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Biolog.py
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Biolog.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Biolog.py
Created by Povilas Norvaisas on 2015-02-26.
Copyright (c) 2015. All rights reserved.
"""
install=False
try:
try:
from pip import main as pipmain
except:
from pip._internal import main as pipmain
except ImportError, e:
print "Module pip not found!"
print "Please install pip manually to proceed!"
sys.exit(1)
def pipinstall(package):
pipmain(['install', package])
for mod in ['pip','string','math','re','csv','sys','os',
'commands','datetime','operator','getopt','subprocess','shutil','glob',
'types','math','copy','pyExcelerator','xlrd','xlwt','xlutils','types','warnings','unicodedata']:
try:
exec "import %(mod)s" % vars()
except ImportError, e:
print "Module not found %(mod)s" % vars()
qstn = "Python module %(mod)s not found, should it be installed? (yes/no): " % vars()
answ = raw_input(qstn)
if answ in ['y','yes']:
install=True
else:
print "Script cannot function without module %(mod)s, quitting!" % vars()
sys.exit(1)
print "\nTrying to install!"
if install:
pipinstall(mod)
#pass # module doesn't exist, deal with it.
#import warnings
#Disable simple warnings
warnings.filterwarnings("ignore")
import unicodedata
import numpy as np
import itertools as IT
import pandas as pd
import textwrap as tw
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
import matplotlib.lines as mlines
from scipy import interpolate as ip
from scipy import signal as sig
from scipy.optimize import curve_fit
help_message = '''
Biolog data preparation
Flags:
-h Display this help message
-v Verbose output
Arguments:
-i <files> Design table
-d <file> Biolog_metabolites.csv
-o <dir> directory to write the output to
Options:
full Return additional figures and data columns
'''
class Usage(Exception):
def __init__(self, msg):
self.msg = msg
def usage():
print help_message % vars()
return
optionsset='''
Options:
<--------------------------------------------->
Files: %(ifile)s
Metabolites: %(dfile)s
Out: %(odir)s
Full: %(full)s
<--------------------------------------------->
'''
def main(argv=None):
mode='biolog'
ifile=""
dfile=""
odir='Output'
full=False
comparison=False
integrals=''
if argv is None:
argv = sys.argv
try:
try:
opts, args = getopt.getopt(argv[1:], "hi:f:d:o:", ["help"])
except getopt.error, msg:
raise Usage(msg)
for option, value in opts:
if option in ("-h", "--help"):
usage()
return
if option in ("-i", "--input"):
ifile=value
if option in ("-d", "--db"):
dfile=value
if option in ("-o", "--out"):
odir=value
for argument in args:
if argument in ("full", "--full"):
full = True
except Usage, err:
print >> sys.stderr, sys.argv[0].split("/")[-1] + ": " + str(err.msg)
print >> sys.stderr, "\t for help use --help"
return 2
#Check for the input integrity
try:
if os.path.isfile(ifile):
ipath, iname, itype = filename(ifile)
info=readinfo(ifile)
else:
raise Exception("No design file specified!")
if dfile!="":
dpath, dname, dtype = filename(dfile)
metabolites=pd.read_csv(dfile)
else:
raise Exception("No Biolog metabolites file specified!")
except Exception, e:
print e
#print "!!!-------------------------------------------!!!"
sys.exit(1)
print optionsset %vars()
#----------------------------
if odir!="":
dirn=dircheck(odir)
ilist=info['File'].values
uniques=uniquecomb(info,['Plate','Strain','Sugar_20mM','Inoculum','Uracil_uM'],'Type')
data=collect(info)
data=analyse(data,full)
sheets=makesheets(data,metabolites,info,mode)
writesheets(sheets,odir)
plot_comparison(data,metabolites,odir,'all',info,uniques)
#-------------Functions------------
def readinfo(ifile):
#print ifile
info=NestedDict()
ilist=[]
dlist=[]
odirs=[]
ipt, inm, itp = filename(ifile)
if itp in ['xlsx','xls']:
data=readxls_s(ifile)
elif itp=='csv':
data=readcsv(ifile)
else:
raise Exception("Unknown design file format!")
