diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 00000000..5574bdb9
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,13 @@
+.github
+.ipynb_checkpoints
+.pytest_cache
+.vscode
+dashboards
+dist
+docs
+examples
+frontend
+playground
+study_template
+teehr-hub
+tests
diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml
index ee61ac5c..db5a27e8 100644
--- a/.github/workflows/docker-publish.yml
+++ b/.github/workflows/docker-publish.yml
@@ -54,7 +54,6 @@ jobs:
with:
cosign-release: 'v2.1.1' # optional
-
# Workaround: https://github.com/docker/build-push-action/issues/461
- name: Setup Docker buildx
uses: docker/setup-buildx-action@79abd3f86f79a9d68a23c75a09a9a85889262adf
diff --git a/.gitignore b/.gitignore
index 9d476b0f..63b72c89 100644
--- a/.gitignore
+++ b/.gitignore
@@ -129,4 +129,4 @@ dmypy.json
.pyre/
# Tests output
-temp/
\ No newline at end of file
+temp/
diff --git a/CHANGELOG.md b/CHANGELOG.md
index fdbc029c..c3780bfc 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,19 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+## [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
diff --git a/Dockerfile b/Dockerfile
index 0fd446d2..bd0ad2a8 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -13,13 +13,14 @@ RUN TEEHR_VERSION=$(cat /teehr/version.txt) && \
# Install TEEHR in the Pangeo Image
# https://hub.docker.com/r/pangeo/pangeo-notebook/tags
-FROM pangeo/pangeo-notebook:2023.07.05
+# Subsequent images use python=3.11
+FROM pangeo/pangeo-notebook:2023.09.11
USER root
ENV DEBIAN_FRONTEND=noninteractive
ENV PATH ${NB_PYTHON_PREFIX}/bin:$PATH
-# Needed for apt-key to work
+# Needed for apt-key to work -- Is this part needed?
RUN apt-get update -qq --yes > /dev/null && \
apt-get install --yes -qq gnupg2 > /dev/null && \
rm -rf /var/lib/apt/lists/*
diff --git a/README.md b/README.md
index 0496bea6..ea5dbe79 100644
--- a/README.md
+++ b/README.md
@@ -14,29 +14,32 @@ assess their skill and performance.
NOTE: THIS PROJECT IS UNDER DEVELOPMENT - EXPECT TO FIND BROKEN AND INCOMPLETE CODE.
## How to Install TEEHR
-Install with from source
-
+Install poetry
+```bash
+$ pipx install poetry
+```
+Install from source
```bash
# Create and activate python environment, requires python >= 3.10
-$ python3 -m venv .venv
-$ source .venv/bin/activate
-$ python3 -m pip install --upgrade pip
-
-# Build and install from source
-$ python3 -m pip install --upgrade build
-$ python -m build
-$ python -m pip install dist/teehr-0.2.9.tar.gz
+$ poetry shell
+
+# Install from source
+$ poetry install
```
Install from GitHub
```bash
+# Using pip
$ pip install 'teehr @ git+https://github.com/RTIInternational/teehr@[BRANCH_TAG]'
+
+# Using poetry
+$ poetry add git+https://github.com/RTIInternational/teehr.git#[BRANCH TAG]
```
Use Docker
```bash
-$ docker build -t teehr:v0.2.9 .
-$ docker run -it --rm --volume $HOME:$HOME -p 8888:8888 teehr:v0.2.9 jupyter lab --ip 0.0.0.0 $HOME
+$ docker build -t teehr:v0.3.0 .
+$ docker run -it --rm --volume $HOME:$HOME -p 8888:8888 teehr:v0.3.0 jupyter lab --ip 0.0.0.0 $HOME
```
## Examples
diff --git a/docs/release_process.md b/docs/release_process.md
index e16c68a3..4059a866 100644
--- a/docs/release_process.md
+++ b/docs/release_process.md
@@ -4,6 +4,7 @@ This document describes the release process which has some manual steps to compl
Create branch with the following updated to the new version (find and replace version number):
- `version.txt`
- `README.md`
+- `pyproject.toml`
Update the `CHANGELOG.md` to reflect the changes included in the release.
diff --git a/examples/loading/create_database.py b/examples/loading/create_database.py
new file mode 100644
index 00000000..7d60b2d3
--- /dev/null
+++ b/examples/loading/create_database.py
@@ -0,0 +1,221 @@
+"""
+This script provides and example of how to create a TEEHR datyabase
+and insert joined timeseries, append attributes, and add user
+defined fields.
+"""
+from pathlib import Path
+from teehr.database.teehr_dataset import TEEHRDatasetDB
+import time
+import datetime
+
+
+TEST_STUDY_DIR = Path("/home/matt/temp/huc1802_retro")
+PRIMARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "usgs.parquet")
+SECONDARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "nwm2*.parquet")
+CROSSWALK_FILEPATH = Path(TEST_STUDY_DIR, "geo", "usgs_nwm2*_crosswalk.parquet") # noqa
+ATTRIBUTES_FILEPATH = Path(TEST_STUDY_DIR, "geo", "usgs_attr_*.parquet")
+GEOMETRY_FILEPATH = Path(TEST_STUDY_DIR, "geo", "usgs_geometry.parquet")
+DATABASE_FILEPATH = Path(TEST_STUDY_DIR, "huc1802_retro.db")
+
+# Test data
+# TEST_STUDY_DIR = Path("tests/data/test_study")
+# PRIMARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "test_short_obs.parquet") # noqa
+# SECONDARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "test_short_fcast.parquet") # noqa
+# CROSSWALK_FILEPATH = Path(TEST_STUDY_DIR, "geo", "crosswalk.parquet")
+# ATTRIBUTES_FILEPATH = Path(TEST_STUDY_DIR, "geo", "test_attr2.parquet")
+# GEOMETRY_FILEPATH = Path(TEST_STUDY_DIR, "geo", "gages.parquet")
+# DATABASE_FILEPATH = Path(TEST_STUDY_DIR, "temp_test.db")
+
+
+def describe_inputs():
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Check the parquet files and report some stats to the user (WIP)
+ df = tds.describe_inputs(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH
+ )
+
+ print(df)
+
+
+def create_db_add_timeseries():
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ # NOTE: Right now this will re-join and overwrite
+ print("Creating joined table")
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH
+ )
+ tds.insert_geometry(geometry_filepath=GEOMETRY_FILEPATH)
+
+
+def add_attributes():
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Join (one or more?) table(s) of attributes to the timeseries table
+ print("Adding attributes")
+ tds.join_attributes(ATTRIBUTES_FILEPATH)
+
+
+def add_fields():
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Calculate and add a field based on some user-defined function (UDF).
+ def test_user_function(arg1: float, arg2: str) -> float:
+ """Function arguments are fields in joined_timeseries, and
+ should have the same data type.
+ Note: In the data model, attribute values are always str type"""
+ return float(arg1) / float(arg2)
+
+ parameter_names = ["primary_value", "upstream_area_km2"]
+ new_field_name = "primary_normalized_discharge"
+ new_field_type = "FLOAT"
+ tds.calculate_field(new_field_name=new_field_name,
+ new_field_type=new_field_type,
+ parameter_names=parameter_names,
+ user_defined_function=test_user_function)
+
+ # Calculate and add a field based on some user-defined function (UDF).
+ def add_month_field(arg1: datetime.datetime) -> int:
+ """Function arguments are fields in joined_timeseries, and
+ should have the same data type.
+ Note: In the data model, attribute values are always str type"""
+ return arg1.month
+
+ parameter_names = ["value_time"]
+ new_field_name = "month"
+ new_field_type = "INTEGER"
+ tds.calculate_field(new_field_name=new_field_name,
+ new_field_type=new_field_type,
+ parameter_names=parameter_names,
+ user_defined_function=add_month_field)
+
+ # Calculate and add a field based on some user-defined function (UDF).
+ def exceed_2yr_recurrence(arg1: float, arg2: float) -> bool:
+ """Function arguments are fields in joined_timeseries, and
+ should have the same data type.
+ Note: In the data model, attribute values are always str type"""
+ return float(arg1) > float(arg2)
+
+ parameter_names = ["primary_value", "retro_2yr_recurrence_flow_cms"]
+ new_field_name = "exceed_2yr_recurrence"
+ new_field_type = "BOOLEAN"
+ tds.calculate_field(new_field_name=new_field_name,
+ new_field_type=new_field_type,
+ parameter_names=parameter_names,
+ user_defined_function=exceed_2yr_recurrence)
+ pass
+
+
+def run_metrics_query():
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+ # schema_df = tds.get_joined_timeseries_schema()
+ # print(schema_df[["column_name", "column_type"]])
+
+ # Get metrics
+ group_by = ["primary_location_id", "configuration"]
+ order_by = ["primary_location_id"]
+ include_metrics = ["mean_error", "bias"]
+ filters = [
+ # {
+ # "column": "primary_location_id",
+ # "operator": "=",
+ # "value": "usgs-11337080"
+ # },
+ # {
+ # "column": "month",
+ # "operator": "=",
+ # "value": 1
+ # },
+ # {
+ # "column": "upstream_area_km2",
+ # "operator": ">",
+ # "value": 1000
+ # },
+ # {
+ # "column": "exceed_2yr_recurrence",
+ # "operator": "=",
+ # "value": True
+ # }
+ ]
+
+ t1 = time.time()
+ df1 = tds.get_metrics(
+ group_by=group_by,
+ order_by=order_by,
+ filters=filters,
+ include_metrics=include_metrics,
+ include_geometry=True,
+ # return_query=True
+ )
+ print(df1)
+ print(f"Database query: {(time.time() - t1):.2f} secs")
+
+ pass
+
+
+def describe_database():
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+ df = tds.get_joined_timeseries_schema()
+ print(df)
+
+
+def run_raw_query():
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+ query = """
+ WITH joined as (
+ SELECT
+ *
+ FROM joined_timeseries
+ )
+ , metrics AS (
+ SELECT
+ joined.primary_location_id,joined.configuration
+ , sum(primary_value - secondary_value)/count(*) as bias
+ , sum(absolute_difference)/count(*) as mean_error
+ FROM
+ joined
+ GROUP BY
+ joined.primary_location_id,joined.configuration
+ )
+ SELECT
+ metrics.*
+ ,gf.geometry as geometry
+ FROM metrics
+ JOIN geometry gf
+ on primary_location_id = gf.id
+ ORDER BY
+ metrics.primary_location_id
+ ;
+ ;"""
+ # query = f"""
+ # COPY (
+ # SELECT * FROM joined_timeseries
+ # )
+ # TO '{str(Path(TEST_STUDY_DIR, "huc1802_retro.parquet"))}' (
+ # FORMAT 'parquet', COMPRESSION 'ZSTD', ROW_GROUP_SIZE 100000
+ # )
+ # ;"""
+ df = tds.query(query, format="df")
+ print(df)
+
+
+if __name__ == "__main__":
+ # create_db_add_timeseries()
+ # describe_inputs()
+ # describe_database()
+ # add_attributes()
+ # describe_database()
+ # add_fields()
+ # describe_database()
+ # run_metrics_query()
+ # run_raw_query()
+ pass
diff --git a/frontend/teehr/.eslintrc.cjs b/frontend/teehr/.eslintrc.cjs
new file mode 100644
index 00000000..4dcb4390
--- /dev/null
+++ b/frontend/teehr/.eslintrc.cjs
@@ -0,0 +1,20 @@
+module.exports = {
+ root: true,
+ env: { browser: true, es2020: true },
+ extends: [
+ 'eslint:recommended',
+ 'plugin:react/recommended',
+ 'plugin:react/jsx-runtime',
+ 'plugin:react-hooks/recommended',
+ ],
+ ignorePatterns: ['dist', '.eslintrc.cjs'],
+ parserOptions: { ecmaVersion: 'latest', sourceType: 'module' },
+ settings: { react: { version: '18.2' } },
+ plugins: ['react-refresh'],
+ rules: {
+ 'react-refresh/only-export-components': [
+ 'warn',
+ { allowConstantExport: true },
+ ],
+ },
+}
diff --git a/frontend/teehr/.gitignore b/frontend/teehr/.gitignore
index c96a04f0..a547bf36 100644
--- a/frontend/teehr/.gitignore
+++ b/frontend/teehr/.gitignore
@@ -1,2 +1,24 @@
-*
-!.gitignore
\ No newline at end of file
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
diff --git a/frontend/teehr/README.md b/frontend/teehr/README.md
new file mode 100644
index 00000000..f768e33f
--- /dev/null
+++ b/frontend/teehr/README.md
@@ -0,0 +1,8 @@
+# React + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
diff --git a/frontend/teehr/index.html b/frontend/teehr/index.html
new file mode 100644
index 00000000..ff47c1e8
--- /dev/null
+++ b/frontend/teehr/index.html
@@ -0,0 +1,19 @@
+
+
+
+
+
+
+
+
+ Vite + React
+
+
+
+
+
+
diff --git a/frontend/teehr/package-lock.json b/frontend/teehr/package-lock.json
new file mode 100644
index 00000000..8ac8cb95
--- /dev/null
+++ b/frontend/teehr/package-lock.json
@@ -0,0 +1,6758 @@
+{
+ "name": "teehr",
+ "version": "0.0.0",
+ "lockfileVersion": 3,
+ "requires": true,
+ "packages": {
+ "": {
+ "name": "teehr",
+ "version": "0.0.0",
+ "dependencies": {
+ "@emotion/react": "^11.11.1",
+ "@emotion/styled": "^11.11.0",
+ "@fontsource/roboto": "^5.0.8",
+ "@mui/icons-material": "^5.14.13",
+ "@mui/material": "^5.14.13",
+ "@mui/styled-engine-sc": "^6.0.0-alpha.1",
+ "@mui/x-data-grid": "^6.16.2",
+ "@mui/x-date-pickers": "^6.18.1",
+ "axios": "^1.5.1",
+ "colormap": "^2.3.2",
+ "leaflet": "^1.9.4",
+ "luxon": "^3.4.4",
+ "ol": "^8.1.0",
+ "react": "^18.2.0",
+ "react-dom": "^18.2.0",
+ "react-leaflet": "^4.2.1",
+ "react-router-dom": "^6.17.0",
+ "styled-components": "^6.0.8"
+ },
+ "devDependencies": {
+ "@types/react": "^18.2.15",
+ "@types/react-dom": "^18.2.7",
+ "@vitejs/plugin-react": "^4.0.3",
+ "eslint": "^8.45.0",
+ "eslint-plugin-react": "^7.32.2",
+ "eslint-plugin-react-hooks": "^4.6.0",
+ "eslint-plugin-react-refresh": "^0.4.3",
+ "vite": "^4.4.5"
+ }
+ },
+ "node_modules/@aashutoshrathi/word-wrap": {
+ "version": "1.2.6",
+ "resolved": "https://registry.npmjs.org/@aashutoshrathi/word-wrap/-/word-wrap-1.2.6.tgz",
+ "integrity": "sha512-1Yjs2SvM8TflER/OD3cOjhWWOZb58A2t7wpE2S9XfBYTiIl+XFhQG2bjy4Pu1I+EAlCNUzRDYDdFwFYUKvXcIA==",
+ "dev": true,
+ "engines": {
+ "node": ">=0.10.0"
+ }
+ },
+ "node_modules/@ampproject/remapping": {
+ "version": "2.2.1",
+ "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.2.1.tgz",
+ "integrity": "sha512-lFMjJTrFL3j7L9yBxwYfCq2k6qqwHyzuUl/XBnif78PWTJYyL/dfowQHWE3sp6U6ZzqWiiIZnpTMO96zhkjwtg==",
+ "dependencies": {
+ "@jridgewell/gen-mapping": "^0.3.0",
+ "@jridgewell/trace-mapping": "^0.3.9"
+ },
+ "engines": {
+ "node": ">=6.0.0"
+ }
+ },
+ "node_modules/@babel/cli": {
+ "version": "7.23.0",
+ "resolved": "https://registry.npmjs.org/@babel/cli/-/cli-7.23.0.tgz",
+ "integrity": "sha512-17E1oSkGk2IwNILM4jtfAvgjt+ohmpfBky8aLerUfYZhiPNg7ca+CRCxZn8QDxwNhV/upsc2VHBCqGFIR+iBfA==",
+ "dependencies": {
+ "@jridgewell/trace-mapping": "^0.3.17",
+ "commander": "^4.0.1",
+ "convert-source-map": "^2.0.0",
+ "fs-readdir-recursive": "^1.1.0",
+ "glob": "^7.2.0",
+ "make-dir": "^2.1.0",
+ "slash": "^2.0.0"
+ },
+ "bin": {
+ "babel": "bin/babel.js",
+ "babel-external-helpers": "bin/babel-external-helpers.js"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ },
+ "optionalDependencies": {
+ "@nicolo-ribaudo/chokidar-2": "2.1.8-no-fsevents.3",
+ "chokidar": "^3.4.0"
+ },
+ "peerDependencies": {
+ "@babel/core": "^7.0.0-0"
+ }
+ },
+ "node_modules/@babel/code-frame": {
+ "version": "7.22.13",
+ "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.13.tgz",
+ "integrity": "sha512-XktuhWlJ5g+3TJXc5upd9Ks1HutSArik6jf2eAjYFyIOf4ej3RN+184cZbzDvbPnuTJIUhPKKJE3cIsYTiAT3w==",
+ "dependencies": {
+ "@babel/highlight": "^7.22.13",
+ "chalk": "^2.4.2"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/compat-data": {
+ "version": "7.23.2",
+ "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.23.2.tgz",
+ "integrity": "sha512-0S9TQMmDHlqAZ2ITT95irXKfxN9bncq8ZCoJhun3nHL/lLUxd2NKBJYoNGWH7S0hz6fRQwWlAWn/ILM0C70KZQ==",
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/core": {
+ "version": "7.23.0",
+ "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.23.0.tgz",
+ "integrity": "sha512-97z/ju/Jy1rZmDxybphrBuI+jtJjFVoz7Mr9yUQVVVi+DNZE333uFQeMOqcCIy1x3WYBIbWftUSLmbNXNT7qFQ==",
+ "dependencies": {
+ "@ampproject/remapping": "^2.2.0",
+ "@babel/code-frame": "^7.22.13",
+ "@babel/generator": "^7.23.0",
+ "@babel/helper-compilation-targets": "^7.22.15",
+ "@babel/helper-module-transforms": "^7.23.0",
+ "@babel/helpers": "^7.23.0",
+ "@babel/parser": "^7.23.0",
+ "@babel/template": "^7.22.15",
+ "@babel/traverse": "^7.23.0",
+ "@babel/types": "^7.23.0",
+ "convert-source-map": "^2.0.0",
+ "debug": "^4.1.0",
+ "gensync": "^1.0.0-beta.2",
+ "json5": "^2.2.3",
+ "semver": "^6.3.1"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ },
+ "funding": {
+ "type": "opencollective",
+ "url": "https://opencollective.com/babel"
+ }
+ },
+ "node_modules/@babel/generator": {
+ "version": "7.23.0",
+ "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.23.0.tgz",
+ "integrity": "sha512-lN85QRR+5IbYrMWM6Y4pE/noaQtg4pNiqeNGX60eqOfo6gtEj6uw/JagelB8vVztSd7R6M5n1+PQkDbHbBRU4g==",
+ "dependencies": {
+ "@babel/types": "^7.23.0",
+ "@jridgewell/gen-mapping": "^0.3.2",
+ "@jridgewell/trace-mapping": "^0.3.17",
+ "jsesc": "^2.5.1"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/helper-annotate-as-pure": {
+ "version": "7.22.5",
+ "resolved": "https://registry.npmjs.org/@babel/helper-annotate-as-pure/-/helper-annotate-as-pure-7.22.5.tgz",
+ "integrity": "sha512-LvBTxu8bQSQkcyKOU+a1btnNFQ1dMAd0R6PyW3arXes06F6QLWLIrd681bxRPIXlrMGR3XYnW9JyML7dP3qgxg==",
+ "dependencies": {
+ "@babel/types": "^7.22.5"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/helper-builder-binary-assignment-operator-visitor": {
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+ "integrity": "sha512-ksNZJlkcU9b0lBwAGZGGaZHCMqHsc8OpgtoYhsQ4/I2v5cnpmmmqe5pM4nv/4Hn6G/2GhTdj0DhZh2e+Er1q5A==",
+ "dev": true,
+ "dependencies": {
+ "esbuild": "^0.18.10",
+ "postcss": "^8.4.27",
+ "rollup": "^3.27.1"
+ },
+ "bin": {
+ "vite": "bin/vite.js"
+ },
+ "engines": {
+ "node": "^14.18.0 || >=16.0.0"
+ },
+ "funding": {
+ "url": "https://github.com/vitejs/vite?sponsor=1"
+ },
+ "optionalDependencies": {
+ "fsevents": "~2.3.2"
+ },
+ "peerDependencies": {
+ "@types/node": ">= 14",
+ "less": "*",
+ "lightningcss": "^1.21.0",
+ "sass": "*",
+ "stylus": "*",
+ "sugarss": "*",
+ "terser": "^5.4.0"
+ },
+ "peerDependenciesMeta": {
+ "@types/node": {
+ "optional": true
+ },
+ "less": {
+ "optional": true
+ },
+ "lightningcss": {
+ "optional": true
+ },
+ "sass": {
+ "optional": true
+ },
+ "stylus": {
+ "optional": true
+ },
+ "sugarss": {
+ "optional": true
+ },
+ "terser": {
+ "optional": true
+ }
+ }
+ },
+ "node_modules/web-worker": {
+ "version": "1.2.0",
+ "resolved": "https://registry.npmjs.org/web-worker/-/web-worker-1.2.0.tgz",
+ "integrity": "sha512-PgF341avzqyx60neE9DD+XS26MMNMoUQRz9NOZwW32nPQrF6p77f1htcnjBSEV8BGMKZ16choqUG4hyI0Hx7mA=="
+ },
+ "node_modules/which": {
+ "version": "2.0.2",
+ "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
+ "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
+ "dev": true,
+ "dependencies": {
+ "isexe": "^2.0.0"
+ },
+ "bin": {
+ "node-which": "bin/node-which"
+ },
+ "engines": {
+ "node": ">= 8"
+ }
+ },
+ "node_modules/which-boxed-primitive": {
+ "version": "1.0.2",
+ "resolved": "https://registry.npmjs.org/which-boxed-primitive/-/which-boxed-primitive-1.0.2.tgz",
+ "integrity": "sha512-bwZdv0AKLpplFY2KZRX6TvyuN7ojjr7lwkg6ml0roIy9YeuSr7JS372qlNW18UQYzgYK9ziGcerWqZOmEn9VNg==",
+ "dev": true,
+ "dependencies": {
+ "is-bigint": "^1.0.1",
+ "is-boolean-object": "^1.1.0",
+ "is-number-object": "^1.0.4",
+ "is-string": "^1.0.5",
+ "is-symbol": "^1.0.3"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/ljharb"
+ }
+ },
+ "node_modules/which-builtin-type": {
+ "version": "1.1.3",
+ "resolved": "https://registry.npmjs.org/which-builtin-type/-/which-builtin-type-1.1.3.tgz",
+ "integrity": "sha512-YmjsSMDBYsM1CaFiayOVT06+KJeXf0o5M/CAd4o1lTadFAtacTUM49zoYxr/oroopFDfhvN6iEcBxUyc3gvKmw==",
+ "dev": true,
+ "dependencies": {
+ "function.prototype.name": "^1.1.5",
+ "has-tostringtag": "^1.0.0",
+ "is-async-function": "^2.0.0",
+ "is-date-object": "^1.0.5",
+ "is-finalizationregistry": "^1.0.2",
+ "is-generator-function": "^1.0.10",
+ "is-regex": "^1.1.4",
+ "is-weakref": "^1.0.2",
+ "isarray": "^2.0.5",
+ "which-boxed-primitive": "^1.0.2",
+ "which-collection": "^1.0.1",
+ "which-typed-array": "^1.1.9"
+ },
+ "engines": {
+ "node": ">= 0.4"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/ljharb"
+ }
+ },
+ "node_modules/which-collection": {
+ "version": "1.0.1",
+ "resolved": "https://registry.npmjs.org/which-collection/-/which-collection-1.0.1.tgz",
+ "integrity": "sha512-W8xeTUwaln8i3K/cY1nGXzdnVZlidBcagyNFtBdD5kxnb4TvGKR7FfSIS3mYpwWS1QUCutfKz8IY8RjftB0+1A==",
+ "dev": true,
+ "dependencies": {
+ "is-map": "^2.0.1",
+ "is-set": "^2.0.1",
+ "is-weakmap": "^2.0.1",
+ "is-weakset": "^2.0.1"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/ljharb"
+ }
+ },
+ "node_modules/which-typed-array": {
+ "version": "1.1.11",
+ "resolved": "https://registry.npmjs.org/which-typed-array/-/which-typed-array-1.1.11.tgz",
+ "integrity": "sha512-qe9UWWpkeG5yzZ0tNYxDmd7vo58HDBc39mZ0xWWpolAGADdFOzkfamWLDxkOWcvHQKVmdTyQdLD4NOfjLWTKew==",
+ "dev": true,
+ "dependencies": {
+ "available-typed-arrays": "^1.0.5",
+ "call-bind": "^1.0.2",
+ "for-each": "^0.3.3",
+ "gopd": "^1.0.1",
+ "has-tostringtag": "^1.0.0"
+ },
+ "engines": {
+ "node": ">= 0.4"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/ljharb"
+ }
+ },
+ "node_modules/wrappy": {
+ "version": "1.0.2",
+ "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
+ "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="
+ },
+ "node_modules/xml-utils": {
+ "version": "1.7.0",
+ "resolved": "https://registry.npmjs.org/xml-utils/-/xml-utils-1.7.0.tgz",
+ "integrity": "sha512-bWB489+RQQclC7A9OW8e5BzbT8Tu//jtAOvkYwewFr+Q9T9KDGvfzC1lp0pYPEQPEoPQLDkmxkepSC/2gIAZGw=="
+ },
+ "node_modules/yallist": {
+ "version": "3.1.1",
+ "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
+ "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g=="
+ },
+ "node_modules/yaml": {
+ "version": "1.10.2",
+ "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz",
+ "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
+ "engines": {
+ "node": ">= 6"
+ }
+ },
+ "node_modules/yocto-queue": {
+ "version": "0.1.0",
+ "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+ "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+ "dev": true,
+ "engines": {
+ "node": ">=10"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/sindresorhus"
+ }
+ }
+ }
+}
diff --git a/frontend/teehr/package.json b/frontend/teehr/package.json
new file mode 100644
index 00000000..30233fba
--- /dev/null
+++ b/frontend/teehr/package.json
@@ -0,0 +1,42 @@
+{
+ "name": "teehr",
+ "private": true,
+ "version": "0.0.0",
+ "type": "module",
+ "scripts": {
+ "dev": "vite",
+ "build": "vite build",
+ "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0",
+ "preview": "vite preview"
+ },
+ "dependencies": {
+ "@emotion/react": "^11.11.1",
+ "@emotion/styled": "^11.11.0",
+ "@fontsource/roboto": "^5.0.8",
+ "@mui/icons-material": "^5.14.13",
+ "@mui/material": "^5.14.13",
+ "@mui/styled-engine-sc": "^6.0.0-alpha.1",
+ "@mui/x-data-grid": "^6.16.2",
+ "@mui/x-date-pickers": "^6.18.1",
+ "axios": "^1.5.1",
+ "colormap": "^2.3.2",
+ "leaflet": "^1.9.4",
+ "luxon": "^3.4.4",
+ "ol": "^8.1.0",
+ "react": "^18.2.0",
+ "react-dom": "^18.2.0",
+ "react-leaflet": "^4.2.1",
+ "react-router-dom": "^6.17.0",
+ "styled-components": "^6.0.8"
+ },
+ "devDependencies": {
+ "@types/react": "^18.2.15",
+ "@types/react-dom": "^18.2.7",
+ "@vitejs/plugin-react": "^4.0.3",
+ "eslint": "^8.45.0",
+ "eslint-plugin-react": "^7.32.2",
+ "eslint-plugin-react-hooks": "^4.6.0",
+ "eslint-plugin-react-refresh": "^0.4.3",
+ "vite": "^4.4.5"
+ }
+}
diff --git a/frontend/teehr/public/vite.svg b/frontend/teehr/public/vite.svg
new file mode 100644
index 00000000..e7b8dfb1
--- /dev/null
+++ b/frontend/teehr/public/vite.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/frontend/teehr/src/App.css b/frontend/teehr/src/App.css
new file mode 100644
index 00000000..e9d7747d
--- /dev/null
+++ b/frontend/teehr/src/App.css
@@ -0,0 +1,30 @@
+
+#root {
+ max-width: 1280px;
+ margin: 0 auto;
+ padding: 2rem;
+ text-align: center;
+}
+
+.leaflet-container {
+ height: calc(100vh - 4rem);
+ width: 100%;
+}
+
+.map-container {
+ height: calc(100vh - 4rem);
+ width: 100%;
+}
+
+.clicked-coord-label{
+ position: absolute;
+ right: 0;
+ bottom: 0;
+ background: white;
+ border-radius: 5px;
+}
+
+.clicked-coord-label p {
+ margin: 10px;
+}
+
diff --git a/frontend/teehr/src/App.jsx b/frontend/teehr/src/App.jsx
new file mode 100644
index 00000000..2c8c83a1
--- /dev/null
+++ b/frontend/teehr/src/App.jsx
@@ -0,0 +1,8 @@
+import { RouterProvider } from "react-router-dom";
+import router from "./Router";
+
+function App() {
+ return ;
+}
+
+export default App;
diff --git a/frontend/teehr/src/Context.js b/frontend/teehr/src/Context.js
new file mode 100644
index 00000000..42e53b3e
--- /dev/null
+++ b/frontend/teehr/src/Context.js
@@ -0,0 +1,6 @@
+// DashboardContext.js
+import { createContext } from "react";
+
+const DashboardContext = createContext(null);
+
+export default DashboardContext;
diff --git a/frontend/teehr/src/Router.jsx b/frontend/teehr/src/Router.jsx
new file mode 100644
index 00000000..06f5b367
--- /dev/null
