{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Explore ERDDAP timeseries data using Jupyter Widgets" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from erddapy import ERDDAP\n", "\n", "\n", "server = 'http://www.neracoos.org/erddap'\n", "e = ERDDAP(server=server)\n", "\n", "e.protocol = 'tabledap'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pendulum\n", "\n", "\n", "initial_standard_name = 'significant_height_of_wind_and_swell_waves'\n", "\n", "zoom = 6\n", "center = [42.5, -68]\n", "nchar = 3 # number of characters for short dataset name\n", "cdm_data_type = 'TimeSeries'\n", "\n", "max_time = pendulum.utcnow()\n", "min_time = pendulum.utcnow().subtract(weeks=2) " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "\n", "url = '{}/categorize/standard_name/index.csv'.format(server)\n", "df = pd.read_csv(url, skiprows=[1, 2])\n", "variables = df['Category'].values" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import ipywidgets\n", "\n", "dpdown = ipywidgets.Dropdown(options=variables, value=initial_standard_name)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def point(dataset, lon, lat, nchar):\n", " geojsonFeature = {\n", " \"type\": \"Feature\",\n", " \"properties\": {\n", " \"datasetID\": dataset,\n", " \"short_dataset_name\": dataset[:nchar]\n", " },\n", " \"geometry\": {\n", " \"type\": \"Point\",\n", " \"coordinates\": [lon, lat]\n", " }\n", " };\n", " geojsonFeature['properties']['style'] = {'color': 'Grey'}\n", " return geojsonFeature" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def adv_search(e, standard_name, cdm_data_type, min_time, max_time):\n", " try:\n", " search_url = e.get_search_url(\n", " response='csv',\n", " cdm_data_type=cdm_data_type.lower(),\n", " items_per_page=100000,\n", " standard_name=standard_name,\n", " min_time=min_time,\n", " max_time=max_time\n", " )\n", " df = pd.read_csv(search_url)\n", " except:\n", " df = []\n", " if len(var)>14:\n", " v = '{}...'.format(standard_name[:15])\n", " else:\n", " v = standard_name\n", " figure.title = 'No {} found in this time range. Pick another variable.'.format(v)\n", " figure.marks[0].y = 0.0 * figure.marks[0].y\n", " return df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def alllonlat(e, cdm_data_type, min_time, max_time):\n", " url='{}/tabledap/allDatasets.csv?datasetID%2CminLongitude%2CminLatitude&cdm_data_type=%22{}%22&minTime%3C={}&maxTime%3E={}'.format(e.server,cdm_data_type,max_time,min_time)\n", " df = pd.read_csv(url, skiprows=[1])\n", " return df" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def stdname2geojson(e, standard_name, min_time, max_time, nchar):\n", " dfa = adv_search(e, standard_name, cdm_data_type, min_time, max_time)\n", " if isinstance(dfa, pd.DataFrame):\n", " datasets = dfa['Dataset ID'].values\n", " dfll = alllonlat(e, cdm_data_type, min_time, max_time)\n", " dfr = dfll[dfll['datasetID'].isin(dfa['Dataset ID'])]\n", " geojson = {'features':[point(row[1],row[2],row[3],nchar) for row in dfr.itertuples()]}\n", " else:\n", " geojson = {'features':[]}\n", " datasets = []\n", " return geojson, datasets" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def click_handler(event=None, id=None, properties=None):\n", " datasetID = properties['datasetID']\n", " kwargs = {'time>=': min_time, 'time<=': max_time}\n", " df, var = get_data(datasetID, dpdown.value, kwargs)\n", " figure.marks[0].x = df.index\n", " figure.marks[0].y = df[var]\n", " figure.title = '{} - {}'.format(properties['short_dataset_name'], var)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import ipyleaflet\n", "\n", "\n", "def update_dpdown(change):\n", " standard_name = change['new']\n", " data, datasets = stdname2geojson(e, standard_name, min_time, max_time, nchar)\n", " feature_layer = ipyleaflet.GeoJSON(data=data)\n", " feature_layer.on_click(click_handler)\n", " m.layers = [m.layers[0], feature_layer]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "dpdown.observe(update_dpdown, names=['value'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def get_data(dataset, standard_name, kwargs):\n", " var = e.get_var_by_attr(\n", " dataset_id=dataset,\n", " standard_name=lambda v: str(v).lower() == standard_name)[0]\n", " e.dataset_id = dataset\n", " e.variables = ['time', var]\n", " e.constraints = kwargs\n", " df = e.to_pandas(index_col='time', parse_dates=True, skiprows=[1])\n", " return df, var" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "m = ipyleaflet.Map(\n", " center=center,\n", " zoom=zoom,\n", ")\n", "\n", "data, datasets = stdname2geojson(e, initial_standard_name, min_time, max_time, nchar)\n", "feature_layer = ipyleaflet.GeoJSON(data=data)\n", "feature_layer.on_click(click_handler)\n", "m.layers = [m.layers[0], feature_layer]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import bqplot\n", "\n", "\n", "dt_x = bqplot.DateScale()\n", "sc_y = bqplot.LinearScale()\n", "\n", "initial_dataset = datasets[0]\n", "kwargs = {'time>=': min_time, 'time<=': max_time}\n", "df, var = get_data(initial_dataset, initial_standard_name, kwargs)\n", "time_series = bqplot.Lines(x=df.index, y=df[var], scales={'x': dt_x, 'y': sc_y})\n", "ax_x = bqplot.Axis(scale=dt_x, label='Time')\n", "ax_y = bqplot.Axis(scale=sc_y, orientation='vertical')\n", "figure = bqplot.Figure(marks=[time_series], axes=[ax_x, ax_y])\n", "figure.title = '{} - {}'.format(initial_dataset[:nchar], var)\n", "figure.layout.height = '300px'\n", "figure.layout.width = '800px'" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "66401cc9002944c283c043fd5d0d2a96", "version_major": 2, "version_minor": 0 }, "text/html": [ "

Failed to display Jupyter Widget of type VBox.

\n", "

\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "

\n", "

\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "

\n" ], "text/plain": [ "VBox(children=(Dropdown(index=110, options=('air_temperature', 'air_temperature_data_quality', 'altitude', 'altitude_data_quality', 'altitude_max', 'altitude_max_data_quality', 'altitude_min', 'altitude_min_data_quality', 'barometric_pressure', 'barometric_pressure_data_quality', 'beam_attenuation_data_quality', 'chlorophyll_concentration_in_sea_water', 'chlorophyll_data_quality', 'concentration_of_chlorophyll_in_sea_water', 'concentration_of_colored_dissolved_organic_matter_in_sea_water_expressed_as_equivalent_mass_fraction_of_quinine_sulfate_dihydrate', 'conductivity_data_quality', 'current_direction_data_quality', 'current_speed_data_quality', 'depth', 'depth_status_flag', 'direction_of_sea_water_velocity', 'direction_of_sea_water_velocity_data_quality', 'dissolved_oxygen_data_quality', 'dominant_wave_period_data_quality', 'eastward_sea_water_velocity', 'eastward_sea_water_velocity_data_quality', 'eastward_sea_water_velocity_status_flag', 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