././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.250823 aplpy-2.1.0/0000775000175000017500000000000000000000000011022 5ustar00tomtom././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.238823 aplpy-2.1.0/.circleci/0000775000175000017500000000000000000000000012655 5ustar00tomtom././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/.circleci/config.yml0000664000175000017500000000516600000000000014655 0ustar00tomtomversion: 2 jobs: image-tests-mpl212: docker: - image: astropy/image-tests-py36-mpl212:1.10 steps: - checkout - run: name: Install wheel command: pip3 install wheel - run: name: Install dependencies command: pip3 install .[test] codecov - run: name: Run tests command: MPLBACKEND=Agg pytest --remote-data=any --mpl - run: name: Report coverage command: codecov image-tests-mpl222: docker: - image: astropy/image-tests-py36-mpl222:1.10 steps: - checkout - run: name: Install wheel command: pip3 install wheel - run: name: Install dependencies command: pip3 install .[test] codecov - run: name: Run tests command: MPLBACKEND=Agg pytest --remote-data=any --mpl - run: name: Report coverage command: codecov image-tests-mpl302: docker: - image: astropy/image-tests-py37-mpl302:1.10 steps: - checkout - run: name: Install wheel command: pip3 install wheel - run: name: Install dependencies command: pip3 install .[test] codecov - run: name: Run tests command: MPLBACKEND=Agg pytest --remote-data=any --mpl - run: name: Report coverage command: codecov image-tests-mpl311: docker: - image: astropy/image-tests-py37-mpl311:1.10 steps: - checkout - run: name: Install wheel command: pip3 install wheel - run: name: Install dependencies command: pip3 install .[test] codecov - run: name: Run tests command: MPLBACKEND=Agg pytest --remote-data=any --mpl - run: name: Report coverage command: codecov image-tests-mpldev: docker: - image: astropy/image-tests-py37-base:1.4 steps: - checkout - run: name: Install wheel command: pip3 install wheel - run: name: Install Matplotlib command: pip3 install git+https://github.com/matplotlib/matplotlib.git - run: name: Install dependencies command: pip3 install .[test] codecov - run: name: Run tests command: MPLBACKEND=Agg pytest --remote-data=any --mpl - run: name: Report coverage command: codecov workflows: version: 2 build_and_test: jobs: - image-tests-mpl212 - image-tests-mpl222 - image-tests-mpl302 - image-tests-mpl311 - image-tests-mpldev ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/.gitignore0000664000175000017500000000100400000000000013005 0ustar00tomtom# Compiled files *.py[co] *.a *.o *.so __pycache__ # Ignore .c files by default to avoid including generated code. If you want to # add a non-generated .c extension, use `git add -f filename.c`. *.c # Other generated files */version.py */cython_version.py htmlcov .coverage # Sphinx _build # Packages/installer info *.egg *.egg-info dist build eggs parts bin var sdist develop-eggs .installed.cfg distribute-*.tar.gz # Other .*.swp *~ .project .pydevproject .settings # Mac OSX .DS_Store .tox docs/api .tmp ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/.readthedocs.yml0000664000175000017500000000025200000000000014107 0ustar00tomtomversion: 2 build: image: latest python: version: 3.7 install: - method: pip path: . extra_requirements: - docs - all formats: [] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646929473.0 aplpy-2.1.0/CHANGES.rst0000664000175000017500000004014200000000000012625 0ustar00tomtomCHANGES -------- 2.1.0 (2022-03-10) ------------------ - Updated package infrastructure to follow guidelines described in APE 17, namely removing astropy-helpers and using tox for setting up testing environments. [#448] - Fix compatibility with astropy 4.x and 5.0. [#448,#471] - Fixed default color for ``show_lines`` (was previously transparent, now black). [#441] - Fixed ``set_tick_direction`` to not error due to incorrect reference to ``ax``. [#450] - Fixed compatibility with Matplotlib 3.4. [#466] 2.0.3 (2019-02-19) ------------------ - Fix 'arcsinh' stretch and fix definition of vmid to match that in 1.x. [#421] 2.0.2 (2019-02-18) ------------------ - Fixed ``set_nan_color``. [#419] 2.0.1 (2019-02-18) ------------------ - Remove unused arguments in ``make_rgb_cube`` and fix behavior of ``north=False``. [#417] - Fixed bug in image extent for non-square RGB images. [#417] 2.0 (2019-02-17) ---------------- - Refactored APLpy to make use of WCSAxes to draw coordinate frames and astropy.visualization to deal with image stretching. [#239, #364, #365] - Use reproject package instead of montage-wrapper and Montage. [#360] 1.1.1 (2016-10-04) ------------------ - Added missing LICENSE and README to tarball. - Fix undefined ``HDUList``. [#317] 1.1 (2016-09-23) ---------------- - Fixed compatibility of Scalebar.set with Matplotlib 1.5.x. [#272, #286] - Fixed compatibility with Python 3.5. - Astropy is now a required dependency, and PyFITS and PyWCS are no longer supported. 1.0 (2015-02-18) ---------------- New features ~~~~~~~~~~~~ - Added new ``show_vectors`` to show vector maps. [#220] Improvements ~~~~~~~~~~~~ - The ``auto_refresh`` option now defaults to ``False`` unless IPython is being used and the Matplotlib backend is interactive. [#238] Bug fixes ~~~~~~~~~ - Fix a bug that caused RGB images to be incorrectly displayed when zooming in. [#235] 0.9.14 (2014-11-05) ------------------- Bug fixes ~~~~~~~~~ - Fix a bug that caused smoothing to fail with integer arrays. [#165] Improvements ~~~~~~~~~~~~ - Fix deprecation warnings from Astropy. [#173] 0.9.13 (2014-10-04) ------------------- New features ~~~~~~~~~~~~ - Added ``FITSFigure.set_title`` method that can be used to set the title of a figure. [#175] Improvements ~~~~~~~~~~~~ - Beams and scalebars can now optionally be instantiated with Astropy angular units and quantities. [#186] - APLpy now includes image tests to ensure reliability over time. [#200] - The code is now all Python 2 and 3 compatible without requiring 2to3. [#198] Bug fixes ~~~~~~~~~ - Fix bug that caused a crash when plotting an image with a single valid pixel [#197] - Fixed a severe bug that caused rotated images to have incorrect pixel scales determined. [#211] - Fixed a bug that caused set_nan_color to modify Matplotlib colormaps globally rather than apply just to the desired FITSFigure. [#214] 0.9.12 (2014-07-17) ------------------- This version fixes compatibility with Astropy 0.4 New features ~~~~~~~~~~~~ - Added the ability to call ``fig.show_contour()`` without arguments and used the data used to initialize ``FITSFigure``. [#170] - Added the ability to format colorbar ticks in exponential notation using the ``log_format`` argument. [#143] - Added the ability to make NaNs transparent in RGB image output, using the ``make_nans_transparent`` argument. [#138] API Changes ~~~~~~~~~~~ - astropy.wcs.WCS no longer contains information about the original image size. Any attempt to instantiate a FITSFigure from a WCS object will raise a DeprecationException. A workaround is to add `naxisn` attributes to your WCS object:: mywcs = wcs.WCS(header) mywcs.naxis1 = header['NAXIS1'] mywcs.naxis2 = header['NAXIS2'] Bug fixes ~~~~~~~~~ - FITSFigure can now be instantiated using an astropy.io.fits.CompImageHDU object. [#188] - The coordinate grid is now plotted on the whole axes, not just the subset containing the image (noticeable when zooming out). [#118] 0.9.11 (2013-11-29) ------------------- Bug fixes ~~~~~~~~~ - Fix a bug that meant that pixel scales were incorrectly extracted for some WCS settings. [#156] 0.9.10 (2013-11-25) ------------------- This version restores compatibility with Astropy 0.3 New Features ~~~~~~~~~~~~ - `FITSFigure.recenter` can now be used with sliced data cubes. [#122] Bug fixes ~~~~~~~~~ - Fixed issues related to bugs in matplotlib in `match_original` when using patch collections. [#124] - Fixed a bug that made images containing NaN or Inf values crash APLpy. [#113] - Fixed a bug that meant that world2pix and pix2world could not take Astropy table columns. [#114] 0.9.9 (2013-04-25) ------------------ The main change in this version is that APLpy is now an Astropy-affiliated package. This means that the Astropy core package is now required, but PyFITS and PyWCS are no longer required as dependencies. This release is now no longer compatible with python-montage, and users should use the montage-wrapper package instead: http://www.astropy.org/montage-wrapper/ Similarly, PyAVM 0.9.1 or later is now required, and APLpy cannot use earlier versions: http://astrofrog.github.io/pyavm/ This release is also the first release fully compatible with Python 3. New features ~~~~~~~~~~~~ - file-like objects can now be passed to ``FITSFigure.save()`` - the subplot= argument to ``FITSFigure`` can now take the tuple syntax ``(2, 2, 1)`` instead of the full axes box, e.g. ``[0.1, 0.1, 0.9, 0.9]`` (thanks to Anika Schmiedeke for a patch). - a colorbar label can now be set (thanks to Daniel Goering for a patch). API changes ~~~~~~~~~~~ - the ``smooth`` argument used for images and contours can no longer take a tuple. It takes either a single value for symmetric kernels (``gauss`` and ``box``), or it can take a Numpy array for any other kernel shape. Bug fixes ~~~~~~~~~ - fixed bug that caused regions read from a ds9 region file to be offset by one pixel. - fixed bug that caused an exception when adding a Beam - fixed bug that caused labels in decimal degrees to disappear for images near the poles. - fixed a bug that caused RGB images to appear vertically flipped with certain versions of Matplotlib. - added a workaround for a bug in Matplotlib that caused patches to appear filled even with ``facecolor='none'``. - fixed bug that caused images with NaN or Inf values to not be smoothed correctly. Version 0.9.8 This version fixes issues that occurred when using APLpy with PyFITS 3.0.5. This version also contains the first unit/regression tests (which we will include many more of in future). New features ~~~~~~~~~~~~ - APLpy now includes regression tests that can be run with: python setup.py test - FITSFigure can now be initialized with files or HDUs with missing WCS information. - FITSFigure can now be initialized directly with Numpy arrays - Added methods to show/hide ticks Bug fixes ~~~~~~~~~ - Fixed a major bug that prevented data cube slicing with PyFITS 3.0.5 - Fixed a few bugs that occurred when plotting a grid for a few specific projections. - Fixed a bug that cause the error: 'Figure' object has no attribute '_auto_refresh' Version 0.9.7 This version sees the re-write of the WCS support to enable plotting of FITS files with arbitrary coordinates rather than just sky coordinates, and to allow slicing of multi-dimensional data cubes. It is now very easy for example to make position-velocity plots from a 3D FITS cube. See http://aplpy.github.com/documentation/slicing.html for details. New features ~~~~~~~~~~~~ - added the ability to plot arbitrary coordinate systems rather than just sky coordinates. - added the ability to slice multi-dimensional data cubes. - added minor ticks. - pressing 'c' toggles between showing world and pixel coordinates in the bottom of the interactive window. - added the ability to specify slice indices for 3D FITS cubes in make_rgb_image. - allow pyregion.ShapeList instance to be passed to show_regions. - the 'logging' module is now used for any stdout output. - added an overlap= argument to show_contour to force only contours with at least one point in the image to be shown. This can result in significantly improved performance, and smaller files, when large FITS files are used to display contours. Changes ~~~~~~~ - the default frame color for the 'pretty' theme (the default) is now black Version 0.9.6 APLpy is now released under an MIT license New Features ~~~~~~~~~~~~ - support for plotting AVM-tagged images (requires PyAVM) - make_rgb_image can now optionally embed AVM Spatial.* meta-data into images (off by default, requires PyAVM) - added support for multiple beams - added method to show arrows - added method to show lines (thanks to Moritz Guenther) - added close() method to FITSFigure to free up memory (useful when making many finder charts for example) - added B1950 <-> J2000 conversion - added axis_labels.set_x/yposition and tick_labels.set_x/yposition to control whether labels are on top/bottom or left/right of the axes. - now uses python-montage wrapper for reprojection - added support for passing a WCS object to FITSFigure instead of a file - allow a custom box to be specified for the colorbar API changes ~~~~~~~~~~~ - vmid has changed meaning for log image scaling. It is now the baseline value to subtract from pixel values before taking the log (defaults to zero) - added interpolation= option to show_grayscale and show_colorscale - added support for zorder= in methods to show shapes (controls which layers appear on top of which) - added north=, system=, and equinox= to make_rgb_cube for more control over the final projection Bug fixes ~~~~~~~~~ - added a few workaround for matplotlib bugs - fixed a major bug with downsampling - fixed bug with import of local modules - other minor bug fixes Version 0.9.5 New Features ~~~~~~~~~~~~ - Support for image and contour smoothing - Support for slicing of n-dimensional datacubes - Support for beam: -> add_beam() -> remove_beam() - Support for colorbar: -> add_colorbar() -> remove_colorbar() - Support for scalebar: -> add_scalebar() -> remove_scalebar() - Support for plotting ds9 region files: -> show_regions() - Support for automatic bounding box adjustments when saving - New method to set the color to use for NaN values: -> set_nan_color() - Auto refreshing has been improved. Figures only refresh once per user command if refreshing is turned on. - Ensure tick spacing / label format consistency - New method to overlay polygons API changes ~~~~~~~~~~~ - The API has been majorly overhauled. Methods that have been deprecated will give instructions on the new methods to use. Bug fixes ~~~~~~~~~ - Fixed a bug with filled contours - Fixed bug with remove_layer - Fixed bug with montage commands Version 0.9.4 Important changes ~~~~~~~~~~~~~~~~~ Matplotlib 0.99 is now required for APLpy New Features ~~~~~~~~~~~~ - methods such as show_contour, show_markers, etc. now return the contour, marker, etc. object - added a method to retrieve the object in a specific layer: -> get_layer() - ability to show the beam for the observations: -> show_beam() -> hide_beam() -> set_beam_properties() - added the ability to show/hide only the x or y axis/tick labels: -> show_xtick_labels() -> hide_xtick_labels() -> show_ytick_labels() -> hide_ytick_labels() -> show_xaxis_labels() -> hide_xaxis_labels() -> show_yaxis_labels() -> hide_yaxis_labels() - convenience functions for world to pixel and pixel to world conversion: -> world2pixel() -> pixel2world() - added a convention= argument to FITSFigure() and show_contour(). This is to be used in cases where the WCS interpretation is ambiguous. For example, a -CAR projection with CRVAL2<>0 can be interpreted in two different ways. If an ambiguous case pops up, APLpy will raise an exception and ask for the convention to be specified. API changes ~~~~~~~~~~~ - set_labels_latex() is now set_system_latex() Bug fixes ~~~~~~~~~ - the current position of the cursor in world coordinates is now correctly shown in interactive mode - fixed an issue which caused RGB FITS cubes to be 64-bit - fixed a bug which meant that the coordinate grid was not updated immediately during pan and zoom Version 0.9.3 New Features ~~~~~~~~~~~~ - added aplpy.make_rgb_cube() that allows users to make a FITS RGB cube from three FITS files with different projections - added aplpy.make_rgb_image() that allows users to make an RGB file in standard image formats from a FITS RGB cube - added width= and height= arguments to aplpy.FITSFigure.recenter() method. - added show_circles(), show_ellipses(), and show_rectangles() to aplpy.FITSFigure - new hide_grayscale() and hide_colorscale() methods API changes ~~~~~~~~~~~ - changes to the API for set_tick_labels_* and set_axis_labels_* methods - percentiles values are now specified between 0 and 100 Bug fixes ~~~~~~~~~ - fixed an issue which ocurred when reading in FITS cubes - fixed an issue which led to the last tick along an axis being missing for coarse images - fixed a bug that occured if set_theme was called before showing the image - fixed a bug that occured when show_contour was called after removing a layer Acknowledgments ~~~~~~~~~~~~~~~ Thanks to Paul Ray, Adam Ginsburg, Gus Muench, and forum user hatchell for bug reports and feature suggestions. Version 0.9.2 Improvements ~~~~~~~~~~~~ - Improved compatibility issues with matplotlib 0.98.6svn - Improved speed of initialization of FITSFigure Version 0.9.1.2 Bug fixes ~~~~~~~~~ - fixed a major bug that occured when reading in Galactic -CAR images Version 0.9.1 SciPy Dependency Dropped ~~~~~~~~~~~~~~~~~~~~~~~~ While SciPy is a great python package, it can be troublesome to build and install from scratch. Thanks to Tom Aldcroft's suggestion and highlighting some of his own code we were able to easily drop the SciPy dependency. New Features ~~~~~~~~~~~~ - Users can now pass a pyfits HDU instance instead of filenames for both FITSFigure() and show_contour() if desired. - Users can now specify an existing figure with the figure= argument. - Users with a recent enough version of matplotlib (0.98.6svn) can now use the subplot= argument to place multiple plots in a single figure. - New hide/show_tick_labels() and hide/show_axis_labels() methods. - Show_grayscale and show_colorscale() now accept percentile_lower= and percentile_higher= as arguments. - Show_grayscale and show_colorscale() now print out vmin and vmax if chosen automatically. - New recenter() method to pan and zoom non-interactively. General Fixes and Optimization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - Added HDU keyword for the show_contour() method. - More robust reading in FITS file for the show_contour() method. - Warning if FITS files do not exist for FITSFigure() or show_contour(). Version 0.9.0 First public beta release ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/CITATION0000644000175000017500000000246200000000000012161 0ustar00tomtomIf you use APLpy for a publication, you can use the following acknowledgment: This research made use of APLpy, an open-source plotting package for Python (Robitaille and Bressert, 2012; Robitaille, 2019) where (Robitaille and Bressert, 2012) is a citation to this ADS/ASCL entry: http://adsabs.harvard.edu/abs/2012ascl.soft08017R and (Robitaille, 2019) is a citation to the following Zenodo entry: https://zenodo.org/record/2567476#.XGmAA5P7RZo The BibTex entries are: @misc{aplpy2012, author = {{Robitaille}, T. and {Bressert}, E.}, title = "{APLpy: Astronomical Plotting Library in Python}", keywords = {Software }, howpublished = {Astrophysics Source Code Library}, year = 2012, month = aug, archivePrefix = "ascl", eprint = {1208.017}, adsurl = {http://adsabs.harvard.edu/abs/2012ascl.soft08017R}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @misc{aplpy2019, author = {Robitaille, Thomas}, title = {{APLpy v2.0: The Astronomical Plotting Library in Python}}, month = feb, year = 2019, doi = {10.5281/zenodo.2567476}, url = {https://doi.org/10.5281/zenodo.2567476} } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/LICENSE.md0000644000175000017500000000216200000000000012425 0ustar00tomtomAPLpy - The Astronomical Plotting Library in Python Copyright (c) 2010-2013 Thomas P. Robitaille and Eli Bressert Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/MANIFEST.in0000664000175000017500000000044700000000000012565 0ustar00tomtominclude README.rst include CHANGES.rst include setup.cfg include LICENSE.rst include pyproject.toml recursive-include aplpy *.pyx *.c *.pxd recursive-include docs * recursive-include licenses * recursive-include scripts * prune build prune docs/_build prune docs/api global-exclude *.pyc *.o ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.250823 aplpy-2.1.0/PKG-INFO0000664000175000017500000000311000000000000012112 0ustar00tomtomMetadata-Version: 2.1 Name: aplpy Version: 2.1.0 Summary: The Astronomical Plotting Library in Python Home-page: http://aplpy.github.io Author: Thomas Robitaille and Eli Bressert Author-email: thomas.robitaille@gmail.com License: MIT Platform: UNKNOWN Requires-Python: >=3.6 Description-Content-Type: text/x-rst Provides-Extra: test Provides-Extra: docs License-File: LICENSE.md |Azure| |CircleCI| |codecov| |Documentation Status| About ----- APLpy (the Astronomical Plotting Library in Python) is a Python module aimed at producing publication-quality plots of astronomical imaging data in FITS format. PyPI: https://pypi.python.org/pypi/APLpy Website: http://aplpy.github.io Documentation: http://aplpy.readthedocs.io APLpy is released under an MIT open-source license. Installing ---------- You can install APLpy and all its dependencies with:: pip install aplpy .. |Azure| image:: https://dev.azure.com/thomasrobitaille/aplpy/_apis/build/status/aplpy.aplpy?repoName=aplpy%2Faplpy&branchName=refs%2Fpull%2F441%2Fmerge :target: https://dev.azure.com/thomasrobitaille/aplpy/_build/latest?definitionId=16&repoName=aplpy%2Faplpy&branchName=refs%2Fpull%2F441%2Fmerge .. |CircleCI| image:: https://circleci.com/gh/aplpy/aplpy/tree/main.svg?style=svg :target: https://circleci.com/gh/aplpy/aplpy/tree/main .. |codecov| image:: https://codecov.io/gh/aplpy/aplpy/branch/main/graph/badge.svg :target: https://codecov.io/gh/aplpy/aplpy .. |Documentation Status| image:: https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat :target: https://aplpy.readthedocs.io/en/latest/ ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646929450.0 aplpy-2.1.0/README.rst0000664000175000017500000000231200000000000012507 0ustar00tomtom|Azure| |CircleCI| |codecov| |Documentation Status| About ----- APLpy (the Astronomical Plotting Library in Python) is a Python module aimed at producing publication-quality plots of astronomical imaging data in FITS format. PyPI: https://pypi.python.org/pypi/APLpy Website: http://aplpy.github.io Documentation: http://aplpy.readthedocs.io APLpy is released under an MIT open-source license. Installing ---------- You can install APLpy and all its dependencies with:: pip install aplpy .. |Azure| image:: https://dev.azure.com/thomasrobitaille/aplpy/_apis/build/status/aplpy.aplpy?repoName=aplpy%2Faplpy&branchName=refs%2Fpull%2F441%2Fmerge :target: https://dev.azure.com/thomasrobitaille/aplpy/_build/latest?definitionId=16&repoName=aplpy%2Faplpy&branchName=refs%2Fpull%2F441%2Fmerge .. |CircleCI| image:: https://circleci.com/gh/aplpy/aplpy/tree/main.svg?style=svg :target: https://circleci.com/gh/aplpy/aplpy/tree/main .. |codecov| image:: https://codecov.io/gh/aplpy/aplpy/branch/main/graph/badge.svg :target: https://codecov.io/gh/aplpy/aplpy .. |Documentation Status| image:: https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat :target: https://aplpy.readthedocs.io/en/latest/ ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.238823 aplpy-2.1.0/aplpy/0000775000175000017500000000000000000000000012147 5ustar00tomtom././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/__init__.py0000644000175000017500000000152600000000000014262 0ustar00tomtom# Licensed under a 3-clause BSD style license - see LICENSE.rst """ APLpy : Astronomical Plotting Library in Python """ # Affiliated packages may add whatever they like to this file, but # should keep this content at the top. # ---------------------------------------------------------------------------- from ._astropy_init import * # noqa # ---------------------------------------------------------------------------- if not _ASTROPY_SETUP_: # noqa from .core import FITSFigure # noqa from .rgb import make_rgb_image, make_rgb_cube # noqa from .frame import Frame # noqa from .overlays import Scalebar, Beam # noqa from .colorbar import Colorbar # noqa from .grid import Grid # noqa from .ticks import Ticks # noqa from .tick_labels import TickLabels # noqa from .axis_labels import AxisLabels # noqa ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/_astropy_init.py0000664000175000017500000000340700000000000015410 0ustar00tomtom# Licensed under a 3-clause BSD style license - see LICENSE.rst __all__ = ['__version__'] # this indicates whether or not we are in the package's setup.py try: _ASTROPY_SETUP_ except NameError: import builtins builtins._ASTROPY_SETUP_ = False try: from .version import version as __version__ except ImportError: __version__ = '' if not _ASTROPY_SETUP_: # noqa import os from warnings import warn from astropy.config.configuration import ( update_default_config, ConfigurationDefaultMissingError, ConfigurationDefaultMissingWarning) # Create the test function for self test from astropy.tests.runner import TestRunner test = TestRunner.make_test_runner_in(os.path.dirname(__file__)) test.__test__ = False __all__ += ['test'] # add these here so we only need to cleanup the namespace at the end config_dir = None if not os.environ.get('ASTROPY_SKIP_CONFIG_UPDATE', False): config_dir = os.path.dirname(__file__) config_template = os.path.join(config_dir, __package__ + ".cfg") if os.path.isfile(config_template): try: update_default_config( __package__, config_dir, version=__version__) except TypeError as orig_error: try: update_default_config(__package__, config_dir) except ConfigurationDefaultMissingError as e: wmsg = (e.args[0] + " Cannot install default profile. If you are " "importing from source, this is expected.") warn(ConfigurationDefaultMissingWarning(wmsg)) del e except Exception: raise orig_error ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/axis_labels.py0000664000175000017500000001513100000000000015010 0ustar00tomtomfrom astropy.wcs.utils import wcs_to_celestial_frame from astropy.coordinates import (ICRS, FK5, FK4, Galactic, HeliocentricTrueEcliptic, BarycentricTrueEcliptic) from .decorators import auto_refresh, fixdocstring __all__ = ['AxisLabels'] class AxisLabels(object): def __init__(self, parent): self._ax = parent.ax self._wcs = parent.ax.wcs self.x = parent.x self.y = parent.y self._ax.coords[self.x].set_axislabel_visibility_rule('always') self._ax.coords[self.y].set_axislabel_visibility_rule('always') xcoord_type = self._ax.coords[self.x].coord_type ycoord_type = self._ax.coords[self.y].coord_type if xcoord_type == 'longitude' and ycoord_type == 'latitude': celestial = True inverted = False elif xcoord_type == 'latitude' and ycoord_type == 'longitude': celestial = True inverted = True else: celestial = inverted = False if celestial: frame = wcs_to_celestial_frame(self._wcs) else: frame = None if isinstance(frame, ICRS): xtext = 'RA (ICRS)' ytext = 'Dec (ICRS)' elif isinstance(frame, FK5): equinox = "{:g}".format(FK5.equinox.jyear) xtext = 'RA (J{0})'.format(equinox) ytext = 'Dec (J{0})'.format(equinox) elif isinstance(frame, FK4): equinox = "{:g}".format(FK4.equinox.byear) xtext = 'RA (B{0})'.format(equinox) ytext = 'Dec (B{0})'.format(equinox) elif isinstance(frame, Galactic): xtext = 'Galactic Longitude' ytext = 'Galactic Latitude' elif isinstance(frame, (HeliocentricTrueEcliptic, BarycentricTrueEcliptic)): # NOTE: once we support only Astropy 2.0+, we can use BaseEclipticFrame xtext = 'Ecliptic Longitude' ytext = 'Ecliptic Latitude' else: cunit_x = self._wcs.wcs.cunit[self.x] cunit_y = self._wcs.wcs.cunit[self.y] cname_x = self._wcs.wcs.cname[self.x] cname_y = self._wcs.wcs.cname[self.y] ctype_x = self._wcs.wcs.ctype[self.x] ctype_y = self._wcs.wcs.ctype[self.y] xunit = " (%s)" % cunit_x if cunit_x not in ["", None] else "" yunit = " (%s)" % cunit_y if cunit_y not in ["", None] else "" if len(cname_x) > 0: xtext = cname_x + xunit else: if len(ctype_x) == 8 and ctype_x[4] == '-': xtext = ctype_x[:4].replace('-', '') + xunit else: xtext = ctype_x + xunit if len(cname_y) > 0: ytext = cname_y + yunit else: if len(ctype_y) == 8 and ctype_y[4] == '-': ytext = ctype_y[:4].replace('-', '') + yunit else: ytext = ctype_y + yunit if inverted: xtext, ytext = ytext, xtext self.set_xtext(xtext) self.set_ytext(ytext) self.set_xposition('bottom') self.set_yposition('left') @auto_refresh def set_xtext(self, label): """ Set the x-axis label text. """ self._x_text = label self._ax.coords[self.x].set_axislabel(label) @auto_refresh def set_ytext(self, label): """ Set the y-axis label text. """ self._y_text = label self._ax.coords[self.y].set_axislabel(label) @auto_refresh def set_xpad(self, pad): """ Set the x-axis label displacement in terms of the axis label font size. """ self._ax.coords[self.x].axislabels.set_minpad(pad) @auto_refresh def set_ypad(self, pad): """ Set the y-axis label displacement in terms of the axis label font size. """ self._ax.coords[self.y].axislabels.set_minpad(pad) @auto_refresh @fixdocstring def set_font(self, **kwargs): """ Set the font of the axis labels. Parameters ---------- common: family, style, variant, stretch, weight, size, fontproperties Notes ----- Default values are set by matplotlib or previously set values if set_font has already been called. Global default values can be set by editing the matplotlibrc file. """ self._ax.coords[self.x].axislabels.set(**kwargs) self._ax.coords[self.y].axislabels.set(**kwargs) @auto_refresh def show(self): """ Show the x- and y-axis labels. """ self.show_x() self.show_y() @auto_refresh def hide(self): """ Hide the x- and y-axis labels. """ self.hide_x() self.hide_y() @auto_refresh def show_x(self): """ Show the x-axis label. """ if self._xposition == 'bottom': self._ax.coords[self.x].set_axislabel_position('b') else: self._ax.coords[self.x].set_axislabel_position('t') @auto_refresh def hide_x(self): """ Hide the x-axis label. """ self._ax.coords[self.x].set_axislabel_position('') @auto_refresh def show_y(self): """ Show the y-axis label. """ if self._yposition == 'left': self._ax.coords[self.y].set_axislabel_position('l') else: self._ax.coords[self.y].set_axislabel_position('r') @auto_refresh def hide_y(self): """ Hide the y-axis label. """ self._ax.coords[self.y].set_axislabel_position('') @auto_refresh def set_xposition(self, position): """ Set the position of the x-axis label ('top' or 'bottom') """ if position == 'bottom': self._ax.coords[self.x].set_axislabel_position('b') elif position == 'top': self._ax.coords[self.x].set_axislabel_position('t') else: raise ValueError("position should be one of 'top' or 'bottom'") self._xposition = position @auto_refresh def set_yposition(self, position): """ Set the position of the y-axis label ('left' or 'right') """ if position == 'left': self._ax.coords[self.y].set_axislabel_position('l') elif position == 'right': self._ax.coords[self.y].set_axislabel_position('r') else: raise ValueError("position should be one of 'left' or 'right'") self._yposition = position ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/colorbar.py0000664000175000017500000003557000000000000014336 0ustar00tomtomimport warnings import matplotlib.axes as maxes from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties from matplotlib.ticker import LogFormatterMathtext from .decorators import auto_refresh, fixdocstring class Colorbar(object): def __init__(self, parent): self._figure = parent._figure self._colorbar_axes = None self._parent = parent self._base_settings = {} self._ticklabel_fontproperties = FontProperties() self._axislabel_fontproperties = FontProperties() @auto_refresh def show(self, location='right', width=0.2, pad=0.05, ticks=None, labels=True, log_format=False, box=None, box_orientation='vertical', axis_label_text=None, axis_label_rotation=None, axis_label_pad=5): """ Show a colorbar on the side of the image. Parameters ---------- location : str, optional Where to place the colorbar. Should be one of 'left', 'right', 'top', 'bottom'. width : float, optional The width of the colorbar relative to the canvas size. pad : float, optional The spacing between the colorbar and the image relative to the canvas size. ticks : list, optional The position of the ticks on the colorbar. labels : bool, optional Whether to show numerical labels. log_format : bool, optional Whether to format ticks in exponential notation box : list, optional A custom box within which to place the colorbar. This should be in the form [xmin, ymin, dx, dy] and be in relative figure units. This overrides the location argument. box_orientation str, optional The orientation of the colorbar within the box. Can be 'horizontal' or 'vertical' axis_label_text str, optional Optional text label of the colorbar. """ self._base_settings['location'] = location self._base_settings['width'] = width self._base_settings['pad'] = pad self._base_settings['ticks'] = ticks self._base_settings['labels'] = labels self._base_settings['log_format'] = log_format self._base_settings['box'] = box self._base_settings['box_orientation'] = box_orientation self._base_settings['axis_label_text'] = axis_label_text self._base_settings['axis_label_rotation'] = axis_label_rotation self._base_settings['axis_label_pad'] = axis_label_pad if self._parent.image: if self._colorbar_axes: self._figure.delaxes(self._colorbar_axes) if box is None: divider = make_axes_locatable(self._parent.ax) if location == 'right': self._colorbar_axes = divider.new_horizontal(size=width, pad=pad, axes_class=maxes.Axes) orientation = 'vertical' elif location == 'top': self._colorbar_axes = divider.new_vertical(size=width, pad=pad, axes_class=maxes.Axes) orientation = 'horizontal' elif location == 'left': warnings.warn("Left colorbar not fully implemented") self._colorbar_axes = divider.new_horizontal(size=width, pad=pad, pack_start=True, axes_class=maxes.Axes) locator = divider.new_locator(nx=0, ny=0) self._colorbar_axes.set_axes_locator(locator) orientation = 'vertical' elif location == 'bottom': warnings.warn("Bottom colorbar not fully implemented") self._colorbar_axes = divider.new_vertical(size=width, pad=pad, pack_start=True, axes_class=maxes.Axes) locator = divider.new_locator(nx=0, ny=0) self._colorbar_axes.set_axes_locator(locator) orientation = 'horizontal' else: raise Exception("location should be one of: right/top") self._figure.add_axes(self._colorbar_axes) else: self._colorbar_axes = self._figure.add_axes(box) orientation = box_orientation if log_format: format = LogFormatterMathtext() else: format = None self._colorbar = self._figure.colorbar(self._parent.image, cax=self._colorbar_axes, orientation=orientation, format=format, ticks=ticks) if axis_label_text: if axis_label_rotation: self._colorbar.set_label(axis_label_text, rotation=axis_label_rotation) else: self._colorbar.set_label(axis_label_text) if location == 'right': for tick in self._colorbar_axes.yaxis.get_major_ticks(): tick.tick1line.set_visible(True) tick.tick2line.set_visible(True) tick.label1.set_visible(False) tick.label2.set_visible(labels) self._colorbar_axes.yaxis.set_label_position('right') self._colorbar_axes.yaxis.labelpad = axis_label_pad elif location == 'top': for tick in self._colorbar_axes.xaxis.get_major_ticks(): tick.tick1line.set_visible(True) tick.tick2line.set_visible(True) tick.label1.set_visible(False) tick.label2.set_visible(labels) self._colorbar_axes.xaxis.set_label_position('top') self._colorbar_axes.xaxis.labelpad = axis_label_pad elif location == 'left': for tick in self._colorbar_axes.yaxis.get_major_ticks(): tick.tick1line.set_visible(True) tick.tick2line.set_visible(True) tick.label1.set_visible(labels) tick.label2.set_visible(False) self._colorbar_axes.yaxis.set_label_position('left') self._colorbar_axes.yaxis.labelpad = axis_label_pad elif location == 'bottom': for tick in self._colorbar_axes.xaxis.get_major_ticks(): tick.tick1line.set_visible(True) tick.tick2line.set_visible(True) tick.label1.set_visible(labels) tick.label2.set_visible(False) self._colorbar_axes.xaxis.set_label_position('bottom') self._colorbar_axes.xaxis.labelpad = axis_label_pad else: warnings.warn("No image is shown, therefore, no colorbar will be plotted") @auto_refresh def update(self): if self._colorbar_axes: self.show(**self._base_settings) @auto_refresh def hide(self): self._figure.delaxes(self._colorbar_axes) self._colorbar_axes = None @auto_refresh def _remove(self): self._figure.delaxes(self._colorbar_axes) # LOCATION AND SIZE @auto_refresh def set_location(self, location): """ Set the location of the colorbar. Should be one of 'left', 'right', 'top', 'bottom'. """ self._base_settings['location'] = location self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) @auto_refresh def set_width(self, width): """ Set the width of the colorbar relative to the canvas size. """ self._base_settings['width'] = width self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) @auto_refresh def set_pad(self, pad): """ Set the spacing between the colorbar and the image relative to the canvas size. """ self._base_settings['pad'] = pad self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) @auto_refresh def set_ticks(self, ticks): """ Set the position of the ticks on the colorbar. """ self._base_settings['ticks'] = ticks self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) @auto_refresh def set_labels(self, labels): """ Set whether to show numerical labels. """ self._base_settings['labels'] = labels self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) @auto_refresh def set_box(self, box, box_orientation='vertical'): """ Set the box within which to place the colorbar. This should be in the form [xmin, ymin, dx, dy] and be in relative figure units. The orientation of the colorbar within the box can be controlled with the box_orientation argument. """ self._base_settings['box'] = box self._base_settings['box_orientation'] = box_orientation self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) @auto_refresh def set_axis_label_text(self, axis_label_text): """ Set the colorbar label text. """ self._base_settings['axis_label_text'] = axis_label_text self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) @auto_refresh def set_axis_label_rotation(self, axis_label_rotation): """ Set the colorbar label rotation. """ self._base_settings['axis_label_rotation'] = axis_label_rotation self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) @auto_refresh def set_axis_label_pad(self, axis_label_pad): """ Set the colorbar label displacement, in points. """ self._base_settings['axis_label_pad'] = axis_label_pad self.show(**self._base_settings) self.set_font(fontproperties=self._ticklabel_fontproperties) self.set_axis_label_font(fontproperties=self._axislabel_fontproperties) # FONT PROPERTIES @auto_refresh def set_label_properties(self, *args, **kwargs): warnings.warn("set_label_properties is deprecated - use set_font instead", DeprecationWarning) self.set_font(*args, **kwargs) @auto_refresh @fixdocstring def set_font(self, family=None, style=None, variant=None, stretch=None, weight=None, size=None, fontproperties=None): """ Set the font of the tick labels. Parameters ---------- common: family, style, variant, stretch, weight, size, fontproperties Notes ----- Default values are set by matplotlib or previously set values if set_font has already been called. Global default values can be set by editing the matplotlibrc file. """ if family: self._ticklabel_fontproperties.set_family(family) if style: self._ticklabel_fontproperties.set_style(style) if variant: self._ticklabel_fontproperties.set_variant(variant) if stretch: self._ticklabel_fontproperties.set_stretch(stretch) if weight: self._ticklabel_fontproperties.set_weight(weight) if size: self._ticklabel_fontproperties.set_size(size) if fontproperties: self._ticklabel_fontproperties = fontproperties # Update the tick label font properties for label in self._colorbar_axes.get_xticklabels(): label.set_fontproperties(self._ticklabel_fontproperties) for label in self._colorbar_axes.get_yticklabels(): label.set_fontproperties(self._ticklabel_fontproperties) # Also update the offset text font properties label = self._colorbar_axes.xaxis.get_offset_text() label.set_fontproperties(self._ticklabel_fontproperties) label = self._colorbar_axes.yaxis.get_offset_text() label.set_fontproperties(self._ticklabel_fontproperties) @auto_refresh @fixdocstring def set_axis_label_font(self, family=None, style=None, variant=None, stretch=None, weight=None, size=None, fontproperties=None): """ Set the font of the tick labels. Parameters ---------- common: family, style, variant, stretch, weight, size, fontproperties Notes ----- Default values are set by matplotlib or previously set values if set_font has already been called. Global default values can be set by editing the matplotlibrc file. """ if family: self._axislabel_fontproperties.set_family(family) if style: self._axislabel_fontproperties.set_style(style) if variant: self._axislabel_fontproperties.set_variant(variant) if stretch: self._axislabel_fontproperties.set_stretch(stretch) if weight: self._axislabel_fontproperties.set_weight(weight) if size: self._axislabel_fontproperties.set_size(size) if fontproperties: self._axislabel_fontproperties = fontproperties # Update the label font properties label = self._colorbar_axes.xaxis.get_label() label.set_fontproperties(self._axislabel_fontproperties) label = self._colorbar_axes.yaxis.get_label() label.set_fontproperties(self._axislabel_fontproperties) # FRAME PROPERTIES @auto_refresh def set_frame_linewidth(self, linewidth): """ Set the linewidth of the colorbar frame, in points. """ warnings.warn("This method is not functional at this time") for key in self._colorbar_axes.spines: self._colorbar_axes.spines[key].set_linewidth(linewidth) @auto_refresh def set_frame_color(self, color): """ Set the color of the colorbar frame, in points. """ warnings.warn("This method is not functional at this time") for key in self._colorbar_axes.spines: self._colorbar_axes.spines[key].set_edgecolor(color) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/compat.py0000644000175000017500000000347400000000000014012 0ustar00tomtom# The simple_norm function in astropy is missing a control for the log # scaling as described in https://github.com/astropy/astropy/issues/8432, # so for now we include a copy of the fixed function here. from astropy.visualization.interval import (PercentileInterval, AsymmetricPercentileInterval, ManualInterval, MinMaxInterval) from astropy.visualization.stretch import (LinearStretch, SqrtStretch, PowerStretch, LogStretch, AsinhStretch) from astropy.visualization.mpl_normalize import ImageNormalize __all__ = ['simple_norm'] def simple_norm(data, stretch='linear', power=1.0, asinh_a=0.1, log_a=1000, min_cut=None, max_cut=None, min_percent=None, max_percent=None, percent=None, clip=True): if percent is not None: interval = PercentileInterval(percent) elif min_percent is not None or max_percent is not None: interval = AsymmetricPercentileInterval(min_percent or 0., max_percent or 100.) elif min_cut is not None or max_cut is not None: interval = ManualInterval(min_cut, max_cut) else: interval = MinMaxInterval() if stretch == 'linear': stretch = LinearStretch() elif stretch == 'sqrt': stretch = SqrtStretch() elif stretch == 'power': stretch = PowerStretch(power) elif stretch == 'log': stretch = LogStretch(log_a) elif stretch == 'asinh': stretch = AsinhStretch(asinh_a) else: raise ValueError('Unknown stretch: {0}.'.format(stretch)) vmin, vmax = interval.get_limits(data) return ImageNormalize(vmin=vmin, vmax=vmax, stretch=stretch, clip=clip) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/conftest.py0000664000175000017500000000407100000000000014350 0ustar00tomtom# This file is used to configure the behavior of pytest when using the Astropy # test infrastructure. It needs to live inside the package in order for it to # get picked up when running the tests inside an interpreter using # packagename.test import os from astropy.version import version as astropy_version # For Astropy 3.0 and later, we can use the standalone pytest plugin if astropy_version < '3.0': from astropy.tests.pytest_plugins import * # noqa del pytest_report_header ASTROPY_HEADER = True else: try: from pytest_astropy_header.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS ASTROPY_HEADER = True except ImportError: ASTROPY_HEADER = False def pytest_configure(config): if ASTROPY_HEADER: config.option.astropy_header = True # Customize the following lines to add/remove entries from the list of # packages for which version numbers are displayed when running the tests. PYTEST_HEADER_MODULES['Astropy'] = 'astropy' PYTEST_HEADER_MODULES['pyregion'] = 'pyregion' PYTEST_HEADER_MODULES['PyAVM'] = 'PyAVM' PYTEST_HEADER_MODULES['reproject'] = 'reproject' del PYTEST_HEADER_MODULES['h5py'] from . import __version__ packagename = os.path.basename(os.path.dirname(__file__)) TESTED_VERSIONS[packagename] = __version__ # Uncomment the last two lines in this block to treat all DeprecationWarnings as # exceptions. For Astropy v2.0 or later, there are 2 additional keywords, # as follow (although default should work for most cases). # To ignore some packages that produce deprecation warnings on import # (in addition to 'compiler', 'scipy', 'pygments', 'ipykernel', and # 'setuptools'), add: # modules_to_ignore_on_import=['module_1', 'module_2'] # To ignore some specific deprecation warning messages for Python version # MAJOR.MINOR or later, add: # warnings_to_ignore_by_pyver={(MAJOR, MINOR): ['Message to ignore']} # from astropy.tests.helper import enable_deprecations_as_exceptions # noqa # enable_deprecations_as_exceptions() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/convolve_util.py0000664000175000017500000000225500000000000015415 0ustar00tomtomimport numpy as np from astropy.convolution import (convolve as astropy_convolve, Gaussian2DKernel, Box2DKernel) def convolve(image, smooth=3, kernel='gauss'): if smooth is None and isinstance(kernel, str) and kernel in ['box', 'gauss']: return image if smooth is not None and not np.isscalar(smooth): raise ValueError("smooth= should be an integer - for more complex " "kernels, pass an array containing the kernel " "to the kernel= option") # The Astropy convolution doesn't treat +/-Inf values correctly yet, so we # convert to NaN here. image_fixed = np.array(image, dtype=float, copy=True) image_fixed[np.isinf(image)] = np.nan if isinstance(kernel, str): if kernel == 'gauss': kernel = Gaussian2DKernel(smooth, x_size=smooth * 5, y_size=smooth * 5) elif kernel == 'box': kernel = Box2DKernel(smooth, x_size=smooth * 5, y_size=smooth * 5) else: raise ValueError("Unknown kernel: {0}".format(kernel)) else: kernel = kernel return astropy_convolve(image, kernel, boundary='extend') ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646929450.0 aplpy-2.1.0/aplpy/core.py0000664000175000017500000022634200000000000013462 0ustar00tomtomfrom distutils import version import os import operator from functools import reduce import matplotlib import matplotlib.pyplot as plt from matplotlib.patches import Circle, Rectangle, Ellipse, Polygon, FancyArrow from matplotlib.collections import PatchCollection, LineCollection import numpy as np from astropy import log from astropy.wcs import WCS from astropy.wcs.utils import proj_plane_pixel_scales from astropy.io import fits try: from astropy.nddata.blocks import block_reduce except ImportError: # astropy<5 from astropy.nddata.utils import block_reduce from astropy.visualization import AsymmetricPercentileInterval from astropy.visualization.wcsaxes import WCSAxes, WCSAxesSubplot from astropy.coordinates import ICRS from . import convolve_util from . import header as header_util from . import slicer from .compat import simple_norm from .layers import Layers from .grid import Grid from .ticks import Ticks from .tick_labels import TickLabels from .axis_labels import AxisLabels from .overlays import Beam, Scalebar from .regions import Regions from .colorbar import Colorbar from .frame import Frame from .decorators import auto_refresh, fixdocstring HDU_TYPES = tuple([fits.PrimaryHDU, fits.ImageHDU, fits.CompImageHDU]) __doctest_skip__ = ['FITSFigure.add_beam', 'FITSFigure.add_colorbar', 'FITSFigure.add_grid', 'FITSFigure.add_scalebar'] def uniformize_1d(*args): if len(args) > 1: return np.broadcast_arrays(np.atleast_1d(args[0]), *args[1:]) elif len(args) == 1: return np.atleast_1d(args[0]) else: raise ValueError("No arguments passed to uniformize_1d") class FITSFigure(Layers, Regions): """ Create a FITSFigure instance. This class is a wrapper around the Astropy WCSAxes class and provides the same API as historical versions of APLpy. Parameters ---------- data : see below The FITS file to open. The following data types can be passed: string astropy.io.fits.PrimaryHDU astropy.io.fits.ImageHDU astropy.wcs.WCS np.ndarray RGB image with AVM meta-data hdu : int, optional By default, the image in the primary HDU is read in. If a different HDU is required, use this argument. figure : ~matplotlib.figure.Figure, optional If specified, a subplot will be added to this existing matplotlib figure() instance, rather than a new figure being created from scratch. subplot : tuple or list, optional If specified, a subplot will be added at this position. If a tuple of three values, the tuple should contain the standard matplotlib subplot parameters, i.e. (ny, nx, subplot). If a list of four values, the list should contain [xmin, ymin, dx, dy] where xmin and ymin are the position of the bottom left corner of the subplot, and dx and dy are the width and height of the subplot respectively. These should all be given in units of the figure width and height. For example, [0.1, 0.1, 0.8, 0.8] will almost fill the entire figure, leaving a 10 percent margin on all sides. downsample : int, optional If this option is specified, the image will be downsampled by a factor *downsample* when reading in the data. north : bool, optional Whether to rotate the image so that north is up. By default, this is assumed to be 'north' in the ICRS frame, but you can also pass any astropy :class:`~astropy.coordinates.BaseCoordinateFrame` to indicate to use the north of that frame. convention : str, optional This is used in cases where a FITS header can be interpreted in multiple ways. For example, for files with a -CAR projection and CRVAL2=0, this can be set to 'wells' or 'calabretta' to choose the appropriate convention. dimensions : tuple or list, optional The index of the axes to use if the data has more than three dimensions. slices : tuple or list, optional If a FITS file with more than two dimensions is specified, then these are the slices to extract. If all extra dimensions only have size 1, then this is not required. auto_refresh : bool, optional Whether to refresh the figure automatically every time a plotting method is called. This can also be set using the set_auto_refresh method. This defaults to `True` if and only if APLpy is being used from IPython and the Matplotlib backend is interactive. kwargs Any additional arguments are passed on to matplotlib's Figure() class. For example, to set the figure size, use the figsize=(xsize, ysize) argument (where xsize and ysize are in inches). For more information on these additional arguments, see the *Optional keyword arguments* section in the documentation for :class:`~matplotlib.figure.Figure`. """ @auto_refresh def __init__(self, data, hdu=0, figure=None, subplot=(1, 1, 1), downsample=False, north=False, convention=None, dimensions=[0, 1], slices=[], auto_refresh=None, **kwargs): self._wcsaxes_slices = ('x', 'y') if 'figsize' not in kwargs: kwargs['figsize'] = (10, 9) # Set the grid type if len(slices) > 0: self.grid_type = 'contours' else: self.grid_type = 'lines' if (isinstance(data, str) and data.split('.')[-1].lower() in ['png', 'jpg', 'tif']): try: from PIL import Image except ImportError: try: import Image except ImportError: raise ImportError("The Python Imaging Library (PIL) is " "required to read in RGB images") try: import pyavm except ImportError: raise ImportError("PyAVM is required to read in AVM " "meta-data from RGB images") if version.LooseVersion(pyavm.__version__) < version.LooseVersion('0.9.1'): raise ImportError("PyAVM installation is not recent enough " "(version 0.9.1 or later is required).") from pyavm import AVM # Remember image filename self._rgb_image = data # Find image size nx, ny = Image.open(data).size # Now convert AVM information to WCS data = AVM.from_image(data).to_wcs() # Need to scale CDELT values sometimes the AVM meta-data is only # really valid for the full-resolution image data.wcs.cdelt = [data.wcs.cdelt[0] * nx / float(nx), data.wcs.cdelt[1] * ny / float(ny)] data.wcs.crpix = [data.wcs.crpix[0] / nx * float(nx), data.wcs.crpix[1] / ny * float(ny)] # Update the NAXIS values with the true dimensions of the RGB image data.nx = nx data.ny = ny data.pixel_shape = (nx, ny) if isinstance(data, WCS): wcs = data if wcs.naxis != 2: raise ValueError("FITSFigure initialization via WCS objects " "can only be done with 2-dimensional WCS " "objects") if wcs.pixel_shape is None: raise ValueError("The WCS object does not contain any size " "information") header = wcs.to_header() header['NAXIS1'], header['NAXIS2'] = wcs.pixel_shape nx = header['NAXIS%i' % (dimensions[0] + 1)] ny = header['NAXIS%i' % (dimensions[1] + 1)] self._data = np.zeros((ny, nx), dtype=float) self._header = header self._wcs = WCS(header, relax=True) self._wcs.nx = nx self._wcs.ny = ny if downsample: log.warning("downsample argument is ignored if data " "passed is a WCS object") downsample = False if north: log.warning("north argument is ignored if data " "passed is a WCS object") north = False else: self._data, self._header, self._wcs, self._wcsaxes_slices = \ self._get_hdu(data, hdu, north, convention=convention, dimensions=dimensions, slices=slices) self._wcs.nx = self._header['NAXIS%i' % (dimensions[0] + 1)] self._wcs.ny = self._header['NAXIS%i' % (dimensions[1] + 1)] # Downsample if requested if downsample: nx_new = self._wcs.nx - np.mod(self._wcs.nx, downsample) ny_new = self._wcs.ny - np.mod(self._wcs.ny, downsample) self._data = self._data[0:ny_new, 0:nx_new] self._data = block_reduce(self._data, downsample, func=np.mean) self._wcs.nx, self._wcs.ny = nx_new, ny_new # Open the figure if figure: self._figure = figure else: self._figure = plt.figure(**kwargs) # Set whether to automatically refresh the display self.set_auto_refresh(auto_refresh) # Initialize axis instance if type(subplot) == list and len(subplot) == 4: self.ax = WCSAxes(self._figure, subplot, wcs=self._wcs, slices=self._wcsaxes_slices, adjustable='datalim') self._figure.add_axes(self.ax) elif type(subplot) == tuple and len(subplot) == 3: self.ax = WCSAxesSubplot(self._figure, *subplot, wcs=self._wcs, slices=self._wcsaxes_slices) self._figure.add_subplot(self.ax) else: raise ValueError("subplot= should be either a tuple of three " "values, or a list of four values") # Turn off autoscaling self.ax.set_autoscale_on(False) # Make sure axes are above everything else self.ax.set_axisbelow(False) # Set view to whole FITS file self._initialize_view() # Set the coordinates for x and y axis self.x = dimensions[0] self.y = dimensions[1] # Initialize tick, label, and frame convenience wrappers (these dispatch # calls to WCSAxes) self.ticks = Ticks(self) self.axis_labels = AxisLabels(self) self.tick_labels = TickLabels(self) self.frame = Frame(self) # Display minor ticks self.ax.coords[self.x].display_minor_ticks(True) self.ax.coords[self.y].display_minor_ticks(True) # Initialize layers list self._initialize_layers() # Set image holder to be empty self.image = None # Set default theme self.set_theme(theme='pretty') def _get_hdu(self, data, hdu, north, convention=None, dimensions=[0, 1], slices=[]): if isinstance(data, str): filename = data # Check file exists if not os.path.exists(filename): raise IOError("File not found: " + filename) # Read in FITS file try: hdulist = fits.open(filename) except Exception: raise IOError("An error occurred while reading the FITS file") # Check whether the HDU specified contains any data, otherwise # cycle through all HDUs to find one that contains valid image data if hdulist[hdu].data is None: found = False for alt_hdu in range(len(hdulist)): if isinstance(hdulist[alt_hdu], HDU_TYPES): if hdulist[alt_hdu].data is not None: log.warning("hdu=%i does not contain any data, " "using hdu=%i instead" % (hdu, alt_hdu)) hdu = hdulist[alt_hdu] found = True break if not found: raise Exception("FITS file does not contain any image data") else: hdu = hdulist[hdu] elif type(data) == np.ndarray: hdu = fits.ImageHDU(data) elif isinstance(data, HDU_TYPES): hdu = data elif isinstance(data, fits.HDUList): hdu = data[hdu] else: raise Exception("data argument should either be a filename, an HDU object from astropy.io.fits, a WCS object from astropy.wcs, or a Numpy array.") # Check that we have at least 2-dimensional data if hdu.header['NAXIS'] < 2: raise ValueError("Data should have at least two dimensions") # Check dimensions= argument if type(dimensions) not in [list, tuple]: raise ValueError('dimensions= should be a list or a tuple') if len(set(dimensions)) != 2 or len(dimensions) != 2: raise ValueError("dimensions= should be a tuple of two different values") if dimensions[0] < 0 or dimensions[0] > hdu.header['NAXIS'] - 1: raise ValueError('values of dimensions= should be between %i and %i' % (0, hdu.header['NAXIS'] - 1)) if dimensions[1] < 0 or dimensions[1] > hdu.header['NAXIS'] - 1: raise ValueError('values of dimensions= should be between %i and %i' % (0, hdu.header['NAXIS'] - 1)) # Reproject to face north if requested if north: # Find rotated WCS frame = ICRS() if north is True else north from reproject.mosaicking import find_optimal_celestial_wcs wcs, shape = find_optimal_celestial_wcs([hdu], frame=frame) from reproject import reproject_interp data, _ = reproject_interp(hdu, wcs, shape_out=shape) header = wcs.to_header() header['NAXIS1'] = shape[1] header['NAXIS2'] = shape[0] else: # Now copy the data and header to new objects, since in astropy.io.fits # the two attributes are linked, which can lead to confusing behavior. # We just need to copy the header to avoid memory issues - as long as # one item is copied, the two variables are decoupled. data = hdu.data header = hdu.header.copy() del hdu # If slices wasn't specified, check if we can guess shape = data.shape if len(shape) > 2: n_total = reduce(operator.mul, shape) n_image = (shape[len(shape) - 1 - dimensions[0]] * shape[len(shape) - 1 - dimensions[1]]) if n_total == n_image: slices = [0 for i in range(1, len(shape) - 1)] log.info("Setting slices=%s" % str(slices)) # Extract slices data, wcsaxes_slices = slicer.slice_hypercube(data, header, dimensions=dimensions, slices=slices) # Check header header = header_util.check(header, convention=convention, dimensions=dimensions) # Parse WCS info wcs = WCS(header, relax=True) return data, header, wcs, wcsaxes_slices @auto_refresh def set_title(self, title, **kwargs): """ Set the figure title """ self.ax.set_title(title, **kwargs) @auto_refresh def set_xaxis_coord_type(self, coord_type): """ Set the type of x coordinate. Options are: * ``scalar``: treat the values are normal decimal scalar values * ``longitude``: treat the values as a longitude in the 0 to 360 range * ``latitude``: treat the values as a latitude in the -90 to 90 range """ self.ax.coords[self.x].set_coord_type(coord_type) @auto_refresh def set_yaxis_coord_type(self, coord_type): """ Set the type of y coordinate. Options are: * ``scalar``: treat the values are normal decimal scalar values * ``longitude``: treat the values as a longitude in the 0 to 360 range * ``latitude``: treat the values as a latitude in the -90 to 90 range """ self.ax.coords[self.y].set_coord_type(coord_type) @auto_refresh def set_system_latex(self, usetex): """ Set whether to use a real LaTeX installation or the built-in matplotlib LaTeX. Parameters ---------- usetex : str Whether to use a real LaTex installation (True) or the built-in matplotlib LaTeX (False). Note that if the former is chosen, an installation of LaTex is required. """ plt.rc('text', usetex=usetex) @auto_refresh def recenter(self, x, y, radius=None, width=None, height=None): """ Center the image on a given position and with a given radius. Either the radius or width/height arguments should be specified. The units of the radius or width/height should be the same as the world coordinates in the WCS. For images of the sky, this is often (but not always) degrees. Parameters ---------- x, y : float Coordinates to center on radius : float, optional Radius of the region to view in degrees. This produces a square plot. width : float, optional Width of the region to view. This should be given in conjunction with the height argument. height : float, optional Height of the region to view. This should be given in conjunction with the width argument. """ xpix, ypix = self.world2pixel(x, y) pix_scale = proj_plane_pixel_scales(self._wcs) sx, sy = pix_scale[self.x], pix_scale[self.y] if radius: dx_pix = radius / sx dy_pix = radius / sy elif width and height: dx_pix = width / sx * 0.5 dy_pix = height / sy * 0.5 else: raise Exception("Need to specify either radius= or width= and height= arguments") if (xpix + dx_pix < -0.5 or xpix - dx_pix > self._wcs.nx - 0.5 or ypix + dy_pix < -0.5 or ypix - dy_pix > self._wcs.ny): raise Exception("Zoom region falls outside the image") self.ax.set_xlim(xpix - dx_pix, xpix + dx_pix) self.ax.set_ylim(ypix - dy_pix, ypix + dy_pix) @auto_refresh def show_grayscale(self, vmin=None, vmid=None, vmax=None, pmin=0.25, pmax=99.75, stretch='linear', exponent=2, invert='default', smooth=None, kernel='gauss', aspect='equal', interpolation='nearest'): """ Show a grayscale image of the FITS file. Parameters ---------- vmin : None or float, optional Minimum pixel value to use for the grayscale. If set to None, the minimum pixel value is determined using pmin (default). vmax : None or float, optional Maximum pixel value to use for the grayscale. If set to None, the maximum pixel value is determined using pmax (default). pmin : float, optional Percentile value used to determine the minimum pixel value to use for the grayscale if vmin is set to None. The default value is 0.25%. pmax : float, optional Percentile value used to determine the maximum pixel value to use for the grayscale if vmax is set to None. The default value is 99.75%. stretch : { 'linear', 'log', 'sqrt', 'arcsinh', 'power' }, optional The stretch function to use vmid : None or float, optional Baseline value used for the log and arcsinh stretches. If set to None, this is set to zero for log stretches and to vmin - (vmax - vmin) / 30. for arcsinh stretches exponent : float, optional If stretch is set to 'power', this is the exponent to use invert : str, optional Whether to invert the grayscale or not. The default is False, unless set_theme is used, in which case the default depends on the theme. smooth : int or tuple, optional Default smoothing scale is 3 pixels across. User can define whether they want an NxN kernel (integer), or NxM kernel (tuple). This argument corresponds to the 'gauss' and 'box' smoothing kernels. kernel : { 'gauss', 'box', numpy.array }, optional Default kernel used for smoothing is 'gauss'. The user can specify if they would prefer 'gauss', 'box', or a custom kernel. All kernels are normalized to ensure flux retention. aspect : { 'auto', 'equal' }, optional Whether to change the aspect ratio of the image to match that of the axes ('auto') or to change the aspect ratio of the axes to match that of the data ('equal'; default) interpolation : str, optional The type of interpolation to use for the image. The default is 'nearest'. Other options include 'none' (no interpolation, meaning that if exported to a postscript file, the grayscale will be output at native resolution irrespective of the dpi setting), 'bilinear', 'bicubic', and many more (see the matplotlib documentation for imshow). """ if invert == 'default': invert = self._get_invert_default() if invert: cmap = 'gist_yarg' else: cmap = 'gray' self.show_colorscale(vmin=vmin, vmid=vmid, vmax=vmax, pmin=pmin, pmax=pmax, stretch=stretch, exponent=exponent, cmap=cmap, smooth=smooth, kernel=kernel, aspect=aspect, interpolation=interpolation) @auto_refresh def hide_grayscale(self, *args, **kwargs): self.hide_colorscale(*args, **kwargs) @auto_refresh def show_colorscale(self, vmin=None, vmid=None, vmax=None, pmin=0.25, pmax=99.75, stretch='linear', exponent=2, cmap='default', smooth=None, kernel='gauss', aspect='equal', interpolation='nearest'): """ Show a colorscale image of the FITS file. Parameters ---------- vmin : None or float, optional Minimum pixel value to use for the colorscale. If set to None, the minimum pixel value is determined using pmin (default). vmax : None or float, optional Maximum pixel value to use for the colorscale. If set to None, the maximum pixel value is determined using pmax (default). pmin : float, optional Percentile value used to determine the minimum pixel value to use for the colorscale if vmin is set to None. The default value is 0.25%. pmax : float, optional Percentile value used to determine the maximum pixel value to use for the colorscale if vmax is set to None. The default value is 99.75%. stretch : { 'linear', 'log', 'sqrt', 'arcsinh', 'power' }, optional The stretch function to use vmid : None or float, optional Baseline value used for the log and arcsinh stretches. If not set, this defaults to zero for log stretches and to vmin - (vmax - vmin) / 30. for arcsinh stretches exponent : float, optional If stretch is set to 'power', this is the exponent to use cmap : str, optional The name of the colormap to use smooth : int or tuple, optional Default smoothing scale is 3 pixels across. User can define whether they want an NxN kernel (integer), or NxM kernel (tuple). This argument corresponds to the 'gauss' and 'box' smoothing kernels. kernel : { 'gauss', 'box', numpy.array }, optional Default kernel used for smoothing is 'gauss'. The user can specify if they would prefer 'gauss', 'box', or a custom kernel. All kernels are normalized to ensure flux retention. aspect : { 'auto', 'equal' }, optional Whether to change the aspect ratio of the image to match that of the axes ('auto') or to change the aspect ratio of the axes to match that of the data ('equal'; default) interpolation : str, optional The type of interpolation to use for the image. The default is 'nearest'. Other options include 'none' (no interpolation, meaning that if exported to a postscript file, the colorscale will be output at native resolution irrespective of the dpi setting), 'bilinear', 'bicubic', and many more (see the matplotlib documentation for imshow). """ if cmap == 'default': cmap = self._get_colormap_default() min_auto = vmin is None max_auto = vmax is None # The set of available functions cmap = plt.cm.get_cmap(cmap) if min_auto or max_auto: interval = AsymmetricPercentileInterval(pmin, pmax, n_samples=10000) try: vmin_auto, vmax_auto = interval.get_limits(self._data) except (IndexError, TypeError): # no valid values vmin_auto = vmax_auto = 0 if min_auto: vmin = vmin_auto if max_auto: vmax = vmax_auto # Prepare normalizer object if stretch == 'arcsinh': stretch = 'asinh' if stretch == 'log': if vmid is None: if vmin < 0: raise ValueError("When using a log stretch, if vmin < 0, then vmid has to be specified") else: vmid = 0. if vmin < vmid: raise ValueError("When using a log stretch, vmin should be larger than vmid") log_a = (vmax - vmid) / (vmin - vmid) norm_kwargs = {'log_a': log_a} elif stretch == 'asinh': if vmid is None: vmid = vmin - (vmax - vmin) / 30. asinh_a = (vmid - vmin) / (vmax - vmin) norm_kwargs = {'asinh_a': abs(asinh_a)} else: norm_kwargs = {} normalizer = simple_norm(self._data, stretch=stretch, power=exponent, min_cut=vmin, max_cut=vmax, clip=False, **norm_kwargs) # Adjust vmin/vmax if auto if min_auto: if stretch == 'linear': vmin = -0.1 * (vmax - vmin) + vmin log.info("Auto-setting vmin to %10.3e" % vmin) if max_auto: if stretch == 'linear': vmax = 0.1 * (vmax - vmin) + vmax log.info("Auto-setting vmax to %10.3e" % vmax) # Update normalizer object normalizer.vmin = vmin normalizer.vmax = vmax if self.image: self.image.set_visible(True) self.image.set_norm(normalizer) self.image.set_cmap(cmap=cmap) self.image.origin = 'lower' self.image.set_interpolation(interpolation) self.image.set_data(convolve_util.convolve(self._data, smooth=smooth, kernel=kernel)) else: extent = -0.5, self._wcs.nx - 0.5, -0.5, self._wcs.ny - 0.5 convolved_data = convolve_util.convolve(self._data, smooth=smooth, kernel=kernel) self.image = self.ax.imshow(convolved_data, cmap=cmap, interpolation=interpolation, origin='lower', norm=normalizer, aspect=aspect, extent=extent) xmin, xmax = self.ax.get_xbound() if xmin == 0.0: self.ax.set_xlim(0.5, xmax) ymin, ymax = self.ax.get_ybound() if