Updated README, CI tests and requirements to drop support to 3.7 but include support to 3.11

.github/workflows/ci.yml

@@ -12,7 +12,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
         group: [1, 2, 3]
 
     steps:

README.rst

@@ -50,16 +50,16 @@ VIP - Vortex Image Processing package
 Introduction
 ------------
 
-``VIP`` is a python package for high-contrast imaging of exoplanets and circumstellar disks. 
-``VIP`` is compatible with Python 3.7, 3.8 and 3.9 (Python 2 compatibility dropped with ``VIP`` 0.9.9).
+VIP is a python package for high-contrast imaging of exoplanets and circumstellar disks. 
+VIP is compatible with Python 3.8, 3.9, 3.10 and 3.11 (Python 2 compatibility dropped with VIP 0.9.9, and Python 3.7 compatibility dropped with VIP 1.4.3).
 
-The goal of ``VIP`` is to integrate open-source, efficient, easy-to-use and
+The goal of VIP is to integrate open-source, efficient, easy-to-use and
 well-documented implementations of high-contrast image processing algorithms to
-the interested scientific community. The main repository of ``VIP`` resides on
+the interested scientific community. The main repository of VIP resides on
 `GitHub <https://github.com/vortex-exoplanet/VIP>`_, the standard for scientific
 open source code distribution, using Git as a version control system.
 
-Most of ``VIP``'s functionalities are mature but
+Most of VIP's functionalities are mature but
 it does not mean it is free from bugs. The code is continuously evolving and
 therefore feedback/contributions are greatly appreciated. Please refer to `these instructions <https://vip.readthedocs.io/en/latest/Contact.html>`_ if you want to report
 a bug, ask a question, suggest a new functionality or contribute to the code (the latter is particularly welcome)!
@@ -69,12 +69,12 @@ a bug, ask a question, suggest a new functionality or contribute to the code (th
 
 Documentation
 -------------
-The documentation for ``VIP`` can be found here: http://vip.readthedocs.io.
+The documentation for VIP can be found here: http://vip.readthedocs.io.
 
 
 Jupyter notebook tutorial
 -------------------------
-Tutorials, in the form of Jupyter notebooks, showcasing ``VIP``'s usage and 
+Tutorials, in the form of Jupyter notebooks, showcasing VIP's usage and 
 other resources such as test datasets are available in the 
 ``VIP-extras`` `repository <https://github.com/vortex-exoplanet/VIP_extras>`_. 
 **In order to execute the notebook tutorials, you will have to download or clone the VIP-extras repository, and open each tutorial locally with jupyter notebook.**
@@ -97,53 +97,52 @@ TL;DR setup guide
 Installation and dependencies
 -----------------------------
 The benefits of using a Python package manager (distribution), such as
-(ana)conda or Canopy, are many. Mainly, it brings easy and robust package
+(ana)conda, are many. Mainly, it brings easy and robust package
 management and avoids messing up with your system's default python. An
 alternative is to use package managers like apt-get for Ubuntu or
 Homebrew/MacPorts/Fink for macOS. We recommend using 
 `Miniconda <https://conda.io/miniconda>`_.
 
-``VIP`` depends on existing packages from the Python ecosystem, such as
+VIP depends on existing packages from the Python ecosystem, such as
 ``numpy``, ``scipy``, ``matplotlib``, ``pandas``, ``astropy``, ``scikit-learn``,
 ``scikit-image``, ``photutils`` and others. There are different ways of
-installing ``VIP`` suitable for different scenarios.
+installing VIP suitable for different scenarios.
 
-
-Using pip
-^^^^^^^^^
-The easiest way to install ``VIP`` is through the Python Package Index, aka
-`PyPI <https://pypi.org/>`_, with the ``pip`` package manager. Simply run:
+Before installing the package, it is **highly recommended to create a dedicated
+conda environment** to not mess up with the package versions in your base 
+environment. This can be done easily with (replace ``vipenv`` by the name you want
+for your environment):
 
 .. code-block:: bash
 
-  $ pip install vip_hci
-
-With ``pip`` you can easily uninstall, upgrade or install a specific version of
-``VIP``. For upgrading the package run:
+  $ conda create -n vipenv python=3.10 ipython
 
-.. code-block:: bash
+.. note::
+  Installing ipython while creating the environment, as in the example above, will
+  avoid a commonly reported issue which stems from trying to import VIP from 
+  within a base python2.7 ipython console.
 
