Source code for emodpy_malaria.vector_config

import emod_api.config.default_from_schema_no_validation as dfs
import csv
import os
from emodpy_malaria.malaria_vector_species_params import species_params


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# PUBLIC API section
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[docs] def set_team_defaults(config, manifest): """ Set configuration defaults using team-wide values, including drugs and vector species. Args: config: schema-backed config smart dict manifest: manifest file containing the schema path Returns: configured config """ # INFECTION config.parameters.Simulation_Type = "VECTOR_SIM" config.parameters.Infection_Updates_Per_Timestep = 8 config.parameters.Incubation_Period_Constant = 7 config.parameters.Infectious_Period_Constant = 7 # VECTOR_SIM parameters (formerly lived in dtk-tools/dtk/vector/params.py) config.parameters.Enable_Vector_Species_Report = 0 config.parameters.Vector_Sampling_Type = "VECTOR_COMPARTMENTS_NUMBER" # config.parameters.Mosquito_Weight = 1 # If this parameter is set, config.parameters.Vector_Sampling_Type is automatically changed to "SAMPLE_IND_VECTORS" config.parameters.Enable_Vector_Aging = 0 config.parameters.Enable_Vector_Mortality = 1 config.parameters.Enable_Vector_Migration = 0 config.parameters.Enable_Vector_Migration_Local = 0 config.parameters.Enable_Vector_Migration_Regional = 0 config.parameters.Vector_Migration_Habitat_Modifier = 0 config.parameters.Vector_Migration_Food_Modifier = 0 config.parameters.Vector_Migration_Stay_Put_Modifier = 0 config.parameters.Age_Dependent_Biting_Risk_Type = "SURFACE_AREA_DEPENDENT" config.parameters.Human_Feeding_Mortality = 0.1 config.parameters.Wolbachia_Infection_Modification = 1.0 config.parameters.Wolbachia_Mortality_Modification = 1.0 config.parameters.x_Temporary_Larval_Habitat = 1 config.parameters.Vector_Species_Params = [] config.parameters.Egg_Hatch_Density_Dependence = "NO_DENSITY_DEPENDENCE" config.parameters.Enable_Temperature_Dependent_Egg_Hatching = 0 config.parameters.Enable_Egg_Mortality = 0 config.parameters.Enable_Drought_Egg_Hatch_Delay = 0 config.parameters.Insecticides = [] # Other defaults from dtk-tools transition #fixme very likely needs pruning config.parameters.Egg_Saturation_At_Oviposition = "SATURATION_AT_OVIPOSITION" config.parameters.Enable_Demographics_Reporting = 0 # config.parameters.Enable_Rainfall_Stochasticity = 1 config.parameters.Node_Grid_Size = 0.042 # config.parameters.Population_Density_C50 = 30 config.parameters.Population_Scale_Type = "FIXED_SCALING" # setting up parameters for climate constant config.parameters.Base_Rainfall = 10 config.parameters.Base_Air_Temperature = 27 config.parameters.Base_Land_Temperature = 27 config.parameters.Base_Relative_Humidity = 0.75 config.parameters.Climate_Model = "CLIMATE_CONSTANT" config.parameters.Climate_Update_Resolution = "CLIMATE_UPDATE_DAY" # not used with "CLIMATE_CONSTANT", nice to have config.parameters.Enable_Climate_Stochasticity = 0 config.parameters.Simulation_Duration = 365 config.parameters.Start_Time = 0 # default is 1, but interventions often start at 0, which will make them not work return config
[docs] def get_species_params(config, species: str = None): """ Returns the species parameters dictionary with the matching species **Name** Args: config: schema-backed config smart dict species: Species to look up Returns: Dictionary of species parameters with the matching name """ for vector_species in config.parameters.Vector_Species_Params: if vector_species.Name == species: return vector_species raise ValueError(f"Species {species} not found.")
