INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
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Add the absolute value of an expression to the model. | def add_absolute_expression(model, expression, name="abs_var", ub=None,
difference=0, add=True):
"""Add the absolute value of an expression to the model.
Also defines a variable for the absolute value that can be used in other
objectives or constraints.
Parameters
-----... |
Fix current objective as an additional constraint. | def fix_objective_as_constraint(model, fraction=1, bound=None,
name='fixed_objective_{}'):
"""Fix current objective as an additional constraint.
When adding constraints to a model, such as done in pFBA which
minimizes total flux, these constraints can become too powerful,
... |
Perform standard checks on a solver s status. | def check_solver_status(status, raise_error=False):
"""Perform standard checks on a solver's status."""
if status == OPTIMAL:
return
elif (status in has_primals) and not raise_error:
warn("solver status is '{}'".format(status), UserWarning)
elif status is None:
raise Optimization... |
Assert model solver status is optimal. | def assert_optimal(model, message='optimization failed'):
"""Assert model solver status is optimal.
Do nothing if model solver status is optimal, otherwise throw
appropriate exception depending on the status.
Parameters
----------
model : cobra.Model
The model to check the solver statu... |
Add a new objective and variables to ensure a feasible solution. | def add_lp_feasibility(model):
"""
Add a new objective and variables to ensure a feasible solution.
The optimized objective will be zero for a feasible solution and otherwise
represent the distance from feasibility (please see [1]_ for more
information).
Parameters
----------
model : c... |
Successively optimize separate targets in a specific order. | def add_lexicographic_constraints(model,
objectives,
objective_direction='max'):
"""
Successively optimize separate targets in a specific order.
For each objective, optimize the model and set the optimal value as a
constraint. Proceed ... |
Create a new numpy array that resides in shared memory. | def shared_np_array(shape, data=None, integer=False):
"""Create a new numpy array that resides in shared memory.
Parameters
----------
shape : tuple of ints
The shape of the new array.
data : numpy.array
Data to copy to the new array. Has to have the same shape.
integer : boolea... |
Sample a new feasible point from the point x in direction delta. | def step(sampler, x, delta, fraction=None, tries=0):
"""Sample a new feasible point from the point `x` in direction `delta`."""
prob = sampler.problem
valid = ((np.abs(delta) > sampler.feasibility_tol) &
np.logical_not(prob.variable_fixed))
# permissible alphas for staying in variable bou... |
Build the matrix representation of the sampling problem. | def __build_problem(self):
"""Build the matrix representation of the sampling problem."""
# Set up the mathematical problem
prob = constraint_matrices(self.model, zero_tol=self.feasibility_tol)
# check if there any non-zero equality constraints
equalities = prob.equalities
... |
Generate the warmup points for the sampler. | def generate_fva_warmup(self):
"""Generate the warmup points for the sampler.
Generates warmup points by setting each flux as the sole objective
and minimizing/maximizing it. Also caches the projection of the
warmup points into the nullspace for non-homogeneous problems (only
if... |
Reproject a point into the feasibility region. | def _reproject(self, p):
"""Reproject a point into the feasibility region.
This function is guaranteed to return a new feasible point. However,
no guarantees in terms of proximity to the original point can be made.
Parameters
----------
p : numpy.array
The c... |
Find an approximately random point in the flux cone. | def _random_point(self):
"""Find an approximately random point in the flux cone."""
idx = np.random.randint(self.n_warmup,
size=min(2, np.ceil(np.sqrt(self.n_warmup))))
return self.warmup[idx, :].mean(axis=0) |
Identify rdeundant rows in a matrix that can be removed. | def _is_redundant(self, matrix, cutoff=None):
"""Identify rdeundant rows in a matrix that can be removed."""
cutoff = 1.0 - self.feasibility_tol
# Avoid zero variances
extra_col = matrix[:, 0] + 1
# Avoid zero rows being correlated with constant rows
extra_col[matrix.s... |
Get the lower and upper bound distances. Negative is bad. | def _bounds_dist(self, p):
"""Get the lower and upper bound distances. Negative is bad."""
prob = self.problem
lb_dist = (p - prob.variable_bounds[0, ]).min()
ub_dist = (prob.variable_bounds[1, ] - p).min()
if prob.bounds.shape[0] > 0:
const = prob.inequalities.dot(... |
Create a batch generator. | def batch(self, batch_size, batch_num, fluxes=True):
"""Create a batch generator.
