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getSact.py
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import re
import numpy as np
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab! - Otherwise figures try to load but -X not necessarily on
import matplotlib.pyplot as plt
import sys
import os
import glob
# setup data
try:
fileName = sys.argv[1]
except Exception:
fileName = "Ca_WT_5phubs_9s_sGJ_100by501.dat"
try:
isImagedCells = bool(int(sys.argv[2]))
except Exception:
isImagedCells = False
try:
nBatch = int(sys.argv[3])
except Exception:
nBatch = 0
fileDir = os.path.dirname(fileName)
fileBase = os.path.basename(fileName)
fileIdx = re.findall(".*model_(\d+)_morphology_(\d+)_seed_(\d+)_mode_(\d+)_.*",fileName)
fileId = "model_%s_morphology_%s_seed_%s_mode_%s"%fileIdx[0]
mode = int(fileIdx[0][3])
shape = re.findall('.*_(\w+)x(\w+)\.dat',fileName)[0]
shapeX = (int(shape[0]),int(shape[1]))
if nBatch>0:
filePrefix,startBatch = re.findall("(.*)_p_(\d+)_.*",fileName)[0]
fileNameBatch = []
shapeXBatch = []
for i in range(1,nBatch+1):
getBatch = int(startBatch)+i
tempfilename = glob.glob(filePrefix+"_p_%d_*"%getBatch)[0]
tempshape = re.findall('.*_(\w+)x(\w+)\.dat',tempfilename)[0]
shapeXBatch.append((int(tempshape[0]),int(tempshape[1])))
fileNameBatch.append(tempfilename)
varName = r"[Ca]$_i$ [mM]" if "Ca" in fileName else r"$V_m$ [mV]"
title = "short simulation homogeneous GJ @ mouse 40-3"
if isImagedCells:
saveName = os.path.join(fileDir,'imaged_'+fileBase[:-4]+'.png')
else:
saveName = os.path.join(fileDir,'whole_'+fileBase[:-4]+'.png')
XX = np.zeros((nBatch+1)*shapeX[1])
# load data
startName = "#dt = "
with open(os.path.join(fileDir,fileId+'.log'),"r") as fi:
for ln in fi:
if ln.startswith(startName):
temp = ln[len(startName):]
dt = float(temp)
startName = "#downSampling = "
with open(os.path.join(fileDir,fileId+'.log'),"r") as fi:
for ln in fi:
if ln.startswith(startName):
temp = ln[len(startName):]
downSampling = float(temp)
tstep = dt*downSampling
X = np.memmap(fileName, dtype='float64', mode='r', shape=shapeX)
t = np.arange(0,shapeX[1]*tstep,tstep) # account dt and downSampling
# hubList (if have)
try:
hubList = np.loadtxt(os.path.join(fileDir,fileId+'.log'),delimiter=',',dtype=int)
except Exception:
hubList = []
# get only imaged cells (if applicable)
if isImagedCells:
imagedCells = []
startName = "#imagedCells = "
with open(os.path.join(fileDir,fileId+'.log'),"r") as fi:
for ln in fi:
if ln.startswith(startName):
imagedCells = ln[len(startName):]
imagedCells = [int(x.strip()) for x in imagedCells.split(',')]
imagedHubs = []
startName = "#imagedHubs = "
with open(os.path.join(fileDir,fileId+'.log'),"r") as fi:
for ln in fi:
if ln.startswith(startName):
imagedHubs = ln[len(startName):]
imagedHubs = [int(x.strip()) for x in imagedHubs.split(',')]
hubList = imagedHubs
# main plot
#fig = plt.figure()
#ax = fig.add_subplot(111)
aveX = np.zeros(X[0].shape)
maxCa = 0
for i in xrange(len(X)):
if isImagedCells:
