Add timeseries_activity_min_span filter argument & floating storage example.

docs/examples/6_medium_sour_floating_storage.py

@@ -0,0 +1,51 @@
+"""
+Let's see how much Medium-Sour Crude is in long term floating storage.
+
+The below script returns:
+
+|     | key                      |    value |   count |
+|----:|:-------------------------|---------:|--------:|
+|   0 | 2019-01-01T00:00:00.000Z |  7381    |       9 |
+|   1 | 2019-01-02T00:00:00.000Z |  8127    |      23 |
+|   2 | 2019-01-03T00:00:00.000Z |  2333    |      32 |
+|   3 | 2019-01-04T00:00:00.000Z |  447759  |      43 |
+|   4 | 2019-01-05T00:00:00.000Z |  7777144 |       4 |
+...
+
+"""
+from datetime import datetime
+
+from docs.utils import to_markdown
+from vortexasdk import CargoTimeSeries, Geographies, Products
+
+if __name__ == "__main__":
+    # Find Medium Sour ID
+    medium_sour = [
+        p.id
+        for p in Products().search(term="Medium-Sour").to_list()
+        if p.name == "Medium-Sour"
+    ]
+    # Check we've only got one ID
+    assert len(medium_sour) == 1
+
+    # Query API
+    search_result = CargoTimeSeries().search(
+        # We're looking at daily storage levels
+        timeseries_frequency="day",
+        # We want 'b' for barrels here
+        timeseries_unit="b",
+        # We're only interested in storage of Medium-Sour Crude
+        filter_products=medium_sour,
+        # We're only included in cargo's that were in floating storage
+        filter_activity="storing_state",
+        # We're only interested in floating storage that lasted longer than 24 days
+        timeseries_activity_time_span_min=1000 * 60 * 60 * 24,
+        # Let's limit the search to 2019 storage events
+        filter_time_min=datetime(2019, 1, 1),
+        filter_time_max=datetime(2019, 12, 31),
+    )
+
+    # Convert search result to dataframe
+    df = search_result.to_df()
+
+    print(to_markdown(df.head()))

pydocmd.yml

@@ -64,6 +64,7 @@ pages:
       3 Crude movements from Saudi Arabia to India: examples/3_crude_from_saudi_arabia_to_india.md
       4 Ballast Movements: examples/4_ballast_movements.md
       5 Chinese Daily Crude Imports: examples/5_chinese_daily_imports.md
+      6 Floating Storage Medium Sour Crude: examples/6_medium_sour_floating_storage.md
 theme:
   name: readthedocs
   collapse_navigation: true

tests/endpoints/test_cargo_movements_real.py

@@ -81,6 +81,22 @@ def test_search_single_filter_origin_name(self):
 
         assert len(df) == 2
 
+    def test_search_filters_on_timeseries_max_activity(self):
+        df = (
+            CargoMovements()
+            .search(
+                filter_activity="storing_state",
+                filter_time_min=datetime(2019, 8, 29),
+                filter_time_max=datetime(2019, 8, 29, 0, 10),
+                timeseries_activity_time_span_min=1000 * 60 * 60 * 24 * 14,
+                timeseries_activity_time_span_max=1000 * 60 * 60 * 24 * 60,
+            )
+            .to_df()
+            .head(2)
+        )
+
+        assert len(df) == 2
+
     def test_search_single_filter_owner_name(self):
         df = (
             CargoMovements()

tests/endpoints/test_cargo_time_series_real.py

@@ -119,3 +119,19 @@ def test_filter_geographies_and_products(self):
         )
 
         print(rotterdam_crude_timeseries.head())
+
+    def test_search_filters_on_timeseries_max_activity(self):
+        df = (
+            CargoTimeSeries()
+            .search(
+                filter_activity="storing_state",
+                filter_time_min=datetime(2019, 8, 1),
+                filter_time_max=datetime(2019, 8, 31),
+                timeseries_activity_time_span_min=1000 * 60 * 60 * 24 * 14,
+                timeseries_activity_time_span_max=1000 * 60 * 60 * 24 * 60,
+            )
+            .to_df()
+            .head(2)
+        )
+
+        assert len(df) == 2

vortexasdk/endpoints/cargo_movements.py

@@ -41,6 +41,8 @@ def search(
         filter_ship_to_ship_locations: Union[ID, List[ID]] = None,
         filter_waypoints: Union[ID, List[ID]] = None,
         disable_geographic_exclusion_rules: bool = None,
+        timeseries_activity_time_span_min: int = None,
+        timeseries_activity_time_span_max: int = None,
     ) -> CargoMovementsResult:
         """
 
@@ -79,6 +81,18 @@ def search(
             disable_geographic_exclusion_rules: This controls a popular industry term "intra-movements" and determines
              the filter behaviour for cargo leaving then entering the same geographic area.
 
