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Change step size defaults on ParametricFunction

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Grant Sanderson 2020-02-12 12:49:46 -08:00
parent 5cfae1ea75
commit ec90bb2e8a
1 changed files with 16 additions and 30 deletions
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46
manimlib/mobject/functions.py
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@ -1,14 +1,15 @@
from manimlib.constants import * from manimlib.constants import *
from manimlib.mobject.types.vectorized_mobject import VMobject from manimlib.mobject.types.vectorized_mobject import VMobject
from manimlib.utils.config_ops import digest_config from manimlib.utils.config_ops import digest_config
import math from manimlib.utils.space_ops import get_norm
class ParametricFunction(VMobject): class ParametricFunction(VMobject):
CONFIG = { CONFIG = {
"t_min": 0, "t_min": 0,
"t_max": 1, "t_max": 1,
"step_size": 0.01, # Use "auto" (lowercase) for automatic step size "step_size": 0.2,
"min_samples": 8,
"dt": 1e-8, "dt": 1e-8,
# TODO, automatically figure out discontinuities # TODO, automatically figure out discontinuities
"discontinuities": [], "discontinuities": [],
@ -25,27 +26,6 @@ class ParametricFunction(VMobject):
def get_point_from_function(self, t): def get_point_from_function(self, t):
return self.function(t) return self.function(t)
def get_step_size(self, t=None):
if self.step_size == "auto":
"""
for x between -1 to 1, return 0.01
else, return log10(x) (rounded)
e.g.: 10.5 -> 0.1 ; 1040 -> 10
"""
if t == 0:
scale = 0
else:
scale = math.log10(abs(t))
if scale < 0:
scale = 0
scale = math.floor(scale)
scale -= 2
return math.pow(10, scale)
else:
return self.step_size
def init_points(self): def init_points(self):
t_min, t_max = self.t_min, self.t_max t_min, t_max = self.t_min, self.t_max
dt = self.dt dt = self.dt
@ -62,13 +42,19 @@ class ParametricFunction(VMobject):
] ]
boundary_times.sort() boundary_times.sort()
for t1, t2 in zip(boundary_times[0::2], boundary_times[1::2]): for t1, t2 in zip(boundary_times[0::2], boundary_times[1::2]):
t_range = list(np.arange(t1, t2, self.get_step_size(t1))) # Get an initial sample of points
if t_range[-1] != t2: t_range = list(np.linspace(t1, t2, self.min_samples + 1))
t_range.append(t2) samples = [self.function(t) for t in t_range]
points = np.array([self.function(t) for t in t_range])
valid_indices = np.apply_along_axis( # Take more samples based on the distances between them
np.all, 1, np.isfinite(points) 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)
full_t_range += list(np.linspace(s1, s2, n_inserts + 1)[1:])
points = np.array([self.function(t) for t in full_t_range])
valid_indices = np.apply_along_axis(np.all, 1, np.isfinite(points))
points = points[valid_indices] points = points[valid_indices]
if len(points) > 0: if len(points) > 0:
self.start_new_path(points[0]) self.start_new_path(points[0])