ymin == 0.0: self.ax.set_ylim(0.5, ymax) if hasattr(self, 'colorbar'): self.colorbar.update() @auto_refresh def hide_colorscale(self): self.image.set_visible(False) @auto_refresh def set_nan_color(self, color): """ Set the color for NaN pixels. Parameters ---------- color : str This can be any valid matplotlib color """ from copy import deepcopy cm = deepcopy(self.image.get_cmap()) cm.set_bad(color) self.image.set_cmap(cm) @auto_refresh def show_rgb(self, filename=None, interpolation='nearest', vertical_flip=False, horizontal_flip=False, flip=False): """ Show a 3-color image instead of the FITS file data. Parameters ---------- filename, optional The 3-color image should have exactly the same dimensions as the FITS file, and will be shown with exactly the same projection. If FITSFigure was initialized with an AVM-tagged RGB image, the filename is not needed here. vertical_flip : str, optional Whether to vertically flip the RGB image horizontal_flip : str, optional Whether to horizontally flip the RGB image """ try: from PIL import Image except ImportError: try: import Image except ImportError: raise ImportError("The Python Imaging Library (PIL) is required to read in RGB images") if flip: log.warning("Note that show_rgb should now correctly flip RGB images, so the flip= argument is now deprecated. If you still need to flip an image vertically or horizontally, you can use the vertical_flip= and horizontal_flip arguments instead.") if filename is None: if hasattr(self, '_rgb_image'): image = Image.open(self._rgb_image) else: raise Exception("Need to specify the filename of an RGB image") else: image = Image.open(filename) if vertical_flip: image = image.transpose(Image.FLIP_TOP_BOTTOM) if horizontal_flip: image = image.transpose(Image.FLIP_LEFT_RIGHT) self.image = self.ax.imshow(image, interpolation=interpolation, origin='lower') @auto_refresh def show_contour(self, data=None, hdu=0, layer=None, levels=5, filled=False, cmap=None, colors=None, returnlevels=False, convention=None, dimensions=[0, 1], slices=[], smooth=None, kernel='gauss', overlap=False, **kwargs): """ Overlay contours on the current plot. Parameters ---------- data : see below The FITS file to plot contours for. The following data types can be passed: string astropy.io.fits.PrimaryHDU astropy.io.fits.ImageHDU astropy.wcs.WCS np.ndarray hdu : int, optional By default, the image in the primary HDU is read in. If a different HDU is required, use this argument. layer : str, optional The name of the contour layer. This is useful for giving custom names to layers (instead of contour_set_n) and for replacing existing layers. levels : int or list, optional This can either be the number of contour levels to compute (if an integer is provided) or the actual list of contours to show (if a list of floats is provided) filled : str, optional Whether to show filled or line contours cmap : str, optional The colormap to use for the contours colors : str or tuple, optional If a single string is provided, all contour levels will be shown in this color. If a tuple of strings is provided, each contour will be colored according to the corresponding tuple element. returnlevels : str, optional Whether to return the list of contours to the caller. convention : str, optional This is used in cases where a FITS header can be interpreted in multiple ways. For example, for files with a -CAR projection and CRVAL2=0, this can be set to 'wells' or 'calabretta' to choose the appropriate convention. dimensions : tuple or list, optional The index of the axes to use if the data has more than three dimensions. slices : tuple or list, optional If a FITS file with more than two dimensions is specified, then these are the slices to extract. If all extra dimensions only have size 1, then this is not required. smooth : int or tuple, optional Default smoothing scale is 3 pixels across. User can define whether they want an NxN kernel (integer), or NxM kernel (tuple). This argument corresponds to the 'gauss' and 'box' smoothing kernels. kernel : { 'gauss' , 'box' , numpy.array }, optional Default kernel used for smoothing is 'gauss'. The user can specify if they would prefer 'gauss', 'box', or a custom kernel. All kernels are normalized to ensure flux retention. overlap str, optional Whether to include only contours that overlap with the image area. This significantly speeds up the drawing of contours and reduces file size when using a file for the contours covering a much larger area than the image. kwargs Additional keyword arguments (such as alpha, linewidths, or linestyles) will be passed on directly to Matplotlib's :meth:`~matplotlib.axes.Axes.contour` or :meth:`~matplotlib.axes.Axes.contourf` methods. For more information on these additional arguments, see the *Optional keyword arguments* sections in the documentation for those methods. """ if layer: self.remove_layer(layer, raise_exception=False) if cmap: cmap = plt.cm.get_cmap(cmap) elif not colors: cmap = plt.cm.get_cmap('viridis') if data is not None: data_contour, header_contour, wcs_contour, wcsaxes_slices = \ self._get_hdu(data, hdu, False, convention=convention, dimensions=dimensions, slices=slices) else: data_contour = self._data header_contour = self._header wcs_contour = self._wcs wcs_contour.nx = header_contour['NAXIS%i' % (dimensions[0] + 1)] wcs_contour.ny = header_contour['NAXIS%i' % (dimensions[1] + 1)] image_contour = convolve_util.convolve(data_contour, smooth=smooth, kernel=kernel) if type(levels) == int: interval = AsymmetricPercentileInterval(0.25, 99.75, n_samples=10000) try: vmin_auto, vmax_auto = interval.get_limits(image_contour) except IndexError: # no valid values vmin_auto = vmax_auto = 0 levels = np.linspace(vmin_auto, vmax_auto, levels) if wcs_contour.wcs.ctype[self.x] == 'PIXEL' or wcs_contour.wcs.ctype[self.y] == 'PIXEL': frame = 'pixel' else: frame = wcs_contour if filled: c = self.ax.contourf(image_contour, levels, transform=self.ax.get_transform(frame), cmap=cmap, colors=colors, **kwargs) else: c = self.ax.contour(image_contour, levels, transform=self.ax.get_transform(frame), cmap=cmap, colors=colors, **kwargs) if layer: contour_set_name = layer else: self._contour_counter += 1 contour_set_name = 'contour_set_' + str(self._contour_counter) self._layers[contour_set_name] = c if returnlevels: return levels @auto_refresh def show_vectors(self, pdata, adata, phdu=0, ahdu=0, step=1, scale=1, rotate=0, cutoff=0, units='degrees', layer=None, convention=None, dimensions=[0, 1], slices=[], **kwargs): """ Overlay vectors on the current plot. Parameters ---------- pdata : see below The FITS file specifying the magnitude of vectors. The following data types can be passed: string astropy.io.fits.PrimaryHDU astropy.io.fits.ImageHDU astropy.wcs.WCS np.ndarray adata : see below The FITS file specifying the angle of vectors. The following data types can be passed: string astropy.io.fits.PrimaryHDU astropy.io.fits.ImageHDU astropy.wcs.WCS np.ndarray phdu : int, optional By default, the image in the primary HDU is read in. If a different HDU is required for pdata, use this argument. ahdu : int, optional By default, the image in the primary HDU is read in. If a different HDU is required for adata, use this argument. step : int, optional Derive a vector only from every 'step' pixels. You will normally want this to be >1 to get sensible vector spacing. scale : int, optional The length, in pixels, of a vector with magnitude 1 in the image specified by pdata. If pdata specifies fractional polarization, make this comparable to step. rotate : float, optional An angle to rotate by, in units the same as those of the angle map. cutoff : float, optional The value of magnitude below which no vectors should be plotted. The default value, zero, excludes negative-length and NaN-masked data. units : str, optional Units to assume for the angle map. Valid values are 'degrees' (the default) or 'radians' (or anything else), which will not apply a scaling factor of pi/180 to the angle data. layer : str, optional The name of the vector layer. This is useful for giving custom names to layers (instead of vector_set_n) and for replacing existing layers. convention : str, optional This is used in cases where a FITS header can be interpreted in multiple ways. For example, for files with a -CAR projection and CRVAL2=0, this can be set to 'wells' or 'calabretta' to choose the appropriate convention. dimensions : tuple or list, optional The index of the axes to use if the data has more than three dimensions. slices : tuple or list, optional If a FITS file with more than two dimensions is specified, then these are the slices to extract. If all extra dimensions only have size 1, then this is not required. kwargs Additional keyword arguments (such as alpha, linewidths, or color) which are passed to Matplotlib's :class:`~matplotlib.collections.LineCollection` class, and can be used to control the appearance of the lines. For more information on these additional arguments, see the *Optional keyword arguments* sections in the documentation for those methods. """ # over-ride default color (none) that will otherwise be set by # show_lines() if 'color' not in kwargs: kwargs.setdefault('color', 'black') if layer: self.remove_layer(layer, raise_exception=False) data_p, header_p, wcs_p, slices_p = \ self._get_hdu(pdata, phdu, False, convention=convention, dimensions=dimensions, slices=slices) data_a, header_a, wcs_a, slices_a = \ self._get_hdu(adata, ahdu, False, convention=convention, dimensions=dimensions, slices=slices) # TODO: use slices correctly wcs_p.nx = header_p['NAXIS%i' % (dimensions[0] + 1)] wcs_p.ny = header_p['NAXIS%i' % (dimensions[1] + 1)] wcs_a.nx = header_a['NAXIS%i' % (dimensions[0] + 1)] wcs_a.ny = header_a['NAXIS%i' % (dimensions[1] + 1)] if (wcs_p.nx != wcs_a.nx or wcs_p.ny != wcs_a.ny): raise Exception("Angle and magnitude images must be same size") angle = data_a + rotate if units == 'degrees': angle = np.radians(angle) linelist = [] for y in range(0, wcs_p.ny, step): for x in range(0, wcs_p.nx, step): if data_p[y, x] > cutoff and np.isfinite(angle[y, x]): r = data_p[y, x] * 0.5 * scale a = angle[y, x] x1 = x + r * np.sin(a) y1 = y - r * np.cos(a) x2 = x - r * np.sin(a) y2 = y + r * np.cos(a) x_world, y_world = self.pixel2world([x1, x2], [y1, y2], wcs=wcs_p) line = np.array([x_world, y_world]) linelist.append(line) if layer: vector_set_name = layer else: self._vector_counter += 1 vector_set_name = 'vector_set_' + str(self._vector_counter) # Use show_lines to finish the process off self.show_lines(linelist, layer=vector_set_name, **kwargs) # This method plots markers. The input should be an Nx2 array with WCS # coordinates in degree format. @auto_refresh def show_markers(self, xw, yw, layer=False, coords_frame='world', **kwargs): """ Overlay markers on the current plot. Parameters ---------- xw : list or `~numpy.ndarray` The x positions of the markers (in world coordinates) yw : list or `~numpy.ndarray` The y positions of the markers (in world coordinates) layer : str, optional The name of the scatter layer. This is useful for giving custom names to layers (instead of marker_set_n) and for replacing existing layers. coords_frame : 'pixel' or 'world' The reference frame in which the coordinates are defined. This is used to interpret the values of ``xw`` and ``yw``. kwargs Additional keyword arguments (such as marker, facecolor, edgecolor, alpha, or linewidth) will be passed on directly to Matplotlib's :meth:`~matplotlib.axes.Axes.scatter` method (in particular, have a look at the *Optional keyword arguments* in the documentation for that method). """ if 'c' not in kwargs: kwargs.setdefault('edgecolor', 'red') kwargs.setdefault('facecolor', 'none') kwargs.setdefault('s', 30) if layer: self.remove_layer(layer, raise_exception=False) s = self.ax.scatter(xw, yw, transform=self.ax.get_transform(coords_frame), **kwargs) if layer: marker_set_name = layer else: self._scatter_counter += 1 marker_set_name = 'marker_set_' + str(self._scatter_counter) self._layers[marker_set_name] = s # Show circles. Different from markers as this method allows more # definitions for the circles. @auto_refresh def show_circles(self, xw, yw, radius, layer=False, coords_frame='world', zorder=None, **kwargs): """ Overlay circles on the current plot. Parameters ---------- xw : list or `~numpy.ndarray` The x positions of the centers of the circles (in world coordinates) yw : list or `~numpy.ndarray` The y positions of the centers of the circles (in world coordinates) radius : int or float or list or `~numpy.ndarray` The radii of the circles (in world coordinates) layer : str, optional The name of the circle layer. This is useful for giving custom names to layers (instead of circle_set_n) and for replacing existing layers. coords_frame : 'pixel' or 'world' The reference frame in which the coordinates are defined. This is used to interpret the values of ``xw`` and ``yw``. kwargs Additional keyword arguments (such as facecolor, edgecolor, alpha, or linewidth) are passed to Matplotlib :class:`~matplotlib.collections.PatchCollection` class, and can be used to control the appearance of the circles. """ xw, yw, radius = uniformize_1d(xw, yw, radius) if 'facecolor' not in kwargs: kwargs.setdefault('facecolor', 'none') if layer: self.remove_layer(layer, raise_exception=False) if coords_frame not in ['pixel', 'world']: raise ValueError("coords_frame should be set to 'pixel' or 'world'") # While we could plot the shape using the get_transform('world') mode # from WCSAxes, the issue is that the rotation angle is also measured in # world coordinates so will not be what the user is expecting. So we allow the user to specify the reference frame for the coordinates and for the rotation. if coords_frame == 'pixel': x, y = xw, yw r = radius else: x, y = self.world2pixel(xw, yw) pix_scale = proj_plane_pixel_scales(self._wcs) sx, sy = pix_scale[self.x], pix_scale[self.y] r = radius / np.sqrt(sx * sy) patches = [] for i in range(len(xw)): patches.append(Circle((x[i], y[i]), radius=r[i])) # Due to bugs in matplotlib, we need to pass the patch properties # directly to the PatchCollection rather than use match_original. p = PatchCollection(patches, **kwargs) if zorder is not None: p.zorder = zorder c = self.ax.add_collection(p) if layer: circle_set_name = layer else: self._circle_counter += 1 circle_set_name = 'circle_set_' + str(self._circle_counter) self._layers[circle_set_name] = c @auto_refresh def show_ellipses(self, xw, yw, width, height, angle=0, layer=False, zorder=None, coords_frame='world', **kwargs): """ Overlay ellipses on the current plot. Parameters ---------- xw : list or `~numpy.ndarray` The x positions of the centers of the ellipses (in world coordinates) yw : list or `~numpy.ndarray` The y positions of the centers of the ellipses (in world coordinates) width : int or float or list or `~numpy.ndarray` The width of the ellipse (in world coordinates) height : int or float or list or `~numpy.ndarray` The height of the ellipse (in world coordinates) angle : int or float or list or `~numpy.ndarray`, optional rotation in degrees (anti-clockwise). Default angle is 0.0. layer : str, optional The name of the ellipse layer. This is useful for giving custom names to layers (instead of ellipse_set_n) and for replacing existing layers. coords_frame : 'pixel' or 'world' The reference frame in which the coordinates are defined. This is used to interpret the values of ``xw``, ``yw``, ``width``, and ``height``. kwargs Additional keyword arguments (such as facecolor, edgecolor, alpha, or linewidth) are passed to Matplotlib :class:`~matplotlib.collections.PatchCollection` class, and can be used to control the appearance of the ellipses. """ xw, yw, width, height, angle = uniformize_1d(xw, yw, width, height, angle) if 'facecolor' not in kwargs: kwargs.setdefault('facecolor', 'none') if 'edgecolor' not in kwargs: kwargs.setdefault('edgecolor', 'black') if layer: self.remove_layer(layer, raise_exception=False) if coords_frame not in ['pixel', 'world']: raise ValueError("coords_frame should be set to 'pixel' or 'world'") # While we could plot the shape using the get_transform('world') mode # from WCSAxes, the issue is that the rotation angle is also measured in # world coordinates so will not be what the user is expecting. So we allow the user to specify the reference frame for the coordinates and for the rotation. if coords_frame == 'pixel': x, y = xw, yw w = width h = height a = angle transform = self.ax.transData else: x, y = self.world2pixel(xw, yw) pix_scale = proj_plane_pixel_scales(self._wcs) sx, sy = pix_scale[self.x], pix_scale[self.y] w = width / sx h = height / sy a = angle transform = self.ax.transData patches = [] for i in range(len(x)): patches.append(Ellipse((x[i], y[i]), width=w[i], height=h[i], angle=a[i])) # Due to bugs in matplotlib, we need to pass the patch properties # directly to the PatchCollection rather than use match_original. p = PatchCollection(patches, transform=transform, **kwargs) if zorder is not None: p.zorder = zorder c = self.ax.add_collection(p) if layer: ellipse_set_name = layer else: self._ellipse_counter += 1 ellipse_set_name = 'ellipse_set_' + str(self._ellipse_counter) self._layers[ellipse_set_name] = c @auto_refresh def show_rectangles(self, xw, yw, width, height, angle=0, layer=False, zorder=None, coords_frame='world', **kwargs): """ Overlay rectangles on the current plot. Parameters ---------- xw : list or `~numpy.ndarray` The x positions of the centers of the rectangles (in world coordinates) yw : list or `~numpy.ndarray` The y positions of the centers of the rectangles (in world coordinates) width : int or float or list or `~numpy.ndarray` The width of the rectangle (in world coordinates) height : int or float or list or `~numpy.ndarray` The height of the rectangle (in world coordinates) angle : int or float or list or `~numpy.ndarray`, optional rotation in degrees (anti-clockwise). Default angle is 0.0. layer : str, optional The name of the rectangle layer. This is useful for giving custom names to layers (instead of rectangle_set_n) and for replacing existing layers. coords_frame : 'pixel' or 'world' The reference frame in which the coordinates are defined. This is used to interpret the values of ``xw``, ``yw``, ``width``, and ``height``. kwargs Additional keyword arguments (such as facecolor, edgecolor, alpha, or linewidth) are passed to Matplotlib :class:`~matplotlib.collections.PatchCollection` class, and can be used to control the appearance of the rectangles. """ xw, yw, width, height, angle = uniformize_1d(xw, yw, width, height, angle) if 'facecolor' not in kwargs: kwargs.setdefault('facecolor', 'none') if layer: self.remove_layer(layer, raise_exception=False) if coords_frame not in ['pixel', 'world']: raise ValueError("coords_frame should be set to 'pixel' or 'world'") # While we could plot the shape using the get_transform('world') mode # from WCSAxes, the issue is that the rotation angle is also measured in # world coordinates so will not be what the user is expecting. So we # allow the user to specify the reference frame for the coordinates and # for the rotation. if coords_frame == 'pixel': x, y = xw, yw w = width h = height a = angle transform = self.ax.transData else: x, y = self.world2pixel(xw, yw) pix_scale = proj_plane_pixel_scales(self._wcs) sx, sy = pix_scale[self.x], pix_scale[self.y] w = width / sx h = height / sy a = angle transform = self.ax.transData x = x - w / 2. y = y - h / 2. patches = [] for i in range(len(x)): patches.append(Rectangle((x[i], y[i]), width=w[i], height=h[i], angle=a[i])) # Due to bugs in matplotlib, we need to pass the patch properties # directly to the PatchCollection rather than use match_original. p = PatchCollection(patches, transform=transform, **kwargs) if zorder is not None: p.zorder = zorder c = self.ax.add_collection(p) if layer: rectangle_set_name = layer else: self._rectangle_counter += 1 rectangle_set_name = 'rectangle_set_' + str(self._rectangle_counter) self._layers[rectangle_set_name] = c @auto_refresh def show_lines(self, line_list, layer=False, zorder=None, **kwargs): """ Overlay lines on the current plot. Parameters ---------- line_list : list A list of one or more 2xN numpy arrays which contain the [x, y] positions of the vertices in world coordinates. layer : str, optional The name of the line(s) layer. This is useful for giving custom names to layers (instead of line_set_n) and for replacing existing layers. kwargs Additional keyword arguments (such as color, offsets, linestyle, or linewidth) are passed to Matplotlib :class:`~matplotlib.collections.LineCollection` class, and can be used to control the appearance of the lines. """ if 'color' not in kwargs: kwargs.setdefault('color', 'none') if layer: self.remove_layer(layer, raise_exception=False) lines = [] for line in line_list: xw, yw = line[0, :], line[1, :] lines.append(np.column_stack((xw, yw))) lc = LineCollection(lines, transform=self.ax.get_transform('world'), **kwargs) if zorder is not None: lc.zorder = zorder c = self.ax.add_collection(lc) if layer: line_set_name = layer else: self._linelist_counter += 1 line_set_name = 'line_set_' + str(self._linelist_counter) self._layers[line_set_name] = c @auto_refresh def show_arrows(self, x, y, dx, dy, width='auto', head_width='auto', head_length='auto', length_includes_head=True, layer=False, zorder=None, **kwargs): """ Overlay arrows on the current plot. Parameters ---------- x, y, dx, dy : float or list or `~numpy.ndarray` Origin and displacement of the arrows in world coordinates. These can either be scalars to plot a single arrow, or lists or arrays to plot multiple arrows. width : float, optional The width of the arrow body, in pixels (default: 2% of the arrow length) head_width : float, optional The width of the arrow head, in pixels (default: 5% of the arrow length) head_length : float, optional The length of the arrow head, in pixels (default: 5% of the arrow length) length_includes_head : bool, optional Whether the head includes the length layer : str, optional The name of the arrow(s) layer. This is useful for giving custom names to layers (instead of line_set_n) and for replacing existing layers. kwargs Additional keyword arguments (such as facecolor, edgecolor, alpha, or linewidth) are passed to Matplotlib :class:`~matplotlib.collections.PatchCollection` class, and can be used to control the appearance of the arrows. """ x, y, dx, dy = uniformize_1d(x, y, dx, dy) if layer: self.remove_layer(layer, raise_exception=False) arrows = [] # Here we don't make use of WCSAxes.get_transform('world') because # otherwise the arrow heads will be distored. Instead, we work in pixel # coordinates. for i in range(len(x)): xp1, yp1 = self.world2pixel(x[i], y[i]) xp2, yp2 = self.world2pixel(x[i] + dx[i], y[i] + dy[i]) if width == 'auto': width = 0.02 * np.sqrt((xp2 - xp1) ** 2 + (yp2 - yp1) ** 2) if head_width == 'auto': head_width = 0.1 * np.sqrt((xp2 - xp1) ** 2 + (yp2 - yp1) ** 2) if head_length == 'auto': head_length = 0.1 * np.sqrt((xp2 - xp1) ** 2 + (yp2 - yp1) ** 2) arrows.append(FancyArrow(xp1, yp1, xp2 - xp1, yp2 - yp1, width=width, head_width=head_width, head_length=head_length, length_includes_head=length_includes_head) ) # Due to bugs in matplotlib, we need to pass the patch properties # directly to the PatchCollection rather than use match_original. p = PatchCollection(arrows, **kwargs) if zorder is not None: p.zorder = zorder c = self.ax.add_collection(p) if layer: line_set_name = layer else: self._linelist_counter += 1 line_set_name = 'arrow_set_' + str(self._linelist_counter) self._layers[line_set_name] = c @auto_refresh def show_polygons(self, polygon_list, layer=False, zorder=None, **kwargs): """ Overlay polygons on the current plot. Parameters ---------- polygon_list : list or tuple A list of one or more 2xN or Nx2 Numpy arrays which contain the [x, y] positions of the vertices in world coordinates. Note that N should be greater than 2. layer : str, optional The name of the circle layer. This is useful for giving custom names to layers (instead of circle_set_n) and for replacing existing layers. kwargs Additional keyword arguments (such as facecolor, edgecolor, alpha, or linewidth) are passed to Matplotlib :class:`~matplotlib.collections.PatchCollection` class, and can be used to control the appearance of the polygons. """ if 'facecolor' not in kwargs: kwargs.setdefault('facecolor', 'none') if layer: self.remove_layer(layer, raise_exception=False) if type(polygon_list) not in [list, tuple]: raise Exception("polygon_list should be a list or tuple of Numpy arrays") pix_polygon_list = [] for polygon in polygon_list: if type(polygon) is not np.ndarray: raise Exception("Polygon should be given as a Numpy array") if polygon.shape[0] == 2 and polygon.shape[1] > 2: xw = polygon[0, :] yw = polygon[1, :] elif polygon.shape[0] > 2 and polygon.shape[1] == 2: xw = polygon[:, 0] yw = polygon[:, 1] else: raise Exception("Polygon should have dimensions 2xN or Nx2 with N>2") pix_polygon_list.append(np.column_stack((xw, yw))) patches = [] for i in range(len(pix_polygon_list)): patches.append(Polygon(pix_polygon_list[i], **kwargs)) # Due to bugs in matplotlib, we need to pass the patch properties # directly to the PatchCollection rather than use match_original. p = PatchCollection(patches, transform=self.ax.get_transform('world'), **kwargs) if zorder is not None: p.zorder = zorder c = self.ax.add_collection(p) if layer: poly_set_name = layer else: self._poly_counter += 1 poly_set_name = 'poly_set_' + str(self._poly_counter) self._layers[poly_set_name] = c @auto_refresh @fixdocstring def add_label(self, x, y, text, relative=False, color='black', family=None, style=None, variant=None, stretch=None, weight=None, size=None, fontproperties=None, horizontalalignment='center', verticalalignment='center', layer=None, **kwargs): """ Add a text label. Parameters ---------- x, y : float Coordinates of the text label text : str The label relative : str, optional Whether the coordinates are to be interpreted as world coordinates (e.g. RA/Dec or longitude/latitude), or coordinates relative to the axes (where 0.0 is left or bottom and 1.0 is right or top). common: color, family, style, variant, stretch, weight, size, fontproperties, horizontalalignment, verticalalignment """ if layer: self.remove_layer(layer, raise_exception=False) # Can't pass fontproperties=None to text. Only pass it if it is not None. if fontproperties: kwargs['fontproperties'] = fontproperties if not np.isscalar(x): raise Exception("x should be a single value") if not np.isscalar(y): raise Exception("y should be a single value") if not np.isscalar(text): raise Exception("text should be a single value") if relative: lc = self.ax.text(x, y, text, color=color, family=family, style=style, variant=variant, stretch=stretch, weight=weight, size=size, horizontalalignment=horizontalalignment, verticalalignment=verticalalignment, transform=self.ax.transAxes, **kwargs) else: lc = self.ax.text(x, y, text, color=color, family=family, style=style, variant=variant, stretch=stretch, weight=weight, size=size, horizontalalignment=horizontalalignment, verticalalignment=verticalalignment, transform=self.ax.get_transform('world'), **kwargs) if layer: label_name = layer else: self._label_counter += 1 label_name = 'label_' + str(self._label_counter) self._layers[label_name] = lc def set_auto_refresh(self, refresh): """ Set whether the display should refresh after each method call. Parameters ---------- refresh : bool Whether to refresh the display every time a FITSFigure method is called. This defaults to `True` if and only if APLpy is being used from IPython and the Matplotlib backend is interactive. """ if refresh is None: if matplotlib.is_interactive(): try: get_ipython() except NameError: refresh = False else: refresh = True else: refresh = False elif not isinstance(refresh, bool): raise TypeError("refresh argument should be boolean or `None`") self._figure._auto_refresh = refresh def refresh(self, force=True): """ Refresh the display. Parameters ---------- force : str, optional If set to False, refresh() will only have an effect if auto refresh is on. If set to True, the display will be refreshed whatever the auto refresh setting is set to. The default is True. """ if self._figure._auto_refresh or force: self._figure.canvas.draw() def save(self, filename, dpi=None, transparent=False, adjust_bbox=True, max_dpi=300, format=None): """ Save the current figure to a file. Parameters ---------- filename : str or fileobj The name of the file to save the plot to. This can be for example a PS, EPS, PDF, PNG, JPEG, or SVG file. Note that it is also possible to pass file-like object. dpi : float, optional The output resolution, in dots per inch. If the output file is a vector graphics format (such as PS, EPS, PDF or SVG) only the image itself will be rasterized. If the output is a PS or EPS file and no dpi is specified, the dpi is automatically calculated to match the resolution of the image. If this value is larger than max_dpi, then dpi is set to max_dpi. transparent : str, optional Whether to preserve transparency adjust_bbox : str, optional Auto-adjust the bounding box for the output max_dpi : float, optional The maximum resolution to output images at. If no maximum is wanted, enter None or 0. format : str, optional By default, APLpy tries to guess the file format based on the file extension, but the format can also be specified explicitly. Should be one of 'eps', 'ps', 'pdf', 'svg', 'png'. """ if isinstance(filename, str) and format is None: format = os.path.splitext(filename)[1].lower()[1:] if dpi is None and format in ['eps', 'ps', 'pdf']: width = self.ax.get_position().width * self._figure.get_figwidth() interval = self.ax.get_xlim() nx = interval[1] - interval[0] if max_dpi: dpi = np.minimum(nx / width, max_dpi) else: dpi = nx / width log.info("Auto-setting resolution to %g dpi" % dpi) if adjust_bbox: self._figure.savefig(filename, dpi=dpi, transparent=transparent, bbox_inches='tight', format=format) else: self._figure.savefig(filename, dpi=dpi, transparent=transparent, format=format) def _initialize_view(self): self.ax.set_xlim(-0.5, self._wcs.nx - 0.5) self.ax.set_ylim(-0.5, self._wcs.ny - 0.5) def _get_invert_default(self): return self._figure.apl_grayscale_invert_default def _get_colormap_default(self): return self._figure.apl_colorscale_cmap_default @auto_refresh def set_theme(self, theme): """ Set the axes, ticks, grid, and image colors to a certain style (experimental). Parameters ---------- theme : str The theme to use. At the moment, this can be 'pretty' (for viewing on-screen) and 'publication' (which makes the ticks and grid black, and displays the image in inverted grayscale) """ if theme == 'pretty': self.frame.set_color('black') self.frame.set_linewidth(1.0) if self.ax.coords[self.x].ticks.get_tick_out(): self.ticks.set_color('black') else: self.ticks.set_color('white') self._figure.apl_grayscale_invert_default = False self._figure.apl_colorscale_cmap_default = 'viridis' if self.image: self.image.set_cmap(cmap=plt.cm.get_cmap('viridis')) elif theme == 'publication': self.frame.set_color('black') self.frame.set_linewidth(1.0) self.ticks.set_color('black') self.ticks.set_length(7) self._figure.apl_grayscale_invert_default = True self._figure.apl_colorscale_cmap_default = 'gist_heat' if self.image: self.image.set_cmap(cmap=plt.cm.get_cmap('gist_yarg')) def world2pixel(self, xw, yw, wcs=None): """ Convert world to pixel coordinates. Parameters ---------- xw : float or iterable x world coordinate yw : float or iterable y world coordinate Returns ------- xp : float or iterable x pixel coordinate yp : float or iterable y pixel coordinate """ if wcs is None: wcs = self._wcs return wcs.wcs_world2pix(xw, yw, 0) def pixel2world(self, xp, yp, wcs=None): """ Convert pixel to world coordinates. Parameters ---------- xp : float or iterable x pixel coordinate yp : float or iterable y pixel coordinate Returns ------- xw : float or iterable x world coordinate yw : float or iterable y world coordinate """ if wcs is None: wcs = self._wcs return wcs.wcs_pix2world(xp, yp, 0) @auto_refresh def add_grid(self): """ Add a coordinate to the current figure. Once this method has been run, a grid attribute becomes available, and can be used to control the aspect of the grid:: >>> f = aplpy.FITSFigure(...) >>> ... >>> f.add_grid() >>> f.grid.set_color('white') >>> f.grid.set_alpha(0.5) >>> ... """ if hasattr(self, 'grid'): raise Exception("Grid already exists") try: self.grid = Grid(self) self.grid.show() except Exception: del self.grid raise @auto_refresh def remove_grid(self): """ Removes the grid from the current figure. """ self.grid.hide() del self.grid @auto_refresh def add_beam(self, *args, **kwargs): """ Add a beam to the current figure. Once this method has been run, a beam attribute becomes available, and can be used to control the aspect of the beam:: >>> f = aplpy.FITSFigure(...) >>> ... >>> f.add_beam() >>> f.beam.set_color('white') >>> f.beam.set_hatch('+') >>> ... If more than one beam is added, the beam object becomes a list. In this case, to control the aspect of one of the beams, you will need to specify the beam index:: >>> ... >>> f.beam[2].set_hatch('/') >>> ... """ # Initalize the beam and set parameters b = Beam(self) b.show(*args, **kwargs) if hasattr(self, 'beam'): if type(self.beam) is list: self.beam.append(b) else: self.beam = [self.beam, b] else: self.beam = b @auto_refresh def remove_beam(self, beam_index=None): """ Removes the beam from the current figure. If more than one beam is present, the index of the beam should be specified using beam_index= """ if type(self.beam) is list: if beam_index is None: raise Exception("More than one beam present - use beam_index= to specify which one to remove") else: b = self.beam.pop(beam_index) b._remove() del b # If only one beam is present, remove containing list if len(self.beam) == 1: self.beam = self.beam[0] else: self.beam._remove() del self.beam @auto_refresh def add_scalebar(self, length, *args, **kwargs): """ Add a scalebar to the current figure. Once this method has been run, a scalebar attribute becomes available, and can be used to control the aspect of the scalebar:: >>> f = aplpy.FITSFigure(...) >>> ... >>> f.add_scalebar(0.01) # length has to be specified >>> f.scalebar.set_label('100 AU') >>> ... Parameters ---------- length : float, or quantity The length of the scalebar in degrees, an angular quantity, or angular unit label : str, optional Label to place below the scalebar corner : int, optional Where to place the scalebar. Acceptable values are:, 'left', 'right', 'top', 'bottom', 'top left', 'top right', 'bottom left' (default), 'bottom right' frame : str, optional Whether to display a frame behind the scalebar (default is False) kwargs Additional arguments are passed to the matplotlib Rectangle and Text classes. See the matplotlib documentation for more details. In cases where the same argument exists for the two objects, the argument is passed to both the Text and Rectangle instance. """ if hasattr(self, 'scalebar'): raise Exception("Scalebar already exists") try: self.scalebar = Scalebar(self) self.scalebar.show(length, *args, **kwargs) except Exception: del self.scalebar raise @auto_refresh def remove_scalebar(self): """ Removes the scalebar from the current figure. """ self.scalebar._remove() del self.scalebar @auto_refresh def add_colorbar(self, *args, **kwargs): """ Add a colorbar to the current figure. Once this method has been run, a colorbar attribute becomes available, and can be used to control the aspect of the colorbar:: >>> f = aplpy.FITSFigure(...) >>> ... >>> f.add_colorbar() >>> f.colorbar.set_width(0.3) >>> f.colorbar.set_location('top') >>> ... """ if hasattr(self, 'colorbar'): raise Exception("Colorbar already exists") if self.image is None: raise Exception("No image is shown, so a colorbar cannot be displayed") try: self.colorbar = Colorbar(self) self.colorbar.show(*args, **kwargs) except Exception: del self.colorbar raise @auto_refresh def remove_colorbar(self): """ Removes the colorbar from the current figure. """ self.colorbar._remove() del self.colorbar def close(self): """ Close the figure and free up the memory. """ plt.close(self._figure) savefig = save ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/decorators.py0000664000175000017500000000543100000000000014671 0ustar00tomtomimport threading from functools import wraps mydata = threading.local() __all__ = ['auto_refresh', 'fixdocstring'] def auto_refresh(f): @wraps(f) def wrapper(*args, **kwargs): if 'refresh' in kwargs: refresh = kwargs.pop('refresh') else: refresh = True # The following is necessary rather than using mydata.nesting = 0 at the # start of the file, because doing the latter caused issues with the Django # development server. mydata.nesting = getattr(mydata, 'nesting', 0) + 1 try: return f(*args, **kwargs) finally: mydata.nesting -= 1 if hasattr(args[0], '_figure'): if refresh and mydata.nesting == 0 and args[0]._figure._auto_refresh: args[0]._figure.canvas.draw() return wrapper doc = {} doc['size'] = """size : str or int or float, optional The size of the font. This can either be a numeric value (e.g. 12), giving the size in points, or one of 'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', or 'xx-large'. """ doc['weight'] = """weight : str or int or float, optional The weight (or boldness) of the font. This can either be a numeric value in the range 0-1000 or one of 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'. """ doc['stretch'] = """stretch : str or int or float, optional The stretching (spacing between letters) for the font. This can either be a numeric value in the range 0-1000 or one of 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded' or 'ultra-expanded'. """ doc['family'] = """family : str, optional The family of the font to use. This can either be a generic font family name, either 'serif', 'sans-serif', 'cursive', 'fantasy', or 'monospace', or a list of font names in decreasing order of priority. """ doc['style'] = """style : str, optional The font style. This can be 'normal', 'italic' or 'oblique'. """ doc['variant'] = """variant : str, optional The font variant. This can be 'normal' or 'small-caps' """ def fixdocstring(func): lines = func.