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#!/usr/bin/python3
r'''Test the broadcasting in C
Uses the "testlib" guinea pig C library
'''
import sys
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path[:0] = dir_path + '/..',
import numpy as np
import numpysane as nps
# Local test harness. The python standard ones all suck
from testutils import *
# The extension module we're testing
import testlib
def check(matching_functions, A, B):
r'''Compare results of pairs of matching functions
matching_functions is a list of pairs of functions that are supposed to
produce identical results (testlib and numpysane implementations of
inner and outer products). A and B are lists of arguments that we try out.
These support broadcasting, so either one is allowed to be a single array,
which is then used for all the checks. I check both dynamically-created and
inlined "out" arrays
'''
N = 1
if type(A) is tuple and len(A) > N:
N = len(A)
if type(B) is tuple and len(B) > N:
N = len(B)
if type(A) is not tuple: A = (A,) * N
if type(B) is not tuple: B = (B,) * N
for what,f0,f1 in matching_functions:
for i in range(N):
out0 = f0(A[i], B[i])
out1 = f1(A[i], B[i])
confirm_equal( out0, out1,
msg = what + ' matches. Dynamically-allocated output' )
outshape = out1.shape
out0 = np.zeros(outshape, dtype=np.array(A[i]).dtype)
out1 = np.ones (outshape, dtype=np.array(A[i]).dtype)
f0(A[i], B[i], out=out0)
f1(A[i], B[i], out=out1)
confirm_equal( out0, out1,
msg = what + ' matches. Pre-allocated output' )
from functools import reduce
def arr(*shape, **kwargs):
dtype = kwargs.get('dtype',float)
r'''Return an arange() array of the given shape.'''
if len(shape) == 0:
return np.array(3, dtype=dtype)
product = reduce( lambda x,y: x*y, shape)
return np.arange(product, dtype=dtype).reshape(*shape)
# Check dimensionality logic. A sanity check:
assertValueShape( np.array((6,6,6),),
(3,),
testlib.sum_Nrows_Ncols,
arr(3,2,4))
# An extra implicity dimension should be added, so the array of shape (4,)
# should be treated as an array of shape (1,4), and we should get 5 out
assertValueShape( np.array(5),
(),
testlib.sum_Nrows_Ncols,
arr(4,))
# pairs of functions that should produce identical results
matching_functions = ( ("inner", testlib.inner, nps.inner),
("outer", testlib.outer, nps.outer) )
# Basic 1D arrays
a0 = np.arange(5, dtype=float)
b = a0+3
# a needs to broadcast; contiguous and strided
a1 = np.arange(10, dtype=float).reshape(2,5)
a2 = nps.transpose(np.arange(10, dtype=float).reshape(5,2))
# Try it!
check(matching_functions, (a0,a1,a2), b)
# Try it again, but use the floating-point version
check( (("inner", nps.inner, testlib.inner),),
tuple([a.astype(int) for a in (a0,a1,a2)]),
b.astype(int))
confirm_raises( lambda: check( (("inner", nps.inner, testlib.inner),),
(a0,a1,a2),
b.astype(int)),
msg = "types must match" )
# Too few input dimensions (passing a scalar where a vector is expected). This
# should be ok. It can be viewed as a length-1 vector
check( (("inner", nps.inner, testlib.inner),),
6.,
(5.,
np.array(5, dtype=float),
np.array((5,), dtype=float),
),)
# Broadcasting. Should be ok. No barf.
confirm_does_not_raise(lambda: testlib.inner(np.arange(10, dtype=float).reshape( 2,5),
np.arange(15, dtype=float).reshape(3,1,5)),
msg='Aligned dimensions')
confirm_raises( lambda: testlib.inner(np.arange(10, dtype=float).reshape(2,5),
np.arange(15, dtype=float).reshape(3,5)) )
confirm_raises( lambda: testlib.inner(np.arange(5), np.arange(6)) )
confirm_does_not_raise( lambda: testlib.outer(a0,b, out=np.zeros((5,5), dtype=float)),
msg = "Basic in-place broadcasting")
confirm_raises(lambda: testlib.outer(a0,b, out=np.zeros((5,5), dtype=int)),
msg = "Output type must match")
confirm_raises(lambda: testlib.outer(a0.astype(int),b.astype(int), out=np.zeros((5,5), dtype=float)),
msg = "Output type must match")
confirm_does_not_raise( lambda: testlib.outer(a0.astype(float),b.astype(float), out=np.zeros((5,5), dtype=float)),
msg = "Output type must match")
confirm_does_not_raise( lambda: testlib.inner(a0.astype(int),b.astype(int), out=np.zeros((), dtype=int)),
msg = "Output type must match")
confirm_raises( lambda: testlib.outer(a0,b, out=np.zeros((3,3), dtype=float)),
msg = "Wrong dimensions on out" )
confirm_raises( lambda: testlib.outer(a0,b, out=np.zeros((4,5), dtype=float)),
msg = "Wrong dimensions on out" )
confirm_raises( lambda: testlib.outer(a0,b, out=np.zeros((5,), dtype=float)),
msg = "Wrong dimensions on out" )
confirm_raises( lambda: testlib.outer(a0,b, out=np.zeros((), dtype=float)),
msg = "Wrong dimensions on out" )
o555 = np.ones((5,5,5), dtype=float)
confirm_does_not_raise( lambda: testlib.outer(a0,b, out=o555),
msg = "Bigger dimensions on out" )
confirm_equal(o555 - nps.outer(a0,b),
0*o555,
eps = 1e-12,
msg = 'out can contain repeated slices')
def test_identity3():
r'''Testing identity3()'''
ref = np.array(((1,0,0),
(0,1,0),
(0,0,1)),dtype=float)
ref_int = np.array(((1,0,0),
(0,1,0),
(0,0,1)),dtype=int)
out = ref*0
out_int = ref_int*0
assertResult_inoutplace( ref,
testlib.identity3 )
confirm_does_not_raise( lambda: testlib.identity3(out = out))
confirm_raises( lambda: testlib.identity3(out = out_int))
out_discontiguous = np.zeros((4,5,6), dtype=float)[:3,:3,0]
confirm(not out_discontiguous.flags['C_CONTIGUOUS'])
testlib.identity3(out = out_discontiguous)
confirm_equal(ref, out_discontiguous)
def test_identity():
r'''Testing identity()
This tests much of the named-dimensions-in-output-only logic
'''
# This i
ref = np.eye(2, dtype=float)
ref_int = np.eye(2, dtype=int)
out = np.zeros((2,2), dtype=float)
out_int = np.zeros((2,2), dtype=int)
out32 = np.zeros((3,2), dtype=float)
out23 = np.zeros((3,2), dtype=float)
confirm_raises(lambda: testlib.identity(),
msg='output-only named dimensions MUST be given in the in-place array')
confirm_raises(lambda: testlib.identity(out=out_int),
msg='types must match')
confirm_equal(ref, testlib.identity(out=out),
msg='basic output-only named dimensions work')
confirm_raises(lambda: testlib.identity(out=out23),
msg='output-only named dimensions must still be self-consistent')
confirm_raises(lambda: testlib.identity(out=out32),
msg='output-only named dimensions must still be self-consistent')
out_discontiguous = np.zeros((4,5,6), dtype=float)[:3,:3,0]
confirm(not out_discontiguous.flags['C_CONTIGUOUS'])
testlib.identity(out = out_discontiguous)
confirm_equal(np.eye(3, dtype=float),
out_discontiguous)
def test_inner():
r'''Testing the broadcasted inner product'''
ref = np.array([[[ 30, 255, 730],
[ 180, 780, 1630]],
[[ 180, 780, 1630],
[1455, 2430, 3655]],
[[ 330, 1305, 2530],
[2730, 4080, 5680]],
[[ 480, 1830, 3430],
[4005, 5730, 7705.0]]])
assertResult_inoutplace( ref,
testlib.inner, arr(2,3,5), arr(4,1,3,5) )
output = np.empty((4,2,3), dtype=int)
confirm_raises( lambda: testlib.inner( arr( 2,3,5, dtype=float),
arr(4,1,3,5, dtype=float),
out=output ),
"inner(out=out, dtype=dtype) have out=dtype==dtype" )
# make sure non-contiguous output works properly
output = np.empty((4,2,3), dtype=float)
confirm(output.flags['C_CONTIGUOUS'])
output = nps.reorder( np.empty((2,3,4), dtype=float),
2,0,1 )
confirm(not output.flags['C_CONTIGUOUS'])
confirm_equal( testlib.inner( arr( 2,3,5, dtype=float),
arr(4,1,3,5, dtype=float),
out=output ),
ref,
msg = 'Noncontiguous output' )
confirm(not output.flags['C_CONTIGUOUS'])
confirm_equal( output,
ref,
msg = 'Noncontiguous output' )
def test_outer():
r'''Testing the broadcasted outer product'''
# comes from PDL. numpy has a reversed axis ordering convention from
# PDL, so I transpose the array before comparing
ref = nps.transpose(
np.array([[[[[0,0,0,0,0],[0,1,2,3,4],[0,2,4,6,8],[0,3,6,9,12],[0,4,8,12,16]],
[[25,30,35,40,45],[30,36,42,48,54],[35,42,49,56,63],[40,48,56,64,72],[45,54,63,72,81]],
[[100,110,120,130,140],[110,121,132,143,154],[120,132,144,156,168],[130,143,156,169,182],[140,154,168,182,196]]],
[[[0,0,0,0,0],[15,16,17,18,19],[30,32,34,36,38],[45,48,51,54,57],[60,64,68,72,76]],
[[100,105,110,115,120],[120,126,132,138,144],[140,147,154,161,168],[160,168,176,184,192],[180,189,198,207,216]],
[[250,260,270,280,290],[275,286,297,308,319],[300,312,324,336,348],[325,338,351,364,377],[350,364,378,392,406]]]],
[[[[0,15,30,45,60],[0,16,32,48,64],[0,17,34,51,68],[0,18,36,54,72],[0,19,38,57,76]],
[[100,120,140,160,180],[105,126,147,168,189],[110,132,154,176,198],[115,138,161,184,207],[120,144,168,192,216]],
[[250,275,300,325,350],[260,286,312,338,364],[270,297,324,351,378],[280,308,336,364,392],[290,319,348,377,406]]],
[[[225,240,255,270,285],[240,256,272,288,304],[255,272,289,306,323],[270,288,306,324,342],[285,304,323,342,361]],
[[400,420,440,460,480],[420,441,462,483,504],[440,462,484,506,528],[460,483,506,529,552],[480,504,528,552,576]],
[[625,650,675,700,725],[650,676,702,728,754],[675,702,729,756,783],[700,728,756,784,812],[725,754,783,812,841]]]],
[[[[0,30,60,90,120],[0,31,62,93,124],[0,32,64,96,128],[0,33,66,99,132],[0,34,68,102,136]],
[[175,210,245,280,315],[180,216,252,288,324],[185,222,259,296,333],[190,228,266,304,342],[195,234,273,312,351]],
[[400,440,480,520,560],[410,451,492,533,574],[420,462,504,546,588],[430,473,516,559,602],[440,484,528,572,616]]],
[[[450,480,510,540,570],[465,496,527,558,589],[480,512,544,576,608],[495,528,561,594,627],[510,544,578,612,646]],
[[700,735,770,805,840],[720,756,792,828,864],[740,777,814,851,888],[760,798,836,874,912],[780,819,858,897,936]],
[[1000,1040,1080,1120,1160],[1025,1066,1107,1148,1189],[1050,1092,1134,1176,1218],[1075,1118,1161,1204,1247],[1100,1144,1188,1232,1276]]]],
[[[[0,45,90,135,180],[0,46,92,138,184],[0,47,94,141,188],[0,48,96,144,192],[0,49,98,147,196]],
[[250,300,350,400,450],[255,306,357,408,459],[260,312,364,416,468],[265,318,371,424,477],[270,324,378,432,486]],
[[550,605,660,715,770],[560,616,672,728,784],[570,627,684,741,798],[580,638,696,754,812],[590,649,708,767,826]]],
[[[675,720,765,810,855],[690,736,782,828,874],[705,752,799,846,893],[720,768,816,864,912],[735,784,833,882,931]],
[[1000,1050,1100,1150,1200],[1020,1071,1122,1173,1224],[1040,1092,1144,1196,1248],[1060,1113,1166,1219,1272],[1080,1134,1188,1242,1296]],
[[1375,1430,1485,1540,1595],[1400,1456,1512,1568,1624],[1425,1482,1539,1596,1653],[1450,1508,1566,1624,1682],[1475,1534,1593,1652,1711]]]]]))
assertResult_inoutplace( ref,
testlib.outer, arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float) )
