diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 01ea0d829f..021fed2c5e 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -172,6 +172,7 @@ jobs:
- name: Run integration tests, trying to use GCS cache if possible
run: |
pip install --no-deps --editable .
+ pudl_datastore --dataset epacems --partition year_quarter=2022q1
make pytest-integration
- name: Upload coverage
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index c4f5537346..555cae568b 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -104,10 +104,10 @@ jobs:
git push
notify-slack:
+ if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v20')
runs-on: ubuntu-latest
needs:
- publish-github
- if: ${{ always() }}
steps:
- name: Inform the Codemonkeys
uses: 8398a7/action-slack@v3
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5e7f4b0344..9b89fec2a2 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -29,7 +29,7 @@ repos:
# Formatters: hooks that re-write Python & documentation files
####################################################################################
- repo: https://github.com/astral-sh/ruff-pre-commit
- rev: v0.1.9
+ rev: v0.1.13
hooks:
- id: ruff
args: [--fix, --exit-non-zero-on-fix]
diff --git a/README.rst b/README.rst
index 2947f4d13c..a4a1908957 100644
--- a/README.rst
+++ b/README.rst
@@ -42,48 +42,54 @@ What is PUDL?
The `PUDL `__ Project is an open source data processing
pipeline that makes US energy data easier to access and use programmatically.
-Hundreds of gigabytes of valuable data are published by US government agencies, but
-it's often difficult to work with. PUDL takes the original spreadsheets, CSV files,
-and databases and turns them into a unified resource. This allows users to spend more
-time on novel analysis and less time on data preparation.
+Hundreds of gigabytes of valuable data are published by US government agencies, but it's
+often difficult to work with. PUDL takes the original spreadsheets, CSV files, and
+databases and turns them into a unified resource. This allows users to spend more time
+on novel analysis and less time on data preparation.
The project is focused on serving researchers, activists, journalists, policy makers,
-and small businesses that might not otherwise be able to afford access to this data
-from commercial sources and who may not have the time or expertise to do all the
-data processing themselves from scratch.
+and small businesses that might not otherwise be able to afford access to this data from
+commercial sources and who may not have the time or expertise to do all the data
+processing themselves from scratch.
We want to make this data accessible and easy to work with for as wide an audience as
-possible: anyone from a grassroots youth climate organizers working with Google
-sheets to university researchers with access to scalable cloud computing
-resources and everyone in between!
+possible: anyone from a grassroots youth climate organizers working with Google sheets
+to university researchers with access to scalable cloud computing resources and everyone
+in between!
PUDL is comprised of three core components:
-- **Raw Data Archives**
-
- - PUDL `archives `__
- all the raw data inputs on `Zenodo `__
- to ensure perminant, versioned access to the data. In the event that an agency
- changes how they publish data or deletes old files, the ETL will still have access
- to the original inputs. Each of the data inputs may have several different versions
- archived, and all are assigned a unique DOI and made available through the REST API.
- You can read more about the Raw Data Archives in the
- `docs `__.
-- **ETL Pipeline**
-
- - The ETL pipeline (this repo) ingests the raw archives, cleans them,
- integrates them, and outputs them to a series of tables stored in SQLite Databases,
- Parquet files, and pickle files (the Data Warehouse). Each release of the PUDL
- Python package is embedded with a set of of DOIs to indicate which version of the
- raw inputs it is meant to process. This process helps ensure that the ETL and it's
- outputs are replicable. You can read more about the ETL in the
- `docs `__.
-- **Data Warehouse**
-
- - The outputs from the ETL, sometimes called "PUDL outputs",
- are stored in a data warehouse as a collection of SQLite and Parquet files so that
- users can access the data without having to run any code. Learn more about how to
- access the data `here `__.
+Raw Data Archives
+^^^^^^^^^^^^^^^^^
+PUDL `archives `__ all our raw
+inputs on `Zenodo
+`__ to ensure
+permanent, versioned access to the data. In the event that an agency changes how they
+publish data or deletes old files, the data processing pipeline will still have access
+to the original inputs. Each of the data inputs may have several different versions
+archived, and all are assigned a unique DOI (digital object identifier) and made
+available through Zenodo's REST API. You can read more about the Raw Data Archives in
+the `docs `__.
+
+Data Pipeline
+^^^^^^^^^^^^^
+The data pipeline (this repo) ingests raw data from the archives, cleans and integrates
+it, and writes the resulting tables to `SQLite `__ and `Apache
+Parquet `__ files, with some acompanying metadata stored as
+JSON. Each release of the PUDL software contains a set of of DOIs indicating which
+versions of the raw inputs it processes. This helps ensure that the outputs are
+replicable. You can read more about our ETL (extract, transform, load) process in the
+`PUDL documentation `__.
+
+Data Warehouse
+^^^^^^^^^^^^^^
+The SQLite, Parquet, and JSON outputs from the data pipeline, sometimes called "PUDL
+outputs", are updated each night by an automated build process, and periodically
+archived so that users can access the data without having to install and run our data
+processing system. These outputs contain hundreds of tables and comprise a small
+file-based data warehouse that can be used for a variety of energy system analyses.
+Learn more about `how to access the PUDL data
+`__.
What data is available?
-----------------------
@@ -98,23 +104,23 @@ PUDL currently integrates data from:
* **EIA Form 861**: 2001-2022
- `Source Docs `__
- `PUDL Docs `__
-* **EIA Form 923**: 2001-2022
+* **EIA Form 923**: 2001-2023
- `Source Docs `__
- `PUDL Docs `__
-* **EPA Continuous Emissions Monitoring System (CEMS)**: 1995-2022
+* **EPA Continuous Emissions Monitoring System (CEMS)**: 1995Q1-2023Q3
- `Source Docs `__
- `PUDL Docs `__
-* **FERC Form 1**: 1994-2021
+* **FERC Form 1**: 1994-2022
- `Source Docs `__
- `PUDL Docs `__
-* **FERC Form 714**: 2006-2020
+* **FERC Form 714**: 2006-2022 (mostly raw)
- `Source Docs `__
- `PUDL Docs `__
-* **FERC Form 2**: 2021 (raw only)
+* **FERC Form 2**: 1996-2022 (raw only)
- `Source Docs `__
-* **FERC Form 6**: 2021 (raw only)
+* **FERC Form 6**: 2000-2022 (raw only)
- `Source Docs `__
-* **FERC Form 60**: 2021 (raw only)
+* **FERC Form 60**: 2006-2022 (raw only)
- `Source Docs `__
* **US Census Demographic Profile 1 Geodatabase**: 2010
- `Source Docs `__
diff --git a/devtools/datasette/publish.py b/devtools/datasette/publish.py
index f9197fdbeb..6ae2bb9076 100644
--- a/devtools/datasette/publish.py
+++ b/devtools/datasette/publish.py
@@ -110,8 +110,19 @@ def metadata(pudl_out: Path) -> str:
flag_value="metadata",
help="Generate the Datasette metadata.yml in current directory, but do not deploy.",
)
-def deploy_datasette(deploy: str) -> int:
- """Generate deployment files and run the deploy."""
+@click.argument(
+ "fly_args",
+ required=False,
+ nargs=-1,
+)
+def deploy_datasette(deploy: str, fly_args: tuple[str]) -> int:
+ """Generate deployment files and deploy Datasette either locally or to fly.io.
+
+ Any additional arguments after -- will be passed through to flyctl if deploying to
+ fly.io. E.g. the following would build ouputs for fly.io, but not actually deploy:
+
+ python publish.py --fly -- --build-only
+ """
pudl_out = PudlPaths().pudl_output
metadata_yml = metadata(pudl_out)
# Order the databases to highlight PUDL
@@ -148,7 +159,10 @@ def deploy_datasette(deploy: str) -> int:
)
logging.info("Running fly deploy...")
- check_call(["/usr/bin/env", "flyctl", "deploy"], cwd=fly_dir) # noqa: S603
+ cmd = ["/usr/bin/env", "flyctl", "deploy"]
+ if fly_args:
+ cmd = cmd + list(fly_args)
+ check_call(cmd, cwd=fly_dir) # noqa: S603
logging.info("Deploy finished!")
elif deploy == "local":
diff --git a/docs/conf.py b/docs/conf.py
index 6935f09761..6721aec6c8 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -137,7 +137,7 @@ def data_dictionary_metadata_to_rst(app):
"""Export data dictionary metadata to RST for inclusion in the documentation."""
# Create an RST Data Dictionary for the PUDL DB:
print("Exporting PUDL DB data dictionary metadata to RST.")
- skip_names = ["datasets", "accumulated_depreciation_ferc1", "entity_types_eia"]
+ skip_names = ["datasets", "accumulated_depreciation_ferc1"]
names = [name for name in RESOURCE_METADATA if name not in skip_names]
package = Package.from_resource_ids(resource_ids=tuple(sorted(names)))
# Sort fields within each resource by name:
diff --git a/docs/data_access.rst b/docs/data_access.rst
index 9c2e799e08..45efc60573 100644
--- a/docs/data_access.rst
+++ b/docs/data_access.rst
@@ -144,18 +144,18 @@ HTTPS using the following links:
* `FERC-714 Datapackage (JSON) describing SQLite derived from XBRL `__
* `FERC-714 XBRL Taxonomy Metadata as JSON (2021-2022) `__
-.. note::
- To reduce network transfer times, we ``gzip`` the SQLite database files, which can
- be quite large when uncompressed. To decompress them locally, at the command line
- on Linux, MacOS, or Windows you can use the ``gunzip`` command.
+To reduce network transfer times, we compress the SQLite databases using ``gzip``. To
+decompress them locally, at the command line on Linux, MacOS, or Windows you can use the
+``gunzip`` command. (Git for Windows installs ``gzip`` / ``gunzip`` by default, and it
+can also be installed using the conda package manager).
- .. code-block:: console
+.. code-block:: console
- $ gunzip *.sqlite.gz
+ $ gunzip *.sqlite.gz
- On Windows you can also use a 3rd party tool like
- `7zip `__.
+If you're not familiar with using Unix command line tools in Windows you can also use a
+3rd party tool like `7zip `__.
.. _access-zenodo:
diff --git a/docs/release_notes.rst b/docs/release_notes.rst
index 8936ff2761..688824bdce 100644
--- a/docs/release_notes.rst
+++ b/docs/release_notes.rst
@@ -24,6 +24,9 @@ v2024.01.XX
protected and automatically updated. Build outputs are now written to
``gs://builds.catalyst.coop`` and retained for 30 days. See issues :issue:`3140,3179`
and PRs :pr:`3195,3206,3212`
+* The :mod:`pudl.analysis.record_linkage.eia_ferc1_record_linkage` module has been
+ refactored to use PUDL record linkage infrastructure and include extra cleaning
+ steps. This resulted in around 500 or 2% of matches changing.
Data Coverage
^^^^^^^^^^^^^
@@ -31,6 +34,13 @@ Data Coverage
CEMS instead of the annual files. Integrates CEMS through 2023q3. See issue
:issue:`2973` & PR :pr:`3096`.
+Data Cleaning
+^^^^^^^^^^^^^
+
+* Filled in null annual balances with fourth-quarter quarterly balances in
+ :ref:`core_ferc1__yearly_balance_sheet_liabilities_sched110`. :issue:`3233` and
+ :pr:`3234`.
+
---------------------------------------------------------------------------------------
v2023.12.01
---------------------------------------------------------------------------------------
diff --git a/environments/conda-linux-64.lock.yml b/environments/conda-linux-64.lock.yml
index f9141f2041..d4abe89e89 100644
--- a/environments/conda-linux-64.lock.yml
+++ b/environments/conda-linux-64.lock.yml
@@ -1,6 +1,6 @@
# Generated by conda-lock.
# platform: linux-64
-# input_hash: 25338bdeb54a81fb064efcdf38a56212bb8da4efc4654985f265ded511dcb38f
+# input_hash: a94ce1081d54c82931f58616985e5261ac1f9a1471c91852e1d0b53e9306b923
channels:
- conda-forge
@@ -24,9 +24,9 @@ dependencies:
- _openmp_mutex=4.5=2_gnu
- fonts-conda-ecosystem=1=0
- libgcc-ng=13.2.0=h807b86a_3
- - aws-c-common=0.9.8=hd590300_0
+ - aws-c-common=0.9.12=hd590300_0
- bzip2=1.0.8=hd590300_5
- - c-ares=1.24.0=hd590300_0
+ - c-ares=1.25.0=hd590300_0
- fribidi=1.0.10=h36c2ea0_0
- geos=3.12.1=h59595ed_0
- gettext=0.21.1=h27087fc_0
@@ -65,7 +65,7 @@ dependencies:
- ncurses=6.4=h59595ed_2
- nspr=4.35=h27087fc_0
- openssl=3.2.0=hd590300_1
- - pixman=0.42.2=h59595ed_0
+ - pixman=0.43.0=h59595ed_0
- pthread-stubs=0.4=h36c2ea0_1001
- snappy=1.1.10=h9fff704_0
- tzcode=2023d=h3f72095_0
@@ -79,10 +79,10 @@ dependencies:
- xorg-xproto=7.0.31=h7f98852_1007
- xz=5.2.6=h166bdaf_0
- yaml=0.2.5=h7f98852_2
- - aws-c-cal=0.6.9=h3b91eb8_1
- - aws-c-compression=0.2.17=hfd9eb17_6
- - aws-c-sdkutils=0.1.12=hfd9eb17_5
- - aws-checksums=0.1.17=hfd9eb17_5
+ - aws-c-cal=0.6.9=h14ec70c_3
+ - aws-c-compression=0.2.17=h572eabf_8
+ - aws-c-sdkutils=0.1.13=h572eabf_1
+ - aws-checksums=0.1.17=h572eabf_7
- expat=2.5.0=hcb278e6_1
- glog=0.6.0=h6f12383_0
- hdf4=4.2.15=h2a13503_7
@@ -101,33 +101,33 @@ dependencies:
- libsqlite=3.44.2=h2797004_0
- libssh2=1.11.0=h0841786_0
- libxcb=1.15=h0b41bf4_0
- - libxml2=2.11.6=h232c23b_0
+ - libxml2=2.12.4=h232c23b_1
- libzip=1.10.1=h2629f0a_3
- pcre2=10.42=hcad00b1_0
- rdma-core=49.0=hd3aeb46_2
- readline=8.2=h8228510_1
- - s2n=1.3.56=h06160fa_0
+ - s2n=1.4.1=h06160fa_0
- tk=8.6.13=noxft_h4845f30_101
- xorg-libsm=1.2.4=h7391055_0
- zeromq=4.3.5=h59595ed_0
- zlib=1.2.13=hd590300_5
- zstd=1.5.5=hfc55251_0
- - aws-c-io=0.13.36=hc23c90e_0
+ - aws-c-io=0.14.0=hf8f278a_1
- blosc=1.21.5=h0f2a231_0
- brotli-bin=1.1.0=hd590300_1
- freetype=2.12.1=h267a509_2
- krb5=1.21.2=h659d440_0
- - libarchive=3.7.2=h039dbb9_0
+ - libarchive=3.7.2=h2aa1ff5_1
- libglib=2.78.3=h783c2da_0
- - libllvm15=15.0.7=h5cf9203_3
+ - libllvm15=15.0.7=hb3ce162_4
- libopenblas=0.3.25=pthreads_h413a1c8_0
- libthrift=0.19.0=hb90f79a_1
- libtiff=4.6.0=ha9c0a0a_2
- - libxslt=1.1.37=h0054252_1
+ - libxslt=1.1.39=h76b75d6_0
- minizip=4.0.4=h0ab5242_0
- nodejs=20.9.0=hb753e55_0
- nss=3.96=h1d7d5a4_0
- - orc=1.9.0=h4b38347_4
+ - orc=1.9.2=h4b38347_0
- pandoc=3.1.3=h32600fe_0
