我们从Python开源项目中，提取了以下35个代码示例，用于说明如何使用numpy.arccosh()。
def test_branch_cuts(self): # check branch cuts and continuity on them yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arccos, [ -2, 2], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arctan, [0-2j, 2j], [1, 1], -1, 1, True yield _check_branch_cut, np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True # check against bogus branch cuts: assert continuity between quadrants yield _check_branch_cut, np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1 yield _check_branch_cut, np.arccos, [0-2j, 2j], [ 1, 1], 1, 1 yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1 yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1 yield _check_branch_cut, np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1 yield _check_branch_cut, np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1
def test_branch_cuts_complex64(self): # check branch cuts and continuity on them yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arccos, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arctan, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64 yield _check_branch_cut, np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64 yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 # check against bogus branch cuts: assert continuity between quadrants yield _check_branch_cut, np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arccos, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1, False, np.complex64 yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1, False, np.complex64 yield _check_branch_cut, np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1, False, np.complex64
def test_against_cmath(self): import cmath points = [-1-1j, -1+1j, +1-1j, +1+1j] name_map = {'arcsin': 'asin', 'arccos': 'acos', 'arctan': 'atan', 'arcsinh': 'asinh', 'arccosh': 'acosh', 'arctanh': 'atanh'} atol = 4*np.finfo(np.complex).eps for func in self.funcs: fname = func.__name__.split('.')[-1] cname = name_map.get(fname, fname) try: cfunc = getattr(cmath, cname) except AttributeError: continue for p in points: a = complex(func(np.complex_(p))) b = cfunc(p) assert_(abs(a - b) < atol, "%s %s: %s; cmath: %s" % (fname, p, a, b))
def _keplerian_to_keplerian_mean(cls, coord, center): """Conversion from Keplerian to Keplerian Mean The difference is the use of Mean anomaly instead of True anomaly """ a, e, i, ?, ?, ? = coord if e < 1: # Elliptic case E = arccos((e + cos(?)) / (1 + e * cos(?))) # Eccentric anomaly M = E - e * sin(E) # Mean anomaly else: # Hyperbolic case H = arccosh((e + cos(?)) / (1 + e * cos(?))) M = e * sinh(H) - H return np.array([a, e, i, ?, ?, M], dtype=float)
def test_it(self): for f in self.funcs: if f is np.arccosh: x = 1.5 else: x = .5 fr = f(x) fz = f(np.complex(x)) assert_almost_equal(fz.real, fr, err_msg='real part %s' % f) assert_almost_equal(fz.imag, 0., err_msg='imag part %s' % f)
def Dn_plane(l, r, N=20): alpha = np.arccosh(l/r) sinh = np.sinh s = 0. for n in range(1, N): n = float(n) K = n*(n+1)/(2*n-1)/(2*n+3) s += K*((2*sinh((2*n+1)*alpha)+(2*n+1)*sinh(2*alpha))/(4*(sinh((n+.5)*alpha))**2-(2*n+1)**2*(sinh(alpha))**2) - 1) return 1./((4./3.)*sinh(alpha)*s)
def test_numpy_method(): # This type of code is used frequently by PyMC3 users x = tt.dmatrix('x') data = np.random.rand(5, 5) x.tag.test_value = data for fct in [np.arccos, np.arccosh, np.arcsin, np.arcsinh, np.arctan, np.arctanh, np.ceil, np.cos, np.cosh, np.deg2rad, np.exp, np.exp2, np.expm1, np.floor, np.log, np.log10, np.log1p, np.log2, np.rad2deg, np.sin, np.sinh, np.sqrt, np.tan, np.tanh, np.trunc]: y = fct(x) f = theano.function([x], y) utt.assert_allclose(np.nan_to_num(f(data)), np.nan_to_num(fct(data)))
def impl(self, x): # If x is an int8 or uint8, numpy.arccosh will compute the result in # half-precision (float16), where we want float32. x_dtype = str(getattr(x, 'dtype', '')) if x_dtype in ('int8', 'uint8'): return numpy.arccosh(x, sig='f') return numpy.arccosh(x)
def arccosh(inp): if isinstance(inp, ooarray) and inp.dtype == object: return ooarray([arccosh(elem) for elem in inp]) if not isinstance(inp, oofun): return np.arccosh(inp) # TODO: move it outside of arccosh definition def interval(arg_inf, arg_sup): raise 'interval for arccosh is unimplemented yet' r = oofun(np.arccosh, inp, d = lambda x: FDmisc.Diag(1.0/sqrt(x**2 - 1)), vectorized = True, interval = interval) return r
def t_from_z(self, z): """ Compute the age of the Universe from redshift. This is based on Enzo's CosmologyComputeTimeFromRedshift.C, but altered to use physical units. Similar to hubble_time, but using an analytical function. Parameters ---------- z : float Redshift. Examples -------- >>> from yt.utilities.cosmology import Cosmology >>> co = Cosmology() >>> print(co.t_from_z(0.).in_units("Gyr")) See Also -------- hubble_time """ omega_curvature = 1.0 - self.omega_matter - self.omega_lambda # 1) For a flat universe with omega_matter = 1, things are easy. if ((self.omega_matter == 1.0) and (self.omega_lambda == 0.0)): t0 = 2.0/3.0/np.power(1+z, 1.5) # 2) For omega_matter < 1 and omega_lambda == 0 see # Peebles 1993, eq. 13-3, 13-10. if ((self.omega_matter < 1) and (self.omega_lambda == 0)): eta = np.arccosh(1 + 2*(1-self.omega_matter)/self.omega_matter/(1+z)) t0 = self.omega_matter/ \ (2*np.power(1.0-self.omega_matter, 1.5))*\ (np.sinh(eta) - eta) # 3) For omega_matter > 1 and omega_lambda == 0, use sin/cos. if ((self.omega_matter > 1) and (self.omega_lambda == 0)): eta = np.arccos(1 - 2*(1-self.omega_matter)/self.omega_matter/(1+z)) t0 = self.omega_matter/(2*np.power(1.0-self.omega_matter, 1.5))*\ (eta - np.sin(eta)) # 4) For flat universe, with non-zero omega_lambda, see eq. 13-20. if ((np.fabs(omega_curvature) < 1.0e-3) and (self.omega_lambda != 0)): t0 = 2.0/3.0/np.sqrt(1-self.omega_matter)*\ np.arcsinh(np.sqrt((1-self.omega_matter)/self.omega_matter)/ \ np.power(1+z, 1.5)) # Now convert from Time * H0 to time. my_time = t0 / self.hubble_constant return my_time.in_base(self.unit_system)
def test_numpy_ufuncs(self): # test ufuncs of numpy 1.9.2. see: # http://docs.scipy.org/doc/numpy/reference/ufuncs.html # some functions are skipped because it may return different result # for unicode input depending on numpy version for name, idx in compat.iteritems(self.indices): for func in [np.exp, np.exp2, np.expm1, np.log, np.log2, np.log10, np.log1p, np.sqrt, np.sin, np.cos, np.tan, np.arcsin, np.arccos, np.arctan, np.sinh, np.cosh, np.tanh, np.arcsinh, np.arccosh, np.arctanh, np.deg2rad, np.rad2deg]: if isinstance(idx, pd.tseries.base.DatetimeIndexOpsMixin): # raise TypeError or ValueError (PeriodIndex) # PeriodIndex behavior should be changed in future version with tm.assertRaises(Exception): func(idx) elif isinstance(idx, (Float64Index, Int64Index)): # coerces to float (e.g. np.sin) result = func(idx) exp = Index(func(idx.values), name=idx.name) self.assert_index_equal(result, exp) self.assertIsInstance(result, pd.Float64Index) else: # raise AttributeError or TypeError if len(idx) == 0: continue else: with tm.assertRaises(Exception): func(idx) for func in [np.isfinite, np.isinf, np.isnan, np.signbit]: if isinstance(idx, pd.tseries.base.DatetimeIndexOpsMixin): # raise TypeError or ValueError (PeriodIndex) with tm.assertRaises(Exception): func(idx) elif isinstance(idx, (Float64Index, Int64Index)): # results in bool array result = func(idx) exp = func(idx.values) self.assertIsInstance(result, np.ndarray) tm.assertNotIsInstance(result, Index) else: if len(idx) == 0: continue else: with tm.assertRaises(Exception): func(idx)
