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cleanPipeline2019-08-21.py
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#!/usr/bin/env python
# coding: utf-8
# In[55]:
import os
import sys
module_path = os.path.abspath(os.path.join('/Users/bacterie/Documents/Experiences/Milli/jupyterScript/'))
if module_path not in sys.path:
sys.path.append(module_path)
from functionsCleanPipeline import *
#define the folder in which the good experiement are
source='/Users/bacterie/Documents/Experiences/Milli/'
#rootPath='/Users/maxime/Documents/experiences/milli/expDilution_5_50_500_3500/2018-02-09_dilution_CAA_WT-stock2/'
#folder=rootPath+'analysis/'
#rootPath='/Users/maxime/Documents/experiences/milli/goodExperiments/'
#rootPath='/Users/maxime/Documents/experiences/milli/experienceDilutionWTstock2/'
#rootPath='/Users/maxime/Documents/experiences/milli/expDilution_5_50_500_3500/'
#rootPath='/Users/maxime/Documents/experiences/milli/experienceGreenRed/'
#rootPath='/Users/maxime/Documents/experiences/milli/expBipyCAA_WT/'
#rootPath='/Users/maxime/Documents/experiences/milli/expBipyCAA_PvdS/'
#rootPathList=['/Users/maxime/Documents/experiences/milli/expBipyCAA_WT/', '/Users/maxime/Documents/experiences/milli/expBipyCAA_PvdS/']
#rootPath=source+'Exp_IronCAA_WT/'
#rootPath=source+'Exp_Incub_4h30-6h30-24h-52h/'
#rootPath=source+'Exp_Incubation_14h-19h-43h/'
#rootPath= source +'Exp_Incubation_merge/'
#rootPath=source+'Exp_DilutionWTstock2/'
rootPath=source+'Exp_Dilution_5_50_500_3500/'
#rootPath=source+'Exp_IronCAA_PvdS/'
#rootPath=source+'Exp_Dilution_PvdS/'
#rootPath=source+'Exp_BipyCAA_WT/'
#rootPath=source+'Exp_BipyCAA_PvdS/'
#rootPath= source +'Exp_IronPvd/'
#rootPath= source +'Exp_GreenRed/'
#rootPath= source +'Exp_Incubation_4h30_6h30_24h_43h13_1bactPerDrop/'
#rootPath='/Users/admin/Documents/Experiences/Milli/Exp_Dilution_M9glycerol/'
rootPathList=[rootPath]
#rootPathList=[source+'Exp_Dilution_PvdS/',\
# source+'Exp_IronCAA_WT/', source+'Exp_Incub_4h30-6h30-24h-52h/', \
# source+'Exp_Incubation_14h-19h-43h/',\
# source+'Exp_Dilution_5_50_500_3500/',source+'Exp_DilutionWTstock2/']
#source+'Exp_IronCAA_PvdS/',\
# source +'Exp_Incubation_4h30_6h30_24h_43h13_1bactPerDrop/',source+'Exp_BipyCAA_WT/',source+'Exp_BipyCAA_PvdS/']
print(rootPathList)
# In[59]:
#label=['SBW25-WT_CAA_1000','SBW25-WT_CAA_7000']#'SBW25-WT_CAA_10', 'SBW25-WT_CAA_100',
for rootPath in rootPathList:
folder=sorted([join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if (o[:3]=='201' or o[:3]=='EXP') and os.path.isdir(os.path.join(rootPath, o))])
#channelList =['PVD','GFP','RFP','PVDoverGFP', 'SpeedSize']
channelList =['GFP']
incCarte=5e-3 #incertitude de la mesure par la carte hardware
timeThresh=30 #heures max d'analyse
threshOutlayer=25 #heures pour mesure de contamination. Pour desactiver mettre une val negative
display = False
startTime=0 #time to start analysing the curves to get ride of the weird shit at the beginning
deletionData=False #delete data in resultIndiv
for channel in channelList:
for path in [folder[2]]:
print(path)
[dropMap, df, label]=loadData(path)
