diff --git a/CITATION.cff b/CITATION.cff
index 993de4f..13d49d5 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -6,6 +6,8 @@ authors:
given-names: "Radovan"
- family-names: "Darst"
given-names: "Richard"
+- family-names: "Tian"
+ given-names: "Yu"
- family-names: "Juselius"
given-names: "Jonas"
- family-names: "Di Remigio Eikås"
diff --git a/content/code/abstracting-plot.py b/content/code/abstracting-plot.py
index 52969bc..c2f7303 100644
--- a/content/code/abstracting-plot.py
+++ b/content/code/abstracting-plot.py
@@ -1,31 +1,66 @@
import pandas as pd
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
-plt.xlabel("measurements")
-plt.ylabel("air temperature (deg C)")
+def plot(column, label, location, color, compute_mean):
+ fig, ax = plt.subplots()
-def plot_temperatures(temperatures):
- plt.plot(temperatures, "r-")
- plt.axhline(y=mean, color="b", linestyle="--")
- plt.show()
- plt.savefig(f"{num_measurements}.png")
- plt.clf()
+ # time series
+ ax.plot(
+ data_month.index,
+ data_month[column],
+ label=label,
+ color=color,
+ )
+ if compute_mean:
+ values = data_month[column].values
+ mean_value = sum(values) / len(values)
-for num_measurements in [25, 100, 500]:
+ # mean (as horizontal dashed line)
+ ax.axhline(
+ y=mean_value,
+ label=f"mean {label}: {mean_value:.1f}",
+ color=color,
+ linestyle="--",
+ )
- # read data from file
- data = pd.read_csv("temperatures.csv", nrows=num_measurements)
- temperatures = data["Air temperature (degC)"]
+ ax.set_title(f"{label} at {location}")
+ ax.set_xlabel("date and time")
+ ax.set_ylabel(label)
+ ax.legend()
+ ax.grid(True)
- # compute statistics
- mean = sum(temperatures) / num_measurements
+ # format x-axis for better date display
+ fig.autofmt_xdate()
- # plot results
- # plt.plot(temperatures, 'r-')
- # plt.axhline(y=mean, color='b', linestyle='--')
- # plt.show()
- # plt.savefig(f'{num_measurements}.png')
- # plt.clf()
- plot_temperatures(temperatures)
+ fig.savefig(f"{month}-{column}.png")
+
+
+# read data
+data = pd.read_csv("weather_data.csv")
+
+# combine 'date' and 'time' into a single datetime column
+data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
+
+# set datetime as index for convenience
+data = data.set_index("datetime")
+
+
+for month in ["2024-01", "2024-02", "2024-03"]:
+ data_month = data.loc[month]
+
+ plot(
+ "air_temperature_celsius",
+ "air temperature (C)",
+ "Helsinki airport",
+ "red",
+ compute_mean=True,
+ )
+ plot(
+ "precipitation_mm",
+ "precipitation (mm)",
+ "Helsinki airport",
+ "blue",
+ compute_mean=False,
+ )
diff --git a/content/code/add-iteration.py b/content/code/add-iteration.py
index f5c7a51..ea262f8 100644
--- a/content/code/add-iteration.py
+++ b/content/code/add-iteration.py
@@ -1,21 +1,69 @@
import pandas as pd
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
-plt.xlabel("measurements")
-plt.ylabel("air temperature (deg C)")
-for num_measurements in [25, 100, 500]:
+# read data
+data = pd.read_csv("weather_data.csv")
- # read data from file
- data = pd.read_csv("temperatures.csv", nrows=num_measurements)
- temperatures = data["Air temperature (degC)"]
+# combine 'date' and 'time' into a single datetime column
+data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
- # compute statistics
- mean = sum(temperatures) / num_measurements
+# set datetime as index for convenience
+data = data.set_index("datetime")
- # plot results
- plt.plot(temperatures, "r-")
- plt.axhline(y=mean, color="b", linestyle="--")
- plt.show()
- plt.savefig(f"{num_measurements}.png")
- plt.clf()
+
+for month in ["2024-01", "2024-02", "2024-03"]:
+ data_month = data.loc[month]
+
+ fig, ax = plt.subplots()
+
+ # temperature time series
+ ax.plot(
+ data_month.index,
+ data_month["air_temperature_celsius"],
+ label="air temperature (C)",
+ color="red",
+ )
+
+ values = data_month["air_temperature_celsius"].values
+ mean_temp = sum(values) / len(values)
+
+ # mean temperature (as horizontal dashed line)
+ ax.axhline(
+ y=mean_temp,
+ label=f"mean air temperature (C): {mean_temp:.1f}",
+ color="red",
+ linestyle="--",
+ )
+
+ ax.set_title("air temperature (C) at Helsinki airport")
+ ax.set_xlabel("date and time")
+ ax.set_ylabel("air temperature (C)")
+ ax.legend()
+ ax.grid(True)
+
+ # format x-axis for better date display
+ fig.autofmt_xdate()
+
+ fig.savefig(f"{month}-temperature.png")
+
+ fig, ax = plt.subplots()
+
+ # precipitation time series
+ ax.plot(
+ data_month.index,
+ data_month["precipitation_mm"],
+ label="precipitation (mm)",
+ color="blue",
+ )
+
+ ax.set_title("precipitation (mm) at Helsinki airport")
+ ax.set_xlabel("date and time")
+ ax.set_ylabel("precipitation (mm)")
+ ax.legend()
+ ax.grid(True)
+
+ # format x-axis for better date display
+ fig.autofmt_xdate()
+
+ fig.savefig(f"{month}-precipitation.png")
diff --git a/content/code/cli.py b/content/code/cli.py
index feb8b5d..2278414 100644
--- a/content/code/cli.py
+++ b/content/code/cli.py
@@ -1,55 +1,109 @@
+from pathlib import Path
+
+
import pandas as pd
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
import pytest
import click
-def plot_data(data, mean, xlabel, ylabel, file_name):
- plt.plot(data, "r-")
- plt.xlabel(xlabel)
- plt.ylabel(ylabel)
- plt.axhline(y=mean, color="b", linestyle="--")
- plt.savefig(file_name)
- plt.clf()
+def read_data(file_name):
+ data = pd.read_csv(file_name)
+
+ # combine 'date' and 'time' into a single datetime column
+ data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
+
+ # set datetime as index for convenience
+ data = data.set_index("datetime")
+ return data
-def compute_mean(data):
- mean = sum(data) / len(data)
- return mean
+def arithmetic_mean(values):
+ mean_value = sum(values) / len(values)
+ return mean_value
-def test_compute_mean():
- result = compute_mean([1.0, 2.0, 3.0, 4.0])
+
+def test_arithmetic_mean():
+ result = arithmetic_mean([1.0, 2.0, 3.0, 4.0])
assert result == pytest.approx(2.5)
-def read_data(file_name, nrows, column):
- data = pd.read_csv(file_name, nrows=nrows)
- return data[column]
+def plot(date_range, values, label, location, color, compute_mean, file_name):
+ fig, ax = plt.subplots()
+
+ # time series
+ ax.plot(
+ date_range,
+ values,
+ label=label,
+ color=color,
+ )
+
+ if compute_mean:
+ mean_value = arithmetic_mean(values)
+
+ # mean (as horizontal dashed line)
+ ax.axhline(
+ y=mean_value,
+ label=f"mean {label}: {mean_value:.1f}",
+ color=color,
+ linestyle="--",
+ )
+
+ ax.set_title(f"{label} at {location}")
+ ax.set_xlabel("date and time")
+ ax.set_ylabel(label)
+ ax.legend()
+ ax.grid(True)
+
+ # format x-axis for better date display
+ fig.autofmt_xdate()
+
+ fig.savefig(file_name)
@click.command()
+@click.option("--month", required=True, type=str, help="Which month (YYYY-MM)?")
@click.option(
- "--num-measurements", required=True, type=int, help="Number of measurements."
+ "--data-file",
+ required=True,
+ type=click.Path(exists=True, path_type=Path),
+ help="Data is read from this file.",
)
-@click.option("--in-file", required=True, help="File name where we read from.")
-@click.option("--out-file", required=True, help="File name where we write to.")
-def main(num_measurements, in_file, out_file):
-
- temperatures = read_data(
- file_name=in_file,
- nrows=num_measurements,
- column="Air temperature (degC)",
- )
+@click.option(
+ "--output-directory",
+ required=True,
+ type=click.Path(exists=True, path_type=Path),
+ help="Figures are written to this directory.",
+)
+def main(
+ month,
+ data_file,
+ output_directory,
+):
+ data = read_data(data_file)
- mean = compute_mean(temperatures)
+ data_month = data.loc[month]
+ date_range = data_month.index
- plot_data(
- data=temperatures,
- mean=mean,
- xlabel="measurements",
- ylabel="air temperature (deg C)",
- file_name=out_file,
+ plot(
+ date_range,
+ data_month["air_temperature_celsius"].values,
+ "air temperature (C)",
+ "Helsinki airport",
+ "red",
+ compute_mean=True,
+ file_name=output_directory / f"{month}-temperature.png",
+ )
+ plot(
+ date_range,
+ data_month["precipitation_mm"].values,
+ "precipitation (mm)",
+ "Helsinki airport",
+ "blue",
+ compute_mean=False,
+ file_name=output_directory / f"{month}-precipitation.png",
)
diff --git a/content/code/initial-version-with-mean.py b/content/code/initial-version-with-mean.py
new file mode 100644
index 0000000..6fe191e
--- /dev/null
+++ b/content/code/initial-version-with-mean.py
@@ -0,0 +1,47 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# read data
+data = pd.read_csv("weather_data.csv")
+
+# combine 'date' and 'time' into a single datetime column
+data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
+
+# set datetime as index for convenience
+data = data.set_index("datetime")
+
+# keep only january data
+january = data.loc["2024-01"]
+
+fig, ax = plt.subplots()
+
+# temperature time series
+ax.plot(
+ january.index,
+ january["air_temperature_celsius"],
+ label="air temperature (C)",
+ color="red",
+)
+
+values = january["air_temperature_celsius"].values
+mean_temp = sum(values) / len(values)
+
+# mean temperature (as horizontal dashed line)
+ax.axhline(
+ y=mean_temp,
+ label=f"mean air temperature (C): {mean_temp:.1f}",
+ color="red",
+ linestyle="--",
+)
+
+ax.set_title("air temperature (C) at Helsinki airport")
+ax.set_xlabel("date and time")
+ax.set_ylabel("air temperature (C)")
+ax.legend()
+ax.grid(True)
+
+# format x-axis for better date display
+fig.autofmt_xdate()
+
+fig.savefig("2024-01-temperature.png")
diff --git a/content/code/initial-version-with-precipitation.py b/content/code/initial-version-with-precipitation.py
new file mode 100644
index 0000000..a50f079
--- /dev/null
+++ b/content/code/initial-version-with-precipitation.py
@@ -0,0 +1,68 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# read data
+data = pd.read_csv("weather_data.csv")
+
+# combine 'date' and 'time' into a single datetime column
+data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
+
+# set datetime as index for convenience
+data = data.set_index("datetime")
+
+# keep only january data
+january = data.loc["2024-01"]
+
+fig, ax = plt.subplots()
+
+# temperature time series
+ax.plot(
+ january.index,
+ january["air_temperature_celsius"],
+ label="air temperature (C)",
+ color="red",
+)
+
+values = january["air_temperature_celsius"].values
+mean_temp = sum(values) / len(values)
+
+# mean temperature (as horizontal dashed line)
+ax.axhline(
+ y=mean_temp,
+ label=f"mean air temperature (C): {mean_temp:.1f}",
+ color="red",
+ linestyle="--",
+)
+
+ax.set_title("air temperature (C) at Helsinki airport")
+ax.set_xlabel("date and time")
+ax.set_ylabel("air temperature (C)")
+ax.legend()
+ax.grid(True)
+
+# format x-axis for better date display
+fig.autofmt_xdate()
+
+fig.savefig("2024-01-temperature.png")
+
+fig, ax = plt.subplots()
+
+# precipitation time series
+ax.plot(
+ january.index,
+ january["precipitation_mm"],
+ label="precipitation (mm)",
+ color="blue",
+)
+
+ax.set_title("precipitation (mm) at Helsinki airport")
+ax.set_xlabel("date and time")
+ax.set_ylabel("precipitation (mm)")
+ax.legend()
+ax.grid(True)
+
+# format x-axis for better date display
+fig.autofmt_xdate()
+
+fig.savefig("2024-01-precipitation.png")
diff --git a/content/code/initial-version.py b/content/code/initial-version.py
index 6912080..b22db27 100644
--- a/content/code/initial-version.py
+++ b/content/code/initial-version.py
@@ -1,18 +1,36 @@
import pandas as pd
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
-num_measurements = 25
-# read data from file
-data = pd.read_csv("temperatures.csv", nrows=num_measurements)
-temperatures = data["Air temperature (degC)"]
+# read data
+data = pd.read_csv("weather_data.csv")
-# compute statistics
-mean = sum(temperatures) / num_measurements
+# combine 'date' and 'time' into a single datetime column
+data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
-# plot results
-plt.plot(temperatures, "r-")
-plt.axhline(y=mean, color="b", linestyle="--")
-plt.show()
-plt.savefig("25.png")
-plt.clf()
+# set datetime as index for convenience
+data = data.set_index("datetime")
+
+# keep only january data
+january = data.loc["2024-01"]
+
+fig, ax = plt.subplots()
+
+# temperature time series
+ax.plot(
+ january.index,
+ january["air_temperature_celsius"],
+ label="air temperature (C)",
+ color="red",
+)
+
+ax.set_title("air temperature (C) at Helsinki airport")
+ax.set_xlabel("date and time")
+ax.set_ylabel("air temperature (C)")
+ax.legend()
+ax.grid(True)
+
+# format x-axis for better date display
+fig.autofmt_xdate()
+
+fig.savefig("2024-01-temperature.png")
diff --git a/content/code/small-improvements.py b/content/code/small-improvements.py
index 0e142b3..322878b 100644
--- a/content/code/small-improvements.py
+++ b/content/code/small-improvements.py
@@ -1,35 +1,74 @@
import pandas as pd
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
-def plot_data(data, xlabel, ylabel):
- plt.plot(data, "r-")
- plt.xlabel(xlabel)
- plt.ylabel(ylabel)
- plt.axhline(y=mean, color="b", linestyle="--")
- plt.show()
- plt.savefig(f"{num_measurements}.png")
- plt.clf()
+def read_data(file_name):
+ data = pd.read_csv(file_name)
+ # combine 'date' and 'time' into a single datetime column
+ data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
-def compute_statistics(data):
- mean = sum(data) / num_measurements
- return mean
+ # set datetime as index for convenience
+ data = data.set_index("datetime")
+ return data
-def read_data(file_name, column):
- data = pd.read_csv(file_name, nrows=num_measurements)
- return data[column]
+def arithmetic_mean(values):
+ mean_value = sum(values) / len(values)
+ return mean_value
-for num_measurements in [25, 100, 500]:
- temperatures = read_data(
- file_name="temperatures.csv", column="Air temperature (degC)"
+def plot(column, label, location, color, compute_mean):
+ fig, ax = plt.subplots()
+
+ # time series
+ ax.plot(
+ data_month.index,
+ data_month[column],
+ label=label,
+ color=color,
)
- mean = compute_statistics(temperatures)
+ if compute_mean:
+ mean_value = arithmetic_mean(data_month[column].values)
+
+ # mean (as horizontal dashed line)
+ ax.axhline(
+ y=mean_value,
+ label=f"mean {label}: {mean_value:.1f}",
+ color=color,
+ linestyle="--",
+ )
+
+ ax.set_title(f"{label} at {location}")
+ ax.set_xlabel("date and time")
+ ax.set_ylabel(label)
+ ax.legend()
+ ax.grid(True)
+
+ # format x-axis for better date display
+ fig.autofmt_xdate()
+
+ fig.savefig(f"{month}-{column}.png")
- plot_data(
- data=temperatures, xlabel="measurements", ylabel="air temperature (deg C)"
+
+data = read_data("weather_data.csv")
+
+for month in ["2024-01", "2024-02", "2024-03"]:
+ data_month = data.loc[month]
+
+ plot(
+ "air_temperature_celsius",
+ "air temperature (C)",
+ "Helsinki airport",
+ "red",
+ compute_mean=True,
+ )
+ plot(
+ "precipitation_mm",
+ "precipitation (mm)",
+ "Helsinki airport",
+ "blue",
+ compute_mean=False,
)
diff --git a/content/code/testing.py b/content/code/testing.py
index 9b6f04c..56d406d 100644
--- a/content/code/testing.py
+++ b/content/code/testing.py
@@ -1,50 +1,88 @@
import pandas as pd
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
import pytest
-def plot_data(data, mean, xlabel, ylabel, file_name):
- plt.plot(data, "r-")
- plt.xlabel(xlabel)
- plt.ylabel(ylabel)
- plt.axhline(y=mean, color="b", linestyle="--")
- # plt.show()
- plt.savefig(file_name)
- plt.clf()
+def read_data(file_name):
+ data = pd.read_csv(file_name)
+ # combine 'date' and 'time' into a single datetime column
+ data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
-def compute_mean(data):
- mean = sum(data) / len(data)
- return mean
+ # set datetime as index for convenience
+ data = data.set_index("datetime")
+ return data
-def test_compute_mean():
- result = compute_mean([1.0, 2.0, 3.0, 4.0])
+
+def arithmetic_mean(values):
+ mean_value = sum(values) / len(values)
+ return mean_value
+
+
+def test_arithmetic_mean():
+ result = arithmetic_mean([1.0, 2.0, 3.0, 4.0])
assert result == pytest.approx(2.5)
-def read_data(file_name, nrows, column):
- data = pd.read_csv(file_name, nrows=nrows)
- return data[column]
+def plot(date_range, values, label, location, color, compute_mean, file_name):
+ fig, ax = plt.subplots()
+ # time series
+ ax.plot(
+ date_range,
+ values,
+ label=label,
+ color=color,
+ )
-def main():
- for num_measurements in [25, 100, 500]:
+ if compute_mean:
+ mean_value = arithmetic_mean(values)
- temperatures = read_data(
- file_name="temperatures.csv",
- nrows=num_measurements,
- column="Air temperature (degC)",
+ # mean (as horizontal dashed line)
+ ax.axhline(
+ y=mean_value,
+ label=f"mean {label}: {mean_value:.1f}",
+ color=color,
+ linestyle="--",
)
- mean = compute_mean(temperatures)
+ ax.set_title(f"{label} at {location}")
+ ax.set_xlabel("date and time")
+ ax.set_ylabel(label)
+ ax.legend()
+ ax.grid(True)
+
+ # format x-axis for better date display
+ fig.autofmt_xdate()
+
+ fig.savefig(file_name)
- plot_data(
- data=temperatures,
- mean=mean,
- xlabel="measurements",
- ylabel="air temperature (deg C)",
- file_name=f"{num_measurements}.png",
+
+def main():
+ data = read_data("weather_data.csv")
+
+ for month in ["2024-01", "2024-02", "2024-03"]:
+ data_month = data.loc[month]
+ date_range = data_month.index
+
+ plot(
+ date_range,
+ data_month["air_temperature_celsius"].values,
+ "air temperature (C)",
+ "Helsinki airport",
+ "red",
+ compute_mean=True,
+ file_name=f"{month}-temperature.png",
+ )
+ plot(
+ date_range,
+ data_month["precipitation_mm"].values,
+ "precipitation (mm)",
+ "Helsinki airport",
+ "blue",
+ compute_mean=False,
+ file_name=f"{month}-precipitation.png",
)
diff --git a/content/code/towards-pure.py b/content/code/towards-pure.py
index c231f24..1cb0ddc 100644
--- a/content/code/towards-pure.py
+++ b/content/code/towards-pure.py
@@ -1,41 +1,84 @@
import pandas as pd
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
-def plot_data(data, mean, xlabel, ylabel, file_name):
- plt.plot(data, "r-")
- plt.xlabel(xlabel)
- plt.ylabel(ylabel)
- plt.axhline(y=mean, color="b", linestyle="--")
- plt.show()
- plt.savefig(file_name)
- plt.clf()
+def read_data(file_name):
+ data = pd.read_csv(file_name)
+ # combine 'date' and 'time' into a single datetime column
+ data["datetime"] = pd.to_datetime(data["date"] + " " + data["time"])
-def compute_mean(data):
- mean = sum(data) / len(data)
- return mean
+ # set datetime as index for convenience
+ data = data.set_index("datetime")
+ return data
-def read_data(file_name, nrows, column):
- data = pd.read_csv(file_name, nrows=nrows)
- return data[column]
+def arithmetic_mean(values):
+ mean_value = sum(values) / len(values)
+ return mean_value
-for num_measurements in [25, 100, 500]:
- temperatures = read_data(
- file_name="temperatures.csv",
- nrows=num_measurements,
- column="Air temperature (degC)",
+def plot(date_range, values, label, location, color, compute_mean, file_name):
+ fig, ax = plt.subplots()
+
+ # time series
+ ax.plot(
+ date_range,
+ values,
+ label=label,
+ color=color,
)
- mean = compute_mean(temperatures)
+ if compute_mean:
+ mean_value = arithmetic_mean(values)
- plot_data(
- data=temperatures,
- mean=mean,
- xlabel="measurements",
- ylabel="air temperature (deg C)",
- file_name=f"{num_measurements}.png",
- )
+ # mean (as horizontal dashed line)
+ ax.axhline(
+ y=mean_value,
+ label=f"mean {label}: {mean_value:.1f}",
+ color=color,
+ linestyle="--",
+ )
+
+ ax.set_title(f"{label} at {location}")
+ ax.set_xlabel("date and time")
+ ax.set_ylabel(label)
+ ax.legend()
+ ax.grid(True)
+
+ # format x-axis for better date display
+ fig.autofmt_xdate()
+
+ fig.savefig(file_name)
+
+
+def main():
+ data = read_data("weather_data.csv")
+
+ for month in ["2024-01", "2024-02", "2024-03"]:
+ data_month = data.loc[month]
+ date_range = data_month.index
+
+ plot(
+ date_range,
+ data_month["air_temperature_celsius"].values,
+ "air temperature (C)",
+ "Helsinki airport",
+ "red",
+ compute_mean=True,
+ file_name=f"{month}-temperature.png",
+ )
+ plot(
+ date_range,
+ data_month["precipitation_mm"].values,
+ "precipitation (mm)",
+ "Helsinki airport",
+ "blue",
+ compute_mean=False,
+ file_name=f"{month}-precipitation.png",
