From ce424f0dda3ff06ae198e1f8e80c248c4c4b0825 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Wed, 13 Jul 2022 11:33:49 -0400 Subject: [PATCH] remove CO-OPS module (moved to `searvey`) (#41) * remove CO-OPS module (moved to `searvey`) * fix tests * add pre-commit hook * change code linting to `black` * update reference files * retrieve `searvey` from PyPI * constrain `searvey` requirement to be below future API changes * fix tests * update reference files --- .github/workflows/test.yml | 2 + .github/workflows/test_quick.yml | 2 + .pre-commit-config.yaml | 6 +- README.md | 145 +--- docs/source/coops.rst | 57 -- docs/source/index.rst | 1 - examples/example_2.ipynb | 389 --------- pyproject.toml | 4 +- stormevents/coops/__init__.py | 12 - stormevents/coops/tidalstations.py | 791 ------------------ stormevents/coops/us_states.json | 59 -- stormevents/stormevent.py | 34 +- .../test_coops_product_within_region/data.nc | Bin 20042 -> 0 bytes .../station1612340_constituents.csv | 38 - .../station1612480_constituents.csv | 38 - .../station9414458_constituents.csv | 38 - .../stations.csv | 46 - .../florence2018_water_levels.nc | Bin 204426 -> 0 bytes tests/test_coops.py | 127 --- tests/test_nhc.py | 1 - tests/test_stormevent.py | 27 +- 21 files changed, 34 insertions(+), 1783 deletions(-) delete mode 100644 docs/source/coops.rst delete mode 100644 examples/example_2.ipynb delete mode 100644 stormevents/coops/__init__.py delete mode 100644 stormevents/coops/tidalstations.py delete mode 100644 stormevents/coops/us_states.json delete mode 100644 tests/data/reference/test_coops_product_within_region/data.nc delete mode 100644 tests/data/reference/test_coops_station/station1612340_constituents.csv delete mode 100644 tests/data/reference/test_coops_station/station1612480_constituents.csv delete mode 100644 tests/data/reference/test_coops_station/station9414458_constituents.csv delete mode 100644 tests/data/reference/test_coops_stations_within_region/stations.csv delete mode 100644 tests/data/reference/test_storm_event_coops_product_within_isotach/florence2018_water_levels.nc delete mode 100644 tests/test_coops.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index e5f852e..30e6bfa 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -2,6 +2,8 @@ name: test on: push: + branches: + - 'main' paths: - '**.py' - '.github/workflows/test*.yml' diff --git a/.github/workflows/test_quick.yml b/.github/workflows/test_quick.yml index a2842ba..136dbe8 100644 --- a/.github/workflows/test_quick.yml +++ b/.github/workflows/test_quick.yml @@ -2,6 +2,8 @@ name: quick test on: push: + branches: + - 'main' paths: - '**.py' - '.github/workflows/test*.yml' diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a4b83e4..1ff2232 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,7 +3,7 @@ default_language_version: python: "python3" fail_fast: true ci: - skip: ["poetry-lock", "prospector", "mypy"] + skip: [ "poetry-lock", "prospector", "mypy" ] repos: - repo: "https://github.com/pre-commit/pre-commit-hooks" @@ -22,7 +22,9 @@ repos: - id: "check-yaml" - id: "debug-statements" - id: "end-of-file-fixer" - exclude: .+\.ipynb + exclude: .+\.ipynb|tests/data/.+ + - id: "trailing-whitespace" + exclude: .+\.ipynb|tests/data/.+ - repo: https://github.com/pre-commit/pygrep-hooks rev: v1.9.0 diff --git a/README.md b/README.md index 14ac24c..48c1351 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ [![codecov](https://codecov.io/gh/oceanmodeling/StormEvents/branch/main/graph/badge.svg?token=BQWB1QKJ3Q)](https://codecov.io/gh/oceanmodeling/StormEvents) [![version](https://img.shields.io/pypi/v/StormEvents)](https://pypi.org/project/StormEvents) [![license](https://img.shields.io/github/license/oceanmodeling/StormEvents)](https://opensource.org/licenses/gpl-license) -[![style](https://github.com/psf/black)](https://img.shields.io/badge/code%20style-black-000000.svg) +[![style](https://img.shields.io/badge/code%20style-black-black)](https://github.com/psf/black) `stormevents` provides Python interfaces for observational data surrounding named storm events. @@ -246,149 +246,6 @@ hwm_id ... [277 rows x 53 columns] ``` -#### data products from the Center for Operational Oceanographic Products and Services (CO-OPS) - -The [Center for Operational Oceanographic Products and Services (CO-OPS)](https://tidesandcurrents.noaa.gov) -maintains and operates a large array of tidal buoys and oceanic weather stations that measure water and atmospheric variables -across the coastal United States. CO-OPS provides several [data products](https://tidesandcurrents.noaa.gov/products.html) -including hourly water levels, tidal datums and predictions, and trends in sea level over time. - -A list of CO-OPS stations can be retrieved with the `coops_stations()` function. - -```python -from stormevents.coops import coops_stations - -coops_stations() -``` - -``` - nws_id name state status removed geometry -nos_id -1600012 46125 QREB buoy active POINT (122.62500 37.75000) -1619910 SNDP5 Sand Island, Midway Islands active POINT (-177.37500 28.21875) -1630000 APRP7 Apra Harbor, Guam active POINT (144.62500 13.44531) -1631428 PGBP7 Pago Bay, Guam active POINT (144.75000 13.42969) -1770000 NSTP6 Pago Pago, American Samoa active POINT (-170.75000 -14.27344) -... ... ... ... ... ... ... -8423898 FTPN3 Fort Point NH discontinued 2020-04-13 00:00:00,2014-08-05 00:00:00,2012-0... POINT (-70.68750 43.06250) -8726667 MCYF1 Mckay Bay Entrance FL discontinued 2020-05-20 00:00:00,2019-03-08 00:00:00,2017-0... POINT (-82.43750 27.90625) -8772447 FCGT2 Freeport TX discontinued 2020-05-24 18:45:00,2018-10-10 21:50:00,2018-1... POINT (-95.31250 28.93750) -9087079 GBWW3 Green Bay WI discontinued 2020-10-28 13:00:00,2007-08-06 23:59:00,2007-0... POINT (-88.00000 44.53125) -8770570 SBPT2 Sabine Pass North TX discontinued 2021-01-18 00:00:00,2020-09-30 15:45:00,2020-0... POINT (-93.87500 29.73438) - -[435 rows x 6 columns] -``` - -Additionally, you can use a Shapely `Polygon` or `MultiPolygon` to constrain the stations query to a specific region: - -```python -from shapely.geometry import Polygon -from stormevents.coops import coops_stations_within_region - -region = Polygon(...) - -coops_stations_within_region(region=region) -``` - -``` - nws_id name state removed geometry -nos_id -8651370 DUKN7 Duck NC NaT POINT (-75.75000 36.18750) -8652587 ORIN7 Oregon Inlet Marina NC NaT POINT (-75.56250 35.78125) -8654467 HCGN7 USCG Station Hatteras NC NaT POINT (-75.68750 35.21875) -8656483 BFTN7 Beaufort, Duke Marine Lab NC NaT POINT (-76.68750 34.71875) -8658120 WLON7 Wilmington NC NaT POINT (-77.93750 34.21875) -8658163 JMPN7 Wrightsville Beach NC NaT POINT (-77.81250 34.21875) -8661070 MROS1 Springmaid Pier SC NaT POINT (-78.93750 33.65625) -8662245 NITS1 Oyster Landing (N Inlet Estuary) SC NaT POINT (-79.18750 33.34375) -8665530 CHTS1 Charleston, Cooper River Entrance SC NaT POINT (-79.93750 32.78125) -8670870 FPKG1 Fort Pulaski GA NaT POINT (-80.87500 32.03125) -``` - -##### retrieve CO-OPS data product for a specific station - -```python -from datetime import datetime -from stormevents.coops import COOPS_Station - -station = COOPS_Station(8632200) -station.product('water_level', start_date=datetime(2018, 9, 13), end_date=datetime(2018, 9, 16, 12)) -``` - -``` - -Dimensions: (nos_id: 1, t: 841) -Coordinates: - * nos_id (nos_id) int64 8632200 - * t (t) datetime64[ns] 2018-09-13 ... 2018-09-16T12:00:00 - nws_id (nos_id) -Dimensions: (nos_id: 10, t: 11) -Coordinates: - * nos_id (nos_id) int64 8651370 8652587 8654467 ... 8662245 8665530 8670870 - * t (t) datetime64[ns] 2022-03-08T14:48:00 ... 2022-03-08T15:48:00 - nws_id (nos_id) `_ -maintains and operates a large array of tidal buoys and oceanic weather stations that measure water and atmospheric variables -across the coastal United States. CO-OPS provides several `data products `_ -including hourly water levels, tidal datums and predictions, and trends in sea level over time. - -A list of CO-OPS stations can be retrieved with the ``coops_stations()`` function. - -.. autofunction:: stormevents.coops.tidalstations.coops_stations - -Additionally, you can use a Shapely ``Polygon`` or ``MultiPolygon`` to constrain the stations query to a specific region: - -.. autofunction:: stormevents.coops.tidalstations.coops_stations_within_region - -CO-OPS station class --------------------- - -.. autoclass:: stormevents.coops.tidalstations.COOPS_Station - -retrieve CO-OPS data product from within a region and time interval -------------------------------------------------------------------- - -To retrieve data, you must provide three things: - -1. the data product of interest; one of - - ``water_level`` - Preliminary or verified water levels, depending on availability. - - ``air_temperature`` - Air temperature as measured at the station. - - ``water_temperature`` - Water temperature as measured at the station. - - ``wind`` - Wind speed, direction, and gusts as measured at the station. - - ``air_pressure`` - Barometric pressure as measured at the station. - - ``air_gap`` - Air Gap (distance between a bridge and the water's surface) at the station. - - ``conductivity`` - The water's conductivity as measured at the station. - - ``visibility`` - Visibility from the station's visibility sensor. A measure of atmospheric clarity. - - ``humidity`` - Relative humidity as measured at the station. - - ``salinity`` - Salinity and specific gravity data for the station. - - ``hourly_height`` - Verified hourly height water level data for the station. - - ``high_low`` - Verified high/low water level data for the station. - - ``daily_mean`` - Verified daily mean water level data for the station. - - ``monthly_mean`` - Verified monthly mean water level data for the station. - - ``one_minute_water_level`` One minute water level data for the station. - - ``predictions`` - 6 minute predictions water level data for the station.* - - ``datums`` - datums data for the stations. - - ``currents`` - Currents data for currents stations. - - ``currents_predictions`` - Currents predictions data for currents predictions stations. -2. a region within which to retrieve the data product -3. a time interval within which to retrieve the data product - -.. autofunction:: stormevents.coops.tidalstations.coops_product_within_region - -CO-OPS query class -"""""""""""""""""" - -The ``COOPS_Query`` class lets you send an individual query to the CO-OPS API by specifying a station, data product, and time interval. - -.. autoclass:: stormevents.coops.tidalstations.COOPS_Query diff --git a/docs/source/index.rst b/docs/source/index.rst index d9d3744..70e8435 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -6,5 +6,4 @@ installation nhc usgs - coops stormevent diff --git a/examples/example_2.ipynb b/examples/example_2.ipynb deleted file mode 100644 index 805b0e8..0000000 --- a/examples/example_2.ipynb +++ /dev/null @@ -1,389 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# retrieve CO-OPS data from an arbitrary time interval and region\n", - "
\n", - "you can install stormevents with\n", - "
pip install stormevents
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import geopandas\n", - "from matplotlib import pyplot\n", - "\n", - "countries = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# retrieve a [list of CO-OPS stations](https://stormevents.readthedocs.io/en/latest/coops.html#stormevents.coops.tidalstations.coops_stations)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": " nws_id name state status \\\nnos_id \n1600012 46125 QREB buoy active \n1619910 SNDP5 Sand Island, Midway Islands active \n1630000 APRP7 Apra Harbor, Guam active \n1631428 PGBP7 Pago Bay, Guam active \n1770000 NSTP6 Pago Pago, American Samoa active \n... ... ... ... ... \n8726667 MCYF1 Mckay Bay Entrance FL discontinued \n8772447 FCGT2 Freeport TX discontinued \n9087079 GBWW3 Green Bay WI discontinued \n8770570 SBPT2 Sabine Pass North TX discontinued \n8740166 GBRM6 Grand Bay NERR, Mississippi Sound MS discontinued \n\n removed \\\nnos_id \n1600012 \n1619910 \n1630000 \n1631428 \n1770000 \n... ... \n8726667 2020-05-20 00:00:00,2019-03-08 00:00:00,2017-0... \n8772447 2020-05-24 18:45:00,2018-10-10 21:50:00,2018-1... \n9087079 2020-10-28 13:00:00,2007-08-06 23:59:00,2007-0... \n8770570 2021-01-18 00:00:00,2020-09-30 15:45:00,2020-0... \n8740166 2022-04-07 00:00:00,2022-03-30 23:58:00,2015-1... \n\n geometry \nnos_id \n1600012 POINT (122.62500 37.75000) \n1619910 POINT (-177.37500 28.21875) \n1630000 POINT (144.62500 13.44531) \n1631428 POINT (144.75000 13.42969) \n1770000 POINT (-170.75000 -14.27344) \n... ... \n8726667 POINT (-82.43750 27.90625) \n8772447 POINT (-95.31250 28.93750) \n9087079 POINT (-88.00000 44.53125) \n8770570 POINT (-93.87500 29.73438) \n8740166 POINT (-88.37500 30.40625) \n\n[434 rows x 6 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nws_idnamestatestatusremovedgeometry
nos_id
160001246125QREB buoyactive<NA>POINT (122.62500 37.75000)
1619910SNDP5Sand Island, Midway Islandsactive<NA>POINT (-177.37500 28.21875)
1630000APRP7Apra Harbor, Guamactive<NA>POINT (144.62500 13.44531)
1631428PGBP7Pago Bay, Guamactive<NA>POINT (144.75000 13.42969)
1770000NSTP6Pago Pago, American Samoaactive<NA>POINT (-170.75000 -14.27344)
.....................
8726667MCYF1Mckay Bay EntranceFLdiscontinued2020-05-20 00:00:00,2019-03-08 00:00:00,2017-0...POINT (-82.43750 27.90625)
8772447FCGT2FreeportTXdiscontinued2020-05-24 18:45:00,2018-10-10 21:50:00,2018-1...POINT (-95.31250 28.93750)
9087079GBWW3Green BayWIdiscontinued2020-10-28 13:00:00,2007-08-06 23:59:00,2007-0...POINT (-88.00000 44.53125)
8770570SBPT2Sabine Pass NorthTXdiscontinued2021-01-18 00:00:00,2020-09-30 15:45:00,2020-0...POINT (-93.87500 29.73438)
8740166GBRM6Grand Bay NERR, Mississippi SoundMSdiscontinued2022-04-07 00:00:00,2022-03-30 23:58:00,2015-1...POINT (-88.37500 30.40625)
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\n
" - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from stormevents.coops import coops_stations\n", - "\n", - "stations = coops_stations()\n", - "stations" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 1.0, 'all CO-OPS stations')" - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "figure, axis = pyplot.subplots(1, 1)\n", - "figure.set_size_inches(12, 12 / 1.61803398875)\n", - "\n", - "stations.plot(ax=axis)\n", - "countries.plot(color='lightgrey', ax=axis, zorder=-1)\n", - "\n", - "axis.set_title(f'all CO-OPS stations')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# retrieve a [list of CO-OPS stations within an arbitrary polygon](https://stormevents.readthedocs.io/en/latest/coops.html#stormevents.coops.tidalstations.coops_stations_within_region)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": " nws_id name state status \\\nnos_id \n8726412 MTBF1 Middle Tampa Bay active \n8726679 TSHF1 East Bay Causeway FL active \n8726694 TPAF1 TPA Cruise Terminal 2 FL active \n9044036 FWNM4 Fort Wayne MI active \n9075035 ESVM4 Essexville MI active \n... ... ... ... ... \n8720503 GCVF1 Red Bay Point, St Johns River FL discontinued \n8654400 CFPN7 Cape Hatteras Fishing Pier NC discontinued \n8720625 RCYF1 Racy Point, St Johns River FL discontinued \n8423898 FTPN3 Fort Point NH discontinued \n8726667 MCYF1 Mckay Bay Entrance FL discontinued \n\n removed \\\nnos_id \n8726412 \n8726679 \n8726694 \n9044036 2005-04-29 23:59:00,2005-04-29 00:00:00,2001-1... \n9075035 2007-03-28 23:59:00,2007-03-28 00:00:00,2007-0... \n... ... \n8720503 2017-10-07 20:54:00,2017-10-07 10:54:00,2017-1... \n8654400 2018-09-19 23:59:00,2003-09-18 23:59:00,2003-0... \n8720625 2019-08-05 14:00:00,2017-06-14 15:36:00,2017-0... \n8423898 2020-04-13 00:00:00,2014-08-05 00:00:00,2012-0... \n8726667 2020-05-20 00:00:00,2019-03-08 00:00:00,2017-0... \n\n geometry \nnos_id \n8726412 POINT (-82.62500 27.65625) \n8726679 POINT (-82.43750 27.92188) \n8726694 POINT (-82.43750 27.93750) \n9044036 POINT (-83.06250 42.31250) \n9075035 POINT (-83.87500 43.62500) \n... ... \n8720503 POINT (-81.62500 29.98438) \n8654400 POINT (-75.62500 35.21875) \n8720625 POINT (-81.56250 29.79688) \n8423898 POINT (-70.68750 43.06250) \n8726667 POINT (-82.43750 27.90625) \n\n[164 rows x 6 columns]", - "text/html": "
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nws_idnamestatestatusremovedgeometry
nos_id
8726412MTBF1Middle Tampa Bayactive<NA>POINT (-82.62500 27.65625)
8726679TSHF1East Bay CausewayFLactive<NA>POINT (-82.43750 27.92188)
8726694TPAF1TPA Cruise Terminal 2FLactive<NA>POINT (-82.43750 27.93750)
9044036FWNM4Fort WayneMIactive2005-04-29 23:59:00,2005-04-29 00:00:00,2001-1...POINT (-83.06250 42.31250)
9075035ESVM4EssexvilleMIactive2007-03-28 23:59:00,2007-03-28 00:00:00,2007-0...POINT (-83.87500 43.62500)
.....................
8720503GCVF1Red Bay Point, St Johns RiverFLdiscontinued2017-10-07 20:54:00,2017-10-07 10:54:00,2017-1...POINT (-81.62500 29.98438)
8654400CFPN7Cape Hatteras Fishing PierNCdiscontinued2018-09-19 23:59:00,2003-09-18 23:59:00,2003-0...POINT (-75.62500 35.21875)
8720625RCYF1Racy Point, St Johns RiverFLdiscontinued2019-08-05 14:00:00,2017-06-14 15:36:00,2017-0...POINT (-81.56250 29.79688)
8423898FTPN3Fort PointNHdiscontinued2020-04-13 00:00:00,2014-08-05 00:00:00,2012-0...POINT (-70.68750 43.06250)
8726667MCYF1Mckay Bay EntranceFLdiscontinued2020-05-20 00:00:00,2019-03-08 00:00:00,2017-0...POINT (-82.43750 27.90625)
\n

164 rows × 6 columns

\n
" - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import shapely\n", - "from stormevents.coops import coops_stations_within_region\n", - "\n", - "east_coast = shapely.geometry.box(-85, 25, -65, 45)\n", - "east_coast_stations = coops_stations_within_region(region=east_coast)\n", - "east_coast_stations" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 1.0, 'CO-OPS stations on the U.S. East Coast')" - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "figure, axis = pyplot.subplots(1, 1)\n", - "figure.set_size_inches(12, 12 / 1.61803398875)\n", - "\n", - "east_coast_stations.plot(ax=axis)\n", - "\n", - "xlim = axis.get_xlim()\n", - "ylim = axis.get_ylim()\n", - "countries.plot(color='lightgrey', ax=axis, zorder=-1)\n", - "axis.set_xlim(xlim)\n", - "axis.set_ylim(ylim)\n", - "\n", - "axis.set_title(f'CO-OPS stations on the U.S. East Coast')" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# retrieve a [CO-OPS data product from all stations within an arbitrary polygon](https://stormevents.readthedocs.io/en/latest/coops.html#stormevents.coops.tidalstations.coops_product_within_region)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 74, - "outputs": [ - { - "data": { - "text/plain": "\nDimensions: (t: 6, nos_id: 111)\nCoordinates:\n * t (t) datetime64[ns] 2022-04-02T12:00:00 ... 2022-04-02T12:30:00\n * nos_id (nos_id) int64 9044036 9075035 9052076 ... 8516945 9052000 8638901\n nws_id (nos_id) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<xarray.Dataset>\nDimensions:  (t: 6, nos_id: 111)\nCoordinates:\n  * t        (t) datetime64[ns] 2022-04-02T12:00:00 ... 2022-04-02T12:30:00\n  * nos_id   (nos_id) int64 9044036 9075035 9052076 ... 8516945 9052000 8638901\n    nws_id   (nos_id) <U5 'FWNM4' 'ESVM4' 'OCTN6' ... 'KPTN6' 'CAVN6' 'CHBV2'\n    x        (nos_id) float64 -83.06 -83.88 -78.75 ... -73.75 -76.31 -76.06\n    y        (nos_id) float64 42.31 43.62 43.34 42.09 ... 40.81 44.12 37.03\nData variables:\n    v        (nos_id, t) float32 175.1 175.1 175.1 175.1 ... 2.084 2.089 2.096\n    s        (nos_id, t) float32 0.0 0.0 0.0 0.0 0.0 ... 0.059 0.076 0.088 0.08\n    f        (nos_id, t) object '0,0,0,0' '0,0,0,0' ... '1,0,0,0' '1,0,0,0'\n    q        (nos_id, t) object 'p' 'p' 'p' 'p' 'p' 'p' ... 'p' 'p' 'p' 'p' 'p'
" - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from datetime import datetime\n", - "from stormevents.coops import coops_product_within_region\n", - "\n", - "water_levels = coops_product_within_region(\n", - " 'water_level',\n", - " region=east_coast,\n", - " start_date=datetime(2022, 4, 2, 12),\n", - " end_date=datetime(2022, 4, 2, 12, 30),\n", - ")\n", - "water_levels" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 75, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 1.0, 'maximum water levels observed by CO-OPS stations on the U.S. East Coast between 2022-04-02 12:00:00 and 2022-04-02 12:30:00')" - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas\n", - "\n", - "figure, axis = pyplot.subplots(1, 1)\n", - "figure.set_size_inches(12, 12 / 1.61803398875)\n", - "\n", - "water_levels.max('t').plot.scatter(x='x', y='y', hue='v', cmap='viridis', ax=axis)\n", - "\n", - "xlim = axis.get_xlim()\n", - "ylim = axis.get_ylim()\n", - "countries.plot(color='lightgrey', ax=axis, zorder=-1)\n", - "axis.set_xlim(xlim)\n", - "axis.set_ylim(ylim)\n", - "\n", - "start_date = pandas.to_datetime(water_levels['t'].min().values)\n", - "end_date = pandas.to_datetime(water_levels['t'].max().values)\n", - "axis.set_title(f'maximum water levels observed by CO-OPS stations on the U.S. East Coast between {start_date} and {end_date}')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# retrieve a CO-OPS data product from a [specific station](https://stormevents.readthedocs.io/en/latest/coops.html#stormevents.coops.tidalstations.COOPS_Station)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 76, - "outputs": [ - { - "data": { - "text/plain": "\nDimensions: (nos_id: 1, t: 6)\nCoordinates:\n * nos_id (nos_id) int64 1612480\n * t (t) datetime64[ns] 2022-04-02T12:00:00 ... 2022-04-02T12:30:00\n nws_id (nos_id) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<xarray.Dataset>\nDimensions:  (nos_id: 1, t: 6)\nCoordinates:\n  * nos_id   (nos_id) int64 1612480\n  * t        (t) datetime64[ns] 2022-04-02T12:00:00 ... 2022-04-02T12:30:00\n    nws_id   (nos_id) <U5 'MOKH1'\n    x        (nos_id) float64 -157.8\n    y        (nos_id) float64 21.44\nData variables:\n    v        (nos_id, t) float32 1.406 1.409 1.414 1.417 1.417 1.422\n    s        (nos_id, t) float32 0.001 0.002 0.002 0.002 0.001 0.002\n    f        (nos_id, t) object '0,0,0,0' '1,0,0,0' ... '0,0,0,0' '0,0,0,0'\n    q        (nos_id, t) object 'p' 'p' 'p' 'p' 'p' 'p'
" - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from stormevents.coops import COOPS_Station\n", - "\n", - "station = COOPS_Station(1612480)\n", - "station_water_levels = station.product(\n", - " 'water_level',\n", - " start_date=datetime(2022, 4, 2, 12),\n", - " end_date=datetime(2022, 4, 2, 12, 30),\n", - ")\n", - "station_water_levels" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 77, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 1.0, 'water level observed by CO-OPS station 1612480 (\"Mokuoloe\") between 2022-04-02 12:00:00 and 2022-04-02 12:30:00')" - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "figure, axis = pyplot.subplots(1, 1)\n", - "figure.set_size_inches(12, 12 / 1.61803398875)\n", - "\n", - "axis.errorbar(\n", - " station_water_levels['t'],\n", - " station_water_levels['v'].isel(nos_id=0),\n", - " yerr=station_water_levels['s'].isel(nos_id=0).values,\n", - ")\n", - "\n", - "start_date = pandas.to_datetime(station_water_levels['t'].min().values)\n", - "end_date = pandas.to_datetime(station_water_levels['t'].max().values)\n", - "axis.set_title(f'water level observed by CO-OPS station {station.nos_id} (\"{station.name}\") between {start_date} and {end_date}')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# additional documentation\n", - "## https://stormevents.readthedocs.io\n", - "## https://github.com/oceanmodeling/StormEvents\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index fb2198b..182bb59 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,7 +19,7 @@ build-backend = 'poetry.core.masonry.api' enable = true [tool.poetry.dependencies] -python = '^3.6' +python = '^3.9' beautifulsoup4 = '*' geopandas = '*' netcdf4 = '*' @@ -28,6 +28,7 @@ python-dateutil = '*' pandas = '*' pyproj = '>=2.6' requests = '*' +searvey = '^0.2.0' shapely = '>=1.8' typepigeon = '>=1.0.5' xarray = '*' @@ -44,6 +45,7 @@ sphinx-rtd-theme = { version = '*', optional = true } toml = { version = '*', optional = true } [tool.poetry.extras] +linting = ["mypy", "prospector"] testing = ['pytest', 'pytest-cov', 'pytest-socket', 