Merge pull request #457 from VChristiaens/readme_update

Major v1.0.0 update
vortex-exoplanet · Sep 11, 2021 · 30ad391 · 30ad391
2 parents f74d1cb + 1666737
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diff --git a/README.rst b/README.rst
@@ -192,9 +192,8 @@ also install the optional dependencies listed below.
 
 Optional dependencies
 ^^^^^^^^^^^^^^^^^^^^^
-
 The following dependencies are not automatically installed upon installation of
-``VIP`` but may significantly improve your experience:
+ ``VIP`` but may significantly improve your experience:
 
 ``VIP`` contains a class ``vip_hci.fits.ds9`` that enables, through ``pyds9``,
 the interaction with a DS9 window (displaying numpy arrays, controlling the
@@ -254,4 +253,4 @@ In addition, if you use one of the following modules, please also cite:
 - negfc: `Wertz et al. (2017) <https://ui.adsabs.harvard.edu/abs/2017A%26A...598A..83W/abstract>`_;
 - nmf: `Ren et al. (2018) <https://ui.adsabs.harvard.edu/abs/2018ApJ...852..104R/abstract>`_;
 - pca: `Amara and Quanz (2012) <https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..948A/abstract>`_ and `Soummer et al. (2012) <https://ui.adsabs.harvard.edu/abs/2012ApJ...755L..28S/abstract>`_;
-- specfit: `Christiaens et al. (2021) <https://ui.adsabs.harvard.edu/abs/2021arXiv210210288C/abstract>`_;
+- specfit: `Christiaens et al. (2021) <https://ui.adsabs.harvard.edu/abs/2021arXiv210210288C/abstract>`_;
diff --git a/docs/source/vip_hci.rst b/docs/source/vip_hci.rst
@@ -24,6 +24,7 @@ Subpackages
     vip_hci.nmf
     vip_hci.pca
     vip_hci.preproc
+    vip_hci.specfit
     vip_hci.stats
     vip_hci.var
 

