mirror of
https://github.com/3b1b/manim.git
synced 2025-04-13 09:47:07 +00:00
141 lines
3.6 KiB
Python
141 lines
3.6 KiB
Python
import itertools as it
|
|
import numpy as np
|
|
|
|
|
|
def remove_list_redundancies(l):
|
|
"""
|
|
Used instead of list(set(l)) to maintain order
|
|
Keeps the last occurrence of each element
|
|
"""
|
|
reversed_result = []
|
|
used = set()
|
|
for x in reversed(l):
|
|
if x not in used:
|
|
reversed_result.append(x)
|
|
used.add(x)
|
|
reversed_result.reverse()
|
|
return reversed_result
|
|
|
|
|
|
def list_update(l1, l2):
|
|
"""
|
|
Used instead of list(set(l1).update(l2)) to maintain order,
|
|
making sure duplicates are removed from l1, not l2.
|
|
"""
|
|
return [e for e in l1 if e not in l2] + list(l2)
|
|
|
|
|
|
def list_difference_update(l1, l2):
|
|
return [e for e in l1 if e not in l2]
|
|
|
|
|
|
def all_elements_are_instances(iterable, Class):
|
|
return all([isinstance(e, Class) for e in iterable])
|
|
|
|
|
|
def adjacent_n_tuples(objects, n):
|
|
return zip(*[
|
|
[*objects[k:], *objects[:k]]
|
|
for k in range(n)
|
|
])
|
|
|
|
|
|
def adjacent_pairs(objects):
|
|
return adjacent_n_tuples(objects, 2)
|
|
|
|
|
|
def batch_by_property(items, property_func):
|
|
"""
|
|
Takes in a list, and returns a list of tuples, (batch, prop)
|
|
such that all items in a batch have the same output when
|
|
put into property_func, and such that chaining all these
|
|
batches together would give the original list (i.e. order is
|
|
preserved)
|
|
"""
|
|
batch_prop_pairs = []
|
|
curr_batch = []
|
|
curr_prop = None
|
|
for item in items:
|
|
prop = property_func(item)
|
|
if prop != curr_prop:
|
|
# Add current batch
|
|
if len(curr_batch) > 0:
|
|
batch_prop_pairs.append((curr_batch, curr_prop))
|
|
# Redefine curr
|
|
curr_prop = prop
|
|
curr_batch = [item]
|
|
else:
|
|
curr_batch.append(item)
|
|
if len(curr_batch) > 0:
|
|
batch_prop_pairs.append((curr_batch, curr_prop))
|
|
return batch_prop_pairs
|
|
|
|
|
|
def listify(obj):
|
|
if isinstance(obj, str):
|
|
return [obj]
|
|
try:
|
|
return list(obj)
|
|
except TypeError:
|
|
return [obj]
|
|
|
|
|
|
def resize_array(nparray, length):
|
|
if len(nparray) == length:
|
|
return nparray
|
|
return np.resize(nparray, (length, *nparray.shape[1:]))
|
|
|
|
|
|
def resize_preserving_order(nparray, length):
|
|
if len(nparray) == 0:
|
|
return np.zeros((length, *nparray.shape[1:]))
|
|
if len(nparray) == length:
|
|
return nparray
|
|
indices = np.arange(length) * len(nparray) // length
|
|
return nparray[indices]
|
|
|
|
|
|
def resize_with_interpolation(nparray, length):
|
|
if len(nparray) == length:
|
|
return nparray
|
|
if length == 0:
|
|
return np.zeros((0, *nparray.shape[1:]))
|
|
cont_indices = np.linspace(0, len(nparray) - 1, length)
|
|
return np.array([
|
|
(1 - a) * nparray[lh] + a * nparray[rh]
|
|
for ci in cont_indices
|
|
for lh, rh, a in [(int(ci), int(np.ceil(ci)), ci % 1)]
|
|
])
|
|
|
|
|
|
def make_even(iterable_1, iterable_2):
|
|
len1 = len(iterable_1)
|
|
len2 = len(iterable_2)
|
|
if len1 == len2:
|
|
return iterable_1, iterable_2
|
|
new_len = max(len1, len2)
|
|
return (
|
|
[iterable_1[(n * len1) // new_len] for n in range(new_len)],
|
|
[iterable_2[(n * len2) // new_len] for n in range(new_len)]
|
|
)
|
|
|
|
|
|
def make_even_by_cycling(iterable_1, iterable_2):
|
|
length = max(len(iterable_1), len(iterable_2))
|
|
cycle1 = it.cycle(iterable_1)
|
|
cycle2 = it.cycle(iterable_2)
|
|
return (
|
|
[next(cycle1) for x in range(length)],
|
|
[next(cycle2) for x in range(length)]
|
|
)
|
|
|
|
|
|
def remove_nones(sequence):
|
|
return [x for x in sequence if x]
|
|
|
|
|
|
# Note this is redundant with it.chain
|
|
|
|
|
|
def concatenate_lists(*list_of_lists):
|
|
return [item for l in list_of_lists for item in l]
|