TimeVariant: Code Optimization

[Tanvi-Jain01]

@nipunbatra , @patel-zeel

Description:

I went through the source code of timeVariantplot where I think that the code can be generalized and can be optimized to work better with any pollutant.

Source Code:

Code Optimization 1

vayu/vayu/timeVariation.py

Line 45 in ef99aef

pollutant = ["pm10", "no2", "pm25", "so2"]

The above list make is particular to the specified pollutants only, so it would be better if we take the list of pollutants from the user.

Proposed solution:
Hence the method signature should look like below:

def timeVariation(df, pollutant=['pm25']):

Code Optimization 2

vayu/vayu/timeVariation.py

Lines 55 to 56 in ef99aef

	df_day_m = df_day.groupby("hour").mean()
	df_day_m = df_day_m.reset_index()

The above line can be optimized like:

Proposed solution:

df_day_m = df_day.groupby("hour").mean().reset_index()

Code Optimization 3

vayu/vayu/timeVariation.py

Line 75 in ef99aef

df_hour = df

vayu/vayu/timeVariation.py

Line 98 in ef99aef

df_month = df

The above initialization should be done at once in the beginning like:
Proposed solution:

    df_days=df
    df_hour=df
    df_month=df
    df_weekday = df

Improved Code:

def timeVariation(df, pollutant=['pm25']):
    """ 
    Plots four plots:
    - The average pollutant level per day by 
    each hour for each day of the week across all of the data
    - The average pollutant level by each hour, 
    across all data
    - The average pollutant level by each month of the
    year for across data
    - The average pollutant level per day of the week 
    across all data
    
    Parameters
    ----------
    df: pandas.DataFrame
      data frame of hourly data. 
      Must include a date field and at least one variable to plot
    pollutant: str as list
      Name of pollutants 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
    
    #print(pollutants)
    df["date"] = pd.to_datetime(df.date)

    df_days=df
    df_hour=df
    df_month=df
    df_weekday = df
    
    df_days["day"] = df_days["date"].dt.day_name()
    df_days = df_days.set_index(keys=["day"])
    df_days = df_days.groupby(["day"])

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

    for i in range(len(dayWeek)):
        plt.figure(1, figsize=(40, 5))
        plt.subplot(1, 7, i + 1)
        plt.grid()

        df_day = df_days.get_group(dayWeek[i])
        df_day["hour"] = df_day["date"].dt.hour

        df_day_m = df_day.groupby("hour").mean().reset_index()
        df_day_s = df_day.groupby("hour").std().reset_index()
       

        for k in range(len(pollutant)):
            plt.plot(df_day_m["hour"], df_day_m[pollutant[k]], label=pollutant[k])
            plt.fill_between(
                df_day_s["hour"],
                df_day_m[pollutant[k]] - 0.5 * df_day_s[pollutant[k]],
                df_day_m[pollutant[k]] + 0.5 * df_day_s[pollutant[k]],
                alpha=0.2,
            )
            plt.xlabel(dayWeek[i])
            plt.legend()
    plt.savefig("TimeVariationPlots1.png", bbox_inches="tight")

    plt.figure(2, figsize=(35, 5))
    plt.subplot(1, 3, 1)
    plt.grid()

    df_hour["hour"] = df_hour["date"].dt.hour
    df_hour_m = df.groupby("hour").mean().reset_index()
    df_hour_s = df.groupby("hour").std().reset_index()
    
    for i in range(len(pollutant)):
        plt.plot(df_hour_m["hour"], df_hour_m[pollutant[i]], label=pollutant[i])
        plt.fill_between(
            df_hour_s["hour"],
            df_hour_m[pollutant[i]] - 0.5 * df_hour_s[pollutant[i]],
            df_hour_m[pollutant[i]] + 0.5 * df_hour_s[pollutant[i]],
            alpha=0.2,
        )
        plt.xlabel("Hour")
        plt.legend()

    plt.subplot(1, 3, 2)
    plt.grid()

    df_month["month"] = df_month["date"].dt.month
    df_month_m = df_month.groupby("month").mean().reset_index()
    df_month_s = df_month.groupby("month").std().reset_index()
    
    for i in range(len(pollutant)):
        plt.plot(df_month_m["month"], df_month_m[pollutant[i]], label=pollutant[i])
        plt.fill_between(
            df_month_s["month"],
            df_month_m[pollutant[i]] - 0.5 * df_month_s[pollutant[i]],
            df_month_m[pollutant[i]] + 0.5 * df_month_s[pollutant[i]],
            alpha=0.2,
        )
        plt.xlabel("Month")
        plt.legend()

    plt.subplot(1, 3, 3)
    plt.grid()
    
    df_weekday["weekday"] = df_weekday["date"].dt.weekday
    df_weekday_m = df_weekday.groupby("weekday").mean().reset_index()
    df_weekday_s = df_weekday.groupby("weekday").std().reset_index()

    for i in range(len(pollutant)):
        plt.plot(
            df_weekday_m["weekday"], df_weekday_m[pollutant[i]], label=pollutant[i]
        )
        plt.fill_between(
            df_weekday_s["weekday"],
            df_weekday_m[pollutant[i]] - 0.5 * df_weekday_s[pollutant[i]],
            df_weekday_m[pollutant[i]] + 0.5 * df_weekday_s[pollutant[i]],
            alpha=0.2,
        )
        plt.xlabel("WeekDay")
        plt.legend()
    plt.savefig("TimeVariationPlots2.png", bbox_inches="tight")
    print("Your plots has also been saved")
    plt.show()
    

timeVariation(df1, pollutant=['pm25','nh3'])

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

NOTE: I'm also adding plt.savefig("TimeVariationPlots2.png", bbox_inches="tight") to save the figure.

Output: