3, self.nyg - self.MYG)])
slices["custom_core_ring"] = custom_core_ring
slices["custom_sol_ring"] = custom_sol_ring
slices["inner_lower_target"] = (slice(self.MXG,-self.MXG), slice(self.MYG, self.MYG + 1))
slices["inner_upper_target"] = (slice(self.MXG,-self.MXG), slice(self.ny_inner+self.MYG -1, self.ny_inner+self.MYG))
slices["outer_upper_target"] = (slice(self.MXG,-self.MXG), slice(self.ny_inner+self.MYG*3, self.ny_inner+self.MYG*3+1))
slices["outer_lower_target"] = (slice(self.MXG,-self.MXG), slice(self.nyg-self.MYG-1, self.nyg - self.MYG))
slices["inner_lower_target_guard"] = (slice(self.MXG,-self.MXG), slice(self.MYG -1, self.MYG))
slices["inner_upper_target_guard"] = (slice(self.MXG,-self.MXG), slice(self.ny_inner+self.MYG , self.ny_inner+self.MYG+1))
slices["outer_upper_target_guard"] = (slice(self.MXG,-self.MXG), slice(self.ny_inner+self.MYG*3-1, self.ny_inner+self.MYG*3))
slices["outer_lower_target_guard"] = (slice(self.MXG,-self.MXG), slice(self.nyg-self.MYG, self.nyg - self.MYG+1))
slices["inner_lower_pfr"] = (slice(0, self.ixseps1), slice(None, self.j1_1g))
slices["outer_lower_pfr"] = (slice(0, self.ixseps1), slice(self.j2_2g+1, self.nyg))
slices["lower_pfr"] = (slice(0, self.ixseps1), np.r_[slice(None, self.j1_1g+1), slice(self.j2_2g+1, self.nyg)])
slices["upper_pfr"] = (slice(0, self.ixseps1), slice(self.j2_1g+1, self.j1_2g+1))
slices["pfr"] = (slice(0, self.ixseps1), np.r_[
np.r_[slice(None, self.j1_1g+1), slice(self.j2_2g+1, self.nyg)],
slice(self.j2_1g+1, self.j1_2g+1)])
slices["lower_pfr_edge"] = (slice(self.MXG, self.MXG+1), np.r_[slice(None, self.j1_1g+1), slice(self.j2_2g+1, self.nyg)])
slices["upper_pfr_edge"] = (slice(self.MXG, self.MXG+1), slice(self.j2_1g+1, self.j1_2g+1))
slices["pfr_edge"] = (slice(self.MXG, self.MXG+1), np.r_[
np.r_[slice(None, self.j1_1g+1), slice(self.j2_2g+1, self.nyg)],
slice(self.j2_1g+1, self.j1_2g+1)])
slices["outer_midplane_a"] = (slice(self.MXG,-self.MXG), int((self.j2_2g - self.j1_2g) / 2) + self.j1_2g)
slices["outer_midplane_b"] = (slice(self.MXG,-self.MXG), int((self.j2_2g - self.j1_2g) / 2) + self.j1_2g + 1)
slices["inner_midplane_a"] = (slice(self.MXG,-self.MXG), int((self.j2_1g - self.j1_1g) / 2) + self.j1_1g + 1)
slices["inner_midplane_b"] = (slice(self.MXG,-self.MXG), int((self.j2_1g - self.j1_1g) / 2) + self.j1_1g)
return slices[name]
def plot_slice(self, slicer, dpi = 100):
"""
Indicates region of cells in X, Y and R, Z space for implementing Hermes-3 sources
You must provide a slice() object for the X and Y dimensions which is a tuple in the form (X,Y)
X is the radial coordinate (excl guards) and Y the poloidal coordinate (incl guards)
WARNING: only developed for a connected double null. Someone can adapt this to a single null or DDN.
