#!/usr/bin/env python3

import datetime

import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.dates as md
from screeninfo import get_monitors
from screeninfo.common import ScreenInfoError

SHOW_DEPLOYMENTS = True
FILTER_DEPLOYMENTS = True

plt.rcParams["figure.autolayout"] = True
plt.rcParams['figure.figsize'] = (16, 10)

data_frame = pd.read_csv("data.csv", delimiter=';')

# Filter rows, so that only the following will be kept:
# First and last rows,
# rows which have a different temperature than the one before,
# and rows which have a different temperature than the one after.
#
# Basically: If the temperatures don't change for a few rows, keep only the first
# and last of that run
last_row = None
timestamps_to_keep = []
for row in data_frame.iterrows():
    # row is actually a tuple with index at 0 and the row data at 1. We care only about the row data
    row = row[1]
    # First row: Always keep it
    if last_row is None:
        last_row = row
        timestamps_to_keep.append(row['timestamp'])
        continue
    # Any temp changed?
    keep_row = (last_row['tempWarmSide1C'] != row['tempWarmSide1C'] or
                last_row['tempWarmSide2C'] != row['tempWarmSide2C'] or
                last_row['tempCoolSide1C'] != row['tempCoolSide1C'] or
                last_row['tempCoolSide2C'] != row['tempCoolSide2C'])
    # Keep this and the previous row
    if keep_row:
        timestamps_to_keep.append(last_row['timestamp'])
        timestamps_to_keep.append(row['timestamp'])
    last_row = row
# Always keep the last row
timestamps_to_keep.append(data_frame.iloc[-1:]['timestamp'])
# Filter rows by the timestamps we just saved
data_frame = data_frame[data_frame.timestamp.isin(timestamps_to_keep)]

# Subplot, to move legend next to plot
# 1 row, 1 col, index: 1
plt.subplot(1, 1, 1)

# Rotate long date text
plt.xticks(rotation=25)


# pyplot doesn't like timestamps
# convert them to "date-numbers" and format
datenums = md.date2num([datetime.datetime.fromtimestamp(ts) for ts in data_frame.timestamp])
xfmt = md.DateFormatter('%Y-%m-%d %H:%M:%S')
ax = plt.gca()
ax.xaxis.set_major_formatter(xfmt)
ax.set_ylabel('Temperature [°C]')

# Data
plt.plot(
    datenums,
    data_frame.tempWarmSide1C,
    label='Sunshield UPS\nAverage Temperature (a)',
    color='orange',
    marker='x'
)
for i, j in zip(datenums, data_frame.tempWarmSide1C):
    ax.annotate(str(round(j, 2)), xy=(i, j), rotation=45)

plt.plot(
    datenums,
    data_frame.tempWarmSide2C,
    label='Spacecraft Equipment Panel\nAverage Temperature (b)',
    color='orangered',
    marker='x'
)
for i, j in zip(datenums, data_frame.tempWarmSide2C):
    ax.annotate(str(round(j, 2)), xy=(i, j-30), rotation=300)

plt.plot(
    datenums,
    data_frame.tempCoolSide1C,
    label='Primary Mirror\nAverage Temperature (c)',
    color='blue',
    marker='x')
for i, j in zip(datenums, data_frame.tempCoolSide1C):
    ax.annotate(str(round(j, 2)), xy=(i, j), rotation=45)

plt.plot(datenums, data_frame.tempCoolSide2C, label='Instrument Radiator\nTemperature', color='navy', marker='x')
for i, j in zip(datenums, data_frame.tempCoolSide2C):
    ax.annotate(str(round(j, 2)), xy=(i, j-30), rotation=300)

plt.axhline(y=-223, color='darkblue', linestyle=':', label='Operating temperature')
plt.ylim(ymin=-273.15)

launch_date = datetime.datetime.fromisoformat('2021-12-25T12:20+00:00')
deployment_names = [
    'Sunshield Pallet',
    'DTA Deployment',
    'Sunshield Covers Release',
    'Sunshield Mid-Boom',
    'Sunshield Layer Tensioning Ongoing',
    'Sunshield Tensioning Complete',
    'Secondary Mirror Deployment',
    'Aft Deployed Instrument Radiator',
    'Port Primary Mirror Wing',
    'WEBB IS FULLY DEPLOYED!',
    'Individual Mirror Segment Movements',
    'L2 Insertion Burn',
    'WEBB IS ORBITING L2'
]

# deployment dates, based on "+ X days"
deployment_dates = [md.date2num(launch_date + datetime.timedelta(days=x)) for x in [
    3,
    4,
    5,
    6,
    9,
    10,
    11,
    12,
    13,
    14,
    15,
    26,
    29.5]
]

latest_available_temp_date = max(datenums)
earliest_available_temp_date = min(datenums)

deployments_data = zip(deployment_names, deployment_dates)
filtered_deployments_data = (filter(lambda tup:
                                    tup[1] >= earliest_available_temp_date and
                                    tup[1] <= latest_available_temp_date, deployments_data)
                             if FILTER_DEPLOYMENTS else deployments_data)

max_temp = max(max(data_frame.tempWarmSide1C), max(data_frame.tempWarmSide2C))

if SHOW_DEPLOYMENTS:
    for label, date in filtered_deployments_data:
        plt.axvline(x=date, color='gray', linestyle=':')
        plt.text(x=date, y=max_temp+30, s=label, rotation=25)

plt.legend(bbox_to_anchor=(1, 1), loc="upper left")

try:
    get_monitors()
    plt.show()
except ScreenInfoError:
    plt.savefig("jwst_tempertature.png", dpi=300)