This is useful to generate n batches of m samples each.
Parameters
----------
batch_size : int
The number of samples contained in each batch (m).
batch_num : int
Th... |
Validate a set of samples for equality and inequality feasibility. | def validate(self, samples):
"""Validate a set of samples for equality and inequality feasibility.
Can be used to check whether the generated samples and warmup points
are feasible.
Parameters
----------
samples : numpy.matrix
Must be of dimension (n_samples... |
Remove metabolites that are not involved in any reactions and returns pruned model | def prune_unused_metabolites(cobra_model):
"""Remove metabolites that are not involved in any reactions and
returns pruned model
Parameters
----------
cobra_model: class:`~cobra.core.Model.Model` object
the model to remove unused metabolites from
Returns
-------
output_model: c... |
Remove reactions with no assigned metabolites returns pruned model | def prune_unused_reactions(cobra_model):
"""Remove reactions with no assigned metabolites, returns pruned model
Parameters
----------
cobra_model: class:`~cobra.core.Model.Model` object
the model to remove unused reactions from
Returns
-------
output_model: class:`~cobra.core.Model... |
Undoes the effects of a call to delete_model_genes in place. | def undelete_model_genes(cobra_model):
"""Undoes the effects of a call to delete_model_genes in place.
cobra_model: A cobra.Model which will be modified in place
"""
if cobra_model._trimmed_genes is not None:
for x in cobra_model._trimmed_genes:
x.functional = True
if cobra_... |
identify reactions which will be disabled when the genes are knocked out | def find_gene_knockout_reactions(cobra_model, gene_list,
compiled_gene_reaction_rules=None):
"""identify reactions which will be disabled when the genes are knocked out
cobra_model: :class:`~cobra.core.Model.Model`
gene_list: iterable of :class:`~cobra.core.Gene.Gene`
... |
delete_model_genes will set the upper and lower bounds for reactions catalysed by the genes in gene_list if deleting the genes means that the reaction cannot proceed according to cobra_model. reactions [: ]. gene_reaction_rule | def delete_model_genes(cobra_model, gene_list,
cumulative_deletions=True, disable_orphans=False):
"""delete_model_genes will set the upper and lower bounds for reactions
catalysed by the genes in gene_list if deleting the genes means that
the reaction cannot proceed according to
c... |
remove genes entirely from the model | def remove_genes(cobra_model, gene_list, remove_reactions=True):
"""remove genes entirely from the model
This will also simplify all gene_reaction_rules with this
gene inactivated."""
gene_set = {cobra_model.genes.get_by_id(str(i)) for i in gene_list}
gene_id_set = {i.id for i in gene_set}
remo... |
Perform gapfilling on a model. | def gapfill(model, universal=None, lower_bound=0.05,
penalties=None, demand_reactions=True, exchange_reactions=False,
iterations=1):
"""Perform gapfilling on a model.
See documentation for the class GapFiller.
Parameters
----------
model : cobra.Model
The model to p... |
Extend gapfilling model. | def extend_model(self, exchange_reactions=False, demand_reactions=True):
"""Extend gapfilling model.
Add reactions from universal model and optionally exchange and
demand reactions for all metabolites in the model to perform
gapfilling on.
Parameters
----------
... |
Update the coefficients for the indicator variables in the objective. | def update_costs(self):
"""Update the coefficients for the indicator variables in the objective.
Done incrementally so that second time the function is called,
active indicators in the current solutions gets higher cost than the
unused indicators.
"""
for var in self.ind... |
Update gapfilling model with switches and the indicator objective. | def add_switches_and_objective(self):
""" Update gapfilling model with switches and the indicator objective.
"""
constraints = list()
big_m = max(max(abs(b) for b in r.bounds)
for r in self.model.reactions)
prob = self.model.problem
for rxn in self.mod... |
Perform the gapfilling by iteratively solving the model updating the costs and recording the used reactions. | def fill(self, iterations=1):
"""Perform the gapfilling by iteratively solving the model, updating
the costs and recording the used reactions.
Parameters
----------
iterations : int
The number of rounds of gapfilling to perform. For every
iteration, the ... |
Find the external compartment in the model. | def find_external_compartment(model):
"""Find the external compartment in the model.
Uses a simple heuristic where the external compartment should be the one
with the most exchange reactions.
Arguments
---------
model : cobra.Model
A cobra model.
Returns
-------
str
... |
Check whether a reaction is an exchange reaction. | def is_boundary_type(reaction, boundary_type, external_compartment):
"""Check whether a reaction is an exchange reaction.
Arguments
---------
reaction : cobra.Reaction
The reaction to check.
boundary_type : str
What boundary type to check for. Must be one of
"exchange", "dem... |
Find specific boundary reactions. | def find_boundary_types(model, boundary_type, external_compartment=None):
"""Find specific boundary reactions.
Arguments
---------
model : cobra.Model
A cobra model.
boundary_type : str
What boundary type to check for. Must be one of
"exchange", "demand", or "sink".
exte... |
Return a valid zero cutoff value. | def normalize_cutoff(model, zero_cutoff=None):
"""Return a valid zero cutoff value."""
if zero_cutoff is None:
return model.tolerance
else:
if zero_cutoff < model.tolerance:
raise ValueError(
"The chosen zero cutoff cannot be less than the model's "
... |
Sample a single chain for OptGPSampler. | def _sample_chain(args):
"""Sample a single chain for OptGPSampler.
center and n_samples are updated locally and forgotten afterwards.
"""
n, idx = args # has to be this way to work in Python 2.7
center = sampler.center
np.random.seed((sampler._seed + idx) % np.iinfo(np.int32).max)
... |
Generate a set of samples. | def sample(self, n, fluxes=True):
"""Generate a set of samples.
This is the basic sampling function for all hit-and-run samplers.
Paramters
---------
n : int
The minimum number of samples that are generated at once
(see Notes).
fluxes : boolean
... |
convert compiled ast to gene_reaction_rule str | def ast2str(expr, level=0, names=None):
"""convert compiled ast to gene_reaction_rule str
Parameters
----------
expr : str
string for a gene reaction rule, e.g "a and b"
level : int
internal use only
names : dict
Dict where each element id a gene identifier and the value... |
evaluate compiled ast of gene_reaction_rule with knockouts | def eval_gpr(expr, knockouts):
"""evaluate compiled ast of gene_reaction_rule with knockouts
Parameters
----------
expr : Expression
The ast of the gene reaction rule
knockouts : DictList, set
Set of genes that are knocked out
Returns
-------
bool
True if the ge... |
parse gpr into AST | def parse_gpr(str_expr):
"""parse gpr into AST
Parameters
----------
str_expr : string
string with the gene reaction rule to parse
Returns
-------
tuple
elements ast_tree and gene_ids as a set
"""
str_expr = str_expr.strip()
if len(str_expr) == 0:
return... |
Knockout gene by marking it as non - functional and setting all associated reactions bounds to zero. | def knock_out(self):
"""Knockout gene by marking it as non-functional and setting all
associated reactions bounds to zero.
The change is reverted upon exit if executed within the model as
context.
"""
self.functional = False
for reaction in self.reactions:
... |
Removes the association | def remove_from_model(self, model=None,
make_dependent_reactions_nonfunctional=True):
"""Removes the association
Parameters
----------
model : cobra model
The model to remove the gene from
make_dependent_reactions_nonfunctional : bool
... |
Compute a single solution based on ( linear ) MOMA. | def moma(model, solution=None, linear=True):
"""
Compute a single solution based on (linear) MOMA.
Compute a new flux distribution that is at a minimal distance to a
previous reference solution. Minimization of metabolic adjustment (MOMA) is
generally used to assess the impact
of knock-outs. Th... |
r Add constraints and objective representing for MOMA. | def add_moma(model, solution=None, linear=True):
r"""Add constraints and objective representing for MOMA.
This adds variables and constraints for the minimization of metabolic
adjustment (MOMA) to the model.
Parameters
----------
model : cobra.Model
The model to add MOMA constraints an... |
convert possible types to str float and bool | def _fix_type(value):
"""convert possible types to str, float, and bool"""
# Because numpy floats can not be pickled to json
if isinstance(value, string_types):
return str(value)
if isinstance(value, float_):
return float(value)
if isinstance(value, bool_):
return bool(value)... |
update new_dict with optional attributes from cobra_object | def _update_optional(cobra_object, new_dict, optional_attribute_dict,
ordered_keys):
"""update new_dict with optional attributes from cobra_object"""
for key in ordered_keys:
default = optional_attribute_dict[key]
value = getattr(cobra_object, key)
if value is None o... |
Convert model to a dict. | def model_to_dict(model, sort=False):
"""Convert model to a dict.
