def create_supplement_figure_8():
""" plots EARM observable """
from models.earm_lopez_embedded_flat import model
time = np.linspace(0, 20000, 101)
observable_name = 'aSmac'
new_params = load_params('Params/earm_parameter_set_one.txt')
save_name = 'supp_figure_8_earm_output_parameter_set_one'
update_param_vals(model, new_params)
solver = pysb.integrate.Solver(model, time, rtol=RTOL, atol=ATOL,
integrator='lsoda', mxstep=mxstep)
solver.run()
ysim_momp_norm = solver.yobs[observable_name] / np.nanmax(
solver.yobs[observable_name])
st, sc, sk = scipy.interpolate.splrep(time, ysim_momp_norm)
try:
t10 = scipy.interpolate.sproot((st, sc - 0.10, sk))[0]
t90 = scipy.interpolate.sproot((st, sc - 0.90, sk))[0]
except IndexError:
t10 = 0
t90 = 0
td = (t10 + t90) / 2
plt.figure()
plt.plot(time / 3600, ysim_momp_norm, 'b-', linewidth=2)
plt.xlabel("Time (hr)", fontsize=16)
plt.ylabel('aSmac / SMAC_0', fontsize=16)
plt.plot(td / 3600, .5, 'ok', ms=15, mfc='none', mew=3)
plt.axvline(td / 3600, linestyle='dashed', color='black')
plt.ylim(-.05, 1.05)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.savefig('%s/%s_%s.png' % ('Figures', save_name, observable_name),
dpi=150)
plt.savefig('%s/%s_%s.eps' % ('Figures', save_name, observable_name))
plt.close()
print_model_stats(model)
outdir = '/Users/lopezlab/temp/EARM/'
outfiles = {}
for name in obj_names:
outfiles[name+'_sens'] = os.path.join(outdir,name+"_sens_scipy.txt")
outfiles[name+'_sens_t'] = os.path.join(outdir,name+"_sens_t_scipy.txt")
outfiles[name+'_sens_2'] = os.path.join(outdir,name+"_sens_2_scipy.txt")
OUTPUT = {key : open(file, 'a') for key,file in outfiles.items()}
for file in OUTPUT.values():
file.write("-----"+str(datetime.datetime.now())+"-----\n")
file.write("n")
for i in range(len(model.parameters_rules())):
file.write("\t"+"p_"+str(i))
file.write("\n")
# Best fit parameters
param_dict = load_params("/Users/lopezlab/git/earm/EARM_2_0_M1a_fitted_params.txt")
for param in model.parameters:
if param.name in param_dict:
param.value = param_dict[param.name]
# Load experimental data file
exp_data = np.genfromtxt('/Users/lopezlab/git/earm/xpdata/forfits/EC-RP_IMS-RP_IC-RP_data_for_models.csv', delimiter=',', names=True)
# Time points (same as for experiments)
tspan = exp_data['Time']
# plt.figure()
# plt.plot(tspan,exp_data['ECRP'],linewidth=3,label='ECRP')
# plt.plot(tspan,exp_data['norm_ECRP'],linewidth=3,label='norm_ECRP')
# plt.legend(loc='lower right')
#
import numpy as np
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
import scipy.interpolate
import pysb
from earm.lopez_embedded import model
from pysb.bng import generate_equations
from pysb.integrate import odesolve
from pysb.util import update_param_vals, load_params
from pysb_cupsoda import set_cupsoda_path, CupsodaSolver
tspan = np.linspace(0, 20000, 2000)
option = '911'
if option == '911':
new_params = load_params('Params/pars_embedded_911.txt')
savename = 'parameters_911_gpu_new'
directory = 'OUT'
if option == '486':
new_params = load_params('Params/pars_embedded_486.txt')
savename = 'parameters_486_gpu_new'
directory = 'OUT'
update_param_vals(model, new_params)
set_cupsoda_path("/home/pinojc/git/cupSODA")
run = 'cupSODA'
#run = 'scipy'
ATOL = 1e-6
RTOL = 1e-6
import numpy as np
import scipy.interpolate
import matplotlib.pyplot as plt
import datetime
from multiprocessing import Pool, Value, Queue
import multiprocessing
import multiprocessing as mp
import math
obj_names = ['emBid', 'ecPARP', 'e2']
# observables = ['mBid', 'aSmac' , 'cPARP']
# Best fit parameters
param_dict = load_params("/home/pinojc/Projects/earm/EARM_2_0_M1a_fitted_params.txt")
for param in model.parameters:
if param.name in param_dict:
param.value = param_dict[param.name]
# Load experimental data file
exp_data = np.genfromtxt('/home/pinojc/Projects/earm/xpdata/forfits/EC-RP_IMS-RP_IC-RP_data_for_models.csv', delimiter=',', names=True)
