OpenDSS_VoltageEstimation/plotting_utils.py at main · HodgeLab/OpenDSS_VoltageEstimation · GitHub

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"""
Plotting utilities for OpenDSS analysis
"""

import os
import numpy as np
import pandas as pd
import matplotlib
from config import MATPLOTLIB_BACKEND, PLOTTING_DAYS, TICK_INCREMENT, VOLTAGE_BINS, HISTOGRAM_COLOR, HISTOGRAM_ALPHA, HISTOGRAM_WIDTH, PERCENTILE_BAR_WIDTH

matplotlib.use(MATPLOTLIB_BACKEND)
import matplotlib.pyplot as plt


def do_plotting(total, res, com, region, substation, feeder, day, output_folder):
    """
    Sequential function to plot the com/res/total profiles for each day
    over each other
    """
    num_days = PLOTTING_DAYS
    times = (day*24*4, (day+1)*24*4*num_days) # Now one day - was 1 week
    tick_inc = TICK_INCREMENT

    if not isinstance(res, pd.core.series.Series):
        res = pd.Series([0 for i in range(len(total))])
    if not isinstance(com, pd.core.series.Series):
        com = pd.Series([0 for i in range(len(total))])

    subset = total[times[0]:times[1]]
    subset_res = res[times[0]:times[1]]
    subset_com = com[times[0]:times[1]]
    max_value = max(total)
    inc = int(len(subset)/tick_inc)
    ticks_pos = [times[0] + inc*i for i in range(tick_inc)]
    ticks = [ticks_pos[i]/4 for i in range(tick_inc)]

    plt.clf()

    ## Overlayed load plot ##
    f, axarr = plt.subplots(1, sharex=True)
    axarr.plot(subset, color='k', label='Total')
    axarr.plot(subset_res, color='r', label='Residential')
    axarr.plot(subset_com, color='b', label='Commercial')
    axarr.set_xticks(ticks_pos)
    axarr.set_xticklabels(ticks, rotation=70)
    axarr.set_ylabel('kW total')
    axarr.set_ylim(0, max_value*1.05)
    axarr.set_xlabel('Hour of Year')
    plt.title('Timeseries Load for '+ region + ' '+substation+' '+feeder+ ' Day '+str(day+1))
    plt.legend()
    plt.tight_layout()
    plt.savefig(os.path.join(output_folder, f'total_load_{day+1}.png'))

    plt.clf()
    plt.close(f)


def create_voltage_histogram(dss_vals_avg, global_min, global_max, region, substation, feeder, output_folder):
    """Create voltage histogram plot"""
    comparison_ticks = []
    comparison_cnt = 0.01
    while comparison_cnt < 1.5:
        comparison_cnt += 0.005
        if comparison_cnt >= global_min-0.01 and comparison_cnt <= global_max + 0.01:
            comparison_ticks.append(round(comparison_cnt, 3))

    f, axarr = plt.subplots(1, sharex=True)
    bins = np.linspace(VOLTAGE_BINS[0], VOLTAGE_BINS[1], VOLTAGE_BINS[2])
    axarr.set_title(region+' '+substation+' '+feeder+' OpenDSS p.u. Voltages')
    axarr.hist(x=dss_vals_avg, bins=bins, color=HISTOGRAM_COLOR,
                                alpha=HISTOGRAM_ALPHA, rwidth=HISTOGRAM_WIDTH)
    axarr.set_xlim(global_min-0.01, global_max+0.01)
    axarr.set_xticks(comparison_ticks)
    axarr.set_xticklabels(comparison_ticks, rotation=70)
    axarr.set_ylabel('Frequency')
    axarr.set_xlabel('p.u. Voltage')

    plt.tight_layout()
    plt.savefig(os.path.join(output_folder, 'pu_voltages_histogram.png'))
    plt.clf()
    plt.close(f)


def create_voltage_percentiles(dss_vals_avg, region, substation, feeder, output_folder):
    """Create voltage percentiles plot"""
    percentiles = []
    for i in range(100):
        percentiles.append(np.percentile(dss_vals_avg, i))

    plt.bar(range(len(percentiles)), percentiles, width=PERCENTILE_BAR_WIDTH)
    max_pct = max(percentiles)
    min_pct = min(percentiles)
    plt.ylim(min_pct - (max_pct-min_pct)/20, max_pct + (max_pct-min_pct)/20)
    plt.grid()

    plt.xlabel('Percentile')
    plt.ylabel('Value')
    plt.title(region+' '+substation+' '+feeder+' All Percentiles')
    plt.tight_layout()
    plt.savefig(os.path.join(output_folder, "pu_voltages_percentiles.png"))
    plt.clf()
    plt.close()