Release Notes#

0.6.1 - 2026-04-01#

This release focuses on (1) making the public Evaluation API safer/clearer by moving lower-level components behind “private” attributes, (2) improving table loading/writing ergonomics (including audit timestamps and safer writes), and (3) updating fetching capability (notably USGS via dataretrieval.waterdata and NWM temperature handling).

### Upgrade notes - Replace any Configuration(…, type=”primary”) with Configuration(…, timeseries_type=”primary”). - Prefer:

  • ev.primary_timeseries.load_parquet(…) over ev.load.primary_timeseries.from_parquet(…)

  • df_accessor.write_to(“my_results”) over df_accessor.write(“my_results”)

  • If you were calling ev.write.*/ ev.load.* directly, expect those to be internal/private (ev._write, ev._load)

and migrate to table methods where possible.

### Breaking / behavior changes - Renamed Configuration.type → Configuration.timeseries_type (and corresponding schema/docs updates). - Internal Evaluation components are now treated as private (ev._write, ev._load, ev._validate, ev._read, ev._extract) and many call sites were updated accordingly. - USGS site IDs are now expected to be prefixed (e.g., “USGS-02449838”) when calling the fetch methods. This happens automatically when using the fetch.usgs_streamflow() method, but users calling the underlying fetch functions directly will need to update their site ID formats.

### Added - Audit + metadata fields

  • Added created_at and updated_at columns (via migrations) across core tables;

warehouse writes now manage these timestamps automatically. - Added properties map columns (via migrations) to locations, location_crosswalks, location_attributes, and configurations.

  • Safer / clearer write APIs - Added write_to() on DataFrame accessors for writing results to Iceberg tables. - Added protections preventing accidental writes to core tables via accessor writes; use table loading methods instead.

  • Table-centric loading ergonomics - Added BaseTable.load_dataframe() so data can be loaded to all tables via ev.<table>.load_dataframe(…). - Added domain-table file loading helpers (e.g., load_csv, load_parquet) using shared single-file extraction utilities.

  • Fetching enhancements - NWM: expanded operational configuration descriptions and added support for T2D with optional Kelvin→Celsius conversion (convert_k_to_c). - USGS: migrated streamflow fetching to dataretrieval.waterdata to support the latests USGS API changes and added description-based time series selection via metadata lookup.

  • Migrations - Added migrations to (1) update units/variables for temperature support, (2) add audit timestamps, (3) add properties map fields, and (4) rename configuration type → timeseries_type.

### Changed - Refactored validation into Validate.dataframe(…) with improved handling for:

  • adding missing nullable columns

  • strict column enforcement

  • duplicate dropping

  • clearer foreign key constraint enforcement

  • Improved GeoDataFrame caching/parquet writing by converting geometry to WKB and writing GeoParquet metadata.

  • LocalReadWriteEvaluation / LocalReadEvaluation now accept namespace_name and use constants for

local/remote namespaces rather than Spark conf. - Updated docs/notebooks to reflect:

  • timeseries_type rename

  • table-first load/write patterns (e.g., ev.primary_timeseries.load_parquet(…))

  • write_to() usage

  • updated USGS ID format

### Fixed - Improved import_evaluation.update_metadata_paths(…) robustness when re-registering imported Iceberg tables and applying migrations. - Updated/expanded test fixtures and added new tests covering:

  • created_at / updated_at behavior

  • properties fields

  • domain table CSV/Parquet loading

  • validation edge cases

### Dependencies - Bumped dataretrieval to >=1.1.2,<2 (and updated lockfile).

### Deprecated - TeehrDataFrameBase.write() is deprecated in favor of write_to() (will be removed in a future release).

0.6.0 - 2026-03-19#

This release introduces major architectural changes and new features. Several breaking changes require updates to existing code; see the Breaking Changes section below.

A core change in this release is the integration of Apache Iceberg as the underlying table format for TEEHR evaluations. Iceberg brings a rich set of capabilities to TEEHR, including ACID transactions, time travel, schema evolution, hidden partitioning, a full suite of write methods (append, upsert, insert, delete, etc.), and more. In addition to local warehouse support, users now have access to the TEEHR-Cloud data warehouse hosted on AWS S3 via the download methods, which provides CONUS-scale historical and real-time NWM simulations and forecasts ready for evaluation.

