#!/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()