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GrpPlotUtil.py
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GrpPlotUtil.py
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import numpy as np
from matplotlib import pyplot
from matplotlib import gridspec
from scipy import optimize
import pop_spike_utilities as psu
######## This function calculates averages across experiments
def exp_avg(datas, datatype,somethreshold=999e6):
if datatype=='none':
alldatavalues=datas
#print "exp_avg: datatype=none, single array passed"
else:
alldatavalues = [celldata[datatype] for celldata in datas]
ln = max(len(celldatavalues) for celldatavalues in alldatavalues)
total = np.zeros(ln)
count = np.zeros(ln)
sumsquares = np.zeros(ln)
for celldatavalues in alldatavalues:
for i,datavalue in enumerate(celldatavalues):
if datavalue < somethreshold and ~np.isnan(datavalue):
total[i]+=datavalue
count[i]+=1
sumsquares[i]+=datavalue**2
#else:
#print (datatype,datavalue)
mn=total/count #cannot use np.nanmean because the lengths of each array in alldatavalues are not identical
meansquares=sumsquares/count
stdev=meansquares-mn**2
return mn,stdev,count
def separate(in_data,sepvar,sepval,in_params):
#make sep_data and sep_params have as many sub arrays as length of sepval?
separate_data=[[],[]]
sep_params=[[],[]]
for i in range(len(sepval)-2):
sep_params.append([])
separate_data.append([])
#print "sep_params",sep_params, "sepval", sepval, sepval[0]
for i in range(len(in_data)):
success=0
if sepvar=='genotype':
attribute=getattr(in_params[i],sepvar)[0:-1]
else:
if sepvar=='cre':
attribute=getattr(in_params[i],'genotype')[-1]
else:
attribute=getattr(in_params[i],sepvar)
if len(sepval)==1:
if isinstance(sepval[0], str):
#two classes, either equal or not equal to specified param
if (str(attribute) == sepval[0]):
separate_data[0].append(in_data[i])
sep_params[0].append(in_params[i])
success=1
else:
separate_data[1].append(in_data[i])
sep_params[1].append(in_params[i])
success=1
else:
if (attribute > sepval[0]):
#print "sepval", sepval[0], '<' ,attribute
separate_data[0].append(in_data[i])
sep_params[0].append(in_params[i])
success=1
else:
#rint "sepval", sepval[0], '>=', attribute
separate_data[1].append(in_data[i])
sep_params[1].append(in_params[i])
success=1
else:
#multiple classes - each value has to be specified
for j in range(len(sepval)):
if (attribute == sepval[j]):
sep_params[j].append(in_params[i])
separate_data[j].append(in_data[i])
success=1
#print "separate on", sepvar, attribute
if success==0:
print ("!!!!!!!!!!data",i," not assigned to group:",in_params[i])
#else:
#print "data",i," assigned", in_params[i]
return separate_data,sep_params
def plot_groups(avg_grp,stderr_grp,minutes_grp,count,filenm,sepvarlist,paramgrp,paramgrpnum):
window_criterion=2 #make this 1 to get 4 windows with 2 separation variables
#maxdur=minutes_grp[0][-1]
pyplot.ion()
fig=pyplot.figure(figsize=(10,15))
fig.canvas.set_window_title('Group Average of Normalized PopSpike')
if len(sepvarlist)>window_criterion:
numwindows=4
else:
numwindows=2
gs=gridspec.GridSpec(numwindows,1)
#Assign graph number based on first two separation variables
graphnum=[]
for num in range(len(avg_grp)):
if len(sepvarlist)<=window_criterion:
if (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[0][0])==sepvarlist[0][1][0]):
graphnum.append(0)
else:
graphnum.append(1)
if len(sepvarlist)>window_criterion:
