- add support for MCA

basico/__init__.py

@@ -20,6 +20,7 @@
 from .task_scan import *
 from .task_optimization import *
 from .task_sensitivities import *
+from .task_mca import *
 
 from .compartment_array_tools import *
 

basico/task_mca.py

@@ -0,0 +1,175 @@
+"""Utility methods for working with the mca task
+
+"""
+import logging
+
+import basico
+import COPASI
+
+from . import model_io
+
+logger = logging.getLogger(__name__)
+
+def run_mca(**kwargs):
+    """Runs the mca task, the result is obtained
+
+    by calling:
+
+    * `get_elasticities`
+    * `get_flux_control_coefficients` or
+    * `get_concentration_control_coefficients`
+
+    :param kwargs: optional arguments
+
+         - | `model`: to specify the data model to be used (if not specified
+           | the one from :func:`.get_current_model` will be taken)
+
+         - | `settings`: a dictionary with the settings to apply first
+
+         - | `use_initial_values`: boolean flag indicating whether initial values should be used (true by default)
+
+    :return: None
+    """
+    model = model_io.get_model_from_dict_or_default(kwargs)
+    assert (isinstance(model, COPASI.CDataModel))
+
+    model.getModel().compileIfNecessary()
+
+    if 'settings' in kwargs:
+        basico.set_task_settings(basico.T.MCA, kwargs['settings'], model=model)
+
+    task = model.getTask(basico.T.MCA)
+    assert (isinstance(task, COPASI.CMCATask))
+
+    if not task.initializeRaw(COPASI.CCopasiTask.OUTPUT_UI):
+        logger.error('Could not initialize Sensitivity Task: {0}'.format(COPASI.CCopasiMessage.getAllMessageText()))
+        return
+
+    use_initial_values = kwargs.get('use_initial_values', True)
+
+    if not task.processRaw(use_initial_values):
+        logger.error('Could not run Sensitivity Task: {0}'.format(COPASI.CCopasiMessage.getAllMessageText()))
+        return
+
+    task.restore()
+
+
+
+def get_mca_matrix(matrix, scaled=True, run_first=False, **kwargs):
+    """Returns the specified mca matrix as pandas data frame
+
+    :param matrix: the matrix to be returned, as string, one of `elasticities`, 
+                   `flux_control_coefficients` or `concentration_control_coefficients`
+    :param scaled: boolean flag indicating whether the scaled matrix should be returned 
+                   defaults to True
+
+    :param run_first: boolean flag indicating that the task should be run first (defaults to false)
+
+    :param kwargs: optional arguments
+
+         - | `model`: to specify the data model to be used (if not specified
+           | the one from :func:`.get_current_model` will be taken)
+
+         - | `settings`: a dictionary with the settings to apply first
+
+         - | `use_initial_values`: boolean flag indicating whether initial values should be used (true by default)
+
+    :return: the selected mca matrix
+    :rtype: pd.DataFrame
+    """
+
+    model = model_io.get_model_from_dict_or_default(kwargs)
+    assert (isinstance(model, COPASI.CDataModel))
+
+    if run_first: 
+        run_mca(**kwargs)
+
+    task = model.getTask(basico.T.MCA)
+    assert (isinstance(task, COPASI.CMCATask))
+    method = task.getMethod()
+    assert (isinstance(method, COPASI.CMCAMethod))
+
+    if matrix == 'elasticities':
+        return basico.model_info._annotated_matrix_to_df(
+            method.getScaledElasticitiesAnn() if scaled else method.getUnscaledElasticitiesAnn()
+        )
+
+    if matrix == 'concentration_control_coefficients':
+        return basico.model_info._annotated_matrix_to_df(
+            method.getScaledConcentrationCCAnn() if scaled else method.getUnscaledConcentrationCCAnn()
+        )
+
+    if matrix == 'flux_control_coefficients':
+        return basico.model_info._annotated_matrix_to_df(
+            method.getScaledFluxCCAnn() if scaled else method.getUnscaledFluxCCAnn()
+        )
+
+    return None
+
+
+
+def get_elasticities(scaled=True, run_first=False, **kwargs):
+    """Returns the elasticity matrix as pandas data frame
+
+    :param scaled: boolean flag indicating whether the scaled matrix should be returned 
+                   defaults to True
+
+    :param run_first: boolean flag indicating that the task should be run first (defaults to false)
+
+    :param kwargs: optional arguments
+
+         - | `model`: to specify the data model to be used (if not specified
+           | the one from :func:`.get_current_model` will be taken)
+
+         - | `settings`: a dictionary with the settings to apply first
+
+         - | `use_initial_values`: boolean flag indicating whether initial values should be used (true by default)
+
+    :return: the elasticity matrix
+    :rtype: pd.DataFrame
+    """
+    return get_mca_matrix('elasticities', scaled, run_first, **kwargs)
+
+def get_flux_control_coefficients(scaled=True, run_first=False, **kwargs):
+    """Returns the flux control coefficient matrix as pandas data frame
+
+    :param scaled: boolean flag indicating whether the scaled matrix should be returned 
+                   defaults to True
+
+    :param run_first: boolean flag indicating that the task should be run first (defaults to false)
+
+    :param kwargs: optional arguments
+
+         - | `model`: to specify the data model to be used (if not specified
+           | the one from :func:`.get_current_model` will be taken)
+
+         - | `settings`: a dictionary with the settings to apply first
+
+         - | `use_initial_values`: boolean flag indicating whether initial values should be used (true by default)
+
+    :return: the flux control coefficient matrix
+    :rtype: pd.DataFrame
+    """
+    return get_mca_matrix('flux_control_coefficients', scaled, run_first, **kwargs)
+
+def get_concentration_control_coefficients(scaled=True, run_first=False, **kwargs):
+    """Returns the concentration control coefficient matrix as pandas data frame
+
+    :param scaled: boolean flag indicating whether the scaled matrix should be returned 
+                   defaults to True
+
+    :param run_first: boolean flag indicating that the task should be run first (defaults to false)
+
+    :param kwargs: optional arguments
+
+         - | `model`: to specify the data model to be used (if not specified
+           | the one from :func:`.get_current_model` will be taken)
+
+         - | `settings`: a dictionary with the settings to apply first
+
+         - | `use_initial_values`: boolean flag indicating whether initial values should be used (true by default)
+
+    :return: the concentration control coefficient matrix
+    :rtype: pd.DataFrame
+    """
+    return get_mca_matrix('concentration_control_coefficients', scaled, run_first, **kwargs)

docs/index.rst

@@ -41,6 +41,7 @@ complexity of the underlying SWIG generated language bindings.
    notebooks/Working_with_PEtab
    notebooks/Working_with_ParameterSets
    notebooks/Profile_likelihood
+   notebooks/MCA
 
 .. toctree::
    :maxdepth: 2