Merge pull request #138 from aradhakrishnanGFDL/CompVal

Comp val (In progress)

cats/gfdl_template.json

@@ -58,7 +58,6 @@
     },
     {
       "column_name": "chunk_freq",
-      "vocabulary": "https://raw.githubusercontent.com/NOAA-GFDL/CMIP6_CVs/master/GFDL_chunk_freq.json",
       "required": false
     },
     {

tests/compval

@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+
+import click
+import json
+from jsondiff import diff
+import pandas as pd
+import sys
+import os
+import re
+import math
+
+@click.command()
+@click.argument('json_path',nargs=1,required=True)
+@click.argument('cv_dir_path',nargs=1,required=False)
+def main(json_path=None, cv_dir_path=None):
+
+    ''' This test validates catalogs against CMIP6 or GFDL controlled vocabulary (CV) as provided by particular JSON schemas per vocabulary type. CMIP6 CV's are found in the WCRP-CMIP/CMIP6_CVs github repository. GFDL CV's are found in the NOAA-GFDL/CMIP6_CVs github repository.
+
+     JSON_PATH = Path to generated catalog JSON schema
+     CV_DIR_PATH = Path to CMIP6 CV Repository.
+
+     USAGE:
+         To validate against GFDL CV's: compval <json_path>
+         (Uses CV found in json's "vocabulary" field)
+
+         To validate against CMIP CV's: compval <json_path> <cv_dir_path>
+         (Must clone WCRP-CMIP/CMIP6_CVs github directory. CV is found automatically given the path to this directory) '''
+    bad_vocab = []
+    nan_list = []
+    vocab_list = []
+
+    #Open catalog json
+    j = json.load(open(json_path))
+
+    #Get CSV from JSON and open it
+    csv_path = str(j["catalog_file"])
+    catalog = pd.read_csv(csv_path)
+
+    if cv_dir_path:
+    #Gather all CV's from CMIP6_CV Dir
+        for filename in os.listdir(cv_dir_path):
+            if str(filename).startswith("CMIP6"):
+                cv_type = re.search('CMIP6_(.*).json',str(filename)).group(1)
+
+                #Open JSON
+                try:
+                    cv = json.load(open(cv_dir_path+'/CMIP6_'+cv_type+'.json'))
+                except:
+                    sys.exit("Unable to open json: " + cv_type)
+
+                #Ignoring directory structure for now
+                if list(cv.keys())[0] == 'DRS':
+                    continue
+                    #vals = list(cv.values())
+                    #dir_structure = list(vals[0].values())[2]
+
+                else:
+                    vals = list(cv.values())
+
+                    #Sometimes the CV formats are a little different :)
+                    if isinstance(vals[0],list):
+                        for vocab in vals[0]:
+                            vocab_list.append(vocab)
+
+                    else: 
+                        for vocab in vals[0].keys():
+                            vocab_list.append(vocab)
+
+                #Sometimes our catalogs don't include the cv type as a column
+                if cv_type in catalog:
+
+                    for y in catalog[cv_type]:
+
+                        #If there's NaN's this whole thing will break so let's get those out of the way
+                        if not isinstance(y,str) and math.isnan(y):
+                            #Add to the nan list 
+                            if cv_type not in nan_list:
+                                nan_list.append(cv_type)
+                                continue
+                            else:
+                                continue
+
+                        #Check for bad vocab
+                        if y not in vocab_list and y not in bad_vocab:
+                            bad_vocab.append(y)
+
+    else:
+        #Parse through the JSON and find which CV is needed
+
+        for x in j["attributes"]:
+
+            #Checks to see if the vocabulary field is filled out. If so, use the vocab. Also, we avoid chunk_freq because it's currently broken.
+            if list(x.values())[1]:
+                cv_url = list(x.values())[1]
+                try:
+                    df = pd.read_json(cv_url)
+                except:
+                    sys.exit("Unable to open json: " + list(x.values())[0])
+
+                #This will probably break if formatting is different. Will adjust if necessary.
+                for y in df[list(x.values())[0]].keys():
+                    vocab_list.append(y)
+
+                #Look for "bad vocab" in the CSV
+                for z in catalog[list(x.values())[0]]:
+                    if z not in vocab_list and z not in bad_vocab:
+                        if not isinstance(z,str) and math.isnan(z):
+                            continue
+                        else:
+                             bad_vocab.append(z)
+
+    if nan_list:
+        print("WARNING: NaN's found in: " + str(nan_list))
+
+    if bad_vocab:
+        print("Found inconsistent value(s): " + str(bad_vocab))
+    else:
+        print("Check passed.")
+
+if __name__ == '__main__':
+    main()
+