Water-Quality-Inference-Non-Linear-Regression/gm_data_viz.py at main · ecorey/Water-Quality-Inference-Non-Linear-Regression · GitHub

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"""
This script loads P90 score data from 2013-2023 and visualizes the GM values for 5 stations over time.
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# paths to the P90 score files
file_list = [
    './data/2013_P90_Scores.csv',
    './data/2014_P90_Scores.csv',
    './data/2015_P90_Scores.csv',
    './data/2016_P90_Scores.csv',
    './data/2017_P90_Scores.csv',
    './data/2018_P90_Scores.csv',
    './data/2019_P90_Scores.csv',
    './data/2020_P90_Scores.csv',
    './data/2021_P90_Scores.csv',
    './data/2022_P90_Scores.csv',
    './data/2023_P90_Scores.csv'
]

# load the data from each file into a list of dataframes
dataframes = []
for file in file_list:
    try:
        year = int(file.split('_')[0].split('/')[-1])
        df = pd.read_csv(file)

        # replace values with infinite with NaN
        df = df.replace([np.inf, -np.inf], np.nan)

        # add a year column
        df['Year'] = year
        dataframes.append(df)
        print(f"Loaded {file} with {len(df)} rows")
    except Exception as e:
        print(f"Could not load {file}: {e}")


# combine all data for the 10 years into a single dataframe so that we can visualize it as a line plot
all_data = pd.concat(dataframes, ignore_index=True)
print(f"Combined dataset has {len(all_data)} rows")

# get 5 stations to visualize the GM values over time
five_stations = all_data['Station'].unique()[1240:1245]
print(f"First 5 stations for visualization: {five_stations}")

# filter the data for just these stations
selected_data = all_data[all_data['Station'].isin(five_stations)]

# create a scatter plot for the GM values across years
plt.figure(figsize=(12, 8))

# x-axis range
x_min, x_max = 2013, 2023

# approved
plt.axhspan(0, 14, facecolor='green', alpha=0.45, zorder=0)
# restricted
plt.axhspan(14, 88, facecolor='orange', alpha=0.45, zorder=0)
# prohibited
plt.axhspan(88, plt.ylim()[1] * 2, facecolor='red', alpha=0.45, zorder=0)

# add horizontal lines at the threshold boundaries
plt.axhline(y=14, color='black', linestyle='--', alpha=0.5, zorder=1)
plt.axhline(y=88, color='black', linestyle='--', alpha=0.5, zorder=1)

# colors for each station
colors = ['red', 'orange', 'yellow', 'green', 'blue']

# plot each station with a different color
for i, station in enumerate(five_stations):
    station_data = selected_data[selected_data['Station'] == station]

    # sort by year
    station_data = station_data.sort_values('Year')

    # plot the scatter points
    plt.scatter(station_data['Year'], station_data['GM'],
                color=colors[i], label=station, alpha=0.7, s=80)

    # add connecting lines for each station
    plt.plot(station_data['Year'], station_data['GM'],
             color=colors[i], alpha=0.5, linestyle='-')

# add labels and a title
plt.xlabel('Year', fontsize=14)
plt.ylabel('GM Value', fontsize=14)
plt.title('GM Values for 5 Stations (2013-2023)', fontsize=16)
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend(title='Station ID')


# adjust the x-axis to show all years
plt.xticks(range(2013, 2023))

# save the figure
plt.tight_layout()
plt.savefig('gm_station_visualization.png', dpi=300)
print("Saved visualization!")

# display the figure
plt.show()

# print a table of the data for these stations
print("\nGM values by station and year:")
pivot_table = selected_data.pivot_table(values='GM', index='Station', columns='Year', aggfunc='mean')
print(pivot_table)