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int64 0
731k
| package
stringlengths 2
98
⌀ | name
stringlengths 1
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| docstring
stringlengths 0
281k
⌀ | code
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8.19k
| signature
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sequencelengths 768
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20,448 | metevolsim.metevolsim | initialize |
Initialize the MCMC algorithm.
Parameters
----------
None
Returns
-------
None
| def initialize( self ):
"""
Initialize the MCMC algorithm.
Parameters
----------
None
Returns
-------
None
"""
success = self.model.compute_wild_type_steady_state()
assert success, "Wild-type model is not stable. Exit."
success = self.model.compute_mutant_steady_state()
assert success, "Wild-type model is not stable. Exit."
self.model.compute_sum_distance()
self.model.compute_moma_distance()
self.initialize_output_file()
self.wild_type_abund = self.model.get_wild_type_species_array()
self.mutant_abund = self.model.get_mutant_species_array()
self.wild_type_flux = self.model.get_wild_type_reaction_array()
self.mutant_flux = self.model.get_mutant_reaction_array()
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20,449 | metevolsim.metevolsim | initialize_output_file |
Initialize the output file (write the header).
Parameters
----------
None
Returns
-------
None
| def initialize_output_file( self ):
"""
Initialize the output file (write the header).
Parameters
----------
None
Returns
-------
None
"""
header = "nb_iterations nb_accepted nb_rejected param_metaid param_id param_previous param_val"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
header += " "+species_id
for reaction_id in self.model.reactions:
header += " "+reaction_id
header += " wild_type_absolute_sum mutant_absolute_sum absolute_sum_dist absolute_moma_dist relative_moma_dist absolute_moma_all_dist relative_moma_all_dist\n"
self.output_file = open("output/iterations.txt", "a")
self.output_file.write(header)
self.output_file.close()
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] |
20,450 | metevolsim.metevolsim | iterate |
Iterate the MCMC algorithm.
Parameters
----------
None
Returns
-------
None
| def iterate( self ):
"""
Iterate the MCMC algorithm.
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Save previous state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.previous_abund = np.copy(self.mutant_abund)
self.previous_flux = np.copy(self.mutant_flux)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Introduce a new random mutation #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.param_metaid = self.model.get_random_parameter()
self.param_id = self.model.parameters[self.param_metaid]["id"]
self.param_previous, self.param_value = self.model.random_parameter_mutation(self.param_metaid, self.sigma)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Compute the new steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
success = self.model.compute_mutant_steady_state()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 4) Evaluate model stability and select the new iteration event #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
if not success:
self.reload_previous_state()
self.nb_unstable += 1
self.param_metaid = "_"
self.param_id = "_"
self.param_value = 0.0
self.param_previous = 0.0
else:
self.nb_unstable = 0
self.model.compute_sum_distance()
self.model.compute_moma_distance()
self.mutant_abund = self.model.get_mutant_species_array()
self.mutant_flux = self.model.get_mutant_reaction_array()
#-----------------------------------------------------------------#
# 4.1) If the simulation is a mutation accumulation experiment, #
# keep all the mutational events. #
#-----------------------------------------------------------------#
if self.selection_scheme == "MUTATION_ACCUMULATION":
self.nb_iterations += 1
self.nb_accepted += 1
self.update_statistics()
self.compute_statistics()
#-----------------------------------------------------------------#
# 4.2) If the simulation is a absolute metabolic sum selection #
# experiment, keep only mutations for which the SUM distance #
# is lower than a given selection threshold. #
#-----------------------------------------------------------------#
elif self.selection_scheme == "ABSOLUTE_METABOLIC_SUM_SELECTION" and self.model.absolute_sum_distance < self.selection_threshold:
self.nb_iterations += 1
self.nb_accepted += 1
self.update_statistics()
self.compute_statistics()
elif self.selection_scheme == "ABSOLUTE_METABOLIC_SUM_SELECTION" and self.model.absolute_sum_distance >= self.selection_threshold:
self.nb_iterations += 1
self.nb_rejected += 1
self.reload_previous_state()
self.update_statistics()
self.compute_statistics()
self.param_metaid = "_"
self.param_id = "_"
self.param_value = 0.0
self.param_previous = 0.0
#-----------------------------------------------------------------#
# 4.3) If the simulation is a absolute target fluxes selection #
# experiment, keep only mutations for which the MOMA #
# distance is lower than a given selection threshold. #
#-----------------------------------------------------------------#
elif self.selection_scheme == "ABSOLUTE_TARGET_FLUXES_SELECTION" and self.model.absolute_moma_distance < self.selection_threshold:
self.nb_iterations += 1
self.nb_accepted += 1
self.update_statistics()
self.compute_statistics()
elif self.selection_scheme == "ABSOLUTE_TARGET_FLUXES_SELECTION" and self.model.absolute_moma_distance >= self.selection_threshold:
self.nb_iterations += 1
self.nb_rejected += 1
self.reload_previous_state()
self.update_statistics()
self.compute_statistics()
self.param_metaid = "_"
self.param_id = "_"
self.param_value = 0.0
self.param_previous = 0.0
#-----------------------------------------------------------------#
# 4.4) If the simulation is a relative target fluxes selection #
# experiment, keep only mutations for which the MOMA #
# distance is lower than a given selection threshold. #
#-----------------------------------------------------------------#
elif self.selection_scheme == "RELATIVE_TARGET_FLUXES_SELECTION" and self.model.relative_moma_distance < self.selection_threshold:
self.nb_iterations += 1
self.nb_accepted += 1
self.update_statistics()
self.compute_statistics()
elif self.selection_scheme == "RELATIVE_TARGET_FLUXES_SELECTION" and self.model.relative_moma_distance >= self.selection_threshold:
self.nb_iterations += 1
self.nb_rejected += 1
self.reload_previous_state()
self.update_statistics()
self.compute_statistics()
self.param_metaid = "_"
self.param_id = "_"
self.param_value = 0.0
self.param_previous = 0.0
#-----------------------------------------------------------------#
# 4.5) If the simulation is a absolute ALL fluxes selection #
# experiment, keep only mutations for which the MOMA #
# distance is lower than a given selection threshold. #
#-----------------------------------------------------------------#
elif self.selection_scheme == "ABSOLUTE_ALL_FLUXES_SELECTION" and self.model.absolute_moma_all_distance < self.selection_threshold:
self.nb_iterations += 1
self.nb_accepted += 1
self.update_statistics()
self.compute_statistics()
elif self.selection_scheme == "ABSOLUTE_ALL_FLUXES_SELECTION" and self.model.absolute_moma_all_distance >= self.selection_threshold:
self.nb_iterations += 1
self.nb_rejected += 1
self.reload_previous_state()
self.update_statistics()
self.compute_statistics()
self.param_metaid = "_"
self.param_id = "_"
self.param_value = 0.0
self.param_previous = 0.0
#-----------------------------------------------------------------#
# 4.6) If the simulation is a relative ALL fluxes selection #
# experiment, keep only mutations for which the MOMA #
# distance is lower than a given selection threshold. #
#-----------------------------------------------------------------#
elif self.selection_scheme == "RELATIVE_ALL_FLUXES_SELECTION" and self.model.relative_moma_all_distance < self.selection_threshold:
self.nb_iterations += 1
self.nb_accepted += 1
self.update_statistics()
self.compute_statistics()
elif self.selection_scheme == "RELATIVE_ALL_FLUXES_SELECTION" and self.model.relative_moma_all_distance >= self.selection_threshold:
self.nb_iterations += 1
self.nb_rejected += 1
self.reload_previous_state()
self.update_statistics()
self.compute_statistics()
self.param_metaid = "_"
self.param_id = "_"
self.param_value = 0.0
self.param_previous = 0.0
#~~~~~~~~~~~~~~~~~~~~~~ | (self) | [
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] |
20,451 | metevolsim.metevolsim | reload_previous_state |
Reload the previous MCMC state.
Parameters
----------
None
Returns
-------
None
| def reload_previous_state( self ):
"""
Reload the previous MCMC state.
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Restore the mutated parameter #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.model.set_mutant_parameter_value(self.param_metaid, self.param_previous)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Retrieve previous values #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.mutant_abund = np.copy(self.previous_abund)
self.mutant_flux = np.copy(self.previous_flux)
index = 0
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
self.model.species[species_id]["mutant_value"] = self.mutant_abund[index]
index += 1
index = 0
for reaction_id in self.model.reactions:
self.model.reactions[reaction_id]["mutant_value"] = self.mutant_flux[index]
index += 1
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] |
20,452 | metevolsim.metevolsim | update_statistics |
Update statistics trackers for the current state.
Parameters
----------
None
Returns
-------
None
| def update_statistics( self ):
"""
Update statistics trackers for the current state.
Parameters
----------
None
Returns
-------
None
"""
###########
self.sum_abund += self.mutant_abund
self.sqsum_abund += self.mutant_abund*self.mutant_abund
for i in range(self.N_abund):
if self.wild_type_abund[i] > 0.0:
self.relsum_abund[i] += (self.mutant_abund[i]/self.wild_type_abund[i])
self.relsqsum_abund[i] += (self.mutant_abund[i]/self.wild_type_abund[i])*(self.mutant_abund[i]/self.wild_type_abund[i])
###########
self.sum_flux += self.mutant_flux
self.sqsum_flux += self.mutant_flux*self.mutant_flux
for i in range(self.N_flux):
if self.wild_type_flux[i] > 0.0:
self.relsum_flux[i] += (self.mutant_flux[i]/self.wild_type_flux[i])
self.relsqsum_flux[i] += (self.mutant_flux[i]/self.wild_type_flux[i])*(self.mutant_flux[i]/self.wild_type_flux[i])
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] |
20,453 | metevolsim.metevolsim | write_output_file |
Write the current MCMC state in the output file.
Parameters
----------
None
Returns
-------
None
| def write_output_file( self ):
"""
Write the current MCMC state in the output file.
Parameters
----------
None
Returns
-------
None
"""
line = ""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write current MCMC state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
line += str(self.nb_iterations)+" "
line += str(self.nb_accepted)+" "
line += str(self.nb_rejected)+" "
line += str(self.param_metaid)+" "
line += str(self.param_id)+" "
line += str(self.param_previous)+" "
line += str(self.param_value)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
line += " "+str(self.model.species[species_id]["mutant_value"])
for reaction_id in self.model.reactions:
line += " "+str(self.model.reactions[reaction_id]["mutant_value"])
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Write scores #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
line += " "+str(self.model.wild_type_absolute_sum)+" "+str(self.model.mutant_absolute_sum)+" "+str(self.model.absolute_sum_distance)+" "+str(self.model.absolute_moma_distance)+" "+str(self.model.relative_moma_distance)+" "+str(self.model.absolute_moma_all_distance)+" "+str(self.model.relative_moma_all_distance)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 4) Write in file #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.output_file = open("output/iterations.txt", "a")
self.output_file.write(line+"\n")
self.output_file.close()
| (self) | [
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] |
20,454 | metevolsim.metevolsim | write_statistics |
Write final statistics in an output file (output/statistics.txt).
Parameters
----------
None
Returns
-------
None
| def write_statistics( self ):
"""
Write final statistics in an output file (output/statistics.txt).
Parameters
----------
None
Returns
-------
None
"""
f = open("output/statistics.txt", "w")
f.write("species_id wild_type mean var CV ER\n")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write species statistics #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
index = 0
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
line = species_id+" "
line += str(self.wild_type_abund[index])+" "
line += str(self.mean_abund[index])+" "
line += str(self.var_abund[index])+" "
line += str(self.CV_abund[index])+" "
line += str(self.ER_abund[index])+"\n"
index += 1
f.write(line)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write fluxes statistics #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
index = 0
for reaction_id in self.model.reactions:
line = reaction_id+" "
line += str(self.wild_type_flux[index])+" "
line += str(self.mean_flux[index])+" "
line += str(self.var_flux[index])+" "
line += str(self.CV_flux[index])+" "
line += str(self.ER_flux[index])+"\n"
index += 1
f.write(line)
f.close()
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20,455 | metevolsim.metevolsim | Model |
The Model class loads, saves and manipulates SBML models.
Manipulations include kinetic parameter mutations, steady-state computing,
metabolic control analysis (MCA), species shortest paths and flux-drop
analysis. Some constraints exist on the SBML model format:
- Species and reactions identifiers must be specified and unique.
- Kinetic parameters must be specified (globally, and/or for each reaction).
All kinetic parameters will be mutable.
- The meta-identifiers are rebuilt automatically by MetEvolSim to avoid
collisions.
Attributes
----------
> sbml_filename : str
Name of the SBML model file.
> reader : libsbml.SBMLReader
SBML model reader.
> wild_type_document : libsbml.SBMLDocument
SBML document of the wild-type model.
> mutant_document : libsbml.SBMLDocument
SBML document of the mutant model.
> wild_type_model : libsbml.Model
Wild-type model.
> mutant_model : libsbml.Model
Mutant model.
> copasi_path: str
Location of Copasi executable.
> species : dict
List of species.
> parameters : dict
List of kinetic parameters.
> reactions : dict
List of reactions.
> species_graph: networkx.Graph
A networkx Graph describing the metabolite-to-metabolite network.
> objective_function : list of [str, float]
Objective function (list of reaction identifiers and coefficients).
> wild_type_absolute_sum : float
Sum of evolvable metabolic absolute abundances in wild-type.
> mutant_absolute_sum : float
Sum of evolvable metabolic abundances in mutant.
> absolute_sum_distance : float
Distance between wild-type and mutant absolute metabolic sums.
> absolute_moma_distance : float
Distance between the wild-type and the mutant, based on the
Minimization Of Metabolic Adjustment on absolute target fluxes.
> relative_moma_distance : float
Distance between the wild-type and the mutant, based on the
Minimization Of Metabolic Adjustment on relative target fluxes.
> absolute_moma_all_distance : float
Distance between the wild-type and the mutant, based on the
Minimization Of Metabolic Adjustment on ALL absolute fluxes.
> relative_moma_all_distance : float
Distance between the wild-type and the mutant, based on the
Minimization Of Metabolic Adjustment on ALL relative fluxes.
Methods
-------
> __init__(sbml_filename, objective_function, copasi_path)
Constructor.
> rebuild_metaids()
Rebuild unique metaids for all model variables.
> build_species_list()
Build the list of metabolites.
> build_parameter_list()
Build the list of parameters.
> build_reaction_list()
Build the list of reactions.
> get_number_of_species()
Get the number of species.
> get_number_of_variable_species()
Get the number of variable species.
> get_number_of_parameters()
Get the number of parameters.
> get_number_of_reactions()
Get the number of reactions.
> get_wild_type_species_value(species_id)
Get wild-type species value.
> get_mutant_species_value(species_id)
Get mutant species value.
> get_wild_type_parameter_value(param_metaid)
Get wild-type parameter value.
> get_mutant_parameter_value(param_metaid)
Get mutant parameter value.
> set_species_initial_value(species_id, value)
Set a species initial value.
> set_wild_type_parameter_value(param_metaid, value)
Set wild-type parameter value.
> set_mutant_parameter_value(param_metaid, value)
Set mutant parameter value.
> get_random_parameter()
Get a random parameter.
> deterministic_parameter_mutation(param_metaid, factor)
Make a deterministic parameter mutation by a given log-scale factor,
centered on the wild-type value.
> random_parameter_mutation(param_metaid, sigma)
Make a random parameter mutation by a given log-scale mutation size
sigma, centered on the previous mutant value.
> write_list_of_variables()
Write the list of variable names, identifiers and compartment in the
file "output/variables.txt".
> write_wild_type_SBML_file()
Write wild-type SBML file.
> write_mutant_SBML_file()
Write mutant SBML file.
> create_wild_type_cps_file()
Create ancestor CPS file.
> create_mutant_cps_file()
Create mutant CPS file.
> edit_wild_type_cps_file(task)
Edit ancestor CPS file to schedule steady-state calculation.
> edit_mutant_cps_file(task)
Edit mutant CPS file to schedule steady-state calculation.
> run_copasi_for_wild_type()
Run Copasi for the wild-type model.
> run_copasi_for_mutant()
Run Copasi for the mutant model.
> parse_copasi_output(filename, task)
Parse Copasi output file.
> compute_wild_type_steady_state()
Compute wild-type steady-state.
> compute_mutant_steady_state()
Compute mutant steady-state.
> update_initial_concentrations()
Update species initial concentrations.
> compute_sum_distance()
Compute the metabolic sum distance between the wild-type and the mutant.
> compute_moma_distance()
Compute the MOMA distance between the wild-type and the mutant, based on
target fluxes (MOMA: Minimization Of Metabolic Adjustment).
> compute_wild_type_metabolic_control_analysis()
Compute and save the wild-type metabolic control analysis (MCA).
> compute_mutant_metabolic_control_analysis()
Compute and save the mutant metabolic control analysis (MCA).
> build_species_graph():
Build the metabolite-to-metabolite graph (mainly to compute shortest
paths afterward).
> save_shortest_paths(filename):
Save the matrix of all pairwise metabolites shortest paths (assuming an
undirected graph).
> flux_drop_analysis(drop_coefficient, filename, overwrite):
Run a flux-drop analysis. Each flux is independently dropped by a factor
"drop_coefficient" and the relative MOMA distance from wild-type target
fluxes is computed. The result is saved in a file "filename", one flux
per line.
| class Model:
"""
The Model class loads, saves and manipulates SBML models.
Manipulations include kinetic parameter mutations, steady-state computing,
metabolic control analysis (MCA), species shortest paths and flux-drop
analysis. Some constraints exist on the SBML model format:
- Species and reactions identifiers must be specified and unique.
- Kinetic parameters must be specified (globally, and/or for each reaction).
All kinetic parameters will be mutable.
- The meta-identifiers are rebuilt automatically by MetEvolSim to avoid
collisions.
Attributes
----------
> sbml_filename : str
Name of the SBML model file.
> reader : libsbml.SBMLReader
SBML model reader.
> wild_type_document : libsbml.SBMLDocument
SBML document of the wild-type model.
> mutant_document : libsbml.SBMLDocument
SBML document of the mutant model.
> wild_type_model : libsbml.Model
Wild-type model.
> mutant_model : libsbml.Model
Mutant model.
> copasi_path: str
Location of Copasi executable.
> species : dict
List of species.
> parameters : dict
List of kinetic parameters.
> reactions : dict
List of reactions.
> species_graph: networkx.Graph
A networkx Graph describing the metabolite-to-metabolite network.
> objective_function : list of [str, float]
Objective function (list of reaction identifiers and coefficients).
> wild_type_absolute_sum : float
Sum of evolvable metabolic absolute abundances in wild-type.
> mutant_absolute_sum : float
Sum of evolvable metabolic abundances in mutant.
> absolute_sum_distance : float
Distance between wild-type and mutant absolute metabolic sums.
> absolute_moma_distance : float
Distance between the wild-type and the mutant, based on the
Minimization Of Metabolic Adjustment on absolute target fluxes.
> relative_moma_distance : float
Distance between the wild-type and the mutant, based on the
Minimization Of Metabolic Adjustment on relative target fluxes.
> absolute_moma_all_distance : float
Distance between the wild-type and the mutant, based on the
Minimization Of Metabolic Adjustment on ALL absolute fluxes.
> relative_moma_all_distance : float
Distance between the wild-type and the mutant, based on the
Minimization Of Metabolic Adjustment on ALL relative fluxes.
Methods
-------
> __init__(sbml_filename, objective_function, copasi_path)
Constructor.
> rebuild_metaids()
Rebuild unique metaids for all model variables.
> build_species_list()
Build the list of metabolites.
> build_parameter_list()
Build the list of parameters.
> build_reaction_list()
Build the list of reactions.
> get_number_of_species()
Get the number of species.
> get_number_of_variable_species()
Get the number of variable species.
> get_number_of_parameters()
Get the number of parameters.
> get_number_of_reactions()
Get the number of reactions.
> get_wild_type_species_value(species_id)
Get wild-type species value.
> get_mutant_species_value(species_id)
Get mutant species value.
> get_wild_type_parameter_value(param_metaid)
Get wild-type parameter value.
> get_mutant_parameter_value(param_metaid)
Get mutant parameter value.
> set_species_initial_value(species_id, value)
Set a species initial value.
> set_wild_type_parameter_value(param_metaid, value)
Set wild-type parameter value.
> set_mutant_parameter_value(param_metaid, value)
Set mutant parameter value.
> get_random_parameter()
Get a random parameter.
> deterministic_parameter_mutation(param_metaid, factor)
Make a deterministic parameter mutation by a given log-scale factor,
centered on the wild-type value.
> random_parameter_mutation(param_metaid, sigma)
Make a random parameter mutation by a given log-scale mutation size
sigma, centered on the previous mutant value.
> write_list_of_variables()
Write the list of variable names, identifiers and compartment in the
file "output/variables.txt".
> write_wild_type_SBML_file()
Write wild-type SBML file.
> write_mutant_SBML_file()
Write mutant SBML file.
> create_wild_type_cps_file()
Create ancestor CPS file.
> create_mutant_cps_file()
Create mutant CPS file.
> edit_wild_type_cps_file(task)
Edit ancestor CPS file to schedule steady-state calculation.
> edit_mutant_cps_file(task)
Edit mutant CPS file to schedule steady-state calculation.
> run_copasi_for_wild_type()
Run Copasi for the wild-type model.
> run_copasi_for_mutant()
Run Copasi for the mutant model.
> parse_copasi_output(filename, task)
Parse Copasi output file.
> compute_wild_type_steady_state()
Compute wild-type steady-state.
> compute_mutant_steady_state()
Compute mutant steady-state.
> update_initial_concentrations()
Update species initial concentrations.
> compute_sum_distance()
Compute the metabolic sum distance between the wild-type and the mutant.
> compute_moma_distance()
Compute the MOMA distance between the wild-type and the mutant, based on
target fluxes (MOMA: Minimization Of Metabolic Adjustment).
> compute_wild_type_metabolic_control_analysis()
Compute and save the wild-type metabolic control analysis (MCA).
> compute_mutant_metabolic_control_analysis()
Compute and save the mutant metabolic control analysis (MCA).
> build_species_graph():
Build the metabolite-to-metabolite graph (mainly to compute shortest
paths afterward).
> save_shortest_paths(filename):
Save the matrix of all pairwise metabolites shortest paths (assuming an
undirected graph).
> flux_drop_analysis(drop_coefficient, filename, overwrite):
Run a flux-drop analysis. Each flux is independently dropped by a factor
"drop_coefficient" and the relative MOMA distance from wild-type target
fluxes is computed. The result is saved in a file "filename", one flux
per line.
"""
### Constructor ###
def __init__( self, sbml_filename, objective_function, copasi_path ):
"""
Model class constructor.
Parameters
----------
sbml_filename : str
Path of the SBML model file. The SBML model is automatically loaded.
objective_function : list of [str, float]
Objective function (list of reaction identifiers and coefficients).
copasi_path : str
Location of Copasi executable.
Returns
-------
None
"""
assert os.path.isfile(sbml_filename), "The SBML file \""+sbml_filename+"\" does not exist. Exit."
assert os.path.isfile(copasi_path), "The executable \""+copasi_path+"\" does not exist. Exit."
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Main SBML data #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.sbml_filename = sbml_filename
self.reader = libsbml.SBMLReader()
self.wild_type_document = self.reader.readSBMLFromFile(self.sbml_filename)
self.mutant_document = self.reader.readSBMLFromFile(self.sbml_filename)
self.wild_type_model = self.wild_type_document.getModel()
self.mutant_model = self.mutant_document.getModel()
self.copasi_path = copasi_path
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) List of model variables (species, reactions, parameters) #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.species = {}
self.parameters = {}
self.reactions = {}
self.rebuild_metaids | (sbml_filename, objective_function, copasi_path) | [
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20,456 | metevolsim.metevolsim | __init__ |
Model class constructor.
Parameters
----------
sbml_filename : str
Path of the SBML model file. The SBML model is automatically loaded.
objective_function : list of [str, float]
Objective function (list of reaction identifiers and coefficients).
copasi_path : str
Location of Copasi executable.
Returns
-------
None
| def __init__( self, sbml_filename, objective_function, copasi_path ):
"""
Model class constructor.
Parameters
----------
sbml_filename : str
Path of the SBML model file. The SBML model is automatically loaded.
objective_function : list of [str, float]
Objective function (list of reaction identifiers and coefficients).
copasi_path : str
Location of Copasi executable.
Returns
-------
None
"""
assert os.path.isfile(sbml_filename), "The SBML file \""+sbml_filename+"\" does not exist. Exit."
assert os.path.isfile(copasi_path), "The executable \""+copasi_path+"\" does not exist. Exit."
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Main SBML data #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.sbml_filename = sbml_filename
self.reader = libsbml.SBMLReader()
self.wild_type_document = self.reader.readSBMLFromFile(self.sbml_filename)
self.mutant_document = self.reader.readSBMLFromFile(self.sbml_filename)
self.wild_type_model = self.wild_type_document.getModel()
self.mutant_model = self.mutant_document.getModel()
self.copasi_path = copasi_path
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) List of model variables (species, reactions, parameters) #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.species = {}
self.parameters = {}
self.reactions = {}
self.rebuild_metaids()
self.build_species_list()
self.build_parameter_list()
self.build_reaction_list()
self.replace_variable_names()
self.write_list_of_variables()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Graph analysis #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.species_graph = nx.Graph()
self.species_to_reaction = {}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 4) Model evaluation #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.objective_function = objective_function
self.wild_type_absolute_sum = 0.0
self.mutant_absolute_sum = 0.0
self.absolute_sum_distance = 0.0
self.absolute_moma_distance = 0.0
self.relative_moma_distance = 0.0
self.absolute_moma_all_distance = 0.0
self.relative_moma_all_distance = 0.0
for target_flux in objective_function:
assert target_flux[0] in self.reactions
| (self, sbml_filename, objective_function, copasi_path) | [
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] |
20,457 | metevolsim.metevolsim | build_parameter_list |
Build the parameters list.
Parameters
----------
None
Returns
-------
None
| def build_parameter_list( self ):
"""
Build the parameters list.
Parameters
----------
None
Returns
-------
None
"""
self.parameters = {}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Parse main parameters #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for parameter in self.wild_type_model.getListOfParameters():
if parameter.getValue() != 0.0 and str(parameter.getValue()) != "nan":
assert parameter.getMetaId() not in list(self.parameters)
self.parameters[parameter.getMetaId()] = {}
self.parameters[parameter.getMetaId()]["metaid"] = parameter.getMetaId()
self.parameters[parameter.getMetaId()]["id"] = parameter.getId()
self.parameters[parameter.getMetaId()]["wild_type_value"] = parameter.getValue()
self.parameters[parameter.getMetaId()]["mutant_value"] = parameter.getValue()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Parse parameters for each reaction #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for reaction in self.wild_type_model.getListOfReactions():
for elmt in reaction.getListOfAllElements():
if elmt.getElementName() == "parameter" or elmt.getElementName() == "localParameter":
if elmt.getValue() != 0.0 and str(elmt.getValue()) != "nan":
assert elmt.getMetaId() not in list(self.parameters)
self.parameters[elmt.getMetaId()] = {}
self.parameters[elmt.getMetaId()]["metaid"] = elmt.getMetaId()
self.parameters[elmt.getMetaId()]["id"] = elmt.getId()
self.parameters[elmt.getMetaId()]["wild_type_value"] = elmt.getValue()
self.parameters[elmt.getMetaId()]["mutant_value"] = elmt.getValue()
| (self) | [
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20,458 | metevolsim.metevolsim | build_reaction_list |
Build the reaction list.
Parameters
----------
None
Returns
-------
None
| def build_reaction_list( self ):
"""
Build the reaction list.
Parameters
----------
None
Returns
-------
None
"""
self.reactions = {}
for reaction in self.wild_type_model.getListOfReactions():
assert reaction.getId() not in list(self.reactions)
self.reactions[reaction.getId()] = {"wild_type_value":0.0, "mutant_value":0.0}
self.reactions[reaction.getId()]["metaid"] = reaction.getMetaId()
self.reactions[reaction.getId()]["id"] = reaction.getId()
self.reactions[reaction.getId()]["name"] = reaction.getName()
self.reactions[reaction.getId()]["compartment"] = reaction.getCompartment()
| (self) | [
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20,459 | metevolsim.metevolsim | build_species_graph |
Build the metabolite-to-metabolite graph (mainly to compute shortest
paths afterward).
Parameters
----------
None
Returns
-------
None
| def build_species_graph( self ):
"""
Build the metabolite-to-metabolite graph (mainly to compute shortest
paths afterward).
Parameters
----------
None
Returns
-------
None
"""
self.species_graph.clear()
self.species_to_reaction = {}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Create nodes from species #
# identifiers #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for species_id in self.species.keys():
self.species_graph.add_node(species_id)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Create edges from reactions #
# (All the species involved in the #
# reaction are connected) #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for reaction in self.wild_type_model.getListOfReactions():
list_of_metabolites = []
for reactant in reaction.getListOfReactants():
species_id = reactant.getSpecies()
if species_id in self.species.keys():
list_of_metabolites.append(species_id)
if species_id not in self.species_to_reaction.keys():
self.species_to_reaction[species_id] = [reaction.getId()]
else:
self.species_to_reaction[species_id].append(reaction.getId())
for product in reaction.getListOfProducts():
species_id = product.getSpecies()
if species_id in self.species.keys():
list_of_metabolites.append(species_id)
if species_id not in self.species_to_reaction.keys():
self.species_to_reaction[species_id] = [reaction.getId()]
else:
self.species_to_reaction[species_id].append(reaction.getId())
#for modifier in reaction.getListOfModifiers():
# list_of_metabolites.append(modifier.getSpecies())
for i in range(len(list_of_metabolites)):
for j in range(i+1, len(list_of_metabolites)):
self.species_graph.add_edge(list_of_metabolites[i], list_of_metabolites[j])
| (self) | [
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] |
20,460 | metevolsim.metevolsim | build_species_list |
Build the species list.
Parameters
----------
None
Returns
-------
None
| def build_species_list( self ):
"""
Build the species list.
Parameters
----------
None
Returns
-------
None
"""
self.species = {}
for species in self.wild_type_model.getListOfSpecies():
assert species.getId() not in list(self.species)
self.species[species.getId()] = {}
self.species[species.getId()]["metaid"] = species.getMetaId()
self.species[species.getId()]["id"] = species.getId()
self.species[species.getId()]["name"] = species.getName()
if species.getName()=="":
self.species[species.getId()]["name"] = "-"
self.species[species.getId()]["compartment"] = species.getCompartment()
self.species[species.getId()]["constant"] = species.getConstant()
self.species[species.getId()]["boundary_condition"] = species.getBoundaryCondition()
self.species[species.getId()]["initial_value"] = species.getInitialConcentration()
self.species[species.getId()]["wild_type_value"] = species.getInitialConcentration()
self.species[species.getId()]["mutant_value"] = species.getInitialConcentration()
s1 = species.getAnnotationString().split("http://identifiers.org/kegg.compound/")
s2 = ""
if len(s1) == 1:
s1 = species.getAnnotationString().split("https://identifiers.org/kegg.compound/")
if len(s1) > 1:
s2 = s1[1].split("\"/>\n")[0]
if s2 != "":
self.species[species.getId()]["kegg_id"] = "https://identifiers.org/kegg.compound/"+s2
else:
self.species[species.getId()]["kegg_id"] = "-"
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20,461 | metevolsim.metevolsim | compute_moma_distance |
Compute the MOMA distance between the wild-type and the mutant, based on
target fluxes.
(MOMA: Minimization Of Metabolic Adjustment)
Parameters
----------
None
Returns
-------
None
| def compute_moma_distance( self ):
"""
Compute the MOMA distance between the wild-type and the mutant, based on
target fluxes.
(MOMA: Minimization Of Metabolic Adjustment)
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Compute absolute and relative MOMA distance on target fluxes #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.absolute_moma_distance = 0.0
self.relative_moma_distance = 0.0
for target_flux in self.objective_function:
reaction_id = target_flux[0]
coefficient = target_flux[1]
wild_type_value = self.reactions[reaction_id]["wild_type_value"]
mutant_value = self.reactions[reaction_id]["mutant_value"]
self.absolute_moma_distance += (wild_type_value-mutant_value)*(wild_type_value-mutant_value)
self.relative_moma_distance += (wild_type_value-mutant_value)/wild_type_value*(wild_type_value-mutant_value)/wild_type_value
self.absolute_moma_distance = np.sqrt(self.absolute_moma_distance)
self.relative_moma_distance = np.sqrt(self.relative_moma_distance)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Compute absolute and relative MOMA distance on all the fluxes #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.absolute_moma_all_distance = 0.0
self.relative_moma_all_distance = 0.0
wild_type_flux_array = self.get_wild_type_reaction_array()
mutant_flux_array = self.get_mutant_reaction_array()
for i in range(self.get_number_of_reactions()):
wild_type_value = wild_type_flux_array[i]
mutant_value = mutant_flux_array[i]
if wild_type_value > 1e-10:
self.absolute_moma_all_distance += (wild_type_value-mutant_value)*(wild_type_value-mutant_value)
self.relative_moma_all_distance += (wild_type_value-mutant_value)/wild_type_value*(wild_type_value-mutant_value)/wild_type_value
self.absolute_moma_all_distance = np.sqrt(self.absolute_moma_all_distance)
self.relative_moma_all_distance = np.sqrt(self.relative_moma_all_distance)
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] |
20,462 | metevolsim.metevolsim | compute_mutant_metabolic_control_analysis |
Compute and save the mutant metabolic control analysis (MCA).
Parameters
----------
None
Returns
-------
None
| def compute_mutant_metabolic_control_analysis( self ):
"""
Compute and save the mutant metabolic control analysis (MCA).
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Compute the steady-state with Copasi #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.write_mutant_SBML_file()
self.create_mutant_cps_file()
self.edit_mutant_cps_file("MCA")
self.run_copasi_for_mutant()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Extract the MCA result #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
rownames, colnames, unscaled, scaled = self.parse_copasi_output("./output/mutant_output.txt", "MCA")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Write control coefficients matrices #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
f = open("./output/mutant_MCA_unscaled.txt", "w")
for colname in colnames:
f.write(" "+colname)
f.write("\n")
for i in range(len(rownames)):
f.write(rowname)
for elmt in unscaled[i]:
f.write(" "+elmt)
f.write("\n")
f.close()
f = open("./output/mutant_MCA_scaled.txt", "w")
for colname in colnames:
f.write(" "+colname)
f.write("\n")
for i in range(len(rownames)):
f.write(rowname)
for elmt in scaled[i]:
f.write(" "+elmt)
f.write("\n")
f.close()
| (self) | [
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20,463 | metevolsim.metevolsim | compute_mutant_steady_state |
Compute and save the mutant steady-state.
Parameters
----------
None
Returns
-------
Boolean
| def compute_mutant_steady_state( self ):
"""
Compute and save the mutant steady-state.
Parameters
----------
None
Returns
-------
Boolean
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Compute the steady-state with Copasi #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.write_mutant_SBML_file()
self.create_mutant_cps_file()
self.edit_mutant_cps_file("STEADY_STATE")
self.run_copasi_for_mutant()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Extract steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
success, concentrations, fluxes = self.parse_copasi_output("./output/mutant_output.txt", "STEADY_STATE")
if not success:
return False
#assert success, "The model is unstable. Exit."
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Update model and lists #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.mutant_absolute_sum = 0.0
for elmt in concentrations:
species_id = elmt[0]
species_value = float(elmt[1])
assert species_id in self.species
if not self.species[species_id]["constant"]:
self.species[species_id]["mutant_value"] = species_value
self.mutant_absolute_sum += species_value
for elmt in fluxes:
reaction_id = elmt[0]
reaction_value = float(elmt[1])
assert reaction_id in self.reactions
self.reactions[reaction_id]["mutant_value"] = reaction_value
return True
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] |
20,464 | metevolsim.metevolsim | compute_sum_distance |
Compute the metabolic sum distance between the wild-type and the mutant.
Parameters
----------
None
Returns
-------
None
| def compute_sum_distance( self ):
"""
Compute the metabolic sum distance between the wild-type and the mutant.
Parameters
----------
None
Returns
-------
None
"""
self.absolute_sum_distance = abs(self.wild_type_absolute_sum-self.mutant_absolute_sum)
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20,465 | metevolsim.metevolsim | compute_wild_type_metabolic_control_analysis |
Compute and save the wild-type metabolic control analysis (MCA).
Parameters
----------
None
Returns
-------
None
| def compute_wild_type_metabolic_control_analysis( self ):
"""
Compute and save the wild-type metabolic control analysis (MCA).
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Compute the steady-state with Copasi #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.write_wild_type_SBML_file()
self.create_wild_type_cps_file()
self.edit_wild_type_cps_file("MCA")
self.run_copasi_for_wild_type()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Extract the MCA result #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
rownames, colnames, unscaled, scaled = self.parse_copasi_output("./output/wild_type_output.txt", "MCA")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Write control coefficients data #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
f = open("./output/wild_type_MCA_unscaled.txt", "w")
f.write("species_id species_value flux_id flux_value control_coef\n")
for i in range(len(rownames)):
for j in range(len(colnames)):
species_id = rownames[i]
flux_id = colnames[j]
species_value = self.species[species_id]["wild_type_value"]
flux_value = self.reactions[flux_id]["wild_type_value"]
control_coef = unscaled[i][j]
f.write(species_id+" "+str(species_value)+" "+flux_id+" "+str(flux_value)+" "+control_coef+"\n")
f.close()
f = open("./output/wild_type_MCA_scaled.txt", "w")
f.write("species_id species_value flux_id flux_value control_coef\n")
for i in range(len(rownames)):
for j in range(len(colnames)):
species_id = rownames[i]
flux_id = colnames[j]
species_value = self.species[species_id]["wild_type_value"]
flux_value = self.reactions[flux_id]["wild_type_value"]
control_coef = scaled[i][j]
f.write(species_id+" "+str(species_value)+" "+flux_id+" "+str(flux_value)+" "+control_coef+"\n")
f.close()
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20,466 | metevolsim.metevolsim | compute_wild_type_steady_state |
Compute and save the wild-type steady-state.
Parameters
----------
None
Returns
-------
Boolean
| def compute_wild_type_steady_state( self ):
"""
Compute and save the wild-type steady-state.
Parameters
----------
None
Returns
-------
Boolean
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Compute the steady-state with Copasi #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.write_wild_type_SBML_file()
self.create_wild_type_cps_file()
self.edit_wild_type_cps_file("STEADY_STATE")
self.run_copasi_for_wild_type()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Extract steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
success, concentrations, fluxes = self.parse_copasi_output("./output/wild_type_output.txt", "STEADY_STATE")
if not success:
return False
#assert success, "The model is unstable. Exit."
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Update model and lists #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.wild_type_absolute_sum = 0.0
for elmt in concentrations:
species_id = elmt[0]
species_value = float(elmt[1])
assert species_id in self.species
if not self.species[species_id]["constant"]:
self.species[species_id]["wild_type_value"] = species_value
self.species[species_id]["mutant_value"] = species_value
self.wild_type_absolute_sum += species_value
for elmt in fluxes:
reaction_id = elmt[0]
reaction_value = float(elmt[1])
assert reaction_id in self.reactions
self.reactions[reaction_id]["wild_type_value"] = reaction_value
self.reactions[reaction_id]["mutant_value"] = reaction_value
return True
| (self) | [
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20,467 | metevolsim.metevolsim | create_mutant_cps_file |
Create a CPS file from the mutant SBML file.
Parameters
----------
None
Returns
-------
None
| def create_mutant_cps_file( self ):
"""
Create a CPS file from the mutant SBML file.
Parameters
----------
None
Returns
-------
None
"""
cmd_line = self.copasi_path+" -i ./output/mutant.xml"
process = subprocess.call([cmd_line], stdout=subprocess.PIPE, shell=True)
| (self) | [
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20,468 | metevolsim.metevolsim | create_wild_type_cps_file |
Create a CPS file from the wild-type SBML file.
Parameters
----------
None
Returns
-------
None
| def create_wild_type_cps_file( self ):
"""
Create a CPS file from the wild-type SBML file.
Parameters
----------
None
Returns
-------
None
"""
cmd_line = self.copasi_path+" -i ./output/wild_type.xml"
process = subprocess.call([cmd_line], stdout=subprocess.PIPE, shell=True)
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20,469 | metevolsim.metevolsim | deterministic_parameter_mutation |
Mutate a given parameter 'param_metaid' by a given log10-scale factor.
The mutation is centered on the wild-type value.
x' = x*10^(factor)
Parameters
----------
param_metaid: str
Parameter meta identifier (as defined in the SBML model).
factor: float
Log10-scale factor.
Returns
-------
int, int
| def deterministic_parameter_mutation( self, param_metaid, factor ):
"""
Mutate a given parameter 'param_metaid' by a given log10-scale factor.
The mutation is centered on the wild-type value.
x' = x*10^(factor)
Parameters
----------
param_metaid: str
Parameter meta identifier (as defined in the SBML model).
factor: float
Log10-scale factor.
Returns
-------
int, int
"""
assert param_metaid in list(self.parameters)
wild_type_value = self.get_wild_type_parameter_value(param_metaid)
mutant_value = wild_type_value*10.0**factor
self.set_mutant_parameter_value(param_metaid, mutant_value)
return wild_type_value, mutant_value
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20,470 | metevolsim.metevolsim | edit_mutant_cps_file |
Edit mutant CPS file to schedule steady-state calculation.
Parameters
----------
task: str
Define Copasi task (STEADY_STATE/MCA).
