{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coastal Ocean Wave Height Assessment\n", "\n", "\n", "This notebook is a follow-up from the [Sea Surface Height notebook](http://ioos.github.io/notebooks_demos/notebooks/2018-03-15-ssh-skillscore), here we use the same workflow to compare observations and models for sea waves height.\n", "\n", "We start with virtually the same configuration from before, except that now we changed the variable names to \"waves\" and the corresponding `standard_names` for waves.\n", "\n", "For more information regarding the workflow presented here see [SSH notebook](http://ioos.github.io/notebooks_demos/notebooks/2018-03-15-ssh-skillscore) and [Signell, Richard P.; Fernandes, Filipe; Wilcox, Kyle. 2016. \"Dynamic Reusable Workflows for Ocean Science.\" *J. Mar. Sci. Eng.* 4, no. 4: 68](http://dx.doi.org/10.3390/jmse4040068)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:23.944692Z", "start_time": "2017-10-27T19:00:23.932666Z" } }, "outputs": [], "source": [ "import warnings\n", "\n", "# Suppresing warnings for a \"pretty output.\"\n", "warnings.simplefilter('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:24.015841Z", "start_time": "2017-10-27T19:00:23.952708Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting wave_config.yaml\n" ] } ], "source": [ "%%writefile wave_config.yaml\n", "\n", "date:\n", " start: 2018-2-28 00:00:00\n", " stop: 2018-3-10 00:00:00\n", "\n", "run_name: 'latest'\n", "\n", "region:\n", " bbox: [-71.20, 41.40, -69.20, 43.74]\n", " crs: 'urn:ogc:def:crs:OGC:1.3:CRS84'\n", "\n", "# try: sea_surface_wave_significant_height\n", "sos_name: 'waves'\n", "\n", "cf_names:\n", " - sea_surface_wave_significant_height\n", " - sea_surface_wind_wave_significant_height\n", "\n", "units: 'm'\n", "\n", "catalogs:\n", " - https://data.ioos.us/csw" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:27.643443Z", "start_time": "2017-10-27T19:00:24.024860Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving data inside directory /home/filipe/IOOS/notebooks_demos/notebooks/latest\n", "*********************** Run information ************************\n", "Run date: 2018-03-12 13:08:47\n", "Start: 2018-02-28 00:00:00\n", "Stop: 2018-03-10 00:00:00\n", "Bounding box: -71.20, 41.40,-69.20, 43.74\n" ] } ], "source": [ "import os\n", "import shutil\n", "from datetime import datetime\n", "from ioos_tools.ioos import parse_config\n", "\n", "config = parse_config('wave_config.yaml')\n", "\n", "save_dir = os.path.abspath(config['run_name'])\n", "if os.path.exists(save_dir):\n", " shutil.rmtree(save_dir)\n", "os.makedirs(save_dir)\n", "\n", "fmt = '{:*^64}'.format\n", "print(fmt('Saving data inside directory {}'.format(save_dir)))\n", "print(fmt(' Run information '))\n", "print('Run date: {:%Y-%m-%d %H:%M:%S}'.format(datetime.utcnow()))\n", "print('Start: {:%Y-%m-%d %H:%M:%S}'.format(config['date']['start']))\n", "print('Stop: {:%Y-%m-%d %H:%M:%S}'.format(config['date']['stop']))\n", "print('Bounding box: {0:3.2f}, {1:3.2f},'\n", " '{2:3.2f}, {3:3.2f}'.format(*config['region']['bbox']))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:27.779728Z", "start_time": "2017-10-27T19:00:27.695552Z" } }, "outputs": [], "source": [ "def make_filter(config):\n", " from owslib import fes\n", " from ioos_tools.ioos import fes_date_filter\n", " kw = dict(wildCard='*', escapeChar='\\\\',\n", " singleChar='?', propertyname='apiso:Subject')\n", "\n", " if len(config['cf_names']) > 1:\n", " or_filt = fes.Or([fes.PropertyIsLike(literal=('*%s*' % val), **kw)\n", " for val in config['cf_names']])\n", " else:\n", " or_filt = fes.PropertyIsLike(literal=('*%s*' % config['cf_names'][0]), **kw) \n", "\n", " not_filt = fes.Not([fes.PropertyIsLike(literal='GRIB-2', **kw)])\n", "\n", " begin, end = fes_date_filter(config['date']['start'],\n", " config['date']['stop'])\n", " bbox_crs = fes.BBox(config['region']['bbox'],\n", " crs=config['region']['crs'])\n", " filter_list = [fes.And([bbox_crs, begin, end, or_filt, not_filt])]\n", " return filter_list\n", "\n", "\n", "filter_list = make_filter(config)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:29.681714Z", "start_time": "2017-10-27T19:00:27.786743Z" }, "code_folding": [], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********************* Catalog information **********************\n", "URL: https://data.ioos.us/csw\n", "Number of datasets available: 12\n", "Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near LOWER COOK INLET, AK from 2016/12/16 00:00:00 to 2018/03/11 18:09:42.\n", "NECOFS GOM3 Wave - Northeast US - Latest Forecast\n", "BIO WW III Latest Forecasts/EastCoast.nc\n", "BIO WW III Latest Forecasts/GulfOfMaine.nc\n", "BIO WW III Latest Forecasts/NorthAtlantic.nc\n", "Coupled Northwest Atlantic Prediction System (CNAPS)\n", "Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near ASTORIA CANYON, OR from 2017/08/03 18:00:00 to 2018/03/11 17:57:11.\n", "Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near CAPE COD BAY, MA from 2017/10/05 20:00:00 to 2018/03/11 18:01:09.\n", "Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near CLATSOP SPIT, OR from 2018/02/07 22:00:00 to 2018/03/11 18:00:33.\n", "Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near GRAYS HARBOR, WA from 2017/06/29 16:00:00 to 2018/03/11 18:00:29.\n", "Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near JEFFREYS LEDGE, NH from 2017/01/31 18:00:00 to 2018/03/11 18:08:17.\n", "Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near LAKESIDE, OR from 2017/03/31 23:00:00 to 2018/03/11 17:30:56.\n", "***************************** DAP ******************************\n", "http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/036p1_rt.nc.html\n", "http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/160p1_rt.nc.html\n", "http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/162p1_rt.nc.html\n", "http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/179p1_rt.nc.html\n", "http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/204p1_rt.nc.html\n", "http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/221p1_rt.nc.html\n", "http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/231p1_rt.nc.html\n", "http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc.html\n", "http://www.neracoos.org/thredds/dodsC/WW3/EastCoast.nc.html\n", "http://www.neracoos.org/thredds/dodsC/WW3/GulfOfMaine.nc.html\n", "http://www.neracoos.org/thredds/dodsC/WW3/NorthAtlantic.nc.html\n", "http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_WAVE_FORECAST.nc.html\n", "\n", "\n" ] } ], "source": [ "from ioos_tools.ioos import service_urls, get_csw_records\n", "from owslib.csw import CatalogueServiceWeb\n", "\n", "\n", "dap_urls = []\n", "print(fmt(' Catalog information '))\n", "for endpoint in config['catalogs']:\n", " print('URL: {}'.format(endpoint))\n", " try:\n", " csw = CatalogueServiceWeb(endpoint, timeout=120)\n", " except Exception as e:\n", " print('{}'.format(e))\n", " continue\n", " csw = get_csw_records(csw, filter_list, esn='full')\n", " OPeNDAP = service_urls(csw.records, identifier='OPeNDAP:OPeNDAP')\n", " odp = service_urls(csw.records, identifier='urn:x-esri:specification:ServiceType:odp:url')\n", " dap = OPeNDAP + odp\n", " dap_urls.extend(dap)\n", "\n", " print('Number of datasets available: {}'.format(len(csw.records.keys())))\n", "\n", " for rec, item in csw.records.items():\n", " print('{}'.format(item.title))\n", " if dap:\n", " print(fmt(' DAP '))\n", " for url in dap:\n", " print('{}.html'.format(url))\n", " print('\\n')\n", "\n", "# Get only unique endpoints.