Breaking Changes#

  • query() method renamed to aggregate(): The query() method on metrics and joined timeseries has been renamed to aggregate() to better reflect its purpose. All user code and notebooks referencing .query() must be updated to .aggregate().

  • Pandas DataFrame accessor removed: The @pd.api.extensions.register_dataframe_accessor TEEHR accessor has been removed. Visualization and utility methods previously accessed via the accessor must now be called directly on the evaluation or table objects.

  • SignatureMetrics renamed to Signatures: Importing SignatureMetrics will no longer work; use Signatures instead.

  • clone_from_s3(), clone_template(), and list_s3_evaluations() removed: These methods have been removed from the Evaluation class. Data from the TEEHR-Cloud data warehouse should now be accessed via the ev.download.* methods.

Added#

  • Views for computed DataFrames: New View classes (JoinedTimeseriesView, PrimaryTimeseriesView, SecondaryTimeseriesView, LocationAttributesView) provide lazy, on-the-fly computed DataFrames that can be filtered, chained with add_calculated_fields() and aggregate(), and materialized to an Iceberg table via write(). Views are accessed via methods on the evaluation object (e.g., ev.joined_timeseries_view()) and complement the existing persisted Table objects.

  • TEEHR API support: New download methods backed by a TEEHR API, with support for fetching locations, timeseries, and other data from the TEEHR-Cloud data warehouse via ev.download.* methods.

  • Automatic pagination for all download methods: Download methods now automatically handle pagination under the hood. A page_size argument is available for tuning. A configurable timeout (default 60 s) has been added to all download methods.

  • RemoteReadOnlyEvaluation and RemoteReadWriteEvaluation classes: New evaluation classes for connecting to a remote TEEHR deployment without requiring a local evaluation directory.

  • INSERT INTO and DELETE FROM write methods: Write class now supports insert_into() and delete_from() operations, and tables expose a delete() method for row-level deletes.

  • User-defined custom tables: Users can now create and manage their own Iceberg tables alongside the core TEEHR tables (e.g. primary_timeseries, locations, etc.) using ev.table("my_table"). Custom tables are backed by the same BaseTable interface, supporting the full read, filter, aggregate, add calculated fields, load, and write workflow. Core TEEHR tables continue to return their specialized table class; any other name returns a generic BaseTable instance, giving users a flexible way to persist and query intermediate or derived datasets within the same Iceberg warehouse.

  • Drop table method for user-created tables: Tables now have a drop() / drop_table() method so users can remove custom tables programmatically.

  • add_attributes() on DataFrame results: A generic add_attributes() method has been added to TeehrDataFrameBase, allowing attribute columns to be joined onto any query result DataFrame.

  • GenericSQL row-level calculated field: Users can now supply arbitrary SQL expressions as row-level calculated fields via the GenericSQL class.

  • name field in geometry joins: The name field is now included when joining geometry to timeseries tables.

  • primary_location_id_prefix and secondary_location_id_prefix for crosswalk downloads: These new parameters allow location ID prefix filtering when downloading location crosswalk data.

  • ID list filtering for location downloads: download.locations() now accepts a list of location IDs for targeted data retrieval.

  • Ability to generate and write partial joined timeseries: Filters can be applied during the create_joined_timeseries() step so only a subset of the joined data is written.

  • Load functions added to BaseTable: load_dataframe(), load_csv(), load_parquet(), and load_spatial() are now available on the base table class, enabling consistent data ingestion across all table types.

  • AWS profile support: create_spark_session() now accepts an AWS profile name and resolves credentials using the standard AWS credential priority chain.

  • Spark decommissioning support: The Spark session helper now gracefully decommissions executor nodes when a session is closed.

  • Additional deterministic and probabilistic metrics: New categorical metrics including SuccessRatio, FrequencyBiasIndex, ProbabilityOfDetection, FalseAlarmRatio, CriticalSuccessIndex, and related metrics have been added.

  • Brier Score and Brier Skill Score: Probabilistic metrics for ensemble evaluation.

