\n",
+ "\n",
+ "# Compare time series from different sensors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Several ocean color sensors have been launched since 1997 to provide continuous global ocean color data. The sensors have differences in design and calibration, and different algorithms may be applied to generate chlorophyll values. Consequently, chlorophyll-a values can vary among the sensors during periods where measurements overlap. \n",
+ "\n",
+ "To examine this phenomenon, we will download and plot time-series of chlorophyll_a concentrations from various sensors from 1997 to the present and see how the measurements compare during periods of overlap. We will use data from the Seawifs (1997-2010), MODIS Aqua (2002-present) and VIIRS SNPP (2012-present) missions.\n",
+ "\n",
+ "### Information on the datasets in this tutorial\n",
+ "\n",
+ "The OceanWatch website has a data catalog containing documentation and links to all the datasets available: \n",
+ "https://oceanwatch.pifsc.noaa.gov/doc.html\n",
+ "\n",
+ "Navigate to the \"Ocean Color\" tab. From there you can access the different datasets using ERDDAP or THREDDS.\n",
+ "\n",
+ "## Load the Python modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import ssl\n",
+ "import urllib\n",
+ "import xarray as xr \n",
+ "import netCDF4 as nc \n",
+ "import pandas as pd \n",
+ "import numpy as np \n",
+ "from matplotlib import pyplot as plt \n",
+ "from matplotlib.colors import LinearSegmentedColormap \n",
+ "np.warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get monthly SeaWiFS data\n",
+ "First, we will visit the ERDDAP web page for our dataset of interest to become familiar with the dataset and to generate a template URL that we will use in Python.\n",
+ "\n",
+ "### Finding the data on ERDDAP\n",
+ "* Use a web browser to qo to the ERDDAP operated by the CoastWatch Central Pacific Node: \n",
+ "https://oceanwatch.pifsc.noaa.gov/erddap/ \n",
+ "\n",
+ "* Find the monthly SeaWiFS chlorophyll dataset by entering the dataset ID (sw_chla_monthly_2018_0) in the search box. \n",
+ "\n",
+ "* Click on the \"data\" link. \n",
+ "* We are interested in the entire length of record for the dataset, so use the sliders to select the entire time range (1997-09-16T12:00:00Z to 2010-12-16T12:00:00Z)\n",
+ "* Set the latitude range to 15 - 25N. Note that requests for this dataset use a reverse order for the latitude (high to low values), so enter the higher value in the first box and the lower value in the second\n",
+ "* Set the longitude range to 198E - 208E\n",
+ "* to reduce the size of the request for this example, we will add a spacing of 10 to the latitude and longitude variables. This pulls every 10th point.\n",
+ "* Set the \"File type\" to \".nc\" and then click the generate the URL button. \n",
+ "* Copy the URL from the web page and paste it into your python script, e.g.\n",
+ "> url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/sw_chla_monthly_2018_0.nc?chlor_a%5B(1997-09-16T12:00:00Z):1:(2010-12-16T12:00:00Z)%5D%5B(40):1:(0)%5D%5B(198):1:(208)%5D' \n",
+ "\n",
+ "### Download the data using the \"urllib\" module \n",
+ "For each dataset we will download the data, bring it into Python and make time-series plots\n",
+ "In this example we are looking at a long time record so the data requests are large.\n",
+ "Be patient, depending on your network speed thiis could take a while. You should see an hourglass in your browser tab showing that the request is processing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "complete\n"
+ ]
+ }
+ ],
+ "source": [
+ "ctx = ssl.create_default_context()\n",
+ "ctx.check_hostname = False\n",
+ "ctx.verify_mode = ssl.CERT_NONE\n",
+ "\n",
+ "url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/sw_chla_monthly_2018_0.nc?chlor_a[(1997-10-16T12:00:00Z):1:(2010-10-16T12:00:00Z)][(25):10:(15)][(198):10:(208)]' \n",
+ "file_name = \"sw.nc\"\n",
+ "\n",
+ "with urllib.request.urlopen(url, context=ctx) as u, \\\n",
+ " open(file_name, 'wb') as f:\n",
+ " f.write(u.read())\n",
+ "print('complete') "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use xarray to bring in the data\n",
+ "* open the dataset\n",
+ "* print information about the data variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Data variables:\n",
+ " chlor_a (time, latitude, longitude) float32 ..."
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sw_ds = xr.open_dataset('sw.nc',decode_cf=False)\n",
+ "sw_ds.data_vars"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The downloaded data contains only one variable: chlor_a. \n",
+ " \n",
+ "### Use Numpy and netCDF4 to prepare the data for comparison\n",
+ "* Compute the mean chlorophyll over the region\n",
+ "* Extract the dates corresponding to each month"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sw_avg = np.mean(sw_ds.chlor_a,axis=(1,2)) \n",
+ "sw_dates = nc.num2date(sw_ds.time,sw_ds.time.units) \n",
+ "sw_ds.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get monthly MODIS data\n",
+ "* Repeat the steps above to get data for the __MODIS Aqua__ chlorophyll dataset\n",
+ "* This dataset is available on the same ERDDAP\n",
+ "* ERDDAP Dataset ID: aqua_chla_monthly_2018_0\n",
+ "* Download the data as an .nc file (remember this may take a minute)\n",
+ "* Use xarray to bring the data into python\n",
+ "* Use numpy and netCDF4 to create a mean for the area and extract the dates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "complete\n"
+ ]
+ }
+ ],
+ "source": [
+ "url2 = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/aqua_chla_monthly_2018_0.nc?chlor_a[(2002-07-16T12:00:00Z):1:(2019-12-16T12:00:00Z)][(25):10:(15)][(198):10:(208)]' \n",
+ "aqua_fn = \"aq.nc\"\n",
+ "\n",
+ "with urllib.request.urlopen(url2, context=ctx) as u, \\\n",
+ " open(aqua_fn, 'wb') as f:\n",
+ " f.write(u.read())\n",
+ "print('complete') "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Data variables:\n",
+ " chlor_a (time, latitude, longitude) float32 ..."
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "aq_ds = xr.open_dataset('aq.nc', decode_cf = False) \n",
+ "aq_ds.data_vars"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aq_avg = np.mean(aq_ds.chlor_a,axis = (1,2)) \n",
+ "aq_dates = nc.num2date(aq_ds.time, aq_ds.time.units) \n",
+ "aq_ds.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get monthly VIIRS data\n",
+ "* Repeat the steps above to get data for the __VIIRS SNPP__ chlorophyll dataset\n",
+ "* This dataset is also vailable on the same ERDDAP\n",
+ "* ERDDAP Dataset ID: noaa_snpp_chla_monthly\n",
+ "* Download the data as an .nc file (remember this may take a minute)\n",
+ "* Use xarray to bring the data into python\n",
+ "* Use numpy and netCDF4 to create a mean for the area and extract the dates "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "complete\n"
+ ]
+ }
+ ],
+ "source": [
+ "url3 = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/noaa_snpp_chla_monthly.nc?chlor_a[(2012-01-02T12:00:00Z):1:(2019-12-01T12:00:00Z)][(25):10:(15)][(198):10:(208)]' \n",
+ "viirs_fn = \"snpp.nc\"\n",
+ "\n",
+ "with urllib.request.urlopen(url3, context=ctx) as u, \\\n",
+ " open(viirs_fn, 'wb') as f:\n",
+ " f.write(u.read())\n",
+ "print('complete') \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Data variables:\n",
+ " chlor_a (time, latitude, longitude) float32 ..."
