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107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
import numpy as np
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import itertools as it
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from scene import Scene
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from animation import Animation
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class RearrangeEquation(Scene):
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def construct(
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self,
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start_terms,
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end_terms,
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index_map,
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size = None,
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path = counterclockwise_path(),
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start_transform = None,
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end_transform = None,
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leave_start_terms = False,
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transform_kwargs = {},
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):
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transform_kwargs["interpolation_function"] = path
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start_mobs, end_mobs = self.get_mobs_from_terms(
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start_terms, end_terms, size
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)
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if start_transform:
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start_mobs = start_transform(CompoundMobject(*start_mobs)).split()
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if end_transform:
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end_mobs = end_transform(CompoundMobject(*end_mobs)).split()
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unmatched_start_indices = set(range(len(start_mobs)))
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unmatched_end_indices = set(range(len(end_mobs)))
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unmatched_start_indices.difference_update(
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[n%len(start_mobs) for n in index_map]
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)
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unmatched_end_indices.difference_update(
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[n%len(end_mobs) for n in index_map.values()]
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)
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mobject_pairs = [
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(start_mobs[a], end_mobs[b])
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for a, b in index_map.iteritems()
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]+ [
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(Point(end_mobs[b].get_center()), end_mobs[b])
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for b in unmatched_end_indices
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]
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if not leave_start_terms:
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mobject_pairs += [
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(start_mobs[a], Point(start_mobs[a].get_center()))
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for a in unmatched_start_indices
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]
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self.add(*start_mobs)
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if leave_start_terms:
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self.add(CompoundMobject(*start_mobs))
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self.dither()
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self.play(*[
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Transform(*pair, **transform_kwargs)
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for pair in mobject_pairs
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])
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self.dither()
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def get_mobs_from_terms(self, start_terms, end_terms, size):
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"""
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Need to ensure that all image mobjects for a tex expression
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stemming from the same string are point-for-point copies of one
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and other. This makes transitions much smoother, and not look
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like point-clouds.
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"""
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num_start_terms = len(start_terms)
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all_mobs = np.array(
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tex_mobject(start_terms, size = size).split() + \
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tex_mobject(end_terms, size = size).split()
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)
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all_terms = np.array(start_terms+end_terms)
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for term in set(all_terms):
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matches = all_terms == term
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if sum(matches) > 1:
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base_mob = all_mobs[list(all_terms).index(term)]
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all_mobs[matches] = [
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deepcopy(base_mob).replace(target_mob)
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for target_mob in all_mobs[matches]
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]
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return all_mobs[:num_start_terms], all_mobs[num_start_terms:]
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class FlipThroughSymbols(Animation):
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DEFAULT_CONFIG = {
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"start_center" : ORIGIN,
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"end_center" : ORIGIN,
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}
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def __init__(self, tex_list, **kwargs):
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digest_config(self, FlipThroughSymbols, kwargs, locals())
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self.curr_tex = self.tex_list[0]
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mobject = tex_mobject(self.curr_tex).shift(start_center)
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Animation.__init__(self, mobject, **kwargs)
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def update_mobject(self, alpha):
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new_tex = self.tex_list[np.ceil(alpha*len(self.tex_list))-1]
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if new_tex != self.curr_tex:
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self.curr_tex = new_tex
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self.mobject = tex_mobject(new_tex).shift(self.start_center)
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if not all(self.start_center == self.end_center):
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self.mobject.center().shift(
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(1-alpha)*self.start_center + alpha*self.end_center
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)
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