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mode 100644 index 0000000000000000000000000000000000000000..2f54bebfdb27d54f436378e4ab6d6c8f2426dd90 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/polynomial/tests/test_chebyshev.py @@ -0,0 +1,619 @@ +"""Tests for chebyshev module. + +""" +from functools import reduce + +import numpy as np +import numpy.polynomial.chebyshev as cheb +from numpy.polynomial.polynomial import polyval +from numpy.testing import ( + assert_almost_equal, assert_raises, assert_equal, assert_, + ) + + +def trim(x): + return cheb.chebtrim(x, tol=1e-6) + +T0 = [1] +T1 = [0, 1] +T2 = [-1, 0, 2] +T3 = [0, -3, 0, 4] +T4 = [1, 0, -8, 0, 8] +T5 = [0, 5, 0, -20, 0, 16] +T6 = [-1, 0, 18, 0, -48, 0, 32] +T7 = [0, -7, 0, 56, 0, -112, 0, 64] +T8 = [1, 0, -32, 0, 160, 0, -256, 0, 128] +T9 = [0, 9, 0, -120, 0, 432, 0, -576, 0, 256] + +Tlist = [T0, T1, T2, T3, T4, T5, T6, T7, T8, T9] + + +class TestPrivate: + + def test__cseries_to_zseries(self): + for i in range(5): + inp = np.array([2] + [1]*i, np.double) + tgt = np.array([.5]*i + [2] + [.5]*i, np.double) + res = cheb._cseries_to_zseries(inp) + assert_equal(res, tgt) + + def test__zseries_to_cseries(self): + for i in range(5): + inp = np.array([.5]*i + [2] + [.5]*i, np.double) + tgt = np.array([2] + [1]*i, np.double) + res = cheb._zseries_to_cseries(inp) + assert_equal(res, tgt) + + +class TestConstants: + + def test_chebdomain(self): + assert_equal(cheb.chebdomain, [-1, 1]) + + def test_chebzero(self): + assert_equal(cheb.chebzero, [0]) + + def test_chebone(self): + assert_equal(cheb.chebone, [1]) + + def test_chebx(self): + assert_equal(cheb.chebx, [0, 1]) + + +class TestArithmetic: + + def test_chebadd(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + tgt = np.zeros(max(i, j) + 1) + tgt[i] += 1 + tgt[j] += 1 + res = cheb.chebadd([0]*i + [1], [0]*j + [1]) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_chebsub(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + tgt = np.zeros(max(i, j) + 1) + tgt[i] += 1 + tgt[j] -= 1 + res = cheb.chebsub([0]*i + [1], [0]*j + [1]) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_chebmulx(self): + assert_equal(cheb.chebmulx([0]), [0]) + assert_equal(cheb.chebmulx([1]), [0, 1]) + for i in range(1, 5): + ser = [0]*i + [1] + tgt = [0]*(i - 1) + [.5, 0, .5] + assert_equal(cheb.chebmulx(ser), tgt) + + def test_chebmul(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + tgt = np.zeros(i + j + 1) + tgt[i + j] += .5 + tgt[abs(i - j)] += .5 + res = cheb.chebmul([0]*i + [1], [0]*j + [1]) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_chebdiv(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + ci = [0]*i + [1] + cj = [0]*j + [1] + tgt = cheb.chebadd(ci, cj) + quo, rem = cheb.chebdiv(tgt, ci) + res = cheb.chebadd(cheb.chebmul(quo, ci), rem) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_chebpow(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + c = np.arange(i + 1) + tgt = reduce(cheb.chebmul, [c]*j, np.array([1])) + res = cheb.chebpow(c, j) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + +class TestEvaluation: + # coefficients of 1 + 2*x + 3*x**2 + c1d = np.array([2.5, 2., 1.5]) + c2d = np.einsum('i,j->ij', c1d, c1d) + c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d) + + # some random values in [-1, 1) + x = np.random.random((3, 5))*2 - 1 + y = polyval(x, [1., 2., 3.]) + + def test_chebval(self): + #check empty input + assert_equal(cheb.chebval([], [1]).size, 0) + + #check normal input) + x = np.linspace(-1, 1) + y = [polyval(x, c) for c in Tlist] + for i in range(10): + msg = f"At i={i}" + tgt = y[i] + res = cheb.chebval(x, [0]*i + [1]) + assert_almost_equal(res, tgt, err_msg=msg) + + #check that shape is preserved + for i in range(3): + dims = [2]*i + x = np.zeros(dims) + assert_equal(cheb.chebval(x, [1]).shape, dims) + assert_equal(cheb.chebval(x, [1, 0]).shape, dims) + assert_equal(cheb.chebval(x, [1, 0, 0]).shape, dims) + + def test_chebval2d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test exceptions + assert_raises(ValueError, cheb.chebval2d, x1, x2[:2], self.c2d) + + #test values + tgt = y1*y2 + res = cheb.chebval2d(x1, x2, self.c2d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = cheb.chebval2d(z, z, self.c2d) + assert_(res.shape == (2, 3)) + + def test_chebval3d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test exceptions + assert_raises(ValueError, cheb.chebval3d, x1, x2, x3[:2], self.c3d) + + #test values + tgt = y1*y2*y3 + res = cheb.chebval3d(x1, x2, x3, self.c3d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = cheb.chebval3d(z, z, z, self.c3d) + assert_(res.shape == (2, 3)) + + def test_chebgrid2d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test values + tgt = np.einsum('i,j->ij', y1, y2) + res = cheb.chebgrid2d(x1, x2, self.c2d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = cheb.chebgrid2d(z, z, self.c2d) + assert_(res.shape == (2, 3)*2) + + def test_chebgrid3d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test values + tgt = np.einsum('i,j,k->ijk', y1, y2, y3) + res = cheb.chebgrid3d(x1, x2, x3, self.c3d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = cheb.chebgrid3d(z, z, z, self.c3d) + assert_(res.shape == (2, 3)*3) + + +class TestIntegral: + + def test_chebint(self): + # check exceptions + assert_raises(TypeError, cheb.chebint, [0], .5) + assert_raises(ValueError, cheb.chebint, [0], -1) + assert_raises(ValueError, cheb.chebint, [0], 1, [0, 0]) + assert_raises(ValueError, cheb.chebint, [0], lbnd=[0]) + assert_raises(ValueError, cheb.chebint, [0], scl=[0]) + assert_raises(TypeError, cheb.chebint, [0], axis=.5) + + # test integration of zero polynomial + for i in range(2, 5): + k = [0]*(i - 2) + [1] + res = cheb.chebint([0], m=i, k=k) + assert_almost_equal(res, [0, 1]) + + # check single integration with integration constant + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + tgt = [i] + [0]*i + [1/scl] + chebpol = cheb.poly2cheb(pol) + chebint = cheb.chebint(chebpol, m=1, k=[i]) + res = cheb.cheb2poly(chebint) + assert_almost_equal(trim(res), trim(tgt)) + + # check single integration with integration constant and lbnd + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + chebpol = cheb.poly2cheb(pol) + chebint = cheb.chebint(chebpol, m=1, k=[i], lbnd=-1) + assert_almost_equal(cheb.chebval(-1, chebint), i) + + # check single integration with integration constant and scaling + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + tgt = [i] + [0]*i + [2/scl] + chebpol = cheb.poly2cheb(pol) + chebint = cheb.chebint(chebpol, m=1, k=[i], scl=2) + res = cheb.cheb2poly(chebint) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with default k + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = cheb.chebint(tgt, m=1) + res = cheb.chebint(pol, m=j) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with defined k + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = cheb.chebint(tgt, m=1, k=[k]) + res = cheb.chebint(pol, m=j, k=list(range(j))) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with lbnd + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = cheb.chebint(tgt, m=1, k=[k], lbnd=-1) + res = cheb.chebint(pol, m=j, k=list(range(j)), lbnd=-1) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with scaling + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = cheb.chebint(tgt, m=1, k=[k], scl=2) + res = cheb.chebint(pol, m=j, k=list(range(j)), scl=2) + assert_almost_equal(trim(res), trim(tgt)) + + def test_chebint_axis(self): + # check that axis keyword works + c2d = np.random.random((3, 4)) + + tgt = np.vstack([cheb.chebint(c) for c in c2d.T]).T + res = cheb.chebint(c2d, axis=0) + assert_almost_equal(res, tgt) + + tgt = np.vstack([cheb.chebint(c) for c in c2d]) + res = cheb.chebint(c2d, axis=1) + assert_almost_equal(res, tgt) + + tgt = np.vstack([cheb.chebint(c, k=3) for c in c2d]) + res = cheb.chebint(c2d, k=3, axis=1) + assert_almost_equal(res, tgt) + + +class TestDerivative: + + def test_chebder(self): + # check exceptions + assert_raises(TypeError, cheb.chebder, [0], .5) + assert_raises(ValueError, cheb.chebder, [0], -1) + + # check that zeroth derivative does nothing + for i in range(5): + tgt = [0]*i + [1] + res = cheb.chebder(tgt, m=0) + assert_equal(trim(res), trim(tgt)) + + # check that derivation is the inverse of integration + for i in range(5): + for j in range(2, 5): + tgt = [0]*i + [1] + res = cheb.chebder(cheb.chebint(tgt, m=j), m=j) + assert_almost_equal(trim(res), trim(tgt)) + + # check derivation with scaling + for i in range(5): + for j in range(2, 5): + tgt = [0]*i + [1] + res = cheb.chebder(cheb.chebint(tgt, m=j, scl=2), m=j, scl=.5) + assert_almost_equal(trim(res), trim(tgt)) + + def test_chebder_axis(self): + # check that axis keyword works + c2d = np.random.random((3, 4)) + + tgt = np.vstack([cheb.chebder(c) for c in c2d.T]).T + res = cheb.chebder(c2d, axis=0) + assert_almost_equal(res, tgt) + + tgt = np.vstack([cheb.chebder(c) for c in c2d]) + res = cheb.chebder(c2d, axis=1) + assert_almost_equal(res, tgt) + + +class TestVander: + # some random values in [-1, 1) + x = np.random.random((3, 5))*2 - 1 + + def test_chebvander(self): + # check for 1d x + x = np.arange(3) + v = cheb.chebvander(x, 3) + assert_(v.shape == (3, 4)) + for i in range(4): + coef = [0]*i + [1] + assert_almost_equal(v[..., i], cheb.chebval(x, coef)) + + # check for 2d x + x = np.array([[1, 2], [3, 4], [5, 6]]) + v = cheb.chebvander(x, 3) + assert_(v.shape == (3, 2, 4)) + for i in range(4): + coef = [0]*i + [1] + assert_almost_equal(v[..., i], cheb.chebval(x, coef)) + + def test_chebvander2d(self): + # also tests chebval2d for non-square coefficient array + x1, x2, x3 = self.x + c = np.random.random((2, 3)) + van = cheb.chebvander2d(x1, x2, [1, 2]) + tgt = cheb.chebval2d(x1, x2, c) + res = np.dot(van, c.flat) + assert_almost_equal(res, tgt) + + # check shape + van = cheb.chebvander2d([x1], [x2], [1, 2]) + assert_(van.shape == (1, 5, 6)) + + def test_chebvander3d(self): + # also tests chebval3d for non-square coefficient array + x1, x2, x3 = self.x + c = np.random.random((2, 3, 4)) + van = cheb.chebvander3d(x1, x2, x3, [1, 2, 3]) + tgt = cheb.chebval3d(x1, x2, x3, c) + res = np.dot(van, c.flat) + assert_almost_equal(res, tgt) + + # check shape + van = cheb.chebvander3d([x1], [x2], [x3], [1, 2, 3]) + assert_(van.shape == (1, 5, 24)) + + +class TestFitting: + + def test_chebfit(self): + def f(x): + return x*(x - 1)*(x - 2) + + def f2(x): + return x**4 + x**2 + 1 + + # Test exceptions + assert_raises(ValueError, cheb.chebfit, [1], [1], -1) + assert_raises(TypeError, cheb.chebfit, [[1]], [1], 0) + assert_raises(TypeError, cheb.chebfit, [], [1], 0) + assert_raises(TypeError, cheb.chebfit, [1], [[[1]]], 0) + assert_raises(TypeError, cheb.chebfit, [1, 2], [1], 0) + assert_raises(TypeError, cheb.chebfit, [1], [1, 2], 0) + assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, cheb.chebfit, [1], [1], [-1,]) + assert_raises(ValueError, cheb.chebfit, [1], [1], [2, -1, 6]) + assert_raises(TypeError, cheb.chebfit, [1], [1], []) + + # Test fit + x = np.linspace(0, 2) + y = f(x) + # + coef3 = cheb.chebfit(x, y, 3) + assert_equal(len(coef3), 4) + assert_almost_equal(cheb.chebval(x, coef3), y) + coef3 = cheb.chebfit(x, y, [0, 1, 2, 3]) + assert_equal(len(coef3), 