+            timeseries_activity_time_span_min: The minimum amount of time in milliseconds accounted for in a time series
+             activity. Can be used to request long-term floating storage. For example, to only return floating storage
+             movements that occured for _more_ than 14 days enter
+             `timeseries_activity_time_span_min=1000 * 60 * 60 * 24 * 14` in conjunction with
+             `filter_activity='storing_state'`.
+
+            timeseries_activity_time_span_max: The maximum amount of time in milliseconds accounted for in a time series
+             activity. Can be used to request short-term floating storage. For example, to only return floating storage
+             movements that occured for _less_ than 14 days enter
+             `timeseries_activity_time_span_max=1000 * 60 * 60 * 24 * 14`
+             in conjunction with `filter_activity='storing_state'`.
+
         # Returns
         `CargoMovementsResult`, containing all the cargo movements matching the given search terms.
 
@@ -164,6 +178,8 @@ def search(
             ),
             "filter_waypoints": convert_to_list(filter_waypoints),
             "disable_geographic_exclusion_rules": disable_geographic_exclusion_rules,
+            "timeseries_activity_time_span_min": timeseries_activity_time_span_min,
+            "timeseries_activity_time_span_max": timeseries_activity_time_span_max,
         }
 
         return CargoMovementsResult(super().search(**params))

vortexasdk/endpoints/cargo_timeseries.py

@@ -32,6 +32,8 @@ def search(
         filter_ship_to_ship_locations: Union[str, List[str]] = None,
         filter_waypoints: Union[str, List[str]] = None,
         disable_geographic_exclusion_rules: bool = None,
+        timeseries_activity_time_span_min: int = None,
+        timeseries_activity_time_span_max: int = None,
     ) -> TimeSeriesResult:
         """
 
@@ -41,6 +43,7 @@ def search(
 
         * _How many Crude/Condensate barrels have been imported into China each day over the last year?_
         * _How many tonnes of Fuel Oil has company X exported from the United States each week over the last 2 years?_
+        * _How have long-term Medium-Sour floating storage levels changed over time?_
 
         # Arguments
             filter_activity: Movement activity on which to base the time filter. Must be one of ['loading_state',
@@ -85,6 +88,18 @@ def search(
             disable_geographic_exclusion_rules: This controls a popular industry term "intra-movements" and determines
              the filter behaviour for cargo leaving then entering the same geographic area.
 
+            timeseries_activity_time_span_min: The minimum amount of time in milliseconds accounted for in a time series
+             activity. Can be used to request long-term floating storage. For example, to only return floating storage
+             movements that occured for _more_ than 14 days enter
+             `timeseries_activity_time_span_min=1000 * 60 * 60 * 24 * 14` in conjunction with
+             `filter_activity='storing_state'`.
+
+            timeseries_activity_time_span_max: The maximum amount of time in milliseconds accounted for in a time series
+             activity. Can be used to request short-term floating storage. For example, to only return floating storage
+             movements that occured for _less_ than 14 days enter
+             `timeseries_activity_time_span_max=1000 * 60 * 60 * 24 * 14`
+             in conjunction with `filter_activity='storing_state'`.
+
         # Returns
         `TimeSeriesResult`
 
@@ -149,6 +164,8 @@ def search(
             ),
             "filter_waypoints": convert_to_list(filter_waypoints),
             "disable_geographic_exclusion_rules": disable_geographic_exclusion_rules,
+            "timeseries_activity_time_span_min": timeseries_activity_time_span_min,
+            "timeseries_activity_time_span_max": timeseries_activity_time_span_max,
         }
 
         return TimeSeriesResult(super().search(**params))

vortexasdk/retry_session.py

@@ -7,10 +7,10 @@
 
 # Inspired by https://www.peterbe.com/plog/best-practice-with-retries-with-requests
 def _requests_retry_session(
-        retries=6,
-        backoff_factor=1,
-        status_forcelist=(500, 502, 504),
-        session=None,
+    retries=6,
+    backoff_factor=1,
+    status_forcelist=(500, 502, 504),
+    session=None,
 ) -> Session:
     """Instantiate a session with Retry backoff."""
     session = session or Session()