Releases: opencobra/cobrapy
0.6.1
0.6.0
Highlights
In this release we have made major changes to pretty much all corners
of cobrapy and we hope that you will enjoy the new features as much as
we do, and that any negative impacts on existing workflows will be
limited.
The major change is the ongoing move away from cobrapy's internal
solver interfaces to those provided by
optlang which provides a
single unified interface to glpk, cplex and gurobi enhanced by the
ability to deal with symbolic expressions. This means formulating
complex constraints no longer implies defining the necessary matrix
algebra, but instead simply writing the expression and assigning that
as an objective to the model.
We feel that this, and the clarified scope and focus attained by
separating the topic of linear programming (optlang) and metabolic
flux analysis (cobrapy) to two packages is natural and makes both of
these tasks less confusing and more fun. We hope that you after
trying, feel the same and that in any case you let us know what you
think by
raising an issue or
talking directly to us on gitter or
google groups.
New features
The optlang solver interface
The main change is the addition of model.solver
which is the optlang
interface to the chosen solver (cplex and glpk are currently well
supported, gurobi interface is at the time of writing mostly working
but improvements are still expected). The solver interface manages
variables, constraints and the objective of the model and the task of
turning these into a model formulation to be optimized by the
solver. From cobrapy's point-of-view, this means that all aspects
concerning generating problems, configuring solvers are handed over to
optlang and consequently the whole cobra.solver
has been deprecated,
slated for removal in the next major release of cobrapy.
Importantly, configuring the solver by passing **solver_args
or
solver='solver'
is now instead done by assigning solver to
model.solver
and then configuring via model.solver.configuration
.
Creating new algorithms has been greatly facilitated as it no longer
requires formulating objectives and constraints by matrix algebra but
instead directly by expressions, e.g. see the implementation of
cobra.flux_analysis.moma.add_moma
and
cobra.flux_analysis.loopless.loopless_solution
.
Instead of having only reactions as variables and metabolites as
constraints, with optlang, cobrapy now supports arbitrary constraints
and variables and these can be added/removed by model.add_cons_vars
and model.remove_cons_vars
which take care of adding these to
model.problem
which is the optlang's mathematical model associated
with the cobra model.
Reactions are now modeled by two variables, forward and reverse, and
these can be seen by accessing reaction.{forward,reverse}_variable
and the combined reaction.flux_expression
.
Objectives can now easily be made quite advanced by simply crafting
the right expression and assigning this as usual to model.objective
,
see the
contraints and objectives notebook.
Temporary changes to a model
Models are large complex objects and copying such objects is
inevitably slow. To avoid that, cobrapy has drawn on the experience
from the development of cameo to introduce the HistoryManager
class
and the concept of models as contexts. Now, most changes that can be
made to a model such as changing the objective, setting reaction
bounds, adding and removing reactions, is reversed upon exit when done
inside a context, see the updated
getting started notebook.
Improved solution handling
Previously, cobra models lugged around their latest solution to enable
providing reaction.{flux,reduced_cost}
(formerly
reaction.{x,y}
). This was problematic because if the model had
changed since last optimization, then this would effectively give the
wrong result. On top of that, it was not easy to make a change,
optimize and get values, and then undo that change to the model
without having to copy the whole model object. To solve this, and many
similar problem, we have completely refactored cobra.Solution
so
that model.optimize()
now returns a solution and it is the user's
responsibility to manage this object. reaction.flux
gets its values
directly from the model.problem
. To sugar the new solution class,
fluxes, reduced costs, and shadow prices are now pandas series! Fluxes
and reduced costs can be returned as a data frame directlt with the
to_frame
method.
Sampling
Cobrapy now has flux sampling supported by
cobra.flux_analysis.sampling
see
the sampling notebook.
Loopless models and solutions
Added implementations of
CycleFreeFlux and
the loopless model of
Schellenberger et al.. See
the
notebook on loopless
and simulating
DataFrames as return values
flux_variability_analysis
, single_{gene,reaction}_deletion
,
cobra.flux_analysis.sampling
and
cobra.util.create_stoichiometric_matrix
now return pandas data frames
instead of nested dicts as these are more convenient and fun to work
with. Pandas (and numpy) are therefore now hard requirements for
cobrapy, which should not be a problem for neither linux, windows or
mac users as there are reliable wheels for these packages now.
Model medium
model.medium
is now a dict and setter for getting boundary feeding
reactions and their bounds
Knocking out genes
Addition of cobra.core.Gene.knock_out
which can be used to evaluate
impact of knocking a gene (and all depending reactions).
Adding boundary reactions
The model class has new method model.add_boundary
which can be used
to add sink, exchange or demand reactions with the appropriate bounds
and prefixes (DM, SK or EX).
Gapfilling
The SMILEY
and growMatch
implementations were refactored to a
single new function cobra.flux_analysis.gapfilling.gapfill
which
handles both use-cases.
New Output Format in YAML
Models can now be round tripped to/from YAML documents. YAML is a file format
that is even more legible than JSON. In the scope of cobrapy, YAML output is
intended for diff
comparisons between models.
