# -*- coding: utf-8 -*-
"""
Created on Mon Jul 22 20:50:49 2019

@author: <NAME>
"""


def timeVariation(df, pollutant):
    """ 4 seperate plots are plotted:
        
        1) the average pollutant level per day by each hour for each day of the 
            week across all of the data
        2) the average pollutant level by each hour, across all data
        3) the average pollutant level by each month of the year for across data
        4) the average pollutant level per day of the week across all data
		
		Parameters
		----------
		df: data frame
			data frame of hourly data. 
			Must include a date field and at least one variable to plot
		pollutant: type string
			Name of variable to plot
    """
    import datetime as dt
    import matplotlib.pyplot as plt
    import matplotlib as mpl
    import numpy as np
    import pandas as pd
    from numpy import array

    dayWeek = [
        "Monday",
        "Tuesday",
        "Wednesday",
        "Thursday",
        "Friday",
        "Saturday",
        "Sunday",
    ]

    # Adds month,day, and hour columns in df for futher pandas manipulation

    df.index = pd.to_datetime(df.date)
    df = df.drop("date", axis=1)
    df_new = df
    df_new = df_new.fillna(method="ffill")
    df_new["month"] = df_new.index.month
    df_new["day"] = df_new.index.dayofweek
    df_new["hour"] = df_new.index.hour

    x = 0
    sub = 1
    # for plot 1
    while x < 7:
        monday = []
        a = df_new[df_new.day == x]
        i = 0
        plt.figure(1, figsize=(50, 3))

        while i < 24:
            b = a[a.hour == i]
            c = b[pollutant].mean()
            i = i + 1
            monday.append(c)
        df_1 = pd.DataFrame(monday)
        df_1.columns = [pollutant]
        # plt.figure(1)
        plt.subplot(1, 7, sub)
        plt.subplots_adjust(wspace=0.2)
        sub = sub + 1
        a = df_1[pollutant].plot.line(color="#ff9999")
        # a.axes.get_xaxis().set_visible(False)
        a.yaxis.set_label_position("left")
        plt.ylabel(pollutant)
        plt.xlabel("hours")
        plt.ylim((20, 70))
        plt.xlim((0, 23))
        plt.title(dayWeek[x])
        plt.grid()

        x = x + 1
    # plot 2
    a = df_new
    hourly = []
    i = 0
    plt.figure(2, figsize=(30, 3))
    while i < 24:
        b = a[a.hour == i]
        c = b[pollutant].mean()
        i = i + 1
        hourly.append(c)
    df_1 = pd.DataFrame(hourly)
    df_1.columns = [pollutant]
    plt.subplot(1, 3, 1)
    a = df_1[pollutant].plot.line(color="#ff9999")
    a.yaxis.set_label_position("left")
    plt.ylabel(pollutant)
    plt.xlabel("hour")
    plt.ylim((20, 45))
    plt.xlim((0, 23))
    plt.grid()

    # plot 3
    a = df_new.fillna(method="ffill")
    monthly = []
    i = 0
    plt.figure(2, figsize=(30, 3))
    while i < 12:
        b = a[a.month == i]
        c = b[pollutant].mean()
        i = i + 1
        monthly.append(c)
    df_1 = pd.DataFrame(monthly)
    df_1.columns = [pollutant]
    plt.subplot(1, 3, 2)
    a = df_1[pollutant].plot.line(color="#ff9999")
    a.yaxis.set_label_position("left")
    plt.ylabel(pollutant)
    plt.xlabel("month")
    plt.ylim((30, 40))
    plt.xlim((0, 12))
    plt.grid()

    # plot 4
    x = 0
    weekday = []
    while x < 7:
        monday = []
        a = df_new[df_new.day == x]
        i = 0
        plt.figure(2, figsize=(30, 3))

        while i < 24:
            b = a[a.hour == i]
            c = b[pollutant].mean()
            i = i + 1
            monday.append(c)
        df_1 = pd.DataFrame(monday)
        df_1.columns = [pollutant]
        weekday.append(df_1.mean())
        x = x + 1
    # print(weekday)
    weekday = pd.DataFrame(weekday)
    weekday.columns = [pollutant]
    plt.subplot(1, 3, 3)
    a = weekday[pollutant].plot.line(color="#ff9999")
    a.yaxis.set_label_position("left")
    plt.ylabel(pollutant)
    plt.xlabel("weekday")
    plt.ylim((26, 40))
    plt.xlim((0, 6))
    plt.grid()


# =============================================================================
# df = pd.read_csv("mydata.csv")
# timeVariation(df,'pm10')
# =============================================================================

# -*- coding: utf-8 -*-
"""
Created on Mon Jul 22 20:50:49 2019

@author: <NAME>
"""


def timeVariation(df, pollutant):
    """ 4 seperate plots are plotted:
        
        1) the average pollutant level per day by each hour for each day of the 
            week across all of the data
        2) the average pollutant level by each hour, across all data
        3) the average pollutant level by each month of the year for across data
        4) the average pollutant level per day of the week across all data
		
		Parameters
		----------
		df: data frame
			data frame of hourly data. 
			Must include a date field and at least one variable to plot
		pollutant: type string
			Name of variable to plot
    """
    import datetime as dt
    import matplotlib.pyplot as plt
    import matplotlib as mpl
    import numpy as np
    import pandas as pd
    from numpy import array

    dayWeek = [
        "Monday",
        "Tuesday",
        "Wednesday",
        "Thursday",
        "Friday",
        "Saturday",
        "Sunday",
    ]

    # Adds month,day, and hour columns in df for futher pandas manipulation

    df.index = pd.to_datetime(df.date)
    df = df.drop("date", axis=1)
    df_new = df
    df_new = df_new.fillna(method="ffill")
    df_new["month"] = df_new.index.month
    df_new["day"] = df_new.index.dayofweek
    df_new["hour"] = df_new.index.hour

    x = 0
    sub = 1
    # for plot 1
    while x < 7:
        monday = []
        a = df_new[df_new.day == x]
        i = 0
        plt.figure(1, figsize=(50, 3))

        while i < 24:
            b = a[a.hour == i]
            c = b[pollutant].mean()
            i = i + 1
            monday.append(c)
        df_1 = pd.DataFrame(monday)
        df_1.columns = [pollutant]
        # plt.figure(1)
        plt.subplot(1, 7, sub)
        plt.subplots_adjust(wspace=0.2)
        sub = sub + 1
        a = df_1[pollutant].plot.line(color="#ff9999")
        # a.axes.get_xaxis().set_visible(False)
        a.yaxis.set_label_position("left")
        plt.ylabel(pollutant)
        plt.xlabel("hours")
        plt.ylim((20, 70))
        plt.xlim((0, 23))
        plt.title(dayWeek[x])
        plt.grid()

        x = x + 1
    # plot 2
    a = df_new
    hourly = []
    i = 0
    plt.figure(2, figsize=(30, 3))
    while i < 24:
        b = a[a.hour == i]
        c = b[pollutant].mean()
        i = i + 1
        hourly.append(c)
    df_1 = pd.DataFrame(hourly)
    df_1.columns = [pollutant]
    plt.subplot(1, 3, 1)
    a = df_1[pollutant].plot.line(color="#ff9999")
    a.yaxis.set_label_position("left")
    plt.ylabel(pollutant)
    plt.xlabel("hour")
    plt.ylim((20, 45))
    plt.xlim((0, 23))
    plt.grid()

    # plot 3
    a = df_new.fillna(method="ffill")
    monthly = []
    i = 0
    plt.figure(2, figsize=(30, 3))
    while i < 12:
        b = a[a.month == i]
        c = b[pollutant].mean()
        i = i + 1
        monthly.append(c)
    df_1 = pd.DataFrame(monthly)
    df_1.columns = [pollutant]
    plt.subplot(1, 3, 2)
    a = df_1[pollutant].plot.line(color="#ff9999")
    a.yaxis.set_label_position("left")
    plt.ylabel(pollutant)
    plt.xlabel("month")
    plt.ylim((30, 40))
    plt.xlim((0, 12))
    plt.grid()

    # plot 4
    x = 0
    weekday = []
    while x < 7:
        monday = []
        a = df_new[df_new.day == x]
        i = 0
        plt.figure(2, figsize=(30, 3))

        while i < 24:
            b = a[a.hour == i]
            c = b[pollutant].mean()
            i = i + 1
            monday.append(c)
        df_1 = pd.DataFrame(monday)
        df_1.columns = [pollutant]
        weekday.append(df_1.mean())
        x = x + 1
    # print(weekday)
    weekday = pd.DataFrame(weekday)
    weekday.columns = [pollutant]
    plt.subplot(1, 3, 3)
    a = weekday[pollutant].plot.line(color="#ff9999")
    a.yaxis.set_label_position("left")
    plt.ylabel(pollutant)
    plt.xlabel("weekday")
    plt.ylim((26, 40))
    plt.xlim((0, 6))
    plt.grid()


# =============================================================================
# df = pd.read_csv("mydata.csv")
# timeVariation(df,'pm10')
# =============================================================================

# -*- coding: utf-8 -*- Created on Mon Jul 22 20:50:49 2019  @author: <NAME> 4 seperate plots are plotted:                  1) the average pollutant level per day by each hour for each day of the              week across all of the data         2) the average pollutant level by each hour, across all data         3) the average pollutant level by each month of the year for across data         4) the average pollutant level per day of the week across all data 		 		Parameters 		---------- 		df: data frame 			data frame of hourly data.  			Must include a date field and at least one variable to plot 		pollutant: type string 			Name of variable to plot # Adds month,day, and hour columns in df for futher pandas manipulation # for plot 1 # plt.figure(1) # a.axes.get_xaxis().set_visible(False) # plot 2 # plot 3 # plot 4 # print(weekday) # ============================================================================= # df = pd.read_csv("mydata.csv") # timeVariation(df,'pm10') # =============================================================================