fetplot now loading all new density data

afmdensityplot.py

@@ -61,9 +61,9 @@ def save_density_metrics(fname,tubedata,juncdata,lengths, splits=0):
  lengths=lengths*5/1024
  lengthMeanCorrected=sum(lengths)/(len(lengths)-splits)
  percolationCorrected = calc_percolation(lengthMeanCorrected)
- basename=os.path.basename(fname)[:-4]
- data=[[basename, np.mean(juncdata), np.std(juncdata), np.mean(tubedata), np.std(tubedata), np.mean(lengths), np.std(lengths), calc_percolation(lengths), splits, np.mean(juncdata)-splits/25, np.mean(tubedata)-splits/25, lengthMeanCorrected,percolationCorrected]]
- columns=["fname","junctionMean","junctionStd","tubeMean","tubeStd","lenthMean","lengthStd","percolation", "splits", "junctionMeanCorrected", "tubeMeanCorrected", "lengthMeanCorrected", "percolationCorrected"]
+ device=os.path.basename(fname)[4:13]
+ data=[[device, np.mean(juncdata), np.std(juncdata), np.mean(tubedata), np.std(tubedata), np.mean(lengths), np.std(lengths), calc_percolation(lengths), splits, np.mean(juncdata)-splits/25, np.mean(tubedata)-splits/25, lengthMeanCorrected,percolationCorrected]]
+ columns=["device","junctionMean","junctionStd","tubeMean","tubeStd","lenthMean","lengthStd","percolation", "splits", "junctionMeanCorrected", "tubeMeanCorrected", "lengthMeanCorrected", "percolationCorrected"]
  df=pd.DataFrame(data, columns=columns)
  df.to_csv( fname.replace("_tubes.txt", "_density.csv"), delimiter=',')
 
@@ -118,10 +118,10 @@ def plot_image(fname,save=False, show=True):
  if show:
  plt.show()
 def combine_data(folder):
- cols=["fname","junctionMean","junctionStd","tubeMean","tubeStd","lenthMean","lengthStd","percolation", "splits", "junctionMeanCorrected", "tubeMeanCorrected", "lengthMeanCorrected", "percolationCorrected"]
+ cols=["device","junctionMean","junctionStd","tubeMean","tubeStd","lenthMean","lengthStd","percolation", "splits", "junctionMeanCorrected", "tubeMeanCorrected", "lengthMeanCorrected", "percolationCorrected"]
  fnames=glob.glob(folder+"*density.csv")
  data=pd.concat([pd.read_csv(fname) for fname in fnames])
- data.sort_values(by="fname",inplace=True)
+ data.sort_values(by="device",inplace=True)
  data.to_csv("all_density_data.csv",index=False,columns=cols)
 
 

fetplot.py

@@ -78,11 +78,7 @@ def find_gate(fname):
  try:return re.search('_([^_]*gate[^_]*)_', fname).group(1)
  except: return "backgate"
 
-def load_AFMdensities(directory='AFM_densities', v=False):
- fnames = glob.glob(os.path.join(directory,"*"))
- densities={os.path.basename(x)[7:13]:float(pd.read_csv(x)['1uMean'][0]) for x in fnames}
- deviations ={os.path.basename(x)[7:13]:float(pd.read_csv(x)['1uStd'][0]) for x in fnames}
- return densities,deviations
+
 def get_metrics(df,fname):
  df['IGmax']=max(df["IG"])
  df['IDmax']=max(df["ID"])
@@ -102,6 +98,12 @@ def get_metrics(df,fname):
  df['ONOFF']=np.nan
  df["Ithresh"]=np.nan
  df["Vthresh"]=np.nan
+
+
+ tubedata=pd.read_csv("all_density_data.csv")
+ for key in list(tubedata):
+ if key!="device":
+ df[key]=float( tubedata["junctionMeanCorrected"][ os.path.basename(fname)[0:9] ==tubedata.device ].values )
  return df
 def gate_area(gate):
  areas={"backgate":3600,"topgate1":600,"topgate2":200,"topgate3":200,"topgate4":600}
@@ -128,7 +130,6 @@ def load_to_dataframe(directory,v=True,force=True):
  for i in range(1,len(directory)):
  fnames=fnames+glob.glob(os.path.join(directory[i],"data/COL*"))
  fnames.sort()
- mean,std=load_AFMdensities(v=v)
  # process fnames
  frames= [ get_metrics(pd.read_csv(fname),fname) for fname in fnames ]
  # sets index to fname and concatenates dataset
@@ -150,8 +151,6 @@ def load_to_dataframe(directory,v=True,force=True):
  df['sweep'] = df['temp'].apply(find_sweep)
  df['timestamp']=df['temp'].apply(get_timestamp)
  df['uuid']=df['temp'].apply(get_runID)
- df['junctionMean']=df['device'].apply(lambda x: mean[x] if x in mean.keys() else np.nan)
- df['junctionStd']=df['device'].apply(lambda x: std[x] if x in std.keys() else np.nan)
  df['fname']=df['temp']
  df.drop(['temp'],axis=1,inplace=True)
  # resets the index to a unique identifier for a datarun