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snpp_ds = xr.open_dataset('snpp.nc', decode_cf=False) \n",
+ "snpp_ds.data_vars"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snpp_avg = np.mean(snpp_ds.chlor_a, axis = (1, 2)) \n",
+ "snpp_dates = nc.num2date(snpp_ds.time, snpp_ds.time.units) \n",
+ "snpp_ds.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot the result for the three datasets \n",
+ "* Make a plot showing the average chlorophyll in the region over time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize = (12, 5)) \n",
+ "plt.plot_date(sw_dates, sw_avg,label='SeaWiFS', c='red', marker='.', linestyle='-') \n",
+ "plt.plot_date(aq_dates, aq_avg, label='MODIS', c='blue', marker='.', linestyle='-') \n",
+ "plt.plot_date(snpp_dates, snpp_avg, label='VIIRS', c='green', marker='.', linestyle='-') \n",
+ "plt.ylabel('Chl-a (mg/m^3)') \n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see from this plot that there are differences between sensors during periods of data overlap. If you need a single time series from 1997 to present, you would have to devise a method to reconcile the difference in values. \n",
+ "\n",
+ "Fortunately there is a dataset that blends ocean color data from many satellite missions, including the three datasets we have just looked at, into a continuous single dataset. That dataset is also available in ERRDAP so we can easily see how it compares to the others.\n",
+ "\n",
+ "The combined dataset is the ESA OC-CCI (ocean color climate change initiative) dataset.\n",
+ "\n",
+ "## Get OC-CCI data\n",
+ "* Repeat the steps above to get data for the __ESA OC-CCI__ chlorophyll dataset\n",
+ "* This dataset is also vailable on the same ERDDAP\n",
+ "* ERDDAP Dataset ID: esa-cci-chla-monthly-v5-0\n",
+ "* Download the data as an .nc file (remember this may take a minute)\n",
+ "* Use xarray to bring the data into python\n",
+ "* Use numpy and netCDF4 to create a mean for the area and extract the dates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "complete\n"
+ ]
+ }
+ ],
+ "source": [
+ "url4 = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-monthly-v5-0.nc?chlor_a[(1997-09-04):1:(2019-12-01T00:00:00Z)][(25):10:(15)][(198):10:(208)]' \n",
+ "cci_fn = \"cci.nc\"\n",
+ "\n",
+ "with urllib.request.urlopen(url4, context=ctx) as u, \\\n",
+ " open(cci_fn, 'wb') as f:\n",
+ " f.write(u.read())\n",
+ "print('complete') "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cci_ds = xr.open_dataset('cci.nc',decode_cf=False) \n",
+ "cci_avg = np.mean(cci_ds.chlor_a,axis=(1,2)) \n",
+ "cci_dates = nc.num2date(cci_ds.time,cci_ds.time.units) \n",
+ "cci_ds.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot all four datasets \n",
+ "Add the ESA OC-CCI dataset to the plot to see how it compares with data from the individual satellite missions. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
- "\n",
- "# Compare time series from different sensors"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Several ocean color sensors have been launched since 1997 to provide continuous global ocean color data. The sensors have differences in design and calibration, and different algorithms may be applied to generate chlorophyll values. Consequently, chlorophyll-a values can vary among the sensors during periods where measurements overlap. \n",
- "\n",
- "To examine this phenomenon, we will download and plot time-series of chlorophyll_a concentrations from various sensors from 1997 to the present and see how the measurements compare during periods of overlap. We will use data from the Seawifs (1997-2010), MODIS Aqua (2002-present) and VIIRS SNPP (2012-present) missions.\n",
- "\n",
- "## Load the Python modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [],
- "source": [
- "import urllib.request \n",
- "import xarray as xr \n",
- "import netCDF4 as nc \n",
- "import pandas as pd \n",
- "import numpy as np \n",
- "from matplotlib import pyplot as plt \n",
- "from matplotlib.colors import LinearSegmentedColormap \n",
- "np.warnings.filterwarnings('ignore')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The OceanWatch website has a data catalog containing documentation and links to all the datasets available: \n",
- "https://oceanwatch.pifsc.noaa.gov/doc.html\n",
- "\n",
- "Navigate to the \"Ocean Color\" tab. From there you can access the different datasets using ERDDAP or THREDDS."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Get monthly SeaWiFS data\n",
- "### Find the data on ERDDAP\n",
- "* Go to the ERDDAP operated by the CoastWatch Central Pacific Node: \n",
- "https://oceanwatch.pifsc.noaa.gov/erddap/ \n",
- "\n",
- "* Find the monthly SeaWiFS chlorophyll dataset by putting the dataset ID (sw_chla_monthly_2018_0) in the search box. \n",
- "\n",
- "* Click on the \"data\" link. \n",
- "* Use the sliders to select the entire time range (1997-09-16T12:00:00Z to 2010-12-16T12:00:00Z)\n",
- "* Set the latitude range to 0E - 40N. This dataset has a reverse order for the latitude (high to low values), so enter the higher value (40) in the first box and the lower value in the second (0)\n",
- "* Set the longitude range to 198E - 208E\n",
- "* Set the \"File type\" to \".nc\" and generate the URL. \n",
- "* Copy the URL and put it into the url variable, e.g.\n",
- "> url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/sw_chla_monthly_2018_0.nc?chlor_a%5B(1997-09-16T12:00:00Z):1:(2010-12-16T12:00:00Z)%5D%5B(40):1:(0)%5D%5B(198):1:(208)%5D' \n",
- "\n",
- "### Download the data using the \"urllib\" module (this could take a while)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "('sw.nc', )"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "url='https://oceanwatch.pifsc.noaa.gov/erddap/griddap/sw_chla_monthly_2018_0.nc?chlor_a[(1997-10-16T12:00:00Z):1:(2010-10-16T12:00:00Z)][(25):1:(15)][(198):1:(208)]' \n",
- "urllib.request.urlretrieve(url, \"sw.nc\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Use xarray to extract the data from the downloaded file and print information about the data variables."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Data variables:\n",
- " chlor_a (time, latitude, longitude) float32 ..."
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sw_ds = xr.open_dataset('sw.nc',decode_cf=False)\n",
- "sw_ds.data_vars"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The downloaded data contains only one variable: chlor_a. \n",
- " \n",
- "### Compute the monthly mean over the region\n",
- "* Extract the dates corresponding to each month, too"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "swAVG=np.mean(sw_ds.chlor_a,axis=(1,2)) \n",
- "swdates=nc.num2date(sw_ds.time,sw_ds.time.units) \n",
- "sw_ds.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Get monthly MODIS data\n",
- "### Repeat the steps above to get data for the MODIS Aqua chlorophyll dataset \n",
- "ERDDAP ID: aqua_chla_monthly_2018_0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [],
- "source": [
- "url2 = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/aqua_chla_monthly_2018_0.nc?chlor_a[(2002-07-16T12:00:00Z):1:(2019-12-16T12:00:00Z)][(25):1:(15)][(198):1:(208)]' \n",
- "urllib.request.urlretrieve(url2, \"aq.nc\") \n",
- "aq_ds = xr.open_dataset('aq.nc', decode_cf = False) \n",
- "aqAVG = np.mean(aq_ds.chlor_a,axis = (1,2)) \n",
- "aqdates = nc.num2date(aq_ds.time, aq_ds.time.units) \n",
- "aq_ds.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Get monthly VIIRS data\n",
- "### Repeat the steps above to get data for the VIIRS SNPP chlorophyll dataset\n",
- "ERDDAP ID: noaa_snpp_chla_monthly"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [],