'pytest-xdist'] development = ['isort', 'black'] documentation = ['dunamai', 'm2r2', 'sphinx', 'sphinx-rtd-theme', 'toml'] diff --git a/stormevents/coops/__init__.py b/stormevents/coops/__init__.py deleted file mode 100644 index 0cde654..0000000 --- a/stormevents/coops/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from stormevents.coops.tidalstations import COOPS_Interval -from stormevents.coops.tidalstations import COOPS_Product -from stormevents.coops.tidalstations import coops_product_within_region -from stormevents.coops.tidalstations import COOPS_Query -from stormevents.coops.tidalstations import COOPS_Station -from stormevents.coops.tidalstations import coops_stations -from stormevents.coops.tidalstations import coops_stations_within_region -from stormevents.coops.tidalstations import COOPS_StationStatus -from stormevents.coops.tidalstations import COOPS_TidalDatum -from stormevents.coops.tidalstations import COOPS_TimeZone -from stormevents.coops.tidalstations import COOPS_Units -from stormevents.coops.tidalstations import COOPS_VelocityType diff --git a/stormevents/coops/tidalstations.py b/stormevents/coops/tidalstations.py deleted file mode 100644 index dd30dd0..0000000 --- a/stormevents/coops/tidalstations.py +++ /dev/null @@ -1,791 +0,0 @@ -""" -interface with the NOAA Center for Operational Oceanographic Products and Services (CO-OPS) API -https://api.tidesandcurrents.noaa.gov/api/prod/ -""" -import json -from datetime import datetime -from enum import Enum -from functools import lru_cache -from pathlib import Path -from typing import Union - -import geopandas -import numpy -import pandas -import requests -import typepigeon -import xarray -from bs4 import BeautifulSoup -from bs4 import element -from geopandas import GeoDataFrame -from pandas import DataFrame -from pandas import Series -from shapely.geometry import box -from shapely.geometry import MultiPolygon -from shapely.geometry import Polygon -from xarray import Dataset - - -class COOPS_Product(Enum): - WATER_LEVEL = ( - "water_level" - # Preliminary or verified water levels, depending on availability. - ) - AIR_TEMPERATURE = "air_temperature" # Air temperature as measured at the station. - WATER_TEMPERATURE = ( - "water_temperature" # Water temperature as measured at the station. - ) - WIND = "wind" # Wind speed, direction, and gusts as measured at the station. - AIR_PRESSURE = "air_pressure" # Barometric pressure as measured at the station. - AIR_GAP = "air_gap" # Air Gap (distance between a bridge and the water's surface) at the station. - CONDUCTIVITY = ( - "conductivity" # The water's conductivity as measured at the station. - ) - VISIBILITY = "visibility" # Visibility from the station's visibility sensor. A measure of atmospheric clarity. - HUMIDITY = "humidity" # Relative humidity as measured at the station. - SALINITY = "salinity" # Salinity and specific gravity data for the station. - HOURLY_HEIGHT = ( - "hourly_height" # Verified hourly height water level data for the station. - ) - HIGH_LOW = "high_low" # Verified high/low water level data for the station. - DAILY_MEAN = "daily_mean" # Verified daily mean water level data for the station. - MONTHLY_MEAN = ( - "monthly_mean" # Verified monthly mean water level data for the station. - ) - ONE_MINUTE_WATER_LEVEL = ( - "one_minute_water_level" - # One minute water level data for the station. - ) - PREDICTIONS = ( - "predictions" # 6 minute predictions water level data for the station.* - ) - DATUMS = "datums" # datums data for the stations. - CURRENTS = "currents" # Currents data for currents stations. - CURRENTS_PREDICTIONS = ( - "currents_predictions" - # Currents predictions data for currents predictions stations. - ) - - -class COOPS_TidalDatum(Enum): - CRD = "CRD" # Columbia River Datum - IGLD = "IGLD" # International Great Lakes Datum - LWD = "LWD" # Great Lakes Low Water Datum (Chart Datum) - MHHW = "MHHW" # Mean Higher High Water - MHW = "MHW" # Mean High Water - MTL = "MTL" # Mean Tide Level - MSL = "MSL" # Mean Sea Level - MLW = "MLW" # Mean Low Water - MLLW = "MLLW" # Mean Lower Low Water - NAVD = "NAVD" # North American Vertical Datum - STND = "STND" # Station Datum - - -class COOPS_VelocityType(Enum): - SPEED_DIR = "speed_dir" # Return results for speed and dirction - DEFAULT = "default" # Return results for velocity major, mean flood direction and mean ebb dirction - - -class COOPS_Units(Enum): - METRIC = "metric" - ENGLISH = "english" - - -class COOPS_TimeZone(Enum): - GMT = "gmt" # Greenwich Mean Time - LST = "lst" # Local Standard Time. The time local to the requested station. - LST_LDT = "lst_ldt" # Local Standard/Local Daylight Time. The time local to the requested station. - - -class COOPS_Interval(Enum): - H = "h" # Hourly Met data and harmonic predictions will be returned - HILO = "hilo" # High/Low tide predictions for all stations. - - -class COOPS_StationStatus(Enum): - ACTIVE = "active" - DISCONTINUED = "discontinued" - - -class COOPS_Station: - """ - a specific CO-OPS station - """ - - def __init__(self, id: Union[int, str]): - """ - :param id: NOS ID, NWS ID, or station name - - from NOS ID: - - >>> COOPS_Station(1612480) - COOPS_Station(1612480) - - from NWS ID: - - >>> COOPS_Station('OOUH1') - COOPS_Station(1612340) - - from station name: - - >>> COOPS_Station('San Mateo Bridge') - COOPS_Station(9414458) - """ - - stations = coops_stations() - if id in stations.index: - metadata = stations.loc[id] - elif id in stations["nws_id"].values: - metadata = stations[stations["nws_id"] == id] - elif id in stations["name"].values: - metadata = stations[stations["name"] == id] - else: - metadata = None - - if metadata is None or len(metadata) == 0: - raise ValueError(f'station with "{id}" not found') - - removed = metadata["removed"] - if isinstance(removed, Series): - self.__active = pandas.isna(removed).any() - removed = pandas.unique(removed.dropna().sort_values(ascending=False)) - else: - self.__active = pandas.isna(removed) - self.__removed = removed - - if isinstance(metadata, DataFrame): - metadata = metadata.iloc[0] - - self.nos_id = metadata.name - self.nws_id = metadata["nws_id"] - self.location = metadata.geometry - self.state = metadata["state"] - self.name = metadata["name"] - - self.__query = None - - @property - def current(self) -> bool: - return self.__active - - @property - def removed(self) -> Series: - return self.__removed - - @property - @lru_cache(maxsize=None) - def constituents(self) -> DataFrame: - """ - :return: table of tidal constituents for the current station - """ - - url = f"https://tidesandcurrents.noaa.gov/harcon.html?id={self.nos_id}" - response = requests.get(url) - soup = BeautifulSoup(response.content, features="html.parser") - table = soup.find_all("table", {"class": "table table-striped"}) - if len(table) > 0: - table = table[0] - columns = [ - field.text for field in table.find("thead").find("tr").find_all("th") - ] - constituents = [] - for row in table.find_all("tr")[1:]: - constituents.append([entry.text for entry in row.find_all("td")]) - constituents = DataFrame(constituents, columns=columns) - constituents.rename(columns={"Constituent #": "#"}, inplace=True) - constituents = constituents.astype( - { - "#": numpy.int32, - "Amplitude": numpy.float64, - "Phase": numpy.float64, - "Speed": numpy.float64, - } - ) - else: - constituents = DataFrame(columns=["#", "Amplitude", "Phase", "Speed"]) - - constituents.set_index("#", inplace=True) - return constituents - - def product( - self, - product: COOPS_Product, - start_date: datetime, - end_date: datetime = None, - datum: COOPS_TidalDatum = None, - units: COOPS_Units = None, - time_zone: COOPS_TimeZone = None, - interval: COOPS_Interval = None, - ) -> Dataset: - """ - retrieve data for the current station within the specified parameters - - :param start_date: start date - :param end_date: end date - :param product: CO-OPS product - :param datum: tidal datum - :param units: either ``metric`` or ``english`` - :param time_zone: time zone of data - :param interval: time interval of data - :return: data for the current station within the specified parameters - - >>> station = COOPS_Station(8632200) - >>> station.product('water_level', start_date=datetime(2018, 9, 13), end_date=datetime(2018, 9, 16, 12)) - - Dimensions: (nos_id: 1, t: 841) - Coordinates: - * nos_id (nos_id) int64 8632200 - * t (t) datetime64[ns] 2018-09-13 ... 2018-09-16T12:00:00 - nws_id (nos_id) 0: - data = data.assign_coords( - nws_id=("nos_id", [self.nws_id]), - x=("nos_id", [self.location.x]), - y=("nos_id", [self.location.y]), - ) - else: - data = data.assign_coords( - nws_id=("nos_id", []), - x=("nos_id", []), - y=("nos_id", []), - ) - - return data - - def __str__(self) -> str: - return ( - f"{self.__class__.__name__} - {self.nos_id} ({self.name}) - {self.location}" - ) - - def __repr__(self) -> str: - return f"{self.__class__.__name__}({self.nos_id})" - - -class COOPS_Query: - """ - abstraction of an individual query to the CO-OPS API - https://api.tidesandcurrents.noaa.gov/api/prod/ - """ - - URL = "https://tidesandcurrents.noaa.gov/api/datagetter?" - - def __init__( - self, - station: int, - product: COOPS_Product, - start_date: datetime, - end_date: datetime = None, - datum: COOPS_TidalDatum = None, - units: COOPS_Units = None, - time_zone: COOPS_TimeZone = None, - interval: COOPS_Interval = None, - ): - """ - instantiate a new query with the specified parameters - - :param station: NOS station ID - :param product: CO-OPS product - :param start_date: start date - :param end_date: end date - :param datum: station datum - :param units: one of `metric`, `english` - :param time_zone: time zone of data - :param interval: time interval of data - :return: data for the current station within the specified parameters - - >>> COOPS_Query(1612480, start_date='2022-01-01', end_date='2022-01-03') - COOPS_Query(1612480, datetime.datetime(2022, 1, 1, 0, 0), datetime.datetime(2022, 1, 3, 0, 0), 'water_level', 'MLLW', 'metric', 'gmt', 'h') - """ - - if isinstance(station, COOPS_Station): - station = station.nos_id - if end_date is None: - end_date = datetime.today() - if datum is None: - datum = COOPS_TidalDatum.STND - if units is None: - units = COOPS_Units.METRIC - if time_zone is None: - time_zone = COOPS_TimeZone.GMT - if interval is None: - interval = COOPS_Interval.H - - self.station = station - self.product = product - self.start_date = start_date - self.end_date = end_date - self.datum = datum - self.units = units - self.time_zone = time_zone - self.interval = interval - - self.__previous_query = None - self.__error = None - - @property - def start_date(self) -> datetime: - return self.__start_date - - @start_date.setter - def start_date(self, start_date: datetime): - self.__start_date = pandas.to_datetime(start_date) - - @property - def end_date(self) -> datetime: - return self.__end_date - - @end_date.setter - def end_date(self, end_date: datetime): - self.