def test_read_write_error_handling(self):
report_1 = Report(
id='report_1',
data_sets=[
DataSet(id='x', label='X'),
DataSet(id='y', label='Y'),
DataSet(id='z', label='Z'),
],
)
data_set_results_1 = data_model.DataSetResults({
'x':
numpy.array([1., 2.]),
'y':
numpy.array([3., 4.]),
'z':
numpy.array([5., 6.]),
})
rel_path_1 = os.path.join('a/b/c.sedml', report_1.id)
io.ReportWriter().run(report_1,
data_set_results_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.h5)
report_1.data_sets.append(DataSet(id='w', label='W'))
with self.assertWarns(MissingDataWarning):
io.ReportReader().run(report_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.h5)
report_1.data_sets.pop()
report_1.data_sets.pop()
with self.assertWarns(ExtraDataWarning):
io.ReportReader().run(report_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.h5)
data_set_results_1['x'] = numpy.array([1., 2.],
dtype=numpy.dtype('object'))
with self.assertRaisesRegex(TypeError, 'NumPy dtype should be '):
io.ReportWriter().run(report_1,
data_set_results_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.h5)
def gen_sedml_2d_3d(omex_file_path, base_out_path):
temp_path = os.path.join(base_out_path, "temp")
if not os.path.exists(temp_path):
os.mkdir(temp_path, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)
# defining archive
archive = CombineArchiveReader().run(
in_file=omex_file_path,
out_dir=temp_path,
try_reading_as_plain_zip_archive=False)
# determine files to execute
sedml_contents = get_sedml_contents(archive)
for i_content, content in enumerate(sedml_contents):
content_filename = os.path.join(temp_path, content.location)
sedml_name = content.location.split('/')[1].split('.')[0]
doc = SedmlSimulationReader().run(content_filename)
for output in doc.outputs:
if isinstance(output, (Plot2D, Plot3D)):
report = Report(id='__plot__' + output.id, name=output.name)
data_generators = {}
if isinstance(output, Plot2D):
for curve in output.curves:
data_generators[
curve.x_data_generator.id] = curve.x_data_generator
data_generators[
curve.y_data_generator.id] = curve.y_data_generator
elif isinstance(output, Plot3D):
for surface in output.surfaces:
data_generators[surface.x_data_generator.
id] = surface.x_data_generator
data_generators[surface.y_data_generator.
id] = surface.y_data_generator
data_generators[surface.z_data_generator.
id] = surface.z_data_generator
for data_generator in data_generators.values():
report.data_sets.append(
DataSet(
id='__data_set__{}_{}'.format(
output.id, data_generator.id),
name=data_generator.name,
label=data_generator.id,
data_generator=data_generator,
))
report.data_sets.sort(key=lambda data_set: data_set.id)
doc.outputs.append(report)
filename_with_reports_for_plots = os.path.join(
temp_path, f'simulation_{sedml_name}.sedml')
SedmlSimulationWriter().run(doc,
filename_with_reports_for_plots,
validate_models_with_languages=False)
def exec_plot_output_sed_doc(omex_file_path, base_out_path):
archive = CombineArchiveReader().run(in_file=omex_file_path,
out_dir=tmp_dir,
try_reading_as_plain_zip_archive=True)
# determine files to execute
sedml_contents = get_sedml_contents(archive)
report_results = ReportResults()
for i_content, content in enumerate(sedml_contents):
content_filename = os.path.join(tmp_dir, content.location)
for report_filename in glob.glob(
os.path.join(base_out_path, content.location, '*.csv')):
if report_filename.find('__plot__') != -1:
report_id = os.path.splitext(
os.path.basename(report_filename))[0]
# read report from CSV file produced by tellurium
# data_set_df = pd.read_csv(report_filename).transpose()
data_set_df = pd.read_csv(report_filename, header=None).T
data_set_df.columns = data_set_df.iloc[0]
data_set_df.drop(0, inplace=True)
data_set_df.reset_index(inplace=True)
data_set_df.drop('index', axis=1, inplace=True)
# create pseudo-report for ReportWriter
datasets = []
for col in list(data_set_df.columns):
datasets.append(DataSet(id=col, label=col, name=col))
#report.data_sets = datasets
report = Report(id=report_id,
name=report_id,
data_sets=datasets)
data_set_results = DataSetResults()
for col in list(data_set_df.columns):
data_set_results[col] = data_set_df[col].values
# append to data structure of report results
# save file in desired BioSimulators format(s)
export_id = report_id.replace('__plot__', '')
report.id = export_id
rel_path = os.path.join(content.location, report.id)
if len(rel_path.split("./")) > 1:
rel_path = rel_path.split("./")[1]
ReportWriter().run(report,
data_set_results,
base_out_path,
rel_path,
format='h5')
os.rename(report_filename,
report_filename.replace('__plot__', ''))
def test_write_error_handling(self):
with self.assertRaisesRegex(NotImplementedError, 'is not supported'):
io.ReportWriter().run(Report(), None, None, 'a', format='TSV')
report = Report(data_sets=[DataSet(id='x', label='x')])
data_set_results = data_model.DataSetResults({'x': numpy.zeros((3, ))})
io.ReportWriter().run(report,
data_set_results,
self.dirname,
'.',
format=data_model.ReportFormat.csv)
data_set_results['x'] = data_set_results['x'].reshape((3, 1))
with self.assertWarnsRegex(
CannotExportMultidimensionalTableWarning,
'Multidimensional reports cannot be exported'):
io.ReportWriter().run(report,
data_set_results,
