Source code for tlo.analysis.utils

General utility functions for TLO analysis
import gzip
import json
import os
import pickle
import warnings
from collections import Counter, defaultdict
from import Mapping
from pathlib import Path
from types import MappingProxyType
from typing import Callable, Dict, Iterable, List, Optional, TextIO, Tuple, Union

import git
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import squarify

from tlo import Date, Simulation, logging, util
from tlo.logging.reader import LogData
from tlo.util import create_age_range_lookup

logger = logging.getLogger(__name__)

def _parse_log_file_inner_loop(filepath, level):
    """Parses the log file and returns dictionary of dataframes"""
    log_data = LogData()
    with open(filepath) as log_file:
        for line in log_file:
            # only parse json entities
            if line.startswith('{'):
                log_data.parse_log_line(line, level)
                print('FAILURE: found old-style log:')
                raise RuntimeError
    # convert dictionaries to dataframes
    output_logs = {**log_data.get_log_dataframes()}
    return output_logs

[docs]def parse_log_file(log_filepath, level: int = logging.INFO): """Parses logged output from a TLO run, split it into smaller logfiles and returns a class containing paths to these split logfiles. :param log_filepath: file path to log file :param level: parse everything from the given level :return: a class containing paths to split logfiles """ print(f'Processing log file {log_filepath}') uuid_to_module_name: Dict[str, str] = dict() # uuid to module name module_name_to_filehandle: Dict[str, TextIO] = dict() # module name to file handle log_directory = Path(log_filepath).parent print(f'Writing module-specific log files to {log_directory}') # iterate over each line in the logfile with open(log_filepath) as log_file: for line in log_file: # only parse lines that are json log lines (old-style logging is not supported) if line.startswith('{'): log_data_json = json.loads(line) uuid = log_data_json['uuid'] # if this is a header line (only header lines have a `type` key) if 'type' in log_data_json: module_name = log_data_json["module"] uuid_to_module_name[uuid] = module_name # we only need to create the file if we don't already have one for this module if module_name not in module_name_to_filehandle: module_name_to_filehandle[module_name] = open(log_directory / f"{module_name}.log", mode="w") # copy line from log file to module-specific log file (both headers and non-header lines) module_name_to_filehandle[uuid_to_module_name[uuid]].write(line) print('Finished writing module-specific log files.') # close all module-specific files for file_handle in module_name_to_filehandle.values(): file_handle.close() # return an object that accepts as an argument a dictionary containing paths to split logfiles return LogsDict({name: for name, handle in module_name_to_filehandle.items()}, level)
[docs]def write_log_to_excel(filename, log_dataframes): """Takes the output of parse_log_file() and creates an Excel file from dataframes""" metadata = list() sheet_count = 0 for module, dataframes in log_dataframes.items(): for key, dataframe in dataframes.items(): if key != '_metadata': sheet_count += 1 metadata.append([module, key, sheet_count, dataframes['_metadata'][module][key]['description']]) writer = pd.ExcelWriter(filename) index = pd.DataFrame(data=metadata, columns=['module', 'key', 'sheet', 'description']) index.to_excel(writer, sheet_name='Index') sheet_count = 0 for module, dataframes in log_dataframes.items(): for key, df in dataframes.items(): if key != '_metadata': sheet_count += 1 df.to_excel(writer, sheet_name=f'Sheet {sheet_count}')
[docs]def make_calendar_period_lookup(): """Returns a dictionary mapping calendar year (in years) to five year period i.e. { 1950: '1950-1954', 1951: '1950-1954, ...} """ # Recycles the code used to make age-range lookups: ranges, lookup = util.create_age_range_lookup(1950, 2100, 5) # Removes the '0-1950' category ranges.remove('0-1950') for year in range(1950): lookup.pop(year) return ranges, lookup
[docs]def make_calendar_period_type(): """ Make an ordered categorical type for calendar periods Returns CategoricalDType """ keys, _ = make_calendar_period_lookup() return pd.CategoricalDtype(categories=keys, ordered=True)
[docs]def make_age_grp_lookup(): """Returns a dictionary mapping age (in years) to five year period i.e. { 0: '0-4', 1: '0-4', ..., 119: '100+', 120: '100+' } """ return create_age_range_lookup(min_age=0, max_age=100, range_size=5)
[docs]def make_age_grp_types(): """ Make an ordered categorical type for age-groups Returns CategoricalDType """ keys, _ = create_age_range_lookup(min_age=0, max_age=100, range_size=5) return pd.CategoricalDtype(categories=keys, ordered=True)
[docs]def to_age_group(_ages: pd.Series): """Return a pd.Series with age-group formatted as a categorical type, created from a pd.Series with exact age.""" _, agegrplookup = make_age_grp_lookup() return
