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test(NumpyTest): numpy.ndarray 裸泛型注解改为显式类型参数 numpy.ndarray[t.CDouble]

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裸泛型类名注解(Foo 无 [T])在 TPC/TPV 均不被支持(TPC 静默降级 i32, TPV fail-fast 报 struct 未注册); 与 SimdTest 已验证模式对齐
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TermiNexus committed 2026-08-16 05:53:56 +08:00
1 parent 9b190ac73e
commit a641cbff63
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+136 -136
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Test/NumpyTest/App/test_numpy.vp
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@@ -69,18 +69,18 @@ def section_footer():
# ============================================================
# Utility: quick array creation
# ============================================================
def vec4(p: memhub.MemPool | t.CPtr, v0: t.CDouble, v1: t.CDouble, v2: t.CDouble, v3: t.CDouble) -> numpy.ndarray | t.CPtr:
a: numpy.ndarray | t.CPtr = numpy.zeros(p, 4)
def vec4(p: memhub.MemPool | t.CPtr, v0: t.CDouble, v1: t.CDouble, v2: t.CDouble, v3: t.CDouble) -> numpy.ndarray[t.CDouble] | t.CPtr:
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(p, 4)
a.data[0] = v0; a.data[1] = v1; a.data[2] = v2; a.data[3] = v3
return a
def vec3(p: memhub.MemPool | t.CPtr, v0: t.CDouble, v1: t.CDouble, v2: t.CDouble) -> numpy.ndarray | t.CPtr:
a: numpy.ndarray | t.CPtr = numpy.zeros(p, 3)
def vec3(p: memhub.MemPool | t.CPtr, v0: t.CDouble, v1: t.CDouble, v2: t.CDouble) -> numpy.ndarray[t.CDouble] | t.CPtr:
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(p, 3)
a.data[0] = v0; a.data[1] = v1; a.data[2] = v2
return a
def vec2(p: memhub.MemPool | t.CPtr, v0: t.CDouble, v1: t.CDouble) -> numpy.ndarray | t.CPtr:
a: numpy.ndarray | t.CPtr = numpy.zeros(p, 2)
def vec2(p: memhub.MemPool | t.CPtr, v0: t.CDouble, v1: t.CDouble) -> numpy.ndarray[t.CDouble] | t.CPtr:
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(p, 2)
a.data[0] = v0; a.data[1] = v1
return a
@@ -91,18 +91,18 @@ def vec2(p: memhub.MemPool | t.CPtr, v0: t.CDouble, v1: t.CDouble) -> numpy.ndar
def test_zeros_ones():
section_header("zeros / ones / full")
a: numpy.ndarray | t.CPtr = numpy.zeros(pool, 5)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 5)
check("zeros not None", a != None, "zeros returned None")
check("zeros size == 5", a.size == 5, "size mismatch")
check("zeros [0]==0", a.data[0] == t.CDouble(0.0), "not zero")
check("zeros [4]==0", a.data[4] == t.CDouble(0.0), "not zero")
b: numpy.ndarray | t.CPtr = numpy.ones(pool, 4)
b: numpy.ndarray[t.CDouble] | t.CPtr = numpy.ones(pool, 4)
check("ones not None", b != None, "ones returned None")
check("ones [0]==1", b.data[0] == t.CDouble(1.0), "not one")
check("ones [3]==1", b.data[3] == t.CDouble(1.0), "not one")
c: numpy.ndarray | t.CPtr = numpy.full(pool, 3, t.CDouble(7.5))
c: numpy.ndarray[t.CDouble] | t.CPtr = numpy.full(pool, 3, t.CDouble(7.5))
check("full not None", c != None, "full returned None")
check("full value", c.data[0] == t.CDouble(7.5), "value mismatch")
@@ -115,13 +115,13 @@ def test_zeros_ones():
def test_arange_linspace():
section_header("arange / linspace")
a: numpy.ndarray | t.CPtr = numpy.arange(pool, t.CDouble(0.0), t.CDouble(5.0), t.CDouble(1.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.arange(pool, t.CDouble(0.0), t.CDouble(5.0), t.CDouble(1.0))
check("arange not None", a != None, "arange returned None")
check("arange size == 5", a.size == 5, "size mismatch")
check("arange [0]==0", approx_eq(a.data[0], t.CDouble(0.0)), "mismatch")
check("arange [4]==4", approx_eq(a.data[4], t.CDouble(4.0)), "mismatch")
b: numpy.ndarray | t.CPtr = numpy.linspace(pool, t.CDouble(0.0), t.CDouble(1.0), 5)
b: numpy.ndarray[t.CDouble] | t.CPtr = numpy.linspace(pool, t.CDouble(0.0), t.CDouble(1.0), 5)
check("linspace not None", b != None, "linspace returned None")
check("linspace size == 5", b.size == 5, "size mismatch")
check("linspace [0]==0", approx_eq(b.data[0], t.CDouble(0.0)), "mismatch")
@@ -135,15 +135,15 @@ def test_arange_linspace():
def test_eye_diag():
section_header("eye / diag")
e: numpy.ndarray | t.CPtr = numpy.eye(pool, 3)
e: numpy.ndarray[t.CDouble] | t.CPtr = numpy.eye(pool, 3)
check("eye not None", e != None, "eye returned None")
check("eye ndim == 2", e.ndim == 2, "ndim mismatch")
check("eye [0,0]==1", e.at2d(0, 0) == t.CDouble(1.0), "mismatch")
check("eye [0,1]==0", e.at2d(0, 1) == t.CDouble(0.0), "mismatch")
check("eye [1,1]==1", e.at2d(1, 1) == t.CDouble(1.0), "mismatch")
vals: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
d: numpy.ndarray | t.CPtr = numpy.diag(pool, vals)
vals: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
