MNT: move piecewise functions to separate file

closes #667

rocketpy/mathutils/__init__.py

@@ -1,7 +1,7 @@
 from .function import (
     Function,
-    PiecewiseFunction,
     funcify_method,
     reset_funcified_methods,
 )
 from .vector_matrix import Matrix, Vector
+from .piecewise_function import PiecewiseFunction

rocketpy/mathutils/function.py

@@ -3419,109 +3419,6 @@ def __validate_extrapolation(self, extrapolation):
         return extrapolation
 
 
-class PiecewiseFunction(Function):
-    """Class for creating piecewise functions. These kind of functions are
-    defined by a dictionary of functions, where the keys are tuples that
-    represent the domain of the function. The domains must be disjoint.
-    """
-
-    def __new__(
-        cls,
-        source,
-        inputs=None,
-        outputs=None,
-        interpolation="spline",
-        extrapolation=None,
-        datapoints=100,
-    ):
-        """
-        Creates a piecewise function from a dictionary of functions. The keys of
-        the dictionary must be tuples that represent the domain of the function.
-        The domains must be disjoint. The piecewise function will be evaluated
-        at datapoints points to create Function object.
-
-        Parameters
-        ----------
-        source: dictionary
-            A dictionary of Function objects, where the keys are the domains.
-        inputs : list of strings
-            A list of strings that represent the inputs of the function.
-        outputs: list of strings
-            A list of strings that represent the outputs of the function.
-        interpolation: str
-            The type of interpolation to use. The default value is 'spline'.
-        extrapolation: str
-            The type of extrapolation to use. The default value is None.
-        datapoints: int
-            The number of points in which the piecewise function will be
-            evaluated to create a base function. The default value is 100.
-        """
-        if inputs is None:
-            inputs = ["Scalar"]
-        if outputs is None:
-            outputs = ["Scalar"]
-        # Check if source is a dictionary
-        if not isinstance(source, dict):
-            raise TypeError("source must be a dictionary")
-        # Check if all keys are tuples
-        for key in source.keys():
-            if not isinstance(key, tuple):
-                raise TypeError("keys of source must be tuples")
-        # Check if all domains are disjoint
-        for key1 in source.keys():
-            for key2 in source.keys():
-                if key1 != key2:
-                    if key1[0] < key2[1] and key1[1] > key2[0]:
-                        raise ValueError("domains must be disjoint")
-
-        # Crate Function
-        def calc_output(func, inputs):
-            """Receives a list of inputs value and a function, populates another
-            list with the results corresponding to the same results.
-
-            Parameters
-            ----------
-            func : Function
-                The Function object to be
-            inputs : list, tuple, np.array
-                The array of points to applied the func to.
-
-            Examples
-            --------
-            >>> inputs = [0, 1, 2, 3, 4, 5]
-            >>> def func(x):
-            ...     return x*10
-            >>> calc_output(func, inputs)
-            [0, 10, 20, 30, 40, 50]
-
-            Notes
-            -----
-            In the future, consider using the built-in map function from python.
-            """
-            output = np.zeros(len(inputs))
-            for j, value in enumerate(inputs):
-                output[j] = func.get_value_opt(value)
-            return output
-
-        input_data = []
-        output_data = []
-        for key in sorted(source.keys()):
-            i = np.linspace(key[0], key[1], datapoints)
-            i = i[~np.isin(i, input_data)]
-            input_data = np.concatenate((input_data, i))
-
-            f = Function(source[key])
-            output_data = np.concatenate((output_data, calc_output(f, i)))
-
-        return Function(
-            np.concatenate(([input_data], [output_data])).T,
-            inputs=inputs,
-            outputs=outputs,
-            interpolation=interpolation,
-            extrapolation=extrapolation,
-        )
-
-
 def funcify_method(*args, **kwargs):  # pylint: disable=too-many-statements
     """Decorator factory to wrap methods as Function objects and save them as
     cached properties.