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/content/code/with-axis-labels.py b/content/code/with-axis-labels.py
deleted file mode 100644
index 333ed79..0000000
--- a/content/code/with-axis-labels.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import pandas as pd
-from matplotlib import pyplot as plt
-
-plt.xlabel("measurements")
-plt.ylabel("air temperature (deg C)")
-
-num_measurements = 25
-
-# read data from file
-data = pd.read_csv("temperatures.csv", nrows=num_measurements)
-temperatures = data["Air temperature (degC)"]
-
-# compute statistics
-mean = sum(temperatures) / num_measurements
-
-# plot results
-plt.plot(temperatures, "r-")
-plt.axhline(y=mean, color="b", linestyle="--")
-plt.show()
-plt.savefig("25.png")
-plt.clf()
diff --git a/content/conf.py b/content/conf.py
index 5fab3ab..68bbc5c 100644
--- a/content/conf.py
+++ b/content/conf.py
@@ -40,6 +40,11 @@
'sphinx_coderefinery_branding',
]
+# MyST extensions
+myst_enable_extensions = [
+ "colon_fence", # ::: can be used instead of ``` for better rendering
+ ]
+
nb_execution_mode = "cache"
# Add any paths that contain templates here, relative to this directory.
diff --git a/content/lesson.md b/content/lesson.md
index 98fc380..719971d 100644
--- a/content/lesson.md
+++ b/content/lesson.md
@@ -3,13 +3,14 @@
## Data
-The file [temperatures.csv](https://github.com/coderefinery/modular-type-along/blob/main/data/temperatures.csv) ([raw csv file](https://raw.githubusercontent.com/coderefinery/modular-type-along/main/data/temperatures.csv))
+The file [weather_data.csv](https://github.com/coderefinery/modular-type-along/blob/main/data/weather_data.csv)
+([raw csv file](https://raw.githubusercontent.com/coderefinery/modular-type-along/main/data/weather_data.csv))
contains hourly air temperature measurements for the observation station
-"Vantaa Helsinki-Vantaan lentoasema" (Helsinki airport) during 2022.
+"Vantaa Helsinki-Vantaan lentoasema" (Helsinki airport) during 2024.
```{admonition} Origin of the data
Data obtained from
- on 2023-09-27.
+ on 2025-09-18.
Data has been provided by the Finnish Meteorological Institute
under the Creative Commons Attribution 4.0 International license (CC BY 4.0):
@@ -19,9 +20,9 @@ under the Creative Commons Attribution 4.0 International license (CC BY 4.0):
## Our initial goal
-Our initial goal for this exercise is to plot a series of temperatures
-for **25 measurements** and to compute and plot the **arithmetic mean**. We
-imagine that we assemble a working script from various StackOverflow/ChatGPT
+Our initial goal for this exercise is to plot a series of temperatures and precipitations
+for **January** and to compute and plot the **arithmetic mean**. We
+imagine that we assemble a working script from various internet research/ AI chat
recommendations and arrive at:
```{literalinclude} code/initial-version.py
@@ -36,11 +37,11 @@ elegant Python.
## Further goals
-- Once we get this working for **25 measurements**, our task changes to also
- plot the **first 100** and the **first 500 measurements** in two additional
+- Once we get this working for **January**, our task changes to also
+ plot the **Febrary** and the **March** in two additional
plots.
- Then we wish to generalize the code so that a user can compute and plot this
- for **any number**, **without changing the code** (with a command line interface).
+ for **any month**, **without changing the code** (with a command line interface).
## How we plan to solve it
diff --git a/content/solution.md b/content/solution.md
index e05effc..9e10bd2 100644
--- a/content/solution.md
+++ b/content/solution.md
@@ -19,6 +19,7 @@ and learners can commend, suggest, and ask questions, and we are either all in
the same video room or everybody is watching via stream. In other words, for
this lesson, **learners are not in separate breakout-rooms**.
+
## Checklist
- Start with notebook
@@ -32,97 +33,147 @@ this lesson, **learners are not in separate breakout-rooms**.
- **Add command line interface**
- Split into multiple files/modules
+
## Our initial version
-We imagine that we assemble a working script from various StackOverflow
+We imagine that we assemble a working script/code
+from various internet research/ AI chat
recommendations and arrive at:
-`````{tabs}
-````{group-tab} Python
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/initial-version.py
+ :language: python
+ :::
+ ::::
-```{literalinclude} code/initial-version.py
-:language: python
-```
-
-````
-`````
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
- We test it out **in a notebook**.
-## We add axis labels
-It's not the best placement but it works and later it will bite us (only the
-first plot will have labels) and we will improve it:
+## We add a dashed line representing the mean temperature
-`````{tabs}
-````{group-tab} Python
+This is still only the January data.
-```{literalinclude} code/with-axis-labels.py
-:language: python
-:emphasize-lines: 4,5
-```
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/initial-version-with-mean.py
+ :language: python
+ :emphasize-lines: 27-36
+ :linenos:
+ :::
+ ::::
-````
-`````
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
-Once we get this working for 25 measurements, our task changes to also
-plot the first 100 and the first 500 measurements in two additional
-plots.
-## Plotting also 100 and 500 measurements
+## We add another plot for the precipitation
-- Next idea is perhaps code duplication.
-- Then a for-loop to iterate over `[25, 100, 500]`:
+As a first go, we achieve this by copy pasting the existing code and adjusting
+it for the precipitation column.
-`````{tabs}
-````{group-tab} Python
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/initial-version-with-precipitation.py
+ :language: python
+ :emphasize-lines: 49-68
+ :linenos:
+ :::
+ ::::
-```{literalinclude} code/add-iteration.py
-:language: python
-:emphasize-lines: 7,20
-```
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
-````
-`````
-## Abstracting the plotting part into a function
+## Plotting also February and March data
-`````{tabs}
-````{group-tab} Python
+- Copy-pasting very similar code 6 times would be too complicated to maintain.
+- We avoid this by iterating over the first 3 months.
+- Instead of reusing `data`, we introduce `data_month`.
-```{literalinclude} code/abstracting-plot.py
-:language: python
-:emphasize-lines: 8-13,26-30
-```
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/add-iteration.py
+ :language: python
+ :emphasize-lines: 15-16,22-23,28,54-55
+ :linenos:
+ :::
+ ::::
-````
-`````
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
+
+
+## Abstracting the plotting part into a function
-- Discuss what we expect before running it (some will expect this not to work
- because variables seem undefined).