Returns
-------
None
| def edit_mutant_cps_file( self, task ):
"""
Edit mutant CPS file to schedule steady-state calculation.
Parameters
----------
task: str
Define Copasi task (STEADY_STATE/MCA).
Returns
-------
None
"""
assert task in ["STEADY_STATE", "MCA"]
f = open("./output/mutant.cps", "r")
cps = f.read()
f.close()
upper_text = cps.split("<ListOfTasks>")[0]
lower_text = cps.split("</ListOfReports>")[1]
edited_file = ""
edited_file += upper_text
if task == "STEADY_STATE":
#~~~~~~~~~~~~~~~~~~#
# Write the task #
#~~~~~~~~~~~~~~~~~~#
edited_file += ' <ListOfTasks>\n'
edited_file += ' <Task key="Task_1" name="Steady-State" type="steadyState" scheduled="true" updateModel="false">\n'
edited_file += ' <Report reference="Report_1" target="mutant_output.txt" append="1" confirmOverwrite="1"/>\n'
edited_file += ' <Problem>\n'
edited_file += ' <Parameter name="JacobianRequested" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="StabilityAnalysisRequested" type="bool" value="1"/>\n'
edited_file += ' </Problem>\n'
edited_file += ' <Method name="Enhanced Newton" type="EnhancedNewton">\n'
edited_file += ' <Parameter name="Resolution" type="unsignedFloat" value="1.0000000000000001e-12"/>\n'
edited_file += ' <Parameter name="Derivation Factor" type="unsignedFloat" value="0.001"/>\n'
edited_file += ' <Parameter name="Use Newton" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Integration" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Back Integration" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Accept Negative Concentrations" type="bool" value="0"/>\n'
edited_file += ' <Parameter name="Iteration Limit" type="unsignedInteger" value="50"/>\n'
edited_file += ' <Parameter name="Maximum duration for forward integration" type="unsignedFloat" value="1000000000"/>\n'
edited_file += ' <Parameter name="Maximum duration for backward integration" type="unsignedFloat" value="1000000"/>\n'
edited_file += ' </Method>\n'
edited_file += ' </Task>\n'
edited_file += ' </ListOfTasks>\n'
#~~~~~~~~~~~~~~~~~~#
# Write the report #
#~~~~~~~~~~~~~~~~~~#
edited_file += ' <ListOfReports>\n'
edited_file += ' <Report key="Report_1" name="Steady-State" taskType="steadyState" separator="	" precision="6">\n'
edited_file += ' <Comment>\n'
edited_file += ' Automatically generated report.\n'
edited_file += ' </Comment>\n'
edited_file += ' <Footer>\n'
edited_file += ' <Object cn="CN=Root,Vector=TaskList[Steady-State]"/>\n'
edited_file += ' </Footer>\n'
edited_file += ' </Report>\n'
edited_file += ' </ListOfReports>\n'
elif task == "MCA":
#~~~~~~~~~~~~~~~~~~#
# Write the task #
#~~~~~~~~~~~~~~~~~~#
edited_file += ' <ListOfTasks>\n'
edited_file += ' <Task key="Task_1" name="Steady-State" type="steadyState" scheduled="false" updateModel="false">\n'
edited_file += ' <Report reference="Report_1" target="" append="1" confirmOverwrite="1"/>\n'
edited_file += ' <Problem>\n'
edited_file += ' <Parameter name="JacobianRequested" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="StabilityAnalysisRequested" type="bool" value="1"/>\n'
edited_file += ' </Problem>\n'
edited_file += ' <Method name="Enhanced Newton" type="EnhancedNewton">\n'
edited_file += ' <Parameter name="Resolution" type="unsignedFloat" value="1.0000000000000001e-09"/>\n'
edited_file += ' <Parameter name="Derivation Factor" type="unsignedFloat" value="0.001"/>\n'
edited_file += ' <Parameter name="Use Newton" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Integration" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Back Integration" type="bool" value="0"/>\n'
edited_file += ' <Parameter name="Accept Negative Concentrations" type="bool" value="0"/>\n'
edited_file += ' <Parameter name="Iteration Limit" type="unsignedInteger" value="50"/>\n'
edited_file += ' <Parameter name="Maximum duration for forward integration" type="unsignedFloat" value="1000000000"/>\n'
edited_file += ' <Parameter name="Maximum duration for backward integration" type="unsignedFloat" value="1000000"/>\n'
edited_file += ' </Method>\n'
edited_file += ' </Task>\n'
edited_file += ' <Task key="Task_2" name="Metabolic Control Analysis" type="metabolicControlAnalysis" scheduled="true" updateModel="false">\n'
edited_file += ' <Report reference="Report_1" target="mutant_output.txt" append="1" confirmOverwrite="1"/>\n'
edited_file += ' <Problem>\n'
edited_file += ' <Parameter name="Steady-State" type="key" value="Task_1"/>\n'
edited_file += ' </Problem>\n'
edited_file += ' <Method name="MCA Method (Reder)" type="MCAMethod(Reder)">\n'
edited_file += ' <Parameter name="Modulation Factor" type="unsignedFloat" value="1.0000000000000001e-09"/>\n'
edited_file += ' <Parameter name="Use Reder" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Smallbone" type="bool" value="1"/>\n'
edited_file += ' </Method>\n'
edited_file += ' </Task>\n'
edited_file += ' </ListOfTasks>\n'
#~~~~~~~~~~~~~~~~~~#
# Write the report #
#~~~~~~~~~~~~~~~~~~#
edited_file += ' <ListOfReports>\n'
edited_file += ' <Report key="Report_1" name="Metabolic Control Analysis" taskType="metabolicControlAnalysis" separator="	" precision="6">\n'
edited_file += ' <Comment>\n'
edited_file += ' Automatically generated report.\n'
edited_file += ' </Comment>\n'
edited_file += ' <Header>\n'
edited_file += ' <Object cn="CN=Root,Vector=TaskList[Metabolic Control Analysis],Object=Description"/>\n'
edited_file += ' </Header>\n'
edited_file += ' <Footer>\n'
edited_file += ' <Object cn="String=
"/>\n'
edited_file += ' <Object cn="CN=Root,Vector=TaskList[Metabolic Control Analysis],Object=Result"/>\n'
edited_file += ' </Footer>\n'
edited_file += ' </Report>\n'
edited_file += ' </ListOfReports>\n'
edited_file += lower_text
f = open("./output/mutant.cps", "w")
f.write(edited_file)
f.close()
| (self, task) | [
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] |
20,471 | metevolsim.metevolsim | edit_wild_type_cps_file |
Edit wild-type CPS file to schedule steady-state calculation.
Parameters
----------
task: str
Define Copasi task (STEADY_STATE/MCA).
Returns
-------
None
| def edit_wild_type_cps_file( self, task ):
"""
Edit wild-type CPS file to schedule steady-state calculation.
Parameters
----------
task: str
Define Copasi task (STEADY_STATE/MCA).
Returns
-------
None
"""
assert task in ["STEADY_STATE", "MCA"]
f = open("./output/wild_type.cps", "r")
cps = f.read()
f.close()
upper_text = cps.split("<ListOfTasks>")[0]
lower_text = cps.split("</ListOfReports>")[1]
edited_file = ""
edited_file += upper_text
if task == "STEADY_STATE":
#~~~~~~~~~~~~~~~~~~#
# Write the task #
#~~~~~~~~~~~~~~~~~~#
edited_file += ' <ListOfTasks>\n'
edited_file += ' <Task key="Task_1" name="Steady-State" type="steadyState" scheduled="true" updateModel="false">\n'
edited_file += ' <Report reference="Report_1" target="wild_type_output.txt" append="1" confirmOverwrite="1"/>\n'
edited_file += ' <Problem>\n'
edited_file += ' <Parameter name="JacobianRequested" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="StabilityAnalysisRequested" type="bool" value="1"/>\n'
edited_file += ' </Problem>\n'
edited_file += ' <Method name="Enhanced Newton" type="EnhancedNewton">\n'
edited_file += ' <Parameter name="Resolution" type="unsignedFloat" value="1.0000000000000001e-12"/>\n'
edited_file += ' <Parameter name="Derivation Factor" type="unsignedFloat" value="0.001"/>\n'
edited_file += ' <Parameter name="Use Newton" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Integration" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Back Integration" type="bool" value="0"/>\n'
edited_file += ' <Parameter name="Accept Negative Concentrations" type="bool" value="0"/>\n'
edited_file += ' <Parameter name="Iteration Limit" type="unsignedInteger" value="50"/>\n'
edited_file += ' <Parameter name="Maximum duration for forward integration" type="unsignedFloat" value="1000000000"/>\n'
edited_file += ' <Parameter name="Maximum duration for backward integration" type="unsignedFloat" value="1000000"/>\n'
edited_file += ' </Method>\n'
edited_file += ' </Task>\n'
edited_file += ' </ListOfTasks>\n'
#~~~~~~~~~~~~~~~~~~#
# Write the report #
#~~~~~~~~~~~~~~~~~~#
edited_file += ' <ListOfReports>\n'
edited_file += ' <Report key="Report_1" name="Steady-State" taskType="steadyState" separator="	" precision="6">\n'
edited_file += ' <Comment>\n'
edited_file += ' Automatically generated report.\n'
edited_file += ' </Comment>\n'
edited_file += ' <Footer>\n'
edited_file += ' <Object cn="CN=Root,Vector=TaskList[Steady-State]"/>\n'
edited_file += ' </Footer>\n'
edited_file += ' </Report>\n'
edited_file += ' </ListOfReports>\n'
elif task == "MCA":
#~~~~~~~~~~~~~~~~~~#
# Write the task #
#~~~~~~~~~~~~~~~~~~#
edited_file += ' <ListOfTasks>\n'
edited_file += ' <Task key="Task_1" name="Steady-State" type="steadyState" scheduled="false" updateModel="false">\n'
edited_file += ' <Report reference="Report_1" target="" append="1" confirmOverwrite="1"/>\n'
edited_file += ' <Problem>\n'
edited_file += ' <Parameter name="JacobianRequested" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="StabilityAnalysisRequested" type="bool" value="1"/>\n'
edited_file += ' </Problem>\n'
edited_file += ' <Method name="Enhanced Newton" type="EnhancedNewton">\n'
edited_file += ' <Parameter name="Resolution" type="unsignedFloat" value="1.0000000000000001e-12"/>\n'
edited_file += ' <Parameter name="Derivation Factor" type="unsignedFloat" value="0.001"/>\n'
edited_file += ' <Parameter name="Use Newton" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Integration" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Back Integration" type="bool" value="0"/>\n'
edited_file += ' <Parameter name="Accept Negative Concentrations" type="bool" value="0"/>\n'
edited_file += ' <Parameter name="Iteration Limit" type="unsignedInteger" value="50"/>\n'
edited_file += ' <Parameter name="Maximum duration for forward integration" type="unsignedFloat" value="1000000000"/>\n'
edited_file += ' <Parameter name="Maximum duration for backward integration" type="unsignedFloat" value="1000000"/>\n'
edited_file += ' </Method>\n'
edited_file += ' </Task>\n'
edited_file += ' <Task key="Task_2" name="Metabolic Control Analysis" type="metabolicControlAnalysis" scheduled="true" updateModel="false">\n'
edited_file += ' <Report reference="Report_1" target="wild_type_output.txt" append="1" confirmOverwrite="1"/>\n'
edited_file += ' <Problem>\n'
edited_file += ' <Parameter name="Steady-State" type="key" value="Task_1"/>\n'
edited_file += ' </Problem>\n'
edited_file += ' <Method name="MCA Method (Reder)" type="MCAMethod(Reder)">\n'
edited_file += ' <Parameter name="Modulation Factor" type="unsignedFloat" value="1.0000000000000001e-09"/>\n'
edited_file += ' <Parameter name="Use Reder" type="bool" value="1"/>\n'
edited_file += ' <Parameter name="Use Smallbone" type="bool" value="1"/>\n'
edited_file += ' </Method>\n'
edited_file += ' </Task>\n'
edited_file += ' </ListOfTasks>\n'
#~~~~~~~~~~~~~~~~~~#
# Write the report #
#~~~~~~~~~~~~~~~~~~#
edited_file += ' <ListOfReports>\n'
edited_file += ' <Report key="Report_1" name="Metabolic Control Analysis" taskType="metabolicControlAnalysis" separator="	" precision="6">\n'
edited_file += ' <Comment>\n'
edited_file += ' Automatically generated report.\n'
edited_file += ' </Comment>\n'
edited_file += ' <Header>\n'
edited_file += ' <Object cn="CN=Root,Vector=TaskList[Metabolic Control Analysis],Object=Description"/>\n'
edited_file += ' </Header>\n'
edited_file += ' <Footer>\n'
edited_file += ' <Object cn="String=
"/>\n'
edited_file += ' <Object cn="CN=Root,Vector=TaskList[Metabolic Control Analysis],Object=Result"/>\n'
edited_file += ' </Footer>\n'
edited_file += ' </Report>\n'
edited_file += ' </ListOfReports>\n'
edited_file += lower_text
f = open("./output/wild_type.cps", "w")
f.write(edited_file)
f.close()
| (self, task) | [
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20,472 | metevolsim.metevolsim | flux_drop_analysis |
Run a flux-drop analysis. Each flux is independently dropped by a factor
"drop_coefficient" and the relative MOMA distance from wild-type target
fluxes is computed. The result is saved in a file "filename", one flux
per line.
Parameters
----------
drop_coefficient: float
Value of the drop coefficient (> 0.0).
filename: str
Name of the output (txt file).
overwrite: bool
Indicates if new data should overwrite or be appended to the file.
Returns
-------
None
| def flux_drop_analysis( self, drop_coefficient, filename, overwrite ):
"""
Run a flux-drop analysis. Each flux is independently dropped by a factor
"drop_coefficient" and the relative MOMA distance from wild-type target
fluxes is computed. The result is saved in a file "filename", one flux
per line.
Parameters
----------
drop_coefficient: float
Value of the drop coefficient (> 0.0).
filename: str
Name of the output (txt file).
overwrite: bool
Indicates if new data should overwrite or be appended to the file.
Returns
-------
None
"""
assert drop_coefficient > 0.0
assert filename != ""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Initialize model states #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.compute_wild_type_steady_state()
self.compute_mutant_steady_state()
self.compute_moma_distance()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Open the output file depending on #
# overwrite parameter #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
if overwrite or not os.path.isfile(filename):
f = open(filename, "w")
f.write("drop_value reaction dist\n")
else:
f = open(filename, 'a')
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Run the flux drop analysis #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for reaction in self.mutant_model.getListOfReactions():
reaction_id = reaction.getId()
#if not reaction_id in fluxes_to_ignore:
form_str_default = reaction.getKineticLaw().getFormula()
form_str_edited = str(drop_coefficient)+" * "+form_str_default
math_exp_default = libsbml.parseL3Formula(form_str_default)
math_exp_edited = libsbml.parseL3Formula(form_str_edited)
reaction.getKineticLaw().setMath(math_exp_edited)
success = self.compute_mutant_steady_state()
if success:
self.compute_moma_distance()
f.write(str(drop_coefficient)+" "+reaction_id+" "+str(self.relative_moma_distance)+"\n")
f.flush()
else:
print(" >>> Failed to find steady-state for "+reaction_id)
f.write(str(drop_coefficient)+" "+reaction_id+" NA\n")
f.flush()
reaction.getKineticLaw().setMath(math_exp_default)
for species_id in self.species:
self.species[species_id]["initial_value"] = self.species[species_id]["wild_type_value"]
self.species[species_id]["mutant_value"] = self.species[species_id]["wild_type_value"]
for parameter_metaid in self.parameters:
self.set_mutant_parameter_value(parameter_metaid, self.get_wild_type_parameter_value(parameter_metaid))
for reaction_id in self.reactions:
self.reactions[reaction_id]["mutant_value"] = self.reactions[reaction_id]["wild_type_value"]
f.close()
| (self, drop_coefficient, filename, overwrite) | [
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20,473 | metevolsim.metevolsim | get_mutant_parameter_value |
Get the mutant value of the parameter 'param_metaid'
Parameters
----------
param_metaid: str
The meta identifier of the parameter (as defined in the SBML model).
Returns
-------
float
| def get_mutant_parameter_value( self, param_metaid ):
"""
Get the mutant value of the parameter 'param_metaid'
Parameters
----------
param_metaid: str
The meta identifier of the parameter (as defined in the SBML model).
Returns
-------
float
"""
assert param_metaid in list(self.parameters)
assert self.parameters[param_metaid]["mutant_value"] == self.mutant_model.getElementByMetaId(param_metaid).getValue()
return self.parameters[param_metaid]["mutant_value"]
| (self, param_metaid) | [
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20,474 | metevolsim.metevolsim | get_mutant_reaction_array |
Get a numpy array of mutant reaction fluxes.
Parameters
----------
None
Returns
-------
numpy array
| def get_mutant_reaction_array( self ):
"""
Get a numpy array of mutant reaction fluxes.
Parameters
----------
None
Returns
-------
numpy array
"""
vec = []
for reaction_id in self.reactions:
vec.append(self.reactions[reaction_id]["mutant_value"])
return np.array(vec)
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20,475 | metevolsim.metevolsim | get_mutant_reaction_value |
Get the wild-type value of the reaction 'reaction_id'.
Parameters
----------
reaction_id: str
The identifier of the reaction (as defined in the SBML model).
Returns
-------
float
| def get_mutant_reaction_value( self, reaction_id ):
"""
Get the wild-type value of the reaction 'reaction_id'.
Parameters
----------
reaction_id: str
The identifier of the reaction (as defined in the SBML model).
Returns
-------
float
"""
assert reaction_id in list(self.reactions)
return self.reactions[reaction_id]["mutant_value"]
| (self, reaction_id) | [
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20,476 | metevolsim.metevolsim | get_mutant_species_array |
Get a numpy array of mutant species abundances.
Parameters
----------
None
Returns
-------
numpy array
| def get_mutant_species_array( self ):
"""
Get a numpy array of mutant species abundances.
Parameters
----------
None
Returns
-------
numpy array
"""
vec = []
for species_id in self.species:
if not self.species[species_id]["constant"]:
vec.append(self.species[species_id]["mutant_value"])
return np.array(vec)
| (self) | [
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20,477 | metevolsim.metevolsim | get_mutant_species_value |
Get the wild-type value of the species 'species_id'.
Parameters
----------
species_id: str
The identifier of the species (as defined in the SBML model).
Returns
-------
float
| def get_mutant_species_value( self, species_id ):
"""
Get the wild-type value of the species 'species_id'.
Parameters
----------
species_id: str
The identifier of the species (as defined in the SBML model).
Returns
-------
float
"""
assert species_id in list(self.species)
return self.species[species_id]["mutant_value"]
| (self, species_id) | [
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20,478 | metevolsim.metevolsim | get_number_of_parameters |
Get the number of kinetic parameters.
Parameters
----------
None
Returns
-------
int
| def get_number_of_parameters( self ):
"""
Get the number of kinetic parameters.
Parameters
----------
None
Returns
-------
int
"""
return len(self.parameters)
| (self) | [
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20,479 | metevolsim.metevolsim | get_number_of_reactions |
Get the number of reactions.
Parameters
----------
None
Returns
-------
int
| def get_number_of_reactions( self ):
"""
Get the number of reactions.
Parameters
----------
None
Returns
-------
int
"""
return len(self.reactions)
| (self) | [
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20,480 | metevolsim.metevolsim | get_number_of_species |
Get the number of species.
Parameters
----------
None
Returns
-------
int
| def get_number_of_species( self ):
"""
Get the number of species.
Parameters
----------
None
Returns
-------
int
"""
return len(self.species)
| (self) | [
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20,481 | metevolsim.metevolsim | get_number_of_variable_species |
Get the number of variable species.
Parameters
----------
None
Returns
-------
int
| def get_number_of_variable_species( self ):
"""
Get the number of variable species.
Parameters
----------
None
Returns
-------
int
"""
count = 0
for species_id in self.species:
if not self.species[species_id]["constant"]:
count += 1
return count
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20,482 | metevolsim.metevolsim | get_random_parameter |
Get a kinetic parameter at random.
Parameters
----------
None
Returns
-------
str
| def get_random_parameter( self ):
"""
Get a kinetic parameter at random.
Parameters
----------
None
Returns
-------
str
"""
param_index = np.random.randint(0, len(self.parameters))
param_metaid = list(self.parameters)[param_index]
return param_metaid
| (self) | [
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20,483 | metevolsim.metevolsim | get_wild_type_parameter_value |
Get the wild-type value of the parameter 'param_metaid'
Parameters
----------
param_metaid: str
The meta identifier of the parameter (as defined in the SBML model).
Returns
-------
float
| def get_wild_type_parameter_value( self, param_metaid ):
"""
Get the wild-type value of the parameter 'param_metaid'
Parameters
----------
param_metaid: str
The meta identifier of the parameter (as defined in the SBML model).