+++ b/frontend/teehr/src/Router.jsx
@@ -0,0 +1,18 @@
+import {
+ createBrowserRouter,
+ createRoutesFromElements,
+ Route,
+} from "react-router-dom";
+import Dashboard from "./components/Dashboard";
+import HomePage from "./components/Home";
+
+const router = createBrowserRouter(
+ createRoutesFromElements(
+
+ } />
+ } />
+
+ )
+);
+
+export default router;
diff --git a/frontend/teehr/src/assets/ciroh.png b/frontend/teehr/src/assets/ciroh.png
new file mode 100644
index 00000000..b6a5688f
Binary files /dev/null and b/frontend/teehr/src/assets/ciroh.png differ
diff --git a/frontend/teehr/src/assets/react.svg b/frontend/teehr/src/assets/react.svg
new file mode 100644
index 00000000..6c87de9b
--- /dev/null
+++ b/frontend/teehr/src/assets/react.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/frontend/teehr/src/assets/teehr.png b/frontend/teehr/src/assets/teehr.png
new file mode 100755
index 00000000..4d054389
Binary files /dev/null and b/frontend/teehr/src/assets/teehr.png differ
diff --git a/frontend/teehr/src/components/Card.jsx b/frontend/teehr/src/components/Card.jsx
new file mode 100644
index 00000000..f20b9da6
--- /dev/null
+++ b/frontend/teehr/src/components/Card.jsx
@@ -0,0 +1,47 @@
+import { NavLink } from "react-router-dom";
+import Card from "@mui/material/Card";
+import CardActionArea from "@mui/material/CardActionArea";
+import CardMedia from "@mui/material/CardMedia";
+import PropTypes from "prop-types";
+import AddchartOutlinedIcon from "@mui/icons-material/AddchartOutlined";
+
+const ClickableCardPropTypes = {
+ title: PropTypes.string.isRequired,
+ image: PropTypes.func,
+ route: PropTypes.string.isRequired,
+};
+
+function ClickableCard({ title, image, route }) {
+ return (
+
+
+
+ {image && (
+
+ )}
+ {!image && }
+
+
+
+ );
+}
+
+ClickableCard.propTypes = ClickableCardPropTypes;
+
+export default ClickableCard;
diff --git a/frontend/teehr/src/components/Dashboard.jsx b/frontend/teehr/src/components/Dashboard.jsx
new file mode 100644
index 00000000..a78f8358
--- /dev/null
+++ b/frontend/teehr/src/components/Dashboard.jsx
@@ -0,0 +1,154 @@
+import { useState } from "react";
+import "../App.css";
+import axios from "axios";
+// import Box from '@mui/material/Box';
+// import Grid from '@mui/material/Grid';
+import { Button, Grid, Box } from "@mui/material";
+
+import StationMap from "./StationMap";
+import MetricSelect from "./MetricSelect.jsx";
+import DatasetSelect from "./DatasetSelect.jsx";
+import DisplayMetricSelect from "./DisplayMetricSelect.jsx";
+import GroupBySelect from "./GroupBySelect";
+import DataGridDemo from "./DataGrid";
+import Filters from "./Filters";
+import DashboardContext from "../Context.js";
+// import MapWrapper from "./MapWrapper.jsx";
+
+function Dashboard() {
+ const [errors, setErrors] = useState(false);
+ const [loading, setLoading] = useState(true);
+ const [data, setData] = useState({});
+
+ const [datasets, setDatasets] = useState([]);
+ const [selectedDataset, setSelectedDataset] = useState("");
+
+ const [metrics, setMetrics] = useState([]);
+ const [selectedMetrics, setSelectedMetrics] = useState([]);
+ const [displayMetric, setDisplayMetric] = useState("");
+
+ const [groupByFields, setGroupByFields] = useState([]);
+ const [selectedGroupByFields, setSelectedGroupByFields] = useState([]);
+
+ const [operators, setOperators] = useState([]);
+
+ const [filters, setFilters] = useState([
+ { column: "", operator: "", value: "" },
+ ]);
+
+ // const [orderByFields, setOrderByFields] = useState([]);
+ // const [selectedOrderByFields, setSelectedOrderByFields] = useState([]);
+
+ const formatGroupByFields = (fields) => {
+ return fields.map((obj) => obj.name);
+ };
+
+ const fetchStations = () => {
+ axios
+ .post(`http://localhost:8000/datasets/${selectedDataset}/get_metrics`, {
+ group_by: formatGroupByFields(selectedGroupByFields),
+ order_by: ["primary_location_id"],
+ include_metrics: selectedMetrics,
+ filters: filters,
+ return_query: false,
+ include_geometry: true,
+ })
+ .then((res) => {
+ setData(res.data);
+ })
+ .catch(function (err) {
+ console.log(err);
+ setErrors(err);
+ });
+ };
+
+ const contextValue = {
+ datasets,
+ metrics,
+ groupByFields,
+ filters,
+ operators,
+ errors,
+ loading,
+ selectedDataset,
+ selectedMetrics,
+ selectedGroupByFields,
+ setDatasets,
+ setMetrics,
+ setGroupByFields,
+ setFilters,
+ setOperators,
+ setErrors,
+ setLoading,
+ };
+
+ return (
+
+
+
+
+
+
+
+ {metrics && selectedDataset && (
+
+
+
+
+
+
+
+
+ )}
+
+ {selectedDataset && (
+
+ )}
+
+
+
+
+ {data && displayMetric && (
+ <>
+
+
+
+ )}
+
+
+
+
+
+
+ );
+}
+
+export default Dashboard;
diff --git a/frontend/teehr/src/components/DataGrid.jsx b/frontend/teehr/src/components/DataGrid.jsx
new file mode 100644
index 00000000..4619130c
--- /dev/null
+++ b/frontend/teehr/src/components/DataGrid.jsx
@@ -0,0 +1,87 @@
+// import * as React from 'react';
+import { useState, useEffect } from 'react'
+import Box from '@mui/material/Box';
+import PropTypes from "prop-types";
+import { DataGrid } from '@mui/x-data-grid';
+
+
+export default function DataGridDemo(props) {
+
+ const [columns, setColumns] = useState(null);
+ const [rows, setRows] = useState(null);
+
+ const { data } = props;
+
+ // function getRowId(row) {
+ // return row.primary_location_id;
+ // }
+
+ useEffect(() => {
+ const getColumns = () => {
+ if (data) {
+ const base = [
+ {
+ field: "id",
+ headerName: "id",
+ editable: false,
+ }
+ ]
+ const arr = Object.keys(data.features[0].properties).map((c) => {
+ return {
+ field: c,
+ headerName: c,
+ editable: false,
+ }
+ })
+ base.push(...arr)
+ // console.log(arr)
+ setColumns(base)
+ }
+ }
+ getColumns()
+ }, [data, setColumns])
+
+ useEffect(() => {
+ const getRows= () => {
+ if (data) {
+ const arr = data.features.map((feat) => {
+ const obj = feat.properties
+ obj["id"] = feat.id
+ return obj
+ })
+ console.log(arr)
+ setRows(arr)
+ }
+ }
+ getRows()
+ }, [data, setRows])
+
+
+ return (
+ (data && columns) && (
+
+
+
+
+
+ )
+ );
+}
+
+DataGridDemo.propTypes = {
+ data: PropTypes.object.isRequired
+};
\ No newline at end of file
diff --git a/frontend/teehr/src/components/DatasetSelect.jsx b/frontend/teehr/src/components/DatasetSelect.jsx
new file mode 100644
index 00000000..4d410c62
--- /dev/null
+++ b/frontend/teehr/src/components/DatasetSelect.jsx
@@ -0,0 +1,89 @@
+import { useState, useEffect } from "react";
+import PropTypes from "prop-types";
+import InputLabel from "@mui/material/InputLabel";
+import MenuItem from "@mui/material/MenuItem";
+import FormControl from "@mui/material/FormControl";
+import Select from "@mui/material/Select";
+import axios from "axios";
+import { OutlinedInput, Box, LinearProgress } from "@mui/material";
+
+export default function DatasetSelect(props) {
+ const [errors, setErrors] = useState(false);
+ const [loading, setLoading] = useState(true);
+ const { datasets, setDatasets, selectedDataset, setSelectedDataset } = props;
+
+ useEffect(() => {
+ const fetchDatasets = () => {
+ axios
+ .get(`http://localhost:8000/datasets/`)
+ .then((res) => {
+ // console.log(res.data);
+ setDatasets(Object.keys(res.data));
+ setLoading(false);
+ })
+ .catch(function (err) {
+ console.log(err);
+ setErrors(err);
+ setLoading(false);
+ });
+ };
+ fetchDatasets();
+ }, [setDatasets, setLoading, setErrors]);
+
+ const handleChange = (event) => {
+ setSelectedDataset(event.target.value);
+ };
+
+ return (
+
+ {!loading ? (
+ !errors ? (
+
+
+ Select Dataset
+
+
+ }
+ renderValue={(selected) => (
+
+ {selected}
+
+ )}
+ // MenuProps={MenuProps}
+ >
+ {datasets.map((name) => (
+
+ ))}
+
+
+ ) : (
+ { errors }
+ )
+ ) : (
+
+ )}
+
+ );
+}
+
+DatasetSelect.propTypes = {
+ datasets: PropTypes.array.isRequired,
+ selectedDataset: PropTypes.string.isRequired,
+ setSelectedDataset: PropTypes.func.isRequired,
+ setDatasets: PropTypes.func.isRequired,
+};
diff --git a/frontend/teehr/src/components/DisplayMetricSelect.jsx b/frontend/teehr/src/components/DisplayMetricSelect.jsx
new file mode 100644
index 00000000..e33092cc
--- /dev/null
+++ b/frontend/teehr/src/components/DisplayMetricSelect.jsx
@@ -0,0 +1,53 @@
+import PropTypes from "prop-types";
+import InputLabel from '@mui/material/InputLabel';
+import MenuItem from '@mui/material/MenuItem';
+import FormControl from '@mui/material/FormControl';
+import Select from '@mui/material/Select';
+import { OutlinedInput, Box } from '@mui/material';
+
+export default function DatasetSelect(props) {
+ // const theme = useTheme();
+ const { selectedMetrics, selectedDisplayMetric, setSelectedDisplayMetric } = props;
+
+
+ const handleChange = (event) => {
+ setSelectedDisplayMetric(event.target.value);
+ };
+
+ return (
+
+
+ Select Display Metric
+ }
+ renderValue={(selected) => (
+
+ {selected}
+
+ )}
+ // MenuProps={MenuProps}
+ >
+ {selectedMetrics.map((name) => (
+
+ ))}
+
+
+
+ );
+}
+
+DatasetSelect.propTypes = {
+ selectedMetrics: PropTypes.array.isRequired,
+ selectedDisplayMetric: PropTypes.string.isRequired,
+ setSelectedDisplayMetric: PropTypes.func.isRequired
+};
\ No newline at end of file
diff --git a/frontend/teehr/src/components/Filters.jsx b/frontend/teehr/src/components/Filters.jsx
new file mode 100644
index 00000000..4cce1bee
--- /dev/null
+++ b/frontend/teehr/src/components/Filters.jsx
@@ -0,0 +1,67 @@
+import { useContext } from "react";
+import { Box, Button, Typography } from "@mui/material";
+import AddOutlinedIcon from "@mui/icons-material/AddOutlined";
+import DeleteOutlineOutlinedIcon from "@mui/icons-material/DeleteOutlineOutlined";
+import DashboardContext from "../Context";
+import Filter from "./SingleFilter";
+
+export default function Filters() {
+ const { filters, setFilters } = useContext(DashboardContext);
+
+ const addNewFilter = () => {
+ setFilters([...filters, { column: "", operator: "", value: "" }]);
+ };
+
+ const updateFilter = (index, key, value) => {
+ const newFilters = [...filters];
+ newFilters[index][key] = value;
+ setFilters(newFilters);
+ };
+
+ const deleteFilter = (indexToRemove) => {
+ const newFilters = filters.filter((_, index) => index !== indexToRemove);
+ setFilters(newFilters);
+ };
+
+ return (
+
+ Filters
+ {filters.map((filter, index) => (
+
+
+ updateFilter(index, "column", value)
+ }
+ setSelectedOperator={(value) =>
+ updateFilter(index, "operator", value)
+ }
+ setValue={(value) => updateFilter(index, "value", value)}
+ />
+
+
+
+
+ ))}
+
+
+ );
+}
diff --git a/frontend/teehr/src/components/GroupBySelect.jsx b/frontend/teehr/src/components/GroupBySelect.jsx
new file mode 100644
index 00000000..f858c21c
--- /dev/null
+++ b/frontend/teehr/src/components/GroupBySelect.jsx
@@ -0,0 +1,134 @@
+import { useState, useEffect } from "react";
+import { useTheme } from "@mui/material/styles";
+import PropTypes from "prop-types";
+import Box from "@mui/material/Box";
+import OutlinedInput from "@mui/material/OutlinedInput";
+import InputLabel from "@mui/material/InputLabel";
+import MenuItem from "@mui/material/MenuItem";
+import FormControl from "@mui/material/FormControl";
+import Select from "@mui/material/Select";
+import Chip from "@mui/material/Chip";
+import LinearProgress from "@mui/material/LinearProgress";
+import axios from "axios";
+
+const ITEM_HEIGHT = 48;
+const ITEM_PADDING_TOP = 8;
+const MenuProps = {
+ PaperProps: {
+ style: {
+ maxHeight: ITEM_HEIGHT * 4.5 + ITEM_PADDING_TOP,
+ width: 250,
+ },
+ },
+};
+
+function getStyles(name, personName, theme) {
+ return {
+ fontWeight:
+ personName.indexOf(name) === -1
+ ? theme.typography.fontWeightRegular
+ : theme.typography.fontWeightMedium,
+ };
+}
+
+export default function GroupBySelect(props) {
+ const theme = useTheme();
+ const [errors, setErrors] = useState(false);
+ const [loading, setLoading] = useState(true);
+ const {
+ groupByFields,
+ setGroupByFields,
+ selectedGroupByFields,
+ setSelectedGroupByFields,
+ selectedDataset,
+ } = props;
+
+ useEffect(() => {
+ const fetchMetricFields = () => {
+ if (selectedDataset && selectedDataset !== "") {
+ axios
+ .get(
+ `http://localhost:8000/datasets/${selectedDataset}/get_data_fields`
+ )
+ .then((res) => {
+ // console.log(res.data);
+ setGroupByFields(res.data);
+ setLoading(false);
+ })
+ .catch(function (err) {
+ console.log(err);
+ setErrors(err);
+ setLoading(false);
+ });
+ }
+ };
+ fetchMetricFields();
+ }, [selectedDataset, setGroupByFields, setLoading, setErrors]);
+
+ const handleChange = (event) => {
+ const {
+ target: { value },
+ } = event;
+ setSelectedGroupByFields(
+ // On autofill we get a stringified value.
+ typeof value === "string" ? value.split(",") : value
+ );
+ };
+
+ return (
+
+ {!loading ? (
+ !errors ? (
+
+
+ Group By Fields
+
+
+ }
+ renderValue={(selected) => (
+
+ {selected.map((obj) => (
+
+ ))}
+
+ )}
+ MenuProps={MenuProps}
+ >
+ {groupByFields.map((obj) => (
+
+ ))}
+
+
+ ) : (
+ { errors }
+ )
+ ) : (
+
+ )}
+
+ );
+}
+
+GroupBySelect.propTypes = {
+ groupByFields: PropTypes.array.isRequired,
+ setGroupByFields: PropTypes.func.isRequired,
+ selectedGroupByFields: PropTypes.array.isRequired,
+ setSelectedGroupByFields: PropTypes.func.isRequired,
+ selectedDataset: PropTypes.string.isRequired,
+};
diff --git a/frontend/teehr/src/components/Home.jsx b/frontend/teehr/src/components/Home.jsx
new file mode 100644
index 00000000..66c66627
--- /dev/null
+++ b/frontend/teehr/src/components/Home.jsx
@@ -0,0 +1,126 @@
+import React from "react";
+import {
+ Box,
+ AppBar,
+ Toolbar,
+ Typography,
+ Drawer,
+ CssBaseline,
+ Grid,
+ Paper,
+} from "@mui/material";
+import ClickableCard from "./Card";
+import teehrLogo from "../assets/teehr.png";
+import cirohLogo from "../assets/ciroh.png";
+
+const appBarStyle = { bgcolor: "#2E8BC0" };
+
+const drawerWidth = 240;
+
+const drawerStyle = {
+ width: drawerWidth,
+ flexShrink: 0,
+ "& .MuiDrawer-paper": {
+ width: drawerWidth,
+ boxSizing: "border-box",
+ mt: "64px",
+ bgcolor: "#0C2D48",
+ },
+ display: { xs: "none", sm: "none", md: "block" },
+};
+
+const sectionHeaderStyle = {
+ height: "100%",
+ display: "flex",
+ alignItems: "center",
+ justifyContent: "center",
+};
+
+const logoStyle = {
+ "& img": {
+ height: { xs: "80px", sm: "120px" },
+ },
+};
+
+const logos = [
+ { src: cirohLogo, alt: "CIROH Logo" },
+ { src: teehrLogo, alt: "TEEHR Logo" },
+];
+
+function createNavlink(title, description, to) {
+ return {
+ title,
+ description,
+ to,
+ };
+}
+
+const navLinksPostEvent = [
+ createNavlink("Event Dashboard Title", "Description", "/map-dashboard"),
+ createNavlink("Event Dashboard Title", "Description", "/map-dashboard"),
+];
+
+const navLinksHistoricalSimulation = [
+ createNavlink("Simulation Dashboard Title", "Description", "/map-dashboard"),
+ createNavlink("Simulation Dashboard Title", "Description", "/map-dashboard"),
+];
+
+const navLinksFIM = [
+ createNavlink("FIM Dashboard Title", "Description", "/map-dashboard"),
+ createNavlink("Simulation Dashboard Title", "Description", "/map-dashboard"),
+];
+
+const sections = {
+ "Post-Event": { links: navLinksPostEvent, cardGridSize: 5 },
+ "Historical Simulation": {
+ links: navLinksHistoricalSimulation,
+ cardGridSize: 5,
+ },
+ FIM: { links: navLinksFIM, cardGridSize: 5 },
+};
+
+export default function HomePage() {
+ return (
+
+
+
+
+ Homepage Text
+
+
+
+
+
+
+ {logos.map((logo, index) => (
+
+ 
+
+ ))}
+
+ {Object.entries(sections).map(
+ ([sectionName, sectionDetails], sectionIndex) => (
+
+
+
+ {sectionName}
+
+
+ {sectionDetails.links.map((link, index) => (
+
+
+
+ ))}
+
+ )
+ )}
+
+
+
+ );
+}
diff --git a/frontend/teehr/src/components/MapWrapper.jsx b/frontend/teehr/src/components/MapWrapper.jsx
new file mode 100644
index 00000000..51b9b0c5
--- /dev/null
+++ b/frontend/teehr/src/components/MapWrapper.jsx
@@ -0,0 +1,189 @@
+// react
+import { useState, useEffect, useRef } from 'react';
+
+// openlayers
+import Map from 'ol/Map';
+import View from 'ol/View';
+import TileLayer from 'ol/layer/Tile';
+import VectorLayer from 'ol/layer/Vector';
+import VectorSource from 'ol/source/Vector';
+import XYZ from 'ol/source/XYZ';
+import GeoJSON from 'ol/format/GeoJSON.js';
+// import { transform } from 'ol/proj';
+import { toStringXY } from 'ol/coordinate';
+import { Circle as CircleStyle, Fill, Stroke, Style, Text } from 'ol/style.js';
+import colormap from 'colormap';
+
+function MapWrapper(props) {
+
+ // set intial state
+ const { stations, metricName} = props;
+ const [map, setMap] = useState()
+ const [featuresLayer, setFeaturesLayer] = useState()
+ const [selectedCoord, setSelectedCoord] = useState()
+
+
+ // pull refs
+ const mapElement = useRef()
+
+ // create state ref that can be accessed in OpenLayers onclick callback function
+ // https://stackoverflow.com/a/60643670
+ const mapRef = useRef()
+ mapRef.current = map
+
+
+ // initialize map on first render - logic formerly put into componentDidMount
+ useEffect(() => {
+
+ // create and add vector source layer
+ const initalFeaturesLayer = new VectorLayer({
+ source: new VectorSource(),
+ })
+
+ // create map
+ const initialMap = new Map({
+ target: mapElement.current,
+ layers: [
+
+ // USGS Topo
+ new TileLayer({
+ source: new XYZ({
+ url: 'https://basemap.nationalmap.gov/arcgis/rest/services/USGSTopo/MapServer/tile/{z}/{y}/{x}',
+ })
+ }),
+
+ // Google Maps Terrain
+ /* new TileLayer({
+ source: new XYZ({
+ url: 'http://mt0.google.com/vt/lyrs=p&hl=en&x={x}&y={y}&z={z}',
+ })
+ }), */
+
+ initalFeaturesLayer
+
+ ],
+ view: new View({
+ // projection: 'EPSG:3857',
+ projection: 'EPSG:4326',
+ center: [0, 0],
+ zoom: 2
+ }),
+ controls: []
+ })
+
+ // set map onclick handler
+ initialMap.on('click', handleMapClick)
+
+ // save map and vector layer references to state
+ setMap(initialMap)
+ setFeaturesLayer(initalFeaturesLayer)
+
+ return () => initialMap.setTarget(null)
+
+ }, [])
+
+ // update map if features prop changes - logic formerly put into componentDidUpdate
+ useEffect(() => {
+
+ if (stations) { // may be null on first render
+
+ const feats = new GeoJSON().readFeatures(stations)
+ const values = feats.map((feat) => feat.get(metricName))
+ const min = Math.min(...values)
+ const max = Math.max(...values)
+
+ const src = new VectorSource({
+ features: feats
+ })
+
+ // set features to map
+ featuresLayer.setSource(src)
+
+ //set style
+ const steps = 50;
+ const ramp = colormap({
+ colormap: 'jet',
+ nshades: steps,
+ });
+ const clamp = (value, low, high) => {
+ return Math.max(low, Math.min(value, high));
+ }
+
+ const getColor = (feature) => {
+ const value = feature.get(metricName);
+ const f = Math.pow(clamp((value - min) / (max - min), 0, 1), 1 / 2);
+ const index = Math.round(f * (steps - 1));
+ return ramp[index];
+ }
+
+ const getMetricValue = (feature) => {
+ return feature.get(metricName).toFixed(4).toString();
+ }
+
+ const styleFunction = (feature) => {
+ return new Style({
+ image: new CircleStyle({
+ radius: 5,
+ fill: new Fill({
+ color: getColor(feature),
+ }),
+ stroke: new Stroke({
+ color: 'rgba(0,0,0,0.5)',
+ }),
+ }),
+ text: new Text({
+ text: getMetricValue(feature),
+ textAlign: "left",
+ fill: new Fill({
+ color: 'rgba(0, 0, 0, 0.6)',
+ }),
+ stroke: new Stroke({
+ color: 'rgba(255, 255, 255, 0.6)',
+ width: 10,
+ }),
+ offsetX: 15,
+ }),
+ });
+ }
+ featuresLayer.setStyle(styleFunction)
+
+ // fit map to feature extent (with 100px of padding)
+ map.getView().fit(featuresLayer.getSource().getExtent(), {
+ padding: [100, 100, 100, 100]
+ })
+
+ }
+
+ }, [featuresLayer, map, metricName, stations])
+
+ // map click handler
+ const handleMapClick = (event) => {
+
+ // get clicked coordinate using mapRef to access current React state inside OpenLayers callback
+ // https://stackoverflow.com/a/60643670
+ const clickedCoord = mapRef.current.getCoordinateFromPixel(event.pixel);
+
+ // transform coord to EPSG 4326 standard Lat Long
+ // const transormedCoord = transform(clickedCoord, 'EPSG:3857', 'EPSG:4326')
+
+ // set React state
+ setSelectedCoord(clickedCoord)
+
+ }
+
+ // render component
+ return (
+
+
+
+
+
+
{(selectedCoord) ? toStringXY(selectedCoord, 5) : ''}
+
+
+
+ {!loading ? (
+ !errors ? (
+
+ Select Metrics
+ }
+ renderValue={(selected) => (
+
+ {selected.map((value) => (
+
+ ))}
+
+ )}
+ MenuProps={MenuProps}
+ >
+ {metrics.map((name) => (
+
+ ))}
+
+
+ ) : (
+ { errors }
+ )) : (
+
+ )
+ }
+
+
+ );
+}
+
+MetricSelect.propTypes = {
+ metrics: PropTypes.array.isRequired,
+ selectedMetrics: PropTypes.array.isRequired,
+ setSelectedMetrics: PropTypes.func.isRequired,
+ setMetrics: PropTypes.func.isRequired,
+ selectedDataset: PropTypes.string.isRequired
+};
\ No newline at end of file
diff --git a/frontend/teehr/src/components/OperatorSelect.jsx b/frontend/teehr/src/components/OperatorSelect.jsx
new file mode 100644
index 00000000..1adeeae7
--- /dev/null
+++ b/frontend/teehr/src/components/OperatorSelect.jsx
@@ -0,0 +1,81 @@
+import { useEffect, useContext } from "react";
+import PropTypes from "prop-types";
+import InputLabel from "@mui/material/InputLabel";
+import MenuItem from "@mui/material/MenuItem";
+import FormControl from "@mui/material/FormControl";
+import Select from "@mui/material/Select";
+import { OutlinedInput, Box } from "@mui/material";
+import axios from "axios";
+import DashboardContext from "../Context";
+
+export default function OperatorSelect(props) {
+ const { operators, selectedDataset, setOperators, setLoading, setErrors } =
+ useContext(DashboardContext);
+ const { selectedOperator, setSelectedOperator } = props;
+
+ const handleChange = (event) => {
+ setSelectedOperator(event.target.value);
+ };
+
+ useEffect(() => {
+ const fetchFilterOperators = () => {
+ if (selectedDataset) {
+ setLoading(true);
+ axios
+ .get(
+ `http://localhost:8000/datasets/${selectedDataset}/get_filter_operators`
+ )
+ .then((res) => {
+ // console.log(res.data);
+ setOperators(res.data);
+ setLoading(false);
+ })
+ .catch(function (err) {
+ console.log(err);
+ setErrors(err);
+ setLoading(false);
+ });
+ }
+ };
+ fetchFilterOperators();
+ }, [selectedDataset, setLoading, setErrors, setOperators]);
+
+ return (
+
+ Operator
+
+ }
+ renderValue={(selected) => (
+
+ {selected}
+
+ )}
+ // MenuProps={MenuProps}
+ >
+ {Object.entries(operators).map(([key, label]) => (
+
+ ))}
+
+
+ );
+}
+
+OperatorSelect.propTypes = {
+ selectedOperator: PropTypes.string,
+ setSelectedOperator: PropTypes.func.isRequired,
+};
diff --git a/frontend/teehr/src/components/SingleFilter.jsx b/frontend/teehr/src/components/SingleFilter.jsx
new file mode 100644
index 00000000..c1e7032e
--- /dev/null
+++ b/frontend/teehr/src/components/SingleFilter.jsx
@@ -0,0 +1,126 @@
+import { useEffect, useState, useContext } from "react";
+import PropTypes from "prop-types";
+import SingleSelect from "./SingleSelect";
+import OperatorSelect from "./OperatorSelect";
+import TextInput from "./TextInput";
+import DashboardContext from "../Context";
+import { LocalizationProvider } from "@mui/x-date-pickers";
+import { AdapterLuxon } from "@mui/x-date-pickers/AdapterLuxon";
+import { TimePicker } from "@mui/x-date-pickers/TimePicker";
+import { DateTime } from "luxon";
+export default function Filter(props) {
+ const { groupByFields } = useContext(DashboardContext);
+ const {
+ selectedGroupByField,
+ selectedOperator,
+ value,
+ setSelectedGroupByField,
+ setSelectedOperator,
+ setValue,
+ } = props;
+
+ const [error, setError] = useState(false);
+ const [errorMessage, setErrorMessage] = useState("");
+ const [type, setType] = useState();
+
+ const getFieldType = (fieldName) => {
+ const field = groupByFields.find((field) => field.name === fieldName);
+ return field ? field.type : null;
+ };
+
+ const validateTypes = (input, type) => {
+ if (type === "FLOAT" && isNaN(Number(input))) {
+ setError(true);
+ setErrorMessage("Input must be a number.");
+ return false;
+ } else if (
+ type === "TIMESTAMP" &&
+ new Date(input).toString() === "Invalid Date"
+ ) {
+ setError(true);
+ setErrorMessage("Invalid date.");
+ return false;
+ }
+ setErrorMessage("");
+ setError(false);
+ };
+
+ useEffect(() => {
+ const validateInput = (value) => {
+ if (!value) return;
+ const type = getFieldType(selectedGroupByField);
+ if (!type) {
+ setErrorMessage("Missing group by field");
+ setError(true);
+ } else if (!selectedOperator) {
+ setErrorMessage("Missing operator");
+ setError(true);
+ } else {