"""
meta = self.ds.metadata
xslice = slicer[0]
yslice = slicer[1]
# Region boundaries
ny = meta["ny"] # Total ny cells (incl guard cells)
nx = meta["nx"] # Total nx cells (excl guard cells)
Rxy = self.ds["R"].values # R coordinate array
Zxy = self.ds["Z"].values # Z coordinate array
MYG = meta["MYG"]
# Array of radial (x) indices and of poloidal (y) indices in the style of Rxy, Zxy
x_idx = np.array([np.array(range(nx))] * int(ny + MYG * 4)).transpose()
y_idx = np.array([np.array(range(ny + MYG*4))] * int(nx))
# Slice the X, Y and R, Z arrays and vectorise them for plotting
xselect = x_idx[xslice,yslice].flatten()
yselect = y_idx[xslice,yslice].flatten()
rselect = Rxy[xslice,yslice].flatten()
zselect = Zxy[xslice,yslice].flatten()
# Plot
fig, axes = plt.subplots(1,3, figsize=(12,5), dpi = dpi, gridspec_kw={'width_ratios': [2.5, 1, 2]})
fig.subplots_adjust(wspace=0.3)
self.plot_xy_grid(axes[0])
axes[0].scatter(yselect, xselect, s = 4, c = "red", marker = "s", edgecolors = "darkorange", linewidths = 0.5)
self.plot_rz_grid(axes[1])
axes[1].scatter(rselect, zselect, s = 20, c = "red", marker = "s", edgecolors = "darkorange", linewidths = 1, zorder = 100)
self.plot_rz_grid(axes[2], ylim=(-1,-0.25))
axes[2].scatter(rselect, zselect, s = 20, c = "red", marker = "s", edgecolors = "darkorange", linewidths = 1, zorder = 100)
def plot_xy_grid(self, ax):
ax.set_title("X, Y index space")
ax.scatter(self.yflat, self.xflat, s = 1, c = "grey")
ax.plot([self.yflat[self.j1_1g]]*np.ones_like(self.xflat), self.xflat, label = "j1_1g", color = self.colors[0])
ax.plot([self.yflat[self.j1_2g]]*np.ones_like(self.xflat), self.xflat, label = "j1_2g", color = self.colors[1])
ax.plot([self.yflat[self.j2_1g]]*np.ones_like(self.xflat), self.xflat, label = "j2_1g", color = self.colors[2])
ax.plot([self.yflat[self.j2_2g]]*np.ones_like(self.xflat), self.xflat, label = "j2_2g", color = self.colors[3])
ax.plot(self.yflat, [self.yflat[self.ixseps1]]*np.ones_like(self.yflat), label = "ixseps1", color = self.colors[4])
ax.plot(self.yflat, [self.yflat[self.ixseps2]]*np.ones_like(self.yflat), label = "ixseps1", color = self.colors[5], ls=":")
ax.legend(loc = "upper center", bbox_to_anchor = (0.5,-0.1), ncol = 3)
ax.set_xlabel("Y index (incl. guards)")
ax.set_ylabel("X index (excl. guards)")
def plot_rz_grid(self, ax, xlim = (None,None), ylim = (None,None)):
ax.set_title("R, Z space")
ax.scatter(self.rflat, self.zflat, s = 0.1, c = "black")
ax.set_axisbelow(True)
ax.grid()
ax.plot(self.Rxy[:,self.j1_1g], self.Zxy[:,self.j1_1g], label = "j1_1g", color = self.colors[0], alpha = 0.7)
ax.plot(self.Rxy[:,self.j1_2g], self.Zxy[:,self.j1_2g], label = "j1_2g", color = self.colors[1], alpha = 0.7)
ax.plot(self.Rxy[:,self.j2_1g], self.Zxy[:,self.j2_1g], label = "j2_1g", color = self.colors[2], alpha = 0.7)
ax.plot(self.Rxy[:,self.j2_2g], self.Zxy[:,self.j2_2g], label = "j2_2g", color = self.colors[3], alpha = 0.7)
ax.plot(self.Rxy[self.ixseps1,:], self.Zxy[self.ixseps1,:], label = "ixseps1", color = self.colors[4], alpha = 0.7, lw = 2)
ax.plot(self.Rxy[self.ixseps2,:], self.Zxy[self.ixseps2,:], label = "ixseps2", color = self.colors[5], alpha = 0.7, lw = 2, ls=":")
if xlim != (None,None):
ax.set_xlim(xlim)
if ylim != (None,None):
ax.set_ylim(ylim)
def extract_1d_tokamak_geometry(self):
ds = self.ds
meta = self.ds.metadata
# Reconstruct grid position from dy
dy = ds.coords["dy"].values
n = len(dy)
pos = np.zeros(n)
pos[0] = -0.5*dy[1]