__doc__.split('\n') for i, line in enumerate(lines): if 'common:' in line: break header = lines[:i] footer = lines[i + 1:] indent = lines[i].index('common:') common = [] for item in lines[i].split(':')[1].split(','): if item.strip() in doc: common.append(" " * indent + doc[item.strip()].replace('\n', '\n' + " " * indent)) docstring = "\n".join(header + common + footer) func.__doc__ = docstring return func ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/frame.py0000664000175000017500000000123600000000000013615 0ustar00tomtomfrom .decorators import auto_refresh class Frame(object): @auto_refresh def __init__(self, parent): self.ax = parent.ax @auto_refresh def set_linewidth(self, linewidth): """ Set line width of the frame. Parameters ---------- linewidth: The linewidth to use for the frame. """ self.ax.coords.frame.set_linewidth(linewidth) @auto_refresh def set_color(self, color): """ Set color of the frame. Parameters ---------- color: The color to use for the frame. """ self.ax.coords.frame.set_color(color) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/grid.py0000664000175000017500000000734500000000000013457 0ustar00tomtomimport numpy as np from .decorators import auto_refresh class Grid(object): @auto_refresh def __init__(self, parent): # Save axes and wcs information self.ax = parent.ax self._wcs = parent._wcs self._figure = parent._figure self.x = parent.x self.y = parent.y self.x_unit = self._wcs.wcs.cunit[self.x] self.y_unit = self._wcs.wcs.cunit[self.y] self.grid_type = parent.grid_type # Set defaults self.default_color = 'white' self.default_alpha = 0.5 @auto_refresh def set_xspacing(self, xspacing): """ Set the grid line spacing in the longitudinal direction Parameters ---------- xspacing : float or str The spacing in the longitudinal direction. To set the spacing to be the same as the ticks, set this to 'tick' """ if xspacing == 'tick': self.ax.coords[self.x].grid(grid_type=self.grid_type) elif np.isreal(xspacing): self.ax.coords[self.x].set_ticks(spacing=xspacing * self.x_unit) else: raise ValueError("Grid spacing should be a scalar or 'tick'") @auto_refresh def set_yspacing(self, yspacing): """ Set the grid line spacing in the latitudinal direction Parameters ---------- yspacing : { float, str } The spacing in the latitudinal direction. To set the spacing to be the same as the ticks, set this to 'tick' """ if yspacing == 'tick': self.ax.coords[self.y].grid(grid_type=self.grid_type) elif np.isreal(yspacing): self.ax.coords[self.y].set_ticks(spacing=yspacing * self.y_unit) else: raise ValueError("Grid spacing should be a scalar or 'tick'") @auto_refresh def set_color(self, color): """ Set the color of the grid lines Parameters ---------- color : str The color of the grid lines """ self.default_color = color self.ax.coords[self.x].grid(color=color, grid_type=self.grid_type) self.ax.coords[self.y].grid(color=color, grid_type=self.grid_type) @auto_refresh def set_alpha(self, alpha): """ Set the alpha (transparency) of the grid lines Parameters ---------- alpha : float The alpha value of the grid. This should be a floating point value between 0 and 1, where 0 is completely transparent, and 1 is completely opaque. """ self.default_alpha = alpha self.ax.coords[self.x].grid(alpha=alpha, grid_type=self.grid_type) self.ax.coords[self.y].grid(alpha=alpha, grid_type=self.grid_type) @auto_refresh def set_linewidth(self, linewidth): self.ax.coords[self.x].grid(linewidth=linewidth, grid_type=self.grid_type) self.ax.coords[self.y].grid(linewidth=linewidth, grid_type=self.grid_type) @auto_refresh def set_linestyle(self, linestyle): self.ax.coords[self.x].grid(linestyle=linestyle, grid_type=self.grid_type) self.ax.coords[self.y].grid(linestyle=linestyle, grid_type=self.grid_type) @auto_refresh def show(self): self.ax.coords[self.x].grid(grid_type=self.grid_type, color=self.default_color, alpha=self.default_alpha) self.ax.coords[self.y].grid(grid_type=self.grid_type, color=self.default_color, alpha=self.default_alpha) @auto_refresh def hide(self): self.ax.coords[self.x].grid(draw_grid=False) self.ax.coords[self.y].grid(draw_grid=False) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/header.py0000664000175000017500000000437500000000000013762 0ustar00tomtomfrom astropy import log PLATE_CAREE_ERROR = """ Projection is Plate Caree (-CAR) and CRVALy is not zero. This can be interpreted either according to Wells (1981) or Calabretta (2002). The former defines the projection as rectilinear regardless of the value of CRVALy, whereas the latter defines the projection as rectilinear only when CRVALy is zero. You will need to specify the convention to assume by setting either convention='wells' or convention='calabretta' when initializing the FITSFigure instance. """.strip() def check(header, convention=None, dimensions=[0, 1]): ix = dimensions[0] + 1 iy = dimensions[1] + 1 # If header does not contain CTYPE keywords, assume that the WCS is # missing or incomplete, and replace it with a 1-to-1 pixel mapping if 'CTYPE%i' % ix not in header or 'CTYPE%i' % iy not in header: log.warning("No WCS information found in header - using pixel coordinates") header['CTYPE%i' % ix] = 'PIXEL' header['CTYPE%i' % iy] = 'PIXEL' header['CRVAL%i' % ix] = 0. header['CRVAL%i' % iy] = 0. header['CRPIX%i' % ix] = 0. header['CRPIX%i' % iy] = 0. header['CDELT%i' % ix] = 1. header['CDELT%i' % iy] = 1. if header['CTYPE%i' % ix][4:] == '-CAR' and header['CTYPE%i' % iy][4:] == '-CAR': if header['CTYPE%i' % ix][:4] == 'DEC-' or header['CTYPE%i' % ix][1:4] == 'LAT': ilat = ix elif header['CTYPE%i' % iy][:4] == 'DEC-' or header['CTYPE%i' % iy][1:4] == 'LAT': ilat = iy else: ilat = None if ilat is not None and header['CRVAL%i' % ilat] != 0: if convention == 'calabretta': pass # we don't need to do anything elif convention == 'wells': if 'CDELT%i' % ilat not in header: raise Exception("Need CDELT%i to be present for wells convention" % ilat) crpix = header['CRPIX%i' % ilat] crval = header['CRVAL%i' % ilat] cdelt = header['CDELT%i' % ilat] crpix = crpix - crval / cdelt header['CRPIX%i' % ilat] = crpix header['CRVAL%i' % ilat] = 0. else: raise Exception(PLATE_CAREE_ERROR) return header ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/layers.py0000664000175000017500000001267200000000000014030 0ustar00tomtomfrom collections import OrderedDict from matplotlib.contour import ContourSet from matplotlib.collections import RegularPolyCollection, \ PatchCollection, CircleCollection, LineCollection from .regions import ArtistCollection from .decorators import auto_refresh class Layers(object): def __init__(self): pass def _layer_type(self, layer): if isinstance(self._layers[layer], ContourSet): return 'contour' elif isinstance(self._layers[layer], RegularPolyCollection): return 'collection' elif isinstance(self._layers[layer], PatchCollection): return 'collection' elif isinstance(self._layers[layer], CircleCollection): return 'collection' elif isinstance(self._layers[layer], LineCollection): return 'collection' elif isinstance(self._layers[layer], ArtistCollection): return 'collection' elif hasattr(self._layers[layer], 'remove') and hasattr(self._layers[layer], 'get_visible') and hasattr(self._layers[layer], 'set_visible'): return 'collection' else: raise Exception("Unknown layer type: " + str(type(self._layers[layer]))) def _initialize_layers(self): self._layers = OrderedDict() self._contour_counter = 0 self._vector_counter = 0 self._scatter_counter = 0 self._circle_counter = 0 self._ellipse_counter = 0 self._rectangle_counter = 0 self._linelist_counter = 0 self._region_counter = 0 self._label_counter = 0 self._poly_counter = 0 def list_layers(self): """ Print a list of layers to standard output. """ layers_list = [] for layer in self._layers: layer_type = self._layer_type(layer) if layer_type == 'contour': visible = self._layers[layer].collections[0].get_visible() elif layer_type == 'collection': visible = self._layers[layer].get_visible() layers_list.append({'name': layer, 'visible': visible}) n_layers = len(layers_list) if n_layers == 0: print("\n There are no layers in this figure") else: if n_layers == 1: print("\n There is one layer in this figure:\n") else: print("\n There are " + str(n_layers) + " layers in this figure:\n") for layer in layers_list: if layer['visible']: print(" -> " + layer['name']) else: print(" -> " + layer['name'] + " (hidden)") @auto_refresh def remove_layer(self, layer, raise_exception=True): """ Remove a layer. Parameters ---------- layer : str The name of the layer to remove """ if layer in self._layers: layer_type = self._layer_type(layer) if layer_type == 'contour': for contour in self._layers[layer].collections: contour.remove() self._layers.pop(layer) elif layer_type == 'collection': self._layers[layer].remove() self._layers.pop(layer) if (layer + '_txt') in self._layers: self._layers[layer + '_txt'].remove() self._layers.pop(layer + '_txt') else: if raise_exception: raise Exception("Layer " + layer + " does not exist") @auto_refresh def hide_layer(self, layer, raise_exception=True): """ Hide a layer. This differs from remove_layer in that if a layer is hidden it can be shown again using show_layer. Parameters ---------- layer : str The name of the layer to hide """ if layer in self._layers: layer_type = self._layer_type(layer) if layer_type == 'contour': for contour in self._layers[layer].collections: contour.set_visible(False) elif layer_type == 'collection': self._layers[layer].set_visible(False) else: if raise_exception: raise Exception("Layer " + layer + " does not exist") @auto_refresh def show_layer(self, layer, raise_exception=True): """ Show a layer. This shows a layer previously hidden with hide_layer Parameters ---------- layer : str The name of the layer to show """ if layer in self._layers: layer_type = self._layer_type(layer) if layer_type == 'contour': for contour in self._layers[layer].collections: contour.set_visible(True) elif layer_type == 'collection': self._layers[layer].set_visible(True) else: if raise_exception: raise Exception("Layer " + layer + " does not exist") def get_layer(self, layer, raise_exception=True): """ Return a layer object. Parameters ---------- layer : str The name of the layer to return """ if layer in self._layers: return self._layers[layer] else: if raise_exception: raise Exception("Layer " + layer + " does not exist") ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/overlays.py0000664000175000017500000004301400000000000014367 0ustar00tomtomimport warnings from mpl_toolkits.axes_grid1.anchored_artists import (AnchoredEllipse, AnchoredSizeBar) import numpy as np from matplotlib.font_manager import FontProperties from astropy import units as u from astropy.wcs.utils import proj_plane_pixel_scales from .decorators import auto_refresh corners = {} corners['top right'] = 1 corners['top left'] = 2 corners['bottom left'] = 3 corners['bottom right'] = 4 corners['right'] = 5 corners['left'] = 6 corners['bottom'] = 8 corners['top'] = 9 class Scalebar(object): def __init__(self, parent): # Retrieve info from parent figure self._ax = parent.ax self._wcs = parent._wcs self._figure = parent._figure self._dimensions = [parent.x, parent.y] # Initialize settings self._base_settings = {} self._scalebar_settings = {} self._label_settings = {} self._label_settings['fontproperties'] = FontProperties() # LAYOUT @auto_refresh def show(self, length, label=None, corner='bottom right', frame=False, borderpad=0.4, pad=0.5, **kwargs): """ Overlay a scale bar on the image. Parameters ---------- length : float, or quantity The length of the scalebar in degrees, an angular quantity, or angular unit label : str, optional Label to place below the scalebar corner : int, optional Where to place the scalebar. Acceptable values are:, 'left', 'right', 'top', 'bottom', 'top left', 'top right', 'bottom left' (default), 'bottom right' frame : str, optional Whether to display a frame behind the scalebar (default is False) kwargs Additional arguments are passed to the matplotlib Rectangle and Text classes. See the matplotlib documentation for more details. In cases where the same argument exists for the two objects, the argument is passed to both the Text and Rectangle instance. """ self._length = length self._base_settings['corner'] = corner self._base_settings['frame'] = frame self._base_settings['borderpad'] = borderpad self._base_settings['pad'] = pad if isinstance(length, u.Quantity): length = length.to(u.degree).value elif isinstance(length, u.Unit): length = length.to(u.degree) if self._wcs.is_celestial: pix_scale = proj_plane_pixel_scales(self._wcs) sx = pix_scale[self._dimensions[0]] sy = pix_scale[self._dimensions[1]] degrees_per_pixel = np.sqrt(sx * sy) else: raise ValueError("Cannot show scalebar when WCS is not celestial") length = length / degrees_per_pixel try: self._scalebar.remove() except Exception: pass if isinstance(corner, str): corner = corners[corner] self._scalebar = AnchoredSizeBar(self._ax.transData, length, label, corner, pad=pad, borderpad=borderpad, sep=5, frameon=frame) self._ax.add_artist(self._scalebar) self.set(**kwargs) @auto_refresh def _remove(self): self._scalebar.remove() @auto_refresh def hide(self): """ Hide the scalebar. """ try: self._scalebar.remove() except Exception: pass @auto_refresh def set_length(self, length): """ Set the length of the scale bar. """ self.show(length, **self._base_settings) self._set_scalebar_properties(**self._scalebar_settings) self._set_label_properties(**self._scalebar_settings) @auto_refresh def set_label(self, label): """ Set the label of the scale bar. """ self._set_label_properties(text=label) @auto_refresh def set_corner(self, corner): """ Set where to place the scalebar. Acceptable values are 'left', 'right', 'top', 'bottom', 'top left', 'top right', 'bottom left' (default), and 'bottom right'. """ self._base_settings['corner'] = corner self.show(self._length, **self._base_settings) self._set_scalebar_properties(**self._scalebar_settings) self._set_label_properties(**self._scalebar_settings) @auto_refresh def set_frame(self, frame): """ Set whether to display a frame around the scalebar. """ self._base_settings['frame'] = frame self.show(self._length, **self._base_settings) self._set_scalebar_properties(**self._scalebar_settings) self._set_label_properties(**self._scalebar_settings) # APPEARANCE @auto_refresh def set_linewidth(self, linewidth): """ Set the linewidth of the scalebar, in points. """ self._set_scalebar_properties(linewidth=linewidth) @auto_refresh def set_linestyle(self, linestyle): """ Set the linestyle of the scalebar. Should be one of 'solid', 'dashed', 'dashdot', or 'dotted'. """ self._set_scalebar_properties(linestyle=linestyle) @auto_refresh def set_alpha(self, alpha): """ Set the alpha value (transparency). This should be a floating point value between 0 and 1. """ self._set_scalebar_properties(alpha=alpha) self._set_label_properties(alpha=alpha) @auto_refresh def set_color(self, color): """ Set the label and scalebar color. """ self._set_scalebar_properties(color=color) self._set_label_properties(color=color) @auto_refresh def set_font(self, family=None, style=None, variant=None, stretch=None, weight=None, size=None, fontproperties=None): """ Set the font of the tick labels Parameters ---------- common: family, style, variant, stretch, weight, size, fontproperties Notes ----- Default values are set by matplotlib or previously set values if set_font has already been called. Global default values can be set by editing the matplotlibrc file. """ if family: self._label_settings['fontproperties'].set_family(family) if style: self._label_settings['fontproperties'].set_style(style) if variant: self._label_settings['fontproperties'].set_variant(variant) if stretch: self._label_settings['fontproperties'].set_stretch(stretch) if weight: self._label_settings['fontproperties'].set_weight(weight) if size: self._label_settings['fontproperties'].set_size(size) if fontproperties: self._label_settings['fontproperties'] = fontproperties self._set_label_properties(fontproperties=self._label_settings['fontproperties']) @auto_refresh def _set_label_properties(self, **kwargs): """ Modify the scalebar label properties. All arguments are passed to the matplotlib Text class. See the matplotlib documentation for more details. """ for kwarg, val in kwargs.items(): try: # Only set attributes that exist kvpair = {kwarg: val} self._scalebar.txt_label.get_children()[0].set(**kvpair) self._label_settings[kwarg] = val except AttributeError: warnings.warn("Text labels do not have attribute {0}. Skipping.".format(kwarg)) @auto_refresh def _set_scalebar_properties(self, **kwargs): """ Modify the scalebar properties. All arguments are passed to the matplotlib Rectangle class. See the matplotlib documentation for more details. """ for kwarg, val in kwargs.items(): try: kvpair = {kwarg: val} self._scalebar.size_bar.get_children()[0].set(**kvpair) self._scalebar_settings[kwarg] = val except AttributeError: warnings.warn("Scalebar does not have attribute {0}. Skipping.".format(kwarg)) @auto_refresh def set(self, **kwargs): """ Modify the scalebar and scalebar properties. All arguments are passed to the matplotlib Rectangle and Text classes. See the matplotlib documentation for more details. In cases where the same argument exists for the two objects, the argument is passed to both the Text and Rectangle instance. """ for kwarg in kwargs: kwargs_single = {kwarg: kwargs[kwarg]} try: self._set_label_properties(**kwargs_single) except (AttributeError, TypeError): pass try: self._set_scalebar_properties(**kwargs_single) except (AttributeError, TypeError): pass # DEPRECATED @auto_refresh def set_font_family(self, family): warnings.warn("scalebar.set_font_family is deprecated - use scalebar.set_font instead", DeprecationWarning) self.set_font(family=family) @auto_refresh def set_font_weight(self, weight): warnings.warn("scalebar.set_font_weight is deprecated - use scalebar.set_font instead", DeprecationWarning) self.set_font(weight=weight) @auto_refresh def set_font_size(self, size): warnings.warn("scalebar.set_font_size is deprecated - use scalebar.set_font instead", DeprecationWarning) self.set_font(size=size) @auto_refresh def set_font_style(self, style): warnings.warn("scalebar.set_font_style is deprecated - use scalebar.set_font instead", DeprecationWarning) self.set_font(style=style) # For backward-compatibility ScaleBar = Scalebar class Beam(object): def __init__(self, parent): # Retrieve info from parent figure self._figure = parent._figure self._header = parent._header self._ax = parent.ax self._wcs = parent._wcs self._dimensions = [parent.x, parent.y] # Initialize settings self._base_settings = {} self._beam_settings = {} # LAYOUT @auto_refresh def show(self, major='BMAJ', minor='BMIN', angle='BPA', corner='bottom left', frame=False, borderpad=0.4, pad=0.5, **kwargs): """ Display the beam shape and size for the primary image. By default, this method will search for the BMAJ, BMIN, and BPA keywords in the FITS header to set the major and minor axes and the position angle on the sky. Parameters ---------- major : float, quantity or unit, optional Major axis of the beam in degrees or an angular quantity (overrides BMAJ if present) minor : float, quantity or unit, optional Minor axis of the beam in degrees or an angular quantity (overrides BMIN if present) angle : float, quantity or unit, optional Position angle of the beam on the sky in degrees or an angular quantity (overrides BPA if present) in the anticlockwise direction. corner : int, optional The beam location. Acceptable values are 'left', 'right', 'top', 'bottom', 'top left', 'top right', 'bottom left' (default), and 'bottom right'. frame : str, optional Whether to display a frame behind the beam (default is False) kwargs Additional arguments are passed to the matplotlib Ellipse class. See the matplotlib documentation for more details. """ if isinstance(major, str): major = self._header[major] if isinstance(minor, str): minor = self._header[minor] if isinstance(angle, str): angle = self._header[angle] if isinstance(major, u.Quantity): major = major.to(u.degree).value elif isinstance(major, u.Unit): major = major.to(u.degree) if isinstance(minor, u.Quantity): minor = minor.to(u.degree).value elif isinstance(minor, u.Unit): minor = minor.to(u.degree) if isinstance(angle, u.Quantity): angle = angle.to(u.degree).value elif isinstance(angle, u.Unit): angle = angle.to(u.degree) if self._wcs.is_celestial: pix_scale = proj_plane_pixel_scales(self._wcs) sx = pix_scale[self._dimensions[0]] sy = pix_scale[self._dimensions[1]] degrees_per_pixel = np.sqrt(sx * sy) else: raise ValueError("Cannot show beam when WCS is not celestial") self._base_settings['minor'] = minor self._base_settings['major'] = major self._base_settings['angle'] = angle self._base_settings['corner'] = corner self._base_settings['frame'] = frame self._base_settings['borderpad'] = borderpad self._base_settings['pad'] = pad minor /= degrees_per_pixel major /= degrees_per_pixel try: self._beam.remove() except Exception: pass if isinstance(corner, str): corner = corners[corner] self._beam = AnchoredEllipse(self._ax.transData, width=minor, height=major, angle=angle, loc=corner, pad=pad, borderpad=borderpad, frameon=frame) self._ax.add_artist(self._beam) self.set(**kwargs) @auto_refresh def _remove(self): self._beam.remove() @auto_refresh def hide(self): """ Hide the beam """ try: self._beam.remove() except Exception: pass @auto_refresh def set_major(self, major): """ Set the major axis of the beam, in degrees. """ self._base_settings['major'] = major self.show(**self._base_settings) self.set(**self._beam_settings) @auto_refresh def set_minor(self, minor): """ Set the minor axis of the beam, in degrees. """ self._base_settings['minor'] = minor self.show(**self._base_settings) self.set(**self._beam_settings) @auto_refresh def set_angle(self, angle): """ Set the position angle of the beam on the sky, in degrees. """ self._base_settings['angle'] = angle self.show(**self._base_settings) self.set(**self._beam_settings) @auto_refresh def set_corner(self, corner): """ Set the beam location. Acceptable values are 'left', 'right', 'top', 'bottom', 'top left', 'top right', 'bottom left' (default), and 'bottom right'. """ self._base_settings['corner'] = corner self.show(**self._base_settings) self.set(**self._beam_settings) @auto_refresh def set_frame(self, frame): """ Set whether to display a frame around the beam. """ self._base_settings['frame'] = frame self.show(**self._base_settings) self.set(**self._beam_settings) @auto_refresh def set_borderpad(self, borderpad): """ Set the amount of padding within the beam object, relative to the canvas size. """ self._base_settings['borderpad'] = borderpad self.show(**self._base_settings) self.set(**self._beam_settings) @auto_refresh def set_pad(self, pad): """ Set the amount of padding between the beam object and the image corner/edge, relative to the canvas size. """ self._base_settings['pad'] = pad self.show(**self._base_settings) self.set(**self._beam_settings) # APPEARANCE @auto_refresh def set_alpha(self, alpha): """ Set the alpha value (transparency). This should be a floating point value between 0 and 1. """ self.set(alpha=alpha) @auto_refresh def set_color(self, color): """ Set the beam color. """ self.set(color=color) @auto_refresh def set_edgecolor(self, edgecolor): """ Set the color for the edge of the beam. """ self.set(edgecolor=edgecolor) @auto_refresh def set_facecolor(self, facecolor): """ Set the color for the interior of the beam. """ self.set(facecolor=facecolor) @auto_refresh def set_linestyle(self, linestyle): """ Set the line style for the edge of the beam. This should be one of 'solid', 'dashed', 'dashdot', or 'dotted'. """ self.set(linestyle=linestyle) @auto_refresh def set_linewidth(self, linewidth): """ Set the line width for the edge of the beam, in points. """ self.set(linewidth=linewidth) @auto_refresh def set_hatch(self, hatch): """ Set the hatch pattern. This should be one of '/', '\', '|', '-', '+', 'x', 'o', 'O', '.', or '*'. """ self.set(hatch=hatch) @auto_refresh def set(self, **kwargs): """ Modify the beam properties. All arguments are passed to the matplotlib Ellipse class. See the matplotlib documentation for more details. """ for kwarg in kwargs: self._beam_settings[kwarg] = kwargs[kwarg] self._beam.ellipse.set(**kwargs) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/regions.py0000664000175000017500000001343100000000000014171 0ustar00tomtomfrom astropy import log from astropy import wcs from .decorators import auto_refresh class Regions(object): """ Regions sub-class of APLpy. Used for overplotting various shapes and annotations on APLpy fitsfigures. Example: # DS9 region file called "test.reg" # (the coordinates are around l=28 in the Galactic Plane) # Filename: test.fits fk5 box(18:42:48.262,-04:01:17.91,505.668",459.714",0) # color=red dash=1 point(18:42:51.797,-03:59:44.82) # point=x color=red dash=1 point(18:42:50.491,-04:03:09.39) # point=box color=red dash=1 # vector(18:42:37.433,-04:02:10.77,107.966",115.201) vector=1 color=red dash=1 ellipse(18:42:37.279,-04:02:11.92,26.4336",40.225",0) # color=red dash=1 polygon(18:42:59.016,-03:58:22.06,18:42:58.219,-03:58:11.30,18:42:57.403,-03:58:35.86,18:42:58.094,-03:58:57.69,18:42:59.861,-03:58:41.60,18:42:59.707,-03:58:23.21) # color=red dash=1 point(18:42:52.284,-04:00:02.80) # point=diamond color=red dash=1 point(18:42:46.561,-03:58:01.57) # point=circle color=red dash=1 point(18:42:42.615,-03:58:25.84) # point=cross color=red dash=1 point(18:42:42.946,-04:01:44.74) # point=arrow color=red dash=1 point(18:42:41.961,-03:57:26.16) # point=boxcircle color=red dash=1 # text(18:42:41.961,-03:57:26.16) text={This is text} color=red Code: import aplpy import regions ff = aplpy.FITSFigure("test.fits") ff.show_grayscale() ff.show_regions('test.reg') """ @auto_refresh def show_regions(self, region_file, layer=False, **kwargs): """ Overplot regions as specified in the region file. Parameters ---------- region_file: string or pyregion.ShapeList Path to a ds9 regions file or a ShapeList already read in by pyregion. layer: str, optional The name of the layer kwargs Additional keyword arguments, e.g. zorder, will be passed to the ds9 call and onto the patchcollections. """ PC, TC = ds9(region_file, flatten_header(self._header), **kwargs) PC.add_to_axes(self.ax) TC.add_to_axes(self.ax) if layer: region_set_name = layer else: self._region_counter += 1 region_set_name = 'region_set_' + str(self._region_counter) self._layers[region_set_name] = PC self._layers[region_set_name + "_txt"] = TC def ds9(region_file, header, zorder=3, **kwargs): """ Wrapper to return a PatchCollection given a ds9 region file and a fits header. zorder - defaults to 3 so that regions are on top of contours """ try: import pyregion except Exception: raise ImportError("The pyregion package is required to load region files") # read region file if isinstance(region_file, str): rr = pyregion.open(region_file) elif isinstance(region_file, pyregion.ShapeList): rr = region_file else: raise Exception("Invalid type for region_file: %s - should be string or pyregion.ShapeList" % type(region_file)) if isinstance(header, wcs.WCS): header = header.to_header() # convert coordinates to image coordinates rrim = rr.as_imagecoord(header) # pyregion and aplpy both correct for the FITS standard origin=1,1 # need to avoid double-correcting. Also, only some items in `coord_list` # are pixel coordinates, so which ones should be corrected depends on the # shape. for r in rrim: if r.name == 'polygon': correct = range(len(r.coord_list)) elif r.name == 'line': correct = range(4) elif r.name in ['rotbox', 'box', 'ellipse', 'annulus', 'circle', 'panda', 'pie', 'epanda', 'text', 'point', 'vector']: correct = range(2) else: log.warning("Unknown region type '{0}' - please report to the developers") correct = range(2) for i in correct: r.coord_list[i] += 1 if 'text_offset' in kwargs: text_offset = kwargs['text_offset'] del kwargs['text_offset'] else: text_offset = 5.0 # grab the shapes to overplot pp, aa = rrim.get_mpl_patches_texts(text_offset=text_offset) PC = ArtistCollection(pp, **kwargs) # preserves line style (dashed) TC = ArtistCollection(aa, **kwargs) PC.set_zorder(zorder) TC.set_zorder(zorder) return PC, TC class ArtistCollection(): """ Matplotlib collections can't handle Text. This is a barebones collection for text objects that supports removing and making (in)visible """ def __init__(self, artistlist): """ Pass in a list of matplotlib.text.Text objects (or possibly any matplotlib Artist will work) """ self.artistlist = artistlist def remove(self): for T in self.artistlist: T.remove() def add_to_axes(self, ax): for T in self.artistlist: ax.add_artist(T) def get_visible(self): visible = True for T in self.artistlist: if not T.get_visible(): visible = False return visible def set_visible(self, visible=True): for T in self.artistlist: T.set_visible(visible) def set_zorder(self, zorder): for T in self.artistlist: T.set_zorder(zorder) def flatten_header(header): """ Attempt to turn an N-dimensional fits header into a 2-dimensional header Turns all CRPIX[>2] etc. into new keywords with suffix 'A' """ orig_wcs = wcs.WCS(header) newheader = orig_wcs.celestial.to_header() newheader['NAXIS'] = 2 newheader['NAXIS1'] = header['NAXIS{0}'.format(orig_wcs.wcs.lng + 1)] newheader['NAXIS2'] = header['NAXIS{0}'.format(orig_wcs.wcs.lat + 1)] return newheader ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/rgb.py0000664000175000017500000002522700000000000013303 0ustar00tomtomfrom distutils import version import os import warnings import numpy as np from astropy import log from astropy.io import fits from astropy.coordinates import ICRS from astropy.visualization import AsymmetricPercentileInterval, simple_norm from reproject import reproject_interp from reproject.mosaicking import find_optimal_celestial_wcs def _data_stretch(image, vmin=None, vmax=None, pmin=0.25, pmax=99.75, stretch='linear', vmid=None, exponent=2): if vmin is None or vmax is None: interval = AsymmetricPercentileInterval(pmin, pmax, n_samples=10000) try: vmin_auto, vmax_auto = interval.get_limits(image) except IndexError: # no valid values vmin_auto = vmax_auto = 0 if vmin is None: log.info("vmin = %10.3e (auto)" % vmin_auto) vmin = vmin_auto else: log.info("vmin = %10.3e" % vmin) if vmax is None: log.info("vmax = %10.3e (auto)" % vmax_auto) vmax = vmax_auto else: log.info("vmax = %10.3e" % vmax) if stretch == 'arcsinh': stretch = 'asinh' normalizer = simple_norm(image, stretch=stretch, power=exponent, asinh_a=vmid, min_cut=vmin, max_cut=vmax, clip=False) data = normalizer(image, clip=True).filled(0) data = np.nan_to_num(data) data = np.clip(data * 255., 0., 255.) return data.astype(np.uint8) def make_rgb_image(data, output, indices=(0, 1, 2), vmin_r=None, vmax_r=None, pmin_r=0.25, pmax_r=99.75, stretch_r='linear', vmid_r=None, exponent_r=2, vmin_g=None, vmax_g=None, pmin_g=0.25, pmax_g=99.75, stretch_g='linear', vmid_g=None, exponent_g=2, vmin_b=None, vmax_b=None, pmin_b=0.25, pmax_b=99.75, stretch_b='linear', vmid_b=None, exponent_b=2, make_nans_transparent=False, embed_avm_tags=True): """ Make an RGB image from a FITS RGB cube or from three FITS files. Parameters ---------- data : str or tuple or list If a string, this is the filename of an RGB FITS cube. If a tuple or list, this should give the filename of three files to use for the red, green, and blue channel. output : str The output filename. The image type (e.g. PNG, JPEG, TIFF, ...) will be determined from the extension. Any image type supported by the Python Imaging Library can be used. indices : tuple, optional If data is the filename of a FITS cube, these indices are the positions in the third dimension to use for red, green, and blue respectively. The default is to use the first three indices. vmin_r, vmin_g, vmin_b : float, optional Minimum pixel value to use for the red, green, and blue channels. If set to None for a given channel, the minimum pixel value for that channel is determined using the corresponding pmin_x argument (default). vmax_r, vmax_g, vmax_b : float, optional Maximum pixel value to use for the red, green, and blue channels. If set to None for a given channel, the maximum pixel value for that channel is determined using the corresponding pmax_x argument (default). pmin_r, pmin_r, pmin_g : float, optional Percentile values used to determine for a given channel the minimum pixel value to use for that channel if the corresponding vmin_x is set to None. The default is 0.25% for all channels. pmax_r, pmax_g, pmax_b : float, optional Percentile values used to determine for a given channel the maximum pixel value to use for that channel if the corresponding vmax_x is set to None. The default is 99.75% for all channels. stretch_r, stretch_g, stretch_b : { 'linear', 'log', 'sqrt', 'arcsinh', 'power' } The stretch function to use for the different channels. vmid_r, vmid_g, vmid_b : float, optional Baseline values used for the log and arcsinh stretches. If set to None, this is set to zero for log stretches and to vmin - (vmax - vmin) / 30. for arcsinh stretches exponent_r, exponent_g, exponent_b : float, optional If stretch_x is set to 'power', this is the exponent to use. make_nans_transparent : bool, optional If set AND output is png, will add an alpha layer that sets pixels containing a NaN to transparent. embed_avm_tags : bool, optional Whether to embed AVM tags inside the image - this can only be done for JPEG and PNG files, and only if PyAVM is installed. """ try: from PIL import Image except ImportError: try: import Image except ImportError: raise ImportError("The Python Imaging Library (PIL) is required to make an RGB image") if isinstance(data, str): image = fits.getdata(data) image_r = image[indices[0], :, :] image_g = image[indices[1], :, :] image_b = image[indices[2], :, :] # Read in header header = fits.getheader(data) # Remove information about third dimension header['NAXIS'] = 2 for key in ['NAXIS', 'CTYPE', 'CRPIX', 'CRVAL', 'CUNIT', 'CDELT', 'CROTA']: for coord in range(3, 6): name = key + str(coord) if name in header: header.