if (i not in hubList) and (i in imagedCells):
#ax.plot(t, X[i], 'k', alpha=0.3)
aveX += X[i]
else:
if i not in hubList:
#ax.plot(t, X[i], 'k', alpha=0.3)
aveX += X[i]
#ax.plot(t, X[i], 'k', alpha=0.3, label='non-hub')
for i in np.array(hubList)[np.array(hubList)>0]:
#ax.plot(t, X[i], 'r')
aveX += X[i]
#ax.plot(t, X[i], 'r', label='hub')
aveX = aveX/float(shapeX[0]) if not isImagedCells else aveX/float(len(imagedCells))
#ax.plot(t, aveX, 'g', linewidth=3.0, label='average all')
maxCa = max(np.max(X),maxCa)
XX[0:len(aveX)] = aveX
# plot the rest if splitted into batches
if nBatch>0:
for iBatch in range(nBatch):
shapeX = shapeXBatch[iBatch]
X = np.memmap(fileNameBatch[iBatch], dtype='float64', mode='r', shape=shapeX)
t = np.arange(t[-1],t[-1]+shapeX[1]*tstep,tstep) # account dt
aveX = np.zeros(X[0].shape)
for i in xrange(len(X)):
if isImagedCells:
if (i not in hubList) and (i in imagedCells):
#ax.plot(t, X[i], 'k', alpha=0.3)
aveX += X[i]
else:
if i not in hubList:
#ax.plot(t, X[i], 'k', alpha=0.3)
aveX += X[i]
for i in np.array(hubList)[np.array(hubList)>0]:
#ax.plot(t, X[i], 'r')
aveX += X[i]
#ax.plot(t, X[i], 'r')
aveX = aveX/float(shapeX[0]) if not isImagedCells else aveX/float(len(imagedCells))
#ax.plot(t, aveX, 'g', linewidth=3.0)
maxCa = max(np.max(X),maxCa)
#print len(XX[(iBatch+1)*len(aveX):(iBatch+2)*len(aveX)]), len(aveX), iBatch
XX[(iBatch+1)*len(aveX):(iBatch+2)*len(aveX)] = aveX
# show where silencing is applied
if mode!=0:
try:
import matplotlib.patches as patches
startName = "#silenceStart = "
with open(os.path.join(fileDir,fileId+'.log'),"r") as fi:
for ln in fi:
if ln.startswith(startName):
temp = ln[len(startName):]
silenceStart = float(temp)
startName = "#silenceDur = "
with open(os.path.join(fileDir,fileId+'.log'),"r") as fi:
for ln in fi:
if ln.startswith(startName):
temp = ln[len(startName):]
silenceDur = float(temp)
if maxCa>0.0005:
rect_y = (0.00095,0.00001) if "Ca" in fileName else (-5, 1)
else:
rect_y = (0.000475,0.000005) if "Ca" in fileName else (-5, 1)
#ax.add_patch(patches.Rectangle( (silenceStart,rect_y[0]), silenceDur, rect_y[1] , alpha=0.6))
except Exception:
pass
####################################
idxx=int(sys.argv[4])
#np.savetxt("tmp%d.txt"%idxx,XX)
sStartIdx = int(silenceStart/tstep)
sEndIdx = int((silenceStart+silenceDur)/tstep)
skipIdx = int(75*1000/tstep)
gStartIdx = int(50*1000/tstep)
XX0 = XX[gStartIdx+skipIdx:sStartIdx-1]
#XX1 = XX[sStartIdx+skipIdx:sEndIdx-1]
minxx0 = np.min(XX0)
#minxx1 = np.min(XX1)
XX0 -= minxx0
#XX1 -= minxx1
outpercent = np.mean(XX0)
inhibPer = idxx*10./np.float(shapeX[0])*100.
nameid = sys.argv[5]
with open("sim%s_act.txt"%nameid, 'a') as f:
f.write("%f %f \n"%(inhibPer,outpercent))
print sStartIdx,sEndIdx,skipIdx,gStartIdx
# plotting setup
#ax.set_xlabel("t [ms]")
#ax.set_ylabel(varName)
#if "Ca" in fileName:
# if maxCa>0.0005:
# ax.set_ylim([0, 0.001])
# else:
# ax.set_ylim([0, 0.0005])
#else:
# ax.set_ylim([-100.0, 0.0])
#ax.set_title(title)
#plt.savefig(saveName)