pos[1] = 0.5*dy[1]
for i in range(2,n):
pos[i] = pos[i-1] + 0.5*dy[i-1] + 0.5*dy[i]
# pos = ds.coords["y"].values.copy()
# Guard replace to get position at boundaries
pos[-2] = (pos[-3] + pos[-2])/2
pos[1] = (pos[1] + pos[2])/2
# Set 0 to be at first cell boundary in domain
pos = pos - pos[1]
# Replace y in dataset with the new one
ds.coords["y"] = pos
self.ds["da"] = self.ds.J / np.sqrt(ds.g_22)
self.ds["da"].attrs.update({
"conversion" : 1,
"units" : "m2",
"standard_name" : "cross-sectional area",
"long_name" : "Cell parallel cross-sectional area"})
self.ds["dV"] = self.ds.J * self.ds.dy
self.ds["dV"].attrs.update({
"conversion" : 1,
"units" : "m3",
"standard_name" : "cell volume",
"long_name" : "Cell Volume"})
def extract_2d_tokamak_geometry(self):
"""
Perpare geometry variables
"""
data = self.ds
meta = self.ds.metadata
# self.Rxy = meta["Rxy"] # R coordinate array
# self.Zxy = meta["Zxy"] # Z coordinate array
self.ixseps1 = meta["ixseps1"]
self.MYG = meta["MYG"]
self.MXG = 2
self.ny_inner = meta["ny_inner"]
self.ny = meta["ny"]
self.nyg = self.ny + self.MYG * 4 # with guard cells
self.nx = meta["nx"]
# Array of radial (x) indices and of poloidal (y) indices in the style of Rxy, Zxy
self.x_idx = np.array([np.array(range(self.nx))] * int(self.nyg)).transpose()
self.y_idx = np.array([np.array(range(self.nyg))] * int(self.nx))
self.yflat = self.y_idx.flatten()
self.xflat = self.x_idx.flatten()
self.rflat = self.ds.coords["R"].values.flatten()
self.zflat = self.ds.coords["Z"].values.flatten()
self.j1_1 = meta["jyseps1_1"]
self.j1_2 = meta["jyseps1_2"]
self.j2_1 = meta["jyseps2_1"]
self.j2_2 = meta["jyseps2_2"]
self.ixseps2 = meta["ixseps2"]
self.ixseps1 = meta["ixseps1"]
self.Rxy = self.ds.coords["R"]
self.Zxy = self.ds.coords["Z"]
self.j1_1g = self.j1_1 + self.MYG
self.j1_2g = self.j1_2 + self.MYG * 3
self.j2_1g = self.j2_1 + self.MYG
self.j2_2g = self.j2_2 + self.MYG * 3
# Cell areas in flux space
# dV = dx * dy * dz * J where dz is assumed to be 2pi in 2D
self.dx = data["dx"]
self.dy = data["dy"]
self.dydx = data["dy"] * data["dx"] # Poloidal surface area
self.J = data["J"]
dz = 2*np.pi # Axisymmetric
self.dv = self.dydx * dz * data["J"] # Cell volume
# Cell areas in real space
# TODO: Check these against dx/dy to ensure volume is the same
# dV = (hthe/Bpol) * (R*Bpol*dr) * dy*2pi = hthe * dy * dr * 2pi * R
self.dr = self.dx / (self.ds.R * self.ds.Bpxy) # Length of cell in radial direction
self.hthe = self.J * self.ds["Bpxy"] # poloidal arc length per radian
self.dl = self.dy * self.hthe # poloidal arc length
self.ds["dr"] = self.ds.dx / (self.ds.R * self.ds.Bpxy)
self.ds["dr"].attrs.update({
"conversion" : 1,
"units" : "m",
"standard_name" : "radial length",
"long_name" : "Length of cell in the radial direction"})
self.ds["hthe"] = self.ds.J * self.ds["Bpxy"] # h_theta
self.ds["hthe"].attrs.update({
"conversion" : 1,
"units" : "m/radian",
"standard_name" : "h_theta: poloidal arc length per radian",
"long_name" : "h_theta: poloidal arc length per radian"})
self.ds["dl"] = self.ds.dy * self.ds["hthe"] # poloidal arc length
self.ds["dl"].attrs.update({
"conversion" : 1,
"units" : "m",
"standard_name" : "poloidal arc length",
"long_name" : "Poloidal arc length"})
self.ds["dv"] = self.dydx * dz * data["J"] # Cell volume
self.ds["dv"].attrs.update({
"conversion" : 1,
"units" : "m3",
"standard_name" : "cell volume",
"long_name" : "Cell volume"})
def mass_balance_1d(self):
"""
