from autumn.tool_kit import schema_builder as sb
from typing import List, Dict
def test_schema_builder():
cerberus_schema = sb.build_schema(**INPUT_SCHEMA)
assert cerberus_schema == EXPECTED_SCHEMA
INPUT_SCHEMA = {
"region": sb.Nullable(str),
"translations": sb.DictGeneric(str, str),
"outputs_to_plot": sb.List(sb.Dict(name=str)),
"pop_distribution_strata": sb.List(str),
"prevalence_combos": sb.List(sb.List(str)),
"input_function": sb.Dict(start_time=float, func_names=sb.List(str)),
"parameter_category_values": sb.Dict(time=float, param_names=sb.List(str)),
}
EXPECTED_SCHEMA = {
"region": {
"type": "string",
"nullable": True
},
"translations": {
"type": "dict",
"valuesrules": {
"type": "string"
},
"keysrules": {
"type": "string"
from autumn.tool_kit.scenarios import Scenario
from autumn.tool_kit import schema_builder as sb
from autumn.tool_kit.uncertainty import export_mcmc_quantiles
from autumn.db.database import Database
from .plotter import Plotter, COLOR_THEME
logger = logging.getLogger(__name__)
# Schema used to validate output plotting configuration data.
validate_plot_config = sb.build_validator(
# A list of translation mappings used to format plot titles.
translations=sb.DictGeneric(str, str),
# List of derived / generated outputs to plot
outputs_to_plot=sb.List(
sb.Dict(name=str,
target_times=sb.List(float),
target_values=sb.List(sb.List(float)))),
)
def plot_mcmc_parameter_trace(plotter: Plotter,
mcmc_tables: List[pd.DataFrame],
param_name: str):
"""
Plot the prameter traces for each MCMC run.
"""
_overwrite_non_accepted_mcmc_runs(mcmc_tables, column_name=param_name)
fig, axis, _, _, _ = plotter.get_figure()
for idx, table_df in enumerate(mcmc_tables):
table_df[param_name].plot.line(ax=axis, alpha=0.8, linewidth=0.7)
from autumn.tool_kit import schema_builder as sb
validate_params = sb.build_validator(
stratify_by=sb.List(str),
# Country info
iso3=str,
region=sb.Nullable(str),
# Running time.
start_time=float,
end_time=float,
time_step=float,
# Compartment construction
compartment_periods=sb.DictGeneric(str, float),
compartment_periods_calculated=dict,
# Infectiousness adjustments (not sure where used)
hospital_props=sb.List(float),
hospital_props_multiplier=float,
# mortality parameters
use_raw_mortality_estimates=bool,
infection_fatality_props=sb.List(float),
ifr_double_exp_model_params=dict,
# Age stratified params
agegroup_breaks=sb.List(float),
age_based_susceptibility=sb.DictGeneric(str, float),
# Clinical status stratified params
clinical_strata=sb.List(str),
non_sympt_infect_multiplier=float,
late_infect_multiplier=sb.Dict(sympt_isolate=float,
hospital_non_icu=float,
icu=float),
icu_mortality_prop=float,
from autumn.demography.social_mixing import load_specific_prem_sheet
from autumn.tool_kit.scenarios import Scenario
from autumn.tool_kit import schema_builder as sb
from .plotter import Plotter, COLOR_THEME
logger = logging.getLogger(__file__)
# Schema used to validate output plotting configuration data.
validate_plot_config = sb.build_validator(
# A list of translation mappings used to format plot titles.
translations=sb.DictGeneric(str, str),
# List of derived / generated outputs to plot
outputs_to_plot=sb.List(
sb.Dict(name=str, target_times=sb.List(float), target_values=sb.List(sb.List(float)))
),
# Plot population distribution across particular strata
pop_distribution_strata=sb.List(str),
# Plot prevalence combinations
prevalence_combos=sb.List(sb.List(str)),
# Visualise input functions over model time range.
input_function=sb.Dict(start_time=float, func_names=sb.List(str)),
# Visualise parameter values across categories for a particular time.
parameter_category_values=sb.Dict(time=float, param_names=sb.List(str)),
)
def plot_mcmc_parameter_trace(plotter: Plotter, mcmc_tables: List[pd.DataFrame], param_name: str):
"""
Plot the prameter traces for each MCMC run.
from copy import deepcopy
from summer.model.strat_model import StratifiedModel
from autumn.tool_kit import schema_builder as sb
from .requested_outputs import RequestedOutput
# FIXME - This data representation can be improved... somehow.
validate_post_process_config = sb.build_validator(
# Outputs to be generated
# Eg. ["prevXinfectiousXamongXage_10Xstrain_sensitive", "distribution_of_strataXstrain"]
requested_outputs=sb.List(str),
# Constants to multiply the generated outputs by.
# Eg. {"prevXinfectiousXamongXage_10Xstrain_sensitive": 1.0e5}
multipliers=sb.DictGeneric(str, float),
# List of compartment, stratification pairs used to generate some more requested outputs
# Eg. [["infectious", "location"], ["latent", "location"]]
collated_combos=sb.List(sb.List(str)),
)
def post_process(model: StratifiedModel,
post_process_config: dict,
add_defaults=True):
"""
Derive generated outputs from a model after the model has run.
Returns a dict of generated outputs.
"""
validate_post_process_config(post_process_config)
# Read config.