diff --git a/docs/source/vip_hci.specfit.rst b/docs/source/vip_hci.specfit.rst
@@ -0,0 +1,52 @@
+vip\_hci.specfit package
+====================
+
+.. automodule:: vip_hci.specfit
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Submodules
+----------
+
+vip\_hci.specfit.chi module
+---------------------------
+
+.. automodule:: vip_hci.specfit.chi
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+vip\_hci.specfit.mcmc\_sampling\_spec module
+--------------------------------------------
+
+.. automodule:: vip_hci.specfit.mcmc_samling_spec
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+vip\_hci.specfit.model\_resampling module
+-----------------------------------------
+
+.. automodule:: vip_hci.specfit.model_resampling
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+vip\_hci.specfit.template\_lib module
+------------------------------------
+
+.. automodule:: vip_hci.specfit.template_lib
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+vip\_hci.specfit.utils\_spec module
+------------------------------
+
+.. automodule:: vip_hci.specfit.utils_spec
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
diff --git a/requirements.txt b/requirements.txt
@@ -1,17 +1,182 @@
-numpy ~= 1.18
-scipy ~= 1.1.0
-astropy ~= 3.2
-photutils ~= 0.6
-scikit-learn ~= 0.17
-scikit-image ~= 0.11
-emcee ~= 2.1
-nestle == 0.2.0
-corner ~= 1.0.2
-pandas ~= 0.18
-matplotlib>=2.2,<=3.3
-psutil
-pyprind ~= 2.11
-munch
-opencv-python >= 3.4
-pytest
-hciplot ~= 0.1.3
+#
+# This file is autogenerated by pip-compile with python 3.7
+# To update, run:
+#
+#    pip-compile requirements.in
+#
+astropy==3.2.3
+    # via
+    #   -r requirements.in
+    #   photutils
+attrs==21.2.0
+    # via pytest
+bleach==4.1.0
+    # via panel
+bokeh==2.3.3
+    # via
+    #   hciplot
+    #   panel
+certifi==2021.5.30
+    # via requests
+charset-normalizer==2.0.4
+    # via requests
+colorcet==2.0.6
+    # via holoviews
+corner==1.0.3
+    # via -r requirements.in
+cycler==0.10.0
+    # via matplotlib
+emcee==2.2.1
+    # via -r requirements.in
+hciplot==0.1.9
+    # via -r requirements.in
+holoviews==1.14.5
+    # via hciplot
+idna==3.2
+    # via requests
+imageio==2.9.0
+    # via scikit-image
+importlib-metadata==4.8.1
+    # via
+    #   markdown
+    #   pluggy
+    #   pytest
+iniconfig==1.1.1
+    # via pytest
+jinja2==3.0.1
+    # via bokeh
+joblib==1.0.1
+    # via scikit-learn
+kiwisolver==1.3.2
+    # via matplotlib
+markdown==3.3.4
+    # via panel
+markupsafe==2.0.1
+    # via jinja2
+matplotlib==3.3.0
+    # via
+    #   -r requirements.in
+    #   hciplot
+    #   scikit-image
+munch==2.5.0
+    # via -r requirements.in
+nestle==0.2.0
+    # via -r requirements.in
+networkx==2.6.3
+    # via scikit-image
+numpy==1.21.2
+    # via
+    #   -r requirements.in
+    #   astropy
+    #   bokeh
+    #   emcee
+    #   hciplot
+    #   holoviews
+    #   imageio
+    #   matplotlib
+    #   opencv-python
+    #   pandas
+    #   photutils
+    #   pywavelets
+    #   scikit-image
+    #   scikit-learn
+    #   scipy
+    #   tifffile
+opencv-python==4.5.3.56
+    # via -r requirements.in
+packaging==21.0
+    # via
+    #   bleach
+    #   bokeh
+    #   pytest
+pandas==0.25.3
+    # via
+    #   -r requirements.in
+    #   holoviews
+panel==0.12.1
+    # via holoviews
+param==1.11.1
+    # via
+    #   colorcet
+    #   holoviews
+    #   panel
+    #   pyct
+    #   pyviz-comms
+photutils==0.7.2
+    # via -r requirements.in
+pillow==8.3.2
+    # via
+    #   bokeh
+    #   imageio
+    #   matplotlib
+    #   scikit-image
+pluggy==1.0.0
+    # via pytest
+psutil==5.8.0
+    # via -r requirements.in
+py==1.10.0
+    # via pytest
+pyct==0.4.8
+    # via
+    #   colorcet
+    #   panel
+pyparsing==2.4.7
+    # via
+    #   matplotlib
+    #   packaging
+pyprind==2.11.3
+    # via -r requirements.in
+pytest==6.2.5
+    # via -r requirements.in
+python-dateutil==2.8.2
+    # via
+    #   bokeh
+    #   matplotlib
+    #   pandas
+pytz==2021.1
+    # via pandas
+pyviz-comms==2.1.0
+    # via
+    #   holoviews
+    #   panel
+pywavelets==1.1.1
+    # via scikit-image
+pyyaml==5.4.1
+    # via bokeh
+requests==2.26.0
+    # via panel
+scikit-image==0.18.3
+    # via -r requirements.in
+scikit-learn==0.24.2
+    # via -r requirements.in
+scipy==1.1.0
+    # via
+    #   -r requirements.in
+    #   scikit-image
+    #   scikit-learn
+six==1.16.0
+    # via
+    #   bleach
+    #   cycler
+    #   munch
+    #   python-dateutil
+threadpoolctl==2.2.0
+    # via scikit-learn
+tifffile==2021.8.30
+    # via scikit-image
+toml==0.10.2
+    # via pytest
+tornado==6.1
+    # via bokeh
+tqdm==4.62.2
+    # via panel
+typing-extensions==3.10.0.2
+    # via
+    #   bokeh
+    #   importlib-metadata
+urllib3==1.26.6
+    # via requests
+webencodings==0.5.1
+    # via bleach
+zipp==3.5.0
+    # via importlib-metadata
diff --git a/vip_hci/andromeda/andromeda.py b/vip_hci/andromeda/andromeda.py
@@ -1098,7 +1098,8 @@ def normalize_snr(snr, nsmooth_snr=1, iwa=None, owa=None, oversampling=None,
                 if prof_snr[i] != 0:
                     k = i
             if k is None:  # error handling not present in IDL version.
-                set_trace()
+                import pdb
+                pdb.set_trace()
                 raise RuntimeError("prof_snr is zero!")
 
             for i in range(j - nsmooth_snr, k):

diff --git a/vip_hci/frdiff/framediff.py b/vip_hci/frdiff/framediff.py
@@ -22,7 +22,8 @@
 
 def frame_diff(cube, angle_list, fwhm=4, metric='manhattan', dist_threshold=50,
                n_similar=None, delta_rot=0.5, radius_int=2, asize=4, ncomp=None,
-               nproc=1, verbose=True, debug=False):
+               imlib='opencv', interpolation='lanczos4', nproc=1, verbose=True, 
+               debug=False):
     """ Frame differencing algorithm. It uses vector distance (depending on
     ``metric``), using separately the pixels from different annuli of ``asize``
     width, to create pairs of most similar images. Then it performs pair-wise
@@ -42,7 +43,7 @@ def frame_diff(cube, angle_list, fwhm=4, metric='manhattan', dist_threshold=50,
         function ``sklearn.metrics.pairwise.pairwise_distances`` (check its
         documentation).
     dist_threshold : int
-        Indices with a distance larger thatn ``dist_threshold`` percentile will
+        Indices with a distance larger than ``dist_threshold`` percentile will
         initially discarded.
     n_similar : None or int, optional
         If a postive integer value is given, then a median combination of
@@ -99,8 +100,8 @@ def frame_diff(cube, angle_list, fwhm=4, metric='manhattan', dist_threshold=50,
 
     res = pool_map(nproc, _pairwise_ann, iterable(range(n_annuli)),
                    n_annuli, fwhm, angle_list, delta_rot, metric,
-                   dist_threshold, n_similar, radius_int, asize, ncomp,
-                   verbose, debug)
+                   dist_threshold, n_similar, radius_int, asize, ncomp, imlib, 
+                   interpolation, verbose, debug)
 
     final_frame = np.sum(res, axis=0)
 
@@ -111,9 +112,9 @@ def frame_diff(cube, angle_list, fwhm=4, metric='manhattan', dist_threshold=50,
     return final_frame
 
 
-def _pairwise_ann(ann, n_annuli, fwhm, angles, delta_rot, metric,
-                  dist_threshold, n_similar, radius_int, asize, ncomp, verbose,
-                  debug=False):
+def _pairwise_ann(ann, n_annuli, fwhm, angles, delta_rot, metric, 
+                  dist_threshold, n_similar, radius_int, asize, ncomp, imlib, 
+                  interpolation, verbose, debug=False):
     """
     Helper functions for pair-wise subtraction for a single annulus.
     """
@@ -219,7 +220,8 @@ def _pairwise_ann(ann, n_annuli, fwhm, angles, delta_rot, metric,
 
     cube_out = np.zeros((cube_res.shape[0], array.shape[1], array.shape[2]))
     cube_out[:, yy, xx] = cube_res
-    cube_der = cube_derotate(cube_out, angles_list)
+    cube_der = cube_derotate(cube_out, angles_list, imlib=imlib, 
+                             interpolation=interpolation)
     frame_der_median = cube_collapse(cube_der, 'median')
 
     return frame_der_median

diff --git a/vip_hci/llsg/llsg.py b/vip_hci/llsg/llsg.py
@@ -336,7 +336,7 @@ def _patch_rlrps(array, array_ref, rank, low_rank_ref, low_rank_mode,
                 PC = get_eigenvectors(rank, L, svdlib, mode=auto_rank_mode,
                                       cevr=cevr, noise_error=residuals_tol,
                                       data_ref=L_ref, debug=debug,
-                                      collapse=True)
+                                      collapse=True, scaling='temp-standard')
                 rank = PC.shape[0]  # so we can use the optimized rank
                 if low_rank_ref:
                     Lnew = np.dot(np.dot(PC, L).T, PC).T

diff --git a/vip_hci/medsub/medsub_source.py b/vip_hci/medsub/medsub_source.py
@@ -151,7 +151,7 @@ def median_sub(cube, angle_list, scale_list=None, fwhm=4, radius_int=0, asize=4,
                            verbose=verbose, msg='Processing annuli:',
                            progressbar_single=True)
 
-            res = np.array(res)
+            res = np.array(res, dtype=object)
             mres = res[:, 0]
             yy = res[:, 1]
             xx = res[:, 2]
@@ -225,7 +225,7 @@ def median_sub(cube, angle_list, scale_list=None, fwhm=4, radius_int=0, asize=4,
                            angle_list, n_annuli, fwhm, radius_int, asize,
                            delta_rot, nframes)
 
-            res = np.array(res)
+            res = np.array(res, dtype=object)
             mres = res[:, 0]
             yy = res[:, 1]
             xx = res[:, 2]