checkfitData(df, dropMap, label, channel,path,startTime,timeThresh,threshOutlayer, incCarte)
fitDataIndiv(df, dropMap, label, channel,path,startTime, timeThresh, threshOutlayer, incCarte, display, deletionData)
####not used anymore
#poolDataInterpolate(dropMap, df, label, channel,path, incCarte, nbReps,startTime, timeThresh, threshOutlayer, display)
#checkDataInterpolated(path, label,channel)
#fitDataLM(path, label,channel) #Not used after lukas discussion
# In[62]:
#plot le diagrame a moustache du lag, gRate, yield en allant chercher les bons label directement et en les ordonnant
#sauve les plot et les grandeurs mesurées dans plotIndiv
#save of the single charte npy file
for rootPath in rootPathList:
folder=[join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if (o[:3]=='201' or o[:3]=='EXP') and os.path.isdir(os.path.join(rootPath, o)) ]
listParam=['stdgRate','gRate', 'lag', 'yld']
#listParam=['yld']
channel='GFP' #GFP RFP PVD
for kindOfData in listParam:
for path in folder:
print(path)
a=path.split('/')
date=a[-3]
data=[]
cleanedData=[]
listData=[]
listLabel=[]
[nn, nnn, label]=loadData(path)
[stdgRate, gRate, lag, stdlag, yld, stdyield]=getDataHistogram(label,path,channel)
for l in label:
#defini le yrange
if kindOfData == 'gRate':
data=gRate[l]
yMin=0
yMax=2
#yMax=1 #GFP
elif kindOfData =='lag':
data=lag[l]
yMin=0
yMax=30
elif kindOfData == 'yld':
data=yld[l]
yMin=0
yMax=8
elif kindOfData == 'stdgRate':
data=stdgRate[l]
yMin=0
yMax=.5
else :
print('do not get kind of data')
cleanedData = [x for x in data if str(x) != 'nan']
listData.append(cleanedData)
listLabel.append(l+' N='+str(len(cleanedData)))
num = []
idx = []
j = 0
print(label)
for a in label:
val=getValueInLabel(a,path)
num.append(val)
idx.append(j)
j = j+1
#ordonne les label selon une concentration croissant
zipped = zip(num,idx)
zz = sorted(zipped,key = lambda t: t[0])
[nn, idx] = [list(t) for t in zip(*zz)]
listLabelOrdered = [listLabel[i] for i in idx]
listDataOrdered = [listData[i] for i in idx]
plt.boxplot(listDataOrdered)
plt.title(date+'_'+channel)
plt.grid()
plt.ylabel(kindOfData)
plt.ylim([yMin, yMax])
if rootPath==source+'Exp_Incubation_14h-19h-43h/' and rootPath==source+'Exp_Incub_4h30-6h30-24h-52h/':
plt.xticks(range(0,60,50),rotation=90)
plt.xlabel('Time of preculture (h)')
else:
plt.xticks(range(1,len(listLabelOrdered)+1), listLabelOrdered)
plt.xticks(rotation=90)
pathPlot = path+'plotIndiv/'
if not os.path.exists(pathPlot):
os.makedirs(pathPlot)
plt.savefig(pathPlot+'plot_'+ kindOfData +'_'+date+'_'+channel+'.pdf',bbox_inches='tight', format='pdf')
plt.show()
dataDict=dict(zip(listLabelOrdered,listDataOrdered))
with open(pathPlot+'result_'+ kindOfData +'_'+ channel+'_'+date+'.csv', "w") as f:
w = csv.writer(f)
w.writerow(dataDict.keys())
w.writerow(dataDict.values())
# Save dict
np.save(pathPlot+ kindOfData+'_'+ channel+'.npy', dataDict)
# =============================================================================
# # In[141]:
#
#
# #plot in one chart the parameters measured as a function of the varying (conc, age, bipy, iron) param for the different experiment. The points correspond to median 5% and 95% bar.