rocketpy/mathutils/piecewise_function.py

@@ -0,0 +1,104 @@
+import numpy as np
+from rocketpy.mathutils.function import Function
+
+class PiecewiseFunction(Function):
+    """Class for creating piecewise functions. These kind of functions are
+    defined by a dictionary of functions, where the keys are tuples that
+    represent the domain of the function. The domains must be disjoint.
+    """
+
+    def __new__(
+        cls,
+        source,
+        inputs=None,
+        outputs=None,
+        interpolation="spline",
+        extrapolation=None,
+        datapoints=100,
+    ):
+        """
+        Creates a piecewise function from a dictionary of functions. The keys of
+        the dictionary must be tuples that represent the domain of the function.
+        The domains must be disjoint. The piecewise function will be evaluated
+        at datapoints points to create Function object.
+
+        Parameters
+        ----------
+        source: dictionary
+            A dictionary of Function objects, where the keys are the domains.
+        inputs : list of strings
+            A list of strings that represent the inputs of the function.
+        outputs: list of strings
+            A list of strings that represent the outputs of the function.
+        interpolation: str
+            The type of interpolation to use. The default value is 'spline'.
+        extrapolation: str
+            The type of extrapolation to use. The default value is None.
+        datapoints: int
+            The number of points in which the piecewise function will be
+            evaluated to create a base function. The default value is 100.
+        """
+        if inputs is None:
+            inputs = ["Scalar"]
+        if outputs is None:
+            outputs = ["Scalar"]
+        # Check if source is a dictionary
+        if not isinstance(source, dict):
+            raise TypeError("source must be a dictionary")
+        # Check if all keys are tuples
+        for key in source.keys():
+            if not isinstance(key, tuple):
+                raise TypeError("keys of source must be tuples")
+        # Check if all domains are disjoint
+        for key1 in source.keys():
+            for key2 in source.keys():
+                if key1 != key2:
+                    if key1[0] < key2[1] and key1[1] > key2[0]:
+                        raise ValueError("domains must be disjoint")
+
+        # Crate Function
+        def calc_output(func, inputs):
+            """Receives a list of inputs value and a function, populates another
+            list with the results corresponding to the same results.
+
+            Parameters
+            ----------
+            func : Function
+                The Function object to be
+            inputs : list, tuple, np.array
+                The array of points to applied the func to.
+
+            Examples
+            --------
+            >>> inputs = [0, 1, 2, 3, 4, 5]
+            >>> def func(x):
+            ...     return x*10
+            >>> calc_output(func, inputs)
+            [0, 10, 20, 30, 40, 50]
+
+            Notes
+            -----
+            In the future, consider using the built-in map function from python.
+            """
+            output = np.zeros(len(inputs))
+            for j, value in enumerate(inputs):
+                output[j] = func.get_value_opt(value)
+            return output
+
+        input_data = []
+        output_data = []
+        for key in sorted(source.keys()):
+            i = np.linspace(key[0], key[1], datapoints)
+            i = i[~np.isin(i, input_data)]
+            input_data = np.concatenate((input_data, i))
+
+            f = Function(source[key])
+            output_data = np.concatenate((output_data, calc_output(f, i)))
+
+        return Function(
+            np.concatenate(([input_data], [output_data])).T,
+            inputs=inputs,
+            outputs=outputs,
+            interpolation=interpolation,
+            extrapolation=extrapolation,
+        )

rocketpy/motors/tank_geometry.py

@@ -2,7 +2,8 @@
 
 import numpy as np
 
-from ..mathutils.function import Function, PiecewiseFunction, funcify_method
+from ..mathutils.function import Function, funcify_method
+from ..mathutils.piecewise_function import PiecewiseFunction
 from ..plots.tank_geometry_plots import _TankGeometryPlots
 from ..prints.tank_geometry_prints import _TankGeometryPrints