# make sure non-contiguous output (in both the broadcasting AND within each
# slice) works properly
output = np.empty((4,2,3,5,5), dtype=float)
confirm(output.flags['C_CONTIGUOUS'])
output = nps.reorder( np.empty((2,3,4,5,5), dtype=float),
2,0,1, 4,3)
confirm(not output.flags['C_CONTIGUOUS'])
confirm_equal( testlib.outer( arr( 2,3,5, dtype=float),
arr(4,1,3,5, dtype=float),
out=output ),
ref,
msg = 'Noncontiguous output (broadcasting and within each slice)' )
confirm(not output.flags['C_CONTIGUOUS'])
confirm_equal( output,
ref,
msg = 'Noncontiguous output (broadcasting and within each slice)' )
def test_innerouter():
r'''Testing the broadcasted inner product'''
# shape (4, 2, 3)
ref_inner = np.array([[[ 30, 255, 730],
[ 180, 780, 1630]],
[[ 180, 780, 1630],
[1455, 2430, 3655]],
[[ 330, 1305, 2530],
[2730, 4080, 5680]],
[[ 480, 1830, 3430],
[4005, 5730, 7705.0]]])
# comes from PDL. numpy has a reversed axis ordering convention from
# PDL, so I transpose the array before comparing
# shape (4, 2, 3, 5, 5)
ref_outer = nps.transpose(
np.array([[[[[0,0,0,0,0],[0,1,2,3,4],[0,2,4,6,8],[0,3,6,9,12],[0,4,8,12,16]],
[[25,30,35,40,45],[30,36,42,48,54],[35,42,49,56,63],[40,48,56,64,72],[45,54,63,72,81]],
[[100,110,120,130,140],[110,121,132,143,154],[120,132,144,156,168],[130,143,156,169,182],[140,154,168,182,196]]],
[[[0,0,0,0,0],[15,16,17,18,19],[30,32,34,36,38],[45,48,51,54,57],[60,64,68,72,76]],
[[100,105,110,115,120],[120,126,132,138,144],[140,147,154,161,168],[160,168,176,184,192],[180,189,198,207,216]],
[[250,260,270,280,290],[275,286,297,308,319],[300,312,324,336,348],[325,338,351,364,377],[350,364,378,392,406]]]],
[[[[0,15,30,45,60],[0,16,32,48,64],[0,17,34,51,68],[0,18,36,54,72],[0,19,38,57,76]],
[[100,120,140,160,180],[105,126,147,168,189],[110,132,154,176,198],[115,138,161,184,207],[120,144,168,192,216]],
[[250,275,300,325,350],[260,286,312,338,364],[270,297,324,351,378],[280,308,336,364,392],[290,319,348,377,406]]],
[[[225,240,255,270,285],[240,256,272,288,304],[255,272,289,306,323],[270,288,306,324,342],[285,304,323,342,361]],
[[400,420,440,460,480],[420,441,462,483,504],[440,462,484,506,528],[460,483,506,529,552],[480,504,528,552,576]],
[[625,650,675,700,725],[650,676,702,728,754],[675,702,729,756,783],[700,728,756,784,812],[725,754,783,812,841]]]],
[[[[0,30,60,90,120],[0,31,62,93,124],[0,32,64,96,128],[0,33,66,99,132],[0,34,68,102,136]],
[[175,210,245,280,315],[180,216,252,288,324],[185,222,259,296,333],[190,228,266,304,342],[195,234,273,312,351]],
[[400,440,480,520,560],[410,451,492,533,574],[420,462,504,546,588],[430,473,516,559,602],[440,484,528,572,616]]],
[[[450,480,510,540,570],[465,496,527,558,589],[480,512,544,576,608],[495,528,561,594,627],[510,544,578,612,646]],
[[700,735,770,805,840],[720,756,792,828,864],[740,777,814,851,888],[760,798,836,874,912],[780,819,858,897,936]],
[[1000,1040,1080,1120,1160],[1025,1066,1107,1148,1189],[1050,1092,1134,1176,1218],[1075,1118,1161,1204,1247],[1100,1144,1188,1232,1276]]]],
[[[[0,45,90,135,180],[0,46,92,138,184],[0,47,94,141,188],[0,48,96,144,192],[0,49,98,147,196]],
[[250,300,350,400,450],[255,306,357,408,459],[260,312,364,416,468],[265,318,371,424,477],[270,324,378,432,486]],
[[550,605,660,715,770],[560,616,672,728,784],[570,627,684,741,798],[580,638,696,754,812],[590,649,708,767,826]]],
[[[675,720,765,810,855],[690,736,782,828,874],[705,752,799,846,893],[720,768,816,864,912],[735,784,833,882,931]],
[[1000,1050,1100,1150,1200],[1020,1071,1122,1173,1224],[1040,1092,1144,1196,1248],[1060,1113,1166,1219,1272],[1080,1134,1188,1242,1296]],
[[1375,1430,1485,1540,1595],[1400,1456,1512,1568,1624],[1425,1482,1539,1596,1653],[1450,1508,1566,1624,1682],[1475,1534,1593,1652,1711]]]]]))
