laser_cholera package

laser_cholera.compute(args)

Subpackages

• laser_cholera.metapop package
  • Analyzer
    • Analyzer.check()
    • Analyzer.plot()
  • Census
    • Census.check()
    • Census.plot()
  • DerivedValues
    • DerivedValues.check()
    • DerivedValues.plot()
  • EnvToHuman
    • EnvToHuman.check()
    • EnvToHuman.plot()
  • EnvToHumanVax
    • EnvToHumanVax.check()
    • EnvToHumanVax.plot()
  • Environmental
    • Environmental.check()
    • Environmental.plot()
  • Exposed
    • Exposed.check()
    • Exposed.plot()
  • HumanToHuman
    • HumanToHuman.__call__()
    • HumanToHuman.check()
    • HumanToHuman.plot()
  • HumanToHumanVax
    • HumanToHumanVax.__call__()
    • HumanToHumanVax.check()
    • HumanToHumanVax.plot()
  • Infectious
    • Infectious.check()
    • Infectious.plot()
  • Parameters
    • Parameters.check()
    • Parameters.plot()
  • Recorder
    • Recorder.check()
  • Recovered
    • Recovered.check()
    • Recovered.plot()
  • Susceptible
    • Susceptible.check()
    • Susceptible.plot()
  • Vaccinated
    • Vaccinated.check()
    • Vaccinated.plot()
  • get_parameters()
  • Submodules
  • laser_cholera.metapop.analyzer module
    • Analyzer
      • Analyzer.check()
      • Analyzer.plot()
  • laser_cholera.metapop.census module
    • Census
      • Census.check()
      • Census.plot()
  • laser_cholera.metapop.derivedvalues module
    • DerivedValues
      • DerivedValues.check()
      • DerivedValues.plot()
  • laser_cholera.metapop.environmental module
    • Environmental
      • Environmental.check()
      • Environmental.plot()
    • map_suitability_to_decay()
  • laser_cholera.metapop.envtohuman module
    • EnvToHuman
      • EnvToHuman.check()
      • EnvToHuman.plot()
  • laser_cholera.metapop.envtohumanvax module
    • EnvToHumanVax
      • EnvToHumanVax.check()
      • EnvToHumanVax.plot()
    • plot_helper()
  • laser_cholera.metapop.exposed module
    • Exposed
      • Exposed.check()
      • Exposed.plot()
  • laser_cholera.metapop.humantohuman module
    • HumanToHuman
      • HumanToHuman.__call__()
      • HumanToHuman.check()
      • HumanToHuman.plot()
  • laser_cholera.metapop.humantohumanvax module
    • HumanToHumanVax
      • HumanToHumanVax.__call__()
      • HumanToHumanVax.check()
      • HumanToHumanVax.plot()
    • plot_helper()
  • laser_cholera.metapop.infectious module
    • Infectious
      • Infectious.check()
      • Infectious.plot()
  • laser_cholera.metapop.logsetup module
    • setup_logging()
  • laser_cholera.metapop.model module
    • Model
      • Model.components
      • Model.plot()
      • Model.run()
      • Model.visualize()
    • run_model()
  • laser_cholera.metapop.params module
    • Parameters
      • Parameters.check()
      • Parameters.plot()
    • PropertySetEx
      • PropertySetEx.__str__()
    • PseEncoder
      • PseEncoder.default()
    • dict_to_propertysetex()
    • get_parameters()
    • load_compressed_hdf5_parameters()
    • load_compressed_json_parameters()
    • load_hdf5()
    • load_hdf5_parameters()
    • load_json_parameters()
    • validate_parameters()
  • laser_cholera.metapop.recorder module
    • Recorder
      • Recorder.check()
    • save_compressed_hdf5_parameters()
    • save_hdf5()
    • save_hdf5_parameters()
  • laser_cholera.metapop.recovered module
    • Recovered
      • Recovered.check()
      • Recovered.plot()
  • laser_cholera.metapop.scenario module
  • laser_cholera.metapop.susceptible module
    • Component
    • Susceptible
      • Susceptible.check()
      • Susceptible.plot()
  • laser_cholera.metapop.utils module
    • fourier_series_double()
    • get_daily_seasonality()
    • get_pi_from_lat_long()
    • override_helper()
  • laser_cholera.metapop.vaccinated module
    • Vaccinated
      • Vaccinated.check()
      • Vaccinated.plot()

Submodules

laser_cholera.cli module

Module that contains the command line app.