Fixes
- Handle multiple IDs in Matlab models
#345 DictList.query
behavior changed so that attribute isNone
if the
search parameter is not a regex or string, to enable
reactions.query(lambda x: x.boundary)
- Set charge from notes if not defined elsewhere
#352 - Warnings are no longer issued on package import if soft requirement
scipy, python-libsbml is not available.
Deprecated features
These features are now deprecated and slated for complete removal in
the next major cobrapy release.
- The whole
cobra.solver
module is now deprecated, see New features. ArrayBasedModel
/Model.to_array_based_model
are
deprecated. This formulation makes little sense when handing over
the matrix algebra to optlang, for the stoichiometry matrix (aka S),
seecobra.util.array.create_stoichiometric_matrix
.Metabolite.y
in favor ofMetabolite.shadow_price
Model.add_reaction
in favor ofModel.add_reactions
Reaction.x
in favor ofReaction.flux
Reaction.y
in favor ofReaction.reduced_cost
Solution.{x, y, x_dict, y_dict, f}
in favor ofSolution.{fluxes, reduced_costs}
. The setters are also deprecated.phenotype_phase_plane
in favor ofproduction_envelope
. The
plotting capabilities are deprecated, to be re-implemented somewhere
else.convert_to_irreverible
,revert_to_irreversible
,canonical_form
deprecated without replacement.check_reaction_bounds
deprecated without replacement.optimize_minimal_flux
was renamed topfba
Backwards incompatible changes
- optknock was completely removed, users are advised to use cameo for
this functionality - dual_problem was removed
cobra.topology
was removed, possibly to be reintroduced in a
different package- flux_variability_analysis results must be transformed to have them
work as the previous nested dict,
i.e.flux_variability_analysis(model).T
should give behavior as
previously. - In a major linting effort we renamed capitalized modules to lower-case,
e.g.cobra.core.Model
tocobra.core.model
. Imports fromcobra
are unchanged though. - objective coefficients of reactions can now only be set once the
reaction is attached to a model. Reaction.{x,y}
,Metabolite.y
are defunct for legacy solvers.SMILEY
andgrowMatch
algorithms are defunct in combination with
the legacy solvers.
0.6.0a7
Release notes for cobrapy 0.6.0
Highlights
In this release we have made major changes to pretty much all corners
of cobrapy and we hope that you will enjoy the new features as much as
we do, and that any negative impacts on existing workflows will be
limited.
The major change is the ongoing move away from cobrapy's internal
solver interfaces to those provided by
optlang which provides a
single unified interface to glpk, cplex and gurobi enhanced by the
ability to deal with symbolic expressions. This means formulating
complex constraints no longer implies defining the necessary matrix
algebra, but instead simply writing the expression and assigning that
as an objective to the model.
We feel that this, and the clarified scope and focus attained by
separating the topic of linear programming (optlang) and metabolic
flux analysis (cobrapy) to two packages is natural and makes both of
these tasks less confusing and more fun. We hope that you after
trying, feel the same and that in any case you let us know what you
think by
raising an issue or
talking directly to us on gitter or
google groups.
New features
The optlang solver interface
The main change is the addition of model.solver
which is the optlang
interface to the chosen solver (cplex and glpk are currently well
supported, gurobi interface is at the time of writing mostly working
but improvements are still expected). The solver interface manages
variables, constraints and the objective of the model and the task of
turning these into a model formulation to be optimized by the
solver. From cobrapy's point-of-view, this means that all aspects
concerning generating problems, configuring solvers are handed over to
optlang and consequently the whole cobra.solver
has been deprecated,
slated for removal in the next major release of cobrapy.
Importantly, configuring the solver by passing **solver_args
or
solver='solver'
is now instead done by assigning solver to
model.solver
and then configuring via model.solver.configuration
.
Creating new algorithms has been greatly facilitated as it no longer
requires formulating objectives and constraints by matrix algebra but
instead directly by expressions, e.g. see the implementation of
cobra.flux_analysis.moma.add_moma
and
cobra.flux_analysis.loopless.loopless_solution
.
Instead of having only reactions as variables and metabolites as
constraints, with optlang, cobrapy now supports arbitrary constraints
and variables and these can be added/removed by model.add_cons_vars
and model.remove_cons_vars
which take care of adding these to
model.problem
which is the optlang's mathematical model associated
with the cobra model.
Reactions are now modeled by two variables, forward and reverse, and
these can be seen by accessing reaction.{forward,reverse}_variable
and the combined reaction.flux_expression
.
Objectives can now easily be made quite advanced by simply crafting
the right expression and assigning this as usual to model.objective
,
see the
contraints and objectives notebook.
Temporary changes to a model
Models are large complex objects and copying such objects is
inevitably slow. To avoid that, cobrapy has drawn on the experience
from the development of cameo to introduce the HistoryManager
class
and the concept of models as contexts. Now, most changes that can be
made to a model such as changing the objective, setting reaction
bounds, adding and removing reactions, is reversed upon exit when done
inside a context, see the updated
getting started notebook.