  • ForecastLeadTimeBins row-level calculated field for grouping forecasts by lead time.

  • Improved Sphinx API documentation: API docs have been reorganized and expanded.

  • Updated Getting Started documentation: The getting-started guide has been refreshed.

Changed#

  • Evaluation initialization revisited: Initialization logic has been cleaned up; the template-based initialization approach has been replaced with a simpler, more explicit flow.

  • Spark session configuration updated: Improved handling of AWS credentials, Iceberg catalog configuration, spot-instance executor support, and Jupyter pod prefix support.

  • Data validation fixed when adding domain data: Validation now correctly catches invalid values when adding domain data to an evaluation.

  • Table filter bug fixed: Applying filters to table queries now works correctly.

  • Foreign key enforcement bug fixed: Fixes an edge case in load_dataframe() where foreign key checks were not enforced properly.

  • Pearson/R-squared updated to use epsilon for near-zero denominators.

  • Metrics refactored: Reduces duplicate code across deterministic, probabilistic, and signature metric modules; bootstrap and probabilistic models cleaned up.

  • Sort order applied to schema migrations: Migrations are now applied in deterministic sort order, preventing out-of-order migration application.

  • Unused S3Path class removed.

  • netcdf4 and PySpark pinned to compatible versions for stability.

  • Conversion workflow updated for migrating v0.4/v0.5 evaluations to v0.6 format.

0.5.3 - 2026-01-07#

Changed#

  • Pins PySpark to 4.0.1 in pyproject.toml

  • Fixes bug using drop_overlapping_assimilation_values in NWM operational fetching methods.

  • Fixes a bug in load_dataframe()

  • Fixes a bug in unpacking metric results

  • Updates the Getting Started sphinx documentation

Added#

  • Check for missing location IDs when cloning from s3

  • Row level calculated fields for forecast lead time bins

  • Brier Score and Brier Skill Score metrics

0.5.2 - 2025-11-11#

Changed#

  • Updates version number in pyproject.toml and init files

  • No changes in functionality from 0.5.1dev10

0.5.1dev10 - 2025-11-10#

Changed#

  • Rename SignatureMetrics to Signatures

  • Updates Spearman correlation to handle repeating values

Added#

  • Flow Duration Curve Slope

  • VariabilityRatio

  • Epsilon to handle division by zero

  • BelowPercentileEventDetection metric

  • Bootstrapping for signatures

0.5.0 - 2025-08-27#

Changed#

  • Many updates and new features are introduced in this release, including but not limited to: - Baseflow separation methods - Better handling of warnings - Ability to generate benchmark timeseries and forecasts - Upgrading to pyspark 4.0 - Documentation updates

  • For a full list see: RTIInternational/teehr

0.4.13 - 2025-06-09#

Changed#

  • Updates logic around reading empty tables to allow for cloning empty timeseries tables from s3. Overrides the read method in the joined_timeseries table class.

  • Removes the pa.Check.isin() pandera validation checks and replaces with the manual “foreign key” enforcement method, _enforce_foreign_keys() to speed up validation.

  • Removes redundant dataframe validation during the write methods.

  • Sets .set(“spark.sql.parquet.enableVectorizedReader”, “false”) in pyspark config to fix type error when reading null parquet fields.

  • Fixes the path conversion error when visualizing locations in the accessor.

  • Sorts timeseries before plotting to fix the visualization error in the accessor.

  • Removes add_configuration_name from user guide doc (#450)

  • Adds tomli to pyproject.toml to dev group to support python 3.12 when building sphinx docs.

  • Drops location_id after joining attributes to the joined_timeseries table.

  • Fixes joining geometry to the secondary timeseries table.

  • Sets timeseries_type to secondary when fetching operational NWM gridded data, unless the configuration_name contains “forcing_analysis_assim” in which case timeseries_type is set to primary.

0.4.12 - 2025-05-22#

Changed#

  • Adds git and vim to docker image for TEEHR-HUB

  • Moves scoringrules and arch imports into the function to speed up import time

  • Removes the repartitioning by self.partition_by of the dataframe in the BaseTable class when writing to parquet

0.4.11 - 2025-05-19#

Changed#

  • Fixes bug in _write_spark_df() method in the BaseTable class that caused writing larger dataframes to fail.