Returns
-------
float
"""
assert param_metaid in list(self.parameters)
assert self.parameters[param_metaid]["wild_type_value"] == self.wild_type_model.getElementByMetaId(param_metaid).getValue()
return self.parameters[param_metaid]["wild_type_value"]
| (self, param_metaid) | [
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20,484 | metevolsim.metevolsim | get_wild_type_reaction_array |
Get a numpy array of wild-type reaction fluxes.
Parameters
----------
None
Returns
-------
numpy array
| def get_wild_type_reaction_array( self ):
"""
Get a numpy array of wild-type reaction fluxes.
Parameters
----------
None
Returns
-------
numpy array
"""
vec = []
for reaction_id in self.reactions:
vec.append(self.reactions[reaction_id]["wild_type_value"])
return np.array(vec)
| (self) | [
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20,485 | metevolsim.metevolsim | get_wild_type_reaction_value |
Get the wild-type value of the reaction 'reaction_id'.
Parameters
----------
reaction_id: str
The identifier of the reaction (as defined in the SBML model).
Returns
-------
float
| def get_wild_type_reaction_value( self, reaction_id ):
"""
Get the wild-type value of the reaction 'reaction_id'.
Parameters
----------
reaction_id: str
The identifier of the reaction (as defined in the SBML model).
Returns
-------
float
"""
assert reaction_id in list(self.reactions)
return self.reactions[reaction_id]["wild_type_value"]
| (self, reaction_id) | [
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] |
20,486 | metevolsim.metevolsim | get_wild_type_species_array |
Get a numpy array of wild-type species abundances.
Parameters
----------
None
Returns
-------
numpy array
| def get_wild_type_species_array( self ):
"""
Get a numpy array of wild-type species abundances.
Parameters
----------
None
Returns
-------
numpy array
"""
vec = []
for species_id in self.species:
if not self.species[species_id]["constant"]:
vec.append(self.species[species_id]["wild_type_value"])
return np.array(vec)
| (self) | [
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20,487 | metevolsim.metevolsim | get_wild_type_species_value |
Get the wild-type value of the species 'species_id'.
Parameters
----------
species_id: str
The identifier of the species (as defined in the SBML model).
Returns
-------
float
| def get_wild_type_species_value( self, species_id ):
"""
Get the wild-type value of the species 'species_id'.
Parameters
----------
species_id: str
The identifier of the species (as defined in the SBML model).
Returns
-------
float
"""
assert species_id in list(self.species)
return self.species[species_id]["wild_type_value"]
| (self, species_id) | [
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20,488 | metevolsim.metevolsim | parse_copasi_output |
Parse the Copasi output 'filename'.
Parameters
----------
filename: str
Name of the Copasi output (txt file).
task: str
Define Copasi task (STEADY_STATE/MCA).
Returns
-------
- boolean, list of [str, float] lists, list of [str, float] lists if task == "STEADY_STATE"
- boolean, list of [str], list of [str], list of list of [str] if task == "MCA"
| def parse_copasi_output( self, filename, task ):
"""
Parse the Copasi output 'filename'.
Parameters
----------
filename: str
Name of the Copasi output (txt file).
task: str
Define Copasi task (STEADY_STATE/MCA).
Returns
-------
- boolean, list of [str, float] lists, list of [str, float] lists if task == "STEADY_STATE"
- boolean, list of [str], list of [str], list of list of [str] if task == "MCA"
"""
assert os.path.isfile(filename), "The Copasi output \""+filename+"\" does not exist. Exit."
assert task in ["STEADY_STATE", "MCA"]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Parse to extract the steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
if task == "STEADY_STATE":
concentrations = []
fluxes = []
parse_concentrations = False
parse_fluxes = False
f = open(filename, "r")
l = f.readline()
if "No steady state with given resolution was found!" in l:
return False, concentrations, fluxes
while l:
if l == "\n" and parse_concentrations:
parse_concentrations = False
if l == "\n" and parse_fluxes:
parse_fluxes = False
if parse_concentrations:
name = l.split("\t")[0]
val = l.split("\t")[1]
concentrations.append([name, val])
if parse_fluxes:
name = l.split("\t")[0]
val = l.split("\t")[1]
fluxes.append([name, val])
if l.startswith("Species"):
parse_concentrations = True
if l.startswith("Reaction"):
parse_fluxes = True
l = f.readline()
f.close()
return True, concentrations, fluxes
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Parse to extract the MCA result #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
elif task == "MCA":
colnames = []
rownames = []
unscaled = []
scaled = []
N = 0
f = open(filename, "r")
l = f.readline()
while l:
if l.startswith("Unscaled concentration control coefficients"):
l = f.readline()
l = f.readline()
l = f.readline()
l = f.readline()
colnames = l.strip("\n\t").split("\t")
N = len(colnames)
l = f.readline()
for i in range(len(colnames)):
colnames[i] = colnames[i].strip("()")
while l != "\n":
l = l.strip("\n\t").split("\t")
rownames.append(l[0])
unscaled.append(l[1:(N+1)])
l = f.readline()
if l.startswith("Scaled concentration control coefficients"):
l = f.readline()
l = f.readline()
l = f.readline()
l = f.readline()
l = f.readline()
while l != "\n":
l = l.strip("\n\t").split("\t")
scaled.append(l[1:(N+1)])
l = f.readline()
break
l = f.readline()
f.close()
return rownames, colnames, unscaled, scaled
| (self, filename, task) | [
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20,489 | metevolsim.metevolsim | random_parameter_mutation |
Mutate a given parameter 'param_metaid' through a log10-normal law of
variance of sigma^2.
x' = x*10^(N(0,sigma))
Parameters
----------
param_metaid: str
Parameter meta identifier (as defined in the SBML model).
sigma: float > 0.0
Log10-scale standard deviation.
Returns
-------
int, int
| def random_parameter_mutation( self, param_metaid, sigma ):
"""
Mutate a given parameter 'param_metaid' through a log10-normal law of
variance of sigma^2.
x' = x*10^(N(0,sigma))
Parameters
----------
param_metaid: str
Parameter meta identifier (as defined in the SBML model).
sigma: float > 0.0
Log10-scale standard deviation.
Returns
-------
int, int
"""
assert param_metaid in list(self.parameters)
assert sigma > 0.0
factor = np.random.normal(0.0, sigma, 1)
wild_type_value = self.get_mutant_parameter_value(param_metaid)
mutant_value = wild_type_value*10**factor[0]
self.set_mutant_parameter_value(param_metaid, mutant_value)
return wild_type_value, mutant_value
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] |
20,490 | metevolsim.metevolsim | rebuild_metaids |
Rebuild the unique metaids for all model variables.
Parameters
----------
None
Returns
-------
None
| def rebuild_metaids( self ):
"""
Rebuild the unique metaids for all model variables.
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Rebuild wild_type metaids #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
metaid = 0
for species in self.wild_type_model.getListOfSpecies():
species.setMetaId("_"+str(metaid))
metaid += 1
for parameter in self.wild_type_model.getListOfParameters():
parameter.setMetaId("_"+str(metaid))
metaid += 1
for reaction in self.wild_type_model.getListOfReactions():
for elmt in reaction.getListOfAllElements():
if elmt.getElementName() == "parameter" or elmt.getElementName() == "localParameter":
parameter = elmt
parameter.setMetaId("_"+str(metaid))
metaid += 1
for reaction in self.wild_type_model.getListOfReactions():
reaction.setMetaId("_"+str(metaid))
metaid += 1
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Rebuild mutant metaids #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
metaid = 0
for species in self.mutant_model.getListOfSpecies():
species.setMetaId("_"+str(metaid))
metaid += 1
for parameter in self.mutant_model.getListOfParameters():
parameter.setMetaId("_"+str(metaid))
metaid += 1
for reaction in self.mutant_model.getListOfReactions():
for elmt in reaction.getListOfAllElements():
if elmt.getElementName() == "parameter" or elmt.getElementName() == "localParameter":
parameter = elmt
parameter.setMetaId("_"+str(metaid))
metaid += 1
for reaction in self.mutant_model.getListOfReactions():
reaction.setMetaId("_"+str(metaid))
metaid += 1
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20,491 | metevolsim.metevolsim | replace_variable_names |
Replace variable names by their identifiers to avoid collisions.
Parameters
----------
None
Returns
-------
None
| def replace_variable_names( self ):
"""
Replace variable names by their identifiers to avoid collisions.
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Replace variables names by identifiers in wild-type #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for species in self.wild_type_model.getListOfSpecies():
species_id = species.getId()
assert species_id in self.species
species.setName(species_id)
for reaction in self.wild_type_model.getListOfReactions():
reaction_id = reaction.getId()
assert reaction_id in self.reactions
reaction.setName(reaction_id)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Replace species names by identifiers in mutant #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for species in self.mutant_model.getListOfSpecies():
species_id = species.getId()
assert species_id in self.species
species.setName(species_id)
for reaction in self.mutant_model.getListOfReactions():
reaction_id = reaction.getId()
assert reaction_id in self.reactions
reaction.setName(reaction_id)
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20,492 | metevolsim.metevolsim | run_copasi_for_mutant |
Run Copasi to compute the mutant steady-state and update model state.
Parameters
----------
None
Returns
-------
None
| def run_copasi_for_mutant( self ):
"""
Run Copasi to compute the mutant steady-state and update model state.
Parameters
----------
None
Returns
-------
None
"""
if os.path.isfile("./output/mutant_output.txt"):
os.system("rm ./output/mutant_output.txt")
cmd_line = self.copasi_path+" ./output/mutant.cps"
process = subprocess.call([cmd_line], stdout=subprocess.PIPE, shell=True)
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20,493 | metevolsim.metevolsim | run_copasi_for_wild_type |
Run Copasi to compute the wild-type steady-state and update model state.
Parameters
----------
None
Returns
-------
None
| def run_copasi_for_wild_type( self ):
"""
Run Copasi to compute the wild-type steady-state and update model state.
Parameters
----------
None
Returns
-------
None
"""
if os.path.isfile("./output/wild_type_output.txt"):
os.system("rm ./output/wild_type_output.txt")
cmd_line = self.copasi_path+" ./output/wild_type.cps"
process = subprocess.call([cmd_line], stdout=subprocess.PIPE, shell=True)
| (self) | [
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20,494 | metevolsim.metevolsim | save_shortest_paths |
Save the matrix of all pairwise metabolites shortest paths (assuming an
undirected graph).
Parameters
----------
filename: str
Name of the Copasi output (txt file).
Returns
-------
None
| def save_shortest_paths( self, filename ):
"""
Save the matrix of all pairwise metabolites shortest paths (assuming an
undirected graph).
Parameters
----------
filename: str
Name of the Copasi output (txt file).
Returns
-------
None
"""
assert filename != ""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Extract shortest path lengths #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
sp = nx.shortest_path_length(self.species_graph)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Build the matrix of shortest paths #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
sp_dict = {}
list_of_metabolites = []
header = ""
for line in sp:
sp_dict[line[0]] = {}
list_of_metabolites.append(line[0])
header += line[0]+" "
for species in line[1]:
sp_dict[line[0]][species] = line[1][species]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Write the file #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
written_reactions = []
f = open(filename, "w")
f.write(header.strip(" ")+"\n")
for sp1 in list_of_metabolites:
for reaction in self.species_to_reaction[sp1]:
if reaction not in written_reactions:
written_reactions.append(reaction)
line = reaction
for sp2 in list_of_metabolites:
line += " "+str(sp_dict[sp1][sp2])
f.write(line+"\n")
f.close()
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20,495 | metevolsim.metevolsim | set_mutant_parameter_value |
Set the mutant value of the parameter 'param_metaid'.
Parameters
----------
param_metaid: str
Parameter meta identifier (as defined in the SBML model).
value: float
Parameter value.
Returns
-------
None
| def set_mutant_parameter_value( self, param_metaid, value ):
"""
Set the mutant value of the parameter 'param_metaid'.
Parameters
----------
param_metaid: str
Parameter meta identifier (as defined in the SBML model).
value: float
Parameter value.
Returns
-------
None
"""
assert param_metaid in list(self.parameters)
self.parameters[param_metaid]["mutant_value"] = value
self.mutant_model.getElementByMetaId(param_metaid).setValue(value)
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20,496 | metevolsim.metevolsim | set_species_initial_value |
Set the initial concentration of the species 'species_id' in the
mutant model.
Parameters
----------
species_id: str
Species identifier (as defined in the SBML model).
value: float >= 0.0
Species abundance.
Returns
-------
None
| def set_species_initial_value( self, species_id, value ):
"""
Set the initial concentration of the species 'species_id' in the
mutant model.
Parameters
----------
species_id: str
Species identifier (as defined in the SBML model).
value: float >= 0.0
Species abundance.
Returns
-------
None
"""
assert species_id in list(self.species)
if value < 0.0:
value = 0.0
self.species[species_id]["wild_type_value"] = value
self.mutant_model.getListOfSpecies().get(species_id).setInitialConcentration(value)
| (self, species_id, value) | [
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20,497 | metevolsim.metevolsim | set_wild_type_parameter_value |
Set the wild-type value of the parameter 'param_metaid'.
Parameters
----------
param_metaid: str
Parameter meta identifier (as defined in the SBML model).
value: float
Parameter value.
Returns
-------
None
| def set_wild_type_parameter_value( self, param_metaid, value ):
"""
Set the wild-type value of the parameter 'param_metaid'.
Parameters
----------
param_metaid: str
Parameter meta identifier (as defined in the SBML model).
value: float
Parameter value.
Returns
-------
None
"""
assert param_metaid in list(self.parameters)
self.parameters[param_metaid]["wild_type_value"] = value
self.wild_type_model.getElementByMetaId(param_metaid).setValue(value)
| (self, param_metaid, value) | [
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] |
20,498 | metevolsim.metevolsim | update_initial_concentrations |
Update initial concentrations in the mutant model.
Parameters
----------
None
Returns
-------
None
| def update_initial_concentrations( self ):
"""
Update initial concentrations in the mutant model.
Parameters
----------
None
Returns
-------
None
"""
for species_item in self.species.items():
species_id = species_item[0]
if not self.species[species_id]["constant"]:
self.set_species_initial_value(species_id, self.species[species_id]["mutant_value"])
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20,499 | metevolsim.metevolsim | write_list_of_variables | null | def write_list_of_variables( self ):
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write the list of species #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
f = open("output/list_of_species.txt", "w")
f.write("id metaid name compartment kegg_id\n")
for species_id in self.species.keys():
f.write(species_id+" "+self.species[species_id]["metaid"]+" "+self.species[species_id]["name"]+" "+self.species[species_id]["compartment"]+" "+self.species[species_id]["kegg_id"]+"\n")
f.close()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write the list of parameters #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
f = open("output/list_of_parameters.txt", "w")
f.write("id metaid\n")
for parameter_metaid in self.parameters.keys():
f.write(self.parameters[parameter_metaid]["id"]+" "+parameter_metaid+"\n")
f.close()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Write the list of reactions #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
f = open("output/list_of_reactions.txt", "w")
f.write("id metaid name compartment\n")
for reaction_id in self.reactions.keys():
f.write(reaction_id+" "+self.reactions[reaction_id]["metaid"]+" "+self.reactions[reaction_id]["name"]+" "+self.reactions[reaction_id]["compartment"]+"\n")
f.close()
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] |
20,500 | metevolsim.metevolsim | write_mutant_SBML_file |
Write the mutant model in a SBML file.
Parameters
----------
None
Returns
-------
None
| def write_mutant_SBML_file( self ):
"""
Write the mutant model in a SBML file.
Parameters
----------
None
Returns
-------
None
"""
libsbml.writeSBMLToFile(self.mutant_document, "output/mutant.xml")
| (self) | [
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20,501 | metevolsim.metevolsim | write_reaction_to_species_map |
Write the reaction-to-species map. For each reaction, the list of
metabolites is written, line by line. The file also indicates if the
metabolite is a substrate or a product of the reaction.
Parameters
----------
filename: str
Name of the output (txt file).
Returns
-------
None
| def write_reaction_to_species_map( self, filename ):
"""
Write the reaction-to-species map. For each reaction, the list of
metabolites is written, line by line. The file also indicates if the
metabolite is a substrate or a product of the reaction.
Parameters
----------
filename: str
Name of the output (txt file).
Returns
-------
None
"""
assert filename != ""
f = open(filename, "w")
for reaction in self.mutant_model.getListOfReactions():
reaction_id = reaction.getId()
nb_reactants = reaction.getNumReactants()
nb_products = reaction.getNumProducts()
for i in range(nb_reactants):
f.write(reaction_id+" substrate "+reaction.getReactant(i).getSpecies()+"\n")
for i in range(nb_products):
f.write(reaction_id+" product "+reaction.getProduct(i).getSpecies()+"\n")
f.close()
| (self, filename) | [
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] |
20,502 | metevolsim.metevolsim | write_wild_type_SBML_file |
Write the wild-type model in a SBML file.
Parameters
----------
None
Returns
-------
None
| def write_wild_type_SBML_file( self ):
"""
Write the wild-type model in a SBML file.
Parameters
----------
None
Returns
-------
None
"""
libsbml.writeSBMLToFile(self.wild_type_document, "output/wild_type.xml")
| (self) | [
0.020546795800328255,
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20,503 | metevolsim.metevolsim | SensitivityAnalysis |
The SensitivityAnalysis class runs a sensitivity analysis by exploring a
range of values for each kinetic parameter and tracking the change for all
fluxes and species.
Attributes
----------
> sbml_filename : str
Path of the SBML model file. The SBML model is automatically loaded.
> copasi_path : str
Location of Copasi executable.
> model : Model
SBML model (automatically loaded).
> factor_range : float > 0.0
Half-range of the log10-scaling factor (total range=2*factor_range)
> factor_step : float > 0.0
Exploration step of the log10-scaling factor.
x' = x*10^(factor)
> param_index : int
Current parameter index.
> param_metaid : str
Current parameter meta identifier.
> param_id : str
Current parameter identifier.
> param_wild_type : float
Current parameter wild-type value.
> param_val : float
Current parameter mutant value.
> sigma : float > 0.0
Kinetic parameters mutation size.
> nb_iterations : int > 0
Number of iterations of the multivariate random analysis.
> iteration : int > 0
Current iteration of the multivariate random analysis.
> OAT_output_file : file
Output file for the One-at-a-time analysis.
> random_output_file : file
Output file for the multivariate random analysis.
Methods
-------
> __init__(sbml_filename, copasi_path)
SensitivityAnalysis class constructor.
> initialize_OAT_output_file()
Initialize the One-At-a-Time sensitivity analysis output file (write the header).
> initialize_random_output_file()
Initialize the random sensitivity analysis output file (write the header).
> write_OAT_output_file()
Write the current One-At-a-Time sensitivity analysis state in the output file.
> write_random_output_file()
Write the current random sensitivity analysis state in the output file.
> initialize_OAT_analysis(factor_range, factor_step)
Initialize the One-At-a-Time sensitivity analysis algorithm.
> initialize_random_analysis(sigma, nb_iterations)
Initialize the random sensitivity analysis algorithm.
> reload_wild_type_state()
Reload the wild-type state into the mutant model.
> next_parameter()
Run a full parameter exploration for the next kinetic parameter.
> next_iteration()
Run the random parametric exploration for the next iteration.
> run_OAT_analysis(factor_range, factor_step)
Run the complete One-At-a-Time sensitivity analysis.
> run_random_analysis(sigma, nb_iterations)
Run the complete random sensitivity analysis.
| class SensitivityAnalysis:
"""
The SensitivityAnalysis class runs a sensitivity analysis by exploring a
range of values for each kinetic parameter and tracking the change for all
fluxes and species.
Attributes
----------
> sbml_filename : str
Path of the SBML model file. The SBML model is automatically loaded.
> copasi_path : str
Location of Copasi executable.
> model : Model
SBML model (automatically loaded).
> factor_range : float > 0.0
Half-range of the log10-scaling factor (total range=2*factor_range)
> factor_step : float > 0.0
Exploration step of the log10-scaling factor.
x' = x*10^(factor)
> param_index : int
Current parameter index.
> param_metaid : str
Current parameter meta identifier.
> param_id : str
Current parameter identifier.
> param_wild_type : float
Current parameter wild-type value.
> param_val : float
Current parameter mutant value.
> sigma : float > 0.0
Kinetic parameters mutation size.
> nb_iterations : int > 0
Number of iterations of the multivariate random analysis.
> iteration : int > 0
Current iteration of the multivariate random analysis.
> OAT_output_file : file
Output file for the One-at-a-time analysis.
> random_output_file : file
Output file for the multivariate random analysis.
Methods
-------
> __init__(sbml_filename, copasi_path)
SensitivityAnalysis class constructor.
> initialize_OAT_output_file()
Initialize the One-At-a-Time sensitivity analysis output file (write the header).
> initialize_random_output_file()
Initialize the random sensitivity analysis output file (write the header).
> write_OAT_output_file()
Write the current One-At-a-Time sensitivity analysis state in the output file.