+ validateTypes(value, type);
+ }
+ };
+ validateInput(value);
+ }, [value, selectedGroupByField, selectedOperator, groupByFields]);
+
+ useEffect(() => {
+ if (selectedGroupByField) {
+ const type = getFieldType(selectedGroupByField);
+ setType(type);
+ setValue("");
+ }
+ }, [selectedGroupByField]);
+
+ return (
+
+
+ o.name)}
+ selectedOption={selectedGroupByField || ""}
+ setSelectedOption={setSelectedGroupByField}
+ />
+
+
+
+
+
+ {type === "TIMESTAMP" && (
+
+ {
+ if (value) {
+ setValue(value.toUTC().toISO());
+ } else {
+ null;
+ }
+ }}
+ sx={{ m: "8px" }}
+ />
+
+ )}
+ {type !== "TIMESTAMP" && (
+
+ )}
+
+
+
+ Group By Field
+
+ }
+ renderValue={(selected) => (
+
+ {selected}
+
+ )}
+ // MenuProps={MenuProps}
+ >
+ {options.map((name) => (
+
+ ))}
+
+
+
+ );
+}
+
+SingleSelect.propTypes = {
+ options: PropTypes.array.isRequired,
+ selectedOption: PropTypes.string,
+ setSelectedOption: PropTypes.func.isRequired,
+};
diff --git a/frontend/teehr/src/components/StationMap.jsx b/frontend/teehr/src/components/StationMap.jsx
new file mode 100644
index 00000000..525a04ed
--- /dev/null
+++ b/frontend/teehr/src/components/StationMap.jsx
@@ -0,0 +1,119 @@
+import { useState, useEffect } from 'react';
+import PropTypes from "prop-types";
+import '../App.css';
+import {
+ MapContainer,
+ TileLayer,
+ GeoJSON
+} from 'react-leaflet';
+import L from "leaflet";
+import colormap from 'colormap';
+
+function StationMap(props) {
+ const position = [39, -98]
+ const { stations, metricName } = props;
+ const [map, setMap] = useState(null);
+ const [metricMin, setMetricMin] = useState(null)
+ const [metricMax, setMetricMax] = useState(null)
+
+ //set style
+ const steps = 50;
+ const ramp = colormap({
+ colormap: 'jet',
+ nshades: steps,
+ });
+ const clamp = (value, low, high) => {
+ return Math.max(low, Math.min(value, high));
+ }
+ const getColor = (feature) => {
+ const value = feature.properties[metricName]
+ const f = Math.pow(clamp((value - metricMin) / (metricMax - metricMin), 0, 1), 1 / 2);
+ const index = Math.round(f * (steps - 1));
+ return ramp[index];
+ }
+
+ useEffect(() => {
+ const setMinMax = (stations, metricName) => {
+ if (stations) {
+ const values = stations.features.map((feature) => feature.properties[metricName]);
+ setMetricMin(Math.min(...values));
+ setMetricMax(Math.max(...values));
+ }
+ };
+ setMinMax(stations, metricName);
+
+ }, [stations, metricName]);
+
+ useEffect(() => {
+ if (map && stations) {
+ const layer = L.geoJSON(stations);
+ map.fitBounds(layer.getBounds());
+ }
+ }, [map, stations]);
+
+ const onEachFeature = (feature, layer) => {
+ layer.on({
+ // click: () => {
+ // // console.log("clicked: " + feature.properties.name + " - " + feature.id)
+ // geoJsonRef.current.getLayers().forEach((layer2) => {
+ // layer2
+ // .getLayers()
+ // .forEach((marker) => marker.setIcon(stationIcon(layer2.feature)));
+ // });
+ // layer.getLayers().forEach((marker) => {
+ // marker.setIcon(selectedStationIcon(feature));
+ // });
+ // setSelectedFeature(feature);
+ // },
+ mouseover: () => {
+ layer.bindTooltip(
+ feature.properties.primary_location_id + " (" + feature.properties[metricName].toFixed(3) + ")",
+ { direction: "right" }
+ );
+ layer.openTooltip();
+ },
+ mouseout: () => {
+ layer.closeTooltip();
+ },
+ });
+ };
+
+ const StationGeoJSON = () => {
+ return stations ? (
+ {
+ return new L.CircleMarker(latlng, {
+ radius: 5,
+ color: getColor(feature),
+ fillOpacity: 1
+ });
+ }}
+ onEachFeature={onEachFeature}
+ />
+ ) : null;
+ };
+
+ return (
+
+
+
+
+ )
+}
+
+StationMap.propTypes = {
+ stations: PropTypes.object.isRequired,
+ metricName: PropTypes.string.isRequired,
+};
+
+export default StationMap
diff --git a/frontend/teehr/src/components/TextInput.jsx b/frontend/teehr/src/components/TextInput.jsx
new file mode 100644
index 00000000..c07428a7
--- /dev/null
+++ b/frontend/teehr/src/components/TextInput.jsx
@@ -0,0 +1,29 @@
+import PropTypes from "prop-types";
+import FormControl from "@mui/material/FormControl";
+import TextField from "@mui/material/TextField";
+
+export default function TextInput(props) {
+ const { value, setValue, label, error, helperText } = props;
+ return (
+
+
+ setValue(e.target.value)}
+ />
+
+
+ );
+}
+
+TextInput.propTypes = {
+ value: PropTypes.string.isRequired,
+ setValue: PropTypes.func.isRequired,
+ label: PropTypes.string,
+ error: PropTypes.bool,
+ helperText: PropTypes.string,
+};
diff --git a/frontend/teehr/src/main.jsx b/frontend/teehr/src/main.jsx
new file mode 100644
index 00000000..e1bee730
--- /dev/null
+++ b/frontend/teehr/src/main.jsx
@@ -0,0 +1,6 @@
+import { createRoot } from "react-dom/client";
+import App from "./App";
+
+const root = createRoot(document.getElementById("root"));
+
+root.render();
diff --git a/frontend/teehr/vite.config.js b/frontend/teehr/vite.config.js
new file mode 100644
index 00000000..5a33944a
--- /dev/null
+++ b/frontend/teehr/vite.config.js
@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vitejs.dev/config/
+export default defineConfig({
+ plugins: [react()],
+})
diff --git a/package-list.txt b/package-list.txt
deleted file mode 100644
index 6cae9e8b..00000000
--- a/package-list.txt
+++ /dev/null
@@ -1,366 +0,0 @@
-# This file may be used to create an environment using:
-# $ conda create --name --file
-# platform: linux-64
-_libgcc_mutex=0.1=conda_forge
-_openmp_mutex=4.5=2_gnu
-affine=2.4.0=pyhd8ed1ab_0
-aiobotocore=2.4.2=pyhd8ed1ab_0
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diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 00000000..ac24aa6c
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3064 @@
+# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
+
+[[package]]
+name = "affine"
+version = "2.4.0"
+description = "Matrices describing affine transformation of the plane"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92"},
+ {file = "affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea"},
+]
+
+[package.extras]
+dev = ["coveralls", "flake8", "pydocstyle"]
+test = ["pytest (>=4.6)", "pytest-cov"]
+
+[[package]]
+name = "aiobotocore"
+version = "2.7.0"
+description = "Async client for aws services using botocore and aiohttp"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "aiobotocore-2.7.0-py3-none-any.whl", hash = "sha256:aec605df77ce4635a0479b50fd849aa6b640900f7b295021ecca192e1140e551"},
+ {file = "aiobotocore-2.7.0.tar.gz", hash = "sha256:506591374cc0aee1bdf0ebe290560424a24af176dfe2ea7057fe1df97c4f0467"},
+]
+
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[[package]]
+name = "zarr"
+version = "2.16.1"
+description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "zarr-2.16.1-py3-none-any.whl", hash = "sha256:de4882433ccb5b42cc1ec9872b95e64ca3a13581424666b28ed265ad76c7056f"},
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+]
+
+[package.dependencies]
+asciitree = "*"
+fasteners = "*"
+numcodecs = ">=0.10.0"
+numpy = ">=1.20,<1.21.0 || >1.21.0"
+
+[package.extras]
+docs = ["numcodecs[msgpack]", "numpydoc", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx-issues", "sphinx-rtd-theme"]
+jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
+
+[[package]]
+name = "zipp"
+version = "3.17.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+ {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "7d9b8392cb0609cc1e0759e6ada994968c955712169371798f256e8b7b7acc5e"
diff --git a/pyproject.toml b/pyproject.toml
index 5cf05f76..63d9083f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,30 +1,45 @@
-[build-system]
-requires = ["setuptools"]
-build-backend = "setuptools.build_meta"
-
-[project]
+[tool.poetry]
name = "teehr"
-
+version = "0.3.0"
+description = "Tools for Exploratory Evaluation in Hydrologic Research"
authors = [
- {name = "RTI International"},
- {name = "Matthew Denno", email = "mdenno@rti.org"},
- {name = "Katie van Werkhoven", email = "kvanwerkhoven@rti.org"},
- {name = "Sam Lamont", email = "slamont@rti.org"},
+ "RTI International",
+ "Matthew Denno ",
+ "Katie van Werkhoven ",
+ "Sam Lamont ",
]
+license = "GNU v3"
+readme = "README.md"
-requires-python = ">=3.10"
-keywords = ["hydrology", "evaluation","visualization"]
-license = {text = "GNU v3"}
+[tool.poetry.dependencies]
+python = "^3.10"
+pandas = "^2.1.3"
+geopandas = "^0.14.0"
+duckdb = "^0.9.2"
+pydantic = "^2.4.2"
+hydrotools-metrics = "^1.3.3"
+hydrotools-nwis-client = "^3.3.1"
+dask = "^2023.11.0"
+fsspec = "^2023.10.0"
+ujson = "^5.8.0"
+kerchunk = "^0.2.2"
+gcsfs = "^2023.10.0"
+s3fs = "^2023.10.0"
+zarr = "^2.16.1"
+rioxarray = "^0.15.0"
+rasterio = "^1.3.9"
+h5py = "^3.10.0"
+pyarrow = "^14.0.1"
+fastapi = "^0.103.0"
+uvicorn = {extras = ["standard"], version = "^0.24.0.post1"}
+httpx = "^0.25.1"
-dynamic = ["version", "readme", "dependencies", "optional-dependencies"]
-[metadata]
-description = "Tools for Exploratory Evaluation in Hydrologic Research"
-
-[tool.setuptools.dynamic]
-dependencies = {file = ["./requirements/minimum.txt"]}
-version = {file = ["./version.txt"]}
-readme = {file = ["README.md"], content-type = "text/markdown"}
+[tool.poetry.group.test.dependencies]
+pytest = "^7.4.3"
+netcdf4 = "^1.6.5"
+flake8 = "^6.1.0"
-# ... other project metadata fields as specified in:
-# https://packaging.python.org/en/latest/specifications/declaring-project-metadata/
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/requirements/minimum.txt b/requirements/minimum.txt
deleted file mode 100644
index e99ff9b2..00000000
--- a/requirements/minimum.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-pandas
-geopandas
-duckdb
-pydantic<2.0
-hydrotools.metrics
-hydrotools.nwis-client
-dask
-fsspec
-ujson
-kerchunk
-gcsfs
-s3fs
-zarr
-rioxarray
-rasterio
-h5py
-pyarrow
diff --git a/requirements/test.txt b/requirements/test.txt
deleted file mode 100644
index 2ab6b8d2..00000000
--- a/requirements/test.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-pytest
-flake8
-netCDF4
\ No newline at end of file
diff --git a/src/teehr/api/data.yaml b/src/teehr/api/data.yaml
new file mode 100644
index 00000000..411a0b3c
--- /dev/null
+++ b/src/teehr/api/data.yaml
@@ -0,0 +1,17 @@
+datasets:
+
+ study-a:
+ primary_filepath: tests/data/test_study/timeseries/*_obs.parquet
+ secondary_filepath: tests/data/test_study/timeseries/*_fcast.parquet
+ crosswalk_filepath: tests/data/test_study/geo/crosswalk.parquet
+ geometry_filepath: tests/data/test_study/geo/gages.parquet
+ database_filepath: tests/data/test_study/temp_test_api.db
+
+ huc1802_retro:
+ primary_filepath: /home/matt/temp/huc1802_retro/timeseries/usgs.parquet
+ secondary_filepath: /home/matt/temp/huc1802_retro/timeseries/nwm20_retro.parquet
+ crosswalk_filepath: /home/matt/temp/huc1802_retro/geo/usgs_nwm22_crosswalk.parquet
+ geometry_filepath: /home/matt/temp/huc1802_retro/geo/usgs_geometry.parquet
+ database_filepath: /home/matt/temp/huc1802_retro/huc1802_retro.db
+
+
diff --git a/src/teehr/api/main.py b/src/teehr/api/main.py
new file mode 100644
index 00000000..44e79aa1
--- /dev/null
+++ b/src/teehr/api/main.py
@@ -0,0 +1,185 @@
+import json
+
+from yaml import load # , dump
+try:
+ from yaml import CLoader as Loader # , CDumper as Dumper
+except ImportError:
+ from yaml import Loader # , Dumper
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from typing import Union
+# from pydantic import validator
+from pathlib import Path
+
+# from teehr.queries.duckdb import get_metrics
+from teehr.database.teehr_dataset import TEEHRDatasetAPI
+from teehr.models.queries import (
+ # JoinedFilterFieldEnum,
+ # JoinedFilter,
+ # BaseModel,
+ FilterOperatorEnum,
+ MetricEnum
+)
+from teehr.models.queries_database import (
+ MetricQuery,
+ TimeseriesQuery,
+ TimeseriesCharQuery,
+ JoinedTimeseriesFieldName
+)
+import pandas as pd
+import geopandas as gpd
+# import duckdb
+
+app = FastAPI()
+
+origins = [
+ "http://localhost",
+ "http://localhost:5173",
+ "http://localhost:8000",
+]
+
+app.add_middleware(
+ CORSMiddleware,
+ allow_origins=origins,
+ allow_credentials=True,
+ allow_methods=["*"],
+ allow_headers=["*"],
+)
+
+
+with open(Path(Path(__file__).resolve().parent, "data.yaml")) as f:
+ datasets = load(f.read(), Loader)
+
+
+def format_response(df: Union[gpd.GeoDataFrame, pd.DataFrame]) -> dict:
+ # print(df.info())
+ if isinstance(df, gpd.GeoDataFrame):
+ # convert datetime/duration to string
+ for col in df.columns:
+ if df[col].dtype in ["datetime64[ns]", "timedelta64[ns]"]:
+ df[col] = df[col].astype(str)
+ return json.loads(df.to_json())
+ elif isinstance(df, pd.DataFrame):
+ return df.to_dict(orient="records", index=True)
+ else:
+ return df
+
+
+@app.get("/")
+def read_root():
+ return {"msg": "Welcome to TEEHR"}
+
+
+@app.get("/datasets/")
+async def get_datasets():
+ return datasets["datasets"]
+
+
+@app.get("/datasets/{dataset_id}")
+async def read_dataset_by_id(dataset_id: str):
+ return datasets["datasets"][dataset_id]
+
+
+@app.get("/datasets/{dataset_id}/get_metric_fields")
+async def get_metric_fields(
+ dataset_id: str,
+):
+ return list(MetricEnum)
+
+
+@app.get("/datasets/{dataset_id}/get_data_fields")
+async def get_data_fields(
+ dataset_id: str,
+):
+ config = datasets["datasets"][dataset_id]
+ tds = TEEHRDatasetAPI(config["database_filepath"])
+ fields = tds.get_joined_timeseries_schema()
+ fields.rename(
+ columns={
+ "column_name": "name",
+ "column_type": "type"
+ },
+ inplace=True
+ )
+ return fields[["name", "type"]].to_dict(orient="records")
+
+
+@app.get("/datasets/{dataset_id}/get_filter_operators")
+async def get_filter_operators(dataset_id: str):
+ return {item.name: item.value for item in FilterOperatorEnum}
+
+
+# @app.get("/datasets/{dataset_id}/get_group_by_fields")
+# async def get_metric_fields(
+# dataset_id: str,
+# ):
+
+# db = TEEHRDataset(filepath="/some/file/path/to/database.db")
+# # df = db.get_database_fields()
+# df = db.get_metrics(APIMetricQuery)
+
+# return {"fields": df}
+
+
+# @app.post("/datasets/{dataset_id}/get_field_values")
+# async def get_metric_fields(
+# dataset_id: str,
+# field_name: str
+# ):
+
+# db = TEEHRDataset(filepath="/some/file/path/to/database.db")
+# list = db.get_distinct_values("field_name")
+
+# return {"fields": df}
+
+
+@app.post("/datasets/{dataset_id}/get_metrics")
+async def get_metrics_by_query(
+ dataset_id: str,
+ api_metrics_query: MetricQuery
+):
+ config = datasets["datasets"][dataset_id]
+ tds = TEEHRDatasetAPI(config["database_filepath"])
+ df = tds.get_metrics(api_metrics_query)
+
+ return format_response(df)
+
+
+@app.post("/datasets/{dataset_id}/get_timeseries")
+async def get_timeseries_by_query(
+ dataset_id: str,
+ api_timeseries_query: TimeseriesQuery
+):
+
+ config = datasets["datasets"][dataset_id]
+ tds = TEEHRDatasetAPI(config["database_filepath"])
+ df = tds.get_timeseries(api_timeseries_query)
+
+ return format_response(df)
+
+
+@app.post("/datasets/{dataset_id}/get_timeseries_chars")
+async def get_timeseries_chars_by_query(
+ dataset_id: str,
+ api_timeseries_char_query: TimeseriesCharQuery
+):
+
+ config = datasets["datasets"][dataset_id]
+ tds = TEEHRDatasetAPI(config["database_filepath"])
+ df = tds.get_timeseries_chars(api_timeseries_char_query)
+
+ return format_response(df)
+
+
+@app.post("/datasets/{dataset_id}/get_unique_field_values")
+async def get_unique_field_vals(
+ dataset_id: str,
+ api_field_name: JoinedTimeseriesFieldName
+):
+ config = datasets["datasets"][dataset_id]
+ tds = TEEHRDatasetAPI(config["database_filepath"])
+ df = tds.get_unique_field_values(api_field_name)
+
+ return format_response(df)
diff --git a/src/teehr/database/teehr_dataset.py b/src/teehr/database/teehr_dataset.py
new file mode 100644
index 00000000..a6a00b66
--- /dev/null
+++ b/src/teehr/database/teehr_dataset.py
@@ -0,0 +1,1272 @@
+"""Defines the TEEHR dataset class and pre-processing methods"""
+from typing import Union, List, Callable, Dict, Any
+from pathlib import Path
+
+import re
+import duckdb
+import pandas as pd
+import geopandas as gpd
+
+import teehr.queries.duckdb_database as tqu_db
+
+import teehr.queries.utils as tqu
+from teehr.models.queries_database import (
+ JoinedFieldNameEnum,
+ JoinedTimeseriesQuery,
+ CalculateField,
+ MetricQuery,
+ TimeseriesQuery,
+ TimeseriesCharQuery,
+ JoinedTimeseriesFieldName,
+)
+from teehr.models.queries import MetricEnum
+
+
+class TEEHRDatasetAPI:
+ """Create an instance of a TEEHRDataset class for API-based queries and
+ initialize a study area database.
+
+ Methods
+ -------
+ __init__(database_filepath: Union[str, Path])
+ Initialize a study area database.
+
+ profile_query(query: str)
+ Helper function to profile query performance (runs EXPLAIN ANALYZE)
+
+ query(query: str, format: str = None, create_function_args: Dict = None)
+ Submit an SQL query string against the database.
+ Return formats include:
+ - a pandas dataframe (format='df')
+ - print results to screen (format='raw')
+ - DuckDBPyRelation, a symbolic representation of
+ the SQL query (format='relation')
+ A user-defined function can be registered to the database by
+ defining parameters in the create_function_args dictionary:
+ create_function_args = {
+ "function": user_defined_function,
+ "function_name": "user_defined_function",
+ "parameter_types": parameter_types,
+ "new_field_type": cf.new_field_type,
+ }
+
+ get_joined_timeseries_schema() -> pd.DataFrame
+ Get field names and field data types from the joined_timeseries,
+ table as a pandas dataframe.
+
+ describe_inputs(
+ primary_filepath: Union[str, Path],
+ secondary_filepath: Union[str, Path]
+ ) -> pd.DataFrame
+ Get descriptive statistics on the primary and secondary
+ timeseries by reading the parquet files as a pandas dataframe.
+
+ get_metrics(mq: MetricQuery) -> Union[pd.DataFrame, gpd.GeoDataFrame, str]
+ Calculate performance metrics using database queries.
+
+ get_timeseries(tq: TimeseriesQuery) -> Union[pd.DataFrame, str]
+ Retrieve timeseries using a database query.
+
+ get_timeseries_chars(tcq: TimeseriesCharQuery)-> Union[str, pd.DataFrame]
+ Retrieve timeseries characteristics using database query
+
+ get_unique_field_values(
+ fn: JoinedTimeseriesFieldName
+ ) -> pd.DataFrame
+ Get unique values for a given field as a pandas dataframe
+
+ """
+
+ def __init__(
+ self,
+ database_filepath: Union[str, Path],
+ ):
+ """Initialize a study area database. Creates the joined_timeseries
+ and geometry tables with a fixed schemas if they do not already
+ exist.
+
+ Parameters
+ ----------
+ database_filepath : Union[str, Path]
+ Filepath to the database
+ """
+ self.database_filepath = str(database_filepath)
+ self._initialize_database_tables()
+
+ def profile_query(self, query: str):
+ """Helper function to profile query performance.
+ (runs EXPLAIN ANALYZE and prints output to screen)"""
+ query = "EXPLAIN ANALYZE " + query
+ with duckdb.connect(self.database_filepath) as con:
+ print(con.sql(query).df().explain_value.values[0])
+
+ def query(
+ self, query: str, format: str = None, create_function_args: Dict = None
+ ):
+ """Run query against the class's database."""
+ if not create_function_args:
+ with duckdb.connect(self.database_filepath) as con:
+ if format == "df":
+ return con.sql(query).df()
+ elif format == "raw":
+ return con.sql(query).show()
+ elif format == "relation":
+ return con.sql(query)
+ con.sql(query)
+ return None
+ else:
+ user_defined_function = create_function_args["function"]
+ function_name = create_function_args["function_name"]
+ parameter_types = create_function_args["parameter_types"]
+ new_field_type = create_function_args["new_field_type"]
+ with duckdb.connect(self.database_filepath) as con:
+ # Register the function
+ con.create_function(
+ function_name,
+ user_defined_function,
+ parameter_types,
+ new_field_type,
+ )
+ # Call the function and add the results to joined_timeseries
+ con.sql(query)
+
+ def _initialize_database_tables(self):
+ """Create the persistent study database and empty table(s)"""
+ create_timeseries_table = """
+ CREATE TABLE IF NOT EXISTS joined_timeseries(
+ reference_time DATETIME,
+ value_time DATETIME,
+ secondary_location_id VARCHAR,
+ secondary_value FLOAT,
+ configuration VARCHAR,
+ measurement_unit VARCHAR,
+ variable_name VARCHAR,
+ primary_value FLOAT,
+ primary_location_id VARCHAR,
+ lead_time INTERVAL,
+ absolute_difference FLOAT
+ );"""
+
+ self.query(create_timeseries_table)
+
+ # Adding a unique index ~ doubles the size of the database on disk.
+ # Doing so might start to get us close to PostgreSQL/TimescaleDB sizes?
+ # add_index = """
+ # CREATE UNIQUE INDEX unique_ts_idx ON joined_timeseries (
+ # reference_time,
+ # value_time,
+ # configuration,
+ # variable_name,
+ # primary_location_id
+ # );"""
+ # self.query(add_index)
+
+ # Also initialize the geometry table (what if multiple geometry types?)
+ create_geometry_table = """
+ CREATE TABLE IF NOT EXISTS geometry(
+ id VARCHAR,
+ name VARCHAR,
+ geometry BLOB
+ );"""
+ self.query(create_geometry_table)
+
+ def get_joined_timeseries_schema(self) -> pd.DataFrame:
+ """Get field names and field data types from the joined_timeseries,
+ table.
+
+ Returns
+ -------
+ pd.DataFrame
+ Includes column_name, column_type, null, key, default,
+ and extra columns
+ """
+ desc = """DESCRIBE SELECT * FROM joined_timeseries;"""
+ joined_df = self.query(desc, format="df")
+
+ return joined_df
+
+ @staticmethod
+ def _sanitize_field_name(field_name: str) -> str:
+ """Replaces unallowed characters from user-defined field names
+ with an underscore.
+ """
+ # I think we will need this
+ allowed_chars = r"[^a-zA-Z0-9_]"
+ search = re.compile(allowed_chars).search
+
+ if bool(search(field_name)):
+ sub = re.compile(allowed_chars).sub
+ return str(sub("_", field_name))
+
+ return field_name
+
+ @staticmethod
+ def describe_inputs(
+ primary_filepath: Union[str, Path],
+ secondary_filepath: Union[str, Path],
+ ) -> pd.DataFrame:
+ """Get descriptive statistics on the primary and secondary
+ timeseries by reading the parquet files.
+
+ Parameters
+ ----------
+ primary_filepath : Union[str, Path]
+ Path to the primary time series parquet file
+ secondary_filepath : Union[str, Path]
+ Path to the primary time series parquet file
+
+ Returns
+ -------
+ pd.DataFrame
+ A dataframe of descriptive statistics for each time series.
+ Currently includes:
+ - Number of unique location IDs
+ - Total number of rows
+ - Start date
+ - End date
+ - Number of duplicate rows
+ - Number of location IDs with duplicate value times
+ - Number of location IDs with missing time steps
+ """
+ primary_dict = tqu_db.describe_timeseries(
+ timeseries_filepath=primary_filepath
+ )
+
+ secondary_dict = tqu_db.describe_timeseries(
+ timeseries_filepath=secondary_filepath
+ )
+
+ df = pd.DataFrame(
+ {
+ "primary": [primary_dict[key] for key in primary_dict.keys()],
+ "secondary": [
+ secondary_dict[key] for key in secondary_dict.keys()
+ ],
+ },
+ index=primary_dict.keys(),
+ )
+
+ return df
+
+ def _check_if_geometry_is_inserted(self):
+ """Make sure the geometry data has been inserted
+ into the geometry table."""
+ df = self.query("SELECT COUNT(geometry) FROM geometry;", format="df")
+ if df["count(geometry)"].values == 0:
+ raise ValueError(
+ "The geometry table is empty! Please insert geometry first"
+ )
+
+ def _validate_query_model(self, query_model: Any) -> Any:
+ """Validate pydantic query models based on the existing fields
+ in the joined_timeseries table.
+ """
+ schema_df = self.get_joined_timeseries_schema()
+ validated_model = query_model.model_validate(
+ query_model.model_dump(),
+ context={"existing_fields": schema_df.column_name.tolist()},
+ )
+ return validated_model
+
+ def get_metrics(
+ self,
+ mq: MetricQuery,
+ ) -> Union[pd.DataFrame, gpd.GeoDataFrame, str]:
+ """Calculate performance metrics using database queries.
+
+ Parameters
+ ----------
+ mq : MetricQuery
+ Pydantic model containing query parameters
+
+ mq Fields
+ ----------
+ group_by : List[str]
+ List of column/field names to group timeseries data by.
+ Must provide at least one.
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ include_metrics = List[str]
+ List of metrics (see below) for allowable list, or "all" to return
+ all. Placeholder, currently ignored -> returns "all"
+ filters : Union[List[dict], None] = None
+ List of dictionaries describing the "where" clause to limit data
+ that is included in metrics.
