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server.py
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server.py
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from http.server import HTTPServer, BaseHTTPRequestHandler
import json
import os
from glob import glob
from pathlib import Path
from urllib.parse import unquote
from scipy.signal import savgol_filter, argrelextrema, find_peaks, resample_poly
from scipy.optimize import differential_evolution, curve_fit
import numpy
import pandas
#Also requires openpyxl
import zipfile
import tempfile
import shutil
import xlsxwriter
import webbrowser
from multiprocessing import Pool, freeze_support
import warnings
import math
#from adjustText import adjust_text
#NEW
from matplotlib import pyplot
import matplotlib
from docx import Document
import argparse
import configparser
np =numpy
#/Users/pau/HTS/
serv_path="webapp/"
data_path="data/"
cache_path="cache/"
persistent_path="persistent/"
plateinfo_path="plateinfo/"
version="8"
#decimation=False
decimation=True
decim_maxpoints=150
formats=["LightCycler","BioRad","QuantStudio 2","QuantStudio 1","Generic DSF"]
def string_escape(s, encoding='utf-8'):
return (s.encode('latin1') # To bytes, required by 'unicode-escape'
.decode('unicode-escape') # Perform the actual octal-escaping decode
.encode('latin1') # 1:1 mapping back to bytes
.decode(encoding))
def remove_cache():
files = glob(cache_path+'/*')
for f in files:
os.remove(f)
def kd_function(x,kd,dH0,Tm0):
R=8.314
n=1
#Tm0=348.98
f=Tm0/(1-n*R*Tm0/dH0*np.log(1+x/kd))
#f=dH0*Tm0/ (dH0-R*Tm0*np.log((kd+x)/kd))
return f
def sumOfSquaredError(parameterTuple,func,xdata,ydata):
warnings.filterwarnings("ignore") # do not print warnings by genetic algorithm
#print(parameterTuple,xdata,ydata)
val = func(xdata, *parameterTuple)
return np.sum((ydata - val) ** 2.0)
#https://stackoverflow.com/questions/56164659/scipy-curve-fit-do-not-converge-even-if-i-iteratively-change-initial-guess
def generate_Initial_Parameters(func,xdata,ydata):
#finds best starting parameters to fit xdata and ydata to func.
minY = np.min(ydata)
maxY = np.max(ydata)
minX = np.min(xdata)
maxX = np.max(xdata)
parameterBounds = []
parameterBounds.append([minX, maxX]) # search bounds for kd
parameterBounds.append([-1e8, 1e8]) # search bounds for dh0
parameterBounds.append([minY, maxY]) # search bounds for tm
# "seed" the numpy random number generator for repeatable results
result = differential_evolution(sumOfSquaredError, parameterBounds, args=([func,xdata,ydata]),seed=3)
return result.x
def getKd(concentrations,tms):
#We need the data at x and y.
#file="AT021-D02.txt"
#df=read_csv(file,sep="\t")
#data=df.values
#x,y=data[:,0], data[:,1]
x,y=concentrations,tms
minY = np.min(y)
maxY = np.max(y)
minX = np.min(x)
maxX = np.max(x)
# by default, differential_evolution completes by calling curve_fit() using parameter bounds
geneticParameters = generate_Initial_Parameters(kd_function,x,y)
#print(geneticParameters)
#geneticParameters.append(maxfev=100000)
# now call curve_fit without passing bounds from the genetic algorithm,
# just in case the best fit parameters are aoutside those bounds
b11=0.9*geneticParameters[0]
b12=1.1*geneticParameters[0]
if (b12>maxX):
b12=maxX
b21=0.9*geneticParameters[1]
b22=1.1*geneticParameters[1]
if geneticParameters[1]<0:
b21=1.1*geneticParameters[1]
b22=0.9*geneticParameters[1]
b31=0.9*geneticParameters[2]
b32=1.1*geneticParameters[2]
try:
#kd,th0,tm0
#fittedParameters, pcov = curve_fit(kd_function, x, y, p0=geneticParameters, method='trf',maxfev=100000, bounds=([minX, -1e12,0.8*minY],[maxX,1e12,1.5*maxY]))
#fittedParameters, pcov = curve_fit(kd_function, x, y, p0=geneticParameters, method='dogbox',maxfev=100000, bounds=([minX, -1e8,0.8*minY],[maxX,1e8,1.5*maxY]))
#fittedParameters, pcov = curve_fit(kd_function, x, y, p0=geneticParameters, method='lm',maxfev=100000)#, bounds=([minX, -1e8,0.8*minY],[maxX,1e8,1.5*maxY]))
fittedParameters, pcov = curve_fit(kd_function, x, y, p0=geneticParameters, method='trf',maxfev=100000, bounds=([b11,b21,b31],[b12,b22,b32]))
except:
#print ("Kd calculation not possible with these values")
return(0,0,"ERROR","ERROR","ERROR")
#print('Fitted parameters:', fittedParameters)
#print('pcov',pcov)
modelPredictions = kd_function(x, *fittedParameters)
absError = modelPredictions - y
SE = np.square(absError) # squared errors
MSE = np.mean(SE) # mean squared errors
RMSE = np.sqrt(MSE) # Root Mean Squared Error, RMSE
Rsquared = 1.0 - (np.var(absError) / np.var(y))
tkd="{:.2e}".format(fittedParameters[0])
if (fittedParameters[0]>1):
tkd="{:.2f}".format(fittedParameters[0])
tdH0="{:.2f}".format(fittedParameters[1])
ttm0="{:.2f}".format(fittedParameters[2])
#print('Kd: '+tkd+" M ; dH0: "+ tdH0+" J ; Tm0: "+ttm0+ " K")
#print('RMSE:', RMSE)
#print('R-squared:', Rsquared)
return(fittedParameters,Rsquared,tkd,tdH0,ttm0)
def get_kd_data_for_molecule(template,plate,tm_ave,molecule):
#print("getting Kd data for molecule",molecule,template,plate,tm_ave)
output_data={}
#output data contains:{wells:{'A1':{'tm':65,'enabled':1,'concentration':250},'A2':...},'datapoints':[{"x":12,"y:7"},{"x":12,"y:7"},...],'kd':0.55,'R2':0.999,'dH0':-544445,'tm0':312,'fittingcurve':[{"x":12,"y:7"},{"x":12,"y:7"},...]}
kd_templatefile=persistent_path+"kdtemplates.templ"
templ={}
if os.path.exists(kd_templatefile):
#print("Categories file found. Serving")
f_templ = open(kd_templatefile,'r')
#return json.loads(f_served.read())
a=json.loads(f_templ.read())
for item in a:
#print(item)
#print(item["filename"],template)
if item["filename"]==template:
templ=item
moleculewells=[]
for item in templ["cells"]:
#print(item)
for key in item:
#print(key,item[key])
splkey=key.split("-")
w=chr(int(splkey[1])+64)+str(splkey[0])
molecul=item[key]
if (molecul==molecule):
moleculewells.append(w)
#print(w)
concentrations={}
for item in templ["concentrations"]:
for key in item:
cl=key[1:]
a=item[key]
if a=="":
a="0"
conc=float(a)
concentrations[cl]=conc
#print(concentrations)
wells={}
tmdata=readtxttm(data_path+plate)
cats_item=json.loads(serve_categories(plate))
tmconc0=0
tmconc50=0
tmconcmax=0
for well in moleculewells:
good_data=1
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=0
tm=float(tmdata[well]["tm"])+273.15
allpeaks=[]
if 'allpeaks' in tmdata[well]:
allpeaks=tmdata[well]['allpeaks']
if len(allpeaks)>1:
#closest value to tm_ave
tm=allpeaks[numpy.abs(numpy.array(allpeaks)-tm_ave).argmin()]+273.15
concentration=concentrations[well[1:]]
wells[well]={'tm':tm,'enabled':good_data,'concentration':concentration}
output_data["wells"]=wells
datapoints=[]
concs_for_kd=[]
tms_for_kd=[]
for item in wells:
datapoints.append({'x':wells[item]['concentration'],'y':wells[item]['tm'],'label':item,'enabled':wells[item]['enabled'],'well':item,'molecule':molecule})
if (wells[item]['enabled']==1):
concs_for_kd.append(wells[item]['concentration'])
tms_for_kd.append(wells[item]['tm'])
output_data["datapoints"]=datapoints
fittedParameters,Rsquared,tkd,tdH0,ttm0=getKd(concs_for_kd,tms_for_kd)