[docs] def set_species_param(config, species, parameter, value, overwrite=False): """ Sets a parameter value for a specific species. Raises value error if species not found Args: config: schema-backed config smart dict species: name of species for which to set the parameter parameter: parameter to set value: value to set the parameter to overwrite: if set to True and parameter is a list, overwrites the parameter with value, appends by default Returns: Nothing """ vector_species = get_species_params(config, species) if parameter in vector_species: if isinstance(vector_species[parameter], list): if overwrite: if isinstance(value, list): vector_species[parameter] = value else: vector_species[parameter] = [value] else: if isinstance(value, list): for val in value: vector_species[parameter].append(val) else: vector_species[parameter].append(value) else: vector_species[parameter] = value else: vector_species[parameter] = value
[docs] def configure_linear_spline(manifest, max_larval_capacity: float = pow(10, 8), capacity_distribution_number_of_years: int = 1, capacity_distribution_over_time: dict = None): """ Configures and returns a ReadOnlyDict of the LINEAR_SPLINE habitat parameters Args: manifest: manifest file containing the schema path max_larval_capacity: The maximum larval capacity. Sets **Max_Larval_Capacity** capacity_distribution_number_of_years: The total length of time in years for the scaling. If the simulation goes longer than this time, the pattern will repeat. Ideally, this value times 365 is the last value in 'Capacity_Distribution_Over_Time'. Sets **Capacity_Distribution_Number_Of_Years** capacity_distribution_over_time: "This allows one to scale the larval capacity over time. The Times and Values arrays must be the same length where Times is in days and Values are a scale factor per degrees squared. The value is multiplied times the max capacity and 'Node_Grid_Size' squared/4. Ideally, you want the last value to equal the first value if they are one day apart. A point will be added if not. Sets **Capacity_Distribution_Over_Time** **Example**:: { "Times": [0, 30, 60, 91, 122, 152, 182, 213, 243, 274, 304, 334, 365 ], "Values": [3, 0.8, 1.25, 0.1, 2.7, 8, 4, 35, 6.8, 6.5, 2.6, 2.1, 2] } Returns: Configured Habitat_Type: "LINEAR_SPLINE" parameters to be passed directly to "set_species_params" function """ if not capacity_distribution_over_time or "Times" not in capacity_distribution_over_time or "Values" not in capacity_distribution_over_time: raise ValueError("Please define capacity_distribution_over_time as a dictionary: {'Times':[], 'Values':[]}.\n") times_length = len(capacity_distribution_over_time["Times"]) values_length = len(capacity_distribution_over_time["Values"]) if not (values_length == times_length): raise ValueError(f"Please make sure the 'Times' and 'Values' lists in the capacity_distribution_over_time " f"dictionary are of equal lengths. Currently 'Times' is {times_length} " f"entrees and 'Values' is {values_length} entrees long.\n") habitat = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:VectorHabitat"]) habitat.parameters.Habitat_Type = "LINEAR_SPLINE" habitat.parameters.Max_Larval_Capacity = max_larval_capacity habitat.parameters.Capacity_Distribution_Number_Of_Years = capacity_distribution_number_of_years # adding larval capacity capacity_distribution = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:InterpolatedValueMap"]) capacity_distribution.parameters.Times = capacity_distribution_over_time["Times"] capacity_distribution.parameters.Values = capacity_distribution_over_time["Values"] habitat.parameters.Capacity_Distribution_Over_Time = capacity_distribution.parameters return habitat.parameters