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/abstracting-plot.py
+ :language: python
+ :emphasize-lines: 5,53-66
+ :linenos:
+ :::
+ ::::
+
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
+
+- Discuss the advantages of what we have done here.
+- Discuss what we expect before running it (we might expect this not to work
+ because `data_month` seems undefined inside the function).
- Then try it out (it actually works).
-- Discuss problems with this solution (what if we copy-paste the function to a different file?).
+- Discuss problems with this solution (what if we copy-paste the function to a
+ different file?).
The point of this step was that abstracting code into functions can be really
-good for reusability but just the fact that we created a function does not mean
-that the function is reusable since in this case it depends on a variable
-defined outside the function and hence there are side-effects.
+good for re-usability but just the fact that we created a function does not
+mean that the function is reusable since in this case it depends on a variable
+defined outside the function and hence there are **side-effects**.
+
## Small improvements
- Abstracting into more functions.
-- Notice how the comments got redundant:
-
-`````{tabs}
-````{group-tab} Python
-
-```{literalinclude} code/small-improvements.py
-:language: python
-:emphasize-lines: 27-35
-```
-
-````
-`````
+- Notice how some code comments got redundant:
+
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/small-improvements.py
+ :language: python
+ :emphasize-lines: 5-14,17-19,34,56
+ :linenos:
+ :::
+ ::::
+
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
Discuss what would happen if we copy-paste the functions to another project
(these functions are stateful/time-dependent).
@@ -131,31 +182,9 @@ Emphasize how stateful functions and order of execution in Jupyter notebooks
can produce unexpected results and explain why we motivate to rerun all cells
before sharing the notebook.
-## Towards functions without side-effects
-
-Improve to more stateless functions:
-
-`````{tabs}
-````{group-tab} Python
-
-```{literalinclude} code/towards-pure.py
-:language: python
-:emphasize-lines: 6,15,20
-```
-
-````
-`````
-
-These functions can now be copy-pasted to a different notebook or project and
-they will still work.
## Move from notebook to script
-Adding unit tests is often the moment when notebook is not the right fit
-anymore.
-
-But before we add tests:
-
- "File" -> "Save and Export Notebook As ..." -> "Executable Script"
- `git init` and commit the working version.
- Add `requirements.txt` and motivate how that can be useful to have later.
@@ -164,59 +193,105 @@ As we continue from here, **create commits after meaningful changes** and later
also share the repository with learners. This nicely connects to other lessons
of the workshop.
-## Unit tests
-
-Design code for testing.
-- Move the main scope code into a main function.
-- Discuss where to add a test and add a test to the statistics function:
+## Towards functions without side-effects
-`````{tabs}
-````{group-tab} Python
+In Python we can detect problems by encapsulating all code into functions and
+when using a code editor with a static checker (instructor can demonstrate
+this by first introducing a main function, then detecting problems, then
+fixing the problems).
+
+We then improve towards:
+
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/towards-pure.py
+ :language: python
+ :emphasize-lines: 22,27-28,34,53,56,61,83-84
+ :linenos:
+ :::
+ ::::
+
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
-```{literalinclude} code/testing.py
-:language: python
-:emphasize-lines: 3,11,21-23
-```
+These functions can now be copy-pasted to a different notebook or project and
+they will still work.
-````
-`````
-## Command-line interface
+## Unit tests
-- Add a CLI for the input data file, the number of measurements, and the output
- file name.
+- Discuss what one could mean with "design code for testing".
+- Discuss when to test and when not to test.
+- Discuss where to add a test and add a test to the `arithmetic_mean` function:
+
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/testing.py
+ :language: python
+ :emphasize-lines: 3,23-25
+ :linenos:
+ :::
+ ::::
+
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
+
+
+## Command-line interface (CLI)
+
+- Add a CLI for the input data file, the month, and the output folder.
+- Instructor demonstrates it, for instance:
+ ```console
+ $ python example.py --month 2024-05 --data-file weather_data.csv --output-directory /home/user/example/results
+ ```
- Example here is using [click](https://click.palletsprojects.com/) but it can
equally well be [optparse](https://docs.python.org/3/library/optparse.html),
[argparse](https://docs.python.org/3/library/argparse.html),
[docopt](http://docopt.org/), or [Typer](https://typer.tiangolo.com/).
- Discuss the motivations for adding a CLI:
- - We are able to modify the behavior without changing the code
+ - We are able to modify the behavior without changing (or needing to
+ understand) the code
- We can run many of such scripts as part of a workflow
-`````{tabs}
-````{group-tab} Python
+:::::{tabs}
+ ::::{group-tab} Python
+ :::{literalinclude} code/cli.py
+ :language: python
+ :emphasize-lines: 1,7,66-84
+ :linenos:
+ :::
+ ::::
-```{literalinclude} code/cli.py
-:language: python
-:emphasize-lines: 4,31-37
-```
+ ::::{group-tab} R
+ Work in progress. You can
+ [help us](https://github.com/coderefinery/modular-type-along/issues/40)
+ by contributing or improving an R solution.
+ ::::
+:::::
-````
-`````
## Split long script into modules
- Discuss how you would move some functions out and organize them into separate
- modules which can be imported to other projects: For instance
- `compute_mean` can be moved to `statistics.py`.
+ modules which can be imported to other projects.
- Discuss naming.
- Discuss interface design.
+
## Summarize in the collaborative document
-- Now return to initial questions on the collaborative document and discuss questions and comments. If
- there is time left, there are additional questions and exercises.
+- Now return to initial questions on the collaborative document and discuss
+ questions and comments. If there is time left, there are additional
+ questions and exercises.
- It is easier and more fun to teach this as a pair with somebody else where
one person can type and the other person helps watching the questions and
commends and relays them to the co-instructor.
diff --git a/data/README.md b/data/README.md
index 32111e2..1d21766 100644
--- a/data/README.md
+++ b/data/README.md
@@ -1,6 +1,9 @@
Data obtained from
- on 2023-09-27.
+ on 2025-09-18.
Data has been provided by the Finnish Meteorological Institute
under the Creative Commons Attribution 4.0 International license (CC BY 4.0):
+
+The data format has been adjusted to make the lesson (which is focused on code
+and less on data cleaning) easier.