# #extraction from the single charte npy file saved in previous cell
# for rootPath in rootPathList:
# folder=[join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if (o[:3]=='201' or o[:3]=='EXP') and os.path.isdir(os.path.join(rootPath, o)) ]
#
# listParam=['stdgRate','gRate', 'lag', 'yld']
# channel='GFP' #GFP RFP PVD
# for kindOfData in listParam:
#
# plt.figure()
# c = matplotlib.cm.rainbow(np.linspace(0, 1, len(folder)))
# plt.rcParams.update({'font.size': 14})
# idxColor=0
#
#
# #for bipy
# if kindOfData=='yld':
# setZero=0
# elif kindOfData=='gRate':
# setZero=0
# elif kindOfData=='lag':
# setZero=0
# else:
# setZero=0
# yMax=0
# #concMax=3000
# #plt.plot([0,concMax], [setZero,setZero], '--') #plot line for non growing condition
#
# #for iron
# concMax=500
#
# # Load
# for path in folder:
# print(path)
# read_dictionary = np.load(path+'plotIndiv/'+kindOfData+'_'+ channel+'.npy',allow_pickle = True).item()
#
# conc=[]
# med=[]
# perc75=[]
# perc25=[]
# stdv=[]
#
#
# for key in read_dictionary.keys():
# tmp = np.array(read_dictionary[key])
# val=getValueInLabel(key,path)
#
#
# if len(tmp)>5:
#
# conc.append(val)
# tmpMed=np.percentile(tmp, 50)
# med.append(tmpMed) #median
# perc25.append(tmpMed-np.percentile(tmp, 25))
# perc75.append(np.percentile(tmp, 75)-tmpMed)
# stdv.append(np.std(tmp))
# else:
# #if did not grow in more than 5 drops over 80 set to zero
# conc.append(val)
# med.append(setZero) #median
# perc75.append(0) #95% bar
# perc25.append(0) #5% bar
# stdv.append(0)
#
# if yMax<np.max(np.array(med)+np.array(perc75)):
# yMax=np.max(np.array(med)+np.array(perc75))
# #plot for every exp with different color
#
# plt.errorbar(conc, med, yerr=[perc25,perc75], fmt='x', color=c[idxColor], label=str(idxColor),uplims=True, lolims=True, markersize=10)
# idxColor=idxColor+1
#
# date=rootPath.split('/')
#
# plt.title(date[-2]+' '+kindOfData+' '+channel)
# #plt.ylim(setZero, yMax*1.1)
#
# plt.grid()
# #plt.legend()
#
# axis_font = {'fontname':'Courrier', 'size':'14'}
# if kindOfData=='yld':
# plt.ylabel('yield (au)',**axis_font)
# plt.ylim([-5,0])
# elif kindOfData=='gRate':
# plt.ylabel('growth rate (1/h)',**axis_font)
# plt.ylim([0.7,.9])
# elif kindOfData=='lag':
# plt.ylabel('lag (h)',**axis_font)
# plt.ylim([0,30])
# elif kindOfData=='stdgRate':
# plt.ylabel('error gRate (1/h)',**axis_font)
# plt.ylim([0,.1])
#
#
# #manip dilution
# #plt.xscale('log')
# #plt.xlabel('inoculum (cell/drop)',**axis_font)
# #manip iron
# #plt.xticks(rotation=90)
# #plt.xlabel('Pre-incubation time (h)')
# #manip iron
# #plt.xticks(range(0,concMax,100),rotation=90)
# #plt.xlabel('Fe2(SO4)3 (µM)')
# #manip bipy
# #plt.xticks(range(0,3000,200),rotation=90)
# #plt.xlabel('Bipyridyl (µM)')
# if rootPath==source+'Exp_GreenRed/':
# plt.xticks(rotation=90)
#
# if rootPath==source+'Exp_Incubation_14h-19h-43h/' or rootPath==source+'Exp_Incub_4h30-6h30-24h-52h/' or rootPath==source+'Exp_Incubation_merge/':
# plt.xticks(range(0,60,10))
# plt.xlabel('Age of preculture (h)')
#
# plt.savefig(rootPath+kindOfData+'_'+ channel+'.pdf',bbox_inches='tight', format='pdf')
# plt.savefig(rootPath+kindOfData+'_'+ channel+'.jpeg',bbox_inches='tight', format='jpeg')
# plt.show()
#
# =============================================================================
# =============================================================================
# # In[75]:
#
#
# #plot correlation between lag and gRate
#
# clr = matplotlib.cm.rainbow(np.linspace(0, 1, 12))
# for rootPath in rootPathList:
# folder=[join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if (o[:3]=='201' or o[:3]=='EXP') and os.path.isdir(os.path.join(rootPath, o)) ]
#
#
# channel='GFP' #GFP RFP PVD
#
#
#
# for path in folder:
# print(path)
#
# a=path.split('/')
# date=a[-3]
#
# idxClr=0
#
# [nn, nnn, label]=loadData(path)
# [stdgRate, gRate, lag, stdlag, yld, stdyield]=getDataHistogram(label,path,channel)
#
#
# plt.figure()
# for l in label:
#
#
# plt.scatter(gRate[l],lag[l], color=clr[idxClr])
# idxClr=1+idxClr
# plt.ylabel('lag')
# plt.xlabel('gRate')
#
# plt.grid()
# plt.xlim([0,1.2])
# plt.ylim([3,30])
# plt.savefig(path+'correlLagGrate_'+kindOfData+'_'+ channel+'.pdf',bbox_inches='tight', format='pdf')
# plt.show()
#
# =============================================================================
# In[]
#plot CUMULATIVE distribution of gRate, lag, yield in a facet for each conditions