Improved solution handling
Previously, cobra models lugged around their latest solution to enable
providing reaction.{flux,reduced_cost}
(formerly
reaction.{x,y}
). This was problematic because if the model had
changed since last optimization, then this would effectively give the
wrong result. On top of that, it was not easy to make a change,
optimize and get values, and then undo that change to the model
without having to copy the whole model object. To solve this, and many
similar problem, we have completely refactored cobra.Solution
so
that model.optimize()
now returns a solution and it is the user's
responsibility to manage this object. reaction.flux
gets its values
directly from the model.problem
. To sugar the new solution class,
fluxes, reduced costs, and shadow prices are now pandas series! Fluxes
and reduced costs can be returned as a data frame directly with the
to_frame
method.
Sampling
Cobrapy now has flux sampling supported by
cobra.flux_analysis.sampling
see
the sampling notebook.
Loopless models and solutions
Added implementations of
CycleFreeFlux and
the loopless model of
Schellenberger et al.. See
the
notebook on loopless
and simulating
DataFrames as return values
flux_variability_analysis
, single_{gene,reaction}_deletion
,
cobra.flux_analysis.sampling
and
cobra.util.create_stoichiometric_matrix
now return pandas data frames
instead of nested dicts as these are more convenient and fun to work
with. Pandas (and numpy) are therefore now hard requirements for
cobrapy, which should not be a problem for neither linux, windows or
mac users as there are reliable wheels for these packages now.
Model medium
model.medium
is now a dict and setter for getting boundary feeding
reactions and their bounds
Knocking out genes
Addition of cobra.core.Gene.knock_out
which can be used to evaluate
impact of knocking a gene (and all depending reactions).
Adding boundary reactions
The model class has new method model.add_boundary
which can be used
to add sink, exchange or demand reactions with the appropriate bounds
and prefixes (DM, SK or EX).
Fixes
- Handle multiple IDs in Matlab models
#345 DictList.query
behavior changed so that attribute isNone
if the
search parameter is not a regex or string, to enable
reactions.query(lambda x: x.boundary)
- Set charge from notes if not defined elsewhere
#352 - Warnings are no longer issued on package import if soft requirement
scipy, python-libsbml is not available.
Deprecated features
These features are now deprecated and slated for complete removal in
the next major cobrapy release.
- The whole
cobra.solver
module is now deprecated, see New features. ArrayBasedModel
/Model.to_array_based_model
are
deprecated. This formulation makes little sense when handing over
the matrix algebra to optlang, for the stoichiometry matrix (aka S),
seecobra.util.array.create_stoichiometric_matrix
.Metabolite.y
in favor ofMetabolite.shadow_price
Model.add_reaction
in favor ofModel.add_reactions
Reaction.x
in favor ofReaction.flux
Reaction.y
in favor ofReaction.reduced_cost
Solution.{x, y, x_dict, y_dict, f}
in favor ofSolution.{fluxes, reduced_costs}
. The setters are also deprecated.phenotype_phase_plane
in favor ofproduction_envelope
. The
plotting capabilities are deprecated, to be re-implemented somewhere
else.convert_to_irreverible
,revert_to_irreversible
,canonical_form
deprecated without replacement.check_reaction_bounds
deprecated without replacement.optimize_minimal_flux
was renamed topfba
Backwards incompatible changes
- optknock was completely removed, users are advised to use cameo for
this functionality - dual_problem was removed
- gapfilling algorithms were removed without replacement, to be
re-introduced in a different package cobra.topology
was removed, possibly to be reintroduced in a
different package- flux_variability_analysis results must be transformed to have them
work as the previous nested dict,
i.e.flux_variability_analysis(model).T
should give behavior as
previously. - In a major linting effort we renamed capitalized modules to lower-case,
e.g.cobra.core.Model
tocobra.core.model
. Imports fromcobra
are unchanged though. - objective coefficients of reactions can now only be set once the
reaction is attached to a model. Reaction.{x,y}
,Metabolite.y
are defunct for legacy solvers.
0.6.0a6
Release notes for cobrapy 0.6.0
Highlights
In this release we have made major changes to pretty much all corners
of cobrapy and we hope that you will enjoy the new features as much as
we do, and that any negative impacts on existing workflows will be
limited.
The major change is the ongoing move away from cobrapy's internal
solver interfaces to those provided by
optlang which provides a
single unified interface to glpk, cplex and gurobi enhanced by the
ability to deal with symbolic expressions. This means formulating
complex constraints no longer implies defining the necessary matrix
algebra, but instead simply writing the expression and assigning that
as an objective to the model.
We feel that this, and the clarified scope and focus attained by
separating the topic of linear programming (optlang) and metabolic
flux analysis (cobrapy) to two packages is natural and makes both of
these tasks less confusing and more fun. We hope that you after
trying, feel the same and that in any case you let us know what you
think by
raising an issue or
talking directly to us on gitter or
google groups.
