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130 lines
3.3 KiB
Python
130 lines
3.3 KiB
Python
import itertools as it
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import numpy as np
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def remove_list_redundancies(l):
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"""
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Used instead of list(set(l)) to maintain order
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Keeps the last occurance of each element
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"""
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reversed_result = []
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used = set()
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for x in reversed(l):
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if x not in used:
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reversed_result.append(x)
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used.add(x)
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reversed_result.reverse()
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return reversed_result
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def list_update(l1, l2):
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"""
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Used instead of list(set(l1).update(l2)) to maintain order,
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making sure duplicates are removed from l1, not l2.
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"""
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return [e for e in l1 if e not in l2] + list(l2)
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def list_difference_update(l1, l2):
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return [e for e in l1 if e not in l2]
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def all_elements_are_instances(iterable, Class):
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return all([isinstance(e, Class) for e in iterable])
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def adjacent_n_tuples(objects, n):
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return zip(*[
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[*objects[k:], *objects[:k]]
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for k in range(n)
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])
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def adjacent_pairs(objects):
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return adjacent_n_tuples(objects, 2)
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def batch_by_property(items, property_func):
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"""
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Takes in a list, and returns a list of tuples, (batch, prop)
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such that all items in a batch have the same output when
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put into property_func, and such that chaining all these
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batches together would give the original list (i.e. order is
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preserved)
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"""
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batch_prop_pairs = []
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def add_batch_prop_pair(batch):
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if len(batch) > 0:
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prop = property_func(batch[0])
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batch_prop_pairs.append((batch, prop))
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curr_batch = []
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curr_prop = None
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for item in items:
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prop = property_func(item)
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if prop != curr_prop:
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add_batch_prop_pair(curr_batch)
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curr_prop = prop
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curr_batch = [item]
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else:
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curr_batch.append(item)
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add_batch_prop_pair(curr_batch)
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return batch_prop_pairs
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def listify(obj):
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if isinstance(obj, str):
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return [obj]
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try:
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return list(obj)
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except TypeError:
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return [obj]
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def stretch_array_to_length(nparray, length):
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curr_len = len(nparray)
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if curr_len > length:
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raise Warning("Trying to stretch array to a length shorter than its own")
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indices = np.arange(0, curr_len, curr_len / length).astype(int)
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return nparray[indices]
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def stretch_array_to_length_with_interpolation(nparray, length):
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curr_len = len(nparray)
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cont_indices = np.linspace(0, curr_len - 1, length)
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return np.array([
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(1 - a) * nparray[lh] + a * nparray[rh]
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for ci in cont_indices
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for lh, rh, a in [(int(ci), int(np.ceil(ci)), ci % 1)]
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])
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def make_even(iterable_1, iterable_2):
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list_1 = list(iterable_1)
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list_2 = list(iterable_2)
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length = max(len(list_1), len(list_2))
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return (
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[list_1[(n * len(list_1)) // length] for n in range(length)],
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[list_2[(n * len(list_2)) // length] for n in range(length)]
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)
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def make_even_by_cycling(iterable_1, iterable_2):
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length = max(len(iterable_1), len(iterable_2))
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cycle1 = it.cycle(iterable_1)
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cycle2 = it.cycle(iterable_2)
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return (
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[next(cycle1) for x in range(length)],
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[next(cycle2) for x in range(length)]
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)
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def remove_nones(sequence):
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return [x for x in sequence if x]
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# Note this is redundant with it.chain
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def concatenate_lists(*list_of_lists):
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return [item for l in list_of_lists for item in l]
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