Fix benchmark code

CHANGELOG.md

@@ -17,6 +17,9 @@
 - Drop click-params dependency
 - Make pyarrow a polars extra
 
+### Fix
+- Fix benchmark code
+
 ## 0.102.0 - 2025-01-17
 
 ### Feature

benchmarks/interpolation.py

@@ -5,7 +5,6 @@
 from dataclasses import dataclass, field
 from zoneinfo import ZoneInfo
 
-import matplotlib.pyplot as plt
 import polars as pl
 import utm
 from scipy import interpolate
@@ -47,16 +46,15 @@ class Data:
 
 
 def request_weather_data(
-    parameter: str,
+    parameters: list[tuple[str, str, str]],
     lat: float,
     lon: float,
     distance: float,
     start_date: dt.datetime,
     end_date: dt.datetime,
 ):
     stations = DwdObservationRequest(
-        parameters=parameter,
-        resolution="hourly",
+        parameters=parameters,
         start_date=start_date,
         end_date=end_date,
     )
@@ -109,29 +107,45 @@ def interpolate_data(latitude: float, longitude: float, data: Data):
 
 
 def visualize_points(data: Data):
-    fig, ax = plt.subplots()
-    ax.scatter(data.utm_y, data.utm_x, color=data.colors)
-
+    try:
+        import plotly.graph_objects as go
+    except ImportError as e:
+        raise ImportError("Please install extra `plotting` with wetterdienst[plotting]") from e
+    fig = go.Figure()
+    fig.add_trace(
+        go.Scatter(
+            x=data.utm_y,
+            y=data.utm_x,
+            mode="markers",
+            marker=dict(color=data.colors),
+            text=[f"id:{station}\nval:{value : .2f}\n" for station, value in zip(data.station_ids, data.values)],
+        )
+    )
     for i, (station, value) in enumerate(zip(data.station_ids, data.values)):
-        ax.annotate(
-            f"id:{station}\nval:{value : .2f}\n",
-            (data.utm_y[i], data.utm_x[i]),
-            horizontalalignment="center",
-            verticalalignment="bottom",
+        fig.add_trace(
+            go.Scatter(
+                x=[data.utm_y[i]],
+                y=[data.utm_x[i]],
+                mode="markers+text",
+                marker=dict(color=data.colors[i]),
+                text=f"id:{station}\nval:{value : .2f}\n",
+                textposition="top center",
+            )
         )
+    fig.update_layout(showlegend=False)
     if "PYTEST_CURRENT_TEST" not in os.environ:
-        plt.show()
+        fig.show()
 
 
 def main():
-    parameter = [("temperature_air_mean_2m", "temperature_air")]
+    parameters = [("hourly", "temperature_air", "temperature_air_mean_2m")]
     latitude = 50.0
     longitude = 8.9
     distance = 21.0
     start_date = dt.datetime(2022, 1, 1, tzinfo=ZoneInfo("UTC"))
     end_date = dt.datetime(2022, 1, 20, tzinfo=ZoneInfo("UTC"))
 
-    data = request_weather_data(parameter, latitude, longitude, distance, start_date, end_date)
+    data = request_weather_data(parameters, latitude, longitude, distance, start_date, end_date)
     interpolate_data(latitude, longitude, data)
     visualize_points(data)
 

benchmarks/interpolation_over_time.py

@@ -1,37 +1,34 @@
 # Copyright (C) 2018-2023, earthobservations developers.
 # Distributed under the MIT License. See LICENSE for more info.
+import datetime as dt
 import os
-from datetime import datetime
 
-import matplotlib.pyplot as plt
 import numpy as np
 import polars as pl
 from sklearn.feature_selection import r_regression
 from sklearn.metrics import root_mean_squared_error
 
-from wetterdienst import Parameter
 from wetterdienst.provider.dwd.observation import (
     DwdObservationRequest,
-    DwdObservationResolution,
 )
 
-plt.style.use("ggplot")
 
-
-def get_interpolated_df(parameter: str, start_date: datetime, end_date: datetime) -> pl.DataFrame:
+def get_interpolated_df(
+    parameters: tuple[str, str, str], start_date: dt.datetime, end_date: dt.datetime
+) -> pl.DataFrame:
     stations = DwdObservationRequest(
-        parameters=parameter,
-        resolution=DwdObservationResolution.HOURLY,
+        parameters=parameters,
         start_date=start_date,
         end_date=end_date,
     )
     return stations.interpolate(latlon=(50.0, 8.9)).df
 
 
-def get_regular_df(parameter: str, start_date: datetime, end_date: datetime, exclude_stations: list) -> pl.DataFrame:
+def get_regular_df(
+    parameters: tuple[str, str, str], start_date: dt.datetime, end_date: dt.datetime, exclude_stations: list
+) -> pl.DataFrame:
     stations = DwdObservationRequest(
-        parameters=parameter,
-        resolution=DwdObservationResolution.HOURLY,
+        parameters=parameters,
         start_date=start_date,
         end_date=end_date,
     )
@@ -56,36 +53,53 @@ def get_corr(regular_values: pl.Series, interpolated_values: pl.Series) -> float
     ).item()
 
