Imputes income field with a light refactor. Needs more refactor and more tests (I spotchecked). Next ticket will check and address but a lot of "narwhal" architecture is here.

Added HOLC indicator (Historic Redlining Score) from NCRC work; included 3.25 cutoff and low income as part of the housing burden category.

* Update PR threshold count to 10

We now show 10 indicators for PR. See the discussion on the github issue for more info: #1621

* Do not use linguistic iso for Puerto Rico

Closes 1350.

Co-authored-by: Shelby Switzer <[email protected]>

* Remove code that drops Guam and USVI from ETL

* Add back code for dropping rows by FIPS code

We may want this functionality, so let's keep it and just make the constant currently be an empty array.

Co-authored-by: Shelby Switzer <[email protected]>

Removing HOLC calculation from score narwhal.

Rescales linguistic isolation to drop puerto rico

adds leaky underground storage tanks

also includes merging / clean up of the release

* added tribalId for Supplemental dataset (#1804)

* Setting zoom levels for tribal map (#1810)

* NRI dataset and initial score YAML configuration (#1534)

* update be staging gha

* NRI dataset and initial score YAML configuration

* checkpoint

* adding data checks for release branch

* passing tests

* adding INPUT_EXTRACTED_FILE_NAME to base class

* lint

* columns to keep and tests

* update be staging gha

* checkpoint

* update be staging gha

* NRI dataset and initial score YAML configuration

* checkpoint

* adding data checks for release branch

* passing tests

* adding INPUT_EXTRACTED_FILE_NAME to base class

* lint

* columns to keep and tests

* checkpoint

* PR Review

* renoving source url

* tests

* stop execution of ETL if there's a YAML schema issue

* update be staging gha

* adding source url as class var again

* clean up

* force cache bust

* gha cache bust

* dynamically set score vars from YAML

* docsctrings

* removing last updated year - optional reverse percentile

* passing tests

* sort order

* column ordening

* PR review

* class level vars

* Updating DatasetsConfig

* fix pylint errors

* moving metadata hint back to code

Co-authored-by: lucasmbrown-usds <[email protected]>

* Correct copy typo (#1809)

* Add basic test suite for COI (#1518)

* Update COI to use new yaml (#1518)

* Add tests for DOE energy budren (1518

* Add dataset config for energy budren (1518)

* Refactor ETL to use datasets.yml (#1518)

* Add fake GEOIDs to COI tests (#1518)

* Refactor _setup_etl_instance_and_run_extract to base (#1518)

For the three classes we've done so far, a generic
_setup_etl_instance_and_run_extract will work fine, for the moment we
can reuse the same setup method until we decide future classes need more
flexibility --- but they can also always subclass so...

* Add output-path tests (#1518)

* Update YAML to match constant (#1518)

* Don't blindly set float format (#1518)

* Add defaults for extract (#1518)

* Run YAML load on all subclasses (#1518)

* Update description fields (#1518)

* Update YAML per final format (#1518)

* Update fixture tract IDs (#1518)

* Update base class refactor (#1518)

Now that NRI is final I needed to make a small number of updates to my
refactored code.

* Remove old comment (#1518)

* Fix type signature and return (#1518)

* Update per code review (#1518)

Co-authored-by: Jorge Escobar <[email protected]>
Co-authored-by: lucasmbrown-usds <[email protected]>
Co-authored-by: Vim <[email protected]>

Yikes! Fixing merge messup!

Imputes income field with a light refactor. Needs more refactor and more tests (I spotchecked). Next ticket will check and address but a lot of "narwhal" architecture is here.

Added HOLC indicator (Historic Redlining Score) from NCRC work; included 3.25 cutoff and low income as part of the housing burden category.

* Update PR threshold count to 10

We now show 10 indicators for PR. See the discussion on the github issue for more info: #1621

* Do not use linguistic iso for Puerto Rico

Closes 1350.

Co-authored-by: Shelby Switzer <[email protected]>

* Remove code that drops Guam and USVI from ETL

* Add back code for dropping rows by FIPS code

We may want this functionality, so let's keep it and just make the constant currently be an empty array.

Co-authored-by: Shelby Switzer <[email protected]>

Removing HOLC calculation from score narwhal.