- "source": [
- "url3 = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/noaa_snpp_chla_monthly.nc?chlor_a[(2012-01-02T12:00:00Z):1:(2019-12-01T12:00:00Z)][(25):1:(15)][(198):1:(208)]' \n",
- "urllib.request.urlretrieve(url3, \"snpp.nc\")\n",
- "snpp_ds = xr.open_dataset('snpp.nc', decode_cf=False) \n",
- "snppAVG = np.mean(snpp_ds.chlor_a, axis = (1, 2)) \n",
- "snppdates = nc.num2date(snpp_ds.time, snpp_ds.time.units) \n",
- "snpp_ds.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plot the result for three datasets "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize = (12, 5)) \n",
- "plt.plot_date(swdates, swAVG,label='SeaWiFS', c='red', marker='.', linestyle='-') \n",
- "plt.plot_date(aqdates, aqAVG, label='MODIS', c='blue', marker='.', linestyle='-') \n",
- "plt.plot_date(snppdates, snppAVG, label='VIIRS', c='green', marker='.', linestyle='-') \n",
- "plt.ylabel('Chl-a (mg/m^3)') \n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Get OC-CCI data\n",
- "If you needed a single time series from 1997 to present, you would have to use the plot above to devise some method to reconcile the difference in values where two datasets overlap. Alternatively, you could use the ESA OC-CCI (ocean color climate change initiative) dataset, which blends data from many satellite missions into a single dataset, including data from SeaWiFS, MODIS, and VIIRS. \n",
- "\n",
- "Add the ESA OC-CCI dataset to the plot above to see how it compares with data from the individual satellite missions. \n",
- "### Repeat the steps above to get data from the ESA OC-CCI chlorophyll dataset \n",
- "ERDDAP ID: esa-cci-chla-monthly-v4-2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "url4='https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-monthly-v4-2.nc?chlor_a[(1997-09-04):1:(2019-12-01T00:00:00Z)][(25):1:(15)][(198):1:(208)]' \n",
- "urllib.request.urlretrieve(url4, \"cci.nc\")\n",
- "cci_ds = xr.open_dataset('cci.nc',decode_cf=False) \n",
- "cciAVG=np.mean(cci_ds.chlor_a,axis=(1,2)) \n",
- "ccidates=nc.num2date(cci_ds.time,cci_ds.time.units) \n",
- "cci_ds.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Replot the result using data from all four datasets "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
\n",
+ "\n",
+ "# Python Basics: A tutorial for the NOAA Satellite Workshop\n",
+ "> history | updated August 2021 \n",
+ "> owner | NOAA CoastWatch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## In this exercise, you will use Python to download data and metadata from ERDDAP.\n",
+ "\n",
+ "### The exercise demonstrates the following skills: \n",
+ "\n",
+ "* Using Python to retrieve information about a dataset from ERDDAP\n",
+ "* Downloading satellite data from ERDDAP in netCDF format\n",
+ "* Extracting data with Python\n",
+ "\n",
+ "### About the dataset used in this exercise\n",
+ "* For the examples in this exercise we will use the NOAA GeoPolar Sea Surface Temperature dataset from the CoastWatch West Coast Node \n",
+ "* ERDDAP ID = nesdisGeoPolarSSTN5SQNRT\n",
+ "* https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph \n",
+ "* The dataset contains monthly composites of SST\n",
+ "* The low spatial resolution (0.05 degrees) will allow for small download size and help prevent overloading the internet bandwidth during the class \n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Look for python modules you might not have installed\n",
+ "* Use the pkg_resources module to check for installed modules\n",
+ "* We will be using the xarray, numpy, and pandas modules for this exercise. \n",
+ "* Make sure that they are installed in your Python 3 environment. \n",
+ "* A quick way to do this is with the script below"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "All modules are installed\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pkg_resources\n",
+ "\n",
+ "# Create a set 'curly brackets' of the modules to look for\n",
+ "# You can put any modules that you want to in the set\n",
+ "required = {'xarray', 'numpy', 'pandas'}\n",
+ "\n",
+ "# look for the installed packages\n",
+ "installed = {pkg.key for pkg in pkg_resources.working_set}\n",
+ "\n",
+ "# Find which modules are missing\n",
+ "missing = required - installed\n",
+ "if len(missing)==0:\n",
+ " print('All modules are installed')\n",
+ "else:\n",
+ " print('These modules are missing', ', '.join(missing))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import the primary modules for this tutorial\n",
+ "* numpy is used for matrix operations\n",
+ "* numpy.ma specifically is used for masked arrays\n",
+ "* pandas is used for tabular data\n",
+ "* xarray is used for opening the gridded dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np \n",
+ "import numpy.ma as ma \n",
+ "import pandas as pd \n",
+ "import xarray as xr "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get information about a dataset from ERDDAP\n",
+ "\n",
+ "We will use the xarray `open_dataset` function to access data and metadata from ERDDAP. Here we describe how to create the url for the `open_dataset` function and demonstrate a function to generate the ERDDAP url.\n",
+ "\n",
+ "### Open a pointer to an ERDDAP dataset, using the xarray `open_dataset` function \n",
+ "> xr.open_dataset('full_url_to_erddap_dataset') \n",
+ "\n",
+ "Where, the `full_url_to_erddap_dataset` is the base url to the ERDDAP you are using and the ERDDAP dataset id. So, for our dataset: \n",
+ "* base_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
+ "* dataset_id = 'nesdisGeoPolarSSTN5SQNRT'\n",
+ "* full_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT'\n",
+ "\n",
+ "__A simple `open_dataset` example__ \n",
+ "```python\n",
+ "base_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
+ "dataset_id = 'nesdisGeoPolarSSTN5SQNRT'\n",
+ "full_URL = '/'.join([base_URL, dataset_id])\n",
+ "print(full_URL)\n",
+ "da = xr.open_dataset(full_URL)\n",
+ "```\n",
+ "\n",
+ "__Make this more versatile by putting it into a function__\n",
+ "\n",
+ "```python\n",
+ "def point_to_dataset(dataset_id, base_url='https://coastwatch.pfeg.noaa.gov/erddap/griddap'):\n",
+ " base_url = base_url.rstrip('/')\n",
+ " full_url = '/'.join([base_url, dataset_id])\n",
+ " return xr.open_dataset(full_url)\n",
+ "``` \n",
+ "* __dataset_id__ is the ERDDAP id for the dataset of interest. For this example: 'nesdisGeoPolarSSTN5SQNRT'\n",
+ "* __base_url__ is the url of the ERDDAP you are pulling data from. For this example, the West Coast Node ERDDAP at 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
+ "* __full_url__ is the full URL to the ERDDAP dataset created by joining base_url and dataset_id\n",
+ "* The pointer to the dataset is returned\n",
+ "* The default base_url is the West Coast Node ERDDAP 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'. \n",
+ "* Examples of passing dataset_id and base_url to the function:\n",
+ "```python\n",
+ "point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
+ "```\n",
+ "```python\n",
+ "point_to_dataset(dataset_id = 'nesdisGeoPolarSSTN5SQNRT')\n",
+ "```\n",
+ "```python\n",
+ "point_to_dataset('nesdisGeoPolarSSTN5SQNRT', 'https://upwell.pfeg.noaa.gov/erddap/griddap')\n",
+ "```\n",
+ "```python\n",
+ "point_to_dataset(dataset_id = 'nesdisGeoPolarSSTN5SQNRT', \n",
+ " base_url = 'https://upwell.pfeg.noaa.gov/erddap/griddap')\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
estimated error standard deviation of analysed_sst
units :
degree_C
valid_max :
5.0
valid_min :
0.0
[183098880000 values with dtype=float32]
sea_ice_fraction
(time, latitude, longitude)
float32
...