# not in-place
try:
i,o = testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float))
except:
confirm(False, msg="broadcasted innerouter succeeded")
else:
confirm_equal(i.shape, ref_inner.shape, msg="broadcasted innerouter produced correct inner.shape")
confirm_equal(i, ref_inner, msg="broadcasted innerouter produced correct inner")
confirm_equal(o.shape, ref_outer.shape, msg="broadcasted innerouter produced correct outer.shape")
confirm_equal(o, ref_outer, msg="broadcasted innerouter produced correct outer")
# in-place
try:
i = np.empty(ref_inner.shape, dtype=float)
o = np.empty(ref_outer.shape, dtype=float)
testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(i,o))
except:
confirm(False, msg="broadcasted in-place innerouter succeeded")
else:
confirm(True, msg="broadcasted in-place innerouter succeeded")
confirm_equal(i.shape, ref_inner.shape, msg="broadcasted in-place innerouter produced correct inner.shape")
confirm_equal(i, ref_inner, msg="broadcasted in-place innerouter produced correct inner")
confirm_equal(o.shape, ref_outer.shape, msg="broadcasted in-place innerouter produced correct outer.shape")
confirm_equal(o, ref_outer, msg="broadcasted in-place innerouter produced correct outer")
# in-place with float scaling
try:
i = np.empty(ref_inner.shape, dtype=float)
o = np.empty(ref_outer.shape, dtype=float)
testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(i,o), scale=3.5)
except:
confirm(False, msg="broadcasted in-place innerouter succeeded")
else:
confirm(True, msg="broadcasted in-place innerouter succeeded")
confirm_equal(i.shape, ref_inner.shape, msg="broadcasted in-place innerouter with scaling produced correct inner.shape")
confirm_equal(i, ref_inner * 3.5, msg="broadcasted in-place innerouter with scaling produced correct inner")
confirm_equal(o.shape, ref_outer.shape, msg="broadcasted in-place innerouter with scaling produced correct outer.shape")
confirm_equal(o, ref_outer * 3.5, msg="broadcasted in-place innerouter with scaling produced correct outer")
# in-place with float scaling and string scaling
try:
i = np.empty(ref_inner.shape, dtype=float)
o = np.empty(ref_outer.shape, dtype=float)
testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(i,o), scale=3.5, scale_string="10.0")
except:
confirm(False, msg="broadcasted in-place innerouter succeeded")
else:
confirm(True, msg="broadcasted in-place innerouter succeeded")
confirm_equal(i.shape, ref_inner.shape, msg="broadcasted in-place innerouter with float and string scaling produced correct inner.shape")
confirm_equal(i, ref_inner * 35., msg="broadcasted in-place innerouter with float and string scaling produced correct inner")
confirm_equal(o.shape, ref_outer.shape, msg="broadcasted in-place innerouter with float and string scaling produced correct outer.shape")
confirm_equal(o, ref_outer * 35., msg="broadcasted in-place innerouter with float and string scaling produced correct outer")
# in-place, with some extra dummy dimensions in the output. Should be allowed
i = np.empty((1,) + ref_inner.shape, dtype=float)
o = np.empty(ref_outer.shape, dtype=float)
confirm_does_not_raise( lambda: testlib.innerouter(arr( 2,3,5, dtype=float),
arr(4,1,3,5, dtype=float),
out=(i,o)),
msg="Extra broadcasted dimensions in the output is allowed")
# in-place, with some extra dummy dimensions in the output. Should be allowed
i = np.empty(ref_inner.shape, dtype=float)
o = np.empty((1,) + ref_outer.shape, dtype=float)
confirm_does_not_raise( lambda: testlib.innerouter(arr( 2,3,5, dtype=float),
arr(4,1,3,5, dtype=float),
out=(i,o)),
msg="Extra broadcasted dimensions in the output is allowed")
