chad_parameter_figures.rst

chad_parameter_figures notebook

# The United States Environmental Protection Agency through its Office of
# Research and Development has developed this software. The code is made
# publicly available to better communicate the research. All input data
# used fora given application should be reviewed by the researcher so
# that the model results are based on appropriate data for any given
# application. This model is under continued development. The model and
# data included herein do not represent and should not be construed to
# represent any Agency determination or policy.
#
# This file was written by Dr. Namdi Brandon
# ORCID: 0000-0001-7050-1538
# March 20, 2018

WARNING:

this code may not be useful

This code plots the histograms of the distributions being sampled from the CHAD data for each activity.

Import

import sys
sys.path.append('..\\source')
sys.path.append('..\\processing')

# plotting capability
import matplotlib.pylab as plt

# zipfile capability
import zipfile

# ABMHAP modules

# general capability
import my_globals as mg
import chad_params as cp
import demography as dmg

import activity, analysis, chad, omni_trial, params

Run

# the demographic
demo = dmg.ADULT_WORK

# sets of activities
keys_all = mg.KEYS_ACTIVITIES

# the activity codes related to not eating
keys_not_eat = [mg.KEY_SLEEP, mg.KEY_WORK, mg.KEY_COMMUTE_TO_WORK, mg.KEY_COMMUTE_FROM_WORK]

# the activity codes of the eating activities
keys_eat = [mg.KEY_EAT_BREAKFAST, mg.KEY_EAT_LUNCH, mg.KEY_EAT_DINNER]

# the chosen group of activities
keys = keys_all

Loop through each activity and plot the histograms of start time, end time, and duration. Note: the limitations for each activity depends on which activity parameters are being sampled

# loop through each activity and plot the histograms of start time, end time, and duration
# Note: the limitations for each activity depends on which activity parameters are being sampled
for k in keys:

    # the CHAD limiting parameters
    s_params = cp.OMNI[k]

    # get the data
    stats_start, stats_end, stats_dt, record = analysis.get_verification_info(demo=demo, key_activity=k,
                                                     sampling_params=[s_params])
    # number of the bins
    num_bins = 24

    # create subplots
    fig, axes = plt.subplots(2, 2)

    # title
    fig.suptitle( activity.INT_2_STR[k] )

    #
    # plot the mean start time distribution
    #
    ax = axes[0, 0]
    if k == mg.KEY_SLEEP:
        ax.hist(mg.to_periodic(stats_start.mu.values, do_hours=True), bins=num_bins, color='blue', label='start')
    else:
        ax.hist(stats_start.mu.values, bins=num_bins, color='blue', label='start')
    ax.set_xlabel('hours')
    ax.legend(loc='best')

    #
    # plot the mean end time distribution
    #
    ax = axes[0, 1]
    ax.hist(stats_end.mu.values, bins=num_bins, color='green', label='end')
    ax.set_xlabel('hours')
    ax.legend(loc='best')

    #
    # plot the mean duration distribution
    #
    ax = axes[1, 0]
    ax.hist(stats_dt.mu.values, bins=num_bins, color='red', label='duration')
    ax.set_xlabel('hours')
    ax.legend(loc='best')

# show plots
plt.show()