fixed bug in unpack_bl

Author: achael

docs/source/conf.py

@@ -56,17 +56,17 @@
 
 # General information about the project.
 project = u'ehtim'
-copyright = u'2021, Andrew Chael'
+copyright = u'2023, Andrew Chael'
 author = u'Andrew Chael'
 
 # 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.
 #
 # The short X.Y version.
-version = u'1.2'
+version = u'1.2.6'
 # The full version, including alpha/beta/rc tags.
-release = u'1.2.3'
+release = u'1.2.6'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/source/index.rst

@@ -5,44 +5,58 @@
 
 ehtim (eht-imaging)
 ===================
+.. image:: https://zenodo.org/badge/42943499.svg
+   :target: https://zenodo.org/badge/latestdoi/42943499
 
 
-Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. This version is an early release so please submit a pull request or email achael@cfa.harvard.edu if you have trouble or need help for your application.
+Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. This version is an early release so please raise an issue, submit a pull request, or email achael@princeton.edu if you have trouble or need help for your application.
 
 The package contains several primary classes for loading, simulating, and manipulating VLBI data. The main classes are the :class:`Image`, :class:`Array`, and :class:`Obsdata`, which provide tools for manipulating images, simulating interferometric data from images, and plotting and analyzing these data. :class:`Movie` and :class:`Vex` provide tools for producing time-variable simulated data and observing with real VLBI tracks from .vex files. :class:`imager` is a generic imager class that can produce images from data sets in various polarizations using various data terms and regularizers.  
 
 .. note::
 
-    This is a pre-release of ehtim. If you have a problem please submit a pull request on the git repository and/or email achael@cfa.harvard.edu
+    This is a pre-release of ehtim. If you have a problem please submit a pull request on the git repository and/or email achael@princeton.edu.edu
 
-Installation
-------------
+The latest stable version (`1.2.6 <https://github.com/achael/eht-imaging/releases/tag/v1.2.6>`_) is available on `PyPi <https://pypi.org/project/ehtim/>`_. Simply install pip and run
 
-Download the latest version from the `GitHub repository <https://github.com/achael/eht-imaging>`_, change to the main directory and run:
+.. code-block:: bash
+
+    pip install ehtim
+
+Incremental updates are developed on the `dev branch <https://github.com/achael/eht-imaging/tree/dev>`_. To use the very latest (unstable) code, checkout the dev branch, change to the main eht-imaging directory, and run:
 
 .. code-block:: bash
 
     pip install .
 
+Installing with pip will update most of the required libraries automatically (`numpy <http://www.numpy.org/>`_, `scipy <http://www.scipy.org/>`_, `matplotlib <http://www.matplotlib.org/>`_, `astropy <http://www.astropy.org/>`_, `ephem <http://pypi.python.org/pypi/pyephem/>`_, `future <http://pypi.python.org/pypi/future>`_, `h5py <http://www.h5py.org/>`_, and `pandas <http://www.pandas.pydata.org/>`_).
 
-It should install most of the required libraries automatically (`astropy <http://www.astropy.org/>`_, `ephem <http://pypi.python.org/pypi/pyephem/>`_, `future <http://pypi.python.org/pypi/future>`_, `h5py <http://www.h5py.org/>`_ , `html <http://www.decalage.info/python/html>`_ , `networkx <https://networkx.github.io/>`_, `numpy <http://www.numpy.org/>`_, `pandas <http://www.pandas.pydata.org/>`_ , `matplotlib <http://www.matplotlib.org/>`_,  `requests <http://docs.python-requests.org/en/master/>`_, `scipy <http://www.scipy.org/>`_, `skimage <https://scikit-image.org/>`_).
+**If you want to use fast fourier transforms, you will also need to separately install** `NFFT <https://github.com/NFFT/nfft>`_ **and its** `pynfft wrapper <https://github.com/ghisvail/pyNFFT/>`__. The simplest way is to use `conda <https://anaconda.org/conda-forge/pynfft/>`__ to install both:
 
-**If you want to use fast fourier transforms, you will also need to separately install** `NFFT <https://github.com/NFFT/nfft>`_ **and its** `pynnft wrapper <https://github.com/ghisvail/pyNFFT/>`_. The simplest way is to use `conda <https://anaconda.org/conda-forge/pynfft/>`__ to to install both:
 
