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https://github.com/kristoferssolo/LU-Data-Visualisation.git
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124 lines
3.2 KiB
Python
Executable File
124 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from loguru import logger
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logger.add(
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Path("logs", "data.log"),
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format="{time} | {level} | {message}",
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level="DEBUG",
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rotation="1 MB",
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compression="zip",
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)
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BASE_PATH = Path(__file__).parent
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WIND_GUSTS_PATH = BASE_PATH.joinpath("data", "vejaAtrumsBrazmas.xlsx")
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WIND_SPEED_PATH = BASE_PATH.joinpath("data", "vejaAtrumsFaktiskais.xlsx")
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AIR_TEMP_PATH = BASE_PATH.joinpath("data", "gaisaTemperatura2022.xlsx")
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BLUE = "#1f77b4"
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ORANGE = "#ff7f0e"
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BLACK = "#000000"
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def read_data(path: Path) -> pd.DataFrame:
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dataframe = pd.read_excel(path, parse_dates=["Datums"], index_col="Datums", date_format="%d.%m.%Y")
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return dataframe
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def bar_chart() -> None:
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df_avg = read_data(WIND_SPEED_PATH).mean(axis=1)
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df_max = read_data(WIND_GUSTS_PATH).max(axis=1) - df_avg
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df_combined = pd.concat(
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[df_avg, df_max],
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axis=1,
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)
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df_combined.columns = ["Vidējais", "Maksimālais"]
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df_combined.plot.bar(stacked=True, figsize=(12, 8), color=[ORANGE, BLUE], width=0.6)
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plt.yticks(np.arange(0, df_combined.max().max() + 2.5, 2.5))
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plt.xticks(rotation=45) # FIX: don't display time
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plt.title("Vidējais un maksimālais vēja ātrums 2023. gada augustā")
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plt.xlabel("Mērījumu Datums")
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plt.ylabel("Vēja ātrums (m/s)")
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plt.show()
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SEASONS = {
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1: "Ziema",
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2: "Pavasaris",
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3: "Vasara",
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4: "Rudens",
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}
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def box_plot() -> None:
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df = read_data(AIR_TEMP_PATH)
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df["Season"] = df.index.month % 12 // 3 + 1
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df["Season"] = df["Season"].map(SEASONS)
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df["Average"] = df.iloc[:, 0:24].mean(axis=1)
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df_melted = pd.melt(df, id_vars=["Season"], value_name="Temperature", var_name="Time") # FIX: should be average temperature
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df_melted["Season"] = pd.Categorical(df_melted["Season"], categories=SEASONS.values(), ordered=True)
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_, ax = plt.subplots(figsize=(12, 8))
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box_props = dict(facecolor=BLUE) # box
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median_props = dict(color=ORANGE) # median line
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whisker_props = dict(color=BLACK) # whiskers (vertical line beween box and min/max)
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width = 0.4
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df_melted[df_melted["Season"] == "Rudens"].boxplot(
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by="Season",
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ax=ax,
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grid=False,
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showfliers=0.5,
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boxprops=box_props,
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medianprops=median_props,
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whiskerprops=whisker_props,
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patch_artist=True,
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widths=width,
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)
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df_melted[df_melted["Season"] != "Rudens"].boxplot(
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by="Season",
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ax=ax,
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grid=False,
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showfliers=False,
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boxprops=box_props,
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medianprops=median_props,
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whiskerprops=whisker_props,
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patch_artist=True,
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widths=width,
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)
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min_value = np.floor(df_melted["Temperature"].min() / 5) * 5
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max_value = np.ceil(df_melted["Temperature"].max() / 5) * 5
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tick_step = 5
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plt.yticks(np.arange(min_value, max_value, tick_step))
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plt.title("Gaisa temperatūra Rīgā četros gadalaikos")
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plt.suptitle("")
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plt.ylabel("Gaisa temperatūra (Celsija grādos)")
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plt.xlabel("")
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plt.show()
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@logger.catch
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def main() -> None:
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# bar_chart()
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box_plot()
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if __name__ == "__main__":
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main()
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