Major rework using rocketpy

spatz/simulations/csv_source.py

@@ -0,0 +1,91 @@
+import pandas as pd
+import numpy as np
+
+from typing import Literal, List
+from numpy.typing import NDArray
+
+from spatz.simulations.data_source import DataSource
+
+
+
+class CSVSource(DataSource):
+    def __init__(self, path: str, time_col: str, interpolation: Literal['linear']='linear') -> None:
+        """A data source that extracts all its data from a csv file.
+
+        Args:
+            time_col (str): The name of the column that contains time data.
+        """
+        super().__init__()
+
+        self._df = pd.read_csv(path)
+        self._time_col = time_col
+        self._idx = 0
+        self._interpolation = interpolation
+
+    def get_length(self) -> float:
+        return max(self._df[self._time_col])
+
+    def _on_reset(self):
+        pass
+
+    def _get_closest_idx(self, t: float) -> int:
+        """Gets an index _idx_ for the dataframe _df_ such that the values at the given time _t_ are somewhere between 
+        _idx_ and _idx+1_. 
+
+        Args:
+            t (float): The requested time.
+
+        Returns:
+            int: The computed index.
+        """
+        idx = (self._df[self._time_col] - t).abs().idxmin()
+        idx = idx if self._df[self._time_col].loc[idx] <= t else idx - 1
+
+        return idx
+
+    def _on_step(self, _: float):
+        self._idx = self._get_closest_idx(self.get_time())
+
+    def fetch_value(self, name: str, t: float | None = None, custom_interpolation=None) -> float:
+        """Get a specific value from the dataframe.
+
+        Args:
+            name (str): The name of the value to fetch.
+            t (float): Allows specification of a different time instead of the current time. None for current time.
+
+        Returns:
+            float: Returns the requested value.
+        """
+        idx = self._idx if t is None else self._get_closest_idx(t)
+
+        if self._interpolation == 'linear':
+            t_min = self._df.at[idx, self._time_col]
+            t_max = self._df.at[idx + 1, self._time_col]
+
+            # Sometimes no time passes in-between two samples.
+            if t_max == t_min:
+                return self._df.at[name, idx]
+            
+            # Compute the weight for interpolation.
+            alpha = (self.get_time() - t_min) / (t_max - t_min)
+
+            if custom_interpolation is not None:
+                a = self._df.at[idx, name]
+                b = self._df.at[idx + 1, name]
+
+                return custom_interpolation(a, b, alpha)
+
+            # Interpolate linearly between the two data points.
+            return (1 - alpha) * self._df.at[idx, name] + alpha * self._df.at[idx + 1, name]
+
+    def fetch_values(self, names: List[str], t: float | None = None, custom_interpolation=None) -> NDArray:
+        """Get specific values from the dataframe.
+
+        Args:
+            names (List[str]): Names of the values to get.
+            t (float): Allows specification of a different time instead of the current time. None for current time.
+
+        Returns:
+            np.array: Returns a numpy array containing the requested values in the same order as in the input list.
+        """
+        return np.asarray([self.fetch_value(name, t, custom_interpolation) for name in names])