added data for data overview

dataoverview.py

@@ -1,11 +1,15 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from pathlib import Path
 from scipy.stats import pearsonr, gaussian_kde
 from scipy.stats import mannwhitneyu
 from thunderlab.tabledata import TableData
 from plotstyle import plot_style, lighter, significance_str
 
 
+data_path = Path('data')
+
+
 def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
               nli_thresh):
     ax.axhline(nli_thresh, color='k', ls=':', lw=0.5)
@@ -169,10 +173,16 @@ def plot_mod_nli_ampul(ax, data, ycol, nli_thresh, color):
 
     
 if __name__ == '__main__':
-    punit_model = TableData('summarychi2noise.csv', sep=';')
+    punit_model = TableData(data_path /
+                            'Apteronotus_leptorhynchus-Punit-models.csv',
+                            sep=';')
     punit_model = punit_model[punit_model('contrast') > 1e-6, :]
-    punit_data = TableData('Apteronotus_leptorhynchus-Punit-data.csv', sep=';')
-    ampul_data = TableData('Apteronotus_leptorhynchus-Ampullary-data.csv')
+    punit_data = TableData(data_path /
+                           'Apteronotus_leptorhynchus-Punit-data.csv',
+                           sep=';')
+    ampul_data = TableData(data_path /
+                           'Apteronotus_leptorhynchus-Ampullary-data.csv',
+                           sep=';')
     nli_thresh = 1.8
 
     u, p = mannwhitneyu(punit_model('cvbase'), punit_data('cvbase'))