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Diffstat (limited to '.venv/lib/python3.12/site-packages/numpy/ma/timer_comparison.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/numpy/ma/timer_comparison.py | 443 |
1 files changed, 443 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/numpy/ma/timer_comparison.py b/.venv/lib/python3.12/site-packages/numpy/ma/timer_comparison.py new file mode 100644 index 00000000..9eb1a23c --- /dev/null +++ b/.venv/lib/python3.12/site-packages/numpy/ma/timer_comparison.py @@ -0,0 +1,443 @@ +import timeit +from functools import reduce + +import numpy as np +from numpy import float_ +import numpy.core.fromnumeric as fromnumeric + +from numpy.testing import build_err_msg + + +pi = np.pi + +class ModuleTester: + def __init__(self, module): + self.module = module + self.allequal = module.allequal + self.arange = module.arange + self.array = module.array + self.concatenate = module.concatenate + self.count = module.count + self.equal = module.equal + self.filled = module.filled + self.getmask = module.getmask + self.getmaskarray = module.getmaskarray + self.id = id + self.inner = module.inner + self.make_mask = module.make_mask + self.masked = module.masked + self.masked_array = module.masked_array + self.masked_values = module.masked_values + self.mask_or = module.mask_or + self.nomask = module.nomask + self.ones = module.ones + self.outer = module.outer + self.repeat = module.repeat + self.resize = module.resize + self.sort = module.sort + self.take = module.take + self.transpose = module.transpose + self.zeros = module.zeros + self.MaskType = module.MaskType + try: + self.umath = module.umath + except AttributeError: + self.umath = module.core.umath + self.testnames = [] + + def assert_array_compare(self, comparison, x, y, err_msg='', header='', + fill_value=True): + """ + Assert that a comparison of two masked arrays is satisfied elementwise. + + """ + xf = self.filled(x) + yf = self.filled(y) + m = self.mask_or(self.getmask(x), self.getmask(y)) + + x = self.filled(self.masked_array(xf, mask=m), fill_value) + y = self.filled(self.masked_array(yf, mask=m), fill_value) + if (x.dtype.char != "O"): + x = x.astype(float_) + if isinstance(x, np.ndarray) and x.size > 1: + x[np.isnan(x)] = 0 + elif np.isnan(x): + x = 0 + if (y.dtype.char != "O"): + y = y.astype(float_) + if isinstance(y, np.ndarray) and y.size > 1: + y[np.isnan(y)] = 0 + elif np.isnan(y): + y = 0 + try: + cond = (x.shape == () or y.shape == ()) or x.shape == y.shape + if not cond: + msg = build_err_msg([x, y], + err_msg + + f'\n(shapes {x.shape}, {y.shape} mismatch)', + header=header, + names=('x', 'y')) + assert cond, msg + val = comparison(x, y) + if m is not self.nomask and fill_value: + val = self.masked_array(val, mask=m) + if isinstance(val, bool): + cond = val + reduced = [0] + else: + reduced = val.ravel() + cond = reduced.all() + reduced = reduced.tolist() + if not cond: + match = 100-100.0*reduced.count(1)/len(reduced) + msg = build_err_msg([x, y], + err_msg + + '\n(mismatch %s%%)' % (match,), + header=header, + names=('x', 'y')) + assert cond, msg + except ValueError as e: + msg = build_err_msg([x, y], err_msg, header=header, names=('x', 'y')) + raise ValueError(msg) from e + + def assert_array_equal(self, x, y, err_msg=''): + """ + Checks the elementwise equality of two masked arrays. + + """ + self.assert_array_compare(self.equal, x, y, err_msg=err_msg, + header='Arrays are not equal') + + @np.errstate(all='ignore') + def test_0(self): + """ + Tests creation + + """ + x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.]) + m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] + xm = self.masked_array(x, mask=m) + xm[0] + + @np.errstate(all='ignore') + def test_1(self): + """ + Tests creation + + """ + x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.]) + y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.]) + m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] + m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1] + xm = self.masked_array(x, mask=m1) + ym = self.masked_array(y, mask=m2) + xf = np.where(m1, 1.e+20, x) + xm.set_fill_value(1.e+20) + + assert((xm-ym).filled(0).any()) + s = x.shape + assert(xm.size == reduce(lambda x, y:x*y, s)) + assert(self.count(xm) == len(m1) - reduce(lambda x, y:x+y, m1)) + + for s in [(4, 3), (6, 2)]: + x.shape = s + y.shape = s + xm.shape = s + ym.shape = s + xf.shape = s + assert(self.count(xm) == len(m1) - reduce(lambda x, y:x+y, m1)) + + @np.errstate(all='ignore') + def test_2(self): + """ + Tests conversions and indexing. + + """ + x1 = np.array([1, 2, 4, 3]) + x2 = self.array(x1, mask=[1, 0, 0, 0]) + x3 = self.array(x1, mask=[0, 1, 0, 1]) + x4 = self.array(x1) + # test conversion to strings, no errors + str(x2) + repr(x2) + # tests of indexing + assert type(x2[1]) is type(x1[1]) + assert x1[1] == x2[1] + x1[2] = 9 + x2[2] = 9 + self.assert_array_equal(x1, x2) + x1[1:3] = 99 + x2[1:3] = 99 + x2[1] = self.masked + x2[1:3] = self.masked + x2[:] = x1 + x2[1] = self.masked + x3[:] = self.masked_array([1, 2, 3, 4], [0, 1, 1, 0]) + x4[:] = self.masked_array([1, 2, 3, 4], [0, 1, 1, 0]) + x1 = np.arange(5)*1.0 + x2 = self.masked_values(x1, 3.0) + x1 = self.array([1, 'hello', 2, 3], object) + x2 = np.array([1, 'hello', 2, 3], object) + # check that no error occurs. + x1[1] + x2[1] + assert x1[1:1].shape == (0,) + # Tests copy-size + n = [0, 0, 1, 0, 0] + m = self.make_mask(n) + m2 = self.make_mask(m) + assert(m is m2) + m3 = self.make_mask(m, copy=1) + assert(m is not m3) + + @np.errstate(all='ignore') + def test_3(self): + """ + Tests resize/repeat + + """ + x4 = self.arange(4) + x4[2] = self.masked + y4 = self.resize(x4, (8,)) + assert self.allequal(self.concatenate([x4, x4]), y4) + assert self.allequal(self.getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0]) + y5 = self.repeat(x4, (2, 2, 2, 2), axis=0) + self.assert_array_equal(y5, [0, 0, 1, 1, 2, 2, 3, 3]) + y6 = self.repeat(x4, 2, axis=0) + assert self.allequal(y5, y6) + y7 = x4.repeat((2, 2, 2, 2), axis=0) + assert self.allequal(y5, y7) + y8 = x4.repeat(2, 0) + assert self.allequal(y5, y8) + + @np.errstate(all='ignore') + def test_4(self): + """ + Test of take, transpose, inner, outer products. + + """ + x = self.arange(24) + y = np.arange(24) + x[5:6] = self.masked + x = x.reshape(2, 3, 4) + y = y.reshape(2, 3, 4) + assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1))) + assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1)) + assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)), + self.inner(x, y)) + assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)), + self.outer(x, y)) + y = self.array(['abc', 1, 'def', 2, 3], object) + y[2] = self.masked + t = self.take(y, [0, 3, 4]) + assert t[0] == 'abc' + assert t[1] == 2 + assert t[2] == 3 + + @np.errstate(all='ignore') + def test_5(self): + """ + Tests inplace w/ scalar + + """ + x = self.arange(10) + y = self.arange(10) + xm = self.arange(10) + xm[2] = self.masked + x += 1 + assert self.allequal(x, y+1) + xm += 1 + assert self.allequal(xm, y+1) + + x = self.arange(10) + xm = self.arange(10) + xm[2] = self.masked + x -= 1 + assert self.allequal(x, y-1) + xm -= 1 + assert self.allequal(xm, y-1) + + x = self.arange(10)*1.0 + xm = self.arange(10)*1.0 + xm[2] = self.masked + x *= 2.0 + assert self.allequal(x, y*2) + xm *= 2.0 + assert self.allequal(xm, y*2) + + x = self.arange(10)*2 + xm = self.arange(10)*2 + xm[2] = self.masked + x /= 2 + assert self.allequal(x, y) + xm /= 2 + assert self.allequal(xm, y) + + x = self.arange(10)*1.0 + xm = self.arange(10)*1.0 + xm[2] = self.masked + x /= 2.0 + assert self.allequal(x, y/2.0) + xm /= self.arange(10) + self.assert_array_equal(xm, self.ones((10,))) + + x = self.arange(10).astype(float_) + xm = self.arange(10) + xm[2] = self.masked + x += 1. + assert self.allequal(x, y + 1.) + + @np.errstate(all='ignore') + def test_6(self): + """ + Tests inplace w/ array + + """ + x = self.arange(10, dtype=float_) + y = self.arange(10) + xm = self.arange(10, dtype=float_) + xm[2] = self.masked + m = xm.mask + a = self.arange(10, dtype=float_) + a[-1] = self.masked + x += a + xm += a + assert self.allequal(x, y+a) + assert self.allequal(xm, y+a) + assert self.allequal(xm.mask, self.mask_or(m, a.mask)) + + x = self.arange(10, dtype=float_) + xm = self.arange(10, dtype=float_) + xm[2] = self.masked + m = xm.mask + a = self.arange(10, dtype=float_) + a[-1] = self.masked + x -= a + xm -= a + assert self.allequal(x, y-a) + assert self.allequal(xm, y-a) + assert self.allequal(xm.mask, self.mask_or(m, a.mask)) + + x = self.arange(10, dtype=float_) + xm = self.arange(10, dtype=float_) + xm[2] = self.masked + m = xm.mask + a = self.arange(10, dtype=float_) + a[-1] = self.masked + x *= a + xm *= a + assert self.allequal(x, y*a) + assert self.allequal(xm, y*a) + assert self.allequal(xm.mask, self.mask_or(m, a.mask)) + + x = self.arange(10, dtype=float_) + xm = self.arange(10, dtype=float_) + xm[2] = self.masked + m = xm.mask + a = self.arange(10, dtype=float_) + a[-1] = self.masked + x /= a + xm /= a + + @np.errstate(all='ignore') + def test_7(self): + "Tests ufunc" + d = (self.array([1.0, 0, -1, pi/2]*2, mask=[0, 1]+[0]*6), + self.array([1.0, 0, -1, pi/2]*2, mask=[1, 0]+[0]*6),) + for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate', +# 'sin', 'cos', 'tan', +# 'arcsin', 'arccos', 'arctan', +# 'sinh', 'cosh', 'tanh', +# 'arcsinh', +# 'arccosh', +# 'arctanh', +# 'absolute', 'fabs', 'negative', +# # 'nonzero', 'around', +# 'floor', 'ceil', +# # 'sometrue', 'alltrue', +# 'logical_not', +# 'add', 'subtract', 'multiply', +# 'divide', 'true_divide', 'floor_divide', +# 'remainder', 'fmod', 'hypot', 'arctan2', +# 'equal', 'not_equal', 'less_equal', 'greater_equal', +# 'less', 'greater', +# 'logical_and', 'logical_or', 'logical_xor', + ]: + try: + uf = getattr(self.umath, f) + except AttributeError: + uf = getattr(fromnumeric, f) + mf = getattr(self.module, f) + args = d[:uf.nin] + ur = uf(*args) + mr = mf(*args) + self.assert_array_equal(ur.filled(0), mr.filled(0), f) + self.assert_array_equal(ur._mask, mr._mask) + + @np.errstate(all='ignore') + def test_99(self): + # test average + ott = self.array([0., 1., 2., 3.], mask=[1, 0, 0, 0]) + self.assert_array_equal(2.0, self.average(ott, axis=0)) + self.assert_array_equal(2.0, self.average(ott, weights=[1., 1., 2., 1.])) + result, wts = self.average(ott, weights=[1., 1., 2., 1.], returned=1) + self.assert_array_equal(2.0, result) + assert(wts == 4.0) + ott[:] = self.masked + assert(self.average(ott, axis=0) is self.masked) + ott = self.array([0., 1., 2., 3.], mask=[1, 0, 0, 0]) + ott = ott.reshape(2, 2) + ott[:, 1] = self.masked + self.assert_array_equal(self.average(ott, axis=0), [2.0, 0.0]) + assert(self.average(ott, axis=1)[0] is self.masked) + self.assert_array_equal([2., 0.], self.average(ott, axis=0)) + result, wts = self.average(ott, axis=0, returned=1) + self.assert_array_equal(wts, [1., 0.]) + w1 = [0, 1, 1, 1, 1, 0] + w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]] + x = self.arange(6) + self.assert_array_equal(self.average(x, axis=0), 2.5) + self.assert_array_equal(self.average(x, axis=0, weights=w1), 2.5) + y = self.array([self.arange(6), 2.0*self.arange(6)]) + self.assert_array_equal(self.average(y, None), np.add.reduce(np.arange(6))*3./12.) + self.assert_array_equal(self.average(y, axis=0), np.arange(6) * 3./2.) + self.assert_array_equal(self.average(y, axis=1), [self.average(x, axis=0), self.average(x, axis=0) * 2.0]) + self.assert_array_equal(self.average(y, None, weights=w2), 20./6.) + self.assert_array_equal(self.average(y, axis=0, weights=w2), [0., 1., 2., 3., 4., 10.]) + self.assert_array_equal(self.average(y, axis=1), [self.average(x, axis=0), self.average(x, axis=0) * 2.0]) + m1 = self.zeros(6) + m2 = [0, 0, 1, 1, 0, 0] + m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]] + m4 = self.ones(6) + m5 = [0, 1, 1, 1, 1, 1] + self.assert_array_equal(self.average(self.masked_array(x, m1), axis=0), 2.5) + self.assert_array_equal(self.average(self.masked_array(x, m2), axis=0), 2.5) + self.assert_array_equal(self.average(self.masked_array(x, m5), axis=0), 0.0) + self.assert_array_equal(self.count(self.average(self.masked_array(x, m4), axis=0)), 0) + z = self.masked_array(y, m3) + self.assert_array_equal(self.average(z, None), 20./6.) + self.assert_array_equal(self.average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5]) + self.assert_array_equal(self.average(z, axis=1), [2.5, 5.0]) + self.assert_array_equal(self.average(z, axis=0, weights=w2), [0., 1., 99., 99., 4.0, 10.0]) + + @np.errstate(all='ignore') + def test_A(self): + x = self.arange(24) + x[5:6] = self.masked + x = x.reshape(2, 3, 4) + + +if __name__ == '__main__': + setup_base = ("from __main__ import ModuleTester \n" + "import numpy\n" + "tester = ModuleTester(module)\n") + setup_cur = "import numpy.ma.core as module\n" + setup_base + (nrepeat, nloop) = (10, 10) + + for i in range(1, 8): + func = 'tester.test_%i()' % i + cur = timeit.Timer(func, setup_cur).repeat(nrepeat, nloop*10) + cur = np.sort(cur) + print("#%i" % i + 50*'.') + print(eval("ModuleTester.test_%i.__doc__" % i)) + print(f'core_current : {cur[0]:.3f} - {cur[1]:.3f}') |