Bug fixes in cpac_correlations

corr_two_1D.py

@@ -7,40 +7,78 @@
 oned_one_file = str(sys.argv[1])
 oned_two_file = str(sys.argv[2])
 
+cols = None
 try:
-    col = str(sys.argv[3])
+    cols = [str(sys.argv[3])]
 except IndexError:
-    col = None
+    cols = None
 
-with open(oned_one_file, 'r') as f:
-    lines = f.readlines()
+if "nuisance_regressors.1D" in oned_one_file:
+    reg_one_df = pd.read_csv(oned_one_file, delimiter="\t", header=2)
+    reg_two_df = pd.read_csv(oned_two_file, delimiter="\t", header=2)
 
-line_idx = 0
-delimiter = ','
-for line in lines:
-    if '#' in line:
-        line_idx += 1
-    else:
-        if ',' in line:
-            delimiter = ','
-        elif '\t' in line:
-            delimiter = '\t'
-        break
+    if not cols:
+        cols = reg_one_df.columns
+
+    corrs = ["{0}: {1}\n".format(x, scipy.stats.pearsonr(reg_one_df[x].values, reg_two_df[x].values)[0]) for x in cols]
+
+elif "roi_stats.csv" in oned_one_file:
+    reg_one_df = pd.read_csv(oned_one_file, header=1)
+    reg_two_df = pd.read_csv(oned_two_file)
+
+    if not cols:
+        cols = reg_one_df.columns
 
-if '.par' in oned_one_file:
-    delimiter = '  '
+    corrs = ["{0}: {1}\n".format(x, scipy.stats.pearsonr(reg_one_df[x].values, reg_two_df[x].values)[0]) for x in cols]
 
-oned_one = pd.read_csv(oned_one_file, delimiter=delimiter, header=line_idx-1).dropna(axis=1)
-oned_two = pd.read_csv(oned_two_file, delimiter=delimiter, header=line_idx-1).dropna(axis=1)
+elif "spatial_map_timeseries.txt" in oned_one_file or ".par" in oned_one_file:
+    reg_one_df = pd.read_csv(oned_one_file, delimiter="  ", header=None)
+    reg_two_df = pd.read_csv(oned_two_file, delimiter="  ", header=None)
 
-print(oned_one)
+    if not cols:
+        cols = reg_one_df.columns
+
+    corrs = ["{0}: {1}\n".format(x, scipy.stats.pearsonr(reg_one_df[x].values, reg_two_df[x].values)[0]) for x in cols]
 
-if col:
-    cols = [col]
 else:
-    cols = oned_one.columns
 
-corrs = np.asarray([scipy.stats.pearsonr(oned_one[x], oned_two[x])[0] for x in cols])
+    with open(oned_one_file, 'r') as f:
+        lines = f.readlines()
+
+    line_idx = 0
+    delimiter = ','
+    for line in lines:
+        if '#' in line or "Mean_" in line:
+            line_idx += 1
+        else:
+            if ',' in line:
+                delimiter = ','
+            elif '\t' in line:
+                delimiter = '\t'
+            else:
+                delimiter = ' '
+            break
+
+    if '.par' in oned_one_file:
+        delimiter = '  '
+
+    header_line = line_idx-1
+    if header_line < 0:
+        header_line = None
+
+    oned_one = pd.read_csv(oned_one_file, delimiter=delimiter, header=header_line).dropna(axis=1)
+    oned_two = pd.read_csv(oned_two_file, delimiter=delimiter, header=header_line).dropna(axis=1)
+
+    if cols:
+        if '#' in cols:
+            cols = [cols.replace('#','')]
+    else:
+        cols = [x.replace('#','') for x in oned_one.columns if isinstance(x, str)]
+
+    if not cols:
+        cols = range(0,oned_one.shape[1])
+
+    corrs = np.asarray([f"{val}: {scipy.stats.pearsonr(oned_one.values.T[x], oned_two.values.T[x])[0]}" for x, val in enumerate(cols)])
 
 for corr in corrs:
     print(corr)