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lapatinib_drs_v4.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 13 22:18:22 2021
@author: arnab
"""
##
import pandas as pd
import numpy as np
# import re
import libsbml
import os
import sys
import importlib
import amici
import argparse
from scipy.signal import find_peaks
import itertools
import time
import multiprocessing
import concurrent.futures
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 300
parser = argparse.ArgumentParser(description='Input doses in uM')
parser.add_argument('--dose', metavar='dose', help='input laptinib dose in uM', default = 0.0)
parser.add_argument('--egf', metavar='egf', help='input EGF conc in nM', default = 100.0)
parser.add_argument('--cellpop', metavar='cellpop', help='starting cell population', default = 5)
parser.add_argument('--td',metavar='td', help='cell line doubling time (hrs) ', default = 48)
parser.add_argument('--sim_name',metavar='sim_name', help='insert exp name', default = 'lapatinib_drs_test')
args = parser.parse_args()
wd = str(os.getcwd()).replace("jupyter_notebooks","")
sim_name = str(args.sim_name)
output_path = os.path.join(wd,'output',sim_name)
if not os.path.exists(output_path):
os.mkdir(output_path)
sbml_file = "SPARCED_au565.xml"
model_name= sbml_file[0:-4]
model_output_dir = model_name
sys.path.insert(0, os.path.join(wd,model_output_dir))
model_module = importlib.import_module(model_name)
model = model_module.getModel()
#%%
sys.path.append(wd+'/bin')
from modules.RunSPARCED import RunSPARCED
omics_input = 'OmicsData_extended_au565.txt'
genereg_input = 'GeneReg_au565.txt'
flagD = 0
# deterministic='GrowthStim_det_', stochastic='GrowthStim_stoc_'
# nmxlsfile = 'U87SPARCED_1nmGMDet_'
ts = 30
# th = 24
Vn = 1.75E-12
Vc = 5.25E-12
# STIMligs = [0.0,0,0,0,0,0,0.0] # EGF, Her, HGF, PDGF, FGF, IGF, INS
#%%
species_all = list(model.getStateIds())
solver = model.getSolver() # Create solver instance
solver.setMaxSteps = 1e10
model.setTimepoints(np.linspace(0,ts)) # np.linspace(0, 30) # set timepoints
cell_pop = int(args.cellpop)
#%% preincubate
STIMligs = [100.0,0,0,0,0,0,0.0]
dose = float(args.dose)*10e2
sp_input = pd.read_csv(os.path.join(wd,'initializer','species_au565.txt'),sep='\t',header=None,index_col=0,squeeze=True)
# sp_input['lapatinib'] = dose
species_initializations = np.array(sp_input)
species_initializations[np.argwhere(species_initializations <= 1e-6)] = 0.0
species_initializations[155:162] = STIMligs
output_dose = os.path.join(output_path,'lapatinib_'+str(float(args.dose)))
if not os.path.exists(output_dose):
os.mkdir(output_dose)
# np.linspace(0, 30) # set timepoint
th = 48
output_dir = output_dose
#%%
def pre_incubate(cell_n,flagD,th,species_initializations,Vn,Vc,model,wd,omics_input,genereg_input):
xoutS_all, xoutG_all, tout_all, flagA = RunSPARCED(flagD,th,species_initializations,Vn,Vc,model,wd,omics_input,genereg_input)
np.savetxt(os.path.join(output_dose,'c'+str(cell_n+1)+'_preinc.txt'),xoutS_all[-1],delimiter='\t')
# start = time.perf_counter()
if __name__ == "__main__":
# flagD = 1
processes = []
for c in range(cell_pop):
p = multiprocessing.Process(target=pre_incubate, args = [c,flagD,th,species_initializations,Vn,Vc,model,wd,omics_input,genereg_input])
p.start()
processes.append(p)
for process in processes:
process.join()
# finish = time.perf_counter()
# print(f'Finished in {round(finish-start,2)} seconds(s)')
#%% pre-incubate with growth factors, random sampling
# dose = 100.0 #temporary
STIMligs = [0.0,0,0,0,0,0,0.0]
# doubling_time = float(args.td)
dose_egf = float(args.egf)
# dose_egf = 100 # temp
STIMligs[0] = dose_egf
th = 48
cellpop_g1 = cell_pop
output_dir = output_dose
def cell_g0(cell_n,flagD,th,Vn,Vc,model,wd,omics_input,genereg_input):
cell_name = 'g0_c'+str(cell_n+1)
s_preinc_i = np.loadtxt(os.path.join(output_dir,'c'+str(cell_n+1)+'_preinc.txt'),delimiter='\t')
sp_input = s_preinc_i
species_initializations = np.array(sp_input)
species_initializations[np.argwhere(species_initializations <= 1e-6)] = 0.0
species_initializations[155:162] = STIMligs
xoutS_all, xoutG_all, tout_all, flagA = RunSPARCED(flagD,th,species_initializations,Vn,Vc,model,wd,omics_input,genereg_input)
np.savetxt(os.path.join(output_dir,str(cell_name)+'_xoutS.txt'),xoutS_all,delimiter='\t')
np.savetxt(os.path.join(output_dir,str(cell_name)+'_xoutG.txt'),xoutG_all,delimiter='\t')
np.savetxt(os.path.join(output_dir,str(cell_name)+'_tout.txt'),tout_all,delimiter='\t')
# start_p = time.perf_counter()
processes = []
for c in range(cellpop_g1):
p = multiprocessing.Process(target=cell_g0, args = [c,flagD,th,Vn,Vc,model,wd,omics_input,genereg_input])
p.start()
processes.append(p)
for process in processes:
process.join()
#%% aux functions
def find_dp(xoutS,tout,species_all=species_all):
data = xoutS[:,list(species_all).index('Mb')]
p,_ = find_peaks(data,height=30)
b = (np.diff(np.sign(np.diff(data))) > 0).nonzero()[0] + 1
if sum(b>p[0]) > 0:
dp = int(b[b>p[0]][0])
else:
dp = np.nan
return(dp)
def find_dp_all(xoutS,species_all=species_all):
data = xoutS[:,list(species_all).index('Mb')]
p,_ = find_peaks(data,height=30)
b = (np.diff(np.sign(np.diff(data))) > 0).nonzero()[0] + 1
dp_all = []
for i in range(len(p)):
b2 = np.where(b>p[i])[0]
if len(b2)!=0:
dp_all.append(b[b2[0]])
return(dp_all)
def find_dp_0(dp_all,tp):
dp0 = np.nan
k = np.where(np.array(dp_all)>tp)[0]
if len(k)!=0:
dp0 = dp_all[k[0]]
return dp0
# k = find_dp_all(x_s)
# l = find_dp_0(k,89)
#%% test / debug
def read_tout(output_dir,tout_file):
tout = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
tmax = max(tout)
return (tmax)
def get_tmax(output_dir,read_tout=read_tout):
outputs_ls = os.listdir(output_dir)
tout_files = list(filter(lambda x:x.endswith('tout.txt'),outputs_ls))
results_tmax = []
with concurrent.futures.ThreadPoolExecutor() as executor:
results_tout = [executor.submit(read_tout, output_dir, tout_file) for tout_file in tout_files]
for f in concurrent.futures.as_completed(results_tout):
results_tmax.append(f.result())
results_tmax = np.array(results_tmax)
tmax = max(results_tmax)
return(tmax)
timecourse_tmax = get_tmax(output_dir)
def timecourse_gc(output_dir,species, gx_cx,get_tmax=get_tmax,read_tout=read_tout,tx='default'):
outputs_ls = os.listdir(os.path.join(wd,output_dir))
outputs_gc = list(filter(lambda x:x.startswith(gx_cx+'_'),outputs_ls))
xoutS_file = list(filter(lambda x:x.endswith('xoutS.txt'),outputs_gc))[0]
tout_file = list(filter(lambda x:x.endswith('tout.txt'),outputs_gc))[0]
if 'timecourse_tmax' not in globals():