- python=3.11.7=hab00c5b_1_cpython
- re2=2023.06.02=h2873b5e_0
@@ -135,14 +135,14 @@ dependencies:
- ucx=1.15.0=h75e419f_2
- xorg-libx11=1.8.7=h8ee46fc_0
- aiofiles=23.2.1=pyhd8ed1ab_0
- - alabaster=0.7.13=pyhd8ed1ab_0
+ - alabaster=0.7.16=pyhd8ed1ab_0
- anyascii=0.3.2=pyhd8ed1ab_0
- appdirs=1.4.4=pyh9f0ad1d_0
- astroid=3.0.2=py311h38be061_0
- atk-1.0=2.38.0=hd4edc92_1
- attrs=23.2.0=pyh71513ae_0
- - aws-c-event-stream=0.3.2=h1fff966_7
- - aws-c-http=0.7.14=hc86c171_2
+ - aws-c-event-stream=0.4.1=h97bb272_2
+ - aws-c-http=0.8.0=h9129f04_2
- backoff=2.2.1=pyhd8ed1ab_0
- backports.zoneinfo=0.2.1=py311h38be061_8
- blinker=1.7.0=pyhd8ed1ab_0
@@ -162,7 +162,7 @@ dependencies:
- colorama=0.4.6=pyhd8ed1ab_0
- crashtest=0.4.1=pyhd8ed1ab_0
- cycler=0.12.1=pyhd8ed1ab_0
- - dagster-pipes=1.5.13=pyhd8ed1ab_0
+ - dagster-pipes=1.6.0=pyhd8ed1ab_0
- dataclasses=0.8=pyhc8e2a94_3
- dbus=1.13.6=h5008d03_3
- debugpy=1.8.0=py311hb755f60_1
@@ -173,7 +173,7 @@ dependencies:
- docutils=0.20.1=py311h38be061_3
- entrypoints=0.4=pyhd8ed1ab_0
- et_xmlfile=1.1.0=pyhd8ed1ab_0
- - exceptiongroup=1.2.0=pyhd8ed1ab_0
+ - exceptiongroup=1.2.0=pyhd8ed1ab_2
- execnet=2.0.2=pyhd8ed1ab_0
- executing=2.0.1=pyhd8ed1ab_0
- filelock=3.13.1=pyhd8ed1ab_0
@@ -182,7 +182,7 @@ dependencies:
- frozenlist=1.4.1=py311h459d7ec_0
- fsspec=2023.12.2=pyhca7485f_0
- gdk-pixbuf=2.42.10=h829c605_4
- - google-cloud-sdk=458.0.1=py311h38be061_0
+ - google-cloud-sdk=459.0.0=py311h38be061_0
- greenlet=3.0.3=py311hb755f60_0
- gts=0.7.6=h977cf35_4
- hpack=4.0.0=pyh9f0ad1d_0
@@ -205,24 +205,24 @@ dependencies:
- lcms2=2.16=hb7c19ff_0
- libblas=3.9.0=20_linux64_openblas
- libcurl=8.5.0=hca28451_0
- - libgrpc=1.59.2=hd6c4280_0
+ - libgrpc=1.59.3=hd6c4280_0
- libpq=16.1=h33b98f1_7
- libwebp=1.3.2=h658648e_1
- llvmlite=0.41.1=py311ha6695c7_0
- locket=1.0.0=pyhd8ed1ab_0
- - lxml=4.9.3=py311h1a07684_3
+ - lxml=4.9.4=py311h9691dec_0
- marko=2.0.2=pyhd8ed1ab_0
- markupsafe=2.1.3=py311h459d7ec_1
- - mdurl=0.1.0=pyhd8ed1ab_0
+ - mdurl=0.1.2=pyhd8ed1ab_0
- mergedeep=1.3.4=pyhd8ed1ab_0
- mistune=3.0.2=pyhd8ed1ab_0
- - more-itertools=10.1.0=pyhd8ed1ab_0
+ - more-itertools=10.2.0=pyhd8ed1ab_0
- msgpack-python=1.0.7=py311h9547e67_0
- multidict=6.0.4=py311h459d7ec_1
- multimethod=1.9.1=pyhd8ed1ab_0
- munkres=1.1.4=pyh9f0ad1d_0
- mypy_extensions=1.0.0=pyha770c72_0
- - nest-asyncio=1.5.8=pyhd8ed1ab_0
+ - nest-asyncio=1.5.9=pyhd8ed1ab_0
- networkx=3.2.1=pyhd8ed1ab_0
- openjpeg=2.5.0=h488ebb8_3
- packaging=23.2=pyhd8ed1ab_0
@@ -235,7 +235,7 @@ dependencies:
- pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
- platformdirs=4.1.0=pyhd8ed1ab_0
- pluggy=1.3.0=pyhd8ed1ab_0
- - prettier=3.1.1=h31abb78_0
+ - prettier=3.2.2=h31abb78_0
- prometheus_client=0.19.0=pyhd8ed1ab_0
- psutil=5.9.7=py311h459d7ec_0
- ptyprocess=0.7.0=pyhd3deb0d_0
@@ -261,12 +261,12 @@ dependencies:
- regex=2023.12.25=py311h459d7ec_0
- rfc3986=2.0.0=pyhd8ed1ab_0
- rfc3986-validator=0.1.1=pyh9f0ad1d_0
- - rpds-py=0.16.2=py311h46250e7_0
+ - rpds-py=0.17.1=py311h46250e7_0
- rtree=1.1.0=py311h3bb2b0f_0
- ruamel.yaml.clib=0.2.7=py311h459d7ec_2
- - ruff=0.1.9=py311h7145743_0
+ - ruff=0.1.13=py311h7145743_1
- send2trash=1.8.2=pyh41d4057_0
- - setuptools=68.2.2=pyhd8ed1ab_0
+ - setuptools=69.0.3=pyhd8ed1ab_0
- shellingham=1.5.4=pyhd8ed1ab_0
- simpleeval=0.9.13=pyhd8ed1ab_1
- six=1.16.0=pyh6c4a22f_0
@@ -277,6 +277,7 @@ dependencies:
- soupsieve=2.5=pyhd8ed1ab_1
- sphinxcontrib-jsmath=1.0.1=pyhd8ed1ab_0
- stringcase=1.2.0=py_0
+ - structlog=23.2.0=pyhd8ed1ab_0
- tabulate=0.9.0=pyhd8ed1ab_1
- text-unidecode=1.3=pyhd8ed1ab_1
- threadpoolctl=3.2.0=pyha21a80b_0
@@ -286,8 +287,8 @@ dependencies:
- toolz=0.12.0=pyhd8ed1ab_0
- toposort=1.10=pyhd8ed1ab_0
- tornado=6.3.3=py311h459d7ec_1
- - traitlets=5.14.0=pyhd8ed1ab_0
- - types-python-dateutil=2.8.19.14=pyhd8ed1ab_0
+ - traitlets=5.14.1=pyhd8ed1ab_0
+ - types-python-dateutil=2.8.19.20240106=pyhd8ed1ab_0
- types-pyyaml=6.0.12.12=pyhd8ed1ab_0
- typing_extensions=4.9.0=pyha770c72_0
- typing_utils=0.1.0=pyhd8ed1ab_0
@@ -295,7 +296,7 @@ dependencies:
- uri-template=1.3.0=pyhd8ed1ab_0
- uvloop=0.19.0=py311h460e60f_0
- validators=0.22.0=pyhd8ed1ab_0
- - wcwidth=0.2.12=pyhd8ed1ab_0
+ - wcwidth=0.2.13=pyhd8ed1ab_0
- webcolors=1.13=pyhd8ed1ab_0
- webencodings=0.5.1=pyhd8ed1ab_2
- websocket-client=1.7.0=pyhd8ed1ab_0
@@ -315,8 +316,9 @@ dependencies:
- asgiref=3.7.2=pyhd8ed1ab_0
- asttokens=2.4.1=pyhd8ed1ab_0
- async-lru=2.0.4=pyhd8ed1ab_0
- - aws-c-auth=0.7.7=h4faf3ed_1
- - aws-c-mqtt=0.9.10=hba57965_1
+ - aws-c-auth=0.7.11=h0b4cabd_1
+ - aws-c-mqtt=0.10.1=h2b97f5f_0
+ - azure-core-cpp=1.10.3=h91d86a7_0
- babel=2.14.0=pyhd8ed1ab_0
- beautifulsoup4=4.12.2=pyha770c72_0
- bleach=6.1.0=pyhd8ed1ab_0
@@ -329,28 +331,28 @@ dependencies:
- cligj=0.7.2=pyhd8ed1ab_1
- clikit=0.6.2=pyhd8ed1ab_2
- coloredlogs=14.0=pyhd8ed1ab_3
- - comm=0.1.4=pyhd8ed1ab_0
+ - comm=0.2.1=pyhd8ed1ab_0
- coverage=7.4.0=py311h459d7ec_0
- curl=8.5.0=hca28451_0
- - fonttools=4.47.0=py311h459d7ec_0
+ - fonttools=4.47.2=py311h459d7ec_0
- gitdb=4.0.11=pyhd8ed1ab_0
- graphql-core=3.2.3=pyhd8ed1ab_0
- - grpcio=1.59.2=py311ha6695c7_0
+ - grpcio=1.59.3=py311ha6695c7_0
- h11=0.14.0=pyhd8ed1ab_0
- h2=4.1.0=pyhd8ed1ab_0
- hdf5=1.14.3=nompi_h4f84152_100
- html5lib=1.1=pyh9f0ad1d_0
- - hypothesis=6.92.2=pyha770c72_0
+ - hypothesis=6.94.0=pyha770c72_0
- importlib-metadata=7.0.1=pyha770c72_0
- importlib_resources=6.1.1=pyhd8ed1ab_0
- isodate=0.6.1=pyhd8ed1ab_0
- janus=1.0.0=pyhd8ed1ab_0
- jaraco.classes=3.3.0=pyhd8ed1ab_0
- jedi=0.19.1=pyhd8ed1ab_0
- - jinja2=3.1.2=pyhd8ed1ab_1
+ - jinja2=3.1.3=pyhd8ed1ab_0
- joblib=1.3.2=pyhd8ed1ab_0
- jsonlines=4.0.0=pyhd8ed1ab_0
- - jupyter_core=5.6.0=py311h38be061_0
+ - jupyter_core=5.7.1=py311h38be061_0
- jupyterlab_pygments=0.3.0=pyhd8ed1ab_0
- latexcodec=2.0.1=pyh9f0ad1d_0
- libcblas=3.9.0=20_linux64_openblas
@@ -365,11 +367,11 @@ dependencies:
- overrides=7.4.0=pyhd8ed1ab_0
- partd=1.4.1=pyhd8ed1ab_0
- pexpect=4.8.0=pyh1a96a4e_2
- - pillow=10.1.0=py311ha6c5da5_0
+ - pillow=10.2.0=py311ha6c5da5_0
- pint=0.23=pyhd8ed1ab_0
- pip=23.3.2=pyhd8ed1ab_0
- - postgresql=16.1=h8972f4a_7
- - proj=9.3.0=h1d62c97_2
+ - postgresql=16.1=h7387d8b_7
+ - proj=9.3.1=h1d62c97_0
- prompt-toolkit=3.0.42=pyha770c72_0
- protobuf=4.24.4=py311h46cbc50_0
- psycopg2=2.9.9=py311h03dec38_0
@@ -380,7 +382,7 @@ dependencies:
- python-slugify=8.0.1=pyhd8ed1ab_2
- pyu2f=0.1.5=pyhd8ed1ab_0
- qtpy=2.4.1=pyhd8ed1ab_0
- - referencing=0.32.0=pyhd8ed1ab_0
+ - referencing=0.32.1=pyhd8ed1ab_0
- restructuredtext_lint=1.4.0=pyhd8ed1ab_0
- rfc3339-validator=0.1.4=pyhd8ed1ab_0
- rsa=4.9=pyhd8ed1ab_0
@@ -397,34 +399,35 @@ dependencies:
- xerces-c=3.2.5=hac6953d_0
- yarl=1.9.3=py311h459d7ec_0
- addfips=0.4.0=pyhd8ed1ab_1
+ - aiohttp=3.9.1=py311h459d7ec_0
- aniso8601=9.0.1=pyhd8ed1ab_0
- annotated-types=0.6.0=pyhd8ed1ab_0
- argon2-cffi-bindings=21.2.0=py311h459d7ec_4
- arrow=1.3.0=pyhd8ed1ab_0
- - async-timeout=4.0.3=pyhd8ed1ab_0
- - aws-c-s3=0.4.1=hfadff92_0
- - botocore=1.34.11=pyhd8ed1ab_0
+ - aws-c-s3=0.4.9=hca09fc5_0
+ - azure-storage-common-cpp=12.5.0=hb858b4b_2
+ - botocore=1.34.19=pyhd8ed1ab_0
- branca=0.7.0=pyhd8ed1ab_1
- croniter=2.0.1=pyhd8ed1ab_0
- cryptography=41.0.7=py311hcb13ee4_1
- fqdn=1.5.1=pyhd8ed1ab_0
- - geotiff=1.7.1=hf074850_14
- - gitpython=3.1.40=pyhd8ed1ab_0
+ - geotiff=1.7.1=h6b2125f_15
+ - gitpython=3.1.41=pyhd8ed1ab_0
- google-crc32c=1.1.2=py311h9b08b9c_5
- googleapis-common-protos=1.62.0=pyhd8ed1ab_0
- - gql=3.4.1=pyhd8ed1ab_0
+ - gql=3.5.0=pyhd8ed1ab_0
- graphql-relay=3.2.0=pyhd8ed1ab_0
- - grpcio-health-checking=1.59.2=pyhd8ed1ab_0
+ - grpcio-health-checking=1.59.3=pyhd8ed1ab_0
- harfbuzz=8.3.0=h3d44ed6_0
- httpcore=1.0.2=pyhd8ed1ab_0
- importlib_metadata=7.0.1=hd8ed1ab_0
- jsonschema-specifications=2023.12.1=pyhd8ed1ab_0
- jupyter_server_terminals=0.5.1=pyhd8ed1ab_0
- kealib=1.5.3=h2f55d51_0
- - libnetcdf=4.9.2=nompi_h80fb2b6_112
- - libspatialite=5.1.0=h7385560_2
+ - libnetcdf=4.9.2=nompi_h9612171_113
+ - libspatialite=5.1.0=h7bd4643_4
- mako=1.3.0=pyhd8ed1ab_0
- - numpy=1.26.2=py311h64a7726_0
+ - numpy=1.26.3=py311h64a7726_0
- pbr=6.0.0=pyhd8ed1ab_0
- pendulum=2.1.2=py311h459d7ec_6
- poppler=23.12.0=h590f24d_0
@@ -432,7 +435,7 @@ dependencies:
- psycopg2-binary=2.9.9=pyhd8ed1ab_0
- pybtex=0.24.0=pyhd8ed1ab_2
- pydantic-core=2.14.6=py311h46250e7_1
- - pyproj=3.6.1=py311h1facc83_4
+ - pyproj=3.6.1=py311hca0b8b9_5
- pytest-console-scripts=1.4.1=pyhd8ed1ab_0
- pytest-cov=4.1.0=pyhd8ed1ab_0
- pytest-mock=3.12.0=pyhd8ed1ab_0
@@ -440,36 +443,34 @@ dependencies:
- python-build=1.0.3=pyhd8ed1ab_0
- requests=2.31.0=pyhd8ed1ab_0
- rich=13.7.0=pyhd8ed1ab_0
- - sqlalchemy=2.0.24=py311h459d7ec_0
+ - sqlalchemy=2.0.25=py311h459d7ec_0
- stack_data=0.6.2=pyhd8ed1ab_0
- - starlette=0.34.0=pyhd8ed1ab_0
- - tiledb=2.18.2=h8c794c1_0
+ - starlette=0.35.0=pyhd8ed1ab_0
- ukkonen=1.0.1=py311h9547e67_4
- uvicorn=0.25.0=py311h38be061_0
- watchfiles=0.21.0=py311h46250e7_0
- - aiohttp=3.8.6=py311h459d7ec_1
- - alembic=1.13.1=pyhd8ed1ab_0
- - arelle-release=2.20.3=pyhd8ed1ab_0
+ - alembic=1.13.1=pyhd8ed1ab_1
+ - arelle-release=2.21.3=pyhd8ed1ab_0
- argon2-cffi=23.1.0=pyhd8ed1ab_0
- - aws-crt-cpp=0.24.7=h97e63c7_6
+ - aws-crt-cpp=0.26.0=h04327c0_8
+ - azure-storage-blobs-cpp=12.10.0=h00ab1b0_0
- bottleneck=1.3.7=py311h1f0f07a_1
- cachecontrol=0.13.1=pyhd8ed1ab_0
- contourpy=1.2.0=py311h9547e67_0
- - dask-core=2023.12.1=pyhd8ed1ab_0
+ - dask-core=2024.1.0=pyhd8ed1ab_0
- dnspython=2.4.2=pyhd8ed1ab_1
- ensureconda=1.4.3=pyhd8ed1ab_0
- folium=0.15.1=pyhd8ed1ab_0
- google-resumable-media=2.7.0=pyhd8ed1ab_0
- graphene=3.3=pyhd8ed1ab_0
- - grpcio-status=1.59.2=pyhd8ed1ab_0
+ - grpcio-status=1.59.3=pyhd8ed1ab_0
- h3-py=3.7.6=py311hb755f60_1
- httpx=0.26.0=pyhd8ed1ab_0
- identify=2.5.33=pyhd8ed1ab_0
- - ipython=8.19.0=pyh707e725_0
+ - ipython=8.20.0=pyh707e725_0
- isoduration=20.11.0=pyhd8ed1ab_0
- jsonschema=4.20.0=pyhd8ed1ab_0
- jupyter_client=8.6.0=pyhd8ed1ab_0
- - libgdal=3.8.1=hd0089ee_2
- numba=0.58.1=py311h96b013e_0
- numexpr=2.8.8=py311h039bad6_100
- oauthlib=3.2.2=pyhd8ed1ab_0
@@ -479,7 +480,7 @@ dependencies:
- pydantic=2.5.3=pyhd8ed1ab_0
- pyopenssl=23.3.0=pyhd8ed1ab_0
- readthedocs-sphinx-ext=2.2.5=pyhd8ed1ab_0
- - requests-toolbelt=0.10.1=pyhd8ed1ab_0
+ - requests-toolbelt=1.0.0=pyhd8ed1ab_0
- responses=0.24.1=pyhd8ed1ab_0
- s3transfer=0.10.0=pyhd8ed1ab_0
- scipy=1.11.4=py311h64a7726_0
@@ -489,24 +490,23 @@ dependencies:
- typeguard=4.1.5=pyhd8ed1ab_1
- typer=0.9.0=pyhd8ed1ab_0
- uvicorn-standard=0.25.0=h38be061_0
- - aws-sdk-cpp=1.11.182=h8beafcf_7
- - boto3=1.34.11=pyhd8ed1ab_0
+ - aws-sdk-cpp=1.11.210=hba3e011_10
+ - boto3=1.34.19=pyhd8ed1ab_0
- cachecontrol-with-filecache=0.13.1=pyhd8ed1ab_0
- - dagster=1.5.13=pyhd8ed1ab_0
- - datasette=0.64.5=pyhd8ed1ab_0
+ - dagster=1.6.0=pyhd8ed1ab_0
+ - datasette=0.64.6=pyhd8ed1ab_0
- doc8=1.1.1=pyhd8ed1ab_0
- email-validator=2.1.0.post1=pyhd8ed1ab_0
- frictionless=4.40.8=pyh6c4a22f_0
- - gdal=3.8.1=py311h67923c1_2
- - geopandas-base=0.14.1=pyha770c72_0
- - google-auth=2.25.2=pyhca7485f_0
- - gql-with-requests=3.4.1=pyhd8ed1ab_0
+ - geopandas-base=0.14.2=pyha770c72_0
+ - google-auth=2.26.2=pyhca7485f_0
+ - gql-with-requests=3.5.0=pyhd8ed1ab_0
- gtk2=2.24.33=h7f000aa_3
- - ipykernel=6.28.0=pyhd33586a_0
+ - ipykernel=6.29.0=pyhd33586a_0
- ipywidgets=8.1.1=pyhd8ed1ab_0
- jsonschema-with-format-nongpl=4.20.0=pyhd8ed1ab_0
- keyring=24.3.0=py311h38be061_0
- - librsvg=2.56.3=h98fae49_0
+ - librsvg=2.56.3=he3f83f7_1
- matplotlib-base=3.8.2=py311h54ef318_0
- nbformat=5.9.2=pyhd8ed1ab_0
- pandera-core=0.17.2=pyhd8ed1ab_1
@@ -514,48 +514,50 @@ dependencies:
- pydantic-settings=2.1.0=pyhd8ed1ab_1
- requests-oauthlib=1.3.1=pyhd8ed1ab_0
- scikit-learn=1.3.2=py311hc009520_2
+ - tiledb=2.19.0=hc1131af_0
- timezonefinder=6.2.0=py311h459d7ec_2
- catalystcoop.ferc_xbrl_extractor=1.3.1=pyhd8ed1ab_0
- conda-lock=2.5.1=pyhd8ed1ab_0
- - dagster-graphql=1.5.13=pyhd8ed1ab_0
- - dagster-postgres=0.21.13=pyhd8ed1ab_0
- - fiona=1.9.5=py311hf8e0aa6_2
+ - dagster-graphql=1.6.0=pyhd8ed1ab_0
+ - dagster-postgres=0.22.0=pyhd8ed1ab_0
- google-api-core=2.15.0=pyhd8ed1ab_0
- google-auth-oauthlib=1.2.0=pyhd8ed1ab_0
- graphviz=9.0.0=h78e8752_1
- jupyter_console=6.6.3=pyhd8ed1ab_0
- jupyter_events=0.9.0=pyhd8ed1ab_0
- - libarrow=14.0.1=h4df1b6a_3_cpu
+ - libarrow=14.0.2=h84dd17c_2_cpu
+ - libgdal=3.8.3=hcd1fc54_0
- mapclassify=2.6.1=pyhd8ed1ab_0
- nbclient=0.8.0=pyhd8ed1ab_0
- qtconsole-base=5.5.1=pyha770c72_0
- - recordlinkage=0.16=pyhd8ed1ab_0
- tabulator=1.53.5=pyhd8ed1ab_0
- - dagster-webserver=1.5.13=pyhd8ed1ab_0
- - geopandas=0.14.1=pyhd8ed1ab_0
+ - dagster-webserver=1.6.0=pyhd8ed1ab_0
+ - gdal=3.8.3=py311h39b4e0e_0
- google-cloud-core=2.4.1=pyhd8ed1ab_0
- - libarrow-acero=14.0.1=h59595ed_3_cpu
- - libarrow-flight=14.0.1=h120cb0d_3_cpu
- - libarrow-gandiva=14.0.1=hacb8726_3_cpu
- - libparquet=14.0.1=h352af49_3_cpu
- - nbconvert-core=7.13.1=pyhd8ed1ab_0
+ - libarrow-acero=14.0.2=h59595ed_2_cpu
+ - libarrow-flight=14.0.2=h120cb0d_2_cpu
+ - libarrow-gandiva=14.0.2=hacb8726_2_cpu
+ - libparquet=14.0.2=h352af49_2_cpu
+ - nbconvert-core=7.14.2=pyhd8ed1ab_0
- pygraphviz=1.11=py311hbf5cbc9_2
- tableschema=1.19.3=pyh9f0ad1d_0
- datapackage=1.15.2=pyh44b312d_0
+ - fiona=1.9.5=py311hf8e0aa6_3
- google-cloud-storage=2.14.0=pyhca7485f_0
- - jupyter_server=2.12.1=pyhd8ed1ab_0
- - libarrow-dataset=14.0.1=h59595ed_3_cpu
- - libarrow-flight-sql=14.0.1=h61ff412_3_cpu
- - nbconvert-pandoc=7.13.1=pyhd8ed1ab_0
+ - jupyter_server=2.12.5=pyhd8ed1ab_0
+ - libarrow-dataset=14.0.2=h59595ed_2_cpu
+ - libarrow-flight-sql=14.0.2=h61ff412_2_cpu
+ - nbconvert-pandoc=7.14.2=pyhd8ed1ab_0
- gcsfs=2023.12.2.post1=pyhd8ed1ab_0
+ - geopandas=0.14.2=pyhd8ed1ab_0
- jupyter-lsp=2.2.1=pyhd8ed1ab_0
- jupyter-resource-usage=1.0.1=pyhd8ed1ab_0
- jupyterlab_server=2.25.2=pyhd8ed1ab_0
- - libarrow-substrait=14.0.1=h61ff412_3_cpu
- - nbconvert=7.13.1=pyhd8ed1ab_0
+ - libarrow-substrait=14.0.2=h61ff412_2_cpu
+ - nbconvert=7.14.2=pyhd8ed1ab_0
- notebook-shim=0.2.3=pyhd8ed1ab_0
- jupyterlab=4.0.10=pyhd8ed1ab_0
- - pyarrow=14.0.1=py311h39c9aba_3_cpu
+ - pyarrow=14.0.2=py311h39c9aba_2_cpu
