Reconfigured ParametricCurve

manimlib/mobject/coordinate_systems.py

@@ -84,16 +84,12 @@ class CoordinateSystem():
         )
         return self.axis_labels
 
-    def get_graph(self, function, x_min=None, x_max=None, **kwargs):
-        if x_min is None:
-            x_min = self.x_min
-        if x_max is None:
-            x_max = self.x_max
-
+    def get_graph(self, function, x_range=None, **kwargs):
+        if x_range is None:
+            x_range = self.x_range
         graph = ParametricCurve(
             lambda t: self.coords_to_point(t, function(t)),
-            t_min=x_min,
-            t_max=x_max,
+            t_range=x_range,
             **kwargs
         )
         graph.underlying_function = function
@@ -102,9 +98,7 @@ class CoordinateSystem():
     def get_parametric_curve(self, function, **kwargs):
         dim = self.dimension
         graph = ParametricCurve(
-            lambda t: self.coords_to_point(
-                *function(t)[:dim]
-            ),
+            lambda t: self.coords_to_point(*function(t)[:dim]),
             **kwargs
         )
         graph.underlying_function = function
@@ -119,8 +113,8 @@ class CoordinateSystem():
                     graph.point_from_proportion(a)
                 )[0],
                 target=x,
-                lower_bound=self.x_min,
-                upper_bound=self.x_max,
+                lower_bound=self.x_range[0],
+                upper_bound=self.x_range[1],
             )
             if alpha is not None:
                 return graph.point_from_proportion(alpha)

manimlib/mobject/functions.py

@@ -6,29 +6,33 @@ from manimlib.utils.space_ops import get_norm
 
 class ParametricCurve(VMobject):
     CONFIG = {
-        "t_min": 0,
-        "t_max": 1,
-        "step_size": 0.2,
+        "t_range": [0, 1, 0.1],
         "min_samples": 8,
-        "dt": 1e-8,
+        "epsilon": 1e-8,
         # TODO, automatically figure out discontinuities
         "discontinuities": [],
     }
 
-    def __init__(self, function=None, **kwargs):
-        # either get a function from __init__ or from CONFIG
-        self.function = function or self.function
+    def __init__(self, t_func, t_range=None, **kwargs):
+        digest_config(self, kwargs)
+        if t_range is not None:
+            self.t_range[:len(t_range)] = t_range
+        # To be backward compatible with all the scenes specifying t_min, t_max, step_size
+        self.t_range = [
+            kwargs.get("t_min", self.t_range[0]),
+            kwargs.get("t_max", self.t_range[1]),
+            kwargs.get("step_size", self.t_range[2]),
+        ]
+        self.t_func = t_func
         VMobject.__init__(self, **kwargs)
 
-    def get_function(self):
-        return self.function
-
     def get_point_from_function(self, t):
-        return self.function(t)
+        return self.t_func(t)
 
     def init_points(self):
-        t_min, t_max = self.t_min, self.t_max
-        dt = self.dt
+        # TODO, this seems like a mess.
+        t_min, t_max, step = self.t_range
+        epsilon = self.epsilon
 
         discontinuities = filter(
             lambda t: t_min <= t <= t_max,
@@ -36,24 +40,24 @@ class ParametricCurve(VMobject):
         )
         discontinuities = np.array(list(discontinuities))
         boundary_times = [
-            self.t_min, self.t_max,
-            *(discontinuities - dt),
-            *(discontinuities + dt),
+            t_min, t_max,
+            *(discontinuities - epsilon),
+            *(discontinuities + epsilon),
         ]
         boundary_times.sort()
         for t1, t2 in zip(boundary_times[0::2], boundary_times[1::2]):
             # Get an initial sample of points
             t_range = list(np.linspace(t1, t2, self.min_samples + 1))
-            samples = [self.function(t) for t in t_range]
+            samples = np.array([self.t_func(t) for t in t_range])
 
             # Take more samples based on the distances between them
             norms = [get_norm(p2 - p1) for p1, p2 in zip(samples, samples[1:])]
             full_t_range = [t1]
             for s1, s2, norm in zip(t_range, t_range[1:], norms):
-                n_inserts = int(norm / self.step_size)
+                n_inserts = int(norm / step)
                 full_t_range += list(np.linspace(s1, s2, n_inserts + 1)[1:])
 
-            points = np.array([self.function(t) for t in full_t_range])
+            points = np.array([self.t_func(t) for t in full_t_range])
             valid_indices = np.isfinite(points).all(1)
             points = points[valid_indices]
             if len(points) > 0:
@@ -66,25 +70,23 @@ class ParametricCurve(VMobject):
 class FunctionGraph(ParametricCurve):
     CONFIG = {
         "color": YELLOW,
-        "x_min": -FRAME_X_RADIUS,
-        "x_max": FRAME_X_RADIUS,
+        "x_range": [-8, 8, 0.1],
     }
 
-    def __init__(self, function, **kwargs):
+    def __init__(self, function, x_range=None, **kwargs):
         digest_config(self, kwargs)
-        self.parametric_function = \
-            lambda t: np.array([t, function(t), 0])
-        ParametricCurve.__init__(
-            self,
-            self.parametric_function,
-            t_min=self.x_min,
-            t_max=self.x_max,
-            **kwargs
-        )
         self.function = function
 
+        if x_range is not None:
+            self.x_range[:len(x_range)] = x_range
+
+        def parametric_function(t):
+            return [t, function(t), 0]
+
+        super().__init__(parametric_function, x_range, **kwargs)
+
     def get_function(self):
         return self.function
 
     def get_point_from_function(self, x):
-        return self.parametric_function(x)
+        return self.t_func(x)