diff --git a/data/temperatures.csv b/data/temperatures.csv
deleted file mode 100644
index 9ab9b7f..0000000
--- a/data/temperatures.csv
+++ /dev/null
@@ -1,8762 +0,0 @@
-Year,m,d,Time,Time zone,Air temperature (degC)
-2022,1,1,00:00,UTC,-2.1
-2022,1,1,01:00,UTC,-1.9
-2022,1,1,02:00,UTC,-1.3
-2022,1,1,03:00,UTC,-2.4
-2022,1,1,04:00,UTC,-3.7
-2022,1,1,05:00,UTC,-4.1
-2022,1,1,06:00,UTC,-3.8
-2022,1,1,07:00,UTC,-4
-2022,1,1,08:00,UTC,-5.4
-2022,1,1,09:00,UTC,-5.4
-2022,1,1,10:00,UTC,-5.4
-2022,1,1,11:00,UTC,-5.7
-2022,1,1,12:00,UTC,-5.9
-2022,1,1,13:00,UTC,-6.6
-2022,1,1,14:00,UTC,-6.9
-2022,1,1,15:00,UTC,-7.4
-2022,1,1,16:00,UTC,-8.1
-2022,1,1,17:00,UTC,-8.3
-2022,1,1,18:00,UTC,-9.8
-2022,1,1,19:00,UTC,-10
-2022,1,1,20:00,UTC,-9.8
-2022,1,1,21:00,UTC,-11
-2022,1,1,22:00,UTC,-11.2
-2022,1,1,23:00,UTC,-10.9
-2022,1,2,00:00,UTC,-11.2
-2022,1,2,01:00,UTC,-12.5
-2022,1,2,02:00,UTC,-11.7
-2022,1,2,03:00,UTC,-11.1
-2022,1,2,04:00,UTC,-11.5
-2022,1,2,05:00,UTC,-10.9
-2022,1,2,06:00,UTC,-9.8
-2022,1,2,07:00,UTC,-8.8
-2022,1,2,08:00,UTC,-7.5
-2022,1,2,09:00,UTC,-5.5
-2022,1,2,10:00,UTC,-3.6
-2022,1,2,11:00,UTC,-3.6
-2022,1,2,12:00,UTC,-3.6
-2022,1,2,13:00,UTC,-3.5
-2022,1,2,14:00,UTC,-3.4
-2022,1,2,15:00,UTC,-3.6
-2022,1,2,16:00,UTC,-3.6
-2022,1,2,17:00,UTC,-3.6
-2022,1,2,18:00,UTC,-3.5
-2022,1,2,19:00,UTC,-3.4
-2022,1,2,20:00,UTC,-3.4
-2022,1,2,21:00,UTC,-3.3
-2022,1,2,22:00,UTC,-3.3
-2022,1,2,23:00,UTC,-3.5
-2022,1,3,00:00,UTC,-3.6
-2022,1,3,01:00,UTC,-3.4
-2022,1,3,02:00,UTC,-3.4
-2022,1,3,03:00,UTC,-3.3
-2022,1,3,04:00,UTC,-3.4
-2022,1,3,05:00,UTC,-3.3
-2022,1,3,06:00,UTC,-3.2
-2022,1,3,07:00,UTC,-3.1
-2022,1,3,08:00,UTC,-3
-2022,1,3,09:00,UTC,-2.8
-2022,1,3,10:00,UTC,-2.6
-2022,1,3,11:00,UTC,-2.6
-2022,1,3,12:00,UTC,-2.6
-2022,1,3,13:00,UTC,-2.7
-2022,1,3,14:00,UTC,-2.7
-2022,1,3,15:00,UTC,-2.6
-2022,1,3,16:00,UTC,-2.5
-2022,1,3,17:00,UTC,-2.4
-2022,1,3,18:00,UTC,-2.3
-2022,1,3,19:00,UTC,-2.3
-2022,1,3,20:00,UTC,-2.3
-2022,1,3,21:00,UTC,-2.2
-2022,1,3,22:00,UTC,-2.3
-2022,1,3,23:00,UTC,-2.1
-2022,1,4,00:00,UTC,-2.2
-2022,1,4,01:00,UTC,-2.2
-2022,1,4,02:00,UTC,-2
-2022,1,4,03:00,UTC,-1.9
-2022,1,4,04:00,UTC,-1.7
-2022,1,4,05:00,UTC,-1.5
-2022,1,4,06:00,UTC,-1.1
-2022,1,4,07:00,UTC,-0.5
-2022,1,4,08:00,UTC,-0.1
-2022,1,4,09:00,UTC,0.3
-2022,1,4,10:00,UTC,0.8
-2022,1,4,11:00,UTC,1.9
-2022,1,4,12:00,UTC,1.8
-2022,1,4,13:00,UTC,1.6
-2022,1,4,14:00,UTC,1.5
-2022,1,4,15:00,UTC,0.1
-2022,1,4,16:00,UTC,-1.4
-2022,1,4,17:00,UTC,-1.7
-2022,1,4,18:00,UTC,-1.4
-2022,1,4,19:00,UTC,-1.3
-2022,1,4,20:00,UTC,0
-2022,1,4,21:00,UTC,-0.8
-2022,1,4,22:00,UTC,-1.6
-2022,1,4,23:00,UTC,-1.6
-2022,1,5,00:00,UTC,-0.4
-2022,1,5,01:00,UTC,-0.4
-2022,1,5,02:00,UTC,-0.3
-2022,1,5,03:00,UTC,-0.2
-2022,1,5,04:00,UTC,-0.3
-2022,1,5,05:00,UTC,-0.2
-2022,1,5,06:00,UTC,-0.2
-2022,1,5,07:00,UTC,-0.2
-2022,1,5,08:00,UTC,-0.5
-2022,1,5,09:00,UTC,-0.5
-2022,1,5,10:00,UTC,-1.3
-2022,1,5,11:00,UTC,-1.1
-2022,1,5,12:00,UTC,-1.5
-2022,1,5,13:00,UTC,-1.7
-2022,1,5,14:00,UTC,-1.6
-2022,1,5,15:00,UTC,-1.5
-2022,1,5,16:00,UTC,-1.3
-2022,1,5,17:00,UTC,-1.3
-2022,1,5,18:00,UTC,-1.1
-2022,1,5,19:00,UTC,-1.7
-2022,1,5,20:00,UTC,-1.7
-2022,1,5,21:00,UTC,-1.9
-2022,1,5,22:00,UTC,-2
-2022,1,5,23:00,UTC,-2
-2022,1,6,00:00,UTC,-2.1
-2022,1,6,01:00,UTC,-2.5
-2022,1,6,02:00,UTC,-2.4
-2022,1,6,03:00,UTC,-1.9
-2022,1,6,04:00,UTC,-4.3
-2022,1,6,05:00,UTC,-6.1
-2022,1,6,06:00,UTC,-7.2
-2022,1,6,07:00,UTC,-7.4
-2022,1,6,08:00,UTC,-7.7
-2022,1,6,09:00,UTC,-7.7
-2022,1,6,10:00,UTC,-8
-2022,1,6,11:00,UTC,-8.3
-2022,1,6,12:00,UTC,-8.4
-2022,1,6,13:00,UTC,-8.9
-2022,1,6,14:00,UTC,-9.8
-2022,1,6,15:00,UTC,-9.2
-2022,1,6,16:00,UTC,-9.3
-2022,1,6,17:00,UTC,-9.4
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-2022,12,15,18:00,UTC,-15.2
-2022,12,15,19:00,UTC,-14.6
-2022,12,15,20:00,UTC,-13.3
-2022,12,15,21:00,UTC,-13.3
-2022,12,15,22:00,UTC,-12.6
-2022,12,15,23:00,UTC,-13.2
-2022,12,16,00:00,UTC,-14.7
-2022,12,16,01:00,UTC,-16.9
-2022,12,16,02:00,UTC,-17.6
-2022,12,16,03:00,UTC,-18
-2022,12,16,04:00,UTC,-17.7
-2022,12,16,05:00,UTC,-15.8
-2022,12,16,06:00,UTC,-15.1
-2022,12,16,07:00,UTC,-14.8
-2022,12,16,08:00,UTC,-13.1
-2022,12,16,09:00,UTC,-12
-2022,12,16,10:00,UTC,-9.6
-2022,12,16,11:00,UTC,-8.6
-2022,12,16,12:00,UTC,-8.8
-2022,12,16,13:00,UTC,-11
-2022,12,16,14:00,UTC,-7.7
-2022,12,16,15:00,UTC,-12.1
