docs fix

doc/install.rst

@@ -15,13 +15,13 @@ The following packages are required to use Elephant:
     * numpy_ >= 1.8.2
     * scipy_ >= 0.14.0
     * quantities_ >= 0.10.1
-    * neo_ >= 0.7.0, <0.8.0
+    * neo_ >= 0.7.1, <0.8.0
 
 The following packages are optional in order to run certain parts of Elephant:
     * For using the pandas_bridge module: 
         * pandas >= 0.14.1
     * For using the ASSET analysis
-    * scikit-learn >= 0.15.1
+        * scikit-learn >= 0.15.1
     * For building the documentation:
         * numpydoc >= 0.5
         * sphinx >= 1.2.2

elephant/asset.py

@@ -811,7 +811,7 @@ def probability_matrix_montecarlo(
 
     Parameters
     ----------
-    sts : list of neo.SpikeTrains
+    spiketrains : list of neo.SpikeTrains
         list of spike trains for which to compute the probability matrix
     binsize : quantities.Quantity
         width of the time bins used to compute the probability matrix
@@ -842,18 +842,20 @@ def probability_matrix_montecarlo(
     n_surr : int, optional
         number of spike_train_surrogates to generate for the bootstrap
         procedure. Default: 100
+    verbose : bool, optional
+        Print the progress bar.
 
     Returns
     -------
-    pmat : ndarray
+    pmat : np.ndarray
         the cumulative probability matrix. pmat[i, j] represents the
         estimated probability of having an overlap between bins i and j
         STRICTLY LOWER than the observed overlap, under the null hypothesis
         of independence of the input spike trains.
 
     See also
     --------
-    probability_matrix_analytical() for analytical derivation of the matrix
+    probability_matrix_analytical : for analytical derivation of the matrix
     '''
 
     # Compute the intersection matrix of the original data
@@ -873,6 +875,7 @@ def probability_matrix_montecarlo(
     # equal to that of the original data
     pmat = np.array(np.zeros(imat.shape), dtype=int)
     if verbose:
+        # todo: move to tqdm
         print('pmat_bootstrap(): begin of bootstrap...')
     for i in _xrange(n_surr):  # For each surrogate id i
         if verbose:
@@ -1655,7 +1658,6 @@ def sse_issub(sse1, sse2):
     Both sse1 and sse2 must be provided as dictionaries of the type
             {(i1, j1): S1, (i2, j2): S2, ..., (iK, jK): SK},
     where each i, j is an integer and each S is a set of neuron ids.
-    (See also: extract_sse() that extracts SSEs from given spiketrains).
 
     Parameters
     ----------
@@ -1668,6 +1670,10 @@ def sse_issub(sse1, sse2):
     is_sub : bool
         returns True if sse1 is a subset of sse2
 
+    See Also
+    -------
+    extract_sse : extracts SSEs from given spiketrains
+
     '''
     # Remove empty links from sse11 and sse22, if any
     sse11 = _remove_empty_events(sse1)

readthedocs.yml

@@ -1,2 +1,7 @@
-conda:
-  file: doc/environment.yml
+name: elephant
+
+build:
+    image: latest
+
+python:
+    version: 3.7