#extraction from the single charte npy file saved in previous cell
for rootPath in rootPathList:
folder=[join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if (o[:3]=='201' or o[:3]=='EXP') and os.path.isdir(os.path.join(rootPath, o))]
listParam=['stdgRate','gRate', 'lag', 'yld']
channel='GFP' #GFP RFP PVD
for kindOfData in listParam:
fig, ax = plt.subplots(1, 1)
for m_path in folder:
print(m_path)
read_dictionary = np.load(m_path+'plotIndiv/'+kindOfData+'_'+ channel+'.npy',allow_pickle = True).item()
df=pddf(dict([ (k,pd.Series(v)) for k,v in read_dictionary.items() ])) #cree un dataframe a partir du dictionnaire
for key in df.keys():
k=key.split(' ')
label=getValueInLabel(k[0],m_path)
print([key, label])
df = df.rename(columns={key: label}) #change le nom des columns pour enlever le N=...
for key in df.keys():
data=df[key][np.isfinite(df[key])] #remove the nan values
# evaluate the histogram
values, base = np.histogram(data, bins='fd')
#evaluate the cumulative
cumulative = np.cumsum(values)
if key==5 or key==4.5 or key==4 or key==14 or key==1:
if '2018-02-21_dilution_CAA_WT-stock2' in m_path:
ax.plot(base[:-1], cumulative/len(data), '-*k', markerSize=10)
else:
ax.plot(base[:-1], cumulative/len(data), '-k')
elif key==50 or key==25 or key==6.5 or key==6 or key==10:
ax.plot(base[:-1], cumulative/len(data), '-b')
elif key==500 or key==100 or key==24 or key==19 or key==100:
ax.plot(base[:-1], cumulative/len(data), '-r')
elif key==3500 or key==1000 or key==52 or key==43 or key==1000:
ax.plot(base[:-1], cumulative/len(data), '-g')
else :
print('not printed:'+str(key))
lgd=my_legend(rootPath)
if kindOfData=='gRate':
xl=0.3
xll=1.5
plt.xlim([xl,xll])
plt.xticks(np.arange(xl,xll,.05),rotation=60)
plt.text(xll*0.8, 0.5,lgd)
if kindOfData=='yld':
xl=5
xll=8
plt.xlim([xl,xll])
plt.xticks(np.arange(xl,xll,.5))
plt.text(xll*0.9, 0.5,lgd)
if kindOfData=='lag':
xl=0
xll=20
plt.xlim([xl,xll])
plt.xticks(np.arange(xl,xll,1),rotation=60)
plt.text(xll*0.8, 0.1,lgd)
plt.yticks(np.arange(0,1.1,.1))
plt.ylim([0,1.1])
plt.xlabel(kindOfData)
plt.ylabel('cumulative frequency of '+kindOfData)
plt.box
plt.grid(which='major', axis='both')
plt.savefig(rootPath+kindOfData+'_cumul'+ channel+'.pdf',bbox_inches='tight', format='pdf')
plt.savefig(rootPath+kindOfData+'_cumul'+ channel+'.jpeg',bbox_inches='tight', format='jpeg')
plt.show()
# In[]
#plot HISTO distribution of gRate, lag, yield in a facet for each conditions
#extraction from the single charte npy file saved in previous cell
for rootPath in rootPathList:
folder=[join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if (o[:3]=='201' or o[:3]=='EXP') and os.path.isdir(os.path.join(rootPath, o))]
listParam=['gRate']#['stdgRate','gRate', 'lag', 'yld']
channel='GFP' #GFP RFP PVD
for kindOfData in listParam:
fig, ax = plt.subplots(1, 1)
for m_path in folder:
print(m_path)
read_dictionary = np.load(m_path+'plotIndiv/'+kindOfData+'_'+ channel+'.npy',allow_pickle = True).item()
df=pddf(dict([ (k,pd.Series(v)) for k,v in read_dictionary.items() ])) #cree un dataframe a partir du dictionnaire
for key in df.keys():
k=key.split(' ')
label=getValueInLabel(k[0],m_path)
print([key, label])
df = df.rename(columns={key: label}) #change le nom des columns pour enlever le N=...