self.dirname,
'.',
format=data_model.ReportFormat.csv)
def exec_plot_output_sed_doc(omex_file_path, base_out_path):
config = Config(VALIDATE_OMEX_MANIFESTS=False)
archive = CombineArchiveReader().run(in_file=omex_file_path,
out_dir=tmp_dir,
config=config)
# determine files to execute
sedml_contents = get_sedml_contents(archive)
report_results = ReportResults()
for i_content, content in enumerate(sedml_contents):
content_filename = os.path.join(tmp_dir, content.location)
for report_filename in glob.glob(
os.path.join(base_out_path, content.location, '*.csv')):
if report_filename.find('__plot__') != -1:
report_id = os.path.splitext(
os.path.basename(report_filename))[0]
# read report from CSV file produced by tellurium
# data_set_df = pd.read_csv(report_filename).transpose()
data_set_df = pd.read_csv(report_filename, header=None).T
datasets = []
for col in data_set_df.columns:
datasets.append(
DataSet(id=data_set_df.loc[0, col],
label=data_set_df.loc[1, col],
name=data_set_df.loc[2, col]))
report = Report(id=report_id,
name=report_id,
data_sets=datasets)
data_set_df.columns = data_set_df.iloc[0]
data_set_df.drop(0, inplace=True)
data_set_df.drop(1, inplace=True)
data_set_df.drop(2, inplace=True)
data_set_df.reset_index(inplace=True)
data_set_df.drop('index', axis=1, inplace=True)
# create pseudo-report for ReportWriter
data_set_results = DataSetResults()
for col in list(data_set_df.columns):
data_set_results[col] = data_set_df[col].to_numpy(
dtype='float64')
# append to data structure of report results
# save file in desired BioSimulators format(s)
export_id = report_id.replace('__plot__', '')
report.id = export_id
rel_path = os.path.join(content.location, report.id)
if len(rel_path.split("./")) > 1:
rel_path = rel_path.split("./")[1]
# print("base: ", base_out_path, file=sys.stdout)
# print("rel: ", rel_path, file=sys.stdout)
ReportWriter().run(report,
data_set_results,
base_out_path,
rel_path,
format='h5',
type=Plot2D)
os.rename(report_filename,
report_filename.replace('__plot__', ''))
else:
print("report : ", report_filename, file=sys.stdout)
report_id = os.path.splitext(
os.path.basename(report_filename))[0]
data_set_df = pd.read_csv(report_filename, header=None).T
datasets = []
for col in data_set_df.columns:
datasets.append(
DataSet(id=data_set_df.loc[0, col],
label=data_set_df.loc[1, col],
name=""))
report = Report(id=report_id,
name=report_id,
data_sets=datasets)
data_set_df.columns = data_set_df.iloc[0] # use ids
data_set_df.drop(0, inplace=True)
data_set_df.drop(1, inplace=True)
data_set_df.drop(2, inplace=True)
data_set_df.reset_index(inplace=True)
data_set_df.drop('index', axis=1, inplace=True)
data_set_results = DataSetResults()
for col in list(data_set_df.columns):
data_set_results[col] = data_set_df[col].to_numpy(
dtype='float64')
rel_path = os.path.join(content.location, report.id)
if len(rel_path.split("./")) > 1:
rel_path = rel_path.split("./")[1]
ReportWriter().run(report,
data_set_results,
base_out_path,
rel_path,
format='h5',
type=Report)
def exec_sed_doc(omex_file_path, base_out_path):
# defining archive
config = Config(VALIDATE_OMEX_MANIFESTS=False)
archive = CombineArchiveReader().run(in_file=omex_file_path,
out_dir=tmp_dir,
config=config)
# determine files to execute
sedml_contents = get_sedml_contents(archive)
report_results = ReportResults()
for i_content, content in enumerate(sedml_contents):
content_filename = os.path.join(tmp_dir, content.location)
doc = SedmlSimulationReader().run(content_filename)
for report_filename in glob.glob(
os.path.join(base_out_path, content.location, '*.csv')):
report_id = os.path.splitext(os.path.basename(report_filename))[0]
# read report from CSV file produced by VCell
data_set_df = pd.read_csv(report_filename).transpose()
data_set_df.columns = data_set_df.iloc[0]
data_set_df = data_set_df.drop(data_set_df.iloc[0].name)
data_set_df = data_set_df.reset_index()
data_set_df = data_set_df.rename(
columns={'index': data_set_df.columns.name})
data_set_df = data_set_df.transpose()
data_set_df.index.name = None
report = next(report for report in doc.outputs
if report.id == report_id)
data_set_results = DataSetResults()
# print("report: ", report, file=sys.stderr)
# print("report Type: ", type(report), file=sys.stderr)
# print("Plot Type: ", Plot2D, file=sys.stderr)
if type(report) != Plot2D and type(report) != Plot3D:
# Considering the scenario where it has the datasets in sedml
for data_set in report.data_sets:
data_set_results[data_set.id] = data_set_df.loc[
data_set.label, :].to_numpy(dtype='float64')
# print("DF for report: ", data_set_results[data_set.id], file=sys.stderr)
# print("df.types: ", data_set_results[data_set.id].dtype, file=sys.stderr)
else:
data_set_df = pd.read_csv(report_filename, header=None).T
data_set_df.columns = data_set_df.iloc[0]
data_set_df.drop(0, inplace=True)
data_set_df.reset_index(inplace=True)
data_set_df.drop('index', axis=1, inplace=True)
# print("DF for plot: ", data_set_df, file=sys.stderr)
# Considering the scenario where it doesn't have datasets in sedml (pseudo sedml for plots)
for col in list(data_set_df.columns):
data_set_results[col] = data_set_df[col].values
# append to data structure of report results
report_results[report_id] = data_set_results