d: numpy.ndarray[t.CDouble] | t.CPtr = numpy.diag(pool, vals)
check("diag not None", d != None, "diag returned None")
check("diag [0,0]==1", d.at2d(0, 0) == t.CDouble(1.0), "mismatch")
check("diag [1,1]==2", d.at2d(1, 1) == t.CDouble(2.0), "mismatch")
@@ -161,7 +161,7 @@ def test_eye_diag():
def test_sum_mean_min_max():
section_header("sum / mean / min / max / argmax / argmin")
a: numpy.ndarray | t.CPtr = numpy.arange(pool, t.CDouble(1.0), t.CDouble(6.0), t.CDouble(1.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.arange(pool, t.CDouble(1.0), t.CDouble(6.0), t.CDouble(1.0))
check("sum == 15", approx_eq(a.sum(), t.CDouble(15.0)), "mismatch")
check("mean == 3", approx_eq(a.mean(), t.CDouble(3.0)), "mismatch")
check("min == 1", approx_eq(a.min(), t.CDouble(1.0)), "mismatch")
@@ -176,12 +176,12 @@ def test_sum_mean_min_max():
def test_fill_copy():
section_header("fill / copy")
a: numpy.ndarray | t.CPtr = numpy.zeros(pool, 3)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 3)
a.fill(t.CDouble(9.0))
check("fill [0]==9", a.data[0] == t.CDouble(9.0), "mismatch")
check("fill [2]==9", a.data[2] == t.CDouble(9.0), "mismatch")
b: numpy.ndarray | t.CPtr = a.copy()
b: numpy.ndarray[t.CDouble] | t.CPtr = a.copy()
check("copy not None", b != None, "copy returned None")
check("copy [0]==9", b.data[0] == t.CDouble(9.0), "mismatch")
@@ -199,21 +199,21 @@ def test_fill_copy():
def test_add_sub_mul_div():
section_header("__add__ / __sub__ / __mul__ / __truediv__")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
b: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(4.0), t.CDouble(5.0), t.CDouble(6.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
b: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(4.0), t.CDouble(5.0), t.CDouble(6.0))
c_add: numpy.ndarray | t.CPtr = a + b
c_add: numpy.ndarray[t.CDouble] | t.CPtr = a + b
check("a+b not None", c_add != None, "add returned None")
check("a+b [0]==5", c_add.data[0] == t.CDouble(5.0), "mismatch")
check("a+b [2]==9", c_add.data[2] == t.CDouble(9.0), "mismatch")
c_sub: numpy.ndarray | t.CPtr = a - b
c_sub: numpy.ndarray[t.CDouble] | t.CPtr = a - b
check("a-b [0]==-3", c_sub.data[0] == t.CDouble(-3.0), "mismatch")
c_mul: numpy.ndarray | t.CPtr = a * b
c_mul: numpy.ndarray[t.CDouble] | t.CPtr = a * b
check("a*b [1]==10", c_mul.data[1] == t.CDouble(10.0), "mismatch")
c_div: numpy.ndarray | t.CPtr = b / a
c_div: numpy.ndarray[t.CDouble] | t.CPtr = b / a
check("b/a [0]==4", c_div.data[0] == t.CDouble(4.0), "mismatch")
check("b/a [2]==2", c_div.data[2] == t.CDouble(2.0), "mismatch")
@@ -229,8 +229,8 @@ def test_add_sub_mul_div():
def test_neg():
section_header("__neg__")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(-2.0), t.CDouble(3.0))
neg_a: numpy.ndarray | t.CPtr = -a
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(-2.0), t.CDouble(3.0))
neg_a: numpy.ndarray[t.CDouble] | t.CPtr = -a
check("neg not None", neg_a != None, "neg returned None")
check("neg [0]==-1", neg_a.data[0] == t.CDouble(-1.0), "mismatch")
check("neg [1]==2", neg_a.data[1] == t.CDouble(2.0), "mismatch")
@@ -243,7 +243,7 @@ def test_neg():
def test_len():
section_header("__len__")
a: numpy.ndarray | t.CPtr = numpy.zeros(pool, 10)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 10)
check("len == 10", len(a) == 10, "mismatch")
a.delete()
@@ -256,18 +256,18 @@ def test_len():
def test_scalar_ops():
section_header("add_scalar / mul_scalar / sub_scalar / div_scalar")
a: numpy.ndarray | t.CPtr = numpy.ones(pool, 3)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.ones(pool, 3)
r1: numpy.ndarray | t.CPtr = numpy.add_scalar(a, t.CDouble(2.0))
r1: numpy.ndarray[t.CDouble] | t.CPtr = numpy.add_scalar(a, t.CDouble(2.0))
check("add_scalar [0]==3", r1.data[0] == t.CDouble(3.0), "mismatch")
r2: numpy.ndarray | t.CPtr = numpy.mul_scalar(a, t.CDouble(5.0))
r2: numpy.ndarray[t.CDouble] | t.CPtr = numpy.mul_scalar(a, t.CDouble(5.0))
check("mul_scalar [0]==5", r2.data[0] == t.CDouble(5.0), "mismatch")
r3: numpy.ndarray | t.CPtr = numpy.sub_scalar(a, t.CDouble(0.5))
r3: numpy.ndarray[t.CDouble] | t.CPtr = numpy.sub_scalar(a, t.CDouble(0.5))
check("sub_scalar [0]==0.5", r3.data[0] == t.CDouble(0.5), "mismatch")
r4: numpy.ndarray | t.CPtr = numpy.div_scalar(a, t.CDouble(2.0))
r4: numpy.ndarray[t.CDouble] | t.CPtr = numpy.div_scalar(a, t.CDouble(2.0))
check("div_scalar [0]==0.5", r4.data[0] == t.CDouble(0.5), "mismatch")
a.delete()
@@ -284,20 +284,20 @@ def test_scalar_ops():
def test_math_funcs():
section_header("np_abs / np_sqrt / np_pow")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(4.0), t.CDouble(9.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(4.0), t.CDouble(9.0))
