Merge branch 'master' into mcmc

PynPoint/ProcessingModules/BackgroundSubtraction.py

@@ -713,7 +713,7 @@ def _dot_product(x_dot, *p):
         star = np.zeros((nimages, 2))
         for i, _ in enumerate(star):
             star[i, :] = locate_star(image=self.m_star_in_port[i, ]-bg_mean,
-                                     center=(None, None),
+                                     center=None,
                                      width=self.m_subframe,
                                      fwhm=self.m_gaussian)
 

PynPoint/ProcessingModules/FrameSelection.py

@@ -277,7 +277,11 @@ def _get_starpos(fwhm, position):
                     width = None
 
                 else:
-                    center = position[0:2]
+                    if position[0] is None and position[1] is None:
+                        center = None
+                    else:
+                        center = position[0:2]
+
                     width = int(math.ceil(position[2]/pixscale))
 
                 for i, _ in enumerate(starpos):

PynPoint/ProcessingModules/StarAlignment.py

@@ -119,6 +119,7 @@ def _crop_around_star(image, position, im_size, fwhm):
                         center = None
                     else:
                         center = (int(position[1]), int(position[0]))
+
                     width = int(math.ceil(position[2]/pixscale))
 
                 elif position.ndim == 2:

PynPoint/Util/ModuleTools.py

@@ -91,8 +91,8 @@ def locate_star(image,
     :rtype: (int, int)
     """
 
-    if center is not None and width is not None:
-        if center[0] is None and center[1] is None:
+    if width is not None:
+        if center is None:
             center = image_center(image)
 
         image = crop_image(image, center, width)

README.rst

@@ -46,7 +46,7 @@ Please subscribe to the `mailing list <https://pynpoint.readthedocs.io/en/latest
 Attribution
 -----------
 
-If you use PynPoint in your publication then please cite Stolker et al. subm. Please also cite `Amara & Quanz (2012) <http://adsabs.harvard.edu/abs/2012MNRAS.427..948A>`_ as the origin of PynPoint, which focused initially on the use of principal component analysis (PCA) as a PSF subtraction method. In case you use specifically the PCA-based background subtraction module or the wavelet based speckle suppression module, please give credit to `Hunziker et al. (2018) <http://adsabs.harvard.edu/abs/2018A%26A...611A..23H>`_ or `Bonse, Quanz & Amara (2018) <http://adsabs.harvard.edu/abs/2018arXiv180405063B>`_, respectively.
+If you use PynPoint in your publication then please cite `Stolker et al. (2018) <http://adsabs.harvard.edu/abs/2018arXiv181103336S>`_. Please also cite `Amara & Quanz (2012) <http://adsabs.harvard.edu/abs/2012MNRAS.427..948A>`_ as the origin of PynPoint, which focused initially on the use of principal component analysis (PCA) as a PSF subtraction method. In case you use specifically the PCA-based background subtraction module or the wavelet based speckle suppression module, please give credit to `Hunziker et al. (2018) <http://adsabs.harvard.edu/abs/2018A%26A...611A..23H>`_ or `Bonse, Quanz & Amara (2018) <http://adsabs.harvard.edu/abs/2018arXiv180405063B>`_, respectively.
 
 License
 -------

docs/about.rst

@@ -33,7 +33,7 @@ If you encounter errors or problems when using PynPoint then please contact Toma
 Attribution
 -----------
 
-If you use PynPoint in your publication then please cite Stolker et al. subm. Please also cite `Amara & Quanz (2012) <http://adsabs.harvard.edu/abs/2012MNRAS.427..948A>`_ as the origin of PynPoint, which focused initially on the use of principal component analysis (PCA) as a PSF subtraction method. In case you use specifically the PCA-based background subtraction module or the wavelet based speckle suppression module, please give credit to `Hunziker et al. (2018) <http://adsabs.harvard.edu/abs/2018A%26A...611A..23H>`_ or `Bonse, Quanz & Amara (2018) <http://adsabs.harvard.edu/abs/2018arXiv180405063B>`_, respectively.
+If you use PynPoint in your publication then please cite `Stolker et al. (2018) <http://adsabs.harvard.edu/abs/2018arXiv181103336S>`_. Please also cite `Amara & Quanz (2012) <http://adsabs.harvard.edu/abs/2012MNRAS.427..948A>`_ as the origin of PynPoint, which focused initially on the use of principal component analysis (PCA) as a PSF subtraction method. In case you use specifically the PCA-based background subtraction module or the wavelet based speckle suppression module, please give credit to `Hunziker et al. (2018) <http://adsabs.harvard.edu/abs/2018A%26A...611A..23H>`_ or `Bonse, Quanz & Amara (2018) <http://adsabs.harvard.edu/abs/2018arXiv180405063B>`_, respectively.
 
 .. _acknowledgements: