changed measure to allow comparison off different junction resistance…

… maps

README.md

@@ -7,19 +7,15 @@ The best and most comprehensive links for MNA on the web:
 [Examples](https://www.swarthmore.edu/NatSci/echeeve1/Ref/mna/MNA4.html), and
 [Matrix formation rules](https://www.swarthmore.edu/NatSci/echeeve1/Ref/mna/MNAMatrixRules.html).
 
-the network is labeled:
-
-|rows/columns->|0|1|2|...|c-1|
-|---|:---:|:---:|:---:|:---:|:---:|
-|0|0 |1  |2  | ... |c-1 |
-|1|c |c+1|c+2|...|c+c-1|
-|2|2c|2c+1|...|...|2c+c-1|
-|...|...|...|...|...|...|
-|(r-1)*c|(r-1)*c+1|...|...|...|(r-1)*c+c-1=r*c-1|
-
 ## electrical junction values:
 Fuhrer: mm=200kOhm, ss=400kOhm
-tombler : mm 400 to 600 ohm from on to off
+tombler : mm 400 to 600 ohm from on to off. compares ms junction to Ti electrode-s junction.
+Nirmalraj : 98kOhm to 2.6MOhm for 1.2nm to 14nm diameter bundles.
+Topinka : 200kOhm junction resistance from tombler. Treats ms junctions and crosses as nonconductive
+Stadermann : 100MOhm at some steps along the scan, attributed to junctions.
+Lyons : estimates interjunction resistance from 70kOhm to 3.5Mohm for bundles and tubes
+E. Lee : "demonstrate that potential barriers
+located at nanotube crossings become dominant in the OFF state of cross-junction devices, as a result of the flattening of Schottky barriers present at the electrical contacts. Consequently, under applied bias, the electrostatic potential drop, and thus the photoresponse, occurs predominantly at these positions."
 ## Optimization
 use cProfile and the [gprof2dot](https://github.com/jrfonseca/gprof2dot).py script to generate profiles of time spent for running the script.
 explicit number to run optimization on is:

measure_perc.py

@@ -7,17 +7,30 @@
 from multiprocessing import Pool
 import uuid as id
 
+#### preset offmappings  #####
+### 0 ###
+# only intertube junctions have a 10^3 on off ratio
+offmap0={'ms':1000,'sm':1000, 'mm':1,'ss':1,'vs':1,'sv':1,'vm':1,'mv':1}
+### 1 ###
+# all ms junctions including electrodes have a 10^3 on off ratio
+offmap1={'ms':1000,'sm':1000, 'mm':1,'ss':1,'vs':1000,'sv':1000,'vm':1000,'mv':1000}
+### 2 ###
+# all junctions including electrodes have a 10^3 on off ratio
+offmap2={'ms':1000,'sm':1000, 'mm':1000,'ss':1000,'vs':1000,'sv':1000,'vm':1000,'mv':1000}
+offmappings=[offmap0,offmap1,offmap2]
+
+
 def checkdir(directoryname):
     if os.path.isdir(directoryname) == False:
         os.system("mkdir " + directoryname)
     pass
 
-
-def measure_fullnet(n,scaling, l='exp', save=False, seed=0, v=True ,remote=False):
+def measure_fullnet(n,scaling, l='exp', save=False, seed=0,offmap=1, v=True ,remote=False):
+    datacol=['sticks', 'size', 'density', 'nclust', 'maxclust', 'ion', 'ioff','gate', 'fname','seed','offmap']
     start = timer()
-    data=pd.DataFrame(columns = ['sticks', 'size', 'density', 'nclust', 'maxclust', 'ion', 'ioff','ioff_totaltop', 'ioff_partialtop', 'runtime', 'fname','seed'])
+    data=pd.DataFrame(columns = datacol)
     try:
-        collection=perc.StickCollection(n,scaling=scaling,notes='run',l=l,seed=seed)
+        collection=perc.StickCollection(n,scaling=scaling,notes='run',l=l,seed=seed,offmap=offmappings[offmap])
         collection.label_clusters()
         nclust=len(collection.sticks.cluster.drop_duplicates())
         maxclust=len(max(nx.connected_components(collection.graph)))
@@ -45,9 +58,9 @@ def measure_fullnet(n,scaling, l='exp', save=False, seed=0, v=True ,remote=False
             collection.cnet.set_global_gate(10)
             collection.cnet.update()
             ioff=sum(collection.cnet.source_currents)
+            gate='back'
+
         except Exception as e:
-            ion=0
-            ioff=0
             print("measurement failed: error global gating")
             print("ERROR for {} sticks:\n".format(n),e)
             traceback.print_exc(file=sys.stdout)
@@ -63,33 +76,42 @@ def measure_fullnet(n,scaling, l='exp', save=False, seed=0, v=True ,remote=False
         except:
             ioff_totaltop=0
             ioff_partialtop=0
+        data.loc[0]=[n,scaling,n/scaling**2,nclust,maxclust,ion,ioff,'back',fname,seed,offmap]
+        data.loc[1]=[n,scaling,n/scaling**2,nclust,maxclust,ion,ioff_totaltop,'total',fname,seed,offmap]
+        data.loc[2]=[n,scaling,n/scaling**2,nclust,maxclust,ion,ioff_partialtop,'partial',fname,seed,offmap]
     else:
         ion=0
         ioff=0
-        ioff_totaltop=0
-        ioff_partialtop=0
+        gate='back'
+        data.loc[0]=[n,scaling,n/scaling**2,nclust,maxclust,ion,ioff,gate,fname,seed,offmap]
     end = timer()
     runtime=end - start
-    data.loc[0]=[n,scaling,n/scaling**2,nclust,maxclust,ion,ioff,ioff_totaltop,ioff_partialtop,runtime,fname,seed]
+    data['runtime']=runtime
     if fname:
         data.to_csv(fname+"_data.csv")
     return data
 