Perform mass balance on 1D case with atomics
Species names are hardcoded for now
May not work with MYG != 2
"""
o = self.ds.options
ds = self.ds
meta = self.ds.metadata
MYG = meta["MYG"]
mass_i = constants("mass_p") * 2
# ----- Recycling
recycle_multiplier = float(o["d+"]["recycle_multiplier"])
# ----- Boundary flux
sheath_area = ds.da[-2]
sheath_ion_flux = ds["NVd+"].isel(y=-MYG) * sheath_area / mass_i
sheath_neutral_flux = ds["NVd"].isel(y=-MYG) * sheath_area / mass_i
intended_recycle_flux = sheath_ion_flux * recycle_multiplier
# ----- Domain integrals
integrals = dict()
for param in ["Sd+_src", "Sd_src", "Sd+_iz", "Sd+_rec", "SNd+", "Nd+", "Nd"]:
if param in ds.data_vars:
integrals[param] = (ds[param].isel(y = slice(MYG,-MYG)) * ds["dV"].isel(y = slice(MYG,-MYG))).sum("y")
else:
integrals[param] = np.zeros_like(sheath_ion_flux)
# ----- Total fluxes
total_in = integrals["Sd+_src"] + integrals["Sd_src"] + integrals["Sd+_iz"]
total_out = sheath_ion_flux + sheath_neutral_flux + (integrals["Sd+_rec"] * -1)
total_balance = total_in - total_out
frac_balance = total_balance / total_in
total_ions = integrals["Nd+"]
total_neutrals = integrals["Nd"]
total_particles = total_ions + total_neutrals
avg_plasma_dens = integrals["Nd+"] / ds["dV"].sum()
upstream_dens = ds["Nd+"].isel(y = MYG-1)
print(">>> System mass balance")
print("- Total in ---------------")
print(f"- Input ion source = {integrals['Sd+_src'][-1]:.3E} [s-1]")
print(f"- Input neutral source = {integrals['Sd_src'][-1]:.3E} [s-1]")
print(f"- Ionisation source = {integrals['Sd+_iz'][-1]:.3E} [s-1]")
print(f"- Intended recycling source = {intended_recycle_flux[-1]:.3E} [s-1]")
print(f"- Total = {total_in[-1]:.3E} [s-1]")
print("\n- Total out ---------------")
print(f"- Sheath ion flux = {sheath_ion_flux[-1]:.3E} [s-1]")
print(f"- Sheath neutral flux = {sheath_neutral_flux[-1]:.3E} [s-1]")
print(f"- Recombination source = {integrals['Sd+_rec'][-1]:.3E} [s-1]")
print(f"- Total = {total_out[-1]:.3E} [s-1]")
print(f"\n- Difference:")
print(f"---> {total_balance[-1]:.3E} [s-1] ({total_balance[-1]/total_in[-1]:.3%})")
fig, axes = plt.subplots(1,3, figsize=(18,4), dpi = 100)
fig.suptitle(self.name)
fig.subplots_adjust(wspace=0.4)
t = ds.coords["t"]
ax = axes[0]
ax.set_title("Domain particle sources/sinks")
ax.plot(t, integrals["Sd+_src"], label = "Input plasma source", marker = "o", markersize = 3, markevery = 20)
ax.plot(t, integrals["Sd_src"], label = "Input neutral source", c = "k", zorder = 100)
ax.plot(t, integrals["Sd+_iz"], label = "Ionisation source")
ax.plot(t, integrals["Sd+_rec"], label = "Recombination sink")
ax.plot(t, sheath_ion_flux, ls = "-", c = "grey", label = "Ion sheath sink")
ax.set_ylabel("Particle flux [s-1]")
ax = axes[1]
ax.set_title("Total in/out, mass imbalance")
ax.plot(t, total_in, lw = 2, ls = "-", c = "k", label = "Total in")
ax.plot(t, total_out, lw = 2, ls = "-", c = "r", label = "Total out")
ax.set_ylabel("Particle flux [s-1]")
ax2 = ax.twinx()
ax2.plot(t, frac_balance, lw = 2, alpha = 0.3, ls = "-", c = "magenta", label = "Imbalance")
ax2.set_ylim(-1,1)
ax2.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter("{x:.0%}"))
ax2.set_ylabel("Mass imbalance [%]", c = "magenta")
ax2.spines["right"].set_color("magenta")
ax2.yaxis.label.set_color("magenta")
ax2.tick_params(axis="y", colors = "magenta")
ax = axes[2]
ax.set_title("Total particle count, upstream density", fontsize = 10)
