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https://github.com/3b1b/manim.git
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602809758e
* Enable setting points to a null list, and adding one point at a time.
* Add refresh_locked_data
* Add presenter mode to scenes with -p option
* Allow for an embed by hitting e during interaction
* Add set_min_height, etc.
* Make sure null parametric curve has at least one point
* Account for edge case where \{ is used in Tex
* Allow for logging notes in wait calls, useful for presenter mode
* Simplify choose, and add gen_choose for fractional amounts
* Default to no top on axes
* Allow match_x, match_y, etc. to take in a point
* Allow wait calls to ignore presenter mode
* Just use math.combo, no caching with choose(n, r)
* Use generator instead of list in bezier
* Bubble init_colors should override
* Account for "px" values read in from an svg
* Stop displaying when writing is happening
* Update the way Bubble override SVG colors
77 lines
1.8 KiB
Python
77 lines
1.8 KiB
Python
import inspect
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import numpy as np
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import math
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from functools import lru_cache
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def sigmoid(x):
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return 1.0 / (1 + np.exp(-x))
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@lru_cache(maxsize=10)
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def choose(n, k):
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return math.comb(n, k)
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def gen_choose(n, r):
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return np.prod(np.arange(n, n - r, -1)) / math.factorial(r)
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def get_num_args(function):
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return len(get_parameters(function))
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def get_parameters(function):
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return inspect.signature(function).parameters
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# Just to have a less heavyweight name for this extremely common operation
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#
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# We may wish to have more fine-grained control over division by zero behavior
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# in the future (separate specifiable values for 0/0 and x/0 with x != 0),
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# but for now, we just allow the option to handle indeterminate 0/0.
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def clip(a, min_a, max_a):
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if a < min_a:
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return min_a
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elif a > max_a:
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return max_a
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return a
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def fdiv(a, b, zero_over_zero_value=None):
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if zero_over_zero_value is not None:
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out = np.full_like(a, zero_over_zero_value)
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where = np.logical_or(a != 0, b != 0)
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else:
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out = None
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where = True
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return np.true_divide(a, b, out=out, where=where)
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def binary_search(function,
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target,
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lower_bound,
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upper_bound,
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tolerance=1e-4):
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lh = lower_bound
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rh = upper_bound
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while abs(rh - lh) > tolerance:
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mh = np.mean([lh, rh])
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lx, mx, rx = [function(h) for h in (lh, mh, rh)]
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if lx == target:
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return lx
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if rx == target:
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return rx
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if lx <= target and rx >= target:
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if mx > target:
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rh = mh
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else:
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lh = mh
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elif lx > target and rx < target:
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lh, rh = rh, lh
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else:
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return None
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return mh
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