refactored MANIFEST to affect changes of new docs (NeuralEnsemble#302)

* refactored MANIFEST to affect changes of new docs
* xrange->range
* get rid of numpy docscrape warnings because of the incorrect docstring fields
* removed unused doc/tutorials/images folder
* upd maintainers guide

.gitignore

@@ -44,8 +44,8 @@ lib
 lib64
 # sphinx build directory
 doc/_build
-doc/reference/statistics/
-doc/reference/unitary_event_analysis/
+doc/reference/toctree/*
+!doc/reference/toctree/kernels/
 *.h5
 # setup.py dist directory
 dist

MANIFEST.in

@@ -5,11 +5,13 @@ include LICENSE.txt
 include elephant/VERSION
 include elephant/current_source_density_src/README.md
 include elephant/current_source_density_src/test_data.mat
-include elephant/neural_trajectory_src/README.md
-include elephant/spade_src/LICENCE
-include elephant/gpfa_src/INFO.md
+include elephant/spade_src/LICENSE
 recursive-include elephant/spade_src *.so *.pyd
 include elephant/test/spike_extraction_test_data.txt
 recursive-include doc *
 prune doc/_build
-recursive-exclude . *~
+prune doc/tutorials/.ipynb_checkpoints
+prune doc/reference/toctree
+include doc/reference/toctree/kernels/*
+recursive-exclude * *.h5
+recursive-exclude * *~

doc/authors.rst

@@ -8,6 +8,9 @@ The following people have contributed code and/or ideas to the current version
 of Elephant. The institutional affiliations are those at the time of the
 contribution, and may not be the current affiliation of a contributor.
 
+Do you want to contribute to Elephant? Please refer to the
+:ref:`developers_guide`.
+
 * Alper Yegenoglu [1]
 * Andrew Davison [2]
 * Björn Müller [1]

doc/documentation_guide.rst

@@ -21,7 +21,7 @@ Building the documentation
 The documentation in :file:`doc/` folder is written in `reStructuredText
 <http://docutils.sourceforge.net/rst.html>`_, using the
 `Sphinx <http://sphinx-doc.org/>`_ documentation system. To build the
-documentation::
+documentation:
 
 1. Install requirements-docs.txt and requirements-tutorials.txt in the same way
    as it's explained in :ref:`developers_guide` step 3::

doc/index.rst

@@ -33,7 +33,7 @@ Table of Contents
     authors
     release_notes
     get_in_touch
-    acknowledgements
+    acknowledgments
 
 
 

doc/maintaners_guide.rst → doc/maintainers_guide.rst

@@ -83,7 +83,7 @@ The module docstring of ``elephant/x.py`` is also standardized in its structure:
     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
     .. autosummary::
-        :toctree: x/
+        :toctree: toctree/x/
 
         function1
         function2
@@ -92,7 +92,7 @@ The module docstring of ``elephant/x.py`` is also standardized in its structure:
     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
     .. autosummary::
-        :toctree: x/
+        :toctree: toctree/x/
 
         function3
         function4
@@ -119,29 +119,35 @@ Making a release
 1. Increment the Elephant package version in :file:`elephant/VERSION`.
 
 2. Add a section in :file:`doc/release_notes.rst`, describing in short the
-changes made from the previous release.
+   changes made from the previous release.
 
 3. Check that the copyright statement (in :file:`LICENSE.txt`,
-:file:`README.md`, and :file:`doc/conf.py`) is correct.
+   :file:`README.md`, and :file:`doc/conf.py`) is correct.
 
-4. If there is a new module do not forget to add the modulename to the
-:file:`doc/modules.rst` and make a file with a short description in
-:file:`doc/reference/<modulename>.rst`.
+4. If there is a new module do not forget to add the module name to the
+   :file:`doc/modules.rst` and make a file with a short description in
+   :file:`doc/reference/<modulename>.rst`.
 
-To build a source package (see `Packaging Python Projects
-<https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archives>`_)::
+5. Remove :file:`elephant/spade_src/fim.so`. Otherwise, it'll be included in
+   the built package (it should be downloaded at pip install).
+
+6. Build a source package and upload it to PyPi.
+
+   Build a source package (see `Packaging Python Projects
+   <https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archives>`_)::
 
     $ pip install --user --upgrade twine
     $ python setup.py sdist
 
-To upload the package to `PyPI <http://pypi.python.org>`_
-(if you have the necessary permissions)::
+   To upload the package to `PyPI <http://pypi.python.org>`_
+   (if you have the necessary permissions)::
 
-    $ python -m twine upload dist/elephant-x.x.x.tar.gz
+    $ python -m twine upload dist/elephant-X.Y.Z.tar.gz
 
-Finally, tag the release in the Git repository and push it::
+7. Finally, make a release on GitHub UI page and copy-paste the release notes.
+   Then tag the release in the Git repository and push it::
 
     $ git tag <version>
     $ git push --tags upstream
 
-Here, version should be of the form ``vX.Y.Z``.
+   Here, version should be of the form ``vX.Y.Z``.

elephant/asset.py

@@ -787,9 +787,9 @@ def cluster_matrix_entries(mat, eps=10, min=2, stretch=5):
 def probability_matrix_montecarlo(
         spiketrains, binsize, dt, t_start_x=None, t_start_y=None,
         surr_method='dither_spike_train', j=None, n_surr=100, verbose=False):
-    '''
+    """
     Given a list of parallel spike trains, estimate the cumulative probability
-     of each entry in their intersection matrix (see: intersection_matrix())
+    of each entry in their intersection matrix (see: intersection_matrix())
     by a Monte Carlo approach using surrogate data.
     Contrarily to the analytical version (see: probability_matrix_analytical())
     the Monte Carlo one does not incorporate the assumptions of Poissonianity
@@ -799,8 +799,8 @@ def probability_matrix_montecarlo(
     at disposal, see below) and calculates their intersection matrix M.
     For each entry (i, j), the intersection cdf P[i, j] is then given by:
 
-    .. centered::  P[i, j] = #(spike_train_surrogates such that M[i, j] < I[i, j]) /
-                        #(spike_train_surrogates)
+    .. centered::  P[i, j] = #(spike_train_surrogates such that M[i, j] <
+                            I[i, j]) / #(spike_train_surrogates)
 
     If P[i, j] is large (close to 1), I[i, j] is statistically significant:
     the probability to observe an overlap equal to or larger then I[i, j]
@@ -853,7 +853,7 @@ def probability_matrix_montecarlo(
     See also
     --------
     probability_matrix_analytical : for analytical derivation of the matrix
-    '''
+    """
 
     # Compute the intersection matrix of the original data
     imat, x_edges, y_edges = intersection_matrix(
@@ -1260,7 +1260,7 @@ def _pmat_neighbors(mat, filter_shape, nr_largest=None, diag=0):
 
 def joint_probability_matrix(
         pmat, filter_shape, nr_largest=None, alpha=0, pvmin=1e-5):
-    '''
+    """
     Map a probability matrix pmat to a joint probability matrix jmat, where
     jmat[i, j] is the joint p-value of the largest neighbors of pmat[i, j].
 
@@ -1302,18 +1302,22 @@ def joint_probability_matrix(
     ----------
     [1] Torre et al (in prep) ...
 
-    Example
-    -------
-    # Assuming to have a list sts of parallel spike trains over 1s recording,
-    # the following code computes the intersection/probability/joint-prob
-    # matrices imat/pmat/jmat using a bin width of 5 ms
+    Examples
+    --------
+    Assuming to have a list sts of parallel spike trains over 1s recording,
+    the following code computes the intersection/probability/joint-prob
+    matrices imat/pmat/jmat using a bin width of 5 ms:
+
+    >>> import quantities as pq
+    >>> from elephant import asset
     >>> T = 1 * pq.s
     >>> binsize = 5 * pq.ms
-    >>> imat, xedges, yedges = intersection_matrix(sts, binsize=binsize, dt=T)
-    >>> pmat = probability_matrix_analytical(sts, binsize, dt=T)
-    >>> jmat = joint_probability_matrix(pmat, filter_shape=(fl, fw))
+    >>> imat, xedges, yedges = asset.intersection_matrix(sts,
+    ...                        binsize=binsize, dt=T)
+    >>> pmat = asset.probability_matrix_analytical(sts, binsize, dt=T)
+    >>> jmat = asset.joint_probability_matrix(pmat, filter_shape=(fl, fw))
 
-    '''
+    """
     # Find for each P_ij in the probability matrix its neighbors and maximize
     # them by the maximum value 1-pvmin
     pmat_neighb = _pmat_neighbors(

elephant/cell_assembly_detection.py

@@ -20,8 +20,8 @@
 applied as demonstrated in this short toy example of 5 parallel spike trains
 that exhibit fully synchronous events of order 5.
 
-Example
--------
+Examples
+--------
 >>> import matplotlib.pyplot as plt
 >>> import elephant.conversion as conv
 >>> import elephant.spike_train_generation

elephant/kernels.py

@@ -9,7 +9,7 @@
 ~~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-    :toctree: kernels/
+    :toctree: toctree/kernels/
 
     Kernel
     SymmetricKernel
@@ -18,7 +18,7 @@
 ~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-    :toctree: kernels/
+    :toctree: toctree/kernels/
 
     RectangularKernel
     TriangularKernel
@@ -30,7 +30,7 @@
 ~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-    :toctree: kernels/
+    :toctree: toctree/kernels/
 
     ExponentialKernel
     AlphaKernel
@@ -186,8 +186,8 @@ def boundary_enclosing_area_fraction(self, fraction):
         whole probability mass of the kernel is concentrated over negative
         support for inverted kernels.
 
-        Parameter
-        ---------
+        Parameters
+        ----------
         fraction : float
             Fraction of the whole area which has to be enclosed.
 

elephant/pandas_bridge.py

@@ -582,8 +582,8 @@ def slice_spiketrain(pdobj, t_start=None, t_stop=None):
     pdobj : scalar, pandas Series, DataFrame, or Panel
             The returned data type is the same as the type of `pdobj`
 
-    Note
-    ----
+    Notes
+    -----
 
     The order of the index and/or column levels of the returned object may
     differ  from the order of the original.