tmax = get_tmax(output_dir)
else:
tmax = timecourse_tmax
# x_s = np.loadtxt(os.path.join(wd,'output','cellpop_test',str(gx_cx+'_xoutS.txt')),delimiter='\t')
# tout_all = np.loadtxt(os.path.join(wd,'output','cellpop_test',str(gx_cx+'_tout.txt')),delimiter='\t')
x_s = np.loadtxt(os.path.join(output_dir,xoutS_file),delimiter='\t')
tout_all = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
x_t = x_s[:, list(species_all).index(species)]
plt.plot(tout_all/3600, x_t)
plt.ylabel('species: '+str(species))
plt.xlabel('time(h)')
plt.ylim(0, max(x_t)*1.25)
if type(tx)==str:
# plt.xlim(0, round(max(tout_all)/3600))
plt.xlim(0, round(tmax/3600))
elif type(tx)==int or type(tx)==float:
plt.xlim(0,tx)
plt.title(gx_cx)
plt.show
#%% test - g0
# timecourse_gc(output_dir,'Mb','g0_c3')
#%%
# def cell_g1(cell_n,flagD,th,Vn,Vc,model,wd,omics_input,genereg_input):
# cell_name = 'g1_c'+str(cell_n+1)
# # s_preinc_i = np.loadtxt(os.path.join(output_dose,'c'+str(cell_n+1)+'_preinc.txt'),delimiter='\t')
# x_s_g0 = np.loadtxt(os.path.join(output_dose,'g0_c'+str(cell_n+1)+'_xoutS.txt'),delimiter='\t')
# np.random.seed()
# tp_g0 = np.random.randint(0,np.shape(x_s_g0)[0])
# sp_input = x_s_g0[:,tp_g0]
# species_initializations = np.array(sp_input)
# species_initializations[np.argwhere(species_initializations <= 1e-6)] = 0.0
# species_initializations[155:162] = STIMligs
# species_initializations[list(model.getStateIds()).index('lapatinib')] = dose
# xoutS_all, xoutG_all, tout_all, flagA = RunSPARCED(flagD,th,species_initializations,Vn,Vc,model,wd,omics_input,genereg_input)
# np.savetxt(os.path.join(output_dir,'g0_c'+str(int(cell_n+1))+'_tp.txt'),np.array([tp_g0]),delimiter='\t')
# np.savetxt(os.path.join(output_dir,str(cell_name)+'_xoutS.txt'),xoutS_all,delimiter='\t')
# np.savetxt(os.path.join(output_dir,str(cell_name)+'_xoutG.txt'),xoutG_all,delimiter='\t')
# np.savetxt(os.path.join(output_dir,str(cell_name)+'_tout.txt'),tout_all,delimiter='\t')
#%% drs no queue - 72 hr
dose_egf = float(args.dose)
STIMligs = [0.0,0,0,0,0,0,0.0]
doubling_time = float(args.td)
STIMligs[0] = dose_egf
th = 72
cellpop_g1 = cell_pop
output_dir = output_dose
def cell_g1(cell_n,flagD,th,Vn,Vc,model,wd,omics_input,genereg_input):
cell_name = 'g1_c'+str(cell_n+1)
# s_preinc_i = np.loadtxt(os.path.join(output_dose,'c'+str(cell_n+1)+'_preinc.txt'),delimiter='\t')
x_s_g0 = np.loadtxt(os.path.join(output_dose,'g0_c'+str(cell_n+1)+'_xoutS.txt'),delimiter='\t')
np.random.seed()
tp_g0 = np.random.randint(0,np.shape(x_s_g0)[0])
sp_input = x_s_g0[tp_g0,:]
species_initializations = np.array(sp_input)
species_initializations[np.argwhere(species_initializations <= 1e-6)] = 0.0
species_initializations[155:162] = STIMligs
species_initializations[list(model.getStateIds()).index('lapatinib')] = dose
xoutS_all, xoutG_all, tout_all, flagA = RunSPARCED(flagD,th,species_initializations,Vn,Vc,model,wd,omics_input,genereg_input)
np.savetxt(os.path.join(output_dir,'g0_c'+str(int(cell_n+1))+'_tp.txt'),np.array([tp_g0]),delimiter='\t')
np.savetxt(os.path.join(output_dir,str(cell_name)+'_xoutS.txt'),xoutS_all,delimiter='\t')
np.savetxt(os.path.join(output_dir,str(cell_name)+'_xoutG.txt'),xoutG_all,delimiter='\t')
np.savetxt(os.path.join(output_dir,str(cell_name)+'_tout.txt'),tout_all,delimiter='\t')
start_p = time.perf_counter()
processes = []
for c in range(cellpop_g1):
p = multiprocessing.Process(target=cell_g1, args = [c,flagD,th,Vn,Vc,model,wd,omics_input,genereg_input])
p.start()
processes.append(p)
for process in processes:
process.join()
#%%% debug
# cell_n = 1
# cell_name = 'g1_c'+str(cell_n+1)
# # # s_preinc_i = np.loadtxt(os.path.join(output_dose,'c'+str(cell_n+1)+'_preinc.txt'),delimiter='\t')
# x_s_g0 = np.loadtxt(os.path.join(output_dose,'g0_c'+str(cell_n+1)+'_xoutS.txt'),delimiter='\t')
# tp_g0 = int(np.loadtxt(os.path.join(output_dir,'g0_c'+str(int(cell_n+1))+'_tp.txt'),delimiter='\t'))
# tout_g0 = np.loadtxt(os.path.join(output_dose,'g0_c'+str(cell_n+1)+'_tout.txt'),delimiter='\t')
# timecourse_gc(output_dir,'Mb','g0_c1')
# gx_cx = 'g1_c2'
# outputs_ls = os.listdir(os.path.join(wd,'output',output_dir))
# outputs_gc = list(filter(lambda x:x.startswith(gx_cx+'_'),outputs_ls))
# xoutS_file = list(filter(lambda x:x.endswith('xoutS.txt'),outputs_gc))[0]
# tout_file = list(filter(lambda x:x.endswith('tout.txt'),outputs_gc))[0]
# xoutG_file = list(filter(lambda x:x.endswith('xoutG.txt'),outputs_gc))[0]
# xoutS_all = np.loadtxt(os.path.join(output_dir,xoutS_file),delimiter='\t')
# # xoutG_all = np.loadtxt(os.path.join(output_dir,xoutG_file),delimiter='\t')
# tout_all = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
# cb_peaks, _ = find_peaks(xoutS_all[:, list(species_all).index('Mb')],height=30)
# dp1 = find_dp(xoutS_all,tout_all)
# dp0_all = find_dp_all(x_s_g0[tp_g0:]) ## wrong output!!
# data = x_s_g0[:,list(species_all).index('Mb')]
# p,_ = find_peaks(data,height=30)
# b = (np.diff(np.sign(np.diff(data))) > 0).nonzero()[0] + 1
# dp_all = []
# for i in range(len(p)):
# b2 = np.where(b>p[i])[0]
# if len(b2)!=0:
# dp_all.append(b[b2[0]])
# dp0 = find_dp_0(dp0_all,tp_g0)
# dp0 = dp0 - tp_g0
# np.random.seed()
# tp_g0 = np.random.randint(0,np.shape(x_s_g0)[0])
# sp_input = x_s_g0[tp_g0,:]
# species_initializations = np.array(sp_input)
# species_initializations[np.argwhere(species_initializations <= 1e-6)] = 0.0
# species_initializations[155:162] = STIMligs
# species_initializations[list(model.getStateIds()).index('lapatinib')] = dose
# xoutS_all, xoutG_all, tout_all, flagA = RunSPARCED(flagD,th,species_initializations,Vn,Vc,model,wd,omics_input,genereg_input)
def find_dp_all(xoutS,species_all=species_all):
data = xoutS[:,list(species_all).index('Mb')]
p,_ = find_peaks(data,height=30)
b = (np.diff(np.sign(np.diff(data))) > 0).nonzero()[0] + 1
dp_all = []
for i in range(len(p)):
b2 = np.where(b>p[i])[0]
if len(b2)!=0:
dp_all.append(b[b2[0]])
return(dp_all)
def find_dp_0(dp_all,tp):
dp0 = np.nan
k = np.where(np.array(dp_all)>tp)[0]
if len(k)!=0:
dp0 = dp_all[k[0]]
return dp0
#%%
def read_cell_g1(output_dir,g,cell_n):
gx_cx = 'g'+str(g)+'_c'+str(int(cell_n+1))
outputs_ls = os.listdir(os.path.join(wd,'output',output_dir))
outputs_gc = list(filter(lambda x:x.startswith(gx_cx+'_'),outputs_ls))
xoutS_file = list(filter(lambda x:x.endswith('xoutS.txt'),outputs_gc))[0]
tout_file = list(filter(lambda x:x.endswith('tout.txt'),outputs_gc))[0]
xoutG_file = list(filter(lambda x:x.endswith('xoutG.txt'),outputs_gc))[0]
xoutS_g1 = np.loadtxt(os.path.join(output_dir,xoutS_file),delimiter='\t')
xoutG_g1 = np.loadtxt(os.path.join(output_dir,xoutG_file),delimiter='\t')
tout_g1 = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
xoutS_g0 = np.loadtxt(os.path.join(output_dir,'g0_c'+str(int(cell_n+1))+'_xoutS.txt'),delimiter='\t')
tout_g0 = np.loadtxt(os.path.join(output_dir,'g0_c'+str(int(cell_n+1))+'_tout.txt'),delimiter='\t')
tp_g0 = int(np.loadtxt(os.path.join(output_dir,'g0_c'+str(int(cell_n+1))+'_tp.txt'),delimiter='\t'))
tneg_g0_min = max(tout_g0[:tp_g0]) - 16*3600
tneg_idx_start = np.where(tout_g0[:tp_g0]>tneg_g0_min)[0][0]
tout_g0_neg = tout_g0[:tp_g0][tneg_idx_start:tp_g0] - tout_g0[tp_g0]
xoutS_new = np.concatenate((xoutS_g0[tneg_idx_start:tp_g0],xoutS_g1),axis=0)
# tout_g0_neg = tout_g0[:tp_g0][tout_g0[:tp_g0]>tneg_g0_min] - tout_g0[tp_g0]
# tout_g0_neg = tout_g0[:tp_g0] - tout_g0[tp_g0]
tout_new = np.concatenate((tout_g0_neg,tout_g1),axis=0)
cb_peaks, _ = find_peaks(xoutS_new[:, list(species_all).index('Mb')],height=30)
results = {}
# xoutS_lite = np.array(list(itertools.islice(xoutS_all,0,(len(xoutS_all)-1),20)))
# xoutG_lite = np.array(list(itertools.islice(xoutG_all,0,(len(xoutG_all)-1),20)))
# tout_lite = np.array(list(itertools.islice(tout_all,0,(len(tout_all)-1),20)))
if len(cb_peaks)>0:
dp_all = find_dp_all(xoutS_new)
dp = np.nan
if len(dp_all)>0:
dp_idx = np.where(tout_new[dp_all]>0)[0][0]
dp = dp_all[dp_idx]
# dp1 = find_dp(xoutS_all,tout_all)
# dp0_all = find_dp_all(x_s_g0[tp_g0:])
# dp0 = find_dp_0(dp0_all,tp_g0)
# # dp0 = dp0 - tp_g0
# dp = min(dp0,dp1)
if ~np.isnan(dp):
parp_dp = float(xoutS_new[dp,list(species_all).index('PARP')])
cparp_dp = float(xoutS_new[dp,list(species_all).index('cPARP')])
if parp_dp > cparp_dp:
tdp_g2_cell = tout_new[dp]/3600
sp_g2_cell = xoutS_new[dp]
lin_g2_cell = 'c'+str(int(cell_n+1))
results['cell'] = int(cell_n+1)
results['dp'] = dp
results['th_g2'] = th- tdp_g2_cell
results['sp_g2'] = sp_g2_cell
results['lin'] = lin_g2_cell
# xoutS_lite = np.array(list(itertools.islice(xoutS_all,0,(dp+1),20)))
# xoutG_lite = np.array(list(itertools.islice(xoutG_all,0,(dp+1),20)))
# tout_lite = np.array(list(itertools.islice(tout_all,0,(dp+1),20)))
# np.savetxt(os.path.join(output_dir,xoutS_file),xoutS_lite,delimiter='\t')
# np.savetxt(os.path.join(output_dir,xoutG_file),xoutG_lite,delimiter='\t')
# np.savetxt(os.path.join(output_dir,tout_file),tout_lite,delimiter='\t')
# temp/diagnostics
np.savetxt(os.path.join(output_dir,'g99_c'+str(cell_n+1)+'_xoutS.txt'),xoutS_new,delimiter='\t')
# np.savetxt(os.path.join(output_dir,xoutG_file),xoutG_lite,delimiter='\t')
np.savetxt(os.path.join(output_dir,'g99_c'+str(cell_n+1)+'_tout.txt'),tout_new,delimiter='\t')
return results
results_all = []
with concurrent.futures.ThreadPoolExecutor() as executor:
results_tpe = [executor.submit(read_cell_g1, output_dir,1,cell_n) for cell_n in range(cellpop_g1)]
for f in concurrent.futures.as_completed(results_tpe):
results_all.append(f)
#%
results_f = [bool(results_all[i].result()) for i in range(len(results_all))]
results_notempty = np.array(results_all)[np.where(results_f)[0]]
results_actual = [r.result() for r in results_notempty]
th_g2 = [r['th_g2'] for r in results_actual]
th_g2 = th_g2 + th_g2
lin_g2 = [r['lin'] for r in results_actual]
lin_g2 = lin_g2 + lin_g2