  • Parallelizes convert_single_timeseries() when a directory is passed to the in_path argument.

  • Fixes doc string in generate_weights_file()

  • Switched to the built-in dropDuplicates() method in the BaseTable class to drop duplicates instead of using a custom implementation.

  • Added option to specify the number of partitions when writing dataframes in the BaseTable class.

  • Added the option to skip the dropDuplicates() method when writing dataframes in the BaseTable class.

0.4.10 - 2025-04-14#

Added#

  • Adds append and upsert functionality without duplicates to loading methods on tables:

    • locations

    • location crosswalk

    • location attributes

    • primary and secondary timeseries

  • Adds upsert argument to fetching methods (append is default).

  • Clears fetching cache before each call.

  • Adds ability to add or update the location id prefix during loading in above tables

  • Adds reference_time as a default partition for secondary and joined timeseries

  • Adds script to re-write timeseries tables partitioned on reference_time

  • Adds function to drop potential duplicates before writing tables (_drop_duplicates())

  • Combines the script to calculate pixel weights per polygon (generate_weights.py) with the NWM gridded Evaluation fetching methods (retro and operational).

    • This allows users to optionally generate the weights from within the fetching methods or to use a pre-created weights file.

    • When run from the Evaluation, the weights file is saved to the evaluation cache and corresponds to ids in the locations table.

  • Adds User Guide notebook for NWM gridded fetching

  • Adds transform functions to metric calculations

  • Adds geoviews dependency to poetry evaluation

  • Adds aws cli and datashader to the TEEHR-HUB docker image

  • Removes duckdb for teehr env

0.4.9 - 2025-03-26#

Added#

  • Adds pandera schema for the weights file and validates weights dataframe on read and write, coercing values into schema data types

  • Adds starting_z_hour and ending_z_hour arguments to operational NWM fetching methods (point, gridded)

  • Adds function to drop NaN values (from value field) when fetching NWM and USGS data

  • Adds a check so that if schema validation fails, the current file is skipped and fetching continues

  • Adds versions 1.2 and 2.0 to operational NWM fetching (version 2.2 (nwm22) is allowed to be used with a note that it is no different from 2.1)

  • Adds a test notebook for testing on remote teehr-hub kernel

  • Adds wrapper functions for deterministic and signature metrics

Changed#

  • Fixes doc strings for fetch.nwm_retrospective_grids()

  • Removes add_configuration_name in fetching and automatically adds if it doesn’t exist

  • Updates dask version

  • Fixes a bug in parsing the z_hour and day from the remote json paths when an ensemble configuration is selected

  • Removes the imports in __init__.py that were for documentation purposes

  • Removes hydrotools as a dependency

  • Updates API documentation, adding evaluation.metrics.Metrics methods

  • Changes base docker image to base-notebook:2025.01.24

0.4.8 - 2025-02-17#

Added#

  • Adds box zoom to location plots.

  • Adds User Guide page for fetching NWM point data.

  • Adds new row level calculated fields, DayOfYear, ThresholdValueExceeded, ForecastLeadTime.

Changed#

  • Changes NWM fetching methods from nwm_forecast_<xxxx> to nwm_operational_<xxxx>.

  • Set use_table_schema to False when cloning the joined_timeseries table from s3, so that extra fields will not be dropped. Note, this will raise an error if the table is empty or does not exist.

  • Made auto-adding of configuration_name in NWM and USGS fetching optional.

  • Removed 2 evaluations from s3 (HEFS, NWM fetching), using TEEHR data module instead.

0.4.7 - 2025-01-08#

Added#

  • Adds RowLevelCalculatedFields and TimeseriesAwareCalculatedFields which are hopefully descriptive enough names.

  • Adds a User Guide page to describe what they are and how to use them.