Parameters
----------
model : cobra.Model
The model to reformulate as a dict.
sort : bool, optional
Whether to sort the metabolites, reactions, and genes or maintain the
order defined in the model.
Return... |
Build a model from a dict. | def model_from_dict(obj):
"""Build a model from a dict.
Models stored in json are first formulated as a dict that can be read to
cobra model using this function.
Parameters
----------
obj : dict
A dictionary with elements, 'genes', 'compartments', 'id',
'metabolites', 'notes' a... |
extract the compartment from the id string | def _get_id_compartment(id):
"""extract the compartment from the id string"""
bracket_search = _bracket_re.findall(id)
if len(bracket_search) == 1:
return bracket_search[0][1]
underscore_search = _underscore_re.findall(id)
if len(underscore_search) == 1:
return underscore_search[0][1... |
translate an array x into a MATLAB cell array | def _cell(x):
"""translate an array x into a MATLAB cell array"""
x_no_none = [i if i is not None else "" for i in x]
return array(x_no_none, dtype=np_object) |
Load a cobra model stored as a. mat file | def load_matlab_model(infile_path, variable_name=None, inf=inf):
"""Load a cobra model stored as a .mat file
Parameters
----------
infile_path: str
path to the file to to read
variable_name: str, optional
The variable name of the model in the .mat file. If this is not
specif... |
Save the cobra model as a. mat file. | def save_matlab_model(model, file_name, varname=None):
"""Save the cobra model as a .mat file.
This .mat file can be used directly in the MATLAB version of COBRA.
Parameters
----------
model : cobra.core.Model.Model object
The model to save
file_name : str or file-like object
T... |
create a dict mapping model attributes to arrays | def create_mat_dict(model):
"""create a dict mapping model attributes to arrays"""
rxns = model.reactions
mets = model.metabolites
mat = OrderedDict()
mat["mets"] = _cell([met_id for met_id in create_mat_metabolite_id(model)])
mat["metNames"] = _cell(mets.list_attr("name"))
mat["metFormulas"... |
create a model from the COBRA toolbox struct | def from_mat_struct(mat_struct, model_id=None, inf=inf):
"""create a model from the COBRA toolbox struct
The struct will be a dict read in by scipy.io.loadmat
"""
m = mat_struct
if m.dtype.names is None:
raise ValueError("not a valid mat struct")
if not {"rxns", "mets", "S", "lb", "ub"... |
send the model to a MATLAB workspace through pymatbridge | def model_to_pymatbridge(model, variable_name="model", matlab=None):
"""send the model to a MATLAB workspace through pymatbridge
This model can then be manipulated through the COBRA toolbox
Parameters
----------
variable_name : str
The variable name to which the model will be assigned in t... |
Search for a context manager | def get_context(obj):
"""Search for a context manager"""
try:
return obj._contexts[-1]
except (AttributeError, IndexError):
pass
try:
return obj._model._contexts[-1]
except (AttributeError, IndexError):
pass
return None |
A decorator to simplify the context management of simple object attributes. Gets the value of the attribute prior to setting it and stores a function to set the value to the old value in the HistoryManager. | def resettable(f):
"""A decorator to simplify the context management of simple object
attributes. Gets the value of the attribute prior to setting it, and stores
a function to set the value to the old value in the HistoryManager.
"""
def wrapper(self, new_value):
context = get_context(self)... |
Generate a solution representation of the current solver state. | def get_solution(model, reactions=None, metabolites=None, raise_error=False):
"""
Generate a solution representation of the current solver state.
Parameters
---------
model : cobra.Model
The model whose reactions to retrieve values for.
reactions : list, optional
An iterable of ... |
Return all metabolites compartments. | def get_metabolite_compartments(self):
"""Return all metabolites' compartments."""
warn('use Model.compartments instead', DeprecationWarning)
return {met.compartment for met in self.metabolites
if met.compartment is not None} |
Get or set the constraints on the model exchanges. | def medium(self, medium):
"""Get or set the constraints on the model exchanges.
`model.medium` returns a dictionary of the bounds for each of the
boundary reactions, in the form of `{rxn_id: bound}`, where `bound`
specifies the absolute value of the bound in direction of metabolite
... |
Provides a partial deepcopy of the Model. All of the Metabolite Gene and Reaction objects are created anew but in a faster fashion than deepcopy | def copy(self):
"""Provides a partial 'deepcopy' of the Model. All of the Metabolite,
Gene, and Reaction objects are created anew but in a faster fashion
than deepcopy
"""
new = self.__class__()
do_not_copy_by_ref = {"metabolites", "reactions", "genes", "notes",
... |
Will add a list of metabolites to the model object and add new constraints accordingly. | def add_metabolites(self, metabolite_list):
"""Will add a list of metabolites to the model object and add new
constraints accordingly.
The change is reverted upon exit when using the model as a context.
Parameters
----------
metabolite_list : A list of `cobra.core.Metab... |
Remove a list of metabolites from the the object. | def remove_metabolites(self, metabolite_list, destructive=False):
"""Remove a list of metabolites from the the object.
The change is reverted upon exit when using the model as a context.
Parameters
----------
metabolite_list : list
A list with `cobra.Metabolite` obj... |
Add a boundary reaction for a given metabolite. | def add_boundary(self, metabolite, type="exchange", reaction_id=None,
lb=None, ub=None, sbo_term=None):
"""
Add a boundary reaction for a given metabolite.
There are three different types of pre-defined boundary reactions:
exchange, demand, and sink reactions.
... |
Add reactions to the model. | def add_reactions(self, reaction_list):
"""Add reactions to the model.
Reactions with identifiers identical to a reaction already in the
model are ignored.
The change is reverted upon exit when using the model as a context.
Parameters
----------
reaction_list :... |
Remove reactions from the model. | def remove_reactions(self, reactions, remove_orphans=False):
"""Remove reactions from the model.
The change is reverted upon exit when using the model as a context.
Parameters
----------
reactions : list
A list with reactions (`cobra.Reaction`), or their id's, to re... |
Add groups to the model. | def add_groups(self, group_list):
"""Add groups to the model.
Groups with identifiers identical to a group already in the model are
ignored.
If any group contains members that are not in the model, these members
are added to the model as well. Only metabolites, reactions, and g... |
Remove groups from the model. | def remove_groups(self, group_list):
"""Remove groups from the model.
Members of each group are not removed
from the model (i.e. metabolites, reactions, and genes in the group
stay in the model after any groups containing them are removed).
Parameters
----------
... |
Returns a list of groups that an element ( reaction metabolite gene ) is associated with. | def get_associated_groups(self, element):
"""Returns a list of groups that an element (reaction, metabolite, gene)
is associated with.
Parameters
----------
element: `cobra.Reaction`, `cobra.Metabolite`, or `cobra.Gene`
Returns
-------
list of `cobra.Gro... |
Populate attached solver with constraints and variables that model the provided reactions. | def _populate_solver(self, reaction_list, metabolite_list=None):
"""Populate attached solver with constraints and variables that
model the provided reactions.
"""
constraint_terms = AutoVivification()
to_add = []
if metabolite_list is not None:
for met in meta... |
Optimize model without creating a solution object. | def slim_optimize(self, error_value=float('nan'), message=None):
"""Optimize model without creating a solution object.
Creating a full solution object implies fetching shadow prices and
flux values for all reactions and metabolites from the solver
object. This necessarily takes some tim... |
Optimize the model using flux balance analysis. | def optimize(self, objective_sense=None, raise_error=False):
"""
Optimize the model using flux balance analysis.
Parameters
----------
objective_sense : {None, 'maximize' 'minimize'}, optional
Whether fluxes should be maximized or minimized. In case of None,
... |
Update all indexes and pointers in a model | def repair(self, rebuild_index=True, rebuild_relationships=True):
"""Update all indexes and pointers in a model
Parameters
----------
rebuild_index : bool
rebuild the indices kept in reactions, metabolites and genes
rebuild_relationships : bool
reset all... |
Print a summary of the input and output fluxes of the model. | def summary(self, solution=None, threshold=1E-06, fva=None, names=False,
floatfmt='.3g'):
"""
Print a summary of the input and output fluxes of the model.
Parameters
----------
solution: cobra.Solution, optional
A previously solved model solution to u... |
Merge two models to create a model with the reactions from both models. | def merge(self, right, prefix_existing=None, inplace=True,
objective='left'):
"""Merge two models to create a model with the reactions from both
models.