> write_random_output_file()
Write the current random sensitivity analysis state in the output file.
> initialize_OAT_analysis(factor_range, factor_step)
Initialize the One-At-a-Time sensitivity analysis algorithm.
> initialize_random_analysis(sigma, nb_iterations)
Initialize the random sensitivity analysis algorithm.
> reload_wild_type_state()
Reload the wild-type state into the mutant model.
> next_parameter()
Run a full parameter exploration for the next kinetic parameter.
> next_iteration()
Run the random parametric exploration for the next iteration.
> run_OAT_analysis(factor_range, factor_step)
Run the complete One-At-a-Time sensitivity analysis.
> run_random_analysis(sigma, nb_iterations)
Run the complete random sensitivity analysis.
"""
### Constructor ###
def __init__( self, sbml_filename, copasi_path ):
"""
SensitivityAnalysis class constructor.
Parameters
----------
sbml_filename : str
Path of the SBML model file. The SBML model is automatically loaded.
copasi_path : str
Location of Copasi executable.
Returns
-------
None
"""
assert os.path.isfile(sbml_filename), "The SBML file \""+sbml_filename+"\" does not exist. Exit."
assert os.path.isfile(copasi_path), "The executable \""+copasi_path+"\" does not exist. Exit."
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Main sensitivity analysis parameters #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.sbml_filename = sbml_filename
self.copasi_path = copasi_path
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) SBML model #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.model = Model(sbml_filename, [], copasi_path)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) One-at-a-time analysis #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.factor_range = 0.0
self.factor_step = 0.0
self.param_index = 0
self.param_metaid = "wild_type"
self.param_id = "wild_type"
self.param_wild_type = 0.0
self.param_val = 0.0
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 4) Multivariate random analysis #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.sigma = 0.0
self.nb_iterations = 0.0
self.iteration = 0
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 5) Output files #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.OAT_output_file = open("output/OAT_sensitivity_analysis.txt", "w")
self.OAT_output_file.close()
self.random_output_file = open("output/random_sensitivity_analysis.txt", "w")
self.random_output_file.close()
### Initialize the OAT output file ###
def initialize_OAT_output_file( self ):
"""
Initialize the OAT output file (write the header).
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write the header #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
header = "param_metaid param_id param_wild_type param_val param_dln"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
header += " "+species_id
for reaction_id in self.model.reactions:
header += " "+reaction_id
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write the wild-type steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
first_line = "wild_type wild_type 0.0 0.0 0.0"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
first_line += " "+str(self.model.species[species_id]["wild_type_value"])
for reaction_id in self.model.reactions:
first_line += " "+str(self.model.reactions[reaction_id]["wild_type_value"])
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Save in output file #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.OAT_output_file = open("output/OAT_sensitivity_analysis.txt", "a")
self.OAT_output_file.write(header+"\n"+first_line+"\n")
self.OAT_output_file.close()
### Initialize the multivariate random output file ###
def initialize_random_output_file( self ):
"""
Initialize the multivariate random output file (write the header).
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write the header #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
header = "iteration"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
header += " "+species_id
for reaction_id in self.model.reactions:
header += " "+reaction_id
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write the wild-type steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
first_line = "wild_type"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
first_line += " "+str(self.model.species[species_id]["wild_type_value"])
for reaction_id in self.model.reactions:
first_line += " "+str(self.model.reactions[reaction_id]["wild_type_value"])
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Save in output file #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.output_file = open("output/random_sensitivity_analysis.txt", "a")
self.output_file.write(header+"\n"+first_line+"\n")
self.output_file.close()
### Write the sensitivity analysis state in the OAT output file ###
def write_OAT_output_file( self ):
"""
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20,504 | metevolsim.metevolsim | __init__ |
SensitivityAnalysis class constructor.
Parameters
----------
sbml_filename : str
Path of the SBML model file. The SBML model is automatically loaded.
copasi_path : str
Location of Copasi executable.
Returns
-------
None
| def __init__( self, sbml_filename, copasi_path ):
"""
SensitivityAnalysis class constructor.
Parameters
----------
sbml_filename : str
Path of the SBML model file. The SBML model is automatically loaded.
copasi_path : str
Location of Copasi executable.
Returns
-------
None
"""
assert os.path.isfile(sbml_filename), "The SBML file \""+sbml_filename+"\" does not exist. Exit."
assert os.path.isfile(copasi_path), "The executable \""+copasi_path+"\" does not exist. Exit."
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Main sensitivity analysis parameters #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.sbml_filename = sbml_filename
self.copasi_path = copasi_path
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) SBML model #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.model = Model(sbml_filename, [], copasi_path)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) One-at-a-time analysis #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.factor_range = 0.0
self.factor_step = 0.0
self.param_index = 0
self.param_metaid = "wild_type"
self.param_id = "wild_type"
self.param_wild_type = 0.0
self.param_val = 0.0
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 4) Multivariate random analysis #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.sigma = 0.0
self.nb_iterations = 0.0
self.iteration = 0
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 5) Output files #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.OAT_output_file = open("output/OAT_sensitivity_analysis.txt", "w")
self.OAT_output_file.close()
self.random_output_file = open("output/random_sensitivity_analysis.txt", "w")
self.random_output_file.close()
| (self, sbml_filename, copasi_path) | [
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20,505 | metevolsim.metevolsim | initialize_OAT_analysis |
Initialize the OAT sensitivity analysis algorithm.
Parameters
----------
factor_range : float > 0.0
Half-range of the log10-scaling factor (total range=2*factor_range)
factor_step : float > 0.0
Exploration step of the log10-scaling factor.
x' = x*10^(factor)
Returns
-------
None
| def initialize_OAT_analysis( self, factor_range, factor_step ):
"""
Initialize the OAT sensitivity analysis algorithm.
Parameters
----------
factor_range : float > 0.0
Half-range of the log10-scaling factor (total range=2*factor_range)
factor_step : float > 0.0
Exploration step of the log10-scaling factor.
x' = x*10^(factor)
Returns
-------
None
"""
assert factor_range > 0.0, "The factor range 'factor_range' must be a positive nonzero value. Exit."
assert factor_step > 0.0, "The factor step 'factor_step' must be a positive nonzero value. Exit."
self.factor_range = factor_range
self.factor_step = factor_step
self.model.compute_wild_type_steady_state()
self.model.compute_mutant_steady_state()
self.initialize_OAT_output_file()
self.param_index = 0
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] |
20,506 | metevolsim.metevolsim | initialize_OAT_output_file |
Initialize the OAT output file (write the header).
Parameters
----------
None
Returns
-------
None
| def initialize_OAT_output_file( self ):
"""
Initialize the OAT output file (write the header).
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write the header #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
header = "param_metaid param_id param_wild_type param_val param_dln"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
header += " "+species_id
for reaction_id in self.model.reactions:
header += " "+reaction_id
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write the wild-type steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
first_line = "wild_type wild_type 0.0 0.0 0.0"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
first_line += " "+str(self.model.species[species_id]["wild_type_value"])
for reaction_id in self.model.reactions:
first_line += " "+str(self.model.reactions[reaction_id]["wild_type_value"])
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Save in output file #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.OAT_output_file = open("output/OAT_sensitivity_analysis.txt", "a")
self.OAT_output_file.write(header+"\n"+first_line+"\n")
self.OAT_output_file.close()
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20,507 | metevolsim.metevolsim | initialize_random_analysis |
Initialize the multivariate random sensitivity analysis algorithm.
Parameters
----------
sigma : float > 0.0
Kinetic parameters mutation size.
nb_iterations : int > 0
Number of iterations of the multivariate random analysis.
Returns
-------
None
| def initialize_random_analysis( self, sigma, nb_iterations ):
"""
Initialize the multivariate random sensitivity analysis algorithm.
Parameters
----------
sigma : float > 0.0
Kinetic parameters mutation size.
nb_iterations : int > 0
Number of iterations of the multivariate random analysis.
Returns
-------
None
"""
assert sigma > 0.0, "The mutation size sigma must be positive. Exit."
assert nb_iterations > 0, "The number of iterations must be positive. Exit."
self.sigma = sigma
self.nb_iterations = nb_iterations
self.model.compute_wild_type_steady_state()
self.model.compute_mutant_steady_state()
self.initialize_random_output_file()
self.iterations = 0
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] |
20,508 | metevolsim.metevolsim | initialize_random_output_file |
Initialize the multivariate random output file (write the header).
Parameters
----------
None
Returns
-------
None
| def initialize_random_output_file( self ):
"""
Initialize the multivariate random output file (write the header).
Parameters
----------
None
Returns
-------
None
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write the header #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
header = "iteration"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
header += " "+species_id
for reaction_id in self.model.reactions:
header += " "+reaction_id
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write the wild-type steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
first_line = "wild_type"
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
first_line += " "+str(self.model.species[species_id]["wild_type_value"])
for reaction_id in self.model.reactions:
first_line += " "+str(self.model.reactions[reaction_id]["wild_type_value"])
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Save in output file #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.output_file = open("output/random_sensitivity_analysis.txt", "a")
self.output_file.write(header+"\n"+first_line+"\n")
self.output_file.close()
| (self) | [
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] |
20,509 | metevolsim.metevolsim | next_iteration |
Run the next multivariate random iteration
Parameters
----------
None
Returns
-------
bool
Returns True if the last iteration has been done. Returns False
else.
| def next_iteration( self ):
"""
Run the next multivariate random iteration
Parameters
----------
None
Returns
-------
bool
Returns True if the last iteration has been done. Returns False
else.
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Mutate each parameter at random with mutation size "sigma" #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
print("> Current iteration: "+str(self.iteration+1)+"/"+str(self.nb_iterations))
param_metaid = self.model.get_random_parameter()
self.model.random_parameter_mutation(param_metaid, self.sigma)
self.model.compute_mutant_steady_state()
self.write_random_output_file()
self.reload_wild_type_state()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Increment the current iteration #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.iteration += 1
if self.iteration == self.nb_iterations:
return True
return False
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20,510 | metevolsim.metevolsim | next_parameter |
Run a full parameter exploration for the next kinetic parameter.
Parameters
----------
None
Returns
-------
bool
Returns True if the last parameter has been explored. Returns False
else.
| def next_parameter( self ):
"""
Run a full parameter exploration for the next kinetic parameter.
Parameters
----------
None
Returns
-------
bool
Returns True if the last parameter has been explored. Returns False
else.
"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Get the next parameter #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.param_metaid = list(self.model.parameters)[self.param_index]
self.param_id = self.model.parameters[self.param_metaid]["id"]
self.param_wild_type = self.model.parameters[self.param_metaid]["wild_type_value"]
self.param_val = 0.0
print("> Current parameter: "+str(self.param_id))
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Explore the upper range #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
factor = 0.0
while factor <= self.factor_range+self.factor_step/2.0: # +step/2 for precision errors
self.param_val = self.param_wild_type*10**factor
self.model.set_mutant_parameter_value(self.param_metaid, self.param_val)
self.model.compute_mutant_steady_state()
self.write_OAT_output_file()
factor += self.factor_step
self.reload_wild_type_state()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Explore the lower range #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
factor = -self.factor_step
while factor >= -self.factor_range-self.factor_step/2.0: # -step/2 for precision errors
self.param_val = self.param_wild_type*10**factor
self.model.set_mutant_parameter_value(self.param_metaid, self.param_val)
self.model.compute_mutant_steady_state()
self.write_OAT_output_file()
factor -= self.factor_step
self.reload_wild_type_state()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 4) Increment the parameter index #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.param_index += 1
if self.param_index == len(self.model.parameters):
return True
return False
| (self) | [
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20,511 | metevolsim.metevolsim | reload_wild_type_state |
Reload the wild-type state into the mutant model.
Parameters
----------
None
Returns
-------
None
| def reload_wild_type_state( self ):
"""
Reload the wild-type state into the mutant model.
Parameters
----------
None
Returns
-------
None
"""
for species_id in self.model.species:
self.model.species[species_id]["initial_value"] = self.model.species[species_id]["wild_type_value"]
self.model.species[species_id]["mutant_value"] = self.model.species[species_id]["wild_type_value"]
#if not self.model.species[species_id]["constant"]:
# self.model.set_species_initial_value(species_id, self.model.species[species_id]["mutant_value"])
for parameter_metaid in self.model.parameters:
self.model.set_mutant_parameter_value(parameter_metaid, self.model.get_wild_type_parameter_value(parameter_metaid))
for reaction_id in self.model.reactions:
self.model.reactions[reaction_id]["mutant_value"] = self.model.reactions[reaction_id]["wild_type_value"]
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20,512 | metevolsim.metevolsim | run_OAT_analysis |
Run the OAT sensitivity analysis algorithm.
Parameters
----------
factor_range : float > 0.0
Half-range of the log10-scaling factor (total range=2*factor_range)
factor_step : float > 0.0
Exploration step of the log10-scaling factor.
x' = x*10^(factor)
Returns
-------
None
| def run_OAT_analysis( self, factor_range, factor_step ):
"""
Run the OAT sensitivity analysis algorithm.
Parameters
----------
factor_range : float > 0.0
Half-range of the log10-scaling factor (total range=2*factor_range)
factor_step : float > 0.0
Exploration step of the log10-scaling factor.
x' = x*10^(factor)
Returns
-------
None
"""
assert factor_range > 0.0, "The factor range 'factor_range' must be a positive nonzero value. Exit."
assert factor_step > 0.0, "The factor step 'factor_step' must be a positive nonzero value. Exit."
self.initialize_OAT_analysis(factor_range, factor_step)
stop_sa = False
start_time = time.time()
while not stop_sa:
stop_sa = self.next_parameter()
ongoing_time = time.time()
estimated_time = (ongoing_time-start_time)*float(self.model.get_number_of_parameters()-self.param_index-1)/float(self.param_index+1)
print(" Estimated remaining time "+str(int(round(estimated_time/60)))+" min.")
| (self, factor_range, factor_step) | [
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20,513 | metevolsim.metevolsim | run_random_analysis |
Run the multivariate random sensitivity analysis algorithm.
Parameters
----------
sigma : float > 0.0
Kinetic parameters mutation size.
nb_iterations : int > 0
Number of iterations of the multivariate random analysis.
Returns
-------
None
| def run_random_analysis( self, sigma, nb_iterations ):
"""
Run the multivariate random sensitivity analysis algorithm.
Parameters
----------
sigma : float > 0.0
Kinetic parameters mutation size.
nb_iterations : int > 0
Number of iterations of the multivariate random analysis.
Returns
-------
None
"""
assert sigma > 0.0, "The mutation size sigma must be positive. Exit."
assert nb_iterations > 0, "The number of iterations must be positive. Exit."
self.initialize_random_analysis(sigma, nb_iterations)
stop_sa = False
start_time = time.time()
while not stop_sa:
stop_sa = self.next_iteration()
ongoing_time = time.time()
estimated_time = (ongoing_time-start_time)*float(self.nb_iterations-self.iteration)/float(self.iteration+1)
print(" Estimated remaining time "+str((round(estimated_time/60,2)))+" min.")
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20,514 | metevolsim.metevolsim | write_OAT_output_file |
Write the current OAT sensitivity analysis state in the output file.
Parameters
----------
None
Returns
-------
None
| def write_OAT_output_file( self ):
"""
Write the current OAT sensitivity analysis state in the output file.
Parameters
----------
None
Returns
-------
None
"""
line = ""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write current OAT state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
line += str(self.param_metaid)+" "
line += str(self.param_id)+" "
line += str(self.param_wild_type)+" "
line += str(self.param_val)+" "
line += str((self.param_val-self.param_wild_type)/self.param_wild_type)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
wild_type_val = self.model.species[species_id]["wild_type_value"]
mutant_val = self.model.species[species_id]["mutant_value"]
dln_val = "NA"
if wild_type_val != 0.0:
dln_val = (mutant_val-wild_type_val)/wild_type_val
line += " "+str(dln_val)
for reaction_id in self.model.reactions:
wild_type_val = self.model.reactions[reaction_id]["wild_type_value"]
mutant_val = self.model.reactions[reaction_id]["mutant_value"]
dln_val = "NA"
if wild_type_val != 0.0:
dln_val = (mutant_val-wild_type_val)/wild_type_val
line += " "+str(dln_val)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Write in file #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.OAT_output_file = open("output/OAT_sensitivity_analysis.txt", "a")
self.OAT_output_file.write(line+"\n")
self.OAT_output_file.close()
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20,515 | metevolsim.metevolsim | write_random_output_file |
Write the current multivariate random sensitivity analysis state in the output file.
Parameters
----------
None
Returns
-------
None
| def write_random_output_file( self ):
"""
Write the current multivariate random sensitivity analysis state in the output file.
Parameters
----------
None
Returns
-------
None
"""
line = ""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 1) Write current multivariate random state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
line += str(self.iteration)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 2) Write steady-state #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for species_id in self.model.species:
if not self.model.species[species_id]["constant"]:
wild_type_val = self.model.species[species_id]["wild_type_value"]
mutant_val = self.model.species[species_id]["mutant_value"]
dln_val = "NA"
if wild_type_val != 0.0:
dln_val = (mutant_val-wild_type_val)/wild_type_val
line += " "+str(dln_val)
for reaction_id in self.model.reactions:
wild_type_val = self.model.reactions[reaction_id]["wild_type_value"]
mutant_val = self.model.reactions[reaction_id]["mutant_value"]
dln_val = "NA"
if wild_type_val != 0.0:
dln_val = (mutant_val-wild_type_val)/wild_type_val
line += " "+str(dln_val)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# 3) Write in file #
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
self.random_output_file = open("output/random_sensitivity_analysis.txt", "a")
self.random_output_file.write(line+"\n")
self.random_output_file.close()
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20,524 | pyobjectify.pyobjectify | Connectivity |
An enumeration of the supported file connectivity types:
- `ONLINE_STATIC` = The URL points to a static file on the Internet.
- `LOCAL` = The URL is a path to a local file.
For example, at the moment, a data stream from the Internet is not supported.
The end-user does not have to interface with this, but it is provided for more granular operations.
| class Connectivity(Enum):
"""
An enumeration of the supported file connectivity types:
- `ONLINE_STATIC` = The URL points to a static file on the Internet.
- `LOCAL` = The URL is a path to a local file.
For example, at the moment, a data stream from the Internet is not supported.
The end-user does not have to interface with this, but it is provided for more granular operations.
"""
ONLINE_STATIC = auto()
LOCAL = auto()
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20,525 | pyobjectify.pyobjectify | InputType |
An enumeration of the input types supported by pyobjectify.
The end-user does not have to interface with this, but it is provided for more granular operations.
| class InputType(Enum):
"""
An enumeration of the input types supported by pyobjectify.
The end-user does not have to interface with this, but it is provided for more granular operations.
"""
JSON = auto()
CSV = auto()
TSV = auto()
XML = auto()
XLSX = auto()
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] |
20,526 | pyobjectify.pyobjectify | Resource |
The Resource class stores some metadata about the resource to simplify the code.
The end-user does not have to interface with this, but it is provided for more granular operations.
| class Resource:
"""
The Resource class stores some metadata about the resource to simplify the code.
The end-user does not have to interface with this, but it is provided for more granular operations.
"""
def __init__(self, url, connectivity):
url = url.replace("file://", "")
self.url = url
self.connectivity = connectivity
if connectivity == Connectivity.ONLINE_STATIC:
response = get(url)
self.response = response
self.plaintext = response.text
elif connectivity == Connectivity.LOCAL:
url = url.replace("file://", "")
file_obj = open(url, "r")
self.response = file_obj
try:
self.plaintext = file_obj.read()
self.response.seek(0, 0)
except Exception: # XLSX data does not like to be read
self.plaintext = None
def __eq__(self, other):
# For Internet resources,
# Resource.response may have stochastic attributes like time elapsed.
# URL, connectivity type, plaintext should be enough to determine different Resources.
return self.url == other.url and self.connectivity == other.connectivity and self.plaintext == other.plaintext
| (url, connectivity) | [
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20,527 | pyobjectify.pyobjectify | __eq__ | null | def __eq__(self, other):
# For Internet resources,
# Resource.response may have stochastic attributes like time elapsed.
# URL, connectivity type, plaintext should be enough to determine different Resources.
return self.url == other.url and self.connectivity == other.connectivity and self.plaintext == other.plaintext
| (self, other) | [
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20,528 | pyobjectify.pyobjectify | __init__ | null | def __init__(self, url, connectivity):
url = url.replace("file://", "")
self.url = url
self.connectivity = connectivity
if connectivity == Connectivity.ONLINE_STATIC:
response = get(url)
self.response = response
self.plaintext = response.text
elif connectivity == Connectivity.LOCAL:
url = url.replace("file://", "")
file_obj = open(url, "r")
self.response = file_obj
try:
self.plaintext = file_obj.read()
self.response.seek(0, 0)
except Exception: # XLSX data does not like to be read
self.plaintext = None
| (self, url, connectivity) | [
0.015738096088171005,
-0.06247352063655853,
-0.04438713937997818,
0.0004414540599100292,
0.010442949831485748,
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-0.03040795400738716,
0.051606956869363785,
0.007086287252604961,
-0.015517081134021282,
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] |
20,530 | pyobjectify.pyobjectify | convert |
Attempts to convert the resource data through possible conversions.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
resource (:obj:`Resource`): he Resource object for the resource.
conversions (list): The list of all possible conversions, filtered if user specified output data type.
Returns:
object: The first successful conversion from the probable resource type to an output data type.
Raises:
TypeError: None of the possible conversions were successful.
| def convert(resource, conversions):
"""
Attempts to convert the resource data through possible conversions.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
resource (:obj:`Resource`): he Resource object for the resource.
conversions (list): The list of all possible conversions, filtered if user specified output data type.
Returns:
object: The first successful conversion from the probable resource type to an output data type.
Raises:
TypeError: None of the possible conversions were successful.
"""
for conversion in conversions:
try:
i_type, o_type = conversion
# Handle each case. Currently, only JSON has multiple options.
# Return the first conversion that works.
if i_type is InputType.JSON:
if o_type is dict:
return json_to_dict(resource)
elif o_type is list:
return json_to_list(resource)
elif o_type is DataFrame:
return json_to_dataframe(resource)
elif i_type is InputType.CSV:
return csv_to_list(resource)
elif i_type is InputType.TSV:
return tsv_to_list(resource)
elif i_type is InputType.XML:
return xml_to_dict(resource)
elif i_type is InputType.XLSX:
return xlsx_to_dict(resource)
except Exception:
continue # Try the next conversion
# Reach here means none of the conversions worked!
raise TypeError("The type of the resource is not supported.")
| (resource, conversions) | [
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20,531 | pyobjectify.pyobjectify | from_url |
This is the main interface that the end-user interacts with.
Given a URL, converts the resource data to a parsable Python object.
Args:
url (str): A URL to a resource.
out_type (:obj:`class`, optional): The user-specified data type of the output.
Returns:
object: A parsable Python object representation of the resource.
Raises:
TypeError: The user-specified data type of the output is not supported.
| def from_url(url, out_type=None):
"""
This is the main interface that the end-user interacts with.
Given a URL, converts the resource data to a parsable Python object.
Args:
url (str): A URL to a resource.
out_type (:obj:`class`, optional): The user-specified data type of the output.
Returns:
object: A parsable Python object representation of the resource.
Raises:
TypeError: The user-specified data type of the output is not supported.
"""
if out_type is not None and out_type not in OUTPUT_TYPES:
raise TypeError(f"The specified output type {out_type} is not supported.")
# (1) Get resource connectivity type
connectivity = url_to_connectivity(url)
# (2) Retrieve resource
resource = retrieve_resource(url, connectivity)
# (3) Determine input type
in_types = get_resource_types(resource)
# (4) Determine possible conversions
conversions = get_conversions(in_types, out_type)
# (5) Convert to output type
output = convert(resource, conversions)
# (6) Close the file object if open()-ed
if connectivity is Connectivity.LOCAL:
resource.response.close()
return output
| (url, out_type=None) | [
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] |
20,532 | pyobjectify.pyobjectify | get_conversions |
Get possible conversions for the probable resource types.
If the user specified a preferred output type, filter out any undesirable conversions to consider.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
in_types (:obj:`list`): A list of calculated possible resource types.
out_type (:obj:`class`, optional): The user-specified data type of the output.
Returns:
list: A list of (in, out) conversion tuples as described above.
Raises:
TypeError: There are no possible conversions.
| def get_conversions(in_types, out_type=None):
"""
Get possible conversions for the probable resource types.
If the user specified a preferred output type, filter out any undesirable conversions to consider.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
in_types (:obj:`list`): A list of calculated possible resource types.
out_type (:obj:`class`, optional): The user-specified data type of the output.
Returns:
list: A list of (in, out) conversion tuples as described above.
Raises:
TypeError: There are no possible conversions.
"""
# There is guaranteed at least one probable in_type
# Go through each probable resource data type.
# Use lists to preserve order.
conversions = [] # To make a list of possible conversions.
poss_out_types = [] # To make list of all output types based on probable input types.
for in_type in in_types:
for poss_out_type in CONVERSIONS[in_type]:
if out_type is None:
if (in_type, poss_out_type) not in conversions:
conversions.append((in_type, poss_out_type))
if poss_out_type not in poss_out_types:
poss_out_types.append(poss_out_type)
elif poss_out_type is out_type: # (and user specified a preferred output type)
if (in_type, poss_out_type) not in conversions:
conversions.append((in_type, poss_out_type))
if poss_out_type not in poss_out_types:
poss_out_types.append(poss_out_type)
if out_type is not None and out_type not in poss_out_types:
raise TypeError(f"The resource cannot be converted into the requested data type {out_type}.")
return conversions
| (in_types, out_type=None) | [
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20,533 | pyobjectify.pyobjectify | get_resource_types |
Get possible resource types of the resource.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
resource (:obj:`Resource`): The Resource object for the resource.
Returns:
list: A list of possible resource types of the resource.
Raises:
TypeError: The resource is of a type that is not supported.
| def get_resource_types(resource):
"""
Get possible resource types of the resource.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
resource (:obj:`Resource`): The Resource object for the resource.
Returns:
list: A list of possible resource types of the resource.
Raises:
TypeError: The resource is of a type that is not supported.
"""
possible = list(InputType)
try:
_ = loads(resource.plaintext)
except Exception:
possible.remove(InputType.JSON)
pass
try:
dicts = DictReader(resource.response)
if resource.connectivity is Connectivity.LOCAL:
resource.response.seek(0, 0)
# Ensure that each row has the same number of fields
nums_fields = set([len(d.items()) for d in list(dicts)])
if resource.connectivity is Connectivity.LOCAL:
resource.response.seek(0, 0)
assert len(nums_fields) == 1
# Ensure that the number of fields is greater than 1
(num_fields,) = nums_fields
assert num_fields > 1 # Data that have only one column will not be interpreted as CSVs
except Exception:
possible.remove(InputType.CSV)
try:
dicts = DictReader(resource.response, delimiter="\t")
if resource.connectivity is Connectivity.LOCAL:
resource.response.seek(0, 0)
# Ensure that each row has the same number of fields
nums_fields = set([len(d.items()) for d in list(dicts)])
if resource.connectivity is Connectivity.LOCAL:
resource.response.seek(0, 0)
assert len(nums_fields) == 1
# Ensure that the number of fields is greater than 1
(num_fields,) = nums_fields
assert num_fields > 1 # Data that have only one column will not be interpreted as TSVs
except Exception:
possible.remove(InputType.TSV)
try:
_ = parse(resource.plaintext)
assert resource.plaintext[0] == "<"
except Exception:
possible.remove(InputType.XML)
try:
df = read_excel(resource.url, sheet_name=None)
sheets_dict = {}
for sheet_name, df in df.items():
sheets_dict[sheet_name] = df.to_dict()
except Exception:
possible.remove(InputType.XLSX)
if len(possible) == 0:
raise TypeError("The type of the resource is not supported.")
return possible
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20,535 | pyobjectify.pyobjectify | retrieve_resource |
Retrieves the resource at the URL using the connectivity type and stores it in a Resource object.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
url (str): The URL to a resource.
connectivity (:obj:`Connectivity`): An attribute in the enumeration Connectivity.
The calculated connectivity type of the resource.
Returns:
Resource: The Resource object for the resource at the URL specified.
Raises:
TypeError: The connectivity type is not supported.
| def retrieve_resource(url, connectivity):
"""
Retrieves the resource at the URL using the connectivity type and stores it in a Resource object.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
url (str): The URL to a resource.
connectivity (:obj:`Connectivity`): An attribute in the enumeration Connectivity.
The calculated connectivity type of the resource.
Returns:
Resource: The Resource object for the resource at the URL specified.
Raises:
TypeError: The connectivity type is not supported.
"""
if not isinstance(connectivity, Connectivity):
raise TypeError(f"The connectivity type {connectivity} is not supported.")
return Resource(url, connectivity)
| (url, connectivity) | [
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20,536 | pyobjectify.pyobjectify | url_to_connectivity |
Get the connectivity type of the resource.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
url (str): The URL to a resource.
Returns:
Connectivity: An attribute in the enumeration `Connectivity`. The calculated connectivity of the resource type.
| def url_to_connectivity(url):
"""
Get the connectivity type of the resource.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
url (str): The URL to a resource.
Returns:
Connectivity: An attribute in the enumeration `Connectivity`. The calculated connectivity of the resource type.
"""
local_conditions = [url.startswith("file:///"), url.startswith("/"), url.startswith(".")]
if any(local_conditions):
return Connectivity.LOCAL
else:
return Connectivity.ONLINE_STATIC
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20,537 | pyratings.utils | _extract_rating_provider | Extract valid rating providers.
It is meant to extract rating providers from the column headings of a
``pd.DataFrame``. For example, let's assume some rating column headers are
["rating_fitch", "S&P rating", "BLOOMBERG composite rating"]. The function would
then return a list of valid rating providers, namely ["Fitch", "SP", "Bloomberg"].
Parameters
----------
rating_provider
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
valid_rtg_provider
List of strings containing the names of valid rating providers. Supported
rating providers are {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
'rating_provider' must be in that list.
Returns
-------
Union[str, List[str]]
str or List[str] with valid rating providers.
Raises
------
AssertionError
If ``rating_provider`` is not a subset of `valid_rtg_provider`.
Examples
--------
>>> _extract_rating_provider(
... rating_provider="S&P",
... valid_rtg_provider=["fitch", "s&p", "moody"],
... )
'SP'
>>> _extract_rating_provider(
... rating_provider="rtg_DBRS",
... valid_rtg_provider=["Fitch", "SP", "DBRS"]
... )
'DBRS'
You can also provide a list of strings.
>>> _extract_rating_provider(
... rating_provider=["Fitch ratings", "rating_SP", "DBRS"],
... valid_rtg_provider=["fitch", "moody", "sp", "bloomberg", "dbrs"]
... )
['Fitch', 'SP', 'DBRS']
| def _extract_rating_provider(
rating_provider: str | list[str] | Hashable,
valid_rtg_provider: list[str],
) -> str | list[str]:
"""Extract valid rating providers.
It is meant to extract rating providers from the column headings of a
``pd.DataFrame``. For example, let's assume some rating column headers are
["rating_fitch", "S&P rating", "BLOOMBERG composite rating"]. The function would
then return a list of valid rating providers, namely ["Fitch", "SP", "Bloomberg"].
Parameters
----------
rating_provider
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
valid_rtg_provider
List of strings containing the names of valid rating providers. Supported
rating providers are {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
'rating_provider' must be in that list.
Returns
-------
Union[str, List[str]]
str or List[str] with valid rating providers.
Raises
------
AssertionError
If ``rating_provider`` is not a subset of `valid_rtg_provider`.
Examples
--------
>>> _extract_rating_provider(
... rating_provider="S&P",
... valid_rtg_provider=["fitch", "s&p", "moody"],
... )
'SP'
>>> _extract_rating_provider(
... rating_provider="rtg_DBRS",
... valid_rtg_provider=["Fitch", "SP", "DBRS"]
... )
'DBRS'
You can also provide a list of strings.
>>> _extract_rating_provider(
... rating_provider=["Fitch ratings", "rating_SP", "DBRS"],
... valid_rtg_provider=["fitch", "moody", "sp", "bloomberg", "dbrs"]
... )
['Fitch', 'SP', 'DBRS']
"""
provider_map = {
"fitch": "Fitch",
"moody": "Moody",
"moody's": "Moody",
"sp": "SP",
"s&p": "SP",
"bloomberg": "Bloomberg",
"dbrs": "DBRS",
}
if isinstance(rating_provider, str):
rating_provider = [rating_provider]
valid_rtg_provider_lowercase = [x.lower() for x in valid_rtg_provider]
for i, provider in enumerate(rating_provider):
if not any(x in provider.lower() for x in valid_rtg_provider_lowercase):
raise AssertionError(
f"{provider!r} is not a valid rating provider. 'rating_provider' must "
f"be in {valid_rtg_provider}."
)
for valid_provider in valid_rtg_provider:
if valid_provider.lower() in provider.lower():
rating_provider[i] = provider_map[valid_provider.lower()]
if len(rating_provider) > 1:
return rating_provider
else:
return rating_provider[0]
| (rating_provider: str | list[str] | collections.abc.Hashable, valid_rtg_provider: list[str]) -> str | list[str] | [
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20,538 | pyratings.utils | _get_translation_dict | Load translation dictionaries from SQLite database. | def _get_translation_dict(
translation_table: str,
rating_provider: str = None,
tenor: str = "long-term",
st_rtg_strategy: str = "base",
) -> dict | pd.DataFrame:
"""Load translation dictionaries from SQLite database."""
def _rtg_to_scores(tenor: str) -> dict[str, int]:
"""Create translation dictionary to translate from ratings to scores."""
if tenor == "long-term":
sql_query = """
SELECT Rating, RatingScore FROM v_ltRatings
WHERE RatingProvider=?
"""
cursor.execute(sql_query, (rating_provider,))
else:
sql_query = """
SELECT Rating, AvgEquivLTScore FROM v_stRatings
WHERE RatingProvider=? and Strategy=?
"""
cursor.execute(sql_query, (rating_provider, st_rtg_strategy))
return dict(cursor.fetchall())
def _scores_to_rtg(tenor: str, strat: str) -> dict[int, str] | pd.DataFrame:
"""Create translation dictionary to translate from scores to ratings."""
if tenor == "long-term":
sql_query = """
SELECT RatingScore, Rating FROM v_ltRatings
WHERE Rating != 'SD' and RatingProvider=?
"""
cursor.execute(sql_query, (rating_provider,))
translation_dict = dict(cursor.fetchall())
else:
sql_query = """
SELECT MinEquivLTScore, MaxEquivLTScore, Rating FROM v_stRatings
WHERE RatingProvider=? and Strategy=?
ORDER BY MaxEquivLTScore
"""
cursor.execute(sql_query, (rating_provider, strat))
translation_dict = pd.DataFrame.from_records(
cursor.fetchall(), columns=["MinScore", "MaxScore", "Rating"]
)
return translation_dict
def _scores_to_warf() -> dict[int, int]:
"""Create translation dictionary to translate from scores to WARFs."""
sql_query = "SELECT RatingScore, WARF FROM WARFs"
cursor.execute(sql_query)
return dict(cursor.fetchall())
# connect to database
connection = sqlite3.connect(str(RATINGS_DB))
cursor = connection.cursor()
if translation_table == "rtg_to_scores":
translation_dict = _rtg_to_scores(tenor=tenor)
elif translation_table == "scores_to_rtg":
translation_dict = _scores_to_rtg(tenor=tenor, strat=st_rtg_strategy)
else:
translation_dict = _scores_to_warf()
# close database connection
connection.close()
return translation_dict
| (translation_table: str, rating_provider: Optional[str] = None, tenor: str = 'long-term', st_rtg_strategy: str = 'base') -> dict | pandas.core.frame.DataFrame | [
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20,542 | pyratings.consolidate | consolidate_ratings | Consolidate ratings on a security level basis across rating agencies .
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
method
Defines the method that will be used in order to consolidate the ratings on a
security level basis across rating agencies.
Valid methods are {"best", "second_best", "worst"}.
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Consolidated ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
Identify the best ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="best",
... rating_provider_input=["S&P", "Moody", "Fitch"],
... rating_provider_output="Moody",
... )
0 Aaa
1 Aa3
2 Aa1
3 Ba3
4 Ca
Name: best_rtg, dtype: object
Identify the second-best ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="second_best",
... rating_provider_input=["S&P", "Moody", "Fitch"],
... rating_provider_output="DBRS",
... )
0 AAH
1 AAL
2 AA
3 BBL
4 C
Name: second_best_rtg, dtype: object
Identify the worst ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="worst",
... rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 AA-
1 AA-
2 AA-
3 B+
4 C
Name: worst_rtg, dtype: object
| def consolidate_ratings(
ratings: pd.DataFrame,
method: Literal["best", "second_best", "worst"] = "worst",
rating_provider_input: list[str] = None,
rating_provider_output: Literal[
"Fitch", "Moody", "S&P", "Bloomberg", "DBRS"
] = "S&P",
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Consolidate ratings on a security level basis across rating agencies .
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
method
Defines the method that will be used in order to consolidate the ratings on a
security level basis across rating agencies.
Valid methods are {"best", "second_best", "worst"}.
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Consolidated ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
Identify the best ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="best",
... rating_provider_input=["S&P", "Moody", "Fitch"],
... rating_provider_output="Moody",
... )
0 Aaa
1 Aa3
2 Aa1
3 Ba3
4 Ca
Name: best_rtg, dtype: object
Identify the second-best ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="second_best",
... rating_provider_input=["S&P", "Moody", "Fitch"],
... rating_provider_output="DBRS",
... )
0 AAH
1 AAL
2 AA
3 BBL
4 C
Name: second_best_rtg, dtype: object
Identify the worst ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="worst",
... rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 AA-
1 AA-
2 AA-
3 B+
4 C
Name: worst_rtg, dtype: object
"""
func = {
"best": get_best_scores,
"second_best": get_second_best_scores,
"worst": get_worst_scores,
}
# translate ratings -> scores
rating_scores_series = func[method](
ratings, rating_provider_input=rating_provider_input, tenor=tenor
)
# convert back to ratings
ratings_series = get_ratings_from_scores(
rating_scores=rating_scores_series,
rating_provider=rating_provider_output,
tenor=tenor,
)
ratings_series.name = f"{method}_rtg"
return ratings_series
| (ratings: pandas.core.frame.DataFrame, method: Literal['best', 'second_best', 'worst'] = 'worst', rating_provider_input: Optional[list[str]] = None, rating_provider_output: Literal['Fitch', 'Moody', 'S&P', 'Bloomberg', 'DBRS'] = 'S&P', tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series | [
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] |
20,543 | pyratings.consolidate | get_best_ratings | Compute the best rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Best ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_best_ratings(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 AAA
1 AA-
2 AA+
3 BB-
4 CC
Name: best_rtg, dtype: object
| def get_best_ratings(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
rating_provider_output: Literal[
"Fitch", "Moody", "S&P", "Bloomberg", "DBRS"
] = "S&P",
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the best rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Best ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_best_ratings(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 AAA
1 AA-
2 AA+
3 BB-
4 CC
Name: best_rtg, dtype: object
"""
ratings_series = consolidate_ratings(
method="best",
ratings=ratings,
rating_provider_input=rating_provider_input,
rating_provider_output=rating_provider_output,
tenor=tenor,
)
return ratings_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, rating_provider_output: Literal['Fitch', 'Moody', 'S&P', 'Bloomberg', 'DBRS'] = 'S&P', tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series | [
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20,544 | pyratings.consolidate | get_best_scores | Compute the best rating scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Best rating scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_best_scores(
... ratings=ratings_df,
... rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 1
1 4
2 2
3 13
4 20
Name: best_scores, dtype: int64
| def get_best_scores(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the best rating scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Best rating scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_best_scores(
... ratings=ratings_df,
... rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 1
1 4
2 2
3 13
4 20
Name: best_scores, dtype: int64
"""
rating_scores_df = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider_input, tenor=tenor
)
rating_scores_series = rating_scores_df.min(axis=1)
rating_scores_series.name = "best_scores"
return rating_scores_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series | [
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] |
20,545 | pyratings.clean | get_pure_ratings | Remove rating watches/outlooks and other non-actual-rating related information.
Ratings may contain watch, such as 'AA- *+', 'BBB+ (CwNegative)'.
Outlook/watch should be seperated by a blank from the actual rating.
Also, ratings may also contain the letter 'u' (unsolicited) or be prefixed by
'(P)' (public information only).
This kind of information will be removed to retrieve the actual rating(s).
Parameters
----------
ratings
Uncleaned rating(s).
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular ratings stripped off of watches. The name of the resulting Series or
the columns of the returning DataFrame will be suffixed with `_clean`.
Examples
--------
Cleaning a single rating:
>>> get_pure_ratings("AA- *+")
'AA-'
>>> get_pure_ratings("Au")
'A'
>>> get_pure_ratings("(P)P-2")
'P-2'
Cleaning a `pd.Series`:
>>> import numpy as np
>>> import pandas as pd
>>> rating_series=pd.Series(
... data=[
... "BB+ *-",
... "(P)BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... name="rtg_SP",
... )
>>> get_pure_ratings(rating_series)
0 BB+
1 BBB
2 NaN
3 AA-
4 NaN
5 CCC+
6 BB+
Name: rtg_SP_clean, dtype: object
Cleaning a `pd.DataFrame`:
>>> rtg_df = pd.DataFrame(
... data={
... "rtg_SP": [
... "BB+ *-",
... "BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... "rtg_Fitch": [
... "BB+ *-",
... "BBB *+",
... pd.NA,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... },
... )
>>> get_pure_ratings(rtg_df)
rtg_SP_clean rtg_Fitch_clean
0 BB+ BB+
1 BBB BBB
2 NaN <NA>
3 AA- AA-
4 NaN NaN
5 CCC+ CCC+
6 BB+ BB+
| def get_pure_ratings(
ratings: str | pd.Series | pd.DataFrame,
) -> str | pd.Series | pd.DataFrame:
"""Remove rating watches/outlooks and other non-actual-rating related information.