output_data['fkd']=tkd
output_data['kd']=fittedParameters[0]
output_data['dH0']=tdH0
output_data['tm0']=ttm0
output_data['R2']=Rsquared
output_data['max_conc']=np.max(concs_for_kd)
output_data['unfinished_curve']=0
if (abs(fittedParameters[0]-np.max(concs_for_kd))<0.001):
output_data['unfinished_curve']=1
#Now generate fitting curve:
output_data['fittingcurve']=[]
xf=[]
yf=[]
if (Rsquared>0):
xf=np.linspace(np.min(concs_for_kd),np.max(concs_for_kd),100)
yf=kd_function(xf,fittedParameters[0],fittedParameters[1],fittedParameters[2])
for i in range(0,len(xf)):
output_data['fittingcurve'].append({'x':xf[i],'y':yf[i],'label':''})
#print(xf)
#print(yf)
#print("\n")
#print(output_data)
return output_data
def get_kd_data_m(post_data):
output_data={}
p=json.loads(post_data)
filename=p["filename"]
template=p["template"]
refs=p["references"]
manual_ref=p["manual_ref"]
molecule=p["molecule"]
tm_ave,tm_std=calculate_ref_tm(filename,refs,manual_ref)
output_data[molecule]=get_kd_data_for_molecule(template,filename,tm_ave,molecule)
return output_data
def get_kd_data(post_data):
output_data={}
p=json.loads(post_data)
filename=p["filename"]
template=p["template"]
refs=p["references"]
manual_ref=p["manual_ref"]
#Load template:
kd_templatefile=persistent_path+"kdtemplates.templ"
templ={}
if os.path.exists(kd_templatefile):
#print("Categories file found. Serving")
f_templ = open(kd_templatefile,'r')
#return json.loads(f_served.read())
a=json.loads(f_templ.read())
for item in a:
#print(item)
#print(item["filename"],template)
if item["filename"]==template:
templ=item
moleculewells={"A":[],"B":[],"C":[],"D":[],"E":[],"F":[],"G":[],"H":[],"I":[],"J":[],"K":[],"L":[],"M":[],"N":[],"O":[],"P":[],"Q":[],"R":[],"S":[],"T":[],"U":[],"V":[],"W":[],"X":[],"Y":[],"Z":[]}
for item in templ["cells"]:
#print(item)
for key in item:
#print(key,item[key])
splkey=key.split("-")
w=chr(int(splkey[1])+64)+str(splkey[0])
molecule=item[key]
if (molecule!=""):
moleculewells[molecule].append(w)
unique_mols=set()
for item in moleculewells:
if len(moleculewells[item])>0:
unique_mols.add(item)
unique_mols=sorted(list(unique_mols))
#For each molecule in template, get_kd_data_for_molecule(template,plate,references,manual_ref,molecule)
tm_ave,tm_std=calculate_ref_tm(filename,refs,manual_ref)
for molec in unique_mols:
output_data[molec]=get_kd_data_for_molecule(template,filename,tm_ave,molec)
#print(output_data)
return(output_data)
def calculate_ref_tm(plate,references,manual_ref):
filename=plate
refs=references
temps_refs=[]
tmdata=readtxttm(data_path+filename)
cats_item=json.loads(serve_categories(filename))
#CAlculate ave_tM
if (manual_ref!=0):
tm_ave=float(manual_ref)
tm_std=0
else:
for rc in refs:
#print(rc)
splrc=rc.split()
#print(splrc)
if (splrc[0]=="Col"):
colnum=int(splrc[1])
for well in tmdata:
#print(well,well[:1],well[1:])
if (well[:1]!="d"):
#print(well,well[:1],well[1:],str(colnum))
if (well[1:]==str(colnum)):
#print(well)
good_data=True
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=False
#print("Bad reference at "+well+" "+item)
if (good_data):
temps_refs.append(float(tmdata[well]["tm"]))
elif (splrc[0]=="Row"):
#print("ROW")
rowletter=splrc[1]
for well in tmdata:
#print(well,well[:1],well[1:])
if (well[:1]!="d"):
#print(well,well[:1],well[1:],str(rowletter))
if (well[:1]==str(rowletter)):
good_data=True
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=False
#print("Bad reference at "+well+" "+item)
if (good_data):
temps_refs.append(float(tmdata[well]["tm"]))
else:
#Single cell.