- notebook=7.0.6=pyhd8ed1ab_0
- jupyter=1.0.0=pyhd8ed1ab_10
- sphinx-autoapi=3.0.0=pyhd8ed1ab_0
@@ -563,10 +565,10 @@ dependencies:
- furo=2023.9.10=pyhd8ed1ab_0
- sphinx-issues=1.2.0=py_0
- sphinx-reredirects=0.1.2=pyhd8ed1ab_0
- - sphinxcontrib-applehelp=1.0.7=pyhd8ed1ab_0
- - sphinxcontrib-bibtex=2.6.1=pyhd8ed1ab_1
- - sphinxcontrib-devhelp=1.0.5=pyhd8ed1ab_0
- - sphinxcontrib-htmlhelp=2.0.4=pyhd8ed1ab_0
- - sphinxcontrib-qthelp=1.0.6=pyhd8ed1ab_0
+ - sphinxcontrib-applehelp=1.0.8=pyhd8ed1ab_0
+ - sphinxcontrib-bibtex=2.6.2=pyhd8ed1ab_0
+ - sphinxcontrib-devhelp=1.0.6=pyhd8ed1ab_0
+ - sphinxcontrib-htmlhelp=2.0.5=pyhd8ed1ab_0
+ - sphinxcontrib-qthelp=1.0.7=pyhd8ed1ab_0
- sphinx=7.2.6=pyhd8ed1ab_0
- - sphinxcontrib-serializinghtml=1.1.9=pyhd8ed1ab_0
+ - sphinxcontrib-serializinghtml=1.1.10=pyhd8ed1ab_0
diff --git a/environments/conda-lock.yml b/environments/conda-lock.yml
index 50d3ac566d..e4790d84dd 100644
--- a/environments/conda-lock.yml
+++ b/environments/conda-lock.yml
@@ -15,9 +15,9 @@
version: 1
metadata:
content_hash:
- linux-64: 25338bdeb54a81fb064efcdf38a56212bb8da4efc4654985f265ded511dcb38f
- osx-64: 0c46f3c7a9c86a583e588c727d6a7e7d3214ce9d048dd4e49df3a792c36b3e9c
- osx-arm64: 3e57b04f7b2300284a5c81502687a5c373c4be31786246211ea23f019d4a513f
+ linux-64: a94ce1081d54c82931f58616985e5261ac1f9a1471c91852e1d0b53e9306b923
+ osx-64: a183514478e1919b8fb62d0d6d4b45a20ca9e0d600412ea1685c6e7d76b79f58
+ osx-arm64: 36ae6768b8517ef3b018f1e8fce32dcb5fea0df8341e951712a7ee4b08b6a361
channels:
- url: conda-forge
used_env_vars: []
@@ -130,64 +130,58 @@ package:
category: main
optional: false
- name: aiohttp
- version: 3.8.6
+ version: 3.9.1
manager: conda
platform: linux-64
dependencies:
aiosignal: ">=1.1.2"
- async-timeout: <5.0,>=4.0.0a3
attrs: ">=17.3.0"
- charset-normalizer: ">=2.0,<4.0"
frozenlist: ">=1.1.1"
libgcc-ng: ">=12"
multidict: ">=4.5,<7.0"
python: ">=3.11,<3.12.0a0"
python_abi: 3.11.*
yarl: ">=1.0,<2.0"
- url: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.8.6-py311h459d7ec_1.conda
+ url: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.9.1-py311h459d7ec_0.conda
hash:
- md5: 7d4b63a745f293029b5689b0b5d8aa15
- sha256: 690f7ca719e99d47728c392ab0f5f362013852800db41702c29d219c8e380976
+ md5: a51ceb9a9219e3c11af56b2b77794839
+ sha256: 3f16a6ff0ce0c137de2bc63ac3758616f7232f75ff630a3955f02af2452b0490
category: main
optional: false
- name: aiohttp
- version: 3.8.6
+ version: 3.9.1
manager: conda
platform: osx-64
dependencies:
aiosignal: ">=1.1.2"
- async-timeout: <5.0,>=4.0.0a3
attrs: ">=17.3.0"
- charset-normalizer: ">=2.0,<4.0"
frozenlist: ">=1.1.1"
multidict: ">=4.5,<7.0"
python: ">=3.11,<3.12.0a0"
python_abi: 3.11.*
yarl: ">=1.0,<2.0"
- url: https://conda.anaconda.org/conda-forge/osx-64/aiohttp-3.8.6-py311he705e18_1.conda
+ url: https://conda.anaconda.org/conda-forge/osx-64/aiohttp-3.9.1-py311he705e18_0.conda
hash:
- md5: 5319ce185be1f2c4d1b19b95488c02a8
- sha256: e2f3b1c8fe44daf016396f6a49e80c84aeb9621d2812bec15e4d1873e5972b58
+ md5: b2b933fedb5534216ff5326f41ed7426
+ sha256: 98241e3eb524fc8d3f67f69faa0c661c56653671996b30a1fef03e58e17ce59a
category: main
optional: false
- name: aiohttp
- version: 3.8.6
+ version: 3.9.1
manager: conda
platform: osx-arm64
dependencies:
aiosignal: ">=1.1.2"
- async-timeout: <5.0,>=4.0.0a3
attrs: ">=17.3.0"
- charset-normalizer: ">=2.0,<4.0"
frozenlist: ">=1.1.1"
multidict: ">=4.5,<7.0"
python: ">=3.11,<3.12.0a0"
python_abi: 3.11.*
yarl: ">=1.0,<2.0"
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+ url: https://conda.anaconda.org/conda-forge/noarch/starlette-0.35.0-pyhd8ed1ab_0.conda
hash:
- md5: 0fcf5fb9e8a6bb5138990731474f710d
- sha256: 4425b1c34338fd27d8a27cb1439e4a7ac56255b2d15a4d82b060c40abb98a7d4
+ md5: e3093d587b422113f298c965e73689b0
+ sha256: dbcbb4e7375f08d8c63b2ac3a48cb6d3d3709f9d34242123b4abb4d55370691f
category: dev
optional: true
- name: starlette
- version: 0.34.0
+ version: 0.35.0
manager: conda
platform: osx-arm64
dependencies:
python: ">=3.8"
typing_extensions: ">=3.10.0"
anyio: <5,>=3.4.0
- url: https://conda.anaconda.org/conda-forge/noarch/starlette-0.34.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/starlette-0.35.0-pyhd8ed1ab_0.conda
hash:
- md5: 0fcf5fb9e8a6bb5138990731474f710d
- sha256: 4425b1c34338fd27d8a27cb1439e4a7ac56255b2d15a4d82b060c40abb98a7d4
+ md5: e3093d587b422113f298c965e73689b0
+ sha256: dbcbb4e7375f08d8c63b2ac3a48cb6d3d3709f9d34242123b4abb4d55370691f
category: dev
optional: true
- name: stevedore
@@ -21221,6 +21241,42 @@ package:
sha256: ebd515c57537799ee7829055fe9aa93d1c4695334b991fe1de9d7947f53f18f2
category: main
optional: false
+ - name: structlog
+ version: 23.2.0
+ manager: conda
+ platform: linux-64
+ dependencies:
+ python: ">=3.7"
+ url: https://conda.anaconda.org/conda-forge/noarch/structlog-23.2.0-pyhd8ed1ab_0.conda
+ hash:
+ md5: 1adf8fd2029f533f70b72cb447512454
+ sha256: 33f8de5028564165bd314e1c2db2578a075cfa3af9d1d936ae6d930bb4d76c1f
+ category: main
+ optional: false
+ - name: structlog
+ version: 23.2.0
+ manager: conda
+ platform: osx-64
+ dependencies:
+ python: ">=3.7"
+ url: https://conda.anaconda.org/conda-forge/noarch/structlog-23.2.0-pyhd8ed1ab_0.conda
+ hash:
+ md5: 1adf8fd2029f533f70b72cb447512454
+ sha256: 33f8de5028564165bd314e1c2db2578a075cfa3af9d1d936ae6d930bb4d76c1f
+ category: main
+ optional: false
+ - name: structlog
+ version: 23.2.0
+ manager: conda
+ platform: osx-arm64
+ dependencies:
+ python: ">=3.7"
+ url: https://conda.anaconda.org/conda-forge/noarch/structlog-23.2.0-pyhd8ed1ab_0.conda
+ hash:
+ md5: 1adf8fd2029f533f70b72cb447512454
+ sha256: 33f8de5028564165bd314e1c2db2578a075cfa3af9d1d936ae6d930bb4d76c1f
+ category: main
+ optional: false
- name: tableschema
version: 1.19.3
manager: conda
@@ -21513,66 +21569,77 @@ package:
category: main
optional: false
- name: tiledb
- version: 2.18.2
+ version: 2.19.0
manager: conda
platform: linux-64
dependencies:
+ azure-core-cpp: ">=1.10.3,<2.0a0"
+ azure-storage-blobs-cpp: ">=12.10.0,<13.0a0"
+ azure-storage-common-cpp: ">=12.5.0,<13.0a0"
bzip2: ">=1.0.8,<2.0a0"
libabseil: ">=20230802.1,<20230803.0a0"
+ libcurl: ">=8.5.0,<9.0a0"
libgcc-ng: ">=12"
libgoogle-cloud: ">=2.12.0,<2.13.0a0"
libstdcxx-ng: ">=12"
- libxml2: ">=2.11.6,<2.12.0a0"
+ libxml2: ">=2.12.3,<3.0.0a0"
libzlib: ">=1.2.13,<1.3.0a0"
lz4-c: ">=1.9.3,<1.10.0a0"
openssl: ">=3.2.0,<4.0a0"
zstd: ">=1.5.5,<1.6.0a0"
- url: https://conda.anaconda.org/conda-forge/linux-64/tiledb-2.18.2-h8c794c1_0.conda
+ url: https://conda.anaconda.org/conda-forge/linux-64/tiledb-2.19.0-hc1131af_0.conda
hash:
- md5: e824b951c15a74d0a2d3e42676370791
- sha256: c73704c36661193a040e4f1c454ef181277b97d3dc225febac9f3093e60f36bf
+ md5: 51f6de9ca86bdeeb92e89e4abccd03e8
+ sha256: b2896839b41289205562b211a6e45ebdbb97018e076c1ae4faecbfcc421810d0
category: main
optional: false
- name: tiledb
- version: 2.18.2
+ version: 2.19.0
manager: conda
platform: osx-64
dependencies:
- __osx: ">=10.9"
+ __osx: ">=10.13"
+ azure-core-cpp: ">=1.10.3,<2.0a0"
+ azure-storage-blobs-cpp: ">=12.10.0,<13.0a0"
+ azure-storage-common-cpp: ">=12.5.0,<13.0a0"
bzip2: ">=1.0.8,<2.0a0"
libabseil: ">=20230802.1,<20230803.0a0"
- libcxx: ">=16.0.6"
+ libcurl: ">=8.5.0,<9.0a0"
+ libcxx: ">=15"
libgoogle-cloud: ">=2.12.0,<2.13.0a0"
- libxml2: ">=2.11.6,<2.12.0a0"
+ libxml2: ">=2.12.3,<3.0.0a0"
libzlib: ">=1.2.13,<1.3.0a0"
lz4-c: ">=1.9.3,<1.10.0a0"
openssl: ">=3.2.0,<4.0a0"
zstd: ">=1.5.5,<1.6.0a0"
- url: https://conda.anaconda.org/conda-forge/osx-64/tiledb-2.18.2-h9fe0a6a_0.conda
+ url: https://conda.anaconda.org/conda-forge/osx-64/tiledb-2.19.0-h4df5763_0.conda
hash:
- md5: 0615fdde1c114affe6a0250b9d342328
- sha256: daccccc133c363360379e9efa525835cd20ed8ca12381641ccb1353c3d7c34c0
+ md5: eab42c6e4909c3901eaca7c5daf30be8
+ sha256: cf5f78eb9f2bd85d0d7eb69c5c816db15abac4010f46c972ba0f8ed1e1033b0c
category: main
optional: false
- name: tiledb
- version: 2.18.2
+ version: 2.19.0
manager: conda
platform: osx-arm64
dependencies:
- __osx: ">=10.9"
+ azure-core-cpp: ">=1.10.3,<2.0a0"
+ azure-storage-blobs-cpp: ">=12.10.0,<13.0a0"
+ azure-storage-common-cpp: ">=12.5.0,<13.0a0"
bzip2: ">=1.0.8,<2.0a0"
libabseil: ">=20230802.1,<20230803.0a0"
- libcxx: ">=16.0.6"
+ libcurl: ">=8.5.0,<9.0a0"
+ libcxx: ">=15"
libgoogle-cloud: ">=2.12.0,<2.13.0a0"
- libxml2: ">=2.11.6,<2.12.0a0"
+ libxml2: ">=2.12.3,<3.0.0a0"
libzlib: ">=1.2.13,<1.3.0a0"
lz4-c: ">=1.9.3,<1.10.0a0"
openssl: ">=3.2.0,<4.0a0"
zstd: ">=1.5.5,<1.6.0a0"
- url: https://conda.anaconda.org/conda-forge/osx-arm64/tiledb-2.18.2-h555b8a3_0.conda
+ url: https://conda.anaconda.org/conda-forge/osx-arm64/tiledb-2.19.0-h567544c_0.conda
hash:
- md5: c223398e541fce9cfe1abe47df687909
- sha256: da9227601b2bf5607fee33b62ed364561363f994ba41fd82e48d45a2f584210e
+ md5: 60e4d67eedc2f19bfbdf964187f52d15
+ sha256: beeeefd593a5f0fa70b25d7d9563dd54c3840e85f6c5d982e2066f7904077191
category: main
optional: false
- name: timezonefinder
@@ -21963,39 +22030,39 @@ package:
category: main
optional: false
- name: traitlets
- version: 5.14.0
+ version: 5.14.1
manager: conda
platform: linux-64
dependencies:
python: ">=3.8"
- url: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.1-pyhd8ed1ab_0.conda
hash:
- md5: 886f4a84ddb49b943b1697ac314e85b3
- sha256: c32412029033264140926be474d327d7fd57c0d11db9b1745396b3d4db78a799
+ md5: 1c6acfdc7ecbfe09954c4216da99c146
+ sha256: fa78d68f74ec8aae5c93f135140bfdbbf0ab60a79c6062b55d73c316068545ec
category: main
optional: false
- name: traitlets
- version: 5.14.0
+ version: 5.14.1
manager: conda
platform: osx-64
dependencies:
python: ">=3.8"
- url: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.1-pyhd8ed1ab_0.conda
hash:
- md5: 886f4a84ddb49b943b1697ac314e85b3
- sha256: c32412029033264140926be474d327d7fd57c0d11db9b1745396b3d4db78a799
+ md5: 1c6acfdc7ecbfe09954c4216da99c146
+ sha256: fa78d68f74ec8aae5c93f135140bfdbbf0ab60a79c6062b55d73c316068545ec
category: main
optional: false
- name: traitlets
- version: 5.14.0
+ version: 5.14.1
manager: conda
platform: osx-arm64
dependencies:
python: ">=3.8"
- url: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.0-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.1-pyhd8ed1ab_0.conda
hash:
- md5: 886f4a84ddb49b943b1697ac314e85b3
- sha256: c32412029033264140926be474d327d7fd57c0d11db9b1745396b3d4db78a799
+ md5: 1c6acfdc7ecbfe09954c4216da99c146
+ sha256: fa78d68f74ec8aae5c93f135140bfdbbf0ab60a79c6062b55d73c316068545ec
category: main
optional: false
- name: typeguard
@@ -22092,39 +22159,39 @@ package:
category: main
optional: false
- name: types-python-dateutil
- version: 2.8.19.14
+ version: 2.8.19.20240106
manager: conda
platform: linux-64
dependencies:
python: ">=3.6"
- url: https://conda.anaconda.org/conda-forge/noarch/types-python-dateutil-2.8.19.14-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/types-python-dateutil-2.8.19.20240106-pyhd8ed1ab_0.conda
hash:
- md5: 4df15c51a543e806d439490b862be1c6
- sha256: 7b0129c72d371fa7a06ed5dd1d701844c20d03bb4641a38a88a982b347d087e2
+ md5: c9096a546660b9079dce531c0039e074
+ sha256: 09ef8cc587bdea80a83b6f820dbae24daadcf82be088fb0a9f6495781653e300
category: main
optional: false
- name: types-python-dateutil
- version: 2.8.19.14
+ version: 2.8.19.20240106
manager: conda
platform: osx-64
dependencies:
python: ">=3.6"
- url: https://conda.anaconda.org/conda-forge/noarch/types-python-dateutil-2.8.19.14-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/types-python-dateutil-2.8.19.20240106-pyhd8ed1ab_0.conda
hash:
- md5: 4df15c51a543e806d439490b862be1c6
- sha256: 7b0129c72d371fa7a06ed5dd1d701844c20d03bb4641a38a88a982b347d087e2
+ md5: c9096a546660b9079dce531c0039e074
+ sha256: 09ef8cc587bdea80a83b6f820dbae24daadcf82be088fb0a9f6495781653e300
category: main
optional: false
- name: types-python-dateutil
- version: 2.8.19.14
+ version: 2.8.19.20240106
manager: conda
platform: osx-arm64
dependencies:
python: ">=3.6"
- url: https://conda.anaconda.org/conda-forge/noarch/types-python-dateutil-2.8.19.14-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/types-python-dateutil-2.8.19.20240106-pyhd8ed1ab_0.conda
hash:
- md5: 4df15c51a543e806d439490b862be1c6
- sha256: 7b0129c72d371fa7a06ed5dd1d701844c20d03bb4641a38a88a982b347d087e2
+ md5: c9096a546660b9079dce531c0039e074
+ sha256: 09ef8cc587bdea80a83b6f820dbae24daadcf82be088fb0a9f6495781653e300
category: main
optional: false
- name: types-pyyaml
@@ -22945,39 +23012,39 @@ package:
category: dev
optional: true
- name: wcwidth
- version: 0.2.12
+ version: 0.2.13
manager: conda
platform: linux-64
dependencies:
python: ">=3.8"
- url: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.12-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.13-pyhd8ed1ab_0.conda
hash:
- md5: bf4a1d1a97ca27b0b65bacd9e238b484
- sha256: ca757d0fc2dbd422af9d3238a8b4b630a6e11df3707a447bd89540656770d1d7
+ md5: 68f0738df502a14213624b288c60c9ad
+ sha256: b6cd2fee7e728e620ec736d8dfee29c6c9e2adbd4e695a31f1d8f834a83e57e3
category: main
optional: false
- name: wcwidth
- version: 0.2.12
+ version: 0.2.13
manager: conda
platform: osx-64
dependencies:
python: ">=3.8"
- url: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.12-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.13-pyhd8ed1ab_0.conda
hash:
- md5: bf4a1d1a97ca27b0b65bacd9e238b484
- sha256: ca757d0fc2dbd422af9d3238a8b4b630a6e11df3707a447bd89540656770d1d7
+ md5: 68f0738df502a14213624b288c60c9ad
+ sha256: b6cd2fee7e728e620ec736d8dfee29c6c9e2adbd4e695a31f1d8f834a83e57e3
category: main
optional: false
- name: wcwidth
- version: 0.2.12
+ version: 0.2.13
manager: conda
platform: osx-arm64
dependencies:
python: ">=3.8"
- url: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.12-pyhd8ed1ab_0.conda
+ url: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.13-pyhd8ed1ab_0.conda
hash:
- md5: bf4a1d1a97ca27b0b65bacd9e238b484
- sha256: ca757d0fc2dbd422af9d3238a8b4b630a6e11df3707a447bd89540656770d1d7
+ md5: 68f0738df502a14213624b288c60c9ad
+ sha256: b6cd2fee7e728e620ec736d8dfee29c6c9e2adbd4e695a31f1d8f834a83e57e3
category: main
optional: false
- name: webcolors
diff --git a/environments/conda-osx-64.lock.yml b/environments/conda-osx-64.lock.yml