New features
The optlang solver interface
The main change is the addition of model.solver
which is the optlang
interface to the chosen solver (cplex and glpk are currently well
supported, gurobi interface is at the time of writing mostly working
but improvements are still expected). The solver interface manages
variables, constraints and the objective of the model and the task of
turning these into a model formulation to be optimized by the
solver. From cobrapy's point-of-view, this means that all aspects
concerning generating problems, configuring solvers are handed over to
optlang and consequently the whole cobra.solver
has been deprecated,
slated for removal in the next major release of cobrapy.
Importantly, configuring the solver by passing **solver_args
or
solver='solver'
is now instead done by assigning solver to
model.solver
and then configuring via model.solver.configuration
.
Creating new algorithms has been greatly facilitated as it no longer
requires formulating objectives and constraints by matrix algebra but
instead directly by expressions, e.g. see the implementation of
cobra.flux_analysis.moma.add_moma
and
cobra.flux_analysis.loopless.loopless_solution
.
Instead of having only reactions as variables and metabolites as
constraints, with optlang, cobrapy now supports arbitrary constraints
and variables and these can be added/removed by model.add_cons_vars
and model.remove_cons_vars
which take care of adding these to
model.problem
which is the optlang's mathematical model associated
with the cobra model.
Reactions are now modeled by two variables, forward and reverse, and
these can be seen by accessing reaction.{forward,reverse}_variable
and the combined reaction.flux_expression
.
Objectives can now easily be made quite advanced by simply crafting
the right expression and assigning this as usual to model.objective
,
see the
contraints and objectives notebook.
Temporary changes to a model
Models are large complex objects and copying such objects is
inevitably slow. To avoid that, cobrapy has drawn on the experience
from the development of cameo to introduce the HistoryManager
class
and the concept of models as contexts. Now, most changes that can be
made to a model such as changing the objective, setting reaction
bounds, adding and removing reactions, is reversed upon exit when done
inside a context, see the updated
getting started notebook.
Improved solution handling
Previously, cobra models lugged around their latest solution to enable
providing reaction.{flux,reduced_cost}
(formerly
reaction.{x,y}
). This was problematic because if the model had
changed since last optimization, then this would effectively give the
wrong result. On top of that, it was not easy to make a change,
optimize and get values, and then undo that change to the model
without having to copy the whole model object. To solve this, and many
similar problem, we have completely refactored cobra.Solution
so
that model.optimize()
now returns a solution and it is the user's
responsibility to manage this object. reaction.flux
gets its values
directly from the model.problem
. To sugar the new solution class,
fluxes, reduced costs, and shadow prices are now pandas series!
Sampling
Cobrapy now has flux sampling supported by
cobra.flux_analysis.sampling
see
the sampling notebook.
Loopless models and solutions
Added implementations of
CycleFreeFlux and
the loopless model of
Schellenberger et al.. See
the
notebook on loopless
and simulating
DataFrames as return values
flux_variability_analysis
, single_{gene,reaction}_deletion
,
cobra.flux_analysis.sampling
and
cobra.util.create_stoichiometric_matrix
now return pandas data frames
instead of nested dicts as these are more convenient and fun to work
with. Pandas (and numpy) are therefore now hard requirements for
cobrapy, which should not be a problem for neither linux, windows or
mac users as there are reliable wheels for these packages now.
Model medium
model.medium
is now a dict and setter for getting boundary feeding
reactions and their bounds
Knocking out genes
Addition of cobra.core.Gene.knock_out
which can be used to evaluate
impact of knocking a gene (and all depending reactions).
Adding boundary reactions
The model class has new method model.add_boundary
which can be used
to add sink, exchange or demand reactions with the appropriate bounds
and prefixes (DM, SK or EX).
Fixes
- Handle multiple IDs in Matlab models
#345 DictList.query
behavior changed so that attribute isNone
if the
search parameter is not a regex or string, to enable
reactions.query(lambda x: x.boundary)
- Set charge from notes if not defined elsewhere
#352 - Warnings are no longer issued on package import if soft requirement
scipy, python-libsbml is not available.
Deprecated features
These features are now deprecated and slated for complete removal in
the next major cobrapy release.
- The whole
cobra.solver
module is now deprecated, see New features. ArrayBasedModel
/Model.to_array_based_model
are
deprecated. This formulation makes little sense when handing over
the matrix algebra to optlang, for the stoichiometry matrix (aka S),
seecobra.util.array.create_stoichiometric_matrix
.Metabolite.y
in favor ofMetabolite.shadow_price
Model.add_reaction
in favor ofModel.add_reactions
Reaction.x
in favor ofReaction.flux
Reaction.y
in favor ofReaction.reduced_cost
Solution.{x, y, x_dict, y_dict, f}
in favor ofSolution.{fluxes, reduced_costs}
. The setters are also deprecated.phenotype_phase_plane
in favor ofproduction_envelope
. The
plotting capabilities are deprecated, to be re-implemented somewhere
else.convert_to_irreverible
,revert_to_irreversible
,canonical_form
deprecated without replacement.check_reaction_bounds
deprecated without replacement.optimize_minimal_flux
was renamed topfba
Backwards incompatible changes
- optknock was completely removed, users are advised to use cameo for
this functionality - dual_problem was removed
- gapfilling algorithms were removed without replacement, to be
re-introduced in a different package cobra.topology
was removed, possibly to be reintroduced in a
different package- flux_variability_analysis results must be transformed to have them
work as the previous nested dict,
i.e.flux_variability_analysis(model).T
should give behavior as
previously. - In a major linting effort we renamed capitalized modules to lower-case,
e.g.cobra.core.Model
tocobra.core.model
. Imports fromcobra
are unchanged though. - objective coefficients of reactions can now only be set once the
reaction is attached to a model. Reaction.{x,y}
,Metabolite.y
are defunct for legacy solvers.