# Build time points for the integrator, using the same time scale as the
# experimental data but with greater resolution to help the integrator converge.
#ntimes = len(exp_data['Time'])
# Factor by which to increase time resolution
#tmul = 20
# Do the sampling such that the original experimental timepoints can be
# extracted with a slice expression instead of requiring interpolation.
#tspan = np.linspace(0.0, exp_data['Time'][-1], (ntimes-1) * tmul + 1)
for name in obj_names:
outfiles[name + '_sens'] = os.path.join(outdir, name + "_sens_scipy.txt")
outfiles[name + '_sens_t'] = os.path.join(outdir,
name + "_sens_t_scipy.txt")
outfiles[name + '_sens_2'] = os.path.join(outdir,
name + "_sens_2_scipy.txt")
OUTPUT = {key: open(file, 'a') for key, file in outfiles.items()}
for file in OUTPUT.values():
file.write("-----" + str(datetime.datetime.now()) + "-----\n")
file.write("n")
for i in range(len(model.parameters_rules())):
file.write("\t" + "p_" + str(i))
file.write("\n")
# Best fit parameters
param_dict = load_params(
"/Users/lopezlab/git/earm/EARM_2_0_M1a_fitted_params.txt")
for param in model.parameters:
if param.name in param_dict:
param.value = param_dict[param.name]
# Load experimental data file
exp_data = np.genfromtxt(
'/Users/lopezlab/git/earm/xpdata/forfits/EC-RP_IMS-RP_IC-RP_data_for_models.csv',
delimiter=',',
names=True)
# Time points (same as for experiments)
tspan = exp_data['Time']
# plt.figure()
# plt.plot(tspan,exp_data['ECRP'],linewidth=3,label='ECRP')
import multiprocessing
import multiprocessing as mp
import math
from earm.lopez_embedded_different_max_pore import createModel
minpore = '5'
maxpore = '5'
model = createModel(int(minpore),int(maxpore))
obj_names = ['emBid', 'ecPARP', 'e2']
# observables = ['mBid', 'aSmac' , 'cPARP']
# Best fit parameters
#param_dict = load_params("/home/pinojc/Projects/earm/EARM_2_0_M1a_fitted_params.txt")
param_dict=load_params('/home/pinojc/Projects/EARM_sensitivity_to_pore/DiffMaxPore/min5_max_5_fitted_params.txt')
for param in model.parameters:
if param.name in param_dict:
param.value = param_dict[param.name]
# Load experimental data file
exp_data = np.genfromtxt('/home/pinojc/Projects/earm/xpdata/forfits/EC-RP_IMS-RP_IC-RP_data_for_models.csv', delimiter=',', names=True)
# # Build time points for the integrator, using the same time scale as the
# # experimental data but with greater resolution to help the integrator converge.
# ntimes = len(exp_data['Time'])
# # Factor by which to increase time resolution
# tmul = 20
# # Do the sampling such that the original experimental timepoints can be
# # extracted with a slice expression instead of requiring interpolation.
import numpy as np
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
import scipy.interpolate
import pysb
from earm.lopez_embedded import model
from pysb.bng import generate_equations
from pysb.integrate import odesolve
from pysb.util import update_param_vals, load_params
from pysb_cupsoda import set_cupsoda_path, CupsodaSolver
tspan = np.linspace(0, 20000, 2000)
option = '911'
if option == '911':
new_params = load_params('Params/pars_embedded_911.txt')
savename = 'parameters_911_gpu_new'
directory = 'OUT'
if option == '486':
new_params = load_params('Params/pars_embedded_486.txt')
savename = 'parameters_486_gpu_new'
directory = 'OUT'
update_param_vals(model, new_params)
set_cupsoda_path("/home/pinojc/git/cupSODA")
run = 'cupSODA'
#run = 'scipy'
ATOL = 1e-6
RTOL = 1e-6