index 9528f5a02c..381eeb8fac 100644
--- a/environments/conda-osx-64.lock.yml
+++ b/environments/conda-osx-64.lock.yml
@@ -1,14 +1,14 @@
# Generated by conda-lock.
# platform: osx-64
-# input_hash: 0c46f3c7a9c86a583e588c727d6a7e7d3214ce9d048dd4e49df3a792c36b3e9c
+# input_hash: a183514478e1919b8fb62d0d6d4b45a20ca9e0d600412ea1685c6e7d76b79f58
channels:
- conda-forge
- defaults
dependencies:
- - aws-c-common=0.9.8=h10d778d_0
+ - aws-c-common=0.9.12=h10d778d_0
- bzip2=1.0.8=h10d778d_5
- - c-ares=1.24.0=h10d778d_0
+ - c-ares=1.25.0=h10d778d_0
- ca-certificates=2023.11.17=h8857fd0_0
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
- font-ttf-inconsolata=3.000=h77eed37_0
@@ -43,10 +43,10 @@ dependencies:
- xorg-libxdmcp=1.1.3=h35c211d_0
- xz=5.2.6=h775f41a_0
- yaml=0.2.5=h0d85af4_2
- - aws-c-cal=0.6.9=h49e9720_1
- - aws-c-compression=0.2.17=hff1f2c8_6
- - aws-c-sdkutils=0.1.12=hff1f2c8_5
- - aws-checksums=0.1.17=hff1f2c8_5
+ - aws-c-cal=0.6.9=he75d6b7_3
+ - aws-c-compression=0.2.17=h45babc2_8
+ - aws-c-sdkutils=0.1.13=h45babc2_1
+ - aws-checksums=0.1.17=h45babc2_7
- expat=2.5.0=hf0c8a7f_1
- fonts-conda-forge=1=0
- geos=3.12.1=h93d8f39_0
@@ -66,48 +66,48 @@ dependencies:
- libspatialindex=1.9.3=he49afe7_4
- libsqlite=3.44.2=h92b6c6a_0
- libxcb=1.15=hb7f2c08_0
- - libxml2=2.11.6=hc0ae0f7_0
+ - libxml2=2.12.4=hc0ae0f7_1
- lz4-c=1.9.4=hf0c8a7f_0
- ncurses=6.4=h93d8f39_2
- nspr=4.35=hea0b92c_0
- openssl=3.2.0=hd75f5a5_1
- pandoc=3.1.3=h9d075a6_0
- pcre2=10.42=h0ad2156_0
- - pixman=0.42.2=he965462_0
+ - pixman=0.43.0=h73e2aa4_0
- snappy=1.1.10=h225ccf5_0
- tk=8.6.13=h1abcd95_1
- uriparser=0.9.7=hf0c8a7f_1
- zeromq=4.3.5=h93d8f39_0
- zlib=1.2.13=h8a1eda9_5
- zstd=1.5.5=h829000d_0
- - aws-c-io=0.13.36=hb98174f_0
+ - aws-c-io=0.14.0=h49ca7b5_1
- blosc=1.21.5=heccf04b_0
- brotli-bin=1.1.0=h0dc2134_1
- fonts-conda-ecosystem=1=0
- freetype=2.12.1=h60636b9_2
- glog=0.6.0=h8ac2a54_0
- - libarchive=3.7.2=h0b5dc4a_0
+ - libarchive=3.7.2=hd35d340_1
- libedit=3.1.20191231=h0678c8f_2
- libevent=2.1.12=ha90c15b_1
- libgfortran=5.0.0=13_2_0_h97931a8_1
- libglib=2.78.3=h198397b_0
- libkml=1.3.0=hab3ca0e_1018
- - libllvm15=15.0.7=he4b1e75_3
+ - libllvm15=15.0.7=hbedff68_4
- libnghttp2=1.58.0=h64cf6d3_1
- libprotobuf=4.24.4=h0ee05dc_0
- libre2-11=2023.06.02=h4694dbf_0
- librttopo=1.1.0=hf05f67e_15
- libssh2=1.11.0=hd019ec5_0
- libtiff=4.6.0=h684deea_2
- - libxslt=1.1.37=h20bfa82_1
+ - libxslt=1.1.39=h03b04e6_0
- libzip=1.10.1=hc158999_3
- minizip=4.0.4=h37d7099_0
- nodejs=20.9.0=h9adec40_0
- nss=3.96=ha05da47_0
- readline=8.2=h9e318b2_1
- atk-1.0=2.38.0=h1d18e73_1
- - aws-c-event-stream=0.3.2=hdb93a3d_7
- - aws-c-http=0.7.14=h99202ee_2
+ - aws-c-event-stream=0.4.1=h3600a39_2
+ - aws-c-http=0.8.0=h19e0e28_2
- brotli=1.1.0=h0dc2134_1
- fontconfig=2.14.2=h5bb23bf_0
- freexl=2.0.0=h3ec172f_0
@@ -119,20 +119,20 @@ dependencies:
- libthrift=0.19.0=h064b379_1
- libwebp=1.3.2=h44782d1_1
- openjpeg=2.5.0=ha4da562_3
- - orc=1.9.0=hd1092d7_4
- - prettier=3.1.1=hbd11d21_0
+ - orc=1.9.2=h9ab30d4_0
+ - prettier=3.2.2=hbd11d21_0
- python=3.11.7=h9f0c242_1_cpython
- re2=2023.06.02=hd34609a_0
- sqlite=3.44.2=h7461747_0
- aiofiles=23.2.1=pyhd8ed1ab_0
- - alabaster=0.7.13=pyhd8ed1ab_0
+ - alabaster=0.7.16=pyhd8ed1ab_0
- anyascii=0.3.2=pyhd8ed1ab_0
- appdirs=1.4.4=pyh9f0ad1d_0
- appnope=0.1.3=pyhd8ed1ab_0
- astroid=3.0.2=py311h6eed73b_0
- attrs=23.2.0=pyh71513ae_0
- - aws-c-auth=0.7.7=h9ac2572_1
- - aws-c-mqtt=0.9.10=h10c2427_1
+ - aws-c-auth=0.7.11=h94c8779_1
+ - aws-c-mqtt=0.10.1=h947eb33_0
- backoff=2.2.1=pyhd8ed1ab_0
- backports.zoneinfo=0.2.1=py311h6eed73b_8
- blinker=1.7.0=pyhd8ed1ab_0
@@ -152,7 +152,7 @@ dependencies:
- colorama=0.4.6=pyhd8ed1ab_0
- crashtest=0.4.1=pyhd8ed1ab_0
- cycler=0.12.1=pyhd8ed1ab_0
- - dagster-pipes=1.5.13=pyhd8ed1ab_0
+ - dagster-pipes=1.6.0=pyhd8ed1ab_0
- dataclasses=0.8=pyhc8e2a94_3
- debugpy=1.8.0=py311hdf8f085_1
- decorator=5.1.1=pyhd8ed1ab_0
@@ -162,13 +162,13 @@ dependencies:
- docutils=0.20.1=py311h6eed73b_3
- entrypoints=0.4=pyhd8ed1ab_0
- et_xmlfile=1.1.0=pyhd8ed1ab_0
- - exceptiongroup=1.2.0=pyhd8ed1ab_0
+ - exceptiongroup=1.2.0=pyhd8ed1ab_2
- execnet=2.0.2=pyhd8ed1ab_0
- executing=2.0.1=pyhd8ed1ab_0
- filelock=3.13.1=pyhd8ed1ab_0
- frozenlist=1.4.1=py311he705e18_0
- fsspec=2023.12.2=pyhca7485f_0
- - google-cloud-sdk=458.0.1=py311h6eed73b_0
+ - google-cloud-sdk=459.0.0=py311h6eed73b_0
- greenlet=3.0.3=py311hdd0406b_0
- hpack=4.0.0=pyh9f0ad1d_0
- httptools=0.6.1=py311he705e18_0
@@ -189,23 +189,23 @@ dependencies:
- libblas=3.9.0=20_osx64_openblas
- libcurl=8.5.0=h726d00d_0
- libgd=2.3.3=h0dceb68_9
- - libgrpc=1.59.2=ha7f534c_0
+ - libgrpc=1.59.3=ha7f534c_0
- libpq=16.1=ha925e61_7
- llvmlite=0.41.1=py311hb5c2e0a_0
- locket=1.0.0=pyhd8ed1ab_0
- - lxml=4.9.3=py311h719c1e2_3
+ - lxml=4.9.4=py311h033124e_0
- marko=2.0.2=pyhd8ed1ab_0
- markupsafe=2.1.3=py311h2725bcf_1
- - mdurl=0.1.0=pyhd8ed1ab_0
+ - mdurl=0.1.2=pyhd8ed1ab_0
- mergedeep=1.3.4=pyhd8ed1ab_0
- mistune=3.0.2=pyhd8ed1ab_0
- - more-itertools=10.1.0=pyhd8ed1ab_0
+ - more-itertools=10.2.0=pyhd8ed1ab_0
- msgpack-python=1.0.7=py311h7bea37d_0
- multidict=6.0.4=py311h5547dcb_1
- multimethod=1.9.1=pyhd8ed1ab_0
- munkres=1.1.4=pyh9f0ad1d_0
- mypy_extensions=1.0.0=pyha770c72_0
- - nest-asyncio=1.5.8=pyhd8ed1ab_0
+ - nest-asyncio=1.5.9=pyhd8ed1ab_0
- networkx=3.2.1=pyhd8ed1ab_0
- packaging=23.2=pyhd8ed1ab_0
- pandocfilters=1.5.0=pyhd8ed1ab_0
@@ -213,7 +213,7 @@ dependencies:
- pastel=0.2.1=pyhd8ed1ab_0
- petl=1.7.14=pyhd8ed1ab_0
- pickleshare=0.7.5=py_1003
- - pillow=10.1.0=py311hea5c87a_0
+ - pillow=10.2.0=py311hea5c87a_0
- pkginfo=1.9.6=pyhd8ed1ab_0
- pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
- platformdirs=4.1.0=pyhd8ed1ab_0
@@ -243,11 +243,11 @@ dependencies:
- regex=2023.12.25=py311he705e18_0
- rfc3986=2.0.0=pyhd8ed1ab_0
- rfc3986-validator=0.1.1=pyh9f0ad1d_0
- - rpds-py=0.16.2=py311h5e0f0e4_0
+ - rpds-py=0.17.1=py311h5e0f0e4_0
- rtree=1.1.0=py311hbc1f44b_0
- ruamel.yaml.clib=0.2.7=py311h2725bcf_2
- - ruff=0.1.9=py311ha071555_0
- - setuptools=68.2.2=pyhd8ed1ab_0
+ - ruff=0.1.13=py311ha071555_1
+ - setuptools=69.0.3=pyhd8ed1ab_0
- shellingham=1.5.4=pyhd8ed1ab_0
- simpleeval=0.9.13=pyhd8ed1ab_1
- six=1.16.0=pyh6c4a22f_0
@@ -258,6 +258,7 @@ dependencies:
- soupsieve=2.5=pyhd8ed1ab_1
- sphinxcontrib-jsmath=1.0.1=pyhd8ed1ab_0
- stringcase=1.2.0=py_0
+ - structlog=23.2.0=pyhd8ed1ab_0
- tabulate=0.9.0=pyhd8ed1ab_1
- text-unidecode=1.3=pyhd8ed1ab_1
- threadpoolctl=3.2.0=pyha21a80b_0
@@ -267,8 +268,8 @@ dependencies:
- toolz=0.12.0=pyhd8ed1ab_0
- toposort=1.10=pyhd8ed1ab_0
- tornado=6.3.3=py311h2725bcf_1
- - traitlets=5.14.0=pyhd8ed1ab_0
- - types-python-dateutil=2.8.19.14=pyhd8ed1ab_0
+ - traitlets=5.14.1=pyhd8ed1ab_0
+ - types-python-dateutil=2.8.19.20240106=pyhd8ed1ab_0
- types-pyyaml=6.0.12.12=pyhd8ed1ab_0
- typing_extensions=4.9.0=pyha770c72_0
- typing_utils=0.1.0=pyhd8ed1ab_0
@@ -276,7 +277,7 @@ dependencies:
- uri-template=1.3.0=pyhd8ed1ab_0
- uvloop=0.19.0=py311ha272bfe_0
- validators=0.22.0=pyhd8ed1ab_0
- - wcwidth=0.2.12=pyhd8ed1ab_0
+ - wcwidth=0.2.13=pyhd8ed1ab_0
- webcolors=1.13=pyhd8ed1ab_0
- webencodings=0.5.1=pyhd8ed1ab_2
- websocket-client=1.7.0=pyhd8ed1ab_0
@@ -294,7 +295,8 @@ dependencies:
- asgiref=3.7.2=pyhd8ed1ab_0
- asttokens=2.4.1=pyhd8ed1ab_0
- async-lru=2.0.4=pyhd8ed1ab_0
- - aws-c-s3=0.4.1=h70a162a_0
+ - aws-c-s3=0.4.9=hee0ca28_0
+ - azure-core-cpp=1.11.0=hbb1e571_0
- babel=2.14.0=pyhd8ed1ab_0
- beautifulsoup4=4.12.2=pyha770c72_0
- bleach=6.1.0=pyhd8ed1ab_0
@@ -306,29 +308,29 @@ dependencies:
- cligj=0.7.2=pyhd8ed1ab_1
- clikit=0.6.2=pyhd8ed1ab_2
- coloredlogs=14.0=pyhd8ed1ab_3
- - comm=0.1.4=pyhd8ed1ab_0
+ - comm=0.2.1=pyhd8ed1ab_0
- coverage=7.4.0=py311he705e18_0
- curl=8.5.0=h726d00d_0
- - fonttools=4.47.0=py311he705e18_0
+ - fonttools=4.47.2=py311he705e18_0
- gitdb=4.0.11=pyhd8ed1ab_0
- graphql-core=3.2.3=pyhd8ed1ab_0
- - grpcio=1.59.2=py311hfd95bfa_0
+ - grpcio=1.59.3=py311hfd95bfa_0
- h11=0.14.0=pyhd8ed1ab_0
- h2=4.1.0=pyhd8ed1ab_0
- harfbuzz=8.3.0=hf45c392_0
- hdf5=1.14.3=nompi_h691f4bf_100
- html5lib=1.1=pyh9f0ad1d_0
- - hypothesis=6.92.2=pyha770c72_0
+ - hypothesis=6.94.0=pyha770c72_0
- importlib-metadata=7.0.1=pyha770c72_0
- importlib_resources=6.1.1=pyhd8ed1ab_0
- isodate=0.6.1=pyhd8ed1ab_0
- janus=1.0.0=pyhd8ed1ab_0
- jaraco.classes=3.3.0=pyhd8ed1ab_0
- jedi=0.19.1=pyhd8ed1ab_0
- - jinja2=3.1.2=pyhd8ed1ab_1
+ - jinja2=3.1.3=pyhd8ed1ab_0
- joblib=1.3.2=pyhd8ed1ab_0
- jsonlines=4.0.0=pyhd8ed1ab_0
- - jupyter_core=5.6.0=py311h6eed73b_0
+ - jupyter_core=5.7.1=py311h6eed73b_0
- jupyterlab_pygments=0.3.0=pyhd8ed1ab_0
- latexcodec=2.0.1=pyh9f0ad1d_0
- libcblas=3.9.0=20_osx64_openblas
@@ -345,8 +347,8 @@ dependencies:
- pint=0.23=pyhd8ed1ab_0
- pip=23.3.2=pyhd8ed1ab_0
- poppler=23.12.0=hdd5a5e8_0
- - postgresql=16.1=h413614c_7
- - proj=9.3.0=h23b96cc_2
+ - postgresql=16.1=hbd19fd8_7
+ - proj=9.3.1=h81faed2_0
- prompt-toolkit=3.0.42=pyha770c72_0
- protobuf=4.24.4=py311h021eaf5_0
- psycopg2=2.9.9=py311h187f0af_0
@@ -358,7 +360,7 @@ dependencies:
- python-slugify=8.0.1=pyhd8ed1ab_2
- pyu2f=0.1.5=pyhd8ed1ab_0
- qtpy=2.4.1=pyhd8ed1ab_0
- - referencing=0.32.0=pyhd8ed1ab_0
+ - referencing=0.32.1=pyhd8ed1ab_0
- restructuredtext_lint=1.4.0=pyhd8ed1ab_0
- rfc3339-validator=0.1.4=pyhd8ed1ab_0
- rsa=4.9=pyhd8ed1ab_0
@@ -375,33 +377,34 @@ dependencies:
- xerces-c=3.2.5=hbbe9ea5_0
- yarl=1.9.3=py311he705e18_0
- addfips=0.4.0=pyhd8ed1ab_1
+ - aiohttp=3.9.1=py311he705e18_0
- aniso8601=9.0.1=pyhd8ed1ab_0
- annotated-types=0.6.0=pyhd8ed1ab_0
- argon2-cffi-bindings=21.2.0=py311h2725bcf_4
- arrow=1.3.0=pyhd8ed1ab_0
- - async-timeout=4.0.3=pyhd8ed1ab_0
- - aws-crt-cpp=0.24.7=hf3941dc_6
- - botocore=1.34.11=pyhd8ed1ab_0
+ - aws-crt-cpp=0.26.0=he4637c3_8
+ - azure-storage-common-cpp=12.5.0=hf4badfb_2
+ - botocore=1.34.19=pyhd8ed1ab_0
- branca=0.7.0=pyhd8ed1ab_1
- croniter=2.0.1=pyhd8ed1ab_0
- cryptography=41.0.7=py311h48c7838_1
- fqdn=1.5.1=pyhd8ed1ab_0
- - geotiff=1.7.1=h889ec99_14
- - gitpython=3.1.40=pyhd8ed1ab_0
+ - geotiff=1.7.1=h509af15_15
+ - gitpython=3.1.41=pyhd8ed1ab_0
- google-crc32c=1.1.2=py311h0b57020_5
- googleapis-common-protos=1.62.0=pyhd8ed1ab_0
- - gql=3.4.1=pyhd8ed1ab_0
+ - gql=3.5.0=pyhd8ed1ab_0
- graphql-relay=3.2.0=pyhd8ed1ab_0
- - grpcio-health-checking=1.59.2=pyhd8ed1ab_0
+ - grpcio-health-checking=1.59.3=pyhd8ed1ab_0
- httpcore=1.0.2=pyhd8ed1ab_0
- importlib_metadata=7.0.1=hd8ed1ab_0
- jsonschema-specifications=2023.12.1=pyhd8ed1ab_0
- jupyter_server_terminals=0.5.1=pyhd8ed1ab_0
- kealib=1.5.3=h5f07ac3_0
- - libnetcdf=4.9.2=nompi_h6a32802_112
- - libspatialite=5.1.0=hf63aa75_2
+ - libnetcdf=4.9.2=nompi_h7760872_113
+ - libspatialite=5.1.0=hebe6af1_4
- mako=1.3.0=pyhd8ed1ab_0
- - numpy=1.26.2=py311h93c810c_0
+ - numpy=1.26.3=py311hc43a94b_0
- pango=1.50.14=h19c1c8a_2
- pbr=6.0.0=pyhd8ed1ab_0
- pendulum=2.1.2=py311h2725bcf_6
@@ -410,7 +413,7 @@ dependencies:
- pybtex=0.24.0=pyhd8ed1ab_2
- pydantic-core=2.14.6=py311h5e0f0e4_1
- pyobjc-framework-cocoa=10.1=py311h9b70068_0
- - pyproj=3.6.1=py311he36daed_4
+ - pyproj=3.6.1=py311hb91e5a3_5
- pytest-console-scripts=1.4.1=pyhd8ed1ab_0
- pytest-cov=4.1.0=pyhd8ed1ab_0
- pytest-mock=3.12.0=pyhd8ed1ab_0
@@ -418,39 +421,37 @@ dependencies:
- python-build=1.0.3=pyhd8ed1ab_0
- requests=2.31.0=pyhd8ed1ab_0
- rich=13.7.0=pyhd8ed1ab_0
- - sqlalchemy=2.0.24=py311he705e18_0
+ - sqlalchemy=2.0.25=py311he705e18_0
- stack_data=0.6.2=pyhd8ed1ab_0
- - starlette=0.34.0=pyhd8ed1ab_0
- - tiledb=2.18.2=h9fe0a6a_0
+ - starlette=0.35.0=pyhd8ed1ab_0
- ukkonen=1.0.1=py311h5fe6e05_4
- uvicorn=0.25.0=py311h6eed73b_0
- watchfiles=0.21.0=py311h5e0f0e4_0
- - aiohttp=3.8.6=py311he705e18_1
- - alembic=1.13.1=pyhd8ed1ab_0
- - arelle-release=2.20.3=pyhd8ed1ab_0
+ - alembic=1.13.1=pyhd8ed1ab_1
+ - arelle-release=2.21.3=pyhd8ed1ab_0
- argon2-cffi=23.1.0=pyhd8ed1ab_0
- - aws-sdk-cpp=1.11.182=h28d282b_7
+ - aws-sdk-cpp=1.11.210=hf51409f_10
+ - azure-storage-blobs-cpp=12.10.0=he51d815_0
- bottleneck=1.3.7=py311h4a70a88_1
- cachecontrol=0.13.1=pyhd8ed1ab_0
- contourpy=1.2.0=py311h7bea37d_0
- - dask-core=2023.12.1=pyhd8ed1ab_0
+ - dask-core=2024.1.0=pyhd8ed1ab_0
- dnspython=2.4.2=pyhd8ed1ab_1
- ensureconda=1.4.3=pyhd8ed1ab_0
- folium=0.15.1=pyhd8ed1ab_0
- google-resumable-media=2.7.0=pyhd8ed1ab_0
- graphene=3.3=pyhd8ed1ab_0
- - grpcio-status=1.59.2=pyhd8ed1ab_0
+ - grpcio-status=1.59.3=pyhd8ed1ab_0
- gtk2=2.24.33=h8ca4665_3
- h3-py=3.7.6=py311hdf8f085_1
- httpx=0.26.0=pyhd8ed1ab_0
- identify=2.5.33=pyhd8ed1ab_0
- - ipython=8.19.0=pyh707e725_0
+ - ipython=8.20.0=pyh707e725_0
- isoduration=20.11.0=pyhd8ed1ab_0
- jsonschema=4.20.0=pyhd8ed1ab_0
- jupyter_client=8.6.0=pyhd8ed1ab_0
- keyring=24.3.0=py311h6eed73b_0
- - libgdal=3.8.1=h7bb8e92_2
- - librsvg=2.56.3=hec3db73_0
+ - librsvg=2.56.3=h1877882_1
- numba=0.58.1=py311h32f2313_0
- numexpr=2.8.8=py311h8f6166a_0
- oauthlib=3.2.2=pyhd8ed1ab_0
@@ -459,7 +460,7 @@ dependencies:
- pydantic=2.5.3=pyhd8ed1ab_0
- pyopenssl=23.3.0=pyhd8ed1ab_0
- readthedocs-sphinx-ext=2.2.5=pyhd8ed1ab_0
- - requests-toolbelt=0.10.1=pyhd8ed1ab_0
+ - requests-toolbelt=1.0.0=pyhd8ed1ab_0
- responses=0.24.1=pyhd8ed1ab_0
- s3transfer=0.10.0=pyhd8ed1ab_0
- scipy=1.11.4=py311he0bea55_0
@@ -469,22 +470,21 @@ dependencies:
- typeguard=4.1.5=pyhd8ed1ab_1
- typer=0.9.0=pyhd8ed1ab_0
- uvicorn-standard=0.25.0=h6eed73b_0
- - boto3=1.34.11=pyhd8ed1ab_0
+ - boto3=1.34.19=pyhd8ed1ab_0
- cachecontrol-with-filecache=0.13.1=pyhd8ed1ab_0
- - dagster=1.5.13=pyhd8ed1ab_0
- - datasette=0.64.5=pyhd8ed1ab_0
+ - dagster=1.6.0=pyhd8ed1ab_0
+ - datasette=0.64.6=pyhd8ed1ab_0
- doc8=1.1.1=pyhd8ed1ab_0
- email-validator=2.1.0.post1=pyhd8ed1ab_0
- frictionless=4.40.8=pyh6c4a22f_0
- - gdal=3.8.1=py311h06f03dd_2
- - geopandas-base=0.14.1=pyha770c72_0
- - google-auth=2.25.2=pyhca7485f_0
- - gql-with-requests=3.4.1=pyhd8ed1ab_0
+ - geopandas-base=0.14.2=pyha770c72_0
+ - google-auth=2.26.2=pyhca7485f_0
+ - gql-with-requests=3.5.0=pyhd8ed1ab_0
- graphviz=9.0.0=hee74176_1
- - ipykernel=6.28.0=pyh3cd1d5f_0
+ - ipykernel=6.29.0=pyh3cd1d5f_0
- ipywidgets=8.1.1=pyhd8ed1ab_0
- jsonschema-with-format-nongpl=4.20.0=pyhd8ed1ab_0
- - libarrow=14.0.1=hd201b0c_3_cpu
+ - libarrow=14.0.2=h1aaacd4_2_cpu
- matplotlib-base=3.8.2=py311hd316c10_0
- nbformat=5.9.2=pyhd8ed1ab_0
- pandera-core=0.17.2=pyhd8ed1ab_1
@@ -492,45 +492,47 @@ dependencies:
- pydantic-settings=2.1.0=pyhd8ed1ab_1
- requests-oauthlib=1.3.1=pyhd8ed1ab_0
- scikit-learn=1.3.2=py311h66081b9_2
+ - tiledb=2.19.0=h4df5763_0
- timezonefinder=6.2.0=py311he705e18_2
- catalystcoop.ferc_xbrl_extractor=1.3.1=pyhd8ed1ab_0
- conda-lock=2.5.1=pyhd8ed1ab_0
- - dagster-graphql=1.5.13=pyhd8ed1ab_0
- - dagster-postgres=0.21.13=pyhd8ed1ab_0
- - fiona=1.9.5=py311h809632c_2
+ - dagster-graphql=1.6.0=pyhd8ed1ab_0
+ - dagster-postgres=0.22.0=pyhd8ed1ab_0
- google-api-core=2.15.0=pyhd8ed1ab_0
- google-auth-oauthlib=1.2.0=pyhd8ed1ab_0
- jupyter_console=6.6.3=pyhd8ed1ab_0
- jupyter_events=0.9.0=pyhd8ed1ab_0
- - libarrow-acero=14.0.1=hc222712_3_cpu
- - libarrow-flight=14.0.1=h440f1c2_3_cpu
- - libarrow-gandiva=14.0.1=heeebe7c_3_cpu
- - libparquet=14.0.1=h27bd29f_3_cpu
+ - libarrow-acero=14.0.2=h000cb23_2_cpu
+ - libarrow-flight=14.0.2=ha1803ca_2_cpu
+ - libarrow-gandiva=14.0.2=h01dce7f_2_cpu
+ - libgdal=3.8.3=h89a805d_0
+ - libparquet=14.0.2=h381d950_2_cpu
- mapclassify=2.6.1=pyhd8ed1ab_0
- nbclient=0.8.0=pyhd8ed1ab_0
- pygraphviz=1.11=py311h03ee4fc_2
- qtconsole-base=5.5.1=pyha770c72_0
- - recordlinkage=0.16=pyhd8ed1ab_0
- tabulator=1.53.5=pyhd8ed1ab_0
- - dagster-webserver=1.5.13=pyhd8ed1ab_0
- - geopandas=0.14.1=pyhd8ed1ab_0
+ - dagster-webserver=1.6.0=pyhd8ed1ab_0
+ - gdal=3.8.3=py311hd4433e8_0
- google-cloud-core=2.4.1=pyhd8ed1ab_0
- - libarrow-dataset=14.0.1=hc222712_3_cpu
- - libarrow-flight-sql=14.0.1=h2cc6c1c_3_cpu
- - nbconvert-core=7.13.1=pyhd8ed1ab_0
+ - libarrow-dataset=14.0.2=h000cb23_2_cpu
+ - libarrow-flight-sql=14.0.2=h8ec153b_2_cpu
+ - nbconvert-core=7.14.2=pyhd8ed1ab_0
- tableschema=1.19.3=pyh9f0ad1d_0
- datapackage=1.15.2=pyh44b312d_0
+ - fiona=1.9.5=py311hd2ff552_3
- google-cloud-storage=2.14.0=pyhca7485f_0
- - jupyter_server=2.12.1=pyhd8ed1ab_0
- - libarrow-substrait=14.0.1=h2cc6c1c_3_cpu
- - nbconvert-pandoc=7.13.1=pyhd8ed1ab_0
+ - jupyter_server=2.12.5=pyhd8ed1ab_0
+ - libarrow-substrait=14.0.2=h8ec153b_2_cpu
+ - nbconvert-pandoc=7.14.2=pyhd8ed1ab_0
- gcsfs=2023.12.2.post1=pyhd8ed1ab_0
+ - geopandas=0.14.2=pyhd8ed1ab_0
- jupyter-lsp=2.2.1=pyhd8ed1ab_0
- jupyter-resource-usage=1.0.1=pyhd8ed1ab_0
- jupyterlab_server=2.25.2=pyhd8ed1ab_0
- - nbconvert=7.13.1=pyhd8ed1ab_0
+ - nbconvert=7.14.2=pyhd8ed1ab_0
- notebook-shim=0.2.3=pyhd8ed1ab_0
- - pyarrow=14.0.1=py311h98a0319_3_cpu
+ - pyarrow=14.0.2=py311h54e7ce8_2_cpu
- jupyterlab=4.0.10=pyhd8ed1ab_0
- notebook=7.0.6=pyhd8ed1ab_0
- jupyter=1.0.0=pyhd8ed1ab_10
@@ -539,10 +541,10 @@ dependencies:
- furo=2023.9.10=pyhd8ed1ab_0
- sphinx-issues=1.2.0=py_0
- sphinx-reredirects=0.1.2=pyhd8ed1ab_0
- - sphinxcontrib-applehelp=1.0.7=pyhd8ed1ab_0
- - sphinxcontrib-bibtex=2.6.1=pyhd8ed1ab_1
- - sphinxcontrib-devhelp=1.0.5=pyhd8ed1ab_0
- - sphinxcontrib-htmlhelp=2.0.4=pyhd8ed1ab_0
- - sphinxcontrib-qthelp=1.0.6=pyhd8ed1ab_0
+ - sphinxcontrib-applehelp=1.0.8=pyhd8ed1ab_0
+ - sphinxcontrib-bibtex=2.6.2=pyhd8ed1ab_0
+ - sphinxcontrib-devhelp=1.0.6=pyhd8ed1ab_0
+ - sphinxcontrib-htmlhelp=2.0.5=pyhd8ed1ab_0
+ - sphinxcontrib-qthelp=1.0.7=pyhd8ed1ab_0
- sphinx=7.2.6=pyhd8ed1ab_0
- - sphinxcontrib-serializinghtml=1.1.9=pyhd8ed1ab_0
+ - sphinxcontrib-serializinghtml=1.1.10=pyhd8ed1ab_0
diff --git a/environments/conda-osx-arm64.lock.yml b/environments/conda-osx-arm64.lock.yml
index 5ea4260ce4..0a47e5c653 100644
--- a/environments/conda-osx-arm64.lock.yml
+++ b/environments/conda-osx-arm64.lock.yml
@@ -1,14 +1,14 @@
# Generated by conda-lock.
# platform: osx-arm64
-# input_hash: 3e57b04f7b2300284a5c81502687a5c373c4be31786246211ea23f019d4a513f
+# input_hash: 36ae6768b8517ef3b018f1e8fce32dcb5fea0df8341e951712a7ee4b08b6a361
channels:
- conda-forge
- defaults
dependencies:
- - aws-c-common=0.9.8=h93a5062_0
+ - aws-c-common=0.9.12=h93a5062_0
- bzip2=1.0.8=h93a5062_5
- - c-ares=1.24.0=h93a5062_0
+ - c-ares=1.25.0=h93a5062_0
- ca-certificates=2023.11.17=hf0a4a13_0
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
- font-ttf-inconsolata=3.000=h77eed37_0
@@ -44,10 +44,10 @@ dependencies:
- xorg-libxdmcp=1.1.3=h27ca646_0
- xz=5.2.6=h57fd34a_0
- yaml=0.2.5=h3422bc3_2
- - aws-c-cal=0.6.9=hea61927_1
- - aws-c-compression=0.2.17=hea61927_6
- - aws-c-sdkutils=0.1.12=hea61927_5
- - aws-checksums=0.1.17=hea61927_5
+ - aws-c-cal=0.6.9=h4fd42c2_3
+ - aws-c-compression=0.2.17=h4fd42c2_8
+ - aws-c-sdkutils=0.1.13=h4fd42c2_1
+ - aws-checksums=0.1.17=h4fd42c2_7
- expat=2.5.0=hb7217d7_1
- fonts-conda-forge=1=0
- geos=3.12.1=h965bd2d_0
@@ -67,47 +67,47 @@ dependencies:
- libspatialindex=1.9.3=hbdafb3b_4
- libsqlite=3.44.2=h091b4b1_0
- libxcb=1.15=hf346824_0
- - libxml2=2.11.6=h0d0cfa8_0
+ - libxml2=2.12.4=h0d0cfa8_1
- lz4-c=1.9.4=hb7217d7_0
- ncurses=6.4=h463b476_2
- nspr=4.35=hb7217d7_0
- openssl=3.2.0=h0d3ecfb_1
- pcre2=10.42=h26f9a81_0
- - pixman=0.42.2=h13dd4ca_0
+ - pixman=0.43.0=hebf3989_0
- snappy=1.1.10=h17c5cce_0
- tk=8.6.13=h5083fa2_1
- uriparser=0.9.7=hb7217d7_1
- zeromq=4.3.5=h965bd2d_0
- zlib=1.2.13=h53f4e23_5
- zstd=1.5.5=h4f39d0f_0
- - aws-c-io=0.13.36=he1b4ce3_0
+ - aws-c-io=0.14.0=h8daa835_1
- blosc=1.21.5=hc338f07_0
- brotli-bin=1.1.0=hb547adb_1
- fonts-conda-ecosystem=1=0
- freetype=2.12.1=hadb7bae_2
- glog=0.6.0=h6da1cb0_0
- - libarchive=3.7.2=h82b9b87_0
+ - libarchive=3.7.2=hcacb583_1
- libedit=3.1.20191231=hc8eb9b7_2
- libevent=2.1.12=h2757513_1
- libgfortran=5.0.0=13_2_0_hd922786_1
- libglib=2.78.3=hb438215_0
- libkml=1.3.0=h1eb4d9f_1018
- - libllvm15=15.0.7=h504e6bf_3
+ - libllvm15=15.0.7=h2621b3d_4
- libnghttp2=1.58.0=ha4dd798_1
- libprotobuf=4.24.4=hc9861d8_0
- libre2-11=2023.06.02=h1753957_0
- librttopo=1.1.0=hc8f776e_15
- libssh2=1.11.0=h7a5bd25_0
- libtiff=4.6.0=ha8a6c65_2
- - libxslt=1.1.37=h1728932_1
+ - libxslt=1.1.39=h223e5b9_0
- libzip=1.10.1=ha0bc3c6_3
- minizip=4.0.4=hc35e051_0
- nodejs=20.9.0=h0950e01_0
- nss=3.96=h5ce2875_0
- readline=8.2=h92ec313_1
- atk-1.0=2.38.0=hcb7b3dd_1
- - aws-c-event-stream=0.3.2=h0574dc0_7
- - aws-c-http=0.7.14=h90b1786_2
+ - aws-c-event-stream=0.4.1=he66824e_2
+ - aws-c-http=0.8.0=hd3d28cd_2
- brotli=1.1.0=hb547adb_1
- fontconfig=2.14.2=h82840c6_0
- freexl=2.0.0=hfbad9fb_0
@@ -119,20 +119,20 @@ dependencies:
- libthrift=0.19.0=h026a170_1
- libwebp=1.3.2=hf30222e_1
- openjpeg=2.5.0=h4c1507b_3
- - orc=1.9.0=h7c018df_4
- - prettier=3.1.1=hb67532b_0
+ - orc=1.9.2=h7c018df_0
+ - prettier=3.2.2=hb67532b_0
- python=3.11.7=hdf0ec26_1_cpython
- re2=2023.06.02=h6135d0a_0
- sqlite=3.44.2=hf2abe2d_0
- aiofiles=23.2.1=pyhd8ed1ab_0
- - alabaster=0.7.13=pyhd8ed1ab_0
+ - alabaster=0.7.16=pyhd8ed1ab_0
- anyascii=0.3.2=pyhd8ed1ab_0
- appdirs=1.4.4=pyh9f0ad1d_0
- appnope=0.1.3=pyhd8ed1ab_0
- astroid=3.0.2=py311h267d04e_0
- attrs=23.2.0=pyh71513ae_0
- - aws-c-auth=0.7.7=h886c30d_1
- - aws-c-mqtt=0.9.10=h8d54690_1
+ - aws-c-auth=0.7.11=ha4ce7b8_1
+ - aws-c-mqtt=0.10.1=h59ff425_0
- backoff=2.2.1=pyhd8ed1ab_0
- backports.zoneinfo=0.2.1=py311h267d04e_8
- blinker=1.7.0=pyhd8ed1ab_0
@@ -152,7 +152,7 @@ dependencies:
- colorama=0.4.6=pyhd8ed1ab_0
- crashtest=0.4.1=pyhd8ed1ab_0
- cycler=0.12.1=pyhd8ed1ab_0
- - dagster-pipes=1.5.13=pyhd8ed1ab_0
+ - dagster-pipes=1.6.0=pyhd8ed1ab_0
- dataclasses=0.8=pyhc8e2a94_3
- debugpy=1.8.0=py311ha891d26_1
- decorator=5.1.1=pyhd8ed1ab_0
@@ -162,13 +162,13 @@ dependencies:
- docutils=0.20.1=py311h267d04e_3
- entrypoints=0.4=pyhd8ed1ab_0
- et_xmlfile=1.1.0=pyhd8ed1ab_0
- - exceptiongroup=1.2.0=pyhd8ed1ab_0
+ - exceptiongroup=1.2.0=pyhd8ed1ab_2
- execnet=2.0.2=pyhd8ed1ab_0
- executing=2.0.1=pyhd8ed1ab_0
- filelock=3.13.1=pyhd8ed1ab_0
- frozenlist=1.4.1=py311h05b510d_0
- fsspec=2023.12.2=pyhca7485f_0
- - google-cloud-sdk=458.0.1=py311h267d04e_0
+ - google-cloud-sdk=459.0.0=py311h267d04e_0
- greenlet=3.0.3=py311h92babd0_0
- hpack=4.0.0=pyh9f0ad1d_0
- httptools=0.6.1=py311h05b510d_0
@@ -189,23 +189,23 @@ dependencies:
- libblas=3.9.0=20_osxarm64_openblas
- libcurl=8.5.0=h2d989ff_0
- libgd=2.3.3=hfdf3952_9
- - libgrpc=1.59.2=hbcf6334_0
+ - libgrpc=1.59.3=hbcf6334_0
- libpq=16.1=h0f8b458_7
- llvmlite=0.41.1=py311hf5d242d_0
- locket=1.0.0=pyhd8ed1ab_0
- - lxml=4.9.3=py311hdef8331_3
+ - lxml=4.9.4=py311h85df328_0
- marko=2.0.2=pyhd8ed1ab_0
- markupsafe=2.1.3=py311heffc1b2_1
- - mdurl=0.1.0=pyhd8ed1ab_0
+ - mdurl=0.1.2=pyhd8ed1ab_0
- mergedeep=1.3.4=pyhd8ed1ab_0
- mistune=3.0.2=pyhd8ed1ab_0
- - more-itertools=10.1.0=pyhd8ed1ab_0
+ - more-itertools=10.2.0=pyhd8ed1ab_0
- msgpack-python=1.0.7=py311hd03642b_0
- multidict=6.0.4=py311he2be06e_1
- multimethod=1.9.1=pyhd8ed1ab_0
- munkres=1.1.4=pyh9f0ad1d_0
- mypy_extensions=1.0.0=pyha770c72_0
- - nest-asyncio=1.5.8=pyhd8ed1ab_0
+ - nest-asyncio=1.5.9=pyhd8ed1ab_0
- networkx=3.2.1=pyhd8ed1ab_0
- packaging=23.2=pyhd8ed1ab_0
- pandocfilters=1.5.0=pyhd8ed1ab_0
@@ -213,7 +213,7 @@ dependencies:
- pastel=0.2.1=pyhd8ed1ab_0
- petl=1.7.14=pyhd8ed1ab_0
- pickleshare=0.7.5=py_1003
- - pillow=10.1.0=py311hb9c5795_0
+ - pillow=10.2.0=py311hb9c5795_0
- pkginfo=1.9.6=pyhd8ed1ab_0
- pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
- platformdirs=4.1.0=pyhd8ed1ab_0
@@ -243,11 +243,11 @@ dependencies:
- regex=2023.12.25=py311h05b510d_0
- rfc3986=2.0.0=pyhd8ed1ab_0
- rfc3986-validator=0.1.1=pyh9f0ad1d_0
- - rpds-py=0.16.2=py311h94f323b_0
+ - rpds-py=0.17.1=py311h94f323b_0
- rtree=1.1.0=py311hd698ff7_0
- ruamel.yaml.clib=0.2.7=py311heffc1b2_2
- - ruff=0.1.9=py311h8c97afb_0
- - setuptools=68.2.2=pyhd8ed1ab_0
+ - ruff=0.1.13=py311h8c97afb_1
+ - setuptools=69.0.3=pyhd8ed1ab_0
- shellingham=1.5.4=pyhd8ed1ab_0
- simpleeval=0.9.13=pyhd8ed1ab_1
- six=1.16.0=pyh6c4a22f_0
@@ -258,6 +258,7 @@ dependencies:
- soupsieve=2.5=pyhd8ed1ab_1
- sphinxcontrib-jsmath=1.0.1=pyhd8ed1ab_0
- stringcase=1.2.0=py_0
+ - structlog=23.2.0=pyhd8ed1ab_0
- tabulate=0.9.0=pyhd8ed1ab_1
- text-unidecode=1.3=pyhd8ed1ab_1
- threadpoolctl=3.2.0=pyha21a80b_0
@@ -267,8 +268,8 @@ dependencies:
- toolz=0.12.0=pyhd8ed1ab_0
- toposort=1.10=pyhd8ed1ab_0
- tornado=6.3.3=py311heffc1b2_1
- - traitlets=5.14.0=pyhd8ed1ab_0
- - types-python-dateutil=2.8.19.14=pyhd8ed1ab_0
+ - traitlets=5.14.1=pyhd8ed1ab_0
+ - types-python-dateutil=2.8.19.20240106=pyhd8ed1ab_0
- types-pyyaml=6.0.12.12=pyhd8ed1ab_0
- typing_extensions=4.9.0=pyha770c72_0
- typing_utils=0.1.0=pyhd8ed1ab_0
@@ -276,7 +277,7 @@ dependencies:
- uri-template=1.3.0=pyhd8ed1ab_0
- uvloop=0.19.0=py311h05b510d_0
- validators=0.22.0=pyhd8ed1ab_0
- - wcwidth=0.2.12=pyhd8ed1ab_0
+ - wcwidth=0.2.13=pyhd8ed1ab_0
- webcolors=1.13=pyhd8ed1ab_0
- webencodings=0.5.1=pyhd8ed1ab_2
- websocket-client=1.7.0=pyhd8ed1ab_0
@@ -294,7 +295,8 @@ dependencies:
- asgiref=3.7.2=pyhd8ed1ab_0
- asttokens=2.4.1=pyhd8ed1ab_0
- async-lru=2.0.4=pyhd8ed1ab_0
- - aws-c-s3=0.4.1=ha5b923c_0
+ - aws-c-s3=0.4.9=h7f99a2c_0
+ - azure-core-cpp=1.11.0=he231e37_0
- babel=2.14.0=pyhd8ed1ab_0
- beautifulsoup4=4.12.2=pyha770c72_0
- bleach=6.1.0=pyhd8ed1ab_0
@@ -306,29 +308,29 @@ dependencies:
- cligj=0.7.2=pyhd8ed1ab_1
- clikit=0.6.2=pyhd8ed1ab_2
- coloredlogs=14.0=pyhd8ed1ab_3
- - comm=0.1.4=pyhd8ed1ab_0
+ - comm=0.2.1=pyhd8ed1ab_0
- coverage=7.4.0=py311h05b510d_0
- curl=8.5.0=h2d989ff_0
- - fonttools=4.47.0=py311h05b510d_0
+ - fonttools=4.47.2=py311h05b510d_0
- gitdb=4.0.11=pyhd8ed1ab_0
- graphql-core=3.2.3=pyhd8ed1ab_0
- - grpcio=1.59.2=py311h79dd126_0
+ - grpcio=1.59.3=py311h79dd126_0
- h11=0.14.0=pyhd8ed1ab_0
- h2=4.1.0=pyhd8ed1ab_0
- harfbuzz=8.3.0=h8f0ba13_0
- hdf5=1.14.3=nompi_h5bb55e9_100
- html5lib=1.1=pyh9f0ad1d_0
- - hypothesis=6.92.2=pyha770c72_0
+ - hypothesis=6.94.0=pyha770c72_0
- importlib-metadata=7.0.1=pyha770c72_0
- importlib_resources=6.1.1=pyhd8ed1ab_0
- isodate=0.6.1=pyhd8ed1ab_0
- janus=1.0.0=pyhd8ed1ab_0
- jaraco.classes=3.3.0=pyhd8ed1ab_0
- jedi=0.19.1=pyhd8ed1ab_0
- - jinja2=3.1.2=pyhd8ed1ab_1
+ - jinja2=3.1.3=pyhd8ed1ab_0
- joblib=1.3.2=pyhd8ed1ab_0
- jsonlines=4.0.0=pyhd8ed1ab_0
- - jupyter_core=5.6.0=py311h267d04e_0
+ - jupyter_core=5.7.1=py311h267d04e_0
- jupyterlab_pygments=0.3.0=pyhd8ed1ab_0
- latexcodec=2.0.1=pyh9f0ad1d_0
- libcblas=3.9.0=20_osxarm64_openblas
@@ -346,7 +348,7 @@ dependencies:
- pip=23.3.2=pyhd8ed1ab_0
- poppler=23.12.0=hcdd998b_0
- postgresql=16.1=hc6ab77f_7
- - proj=9.3.0=h52fb9d0_2
+ - proj=9.3.1=h93d94ba_0
- prompt-toolkit=3.0.42=pyha770c72_0
- protobuf=4.24.4=py311h4d1eceb_0
- psycopg2=2.9.9=py311h589e011_0
@@ -358,7 +360,7 @@ dependencies:
- python-slugify=8.0.1=pyhd8ed1ab_2
- pyu2f=0.1.5=pyhd8ed1ab_0
- qtpy=2.4.1=pyhd8ed1ab_0
- - referencing=0.32.0=pyhd8ed1ab_0
+ - referencing=0.32.1=pyhd8ed1ab_0
- restructuredtext_lint=1.4.0=pyhd8ed1ab_0
- rfc3339-validator=0.1.4=pyhd8ed1ab_0
- rsa=4.9=pyhd8ed1ab_0
@@ -375,33 +377,34 @@ dependencies:
- xerces-c=3.2.5=hf393695_0
- yarl=1.9.3=py311h05b510d_0
- addfips=0.4.0=pyhd8ed1ab_1
+ - aiohttp=3.9.1=py311h05b510d_0
- aniso8601=9.0.1=pyhd8ed1ab_0
- annotated-types=0.6.0=pyhd8ed1ab_0
- argon2-cffi-bindings=21.2.0=py311heffc1b2_4
- arrow=1.3.0=pyhd8ed1ab_0
- - async-timeout=4.0.3=pyhd8ed1ab_0
- - aws-crt-cpp=0.24.7=hba4ac3b_6
- - botocore=1.34.11=pyhd8ed1ab_0
+ - aws-crt-cpp=0.26.0=hfff802b_8
+ - azure-storage-common-cpp=12.5.0=h607ffeb_2
+ - botocore=1.34.19=pyhd8ed1ab_0
- branca=0.7.0=pyhd8ed1ab_1
- croniter=2.0.1=pyhd8ed1ab_0
- cryptography=41.0.7=py311h08c85a6_1
- fqdn=1.5.1=pyhd8ed1ab_0
- - geotiff=1.7.1=h71398c0_14
- - gitpython=3.1.40=pyhd8ed1ab_0
+ - geotiff=1.7.1=h7bcba05_15
+ - gitpython=3.1.41=pyhd8ed1ab_0
- google-crc32c=1.1.2=py311h533d1a3_5
- googleapis-common-protos=1.62.0=pyhd8ed1ab_0
- - gql=3.4.1=pyhd8ed1ab_0
+ - gql=3.5.0=pyhd8ed1ab_0
- graphql-relay=3.2.0=pyhd8ed1ab_0
- - grpcio-health-checking=1.59.2=pyhd8ed1ab_0
+ - grpcio-health-checking=1.59.3=pyhd8ed1ab_0
- httpcore=1.0.2=pyhd8ed1ab_0
- importlib_metadata=7.0.1=hd8ed1ab_0
- jsonschema-specifications=2023.12.1=pyhd8ed1ab_0
- jupyter_server_terminals=0.5.1=pyhd8ed1ab_0
- kealib=1.5.3=h210d843_0
- - libnetcdf=4.9.2=nompi_hb2fb864_112
- - libspatialite=5.1.0=h66af7d6_2
+ - libnetcdf=4.9.2=nompi_h291a7c2_113
+ - libspatialite=5.1.0=h69abc6b_4
- mako=1.3.0=pyhd8ed1ab_0
- - numpy=1.26.2=py311h6d074dd_0
+ - numpy=1.26.3=py311h7125741_0
- pango=1.50.14=hcf40dda_2
- pbr=6.0.0=pyhd8ed1ab_0
- pendulum=2.1.2=py311heffc1b2_6
@@ -410,7 +413,7 @@ dependencies:
- pybtex=0.24.0=pyhd8ed1ab_2
- pydantic-core=2.14.6=py311h94f323b_1
- pyobjc-framework-cocoa=10.1=py311h665608e_0
- - pyproj=3.6.1=py311h20a9b75_4
+ - pyproj=3.6.1=py311h9a031f7_5
- pytest-console-scripts=1.4.1=pyhd8ed1ab_0
- pytest-cov=4.1.0=pyhd8ed1ab_0
- pytest-mock=3.12.0=pyhd8ed1ab_0
@@ -418,39 +421,37 @@ dependencies:
- python-build=1.0.3=pyhd8ed1ab_0
- requests=2.31.0=pyhd8ed1ab_0
- rich=13.7.0=pyhd8ed1ab_0
- - sqlalchemy=2.0.24=py311h05b510d_0
+ - sqlalchemy=2.0.25=py311h05b510d_0
- stack_data=0.6.2=pyhd8ed1ab_0
- - starlette=0.34.0=pyhd8ed1ab_0
- - tiledb=2.18.2=h555b8a3_0
+ - starlette=0.35.0=pyhd8ed1ab_0
- ukkonen=1.0.1=py311he4fd1f5_4
- uvicorn=0.25.0=py311h267d04e_0
- watchfiles=0.21.0=py311h94f323b_0
- - aiohttp=3.8.6=py311h05b510d_1
- - alembic=1.13.1=pyhd8ed1ab_0
- - arelle-release=2.20.3=pyhd8ed1ab_0
+ - alembic=1.13.1=pyhd8ed1ab_1
+ - arelle-release=2.21.3=pyhd8ed1ab_0
- argon2-cffi=23.1.0=pyhd8ed1ab_0
- - aws-sdk-cpp=1.11.182=h31542fa_7
+ - aws-sdk-cpp=1.11.210=he93ac2d_10
+ - azure-storage-blobs-cpp=12.10.0=h6aa02a4_0
- bottleneck=1.3.7=py311hb49d859_1
- cachecontrol=0.13.1=pyhd8ed1ab_0
- contourpy=1.2.0=py311hd03642b_0
- - dask-core=2023.12.1=pyhd8ed1ab_0
+ - dask-core=2024.1.0=pyhd8ed1ab_0
- dnspython=2.4.2=pyhd8ed1ab_1
- ensureconda=1.4.3=pyhd8ed1ab_0
- folium=0.15.1=pyhd8ed1ab_0
- google-resumable-media=2.7.0=pyhd8ed1ab_0
- graphene=3.3=pyhd8ed1ab_0
- - grpcio-status=1.59.2=pyhd8ed1ab_0
+ - grpcio-status=1.59.3=pyhd8ed1ab_0
- gtk2=2.24.33=h7895bb2_3
- h3-py=3.7.6=py311ha891d26_1
- httpx=0.26.0=pyhd8ed1ab_0
- identify=2.5.33=pyhd8ed1ab_0
- - ipython=8.19.0=pyh707e725_0
+ - ipython=8.20.0=pyh707e725_0
- isoduration=20.11.0=pyhd8ed1ab_0
- jsonschema=4.20.0=pyhd8ed1ab_0
- jupyter_client=8.6.0=pyhd8ed1ab_0
- keyring=24.3.0=py311h267d04e_0
- - libgdal=3.8.1=h1ac08d9_2
- - librsvg=2.56.3=h0db3404_0
+ - librsvg=2.56.3=h55a2576_1
- numba=0.58.1=py311h9ec4793_0
- numexpr=2.8.8=py311hfbe21a1_0
- oauthlib=3.2.2=pyhd8ed1ab_0
@@ -459,7 +460,7 @@ dependencies:
- pydantic=2.5.3=pyhd8ed1ab_0
- pyopenssl=23.3.0=pyhd8ed1ab_0
- readthedocs-sphinx-ext=2.2.5=pyhd8ed1ab_0
- - requests-toolbelt=0.10.1=pyhd8ed1ab_0
+ - requests-toolbelt=1.0.0=pyhd8ed1ab_0
- responses=0.24.1=pyhd8ed1ab_0
- s3transfer=0.10.0=pyhd8ed1ab_0
- scipy=1.11.4=py311h2b215a9_0
@@ -469,22 +470,21 @@ dependencies:
- typeguard=4.1.5=pyhd8ed1ab_1
- typer=0.9.0=pyhd8ed1ab_0
- uvicorn-standard=0.25.0=ha1ab1f8_0
- - boto3=1.34.11=pyhd8ed1ab_0
+ - boto3=1.34.19=pyhd8ed1ab_0
- cachecontrol-with-filecache=0.13.1=pyhd8ed1ab_0
- - dagster=1.5.13=pyhd8ed1ab_0
- - datasette=0.64.5=pyhd8ed1ab_0
+ - dagster=1.6.0=pyhd8ed1ab_0
+ - datasette=0.64.6=pyhd8ed1ab_0
- doc8=1.1.1=pyhd8ed1ab_0
- email-validator=2.1.0.post1=pyhd8ed1ab_0
- frictionless=4.40.8=pyh6c4a22f_0
- - gdal=3.8.1=py311hff5d751_2
- - geopandas-base=0.14.1=pyha770c72_0
- - google-auth=2.25.2=pyhca7485f_0
- - gql-with-requests=3.4.1=pyhd8ed1ab_0
+ - geopandas-base=0.14.2=pyha770c72_0
+ - google-auth=2.26.2=pyhca7485f_0
+ - gql-with-requests=3.5.0=pyhd8ed1ab_0
- graphviz=9.0.0=h3face73_1
- - ipykernel=6.28.0=pyh3cd1d5f_0
+ - ipykernel=6.29.0=pyh3cd1d5f_0
- ipywidgets=8.1.1=pyhd8ed1ab_0
- jsonschema-with-format-nongpl=4.20.0=pyhd8ed1ab_0
- - libarrow=14.0.1=h8dffd16_3_cpu
+ - libarrow=14.0.2=h4ce3932_2_cpu
- matplotlib-base=3.8.2=py311hfdba5f6_0
- nbformat=5.9.2=pyhd8ed1ab_0
- pandera-core=0.17.2=pyhd8ed1ab_1
@@ -492,45 +492,47 @@ dependencies:
- pydantic-settings=2.1.0=pyhd8ed1ab_1
- requests-oauthlib=1.3.1=pyhd8ed1ab_0
- scikit-learn=1.3.2=py311ha25ca4d_2
+ - tiledb=2.19.0=h567544c_0
- timezonefinder=6.2.0=py311h05b510d_2
- catalystcoop.ferc_xbrl_extractor=1.3.1=pyhd8ed1ab_0
- conda-lock=2.5.1=pyhd8ed1ab_0
- - dagster-graphql=1.5.13=pyhd8ed1ab_0
- - dagster-postgres=0.21.13=pyhd8ed1ab_0
- - fiona=1.9.5=py311h4760b73_2
+ - dagster-graphql=1.6.0=pyhd8ed1ab_0
+ - dagster-postgres=0.22.0=pyhd8ed1ab_0
- google-api-core=2.15.0=pyhd8ed1ab_0
- google-auth-oauthlib=1.2.0=pyhd8ed1ab_0
- jupyter_console=6.6.3=pyhd8ed1ab_0
- jupyter_events=0.9.0=pyhd8ed1ab_0
- - libarrow-acero=14.0.1=had9dd58_3_cpu
- - libarrow-flight=14.0.1=h1011bfc_3_cpu
- - libarrow-gandiva=14.0.1=h2b96968_3_cpu
- - libparquet=14.0.1=heaab74a_3_cpu
+ - libarrow-acero=14.0.2=h13dd4ca_2_cpu
+ - libarrow-flight=14.0.2=ha94d253_2_cpu
+ - libarrow-gandiva=14.0.2=hf757142_2_cpu
+ - libgdal=3.8.3=h7e86f1f_0
+ - libparquet=14.0.2=hf6ce1d5_2_cpu
- mapclassify=2.6.1=pyhd8ed1ab_0
- nbclient=0.8.0=pyhd8ed1ab_0
- pygraphviz=1.11=py311h1df6e61_2
- qtconsole-base=5.5.1=pyha770c72_0
- - recordlinkage=0.16=pyhd8ed1ab_0
- tabulator=1.53.5=pyhd8ed1ab_0
- - dagster-webserver=1.5.13=pyhd8ed1ab_0
- - geopandas=0.14.1=pyhd8ed1ab_0
+ - dagster-webserver=1.6.0=pyhd8ed1ab_0
+ - gdal=3.8.3=py311h4a095a9_0
- google-cloud-core=2.4.1=pyhd8ed1ab_0
- - libarrow-dataset=14.0.1=had9dd58_3_cpu
- - libarrow-flight-sql=14.0.1=h660fe36_3_cpu
- - nbconvert-core=7.13.1=pyhd8ed1ab_0
+ - libarrow-dataset=14.0.2=h13dd4ca_2_cpu
+ - libarrow-flight-sql=14.0.2=h39a9b85_2_cpu
+ - nbconvert-core=7.14.2=pyhd8ed1ab_0
- tableschema=1.19.3=pyh9f0ad1d_0
- datapackage=1.15.2=pyh44b312d_0
+ - fiona=1.9.5=py311h1c26527_3
- google-cloud-storage=2.14.0=pyhca7485f_0
- - jupyter_server=2.12.1=pyhd8ed1ab_0
- - libarrow-substrait=14.0.1=h594d712_3_cpu
- - nbconvert-pandoc=7.13.1=pyhd8ed1ab_0
+ - jupyter_server=2.12.5=pyhd8ed1ab_0
+ - libarrow-substrait=14.0.2=h7fd9903_2_cpu
+ - nbconvert-pandoc=7.14.2=pyhd8ed1ab_0
- gcsfs=2023.12.2.post1=pyhd8ed1ab_0
+ - geopandas=0.14.2=pyhd8ed1ab_0
- jupyter-lsp=2.2.1=pyhd8ed1ab_0
- jupyter-resource-usage=1.0.1=pyhd8ed1ab_0
- jupyterlab_server=2.25.2=pyhd8ed1ab_0
- - nbconvert=7.13.1=pyhd8ed1ab_0
+ - nbconvert=7.14.2=pyhd8ed1ab_0
- notebook-shim=0.2.3=pyhd8ed1ab_0
- - pyarrow=14.0.1=py311h637fcfe_3_cpu
+ - pyarrow=14.0.2=py311hd7bc329_2_cpu
- jupyterlab=4.0.10=pyhd8ed1ab_0
- notebook=7.0.6=pyhd8ed1ab_0
- jupyter=1.0.0=pyhd8ed1ab_10
@@ -539,10 +541,10 @@ dependencies:
- furo=2023.9.10=pyhd8ed1ab_0
- sphinx-issues=1.2.0=py_0
- sphinx-reredirects=0.1.2=pyhd8ed1ab_0
- - sphinxcontrib-applehelp=1.0.7=pyhd8ed1ab_0
- - sphinxcontrib-bibtex=2.6.1=pyhd8ed1ab_1
- - sphinxcontrib-devhelp=1.0.5=pyhd8ed1ab_0
- - sphinxcontrib-htmlhelp=2.0.4=pyhd8ed1ab_0
- - sphinxcontrib-qthelp=1.0.6=pyhd8ed1ab_0
+ - sphinxcontrib-applehelp=1.0.8=pyhd8ed1ab_0
+ - sphinxcontrib-bibtex=2.6.2=pyhd8ed1ab_0
+ - sphinxcontrib-devhelp=1.0.6=pyhd8ed1ab_0
+ - sphinxcontrib-htmlhelp=2.0.5=pyhd8ed1ab_0
+ - sphinxcontrib-qthelp=1.0.7=pyhd8ed1ab_0
- sphinx=7.2.6=pyhd8ed1ab_0
- - sphinxcontrib-serializinghtml=1.1.9=pyhd8ed1ab_0
+ - sphinxcontrib-serializinghtml=1.1.10=pyhd8ed1ab_0
diff --git a/migrations/versions/997d2c7bc7de_add_utility_name_to_plant_parts_list.py b/migrations/versions/997d2c7bc7de_add_utility_name_to_plant_parts_list.py
new file mode 100644
index 0000000000..b267f3285c
--- /dev/null
+++ b/migrations/versions/997d2c7bc7de_add_utility_name_to_plant_parts_list.py
@@ -0,0 +1,31 @@
+"""add utility name to plant parts list
+
+Revision ID: 997d2c7bc7de
+Revises: 2e5b623ab40b
+Create Date: 2024-01-10 16:18:35.723544
+
+"""
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = '997d2c7bc7de'
+down_revision = '2e5b623ab40b'
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+ # ### commands auto generated by Alembic - please adjust! ###
+ with op.batch_alter_table('out_eia__yearly_plant_parts', schema=None) as batch_op:
+ batch_op.add_column(sa.Column('utility_name_eia', sa.Text(), nullable=True, comment='The name of the utility.'))
+
+ # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+ # ### commands auto generated by Alembic - please adjust! ###
+ with op.batch_alter_table('out_eia__yearly_plant_parts', schema=None) as batch_op:
+ batch_op.drop_column('utility_name_eia')
+
+ # ### end Alembic commands ###
diff --git a/notebooks/.gitignore b/notebooks/.gitignore
index a78b32c55d..4154357b42 100644
--- a/notebooks/.gitignore
+++ b/notebooks/.gitignore
@@ -6,3 +6,5 @@
*.zip
ef_nat_gas/
/**/*.jpg
+local/**
+!local/README.md
diff --git a/notebooks/local/README.md b/notebooks/local/README.md
new file mode 100644
index 0000000000..ef91c0cd37
--- /dev/null
+++ b/notebooks/local/README.md
@@ -0,0 +1,2 @@
+## Local Notebooks
+This directory is a placeholder directory to store notebooks you might want to keep locally but shouldn't be tracked in git.
diff --git a/pyproject.toml b/pyproject.toml
index 3d9707c8a7..84b2ef2833 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,8 +22,8 @@ dependencies = [
"coloredlogs>=14.0", # Dagster requires 14.0
"conda-lock>=2.5.1",
"coverage>=7",
- "dagster>=1.5.6",
- "dagster-postgres>=0.21.6,<1", # Update when dagster-postgres graduates to 1.x
+ "dagster>=1.6",
+ "dagster-postgres>=0.22,<1", # Update when dagster-postgres graduates to 1.x
"dask>=2023",
"datapackage>=1.15,<2", # Transition datastore to use frictionless.
"datasette>=0.64",
@@ -32,11 +32,13 @@ dependencies = [
"fsspec>=2023",
"furo>=2022.4.7",
"gcsfs>=2023",
+ "gdal>=3.8.3",
"geopandas>=0.14",
- "grpcio==1.59.2", # Required by dagster, binary dependencies are flaky
- "grpcio-health-checking==1.59.2", # Required by dagster, binary dependencies are flaky
- "grpcio-status==1.59.2", # Required by dagster, binary dependencies are flaky
+ "grpcio==1.59.3", # Required by dagster, binary dependencies are flaky
+ "grpcio-health-checking==1.59.3", # Required by dagster, binary dependencies are flaky
+ "grpcio-status==1.59.3", # Required by dagster, binary dependencies are flaky
"hypothesis>=6.87",
+ "jellyfish>=1",
"jinja2>=3.1",
"jupyter",
"matplotlib>=3.8",
@@ -62,7 +64,6 @@ dependencies = [
"pyxlsb>=1.0.9", # pandas[excel]
"pyyaml>=6",
"readthedocs-sphinx-ext>=2",
- "recordlinkage",
"responses>=0.23",
"ruff>=0.1",
"scikit-learn>=1.3",
@@ -137,7 +138,7 @@ pudl_service_territories = "pudl.analysis.service_territory:pudl_service_territo
[project.optional-dependencies]
dev = [
- "dagster-webserver>=1.5.6",
+ "dagster-webserver>=1.6",
"jupyterlab>3.2",
"jupyter-resource-usage",
"pygraphviz",
diff --git a/src/pudl/analysis/record_linkage/__init__.py b/src/pudl/analysis/record_linkage/__init__.py
index fbc62f4b3b..330a0e46b7 100644
--- a/src/pudl/analysis/record_linkage/__init__.py
+++ b/src/pudl/analysis/record_linkage/__init__.py
@@ -1,4 +1,4 @@
-"""This module impolements models for various forms of record linkage."""
+"""This module implements models for various forms of record linkage."""
from . import (
classify_plants_ferc1,
eia_ferc1_record_linkage,
diff --git a/src/pudl/analysis/record_linkage/eia_ferc1_record_linkage.py b/src/pudl/analysis/record_linkage/eia_ferc1_record_linkage.py
index 11a49b3ebc..b37895112e 100644
--- a/src/pudl/analysis/record_linkage/eia_ferc1_record_linkage.py
+++ b/src/pudl/analysis/record_linkage/eia_ferc1_record_linkage.py
@@ -14,9 +14,7 @@
In this module we infer which of the many ``plant_parts_eia`` records is most likely to
correspond to an actually reported FERC Form 1 plant record. this is done with a
-logistic regression model. The :mod:`recordlinkage` package helps us create feature
-vectors (via :meth:`Features.make_features`) for each candidate match between FERC and
-EIA.
+logistic regression model.
We train the logistic regression model using manually labeled training data that links
together several thousand EIA and FERC plant records, and use grid search cross
@@ -35,7 +33,7 @@
import numpy as np
import pandas as pd
-from dagster import AssetOut, graph_multi_asset, op
+from dagster import Out, graph_asset, op
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_recall_fscore_support
from sklearn.model_selection import GridSearchCV, train_test_split
@@ -207,8 +205,8 @@ def get_compiled_input_manager(plants_all_ferc1, fbp_ferc1, plant_parts_eia):
return inputs
-@op
-def get_all_pairs_df(inputs):
+@op(out={"all_pairs_df": Out(), "train_pairs_df": Out()})
+def get_pairs_dfs(inputs):
"""Get a dataframe with all possible FERC to EIA record pairs.
Merge the FERC and EIA records on ``block_col`` to generate possible
@@ -216,33 +214,25 @@ def get_all_pairs_df(inputs):
Arguments:
inputs: :class:`InputManager` object.
+
+ Returns:
+ A dataframe with all possible record pairs from all the input
+ data and a dataframe with all possible record pairs from the
+ training data.
"""
ferc1_df = inputs.get_plants_ferc1().reset_index()
eia_df = inputs.get_plant_parts_eia_true().reset_index()
block_col = "plant_id_report_year_util_id"
- out = ferc1_df.merge(
+ all_pairs_df = ferc1_df.merge(
eia_df, how="inner", on=block_col, suffixes=("_ferc1", "_eia")
).set_index(["record_id_ferc1", "record_id_eia"])
- return out
-
-
-@op
-def get_train_pairs_df(inputs):
- """Get a dataframe with possible FERC to EIA record pairs from training data.
-
- Merge the FERC and EIA records on ``block_col`` to generate possible
- record pairs for the matching model.
-
- Arguments:
- inputs: :class:`InputManager` object.
- """
- ferc1_df = inputs.get_train_ferc1().reset_index()
- eia_df = inputs.get_train_eia().reset_index()
+ ferc1_train_df = inputs.get_train_ferc1().reset_index()
+ eia_train_df = inputs.get_train_eia().reset_index()
block_col = "plant_id_report_year_util_id"
- out = ferc1_df.merge(
- eia_df, how="inner", on=block_col, suffixes=("_ferc1", "_eia")
+ train_pairs_df = ferc1_train_df.merge(
+ eia_train_df, how="inner", on=block_col, suffixes=("_ferc1", "_eia")
).set_index(["record_id_ferc1", "record_id_eia"])
- return out
+ return (all_pairs_df, train_pairs_df)
@op
@@ -283,7 +273,13 @@ def run_matching_model(features_train, features_all, y_df):
)
-@op
+@op(
+ out={
+ "out_pudl__yearly_assn_eia_ferc1_plant_parts": Out(
+ io_manager_key="pudl_sqlite_io_manager"
+ )
+ }
+)
def get_match_full_records(best_match_df, inputs):
"""Join full dataframe onto matches to make usable and get stats."""
connected_df = prettyify_best_matches(
@@ -297,13 +293,7 @@ def get_match_full_records(best_match_df, inputs):
).enforce_schema(connected_df)
-@graph_multi_asset(
- outs={
- "out_pudl__yearly_assn_eia_ferc1_plant_parts": AssetOut(
- io_manager_key="pudl_sqlite_io_manager"
- )
- }
-)
+@graph_asset
def out_pudl__yearly_assn_eia_ferc1_plant_parts(
out_ferc1__yearly_all_plants: pd.DataFrame,
out_ferc1__yearly_steam_plants_fuel_by_plant_sched402: pd.DataFrame,
@@ -322,8 +312,7 @@ def out_pudl__yearly_assn_eia_ferc1_plant_parts(
out_ferc1__yearly_steam_plants_fuel_by_plant_sched402,
out_eia__yearly_plant_parts,
)
- all_pairs_df = get_all_pairs_df(inputs)
- train_pairs_df = get_train_pairs_df(inputs)
+ all_pairs_df, train_pairs_df = get_pairs_dfs(inputs)
features_all = pair_vectorizers(all_pairs_df)
features_train = pair_vectorizers(train_pairs_df)
y_df = get_y_label_df(train_pairs_df, inputs)
@@ -393,7 +382,7 @@ def get_plant_parts_eia_true(self, clobber: bool = False) -> pd.DataFrame:
def get_plants_ferc1(self, clobber: bool = False) -> pd.DataFrame:
"""Prepare FERC1 plants data for record linkage with EIA plant-parts.