[docs]def get_scenario_outputs(scenario_filename: str, outputs_dir: Path) -> list: """Returns paths of folders associated with a batch_file, in chronological order.""" stub = scenario_filename.rstrip('.py') folders = [Path(f.path) for f in os.scandir(outputs_dir) if f.is_dir() and] folders.sort() return folders
[docs]def get_scenario_info(scenario_output_dir: Path) -> dict: """Utility function to get the the number draws and the number of runs in a batch set. TODO: read the JSON file to get further information """ info = dict() draw_folders = [f for f in os.scandir(scenario_output_dir) if f.is_dir()] info['number_of_draws'] = len(draw_folders) run_folders = [f for f in os.scandir(draw_folders[0]) if f.is_dir()] info['runs_per_draw'] = len(run_folders) return info
[docs]def load_pickled_dataframes(results_folder: Path, draw=0, run=0, name=None) -> dict: """Utility function to create a dict contaning all the logs from the specified run within a batch set""" folder = results_folder / str(draw) / str(run) p: os.DirEntry pickles = [p for p in os.scandir(folder) if'.pickle')] if name is not None: pickles = [p for p in pickles if in f"{name}.pickle"] output = dict() for p in pickles: name = os.path.splitext([0] with open(p.path, "rb") as f: output[name] = pickle.load(f) return output
[docs]def extract_params(results_folder: Path) -> Optional[pd.DataFrame]: """Utility function to get overridden parameters from scenario runs Returns dateframe summarizing parameters that change across the draws. It produces a dataframe with index of draw and columns of each parameters that is specified to be varied in the batch. NB. This does the extraction from run 0 in each draw, under the assumption that the over-written parameters are the same in each run. """ try: # Get the paths for the draws draws = [f for f in os.scandir(results_folder) if f.is_dir()] list_of_param_changes = list() for d in draws: p = load_pickled_dataframes(results_folder,, 0, name="tlo.scenario") p = p["tlo.scenario"]["override_parameter"] p['module_param'] = p['module'] + ':' + p['name'] p.index = [int(] * len(p.index) list_of_param_changes.append(p[['module_param', 'new_value']]) params = pd.concat(list_of_param_changes) = 'draw' params = params.rename(columns={'new_value': 'value'}) params = params.sort_index() return params except KeyError: print("No parameters changed between the runs") return None
[docs]def extract_results(results_folder: Path, module: str, key: str, column: str = None, index: str = None, custom_generate_series=None, do_scaling: bool = False, ) -> pd.DataFrame: """Utility function to unpack results. Produces a dataframe from extracting information from a log with the column multi-index for the draw/run. If the column to be extracted exists in the log, the name of the `column` is provided as `column`. If the resulting dataframe should be based on another column that exists in the log, this can be provided as 'index'. If instead, some work must be done to generate a new column from log, then a function can be provided to do this as `custom_generate_series`. Optionally, with `do_scaling=True`, each element is multiplied by the scaling_factor recorded in the simulation. Note that if runs in the batch have failed (such that logs have not been generated), these are dropped silently. """ def get_multiplier(_draw, _run): """Helper function to get the multiplier from the simulation, if do_scaling=True. Note that if the scaling factor cannot be found a `KeyError` is thrown.""" if not do_scaling: return 1.0 else: return load_pickled_dataframes(results_folder, _draw, _run, 'tlo.methods.population' )['tlo.methods.population']['scaling_factor']['scaling_factor'].values[0] if custom_generate_series is None: # If there is no `custom_generate_series` provided, it implies that function required selects a the specified # column from the dataframe. assert column is not None, "Must specify which column to extract" if index is not None: _gen_series = lambda _df: _df.set_index(index)[column] # noqa: 731 else: _gen_series = lambda _df: _df.reset_index(drop=True)[column] # noqa: 731 else: assert index is None, "Cannot specify an index if using custom_generate_series" assert column is None, "Cannot specify a column if using custom_generate_series" _gen_series = custom_generate_series # get number of draws and numbers of runs info = get_scenario_info(results_folder) # Collect results from each draw/run res = dict() for draw in range(info['number_of_draws']): for run in range(info['runs_per_draw']): draw_run = (draw, run) try: df: pd.DataFrame = load_pickled_dataframes(results_folder, draw, run, module)[module][key] output_from_eval: pd.Series = _gen_series(df) assert pd.Series == type(output_from_eval), 'Custom command does not generate a pd.Series' res[draw_run] = output_from_eval * get_multiplier(draw, run) except KeyError: # Some logs could not be found - probably because this run failed. res[draw_run] = None # Use pd.concat to compile results (skips dict items where the values is None) _concat = pd.concat(res, axis=1) _concat.columns.names = ['draw', 'run'] # name the levels of the columns multi-index return _concat