-  $ pip install --upgrade vip_hci
 
-Alternatively, you can use ``pip install`` and point to the GitHub repo:
+For users (not planning to contribute):
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Once within your new environment, the easiest way to install VIP is 
+through the Python Package Index, aka `PyPI <https://pypi.org/>`_, with 
+the ``pip`` package manager. Simply run:
 
 .. code-block:: bash
 
-  $ pip install git+https://github.com/vortex-exoplanet/VIP.git
+  $ pip install vip_hci
 
-Using the setup.py file
-^^^^^^^^^^^^^^^^^^^^^^^
-You can download ``VIP`` from its GitHub repository as a zip file. A ``setup.py``
-file (setuptools) is included in the root folder of ``VIP``. Enter the package's
-root folder and run:
+With ``pip`` you can easily uninstall, upgrade or install a specific version of
+VIP. For upgrading the package, run:
 
 .. code-block:: bash
 
-  $ python setup.py install
+  $ pip install --upgrade vip_hci
 
 
-Using Git
-^^^^^^^^^
+For potential contributors:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
 If you plan to contribute or experiment with the code you need to make a 
 fork of the repository (click on the fork button in the top right corner) and 
 clone it:
@@ -159,26 +158,13 @@ functionalities by cloning the repository (but will not be able to contribute):
 
   $ git clone https://github.com/vortex-exoplanet/VIP.git
 
-Before installing the package, it is highly recommended to create a dedicated
-conda environment to not mess up with the package versions in your base 
-environment. This can be done easily with (replace vipenv by the name you want
-for your environment):
-
-.. code-block:: bash
-
-  $ conda create -n vipenv python=3.9 ipython
-
-Note: installing ipython while creating the environment with the above line will
-avoid a commonly reported issue which stems from trying to import VIP from 
-within a base python2.7 ipython console.
 
-To install VIP, simply cd into the VIP directory and run the setup file 
-in 'develop' mode:
+To install VIP, then simply cd into your local VIP directory and run the installation in editable mode:
 
 .. code-block:: bash
 
   $ cd VIP
-  $ python setup.py develop
+  $ pip install -e .
 
 If cloned from your fork, make sure to link your VIP directory to the upstream 
 source, to be able to easily update your local copy when a new version comes 
@@ -194,26 +180,25 @@ 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:
+The following dependencies are not automatically installed upon installation of VIP but may significantly improve your experience:
 
-- ``VIP`` contains a class ``vip_hci.vip_ds9`` that enables, through ``pyds9``, the interaction with a DS9 window (displaying numpy arrays, controlling the display options, etc). To enable this feature, ``pyds9`` must be installed from the latest development version: ``pip install git+git://github.com/ericmandel/pyds9.git#egg=pyds9``
-- ``VIP`` image operations (e.g. shifts, rotations, scaling) can be performed using ``OpenCV`` instead of the default FFT-based methods. While flux are less well preserved, ``OpenCV`` offers a significant speed improvement (up to a factor 50x), in particular for image rotations, which can be useful to get quick results. Installation: ``pip install opencv-python``.
+- VIP contains a class ``vip_hci.vip_ds9`` that enables, through ``pyds9``, the interaction with a DS9 window (displaying numpy arrays, controlling the display options, etc). To enable this feature, ``pyds9`` must be installed from the latest development version: ``pip install git+git://github.com/ericmandel/pyds9.git#egg=pyds9``
+- VIP image operations (e.g. shifts, rotations, scaling) can be performed using ``OpenCV`` instead of the default FFT-based methods. While flux are less well preserved, ``OpenCV`` offers a significant speed improvement (up to a factor 50x), in particular for image rotations, which can be useful to get quick results. Installation: ``pip install opencv-python``.
 - Also, you can install the Intel Math Kernel Library (``mkl``) optimizations (provided that you have a recent version of ``conda``) or ``openblas`` libraries. Either of them can be installed with ``conda install``.
-- ``VIP`` offers the possibility of computing SVDs on GPU by using ``CuPy`` (starting from version 0.8.0) or ``PyTorch`` (from version 0.9.2). These remain as optional requirements, to be installed by the user, as well as a proper CUDA environment (and a decent GPU card).
+- VIP offers the possibility of computing SVDs on GPU by using ``CuPy`` (starting from version 0.8.0) or ``PyTorch`` (from version 0.9.2). These remain as optional requirements, to be installed by the user, as well as a proper CUDA environment (and a decent GPU card).
 - Finally, bad pixel correction routines can be optimised with ``Numba``, which  converts some Python code, particularly ``NumPy``, into fast machine code. A factor up to ~50x times speed improvement can be obtained on large images compared to NumPy. Numba can be installed with ``conda install numba``.
 