for key in df.keys():
data=df[key][np.isfinite(df[key])] #remove the nan values
# evaluate the histogram
values, base = np.histogram(data, bins='fd')
#evaluate the cumulative
plot_distribution(ax,key,base,values/len(values),m_path)
# =============================================================================
#
# if key==5 or key==4.5 or key==4:
# if '2018-02-21_dilution_CAA_WT-stock2' in m_path:
# ax.plot(base[:-1], values/len(values), '-*k', markerSize=10)
# else:
# ax.plot(base[:-1], values/len(values), '-ok')
# elif key==50 or key==25 or key==6.5 or key==6:
# ax.plot(base[:-1], values/len(values), '-ob')
# elif key==500 or key==100 or key==24:
# ax.plot(base[:-1], values/len(values), '-or')
# elif key==3500 or key==1000 or key==52 or key==43:
# ax.plot(base[:-1], values/len(values), '-og')
# else :
# print('not printed:'+str(key))
# =============================================================================
lgd=my_legend(rootPath)
if kindOfData=='gRate':
plt.xlim([0.6,0.9])
plt.xticks(np.arange(0.6,1,.02),rotation=60)
plt.text(0.61, 2,lgd)
if kindOfData=='yld':
plt.xlim([6,9])
plt.xticks(np.arange(6,9,.5))
plt.text(6.5, 2,lgd)
if kindOfData=='lag':
plt.xlim([0,20])
plt.xticks(np.arange(0,20,1),rotation=60)
plt.text(13, 2,lgd)
plt.yticks(np.arange(0,4))
plt.ylim([0,4])
plt.xlabel(kindOfData)
plt.ylabel('cumulative frequency of '+kindOfData)
plt.box
plt.grid(which='major', axis='both')
plt.savefig(rootPath+kindOfData+'_histo'+ channel+'.pdf',bbox_inches='tight', format='pdf')
plt.savefig(rootPath+kindOfData+'_histo'+ channel+'.png',bbox_inches='tight', format='png')
plt.show()
# In[]
ax=df[3500.0].plot.hist(color='b',edgecolor='black', linewidth=1.2)
ax.set_xlabel('growth rate')
ax.set_ylabel('frequency')
ax.set_xlim([0.78, 0.92])
plt.savefig(rootPath+kindOfData+'_histo3500'+ channel+'.png',bbox_inches='tight', format='png')
# In[]
#plot distribution of gRate, lag, yield in a facet for each conditions
#extraction from the single charte npy file saved in previous cell
for rootPath in rootPathList:
folder=[join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if (o[:3]=='201' or o[:3]=='EXP') and os.path.isdir(os.path.join(rootPath, o))]
listParam=['stdgRate','gRate', 'lag', 'yld']
channel='GFP' #GFP RFP PVD
for kindOfData in listParam:
df_concat=pddf()
rep=0
#eqRepDate=pddf()
for m_path in folder:
print(m_path)
print(rep)
read_dictionary = np.load(m_path+'plotIndiv/'+kindOfData+'_'+ channel+'.npy',allow_pickle = True).item()
df=pddf(dict([ (k,pd.Series(v)) for k,v in read_dictionary.items() ])) #cree un dataframe a partir du dictionnaire
for key in df.keys():
k=key.split(' ')
label=getValueInLabel(k[0],m_path)
print([key, label])
df = df.rename(columns={key: label}) #change le nom des columns pour enlever le N=...