# save file in desired BioSimulators format(s)
# for report_format in report_formats:
# print("HDF report: ", report, file=sys.stderr)
# print("HDF dataset results: ", data_set_results, file=sys.stderr)
# print("HDF base_out_path: ", base_out_path,file=sys.stderr)
# print("HDF path: ", os.path.join(content.location, report.id), file=sys.stderr)
rel_path = os.path.join(content.location, report.id)
if len(rel_path.split("./")) > 1:
rel_path = rel_path.split("./")[1]
if type(report) != Plot2D and type(report) != Plot3D:
ReportWriter().run(report,
data_set_results,
base_out_path,
rel_path,
format='h5')
else:
datasets = []
for col in list(data_set_df.columns):
datasets.append(DataSet(id=col, label=col, name=col))
report.data_sets = datasets
ReportWriter().run(report,
data_set_results,
base_out_path,
rel_path,
format='h5')
# Remove temp directory
shutil.rmtree(tmp_dir)
def test_get_ids(self):
report = Report(data_sets=[
DataSet(id='A', label='A'),
DataSet(id='B', label='A'),
], )
results = data_model.DataSetResults({
report.data_sets[0].id:
numpy.array([1, 2, 3]),
report.data_sets[1].id:
numpy.array([4, 5, 6]),
})
for format in [
data_model.ReportFormat.h5, data_model.ReportFormat.csv
]:
filename = os.path.join(self.dirname, 'test')
io.ReportWriter().run(report,
results,
filename,
'a/b/c.sedml/report1',
format=format)
io.ReportWriter().run(report,
results,
filename,
'a/b/c.sedml/report2',
format=format)
io.ReportWriter().run(report,
results,
filename,
'a/b/c.sedml/report3',
format=format)
io.ReportWriter().run(report,
results,
filename,
'a/b/d.sedml/report4',
format=format)
io.ReportWriter().run(report,
results,
filename,
'a/b/report5',
format=format)
io.ReportWriter().run(report,
results,
filename,
'a/b/report6',
format=format)
self.assertEqual(
set(io.ReportReader().get_ids(filename, format=format)),
set([
'a/b/c.sedml/report1',
'a/b/c.sedml/report2',
'a/b/c.sedml/report3',
'a/b/d.sedml/report4',
'a/b/report5',
'a/b/report6',
]))
with self.assertRaisesRegex(NotImplementedError, 'is not supported'):
io.ReportReader().get_ids(filename, format=None)
def test_overwrite_report(self):
report_1 = Report(
id='report_1',
data_sets=[
DataSet(id='x', label='X'),
DataSet(id='y', label='Y'),
DataSet(id='z', label='Z'),
],
)
data_set_results_1 = data_model.DataSetResults({
'x':
numpy.array([1., 2.]),
'y':
numpy.array([3., 4.]),
'z':
numpy.array([5., 6.]),
})
rel_path_1 = os.path.join('a/b/c.sedml', report_1.id)
io.ReportWriter().run(report_1,
data_set_results_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.h5)
data_set_results_2 = io.ReportReader().run(
report_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.h5)
numpy.testing.assert_allclose(data_set_results_2['x'],
numpy.array([1., 2.]))
numpy.testing.assert_allclose(data_set_results_2['y'],
numpy.array([3., 4.]))
numpy.testing.assert_allclose(data_set_results_2['z'],
numpy.array([5., 6.]))
data_set_results_1 = data_model.DataSetResults({
'x':
numpy.array([1., 2.]) + 1.,
'y':
numpy.array([3., 4.]) + 1.,
'z':
numpy.array([5., 6.]) + 1.,
})
io.ReportWriter().run(report_1,
data_set_results_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.h5)
data_set_results_2 = io.ReportReader().run(
report_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.h5)
numpy.testing.assert_allclose(data_set_results_2['x'],
numpy.array([1., 2.]) + 1.)
numpy.testing.assert_allclose(data_set_results_2['y'],
numpy.array([3., 4.]) + 1.)
numpy.testing.assert_allclose(data_set_results_2['z'],
numpy.array([5., 6.]) + 1.)
def test_read_write_duplicate_labels(self):
# labels in same order
report_1 = Report(
id='report_1',
data_sets=[
DataSet(id='x', label='A'),
DataSet(id='y', label='A'),
DataSet(id='z', label='A'),
],
)
data_set_results_1 = data_model.DataSetResults({
'x':
numpy.array([1., 2.]),
'y':
numpy.array([3., 4.]),
'z':
numpy.array([5., 6.]),
})
rel_path_1 = os.path.join('a/b/c.sedml', report_1.id)
io.ReportWriter().run(report_1,
data_set_results_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.csv)
data_set_results_2 = io.ReportReader().run(
report_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.csv)
numpy.testing.assert_allclose(data_set_results_2['x'],
numpy.array([1., 2.]))
numpy.testing.assert_allclose(data_set_results_2['y'],
numpy.array([3., 4.]))
numpy.testing.assert_allclose(data_set_results_2['z'],
numpy.array([5., 6.]))
# labels in different order
report_1 = Report(
id='report_1',
data_sets=[
DataSet(id='x', label='X'),
DataSet(id='y', label='X'),
DataSet(id='z', label='Z'),
],
)
data_set_results_1 = data_model.DataSetResults({
'x':
numpy.array([1., 2.]),
'y':
numpy.array([3., 4.]),
'z':
numpy.array([5., 6.]),
})
rel_path_1 = os.path.join('a/b/c.sedml', report_1.id)
io.ReportWriter().run(report_1,
data_set_results_1,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.csv)
report_2 = Report(
id='report_1',
data_sets=[
DataSet(id='x', label='X'),
DataSet(id='z', label='Z'),
DataSet(id='y', label='X'),
],
)
data_set_results_2 = io.ReportReader().run(
report_2,
self.dirname,
rel_path_1,
format=data_model.ReportFormat.csv)
self.assertEqual(set(data_set_results_2.keys()), set(['z']))
numpy.testing.assert_allclose(data_set_results_2['z'],
numpy.array([5., 6.]))