- This method grabs two tables (``plants_all_ferc1`` and ``fuel_by_plant_ferc1`` -
+ This method grabs two tables (``plants_all_ferc1`` and ``fuel_by_plant_ferc1``,
accessed originally via :meth:`pudl.output.pudltabl.PudlTabl.plants_all_ferc1`
and :meth:`pudl.output.pudltabl.PudlTabl.fbp_ferc1` respectively) and ensures
that the columns the same as their EIA counterparts, because the output of this
@@ -863,7 +852,7 @@ def prep_train_connections(
"record_id_eia": pd.NA,
}
)
- .set_index( # recordlinkage and sklearn wants MultiIndexs to do the stuff
+ .set_index( # sklearn wants a MultiIndex to do the stuff
[
"record_id_ferc1",
"record_id_eia",
diff --git a/src/pudl/analysis/record_linkage/embed_dataframe.py b/src/pudl/analysis/record_linkage/embed_dataframe.py
index cabbc0ab54..128f4027ab 100644
--- a/src/pudl/analysis/record_linkage/embed_dataframe.py
+++ b/src/pudl/analysis/record_linkage/embed_dataframe.py
@@ -232,7 +232,8 @@ def _exp_sim(df, col1, col2, scale, offset, origin, missing_value, label):
raise ValueError("The offset must be positive.")
if scale <= 0:
raise ValueError("The scale must be larger than 0.")
- d = abs(df[col1] - df[col2] - origin).clip(offset, None)
+ diff = df[col1] - df[col2]
+ d = (abs(diff - origin)).clip(offset, None)
return (
(2 ** (-(d - offset) / scale))
.fillna(missing_value)
@@ -245,8 +246,14 @@ def _linear_sim(df, col1, col2, scale, offset, origin, missing_value, label):
raise ValueError("The offset must be positive.")
if scale <= 0:
raise ValueError("The scale must be larger than 0.")
- d = (abs(df[col1] - df[col2] - origin)).clip(offset, offset + 2 * scale)
- return (1 - (d - offset) / (2 * scale)).fillna(missing_value).to_frame()
+ diff = df[col1] - df[col2]
+ d = (abs(diff - origin)).clip(offset, offset + 2 * scale)
+ return (
+ (1 - (d - offset) / (2 * scale))
+ .fillna(missing_value)
+ .rename(label)
+ .to_frame()
+ )
def _exact(df, missing_value, label):
compare = pd.Series(0, index=df.index)
@@ -257,25 +264,61 @@ def _exact(df, missing_value, label):
function_transforms = {
"exponential": lambda df: _exp_sim(
- df, col1, col2, scale, offset, origin, missing_value, label
+ df=df,
+ col1=col1,
+ col2=col2,
+ scale=scale,
+ offset=offset,
+ origin=origin,
+ missing_value=missing_value,
+ label=label,
),
"linear": lambda df: _linear_sim(
- df, col1, col2, scale, offset, origin, missing_value, label
+ df=df,
+ col1=col1,
+ col2=col2,
+ scale=scale,
+ offset=offset,
+ origin=origin,
+ missing_value=missing_value,
+ label=label,
),
- "exact": lambda df: _exact(df, missing_value, label),
+ "exact": lambda df: _exact(df=df, missing_value=missing_value, label=label),
}
return function_transforms[function_key](df)
class NumericSimilarityScorer(TransformStep):
- """Vectorize two numeric columns with an expoential similarity score."""
+ """Vectorize two numeric columns with a similarity score.
+
+ If two values are the same the similarity is 1 and in case of complete
+ disagreement it is 0. The implementation is adapted from the recordlinkage
+ Python package Numeric comparison library and is similar with numeric
+ comparing in ElasticSearch, a full-text search tool.
+
+ Arguments:
+ name: The name of the transformation step. Default is numeric_sim.
+ col1: The name of the first column to compare. Must be a numeric column.
+ col2: The name of the second column to compare. Must be a numeric column.
+ output_name: The name of the output Series of compared values.
+ method: The metric used. Options are "exponential", "linear", "exact".
+ scale : The rate of decay, how quickly the score should drop the further
+ from the origin that a value lies. Default is 1.0.
+ offset: Setting a nonzero offset expands the central point to cover a
+ range of values instead of just the single point specified by the
+ origin. Default is 0.
+ origin : The central point, or the best possible value for the difference
+ between records. Differences that fall at the origin will get a
+ similarity score of 1.0. The default is 0.
+ missing_value: The value if one or both records have a missing value on the
+ compared field. Default 0.
+ """
- # TODO: include docstring about what they parameters are
name: str = "numeric_sim"
- col1: str # name of the first column to compare
- col2: str # name of the second column to compare
+ col1: str
+ col2: str
output_name: str
- method: str = "linear" # name of the function transform method
+ method: str = "linear"
scale: float = 1.0
offset: float = 0.0
origin: float = 0.0
diff --git a/src/pudl/etl/__init__.py b/src/pudl/etl/__init__.py
index 3f4e807f1b..591dd05e2e 100644
--- a/src/pudl/etl/__init__.py
+++ b/src/pudl/etl/__init__.py
@@ -34,6 +34,7 @@
default_assets = (
*load_assets_from_modules([eia_bulk_elec_assets], group_name="core_eia_bulk_elec"),
*load_assets_from_modules([epacems_assets], group_name="core_epacems"),
+ *load_assets_from_modules([pudl.extract.eia176], group_name="raw_eia176"),
*load_assets_from_modules([pudl.extract.eia860], group_name="raw_eia860"),
*load_assets_from_modules([pudl.transform.eia860], group_name="_core_eia860"),
*load_assets_from_modules([pudl.extract.eia861], group_name="raw_eia861"),
diff --git a/src/pudl/extract/__init__.py b/src/pudl/extract/__init__.py
index a25e4b6fd2..7e889c6d0c 100644
--- a/src/pudl/extract/__init__.py
+++ b/src/pudl/extract/__init__.py
@@ -8,6 +8,7 @@
:mod:`pudl.transform` subpackage.
"""
from . import (
+ eia176,
eia860,
eia860m,
eia861,
diff --git a/src/pudl/extract/csv.py b/src/pudl/extract/csv.py
new file mode 100644
index 0000000000..af7718e8c0
--- /dev/null
+++ b/src/pudl/extract/csv.py
@@ -0,0 +1,72 @@
+"""Extractor for CSV data."""
+from csv import DictReader
+from importlib import resources
+from zipfile import ZipFile
+
+import pandas as pd
+
+import pudl.logging_helpers
+
+logger = pudl.logging_helpers.get_logger(__name__)
+
+
+def open_csv_resource(dataset: str, base_filename: str) -> DictReader:
+ """Open the given resource file as :class:`csv.DictReader`.
+
+ Args:
+ dataset: used to load metadata from package_data/{dataset} subdirectory.
+ base_filename: the name of the file in the subdirectory to open.
+ """
+ csv_path = resources.files(f"pudl.package_data.{dataset}") / base_filename
+ return DictReader(csv_path.open())
+
+
+def get_table_file_map(dataset: str) -> dict[str, str]:
+ """Return a dictionary of table names and filenames for the dataset.
+
+ Args:
+ dataset: used to load metadata from package_data/{dataset} subdirectory.
+ """
+ return {
+ row["table"]: row["filename"]
+ for row in open_csv_resource(dataset, "table_file_map.csv")
+ }
+
+
+class CsvExtractor:
+ """Generalized class for extracting dataframes from CSV files.
+
+ The extraction logic is invoked by calling extract() method of this class.
+ """
+
+ def __init__(self, zipfile: ZipFile, table_file_map: dict[str, str]):
+ """Create a new instance of CsvExtractor.
+
+ This can be used for retrieving data from CSV files.
+
+ Args:
+ zipfile: zipfile object containing source files
+ table_file_map: map of table name to source file in zipfile archive
+ """
+ self._zipfile = zipfile
+ self._table_file_map = table_file_map
+
+ def get_table_names(self) -> list[str]:
+ """Returns list of tables that this extractor provides access to."""
+ return list(self._table_file_map)
+
+ def extract_one(self, table_name: str) -> pd.DataFrame:
+ """Read the data from the CSV source file and return as a dataframe."""
+ logger.info(f"Extracting {table_name} from CSV into pandas DataFrame.")
+ filename = self._table_file_map[table_name]
+ with self._zipfile.open(filename) as f:
+ df = pd.read_csv(f)
+ return df
+
+ def extract_all(self) -> dict[str, pd.DataFrame]:
+ """Extracts a dictionary of table names and dataframes from CSV source files."""
+ data = {}
+ for table_name in self.get_table_names():
+ df = self.extract_one(table_name)
+ data[table_name] = df
+ return data
diff --git a/src/pudl/extract/dbf.py b/src/pudl/extract/dbf.py
index 1c296aaea6..e34602e19a 100644
--- a/src/pudl/extract/dbf.py
+++ b/src/pudl/extract/dbf.py
@@ -160,7 +160,7 @@ def get_table_schema(self, table_name: str) -> DbfTableSchema:
table_columns = self.get_db_schema()[table_name]
dbf = self.get_table_dbf(table_name)
dbf_fields = [field for field in dbf.fields if field.name != "_NullFlags"]
- if len(table_columns) != len(table_columns):
+ if len(dbf_fields) != len(table_columns):
return ValueError(
f"Number of DBF fields in {table_name} does not match what was "
f"found in the DBC index file for {self.partition}."
diff --git a/src/pudl/extract/eia176.py b/src/pudl/extract/eia176.py
new file mode 100644
index 0000000000..aeab25026f
--- /dev/null
+++ b/src/pudl/extract/eia176.py
@@ -0,0 +1,26 @@
+"""Extract EIA Form 176 data from CSVs.
+
+The EIA Form 176 archive also contains CSVs for EIA Form 191 and EIA Form 757.
+"""
+
+from dagster import asset
+
+from pudl.extract.csv import CsvExtractor, get_table_file_map
+
+DATASET = "eia176"
+
+
+@asset(required_resource_keys={"datastore"})
+def raw_eia176__company(context):
+ """Extract raw EIA company data from CSV sheets into dataframes.
+
+ Args:
+ context: dagster keyword that provides access to resources and config.
+
+ Returns:
+ An extracted EIA dataframe with company data.
+ """
+ zipfile = context.resources.datastore.get_zipfile_resource(DATASET)
+ table_file_map = get_table_file_map(DATASET)
+ extractor = CsvExtractor(zipfile, table_file_map)
+ extractor.extract_one("company")
diff --git a/src/pudl/extract/epacems.py b/src/pudl/extract/epacems.py
index ea38f04719..c874362a0f 100644
--- a/src/pudl/extract/epacems.py
+++ b/src/pudl/extract/epacems.py
@@ -97,6 +97,44 @@
}
"""Set: The set of EPA CEMS columns to ignore when reading data."""
+API_DTYPE_DICT = {
+ "State": pd.CategoricalDtype(),
+ "Facility Name": pd.StringDtype(), # Not reading from CSV
+ "Facility ID": pd.Int32Dtype(), # unique facility id for internal EPA database management (ORIS code)
+ "Unit ID": pd.StringDtype(),
+ "Associated Stacks": pd.StringDtype(),
+ # These op_date, op_hour, and op_time variables get converted to
+ # operating_date, operating_datetime and operating_time_interval in
+ # transform/epacems.py
+ "Date": pd.StringDtype(),
+ "Hour": pd.Int16Dtype(),
+ "Operating Time": pd.Float32Dtype(),
+ "Gross Load (MW)": pd.Float32Dtype(),
+ "Steam Load (1000 lb/hr)": pd.Float32Dtype(),
+ "SO2 Mass (lbs)": pd.Float32Dtype(),
+ "SO2 Mass Measure Indicator": pd.CategoricalDtype(),
+ "SO2 Rate (lbs/mmBtu)": pd.Float32Dtype(), # Not reading from CSV
+ "SO2 Rate Measure Indicator": pd.CategoricalDtype(), # Not reading from CSV
+ "NOx Rate (lbs/mmBtu)": pd.Float32Dtype(), # Not reading from CSV
+ "NOx Rate Measure Indicator": pd.CategoricalDtype(), # Not reading from CSV
+ "NOx Mass (lbs)": pd.Float32Dtype(),
+ "NOx Mass Measure Indicator": pd.CategoricalDtype(),
+ "CO2 Mass (short tons)": pd.Float32Dtype(),
+ "CO2 Mass Measure Indicator": pd.CategoricalDtype(),
+ "CO2 Rate (short tons/mmBtu)": pd.Float32Dtype(), # Not reading from CSV
+ "CO2 Rate Measure Indicator": pd.CategoricalDtype(), # Not reading from CSV
+ "Heat Input (mmBtu)": pd.Float32Dtype(),
+ "Heat Input Measure Indicator": pd.CategoricalDtype(),
+ "Primary Fuel Type": pd.CategoricalDtype(),
+ "Secondary Fuel Type": pd.CategoricalDtype(),
+ "Unit Type": pd.CategoricalDtype(),
+ "SO2 Controls": pd.CategoricalDtype(),
+ "NOx Controls": pd.CategoricalDtype(),
+ "PM Controls": pd.CategoricalDtype(),
+ "Hg Controls": pd.CategoricalDtype(),
+ "Program Code": pd.CategoricalDtype(),
+}
+
class EpaCemsPartition(BaseModel):
"""Represents EpaCems partition identifying unique resource file."""
@@ -117,7 +155,7 @@ def get_filters(self):
def get_quarterly_file(self) -> Path:
"""Return the name of the CSV file that holds annual hourly data."""
return Path(
- f"epacems-{self.year}-{pd.to_datetime(self.year_quarter).quarter}.csv"
+ f"epacems-{self.year}q{pd.to_datetime(self.year_quarter).quarter}.csv"
)
@@ -138,30 +176,43 @@ def get_data_frame(self, partition: EpaCemsPartition) -> pd.DataFrame:
archive = self.datastore.get_zipfile_resource(
"epacems", **partition.get_filters()
)
-
with archive.open(str(partition.get_quarterly_file()), "r") as csv_file:
df = self._csv_to_dataframe(
- csv_file, ignore_cols=API_IGNORE_COLS, rename_dict=API_RENAME_DICT
+ csv_file,
+ ignore_cols=API_IGNORE_COLS,
+ rename_dict=API_RENAME_DICT,
+ dtype_dict=API_DTYPE_DICT,
)
return df
def _csv_to_dataframe(
- self, csv_file: Path, ignore_cols: dict[str, str], rename_dict: dict[str, str]
+ self,
+ csv_path: Path,
+ ignore_cols: dict[str, str],
+ rename_dict: dict[str, str],
+ dtype_dict: dict[str, type],
+ chunksize: int = 100_000,
) -> pd.DataFrame:
"""Convert a CEMS csv file into a :class:`pandas.DataFrame`.
Args:
- csv (file-like object): data to be read
+ csv_path: Path to CSV file containing data to read.
Returns:
- A DataFrame containing the contents of the CSV file.
+ A DataFrame containing the filtered and dtyped contents of the CSV file.
"""
- return pd.read_csv(
- csv_file,
+ chunk_iter = pd.read_csv(
+ csv_path,
index_col=False,
usecols=lambda col: col not in ignore_cols,
- low_memory=False,
- ).rename(columns=rename_dict)
+ dtype=dtype_dict,
+ chunksize=chunksize,
+ low_memory=True,
+ parse_dates=["Date"],
+ )
+ df = pd.concat(chunk_iter)
+ dtypes = {k: v for k, v in dtype_dict.items() if k in df.columns}
+ return df.astype(dtypes).rename(columns=rename_dict)
def extract(year_quarter: str, ds: Datastore) -> pd.DataFrame:
@@ -178,6 +229,7 @@ def extract(year_quarter: str, ds: Datastore) -> pd.DataFrame:
year = partition.year
# We have to assign the reporting year for partitioning purposes
try:
+ logger.info(f"Extracting data frame for {year_quarter}")
df = ds.get_data_frame(partition).assign(year=year)
# If the requested quarter is not found, return an empty df with expected columns:
except KeyError:
diff --git a/src/pudl/metadata/classes.py b/src/pudl/metadata/classes.py
index 17179b41e3..827ccfc49e 100644
--- a/src/pudl/metadata/classes.py
+++ b/src/pudl/metadata/classes.py
@@ -521,15 +521,7 @@ class Field(PudlMeta):
name: SnakeCase
# Shadows built-in type.
- type: Literal[ # noqa: A003
- "string",
- "number",
- "integer",
- "boolean",
- "date",
- "datetime",
- "year",
- ]
+ type: Literal["string", "number", "integer", "boolean", "date", "datetime", "year"] # noqa: A003
title: String | None = None
# Alias required to avoid shadowing Python built-in format()
format_: Literal["default"] = pydantic.Field(alias="format", default="default")
@@ -606,7 +598,7 @@ def to_pandas_dtype(self, compact: bool = False) -> str | pd.CategoricalDtype:
return "float32"
return FIELD_DTYPES_PANDAS[self.type]
- def to_sql_dtype(self) -> type:
+ def to_sql_dtype(self) -> type: # noqa: A003
"""Return SQLAlchemy data type."""
if self.constraints.enum and self.type == "string":
return sa.Enum(*self.constraints.enum)
@@ -624,7 +616,9 @@ def to_pyarrow(self) -> pa.Field:
name=self.name,
type=self.to_pyarrow_dtype(),
nullable=(not self.constraints.required),
- metadata={"description": self.description},
+ metadata={
+ "description": self.description if self.description is not None else ""
+ },
)
def to_sql( # noqa: C901
@@ -684,7 +678,7 @@ def to_sql( # noqa: C901
comment=self.description,
)
- def encode(self, col: pd.Series, dtype: type | None = None) -> pd.Series:
+ def encode(self, col: pd.Series, dtype: type | None = None) -> pd.Series: # noqa: A003
"""Recode the Field if it has an associated encoder."""
return self.encoder.encode(col, dtype=dtype) if self.encoder else col
@@ -1318,9 +1312,10 @@ def to_pyarrow(self) -> pa.Schema:
"""Construct a PyArrow schema for the resource."""
fields = [field.to_pyarrow() for field in self.schema.fields]
metadata = {
- "description": self.description,
- "primary_key": ",".join(self.schema.primary_key),
+ "description": self.description if self.description is not None else ""
}
+ if self.schema.primary_key is not None:
+ metadata |= {"primary_key": ",".join(self.schema.primary_key)}
return pa.schema(fields=fields, metadata=metadata)
def to_pandas_dtypes(self, **kwargs: Any) -> dict[str, str | pd.CategoricalDtype]:
diff --git a/src/pudl/metadata/codes.py b/src/pudl/metadata/codes.py
index c83208bf9f..61ed980ee8 100644
--- a/src/pudl/metadata/codes.py
+++ b/src/pudl/metadata/codes.py
@@ -795,101 +795,6 @@
"code_fixes": {"5": "F"},
"ignored_codes": [],
},
- "entity_types_eia": {
- "df": pd.DataFrame(
- columns=[
- "code",
- "label",
- "description",
- ],
- data=[
- (
- "A",
- "municipal_marketing_authority",
- "Municipal Marketing Authority. Voted into existence by the residents of a municipality and given authority for creation by the state government. They are nonprofit organizations",
- ),
- (
- "B",
- "behind_the_meter",
- "Behind the Meter. Entities that install, own, and/or operate a system (usually photovoltaic), and sell, under a long term power purchase agreement (PPA) or lease, all the production from the system to the homeowner or business with which there is a net metering agreement. Third Party Owners (TPOs) of PV solar installations use this ownership code.",
- ),
- ("C", "cooperative", "Cooperative. Member-owned organizations."),
- ("COM", "commercial", "Commercial facility."),
- (
- "D",
- "nonutility_dsm_administrator",
- "Non-utility DSM Administrator. Only involved with Demand-Side Management activities.",
- ),
- (
- "F",
- "federal",
- "Federal. Government agencies with the authority to deliver energy to end-use customers.",
- ),
- ("G", "community_choice_aggregator", "Community Choice Aggregator."),
- (
- "I",
- "investor_owned",
- "Investor-owned Utilities. Entities that are privately owned and provide a public service.",
- ),
- ("IND", "industrial", "Industrial facility."),
- (
- "M",
- "municipal",
- "Municipal: Entities that are organized under authority of state statute to provide a public service to residents of that area.",
- ),
- ("O", "other", "Other entity type."),
- (
- "P",
- "political_subdivision",
- 'Political Subdivision. (also called "public utility district"): Independent of city or county government and voted into existence by a majority of the residents of any given area for the specific purpose of providing utility service to the voters. State laws provide for the formation of such districts.',
- ),
- ("PO", "power_marketer", "Power marketer."),
- ("PR", "private", "Private entity."),
- (
- "Q",
- "independent_power_producer",
- "Independent Power Producer or Qualifying Facility. Entities that own power plants and sell their power into the wholesale market.",
- ),
- (
- "R",
- "retail_power_marketer",
- "Retail Power Marketer or Energy Service Provider: Entities that market power to customers in restructured markets.",
- ),
- (
- "S",
- "state",
- "State entities that own or operate facilities or provide a public service.",
- ),
- (
- "T",
- "transmission",
- "Transmission: Entities that operate or own high voltage transmission wires that provide bulk power services.",
- ),
- ("U", "unknown", "Unknown entity type."),
- (
- "W",
- "wholesale_power_marketer",
- "Wholesale Power Marketer: Entities that buy and sell power in the wholesale market.",
- ),
- ],
- ).convert_dtypes(),
- "code_fixes": {
- "Behind the Meter": "B",
- "Community Choice Aggregator": "G",
- "Cooperative": "C",
- "Facility": "Q",
- "Federal": "F",
- "Investor Owned": "I",
- "Municipal": "M",
- "Political Subdivision": "P",
- "Power Marketer": "PO",
- "Retail Power Marketer": "R",
- "State": "S",
- "Unregulated": "Q",
- "Wholesale Power Marketer": "W",
- },
- "ignored_codes": [],
- },
"core_eia__codes_boiler_generator_assn_types": {
"df": pd.DataFrame(
columns=[
@@ -2391,3 +2296,103 @@
"ignored_codes": [],
},
}
+
+# The entity type codes were never fully reconciled. Preserving this work for reference.