Why does this file exist, and why not put this in __main__?

You might be tempted to import things from __main__ later, but that will cause problems: the code will get executed twice:

• When you run python -mlaser_cholera python will execute __main__.py as a script. That means there will not be any laser_cholera.__main__ in sys.modules.

• When you import __main__ it will get executed again (as a module) because there”s no laser_cholera.__main__ in sys.modules.

Also see (1) from https://click.palletsprojects.com/en/stable/setuptools/

laser_cholera.core module

laser_cholera.core.compute(args)

laser_cholera.iso_codes module

laser_cholera.likelihood module

laser_cholera.likelihood.calc_log_likelihood(observed, estimated, family, weights=None, **kwargs)

Calculate the log-likelihood of the observed data given the estimated data.

Parameters:

• observed (np.ndarray) – Observed data.

• estimated (np.ndarray) – Estimated data.

• family (str) – The family of the distribution (e.g., “poisson”, “negbin”).

• weights (np.ndarray, optional) – Weights for the data. If None, all weights are set to 1.

• **kwargs (dict) – Additional arguments for the likelihood calculation.

Returns:

float – The log-likelihood of the observed data given the estimated data.

laser_cholera.likelihood.calc_log_likelihood_beta(observed, estimated, mean_precision=True, weights=None, verbose=True)

Calculate the log-likelihood for the Beta distribution.

Parameters:

• observed (np.ndarray) – Observed values (must be strictly between 0 and 1).

• estimated (np.ndarray) – Estimated values (must be strictly between 0 and 1).

• mean_precision (bool, optional) – Whether to use mean-precision parameterization. Default is True.

• weights (np.ndarray, optional) – Weights for the observations. If None, all weights are set to 1.

• verbose (bool, optional) – If True, print additional information.

Returns:

float – The log-likelihood of the observed data given the estimated data.

laser_cholera.likelihood.calc_log_likelihood_binomial(observed, estimated, trials, weights=None, verbose=True)

Calculate the log-likelihood for the Binomial distribution.

Parameters:

• observed (np.ndarray) – Observed counts (must be integers between 0 and trials).

• estimated (np.ndarray) – Estimated probabilities (must be strictly between 0 and 1).

• trials (np.ndarray) – Number of trials (must be positive integers).

• weights (np.ndarray, optional) – Weights for the observations. If None, all weights are set to 1.

• verbose (bool, optional) – If True, print additional information.

Returns:

float – The log-likelihood of the observed data given the estimated data.

laser_cholera.likelihood.calc_log_likelihood_gamma(observed, estimated, weights=None, verbose=True)

Calculate the log-likelihood for the Gamma distribution.

Parameters:

• observed (np.ndarray) – Observed values (must be strictly positive).

• estimated (np.ndarray) – Estimated values (must be strictly positive).

• weights (np.ndarray, optional) – Weights for the observations. If None, all weights are set to 1.

• verbose (bool, optional) – If True, print additional information.

Returns:

float – The log-likelihood of the observed data given the estimated data.

laser_cholera.likelihood.calc_log_likelihood_negbin(observed, estimated, k=None, weights=None, verbose=True)

Calculate the log-likelihood for the Negative Binomial distribution.

Parameters:

• observed (np.ndarray) – Observed counts (must be non-negative integers).

• estimated (np.ndarray) – Estimated means (must be strictly positive).

• k (float, optional) – Dispersion parameter. If None, it will be estimated from the data.

• weights (np.ndarray, optional) – Weights for the observations. If None, all weights are set to 1.

• verbose (bool, optional) – If True, print additional information.

Returns:

float – The log-likelihood of the observed data given the estimated data.

laser_cholera.likelihood.calc_log_likelihood_normal(observed, estimated, weights=None, verbose=True)

Calculate the log-likelihood for the Normal distribution.

Parameters:

• observed (np.ndarray) – Observed values.

• estimated (np.ndarray) – Estimated values.