\n", "dap_urls = list(set(dap_urls))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:36.030018Z", "start_time": "2017-10-27T19:00:29.692737Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "************************* Filtered DAP *************************\n", "http://www.neracoos.org/thredds/dodsC/WW3/GulfOfMaine.nc.html\n", "http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_WAVE_FORECAST.nc.html\n", "http://www.neracoos.org/thredds/dodsC/WW3/EastCoast.nc.html\n", "http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc.html\n", "http://www.neracoos.org/thredds/dodsC/WW3/NorthAtlantic.nc.html\n" ] } ], "source": [ "from ioos_tools.ioos import is_station\n", "\n", "non_stations = []\n", "for url in dap_urls:\n", " try:\n", " if not is_station(url):\n", " non_stations.append(url)\n", " except (RuntimeError, OSError, IOError) as e:\n", " print('Could not access URL {}. {!r}'.format(url, e))\n", "\n", "dap_urls = non_stations\n", "\n", "print(fmt(' Filtered DAP '))\n", "for url in dap_urls:\n", " print('{}.html'.format(url))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:39.322918Z", "start_time": "2017-10-27T19:00:36.038034Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "******************* NDBC Collector offerings *******************\n", "National Data Buoy Center SOS: 1017 offerings\n" ] } ], "source": [ "from pyoos.collectors.ndbc.ndbc_sos import NdbcSos\n", "\n", "collector_ndbc = NdbcSos()\n", "\n", "collector_ndbc.set_bbox(config['region']['bbox'])\n", "collector_ndbc.end_time = config['date']['stop']\n", "collector_ndbc.start_time = config['date']['start']\n", "collector_ndbc.variables = [config['sos_name']]\n", "\n", "ofrs = collector_ndbc.server.offerings\n", "title = collector_ndbc.server.identification.title\n", "print(fmt(' NDBC Collector offerings '))\n", "print('{}: {} offerings'.format(title, len(ofrs)))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:42.837283Z", "start_time": "2017-10-27T19:00:39.328931Z" } }, "outputs": [ { "data": { "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", "
depthlatlonsensorstation_name
station_code
44007None43.525-70.141urn:ioos:sensor:wmo:44007::wpm1PORTLAND - 12 NM Southeast of Portland,ME
44013None42.346-70.651urn:ioos:sensor:wmo:44013::wpm1BOSTON 16 NM East of Boston, MA
44020None41.439-70.186urn:ioos:sensor:wmo:44020::wpm1NANTUCKET SOUND
44029None42.523-70.566urn:ioos:sensor:wmo:44029::summarywav1Buoy A01 - Massachusetts Bay
44030None43.181-70.428urn:ioos:sensor:wmo:44030::summarywav1Buoy B01 - Western Maine Shelf
44032None43.716-69.355urn:ioos:sensor:wmo:44032::summarywav1Buoy E01 - Central Maine Shelf
44090None41.840-70.329urn:ioos:sensor:wmo:44090::summarywav1Cape Cod Bay, MA (221)
44098None42.798-70.168urn:ioos:sensor:wmo:44098::summarywav1Jeffrey's Ledge, NH (160)
\n", "
" ], "text/plain": [ " depth lat lon sensor \\\n", "station_code \n", "44007 None 43.525 -70.141 urn:ioos:sensor:wmo:44007::wpm1 \n", "44013 None 42.346 -70.651 urn:ioos:sensor:wmo:44013::wpm1 \n", "44020 None 41.439 -70.186 urn:ioos:sensor:wmo:44020::wpm1 \n", "44029 None 42.523 -70.566 urn:ioos:sensor:wmo:44029::summarywav1 \n", "44030 None 43.181 -70.428 urn:ioos:sensor:wmo:44030::summarywav1 \n", "44032 None 43.716 -69.355 urn:ioos:sensor:wmo:44032::summarywav1 \n", "44090 None 41.840 -70.329 urn:ioos:sensor:wmo:44090::summarywav1 \n", "44098 None 42.798 -70.168 urn:ioos:sensor:wmo:44098::summarywav1 \n", "\n", " station_name \n", "station_code \n", "44007 PORTLAND - 12 NM Southeast of Portland,ME \n", "44013 BOSTON 16 NM East of Boston, MA \n", "44020 NANTUCKET SOUND \n", "44029 Buoy A01 - Massachusetts Bay \n", "44030 Buoy B01 - Western Maine Shelf \n", "44032 Buoy E01 - Central Maine Shelf \n", "44090 Cape Cod Bay, MA (221) \n", "44098 Jeffrey's Ledge, NH (160) " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from ioos_tools.ioos import collector2table\n", "\n", "ndbc = collector2table(\n", " collector=collector_ndbc,\n", " config=config,\n", " col='sea_surface_wave_significant_height (m)'\n", ")\n", "\n", "if ndbc:\n", " data = dict(\n", " station_name=[s._metadata.get('station_name') for s in ndbc],\n", " station_code=[s._metadata.get('station_code') for s in ndbc],\n", " sensor=[s._metadata.get('sensor') for s in ndbc],\n", " lon=[s._metadata.get('lon') for s in ndbc],\n", " lat=[s._metadata.get('lat') for s in ndbc],\n", " depth=[s._metadata.get('depth') for s in ndbc],\n", " )\n", "\n", "table = pd.DataFrame(data).set_index('station_code')\n", "table" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:42.908432Z", "start_time": "2017-10-27T19:00:42.845300Z" }, "scrolled": true }, "outputs": [], "source": [ "data = ndbc\n", "\n", "index = pd.date_range(\n", " start=config['date']['start'].replace(tzinfo=None),\n", " end=config['date']['stop'].replace(tzinfo=None),\n", " freq='1H'\n", ")\n", "\n", "# Preserve metadata with `reindex`.\n", "observations = []\n", "for series in data:\n", " _metadata = series._metadata\n", " obs = series.reindex(index=index, limit=1, method='nearest')\n", " obs._metadata = _metadata\n", " # FIXME: remove this!\n", " obs._metadata['standard_name']='sea_surface_wave_significant_height'\n", "\n", " observations.append(obs)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "fig, ax = plt.subplots(figsize=(13, 2.75))\n", "for series in data:\n", " series.plot(ax=ax, label=series._metadata['station_name'])\n", " plt.legend(bbox_to_anchor=(1.38, 0.95), loc='upper right')\n", " ax.grid(True)\n", " ax.set_ylabel('Wave height (m)')\n", " ax.set_xlabel('')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:00:43.182005Z", "start_time": "2017-10-27T19:00:43.003632Z" }, "scrolled": false }, "outputs": [], "source": [ "import iris\n", "from ioos_tools.tardis import series2cube\n", "\n", "attr = dict(\n", " featureType='timeSeries',\n", " Conventions='CF-1.6',\n", " standard_name_vocabulary='CF-1.6',\n", " cdm_data_type='Station',\n", " comment='Data from http://opendap.co-ops.nos.noaa.gov'\n", ")\n", "\n", "\n", "cubes = iris.cube.CubeList(\n", " [series2cube(obs, attr=attr) for obs in observations]\n", ")\n", "\n", "outfile = os.path.join(save_dir, 'OBS_DATA.nc')\n", "iris.save(cubes, outfile)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def check_standard_name(url, standard_names):\n", " from netCDF4 import Dataset\n", " standard_name = lambda v: v in standard_names\n", "\n", " with Dataset(url) as nc:\n", " variables = nc.get_variables_by_attributes(standard_name=standard_name)\n", " if variables:\n", " return True\n", " else:\n", " return False" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:02:43.749667Z", "start_time": "2017-10-27T19:00:43.189020Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "**************************** Models ****************************\n", "\n", "[Reading url 1/5]: http://www.neracoos.org/thredds/dodsC/WW3/GulfOfMaine.nc\n", "\n", "[Reading