ax.plot(t, integrals["Nd"] + integrals["Nd+"], label = "Total domain particle count")
ax.plot(t, integrals["Nd+"], label = "Total domain ion count")
ax.plot(t, integrals["Nd"], label = "Total domain neutral count")
ax.set_ylabel("Particle count")
ax2 = ax.twinx()
ax2.plot(t, upstream_dens, c = "r", ls = "-")
ax2.set_ylabel("Upstream plasma density")
ax2.spines["right"].set_color("red")
ax2.yaxis.label.set_color("red")
ax2.tick_params(axis="y", colors = "red")
for ax in axes:
ax.grid(which = "both")
ax.set_xlabel("Timestep")
axes[0].legend(loc="upper center", bbox_to_anchor = (0.5,-0.15), ncol = 2)
axes[1].legend(loc="upper center", bbox_to_anchor = (0.5,-0.15), ncol = 1)
axes[2].legend(loc="upper center", bbox_to_anchor = (0.5,-0.15), ncol = 1)
def heat_balance_2d(self):
case = self
domain = Region(case, case.slices("all"))
core_ring = CoreRing(case, ring_index = 0)
targets = {
"outer_lower" : Target(case, "outer_lower_target"),
"outer_upper" : Target(case, "outer_upper_target"),
"inner_lower" : Target(case, "inner_lower_target"),
"inner_upper" : Target(case, "inner_upper_target"),
}
list_targets = list(targets.values())
core_i_source = domain.integrals["Pd+_src"] * 1e-6
core_e_source = domain.integrals["Pe_src"] * 1e-6
rad_iz = domain.integrals["Rd+_ex"] * 1e-6 * -1 # MW
rad_rec = domain.integrals["Rd+_rec"] * 1e-6 * -1 # MW
core_i_power = core_ring.total_heat_flux["d+"].squeeze() * 1e-6 # MW
core_e_power = core_ring.total_heat_flux["e"].squeeze() * 1e-6 # MW
outer_target_flux = np.sum([x.total_heat_flux_all for x in [targets["outer_lower"], targets["outer_upper"]]]) * 1e-6
inner_target_flux = np.sum([x.total_heat_flux_all for x in [targets["inner_lower"], targets["inner_upper"]]]) * 1e-6
print(f"- Ion power source: {core_i_source[-1]:,.1f}MW")
print(f"- Electron power source: {core_e_source[-1]:,.1f}MW")
print(f"- Ion power leaving core: {core_i_power[-1]:,.1f}MW")
print(f"- Electron power leaving core: {core_e_power[-1]:,.1f}MW")
print(f"- Total ionisation radiation: {rad_iz[-1]:,.1f}MW")
print(f"- Total recombination radiation: {rad_rec[-1]:,.1f}MW")
print(f"- Total outer target heat flux: {outer_target_flux:,.1f}MW")
print(f"- Total inner target heat flux: {inner_target_flux:,.1f}MW")
def mass_balance_2d(self):
case = self
domain = Region(case, case.slices("all"))
core_ring = CoreRing(case, ring_index = 0)
targets = {
"outer_lower" : Target(case, "outer_lower_target"),
"outer_upper" : Target(case, "outer_upper_target"),
"inner_lower" : Target(case, "inner_lower_target"),
"inner_upper" : Target(case, "inner_upper_target"),
}
list_targets = list(targets.values())
core_source = domain.integrals["Sd+_src"] * 1e-6
s_iz = domain.integrals["Sd+_iz"]
s_rec = domain.integrals["Sd+_rec"] * -1
core_flux = core_ring.particle_flux["d+"].squeeze()
outer_target_flux = np.sum([x.particle_flux for x in [targets["outer_lower"], targets["outer_upper"]]]) * 1e-6
inner_target_flux = np.sum([x.particle_flux for x in [targets["inner_lower"], targets["inner_upper"]]]) * 1e-6
print(f"- Total particle source [s-1]: {core_source[-1]:,.3E} ")
print(f"- Particles leaving core [s-1]: {core_flux[-1]:,.3E} ")
print(f"- Total ionisation source [s-1]: {s_iz[-1]:,.3E} ")
print(f"- Total recombination source [s-1]: {s_rec[-1]:,.3E} ")
print(f"- Total outer target flux [s-1]: {outer_target_flux:,.3E} ")
print(f"- Total inner target flux [s-1]: {inner_target_flux:,.3E} ")
def collect_boundaries(self):
self.boundaries = dict()
