Source code for pysb.kappa

"""
Wrapper functions for running the Kappa programs *KaSim* and *KaSa*.

The path to the directory containing the KaSim and KaSa executables can be
specified in one of three ways:

- set the KAPPAPATH environment variable to the KaSim directory
- move Kappa to /usr/local/share/KaSim (macOS, Linux) or
  c:\Program Files\KaSim (Windows)
- set the path using the :py:func:`pysb.pathfinder.set_path` function at
  runtime
"""

from __future__ import print_function as _
import pysb.pathfinder as pf
from pysb.generator.kappa import KappaGenerator
import os
import subprocess
import re
import numpy as np
import tempfile
import shutil
import warnings
from collections import namedtuple
from pysb.util import read_dot
import pysb.logging

try:
    from future_builtins import zip
except ImportError:
    pass

logger = pysb.logging.get_logger(__name__)


[docs]def set_kappa_path(path): """Set the path to the KaSim and KaSa executables. Deprecated. Use pysb.pathfinder.set_path() instead. Parameters ---------- path: string Directory containing KaSim and KaSa executables. """ warnings.warn("Function %s() is deprecated; use " "pysb.pathfinder.set_path() instead" % set_kappa_path.__name__, category=DeprecationWarning, stacklevel=2) pf.set_path('kasim', path) pf.set_path('kasa', path)
[docs]class KasimInterfaceError(RuntimeError): pass
[docs]class KasaInterfaceError(RuntimeError): pass
StaticAnalysisResult = namedtuple('StaticAnalysisResult', ['contact_map', 'influence_map']) SimulationResult = namedtuple('SimulationResult', ['timecourse', 'flux_map'])
[docs]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
[docs]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)
[docs]def contact_map(model, **kwargs): """Generates the contact map via KaSa. Parameters ---------- model : pysb.core.Model The model for generating the influence map. **kwargs : other keyword arguments Any other keyword arguments are passed to the function :py:func:`run_static_analysis`. Returns ------- networkx MultiGraph object containing the contact map. For details on viewing the contact map graphically see :func:`run_static_analysis` (notes section). """ kasa_result = run_static_analysis(model, influence_map=False, contact_map=True, **kwargs) return kasa_result.contact_map
[docs]def influence_map(model, **kwargs): """Generates the influence map via KaSa. Parameters ---------- model : pysb.core.Model The model for generating the influence map. **kwargs : other keyword arguments Any other keyword arguments are passed to the function :py:func:`run_static_analysis`. Returns ------- networkx MultiGraph object containing the influence map. For details on viewing the influence map graphically see :func:`run_static_analysis` (notes section). """ kasa_result = run_static_analysis(model, influence_map=True, contact_map=False, **kwargs) return kasa_result.influence_map
### "PRIVATE" Functions ############################################### def _parse_kasim_outfile(out_filename): """ Parses the KaSim .out file into a Numpy ndarray. Parameters ---------- out_filename : string String specifying the location of the .out filename produced by KaSim. Returns ------- numpy.ndarray Returns the KaSim simulation data as a Numpy ndarray. Data is accessed using the syntax:: results[index_name] The index 'time' gives the data for the time coordinates of the simulation. Data for the observables can be accessed by indexing the array with the names of the observables. """ try: with open(out_filename, 'r') as fh: for header_line in fh: if header_line[0] != '#': break # Load the output file as a numpy record array, skip the name row arr = np.loadtxt(fh, dtype=float, delimiter=',') raw_names = [term.strip() for term in re.split(',', header_line)] column_names = [] # Get rid of the quotes surrounding the observable names for raw_name in raw_names: mo = re.match('"(.*)"', raw_name) if mo: name = mo.group(1) # Rename the time column to remain backwards compatible if name == '[T]': name = 'time' column_names.append(name) else: column_names.append(raw_name) # Create the dtype argument for the numpy record array dt = list(zip(column_names, ('float', ) * len(column_names))) recarr = arr.view(dt) except Exception as e: raise Exception("problem parsing KaSim outfile: " + str(e)) return recarr