url 2/5]: http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_WAVE_FORECAST.nc\n", "\n", "[Reading url 3/5]: http://www.neracoos.org/thredds/dodsC/WW3/EastCoast.nc\n", "\n", "[Reading url 4/5]: http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc\n", "\n", "[Reading url 5/5]: http://www.neracoos.org/thredds/dodsC/WW3/NorthAtlantic.nc\n" ] } ], "source": [ "from iris.exceptions import (CoordinateNotFoundError, ConstraintMismatchError,\n", " MergeError)\n", "from ioos_tools.ioos import get_model_name\n", "from ioos_tools.tardis import quick_load_cubes, proc_cube, is_model, get_surface\n", "\n", "print(fmt(' Models '))\n", "cubes = dict()\n", "for k, url in enumerate(dap_urls):\n", " print('\\n[Reading url {}/{}]: {}'.format(k+1, len(dap_urls), url))\n", " if not check_standard_name(url, config['cf_names']):\n", " print('Could not find {} in {}'.format(config['cf_names'], url))\n", " continue\n", " try:\n", " cube = quick_load_cubes(url, config['cf_names'],\n", " callback=None, strict=True)\n", " if is_model(cube):\n", " cube = proc_cube(cube,\n", " bbox=config['region']['bbox'],\n", " time=(config['date']['start'],\n", " config['date']['stop']),\n", " units=config['units'])\n", " else:\n", " print('[Not model data]: {}'.format(url))\n", " continue\n", " mod_name = get_model_name(url)\n", " cubes.update({mod_name: cube})\n", " except (RuntimeError, ValueError,\n", " ConstraintMismatchError, CoordinateNotFoundError,\n", " IndexError) as e:\n", " print('Cannot get cube for: {}\\n{}'.format(url, e))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:24.768626Z", "start_time": "2017-10-27T19:02:43.754677Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/WW3-GulfOfMaine.nc \n", "[Water ] PORTLAND - 12 NM Southeast of Portland,ME\n", "[Water ] BOSTON 16 NM East of Boston, MA\n", "[Water ] NANTUCKET SOUND\n", "[Water ] Buoy A01 - Massachusetts Bay\n", "[Water ] Buoy B01 - Western Maine Shelf\n", "[Water ] Buoy E01 - Central Maine Shelf\n", "[Water ] Cape Cod Bay, MA (221)\n", "[Water ] Jeffrey's Ledge, NH (160)\n", "Finished processing [WW3-GulfOfMaine]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/FVCOM_Forecasts-NECOFS_WAVE_FORECAST.nc \n", "[Water ] PORTLAND - 12 NM Southeast of Portland,ME\n", "[Water ] BOSTON 16 NM East of Boston, MA\n", "[Water ] NANTUCKET SOUND\n", "[Water ] Buoy A01 - Massachusetts Bay\n", "[Water ] Buoy B01 - Western Maine Shelf\n", "[Water ] Buoy E01 - Central Maine Shelf\n", "[Water ] Cape Cod Bay, MA (221)\n", "[Water ] Jeffrey's Ledge, NH (160)\n", "Finished processing [FVCOM_Forecasts-NECOFS_WAVE_FORECAST]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/WW3-EastCoast.nc \n", "[Water ] PORTLAND - 12 NM Southeast of Portland,ME\n", "[Water ] BOSTON 16 NM East of Boston, MA\n", "[No Data] NANTUCKET SOUND\n", "[Water ] Buoy A01 - Massachusetts Bay\n", "[No Data] Buoy B01 - Western Maine Shelf\n", "[Water ] Buoy E01 - Central Maine Shelf\n", "[Water ] Cape Cod Bay, MA (221)\n", "[No Data] Jeffrey's Ledge, NH (160)\n", "Finished processing [WW3-EastCoast]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/SECOORA_NCSU_CNAPS.nc \n", "[Water ] PORTLAND - 12 NM Southeast of Portland,ME\n", "[Water ] BOSTON 16 NM East of Boston, MA\n", "[Water ] NANTUCKET SOUND\n", "[Water ] Buoy A01 - Massachusetts Bay\n", "[Water ] Buoy B01 - Western Maine Shelf\n", "[Water ] Buoy E01 - Central Maine Shelf\n", "[Water ] Cape Cod Bay, MA (221)\n", "[Water ] Jeffrey's Ledge, NH (160)\n", "Finished processing [SECOORA_NCSU_CNAPS]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/WW3-NorthAtlanti.nc \n", "[No Data] PORTLAND - 12 NM Southeast of Portland,ME\n", "[No Data] BOSTON 16 NM East of Boston, MA\n", "[No Data] NANTUCKET SOUND\n", "[Water ] Buoy A01 - Massachusetts Bay\n", "[No Data] Buoy B01 - Western Maine Shelf\n", "[No Data] Buoy E01 - Central Maine Shelf\n", "[No Data] Cape Cod Bay, MA (221)\n", "[No Data] Jeffrey's Ledge, NH (160)\n", "Finished processing [WW3-NorthAtlanti]\n" ] } ], "source": [ "import iris\n", "from iris.pandas import as_series\n", "from ioos_tools.tardis import (make_tree, get_nearest_water,\n", " add_station, ensure_timeseries, remove_ssh)\n", "\n", "for mod_name, cube in cubes.items():\n", " fname = '{}.nc'.format(mod_name)\n", " fname = os.path.join(save_dir, fname)\n", " print(fmt(' Downloading to file {} '.format(fname)))\n", " try:\n", " tree, lon, lat = make_tree(cube)\n", " except CoordinateNotFoundError as e:\n", " print('Cannot make KDTree for: {}'.format(mod_name))\n", " continue\n", " # Get model series at observed locations.\n", " raw_series = dict()\n", " for obs in observations:\n", " obs = obs._metadata\n", " station = obs['station_code']\n", " try:\n", " kw = dict(k=10, max_dist=0.08, min_var=0.01)\n", " args = cube, tree, obs['lon'], obs['lat']\n", " try:\n", " series, dist, idx = get_nearest_water(*args, **kw)\n", " except RuntimeError as e:\n", " print('Cannot download {!r}.\\n{}'.format(cube, e))\n", " series = None\n", " except ValueError as e:\n", " status = 'No Data'\n", " print('[{}] {}'.format(status, obs['station_name']))\n", " continue\n", " if not series:\n", " status = 'Land '\n", " else:\n", " raw_series.update({station: series})\n", " series = as_series(series)\n", " status = 'Water '\n", " print('[{}] {}'.format(status, obs['station_name']))\n", " if raw_series: # Save cube.\n", " for station, cube in raw_series.items():\n", " cube = add_station(cube, station)\n", " try:\n", " cube = iris.cube.CubeList(raw_series.values()).merge_cube()\n", " except MergeError as e:\n", " print(e)\n", " ensure_timeseries(cube)\n", " try:\n", " iris.save(cube, fname)\n", " except AttributeError:\n", " # FIXME: we should patch the bad attribute instead of removing everything.\n", " cube.attributes = {}\n", " iris.save(cube, fname)\n", " del cube\n", " print('Finished processing [{}]'.format(mod_name))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:24.797687Z", "start_time": "2017-10-27T19:03:24.780651Z" } }, "outputs": [], "source": [ "from ioos_tools.ioos import stations_keys\n", "\n", "\n", "def rename_cols(df, config):\n", " cols = stations_keys(config, key='station_name')\n", " return df.rename(columns=cols)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:26.031272Z", "start_time": "2017-10-27T19:03:24.808710Z" }, "scrolled": false }, "outputs": [], "source": [ "from ioos_tools.ioos import load_ncs\n", "from ioos_tools.skill_score import mean_bias, apply_skill\n", "\n", "dfs = load_ncs(config)\n", "\n", "df = apply_skill(dfs, mean_bias, remove_mean=False, filter_tides=False)\n", "skill_score = dict(mean_bias=df.to_dict())\n", "\n", "# Filter out stations with no valid comparison.\n", "df.dropna(how='all', axis=1, inplace=True)\n", "df = df.applymap('{:.2f}'.format).replace('nan', '--')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:26.916126Z", "start_time": "2017-10-27T19:03:26.042295Z" } }, "outputs": [], "source": [ "from ioos_tools.skill_score import rmse\n", "\n", "dfs = load_ncs(config)\n", "\n", "df = apply_skill(dfs, rmse, remove_mean=True, filter_tides=False)\n", "skill_score['rmse'] = df.to_dict()\n", "\n", "# Filter out stations with no valid comparison.