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View convert_label_to_indexer.py
import numpy as np
def convert_label_to_indexer(index, label):
"""Given a pandas.Index and labels (e.g., from __getitem__) for one dimension,
return an indexer suitable for indexing an ndarray along that dimension
"""
if isinstance(label, slice):
indexer = index.slice_indexer(label.start, label.stop, label.step)
elif np.isscalar(label):
indexer = indexer.get_loc(label)
View xray-pydata-pv2014.ipynb
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View xray-repr-examples.ipynb
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View xray-demo-python-workers-party.ipynb
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View numba ufunc for bottleneck.ipynb
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View urbanareas1_1.tsv
We can make this file beautiful and searchable if this error is corrected: It looks like row 6 should actually have 23 columns, instead of 19. in line 5.
"City" "City_Alternate" "Country" "Latitude" "Longitude" "Country_ISO3" "pop1950" "pop1955" "pop1960" "pop1965" "pop1970" "pop1975" "pop1980" "pop1985" "pop1990" "pop1995" "pop2000" "pop2005" "pop2010" "pop2015" "pop2020" "pop2025" "pop2050"
"Sofia" "" "Bulgaria" 42.70 23.33 "BGR" 520.00 620.00 710.00 810.00 890.00 980.00 1070.00 1180.00 1190.00 1170.00 1130.00 1170.00 1180.00 1210.00 1230.00 1240.00 1236
"Mandalay" "" "Myanmar" 21.97 96.08 "MMR" 170.00 200.00 250.00 310.00 370.00 440.00 500.00 560.00 640.00 720.00 810.00 920.00 960.00 1030.00 1170.00 1310.00 1446
"Nay Pyi Taw" "Myanmar" 19.75 96.10 "MMR" 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 60.00 930.00 1020.00 1180.00 1320.00 1461
"Yangon" "Rangoon" "Myanmar" 16.87 96.12 "MMR" 1300.00 1440.00 1590.00 1760.00 1950.00 2150.00 2380.00 2630.00 2910.00 3210.00 3550.00 3930.00 4090.00 4350.00 4840.00 5360.00 5869
"Minsk" "" "Belarus" 53.89 27.57 "BLR" 280.00 410.00 550.00 720.00 930.00 1120.00 1320.00 1470.00 1610.00 1650.00 1700.00 1780.00 180
View numba_ndreducers.py
"""Proof of concept for implementing bottleneck style aggregators with numba
These functions aggregate over any number of axes, just like the built-in
numpy functions, e.g.,
- nansum(x) # aggregates over all axes
- nansum(x, axis=1) # aggregates over axis 1
- nansum(x, axis=(0, 2)) # aggregtes over axes 0 and 2
"""
import functools
View canonicalize_gufunc_axis.ipynb
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View gist:92f8beb8bf9b0a66da69
def transform_state_vector(rho, U):
N = len(U)
if rho.shape[-1] == N:
# rho is in Hilbert space
pass
elif rho.shape[-1] == N ** 2:
# rho is in Liouville space
U = np.kron(U, U)
else:
raise ValueError('basis transformation incompatible with '
View simpler_stride_ticks.py
import numpy as np
from numpy.lib.stride_tricks import as_strided
def broadcast_to(array, shape):
"""Expand a numpy.ndarray to a new shape according to broadcasting rules
"""
array = np.asarray(array)
# will raise ValueError if shapes incompatible
np.nditer((array,), itershape=shape)
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