def run_simulation(model,
time=10000,
points=200,
cleanup=True,
output_prefix=None,
output_dir=None,
flux_map=False,
perturbation=None,
seed=None,
verbose=False):
"""Runs the given model using KaSim and returns the parsed results.
.. deprecated:: 1.10
Use :func:`pysb.simulator.KappaSimulator` instead
Parameters
----------
model : pysb.core.Model
The model to simulate/analyze using KaSim.
time : number
The amount of time (in arbitrary units) to run a simulation.
Identical to the -u time -l argument when using KaSim at the command
line.
Default value is 10000. If set to 0, no simulation will be run.
points : integer
The number of data points to collect for plotting.
Note that this is not identical to the -p argument of KaSim when
called from the command line, which denotes plot period (time interval
between points in plot).
Default value is 200. Note that the number of points actually returned
by the simulator will be points + 1 (including the 0 point).
cleanup : boolean
Specifies whether output files produced by KaSim should be deleted
after execution is completed. Default value is True.
output_prefix: str
Prefix of the temporary directory name. Default is
'tmpKappa_<model name>_'.
output_dir : string
The directory in which to create the temporary directory for
the .ka and other output files. Defaults to the system temporary file
directory (e.g. /tmp). If the specified directory does not exist,
an Exception is thrown.
flux_map: boolean
Specifies whether or not to produce the flux map (generated over the
full duration of the simulation). Default value is False.
perturbation : string or None
Optional perturbation language syntax to be appended to the Kappa file.
See KaSim manual for more details. Default value is None (no
perturbation).
seed : integer
A seed integer for KaSim random number generator. Set to None to
allow KaSim to use a random seed (default) or supply a seed for
deterministic behaviour (e.g. for testing)
verbose : boolean
Whether to pass the output of KaSim through to stdout/stderr.
Returns
-------
If flux_map is False, returns the kasim simulation data as a Numpy ndarray.
Data is accessed using the syntax::
results[index_name]
The index 'time' gives the time coordinates of the simulation. Data for the
observables can be accessed by indexing the array with the names of the
observables. Each entry in the ndarray has length points + 1, due to the
inclusion of both the zero point and the final timepoint.
If flux_map is True, returns an instance of SimulationResult, a namedtuple
with two members, `timecourse` and `flux_map`. The `timecourse` field
contains the simulation ndarray, and the `flux_map` field is an instance of
a networkx MultiGraph containing the flux map. For details on viewing
the flux map graphically see :func:`run_static_analysis` (notes section).
"""
warnings.warn(
'run_simulation will be removed in a future version of PySB. '
'Use pysb.simulator.KappaSimulator instead.', DeprecationWarning)
gen = KappaGenerator(model)
if output_prefix is None:
output_prefix = 'tmpKappa_%s_' % model.name
base_directory = tempfile.mkdtemp(prefix=output_prefix, dir=output_dir)
base_filename = os.path.join(base_directory, model.name)
kappa_filename = base_filename + '.ka'
fm_filename = base_filename + '_fm.dot'
out_filename = base_filename + '.out'
if points == 0:
raise ValueError('The number of data points cannot be zero.')
plot_period = (float(time) / points) if time > 0 else 1.0
args = [
'-i', kappa_filename, '-u', 'time', '-l',
str(time), '-p',
'%.5f' % plot_period, '-o', out_filename
]
if seed:
args.extend(['-seed', str(seed)])
# Generate the Kappa model code from the PySB model and write it to
# the Kappa file:
with open(kappa_filename, 'w') as kappa_file:
file_data = gen.get_content()
# If desired, add instructions to the kappa file to generate the
# flux map:
if flux_map:
file_data += '%%mod: [true] do $DIN "%s" [true];\n' % fm_filename
# If any perturbation language code has been passed in, add it to
# the Kappa file:
if perturbation:
file_data += '\n%s\n' % perturbation
logger.debug('Kappa file contents:\n\n' + file_data)
kappa_file.write(file_data)
# Run KaSim
kasim_path = pf.get_path('kasim')
p = subprocess.Popen([kasim_path] + args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=base_directory)
if verbose:
for line in iter(p.stdout.readline, b''):
print('@@', line, end='')
(p_out, p_err) = p.communicate()
if p.returncode:
raise KasimInterfaceError(
p_out.decode('utf8') + '\n' + p_err.decode('utf8'))
# The simulation data, as a numpy array
data = _parse_kasim_outfile(out_filename)
if flux_map:
try:
flux_graph = read_dot(fm_filename)
except ImportError:
if cleanup:
raise
else:
warnings.warn("The pydot library could not be "
"imported, so no MultiGraph "
"object returned (returning None); flux map "
"dot file available at %s" % fm_filename)
flux_graph = None
if cleanup:
shutil.rmtree(base_directory)
# If a flux map was generated, return both the simulation output and the
# flux map as a networkx multigraph
if flux_map:
return SimulationResult(data, flux_graph)
# If no flux map was requested, return only the simulation data
else:
return data
def run_simulation(model, time=10000, points=200, cleanup=True,
output_prefix=None, output_dir=None, flux_map=False,
perturbation=None, seed=None, verbose=False):
"""Runs the given model using KaSim and returns the parsed results.
Parameters
----------
model : pysb.core.Model
The model to simulate/analyze using KaSim.
time : number
The amount of time (in arbitrary units) to run a simulation.
Identical to the -u time -l argument when using KaSim at the command
line.
Default value is 10000. If set to 0, no simulation will be run.
points : integer
The number of data points to collect for plotting.
Note that this is not identical to the -p argument of KaSim when
called from the command line, which denotes plot period (time interval
between points in plot).
Default value is 200. Note that the number of points actually returned
by the simulator will be points + 1 (including the 0 point).
cleanup : boolean
Specifies whether output files produced by KaSim should be deleted
after execution is completed. Default value is True.
output_prefix: str
Prefix of the temporary directory name. Default is
'tmpKappa_<model name>_'.
output_dir : string
The directory in which to create the temporary directory for
the .ka and other output files. Defaults to the system temporary file
directory (e.g. /tmp). If the specified directory does not exist,
an Exception is thrown.
flux_map: boolean
Specifies whether or not to produce the flux map (generated over the
full duration of the simulation). Default value is False.
perturbation : string or None
Optional perturbation language syntax to be appended to the Kappa file.
See KaSim manual for more details. Default value is None (no
perturbation).
seed : integer
A seed integer for KaSim random number generator. Set to None to
allow KaSim to use a random seed (default) or supply a seed for
deterministic behaviour (e.g. for testing)
verbose : boolean
Whether to pass the output of KaSim through to stdout/stderr.
Returns
-------
If flux_map is False, returns the kasim simulation data as a Numpy ndarray.
Data is accessed using the syntax::
results[index_name]
The index 'time' gives the time coordinates of the simulation. Data for the
observables can be accessed by indexing the array with the names of the
observables. Each entry in the ndarray has length points + 1, due to the
inclusion of both the zero point and the final timepoint.
If flux_map is True, returns an instance of SimulationResult, a namedtuple
with two members, `timecourse` and `flux_map`. The `timecourse` field
contains the simulation ndarray, and the `flux_map` field is an instance of
a networkx MultiGraph containing the flux map. For details on viewing
the flux map graphically see :func:`run_static_analysis` (notes section).
"""
gen = KappaGenerator(model)
if output_prefix is None:
output_prefix = 'tmpKappa_%s_' % model.name
base_directory = tempfile.mkdtemp(prefix=output_prefix, dir=output_dir)
base_filename = os.path.join(base_directory, model.name)
kappa_filename = base_filename + '.ka'
fm_filename = base_filename + '_fm.dot'
out_filename = base_filename + '.out'
if points == 0:
raise ValueError('The number of data points cannot be zero.')
plot_period = (float(time) / points) if time > 0 else 1.0
args = ['-i', kappa_filename, '-u', 'time', '-l', str(time),
'-p', '%.5f' % plot_period, '-o', out_filename]
if seed:
args.extend(['-seed', str(seed)])
# Generate the Kappa model code from the PySB model and write it to
# the Kappa file:
with open(kappa_filename, 'w') as kappa_file:
file_data = gen.get_content()
# If desired, add instructions to the kappa file to generate the
# flux map:
if flux_map:
file_data += '%%mod: [true] do $DIN "%s" [true];\n' % fm_filename
# If any perturbation language code has been passed in, add it to
# the Kappa file:
if perturbation:
file_data += '\n%s\n' % perturbation
logger.debug('Kappa file contents:\n\n' + file_data)
kappa_file.write(file_data)
# Run KaSim
kasim_path = pf.get_path('kasim')
p = subprocess.Popen([kasim_path] + args,
stdout=subprocess.PIPE, stderr=subprocess.PIPE,
cwd=base_directory)
if verbose:
for line in iter(p.stdout.readline, b''):
print('@@', line, end='')
(p_out, p_err) = p.communicate()
if p.returncode:
raise KasimInterfaceError(
p_out.decode('utf8') + '\n' + p_err.decode('utf8'))
# The simulation data, as a numpy array
data = _parse_kasim_outfile(out_filename)
if flux_map:
try:
flux_graph = read_dot(fm_filename)
except ImportError:
if cleanup:
raise
else:
warnings.warn(
"The pydot library could not be "
"imported, so no MultiGraph "
"object returned (returning None); flux map "
"dot file available at %s" % fm_filename)
flux_graph = None
if cleanup:
shutil.rmtree(base_directory)
# If a flux map was generated, return both the simulation output and the
# flux map as a networkx multigraph
if flux_map:
return SimulationResult(data, flux_graph)
# If no flux map was requested, return only the simulation data
else:
return data
def run_static_analysis(model,
influence_map=False,
contact_map=False,
cleanup=True,
output_prefix=None,
output_dir=None,
verbose=False):
"""Run static analysis (KaSa) on to get the contact and influence maps.