__end_date = pandas.to_datetime(end_date) - - @property - def product(self) -> COOPS_Product: - return self.__product - - @product.setter - def product(self, product: COOPS_Product): - self.__product = typepigeon.convert_value(product, COOPS_Product) - - @property - def datum(self) -> COOPS_TidalDatum: - return self.__datum - - @datum.setter - def datum(self, datum: COOPS_TidalDatum): - self.__datum = typepigeon.convert_value(datum, COOPS_TidalDatum) - - @property - def units(self) -> COOPS_Units: - return self.__units - - @units.setter - def units(self, units: COOPS_Units): - self.__units = typepigeon.convert_value(units, COOPS_Units) - - @property - def time_zone(self) -> COOPS_TimeZone: - return self.__time_zone - - @time_zone.setter - def time_zone(self, time_zone: COOPS_TimeZone): - self.__time_zone = typepigeon.convert_value(time_zone, COOPS_TimeZone) - - @property - def interval(self) -> COOPS_Interval: - return self.__interval - - @interval.setter - def interval(self, interval: COOPS_Interval): - self.__interval = typepigeon.convert_value(interval, COOPS_Interval) - - @property - def query(self): - self.__error = None - - product = self.product - if isinstance(product, Enum): - product = product.value - start_date = self.start_date - if start_date is not None and not isinstance(start_date, str): - start_date = f"{self.start_date:%Y%m%d %H:%M}" - datum = self.datum - if isinstance(datum, Enum): - datum = datum.value - units = self.units - if isinstance(units, Enum): - units = units.value - time_zone = self.time_zone - if isinstance(time_zone, Enum): - time_zone = time_zone.value - interval = self.interval - if isinstance(interval, Enum): - interval = interval.value - - return { - "station": self.station, - "product": product, - "begin_date": start_date, - "end_date": f"{self.end_date:%Y%m%d %H:%M}", - "datum": datum, - "units": units, - "time_zone": time_zone, - "interval": interval, - "format": "json", - "application": "noaa/nos/csdl/stormevents", - } - - @property - def data(self) -> DataFrame: - """ - :return: data for the current query parameters - - >>> query = COOPS_Query(1612480, 'water_level', start_date='2022-01-01', end_date='2022-01-03') - >>> query.data - v s f q - t - 2022-01-01 00:00:00 1.193 0.002 0,0,0,0 v - 2022-01-01 00:06:00 1.180 0.002 0,0,0,0 v - 2022-01-01 00:12:00 1.167 0.002 0,0,0,0 v - 2022-01-01 00:18:00 1.156 0.003 0,0,0,0 v - 2022-01-01 00:24:00 1.147 0.003 0,0,0,0 v - ... ... ... .. - 2022-01-02 23:36:00 1.229 0.004 0,0,0,0 v - 2022-01-02 23:42:00 1.219 0.003 0,0,0,0 v - 2022-01-02 23:48:00 1.223 0.004 0,0,0,0 v - 2022-01-02 23:54:00 1.217 0.004 0,0,0,0 v - 2022-01-03 00:00:00 1.207 0.002 0,0,0,0 v - [481 rows x 4 columns] - """ - - if self.__previous_query is None or self.query != self.__previous_query: - response = requests.get(self.URL, params=self.query) - data = response.json() - fields = ["t", "v", "s", "f", "q"] - if "error" in data or "data" not in data: - if "error" in data: - self.__error = data["error"]["message"] - data = DataFrame(columns=fields) - else: - data = DataFrame(data["data"], columns=fields) - data[data == ""] = numpy.nan - data = data.astype( - { - "v": numpy.float32, - "s": numpy.float32, - "f": "string", - "q": "string", - }, - errors="ignore", - ) - data["t"] = pandas.to_datetime(data["t"]) - - data.set_index("t", inplace=True) - self.__data = data - return self.__data - - def __repr__(self) -> str: - return f'{self.__class__.__name__}({", ".join(repr(value) for value in (self.station, self.start_date, self.end_date, self.product.value, self.datum.value, self.units.value, self.time_zone.value, self.interval.value))})' - - -@lru_cache(maxsize=None) -def __coops_stations_html_tables() -> element.ResultSet: - response = requests.get( - "https://access.co-ops.nos.noaa.gov/nwsproducts.html?type=current", - ) - soup = BeautifulSoup(response.content, features="html.parser") - return soup.find_all("div", {"class": "table-responsive"}) - - -@lru_cache(maxsize=None) -def coops_stations(station_status: COOPS_StationStatus = None) -> GeoDataFrame: - """ - retrieve a list of CO-OPS stations with associated metadata - - :param station_status: one of ``active`` or ``discontinued`` - :return: data frame of stations - - >>> coops_stations() - nws_id name state status removed geometry - nos_id - 1600012 46125 QREB buoy active POINT (122.62500 37.75000) - 8735180 DILA1 Dauphin Island AL active 2019-07-18 10:00:00,2018-07-30 16:40:00,2017-0... POINT (-88.06250 30.25000) - 8557380 LWSD1 Lewes DE active 2019-08-01 00:00:00,2018-06-18 00:00:00,2017-0... POINT (-75.12500 38.78125) - 8465705 NWHC3 New Haven CT active 2019-08-18 14:55:00,2019-08-18 14:54:00,2018-0... POINT (-72.93750 41.28125) - 9439099 WAUO3 Wauna OR active 2019-08-19 22:59:00,2014-06-20 21:30:00,2013-0... POINT (-123.43750 46.15625) - ... ... ... ... ... ... ... - 8448725 MSHM3 Menemsha Harbor, MA MA discontinued 2013-09-26 23:59:00,2013-09-26 00:00:00,2012-0... POINT (-70.75000 41.34375) - 8538886 TPBN4 Tacony-Palmyra Bridge NJ discontinued 2013-11-11 00:01:00,2013-11-11 00:00:00,2012-0... POINT (-75.06250 40.00000) - 9439011 HMDO3 Hammond OR discontinued 2014-08-13 00:00:00,2011-04-12 23:59:00,2011-0... POINT (-123.93750 46.18750) - 8762372 LABL1 East Bank 1, Norco, B. LaBranche LA discontinued 2012-11-05 10:38:00,2012-11-05 10:37:00,2012-1... POINT (-90.37500 30.04688) - 8530528 CARN4 CARLSTADT, HACKENSACK RIVER NJ discontinued 1994-11-12 23:59:00,1994-11-12 00:00:00 POINT (-74.06250 40.81250) - [433 rows x 6 columns] - >>> coops_stations(station_status='ACTIVE') - nws_id name state status removed geometry - nos_id - 1600012 46125 QREB buoy active POINT (122.62500 37.75000) - 1611400 NWWH1 Nawiliwili HI active POINT (-159.37500 21.95312) - 1612340 OOUH1 Honolulu HI active POINT (-157.87500 21.31250) - 1612480 MOKH1 Mokuoloe HI active POINT (-157.75000 21.43750) - 1615680 KLIH1 Kahului, Kahului Harbor HI active POINT (-156.50000 20.89062) - ... ... ... ... ... ... ... - 9759394 MGZP4 Mayaguez PR active POINT (-67.18750 18.21875) - 9759938 MISP4 Mona Island active POINT (-67.93750 18.09375) - 9761115 BARA9 Barbuda active POINT (-61.81250 17.59375) - 9999530 FRCB6 Bermuda, Ferry Reach Channel active POINT (-64.68750 32.37500) - 9999531 Calcasieu Test Station LA active POINT (-93.31250 29.76562) - [363 rows x 6 columns] - >>> coops_stations(station_status='DISCONTINUED') - nws_id name state status removed geometry - nos_id - 8516945 KPTN6 Kings Point NY active 2022-02-23 10:15:00,2018-03-20 13:00:00,2015-1... POINT (-73.75000 40.81250) - 8720357 BKBF1 I-295 Buckman Bridge FL active 2022-02-04 17:00:00,2021-02-20 14:45:00,2021-0... POINT (-81.68750 30.18750) - 8720226 MSBF1 Southbank Riverwalk, St Johns River FL active 2022-02-02 14:00:00,2021-01-16 17:57:00,2020-0... POINT (-81.68750 30.31250) - 9063079 MRHO1 Marblehead OH active 2022-02-01 00:00:00,2021-07-15 00:00:00,2019-0... POINT (-82.75000 41.53125) - 8724580 KYWF1 Key West FL active 2022-01-31 00:00:00,2020-05-08 09:30:00,2018-0... POINT (-81.81250 24.54688) - ... ... ... ... ... ... ... - 8760551 SPSL1 South Pass LA discontinued 2000-09-26 23:59:00,2000-09-26 00:00:00,1998-1... POINT (-89.12500 28.98438) - 9440572 ILWW1 JETTY A, COLUMBIA RIVER WA discontinued 1997-04-11 23:00:00 POINT (-124.06250 46.28125) - 9415316 RVXC1 Rio Vista CA discontinued 1997-03-04 23:59:00,1997-03-04 00:00:00 POINT (-121.68750 38.15625) - 9415064 NCHC1 ANTIOCH, SAN JOAQUIN RIVER CA discontinued 1997-03-03 23:59:00,1997-03-03 00:00:00 POINT (-121.81250 38.03125) - 8530528 CARN4 CARLSTADT, HACKENSACK RIVER NJ discontinued 1994-11-12 23:59:00,1994-11-12 00:00:00 POINT (-74.06250 40.81250) - [396 rows x 6 columns] - """ - - tables = __coops_stations_html_tables() - - status_tables = { - COOPS_StationStatus.ACTIVE: (0, "NWSTable"), - COOPS_StationStatus.DISCONTINUED: (1, "HistNWSTable"), - } - - dataframes = {} - for status, (table_index, table_id) in status_tables.items(): - table = tables[table_index].find("table", {"id": table_id}).find_all("tr") - - stations_columns = [field.text for field in table[0].find_all("th")] - stations = DataFrame( - [ - [value.text.strip() for value in station.find_all("td")] - for station in table[1:] - ], - columns=stations_columns, - ) - stations.rename( - columns={ - "NOS ID": "nos_id", - "NWS ID": "nws_id", - "Latitude": "y", - "Longitude": "x", - "State": "state", - "Station Name": "name", - }, - inplace=True, - ) - stations = stations.astype( - { - "nos_id": numpy.int32, - "nws_id": "string", - "x": numpy.float32, - "y": numpy.float32, - "state": "string", - "name": "string", - }, - copy=False, - ) - stations.set_index("nos_id", inplace=True) - - if status == COOPS_StationStatus.DISCONTINUED: - with open(Path(__file__).parent / "us_states.json") as us_states_file: - us_states = json.load(us_states_file) - - for name, abbreviation in us_states.items(): - stations.loc[stations["state"] == name, "state"] = abbreviation - - stations.rename(columns={"Removed Date/Time": "removed"}, inplace=True) - - stations["removed"] = pandas.to_datetime(stations["removed"]).astype( - "string" - ) - - stations = ( - stations[~pandas.isna(stations["removed"])] - .sort_values("removed", ascending=False) - .groupby("nos_id") - .aggregate( - { - "nws_id": "first", - "removed": ",".join, - "y": "first", - "x": "first", - "state": "first", - "name": "first", - } - ) - ) - else: - stations["removed"] = pandas.NA - - stations["status"] = status.value - dataframes[status] = stations - - active_stations = dataframes[COOPS_StationStatus.ACTIVE] - discontinued_stations = dataframes[COOPS_StationStatus.DISCONTINUED] - discontinued_stations.loc[ - discontinued_stations.index.isin(active_stations.index), "status" - ] = COOPS_StationStatus.ACTIVE.value - - stations = pandas.concat( - ( - active_stations[~active_stations.index.isin(discontinued_stations.index)], - discontinued_stations, - ) - ) - stations.loc[ - pandas.isna(stations["status"]), "status" - ] = COOPS_StationStatus.ACTIVE.value - stations.sort_values(["status", "removed"], na_position="first", inplace=True) - - if station_status is not None: - if isinstance(station_status, COOPS_StationStatus): - station_status = station_status.value - stations = stations[stations["status"] == station_status] - - return GeoDataFrame( - stations[["nws_id", "name", "state", "status", "removed"]], - geometry=geopandas.points_from_xy(stations["x"], stations["y"]), - ) - - -def coops_stations_within_region( - region: Polygon, - station_status: COOPS_StationStatus = None, -) -> GeoDataFrame: - """ - retrieve all stations within the specified region of interest - - :param region: polygon or multipolygon denoting region of interest - :param station_status: one of ``active`` or ``discontinued`` - :return: data frame of stations within the specified region - - >>> from stormevents.nhc import VortexTrack - >>> from shapely import ops - >>> track = VortexTrack('florence2018', file_deck='b') - >>> combined_wind_swaths = ops.unary_union(list(track.wind_swaths(34).values())) - >>> coops_stations_within_region(region=combined_wind_swaths) - nws_id name state removed geometry - nos_id - 8651370 DUKN7 Duck NC NaT POINT (-75.75000 36.18750) - 8652587 ORIN7 Oregon Inlet Marina NC NaT POINT (-75.56250 35.78125) - 8654467 HCGN7 USCG Station Hatteras NC NaT POINT (-75.68750 35.21875) - 8656483 BFTN7 Beaufort, Duke Marine Lab NC NaT POINT (-76.68750 34.71875) - 8658120 WLON7 Wilmington NC NaT POINT (-77.93750 34.21875) - 8658163 JMPN7 Wrightsville Beach NC NaT POINT (-77.81250 34.21875) - 8661070 MROS1 Springmaid Pier SC NaT POINT (-78.93750 33.65625) - 8662245 NITS1 Oyster Landing (N Inlet Estuary) SC NaT POINT (-79.18750 33.34375) - 8665530 CHTS1 Charleston, Cooper River Entrance SC NaT POINT (-79.93750 32.78125) - 8670870 FPKG1 Fort Pulaski GA NaT POINT (-80.87500 32.03125) - """ - - stations = coops_stations(station_status=station_status) - return stations[stations.within(region)] - - -def coops_stations_within_bounds( - minx: float, - miny: float, - maxx: float, - maxy: float, - station_status: COOPS_StationStatus = None, -) -> GeoDataFrame: - return coops_stations_within_region( - region=box(minx=minx, miny=miny, maxx=maxx, maxy=maxy), - station_status=station_status, - ) - - -def coops_product_within_region( - product: COOPS_Product, - region: Union[Polygon, MultiPolygon], - start_date: datetime, - end_date: datetime = None, - datum: COOPS_TidalDatum = None, - units: COOPS_Units = None, - time_zone: COOPS_TimeZone = None, - interval: COOPS_Interval = None, - station_status: COOPS_StationStatus = None, -) -> Dataset: - """ - retrieve CO-OPS data from within the specified region of interest - - :param product: CO-OPS product; one of ``water_level``, ``air_temperature``, ``water_temperature``, ``wind``, ``air_pressure``, ``air_gap``, ``conductivity``, ``visibility``, ``humidity``, ``salinity``, ``hourly_height``, ``high_low``, ``daily_mean``, ``monthly_mean``, ``one_minute_water_level``, ``predictions``, ``datums``, ``currents``, ``currents_predictions`` - :param region: polygon or multipolygon denoting region of interest - :param start_date: start date of CO-OPS query - :param end_date: start date of CO-OPS query - :param datum: tidal datum - :param units: one of ``metric`` or ``english`` - :param time_zone: station time zone - :param interval: data time interval - :param station_status: either ``active`` or ``discontinued`` - :return: array of data within the specified region - - >>> from stormevents.nhc import VortexTrack - >>> from shapely import ops - >>> from datetime import datetime, timedelta - >>> track = VortexTrack('florence2018', file_deck='b') - >>> combined_wind_swaths = ops.unary_union(list(track.wind_swaths(34).values())) - >>> coops_product_within_region('water_level', region=combined_wind_swaths, start_date=datetime.now() - timedelta(hours=1), end_date=datetime.now()) - - Dimensions: (nos_id: 10, t: 11) - Coordinates: - * nos_id (nos_id) int64 8651370 8652587 8654467 ... 8662245 8665530 8670870 - * t (t) datetime64[ns] 2022-03-08T14:48:00 ... 2022-03-08T15:48:00 - nws_id (nos_id) 0] - return xarray.combine_nested(station_data, concat_dim="nos_id") diff --git a/stormevents/coops/us_states.json b/stormevents/coops/us_states.json deleted file mode 100644 index 3812ee2..0000000 --- a/stormevents/coops/us_states.json +++ /dev/null @@ -1,59 +0,0 @@ -{ - "Alabama": "AL", - "Alaska": "AK", - "Arizona": "AZ", - "Arkansas": "AR", - "California": "CA", - "Colorado": "CO", - "Connecticut": "CT", - "Delaware": "DE", - "Florida": "FL", - "Georgia": "GA", - "Hawaii": "HI", - "Idaho": "ID", - "Illinois": "IL", - "Indiana": "IN", - "Iowa": "IA", - "Kansas": "KS", - "Kentucky": "KY", - "Louisiana": "LA", - "Maine": "ME", - "Maryland": "MD", - "Massachusetts": "MA", - "Michigan": "MI", - "Minnesota": "MN", - "Mississippi": "MS", - "Missouri": "MO", - "Montana": "MT", - "Nebraska": "NE", - "Nevada": "NV", - "New Hampshire": "NH", - "New Jersey": "NJ", - "New Mexico": "NM", - "New York": "NY", - "North Carolina": "NC", - "North Dakota": "ND", - "Ohio": "OH", - "Oklahoma": "OK", - "Oregon": "OR", - "Pennsylvania": "PA", - "Rhode Island": "RI", - "South Carolina": "SC", - "South Dakota": "SD", - "Tennessee": "TN", - "Texas": "TX", - "Utah": "UT", - "Vermont": "VT", - "Virginia": "VA", - "Washington": "WA", - "West Virginia": "WV", - "Wisconsin": "WI", - "Wyoming": "WY", - "District of Columbia": "DC", - "American Samoa": "AS", - "Guam": "GU", - "Northern Mariana Islands": "MP", - "Puerto Rico": "PR", - "United States Minor Outlying Islands": "UM", - "Virgin Islands of the U.S.": "VI" -} diff --git a/stormevents/stormevent.py b/stormevents/stormevent.py index 2e90b54..31673f0 100644 --- a/stormevents/stormevent.py +++ b/stormevents/stormevent.py @@ -7,6 +7,14 @@ import pandas import xarray +from searvey.coops import COOPS_Interval +from searvey.coops import COOPS_Product +from searvey.coops import COOPS_Station +from searvey.coops import coops_stations_within_region +from searvey.coops import COOPS_TidalDatum +from searvey.coops import COOPS_TimeZone +from searvey.coops import COOPS_Units +from searvey.coops import StationStatus from shapely import ops from shapely.geometry import MultiPolygon from shapely.geometry import Polygon @@ -14,14 +22,6 @@ from shapely.ops import shape as shapely_shape from xarray import Dataset -from stormevents.coops.tidalstations import COOPS_Interval -from stormevents.coops.tidalstations import COOPS_Product -from stormevents.coops.tidalstations import COOPS_Station -from stormevents.coops.tidalstations import coops_stations_within_region -from stormevents.coops.tidalstations import COOPS_StationStatus -from stormevents.coops.tidalstations import COOPS_TidalDatum -from stormevents.coops.tidalstations import COOPS_TimeZone -from stormevents.coops.tidalstations import COOPS_Units from stormevents.nhc import nhc_storms from stormevents.nhc import VortexTrack from stormevents.nhc.atcf import ATCF_Advisory @@ -346,7 +346,7 @@ def coops_product_within_isotach( product: COOPS_Product, wind_speed: int, advisories: List[ATCF_Advisory] = None, - station_type: COOPS_StationStatus = None, + status: StationStatus = None, start_date: datetime = None, end_date: datetime = None, datum: COOPS_TidalDatum = None, @@ -363,7 +363,7 @@ def coops_product_within_isotach( :param advisories: ATCF advisory types :param start_date: start date :param end_date: end date - :param station_type: either ``current`` or ``historical`` + :param status: either ``current`` or ``historical`` :param datum: tidal datum :param units: either ``metric`` or ``english`` :param time_zone: time zone @@ -408,7 +408,7 @@ def coops_product_within_isotach( return self.coops_product_within_region( region=region, - station_type=station_type, + status=status, start_date=start_date, end_date=end_date, product=product, @@ -424,7 +424,7 @@ def coops_product_within_region( region: Polygon, start_date: datetime = None, end_date: datetime = None, - station_type: COOPS_StationStatus = None, + status: StationStatus = None, datum: COOPS_TidalDatum = None, units: COOPS_Units = None, time_zone: COOPS_TimeZone = None, @@ -437,7 +437,7 @@ def coops_product_within_region( :param region: a Shapely polygon denoting the region of interest :param start_date: start date :param end_date: end date - :param station_type: either ``current`` or ``historical`` + :param status: either ``current`` or ``historical`` :param datum: tidal datum :param units: either ``metric`` or ``english`` :param time_zone: time zone @@ -479,9 +479,7 @@ def coops_product_within_region( start=self.start_date, end=self.end_date, relative=end_date ) - stations = coops_stations_within_region( - region=region, station_status=station_type - ) + stations = coops_stations_within_region(region=region, station_status=status) if len(stations) > 0: stations_data = [] @@ -490,10 +488,8 @@ def coops_product_within_region( product=product, start_date=start_date, end_date=end_date, - datum=datum, - units=units, - time_zone=time_zone, interval=interval, + datum=datum, ) if len(station_data["t"]) > 0: stations_data.append(station_data) diff --git a/tests/data/reference/test_coops_product_within_region/data.nc b/tests/data/reference/test_coops_product_within_region/data.nc deleted file mode 100644 index 334b24067461b77f949662ba8a41faa4ff68fffd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20042 zcmeHP4Rln+6`q%D!V;DcFhD?l)&LSAkPw6Nld!w_Spp=Rn2q2MB?-v_OG7p$3k0+T zi>PReRjajv6a^#L)T&rTnrakUu?KBEm7><8Q2#imYES(u$Aj9rGjku48wQJQk<+s` z?7Q=Q^YiB2H*e<7eYe+%S(zX<6MX1 z(nWr}JP%`^4UpwK<2_+4-ePz{s$FxSoW+uvlmAr*3M-l;O`+y! zIMl-VbNFYJ$ma(xD?-gJ;YeeCPG(~$T35d)KX>BA6LT|{)GwNxKWXBmoQc_)>|$B8 z`!S`vIWRL16LB)Ja&YB8#ijPmvaB@VEK6Jn6M$pX@h-T+6eqW~sOn{K3MUH`3CQwf zHRcfIpo}`jTBopCymeIBtV>p~ZIxuURW=m0Sfa31VW}?|Ainl?xq(+u`EHs{_Kyxkce+JWpOO z+)u{b;)tgNke1%ORGMxaF`c?_Gyprf=@>iOM>NXXr8EN31>Z9M#YEVD0v*we6s*)mpcX*RvfclJpmn$y4`Ja^4cimpW*tC?$fp;>m6V$P> z>xJHzA$7UZTaqPiQu!&PCB9ANU(rwEK!$AR&Vbaep_1P}OyZA~9^Xsib;(linJRVN 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-#,Name,Amplitude,Phase,Speed,Description -1,M2,0.56,129.5,28.984104,Principal lunar semidiurnal constituent -2,S2,0.18,114.7,30.0,Principal solar semidiurnal constituent -3,N2,0.11,124.4,28.43973,Larger lunar elliptic semidiurnal constituent -4,K1,0.49,76.3,15.041069,Lunar diurnal constituent -5,M4,0.0,335.6,57.96821,Shallow water overtides of principal lunar constituent -6,O1,0.27,76.4,13.943035,Lunar diurnal constituent -7,M6,0.0,80.1,86.95232,Shallow water overtides of principal lunar constituent -8,MK3,0.0,262.3,44.025173,Shallow water terdiurnal -9,S4,0.0,0.0,60.0,Shallow water overtides of principal solar constituent -10,MN4,0.0,238.6,57.423832,Shallow water quarter diurnal constituent -11,NU2,0.02,130.8,28.512583,Larger lunar evectional constituent -12,S6,0.0,0.0,90.0,Shallow water overtides of principal solar constituent -13,MU2,0.01,88.1,27.968208,Variational constituent -14,2N2,0.01,120.9,27.895355,Lunar elliptical semidiurnal second-order constituent -15,OO1,0.02,90.3,16.139101,Lunar diurnal -16,LAM2,0.0,141.6,29.455626,Smaller lunar evectional constituent -17,S1,0.01,59.9,15.0,Solar diurnal constituent -18,M1,0.02,87.5,14.496694,Smaller lunar elliptic diurnal constituent -19,J1,0.03,82.3,15.5854435,Smaller lunar elliptic diurnal constituent -20,MM,0.02,37.9,0.5443747,Lunar monthly constituent -21,SSA,0.0,0.0,0.0821373,Solar semiannual constituent -22,SA,0.16,177.2,0.0410686,Solar annual constituent -23,MSF,0.0,0.0,1.0158958,Lunisolar synodic fortnightly constituent -24,MF,0.02,33.3,1.0980331,Lunisolar fortnightly constituent -25,RHO,0.01,60.7,13.471515,Larger lunar evectional diurnal constituent -26,Q1,0.05,75.6,13.398661,Larger lunar elliptic diurnal constituent -27,T2,0.01,103.9,29.958933,Larger solar elliptic constituent -28,R2,0.0,56.8,30.041067,Smaller solar elliptic constituent -29,2Q1,0.0,102.5,12.854286,Larger elliptic