Rescales linguistic isolation to drop puerto rico

adds leaky underground storage tanks

also includes merging / clean up of the release

* added tribalId for Supplemental dataset (#1804)

* Setting zoom levels for tribal map (#1810)

* NRI dataset and initial score YAML configuration (#1534)

* update be staging gha

* NRI dataset and initial score YAML configuration

* checkpoint

* adding data checks for release branch

* passing tests

* adding INPUT_EXTRACTED_FILE_NAME to base class

* lint

* columns to keep and tests

* update be staging gha

* checkpoint

* update be staging gha

* NRI dataset and initial score YAML configuration

* checkpoint

* adding data checks for release branch

* passing tests

* adding INPUT_EXTRACTED_FILE_NAME to base class

* lint

* columns to keep and tests

* checkpoint

* PR Review

* renoving source url

* tests

* stop execution of ETL if there's a YAML schema issue

* update be staging gha

* adding source url as class var again

* clean up

* force cache bust

* gha cache bust

* dynamically set score vars from YAML

* docsctrings

* removing last updated year - optional reverse percentile

* passing tests

* sort order

* column ordening

* PR review

* class level vars

* Updating DatasetsConfig

* fix pylint errors

* moving metadata hint back to code

Co-authored-by: lucasmbrown-usds <[email protected]>

* Correct copy typo (#1809)

* Add basic test suite for COI (#1518)

* Update COI to use new yaml (#1518)

* Add tests for DOE energy budren (1518

* Add dataset config for energy budren (1518)

* Refactor ETL to use datasets.yml (#1518)

* Add fake GEOIDs to COI tests (#1518)

* Refactor _setup_etl_instance_and_run_extract to base (#1518)

For the three classes we've done so far, a generic
_setup_etl_instance_and_run_extract will work fine, for the moment we
can reuse the same setup method until we decide future classes need more
flexibility --- but they can also always subclass so...

* Add output-path tests (#1518)

* Update YAML to match constant (#1518)

* Don't blindly set float format (#1518)

* Add defaults for extract (#1518)

* Run YAML load on all subclasses (#1518)

* Update description fields (#1518)

* Update YAML per final format (#1518)

* Update fixture tract IDs (#1518)

* Update base class refactor (#1518)

Now that NRI is final I needed to make a small number of updates to my
refactored code.

* Remove old comment (#1518)

* Fix type signature and return (#1518)

* Update per code review (#1518)

Co-authored-by: Jorge Escobar <[email protected]>
Co-authored-by: lucasmbrown-usds <[email protected]>
Co-authored-by: Vim <[email protected]>

Yikes! Fixing merge messup!

Cleans and updates base branch

* Add spatial join method (#1871)

Since we'll need to figure out the tracts for a large number of points
in future tickets, add a utility to handle grabbing the tract geometries
and adding tract data to a point dataset.

* Add FUDS, also jupyter lab (#1871)

* Add YAML configs for FUDS (#1871)

* Allow input geoid to be optional (#1871)

* Add FUDS ETL, tests, test-datae noteobook (#1871)

This adds the ETL class for Formerly Used Defense Sites (FUDS). This is
different from most other ETLs since these FUDS are not provided by
tract, but instead by geographic point, so we need to assign FUDS to
tracts and then do calculations from there.

* Floats -> Ints, as I intended (#1871)

* Floats -> Ints, as I intended (#1871)

* Formatting fixes (#1871)

* Add test false positive GEOIDs (#1871)

* Add gdal binaries (#1871)

* Refactor pandas code to be more idiomatic (#1871)

Per Emma, the more pandas-y way of doing my counts is using np.where to
add the values i need, then groupby and size. It is definitely more
compact, and also I think more correct!

* Update configs per Emma suggestions (#1871)

* Type fixed! (#1871)

* Remove spurious import from vscode (#1871)

* Snapshot update after changing col name (#1871)

* Move up GDAL (#1871)

* Adjust geojson strategy (#1871)

* Try running census separately first (#1871)

* Fix import order (#1871)

* Cleanup cache strategy (#1871)

* Download census data from S3 instead of re-calculating (#1871)

* Clarify pandas code per Emma (#1871)

….com/usds/justice40-tool into emma-nechamkin/release/score-narwhal

This adds the DOT dataset to the ETL and to the score. Note that currently we take a percentile of an average of percentiles.

Adding FSF flood and wildfire risk datasets to the score.

* Add notebook to generate test data (#1780)

* Add Abandoned Mine Land data (#1780)

Using a similar structure but simpler apporach compared to FUDs, add an
indicator for whether a tract has an abandonded mine.

* Adding some detail to dataset readmes

Just a thought!

