Don't check if file is valid

daqviewer/daqviewer.py

@@ -50,29 +50,25 @@ def parse_contents(contents, filename):
 
     # Check instrument type
     inst_type = basename.split('_')[-1]
-    try:
-        if inst_type == 'WP':
-            df = pd.read_csv(io.StringIO(decoded.decode('utf-8')),
-                             index_col=0,
-                             header=5,
-                             skiprows=[6])
-
-            # Rename columns based on probe locations
-            suffixes = ['P1', 'P2', 'P3', 'incident', 'reflected']
-            col_names = list(df.columns)
-            for i, col in enumerate(col_names[:-4]):
-                if ('.' not in col) and (col_names[i + 4] == col + '.4'):
-                    for j, suffix in enumerate(suffixes):
-                        col_names[i + j] = '{}-{}'.format(col, suffix)
-            df.columns = col_names
-        else:
-            df = pd.read_csv(io.StringIO(decoded.decode('utf-8')),
-                             index_col=0,
-                             header=3,
-                             skiprows=[4])
-
-    except Exception as e:
-        return html.Div(['There was an error processing this file.'])
+    if inst_type == 'WP':
+        df = pd.read_csv(io.StringIO(decoded.decode('utf-8')),
+                         index_col=0,
+                         header=5,
+                         skiprows=[6])
+
+        # Rename columns based on probe locations
+        suffixes = ['P1', 'P2', 'P3', 'incident', 'reflected']
+        col_names = list(df.columns)
+        for i, col in enumerate(col_names[:-4]):
+            if ('.' not in col) and (col_names[i + 4] == col + '.4'):
+                for j, suffix in enumerate(suffixes):
+                    col_names[i + j] = '{}-{}'.format(col, suffix)
+        df.columns = col_names
+    else:
+        df = pd.read_csv(io.StringIO(decoded.decode('utf-8')),
+                         index_col=0,
+                         header=3,
+                         skiprows=[4])
 
     # Zero time series based on first 5s
     df -= df[:5].mean()