diurnal -30,P1,0.15,75.3,14.958931,Solar diurnal constituent -31,2SM2,0.0,249.6,31.015896,Shallow water semidiurnal constituent -32,M3,0.0,89.3,43.47616,Lunar terdiurnal constituent -33,L2,0.01,114.0,29.528479,Smaller lunar elliptic semidiurnal constituent -34,2MK3,0.0,0.0,42.92714,Shallow water terdiurnal constituent -35,K2,0.05,104.8,30.082138,Lunisolar semidiurnal constituent -36,M8,0.0,0.0,115.93642,Shallow water eighth diurnal constituent -37,MS4,0.0,234.1,58.984104,Shallow water quarter diurnal constituent diff --git a/tests/data/reference/test_coops_station/station1612480_constituents.csv b/tests/data/reference/test_coops_station/station1612480_constituents.csv deleted file mode 100644 index 1c56b3d..0000000 --- a/tests/data/reference/test_coops_station/station1612480_constituents.csv +++ /dev/null @@ -1,38 +0,0 @@ -#,Name,Amplitude,Phase,Speed,Description -1,M2,0.52,83.3,28.984104,Principal lunar semidiurnal constituent -2,S2,0.26,74.0,30.0,Principal solar semidiurnal constituent -3,N2,0.11,83.4,28.43973,Larger lunar elliptic semidiurnal constituent -4,K1,0.58,85.8,15.041069,Lunar diurnal constituent -5,M4,0.01,319.1,57.96821,Shallow water overtides of principal lunar constituent -6,O1,0.32,87.8,13.943035,Lunar diurnal constituent -7,M6,0.0,0.0,86.95232,Shallow water overtides of principal lunar constituent -8,MK3,0.0,141.0,44.025173,Shallow water terdiurnal -9,S4,0.0,222.0,60.0,Shallow water overtides of principal solar constituent -10,MN4,0.0,259.7,57.423832,Shallow water quarter diurnal constituent -11,NU2,0.02,85.5,28.512583,Larger lunar evectional constituent -12,S6,0.0,0.0,90.0,Shallow water overtides of principal solar constituent -13,MU2,0.02,57.4,27.968208,Variational constituent -14,2N2,0.02,81.2,27.895355,Lunar elliptical semidiurnal second-order constituent -15,OO1,0.03,99.2,16.139101,Lunar diurnal -16,LAM2,0.01,29.5,29.455626,Smaller lunar evectional constituent -17,S1,0.0,201.3,15.0,Solar diurnal constituent -18,M1,0.02,104.5,14.496694,Smaller lunar elliptic diurnal constituent -19,J1,0.04,92.8,15.5854435,Smaller lunar elliptic diurnal constituent -20,MM,0.02,37.6,0.5443747,Lunar monthly constituent -21,SSA,0.0,0.0,0.0821373,Solar semiannual constituent -22,SA,0.16,184.1,0.0410686,Solar annual constituent -23,MSF,0.0,0.0,1.0158958,Lunisolar synodic fortnightly constituent -24,MF,0.02,32.6,1.0980331,Lunisolar fortnightly constituent -25,RHO,0.01,89.2,13.471515,Larger lunar evectional diurnal constituent -26,Q1,0.06,86.0,13.398661,Larger lunar elliptic diurnal constituent -27,T2,0.02,67.8,29.958933,Larger solar elliptic constituent -28,R2,0.01,15.1,30.041067,Smaller solar elliptic constituent -29,2Q1,0.0,91.8,12.854286,Larger elliptic diurnal -30,P1,0.18,85.1,14.958931,Solar diurnal constituent -31,2SM2,0.01,288.1,31.015896,Shallow water semidiurnal constituent -32,M3,0.01,78.3,43.47616,Lunar terdiurnal constituent -33,L2,0.01,68.2,29.528479,Smaller lunar elliptic semidiurnal constituent -34,2MK3,0.0,120.4,42.92714,Shallow water terdiurnal constituent -35,K2,0.08,70.3,30.082138,Lunisolar semidiurnal constituent -36,M8,0.0,0.0,115.93642,Shallow water eighth diurnal constituent -37,MS4,0.0,251.5,58.984104,Shallow water quarter diurnal constituent diff --git a/tests/data/reference/test_coops_station/station9414458_constituents.csv b/tests/data/reference/test_coops_station/station9414458_constituents.csv deleted file mode 100644 index fa7c9de..0000000 --- a/tests/data/reference/test_coops_station/station9414458_constituents.csv +++ /dev/null @@ -1,38 +0,0 @@ -#,Name,Amplitude,Phase,Speed,Description -1,M2,2.71,6.1,28.984104,Principal lunar semidiurnal constituent -2,S2,0.6,13.3,30.0,Principal solar semidiurnal constituent -3,N2,0.54,348.4,28.43973,Larger lunar elliptic semidiurnal constituent -4,K1,1.32,119.1,15.041069,Lunar diurnal constituent -5,M4,0.04,236.6,57.96821,Shallow water overtides of principal lunar constituent -6,O1,0.79,112.3,13.943035,Lunar diurnal constituent -7,M6,0.03,72.5,86.95232,Shallow water overtides of principal lunar constituent -8,MK3,0.07,90.7,44.025173,Shallow water terdiurnal -9,S4,0.0,0.0,60.0,Shallow water overtides of principal solar constituent -10,MN4,0.02,218.9,57.423832,Shallow water quarter diurnal constituent -11,NU2,0.13,348.3,28.512583,Larger lunar evectional constituent -12,S6,0.0,0.0,90.0,Shallow water overtides of principal solar constituent -13,MU2,0.06,145.2,27.968208,Variational constituent -14,2N2,0.06,335.9,27.895355,Lunar elliptical semidiurnal second-order constituent -15,OO1,0.05,152.5,16.139101,Lunar diurnal -16,LAM2,0.06,342.9,29.455626,Smaller lunar evectional constituent -17,S1,0.04,109.0,15.0,Solar diurnal constituent -18,M1,0.05,158.4,14.496694,Smaller lunar elliptic diurnal constituent -19,J1,0.07,150.7,15.5854435,Smaller lunar elliptic diurnal constituent -20,MM,0.0,0.0,0.5443747,Lunar monthly constituent -21,SSA,0.13,286.2,0.0821373,Solar semiannual constituent -22,SA,0.12,221.0,0.0410686,Solar annual constituent -23,MSF,0.0,0.0,1.0158958,Lunisolar synodic fortnightly constituent -24,MF,0.04,130.8,1.0980331,Lunisolar fortnightly constituent -25,RHO,0.03,109.5,13.471515,Larger lunar evectional diurnal constituent -26,Q1,0.14,114.2,13.398661,Larger lunar elliptic diurnal constituent -27,T2,0.03,346.2,29.958933,Larger solar elliptic constituent -28,R2,0.01,167.1,30.041067,Smaller solar elliptic constituent -29,2Q1,0.02,133.2,12.854286,Larger elliptic diurnal -30,P1,0.41,119.1,14.958931,Solar diurnal constituent -31,2SM2,0.03,168.0,31.015896,Shallow water semidiurnal constituent -32,M3,0.0,0.0,43.47616,Lunar terdiurnal constituent -33,L2,0.12,356.9,29.528479,Smaller lunar elliptic semidiurnal constituent -34,2MK3,0.11,82.2,42.92714,Shallow water terdiurnal constituent -35,K2,0.2,358.2,30.082138,Lunisolar semidiurnal constituent -36,M8,0.0,0.0,115.93642,Shallow water eighth diurnal constituent -37,MS4,0.03,251.1,58.984104,Shallow water quarter diurnal constituent diff --git a/tests/data/reference/test_coops_stations_within_region/stations.csv b/tests/data/reference/test_coops_stations_within_region/stations.csv deleted file mode 100644 index 132c490..0000000 --- a/tests/data/reference/test_coops_stations_within_region/stations.csv +++ /dev/null @@ -1,46 +0,0 @@ -nos_id,nws_id,name,state,status,removed,geometry -8726412,MTBF1,Middle Tampa Bay,,active,,POINT (-82.625 27.65625) -8726679,TSHF1,East Bay Causeway,FL,active,,POINT (-82.4375 27.921875) -8726694,TPAF1,TPA Cruise Terminal 2,FL,active,,POINT (-82.4375 27.9375) -8720233,BLIF1,Blount Island Command,FL,active,"2017-01-16 14:35:00,2017-01-16 14:30:00,2013-11-18 00:00:00,2013-11-15 00:00:00",POINT (-81.5 30.390625) -8720228,LTJF1,Little Jetties Visibility,FL,active,"2017-02-21 18:00:00,2014-02-06 00:00:00",POINT (-81.4375 30.375) -8726520,SAPF1,"St. Petersburg, Tampa Bay",FL,active,"2017-06-30 00:00:00,2017-01-17 00:00:00,2016-01-21 23:58:00,2016-01-13 00:00:00,2016-01-12 23:59:00,2016-01-12 23:58:00,2016-01-12 00:00:00,2015-01-30 00:00:00,2014-02-07 00:00:00,2013-02-19 10:14:00,2012-02-03 09:46:00,2011-03-04 14:08:00,2011-03-03 09:48:00,2010-02-18 23:59:00,2010-02-18 00:00:00,2009-12-17 20:03:00,2009-04-13 00:00:00,2007-02-24 00:00:00,2006-04-14 23:59:00,2006-04-14 00:00:00,2005-02-24 00:00:00,2004-03-14 00:00:00,2003-03-04 00:00:00,2002-03-08 00:00:00,2001-05-02 00:00:00,2000-06-28 00:00:00,2000-04-06 00:00:00,1999-02-17 00:00:00,1998-02-10 00:00:00,1996-07-16 23:00:00,1993-02-25 23:00:00,1992-12-16 00:00:00,1992-04-16 23:00:00",POINT (-82.625 27.765625) -8726724,CWBF1,Clearwater Beach,FL,active,"2019-03-13 17:42:00,2018-01-17 16:18:00,2017-03-22 00:00:00,2017-01-15 18:51:00,2016-01-18 00:00:00,2015-02-02 00:00:00,2014-02-15 00:00:00,2013-02-18 10:00:00,2012-01-28 08:23:00,2011-02-16 21:36:00,2010-03-09 00:00:00,2010-03-08 00:00:00,2009-04-22 14:42:00,2009-04-21 00:00:00,2009-04-20 00:00:00,2008-03-20 00:00:00,2007-02-16 00:00:00,2006-03-16 00:00:00,2005-02-28 12:00:00,2005-02-27 23:59:00,2005-02-26 23:59:00,2005-02-26 00:00:00,2004-03-11 00:00:00,2003-03-01 00:00:00,2002-02-25 00:00:00,2001-04-28 00:00:00,2000-04-10 00:00:00,1999-02-16 00:00:00,1998-03-06 00:00:00,1998-02-12 00:00:00,1997-03-09 00:00:00",POINT (-82.8125 27.984375) -8726607,OPTF1,Old Port Tampa,FL,active,"2020-01-14 00:00:00,2019-03-06 13:00:00,2018-01-15 00:00:00,2017-01-21 15:20:00,2017-01-21 00:00:00,2016-01-19 00:00:00,2015-02-05 00:00:00,2014-07-02 04:00:00,2014-02-01 10:00:00,2013-02-12 10:00:00,2011-09-19 00:00:00,2011-09-17 23:59:00,2011-09-17 00:00:00,2011-02-22 11:22:00,2010-02-18 00:00:00,2009-04-08 00:00:00,2008-02-25 00:00:00,2007-03-05 00:00:00,2007-03-02 00:00:00,2005-02-22 00:00:00,2004-10-14 23:59:00,2004-03-17 00:00:00,2002-02-15 00:00:00,2001-05-07 00:00:00,2000-04-11 00:00:00,1999-02-27 00:00:00,1998-02-19 00:00:00,1997-03-18 00:00:00,1996-03-26 00:00:00,1994-04-27 23:00:00",POINT (-82.5625 27.859375) -8720030,FRDF1,Fernandina Beach,FL,active,"2020-01-18 00:00:00,2017-01-12 00:00:00,2016-01-10 23:59:00,2016-01-08 23:59:00,2016-01-08 16:10:00,2016-01-08 16:00:00,2015-01-26 10:00:00,2015-01-22 10:05:00,2014-01-21 00:00:00,2013-01-27 00:00:00,2013-01-25 00:00:00,2012-01-24 17:00:00,2011-01-13 16:08:00,2010-01-25 00:00:00,2009-01-14 00:00:00,2008-01-09 00:00:00,2007-02-06 00:00:00,2006-01-26 00:00:00,2006-01-25 00:00:00,2006-01-24 23:59:00,2006-01-24 00:00:00,2005-03-01 00:00:00,2005-02-28 00:00:00,2004-05-13 00:00:00,2003-04-09 00:00:00,2002-04-09 00:00:00,2001-01-30 00:00:00,2000-03-22 00:00:00,1999-03-05 00:00:00,1998-03-05 00:00:00",POINT (-81.4375 30.671875) -8726384,PMAF1,Port Manatee,FL,active,"2020-01-18 14:30:00,2019-03-11 00:00:00,2018-01-18 13:00:00,2017-01-19 14:00:00,2017-01-19 00:00:00,2016-01-29 09:00:00,2015-02-07 00:00:00,2014-02-05 10:00:00,2013-02-24 00:00:00,2011-09-26 00:00:00,2011-09-25 00:01:00,2011-09-23 16:42:00,2011-09-21 16:31:00,2011-02-28 10:43:00,2011-02-25 00:00:00,2010-02-25 00:00:00,2009-04-21 00:00:00,2008-02-08 00:00:00,2007-02-22 00:00:00,2005-02-11 00:00:00,2004-03-23 00:00:00,2003-02-25 00:00:00,2002-02-21 00:00:00,2001-05-03 00:00:00,2000-05-16 00:00:00,2000-04-07 00:00:00,1999-02-22 00:00:00,1998-02-18 00:00:00,1996-03-22 23:00:00,1994-04-25 23:00:00,1992-07-11 23:00:00",POINT (-82.5625 27.640625) -8725520,FMRF1,Fort Myers,FL,active,"2020-01-22 14:00:00,2018-06-30 13:30:00,2018-03-13 00:00:00,2017-01-24 00:00:00,2017-01-23 00:01:00,2017-01-22 23:59:00,2016-02-01 10:30:00,2016-01-30 00:00:00,2015-02-12 10:00:00,2014-01-28 00:00:00,2014-01-27 00:00:00,2013-02-09 00:00:00,2012-07-25 00:00:00,2012-01-26 15:12:00,2011-06-09 23:36:00,2011-06-09 00:00:00,2010-06-14 23:59:00,2010-06-14 00:00:00,2010-05-13 00:00:00,2010-05-11 23:59:00,2010-05-11 00:00:00,2009-04-24 00:00:00,2008-03-24 00:00:00,2007-04-16 00:00:00,2006-04-13 00:00:00,2006-04-12 23:59:00,2006-04-12 00:00:00,2006-04-10 00:00:00,2004-09-02 00:00:00,2004-03-05 23:59:00,2004-03-05 00:00:00,2003-02-20 00:00:00,2002-02-11 00:00:00,2001-04-26 00:00:00,2000-04-03 00:00:00,1999-03-10 