* 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)
* 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 <lucas.m.brown@omb.eop.gov>
* 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 <83969469+esfoobar-usds@users.noreply.github.com>
Co-authored-by: lucasmbrown-usds <lucas.m.brown@omb.eop.gov>
Co-authored-by: Vim <86254807+vim-usds@users.noreply.github.com>
* 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 <lucas.m.brown@omb.eop.gov>
* PyPi Packaging of Data Pipeline
* package rename
* adding python version
* trigger data checks
* print env vars
* python version 2
* trigger data check
* python version 3
* update caching for other GHAs
* installation step
* trigger action
* installing to home dir
* dry-run
* pyenv
* py 2.8
* trying s4cmd
* removing pyenv
* poetry s4cmd
* num-threads
* public read
* poetry cache
* s4cmd all around
* poetry cache
* poetry cache
* install poetry packages
* poetry echo
* let's do this
* s4cmd install on run
* s4cmd
* ad aws back
* add aws back
* testing census api key and poetry caching
* census api key
* census api
* census api key #3
* 250
* poetry update
* poetry change
* check census api key
* force flag
* update score gen and tilefy; remove cached fips
* small gdal update
* invalidation
* missing cache ids
* WIP on parallelizing
* switching to get_tmp_path for nri
* switching to get_tmp_path everywhere necessary
* fixing linter errors
* moving heavy ETLs to front of line
* add hold
* moving cdc places up
* removing unnecessary print
* moving h&t up
* adding parallel to geo post
* better census labels
* switching to concurrent futures
* fixing output
This ended up being a pretty large task. Here's what this PR does:
1. Pulls in Vincent's data from island areas into the score ETL. This is from the 2010 decennial census, the last census of any kind in the island areas.
2. Grabs a few new fields from 2010 island areas decennial census.
3. Calculates area median income for island areas.
4. Stops using EJSCREEN as the source of our high school education data and directly pulls that from census (this was related to this project so I went ahead and fixed it).
5. Grabs a bunch of data from the 2010 ACS in the states/Puerto Rico/DC, so that we can create percentiles comparing apples-to-apples (ish) from 2010 island areas decennial census data to 2010 ACS data. This required creating a new class because all the ACS fields are different between 2010 and 2019, so it wasn't as simple as looping over a year parameter.
6. Creates a combined population field of island areas and mainland so we can use those stats in our comparison tool, and updates the comparison tool accordingly.
* Adds dev dependencies to requirements.txt and re-runs black on codebase
* Adds test and code for national risk index etl, still in progress
* Removes test_data from .gitignore
* Adds test data to nation_risk_index tests
* Creates tests and ETL class for NRI data
* Adds tests for load() and transform() methods of NationalRiskIndexETL
* Updates README.md with info about the NRI dataset
* Adds to dos
* Moves tests and test data into a tests/ dir in national_risk_index
* Moves tmp_dir for tests into data/tmp/tests/
* Promotes fixtures to conftest and relocates national_risk_index tests:
The relocation of national_risk_index tests is necessary because tests
can only use fixtures specified in conftests within the same package
* Fixes issue with df.equals() in test_transform()
* Files reformatted by black
* Commit changes to other files after re-running black
* Fixes unused import that caused lint checks to fail
* Moves tests/ directory to app root for data_pipeline
* Adds new methods to ExtractTransformLoad base class:
- __init__() Initializes class attributes
- _get_census_fips_codes() Loads a dataframe with the fips codes for
census block group and tract
- validate_init() Checks that the class was initialized correctly
- validate_output() Checks that the output was loaded correctly
* Adds test for ExtractTransformLoad.__init__() and base.py
* Fixes failing flake8 test
* Changes geo_col to geoid_col and changes is_dataset to is_census in yaml
* Adds test for validate_output()
* Adds remaining tests
* Removes is_dataset from init method
* Makes CENSUS_CSV a class attribute instead of a class global:
This ensures that CENSUS_CSV is only set when the ETL class is for a
non-census dataset and removes the need to overwrite the value in
mock_etl fixture
* Re-formats files with black and fixes broken tox tests
* Add pytest to tox run in CI/CD
* Try fixing tox dependencies for pytest
* update poetry to get ci/cd passing
* Run poetry export with --dev flag to include dev dependencies such as pytest
* WIP updating test fixtures to include PDF
* Remove dev dependencies from reqs and add pytest to envlist to make build faster
* passing score_post tests
* Add pytest tox (#729)
* Fix failing pytest
* Fixes failing tox tests and updates requirements.txt to include dev deps
* pickle protocol 4
Co-authored-by: Shelby Switzer <shelby.switzer@cms.hhs.gov>
Co-authored-by: Jorge Escobar <jorge.e.escobar@omb.eop.gov>
Co-authored-by: Billy Daly <williamdaly422@gmail.com>
Co-authored-by: Jorge Escobar <83969469+esfoobar-usds@users.noreply.github.com>
* Fixes#456 - Our data directory should adopt standard python package structure
* a few missed references
* updating readme
* updating requirements
* Running Black
* Fixes for flake8
* updating pylint
2021-08-05 15:35:54 -04:00
Renamed from data/data-pipeline/etl/base.py (Browse further)