def test_read_write(self):
report_1 = Report(
id='report_1',
name='report 1',
data_sets=[
DataSet(id='w', label='W'),
DataSet(id='x', label='X'),
DataSet(id='y', label='Y'),
DataSet(id='z', label='Z'),
],
)
report_2 = Report(
id='report_2',
name='report 2',
data_sets=[
DataSet(id='a', label='A'),
DataSet(id='b', label='B'),
DataSet(id='c', label='C'),
DataSet(id='d', label='D'),
],
)
report_3 = Report(
id='report_3',
data_sets=[
DataSet(id='a', label='A'),
DataSet(id='b', label='B'),
DataSet(id='c', label='C'),
DataSet(id='d', label='D'),
],
)
data_set_results_1 = data_model.DataSetResults({
'w':
None,
'x':
numpy.array([1, 2, 3]),
'y':
numpy.array([4., numpy.nan]),
'z':
numpy.array(6.),
})
data_set_results_2 = data_model.DataSetResults({
'a':
numpy.array([1, 2]),
'b':
numpy.array([7., 8., 9.]),
'c':
numpy.array(True),
'd':
None,
})
data_set_results_3 = data_model.DataSetResults({
'a':
numpy.array([[1, 2], [3, 4], [5, 6]]),
'b':
numpy.array([7., 8., 9.]),
'c':
numpy.array(True),
'd':
None,
})
# CSV, TSV
for format in [
data_model.ReportFormat.csv, data_model.ReportFormat.tsv,
data_model.ReportFormat.xlsx
]:
rel_path_1 = os.path.join(format.value, 'a/b/c.sedml', report_1.id)
rel_path_2 = os.path.join(format.value, 'a/d.sedml', report_2.id)
rel_path_3 = os.path.join(format.value, 'e.sedml', report_2.id)
io.ReportWriter().run(report_1,
data_set_results_1,
self.dirname,
rel_path_1,
format=format)
io.ReportWriter().run(report_2,
data_set_results_2,
self.dirname,
rel_path_2,
format=format)
with self.assertWarnsRegex(
CannotExportMultidimensionalTableWarning,
'Multidimensional reports cannot be exported'):
io.ReportWriter().run(report_3,
data_set_results_3,
self.dirname,
rel_path_3,
format=format)
data_set_results_1_b = io.ReportReader().run(report_1,
self.dirname,
rel_path_1,
format=format)
data_set_results_2_b = io.ReportReader().run(report_2,
self.dirname,
rel_path_2,
format=format)
self.assertEqual(
set(io.ReportReader().get_ids(self.dirname, format=format)),
set([rel_path_1, rel_path_2]))
numpy.testing.assert_allclose(
data_set_results_1_b['w'],
numpy.array([numpy.nan, numpy.nan, numpy.nan]))
numpy.testing.assert_allclose(data_set_results_1_b['x'],
numpy.array([1., 2., 3.]))
numpy.testing.assert_allclose(
data_set_results_1_b['y'],
numpy.array([4., numpy.nan, numpy.nan]))
numpy.testing.assert_allclose(
data_set_results_1_b['z'],
numpy.array([6., numpy.nan, numpy.nan]))
self.assertEqual(data_set_results_1_b['w'].dtype.name, 'float64')
self.assertEqual(data_set_results_1_b['x'].dtype.name, 'float64')
self.assertEqual(data_set_results_1_b['y'].dtype.name, 'float64')
self.assertEqual(data_set_results_1_b['z'].dtype.name, 'float64')
numpy.testing.assert_allclose(data_set_results_2_b['a'],
numpy.array([1., 2., numpy.nan]))
numpy.testing.assert_allclose(data_set_results_2_b['b'],
numpy.array([7., 8., 9.]))
numpy.testing.assert_allclose(
data_set_results_2_b['c'],
numpy.array([1., numpy.nan, numpy.nan]))
numpy.testing.assert_allclose(
data_set_results_2_b['d'],
numpy.array([numpy.nan, numpy.nan, numpy.nan]))
self.assertEqual(data_set_results_2_b['a'].dtype.name, 'float64')
self.assertEqual(data_set_results_2_b['b'].dtype.name, 'float64')
self.assertEqual(data_set_results_2_b['c'].dtype.name, 'float64')
self.assertEqual(data_set_results_2_b['d'].dtype.name, 'float64')
# HDF
for format in [data_model.ReportFormat.h5]:
rel_path_1 = os.path.join(format.value, 'a/b/c.sedml', report_1.id)
rel_path_2 = os.path.join(format.value, 'a/d.sedml', report_2.id)
rel_path_3 = os.path.join(format.value, 'e.sedml', report_3.id)
io.ReportWriter().run(report_1,
data_set_results_1,
self.dirname,
rel_path_1,
format=format)
io.ReportWriter().run(report_2,
data_set_results_2,
self.dirname,
rel_path_2,
format=format)
io.ReportWriter().run(report_3,
data_set_results_3,
self.dirname,
rel_path_3,
format=format)
data_set_results_1_b = io.ReportReader().run(report_1,
self.dirname,
rel_path_1,
format=format)
data_set_results_2_b = io.ReportReader().run(report_2,
self.dirname,
rel_path_2,
format=format)
data_set_results_3_b = io.ReportReader().run(report_3,
self.dirname,
rel_path_3,
format=format)
self.assertEqual(
set(io.ReportReader().get_ids(self.dirname, format=format)),
set([rel_path_1, rel_path_2, rel_path_3]))
self.assertEqual(data_set_results_1_b['w'], None)
numpy.testing.assert_allclose(data_set_results_1_b['x'],
numpy.array([1, 2, 3]))
numpy.testing.assert_allclose(data_set_results_1_b['y'],
numpy.array([4., numpy.nan]))
numpy.testing.assert_allclose(data_set_results_1_b['z'],
numpy.array(6.))
self.assertEqual(data_set_results_1_b['x'].dtype.name, 'int64')
self.assertEqual(data_set_results_1_b['y'].dtype.name, 'float64')
self.assertEqual(data_set_results_1_b['z'].dtype.name, 'float64')
numpy.testing.assert_allclose(data_set_results_2_b['a'],
numpy.array([1, 2]))
numpy.testing.assert_allclose(data_set_results_2_b['b'],
numpy.array([7., 8., 9.]))
numpy.testing.assert_allclose(data_set_results_2_b['c'],
numpy.array(True))
self.assertEqual(data_set_results_2_b['d'], None)
self.assertEqual(data_set_results_2_b['a'].dtype.name, 'int64')
self.assertEqual(data_set_results_2_b['b'].dtype.name, 'float64')
self.assertEqual(data_set_results_2_b['c'].dtype.name, 'bool')
numpy.testing.assert_allclose(
data_set_results_3_b['a'], numpy.array([[1, 2], [3, 4], [5,
6]]))
numpy.testing.assert_allclose(data_set_results_3_b['b'],
numpy.array([7., 8., 9.]))