for target_name in ["inner_lower_target", "inner_upper_target", "outer_upper_target", "outer_lower_target"]:
self.boundaries[target_name] = Target(self, target_name)
def summarise_grid(self):
meta = self.ds.metadata
print(f' - ixseps1: {meta["ixseps1"]} // id of first cell after separatrix 1')
print(f' - ixseps2: {meta["ixseps2"]} // id of first cell after separatrix 2')
print(f' - jyseps1_1: {meta["jyseps1_1"]} // near lower inner')
print(f' - jyseps1_2: {meta["jyseps1_2"]} // near lower outer')
print(f' - jyseps2_1: {meta["jyseps2_1"]} // near upper outer')
print(f' - jyseps2_2: {meta["jyseps2_2"]} // near lower outer')
print(f' - ny_inner: {meta["ny_inner"]} // no. poloidal cells in-between divertor regions')
print(f' - ny: {meta["ny"]} // total cells in Y (poloidal, does not include guard cells)')
print(f' - nx: {meta["nx"]} // total cells in X (radial, includes guard cells)')
def diagnose_cvode(self, lims = (0,0), scale = "log"):
ds = self.ds
fig, axes = plt.subplots(2,2, figsize = (8,6))
ax = axes[0,0]; ax.set_yscale(scale)
ax.plot(ds.coords["t"], ds.data_vars["cvode_nsteps"].values, label = "nsteps")
ax.plot(ds.coords["t"], ds.data_vars["cvode_nfevals"].values, label = "nfevals")
ax.plot(ds.coords["t"], ds.data_vars["cvode_npevals"].values, label = "npevals")
ax.plot(ds.coords["t"], ds.data_vars["cvode_nliters"].values, label = "nliters")
ax = axes[0,1]
ax.plot(ds.coords["t"], ds.data_vars["cvode_last_order"].values, label = "last_order", lw = 1)
ax = axes[1,0]; ax.set_yscale(scale)
ax.plot(ds.coords["t"], ds.data_vars["cvode_num_fails"].values, label = "num_fails")
ax.plot(ds.coords["t"], ds.data_vars["cvode_nonlin_fails"].values, label = "nonlin_fails")
ax = axes[1,1]; ax.set_yscale(scale)
ax.plot(ds.coords["t"], ds.data_vars["cvode_stab_lims"].values, label = "stab_lims")
ax.plot(ds.coords["t"], ds.data_vars["cvode_stab_lims"].values, label = "last_step")
for i in range(2):
for j in range(2):
axes[i,j].grid()
axes[i,j].legend()
if lims != (0,0):
axes[i,j].set_xlim(lims)
def plot_residuals(self):
"""
Scaled residual calculation based on ANSYS Fluent
From 26.13-19 in https://www.afs.enea.it/project/neptunius/docs/fluent/html/ug/node812.htm
- Take RMS of rate of change in each parameter over whole domain
- Normalise by maximum value of this parameter within first 5 iterations
- Plots parameters corresponding to equations solved (density, momentum, pressure)
"""
# Find parameters (species dependent)
list_params = ["Ne", "Pe"]
for var in self.ds.data_vars:
if "NV" in var and not any([x in var for x in ["S", ")", "_"]]):
list_params.append(var)
if "P" in var and not any([x in var for x in ["S", ")", "_", "e"]]):
list_params.append(var)
list_params.sort()
res = dict()
for param in list_params:
res[param] = np.diff(self.ds[param], axis = 0) # Rate of change
res[param] = np.sqrt(res[param]**2) # RMS of rate of change
res[param] = np.mean(res[param], axis = (1,2))
res[param] = res[param] / np.max(res[param][:4]) # Normalise by max in first 5 iterations
fig, ax = plt.subplots(dpi = 100)
for param in list_params:
ax.plot(res[param], label = param)
ax.set_yscale("log")
ax.grid(which = "major", lw = 1)
ax.grid(which = "minor", lw = 1, alpha = 0.3)
ax.legend()
ax.set_xlabel("Timestep")
ax.set_ylabel("Normalised residual")
ax.set_title(f"Residual plot: {self.name}")
def plot_ddt(self, smoothing = 50, volume_weighted = True, dpi = 100, plot_from=None):
"""
RMS of all the ddt parameters, which are convergence metrics.