 .. code-block:: bash
 
     conda install -c conda-forge pynfft
 
-Alternatively, first install NFFT manually following the instructions on the `readme <https://github.com/NFFT/nfft>`_, making sure to use the :code:`--enable-openmp` flag in compilation. Then install `pynft <https://github.com/ghisvail/pyNFFT/>`_, with pip, following the readme instructions to link the installation to where you installed NFFT. Finally, reinstall ehtim.
+Alternatively, first install NFFT manually following the instructions on the `readme <https://github.com/NFFT/nfft>`__, making sure to use the ``--enable-openmp`` flag in compilation. Then install `pynfft <https://github.com/ghisvail/pyNFFT/>`__, with pip, following the readme instructions to link the installation to where you installed NFFT. Finally, reinstall ehtim.
 
+**For M1 Macs (OS >= v12.0)**, install the M1 Mac version of `pynfft <https://github.com/rohandahale/pyNFFT.git>`__ and follow the instructions on the `readme  <https://github.com/rohandahale/pyNFFT.git>`__. It has the instructions to install `fftw <http://www.fftw.org>`_, `nfft <https://github.com/NFFT/nfft>`__ and then `pynfft <https://github.com/rohandahale/pyNFFT.git>`__.
 
-Tutorials
--------------
+**Certain eht-imaging functions require other external packages that are not automatically installed.** In addition to pynfft, these include  `networkx <https://networkx.github.io/>`_ (for image comparison functions), `requests <http://docs.python-requests.org/en/master/>`_ (for dynamical imaging), and `scikit-image <https://scikit-image.org/>`_ (for a few image analysis functions). However, the vast majority of the code will work without these dependencies.
+
+Documentation and Tutorials
+---------------------------
+Documentation is  `here <https://achael.github.io/eht-imaging>`_.
+
+A intro to imaging tutorial jupyter notebook can be found in the repo at `tutorials/ehtim_tutorial.ipynb <https://github.com/achael/eht-imaging/blob/main/tutorials/ehtim_tutorial.ipynb>`__
+
+`Slides <https://docs.google.com/presentation/d/1A0y9omYI2ueSUa6_t5reylBhw6eiLwjqDzw-HUOk8Ac/edit?usp=sharing>`__ for the included tutorial walk through the basic steps of reconstructing EHT images with the code
+
+Here are some other ways to learn to use the code:
 
-Tutorials are in progress, but here are some ways to learn the code
+- Start with the script examples/example.py, which contains a series of sample commands to load an image and array, generate data, and produce an image with various imaging algorithms.
 
-- The script in `examples/example.py <https://github.com/achael/eht-imaging/blob/master/examples/example.py>`_ has a series of sample commands to load an image and array, generate data, and produce an image with regularized maximum likelihood on closure quantities.
-- `Slides <https://www.dropbox.com/s/7533ucj8bt54yh7/Bouman_Chael.pdf?dl=0>`_ from the EHT 2016 conference data generation and imaging workshop contain a tutorial on generating data externally with the vlbi imaging `website <http://vlbiimaging.csail.mit.edu>`_, loading into the library, and producing an image.
+- Older `Slides <https://www.dropbox.com/s/7533ucj8bt54yh7/Bouman_Chael.pdf?dl=0>`__ from the EHT2016 data generation and imaging workshop contain a tutorial on generating data with the VLBI imaging `website <http://vlbiimaging.csail.mit.edu>`_, loading into the library, and producing an image.
 
 Documentation
 -------------

ehtim/obsdata.py

@@ -715,8 +715,9 @@ def unpack_bl(self, site1, site2, fields, ang_unit='deg', debias=False, timetype
                                           debias=debias, timetype=timetype)
 
                     allout.append(out)
-
-        return np.array(allout, dtype=object)
+        
+        #return np.array(allout, dtype=object)
+        return np.array(allout)
 
     def unpack(self, fields, mode='all', ang_unit='deg', debias=False, conj=False, timetype=False):
         """Unpack the data for the whole observation .