diff options
Diffstat (limited to 'numpy/lib/polynomial.py')
| -rw-r--r-- | numpy/lib/polynomial.py | 67 |
1 files changed, 34 insertions, 33 deletions
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index 10ae32a60..6a1adc773 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -143,7 +143,7 @@ def poly(seq_of_zeros): neg_roots = NX.conjugate(sort_complex( NX.compress(roots.imag < 0, roots))) if (len(pos_roots) == len(neg_roots) and - NX.alltrue(neg_roots == pos_roots)): + NX.alltrue(neg_roots == pos_roots)): a = a.real.copy() return a @@ -412,15 +412,14 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): deg : int Degree of the fitting polynomial rcond : float, optional - Relative condition number of the fit. Singular values smaller than this - relative to the largest singular value will be ignored. The default - value is len(x)*eps, where eps is the relative precision of the float - type, about 2e-16 in most cases. + Relative condition number of the fit. Singular values smaller than + this relative to the largest singular value will be ignored. The + default value is len(x)*eps, where eps is the relative precision of + the float type, about 2e-16 in most cases. full : bool, optional - Switch determining nature of return value. When it is - False (the default) just the coefficients are returned, when True - diagnostic information from the singular value decomposition is also - returned. + Switch determining nature of return value. When it is False (the + default) just the coefficients are returned, when True diagnostic + information from the singular value decomposition is also returned. w : array_like, shape (M,), optional weights to apply to the y-coordinates of the sample points. cov : bool, optional @@ -430,18 +429,21 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): Returns ------- p : ndarray, shape (M,) or (M, K) - Polynomial coefficients, highest power first. - If `y` was 2-D, the coefficients for `k`-th data set are in ``p[:,k]``. + Polynomial coefficients, highest power first. If `y` was 2-D, the + coefficients for `k`-th data set are in ``p[:,k]``. - residuals, rank, singular_values, rcond : present only if `full` = True - Residuals of the least-squares fit, the effective rank of the scaled - Vandermonde coefficient matrix, its singular values, and the specified - value of `rcond`. For more details, see `linalg.lstsq`. + residuals, rank, singular_values, rcond : + Present only if `full` = True. Residuals of the least-squares fit, + the effective rank of the scaled Vandermonde coefficient matrix, + its singular values, and the specified value of `rcond`. For more + details, see `linalg.lstsq`. - V : ndaray, shape (M,M) or (M,M,K) : present only if `full` = False and `cov`=True - The covariance matrix of the polynomial coefficient estimates. The diagonal - of this matrix are the variance estimates for each coefficient. If y is a 2-d - array, then the covariance matrix for the `k`-th data set are in ``V[:,:,k]`` + V : ndarray, shape (M,M) or (M,M,K) + Present only if `full` = False and `cov`=True. The covariance + matrix of the polynomial coefficient estimates. The diagonal of + this matrix are the variance estimates for each coefficient. If y + is a 2-D array, then the covariance matrix for the `k`-th data set + are in ``V[:,:,k]`` Warns @@ -531,8 +533,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): >>> import matplotlib.pyplot as plt >>> xp = np.linspace(-2, 6, 100) - >>> plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--') - [<matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>] + >>> _ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--') >>> plt.ylim(-2,2) (-2, 2) >>> plt.show() @@ -543,19 +544,19 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): y = NX.asarray(y) + 0.0 # check arguments. - if deg < 0 : + if deg < 0: raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2 : + if y.ndim < 1 or y.ndim > 2: raise TypeError("expected 1D or 2D array for y") - if x.shape[0] != y.shape[0] : + if x.shape[0] != y.shape[0]: raise TypeError("expected x and y to have same length") # set rcond - if rcond is None : + if rcond is None: rcond = len(x)*finfo(x.dtype).eps # set up least squares equation for powers of x @@ -567,7 +568,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): w = NX.asarray(w) + 0.0 if w.ndim != 1: raise TypeError("expected a 1-d array for weights") - if w.shape[0] != y.shape[0] : + if w.shape[0] != y.shape[0]: raise TypeError("expected w and y to have the same length") lhs *= w[:, NX.newaxis] if rhs.ndim == 2: @@ -586,9 +587,9 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): msg = "Polyfit may be poorly conditioned" warnings.warn(msg, RankWarning) - if full : + if full: return c, resids, rank, s, rcond - elif cov : + elif cov: Vbase = inv(dot(lhs.T, lhs)) Vbase /= NX.outer(scale, scale) # Some literature ignores the extra -2.0 factor in the denominator, but @@ -600,7 +601,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): return c, Vbase * fac else: return c, Vbase[:,:, NX.newaxis] * fac - else : + else: return c @@ -917,8 +918,8 @@ def _raise_power(astr, wrap=70): n = span[1] toadd2 = partstr + ' '*(len(power)-1) toadd1 = ' '*(len(partstr)-1) + power - if ((len(line2)+len(toadd2) > wrap) or \ - (len(line1)+len(toadd1) > wrap)): + if ((len(line2) + len(toadd2) > wrap) or + (len(line1) + len(toadd1) > wrap)): output += line1 + "\n" + line2 + "\n " line1 = toadd1 line2 = toadd2 @@ -1126,7 +1127,6 @@ class poly1d(object): thestr = newstr return _raise_power(thestr) - def __call__(self, val): return polyval(self.coeffs, val) @@ -1214,7 +1214,8 @@ class poly1d(object): try: return self.__dict__[key] except KeyError: - raise AttributeError("'%s' has no attribute '%s'" % (self.__class__, key)) + raise AttributeError( + "'%s' has no attribute '%s'" % (self.__class__, key)) def __getitem__(self, val): ind = self.order - val |