* Apply feedback from revieiw (#1780)

* Fixup bad string that broke test (#1780)

* Update a string that I should have renamed (#1780)

* Reduce number of threads to reduce memory pressure (#1780)

* Try not running geo data (#1780)

* Run the high-memory sets separately (#1780)

* Actually deduplicate (#1780)

* Add flag for memory intensive ETLs (#1780)

* Document new flag for datasets (#1780)

* Add flag for new datasets fro rebase (#1780)

Co-authored-by: Emma Nechamkin <[email protected]>

…s/justice40-tool into emma-nechamkin/release/score-narwhal

Adding NLCD's natural space indicator end to end to the score.

Adds adjacency index to the pipeline. Requires thorough QA

Update to add EAMLIS and FUDS data to score

backend is partially done!

Yikes! I think I messed something up and dropped the pctile field suffix from when the KP score gets calculated. Fixing right quick.

Added additional flags for the front end based on our conversation in stand up this morning.

Fixes score-geo and adds flags

* Adding demographics for use in sidebar and download files

* update Python version on README; tuple typing fix

* Alaska tribal points fix (#1821)

* Bump mistune from 0.8.4 to 2.0.3 in /data/data-pipeline (#1777)

Bumps [mistune](https://github.com/lepture/mistune) from 0.8.4 to 2.0.3.
- [Release notes](https://github.com/lepture/mistune/releases)
- [Changelog](https://github.com/lepture/mistune/blob/master/docs/changes.rst)
- [Commits](lepture/mistune@v0.8.4...v2.0.3)

---
updated-dependencies:
- dependency-name: mistune
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <[email protected]>

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* poetry update

* initial pass of score tests

* add threshold tests

* added ses threshold (not donut, not island)

* testing suite -- stopping for the day

* added test for lead proxy indicator

* Refactor score tests to make them less verbose and more direct (#1865)

* Cleanup tests slightly before refactor (#1846)

* Refactor score calculations tests

* Feedback from review

* Refactor output tests like calculatoin tests (#1846) (#1870)

* Reorganize files (#1846)

* Switch from lru_cache to fixture scorpes (#1846)

* Add tests for all factors (#1846)

* Mark smoketests and run as part of be deply (#1846)

* Update renamed var (#1846)

* Switch from named tuple to dataclass (#1846)

This is annoying, but pylint in python3.8 was crashing parsing the named
tuple. We weren't using any namedtuple-specific features, so I made the
type a dataclass just to get pylint to behave.

* Add default timout to requests (#1846)

* Fix type (#1846)

* Fix merge mistake on poetry.lock (#1846)

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: Jorge Escobar <[email protected]>
Co-authored-by: Jorge Escobar <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Matt Bowen <[email protected]>
Co-authored-by: matt bowen <[email protected]>

* updated with hopefully a fix; coercing aml, fuds, hrs to booleans for the raw value to preserve null character.

* just testing that the boolean is preserved on gha
* checking drop tracts works
* adding a check to the agvalue calculation for nri
* updated with error messages

* Alaska tribal points fix (#1821)

* tribal tiles fix

* disabling child opportunity

* lint

* removing COI

* removing commented out code

* temp update

* updating with fips check

* adding check on pfs

* updating with pfs test

* Update test_tiles_smoketests.py

* Fix lint errors (#1848)

* Add column names test (#1848)

* Mark tests as smoketests (#1848)

* Move to other score-related tests (#1848)

* Recast Total threshold criteria exceeded to int (#1848)

In writing tests to verify the output of the tiles csv matches the final
score CSV, I noticed TC/Total threshold criteria exceeded was getting
cast from an int64 to a float64 in the process of PostScoreETL. I
tracked it down to the line where we merge the score dataframe with
constants.DATA_CENSUS_CSV_FILE_PATH --- there where > 100 tracts in the
national census CSV that don't exist in the score, so those ended up
with a Total threshhold count of np.nan, which is a float, and thereby
cast those columns to float. For the moment I just cast it back.

* No need for low memeory (#1848)

* Add additional tests of tiles.csv (#1848)

* Drop pre-2010 rows before computing score (#1848)

Note this is probably NOT the optimal place for this change; it might
make more sense for each source to filter its own tracts down to the
acceptable tract list. However, that would be a pretty invasive change,
where this is central and plenty of other things are happening in score
transform that could be moved to sources, so for today, here's where the
change will live.

* Fix typo (#1848)

* Switch from filter to inner join (#1848)

* Remove no-op lines from tiles (#1848)

* Apply feedback from review, linter (#1848)

* Check the values oeverything in the frame (#1848)

* Refactor checker class (#1848)

* Add test for state names (#1848)

* cleanup from reviewing my own code (#1848)

* Fix lint error (#1858)

* Apply Emma's feedback from review (#1848)

* Remove refs to national_df (#1848)

* Account for new, fake nullable bools in tiles (#1848)

To handle a geojson limitation, Emma converted some nullable boolean
colunms to float64 in the tiles export with the values {0.0, 1.0, nan},
giving us the same expressiveness. Sadly, this broke my assumption that
all columns between the score and tiles csvs would have the same dtypes,
so I need to account for these new, fake bools in my test.