[docs]def summarize(results: pd.DataFrame, only_mean: bool = False, collapse_columns: bool = False) -> pd.DataFrame: """Utility function to compute summary statistics Finds mean value and 95% interval across the runs for each draw. """ summary = pd.concat( { 'mean': results.groupby(axis=1, by='draw', sort=False).mean(), 'lower': results.groupby(axis=1, by='draw', sort=False).quantile(0.025), 'upper': results.groupby(axis=1, by='draw', sort=False).quantile(0.975), }, axis=1 ) summary.columns = summary.columns.swaplevel(1, 0) summary.columns.names = ['draw', 'stat'] summary = summary.sort_index(axis=1) if only_mean and (not collapse_columns): # Remove other metrics and simplify if 'only_mean' across runs for each draw is required: om: pd.DataFrame = summary.loc[:, (slice(None), "mean")] om.columns = [c[0] for c in om.columns.to_flat_index()] = 'draw' return om elif collapse_columns and (len(summary.columns.levels[0]) == 1): # With 'collapse_columns', if number of draws is 1, then collapse columns multi-index: summary_droppedlevel = summary.droplevel('draw', axis=1) if only_mean: return summary_droppedlevel['mean'] else: return summary_droppedlevel else: return summary
[docs]def get_grid(params: pd.DataFrame, res: pd.Series): """Utility function to create the arrays needed to plot a heatmap. :param pd.DataFrame params: the dataframe of parameters with index=draw (made using `extract_params()`). :param pd.Series res: results of interest with index=draw (can be made using `extract_params()`) :returns: grid as dictionary """ res = pd.concat([params.pivot(columns='module_param', values='value'), res], axis=1) piv = res.pivot_table(index=res.columns[0], columns=res.columns[1], values=res.columns[2]) grid = dict() grid[res.columns[0]], grid[res.columns[1]] = np.meshgrid(piv.index, piv.columns) grid[res.columns[2]] = piv.values return grid
[docs]def format_gbd(gbd_df: pd.DataFrame): """Format GBD data to give standarize categories for age_group and period""" # Age-groups: gbd_df['Age_Grp'] = gbd_df['Age_Grp'].astype(make_age_grp_types()) # label periods: calperiods, calperiodlookup = make_calendar_period_lookup() gbd_df['Period'] = gbd_df['Year'].map(calperiodlookup).astype(make_calendar_period_type()) return gbd_df
[docs]def create_pickles_locally(scenario_output_dir, compressed_file_name_prefix=None): """For a run from the Batch system that has not resulted in the creation of the pickles, reconstruct the pickles locally.""" def turn_log_into_pickles(logfile): print(f"Opening {logfile}") outputs = parse_log_file(logfile) for key, output in outputs.items(): if key.startswith("tlo."): print(f" - Writing {key}.pickle") with open(logfile.parent / f"{key}.pickle", "wb") as f: pickle.dump(output, f) def uncompress_and_save_logfile(compressed_file) -> Path: """Uncompress and save a log file and return its path.""" target = compressed_file.parent / str([0:-3]) with open(target, "wb") as t: with, 'rb') as s: t.write( return target draw_folders = [f for f in os.scandir(scenario_output_dir) if f.is_dir()] for draw_folder in draw_folders: run_folders = [f for f in os.scandir(draw_folder) if f.is_dir()] for run_folder in run_folders: # Find the original log-file written by the simulation if compressed_file_name_prefix is None: logfile = [x for x in os.listdir(run_folder) if x.endswith('.log')][0] else: compressed_file_name = [ x for x in os.listdir(run_folder) if x.startswith(compressed_file_name_prefix) ][0] logfile = uncompress_and_save_logfile(Path(run_folder) / compressed_file_name) turn_log_into_pickles(logfile)
[docs]def compare_number_of_deaths(logfile: Path, resourcefilepath: Path): """Helper function to produce tables summarising deaths in the model run (given be a logfile) and the corresponding number of deaths in the GBD dataset. NB. * Requires output from the module `tlo.methods.demography` * Will do scaling automatically if the scaling-factor has been computed in the simulation (but not otherwise). """ output = parse_log_file(logfile) # 1) Get model outputs: # - get scaling factor: if 'scaling_factor' in output['tlo.methods.population']: sf = output['tlo.methods.population']['scaling_factor']['scaling_factor'].values[0] else: sf = 1.0 # - extract number of death by period/sex/age-group model = output['tlo.methods.demography']['death'].assign( year=lambda x: x['date'].dt.year ).groupby( ['sex', 'year', 'age', 'label'] )['person_id'].count().mul(sf) # - format categories: agegrps, agegrplookup = make_age_grp_lookup() calperiods, calperiodlookup = make_calendar_period_lookup() model = model.reset_index() model['age_grp'] = model['age'].map(agegrplookup).astype(make_age_grp_types()) model['period'] = model['year'].map(calperiodlookup).astype(make_calendar_period_type()) model = model.drop(columns=['age', 'year']) # - sum over period and divide by five to give yearly averages model = model.groupby(['period', 'sex', 'age_grp', 'label']).sum().div(5.0).rename( columns={'person_id': 'model'}).replace({0: np.nan}) # 2) Load comparator GBD datasets # - Load data, format and limit to deaths only: gbd_dat = format_gbd(pd.read_csv(resourcefilepath / 'gbd' / 'ResourceFile_Deaths_And_DALYS_GBD2019.csv')) gbd_dat = gbd_dat.loc[gbd_dat['measure_name'] == 'Deaths'] gbd_dat = gbd_dat.rename(columns={ 'Sex': 'sex', 'Age_Grp': 'age_grp', 'Period': 'period', 'GBD_Est': 'mean', 'GBD_Lower': 'lower', 'GBD_Upper': 'upper'}) # - Label GBD causes of death by 'label' defined in the simulation mapper_from_gbd_causes = pd.Series( output['tlo.methods.demography']['mapper_from_gbd_cause_to_common_label'].drop(columns={'date'}).loc[0] ).to_dict() gbd_dat['label'] = gbd_dat['cause_name'].map(mapper_from_gbd_causes) assert not gbd_dat['label'].isna().any() # - Create comparable data structure: gbd = gbd_dat.groupby(['period', 'sex', 'age_grp', 'label'])[['mean', 'lower', 'upper']].sum().div(5.0) gbd = gbd.add_prefix('GBD_') # 3) Return summary return gbd.merge(model, on=['period', 'sex', 'age_grp', 'label'], how='left')
[docs]def flatten_multi_index_series_into_dict_for_logging(ser: pd.Series) -> dict: """Helper function that converts a pd.Series with multi-index into a dict format that is suitable for logging. It does this by converting the multi-index into keys of type `str` in a format that later be used to reconstruct the multi-index (using `unflatten_flattened_multi_index_in_logging`).""" assert not ser.index.has_duplicates, "There should not be any duplicates in the multi-index. These will be lost" \ "in the conversion to a dict." names_of_multi_index = ser.index.names _df = ser.reset_index() flat_index = list() for _, row in _df.iterrows(): flat_index.append('|'.join([f"{col}={row[col]}" for col in names_of_multi_index])) return dict(zip(flat_index, ser.values))
[docs]def unflatten_flattened_multi_index_in_logging(_x: [pd.DataFrame, pd.Index]) -> [pd.DataFrame, pd.Index]: """Helper function that recreate the multi-index of logged results from a pd.DataFrame that is generated by `parse_log`. If a pd.DataFrame created by `parse_log` is the result of repeated logging of a pd.Series with a multi-index that was transformed before logging using `flatten_multi_index_series_into_dict_for_logging`, then the pd.DataFrame's columns will be those flattened labels. This helper function recreates the original multi-index from which the flattened labels were created and applies it to the pd.DataFrame. Alternatively, if jus the index of the "flattened" labels is provided, then the equivalent multi-index is returned. """ def gen_mutli_index(_idx: pd.Index): """Returns the multi-index represented by the flattened index.""" index_value_list = list() for col in _idx.str.split('|'): index_value_list.append(tuple(component.split('=')[1] for component in col)) index_name_list = tuple(component.split('=')[0] for component in _idx[0].split('|')) return pd.MultiIndex.from_tuples(index_value_list, names=index_name_list) if isinstance(_x, pd.DataFrame): _y = _x.copy() _y.columns = gen_mutli_index(_x.columns) return _y else: return gen_mutli_index(_x)
[docs]class LogsDict(Mapping): """Parses module-specific log files and returns Pandas dataframes. The dictionary returned has the format:: { <logger 1 name>: { <log key 1>: <pandas dataframe>, <log key 2>: <pandas dataframe>, <log key 3>: <pandas dataframe> }, <logger 2 name>: { <log key 4>: <pandas dataframe>, <log key 5>: <pandas dataframe>, <log key 6>: <pandas dataframe> }, ... } """ def __init__(self, file_names_and_paths, level): super().__init__() # initialise class with module-specific log files paths self._logfile_names_and_paths: Dict[str, str] = file_names_and_paths # create a dictionary that will contain cached data self._results_cache: Dict[str, Dict] = dict() self._level = level def __getitem__(self, key, cache=True): # check if the requested key is found in a dictionary containing module name and log file paths. if key # is found, return parsed logs else return KeyError if key in self._logfile_names_and_paths: # check if key is found in cache if key not in self._results_cache: result_df = _parse_log_file_inner_loop(self._logfile_names_and_paths[key], self._level) # get metadata for the selected log file and merge it all with the selected key result_df[key]['_metadata'] = result_df['_metadata'] if not cache: # check if caching is disallowed return result_df[key] self._results_cache[key] = result_df[key] # add key specific parsed results to cache return self._results_cache[key] # return the added results else: raise KeyError def __contains__(self, k): # if key k is a valid logfile entry return k in self._logfile_names_and_paths
[docs] def items(self): # parse module-specific log file and return results as a generator for key in self._logfile_names_and_paths.keys(): module_specific_logs = self.__getitem__(key, cache=False) yield key, module_specific_logs
def __repr__(self): return repr(self._logfile_names_and_paths) def __len__(self): return len(self._logfile_names_and_paths)
[docs] def keys(self): # return dictionary keys return self._logfile_names_and_paths.keys()
[docs] def values(self): # parse module-specific log file and yield the results for key in self._logfile_names_and_paths.keys(): module_specific_logs = self.__getitem__(key, cache=False) yield module_specific_logs
def __iter__(self): return iter(self._logfile_names_and_paths) def __getstate__(self): # Ensure all items cached before pickling for key in self.keys(): self.__getitem__(key, cache=True) return self.__dict__
[docs]def get_filtered_treatment_ids(depth: Optional[int] = None) -> List[str]: """Return a list of treatment_ids that are defined in the model, filtered to a specified depth.""" def filter_treatments(_treatments: Iterable[str], depth: int = 1) -> List[str]: """Reduce an iterable of `TREATMENT_IDs` by ignoring difference beyond a certain depth of specification and adding '_*' to the end to serve as a wild-card. N.B., The TREATMENT_ID is defined with each increasing level of specification separated by a `_`. """ return sorted(list(set( [ "".join(f"{x}_" for i, x in enumerate(t.split('_')) if i < depth).rstrip('_') + '_*' for t in set(_treatments) ] ))) # Get pd.DataFrame with information of all the defined HSI # Import within function to avoid circular import error from tlo.analysis.hsi_events import get_all_defined_hsi_events_as_dataframe hsi_event_details = get_all_defined_hsi_events_as_dataframe() # Return list of TREATMENT_IDs and filter to the resolution needed return filter_treatments(hsi_event_details['treatment_id'], depth=depth if depth is not None else np.inf)
[docs]def colors_in_matplotlib() -> tuple: """Return tuple of the strings for all the colours defined in Matplotlib.""" return tuple( set().union( mcolors.BASE_COLORS.keys(), mcolors.TABLEAU_COLORS.keys(), mcolors.CSS4_COLORS.keys(), ) )
APPT_TYPE_TO_COARSE_APPT_TYPE_MAP = MappingProxyType({ 'Under5OPD': 'Outpatient', 'Over5OPD': 'Outpatient', 'ConWithDCSA': 'Con w/ DCSA', 'AccidentsandEmerg': 'A & E', 'InpatientDays': 'Inpatient', 'IPAdmission': 'Inpatient', 'AntenatalFirst': 'RMNCH', 'ANCSubsequent': 'RMNCH', 'NormalDelivery': 'RMNCH', 'CompDelivery': 'RMNCH', 'Csection': 'RMNCH', 'EPI': 'RMNCH', 'FamPlan': 'RMNCH', 'U5Malnutr': 'RMNCH', 'VCTNegative': 'HIV/AIDS', 'VCTPositive': 'HIV/AIDS', 'MaleCirc': 'HIV/AIDS', 'NewAdult': 'HIV/AIDS', 'EstMedCom': 'HIV/AIDS', 'EstNonCom': 'HIV/AIDS', 'PMTCT': 'HIV/AIDS', 'Peds': 'HIV/AIDS', 'TBNew': 'Tb', 'TBFollowUp': 'Tb', 'DentAccidEmerg': 'Dental', 'DentSurg': 'Dental', 'DentalU5': 'Dental', 'DentalO5': 'Dental', 'MentOPD': 'Mental Health', 'MentClinic': 'Mental Health', 'MajorSurg': 'Surgery / Radiotherapy', 'MinorSurg': 'Surgery / Radiotherapy', 'Radiotherapy': 'Surgery / Radiotherapy', 'STI': 'STI', 'LabHaem': 'Lab / Diagnostics', 'LabPOC': 'Lab / Diagnostics', 'LabParasit': 'Lab / Diagnostics', 'LabBiochem': 'Lab / Diagnostics', 'LabMicrobio': 'Lab / Diagnostics', 'LabMolec': 'Lab / Diagnostics', 'LabTBMicro': 'Lab / Diagnostics', 'LabSero': 'Lab / Diagnostics', 'LabCyto': 'Lab / Diagnostics', 'LabTrans': 'Lab / Diagnostics', 'Ultrasound': 'Lab / Diagnostics', 'Mammography': 'Lab / Diagnostics', 'MRI': 'Lab / Diagnostics', 'Tomography': 'Lab / Diagnostics', 'DiagRadio': 'Lab / Diagnostics', 'PharmDispensing': 'Pharm Dispensing' }) COARSE_APPT_TYPE_TO_COLOR_MAP = MappingProxyType({ 'Outpatient': 'magenta', 'Con w/ DCSA': 'crimson', 'A & E': 'forestgreen', 'Inpatient': 'mediumorchid', 'RMNCH': 'gold', 'HIV/AIDS': 'darkturquoise', 'Tb': 'y', 'Dental': 'rosybrown', 'Mental Health': 'lightsalmon', 'Surgery / Radiotherapy': 'orange', 'STI': 'slateblue', 'Lab / Diagnostics': 'dodgerblue', 'Pharm Dispensing': 'springgreen' })
[docs]def get_coarse_appt_type(appt_type: str) -> str: """Return the `coarser` categorization of appt_types for a given appt_type.""" return APPT_TYPE_TO_COARSE_APPT_TYPE_MAP.get(appt_type, None)
[docs]def order_of_coarse_appt(_coarse_appt: Union[str, pd.Index]) -> Union[int, pd.Index]: """Define a standard order for the coarse appointment types.""" ordered_coarse_appts = list(COARSE_APPT_TYPE_TO_COLOR_MAP.keys()) if isinstance(_coarse_appt, str): return ordered_coarse_appts.index(_coarse_appt) else: return pd.Index(ordered_coarse_appts.index(c) for c in _coarse_appt)
[docs]def get_color_coarse_appt(coarse_appt_type: str) -> str: """Return the colour (as matplotlib string) assigned to this appointment type. Returns `np.nan` if appointment-type is not recognised. Names of colors are selected with reference to: """ return COARSE_APPT_TYPE_TO_COLOR_MAP.get(coarse_appt_type, np.nan)
SHORT_TREATMENT_ID_TO_COLOR_MAP = MappingProxyType({ '*': 'black', 'FirstAttendance*': 'darkgrey', 'Inpatient*': 'silver', 'Contraception*': 'darkseagreen', 'AntenatalCare*': 'green', 'DeliveryCare*': 'limegreen', 'PostnatalCare*': 'springgreen', 'Alri*': 'darkorange', 'Diarrhoea*': 'tan', 'Undernutrition*': 'gold', 'Epi*': 'darkgoldenrod', 'Hiv*': 'deepskyblue', 'Malaria*': 'lightsteelblue', 'Measles*': 'cornflowerblue', 'Tb*': 'mediumslateblue', 'Schisto*': 'skyblue', 'CardioMetabolicDisorders*': 'brown', 'BladderCancer*': 'orchid', 'BreastCancer*': 'mediumvioletred', 'OesophagealCancer*': 'deeppink', 'ProstateCancer*': 'hotpink', 'OtherAdultCancer*': 'palevioletred', 'Depression*': 'indianred', 'Epilepsy*': 'red', 'Copd*': 'lightcoral', 'Rti*': 'lightsalmon', }) def _standardize_short_treatment_id(short_treatment_id): return short_treatment_id.replace('_*', '*').rstrip('*') + '*'