+# See https://github.com/catalyst-cooperative/pudl/issues/1392
+DISABLED_CODE_METADATA = {
+ "core_eia__codes_entity_types": {
+ "df": pd.DataFrame(
+ columns=[
+ "code",
+ "label",
+ "description",
+ ],
+ data=[
+ (
+ "A",
+ "municipal_marketing_authority",
+ "Municipal Marketing Authority. Voted into existence by the residents of a municipality and given authority for creation by the state government. They are nonprofit organizations",
+ ),
+ (
+ "B",
+ "behind_the_meter",
+ "Behind the Meter. Entities that install, own, and/or operate a system (usually photovoltaic), and sell, under a long term power purchase agreement (PPA) or lease, all the production from the system to the homeowner or business with which there is a net metering agreement. Third Party Owners (TPOs) of PV solar installations use this ownership code.",
+ ),
+ ("C", "cooperative", "Cooperative. Member-owned organizations."),
+ ("COM", "commercial", "Commercial facility."),
+ (
+ "D",
+ "nonutility_dsm_administrator",
+ "Non-utility DSM Administrator. Only involved with Demand-Side Management activities.",
+ ),
+ (
+ "F",
+ "federal",
+ "Federal. Government agencies with the authority to deliver energy to end-use customers.",
+ ),
+ ("G", "community_choice_aggregator", "Community Choice Aggregator."),
+ (
+ "I",
+ "investor_owned",
+ "Investor-owned Utilities. Entities that are privately owned and provide a public service.",
+ ),
+ ("IND", "industrial", "Industrial facility."),
+ (
+ "M",
+ "municipal",
+ "Municipal: Entities that are organized under authority of state statute to provide a public service to residents of that area.",
+ ),
+ ("O", "other", "Other entity type."),
+ (
+ "P",
+ "political_subdivision",
+ 'Political Subdivision. (also called "public utility district"): Independent of city or county government and voted into existence by a majority of the residents of any given area for the specific purpose of providing utility service to the voters. State laws provide for the formation of such districts.',
+ ),
+ ("PO", "power_marketer", "Power marketer."),
+ ("PR", "private", "Private entity."),
+ (
+ "Q",
+ "independent_power_producer",
+ "Independent Power Producer or Qualifying Facility. Entities that own power plants and sell their power into the wholesale market.",
+ ),
+ (
+ "R",
+ "retail_power_marketer",
+ "Retail Power Marketer or Energy Service Provider: Entities that market power to customers in restructured markets.",
+ ),
+ (
+ "S",
+ "state",
+ "State entities that own or operate facilities or provide a public service.",
+ ),
+ (
+ "T",
+ "transmission",
+ "Transmission: Entities that operate or own high voltage transmission wires that provide bulk power services.",
+ ),
+ ("U", "unknown", "Unknown entity type."),
+ (
+ "W",
+ "wholesale_power_marketer",
+ "Wholesale Power Marketer: Entities that buy and sell power in the wholesale market.",
+ ),
+ ],
+ ).convert_dtypes(),
+ "code_fixes": {
+ "Behind the Meter": "B",
+ "Community Choice Aggregator": "G",
+ "Cooperative": "C",
+ "Facility": "Q",
+ "Federal": "F",
+ "Investor Owned": "I",
+ "Municipal": "M",
+ "Political Subdivision": "P",
+ "Power Marketer": "PO",
+ "Retail Power Marketer": "R",
+ "State": "S",
+ "Unregulated": "Q",
+ "Wholesale Power Marketer": "W",
+ },
+ "ignored_codes": [],
+ }
+}
diff --git a/src/pudl/metadata/fields.py b/src/pudl/metadata/fields.py
index ca0a1cdf9d..bc7f22db8f 100644
--- a/src/pudl/metadata/fields.py
+++ b/src/pudl/metadata/fields.py
@@ -372,6 +372,7 @@
"city": {
"type": "string",
# TODO: Disambiguate column. City means different things in different tables.
+ "description": "Name of the city.",
},
"co2_mass_measurement_code": {
"type": "string",
@@ -1728,7 +1729,7 @@
},
"operator_name": {
"type": "string",
- "descripion": "The name of the EIA operator utility.",
+ "description": "The name of the EIA operator utility.",
},
"operator_state": {
"type": "string",
@@ -1736,7 +1737,7 @@
},
"operator_utility_id_eia": {
"type": "integer",
- "descrption": "The EIA utility Identification number for the operator utility.",
+ "description": "The EIA utility Identification number for the operator utility.",
},
"opex_allowances": {"type": "number", "description": "Allowances.", "unit": "USD"},
"opex_boiler": {
@@ -2581,6 +2582,7 @@
"street_address": {
"type": "string",
# TODO: Disambiguate as this means different things in different tables.
+ "description": "Physical street address.",
},
"subcritical_tech": {
"type": "boolean",
diff --git a/src/pudl/metadata/resources/eia.py b/src/pudl/metadata/resources/eia.py
index 72efd02d5f..6b0721b41c 100644
--- a/src/pudl/metadata/resources/eia.py
+++ b/src/pudl/metadata/resources/eia.py
@@ -444,15 +444,17 @@
"etl_group": "static_eia",
"field_namespace": "eia",
},
- "entity_types_eia": {
- "description": "Descriptive labels for EIA entity type and ownership codes, taken from the EIA-861 form instructions, valid through 2023-05-31.",
- "schema": {"fields": ["code", "label", "description"], "primary_key": ["code"]},
- "encoder": CODE_METADATA["entity_types_eia"],
- "sources": ["eia861"],
- "etl_group": "static_eia_disabled", # currently not being loaded into the db
- "field_namespace": "eia",
- "create_database_schema": False,
- },
+ # The entity types were never fully reconciled. Preserving for future reference.
+ # See https://github.com/catalyst-cooperative/pudl/issues/1392
+ # "core_eia__codes_entity_types": {
+ # "description": "Descriptive labels for EIA entity type and ownership codes, taken from the EIA-861 form instructions, valid through 2023-05-31.",
+ # "schema": {"fields": ["code", "label", "description"], "primary_key": ["code"]},
+ # "encoder": CODE_METADATA["core_eia__codes_entity_types"],
+ # "sources": ["eia861"],
+ # "etl_group": "static_eia_disabled", # currently not being loaded into the db
+ # "field_namespace": "eia",
+ # "create_database_schema": False,
+ # },
"core_eia__codes_fuel_transportation_modes": {
"description": "Long descriptions of the fuel transportation modes reported in the EIA-860 and EIA-923.",
"schema": {
diff --git a/src/pudl/metadata/resources/pudl.py b/src/pudl/metadata/resources/pudl.py
index a58f20f786..7ee3e73216 100644
--- a/src/pudl/metadata/resources/pudl.py
+++ b/src/pudl/metadata/resources/pudl.py
@@ -56,6 +56,8 @@
"sources": ["pudl"],
},
"core_pudl__assn_utilities_plants": {
+ "title": "PUDL Utility-Plant Associations",
+ "description": "Associations between PUDL utility IDs and PUDL plant IDs. This table is read in from a spreadsheet stored in the PUDL repository: src/pudl/package_data/glue/pudl_id_mapping.xlsx",
"schema": {
"fields": ["utility_id_pudl", "plant_id_pudl"],
"primary_key": ["utility_id_pudl", "plant_id_pudl"],
@@ -65,6 +67,8 @@
"sources": ["pudl"],
},
"core_pudl__codes_datasources": {
+ "title": "PUDL Data Sources",
+ "description": "Static table defining codes associated with the data sources that PUDL integrates.",
"schema": {
"fields": [
"datasource",
@@ -79,6 +83,8 @@
"sources": ["pudl"],
},
"out_ferc714__hourly_predicted_state_demand": {
+ "title": "Estimated Hourly State Electricity Demand",
+ "description": "Estimated hourly electricity demand for each state, scaled such that it matches the total electricity sales by state reported in EIA 861.",
"schema": {
"fields": [
"state_id_fips",
diff --git a/src/pudl/output/ferc1.py b/src/pudl/output/ferc1.py
index edf6f68247..0bd5594721 100644
--- a/src/pudl/output/ferc1.py
+++ b/src/pudl/output/ferc1.py
@@ -110,7 +110,7 @@
null_calculated_value_frequency=1.0,
),
xbrl_factoid=MetricTolerances(
- error_frequency=0.028,
+ error_frequency=0.035,
relative_error_magnitude=0.019,
null_calculated_value_frequency=1.0,
),
diff --git a/src/pudl/package_data/eia176/table_file_map.csv b/src/pudl/package_data/eia176/table_file_map.csv
new file mode 100644
index 0000000000..343bdb33c0
--- /dev/null
+++ b/src/pudl/package_data/eia176/table_file_map.csv
@@ -0,0 +1,2 @@
+table,filename
+company,all_company_176.csv
diff --git a/src/pudl/transform/ferc1.py b/src/pudl/transform/ferc1.py
index 30a2471ce2..45f67a015a 100644
--- a/src/pudl/transform/ferc1.py
+++ b/src/pudl/transform/ferc1.py
@@ -4720,6 +4720,31 @@ def process_xbrl_metadata(
.assign(utility_type="total")
)
+ def process_dbf_test(self: Self, df: pd.DataFrame):
+ """Standard dbf process plus converting quarterly data to annual.
+
+ For some reason in the dbf data for this table reported all of the
+ balance data as quarterly data between 2005 and 2020. We already choose
+ the end of the year in :meth:`select_annual_rows_dbf`.
+
+ https://github.com/catalyst-cooperative/pudl/issues/3233
+ """
+ df = super().process_dbf(df)
+ annual_cols = ["starting_balance", "ending_balance"]
+ bad_years_mask = df.report_year.between(2005, 2020)
+ # ensure this filling in treatment is necessary!
+ if not df.loc[bad_years_mask, annual_cols].isnull().all(axis=None):
+ raise AssertionError(
+ "We expected that all balance data between 2005 and 2020 are all null. "
+ "Found non-null records, so the annual columns may no longer need to "
+ "be filled in with quarterly data."
+ )
+
+ df.loc[bad_years_mask, annual_cols] = df.loc[
+ bad_years_mask, ["pri_yr_q4_bal", "end_qtr_bal"]
+ ].to_numpy()
+ return df
+
@cache_df(key="main")
def transform_main(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Duplicate data that appears in multiple distinct calculations.
diff --git a/src/pudl/workspace/datastore.py b/src/pudl/workspace/datastore.py
index 144c187aeb..052fd98a41 100644
--- a/src/pudl/workspace/datastore.py
+++ b/src/pudl/workspace/datastore.py
@@ -104,6 +104,13 @@ def _matches(self, res: dict, **filters: Any):
f"Resource filter values should be all lowercase: {k}={v}"
)
parts = res.get("parts", {})
+ # If partitions are list, match whole list if it contains desired element
+ if set(map(type, parts.values())) == {list}:
+ return all(
+ any(part.lower() == str(v).lower() for part in parts.get(k))
+ for k, v in filters.items()
+ )
+ # Otherwise return matches to int/str partitions
return all(
str(parts.get(k)).lower() == str(v).lower() for k, v in filters.items()
)
@@ -134,7 +141,10 @@ def get_partitions(self, name: str = None) -> dict[str, set[str]]:
if name and res["name"] != name:
continue
for k, v in res.get("parts", {}).items():
- partitions[k].add(v)
+ if isinstance(v, list):
+ partitions[k] |= set(v) # Add all items from list
+ else:
+ partitions[k].add(v)
return partitions
def get_partition_filters(self, **filters: Any) -> Iterator[dict[str, str]]:
@@ -171,13 +181,14 @@ class ZenodoDoiSettings(BaseSettings):
"""Digital Object Identifiers pointing to currently used Zenodo archives."""
censusdp1tract: ZenodoDoi = "10.5281/zenodo.4127049"
+ eia176: ZenodoDoi = "10.5281/zenodo.7682358"
eia860: ZenodoDoi = "10.5281/zenodo.10067566"
- eia860m: ZenodoDoi = "10.5281/zenodo.10204686"
+ eia860m: ZenodoDoi = "10.5281/zenodo.10423813"
eia861: ZenodoDoi = "10.5281/zenodo.10204708"
eia923: ZenodoDoi = "10.5281/zenodo.10067550"
eia_bulk_elec: ZenodoDoi = "10.5281/zenodo.7067367"
epacamd_eia: ZenodoDoi = "10.5281/zenodo.7900974"
- epacems: ZenodoDoi = "10.5281/zenodo.10306114"
+ epacems: ZenodoDoi = "10.5281/zenodo.10425497"
ferc1: ZenodoDoi = "10.5281/zenodo.8326634"
ferc2: ZenodoDoi = "10.5281/zenodo.8326697"
ferc6: ZenodoDoi = "10.5281/zenodo.8326696"
@@ -366,8 +377,8 @@ def get_resources(
logger.info(f"{res} is already optimally cached.")
continue
if self._cache.contains(res):
- logger.info(f"Retrieved {res} from cache.")
contents = self._cache.get(res)
+ logger.info(f"Retrieved {res} from cache.")
if not self._cache.is_optimally_cached(res):
logger.info(f"{res} was not optimally cached yet, adding.")
self._cache.add(res, contents)
diff --git a/src/pudl/workspace/resource_cache.py b/src/pudl/workspace/resource_cache.py
index 0eb8e72019..9d7fb5b40a 100644
--- a/src/pudl/workspace/resource_cache.py
+++ b/src/pudl/workspace/resource_cache.py
@@ -98,6 +98,7 @@ def _resource_path(self, resource: PudlResourceKey) -> Path:
def get(self, resource: PudlResourceKey) -> bytes:
"""Retrieves value associated with a given resource."""
+ logger.debug(f"Getting {resource} from local file cache.")
return self._resource_path(resource).open("rb").read()
def add(self, resource: PudlResourceKey, content: bytes):
@@ -151,6 +152,7 @@ def _blob(self, resource: PudlResourceKey) -> Blob:
def get(self, resource: PudlResourceKey) -> bytes:
"""Retrieves value associated with given resource."""
+ logger.debug(f"Getting {resource} from {self._blob.__name__}")
return self._blob(resource).download_as_bytes(retry=gcs_retry)
def add(self, resource: PudlResourceKey, value: bytes):
@@ -218,9 +220,10 @@ def add(self, resource: PudlResourceKey, value):
for cache_layer in self._caches:
if cache_layer.is_read_only():
continue
+ logger.debug(f"Adding {resource} to cache {cache_layer.__class__.__name__}")
cache_layer.add(resource, value)
logger.debug(
- f"Add {resource} to cache layer {cache_layer.__class__.__name__})"
+ f"Added {resource} to cache layer {cache_layer.__class__.__name__})"
)
break
diff --git a/test/integration/etl_test.py b/test/integration/etl_test.py
index affa1d813b..4ab7887409 100644
--- a/test/integration/etl_test.py
+++ b/test/integration/etl_test.py
@@ -75,6 +75,20 @@ def test_ferc1_xbrl2sqlite(ferc1_engine_xbrl: sa.Engine, ferc1_xbrl_taxonomy_met
)
+class TestCsvExtractor:
+ """Verify that we can lead CSV files as provided via the datastore."""
+
+ def test_extract_eia176(self, pudl_datastore_fixture):
+ """Spot check extraction of eia176 csv files."""
+ dataset = "eia176"
+ zipfile = pudl_datastore_fixture.get_zipfile_resource(dataset)
+ table_file_map = pudl.extract.csv.get_table_file_map(dataset)
+ extractor = pudl.extract.csv.CsvExtractor(zipfile, table_file_map)
+ table = "company"
+ if table not in extractor.extract_all():
+ raise AssertionError(f"table {table} not found in datastore")
+
+
class TestExcelExtractor:
"""Verify that we can lead excel files as provided via the datastore."""
diff --git a/test/integration/output_test.py b/test/integration/output_test.py
index 9452cf62a3..fcc204ca1c 100644
--- a/test/integration/output_test.py
+++ b/test/integration/output_test.py
@@ -1,4 +1,4 @@
-"""PyTest cases related to the integration between FERC1 & EIA 860/923."""
+"""PyTest cases related to generating dervied outputs."""
import logging
import pandas as pd
diff --git a/test/unit/extract/csv_test.py b/test/unit/extract/csv_test.py
new file mode 100644
index 0000000000..2b07b0bf30
--- /dev/null
+++ b/test/unit/extract/csv_test.py
@@ -0,0 +1,52 @@
+"""Unit tests for pudl.extract.csv module."""
+from unittest.mock import MagicMock, patch
+
+from pudl.extract.csv import CsvExtractor, get_table_file_map, open_csv_resource
+
+DATASET = "eia176"
+BASE_FILENAME = "table_file_map.csv"
+TABLE_NAME = "company"
+FILENAME = "all_company_176.csv"
+TABLE_FILE_MAP = {TABLE_NAME: FILENAME}
+
+
+def get_csv_extractor():
+ zipfile = MagicMock()
+ return CsvExtractor(zipfile, TABLE_FILE_MAP)
+
+
+def test_open_csv_resource():
+ csv_resource = open_csv_resource(DATASET, BASE_FILENAME)
+ assert ["table", "filename"] == csv_resource.fieldnames
+
+
+def test_get_table_file_map():
+ table_file_map = get_table_file_map(DATASET)
+ assert table_file_map == TABLE_FILE_MAP
+
+
+def test_get_table_names():
+ extractor = get_csv_extractor()
+ table_names = extractor.get_table_names()
+ assert [TABLE_NAME] == table_names
+
+
+@patch("pudl.extract.csv.pd")
+def test_csv_extractor_read_source(mock_pd):
+ extractor = get_csv_extractor()
+ res = extractor.extract_one(TABLE_NAME)
+ mock_zipfile = extractor._zipfile
+ mock_zipfile.open.assert_called_once_with(FILENAME)
+ f = mock_zipfile.open.return_value.__enter__.return_value
+ mock_pd.read_csv.assert_called_once_with(f)
+ df = mock_pd.read_csv()
+ assert df == res
+
+
+def test_csv_extractor_extract():
+ extractor = get_csv_extractor()
+ df = MagicMock()
+ with patch.object(CsvExtractor, "extract_one", return_value=df) as mock_read_source:
+ raw_dfs = extractor.extract_all()
+ mock_read_source.assert_called_once_with(TABLE_NAME)
+ assert {TABLE_NAME: df} == raw_dfs
diff --git a/test/unit/metadata_test.py b/test/unit/metadata_test.py
index 9ef285a147..9f6eb17738 100644
--- a/test/unit/metadata_test.py
+++ b/test/unit/metadata_test.py
@@ -1,11 +1,14 @@
"""Tests for metadata not covered elsewhere."""
import pytest
-from pudl.metadata.classes import DataSource, Package
+from pudl.metadata.classes import DataSource, Field, Package
+from pudl.metadata.fields import FIELD_METADATA
from pudl.metadata.helpers import format_errors
from pudl.metadata.resources import RESOURCE_METADATA
from pudl.metadata.sources import SOURCES
+PUDL_RESOURCES = {r.name: r for r in Package.from_resource_ids().resources}
+
def test_all_resources_valid() -> None:
"""All resources in metadata pass validation tests."""
@@ -39,3 +42,60 @@ def test_get_etl_group_tables() -> None:
"""Test that a Value error is raised for non existent etl group."""
with pytest.raises(ValueError):
Package.get_etl_group_tables("not_an_etl_group")
+
+
+@pytest.mark.parametrize("resource_name", sorted(PUDL_RESOURCES.keys()))
+def test_pyarrow_schemas(resource_name: str):
+ """Verify that we can produce pyarrow schemas for all defined Resources."""
+ _ = PUDL_RESOURCES[resource_name].to_pyarrow()
+
+
+@pytest.mark.parametrize("field_name", sorted(FIELD_METADATA.keys()))
+def test_field_definitions(field_name: str):
+ """Check that all defined fields are valid."""
+ _ = Field(name=field_name, **FIELD_METADATA[field_name])
+
+
+@pytest.mark.xfail(reason="Need to purge unused fields. See issue #3224")
+def test_defined_fields_are_used():
+ """Check that all fields which are defined are actually used."""
+ used_fields = set()
+ for resource in PUDL_RESOURCES.values():
+ used_fields |= {f.name for f in resource.schema.fields}
+ defined_fields = set(FIELD_METADATA.keys())
+ unused_fields = sorted(defined_fields - used_fields)
+ if len(unused_fields) > 0:
+ raise AssertionError(
+ f"Found {len(unused_fields)} unused fields: {unused_fields}"
+ )
+
+
+@pytest.mark.xfail(reason="Incomplete descriptions. See issue #3224")
+def test_fields_have_descriptions():
+ """Check that all fields have a description and report any that do not."""
+ fields_without_description = []
+ for field_name in FIELD_METADATA:
+ field = Field(name=field_name, **FIELD_METADATA[field_name])
+ if field.description is None:
+ fields_without_description.append(field_name)
+ fields_without_description = sorted(fields_without_description)
+ if len(fields_without_description) > 0:
+ raise AssertionError(
+ f"Found {len(fields_without_description)} fields without descriptions: "
+ f"{fields_without_description}"
+ )
+
+
+@pytest.mark.xfail(reason="Incomplete descriptions. See issue #3224")
+def test_resources_have_descriptions():
+ """Check that all resources have a description and report any that do not."""
+ resources_without_description = []
+ for resource_name, resource in PUDL_RESOURCES.items():
+ if resource.description is None:
+ resources_without_description.append(resource_name)
+ resources_without_description = sorted(resources_without_description)
+ if len(resources_without_description) > 0:
+ raise AssertionError(
+ f"Found {len(resources_without_description)} resources without descriptions: "
+ f"{resources_without_description}"
+ )