[ENH 🐛] bug fixes in fetch_ahba and v1.0.2

docs/_static/css/saratheriver_nomaxwidth.css

@@ -6,3 +6,4 @@
 
 @media screen and (max-width:768px){.tablet-hide{display:none}}
 @media screen and (max-width:480px){.mobile-hide{display:none}}
+

docs/build/html/_static/css/saratheriver_nomaxwidth.css

@@ -6,3 +6,4 @@
 
 @media screen and (max-width:768px){.tablet-hide{display:none}}
 @media screen and (max-width:480px){.mobile-hide{display:none}}
+

docs/pages/01.install/index.rst

@@ -35,7 +35,7 @@ Installation
 
         **ENIGMA TOOLBOX** was tested with MATLAB R2017b.
 
-        To install the MATLAB toolbox simply `download <https://github.com/MICA-MNI/ENIGMA/archive/1.0.1.zip>`_ 
+        To install the MATLAB toolbox simply `download <https://github.com/MICA-MNI/ENIGMA/archive/1.0.2.zip>`_ 
         and unzip the GitHub toolbox (slow 🐢) or run the following command in your terminal (fast 🐅): ::
             
             git clone https://github.com/MICA-MNI/ENIGMA.git

docs/pages/02.whatsnew/index.rst

@@ -5,6 +5,17 @@
 What's new?
 ======================================
 
+v1.0.2 (February 23, 2021)
+------------------------------------------
+Bug fixes in the ``fetch_ahba`` function. Users who wish to use this function (or have used it in a previous version) 
+must update their **ENIGMA Toolbox** to this release; ``fetch_ahba`` from previous releases will now error.
+
+::
+
+    ↪ [ENH 🔧] Fix python version mislabeling                |  @saratheriver
+    ↪ [ENH 🔧] Fix bug and replace stable gene lists         |  @saratheriver
+    ↪ [DOC 📄] Update documentations                         |  @saratheriver
+
 v1.0.1 (January 11, 2021)
 ------------------------------------------
 Bug fixes and improvement

docs/pages/07.epicenter/index.rst

@@ -64,7 +64,7 @@ derived from cortical thickness decreases in individuals with left TLE.
 
    .. code-tab:: matlab **Matlab** | meta
 
-        % Computing cortical epicenter values (from functional connectivity)
+        % Identify cortical epicenter values (from functional connectivity)
         fc_ctx_epi              = zeros(size(fc_ctx, 1), 1);
         fc_ctx_epi_p            = zeros(size(fc_ctx, 1), 1);
         for seed = 1:size(fc_ctx, 1)
@@ -75,7 +75,7 @@ derived from cortical thickness decreases in individuals with left TLE.
                                             'aparc', 'n_rot', 1000, 'type', 'pearson');
         end
 
-        % Computing cortical epicenter values (from structural connectivity)
+        % Identify cortical epicenter values (from structural connectivity)
         sc_ctx_epi              = zeros(size(sc_ctx, 1), 1);
         sc_ctx_epi_p            = zeros(size(sc_ctx, 1), 1);
         for seed = 1:size(sc_ctx, 1)
@@ -118,7 +118,7 @@ derived from cortical thickness decreases in individuals with left TLE.
 
    .. code-tab:: matlab **Matlab** | mega
 
-        % Computing cortical epicenter values (from functional connectivity)
+        % Identify cortical epicenter values (from functional connectivity)
         fc_ctx_epi              = zeros(size(fc_ctx, 1), 1);
         fc_ctx_epi_p            = zeros(size(fc_ctx, 1), 1);
         for seed = 1:size(fc_ctx, 1)
@@ -129,7 +129,7 @@ derived from cortical thickness decreases in individuals with left TLE.
                                             'parcellation_name', 'aparc', 'n_rot', 1000, 'type', 'pearson');
         end
 
-        % Computing cortical epicenter values (from structural connectivity)
+        % Identify cortical epicenter values (from structural connectivity)
         sc_ctx_epi              = zeros(size(sc_ctx, 1), 1);
         sc_ctx_epi_p            = zeros(size(sc_ctx, 1), 1);
         for seed = 1:size(sc_ctx, 1)
@@ -232,7 +232,7 @@ derived from cortical thickness decreases in individuals with left TLE.
 
    .. code-tab:: matlab **Matlab** | meta
 
-        % Computing subcortical epicenter values (from functional connectivity)
+        % Identify subcortical epicenter values (from functional connectivity)
         fc_sctx_epi             = zeros(size(fc_sctx, 1), 1);
         fc_sctx_epi_p           = zeros(size(fc_sctx, 1), 1);
         for seed = 1:size(fc_sctx, 1)
@@ -243,7 +243,7 @@ derived from cortical thickness decreases in individuals with left TLE.
                                             'aparc', 'n_rot', 1000, 'type', 'pearson');
         end
 
-        % Computing subcortical epicenter values (from structural connectivity)
+        % Identify subcortical epicenter values (from structural connectivity)
         sc_sctx_epi             = zeros(size(sc_sctx, 1), 1);
         sc_sctx_epi_p           = zeros(size(sc_sctx, 1), 1);
         for seed = 1:size(sc_sctx, 1)
@@ -284,7 +284,7 @@ derived from cortical thickness decreases in individuals with left TLE.
 
    .. code-tab:: matlab **Matlab** | mega
 
-        % Computing subcortical epicenter values (from functional connectivity)
+        % Identify subcortical epicenter values (from functional connectivity)
         fc_sctx_epi             = zeros(size(fc_sctx, 1), 1);
         fc_sctx_epi_p           = zeros(size(fc_sctx, 1), 1);
         for seed = 1:size(fc_sctx, 1)
@@ -295,7 +295,7 @@ derived from cortical thickness decreases in individuals with left TLE.
                                             'parcellation_name', 'aparc', 'n_rot', 1000, 'type', 'pearson');
         end
 
-        % Computing subcortical epicenter values (from structural connectivity)
+        % Identify subcortical epicenter values (from structural connectivity)
         sc_sctx_epi             = zeros(size(sc_sctx, 1), 1);
         sc_sctx_epi_p           = zeros(size(sc_sctx, 1), 1);
         for seed = 1:size(sc_sctx, 1)

docs/pages/10.genemaps/index.rst

@@ -30,8 +30,8 @@ Following the examples below, we can fetch microarray expression data.
 
      The command ``fetch_ahba()`` fetches a large (~24 MB) microarray dataset from the internet and may thus be 
      incredibly slow to load if you lack a good connection. But don't you worry: you can download the
-     relevant file by typing this command in your terminal ``wget https://github.com/saratheriver/enigma-extra/raw/master/ahba/allgenes_stable.csv``
-     and specifying its path in the ``fetch_ahba()`` function as follows:``fetch_ahba('/path/to/allgenes.csv')``
+     relevant file by typing this command in your terminal ``wget https://github.com/saratheriver/enigma-extra/raw/master/ahba/allgenes_stable_r0.2.csv``
+     and specifying its path in the ``fetch_ahba()`` function as follows:``fetch_ahba('/path/to/allgenes_stable_r0.2.csv')``
 
 .. _fetch_genes:
 

enigmatoolbox/_version.py

@@ -1,3 +1,3 @@
 """enigmatoolbox version"""
 
-__version__ = '1.0.1'
+__version__ = '1.0.2'

enigmatoolbox/datasets/base.py

@@ -389,7 +389,7 @@ def fetch_ahba(csvfile=None):
             Table of gene co-expression data, shape = (82, 15633)
      """
     if csvfile is None:
-        url = 'https://raw.githubusercontent.com/saratheriver/enigma-extra/master/ahba/allgenes_stable20.csv'
+        url = 'https://raw.githubusercontent.com/saratheriver/enigma-extra/master/ahba/allgenes_stable_r0.2.csv'
         return pd.read_csv(url, error_bad_lines=False)
     else:
         return pd.read_csv(csvfile, error_bad_lines=False)