r_sqrt: numpy.ndarray | t.CPtr = numpy.np_sqrt(a)
r_sqrt: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_sqrt(a)
check("sqrt [0]==1", approx_eq(r_sqrt.data[0], t.CDouble(1.0)), "mismatch")
check("sqrt [1]==2", approx_eq(r_sqrt.data[1], t.CDouble(2.0)), "mismatch")
check("sqrt [2]==3", approx_eq(r_sqrt.data[2], t.CDouble(3.0)), "mismatch")
b: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(-1.0), t.CDouble(-4.0), t.CDouble(9.0))
r_abs: numpy.ndarray | t.CPtr = numpy.np_abs(b)
b: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(-1.0), t.CDouble(-4.0), t.CDouble(9.0))
r_abs: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_abs(b)
check("abs [0]==1", approx_eq(r_abs.data[0], t.CDouble(1.0)), "mismatch")
check("abs [1]==4", approx_eq(r_abs.data[1], t.CDouble(4.0)), "mismatch")
c: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
r_pow: numpy.ndarray | t.CPtr = numpy.np_pow(c, t.CDouble(2.0))
c: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
r_pow: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_pow(c, t.CDouble(2.0))
check("pow [0]==1", approx_eq(r_pow.data[0], t.CDouble(1.0)), "mismatch")
check("pow [1]==4", approx_eq(r_pow.data[1], t.CDouble(4.0)), "mismatch")
check("pow [2]==9", approx_eq(r_pow.data[2], t.CDouble(9.0)), "mismatch")
@@ -318,15 +318,15 @@ def test_matmul():
section_header("matmul")
# [1,2;3,4] x [5,6;7,8] = [19,22;43,50]
a: numpy.ndarray | t.CPtr = numpy.empty2d(pool, 2, 2)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.empty2d(pool, 2, 2)
a.set2d(0, 0, t.CDouble(1.0)); a.set2d(0, 1, t.CDouble(2.0))
a.set2d(1, 0, t.CDouble(3.0)); a.set2d(1, 1, t.CDouble(4.0))
b: numpy.ndarray | t.CPtr = numpy.empty2d(pool, 2, 2)
b: numpy.ndarray[t.CDouble] | t.CPtr = numpy.empty2d(pool, 2, 2)
b.set2d(0, 0, t.CDouble(5.0)); b.set2d(0, 1, t.CDouble(6.0))
b.set2d(1, 0, t.CDouble(7.0)); b.set2d(1, 1, t.CDouble(8.0))
c: numpy.ndarray | t.CPtr = numpy.matmul(a, b)
c: numpy.ndarray[t.CDouble] | t.CPtr = numpy.matmul(a, b)
check("matmul not None", c != None, "matmul returned None")
check("matmul [0,0]==19", c.at2d(0, 0) == t.CDouble(19.0), "mismatch")
check("matmul [0,1]==22", c.at2d(0, 1) == t.CDouble(22.0), "mismatch")
@@ -342,8 +342,8 @@ def test_matmul():
def test_dot():
section_header("dot / dot_product")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
b: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(4.0), t.CDouble(5.0), t.CDouble(6.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
b: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(4.0), t.CDouble(5.0), t.CDouble(6.0))
d: t.CDouble = a.dot(b)
check("dot == 32", approx_eq(d, t.CDouble(32.0)), "mismatch")
@@ -359,11 +359,11 @@ def test_dot():
def test_transpose():
section_header("T (transpose)")
a: numpy.ndarray | t.CPtr = numpy.empty2d(pool, 2, 3)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.empty2d(pool, 2, 3)
a.set2d(0, 0, t.CDouble(1.0)); a.set2d(0, 1, t.CDouble(2.0)); a.set2d(0, 2, t.CDouble(3.0))
a.set2d(1, 0, t.CDouble(4.0)); a.set2d(1, 1, t.CDouble(5.0)); a.set2d(1, 2, t.CDouble(6.0))
at: numpy.ndarray | t.CPtr = a.T()
at: numpy.ndarray[t.CDouble] | t.CPtr = a.T()
check("T not None", at != None, "T returned None")
check("T shape[0]==3", at.shape[0] == 3, "shape mismatch")
check("T shape[1]==2", at.shape[1] == 2, "shape mismatch")
@@ -383,7 +383,7 @@ def test_var_std_norm():
section_header("var / std / norm")
# [2,4,4,4,5,5,7,9] mean=5, var=4, std=2
a: numpy.ndarray | t.CPtr = numpy.zeros(pool, 8)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 8)
a.data[0] = t.CDouble(2.0); a.data[1] = t.CDouble(4.0)
a.data[2] = t.CDouble(4.0); a.data[3] = t.CDouble(4.0)
a.data[4] = t.CDouble(5.0); a.data[5] = t.CDouble(5.0)
@@ -397,7 +397,7 @@ def test_var_std_norm():
printf(" std = %.6f (expect 2.0)\n", s)
check("std ~= 2", approx_eq(s, t.CDouble(2.0)), "mismatch")
b: numpy.ndarray | t.CPtr = vec2(pool, t.CDouble(3.0), t.CDouble(4.0))
b: numpy.ndarray[t.CDouble] | t.CPtr = vec2(pool, t.CDouble(3.0), t.CDouble(4.0))
n: t.CDouble = numpy.norm(b)
printf(" norm = %.6f (expect 5.0)\n", n)
check("norm ~= 5", approx_eq(n, t.CDouble(5.0)), "mismatch")
@@ -410,15 +410,15 @@ def test_var_std_norm():
def test_clip_concatenate():
section_header("clip / concatenate")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(5.0), t.CDouble(10.0))
r: numpy.ndarray | t.CPtr = numpy.clip(a, t.CDouble(2.0), t.CDouble(8.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(5.0), t.CDouble(10.0))
r: numpy.ndarray[t.CDouble] | t.CPtr = numpy.clip(a, t.CDouble(2.0), t.CDouble(8.0))
check("clip [0]==2", r.data[0] == t.CDouble(2.0), "mismatch")
check("clip [1]==5", r.data[1] == t.CDouble(5.0), "mismatch")