 
 Loading VIP
 ^^^^^^^^^^^
 Finally, start Python (or IPython or a Jupyter notebook if you prefer) and check
-that you are able to import ``VIP``:
+that you are able to import VIP:
 
 .. code-block:: python
 
   import vip_hci as vip
 
-If everything went fine with the installation, you will see a welcome message.
-Now you can start finding exoplanets!
+If everything went fine with the installation, you should not get any error message upon importation, and you can start finding exoplanets!
 
 
 Image conventions
@@ -242,13 +227,13 @@ If you wish to be kept informed about major VIP updates and on-going/future deve
 Attribution
 -----------
 
-``VIP`` started as the effort of `Carlos Alberto Gomez Gonzalez <https://github.com/carlos-gg>`_,
+VIP started as the effort of `Carlos Alberto Gomez Gonzalez <https://github.com/carlos-gg>`_,
 a former PhD student of `PSILab <https://sites.google.com/site/olivierabsil/psilab>`_
 (ULiege, Belgium), who has led the development of VIP from 2015 to 2020.
 Maintenance and current development is now led by `Valentin Christiaens <https://github.com/VChristiaens>`_.
 VIP benefitted from contributions made by collaborators from several teams, including: Ralf Farkas, Julien Milli, Olivier Wertz, Henry Ngo, Alan Rainot, Gary Ruane, Corentin Doco, Miles Lucas, Gilles Orban de Xivry, Lewis Picker, Faustine Cantalloube, Iain Hammond, Christian Delacroix, Arthur Vigan, Dimitri Mawet and Olivier Absil.
 More details about the respective contributions are available `here <https://github.com/vortex-exoplanet/VIP/graphs/contributors?from=2015-07-26&to=2022-03-29&type=a>`_.
 
 Please cite `Gomez Gonzalez et al. (2017) <https://ui.adsabs.harvard.edu/abs/2017AJ....154....7G/abstract>`_ and `Christiaens et al. (2023) <https://ui.adsabs.harvard.edu/abs/2023JOSS....8.4774C/abstract>`_ whenever 
-you publish data reduced with ``VIP`` (Astrophysics Source Code Library reference `ascl:1603.003`).
+you publish data reduced with VIP (Astrophysics Source Code Library reference `ascl:1603.003`).
 In addition, please cite the relevant publication(s) for the algorithms you use within VIP (usually mentioned in the documentation, e.g. `Marois et al. 2006 <https://ui.adsabs.harvard.edu/abs/2006ApJ...641..556M/abstract>`_ for median-ADI).

requirements.txt

@@ -8,12 +8,11 @@ emcee==2.2.1
 nestle
 corner
 pandas
-matplotlib<=3.4.3
+matplotlib
 psutil
 pyprind
 munch
 nbsphinx
 hciplot>=0.2.4
 typing
-dataclass_builder
-urllib3 < 2.0
+dataclass_builder

vip_hci/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.4.2"
+__version__ = "1.4.3"
 
 
 from . import preproc

vip_hci/config/utils_conf.py

@@ -17,7 +17,7 @@
 import numpy as np
 
 import itertools as itt
-from inspect import getargspec, signature, Parameter
+from inspect import signature, Parameter
 from functools import wraps
 import multiprocessing
 from vip_hci import __version__
@@ -246,11 +246,6 @@ def wrapper(self, *args, **kwargs):
 
