First implementation of data chart.

src/corchestrate/chart.py

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+from datetime import datetime
+from plotly.subplots import make_subplots
+
+from corchestrate.chart_data import ChartData
+from corchestrate.indexed_input import IndexedInput
+from corchestrate.measurement import Measurement
+
+
+def plot_over_time(input_data: list[ChartData]):
+    indexed_inputs = [
+        IndexedInput(data.name, data.measurements) for data in input_data
+    ]
+
+    datetime_indices = []
+
+    fig = make_subplots(5, 1)
+
+    while True:
+        min_datetime: datetime | None = None
+        # Find min_datetime
+        for indexed_input in indexed_inputs:
+            current_measurement = indexed_input.current()
+
+            if current_measurement == None:
+                continue
+
+            if min_datetime == None:
+                min_datetime = current_measurement.measured_at
+                continue
+
+            if current_measurement.measured_at < min_datetime:
+                min_datetime = current_measurement.measured_at
+
+        # If no valid measurement is available, break the loop
+        if min_datetime == None:
+            break
+
+        datetime_indices.append(min_datetime)
+
+        # Extract data at minimum datetime
+        for indexed_input in indexed_inputs:
+            current_measurement = indexed_input.current()
+            if current_measurement is None:
+                continue
+
+            if current_measurement.measured_at == min_datetime:
+                indexed_input.extracted_heart_rate.append(
+                    current_measurement.heart_rate)
+                indexed_input.extracted_speed.append(current_measurement.speed)
+                indexed_input.extracted_cadence.append(
+                    current_measurement.cadence)
+                indexed_input.extracted_power.append(current_measurement.power)
+                indexed_input.extracted_temperature.append(
+                    current_measurement.temperature)
+                # Advance to next index
+                indexed_input.next()
+
+    # Plot
+    for indexed_input in indexed_inputs:
+        fig.add_scatter(x=datetime_indices,
+                        y=indexed_input.extracted_heart_rate,
+                        row=1,
+                        col=1,
+                        name=f"Heart rate - {indexed_input.name}")
+        fig.add_scatter(x=datetime_indices,
+                        y=indexed_input.extracted_speed,
+                        row=2,
+                        col=1,
+                        name=f"Speed - {indexed_input.name}")
+        fig.add_scatter(x=datetime_indices,
+                        y=indexed_input.extracted_cadence,
+                        row=3,
+                        col=1,
+                        name=f"Cadence - {indexed_input.name}")
+        fig.add_scatter(x=datetime_indices,
+                        y=indexed_input.extracted_power,
+                        row=4,
+                        col=1,
+                        name=f"Power - {indexed_input.name}")
+        fig.add_scatter(x=datetime_indices,
+                        y=indexed_input.extracted_temperature,
+                        row=5,
+                        col=1,
+                        name=f"Temperature - {indexed_input.name}")
+
+    fig.update_layout(height=3000)
+    fig.show()

src/corchestrate/chart_data.py

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+from dataclasses import dataclass
+
+from corchestrate.measurement import Measurement
+
+
+@dataclass
+class ChartData:
+    name: str
+    measurements: list[Measurement]

src/corchestrate/indexed_input.py

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+from numpy import double
+from corchestrate.measurement import Measurement
+
+
+class IndexedInput:
+
+    def __init__(self, name: str, measurements: list[Measurement]) -> None:
+        self.index: int = 0
+        self.name = name
+        self.measurements: list[Measurement] = measurements
+
+        self.extracted_heart_rate: list[float | None] = []
+        self.extracted_speed: list[float | None] = []
+        self.extracted_cadence: list[float | None] = []
+        self.extracted_power: list[float | None] = []
+        self.extracted_temperature: list[float | None] = []
+
+    def is_out_of_range(self) -> bool:
+        return len(self.measurements) <= self.index
+
+    def current(self):
+        if self.is_out_of_range():
+            return None
+
+        return self.measurements[self.index]
+
+    def next(self):
+        self.index += 1
+        self.current()