If neither influence_map nor contact_map are set to True, then a ValueError
is raised.
Parameters
----------
model : pysb.core.Model
The model to simulate/analyze using KaSa.
influence_map : boolean
Whether to compute the influence map.
contact_map : boolean
Whether to compute the contact map.
cleanup : boolean
Specifies whether output files produced by KaSa should be deleted
after execution is completed. Default value is True.
output_prefix: str
Prefix of the temporary directory name. Default is
'tmpKappa_<model name>_'.
output_dir : string
The directory in which to create the temporary directory for
the .ka and other output files. Defaults to the system temporary file
directory (e.g. /tmp). If the specified directory does not exist,
an Exception is thrown.
verbose : boolean
Whether to pass the output of KaSa through to stdout/stderr.
Returns
-------
StaticAnalysisResult, a namedtuple with two fields, `contact_map` and
`influence_map`, each containing the respective result as an instance
of a networkx MultiGraph. If the either the contact_map or influence_map
argument to the function is False, the corresponding entry in the
StaticAnalysisResult returned by the function will be None.
Notes
-----
To view a networkx file graphically, use `draw_network`::
import networkx as nx
nx.draw_networkx(g, with_labels=True)
You can use `graphviz_layout` to use graphviz for layout (requires pydot
library)::
import networkx as nx
pos = nx.drawing.nx_pydot.graphviz_layout(g, prog='dot')
nx.draw_networkx(g, pos, with_labels=True)
For further information, see the networkx documentation on visualization:
https://networkx.github.io/documentation/latest/reference/drawing.html
"""
# Make sure the user has asked for an output!
if not influence_map and not contact_map:
raise ValueError('Either contact_map or influence_map (or both) must '
'be set to True in order to perform static analysis.')
gen = KappaGenerator(model, _warn_no_ic=False)
if output_prefix is None:
output_prefix = 'tmpKappa_%s_' % model.name
base_directory = tempfile.mkdtemp(prefix=output_prefix, dir=output_dir)
base_filename = os.path.join(base_directory, str(model.name))
kappa_filename = base_filename + '.ka'
im_filename = base_filename + '_im.dot'
cm_filename = base_filename + '_cm.dot'
# NOTE: in the args passed to KaSa, the directory for the .dot files is
# specified by the --output_directory option, and the output_contact_map
# and output_influence_map should only be the base filenames (without
# a directory prefix).
# Contact map args:
if contact_map:
cm_args = [
'--compute-contact-map', '--output-contact-map',
os.path.basename(cm_filename), '--output-contact-map-directory',
base_directory
]
else:
cm_args = ['--no-compute-contact-map']
# Influence map args:
if influence_map:
im_args = [
'--compute-influence-map', '--output-influence-map',
os.path.basename(im_filename), '--output-influence-map-directory',
base_directory
]
else:
im_args = ['--no-compute-influence-map']
# Full arg list
args = [kappa_filename] + cm_args + im_args
# Generate the Kappa model code from the PySB model and write it to
# the Kappa file:
with open(kappa_filename, 'w') as kappa_file:
file_data = gen.get_content()
logger.debug('Kappa file contents:\n\n' + file_data)
kappa_file.write(file_data)
# Run KaSa using the given args
kasa_path = pf.get_path('kasa')
p = subprocess.Popen([kasa_path] + args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=base_directory)
if verbose:
for line in iter(p.stdout.readline, b''):
print('@@', line, end='')
(p_out, p_err) = p.communicate()
if p.returncode:
raise KasaInterfaceError(
p_out.decode('utf8') + '\n' + p_err.decode('utf8'))
# Try to create the graphviz objects from the .dot files created
try:
# Convert the contact map to a Graph
cmap = read_dot(cm_filename) if contact_map else None
imap = read_dot(im_filename) if influence_map else None
except ImportError:
if cleanup:
raise
else:
warnings.warn("The pydot library could not be "
"imported, so no MultiGraph "
"object returned (returning None); "
"contact/influence maps available at %s" %
base_directory)
cmap = None
imap = None
# Clean up the temp directory if desired
if cleanup:
shutil.rmtree(base_directory)
return StaticAnalysisResult(cmap, imap)
def run_static_analysis(model, influence_map=False, contact_map=False,
cleanup=True, output_prefix=None, output_dir=None,
verbose=False):
"""Run static analysis (KaSa) on to get the contact and influence maps.