check("clip [2]==8", r.data[2] == t.CDouble(8.0), "mismatch")
b: numpy.ndarray | t.CPtr = vec2(pool, t.CDouble(1.0), t.CDouble(2.0))
c: numpy.ndarray | t.CPtr = vec2(pool, t.CDouble(3.0), t.CDouble(4.0))
cat: numpy.ndarray | t.CPtr = numpy.concatenate(b, c)
b: numpy.ndarray[t.CDouble] | t.CPtr = vec2(pool, t.CDouble(1.0), t.CDouble(2.0))
c: numpy.ndarray[t.CDouble] | t.CPtr = vec2(pool, t.CDouble(3.0), t.CDouble(4.0))
cat: numpy.ndarray[t.CDouble] | t.CPtr = numpy.concatenate(b, c)
check("concatenate not None", cat != None, "concatenate returned None")
check("concatenate size == 4", cat.size == 4, "size mismatch")
check("concatenate [0]==1", cat.data[0] == t.CDouble(1.0), "mismatch")
@@ -435,14 +435,14 @@ def test_clip_concatenate():
def test_sort_reverse():
section_header("sort_arr / reverse")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(3.0), t.CDouble(1.0), t.CDouble(2.0))
s: numpy.ndarray | t.CPtr = numpy.sort_arr(a)
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(3.0), t.CDouble(1.0), t.CDouble(2.0))
s: numpy.ndarray[t.CDouble] | t.CPtr = numpy.sort_arr(a)
check("sort not None", s != None, "sort returned None")
check("sort [0]==1", s.data[0] == t.CDouble(1.0), "mismatch")
check("sort [1]==2", s.data[1] == t.CDouble(2.0), "mismatch")
check("sort [2]==3", s.data[2] == t.CDouble(3.0), "mismatch")
r: numpy.ndarray | t.CPtr = numpy.reverse(a)
r: numpy.ndarray[t.CDouble] | t.CPtr = numpy.reverse(a)
check("reverse not None", r != None, "reverse returned None")
check("reverse [0]==2", r.data[0] == t.CDouble(2.0), "mismatch")
check("reverse [1]==1", r.data[1] == t.CDouble(1.0), "mismatch")
@@ -457,11 +457,11 @@ def test_sort_reverse():
def test_print_arr():
section_header("print_arr")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
printf(" 1D array: ")
a.print_arr()
b: numpy.ndarray | t.CPtr = numpy.eye(pool, 3)
b: numpy.ndarray[t.CDouble] | t.CPtr = numpy.eye(pool, 3)
printf(" 2D eye(3):\n")
b.print_arr()
@@ -476,15 +476,15 @@ def test_print_arr():
def test_floordiv_mod():
section_header("__floordiv__ / __mod__")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(7.0), t.CDouble(10.0), t.CDouble(15.0))
b: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(2.0), t.CDouble(3.0), t.CDouble(4.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(7.0), t.CDouble(10.0), t.CDouble(15.0))
b: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(2.0), t.CDouble(3.0), t.CDouble(4.0))
c_fd: numpy.ndarray | t.CPtr = a // b
c_fd: numpy.ndarray[t.CDouble] | t.CPtr = a // b
check("7//2==3", c_fd.data[0] == t.CDouble(3.0), "mismatch")
check("10//3==3", c_fd.data[1] == t.CDouble(3.0), "mismatch")
check("15//4==3", c_fd.data[2] == t.CDouble(3.0), "mismatch")
c_mod: numpy.ndarray | t.CPtr = a % b
c_mod: numpy.ndarray[t.CDouble] | t.CPtr = a % b
check("7%%2==1", c_mod.data[0] == t.CDouble(1.0), "mismatch")
check("10%%3==1", c_mod.data[1] == t.CDouble(1.0), "mismatch")
check("15%%4==3", c_mod.data[2] == t.CDouble(3.0), "mismatch")
@@ -502,35 +502,35 @@ def test_floordiv_mod():
def test_additional_math():
section_header("np_log10 / np_log2 / np_floor / np_ceil / np_round / np_sign")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(10.0), t.CDouble(100.0), t.CDouble(1000.0))
r_log10: numpy.ndarray | t.CPtr = numpy.np_log10(a)
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(10.0), t.CDouble(100.0), t.CDouble(1000.0))
r_log10: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_log10(a)
check("log10(10)~=1", approx_loose(r_log10.data[0], t.CDouble(1.0)), "mismatch")
check("log10(100)~=2", approx_loose(r_log10.data[1], t.CDouble(2.0)), "mismatch")
b: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(2.0), t.CDouble(4.0), t.CDouble(8.0))
r_log2: numpy.ndarray | t.CPtr = numpy.np_log2(b)
b: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(2.0), t.CDouble(4.0), t.CDouble(8.0))
r_log2: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_log2(b)
check("log2(2)~=1", approx_loose(r_log2.data[0], t.CDouble(1.0)), "mismatch")
check("log2(4)~=2", approx_loose(r_log2.data[1], t.CDouble(2.0)), "mismatch")
c: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.5), t.CDouble(2.7), t.CDouble(-1.3))
r_floor: numpy.ndarray | t.CPtr = numpy.np_floor(c)
c: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.5), t.CDouble(2.7), t.CDouble(-1.3))
r_floor: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_floor(c)
check("floor(1.5)==1", r_floor.data[0] == t.CDouble(1.0), "mismatch")
check("floor(2.7)==2", r_floor.data[1] == t.CDouble(2.0), "mismatch")
check("floor(-1.3)==-2", r_floor.data[2] == t.CDouble(-2.0), "mismatch")
r_ceil: numpy.ndarray | t.CPtr = numpy.np_ceil(c)