+ include_geometry : bool
+ True joins the geometry to the query results.
+ Only works if `primary_location_id`
+ is included as a group_by field.
+ return_query: bool = False
+ True returns the query string instead of the data
+
+ Returns
+ -------
+ Union[pd.DataFrame, gpd.GeoDataFrame, str]
+ A DataFrame or optionally a GeoDataFrame containing query results,
+ or the query itself as a string
+
+ Filter, Order By and Group By Fields
+ -----------------------------------
+ * reference_time
+ * primary_location_id
+ * secondary_location_id
+ * primary_value
+ * secondary_value
+ * value_time
+ * configuration
+ * measurement_unit
+ * variable_name
+ * lead_time
+ * [any user-added fields]
+
+ Available Metrics
+ -----------------------
+ Basic
+ * primary_count
+ * secondary_count
+ * primary_minimum
+ * secondary_minimum
+ * primary_maximum
+ * secondary_maximum
+ * primary_average
+ * secondary_average
+ * primary_sum
+ * secondary_sum
+ * primary_variance
+ * secondary_variance
+ * max_value_delta
+ max(secondary_value) - max(primary_value)
+ * bias
+ sum(primary_value - secondary_value)/count(*)
+
+ HydroTools Metrics
+ * nash_sutcliffe_efficiency
+ * kling_gupta_efficiency
+ * coefficient_of_extrapolation
+ * coefficient_of_persistence
+ * mean_error
+ * mean_squared_error
+ * root_mean_squared_error
+
+ Time-based Metrics
+ * primary_max_value_time
+ * secondary_max_value_time
+ * max_value_timedelta
+
+ Examples:
+ order_by = ["lead_time", "primary_location_id"]
+ group_by = ["lead_time", "primary_location_id"]
+ filters = [
+ {
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "gage-A",
+ },
+ {
+ "column": "reference_time",
+ "operator": "=",
+ "value": "2022-01-01 00:00:00",
+ },
+ {"column": "lead_time", "operator": "<=", "value": "10 hours"},
+ ]
+
+ """
+
+ mq = self._validate_query_model(mq)
+
+ query = tqu_db.create_get_metrics_query(mq)
+
+ if mq.return_query:
+ return tqu.remove_empty_lines(query)
+ elif mq.include_geometry:
+ self._check_if_geometry_is_inserted()
+ df = self.query(query, format="df")
+ return tqu.df_to_gdf(df)
+ else:
+ df = self.query(query, format="df")
+ return df
+
+ def get_timeseries(
+ self,
+ tq: TimeseriesQuery
+ ) -> Union[pd.DataFrame, str]:
+ """Retrieve timeseries using database query.
+
+ Parameters
+ ----------
+ tq : TimeseriesQuery
+ Pydantic model containing query parameters
+
+ tq Fields
+ ----------
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ timeseries_name: TimeseriesNameEnum
+ Name of the time series to query (primary or secondary)
+ filters : Union[List[dict], None] = None
+ List of dictionaries describing the "where" clause to limit data
+ that is included in metrics.
+ return_query: bool = False
+ True returns the query string instead of the data
+
+
+ Returns
+ -------
+ Union[pd.DataFrame, str]
+ A DataFrame of query results or the query itself as a string
+
+ Filter By Fields
+ -----------------------------------
+ * reference_time
+ * primary_location_id
+ * secondary_location_id
+ * primary_value
+ * secondary_value
+ * value_time
+ * configuration
+ * measurement_unit
+ * variable_name
+ * lead_time
+ * absolute_difference
+ * [any user-added fields]
+
+ Order By Fields
+ ---------------
+ * reference_time
+ * primary_location_id
+ * secondary_location_id
+ * primary_value
+ * secondary_value
+ * value_time
+ * configuration
+ * measurement_unit
+ * variable_name
+
+ """
+
+ tq = self._validate_query_model(tq)
+
+ query = tqu_db.create_get_timeseries_query(tq)
+
+ if tq.return_query:
+ return tqu.remove_empty_lines(query)
+ else:
+ df = self.query(query, format="df")
+ return df
+
+ def get_timeseries_chars(
+ self, tcq: TimeseriesCharQuery
+ ) -> Union[str, pd.DataFrame]:
+ """Retrieve timeseries characteristics using database query.
+
+ Parameters
+ ----------
+ tq : TimeseriesQuery
+ Pydantic model containing query parameters
+
+ tcq Fields
+ ----------
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ filters : Union[List[dict], None] = None
+ List of dictionaries describing the "where" clause to limit data
+ that is included in metrics.
+ group_by : List[str]
+ List of column/field names to group timeseries data by.
+ Must provide at least one.
+ timeseries_name: TimeseriesNameEnum
+ Name of the time series to query (primary or secondary)
+ return_query: bool = False
+ True returns the query string instead of the data
+
+ Returns
+ -------
+ Union[str, pd.DataFrame]
+ A DataFrame of time series characteristics including:
+ - location_id
+ - count
+ - min
+ - max
+ - average
+ - sum
+ - variance
+ or, the query itself as a string
+
+ Group By Fields
+ ---------------
+ * any existing joined_timeseries field
+ * must include primary_location_id or
+ secondary_location_id
+
+ Order By Fields
+ ---------------
+ * Group By Fields plus
+ * count
+ * min
+ * max
+ * average
+ * sum
+ * variance
+
+ Examples:
+ order_by = ["lead_time", "primary_location_id"]
+ filters = [
+ {
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "'123456'"
+ },
+ {
+ "column": "reference_time",
+ "operator": "=",
+ "value": "'2022-01-01 00:00'"
+ },
+ {
+ "column": "lead_time",
+ "operator": "<=",
+ "value": "'10 days'"
+ }
+ ]
+ """
+
+ tcq = self._validate_query_model(tcq)
+
+ query = tqu_db.create_get_timeseries_char_query(tcq)
+
+ if tcq.return_query:
+ return tqu.remove_empty_lines(query)
+ else:
+ df = self.query(query, format="df")
+ return df
+
+ pass
+
+ def get_unique_field_values(
+ self,
+ fn: JoinedTimeseriesFieldName
+ ) -> pd.DataFrame:
+ """Get unique values for a given field.
+
+ Parameters
+ ----------
+ fn : JoinedTimeseriesFieldName
+ Pydantic model containing the joined_timeseries table field name
+
+ Returns
+ -------
+ pd.DataFrame
+ A dataframe containing unique values for the given field
+ """
+ fn = self._validate_query_model(fn)
+ query = tqu_db.create_unique_field_values_query(fn)
+ df = self.query(query, format="df")
+ return df
+
+
+class TEEHRDatasetDB(TEEHRDatasetAPI):
+ """Extends TEEHRDatasetAPI class with additional functionality
+ for local database-based queries.
+
+ Methods
+ -------
+ insert_geometry(geometry_filepath: Union[str, Path])
+ Inserts geometry from a parquet file into a separate
+ database table named 'geometry'.
+
+ insert_joined_timeseries(
+ primary_filepath: Union[str, Path],
+ secondary_filepath: Union[str, Path],
+ crosswalk_filepath: Union[str, Path],
+ order_by: List[str] = [
+ "reference_time",
+ "primary_location_id",
+ ],
+ drop_added_fields=False,
+ )
+ Joins the primary and secondary timeseries read from parquet files
+ and inserts into the database as the joined_timeseries table.
+
+ join_attributes(attributes_filepath: Union[str, Path])
+ Joins attributes from the provided attribute table(s) to new
+ fields in the joined_timeseries table
+
+ calculate_field(
+ self,
+ new_field_name: str,
+ new_field_type: str,
+ parameter_names: List[str],
+ user_defined_function: Callable,
+ replace: bool = True,
+ )
+ Calculate a new field in joined_timeseries based on existing
+ fields and a user-defined function.
+
+ profile_query(query: str)
+ Helper function to profile query performance (runs EXPLAIN ANALYZE).
+ Inherited from TEEHRDatasetAPI.
+
+ query(query: str, format: str = None, create_function_args: Dict = None)
+ Submit an SQL query string against the database.
+ Inherited from TEEHRDatasetAPI.
+
+ Return formats include:
+ - a pandas dataframe (format='df')
+ - print results to screen (format='raw')
+ - DuckDBPyRelation, a symbolic representation of
+ the SQL query (format='relation')
+ A user-defined function can be registered to the database by
+ defining parameters in the create_function_args dictionary:
+ create_function_args = {
+ "function": user_defined_function,
+ "function_name": "user_defined_function",
+ "parameter_types": parameter_types,
+ "new_field_type": cf.new_field_type,
+ }
+
+ get_joined_timeseries_schema() -> pd.DataFrame
+ Get field names and field data types from the joined_timeseries,
+ table as a pandas dataframe. Inherited from TEEHRDatasetAPI.
+
+ describe_inputs(
+ primary_filepath: Union[str, Path],
+ secondary_filepath: Union[str, Path]
+ ) -> pd.DataFrame
+ Get descriptive statistics on the primary and secondary
+ timeseries by reading the parquet files as a pandas dataframe.
+ Inherited from TEEHRDatasetAPI.
+
+ get_metrics(
+ group_by: List[str],
+ order_by: List[str],
+ include_metrics: Union[List[MetricEnum], "all"],
+ filters: Union[List[dict], None] = None,
+ include_geometry: bool = False,
+ return_query: bool = False,
+ ) -> Union[str, pd.DataFrame, gpd.GeoDataFrame]:
+ Calculate performance metrics using database queries
+ Overrides TEEHRDatasetAPI.
+
+ get_timeseries(
+ self,
+ order_by: List[str],
+ timeseries_name: str,
+ filters: Union[List[dict], None] = None,
+ return_query: bool = False,
+ ) -> Union[pd.DataFrame, str]
+ Retrieve timeseries using a database query.
+ Overrides TEEHRDatasetAPI.
+
+ get_timeseries_chars(
+ self,
+ group_by: List[str],
+ order_by: List[str],
+ timeseries_name: str,
+ filters: Union[List[dict], None] = None,
+ return_query: bool = False,
+ ) -> Union[str, pd.DataFrame]
+ Retrieve timeseries characteristics using database query
+ Overrides TEEHRDatasetAPI.
+
+ get_unique_field_values(
+ fn: JoinedTimeseriesFieldName
+ ) -> pd.DataFrame
+ Get unique values for a given field as a pandas dataframe.
+ Overrides TEEHRDatasetAPI.
+ Use get_joined_timeseries_schema() to see existing table fields.
+
+ """
+
+ def _drop_joined_timeseries_field(self, field_name: str):
+ """Drops the specified field by name from joined_timeseries table"""
+ query = f"""
+ ALTER TABLE joined_timeseries
+ DROP COLUMN {field_name}
+ ;"""
+ self.query(query)
+
+ def _validate_joined_timeseries_base_fields(self, drop_added_fields: bool):
+ """Ensures that no user-defined fields have been added or base fields
+ have been dropped. This is necessary in order to add multiple
+ configurations into the joined_timeseries table.
+ """
+ schema_df = self.get_joined_timeseries_schema()
+ if schema_df.index.size < len(JoinedFieldNameEnum) - 1:
+ raise ValueError(
+ "There are missing fields in the joined_timeseries schema"
+ )
+ for field_name in schema_df.column_name.tolist():
+ if field_name not in JoinedFieldNameEnum.__members__:
+ if drop_added_fields:
+ print(f"Dropping added field {field_name}")
+ self._drop_joined_timeseries_field(field_name)
+ else:
+ raise ValueError(
+ f"An added field '{field_name}' exists,"
+ "please drop it before joining timeseries"
+ )
+
+ def insert_geometry(self, geometry_filepath: Union[str, Path]):
+ """Inserts geometry from a parquet file into a separate
+ database table named 'geometry'.
+
+ Parameters
+ ----------
+ geometry_filepath : Union[str, Path]
+ Path to the geometry file
+ """
+ # Load the geometry data into a separate table
+ if geometry_filepath:
+ query = f"""
+ INSERT INTO
+ geometry
+ SELECT
+ pq.id, pq.name, pq.geometry
+ FROM
+ read_parquet('{str(geometry_filepath)}') pq
+ WHERE NOT EXISTS (
+ SELECT
+ id, name, geometry
+ FROM
+ geometry
+ WHERE
+ pq.id = id AND pq.name = name
+ AND pq.geometry = geometry
+ )
+ ;"""
+ self.query(query)
+
+ def insert_joined_timeseries(
+ self,
+ primary_filepath: Union[str, Path],
+ secondary_filepath: Union[str, Path],
+ crosswalk_filepath: Union[str, Path],
+ order_by: List[str] = [
+ "reference_time",
+ "primary_location_id",
+ ],
+ drop_added_fields=False,
+ ):
+ """Joins the primary and secondary timeseries read from parquet files
+ and inserts into the database as the joined_timeseries table.
+
+ Parameters
+ ----------
+ primary_filepath : Union[str, Path]
+ File path to the "observed" data. String must include path to
+ file(s) and can include wildcards. For example,
+ "/path/to/parquet/*.parquet"
+ secondary_filepath : Union[str, Path]
+ File path to the "forecast" data. String must include path to
+ file(s) and can include wildcards. For example,
+ "/path/to/parquet/*.parquet"
+ crosswalk_filepath : Union[str, Path]
+ File path to single crosswalk file.
+ order_by : List[str], optional
+ List of column/field names to order results by,
+ by default ["reference_time", "primary_location_id"]
+ drop_added_fields : bool, optional
+ A flag to determine whether to drop any user-defined fields that
+ have been added to the table (True), or raise an error if added
+ fields exist (False). By default False
+ """
+ self._validate_joined_timeseries_base_fields(drop_added_fields)
+
+ jtq = JoinedTimeseriesQuery.model_validate(
+ {
+ "primary_filepath": primary_filepath,
+ "secondary_filepath": secondary_filepath,
+ "crosswalk_filepath": crosswalk_filepath,
+ "order_by": order_by,
+ }
+ )
+
+ query = tqu_db.create_join_and_save_timeseries_query(jtq)
+ self.query(query)
+
+ def _get_unique_attributes(self, attributes_filepath: str) -> List:
+ """Gets a list of unique attributes and attribute units from the
+ provided attribute table(s)
+ """
+
+ query = f"""
+ SELECT
+ DISTINCT attribute_name, attribute_unit
+ FROM
+ read_parquet('{attributes_filepath}')
+ ;"""
+ attr_list = self.query(query, format="df").to_dict(orient="records")
+ return attr_list
+
+ def _pivot_attribute_table(
+ self, attributes_filepath: str, attr: duckdb.DuckDBPyRelation
+ ) -> duckdb.DuckDBPyRelation:
+ """Pivots an attribute table selected as a name-unit pair.
+ The schema of the returned table consists of a field whose name
+ is a combination of attribute_name and attribute_unit, and whose
+ values are attribute_value"""
+
+ query = f"""
+ WITH attribute AS (
+ SELECT *
+ FROM
+ read_parquet('{attributes_filepath}')
+ WHERE
+ attribute_name = '{attr["attribute_name"]}'
+ AND
+ attribute_unit = '{attr["attribute_unit"]}'
+ )
+ PIVOT
+ attribute
+ ON
+ attribute_name, attribute_unit
+ USING
+ FIRST(attribute_value)
+ ;"""
+ attr_pivot = self.query(query=query, format="relation")
+
+ return attr_pivot
+
+ def _add_field_name_to_joined_timeseries(
+ self, field_name: str, field_dtype="VARCHAR"
+ ):
+ """Adds a field name to joined_timeseries
+ if it does not already exist"""
+ query = f"""
+ ALTER TABLE
+ joined_timeseries
+ ADD IF NOT EXISTS
+ {field_name} {field_dtype}
+ ;"""
+ self.query(query)
+
+ def _join_attribute_values(self, field_name: str):
+ """Join values of the new attr field on location_id"""
+ update_query = f"""
+ UPDATE
+ joined_timeseries
+ SET
+ {field_name} = (
+ SELECT
+ attr_pivot.{field_name}
+ FROM
+ attr_pivot
+ WHERE
+ joined_timeseries.primary_location_id = attr_pivot.location_id)
+ ;"""
+ print(f"Joining {field_name} values to joined_timeseries")
+ self.query(update_query)
+
+ def join_attributes(self, attributes_filepath: Union[str, Path]):
+ """Joins attributes from the provided attribute table(s) to new
+ fields in the joined_timeseries table
+
+ Parameters
+ ----------
+ attributes_filepath : Union[str, Path]
+ File path to the "attributes" data. String must include path to
+ file(s) and can include wildcards. For example,
+ "/path/to/parquet/*.parquet"
+ """
+ attr_list = self._get_unique_attributes(str(attributes_filepath))
+
+ for attr in attr_list:
+ # Pivot the single attribute
+ attr_pivot = self._pivot_attribute_table(
+ str(attributes_filepath), attr
+ )
+
+ # Add the attr field name to joined_timeseries
+ field_name = attr_pivot.columns
+ field_name.remove("location_id")
+ field_name = self._sanitize_field_name(field_name[0])
+
+ self._add_field_name_to_joined_timeseries(field_name)
+
+ # Join the attribute values to the new field, attr_pivot
+ self._join_attribute_values(field_name)
+
+ def calculate_field(
+ self,
+ new_field_name: str,
+ new_field_type: str,
+ parameter_names: List[str],
+ user_defined_function: Callable,
+ replace: bool = True,
+ ):
+ """Calculate a new field in joined_timeseries based on existing
+ fields and a user-defined function.
+
+ Parameters
+ ----------
+ parameter_names: List[str]
+ Arguments to your user function,
+ must be exisiting joined_timeseries fields
+ new_field_name: str
+ Name of new field to be added to joined_timeseries
+ new_field_type: str
+ Data type of the new field
+ user_defined_function: Callable
+ Function to apply
+ replace: bool
+ If replace is True and new_field_name already exists, it is
+ dropped before being recalculated and re-added
+
+ """
+ schema_df = self.get_joined_timeseries_schema()
+
+ sanitized_field_name = self._sanitize_field_name(new_field_name)
+
+ cf = CalculateField.model_validate(
+ {
+ "new_field_name": sanitized_field_name,
+ "new_field_type": new_field_type,
+ "parameter_names": parameter_names,
+ },
+ context={"existing_fields": schema_df.column_name.tolist()},
+ )
+
+ # Get data types of function parameters
+ schema_dict = dict(zip(schema_df.column_name, schema_df.column_type))
+ parameter_types = [schema_dict[param] for param in parameter_names]
+
+ if replace and (
+ sanitized_field_name in schema_df.column_name.tolist()
+ ):
+ self._drop_joined_timeseries_field(new_field_name)
+
+ self._add_field_name_to_joined_timeseries(
+ field_name=sanitized_field_name, field_dtype=new_field_type
+ )
+
+ # Build the query using the parameter names
+ args = ""
+ for name in cf.parameter_names:
+ args += f"{name}, "
+ args = args[:-2]
+
+ query = f"""
+ UPDATE
+ joined_timeseries
+ SET
+ {cf.new_field_name} = (
+ SELECT
+ user_defined_function({args})
+ );
+ """
+
+ create_function_args = {
+ "function": user_defined_function,
+ "function_name": "user_defined_function",
+ "parameter_types": parameter_types,
+ "new_field_type": cf.new_field_type,
+ }
+
+ self.query(query=query, create_function_args=create_function_args)
+
+ def _validate_query_model(self, query_model: Any, data: Dict) -> Any:
+ """Validate the query based on existing table fields."""
+ schema_df = self.get_joined_timeseries_schema()
+ validated_model = query_model.model_validate(
+ data,
+ context={"existing_fields": schema_df.column_name.tolist()},
+ )
+ return validated_model
+
+ def get_metrics(
+ self,
+ group_by: List[str],
+ order_by: List[str],
+ include_metrics: Union[List[MetricEnum], "all"],
+ filters: Union[List[dict], None] = None,
+ include_geometry: bool = False,
+ return_query: bool = False,
+ ) -> Union[str, pd.DataFrame, gpd.GeoDataFrame]:
+ """Calculate performance metrics using database queries.
+
+ Parameters
+ ----------
+ group_by : List[str]
+ List of column/field names to group timeseries data by.
+ Must provide at least one.
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ include_metrics = List[str]
+ List of metrics (see below) for allowable list, or "all" to return
+ all. Placeholder, currently ignored -> returns "all"
+ filters : Union[List[dict], None] = None
+ List of dictionaries describing the "where" clause to limit data
+ that is included in metrics.
+ include_geometry : bool, optional
+ True joins the geometry to the query results.
+ Only works if `primary_location_id`
+ is included as a group_by field, by default False
+ return_query : bool, optional
+ True returns the query string instead of the data, by default False
+
+ Returns
+ -------
+ Union[pd.DataFrame, gpd.GeoDataFrame, str]
+ A DataFrame or optionally a GeoDataFrame containing query results,
+ or the query itself as a string
+
+ Filter, Order By and Group By Fields
+ -----------------------------------
+ * reference_time
+ * primary_location_id
+ * secondary_location_id
+ * primary_value
+ * secondary_value
+ * value_time
+ * configuration
+ * measurement_unit
+ * variable_name
+ * lead_time
+ * [any user-added fields]
+
+ Available Metrics
+ -----------------------
+ Basic
+ * primary_count
+ * secondary_count
+ * primary_minimum
+ * secondary_minimum
+ * primary_maximum
+ * secondary_maximum
+ * primary_average
+ * secondary_average
+ * primary_sum
+ * secondary_sum
+ * primary_variance
+ * secondary_variance
+ * max_value_delta
+ max(secondary_value) - max(primary_value)
+ * bias
+ sum(primary_value - secondary_value)/count(*)
+
+ HydroTools Metrics
+ * nash_sutcliffe_efficiency
+ * kling_gupta_efficiency
+ * coefficient_of_extrapolation
+ * coefficient_of_persistence
+ * mean_error
+ * mean_squared_error
+ * root_mean_squared_error
+
+ Time-based Metrics
+ * primary_max_value_time
+ * secondary_max_value_time
+ * max_value_timedelta
+
+ Examples:
+ order_by = ["lead_time", "primary_location_id"]
+ group_by = ["lead_time", "primary_location_id"]
+ filters = [
+ {
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "gage-A",
+ },
+ {
+ "column": "reference_time",
+ "operator": "=",
+ "value": "2022-01-01 00:00:00",
+ },
+ {"column": "lead_time", "operator": "<=", "value": "10 hours"},
+ ]
+
+ """
+
+ data = {
+ "group_by": group_by,
+ "order_by": order_by,
+ "include_metrics": include_metrics,
+ "filters": filters,
+ "include_geometry": include_geometry,
+ "return_query": return_query,
+ }
+ mq = self._validate_query_model(MetricQuery, data)
+
+ query = tqu_db.create_get_metrics_query(mq)
+
+ if mq.return_query:
+ return tqu.remove_empty_lines(query)
+ elif mq.include_geometry:
+ self._check_if_geometry_is_inserted()
+ df = self.query(query, format="df")
+ return tqu.df_to_gdf(df)
+ else:
+ df = self.query(query, format="df")
+ return df
+
+ def get_timeseries(
+ self,
+ order_by: List[str],
+ timeseries_name: str,
+ filters: Union[List[dict], None] = None,
+ return_query: bool = False,
+ ) -> Union[pd.DataFrame, str]:
+ """Retrieve timeseries using database query.
+
+ Parameters
+ ----------
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ timeseries_name: str
+ Name of the time series to query ('primary' or 'secondary')
+ filters : Union[List[dict], None], optional
+ List of dictionaries describing the "where" clause to limit data
+ that is included in metrics, by default None
+ return_query : bool, optional
+ True returns the query string instead of the data, by default False
+
+ Returns
+ -------
+ Union[pd.DataFrame, str]
+ A DataFrame of query results or the query itself as a string
+
+ Filter and Order By Fields
+ -----------------------------------
+ * reference_time
+ * primary_location_id
+ * secondary_location_id
+ * primary_value
+ * secondary_value
+ * value_time
+ * configuration
+ * measurement_unit
+ * variable_name
+ * lead_time
+ * [any user-added fields]
+
+ """
+ data = {
+ "order_by": order_by,
+ "filters": filters,
+ "return_query": return_query,
+ "timeseries_name": timeseries_name,
+ }
+ tq = self._validate_query_model(TimeseriesQuery, data)
+
+ query = tqu_db.create_get_timeseries_query(tq)
+
+ if tq.return_query:
+ return tqu.remove_empty_lines(query)
+ else:
+ df = self.query(query, format="df")
+ return df
+
+ def get_timeseries_chars(
+ self,
+ group_by: List[str],
+ order_by: List[str],
+ timeseries_name: str,
+ filters: Union[List[dict], None] = None,
+ return_query: bool = False,
+ ) -> Union[str, pd.DataFrame]:
+ """Retrieve timeseries characteristics using database query.
+
+ Parameters
+ ----------
+ group_by : List[str]
+ List of column/field names to group timeseries data by.
+ Must provide at least one.
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ timeseries_name : str
+ Name of the time series to query (primary or secondary)
+ filters : Union[List[dict], None], optional
+ List of dictionaries describing the "where" clause to limit data
+ that is included in metrics., by default None
+ return_query : bool, optional
+ True returns the query string instead of the data, by default False
+
+ Returns
+ -------
+ Union[str, pd.DataFrame]
+ A DataFrame of time series characteristics including:
+ - count
+ - min
+ - max
+ - average
+ - sum
+ - variance
+ or, the query itself as a string
+
+ Filter, Order By and Group By Fields
+ -----------------------------------
+ * reference_time
+ * primary_location_id
+ * secondary_location_id
+ * primary_value
+ * secondary_value
+ * value_time
+ * configuration
+ * measurement_unit
+ * variable_name
+ * lead_time
+ * [any user-added fields]
+
+ Examples:
+ order_by = ["lead_time", "primary_location_id"]
+ filters = [
+ {
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "'123456'"
+ },
+ {
+ "column": "reference_time",
+ "operator": "=",
+ "value": "'2022-01-01 00:00'"
+ },
+ {
+ "column": "lead_time",
+ "operator": "<=",
+ "value": "'10 days'"
+ }
+ ]
+
+ """
+ data = {
+ "order_by": order_by,
+ "group_by": group_by,
+ "timeseries_name": timeseries_name,
+ "filters": filters,
+ "return_query": return_query,
+ }
+ tcq = self._validate_query_model(TimeseriesCharQuery, data)
+
+ query = tqu_db.create_get_timeseries_char_query(tcq)
+
+ if tcq.return_query:
+ return tqu.remove_empty_lines(query)
+ else:
+ df = self.query(query, format="df")
+ return df
+
+ def get_unique_field_values(self, field_name: str) -> pd.DataFrame:
+ """Get unique values for a given field.
