def get_parametric_curve(self, function: Callable[[float], np.ndarray],
**kwargs) -> ParametricCurve:
dim = self.dimension
graph = ParametricCurve(
lambda t: self.coords_to_point(*function(t)[:dim]), **kwargs)
graph.underlying_function = function
return graph
def get_graph(self, function, x_range=None, **kwargs):
t_range = np.array(self.x_range, dtype=float)
if x_range is not None:
t_range[:len(x_range)] = x_range
# For axes, the third coordinate of x_range indicates
# tick frequency. But for functions, it indicates a
# sample frequency
if x_range is None or len(x_range) < 3:
t_range[2] /= self.num_sampled_graph_points_per_tick
t_range[0] = kwargs.pop("x_min", t_range[0])
t_range[1] = kwargs.pop("x_max", t_range[1])
'''
x_min = kwargs.pop("x_min", self.x_min)
x_max = kwargs.pop("x_max", self.x_max)
'''
graph = ParametricCurve(lambda t: self.c2p(t, function(t)),
t_range=t_range,
**kwargs)
'''
graph = ParametricFunction(
lambda t: self.coords_to_point(t, function(t)),
t_min=x_min,
t_max=x_max,
**kwargs
)
'''
graph.underlying_function = function
return graph
def get_parametric_curve(self, function, **kwargs):
dim = self.dimension
graph = ParametricCurve(
lambda t: self.coords_to_point(*function(t)[:dim]),
**kwargs
)
graph.underlying_function = function
return graph
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_range=x_range,
**kwargs)
graph.underlying_function = function
return graph
def get_graph(self, function, x_range=None, **kwargs):
t_range = np.array(self.x_range, dtype=float)
if x_range is not None:
t_range[:len(x_range)] = x_range
# For axes, the third coordinate of x_range indicates
# tick frequency. But for functions, it indicates a
# sample frequency
if x_range is None or len(x_range) < 3:
t_range[2] /= self.num_sampled_graph_points_per_tick
graph = ParametricCurve(lambda t: self.c2p(t, function(t)),
t_range=t_range,
**kwargs)
graph.underlying_function = function
return graph
def input_to_graph_point(self, x: float,
graph: ParametricCurve) -> np.ndarray | None:
if hasattr(graph, "underlying_function"):
return self.coords_to_point(x, graph.underlying_function(x))
else:
alpha = binary_search(
function=lambda a: self.point_to_coords(
graph.quick_point_from_proportion(a))[0],
target=x,
lower_bound=self.x_range[0],
upper_bound=self.x_range[1],
)
if alpha is not None:
return graph.quick_point_from_proportion(alpha)
else:
return None
def get_graph(self, func, color=None, x_min=None, x_max=None, **kwargs):
if color is None:
color = next(self.default_graph_colors_cycle)
if x_min is None:
x_min = self.x_min
if x_max is None:
x_max = self.x_max
def parameterized_function(alpha):
x = interpolate(x_min, x_max, alpha)
y = func(x)
if not np.isfinite(y):
y = self.y_max
return self.coords_to_point(x, y)
graph = ParametricCurve(parameterized_function, color=color, **kwargs)
graph.underlying_function = func
return graph