• weights (np.ndarray, optional) – Weights for the observations. If None, all weights are set to 1.

• verbose (bool, optional) – If True, print additional information.

Returns:

float – The log-likelihood of the observed data given the estimated data.

laser_cholera.likelihood.calc_log_likelihood_poisson(observed, estimated, weights=None, verbose=True)

Calculate the log-likelihood for the Poisson distribution.

Parameters:

• observed (np.ndarray) – Observed counts (must be non-negative integers).

• estimated (np.ndarray) – Estimated means (must be strictly positive).

• weights (np.ndarray, optional) – Weights for the observations. If None, all weights are set to 1.

• verbose (bool, optional) – If True, print additional information.

Returns:

float – The log-likelihood of the observed data given the estimated data.

laser_cholera.likelihood.calc_log_likelihood_validation(observed, estimated, weights, trials=None, verbose=True)

Validate and preprocess inputs for log-likelihood calculation. This function performs validation and preprocessing of input arrays for calculating log-likelihood. It removes NaN values, checks for consistency in array lengths, and ensures weights are non-negative. The function returns the processed inputs or raises errors for invalid inputs.

Parameters:

• observed (np.ndarray) – Array of observed values.

• estimated (np.ndarray) – Array of estimated values.

• weights (np.ndarray or None) – Array of weights. If None, defaults to an array of ones with the same shape as observed.

• trials (np.ndarray or None, optional) – Array of trial counts. If provided, it is also validated and returned. Defaults to None.

• verbose (bool, optional) – If True, prints warnings for empty or invalid inputs. Defaults to True.

Returns:

tuple –

A tuple containing the validated and preprocessed arrays:

• (observed, estimated, weights) if trials is None.

• (observed, estimated, trials, weights) if trials is provided.

Raises:

• ValueError – If the lengths of observed, estimated, and weights do not match after preprocessing.

• ValueError – If any weight is negative.

• ValueError – If all weights are zero.

Notes

• If all input values are NaN after preprocessing, the function returns None and optionally prints a warning if verbose is True.

laser_cholera.likelihood.get_model_likelihood(obs_cases, est_cases, obs_deaths, est_deaths, weight_cases=None, weight_deaths=None, weights_location=None, weights_time=None, verbose=False)

Calculate the likelihood of the model given the observed and estimated cases and deaths.

Parameters:

• obs_cases (np.ndarray) – Observed cases.

• est_cases (np.ndarray) – Estimated cases.

• obs_deaths (np.ndarray) – Observed deaths.

• est_deaths (np.ndarray) – Estimated deaths.

• weight_cases (np.ndarray, optional) – Weights for the cases. If None, all weights are set to 1.

• weight_deaths (np.ndarray, optional) – Weights for the deaths. If None, all weights are set to 1.

• weights_location (np.ndarray, optional) – Weights for the locations. If None, all weights are set to 1.

• weights_time (np.ndarray, optional) – Weights for the time. If None, all weights are set to 1.

• verbose (bool, optional) – If True, print additional information.

Returns:

float – The likelihood of the model given the observed and estimated cases and deaths.

laser_cholera.sc module

laser_cholera.sc.color(s, fg=None, bg=None, style=None)

Add ANSI colors and styles to a string.

Parameters:

• s (str) – String to format.

• fg (str|int|tuple) – Foreground color specification.

• bg (str|int|tuple( bg) – Background color specification.

• style (str) – Style names, separated by ‘+’

Returns:

Formatted string.

laser_cholera.sc.parse_rgb(s)

Convert string to an RGB color

laser_cholera.sc.printcyan(s, **kwargs)

Alias to print(colors.cyan(s))

laser_cholera.sc.printgreen(s, **kwargs)

Alias to print(colors.green(s))

laser_cholera.sc.printred(s, **kwargs)

Alias to print(colors.red(s))

laser_cholera.test module

class laser_cholera.test.Eradication(model)

Bases: object

check()

plot(fig: Figure | None = None)

laser_cholera.utils module

laser_cholera.utils.sim_duration(start: datetime = datetime.datetime(2025, 3, 24, 0, 0), stop: datetime = datetime.datetime(2025, 4, 24, 0, 0))