sp_g2 = [r['sp_g2'] for r in results_actual]
sp_g2 = sp_g2 + sp_g2
#%
sp_gn0 = np.array(sp_g2)
lin_gn0 = lin_g2
th_gn0 = th_g2
cellpop_gn0 = len(th_g2)
g = 2
def cell_gn(cell_n,lin_gn0,flagD,th,th_gc,species_initializations,Vn,Vc,model,wd,omics_input,genereg_input):
cell_name = 'g'+str(g)+'_c'+str(cell_n+1)+'_lin_'+str(lin_gn0[cell_n])
xoutS_all, xoutG_all, tout_all, flagA = RunSPARCED(flagD,th_gc[cell_n],species_initializations,Vn,Vc,model,wd,omics_input,genereg_input)
tout_all = tout_all + (th-th_gc[cell_n])*3600
np.savetxt(os.path.join(output_dir,str(cell_name)+'_xoutS.txt'),xoutS_all,delimiter='\t')
np.savetxt(os.path.join(output_dir,str(cell_name)+'_xoutG.txt'),xoutG_all,delimiter='\t')
np.savetxt(os.path.join(output_dir,str(cell_name)+'_tout.txt'),tout_all,delimiter='\t')
def read_cell_gn(output_dir,g,cell_n):
gx_cx = 'g'+str(g)+'_c'+str(int(cell_n+1))
outputs_ls = os.listdir(os.path.join(wd,'output',output_dir))
outputs_gc = list(filter(lambda x:x.startswith(gx_cx+'_'),outputs_ls))
xoutS_file = list(filter(lambda x:x.endswith('xoutS.txt'),outputs_gc))[0]
tout_file = list(filter(lambda x:x.endswith('tout.txt'),outputs_gc))[0]
xoutG_file = list(filter(lambda x:x.endswith('xoutG.txt'),outputs_gc))[0]
xoutS_all = np.loadtxt(os.path.join(output_dir,xoutS_file),delimiter='\t')
xoutG_all = np.loadtxt(os.path.join(output_dir,xoutG_file),delimiter='\t')
tout_all = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
# xoutS_lite = np.array(list(itertools.islice(xoutS_all,0,(len(xoutS_all)-1),20)))
# xoutG_lite = np.array(list(itertools.islice(xoutG_all,0,(len(xoutG_all)-1),20)))
# tout_lite = np.array(list(itertools.islice(tout_all,0,(len(tout_all)-1),20)))
cb_peaks, _ = find_peaks(xoutS_all[:, list(species_all).index('Mb')],height=30)
results = {}
if len(cb_peaks)>0:
dp = find_dp(xoutS_all,tout_all)
if ~np.isnan(dp):
parp_dp = float(xoutS_all[dp,list(species_all).index('PARP')])
cparp_dp = float(xoutS_all[dp,list(species_all).index('cPARP')])
if parp_dp > cparp_dp:
tdp_gn_cell = tout_all[dp]/3600
sp_gn_cell = xoutS_all[dp]
lin_gn_cell = str(lin_gn0[cell_n])+'c'+str(cell_n+1)
results['cell'] = int(cell_n+1)
results['dp'] = dp
results['th_gn'] = th- tdp_gn_cell
results['sp_gn'] = sp_gn_cell
results['lin'] = lin_gn_cell
# xoutS_lite = np.array(list(itertools.islice(xoutS_all,0,(dp+1),20)))
# xoutG_lite = np.array(list(itertools.islice(xoutG_all,0,(dp+1),20)))
# tout_lite = np.array(list(itertools.islice(tout_all,0,(dp+1),20)))
# np.savetxt(os.path.join(output_dir,xoutS_file),xoutS_lite,delimiter='\t')
# np.savetxt(os.path.join(output_dir,xoutG_file),xoutG_lite,delimiter='\t')
# np.savetxt(os.path.join(output_dir,tout_file),tout_lite,delimiter='\t')
return results
#%
while cellpop_gn0 > 0:
processes = []
for c in range(cellpop_gn0):
p = multiprocessing.Process(target=cell_gn, args = [c,lin_gn0,flagD,th,th_gn0,sp_gn0[c],Vn,Vc,model,wd,omics_input,genereg_input])
p.start()
processes.append(p)
for process in processes:
process.join()
results_all = []
with concurrent.futures.ThreadPoolExecutor() as executor:
results_tpe = [executor.submit(read_cell_gn, output_dir,g,cell_n) for cell_n in range(cellpop_gn0)]
for f in concurrent.futures.as_completed(results_tpe):
results_all.append(f)
results_f = [bool(results_all[i].result()) for i in range(len(results_all))]