# now some bogus shapes and types that should fail
i = np.empty(ref_inner.shape, dtype=float)
o = np.empty(ref_outer.shape, dtype=float)
confirm_does_not_raise( lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(i,o)),
msg = "basic broadcasted innerouter works")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(i,i)),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(o,o)),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(o,i)),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(i,o,i)),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(i,i,o)),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=int), out=(i,o)),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=o),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=i),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(i,None)),
msg = "in-place broadcasting output dimensions match")
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(None,i)),
msg = "in-place broadcasting output dimensions match")
iint = np.empty(ref_inner.shape, dtype=int)
i1 = np.empty((1,) + ref_inner.shape, dtype=float)
i2 = np.empty((2,) + ref_inner.shape, dtype=float)
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), out=(iint,o)),
msg = "in-place broadcasting output types match")
confirm_does_not_raise(lambda: testlib.innerouter(arr( 2,3,5, dtype=float),
arr(4,1,3,5, dtype=float),
out=(i1,o)),
msg = "broadcasted innerouter: extra output dims are allowed")
confirm_does_not_raise(lambda: testlib.innerouter(arr( 2,3,5, dtype=float),
arr(4,1,3,5, dtype=float),
out=(i2,o)),
msg = "broadcasted innerouter: extra output dims are allowed")
confirm_does_not_raise(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), scale=3.5),
msg = 'Validation looks at the cookie')
confirm_raises(lambda: testlib.innerouter(arr(2,3,5, dtype=float), arr(4,1,3,5, dtype=float), scale=-3.5),
msg = 'Validation looks at the cookie')
def test_sorted_indices():
x64 = np.array((1., 5., 3, 2.5, 3.5, 2.9), dtype=float)
x32 = np.array((1., 5., 3, 2.5, 3.5, 2.9), dtype=np.float32)
iref = np.array((0, 3, 5, 2, 4, 1), dtype=np.int64)
confirm_raises(lambda: testlib.sorted_indices(np.arange(5, dtype=np.int64)))
confirm_does_not_raise(lambda: testlib.sorted_indices(np.arange(5, dtype=np.float32)))
confirm_does_not_raise(lambda: testlib.sorted_indices(np.arange(5, dtype=np.float32),
out=np.arange(5, dtype=np.int32)))
confirm_raises(lambda: testlib.sorted_indices(np.arange(5, dtype=np.float32),
out=np.arange(5, dtype=np.int64)))
confirm_raises(lambda: testlib.sorted_indices(np.arange(5, dtype=np.float32),
out=np.arange(5, dtype=float)))
assertResult_inoutplace( iref,
testlib.sorted_indices, x64, out_inplace_dtype=np.int32)
assertResult_inoutplace( iref,
testlib.sorted_indices, x32, out_inplace_dtype=np.int32)
def test_broadcasting():
assertValueShape( np.array(5), (), testlib.inner, arr(3), arr(3))
assertValueShape( np.array((5,14)), (2,), testlib.inner, arr(2,3), arr(3))
assertValueShape( np.array((5,14)), (2,), testlib.inner, arr(3), arr(2,3))
assertValueShape( np.array(((5,14),)), (1,2,), testlib.inner, arr(1,2,3), arr(3))
assertValueShape( np.array(((5,),(14,))), (2,1,), testlib.inner, arr(2,1,3), arr(3))
assertValueShape( np.array((5,14)), (2,), testlib.inner, arr(2,3), arr(1,3))
assertValueShape( np.array((5,14)), (2,), testlib.inner, arr(1,3), arr(2,3))
assertValueShape( np.array(((5,14),)), (1,2,), testlib.inner, arr(1,2,3), arr(1,3))
assertValueShape( np.array(((5,),(14,))), (2,1,), testlib.inner, arr(2,1,3), arr(1,3))
assertValueShape( np.array(((5,14),(14,50))), (2,2,), testlib.inner, arr(2,1,3), arr(2,3))
assertValueShape( np.array(((5,14),(14,50))), (2,2,), testlib.inner, arr(2,1,3), arr(1,2,3))