+
+ Parameters
+ ----------
+ field_name : str
+ Name of the joined_timeseries field
+
+ Returns
+ -------
+ pd.DataFrame
+ A dataframe containing unique values for the given field
+
+ """
+
+ data = {"field_name": field_name}
+ fn = self._validate_query_model(JoinedTimeseriesFieldName, data)
+ query = tqu_db.create_unique_field_values_query(fn)
+ df = self.query(query, format="df")
+ return df
diff --git a/src/teehr/loading/nwm/nwm_grids.py b/src/teehr/loading/nwm/nwm_grids.py
index 7e63aa86..3b17a2f7 100644
--- a/src/teehr/loading/nwm/nwm_grids.py
+++ b/src/teehr/loading/nwm/nwm_grids.py
@@ -119,7 +119,7 @@ def nwm_grids_to_parquet(
},
}
- cm = GridConfigurationModel.parse_obj(vars)
+ cm = GridConfigurationModel.model_validate(vars)
configuration = cm.configuration.name
forecast_obj = getattr(cm, configuration)
output_type = forecast_obj.output_type.name
diff --git a/src/teehr/loading/nwm/nwm_points.py b/src/teehr/loading/nwm/nwm_points.py
index 5886768f..0b9101ff 100644
--- a/src/teehr/loading/nwm/nwm_points.py
+++ b/src/teehr/loading/nwm/nwm_points.py
@@ -2,7 +2,7 @@
from datetime import datetime
from pathlib import Path
-from pydantic import validate_arguments # validate_call in > 2
+from pydantic import validate_call
from teehr.loading.nwm.point_utils import (
fetch_and_format_nwm_points,
@@ -24,7 +24,7 @@
)
-@validate_arguments
+@validate_call()
def nwm_to_parquet(
configuration: str,
output_type: str,
@@ -135,7 +135,7 @@ def nwm_to_parquet(
output_type: variable_name,
},
}
- cm = PointConfigurationModel.parse_obj(vars)
+ cm = PointConfigurationModel.model_validate(vars)
configuration = cm.configuration.name
forecast_obj = getattr(cm, configuration)
output_type = forecast_obj.output_type.name
diff --git a/src/teehr/loading/usgs/usgs.py b/src/teehr/loading/usgs/usgs.py
index 3eee8f87..e8d72e93 100644
--- a/src/teehr/loading/usgs/usgs.py
+++ b/src/teehr/loading/usgs/usgs.py
@@ -5,7 +5,7 @@
from datetime import datetime, timedelta
from hydrotools.nwis_client.iv import IVDataService
from teehr.models.loading.utils import ChunkByEnum
-from pydantic import validate_arguments
+from pydantic import validate_call, ConfigDict
from teehr.loading.nwm.utils import write_parquet_file
DATETIME_STR_FMT = "%Y-%m-%dT%H:%M:00+0000"
@@ -106,7 +106,7 @@ def _fetch_usgs(
return usgs_df
-@validate_arguments()
+@validate_call(config=ConfigDict(arbitrary_types_allowed=True))
def usgs_to_parquet(
sites: List[str],
start_date: Union[str, datetime, pd.Timestamp],
diff --git a/src/teehr/models/loading/nwm22_grid.py b/src/teehr/models/loading/nwm22_grid.py
index ae8f0b27..88742bf6 100644
--- a/src/teehr/models/loading/nwm22_grid.py
+++ b/src/teehr/models/loading/nwm22_grid.py
@@ -109,36 +109,36 @@ class ForcingOutputEnum(str, Enum):
# OUTPUT TYPE MODELS
class Analysis(BaseModel):
output_type: ShortAndAnalysisOutputEnum
- land: Optional[LandAssimVariablesEnum]
+ land: Optional[LandAssimVariablesEnum] = None
class ShortRange(BaseModel):
output_type: ShortAndAnalysisOutputEnum
- land: Optional[LandShortVariablesEnum]
+ land: Optional[LandShortVariablesEnum] = None
class MediumRange(BaseModel):
output_type: MediumOutputEnum
- land_1: Optional[LandMediumVariablesEnum]
- land_2: Optional[LandMediumVariablesEnum]
- land_3: Optional[LandMediumVariablesEnum]
- land_4: Optional[LandMediumVariablesEnum]
- land_5: Optional[LandMediumVariablesEnum]
- land_6: Optional[LandMediumVariablesEnum]
- land_7: Optional[LandMediumVariablesEnum]
+ land_1: Optional[LandMediumVariablesEnum] = None
+ land_2: Optional[LandMediumVariablesEnum] = None
+ land_3: Optional[LandMediumVariablesEnum] = None
+ land_4: Optional[LandMediumVariablesEnum] = None
+ land_5: Optional[LandMediumVariablesEnum] = None
+ land_6: Optional[LandMediumVariablesEnum] = None
+ land_7: Optional[LandMediumVariablesEnum] = None
class LongRange(BaseModel):
output_type: LongOutputEnum
- land_1: Optional[LandLongVariablesEnum]
- land_2: Optional[LandLongVariablesEnum]
- land_3: Optional[LandLongVariablesEnum]
- land_4: Optional[LandLongVariablesEnum]
+ land_1: Optional[LandLongVariablesEnum] = None
+ land_2: Optional[LandLongVariablesEnum] = None
+ land_3: Optional[LandLongVariablesEnum] = None
+ land_4: Optional[LandLongVariablesEnum] = None
class Forcing(BaseModel):
output_type: ForcingOutputEnum
- forcing: Optional[ForcingVariablesEnum]
+ forcing: Optional[ForcingVariablesEnum] = None
# CONFIGURATIONS ENUM
@@ -182,41 +182,41 @@ class ConfigurationsEnum(str, Enum):
# CONFIGURATION MODEL
class GridConfigurationModel(BaseModel):
configuration: ConfigurationsEnum
- analysis_assim: Optional[Analysis]
- analysis_assim_no_da: Optional[Analysis]
- analysis_assim_extend: Optional[Analysis]
- analysis_assim_extend_no_da: Optional[Analysis]
- analysis_assim_long: Optional[Analysis]
- analysis_assim_long_no_da: Optional[Analysis]
- analysis_assim_hawaii: Optional[Analysis]
- analysis_assim_hawaii_no_da: Optional[Analysis]
- analysis_assim_puertorico: Optional[Analysis]
- analysis_assim_puertorico_no_da: Optional[Analysis]
- short_range: Optional[ShortRange]
- short_range_hawaii: Optional[ShortRange]
- short_range_puertorico: Optional[ShortRange]
- short_range_hawaii_no_da: Optional[ShortRange]
- short_range_puertorico_no_da: Optional[ShortRange]
- medium_range_mem1: Optional[MediumRange]
- medium_range_mem2: Optional[MediumRange]
- medium_range_mem3: Optional[MediumRange]
- medium_range_mem4: Optional[MediumRange]
- medium_range_mem5: Optional[MediumRange]
- medium_range_mem6: Optional[MediumRange]
- medium_range_mem7: Optional[MediumRange]
- medium_range_no_da: Optional[MediumRange]
- long_range_mem1: Optional[LongRange]
- long_range_mem2: Optional[LongRange]
- long_range_mem3: Optional[LongRange]
- long_range_mem4: Optional[LongRange]
- forcing_medium_range: Optional[Forcing]
- forcing_short_range: Optional[Forcing]
- forcing_short_range_hawaii: Optional[Forcing]
- forcing_short_range_puertorico: Optional[Forcing]
- forcing_analysis_assim: Optional[Forcing]
- forcing_analysis_assim_extend: Optional[Forcing]
- forcing_analysis_assim_hawaii: Optional[Forcing]
- forcing_analysis_assim_puertorico: Optional[Forcing]
+ analysis_assim: Optional[Analysis] = None
+ analysis_assim_no_da: Optional[Analysis] = None
+ analysis_assim_extend: Optional[Analysis] = None
+ analysis_assim_extend_no_da: Optional[Analysis] = None
+ analysis_assim_long: Optional[Analysis] = None
+ analysis_assim_long_no_da: Optional[Analysis] = None
+ analysis_assim_hawaii: Optional[Analysis] = None
+ analysis_assim_hawaii_no_da: Optional[Analysis] = None
+ analysis_assim_puertorico: Optional[Analysis] = None
+ analysis_assim_puertorico_no_da: Optional[Analysis] = None
+ short_range: Optional[ShortRange] = None
+ short_range_hawaii: Optional[ShortRange] = None
+ short_range_puertorico: Optional[ShortRange] = None
+ short_range_hawaii_no_da: Optional[ShortRange] = None
+ short_range_puertorico_no_da: Optional[ShortRange] = None
+ medium_range_mem1: Optional[MediumRange] = None
+ medium_range_mem2: Optional[MediumRange] = None
+ medium_range_mem3: Optional[MediumRange] = None
+ medium_range_mem4: Optional[MediumRange] = None
+ medium_range_mem5: Optional[MediumRange] = None
+ medium_range_mem6: Optional[MediumRange] = None
+ medium_range_mem7: Optional[MediumRange] = None
+ medium_range_no_da: Optional[MediumRange] = None
+ long_range_mem1: Optional[LongRange] = None
+ long_range_mem2: Optional[LongRange] = None
+ long_range_mem3: Optional[LongRange] = None
+ long_range_mem4: Optional[LongRange] = None
+ forcing_medium_range: Optional[Forcing] = None
+ forcing_short_range: Optional[Forcing] = None
+ forcing_short_range_hawaii: Optional[Forcing] = None
+ forcing_short_range_puertorico: Optional[Forcing] = None
+ forcing_analysis_assim: Optional[Forcing] = None
+ forcing_analysis_assim_extend: Optional[Forcing] = None
+ forcing_analysis_assim_hawaii: Optional[Forcing] = None
+ forcing_analysis_assim_puertorico: Optional[Forcing] = None
if __name__ == "__main__":
@@ -237,7 +237,7 @@ class GridConfigurationModel(BaseModel):
# import sys
# Check input parameters
# try:
- cm = GridConfigurationModel.parse_obj(vars)
+ cm = GridConfigurationModel.model_validate(vars)
# except ValidationError as e:
# print(e.errors()[0]["msg"])
# sys.exit()
diff --git a/src/teehr/models/loading/nwm22_point.py b/src/teehr/models/loading/nwm22_point.py
index c55aa36c..5cb82965 100644
--- a/src/teehr/models/loading/nwm22_point.py
+++ b/src/teehr/models/loading/nwm22_point.py
@@ -116,94 +116,94 @@ class Long4OutputEnum(str, Enum):
# POINT OUTPUT TYPE MODELS (needed for each configuration enum)
class ShortAndAnalysis(BaseModel):
output_type: ShortAndAnalysisOutputEnum
- channel_rt: Optional[ChannelRtVariableEnum]
- terrain_rt: Optional[TerrainRtVariableEnum]
- reservoir: Optional[ReservoirVariableEnum]
+ channel_rt: Optional[ChannelRtVariableEnum] = None
+ terrain_rt: Optional[TerrainRtVariableEnum] = None
+ reservoir: Optional[ReservoirVariableEnum] = None
class ShortAndAnalysisNoDA(BaseModel):
output_type: ShortAndAnalysisOutputEnum
- channel_rt: Optional[ChannelRtNoDAVariableEnum]
- terrain_rt: Optional[TerrainRtVariableEnum]
- reservoir: Optional[ReservoirVariableEnum]
+ channel_rt: Optional[ChannelRtNoDAVariableEnum] = None
+ terrain_rt: Optional[TerrainRtVariableEnum] = None
+ reservoir: Optional[ReservoirVariableEnum] = None
class MediumRangeMem1(BaseModel):
output_type: Medium1OutputEnum
- channel_rt_1: Optional[ChannelRtVariableEnum]
- terrain_rt_1: Optional[TerrainRtVariableEnum]
- reservoir_1: Optional[ReservoirVariableEnum]
+ channel_rt_1: Optional[ChannelRtVariableEnum] = None
+ terrain_rt_1: Optional[TerrainRtVariableEnum] = None
+ reservoir_1: Optional[ReservoirVariableEnum] = None
class MediumRangeMem2(BaseModel):
output_type: Medium2OutputEnum
- channel_rt_2: Optional[ChannelRtVariableEnum]
- terrain_rt_2: Optional[TerrainRtVariableEnum]
- reservoir_2: Optional[ReservoirVariableEnum]
+ channel_rt_2: Optional[ChannelRtVariableEnum] = None
+ terrain_rt_2: Optional[TerrainRtVariableEnum] = None
+ reservoir_2: Optional[ReservoirVariableEnum] = None
class MediumRangeMem3(BaseModel):
output_type: Medium3OutputEnum
- channel_rt_3: Optional[ChannelRtVariableEnum]
- terrain_rt_3: Optional[TerrainRtVariableEnum]
- reservoir_3: Optional[ReservoirVariableEnum]
+ channel_rt_3: Optional[ChannelRtVariableEnum] = None
+ terrain_rt_3: Optional[TerrainRtVariableEnum] = None
+ reservoir_3: Optional[ReservoirVariableEnum] = None
class MediumRangeMem4(BaseModel):
output_type: Medium4OutputEnum
- channel_rt_4: Optional[ChannelRtVariableEnum]
- terrain_rt_4: Optional[TerrainRtVariableEnum]
- reservoir_4: Optional[ReservoirVariableEnum]
+ channel_rt_4: Optional[ChannelRtVariableEnum] = None
+ terrain_rt_4: Optional[TerrainRtVariableEnum] = None
+ reservoir_4: Optional[ReservoirVariableEnum] = None
class MediumRangeMem5(BaseModel):
output_type: Medium5OutputEnum
- channel_rt_5: Optional[ChannelRtVariableEnum]
- terrain_rt_5: Optional[TerrainRtVariableEnum]
- reservoir_5: Optional[ReservoirVariableEnum]
+ channel_rt_5: Optional[ChannelRtVariableEnum] = None
+ terrain_rt_5: Optional[TerrainRtVariableEnum] = None
+ reservoir_5: Optional[ReservoirVariableEnum] = None
class MediumRangeMem6(BaseModel):
output_type: Medium6OutputEnum
- channel_rt_6: Optional[ChannelRtVariableEnum]
- terrain_rt_6: Optional[TerrainRtVariableEnum]
- reservoir_6: Optional[ReservoirVariableEnum]
+ channel_rt_6: Optional[ChannelRtVariableEnum] = None
+ terrain_rt_6: Optional[TerrainRtVariableEnum] = None
+ reservoir_6: Optional[ReservoirVariableEnum] = None
class MediumRangeMem7(BaseModel):
output_type: Medium7OutputEnum
- channel_rt_7: Optional[ChannelRtVariableEnum]
- terrain_rt_7: Optional[TerrainRtVariableEnum]
- reservoir_7: Optional[ReservoirVariableEnum]
+ channel_rt_7: Optional[ChannelRtVariableEnum] = None
+ terrain_rt_7: Optional[TerrainRtVariableEnum] = None
+ reservoir_7: Optional[ReservoirVariableEnum] = None
class MediumRangeNoDA(BaseModel):
- output_type = MediumNoDAEnum
- channel_rt: Optional[ChannelRtNoDAVariableEnum]
+ output_type: MediumNoDAEnum
+ channel_rt: Optional[ChannelRtNoDAVariableEnum] = None
class LongRangeMem1(BaseModel):
output_type: Long1OutputEnum
- channel_rt_1: Optional[ChannelRtLongVariableEnum]
- reservoir_1: Optional[ReservoirVariableEnum]
+ channel_rt_1: Optional[ChannelRtLongVariableEnum] = None
+ reservoir_1: Optional[ReservoirVariableEnum] = None
class LongRangeMem2(BaseModel):
output_type: Long2OutputEnum
- channel_rt_2: Optional[ChannelRtLongVariableEnum]
- reservoir_2: Optional[ReservoirVariableEnum]
+ channel_rt_2: Optional[ChannelRtLongVariableEnum] = None
+ reservoir_2: Optional[ReservoirVariableEnum] = None
class LongRangeMem3(BaseModel):
output_type: Long3OutputEnum
- channel_rt_3: Optional[ChannelRtLongVariableEnum]
- reservoir_3: Optional[ReservoirVariableEnum]
+ channel_rt_3: Optional[ChannelRtLongVariableEnum] = None
+ reservoir_3: Optional[ReservoirVariableEnum] = None
class LongRangeMem4(BaseModel):
output_type: Long4OutputEnum
- channel_rt_4: Optional[ChannelRtLongVariableEnum]
- reservoir_4: Optional[ReservoirVariableEnum]
+ channel_rt_4: Optional[ChannelRtLongVariableEnum] = None
+ reservoir_4: Optional[ReservoirVariableEnum] = None
# POINT CONFIGURATION ENUM: Potential configuration names
@@ -239,34 +239,34 @@ class ConfigurationsEnum(str, Enum):
# POINT CONFIGURATION MODEL
class PointConfigurationModel(BaseModel):
- configuration: ConfigurationsEnum
- analysis_assim: Optional[ShortAndAnalysis]
- analysis_assim_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_extend: Optional[ShortAndAnalysis]
- analysis_assim_extend_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_long: Optional[ShortAndAnalysis]
- analysis_assim_long_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_hawaii: Optional[ShortAndAnalysis]
- analysis_assim_hawaii_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_puertorico: Optional[ShortAndAnalysis]
- analysis_assim_puertorico_no_da: Optional[ShortAndAnalysisNoDA]
- short_range: Optional[ShortAndAnalysis]
- short_range_hawaii: Optional[ShortAndAnalysis]
- short_range_puertorico: Optional[ShortAndAnalysis]
- short_range_hawaii_no_da: Optional[ShortAndAnalysisNoDA]
- short_range_puertorico_no_da: Optional[ShortAndAnalysisNoDA]
- medium_range_mem1: Optional[MediumRangeMem1]
- medium_range_mem2: Optional[MediumRangeMem2]
- medium_range_mem3: Optional[MediumRangeMem3]
- medium_range_mem4: Optional[MediumRangeMem4]
- medium_range_mem5: Optional[MediumRangeMem5]
- medium_range_mem6: Optional[MediumRangeMem6]
- medium_range_mem7: Optional[MediumRangeMem7]
- medium_range_no_da: Optional[MediumRangeNoDA]
- long_range_mem1: Optional[LongRangeMem1]
- long_range_mem2: Optional[LongRangeMem2]
- long_range_mem3: Optional[LongRangeMem3]
- long_range_mem4: Optional[LongRangeMem4]
+ configuration: ConfigurationsEnum = None
+ analysis_assim: Optional[ShortAndAnalysis] = None
+ analysis_assim_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_extend: Optional[ShortAndAnalysis] = None
+ analysis_assim_extend_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_long: Optional[ShortAndAnalysis] = None
+ analysis_assim_long_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_hawaii: Optional[ShortAndAnalysis] = None
+ analysis_assim_hawaii_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_puertorico: Optional[ShortAndAnalysis] = None
+ analysis_assim_puertorico_no_da: Optional[ShortAndAnalysisNoDA] = None
+ short_range: Optional[ShortAndAnalysis] = None
+ short_range_hawaii: Optional[ShortAndAnalysis] = None
+ short_range_puertorico: Optional[ShortAndAnalysis] = None
+ short_range_hawaii_no_da: Optional[ShortAndAnalysisNoDA] = None
+ short_range_puertorico_no_da: Optional[ShortAndAnalysisNoDA] = None
+ medium_range_mem1: Optional[MediumRangeMem1] = None
+ medium_range_mem2: Optional[MediumRangeMem2] = None
+ medium_range_mem3: Optional[MediumRangeMem3] = None
+ medium_range_mem4: Optional[MediumRangeMem4] = None
+ medium_range_mem5: Optional[MediumRangeMem5] = None
+ medium_range_mem6: Optional[MediumRangeMem6] = None
+ medium_range_mem7: Optional[MediumRangeMem7] = None
+ medium_range_no_da: Optional[MediumRangeNoDA] = None
+ long_range_mem1: Optional[LongRangeMem1] = None
+ long_range_mem2: Optional[LongRangeMem2] = None
+ long_range_mem3: Optional[LongRangeMem3] = None
+ long_range_mem4: Optional[LongRangeMem4] = None
if __name__ == "__main__":
@@ -285,7 +285,7 @@ class PointConfigurationModel(BaseModel):
}
# Check input parameters
- cm = PointConfigurationModel.parse_obj(vars)
+ cm = PointConfigurationModel.model_validate(vars)
config = cm.configuration.name
forecast_obj = getattr(cm, config)
diff --git a/src/teehr/models/loading/nwm30_grid.py b/src/teehr/models/loading/nwm30_grid.py
index da2b123b..285944c0 100644
--- a/src/teehr/models/loading/nwm30_grid.py
+++ b/src/teehr/models/loading/nwm30_grid.py
@@ -58,46 +58,46 @@ class ConfigurationsEnum(str, Enum):
# CONFIGURATION MODEL
class GridConfigurationModel(BaseModel):
configuration: ConfigurationsEnum
- analysis_assim: Optional[Analysis]
- analysis_assim_no_da: Optional[Analysis]
- analysis_assim_extend: Optional[Analysis]
- analysis_assim_extend_no_da: Optional[Analysis]
- analysis_assim_long: Optional[Analysis]
- analysis_assim_long_no_da: Optional[Analysis]
- analysis_assim_hawaii: Optional[Analysis]
- analysis_assim_hawaii_no_da: Optional[Analysis]
- analysis_assim_puertorico: Optional[Analysis]
- analysis_assim_puertorico_no_da: Optional[Analysis]
- short_range: Optional[ShortRange]
- short_range_hawaii: Optional[ShortRange]
- short_range_puertorico: Optional[ShortRange]
- short_range_hawaii_no_da: Optional[ShortRange]
- short_range_puertorico_no_da: Optional[ShortRange]
- medium_range_mem1: Optional[MediumRange]
- medium_range_mem2: Optional[MediumRange]
- medium_range_mem3: Optional[MediumRange]
- medium_range_mem4: Optional[MediumRange]
- medium_range_mem5: Optional[MediumRange]
- medium_range_mem6: Optional[MediumRange]
- medium_range_mem7: Optional[MediumRange]
- medium_range_no_da: Optional[MediumRange]
- long_range_mem1: Optional[LongRange]
- long_range_mem2: Optional[LongRange]
- long_range_mem3: Optional[LongRange]
- long_range_mem4: Optional[LongRange]
- forcing_medium_range: Optional[Forcing]
- forcing_medium_range_blend: Optional[Forcing]
- forcing_medium_range_alaska: Optional[Forcing]
- forcing_medium_range_blend_alaska: Optional[Forcing]
- forcing_short_range: Optional[Forcing]
- forcing_short_range_hawaii: Optional[Forcing]
- forcing_short_range_puertorico: Optional[Forcing]
- forcing_short_range_alaska: Optional[Forcing]
- forcing_analysis_assim: Optional[Forcing]
- forcing_analysis_assim_extend: Optional[Forcing]
- forcing_analysis_assim_hawaii: Optional[Forcing]
- forcing_analysis_assim_puertorico: Optional[Forcing]
- forcing_analysis_assim_alaska: Optional[Forcing]
+ analysis_assim: Optional[Analysis] = None
+ analysis_assim_no_da: Optional[Analysis] = None
+ analysis_assim_extend: Optional[Analysis] = None
+ analysis_assim_extend_no_da: Optional[Analysis] = None
+ analysis_assim_long: Optional[Analysis] = None
+ analysis_assim_long_no_da: Optional[Analysis] = None
+ analysis_assim_hawaii: Optional[Analysis] = None
+ analysis_assim_hawaii_no_da: Optional[Analysis] = None
+ analysis_assim_puertorico: Optional[Analysis] = None
+ analysis_assim_puertorico_no_da: Optional[Analysis] = None
+ short_range: Optional[ShortRange] = None
+ short_range_hawaii: Optional[ShortRange] = None
+ short_range_puertorico: Optional[ShortRange] = None
+ short_range_hawaii_no_da: Optional[ShortRange] = None
+ short_range_puertorico_no_da: Optional[ShortRange] = None
+ medium_range_mem1: Optional[MediumRange] = None
+ medium_range_mem2: Optional[MediumRange] = None
+ medium_range_mem3: Optional[MediumRange] = None
+ medium_range_mem4: Optional[MediumRange] = None
+ medium_range_mem5: Optional[MediumRange] = None
+ medium_range_mem6: Optional[MediumRange] = None
+ medium_range_mem7: Optional[MediumRange] = None
+ medium_range_no_da: Optional[MediumRange] = None
+ long_range_mem1: Optional[LongRange] = None
+ long_range_mem2: Optional[LongRange] = None
+ long_range_mem3: Optional[LongRange] = None
+ long_range_mem4: Optional[LongRange] = None
+ forcing_medium_range: Optional[Forcing] = None
+ forcing_medium_range_blend: Optional[Forcing] = None
+ forcing_medium_range_alaska: Optional[Forcing] = None
+ forcing_medium_range_blend_alaska: Optional[Forcing] = None
+ forcing_short_range: Optional[Forcing] = None
+ forcing_short_range_hawaii: Optional[Forcing] = None
+ forcing_short_range_puertorico: Optional[Forcing] = None
+ forcing_short_range_alaska: Optional[Forcing] = None
+ forcing_analysis_assim: Optional[Forcing] = None
+ forcing_analysis_assim_extend: Optional[Forcing] = None
+ forcing_analysis_assim_hawaii: Optional[Forcing] = None
+ forcing_analysis_assim_puertorico: Optional[Forcing] = None
+ forcing_analysis_assim_alaska: Optional[Forcing] = None
if __name__ == "__main__":
@@ -118,7 +118,7 @@ class GridConfigurationModel(BaseModel):
# import sys
# Check input parameters
# try:
- cm = GridConfigurationModel.parse_obj(vars)
+ cm = GridConfigurationModel.model_validate(vars)
# except ValidationError as e:
# print(e.errors()[0]["msg"])
# sys.exit()
diff --git a/src/teehr/models/loading/nwm30_point.py b/src/teehr/models/loading/nwm30_point.py
index a906f571..76b33caf 100644
--- a/src/teehr/models/loading/nwm30_point.py
+++ b/src/teehr/models/loading/nwm30_point.py
@@ -68,48 +68,48 @@ class ConfigurationsEnum(str, Enum):
# POINT CONFIGURATION MODEL
class PointConfigurationModel(BaseModel):
- configuration: ConfigurationsEnum
- analysis_assim: Optional[ShortAndAnalysis]
- analysis_assim_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_extend: Optional[ShortAndAnalysis]
- analysis_assim_extend_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_long: Optional[ShortAndAnalysis]
- analysis_assim_long_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_hawaii: Optional[ShortAndAnalysis]
- analysis_assim_hawaii_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_puertorico: Optional[ShortAndAnalysis]
- analysis_assim_puertorico_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_alaska: Optional[ShortAndAnalysis]
- analysis_assim_alaska_no_da: Optional[ShortAndAnalysisNoDA]
- analysis_assim_extend_alaska: Optional[ShortAndAnalysis]
- analysis_assim_extend_alaska_no_da: Optional[ShortAndAnalysisNoDA]
- short_range: Optional[ShortAndAnalysis]
- short_range_hawaii: Optional[ShortAndAnalysis]
- short_range_puertorico: Optional[ShortAndAnalysis]
- short_range_hawaii_no_da: Optional[ShortAndAnalysisNoDA]
- short_range_puertorico_no_da: Optional[ShortAndAnalysisNoDA]
- short_range_alaska: Optional[ShortAndAnalysis]
- medium_range_mem1: Optional[MediumRangeMem1]
- medium_range_mem2: Optional[MediumRangeMem2]
- medium_range_mem3: Optional[MediumRangeMem3]
- medium_range_mem4: Optional[MediumRangeMem4]
- medium_range_mem5: Optional[MediumRangeMem5]
- medium_range_mem6: Optional[MediumRangeMem6]
- medium_range_mem7: Optional[MediumRangeMem7]
- medium_range_no_da: Optional[MediumRangeNoDA]
- medium_range_alaska_mem1: Optional[MediumRangeMem1]
- medium_range_alaska_mem2: Optional[MediumRangeMem2]
- medium_range_alaska_mem3: Optional[MediumRangeMem3]
- medium_range_alaska_mem4: Optional[MediumRangeMem4]
- medium_range_alaska_mem5: Optional[MediumRangeMem5]
- medium_range_alaska_mem6: Optional[MediumRangeMem6]
- medium_range_alaska_no_da: Optional[ShortAndAnalysisNoDA]
- medium_range_blend: Optional[ShortAndAnalysis]
- medium_range_blend_alaska: Optional[ShortAndAnalysis]
- long_range_mem1: Optional[LongRangeMem1]
- long_range_mem2: Optional[LongRangeMem2]
- long_range_mem3: Optional[LongRangeMem3]
- long_range_mem4: Optional[LongRangeMem4]
+ configuration: ConfigurationsEnum = None
+ analysis_assim: Optional[ShortAndAnalysis] = None
+ analysis_assim_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_extend: Optional[ShortAndAnalysis] = None
+ analysis_assim_extend_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_long: Optional[ShortAndAnalysis] = None
+ analysis_assim_long_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_hawaii: Optional[ShortAndAnalysis] = None
+ analysis_assim_hawaii_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_puertorico: Optional[ShortAndAnalysis] = None
+ analysis_assim_puertorico_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_alaska: Optional[ShortAndAnalysis] = None
+ analysis_assim_alaska_no_da: Optional[ShortAndAnalysisNoDA] = None
+ analysis_assim_extend_alaska: Optional[ShortAndAnalysis] = None
+ analysis_assim_extend_alaska_no_da: Optional[ShortAndAnalysisNoDA] = None
+ short_range: Optional[ShortAndAnalysis] = None
+ short_range_hawaii: Optional[ShortAndAnalysis] = None
+ short_range_puertorico: Optional[ShortAndAnalysis] = None
+ short_range_hawaii_no_da: Optional[ShortAndAnalysisNoDA] = None
+ short_range_puertorico_no_da: Optional[ShortAndAnalysisNoDA] = None
+ short_range_alaska: Optional[ShortAndAnalysis] = None
+ medium_range_mem1: Optional[MediumRangeMem1] = None
+ medium_range_mem2: Optional[MediumRangeMem2] = None
+ medium_range_mem3: Optional[MediumRangeMem3] = None
+ medium_range_mem4: Optional[MediumRangeMem4] = None
+ medium_range_mem5: Optional[MediumRangeMem5] = None
+ medium_range_mem6: Optional[MediumRangeMem6] = None
+ medium_range_mem7: Optional[MediumRangeMem7] = None
+ medium_range_no_da: Optional[MediumRangeNoDA] = None
+ medium_range_alaska_mem1: Optional[MediumRangeMem1] = None
+ medium_range_alaska_mem2: Optional[MediumRangeMem2] = None
+ medium_range_alaska_mem3: Optional[MediumRangeMem3] = None
+ medium_range_alaska_mem4: Optional[MediumRangeMem4] = None
+ medium_range_alaska_mem5: Optional[MediumRangeMem5] = None
+ medium_range_alaska_mem6: Optional[MediumRangeMem6] = None
+ medium_range_alaska_no_da: Optional[ShortAndAnalysisNoDA] = None
+ medium_range_blend: Optional[ShortAndAnalysis] = None
+ medium_range_blend_alaska: Optional[ShortAndAnalysis] = None
+ long_range_mem1: Optional[LongRangeMem1] = None
+ long_range_mem2: Optional[LongRangeMem2] = None
+ long_range_mem3: Optional[LongRangeMem3] = None
+ long_range_mem4: Optional[LongRangeMem4] = None
if __name__ == "__main__":
@@ -128,7 +128,7 @@ class PointConfigurationModel(BaseModel):
}
# Check input parameters
- cm = PointConfigurationModel.parse_obj(vars)
+ cm = PointConfigurationModel.model_validate(vars)
config = cm.configuration.name
forecast_obj = getattr(cm, config)
diff --git a/src/teehr/models/queries.py b/src/teehr/models/queries.py
index f1e3a37a..a3dc666a 100644
--- a/src/teehr/models/queries.py
+++ b/src/teehr/models/queries.py
@@ -1,17 +1,17 @@
from collections.abc import Iterable
from datetime import datetime
-from enum import Enum
+from enum import Enum # StrEnum
from typing import List, Optional, Union
from pydantic import BaseModel as PydanticBaseModel
-from pydantic import validator
+from pydantic import ValidationInfo, field_validator
from pathlib import Path
class BaseModel(PydanticBaseModel):
- class Config:
+ class ConfigDict:
arbitrary_types_allowed = True
- smart_union = True
+ # smart_union = True # deprecated in v2
class FilterOperatorEnum(str, Enum):
@@ -84,8 +84,7 @@ class JoinedFilter(BaseModel):
column: JoinedFilterFieldEnum
operator: FilterOperatorEnum
value: Union[
- str, int, float, datetime,
- List[Union[str, int, float, datetime]]
+ str, int, float, datetime, List[Union[str, int, float, datetime]]
]
def is_iterable_not_str(obj):
@@ -93,12 +92,12 @@ def is_iterable_not_str(obj):
return True
return False
- @validator("value")
- def in_operator_must_have_iterable(cls, v, values):
- if cls.is_iterable_not_str(v) and values["operator"] != "in":
+ @field_validator("value")
+ def in_operator_must_have_iterable(cls, v, info: ValidationInfo):
+ if cls.is_iterable_not_str(v) and info.data["operator"] != "in":
raise ValueError("iterable value must be used with 'in' operator")
- if values["operator"] == "in" and not cls.is_iterable_not_str(v):
+ if info.data["operator"] == "in" and not cls.is_iterable_not_str(v):
raise ValueError(
"'in' operator can only be used with iterable value"
)
@@ -110,8 +109,7 @@ class TimeseriesFilter(BaseModel):
column: TimeseriesFilterFieldEnum
operator: FilterOperatorEnum
value: Union[
- str, int, float, datetime,
- List[Union[str, int, float, datetime]]
+ str, int, float, datetime, List[Union[str, int, float, datetime]]
]
def is_iterable_not_str(obj):
@@ -119,16 +117,15 @@ def is_iterable_not_str(obj):
return True
return False
- @validator("value")
- def in_operator_must_have_iterable(cls, v, values):
- if cls.is_iterable_not_str(v) and values["operator"] != "in":
+ @field_validator("value")
+ def in_operator_must_have_iterable(cls, v, info: ValidationInfo):
+ if cls.is_iterable_not_str(v) and info.data["operator"] != "in":
raise ValueError("iterable value must be used with 'in' operator")
- if values["operator"] == "in" and not cls.is_iterable_not_str(v):
+ if info.data["operator"] == "in" and not cls.is_iterable_not_str(v):
raise ValueError(
"'in' operator can only be used with iterable value"
)
-
return v
@@ -138,32 +135,34 @@ class MetricQuery(BaseModel):
crosswalk_filepath: Union[str, Path]
group_by: List[JoinedFilterFieldEnum]
order_by: List[JoinedFilterFieldEnum]
- include_metrics: Union[List[str], str]
+ include_metrics: Union[List[MetricEnum], MetricEnum, str]
filters: Optional[List[JoinedFilter]] = []
return_query: bool
geometry_filepath: Optional[Union[str, Path]]
include_geometry: bool
- @validator("include_geometry")
- def include_geometry_must_group_by_primary_location_id(cls, v, values):
+ @field_validator("include_geometry")
+ def include_geometry_must_group_by_primary_location_id(
+ cls, v, info: ValidationInfo
+ ):
if (
v is True
and JoinedFilterFieldEnum.primary_location_id
- not in values["group_by"] # noqa
+ not in info.data["group_by"] # noqa
):
raise ValueError(
"`group_by` must contain `primary_location_id` "
"to include geometry in returned data"
)
- if v is True and not values["geometry_filepath"]:
+ if v is True and not info.data["geometry_filepath"]:
raise ValueError(
"`geometry_filepath` must be provided to include geometry "
"in returned data"
)
if (
- JoinedFilterFieldEnum.geometry in values["group_by"]
+ JoinedFilterFieldEnum.geometry in info.data["group_by"]
and v is False
):
raise ValueError(
@@ -173,7 +172,7 @@ def include_geometry_must_group_by_primary_location_id(cls, v, values):
return v
- @validator("filters")
+ @field_validator("filters")
def filter_must_be_list(cls, v):
if v is None:
return []
@@ -190,9 +189,11 @@ class JoinedTimeseriesQuery(BaseModel):
geometry_filepath: Optional[Union[str, Path]]
include_geometry: bool
- @validator("include_geometry")
- def include_geometry_must_group_by_primary_location_id(cls, v, values):
- if v is True and not values["geometry_filepath"]:
+ @field_validator("include_geometry")
+ def include_geometry_must_group_by_primary_location_id(
+ cls, v, info: ValidationInfo
+ ):
+ if v is True and not info.data["geometry_filepath"]:
raise ValueError(
"`geometry_filepath` must be provided to include geometry "
"in returned data"
@@ -200,7 +201,7 @@ def include_geometry_must_group_by_primary_location_id(cls, v, values):
return v
- @validator("filters")
+ @field_validator("filters")
def filter_must_be_list(cls, v):
if v is None:
return []
@@ -213,7 +214,7 @@ class TimeseriesQuery(BaseModel):
filters: Optional[List[TimeseriesFilter]] = []
return_query: bool
- @validator("filters")
+ @field_validator("filters")
def filter_must_be_list(cls, v):
if v is None:
return []
@@ -227,7 +228,7 @@ class TimeseriesCharQuery(BaseModel):
filters: Optional[List[TimeseriesFilter]] = []
return_query: bool
- @validator("filters")
+ @field_validator("filters")
def filter_must_be_list(cls, v):
if v is None:
return []
diff --git a/src/teehr/models/queries_database.py b/src/teehr/models/queries_database.py
new file mode 100644
index 00000000..481bd7ed
--- /dev/null
+++ b/src/teehr/models/queries_database.py
@@ -0,0 +1,309 @@
+from collections.abc import Iterable
+from datetime import datetime
+from enum import Enum # , StrEnum if 3.11
+from typing import List, Optional, Union
+
+from pydantic import BaseModel as PydanticBaseModel
+from pydantic import ValidationInfo, field_validator, model_validator
+from pathlib import Path
+
+from teehr.models.queries import FilterOperatorEnum, MetricEnum
+
+
+class BaseModel(PydanticBaseModel):
+ class ConfigDict:
+ arbitrary_types_allowed = True
+ # smart_union = True # deprecated in v2
+
+
+class FieldTypeEnum(str, Enum):
+ """Allowable duckdb data types."""