0.6.0a4
Release notes for cobrapy 0.6.0
Highlights
In this release we have made major changes to pretty much all corners
of cobrapy and we hope that you will enjoy the new features as much as
we do, and that any negative impacts on existing workflows will be
limited.
The major change is the ongoing move away from cobrapy's internal
solver interfaces to those provided by
optlang which provides a
single unified interface to glpk, cplex and gurobi enhanced by the
ability to deal with symbolic expressions. This means formulating
complex constraints no longer implies defining the necessary matrix
algebra, but instead simply writing the expression and assigning that
as an objective to the model.
We feel that this, and the clarified scope and focus attained by
separating the topic of linear programming (optlang) and metabolic
flux analysis (cobrapy) to two packages is natural and makes both of
these tasks less confusing and more fun. We hope that you after
trying, feel the same and that in any case you let us know what you
think by
raising an issue or
talking directly to us on gitter or
google groups.
New features
The optlang solver interface
The main change is the addition of model.solver
which is the optlang
interface to the chosen solver (cplex and glpk are currently well
supported, gurobi interface is at the time of writing mostly working
but improvements are still expected). The solver interface manages
variables, constraints and the objective of the model and the task of
turning these into a model formulation to be optimized by the
solver. From cobrapy's point-of-view, this means that all aspects
concerning generating problems, configuring solvers are handed over to
optlang and consequently the whole cobra.solver
has been deprecated,
slated for removal in the next major release of cobrapy.
Importantly, configuring the solver by passing **solver_args
or
solver='solver'
is now instead done by assigning solver to
model.solver
and then configuring via model.solver.configuration
.
Creating new algorithms has been greatly facilitated as it no longer
requires formulating objectives and constraints by matrix algebra but
instead directly by expressions, e.g. see the implementation of
cobra.flux_analysis.moma.add_moma
and
cobra.flux_analysis.loopless.loopless_solution
.
Instead of having only reactions as variables and metabolites as
constraints, with optlang, cobrapy now supports arbitrary constraints
and variables and these can be added/removed by model.add_cons_vars
and model.remove_cons_vars
which take care of adding these to
model.problem
which is the optlang's mathematical model associated
with the cobra model.
Reactions are now modeled by two variables, forward and reverse, and
these can be seen by accessing reaction.{forward,reverse}_variable
and the combined reaction.flux_expression
.
Objectives can now easily be made quite advanced by simply crafting
the right expression and assigning this as usual to model.objective
,
see the
contraints and objectives notebook.
Temporary changes to a model
Models are large complex objects and copying such objects is
inevitably slow. To avoid that, cobrapy has drawn on the experience
from the development of cameo to introduce the HistoryManager
class
and the concept of models as contexts. Now, most changes that can be
made to a model such as changing the objective, setting reaction
bounds, adding and removing reactions, is reversed upon exit when done
inside a context, see the updated
getting started notebook.
Improved solution handling
Previously, cobra models lugged around their latest solution to enable
providing reaction.{flux,reduced_cost}
(formerly
reaction.{x,y}
). This was problematic because if the model had
changed since last optimization, then this would effectively give the
wrong result. On top of that, it was not easy to make a change,
optimize and get values, and then undo that change to the model
without having to copy the whole model object. To solve this, and many
similar problem, we have completely refactored cobra.Solution
so
that model.optimize()
now returns a solution and it is the user's
responsibility to manage this object. reaction.flux
gets its values
directly from the model.problem
. To sugar the new solution class,
fluxes, reduced costs, and shadow prices are now pandas series!
Sampling
Cobrapy now has flux sampling supported by
cobra.flux_analysis.sampling
see
the sampling notebook.
Loopless models and solutions
Added implementations of
CycleFreeFlux and
the loopless model of
Schellenberger et al.. See
the
notebook on loopless
and simulating
DataFrames as return values
flux_variability_analysis
, single_{gene,reaction}_deletion
,
cobra.flux_analysis.sampling
and
cobra.util.create_stoichiometric_matrix
now return pandas data frames
instead of nested dicts as these are more convenient and fun to work
with. Pandas (and numpy) are therefore now hard requirements for
cobrapy, which should not be a problem for neither linux, windows or
mac users as there are reliable wheels for these packages now.