df=df.join(pddf(data=np.full(len(df.index), str(m_path.split('/')[-3].split('_')[0])),columns=['rep'])) #ajoute une column numero pour la manip (jour)
#eqRepDate=eqRepDate.append(pddf({'file':m_path.split('/')[-3],'rep':pd.Series(rep)}), ignore_index=True)
rep+=1
if rep==0:
df_concat=df
else:
df_concat=df_concat.append(df) #stock toute les experiences dans un même dataframe
#eqRepDate.to_csv(rootPath+'Distri_eqRepToDate.csv')
if df_concat.size>0:
#supprime des columns mauvaise
if rootPath=='/Users/admin/Documents/Experiences/Milli/Exp_Incubation_4h30_6h30_24h_43h13_1bactPerDrop/':
df_concat=df_concat.drop([0.0, 6.0], axis=1)
if rootPath=='/Users/admin/Documents/Experiences/Milli/Exp_Dilution_5_50_500_3500/':
df_concat=df_concat.drop([0.5, 350], axis=1)
df_melt=pddf()
df_melt=pd.melt(df_concat, id_vars="rep") #transform le dataframe avec les replicats en colomuns en un dataframe avec une trois columns: nom de la condition(entête des columns), numero de manip, valeur
df_melt=df_melt[np.isfinite(df_melt['value'])] #remove the nan values
if kindOfData=='gRate':
xlow = 0
xhight = 1.3
xlabel = 'growth rate (1/h)'
if kindOfData== 'stdgRate':
xlow = 0
xhight = .1
xlabel = 'error growth rate (1/h)'
elif kindOfData=='yld':
xhight = -1
xlow = -3
xlabel = 'yield (au)'
elif kindOfData=='lag':
xlabel = 'lag (h)'
xlow = 3
xhight = 30
else :
xlow = None
xhight = None
xlabel = kindOfData
if 'p' in locals():
del p
if len(df_melt['value']) > 0:
bwth=2 * scipy.stats.iqr(df_melt['value']) / (len(df_melt['value'])**(1/3))
else:
bwth=None
#geom_density(bw=1/100) \
# p=ggplot(aes(x='value', fill='rep', colour='rep'), data=df_melt) \
# + geom_histogram(binwidth=bwth, alpha=.5) \
# + facet_wrap('variable') \
# + xlim(low=xlow, high=xhight) \
# + ylab('occurence') \
# + xlab(xlabel) \
m_name = rootPath+'distri_' + kindOfData + '_'+ channel +'.pdf'
#p.save(m_name)
# In[405]:
#plot the difference of lag of PVD and GFP.