  • Adds hvplot dependency to poetry

  • Adds add_calculated_fields() methods to joined_timeseries and metrics “tables”

  • Adds the Continuous Rank Probability Score CRPS ensemble metric using the scoringrules package

  • Adds a script to create an example ensemble evaluation using data in the test directory

  • Adds an example notebook to demo CRPS metric query

  • Adds user guide notebook page for ensembles, reading a test ensemble evaluation from S3

  • Adds ability to unpack metric dictionary results into separate columns (ie, bootstrap quantiles)

Changed#

  • Splits metric models and functions into three categories: Deterministic, Probabilistic, Signature. This is a breaking change requiring import of specific metric classes (Deterministic, Probabilistic, Signature) rather than just Metrics.

    • Functions are moved to separate modules

    • Models are moved to separate classes

    • Basemodels and metric enums are moved to a separate basemodel module

  • Updates API docs, removes unused files and the autoapi directory.

0.4.6 - 2024-12-17#

Added#

  • Adds add_missing_columns to the _validate method in the BaseTable class to allow for adding missing columns to the schema.

  • When upgrading from 0.4.4 or earlier, you may need to run the following to add the missing columns to the secondary_timeseries if you have existing datasets:

sdf = ev.secondary_timeseries.to_sdf()
validated_sdf = ev.secondary_timeseries._validate(sdf, add_missing_columns=True)
ev.secondary_timeseries._write_spark_df(validated_sdf)

Changed#

  • None

0.4.5 - 2024-12-09#

Added#

  • Fixes issues with sphinx docs and run the install_spark_jars.py script in the build container.

  • Adds location plotting to accessor.

  • Adds loading from FEWS XML files.

  • Adds member to secondary timeseries schema for ensembles.

Changed#

  • Fixes issues with sphinx docs and run the install_spark_jars.py script in the build container.

0.4.4 - 2024-12-02#

Added#

  • Added ability to read an Evaluation dataset directly from an S3 bucket.

  • When path to an Evaluation dataset is an S3 bucket, the Evaluation is read-only.

Changed#

  • Pretty significant refactor of the Table classes to make them more flexible and easier to use.

  • Added more robust Pandera validation to the Table classes.

  • Updated docs to reflect changes and added read_from_s3 example.

0.4.3 - 2024-10-19#

Added#

  • None

Changed#

  • Changed paths to the S3 bucket evaluations to reference “e*…” instead of “p*…” naming convention.

0.4.2 - 2024-10-18#

Added#

  • A test-build-publish workflow to push to PyPI

Changed#

  • None

0.4.1 - 2024-10-15#

Added#

  • Updated docs to include pages for grouping, filtering and Joining in the User Guide.

Changed#

  • Fixed some broken data download links in the User Guide.

  • Fixed the post-install script to install the AWS Spark Jars.

  • Fixed the API doc build.

0.4.0 - 2024-10-13#

Added#

  • This is a major (although still less that version 1) release that includes a number of new features and changes.

  • Some of the more significant changes: - Added a new Evaluation class that is the primary interface for working with TEEHR data. - Switched from DuckDB to PySpark to enable horizonal scaling for the computational workloads. - Formalized the structure of the TEEHR dataset. - Added data validation of values referenced from domain and location tables to the timeseries tables. - Updated docs to include new features and changes.

Changed#

  • Many changes have been made between v0.3.28 and v0.4.0.

0.3.28 - 2024-07-10#

Added#

  • pandas DataFrame accessor classes for metrics and timeseries queries, including some simple methods for plotting and summarizing data.

  • Added Bokeh as a dependency for visualization.

Changed#

  • None

0.3.27 - 2024-07-08#

Added#

  • Documentation updates primarly to Getting Started and User Guide sections.

Changed#

  • None

0.3.26 - 2024-06-27#

Added#

  • Dark theme logo for sphinx documentation.

  • Added the pickleshare package to dev dependency group to fix ipython directive in sphinx documentation.

Changed#

  • Pinned sphinx-autodoc to v3.0.0 and numpy to v1.26.4 in documentation-publish.yml to fix the API documentation build.

  • Removed unused documentation dependencies from dev group.

0.3.25 - 2024-06-06#

Added#

  • Added PySpark to TEEHR-HUB (including openjdk-17-jdk and jar files)

Changed#

  • None

0.3.24 - 2024-05-29#

Added#

  • Added metrics documentation to the Sphinx documentation.