+
+ BIGINT = "BIGINT"
+ BIT = "BIT"
+ BOOLEAN = "BOOLEAN"
+ BLOB = "BLOB"
+ DATE = "DATE"
+ DOUBLE = "DOUBLE"
+ DECIMAL = "DECIMAL"
+ FLOAT = "FLOAT"
+ HUGEINT = "HUGEINT"
+ INTEGER = "INTEGER"
+ INTERVAL = "INTEGER"
+ REAL = "REAL"
+ SMALLINT = "SMALLINT"
+ TIME = "TIME"
+ TIMESTAMP = "TIMESTAMP"
+ TINYINT = "TINYINT"
+ UBIGINT = "UBIGINT"
+ UINTEGER = "UINTEGER"
+ USMALLINT = "USMALLINT"
+ UTINYINT = "UTINYINT"
+ UUID = "UUID"
+ VARCHAR = "VARCHAR"
+
+
+class JoinedFieldNameEnum(str, Enum):
+ """Names of fields in base joined_timeseries table."""
+
+ reference_time = "reference_time"
+ value_time = "value_time"
+ secondary_location_id = "secondary_location_id"
+ secondary_value = "secondary_value"
+ configuration = "configuration"
+ measurement_unit = "measurement_unit"
+ variable_name = "variable_name"
+ primary_value = "primary_value"
+ primary_location_id = "primary_location_id"
+ lead_time = "lead_time"
+ absolute_difference = "absolute_difference"
+ geometry = "geometry"
+
+
+class TimeseriesNameEnum(str, Enum):
+ primary = "primary"
+ secondary = "secondary"
+
+
+class JoinedTimeseriesFieldName(BaseModel):
+ field_name: str
+
+ @field_validator("field_name")
+ def field_name_must_exist_in_timeseries_table(cls, v, info: ValidationInfo): # noqa
+ context = info.context
+ if context:
+ existing_fields = context.get("existing_fields", set())
+ if v not in existing_fields:
+ raise ValueError(
+ f"The field name {v} does not exist in"
+ "the joined_timseries table"
+ )
+ return v
+
+
+class CalculateField(BaseModel):
+ parameter_names: List[str]
+ new_field_name: str
+ new_field_type: FieldTypeEnum
+
+ # TODO: Add field_name validator? (has already been sanitized)
+ @field_validator("new_field_name")
+ def field_name_must_be_valid(cls, v):
+ # Must not contain special characters
+ return v
+
+ @field_validator("parameter_names")
+ def parameter_names_must_exist_as_fields(cls, v, info: ValidationInfo):
+ context = info.context
+ if context:
+ existing_fields = context.get("existing_fields", set())
+ for val in v:
+ if val not in existing_fields:
+ raise ValueError(
+ f"The function parameter {val} does not exist in the database" # noqa
+ )
+ return v
+
+
+class Filter(BaseModel):
+ column: str
+ operator: FilterOperatorEnum
+ value: Union[
+ str, int, float, datetime, List[Union[str, int, float, datetime]]
+ ]
+
+ def is_iterable_not_str(obj):
+ if isinstance(obj, Iterable) and not isinstance(obj, str):
+ return True
+ return False
+
+ @field_validator("value")
+ def in_operator_must_have_iterable(
+ cls, v: str, info: ValidationInfo
+ ) -> str:
+ if cls.is_iterable_not_str(v) and info.data["operator"] != "in":
+ raise ValueError("iterable value must be used with 'in' operator")
+
+ if info.data["operator"] == "in" and not cls.is_iterable_not_str(v):
+ raise ValueError(
+ "'in' operator can only be used with iterable value"
+ )
+
+ return v
+
+
+class JoinedTimeseriesQuery(BaseModel):
+ primary_filepath: Union[str, Path]
+ secondary_filepath: Union[str, Path]
+ crosswalk_filepath: Union[str, Path]
+ order_by: Optional[List[JoinedFieldNameEnum]] = []
+
+
+class TimeseriesQuery(BaseModel):
+ order_by: List[str]
+ filters: Optional[List[Filter]] = []
+ return_query: Optional[bool] = False
+ timeseries_name: TimeseriesNameEnum
+
+ @field_validator("filters")
+ def filter_must_be_list(cls, v):
+ if v is None:
+ return []
+ return v
+
+ @field_validator("order_by")
+ def order_by_must_exist_as_fields(cls, v, info: ValidationInfo):
+ """order_by fields must currently exist in the database"""
+ context = info.context
+ if context:
+ existing_fields = context.get("existing_fields", set())
+ for val in v:
+ if val not in existing_fields:
+ raise ValueError(
+ f"The order_by field '{val}' does not"
+ "exist in the database"
+ )
+ return v
+
+ @field_validator("filters")
+ def filters_must_exist_as_fields(cls, v, info: ValidationInfo):
+ """filters fields must currently exist in the database"""
+ context = info.context
+ if context:
+ existing_fields = context.get("existing_fields", set())
+ for val in v:
+ if val.column not in existing_fields:
+ raise ValueError(
+ f"The filters field {val.column} does not"
+ "exist in the database"
+ )
+ return v
+
+
+class TimeseriesCharQuery(BaseModel):
+ order_by: List[str]
+ group_by: List[str]
+ filters: Optional[List[Filter]] = []
+ return_query: Optional[bool] = False
+ timeseries_name: TimeseriesNameEnum
+
+ @field_validator("filters")
+ def filter_must_be_list(cls, v):
+ if v is None:
+ return []
+ return v
+
+ @field_validator("order_by")
+ def order_by_must_exist_as_fields_or_chars(cls, v, info: ValidationInfo):
+ """order_by fields must currently exist in the database or be one of
+ the calculated stats."""
+ context = info.context
+ if context:
+ existing_fields = context.get("existing_fields", set())
+ existing_fields.extend(["count",
+ "min",
+ "max",
+ "average",
+ "sum",
+ "variance"])
+ for val in v:
+ if val not in existing_fields:
+ raise ValueError(
+ f"The order_by or group_by field '{val}' does not"
+ "exist in the database"
+ )
+ return v
+
+ @field_validator("group_by")
+ def group_by_must_exist_as_fields(cls, v, info: ValidationInfo):
+ """group_by fields must currently exist in the database"""
+ context = info.context
+ if context:
+ existing_fields = context.get("existing_fields", set())
+ for val in v:
+ if val not in existing_fields:
+ raise ValueError(
+ f"The order_by or group_by field '{val}' does not"
+ "exist in the database"
+ )
+ return v
+
+ @field_validator("group_by")
+ def group_by_must_contain_primary_or_secondary_id(cls, v):
+ id_list = ["primary_location_id", "secondary_location_id"]
+ if not any([val in id_list for val in v]):
+ raise ValueError(
+ "Group By must contain primary or secondary"
+ " location id"
+ )
+ return v
+
+ @field_validator("filters")
+ def filters_must_exist_as_fields(cls, v, info: ValidationInfo):
+ """filters fields must currently exist in the database"""
+ context = info.context
+ if context:
+ existing_fields = context.get("existing_fields", set())
+ for val in v:
+ if val.column not in existing_fields:
+ raise ValueError(
+ f"The filters field {val.column} does not"
+ "exist in the database"
+ )
+ return v
+
+
+class MetricQuery(BaseModel):
+ include_geometry: bool
+ group_by: List[str]
+ order_by: List[str]
+ include_metrics: Union[List[MetricEnum], MetricEnum, str]
+ filters: Optional[List[Filter]] = None
+ return_query: Optional[bool] = False
+
+ @field_validator("filters")
+ def filter_must_be_list(cls, v):
+ if v is None:
+ return []
+ return v
+
+ @model_validator(mode="before")
+ @classmethod
+ def validate_include_geometry_and_specified_fields(
+ cls, data, info: ValidationInfo
+ ):
+ if data["include_geometry"]:
+ # If geometry is included, group_by must
+ # contain 'primary_location_id'
+ if JoinedFieldNameEnum.primary_location_id not in data["group_by"]:
+ raise ValueError(
+ "`group_by` must contain `primary_location_id` "
+ "to include geometry in returned data"
+ )
+
+ # order_by, group_by, and filter fields must
+ # currently exist in the database
+ context = info.context
+ if context:
+ existing_fields = context.get("existing_fields", set())
+ for val in data["group_by"]:
+ if val not in existing_fields:
+ raise ValueError(
+ f"The group_by field '{val}' does not"
+ "exist in the database"
+ )
+ for val in data["order_by"]:
+ if val not in existing_fields:
+ raise ValueError(
+ f"The order_by field '{val}' does not"
+ "exist in the database"
+ )
+ if data["filters"]:
+ for val in data["filters"]:
+ if val["column"] not in existing_fields:
+ raise ValueError(
+ f"The filter field '{val}' does not"
+ "exist in the database"
+ )
+
+ return data
diff --git a/src/teehr/queries/duckdb.py b/src/teehr/queries/duckdb.py
index a453677f..6498eeb4 100644
--- a/src/teehr/queries/duckdb.py
+++ b/src/teehr/queries/duckdb.py
@@ -134,7 +134,7 @@ def get_metrics(
]
"""
- mq = MetricQuery.parse_obj(
+ mq = MetricQuery.model_validate(
{
"primary_filepath": primary_filepath,
"secondary_filepath": secondary_filepath,
@@ -145,7 +145,7 @@ def get_metrics(
"filters": filters,
"return_query": return_query,
"include_geometry": include_geometry,
- "geometry_filepath": geometry_filepath
+ "geometry_filepath": geometry_filepath,
}
)
@@ -227,8 +227,8 @@ def get_metrics(
{tqu._join_primary_join_max_time(mq)}
{tqu._join_secondary_join_max_time(mq)}
ORDER BY
- {",".join([f"metrics.{gb}" for gb in mq.group_by])}
- ;"""
+ {",".join([f"metrics.{ob}" for ob in mq.order_by])}
+ ;""" # noqa
if mq.return_query:
return tqu.remove_empty_lines(query)
@@ -314,7 +314,7 @@ def get_joined_timeseries(
]
"""
- jtq = JoinedTimeseriesQuery.parse_obj(
+ jtq = JoinedTimeseriesQuery.model_validate(
{
"primary_filepath": primary_filepath,
"secondary_filepath": secondary_filepath,
@@ -323,7 +323,7 @@ def get_joined_timeseries(
"filters": filters,
"return_query": return_query,
"include_geometry": include_geometry,
- "geometry_filepath": geometry_filepath
+ "geometry_filepath": geometry_filepath,
}
)
@@ -370,7 +370,7 @@ def get_joined_timeseries(
joined
ORDER BY
{",".join(jtq.order_by)}
- ;"""
+ ;""" # noqa
if jtq.return_query:
return tqu.remove_empty_lines(query)
@@ -378,7 +378,9 @@ def get_joined_timeseries(
df = duckdb.query(query).to_df()
df["primary_location_id"] = df["primary_location_id"].astype("category")
- df["secondary_location_id"] = df["secondary_location_id"].astype("category") # noqa
+ df["secondary_location_id"] = df["secondary_location_id"].astype(
+ "category"
+ ) # noqa
df["configuration"] = df["configuration"].astype("category")
df["measurement_unit"] = df["measurement_unit"].astype("category")
df["variable_name"] = df["variable_name"].astype("category")
@@ -435,12 +437,12 @@ def get_timeseries(
},
]
"""
- tq = TimeseriesQuery.parse_obj(
+ tq = TimeseriesQuery.model_validate(
{
"timeseries_filepath": timeseries_filepath,
"order_by": order_by,
"filters": filters,
- "return_query": return_query
+ "return_query": return_query,
}
)
@@ -538,23 +540,22 @@ def get_timeseries_chars(
]
"""
- tcq = TimeseriesCharQuery.parse_obj(
+ tcq = TimeseriesCharQuery.model_validate(
{
"timeseries_filepath": timeseries_filepath,
"order_by": order_by,
"group_by": group_by,
"filters": filters,
- "return_query": return_query
+ "return_query": return_query,
}
)
join_max_time_on = tqu._join_time_on(
- join="mxt",
- join_to="chars",
- join_on=tcq.group_by
+ join="mxt", join_to="chars", join_on=tcq.group_by
)
- chars_order_by = [f"chars.{val}" for val in tcq.order_by]
+ order_by = [f"chars.{val}" for val in tcq.order_by]
+
query = f"""
WITH fts AS (
SELECT sf.* FROM
@@ -590,9 +591,9 @@ def get_timeseries_chars(
chars.*
,mxt.value_time as max_value_time
FROM chars
- {join_max_time_on}
+ {join_max_time_on}
ORDER BY
- {",".join(chars_order_by)}
+ {",".join(order_by)}
;"""
if tcq.return_query:
diff --git a/src/teehr/queries/duckdb_database.py b/src/teehr/queries/duckdb_database.py
new file mode 100644
index 00000000..cef27aa8
--- /dev/null
+++ b/src/teehr/queries/duckdb_database.py
@@ -0,0 +1,612 @@
+import duckdb
+
+from typing import Dict
+
+from teehr.models.queries_database import (
+ MetricQuery,
+ JoinedTimeseriesQuery,
+ TimeseriesQuery,
+ TimeseriesCharQuery,
+)
+
+import teehr.queries.utils as tqu
+
+SQL_DATETIME_STR_FORMAT = "%Y-%m-%d %H:%M:%S"
+
+
+def create_get_metrics_query(mq: MetricQuery) -> str:
+ """Build the query string to calculate performance metrics
+ using database queries.
+
+ mq Fields
+ ----------
+ group_by : List[str]
+ List of column/field names to group timeseries data by.
+ Must provide at least one.
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ include_metrics = List[str]
+ List of metrics (see below) for allowable list, or "all" to return all
+ Placeholder, currently ignored -> returns "all"
+ filters : Union[List[dict], None] = None
+ List of dictionaries describing the "where" clause to limit data that
+ is included in metrics.
+
+ Returns
+ -------
+ query : str
+
+ Filter, Order By and Group By Fields
+ -----------------------------------
+ * reference_time
+ * primary_location_id
+ * secondary_location_id
+ * primary_value
+ * secondary_value
+ * value_time
+ * configuration
+ * measurement_unit
+ * variable_name
+ * lead_time
+
+ Available Metrics
+ -----------------------
+ Basic
+ * primary_count
+ * secondary_count
+ * primary_minimum
+ * secondary_minimum
+ * primary_maximum
+ * secondary_maximum
+ * primary_average
+ * secondary_average
+ * primary_sum
+ * secondary_sum
+ * primary_variance
+ * secondary_variance
+ * max_value_delta
+ max(secondary_value) - max(primary_value)
+ * bias
+ sum(primary_value - secondary_value)/count(*)
+
+ HydroTools Metrics
+ * nash_sutcliffe_efficiency
+ * kling_gupta_efficiency
+ * coefficient_of_extrapolation
+ * coefficient_of_persistence
+ * mean_error
+ * mean_squared_error
+ * root_mean_squared_error
+
+ Time-based Metrics
+ * primary_max_value_time
+ * secondary_max_value_time
+ * max_value_timedelta
+
+ Examples:
+ order_by = ["lead_time", "primary_location_id"]
+ group_by = ["lead_time", "primary_location_id"]
+ filters = [
+ {
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "gage-A",
+ },
+ {
+ "column": "reference_time",
+ "operator": "=",
+ "value": "2022-01-01 00:00:00",
+ },
+ {"column": "lead_time", "operator": "<=", "value": "10 hours"},
+ ]
+ """
+
+ query = f"""
+ WITH joined as (
+ SELECT
+ *
+ FROM joined_timeseries
+ {tqu.filters_to_sql_db(mq.filters)}
+ )
+ {tqu._nse_cte(mq)}
+ {tqu._pmxt_cte(mq)}
+ {tqu._smxt_cte(mq)}
+ , metrics AS (
+ SELECT
+ {",".join([f"joined.{gb}" for gb in mq.group_by])}
+ {tqu._select_primary_count(mq)}
+ {tqu._select_secondary_count(mq)}
+ {tqu._select_primary_minimum(mq)}
+ {tqu._select_secondary_minimum(mq)}
+ {tqu._select_primary_maximum(mq)}
+ {tqu._select_secondary_maximum(mq)}
+ {tqu._select_primary_average(mq)}
+ {tqu._select_secondary_average(mq)}
+ {tqu._select_primary_sum(mq)}
+ {tqu._select_secondary_sum(mq)}
+ {tqu._select_primary_variance(mq)}
+ {tqu._select_secondary_variance(mq)}
+ {tqu._select_max_value_delta(mq)}
+ {tqu._select_bias(mq)}
+ {tqu._select_nash_sutcliffe_efficiency(mq)}
+ {tqu._select_kling_gupta_efficiency(mq)}
+ {tqu._select_mean_error(mq)}
+ {tqu._select_mean_squared_error(mq)}
+ {tqu._select_root_mean_squared_error(mq)}
+ FROM
+ joined
+ {tqu._join_nse_cte(mq)}
+ GROUP BY
+ {",".join([f"joined.{gb}" for gb in mq.group_by])}
+ )
+ SELECT
+ metrics.*
+ {tqu._select_primary_max_value_time(mq)}
+ {tqu._select_secondary_max_value_time(mq)}
+ {tqu._select_max_value_timedelta(mq)}
+ {tqu.geometry_select_clause(mq)}
+ FROM metrics
+ {tqu.metric_geometry_join_clause_db(mq)}
+ {tqu._join_primary_join_max_time(mq)}
+ {tqu._join_secondary_join_max_time(mq)}
+ ORDER BY
+ {",".join([f"metrics.{ob}" for ob in mq.order_by])}
+ ;"""
+
+ return query
+
+
+def create_join_and_save_timeseries_query(jtq: JoinedTimeseriesQuery) -> str:
+ """Load joined timeseries into a duckdb persistent database
+ using database query.
+
+ jtq Fields
+ ----------
+ primary_filepath : str
+ File path to the "observed" data. String must include path to file(s)
+ and can include wildcards. For example, "/path/to/parquet/*.parquet"
+ secondary_filepath : str
+ File path to the "forecast" data. String must include path to file(s)
+ and can include wildcards. For example, "/path/to/parquet/*.parquet"
+ crosswalk_filepath : str
+ File path to single crosswalk file.
+ order_by : Optional[List[str]]
+ List of column/field names to order results by.
+ Must provide at least one.
+
+
+ Returns
+ -------
+ query : str
+ """
+
+ query = f"""
+ WITH filtered_primary AS (
+ SELECT * FROM(
+ SELECT *,
+ row_number() OVER(
+ PARTITION BY value_time,
+ location_id,
+ configuration,
+ measurement_unit,
+ variable_name
+ ORDER BY reference_time desc)
+ AS rn
+ FROM read_parquet("{str(jtq.primary_filepath)}")
+ ) t
+ WHERE rn = 1
+ ),
+ joined as (
+ SELECT
+ sf.reference_time,
+ sf.value_time,
+ sf.location_id as secondary_location_id,
+ sf.value as secondary_value,
+ sf.configuration,
+ sf.measurement_unit,
+ sf.variable_name,
+ pf.value as primary_value,
+ pf.location_id as primary_location_id,
+ sf.value_time - sf.reference_time as lead_time,
+ abs(primary_value - secondary_value) as absolute_difference
+ FROM read_parquet('{str(jtq.secondary_filepath)}') sf
+ JOIN read_parquet('{str(jtq.crosswalk_filepath)}') cf
+ on cf.secondary_location_id = sf.location_id
+ JOIN filtered_primary pf
+ on cf.primary_location_id = pf.location_id
+ and sf.value_time = pf.value_time
+ and sf.measurement_unit = pf.measurement_unit
+ and sf.variable_name = pf.variable_name
+ )
+ INSERT INTO joined_timeseries
+ SELECT
+ *
+ FROM
+ joined
+ ORDER BY
+ {",".join(jtq.order_by)}
+ ;""" # noqa
+
+ return query
+
+
+def describe_timeseries(timeseries_filepath: str) -> Dict:
+ """Retrieve descriptive stats for a time series.
