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https://github.com/3b1b/manim.git
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After running 2to3
This commit is contained in:
Grant Sanderson
@ -369,8 +369,8 @@ class ExampleThrees(PiCreatureScene):
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),
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randy.change, "sassy",
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*it.chain(
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map(GrowFromCenter, braces),
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map(FadeIn, brace_labels)
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list(map(GrowFromCenter, braces)),
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list(map(FadeIn, brace_labels))
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)
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)
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self.wait()
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@ -415,9 +415,9 @@ class ExampleThrees(PiCreatureScene):
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self.wait(0.5)
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self.play(
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randy.change, "plain",
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*map(FadeOut, [
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*list(map(FadeOut, [
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self.bubble, self.arrow, self.real_three
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]) + [MoveToTarget(three)]
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])) + [MoveToTarget(three)]
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)
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for alt_three in alt_threes[:2]:
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self.play(three.replace, alt_three)
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@ -975,11 +975,11 @@ class BreakDownName(Scene):
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)
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self.wait(2)
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self.play(*map(FadeOut, [
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self.play(*list(map(FadeOut, [
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name, randy, brain,
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q2, a1, a2,
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q1[0], q1[2]
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]))
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])))
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self.neuron_word = q1[1]
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@ -1335,7 +1335,7 @@ class DiscussChoiceForHiddenLayers(TeacherStudentsScene):
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network_mob.set_height(4)
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network_mob.to_edge(UP, buff = LARGE_BUFF)
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layers = VGroup(*network_mob.layers[1:3])
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rects = VGroup(*map(SurroundingRectangle, layers))
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rects = VGroup(*list(map(SurroundingRectangle, layers)))
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self.add(network_mob)
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two_words = TextMobject("2 hidden layers")
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@ -1463,7 +1463,7 @@ class AskAboutLayers(PreviewMNistNetwork):
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)
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for group in neuron_groups
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])
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rects = map(SurroundingRectangle, neuron_groups[1:3])
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rects = list(map(SurroundingRectangle, neuron_groups[1:3]))
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self.play(
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Write(question, run_time = 1),
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@ -1474,7 +1474,7 @@ class AskAboutLayers(PreviewMNistNetwork):
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)
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)
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self.wait()
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self.play(*map(ShowCreation, rects))
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self.play(*list(map(ShowCreation, rects)))
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self.wait()
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class BreakUpMacroPatterns(IntroduceEachLayer):
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@ -1506,7 +1506,7 @@ class BreakUpMacroPatterns(IntroduceEachLayer):
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))[:,:,0].flatten()/255.0
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for p in prefixes
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]
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mobjects = map(MNistMobject, vects)
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mobjects = list(map(MNistMobject, vects))
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for mob in mobjects:
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image = mob[1]
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self.make_transparent(image)
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@ -1549,7 +1549,7 @@ class BreakUpMacroPatterns(IntroduceEachLayer):
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self.play(FadeIn(nine))
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self.wait()
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self.play(*map(FadeIn, parts))
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self.play(*list(map(FadeIn, parts)))
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self.wait()
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self.play(
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Write(equation[1]),
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@ -1651,7 +1651,7 @@ class BreakUpMacroPatterns(IntroduceEachLayer):
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for pattern in patterns[5:]:
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pattern.fade(1)
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self.play(*map(FadeOut, everything))
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self.play(*list(map(FadeOut, everything)))
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self.play(
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FadeIn(
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network_mob,
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@ -1732,9 +1732,9 @@ class BreakUpMacroPatterns(IntroduceEachLayer):
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class GenerallyLoopyPattern(Scene):
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def construct(self):
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image_map = get_organized_images()
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images = map(MNistMobject, it.chain(
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images = list(map(MNistMobject, it.chain(
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image_map[8], image_map[9],
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))
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)))
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random.shuffle(images)
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for image in images:
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@ -1914,7 +1914,7 @@ class SecondLayerIsLittleEdgeLayer(IntroduceEachLayer):
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nine_pa = self.nine[1].pixel_array
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n, k = 6, 4
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colors = color_gradient([BLUE, YELLOW, RED, MAROON_B, GREEN], 10)
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for i, j in it.product(range(n), range(k)):
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for i, j in it.product(list(range(n)), list(range(k))):
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mob = ImageMobject(np.zeros((28, 28, 4), dtype = 'uint8'))
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mob.replace(self.nine[1])
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pa = mob.pixel_array
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@ -1955,7 +1955,7 @@ class SecondLayerIsLittleEdgeLayer(IntroduceEachLayer):
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Write(words, run_time = 2)
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)
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self.wait()