headers=data[0]
#Automatically find variables
headin={ hd : headers.index(hd) for hd in headers}
nec=['File','Plate','Type']
addhead=[key for key in headin.keys() if key not in nec]
if not all(n in headers for n in nec):
print [not n in headers for n in nec]
raise Exception('Missing essential headers in description file!')
info=pd.DataFrame(data[1:],columns=headers)
info=info[info['File']!='']
#info=info.set_index('File')
return info
def runcmd(cmd):
failure, output = commands.getstatusoutput(cmd)
if failure:
print '''Running failed \n %(cmd)s \n %(output)s'''.encode('utf-8') % vars();
return failure, output
class NestedDict(dict):
def __getitem__(self, key):
if key in self: return self.get(key)
return self.setdefault(key, NestedDict())
def numerize(s):
try:
if s=='NAN':
return s
float(s)
if float(s).is_integer():
return int(float(s))
elif float(s)==0:
return float(s)
else:
return float(s)
except ValueError:
return s
def round_to(n, precission):
#Round a number to desired precision
correction = 0.5 if n >= 0 else -0.5
return int(n/precission+correction)*precission
def filename(ifile):
if ifile.split('.')[0]=='':
ipat=''
iname=''
itype=ifile.split('.')[1]
else:
if "\\" in ifile.split('.')[0]:
sep="\\"
elif "/" in ifile.split('.')[0]:
sep="/"
else:
ipat=''
iname=ifile.split('.')[0]
itype=ifile.split('.')[1]
return ipat, iname, itype
allpath=ifile.split('.')[0]
iname=allpath.split(sep)[-1]
ipath=allpath.split(sep)[:-1]
ipat='/'.join(ipath)
itype=ifile.split('.')[1]
return ipat, iname, itype
def dircheck(somedir):
while True:
if os.path.exists(somedir):
qstn = "Directory %(somedir)s already exists! Delete, quit, continue or provide a new name (d/q/c/<type name>): " % vars()
answ = raw_input(qstn)
if answ == "d":
shutil.rmtree(somedir)
os.makedirs(somedir)
break
elif answ == "q":
sys.exit("Have a nice day!")
elif answ == "c":
break
else:
somedir=answ
continue
else:
os.makedirs(somedir)
break
return somedir
def readxls(ifile):
book=xlrd.open_workbook(ifile,formatting_info=False)
#print book.sheet_names()
data=book.sheet_by_name([nm for nm in book.sheet_names() if 'Magellan' in nm or 'RAW data' in nm][0])
sheet=[]
for r in range(0,data.nrows):
if len(data.row_values(r))>200:
#print set(data.row_values(r))
sheet.append(data.row_values(r))
return sheet
def readxls_s(ifile):
book=xlrd.open_workbook(ifile,formatting_info=False)
data=book.sheet_by_name(book.sheet_names()[0])
sheet=[]
for r in range(0,data.nrows):
#print set(data.row_values(r))
sheet.append(data.row_values(r))
return sheet
def readcsv(ifile):
f=open(ifile,'r')
sheet=[]
rdr=csv.reader(f, delimiter=',')
data=[ln for ln in rdr]
f.close()
return data
def readtxt(ifile):
with open(ifile,'r') as f:
contents=f.read()
defdlmt=','
maxdlmt=0
for dlmt in [',','\t']:
dlmtcount=contents.count(dlmt)
if dlmtcount>maxdlmt:
defdlmt=dlmt
maxdlmt=dlmtcount
#print 'Delimiter: {} with {} instances'.format(defdlmt,maxdlmt)
rdr = csv.reader(contents.splitlines(), delimiter=defdlmt)
sheet=[[numerize(cell) for cell in ln] for ln in rdr]
#print dialect.delimiter
#rdr = csv.reader(f, delimiter=',')
#f.close()
#print len(sheet)
return sheet
def uniquecomb(info,sortby,compareby):
summ=[]
uns=NestedDict()
preskeys=info.columns.values
inkeys=[k for k in sortby if k in preskeys]
missing=[k for k in sortby if k not in preskeys]
if len(inkeys)>0:
if len(inkeys)==len(sortby):
print 'All defined keys have been found!'
else:
print 'Some keys were found!\n{}'.format(inkeys)
#print 'Keys for unique sets have not been found in Design file!\n{}'.format(missing)
for index,row in info.iterrows():
#Collect values?
summ.append(row[inkeys].values)
#print summ
uniqs=[list(i) for i in set(tuple(i) for i in summ)]
for unl in uniqs:
unin='|'.join(unl)
uns[unin]['Unique-keys']=inkeys
uns[unin]['Unique-values']=unl
uns[unin]['Compare-by']=compareby
uns[unin]['Compare-by-values']=[]
for index,row in info.iterrows():
# print sortby
# print unl
# print type(unl)
check=[row[uk]==uv for uk,uv in IT.izip(inkeys,unl)]
# print check
#print compareby
if all(check):
if row[compareby] in uns[unin].keys():
uns[unin][row[compareby]]=uns[unin][row[compareby]]+[row['File']]
else:
uns[unin]['Compare-by-values']=uns[unin]['Compare-by-values']+[row[compareby]]
uns[unin][row[compareby]]=[row['File']]
else:
raise Exception('None of the defined sorting keys have been found in Design file!\n{}'.format(missing))
return uns
def collect(info):
data=NestedDict()
ilist=info['File']
for index,row in info.iterrows():
ifl=row['File']
ipt, inm, itp = filename(ifl)
print ifl
if 'Reader' in info.columns.values:
reader=row['Reader']
if reader=='Tecan':
if itp == 'xlsx':
sheet = readxls(ifl)
data[ifl] = collect_Tecan(sheet)
elif itp in ['asc','txt']:
sheet = readtxt(ifl)
data[ifl] = collect_Tecan(sheet)
else:
raise Exception('Unknown file format: {}!'.format(itp))
elif reader=='Biotek':
sheet=readtxt(ifl)
data[ifl]=collect_Biotek(sheet)
else:
raise Exception('Unknown reader: {}!'.format(reader))
else:
if itp in 'xlsx':
sheet=readxls(ifl)
data[ifl]=collect_Tecan(sheet)
elif itp=='asc':
sheet=readtxt(ifl)
data[ifl]=collect_Tecan(sheet)
elif itp=='txt':
sheet=readtxt(ifl)
data[ifl]=collect_Biotek(sheet)
else:
raise Exception('Unknown file format: {}!'.format(itp))
data[ifl]['File']=ifl
return data
def collect_Tecan(sheet):
sheetdata=NestedDict()
datarange=sheet[0].index('Well positions')
stdrow=max([len(r) for r in sheet])
sheet=[r for r in sheet if len(r)==stdrow]
nrows=len(sheet)
datarange=sheet[0].index('Well positions')
nm_labels=[lab for lab in sheet[0] if lab not in ['Layout','Well positions','','Replicate Info']]
if len(nm_labels)>1:
starts=[sheet[0].index(lab) for lab in nm_labels]
else:
starts=[sheet[0].index(nm_labels[0])]
if nm_labels[0]=='Raw data':
nm_labels[0]='600'
waves=[numerize(str(wlen).replace('nm','')) for wlen in nm_labels]
#print 'Identified wavelengths: {}'.format(waves)
#print datarange
length=(datarange)/len(waves)
#Extract time
time_row=sheet[1][:length]
if list(set([s for s in time_row if isinstance(s,float)])):
time_t=time_row
else:
time_t=[int(str(t).replace('s','')) for t in time_row]
#length=time_t.index(max(time_t))+1
temp=[float(t.split()[0]) for t in sheet[2][:length]]
timemax_min=int(round_to(float(time_t[-1])/60,5))
timemax_h,timemax_remmin=divmod(timemax_min,60)
#Time in seconds
time=np.linspace(0,timemax_min*60,length,dtype=np.dtype(int))
timestep=round_to(float(time_t[-1])/(length-1),1)
alllabels=[r[datarange] for r in sheet if r[datarange] not in ['Well positions']]
#Find first named well
dstart=map(bool, alllabels).index(True)
cleanlabels=alllabels[dstart:]
cleandata=sheet[dstart+1:]
#print "Non zero start {}".format(dstart)
#print alllabels[dstart]
#print sheet[dstart+1]
#print alllabels
labels=[l for l in cleanlabels if l!='']#Sheet
#print labels
plsize=len(labels)
if plsize not in [12,48,96,384]:
buffer=True
else:
buffer=False
sheetdata['Labels']=labels
sheetdata['Spectra']=[str(int(w))+'nm' for w in waves]
sheetdata['Time']=time
sheetdata['Temp']=temp
sheetdata['Time_max']=timemax_min
sheetdata['Time_step']=timestep
sheetdata['Wells']=len(labels)
sheetdata['Used wells']=plsize
sheetdata['Buffered']=str(buffer)
sheetdata['Figures']=[str(w)+'nm' for w in waves]
#sheetdata['File']=inm
print "Wavelengths: {}".format(waves)
print "Run time {}, step {}min in {} wells\n".format(str(datetime.timedelta(minutes=timemax_min)),timestep/60, len(labels))
for wave in waves:
swave=str(int(wave))
scol=(length)*(waves.index(wave))
ecol=(length)*(waves.index(wave)+1)
#print scol,ecol
sheetvalues=[]
for lab,well in enumerate(cleanlabels):
if well!='':
data_row=cleandata[lab][scol:ecol]
sheetvalues.append(data_row)
sheetdata[swave+'nm']=pd.DataFrame(sheetvalues,columns=time,index=labels)
return sheetdata
def collect_Biotek(sheet):
sheetdata=NestedDict()
allwells=getallwells()
rownames=[row[0] if len(row)!=0 else '' for row in sheet]
time_ids=[ rin for rin,r in enumerate(rownames) if 'Time' in str(r) ]
OD_ids=[ rin for rin,r in enumerate(rownames) if 'T' in str(r) and 'OD:' in str(r) ]
time_row=sheet[time_ids[0]]
temp_row=sheet[OD_ids[0]]
nm_labels=[ str(sheet[rin][0].split()[1]) for rin in OD_ids]
nm_labelsm={ rin:str(sheet[rin][0].split()[1]) for rin in OD_ids}
waves_nmm={ rin: numerize(re.sub('OD:|GFP:','',str(sheet[rin][0].split()[1])) ) for rin in OD_ids}
waves=[ numerize(re.sub('OD:|GFP:','',wlen)) for wlen in nm_labels if wlen!='']
waves_nm=[str(int(w))+'nm' for w in waves]
time_t=[time_to_sec(tval) for tval in time_row if tval not in ['Time','','0:00:00']]
length=len(time_t)
timestep=round_to(float(time_t[-1]-time_t[0])/(length-1),1)
timemax_min=int((length-1)*timestep/60)
timemax_h,timemax_remmin=divmod(timemax_min,60)
time=np.linspace(0,timemax_min*60,length,dtype=np.dtype(int))
temp=[float(t) for t in temp_row[1:] if t not in ['']]
#print time_t[-1],timemax_h,time[-1],timestep
alllabels=[rn if rn in allwells else '' for rn in rownames]
labels=list(set([l for l in alllabels if l!='']))
plsize=len(labels)
if plsize not in [12,48,96,384]:
buffer=True
else:
buffer=False
sheetdata['Labels']=labels
sheetdata['Spectra']=waves_nm
sheetdata['Time']=time
sheetdata['Temp']=temp
sheetdata['Time_max']=timemax_min
sheetdata['Time_step']=timestep
sheetdata['Wells']=len(labels)
sheetdata['Used wells']=plsize
sheetdata['Buffered']=str(buffer)
sheetdata['Figures']=waves_nm
print "Wavelengths: {}".format(waves)
print "Run time {}, step {}min in {} wells".format(str(datetime.timedelta(minutes=timemax_min)),timestep/60, len(labels))
sheetvalues=[]
wells=[]
for rid,row in enumerate(sheet):
if rid>min(OD_ids) and len(row)>0:
well=str(row[0])
OD_sel=max([ODid for ODid in OD_ids if rid>ODid])
if well in allwells:
data_row=[val for val in row[1:] if val not in ['']]
sheetvalues.append(data_row)
wells.append(well)
if rid==len(sheet)-1 or rid in [ODid for ODid in OD_ids[1:]]:
#print 'Collecting table with {} rows at row {}'.format(len(sheetvalues),rid)
#print row
swave=str(waves_nmm[OD_sel])+'nm'
sheetDF=pd.DataFrame(sheetvalues,columns=time,index=wells)
sheetvalues=[]
wells=[]
sheetdata[swave]=sheetDF
print "\n"
return sheetdata
def myround(a, decimals=1):
return np.around(a-10**(-(decimals+5)), decimals=decimals)
def greek_check(text,slen):
text=unicode(text)
greek_alphabet ={'alpha':u'\u03B1','beta':u'\u03B2','gamma':u'\u03B3','delta':u'\u03B4','epsilon':u'\u03B5'}
greek_alphabet2 ={'alpha':u'α','beta':u'β','gamma':u'γ','delta':u'δ','epsilon':u'ε'}
tex_alphabet ={u'α':u'$α$',u'β':u'$β$',u'γ':u'$γ$',u'δ':u'$δ$',u'ε':u'$ε$'}
uni_alphabet ={'alpha':u'α','beta':u'β','gamma':u'γ','delta':u'δ','epsilon':u'ε'}
for k in uni_alphabet.keys():
if k in text:
text=text.replace(k,uni_alphabet[k])
text='\n'.join(tw.wrap(text,slen))
for k in tex_alphabet.keys():
if k in text:
text=text.replace(k,tex_alphabet[k])
return text
def Wiener(y, n):
wi = sig.wiener(y, mysize=n)
if sum(np.isnan(wi))>0:
return y
return wi
def Butter(x, y, par1, par2):
b, a = sig.butter(par1, par2)
fl = sig.filtfilt(b, a, y)
return fl
def setbar(x,bar):
x2=[xi if xi>bar else bar for xi in x]
x2=np.array(x2)
return x2
def cut(x, y, a,b,equalize=False):
x2=[]
y2=[]
maxy=max(y)
maxind=y.tolist().index(maxy)
last=0
for xt, yt in IT.izip(enumerate(x),enumerate(y)):
df=yt[0]-last
#print df
if yt[1]>a and yt[1]<=b:# and df<3
x2.append(xt[1])
last=yt[0]
if yt[0]<maxind:
y2.append(yt[1])
else:
if equalize:
y2.append(maxy)
else:
y2.append(yt[1])
y2=np.asarray(y2)
x2=np.asarray(x2)
return x2,y2
def getallwells():
r = [l for l in string.ascii_uppercase[0:16]]
c = [str(i) for i in range(1,25)]
allwells=[]
for rn in r:
for cn in c:
#print rn+cn
allwells.append(rn+cn)
return allwells
def time_to_sec(tstr):
h,m,s=tstr.split(':')
seconds=int(s)+60*int(m)+3600*int(h)
return seconds
def tableout(inp):
#Read file to a list
ifl=open(inp, 'r')
idata=ifl.read()
ifl.close()
table=[]
for row in idata.split('\n'):
data=[it.strip() for it in row.split()]
data=[numerize(it) for it in data]
if len(data)!=0:
table.append(data)
return table
#Log growth model
def growth(x,A,lam,u):
return A/(1+np.exp((4*u/A)*(lam-x)+2))
def lin(x,a,c):
y=x*a+c
return y
def loggrowth(x,y,y0=np.power(2.0,-4),thres=-5):
#thres=-4
#y=setbar(y,np.power(2,-5))
#maxy=max(y)
#miny=min(y)
#yl=np.log2(y)
#print l,y
miny=min(y)
maxy=max(y)
gscale=maxy-miny
#print miny, maxy
x2,y2=cut(x, y, thres+(maxy-thres)*0.1, thres+(maxy-thres)*0.6) #0.6
if len(y2)>0 and gscale>0.5 and len(set(y))>2:
try:
popt, pcov = curve_fit(lin, x2, y2)
a=popt[0]
c=popt[1]
t0=(np.log2(y0)-c)/(a)
tmax=(maxy-c)/(a)
except TypeError:
#print 'Curve_fit encountered an error!'
a,c,t0,tmax=[np.nan,np.nan,np.nan,np.nan]
else:
a,c,t0,tmax=[np.nan,np.nan,np.nan,np.nan]
# for par,nm in IT.izip([a,c,t0],['a','c','t0']):
# if allfit[tp]['Log'][nm]:
# allfit[tp]['Log'][nm]=allfit[tp]['Log'][nm]+[par]
# else:
# allfit[tp]['Log'][nm]=[par]
return a,c,t0,tmax
def absgrowth(xvars,y,margin=0.01):
maxg=max(y)
scaleg=maxg-min(y)
timec,growc=cut(xvars, y, 0, maxg,equalize=True)
if scaleg>0.1 and len(timec)>10 and len(growc)>10 and len(np.unique(y))>2:
#print plate,well
#print len(timec), len(growc)
#print min(growc),max(growc)
#popt, pcov = curve_fit(growth, timec, growc,bounds=(0,np.inf),p0=[0.5, 5, 0.1],max_nfev=5000)
#A,lam,u=popt
try:
popt, pcov = curve_fit(growth,xvars, y,bounds=(0,np.inf),p0=[0.5, 5, 0.1],max_nfev=5000)#,p0=[0.1,10,1]maxfev=5000
A,lam,u=popt
#print popt,tmaxf
except (RuntimeError, ValueError, RuntimeWarning, UnboundLocalError) as e:
#print e
#print 'Curve_fit encountered an error in well {}!'.format(well)
A,lam,u=[0,np.nan,0]
if A>0 and lam<np.inf and u>0:
yreducedf = growth(xvars,*popt) - max(growth(xvars,*popt))*(1-margin) #maxg#
freducedf = ip.UnivariateSpline(xvars, yreducedf, s=0)
if len(freducedf.roots())>0:
tmaxf=freducedf.roots()[0]
else:
tmaxf=np.nan
else:
tmaxf=np.nan
yreduced = growc - maxg*(1-margin)
try:
freduced = ip.UnivariateSpline(timec, yreduced, s=0)
if len(freduced.roots()>0):
tmax=freduced.roots()[0]
else:
tmax=np.nan
except TypeError:
#print 'Integration encountered an error in well {}!'.format(well)
tmax=np.nan
else:
A,lam,u=[0,np.nan,0]
tmaxf=np.nan
tmax=np.nan
#data[plate]['Summary']['GrowthFit'][well]=[]
return A,lam,u,tmax
def analyse(data,full):
window_h=2
thres=np.power(2.0,-5)
for plate in sorted(data.keys()):
time=data[plate]['Time']
time_h=time/3600.0
msize=len(time)//10
maxt=max(time_h)
dt=time[1]-time[0]
wells=data[plate]['Labels']
#Estimate window for background subtraction
if maxt>2:
window=window_h*3600//dt
else:
window=2
#print window
#Needs to be checked
waves=data[plate]['Spectra']
#print waves
#print wells
for wave in waves:
wv=wave.replace('nm','')
print 'Analysing data in {}: {}'.format(plate,wave)
#print time
rawdata=data[plate][wave]#[well]
#Change windows size
nobagall=rawdata.apply( func=lambda row: pd.Series( setbar(row-np.mean(row[:window]) ,0.0),index=time),axis=1 )
wfilt=nobagall.apply( func=lambda row: pd.Series( Wiener(row,msize), index=time) ,axis=1)
logfilt=np.log2( wfilt )
#logfilt=logfilt.apply(func=lambda row: pd.Series(np.where(np.isinf(row), None, row),index=time), axis=1)
dtts=wfilt.apply(func=lambda row: pd.Series(ip.UnivariateSpline(time_h, row, s=0).derivative(1)(time_h),index=time),axis=1 )
# logfiltdt=logfilt.apply( func=lambda row: pd.Series(ip.UnivariateSpline(time_h, row.replace(-np.inf, -5) ,s=0).derivative(1)(time_h) ,index=time),axis=1)
#dtfilt=dtts.apply( lambda row: Wiener(row,msize//2),axis=1)
data[plate][wave+'_b']=nobagall
data[plate][wave+'_f']=wfilt
data[plate][wave+'_log']=logfilt
data[plate][wave+'_dt']=dtts
#data[plate][wave+'_logdt']=logfiltdt
if full:
data[plate]['Figures']=data[plate]['Figures']+[wave+'_b',wave+'_f',wave+'_log',wave+'_dt']
else:
data[plate]['Figures']=data[plate]['Figures']+[wave+'_f',wave+'_log']
summary=pd.DataFrame([],index=wells)
for fg in data[plate]['Figures']:
summaries=[summary]
fgdata=data[plate][fg]
if full:
maxs=pd.DataFrame({ '{}_Max'.format(fg) : fgdata.apply(max,axis=1) })
mins=pd.DataFrame({ '{}_Min'.format(fg) : fgdata.apply(min,axis=1) })
summaries.extend([maxs,mins])
if re.findall('_f$',fg):
#print fgdata
ints=fgdata.apply( lambda x: pd.Series({ '{}_AUC'.format(fg):ip.UnivariateSpline(time_h,x,s=0).integral(0, maxt)}),axis=1)
logints=np.log2(ints.copy(deep=True)).rename(columns={'{}_AUC'.format(fg):'{}_logAUC'.format(fg)})
summaries.extend([ints,logints])
if full:
absgfit=fgdata.apply(lambda x: pd.Series( absgrowth(time_h,x), index=['{}_AG_A'.format(fg), '{}_AG_lamda'.format(fg), '{}_AG_u'.format(fg), '{}_AG_tmax'.format(fg)]),axis=1)
summaries.append(absgfit)
if re.findall('_log$',fg):
loggfit=fgdata.apply(lambda x: pd.Series( loggrowth(time_h,x), index=['{}_LG_a'.format(fg), '{}_LG_c'.format(fg), '{}_LG_t0'.format(fg), '{}_LG_tmax'.format(fg)]),axis=1)
if full:
summaries.append(loggfit)
else:
summaries.append(loggfit[['{}_LG_a'.format(fg),'{}_LG_c'.format(fg)]])
summary=pd.concat(summaries, axis=1)
data[plate]['Summary']=summary
return data
def makesheets(data,descriptors,info,mode):
header_temp=['File','Well','Data']
header=header_temp
if len(info.keys())>0:
hasinfo=[pl for pl in info.keys() if len(info[pl].keys())>0]
if len(hasinfo)>0:
infokeys=sorted(info[hasinfo[0]].keys())
header+=infokeys
if len(descriptors.keys())>0:
hasdesc=[pl for pl in descriptors.keys() if len(descriptors[pl].keys())>0]
if len(hasdesc)>0:
desckeys=sorted(descriptors[hasdesc[0]].keys())
header+=desckeys
allsummary=[]
alldatats=[]
for file in data.keys():
wells=data[file]['Labels']
summary = data[file]['Summary'].copy(deep=True)
summary['File']=file
summary['Data']='Summary'
allsummary.append(summary)
output = data[file]['Figures']
for fig in output:
datats=data[file][fig].copy(deep=True)
datats['File']=file
datats['Data']=fig
alldatats.append(datats)
allsummaryDF=pd.concat(allsummary)
alldatatsDF=pd.concat(alldatats)
allsummaryDF['Well']=allsummaryDF.index
alldatatsDF['Well']=alldatatsDF.index