8.1.1.1.1.2. skimpy.analysis.mca

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Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB), Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

8.1.1.1.1.2.1. Submodules

8.1.1.1.1.2.2. Attributes

SPLIT

Item types

NET

SPLIT

Item types

NET

8.1.1.1.1.2.3. Classes

JacobianFunction

Tensor

ConcentrationControlFunction

Tensor

FluxControlFunction

8.1.1.1.1.2.4. Functions

get_reversible_fluxes(net_fluxes, displacements, reactions)

8.1.1.1.1.2.5. Package Contents

class skimpy.JacobianFunction(reduced_stoichometry, independent_elasticity_function, dependent_elasticity_function, volume_ratio_function, conservation_relation, independent_variable_ix, dependent_variable_ix)
reduced_stoichometry
dependent_elasticity_function
independent_elasticity_function
volume_ratio_function
independent_variable_ix
dependent_variable_ix
conservation_relation
__call__(fluxes, concentrations, parameters, flux_jacobian=False)
Parameters:

  • fluxes – Dict or pd.Series of reference flux vector

  • concentrations – Dict or pd.Series of reference concentration vector

  • parameters – Dict or pd.Series of reference parameters vector

class skimpy.Tensor(data, indexes, *args, **kwargs)

Bases: object

_data
complementary_indexes
_i
_j
_k
build_index_dict(index_names)
slice_by(slicer, value)

Returns a slice of the tensor along a value on a specific index

Parameters:

  • slicer (str or pandas.Index)

  • value

Returns:

get_slice_index(slicer)

Utility function for getting the integer number of the index (0,1, or 2)

Parameters:

slicer

Returns:

mean(slicer, *args, **kwargs)

Flatten using the mean along an index

Parameters:

  • slicer

  • args

  • kwargs

Returns:

std(slicer, *args, **kwargs)

Flatten using the standard deviation along an index :param slicer: :param args: :param kwargs: :return:

quantile(slicer, quantile, *args, **kwargs)

Flatten using the standard deviation along an index :param slicer: :param args: :param kwargs: :return:

make_df(data, index1, index2)
skimpy.SPLIT = 'split'

Item types

skimpy.NET = 'net'
skimpy.get_reversible_fluxes(net_fluxes, displacements, reactions)
Parameters:

  • net_fluxes – dict of net-fluxes

  • displacements – dict of displacements for reversible reactions

  • reactions – list of reaction names

Returns:

class skimpy.ConcentrationControlFunction(model, reduced_stoichometry, independent_elasticity_function, dependent_elasticity_function, parameter_elasticity_function, volume_ratio_function, conservation_relation, independent_variable_ix, dependent_variable_ix, mca_type=NET, displacement_function=None)
model
reduced_stoichometry
dependent_elasticity_function
independent_elasticity_function
parameter_elasticity_function
independent_variable_ix
dependent_variable_ix
conservation_relation
volume_ratio_function
displacement_function = None
mca_type = 'net'
__call__(flux_dict, concentration_dict, parameter_population)
class skimpy.Tensor(data, indexes, *args, **kwargs)

Bases: object

_data
complementary_indexes
_i
_j
_k
build_index_dict(index_names)
slice_by(slicer, value)

Returns a slice of the tensor along a value on a specific index

Parameters:

  • slicer (str or pandas.Index)

  • value

Returns:

get_slice_index(slicer)

Utility function for getting the integer number of the index (0,1, or 2)

Parameters:

slicer

Returns:

mean(slicer, *args, **kwargs)

Flatten using the mean along an index

Parameters:

  • slicer

  • args

  • kwargs

Returns:

std(slicer, *args, **kwargs)

Flatten using the standard deviation along an index :param slicer: :param args: :param kwargs: :return:

quantile(slicer, quantile, *args, **kwargs)

Flatten using the standard deviation along an index :param slicer: :param args: :param kwargs: :return:

make_df(data, index1, index2)
skimpy.SPLIT = 'split'

Item types

skimpy.NET = 'net'
class skimpy.FluxControlFunction(model, reduced_stoichometry, independent_elasticity_function, dependent_elasticity_function, parameter_elasticity_function, conservation_relation, independent_variable_ix, dependent_variable_ix, concentration_control_fun, mca_type=NET)
model
reduced_stoichometry
dependent_elasticity_function
independent_elasticity_function
parameter_elasticity_function
independent_variable_ix
dependent_variable_ix
conservation_relation
concentration_control_fun
mca_type = 'net'
__call__(flux_dict, concentration_dict, parameter_population)