numpy.testing.assert_allclose(data_set_results_3_b['c'],
numpy.array(True))
self.assertEqual(data_set_results_3_b['d'], None)
self.assertEqual(data_set_results_3_b['a'].dtype.name, 'int64')
self.assertEqual(data_set_results_3_b['b'].dtype.name, 'float64')
self.assertEqual(data_set_results_3_b['c'].dtype.name, 'bool')
with h5py.File(os.path.join(self.dirname, 'reports.h5'),
'r') as file:
self.assertEqual(
file[format.value + '/a'].attrs, {
'uri': format.value + '/a',
'combineArchiveLocation': format.value + '/a',
})
self.assertEqual(
file[format.value + '/a/b'].attrs, {
'uri': format.value + '/a/b',
'combineArchiveLocation': format.value + '/a/b',
})
self.assertEqual(
file[format.value + '/a/b/c.sedml'].attrs, {
'uri': format.value + '/a/b/c.sedml',
'combineArchiveLocation':
format.value + '/a/b/c.sedml',
})
self.assertEqual(
file[format.value + '/a/d.sedml'].attrs, {
'uri': format.value + '/a/d.sedml',
'combineArchiveLocation': format.value + '/a/d.sedml',
})
self.assertEqual(
file[format.value + '/e.sedml'].attrs, {
'uri': format.value + '/e.sedml',
'combineArchiveLocation': format.value + '/e.sedml',
})
self.assertEqual(
file[format.value + '/a/b/c.sedml/' +
report_1.id].attrs['uri'],
format.value + '/a/b/c.sedml/' + report_1.id)
self.assertEqual(
file[format.value + '/a/b/c.sedml/' +
report_1.id].attrs['sedmlId'], report_1.id)
self.assertEqual(
file[format.value + '/a/b/c.sedml/' +
report_1.id].attrs['sedmlName'], report_1.name)
self.assertEqual(
file[format.value + '/a/d.sedml/' +
report_2.id].attrs['uri'],
format.value + '/a/d.sedml/' + report_2.id)
self.assertEqual(
file[format.value + '/a/d.sedml/' +
report_2.id].attrs['sedmlId'], report_2.id)
self.assertEqual(
file[format.value + '/a/d.sedml/' +
report_2.id].attrs['sedmlName'], report_2.name)
self.assertEqual(
file[format.value + '/e.sedml/' +
report_3.id].attrs['uri'],
format.value + '/e.sedml/' + report_3.id)
self.assertEqual(
file[format.value + '/e.sedml/' +
report_3.id].attrs['sedmlId'], report_3.id)
self.assertNotIn(
'sedmlName',
file[format.value + '/e.sedml/' + report_3.id].attrs)
def test_exec_sedml_docs_in_archive_without_log(self):
archive = CombineArchive(contents=[
CombineArchiveContent(
location='sim.sedml',
format='http://identifiers.org/combine.specifications/sed-ml',
),
CombineArchiveContent(
location='model.xml',
format='http://identifiers.org/combine.specifications/sbml',
),
], )
sed_doc = SedDocument()
model = Model(id='model_1',
source='model.xml',
language=ModelLanguage.SBML.value)
sed_doc.models.append(model)
sim = UniformTimeCourseSimulation(
id='sim_1',
initial_time=0.,
output_start_time=0.,
output_end_time=10.,
number_of_points=10,
algorithm=Algorithm(kisao_id='KISAO_0000019'))
sed_doc.simulations.append(sim)
task = Task(id='task_1', model=model, simulation=sim)
sed_doc.tasks.append(task)
sed_doc.data_generators.append(
DataGenerator(
id='data_gen_1',
variables=[
Variable(
id='var_1',
target=
"/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='Trim']",
target_namespaces={
'sbml': 'http://www.sbml.org/sbml/level2/version4'
},
task=task)
],
math='var_1',
))
sed_doc.data_generators.append(
DataGenerator(
id='data_gen_2',
variables=[
Variable(
id='var_2',
target=
"/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='Clb']",
target_namespaces={
'sbml': 'http://www.sbml.org/sbml/level2/version4'
},
task=task)
],
math='var_2',
))
report = Report(id='output_1')
sed_doc.outputs.append(report)
report.data_sets.append(
DataSet(id='data_set_1',
label='data_set_1',
data_generator=sed_doc.data_generators[0]))
report.data_sets.append(
DataSet(id='data_set_2',
label='data_set_2',
data_generator=sed_doc.data_generators[1]))
archive_dirname = os.path.join(self.tmp_dir, 'archive')
os.makedirs(archive_dirname)
shutil.copyfile(
os.path.join(os.path.dirname(__file__), '..', 'fixtures',
'BIOMD0000000297.xml'),
os.path.join(archive_dirname, 'model.xml'))
SedmlSimulationWriter().run(sed_doc,
os.path.join(archive_dirname, 'sim.sedml'))
archive_filename = os.path.join(self.tmp_dir, 'archive.omex')
CombineArchiveWriter().run(archive, archive_dirname, archive_filename)
def sed_task_executer(task, variables, log=None, config=None):
if log:
log.algorithm = task.simulation.algorithm.kisao_id
log.simulator_details = {
'attrib': 'value',
}
return VariableResults({
'var_1':
numpy.linspace(0., 10., task.simulation.number_of_points + 1),
'var_2':
numpy.linspace(10., 20., task.simulation.number_of_points + 1),
}), log
def sed_task_executer_error(task, variables, log=None, config=None):
raise ValueError('Big error')
out_dir = os.path.join(self.tmp_dir, 'outputs')
config = get_config()
config.REPORT_FORMATS = []
config.VIZ_FORMATS = []
config.COLLECT_COMBINE_ARCHIVE_RESULTS = True
config.LOG = True
# with log
sed_doc_executer = functools.partial(sedml_exec.exec_sed_doc,
sed_task_executer)
results, log = exec.exec_sedml_docs_in_archive(
sed_doc_executer,
archive_filename,
out_dir,
apply_xml_model_changes=False,
config=config)
self.assertEqual(set(results.keys()), set(['sim.sedml']))
self.assertEqual(set(results['sim.sedml'].keys()), set(['output_1']))
self.assertEqual(set(results['sim.sedml']['output_1'].keys()),
set(['data_set_1', 'data_set_2']))
numpy.testing.assert_allclose(
results['sim.sedml']['output_1']['data_set_1'],
numpy.linspace(0., 10., 11))
numpy.testing.assert_allclose(
results['sim.sedml']['output_1']['data_set_2'],
numpy.linspace(10., 20., 11))
self.assertEqual(log.exception, None)
self.assertEqual(
log.sed_documents['sim.sedml'].tasks['task_1'].algorithm,
task.simulation.algorithm.kisao_id)
self.assertEqual(
log.sed_documents['sim.sedml'].tasks['task_1'].simulator_details,
{'attrib': 'value'})
sed_doc_executer = functools.partial(sedml_exec.exec_sed_doc,
sed_task_executer_error)
results, log = exec.exec_sedml_docs_in_archive(
sed_doc_executer,
archive_filename,
out_dir,
apply_xml_model_changes=False,
config=config)
self.assertIsInstance(log.exception, CombineArchiveExecutionError)
config.DEBUG = True
sed_doc_executer = functools.partial(sedml_exec.exec_sed_doc,
sed_task_executer_error)
with self.assertRaisesRegex(ValueError, 'Big error'):
exec.exec_sedml_docs_in_archive(sed_doc_executer,
archive_filename,
out_dir,
apply_xml_model_changes=False,
config=config)
# without log
config.COLLECT_COMBINE_ARCHIVE_RESULTS = False
config.LOG = False
config.DEBUG = False
sed_doc_executer = functools.partial(sedml_exec.exec_sed_doc,
sed_task_executer)
results, log = exec.exec_sedml_docs_in_archive(
sed_doc_executer,
archive_filename,
out_dir,
apply_xml_model_changes=False,
config=config)
self.assertEqual(results, None)
self.assertEqual(log, None)
sed_doc_executer = functools.partial(sedml_exec.exec_sed_doc,
sed_task_executer_error)
with self.assertRaisesRegex(CombineArchiveExecutionError, 'Big error'):
exec.exec_sedml_docs_in_archive(sed_doc_executer,
archive_filename,
out_dir,
apply_xml_model_changes=False,
config=config)
config.DEBUG = True
sed_doc_executer = functools.partial(sedml_exec.exec_sed_doc,
sed_task_executer_error)
with self.assertRaisesRegex(ValueError, 'Big error'):
exec.exec_sedml_docs_in_archive(sed_doc_executer,
archive_filename,
out_dir,
apply_xml_model_changes=False,
config=config)
def _build_combine_archive(self):
task = Task(
id='task',
model=Model(
id='model',
source='bounce1.txt',
language=ModelLanguage.Smoldyn.value,
),
simulation=UniformTimeCourseSimulation(
id='sim',
initial_time=0.,
output_start_time=0.1,
output_end_time=0.2,
number_of_points=10,
algorithm=Algorithm(kisao_id='KISAO_0000057',
changes=[
AlgorithmParameterChange(
kisao_id='KISAO_0000488',
new_value='10'),
])),
)
variables = [
Variable(id='time', symbol=Symbol.time.value, task=task),
Variable(id='red', target='molcount red', task=task),
Variable(id='green', target='molcount green', task=task),
]
doc = SedDocument(
models=[task.model],
simulations=[task.simulation],
tasks=[task],
data_generators=[
DataGenerator(
id='data_gen_time',
variables=[
Variable(id='var_time',
symbol=Symbol.time.value,
task=task)
],
math='var_time',
),
DataGenerator(
id='data_gen_red',
variables=[
Variable(id='var_red',
target='molcount red',
task=task)
],
math='var_red',
),
DataGenerator(
id='data_gen_green',
variables=[
Variable(id='var_green',
target='molcount green',
task=task)
],
math='var_green',
),
],
)
doc.outputs.append(
Report(id='report',
data_sets=[
DataSet(id='data_set_time',
label='time',
data_generator=doc.data_generators[0]),
DataSet(id='data_set_red',
label='red',
data_generator=doc.data_generators[1]),
DataSet(id='data_set_green',
label='green',
data_generator=doc.data_generators[2]),
]))
archive_dirname = os.path.join(self.dirname, 'archive')
os.makedirs(archive_dirname)
shutil.copyfile(
os.path.join(self.EXAMPLES_DIRNAME, 'S1_intro', 'bounce1.txt'),
os.path.join(archive_dirname, 'bounce1.txt'))
sim_filename = os.path.join(archive_dirname, 'sim_1.sedml')
SedmlSimulationWriter().run(doc, sim_filename)
archive = CombineArchive(contents=[
CombineArchiveContent('bounce1.txt',
CombineArchiveContentFormat.Smoldyn.value),
CombineArchiveContent('sim_1.sedml',
CombineArchiveContentFormat.SED_ML.value),
], )
archive_filename = os.path.join(self.dirname, 'archive.omex')
CombineArchiveWriter().run(archive, archive_dirname, archive_filename)
return doc, archive_filename
def export_sed_doc(sed_doc_specs):
""" Export the specifications of SED-ML document to SED-ML
Args:
sed_doc_specs (``SedDocument``)
Returns:
:obj:`SedDocument`
"""
sed_doc = SedDocument(
level=sed_doc_specs['level'],
version=sed_doc_specs['version'],
)
# add styles to SED-ML document
style_id_map = {}
for style_spec in sed_doc_specs['styles']:
style = Style(
id=style_spec.get('id'),
name=style_spec.get('name', None),
)
sed_doc.styles.append(style)
style_id_map[style.id] = style
if style_spec.get('line', None) is not None:
style.line = LineStyle(
type=style_spec['line'].get('type', None),
color=style_spec['line'].get('color', None),
thickness=style_spec['line'].get('thickness', None),
)
if style_spec['line'].get('type', None) is not None:
style.line.type = LineStyleType[style_spec['line']['type']]
if style_spec['line'].get('color', None) is not None:
style.line.color = Color(style_spec['line']['color'])
if style_spec.get('marker', None) is not None:
style.marker = MarkerStyle(
type=style_spec['marker'].get('type', None),
size=style_spec['marker'].get('size', None),
line_color=style_spec['marker'].get('lineColor', None),
line_thickness=style_spec['marker'].get('lineThickness', None),
fill_color=style_spec['marker'].get('fillColor', None),
)
if style_spec['marker'].get('type', None) is not None:
style.marker.type = MarkerStyleType[style_spec['marker']
['type']]
if style_spec['marker'].get('lineColor', None) is not None:
style.marker.line_color = Color(
style_spec['marker']['lineColor'])
if style_spec['marker'].get('fillColor', None) is not None:
style.marker.fill_color = Color(
style_spec['marker']['fillColor'])
if style_spec.get('fill', None) is not None:
style.fill = FillStyle(color=style_spec['fill'].get('color',
None), )
if style_spec['fill'].get('color', None) is not None:
style.fill.color = Color(style_spec['fill']['color'])
for style_spec, style in zip(sed_doc_specs['styles'], sed_doc.styles):
if style_spec.get('base', None) is not None:
style.base = style_id_map.get(style_spec['base'], None)
if style.base is None:
raise BadRequestException(
title='Base style `{}` for style `{}` does not exist'.