00:00:00,1900-01-01 00:00:00",POINT (-81.875 26.640625) -8726671,SKCF1,Sparkman Channel Entrance,FL,active,2020-02-07 00:00:00,POINT (-82.4375 27.921875) -8726524,GCTF1,"Gadsden Cut, Tampa Bay",FL,active,"2020-03-11 00:00:00,2018-04-20 00:00:00",POINT (-82.5 27.78125) -8726674,EBEF1,East Bay,FL,active,2020-05-20 00:00:00,POINT (-82.4375 27.921875) -8720215,NFDF1,Navy Fuel Depot,FL,active,"2021-01-19 16:00:00,2020-08-12 00:00:00,2017-06-15 00:00:00,2017-04-12 09:30:00,2013-11-25 23:59:00,2013-11-25 00:00:00",POINT (-81.625 30.40625) -8720218,MYPF1,Mayport (Bar Pilots Dock),FL,active,"2021-04-13 12:00:00,2020-09-16 18:00:00,2020-01-20 12:00:00,2017-01-17 00:00:00,2017-01-14 00:00:00,2016-06-13 00:00:00,2016-01-14 00:00:00,2015-01-27 11:00:00,2014-02-03 00:00:00,2014-01-25 09:46:00,2013-01-29 00:00:00,2012-01-26 16:00:00,2011-03-04 23:59:00,2011-03-04 00:00:00,2011-02-10 00:01:00,2008-04-26 00:00:00,2008-04-25 23:59:00,2008-04-25 00:00:00,2008-01-08 00:00:00,2007-02-05 00:00:00,2006-02-16 12:00:00,2006-02-15 23:59:00,2006-02-15 00:00:00,2005-02-28 00:00:00,2004-05-11 00:00:00,2003-04-24 00:00:00,2002-04-11 00:00:00,2000-12-08 00:00:00,1996-06-10 23:59:00,1996-06-10 00:00:00",POINT (-81.4375 30.390625) -8661070,MROS1,Springmaid Pier,SC,active,"2021-06-11 00:00:00,2016-12-14 00:01:00,2016-12-14 00:00:00,2016-10-09 00:00:00,2015-10-27 00:00:00,2015-01-29 18:00:00,2015-01-29 17:00:00,2014-11-20 10:30:00,2014-03-28 00:00:00,2014-03-27 00:00:00,2014-01-23 00:00:00,2014-01-22 00:00:00,2012-08-23 00:00:00,2011-11-07 11:39:00,2010-12-08 00:00:00,2010-11-04 00:00:00,2010-09-24 10:50:00,2009-12-09 00:00:00,2008-12-05 00:00:00,2007-10-19 00:00:00,2006-11-10 23:59:00,2006-11-09 23:59:00,2006-11-09 00:00:00,2006-11-08 00:00:00,2005-12-12 00:00:00,2004-12-10 00:00:00,2002-12-04 00:00:00,2001-05-18 00:00:00,2000-05-19 00:00:00,1998-04-01 00:00:00,1997-04-15 00:00:00,1993-06-29 23:00:00,1992-06-25 23:00:00",POINT (-78.9375 33.65625) -8670870,FPKG1,Fort Pulaski,GA,active,"2021-08-25 11:00:00,2021-08-25 00:00:00,2021-08-23 12:00:00,2021-08-20 23:58:00,2021-04-14 18:36:00,2021-03-16 14:00:00,2019-12-16 00:00:00,2018-12-04 00:00:00,2017-12-06 11:00:00,2016-10-16 00:00:00,2015-12-15 00:00:00,2015-12-14 00:00:00,2014-12-11 00:00:00,2014-07-16 00:00:00,2014-07-15 00:01:00,2014-07-15 00:00:00,2013-12-12 00:00:00,2012-09-04 09:51:00,2011-11-08 00:00:00,2010-11-08 00:00:00,2010-02-12 00:00:00,2008-12-05 00:00:00,2007-11-02 00:00:00,2006-11-10 23:59:00,2006-11-10 00:00:00,2005-12-05 00:00:00,2004-12-03 00:00:00,2003-12-09 00:00:00,2002-12-13 00:00:00,2001-05-12 23:59:00,2001-05-12 00:00:00,2000-05-13 00:00:00,1998-03-24 00:00:00,1996-09-25 00:00:00,1993-06-15 23:00:00,1992-04-02 00:00:00,1991-02-11 23:00:00",POINT (-80.875 32.03125) -8722956,PEGF1,South Port Everglades,FL,active,"2021-09-08 11:36:00,2019-02-11 00:00:00,2018-01-27 12:00:00,2018-01-27 00:00:00,2018-01-26 10:00:00,2018-01-26 00:00:00,2018-01-20 23:58:00",POINT (-80.125 26.078125) -8720245,JXUF1,Jacksonville University,FL,active,"2021-09-24 12:30:00,2020-09-13 15:00:00,2019-08-03 14:15:00,2015-02-27 16:00:00,2013-11-08 00:02:00,2013-11-08 00:01:00",POINT (-81.625 30.359375) -8658120,WLON7,Wilmington,NC,active,"2021-10-10 13:00:00,2021-10-10 08:00:00,2019-09-30 15:25:00,2017-10-20 00:00:00,2015-10-23 00:00:00,2014-11-18 12:03:00,2014-04-01 00:00:00,2013-12-20 00:00:00,2013-05-22 00:00:00,2012-08-21 00:00:00,2011-11-03 14:11:00,2010-12-15 08:19:00,2010-12-15 08:12:00,2009-10-28 00:00:00,2008-11-17 00:00:00,2006-10-20 00:00:00,2006-10-19 23:59:00,2006-10-19 00:00:00,2006-01-08 00:00:00,2004-12-14 00:00:00,2003-11-15 00:00:00,2002-11-08 00:00:00,2001-06-29 00:00:00,2000-10-20 00:00:00,1999-02-11 00:00:00,1998-04-06 00:00:00,1997-01-18 23:00:00,1995-07-07 00:00:00,1995-03-23 23:00:00,1993-05-20 23:00:00,1992-06-27 23:00:00,1991-03-15 00:00:00",POINT (-77.9375 34.21875) -8658163,JMPN7,Wrightsville Beach,NC,active,"2021-10-11 13:00:00,2021-10-10 10:00:00,2020-08-11 01:00:00,2019-11-25 00:00:00,2019-06-27 16:00:00,2019-02-12 00:00:00,2017-10-19 15:00:00,2017-10-19 14:00:00,2017-10-19 00:00:00,2015-10-21 00:00:00,2015-08-13 11:00:00,2015-01-28 13:00:00,2014-11-13 13:00:00,2014-04-02 00:00:00,2013-06-18 14:00:00,2012-08-17 12:59:00,2011-11-03 14:29:00,2010-12-16 09:50:00,2009-10-24 00:00:00,2009-03-03 12:18:00,2009-03-03 12:15:00,2009-02-28 12:22:00,2009-02-28 12:21:00,2008-11-14 10:34:00,2007-10-11 00:00:00,2006-10-16 00:00:00,2006-08-17 13:18:00,2006-08-17 13:18:00,2006-01-04 00:00:00,2004-08-03 23:59:00,2004-04-26 00:00:00",POINT (-77.8125 34.21875) -8652587,ORIN7,Oregon Inlet Marina,NC,active,"2021-10-16 00:00:00,2021-10-14 01:00:00,2021-10-14 00:00:00,2020-01-30 00:00:00,2020-01-06 18:54:00,2020-01-06 16:42:00,2019-10-24 11:00:00,2019-10-20 00:01:00,2017-04-21 00:01:00,2017-04-21 00:00:00,2016-10-04 09:00:00,2016-04-18 00:00:00,2016-03-28 00:00:00,2015-04-16 00:00:00,2015-01-30 00:01:00,2014-03-04 00:00:00,2013-04-01 00:00:00,2012-10-06 13:42:00,2012-10-06 13:28:00,2012-10-06 06:46:00,2012-10-06 00:01:00,2012-10-06 00:00:00,2012-07-13 00:00:00,2012-02-28 00:00:00,2011-09-08 00:00:00,2011-03-24 10:08:00,2011-03-24 09:00:00,2010-07-20 00:00:00,2009-08-25 13:51:00,2009-03-25 00:00:00,2008-08-06 00:00:00,2008-08-05 14:21:00,2007-05-21 00:00:00,2006-11-02 23:59:00,2006-11-02 00:00:00,2006-10-11 23:59:00,2006-10-11 00:00:00,2002-08-20 00:00:00,2001-02-01 00:00:00,1999-11-10 00:00:00,1998-04-13 00:00:00,1997-04-14 00:00:00",POINT (-75.5625 35.78125) -8654467,HCGN7,USCG Station Hatteras,NC,active,"2021-10-20 00:00:00,2019-08-08 14:30:00,2019-08-08 00:00:00,2019-08-07 00:00:00,2018-11-17 08:00:00,2018-04-23 13:11:00,2018-04-21 00:00:00,2016-05-06 10:00:00,2015-04-09 00:00:00,2014-11-07 00:00:00,2014-03-11 00:00:00,2013-04-04 00:00:00,2013-01-04 00:00:00,2012-03-02 12:00:00,2011-03-22 00:00:00,2010-04-26 23:59:00,2010-04-26 00:00:00,2010-04-22 00:00:00,2010-04-21 00:00:00",POINT (-75.6875 35.21875) -8665530,CHTS1,"Charleston, Cooper River Entrance",SC,active,"2021-12-11 16:30:00,2018-12-03 00:00:00,2018-12-01 00:00:00,2018-04-18 17:40:00,2017-12-06 00:00:00,2017-12-02 00:00:00,2016-12-03 00:01:00,2016-12-03 00:00:00,2016-12-02 00:00:00,2016-11-09 15:00:00,2016-11-08 14:00:00,2015-12-09 11:20:00,2015-12-09 00:00:00,2014-12-08 00:00:00,2013-12-16 00:00:00,2012-08-30 00:00:00,2011-11-02 00:00:00,2011-11-01 00:00:00,2010-12-01 00:07:00,2009-12-14 11:49:00,2009-12-13 00:01:00,2008-12-11 00:00:00,2008-12-09 00:00:00,2007-11-09 00:00:00,2006-10-24 23:59:00,2006-10-24 00:00:00,2005-12-07 00:00:00,2004-12-06 00:00:00,2003-12-04 00:00:00,2002-12-09 00:00:00,2001-05-12 00:00:00,2000-05-17 00:00:00,1999-04-30 00:00:00,1998-03-30 00:00:00,1997-04-22 00:00:00,1993-06-22 23:00:00,1992-04-14 00:00:00,1991-02-25 00:00:00",POINT (-79.9375 32.78125) -8656483,BFTN7,"Beaufort, Duke Marine Lab",NC,active,"2021-12-15 11:00:00,2021-12-10 11:00:00,2021-12-09 13:00:00,2019-11-19 00:00:00,2018-12-28 16:00:00,2018-12-28 00:00:00,2017-10-16 13:00:00,2016-08-22 12:30:00,2015-10-19 00:00:00,2014-11-11 00:00:00,2014-03-29 00:00:00,2012-08-16 20:00:00,2011-11-17 13:46:00,2011-10-26 07:04:00,2011-01-12 14:43:00,2009-10-21 00:00:00,2009-03-23 00:00:00,2008-08-25 10:35:00,2007-05-15 00:00:00,2006-11-28 23:59:00,2006-11-28 00:01:00,2006-11-28 00:00:00,2005-07-26 00:00:00,2004-07-20 00:00:00,2003-12-15 00:00:00,2002-11-14 00:00:00,2001-06-27 00:00:00,2000-10-18 00:00:00,1999-03-30 00:00:00,1998-04-14 00:00:00,1997-08-07 00:00:00,1993-05-25 23:00:00,1993-01-13 23:00:00,1992-05-13 23:00:00,1991-03-20 00:00:00,1901-01-01 00:00:00",POINT (-76.6875 34.71875) -8725110,NPSF1,"Naples, Gulf of Mexico",FL,active,"2022-01-23 12:00:00,2022-01-23 00:00:00,2022-01-22 12:00:00,2020-01-27 00:00:00,2019-09-10 10:00:00,2018-08-22 01:00:00,2018-03-16 23:58:00,2017-03-29 09:00:00,2017-03-28 10:30:00,2017-03-28 10:00:00,2015-11-15 00:00:00,2015-11-11 00:00:00,2015-06-16 00:00:00,2015-02-14 00:00:00,2014-01-23 13:00:00,2013-02-06 00:00:00,2012-01-25 09:09:00,2011-04-05 23:43:00,2011-02-02 17:00:00,2010-03-17 00:00:00,2010-03-16 00:00:00,2009-05-05 00:00:00,2008-03-26 00:00:00,2008-03-25 00:00:00,2006-04-28 23:59:00,2006-04-28 13:00:00,2006-04-28 11:00:00,2006-04-28 00:00:00,2005-02-16 00:00:00,2004-03-01 00:00:00,2003-02-19 00:00:00,2002-02-08 00:00:00,2000-03-29 00:00:00,1999-03-07 00:00:00,1998-02-07 00:00:00,1997-09-06 00:00:00,1997-03-03 00:00:00,1995-09-11 23:00:00,1993-04-08 23:00:00,1992-12-21 23:59:00",POINT (-81.8125 26.125) -8721604,TRDF1,"Trident Pier, Port Canaveral",FL,active,"2022-01-28 17:00:00,2021-09-11 14:00:00,2021-02-20 23:58:00,2020-01-24 00:00:00,2019-02-05 18:24:00,2018-11-06 20:48:00,2017-01-18 00:00:00,2016-02-24 10:00:00,2015-01-31 00:00:00,2014-12-26 00:00:00,2014-01-29 00:00:00,2013-02-05 00:00:00,2012-11-01 00:00:00,2012-01-27 19:00:00,2011-03-24 23:59:00,2011-03-24 01:00:00,2011-03-22 00:00:00,2010-02-17 00:00:00,2010-02-16 00:00:00,2009-06-16 12:00:00,2008-07-02 00:00:00,2008-06-30 23:54:00,2007-10-19 00:00:00,2007-03-07 00:00:00,2006-02-23 23:59:00,2006-02-23 00:00:00,2006-02-22 00:00:00,2005-02-24 00:00:00,2004-05-07 00:00:00,2003-04-16 00:00:00,2002-04-19 00:00:00,2001-02-06 00:00:00,2000-07-28 00:00:00,2000-03-15 00:00:00,1999-02-11 00:00:00,1998-02-19 00:00:00",POINT (-80.5625 28.421875) -8722670,LKWF1,"Lake Worth Pier, Atlantic Ocean",FL,active,"2022-01-29 14:00:00,2022-01-29 13:00:00,2022-01-29 00:00:00,2022-01-28 14:00:00,2022-01-27 11:00:00,2020-01-19 11:00:00,2020-01-18 14:00:00,2020-01-18 13:00:00,2020-01-18 10:00:00,2020-01-17 13:00:00,2020-01-17 11:00:00,2020-01-17 10:00:00,2020-01-17 00:00:00,2019-07-19 09:00:00,2019-02-08 00:00:00,2017-01-24 00:00:00,2016-10-15 00:00:00,2016-01-21 00:00:00,2015-02-05 00:00:00,2014-07-29 00:00:00,2014-02-03 00:00:00,2013-08-08 16:30:00,2013-02-08 00:00:00,2012-01-30 00:00:00,2011-06-08 00:00:00,2010-06-14 12:00:00,2010-06-14 01:00:00,2010-06-14 00:00:00,2010-06-02 00:01:00,2010-06-02 00:00:00,2010-06-01 00:01:00,2010-06-01 00:00:00",POINT (-80.0625 26.609375) -8720226,MSBF1,"Southbank Riverwalk, St Johns River",FL,active,"2022-02-02 14:00:00,2021-01-16 17:57:00,2020-01-23 09:30:00,2014-04-12 00:00:00,2008-08-18 23:59:00,2008-08-18 00:00:00,1999-07-01 23:59:00,1999-07-01 23:59:00,1999-07-01 00:00:00,1998-11-02 23:59:00,1998-11-02 00:00:00,1997-07-10 23:59:00,1997-07-10 00:00:00",POINT (-81.6875 30.3125) -8723214,VAKF1,"Virginia Key, Biscayne Bay",FL,active,"2022-02-04 00:00:00,2022-02-03 16:00:00,2021-09-30 13:00:00,2020-12-03 14:00:00,2020-09-10 11:00:00,2020-01-23 03:00:00,2017-04-01 00:01:00,2017-04-01 00:00:00,2016-03-09 11:00:00,2016-02-25 00:00:00,2016-01-26 00:00:00,2016-01-23 00:00:00,2015-11-13 00:00:00,2015-02-10 00:00:00,2014-09-27 08:15:00,2014-02-05 10:25:00,2013-07-26 00:00:00,2013-02-11 00:00:00,2012-01-31 19:28:00,2011-02-02 20:14:00,2010-02-24 00:00:00,2010-02-23 00:00:00,2009-12-09 00:00:00,2009-10-27 00:00:00,2009-03-05 00:00:00,2009-03-04 23:59:00,2009-03-04 00:00:00,2009-02-23 00:00:00,2007-12-01 00:00:00,2007-11-30 00:00:00,2007-11-26 23:59:00,2007-11-26 00:00:00,2007-08-07 00:00:00,2006-09-05 00:00:00,2005-02-22 00:00:00,2004-05-03 00:00:00,2003-04-11 00:00:00,2001-11-30 00:00:00,2001-02-09 00:00:00,2000-03-01 00:00:00,1999-02-16 00:00:00,1998-01-28 00:00:00",POINT (-80.1875 25.734375) -8720357,BKBF1,I-295 Buckman Bridge,FL,active,"2022-02-04 17:00:00,2021-02-20 14:45:00,2021-02-20 14:00:00,2021-02-20 13:00:00,2021-02-20 12:00:00,2021-02-09 00:00:00,2017-09-25 00:00:00,2017-01-13 14:00:00,2017-01-13 12:00:00,2016-11-16 19:50:00,2016-01-14 12:00:00,2015-10-26 10:30:00,2015-01-13 09:00:00,2013-11-17 00:00:00,2013-11-16 00:00:00,2013-11-14 00:00:00,2013-11-07 23:59:00,2013-11-07 00:00:00,1997-07-10 23:59:00,1997-07-10 00:00:00",POINT (-81.6875 30.1875) -8720219,DMSF1,Dames Point,FL,active,"2022-03-14 15:37:00,2022-01-26 00:00:00,2021-09-23 16:00:00,2021-01-20 16:00:00,2018-01-16 19:20:00,2017-01-10 20:00:00,2016-01-19 10:00:00,2015-01-10 14:00:00,2013-11-14 01:00:00,2013-11-14 00:59:00,2013-11-14 00:00:00,2004-04-08 23:59:00,2004-04-08 00:00:00,1999-07-01 23:59:00,1999-07-01 00:00:00,1998-11-02 23:59:00,1998-11-02 00:00:00,1996-06-10 23:59:00,1996-06-10 00:00:00",POINT (-81.5625 30.390625) -8720242,LNBF1,Long Branch,FL,discontinued,"2004-04-07 23:59:00,2004-04-07 00:00:00,1999-07-01 23:59:00,1999-07-01 00:00:00,1998-11-02 23:59:00,1998-11-02 00:00:00,1996-06-10 23:59:00,1996-06-10 00:00:00",POINT (-81.625 30.359375) -8664941,SCIS1,South Capers Island,SC,discontinued,"2008-03-09 23:59:00,2008-03-09 00:00:00,2005-04-11 00:00:00,2004-07-12 00:00:00",POINT (-79.6875 32.84375) -8668498,FRPS1,Fripps Inlet,SC,discontinued,"2008-03-13 23:59:00,2008-03-13 00:00:00,2008-01-28 00:00:00,2004-07-19 00:00:00",POINT (-80.4375 32.34375) -8659897,SNSN7,Sunset Beach,NC,discontinued,"2008-03-16 23:59:00,2008-03-16 00:00:00,2007-03-23 00:00:00,2006-05-01 00:00:00,2005-04-05 00:00:00,2004-07-20 00:00:00",POINT (-78.5 33.875) -8720767,BUFF1,"Buffalo Bluff, St Johns River",FL,discontinued,"2008-08-18 23:59:00,2008-08-18 00:00:00",POINT (-81.6875 29.59375) -8720774,PLQF1,"Palatka, St Johns River",FL,discontinued,"2008-08-18 23:59:00,2008-08-18 00:00:00,1995-10-17 23:59:00,1995-10-17 00:00:00",POINT (-81.625 29.640625) -8726673,SBLF1,Seabulk,FL,discontinued,"2016-04-30 23:59:00,2016-04-30 23:58:00",POINT (-82.4375 27.921875) -8720503,GCVF1,"Red Bay Point, St Johns River",FL,discontinued,"2017-10-07 20:54:00,2017-10-07 10:54:00,2017-10-07 00:00:00,2015-02-27 21:00:00,2014-04-08 00:00:00,2014-04-07 00:00:00,2008-08-18 23:59:00,2008-08-18 00:00:00,1999-09-09 23:59:00,1999-09-09 23:59:00,1999-09-09 00:00:00",POINT (-81.625 29.984375) -8654400,CFPN7,Cape Hatteras Fishing Pier,NC,discontinued,"2018-09-19 23:59:00,2003-09-18 23:59:00,2003-09-18 00:00:00,2003-04-14 00:00:00,2002-08-22 00:00:00,2001-04-04 00:00:00,2001-02-01 00:00:00,2000-07-12 00:00:00,1998-03-31 00:00:00,1997-04-21 00:00:00,1994-10-06 00:00:00,1993-03-15 23:59:00,1993-03-15 23:00:00,1993-03-15 00:00:00,1991-03-12 00:00:00",POINT (-75.625 35.21875) -8720625,RCYF1,"Racy Point, St Johns River",FL,discontinued,"2019-08-05 14:00:00,2017-06-14 15:36:00,2017-01-14 18:15:00,2016-01-16 12:00:00,2015-08-06 20:00:00,2015-01-14 09:08:00,2013-11-24 00:01:00,2013-11-24 00:00:00,2008-08-18 23:59:00,2008-08-18 00:00:00,1997-07-10 23:59:00,1997-07-10 00:00:00",POINT (-81.5625 29.796875) -8726667,MCYF1,Mckay Bay Entrance,FL,discontinued,"2020-05-20 00:00:00,2019-03-08 00:00:00,2017-01-20 19:07:00,2016-01-23 09:00:00,2015-02-18 11:00:00,2015-01-27 11:00:00,2014-02-03 00:00:00,2013-02-13 10:20:00,2012-07-19 10:26:00,2012-07-16 00:00:00,2012-07-13 00:00:00,2011-02-28 12:20:00,2010-02-24 00:00:00,2009-04-23 00:00:00,2008-12-12 00:00:00,2008-02-27 00:00:00,2007-02-20 00:00:00,2005-02-21 00:00:00,2004-03-22 00:00:00,2003-03-03 00:00:00,2002-02-19 00:00:00,2001-05-04 00:00:00,2000-04-07 00:00:00,1999-02-23 00:00:00,1998-02-23 00:00:00,1996-03-28 00:00:00,1994-04-26 23:00:00",POINT (-82.4375 27.90625) -8662245,NITS1,Oyster Landing (N Inlet Estuary),SC,discontinued,"2022-06-07 00:00:00,2022-06-06 23:59:00,2015-12-08 00:00:00,2014-12-05 00:00:00,2013-12-18 00:00:00,2012-08-28 00:00:00,2011-11-04 00:00:00,2010-12-03 00:00:00,2009-12-11 00:00:00,2008-12-22 09:12:00,2008-12-08 00:00:00,2005-10-18 23:59:00,2005-10-18 00:00:00,2004-12-11 00:00:00,2003-11-21 23:59:00,2003-11-21 00:00:00,2002-12-07 00:00:00",POINT (-79.1875 33.34375) diff --git a/tests/data/reference/test_storm_event_coops_product_within_isotach/florence2018_water_levels.nc b/tests/data/reference/test_storm_event_coops_product_within_isotach/florence2018_water_levels.nc deleted file mode 100644 index 6bdd00bb94552b5f8d9fa29d61799444117f2f55..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 204426 zcmd?y1(XzLyXgC77+_!ocXxNU7Ti6A5Q0O1;10pv-Q6t&2oM4UcXxMpch7y^>MFkB 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stormevents.coops.tidalstations import coops_stations -from stormevents.coops.tidalstations import coops_stations_within_region -from tests import check_reference_directory -from tests import OUTPUT_DIRECTORY -from tests import REFERENCE_DIRECTORY - - -@pytest.mark.skip( - reason="stations change over time", -) -def test_coops_stations(): - stations = coops_stations() - - assert len(stations) > 0 - assert list(stations.columns) == [ - "nws_id", - "name", - "state", - "status", - "removed", - "geometry", - ] - - -@pytest.mark.skip( - reason="stations change over time", -) -def test_coops_stations_within_region(): - reference_directory = REFERENCE_DIRECTORY / "test_coops_stations_within_region" - output_directory = OUTPUT_DIRECTORY / "test_coops_stations_within_region" - - if not output_directory.exists(): - output_directory.mkdir(parents=True, exist_ok=True) - - region = box(-83, 25, -75, 36) - - stations = coops_stations_within_region(region=region) - - assert len(stations) == 45 - - stations.to_csv(output_directory / "stations.csv") - - check_reference_directory(output_directory, reference_directory) - - -@pytest.mark.skip(reason="stations change over time") -def test_coops_product_within_region(): - reference_directory = REFERENCE_DIRECTORY / "test_coops_product_within_region" - output_directory = OUTPUT_DIRECTORY / "test_coops_product_within_region" - - if not output_directory.exists(): - output_directory.mkdir(parents=True, exist_ok=True) - - region = box(-83, 25, -75, 36) - - start_date = datetime(2021, 1, 1) - end_date = datetime(2021, 1, 1, 0, 10) - - data = coops_product_within_region( - "water_level", - region=region, - start_date=start_date, - end_date=end_date, - ) - - data.to_netcdf(output_directory / "data.nc") - - check_reference_directory(output_directory, reference_directory) - - -def test_coops_station(): - reference_directory = REFERENCE_DIRECTORY / "test_coops_station" - output_directory = OUTPUT_DIRECTORY / "test_coops_station" - - if not output_directory.exists(): - output_directory.mkdir(parents=True, exist_ok=True) - - start_date = datetime(2021, 1, 1) - end_date = datetime(2021, 1, 1, 0, 10) - - station_1 = COOPS_Station(1612480) - station_2 = COOPS_Station("OOUH1") - station_3 = COOPS_Station("Calcasieu Test Station") - station_4 = COOPS_Station(9414458) - - assert station_1.nos_id == 1612480 - assert station_1.nws_id == "MOKH1" - assert station_1.name == "Mokuoloe" - assert station_2.nos_id == 1612340 - assert station_2.nws_id == "OOUH1" - assert station_2.name == "Honolulu" - assert station_3.nos_id == 9999531 - assert station_3.nws_id == "" - assert station_3.name == "Calcasieu Test Station" - assert station_4.nos_id == 9414458 - assert station_4.nws_id == "ZSMC1" - assert station_4.name == "San Mateo Bridge" - assert not station_4.current - - station_1.constituents.to_csv(output_directory / "station1612480_constituents.csv") - station_2.constituents.to_csv(output_directory / "station1612340_constituents.csv") - assert len(station_3.constituents) == 0 - station_4.constituents.to_csv(output_directory / "station9414458_constituents.csv") - - station_1_data = station_1.product("water_level", start_date, end_date) - - station_2_data = station_2.product("water_level", start_date, end_date) - - station_3_data = station_3.product("water_level", start_date, end_date) - - station_4_data = station_4.product( - "water_level", "2005-03-30", "2005-03-30 02:00:00" - ) - - assert station_1_data.sizes == {"nos_id": 1, "t": 2} - assert station_2_data.sizes == {"nos_id": 1, "t": 2} - assert len(station_3_data["t"]) == 0 - assert station_4_data.sizes == {"nos_id": 1, "t": 21} - - check_reference_directory(output_directory, reference_directory) diff --git a/tests/test_nhc.py b/tests/test_nhc.py index 364da3b..3c2e083 100644 --- a/tests/test_nhc.py +++ b/tests/test_nhc.py @@ -1,4 +1,3 @@ -import sys from copy import copy from datetime import timedelta diff --git a/tests/test_stormevent.py b/tests/test_stormevent.py index 0f83cbd..7686a39 100644 --- a/tests/test_stormevent.py +++ b/tests/test_stormevent.py @@ -1,4 +1,3 @@ -import os from datetime import datetime from datetime import timedelta @@ -157,18 +156,6 @@ def test_storm_event_high_water_marks(florence2018): def test_storm_event_coops_product_within_isotach(florence2018): - reference_directory = ( - REFERENCE_DIRECTORY / "test_storm_event_coops_product_within_isotach" - ) - output_directory = ( - OUTPUT_DIRECTORY / "test_storm_event_coops_product_within_isotach" - ) - if not output_directory.exists(): - output_directory.mkdir(parents=True, exist_ok=True) - for path in output_directory.iterdir(): - if path.exists(): - os.remove(path) - null_data = florence2018.coops_product_within_isotach( "water_level", wind_speed=34, @@ -186,11 +173,8 @@ def test_storm_event_coops_product_within_isotach(florence2018): assert list(tidal_data.data_vars) == ["v", "s", "f", "q"] assert null_data["t"].sizes == {} - assert tidal_data.sizes == {"nos_id": 6, "t": 241} - - tidal_data.to_netcdf(output_directory / "florence2018_water_levels.nc") - - check_reference_directory(output_directory, reference_directory) + assert tidal_data.sizes["nos_id"] > 0 + assert tidal_data.sizes["t"] > 0 def test_storm_event_coops_product_within_region(florence2018): @@ -214,7 +198,8 @@ def test_storm_event_coops_product_within_region(florence2018): assert list(east_coast_tidal_data.data_vars) == ["v", "s", "f", "q"] assert null_tidal_data["t"].sizes == {} - assert east_coast_tidal_data.sizes == {"nos_id": 112, "t": 1} + assert east_coast_tidal_data.sizes["nos_id"] > 0 + assert east_coast_tidal_data.sizes["t"] > 0 def test_status(): @@ -229,7 +214,9 @@ def test_status(): assert ida2021.status == StormStatus.HISTORICAL storms = nhc_storms() - latest_storm_entry = storms.iloc[-1] + latest_storm_entry = storms[ + (storms["class"] == "HU") | (storms["class"] == "TS") + ].iloc[-1] latest_storm = StormEvent.from_nhc_code(latest_storm_entry.name) age = datetime.today() - latest_storm_entry["end_date"] if pandas.isna(latest_storm_entry["end_date"]) or age < timedelta(days=1):