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self.play(*map(FadeOut, [rect, words]))
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self.play(*list(map(FadeOut, [rect, words])))
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def show_propogation(self):
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nine = self.nine
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@ -2464,7 +2464,7 @@ class IntroduceWeights(IntroduceEachLayer):
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)
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] + [
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Write(weighted_sum[i])
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for i in range(2, 12, 3) + [-4, -3]
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for i in list(range(2, 12, 3)) + [-4, -3]
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],
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run_time = 1.5
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)
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@ -2715,8 +2715,8 @@ class IntroduceSigmoid(GraphScene):
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"y_max" : 2,
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"y_axis_label" : "",
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"graph_origin" : DOWN,
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"x_labeled_nums" : range(-4, 5),
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"y_labeled_nums" : range(-1, 3),
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"x_labeled_nums" : list(range(-4, 5)),
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"y_labeled_nums" : list(range(-1, 3)),
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}
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def construct(self):
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self.setup_axes()
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@ -3277,7 +3277,7 @@ class WhenTheNetworkFails(MoreHonestMNistNetworkPreview):
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DrawBorderThenFill(box)
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)
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self.wait()
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self.play(*map(FadeOut, [words, box]))
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self.play(*list(map(FadeOut, [words, box])))
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def incorrect_classification(self):
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network = self.network
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@ -3567,17 +3567,17 @@ class IntroduceWeightMatrix(NetworkScene):
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lwb.align_to(rwb, UP)
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row_1, row_k = [
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VGroup(*map(TexMobject, [
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VGroup(*list(map(TexMobject, [
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"w_{%s, 0}"%i,
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"w_{%s, 1}"%i,
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"\\cdots",
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"w_{%s, n}"%i,
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]))
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])))
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for i in ("1", "k")
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]
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dots_row = VGroup(*map(TexMobject, [
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dots_row = VGroup(*list(map(TexMobject, [
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"\\vdots", "\\vdots", "\\ddots", "\\vdots"
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]))
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])))
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lower_rows = VGroup(row_1, dots_row, row_k)
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lower_rows.scale(0.75)
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@ -3671,9 +3671,9 @@ class IntroduceWeightMatrix(NetworkScene):
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self.show_meaning_of_lower_rows(
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arrow, brace, top_row_rect, result_terms
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)
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self.play(*map(FadeOut, [
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self.play(*list(map(FadeOut, [
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result_terms, result_brackets, equals, column_rect
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]))
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])))
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def show_meaning_of_lower_rows(self, arrow, brace, row_rect, result_terms):
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n1, n2, nk = neurons = VGroup(*[
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@ -3735,9 +3735,9 @@ class IntroduceWeightMatrix(NetworkScene):
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plus = TexMobject("+")
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b_brackets = a_column_brackets.copy()
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b_column = VGroup(*map(TexMobject, [
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b_column = VGroup(*list(map(TexMobject, [
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"b_0", "b_1", "\\vdots", "b_n",
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]))
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])))
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b_column.scale(0.85)
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b_column.arrange_submobjects(DOWN, buff = 0.35)
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b_column.move_to(a_column)
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@ -3855,13 +3855,13 @@ class IntroduceWeightMatrix(NetworkScene):
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self.wait(2)
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def fade_weighted_sum(self):
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self.play(*map(FadeOut, [
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self.play(*list(map(FadeOut, [
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self.a1_label, self.a1_equals,
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self.sigma, self.sigma_parens,
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self.weighted_sum,
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self.bias_name,
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self.sigmoid_name,
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]))
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])))
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###
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@ -3903,10 +3903,10 @@ class SigmoidAppliedToVector(Scene):
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tex.set_width(FRAME_WIDTH - 1)
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tex.to_edge(DOWN)
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indices = it.chain(
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[0], range(1, 5), range(16, 16+4),
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range(25, 25+2), [25+3],
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range(29, 29+2), [29+3],
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range(33, 33+2), [33+3],
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[0], list(range(1, 5)), list(range(16, 16+4)),
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list(range(25, 25+2)), [25+3],
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list(range(29, 29+2)), [29+3],
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list(range(33, 33+2)), [33+3],
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)
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for i in indices:
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tex[i].set_color(YELLOW)
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@ -4050,7 +4050,7 @@ class NeuronIsFunction(MoreHonestMNistNetworkPreview):
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self.play(
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FadeOut(self.network_mob),
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*map(Animation, [neuron, edges, prev_layer])
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*list(map(Animation, [neuron, edges, prev_layer]))
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)
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self.play(LaggedStart(
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ShowCreationThenDestruction,
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