def to_copasi(self) -> model.Model:
"""
Build a copasi file from the sbml generated from tellurium
Returns:
A :py:class:`tasks.Model`
"""
return model.loada(self.to_antimony(), self.copasi_file)
def load_model(k1, k_minus1, k2):
# create a base directory to work from
working_directory = os.path.abspath('')
filename = f'Model_k1={k1}_kminus1={k_minus1}_k2={k2}.cps'
# create a string to a copasi file on system
copasi_filename = os.path.join(working_directory, filename)
antimony_string = f'''
model negative_feedback()
// define compartments
compartment cell = 1.0
//define species
S in cell
P in cell
C in cell
I in cell
//define initial conditions
S = 1
C = 1
I = 0
P = 0
// reaction parameters
k1 = {k1}
k_minus1 = {k_minus1}
k2 = {k2}
//define reactions
R1: S + C => I; cell * k1 * S * C
R_minus1: I => S + C; cell * k_minus1 * I
R3: I => P + C; cell * k2 * I
end
'''
return model.loada(antimony_string, copasi_filename) # load the model
def setUp(self):
super(ProfileLikelihoodTests, self).setUp()
ant_str = """
model new_model
R1: A -> B ; _k1*A;
R2: B -> A; k2*B;
R3: C -> D; _k3*C*B;
R4: D -> C; k4*D;
A = 100;
B = 0;
_k1=0.1;
k2 = 0.01
_k3 = 0.01
k4 = 1
end
"""
self.copasi_file = os.path.join(os.path.dirname(__file__),
'test_model.cps')
self.model = model.loada(ant_str, self.copasi_file)
self.fname = os.path.join(os.path.dirname(__file__), 'timecourse.txt')
self.data = self.model.simulate(0, 10, 1, report_name=self.fname)
with tasks.ParameterEstimation.Context(self.model,
self.fname,
context='s',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('run_mode', True)
context.set('prefix', '_')
context.set('randomize_start_values', True)
config = context.get_config()
self.pe = tasks.ParameterEstimation(config)
data = viz.Parse(self.pe).data['test_model']
self.rss = data.loc[0, 'RSS']
self.pe_mod = self.pe.models['test_model'].model
self.pe_mod.insert_parameters(df=data, index=0, inplace=True)
#context.set('number_of_generations', 300)
context.set('run_mode', True) ##defaults to False
context.set('pe_number', 3) ## number of repeat items in scan task
#context.set('copy_number', 2) ## number of times to copy model
#context.set('problem', 'Problem1')
#context.set('fit', 3)
#context.set('prefix', 'k')
config = context.get_config()
pe = tasks.ParameterEstimation(config)
#myPctMod.open()
data = viz.Parse(pe)
print(data)
myExMod = model.loada(model_string_ex, copasi_ex_filename)
def my_add_cols(inFName, outFName, sState, i1State, i2State):
myData = pd.read_csv(
os.path.join(os.path.dirname(working_directory), 'data', inFName))
myData.insert(len(myData.columns), 'S_indep', sState)
myData.insert(len(myData.columns), 'I1_indep', i1State)
myData.insert(len(myData.columns), 'I2_indep', i2State)
myData.rename(columns={
"pN": "Np",
"pG": "Gp",
"pD": "Dp",
"pK": "Kp"
},
inplace=True)
myData.to_csv(os.path.join(working_directory, outFName))
ADeg_k1 = 0.1;
// Other declarations:
var ThisIsAssignment;
const nuc, cyt, A2B, B2C;
// Unit definitions:
unit volume = 1e-3 litre;
unit substance = 1e-3 mole;
// Display Names:
A2B_0 is "A2B";
B2C_0 is "B2C";
end"""
copasi_file = os.path.join(os.getcwd(), 'copasi_file.cps')
mod = model.loada(ant, copasi_file=copasi_file)
TC1 = tasks.TimeCourse(mod,
end=1000,
step_size=100,
intervals=10,
report_name='report1.txt')
TC2 = tasks.TimeCourse(mod,
end=1000,
step_size=100,
intervals=10,
report_name='report2.txt')
TC3 = tasks.TimeCourse(mod,
end=1000,
os.environ["PATH"] += os.pathsep + "/Applications/copasi"
working_dir = os.path.abspath('')
copasi_filename = os.path.join(working_dir, "testNoRun.cps")
test_string = """
model test_model()
var A
R1: ->A; 1
R2: A->; 1
end
"""
theModel = model.loada(test_string, copasi_filename)
theTimeCourse = tasks.TimeCourse(theModel, run=False)
shellString = """#!/bin/bash
# Example SLURM job script for serial (non-parallel) jobs
#
#
# Tell SLURM if you want to be emailed when your job starts, ends, etc.
# Currently mail can only be sent to addresses @ncl.ac.uk
#
#SBATCH --mail-type=ALL
#SBATCH [email protected]
#
def antStringToDiagramSBML(antString, filePath, tempPath):
myAntList = oldAntStr.splitlines()
myAntList = [antLineReactionReducer(line) for line in myAntList]
newAntStr = "\n".join(myAntList)
newModel = model.loada(newAntStr, tempPath)
newModel.to_sbml(sbml_file=filePath)
Created on Wed Jan 22 16:04:37 2020
@author: peter
"""
from pycotools3 import model
import re, os
copasiPath = "/Applications/copasi"
if not re.search(copasiPath, os.environ["PATH"]):
os.environ["PATH"] += os.pathsep + copasiPath
working_directory = os.path.dirname(os.path.abspath(__file__))
f = open(os.path.join(working_directory,'antFile.txt'), "r" )
oldAntStr = f.read()
f.close()
copasi_filename = os.path.join(working_directory,'temp.cps')
model = model.loada(oldAntStr, copasi_filename)
kineticNames = model.get_variable_names(which='gl',
include_assignments=False)
metaboliteNames = model.get_variable_names(which='m',
include_assignments=False)
model.parameters[kineticNames].to_csv(
path_or_buf=os.path.join(working_directory,"oldKVals.csv"))
A = 100;
B = 1;
C = 1;
// reaction parameters
k1 = 0.1;
k2 = 0.1;
k3 = 0.1;
k4 = 0.1;
end
"""
copasi_file = os.path.join(working_directory, 'example_model.cps')
## build model
mod = model.loada(antimony_string, copasi_file)
assert isinstance(mod, model.Model)
## simulate some data, returns a pandas.DataFrame
data = mod.simulate(0, 20, 1)
## write data to file
experiment_filename = os.path.join(working_directory, 'experiment_data.txt')
data.to_csv(experiment_filename)
with tasks.ParameterEstimation.Context(
mod, experiment_filename,
context='pl', parameters='gm'
) as context:
context.set('method', 'hooke_jeeves')