__delitem__(name) elif (type(data) == list or type(data) == tuple) and len(data) == 3: filename_r, filename_g, filename_b = data image_r = fits.getdata(filename_r) image_g = fits.getdata(filename_g) image_b = fits.getdata(filename_b) # Read in header header = fits.getheader(filename_r) else: raise Exception("data should either be the filename of a FITS cube or a list/tuple of three images") # are we making a transparent layer? do_alpha = make_nans_transparent and output.lower().endswith('.png') if do_alpha: log.info("Making alpha layer") # initialize alpha layer image_alpha = np.empty_like(image_r, dtype=np.uint8) image_alpha[:] = 255 # look for nans in images for im in [image_r, image_g, image_b]: image_alpha[np.isnan(im)] = 0 log.info("Red:") image_r = Image.fromarray(_data_stretch(image_r, vmin=vmin_r, vmax=vmax_r, pmin=pmin_r, pmax=pmax_r, stretch=stretch_r, vmid=vmid_r, exponent=exponent_r)) log.info("Green:") image_g = Image.fromarray(_data_stretch(image_g, vmin=vmin_g, vmax=vmax_g, pmin=pmin_g, pmax=pmax_g, stretch=stretch_g, vmid=vmid_g, exponent=exponent_g)) log.info("Blue:") image_b = Image.fromarray(_data_stretch(image_b, vmin=vmin_b, vmax=vmax_b, pmin=pmin_b, pmax=pmax_b, stretch=stretch_b, vmid=vmid_b, exponent=exponent_b)) img = Image.merge("RGB", (image_r, image_g, image_b)) if do_alpha: # convert to RGBA and add alpha layer image_alpha = Image.fromarray(image_alpha) img.convert("RGBA") img.putalpha(image_alpha) img = img.transpose(Image.FLIP_TOP_BOTTOM) img.save(output) if embed_avm_tags: try: import pyavm except ImportError: warnings.warn("PyAVM 0.9.1 or later is not installed, so AVM tags will not be embedded in RGB image") return if version.LooseVersion(pyavm.__version__) < version.LooseVersion('0.9.1'): warnings.warn("PyAVM 0.9.1 or later is not installed, so AVM tags will not be embedded in RGB image") return from pyavm import AVM if output.lower().endswith(('.jpg', '.jpeg', '.png')): avm = AVM.from_header(header) avm.embed(output, output) else: warnings.warn("AVM tags will not be embedded in RGB image, as only JPEG and PNG files are supported") def make_rgb_cube(files, output, north=True): """ Make an RGB data cube from a list of three FITS images. This method can read in three FITS files with different projections/sizes/resolutions and uses the `reproject `_ package to reproject them all to the same projection. Two files are produced by this function. The first is a three-dimensional FITS cube with a filename give by ``output``, where the third dimension contains the different channels. The second is a two-dimensional FITS image with a filename given by ``output`` with a `_2d` suffix. This file contains the mean of the different channels, and is required as input to FITSFigure if show_rgb is subsequently used to show a color image generated from the FITS cube (to provide the correct WCS information to FITSFigure). Parameters ---------- files : tuple or list A list of the filenames of three FITS filename to reproject. The order is red, green, blue. output : str The filename of the output RGB FITS cube. north : bool, optional Whether to rotate the image so that north is up. By default, this is assumed to be 'north' in the ICRS frame, but you can also pass any astropy :class:`~astropy.coordinates.BaseCoordinateFrame` to indicate to use the north of that frame. """ # Check that input files exist for f in files: if not os.path.exists(f): raise Exception("File does not exist : " + f) if north is not False: frame = ICRS() if north is True else north auto_rotate = False else: frame = None auto_rotate = True # Find optimal WCS and shape based on input images wcs, shape = find_optimal_celestial_wcs(files, frame=frame, auto_rotate=auto_rotate) header = wcs.to_header() # Generate empty datacube image_cube = np.zeros((len(files),) + shape, dtype=np.float32) # Loop through files and reproject for i, filename in enumerate(files): image_cube[i, :, :] = reproject_interp(filename, wcs, shape_out=shape)[0] # Write out final cube fits.writeto(output, image_cube, header, overwrite=True) # Write out collapsed version of cube fits.writeto(output.replace('.fits', '_2d.fits'), np.mean(image_cube, axis=0), header, overwrite=True) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/slicer.py0000664000175000017500000000414300000000000014004 0ustar00tomtom def slice_hypercube(data, header, dimensions=[0, 1], slices=[]): """ Extract a slice from an n-dimensional HDU data/header pair, and return the new data (without changing the header). """ if type(slices) == int: slices = (slices, ) else: slices = slices[:] shape = data.shape if len(shape) < 2: raise Exception("FITS file does not have enough dimensions") elif len(shape) == 2: wcsaxes_slices = ('x', 'y') if dimensions[1] < dimensions[0]: data = data.transpose() wcsaxes_slices = ('y', 'x') return data, wcsaxes_slices else: if slices: wcsaxes_slices = slices[:] if dimensions[0] < dimensions[1]: slices.insert(dimensions[0], slice(None, None, None)) slices.insert(dimensions[1], slice(None, None, None)) wcsaxes_slices.insert(dimensions[0], 'x') wcsaxes_slices.insert(dimensions[1], 'y') else: slices.insert(dimensions[1], slice(None, None, None)) slices.insert(dimensions[0], slice(None, None, None)) wcsaxes_slices.insert(dimensions[1], 'y') wcsaxes_slices.insert(dimensions[0], 'x') if type(slices) == list: slices = tuple(slices) wcsaxes_slices = tuple(wcsaxes_slices) data = data[slices[::-1]] if dimensions[1] < dimensions[0]: data = data.transpose() else: message = """ Attempted to read in %i-dimensional FITS cube, but dimensions and slices were not specified. Please specify these using the dimensions= and slices= argument. The cube dimensions are:\n\n""" % len(shape) for i in range(1, len(shape) + 1): message += " " * 10 message += " %i %s %i\n" % (i - 1, header["CTYPE%i" % i], header["NAXIS%i" % i]) raise Exception(message) return data, wcsaxes_slices ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.242823 aplpy-2.1.0/aplpy/tests/0000775000175000017500000000000000000000000013311 5ustar00tomtom././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/__init__.py0000664000175000017500000000061500000000000015424 0ustar00tomtomimport matplotlib MPL_VERSION = matplotlib.__version__ ROOT = "https://aplpy.github.io/aplpy-data/2.x/2018-10-28T17:43:23.106" # The developer versions of the form 3.2.x+... contain changes that will only # be included in the 3.3.x release, so we update this here. if MPL_VERSION[:3] == '3.2' and '+' in MPL_VERSION: MPL_VERSION = '3.3' baseline_dir = ROOT + '/' + MPL_VERSION[:3] + '.x/' ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/coveragerc0000644000175000017500000000140000000000000015345 0ustar00tomtom[run] source = {packagename} omit = {packagename}/_astropy_init* {packagename}/conftest* {packagename}/cython_version* {packagename}/setup_package* {packagename}/*/setup_package* {packagename}/*/*/setup_package* {packagename}/tests/* {packagename}/*/tests/* {packagename}/*/*/tests/* {packagename}/version* [report] exclude_lines = # Have to re-enable the standard pragma pragma: no cover # Don't complain about packages we have installed except ImportError # Don't complain if tests don't hit assertions raise AssertionError raise NotImplementedError # Don't complain about script hooks def main\(.*\): # Ignore branches that don't pertain to this version of Python pragma: py{ignore_python_version}././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.242823 aplpy-2.1.0/aplpy/tests/data/0000775000175000017500000000000000000000000014222 5ustar00tomtom././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.246823 aplpy-2.1.0/aplpy/tests/data/2d_fits/0000775000175000017500000000000000000000000015554 5ustar00tomtom././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_AIR.hdr0000644000175000017500000000166700000000000017604 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---AIR' CRPIX1 = 8.339330824421999 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--AIR' CRPIX2 = -234.7545010835 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole PV2_1 = 4.500000000000E+01 / Projection parameter 1 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_AIT.hdr0000644000175000017500000000157700000000000017606 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---AIT' CRPIX1 = 7.115850027049 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--AIT' CRPIX2 = -246.2317116277 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_ARC.hdr0000644000175000017500000000157700000000000017576 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---ARC' CRPIX1 = 5.082274450444 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--ARC' CRPIX2 = -246.941901905 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_AZP.hdr0000644000175000017500000000175700000000000017623 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---AZP' CRPIX1 = -11.34948542534 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--AZP' CRPIX2 = -254.1100848779 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole PV2_1 = 2.000000000000E+00 / Projection parameter 1 PV2_2 = 3.000000000000E+01 / Projection parameter 2 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_BON.hdr0000644000175000017500000000166700000000000017607 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---BON' CRPIX1 = -33.0741266819 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--BON' CRPIX2 = -243.1263982441 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole PV2_1 = 4.500000000000E+01 / Projection parameter 1 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_CAR.hdr0000644000175000017500000000157700000000000017576 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---CAR' CRPIX1 = 7.527038199745 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--CAR' CRPIX2 = -248.2173814412 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_CEA.hdr0000644000175000017500000000166700000000000017561 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---CEA' CRPIX1 = 7.688571124876 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--CEA' CRPIX2 = -248.2173814412 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole PV2_1 = 1.000000000000E+00 / Projection parameter 1 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_COD.hdr0000644000175000017500000000175700000000000017576 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---COD' CRPIX1 = 15.61302682707 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--COD' CRPIX2 = -215.3431714695 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -4.500000000000E+01 / Native latitude of celestial pole PV2_1 = 4.500000000000E+01 / Projection parameter 1 PV2_2 = 2.500000000000E+01 / Projection parameter 2 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_COE.hdr0000644000175000017500000000175700000000000017577 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---COE' CRPIX1 = -14.35249668783 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--COE' CRPIX2 = -223.0375366798 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 4.500000000000E+01 / Native latitude of celestial pole PV2_1 = -4.500000000000E+01 / Projection parameter 1 PV2_2 = 2.500000000000E+01 / Projection parameter 2 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_COO.hdr0000644000175000017500000000175700000000000017611 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---COO' CRPIX1 = 12.92640949564 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--COO' CRPIX2 = -213.6486051767 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -4.500000000000E+01 / Native latitude of celestial pole PV2_1 = 4.500000000000E+01 / Projection parameter 1 PV2_2 = 2.500000000000E+01 / Projection parameter 2 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_COP.hdr0000644000175000017500000000175700000000000017612 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---COP' CRPIX1 = 15.05768272737 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--COP' CRPIX2 = -215.1923139086 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -4.500000000000E+01 / Native latitude of celestial pole PV2_1 = 4.500000000000E+01 / Projection parameter 1 PV2_2 = 2.500000000000E+01 / Projection parameter 2 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_CSC.hdr0000644000175000017500000000157700000000000017601 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---CSC' CRPIX1 = -7.043520126533 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--CSC' CRPIX2 = -268.6531829635 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_CYP.hdr0000644000175000017500000000175700000000000017624 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---CYP' CRPIX1 = 20.56099939277 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--CYP' CRPIX2 = -147.1055514007 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole PV2_1 = 1.000000000000E+00 / Projection parameter 1 PV2_2 = 7.071067811870E-01 / Projection parameter 2 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_HPX.hdr0000644000175000017500000000240300000000000017615 0ustar00tomtomSIMPLE = T / file does conform to FITS standard BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 / number of data axes NAXIS1 = 192 / length of data axis 1 NAXIS2 = 192 / length of data axis 2 EXTEND = T / FITS dataset may contain extensions COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H BUNIT = 'Jy/beam ' / Pixel value is surface brightness CTYPE1 = 'RA---HPX' CRPIX1 = -8.21754831338666 CDELT1 = -0.0666666666666667 CRVAL1 = 0. CTYPE2 = 'DEC--HPX' CRPIX2 = -248.217381441188 CDELT2 = 0.0666666666666667 CRVAL2 = -90. LONPOLE = 180. / Native longitude of celestial pole LATPOLE = 0. / Native latitude of celestial pole RADESYS = 'FK5 ' / Equatorial coordinate system EQUINOX = 2000.0 / Equinox of equatorial coordinates BMAJ = 0.24000 / Beam major axis in degrees BMIN = 0.24000 / Beam minor axis in degrees BPA = 0.0 / Beam position angle in degrees ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_MER.hdr0000644000175000017500000000157700000000000017614 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---MER' CRPIX1 = 7.364978412864 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--MER' CRPIX2 = -248.2173814412 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_MOL.hdr0000644000175000017500000000157700000000000017620 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---MOL' CRPIX1 = -2.310670994515 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--MOL' CRPIX2 = -212.7655947497 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_NCP.hdr0000644000175000017500000000175700000000000017611 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---SIN' CRPIX1 = 7.688572009351 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--SIN' CRPIX2 = -237.1895431541 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole PV2_1 = 0.000000000000E+00 / Projection parameter 1 PV2_2 = -1.216796447506E-08 / Projection parameter 2 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_PAR.hdr0000644000175000017500000000157700000000000017613 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---PAR' CRPIX1 = 3.322937769653 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--PAR' CRPIX2 = -246.5551494284 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_PCO.hdr0000644000175000017500000000157700000000000017612 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---PCO' CRPIX1 = 0.3620782775517 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--PCO' CRPIX2 = -246.2486098896 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_QSC.hdr0000644000175000017500000000157700000000000017617 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---QSC' CRPIX1 = -8.258194421088 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--QSC' CRPIX2 = -258.3408175994 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_SFL.hdr0000644000175000017500000000157700000000000017615 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---SFL' CRPIX1 = 7.527038199745 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--SFL' CRPIX2 = -246.3483086237 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_SIN.hdr0000644000175000017500000000175700000000000017622 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---SIN' CRPIX1 = 7.688571124876 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--SIN' CRPIX2 = -237.1895431541 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole PV2_1 = 0.000000000000E+00 / Projection parameter 1 PV2_2 = 0.000000000000E+00 / Projection parameter 2 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_STG.hdr0000644000175000017500000000157700000000000017626 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---STG' CRPIX1 = 3.744942537739 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--STG' CRPIX2 = -251.945990929 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_SZP.hdr0000644000175000017500000000204700000000000017636 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---SZP' CRPIX1 = -22.62051956373 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--SZP' CRPIX2 = -247.8656972779 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole PV2_1 = 2.000000000000E+00 / Projection parameter 1 PV2_2 = 1.800000000000E+02 / Projection parameter 2 PV2_3 = 6.000000000000E+01 / Projection parameter 3 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_TAN.hdr0000644000175000017500000000157700000000000017613 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---TAN' CRPIX1 = -0.5630437201085 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--TAN' CRPIX2 = -268.0658087122 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_TSC.hdr0000644000175000017500000000157700000000000017622 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---TSC' CRPIX1 = 20.37416464676 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--TSC' CRPIX2 = -189.7220156818 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = 0.000000000000E+00 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_ZEA.hdr0000644000175000017500000000157700000000000017610 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---ZEA' CRPIX1 = 5.738055949994 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--ZEA' CRPIX2 = -244.4880690361 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/1904-66_ZPN.hdr0000644000175000017500000000375100000000000017634 0ustar00tomtomSIMPLE = T BITPIX = -32 / IEEE (big-endian) 32-bit floating point data NAXIS = 2 NAXIS1 = 192 NAXIS2 = 192 BUNIT = 'JY/BEAM ' CTYPE1 = 'RA---ZPN' CRPIX1 = 22.09211120575 CDELT1 = -6.666666666667E-02 CRVAL1 = 0.000000000000E+00 CTYPE2 = 'DEC--ZPN' CRPIX2 = -183.2937255632 CDELT2 = 6.666666666667E-02 CRVAL2 = -9.000000000000E+01 LONPOLE = 1.800000000000E+02 / Native longitude of celestial pole LATPOLE = -9.000000000000E+01 / Native latitude of celestial pole PV2_0 = 5.000000000000E-02 / Projection parameter 0 PV2_1 = 9.750000000000E-01 / Projection parameter 1 PV2_2 = -8.070000000000E-01 / Projection parameter 2 PV2_3 = 3.370000000000E-01 / Projection parameter 3 PV2_4 = -6.500000000000E-02 / Projection parameter 4 PV2_5 = 1.000000000000E-02 / Projection parameter 5 PV2_6 = 3.000000000000E-03 / Projection parameter 6 PV2_7 = -1.000000000000E-03 / Projection parameter 7 PV2_8 = 0.000000000000E+00 / Projection parameter 8 PV2_9 = 0.000000000000E+00 / Projection parameter 9 PV2_10 = 0.000000000000E+00 / Projection parameter 10 PV2_11 = 0.000000000000E+00 / Projection parameter 11 PV2_12 = 0.000000000000E+00 / Projection parameter 12 PV2_13 = 0.000000000000E+00 / Projection parameter 13 PV2_14 = 0.000000000000E+00 / Projection parameter 14 PV2_15 = 0.000000000000E+00 / Projection parameter 15 PV2_16 = 0.000000000000E+00 / Projection parameter 16 PV2_17 = 0.000000000000E+00 / Projection parameter 17 PV2_18 = 0.000000000000E+00 / Projection parameter 18 PV2_19 = 0.000000000000E+00 / Projection parameter 19 EQUINOX = 2.000000000000E+03 / Equinox of equatorial coordinates BMAJ = 2.399999936422E-01 / Beam major axis in degrees BMIN = 2.399999936422E-01 / Beam minor axis in degrees BPA = 0.000000000000E+00 / Beam position angle in degrees RESTFRQ = 1.420405750000E+09 / Line rest frequency, Hz ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/2MASS_k.hdr0000644000175000017500000000125600000000000017414 0ustar00tomtomSIMPLE = T BITPIX = 16 NAXIS = 2 NAXIS1 = 721 NAXIS2 = 720 EXTEND = T / FITS dataset may contain extensions DATASET = '2MASS ' BAND = 'K ' CDATE = 'Wed Feb 25 11:57:21 2009' CTYPE1 = 'RA---TAN' CTYPE2 = 'DEC--TAN' CRVAL1 = 266.400000 CRVAL2 = -28.933330 CRPIX1 = 361. CRPIX2 = 360.5 CDELT1 = -0.001388889 CDELT2 = 0.001388889 CROTA2 = 0.000000 EQUINOX = 2000.0 MAGZP = 19.9757 BSCALE = 0.045777764213996 BZERO = 1500. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/2d_fits/2MASS_k_rot.hdr0000644000175000017500000000125600000000000020300 0ustar00tomtomSIMPLE = T BITPIX = 16 NAXIS = 2 NAXIS1 = 721 NAXIS2 = 720 EXTEND = T / FITS dataset may contain extensions DATASET = '2MASS ' BAND = 'K ' CDATE = 'Wed Feb 25 11:57:21 2009' CTYPE1 = 'RA---TAN' CTYPE2 = 'DEC--TAN' CRVAL1 = 266.400000 CRVAL2 = -28.933330 CRPIX1 = 361. CRPIX2 = 360.5 CDELT1 = -0.001388889 CDELT2 = 0.001388889 CROTA2 = 30.000000 EQUINOX = 2000.0 MAGZP = 19.9757 BSCALE = 0.045777764213996 BZERO = 1500. ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.246823 aplpy-2.1.0/aplpy/tests/data/3d_fits/0000775000175000017500000000000000000000000015555 5ustar00tomtom././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/3d_fits/cube.hdr0000644000175000017500000000136200000000000017172 0ustar00tomtomSIMPLE = T / Written by IDL: Fri Oct 27 10:49:59 2006 BITPIX = -32 / bits per data value NAXIS = 3 / number of axes NAXIS1 = 11 NAXIS2 = 12 NAXIS3 = 32 EXTEND = T /file may contain extensions CRVAL1 = 57.6599999999 / CRPIX1 = -799.000000000 / CDELT1 = -0.00638888900000 CTYPE1 = 'RA---SFL' / CRVAL2 = 0.00000000000 / CRPIX2 = -4741.91300000 / CDELT2 = 0.00638888900000 CTYPE2 = 'DEC--SFL' / CRVAL3 = -9959.44378305 / CRPIX3 = 1.00000 / CDELT3 = 66.4236100000 / CTYPE3 = 'VOPT' / SPECSYS = 'LSRK' / ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/__init__.py0000644000175000017500000000000000000000000016317 0ustar00tomtom././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tests/data/shapes.reg0000644000175000017500000000131600000000000016203 0ustar00tomtom# Region file format: DS9 version 4.1 global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 fk5 circle(17:46:26.948,-28:45:11.89,330.498") ellipse(17:44:59.122,-28:44:47.18,505",185",360) # color=red width=2 box(17:45:50.090,-28:55:04.94,320",485",360) # color=blue width=4 line(17:46:39.285,-29:01:56.56,17:45:47.446,-29:06:42.46) # line=0 0 # vector(17:45:32.185,-29:06:07.48,924.175",13.1336) vector=1 color=yellow width=3 # text(17:45:40.580,-29:09:07.48) text={All you regions are belong to us} panda(17:46:26.192,-28:45:41.91,360,720,4,39.1095",78.2191",1) ellipse(17:45:09.384,-28:45:32.33,120",37.5",240",75",360) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/helpers.py0000664000175000017500000000247500000000000015335 0ustar00tomtomimport string import random import os import numpy as np from astropy.io import fits from astropy.wcs import WCS def random_id(): return ''.join(random.sample(string.ascii_letters + string.digits, 16)) def generate_header(header_file): # Read in header header = fits.Header.fromtextfile(header_file) return header def generate_data(header_file): # Read in header header = generate_header(header_file) # Find shape of array shape = [] for i in range(header['NAXIS']): shape.append(header['NAXIS%i' % (i + 1)]) # Generate data array data = np.zeros(shape[::-1]) return data def generate_hdu(header_file): # Read in header header = generate_header(header_file) # Generate data array data = generate_data(header_file) # Generate primary HDU hdu = fits.PrimaryHDU(data=data, header=header) return hdu def generate_wcs(header_file): # Read in header header = generate_header(header_file) # Compute WCS object wcs = WCS(header) return wcs def generate_file(header_file, directory): # Generate HDU object hdu = generate_hdu(header_file) # Write out to a temporary file in the specified directory filename = os.path.join(directory, random_id() + '.fits') hdu.writeto(filename) return filename ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/setup_package.py0000664000175000017500000000017600000000000016502 0ustar00tomtom def get_package_data(): return {_ASTROPY_PACKAGE_NAME_ + '.tests': ['coveragerc', 'data/*.reg', 'data/*/*.hdr']} # noqa ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_axis_labels.py0000664000175000017500000000313500000000000017212 0ustar00tomtomimport numpy as np from astropy.tests.helper import pytest from .. import FITSFigure def test_axis_labels_show_hide(): data = np.zeros((16, 16)) f = FITSFigure(data) f.axis_labels.hide() f.axis_labels.show() f.axis_labels.hide_x() f.axis_labels.show_x() f.axis_labels.hide_y() f.axis_labels.show_y() f.close() def test_axis_labels_text(): data = np.zeros((16, 16)) f = FITSFigure(data) f.axis_labels.set_xtext('x') f.axis_labels.set_ytext('y') f.close() def test_axis_labels_pad(): data = np.zeros((16, 16)) f = FITSFigure(data) f.axis_labels.set_xpad(-1.) f.axis_labels.set_ypad(0.5) f.close() def test_axis_labels_position(): data = np.zeros((16, 16)) f = FITSFigure(data) f.axis_labels.set_xposition('top') f.axis_labels.set_xposition('bottom') f.axis_labels.set_yposition('right') f.axis_labels.set_yposition('left') f.close() def test_axis_labels_position_invalid(): data = np.zeros((16, 16)) f = FITSFigure(data) with pytest.raises(ValueError): f.axis_labels.set_xposition('right') with pytest.raises(ValueError): f.axis_labels.set_xposition('left') with pytest.raises(ValueError): f.axis_labels.set_yposition('top') with pytest.raises(ValueError): f.axis_labels.set_yposition('bottom') f.close() def test_axis_labels_font(): data = np.zeros((16, 16)) f = FITSFigure(data) f.axis_labels.set_font(size='small', weight='bold', stretch='normal', family='serif', style='normal', variant='normal') f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_beam.py0000664000175000017500000001207600000000000015634 0ustar00tomtomimport os import pytest import numpy as np from astropy import units as u from astropy.io import fits from .. import FITSFigure from ..overlays import Beam header_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data/2d_fits') HEADER = fits.Header.fromtextfile(os.path.join(header_dir, '1904-66_TAN.hdr')) HDU = fits.PrimaryHDU(np.zeros((16, 16)), HEADER) def test_beam_add_remove(): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=0.1, minor=0.04, angle=10.) f.remove_beam() f.add_beam(major=0.1, minor=0.04, angle=10.) f.remove_beam() f.close() def test_beam_show_hide(): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.hide() f.beam.show(major=0.1, minor=0.04, angle=10.) f.close() def test_beam_major(): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_major(0.5) f.beam.set_major(1.0) f.close() @pytest.mark.parametrize('quantity', [u.arcsec, 5 * u.arcsec, 1 * u.degree, 1 * u.radian]) def test_beam_major_quantity(quantity): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=quantity, minor=0.04, angle=10.) f.beam.set_major(quantity) f.close() def test_beam_minor(): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_minor(0.05) f.beam.set_minor(0.08) f.close() @pytest.mark.parametrize('quantity', [u.arcsec, 5 * u.arcsec, 1 * u.degree, 1 * u.radian]) def test_beam_minor_quantity(quantity): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=0.1, minor=quantity, angle=10.) assert type(f.beam) != list f.beam.set_minor(quantity) f.close() def test_beam_angle(): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_angle(0.) f.beam.set_angle(55.) f.close() @pytest.mark.parametrize('quantity', [u.arcsec, 5 * u.arcsec, 1 * u.degree, 1 * u.radian]) def test_beam_angle_quantity(quantity): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=0.1, minor=0.04, angle=quantity) f.beam.set_angle(quantity) f.close() def test_beam_corner(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) for corner in ['top', 'bottom', 'left', 'right', 'top left', 'top right', 'bottom left', 'bottom right']: f.beam.set_corner(corner) f.close() def test_beam_frame(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_frame(True) f.beam.set_frame(False) f.close() def test_beam_borderpad(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_borderpad(0.1) f.beam.set_borderpad(0.3) f.close() def test_beam_pad(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_pad(0.1) f.beam.set_pad(0.3) f.close() def test_beam_alpha(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_alpha(0.1) f.beam.set_alpha(0.2) f.beam.set_alpha(0.5) f.close() def test_beam_color(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_color('black') f.beam.set_color('#003344') f.beam.set_color((1.0, 0.4, 0.3)) f.close() def test_beam_facecolor(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_facecolor('black') f.beam.set_facecolor('#003344') f.beam.set_facecolor((1.0, 0.4, 0.3)) f.close() def test_beam_edgecolor(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_edgecolor('black') f.beam.set_edgecolor('#003344') f.beam.set_edgecolor((1.0, 0.4, 0.3)) f.close() def test_beam_linestyle(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_linestyle('solid') f.beam.set_linestyle('dotted') f.beam.set_linestyle('dashed') f.close() def test_beam_linewidth(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) f.beam.set_linewidth(0) f.beam.set_linewidth(1) f.beam.set_linewidth(5) f.close() def test_beam_hatch(): f = FITSFigure(HDU) f.add_beam(major=0.1, minor=0.04, angle=10.) for hatch in ['/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*']: f.beam.set_hatch(hatch) f.close() def test_beam_multiple(): f = FITSFigure(HDU) f.show_grayscale() f.add_beam(major=0.1, minor=0.04, angle=10.) f.add_beam(major=0.2, minor=0.08, angle=10.) f.add_beam(major=0.15, minor=0.08, angle=10.) assert len(f.beam) == 3 with pytest.raises(Exception) as exc: f.remove_beam() assert exc.value.args[0] == ('More than one beam present - use beam_index= ' 'to specify which one to remove') f.remove_beam(beam_index=2) assert len(f.beam) == 2 f.remove_beam(beam_index=1) assert isinstance(f.beam, Beam) f.remove_beam() assert not hasattr(f, 'beam') f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_colorbar.py0000664000175000017500000000411600000000000016527 0ustar00tomtomimport numpy as np from astropy.tests.helper import pytest from .. import FITSFigure ARRAY = np.arange(256).reshape((16, 16)) def test_colorbar_invalid(): f = FITSFigure(ARRAY) with pytest.raises(Exception): f.add_colorbar() # no grayscale/colorscale was shown def test_colorbar_addremove(): f = FITSFigure(ARRAY) f.show_grayscale() f.add_colorbar() f.remove_colorbar() f.add_colorbar() f.close() def test_colorbar_showhide(): f = FITSFigure(ARRAY) f.show_grayscale() f.add_colorbar() f.colorbar.hide() f.colorbar.show() f.close() def test_colorbar_location(): f = FITSFigure(ARRAY) f.show_grayscale() f.add_colorbar() f.colorbar.set_location('top') with pytest.warns(UserWarning, match='Bottom colorbar not fully implemented'): f.colorbar.set_location('bottom') with pytest.warns(UserWarning, match='Left colorbar not fully implemented'): f.colorbar.set_location('left') f.colorbar.set_location('right') f.close() def test_colorbar_width(): f = FITSFigure(ARRAY) f.show_grayscale() f.add_colorbar() f.colorbar.set_width(0.1) f.colorbar.set_width(0.2) f.colorbar.set_width(0.5) f.close() def test_colorbar_pad(): f = FITSFigure(ARRAY) f.show_grayscale() f.add_colorbar() f.colorbar.set_pad(0.1) f.colorbar.set_pad(0.2) f.colorbar.set_pad(0.5) f.close() def test_colorbar_font(): f = FITSFigure(ARRAY) f.show_grayscale() f.add_colorbar() f.colorbar.set_font(size='small', weight='bold', stretch='normal', family='serif', style='normal', variant='normal') f.close() def test_colorbar_axis_label(): f = FITSFigure(ARRAY) f.show_grayscale() f.add_colorbar() f.colorbar.set_axis_label_text('Surface Brightness (MJy/sr)') f.colorbar.set_axis_label_rotation(45.) f.colorbar.set_axis_label_font(size='small', weight='bold', stretch='normal', family='serif', style='normal', variant='normal') f.colorbar.set_axis_label_pad(5.) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_contour.py0000664000175000017500000000057300000000000016420 0ustar00tomtomimport pytest import numpy as np from .. import FITSFigure # Test simple contour generation with Numpy example @pytest.mark.parametrize(('filled'), [True, False]) def test_numpy_contour(filled): data = np.arange(256).reshape((16, 16)) f = FITSFigure(data) f.show_grayscale() f.show_contour(data, levels=np.linspace(1., 254., 10), filled=filled) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_convolve.py0000664000175000017500000000153000000000000016554 0ustar00tomtomimport numpy as np from astropy.io import fits from .. import FITSFigure ARRAY = np.arange(256).reshape((16, 16)) def test_convolve_default(): hdu = fits.PrimaryHDU(ARRAY) f = FITSFigure(hdu) f.show_grayscale(smooth=3) f.close() def test_convolve_gauss(): hdu = fits.PrimaryHDU(ARRAY) f = FITSFigure(hdu) f.show_grayscale(kernel='gauss', smooth=3) f.close() def test_convolve_box(): hdu = fits.PrimaryHDU(ARRAY) f = FITSFigure(hdu) f.show_grayscale(kernel='box', smooth=3) f.close() def test_convolve_custom(): hdu = fits.PrimaryHDU(ARRAY) f = FITSFigure(hdu) f.show_grayscale(kernel=np.ones((3, 3))) f.close() def test_convolve_int(): # Regression test for aplpy/aplpy#165 hdu = fits.PrimaryHDU(ARRAY) f = FITSFigure(hdu) f.show_grayscale(smooth=3) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_downsample.py0000664000175000017500000000030500000000000017071 0ustar00tomtomimport numpy as np from .. import FITSFigure def test_numpy_downsample(): data = np.arange(256).reshape((16, 16)) f = FITSFigure(data, downsample=2) f.show_grayscale() f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_frame.py0000664000175000017500000000065700000000000016024 0ustar00tomtomimport numpy as np from .. import FITSFigure def test_frame_linewidth(): data = np.zeros((16, 16)) f = FITSFigure(data) f.frame.set_linewidth(0) f.frame.set_linewidth(1) f.frame.set_linewidth(10) f.close() def test_frame_color(): data = np.zeros((16, 16)) f = FITSFigure(data) f.frame.set_color('black') f.frame.set_color('#003344') f.frame.set_color((1.0, 0.4, 0.3)) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_grid.py0000664000175000017500000000302500000000000015647 0ustar00tomtomimport pytest import numpy as np from .. import FITSFigure def test_grid_addremove(): data = np.zeros((16, 16)) f = FITSFigure(data) f.add_grid() f.remove_grid() f.add_grid() f.close() def test_grid_showhide(): data = np.zeros((16, 16)) f = FITSFigure(data) f.add_grid() f.grid.hide() f.grid.show() f.close() def test_grid_spacing(): data = np.zeros((16, 16)) f = FITSFigure(data) f.add_grid() f.grid.set_xspacing(1.) f.grid.set_xspacing('tick') with pytest.raises(ValueError): f.grid.set_xspacing('auto') f.grid.set_yspacing(2.) f.grid.set_yspacing('tick') with pytest.raises(ValueError): f.grid.set_yspacing('auto') f.close() def test_grid_color(): data = np.zeros((16, 16)) f = FITSFigure(data) f.add_grid() f.grid.set_color('black') f.grid.set_color('#003344') f.grid.set_color((1.0, 0.4, 0.3)) f.close() def test_grid_alpha(): data = np.zeros((16, 16)) f = FITSFigure(data) f.add_grid() f.grid.set_alpha(0.0) f.grid.set_alpha(0.3) f.grid.set_alpha(1.0) f.close() def test_grid_linestyle(): data = np.zeros((16, 16)) f = FITSFigure(data) f.add_grid() f.grid.set_linestyle('solid') f.grid.set_linestyle('dashed') f.grid.set_linestyle('dotted') f.close() def test_grid_linewidth(): data = np.zeros((16, 16)) f = FITSFigure(data) f.add_grid() f.grid.set_linewidth(0) f.grid.set_linewidth(2) f.grid.set_linewidth(5) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_images.py0000664000175000017500000002053100000000000016170 0ustar00tomtomimport os import tempfile import pytest import numpy as np from .. import FITSFigure from .helpers import generate_file from . import baseline_dir MODULEDIR = os.path.dirname(__file__) DATADIR = os.path.abspath(os.path.join(MODULEDIR, 'data')) class BaseImageTests(object): @classmethod def setup_class(cls): cls._baseline_images_dir = os.path.abspath(os.path.join(MODULEDIR, 'baseline_images')) header_1 = os.path.join(DATADIR, '2d_fits/1904-66_AIR.hdr') cls.filename_1 = generate_file(header_1, str(tempfile.mkdtemp())) header_2 = os.path.join(DATADIR, '2d_fits/2MASS_k.hdr') cls.filename_2 = generate_file(header_2, str(tempfile.mkdtemp())) header_3 = os.path.join(DATADIR, '3d_fits/cube.hdr') cls.filename_3 = generate_file(header_3, str(tempfile.mkdtemp())) header_4 = os.path.join(DATADIR, '2d_fits/2MASS_k_rot.hdr') cls.filename_4 = generate_file(header_4, str(tempfile.mkdtemp())) class TestBasic(BaseImageTests): # Test for showing grayscale @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=7.5) def test_basic_image(self): f = FITSFigure(self.filename_2, figsize=(7, 5)) f.show_grayscale(vmin=0, vmax=1) return f._figure @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=7.5) def test_ticks_labels_options(self): f = FITSFigure(self.filename_2, figsize=(7, 5)) # Force aspect ratio f.show_grayscale() f.hide_grayscale() f.ticks.set_color('black') f.axis_labels.set_xposition('top') f.axis_labels.set_yposition('right') f.axis_labels.set_font(size='medium', weight='medium', stretch='normal', style='normal') f.tick_labels.set_xformat('dd:mm:ss.ss') f.tick_labels.set_yformat('hh:mm:ss.ss') f.tick_labels.set_style('colons') f.ticks.set_xspacing(0.2) f.ticks.set_yspacing(0.2) f.ticks.set_minor_frequency(10) return f._figure # Test for showing colorscale @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=5) def test_show_colorbar_scalebar_beam(self): f = FITSFigure(self.filename_1, figsize=(7, 5)) f.ticks.set_color('black') f.show_colorscale(vmin=-0.1, vmax=0.1) f.add_colorbar() f.add_scalebar(7.5) f.add_beam(major=0.5, minor=0.2, angle=10.) f.tick_labels.hide() return f._figure # Test for overlaying shapes @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=1.5) def test_overlay_shapes(self): f = FITSFigure(self.filename_1, figsize=(7, 5)) # Force aspect ratio f.show_grayscale() f.hide_grayscale() f.ticks.set_color('black') # Markers f.show_markers([360., 350., 340.], [-61., -62., -63], color='cyan') f.show_markers([30, 40], [50, 70], coords_frame='pixel', edgecolor='blue') # Circles f.show_circles([360., 350., 340.], [-61., -62., -63], [0.5, 0.4, 0.3], edgecolor='purple') f.show_circles([30, 40], [50, 70], [10, 20], coords_frame='pixel', edgecolor='orange') # Ellipses f.show_ellipses(340., -66., 1.5, 2., 10., edgecolor='red') f.show_ellipses(120, 60, 20, 40, 20., coords_frame='pixel', edgecolor='green') # Rectangles f.show_rectangles([355., 350.], [-71, -72], [0.5, 1], [2, 1], angle=[20, 30], edgecolor='magenta') f.show_rectangles([66, 80], [20, 30], [10, 14], [20, 22], angle=[20, 30], coords_frame='pixel', edgecolor='yellow') # Arrows f.show_arrows([340., 360], [-72, -68], [2, -2], [2, 2]) # Polygons poly = np.array([[330, 340, 360], [-65, -61, -63]]) f.show_polygons([poly], edgecolor='0.3', zorder=10) # Lines f.show_lines([poly], zorder=9, lw=5, color='red', alpha=0.5) # Labels f.add_label(350., -66., 'text') f.add_label(0.4, 0.25, 'text', relative=True) f.frame.set_linewidth(1) # points f.frame.set_color('black') f.axis_labels.hide() return f._figure # Test for grid @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=7.5) def test_grid(self): f = FITSFigure(self.filename_1, figsize=(7, 5)) # Force aspect ratio f.show_grayscale() f.hide_grayscale() f.ticks.set_color('black') f.add_grid() f.grid.set_color('red') f.grid.set_alpha(0.8) f.grid.set_linestyle('solid') f.grid.set_xspacing('tick') f.grid.set_yspacing(3) return f._figure # Test recenter @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=1.5) def test_recenter(self): f = FITSFigure(self.filename_2, figsize=(7, 5)) # Force aspect ratio f.show_grayscale() f.hide_grayscale() f.ticks.set_color('black') f.recenter(266.5, -29.0, width=0.1, height=0.1) f.axis_labels.set_xpad(20) f.axis_labels.set_ypad(20) return f._figure # Test overlaying contours @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=5) def test_contours(self): data = np.arange(256).reshape((16, 16)) f = FITSFigure(data, figsize=(7, 5)) # Force aspect ratio f.show_grayscale() f.hide_grayscale() f.ticks.set_color('black') f.show_contour(data, levels=np.linspace(1., 254., 10), filled=False) return f._figure # Test cube slice @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=5) def test_cube_slice(self): f = FITSFigure(self.filename_3, dimensions=[2, 0], slices=[10], figsize=(7, 5), subplot=[0.25, 0.1, 0.7, 0.8]) f.ticks.set_color('black') f.add_grid() f.grid.set_color('black') f.grid.set_linestyle('solid') f.grid.set_xspacing(250) f.grid.set_yspacing(0.01) f.tick_labels.set_xformat('%g') f.tick_labels.set_yformat('dd:mm:ss.ss') return f._figure # Test for ds9 regions @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=5) def test_regions(self): f = FITSFigure(self.filename_2, figsize=(7, 5)) # Force aspect ratio f.show_grayscale() f.hide_grayscale() f.show_regions(os.path.join(DATADIR, 'shapes.reg')) f.axis_labels.hide() f.tick_labels.hide() f.ticks.hide() return f._figure @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=5) def test_north(self): f = FITSFigure(self.filename_4, figsize=(3, 3), north=True) f.show_grayscale(vmin=-1, vmax=1) f.axis_labels.hide() f.tick_labels.hide() f.ticks.hide() return f._figure @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=5) def test_downsample(self): data = np.arange(256).reshape((16, 16)) f = FITSFigure(data, downsample=2) f.show_grayscale() return f._figure @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=5) def test_set_nan_color(self): data = np.arange(56, dtype=float).reshape((8, 7)) data[3, :] = np.nan f = FITSFigure(data, figsize=(3, 3)) f.show_colorscale() f.axis_labels.hide() f.tick_labels.hide() f.ticks.hide() f.set_nan_color('black') return f._figure ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_init_cube.py0000664000175000017500000000741100000000000016666 0ustar00tomtomimport os import pytest import numpy as np from astropy.io import fits from .helpers import generate_file, generate_hdu, generate_wcs from .. import FITSFigure # The tests in this file check that the initialization and basic plotting do # not crash for FITS files with 3+ dimensions. No reference images are # required here. ROOT = os.path.dirname(os.path.abspath(__file__)) HEADER_DIR = os.path.join(ROOT, 'data/3d_fits') HEADERS = [os.path.join(HEADER_DIR, 'cube.hdr')] REFERENCE = os.path.join(HEADER_DIR, 'cube.hdr') VALID_DIMENSIONS = [(0, 1), (1, 0), (0, 2), (2, 0), (1, 2), (2, 1)] INVALID_DIMENSIONS = [None, (1,), (0, 3), (-4, 2), (1, 1), (2, 2), (3, 3), (1, 2, 3), (3, 5, 3, 2)] # Test initialization through a filename def test_file_init(tmpdir): filename = generate_file(REFERENCE, str(tmpdir)) f = FITSFigure(filename, slices=[5]) f.show_grayscale() f.close() # Test initialization through an HDU object def test_hdu_init(): hdu = generate_hdu(REFERENCE) f = FITSFigure(hdu, slices=[5]) f.show_grayscale() f.close() # Test initialization through a WCS object def test_wcs_init(): wcs = generate_wcs(REFERENCE) with pytest.raises(ValueError) as exc: FITSFigure(wcs, slices=[5]) assert exc.value.args[0] == "FITSFigure initialization via WCS objects can only be done with 2-dimensional WCS objects" # Test initialization through an HDU object (no WCS) def test_hdu_nowcs_init(): data = np.zeros((16, 16, 16)) hdu = fits.PrimaryHDU(data) f = FITSFigure(hdu, slices=[5]) f.show_grayscale() f.close() # Test initalization through a Numpy array (no WCS) def test_numpy_nowcs_init(): data = np.zeros((16, 16, 16)) f = FITSFigure(data, slices=[5]) f.show_grayscale() f.close() # Test that initialization without specifying slices raises an exception for a # true 3D cube def test_hdu_noslices(): hdu = generate_hdu(REFERENCE) with pytest.raises(Exception): FITSFigure(hdu) # Test that initialization without specifying slices does *not* raise an # exception if the remaining dimensions have size 1. def test_hdu_noslices_2d(): data = np.zeros((1, 16, 16)) f = FITSFigure(data) f.show_grayscale() f.close() # Now check initialization with valid and invalid dimensions. We just need to # tes with HDU objects since we already tested that reading from files is ok. # Test initialization with valid dimensions @pytest.mark.parametrize(('dimensions'), VALID_DIMENSIONS) def test_init_dimensions_valid(dimensions): hdu = generate_hdu(REFERENCE) f = FITSFigure(hdu, dimensions=dimensions, slices=[5]) f.show_grayscale() f.close() # Test initialization with invalid dimensions @pytest.mark.parametrize(('dimensions'), INVALID_DIMENSIONS) def test_init_dimensions_invalid(dimensions): hdu = generate_hdu(REFERENCE) with pytest.raises(ValueError): FITSFigure(hdu, dimensions=dimensions, slices=[5]) # Now check initialization of different WCS projections, and we check only # valid dimensions valid_parameters = [] for h in HEADERS: for d in VALID_DIMENSIONS: valid_parameters.append((h, d)) @pytest.mark.parametrize(('header', 'dimensions'), valid_parameters) def test_init_extensive_wcs(tmpdir, header, dimensions): filename = generate_file(header, str(tmpdir)) f = FITSFigure(filename, dimensions=dimensions, slices=[5]) f.show_grayscale() f.close() # Test that recenter works for cube slices # TODO: remove xfail once pix2world transformations work for multidimensional # datasets @pytest.mark.xfail def test_recenter_cube_slices(): data = np.zeros((16, 16, 16)) hdu = fits.PrimaryHDU(data) f = FITSFigure(hdu, slices=[5]) f.show_grayscale() f.recenter(5., 5., width=3., height=3.) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_init_image.py0000664000175000017500000001365200000000000017036 0ustar00tomtomimport os import pytest import numpy as np from astropy.io import fits from astropy.wcs import WCS as AstropyWCS from .helpers import generate_file, generate_hdu, generate_wcs from .. import FITSFigure # The tests in this file check that the initialization and basic plotting do # not crash for FITS files with 2 dimensions. No reference images are # required here. header_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data/2d_fits') HEADERS = [os.path.join(header_dir, '1904-66_AIR.hdr'), os.path.join(header_dir, '1904-66_AIT.hdr'), os.path.join(header_dir, '1904-66_ARC.hdr'), os.path.join(header_dir, '1904-66_AZP.hdr'), os.path.join(header_dir, '1904-66_BON.hdr'), os.path.join(header_dir, '1904-66_CAR.hdr'), os.path.join(header_dir, '1904-66_CEA.hdr'), os.path.join(header_dir, '1904-66_COD.hdr'), os.path.join(header_dir, '1904-66_COE.hdr'), os.path.join(header_dir, '1904-66_COO.hdr'), os.path.join(header_dir, '1904-66_COP.hdr'), os.path.join(header_dir, '1904-66_CSC.hdr'), os.path.join(header_dir, '1904-66_CYP.hdr'), os.path.join(header_dir, '1904-66_HPX.hdr'), os.path.join(header_dir, '1904-66_MER.hdr'), os.path.join(header_dir, '1904-66_MOL.hdr'), os.path.join(header_dir, '1904-66_NCP.hdr'), os.path.join(header_dir, '1904-66_PAR.hdr'), os.path.join(header_dir, '1904-66_PCO.hdr'), os.path.join(header_dir, '1904-66_QSC.hdr'), os.path.join(header_dir, '1904-66_SFL.hdr'), os.path.join(header_dir, '1904-66_SIN.hdr'), os.path.join(header_dir, '1904-66_STG.hdr'), os.path.join(header_dir, '1904-66_SZP.hdr'), os.path.join(header_dir, '1904-66_TAN.hdr'), os.path.join(header_dir, '1904-66_TSC.hdr'), os.path.join(header_dir, '1904-66_ZEA.hdr'), os.path.join(header_dir, '1904-66_ZPN.hdr')] REFERENCE = os.path.join(header_dir, '1904-66_TAN.hdr') CAR_REFERENCE = os.path.join(header_dir, '1904-66_CAR.hdr') VALID_DIMENSIONS = [(0, 1), (1, 0)] INVALID_DIMENSIONS = [None, (1,), (0, 2), (-4, 2), (1, 1), (2, 2), (1, 2, 3)] # Test initialization through a filename def test_file_init(tmpdir): filename = generate_file(REFERENCE, str(tmpdir)) f = FITSFigure(filename) f.show_grayscale() f.close() # Test initialization through an HDU object def test_hdu_init(): hdu = generate_hdu(REFERENCE) f = FITSFigure(hdu) f.show_grayscale() f.close() # Test initialization through a WCS object def test_wcs_init(): wcs = generate_wcs(REFERENCE) f = FITSFigure(wcs) f.show_grayscale() f.close() # Test initialization through a WCS object with wcs.to_header() as a go-between # specifically for testing the cd -> pc -> cd hack, and has particular importance # for AVM-generated headers def test_wcs_toheader_init(): wcs = generate_wcs(REFERENCE) header_ = fits.Header.fromtextfile(REFERENCE) header = wcs.to_header() wcs2 = AstropyWCS(header) wcs2.pixel_shape = wcs.pixel_shape = (header_['NAXIS1'], header_['NAXIS2']) f = FITSFigure(wcs2) f.show_grayscale() f.add_grid() f.close() # Test initialization through an HDU object (no WCS) def test_hdu_nowcs_init(): data = np.zeros((16, 16)) hdu = fits.PrimaryHDU(data) f = FITSFigure(hdu) f.show_grayscale() f.close() # Test initalization through a Numpy array (no WCS) def test_numpy_nowcs_init(): data = np.zeros((16, 16)) f = FITSFigure(data) f.show_grayscale() f.close() # Now check initialization with valid and invalid dimensions. We just need to # tes with HDU objects since we already tested that reading from files is ok. # Test initialization with valid dimensions @pytest.mark.parametrize(('dimensions'), VALID_DIMENSIONS) def test_init_dimensions_valid(dimensions): hdu = generate_hdu(REFERENCE) f = FITSFigure(hdu, dimensions=dimensions) f.show_grayscale() f.close() # Test initialization with invalid dimensions @pytest.mark.parametrize(('dimensions'), INVALID_DIMENSIONS) def test_init_dimensions_invalid(dimensions): hdu = generate_hdu(REFERENCE) with pytest.raises(ValueError): FITSFigure(hdu, dimensions=dimensions) # Now check initialization of different WCS projections, and we check only # valid dimensions valid_parameters = [] for h in HEADERS: for d in VALID_DIMENSIONS: valid_parameters.append((h, d)) @pytest.mark.parametrize(('header', 'dimensions'), valid_parameters) def test_init_extensive_wcs(header, dimensions): hdu = generate_hdu(header) if 'CAR' in header: f = FITSFigure(hdu, dimensions=dimensions, convention='calabretta') else: f = FITSFigure(hdu, dimensions=dimensions) f.show_grayscale() f.add_grid() f.close() # Check that for CAR projections, an exception is raised if no convention is specified @pytest.mark.parametrize(('dimensions'), VALID_DIMENSIONS) def test_init_car_invalid(dimensions): hdu = generate_hdu(CAR_REFERENCE) with pytest.raises(Exception): FITSFigure(hdu, dimensions=dimensions) # Check that images containing only NaN or Inf values don't crash FITSFigure def test_init_only_naninf(): data = np.ones((10, 10)) * np.nan data[::2, ::2] = np.inf f = FITSFigure(data) f.show_grayscale() f.show_colorscale() def test_init_single_pixel(): data = np.zeros((4, 4)) data[...] = np.nan data[2, 2] = 1 f = FITSFigure(data) f.show_grayscale() def test_not_first_hdu(tmpdir): # Test that data is fetched from the first compatible HDU with data filename = tmpdir.join('test.fits').strpath hdu0 = fits.PrimaryHDU() hdu1 = fits.BinTableHDU() hdu2 = fits.ImageHDU(np.zeros((12, 12))) hdulist = fits.HDUList([hdu0, hdu1, hdu2]) hdulist.writeto(filename) f = FITSFigure(filename) assert f._data.shape == (12, 12) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_layers.py0000664000175000017500000001221400000000000016221 0ustar00tomtomimport pytest import numpy as np from .. import FITSFigure def test_layers(capsys): f = FITSFigure(np.arange(256).reshape((16, 16))) capsys.readouterr() # No layers f.list_layers() captured = capsys.readouterr() assert captured.out.strip() == ('There are no layers in this figure') # Two layers f.show_markers([360., 350., 340.], [-61., -62., -63], color='cyan', layer='markers') f.show_markers([360., 350., 340.], [-61., -62., -63], color='cyan', layer='extra_markers') capsys.readouterr() f.list_layers() captured = capsys.readouterr() assert captured.out.strip() == ('There are 2 layers in this figure:\n\n' ' -> markers\n' ' -> extra_markers') # Check removing layers f.remove_layer('extra_markers') f.list_layers() captured = capsys.readouterr() assert captured.out.strip() == ('There is one layer in this figure:\n\n' ' -> markers') # Check all layer types and overwriting layers f.show_markers([30, 40], [50, 70], layer='markers') f.show_circles([360., 350., 340.], [-61., -62., -63], [0.5, 0.4, 0.3], layer='circles') f.show_circles([30, 40], [50, 70], [10, 20], layer='circles') f.show_ellipses(340., -66., 1.5, 2., 10., layer='ellipses') f.show_ellipses(120, 60, 20, 40, 20., layer='ellipses') f.show_rectangles([355., 350.], [-71, -72], [0.5, 1], [2, 1], angle=[20, 30], layer='rectangles') f.show_rectangles([66, 80], [20, 30], [10, 14], [20, 22], angle=[20, 30], layer='rectangles') f.show_arrows([340., 360], [-72, -68], [2, -2], [2, 2], layer='arrows') f.show_arrows([340., 360], [-72, -68], [2, -2], [2, 2], layer='arrows') poly = np.array([[330, 340, 360], [-65, -61, -63]]) f.show_polygons([poly], layer='polygons') f.show_polygons([poly], layer='polygons') f.show_lines([poly], layer='lines') f.show_lines([poly], layer='lines') f.add_label(350., -66., 'text', layer='label') f.add_label(0.4, 0.25, 'text', relative=True, layer='label') # FIXME: Uncomment the following once # https://github.com/astropy/astropy/pull/8321 is merged into Astropy and in # a bug fix release. # f.show_contour(layer='contours') f.list_layers() captured = capsys.readouterr() assert captured.out.strip() == ('There are 8 layers in this figure:\n\n' ' -> markers\n' ' -> circles\n' ' -> ellipses\n' ' -> rectangles\n' ' -> arrows\n' ' -> polygons\n' ' -> lines\n' ' -> label') # Hiding/showing layers f.hide_layer('ellipses') f.hide_layer('polygons') f.list_layers() captured = capsys.readouterr() assert captured.out.strip() == ('There are 8 layers in this figure:\n\n' ' -> markers\n' ' -> circles\n' ' -> ellipses (hidden)\n' ' -> rectangles\n' ' -> arrows\n' ' -> polygons (hidden)\n' ' -> lines\n' ' -> label') layer = f.get_layer('circles') assert layer.get_visible() layer = f.get_layer('ellipses') assert not layer.get_visible() f.show_layer('ellipses') f.list_layers() captured = capsys.readouterr() assert captured.out.strip() == ('There are 8 layers in this figure:\n\n' ' -> markers\n' ' -> circles\n' ' -> ellipses\n' ' -> rectangles\n' ' -> arrows\n' ' -> polygons (hidden)\n' ' -> lines\n' ' -> label') layer = f.get_layer('ellipses') assert layer.get_visible() def test_non_existent_layers(): # Handling non-existent layers f = FITSFigure(np.arange(256).reshape((16, 16))) with pytest.raises(Exception) as exc: f.get_layer('banana') assert exc.value.args[0] == 'Layer banana does not exist' assert f.get_layer('banana', raise_exception=False) is None with pytest.raises(Exception) as exc: f.show_layer('banana') assert exc.value.args[0] == 'Layer banana does not exist' f.show_layer('banana', raise_exception=False) with pytest.raises(Exception) as exc: f.hide_layer('banana') assert exc.value.args[0] == 'Layer banana does not exist' f.hide_layer('banana', raise_exception=False) with pytest.raises(Exception) as exc: f.remove_layer('banana') assert exc.value.args[0] == 'Layer banana does not exist' f.remove_layer('banana', raise_exception=False) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_misc.py0000664000175000017500000000204000000000000015651 0ustar00tomtomimport numpy as np from ..core import FITSFigure def test_nan_color_copy(): """ Regression test to ensure that NaN values set in one image don't affect global Matplotlib colormap. """ data = np.zeros((16, 16)) f1 = FITSFigure(data) f1.show_grayscale() f1.set_nan_color('blue') f2 = FITSFigure(data) f2.show_grayscale() f2.set_nan_color('red') assert f1.image.get_cmap()._rgba_bad == (0.0, 0.0, 1.0, 1.0) assert f2.image.get_cmap()._rgba_bad == (1.0, 0.0, 0.0, 1.0) def test_stretches(): # Regression test to make sure none of the stretches crash data = np.arange(256).reshape((16, 16)) f = FITSFigure(data) f.show_grayscale() f.show_grayscale(stretch='linear') f.show_grayscale(stretch='sqrt') f.show_grayscale(stretch='log') f.show_grayscale(stretch='arcsinh') f.show_grayscale(stretch='power') f.show_grayscale(stretch='log', vmid=-10) f.show_grayscale(stretch='arcsinh', vmid=10) f.show_grayscale(stretch='power', exponent=3.0) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_pixworldmarkers.py0000664000175000017500000000346000000000000020162 0ustar00tomtomimport os import pytest import numpy as np from astropy.table import Table from astropy.io import fits from .helpers import generate_wcs from .. import FITSFigure HEADER = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data/2d_fits', '1904-66_TAN.hdr') tab = Table({'RA': [347., 349.], 'DEC': [-68., -68]}) GOOD_INPUT = [[1, 2], [[1, 2], [3, 4]], [np.arange(2), np.arange(2)], [tab['RA'], tab['DEC']] ] BAD_INPUT = [[1, ['s', 'e']], [np.arange(2), np.sum], [tab['RA'], 'ewr'] ] @pytest.mark.parametrize(('inputval'), GOOD_INPUT) def test_pixel_coords(inputval): data = np.zeros((16, 16)) f = FITSFigure(data) f.show_markers(inputval[0], inputval[1]) f.close() @pytest.mark.parametrize(('inputval'), GOOD_INPUT) def test_wcs_coords(inputval): wcs = generate_wcs(HEADER) header = fits.Header.fromtextfile(HEADER) wcs.naxis1 = header['NAXIS1'] wcs.naxis2 = header['NAXIS2'] f = FITSFigure(wcs) f.show_markers(inputval[0], inputval[1]) f.close() @pytest.mark.parametrize(('inputval'), BAD_INPUT) def test_pixel_coords_bad(inputval): data = np.zeros((16, 16)) f = FITSFigure(data) with pytest.raises(Exception) as exc: f.show_markers(inputval[0], inputval[1]) assert exc.value.args[0] == "x and y must be the same size" f.close() @pytest.mark.parametrize(('inputval'), BAD_INPUT) def test_wcs_coords_bad(inputval): wcs = generate_wcs(HEADER) header = fits.Header.fromtextfile(HEADER) wcs.naxis1 = header['NAXIS1'] wcs.naxis2 = header['NAXIS2'] f = FITSFigure(wcs) with pytest.raises(Exception) as exc: f.show_markers(inputval[0], inputval[1]) f.close() assert exc.value.args[0] == "x and y must be the same size" ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_rgb.py0000664000175000017500000000752200000000000015502 0ustar00tomtomimport os import warnings import pytest import numpy as np from astropy.io import fits from astropy.coordinates import Galactic from .. import FITSFigure from ..rgb import make_rgb_image, make_rgb_cube from .test_images import BaseImageTests from . import baseline_dir from .helpers import generate_header ROOT = os.path.dirname(os.path.abspath(__file__)) HEADER = os.path.join(ROOT, 'data/2d_fits', '1904-66_TAN.hdr') class TestRGB(BaseImageTests): @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=7.5, filename='test_rgb.png') @pytest.mark.parametrize('embed_avm_tags', (False, True)) def test_rgb(self, tmpdir, embed_avm_tags): # Regression test to check that RGB recenter works properly r_file = tmpdir.join('r.fits').strpath g_file = tmpdir.join('g.fits').strpath b_file = tmpdir.join('b.fits').strpath rgb_file = tmpdir.join('rgb.png').strpath np.random.seed(12345) header = fits.Header.fromtextfile(HEADER) r = fits.PrimaryHDU(np.random.random((12, 12)), header) r.writeto(r_file) g = fits.PrimaryHDU(np.random.random((12, 12)), header) g.writeto(g_file) b = fits.PrimaryHDU(np.random.random((12, 12)), header) b.writeto(b_file) make_rgb_image([r_file, g_file, b_file], rgb_file, embed_avm_tags=embed_avm_tags) if embed_avm_tags: f = FITSFigure(rgb_file, figsize=(7, 5)) else: f = FITSFigure(r_file, figsize=(7, 5)) with warnings.catch_warnings(): warnings.simplefilter("ignore") f.show_rgb(rgb_file) f.tick_labels.set_xformat('dd.d') f.tick_labels.set_yformat('dd.d') f.recenter(359.3, -72.1, radius=0.05) return f._figure @pytest.mark.remote_data @pytest.mark.parametrize('north', ['default', 'galactic', 'false']) @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=7.5) def test_make_rgb_cube(self, tmpdir, north): # Regression test to check that RGB recenter works properly header = generate_header(os.path.join(ROOT, 'data', '2d_fits', '2MASS_k_rot.hdr')) header['CRPIX1'] = 6.5 header['CRPIX2'] = 6.5 header_r = header.copy() header_r['CROTA2'] = 5 header_g = header.copy() header_g['CROTA2'] = 35 header_b = header.copy() header_b['CROTA2'] = 70 r_file = tmpdir.join('r.fits').strpath g_file = tmpdir.join('g.fits').strpath b_file = tmpdir.join('b.fits').strpath rgb_cube = tmpdir.join('rgb.fits').strpath rgb_file = tmpdir.join('rgb.png').strpath header = fits.Header.fromtextfile(HEADER) r = fits.PrimaryHDU(np.ones((128, 128)), header_r) r.writeto(r_file) g = fits.PrimaryHDU(np.ones((128, 128)), header_g) g.writeto(g_file) b = fits.PrimaryHDU(np.ones((128, 128)), header_b) b.writeto(b_file) if north == 'default': kwargs = {} elif north == 'galactic': kwargs = {'north': Galactic()} elif north == 'false': kwargs = {'north': False} make_rgb_cube([r_file, g_file, b_file], rgb_cube, **kwargs) make_rgb_image(rgb_cube, rgb_file, embed_avm_tags=True, vmin_r=0, vmax_r=1, vmin_g=0, vmax_g=1, vmin_b=0, vmax_b=1) f = FITSFigure(rgb_file, figsize=(3, 3)) with warnings.catch_warnings(): warnings.simplefilter("ignore") f.show_rgb(rgb_file) f.tick_labels.hide() f.axis_labels.hide() f.add_grid() return f._figure ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_save.py0000664000175000017500000000527700000000000015673 0ustar00tomtomimport os import sys from io import BytesIO as StringIO import pytest import numpy as np from .. import FITSFigure FORMATS = [None, 'png', 'pdf', 'eps', 'ps', 'svg'] ARRAY = np.arange(256).reshape((16, 16)) def is_format(filename, format): if isinstance(filename, str): f = open(filename, 'rb') else: f = filename if format == 'png': return f.read(8) == b'\x89\x50\x4e\x47\x0d\x0a\x1a\x0a' elif format == 'pdf': return f.read(4) == b'\x25\x50\x44\x46' elif format == 'eps': return f.read(23) == b'%!PS-Adobe-3.0 EPSF-3.0' elif format == 'ps': return f.read(14) == b'%!PS-Adobe-3.0' elif format == 'svg': from xml.dom import minidom return minidom.parse(f).childNodes[-1].attributes['xmlns'].value == 'http://www.w3.org/2000/svg' else: raise Exception("Unknown format: %s" % format) @pytest.mark.parametrize(('format'), FORMATS) def test_write_png(tmpdir, format): filename = os.path.join(str(tmpdir), 'test_output.png') f = FITSFigure(ARRAY) f.show_grayscale() try: f.save(filename, format=format) except TypeError: pytest.xfail() finally: f.close() if format is None: assert is_format(filename, 'png') else: assert is_format(filename, format) @pytest.mark.parametrize(('format'), FORMATS) def test_write_pdf(tmpdir, format): filename = os.path.join(str(tmpdir), 'test_output.pdf') f = FITSFigure(ARRAY) f.show_grayscale() try: f.save(filename, format=format) except TypeError: pytest.xfail() finally: f.close() if format is None: assert is_format(filename, 'pdf') else: assert is_format(filename, format) @pytest.mark.parametrize(('format'), FORMATS) def test_write_eps(tmpdir, format): filename = os.path.join(str(tmpdir), 'test_output.eps') f = FITSFigure(ARRAY) f.show_grayscale() try: f.save(filename, format=format) except TypeError: pytest.xfail() finally: f.close() if format is None: assert is_format(filename, 'eps') else: assert is_format(filename, format) @pytest.mark.parametrize(('format'), FORMATS) def test_write_stringio(tmpdir, format): s = StringIO() f = FITSFigure(ARRAY) f.show_grayscale() try: f.save(s, format=format) except TypeError: pytest.xfail() finally: f.close() try: s.seek(0) except ValueError: if format == 'svg' and sys.version_info[:2] >= (3, 3): pytest.xfail() else: raise if format is None: assert is_format(s, 'png') else: assert is_format(s, format) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_scalebar.py0000664000175000017500000000525500000000000016505 0ustar00tomtomimport os import pytest import numpy as np from astropy import units as u from astropy.io import fits from .. import FITSFigure ROOT = os.path.dirname(os.path.abspath(__file__)) HEADER_DIR = os.path.join(ROOT, 'data/2d_fits') HEADER = fits.Header.fromtextfile(os.path.join(HEADER_DIR, '1904-66_TAN.hdr')) HDU = fits.PrimaryHDU(np.zeros((16, 16)), HEADER) def test_scalebar_add_invalid(): f = FITSFigure(HDU) with pytest.raises(TypeError): f.add_scalebar() def test_scalebar_addremove(): f = FITSFigure(HDU) f.add_scalebar(0.1) f.remove_scalebar() f.add_scalebar(0.1) f.close() def test_scalebar_showhide(): f = FITSFigure(HDU) f.add_scalebar(0.1) f.scalebar.hide() f.scalebar.show(0.1) f.close() def test_scalebar_length(): f = FITSFigure(HDU) f.add_scalebar(0.1) f.scalebar.set_length(0.01) f.scalebar.set_length(0.1) f.close() @pytest.mark.parametrize('quantity', [1 * u.arcsec, u.arcsec, 2 * u.degree, 5 * u.radian]) def test_scalebar_length_quantity(quantity): f = FITSFigure(HDU) f.add_scalebar(quantity) f.scalebar.set_length(quantity) f.close() def test_scalebar_label(): f = FITSFigure(HDU) f.add_scalebar(0.1) f.scalebar.set_label('1 pc') f.scalebar.set_label('5 AU') f.scalebar.set_label('2"') f.close() def test_scalebar_corner(): f = FITSFigure(HDU) f.add_scalebar(0.1) for corner in ['top', 'bottom', 'left', 'right', 'top left', 'top right', 'bottom left', 'bottom right']: f.scalebar.set_corner(corner) f.close() def test_scalebar_frame(): f = FITSFigure(HDU) f.add_scalebar(0.1) f.scalebar.set_frame(True) f.scalebar.set_frame(False) f.close() def