             # get the kwargs the fkt sees. Note that this is a combination of
             # the *default* kwargs and the kwargs *passed* by the user
-            """Note : this is deprecated for a while now, and signature should be
-            the only way to do this task. See below."""
-            # a = getargspec(fkt)
-            # all_kwargs = dict(zip(a.args[-len(a.defaults):], a.defaults))
-            # all_kwargs.update(kwargs)
             sig = signature(fkt)
             params = sig.parameters
             all_kwargs = {

vip_hci/metrics/detection.py

@@ -366,7 +366,8 @@ def print_abort():
     xx_final = np.array(xx_final)
     yy_out = np.array(yy_out)
     xx_out = np.array(xx_out)
-    table = pn.DataFrame({'y': yy_final.tolist(), 'x': xx_final.tolist(), 'px_snr': snr_final})
+    table = pn.DataFrame(
+        {'y': yy_final.tolist(), 'x': xx_final.tolist(), 'px_snr': snr_final})
 
     if plot:
         coords = tuple(zip(xx_out.tolist() + xx_final.tolist(),
@@ -440,12 +441,12 @@ def peak_coordinates(obj_tmp, fwhm, approx_peak=None, search_box=None,
         med_filt_tmp = frame_filter_lowpass(obj_tmp, 'median',
                                             median_size=int(fwhm))
         if approx_peak is None:
-            ind_max = np.unravel_index(med_filt_tmp.argmax(),
+            ind_max = np.unravel_index(np.nanargmax(med_filt_tmp),
                                        med_filt_tmp.shape)
         else:
             sbox = med_filt_tmp[approx_peak[0]-sbox_y:approx_peak[0]+sbox_y+1,
                                 approx_peak[1]-sbox_x:approx_peak[1]+sbox_x+1]
-            ind_max_sbox = np.unravel_index(sbox.argmax(), sbox.shape)
+            ind_max_sbox = np.unravel_index(np.nanargmax(sbox), sbox.shape)
             ind_max = (approx_peak[0]-sbox_y+ind_max_sbox[0],
                        approx_peak[1]-sbox_x+ind_max_sbox[1])
 
@@ -462,23 +463,23 @@ def peak_coordinates(obj_tmp, fwhm, approx_peak=None, search_box=None,
             med_filt_tmp[zz] = frame_filter_lowpass(obj_tmp[zz], 'median',
                                                     median_size=int(fwhm[zz]))
             if approx_peak is None:
-                ind_ch_max[zz] = np.unravel_index(med_filt_tmp[zz].argmax(),
+                ind_ch_max[zz] = np.unravel_index(np.nanargmax(med_filt_tmp[zz]),
                                                   med_filt_tmp[zz].shape)
             else:
                 sbox[zz] = med_filt_tmp[zz, approx_peak[0]-sbox_y:
                                         approx_peak[0]+sbox_y+1,
                                         approx_peak[1]-sbox_x:
                                         approx_peak[1]+sbox_x+1]
-                ind_max_sbox = np.unravel_index(sbox[zz].argmax(),
+                ind_max_sbox = np.unravel_index(np.nanargmax(sbox[zz]),
                                                 sbox[zz].shape)
                 ind_ch_max[zz] = (approx_peak[0]-sbox_y+ind_max_sbox[0],
                                   approx_peak[1]-sbox_x+ind_max_sbox[1])
 
         if approx_peak is None:
-            ind_max = np.unravel_index(med_filt_tmp.argmax(),
+            ind_max = np.unravel_index(np.nanargmax(med_filt_tmp),
                                        med_filt_tmp.shape)
         else:
-            ind_max_tmp = np.unravel_index(sbox.argmax(),
+            ind_max_tmp = np.unravel_index(np.nanargmax(sbox),
                                            sbox.shape)
             ind_max = (ind_max_tmp[0]+approx_peak[0]-sbox_y,
                        ind_max_tmp[1]+approx_peak[1]-sbox_x)
@@ -561,4 +562,4 @@ def mask_sources(mask, ap_rad):
             rad_arr = dist_matrix(ny, cx=s_coords[1][s], cy=s_coords[0][s])
             mask_out[np.where(rad_arr < ap_rad)] = 0
 
-        return mask_out
+        return mask_out