Changed#

  • None

0.3.23 - 2024-05-28#

Added#

  • None

Changed#

  • Docstring updates in duckdb_database.py.

  • Changelog update for 0.3.22.

  • Updates insert_attributes() in duckdb_database.py to better handle None/Null attribute units.

  • Test updates in convert.py.

0.3.22 - 2024-05-22#

Added#

  • None

Changed#

  • Cleaned up the DuckDB* classes. Don’t think any public interfaces changed.

  • Import of DuckDBDatabase, DuckDBDatabaseAPI, and DuckDBJoinedParquet now use from teehr.classes import DuckDBDatabase, DuckDBDatabaseAPI, DuckDBJoinedParquet

  • the calculate_field` method was renamed to insert_calculated_field`

0.3.21 - 2024-05-21#

Added#

  • Added the DuckDBJoinedParquet class for metric queries on pre-joined parquet files.

  • Added the DuckDBBase class for common methods between the DuckDBDatabase, DuckDBAPI, and DuckDBJoinedParquet classes.

Changed#

  • Renamed the database directory to classes.

  • Renamed the teehr_dataset.py to teehr_duckdb.py.

  • Renamed the TEEHRDatasetDB and TEEHRDatasetAPI classes to DuckDBDatabase and DuckDBAPI respectively.

  • Removed lead_time and absolute_value from joined table

0.3.20 - 2024-05-18#

Added#

  • None

Changed#

  • Update queries to accept a list of paths for example, primary_filepath and secondary_filepath Includes get_metrics(), get_joined_timeseries(), get_timeseries(), and get_timeseries_chars()

0.3.19 - 2024-05-18#

Added#

  • None

Changed#

  • Update SQL queries to allow reference_time to be NULL.

  • Updated tests for NULL reference_time

0.3.18 - 2024-05-10#

Added#

  • Added documentation regarding best practices for specifying the chunk_by parameter when fetching NWM retrospective and USGS data.

Changed#

  • Fixed a bug in the NWM retrospective grid loading weighted average calculation.

  • Changed the method of fetching NWM gridded data to read only a subset of the grid (given by the row/col bounds from the weights file) into memory rather than the entire grid.

  • Removed ‘day’ and ‘location_id’ chunk_by options to reduce redundant data transfer costs.

0.3.17 - 2024-04-22#

Added#

  • None

Changed#

  • Dropped “Z” from the file name in the NWM loading functions, adding a note in the docstrings that all times are in UTC.

  • Changed data type of zonal_weights_filepath to Union[str, Path] in nwm_grids.py.

  • Fixed SettingWithCopyWarning in NWM grid loading.

  • Fixed the end_date in NWM retrospective loading to include the entirety of the last day and not fail when last available day is specfified.

  • Removed “elevation”, “gage_id”, “order” from NWM v3.0 retrospective point loading.

0.3.16 - 2024-04-11#

Added#

  • Adds a few new metrics to the queries: * annual_peak_relative_bias * spearman_correlation * kling_gupta_efficiency_mod1 * kling_gupta_efficiency_mod2

Changed#

  • None

0.3.15 - 2024-04-08#

Added#

  • location_id_prefix as an optional argument to generate_weights_file() to allow for the prefixing of the location ID with a string.

Changed#

  • Updated the NWM operational and retrospective grid loading functions so that the location ID as defined in the zonal weights file is used as the location ID in the output parquet files.

0.3.14 - 2024-03-29#

Added#

  • relative_bias

  • multiplicative_bias

  • mean_squared_error

  • mean_absolute_relative_error

  • pearson_correlation

  • r_squared

  • nash_sutcliffe_efficiency_normalized

Changed#

  • mean_error (rename current bias to mean_error)

  • mean_absolute_error (rename current mean_error to mean_absolute_error)

0.3.13 - 2024-03-22#

Added#

  • None

Changed#

  • Updated from Enum to StrEnum and added a fix for backwards incompatibility described here: https://tomwojcik.com/posts/2023-01-02/python-311-str-enum-breaking-change. This is required to support both python 3.10 and python 3.11.