if (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[1][0])==sepvarlist[1][1][0]) and (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[0][0])==sepvarlist[0][1][0]):
graphnum.append(0)
if (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[1][0])==sepvarlist[1][1][0]) and (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[0][0])!=sepvarlist[0][1][0]):
graphnum.append(1)
if (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[1][0])!=sepvarlist[1][1][0]) and (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[0][0])==sepvarlist[0][1][0]):
graphnum.append(2)
if (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[1][0])!=sepvarlist[1][1][0]) and (getattr(paramgrp[paramgrpnum[num]][0],sepvarlist[0][0])!=sepvarlist[0][1][0]):
graphnum.append(3)
#plot the data
for gridnum in range(numwindows):
axes=fig.add_subplot(gs[gridnum,0])
axes.axis([-15,60,.6,1.6])
axes.axhline(1)
axes.set_ylabel('normPopSpike')
axes.set_xlabel('Time (min)')
for num in range(len(avg_grp)):
if graphnum[num]==gridnum:
axes.errorbar(minutes_grp[num],avg_grp[num],stderr_grp[num],label=filenm[num]+',n='+str(count[num][0]))
axes.legend(fontsize=10, loc='best')
fig.canvas.draw()
pyplot.show()
return
def construct_filename(sepvarlist,paramgrp):
filnm=''
for sepnum in range(len(sepvarlist)):
sepvar=sepvarlist[sepnum][0]
attr=getattr(paramgrp[0],sepvar)
filnm=filnm+sepvar+str(attr)
#filnm=filnm+str(attr)
return filnm
def opto_filename(sepvarlist,paramgrp,llval):
filnm=''
for sepnum in range(len(sepvarlist)):
sepvar=sepvarlist[sepnum][0]
if sepvar=='genotype':
sep_phrase='geno'
attr=getattr(paramgrp[0],sepvar)[0:-1]
elif sepvar=='cre':
sep_phrase=sepvar
attr=getattr(paramgrp[0],'genotype')[-1]
else:
attr=getattr(paramgrp[0],sepvar)
sep_phrase=sepvar
if sepvar=='lightlevel':
#print 'filename', sepvar,attr,
sep_phrase='LL'
if attr> llval:
attr=str(llval+1)+'-100'
else:
attr='0-'+str(llval)
if sepvar=='compound':
sep_phrase="comp"
if attr>0:
attr='C'
else:
attr='S'
#filnm=filnm+sep_phrase+str(attr)
filnm=filnm+str(attr)
#print filnm
return filnm
def plot_onegroup(dict_group,param_group,group_name):
fig=pyplot.figure(figsize=(10,15))
#fig,axes=pyplot.subplots(1,1)
fig.canvas.set_window_title('popspike vs time for traces of '+group_name)
ps=[p['popspikenorm'] for p in dict_group]
pstime=[p['popspikeminutes'] for p in dict_group]
expername=[p.exper for p in param_group]
for expname,index in zip(expername,range(len(ps))):
pyplot.plot(pstime[index],ps[index],label=expname)
pyplot.legend()
fig.canvas.draw()
pyplot.show()
def plot_corr(dict_group,param_group,paramgrpnum,numgroups,filenm,firstpt,lastpt,base_min):
fig,axes=pyplot.subplots(2,1)
fig.canvas.set_window_title('normpopspike vs ')
for gnum in range(numgroups):
ps=[np.nanmean(p['popspikenorm'][firstpt:lastpt]) for p in dict_group[paramgrpnum[gnum]]]
epsp=[np.nanmean(p['amp'][0:base_min]) for p in dict_group[paramgrpnum[gnum]]]
age=[p.age for p in param_group[paramgrpnum[gnum]]]
for ax,item in enumerate([epsp,age]):
if np.isnan(ps).any():
print ("group", filenm[gnum],ps)
else:
popt,pcov=optimize.curve_fit(psu.line,item,ps)
Aopt,Bopt=popt
Astd,Bstd=np.sqrt(np.diag(pcov))
labl=str(np.round(Bopt,3))+'+/-'+str(np.round(Bstd,3))
axes[ax].plot(item,ps,'o',label=filenm[gnum][6:]+labl)
axes[0].set_xlabel('baseline epsp (mV)')
axes[1].set_xlabel('age (days)')
for axis in axes:
axis.set_ylabel('normPopSpike')
axis.legend(fontsize=10, loc='best')
fig.canvas.draw()
pyplot.show()