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/fsf_flood_risk/data/extract.csv

@@ -0,0 +1,17 @@
+GEOID,count_properties,mid_depth_100_year00,mid_depth_100_year30
+6027000800,942,214,215
+6069000802,1131,283,292
+6061021322,1483,100,108
+15001021010,1888,179,186
+15001021101,3463,130,137
+15007040603,1557,152,181
+15007040700,1533,177,191
+15009030100,1658,232,242
+15009030201,6144,431,447
+15001021402,4118,321,329
+15001021800,2813,350,356
+15009030402,3374,852,888
+15009030800,4847,1003,1019
+15003010201,2335,220,227
+15007040604,5364,630,641
+2290000400,1,1,1

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

@@ -0,0 +1,17 @@
+GEOID10_TRACT,Count of properties eligible for flood risk calculation within tract (floor of 250),Count of properties at risk of flood today,Count of properties at risk of flood in 30 years,Share of properties at risk of flood today,Share of properties at risk of flood in 30 years
+06027000800,942,214,215,0.2271762208,0.2282377919
+06069000802,1131,283,292,0.2502210433,0.2581786030
+06061021322,1483,100,108,0.0674308833,0.0728253540
+15001021010,1888,179,186,0.0948093220,0.0985169492
+15001021101,3463,130,137,0.0375397055,0.0395610742
+15007040603,1557,152,181,0.0976236352,0.1162491972
+15007040700,1533,177,191,0.1154598826,0.1245923027
+15009030100,1658,232,242,0.1399276236,0.1459589867
+15009030201,6144,431,447,0.0701497396,0.0727539062
+15001021402,4118,321,329,0.0779504614,0.0798931520
+15001021800,2813,350,356,0.1244223249,0.1265552791
+15009030402,3374,852,888,0.2525192650,0.2631890931
+15009030800,4847,1003,1019,0.2069321230,0.2102331339
+15003010201,2335,220,227,0.0942184154,0.0972162741
+15007040604,5364,630,641,0.1174496644,0.1195003729
+02290000400,250,1,1,0.0040000000,0.0040000000

data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/transform.csv

@@ -0,0 +1,17 @@
+GEOID,count_properties,Count of properties at risk of flood today,Count of properties at risk of flood in 30 years,GEOID10_TRACT,Count of properties eligible for flood risk calculation within tract (floor of 250),Share of properties at risk of flood today,Share of properties at risk of flood in 30 years
+06027000800,942,214,215,06027000800,942,0.2271762208,0.2282377919
+06069000802,1131,283,292,06069000802,1131,0.2502210433,0.2581786030
+06061021322,1483,100,108,06061021322,1483,0.0674308833,0.0728253540
+15001021010,1888,179,186,15001021010,1888,0.0948093220,0.0985169492
+15001021101,3463,130,137,15001021101,3463,0.0375397055,0.0395610742
+15007040603,1557,152,181,15007040603,1557,0.0976236352,0.1162491972
+15007040700,1533,177,191,15007040700,1533,0.1154598826,0.1245923027
+15009030100,1658,232,242,15009030100,1658,0.1399276236,0.1459589867
+15009030201,6144,431,447,15009030201,6144,0.0701497396,0.0727539062
+15001021402,4118,321,329,15001021402,4118,0.0779504614,0.0798931520
+15001021800,2813,350,356,15001021800,2813,0.1244223249,0.1265552791
+15009030402,3374,852,888,15009030402,3374,0.2525192650,0.2631890931
+15009030800,4847,1003,1019,15009030800,4847,0.2069321230,0.2102331339
+15003010201,2335,220,227,15003010201,2335,0.0942184154,0.0972162741
+15007040604,5364,630,641,15007040604,5364,0.1174496644,0.1195003729
+2290000400,1,1,1,02290000400,250,0.0040000000,0.0040000000

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

@@ -0,0 +1,22 @@
+import pathlib
+from data_pipeline.tests.sources.example.test_etl import TestETL
+from data_pipeline.etl.sources.fsf_flood_risk.etl import FloodRiskETL
+
+
+class TestFloodRiskETL(TestETL):
+    _ETL_CLASS = FloodRiskETL
+
+    _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
+    _SAMPLE_DATA_FILE_NAME = "fsf_flood/flood-tract2010.csv"
+    _SAMPLE_DATA_ZIP_FILE_NAME = "fsf_flood.zip"
+    _EXTRACT_TMP_FOLDER_NAME = "FloodRiskETL"
+    _FIXTURES_SHARED_TRACT_IDS = TestETL._FIXTURES_SHARED_TRACT_IDS + [
+        "02290000400"  # A tract with 1 property
+    ]
+
+    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)