colorBarMaximum :
1.0
colorBarMinimum :
0.0
comment :
Percentage of ice
ioos_category :
Ice Distribution
long_name :
sea ice fraction
source :
NCEP 1/12th degree ice mask
standard_name :
sea_ice_area_fraction
units :
1
valid_max :
1.0
valid_min :
0.0
[183098880000 values with dtype=float32]
mask
(time, latitude, longitude)
float32
...
colorBarMaximum :
5.0
colorBarMinimum :
0.0
comment :
b0: 1=grid cell is water, b1: 1=grid cell is land, b2: 1=grid cell is ice
flag_meanings :
water land ice
flag_values :
[1 2 4]
ioos_category :
Ice Distribution
long_name :
sea/land/ice bit mask
source :
NCEP 1/12th degree ice mask, OSTIA land mask
standard_name :
sea_land_ice_bit_mask
valid_max :
4
valid_min :
1
[183098880000 values with dtype=float32]
acknowledgement :
NOAA/NESDIS
cdm_data_type :
Grid
comment :
The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis includes only nighttime data.
This dataset is an aggregation of Science Quality STAR data (2002-2016) and Near Real Time OSPO data (2017-present). Analysed blended sea surface temperature over the global ocean using night only input data. An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary InfraRed (IR) and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05� spatial resolution.
testOutOfDate :
now-17days
time_coverage_end :
2022-01-09T12:00:00Z
time_coverage_start :
2002-09-01T12:00:00Z
title :
SST, NOAA Geo-Polar Blended Global Level 4, STAR (Science Quality, 2002-2016) + OSPO (Near Real Time, 2017-present), 0.05�, Nighttime
Westernmost_Easting :
-179.975
"
+ ],
+ "text/plain": [
+ "\n",
+ "Dimensions: (time: 7064, latitude: 3600, longitude: 7200)\n",
+ "Coordinates:\n",
+ " * time (time) datetime64[ns] 2002-09-01T12:00:00 ... 2022-01-0...\n",
+ " * latitude (latitude) float32 -89.97 -89.93 -89.88 ... 89.93 89.97\n",
+ " * longitude (longitude) float32 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+ "Data variables:\n",
+ " analysed_sst (time, latitude, longitude) float32 ...\n",
+ " analysis_error (time, latitude, longitude) float32 ...\n",
+ " sea_ice_fraction (time, latitude, longitude) float32 ...\n",
+ " mask (time, latitude, longitude) float32 ...\n",
+ "Attributes: (12/50)\n",
+ " acknowledgement: NOAA/NESDIS\n",
+ " cdm_data_type: Grid\n",
+ " comment: The Geo-Polar Blended Sea Surface Temperature...\n",
+ " Conventions: CF-1.6, Unidata Observation Dataset v1.0, COA...\n",
+ " creator_email: john.sapper@noaa.gov\n",
+ " creator_name: Office of Satellite Products and Operations\n",
+ " ... ...\n",
+ " summary: This dataset is an aggregation of Science Qua...\n",
+ " testOutOfDate: now-17days\n",
+ " time_coverage_end: 2022-01-09T12:00:00Z\n",
+ " time_coverage_start: 2002-09-01T12:00:00Z\n",
+ " title: SST, NOAA Geo-Polar Blended Global Level 4, S...\n",
+ " Westernmost_Easting: -179.975"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def point_to_dataset(dataset_id, base_url='https://coastwatch.pfeg.noaa.gov/erddap/griddap'):\n",
+ " base_url = base_url.rstrip('/')\n",
+ " full_url = '/'.join([base_url, dataset_id])\n",
+ " return xr.open_dataset(full_url)\n",
+ "\n",
+ "da = point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Examine the metadata\n",
+ "### Examine the coordinate variables and dimensions\n",
+ "* The code below lists the coordinate variables and their sizes. \n",
+ "* The dataset is a 3D array with:\n",
+ " * 6793 values in the time dimension (as of 4/15/2021 but that increases each day) \n",
+ " * 3600 values in the latitude dimension\n",
+ " * 7200 values in the longitude dimension\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Coordinates:\n",
+ " * time (time) datetime64[ns] 2002-09-01T12:00:00 ... 2022-01-09T12:00:00\n",
+ " * latitude (latitude) float32 -89.97 -89.93 -89.88 ... 89.88 89.93 89.97\n",
+ " * longitude (longitude) float32 -180.0 -179.9 -179.9 ... 179.9 179.9 180.0"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Frozen({'time': 7064, 'latitude': 3600, 'longitude': 7200})"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(da.coords)\n",
+ "display(da.dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Examine the data variables\n",
+ "* The code below lists the data variables. \n",
+ "* There are several variables in the dataset. We are interested in \"analysed_sst\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "data variables ['analysed_sst', 'analysis_error', 'sea_ice_fraction', 'mask']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print ('data variables', list(da.keys()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Examine the Global Attributes\n",
+ "Global attributes provide information about a dataset as a whole. A few of the global attributes are important for helping you to decide if the dataset will work for your application: \n",
+ "\n",
+ "* `geospatial_lat_min`, `geospatial_lat_max`, `geospatial_lon_min` and `geospatial_lon_max` provide the geographical range of the dataset \n",
+ "\n",
+ "\n",
+ "* `time_coverage_start` and `time_coverage_end` provide the time range covered by the dataset \n",
+ "\n",
+ "\n",
+ "* `geospatial_lat_resolution` and `geospatial_lon_resolution` povide the spatial resolution\n",
+ "\n",
+ "\n",
+ "* Attributes like `comment`, `summary`, and `references` provide more information about:\n",
+ " * how the dataset was generated\n",
+ " * how you may use the data\n",
+ " * the people and organizations to acknowledge if you use the data\n",
+ "\n",
+ "The code below lists these global attributes.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Latitude range: -89.975 to 89.975\n",
+ "Longitude range: -179.975 to 179.975\n",
+ "Time range: 2002-09-01T12:00:00Z to 2022-01-09T12:00:00Z\n",
+ "Spatial resolution (degrees): lat 0.05 lon 0.05\n",
+ " \n",
+ "Dataset summary:\n",
+ "This dataset is an aggregation of Science Quality STAR data (2002-2016) and Near Real Time OSPO data (2017-present). Analysed blended sea surface temperature over the global ocean using night only input data. An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary InfraRed (IR) and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05� spatial resolution.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Latitude range:', da.geospatial_lat_min, 'to', da.geospatial_lat_max)\n",
+ "print('Longitude range:', da.geospatial_lon_min, 'to', da.geospatial_lon_max)\n",
+ "print('Time range:', da.time_coverage_start, 'to', da.time_coverage_end)\n",
+ "print('Spatial resolution (degrees):', \n",
+ " 'lat', np.around(da.geospatial_lat_resolution, decimals=3), \n",
+ " 'lon', np.around(da.geospatial_lon_resolution, decimals=3) \n",
+ " )\n",
+ "\n",
+ "print(' ')\n",
+ "print('Dataset summary:')\n",
+ "print(da.summary)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Download data from ERDDAP\n",
+ "\n",
+ "For this exercise, the area we are interested in includes Monterey Bay, CA: \n",
+ "* Latitude range: 32N, 39N\n",
+ "* Longitude range: -124E, -117E\n",
+ "* Time range June 3, 2020 to June 7, 2020 \n",
+ "\n",
+ "The Xarray module makes it really easy to request a subset of a dataset using latitude, longitude, and time ranges. Here is an example using the `sel` method with the `slice` function. \n",
+ "```python\n",
+ "sst = da['analysed_sst'].sel( \n",
+ " latitude=slice(32., 39.), \n",
+ " longitude=slice(-124, -117), \n",
+ " time=slice('2020-06-03T12:00:00', '2020-06-07T12:00:00') \n",
+ " ) \n",
+ "```\n",
+ "\n",
+ ">Note: If the dataset has an altitude dimension, an altitude slice would need to be added, e.g. \n",
+ ">```python \n",
+ " altitude=slice(0.0), \n",
+ ">```\n",
+ "\n",
+ "### Create a subsetting function\n",
+ "* Use these xarray features in a function to make it more versatile. \n",
+ "* `my_da` is the data array produced from `open_dataset`\n",
+ "* `my_var` is the name of the variable\n",
+ "* the other inputs are the geographic and time ranges"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_data(my_da, my_var,\n",
+ " my_lt_min, my_lt_max, \n",
+ " my_ln_min, my_ln_max, \n",
+ " my_tm_min, my_tm_max\n",
+ " ):\n",
+ " \n",
+ " my_data = my_da[my_var].sel(\n",
+ " latitude=slice(my_lt_min, my_lt_max), \n",
+ " longitude=slice(my_ln_min, my_ln_max), \n",
+ " time=slice(my_tm_min, my_tm_max)\n",
+ " )\n",
+ " return my_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Run the subsetting function with our geographical and time ranges\n",
+ "The returned sst data array is a subset of `da`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('time', 'latitude', 'longitude')\n",
+ "Dimension size: (5, 140, 140)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "for i in range(0,5):\n",
+ " ax = plt.subplot()\n",
+ " sst.isel(time=i).plot.imshow()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Calculate the mean SST over the region for each day \n",
+ "* Use the `numpy mean()` method to take the mean of the latitude longitude grid ( axis=(1,2) ) for each time slice.\n",
+ "* Use the `matplotlib.pyplot.plot_date()` plot routine, which formats the x axis labels as dates\n",
+ "* Use `sst.time` as the x axis values and the mean as the Y axis values\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "# Extracting Satellite Data within an Irregular Boundary from ERRDAP\n",
+ "> history | updated August 2021 \n",
+ "> owner | NOAA CoastWatch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## In this exercise, you will use Python to download data from ERDDAP using an irregular boundary.\n",
+ "\n",
+ "### The exercise demonstrates the following skills: \n",
+ "\n",
+ "* Using Python to retrieve information about a dataset from ERDDAP\n",
+ "* Getting boundary data from a local CSV file\n",
+ "* Downloading satellite data from ERDDAP in netCDF format\n",
+ "* Extracting data within an irregular boundary with Python\n",
+ "\n",
+ "*The scripts in this exercise are written in Python 3.7.* "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get set up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Look for python modules you might not have installed\n",
+ "We will be using the xarray, numpy, and pandas modules for this exercise. Make sure that they are installed in your Python 3 environment. \n",
+ "* A quick way to do this is with the script below\n",
+ "* If you find missing modules, please go to the appendix at the bottom of the page for installation instructions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "All modules are installed\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pkg_resources\n",
+ "\n",
+ "# Create a set 'curly brackets' of the modules to look for\n",
+ "# You can put any modules that you want to in the set\n",
+ "required = {'xarray', 'numpy', 'pandas','shapely'}\n",
+ "\n",
+ "# look for the installed packages\n",
+ "installed = {pkg.key for pkg in pkg_resources.working_set}\n",
+ "\n",
+ "# Find which modules are missing\n",
+ "missing = required - installed\n",
+ "if len(missing)==0:\n",
+ " print('All modules are installed')\n",
+ "else:\n",
+ " print('These modules are missing:', ', '.join(missing))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import the modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np # for matrix operations\n",
+ "import numpy.ma as ma # for masking arrays\n",
+ "import pandas as pd # for tabular data\n",
+ "import xarray as xr # for gridded data\n",
+ "import shapely"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extract data from an irregular bounding box\n",
+ "Extract a timeseries of environmental data from an ERDDAP server within an irregular geographical bounding box (polygon), e.g. the boundaries of marine protected area. This is similar to the R rerddap-Xtractogon function\n",
+ "\n",
+ "The example uses a polygon that outlines the Monterey Bay Marine Protected Area. \n",
+ "\n",
+ "* **Load a csv file (mbnms.csv) that defines the polygon's latitude and longitude coordinates** \n",
+ "The polygon is a series of longitude and latitude coordinates. If you connect the coordinate pairs, like a dot-to-dot puzzle, you create an outline of the Monterey Bay Marine Protected Area. If running this notebook locally you may need to update the path to the location of the shapes directory in the repo on your computer ie. /Users/your_username/Downloads/satellite_course_python_code-main/shapes/mbnms.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