  • Updated TEEHR-HUB to Python 3.11 and pangeo/pangeo-notebook:2024.03.13

  • Made all packages that use YYYY.MM.DD versioning >= instead of ^ in pyproject.toml

0.3.12 - 2024-03-22#

Added#

  • None

Changed#

  • Changed the chunking method for USGS and NWM retrospective data loading to iterate over pandas period_range rather than using groupby or date_range to fix a bug when fetching data over multiple years.

0.3.11 - 2024-03-19#

Added#

  • None

Changed#

  • Downgraded required Dask version to dask = “^2023.8.1” to match pangeo/pangeo-notebook:2023.09.11

0.3.10 - 2024-03-07#

Added#

  • Added test_zonal_mean_results.py

Changed#

  • Fixed the calculation of the zonal mean of pixel values in compute_zonal_mean() so it caculates the weighted average (divides by the sum of weight values).

  • Updated grid loading tests and data to reflect the fixed method.

0.3.9 - 2024-02-15#

Added#

  • Adds sphinx documentation framework and initial docs.

  • The documentation-publish.yml workflow is set to build the docs and push to github pages on every tag.

  • The pre-commit-config.yml github hook runs on each commit and checks docstring formatting, trailing whitespaces, and the presence of large files.

  • Added documenation-related python dependencies to [tool.poetry.group.dev.dependencies]

Changed#

  • Example notebooks have been moved to docs/sphinx/user_guide/notebooks.

  • The CHANGELOG.md is now the index.rst file in docs/sphinx/changelog.

  • The CONTRIBUTE.md and release_process.md files now part of the index.rst file in docs/sphinx/development.

  • The data_models.md and queries.md are now the data_models.rst and queries.rst files in docs/sphinx/getting_started.

0.3.8 - 2024-02-14#

Added#

  • Adds logging with a NullHandler() that can be implemented by the parent app using teehr.

0.3.7 - 2024-02-09#

Changed#

  • Upgraded pandas to ^2.2.0

  • Changed unit=”H” in pandas.time_delta to unit=”h”

  • Updated assert statements in test_weight_generation.py

0.3.6 - 2024-02-07#

Added#

  • Adds an exception to catch an error when a corrupted file is encountered while building the Kerchunk reference file using SingleHdf5ToZarr.

  • The behavior determining whether to raise an exception is controlled by the ignore_missing_file flag.

0.3.5 - 2023-12-18#

Added#

  • Adds additional chunking methods for USGS and NWM retrospective loading to allow week, month and year chunking.

  • Adds mean areal summaries for NWM retrospective gridded forcing variables

  • Adds NWM v3.0 to retrospective loading

Changed#

  • Fixes USGS loading to include last date of range

  • Removes extra fields from v2.1 retro output

0.3.4 - 2023-12-18#

Added#

  • Adds the read_only argument to the query method in the TEEHRDatasetDB class with default values specified in the query methods.

Changed#

  • Establishes a read-only database connection as a class variable to the TEEHRDatasetAPI class so it can be re-used for each class instance.

0.3.3 - 2023-12-13#

Added#

  • Adds get_joined_timeseries method to TEEHR Dataset classes.

Changed#

  • Updated validation fields in the TimeSeriesQuery pydantic model to accept only selected fields rather than existing database fields.

  • Updated function argument typing in queries/utils.py to be more explicit

0.3.2 - 2023-12-12#

Added#

  • None

Changed#

  • Fixed the bias metric so that it is sum(secondary_value - primary_value)/count(*) instead of sum(primary_value - secondary_value)/count(*) which resulted in the wrong sign.

  • Changed primary_max_value_time, secondary_max_value_time and max_value_timedelta queries to use built-in functions instead of CTEs. This improves speed significantly.

  • Fixed bug in queries when filtering by configuration, measurement_unit and variable.

  • Refactored join_attributes in TEEHRDatasetDB to better handle attributes with no units.

  • Refactored create_join_and_save_timeseries_query queries so that the de-duplication CTE is after the intial join CTE for improved performance.

  • Changes default list of order_by variables in insert_joined_timeseries to improve query performance

0.3.1 - 2023-12-08#

Added#

  • Adds a boolean flag to parquet-based metric query control whether or not to de-duplicate.