r_ceil: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_ceil(c)
check("ceil(1.5)==2", r_ceil.data[0] == t.CDouble(2.0), "mismatch")
check("ceil(2.7)==3", r_ceil.data[1] == t.CDouble(3.0), "mismatch")
check("ceil(-1.3)==-1", r_ceil.data[2] == t.CDouble(-1.0), "mismatch")
d: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.4), t.CDouble(2.5), t.CDouble(3.6))
r_round: numpy.ndarray | t.CPtr = numpy.np_round(d)
d: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.4), t.CDouble(2.5), t.CDouble(3.6))
r_round: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_round(d)
check("round(1.4)==1", r_round.data[0] == t.CDouble(1.0), "mismatch")
check("round(3.6)==4", r_round.data[2] == t.CDouble(4.0), "mismatch")
e: numpy.ndarray | t.CPtr = numpy.zeros(pool, 4)
e: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 4)
e.data[0] = t.CDouble(-3.0); e.data[1] = t.CDouble(0.0); e.data[2] = t.CDouble(5.0); e.data[3] = t.CDouble(-0.1)
r_sign: numpy.ndarray | t.CPtr = numpy.np_sign(e)
r_sign: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_sign(e)
check("sign(-3)==-1", r_sign.data[0] == t.CDouble(-1.0), "mismatch")
check("sign(0)==0", r_sign.data[1] == t.CDouble(0.0), "mismatch")
check("sign(5)==1", r_sign.data[2] == t.CDouble(1.0), "mismatch")
@@ -552,25 +552,25 @@ def test_additional_math():
def test_trig_hyperbolic():
section_header("np_tanh / np_sinh / np_cosh / np_arcsin / np_arccos / np_arctan")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(1.0), t.CDouble(-1.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(1.0), t.CDouble(-1.0))
r_tanh: numpy.ndarray | t.CPtr = numpy.np_tanh(a)
r_tanh: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_tanh(a)
check("tanh(0)~=0", approx_loose(r_tanh.data[0], t.CDouble(0.0)), "mismatch")
r_sinh: numpy.ndarray | t.CPtr = numpy.np_sinh(a)
r_sinh: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_sinh(a)
check("sinh(0)~=0", approx_loose(r_sinh.data[0], t.CDouble(0.0)), "mismatch")
r_cosh: numpy.ndarray | t.CPtr = numpy.np_cosh(a)
r_cosh: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_cosh(a)
check("cosh(0)~=1", approx_loose(r_cosh.data[0], t.CDouble(1.0)), "mismatch")
b: numpy.ndarray | t.CPtr = vec2(pool, t.CDouble(0.0), t.CDouble(0.5))
r_asin: numpy.ndarray | t.CPtr = numpy.np_arcsin(b)
b: numpy.ndarray[t.CDouble] | t.CPtr = vec2(pool, t.CDouble(0.0), t.CDouble(0.5))
r_asin: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_arcsin(b)
check("arcsin(0)~=0", approx_loose(r_asin.data[0], t.CDouble(0.0)), "mismatch")
r_acos: numpy.ndarray | t.CPtr = numpy.np_arccos(b)
r_acos: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_arccos(b)
check("arccos(0)~=pi/2", approx_loose(r_acos.data[0], t.CDouble(1.5707963)), "mismatch")
r_atan: numpy.ndarray | t.CPtr = numpy.np_arctan(b)
r_atan: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_arctan(b)
check("arctan(0)~=0", approx_loose(r_atan.data[0], t.CDouble(0.0)), "mismatch")
a.delete()
@@ -587,14 +587,14 @@ def test_trig_hyperbolic():
def test_degrees_radians():
section_header("np_degrees / np_radians")
a: numpy.ndarray | t.CPtr = vec2(pool, t.CDouble(0.0), t.CDouble(1.5707963267948966))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec2(pool, t.CDouble(0.0), t.CDouble(1.5707963267948966))
r_deg: numpy.ndarray | t.CPtr = numpy.np_degrees(a)
r_deg: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_degrees(a)
check("deg(0)~=0", approx_loose(r_deg.data[0], t.CDouble(0.0)), "mismatch")
check("deg(pi/2)~=90", approx_loose(r_deg.data[1], t.CDouble(90.0)), "mismatch")
b: numpy.ndarray | t.CPtr = vec2(pool, t.CDouble(0.0), t.CDouble(90.0))
r_rad: numpy.ndarray | t.CPtr = numpy.np_radians(b)
b: numpy.ndarray[t.CDouble] | t.CPtr = vec2(pool, t.CDouble(0.0), t.CDouble(90.0))
r_rad: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_radians(b)
check("rad(0)~=0", approx_loose(r_rad.data[0], t.CDouble(0.0)), "mismatch")
check("rad(90)~=pi/2", approx_loose(r_rad.data[1], t.CDouble(1.5707963)), "mismatch")
@@ -611,17 +611,17 @@ def test_degrees_radians():
def test_cumsum_diff():
section_header("cumsum / diff")
a: numpy.ndarray | t.CPtr = numpy.zeros(pool, 4)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 4)
a.data[0] = t.CDouble(1.0); a.data[1] = t.CDouble(2.0); a.data[2] = t.CDouble(3.0); a.data[3] = t.CDouble(4.0)
r_cs: numpy.ndarray | t.CPtr = numpy.cumsum(a)
r_cs: numpy.ndarray[t.CDouble] | t.CPtr = numpy.cumsum(a)
check("cumsum not None", r_cs != None, "cumsum returned None")
check("cumsum[0]==1", r_cs.data[0] == t.CDouble(1.0), "mismatch")
check("cumsum[1]==3", r_cs.data[1] == t.CDouble(3.0), "mismatch")
check("cumsum[2]==6", r_cs.data[2] == t.CDouble(6.0), "mismatch")
check("cumsum[3]==10", r_cs.data[3] == t.CDouble(10.0), "mismatch")
r_diff: numpy.ndarray | t.CPtr = numpy.diff(a)
r_diff: numpy.ndarray[t.CDouble] | t.CPtr = numpy.diff(a)
check("diff not None", r_diff != None, "diff returned None")
check("diff size==3", r_diff.size == 3, "size mismatch")
check("diff[0]==1", r_diff.data[0] == t.CDouble(1.0), "mismatch")
@@ -636,22 +636,22 @@ def test_cumsum_diff():
def test_max_min_where():
section_header("np_maximum / np_minimum / np_where")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(5.0), t.CDouble(3.0))
b: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(4.0), t.CDouble(2.0), t.CDouble(6.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(5.0), t.CDouble(3.0))
b: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(4.0), t.CDouble(2.0), t.CDouble(6.0))
r_max: numpy.ndarray | t.CPtr = numpy.np_maximum(a, b)
r_max: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_maximum(a, b)
check("maximum[0]==4", r_max.data[0] == t.CDouble(4.0), "mismatch")
check("maximum[1]==5", r_max.data[1] == t.CDouble(5.0), "mismatch")
check("maximum[2]==6", r_max.data[2] == t.CDouble(6.0), "mismatch")
r_min: numpy.ndarray | t.CPtr = numpy.np_minimum(a, b)
r_min: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_minimum(a, b)
check("minimum[0]==1", r_min.data[0] == t.CDouble(1.0), "mismatch")
check("minimum[1]==2", r_min.data[1] == t.CDouble(2.0), "mismatch")
check("minimum[2]==3", r_min.data[2] == t.CDouble(3.0), "mismatch")
# where: cond > 0 ? a : b
cond: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(1.0), t.CDouble(0.0))
r_where: numpy.ndarray | t.CPtr = numpy.np_where(cond, a, b)
cond: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(1.0), t.CDouble(0.0))
r_where: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_where(cond, a, b)
check("where[0]==4", r_where.data[0] == t.CDouble(4.0), "mismatch")
check("where[1]==5", r_where.data[1] == t.CDouble(5.0), "mismatch")
check("where[2]==6", r_where.data[2] == t.CDouble(6.0), "mismatch")
@@ -668,18 +668,18 @@ def test_max_min_where():
def test_flatten_trace():
section_header("flatten / trace")
a: numpy.ndarray | t.CPtr = numpy.empty2d(pool, 2, 3)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.empty2d(pool, 2, 3)
a.set2d(0, 0, t.CDouble(1.0)); a.set2d(0, 1, t.CDouble(2.0)); a.set2d(0, 2, t.CDouble(3.0))
a.set2d(1, 0, t.CDouble(4.0)); a.set2d(1, 1, t.CDouble(5.0)); a.set2d(1, 2, t.CDouble(6.0))
r_flat: numpy.ndarray | t.CPtr = numpy.flatten(a)
r_flat: numpy.ndarray[t.CDouble] | t.CPtr = numpy.flatten(a)
check("flatten not None", r_flat != None, "flatten returned None")
check("flatten ndim==1", r_flat.ndim == 1, "ndim mismatch")
check("flatten size==6", r_flat.size == 6, "size mismatch")
check("flatten[0]==1", r_flat.data[0] == t.CDouble(1.0), "mismatch")
# trace of [[1,2],[3,4]] = 1+4 = 5
b: numpy.ndarray | t.CPtr = numpy.empty2d(pool, 2, 2)
b: numpy.ndarray[t.CDouble] | t.CPtr = numpy.empty2d(pool, 2, 2)
b.set2d(0, 0, t.CDouble(1.0)); b.set2d(0, 1, t.CDouble(2.0))
b.set2d(1, 0, t.CDouble(3.0)); b.set2d(1, 1, t.CDouble(4.0))
tr: t.CDouble = numpy.trace(b)
@@ -694,10 +694,10 @@ def test_flatten_trace():
def test_outer():
section_header("outer")
a: numpy.ndarray | t.CPtr = vec2(pool, t.CDouble(1.0), t.CDouble(2.0))
b: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(3.0), t.CDouble(4.0), t.CDouble(5.0))
a: numpy.ndarray[t.CDouble] | t.CPtr = vec2(pool, t.CDouble(1.0), t.CDouble(2.0))
b: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(3.0), t.CDouble(4.0), t.CDouble(5.0))
r: numpy.ndarray | t.CPtr = numpy.outer(a, b)
r: numpy.ndarray[t.CDouble] | t.CPtr = numpy.outer(a, b)
check("outer not None", r != None, "outer returned None")
check("outer ndim==2", r.ndim == 2, "ndim mismatch")
check("outer[0,0]==3", r.at2d(0, 0) == t.CDouble(3.0), "mismatch")
@@ -717,30 +717,30 @@ def test_outer():
def test_bool_comparison():
section_header("np_all / np_any / np_count_nonzero / np_equal / np_less / np_greater")
a: numpy.ndarray | t.CPtr = numpy.zeros(pool, 4)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 4)
a.data[0] = t.CDouble(1.0); a.data[1] = t.CDouble(0.0); a.data[2] = t.CDouble(3.0); a.data[3] = t.CDouble(0.0)
check("count_nonzero==2", numpy.np_count_nonzero(a) == 2, "mismatch")
check("any==1", numpy.np_any(a) == 1, "mismatch")
b: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
b: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
check("all(1,2,3)==1", numpy.np_all(b) == 1, "mismatch")
c: numpy.ndarray | t.CPtr = numpy.zeros(pool, 3)
c: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 3)
check("all(0,0,0)==0", numpy.np_all(c) == 0, "mismatch")
check("any(0,0,0)==0", numpy.np_any(c) == 0, "mismatch")