[docs] def add_species(config, manifest, species_to_select): """ Adds species with preset parameters from 'malaria_vector_species_params.py', if species name not found - "gambiae" parameters are added and the new species name assigned. Args: config: schema-backed config smart dict manifest: manifest file containing the schema path species_to_select: a list of species or a name of a single species you'd like to set from malaria_vector_species_params.py Returns: configured config """ if type(species_to_select) is str: species_to_select = [species_to_select] for species in species_to_select: vector_species_parameters = species_params(manifest, species) if isinstance(vector_species_parameters, list): raise ValueError( f"'{species}' species not found in list, available species are: {vector_species_parameters}. " f"We suggest adding 'gambiae' species and changing " f"the name and relevant parameters with set_species_params() or " f"adding your species to malaria_vector_species_params.py.\n") else: config.parameters.Vector_Species_Params.append(vector_species_parameters) return config
[docs] def add_genes_and_alleles(config, manifest, species: str = None, alleles: list = None): """ Adds alleles to a species **Example**:: "Genes": [ { "Alleles": [ { "Name": "X1", "Initial_Allele_Frequency": 0.5, "Is_Y_Chromosome": 0 }, { "Name": "X2", "Initial_Allele_Frequency": 0.25, "Is_Y_Chromosome": 0 }, { "Name": "Y1", "Initial_Allele_Frequency": 0.15, "Is_Y_Chromosome": 1 }, { "Name": "Y2", "Initial_Allele_Frequency": 0.1, "Is_Y_Chromosome": 1 } ], "Is_Gender_Gene": 1, "Mutations": [] } ] Args: config: schema-backed config smart dict manifest: manifest file containing the schema path species: species to which to assign the alleles alleles: List of tuples of (**Name**, **Initial_Allele_Frequency**, **Is_Y_Chromosome**) for a set of alleles or (**Name**, **Initial_Allele_Frequency**), 1/0 or True/False can be used for Is_Y_Chromosome, third parameter is assumed False (0). If the third parameter is set to 1 in any of the tuples, we assume, this is a gender gene. **Example**:: [("X1", 0.25), ("X2", 0.35), ("Y1", 0.15), ("Y2", 0.25)] [("X1", 0.25, 0), ("X2", 0.35, 0), ("Y1", 0.15, 1), ("Y2", 0.25, 1)] Returns: configured config """ if not species or not alleles or not config or not manifest: raise ValueError("Please set all parameters, 'alleles' needs to be a list of tuples.\n") gene = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:VectorGene"]) for allele in alleles: vector_allele = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:VectorAllele"]) vector_allele.parameters.Name = allele[0] vector_allele.parameters.Initial_Allele_Frequency = allele[1] if len(allele) == 3: if allele[2]: gene.parameters.Is_Gender_Gene = 1 vector_allele.parameters.Is_Y_Chromosome = 1 gene.parameters.Alleles.append(vector_allele.parameters) species_params = get_species_params(config, species) species_params.Genes.append(gene.parameters) return config
[docs] def add_mutation(config, manifest, species, mutate_from, mutate_to, probability): """ Adds to **Mutations** parameter in a Gene which has the matching **Alleles** Args: config: schema-backed config smart dict manifest: manifest file containing the schema path species: Name of vector species to which we're adding mutations mutate_from: The allele in the gamete that could mutate mutate_to: The allele that this locus will change to during gamete generation probability: The probability that the allele will mutate from one allele to the other during the creation of the gametes Returns: configured config """ species_params = get_species_params(config, species) found = False for gene in species_params["Genes"]: allele_names = [] for allele in gene["Alleles"]: allele_names.append(allele["Name"]) if mutate_from in allele_names and mutate_to in allele_names: found = True mutations = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:VectorAlleleMutation"]) mutations.parameters.Mutate_From = mutate_from mutations.parameters.Mutate_To = mutate_to mutations.parameters.Probability_Of_Mutation = probability gene.Mutations.append(mutations.parameters) if not found: raise ValueError(f"Allele name(s) '{mutate_from}' and/or '{mutate_to}' were not found for {species}.\n") return config
[docs] def create_trait(manifest, trait: str = None, modifier: float = None, sporozoite_barcode_string: str = None, gametocyte_a_barcode_string: str = None, gametocyte_b_barcode_string: str = None): """ Configures and returns a modifier trait. Args: manifest: manifest file containing the schema path trait: The trait to be modified of vectors with the given allele combination. Available traits are: "INFECTED_BY_HUMAN", "FECUNDITY", "FEMALE_EGG_RATIO", "STERILITY", "TRANSMISSION_TO_HUMAN", "ADJUST_FERTILE_EGGS", "MORTALITY", "INFECTED_PROGRESS", "OOCYST_PROGRESSION", "SPOROZOITE_MORTALITY" modifier: The multiplier to use to modify the given trait for vectors with the given allele combination. sporozoite_barcode_string: TBD gametocyte_a_barcode_string: TBD gametocyte_b_barcode_string: TBD Returns: trait parameters that can be added to a list and passed to add_trait() function """ traits_available = ["INFECTED_BY_HUMAN", "FECUNDITY", "FEMALE_EGG_RATIO", "STERILITY", "TRANSMISSION_TO_HUMAN", "ADJUST_FERTILE_EGGS", "MORTALITY", "INFECTED_PROGRESS", "OOCYST_PROGRESSION", "SPOROZOITE_MORTALITY"] if not trait or modifier is None: raise ValueError(f'Please define both trait and modifier. Available traits are: {traits_available}.\n') if trait == "OOCYST_PROGRESSION" and not (gametocyte_a_barcode_string and gametocyte_b_barcode_string): raise ValueError("With trait = 'OOCYST_PROGRESSION', you need to define gametocyte_a_barcode_string and " "gametocyte_b_barcode_string. \n") if trait == "SPOROZOITE_MORTALITY" and not sporozoite_barcode_string: raise ValueError("With trait = 'SPOROZOITE_MORTALITY', you need to define sporozoite_barcode_string.\n") if trait not in traits_available: raise ValueError(f"Can't find trait '{trait}' in available traits. Traits available for use " f"are {traits_available}") trait_modifier = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:TraitModifier"]) trait_modifier.parameters.Trait = trait trait_modifier.parameters.Modifier = modifier if trait == "SPOROZOITE_MORTALITY": trait_modifier.parameters.Sporozoite_Barcode_String = sporozoite_barcode_string if trait == "OOCYST_PROGRESSION": trait_modifier.parameters.Gametocyte_A_Barcode_String = gametocyte_a_barcode_string trait_modifier.parameters.Gametocyte_B_Barcode_String = gametocyte_b_barcode_string return trait_modifier.parameters
[docs] def add_trait(config, manifest, species, allele_combo: list = None, trait_modifiers: list = None): """ Use this function to add traits as part of vector genetics configuration, the trait is assigned to the species' **Gene_To_Trait_Modifiers** parameter Should produce something like **Example**:: { "Allele_Combinations" : [ [ "X", "X" ], [ "a0", "a1" ] ], "Trait_Modifiers" : [ { "Trait" : "FECUNDITY", "Modifier": 0.7 } ] } Args: config: schema-backed config smart dict manifest: manifest file containing the schema path species: **Name** of species for which to add this **Gene_To_Trait_Modifiers** allele_combo: List of lists, This defines a possible subset of allele pairs that a vector could have. Each pair are alleles from one gene. If the vector has this subset, then the associated traits will be adjusted. Order does not matter. '*' is allowed when only the occurrence of one allele is important. **Example**:: [[ "X", "X" ], [ "a0", "a1" ]] trait_modifiers: list of trait modifier parameters created with create_trait() function. Returns: configured config """ if len(allele_combo) == 0: raise ValueError("allele_combo must define some alleles to target") for combo in allele_combo: if len(combo) != 2: raise ValueError( "Each combo in allele_combo must have two values - one for each chromosome, '*' is acceptable. \n") if not trait_modifiers or not isinstance(trait_modifiers, list): raise ValueError("Please make sure to pass in a list of trait modifiers created by create_trait() funciton.\n") species_params = get_species_params(config, species) # Check that the alleles referenced here have been 'declared' previously allele_names = [] allele_names_in_combo = [] for gene in species_params.Genes: for allele in gene["Alleles"]: allele_names.append(allele["Name"]) for combo in allele_combo: for allele_name in combo: if allele_name != "X" and allele_name != "Y" and allele_name != "*": allele_names_in_combo.append(allele_name) for alnic in allele_names_in_combo: if alnic not in allele_names: raise ValueError(f"Allele name {alnic} submitted in one of the allele_combos is not found" f" in Genes parameterf for {species}.\n") trait = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:GeneToTraitModifierConfig"]) trait.parameters.Allele_Combinations = allele_combo trait.parameters.Trait_Modifiers = trait_modifiers species_params.Gene_To_Trait_Modifiers.append(trait.parameters) return config