\n", "df.dropna(how='all', axis=1, inplace=True)\n", "df = df.applymap('{:.2f}'.format).replace('nan', '--')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:26.994290Z", "start_time": "2017-10-27T19:03:26.924143Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "# Stringfy keys.\n", "for key in skill_score.keys():\n", " skill_score[key] = {str(k): v for k, v in skill_score[key].items()}\n", "\n", "mean_bias = pd.DataFrame.from_dict(skill_score['mean_bias'])\n", "mean_bias = mean_bias.applymap('{:.2f}'.format).replace('nan', '--')\n", "\n", "skill_score = pd.DataFrame.from_dict(skill_score['rmse'])\n", "skill_score = skill_score.applymap('{:.2f}'.format).replace('nan', '--')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:27.498347Z", "start_time": "2017-10-27T19:03:27.003309Z" } }, "outputs": [], "source": [ "import folium\n", "from ioos_tools.ioos import get_coordinates\n", "\n", "\n", "def make_map(bbox, **kw):\n", " line = kw.pop('line', True)\n", " layers = kw.pop('layers', True)\n", " zoom_start = kw.pop('zoom_start', 5)\n", "\n", " lon = (bbox[0] + bbox[2]) / 2\n", " lat = (bbox[1] + bbox[3]) / 2\n", " m = folium.Map(width='100%', height='100%',\n", " location=[lat, lon], zoom_start=zoom_start)\n", "\n", " if layers:\n", " url = 'http://geoport-dev.whoi.edu/thredds/wms/coawst_4/use/fmrc/coawst_4_use_best.ncd'\n", " w = folium.WmsTileLayer(url, name='COAWST Wave Height', fmt='image/png', layers='Hwave',\n", " style='boxfill/rainbow', COLORSCALERANGE='0,5', overlay=True, transparent=True)\n", "\n", " w.add_to(m)\n", "\n", " if line:\n", " p = folium.PolyLine(get_coordinates(bbox), color='#FF0000', weight=2, opacity=0.9, latlon=True)\n", " p.add_to(m)\n", " return m" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:27.639643Z", "start_time": "2017-10-27T19:03:27.504359Z" } }, "outputs": [], "source": [ "bbox = config['region']['bbox']\n", "\n", "m = make_map(\n", " bbox,\n", " zoom_start=9,\n", " line=True,\n", " layers=True\n", ")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:29.239996Z", "start_time": "2017-10-27T19:03:27.645655Z" } }, "outputs": [], "source": [ "all_obs = stations_keys(config)\n", "\n", "from glob import glob\n", "from operator import itemgetter\n", "\n", "import iris\n", "from folium.plugins import MarkerCluster\n", "\n", "iris.FUTURE.netcdf_promote = True\n", "\n", "big_list = []\n", "for fname in glob(os.path.join(save_dir, '*.nc')):\n", " if 'OBS_DATA' in fname:\n", " continue\n", " cube = iris.load_cube(fname)\n", " model = os.path.split(fname)[1].split('-')[-1].split('.')[0]\n", " lons = cube.coord(axis='X').points\n", " lats = cube.coord(axis='Y').points\n", " stations = cube.coord('station_code').points\n", " models = [model]*lons.size\n", " lista = zip(models, lons.tolist(), lats.tolist(), stations.tolist())\n", " big_list.extend(lista)\n", "\n", "big_list.sort(key=itemgetter(3))\n", "df = pd.DataFrame(big_list, columns=['name', 'lon', 'lat', 'station'])\n", "df.set_index('station', drop=True, inplace=True)\n", "groups = df.groupby(df.index)\n", "\n", "\n", "locations, popups = [], []\n", "for station, info in groups:\n", " sta_name = all_obs[station].replace(\"'\",\"\")\n", " for lat, lon, name in zip(info.lat, info.lon, info.name):\n", " locations.append([lat, lon])\n", " popups.append('[{}]: {}'.format(name, sta_name))\n", "\n", "MarkerCluster(locations=locations, popups=popups, name='Cluster').add_to(m);" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:29.260038Z", "start_time": "2017-10-27T19:03:29.249015Z" } }, "outputs": [], "source": [ "# Some known models names. Unknonwn models will use the title metadata or the URL.