 
-def visualize(parameter: str, unit: str, regular_df: pl.DataFrame, interpolated_df: pl.DataFrame):
+def visualize(parameter: tuple[str, str, str], unit: str, regular_df: pl.DataFrame, interpolated_df: pl.DataFrame):
+    try:
+        import plotly.graph_objects as go
+    except ImportError as e:
+        raise ImportError("Please install extra `plotting` with wetterdienst[plotting]") from e
+
     rmse = get_rmse(regular_df.get_column("value"), interpolated_df.get_column("value"))
     corr = get_corr(regular_df.get_column("value"), interpolated_df.get_column("value"))
-    factor = 0.5
-    plt.figure(figsize=(factor * 19.2, factor * 10.8))
-    plt.plot(regular_df.get_column("date"), regular_df.get_column("value"), color="red", label="regular")
-    plt.plot(
-        interpolated_df.get_column("date"),
-        interpolated_df.get_column("value"),
-        color="black",
-        label="interpolated",
+    fig = go.Figure()
+    fig.add_trace(
+        go.Scatter(
+            x=regular_df.get_column("date"),
+            y=regular_df.get_column("value"),
+            mode="lines",
+            name="regular",
+            line=dict(color="red"),
+        )
     )
-    ylabel = f"{parameter.lower()} [{unit}]"
-    plt.ylabel(ylabel)
+    fig.add_trace(
+        go.Scatter(
+            x=interpolated_df.get_column("date"),
+            y=interpolated_df.get_column("value"),
+            mode="lines",
+            name="interpolated",
+            line=dict(color="black"),
+        )
+    )
+
+    ylabel = f"{parameter[-1].lower()} [{unit}]"
     title = (
         f"rmse: {np.round(rmse, 2)}, corr: {np.round(corr, 2)}\n"
         f"station_ids: {interpolated_df.get_column('taken_station_ids').to_list()[0]}"
     )
-    plt.title(title)
-    plt.legend()
-    plt.tight_layout()
+
+    fig.update_layout(
+        title=title, xaxis_title="Date", yaxis_title=ylabel, legend=dict(x=0, y=1), margin=dict(l=40, r=40, t=40, b=40)
+    )
+
     if "PYTEST_CURRENT_TEST" not in os.environ:
-        plt.show()
+        fig.show()
 
 
 def main():
-    parameter = Parameter.TEMPERATURE_AIR_MEAN_2M.name
+    parameter = ("hourly", "air_temperature", "temperature_air_mean_2m")
     unit = "K"
-    start_date = datetime(2022, 3, 1)
-    end_date = datetime(2022, 3, 31)
+    start_date = dt.datetime(2022, 3, 1)
+    end_date = dt.datetime(2022, 3, 31)
     interpolated_df = get_interpolated_df(parameter, start_date, end_date)
     exclude_stations = interpolated_df.get_column("taken_station_ids")[0]
     regular_df = get_regular_df(parameter, start_date, end_date, exclude_stations)

benchmarks/interpolation_precipitation_difference.py

@@ -1,32 +1,28 @@
 # Copyright (C) 2018-2023, earthobservations developers.
 # Distributed under the MIT License. See LICENSE for more info.
-from datetime import datetime
+import datetime as dt
 
 import polars as pl
 
-from wetterdienst import Parameter
 from wetterdienst.provider.dwd.observation import (
     DwdObservationRequest,
-    DwdObservationResolution,
 )
 
 LATLON = (52.52, 13.40)
 
 
-def get_interpolated_df(start_date: datetime, end_date: datetime) -> pl.DataFrame:
+def get_interpolated_df(start_date: dt.datetime, end_date: dt.datetime) -> pl.DataFrame:
     stations = DwdObservationRequest(
-        parameters=Parameter.PRECIPITATION_HEIGHT,
-        resolution=DwdObservationResolution.DAILY,
+        parameters=[("daily", "climate_summary", "precipitation_height")],
         start_date=start_date,
         end_date=end_date,
     )
     return stations.interpolate(latlon=LATLON).df
 
 
-def get_regular_df(start_date: datetime, end_date: datetime, exclude_stations: list) -> pl.DataFrame:
+def get_regular_df(start_date: dt.datetime, end_date: dt.datetime, exclude_stations: list) -> pl.DataFrame:
     stations = DwdObservationRequest(
-        parameters=Parameter.PRECIPITATION_HEIGHT.name,
-        resolution=DwdObservationResolution.DAILY,
+        parameters=[("daily", "climate_summary", "precipitation_height")],
         start_date=start_date,
         end_date=end_date,
     )
@@ -46,8 +42,8 @@ def calculate_percentage_difference(df: pl.DataFrame, text: str = "") -> float:
 
 
 def main():
-    start_date = datetime(2021, 1, 1)
-    end_date = datetime(2022, 1, 1)
+    start_date = dt.datetime(2021, 1, 1)
+    end_date = dt.datetime(2022, 1, 1)
     interpolated_df = get_interpolated_df(start_date, end_date)
     print(interpolated_df)
     exclude_stations = interpolated_df.get_column("taken_station_ids")[0]