[docs] def add_insecticide_resistance(config, manifest, insecticide_name: str = "", species: str = "", allele_combo: list = None, blocking: float = 1.0, killing: float = 1.0, repelling: float = 1.0, larval_killing: float = 1.0): """ Use this function to add to the list of **Resistances** parameter for a specific insecticide Add each resistance separately. **Example**:: Insecticides = [ { "Name": "pyrethroid", "Resistances": [ { "Allele_Combinations": [ [ "a1", "a1" ] ], "Blocking_Modifier": 1.0, "Killing_Modifier": 0.85, "Repelling_Modifier": 0.72, "Larval_Killing_Modifier": 0, "Species": "gambiae" } ] }, {..} Args: config: schema-backed config smart dict manifest: manifest file containing the schema path insecticide_name: The name of the insecticide to which attach the resistance. species: Name of the species of vectors. allele_combo: List of combination of alleles that vectors must have in order to be resistant. blocking: The value used to modify (multiply) the blocking effectivity of an intervention. killing: The value used to modify (multiply) the killing effectivity of an intervention. repelling: The value used to modify (multiply) the repelling effectivity of an intervention. larval_killing: The value used to modify (multiply) the larval killing effectivity of an intervention. Returns: configured config """ resistance = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:AlleleComboProbabilityConfig"]) resistance.parameters.Blocking_Modifier = blocking resistance.parameters.Killing_Modifier = killing resistance.parameters.Repelling_Modifier = repelling resistance.parameters.Larval_Killing_Modifier = larval_killing resistance.parameters.Species = species resistance.parameters.Allele_Combinations = allele_combo insecticides = config.parameters.Insecticides for an_insecticide in insecticides: if an_insecticide.Name == insecticide_name: an_insecticide.Resistances.append(resistance.parameters) return config new_insecticide = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:Insecticide"]) new_insecticide.parameters.Name = insecticide_name new_insecticide.parameters.Resistances.append(resistance.parameters) config.parameters.Insecticides.append(new_insecticide.parameters) return config
[docs] def add_species_drivers(config, manifest, species: str = None, driving_allele: str = None, driver_type: str = "CLASSIC", to_copy: str = None, to_replace: str = None, likelihood_list: list = None, shredding_allele_required: str = None, allele_to_shred: str = None, allele_to_shred_to: str = None, allele_shredding_fraction: float = None, allele_to_shred_to_surviving_fraction: float = None): """ Add a gene drive that propagates a particular set of alleles. Adds one **Alleles_Driven** item to the **Alleles_Driven** list, using 'driving_allele' as key if matching one already exists. **Example**:: { "Driver_Type": "INTEGRAL_AUTONOMOUS", "Driving_Allele": "Ad", "Alleles_Driven": [ { "Allele_To_Copy": "Ad", "Allele_To_Replace": "Aw", "Copy_To_Likelihood": [ { "Copy_To_Allele": "Aw", "Likelihood": 0.1 }, { "Copy_To_Allele": "Ad", "Likelihood": 0.3 }, { "Copy_To_Allele": "Am", "Likelihood": 0.6 } ] }, { "Driver_Type" : "X_SHRED", "Driving_Allele" : "Ad", "Driving_Allele_Params" : { "Allele_To_Copy" : "Ad", "Allele_To_Replace" : "Aw", "Copy_To_Likelihood" : [ { "Copy_To_Allele" : "Ad", "Likelihood" : 1.0 }, { "Copy_To_Allele" : "Aw", "Likelihood" : 0.0 } ] }, "Shredding_Alleles" : { "Allele_Required" : "Yw", "Allele_To_Shred" : "Xw", "Allele_To_Shred_To" : "Xm", "Allele_Shredding_Fraction": 0.97, "Allele_To_Shred_To_Surviving_Fraction" : 0.05 } ] } Args: config: schema-backed config smart dict manifest: manifest file containing the schema path species: Name of the species for which we're setting the drivers driving_allele: This is the allele that is known as the driver driver_type: This indicates the type of driver. CLASSIC - The driver can only drive if the one gamete has the driving allele and the other has a specific allele to be replaced INTEGRAL_AUTONOMOUS - At least one of the gametes must have the driver. Alleles can still be driven if the driving allele is in both gametes or even if the driving allele cannot replace the allele in the other gamete X_SHRED, Y_SHRED - cannot be used in the same species during one simulation/realization. The driving_allele must exist at least once in the genome for shredding to occur. If there is only one, it can exist in either half of the genome. DAISY_CHAIN - can be used for drives that do not drive themselves but can be driven by another allele. to_copy: The main allele to be copied **Allele_To_Copy** to_replace: The allele that must exist and will be replaced by the copy **Allele_To_Replace** likelihood_list: A list of tuples in format: [(**Copy_To_Allele**, **Likelihood**),(),()] to assign to **Copy_To_Likelyhood** list shredding_allele_required: The genome must have this gender allele in order for shredding to occur. If the driver is X_SHRED, then the allele must be designated as a Y chromosome. If the driver is Y_SHRED, then the allele must NOT be designated as a Y chromosome allele_to_shred: The genome must have this gender allele in order for shredding to occur. If the driver is X_SHRED, then the allele must NOT be designated as a Y chromosome. If the driver is Y_SHRED, then the allele must be designated as a Y chromosome allele_to_shred_to: This is a gender allele that the 'shredding' will change the allele_to_shred into. It can be a temporary allele that never exists in the output or could be something that appears due to resistance/failures allele_shredding_fraction: This is the fraction of the alleles_to_Shred that will be converted to allele_to_shred_to. Values 0 to 1. If this value is less than 1, then some of the allele_to_shred will remain and be part of the gametes. allele_to_shred_to_surviving_fraction: A trait modifier will automatically generated for [ Allele_To_Shred_To, * ], the trait ADJUST_FERTILE_EGGS, and this value as its modifier. Values 0 to 1. A value of 0 implies perfect shredding such that no allele_to_Shred_To survive in the eggs. A value of 1 means all of the 'shredded' alleles survive. Returns: configured config """ if not config or not manifest or not species or not driving_allele or not to_copy or not to_replace or not likelihood_list: raise ValueError(f"Please define all the parameters for this function (except shredding," f"unless you're using them).\n") if (driver_type != "X_SHRED" and driver_type != "Y_SHRED") and (shredding_allele_required or allele_to_shred or allele_to_shred_to or allele_shredding_fraction or allele_to_shred_to_surviving_fraction): raise ValueError(f"Please do not define any shredding parameters if you're not using 'driver_type' = X_SHRED or" f"Y_SHRED.\n") elif driver_type == "DAISY_CHAIN": for (copy_to_allele, likelihood) in likelihood_list: if copy_to_allele == driving_allele: raise ValueError(f"For DAISY_CHAIN driver_type, you cannot have the Driving_Allele (driving_allele) " f"= '{driving_allele}' be the same as any of the Copy_To_Allele (in likelihood_list) = " f"'({copy_to_allele}, {likelihood})'.\n") species_params = get_species_params(config, species) gender_allele_required = False gender_allele_to_shred = False gender_allele_to_shred_to = False gene_driver = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:VectorGeneDriver"]) gene_driver.parameters.Driving_Allele = driving_allele gene_driver.parameters.Driver_Type = driver_type if driver_type == "X_SHRED" or driver_type == "Y_SHRED": if not allele_to_shred or not allele_to_shred_to or not shredding_allele_required: raise ValueError(f"For 'driver_type'= X_SHRED or Y_SHRED, please define all the shredding parameters.\n") for gene in species_params.Genes: if gene["Is_Gender_Gene"] == 1: for allele in gene["Alleles"]: if allele["Name"] == shredding_allele_required: gender_allele_required = True if driver_type == "X_SHRED" and allele["Is_Y_Chromosome"] == 0: raise ValueError( f"For 'driver_type' = X_SHRED, 'shredding_allele_required' should be a Y chromosome.