\n", "titles = {\n", " 'coawst_4_use_best': 'COAWST_4',\n", " 'global': 'HYCOM',\n", " 'NECOFS_GOM3_FORECAST': 'NECOFS_GOM3',\n", " 'NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST': 'NECOFS_MassBay',\n", " 'OBS_DATA': 'Observations'\n", "}" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:31.045781Z", "start_time": "2017-10-27T19:03:29.273066Z" } }, "outputs": [], "source": [ "from bokeh.resources import CDN\n", "from bokeh.plotting import figure\n", "from bokeh.embed import file_html\n", "from bokeh.models import HoverTool, Legend\n", "from itertools import cycle\n", "from bokeh.palettes import Category20\n", "\n", "from folium import IFrame\n", "\n", "# Plot defaults.\n", "colors = Category20[20]\n", "colorcycler = cycle(colors)\n", "tools = 'pan,box_zoom,reset'\n", "width, height = 750, 250\n", "\n", "\n", "def make_plot(df, station):\n", " p = figure(\n", " toolbar_location='above',\n", " x_axis_type='datetime',\n", " width=width,\n", " height=height,\n", " tools=tools,\n", " title=str(station)\n", " )\n", " leg = []\n", " for column, series in df.iteritems():\n", " series.dropna(inplace=True)\n", " if not series.empty:\n", " if 'OBS_DATA' not in column:\n", " bias = mean_bias[str(station)][column]\n", " skill = skill_score[str(station)][column]\n", " line_color = next(colorcycler)\n", " kw = dict(alpha=0.65, line_color=line_color)\n", " else:\n", " skill = bias = 'NA'\n", " kw = dict(alpha=1, color='crimson')\n", " line = p.line(\n", " x=series.index,\n", " y=series.values,\n", " line_width=5,\n", " line_cap='round',\n", " line_join='round',\n", " **kw\n", " )\n", " leg.append(('{}'.format(titles.get(column, column)), [line]))\n", " p.add_tools(HoverTool(tooltips=[('Name', '{}'.format(titles.get(column, column))),\n", " ('Bias', bias),\n", " ('Skill', skill)],\n", " renderers=[line]))\n", " legend = Legend(items=leg, location=(0, 60))\n", " legend.click_policy = 'mute'\n", " p.add_layout(legend, 'right')\n", " p.yaxis[0].axis_label = 'Wave Height (m)'\n", " p.xaxis[0].axis_label = 'Date/time'\n", " return p\n", "\n", "\n", "def make_marker(p, station):\n", " lons = stations_keys(config, key='lon')\n", " lats = stations_keys(config, key='lat')\n", "\n", " lon, lat = lons[station], lats[station]\n", " html = file_html(p, CDN, station)\n", " iframe = IFrame(html, width=width+40, height=height+80)\n", "\n", " popup = folium.Popup(iframe, max_width=2650)\n", " icon = folium.Icon(color='green', icon='stats')\n", " marker = folium.Marker(location=[lat, lon],\n", " popup=popup,\n", " icon=icon)\n", " return marker" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T19:03:37.481267Z", "start_time": "2017-10-27T19:03:31.049789Z" }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs = load_ncs(config)\n", "\n", "for station in dfs:\n", " sta_name = all_obs[station].replace(\"'\",\"\")\n", " df = dfs[station]\n", " if df.empty:\n", " continue\n", " p = make_plot(df, station)\n", " marker = make_marker(p, station)\n", " marker.add_to(m)\n", "\n", "folium.LayerControl().add_to(m)\n", "\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can observe a noteworthy increase in the significant wave height starting February 2nd and peaking around March 3rd and followed but a second smaller peak after March 8th." ] } ], "metadata": { "_draft": { "nbviewer_url": "https://gist.github.com/fbf3b8c50a0e0a4a61438470cdb11523" }, "anaconda-cloud": {}, "gist": { "data": { "description": "notebooks_demos/notebooks/wave_height_assessment.ipynb", "public": true }, "id": "fbf3b8c50a0e0a4a61438470cdb11523" }, "kernelspec": { "display_name": "Python 3", "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }