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time_course_tests.py
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# -*- coding: utf-8 -*-
'''
This file is part of pycotools.
pycotools is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
pycotools is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with pycotools. If not, see <http://www.gnu.org/licenses/>.
Author:
Ciaran Welsh
Date:
19-08-2017
'''
import pycotools
import unittest
from Tests import _test_base
class DeterministicTimeCourseTests(_test_base._BaseTest):
def setUp(self):
super(DeterministicTimeCourseTests, self).setUp()
self.TC = pycotools.tasks.TimeCourse(self.model, end=1000,
step_size=100, intervals=10,
max_internal_steps=50000,
report_name='test_time_course.csv')
self.timecourse = self.TC.model # self.TC.set_timecourse()
self.timecourse.save()
self.new_model = pycotools.tasks.CopasiMLParser(self.copasi_file).copasiML
self.list_of_tasks = '{http://www.copasi.org/static/schema}ListOfTasks'
self.list_of_reports = '{http://www.copasi.org/static/schema}ListOfReports'
def test_report_definition(self):
for i in self.new_model.find(self.list_of_reports):
if i.attrib['name'] == 'Time-Course':
self.assertTrue(i.attrib['name'] == 'Time-Course')
def test_report_definition2(self):
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in i:
if 'target' in list(j.attrib.keys()):
self.assertEqual(j.attrib['target'], self.TC.report_name)
def test_deterministic_options1(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
self.assertTrue(i[2].attrib['name'] == 'Deterministic (LSODA)')
def test_deterministic_options2(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in list(i[1]):
if j.attrib['name'] == 'Relative Tolerance':
self.assertTrue(j.attrib['value'] == str(self.TC.relative_tolerance))
def test_deterministic_options3(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in list(i[1]):
if j.attrib['name'] == 'Max Internal Steps':
self.assertTrue(j.attrib['value'] == str(self.TC.max_internal_steps))
def test_deterministic_write_data(self):
"""
Check that the data containing data is actually produced
:return:
"""
self.assertTrue(os.path.isfile(self.TC.report_name))
def test_deterministic_read_output(self):
"""
Ensure that pandas can read the output from TimeCourse
and that it looks like what we expect
:return:
"""
df = pandas.read_csv(self.TC.report_name, sep='\t', index_col=0)
self.assertEqual(df.shape, (11, 6))
def test_specific_variables(self):
"""
Test being selective about which output user wants
:return:
"""
TC = pycotools.tasks.TimeCourse(
self.model, intervals=10, step_size=100, end=1000,
metabolites=['A', 'B'], global_quantities=['B2C']
)
df = pandas.read_csv(TC.report_name, sep='\t', index_col=0)
self.assertEqual(df.shape[1], 3)
def test_parser_in_viz(self):
"""
:return:
"""
p = pycotools.viz.Parse(self.TC)
df = p.from_timecourse()
self.assertTrue(isinstance(df, pandas.core.frame.DataFrame))
def test_parser_in_viz2(self):
"""
:return:
"""
p = pycotools.viz.Parse(self.TC)
df = p.from_timecourse()
boolean = True
## set boolean to false if square brackets still in timecourse
for i in list(df.columns):
if '[' in i:
boolean = False
self.assertTrue(boolean)
def test_parser_in_viz3(self):
"""
:return:
"""
pycotools.viz.PlotTimeCourse(self.TC, y='metabolites')
class GibsonBruckTimeCourseTests(_test_base._BaseTest):
def setUp(self):
super(GibsonBruckTimeCourseTests, self).setUp()
self.TC = pycotools.tasks.TimeCourse(self.model, end=1000,
step_size=100, intervals=10,
max_internal_steps=50000,
method='gibson_bruck')
self.timecourse = self.TC.model # self.TC.set_timecourse()
self.timecourse.save()
self.new_model = pycotools.tasks.CopasiMLParser(self.copasi_file).xml
self.list_of_tasks = '{http://www.copasi.org/static/schema}ListOfTasks'
self.list_of_reports = '{http://www.copasi.org/static/schema}ListOfReports'
# TODO test stochastic algorithms
def test_report_definition(self):
for i in self.new_model.find(self.list_of_reports):
if i.attrib['name'] == 'Time-Course':
self.assertTrue(i.attrib['name'] == 'Time-Course')
def test_gibson_bruck1(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
self.assertTrue(i[2].attrib['name'] == 'Stochastic (Gibson + Bruck)')
def test_gibson_bruck2(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in list(i[1]):
if j.attrib['name'] == 'Max Internal Steps':
self.assertTrue(j.attrib['value'] == str(self.TC.max_internal_steps))
def test_gibson_bruck3(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in list(i[1]):
if j.attrib['name'] == 'Subtype':
self.assertTrue(j.attrib['value'] == str(self.TC.subtype))
def test_gibson_bruck5(self):
"""
Check that the data containing data is actually produced
:return:
"""
self.assertTrue(os.path.isfile(self.TC.report_name))
def test_gibson_bruck_read_output(self):
"""
Ensure that pandas can read the output from TimeCourse
and that it looks like what we expect
:return:
"""
df = pandas.read_csv(self.TC.report_name, sep='\t', index_col=0)
self.assertEqual(df.shape, (11, 6))
if __name__ == '__main__':
unittest.main() # -*- coding: utf-8 -*-
'''
This file is part of pycotools.
pycotools is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
pycotools is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with pycotools. If not, see <http://www.gnu.org/licenses/>.