\n") elif driver_type == "Y_SHRED" and allele["Is_Y_Chromosome"] == 1: raise ValueError( f"For 'driver_type' = Y_SHRED, 'shredding_allele_required' should be an X chromosome.\n") elif allele["Name"] == allele_to_shred: gender_allele_to_shred = True if driver_type == "X_SHRED" and allele["Is_Y_Chromosome"] == 1: raise ValueError( f"For 'driver_type'= X_SHRED, 'allele_to_shred' should be X chromosome.\n") elif driver_type == "Y_SHRED" and allele["Is_Y_Chromosome"] == 0: raise ValueError( f"For 'driver_type'= Y_SHRED, 'allele_to_shred' should be Y chromosome.\n") elif allele["Name"] == allele_to_shred_to: gender_allele_to_shred_to = True if driver_type == "X_SHRED" and allele["Is_Y_Chromosome"] == 1: raise ValueError( f"For 'driver_type'= X_SHRED, 'allele_to_shred' should be X chromosome.\n") elif driver_type == "Y_SHRED" and allele["Is_Y_Chromosome"] == 0: raise ValueError( f"For 'driver_type'= Y_SHRED, 'allele_to_shred_to' should be Y chromosome.\n") if not (gender_allele_required and gender_allele_to_shred and gender_allele_to_shred_to): raise ValueError(f"Looks like shredding_allele_required or allele_to_shred or allele_to_shred_to are not " f"on a gender gene, " f"but they all should be. Please verify your settings.\n") shredding_alleles = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:ShreddingAlleles"]) shredding_alleles.parameters.Allele_Required = shredding_allele_required shredding_alleles.parameters.Allele_Shredding_Fraction = allele_shredding_fraction shredding_alleles.parameters.Allele_To_Shred = allele_to_shred shredding_alleles.parameters.Allele_To_Shred_To = allele_to_shred_to shredding_alleles.parameters.Allele_To_Shred_To_Surviving_Fraction = allele_to_shred_to_surviving_fraction gene_driver.parameters.Shredding_Alleles = shredding_alleles.parameters allele_driven = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:AlleleDriven"]) allele_driven.parameters.Allele_To_Copy = to_copy allele_driven.parameters.Allele_To_Replace = to_replace for index, likely in enumerate(likelihood_list): c2likelyhood = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:CopyToAlleleLikelihood"]) c2likelyhood.parameters.Copy_To_Allele = likely[0] c2likelyhood.parameters.Likelihood = likely[1] allele_driven.parameters.Copy_To_Likelihood.append(c2likelyhood.parameters) # check if the Driving_Allele already exists if "Drivers" in species_params: for driver in species_params.Drivers: if driving_allele == driver["Driving_Allele"]: if driver_type == driver["Driver_Type"]: driver["Alleles_Driven"].append(allele_driven.parameters) return config else: raise ValueError(f"The gene driver with 'driving_allele'={driving_allele} must have exactly one " f"entry in 'Alleles_Driven' for this allele and therefore cannot be used for " f"multiple 'driver_type's.\n") if driver_type == "X_SHRED" or driver_type == "Y_SHRED": gene_driver.parameters.Driving_Allele_Params = allele_driven.parameters else: gene_driver.parameters.Alleles_Driven = [allele_driven.parameters] gene_driver.parameters.Driver_Type = driver_type # to circumvent the implicit settings species_params.Drivers.append(gene_driver.parameters) return config
[docs] def set_max_larval_capacity(config, species_name, habitat_type, max_larval_capacity): """ Set the Max_Larval_Capacity for a given species and habitat. Effectively doing something like: simulation.task.config.parameters.Vector_Species_Params[i]["Habitats"][j]["Max_Larval_Capacity"] = max_larval_capacity where i is index of species_name and j is index of habitat_type. Args: config: schema-backed config smart dict species_name: string. Species_Name to target. habitat_type: enum. Habitat_Type to target. max_larval_capacity: integer. New value of Max_Larval_Capacity. Returns: Nothing. """ habitats = get_species_params(config, species_name).Habitats # g_s_p raises a ValueError so if we get this far, we can use habitats unconditionally. for hab in habitats: if hab['Habitat_Type'] == habitat_type: hab['Max_Larval_Capacity'] = max_larval_capacity return raise ValueError(f"Failed to find habitat_type {habitat_type} for species {species_name}.")
