diff options
Diffstat (limited to 'lib/stwcs/wcsutil/hstwcs.py')
| -rw-r--r-- | lib/stwcs/wcsutil/hstwcs.py | 66 |
1 files changed, 31 insertions, 35 deletions
diff --git a/lib/stwcs/wcsutil/hstwcs.py b/lib/stwcs/wcsutil/hstwcs.py index 978ab4f..2b14d58 100644 --- a/lib/stwcs/wcsutil/hstwcs.py +++ b/lib/stwcs/wcsutil/hstwcs.py @@ -81,12 +81,12 @@ class NoConvergence(Exception): solution appears to be divergent. If the solution does not diverge, `divergent` will be set to `None`. - failed2converge : None, numpy.array + slow_conv : None, numpy.array Indices of the points in :py:attr:`best_solution` array for which the solution failed to converge within the specified maximum number - of iterations. If there are no non-converging poits (i.e., if + of iterations. If there are no non-converging points (i.e., if the required accuracy has been achieved for all points) then - `failed2converge` will be set to `None`. + `slow_conv` will be set to `None`. """ def __init__(self, *args, **kwargs): @@ -96,7 +96,7 @@ class NoConvergence(Exception): self.accuracy = kwargs.pop('accuracy', None) self.niter = kwargs.pop('niter', None) self.divergent = kwargs.pop('divergent', None) - self.failed2converge= kwargs.pop('failed2converge', None) + self.slow_conv = kwargs.pop('slow_conv', None) # @@ -164,22 +164,6 @@ class HSTWCS(WCS): self.setPscale() self.setOrient() - @property - def naxis1(self): - return self._naxis1 - - @naxis1.setter - def naxis1(self, value): - self._naxis1 = value - - @property - def naxis2(self): - return self._naxis2 - - @naxis2.setter - def naxis2(self, value): - self._naxis2 = value - def readIDCCoeffs(self, header): """ Reads in first order IDCTAB coefficients if present in the header @@ -384,7 +368,7 @@ class HSTWCS(WCS): If True - include SIP coefficients """ - h = self.to_header(key=wcskey, relax=relax) + h = self.to_header(wkey=wcskey, relax=relax) if not wcskey: wcskey = self.wcs.alt if self.wcs.has_cd(): @@ -570,7 +554,7 @@ detect_divergence=True, quiet=False) .. note:: Indices of the diverging inverse solutions will be reported in the `divergent` attribute of the - raised :py:class:`NoConvergence` object. + raised :py:class:`NoConvergence` exception object. quiet : bool, optional (Default = False) Do not throw :py:class:`NoConvergence` exceptions when the method @@ -604,6 +588,12 @@ detect_divergence=True, quiet=False) to :py:meth:`all_sky2pix` instead of calling :py:meth:`all_sky2pix` for each data point. Also see the note to the `adaptive` parameter. + See Also + -------- + A detailed description of the algorithm is available on astropy's + GitHub page, + issue `#2373 <https://github.com/astropy/astropy/pull/2373>`_\ . + Examples -------- >>> import stwcs, pyfits @@ -646,7 +636,8 @@ accuracy after 3 iterations. adaptive=False, detect_divergence=True, quiet=False) >>> except stwcs.wcsutil.hstwcs.NoConvergence as e: >>> print("Indices of diverging points: {}".format(e.divergent)) - >>> print("Indices of poorly converging points: {}".format(e.failed2converge)) + >>> print("Indices of poorly converging points: {}"\ +.format(e.slow_conv)) >>> print("Best solution: {}".format(e.best_solution)) >>> print("Achieved accuracy: {}".format(e.accuracy)) >>> raise e @@ -664,15 +655,18 @@ adaptive=False, detect_divergence=True, quiet=False) [ 6.02334592e-05 6.59713067e-07]] Traceback (innermost last): File "<console>", line 8, in <module> - NoConvergence: 'HSTWCS.all_sky2pix' failed to converge to the requested accuracy. - After 5 iterations, the solution is diverging at least for one input point. + NoConvergence: 'HSTWCS.all_sky2pix' failed to converge to the \ +requested accuracy. + After 5 iterations, the solution is diverging at least for one \ +input point. >>> try: >>> xy = w.all_sky2pix(divradec,1, maxiter=20, accuracy=1.0e-4, \ adaptive=False, detect_divergence=False, quiet=False) >>> except stwcs.wcsutil.hstwcs.NoConvergence as e: >>> print("Indices of diverging points: {}".format(e.divergent)) - >>> print("Indices of poorly converging points: {}".format(e.failed2converge)) + >>> print("Indices of poorly converging points: {}"\ +.format(e.slow_conv)) >>> print("Best solution: {}".format(e.best_solution)) >>> print("Achieved accuracy: {}".format(e.accuracy)) >>> raise e @@ -690,8 +684,10 @@ adaptive=False, detect_divergence=False, quiet=False) [ 0. 0.]] Traceback (innermost last): File "<console>", line 8, in <module> - NoConvergence: 'HSTWCS.all_sky2pix' failed to converge to the requested accuracy. - After 20 iterations, the solution is diverging at least for one input point. + NoConvergence: 'HSTWCS.all_sky2pix' failed to converge to the \ +requested accuracy. + After 20 iterations, the solution is diverging at least for one \ +input point. """ ##################################################################### @@ -767,7 +763,7 @@ adaptive=False, detect_divergence=False, quiet=False) x -= dx y -= dy - # norn (L2) squared of the correction: + # norm (L2) squared of the correction: dn2prev = dx**2+dy**2 dn2 = dn2prev @@ -802,15 +798,14 @@ adaptive=False, detect_divergence=False, quiet=False) dx -= x0 dy -= y0 - # update norn (L2) squared of the correction: + # update norm (L2) squared of the correction: dn2 = dx**2+dy**2 # check for divergence (we do this in two stages # to optimize performance for the most common # scenario when succesive approximations converge): if detect_divergence: - ind, = np.where(dn2 <= dn2prev) - if ind.shape[0] < npts: + if np.any(dn2 > dn2prev): inddiv, = np.where( np.logical_and(dn2 > dn2prev, dn2 >= accuracy2)) if inddiv.shape[0] > 0: @@ -851,7 +846,7 @@ adaptive=False, detect_divergence=False, quiet=False) dx[ind] -= x0[ind] dy[ind] -= y0[ind] - # update norn (L2) squared of the correction: + # update norm (L2) squared of the correction: dn2 = dx**2+dy**2 # update indices of elements that still need correction: @@ -909,7 +904,7 @@ adaptive=False, detect_divergence=False, quiet=False) raise NoConvergence("'HSTWCS.all_sky2pix' failed to " \ "converge to the requested accuracy after {:d} " \ "iterations.".format(k), best_solution = sol, \ - accuracy = err, niter = k, failed2converge = ind, \ + accuracy = err, niter = k, slow_conv = ind, \ divergent = None) else: raise NoConvergence("'HSTWCS.all_sky2pix' failed to " \ @@ -917,7 +912,7 @@ adaptive=False, detect_divergence=False, quiet=False) "After {1:d} iterations, the solution is diverging " \ "at least for one input point." \ .format(os.linesep, k), best_solution = sol, \ - accuracy = err, niter = k, failed2converge = ind, \ + accuracy = err, niter = k, slow_conv = ind, \ divergent = inddiv) ##################################################################### @@ -931,6 +926,7 @@ adaptive=False, detect_divergence=False, quiet=False) else: return np.dstack( [x, y] )[0] + def _updatehdr(self, ext_hdr): #kw2add : OCX10, OCX11, OCY10, OCY11 # record the model in the header for use by pydrizzle |