kindOfData='lag'
setZero=0
dfLag=pddf()
dfLagStd=pddf()
clm=0
flag=True
for rootPath in rootPathList:
folder=[join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if o[:3]=='201' or o[:3]=='EXP']
for path in folder:
print(path)
concGFP=[]
medGFP=[]
medPVD=[]
concPVD=[]
stdvGFP=[]
stdvPVD=[]
channel='GFP'
read_dictionary = np.load(path+'plotIndiv/'+kindOfData+'_'+ channel+'.npy').item()
for key in read_dictionary.keys():
tmp = np.array(read_dictionary[key])
print(key)
val=getValueInLabel(key,path)
if len(tmp)>5:
concGFP.append(val)
tmpMed=np.mean(tmp)
medGFP.append(tmpMed) #median
stdvGFP.append(np.std(tmp)/len(tmp))
else:
#if did not grow in more than 5 drops over 80 set to zero
concGFP.append(val)
medGFP.append(setZero) #median
stdvGFP.append(0)
channel='PVD'
read_dictionary = np.load(path+'plotIndiv/'+kindOfData+'_'+ channel+'.npy').item()
for key in read_dictionary.keys():
tmp = np.array(read_dictionary[key])
val=getValueInLabel(key,path)
if len(tmp)>5:
concPVD.append(val)
tmpMed=np.mean(tmp)
medPVD.append(tmpMed) #median
stdvPVD.append(np.std(tmp)/np.sqrt(len(tmp)))
else:
#if did not grow in more than 5 drops over 80 set to zero
concPVD.append(val)
medPVD.append(np.nan) #median
stdvPVD.append(np.nan)
if flag:
flag=False
else:
clm=clm+1
sLag=pd.Series(np.array(medPVD)-np.array(medGFP), index=np.array(concPVD))
dfLag[clm]=sLag
sLag=pd.Series(np.array(stdvPVD)+np.array(stdvGFP), index=np.array(concPVD))
dfLagStd[clm]=sLag
print(dfLagStd)
m=dfLag.mean(axis = 1)
s=dfLagStd.mean(axis = 1)
nbsample=len(dfLagStd.columns.values)
plt.figure()
plt.errorbar(m.index.values,m.values,s.values,fmt='x',markersize=20,c='k')
plt.grid()
plt.ylabel('lagPVD-lagGFP (h)')
plt.xlabel('bacterial concentration (cell/drop)')
plt.ylim([0,6])
plt.xscale('log')
plt.savefig(rootPath+'diffLag'+ '.pdf',bbox_inches='tight', format='pdf')
plt.show()
# In[58]:
#get the diversity of pvd expression per bact within a drop as a function of the controle parameters of the exp
folder=sorted([join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if o[:3]=='201' or o[:3]=='EXP'])
channel='PVDoverGFP'
timeThresh=35 #heures max d'analyse
threshOutlayer=20#heures pour mesure de contamination
startTime=6
seuilProd=0.5
for path in folder:
print(path)
[dropMap, df, label]=loadData(path)
dfDiv=pddf()
for k,dataFile in enumerate(label):
sr = pd.Series()
tmp= pddf()
for j,content in enumerate(dropMap[:,1]):
if content==dataFile:
y=[]
x=[]
if dataFile=='CAA-WT-100':
startTime=10
elif dataFile=='CAA-WT-25':
startTime=15
else:
startTime=6
[x,y,flag] = getfitData(df, j, channel,startTime,timeThresh,threshOutlayer)
if flag and len(x)>0:
idxProd=bisect.bisect(y,seuilProd)
sr=sr.append(pd.Series(x[idxProd]),ignore_index=True)
tmp[dataFile]=sr
dfDiv = pd.concat([dfDiv,tmp], ignore_index=True)
print(dfDiv.std(axis = 0))
# In[730]:
#Exp_Dilution_5_50_500_3500
conc0=5
val7=1.166799
conc1=np.array([3500,5,50,500])
val5=np.array([0.525821,1.418746, 0.645083, 0.570822])
val6=np.array([0.430209,1.486276,0.568323,0.388596])
#Exp_DilutionWTstock2
conc2=np.array([100,1000,25])
val1=np.array([1.008576, 0.530170, -10])
val2=np.array([0.566269, 0.466830, 1.089643])
val3=np.array([0.643435, 0.561668, 1.051770])
val4=np.array([0.641859, 0.646762, 1.700297])
rootPath==source+'Exp_Dilution_5_50_500_3500/'
if False:
plt.figure()
plt.scatter(conc0,val7)
plt.scatter(conc1,val5)
plt.scatter(conc1,val6)
plt.xscale('log')
plt.grid('on')
plt.ylim([0,2])
plt.xlabel('bacteria inoculum (cell/drop)')