enigmatoolbox/plotting/colormaps.py

@@ -23,6 +23,41 @@
                          [235, 235, 181, 255],
                          [253, 174, 97, 255],
                          [213, 62, 79, 255]], dtype=np.uint8)
+fs_colours2 = np.array([[ 0, 0,  0, 255],
+                        [ 25, 100,  40, 255],
+       [125, 100, 160, 255],
+       [100,  25,   0, 255],
+       [220,  20, 100, 255],
+       [220,  20,  10, 255],
+       [180, 220, 140, 255],
+       [220,  60, 220, 255],
+       [180,  40, 120, 255],
+       [140,  20, 140, 255],
+       [ 20,  30, 140, 255],
+       [ 35,  75,  50, 255],
+       [225, 140, 140, 255],
+       [200,  35,  75, 255],
+       [160, 100,  50, 255],
+       [ 20, 220,  60, 255],
+       [ 60, 220,  60, 255],
+       [220, 180, 140, 255],
+       [ 20, 100,  50, 255],
+       [220,  60,  20, 255],
+       [120, 100,  60, 255],
+       [220,  20,  20, 255],
+       [220, 180, 220, 255],
+       [ 60,  20, 220, 255],
+       [160, 140, 180, 255],
+       [ 80,  20, 140, 255],
+       [ 75,  50, 125, 255],
+       [ 20, 220, 160, 255],
+       [ 20, 180, 140, 255],
+       [140, 220, 220, 255],
+       [ 80, 160,  20, 255],
+       [100,   0, 100, 255],
+       [ 70,  70,  70, 255],
+       [150, 150, 200, 255],
+       [255, 192,  32, 255]], dtype=np.uint8)
 
 root_pth = os.path.dirname(__file__)
 TealRd_colors = np.loadtxt(os.path.join(root_pth, 'cmaps', 'TealRd.csv'))
@@ -45,4 +80,4 @@
              'GyRd_r': GyRd_r_colors, 'GyBu': GyBu_colors, 'GyBu_r': GyBu_r_colors,
              'eco_kos': eco_kos_colors, 'bb_r': bb_r_colors, 'bb_o': bb_o_colors,
              'bb_g': bb_g_colors, 'bb_p': bb_p_colors, 'bb_r_r': bb_r_r_colors, 'bb_o_r': bb_o_r_colors,
-             'bb_g_r': bb_g_r_colors, 'bb_p_r': bb_p_r_colors, 'spec_5': spec_5_colors}
+             'bb_g_r': bb_g_r_colors, 'bb_p_r': bb_p_r_colors, 'spec_5': spec_5_colors, 'fs': fs_colours2}

matlab/scripts/ahba/fetch_ahba.m

@@ -20,7 +20,7 @@
 
 if nargin < 1
     % Fetch the csv table from github and load it locally | option 1
-    url   = 'https://raw.githubusercontent.com/saratheriver/enigma-extra/master/ahba/allgenes_stable20.csv';
+    url   = 'https://raw.githubusercontent.com/saratheriver/enigma-extra/master/ahba/allgenes_stable_r0.2.csv';
     urlwrite(url, '.gtmp.csv');
     genes = readtable('.gtmp.csv');
 

matlab/scripts/permutation_testing/spin_test.m

@@ -11,8 +11,8 @@
 %   map2 (double array) - The other map to be correlated
 %
 % Name/value pairs:
-%   Surface name (string, optional) ? Surface name {?fsa5?, ?fsa5_with_sctx?}. 
-%       Use 'fsa5' for Conte69. Default is ?fsa5?.
+%   Surface name (string, optional) - Surface name {'fsa5', 'fsa5_with_sctx'}. 
+%       Use 'fsa5' for Conte69. Default is 'fsa5'.
 %   parcellation_name (string, optional) - Parcellation name {'aparc', 
 %       'aparc_aseg', 'schaefer_100', 'schaefer_200', 'schaefer_300', 
 %       'schaefer_400', 'schaefer_500', 'schaefer_600', 'schaefer_700',