d: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
e: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(0.0), t.CDouble(4.0))
d: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
e: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(0.0), t.CDouble(4.0))
r_eq: numpy.ndarray | t.CPtr = numpy.np_equal(d, e)
r_eq: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_equal(d, e)
check("equal[0]==1", r_eq.data[0] == t.CDouble(1.0), "mismatch")
check("equal[1]==0", r_eq.data[1] == t.CDouble(0.0), "mismatch")
r_lt: numpy.ndarray | t.CPtr = numpy.np_less(d, e)
r_lt: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_less(d, e)
check("less[2]==1", r_lt.data[2] == t.CDouble(1.0), "mismatch")
check("less[0]==0", r_lt.data[0] == t.CDouble(0.0), "mismatch")
r_gt: numpy.ndarray | t.CPtr = numpy.np_greater(d, e)
r_gt: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_greater(d, e)
check("greater[1]==1", r_gt.data[1] == t.CDouble(1.0), "mismatch")
a.delete()
@@ -761,32 +761,32 @@ def test_linalg():
section_header("det2x2 / inv2x2 / cross3 / prod / median / percentile")
# det2x2: [[1,2],[3,4]] -> 1*4-2*3 = -2
a: numpy.ndarray | t.CPtr = numpy.empty2d(pool, 2, 2)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.empty2d(pool, 2, 2)
a.set2d(0, 0, t.CDouble(1.0)); a.set2d(0, 1, t.CDouble(2.0))
a.set2d(1, 0, t.CDouble(3.0)); a.set2d(1, 1, t.CDouble(4.0))
d: t.CDouble = numpy.det2x2(a)
check("det2x2==-2", d == t.CDouble(-2.0), "mismatch")
# inv2x2 of [[1,2],[3,4]]
inv: numpy.ndarray | t.CPtr = numpy.inv2x2(a)
inv: numpy.ndarray[t.CDouble] | t.CPtr = numpy.inv2x2(a)
check("inv2x2 not None", inv != None, "inv2x2 returned None")
check("inv[0,0]==-2", approx_eq(inv.at2d(0, 0), t.CDouble(-2.0)), "mismatch")
check("inv[0,1]==1", approx_eq(inv.at2d(0, 1), t.CDouble(1.0)), "mismatch")
# cross3: [1,0,0] x [0,1,0] = [0,0,1]
x: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(0.0), t.CDouble(0.0))
y: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(1.0), t.CDouble(0.0))
cr: numpy.ndarray | t.CPtr = numpy.cross3(x, y)
x: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(0.0), t.CDouble(0.0))
y: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(1.0), t.CDouble(0.0))
cr: numpy.ndarray[t.CDouble] | t.CPtr = numpy.cross3(x, y)
check("cross not None", cr != None, "cross returned None")
check("cross[2]==1", cr.data[2] == t.CDouble(1.0), "mismatch")
# prod
p: numpy.ndarray | t.CPtr = numpy.zeros(pool, 4)
p: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 4)
p.data[0] = t.CDouble(1.0); p.data[1] = t.CDouble(2.0); p.data[2] = t.CDouble(3.0); p.data[3] = t.CDouble(4.0)
check("prod==24", numpy.prod(p) == t.CDouble(24.0), "mismatch")
# median
m: numpy.ndarray | t.CPtr = numpy.zeros(pool, 5)
m: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 5)
m.data[0] = t.CDouble(3.0); m.data[1] = t.CDouble(1.0); m.data[2] = t.CDouble(2.0)
m.data[3] = t.CDouble(5.0); m.data[4] = t.CDouble(4.0)
check("median==3", numpy.median(m) == t.CDouble(3.0), "mismatch")
@@ -810,23 +810,23 @@ def test_linalg():
def test_additional_creation():
section_header("full2d / zeros_like / ones_like / arange1 / constants")
f: numpy.ndarray | t.CPtr = numpy.full2d(pool, 2, 3, t.CDouble(7.0))
f: numpy.ndarray[t.CDouble] | t.CPtr = numpy.full2d(pool, 2, 3, t.CDouble(7.0))
check("full2d not None", f != None, "full2d returned None")
check("full2d ndim==2", f.ndim == 2, "ndim mismatch")
check("full2d[0,0]==7", f.at2d(0, 0) == t.CDouble(7.0), "mismatch")
check("full2d[1,2]==7", f.at2d(1, 2) == t.CDouble(7.0), "mismatch")
a: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
zl: numpy.ndarray | t.CPtr = numpy.zeros_like(a)
a: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(1.0), t.CDouble(2.0), t.CDouble(3.0))
zl: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros_like(a)
check("zeros_like not None", zl != None, "returned None")
check("zeros_like size==3", zl.size == 3, "size mismatch")
check("zeros_like[0]==0", zl.data[0] == t.CDouble(0.0), "mismatch")
ol: numpy.ndarray | t.CPtr = numpy.ones_like(a)
ol: numpy.ndarray[t.CDouble] | t.CPtr = numpy.ones_like(a)
check("ones_like not None", ol != None, "returned None")
check("ones_like[0]==1", ol.data[0] == t.CDouble(1.0), "mismatch")
ar: numpy.ndarray | t.CPtr = numpy.arange1(pool, t.CDouble(4.0))
ar: numpy.ndarray[t.CDouble] | t.CPtr = numpy.arange1(pool, t.CDouble(4.0))
check("arange1 not None", ar != None, "returned None")
check("arange1 size==4", ar.size == 4, "size mismatch")
check("arange1[0]==0", ar.data[0] == t.CDouble(0.0), "mismatch")
@@ -850,8 +850,8 @@ def test_additional_creation():
def test_interp():
section_header("np_interp")
xp: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(1.0), t.CDouble(2.0))
