Add tests for all non-census sources (#1899)

* Refactor CDC life-expectancy (1554)

* Update to new tract list (#1554)

* Adjust for tests (#1848)

* Add tests for cdc_places (#1848)

* Add EJScreen tests (#1848)

* Add tests for HUD housing (#1848)

* Add tests for GeoCorr (#1848)

* Add persistent poverty tests (#1848)

* Update for sources without zips, for new validation (#1848)

* Update tests for new multi-CSV but (#1848)

Lucas updated the CDC life expectancy data to handle a bug where two
states are missing from the US Overall download. Since virtually none of
our other ETL classes download multiple CSVs directly like this, it
required a pretty invasive new mocking strategy.

* Add basic tests for nature deprived (#1848)

* Add wildfire tests (#1848)

* Add flood risk tests (#1848)

* Add DOT travel tests (#1848)

* Add historic redlining tests (#1848)

* Add tests for ME and WI (#1848)

* Update now that validation exists (#1848)

* Adjust for validation (#1848)

* Add health insurance back to cdc places (#1848)

Ooops

* Update tests with new field (#1848)

* Test for blank tract removal (#1848)

* Add tracts for clipping behavior

* Test clipping and zfill behavior (#1848)

* Fix bad test assumption (#1848)

* Simplify class, add test for tract padding (#1848)

* Fix percentage inversion, update tests (#1848)

Looking through the transformations, I noticed that we were subtracting
a percentage that is usually between 0-100 from 1 instead of 100, and so
were endind up with some surprising results. Confirmed with lucasmbrown-usds

* Add note about first street data (#1848)

data/data-pipeline/data_pipeline/tests/sources/dot_travel_composite/data/output.csv

@@ -0,0 +1,16 @@
+GEOID10_TRACT,DOT Travel Barriers Score
+06061021322,52.3684736425
+06069000802,67.6807523475
+15001021101,65.6905624925
+15001021800,64.6348560575
+15007040603,47.3085751425
+15007040604,48.7634318775
+15007040700,56.8031262775
+15009030201,64.1950173025
+15009030402,50.2530948600
+15009030800,56.1490333775
+15001021010,69.4901838075
+15001021402,53.4854747375
+15003010201,54.7191133125
+15009030100,37.8950511525
+06027000800,38.5533081475

data/data-pipeline/data_pipeline/tests/sources/dot_travel_composite/test_etl.py

@@ -0,0 +1,45 @@
+import pathlib
+import geopandas as gpd
+from data_pipeline.tests.sources.example.test_etl import TestETL
+from data_pipeline.etl.sources.dot_travel_composite.etl import (
+    TravelCompositeETL,
+)
+
+
+class TestTravelCompositeETL(TestETL):
+    _ETL_CLASS = TravelCompositeETL
+
+    _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
+    _SAMPLE_DATA_FILE_NAME = "DOT_Disadvantage_Layer_Final_April2022.shp"
+    _SAMPLE_DATA_ZIP_FILE_NAME = "Shapefile_and_Metadata.zip"
+    _EXTRACT_TMP_FOLDER_NAME = "TravelCompositeETL"
+
+    def setup_method(self, _method, filename=__file__):
+        """Invoke `setup_method` from Parent, but using the current file name.
+
+        This code can be copied identically between all child classes.
+        """
+        super().setup_method(_method=_method, filename=filename)
+
+    def test_extract_produces_valid_data(self, mock_etl, mock_paths):
+        etl = self._setup_etl_instance_and_run_extract(
+            mock_etl=mock_etl,
+            mock_paths=mock_paths,
+        )
+        df = gpd.read_file(
+            etl.get_tmp_path() / self._SAMPLE_DATA_FILE_NAME,
+            dtype={etl.GEOID_TRACT_FIELD_NAME: str},
+        )
+        assert df.shape[0] == 30
+        assert df.shape[1] == 86
+
+    def test_transform_removes_blank_tracts(self, mock_etl, mock_paths):
+        etl: TravelCompositeETL = self._setup_etl_instance_and_run_extract(
+            mock_etl=mock_etl,
+            mock_paths=mock_paths,
+        )
+        etl.transform()
+        etl.load()
+        df = etl.get_data_frame()
+        assert df.shape[0] == 15
+        assert df.shape[1] == len(etl.COLUMNS_TO_KEEP)