confirm_raises( lambda: testlib.inner(arr(3)), msg='right number of args' )
confirm_raises( lambda: testlib.inner(arr(3),arr(5)), msg='matching args')
confirm_raises( lambda: testlib.inner(arr(2,3),arr(4,3)), msg='matching args')
confirm_raises( lambda: testlib.inner(arr(3,3,3),arr(2,1,3)), msg='matching args')
confirm_raises( lambda: testlib.inner(arr(1,2,4),arr(2,1,3)), msg='matching args')
# make sure the output COUNTS are checked (if I expect 2 outputs, but get
# only 1, that's an error
confirm( testlib.innerouter(arr(5), arr( 5)) is not None, msg='output count check' )
confirm( testlib.innerouter(arr(5), arr(2,5)) is not None, msg='output count check' )
confirm( testlib.innerouter(arr(5), arr( 5)) is not None,
msg='output dimensionality check with given out' )
# Basic out_kwarg tests. More thorough ones later, in
# test_broadcasting_into_output())
a5 = arr( 5, dtype=float)
a25 = arr(2, 5, dtype=float)
a125 = arr(1, 2, 5, dtype=float)
o = np.zeros((), dtype=float)
o2 = np.zeros((2,), dtype=float)
o5 = np.zeros((5,), dtype=float)
o12 = np.zeros((1,2), dtype=float)
o22 = np.zeros((2,2), dtype=float)
o55 = np.zeros((5,5), dtype=float)
o25 = np.zeros((2,5), dtype=float)
o255 = np.zeros((2,5,5), dtype=float)
o1255= np.zeros((1,2,5,5),dtype=float)
o2255= np.zeros((2,2,5,5),dtype=float)
o2155= np.zeros((2,1,5,5),dtype=float)
# no broadcasting
confirm_raises( lambda: \
testlib.innerouter(a5, a5, out=o), \
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a5, out=o2), \
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a5, out=(o,)), \
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a5, out=(o55,)), \
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a5, out=(o55,o)), \
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a5, out=(o,o2)), \
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a5, out=(o,o5)), \
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a5, out=(o,o55,o)), \
msg='output dimensionality check with given out' )
confirm( testlib.innerouter(a5, a5, out=(o,o55)) is not None,
msg='output dimensionality check with given out' )
confirm_equal(o, a5.dot(a5), msg='in-place broadcasting computed the right value')
confirm_equal(o55, np.outer(a5,a5), msg='in-place broadcasting computed the right value')
confirm_does_not_raise( lambda: \
testlib.innerouter(a5, a5, out=(o2,o55)), \
msg='output dimensionality check with given out' )
confirm_equal(o2[0], nps.inner(a5,a5), msg='in-place broadcasting computed the right value')
confirm_equal(o2[1], nps.inner(a5,a5), msg='in-place broadcasting computed the right value')
confirm_equal(o55, nps.outer(a5,a5), msg='in-place broadcasting computed the right value')
# two broadcasted slices
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=o),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=o2),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o,)),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o55,)),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o55,o)),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o,o2)),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o,o5)),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o2,o55)),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o,o55,o)),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o,o55)),
msg='output dimensionality check with given out' )
confirm_raises( lambda: \
testlib.innerouter(a5, a25, out=(o,o255)),
msg='output dimensionality check with given out' )
confirm( testlib.innerouter(a5, a25, out=(o2,o255)) is not None,
msg='output dimensionality check with given out' )
confirm_equal(o2, nps.inner(a5,a25), msg='in-place broadcasting computed the right value')
