#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Given:
- CSV file with path: <INPUT_DIR>/heartRate-<YYYYMMDD>.csv
- First line of CSV file: <COLUMN_TIME>,<COLUMN_HEARTRATE>
- Rest of the lines: <Timestamp with format YYYY-MM-DD HH:mm:ss>,<Beats per minute>

Then:
- Create a new CSV file in: <OUTPUT_DIR>/heartRate-<YYYYMMDD>.csv
- First line of CSV file: <COLUMN_TIME>,<COLUMN_HEARTRATE>,<COLUMN_WORKINGOUT>
- Rest of the lines: <Timestamp with format YYYY-MM-DD HH:mm:ss>,<Beats per minute>,<True or False>

The new column in the new CSV file indicates if, at each moment, the person
which heart rate is recorded in this file, was working out or not.

The values for the new column will be set to True between
<WORKOUT_INIT_HOUR>:<WORKOUT_INIT_MIN>:<WORKOUT_INIT_SEC> and
<WORKOUT_END_HOUR>:<WORKOUT_END_MIN>:<WORKOUT_END_SEC>.

TIP: To set all values to False, set
<WORKOUT_INIT_HOUR>:<WORKOUT_INIT_MIN>:<WORKOUT_INIT_SEC> to 02:00:00 and
<WORKOUT_END_HOUR>:<WORKOUT_END_MIN>:<WORKOUT_END_SEC> to 01:00:00.

NOTICE: This code plots 2 graphs, the first one based on the data in the input
file and the second one equal to the first but coloring each dot with a
different color depending on the value in the 3 column.

TIP: Execute the first half of the script to find the range where the person
if exercising, then update
<WORKOUT_INIT_HOUR>:<WORKOUT_INIT_MIN>:<WORKOUT_INIT_SEC> and
<WORKOUT_END_HOUR>:<WORKOUT_END_MIN>:<WORKOUT_END_SEC> and execute all the
code.
"""

# Imports
from datetime import datetime as dt
from os.path import join

import matplotlib.pyplot as plt
import pandas as pd

# Constants
COLUMN_TIME = 'Time'
COLUMN_HEARTRATE = 'BPM'
COLUMN_WORKINGOUT = 'WorkingOut'

YEAR = 2016
MONTH = 12
DAY = 10

WORKOUT_INIT_HOUR = 2
WORKOUT_INIT_MIN = 0
WORKOUT_INIT_SEC = 0

WORKOUT_END_HOUR = 1
WORKOUT_END_MIN = 0
WORKOUT_END_SEC = 0

DATE_FORMAT = '%Y-%m-%d %H:%M:%S'

INPUT_DIR = './Raw'
OUTPUT_DIR = './Processed'

FILENAME = 'heartRate-%d%02d%02d.csv' % (YEAR, MONTH, DAY)

# Read dataset
input_file = join(INPUT_DIR, FILENAME)

dataset = pd.read_csv(input_file,
                      parse_dates = [COLUMN_TIME],
                      date_parser = lambda x: dt.strptime(x, DATE_FORMAT))
dataset.dtypes

# Plot dataset
plt.plot(dataset[COLUMN_TIME].values,
         dataset[COLUMN_HEARTRATE].values,
         color = 'black')
plt.scatter(dataset[COLUMN_TIME].values,
            dataset[COLUMN_HEARTRATE].values,
            color = 'black')
plt.title(COLUMN_TIME + ' vs ' + COLUMN_HEARTRATE + ' (' + FILENAME + ')')
plt.xlabel(COLUMN_TIME)
plt.ylabel(COLUMN_HEARTRATE)
plt.show()





# Label dataset
init_time = dt(YEAR, MONTH, DAY,
               WORKOUT_INIT_HOUR, WORKOUT_INIT_MIN, WORKOUT_INIT_SEC)
end_time = dt(YEAR, MONTH, DAY,
              WORKOUT_END_HOUR, WORKOUT_END_MIN, WORKOUT_END_SEC)
dataset[COLUMN_WORKINGOUT] = ((dataset[COLUMN_TIME] > init_time) &
                              (dataset[COLUMN_TIME] < end_time))

# Plot dataset
plt.plot(dataset[COLUMN_TIME].values,
         dataset[COLUMN_HEARTRATE].values,
         color = 'black')
plt.scatter(dataset[dataset[COLUMN_WORKINGOUT] == False][COLUMN_TIME].values,
            dataset[dataset[COLUMN_WORKINGOUT] == False][COLUMN_HEARTRATE].values,
            color = 'blue')
plt.scatter(dataset[dataset[COLUMN_WORKINGOUT] == True][COLUMN_TIME].values,
            dataset[dataset[COLUMN_WORKINGOUT] == True][COLUMN_HEARTRATE].values,
            color = 'red')
plt.title(COLUMN_TIME + ' vs ' + COLUMN_HEARTRATE + ' (' + FILENAME + ')')
plt.xlabel(COLUMN_TIME)
plt.ylabel(COLUMN_HEARTRATE)
plt.show()

# Export dataset
output_file = join(OUTPUT_DIR, FILENAME)

dataset.to_csv(output_file,
               index = False,
               date_format = DATE_FORMAT)