def make_item_str(key, val):
if explicit or kwargs.get('strkeys', False):
key_str = six.text_type(key)
else:
key_str = repr2(key, precision=precision, newlines=0)
prefix = key_str + kvsep
kwargs['_return_info'] = True
val_str, _leaf_info = repr2(val, **kwargs)
# If the first line does not end with an open nest char
# (e.g. for ndarrays), otherwise we need to worry about
# residual indentation.
pos = val_str.find('\n')
first_line = val_str if pos == -1 else val_str[:pos]
compact_brace = kwargs.get('cbr', kwargs.get('compact_brace', False))
if compact_brace or not first_line.rstrip().endswith(tuple('([{<')):
rest = '' if pos == -1 else val_str[pos:]
val_str = first_line.lstrip() + rest
if '\n' in prefix:
# Fix issue with keys that span new lines
item_str = prefix + val_str
else:
item_str = ub.hzcat([prefix, val_str])
else:
item_str = prefix + val_str
return item_str, _leaf_info
def main():
import ubelt as ub
dis_results = [
dis2(loop_first),
' | ',
dis2(loop_first2),
]
print(ub.hzcat(dis_results))
rows = benchmark_loop_first_variants()
import pandas as pd
df = pd.DataFrame(rows)
import kwplot
plt = kwplot.autoplt()
# import matplotlib as mpl
# mpl.use('Qt5Agg')
# from matplotlib import pyplot as plt
import seaborn as sns
sns.set()
sns.lineplot(data=df, x='num_items', y='mean', hue='method_name',
markers='method_name')
plt.show()
def compare_dis():
"""
Look at the disasembly of the methods in question to gain insight
46 0 LOAD_CONST 1 (0) | 54 0 LOAD_CONST 1 (0)
2 STORE_FAST 1 (x) 2 STORE_FAST 1 (x)
47 4 LOAD_GLOBAL 0 (loop_first) 55 4 LOAD_GLOBAL 0 (enumerate)
6 LOAD_FAST 0 (items) 6 LOAD_FAST 0 (items)
8 CALL_FUNCTION 1 8 CALL_FUNCTION 1
10 GET_ITER 10 GET_ITER
>> 12 FOR_ITER 28 (to 42) >> 12 FOR_ITER 32 (to 46)
14 UNPACK_SEQUENCE 2 14 UNPACK_SEQUENCE 2
16 STORE_FAST 2 (is_first) 16 STORE_FAST 2 (idx)
18 STORE_FAST 3 (item) 18 STORE_FAST 3 (item)
48 20 LOAD_FAST 2 (is_first) 56 20 LOAD_FAST 2 (idx)
22 POP_JUMP_IF_FALSE 32 22 LOAD_CONST 1 (0)
24 COMPARE_OP 2 (==)
49 24 LOAD_FAST 1 (x) 26 POP_JUMP_IF_FALSE 36
26 LOAD_CONST 2 (1)
28 INPLACE_ADD 57 28 LOAD_FAST 1 (x)
30 STORE_FAST 1 (x) 30 LOAD_CONST 2 (1)
32 INPLACE_ADD
50 >> 32 LOAD_FAST 1 (x) 34 STORE_FAST 1 (x)
34 LOAD_CONST 3 (2)
36 INPLACE_ADD 58 >> 36 LOAD_FAST 1 (x)
38 STORE_FAST 1 (x) 38 LOAD_CONST 3 (2)
40 JUMP_ABSOLUTE 12 40 INPLACE_ADD
>> 42 LOAD_CONST 0 (None) 42 STORE_FAST 1 (x)
44 RETURN_VALUE 44 JUMP_ABSOLUTE 12
>> 46 LOAD_CONST 0 (None)
48 RETURN_VALUE
"""
import ubelt as ub
dis_results = [
dis2(method_loop_first),
' | ',
dis2(method_enumerate),
]
print(ub.hzcat(dis_results))
# An odd broadcast behavior
import nprime
import numpy as np
import ubelt as ub
primes = list(nprime.generate_primes(100))
# Shapes of [X, 1, Y] and [X, Y] broadcast to [X, X, Y]
a = np.array(primes[0:6]).reshape(3, 1, 2)
b = np.array(primes[6:12]).reshape(3, 2)
c = a * b
c_str = ('c \n{!r}'.format(c))
a_str = ('a \n{!r}'.format(a))
b_str = ('b \n{!r}'.format(b))
print(ub.hzcat([c_str, ' = ', a_str, ' * ', b_str]))
print('c.shape = {!r}'.format(c.shape))
print('a.shape = {!r}'.format(a.shape))
print('b.shape = {!r}'.format(b.shape))
print('\n ---- \n Look at components \n')
# look at components
b_comp = c / a
a_comp = c / b
a_comp_str = ('a_comp \n{!r}'.format(a_comp))
b_comp_str = ('b_comp \n{!r}'.format(b_comp))
print(ub.hzcat([c_str, ' = ', a_comp_str, ' * ', b_comp_str]))
print('c.shape = {!r}'.format(c.shape))
print('b_comp.shape = {!r}'.format(b_comp.shape))
