--- a
+++ b/genInfos.py
@@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Aug 13 21:35:28 2015.
+
+@author: fornax
+"""
+import numpy as np
+import pandas as pd
+from glob import glob
+from mne import concatenate_raws
+
+from preprocessing.aux import creat_mne_raw_object
+
+# #### define lists #####
+subjects = range(1, 13)
+
+lbls_tot = []
+subjects_val_tot = []
+series_val_tot = []
+
+ids_tot = []
+subjects_test_tot = []
+series_test_tot = []
+
+# #### generate predictions #####
+for subject in subjects:
+    print 'Loading data for subject %d...' % subject
+    # ############### READ DATA ###############################################
+    fnames = glob('data/train/subj%d_series*_data.csv' % (subject))
+    fnames.sort()
+    fnames_val = fnames[-2:]
+
+    fnames_test = glob('data/test/subj%d_series*_data.csv' % (subject))
+    fnames_test.sort()
+
+    raw_val = concatenate_raws([creat_mne_raw_object(fname, read_events=True)
+                                for fname in fnames_val])
+    raw_test = concatenate_raws([creat_mne_raw_object(fname, read_events=False)
+                                for fname in fnames_test])
+
+    # extract labels for series 7&8
+    labels = raw_val._data[32:]
+    lbls_tot.append(labels.transpose())
+
+    # aggregate infos for validation (series 7&8)
+    raw_series7 = creat_mne_raw_object(fnames_val[0])
+    raw_series8 = creat_mne_raw_object(fnames_val[1])
+    series = np.array([7] * raw_series7.n_times +
+                      [8] * raw_series8.n_times)
+    series_val_tot.append(series)
+
+    subjs = np.array([subject]*labels.shape[1])
+    subjects_val_tot.append(subjs)
+
+    # aggregate infos for test (series 9&10)
+    ids = np.concatenate([np.array(pd.read_csv(fname)['id'])
+                         for fname in fnames_test])
+    ids_tot.append(ids)
+    raw_series9 = creat_mne_raw_object(fnames_test[1], read_events=False)
+    raw_series10 = creat_mne_raw_object(fnames_test[0], read_events=False)
+    series = np.array([10] * raw_series10.n_times +
+                      [9] * raw_series9.n_times)
+    series_test_tot.append(series)
+
+    subjs = np.array([subject]*raw_test.n_times)
+    subjects_test_tot.append(subjs)
+
+
+# save validation infos
+subjects_val_tot = np.concatenate(subjects_val_tot)
+series_val_tot = np.concatenate(series_val_tot)
+lbls_tot = np.concatenate(lbls_tot)
+toSave = np.c_[lbls_tot, subjects_val_tot, series_val_tot]
+np.save('infos_val.npy', toSave)
+
+# save test infos
+subjects_test_tot = np.concatenate(subjects_test_tot)
+series_test_tot = np.concatenate(series_test_tot)
+ids_tot = np.concatenate(ids_tot)
+toSave = np.c_[ids_tot, subjects_test_tot, series_test_tot]
+np.save('infos_test.npy', toSave)