[docs]def order_of_short_treatment_ids( short_treatment_id: Union[str, pd.Index] ) -> Union[int, pd.Index]: """Define a standard order for short treatment_ids.""" ordered_short_treatment_ids = list(SHORT_TREATMENT_ID_TO_COLOR_MAP.keys()) if isinstance(short_treatment_id, str): return ordered_short_treatment_ids.index( _standardize_short_treatment_id(short_treatment_id) ) else: return pd.Index( ordered_short_treatment_ids.index(_standardize_short_treatment_id(i)) for i in short_treatment_id )
[docs]def get_color_short_treatment_id(short_treatment_id: str) -> str: """Return the colour (as matplotlib string) assigned to this shorted TREATMENT_ID. Returns `np.nan` if treatment_id is not recognised. """ return SHORT_TREATMENT_ID_TO_COLOR_MAP.get( _standardize_short_treatment_id(short_treatment_id), np.nan )
CAUSE_OF_DEATH_OR_DALY_LABEL_TO_COLOR_MAP = MappingProxyType({ 'Maternal Disorders': 'green', 'Neonatal Disorders': 'springgreen', 'Congenital birth defects': 'mediumaquamarine', 'Lower respiratory infections': 'darkorange', 'Childhood Diarrhoea': 'tan', 'AIDS': 'deepskyblue', 'Malaria': 'lightsteelblue', 'Measles': 'cornflowerblue', 'TB (non-AIDS)': 'mediumslateblue', 'Schistosomiasis': 'skyblue', 'Heart Disease': 'sienna', 'Kidney Disease': 'chocolate', 'Diabetes': 'peru', 'Stroke': 'burlywood', 'Cancer (Bladder)': 'deeppink', 'Cancer (Breast)': 'darkmagenta', 'Cancer (Oesophagus)': 'mediumvioletred', 'Cancer (Other)': 'crimson', 'Cancer (Prostate)': 'hotpink', 'Depression / Self-harm': 'goldenrod', 'Epilepsy': 'gold', 'COPD': 'khaki', 'Transport Injuries': 'lightsalmon', 'Lower Back Pain': 'slategray', 'Other': 'dimgrey', })
[docs]def order_of_cause_of_death_or_daly_label( cause_of_death_label: Union[str, pd.Index] ) -> Union[int, pd.Index]: """Define a standard order for Cause-of-Death labels.""" ordered_cause_of_death_labels = list(CAUSE_OF_DEATH_OR_DALY_LABEL_TO_COLOR_MAP.keys()) if isinstance(cause_of_death_label, str): return ordered_cause_of_death_labels.index(cause_of_death_label) else: return pd.Index( ordered_cause_of_death_labels.index(c) for c in cause_of_death_label )
[docs]def get_color_cause_of_death_or_daly_label(cause_of_death_label: str) -> str: """Return the colour (as matplotlib string) assigned to this Cause-of-Death Label. Returns `np.nan` if label is not recognised. """ return CAUSE_OF_DEATH_OR_DALY_LABEL_TO_COLOR_MAP.get(cause_of_death_label, np.nan)
[docs]def squarify_neat(sizes: np.array, label: np.array, colormap: Callable, numlabels=5, **kwargs): """Pass through to squarify, with some customisation: ... * Apply the colormap specified * Only give label a selection of the segments N.B. The package `squarify` is required. """ # Suppress labels for all but the `numlabels` largest entries. to_label = set(pd.Series(index=label, data=sizes).sort_values(ascending=False).iloc[0:numlabels].index) squarify.plot( sizes=sizes, label=[_label if _label in to_label else '' for _label in label], color=[colormap(_x) for _x in label], **kwargs, )
[docs]def get_root_path(starter_path: Optional[Path] = None) -> Path: """Returns the absolute path of the top level of the repository. `starter_path` optionally gives a reference location from which to begin search; if omitted the location of this file is used.""" def get_git_root(path: Path) -> Path: """Return path of git repo. Based on:""" git_repo = git.Repo(path, search_parent_directories=True) git_root = git_repo.working_dir return Path(git_root) if starter_path is None: return get_git_root(__file__) elif Path(starter_path).exists() and Path(starter_path).is_absolute(): return get_git_root(starter_path) else: raise OSError("File Not Found")
[docs]def bin_hsi_event_details( results_folder: Path, get_counter_from_event_details: callable, start_date: Date, end_date: Date, do_scaling: bool = False ) -> Dict[Tuple[int, int], Counter]: """Bin logged HSI event details into dictionary of counters for each draw and run. :param results_folder: Path to folder containing scenario outputs. :param get_counter_from_event_details: Callable which when passed and event details dictionary and count returns a Counter instance keyed by properties to bin over. :param start_date: Start date to filter log entries by when accumulating counts. :param end_date: End date to filter log entries by when accumulating counts. :param do_scaling: Whether to scale counts by population scaling factor value recorded in `tlo.methods.population` log. :return: Dictionary keyed by `(draw, run)` tuples with corresponding values the counters containing the binned event detail property counts for the corresponding scenario draw and run. """ scenario_info = get_scenario_info(results_folder) binned_counts_by_draw_and_run = {} for draw in range(scenario_info["number_of_draws"]): for run in range(scenario_info["runs_per_draw"]): scaling_factor = 1 if not do_scaling else load_pickled_dataframes( results_folder, draw, run, 'tlo.methods.population' )['tlo.methods.population']['scaling_factor']['scaling_factor'].values[0] hsi_event_counts = load_pickled_dataframes( results_folder, draw, run, "tlo.methods.healthsystem.summary" )["tlo.methods.healthsystem.summary"]["hsi_event_counts"] hsi_event_counts = hsi_event_counts[ hsi_event_counts['date'].between(start_date, end_date) ] hsi_event_counts_sum = sum( [ Counter(d) for d in hsi_event_counts["hsi_event_key_to_counts"].values ], start=Counter() ) hsi_event_details = load_pickled_dataframes( results_folder, draw, run, "tlo.methods.healthsystem.summary" )[ "tlo.methods.healthsystem.summary" ]["hsi_event_details"]["hsi_event_key_to_event_details"][0] binned_counts_by_draw_and_run[draw, run] = sum( ( get_counter_from_event_details( hsi_event_details[key], count * scaling_factor ) for key, count in hsi_event_counts_sum.items() ), Counter() ) return binned_counts_by_draw_and_run
[docs]def compute_mean_across_runs( counters_by_draw_and_run: Dict[Tuple[int, int], Counter] ) -> Dict[int, Counter]: """Compute mean across scenario runs of dict of counters keyed by draw and run. :param counters_by_draw_and_run: Dictionary keyed by `(draw, run)` tuples with counter values. :return: Dictionary keyed by `draw` with counter values corresponding to mean of counters across all runs for each draw. """ summed_counters_by_draw = defaultdict(Counter) num_runs_by_draw = Counter() for (draw, _), counter in counters_by_draw_and_run.items(): summed_counters_by_draw[draw] += counter num_runs_by_draw[draw] += 1 return { draw: Counter( {key: count / num_runs_by_draw[draw] for key, count in counter.items()} ) for draw, counter in summed_counters_by_draw.items() }
[docs]def plot_stacked_bar_chart( ax: plt.Axes, binned_counts: Counter, inner_group_cmap: Optional[Dict] = None, bar_width: float = 0.5, count_scale: float = 1. ): """Plot a stacked bar chart using count data binned over two levels of grouping. :param ax: Matplotlib axis to add bar chart to. :param binned_counts: Counts keyed by pair of string keys corresponding to inner and outer groups binning performed over. :param inner_group_cmap: Map from inner group keys to colors to plot corresponding bars with. If ``None`` the default color cycle will be used. :param bar_width: Width of each bar as a proportion of space between bars. :param count_scale: Scaling factor to multiply all counts by. """ outer_groups = sorted(set(outer_group for outer_group, _ in binned_counts)) if inner_group_cmap is None: inner_groups = sorted(set(inner_group for _, inner_group in binned_counts)) else: inner_groups = list(inner_group_cmap.keys()) cumulative_counts = Counter({outer_group: 0 for outer_group in outer_groups}) for inner_group in inner_groups: counts = Counter( { outer_group: binned_counts[outer_group, inner_group] * count_scale for outer_group in outer_groups } ) if sum(counts.values()) > 0: list(counts.keys()), list(counts.values()), bottom=list( cumulative_counts[outer_group] for outer_group in outer_groups ), label=inner_group, color=( None if inner_group_cmap is None else inner_group_cmap[inner_group] ), width=bar_width, ) cumulative_counts += counts ax.legend()
[docs]def plot_clustered_stacked(dfall, ax, color_for_column_map=None, scaled=False, legends=True, H="/", **kwargs): """Given a dict of dataframes, with identical columns and index, create a clustered stacked bar plot. * H is the hatch used for identification of the different dataframe. * color_for_column_map should return a color for every column in the dataframes * legends=False, suppresses generation of the legends With `scaled=True`, the height of the stacked-bar is scaled to 1.0. From:""" n_df = len(dfall) n_col = len(list(dfall.values())[0].columns) n_ind = len(list(dfall.values())[0].index) for i, df in enumerate(dfall.values()): # for each data frame if scaled: df = df.apply(lambda row: (row / row.sum()).fillna(0.0), axis=1) ax = stacked=True, ax=ax, legend=False, color=[color_for_column_map(_label) for _label in df.columns], **kwargs ) _handles, _labels = ax.get_legend_handles_labels() # get the handles we want to modify for i in range(0, n_df * n_col, n_col): # len(h) = n_col * n_df for j, pa in enumerate(_handles[i: i+n_col]): for rect in pa.patches: # for each index rect.set_x(rect.get_x() + 1 / float(n_df + 1) * i / float(n_col)) rect.set_hatch(H * int(i / n_col)) # edited part rect.set_width(1 / float(n_df + 1)) ax.set_xticks((np.arange(0, 2 * n_ind, 2) + 1 / float(n_df + 1)) / 2.) ax.set_xticklabels(df.index, rotation=0) if legends: # Add invisible data to add another legend n = [] for i in range(n_df): n.append(, 0, color="gray", hatch=H * i)) l1 = ax.legend(_handles[:n_col], _labels[:n_col], loc=[1.01, 0.5]) _ = plt.legend(n, dfall.keys(), loc=[1.01, 0.1]) ax.add_artist(l1)