Custom constraints and variables from right models are also copied
to left model, however note that, constraints and var... |
make a single string id SBML compliant | def _escape_str_id(id_str):
"""make a single string id SBML compliant"""
for c in ("'", '"'):
if id_str.startswith(c) and id_str.endswith(c) \
and id_str.count(c) == 2:
id_str = id_str.strip(c)
for char, escaped_char in _renames:
id_str = id_str.replace(char, esca... |
makes all ids SBML compliant | def escape_ID(cobra_model):
"""makes all ids SBML compliant"""
for x in chain([cobra_model],
cobra_model.metabolites,
cobra_model.reactions,
cobra_model.genes):
x.id = _escape_str_id(x.id)
cobra_model.repair()
gene_renamer = _GeneEscaper()... |
renames genes in a model from the rename_dict | def rename_genes(cobra_model, rename_dict):
"""renames genes in a model from the rename_dict"""
recompute_reactions = set() # need to recomptue related genes
remove_genes = []
for old_name, new_name in iteritems(rename_dict):
# undefined if there a value matches a different key
# becaus... |
Return the model as a JSON document. | def to_json(model, sort=False, **kwargs):
"""
Return the model as a JSON document.
``kwargs`` are passed on to ``json.dumps``.
Parameters
----------
model : cobra.Model
The cobra model to represent.
sort : bool, optional
Whether to sort the metabolites, reactions, and genes... |
Write the cobra model to a file in JSON format. | def save_json_model(model, filename, sort=False, pretty=False, **kwargs):
"""
Write the cobra model to a file in JSON format.
``kwargs`` are passed on to ``json.dump``.
Parameters
----------
model : cobra.Model
The cobra model to represent.
filename : str or file-like
File ... |
Load a cobra model from a file in JSON format. | def load_json_model(filename):
"""
Load a cobra model from a file in JSON format.
Parameters
----------
filename : str or file-like
File path or descriptor that contains the JSON document describing the
cobra model.
Returns
-------
cobra.Model
The cobra model as... |
Add a linear version of a minimal medium to the model solver. | def add_linear_obj(model):
"""Add a linear version of a minimal medium to the model solver.
Changes the optimization objective to finding the growth medium requiring
the smallest total import flux::
minimize sum |r_i| for r_i in import_reactions
Arguments
---------
model : cobra.Model... |
Add a mixed - integer version of a minimal medium to the model. | def add_mip_obj(model):
"""Add a mixed-integer version of a minimal medium to the model.
Changes the optimization objective to finding the medium with the least
components::
minimize size(R) where R part of import_reactions
Arguments
---------
model : cobra.model
The model to ... |
Convert a solution to medium. | def _as_medium(exchanges, tolerance=1e-6, exports=False):
"""Convert a solution to medium.
Arguments
---------
exchanges : list of cobra.reaction
The exchange reactions to consider.
tolerance : positive double
The absolute tolerance for fluxes. Fluxes with an absolute value
... |
Find the minimal growth medium for the model. | def minimal_medium(model, min_objective_value=0.1, exports=False,
minimize_components=False, open_exchanges=False):
"""
Find the minimal growth medium for the model.
Finds the minimal growth medium for the model which allows for
model as well as individual growth. Here, a minimal med... |
Initialize a global model object for multiprocessing. | def _init_worker(model, loopless, sense):
"""Initialize a global model object for multiprocessing."""
global _model
global _loopless
_model = model
_model.solver.objective.direction = sense
_loopless = loopless |
Determine the minimum and maximum possible flux value for each reaction. | def flux_variability_analysis(model, reaction_list=None, loopless=False,
fraction_of_optimum=1.0, pfba_factor=None,
processes=None):
"""
Determine the minimum and maximum possible flux value for each reaction.
Parameters
----------
model :... |
Find reactions that cannot carry any flux. | def find_blocked_reactions(model,
reaction_list=None,
zero_cutoff=None,
open_exchanges=False,
processes=None):
"""
Find reactions that cannot carry any flux.
The question whether or not a reaction is... |
Return a set of essential genes. | def find_essential_genes(model, threshold=None, processes=None):
"""
Return a set of essential genes.
A gene is considered essential if restricting the flux of all reactions
that depend on it to zero causes the objective, e.g., the growth rate,
to also be zero, below the threshold, or infeasible.
... |
Return a set of essential reactions. | def find_essential_reactions(model, threshold=None, processes=None):
"""Return a set of essential reactions.