#print ("CELL")
well=rc
good_data=True
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=False
#print("Bad reference at "+well+" "+item)
if (good_data):
temps_refs.append(float(tmdata[well]["tm"]))
#print(temps_refs)
tm_ave=numpy.average(temps_refs)
tm_std=numpy.std(temps_refs)
return(tm_ave,tm_std)
def create_kd_report(post_data):
matplotlib.use('agg')
tm_ave=0
report_path=tempfile.mkdtemp()
report_path_fil=report_path+os.path.sep+'report.docx'
document= Document()
#print(report_path_fil)
p=json.loads(post_data)
filename=p["filename"]
template=p["template"]
refs=p["references"]
manual_ref=p["manual_ref"]
temps_refs=[]
#Calculate ref_temp to choose best tm maximum for multimaxima wells
document.add_heading('KD Report for plate '+filename,0)
document.add_page_break()
#print(filename,template)
#Open template:
kd_templatefile=persistent_path+"kdtemplates.templ"
templ={}
if os.path.exists(kd_templatefile):
#print("Categories file found. Serving")
f_templ = open(kd_templatefile,'r')
#return json.loads(f_served.read())
a=json.loads(f_templ.read())
for item in a:
#print(item)
#print(item["filename"],template)
if item["filename"]==template:
templ=item
#print(templ)
moleculewells={"A":[],"B":[],"C":[],"D":[],"E":[],"F":[],"G":[],"H":[],"I":[],"J":[],"K":[],"L":[],"M":[],"N":[],"O":[],"P":[],"Q":[],"R":[],"S":[],"T":[],"U":[],"V":[],"W":[],"X":[],"Y":[],"Z":[]}
concentrations={"1":0,"2":0,"3":0,"4":0,"5":0,"6":0,"7":0,"8":0,"9":0,"10":0,"11":0,"12":0,"13":0,"14":0,"15":0,"16":0,"17":0,"18":0,"19":0,"20":0,"21":0,"22":0,"23":0,"24":0}
for item in templ["cells"]:
#print(item)
for key in item:
#print(key,item[key])
splkey=key.split("-")
w=chr(int(splkey[1])+64)+str(splkey[0])
molecule=item[key]
if (molecule!=""):
moleculewells[molecule].append(w)
#print(w)
for item in templ["concentrations"]:
for key in item:
cl=key[1:]
a=item[key]
if a=="":
a="0"
conc=float(a)
concentrations[cl]=conc
#Now, get Tms from filename.
tmdata=readtxttm(data_path+filename)
#Calculate Tm and stdev for references.
cats_item=json.loads(serve_categories(filename))
#Get tm_Ave and tm_std
tm_ave,tm_std=calculate_ref_tm(filename,refs,manual_ref)
#print(moleculewells)
#print(concentrations)
for item in moleculewells:
#For each molecule
concs_for_kd=[]
tms_for_kd=[]
wells_for_kd=[]
if len(moleculewells[item])>0:
#print ("Calculating molecule "+ item)
document.add_heading('Molecule '+item, level=1)
for well in moleculewells[item]:
good_data=True
concentration=concentrations[well[1:]]
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=False
tm=float(tmdata[well]["tm"])+273.15
allpeaks=[]
if 'allpeaks' in tmdata[well]:
allpeaks=tmdata[well]['allpeaks']
if len(allpeaks)>1:
#closest value to tm_ave
tm=allpeaks[numpy.abs(numpy.array(allpeaks)-tm_ave).argmin()]+273.15
#print(well,tm,concentration,good_data)
if (good_data):
concs_for_kd.append(concentration)
tms_for_kd.append(tm)
wells_for_kd.append(well)
#print ("Molecule ",item, " ; wells ",moleculewells[item])
#print("CONCS:",concs_for_kd)
#print("TMS:",tms_for_kd)
fittedParameters,Rsquared,tkd,tdH0,ttm0=getKd(concs_for_kd,tms_for_kd)
#print("Molecule:",item," KD:",tkd, " dH0: ",tdH0," tm0: ",ttm0, 'R2: ',Rsquared)
t=""
for item2 in moleculewells[item]:
if (t!=""):
t=t+" ,"
t=t+item2
document.add_heading("Wells: ",level=3)
document.add_paragraph(t)
if (abs(fittedParameters[0]-np.max(concs_for_kd))<0.001):
tkd=">"+tkd
document.add_heading("Fitting results:",level=3)
document.add_paragraph("Kd: "+str(tkd) + "uM")
document.add_paragraph("dH0: "+str(tdH0)+"J")
document.add_paragraph("tm0: "+str(ttm0)+ 'K')
document.add_paragraph("R2: "+str(Rsquared))
#print(fittedParameters[0],np.max(concs_for_kd),abs(fittedParameters[0]-np.max(concs_for_kd)))
if (abs(fittedParameters[0]-np.max(concs_for_kd))<0.001):
document.add_heading("Unfinished curve. Kd is higher than maximum used concentration. Please use higher concentrations.",level=3)
#Plot:
x,y=concs_for_kd,tms_for_kd
points=[]
for i in range(0,len(x)):
points.append((x[i],y[i],wells_for_kd[i]))
pointss=sorted(points,key=lambda x:x[0])
#print(pointss)
#Set different color depending on wells_for_kd first letter.
px,py,pl=[],[],[]
for i in range(0,len(x)):
px.append(pointss[i][0])
py.append(pointss[i][1])
pl.append(pointss[i][2])
pyplot.ioff()
pyplot.xscale('log')
points_for_row={}
for x,y,s in zip(px,py,pl):
row=s[0]
if row in points_for_row:
points_for_row[row].append((x,y,s))
else:
points_for_row[row]=[(x,y,s)]
#print (points_for_row)
rows_set=set()
for it in points_for_row:
#print (it)
rows_set.add(it)
rows_list=sorted(list(rows_set))
num_of_rows=len(rows_list)
colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf','#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf','#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf','#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
colors=['#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4','#1f77b4']
ccount=0
for it in rows_list:
#print(points_for_row[it])
ppx,ppy=[],[]
for it2 in points_for_row[it]:
#print (it2)
ppx.append(it2[0])
ppy.append(it2[1])
pyplot.scatter(ppx,ppy,c=colors[ccount],marker=".")
ccount=ccount+1
#Write labels
#texts=[]
#for x,y,s in zip(px,py,pl):
# texts.append(pyplot.text(x,y,s))
#adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle="->", color='r', lw=0.5))
#Point annotation
#for i, txt in enumerate(pl):
# pyplot.annotate(txt, (px[i], py[i]))
if (Rsquared>0):
Kd,dH,T0=fittedParameters
x_line = px
y_line= kd_function(x_line,Kd,dH,T0)
pyplot.plot(x_line,y_line)
pyplot.savefig(report_path+os.path.sep+"molecule"+item+".png",format='png')
document.add_picture(report_path+os.path.sep+"molecule"+item+".png")
pyplot.clf()
document.add_page_break()
#pyplot.show()
#Now, generate the tables at the end of the document:
document.add_heading('Data for plate '+filename,0)
for item in moleculewells:
#For each molecule
concs_for_kd=[]
tms_for_kd=[]
wells_for_kd=[]
if len(moleculewells[item])>0:
#print ("Calculating molecule "+ item)
document.add_heading('Molecule '+item, level=1)
for well in moleculewells[item]:
good_data=True
concentration=concentrations[well[1:]]
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=False
