Simplify example indicator_datasets.py

Author: joserc87

ravenpackapi/examples/indicator_datasets.py

@@ -12,34 +12,7 @@
     RELEVANCE = "RELEVANCE"
     ESS = "EVENT_SENTIMENT_SCORE"
 
-# In EDGE, it's not allowed to have a lookback lower than 2 days
-# But in RPA it's possible to have a lookback of 1 day:
-if PRODUCT == "edge":
-    fields = [
-        {"avg_2d": {"avg": {"field": ESS, "mode": "granular"}}},
-        {"avg_7d": {"avg": {"field": "avg_2d", "lookback": 7, "mode": "granular"}}},
-        {
-            "buzz_30d": {
-                "buzz": {"field": "RP_ENTITY_ID", "lookback": 30, "mode": "granular"}
-            }
-        },
-        {"newsvolume_2d": {"count": {"field": "RP_ENTITY_ID", "lookback": 2}}},
-    ]
-else:
-    fields = [
-        {"avg_1d": {"avg": {"field": ESS, "lookback": 1, "mode": "granular"}}},
-        {"avg_7d": {"avg": {"field": "avg_1d", "lookback": 7, "mode": "granular"}}},
-        {"buzz_365d": {"buzz": {"field": "RP_ENTITY_ID", "lookback": 365}}},
-        {"newsvolume_1d": {"count": {"field": "RP_ENTITY_ID", "lookback": 1}}},
-        {
-            "newsvolume_365d": {
-                "avg": {"field": "newsvolume_1d", "lookback": 365, "mode": "granular"}
-            }
-        },
-    ]
-
-
-# Begin creating a dataset with your desired filters (see the RPA user guide for syntax)
+# Begin creating a dataset with your desired filters (see the RPA/EDGE user guide for syntax)
 # You can then add functions (https://app.ravenpack.com/api-documentation/#indicator-syntax)
 # Alternatively you can also create the dataset via the query builder and just use the dataset_uuid
 print("Creating a dataset with a few functions...")
@@ -50,13 +23,32 @@
     name="My Indicator dataset",
     filters={"$and": [{"entity_type": {"$in": ["COMP"]}}, {RELEVANCE: {"$gte": 90}}]},
     frequency="daily",
-    fields=fields,
+    custom_fields=[
+        {"avg_d": {"avg": {"field": ESS, "mode": "daily"}}},
+        {"avg_7d": {"avg": {"field": "avg_d", "lookback": 7, "mode": "granular"}}},
+        {
+            "buzz_30d": {
+                "buzz": {"field": "RP_ENTITY_ID", "lookback": 30, "mode": "granular"}
+            }
+        },
+        {"newsvolume_d": {"count": {"field": "RP_ENTITY_ID", "mode": "daily"}}},
+        {
+            "newsvolume_30d": {
+                "avg": {"field": "newsvolume_d", "lookback": 30, "mode": "granular"}
+            }
+        },
+    ],
 )
 dataset.save()
 
-# you can also change the fields, (remember to save afterward)
-print("Updating fields...")
-dataset.fields = [
+job = dataset.request_datafile(
+    start_date="2018-04-10", end_date="2018-04-11", output_format="csv"
+)
+job.save_to_file("output.csv")  # This will poll until the file is ready for download
+
+# you can also change the custom_fields, (remember to save afterward)
+print("Updating custom_fields...")
+dataset.custom_fields = [
     {"avg": {"avg": {"field": ESS, "lookback": 30}}},
 ]
 dataset.save()
@@ -78,6 +70,7 @@
     Dataset.from_dict(
         {
             "name": "Dataset with functions and conditions",
+            "product": PRODUCT,
             "fields": ["timestamp_utc", "rp_entity_id", "entity_name", "AVG_REL"],
             "filters": {},
             "custom_fields": [