estimated error standard deviation of analysed_sst
units :
degree_C
valid_max :
5.0
valid_min :
0.0
[183098880000 values with dtype=float32]
sea_ice_fraction
(time, latitude, longitude)
float32
...
colorBarMaximum :
1.0
colorBarMinimum :
0.0
comment :
Percentage of ice
ioos_category :
Ice Distribution
long_name :
sea ice fraction
source :
NCEP 1/12th degree ice mask
standard_name :
sea_ice_area_fraction
units :
1
valid_max :
1.0
valid_min :
0.0
[183098880000 values with dtype=float32]
mask
(time, latitude, longitude)
float32
...
colorBarMaximum :
5.0
colorBarMinimum :
0.0
comment :
b0: 1=grid cell is water, b1: 1=grid cell is land, b2: 1=grid cell is ice
flag_meanings :
water land ice
flag_values :
[1 2 4]
ioos_category :
Ice Distribution
long_name :
sea/land/ice bit mask
source :
NCEP 1/12th degree ice mask, OSTIA land mask
standard_name :
sea_land_ice_bit_mask
valid_max :
4
valid_min :
1
[183098880000 values with dtype=float32]
acknowledgement :
NOAA/NESDIS
cdm_data_type :
Grid
comment :
The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis includes only nighttime data.
This dataset is an aggregation of Science Quality STAR data (2002-2016) and Near Real Time OSPO data (2017-present). Analysed blended sea surface temperature over the global ocean using night only input data. An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary InfraRed (IR) and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05� spatial resolution.
testOutOfDate :
now-17days
time_coverage_end :
2022-01-09T12:00:00Z
time_coverage_start :
2002-09-01T12:00:00Z
title :
SST, NOAA Geo-Polar Blended Global Level 4, STAR (Science Quality, 2002-2016) + OSPO (Near Real Time, 2017-present), 0.05�, Nighttime
Westernmost_Easting :
-179.975
"
+ ],
+ "text/plain": [
+ "\n",
+ "Dimensions: (time: 7064, latitude: 3600, longitude: 7200)\n",
+ "Coordinates:\n",
+ " * time (time) datetime64[ns] 2002-09-01T12:00:00 ... 2022-01-0...\n",
+ " * latitude (latitude) float32 -89.97 -89.93 -89.88 ... 89.93 89.97\n",
+ " * longitude (longitude) float32 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+ "Data variables:\n",
+ " analysed_sst (time, latitude, longitude) float32 ...\n",
+ " analysis_error (time, latitude, longitude) float32 ...\n",
+ " sea_ice_fraction (time, latitude, longitude) float32 ...\n",
+ " mask (time, latitude, longitude) float32 ...\n",
+ "Attributes: (12/50)\n",
+ " acknowledgement: NOAA/NESDIS\n",
+ " cdm_data_type: Grid\n",
+ " comment: The Geo-Polar Blended Sea Surface Temperature...\n",
+ " Conventions: CF-1.6, Unidata Observation Dataset v1.0, COA...\n",
+ " creator_email: john.sapper@noaa.gov\n",
+ " creator_name: Office of Satellite Products and Operations\n",
+ " ... ...\n",
+ " summary: This dataset is an aggregation of Science Qua...\n",
+ " testOutOfDate: now-17days\n",
+ " time_coverage_end: 2022-01-09T12:00:00Z\n",
+ " time_coverage_start: 2002-09-01T12:00:00Z\n",
+ " title: SST, NOAA Geo-Polar Blended Global Level 4, S...\n",
+ " Westernmost_Easting: -179.975"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "da = point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
+ "\n",
+ "# uncomment the \"da\" below to see the full data array info\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Get satellite data with our geographical and time ranges"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('time', 'latitude', 'longitude')\n",
+ "dimension size (5, 140, 140)\n"
+ ]
+ }
+ ],
+ "source": [
+ "lat_min = 32.\n",
+ "lat_max = 39.\n",
+ "lon_min = -124.\n",
+ "lon_max = -117.\n",
+ "time_min = '2020-06-03T12:00:00' # written in ISO format\n",
+ "time_max = '2020-06-07T12:00:00' # written in ISO format\n",
+ "my_var = 'analysed_sst'\n",
+ "\n",
+ "sst = get_data(\n",
+ " da, my_var,\n",
+ " lat_min, lat_max,\n",
+ " lon_min, lon_max,\n",
+ " time_min, time_max\n",
+ " )\n",
+ "\n",
+ "# the sst data array is a subset if da\n",
+ "print(sst.dims)\n",
+ "print('dimension size', sst.shape)\n",
+ "\n",
+ "# uncomment the \"sst\" below to see the full data array info\n",
+ "#sst"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a mask from the polygon object\n",
+ "* Make a copy of the SST data array\n",
+ "* Turn the array into a mask layer by finding all pixels that are inside the polygon. Give these pixels a value of True. Cells outside the polygon have a value of False.\n",
+ "* Plot the mask on a map to see its location."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/45/98hpd1j11gxbpzfrcmk643500000gn/T/ipykernel_70382/3854919394.py:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+ "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+ " mask = shapely.vectorized.contains(polyshape, p_lons, p_lats)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Flatten and make a list of lon/lat pairs. \n",
+ "# Make 2D grids of the lat and lon vectors\n",
+ "p_lons, p_lats = np.meshgrid(sst.longitude, sst.latitude)\n",
+ "\n",
+ "mask = shapely.vectorized.contains(polyshape, p_lons, p_lats)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a masked version of the sst data area \n",
+ "* Data outside of the Monterey Bay National Marine Sanctuary will be masked out\n",
+ "* Plot the masked SST (SST within the shapefile boundary)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "# Extracting data at ship stations tutorial\n",
+ "> history | updated August 2021 \n",
+ "> owner | NOAA CoastWatch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this exercise, you will import a ship track and extract satellite SST values at locations along the track.\n",
+ "The exercise demonstrates the following skills:\n",
+ "\n",
+ "* Getting data from a remote CSV file\n",
+ "* Downloading satellite data from ERDDAP in netCDF format\n",
+ "* Extracting data with Python\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Look for python modules you might not have installed\n",
+ "* We will be using the xarray, numpy, and pandas modules for this exercise. \n",
+ "* Make sure that they are installed in your Python 3 environment. \n",
+ "* A quick way to do this is with the script below. \n",
+ "* If you find missing modules, please go to the appendix at the bottom of the page for installation instructions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "All modules are installed\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pkg_resources\n",
+ "\n",
+ "# Create a set 'curly brackets' of the modules to look for\n",
+ "# You can put any modules that you want to in the set\n",
+ "required = {'xarray', 'numpy', 'pandas'}\n",
+ "\n",
+ "# look for the installed packages\n",
+ "installed = {pkg.key for pkg in pkg_resources.working_set}\n",
+ "\n",
+ "# Find which modules are missing\n",
+ "missing = required - installed\n",
+ "if len(missing)==0:\n",
+ " print('All modules are installed')\n",
+ "else:\n",
+ " print('These modules are missing', ', '.join(missing))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import the primary modules used in this tutorial\n",
+ "* numpy is used for matrix operations\n",
+ "* numpy.ma specifically is used for masked arrays\n",
+ "* pandas is used for tabular data\n",
+ "* xarray is used for opening the gridded dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np \n",
+ "import numpy.ma as ma \n",
+ "import pandas as pd \n",
+ "import xarray as xr "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Generate means for a ship station track \n",
+ "\n",
+ "In the notebook repo you will see a .csv file file in the /data directory named my_monterey_track.csv. Load a station location file (.csv) with the time, latitude, and longitude coordinates for ship station locations.\n",
+ "\n",
+ "For this example the CSV file has three columns, with each row locating a station in time and space: \n",
+ "`sample_date\tsample_time\tsample_lat\tsample_lon` \n",
+ "`6/3/20\t 18:00\t 37.3\t -123.2`\n",
+ "\n",
+ "### Load the CSV file data using the \"read_csv\" function from the Pandas module. \n",
+ "To use the date and time, we will need to create a Pandas date object. Pandas has a nice option (parse_dates) to create a column date object from the date and time fields as the CSV file is loaded. We will also keep the original sample_date and sample_time by setting the keep_date_col to True.\n",
+ "\n",
+ "### The Xarray module requires dates be formatted in a ISO 8601 date format, so we will also create an iso_date column"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
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+ " sample_date_sample_time sample_date sample_time sample_lat sample_lon \\\n",
+ "0 2020-06-03 18:00:00 6/3/20 18:00 37.3 236.8 \n",
+ "1 2020-06-04 19:34:00 6/4/20 19:34 36.1 237.5 \n",
+ "2 2020-06-05 20:15:00 6/5/20 20:15 34.4 238.2 \n",
+ "3 2020-06-06 18:45:00 6/6/20 18:45 32.2 239.8 \n",
+ "4 2020-06-07 12:52:00 6/7/20 12:52 31.9 240.9 \n",
+ "5 2020-06-08 14:23:00 6/8/20 14:23 31.9 241.8 \n",
+ "\n",
+ " iso_date \n",
+ "0 2020-06-03 \n",
+ "1 2020-06-04 \n",
+ "2 2020-06-05 \n",
+ "3 2020-06-06 \n",
+ "4 2020-06-07 \n",
+ "5 2020-06-08 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load the file into a Pandas data frame\n",
+ "# This creates a date object column named 'sample_date_sample_time' \n",
+ "# If running this notebook on your local machine you may need to update the csv file path\n",
+ "# ie. /Users/your_username/Downloads/satellite_course_python_code-main/data/my_monterey_track.csv\n",
+ "track_DF = pd.read_csv('./data/my_monterey_track.csv', parse_dates=[['sample_date', 'sample_time']], keep_date_col=True) \n",
+ "\n",
+ "\n",
+ "# track_DF['iso_date'] = track_DF.sample_date_sample_time.dt.strftime('%Y-%m-%dT12:%M:%S')\n",
+ "track_DF['iso_date'] = track_DF.sample_date_sample_time.dt.strftime('%Y-%m-%d')\n",
+ "# Display the data\n",
+ "display(track_DF)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load the satellite SST data from ERDDAP \n",
+ "* We use two functions to get the data, these are described in greater detail in Tutotial 1\n",
+ "* point_to_dataset sets up the request url and returns a dataset array\n",
+ "* get_data makes a subset request and returns the satellite data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def point_to_dataset(dataset_id, base_url='https://coastwatch.pfeg.noaa.gov/erddap/griddap'):\n",
+ " base_url = base_url.rstrip('/')\n",
+ " full_url = '/'.join([base_url, dataset_id])\n",
+ " return xr.open_dataset(full_url)\n",
+ "\n",
+ "def get_data(my_da, my_var,\n",
+ " my_lt_min, my_lt_max, \n",
+ " my_ln_min, my_ln_max, \n",
+ " my_tm_min, my_tm_max\n",
+ " ):\n",
+ " \n",
+ " my_data = my_da[my_var].sel(\n",
+ " latitude=slice(my_lt_min, my_lt_max), \n",
+ " longitude=slice(my_ln_min, my_ln_max), \n",
+ " time=slice(my_tm_min, my_tm_max)\n",
+ " )\n",
+ " return my_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
estimated error standard deviation of analysed_sst
units :
degree_C
valid_max :
5.0
valid_min :
0.0
[183098880000 values with dtype=float32]
sea_ice_fraction
(time, latitude, longitude)
float32
...
colorBarMaximum :
1.0
colorBarMinimum :
0.0
comment :
Percentage of ice
ioos_category :
Ice Distribution
long_name :
sea ice fraction
source :
NCEP 1/12th degree ice mask
standard_name :
sea_ice_area_fraction
units :
1
valid_max :
1.0
valid_min :
0.0
[183098880000 values with dtype=float32]
mask
(time, latitude, longitude)
float32
...
colorBarMaximum :
5.0
colorBarMinimum :
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comment :
b0: 1=grid cell is water, b1: 1=grid cell is land, b2: 1=grid cell is ice
flag_meanings :
water land ice
flag_values :
[1 2 4]
ioos_category :
Ice Distribution
long_name :
sea/land/ice bit mask
source :
NCEP 1/12th degree ice mask, OSTIA land mask
standard_name :
sea_land_ice_bit_mask
valid_max :
4
valid_min :
1
[183098880000 values with dtype=float32]
acknowledgement :
NOAA/NESDIS
cdm_data_type :
Grid
comment :
The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis includes only nighttime data.
This dataset is an aggregation of Science Quality STAR data (2002-2016) and Near Real Time OSPO data (2017-present). Analysed blended sea surface temperature over the global ocean using night only input data. An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary InfraRed (IR) and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05� spatial resolution.
testOutOfDate :
now-17days
time_coverage_end :
2022-01-09T12:00:00Z
time_coverage_start :
2002-09-01T12:00:00Z
title :
SST, NOAA Geo-Polar Blended Global Level 4, STAR (Science Quality, 2002-2016) + OSPO (Near Real Time, 2017-present), 0.05�, Nighttime
Westernmost_Easting :
-179.975
"
+ ],
+ "text/plain": [
+ "\n",
+ "Dimensions: (time: 7064, latitude: 3600, longitude: 7200)\n",
+ "Coordinates:\n",
+ " * time (time) datetime64[ns] 2002-09-01T12:00:00 ... 2022-01-0...\n",
+ " * latitude (latitude) float32 -89.97 -89.93 -89.88 ... 89.93 89.97\n",
+ " * longitude (longitude) float32 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+ "Data variables:\n",
+ " analysed_sst (time, latitude, longitude) float32 ...\n",
+ " analysis_error (time, latitude, longitude) float32 ...\n",
+ " sea_ice_fraction (time, latitude, longitude) float32 ...\n",
+ " mask (time, latitude, longitude) float32 ...\n",
+ "Attributes: (12/50)\n",
+ " acknowledgement: NOAA/NESDIS\n",
+ " cdm_data_type: Grid\n",
+ " comment: The Geo-Polar Blended Sea Surface Temperature...\n",
+ " Conventions: CF-1.6, Unidata Observation Dataset v1.0, COA...\n",
+ " creator_email: john.sapper@noaa.gov\n",
+ " creator_name: Office of Satellite Products and Operations\n",
+ " ... ...\n",
+ " summary: This dataset is an aggregation of Science Qua...\n",
+ " testOutOfDate: now-17days\n",
+ " time_coverage_end: 2022-01-09T12:00:00Z\n",
+ " time_coverage_start: 2002-09-01T12:00:00Z\n",
+ " title: SST, NOAA Geo-Polar Blended Global Level 4, S...\n",
+ " Westernmost_Easting: -179.975"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "da = point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Prepare the track data\n",
+ "### Check that longitudes are in the same format\n",
+ "\n",
+ "Ensure the longitude format of the track data is compatible with the SST dataset. Some datasets in ERDDAP use a -180°E to +180°E format, others use 0-360°.\n",
+ "\n",
+ "* We can see from the metadata printed above that the satellite SST data product has longitudes in a -180 to +180 format. \n",
+ " longitude range: -179.975 to 179.975\n",
+ "\n",
+ "We can use the following function to check the longitude formats in the satellite and ship track datasets and adjust the track longitudes if needed. Here is how the function works: \n",
+ "* If both longitude lists have the same format, then the station location longitudes are returned unchanged.\n",
+ "* If the SST longitudes are -180 to 180 and the station location longitudes are 0-360, then 360 is subtracted from any station location longitude > 180\n",
+ "* If the SST longitudes are 0 to 360 and the station location longitudes are -180 to 180, then 360 is added to any station location longitude < 0 \n",
+ "* `dataset_lon` is the longitude column from the xarray data array ( sst['longitude'] ) \n",
+ "* `location_lon` is the longitude column from the Pandas data frame, ( track_DF['sample_lon'] ) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def is_it_180_to_180(dataset_lon, location_lon):\n",
+ " datasat_is = np.any((dataset_lon < 0))\n",
+ " location_is = np.any((location_lon < 0))\n",
+ " if datasat_is and location_is:\n",
+ " return location_lon \n",
+ " elif not datasat_is and location_is:\n",
+ " return [ x if x>0 else x+360 for x in location_lon ]\n",
+ " elif datasat_is and not location_is:\n",
+ " return [ x if x<=180 else x-360 for x in location_lon ]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "__Call the `is_it_180_to_180` function__\n",
+ "* Pass the dataset longitudes and the station location longitudes to the function \n",
+ "* Add the results to the Pandas track data frame with the ship station information"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
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+ " sample_date_sample_time sample_date sample_time sample_lat sample_lon \\\n",
+ "0 2020-06-03 18:00:00 6/3/20 18:00 37.3 236.8 \n",
+ "1 2020-06-04 19:34:00 6/4/20 19:34 36.1 237.5 \n",
+ "2 2020-06-05 20:15:00 6/5/20 20:15 34.4 238.2 \n",
+ "3 2020-06-06 18:45:00 6/6/20 18:45 32.2 239.8 \n",
+ "4 2020-06-07 12:52:00 6/7/20 12:52 31.9 240.9 \n",
+ "5 2020-06-08 14:23:00 6/8/20 14:23 31.9 241.8 \n",
+ "\n",
+ " iso_date sample_lon_corrected \n",
+ "0 2020-06-03 -123.2 \n",
+ "1 2020-06-04 -122.5 \n",
+ "2 2020-06-05 -121.8 \n",
+ "3 2020-06-06 -120.2 \n",
+ "4 2020-06-07 -119.1 \n",
+ "5 2020-06-08 -118.2 "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "track_DF['sample_lon_corrected'] = is_it_180_to_180(\n",
+ " da['longitude'], \n",
+ " track_DF['sample_lon']\n",
+ " )\n",
+ "\n",
+ "track_DF"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Select an area around each ship station to get matching satellite data\n",
+ "Here we have a function for extracting satellite (or any gridded data) from ERDDAP at a ship track point. There are options to return only the exact match as a single point or to extract all satellite data within a distance from the ship location.\n",
+ "\n",
+ "* Use a single latitude and longitude pair to extract SST data, then the data from a single point will be returned. \n",
+ "* To increase to chances of getting data (if there are gaps due to clouds for example) and improve your statistics, you can request a box of SST data around the station location, e.g. a latitude range and a longitude range. \n",
+ "\n",
+ "* The box can increase by multiples of the spatial resolution of the dataset. \n",
+ " > For this dataset the spatial resolution was 0.05 degrees for the latitude and longitude. That is approximately 5 km resolution. So, a 5 pixel X 5 pixel box around each station location would result in a 25 x 25 km box (625 km^2) of data. \n",
+ "\n",
+ "The following `data_box` function generates a latitude and longitude range box for each station location. The function asks for:\n",
+ "\n",
+ "* Station latitude and longitude\n",
+ "* Spatial resolution of satellite dataset. (`da.geospatial_lat_resolution`, `da.geospatial_lon_resolution`) \n",
+ "* Number of pixels you want added to each side of the latitude and longitude center point \n",
+ " * Choosing `2` for latitude, results in a range of five pixels: the station latitude for the center plus two pixels on each side. If you also choose `2` for the longitude, then you would get a 5x5 box around the station location. The default value for the function is `1`, which is a 3x3 box "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def data_box(\n",
+ " st_lat, st_lon, \n",
+ " lat_res, lon_res, \n",
+ " lat_pixels = 1, lon_pixels = 1\n",
+ " ):\n",
+ " \n",
+ " lt_min = st_lat - lat_res * lat_pixels\n",
+ " lt_max = st_lat + lat_res * lat_pixels\n",
+ " ln_min = st_lon - lon_res * lon_pixels\n",
+ " ln_max = st_lon + lon_res * lon_pixels\n",
+ "\n",
+ " return lt_min, lt_max, ln_min, ln_max\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Call the data_box function \n",
+ "\n",
+ "Add the satellite data request areas to the ship track data frame:\n",
+ "\n",
+ "```new_Pandas_dataframe_column = data_box(st_lat, st_lon, lat_res, lon_res, lat_pixels = 1, lon_pixels = 1)```\n",
+ "\n",
+ "Results in the following new columns in the pandas data frame:\n",
+ "\n",
+ "`track_DF['lat_min']`, `track_DF['lat_min']`, `track_DF['lon_min']`, `track_DF['lon_min']`\n",
+ "\n",
+ "Inputs: \n",
+ "* st_lat = the latitude column from the Pandas station location data frame, track_DF['sample_lat']\n",
+ "* st_lon = the longitude column from the Pandas station location data frame, track_DF['sample_lon_corrected']\n",
+ " * make sure to use the corrected longitude column\n",
+ "* lat_res = the spatial resolution for satellite latitude, da.geospatial_lat_resolution\n",
+ "* lon_res = the spatial resolution for satellite longitude, da.geospatial_lon_resolution \n",
+ "* lat_pixels = the number of pixels to add to each side of the latitude center point (default = 1) \n",
+ "* lon_pixels = the number of pixels to add to each side of the longitude center point (default = 1)"
+ ]
+ },
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+ "execution_count": 11,
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+ "
6/4/20
\n",
+ "
19:34
\n",
+ "
36.1
\n",
+ "
237.5
\n",
+ "
2020-06-04
\n",
+ "
-122.5
\n",
+ "
36.0
\n",
+ "
36.2
\n",
+ "
-122.6
\n",
+ "
-122.4
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
+ "
2020-06-05 20:15:00
\n",
+ "
6/5/20
\n",
+ "
20:15
\n",
+ "
34.4
\n",
+ "
238.2
\n",
+ "
2020-06-05
\n",
+ "
-121.8
\n",
+ "
34.3
\n",
+ "
34.5
\n",
+ "
-121.9
\n",
+ "
-121.7
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
2020-06-06 18:45:00
\n",
+ "
6/6/20
\n",
+ "
18:45
\n",
+ "
32.2
\n",
+ "
239.8
\n",
+ "
2020-06-06
\n",
+ "
-120.2
\n",
+ "
32.1
\n",
+ "
32.3
\n",
+ "
-120.3
\n",
+ "
-120.1
\n",
+ "
\n",
+ "
\n",
+ "
4
\n",
+ "
2020-06-07 12:52:00
\n",
+ "
6/7/20
\n",
+ "
12:52
\n",
+ "
31.9
\n",
+ "
240.9
\n",
+ "
2020-06-07
\n",
+ "
-119.1
\n",
+ "
31.8
\n",
+ "
32.0
\n",
+ "
-119.2
\n",
+ "
-119.0
\n",
+ "
\n",
+ "
\n",
+ "
5
\n",
+ "
2020-06-08 14:23:00
\n",
+ "
6/8/20
\n",
+ "
14:23
\n",
+ "
31.9
\n",
+ "
241.8
\n",
+ "
2020-06-08
\n",
+ "
-118.2
\n",
+ "
31.8
\n",
+ "
32.0
\n",
+ "
-118.3
\n",
+ "
-118.1
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sample_date_sample_time sample_date sample_time sample_lat sample_lon \\\n",
+ "0 2020-06-03 18:00:00 6/3/20 18:00 37.3 236.8 \n",
+ "1 2020-06-04 19:34:00 6/4/20 19:34 36.1 237.5 \n",
+ "2 2020-06-05 20:15:00 6/5/20 20:15 34.4 238.2 \n",
+ "3 2020-06-06 18:45:00 6/6/20 18:45 32.2 239.8 \n",
+ "4 2020-06-07 12:52:00 6/7/20 12:52 31.9 240.9 \n",
+ "5 2020-06-08 14:23:00 6/8/20 14:23 31.9 241.8 \n",
+ "\n",
+ " iso_date sample_lon_corrected lat_min lat_max lon_min lon_max \n",