results_notempty = np.array(results_all)[np.where(results_f)[0]]
results_actual = [r.result() for r in results_notempty]
th_gn = [r['th_gn'] for r in results_actual]
th_gn = th_gn + th_gn
lin_gn = [r['lin'] for r in results_actual]
lin_gn = lin_gn + lin_gn
sp_gn = [r['sp_gn'] for r in results_actual]
sp_gn = sp_gn + sp_gn
cellpop_gn = len(th_gn)
cellpop_gn0 = cellpop_gn
if cellpop_gn0 > 0:
g += 1
lin_gn0 = lin_gn
sp_gn0 = sp_gn
th_gn0 = th_gn
#%%
# results_tmax = []
# with concurrent.futures.ThreadPoolExecutor() as executor:
# results_tout = [executor.submit(read_tout, output_dir, tout_file) for tout_file in tout_files]
# for f in concurrent.futures.as_completed(results_tout):
# results_tmax.append(f.result())
#%% debug
def read_tout(output_dir,tout_file):
tout = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
tmax = max(tout)
return (tmax)
def read_tmin(output_dir,tout_file):
tout = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
tmin = min(tout)
return (tmin)
def get_tmax(output_dir,read_tout=read_tout):
outputs_ls = os.listdir(output_dir)
tout_files = list(filter(lambda x:x.endswith('tout.txt'),outputs_ls))
results_tmax = []
with concurrent.futures.ThreadPoolExecutor() as executor:
results_tout = [executor.submit(read_tout, output_dir, tout_file) for tout_file in tout_files]
for f in concurrent.futures.as_completed(results_tout):
results_tmax.append(f.result())
results_tmax = np.array(results_tmax)
tmax = max(results_tmax)
return(tmax)
def get_tmin(output_dir,read_tmin=read_tmin):
outputs_ls = os.listdir(output_dir)
tout_files = list(filter(lambda x:x.endswith('tout.txt'),outputs_ls))
results_tmin = []
with concurrent.futures.ThreadPoolExecutor() as executor:
results_tout = [executor.submit(read_tmin, output_dir, tout_file) for tout_file in tout_files]
for f in concurrent.futures.as_completed(results_tout):
results_tmin.append(f.result())
results_tmin = np.array(results_tmin)
tmin = min(results_tmin)
return(tmin)
timecourse_tmax = get_tmax(output_dir)
timecourse_tmin = get_tmin(output_dir)
def timecourse_gc(output_dir,species, gx_cx,get_tmax=get_tmax,read_tout=read_tout,tx='default'):
outputs_ls = os.listdir(os.path.join(wd,output_dir))
outputs_gc = list(filter(lambda x:x.startswith(gx_cx+'_'),outputs_ls))
xoutS_file = list(filter(lambda x:x.endswith('xoutS.txt'),outputs_gc))[0]
tout_file = list(filter(lambda x:x.endswith('tout.txt'),outputs_gc))[0]
if 'timecourse_tmax' not in globals():
tmax = get_tmax(output_dir)
else:
tmax = timecourse_tmax
# x_s = np.loadtxt(os.path.join(wd,'output','cellpop_test',str(gx_cx+'_xoutS.txt')),delimiter='\t')
# tout_all = np.loadtxt(os.path.join(wd,'output','cellpop_test',str(gx_cx+'_tout.txt')),delimiter='\t')
x_s = np.loadtxt(os.path.join(output_dir,xoutS_file),delimiter='\t')
tout_all = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
x_t = x_s[:, list(species_all).index(species)]
plt.plot(tout_all/3600, x_t)
plt.ylabel('species: '+str(species))
plt.xlabel('time(h)')
plt.ylim(0, max(x_t)*1.25)
if type(tx)==str:
# plt.xlim(0, round(max(tout_all)/3600))
plt.xlim(0, round(tmax/3600))
elif type(tx)==int or type(tx)==float:
plt.xlim(0,tx)
plt.title(gx_cx)
plt.show
def timecourse_gc2(output_dir,species, gx_cx,get_tmax=get_tmax,read_tout=read_tout,tx='default'):