test_scalebar_color(): f = FITSFigure(HDU) f.add_scalebar(0.1) f.scalebar.set_color('black') f.scalebar.set_color('#003344') f.scalebar.set_color((1.0, 0.4, 0.3)) f.close() def test_scalebar_alpha(): f = FITSFigure(HDU) f.add_scalebar(0.1) f.scalebar.set_alpha(0.1) f.scalebar.set_alpha(0.2) f.scalebar.set_alpha(0.5) f.close() def test_scalebar_font(): f = FITSFigure(HDU) f.add_scalebar(0.1) f.scalebar.set_font(size='small', weight='bold', stretch='normal', family='serif', style='normal', variant='normal') f.close() def test_regression_exception_type(): # In Matplotlib 1.5, the exception type changed for when a property doesn't # exist, so we need to catch both AttributeError and TypeError. f = FITSFigure(HDU) with pytest.warns(UserWarning, match='Scalebar does not have attribute family'): f.add_scalebar(0.1, family='serif') f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_subplot.py0000664000175000017500000000117300000000000016414 0ustar00tomtomimport pytest import numpy as np from .. import FITSFigure def test_subplot_grid(): f = FITSFigure(np.zeros((10, 10)), subplot=(2, 2, 1)) f.show_grayscale() f.close() def test_subplot_box(): f = FITSFigure(np.zeros((10, 10)), subplot=[0.1, 0.1, 0.8, 0.8]) f.show_grayscale() f.close() @pytest.mark.parametrize('subplot', [(1, 2, 3, 4), [1, 2, 3], '111', 1.2]) def test_subplot_invalid(subplot): with pytest.raises(ValueError) as exc: FITSFigure(np.zeros((10, 10)), subplot=subplot) assert exc.value.args[0] == "subplot= should be either a tuple of three values, or a list of four values" ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_tick_labels.py0000664000175000017500000000347500000000000017207 0ustar00tomtomimport os import pytest import numpy as np from .helpers import generate_hdu from .. import FITSFigure ROOT = os.path.dirname(os.path.abspath(__file__)) HEADER_DIR = os.path.join(ROOT, 'data/2d_fits') REFERENCE = os.path.join(HEADER_DIR, '1904-66_TAN.hdr') def test_tick_labels_show_hide(): data = np.zeros((16, 16)) f = FITSFigure(data) f.tick_labels.hide() f.tick_labels.show() f.tick_labels.hide_x() f.tick_labels.show_x() f.tick_labels.hide_y() f.tick_labels.show_y() f.close() def test_tick_labels_format_scalar(): data = np.zeros((16, 16)) f = FITSFigure(data) f.tick_labels.set_xformat('%i') f.tick_labels.set_yformat('%i') f.close() def test_tick_labels_position(): data = np.zeros((16, 16)) f = FITSFigure(data) f.tick_labels.set_xposition('top') f.tick_labels.set_xposition('bottom') f.tick_labels.set_yposition('right') f.tick_labels.set_yposition('left') f.close() def test_tick_labels_position_invalid(): data = np.zeros((16, 16)) f = FITSFigure(data) with pytest.raises(ValueError): f.tick_labels.set_xposition('right') with pytest.raises(ValueError): f.tick_labels.set_xposition('left') with pytest.raises(ValueError): f.tick_labels.set_yposition('top') with pytest.raises(ValueError): f.tick_labels.set_yposition('bottom') f.close() def test_tick_labels_font(): data = np.zeros((16, 16)) f = FITSFigure(data) f.tick_labels.set_font(size='small', weight='bold', stretch='normal', family='serif', style='normal', variant='normal') f.close() def test_single_d_format(): hdu = generate_hdu(REFERENCE) f = FITSFigure(hdu) f.show_grayscale(vmin=0, vmax=1) f.tick_labels.set_yformat('d.d') f.save('test_label_format.png') ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_ticks.py0000664000175000017500000000234500000000000016043 0ustar00tomtomimport numpy as np from .. import FITSFigure def test_ticks_show_hide(): data = np.zeros((16, 16)) f = FITSFigure(data) f.ticks.hide() f.ticks.show() f.ticks.hide_x() f.ticks.show_x() f.ticks.hide_y() f.ticks.show_y() f.close() def test_ticks_spacing(): data = np.zeros((16, 16)) f = FITSFigure(data) f.ticks.set_xspacing(0.5) f.ticks.set_xspacing(1.) f.ticks.set_yspacing(0.5) f.ticks.set_yspacing(1.) f.close() def test_ticks_length(): data = np.zeros((16, 16)) f = FITSFigure(data) f.ticks.set_length(0) f.ticks.set_length(1) f.ticks.set_length(10) f.close() def test_ticks_color(): data = np.zeros((16, 16)) f = FITSFigure(data) f.ticks.set_color('black') f.ticks.set_color('#003344') f.ticks.set_color((1.0, 0.4, 0.3)) f.close() def test_ticks_linewidth(): data = np.zeros((16, 16)) f = FITSFigure(data) f.ticks.set_linewidth(1) f.ticks.set_linewidth(3) f.ticks.set_linewidth(10) f.close() def test_ticks_minor_frequency(): data = np.zeros((16, 16)) f = FITSFigure(data) f.ticks.set_minor_frequency(1) f.ticks.set_minor_frequency(5) f.ticks.set_minor_frequency(10) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/tests/test_vectors.py0000664000175000017500000000210500000000000016405 0ustar00tomtomimport pytest import numpy as np from ..core import FITSFigure from .test_images import BaseImageTests from . import baseline_dir x = np.linspace(-1., 1., 10) y = np.linspace(-1., 1., 10) X, Y = np.meshgrid(x, y) np.random.seed(12345) IMAGE = np.random.random((10, 10)) PDATA = np.arange(100).reshape((10, 10)) / 50. + 0.5 ADATA = np.degrees(np.arctan2(Y, X)) class TestVectors(BaseImageTests): @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=1.5) def test_default(self): f = FITSFigure(IMAGE, figsize=(4, 4)) f.show_grayscale() f.show_vectors(PDATA, ADATA, color='orange') return f._figure @pytest.mark.remote_data @pytest.mark.mpl_image_compare(style={}, savefig_kwargs={'adjust_bbox': False}, baseline_dir=baseline_dir, tolerance=1.5) def test_step_scale(self): f = FITSFigure(IMAGE, figsize=(4, 4)) f.show_grayscale() f.show_vectors(PDATA, ADATA, step=2, scale=0.8, color='orange') return f._figure ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/aplpy/tick_labels.py0000644000175000017500000001351100000000000014774 0ustar00tomtom# -*- coding: utf-8 -*- from __future__ import absolute_import, print_function, division, unicode_literals from .decorators import auto_refresh, fixdocstring __all__ = ['TickLabels'] class TickLabels(object): def __init__(self, parent): self._figure = parent._figure self._ax = parent.ax self._wcs = parent.ax.wcs self.x = parent.x self.y = parent.y self.set_style('plain') @auto_refresh def set_xformat(self, xformat): """ Set the format of the x-axis tick labels. If the x-axis type is ``longitude`` or ``latitude``, then the options are: * ``d.ddddd`` - decimal degrees, where the number of decimal places can be varied * ``hh`` or ``dd`` - hours (or degrees) * ``hh:mm`` or ``dd:mm`` - hours and minutes (or degrees and arcminutes) * ``hh:mm:ss`` or ``dd:mm:ss`` - hours, minutes, and seconds (or degrees, arcminutes, and arcseconds) * ``hh:mm:ss.ss`` or ``dd:mm:ss.ss`` - hours, minutes, and seconds (or degrees, arcminutes, and arcseconds), where the number of decimal places can be varied. If the x-axis type is ``scalar``, then the format should be a valid python string format beginning with a ``%``. If one of these arguments is not specified, the format for that axis is left unchanged. """ if 'dd.' in xformat: xformat = xformat.replace('ddd.', 'd.').replace('dd.', 'd.') self._ax.coords[self.x].set_major_formatter(xformat) @auto_refresh def set_yformat(self, yformat): """ Set the format of the y-axis tick labels. If the y-axis type is ``longitude`` or ``latitude``, then the options are: * ``d.ddddd`` - decimal degrees, where the number of decimal places can be varied * ``hh`` or ``dd`` - hours (or degrees) * ``hh:mm`` or ``dd:mm`` - hours and minutes (or degrees and arcminutes) * ``hh:mm:ss`` or ``dd:mm:ss`` - hours, minutes, and seconds (or degrees, arcminutes, and arcseconds) * ``hh:mm:ss.ss`` or ``dd:mm:ss.ss`` - hours, minutes, and seconds (or degrees, arcminutes, and arcseconds), where the number of decimal places can be varied. If the y-axis type is ``scalar``, then the format should be a valid python string format beginning with a ``%``. If one of these arguments is not specified, the format for that axis is left unchanged. """ if 'dd.' in yformat: yformat = yformat.replace('ddd.', 'd.').replace('dd.', 'd.') self._ax.coords[self.y].set_major_formatter(yformat) @auto_refresh def set_style(self, style): """ Set the format of the x-axis tick labels. This can be 'colons' or 'plain': * 'colons' uses colons as separators, for example 31:41:59.26 +27:18:28.1 * 'plain' uses letters and symbols as separators, for example 31h41m59.26s +27º18'28.1" """ if style not in ['colons', 'plain']: raise Exception("Label style should be one of colons/plain") self.style = style for coord in [self.x, self.y]: coord_type = self._ax.coords[coord].coord_type if coord_type in ['longitude', 'latitude']: if style == 'colons': sep = (':', ':', '') else: sep = None self._ax.coords[coord].set_separator(sep) @auto_refresh @fixdocstring def set_font(self, **kwargs): """ Set the font of the tick labels. Parameters ---------- common: family, style, variant, stretch, weight, size, fontproperties Notes ----- Default values are set by matplotlib or previously set values if set_font has already been called. Global default values can be set by editing the matplotlibrc file. """ self._ax.coords[self.x].set_ticklabel(**kwargs) self._ax.coords[self.y].set_ticklabel(**kwargs) @auto_refresh def show(self): """ Show the x- and y-axis tick labels. """ self.show_x() self.show_y() @auto_refresh def hide(self): """ Hide the x- and y-axis tick labels. """ self.hide_x() self.hide_y() @auto_refresh def show_x(self): """ Show the x-axis tick labels. """ self._ax.coords[self.x].set_ticklabel_visible(True) @auto_refresh def hide_x(self): """ Hide the x-axis tick labels. """ self._ax.coords[self.x].set_ticklabel_visible(False) @auto_refresh def show_y(self): """ Show the y-axis tick labels. """ self._ax.coords[self.y].set_ticklabel_visible(True) @auto_refresh def hide_y(self): """ Hide the y-axis tick labels. """ self._ax.coords[self.y].set_ticklabel_visible(False) @auto_refresh def set_xposition(self, position): """ Set the position of the x-axis tick labels ('top' or 'bottom') """ if position == 'bottom': self._ax.coords[self.x].set_ticklabel_position('b') elif position == 'top': self._ax.coords[self.x].set_ticklabel_position('t') else: raise ValueError("position should be one of 'top' or 'bottom'") @auto_refresh def set_yposition(self, position): """ Set the position of the y-axis tick labels ('left' or 'right') """ if position == 'left': self._ax.coords[self.y].set_ticklabel_position('l') elif position == 'right': self._ax.coords[self.y].set_ticklabel_position('r') else: raise ValueError("position should be one of 'left' or 'right'") ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/aplpy/ticks.py0000664000175000017500000000736700000000000013653 0ustar00tomtomfrom .decorators import auto_refresh class Ticks(object): @auto_refresh def __init__(self, parent): self._figure = parent._figure self._ax = parent.ax self.x = parent.x self.y = parent.y self._wcs = self._ax.wcs def set_tick_direction(self, direction): """ Set the direction of the ticks to be facing out of the axes (``out``) or into the axes (``in``). """ if direction in ('in', 'out'): self._ax.coords[self.x].ticks.set_tick_out(direction == 'out') self._ax.coords[self.y].ticks.set_tick_out(direction == 'out') else: raise ValueError("direction should be 'in' or 'out'") @auto_refresh def set_xspacing(self, spacing): """ Set the x-axis tick spacing, in degrees. To set the tick spacing to be automatically determined, set this to 'auto'. """ self._set_spacing(self.x, spacing) @auto_refresh def set_yspacing(self, spacing): """ Set the y-axis tick spacing, in degrees. To set the tick spacing to be automatically determined, set this to 'auto'. """ self._set_spacing(self.y, spacing) @auto_refresh def _set_spacing(self, coord, spacing): if spacing == 'auto': self._ax.coords[coord].set_ticks(spacing=None) else: coord_unit = self._wcs.wcs.cunit[coord] self._ax.coords[coord].set_ticks(spacing=spacing * coord_unit) @auto_refresh def set_color(self, color): """ Set the color of the ticks """ self._ax.coords[self.x].set_ticks(color=color) self._ax.coords[self.y].set_ticks(color=color) @auto_refresh def set_length(self, length, minor_factor=0.5): """ Set the length of the ticks (in points) """ # TODO: Can't set minor ticksize. Should we just remove that? # Not mentioned in the APLpy in docs either self._ax.coords[self.x].set_ticks(size=length) self._ax.coords[self.y].set_ticks(size=length) @auto_refresh def set_linewidth(self, linewidth): """ Set the linewidth of the ticks (in points) """ self._ax.coords[self.x].set_ticks(width=linewidth) self._ax.coords[self.y].set_ticks(width=linewidth) @auto_refresh def set_minor_frequency(self, xfrequency, yfrequency=None): ''' Set the number of subticks per major tick. Set to one to hide minor ticks. If not yfrequency given, frequency is the same in both axis. Otherwise, xfrequency represents the frequency in the xaxis and yfrequency the one in the yaxis. ''' if yfrequency is None: yfrequency = xfrequency self._ax.coords[self.x].set_minor_frequency(xfrequency) self._ax.coords[self.y].set_minor_frequency(yfrequency) @auto_refresh def show(self): """ Show the x- and y-axis ticks """ self.show_x() self.show_y() @auto_refresh def hide(self): """ Hide the x- and y-axis ticks """ self.hide_x() self.hide_y() @auto_refresh def show_x(self): """ Show the x-axis ticks """ self._ax.coords[self.x].set_ticks_visible(True) @auto_refresh def hide_x(self): """ Hide the x-axis ticks """ self._ax.coords[self.x].set_ticks_visible(False) @auto_refresh def show_y(self): """ Show the y-axis ticks """ self._ax.coords[self.y].set_ticks_visible(True) @auto_refresh def hide_y(self): """ Hide the y-axis ticks """ self._ax.coords[self.y].set_ticks_visible(False) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646929514.0 aplpy-2.1.0/aplpy/version.py0000664000175000017500000000052100000000000014204 0ustar00tomtom# Note that we need to fall back to the hard-coded version if either # setuptools_scm can't be imported or setuptools_scm can't determine the # version, so we catch the generic 'Exception'. try: from setuptools_scm import get_version version = get_version(root='..', relative_to=__file__) except Exception: version = '2.1.0' ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.238823 aplpy-2.1.0/aplpy.egg-info/0000775000175000017500000000000000000000000013641 5ustar00tomtom././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646929514.0 aplpy-2.1.0/aplpy.egg-info/PKG-INFO0000664000175000017500000000311000000000000014731 0ustar00tomtomMetadata-Version: 2.1 Name: aplpy Version: 2.1.0 Summary: The Astronomical Plotting Library in Python Home-page: http://aplpy.github.io Author: Thomas Robitaille and Eli Bressert Author-email: thomas.robitaille@gmail.com License: MIT Platform: UNKNOWN Requires-Python: >=3.6 Description-Content-Type: text/x-rst Provides-Extra: test Provides-Extra: docs License-File: LICENSE.md |Azure| |CircleCI| |codecov| |Documentation Status| About ----- APLpy (the Astronomical Plotting Library in Python) is a Python module aimed at producing publication-quality plots of astronomical imaging data in FITS format. PyPI: https://pypi.python.org/pypi/APLpy Website: http://aplpy.github.io Documentation: http://aplpy.readthedocs.io APLpy is released under an MIT open-source license. Installing ---------- You can install APLpy and all its dependencies with:: pip install aplpy .. |Azure| image:: https://dev.azure.com/thomasrobitaille/aplpy/_apis/build/status/aplpy.aplpy?repoName=aplpy%2Faplpy&branchName=refs%2Fpull%2F441%2Fmerge :target: https://dev.azure.com/thomasrobitaille/aplpy/_build/latest?definitionId=16&repoName=aplpy%2Faplpy&branchName=refs%2Fpull%2F441%2Fmerge .. |CircleCI| image:: https://circleci.com/gh/aplpy/aplpy/tree/main.svg?style=svg :target: https://circleci.com/gh/aplpy/aplpy/tree/main .. |codecov| image:: https://codecov.io/gh/aplpy/aplpy/branch/main/graph/badge.svg :target: https://codecov.io/gh/aplpy/aplpy .. |Documentation Status| image:: https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat :target: https://aplpy.readthedocs.io/en/latest/ ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646929515.0 aplpy-2.1.0/aplpy.egg-info/SOURCES.txt0000664000175000017500000000624200000000000015531 0ustar00tomtom.gitignore .readthedocs.yml CHANGES.rst CITATION LICENSE.md MANIFEST.in README.rst azure-pipelines.yml conftest.py pyproject.toml setup.cfg setup.py tox.ini .circleci/config.yml aplpy/__init__.py aplpy/_astropy_init.py aplpy/axis_labels.py aplpy/colorbar.py aplpy/compat.py aplpy/conftest.py aplpy/convolve_util.py aplpy/core.py aplpy/decorators.py aplpy/frame.py aplpy/grid.py aplpy/header.py aplpy/layers.py aplpy/overlays.py aplpy/regions.py aplpy/rgb.py aplpy/slicer.py aplpy/tick_labels.py aplpy/ticks.py aplpy/version.py aplpy.egg-info/PKG-INFO aplpy.egg-info/SOURCES.txt aplpy.egg-info/dependency_links.txt aplpy.egg-info/not-zip-safe aplpy.egg-info/requires.txt aplpy.egg-info/top_level.txt aplpy/tests/__init__.py aplpy/tests/coveragerc aplpy/tests/helpers.py aplpy/tests/setup_package.py aplpy/tests/test_axis_labels.py aplpy/tests/test_beam.py aplpy/tests/test_colorbar.py aplpy/tests/test_contour.py aplpy/tests/test_convolve.py aplpy/tests/test_downsample.py aplpy/tests/test_frame.py aplpy/tests/test_grid.py aplpy/tests/test_images.py aplpy/tests/test_init_cube.py aplpy/tests/test_init_image.py aplpy/tests/test_layers.py aplpy/tests/test_misc.py aplpy/tests/test_pixworldmarkers.py aplpy/tests/test_rgb.py aplpy/tests/test_save.py aplpy/tests/test_scalebar.py aplpy/tests/test_subplot.py aplpy/tests/test_tick_labels.py aplpy/tests/test_ticks.py aplpy/tests/test_vectors.py aplpy/tests/data/__init__.py aplpy/tests/data/shapes.reg aplpy/tests/data/2d_fits/1904-66_AIR.hdr aplpy/tests/data/2d_fits/1904-66_AIT.hdr aplpy/tests/data/2d_fits/1904-66_ARC.hdr aplpy/tests/data/2d_fits/1904-66_AZP.hdr aplpy/tests/data/2d_fits/1904-66_BON.hdr aplpy/tests/data/2d_fits/1904-66_CAR.hdr aplpy/tests/data/2d_fits/1904-66_CEA.hdr aplpy/tests/data/2d_fits/1904-66_COD.hdr aplpy/tests/data/2d_fits/1904-66_COE.hdr aplpy/tests/data/2d_fits/1904-66_COO.hdr aplpy/tests/data/2d_fits/1904-66_COP.hdr aplpy/tests/data/2d_fits/1904-66_CSC.hdr 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Some values are defined in # the global Astropy configuration which is loaded here before anything else. # See astropy.sphinx.conf for which values are set there. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('..')) # IMPORTANT: the above commented section was generated by sphinx-quickstart, but # is *NOT* appropriate for astropy or Astropy affiliated packages. It is left # commented out with this explanation to make it clear why this should not be # done. If the sys.path entry above is added, when the astropy.sphinx.conf # import occurs, it will import the *source* version of astropy instead of the # version installed (if invoked as "make html" or directly with sphinx), or the # version in the build directory (if "python setup.py build_sphinx" is used). # Thus, any C-extensions that are needed to build the documentation will *not* # be accessible, and the documentation will not build correctly. import os import sys import datetime from importlib import import_module try: from sphinx_astropy.conf.v1 import * # noqa except ImportError: print('ERROR: the documentation requires the sphinx-astropy package to be installed') sys.exit(1) # Get configuration information from setup.cfg from configparser import ConfigParser conf = ConfigParser() conf.read([os.path.join(os.path.dirname(__file__), '..', 'setup.cfg')]) setup_cfg = dict(conf.items('metadata')) # -- General configuration ---------------------------------------------------- # By default, highlight as Python 3. highlight_language = 'python3' # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.2' # To perform a Sphinx version check that needs to be more specific than # major.minor, call `check_sphinx_version("x.y.z")` here. # check_sphinx_version("1.2.1") # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns.append('_templates') # This is added to the end of RST files - a good place to put substitutions to # be used globally. rst_epilog += """ """ # -- Project information ------------------------------------------------------ # This does not *have* to match the package name, but typically does project = setup_cfg['name'] author = setup_cfg['author'] copyright = '{0}, {1}'.format( datetime.datetime.now().year, setup_cfg['author']) # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. import_module(setup_cfg['name']) package = sys.modules[setup_cfg['name']] # The short X.Y version. version = package.__version__.split('-', 1)[0] # The full version, including alpha/beta/rc tags. release = package.__version__ # -- Options for HTML output -------------------------------------------------- # A NOTE ON HTML THEMES # The global astropy configuration uses a custom theme, 'bootstrap-astropy', # which is installed along with astropy. A different theme can be used or # the options for this theme can be modified by overriding some of the # variables set in the global configuration. The variables set in the # global configuration are listed below, commented out. # Add any paths that contain custom themes here, relative to this directory. # To use a different custom theme, add the directory containing the theme. #html_theme_path = [] # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. To override the custom theme, set this to the # name of a builtin theme or the name of a custom theme in html_theme_path. html_theme = 'alabaster' # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = '' # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = '' # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '' # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". html_title = '{0} v{1}'.format(project, release) # Output file base name for HTML help builder. htmlhelp_basename = project + 'doc' html_logo = 'aplpy_logo.png' # -- Options for LaTeX output -------------------------------------------------- # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [('index', project + '.tex', project + u' Documentation', author, 'manual')] # -- Options for manual page output ------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [('index', project.lower(), project + u' Documentation', [author], 1)] # -- Options for the edit_on_github extension --------------------------------- if setup_cfg.get('edit_on_github').lower() == 'true': extensions += ['sphinx_astropy.ext.edit_on_github'] edit_on_github_project = setup_cfg['github_project'] edit_on_github_branch = "master" edit_on_github_source_root = "" edit_on_github_doc_root = "docs" # -- Resolving issue number to links in changelog ----------------------------- github_issues_url = 'https://github.com/{0}/issues/'.format(setup_cfg['github_project']) # Use nitpicky mode to avoid broken links in docs nitpicky = True nitpick_ignore = [('py:class', 'aplpy.layers.Layers'), ('py:class', 'aplpy.regions.Regions')] # -- Turn on nitpicky mode for sphinx (to warn about references not found) ---- # # nitpicky = True # nitpick_ignore = [] # # Some warnings are impossible to suppress, and you can list specific references # that should be ignored in a nitpick-exceptions file which should be inside # the docs/ directory. The format of the file should be: # # # # for example: # # py:class astropy.io.votable.tree.Element # py:class astropy.io.votable.tree.SimpleElement # py:class astropy.io.votable.tree.SimpleElementWithContent # # Uncomment the following lines to enable the exceptions: # # for line in open('nitpick-exceptions'): # if line.strip() == "" or line.startswith("#"): # continue # dtype, target = line.split(None, 1) # target = target.strip() # nitpick_ignore.append((dtype, six.u(target))) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.250823 aplpy-2.1.0/docs/fitsfigure/0000775000175000017500000000000000000000000014121 5ustar00tomtom././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/fitsfigure/arbitrary_coordinate_systems.rst0000644000175000017500000000527100000000000022653 0ustar00tomtom.. _arbitrary: Arbitrary coordinate systems ============================ Coordinate type --------------- In addition to standard longitude/latitude coordinates, APLpy supports any valid quantities for coordinates (e.g. velocity, frequency, ...) as long as the WCS header information is valid. To differentiate between longitudes, latitudes, and arbitrary scalar values, APLpy keeps track of the axis coordinate 'type' for each axis, and will try and guess these based on the header. However, it is possible to explicitly specify the coordinate type with the :meth:`~aplpy.FITSFigure.set_xaxis_coord_type` and :meth:`~aplpy.FITSFigure.set_yaxis_coord_type` methods in the :class:`~aplpy.FITSFigure` object:: f = FITSFigure('2MASS_k.fits') f.set_xaxis_coord_type('scalar') f.set_xaxis_coord_type('latitude') Valid coordinate types are ``longitude``, ``latitude``, and ``scalar``. Longitudes are forced to be in the 0 to 360 degree range, latitudes are forced to be in the -90 to 90 degree range, and scalars are not constrained. Label formatting ---------------- How label formats are specified depends on the coordinate type. If the coordinate is a longitude or latitude, then the label format is specified using a special syntax which describes whether the label should be decimal or sexagesimal, in hours or degrees, and indicates the number of decimal places. For example: * ``ddd.ddddd`` means decimal degrees, where the number of decimal places can be varied * ``hh`` or ``dd`` means hours (or degrees) * ``hh:mm`` or ``dd:mm`` means hours and minutes (or degrees and arcminutes) * ``hh:mm:ss`` or ``dd:mm:ss`` means hours, minutes, and seconds (or degrees, arcminutes, and arcseconds) * ``hh:mm:ss.ss`` or ``dd:mm:ss.ss`` means hours, minutes, and seconds (or degrees, arcminutes, and arcseconds), where the number of decimal places can be varied. If the coordinate type is scalar, then the format should be specified as a valid Python format. For example, ``%g`` is the default Python format, ``%10.3f`` means decimal notation with three decimal places, etc. For more information, see `String Formatting Operations `_. In both cases, the default label format can be overridden:: f.tick_labels.set_xformat('dd.ddddd') f.tick_labels.set_yformat('%11.3f') Aspect ratio ------------ When plotting images in sky coordinates, APLpy makes pixel square by default, but it is possible to change this, which can be useful for non-sky coordinates. 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To make a plot with an AVM-tagged RGB image, say 'example.jpg', you can use:: import aplpy f = aplpy.FITSFigure('example.jpg') f.show_rgb() Note that no filename is required for :meth:`~aplpy.FITSFigure.show_rgb` in this case. If PyAVM is installed, :func:`~aplpy.make_rgb_image` can embed AVM meta-data into RGB images it creates, although only JPEG and PNG files support this:: import aplpy aplpy.make_rgb_image('2mass_cube.fits', '2mass_rgb.jpg') If PyAVM 0.9.1 or later is installed, the above file ``2mass_rgb.jpg`` will include AVM meta-data and can then be plotted with:: import aplpy f = aplpy.FITSFigure('2mass_rgb.jpg') f.show_rgb() In other words, this means that when creating an RGB image with APLpy, it is no longer necessary to initialize :class:`~aplpy.FITSFigure` with a FITS file and then use :meth:`~aplpy.FITSFigure.show_rgb` with the RGB image filename - instead, :class:`~aplpy.FITSFigure` can be directly initialized with the AVM-tagged image. To disable the embedding of AVM tags, you can use the ``embed_avm_tags=False`` option for :func:`~aplpy.make_rgb_image`. These features require `PyAVM 0.9.1 `_ or later. Please report any issues you encounter `here `_. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/fitsfigure/howto_noninteractive.rst0000644000175000017500000000474400000000000021132 0ustar00tomtomAPLpy in non-interactive mode ----------------------------- While APLpy can be easily used to interactively make plots, it is also possible to make plots without opening up a display. This can be useful to run APLpy remotely, or to write non-interactive scripts to plot one or many FITS files. Running APLpy in non-interactive mode ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To run APLpy in non-interactive mode, you will need to change the 'backend' used by matplotlib from an interactive (e.g. WxAgg, TkAgg, MacOS X) to a non-interactive (e.g. Agg, Cairo, PS, PDF) backend. The default backend is typically set in your ``.matplotlibrc`` file (or if you do not have such a file, an interactive backend is usually chosen by default). The easiest way to change the backend temporarily is to use the ``matplotlib.use()`` function:: import matplotlib matplotlib.use('Agg') It is important to change the backend via ``matplotlib.use`` before the ``aplpy`` module is imported. Once the backend has been changed, any call to ``FITSFigure()`` will no longer make a figure window appear. The following script can be used to make a PNG plot:: import matplotlib matplotlib.use('Agg') import aplpy f = aplpy.FITSFigure('mips_24micron.fits') f.show_grayscale() f.save('mips_24.png') Pan/Zoom in non-interactive mode ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ One of the advantages of using APLpy in interactive mode is the ability to zoom in on a given region of interest. To replicate this functionality in non-interactive mode, the ``FITSFigure.recenter()`` method can be used. This method takes a central position, and either a radius (to make a square plot) or a width/height (to make a rectangular plot). This is illustrated in the following example:: import matplotlib matplotlib.use('Agg') import aplpy f = aplpy.FITSFigure('mips_24micron.fits') f.show_grayscale() f.recenter(266.2142,-29.1832,width=0.5,height=0.3) f.save('mips_24_zoomin.png') Batch scripting ^^^^^^^^^^^^^^^ Using APLpy non-interactively can be useful for making plots of many FITS files. Given a directory ``fits/`` containing FITS files, the following code will generate on plot for each ``.fits`` file:: import matplotlib matplotlib.use('Agg') import aplpy import glob import os for fits_file in glob.glob(os.path.join('fits/','*.fits')): f = aplpy.FITSFigure(fits_file) f.show_grayscale() f.save(fits_file.replace('.fits','.png')) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/fitsfigure/howto_subplot.rst0000644000175000017500000000543400000000000017567 0ustar00tomtomCreating subplots ----------------- By default, :class:`~aplpy.FITSFigure` creates a figure with a single subplot that occupies the entire figure. However, APLpy can be used to place a subplot in an existing matplotlib figure instance. To do this, :class:`~aplpy.FITSFigure` should be called with the ``figure=`` argument as follows:: import aplpy import matplotlib.pyplot as mpl fig = mpl.figure() f = aplpy.FITSFigure('some_image.fits', figure=fig) The above will place a subplot inside the ``fig`` figure instance. The ``f`` object can be used as normal to control the FITS figure inside the subplot. The above however is not very interesting compared to just creating a FITSFigure instance from scratch. What this is useful for is only using sub-regions of the figure to display the FITS data, to leave place for other subplots, whether histograms, scatter, or other matplotlib plots, or another FITS Figure. This can be done using the ``subplot`` argument. From the docstring for FITSFigure:: *subplot*: [ list of four floats ] If specified, a subplot will be added at this position. The list should contain [xmin, ymin, dx, dy] where xmin and ymin are the position of the bottom left corner of the subplot, and dx and dy are the width and height of the subplot respectively. These should all be given in units of the figure width and height. For example, [0.1, 0.1, 0.8, 0.8] will almost fill the entire figure, leaving a 10 percent margin on all sides. The following code outline illustrates how to create a rectangular figure with two FITS images:: import aplpy import matplotlib.pyplot as mpl fig = mpl.figure(figsize=(15, 7)) f1 = aplpy.FITSFigure('image_1.fits', figure=fig, subplot=[0.1,0.1,0.35,0.8]) f1.set_tick_labels_font(size='x-small') f1.set_axis_labels_font(size='small') f1.show_grayscale() f2 = aplpy.FITSFigure('image_2.fits', figure=fig, subplot=[0.5,0.1,0.35,0.8]) f2.set_tick_labels_font(size='x-small') f2.set_axis_labels_font(size='small') f2.show_grayscale() f2.hide_yaxis_label() f2.hide_ytick_labels() fig.canvas.draw() The ``hide`` methods shown above are especially useful when working with subplots, as in some cases there is no need to repeat the tick labels. Alternatively figures can be constructed from both APLpy figures and normal matplotlib axes:: import aplpy import matplotlib.pyplot as mpl fig = mpl.figure(figsize=(15, 7)) f1 = aplpy.FITSFigure('image_1.fits', figure=fig, subplot=[0.1,0.1,0.35,0.8]) f1.set_tick_labels_font(size='x-small') f1.set_axis_labels_font(size='small') f1.show_grayscale() ax2 = fig.add_axes([0.5,0.1,0.35,0.8]) # some code here with ax2 fig.canvas.draw() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/fitsfigure/myfirstplot.png0000644000175000017500000451234500000000000017237 0ustar00tomtom‰PNG  IHDRójiZfsBIT|dˆ pHYsaa¨?§i9tEXtSoftwarematplotlib version 3.0.2, http://matplotlib.org/8€;­ IDATxœì½i¦×Uçù»Ë³¼[îY•µªJUÚ%K²,ÛƃAFØnÀ¸±» » ‚‰™&ˆi˜0ÝA7Ý30Ì€išÆf€0vŒñÖÆ Û²dkßJ¥Z3+÷Ìw{¶{ï™7å˜/ÐÝn£ì1ïÿKUe½ñž»<÷ÜsÎÿžT""L0ÁL0ÁL0ÁüBï÷&˜`‚ &˜`‚ &˜`‚ÿb’LL0ÁL0ÁL0Á_&ÉÄL0ÁL0ÁLðUa’LL0ÁL0ÁL0Á_&ÉÄü¢ßïsß}÷Q–å~e‚¯S 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In addition, not all of the optional keywords are mentioned here. For more information on a specific command and all the options available, use the ``help()`` function, e.g. ``help(fig.show_grayscale)``. When multiple optional arguments are given, this does not mean that all of them have to be specified, but just shows all the options available. Introduction ^^^^^^^^^^^^ To import APLpy:: import aplpy To create a figure of a FITS file:: fig = aplpy.FITSFigure('myimage.fits') Here and in the remainder of this reference guide, we use the variable name ``fig`` for the figure object, but any name can be used. A grayscale or colorscale representation of the FITS image can be shown and hidden using the following methods:: fig.show_grayscale() fig.hide_grayscale() fig.show_colorscale() fig.hide_colorscale() To show a three-color image, use the following method, specifying the filename of the color image:: fig.show_rgb('m17.jpeg') The figure can be interactively explored by zooming and panning. To recenter on a specific region programmatically, use the following method, specifying either a radius:: fig.recenter(33.23, 55.33, radius=0.3) # degrees or a separate width and height:: fig.recenter(33.23, 55.33, width=0.3, height=0.2) # degrees To overlay contours, use:: fig.show_contour('co_data.fits') To save the current figure, use:: fig.save('myplot.eps') Other formats such as PDF, PNG, etc. can be used. Labels ^^^^^^ Labels can be added either in world coordinates:: fig.add_label(34.455, 54.112, 'My favorite star') or relative to the axes:: fig.add_label(0.1, 0.9, '(a)', relative=True) Shapes ^^^^^^ To overlay different shapes, the following methods are available:: fig.show_markers(x_world, y_world) fig.show_circles(x_world, y_world, radius) fig.show_ellipses(x_world, y_world, width, height) fig.show_rectangles(x_world, y_world, width, height) fig.show_arrows(x_world, y_world, dx, dy) where ``x_world``, ``y_world``, ``radius``, ``width``, ``height``, ``dx``, and ``dy`` should be 1D arrays specified in degrees. It is also possible to plot lines and polygons using:: fig.show_lines(line_list) fig.show_polygons(polygon_list) For these methods, ``line_list`` and ``polygon_list`` should be lists of 2xN Numpy arrays describing the coordinates of the vertices in degrees. DS9 Regions ^^^^^^^^^^^ DS9 region files can be overlaid with:: fig.show_regions('myregions.reg') Layers ^^^^^^ Markers, shapes, regions and text labels are stored in layers. These layers can be listed using:: fig.list_layers() Layers can be hidden and shown with the following methods:: fig.hide_layer('regions') fig.show_layer('regions') Any layer can be retrieved using:: layer = fig.get_layer('circles') Finally, layers can be removed using:: fig.remove_layer('rectangles') Coordinates ^^^^^^^^^^^ Two methods are provided to help transform coordinates between world and pixel coordinates. These accept either scalars or arrays in degrees:: x_pix, y_pix = fig.world2pixel(45.3332, 22.1932) x_world, y_world = fig.pixel2world(np.array([1., 2., 3]), np.array([1., 3., 5.])) Frame ^^^^^ To set the look of the frame around the figure, use:: fig.frame.set_linewidth(1) # points fig.frame.set_color('black') Colorbar ^^^^^^^^ A grid can be added and removed using the following commands:: fig.add_colorbar() fig.remove_colorbar() Once :meth:`~aplpy.FITSFigure.add_colorbar` has been called, the ``fig.colorbar`` object is created and the following methods are then available: * Show and hide the colorbar:: fig.colorbar.show() fig.colorbar.hide() * Set where to place the colorbar:: fig.colorbar.set_location('right') This can be one of ``left``, ``right``, ``bottom`` or ``top``. * Set the width of the colorbar:: fig.colorbar.set_width(0.1) # arbitrary units, default is 0.2 * Set the amount of padding between the colorbar and the parent axes:: fig.colorbar.set_pad(0.03) # arbitrary units, default is 0.05 * Set the font properties of the labels:: fig.colorbar.set_font(size='medium', weight='medium', \ stretch='normal', family='sans-serif', \ style='normal', variant='normal') * Add a colorbar label:: f.colorbar.set_axis_label_text('Surface Brightness (Jy/beam)') * Set some of the colorbar label properties:: f.colorbar.set_axis_label_font(size=12, weight='bold') * Set the padding between the colorbar and the label in points:: f.colorbar.set_axis_label_pad(10) * Change the rotation of the colorbar label, in degrees:: f.colorbar.set_axis_label_rotation(270) Coordinate Grid ^^^^^^^^^^^^^^^ A coordinate grid can be added and removed using the following commands:: fig.add_grid() fig.remove_grid() Once :meth:`~aplpy.FITSFigure.add_grid` has been called, the ``fig.grid`` object is created and the following methods are then available: * Show and hide the grid:: fig.grid.show() fig.grid.hide() * Set the x and y spacing for the grid:: fig.grid.set_xspacing(0.2) # degrees fig.grid.set_yspacing(0.2) # degrees * Set the color of the grid lines:: fig.grid.set_color('white') * Set the transparency level of the grid lines:: fig.grid.set_alpha(0.8) * Set the line style and width for the grid lines:: fig.grid.set_linestyle('solid') fig.grid.set_linewidth(1) # points Scalebar ^^^^^^^^ A scalebar can be added and removed using the following commands:: fig.add_scalebar() fig.remove_scalebar() Once :meth:`~aplpy.FITSFigure.add_scalebar` has been called, the ``fig.scalebar`` object is created and the following methods are then available: * Show and hide the scalebar:: fig.scalebar.show(0.2) # length in degrees fig.scalebar.hide() * Change the length of the scalebar:: fig.scalebar.set_length(0.02) # degrees * Specify the length of the scalebar in different units:: from astropy import units as u fig.scalebar.set_length(72 * u.arcsecond) * Change the label of the scalebar:: fig.scalebar.set_label('5 pc') * Change the corner that the beam is shown in:: fig.scalebar.set_corner('top right') This can be one of ``top right``, ``top left``, ``bottom right``, ``bottom left``, ``left``, ``right``, ``bottom`` or ``top``. * Set whether or not to show a frame around the beam:: fig.scalebar.set_frame(False) * Set the transparency level of the scalebar and label:: fig.scalebar.set_alpha(0.7) * Set the color of the scalebar and label:: fig.scalebar.set_color('white') * Set the font properties of the label:: fig.scalebar.set_font(size='medium', weight='medium', \ stretch='normal', family='sans-serif', \ style='normal', variant='normal') * Set the line style and width for the scalebar:: fig.scalebar.set_linestyle('solid') fig.scalebar.set_linewidth(3) # points * Set multiple properties at once:: fig.scalebar.set(linestyle='solid', color='red', ...) Beam ^^^^ A beam can be added and removed using the following commands:: fig.add_beam() fig.remove_beam() Once :meth:`~aplpy.FITSFigure.add_beam` has been called, the ``fig.beam`` object is created and the following methods are then available: * Show and hide the beam:: fig.beam.show() fig.beam.hide() * Change the major and minor axes, and the position angle:: fig.beam.set_major(0.03) # degrees fig.beam.set_minor(0.02) # degrees fig.beam.set_angle(45.) # degrees * Specify the major and minor axes, and the position angle, in explicit units:: from astropy import units as u fig.beam.set_major(108 * u.arcsecond) fig.beam.set_minor(349 * u.microradian) fig.beam.set_angle(45 * u.degree) * Change the corner that the beam is shown in:: fig.beam.set_corner('top left') This can be one of ``top right``, ``top left``, ``bottom right``, ``bottom left``, ``left``, ``right``, ``bottom`` or ``top``. * Set whether or not to show a frame around the beam:: fig.beam.set_frame(False) * Set the transparency level of the beam:: fig.beam.set_alpha(0.5) * Set the color of the whole beam, or the edge and face color individually:: fig.beam.set_color('white') fig.beam.set_edgecolor('white') fig.beam.set_facecolor('green') * Set the line style and width for the edge of the beam:: fig.beam.set_linestyle('dashed') fig.beam.set_linewidth(2) # points * Set the hatch style of the beam:: fig.beam.set_hatch('/') * Set multiple properties at once:: fig.beam.set(facecolor='red', linestyle='dashed', ...) Coordinate types ^^^^^^^^^^^^^^^^ APLpy supports three types of coordinates: longitudes (in the range 0 to 360 with wrap-around), latitudes (in the range -90 to 90), and scalars (any arbitrary value). APLpy tries to guess the correct type of coordinate for each axis, but it is possible to override this:: fig.set_xaxis_coord_type('scalar') fig.set_yaxis_coord_type('longitude') Valid options are ``longitude``, ``latitude``, and ``scalar``. Axis labels ^^^^^^^^^^^ The methods to control the x- and y- axis labels are the following * Show/hide both axis labels:: fig.axis_labels.show() fig.axis_labels.hide() * Show/hide the x-axis label:: fig.axis_labels.show_x() fig.axis_labels.hide_x() * Show/hide the y-axis label:: fig.axis_labels.show_y() fig.axis_labels.hide_y() * Set the text for the x- and y-axis labels:: fig.axis_labels.set_xtext('Right Ascension (J2000)') fig.axis_labels.set_ytext('Declination (J2000)') * Set the displacement of the x- and y-axis labels from the x- and y-axis respectively:: fig.axis_labels.set_xpad(...) fig.axis_labels.set_ypad(...) * Set where to place the x-axis label:: fig.axis_labels.set_xposition('bottom') * Set where to place the y-axis label:: fig.axis_labels.set_yposition('right') * Set the font properties of the labels:: fig.axis_labels.set_font(size='medium', weight='medium', \ stretch='normal', family='sans-serif', \ style='normal', variant='normal') Tick labels ^^^^^^^^^^^ The methods to control the numerical labels below each tick are the following * Show/hide all tick labels:: fig.tick_labels.show() fig.tick_labels.hide() * Show/hide the x-axis labels:: fig.tick_labels.show_x() fig.tick_labels.hide_x() * Show/hide the y-axis labels:: fig.tick_labels.show_y() fig.tick_labels.hide_y() * Set the format for the x-axis labels (e.g hh:mm, hh:mm:ss.s, etc.):: fig.tick_labels.set_xformat('hh:mm:ss.ss') * Set the format for the y-axis labels (e.g dd:mm, dd:mm:ss.s, etc.):: fig.tick_labels.set_yformat('dd:mm:ss.s') * Set where to place the x-axis tick labels:: fig.tick_labels.set_xposition('top') * Set where to place the y-axis tick labels:: fig.tick_labels.set_yposition('left') * Set the style of the labels ('colons' or 'plain'):: fig.tick_labels.set_style('colons') * Set the font properties of the labels:: fig.tick_labels.set_font(size='medium', weight='medium', \ stretch='normal', family='sans-serif', \ style='normal', variant='normal') Ticks ^^^^^ The methods to control properties relating to the tick marks are the following: * Show/hide all ticks:: fig.ticks.show() fig.ticks.hide() * Show/hide the x-axis ticks:: fig.ticks.show_x() fig.ticks.hide_x() * Show/hide the y-axis ticks:: fig.ticks.show_y() fig.ticks.hide_y() * Change the tick spacing for the x- and y-axis:: fig.ticks.set_xspacing(0.04) # degrees fig.ticks.set_yspacing(0.03) # degrees * Change the length of the ticks:: fig.ticks.set_length(10) # points * Change the color of the ticks:: fig.ticks.set_color('black') * Set the line width for the ticks:: fig.ticks.set_linewidth(2) # points * Set the number of minor ticks per major tick:: fig.ticks.set_minor_frequency(5) Set this to ``1`` to get rid of minor ticks Advanced ^^^^^^^^ To control whether to refresh the display after each command, use the following method:: fig.set_auto_refresh(False) To force a refresh, use:: fig.refresh() To use the system LaTeX instead of the matplotlib LaTeX, use:: fig.set_system_latex(True) The color for NaN values can be controlled using the following method:: fig.set_nan_color('black') The order of figure elements (eg lines, shapes, imagemap) can be controlled using the zorder parameter using for example:: fig.show_ellipses(x_world, y_world, width, height, zorder=1) Increasing integers from 0 will place elements towards the front. Setting zorder=0 places layer at the bottom. Finally, to change the look of the plot using pre-set themes, use:: fig.set_theme('publication') ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/fitsfigure/quickstart.rst0000644000175000017500000001317000000000000017045 0ustar00tomtomBeginner Tutorial ----------------- .. toctree:: :maxdepth: 1 The following tutorial will take you, step by step, through the main features of APLpy. You will need to download the following `file `_. First, unpack the example files and go to the ``tutorial`` directory:: tar -xvzf tutorial.tar.gz cd tutorial Now, start up ``ipython``:: ipython --pylab and import the ``aplpy`` module as follows:: import aplpy To start off, use the :class:`~aplpy.FITSFigure` class to create a canvas to where your data will be plotted:: gc = aplpy.FITSFigure('fits/2MASS_k.fits') and display the image using a grayscale stretch:: gc.show_grayscale() Check out the buttons at the top or bottom of the canvas (depending on the version of matplotlib). You will see seven buttons: .. image:: buttons.png The first five are of interest to us here. The button with the magnifying glass will allow you to select an area on the plot and zoom in. To zoom out you can click on the home button (first one on the left). When you're zoomed in you can pan around by clicking on the button with the arrows (fourth button from the left). Use the following command to show a colorscale image instead:: gc.show_colorscale() The colormap used for the colorscale image can be changed. Try the following:: gc.show_colorscale(cmap='gist_heat') to show the image showing a 'heat' map. You can find more information in the :meth:`~aplpy.FITSFigure.show_grayscale` and :meth:`~aplpy.FITSFigure.show_colorscale` documentation. For example, you can control the minimum and maximum pixel values to show and the stretch function to use. It is possible to use APLpy to show 3-color images. To do this, you need a FITS file with the image - this is used for the information relating to the coordinates - and a PNG file containing the 3-color image. Both files need to have exactly the same dimensions and the pixels from the PNG file have to match those from the FITS file. An example is provided in the tutorial files. Try the following:: gc.show_rgb('graphics/2MASS_arcsinh_color.png') It is very easy to modify the font properties of the various labels. For example, in this case, we can change the font size of the tick labels to be smaller than the default:: gc.tick_labels.set_font(size='small') APLpy can be used to overlay contours onto a grayscale/colorscale/3-color image. Try typing the following command:: gc.show_contour('fits/mips_24micron.fits', colors='white') There are a number of arguments that can be passed to :meth:`~aplpy.FITSFigure.show_contour` to control the appearance of the contours as well as the number of levels to show. For more information, see the see the :meth:`~aplpy.FITSFigure.show_contour` documentation. Display a coordinate grid using:: gc.add_grid() and hide it again using:: gc.remove_grid() Let's overplot positions from a source list. Here we will use loadtxt to read in the coordinates from a file, but in general you can pass any pair of lists or numpy arrays that are already defined:: import numpy ra, dec = numpy.loadtxt('data/yso_wcs_only.txt', unpack=True) gc.show_markers(ra, dec, edgecolor='green', facecolor='none', marker='o', s=10, alpha=0.5) For more information, see the :meth:`~aplpy.FITSFigure.show_markers` documentation. It's often the case that you might want to change the look of a contour or markers, but if you run :meth:`~aplpy.FITSFigure.show_contour` or :meth:`~aplpy.FITSFigure.show_markers` a second time, it will overplot rather than replacing. To solve this problem APLpy has 'layers' which can be manipulated in a basic way. Type:: gc.list_layers() which will print out something like this:: There are 2 layers in this figure: -> contour_set_1 -> marker_set_1 You can use :meth:`~aplpy.FITSFigure.remove_layer`, :meth:`~aplpy.FITSFigure.hide_layer`, and :meth:`~aplpy.FITSFigure.show_layer` to manipulate the layers, and you can also specify the ``layer=name`` argument to :meth:`~aplpy.FITSFigure.show_contour` or :meth:`~aplpy.FITSFigure.show_markers`. Using the latter forces APLpy to name the layer you are creating with the name provided, and can also be used to replace an existing layer. For example, let's change the color of the markers from green to red:: gc.show_markers(ra, dec, layer='marker_set_1', edgecolor='red', facecolor='none', marker='o', s=10, alpha=0.5) Note the presence of the ``layer='marker_set_1'`` which means that the current markers plot will be replaced. To view active layers, you can use the :meth:`~aplpy.FITSFigure.list_layers` documentation. To wrap up this tutorial, we will save the plot to a file. Type the following:: gc.save('myfirstplot.png') This will produce the following plot: .. image:: myfirstplot.png You can of course save it as a PS/EPS, PDF, or SVG file instead. The EPS output is suitable for publication. To summarize, the above plot was made using the following commands:: import aplpy import numpy gc = aplpy.FITSFigure('fits/2MASS_k.fits') gc.show_rgb('graphics/2MASS_arcsinh_color.png') gc.tick_labels.set_font(size='small') gc.show_contour('fits/mips_24micron.fits', colors='white') ra, dec = numpy.loadtxt('data/yso_wcs_only.txt', unpack=True) gc.show_markers(ra, dec, layer='marker_set_1', edgecolor='red', facecolor='none', marker='o', s=10, alpha=0.5) gc.save('myfirstplot.png') There are many more methods and options, from setting the tick spacing and format to controlling the label fonts. For more information, see the :doc:`quick_reference` or the :ref:`api`. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/fitsfigure/slicing.rst0000644000175000017500000000712000000000000016301 0ustar00tomtomSlicing multi-dimensional data cubes ==================================== How to slice data cubes ----------------------- APLpy supports extracting a slice from n-dimensional FITS cubes, and re-ordering dimensions. The two key arguments to :class:`~aplpy.FITSFigure` to control this are ``dimensions`` and ``slices``. These arguments can also be passed to :meth:`~aplpy.FITSFigure.show_contour`. The ``dimensions`` argument is used to specify which dimensions should be used for the x- and y-axis respectively (zero based). The default values are ``[0, 1]`` which means that the x-axis should use the first dimension in the FITS cube, and the y-axis should use the second dimension. For a 2-dimensional FITS file, this means that one can use ``[1, 0]`` to flip the axes. For a FITS cube with R.A., Declination, and Velocity, ``[0, 2]`` would make a R.A.-Velocity plot. The ``slices`` argument gives the pixels slice to extract from the remaining dimensions, skipping the dimensions used, so ``slices`` should be a list with length n-2 where n is the number of dimensions in the FITS file. For example, if one has a FITS file with R.A., Declination, Velocity, and Time (in that order), then: * ``dimensions=[0, 1]`` means the plot will be an R.A-Declination plot, and ``slices=[33, 56]`` means that pixel slices 33 and 56 will be used in Velocity and Time respectively (in this case, ``dimensions`` does not need to be specified since it defaults to ``[0, 1]``) * ``dimensions=[0, 2]`` means the plot will be an R.A-Velocity plot, and ``slices=[22, 56]`` means that pixel slices 22 and 56 will be used in Declination and Time respectively. * ``dimensions=[3, 2]`` means the plot will be a Time-Velocity plot, and ``slices=[10, 22]`` means that pixel slices 10 and 22 will be used in R.A and Declination respectively. See :ref:`arbitrary` for information on formatting the labels when non-longitude/latitude coordinates are used. Aspect ratio ------------ When plotting images in sky coordinates, APLpy makes pixel square by default, but it is possible to change this. When calling :meth:`~aplpy.FITSFigure.show_grayscale` or :meth:`~aplpy.FITSFigure.show_colorscale`, simply add ``aspect='auto'`` which will override the ``aspect='equal'`` default. The ``aspect='auto'`` is demonstrated below. Example ------- The following examples demonstrates this functionality in use .. plot:: :context: reset :include-source: from astropy.utils.data import get_pkg_data_filename co_cube = get_pkg_data_filename('l1448/l1448_13co.fits') import aplpy f = aplpy.FITSFigure(co_cube, slices=[30], figsize=(8, 6)) f.show_colorscale() f.add_grid() f.tick_labels.set_font(size='x-small') f.axis_labels.set_font(size='small') .. plot:: :context: reset :include-source: from astropy.utils.data import get_pkg_data_filename co_cube = get_pkg_data_filename('l1448/l1448_13co.fits') import aplpy f = aplpy.FITSFigure(co_cube, slices=[30], dimensions=[1, 0], figsize=(8, 6)) f.show_colorscale() f.add_grid() f.tick_labels.set_font(size='x-small') f.axis_labels.set_font(size='small') .. plot:: :context: reset :include-source: from astropy.utils.data import get_pkg_data_filename co_cube = get_pkg_data_filename('l1448/l1448_13co.fits') import aplpy f = aplpy.FITSFigure(co_cube, dimensions=[2, 1], slices=[50], figsize=(8, 6)) f.show_colorscale(aspect='auto') f.add_grid() f.tick_labels.set_font(size='x-small') f.axis_labels.set_font(size='small') f.tick_labels.set_xformat('%.1f') ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/index.rst0000644000175000017500000000714500000000000013620 0ustar00tomtom******************* APLpy documentation ******************* `APLpy `_ (the Astronomical Plotting Library in Python - pronounced *apple pie*) is a Python package module aimed at producing publication-quality plots of astronomical imaging data, and has been around since 2009. Since APLpy 2.0, APLpy relies on Astropy's `WCSAxes `_ package to draw the coordinate axes and grids, which in turn uses `Matplotlib `_ to do the rendering. This is a big change compared to previous versions of APLpy, which handled the drawing of the coordinate axes and grids directly. For information about backward-compatibility, see the `Backward-compatibility notes`_. Note that APLpy 2.x is only compatible with Python 3.5 and later. If you use APLpy for a publication, please consider citing it as described in the `CITATION `__ file. Should you use APLpy? ===================== The `WCSAxes`_ package in Astropy can be used without APLpy to produce plots of astronomical images. It provides a programmatic interface that is conceptually very close to Matplotlib, and can be used to make plots that are more complex than what APLpy has historically been able to do. If you want to be able to control the appearance of your plots in detail, including for example adding overlay coordinate systems, or if you simply want a programmatic interface that is consistent with Matplotlib, then we recommend you use `WCSAxes`_ directly. If you have a FITS file and just want to make it into a plot in a couple of lines, then APLpy is the package for you. Installing APLpy ================ You can install APLpy with pip using:: pip install aplpy If you prefer to use conda, you can install APLpy with:: conda install -c astropy aplpy Using APLpy =========== APLpy includes a :class:`~aplpy.FITSFigure` class that provides a simple interface to make plots and provides easy methods for overlaying shapes, beams, adding colorbars, and overplotting regions. .. toctree:: :maxdepth: 1 fitsfigure/quickstart.rst fitsfigure/quick_reference.rst fitsfigure/arbitrary_coordinate_systems.rst fitsfigure/slicing.rst fitsfigure/howto_noninteractive.rst fitsfigure/howto_avm.rst fitsfigure/howto_subplot.rst rgb .. _backcompat: Backward-compatibility notes ============================ APLpy continues to include a :class:`~aplpy.FITSFigure` class that provides a simpler interface and deals with the reading of FITS files and constructing WCS objects. This class uses `WCSAxes`_ behind the scenes, but provides easy methods for overlaying shapes, beams, adding colorbars, and overplotting regions. The interface of this class is intended to be backward-compatible with existing scripts that used APLpy 1.0 and earlier. If you run into issues with old scripts, please report the issues in the `issue tracker `_. Note however that if you have made adjustments to figures in the past using private attributes (e.g. ``fig._ax1``), these will no longer work and this cannot be fixed in APLpy 2.x. In any case, if you have issues with APLpy 2.x and want to continue using APLpy 1.x, you can always restrict the version of APLpy when installing it using e.g.:: pip install "aplpy<2" or:: conda install "aplpy<2" If critical bugs are found in the 1.x releases and can easily be fixed, we may release new 1.x versions. .. _api: Reference/API ============= .. automodapi:: aplpy :no-main-docstr: :no-heading: :no-inheritance-diagram: :inherited-members: ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/index.txt0000644000175000017500000000042400000000000013620 0ustar00tomtomAPLpy documentation =================== This is the documentation for APLpy. The APLpy homepage is located at http://aplpy.github.io .. toctree:: :maxdepth: 1 quickstart.rst quick_reference.rst arbitrary_coordinate_systems.rst slicing.rst howtos.rst api.rst ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/make.bat0000644000175000017500000001064100000000000013357 0ustar00tomtom@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=_build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\Astropy.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\Astropy.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1581700678.0 aplpy-2.1.0/docs/rgb.rst0000644000175000017500000000667100000000000013266 0ustar00tomtomMaking RGB images ----------------- APLpy includes helper functions to generate RGB images from scratch, even for files with initially different projections/resolutions. Reprojecting three images to the same projection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you are starting from three images with different projections/resolutions, the first step is to reproject these to a common projection/resolution. The following code shows how this can be done using the :func:`~aplpy.make_rgb_cube` function:: aplpy.make_rgb_cube(['2mass_k.fits', '2mass_h.fits', '2mass_j.fits'], '2mass_cube.fits') This method makes use of the `reproject `_ package to reproject the images to a common projection. The above example produces a FITS cube named ``2mass_cube.fits`` which contains the three channels in the same projection. This can be used to then produce an RGB image (see next section) Producing an RGB image from images in a common projection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The :func:`~aplpy.make_rgb_image` function can be used to produce an RGB image from either three FITS files in the exact same projection, or a FITS cube containing the three channels (such as that output by :func:`~aplpy.make_rgb_cube`). The following example illustrates how to do this:: aplpy.make_rgb_image('2mass_cube.fits','2mass_rgb.png') In the above example, APLpy will automatically adjust the stretch of the different channels to try and produce a reasonable image, but it is of course possible to specify one or more of the lower/upper limits of the scale to use for each channel. For each lower/upper scale limit, this can be done in two ways. The first is to use the vmin_r/g/b or vmax_r/g/b argument which specifies the pixel value to use for the lower or upper end of the scale respectively. The alternative is to use the pmin_r/g/b or pmax_r/g/b arguments, which specify the percentile value to use instead of an absolute value. The two can of course be mixed, such as in the following example:: aplpy.make_rgb_image('2mass_cube.fits', '2mass_rgb.png', vmin_r=0., pmax_g=90.) Plotting the resulting RGB image ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If `PyAVM `_ is installed (which is recommended but not required), then if you produce a JPEG or PNG image, you can plot it directly using the :class:`~aplpy.FITSFigure` class with:: f = aplpy.FITSFigure('2mass_rgb.png') f.show_rgb() For more information, see :ref:`howto-avm`. On the other hand, if you are not able to use PyAVM, or need to use a format other than JPEG or PNG, then you need to instantiate the :class:`~aplpy.FITSFigure` class with a FITS file with exactly the same dimensions and WCS as the RGB image. The :func:`~aplpy.make_rgb_cube` function will in fact produce a file with the same name as the main output, but including a ``_2d`` suffix, and this can be used to instantiate :class:`~aplpy.FITSFigure`:: # Reproject the images to a common projection - this will also # produce 2mass_cube_2d.fits aplpy.make_rgb_cube(['2mass_k.fits', '2mass_h.fits', '2mass_j.fits'], '2mass_cube.fits') # Make an RGB image aplpy.make_rgb_image('2mass_cube.fits', '2mass_rgb.png') # Plot the RGB image using the 2d image to indicate the projection f = aplpy.FITSFigure('2mass_cube_2d.fits') f.show_rgb('2mass_rgb.png') ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/pyproject.toml0000664000175000017500000000020600000000000013734 0ustar00tomtom[build-system] requires = ["setuptools", "setuptools_scm", "wheel"] build-backend = 'setuptools.build_meta' ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1646929515.250823 aplpy-2.1.0/setup.cfg0000664000175000017500000000262000000000000012643 0ustar00tomtom[metadata] name = aplpy author = Thomas Robitaille and Eli Bressert author_email = thomas.robitaille@gmail.com license = MIT license_file = LICENSE.md url = http://aplpy.github.io description = The Astronomical Plotting Library in Python long_description = file: README.rst long_description_content_type = text/x-rst edit_on_github = False github_project = APLpy/aplpy [options] zip_safe = False packages = find: python_requires = >=3.6 setup_requires = setuptools_scm install_requires = numpy astropy>=3.1 matplotlib>=2.0 reproject>=0.4 pyregion>=2.0 pillow>=4.3 pyavm>=0.9.4 scikit-image>=0.14 shapely>=1.7 [options.extras_require] test = pytest-astropy pytest-mpl docs = sphinx-astropy [options.package_data] aplpy = *.reg, *.hdr [tool:pytest] testpaths = "aplpy" "docs" astropy_header = true doctest_plus = enabled text_file_format = rst addopts = --doctest-rst [coverage:run] omit = aplpy/_astropy_init* aplpy/conftest.py aplpy/*setup_package* aplpy/tests/* aplpy/*/tests/* aplpy/extern/* aplpy/version* */aplpy/_astropy_init* */aplpy/conftest.py */aplpy/*setup_package* */aplpy/tests/* */aplpy/*/tests/* */aplpy/extern/* */aplpy/version* [coverage:report] exclude_lines = pragma: no cover except ImportError raise AssertionError raise NotImplementedError def main\(.*\): pragma: py{ignore_python_version} def _ipython_key_completions_ [egg_info] tag_build = tag_date = 0 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/setup.py0000775000175000017500000000364200000000000012544 0ustar00tomtom#!/usr/bin/env python # Licensed under a 3-clause BSD style license - see LICENSE.rst # NOTE: The configuration for the package, including the name, version, and # other information are set in the setup.cfg file. import os import sys from setuptools import setup # First provide helpful messages if contributors try and run legacy commands # for tests or docs. TEST_HELP = """ Note: running tests is no longer done using 'python setup.py test'. Instead you will need to run: tox -e test If you don't already have tox installed, you can install it with: pip install tox If you only want to run part of the test suite, you can also use pytest directly with:: pip install -e .[test] pytest For more information, see: http://docs.astropy.org/en/latest/development/testguide.html#running-tests """ if 'test' in sys.argv: print(TEST_HELP) sys.exit(1) DOCS_HELP = """ Note: building the documentation is no longer done using 'python setup.py build_docs'. Instead you will need to run: tox -e build_docs If you don't already have tox installed, you can install it with: pip install tox You can also build the documentation with Sphinx directly using:: pip install -e .[docs] cd docs make html For more information, see: http://docs.astropy.org/en/latest/install.html#builddocs """ if 'build_docs' in sys.argv or 'build_sphinx' in sys.argv: print(DOCS_HELP) sys.exit(1) VERSION_TEMPLATE = """ # Note that we need to fall back to the hard-coded version if either # setuptools_scm can't be imported or setuptools_scm can't determine the # version, so we catch the generic 'Exception'. try: from setuptools_scm import get_version version = get_version(root='..', relative_to=__file__) except Exception: version = '{version}' """.lstrip() setup(use_scm_version={'write_to': os.path.join('aplpy', 'version.py'), 'write_to_template': VERSION_TEMPLATE}) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1646928934.0 aplpy-2.1.0/tox.ini0000664000175000017500000000541700000000000012344 0ustar00tomtom[tox] envlist = py{36,37,38}-test{,-alldeps,-devdeps}{,-cov} py{36,37,38}-test-numpy{116,117,118} py{36,37,38}-test-astropy{30,40,lts} build_docs linkcheck codestyle requires = setuptools >= 30.3.0 pip >= 19.3.1 isolated_build = true [testenv] # Pass through the following environment variables which may be needed for the CI passenv = HOME WINDIR LC_ALL LC_CTYPE CC CI TRAVIS # Run the tests in a temporary directory to make sure that we don't import # this package from the source tree changedir = .tmp/{envname} # tox environments are constructed with so-called 'factors' (or terms) # separated by hyphens, e.g. test-devdeps-cov. Lines below starting with factor: # will only take effect if that factor is included in the environment name. To # see a list of example environments that can be run, along with a description, # run: # # tox -l -v # description = run tests alldeps: with all optional dependencies devdeps: with the latest developer version of key dependencies oldestdeps: with the oldest supported version of key dependencies cov: and test coverage numpy116: with numpy 1.16.* numpy117: with numpy 1.17.* numpy118: with numpy 1.18.* astropy30: with astropy 3.0.* astropy40: with astropy 4.0.* astropylts: with the latest astropy LTS # The following provides some specific pinnings for key packages deps = numpy116: numpy==1.16.* numpy117: numpy==1.17.* numpy118: numpy==1.18.* astropy30: astropy==3.0.* astropy40: astropy==4.0.* astropylts: astropy==4.0.* devdeps: git+https://github.com/numpy/numpy.git#egg=numpy devdeps: git+https://github.com/astropy/astropy.git#egg=astropy oldestdeps: astropy==3.1.* oldestdeps: matplotlib==2.0.* oldestdeps: reproject==0.4.* oldestdeps: pyregion==2.0.* oldestdeps: pillow==4.3.* oldestdeps: pyavm==0.9.4 oldestdeps: scikit-image==0.14.* oldestdeps: shapely==1.7.* oldestdeps: pytest==4.* # The following indicates which extras_require from setup.cfg will be installed extras = test alldeps: all commands = pip freeze !cov: pytest --pyargs aplpy {toxinidir}/docs {posargs} cov: pytest --pyargs aplpy {toxinidir}/docs --cov aplpy --cov-config={toxinidir}/setup.cfg {posargs} [testenv:build_docs] changedir = docs description = invoke sphinx-build to build the HTML docs extras = docs commands = pip freeze sphinx-build -W -b html . _build/html [testenv:linkcheck] changedir = docs description = check the links in the HTML docs extras = docs commands = pip freeze sphinx-build -W -b linkcheck . _build/html [testenv:codestyle] skip_install = true changedir = . description = check code style, e.g. with flake8 deps = flake8 commands = flake8 aplpy --count --ignore=E501,W503,W504