+
+ Parameters
+ ----------
+ timeseries_filepath : str
+ File path to the "observed" data. String must include path to file(s)
+ and can include wildcards. For example, "/path/to/parquet/*.parquet"
+
+ Returns
+ -------
+ output_report : Union[str, pd.DataFrame, gpd.GeoDataFrame]
+ """
+
+ # TEST QUERIES
+
+ # Find number of rows and unique locations
+ query = f"""
+ SELECT
+ COUNT ( DISTINCT
+ location_id
+ )
+ AS num_location_ids,
+ COUNT(*) AS num_rows,
+ MAX(value_time) as end_date,
+ MIN(value_time) as start_date
+ FROM read_parquet("{timeseries_filepath}")
+ """
+ df = duckdb.sql(query).to_df()
+ num_location_ids = df["num_location_ids"][0]
+ total_num_rows = df["num_rows"][0]
+ start_date = df["start_date"][0]
+ end_date = df["end_date"][0]
+
+ # Find number of duplicates from all columns
+ query = f"""
+ SELECT
+ value_time,
+ location_id,
+ value,
+ measurement_unit,
+ reference_time,
+ configuration,
+ variable_name,
+ COUNT(*)
+ FROM read_parquet("{timeseries_filepath}")
+ GROUP BY
+ value_time,
+ location_id,
+ value,
+ measurement_unit,
+ reference_time,
+ configuration,
+ variable_name,
+ HAVING COUNT(*) > 1
+ """
+ df = duckdb.sql(query).to_df()
+ num_duplicate_rows = df.index.size
+
+ # Find number of duplicate value_times per location_id
+ query = f"""
+ WITH find_duplicates AS (
+ SELECT
+ value_time,
+ location_id,
+ measurement_unit,
+ configuration,
+ variable_name,
+ COUNT(*) AS num_duplicates
+ FROM read_parquet("{timeseries_filepath}")
+ GROUP BY
+ value_time,
+ location_id,
+ measurement_unit,
+ configuration,
+ variable_name,
+ HAVING COUNT(*) > 1
+ )
+ SELECT
+ COUNT( DISTINCT location_id)
+ AS num_locations_with_duplicates
+ FROM find_duplicates
+ WHERE num_duplicates > 1
+ """
+ df = duckdb.sql(query).to_df()
+ num_locations_with_duplicate_value_times = df[
+ "num_locations_with_duplicates"
+ ][0]
+
+ # Check time step integrity by reference_time and location_id
+ query = f"""
+ WITH value_time_diff AS (
+ SELECT *,
+ value_time - LAG(value_time)
+ OVER(PARTITION BY
+ location_id,
+ reference_time
+ ORDER BY value_time)
+ AS value_time_step
+ FROM read_parquet("{timeseries_filepath}")
+ ),
+ missing_timesteps AS (
+ SELECT
+ location_id,
+ reference_time,
+ MAX(value_time_step) AS max_timestep,
+ MIN(value_time_step) AS min_timestep,
+ max_timestep = min_timestep AS none_missing
+ FROM value_time_diff
+ GROUP BY location_id, reference_time
+ )
+ SELECT
+ location_id,
+ reference_time,
+ none_missing
+ FROM missing_timesteps
+ WHERE none_missing != TRUE
+ """
+ df = duckdb.sql(query).to_df()
+ num_locations_with_missing_timesteps = df.index.size
+
+ output_report = {
+ "Number of unique location IDs": num_location_ids,
+ "Total number of rows": total_num_rows,
+ "Start Date": start_date,
+ "End Date": end_date,
+ "Number of duplicate rows": num_duplicate_rows,
+ "Number of location IDs with duplicate value times":
+ num_locations_with_duplicate_value_times,
+ "Number of location IDs with missing time steps":
+ num_locations_with_missing_timesteps,
+ }
+
+ return output_report
+
+
+def create_get_timeseries_query(
+ tq: TimeseriesQuery
+) -> str:
+ """Retrieve joined timeseries using database query.
+
+ tq fields
+ ----------
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ timeseries_name: TimeseriesNameEnum
+ Name of the time series to query (primary or secondary)
+ filters : Union[List[dict], None] = None
+ List of dictionaries describing the "where" clause to limit data that
+ is included in metrics.
+ return_query: bool = False
+ True returns the query string instead of the data
+
+ Returns
+ -------
+ results : Union[str, pd.DataFram]
+
+ Filter and Order By Fields
+ --------------------------
+ * value_time
+ * location_id
+ * value
+ * measurement_unit
+ * reference_time
+ * configuration
+ * variable_name
+
+ Examples:
+ order_by = ["lead_time", "primary_location_id"]
+ filters = [
+ {
+ "column": "location_id",
+ "operator": "in",
+ "value": [12345, 54321]
+ },
+ ]
+ """
+
+ if tq.timeseries_name == "primary":
+ query = f"""
+ SELECT
+ ANY_VALUE(reference_time) AS reference_time,
+ ANY_VALUE(primary_value) AS value,
+ value_time,
+ primary_location_id AS location_id,
+ 'primary' as configuration,
+ measurement_unit,
+ variable_name,
+ FROM
+ joined_timeseries
+ {tqu.filters_to_sql_db(tq.filters)}
+ GROUP BY
+ value_time,
+ primary_location_id,
+ --configuration,
+ measurement_unit,
+ variable_name,
+ ORDER BY
+ {",".join(tq.order_by)}
+ ;"""
+ else:
+ query = f"""
+ SELECT
+ reference_time,
+ value_time,
+ secondary_location_id AS location_id,
+ secondary_value AS value,
+ configuration,
+ measurement_unit,
+ variable_name,
+ FROM
+ joined_timeseries
+ {tqu.filters_to_sql_db(tq.filters)}
+ ORDER BY
+ {",".join(tq.order_by)}
+ ;"""
+
+ return query
+
+
+def create_get_timeseries_char_query(tcq: TimeseriesCharQuery) -> str:
+ """Retrieve joined timeseries using database query.
+
+ tcq fields
+ ----------
+ order_by : List[str]
+ List of column/field names to order results by.
+ Must provide at least one.
+ group_by : List[str]
+ List of column/field names to group timeseries data by.
+ Must provide at least one.
+ timeseries_name: TimeseriesNameEnum
+ Name of the time series to query (primary or secondary)
+ filters : Union[List[dict], None] = None
+ List of dictionaries describing the "where" clause to limit data that
+ is included in metrics.
+ return_query: bool = False
+ True returns the query string instead of the data
+
+ Returns
+ -------
+ query : str
+
+ Examples:
+ order_by = ["lead_time", "primary_location_id"]
+ filters = [
+ {
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "'123456'"
+ },
+ {
+ "column": "reference_time",
+ "operator": "=",
+ "value": "'2022-01-01 00:00'"
+ },
+ {
+ "column": "lead_time",
+ "operator": "<=",
+ "value": "'10 days'"
+ }
+ ]
+ """
+
+ join_max_time_on = tqu._join_time_on(
+ join="mxt", join_to="chars", join_on=tcq.group_by
+ )
+
+ order_by = [f"chars.{val}" for val in tcq.order_by]
+
+ # Create the fts_clause to remove duplicates if primary time series
+ # has been specified
+ if tcq.timeseries_name == "primary":
+
+ selected_primary_fields = ["reference_time",
+ "primary_value",
+ "value_time",
+ "primary_location_id",
+ "configuration",
+ "measurement_unit",
+ "variable_name"]
+ # Format any group by fields that are not selected
+ # by default
+ gb_fields = ""
+ for gb_fld in tcq.group_by:
+ if gb_fld not in selected_primary_fields:
+ gb_fields += f"ANY_VALUE({gb_fld}) as {gb_fld}, "
+
+ fts_clause = f"""
+ SELECT
+ ANY_VALUE(reference_time) AS reference_time,
+ ANY_VALUE(primary_value) AS primary_value,
+ value_time,
+ primary_location_id,
+ 'primary' as configuration,
+ measurement_unit,
+ variable_name,
+ {gb_fields}
+ FROM
+ joined_timeseries
+ {tqu.filters_to_sql_db(tcq.filters)}
+ GROUP BY
+ value_time,
+ primary_location_id,
+ --configuration,
+ measurement_unit,
+ variable_name,
+ """
+ else:
+ fts_clause = f"""
+ SELECT
+ *
+ FROM
+ joined_timeseries
+ {tqu.filters_to_sql_db(tcq.filters)}
+ """
+
+ query = f"""
+ WITH fts AS (
+ {fts_clause}
+ ),
+ mxt AS (
+ SELECT
+ {",".join(tcq.group_by)}
+ , {tcq.timeseries_name}_value
+ , value_time
+ , ROW_NUMBER() OVER(
+ PARTITION BY {",".join(tcq.group_by)}
+ ORDER BY {tcq.timeseries_name}_value DESC, value_time
+ ) as n
+ FROM fts
+ ),
+ chars AS (
+ SELECT
+ {",".join(tcq.group_by)}
+ ,count(fts.{tcq.timeseries_name}_value) as count
+ ,min(fts.{tcq.timeseries_name}_value) as min
+ ,max(fts.{tcq.timeseries_name}_value) as max
+ ,avg(fts.{tcq.timeseries_name}_value) as average
+ ,sum(fts.{tcq.timeseries_name}_value) as sum
+ ,var_pop(fts.{tcq.timeseries_name}_value) as variance
+ FROM
+ fts
+ GROUP BY
+ {",".join(tcq.group_by)}
+ )
+ SELECT
+ chars.{tcq.timeseries_name}_location_id as location_id,
+ chars.count,
+ chars.min,
+ chars.max,
+ chars.average,
+ chars.sum,
+ chars.variance,
+ mxt.value_time as max_value_time
+ FROM chars
+ {join_max_time_on}
+ ORDER BY
+ {",".join(order_by)}
+ ;"""
+
+ return query
+
+
+def create_unique_field_values_query(fn) -> str:
+
+ query = f"""
+ SELECT
+ DISTINCT
+ {fn.field_name}
+ AS unique_{fn.field_name}_values,
+ FROM
+ joined_timeseries
+ ORDER BY
+ {fn.field_name}
+ """
+
+ return query
diff --git a/src/teehr/queries/pandas.py b/src/teehr/queries/pandas.py
index 2c3d105b..07e0d4b2 100644
--- a/src/teehr/queries/pandas.py
+++ b/src/teehr/queries/pandas.py
@@ -118,7 +118,7 @@ def get_metrics(
include_metrics=["nash_sutcliffe_efficiency"]
"""
- mq = tmq.MetricQuery.parse_obj(
+ mq = tmq.MetricQuery.model_validate(
{
"primary_filepath": primary_filepath,
"secondary_filepath": secondary_filepath,
@@ -150,7 +150,7 @@ def get_metrics(
)
# Pandas DataFrame GroupBy approach (works).
- grouped = df.groupby(mq.group_by, as_index=False)
+ grouped = df.groupby(mq.group_by, as_index=False, observed=False)
calculated_metrics = grouped.apply(
calculate_group_metrics,
diff --git a/src/teehr/queries/utils.py b/src/teehr/queries/utils.py
index 5d239daa..f79b0402 100644
--- a/src/teehr/queries/utils.py
+++ b/src/teehr/queries/utils.py
@@ -21,7 +21,7 @@ def _get_datetime_list_string(values):
def _format_iterable_value(
- values: Iterable[Union[str, int, float, datetime]]
+ values: Iterable[Union[str, int, float, datetime]]
) -> str:
"""Returns an SQL formatted string from list of values.
@@ -43,10 +43,7 @@ def _format_iterable_value(
if isinstance(values[0], str):
return f"""({",".join([f"'{v}'" for v in values])})"""
# int or float
- elif (
- isinstance(values[0], int)
- or isinstance(values[0], float)
- ):
+ elif isinstance(values[0], int) or isinstance(values[0], float):
return f"""({",".join([f"{v}" for v in values])})"""
# datetime
elif isinstance(values[0], datetime):
@@ -58,6 +55,65 @@ def _format_iterable_value(
return f"""({",".join([f"'{str(v)}'" for v in values])})"""
+def _format_filter_item_db(
+ filter: Union[JoinedFilter, TimeseriesFilter]
+) -> str:
+ """Returns an SQL formatted string for single filter object.
+
+ Parameters
+ ----------
+ filter: models.*Filter
+ A single *Filter object.
+
+ Returns
+ -------
+ formatted_string : str
+
+ """
+ column = filter.column
+
+ if isinstance(filter.value, str):
+ return f"""{column} {filter.operator} '{filter.value}'"""
+ elif isinstance(filter.value, int) or isinstance(filter.value, float):
+ return f"""{column} {filter.operator} {filter.value}"""
+ elif isinstance(filter.value, datetime):
+ dt_str = filter.value.strftime(SQL_DATETIME_STR_FORMAT)
+ return f"""{column} {filter.operator} '{dt_str}'"""
+ elif isinstance(filter.value, Iterable) and not isinstance(
+ filter.value, str
+ ):
+ value = _format_iterable_value(filter.value)
+ return f"""{column} {filter.operator} {value}"""
+ else:
+ warnings.warn(
+ "treating value as string because didn't know what else to do."
+ )
+ return f"""{column} {filter.operator} '{str(filter.value)}'"""
+
+
+def filters_to_sql_db(filters: List[JoinedFilter]) -> List[str]:
+ """Generate SQL where clause string from filters.
+
+ Parameters
+ ----------
+ filters : List[MetricFilter]
+ A list of MetricFilter objects describing the filters.
+
+ Returns
+ -------
+ where_clause : str
+ A where clause formatted string
+ """
+ if len(filters) > 0:
+ filter_strs = []
+ for f in filters:
+ filter_strs.append(_format_filter_item_db(f))
+ qry = f"""WHERE {f" AND ".join(filter_strs)}"""
+ return qry
+
+ return "--no where clause"
+
+
def _format_filter_item(filter: Union[JoinedFilter, TimeseriesFilter]) -> str:
"""Returns an SQL formatted string for single filter object.
@@ -123,9 +179,18 @@ def filters_to_sql(filters: List[JoinedFilter]) -> List[str]:
return "--no where clause"
-def geometry_join_clause(
- q: Union[MetricQuery, JoinedTimeseriesQuery]
+def geometry_join_clause_db(
+ q: Union[MetricQuery, JoinedTimeseriesQuery]
) -> str:
+ """Generate the join clause for"""
+ if q.include_geometry:
+ return """JOIN geometry gf
+ on pf.location_id = gf.id
+ """
+ return ""
+
+
+def geometry_join_clause(q: Union[MetricQuery, JoinedTimeseriesQuery]) -> str:
"""Generate the join clause for"""
if q.include_geometry:
return f"""JOIN read_parquet('{str(q.geometry_filepath)}') gf
@@ -135,15 +200,34 @@ def geometry_join_clause(
def geometry_select_clause(
- q: Union[MetricQuery, JoinedTimeseriesQuery]
+ q: Union[MetricQuery, JoinedTimeseriesQuery]
) -> str:
if q.include_geometry:
return ",gf.geometry as geometry"
return ""
+def geometry_select_clause_db(
+ q: Union[MetricQuery, JoinedTimeseriesQuery]
+) -> str:
+ if q.include_geometry:
+ return ", geometry"
+ return ""
+
+
+def metric_geometry_join_clause_db(
+ q: Union[MetricQuery, JoinedTimeseriesQuery]
+) -> str:
+ """Generate the join clause for"""
+ if q.include_geometry:
+ return """JOIN geometry gf
+ on primary_location_id = gf.id
+ """
+ return ""
+
+
def metric_geometry_join_clause(
- q: Union[MetricQuery, JoinedTimeseriesQuery]
+ q: Union[MetricQuery, JoinedTimeseriesQuery]
) -> str:
"""Generate the join clause for"""
if q.include_geometry:
@@ -300,10 +384,7 @@ def _select_mean_squared_error(mq: MetricQuery) -> str:
def _select_mean_error(mq: MetricQuery) -> str:
- if (
- "mean_error" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "mean_error" in mq.include_metrics or mq.include_metrics == "all":
return """, sum(absolute_difference)/count(*) as mean_error"""
return ""
@@ -337,19 +418,13 @@ def _select_nash_sutcliffe_efficiency(mq: MetricQuery) -> str:
def _select_bias(mq: MetricQuery) -> str:
- if (
- "bias" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "bias" in mq.include_metrics or mq.include_metrics == "all":
return """, sum(primary_value - secondary_value)/count(*) as bias"""
return ""
def _select_max_value_delta(mq: MetricQuery) -> str:
- if (
- "max_value_delta" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "max_value_delta" in mq.include_metrics or mq.include_metrics == "all":
return """, max(secondary_value) - max(primary_value)
as max_value_delta
"""
@@ -357,28 +432,19 @@ def _select_max_value_delta(mq: MetricQuery) -> str:
def _select_primary_count(mq: MetricQuery) -> str:
- if (
- "primary_count" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "primary_count" in mq.include_metrics or mq.include_metrics == "all":
return """, count(primary_value) as primary_count"""
return ""
def _select_secondary_count(mq: MetricQuery) -> str:
- if (
- "secondary_count" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "secondary_count" in mq.include_metrics or mq.include_metrics == "all":
return """, count(secondary_value) as secondary_count"""
return ""
def _select_primary_minimum(mq: MetricQuery) -> str:
- if (
- "primary_minimum" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "primary_minimum" in mq.include_metrics or mq.include_metrics == "all":
return """, min(primary_value) as primary_minimum"""
return ""
@@ -393,10 +459,7 @@ def _select_secondary_minimum(mq: MetricQuery) -> str:
def _select_primary_maximum(mq: MetricQuery) -> str:
- if (
- "primary_maximum" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "primary_maximum" in mq.include_metrics or mq.include_metrics == "all":
return """, max(primary_value) as primary_maximum"""
return ""
@@ -411,10 +474,7 @@ def _select_secondary_maximum(mq: MetricQuery) -> str:
def _select_primary_average(mq: MetricQuery) -> str:
- if (
- "primary_average" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "primary_average" in mq.include_metrics or mq.include_metrics == "all":
return """, avg(primary_value) as primary_average"""
return ""
@@ -429,28 +489,19 @@ def _select_secondary_average(mq: MetricQuery) -> str:
def _select_primary_sum(mq: MetricQuery) -> str:
- if (
- "primary_sum" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "primary_sum" in mq.include_metrics or mq.include_metrics == "all":
return """, sum(primary_value) as primary_sum"""
return ""
def _select_secondary_sum(mq: MetricQuery) -> str:
- if (
- "secondary_sum" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "secondary_sum" in mq.include_metrics or mq.include_metrics == "all":
return """, sum(secondary_value) as secondary_sum"""
return ""
def _select_primary_variance(mq: MetricQuery) -> str:
- if (
- "primary_variance" in mq.include_metrics
- or mq.include_metrics == "all"
- ):
+ if "primary_variance" in mq.include_metrics or mq.include_metrics == "all":
return """, var_pop(primary_value) as primary_variance"""
return ""
@@ -471,9 +522,7 @@ def _join_primary_join_max_time(mq: MetricQuery) -> str:
or mq.include_metrics == "all"
):
return _join_time_on(
- join="pmxt",
- join_to="metrics",
- join_on=mq.group_by
+ join="pmxt", join_to="metrics", join_on=mq.group_by
)
return ""
@@ -485,9 +534,7 @@ def _join_secondary_join_max_time(mq: MetricQuery) -> str:
or mq.include_metrics == "all"
):
return _join_time_on(
- join="smxt",
- join_to="metrics",
- join_on=mq.group_by
+ join="smxt", join_to="metrics", join_on=mq.group_by
)
return ""
@@ -514,10 +561,10 @@ def df_to_gdf(df: pd.DataFrame) -> gpd.GeoDataFrame:
"""
df["geometry"] = gpd.GeoSeries.from_wkb(
df["geometry"].apply(lambda x: bytes(x))
- )
+ )
return gpd.GeoDataFrame(df, crs="EPSG:4326", geometry="geometry")
def remove_empty_lines(text: str) -> str:
"""Remove empty lines from string."""
- return "".join([s for s in text.splitlines(True) if s.strip()])
\ No newline at end of file
+ return "".join([s for s in text.splitlines(True) if s.strip()])
diff --git a/teehr-hub/helm-chart/config.yaml b/teehr-hub/helm-chart/config.yaml
index c0849c2d..6ae480a5 100644
--- a/teehr-hub/helm-chart/config.yaml
+++ b/teehr-hub/helm-chart/config.yaml
@@ -44,10 +44,23 @@ jupyterhub:
- display_name: "TEEHR Evaluation System (16 vCPU/128GB memory)"
description: "A r5.4xlarge EC2 instance @ $1.008/hour"
kubespawner_override:
- # mem_limit: 128G
- # mem_guarantee: 128G
- # cpu_limit: 16
- # cpu_guarantee: 16
+ cpu_limit: null
+ mem_limit: null
+ node_selector:
+ node.kubernetes.io/instance-type: r5.4xlarge
+ - display_name: "TEEHR Evaluation System (4 vCPU/32GB memory) v0.3.0"
+ default: True
+ description: "A r5.xlarge EC2 instance $0.252/hour"
+ kubespawner_override:
+ image: 935462133478.dkr.ecr.us-east-2.amazonaws.com/teehr:v0.3.0
+ cpu_limit: null
+ mem_limit: null
+ node_selector:
+ node.kubernetes.io/instance-type: r5.xlarge
+ - display_name: "TEEHR Evaluation System (16 vCPU/128GB memory) v0.3.0"
+ description: "A r5.4xlarge EC2 instance @ $1.008/hour"
+ kubespawner_override:
+ image: 935462133478.dkr.ecr.us-east-2.amazonaws.com/teehr:v0.3.0
cpu_limit: null
mem_limit: null
node_selector:
diff --git a/temp.json b/temp.json
new file mode 100644
index 00000000..72f0b7ed
--- /dev/null
+++ b/temp.json
@@ -0,0 +1,50 @@
+{
+ "type": "FeatureCollection",
+ "features": [
+ {
+ "id": "0",
+ "type": "Feature",
+ "properties": {
+ "primary_location_id": "gage-A",
+ "primary_count": 72
+ },
+ "geometry": {
+ "type": "Point",
+ "coordinates": [
+ -80.71918431799998,
+ 35.1761134781112
+ ]
+ }
+ },
+ {
+ "id": "1",
+ "type": "Feature",
+ "properties": {
+ "primary_location_id": "gage-B",
+ "primary_count": 72
+ },
+ "geometry": {
+ "type": "Point",
+ "coordinates": [
+ -80.71308451799996,
+ 35.15839061111134
+ ]
+ }
+ },
+ {
+ "id": "2",
+ "type": "Feature",
+ "properties": {
+ "primary_location_id": "gage-C",
+ "primary_count": 72
+ },
+ "geometry": {
+ "type": "Point",
+ "coordinates": [
+ -80.73677251799995,
+ 35.18693221111115
+ ]
+ }
+ }
+ ]
+}
\ No newline at end of file
diff --git a/tests/data/test_study/geo/convert.py b/tests/data/test_study/geo/convert.py
index eda8725a..bc96d2df 100644
--- a/tests/data/test_study/geo/convert.py
+++ b/tests/data/test_study/geo/convert.py
@@ -1,5 +1,5 @@
"""
-This simple script converts `crosswalk.csv` and `gages.geojson`
+This simple script converts `crosswalk.csv` and `gages.geojson`
to parquet files.