tm=float(tmdata[well]["tm"])+273.15
allpeaks=[]
if 'allpeaks' in tmdata[well]:
allpeaks=tmdata[well]['allpeaks']
if len(allpeaks)>1:
#closest value to tm_ave
tm=allpeaks[numpy.abs(numpy.array(allpeaks)-tm_ave).argmin()]+273.15
#print(well,tm,concentration,good_data)
if (good_data):
concs_for_kd.append(concentration)
tms_for_kd.append(tm)
wells_for_kd.append(well)
#Make table for molecule
number_of_wells=len(wells_for_kd)
if (number_of_wells)>0:
numcols=8
numrows=(int( (number_of_wells-1)/(numcols-1) )+1)*3
#print("numcols",numcols,"numrows",numrows)
table = document.add_table(rows=numrows, cols=numcols)
table.style="TableGrid"
for i in range(0,number_of_wells):
c="{:.2e}".format(concs_for_kd[i])
if (concs_for_kd[i]>1):
c="{:.2f}".format(concs_for_kd[i])
row_num=(int(i/(numcols-1))*3)
col_num=(i % (numcols-1))+1
#print(row_num,col_num,wells_for_kd[i])
#print("row_num",row_num,"col_num",col_num,"i",i)
table.rows[0 + row_num].cells[col_num].text=wells_for_kd[i]
table.rows[1 + row_num].cells[col_num].text=c
table.rows[2 + row_num].cells[col_num].text="{:.3f}".format(tms_for_kd[i])
table.rows[0 + row_num].cells[0].text="Well"
table.rows[1 + row_num].cells[0].text="Conc."
table.rows[2 + row_num].cells[0].text="Tm"
document.add_page_break()
document.save(report_path_fil)
#f=open(report_path_fil,"w")
#f.write(filename)
#f.write(template)
#f.close()
return report_path_fil
def create_report(post_data):
#Bad data ("Warning") and "Validated" are exclusive
report_path=tempfile.mkdtemp()
report_path_fil=report_path+os.path.sep+'report.xlsx'
#print(report_path_fil)
report=xlsxwriter.Workbook(report_path_fil)
worksheet1 = report.add_worksheet("Validated")
worksheet2 = report.add_worksheet("Effect")
worksheet3 = report.add_worksheet("All")
worksheet4 = report.add_worksheet("Data")
header_format= report.add_format()
header_format.set_bold()
neutral_format=report.add_format()
neutral_format.set_bg_color("#FFFFFF")
neutral_format.set_border(1)
stab_format=report.add_format()
stab_format.set_bg_color("#D5F5E3")
stab_format.set_border(1)
destab_format=report.add_format()
destab_format.set_bg_color("#FDEBD0")
destab_format.set_border(1)
uncert_format=report.add_format()
uncert_format.set_bg_color("#FDD0F5")
uncert_format.set_border(1)
report_data=[]
#print(post_data)
p=json.loads(post_data)
#print(p)
p_filenames=p["filenames"]
threshold=p["threshold"]
refs=p["references"]
manual_ref=p["manual_ref"]
spk_counter=1
#print(p)
#Read template database.
templatesdf=[]
#for path in Path(data_path).rglob(('*.txt','*.xlsx')):
extensions=['*.txt']
all_files = []
for ext in extensions:
all_files.extend(Path(plateinfo_path).rglob(ext))
for filename in all_files:
#print(filename)
df = pandas.read_csv(filename, index_col=None, sep="\t",header=0)
templatesdf.append(df)
templatesdf=pandas.concat(templatesdf, axis=0, ignore_index=True)
templatesdf=templatesdf.iloc[:,0:5]
print(templatesdf)
templatesdf.columns=["platewell","eos","smiles","plate","well"]
#Columns used: 1,2,3,4
#Columns ignored: 0
#Skips header.
for item in p_filenames:
#print(item)
template=serve_template(item)
b=readtxttm(data_path+item)
#Calculate Tm and stdev for references.
cats_item=json.loads(serve_categories(item))
#print(refs)
temps_refs=[]
if (manual_ref!=0):
tm_ave=float(manual_ref)
tm_std=0
else:
for rc in refs:
#print(rc)
splrc=rc.split()
#print(splrc)
if (splrc[0]=="Col"):
colnum=int(splrc[1])
for well in b:
#print(well,well[:1],well[1:])
if (well[:1]!="d"):
#print(well,well[:1],well[1:],str(colnum))
if (well[1:]==str(colnum)):
#print(well)
good_data=True
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=False
#print("Bad reference at "+well+" "+item)
if (good_data):
temps_refs.append(float(b[well]["tm"]))
elif (splrc[0]=="Row"):
#print("ROW")
rowletter=splrc[1]
for well in b:
#print(well,well[:1],well[1:])
if (well[:1]!="d"):
#print(well,well[:1],well[1:],str(rowletter))
if (well[:1]==str(rowletter)):
good_data=True
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=False
#print("Bad reference at "+well+" "+item)
if (good_data):
temps_refs.append(float(b[well]["tm"]))
else:
#Single cell.