fp: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(10.0), t.CDouble(20.0))
xp: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(1.0), t.CDouble(2.0))
fp: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(10.0), t.CDouble(20.0))
v: t.CDouble = numpy.np_interp(t.CDouble(0.5), xp, fp)
check("interp(0.5)==5", approx_eq(v, t.CDouble(5.0)), "mismatch")
@@ -877,33 +877,33 @@ def test_numpy_edge():
section_header("edge: size=0 / size=1 / non-SIMD-block / negative / div-zero")
# size=0: pool.alloc(0) returns None, so zeros(0) returns None (expected)
a0: numpy.ndarray | t.CPtr = numpy.zeros(pool, 0)
a0: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 0)
check("zeros(0) returns None", a0 == None, "expected None for size=0")
# size=1
a1: numpy.ndarray | t.CPtr = numpy.zeros(pool, 1)
a1: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 1)
check("zeros(1) not None", a1 != None, "returned None")
check("zeros(1) size==1", a1.size == 1, "size mismatch")
check("zeros(1) [0]==0", a1.data[0] == t.CDouble(0.0), "mismatch")
a1.delete()
# non-SIMD-block sizes (1,2,3,5,6,7)
a5: numpy.ndarray | t.CPtr = numpy.arange(pool, t.CDouble(0.0), t.CDouble(5.0), t.CDouble(1.0))
a5: numpy.ndarray[t.CDouble] | t.CPtr = numpy.arange(pool, t.CDouble(0.0), t.CDouble(5.0), t.CDouble(1.0))
check("arange(5) size==5", a5.size == 5, "size mismatch")
check("arange(5) sum==10", approx_eq(a5.sum(), t.CDouble(10.0)), "mismatch")
a5.delete()
a6: numpy.ndarray | t.CPtr = numpy.arange(pool, t.CDouble(0.0), t.CDouble(6.0), t.CDouble(1.0))
a6: numpy.ndarray[t.CDouble] | t.CPtr = numpy.arange(pool, t.CDouble(0.0), t.CDouble(6.0), t.CDouble(1.0))
check("arange(6) sum==15", approx_eq(a6.sum(), t.CDouble(15.0)), "mismatch")
a6.delete()
a7: numpy.ndarray | t.CPtr = numpy.arange(pool, t.CDouble(0.0), t.CDouble(7.0), t.CDouble(1.0))
a7: numpy.ndarray[t.CDouble] | t.CPtr = numpy.arange(pool, t.CDouble(0.0), t.CDouble(7.0), t.CDouble(1.0))
check("arange(7) sum==21", approx_eq(a7.sum(), t.CDouble(21.0)), "mismatch")
a7.delete()
# negative values in sqrt
neg_arr: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(-1.0), t.CDouble(0.0), t.CDouble(4.0))
r_sqrt_neg: numpy.ndarray | t.CPtr = numpy.np_sqrt(neg_arr)
neg_arr: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(-1.0), t.CDouble(0.0), t.CDouble(4.0))
r_sqrt_neg: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_sqrt(neg_arr)
check("sqrt(neg) not None", r_sqrt_neg != None, "returned None")
# sqrt(4)=2, sqrt(0)=0; sqrt(-1) is NaN (implementation-defined)
check("sqrt(4)==2", approx_eq(r_sqrt_neg.data[2], t.CDouble(2.0)), "mismatch")
@@ -911,9 +911,9 @@ def test_numpy_edge():
r_sqrt_neg.delete()
# div-by-zero: b/a where a has zeros
za: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(2.0), t.CDouble(0.0))
zb: numpy.ndarray | t.CPtr = vec3(pool, t.CDouble(6.0), t.CDouble(4.0), t.CDouble(8.0))
r_divz: numpy.ndarray | t.CPtr = za / zb
za: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(0.0), t.CDouble(2.0), t.CDouble(0.0))
zb: numpy.ndarray[t.CDouble] | t.CPtr = vec3(pool, t.CDouble(6.0), t.CDouble(4.0), t.CDouble(8.0))
r_divz: numpy.ndarray[t.CDouble] | t.CPtr = za / zb
check("0/6==0", r_divz.data[0] == t.CDouble(0.0), "mismatch")
check("2/4==0.5", r_divz.data[1] == t.CDouble(0.5), "mismatch")
za.delete()
@@ -921,7 +921,7 @@ def test_numpy_edge():
r_divz.delete()
# large array (non-aligned, non-SIMD-block)
big: numpy.ndarray | t.CPtr = numpy.zeros(pool, 1000)
big: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, 1000)
check("zeros(1000) not None", big != None, "returned None")
check("zeros(1000) size==1000", big.size == 1000, "size mismatch")
big.delete()
@@ -938,8 +938,8 @@ def bench_numpy_perf():
N: t.CSizeT = 10000
ITERS: t.CInt = 5
a: numpy.ndarray | t.CPtr = numpy.zeros(pool, N)
b: numpy.ndarray | t.CPtr = numpy.zeros(pool, N)
a: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, N)
b: numpy.ndarray[t.CDouble] | t.CPtr = numpy.zeros(pool, N)
i: t.CSizeT = 0
while i < N:
a.data[i] = t.CDouble(1.0)
@@ -950,7 +950,7 @@ def bench_numpy_perf():
t0: t.CDouble = c.Timer()
j: t.CInt = 0
while j < ITERS:
r: numpy.ndarray | t.CPtr = a + b
r: numpy.ndarray[t.CDouble] | t.CPtr = a + b
r.delete()
j += 1
t1: t.CDouble = c.Timer()
@@ -960,7 +960,7 @@ def bench_numpy_perf():
t0 = c.Timer()
j = 0
while j < ITERS:
r2: numpy.ndarray | t.CPtr = a * b
r2: numpy.ndarray[t.CDouble] | t.CPtr = a * b
r2.delete()
j += 1
t1 = c.Timer()
@@ -970,7 +970,7 @@ def bench_numpy_perf():
t0 = c.Timer()
j = 0
while j < ITERS:
r3: numpy.ndarray | t.CPtr = numpy.np_sqrt(a)
r3: numpy.ndarray[t.CDouble] | t.CPtr = numpy.np_sqrt(a)
r3.delete()
j += 1
t1 = c.Timer()