[docs] def add_microsporidia(config, manifest, species_name: str = None, strain_name: str = "Strain_A", female_to_male_probability: float = 0, female_to_egg_probability: float = 0, male_to_female_probability: float = 0, male_to_egg_probability: float = 0, duration_to_disease_acquisition_modification: dict = None, duration_to_disease_transmission_modification: dict = None, larval_growth_modifier: float = 1, female_mortality_modifier: float = 1, male_mortality_modifier: float = 1): """ Adds microsporidia parameters to the named species' parameters. Args: config: schema-backed config dictionary, written to config.json manifest: file that contains path to the schema file species_name: Species to target, **Name** parameter strain_name: **Strain_Name** The name/identifier of the collection of transmission parameters. Cannot be empty string female_to_male_probability: **Microsporidia_Female_to_Male_Transmission_Probability** The probability an infected female will infect an uninfected male. female_to_egg_probability: **Microsporidia_Female_To_Egg_Transmission_Probability** The probability an infected female will infect her eggs when laying them. male_to_female_probability: **Microsporidia_Male_To_Female_Transmission_Probability** The probability an infected male will infect an uninfected female male_to_egg_probability: **Microsporidia_Male_To_Egg_Transmission_Probability** The probability a female that mated with an infected male will infect her eggs when laying them, independent of her being infected and transmitting to her offspring. duration_to_disease_acquisition_modification: **Microsporidia_Duration_To_Disease_Acquisition_Modification**, A dictionary for "Times" and "Values" as an age-based modification that the female will acquire malaria. **Times** is an array of days in ascending order that represent the number of days since the vector became infected. **Values** is an array of probabilities with values from 0 to 1 where each probability is the probability that the vector will acquire malaria due to Microsporidia. **Example**:: { "Times": [ 0, 3, 6, 9 ], "Values": [ 1.0, 1.0, 0.5, 0.0 ] } duration_to_disease_transmission_modification: **Microsporidia_Duration_To_Disease_Transmission_Modification**, A dictionary for "Times" and "Values" as an age-based modification that the female will transmit malaria. **Times** is an array of days in ascending order that represent the number of days since the vector became infected. **Values** is an array of probabilities with values from 0 to 1 where each probability is the probability that the vector will acquire malaria due to Microsporidia. **Example**:: { "Times": [ 0, 3, 6, 9 ], "Values": [ 1.0, 1.0, 0.75, 0.5] } larval_growth_modifier: **Microsporidia_Larval_Growth_Modifier** A multiplier modifier to the daily, temperature dependent, larval growth progress. female_mortality_modifier: **Microsporidia_Female_Mortality_Modifier** A multiplier modifier on the death rate for female vectors due to general life expectancy, age, and dry heat male_mortality_modifier: **Microsporidia_Male_Mortality_Modifier** A multiplier modifier on the death rate for male vectors due to general life expectancy, age, and dry heat Returns: Nothing """ if not species_name: raise ValueError("Please define species_name.\n") if not strain_name: raise ValueError("Please define strain_name.\n") if not duration_to_disease_acquisition_modification: duration_to_disease_acquisition_modification = {"Times": [0, 3, 6, 9], "Values": [1.0, 1.0, 0.5, 0.0]} if not duration_to_disease_transmission_modification: duration_to_disease_transmission_modification = {"Times": [0, 3, 6, 9], "Values": [1.0, 1.0, 0.75, 0.5] } species_parameters = get_species_params(config, species_name) d_t_d_a_m = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:InterpolatedValueMap"]) d_t_d_a_m.parameters.Times = duration_to_disease_acquisition_modification["Times"] d_t_d_a_m.parameters.Values = duration_to_disease_acquisition_modification["Values"] d_t_d_t_m = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:InterpolatedValueMap"]) d_t_d_t_m.parameters.Times = duration_to_disease_transmission_modification["Times"] d_t_d_t_m.parameters.Values = duration_to_disease_transmission_modification["Values"] microsporidia = dfs.schema_to_config_subnode(manifest.schema_file, ["idmTypes", "idmType:MicrosporidiaParameters"]) microsporidia.parameters.Duration_To_Disease_Acquisition_Modification = d_t_d_a_m.parameters microsporidia.parameters.Duration_To_Disease_Transmission_Modification = d_t_d_t_m.parameters microsporidia.parameters.Female_To_Male_Transmission_Probability = female_to_male_probability microsporidia.parameters.Male_To_Female_Transmission_Probability = male_to_female_probability microsporidia.parameters.Larval_Growth_Modifier = larval_growth_modifier microsporidia.parameters.Female_To_Egg_Transmission_Probability = female_to_egg_probability microsporidia.parameters.Female_Mortality_Modifier = female_mortality_modifier microsporidia.parameters.Male_Mortality_Modifier = male_mortality_modifier microsporidia.parameters.Male_To_Egg_Transmission_Probability = male_to_egg_probability microsporidia.parameters.Strain_Name = strain_name species_parameters.Microsporidia = species_parameters.Microsporidia.append(microsporidia.parameters)