#print ("CELL")
well=rc
good_data=True
if well in cats_item:
dat=cats_item[well]
if ("Warning") in dat:
good_data=False
#print("Bad reference at "+well+" "+item)
if (good_data):
temps_refs.append(float(b[well]["tm"]))
temps_refs=[i for i in temps_refs if i> 1]
print(temps_refs)
tm_ave=numpy.average(temps_refs)
tm_std=numpy.std(temps_refs)
#print(tm_ave,tm_std)
for well in b:
if well[0]=="d":
continue
#print(well)
if well=="format":
continue
wd={"File":item,"Well":"","Validated":False,"Effect":"Neutral","Warning":False,"tm":0, "dtm":0,"ref":0,"ref_std":0,"spkline1":spk_counter,"spkline2":spk_counter+1,"num_datum":10,"extra_tm_peaks":"","template":template,"eos":"","smiles":"","absdtm":0}
#Add leading zero to well
w3=well
if (len(w3)==2):
w3=w3[0]+"0"+w3[1]
wd["Well"]=well
#First, calculate values
#tm may be the best value in allpeaks. Compare with tmref
t=templatesdf[ (templatesdf["plate"]==template) & (templatesdf["well"]==w3) ]
if (len(t)!=0):
#print(t.head(1)["smiles"])
wd["smiles"]=t.iloc[0]["smiles"]
wd["eos"]=t.iloc[0]["eos"]
wd["tm"]=b[well]["tm"]
allpeaks=[]
if 'allpeaks' in b[well]:
allpeaks=b[well]['allpeaks']
if len(allpeaks)>1:
wd["extra_tm_peaks"]=" ,".join(map(str,allpeaks))
#closest value to tm_ave
wd["tm"]=allpeaks[numpy.abs(numpy.array(allpeaks)-tm_ave).argmin()]
wd["ref"]=float("{:.2f}".format(tm_ave))
wd["ref_std"]=float("{:.2f}".format(tm_std))
wd["dtm"]=float("{:.2f}".format(float(wd["tm"]) - tm_ave))
wd["absdtm"]=abs(wd["dtm"])
#If tm==0, skip.
#print(str(wd["dtm"]))
if (wd["tm"]==0):
#print("ZERO")
continue
#print(well,wd["tm"],tm_ave,threshold)
if ((wd["tm"]-threshold)>tm_ave):
wd["Effect"]="Stabilizing"
if (wd["tm"]+threshold<tm_ave):
wd["Effect"]="Destabilizing"
#print(wd["Effect"])
#then check what in server
a=json.loads(serve_categories(item))
#print(a)
#print (well,a[well])
if well in a:
dat=a[well]
#print(well,a[well])
#Found in persistent data
if ("Validated") in dat:
wd["Validated"]=True
if ("Stabilizing") in dat:
wd["Effect"]="Stabilizing"
if ("Destabilizing") in dat:
wd["Effect"]="Destabilizing"
if ("Uncertain") in dat:
wd["Effect"]="Uncertain"
if ("Warning") in dat:
wd["Warning"]=True
#else:
#Not found, set categories from calculations.