Ratings may contain watch, such as 'AA- *+', 'BBB+ (CwNegative)'.
Outlook/watch should be seperated by a blank from the actual rating.
Also, ratings may also contain the letter 'u' (unsolicited) or be prefixed by
'(P)' (public information only).
This kind of information will be removed to retrieve the actual rating(s).
Parameters
----------
ratings
Uncleaned rating(s).
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular ratings stripped off of watches. The name of the resulting Series or
the columns of the returning DataFrame will be suffixed with `_clean`.
Examples
--------
Cleaning a single rating:
>>> get_pure_ratings("AA- *+")
'AA-'
>>> get_pure_ratings("Au")
'A'
>>> get_pure_ratings("(P)P-2")
'P-2'
Cleaning a `pd.Series`:
>>> import numpy as np
>>> import pandas as pd
>>> rating_series=pd.Series(
... data=[
... "BB+ *-",
... "(P)BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... name="rtg_SP",
... )
>>> get_pure_ratings(rating_series)
0 BB+
1 BBB
2 NaN
3 AA-
4 NaN
5 CCC+
6 BB+
Name: rtg_SP_clean, dtype: object
Cleaning a `pd.DataFrame`:
>>> rtg_df = pd.DataFrame(
... data={
... "rtg_SP": [
... "BB+ *-",
... "BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... "rtg_Fitch": [
... "BB+ *-",
... "BBB *+",
... pd.NA,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... },
... )
>>> get_pure_ratings(rtg_df)
rtg_SP_clean rtg_Fitch_clean
0 BB+ BB+
1 BBB BBB
2 NaN <NA>
3 AA- AA-
4 NaN NaN
5 CCC+ CCC+
6 BB+ BB+
"""
if isinstance(ratings, str):
ratings = (
ratings.split()[0].rstrip("uU").removeprefix("(p)").removeprefix("(P)")
)
return ratings
elif isinstance(ratings, pd.Series):
# identify string occurrences
isstring = ratings.apply(type).eq(str)
# strip string after occurrence of very first blank and strip character 'u',
# which has usually been added without a blank;
# also remove suffix '(p)' and '(P)'
ratings[isstring] = ratings[isstring].str.split().str[0]
ratings[isstring] = ratings[isstring].str.rstrip("uU")
ratings[isstring] = ratings[isstring].str.removeprefix("(p)")
ratings[isstring] = ratings[isstring].str.removeprefix("(P)")
ratings.name = f"{ratings.name}_clean"
return ratings
elif isinstance(ratings, pd.DataFrame):
# Recursive call of `get_pure_ratings`
return pd.concat(
[get_pure_ratings(ratings=ratings[col]) for col in ratings.columns], axis=1
)
| (ratings: str | pandas.core.series.Series | pandas.core.frame.DataFrame) -> str | pandas.core.series.Series | pandas.core.frame.DataFrame | [
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20,547 | pyratings.get_ratings | get_ratings_from_scores | Convert numerical rating scores into regular ratings.
Parameters
----------
rating_scores
Numerical rating score(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}.
short_term_strategy
Will only be used, if `tenor` is "short-term". Choose between three distinct
strategies in order to translate a long-term rating score into a short-term
rating. Must be in {"best", "base", "worst"}.
Compare
https://hsbc.github.io/pyratings/short-term-rating/#there's-one-more-catch...
- Strategy 1 (best):
Always choose the best possible short-term rating. That's the optimistic
approach.
- Strategy 2 (base-case):
Always choose the short-term rating that a rating agency would usually assign
if there aren't any special liquidity issues (positive or negative). That's
the base-case approach.
- Strategy 3 (worst):
Always choose the worst possible short-term rating. That's the conservative
approach.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular ratings according to `rating_provider`'s rating scale.
Raises
------
ValueError
If providing a single rating score and `rating_provider` is None.
Examples
--------
Converting a single long-term rating score:
>>> get_ratings_from_scores(rating_scores=9, rating_provider="Fitch")
'BBB'
Converting a single short-term rating score with different `short_term_strategy`
arguments:
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="best",
... )
'R-2M'
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="base",
... )
'R-3'
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="worst",
... )
'R-3'
Converting a ``pd.Series`` with scores:
>>> import pandas as pd
>>> rating_scores_series = pd.Series(data=[5, 7, 1, np.nan, 22, pd.NA])
>>> get_ratings_from_scores(
... rating_scores=rating_scores_series,
... rating_provider="Moody's",
... tenor="long-term",
... )
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> rating_scores_series = pd.Series(
... data=[5, 7, 1, np.nan, 22, pd.NA],
... name="Moody",
... )
>>> get_ratings_from_scores(rating_scores=rating_scores_series)
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Converting a ``pd.DataFrame`` with scores:
>>> rating_scores_df = pd.DataFrame(
... data=[[11, 16, "foo"], [4, 2, 1], [22, "bar", 22]]
... )
>>> get_ratings_from_scores(
... rating_scores=rating_scores_df,
... rating_provider=["Fitch", "Bloomberg", "DBRS"],
... tenor="long-term",
... )
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN D
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred by the dataframe's columns.
>>> rating_scores_df = pd.DataFrame(
... data={
... "rtg_fitch": [11, 4, 22],
... "rtg_Bloomberg": [16, 2, "foo"],
... "DBRS Ratings": ["bar", 1, 22],
... }
... )
>>> get_ratings_from_scores(rating_scores=rating_scores_df)
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN D
| def get_ratings_from_scores(
rating_scores: int | float | pd.Series | pd.DataFrame,
rating_provider: str | list[str] | None = None,
tenor: str = "long-term",
short_term_strategy: str | None = None,
) -> str | pd.Series | pd.DataFrame:
"""Convert numerical rating scores into regular ratings.
Parameters
----------
rating_scores
Numerical rating score(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}.
short_term_strategy
Will only be used, if `tenor` is "short-term". Choose between three distinct
strategies in order to translate a long-term rating score into a short-term
rating. Must be in {"best", "base", "worst"}.
Compare
https://hsbc.github.io/pyratings/short-term-rating/#there's-one-more-catch...
- Strategy 1 (best):
Always choose the best possible short-term rating. That's the optimistic
approach.
- Strategy 2 (base-case):
Always choose the short-term rating that a rating agency would usually assign
if there aren't any special liquidity issues (positive or negative). That's
the base-case approach.
- Strategy 3 (worst):
Always choose the worst possible short-term rating. That's the conservative
approach.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular ratings according to `rating_provider`'s rating scale.
Raises
------
ValueError
If providing a single rating score and `rating_provider` is None.
Examples
--------
Converting a single long-term rating score:
>>> get_ratings_from_scores(rating_scores=9, rating_provider="Fitch")
'BBB'
Converting a single short-term rating score with different `short_term_strategy`
arguments:
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="best",
... )
'R-2M'
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="base",
... )
'R-3'
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="worst",
... )
'R-3'
Converting a ``pd.Series`` with scores:
>>> import pandas as pd
>>> rating_scores_series = pd.Series(data=[5, 7, 1, np.nan, 22, pd.NA])
>>> get_ratings_from_scores(
... rating_scores=rating_scores_series,
... rating_provider="Moody's",
... tenor="long-term",
... )
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> rating_scores_series = pd.Series(
... data=[5, 7, 1, np.nan, 22, pd.NA],
... name="Moody",
... )
>>> get_ratings_from_scores(rating_scores=rating_scores_series)
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Converting a ``pd.DataFrame`` with scores:
>>> rating_scores_df = pd.DataFrame(
... data=[[11, 16, "foo"], [4, 2, 1], [22, "bar", 22]]
... )
>>> get_ratings_from_scores(
... rating_scores=rating_scores_df,
... rating_provider=["Fitch", "Bloomberg", "DBRS"],
... tenor="long-term",
... )
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN D
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred by the dataframe's columns.
>>> rating_scores_df = pd.DataFrame(
... data={
... "rtg_fitch": [11, 4, 22],
... "rtg_Bloomberg": [16, 2, "foo"],
... "DBRS Ratings": ["bar", 1, 22],
... }
... )
>>> get_ratings_from_scores(rating_scores=rating_scores_df)
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN D
"""
if tenor == "short-term" and short_term_strategy is None:
short_term_strategy = "base"
if tenor == "short-term" and short_term_strategy not in ["best", "base", "worst"]:
raise ValueError(
"Invalid short_term_strategy. Must be in ['best', 'base', 'worst']."
)
if isinstance(rating_scores, (int, float, np.number)):
if rating_provider is None:
raise ValueError(VALUE_ERROR_PROVIDER_MANDATORY)
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
rtg_dict = _get_translation_dict(
"scores_to_rtg",
rating_provider=rating_provider,
tenor=tenor,
st_rtg_strategy=short_term_strategy,
)
if not np.isnan(rating_scores):
rating_scores = int(Decimal(f"{rating_scores}").quantize(0, ROUND_HALF_UP))
if tenor == "long-term":
return rtg_dict.get(rating_scores, pd.NA)
else:
try:
return rtg_dict.loc[
(rating_scores >= rtg_dict["MinScore"])
& (rating_scores <= rtg_dict["MaxScore"]),
"Rating",
].iloc[0]
except IndexError:
return np.nan
elif isinstance(rating_scores, pd.Series):
if rating_provider is None:
rating_provider = _extract_rating_provider(
rating_provider=rating_scores.name,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
else:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
rtg_dict = _get_translation_dict(
"scores_to_rtg",
rating_provider,
tenor=tenor,
st_rtg_strategy=short_term_strategy,
)
# round element to full integer, if element is number
rating_scores = rating_scores.apply(
lambda x: np.round(x, 0) if isinstance(x, (int, float, np.number)) else x
)
if tenor == "long-term":
return pd.Series(
data=rating_scores.map(rtg_dict), name=f"rtg_{rating_provider}"
)
else:
out = []
for score in rating_scores:
try:
out.append(
rtg_dict.loc[
(score >= rtg_dict["MinScore"])
& (score <= rtg_dict["MaxScore"]),
"Rating",
].iloc[0]
)
except (IndexError, TypeError):
out.append(pd.NA)
return pd.Series(data=out, name=f"rtg_{rating_provider}")
elif isinstance(rating_scores, pd.DataFrame):
if rating_provider is None:
rating_provider = _extract_rating_provider(
rating_provider=rating_scores.columns.to_list(),
valid_rtg_provider=valid_rtg_agncy[tenor],
)
else:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
# Recursive call of 'get_ratings_from_score' for every column in dataframe
return pd.concat(
[
get_ratings_from_scores(
rating_scores=rating_scores[c | (rating_scores: int | float | pandas.core.series.Series | pandas.core.frame.DataFrame, rating_provider: Union[str, list[str], NoneType] = None, tenor: str = 'long-term', short_term_strategy: Optional[str] = None) -> str | pandas.core.series.Series | pandas.core.frame.DataFrame | [
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20,548 | pyratings.get_ratings | get_ratings_from_warf | Convert WARFs into regular ratings.
Parameters
----------
warf
Numerical WARF(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular rating(s) according to `rating_provider`'s rating scale.
Examples
--------
Converting a single WARF:
>>> get_ratings_from_warf(warf=610, rating_provider="DBRS")
'BBBL'
>>> get_ratings_from_warf(warf=1234.5678, rating_provider="SP")
'BB'
Converting a ``pd.Series`` with WARFs:
>>> import pandas as pd
>>> warf_series = pd.Series(data=[90, 218.999, 1, np.nan, 10000, pd.NA])
>>> get_ratings_from_warf(
... warf=warf_series,
... rating_provider="Moody's",
... )
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Converting a ``pd.DataFrame`` with WARFs:
>>> warf_df = pd.DataFrame(
... data=[[940, 4000, "foo"], [54, 13.5, 1], [10000, "bar", 9999]]
... )
>>> get_ratings_from_warf(
... warf=warf_df,
... rating_provider=["Fitch", "Bloomberg", "DBRS"],
... )
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN C
| def get_ratings_from_warf(
warf: int | float | pd.Series | pd.DataFrame,
rating_provider: str | list[str] | None = None,
) -> str | pd.Series | pd.DataFrame:
"""Convert WARFs into regular ratings.
Parameters
----------
warf
Numerical WARF(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular rating(s) according to `rating_provider`'s rating scale.
Examples
--------
Converting a single WARF:
>>> get_ratings_from_warf(warf=610, rating_provider="DBRS")
'BBBL'
>>> get_ratings_from_warf(warf=1234.5678, rating_provider="SP")
'BB'
Converting a ``pd.Series`` with WARFs:
>>> import pandas as pd
>>> warf_series = pd.Series(data=[90, 218.999, 1, np.nan, 10000, pd.NA])
>>> get_ratings_from_warf(
... warf=warf_series,
... rating_provider="Moody's",
... )
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Converting a ``pd.DataFrame`` with WARFs:
>>> warf_df = pd.DataFrame(
... data=[[940, 4000, "foo"], [54, 13.5, 1], [10000, "bar", 9999]]
... )
>>> get_ratings_from_warf(
... warf=warf_df,
... rating_provider=["Fitch", "Bloomberg", "DBRS"],
... )
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN C
"""
if isinstance(warf, (int, float, np.number)):
if rating_provider is None:
raise ValueError(VALUE_ERROR_PROVIDER_MANDATORY)
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy["long-term"],
)
rating_scores = get_scores_from_warf(warf=warf)
return get_ratings_from_scores(
rating_scores=rating_scores,
rating_provider=rating_provider,
tenor="long-term",
)
elif isinstance(warf, (pd.Series, pd.DataFrame)):
rating_scores = get_scores_from_warf(warf=warf)
return get_ratings_from_scores(
rating_scores=rating_scores,
rating_provider=rating_provider,
tenor="long-term",
)
| (warf: int | float | pandas.core.series.Series | pandas.core.frame.DataFrame, rating_provider: Union[str, list[str], NoneType] = None) -> str | pandas.core.series.Series | pandas.core.frame.DataFrame | [
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20,550 | pyratings.get_scores | get_scores_from_ratings | Convert regular ratings into numerical rating scores.
Parameters
----------
ratings
Rating(s) to be translated into rating score(s).
rating_provider
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
Union[int, pd.Series, pd.DataFrame]
Numerical rating score(s)
If returns a ``pd.Series``, the series name will be `rtg_score`
suffixed by `ratings.name`.
If return a ``pd.DataFrame``, the column names will be `rtg_score` suffixed
by the respective `ratings.columns`.
Raises
------
ValueError
If providing a single rating and `rating_provider` is None.
Examples
--------
Converting a single long-term rating:
>>> get_scores_from_ratings(
... ratings="BBB-", rating_provider="S&P", tenor="long-term"
... )
10
Converting a single short-term rating score:
>>> get_scores_from_ratings(
... ratings="P-1", rating_provider="Moody", tenor="short-term"
... )
3.5
Converting a ``pd.Series`` of ratings:
>>> import pandas as pd
>>> ratings_series = pd.Series(
... data=["Baa1", "C", "NR", "WD", "D", "B1", "SD"], name='Moody'
... )
>>> get_scores_from_ratings(
... ratings=ratings_series, rating_provider="Moody's", tenor="long-term"
... )
0 8.0
1 21.0
2 NaN
3 NaN
4 22.0
5 14.0
6 22.0
Name: rtg_score_Moody, dtype: float64
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> ratings_series = pd.Series(
... data=["Baa1", "C", "NR", "WD", "D", "B1", "SD"], name="Moody"
... )
>>> get_scores_from_ratings(ratings=ratings_series)
0 8.0
1 21.0
2 NaN
3 NaN
4 22.0
5 14.0
6 22.0
Name: rtg_score_Moody, dtype: float64
Converting a ``pd.DataFrame`` with ratings:
>>> ratings_df = pd.DataFrame(
... data=[["BB+", "B3", "BBB-"], ["AA-", "Aa1", "AAA"], ["D", "NR", "D"]],
... columns=["SP", "Moody", "DBRS"],
... )
>>> get_scores_from_ratings(
... ratings=ratings_df,
... rating_provider=["S&P", "Moody's", "DBRS"],
... tenor="long-term",
... )
rtg_score_SP rtg_score_Moody rtg_score_DBRS
0 11 16.0 NaN
1 4 2.0 1.0
2 22 NaN 22.0
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred from the dataframe's columns.
>>> ratings_df = pd.DataFrame(
... data={
... "rtg_fitch": ["BB+", "AA-", "D"],
... "rtg_Bloomberg": ["B-", "AA+", "NR"],
... "DBRS Ratings": ["BBB-", "AAA", "D"],
... }
... )
>>> get_scores_from_ratings(ratings=ratings_df)
rtg_score_rtg_fitch rtg_score_rtg_Bloomberg rtg_score_DBRS Ratings
0 11 16.0 NaN
1 4 2.0 1.0
2 22 NaN 22.0
| def get_scores_from_ratings(
ratings: str | pd.Series | pd.DataFrame,
rating_provider: str | list[str] | None = None,
tenor: str = "long-term",
) -> int | pd.Series | pd.DataFrame:
"""Convert regular ratings into numerical rating scores.
Parameters
----------
ratings
Rating(s) to be translated into rating score(s).
rating_provider
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
Union[int, pd.Series, pd.DataFrame]
Numerical rating score(s)
If returns a ``pd.Series``, the series name will be `rtg_score`
suffixed by `ratings.name`.
If return a ``pd.DataFrame``, the column names will be `rtg_score` suffixed
by the respective `ratings.columns`.
Raises
------
ValueError
If providing a single rating and `rating_provider` is None.
Examples
--------
Converting a single long-term rating:
>>> get_scores_from_ratings(
... ratings="BBB-", rating_provider="S&P", tenor="long-term"
... )
10
Converting a single short-term rating score:
>>> get_scores_from_ratings(
... ratings="P-1", rating_provider="Moody", tenor="short-term"
... )
3.5
Converting a ``pd.Series`` of ratings:
>>> import pandas as pd
>>> ratings_series = pd.Series(
... data=["Baa1", "C", "NR", "WD", "D", "B1", "SD"], name='Moody'
... )
>>> get_scores_from_ratings(
... ratings=ratings_series, rating_provider="Moody's", tenor="long-term"
... )
0 8.0
1 21.0
2 NaN
3 NaN
4 22.0
5 14.0
6 22.0
Name: rtg_score_Moody, dtype: float64
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> ratings_series = pd.Series(
... data=["Baa1", "C", "NR", "WD", "D", "B1", "SD"], name="Moody"
... )
>>> get_scores_from_ratings(ratings=ratings_series)
0 8.0
1 21.0
2 NaN
3 NaN
4 22.0
5 14.0
6 22.0
Name: rtg_score_Moody, dtype: float64
Converting a ``pd.DataFrame`` with ratings:
>>> ratings_df = pd.DataFrame(
... data=[["BB+", "B3", "BBB-"], ["AA-", "Aa1", "AAA"], ["D", "NR", "D"]],
... columns=["SP", "Moody", "DBRS"],
... )
>>> get_scores_from_ratings(
... ratings=ratings_df,
... rating_provider=["S&P", "Moody's", "DBRS"],
... tenor="long-term",
... )
rtg_score_SP rtg_score_Moody rtg_score_DBRS
0 11 16.0 NaN
1 4 2.0 1.0
2 22 NaN 22.0
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred from the dataframe's columns.
>>> ratings_df = pd.DataFrame(
... data={
... "rtg_fitch": ["BB+", "AA-", "D"],
... "rtg_Bloomberg": ["B-", "AA+", "NR"],
... "DBRS Ratings": ["BBB-", "AAA", "D"],
... }
... )
>>> get_scores_from_ratings(ratings=ratings_df)
rtg_score_rtg_fitch rtg_score_rtg_Bloomberg rtg_score_DBRS Ratings
0 11 16.0 NaN
1 4 2.0 1.0
2 22 NaN 22.0
"""
if isinstance(ratings, str):
if rating_provider is None:
raise ValueError(VALUE_ERROR_PROVIDER_MANDATORY)
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
rtg_dict = _get_translation_dict(
"rtg_to_scores",
rating_provider,
tenor=tenor,
st_rtg_strategy="base",
)
return rtg_dict.get(ratings, pd.NA)
elif isinstance(ratings, pd.Series):
if rating_provider is None:
rating_provider = _extract_rating_provider(
rating_provider=ratings.name,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
else:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
rtg_dict = _get_translation_dict(
"rtg_to_scores",
rating_provider,
tenor=tenor,
st_rtg_strategy="base",
)
return pd.Series(data=ratings.map(rtg_dict), name=f"rtg_score_{ratings.name}")
elif isinstance(ratings, pd.DataFrame):
if rating_provider is None:
rating_provider = _extract_rating_provider(
rating_provider=ratings.columns.to_list(),
valid_rtg_provider=valid_rtg_agncy[tenor],
)
else:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
# Recursive call of `get_scores_from_ratings`
return pd.concat(
[
get_scores_from_ratings(
ratings=ratings[col],
rating_provider=provider,
tenor=tenor,
)
for col, provider in zip(ratings.columns, rating_provider) # noqa: B905
],
axis=1,
)
| (ratings: str | pandas.core.series.Series | pandas.core.frame.DataFrame, rating_provider: Union[str, list[str], NoneType] = None, tenor: str = 'long-term') -> int | pandas.core.series.Series | pandas.core.frame.DataFrame | [
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20,551 | pyratings.get_scores | get_scores_from_warf | Convert weighted average rating factors (WARFs) into numerical rating scores.
