diff options
Diffstat (limited to 'numpy/polynomial/polynomial.py')
| -rw-r--r-- | numpy/polynomial/polynomial.py | 96 |
1 files changed, 6 insertions, 90 deletions
diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index 8a2cffc15..44e1cbb3f 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -511,14 +511,10 @@ def polyder(c, m=1, scl=1, axis=0): # astype fails with NA c = c + 0.0 cdt = c.dtype - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of derivation must be integer") + cnt = pu._deprecate_as_int(m, "the order of derivation") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of derivation must be non-negative") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -625,10 +621,8 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0): cdt = c.dtype if not np.iterable(k): k = [k] - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of integration must be integer") + cnt = pu._deprecate_as_int(m, "the order of integration") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of integration must be non-negative") if len(k) > cnt: @@ -637,8 +631,6 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0): raise ValueError("lbnd must be a scalar.") if np.ndim(scl) != 0: raise ValueError("scl must be a scalar.") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -1095,9 +1087,7 @@ def polyvander(x, deg): polyvander2d, polyvander3d """ - ideg = int(deg) - if ideg != deg: - raise ValueError("deg must be integer") + ideg = pu._deprecate_as_int(deg, "deg") if ideg < 0: raise ValueError("deg must be non-negative") @@ -1358,81 +1348,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): 0.50443316, 0.28853036]), 1.1324274851176597e-014] """ - x = np.asarray(x) + 0.0 - y = np.asarray(y) + 0.0 - deg = np.asarray(deg) - - # check arguments. - if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0: - raise TypeError("deg must be an int or non-empty 1-D array of int") - if deg.min() < 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: - raise TypeError("expected 1D or 2D array for y") - if len(x) != len(y): - raise TypeError("expected x and y to have same length") - - if deg.ndim == 0: - lmax = deg - order = lmax + 1 - van = polyvander(x, lmax) - else: - deg = np.sort(deg) - lmax = deg[-1] - order = len(deg) - van = polyvander(x, lmax)[:, deg] - - # set up the least squares matrices in transposed form - lhs = van.T - rhs = y.T - if w is not None: - w = np.asarray(w) + 0.0 - if w.ndim != 1: - raise TypeError("expected 1D vector for w") - if len(x) != len(w): - raise TypeError("expected x and w to have same length") - # apply weights. Don't use inplace operations as they - # can cause problems with NA. - lhs = lhs * w - rhs = rhs * w - - # set rcond - if rcond is None: - rcond = len(x)*np.finfo(x.dtype).eps - - # Determine the norms of the design matrix columns. - if issubclass(lhs.dtype.type, np.complexfloating): - scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) - else: - scl = np.sqrt(np.square(lhs).sum(1)) - scl[scl == 0] = 1 - - # Solve the least squares problem. - c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) - c = (c.T/scl).T - - # Expand c to include non-fitted coefficients which are set to zero - if deg.ndim == 1: - if c.ndim == 2: - cc = np.zeros((lmax + 1, c.shape[1]), dtype=c.dtype) - else: - cc = np.zeros(lmax + 1, dtype=c.dtype) - cc[deg] = c - c = cc - - # warn on rank reduction - if rank != order and not full: - msg = "The fit may be poorly conditioned" - warnings.warn(msg, pu.RankWarning, stacklevel=2) - - if full: - return c, [resids, rank, s, rcond] - else: - return c + return pu._fit(polyvander, x, y, deg, rcond, full, w) def polycompanion(c): |