Model medium
model.medium
is now a dict and setter for getting boundary feeding
reactions and their bounds
Fixes
- Handle multiple IDs in Matlab models
#345 DictList.query
behavior changed so that attribute isNone
if the
search parameter is not a regex or string, to enable
reactions.query(lambda x: x.boundary)
- Set charge from notes if not defined elsewhere
#352 - Warnings are no longer issued on package import if soft requirement
scipy, python-libsbml is not available.
Deprecated features
These features are now deprecated and slated for complete removal in
the next major cobrapy release.
- The whole
cobra.solver
module is now deprecated, see New features. ArrayBasedModel
/Model.to_array_based_model
are
deprecated. This formulation makes little sense when handing over
the matrix algebra to optlang, for the stoichiometry matrix (aka S),
seecobra.util.array.create_stoichiometric_matrix
.Metabolite.y
in favor ofMetabolite.shadow_price
Model.add_reaction
in favor ofModel.add_reactions
Reaction.x
in favor ofReaction.flux
Reaction.y
in favor ofReaction.reduced_cost
Solution.{x, y, x_dict, y_dict, f}
in favor ofSolution.{fluxes, reduced_costs}
. The setters are also deprecated.phenotype_phase_plane
in favor ofproduction_envelope
. The
plotting capabilities are deprecated, to be re-implemented somewhere
else.convert_to_irreverible
,revert_to_irreversible
,canonical_form
deprecated without replacement.check_reaction_bounds
deprecated without replacement.optimize_minimal_flux
was renamed topfba
Backwards incompatible changes
- optknock was completely removed, users are advised to use cameo for
this functionality - dual_problem was removed
- gapfilling algorithms were removed without replacement, to be
re-introduced in a different package cobra.topology
was removed, possibly to be reintroduced in a
different package- flux_variability_analysis results must be transformed to have them
work as the previous nested dict,
i.e.flux_variability_analysis(model).T
should give behavior as
previously. - In a major linting effort we renamed capitalized modules to lower-case,
e.g.cobra.core.Model
tocobra.core.model
. Imports fromcobra
are unchanged though. - objective coefficients of reactions can now only be set once the
reaction is attached to a model. Reaction.{x,y}
,Metabolite.y
are defunct for legacy solvers.
0.6.0a2
Release notes for cobrapy 0.6.0
Highlights
In this release we have made major changes to pretty much all corners
of cobrapy and we hope that you will enjoy the new features as much as
we do, and that any negative impacts on existing workflows will be
limited.
The major change is the ongoing move away from cobrapy's internal
solver interfaces to those provided by
optlang which provides a
single unified interface to glpk, cplex and gurobi enhanced by the
ability to deal with symbolic expressions. This means formulating
complex constraints no longer implies defining the necessary matrix
algebra, but instead simply writing the expression and assigning that
as an objective to the model.
We feel that this, and the clarified scope and focus attained by
separating the topic of linear programming (optlang) and metabolic
flux analysis (cobrapy) to two packages is natural and makes both of
these tasks less confusing and more fun. We hope that you after
trying, feel the same and that in any case you let us know what you
think by
raising an issue or
talking directly to us on gitter or
google groups.
New features
The optlang solver interface
The main change is the addition of model.solver
which is the optlang
interface to the chosen solver (cplex and glpk are currently well
supported, gurobi interface is at the time of writing mostly working
but improvements are still expected). The solver interface manages
variables, constraints and the objective of the model and the task of
turning these into a model formulation to be optimized by the
solver. From cobrapy's point-of-view, this means that all aspects
concerning generating problems, configuring solvers are handed over to
optlang and consequently the whole cobra.solver
has been deprecated,
slated for removal in the next major release of cobrapy.
Importantly, configuring the solver by passing **solver_args
or
solver='solver'
is now instead done by assigning solver to
model.solver
and then configuring via model.solver.configuration
.
Creating new algorithms has been greatly facilitated as it no longer
requires formulating objectives and constraints by matrix algebra but
instead directly by expressions, e.g. see the implementation of
cobra.flux_analysis.moma.add_moma
and
cobra.flux_analysis.loopless.loopless_solution
.
Instead of having only reactions as variables and metabolites as
constraints, with optlang, cobrapy now supports arbitrary constraints
and variables and these can be added/removed by model.add_cons_vars
and model.remove_cons_vars
which take care of adding these to
model.problem
which is the optlang's mathematical model associated
with the cobra model.
Reactions are now modeled by two variables, forward and reverse, and
these can be seen by accessing reaction.{forward,reverse}_variable
and the combined reaction.flux_expression
.
Objectives can now easily be made quite advanced by simply crafting
the right expression and assigning this as usual to model.objective
,
see the
contraints and objectives notebook.
Temporary changes to a model
Models are large complex objects and copying such objects is
inevitably slow. To avoid that, cobrapy has drawn on the experience
from the development of cameo to introduce the HistoryManager
class
and the concept of models as contexts. Now, most changes that can be
made to a model such as changing the objective, setting reaction
bounds, adding and removing reactions, is reversed upon exit when done
inside a context, see the updated
getting started notebook.