  • Adds a test primary timeseries file including duplicate values for testing.

Changed#

  • Refactored parquet-based get_metrics and get_joined_timeseries queries to that so that the de-duplication CTE is after the intial join CTE for improved performance.

0.3.0 - 2023-12-08#

Added#

  • Adds a dataclass and database that allows preprocessing of joined timeseries and attributes as well as the addition of user defined functions.

  • Adds an initial web service API that serves out timeseries and metrics along with some other supporting data.

  • Adds an initial interactive web application using the web service API.

Changed#

  • Switches to poetry to manage Python venv

  • Upgrades to Pydantic 2+

  • Upgrades to Pangeo image pangeo/pangeo-notebook:2023.09.11

0.2.9 - 2023-12-08#

Added#

  • Three options related to kerchunk jsons * local - (default) previous behavior, manually creates the jsons based on GCS netcdf files using Kerchunk’s SingleHdf5ToZarr. Any locally existing files will be used before creating new jsons from the remote store. * remote - use pre-created jsons, skipping any that do not exist within the specified time frame. Jsons are read directly from s3 using fsspec * auto - use pre-created jsons, creating any that do not exist within the specified time frame

  • Adds nwm_version (nwm22 or nwm30) and data_source (GCS, NOMADS, DSTOR - currently on GCS implemented) as loading arguments

Changed#

  • Combines loading modules into one directory loading/nwm

  • Updates to loading example notebooks

  • Updates to loading tests

0.2.8 - 2023-11-14#

Added#

  • NWM v3.0 data loading and configuration models

  • Added check for duplicate rows in get_metrics and get_joined_timeseries queries (#69)

  • Added control for overwrite file behavior in loading (#77)

  • Significant refactor of the loading libraries

  • Added ability to select which retrospective version to download (v2.0 or v2.1) (#80)

Changed#

  • Fixed NWM pydantic configurations models for v2.2

  • Refactored models/loading directory

0.2.7 - 2023-09-14#

Added#

  • More testing to NWM point and grid loading functions

0.2.6 - 2023-09-14#

Changed#

  • Fixed some sloppy bugs in nwm_grid_data.py

Added#

  • ValueError handling when encountering a corrupt zarr json file

0.2.5 - 2023-09-11#

Changed#

  • None

Added#

  • Added ability to use holoviz export to TEEHR-HUB:

    • Installed firefox (and a bunch of dependencies) to the Docker container (using apt)

    • Installed selenium and the geckodriver using conda

0.2.4 - 2023-08-30#

Changed#

  • Behavior of loading when encountering missing files

  • Renamed field zone to location_id in nwm_grid_data.py and generate_weights.py

Added#

  • The boolean flag ignore_missing_files to point and grid loading to determine whether to fail or continue on missing NWM files

  • Added a check to skip locally existing zarr json files when loading NWM data

0.2.3 - 2023-08-23#

Changed#

  • Removed pyarrow from time calculations in nwm_point_data.py loading due to windows bug

  • Updated output file name in nwm_point_data.py to include forecast hour if process_by_z_hour=False

0.2.2 - 2023-08-23#

Added#

  • nodejs to the jupyterhub build so the extensions will load (not 100% sure this was needed)

Changed#

  • Updated TEEHR to v0.2.2, including TEEHR-HUB

  • Updated the TEEHR-HUB baseimage to pangeo/pangeo-notebook:2023.07.05

0.2.1 - 2023-08-21#

Added#

  • Nothing

Changed#

  • Updated TEEHR version in TEEHR-HUB to v0.2.1

  • Converts nwm feature id’s to numpy array in loading

0.2.0 - 2023-08-17#

Added#

  • This changelog

Changed#

  • Loading directory refactor changed import paths to loading modules

  • Changed directory of generate_weights.py utility

  • Replaced NWM config parameter dictionary with pydantic models

  • NWM reference time used by TEEHR is now taken directly from the file name rather than the “reference time” embedded in the file

  • Use of the term run updated to configuration for NWM

0.1.3 - 2023-06-17#

Added#

  • Initial release

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