V2prime sample script

api/client/samples/v2prime_demo.py

@@ -0,0 +1,179 @@
+import os
+from groclient.experimental import Experimental
+from groclient import GroClient
+from datetime import datetime
+import pandas as pd
+
+API_HOST = "apistage11201.gro-intelligence.com"
+ACCESS_TOKEN = os.environ["GROAPI_TOKEN_STAGE"]
+
+
+exp_client = Experimental(API_HOST, ACCESS_TOKEN)
+client = GroClient(API_HOST, ACCESS_TOKEN)
+
+
+def simple_example():
+    exp_client = Experimental(API_HOST, ACCESS_TOKEN)
+    selection = {
+        "metric_id": 2540047,
+        "item_ids": [3457],
+        "region_ids": [1215],
+        "source_id": 26,
+        "frequency_id": 1,
+        "start_date": "2023-03-01",
+    }
+
+    res = exp_client.get_data_points(**selection)
+    sample_ds_res = res["data_series"][0]
+    print("------sample results (simple request)------")
+    print("1st series info:")
+    print(sample_ds_res["series_description"])
+    data_df = pd.DataFrame(
+        sample_ds_res["data_points"],
+        columns=["start_timestamp", "end_timestamp", "value"],
+    )
+    print("1st series data:")
+    print(data_df.head(10))
+
+
+def print_start_msg(idx, tc):
+    print(
+        f"------example {idx+1}: (source_id: {tc['source']}, region_num: {len(tc['region'])}, {tc['start']} to {tc['end']} )-----"
+    )
+
+
+def print_stats(endpoint, start, end, data_count):
+    time_diff = (end - start).total_seconds()
+    ms_per_point = (1000 * time_diff) / data_count
+    print(
+        f"version: {endpoint}\ntotal_data_points: {data_count}\ntime_spent(s): {time_diff}\nms per points: {ms_per_point}\n"
+    )
+
+
+def get_test_cases(apiclient):
+    L5_US = apiclient.get_descendant(
+        entity_type="regions",
+        entity_id=1215,
+        descendant_level=5,
+        include_details=False,
+        include_historical=False,
+    )
+    L5_US_IDS = [r["id"] for r in L5_US]
+
+    return [
+        {
+            "metric": 431132,
+            "item": 321,
+            "source": 3,
+            "frequency": 3,
+            "start": "2022-01-01",
+            "end": "2023-01-01",
+            "region": [
+                11011,
+                11012,
+                11013,
+                11014,
+                11015,
+                11016,
+                11017,
+                11018,
+                11019,
+                11020,
+            ],
+        },
+        {
+            "metric": 2540047,
+            "item": 3457,
+            "source": 26,
+            "frequency": 1,
+            "start": "2000-01-01",
+            "end": "2023-04-01",
+            "region": [1215],
+        },
+        {
+            "metric": 2540047,
+            "item": 3457,
+            "source": 26,
+            "frequency": 1,
+            "start": "2022-8-01",
+            "end": "2022-12-01",
+            "region": L5_US_IDS[0:500],
+        },
+        # {
+        #     "metric": 15851824,
+        #     "item": 780,
+        #     "source": 235,
+        #     "frequency": 1,
+        #     "start": "2005-01-03",
+        #     "end": "2023-01-03",
+        #     "region": [
+        #         137446,
+        #         137380,
+        #         139273,
+        #         138266,
+        #         138848,
+        #         137016,
+        #         139224,
+        #         138435,
+        #         138418,
+        #         139130,
+        #     ],
+        # },
+    ]
+
+
+def build_v1_selection(tc):
+    return {
+        "item_id": tc["item"],
+        "metric_id": tc["metric"],
+        "source_id": tc["source"],
+        "frequency_id": tc["frequency"],
+        "start_date": tc["start"],
+        "end_date": tc["end"],
+        "region_id": tc["region"],
+    }
+
+
+def build_v2_selection(tc):
+    return {
+        "item_ids": tc["item"],
+        "metric_id": tc["metric"],
+        "source_id": tc["source"],
+        "frequency_id": tc["frequency"],
+        "start_date": tc["start"],
+        "end_date": tc["end"],
+        "region_ids": tc["region"],
+    }
+
+
+def perf_comparison():
+    exp_client = Experimental(API_HOST, ACCESS_TOKEN)
+    client = GroClient(API_HOST, ACCESS_TOKEN)
+
+    END_POINTS = ["v2/data", "v2prime/data"]
+    for idx, tc in enumerate(get_test_cases(client)):
+        print_start_msg(idx, tc)
+        for endpoint in END_POINTS:
+            start = datetime.now()
+            data_count = 0
+            if endpoint == "v2/data":
+                selection = build_v1_selection(tc)
+                res = client.get_data_points(**selection)
+                data_count = len(res)
+            else:
+                selection = build_v2_selection(tc)
+                res = exp_client.get_data_points(**selection)
+                for ds in res["data_series"]:
+                    data_count += len(ds["data_points"])
+
+            end = datetime.now()
+            print_stats(endpoint, start, end, data_count)
+
+
+def main():
+    simple_example()
+    # perf_comparison()
+
+
+if __name__ == "__main__":
+    main()