Author:
Ciaran Welsh
Date:
19-08-2017
'''
import site
site.addsitedir('/home/b3053674/Documents/pycotools')
# site.addsitedir('C:\Users\Ciaran\Documents\pycotools')
import pycotools
import unittest
import os
import pandas
from Tests import _test_base
class DeterministicTimeCourseTests(_test_base._BaseTest):
def setUp(self):
super(DeterministicTimeCourseTests, self).setUp()
self.TC = pycotools.tasks.TimeCourse(self.model, end=1000,
step_size=100, intervals=10,
max_internal_steps=50000,
report_name='test_time_course.csv')
self.timecourse = self.TC.model # self.TC.set_timecourse()
self.timecourse.save()
self.new_model = pycotools.tasks.CopasiMLParser(self.copasi_file).copasiML
self.list_of_tasks = '{http://www.copasi.org/static/schema}ListOfTasks'
self.list_of_reports = '{http://www.copasi.org/static/schema}ListOfReports'
def test_report_definition(self):
for i in self.new_model.find(self.list_of_reports):
if i.attrib['name'] == 'Time-Course':
self.assertTrue(i.attrib['name'] == 'Time-Course')
def test_report_definition2(self):
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in i:
if 'target' in list(j.attrib.keys()):
self.assertEqual(j.attrib['target'], self.TC.report_name)
def test_deterministic_options1(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
self.assertTrue(i[2].attrib['name'] == 'Deterministic (LSODA)')
def test_deterministic_options2(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in list(i[1]):
if j.attrib['name'] == 'Relative Tolerance':
self.assertTrue(j.attrib['value'] == str(self.TC.relative_tolerance))
def test_deterministic_options3(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in list(i[1]):
if j.attrib['name'] == 'Max Internal Steps':
self.assertTrue(j.attrib['value'] == str(self.TC.max_internal_steps))
def test_deterministic_write_data(self):
"""
Check that the data containing data is actually produced
:return:
"""
self.assertTrue(os.path.isfile(self.TC.report_name))
def test_deterministic_read_output(self):
"""
Ensure that pandas can read the output from TimeCourse
and that it looks like what we expect
:return:
"""
df = pandas.read_csv(self.TC.report_name, sep='\t', index_col=0)
self.assertEqual(df.shape, (11, 6))
# def test_correct_output(self):
# """
#
# :return:
# """
# self.TC.correct_output_headers()
# df = pandas.read_csv(self.TC.report_name, sep='\t')
# check = True
# for i in df.keys():
# if '[' in i:
# check = False
# self.assertTrue(check)
def test_parser_in_viz(self):
"""
:return:
"""
p = pycotools.viz.Parse(self.TC)
df = p.parse()
self.assertTrue(isinstance(df, pandas.core.frame.DataFrame))
def test_parser_in_viz2(self):
"""
:return:
"""
p = pycotools.viz.Parse(self.TC)
df = p.parse()
boolean = True
## set boolean to false if square brackets still in timecourse
for i in list(df.columns):
if '[' in i:
boolean = False
self.assertTrue(boolean)
class GibsonBruckTimeCourseTests(_test_base._BaseTest):
def setUp(self):
super(GibsonBruckTimeCourseTests, self).setUp()
self.TC = pycotools.tasks.TimeCourse(self.model, end=1000,
step_size=100, intervals=10,
max_internal_steps=50000,
method='gibson_bruck')
self.timecourse = self.TC.model # self.TC.set_timecourse()
self.timecourse.save()
self.new_model = pycotools.tasks.CopasiMLParser(self.copasi_file).xml
self.list_of_tasks = '{http://www.copasi.org/static/schema}ListOfTasks'
self.list_of_reports = '{http://www.copasi.org/static/schema}ListOfReports'
# TODO test stochastic algorithms
def test_report_definition(self):
for i in self.new_model.find(self.list_of_reports):
if i.attrib['name'] == 'Time-Course':
self.assertTrue(i.attrib['name'] == 'Time-Course')
def test_gibson_bruck1(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
self.assertTrue(i[2].attrib['name'] == 'Stochastic (Gibson + Bruck)')
def test_gibson_bruck2(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in list(i[1]):
if j.attrib['name'] == 'Max Internal Steps':
self.assertTrue(j.attrib['value'] == str(self.TC.max_internal_steps))
def test_gibson_bruck3(self):
"""
"""
for i in self.new_model.find(self.list_of_tasks):
if i.attrib['name'] == 'Time-Course':
for j in list(i[1]):
if j.attrib['name'] == 'Subtype':
self.assertTrue(j.attrib['value'] == str(self.TC.subtype))
def test_gibson_bruck5(self):
"""
Check that the data containing data is actually produced
:return:
"""
self.assertTrue(os.path.isfile(self.TC.report_name))
def test_gibson_bruck_read_output(self):
"""
Ensure that pandas can read the output from TimeCourse
and that it looks like what we expect
:return:
"""
df = pandas.read_csv(self.TC.report_name, sep='\t', index_col=0)
self.assertEqual(df.shape, (11, 6))
if __name__ == '__main__':
unittest.main()