If neither influence_map nor contact_map are set to True, then a ValueError
is raised.
Parameters
----------
model : pysb.core.Model
The model to simulate/analyze using KaSa.
influence_map : boolean
Whether to compute the influence map.
contact_map : boolean
Whether to compute the contact map.
cleanup : boolean
Specifies whether output files produced by KaSa should be deleted
after execution is completed. Default value is True.
output_prefix: str
Prefix of the temporary directory name. Default is
'tmpKappa_<model name>_'.
output_dir : string
The directory in which to create the temporary directory for
the .ka and other output files. Defaults to the system temporary file
directory (e.g. /tmp). If the specified directory does not exist,
an Exception is thrown.
verbose : boolean
Whether to pass the output of KaSa through to stdout/stderr.
Returns
-------
StaticAnalysisResult, a namedtuple with two fields, `contact_map` and
`influence_map`, each containing the respective result as an instance
of a networkx MultiGraph. If the either the contact_map or influence_map
argument to the function is False, the corresponding entry in the
StaticAnalysisResult returned by the function will be None.
Notes
-----
To view a networkx file graphically, use `draw_network`::
import networkx as nx
nx.draw_networkx(g, with_labels=True)
You can use `graphviz_layout` to use graphviz for layout (requires pydot
library)::
import networkx as nx
pos = nx.drawing.nx_pydot.graphviz_layout(g, prog='dot')
nx.draw_networkx(g, pos, with_labels=True)
For further information, see the networkx documentation on visualization:
https://networkx.github.io/documentation/latest/reference/drawing.html
"""
# Make sure the user has asked for an output!
if not influence_map and not contact_map:
raise ValueError('Either contact_map or influence_map (or both) must '
'be set to True in order to perform static analysis.')
gen = KappaGenerator(model, _warn_no_ic=False)
if output_prefix is None:
output_prefix = 'tmpKappa_%s_' % model.name
base_directory = tempfile.mkdtemp(prefix=output_prefix, dir=output_dir)
base_filename = os.path.join(base_directory, str(model.name))
kappa_filename = base_filename + '.ka'
im_filename = base_filename + '_im.dot'
cm_filename = base_filename + '_cm.dot'
# NOTE: in the args passed to KaSa, the directory for the .dot files is
# specified by the --output_directory option, and the output_contact_map
# and output_influence_map should only be the base filenames (without
# a directory prefix).
# Contact map args:
if contact_map:
cm_args = ['--compute-contact-map', '--output-contact-map',
os.path.basename(cm_filename),
'--output-contact-map-directory', base_directory]
else:
cm_args = ['--no-compute-contact-map']
# Influence map args:
if influence_map:
im_args = ['--compute-influence-map', '--output-influence-map',
os.path.basename(im_filename),
'--output-influence-map-directory', base_directory]
else:
im_args = ['--no-compute-influence-map']
# Full arg list
args = [kappa_filename] + cm_args + im_args
# Generate the Kappa model code from the PySB model and write it to
# the Kappa file:
with open(kappa_filename, 'w') as kappa_file:
file_data = gen.get_content()
logger.debug('Kappa file contents:\n\n' + file_data)
kappa_file.write(file_data)
# Run KaSa using the given args
kasa_path = pf.get_path('kasa')
p = subprocess.Popen([kasa_path] + args,
stdout=subprocess.PIPE, stderr=subprocess.PIPE,
cwd=base_directory)
if verbose:
for line in iter(p.stdout.readline, b''):
print('@@', line, end='')
(p_out, p_err) = p.communicate()
if p.returncode:
raise KasaInterfaceError(
p_out.decode('utf8') + '\n' + p_err.decode('utf8'))
# Try to create the graphviz objects from the .dot files created
try:
# Convert the contact map to a Graph
cmap = read_dot(cm_filename) if contact_map else None
imap = read_dot(im_filename) if influence_map else None
except ImportError:
if cleanup:
raise
else:
warnings.warn(
"The pydot library could not be "
"imported, so no MultiGraph "
"object returned (returning None); "
"contact/influence maps available at %s" %
base_directory)
cmap = None
imap = None
# Clean up the temp directory if desired
if cleanup:
shutil.rmtree(base_directory)
return StaticAnalysisResult(cmap, imap)