[docs]def get_mappers_in_fullmodel(resourcefilepath: Path, outputpath: Path): """Returns the cause-of-death, cause-of-disability and cause-of-DALYS mappers that are created in a run of the fullmodel.""" start_date = Date(2010, 1, 1) sim = Simulation(start_date=start_date, seed=0, log_config={'filename': 'test_log', 'directory': outputpath}) from tlo.methods.fullmodel import fullmodel sim.register(*fullmodel(resourcefilepath=resourcefilepath)) sim.make_initial_population(n=10_000) sim.simulate(end_date=start_date) demog_log = parse_log_file(sim.log_filepath)['tlo.methods.demography'] hb_log = parse_log_file(sim.log_filepath)['tlo.methods.healthburden'] keys = [ (demog_log, 'mapper_from_tlo_cause_to_common_label'), (demog_log, 'mapper_from_gbd_cause_to_common_label'), (hb_log, 'disability_mapper_from_tlo_cause_to_common_label'), (hb_log, 'disability_mapper_from_gbd_cause_to_common_label'), (hb_log, 'daly_mapper_from_gbd_cause_to_common_label'), (hb_log, 'daly_mapper_from_tlo_cause_to_common_label'), ] def extract_mapper(key_tuple): return pd.Series(key_tuple[0].get(key_tuple[1]).drop(columns={'date'}).loc[0]).to_dict() return {k[1]: extract_mapper(k) for k in keys}
[docs]def get_parameters_for_status_quo() -> Dict: """ Returns a dictionary of parameters and their updated values to indicate the "Status Quo" scenario. This is the configuration that is the target of calibrations. The return dict is in the form: e.g. { 'Depression': { 'pr_assessed_for_depression_for_perinatal_female': 1.0, 'pr_assessed_for_depression_in_generic_appt_level1': 1.0, }, 'Hiv': { 'prob_start_art_or_vs': 1.0, } } """ return { "SymptomManager": { "spurious_symptoms": True, }, "HealthSystem": { 'Service_Availability': ['*'], "use_funded_or_actual_staffing": "actual", "mode_appt_constraints": 1, "cons_availability": "default", "beds_availability": "default", }, }
[docs]def get_parameters_for_improved_healthsystem_and_healthcare_seeking( resourcefilepath: Path, max_healthsystem_function: Optional[bool] = False, max_healthcare_seeking: Optional[bool] = False, ) -> Dict: """ Returns a dictionary of parameters and their updated values to indicate an ideal healthcare system in terms of maximum health system function, and/or maximum healthcare seeking. The return dict is in the form: e.g. { 'Depression': { 'pr_assessed_for_depression_for_perinatal_female': 1.0, 'pr_assessed_for_depression_in_generic_appt_level1': 1.0 }, 'Hiv': { 'prob_start_art_or_vs': <<the dataframe named in the corresponding cell in the ResourceFile>> } } """ def read_value(_value): """Returns the value, or a dataframe if the value point to a different sheet in the workbook, or a series if the value points to sheet in the workbook with only two columns (which become the index and the values).""" drop_extra_columns = lambda df: df.dropna(how='all', axis=1) # noqa E731 squeeze_single_col_df_to_series = lambda df: \ df.set_index(df[df.columns[0]])[df.columns[1]] if len(df.columns) == 2 else df # noqa E731 def construct_multiindex_if_implied(df): """Detect if a multi-index is implied (by the first column header having a "/" in it) and construct this.""" if isinstance(df, pd.DataFrame) and (len(df.columns) > 1) and ('/' in df.columns[0]): idx = df[df.columns[0]].str.split('/', expand=True) idx.columns = tuple(df.columns[0].split('/')) # Make the dtype as `int` if possible for col in idx.columns: try: idx[col] = idx[col].astype(int) except ValueError: pass df.index = pd.MultiIndex.from_frame(idx) return df.drop(columns=df.columns[0]) else: return df if isinstance(_value, str) and _value.startswith("#"): sheet_name = _value.lstrip("#").split('!')[0] return \ squeeze_single_col_df_to_series( drop_extra_columns( construct_multiindex_if_implied( pd.read_excel(workbook, sheet_name=sheet_name)))) elif isinstance(_value, str) and _value.startswith("["): # this looks like its intended to be a list return eval(_value) else: return _value workbook = pd.ExcelFile( resourcefilepath / 'ResourceFile_Improved_Healthsystem_And_Healthcare_Seeking.xlsx') # Load the ResourceFile for the list of parameters that may change mainsheet = pd.read_excel(workbook, 'main').set_index(['Module', 'Parameter']) # Select which columns for parameter changes to extract cols = [] if max_healthsystem_function: cols.append('max_healthsystem_function') if max_healthcare_seeking: cols.append('max_healthcare_seeking') # Collect parameters that will be changed (collecting the first encountered non-NAN value) params_to_change = mainsheet[cols].dropna(axis=0, how='all')\ .apply(lambda row: [v for v in row if not pd.isnull(v)][0], axis=1) # Convert to dictionary params = defaultdict(lambda: defaultdict(dict)) for idx, value in params_to_change.items(): params[idx[0]][idx[1]] = read_value(value) return params
[docs]def mix_scenarios(*dicts) -> Dict: """Helper function to combine a Dicts that show which parameters should be over-written. * Warnings are generated if a parameter appears in more than one Dict with a different value; * Items under the same top-level key (i.e., for the Module) are merged rather than being over-written.""" d = defaultdict(lambda: defaultdict(dict)) for _d in dicts: for mod, params_in_mod in _d.items(): for param, value in params_in_mod.items(): if param in d[mod]: if d[mod][param] != value: warnings.warn(f'Parameter is being updated more than once: module={mod}, parameter={param}', UserWarning,) d[mod].update({param: value}) return d