Improved solution handling
Previously, cobra models lugged around their latest solution to enable
providing reaction.{flux,reduced_cost}
(formerly
reaction.{x,y}
). This was problematic because if the model had
changed since last optimization, then this would effectively give the
wrong result. On top of that, it was not easy to make a change,
optimize and get values, and then undo that change to the model
without having to copy the whole model object. To solve this, and many
similar problem, we have completely refactored cobra.Solution
so
that model.optimize()
now returns a solution and it is the user's
responsibility to manage this object. reaction.flux
gets its values
directly from the model.problem
. To sugar the new solution class,
fluxes, reduced costs, and shadow prices are now pandas series!
Sampling
Cobrapy now has flux sampling supported by
cobra.flux_analysis.sampling
see
the sampling notebook.
Loopless models and solutions
Added implementations of
CycleFreeFlux and
the loopless model of
Schellenberger et al.. See
the
notebook on loopless
and simulating
DataFrames as return values
flux_variability_analysis
, single_{gene,reaction}_deletion
,
cobra.flux_analysis.sampling
and
cobra.util.create_stoichiometric_matrix
now return pandas data frames
instead of nested dicts as these are more convenient and fun to work
with. Pandas (and numpy) are therefore now hard requirements for
cobrapy, which should not be a problem for neither linux, windows or
mac users as there are reliable wheels for these packages now.
Model medium
model.medium
is now a dict and setter for getting boundary feeding
reactions and their bounds
Fixes
- Handle multiple IDs in Matlab models
#345 DictList.query
behavior changed so that attribute isNone
if the
search parameter is not a regex or string, to enable
reactions.query(lambda x: x.boundary)
- Set charge from notes if not defined elsewhere
#352 - Warnings are no longer issued on package import if soft requirement
scipy, python-libsbml is not available.
Deprecated features
These features are now deprecated and slated for complete removal in
the next major cobrapy release.
- The whole
cobra.solver
module is now deprecated, see New features. ArrayBasedModel
/Model.to_array_based_model
are
deprecated. This formulation makes little sense when handing over
the matrix algebra to optlang, for the stoichiometry matrix (aka S),
seecobra.util.array.create_stoichiometric_matrix
.Metabolite.y
in favor ofMetabolite.shadow_price
Model.add_reaction
in favor ofModel.add_reactions
Reaction.x
in favor ofReaction.flux
Reaction.y
in favor ofReaction.reduced_cost
Solution.{x, y, x_dict, y_dict, f}
in favor ofSolution.{fluxes, reduced_costs}
. The setters are also deprecated.phenotype_phase_plane
in favor ofproduction_envelope
. The
plotting capabilities are deprecated, to be re-implemented somewhere
else.convert_to_irreverible
,revert_to_irreversible
,canonical_form
deprecated without replacement.check_reaction_bounds
deprecated without replacement.
Backwards incompatible changes
- optknock was completely removed, users are advised to use cameo for
this functionality - dual_problem was removed
- gapfilling algorithms were removed without replacement, to be
re-introduced in a different package cobra.topology
was removed, possibly to be reintroduced in a
different package- flux_variability_analysis results must be transformed to have them
work as the previous nested dict,
i.e.flux_variability_analysis(model).T
should give behavior as
previously. - In a major linting effort we renamed capitalized modules to lower-case,
e.g.cobra.core.Model
tocobra.core.model
. Imports fromcobra
are unchanged though. - objective coefficients of reactions can now only be set once the
reaction is attached to a model. Reaction.{x,y}
,Metabolite.y
are defunct for legacy solvers.
0.6.0a1
Release notes for cobrapy 0.6.0
Highlights
In this release we have made major changes to pretty much all corners
of cobrapy but we hope that any negative impact on existing workflows
will still be limited. The major change is the ongoing move away from
cobrapy's internal solver interfaces to those provided by
optlang which provides a
single unified interface to glpk, cplex and gurobi enhanced by the
ability to deal with symbolic expressions. This means formulating
complex constraints no longer implies defining the necessary matrix
algebra, but instead simply writing the expression and assigning that
as on objective to the model.
We feel that the this, and the clarified scope and focus attained by
separating the topic of linear programming (optlang) and metabolic
flux analysis (cobrapy) to two packages is natural and makes both of
these tasks less confusing and more fun. We hope that you after
trying, feel the same and that in any case you let us know what you
think by
raising an issue or
talking directly to us on gitter or
google groups.
New features
The optlang solver interface
The main change is the addition of model.solver
which is the optlang
interface to the chosen solver (cplex and glpk are currently well
supported, gurobi interface is at the time of writing mostly working
but improvements are still expected). The solver interface manages
variables, constraints and the objective of the model and the task of
turning these into a model formulation to be optimized by the
solver. From cobrapy's point-of-view, this means that all aspects
concerning generating problems, configuring solvers are handed over to
optlang and consequently the whole cobra.solver
has been deprecated,
slated for removal in the next major release of cobrapy.