To run this:
@@ -18,4 +18,8 @@
print(f"gages.geojson")
gdf = gpd.read_file("gages.geojson")
-gdf.to_parquet("gages.parquet")
\ No newline at end of file
+gdf.to_parquet("gages.parquet")
+
+print("test_attr.csv")
+df = pd.read_csv("test_attr.csv")
+df.to_parquet("test_attr.parquet")
\ No newline at end of file
diff --git a/tests/data/test_study/geo/test_attr.csv b/tests/data/test_study/geo/test_attr.csv
new file mode 100644
index 00000000..f97e8890
--- /dev/null
+++ b/tests/data/test_study/geo/test_attr.csv
@@ -0,0 +1,7 @@
+location_id,attribute_name,attribute_value,attribute_unit
+gage-A,drainage_area,50,sq_km
+gage-B,drainage_area,20,sq_km
+gage-C,drainage_area,30,sq_km
+gage-A,year_2_discharge,500,cfs
+gage-B,year_2_discharge,200,cfs
+gage-C,year_2_discharge,300,cfs
\ No newline at end of file
diff --git a/tests/data/test_study/geo/test_attr.parquet b/tests/data/test_study/geo/test_attr.parquet
new file mode 100644
index 00000000..7a61127c
Binary files /dev/null and b/tests/data/test_study/geo/test_attr.parquet differ
diff --git a/tests/data/test_study/temp_test_api.db b/tests/data/test_study/temp_test_api.db
new file mode 100644
index 00000000..13388baf
Binary files /dev/null and b/tests/data/test_study/temp_test_api.db differ
diff --git a/tests/test_duckdb_metric_queries.py b/tests/test_duckdb_metric_queries.py
index 0c97c476..86a5238b 100644
--- a/tests/test_duckdb_metric_queries.py
+++ b/tests/test_duckdb_metric_queries.py
@@ -20,15 +20,15 @@ def test_metric_query_str():
geometry_filepath=GEOMETRY_FILEPATH,
group_by=["primary_location_id"],
order_by=["primary_location_id"],
- include_metrics="all",
- return_query=True
+ include_metrics=["nash_sutcliffe_efficiency"],
+ return_query=True,
+ include_geometry=True,
)
# print(query_str)
assert isinstance(query_str, str)
def test_metric_query_df():
-
query_df = tqu.get_metrics(
primary_filepath=PRIMARY_FILEPATH,
secondary_filepath=SECONDARY_FILEPATH,
@@ -44,7 +44,6 @@ def test_metric_query_df():
def test_metric_query_gdf():
-
query_df = tqu.get_metrics(
primary_filepath=PRIMARY_FILEPATH,
secondary_filepath=SECONDARY_FILEPATH,
@@ -62,7 +61,6 @@ def test_metric_query_gdf():
def test_metric_query_gdf_2():
-
query_df = tqu.get_metrics(
primary_filepath=PRIMARY_FILEPATH,
secondary_filepath=SECONDARY_FILEPATH,
@@ -151,7 +149,7 @@ def test_metric_query_df_time_metrics():
include_metrics = [
"primary_max_value_time",
"secondary_max_value_time",
- "max_value_timedelta"
+ "max_value_timedelta",
]
group_by = ["primary_location_id", "reference_time"]
query_df = tqu.get_metrics(
@@ -206,13 +204,13 @@ def test_metric_query_value_time_filter():
{
"column": "value_time",
"operator": ">=",
- "value": f"{'2022-01-01 13:00:00'}"
+ "value": f"{'2022-01-01 13:00:00'}",
},
{
"column": "reference_time",
"operator": ">=",
- "value": f"{'2022-01-01 02:00:00'}"
- }
+ "value": f"{'2022-01-01 02:00:00'}",
+ },
],
)
# print(query_df)
diff --git a/tests/test_duckdb_pandas_compare.py b/tests/test_duckdb_pandas_compare.py
deleted file mode 100644
index f0a77003..00000000
--- a/tests/test_duckdb_pandas_compare.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import numpy as np
-import teehr.queries.pandas as tqk
-import teehr.queries.duckdb as tqu
-from pathlib import Path
-
-TEST_STUDY_DIR = Path("tests", "data", "test_study")
-PRIMARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "*_obs.parquet")
-SECONDARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "*_fcast.parquet")
-CROSSWALK_FILEPATH = Path(TEST_STUDY_DIR, "geo", "crosswalk.parquet")
-GEOMETRY_FILEPATH = Path(TEST_STUDY_DIR, "geo", "gages.parquet")
-
-
-def test_metric_compare_1():
- include_metrics = [
- "primary_count",
- "secondary_count",
- "primary_minimum",
- "secondary_minimum",
- "primary_maximum",
- "secondary_maximum",
- "primary_average",
- "secondary_average",
- "primary_sum",
- "secondary_sum",
- "primary_variance",
- "secondary_variance",
- "max_value_delta",
- "bias",
- "nash_sutcliffe_efficiency",
- "kling_gupta_efficiency",
- "mean_error",
- "mean_squared_error",
- "root_mean_squared_error",
- ]
- group_by = [
- "primary_location_id",
- "reference_time"
- ]
- args = {
- "primary_filepath": PRIMARY_FILEPATH,
- "secondary_filepath": SECONDARY_FILEPATH,
- "crosswalk_filepath": CROSSWALK_FILEPATH,
- "geometry_filepath": GEOMETRY_FILEPATH,
- "group_by": group_by,
- "order_by": ["primary_location_id"],
- "include_metrics": include_metrics,
- "return_query": False
- }
- pandas_df = tqk.get_metrics(**args)
- duckdb_df = tqu.get_metrics(**args)
-
- for m in include_metrics:
- # print(m)
- duckdb_np = duckdb_df[m].to_numpy()
- pandas_np = pandas_df[m].to_numpy()
- assert np.allclose(duckdb_np, pandas_np)
-
-
-def test_metric_compare_time_metrics():
- include_metrics = [
- "primary_max_value_time",
- "secondary_max_value_time",
- "max_value_timedelta",
- ]
- group_by = [
- "primary_location_id",
- "reference_time"
- ]
- args = {
- "primary_filepath": PRIMARY_FILEPATH,
- "secondary_filepath": SECONDARY_FILEPATH,
- "crosswalk_filepath": CROSSWALK_FILEPATH,
- "geometry_filepath": GEOMETRY_FILEPATH,
- "group_by": group_by,
- "order_by": ["primary_location_id"],
- "include_metrics": include_metrics,
- "return_query": False
- }
- pandas_df = tqk.get_metrics(**args)
- duckdb_df = tqu.get_metrics(**args)
-
- # Difference in significant digits
- # for m in include_metrics:
- # duckdb_np = duckdb_df[m].astype('int64').to_numpy()
- # pandas_np = pandas_df[m].astype('int64').to_numpy()
- # assert np.allclose(duckdb_np, pandas_np)
-
- diff_df = pandas_df[include_metrics].compare(duckdb_df[include_metrics])
- assert diff_df.index.size == 0
-
-
-if __name__ == "__main__":
- test_metric_compare_1()
- test_metric_compare_time_metrics()
- pass
diff --git a/tests/test_duckdb_pandas_tds_compare.py b/tests/test_duckdb_pandas_tds_compare.py
new file mode 100644
index 00000000..af40df5d
--- /dev/null
+++ b/tests/test_duckdb_pandas_tds_compare.py
@@ -0,0 +1,239 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+import teehr.queries.pandas as tqk
+import teehr.queries.duckdb as tqu
+from teehr.database.teehr_dataset import TEEHRDatasetDB
+
+TEST_STUDY_DIR = Path("tests", "data", "test_study")
+PRIMARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "*_obs.parquet")
+SECONDARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "*_fcast.parquet")
+CROSSWALK_FILEPATH = Path(TEST_STUDY_DIR, "geo", "crosswalk.parquet")
+GEOMETRY_FILEPATH = Path(TEST_STUDY_DIR, "geo", "gages.parquet")
+DATABASE_FILEPATH = Path("tests", "data", "temp", "temp_test.db")
+
+if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+TDS = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+# Perform the join and insert into duckdb database
+TDS.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+)
+
+
+def test_metric_compare_1():
+ include_metrics = [
+ "primary_count",
+ "secondary_count",
+ "primary_minimum",
+ "secondary_minimum",
+ "primary_maximum",
+ "secondary_maximum",
+ "primary_average",
+ "secondary_average",
+ "primary_sum",
+ "secondary_sum",
+ "primary_variance",
+ "secondary_variance",
+ "max_value_delta",
+ "bias",
+ "nash_sutcliffe_efficiency",
+ "kling_gupta_efficiency",
+ "mean_error",
+ "mean_squared_error",
+ "root_mean_squared_error",
+ ]
+ group_by = [
+ "primary_location_id",
+ "reference_time"
+ ]
+ args = {
+ "primary_filepath": PRIMARY_FILEPATH,
+ "secondary_filepath": SECONDARY_FILEPATH,
+ "crosswalk_filepath": CROSSWALK_FILEPATH,
+ "geometry_filepath": GEOMETRY_FILEPATH,
+ "group_by": group_by,
+ "order_by": ["primary_location_id", "reference_time"],
+ "include_metrics": include_metrics,
+ "return_query": False
+ }
+
+ order_by = ["primary_location_id", "reference_time"]
+ tds_df = TDS.get_metrics(group_by=group_by,
+ order_by=order_by,
+ include_metrics=include_metrics)
+
+ pandas_df = tqk.get_metrics(**args)
+ duckdb_df = tqu.get_metrics(**args)
+
+ pandas_df["primary_count"] = pandas_df.primary_count.astype(int)
+ pandas_df["secondary_count"] = pandas_df.secondary_count.astype(int)
+
+ for m in include_metrics:
+ # print(m)
+ duckdb_np = duckdb_df[m].to_numpy()
+ pandas_np = pandas_df[m].to_numpy()
+ tds_np = tds_df[m].to_numpy()
+ assert np.allclose(duckdb_np, pandas_np)
+ assert np.allclose(tds_np, pandas_np)
+ assert np.allclose(tds_np, duckdb_np)
+
+
+def test_metric_compare_time_metrics():
+ include_metrics = [
+ "primary_max_value_time",
+ "secondary_max_value_time",
+ "max_value_timedelta",
+ ]
+ group_by = [
+ "primary_location_id",
+ "reference_time"
+ ]
+ args = {
+ "primary_filepath": PRIMARY_FILEPATH,
+ "secondary_filepath": SECONDARY_FILEPATH,
+ "crosswalk_filepath": CROSSWALK_FILEPATH,
+ "geometry_filepath": GEOMETRY_FILEPATH,
+ "group_by": group_by,
+ "order_by": ["primary_location_id", "reference_time"],
+ "include_metrics": include_metrics,
+ "return_query": False
+ }
+
+ order_by = ["primary_location_id", "reference_time"]
+ tds_df = TDS.get_metrics(group_by=group_by,
+ order_by=order_by,
+ include_metrics=include_metrics)
+
+ pandas_df = tqk.get_metrics(**args)
+ duckdb_df = tqu.get_metrics(**args)
+
+ diff_df1 = pandas_df[include_metrics].compare(duckdb_df[include_metrics])
+ assert diff_df1.index.size == 0
+ diff_df2 = pandas_df[include_metrics].compare(tds_df[include_metrics])
+ assert diff_df2.index.size == 0
+
+
+def test_primary_timeseries_compare():
+ query_df = tqu.get_timeseries(
+ timeseries_filepath=PRIMARY_FILEPATH,
+ order_by=["location_id"],
+ return_query=False,
+ )
+
+ tds_df = TDS.get_timeseries(
+ order_by=["primary_location_id"],
+ timeseries_name="primary",
+ )
+
+ # print(query_df)
+ assert len(query_df) == 26 * 3
+ assert len(query_df) == len(tds_df)
+
+
+def test_secondary_timeseries_compare():
+ query_df = tqu.get_timeseries(
+ timeseries_filepath=SECONDARY_FILEPATH,
+ order_by=["location_id"],
+ return_query=False,
+ )
+ tds_df = TDS.get_timeseries(
+ order_by=["secondary_location_id"],
+ timeseries_name="secondary"
+ )
+ assert len(query_df) == 24 * 3 * 3
+ assert len(query_df) == len(tds_df)
+
+
+def test_primary_timeseries_char_compare():
+ query_df = tqu.get_timeseries_chars(
+ timeseries_filepath=PRIMARY_FILEPATH,
+ group_by=["location_id"],
+ order_by=["location_id"],
+ return_query=False,
+ )
+
+ tds_df = TDS.get_timeseries_chars(
+ order_by=["primary_location_id"],
+ group_by=["primary_location_id"],
+ timeseries_name="primary",
+ )
+
+ df = pd.DataFrame(
+ {
+ "location_id": {0: "gage-A", 1: "gage-B", 2: "gage-C"},
+ "count": {0: 26, 1: 26, 2: 26},
+ "min": {0: 0.1, 1: 10.1, 2: 0.0},
+ "max": {0: 5.0, 1: 15.0, 2: 180.0},
+ "average": {
+ 0: 1.2038461538461542,
+ 1: 11.203846153846156,
+ 2: 100.38461538461539,
+ },
+ "sum": {0: 31.300000000000008, 1: 291.30000000000007, 2: 2610.0},
+ "variance": {
+ 0: 1.9788313609467447,
+ 1: 1.9788313609467456,
+ 2: 2726.7751479289923,
+ },
+ "max_value_time": {
+ 0: pd.Timestamp("2022-01-01 15:00:00"),
+ 1: pd.Timestamp("2022-01-01 15:00:00"),
+ 2: pd.Timestamp("2022-01-01 06:00:00"),
+ },
+ }
+ )
+ # Check if contents are the same
+ diff_df = df.compare(query_df)
+ assert diff_df.index.size == 0
+
+ for i, col in enumerate(df.columns):
+ # print(m)
+ if (col == "location_id") or (col == "max_value_time"):
+ assert (df[col] == tds_df.iloc[:, i]).all()
+ continue
+ df_np = df[col].to_numpy()
+ tds_np = tds_df.iloc[:, i].to_numpy()
+ assert np.allclose(tds_np, df_np)
+
+
+def test_secondary_timeseries_char_compare():
+ query_df = tqu.get_timeseries_chars(
+ timeseries_filepath=SECONDARY_FILEPATH,
+ group_by=["location_id"],
+ order_by=["location_id"],
+ return_query=False,
+ )
+
+ tds_df = TDS.get_timeseries_chars(
+ order_by=["secondary_location_id"],
+ group_by=["secondary_location_id"],
+ timeseries_name="secondary"
+ )
+
+ # Check if contents are the same
+ for i, col in enumerate(query_df.columns):
+ # print(m)
+ if (col == "location_id") or (col == "max_value_time"):
+ assert (query_df[col] == tds_df.iloc[:, i]).all()
+ continue
+ df_np = query_df[col].to_numpy()
+ tds_np = tds_df.iloc[:, i].to_numpy()
+ assert np.allclose(tds_np, df_np)
+
+
+if __name__ == "__main__":
+ test_metric_compare_1()
+ test_metric_compare_time_metrics()
+ test_primary_timeseries_compare()
+ test_secondary_timeseries_compare()
+ test_primary_timeseries_char_compare()
+ test_secondary_timeseries_char_compare()
+ pass
diff --git a/tests/test_duckdb_timeseries_queries.py b/tests/test_duckdb_timeseries_queries.py
index 47e91adf..e23f55aa 100644
--- a/tests/test_duckdb_timeseries_queries.py
+++ b/tests/test_duckdb_timeseries_queries.py
@@ -18,7 +18,7 @@ def test_joined_timeseries_query_df():
crosswalk_filepath=CROSSWALK_FILEPATH,
geometry_filepath=GEOMETRY_FILEPATH,
order_by=["primary_location_id", "lead_time"],
- return_query=False
+ return_query=False,
)
# print(query_df.info())
@@ -54,14 +54,14 @@ def test_joined_timeseries_query_df_filter():
{
"column": "reference_time",
"operator": "=",
- "value": "2022-01-01 00:00:00"
+ "value": "2022-01-01 00:00:00",
},
{
"column": "primary_location_id",
"operator": "=",
- "value": "gage-A"
+ "value": "gage-A",
},
- ]
+ ],
)
# print(query_df.info())
@@ -75,8 +75,8 @@ def test_timeseries_query_df():
order_by=["location_id"],
return_query=False,
)
- # print(query_df)
- assert len(query_df) == 26*3
+ print(query_df)
+ assert len(query_df) == 26 * 3
def test_timeseries_query_df2():
@@ -85,18 +85,16 @@ def test_timeseries_query_df2():
order_by=["location_id"],
return_query=False,
)
- assert len(query_df) == 24*3*3
+ assert len(query_df) == 24 * 3 * 3
def test_timeseries_query_one_site_df():
query_df = tqu.get_timeseries(
timeseries_filepath=PRIMARY_FILEPATH,
order_by=["location_id"],
- filters=[{
- "column": "location_id",
- "operator": "=",
- "value": "gage-C"
- }],
+ filters=[
+ {"column": "location_id", "operator": "=", "value": "gage-C"}
+ ],
return_query=False,
)
assert len(query_df) == 26
@@ -107,15 +105,11 @@ def test_timeseries_query_one_site_one_ref_df():
timeseries_filepath=SECONDARY_FILEPATH,
order_by=["value_time"],
filters=[
- {
- "column": "location_id",
- "operator": "=",
- "value": "fcst-1"
- },
+ {"column": "location_id", "operator": "=", "value": "fcst-1"},
{
"column": "reference_time",
"operator": "=",
- "value": datetime(2022, 1, 1)
+ "value": datetime(2022, 1, 1),
},
],
return_query=False,
@@ -132,30 +126,26 @@ def test_timeseries_char_query_df():
)
df = pd.DataFrame(
{
- 'location_id': {0: 'gage-A', 1: 'gage-B', 2: 'gage-C'},
- 'count': {0: 26, 1: 26, 2: 26},
- 'min': {0: 0.1, 1: 10.1, 2: 0.0},
- 'max': {0: 5.0, 1: 15.0, 2: 180.0},
- 'average': {
+ "location_id": {0: "gage-A", 1: "gage-B", 2: "gage-C"},
+ "count": {0: 26, 1: 26, 2: 26},
+ "min": {0: 0.1, 1: 10.1, 2: 0.0},
+ "max": {0: 5.0, 1: 15.0, 2: 180.0},
+ "average": {
0: 1.2038461538461542,
1: 11.203846153846156,
- 2: 100.38461538461539
- },
- 'sum': {
- 0: 31.300000000000008,
- 1: 291.30000000000007,
- 2: 2610.0
+ 2: 100.38461538461539,
},
- 'variance': {
+ "sum": {0: 31.300000000000008, 1: 291.30000000000007, 2: 2610.0},
+ "variance": {
0: 1.9788313609467447,
1: 1.9788313609467456,
- 2: 2726.7751479289923
+ 2: 2726.7751479289923,
+ },
+ "max_value_time": {
+ 0: pd.Timestamp("2022-01-01 15:00:00"),
+ 1: pd.Timestamp("2022-01-01 15:00:00"),
+ 2: pd.Timestamp("2022-01-01 06:00:00"),
},
- 'max_value_time': {
- 0: pd.Timestamp('2022-01-01 15:00:00'),
- 1: pd.Timestamp('2022-01-01 15:00:00'),
- 2: pd.Timestamp('2022-01-01 06:00:00')
- }
}
)
diff_df = df.compare(query_df)
@@ -182,15 +172,11 @@ def test_timeseries_char_query_filter_df():
order_by=["location_id"],
return_query=False,
filters=[
- {
- "column": "location_id",
- "operator": "=",
- "value": "fcst-1"
- },
+ {"column": "location_id", "operator": "=", "value": "fcst-1"},
{
"column": "reference_time",
"operator": "=",
- "value": datetime(2022, 1, 1)
+ "value": datetime(2022, 1, 1),
},
],
)
diff --git a/tests/test_fastapi_endpoints.py b/tests/test_fastapi_endpoints.py
new file mode 100644
index 00000000..20295cef
--- /dev/null
+++ b/tests/test_fastapi_endpoints.py
@@ -0,0 +1,148 @@
+from fastapi.testclient import TestClient
+from teehr.api.main import app
+
+client = TestClient(app)
+
+
+def test_read_root():
+ response = client.get("/")
+ assert response.status_code == 200
+ assert response.json() == {"msg": "Welcome to TEEHR"}
+
+
+def test_read_datasets():
+ response = client.get("/datasets/")
+ assert response.status_code == 200
+ print(response.json())
+
+
+def test_get_metrics():
+
+ post = {
+ "group_by": ["primary_location_id"],
+ "order_by": ["primary_location_id"],
+ "include_metrics": "primary_count",
+ "return_query": False,
+ "include_geometry": False
+ }
+
+ response = client.post(
+ "/datasets/study-a/get_metrics",
+ json=post
+ )
+ print(response.json())
+ assert response.status_code == 200
+ # To unpack error msg from pydantic (only present if fails?)
+ # print(response.json()["detail"][0]["msg"])
+
+
+def test_get_metrics_filter():
+
+ post = {
+ "group_by": ["primary_location_id"],
+ "order_by": ["primary_location_id"],
+ "include_metrics": "all",
+ "return_query": False,
+ "include_geometry": False,
+ "filters": [{
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "gage-A"
+ }]
+ }
+
+ response = client.post(
+ "/datasets/study-a/get_metrics",
+ json=post
+ )
+ assert response.status_code == 200
+ print(response.json())
+
+
+def test_get_metric_fields():
+ response = client.get(
+ "/datasets/study-a/get_metric_fields"
+ )
+ assert response.status_code == 200
+ print(response.json())
+
+
+def test_get_data_fields():
+ response = client.get(
+ "/datasets/study-a/get_data_fields"
+ )
+ assert response.status_code == 200
+ print(response.json())
+
+
+def test_get_timeseries():
+
+ post = {
+ "order_by": ["primary_location_id"],
+ "return_query": False,
+ "filters": [{
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "gage-A"
+ }],
+ "timeseries_name": "primary"
+ }
+
+ response = client.post(
+ "/datasets/study-a/get_timeseries",
+ json=post
+ )
+ assert response.status_code == 200
+ print(response.json())
+
+
+def test_get_timeseries_chars():
+
+ # post = {
+ # "order_by": ["primary_location_id"],
+ # "return_query": False,
+ # "filters": [{
+ # "column": "primary_location_id",
+ # "operator": "=",
+ # "value": "gage-A"
+ # }]
+ # }
+
+ post = {
+ "filters": [],
+ "group_by": ["primary_location_id"],
+ "order_by": ["primary_location_id"],
+ "timeseries_name": "primary",
+ # "return_query": False
+ }
+
+ response = client.post(
+ "/datasets/study-a/get_timeseries_chars",
+ json=post
+ )
+ assert response.status_code == 200
+ print(response.json())
+
+
+def test_get_unique_field_values():
+ post = {
+ "field_name": "primary_location_id"
+ }
+ response = client.post(
+ "/datasets/study-a/get_unique_field_values",
+ json=post
+ )
+ assert response.status_code == 200
+ print(response.json())
+
+
+if __name__ == "__main__":
+ # test_read_root()
+ # test_read_datasets()
+ # test_get_metrics()
+ # test_get_metrics_filter()
+ # test_get_metric_fields()
+ # test_get_data_fields()
+ # test_get_timeseries()
+ test_get_timeseries_chars()
+ # test_get_unique_field_values()
diff --git a/tests/test_nwm22_loading_models.py b/tests/test_nwm22_loading_models.py
index af7b12e8..d11d5d07 100644
--- a/tests/test_nwm22_loading_models.py
+++ b/tests/test_nwm22_loading_models.py
@@ -16,7 +16,7 @@ def test_point_model():
},
}
- cm = PointConfigurationModel.parse_obj(vars)
+ cm = PointConfigurationModel.model_validate(vars)
config = cm.configuration.name
forecast_obj = getattr(cm, config)
@@ -42,8 +42,7 @@ def test_grid_model():
},
}
- # Check input parameters
- cm = GridConfigurationModel.parse_obj(vars)
+ cm = GridConfigurationModel.model_validate(vars)
config = cm.configuration.name
forecast_obj = getattr(cm, config)
diff --git a/tests/test_nwm30_loading_models.py b/tests/test_nwm30_loading_models.py
index f972d4c7..96f2355c 100644
--- a/tests/test_nwm30_loading_models.py
+++ b/tests/test_nwm30_loading_models.py
@@ -16,7 +16,7 @@ def test_point_model():
},
}
- cm = PointConfigurationModel.parse_obj(vars)
+ cm = PointConfigurationModel.model_validate(vars)
config = cm.configuration.name
forecast_obj = getattr(cm, config)
@@ -43,7 +43,7 @@ def test_grid_model():
}
# Check input parameters
- cm = GridConfigurationModel.parse_obj(vars)
+ cm = GridConfigurationModel.model_validate(vars)
config = cm.configuration.name
forecast_obj = getattr(cm, config)
diff --git a/tests/test_pandas_metric_queries.py b/tests/test_pandas_metric_queries.py
index 65782b0f..c096bf82 100644
--- a/tests/test_pandas_metric_queries.py
+++ b/tests/test_pandas_metric_queries.py
@@ -133,10 +133,10 @@ def test_metric_query_df_time_metrics():
if __name__ == "__main__":
- # test_metric_query_df()
- # test_metric_query_df2()
- # test_metric_query_filter_df()
- # test_metric_query_gdf()
- # test_metric_query_df_limit_metrics()
+ test_metric_query_df()
+ test_metric_query_df2()
+ test_metric_query_filter_df()
+ test_metric_query_gdf()
+ test_metric_query_df_limit_metrics()
test_metric_query_df_time_metrics()
pass
diff --git a/tests/test_teehr_dataset_api.py b/tests/test_teehr_dataset_api.py
new file mode 100644
index 00000000..669cc6cf
--- /dev/null
+++ b/tests/test_teehr_dataset_api.py
@@ -0,0 +1,80 @@
+from pathlib import Path
+# import numpy as np
+
+from teehr.database.teehr_dataset import TEEHRDatasetAPI
+from teehr.models.queries_database import (
+ MetricQuery,
+ JoinedTimeseriesFieldName
+)
+
+# Test data
+TEST_STUDY_DIR = Path("tests", "data", "test_study")
+PRIMARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "*_obs.parquet")
+SECONDARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "*_fcast.parquet")
+CROSSWALK_FILEPATH = Path(TEST_STUDY_DIR, "geo", "crosswalk.parquet")
+GEOMETRY_FILEPATH = Path(TEST_STUDY_DIR, "geo", "gages.parquet")
+ATTRIBUTES_FILEPATH = Path(TEST_STUDY_DIR, "geo", "test_attr.parquet")
+DATABASE_FILEPATH = Path("tests", "data", "temp", "temp_test.db")
+
+# NOTE: These tests require the db and joined_timeseries values to exist
+
+
+def test_unique_field_values():
+ tds = TEEHRDatasetAPI(DATABASE_FILEPATH)
+
+ jtn = JoinedTimeseriesFieldName.model_validate(
+ {"field_name": "primary_location_id"}
+ )
+
+ df = tds.get_unique_field_values(jtn)
+ assert sorted(df["unique_primary_location_id_values"].tolist()) == [
+ "gage-A",
+ "gage-B",
+ "gage-C",
+ ]
+ pass
+
+
+def test_metrics_query():
+ tds = TEEHRDatasetAPI(DATABASE_FILEPATH)
+
+ # Get metrics
+ order_by = ["lead_time", "primary_location_id"]
+ group_by = ["lead_time", "primary_location_id"]
+ filters = [
+ {
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "gage-A",
+ },
+ {
+ "column": "reference_time",
+ "operator": "=",
+ "value": "2022-01-01 00:00:00",
+ },
+ {"column": "lead_time", "operator": "<=", "value": "10 hours"},
+ ]
+
+ mq = MetricQuery.model_validate(
+ {
+ "group_by": group_by,
+ "order_by": order_by,
+ "include_metrics": "all",
+ "filters": filters,
+ "include_geometry": False,
+ },
+ )
+ df = tds.get_metrics(mq)
+ assert df.index.size == 11
+
+
+def test_describe_inputs():
+ tds = TEEHRDatasetAPI(DATABASE_FILEPATH)
+ df = tds.describe_inputs(PRIMARY_FILEPATH, SECONDARY_FILEPATH)
+ assert df.index.size == 7
+
+
+if __name__ == "__main__":
+ # test_unique_field_values()
+ # test_describe_inputs()
+ test_metrics_query()
diff --git a/tests/test_teehr_dataset_db.py b/tests/test_teehr_dataset_db.py
new file mode 100644
index 00000000..4b7b4827
--- /dev/null
+++ b/tests/test_teehr_dataset_db.py
@@ -0,0 +1,340 @@
+from pathlib import Path
+import numpy as np
+import pandas as pd
+
+from teehr.database.teehr_dataset import TEEHRDatasetDB
+
+# Test data
+TEST_STUDY_DIR = Path("tests", "data", "test_study")
+PRIMARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "*_obs.parquet")
+SECONDARY_FILEPATH = Path(TEST_STUDY_DIR, "timeseries", "*_fcast.parquet")
+CROSSWALK_FILEPATH = Path(TEST_STUDY_DIR, "geo", "crosswalk.parquet")
+GEOMETRY_FILEPATH = Path(TEST_STUDY_DIR, "geo", "gages.parquet")
+ATTRIBUTES_FILEPATH = Path(TEST_STUDY_DIR, "geo", "test_attr.parquet")
+DATABASE_FILEPATH = Path("tests", "data", "temp", "temp_test.db")
+
+
+def test_insert_joined_timeseries():
+
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+
+ tds.insert_geometry(GEOMETRY_FILEPATH)
+
+ df = tds.query("SELECT * FROM joined_timeseries", format="df")
+ np.testing.assert_approx_equal(
+ df.absolute_difference.sum(), 283.7, significant=4
+ )
+ pass
+
+
+def test_unique_field_values():
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+ df = tds.get_unique_field_values("primary_location_id")
+ assert sorted(df["unique_primary_location_id_values"].tolist()) == [
+ "gage-A",
+ "gage-B",
+ "gage-C",
+ ]
+ pass
+
+
+def test_metrics_query():
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+
+ # Insert geometry
+ tds.insert_geometry(GEOMETRY_FILEPATH)
+
+ # Get metrics
+ filters = [
+ {
+ "column": "primary_location_id",
+ "operator": "=",
+ "value": "gage-A",
+ },
+ {
+ "column": "reference_time",
+ "operator": "=",
+ "value": "2022-01-01 00:00:00",
+ },
+ {"column": "lead_time", "operator": "<=", "value": "10 hours"},
+ ]
+ # filters = [
+ # {
+ # "column": "configuration",
+ # "operator": "=",
+ # "value": "nwm22",
+ # }
+ # ]
+
+ group_by = ["primary_location_id"]
+ order_by = ["primary_location_id"]
+ include_metrics = "all"
+ # filters = []
+
+ df = tds.get_metrics(
+ group_by=group_by,
+ order_by=order_by,
+ include_metrics=include_metrics,
+ filters=filters,
+ include_geometry=True,
+ )
+
+ # print(df)
+ assert df.index.size == 1
+ assert df.columns.size == 24
+
+
+def test_describe_inputs():
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+ df = tds.describe_inputs(PRIMARY_FILEPATH, SECONDARY_FILEPATH)
+
+ base_df = pd.DataFrame(
+ index=[
+ "Number of unique location IDs",
+ "Total number of rows",
+ "Start Date",
+ "End Date",
+ "Number of duplicate rows",
+ "Number of location IDs with duplicate value times",
+ "Number of location IDs with missing time steps",
+ ],
+ data={
+ "primary": [
+ 3,
+ 78,
+ pd.to_datetime("2022-01-01 00:00:00"),
+ pd.to_datetime("2022-01-02 01:00:00"),
+ 0,
+ 0,
+ 0,
+ ],
+ "secondary": [
+ 3,
+ 216,
+ pd.to_datetime("2022-01-01 00:00:00"),
+ pd.to_datetime("2022-01-02 01:00:00"),
+ 0,
+ 3,
+ 0,
+ ],
+ },
+ )
+
+ assert base_df.equals(df)
+
+
+def test_calculate_field():
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+ # Add attributes
+ tds.join_attributes(ATTRIBUTES_FILEPATH)
+
+ # Calculate and add a field based on some user-defined function (UDF).
+ def my_user_function(arg1: float, arg2: str) -> float:
+ """Function arguments are fields in joined_timeseries, and
+ should have the same data type.
+ Note: In the data model, attribute values are always str type"""
+ return float(arg1) / float(arg2)
+
+ parameter_names = ["primary_value", "drainage_area_sq_km"]
+ new_field_name = "primary_normalized_discharge"
+ new_field_type = "FLOAT"
+ tds.calculate_field(
+ new_field_name=new_field_name,
+ new_field_type=new_field_type,
+ parameter_names=parameter_names,
+ user_defined_function=my_user_function,
+ )
+
+
+def test_join_attributes():
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+
+ # Add attributes
+ tds.join_attributes(ATTRIBUTES_FILEPATH)
+
+ df = tds.get_joined_timeseries_schema()
+
+ cols = [
+ "reference_time",
+ "value_time",
+ "secondary_location_id",
+ "secondary_value",
+ "configuration",
+ "measurement_unit",
+ "variable_name",
+ "primary_value",
+ "primary_location_id",
+ "lead_time",
+ "absolute_difference",
+ "drainage_area_sq_km",
+ "year_2_discharge_cfs",
+ ]
+ # Make sure attribute fields have been added
+ assert sorted(df.column_name.tolist()) == sorted(cols)
+
+ # Make sure attribute values are correct
+ df = tds.query("SELECT * FROM joined_timeseries", format="df")
+ np.testing.assert_approx_equal(
+ df.year_2_discharge_cfs.astype(float).sum(), 72000.0, significant=6
+ )
+
+ np.testing.assert_approx_equal(
+ df.drainage_area_sq_km.astype(float).sum(), 7200.0, significant=5
+ )
+
+ pass
+
+
+def test_get_joined_timeseries_schema():
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+
+ df = tds.get_joined_timeseries_schema()
+
+ assert df.index.size == 11
+ pass
+
+
+def test_timeseries_query():
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+
+ order_by = ["primary_location_id"]
+
+ df = tds.get_timeseries(
+ order_by=order_by,
+ timeseries_name="primary",
+ return_query=False
+ )
+
+ assert df.index.size == 26 * 3
+
+
+def test_timeseries_char_query():
+ if DATABASE_FILEPATH.is_file():
+ DATABASE_FILEPATH.unlink()
+
+ tds = TEEHRDatasetDB(DATABASE_FILEPATH)
+
+ # Perform the join and insert into duckdb database
+ tds.insert_joined_timeseries(
+ primary_filepath=PRIMARY_FILEPATH,
+ secondary_filepath=SECONDARY_FILEPATH,
+ crosswalk_filepath=CROSSWALK_FILEPATH,
+ drop_added_fields=True,
+ )
+
+ filters = []
+ group_by = ["primary_location_id"]
+ order_by = ["primary_location_id"]
+ timeseries_name = "primary" # "primary, secondary"
+
+ df = tds.get_timeseries_chars(
+ order_by=order_by,
+ group_by=group_by,
+ timeseries_name=timeseries_name,
+ filters=filters
+ )
+
+ assert df.index.size == 3
+ pass
+
+
+if __name__ == "__main__":
+
+ test_insert_joined_timeseries()
+ test_join_attributes()
+ test_unique_field_values()
+ test_describe_inputs()
+ test_calculate_field()
+ test_unique_field_values()
+ test_metrics_query()
+ test_get_joined_timeseries_schema()
+ test_timeseries_query()
+ test_timeseries_char_query()
diff --git a/version.txt b/version.txt
index d81f1c3f..0d91a54c 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-0.2.9
\ No newline at end of file
+0.3.0