-2022,12,16,16:00,UTC,-11.4
-2022,12,16,17:00,UTC,-9.4
-2022,12,16,18:00,UTC,-8.2
-2022,12,16,19:00,UTC,-9.6
-2022,12,16,20:00,UTC,-8.8
-2022,12,16,21:00,UTC,-6.8
-2022,12,16,22:00,UTC,-5.4
-2022,12,16,23:00,UTC,-4
-2022,12,17,00:00,UTC,-5.8
-2022,12,17,01:00,UTC,-5.9
-2022,12,17,02:00,UTC,-6.3
-2022,12,17,03:00,UTC,-6.8
-2022,12,17,04:00,UTC,-6.4
-2022,12,17,05:00,UTC,-6.6
-2022,12,17,06:00,UTC,-9.8
-2022,12,17,07:00,UTC,-10.7
-2022,12,17,08:00,UTC,-9.7
-2022,12,17,09:00,UTC,-11.1
-2022,12,17,10:00,UTC,-9
-2022,12,17,11:00,UTC,-7.1
-2022,12,17,12:00,UTC,-6.1
-2022,12,17,13:00,UTC,-4.1
-2022,12,17,14:00,UTC,-3.4
-2022,12,17,15:00,UTC,-3.7
-2022,12,17,16:00,UTC,-2.6
-2022,12,17,17:00,UTC,-1.9
-2022,12,17,18:00,UTC,-1.9
-2022,12,17,19:00,UTC,-2.5
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-2022,12,17,21:00,UTC,-1.6
-2022,12,17,22:00,UTC,-1.5
-2022,12,17,23:00,UTC,-1.3
-2022,12,18,00:00,UTC,-1
-2022,12,18,01:00,UTC,-0.9
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-2022,12,18,03:00,UTC,-0.8
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-2022,12,18,06:00,UTC,0.6
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-2022,12,18,12:00,UTC,1.5
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-2022,12,18,18:00,UTC,1.4
-2022,12,18,19:00,UTC,1.4
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-2022,12,18,21:00,UTC,1.7
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-2022,12,21,20:00,UTC,1.8
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-2022,12,22,01:00,UTC,2.5
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-2022,12,22,04:00,UTC,1.9
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-2022,12,22,07:00,UTC,2.7
-2022,12,22,08:00,UTC,2.8
-2022,12,22,09:00,UTC,2.6
-2022,12,22,10:00,UTC,2.8
-2022,12,22,11:00,UTC,2.7
-2022,12,22,12:00,UTC,2.4
-2022,12,22,13:00,UTC,2.1
-2022,12,22,14:00,UTC,2.1
-2022,12,22,15:00,UTC,2.2
-2022,12,22,16:00,UTC,2.4
-2022,12,22,17:00,UTC,2.4
-2022,12,22,18:00,UTC,2.5
-2022,12,22,19:00,UTC,2.7
-2022,12,22,20:00,UTC,2.5
-2022,12,22,21:00,UTC,2.6
-2022,12,22,22:00,UTC,2.8
-2022,12,22,23:00,UTC,2.5
-2022,12,23,00:00,UTC,2.5
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-2022,12,23,02:00,UTC,2.3
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-2022,12,23,04:00,UTC,2.4
-2022,12,23,05:00,UTC,2.2
-2022,12,23,06:00,UTC,2.1
-2022,12,23,07:00,UTC,2
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-2022,12,23,22:00,UTC,-2.9
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-2022,12,24,01:00,UTC,-4
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-2022,12,24,03:00,UTC,-4.2
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-2022,12,24,12:00,UTC,-2.6
-2022,12,24,13:00,UTC,-3.1
-2022,12,24,14:00,UTC,-2.9
-2022,12,24,15:00,UTC,-2.6
-2022,12,24,16:00,UTC,-2.5
-2022,12,24,17:00,UTC,-3.7
-2022,12,24,18:00,UTC,-4.6
-2022,12,24,19:00,UTC,-5.3
-2022,12,24,20:00,UTC,-5.1
-2022,12,24,21:00,UTC,-6.2
-2022,12,24,22:00,UTC,-5.7
-2022,12,24,23:00,UTC,-6.7
-2022,12,25,00:00,UTC,-7.3
-2022,12,25,01:00,UTC,-6.6
-2022,12,25,02:00,UTC,-6.8
-2022,12,25,03:00,UTC,-9.8
-2022,12,25,04:00,UTC,-10.2
-2022,12,25,05:00,UTC,-10.5
-2022,12,25,06:00,UTC,-11.4
-2022,12,25,07:00,UTC,-11.4
-2022,12,25,08:00,UTC,-11.5
-2022,12,25,09:00,UTC,-9.9
-2022,12,25,10:00,UTC,-9
-2022,12,25,11:00,UTC,-8
-2022,12,25,12:00,UTC,-8.2
-2022,12,25,13:00,UTC,-8.9
-2022,12,25,14:00,UTC,-9.8
-2022,12,25,15:00,UTC,-10.7
-2022,12,25,16:00,UTC,-11.2
-2022,12,25,17:00,UTC,-10.9
-2022,12,25,18:00,UTC,-9.1
-2022,12,25,19:00,UTC,-10.5
-2022,12,25,20:00,UTC,-9.5
-2022,12,25,21:00,UTC,-9.9
-2022,12,25,22:00,UTC,-8.6
-2022,12,25,23:00,UTC,-6.6
-2022,12,26,00:00,UTC,-5.8
-2022,12,26,01:00,UTC,-3.4
-2022,12,26,02:00,UTC,-3.4
-2022,12,26,03:00,UTC,-3.3
-2022,12,26,04:00,UTC,-3.5
-2022,12,26,05:00,UTC,-3.3
-2022,12,26,06:00,UTC,-4.1
-2022,12,26,07:00,UTC,-4.4
-2022,12,26,08:00,UTC,-4.9
-2022,12,26,09:00,UTC,-5.3
-2022,12,26,10:00,UTC,-6.1
-2022,12,26,11:00,UTC,-5.6
-2022,12,26,12:00,UTC,-5.3
-2022,12,26,13:00,UTC,-5.2
-2022,12,26,14:00,UTC,-5.3
-2022,12,26,15:00,UTC,-5.3
-2022,12,26,16:00,UTC,-5.4
-2022,12,26,17:00,UTC,-5.1
-2022,12,26,18:00,UTC,-4.7
-2022,12,26,19:00,UTC,-4.7
-2022,12,26,20:00,UTC,-4.3
-2022,12,26,21:00,UTC,-4.3
-2022,12,26,22:00,UTC,-4.6
-2022,12,26,23:00,UTC,-5.3