Importantly, configuring the solver by passing **solver_args
or
solver='solver'
is now instead done by assigning solver to
model.solver
and then configuring via model.solver.configuration
.
Creating new algorithms has been greatly facilitated as it no longer
requires formulating objectives and constraints by matrix algebra but
instead directly by expressions, e.g. see the implementation of
cobra.flux_analysis.moma.add_moma
and
cobra.flux_analysis.loopless.loopless_solution
.
Instead of having only reactions as variables and metabolites as
constraints, with optlang, cobrapy now supports arbitrary constraints
and variables and these can be added/removed by model.add_cons_vars
and model.remove_cons_vars
which take care of adding these to
model.problem
which is the optlang's mathematical model associated
with the cobra model.
Reactions are now modeled by two variables, forward and reverse, and
these can be seen by accessing reaction.{forward,reverse}_variable
and the combined reaction.flux_expression
.
Objectives can now easily be made quite advanced by simply crafting
the right expression and assigning this as usual to model.objective
,
see the
contraints and objectives notebook.
Temporary changes to a model
Models are large complex objects and copying such objects is
inevitably slow. To avoid that, cobrapy has drawn on the experience
from the development of cameo to introduce the HistoryManager
class
and the concept of models as contexts. Now, most changes that can be
made to a model such as changing the objective, setting reaction
bounds, adding and removing reactions, is reversed upon exit when done
inside a context, see the updated
getting started notebook.
Improved solution handling
Previously, cobra models lugged around their latest solution to enable
providing reaction.{flux,reduced_cost}
(formerly
reaction.{x,y}
). This was problematic because if the model had
changed since last optimization, then this would effectively give the
wrong result. On top of that, it was not easy to make a change,
optimize and get values, and then undo that change to the model
without having to copy the whole model object. To solve this, and many
similar problem, we have completely refactored cobra.Solution
so
that model.optimize()
now returns a solution and it is the user's
responsibility to manage this object. reaction.flux
gets its values
directly from the model.problem
. To sugar the new solution class,
fluxes, reduced costs, and shadow prices are now pandas series!
Sampling
Cobrapy now has flux sampling supported by
cobra.flux_analysis.sampling
see
the sampling notebook.
Loopless models and solutions
Added implementations of
CycleFreeFlux and
the loopless model of
Schellenberger et al.. See
the
notebook on loopless
and simulating
DataFrames as return values
flux_variability_analysis
, single_{gene,rection}_deletion
,
cobra.flux_analysis.sampling
and
cobra.util.create_stoichiometric_array
now return a pandas data frame
instead of nested dicts as these are more convenient and fun to work
with. Pandas (and numpy) are therefore now hard requirements for
cobrapy, which should not be a problem for neither linux, windows or
mac users as there are reliable wheels for these packages now.
Model medium
model.medium
is now a dict and setter for getting boundary feeding
reactions and their bounds
Fixes
- Handle multiple IDs in Matlab models
#345 DictList.query
behavior changed so that attribute isNone
if the
search parameter is not a regex or string, to enable
reactions.query(lambda x: x.boundary)
- Set charge from notes if not defined elsewhere
#352 - Warnings are no longer issued on package import if soft requirement
scipy, python-libsbml is not available.
Deprecated features
These features are now deprecated and slated for complete removal in
the next major cobrapy release.
- The whole
cobra.solver
module is now deprecated, see New features. ArrayBasedModel
/Model.to_array_based_model
are
deprecated. This formulation makes little sense when handing over
the matrix algebra to optlang, for the stoichiometry matrix (aka S),
seecobra.util.array.create_stoichiometric_array
.Metabolite.y
in favor ofMetabolite.shadow_price
Model.add_reaction
in favor ofModel.add_reactions
Reaction.x
in favor ofReaction.flux
Reaction.y
in favor ofReaction.reduced_cost
Solution.{x, y, x_dict, y_dict, f}
in favor ofSolution.{fluxes, reduced_costs}
. The setters are also deprecated.phenotype_phase_plane
in favor ofproduction_envelope
. The
plotting capabilities are deprecated, to be re-implemented somewhere
else.convert_to_irreverible
,revert_to_irreversible
,canonical_form
deprecated without replacement.check_reaction_bounds
deprecated without replacement.
Backwards incompatible changes
- optknock was completely removed, users are advised to use cameo for
this functionality - dual_problem was removed
- gapfilling algorithms were removed without replacement, to be
re-introduced in a different package cobra.topology
was removed, possibly to be reintroduced in a
different package- flux_variability_analysis results must be transformed to have them
work as the previous nested dict,
i.e.flux_variability_analysis(model).T
should give behavior as
previously. - In a major linting effort we renamed capitalized modules to lower-case,
e.g.cobra.core.Model
tocobra.core.model
. Imports fromcobra
are unchanged though. - objective coefficients of reactions can now only be set once the
reaction is attached to a model. Reaction.{x,y}
,Metabolite.y
are defunct for legacy solvers.