A reaction is considered essential if restricting its flux to zero
causes the objective, e.g., the growth rate, to also be zero, below the
threshold, or infeasible.
Parameters
--------... |
adds SBO terms for demands and exchanges | def add_SBO(model):
"""adds SBO terms for demands and exchanges
This works for models which follow the standard convention for
constructing and naming these reactions.
The reaction should only contain the single metabolite being exchanged,
and the id should be EX_metid or DM_metid
"""
for ... |
Calculate the mol mass of the compound | def weight(self):
"""Calculate the mol mass of the compound
Returns
-------
float
the mol mass
"""
try:
return sum([count * elements_and_molecular_weights[element]
for element, count in self.elements.items()])
excep... |
Insert a <! -- more -- > tag for larger release notes. | def insert_break(lines, break_pos=9):
"""
Insert a <!--more--> tag for larger release notes.
Parameters
----------
lines : list of str
The content of the release note.
break_pos : int
Line number before which a break should approximately be inserted.
Returns
-------
... |
Build the markdown release notes for Hugo. | def build_hugo_md(filename, tag, bump):
"""
Build the markdown release notes for Hugo.
Inserts the required TOML header with specific values and adds a break
for long release notes.
Parameters
----------
filename : str, path
The release notes file.
tag : str
The tag, fo... |
Identify the kind of release by comparing to existing ones. | def find_bump(target, tag):
"""Identify the kind of release by comparing to existing ones."""
tmp = tag.split(".")
existing = [intify(basename(f)) for f in glob(join(target, "[0-9]*.md"))]
latest = max(existing)
if int(tmp[0]) > latest[0]:
return "major"
elif int(tmp[1]) > latest[1]:
... |
Identify the release type and create a new target file with TOML header. | def main(argv):
"""
Identify the release type and create a new target file with TOML header.
Requires three arguments.
"""
source, target, tag = argv
if "a" in tag:
bump = "alpha"
if "b" in tag:
bump = "beta"
else:
bump = find_bump(target, tag)
filename = "{... |
Provide a common interface for single or multiple knockouts. | def _multi_deletion(model, entity, element_lists, method="fba",
solution=None, processes=None, **kwargs):
"""
Provide a common interface for single or multiple knockouts.
Parameters
----------
model : cobra.Model
The metabolic model to perform deletions in.
entity : ... |
Knock out each reaction from a given list. | def single_reaction_deletion(model, reaction_list=None, method="fba",
solution=None, processes=None, **kwargs):
"""
Knock out each reaction from a given list.
Parameters
----------
model : cobra.Model
The metabolic model to perform deletions in.
reaction_lis... |
Knock out each gene from a given list. | def single_gene_deletion(model, gene_list=None, method="fba", solution=None,
processes=None, **kwargs):
"""
Knock out each gene from a given list.
Parameters
----------
model : cobra.Model
The metabolic model to perform deletions in.
gene_list : iterable
... |
Knock out each reaction pair from the combinations of two given lists. | def double_reaction_deletion(model, reaction_list1=None, reaction_list2=None,
method="fba", solution=None, processes=None,
**kwargs):
"""
Knock out each reaction pair from the combinations of two given lists.
We say 'pair' here but the order order d... |
Knock out each gene pair from the combination of two given lists. | def double_gene_deletion(model, gene_list1=None, gene_list2=None, method="fba",
solution=None, processes=None, **kwargs):
"""
Knock out each gene pair from the combination of two given lists.
We say 'pair' here but the order order does not matter.
Parameters
----------
... |
Generate the id of reverse_variable from the reaction s id. | def reverse_id(self):
"""Generate the id of reverse_variable from the reaction's id."""
return '_'.join((self.id, 'reverse',
hashlib.md5(
self.id.encode('utf-8')).hexdigest()[0:5])) |
The flux value in the most recent solution. | def flux(self):
"""
The flux value in the most recent solution.
Flux is the primal value of the corresponding variable in the model.
Warnings
--------
* Accessing reaction fluxes through a `Solution` object is the safer,
preferred, and only guaranteed to be co... |
Display gene_reaction_rule with names intead. | def gene_name_reaction_rule(self):
"""Display gene_reaction_rule with names intead.
Do NOT use this string for computation. It is intended to give a
representation of the rule using more familiar gene names instead of
the often cryptic ids.
"""
names = {i.id: i.name for... |
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