Inputs:
smoothing: moving average period used for plot smoothing (default = 20. 1 is no smoothing)
volume_weighted: weigh the ddt by cell volume
"""
# Find parameters (species dependent)
list_params = []
for var in self.ds.data_vars:
if "ddt" in var and not any([x in var for x in []]):
list_params.append(var)
list_params.sort()
# Account for case if not enough timesteps for smoothing
if len(self.ds.coords["t"]) < smoothing:
smoothing = len(self.ds.coords) / 10
res = dict()
ma = dict()
for param in list_params:
if volume_weighted:
res[param] = (self.ds[param] * self.dv) / np.sum(self.dv) # Cell volume weighted
else:
res[param] = self.ds[param]
res[param] = np.sqrt(np.mean(res[param]**2, axis = (1,2))) # Root mean square
res[param] = np.convolve(res[param], np.ones(smoothing), "same") # Moving average with window = smoothing
fig, ax = plt.subplots(figsize = (6,4), dpi = dpi)
for param in list_params:
ax.plot(self.ds["t"], res[param], label = param, lw = 1)
ax.set_yscale("log")
ax.grid(which = "major", lw = 1)
ax.grid(which = "minor", lw = 1, alpha = 0.3)
ax.legend(loc = "upper left", bbox_to_anchor=(1,1))
ax.set_xlabel("Timestep")
ax.set_ylabel("Normalised residual")
ax.set_title(f"Residual plot: {self.name}")
if plot_from is not None:
ax.set_xlim(plot_from, self.ds["t"][-1])
def plot_monitors(self, to_plot, what = ["integral", "mean", "max", "min"], ignore = [], dpi = 100):
"""
Plot time histories of parameters (density, pressure, or momentum)
In each plot the solid line is the mean and dashed lines
represent the min/max at each timestep.
Momentum is shown as an absolute value
TODO: make this interrogate an object such as Region just like Fluent does
"""
list_params = []
if to_plot == "pressure":
for var in self.ds.data_vars:
if "P" in var and not any([x in var for x in ignore+["S", ")", "_", ]]):
list_params.append(var)
elif to_plot == "density":
for var in self.ds.data_vars:
if "N" in var and not any([x in var for x in ignore+["S", ")", "_", "V"]]):
list_params.append(var)
elif to_plot == "momentum":
for var in self.ds.data_vars:
if "NV" in var and not any([x in var for x in ignore+["S", ")", "_"]]):
list_params.append(var)
else:
list_params = to_plot
list_params.sort()
data = dict()
for param in list_params:
data[param] = dict()
# if self.collect_boundaries ==
if "integral" in what:
if self.ds.metadata["keep_xboundaries"] == 1 and self.ds.metadata["keep_xboundaries"] == 1:
slicer = self.slices("all")
domain = self.ds.isel(x = slicer[0], theta = slicer[1])
data[param]["integral"] = (domain[param] * domain["dv"]).sum("x").sum("theta") # Wm-3
if "mean" in what:
data[param]["mean"] = np.mean(self.ds[param], axis = (1,2))
if "max" in what:
data[param]["max"] = np.max(self.ds[param], axis = (1,2))
if "min" in what:
data[param]["min"] = np.min(self.ds[param], axis = (1,2))
# if to_plot == "momentum":
for key in data[param].keys():