* Use equals instead of my worse version (#1848)

* Missed a spot where we called _create_score_data (#1848)

* Update per safety (#1848)

Co-authored-by: matt bowen <[email protected]>

…1878)

* Remove unused persistent poverty from score (#1835)

* Test a few datasets for overlap in the final score (#1835)

* Add remaining data sources (#1853)

* Apply code-review feedback (#1835)

* Rearrange a little for readabililty (#1835)

* Add tract test (#1835)

* Add test for score values (#1835)

* Check for unmatched source tracts (#1835)

* Cleanup numeric code to plaintext (#1835)

* Make import more obvious (#1835)

Changing the traffic barriers to only be included for places with recorded population

remove from map

)

* Fixing missing states and adding tests for states to all classes

dropping 0 population from FEMA

This code causes no functional change to the code. It does two things:

1. Uses difference instead of - to improve code style for working with sets.

2. Removes the line EXPECTED_MISSING_STATES = ["02", "15"], which is now redundant because of the line I added (in a previous pull request) of ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False.

* 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)

* working notebook

* updating notebook

* wip

* fixing broken tests

* adding tribal overlap files

* WIP

* WIP

* WIP, calculated count and names

* working

* partial cleanup

* partial cleanup

* updating field names

* fixing bug

* removing pyogrio

* removing unused imports

* updating test fixtures to be more realistic

* cleaning up notebook

* fixing black

* fixing flake8 errors

* adding tox instructions

* updating etl_score

* suppressing warning

* Use projected CRSes, ignore geom types (#1900)

I looked into this a bit, and in general the geometry type mismatch
changes very little about the calculation; we have a mix of
multipolygons and polygons. The fastest thing to do is just not keep
geom type; I did some runs with it set to both True and False, and
they're the same within 9 digits of precision. Logically we just want to
overlaps, regardless of how the actual geometries are encoded between
the frames, so we can in this case ignore the geom types and feel OKAY.

I also moved to projected CRSes, since we are actually trying to do area
calculations and so like, we should. Again, the change is small in
magnitude but logically more sound.

* Readd CDC dataset config (#1900)

* adding comments to fips code

* delete unnecessary loggers

Co-authored-by: matt bowen <[email protected]>

…1914)

* Better document base on Lucas's feedback (#1835)

* Fix typo (#1835)

* Add test to verify GEOJSON matches tiles (#1835)

* Remove NOOP line (#1835)

* Move GEOJSON generation up for new smoketest (#1835)

* Fixup code format (#1835)

* Update readme for new somketest (#1835)

* Move test to base for broader coverage (#1848)

* Remove duplicate line (#1848)

* FUDS needed an extra mock (#1848)

* Add tribal count notebook (#1917)

* test without caching

* added comment

Co-authored-by: lucasmbrown-usds <[email protected]>

* Add tribal data to downloads (#1904)

* Update test pickle with current cols (#1904)

* Remove text of tribe names from GeoJSON (#1904)

* Update test data (#1904)

* Add tribal overlap to smoketests (#1904)

* should be working, has unnecessary loggers

* removing loggers and cleaning up

* updating ejscreen tests

* adding tests and responding to PR feedback

* fixing broken smoke test

* delete smoketest docs

Co-authored-by: matt bowen <[email protected]>

* Backfill population in island areas (#1882)

* Update smoketest to account for backfills (#1882)

As I wrote in the commend:
We backfill island areas with data from the 2010 census, so if THOSE tracts
have data beyond the data source, that's to be expected and is fine to pass.
If some other state or territory does though, this should fail

This ends up being a nice way of documenting that behavior i guess!

* Fixup lint issues (#1882)

* Add in race demos to 2010 census pull (#1851)

* Add backfill data to score (#1851)

* Change column name (#1851)

* Fill demos after the score (#1851)

* Add income back, adjust test (#1882)

* Apply code-review feedback (#1851)

* Add test for island area backfill (#1851)

* Fix bad rename (#1851)

* Add back lack of plumbing fields (#1920)

* Reorder fields for excel (#1921)

* Reorder excel fields (#1921)

* Fix formating, lint errors, pickes (#1921)

* Add missing plumbing col, fix order again (#1921)

* Update that pickle (#1921)