format(style_spec['base'], style.id),
instance=ValueError('Style does not exist'),
)
# add models to SED-ML document
model_id_map = {}
for model_spec in sed_doc_specs['models']:
model = Model(
id=model_spec.get('id'),
name=model_spec.get('name', None),
language=model_spec.get('language'),
source=model_spec.get('source'),
)
sed_doc.models.append(model)
model_id_map[model.id] = model
for change_spec in model_spec['changes']:
if change_spec['_type'] == 'SedModelAttributeChange':
change = ModelAttributeChange(
new_value=change_spec.get('newValue'), )
elif change_spec['_type'] == 'SedAddElementModelChange':
change = AddElementModelChange(
new_elements=change_spec.get('newElements'), )
elif change_spec['_type'] == 'SedReplaceElementModelChange':
change = ReplaceElementModelChange(
new_elements=change_spec.get('newElements'), )
elif change_spec['_type'] == 'SedRemoveElementModelChange':
change = RemoveElementModelChange()
elif change_spec['_type'] == 'SedComputeModelChange':
change = ComputeModelChange(
parameters=[],
variables=[],
math=change_spec.get('math'),
)
for parameter_spec in change_spec.get('parameters', []):
change.parameters.append(
Parameter(
id=parameter_spec.get('id'),
name=parameter_spec.get('name', None),
value=parameter_spec.get('value'),
))
for variable_spec in change_spec.get('variables', []):
change.variables.append(
Variable(
id=variable_spec.get('id'),
name=variable_spec.get('name', None),
model=variable_spec.get('model', None),
target=variable_spec.get('target',
{}).get('value', None),
target_namespaces={
namespace['prefix']: namespace['uri']
for namespace in variable_spec.get(
'target', {}).get('namespaces', [])
},
symbol=variable_spec.get('symbol', None),
task=variable_spec.get('task', None),
))
else:
raise BadRequestException(
title='Changes of type `{}` are not supported'.format(
change_spec['_type']),
instance=NotImplementedError('Invalid change'))
change.target = change_spec.get('target').get('value')
for ns in change_spec.get('target').get('namespaces', []):
change.target_namespaces[ns.get('prefix', None)] = ns['uri']
model.changes.append(change)
# add simulations to SED-ML document
simulation_id_map = {}
for sim_spec in sed_doc_specs['simulations']:
if sim_spec['_type'] == 'SedOneStepSimulation':
sim = OneStepSimulation(
id=sim_spec.get('id'),
name=sim_spec.get('name', None),
step=sim_spec.get('step'),
)
elif sim_spec['_type'] == 'SedSteadyStateSimulation':
sim = SteadyStateSimulation(
id=sim_spec.get('id'),
name=sim_spec.get('name', None),
)
elif sim_spec['_type'] == 'SedUniformTimeCourseSimulation':
sim = UniformTimeCourseSimulation(
id=sim_spec.get('id'),
name=sim_spec.get('name', None),
initial_time=sim_spec.get('initialTime'),
output_start_time=sim_spec.get('outputStartTime'),
output_end_time=sim_spec.get('outputEndTime'),
number_of_steps=sim_spec.get('numberOfSteps'),
)
else:
raise BadRequestException(
title='Simulations of type `{}` are not supported'.format(
sim_spec['_type']),
instance=NotImplementedError('Invalid simulation')
) # pragma: no cover: unreachable due to schema validation
alg_spec = sim_spec.get('algorithm')
sim.algorithm = Algorithm(kisao_id=alg_spec.get('kisaoId'))
for change_spec in alg_spec.get('changes'):
sim.algorithm.changes.append(
AlgorithmParameterChange(
kisao_id=change_spec.get('kisaoId'),
new_value=change_spec.get('newValue'),
))
sed_doc.simulations.append(sim)
simulation_id_map[sim.id] = sim
# add tasks to SED-ML document
task_id_map = {}
for task_spec in sed_doc_specs['tasks']:
if task_spec['_type'] == 'SedTask':
model_id = task_spec.get('model')
sim_id = task_spec.get('simulation')
model = model_id_map.get(model_id, None)
sim = simulation_id_map.get(sim_id, None)
if not model:
raise BadRequestException(
title='Model `{}` for task `{}` does not exist'.format(
model_id, task_spec.get('id')),
instance=ValueError('Model does not exist'),
)
if not sim:
raise BadRequestException(
title='Simulation `{}` for task `{}` does not exist'.
