allow point csv files to be compressed

Author: robertjwilson

oceanval/matchall.py

@@ -29,6 +29,23 @@
 from oceanval.gridded import gridded_matchup
 
 
+def read_point(ff):
+    try:
+        df = pd.read_csv(ff)   
+        return df
+    except:
+        pass 
+    compressions = ['infer', 'gzip', 'bz2', 'zip', 'xz']
+    for compression in compressions: 
+        try:
+            df = pd.read_csv(ff, compression = compression)   
+            return df
+        except:
+            pass
+    raise ValueError(f"Could not read file {ff} with any of the following compression types: {compressions}. Error: {e}")
+
+
+
 def is_z_up(ff, variable=None):
     import netCDF4 as nc4
 
@@ -1260,7 +1277,7 @@ def point_match(
                                 ]
 
                             def read_csv_simyears(ff, layer = None):
-                                df = pd.read_csv(ff)
+                                df = read_point(ff)
                                 min_year = session_info["min_year"]
                                 max_year = session_info["max_year"]
                                 if "year" in df.columns:

oceanval/parsers.py

@@ -6,6 +6,22 @@
 import warnings
 from oceanval.session import session_info
 
+def read_point(ff):
+    try:
+        df = pd.read_csv(ff)   
+        return df
+    except:
+        pass 
+    compressions = ['infer', 'gzip', 'bz2', 'zip', 'xz']
+    for compression in compressions: 
+        try:
+            df = pd.read_csv(ff, compression = compression)   
+            return df
+        except:
+            pass
+    raise ValueError(f"Could not read file {ff} with any of the following compression types: {compressions}. Error: {e}")
+
+
 #session_info["keys"] = []
 
 recipe_list = [
@@ -757,7 +773,7 @@ def add_point_comparison(self,
         vertical_option = False
         for vv in point_files:
             # read in the first row
-            df = pd.read_csv(vv, nrows=1)
+            df = read_point(vv, nrows=1)
             # throw error something else is in there
             bad_cols = [col for col in df.columns if col not in valid_vars]
             if len(bad_cols) > 0: