concise usage to not overcrowd readme [skip ci]

README.md

@@ -37,73 +37,20 @@ This package computes single-subject networks, hence you may need loop over samp
 A rough example of usage can be:
 
 ```python
-from hiwenet import extract as hiwenet
+from hiwenet
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import cross_val_score
 import numpy as np
 import nibabel
 import os
 
-# ----------------------------------------------------
-# toy examples - modify for your application/need
-my_project = '/data/myproject'
-subject_list = ['a1', 'b2', 'c3', 'd4']
-subject_labels = [1, 1, -1, -1]
-
-num_subjects = len(subject_list)
-# number of features (imaging vertex-wise cortical thickness values over the whole brain)
-feature_dimensionality = 1000 
-num_ROIs = 50
-edge_weights = np.empty(num_subjects, num_ROIs*(num_ROIs-1)/2.0)
-
-atlas = 'fsaverage'
-# ----------------------------------------------------
-
-def get_parcellation(atlas, parcel_param):
- "Placeholder to insert your own function to return parcellation in reference space."
- parc_path = os.path.join(atlas, 'parcellation_param{}.mgh'.format(parcel_param))
- parcel = nibabel.freesurfer.io.read_geometry(parc_path)
-
- return parcel
-
-
-groups = get_parcellation(atlas, feature_dimensionality)
-
-out_folder = os.path.join(my_project, 'hiwenet')
-
-# choose a method from one among the three groups (metrics, semi-metrics and similarity functions)
-metrics = [ 'manhattan', 'minowski', 'euclidean', 'noelle_2', 'noelle_4', 'noelle_5' ]
-
-semi_metric_list = [
- 'kullback_leibler', 'cosine_1', 
- 'jensen_shannon', 'chi_square',
- 'chebyshev', 'chebyshev_neg',
- 'histogram_intersection_1',
- 'relative_deviation', 'relative_bin_deviation',
- 'noelle_1', 'noelle_3',
- 'correlate_1']
-similarity_func = ['correlate', 'cosine', 'cosine_2', 'cosine_alt', 'fidelity_based']
-
-
-
-def get_features(subject_id):
- "Placeholder to insert your own function to read subject-wise features."
- features_path = os.path.join(my_project,'base_features', subject_id, 'features.txt')
- feature_vector = np.loadtxt(features_path)
-
- return feature_vector
-
-
-def upper_tri_vec(matrix):
- "Returns the vectorized values of upper triangular part of a matrix"
-
- triu_idx = np.triu_indices_from(matrix, 1)
- return matrix[triu_idx]
-
+# ------------------------------------------------------------------------------------
+# see docs/example_thickness_hiwenet.py for a concrete example
+# ------------------------------------------------------------------------------------
 
 for ss, subject in enumerate(subject_list):
  features = get_features(subject)
- edge_weights_subject = hiwenet(features, groups) # or add weight_method = metrics[ii] to use a various other metrics 
+ edge_weights_subject = hiwenet.extract(features, groups, weight_method = 'kullback_leibler')
  edge_weights[ss,:] = upper_tri_vec(edge_weights_subject)
 
  out_file = os.path.join(out_folder, 'hiwenet_{}.txt'.format(subject))
@@ -120,6 +67,9 @@ scores = cross_val_score(rf, edge_weights, subject_labels)
 
 ```
 
+A longer version of this example with full details and concrete details is available [here](docs/example_thickness_hiwenet.py).
+
+
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