plt.ylabel('std production of pvd per cell')
plt.savefig(rootPath+'diversityPVD'+ '.pdf',bbox_inches='tight', format='pdf')
plt.show()
rootPath==source+'Exp_DilutionWTstock2/'
if False:
plt.figure()
plt.scatter(conc2,val1)
plt.scatter(conc2,val2)
plt.scatter(conc2,val3)
plt.scatter(conc2,val4)
plt.xscale('log')
plt.grid('on')
plt.ylim([0,2])
plt.xlabel('bacteria inoculum (cell/drop)')
plt.ylabel('std production of pvd per cell')
plt.savefig(rootPath+'diversityPVD'+ '.pdf',bbox_inches='tight', format='pdf')
plt.show()
#All
plt.figure()
plt.scatter(conc0,val7)
plt.scatter(conc1,val5)
plt.scatter(conc1,val6)
plt.scatter(conc2,val1)
plt.scatter(conc2,val2)
plt.scatter(conc2,val3)
plt.scatter(conc2,val4)
plt.xscale('log')
plt.grid('on')
plt.ylim([0,2])
plt.xlim([-1,4000])
plt.xlabel('bacteria inoculum (cell/drop)')
plt.ylabel("std production \nof pvd per cell (h)")
plt.savefig(rootPath+'diversityPVD_all'+ '.pdf',bbox_inches='tight', format='pdf')
plt.title('all')
plt.show()
# In[60]:
#plot the probability of growing as a function of bipy concentration
channel='GFP' #GFP RFP
for rootPath in rootPathList:
folder=[join(join(rootPath,o),'analysis/') for o in listdir(rootPath) if o[:3]=='201' or o[:3]=='EXP']
plt.figure()
clr = matplotlib.cm.rainbow(np.linspace(0, 1, len(folder)))
mkr=['x','+','<','>','.','x','+','<','>','.','x','+','<','>','.']
idxColor=0
for path in folder:
pathPlot = path+'plotIndiv/'
[dropMap, nnn, label]=loadData(path)
[stdgRate, gRate, lag, stdlag, yld, stdyield]=getDataHistogram(label,path,channel)
dfProba=pddf()
for l in label:
data=yld[l]
cleanedData = [x for x in data if str(x) != 'nan']
conc=getValueInLabel(l,path)
nbTot=np.sum(dropMap[:,1]==l)
dfProba[conc] = pd.Series([len(cleanedData), nbTot, len(cleanedData)/nbTot], index=[0, 1, 2])
#save number of probability drop
with open(pathPlot+'result_'+ 'proba' + channel+'.csv', "w") as f:
w = csv.writer(f)
#w.writerow(dataN.keys())
#w.writerow(dataN.values())
w.writerow(dfProba.columns.values)
w.writerow(dfProba.iloc[2].values)
#setZero=setZero-setZero/10 #change the position of the non growing drop on the figure to see when they stack on eacho other
conc = list(map(int, dfProba.columns.values))#convert list of string to list of int
proba=dfProba.iloc[2].values
plt.plot(conc, proba,mkr[idxColor], color=clr[idxColor], label=str(idxColor))
idxColor=idxColor+1
date=rootPath.split('/')
print(date[-2])
plt.title(date[-2]+' proba of growing')
plt.ylim(-0.5,1.5)
plt.grid()
plt.ylabel('# growing/# total')
if rootPath==source+'Exp_Incubation_14h-19h-43h/' or rootPath==source+'Exp_Incub_4h30-6h30-24h-52h/' or rootPath==source+'Exp_Incubation_merge/':
plt.xticks(range(0,60,10))
plt.xlabel('Age of preculture (h)')
elif rootPath==source+'Exp_IronCAA_WT/':
plt.xticks(range(0,500,200),rotation=90)
plt.xscale('log')
plt.xlabel('Fe2(SO4)3 (µM)')
else:
#manip bipy
plt.xticks(range(0,3000,200),rotation=90)
plt.xlabel('Bipyridyl (µM)')
#plt.legend()
plt.savefig(rootPath+'proba'+channel+'.pdf',bbox_inches='tight', format='pdf')
plt.show()
# In[54]:
#define fonction
# In[10]:
gRateList=[]
stdgRateList=[]
lagList=[]
stdlagList=[]
yieldList=[]
stdyieldList=[]
labelList=[]
channel='GFP'
#WORKS FOR fitDataLM!
for path in folder:
#print(path)
#path='/Users/maxime/Documents/experiences/milli/expDilution_5_50_500_3500/2018-02-09_dilution_CAA_WT-stock2/analysis/'
fileList=glob.glob(path+'*'+channel+'resultInterp.csv')
for file in fileList:
with open(file, 'r') as f:
reader = csv.reader(f)
your_list = list(reader)