Parameters
----------
warf
Weighted average rating factor (WARF).
Returns
-------
Union[int, float, pd.Series, pd.DataFrame]
Numerical rating score(s).
Examples
--------
Converting a single WARF:
>>> get_scores_from_warf(500)
10
>>> get_scores_from_warf(1992.9999)
13
Converting a ``pd.Series`` of WARFs:
>>> import numpy as np
>>> import pandas as pd
>>> warf_series = pd.Series(data=[260, 9999.49, np.nan, 10000, 2469.99, 2470])
>>> get_scores_from_warf(warf=warf_series)
0 8.0
1 21.0
2 NaN
3 22.0
4 14.0
5 15.0
Name: rtg_score, dtype: float64
Converting a ``pd.DataFrame`` of WARFs:
>>> warf_df = pd.DataFrame(
... data={
... "provider1": [900, 40, 10000],
... "provider2": [3000, 10, np.nan],
... "provider3": [610, 1, 9999.49],
... }
... )
>>> get_scores_from_warf(warf=warf_df)
rtg_score_provider1 rtg_score_provider2 rtg_score_provider3
0 11 15.0 10
1 4 2.0 1
2 22 NaN 21
| def get_scores_from_warf(
warf: int | float | pd.Series | pd.DataFrame,
) -> int | float | pd.Series | pd.DataFrame:
"""Convert weighted average rating factors (WARFs) into numerical rating scores.
Parameters
----------
warf
Weighted average rating factor (WARF).
Returns
-------
Union[int, float, pd.Series, pd.DataFrame]
Numerical rating score(s).
Examples
--------
Converting a single WARF:
>>> get_scores_from_warf(500)
10
>>> get_scores_from_warf(1992.9999)
13
Converting a ``pd.Series`` of WARFs:
>>> import numpy as np
>>> import pandas as pd
>>> warf_series = pd.Series(data=[260, 9999.49, np.nan, 10000, 2469.99, 2470])
>>> get_scores_from_warf(warf=warf_series)
0 8.0
1 21.0
2 NaN
3 22.0
4 14.0
5 15.0
Name: rtg_score, dtype: float64
Converting a ``pd.DataFrame`` of WARFs:
>>> warf_df = pd.DataFrame(
... data={
... "provider1": [900, 40, 10000],
... "provider2": [3000, 10, np.nan],
... "provider3": [610, 1, 9999.49],
... }
... )
>>> get_scores_from_warf(warf=warf_df)
rtg_score_provider1 rtg_score_provider2 rtg_score_provider3
0 11 15.0 10
1 4 2.0 1
2 22 NaN 21
"""
def _get_scores_from_warf_db(
wrf: int | float | pd.Series | pd.DataFrame,
) -> int | float:
if not isinstance(wrf, (int, float, np.number) or np.isnan(wrf)) or not (
1 <= wrf <= 10_000
):
return np.nan
else:
if wrf == 10_000:
return 22
else:
# connect to database
connection = sqlite3.connect(RATINGS_DB)
cursor = connection.cursor()
# create SQL query
sql_query = (
"SELECT RatingScore FROM WARFs WHERE ? >= MinWARF and ? < MaxWARF"
)
# execute SQL query
cursor.execute(sql_query, (wrf, wrf))
rtg_score = cursor.fetchall()
# close database connection
connection.close()
return rtg_score[0][0]
if isinstance(warf, (int, float, np.number)):
return _get_scores_from_warf_db(warf)
elif isinstance(warf, pd.Series):
rating_scores = warf.apply(_get_scores_from_warf_db)
rating_scores.name = "rtg_score"
return rating_scores
elif isinstance(warf, pd.DataFrame):
return warf.applymap(_get_scores_from_warf_db).add_prefix("rtg_score_")
| (warf: int | float | pandas.core.series.Series | pandas.core.frame.DataFrame) -> int | float | pandas.core.series.Series | pandas.core.frame.DataFrame | [
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20,552 | pyratings.consolidate | get_second_best_ratings | Compute the second-best rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Second-best ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_second_best_ratings(
... ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 AA+
1 AA-
2 AA
3 BB-
4 C
Name: second_best_rtg, dtype: object
| def get_second_best_ratings(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
rating_provider_output: Literal[
"Fitch", "Moody", "S&P", "Bloomberg", "DBRS"
] = "S&P",
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the second-best rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Second-best ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_second_best_ratings(
... ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 AA+
1 AA-
2 AA
3 BB-
4 C
Name: second_best_rtg, dtype: object
"""
ratings_series = consolidate_ratings(
method="second_best",
ratings=ratings,
rating_provider_input=rating_provider_input,
rating_provider_output=rating_provider_output,
tenor=tenor,
)
return ratings_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, rating_provider_output: Literal['Fitch', 'Moody', 'S&P', 'Bloomberg', 'DBRS'] = 'S&P', tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series | [
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20,553 | pyratings.consolidate | get_second_best_scores | Compute the second-best scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Second-best scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_second_best_scores(
... ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 2.0
1 4.0
2 3.0
3 13.0
4 21.0
Name: second_best_scores, dtype: float64
| def get_second_best_scores(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the second-best scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Second-best scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_second_best_scores(
... ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 2.0
1 4.0
2 3.0
3 13.0
4 21.0
Name: second_best_scores, dtype: float64
"""
rating_scores_df = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider_input, tenor=tenor
)
# rank scores per security (axis=1)
scores_ranked_df = rating_scores_df.rank(axis=1, method="first", numeric_only=False)
# get column with rank of 2, if available, otherwise get column with rank 1
rating_scores_ranked_series = rating_scores_df[scores_ranked_df <= 2].max(axis=1)
rating_scores_ranked_series.name = "second_best_scores"
return rating_scores_ranked_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series | [
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] |
20,555 | pyratings.warf | get_warf_buffer | Compute WARF buffer.
The WARF buffer is the distance from current WARF to the next maxWARF level. It
determines the room until a further rating downgrade.
Parameters
----------
warf
Numerical WARF.
Returns
-------
Union[float, int]
WARF buffer.
Examples
--------
>>> get_warf_buffer(warf=480)
5.0
>>> get_warf_buffer(warf=54)
1.0
| def get_warf_buffer(warf: float | int) -> float | int:
"""Compute WARF buffer.
The WARF buffer is the distance from current WARF to the next maxWARF level. It
determines the room until a further rating downgrade.
Parameters
----------
warf
Numerical WARF.
Returns
-------
Union[float, int]
WARF buffer.
Examples
--------
>>> get_warf_buffer(warf=480)
5.0
>>> get_warf_buffer(warf=54)
1.0
"""
# connect to database
connection = sqlite3.connect(RATINGS_DB)
cursor = connection.cursor()
# create SQL query
sql_query = "SELECT MaxWARF FROM WARFs WHERE ? >= MinWARF and ? < MaxWARF"
# execute SQL query
cursor.execute(sql_query, (warf, warf))
max_warf = cursor.fetchall()
# close database connection
connection.close()
return max_warf[0][0] - warf
| (warf: float | int) -> float | int | [
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20,556 | pyratings.get_warf | get_warf_from_ratings | Convert regular rating(s) to numerical WARF(s).
Parameters
----------
ratings
Regular rating(s) to be translated into WARF(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
Returns
-------
Union[int, pd.Series, pd.DataFrame]
Numerical WARF.
If returns a ``pd.Series``, the series name will be `warf` suffixed by
`ratings.name`.
If return a ``pd.DataFrame``, the column names will be `warf` suffixed
by the respective `ratings.columns`.
Examples
--------
Converting a single rating:
>>> get_warf_from_ratings(ratings="BB-", rating_provider="Fitch")
1766
Converting a ``pd.Series`` with ratings:
>>> import numpy as np
>>> import pandas as pd
>>> ratings_series = pd.Series(data=["A1", "A3", "Aaa", np.nan, "D", pd.NA])
>>> get_warf_from_ratings(
... ratings=ratings_series, rating_provider="Moody's"
... )
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf, dtype: float64
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> ratings_series = pd.Series(
... data=["A1", "A3", "Aaa", np.nan, "D", pd.NA],
... name="Moody's"
... )
>>> get_warf_from_ratings(ratings=ratings_series)
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf_Moody's, dtype: float64
Converting a ``pd.DataFrame`` with ratings:
>>> ratings_df = pd.DataFrame(
... data=[["BB+", "B-", "foo"], ["AA-", "AA+", "AAA"], ["D", "bar", "C"]],
... columns=["Fitch", "Bloomberg", "DBRS"],
... )
>>> get_warf_from_ratings(
... ratings= ratings_df, rating_provider=["Fitch", "Bloomberg", "DBRS"]
... )
warf_Fitch warf_Bloomberg warf_DBRS
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 9999.0
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred by the dataframe's columns.
>>> ratings_df = pd.DataFrame(
... data={
... "rtg_fitch": ["BB+", "AA-", "D"],
... "rtg_Bloomberg": ["B-", "AA+", "bar"],
... "DBRS Ratings": ["foo", "AAA", "C"]
... }
... )
>>> get_warf_from_ratings(ratings=ratings_df)
warf_rtg_fitch warf_rtg_Bloomberg warf_DBRS Ratings
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 9999.0
| def get_warf_from_ratings(
ratings: str | pd.Series | pd.DataFrame,
rating_provider: str | list[str] | None = None,
) -> int | pd.Series | pd.DataFrame:
"""Convert regular rating(s) to numerical WARF(s).
Parameters
----------
ratings
Regular rating(s) to be translated into WARF(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
Returns
-------
Union[int, pd.Series, pd.DataFrame]
Numerical WARF.
If returns a ``pd.Series``, the series name will be `warf` suffixed by
`ratings.name`.
If return a ``pd.DataFrame``, the column names will be `warf` suffixed
by the respective `ratings.columns`.
Examples
--------
Converting a single rating:
>>> get_warf_from_ratings(ratings="BB-", rating_provider="Fitch")
1766
Converting a ``pd.Series`` with ratings:
>>> import numpy as np
>>> import pandas as pd
>>> ratings_series = pd.Series(data=["A1", "A3", "Aaa", np.nan, "D", pd.NA])
>>> get_warf_from_ratings(
... ratings=ratings_series, rating_provider="Moody's"
... )
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf, dtype: float64
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> ratings_series = pd.Series(
... data=["A1", "A3", "Aaa", np.nan, "D", pd.NA],
... name="Moody's"
... )
>>> get_warf_from_ratings(ratings=ratings_series)
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf_Moody's, dtype: float64
Converting a ``pd.DataFrame`` with ratings:
>>> ratings_df = pd.DataFrame(
... data=[["BB+", "B-", "foo"], ["AA-", "AA+", "AAA"], ["D", "bar", "C"]],
... columns=["Fitch", "Bloomberg", "DBRS"],
... )
>>> get_warf_from_ratings(
... ratings= ratings_df, rating_provider=["Fitch", "Bloomberg", "DBRS"]
... )
warf_Fitch warf_Bloomberg warf_DBRS
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 9999.0
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred by the dataframe's columns.
>>> ratings_df = pd.DataFrame(
... data={
... "rtg_fitch": ["BB+", "AA-", "D"],
... "rtg_Bloomberg": ["B-", "AA+", "bar"],
... "DBRS Ratings": ["foo", "AAA", "C"]
... }
... )
>>> get_warf_from_ratings(ratings=ratings_df)
warf_rtg_fitch warf_rtg_Bloomberg warf_DBRS Ratings
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 9999.0
"""
if rating_provider is not None:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy["long-term"],
)
warf_dict = _get_translation_dict("scores_to_warf")
if isinstance(ratings, str):
rating_scores = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider, tenor="long-term"
)
return warf_dict.get(rating_scores, np.nan)
elif isinstance(ratings, (pd.Series, pd.DataFrame)):
rating_scores = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider, tenor="long-term"
)
if isinstance(ratings, pd.Series):
rating_scores.name = ratings.name
elif isinstance(ratings, pd.DataFrame):
rating_scores.columns = ratings.columns
return get_warf_from_scores(rating_scores=rating_scores)
| (ratings: str | pandas.core.series.Series | pandas.core.frame.DataFrame, rating_provider: Union[str, list[str], NoneType] = None) -> int | pandas.core.series.Series | pandas.core.frame.DataFrame | [
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20,557 | pyratings.get_warf | get_warf_from_scores | Convert numerical rating score(s) to numerical WARF(s).
Parameters
----------
rating_scores
Numerical rating score(s).
Returns
-------
Union[int, pd.Series, pd.DataFrame
Numerical WARF(s).
If returns a ``pd.Series``, the series name will be `warf` suffixed by
`rating_scores.name`.
If return a ``pd.DataFrame``, the column names will be `warf` suffixed
by the respective `rating_scores.columns`.
Examples
--------
Converting a single rating score:
>>> get_warf_from_scores(10)
610
Converting a ``pd.Series`` with rating scores:
>>> import pandas as pd
>>> rating_scores_series = pd.Series(data=[5, 7, 1, np.nan, 22, pd.NA])
>>> get_warf_from_scores(rating_scores=rating_scores_series)
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf, dtype: float64
Converting a ``pd.DataFrame`` with rating scores:
>>> rating_scores_df = pd.DataFrame(
... data=[[11, 16, "foo"], [4, 2, 1], [22, "bar", 22]],
... columns=["provider1", "provider2", "provider3"],
... )
>>> get_warf_from_scores(rating_scores=rating_scores_df)
warf_provider1 warf_provider2 warf_provider3
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 10000.0
| def get_warf_from_scores(
rating_scores: int | float | pd.Series | pd.DataFrame,
) -> int | pd.Series | pd.DataFrame:
"""Convert numerical rating score(s) to numerical WARF(s).
Parameters
----------
rating_scores
Numerical rating score(s).
Returns
-------
Union[int, pd.Series, pd.DataFrame
Numerical WARF(s).
If returns a ``pd.Series``, the series name will be `warf` suffixed by
`rating_scores.name`.
If return a ``pd.DataFrame``, the column names will be `warf` suffixed
by the respective `rating_scores.columns`.
Examples
--------
Converting a single rating score:
>>> get_warf_from_scores(10)
610
Converting a ``pd.Series`` with rating scores:
>>> import pandas as pd
>>> rating_scores_series = pd.Series(data=[5, 7, 1, np.nan, 22, pd.NA])
>>> get_warf_from_scores(rating_scores=rating_scores_series)
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf, dtype: float64
Converting a ``pd.DataFrame`` with rating scores:
>>> rating_scores_df = pd.DataFrame(
... data=[[11, 16, "foo"], [4, 2, 1], [22, "bar", 22]],
... columns=["provider1", "provider2", "provider3"],
... )
>>> get_warf_from_scores(rating_scores=rating_scores_df)
warf_provider1 warf_provider2 warf_provider3
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 10000.0
"""
warf_dict = _get_translation_dict("scores_to_warf")
if isinstance(rating_scores, (int, float, np.number)):
return warf_dict.get(rating_scores, np.nan)
elif isinstance(rating_scores, pd.Series):
warf = pd.Series(data=rating_scores.map(warf_dict))
if rating_scores.name is not None:
warf.name = "warf_" + str(rating_scores.name)
else:
warf.name = "warf"
return warf
elif isinstance(rating_scores, pd.DataFrame):
return rating_scores.apply(lambda x: x.map(warf_dict)).add_prefix("warf_")
| (rating_scores: int | float | pandas.core.series.Series | pandas.core.frame.DataFrame) -> int | pandas.core.series.Series | pandas.core.frame.DataFrame | [
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] |
20,558 | pyratings.aggregate | get_weighted_average | Compute weighted average.
Parameters
----------
data
Contains numerical values.
weights
Contains weights (between 0 and 1) with respect to `data`.
Returns
-------
float
Weighted average data.
Notes
-----
Computing the weighted average is simply the sumproduct of `data` and `weights`.
``nan`` in `data` will be excluded from calculating the weighted average. All
corresponding weights will be ignored. As a matter of fact, the remaining
weights will be upscaled so that the weights of all ``non-nan`` rows in `data` will
sum up to 1 (100%).
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> rtg_scores = pd.Series(data=[5, 7, 9])
>>> wgt = pd.Series(data=[0.5, 0.3, 0.2])
>>> get_weighted_average(data=rtg_scores, weights=wgt)
6.4
>>> warf = pd.Series(data=[500, 735, np.nan, 93, np.nan])
>>> wgt = pd.Series(data=[0.4, 0.1, 0.1, 0.2, 0.2])
>>> get_weighted_average(data=warf, weights=wgt)
417.29
| def get_weighted_average(data: pd.Series, weights: pd.Series) -> float:
"""Compute weighted average.
Parameters
----------
data
Contains numerical values.
weights
Contains weights (between 0 and 1) with respect to `data`.
Returns
-------
float
Weighted average data.
Notes
-----
Computing the weighted average is simply the sumproduct of `data` and `weights`.
``nan`` in `data` will be excluded from calculating the weighted average. All
corresponding weights will be ignored. As a matter of fact, the remaining
weights will be upscaled so that the weights of all ``non-nan`` rows in `data` will
sum up to 1 (100%).
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> rtg_scores = pd.Series(data=[5, 7, 9])
>>> wgt = pd.Series(data=[0.5, 0.3, 0.2])
>>> get_weighted_average(data=rtg_scores, weights=wgt)
6.4
>>> warf = pd.Series(data=[500, 735, np.nan, 93, np.nan])
>>> wgt = pd.Series(data=[0.4, 0.1, 0.1, 0.2, 0.2])
>>> get_weighted_average(data=warf, weights=wgt)
417.29
"""
# find indices in warf that correspond to np.nan
idx_nan = data[pd.isna(data)].index
# sum weights of securities with an actual rating, i.e. rating is not NaN
weights_non_nan = 1 - sum(weights.loc[idx_nan])
# upscale to 100%
weights_upscaled = weights / weights_non_nan
return data.fillna(0).dot(weights_upscaled)
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20,559 | pyratings.consolidate | get_worst_ratings | Compute the worst rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_innput` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Worst ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_worst_ratings(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 AA-
1 AA-
2 AA-
3 B+
4 C
Name: worst_rtg, dtype: object
| def get_worst_ratings(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
rating_provider_output: Literal[
"Fitch", "Moody", "S&P", "Bloomberg", "DBRS"
] = "S&P",
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the worst rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_innput` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Worst ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_worst_ratings(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 AA-
1 AA-
2 AA-
3 B+
4 C
Name: worst_rtg, dtype: object
"""
ratings_series = consolidate_ratings(
method="worst",
ratings=ratings,
rating_provider_input=rating_provider_input,
rating_provider_output=rating_provider_output,
tenor=tenor,
)
return ratings_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, rating_provider_output: Literal['Fitch', 'Moody', 'S&P', 'Bloomberg', 'DBRS'] = 'S&P', tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series | [
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20,560 | pyratings.consolidate | get_worst_scores | Compute the worst scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_innput` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Worst scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_worst_scores(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 4
1 4
2 4
3 14
4 21
Name: worst_scores, dtype: int64
| def get_worst_scores(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the worst scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_innput` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Worst scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_worst_scores(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 4
1 4
2 4
3 14
4 21
Name: worst_scores, dtype: int64
"""
rating_scores_df = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider_input, tenor=tenor
)
rating_scores_series = rating_scores_df.max(axis=1)
rating_scores_series.name = "worst_scores"
return rating_scores_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series | [
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20,564 | tabledata.error | DataError |
Exception raised when data is invalid as tabular data.
| class DataError(ValueError):
"""
Exception raised when data is invalid as tabular data.
"""
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] |
20,565 | tabledata.error | InvalidHeaderNameError |
Exception raised when a table header name is invalid.
| class InvalidHeaderNameError(NameValidationError):
"""
Exception raised when a table header name is invalid.
"""
| null | [
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20,566 | tabledata.error | InvalidTableNameError |
Exception raised when a table name is invalid.
| class InvalidTableNameError(NameValidationError):
"""
Exception raised when a table name is invalid.
"""
| null | [
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