Comments (2)
If that helps, the slwodown is much less noticeable with multidimensional arrays:
import blaze as blz
import numpy as np
from time import time
shape = (100,100,100)
len_ = np.prod(shape)
print "len for array:", len_
t0 = time()
a = np.arange(len_).reshape(shape)
print "numpy creation time: %.3f" % (time() - t0,)
t0 = time()
b = blz.Array(a, dshape='%d,%d,%d, int32' % shape)
t1 = time() - t0
print "Final datashape:", b.datashape
print "blaze.Array creation time: %.3f" % (t1,)
t0 = time()
c = blz.zeros(dshape='%d,%d,%d, int32'% shape)
t2 = time() - t0
print "Final datashape:", c.datashape
print "blaze.zeros creation time: %.3f" % (t2,)
print "time ratio blaze.Array vs blaze.zeros: %.1fx" % (t2 / t1,)
and the output:
len for array: 1000000
numpy creation time: 0.008
Final datashape: 100, 100, 100, int32
blaze.Array creation time: 0.030
Final datashape: 100, 100, 100, int32
blaze.zeros creation time: 0.818
time ratio blaze.Array vs blaze.zeros: 27.7x
Although the slowdown is still important...
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This looks to be resolved:
In [6]: run blaze_zeros_test.py
len for array: 1000000
numpy creation time: 0.004
Final datashape: 100, 100, 100, int32
blaze.Array creation time: 0.397
Final datashape: 100, 100, 100, int32
blaze.zeros creation time: 0.002
time ratio blaze.Array vs blaze.zeros: 0.0x
from blaze.
Related Issues (20)
- networkx 2.0 api changes HOT 2
- Transitioning the Blaze project HOT 11
- Separate backends from core HOT 1
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- Blaze to RESTful endpoint
- (sqlite3.OperationalError) no such function: greatest HOT 1
- ValueError: numpy.ufunc has the wrong size, try recompiling. Expected 192, got 216
- removing redundancy
- Filtering with `by` ignores the group by condition HOT 1
- Dependency on pandas tslib HOT 1
- Fix simple typo: absense -> absence
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- Blaze
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