4) + assert_almost_equal(cheb.chebval(x, coef3), y) + # + coef4 = cheb.chebfit(x, y, 4) + assert_equal(len(coef4), 5) + assert_almost_equal(cheb.chebval(x, coef4), y) + coef4 = cheb.chebfit(x, y, [0, 1, 2, 3, 4]) + assert_equal(len(coef4), 5) + assert_almost_equal(cheb.chebval(x, coef4), y) + # check things still work if deg is not in strict increasing + coef4 = cheb.chebfit(x, y, [2, 3, 4, 1, 0]) + assert_equal(len(coef4), 5) + assert_almost_equal(cheb.chebval(x, coef4), y) + # + coef2d = cheb.chebfit(x, np.array([y, y]).T, 3) + assert_almost_equal(coef2d, np.array([coef3, coef3]).T) + coef2d = cheb.chebfit(x, np.array([y, y]).T, [0, 1, 2, 3]) + assert_almost_equal(coef2d, np.array([coef3, coef3]).T) + # test weighting + w = np.zeros_like(x) + yw = y.copy() + w[1::2] = 1 + y[0::2] = 0 + wcoef3 = cheb.chebfit(x, yw, 3, w=w) + assert_almost_equal(wcoef3, coef3) + wcoef3 = cheb.chebfit(x, yw, [0, 1, 2, 3], w=w) + assert_almost_equal(wcoef3, coef3) + # + wcoef2d = cheb.chebfit(x, np.array([yw, yw]).T, 3, w=w) + assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T) + wcoef2d = cheb.chebfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w) + assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T) + # test scaling with complex values x points whose square + # is zero when summed. + x = [1, 1j, -1, -1j] + assert_almost_equal(cheb.chebfit(x, x, 1), [0, 1]) + assert_almost_equal(cheb.chebfit(x, x, [0, 1]), [0, 1]) + # test fitting only even polynomials + x = np.linspace(-1, 1) + y = f2(x) + coef1 = cheb.chebfit(x, y, 4) + assert_almost_equal(cheb.chebval(x, coef1), y) + coef2 = cheb.chebfit(x, y, [0, 2, 4]) + assert_almost_equal(cheb.chebval(x, coef2), y) + assert_almost_equal(coef1, coef2) + + +class TestInterpolate: + + def f(self, x): + return x * (x - 1) * (x - 2) + + def test_raises(self): + assert_raises(ValueError, cheb.chebinterpolate, self.f, -1) + assert_raises(TypeError, cheb.chebinterpolate, self.f, 10.) + + def test_dimensions(self): + for deg in range(1, 5): + assert_(cheb.chebinterpolate(self.f, deg).shape == (deg + 1,)) + + def test_approximation(self): + + def powx(x, p): + return x**p + + x = np.linspace(-1, 1, 10) + for deg in range(0, 10): + for p in range(0, deg + 1): + c = cheb.chebinterpolate(powx, deg, (p,)) + assert_almost_equal(cheb.chebval(x, c), powx(x, p), decimal=12) + + +class TestCompanion: + + def test_raises(self): + assert_raises(ValueError, cheb.chebcompanion, []) + assert_raises(ValueError, cheb.chebcompanion, [1]) + + def test_dimensions(self): + for i in range(1, 5): + coef = [0]*i + [1] + assert_(cheb.chebcompanion(coef).shape == (i, i)) + + def test_linear_root(self): + assert_(cheb.chebcompanion([1, 2])[0, 0] == -.5) + + +class TestGauss: + + def test_100(self): + x, w = cheb.chebgauss(100) + + # test orthogonality. Note that the results need to be normalized, + # otherwise the huge values that can arise from fast growing + # functions like Laguerre can be very confusing. + v = cheb.chebvander(x, 99) + vv = np.dot(v.T * w, v) + vd = 1/np.sqrt(vv.diagonal()) + vv = vd[:, None] * vv * vd + assert_almost_equal(vv, np.eye(100)) + + # check that the integral of 1 is correct + tgt = np.pi + assert_almost_equal(w.sum(), tgt) + + +class TestMisc: + + def test_chebfromroots(self): + res = cheb.chebfromroots([]) + assert_almost_equal(trim(res), [1]) + for i in range(1, 5): + roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2]) + tgt = [0]*i + [1] + res = cheb.chebfromroots(roots)*2**(i-1) + assert_almost_equal(trim(res), trim(tgt)) + + def test_chebroots(self): + assert_almost_equal(cheb.chebroots([1]), []) + assert_almost_equal(cheb.chebroots([1, 2]), [-.5]) + for i in range(2, 5): + tgt = np.linspace(-1, 1, i) + res = cheb.chebroots(cheb.chebfromroots(tgt)) + assert_almost_equal(trim(res), trim(tgt)) + + def test_chebtrim(self): + coef = [2, -1, 1, 0] + + # Test exceptions + assert_raises(ValueError, cheb.chebtrim, coef, -1) + + # Test results + assert_equal(cheb.chebtrim(coef), coef[:-1]) + assert_equal(cheb.chebtrim(coef, 1), coef[:-3]) + assert_equal(cheb.chebtrim(coef, 2), [0]) + + def test_chebline(self): + assert_equal(cheb.chebline(3, 4), [3, 4]) + + def test_cheb2poly(self): + for i in range(10): + assert_almost_equal(cheb.cheb2poly([0]*i + [1]), Tlist[i]) + + def test_poly2cheb(self): + for i in range(10): + assert_almost_equal(cheb.poly2cheb(Tlist[i]), [0]*i + [1]) + + def test_weight(self): + x = np.linspace(-1, 1, 11)[1:-1] + tgt = 1./(np.sqrt(1 + x) * np.sqrt(1 - x)) + res = cheb.chebweight(x) + assert_almost_equal(res, tgt) + + def test_chebpts1(self): + #test exceptions + assert_raises(ValueError, cheb.chebpts1, 1.5) + assert_raises(ValueError, cheb.chebpts1, 0) + + #test points + tgt = [0] + assert_almost_equal(cheb.chebpts1(1), tgt) + tgt = [-0.70710678118654746, 0.70710678118654746] + assert_almost_equal(cheb.chebpts1(2), tgt) + tgt = [-0.86602540378443871, 0, 0.86602540378443871] + assert_almost_equal(cheb.chebpts1(3), tgt) + tgt = [-0.9238795325, -0.3826834323, 0.3826834323, 0.9238795325] + assert_almost_equal(cheb.chebpts1(4), tgt) + + def test_chebpts2(self): + #test exceptions + assert_raises(ValueError, cheb.chebpts2, 1.5) + assert_raises(ValueError, cheb.chebpts2, 1) + + #test points + tgt = [-1, 1] + assert_almost_equal(cheb.chebpts2(2), tgt) + tgt = [-1, 0, 1] + assert_almost_equal(cheb.chebpts2(3), tgt) + tgt = [-1, -0.5, .5, 1] + assert_almost_equal(cheb.chebpts2(4), tgt) + tgt = [-1.0, -0.707106781187, 0, 0.707106781187, 1.0] + assert_almost_equal(cheb.chebpts2(5), tgt) diff --git a/parrot/lib/python3.10/site-packages/numpy/polynomial/tests/test_hermite.py b/parrot/lib/python3.10/site-packages/numpy/polynomial/tests/test_hermite.py new file mode 100644 index 0000000000000000000000000000000000000000..53ee0844e3c58456807bfd7828bdb9cf58f8ed76 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/polynomial/tests/test_hermite.py @@ -0,0 +1,555 @@ +"""Tests for hermite module. + +""" +from functools import reduce + +import numpy as np +import numpy.polynomial.hermite as herm +from numpy.polynomial.polynomial import polyval +from numpy.testing import ( + assert_almost_equal, assert_raises, assert_equal, assert_, + ) + +H0 = np.array([1]) +H1 = np.array([0, 2]) +H2 = np.array([-2, 0, 4]) +H3 = np.array([0, -12, 0, 8]) +H4 = np.array([12, 0, -48, 0, 16]) +H5 = np.array([0, 120, 0, -160, 0, 32]) +H6 = np.array([-120, 0, 720, 0, -480, 0, 64]) +H7 = np.array([0, -1680, 0, 3360, 0, -1344, 0, 128]) +H8 = np.array([1680, 0, -13440, 0, 13440, 0, -3584, 0, 256]) +H9 = np.array([0, 30240, 0, -80640, 0, 48384, 0, -9216, 0, 512]) + +Hlist = [H0, H1, H2, H3, H4, H5, H6, H7, H8, H9] + + +def trim(x): + return herm.hermtrim(x, tol=1e-6) + + +class TestConstants: + + def test_hermdomain(self): + assert_equal(herm.hermdomain, [-1, 1]) + + def test_hermzero(self): + assert_equal(herm.hermzero, [0]) + + def test_hermone(self): + assert_equal(herm.hermone, [1]) + + def test_hermx(self): + assert_equal(herm.hermx, [0, .5]) + + +class TestArithmetic: + x = np.linspace(-3, 3, 100) + + def test_hermadd(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + tgt = np.zeros(max(i, j) + 1) + tgt[i] += 1 + tgt[j] += 1 + res = herm.hermadd([0]*i + [1], [0]*j + [1]) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_hermsub(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + tgt = np.zeros(max(i, j) + 1) + tgt[i] += 1 + tgt[j] -= 1 + res = herm.hermsub([0]*i + [1], [0]*j + [1]) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_hermmulx(self): + assert_equal(herm.hermmulx([0]), [0]) + assert_equal(herm.hermmulx([1]), [0, .5]) + for i in range(1, 5): + ser = [0]*i + [1] + tgt = [0]*(i - 1) + [i, 0, .5] + assert_equal(herm.hermmulx(ser), tgt) + + def test_hermmul(self): + # check values of result + for i in range(5): + pol1 = [0]*i + [1] + val1 = herm.hermval(self.x, pol1) + for j in range(5): + msg = f"At i={i}, j={j}" + pol2 = [0]*j + [1] + val2 = herm.hermval(self.x, pol2) + pol3 = herm.hermmul(pol1, pol2) + val3 = herm.hermval(self.x, pol3) + assert_(len(pol3) == i + j + 1, msg) + assert_almost_equal(val3, val1*val2, err_msg=msg) + + def test_hermdiv(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + ci = [0]*i + [1] + cj = [0]*j + [1] + tgt = herm.hermadd(ci, cj) + quo, rem = herm.hermdiv(tgt, ci) + res = herm.hermadd(herm.hermmul(quo, ci), rem) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_hermpow(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + c = np.arange(i + 1) + tgt = reduce(herm.hermmul, [c]*j, np.array([1])) + res = herm.hermpow(c, j) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + +class TestEvaluation: + # coefficients of 1 + 2*x + 3*x**2 + c1d = np.array([2.5, 1., .75]) + c2d = np.einsum('i,j->ij', c1d, c1d) + c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d) + + # some random values in [-1, 1) + x = np.random.random((3, 5))*2 - 1 + y = polyval(x, [1., 2., 3.]) + + def test_hermval(self): + #check empty input + assert_equal(herm.hermval([], [1]).size, 0) + + #check normal input) + x = np.linspace(-1, 1) + y = [polyval(x, c) for c in Hlist] + for i in range(10): + msg = f"At i={i}" + tgt = y[i] + res = herm.hermval(x, [0]*i + [1]) + assert_almost_equal(res, tgt, err_msg=msg) + + #check that shape is preserved + for i in range(3): + dims = [2]*i + x = np.zeros(dims) + assert_equal(herm.hermval(x, [1]).shape, dims) + assert_equal(herm.hermval(x, [1, 0]).shape, dims) + assert_equal(herm.hermval(x, [1, 0, 0]).shape, dims) + + def test_hermval2d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test exceptions + assert_raises(ValueError, herm.hermval2d, x1, x2[:2], self.c2d) + + #test values + tgt = y1*y2 + res = herm.hermval2d(x1, x2, self.c2d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = herm.hermval2d(z, z, self.c2d) + assert_(res.shape == (2, 3)) + + def test_hermval3d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test exceptions + assert_raises(ValueError, herm.hermval3d, x1, x2, x3[:2], self.c3d) + + #test values + tgt = y1*y2*y3 + res = herm.hermval3d(x1, x2, x3, self.c3d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = herm.hermval3d(z, z, z, self.c3d) + assert_(res.shape == (2, 3)) + + def test_hermgrid2d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test values + tgt = np.einsum('i,j->ij', y1, y2) + res = herm.hermgrid2d(x1, x2, self.c2d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = herm.hermgrid2d(z, z, self.c2d) + assert_(res.shape == (2, 3)*2) + + def test_hermgrid3d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test values + tgt = np.einsum('i,j,k->ijk', y1, y2, y3) + res = herm.hermgrid3d(x1, x2, x3, self.c3d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = herm.hermgrid3d(z, z, z, self.c3d) + assert_(res.shape == (2, 3)*3) + + +class TestIntegral: + + def test_hermint(self): + # check exceptions + assert_raises(TypeError, herm.hermint, [0], .5) + assert_raises(ValueError, herm.hermint, [0], -1) + assert_raises(ValueError, herm.hermint, [0], 1, [0, 0]) + assert_raises(ValueError, herm.hermint, [0], lbnd=[0]) + assert_raises(ValueError, herm.hermint, [0], scl=[0]) + assert_raises(TypeError, herm.hermint, [0], axis=.5) + + # test integration of zero polynomial + for i in range(2, 5): + k = [0]*(i - 2) + [1] + res = herm.hermint([0], m=i, k=k) + assert_almost_equal(res, [0, .5]) + + # check single integration with integration constant + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + tgt = [i] + [0]*i + [1/scl] + hermpol = herm.poly2herm(pol) + hermint = herm.hermint(hermpol, m=1, k=[i]) + res = herm.herm2poly(hermint) + assert_almost_equal(trim(res), trim(tgt)) + + # check single integration with integration constant and lbnd + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + hermpol = herm.poly2herm(pol) + hermint = herm.hermint(hermpol, m=1, k=[i], lbnd=-1) + assert_almost_equal(herm.hermval(-1, hermint), i) + + # check single integration with integration constant and scaling + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + tgt = [i] + [0]*i + [2/scl] + hermpol = herm.poly2herm(pol) + hermint = herm.hermint(hermpol, m=1, k=[i], scl=2) + res = herm.herm2poly(hermint) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with default k + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = herm.hermint(tgt, m=1) + res = herm.hermint(pol, m=j) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with defined k + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = herm.hermint(tgt, m=1, k=[k]) + res = herm.hermint(pol, m=j, k=list(range(j))) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with lbnd + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = herm.hermint(tgt, m=1, k=[k], lbnd=-1) + res = herm.hermint(pol, m=j, k=list(range(j)), lbnd=-1) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with scaling + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = herm.hermint(tgt, m=1, k=[k], scl=2) + res = herm.hermint(pol, m=j, k=list(range(j)), scl=2) + assert_almost_equal(trim(res), trim(tgt)) + + def test_hermint_axis(self): + # check that axis keyword works + c2d = np.random.random((3, 4)) + + tgt = np.vstack([herm.hermint(c) for c in c2d.T]).T + res = herm.hermint(c2d, axis=0) + assert_almost_equal(res, tgt) + + tgt = np.vstack([herm.hermint(c) for c in c2d]) + res = herm.hermint(c2d, axis=1) + assert_almost_equal(res, tgt) + + tgt = np.vstack([herm.hermint(c, k=3) for c in c2d]) + res = herm.hermint(c2d, k=3, axis=1) + assert_almost_equal(res, tgt) + + +class TestDerivative: + + def test_hermder(self): + # check exceptions + assert_raises(TypeError, herm.hermder, [0], .5) + assert_raises(ValueError, herm.hermder, [0], -1) + + # check that zeroth derivative does nothing + for i in range(5): + tgt = [0]*i + [1] + res = herm.hermder(tgt, m=0) + assert_equal(trim(res), trim(tgt)) + + # check that derivation is the inverse of integration + for i in range(5): + for j in range(2, 5): + tgt = [0]*i + [1] + res = herm.hermder(herm.hermint(tgt, m=j), m=j) + assert_almost_equal(trim(res), trim(tgt)) + + # check derivation with scaling + for i in range(5): + for j in range(2, 5): + tgt = [0]*i + [1] + res = herm.hermder(herm.hermint(tgt, m=j, scl=2), m=j, scl=.5) + assert_almost_equal(trim(res), trim(tgt)) + + def test_hermder_axis(self): + # check that axis keyword works + c2d = np.random.random((3, 4)) + + tgt = np.vstack([herm.hermder(c) for c in c2d.T]).T + res = herm.hermder(c2d, axis=0) + assert_almost_equal(res, tgt) + + tgt = np.vstack([herm.hermder(c) for c in c2d]) + res = herm.hermder(c2d, axis=1) + assert_almost_equal(res, tgt) + + +class TestVander: + # some random values in [-1, 1) + x = np.random.random((3, 5))*2 - 1 + + def test_hermvander(self): + # check for 1d x + x = np.arange(3) + v = herm.hermvander(x, 3) + assert_(v.shape == (3, 4)) + for i in range(4): + coef = [0]*i + [1] + assert_almost_equal(v[..., i], herm.hermval(x, coef)) + + # check for 2d x + x = np.array([[1, 2], [3, 4], [5, 6]]) + v = herm.hermvander(x, 3) + assert_(v.shape == (3, 2, 4)) + for i in range(4): + coef = [0]*i + [1] + assert_almost_equal(v[..., i], herm.hermval(x, coef)) + + def test_hermvander2d(self): + # also tests hermval2d for non-square coefficient array + x1, x2, x3 = self.x + c = np.random.random((2, 3)) + van = herm.hermvander2d(x1, x2, [1, 2]) + tgt = herm.hermval2d(x1, x2, c) + res = np.dot(van, c.flat) + assert_almost_equal(res, tgt) + + # check shape + van = herm.hermvander2d([x1], [x2], [1, 2]) + assert_(van.shape == (1, 5, 6)) + + def test_hermvander3d(self): + # also tests hermval3d for non-square coefficient array + x1, x2, x3 = self.x + c = np.random.random((2, 3, 4)) + van = herm.hermvander3d(x1, x2, x3, [1, 2, 3]) + tgt = herm.hermval3d(x1, x2, x3, c) + res = np.dot(van, c.flat) + assert_almost_equal(res, tgt) + + # check shape + van = herm.hermvander3d([x1], [x2], [x3], [1, 2, 3]) + assert_(van.shape == (1, 5, 24)) + + +class TestFitting: + + def test_hermfit(self): + def f(x): + return x*(x - 1)*(x - 2) + + def f2(x): + return x**4 + x**2 + 1 + + # Test exceptions + assert_raises(ValueError, herm.hermfit, [1], [1], -1) + assert_raises(TypeError, herm.hermfit, [[1]], [1], 0) + assert_raises(TypeError, herm.hermfit, [], [1], 0) + assert_raises(TypeError, herm.hermfit, [1], [[[1]]], 0) + assert_raises(TypeError, herm.hermfit, [1, 2], [1], 0) + assert_raises(TypeError, herm.hermfit, [1], [1, 2], 0) + assert_raises(TypeError, herm.hermfit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, herm.hermfit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, herm.hermfit, [1], [1], [-1,]) + assert_raises(ValueError, herm.hermfit, [1], [1], [2, -1, 6]) + assert_raises(TypeError, herm.hermfit, [1], [1], []) + + # Test fit + x = np.linspace(0, 2) + y = f(x) + # + coef3 = herm.hermfit(x, y, 3) + assert_equal(len(coef3), 4) + assert_almost_equal(herm.hermval(x, coef3), y) + coef3 = herm.hermfit(x, y, [0, 1, 2, 3]) + assert_equal(len(coef3), 4) + assert_almost_equal(herm.hermval(x, coef3), y) + # + coef4 = herm.hermfit(x, y, 4) + assert_equal(len(coef4), 5) + assert_almost_equal(herm.hermval(x, coef4), y) + coef4 = herm.hermfit(x, y, [0, 1, 2, 3, 4]) + assert_equal(len(coef4), 5) + assert_almost_equal(herm.hermval(x, coef4), y) + # check things still work if deg is not in strict increasing + coef4 = herm.hermfit(x, y, [2, 3, 4, 1, 0]) + assert_equal(len(coef4), 5) + assert_almost_equal(herm.hermval(x, coef4), y) + # + coef2d = herm.hermfit(x, np.array([y, y]).T, 3) + assert_almost_equal(coef2d, np.array([coef3, coef3]).T) + coef2d = herm.hermfit(x, np.array([y, y]).T, [0, 1, 2, 3]) + assert_almost_equal(coef2d, np.array([coef3, coef3]).T) + # test weighting + w = np.zeros_like(x) + yw = y.copy() + w[1::2] = 1 + y[0::2] = 0 + wcoef3 = herm.hermfit(x, yw, 3, w=w) + assert_almost_equal(wcoef3, coef3) + wcoef3 = herm.hermfit(x, yw, [0, 1, 2, 3], w=w) + assert_almost_equal(wcoef3, coef3) + # + wcoef2d = herm.hermfit(x, np.array([yw, yw]).T, 3, w=w) + assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T) + wcoef2d = herm.hermfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w) + assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T) + # test scaling with complex values x points whose square + # is zero when summed. + x = [1, 1j, -1, -1j] + assert_almost_equal(herm.hermfit(x, x, 1), [0, .5]) + assert_almost_equal(herm.hermfit(x, x, [0, 1]), [0, .5]) + # test fitting only even Legendre polynomials + x = np.linspace(-1, 1) + y = f2(x) + coef1 = herm.hermfit(x, y, 4) + assert_almost_equal(herm.hermval(x, coef1), y) + coef2 = herm.hermfit(x, y, [0, 2, 4]) + assert_almost_equal(herm.hermval(x, coef2), y) + assert_almost_equal(coef1, coef2) + + +class TestCompanion: + + def test_raises(self): + assert_raises(ValueError, herm.hermcompanion, []) + assert_raises(ValueError, herm.hermcompanion, [1]) + + def test_dimensions(self): + for i in range(1, 5): + coef = [0]*i + [1] + assert_(herm.hermcompanion(coef).shape == (i, i)) + + def test_linear_root(self): + assert_(herm.hermcompanion([1, 2])[0, 0] == -.25) + + +class TestGauss: + + def test_100(self): + x, w = herm.hermgauss(100) + + # test orthogonality. Note that the results need to be normalized, + # otherwise the huge values that can arise from fast growing + # functions like Laguerre can be very confusing. + v = herm.hermvander(x, 99) + vv = np.dot(v.T * w, v) + vd = 1/np.sqrt(vv.diagonal()) + vv = vd[:, None] * vv * vd + assert_almost_equal(vv, np.eye(100)) + + # check that the integral of 1 is correct + tgt = np.sqrt(np.pi) + assert_almost_equal(w.sum(), tgt) + + +class TestMisc: + + def test_hermfromroots(self): + res = herm.hermfromroots([]) + assert_almost_equal(trim(res), [1]) + for i in range(1, 5): + roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2]) + pol = herm.hermfromroots(roots) + res = herm.hermval(roots, pol) + tgt = 0 + assert_(len(pol) == i + 1) + assert_almost_equal(herm.herm2poly(pol)[-1], 1) + assert_almost_equal(res, tgt) + + def test_hermroots(self): + assert_almost_equal(herm.hermroots([1]), []) + assert_almost_equal(herm.hermroots([1, 1]), [-.5]) + for i in range(2, 5): + tgt = np.linspace(-1, 1, i) + res = herm.hermroots(herm.hermfromroots(tgt)) + assert_almost_equal(trim(res), trim(tgt)) + + def test_hermtrim(self): + coef = [2, -1, 1, 0] + + # Test exceptions + assert_raises(ValueError, herm.hermtrim, coef, -1) + + # Test results + assert_equal(herm.hermtrim(coef), coef[:-1]) + assert_equal(herm.hermtrim(coef, 1), coef[:-3]) + assert_equal(herm.hermtrim(coef, 2), [0]) + + def test_hermline(self): + assert_equal(herm.hermline(3, 4), [3, 2]) + + def test_herm2poly(self): + for i in range(10): + assert_almost_equal(herm.herm2poly([0]*i + [1]), Hlist[i]) + + def test_poly2herm(self): + for i in range(10): + assert_almost_equal(herm.poly2herm(Hlist[i]), [0]*i + [1]) + + def test_weight(self): + x = np.linspace(-5, 5, 11) + tgt = np.exp(-x**2) + res = herm.hermweight(x) + assert_almost_equal(res, tgt) diff --git a/parrot/lib/python3.10/site-packages/numpy/polynomial/tests/test_laguerre.py b/parrot/lib/python3.10/site-packages/numpy/polynomial/tests/test_laguerre.py new file mode 100644 index 0000000000000000000000000000000000000000..227ef3c5576dd666e2eb76576eb260d5ba48cb0e --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/polynomial/tests/test_laguerre.py @@ -0,0 +1,537 @@ +"""Tests for laguerre module. + +""" +from functools import reduce + +import numpy as np +import numpy.polynomial.laguerre as lag +from numpy.polynomial.polynomial import polyval +from numpy.testing import ( + assert_almost_equal, assert_raises, assert_equal, assert_, + ) + +L0 = np.array([1])/1 +L1 = np.array([1, -1])/1 +L2 = np.array([2, -4, 1])/2 +L3 = np.array([6, -18, 9, -1])/6 +L4 = np.array([24, -96, 72, -16, 1])/24 +L5 = np.array([120, -600, 600, -200, 25, -1])/120 +L6 = np.array([720, -4320, 5400, -2400, 450, -36, 1])/720 + +Llist = [L0, L1, L2, L3, L4, L5, L6] + + +def trim(x): + return lag.lagtrim(x, tol=1e-6) + + +class TestConstants: + + def test_lagdomain(self): + assert_equal(lag.lagdomain, [0, 1]) + + def test_lagzero(self): + assert_equal(lag.lagzero, [0]) + + def test_lagone(self): + assert_equal(lag.lagone, [1]) + + def test_lagx(self): + assert_equal(lag.lagx, [1, -1]) + + +class TestArithmetic: + x = np.linspace(-3, 3, 100) + + def test_lagadd(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + tgt = np.zeros(max(i, j) + 1) + tgt[i] += 1 + tgt[j] += 1 + res = lag.lagadd([0]*i + [1], [0]*j + [1]) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_lagsub(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + tgt = np.zeros(max(i, j) + 1) + tgt[i] += 1 + tgt[j] -= 1 + res = lag.lagsub([0]*i + [1], [0]*j + [1]) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + def test_lagmulx(self): + assert_equal(lag.lagmulx([0]), [0]) + assert_equal(lag.lagmulx([1]), [1, -1]) + for i in range(1, 5): + ser = [0]*i + [1] + tgt = [0]*(i - 1) + [-i, 2*i + 1, -(i + 1)] + assert_almost_equal(lag.lagmulx(ser), tgt) + + def test_lagmul(self): + # check values of result + for i in range(5): + pol1 = [0]*i + [1] + val1 = lag.lagval(self.x, pol1) + for j in range(5): + msg = f"At i={i}, j={j}" + pol2 = [0]*j + [1] + val2 = lag.lagval(self.x, pol2) + pol3 = lag.lagmul(pol1, pol2) + val3 = lag.lagval(self.x, pol3) + assert_(len(pol3) == i + j + 1, msg) + assert_almost_equal(val3, val1*val2, err_msg=msg) + + def test_lagdiv(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + ci = [0]*i + [1] + cj = [0]*j + [1] + tgt = lag.lagadd(ci, cj) + quo, rem = lag.lagdiv(tgt, ci) + res = lag.lagadd(lag.lagmul(quo, ci), rem) + assert_almost_equal(trim(res), trim(tgt), err_msg=msg) + + def test_lagpow(self): + for i in range(5): + for j in range(5): + msg = f"At i={i}, j={j}" + c = np.arange(i + 1) + tgt = reduce(lag.lagmul, [c]*j, np.array([1])) + res = lag.lagpow(c, j) + assert_equal(trim(res), trim(tgt), err_msg=msg) + + +class TestEvaluation: + # coefficients of 1 + 2*x + 3*x**2 + c1d = np.array([9., -14., 6.]) + c2d = np.einsum('i,j->ij', c1d, c1d) + c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d) + + # some random values in [-1, 1) + x = np.random.random((3, 5))*2 - 1 + y = polyval(x, [1., 2., 3.]) + + def test_lagval(self): + #check empty input + assert_equal(lag.lagval([], [1]).size, 0) + + #check normal input) + x = np.linspace(-1, 1) + y = [polyval(x, c) for c in Llist] + for i in range(7): + msg = f"At i={i}" + tgt = y[i] + res = lag.lagval(x, [0]*i + [1]) + assert_almost_equal(res, tgt, err_msg=msg) + + #check that shape is preserved + for i in range(3): + dims = [2]*i + x = np.zeros(dims) + assert_equal(lag.lagval(x, [1]).shape, dims) + assert_equal(lag.lagval(x, [1, 0]).shape, dims) + assert_equal(lag.lagval(x, [1, 0, 0]).shape, dims) + + def test_lagval2d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test exceptions + assert_raises(ValueError, lag.lagval2d, x1, x2[:2], self.c2d) + + #test values + tgt = y1*y2 + res = lag.lagval2d(x1, x2, self.c2d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = lag.lagval2d(z, z, self.c2d) + assert_(res.shape == (2, 3)) + + def test_lagval3d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test exceptions + assert_raises(ValueError, lag.lagval3d, x1, x2, x3[:2], self.c3d) + + #test values + tgt = y1*y2*y3 + res = lag.lagval3d(x1, x2, x3, self.c3d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = lag.lagval3d(z, z, z, self.c3d) + assert_(res.shape == (2, 3)) + + def test_laggrid2d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test values + tgt = np.einsum('i,j->ij', y1, y2) + res = lag.laggrid2d(x1, x2, self.c2d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = lag.laggrid2d(z, z, self.c2d) + assert_(res.shape == (2, 3)*2) + + def test_laggrid3d(self): + x1, x2, x3 = self.x + y1, y2, y3 = self.y + + #test values + tgt = np.einsum('i,j,k->ijk', y1, y2, y3) + res = lag.laggrid3d(x1, x2, x3, self.c3d) + assert_almost_equal(res, tgt) + + #test shape + z = np.ones((2, 3)) + res = lag.laggrid3d(z, z, z, self.c3d) + assert_(res.shape == (2, 3)*3) + + +class TestIntegral: + + def test_lagint(self): + # check exceptions + assert_raises(TypeError, lag.lagint, [0], .5) + assert_raises(ValueError, lag.lagint, [0], -1) + assert_raises(ValueError, lag.lagint, [0], 1, [0, 0]) + assert_raises(ValueError, lag.lagint, [0], lbnd=[0]) + assert_raises(ValueError, lag.lagint, [0], scl=[0]) + assert_raises(TypeError, lag.lagint, [0], axis=.5) + + # test integration of zero polynomial + for i in range(2, 5): + k = [0]*(i - 2) + [1] + res = lag.lagint([0], m=i, k=k) + assert_almost_equal(res, [1, -1]) + + # check single integration with integration constant + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + tgt = [i] + [0]*i + [1/scl] + lagpol = lag.poly2lag(pol) + lagint = lag.lagint(lagpol, m=1, k=[i]) + res = lag.lag2poly(lagint) + assert_almost_equal(trim(res), trim(tgt)) + + # check single integration with integration constant and lbnd + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + lagpol = lag.poly2lag(pol) + lagint = lag.lagint(lagpol, m=1, k=[i], lbnd=-1) + assert_almost_equal(lag.lagval(-1, lagint), i) + + # check single integration with integration constant and scaling + for i in range(5): + scl = i + 1 + pol = [0]*i + [1] + tgt = [i] + [0]*i + [2/scl] + lagpol = lag.poly2lag(pol) + lagint = lag.lagint(lagpol, m=1, k=[i], scl=2) + res = lag.lag2poly(lagint) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with default k + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = lag.lagint(tgt, m=1) + res = lag.lagint(pol, m=j) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with defined k + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = lag.lagint(tgt, m=1, k=[k]) + res = lag.lagint(pol, m=j, k=list(range(j))) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with lbnd + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = lag.lagint(tgt, m=1, k=[k], lbnd=-1) + res = lag.lagint(pol, m=j, k=list(range(j)), lbnd=-1) + assert_almost_equal(trim(res), trim(tgt)) + + # check multiple integrations with scaling + for i in range(5): + for j in range(2, 5): + pol = [0]*i + [1] + tgt = pol[:] + for k in range(j): + tgt = lag.lagint(tgt, m=1, k=[k], scl=2) + res = lag.lagint(pol, m=j, k=list(range(j)), scl=2) + assert_almost_equal(trim(res), trim(tgt)) + + def test_lagint_axis(self): + # check that axis keyword works + c2d = np.random.random((3, 4)) + + tgt = np.vstack([lag.lagint(c) for c in c2d.T]).T + res = lag.lagint(c2d, axis=0) + assert_almost_equal(res, tgt) + + tgt = np.vstack([lag.lagint(c) for c in c2d]) + res = lag.lagint(c2d, axis=1) + assert_almost_equal(res, tgt) + + tgt = np.vstack([lag.lagint(c, k=3) for c in c2d]) + res = lag.lagint(c2d, k=3, axis=1) + assert_almost_equal(res, tgt) + + +class TestDerivative: + + def test_lagder(self): + # check exceptions + assert_raises(TypeError, lag.lagder, [0], .5) + assert_raises(ValueError, lag.lagder, [0], -1) + + # check that zeroth derivative does nothing + for i in range(5): + tgt = [0]*i + [1] + res = lag.lagder(tgt, m=0) + assert_equal(trim(res), trim(tgt)) + + # check that derivation is the inverse of integration + for i in range(5): + for j in range(2, 5): + tgt = [0]*i + [1] + res = lag.lagder(lag.lagint(tgt, m=j), m=j) + assert_almost_equal(trim(res), trim(tgt)) + + # check derivation with scaling + for i in range(5): + for j in range(2, 5): + tgt = [0]*i + [1] + res = lag.lagder(lag.lagint(tgt, m=j, scl=2), m=j, scl=.5) + assert_almost_equal(trim(res), trim(tgt)) + + def test_lagder_axis(self): + # check that axis keyword works + c2d = np.random.random((3, 4)) + + tgt = np.vstack([lag.lagder(c) for c in c2d.T]).T + res = lag.lagder(c2d, axis=0) + assert_almost_equal(res, tgt) + + tgt = np.vstack([lag.lagder(c) for c in c2d]) + res = lag.lagder(c2d, axis=1) + assert_almost_equal(res, tgt) + + +class TestVander: + # some random values in [-1, 1) + x = np.random.random((3, 5))*2 - 1 + + def test_lagvander(self): + # check for 1d x + x = np.arange(3) + v = lag.lagvander(x, 3) + assert_(v.shape == (3, 4)) + for i in range(4): + coef = [0]*i + [1] + assert_almost_equal(v[..., i], lag.lagval(x, coef)) + + # check for 2d x + x = np.array([[1, 2], [3, 4], [5, 6]]) + v = lag.lagvander(x, 3) + assert_(v.shape == (3, 2, 4)) + for i in range(4): + coef = [0]*i + [1] + assert_almost_equal(v[..., i], lag.lagval(x, coef)) + + def test_lagvander2d(self): + # also tests lagval2d for non-square coefficient array + x1, x2, x3 = self.x + c = np.random.random((2, 3)) + van = lag.lagvander2d(x1, x2, [1, 2]) + tgt = lag.lagval2d(x1, x2, c) + res = np.dot(van, c.flat) + assert_almost_equal(res, tgt) + + # check shape + van = lag.lagvander2d([x1], [x2], [1, 2]) + assert_(van.shape == (1, 5, 6)) + + def test_lagvander3d(self): + # also tests lagval3d for non-square coefficient array + x1, x2, x3 = self.x + c = np.random.random((2, 3, 4)) + van = lag.lagvander3d(x1, x2, x3, [1, 2, 3]) + tgt = lag.lagval3d(x1, x2, x3, c) + res = np.dot(van, c.flat) + assert_almost_equal(res, tgt) + + # check shape + van = lag.lagvander3d([x1], [x2], [x3], [1, 2, 3]) + assert_(van.shape == (1, 5, 24)) + + +class TestFitting: + + def test_lagfit(self): + def f(x): + return x*(x - 1)*(x - 2) + + # Test exceptions + assert_raises(ValueError, lag.lagfit, [1], [1], -1) + assert_raises(TypeError, lag.lagfit, [[1]], [1], 0) + assert_raises(TypeError, lag.lagfit, [], [1], 0) + assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0) + assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0) + assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0) + assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, lag.lagfit, [1], [1], [-1,]) + assert_raises(ValueError, lag.lagfit, [1], [1], [2, -1, 6]) + assert_raises(TypeError, lag.lagfit, [1], [1], []) + + # Test fit + x = np.linspace(0, 2) + y = f(x) + # + coef3 = lag.lagfit(x, y, 3) + assert_equal(len(coef3), 4) + assert_almost_equal(lag.lagval(x, coef3), y) + coef3 = lag.lagfit(x, y, [0, 1, 2, 3]) + assert_equal(len(coef3), 4) + assert_almost_equal(lag.lagval(x, coef3), y) + # + coef4 = lag.lagfit(x, y, 4) + assert_equal(len(coef4), 5) + assert_almost_equal(lag.lagval(x, coef4), y) + coef4 = lag.lagfit(x, y, [0, 1, 2, 3, 4]) + assert_equal(len(coef4), 5) + assert_almost_equal(lag.lagval(x, coef4), y) + # + coef2d = lag.lagfit(x, np.array([y, y]).T, 3) + assert_almost_equal(coef2d, np.array([coef3, coef3]).T) + coef2d = lag.lagfit(x, np.array([y, y]).T, [0, 1, 2, 3]) + assert_almost_equal(coef2d, np.array([coef3, coef3]).T) + # test weighting + w = np.zeros_like(x) + yw = y.copy() + w[1::2] = 1 + y[0::2] = 0 + wcoef3 = lag.lagfit(x, yw, 3, w=w) + assert_almost_equal(wcoef3, coef3) + wcoef3 = lag.lagfit(x, yw, [0, 1, 2, 3], w=w) + assert_almost_equal(wcoef3, coef3) + # + wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w) + assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T) + wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w) + assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T) + # test scaling with complex values x points whose square + # is zero when summed. + x = [1, 1j, -1, -1j] + assert_almost_equal(lag.lagfit(x, x, 1), [1, -1]) + assert_almost_equal(lag.lagfit(x, x, [0, 1]), [1, -1]) + + +class TestCompanion: + + def test_raises(self): + assert_raises(ValueError, lag.lagcompanion, []) + assert_raises(ValueError, lag.lagcompanion, [1]) + + def test_dimensions(self): + for i in range(1, 5): + coef = [0]*i + [1] + assert_(lag.lagcompanion(coef).shape == (i, i)) + + def test_linear_root(self): + assert_(lag.lagcompanion([1, 2])[0, 0] == 1.5) + + +class TestGauss: + + def test_100(self): + x, w = lag.laggauss(100) + + # test orthogonality. Note that the results need to be normalized, + # otherwise the huge values that can arise from fast growing + # functions like Laguerre can be very confusing. + v = lag.lagvander(x, 99) + vv = np.dot(v.T * w, v) + vd = 1/np.sqrt(vv.diagonal()) + vv = vd[:, None] * vv * vd + assert_almost_equal(vv, np.eye(100)) + + # check that the integral of 1 is correct + tgt = 1.0 + assert_almost_equal(w.sum(), tgt) + + +class TestMisc: + + def test_lagfromroots(self): + res = lag.lagfromroots([]) + assert_almost_equal(trim(res), [1]) + for i in range(1, 5): + roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2]) + pol = lag.lagfromroots(roots) + res = lag.lagval(roots, pol) + tgt = 0 + assert_(len(pol) == i + 1) + assert_almost_equal(lag.lag2poly(pol)[-1], 1) + assert_almost_equal(res, tgt) + + def test_lagroots(self): + assert_almost_equal(lag.lagroots([1]), []) + assert_almost_equal(lag.lagroots([0, 1]), [1]) + for i in range(2, 5): + tgt = np.linspace(0, 3, i) + res = lag.lagroots(lag.lagfromroots(tgt)) + assert_almost_equal(trim(res), trim(tgt)) + + def test_lagtrim(self): + coef = [2, -1, 1, 0] + + # Test exceptions + assert_raises(ValueError, lag.lagtrim, coef, -1) + + # Test results + assert_equal(lag.lagtrim(coef), coef[:-1]) + assert_equal(lag.lagtrim(coef, 1), coef[:-3]) + assert_equal(lag.lagtrim(coef, 2), [0]) + + def test_lagline(self): + assert_equal(lag.lagline(3, 4), [7, -4]) + + def test_lag2poly(self): + for i in range(7): + assert_almost_equal(lag.lag2poly([0]*i + [1]), Llist[i]) + + def test_poly2lag(self): + for i in range(7): + assert_almost_equal(lag.poly2lag(Llist[i]), [0]*i + [1]) + + def test_weight(self): + x = np.linspace(0, 10, 11) + tgt = np.exp(-x) + res = lag.lagweight(x) + assert_almost_equal(res, tgt) diff --git a/parrot/lib/python3.10/site-packages/numpy/random/__init__.pxd b/parrot/lib/python3.10/site-packages/numpy/random/__init__.pxd new file mode 100644 index 0000000000000000000000000000000000000000..1f9057296ba9475574a191cf231dc04ace3f910c --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/random/__init__.pxd @@ -0,0 +1,14 @@ +cimport numpy as np +from libc.stdint cimport uint32_t, uint64_t + +cdef extern from "numpy/random/bitgen.h": + struct bitgen: + void *state + uint64_t (*next_uint64)(void *st) nogil + uint32_t (*next_uint32)(void *st) nogil + double (*next_double)(void *st) nogil + uint64_t (*next_raw)(void *st) nogil + + ctypedef bitgen bitgen_t + +from numpy.random.bit_generator cimport BitGenerator, SeedSequence diff --git a/parrot/lib/python3.10/site-packages/numpy/random/__init__.pyi b/parrot/lib/python3.10/site-packages/numpy/random/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..26cba3c905026846a448cc0ce2d1d299d5de602a --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/random/__init__.pyi @@ -0,0 +1,71 @@ +from numpy._pytesttester import PytestTester + +from numpy.random._generator import Generator as Generator +from numpy.random._generator import default_rng as default_rng +from numpy.random._mt19937 import MT19937 as MT19937 +from numpy.random._pcg64 import ( + PCG64 as PCG64, + PCG64DXSM as PCG64DXSM, +) +from numpy.random._philox import Philox as Philox +from numpy.random._sfc64 import SFC64 as SFC64 +from numpy.random.bit_generator import BitGenerator as BitGenerator +from numpy.random.bit_generator import SeedSequence as SeedSequence +from numpy.random.mtrand import ( + RandomState as RandomState, + beta as beta, + binomial as binomial, + bytes as bytes, + chisquare as chisquare, + choice as choice, + dirichlet as dirichlet, + exponential as exponential, + f as f, + gamma as gamma, + geometric as geometric, + get_bit_generator as get_bit_generator, + get_state as get_state, + gumbel as gumbel, + hypergeometric as hypergeometric, + laplace as laplace, + logistic as logistic, + lognormal as lognormal, + logseries as logseries, + multinomial as multinomial, + multivariate_normal as multivariate_normal, + negative_binomial as negative_binomial, + noncentral_chisquare as noncentral_chisquare, + noncentral_f as noncentral_f, + normal as normal, + pareto as pareto, + permutation as permutation, + poisson as poisson, + power as power, + rand as rand, + randint as randint, + randn as randn, + random as random, + random_integers as random_integers, + random_sample as random_sample, + ranf as ranf, + rayleigh as rayleigh, + sample as sample, + seed as seed, + set_bit_generator as set_bit_generator, + set_state as set_state, + shuffle as shuffle, + standard_cauchy as standard_cauchy, + standard_exponential as standard_exponential, + standard_gamma as standard_gamma, + standard_normal as standard_normal, + standard_t as standard_t, + triangular as triangular, + uniform as uniform, + vonmises as vonmises, + wald as wald, + weibull as weibull, + zipf as zipf, +) + +__all__: list[str] +test: PytestTester diff --git a/parrot/lib/python3.10/site-packages/numpy/random/_bounded_integers.pxd b/parrot/lib/python3.10/site-packages/numpy/random/_bounded_integers.pxd new file mode 100644 index 0000000000000000000000000000000000000000..607014cbf5b42737669f699471082ab5642910d1 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/random/_bounded_integers.pxd @@ -0,0 +1,29 @@ +from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t, + int8_t, int16_t, int32_t, int64_t, intptr_t) +import numpy as np +cimport numpy as np +ctypedef np.npy_bool bool_t + +from numpy.random cimport bitgen_t + +cdef inline uint64_t _gen_mask(uint64_t max_val) noexcept nogil: + """Mask generator for use in bounded random numbers""" + # Smallest bit mask >= max + cdef uint64_t mask = max_val + mask |= mask >> 1 + mask |= mask >> 2 + mask |= mask >> 4 + mask |= mask >> 8 + mask |= mask >> 16 + mask |= mask >> 32 + return mask + +cdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) +cdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) +cdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) +cdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) +cdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) +cdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) +cdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) +cdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) +cdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock) diff --git a/parrot/lib/python3.10/site-packages/numpy/random/_generator.pyi b/parrot/lib/python3.10/site-packages/numpy/random/_generator.pyi new file mode 100644 index 0000000000000000000000000000000000000000..16a0e5e0ff8dd6ef2ae40a9c2114823c78b57100 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/random/_generator.pyi @@ -0,0 +1,784 @@ +from collections.abc import Callable +from typing import Any, overload, TypeVar, Literal + +import numpy as np +from numpy import ( + dtype, + float32, + float64, + int8, + int16, + int32, + int64, + int_, + uint, + uint8, + uint16, + uint32, + uint64, +) +from numpy.random import BitGenerator, SeedSequence +from numpy._typing import ( + ArrayLike, + NDArray, + _ArrayLikeFloat_co, + _ArrayLikeInt_co, + _DoubleCodes, + _DTypeLikeBool, + _DTypeLikeInt, + _DTypeLikeUInt, + _Float32Codes, + _Float64Codes, + _FloatLike_co, + _Int8Codes, + _Int16Codes, + _Int32Codes, + _Int64Codes, + _IntCodes, + _ShapeLike, + _SingleCodes, + _SupportsDType, + _UInt8Codes, + _UInt16Codes, + _UInt32Codes, + _UInt64Codes, + _UIntCodes, +) + +_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any]) + +_DTypeLikeFloat32 = ( + dtype[float32] + | _SupportsDType[dtype[float32]] + | type[float32] + | _Float32Codes + | _SingleCodes +) + +_DTypeLikeFloat64 = ( + dtype[float64] + | _SupportsDType[dtype[float64]] + | type[float] + | type[float64] + | _Float64Codes + | _DoubleCodes +) + +class Generator: + def __init__(self, bit_generator: BitGenerator) -> None: ... + def __repr__(self) -> str: ... + def __str__(self) -> str: ... + def __getstate__(self) -> None: ... + def __setstate__(self, state: dict[str, Any] | None) -> None: ... + def __reduce__(self) -> tuple[ + Callable[[BitGenerator], Generator], + tuple[BitGenerator], + None]: ... + @property + def bit_generator(self) -> BitGenerator: ... + def spawn(self, n_children: int) -> list[Generator]: ... + def bytes(self, length: int) -> bytes: ... + @overload + def standard_normal( # type: ignore[misc] + self, + size: None = ..., + dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., + out: None = ..., + ) -> float: ... + @overload + def standard_normal( # type: ignore[misc] + self, + size: _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def standard_normal( # type: ignore[misc] + self, + *, + out: NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def standard_normal( # type: ignore[misc] + self, + size: _ShapeLike = ..., + dtype: _DTypeLikeFloat32 = ..., + out: None | NDArray[float32] = ..., + ) -> NDArray[float32]: ... + @overload + def standard_normal( # type: ignore[misc] + self, + size: _ShapeLike = ..., + dtype: _DTypeLikeFloat64 = ..., + out: None | NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def permutation(self, x: int, axis: int = ...) -> NDArray[int64]: ... + @overload + def permutation(self, x: ArrayLike, axis: int = ...) -> NDArray[Any]: ... + @overload + def standard_exponential( # type: ignore[misc] + self, + size: None = ..., + dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., + method: Literal["zig", "inv"] = ..., + out: None = ..., + ) -> float: ... + @overload + def standard_exponential( + self, + size: _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def standard_exponential( + self, + *, + out: NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def standard_exponential( + self, + size: _ShapeLike = ..., + *, + method: Literal["zig", "inv"] = ..., + out: None | NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def standard_exponential( + self, + size: _ShapeLike = ..., + dtype: _DTypeLikeFloat32 = ..., + method: Literal["zig", "inv"] = ..., + out: None | NDArray[float32] = ..., + ) -> NDArray[float32]: ... + @overload + def standard_exponential( + self, + size: _ShapeLike = ..., + dtype: _DTypeLikeFloat64 = ..., + method: Literal["zig", "inv"] = ..., + out: None | NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def random( # type: ignore[misc] + self, + size: None = ..., + dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., + out: None = ..., + ) -> float: ... + @overload + def random( + self, + *, + out: NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def random( + self, + size: _ShapeLike = ..., + *, + out: None | NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def random( + self, + size: _ShapeLike = ..., + dtype: _DTypeLikeFloat32 = ..., + out: None | NDArray[float32] = ..., + ) -> NDArray[float32]: ... + @overload + def random( + self, + size: _ShapeLike = ..., + dtype: _DTypeLikeFloat64 = ..., + out: None | NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def beta( + self, + a: _FloatLike_co, + b: _FloatLike_co, + size: None = ..., + ) -> float: ... # type: ignore[misc] + @overload + def beta( + self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def exponential( + self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + ) -> int: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: type[bool] = ..., + endpoint: bool = ..., + ) -> bool: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: type[np.bool] = ..., + endpoint: bool = ..., + ) -> np.bool: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: type[int] = ..., + endpoint: bool = ..., + ) -> int: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., + endpoint: bool = ..., + ) -> uint8: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., + endpoint: bool = ..., + ) -> uint16: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., + endpoint: bool = ..., + ) -> uint32: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., + endpoint: bool = ..., + ) -> uint: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., + endpoint: bool = ..., + ) -> uint64: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., + endpoint: bool = ..., + ) -> int8: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., + endpoint: bool = ..., + ) -> int16: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., + endpoint: bool = ..., + ) -> int32: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., + endpoint: bool = ..., + ) -> int_: ... + @overload + def integers( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., + endpoint: bool = ..., + ) -> int64: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[int64]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: _DTypeLikeBool = ..., + endpoint: bool = ..., + ) -> NDArray[np.bool]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., + endpoint: bool = ..., + ) -> NDArray[int8]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., + endpoint: bool = ..., + ) -> NDArray[int16]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., + endpoint: bool = ..., + ) -> NDArray[int32]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., + endpoint: bool = ..., + ) -> NDArray[int64]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., + endpoint: bool = ..., + ) -> NDArray[uint8]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., + endpoint: bool = ..., + ) -> NDArray[uint16]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., + endpoint: bool = ..., + ) -> NDArray[uint32]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., + endpoint: bool = ..., + ) -> NDArray[uint64]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., + endpoint: bool = ..., + ) -> NDArray[int_]: ... + @overload + def integers( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., + endpoint: bool = ..., + ) -> NDArray[uint]: ... + # TODO: Use a TypeVar _T here to get away from Any output? Should be int->NDArray[int64], ArrayLike[_T] -> _T | NDArray[Any] + @overload + def choice( + self, + a: int, + size: None = ..., + replace: bool = ..., + p: None | _ArrayLikeFloat_co = ..., + axis: int = ..., + shuffle: bool = ..., + ) -> int: ... + @overload + def choice( + self, + a: int, + size: _ShapeLike = ..., + replace: bool = ..., + p: None | _ArrayLikeFloat_co = ..., + axis: int = ..., + shuffle: bool = ..., + ) -> NDArray[int64]: ... + @overload + def choice( + self, + a: ArrayLike, + size: None = ..., + replace: bool = ..., + p: None | _ArrayLikeFloat_co = ..., + axis: int = ..., + shuffle: bool = ..., + ) -> Any: ... + @overload + def choice( + self, + a: ArrayLike, + size: _ShapeLike = ..., + replace: bool = ..., + p: None | _ArrayLikeFloat_co = ..., + axis: int = ..., + shuffle: bool = ..., + ) -> NDArray[Any]: ... + @overload + def uniform( + self, + low: _FloatLike_co = ..., + high: _FloatLike_co = ..., + size: None = ..., + ) -> float: ... # type: ignore[misc] + @overload + def uniform( + self, + low: _ArrayLikeFloat_co = ..., + high: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def normal( + self, + loc: _FloatLike_co = ..., + scale: _FloatLike_co = ..., + size: None = ..., + ) -> float: ... # type: ignore[misc] + @overload + def normal( + self, + loc: _ArrayLikeFloat_co = ..., + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def standard_gamma( # type: ignore[misc] + self, + shape: _FloatLike_co, + size: None = ..., + dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., + out: None = ..., + ) -> float: ... + @overload + def standard_gamma( + self, + shape: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def standard_gamma( + self, + shape: _ArrayLikeFloat_co, + *, + out: NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def standard_gamma( + self, + shape: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + dtype: _DTypeLikeFloat32 = ..., + out: None | NDArray[float32] = ..., + ) -> NDArray[float32]: ... + @overload + def standard_gamma( + self, + shape: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + dtype: _DTypeLikeFloat64 = ..., + out: None | NDArray[float64] = ..., + ) -> NDArray[float64]: ... + @overload + def gamma(self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def gamma( + self, + shape: _ArrayLikeFloat_co, + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def f( + self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def noncentral_f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def noncentral_f( + self, + dfnum: _ArrayLikeFloat_co, + dfden: _ArrayLikeFloat_co, + nonc: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def chisquare( + self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def noncentral_chisquare(self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def noncentral_chisquare( + self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def standard_t( + self, df: _ArrayLikeFloat_co, size: None = ... + ) -> NDArray[float64]: ... + @overload + def standard_t( + self, df: _ArrayLikeFloat_co, size: _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def vonmises(self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def vonmises( + self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def pareto( + self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def weibull( + self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def power(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def power( + self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def standard_cauchy(self, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ... + @overload + def laplace( + self, + loc: _FloatLike_co = ..., + scale: _FloatLike_co = ..., + size: None = ..., + ) -> float: ... # type: ignore[misc] + @overload + def laplace( + self, + loc: _ArrayLikeFloat_co = ..., + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def gumbel( + self, + loc: _FloatLike_co = ..., + scale: _FloatLike_co = ..., + size: None = ..., + ) -> float: ... # type: ignore[misc] + @overload + def gumbel( + self, + loc: _ArrayLikeFloat_co = ..., + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def logistic( + self, + loc: _FloatLike_co = ..., + scale: _FloatLike_co = ..., + size: None = ..., + ) -> float: ... # type: ignore[misc] + @overload + def logistic( + self, + loc: _ArrayLikeFloat_co = ..., + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def lognormal( + self, + mean: _FloatLike_co = ..., + sigma: _FloatLike_co = ..., + size: None = ..., + ) -> float: ... # type: ignore[misc] + @overload + def lognormal( + self, + mean: _ArrayLikeFloat_co = ..., + sigma: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def rayleigh( + self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def wald(self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def wald( + self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def triangular( + self, + left: _FloatLike_co, + mode: _FloatLike_co, + right: _FloatLike_co, + size: None = ..., + ) -> float: ... # type: ignore[misc] + @overload + def triangular( + self, + left: _ArrayLikeFloat_co, + mode: _ArrayLikeFloat_co, + right: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def binomial( + self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[int64]: ... + @overload + def negative_binomial(self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def negative_binomial( + self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[int64]: ... + @overload + def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int: ... # type: ignore[misc] + @overload + def poisson( + self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... + ) -> NDArray[int64]: ... + @overload + def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def zipf( + self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[int64]: ... + @overload + def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def geometric( + self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[int64]: ... + @overload + def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def hypergeometric( + self, + ngood: _ArrayLikeInt_co, + nbad: _ArrayLikeInt_co, + nsample: _ArrayLikeInt_co, + size: None | _ShapeLike = ..., + ) -> NDArray[int64]: ... + @overload + def logseries(self, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def logseries( + self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[int64]: ... + def multivariate_normal( + self, + mean: _ArrayLikeFloat_co, + cov: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + check_valid: Literal["warn", "raise", "ignore"] = ..., + tol: float = ..., + *, + method: Literal["svd", "eigh", "cholesky"] = ..., + ) -> NDArray[float64]: ... + def multinomial( + self, n: _ArrayLikeInt_co, + pvals: _ArrayLikeFloat_co, + size: None | _ShapeLike = ... + ) -> NDArray[int64]: ... + def multivariate_hypergeometric( + self, + colors: _ArrayLikeInt_co, + nsample: int, + size: None | _ShapeLike = ..., + method: Literal["marginals", "count"] = ..., + ) -> NDArray[int64]: ... + def dirichlet( + self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + def permuted( + self, x: ArrayLike, *, axis: None | int = ..., out: None | NDArray[Any] = ... + ) -> NDArray[Any]: ... + def shuffle(self, x: ArrayLike, axis: int = ...) -> None: ... + +def default_rng( + seed: None | _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator = ... +) -> Generator: ... diff --git a/parrot/lib/python3.10/site-packages/numpy/random/bit_generator.pxd b/parrot/lib/python3.10/site-packages/numpy/random/bit_generator.pxd new file mode 100644 index 0000000000000000000000000000000000000000..dfa7d0a71c085dfa3dfb2819f47493cb8501d198 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/random/bit_generator.pxd @@ -0,0 +1,35 @@ +cimport numpy as np +from libc.stdint cimport uint32_t, uint64_t + +cdef extern from "numpy/random/bitgen.h": + struct bitgen: + void *state + uint64_t (*next_uint64)(void *st) nogil + uint32_t (*next_uint32)(void *st) nogil + double (*next_double)(void *st) nogil + uint64_t (*next_raw)(void *st) nogil + + ctypedef bitgen bitgen_t + +cdef class BitGenerator(): + cdef readonly object _seed_seq + cdef readonly object lock + cdef bitgen_t _bitgen + cdef readonly object _ctypes + cdef readonly object _cffi + cdef readonly object capsule + + +cdef class SeedSequence(): + cdef readonly object entropy + cdef readonly tuple spawn_key + cdef readonly Py_ssize_t pool_size + cdef readonly object pool + cdef readonly uint32_t n_children_spawned + + cdef mix_entropy(self, np.ndarray[np.npy_uint32, ndim=1] mixer, + np.ndarray[np.npy_uint32, ndim=1] entropy_array) + cdef get_assembled_entropy(self) + +cdef class SeedlessSequence(): + pass diff --git a/parrot/lib/python3.10/site-packages/numpy/random/bit_generator.pyi b/parrot/lib/python3.10/site-packages/numpy/random/bit_generator.pyi new file mode 100644 index 0000000000000000000000000000000000000000..d99278e861eabe78170094f7746ad4e37898f178 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/random/bit_generator.pyi @@ -0,0 +1,124 @@ +import abc +from threading import Lock +from collections.abc import Callable, Mapping, Sequence +from typing import ( + Any, + NamedTuple, + TypedDict, + TypeVar, + overload, + Literal, +) + +from numpy import dtype, uint32, uint64 +from numpy._typing import ( + NDArray, + _ArrayLikeInt_co, + _ShapeLike, + _SupportsDType, + _UInt32Codes, + _UInt64Codes, +) + +_T = TypeVar("_T") + +_DTypeLikeUint32 = ( + dtype[uint32] + | _SupportsDType[dtype[uint32]] + | type[uint32] + | _UInt32Codes +) +_DTypeLikeUint64 = ( + dtype[uint64] + | _SupportsDType[dtype[uint64]] + | type[uint64] + | _UInt64Codes +) + +class _SeedSeqState(TypedDict): + entropy: None | int | Sequence[int] + spawn_key: tuple[int, ...] + pool_size: int + n_children_spawned: int + +class _Interface(NamedTuple): + state_address: Any + state: Any + next_uint64: Any + next_uint32: Any + next_double: Any + bit_generator: Any + +class ISeedSequence(abc.ABC): + @abc.abstractmethod + def generate_state( + self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ... + ) -> NDArray[uint32 | uint64]: ... + +class ISpawnableSeedSequence(ISeedSequence): + @abc.abstractmethod + def spawn(self: _T, n_children: int) -> list[_T]: ... + +class SeedlessSeedSequence(ISpawnableSeedSequence): + def generate_state( + self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ... + ) -> NDArray[uint32 | uint64]: ... + def spawn(self: _T, n_children: int) -> list[_T]: ... + +class SeedSequence(ISpawnableSeedSequence): + entropy: None | int | Sequence[int] + spawn_key: tuple[int, ...] + pool_size: int + n_children_spawned: int + pool: NDArray[uint32] + def __init__( + self, + entropy: None | int | Sequence[int] | _ArrayLikeInt_co = ..., + *, + spawn_key: Sequence[int] = ..., + pool_size: int = ..., + n_children_spawned: int = ..., + ) -> None: ... + def __repr__(self) -> str: ... + @property + def state( + self, + ) -> _SeedSeqState: ... + def generate_state( + self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ... + ) -> NDArray[uint32 | uint64]: ... + def spawn(self, n_children: int) -> list[SeedSequence]: ... + +class BitGenerator(abc.ABC): + lock: Lock + def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ... + def __getstate__(self) -> tuple[dict[str, Any], ISeedSequence]: ... + def __setstate__( + self, state_seed_seq: dict[str, Any] | tuple[dict[str, Any], ISeedSequence] + ) -> None: ... + def __reduce__( + self, + ) -> tuple[ + Callable[[str], BitGenerator], + tuple[str], + tuple[dict[str, Any], ISeedSequence] + ]: ... + @abc.abstractmethod + @property + def state(self) -> Mapping[str, Any]: ... + @state.setter + def state(self, value: Mapping[str, Any]) -> None: ... + @property + def seed_seq(self) -> ISeedSequence: ... + def spawn(self, n_children: int) -> list[BitGenerator]: ... + @overload + def random_raw(self, size: None = ..., output: Literal[True] = ...) -> int: ... # type: ignore[misc] + @overload + def random_raw(self, size: _ShapeLike = ..., output: Literal[True] = ...) -> NDArray[uint64]: ... # type: ignore[misc] + @overload + def random_raw(self, size: None | _ShapeLike = ..., output: Literal[False] = ...) -> None: ... # type: ignore[misc] + def _benchmark(self, cnt: int, method: str = ...) -> None: ... + @property + def ctypes(self) -> _Interface: ... + @property + def cffi(self) -> _Interface: ... diff --git a/parrot/lib/python3.10/site-packages/numpy/random/mtrand.pyi b/parrot/lib/python3.10/site-packages/numpy/random/mtrand.pyi new file mode 100644 index 0000000000000000000000000000000000000000..dbd3cd609495242e729affd801d18fabc556ed6f --- /dev/null +++ b/parrot/lib/python3.10/site-packages/numpy/random/mtrand.pyi @@ -0,0 +1,681 @@ +import builtins +from collections.abc import Callable +from typing import Any, overload, Literal + +import numpy as np +from numpy import ( + dtype, + float32, + float64, + int8, + int16, + int32, + int64, + int_, + long, + uint8, + uint16, + uint32, + uint64, + uint, + ulong, +) +from numpy.random.bit_generator import BitGenerator +from numpy._typing import ( + ArrayLike, + NDArray, + _ArrayLikeFloat_co, + _ArrayLikeInt_co, + _DoubleCodes, + _DTypeLikeBool, + _DTypeLikeInt, + _DTypeLikeUInt, + _Float32Codes, + _Float64Codes, + _Int8Codes, + _Int16Codes, + _Int32Codes, + _Int64Codes, + _IntCodes, + _LongCodes, + _ShapeLike, + _SingleCodes, + _SupportsDType, + _UInt8Codes, + _UInt16Codes, + _UInt32Codes, + _UInt64Codes, + _UIntCodes, + _ULongCodes, +) + +_DTypeLikeFloat32 = ( + dtype[float32] + | _SupportsDType[dtype[float32]] + | type[float32] + | _Float32Codes + | _SingleCodes +) + +_DTypeLikeFloat64 = ( + dtype[float64] + | _SupportsDType[dtype[float64]] + | type[float] + | type[float64] + | _Float64Codes + | _DoubleCodes +) + +class RandomState: + _bit_generator: BitGenerator + def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None: ... + def __repr__(self) -> str: ... + def __str__(self) -> str: ... + def __getstate__(self) -> dict[str, Any]: ... + def __setstate__(self, state: dict[str, Any]) -> None: ... + def __reduce__(self) -> tuple[Callable[[BitGenerator], RandomState], tuple[BitGenerator], dict[str, Any]]: ... + def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ... + @overload + def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ... + @overload + def get_state( + self, legacy: Literal[True] = ... + ) -> dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]: ... + def set_state( + self, state: dict[str, Any] | tuple[str, NDArray[uint32], int, int, float] + ) -> None: ... + @overload + def random_sample(self, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def random_sample(self, size: _ShapeLike) -> NDArray[float64]: ... + @overload + def random(self, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def random(self, size: _ShapeLike) -> NDArray[float64]: ... + @overload + def beta(self, a: float, b: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def beta( + self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def exponential(self, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def exponential( + self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def standard_exponential(self, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def standard_exponential(self, size: _ShapeLike) -> NDArray[float64]: ... + @overload + def tomaxint(self, size: None = ...) -> int: ... # type: ignore[misc] + @overload + # Generates long values, but stores it in a 64bit int: + def tomaxint(self, size: _ShapeLike) -> NDArray[int64]: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + ) -> int: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: type[bool] = ..., + ) -> bool: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: type[np.bool] = ..., + ) -> np.bool: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: type[int] = ..., + ) -> int: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., + ) -> uint8: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., + ) -> uint16: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., + ) -> uint32: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., + ) -> uint: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ..., + ) -> ulong: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., + ) -> uint64: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., + ) -> int8: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., + ) -> int16: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., + ) -> int32: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int_] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., + ) -> int_: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[long] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ..., + ) -> long: ... + @overload + def randint( # type: ignore[misc] + self, + low: int, + high: None | int = ..., + size: None = ..., + dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., + ) -> int64: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[long]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: _DTypeLikeBool = ..., + ) -> NDArray[np.bool]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., + ) -> NDArray[int8]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., + ) -> NDArray[int16]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., + ) -> NDArray[int32]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., + ) -> NDArray[int64]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., + ) -> NDArray[uint8]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., + ) -> NDArray[uint16]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., + ) -> NDArray[uint32]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., + ) -> NDArray[uint64]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[long] | type[int] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ..., + ) -> NDArray[long]: ... + @overload + def randint( # type: ignore[misc] + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ..., + ) -> NDArray[ulong]: ... + def bytes(self, length: int) -> builtins.bytes: ... + @overload + def choice( + self, + a: int, + size: None = ..., + replace: bool = ..., + p: None | _ArrayLikeFloat_co = ..., + ) -> int: ... + @overload + def choice( + self, + a: int, + size: _ShapeLike = ..., + replace: bool = ..., + p: None | _ArrayLikeFloat_co = ..., + ) -> NDArray[long]: ... + @overload + def choice( + self, + a: ArrayLike, + size: None = ..., + replace: bool = ..., + p: None | _ArrayLikeFloat_co = ..., + ) -> Any: ... + @overload + def choice( + self, + a: ArrayLike, + size: _ShapeLike = ..., + replace: bool = ..., + p: None | _ArrayLikeFloat_co = ..., + ) -> NDArray[Any]: ... + @overload + def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def uniform( + self, + low: _ArrayLikeFloat_co = ..., + high: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def rand(self) -> float: ... + @overload + def rand(self, *args: int) -> NDArray[float64]: ... + @overload + def randn(self) -> float: ... + @overload + def randn(self, *args: int) -> NDArray[float64]: ... + @overload + def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ... # type: ignore[misc] + @overload + def random_integers( + self, + low: _ArrayLikeInt_co, + high: None | _ArrayLikeInt_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[long]: ... + @overload + def standard_normal(self, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def standard_normal( # type: ignore[misc] + self, size: _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def normal( + self, + loc: _ArrayLikeFloat_co = ..., + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def standard_gamma( # type: ignore[misc] + self, + shape: float, + size: None = ..., + ) -> float: ... + @overload + def standard_gamma( + self, + shape: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def gamma( + self, + shape: _ArrayLikeFloat_co, + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def f( + self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def noncentral_f( + self, + dfnum: _ArrayLikeFloat_co, + dfden: _ArrayLikeFloat_co, + nonc: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def chisquare(self, df: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def chisquare( + self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def noncentral_chisquare( + self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def standard_t(self, df: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def standard_t( + self, df: _ArrayLikeFloat_co, size: None = ... + ) -> NDArray[float64]: ... + @overload + def standard_t( + self, df: _ArrayLikeFloat_co, size: _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def vonmises( + self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def pareto(self, a: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def pareto( + self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def weibull(self, a: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def weibull( + self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def power(self, a: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def power( + self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def standard_cauchy(self, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ... + @overload + def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def laplace( + self, + loc: _ArrayLikeFloat_co = ..., + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def gumbel( + self, + loc: _ArrayLikeFloat_co = ..., + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def logistic( + self, + loc: _ArrayLikeFloat_co = ..., + scale: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def lognormal( + self, + mean: _ArrayLikeFloat_co = ..., + sigma: _ArrayLikeFloat_co = ..., + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def rayleigh(self, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc] + @overload + def rayleigh( + self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def wald(self, mean: float, scale: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def wald( + self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + @overload + def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ... # type: ignore[misc] + @overload + def triangular( + self, + left: _ArrayLikeFloat_co, + mode: _ArrayLikeFloat_co, + right: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + ) -> NDArray[float64]: ... + @overload + def binomial(self, n: int, p: float, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def binomial( + self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[long]: ... + @overload + def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def negative_binomial( + self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[long]: ... + @overload + def poisson(self, lam: float = ..., size: None = ...) -> int: ... # type: ignore[misc] + @overload + def poisson( + self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... + ) -> NDArray[long]: ... + @overload + def zipf(self, a: float, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def zipf( + self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[long]: ... + @overload + def geometric(self, p: float, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def geometric( + self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[long]: ... + @overload + def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def hypergeometric( + self, + ngood: _ArrayLikeInt_co, + nbad: _ArrayLikeInt_co, + nsample: _ArrayLikeInt_co, + size: None | _ShapeLike = ..., + ) -> NDArray[long]: ... + @overload + def logseries(self, p: float, size: None = ...) -> int: ... # type: ignore[misc] + @overload + def logseries( + self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[long]: ... + def multivariate_normal( + self, + mean: _ArrayLikeFloat_co, + cov: _ArrayLikeFloat_co, + size: None | _ShapeLike = ..., + check_valid: Literal["warn", "raise", "ignore"] = ..., + tol: float = ..., + ) -> NDArray[float64]: ... + def multinomial( + self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[long]: ... + def dirichlet( + self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ... + ) -> NDArray[float64]: ... + def shuffle(self, x: ArrayLike) -> None: ... + @overload + def permutation(self, x: int) -> NDArray[long]: ... + @overload + def permutation(self, x: ArrayLike) -> NDArray[Any]: ... + +_rand: RandomState + +beta = _rand.beta +binomial = _rand.binomial +bytes = _rand.bytes +chisquare = _rand.chisquare +choice = _rand.choice +dirichlet = _rand.dirichlet +exponential = _rand.exponential +f = _rand.f +gamma = _rand.gamma +get_state = _rand.get_state +geometric = _rand.geometric +gumbel = _rand.gumbel +hypergeometric = _rand.hypergeometric +laplace = _rand.laplace +logistic = _rand.logistic +lognormal = _rand.lognormal +logseries = _rand.logseries +multinomial = _rand.multinomial +multivariate_normal = _rand.multivariate_normal +negative_binomial = _rand.negative_binomial +noncentral_chisquare = _rand.noncentral_chisquare +noncentral_f = _rand.noncentral_f +normal = _rand.normal +pareto = _rand.pareto +permutation = _rand.permutation +poisson = _rand.poisson +power = _rand.power +rand = _rand.rand +randint = _rand.randint +randn = _rand.randn +random = _rand.random +random_integers = _rand.random_integers +random_sample = _rand.random_sample +rayleigh = _rand.rayleigh +seed = _rand.seed +set_state = _rand.set_state +shuffle = _rand.shuffle +standard_cauchy = _rand.standard_cauchy +standard_exponential = _rand.standard_exponential +standard_gamma = _rand.standard_gamma +standard_normal = _rand.standard_normal +standard_t = _rand.standard_t +triangular = _rand.triangular +uniform = _rand.uniform +vonmises = _rand.vonmises +wald = _rand.wald +weibull = _rand.weibull +zipf = _rand.zipf +# Two legacy that are trivial wrappers around random_sample +sample = _rand.random_sample +ranf = _rand.random_sample + +def set_bit_generator(bitgen: BitGenerator) -> None: + ... + +def get_bit_generator() -> BitGenerator: + ... diff --git a/vllm/lib/python3.10/site-packages/wandb/vendor/promise-2.3.0/__pycache__/conftest.cpython-310.pyc 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0000000000000000000000000000000000000000..394a85f2a4c1c7f84b47f2e17420c5a1ab761b00 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/wandb/vendor/pygments/__init__.py @@ -0,0 +1,90 @@ +# -*- coding: utf-8 -*- +""" + Pygments + ~~~~~~~~ + + Pygments is a syntax highlighting package written in Python. + + It is a generic syntax highlighter for general use in all kinds of software + such as forum systems, wikis or other applications that need to prettify + source code. Highlights are: + + * a wide range of common languages and markup formats is supported + * special attention is paid to details, increasing quality by a fair amount + * support for new languages and formats are added easily + * a number of output formats, presently HTML, LaTeX, RTF, SVG, all image + formats that PIL supports, and ANSI sequences + * it is usable as a command-line tool and as a library + * ... and it highlights even Brainfuck! + + The `Pygments tip`_ is installable with ``easy_install Pygments==dev``. + + .. _Pygments tip: + http://bitbucket.org/birkenfeld/pygments-main/get/tip.zip#egg=Pygments-dev + + :copyright: Copyright 2006-2017 by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" +import sys + +from pygments.util import StringIO, BytesIO + +__version__ = '2.2.0' +__docformat__ = 'restructuredtext' + +__all__ = ['lex', 'format', 'highlight'] + + +def lex(code, lexer): + """ + Lex ``code`` with ``lexer`` and return an iterable of tokens. + """ + try: + return lexer.get_tokens(code) + except TypeError as err: + if (isinstance(err.args[0], str) and + ('unbound method get_tokens' in err.args[0] or + 'missing 1 required positional argument' in err.args[0])): + raise TypeError('lex() argument must be a lexer instance, ' + 'not a class') + raise + + +def format(tokens, formatter, outfile=None): # pylint: disable=redefined-builtin + """ + Format a tokenlist ``tokens`` with the formatter ``formatter``. + + If ``outfile`` is given and a valid file object (an object + with a ``write`` method), the result will be written to it, otherwise + it is returned as a string. + """ + try: + if not outfile: + realoutfile = getattr(formatter, 'encoding', None) and BytesIO() or StringIO() + formatter.format(tokens, realoutfile) + return realoutfile.getvalue() + else: + formatter.format(tokens, outfile) + except TypeError as err: + if (isinstance(err.args[0], str) and + ('unbound method format' in err.args[0] or + 'missing 1 required positional argument' in err.args[0])): + raise TypeError('format() argument must be a formatter instance, ' + 'not a class') + raise + + +def highlight(code, lexer, formatter, outfile=None): + """ + Lex ``code`` with ``lexer`` and format it with the formatter ``formatter``. + + If ``outfile`` is given and a valid file object (an object + with a ``write`` method), the result will be written to it, otherwise + it is returned as a string. + """ + return format(lex(code, lexer), formatter, outfile) + + +if __name__ == '__main__': # pragma: no cover + from pygments.cmdline import main + sys.exit(main(sys.argv)) diff --git a/vllm/lib/python3.10/site-packages/wandb/vendor/pygments/cmdline.py b/vllm/lib/python3.10/site-packages/wandb/vendor/pygments/cmdline.py new file mode 100644 index 0000000000000000000000000000000000000000..5e1f39e2aa4c1ca05d2e8a5ea1700890460e24e1 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/wandb/vendor/pygments/cmdline.py @@ -0,0 +1,568 @@ +# -*- coding: utf-8 -*- +""" + pygments.cmdline + ~~~~~~~~~~~~~~~~ + + Command line interface. + + :copyright: Copyright 2006-2017 by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +from __future__ import print_function + +import sys +import getopt +from textwrap import dedent + +from pygments import __version__, highlight +from pygments.util import ClassNotFound, OptionError, docstring_headline, \ + guess_decode, guess_decode_from_terminal, terminal_encoding +from pygments.lexers import get_all_lexers, get_lexer_by_name, guess_lexer, \ + load_lexer_from_file, get_lexer_for_filename, find_lexer_class_for_filename +from pygments.lexers.special import TextLexer +from pygments.formatters.latex import LatexEmbeddedLexer, LatexFormatter +from pygments.formatters import get_all_formatters, get_formatter_by_name, \ + load_formatter_from_file, get_formatter_for_filename, find_formatter_class +from pygments.formatters.terminal import TerminalFormatter +from pygments.filters import get_all_filters, find_filter_class +from pygments.styles import get_all_styles, get_style_by_name + + +USAGE = """\ +Usage: %s [-l | -g] [-F [:]] [-f ] + [-O ] [-P ] [-s] [-v] [-x] [-o ] [] + + %s -S + + +

%(title)s

+ +''' + +DOC_HEADER_EXTERNALCSS = '''\ + + + + + %(title)s + + + + +

%(title)s

+ +''' + +DOC_FOOTER = '''\ + + +''' + + +class HtmlFormatter(Formatter): + r""" + Format tokens as HTML 4 ```` tags within a ``
`` tag, wrapped
+    in a ``
`` tag. The ``
``'s CSS class can be set by the `cssclass` + option. + + If the `linenos` option is set to ``"table"``, the ``
`` is
+    additionally wrapped inside a ```` which has one row and two
+    cells: one containing the line numbers and one containing the code.
+    Example:
+
+    .. sourcecode:: html
+
+        
+
+ + +
+
1
+            2
+
+
def foo(bar):
+              pass
+            
+
+ + (whitespace added to improve clarity). + + Wrapping can be disabled using the `nowrap` option. + + A list of lines can be specified using the `hl_lines` option to make these + lines highlighted (as of Pygments 0.11). + + With the `full` option, a complete HTML 4 document is output, including + the style definitions inside a ``