-2022,12,27,00:00,UTC,-6
-2022,12,27,01:00,UTC,-5.6
-2022,12,27,02:00,UTC,-4.9
-2022,12,27,03:00,UTC,-4.9
-2022,12,27,04:00,UTC,-4.7
-2022,12,27,05:00,UTC,-4.6
-2022,12,27,06:00,UTC,-4.2
-2022,12,27,07:00,UTC,-4
-2022,12,27,08:00,UTC,-3.5
-2022,12,27,09:00,UTC,-3.1
-2022,12,27,10:00,UTC,-2.3
-2022,12,27,11:00,UTC,-1.7
-2022,12,27,12:00,UTC,-0.3
-2022,12,27,13:00,UTC,1.3
-2022,12,27,14:00,UTC,1.4
-2022,12,27,15:00,UTC,1.6
-2022,12,27,16:00,UTC,1.6
-2022,12,27,17:00,UTC,1.8
-2022,12,27,18:00,UTC,1.4
-2022,12,27,19:00,UTC,1.2
-2022,12,27,20:00,UTC,1
-2022,12,27,21:00,UTC,0.9
-2022,12,27,22:00,UTC,0.7
-2022,12,27,23:00,UTC,0
-2022,12,28,00:00,UTC,0
-2022,12,28,01:00,UTC,-0.2
-2022,12,28,02:00,UTC,-0.3
-2022,12,28,03:00,UTC,-1.6
-2022,12,28,04:00,UTC,-0.8
-2022,12,28,05:00,UTC,-0.8
-2022,12,28,06:00,UTC,-0.6
-2022,12,28,07:00,UTC,-0.7
-2022,12,28,08:00,UTC,-1.2
-2022,12,28,09:00,UTC,-1.1
-2022,12,28,10:00,UTC,-1.1
-2022,12,28,11:00,UTC,-1.4
-2022,12,28,12:00,UTC,-1.4
-2022,12,28,13:00,UTC,-1.4
-2022,12,28,14:00,UTC,-1.4
-2022,12,28,15:00,UTC,-1.2
-2022,12,28,16:00,UTC,-1.5
-2022,12,28,17:00,UTC,-2
-2022,12,28,18:00,UTC,-2.2
-2022,12,28,19:00,UTC,-2.7
-2022,12,28,20:00,UTC,-3.1
-2022,12,28,21:00,UTC,-2.9
-2022,12,28,22:00,UTC,-3.2
-2022,12,28,23:00,UTC,-3.5
-2022,12,29,00:00,UTC,-2.8
-2022,12,29,01:00,UTC,-2.5
-2022,12,29,02:00,UTC,-2.1
-2022,12,29,03:00,UTC,-1.9
-2022,12,29,04:00,UTC,-1.3
-2022,12,29,05:00,UTC,-0.3
-2022,12,29,06:00,UTC,0.2
-2022,12,29,07:00,UTC,0.4
-2022,12,29,08:00,UTC,0.5
-2022,12,29,09:00,UTC,0.4
-2022,12,29,10:00,UTC,0.3
-2022,12,29,11:00,UTC,0.3
-2022,12,29,12:00,UTC,0.6
-2022,12,29,13:00,UTC,0.4
-2022,12,29,14:00,UTC,0.3
-2022,12,29,15:00,UTC,0.5
-2022,12,29,16:00,UTC,0.7
-2022,12,29,17:00,UTC,0.8
-2022,12,29,18:00,UTC,0.6
-2022,12,29,19:00,UTC,0.3
-2022,12,29,20:00,UTC,0.5
-2022,12,29,21:00,UTC,0.3
-2022,12,29,22:00,UTC,0.4
-2022,12,29,23:00,UTC,0.3
-2022,12,30,00:00,UTC,0.4
-2022,12,30,01:00,UTC,0.6
-2022,12,30,02:00,UTC,1.2
-2022,12,30,03:00,UTC,1.9
-2022,12,30,04:00,UTC,2.5
-2022,12,30,05:00,UTC,2.2
-2022,12,30,06:00,UTC,2.9
-2022,12,30,07:00,UTC,2.8
-2022,12,30,08:00,UTC,2.8
-2022,12,30,09:00,UTC,3
-2022,12,30,10:00,UTC,2.7
-2022,12,30,11:00,UTC,2.9
-2022,12,30,12:00,UTC,2.7
-2022,12,30,13:00,UTC,3.5
-2022,12,30,14:00,UTC,3.1
-2022,12,30,15:00,UTC,2.6
-2022,12,30,16:00,UTC,2.5
-2022,12,30,17:00,UTC,2.8
-2022,12,30,18:00,UTC,2.6
-2022,12,30,19:00,UTC,2.9
-2022,12,30,20:00,UTC,2.7
-2022,12,30,21:00,UTC,2.2
-2022,12,30,22:00,UTC,2.5
-2022,12,30,23:00,UTC,2.6
-2022,12,31,00:00,UTC,2.7
-2022,12,31,01:00,UTC,3.3
-2022,12,31,02:00,UTC,2.9
-2022,12,31,03:00,UTC,3.1
-2022,12,31,04:00,UTC,3.1
-2022,12,31,05:00,UTC,2.2
-2022,12,31,06:00,UTC,2.4
-2022,12,31,07:00,UTC,2.3
-2022,12,31,08:00,UTC,2.7
-2022,12,31,09:00,UTC,2.4
-2022,12,31,10:00,UTC,2.6
-2022,12,31,11:00,UTC,2.9
-2022,12,31,12:00,UTC,3.2
-2022,12,31,13:00,UTC,3.6
-2022,12,31,14:00,UTC,3.6
-2022,12,31,15:00,UTC,4.3
-2022,12,31,16:00,UTC,3.8
-2022,12,31,17:00,UTC,4.4
-2022,12,31,18:00,UTC,4.4
-2022,12,31,19:00,UTC,5
-2022,12,31,20:00,UTC,4.6
-2022,12,31,21:00,UTC,4.6
-2022,12,31,22:00,UTC,4.5
-2022,12,31,23:00,UTC,4.2
-2023,1,1,00:00,UTC,3.6
\ No newline at end of file
diff --git a/data/weather_data.csv b/data/weather_data.csv
new file mode 100644
index 0000000..534522e
--- /dev/null
+++ b/data/weather_data.csv
@@ -0,0 +1,8716 @@
+date,time,air_temperature_celsius,precipitation_mm
+2024-01-01,00:00,-16.1,0.0
+2024-01-01,01:00,-16.1,0.0
+2024-01-01,02:00,-16.0,0.0
+2024-01-01,03:00,-15.9,0.0
+2024-01-01,04:00,-16.1,0.0
+2024-01-01,05:00,-16.6,0.0
+2024-01-01,06:00,-17.0,0.0
+2024-01-01,07:00,-16.6,0.0
+2024-01-01,08:00,-16.8,0.0
+2024-01-01,09:00,-16.9,0.0
+2024-01-01,10:00,-16.9,0.0
+2024-01-01,11:00,-17.0,0.0
+2024-01-01,12:00,-17.0,0.0
+2024-01-01,13:00,-17.3,0.0
+2024-01-01,14:00,-18.0,0.0
+2024-01-01,15:00,-18.6,0.0
+2024-01-01,16:00,-18.9,0.0
+2024-01-01,17:00,-19.1,0.0
+2024-01-01,18:00,-19.1,0.0
+2024-01-01,19:00,-18.9,0.0
+2024-01-01,20:00,-18.4,0.0
+2024-01-01,21:00,-18.3,0.0
+2024-01-01,22:00,-18.3,0.0
+2024-01-01,23:00,-18.3,0.0
+2024-01-02,00:00,-18.1,0.0
+2024-01-02,01:00,-17.8,0.0
+2024-01-02,02:00,-17.7,0.0
+2024-01-02,03:00,-17.5,0.0
+2024-01-02,04:00,-17.5,0.0
+2024-01-02,05:00,-17.9,0.0
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