confirm_equal(o255, nps.outer(a5,a25), msg='in-place broadcasting computed the right value')
confirm( testlib.innerouter(a5, a25, out=(o22,o2255)) is not None,
msg='output dimensionality check with given out' )
confirm_equal(o22[0], nps.inner(a5,a25), msg='in-place broadcasting computed the right value')
confirm_equal(o22[1], nps.inner(a5,a25), msg='in-place broadcasting computed the right value')
confirm_equal(o2255[0], nps.outer(a5,a25), msg='in-place broadcasting computed the right value')
confirm_equal(o2255[1], nps.outer(a5,a25), msg='in-place broadcasting computed the right value')
# Non-contiguous data should work with inner and outer, but not innerouter
# (that's what the underlying C library does/does not support)
a2 = arr(2, dtype=float)
a25_noncontiguous = arr(5, 2, dtype=float).T
o255_noncontiguous = nps.transpose(np.zeros((2,5,5), dtype=float))
o255_noncontiguous_in_broadcast = np.zeros((2,2,5,5), dtype=float)[:,0,:,:]
confirm_does_not_raise(lambda: testlib.inner (a25_noncontiguous, a5),
msg='Validation: noncontiguous in the function slice')
confirm_does_not_raise(lambda: testlib.outer (a25_noncontiguous, a5),
msg='Validation: noncontiguous in the function slice')
confirm_does_not_raise(lambda: testlib.outer (a25_noncontiguous, a5, out=o255_noncontiguous),
msg='Validation: noncontiguous in the function slice')
confirm_raises (lambda: testlib.innerouter(a25_noncontiguous, a5),
msg='Validation: noncontiguous in the function slice')
confirm_does_not_raise(lambda: testlib.innerouter(a25, a5, out=(a2, o255)),
msg='Validation: noncontiguous in the function slice')
confirm_raises (lambda: testlib.innerouter(a25, a5, out=(a2, o255_noncontiguous)),
msg='Validation: noncontiguous in the function slice')
confirm_does_not_raise(lambda: testlib.innerouter(a25, a5, out=(a2, o255_noncontiguous_in_broadcast)),
msg='Validation: noncontiguous array that are noncontiguous ONLY in the broadcasted dimensions (i.e. each slice IS contiguous)')
# Extra slices in the output produce duplicates
confirm_does_not_raise( lambda: \
testlib.innerouter(a5, a25, out=(o2,o255)),
msg='output dimensionality check with given out' )
# slightly mismatched output dims: extra dummy dimensions in the inputs or
# outputs
try:
o2[:] = 0
o255[:] = 0
testlib.innerouter(a5, a125, out=(o2,o255))
confirm_equal(o2, nps.inner(a5,a125[0]), "inner value matches")
confirm_equal(o255, nps.outer(a5,a125[0]), "outer value matches")
except Exception as e:
confirm(False, f"Broadcasted innerouter() failed: {e}")
confirm_does_not_raise( lambda: \
testlib.innerouter(a5, a125, out=(o12,o1255)),
msg='output dimensionality check with given out' )
try:
o12[:] = 0
o2255[:] = 0
testlib.innerouter(a5, a125, out=(o12,o2255))
confirm_equal(o12[0], nps.inner(a5,a125[0]), "inner value matches")
confirm_equal(o2255[0], nps.outer(a5,a125[0]), "outer value matches")
confirm_equal(o2255[1], nps.outer(a5,a125[0]), "outer value matches")
except Exception as e:
confirm(False, f"Broadcasted innerouter() failed: {e}")
confirm_raises(lambda: \
testlib.innerouter(a5, a125, out=(o12,o2155)),
msg = 'Output should be big-enough to hold the results, even if it matches the broadcasting rules')
confirm_does_not_raise(lambda: \
testlib.innerouter(a5, a25, out=(o22,o255)),
msg = 'Extra output dimensions should be ok. Output 1 has extras')
confirm_does_not_raise(lambda: \
testlib.innerouter(a5, a25, out=(o2,o2255)),
msg = 'Extra output dimensions should be ok. Output 2 has extras')
test_identity3()
test_identity()
test_inner()
test_outer()
test_innerouter()
test_broadcasting()
test_sorted_indices()
finish()
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