outputs_ls = os.listdir(os.path.join(wd,output_dir))
outputs_gc = list(filter(lambda x:x.startswith(gx_cx+'_'),outputs_ls))
xoutS_file = list(filter(lambda x:x.endswith('xoutS.txt'),outputs_gc))[0]
tout_file = list(filter(lambda x:x.endswith('tout.txt'),outputs_gc))[0]
if 'timecourse_tmax' not in globals():
tmax = get_tmax(output_dir)
else:
tmax = timecourse_tmax
# if 'timecourse_tmin' not in globals():
tmin = get_tmin(output_dir)
# else:
# tmin = timecourse_tmin
# x_s = np.loadtxt(os.path.join(wd,'output','cellpop_test',str(gx_cx+'_xoutS.txt')),delimiter='\t')
# tout_all = np.loadtxt(os.path.join(wd,'output','cellpop_test',str(gx_cx+'_tout.txt')),delimiter='\t')
x_s = np.loadtxt(os.path.join(output_dir,xoutS_file),delimiter='\t')
tout_all = np.loadtxt(os.path.join(output_dir,tout_file),delimiter='\t')
x_t = x_s[:, list(species_all).index(species)]
plt.plot(tout_all/3600, x_t)
plt.ylabel('species: '+str(species))
plt.xlabel('time(h)')
plt.ylim(0, max(x_t)*1.25)
if type(tx)==str:
# plt.xlim(0, round(max(tout_all)/3600))
plt.xlim(round(tmin/3600), round(tmax/3600))
elif type(tx)==int or type(tx)==float:
plt.xlim(round(tmin/3600),tx)
plt.title(gx_cx)
plt.show
#%% debug - timecourse_gc
#%%
def find_dp_gc(x_s,species_all=species_all):
data = x_s[:,list(species_all).index('Mb')]
p,_ = find_peaks(data,height=30)
if len(p)!=0:
b = (np.diff(np.sign(np.diff(data))) > 0).nonzero()[0] + 1
if len(b)!=0:
dp = int(b[b>p[0]][0])
else:
dp = np.nan
else:
dp = np.nan
return(dp)
def timecourse_dp(outout_dir,species,gx_cx,species_all=species_all):
output_ls = os.listdir(os.path.join(wd,'output',output_dir))
outputs_gc = list(filter(lambda x:x.startswith(gx_cx+'_'),output_ls))
xoutS_file = list(filter(lambda x:x.endswith('xoutS.txt'),outputs_gc))[0]
tout_file = list(filter(lambda x:x.endswith('tout.txt'),outputs_gc))[0]
x_s = np.loadtxt(os.path.join(wd,'output',output_dir,xoutS_file),delimiter='\t')
dp = find_dp_gc(x_s,species_all=species_all)
tout_all = np.loadtxt(os.path.join(wd,'output',output_dir,tout_file),delimiter='\t')
if ~np.isnan(dp):
x_s = x_s[:(dp+1)]
tout_all = tout_all[:(dp+1)]
x_t = x_s[:, list(species_all).index(species)]
plt.plot(tout_all/3600, x_t)
plt.ylabel('species: '+str(species))
plt.xlabel('time(h)')
plt.ylim(0, max(x_t)*1.25)
plt.xlim(0, th)
plt.title(gx_cx)
plt.show
#%% #%% test - find dp for randomly sampled cycling cells
# g1_c4, cellpop_test_0
# def xs_cut(x_s,tout,th):
# i = np.where(tout/3600> th)[0][0]
# xs_new = x_s[i:,:]
# tout_new = tout[i:]
# return(xs_new,tout_new)
# def find_dp(xoutS,tout,species_all=species_all):
# data = xoutS[:,list(species_all).index('Mb')]
# p,_ = find_peaks(data,height=30)
# b = (np.diff(np.sign(np.diff(data))) > 0).nonzero()[0] + 1
# if sum(b>p[0]) > 0:
# dp = int(b[b>p[0]][0])
# else:
# dp = np.nan
# return(dp)
# def find_dp_all(xoutS,species_all=species_all):
# data = xoutS[:,list(species_all).index('Mb')]
# p,_ = find_peaks(data,height=30)
# b = (np.diff(np.sign(np.diff(data))) > 0).nonzero()[0] + 1
# dp_all = []
# for i in range(len(p)):
# b2 = np.where(b>p[i])[0]
# if len(b2)!=0:
# dp_all.append(b[b2[0]])
# return(dp_all)
# def find_dp_0(dp_all,tp):
# dp0 = np.nan
# k = np.where(np.array(dp_all)>tp)[0]
# if len(k)!=0:
# dp0 = dp_all[k[0]]