format(sim_id, task_spec.get('id')),
instance=ValueError('Simulation does not exist'),
)
task = Task(
id=task_spec.get('id'),
name=task_spec.get('name', None),
model=model,
simulation=sim,
)
else:
# TODO: support repeated tasks
raise BadRequestException(
title='Tasks of type `{}` are not supported'.format(
task_spec['_type']),
instance=NotImplementedError('Invalid task')
) # pragma: no cover: unreachable due to schema validation
sed_doc.tasks.append(task)
task_id_map[task.id] = task
# add data generators to SED-ML document
data_gen_id_map = {}
for data_gen_spec in sed_doc_specs['dataGenerators']:
data_gen = DataGenerator(
id=data_gen_spec.get('id'),
name=data_gen_spec.get('name', None),
math=data_gen_spec.get('math'),
)
for var_spec in data_gen_spec['variables']:
task_id = var_spec.get('task')
task = task_id_map.get(task_id, None)
if not task:
raise BadRequestException(
title='Task `{}` for variable `{}` does not exist'.format(
task_id, var_spec.get('id')),
instance=ValueError('Task does not exist'),
)
var = Variable(
id=var_spec.get('id'),
name=var_spec.get('name', None),
task=task,
symbol=var_spec.get('symbol', None),
)
target_spec = var_spec.get('target', None)
if target_spec:
var.target = target_spec['value']
for ns in target_spec.get('namespaces', []):
var.target_namespaces[ns.get('prefix', None)] = ns['uri']
data_gen.variables.append(var)
sed_doc.data_generators.append(data_gen)
data_gen_id_map[data_gen.id] = data_gen
# add outputs to SED-ML document
for output_spec in sed_doc_specs['outputs']:
if output_spec['_type'] == 'SedReport':
output = Report(
id=output_spec.get('id'),
name=output_spec.get('name', None),
)
for data_set_spec in output_spec['dataSets']:
data_gen_id = data_set_spec['dataGenerator']
data_gen = data_gen_id_map.get(data_gen_id, None)
if not data_gen:
raise BadRequestException(
title=
'Data generator `{}` for output `{}` does not exist'.
format(data_gen_id, output_spec.get('id')),
instance=ValueError('Data generator does not exist'),
)
data_set = DataSet(
id=data_set_spec.get('id'),
name=data_set_spec.get('name', None),
label=data_set_spec.get('label', None),
data_generator=data_gen,
)
output.data_sets.append(data_set)
elif output_spec['_type'] == 'SedPlot2D':
output = Plot2D(
id=output_spec.get('id'),
name=output_spec.get('name', None),
)
for curve_spec in output_spec['curves']:
x_data_gen_id = curve_spec['xDataGenerator']
y_data_gen_id = curve_spec['yDataGenerator']
style_id = curve_spec.get('style', None)
x_data_gen = data_gen_id_map.get(x_data_gen_id, None)
y_data_gen = data_gen_id_map.get(y_data_gen_id, None)
style = style_id_map.get(style_id, None)
if not x_data_gen:
raise BadRequestException(
title=
'X data generator `{}` for curve `{}` does not exist'.
format(x_data_gen_id, output_spec.get('id')),
instance=ValueError('Data generator does not exist'),
)
if not y_data_gen:
raise BadRequestException(
title=
'Y data generator `{}` for curve `{}` does not exist'.
format(y_data_gen_id, output_spec.get('id')),
instance=ValueError('Data generator does not exist'),
)
if style_id is not None and style is None:
raise BadRequestException(
title='Style `{}` for curve `{}` does not exist'.
format(style_id, output_spec.get('id')),
instance=ValueError('Style does not exist'),
)
curve = Curve(
id=curve_spec.get('id'),
name=curve_spec.get('name', None),
x_data_generator=x_data_gen,
y_data_generator=y_data_gen,
x_scale=AxisScale[output_spec['xScale']],
y_scale=AxisScale[output_spec['yScale']],
style=style,
)
output.curves.append(curve)
elif output_spec['_type'] == 'SedPlot3D':
output = Plot3D(
id=output_spec.get('id'),
name=output_spec.get('name', None),
)
for surface_spec in output_spec['surfaces']:
x_data_gen_id = surface_spec['xDataGenerator']
y_data_gen_id = surface_spec['yDataGenerator']
z_data_gen_id = surface_spec['zDataGenerator']
style_id = surface_spec.get('style', None)
x_data_gen = data_gen_id_map.get(x_data_gen_id, None)
y_data_gen = data_gen_id_map.get(y_data_gen_id, None)
z_data_gen = data_gen_id_map.get(z_data_gen_id, None)
style = style_id_map.get(style_id, None)
if not x_data_gen:
raise BadRequestException(
title=
'X data generator `{}` for surface `{}` does not exist'
.format(x_data_gen_id, output_spec.get('id')),
instance=ValueError('Data generator does not exist'),
)
if not y_data_gen:
raise BadRequestException(
title=
'Y data generator `{}` for surface `{}` does not exist'
.format(y_data_gen_id, output_spec.get('id')),
instance=ValueError('Data generator does not exist'),
)
if not z_data_gen:
raise BadRequestException(
title=
'X data generator `{}` for surface `{}` does not exist'
.format(z_data_gen_id, output_spec.get('id')),
instance=ValueError('Data generator does not exist'),
)
if style_id is not None and style is None:
raise BadRequestException(
title='Style `{}` for surface `{}` does not exist'.
format(style_id, output_spec.get('id')),
instance=ValueError('Style does not exist'),
)
surface = Surface(
id=surface_spec.get('id'),
name=surface_spec.get('name', None),
x_data_generator=x_data_gen,
y_data_generator=y_data_gen,
z_data_generator=z_data_gen,
x_scale=AxisScale[output_spec['xScale']],
y_scale=AxisScale[output_spec['yScale']],
z_scale=AxisScale[output_spec['zScale']],
style=style,
)
output.surfaces.append(surface)
else:
raise BadRequestException(
title='Outputs of type `{}` are not supported'.format(
output_spec['_type']),
instance=NotImplementedError('Invalid output')
) # pragma: no cover: unreachable due to schema validation
sed_doc.outputs.append(output)
# deserialize references
model_map = {}
for model in sed_doc.models:
model_map[model.id] = model
task_map = {}
for task in sed_doc.tasks:
task_map[task.id] = task
for model in sed_doc.models:
for change in model.changes:
if isinstance(change, ComputeModelChange):
for variable in change.variables:
if variable.model:
variable.model = model_map[variable.model]
if variable.task:
variable.task = task_map[variable.task]
return sed_doc