-def measure_async(cores,start,step,number,scaling,save=False):
+def measure_async(cores, start, step, number, scaling, save=False, offmap=1, seeds=[]):
     uuid=id.uuid4()
     starttime = timer()
     nrange=[int(start+i*step) for i in range(number)]
-    seeds=np.random.randint(low=0,high=2**32,size=number)
-    np.savetxt("seeds_{}.csv".format(uuid), seeds, delimiter=",")
+    if not(len(seeds)==number):
+        seeds=np.random.randint(low=0,high=2**32,size=number)
+    if not(os.path.isfile("seeds_{}.csv".format(uuid))):
+        np.savetxt("seeds_{}.csv".format(uuid), seeds, delimiter=",")
     pool=Pool(cores)
-    results=[pool.apply_async(measure_fullnet,args=(nrange[i],scaling,'exp',save,seeds[i])) for i in range(number)]
+    results=[]
+    for omap in offmap:
+        results= results+[pool.apply_async(measure_fullnet, args=(nrange[i],scaling,'exp',save,seeds[i],omap)) for i in range(number)]
+    print(len(results))
     output=[res.get() for res in results]
     endtime = timer()
     runtime=endtime - starttime
     print('finished with a runtime of {:.0f} seconds'.format(runtime))
     data=pd.concat(output)
     data.to_csv('measurement_batch_{}.csv'.format(uuid))
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument("-t",'--test',action="store_true",default=False)
@@ -99,9 +121,10 @@ def measure_async(cores,start,step,number,scaling,save=False):
     parser.add_argument("--step",type=int,default=0)
     parser.add_argument("--number",type=int)
     parser.add_argument("--scaling",type=int,default=5)
+    parser.add_argument("--offmap",nargs='*',type=int)
     args = parser.parse_args()
     checkdir('data')
     if args.test:
         measure_async(2,500,0,10,5,save=True)
     else:
-        measure_async(args.cores, args.start, args.step, args.number,args.scaling, args.save)
+        measure_async(args.cores, args.start, args.step, args.number,args.scaling, args.save,args.offmap)

percolation.py

@@ -11,15 +11,16 @@
 
 
 class StickCollection(object):
-    def __init__(self, n=2, l='exp', pm=0.135, scaling=5, fname='', directory='data', notes='',seed=0):
+    def __init__(self, n=2, l='exp', pm=0.135, scaling=5, fname='', directory='data', notes='', seed=0,
+    offmap={'ms':1000, 'sm':1000, 'mm':1, 'ss':1, 'vs':1000, 'sv':1000, 'vm':1000, 'mv':1000}):
         self.scaling=scaling
         self.n=n
         self.pm=pm
         self.l=l
         self.notes=notes
         self.directory=directory
         self.percolating=False
-
+        self.offmap=offmap
         #seeds are included to ensure proper randomness on slurm
         if seed:
             self.seed=seed
@@ -141,7 +142,7 @@ def make_graph(self):
         if self.percolating:
             self.ground_nodes=[1]
             self.voltage_sources=[[0,0.1]]
-            self.populate_graph()
+            self.populate_graph(self.offmap)
             for node in connected_graph.nodes():
                 connected_graph.nodes[node]['pos'] = [self.sticks.loc[node,'xc'], self.sticks.loc[node,'yc']]
             for edge in connected_graph.edges():
@@ -150,8 +151,8 @@ def make_graph(self):
         else:
             return False,False,False
 
-    def populate_graph(self):
-        offmap={'ms':1000,'sm':1000, 'mm':1,'ss':1,'vs':1000,'sv':1000,'vm':1000,'mv':1000}
+    def populate_graph(self,offmap):
+
         for edge in self.graph.edges():
             self.graph.edges[edge]['component']=Transistor( off_resistance=offmap[self.graph.edges[edge]['kind']])