62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
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import matplotlib.pyplot as plt
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import pandas as pd
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import os
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# Generowanie przykładowych danych
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def generate_sample_data(num_records=100):
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import random
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from datetime import datetime, timedelta
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station_ids = [101, 102, 103, 104]
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pollutants = ["PM10", "WWA"]
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start_date = datetime(2023, 1, 1)
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end_date = datetime(2023, 12, 31)
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data = []
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for _ in range(num_records):
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record = {
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"station_id": random.choice(station_ids),
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"pollutant": random.choice(pollutants),
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"date": (start_date + timedelta(days=random.randint(0, (end_date - start_date).days))).strftime("%Y-%m-%d"),
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"value": round(random.uniform(5, 50), 2), # Example values
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}
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data.append(record)
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return pd.DataFrame(data)
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# Generowanie danych
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data = generate_sample_data(100)
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# Tworzenie katalogu na wykresy
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output_dir = "figs"
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os.makedirs(output_dir, exist_ok=True)
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# Generowanie wykresów
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# Histogramy stężeń dla każdego zanieczyszczenia
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pollutants = data["pollutant"].unique()
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for pollutant in pollutants:
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subset = data[data["pollutant"] == pollutant]
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plt.figure(figsize=(8, 6))
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plt.hist(subset["value"], bins=10, edgecolor="black", alpha=0.7)
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plt.title(f"Histogram of {pollutant} concentrations")
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plt.xlabel("Concentration")
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plt.ylabel("Frequency")
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plt.grid(True)
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plt.savefig(os.path.join(output_dir, f"histogram_{pollutant}.png"))
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plt.close()
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# Średnie stężenia w czasie
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data["date"] = pd.to_datetime(data["date"])
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mean_over_time = data.groupby(["date", "pollutant"])["value"].mean().unstack()
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mean_over_time.plot(figsize=(10, 6), marker="o")
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plt.title("Mean Concentrations Over Time")
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plt.xlabel("Date")
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plt.ylabel("Mean Concentration")
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plt.legend(title="Pollutant")
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plt.grid(True)
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plt.savefig(os.path.join(output_dir, "mean_concentration_over_time.png"))
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plt.close()
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print(f"Figures have been saved to the '{output_dir}' directory.")
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