#print ("NF")
report_data.append(wd)
spk_counter=spk_counter+3
#print(b)
#print(report_data)
#write headers:
row=0
col=0
for header in ["File","Well","Plate template","Validated","Effect","Warning","Tm","dTm","ref Tm","ref stddev","tm plot","derivative","Multiple Tm peaks","Mol. ID.","SMILES","abs dTM"]:
worksheet1.write(row,col,header,header_format)
worksheet2.write(row,col,header,header_format)
worksheet3.write(row,col,header,header_format)
col=col+1
row=1
rowv=1
rowe=1
j={}
worksheet1.set_column(0, 0, 40)
worksheet2.set_column(0, 0, 40)
worksheet3.set_column(0, 0, 40)
worksheet4.set_column(0, 0, 40)
worksheet1.set_column(4, 4, 12)
worksheet2.set_column(4, 4, 12)
worksheet3.set_column(4, 4, 12)
worksheet1.set_column(2, 2, 20)
worksheet2.set_column(2, 2, 20)
worksheet3.set_column(2, 2, 20)
worksheet1.set_column(12, 13, 15)
worksheet2.set_column(12, 13, 15)
worksheet3.set_column(12, 13, 15)
worksheet1.set_column(14, 14, 120)
worksheet2.set_column(14, 14, 120)
worksheet3.set_column(14, 14, 120)
for item in report_data:
myformat=neutral_format
if (item["Effect"]=="Stabilizing"):
myformat=stab_format
if (item["Effect"]=="Destabilizing"):
myformat=destab_format
if (item["Effect"]=="Uncertain"):
myformat=uncert_format
worksheet3.write(row,0,item["File"],myformat)
worksheet3.write(row,1,item["Well"],myformat)
worksheet3.write(row,3,item["Validated"],myformat)
worksheet3.write(row,4,item["Effect"],myformat)
worksheet3.write(row,5,item["Warning"],myformat)
worksheet3.write(row,6,item["tm"],myformat)
worksheet3.write(row,7,item["dtm"],myformat)
worksheet3.write(row,8,item["ref"],myformat)
worksheet3.write(row,9,item["ref_std"],myformat)
worksheet3.write(row,12,item["extra_tm_peaks"],myformat)
worksheet3.write(row,2,item["template"],myformat)
worksheet3.write(row,13,item["eos"],myformat)
worksheet3.write(row,14,item["smiles"],myformat)
worksheet3.write(row,15,item["absdtm"],myformat)
#Write sparkline data
if (not(item["File"] in j)):
j[item["File"]]=readtxt(data_path+item["File"])
max_count=0
worksheet4.write(item["spkline1"],0,item["File"])
worksheet4.write(item["spkline1"],1,item["Well"])
for position,point in enumerate(j[item["File"]][item["Well"]]["data"]):
#print (item["spkline1"])
#print(position)
#print(point["y"])
#print (point["data"]["y"])
worksheet4.write(item["spkline1"],position+2,point["y"])
max_count=position
worksheet4.write(item["spkline2"],0,item["File"])
worksheet4.write(item["spkline2"],1,"d"+item["Well"])
for position,point in enumerate(j[item["File"]]["d"+item["Well"]]["data"]):
#print (item["spkline1"])
#print(position)
#print(point["y"])
#print (point["data"]["y"])
worksheet4.write(item["spkline2"],position+2,point["y"])
max_count=position
#Temperatures
worksheet4.write(item["spkline1"]-1,0,item["File"])
worksheet4.write(item["spkline1"]-1,1,item["Well"]+" temp")
for position,point in enumerate(j[item["File"]][item["Well"]]["data"]):
#print (item["spkline1"])
#print(position)
#print(point["y"])
#print (point["data"]["y"])
worksheet4.write(item["spkline1"]-1,position+2,point["x"])
max_count=position
rang1='Data'+"!"+xlsxwriter.utility.xl_rowcol_to_cell(item["spkline1"],2)+":"+xlsxwriter.utility.xl_rowcol_to_cell(item["spkline1"],max_count+2)
worksheet3.add_sparkline(row,10,{'range':rang1})
rang2='Data'+"!"+xlsxwriter.utility.xl_rowcol_to_cell(item["spkline2"],2)+":"+xlsxwriter.utility.xl_rowcol_to_cell(item["spkline2"],max_count+2)
worksheet3.add_sparkline(row,11,{'range':rang2})
if ((item["Validated"] & (not(item["Warning"])))):
worksheet1.add_sparkline(rowv,11,{'range':rang2})
worksheet1.add_sparkline(rowv,10,{'range':rang1})