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8 changes: 5 additions & 3 deletions README.rst
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@@ -15,15 +15,17 @@ Download the latest version from the `GitHub repository <https://github.com/acha
pip install .
It should install the necessary libraries `astropy <http://www.astropy.org/>`_, `ephem <http://pypi.python.org/pypi/pyephem/>`_, `future <http://pypi.python.org/pypi/future>`_, `matplotlib <http://www.matplotlib.org/>`_, `numpy <http://www.numpy.org/>`_, `scipy <http://www.scipy.org/>`_, `pandas <http://www.pandas.pydata.org/>`_ automatically.
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` <https://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** `**pynnft wrapper** <https://github.com/ghisvail/pyNFFT/>`_. The simplest way is to use `conda <https://anaconda.org/conda-forge/pynfft/>`__ to to install both:

If you want to use fast fourier transforms, you will also need to install `NFFT <https://github.com/NFFT/nfft>`_ and the `pynnft wrapper <https://github.com/ghisvail/pyNFFT/>`_ before installing ehtim. The simplest way is to use `conda <https://anaconda.org/conda-forge/pynfft/>`__ to to install both NFFT and the pynfft wrapper.

.. code-block:: bash
conda install -c conda-forge pynfft
Alternatively, first manually install NFFT following the instructions `here <https://github.com/NFFT/nfft>`_, making sure to use the --enable-openmp flag in compilation. Then install `pynnft <https://github.com/ghisvail/pyNFFT/>`_ with pip, following the instructions to link the installation to where you installed NFFT.
Alternatively, first install NFFT manually following the instructions on the `readme <https://github.com/NFFT/nfft>`_, making sure to use the :bash:`--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.

Documentation
-------------
2 changes: 1 addition & 1 deletion docs/source/imager.rst
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.. _imager:

Stokes I Imager
Imager
===============

.. autofunction:: ehtim.imaging.imager_utils.imager_func
30 changes: 15 additions & 15 deletions docs/source/index.rst
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@@ -6,15 +6,14 @@
ehtim (eht-imaging)
===================

Python modules for simulating and manipulating VLBI data and producing images with regularized gradient descent methods.

The code can be found `GitHub <https://github.com/achael/eht-imaging>`_
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.

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`. :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 Stokes I imager class that can produce images from data sets using various data terms and regularizers.
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 polarizationsusing various data terms and regularizers.

.. note::

If you have a problem please submit a pull request on the git repository and/or email [email protected]
This is a pre-release of ehtim. If you have a problem please submit a pull request on the git repository and/or email [email protected]

Installation
------------
@@ -25,21 +24,28 @@ Download the latest version from the `GitHub repository <https://github.com/acha
pip install .
It should install the depended libraries `astropy <http://www.astropy.org/>`_, `ephem <http://pypi.python.org/pypi/pyephem/>`_, `future <http://pypi.python.org/pypi/future>`_, `matplotlib <http://www.matplotlib.org/>`_, `numpy <http://www.numpy.org/>`_, `scipy <http://www.scipy.org/>`_, `pandas <http://www.pandas.pydata.org/>`_ automatically.
If you want to use fast fourier transforms, you will also need to install `NFFT <https://github.com/NFFT/nfft>`_ and the `pynnft wrapper <https://github.com/ghisvail/pyNFFT/>`_ before installing ehtim. The simplest way is to use `conda <https://anaconda.org/conda-forge/pynfft/>`__ to to install both NFFT and the pynfft wrapper.
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 <https://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** `**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 following the instructions on the `github readme <https://github.com/NFFT/nfft>`_, making sure to use the --enable-openmp flag in compilation. Then install `pynnft <https://github.com/ghisvail/pyNFFT/>`_, with pip, following the readme instructions to link the installation to where you installed NFFT.
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.


Documentation
Tutorials
-------------

Tutorials are in progress, but here are some ways to learn the code

- 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.

Documentation
-------------
.. toctree::
:maxdepth: 2
:caption: Contents:
@@ -56,15 +62,9 @@ Documentation
statistics


The documentation is in progress, but here are some other ways to learn to use the code:

- The file examples/example.py has a series of sample commands to load an image and array, generate data, and produce an image.
- `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.


Acknowledgements
----------------
The oifits_new code used for reading/writing .oifits files is a slightly modified version of Paul Boley's package at `<http://astro.ins.urfu.ru/pages/~pboley/oifits>`_. The oifits read/write functionality is still being tested and may not work with all versions of python or astropy.io.fits.
The :code:`oifits_new` code used for reading/writing .oifits files is a slightly modified version of Paul Boley's package at `<http://astro.ins.urfu.ru/pages/~pboley/oifits>`_. The oifits read/write functionality is still being tested and may not work with all versions of python.

This documentation is styled after `dfm's projects <https://github.com/dfm>`_ and the documentation for `scatterbrane <https://github.com/krosenfeld/scatterbrane>`_

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