<gh_stars>0
#-----------------------------------------------------------------------------
# Copyright (c) 2016, Machiavellian Project.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
#-----------------------------------------------------------------------------

import numpy as np
import pandas as pd

import machivellian.effects as eff


def summarize_power(power_summary, sim_num, test, colors):
    """Generates a dataframe describing a power calculation run

    Please note, this function is primarily designed for use with the
    simulation notebooks.

    Parameters
    ----------
    power_summary : dict
        A dictionary summarizing the power simulation outputs generated in
        the monte_carlo_power simulation notebooks.
    sim_num : int
        Idenifier for the simulation
    test : str
        The name of the test applied
    colors : dictionary
        A dictionary linking the counts to the colors to be used in regression
        and residual gradient plots

    Returns
    -------
    ndarray
        The summary of the power analysis where each row contains data from a
        single observation. Columns include `counts`, `traditional`
        (distribution-based power), `empirical` (empirically based power),
        `sim_position` (a unique identifier for each simulation), `test`,
        `alpha` (the critical value), `sim_num` (the simulation id), and
        `colors`,
    """

    # Builds the initia data frame
    run_summary = _build_summary_frame(power_summary)

    #  Adds static information
    run_summary['test'] = test
    run_summary['ori_alpha'] = power_summary['alpha']
    run_summary['alpha'] = power_summary['alpha_adj'] * power_summary['alpha']
    run_summary['alpha_adj'] = power_summary['alpha_adj']
    run_summary['sim_num'] = sim_num
    run_summary['p_all'] = power_summary['original_p']
    run_summary['statistic'] = power_summary['statistic']

    # Includes the count values to be plotted
    run_summary['colors'] = run_summary['counts'].apply(lambda x: colors[x])

    run_summary['index'] = (
        run_summary['test'] + '.' +
        run_summary['sim_num'].apply(lambda x: '%i' % x) + '.' +
        run_summary['sim_position'].apply(lambda x: '%i' % x)
        )
    run_summary.set_index("index", inplace=True)

    return run_summary


def calc_z_power(x, col2, num_groups=2):
    """Wraps the z-based power calulation"""
    power = eff.z_power(counts=x['counts'],
                        effect=x[col2],
                        alpha=x['alpha'])
    return power


def _build_summary_frame(sim):
    """Builds the intial dataframe summarizing the run"""
    counts = sim['counts']
    empirical = sim['empirical']

    # Determines the number of samples handled in the summary
    (empr_r, empr_c) = empirical.shape

    # Draws the traditional power
    if ('traditional' in sim.keys() and
            (sim['traditional'] is not None)):
        traditional = sim['traditional']
    else:
        traditional = np.nan * np.ones(counts.shape)

    # Sets up the summary dictionary
    run_summary = pd.DataFrame.from_dict(
        {'counts': np.hstack(np.array([counts] * empr_r)),
         'empirical': np.hstack(empirical),
         'traditional':
            np.hstack(np.array([traditional] * empr_r)),
         'sim_position': np.hstack([np.arange(empr_c) + i * 10
                                    for i in np.arange(empr_r)]),
         })

    return run_summary

#-----------------------------------------------------------------------------
# Copyright (c) 2016, Machiavellian Project.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
#-----------------------------------------------------------------------------

import numpy as np
import pandas as pd

import machivellian.effects as eff


def summarize_power(power_summary, sim_num, test, colors):
    """Generates a dataframe describing a power calculation run

    Please note, this function is primarily designed for use with the
    simulation notebooks.

    Parameters
    ----------
    power_summary : dict
        A dictionary summarizing the power simulation outputs generated in
        the monte_carlo_power simulation notebooks.
    sim_num : int
        Idenifier for the simulation
    test : str
        The name of the test applied
    colors : dictionary
        A dictionary linking the counts to the colors to be used in regression
        and residual gradient plots

    Returns
    -------
    ndarray
        The summary of the power analysis where each row contains data from a
        single observation. Columns include `counts`, `traditional`
        (distribution-based power), `empirical` (empirically based power),
        `sim_position` (a unique identifier for each simulation), `test`,
        `alpha` (the critical value), `sim_num` (the simulation id), and
        `colors`,
    """

    # Builds the initia data frame
    run_summary = _build_summary_frame(power_summary)

    #  Adds static information
    run_summary['test'] = test
    run_summary['ori_alpha'] = power_summary['alpha']
    run_summary['alpha'] = power_summary['alpha_adj'] * power_summary['alpha']
    run_summary['alpha_adj'] = power_summary['alpha_adj']
    run_summary['sim_num'] = sim_num
    run_summary['p_all'] = power_summary['original_p']
    run_summary['statistic'] = power_summary['statistic']

    # Includes the count values to be plotted
    run_summary['colors'] = run_summary['counts'].apply(lambda x: colors[x])

    run_summary['index'] = (
        run_summary['test'] + '.' +
        run_summary['sim_num'].apply(lambda x: '%i' % x) + '.' +
        run_summary['sim_position'].apply(lambda x: '%i' % x)
        )
    run_summary.set_index("index", inplace=True)

    return run_summary


def calc_z_power(x, col2, num_groups=2):
    """Wraps the z-based power calulation"""
    power = eff.z_power(counts=x['counts'],
                        effect=x[col2],
                        alpha=x['alpha'])
    return power


def _build_summary_frame(sim):
    """Builds the intial dataframe summarizing the run"""
    counts = sim['counts']
    empirical = sim['empirical']

    # Determines the number of samples handled in the summary
    (empr_r, empr_c) = empirical.shape

    # Draws the traditional power
    if ('traditional' in sim.keys() and
            (sim['traditional'] is not None)):
        traditional = sim['traditional']
    else:
        traditional = np.nan * np.ones(counts.shape)

    # Sets up the summary dictionary
    run_summary = pd.DataFrame.from_dict(
        {'counts': np.hstack(np.array([counts] * empr_r)),
         'empirical': np.hstack(empirical),
         'traditional':
            np.hstack(np.array([traditional] * empr_r)),
         'sim_position': np.hstack([np.arange(empr_c) + i * 10
                                    for i in np.arange(empr_r)]),
         })

    return run_summary
#----------------------------------------------------------------------------- # Copyright (c) 2016, Machiavellian Project. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. #----------------------------------------------------------------------------- Generates a dataframe describing a power calculation run      Please note, this function is primarily designed for use with the     simulation notebooks.      Parameters     ----------     power_summary : dict         A dictionary summarizing the power simulation outputs generated in         the monte_carlo_power simulation notebooks.     sim_num : int         Idenifier for the simulation     test : str         The name of the test applied     colors : dictionary         A dictionary linking the counts to the colors to be used in regression         and residual gradient plots      Returns     -------     ndarray         The summary of the power analysis where each row contains data from a         single observation. Columns include `counts`, `traditional`         (distribution-based power), `empirical` (empirically based power),         `sim_position` (a unique identifier for each simulation), `test`,         `alpha` (the critical value), `sim_num` (the simulation id), and         `colors`, # Builds the initia data frame #  Adds static information # Includes the count values to be plotted Wraps the z-based power calulation Builds the intial dataframe summarizing the run # Determines the number of samples handled in the summary # Draws the traditional power # Sets up the summary dictionary