OCR_Dome/show_data.py

#!/usr/bin/python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.patches as mpatches

from astral import LocationInfo
from astral.sun import sun

import datetime
from json_extensions import load_from_json_file


city = LocationInfo("Rome", "Italy", "Europe/Rome", 44.448066, 11.270639)


def main(data):
    # Sample data
    # epoch_times = [1696000000, 1696086400, 1696172800, 1696259200]  # Epoch seconds
    # y_values = [10, 20, 15, 25]  # Some y-axis values

    # Create the plot
    fig, ax = plt.subplots(tight_layout=False, figsize=(30, 10))
    earliest = None
    latest = None
    for name_raw, serie in data.items():
        if name_raw in ('Security Camera', 'Ext Front'):
            continue
        name = {'Front door': 'Sample #1 No PV panel', 'Ext2': 'Sample #2 with PV Panel', }.get(name_raw, name_raw)
        dates = [datetime.datetime.fromtimestamp(row['epoch']) for row in serie]
        soc_v = [row['soc'] for row in serie]
        if name_raw == 'Front door':
            dates = dates[3:]
            soc_v = soc_v[3:]
        mi = min(dates)
        ma = max(dates)
        if earliest is None:
            earliest = mi
            latest = ma
        else:
            earliest = min(earliest, mi)
            latest = max(latest, ma)

        # charging = [row['charging'] for row in serie]
        # batt_full = [row['battery_full'] for row in serie]
        #
        # sizes = []
        # for c1, c2 in zip(charging, batt_full):
        #     if c1 and c2:
        #         sizes.append(100)  # Example: Both conditions true
        #     elif c1:
        #         sizes.append(200)  # Example: Only condition 1 true
        #     elif c2:
        #         sizes.append(300)  # Example: Only condition 2 true
        #     else:
        #         sizes.append(400)  # Example: Both conditions false
        #

        ax.plot(dates, soc_v, '-o', lw=3, label=name)

        # for x, y, color in zip(dates, soc_v, sizes):

        # ax.plot(dates, y_values, marker='o', linestyle='-')

    print(earliest, latest)
    current_day = earliest
    first_time = True
    while current_day < latest:
        # print(current_day)
        # Get solar times
        s = sun(city.observer, date=current_day, tzinfo=city.timezone)
        dawn = s['dawn']
        sunrise = s['sunrise']
        sunset = s['sunset']
        dusk = s['dusk']
        if first_time:
            ax.axvspan(dawn, sunrise, color='deepskyblue', alpha=0.3, label='Twilight (-6°)')
            ax.axvspan(sunset, dusk, color='deepskyblue', alpha=0.3)
            ax.axvline(sunrise, color='orange', linestyle='--', label='Sunrise')
            ax.axvline(sunset, color='red', linestyle='--', label='Sunset')
            first_time = False
        else:
            ax.axvspan(dawn, sunrise, color='deepskyblue', alpha=0.3)
            ax.axvspan(sunset, dusk, color='deepskyblue', alpha=0.3)
            ax.axvline(sunrise, color='orange', linestyle='--')
            ax.axvline(sunset, color='red', linestyle='--')

        current_day += datetime.timedelta(days=1)

    # Format the x-axis as date
    # ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d\n%H:%M:%S'))  # Customize as needed
    ax.xaxis.set_major_locator(
        mdates.AutoDateLocator(minticks=30, maxticks=60))  # Automatically adjusts for readability
    ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))

    x_tail = datetime.datetime(year=2025, month=4, day=7, hour=7, minute=20)
    y_tail = 85
    x_head = datetime.datetime(year=2025, month=4, day=7, hour=6, minute=59)
    y_head = 100
    dx = x_head - x_tail
    dy = y_head - y_tail

    # Rotate date labels for better visibility
    # plt.xticks(rotation=45)

    arrow = mpatches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head), mutation_scale=50)
    ax.add_patch(arrow)

    # Labels and title
    ax.grid()
    ax.legend(loc='lower right', framealpha=1.0)
    plt.xlabel("Date & Time")
    plt.ylabel("Reported State of Charge [%]")
    plt.title('Camera battery behaviour')

    img_num = 1
    out_name = f'{img_num}_f11.png'
    img_num += 1
    fig.savefig(out_name, dpi=150)

    st1 = datetime.datetime(year=2025, month=3, day=29)
    end1 = datetime.datetime(year=2025, month=4, day=8, hour=12)
    ax.set_xlim(st1, end1)
    txt = f'Problems:\nSample#2: After FW upgrade the problem looks the same\nSample#1: Discharge is not monotonic.'
    annt = ax.text(0.3, 0.1, txt, ha='left', fontsize=20, bbox={'facecolor': 'lightgray', 'alpha': 0.85, 'pad': 5},
                   transform=ax.transAxes)
    # annt.set_text(txt)
    # print(txt)
    out_name = f'{img_num}_overview.png'
    # out_name_svg = f'{img_num}_overview.svg'
    out_name_pdf = f'{img_num}_overview.pdf'
    # fig.savefig(out_name_svg)
    fig.savefig(out_name_pdf)
    img_num += 1
    fig.savefig(out_name, dpi=200)

    #
    # st1 = datetime.datetime(year=2025, month=3, day=1)
    # end1 = datetime.datetime(year=2025, month=3, day=3)
    # ax.set_xlim(st1, end1)
    # txt = f'We noticed battery level anomaly'
    # # print(txt)
    # annt = ax.text(0.1, 0.2, txt, ha='left', fontsize=20, bbox={'facecolor': 'cyan', 'alpha': 0.7, 'pad': 5},
    #         transform=ax.transAxes)
    # print(annt, type(annt))
    # out_name = f'{img_num}_first_anomaly.png'
    # img_num += 1
    # fig.savefig(out_name, dpi=150)
    # # annt.remove()
    #
    # st1 = datetime.datetime(year=2025, month=3, day=17)
    # end1 = datetime.datetime(year=2025, month=3, day=19)
    # ax.set_xlim(st1, end1)
    # txt = f'We noticed battery level anomaly Again!'
    # annt.set_text(txt)
    # # print(txt)
    # out_name = f'{img_num}_second_anomaly.png'
    # img_num += 1
    # fig.savefig(out_name, dpi=150)
    #
    # st1 = datetime.datetime(year=2025, month=3, day=19)
    # end1 = datetime.datetime(year=2025, month=4, day=1)
    # ax.set_xlim(st1, end1)
    # txt = f'We unplugged the PV panel as asked by supplier'
    # annt.set_text(txt)
    # # print(txt)
    # out_name = f'{img_num}_unplugged.png'
    # img_num += 1
    # fig.savefig(out_name, dpi=150)
    #
    # st1 = datetime.datetime(year=2025, month=3, day=29)
    # end1 = datetime.datetime(year=2025, month=4, day=1)
    # ax.set_xlim(st1, end1)
    # txt = f'We started testing a second sample'
    # annt.set_text(txt)
    # # print(txt)
    # out_name = f'{img_num}_Sample2.png'
    # img_num += 1
    # fig.savefig(out_name, dpi=150)
    #
    # st1 = datetime.datetime(year=2025, month=3, day=30, hour=17, minute=30)
    # end1 = datetime.datetime(year=2025, month=3, day=31, hour=9, minute=30)
    # ax.set_xlim(st1, end1)
    # txt = f'There are Anomalies in the evening and in the morning'
    # annt.set_text(txt)
    # # print(txt)
    # out_name = f'{img_num}_Sample2_detail1.png'
    # img_num += 1
    # fig.savefig(out_name, dpi=150)
    #
    # st1 = datetime.datetime(year=2025, month=3, day=31, hour=15, minute=30)
    # end1 = datetime.datetime(year=2025, month=4, day=1, hour=10)
    # ax.set_xlim(st1, end1)
    # txt = f'Second day Sample#2 still show anomaly\nSample#1 has increasing battery level (Impossible)'
    # annt.set_text(txt)
    # # print(txt)
    # out_name = f'{img_num}_Sample2_detail2.png'
    # img_num += 1
    # fig.savefig(out_name, dpi=150)

    # Show the plot
    # plt.show()


if __name__ == '__main__':
    cln_data = load_from_json_file('clean_data.json')
    main(cln_data)