+ "0 2020-06-03 -123.2 37.2 37.4 -123.3 -123.1 \n",
+ "1 2020-06-04 -122.5 36.0 36.2 -122.6 -122.4 \n",
+ "2 2020-06-05 -121.8 34.3 34.5 -121.9 -121.7 \n",
+ "3 2020-06-06 -120.2 32.1 32.3 -120.3 -120.1 \n",
+ "4 2020-06-07 -119.1 31.8 32.0 -119.2 -119.0 \n",
+ "5 2020-06-08 -118.2 31.8 32.0 -118.3 -118.1 "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[\n",
+ " track_DF['lat_min'], track_DF['lat_max'],\n",
+ " track_DF['lon_min'], track_DF['lon_max']\n",
+ "] = data_box(\n",
+ " track_DF['sample_lat'], track_DF['sample_lon_corrected'],\n",
+ " da.geospatial_lat_resolution, da.geospatial_lon_resolution,\n",
+ " 2, 2\n",
+ " )\n",
+ " \n",
+ "track_DF "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extract SST data for each station location and generate some basic statistics\n",
+ "* We now have the input parameters needed to obtain satellite data for the cruise track\n",
+ "* Extract data for each station in the ship track\n",
+ " * Build a __for__ loop to use the `sample_date_sample_time`, `lat_min`, `lat_max`, `lon_min`, and `lon_max` columns from `track_DF` \n",
+ " * Use the `get_data()` function to download satellite data for each station.\n",
+ "* Calculate the mean, standard deviation, and n stats, then add them to `track_DF`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "means [14.251244, 14.836244, 16.224995, 16.861244, 17.026245, 17.41437]\n",
+ "stdevs [0.3615224, 0.12873788, 0.09499985, 0.048589565, 0.09873407, 0.10271109]\n",
+ "ns [16, 16, 16, 16, 16, 16]\n"
+ ]
+ }
+ ],
+ "source": [
+ "means = []\n",
+ "stdevs = []\n",
+ "ns = []\n",
+ "my_var = 'analysed_sst'\n",
+ "\n",
+ "for index, row in track_DF.iterrows():\n",
+ " \n",
+ " my_data = get_data(da, my_var,\n",
+ " row['lat_min'], row['lat_max'], \n",
+ " row['lon_min'], row['lon_max'], \n",
+ " row['iso_date'], row['iso_date']\n",
+ " )\n",
+ " \n",
+ " means.append(np.nanmean(my_data, axis=(1,2))[0])\n",
+ " stdevs.append(np.nanstd(my_data, axis=(1,2))[0])\n",
+ " ns.append(np.count_nonzero(my_data, axis=(1,2))[0])\n",
+ "\n",
+ "print('means', means)\n",
+ "print('stdevs', stdevs)\n",
+ "print('ns', ns)\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ">means = the average SST over the selected area at the ship location\n",
+ ">\n",
+ ">stdevs = An indicator of variability over the selected satellite data cells\n",
+ ">\n",
+ ">ns = The number of non-zero data points used to calculate the average\n",
+ "\n",
+ "\n",
+ "Add the statistics to the track_DF dataframe and export as a csv\n",
+ "* Uncomment the last line to save the dataframe as a CSV file called 'mbnms_results.csv'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
- "\n",
- "# Python Basics: a tutorial for the NOAA Satellite Workshop\n",
- "> history | uodated May 2021 \n",
- "> owner | NOAA CoastWatch West Coast Node"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## In this exercise, you will use Python to download data and metadata from ERDDAP.\n",
- "\n",
- "### The exercise demonstrates the following skills: \n",
- "\n",
- "* Using Python to retrieve information about a dataset from ERDDAP\n",
- "* Getting data from a remote CSV file\n",
- "* Downloading satellite data from ERDDAP in netCDF format\n",
- "* Extracting data with Python\n",
- "\n",
- "For the examples in this exercise we will use the NOAA GeoPolar Sea Surface temperature dataset from the CoastWatch West Coast Node (ERDDAP ID = nesdisGeoPolarSSTN5SQNRT). \n",
- "https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph \n",
- "\n",
- "The dataset contains monthly composites of SSS and has low spatial resolution (0.25 degrees), which will help prevent overloading the local intenet bandwidth during the class. \n",
- "\n",
- "### The scripts in this exercise are written in Python 3.7. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "1. Get set up\n",
- "Import python modules\n",
- "Make sure that the following libraries are installed in your Python 3 environment. The only library that might be difficult is Basemap. To use it you might have to define the environmental variable 'PROJ_LIB'. Search for the file 'epsg'. It should be somewhere in your Python folders. Use the path to that folder in the 'PROJ_LIB' environmental variable (see below)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Get set up\n",
- "## Import some basic python modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pkg_resources\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Look for python modules you might not have installed\n",
- "We will be using the xarray, numpy, and pandas modules for this exercise. Make sure that they are installed in your Python 3 environment. A quick way to do this is with the script below"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "All modules are installed\n"
- ]
- }
- ],
- "source": [
- "# Create a set 'curly brackets' of the modules to look for\n",
- "# You can put any modules that you want to in the set\n",
- "required = {'xarray', 'numpy', 'pandas'}\n",
- "\n",
- "# look for the installed packages\n",
- "installed = {pkg.key for pkg in pkg_resources.working_set}\n",
- "\n",
- "# Find which modules are missing\n",
- "missing = required - installed\n",
- "if len(missing)==0:\n",
- " print('All modules are installed')\n",
- "else:\n",
- " print('These modules are missing', ', '.join(missing))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### If you find missing modules, please go to the appendix at the bottom of the page for installation instructions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Import the modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np # for matrix operations\n",
- "import numpy.ma as ma # for masking arrays\n",
- "import pandas as pd # for tabular data\n",
- "import xarray as xr # for gridded data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Getting information about a dataset \n",
- "## First, open a pointer to an ERDDAP dataset, using the xarray `open_dataset` function. \n",
- "> xr.open_dataset('full_url_to_erddap_dataset') \n",
- "\n",
- "__`'full_url_to_erddap_dataset'` is full URL to the dataset, which is comprised of:__ \n",
- "* the base url to the ERDDAP you are using, plus\n",
- "* the dataset ID. \n",
- "\n",
- "### So, for our dataset: \n",
- "* base_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
- "* dataset_id = 'jplSMAPSSSv42_mday'\n",
- "* full_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplSMAPSSSv42_mday'\n",
- "\n",
- "### Code that works is listed below: \n",
- "```python\n",
- "base_URL = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
- "dataset_id = 'nesdisGeoPolarSSTN5SQNRT'\n",
- "full_URL = '/'.join([base_URL, dataset_id])\n",
- "print(full_URL)\n",
- "da = xr.open_dataset(full_URL)\n",
- "```\n",
- "\n",
- "### But, let's put it in a function to make it more versatile\n",
- "\n",
- "```python\n",
- "def point_to_dataset(dataset_id, base_url='https://coastwatch.pfeg.noaa.gov/erddap/griddap'):\n",
- " base_url = base_url.rstrip('/')\n",
- " full_url = '/'.join([base_url, dataset_id])\n",
- " #print(full_url)\n",
- " # xr.open_dataset('https://coastwatch.pfeg.noaa.gov/erddap/griddap/coastwatchSMOSv662SSS3day')\n",
- " return xr.open_dataset(full_url)\n",
- "```\n",
- "\n",
- "#### Where: \n",
- "* dataset_id is the ERDDAP id for the dataset of interest. For this example: 'nesdisGeoPolarSSTN5SQNRT'\n",
- "* base_url is the url of the ERDDAP you are pulling data from. For this example, the West Coast Node ERDDAP at 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'\n",
- "* full_url is the full URL to the ERDDAP dataset created by joining base_url and dataset_id\n",
- "* the pointer to the dataset is returned\n",
- "\n",
- "The default base_url the West Coast Node ERDDAP 'https://coastwatch.pfeg.noaa.gov/erddap/griddap'. So, you get our dataset, the function call could be as simple as:\n",
- "```python\n",
- "point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
- "``` \n",
- "Or you could specify the dataset_id\n",
- "```python\n",
- "point_to_dataset(dataset_id = 'nesdisGeoPolarSSTN5SQNRT')\n",
- "```\n",
- "You could also specify a different base_url.\n",
- "```python\n",
- "point_to_dataset('nesdisGeoPolarSSTN5SQNRT', 'https://upwell.pfeg.noaa.gov/erddap/griddap')\n",
- "\n",
- "point_to_dataset(dataset_id = 'nesdisGeoPolarSSTN5SQNRT', \n",
- " base_url = 'https://upwell.pfeg.noaa.gov/erddap/griddap')\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 101,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Define the function\n",
- "def point_to_dataset(dataset_id, base_url='https://coastwatch.pfeg.noaa.gov/erddap/griddap'):\n",
- " base_url = base_url.rstrip('/')\n",
- " full_url = '/'.join([base_url, dataset_id])\n",
- " #print(full_url)\n",
- " # xr.open_dataset('https://coastwatch.pfeg.noaa.gov/erddap/griddap/coastwatchSMOSv662SSS3day')\n",
- " return xr.open_dataset(full_url)\n",
- "\n",
- "# Call the function\n",
- "da = point_to_dataset('nesdisGeoPolarSSTN5SQNRT')\n",
- "\n",
- "# uncomment the \"da\" below to see the full data array info\n",
- "# da"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Examine the metadata\n",
- "### Coordiante variables and dimensions\n",
- "### The code below list the coordinate variables and their sizes. \n",
- "The dataset is a 3D array with:\n",
- "* 6793 values in the time dimension (on 4/15/2021 but that increases each day) \n",
- "* 3600 values in the latitude dimension\n",
- "* 7200 values in the longitude dimension\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 102,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Coordinates:\n",
- " * time (time) object 2002-09-01 12:00:00 ... 2021-04-23 12:00:00\n",
- " * latitude (latitude) float32 -89.975 -89.925 -89.875 ... 89.925 89.975\n",
- " * longitude (longitude) float32 -179.975 -179.925 ... 179.925 179.975"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "Frozen(SortedKeysDict({'time': 6804, 'latitude': 3600, 'longitude': 7200}))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(da.coords)\n",
- "display(da.dims)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Data variables\n",
- "### The code below list the data variables. \n",
- "There are a several in the dataset. We are interested in \"analysed_sst\"."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "data variables ['analysed_sst', 'analysis_error', 'sea_ice_fraction', 'mask']\n"
- ]
- }
- ],
- "source": [
- "print ('data variables', list(da.keys()))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Global Attributes\n",
- "#### Global attributes provide information about dataset as a whole. \n",
- "\n",
- "A few of the global attrubutes are important for you to decide if the dataset will work for your application: \n",
- "* \"geospatial_lat_min\" and \"geospatial_lat_max\" plus \"geospatial_lon_min\" and \"geospatial_lon_max\" give you the geographical range of the dataset. \n",
- "* \"time_coverage_start\" and \"time_coverage_end\" give you the time range covered by the dataset.\n",
- "* \"geospatial_lat_resolution and geospatial_lon_resolution\" the spatial resolution\n",
- "\n",
- "Attributes like \"comment\", \"summary\", and \"references\" can give you more information about how the dataset was made, how you may use the data, and the people and organizations to acknowledge if you use the data\n",
- "\n",
- "The code below lists these global attributes.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "latitude range: -89.975 to 89.975\n",
- "longitude range: -179.975 to 179.975\n",
- "time range: 2002-09-01T12:00:00Z to 2021-04-23T12:00:00Z\n",
- "spatial resolution-degrees: lat 0.05 lon 0.05\n",
- " \n",
- "The dataset summary is below\n",
- "This dataset is an aggregation of Science Quality STAR data (2002-2016) and Near Real Time OSPO data (2017-present). Analysed blended sea surface temperature over the global ocean using night only input data. An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary InfraRed (IR) and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05� spatial resolution.\n"
- ]
- }
- ],
- "source": [
- "print('latitude range:', da.geospatial_lat_min, 'to', da.geospatial_lat_max)\n",
- "print('longitude range:', da.geospatial_lon_min, 'to', da.geospatial_lon_max)\n",
- "print('time range:', da.time_coverage_start, 'to', da.time_coverage_end)\n",
- "print('spatial resolution-degrees:', \n",
- " 'lat', np.around(da.geospatial_lat_resolution, decimals=3), \n",
- " 'lon', np.around(da.geospatial_lon_resolution, decimals=3) \n",
- " )\n",
- "\n",
- "print(' ')\n",
- "print('The dataset summary is below')\n",
- "print(da.summary)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Download data from ERDDAP\n",
- "\n",
- "## For this exercise, the area we are interested in includes Monterey Bay, CA: \n",
- "* Latitude range: 32N, 39N\n",
- "* Longitude range: -124E, -117E\n",
- "* Time range June 3, 2020 to June 7, 2020 \n",
- "\n",
- "### Xarray makes it really easy to request a subset of a dataset using latitude, longitude, and time ranges using: \n",
- "* the \"sel\" function with the \"slice\" function \n",
- "\n",
- "```python\n",
- "sss=da['analysed_sst'].sel( \n",
- " latitude=slice(32., 39.), \n",
- " longitude=slice(-124, -117), \n",
- " time=slice('2020-06-03T12:00:00', '2020-06-07T12:00:00') \n",
- " ) \n",
- "```\n",
- "\n",
- "__Note: If the dataset had an altitude dimension, then an altitude slice would need to be added to the code, e.g.__ \n",
- "\n",
- "\n",
- "```python \n",
- " altitude=slice(0.0), \n",
- ")\n",
- "```\n",
- "\n",
- "### Let's put it in a function to make it more versatile. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_data(my_da, my_var,\n",
- " my_lt_min, my_lt_max, \n",
- " my_ln_min, my_ln_max, \n",
- " my_tm_min, my_tm_max\n",
- " ):\n",
- " \n",
- " my_data = my_da[my_var].sel(\n",
- " latitude=slice(my_lt_min, my_lt_max), \n",
- " longitude=slice(my_ln_min, my_ln_max), \n",
- " time=slice(my_tm_min, my_tm_max)\n",
- " )\n",
- " return my_data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Run the function with our geographical and time ranges"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "('time', 'latitude', 'longitude')\n",
- "dimension size (5, 140, 140)\n"
- ]
- }
- ],
- "source": [
- "\n",
- "lat_min = 32.\n",
- "lat_max = 39.\n",
- "lon_min = -124.\n",
- "lon_max = -117.\n",
- "time_min = '2020-06-03T12:00:00' # written in ISO format\n",
- "time_max = '2020-06-07T12:00:00' # written in ISO format\n",
- "my_var = 'analysed_sst'\n",
- "\n",
- "sst = get_data(\n",
- " da, my_var,\n",
- " lat_min, lat_max,\n",
- " lon_min, lon_max,\n",
- " time_min, time_max\n",
- " )\n",
- "\n",
- "# the sst data array is a subset if da\n",
- "print(sst.dims)\n",
- "print('dimension size', sst.shape)\n",
- "\n",
- "# uncomment the \"sst\" below to see the full data array info\n",
- "#sst"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualizing the data\n",
- "\n",
- "### Make a simple plot\n",
- "Xarray makes it easy to quickly view the data as a map. \n",
- "* Use isel to pick a time slice by its index number \n",
- "* imshow to plot the data\n",
- "* We have 5 time steps so the index numbers are 0 to 4. \n",
- "\n",
- "Plot the first time step: \n",
- "```\n",
- "sst.isel(time=0).plot.imshow()\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 72,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "for i in range(0,4):\n",
- " ax = plt.subplot()\n",
- " sst.isel(time=i).plot.imshow()\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Calculate the mean value for each day \n",
- "* Use matplotlib.pyplot.plot_date() plot routine, which formats the x axis as a date\n",
- "* Input sst.time for the x axis values\n",
- "* Use the numpy mean() function to take the mean of the latitude longitude grid ( axis=(1,2) ) each time slice."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 74,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot_date(sst.time, sst.mean(axis=(1,2)), 'o')\n",
- "\n",
- "# auto format the date label position on the x axis\n",
- "plt.gcf().autofmt_xdate()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Generate means for a ship station track \n",
- "\n",
- "## Load a station location file (.csv) with the time, latitude, and longitude coordinates for station locations\n",
- "\n",
- "For this example the CSV file has three columns, with each row locating a station in time and space: \n",
- "`sample_date\tsample_time\tsample_lat\tsample_lon` \n",
- "`6/3/20\t 18:00\t 37.3\t -123.2`\n",
- "\n",
- "### Load the CSV file data using the Pandas module \"read_csv\". \n",
- "To use the date and time, we will need to create a Pandas date object. Pandas has a nice option (parse_dates) to create a column date objects from date and time field as the CSV file is loaded. We will also keep the original sample_date and sample_time by setting the keep_date_col to True.\n",
- "\n",
- "### Xarray likes the date in a ISO 8601 date format, so we will also create an iso_date column"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
sample_date_sample_time
\n",
- "
sample_date
\n",
- "
sample_time
\n",
- "
sample_lat
\n",
- "
sample_lon
\n",
- "
iso_date
\n",
- "
\n",
- " \n",
- " \n",
- "
\n",
- "
0
\n",
- "
2020-06-03 18:00:00
\n",
- "
6/3/20
\n",
- "
18:00
\n",
- "
37.3
\n",
- "
236.8
\n",
- "
2020-06-03
\n",
- "
\n",
- "
\n",
- "
1
\n",
- "
2020-06-04 19:34:00
\n",
- "
6/4/20
\n",
- "
19:34
\n",
- "
36.1
\n",
- "
237.5
\n",
- "
2020-06-04
\n",
- "
\n",
- "
\n",
- "
2
\n",
- "
2020-06-05 20:15:00
\n",
- "
6/5/20
\n",
- "
20:15
\n",
- "
34.4
\n",
- "
238.2
\n",
- "
2020-06-05
\n",
- "
\n",
- "
\n",
- "
3
\n",
- "
2020-06-06 18:45:00
\n",
- "
6/6/20
\n",
- "
18:45
\n",
- "
32.2
\n",
- "
239.8
\n",
- "
2020-06-06
\n",
- "
\n",
- "
\n",
- "
4
\n",
- "
2020-06-07 12:52:00
\n",
- "
6/7/20
\n",
- "
12:52
\n",
- "
31.9
\n",
- "
240.9
\n",
- "
2020-06-07
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- " sample_date_sample_time sample_date sample_time sample_lat sample_lon \\\n",
- "0 2020-06-03 18:00:00 6/3/20 18:00 37.3 236.8 \n",
- "1 2020-06-04 19:34:00 6/4/20 19:34 36.1 237.5 \n",
- "2 2020-06-05 20:15:00 6/5/20 20:15 34.4 238.2 \n",
- "3 2020-06-06 18:45:00 6/6/20 18:45 32.2 239.8 \n",
- "4 2020-06-07 12:52:00 6/7/20 12:52 31.9 240.9 \n",
- "5 2020-06-08 14:23:00 6/8/20 14:23 31.9 241.8 \n",
- "\n",
- " iso_date \n",
- "0 2020-06-03 \n",
- "1 2020-06-04 \n",
- "2 2020-06-05 \n",
- "3 2020-06-06 \n",
- "4 2020-06-07 \n",
- "5 2020-06-08 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Load the file into a Pandas data frame\n",
- "# This create a date object column named 'sample_date_sample_time' \n",
- "track_DF = pd.read_csv('data/my_monterey_track.csv', parse_dates=[['sample_date', 'sample_time']], keep_date_col=True) \n",
- "\n",
- "\n",
- "# track_DF['iso_date'] = track_DF.sample_date_sample_time.dt.strftime('%Y-%m-%dT12:%M:%S')\n",
- "track_DF['iso_date'] = track_DF.sample_date_sample_time.dt.strftime('%Y-%m-%d')\n",
- "# Display the data\n",
- "display(track_DF)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Make the longitude format compatible with your dataset\n",
- "### Some datasets use a -180°E to +180°E format \n",
- "* The 0° longitude is at the prime meridian (the location of the Royal Observatory, Greenwich, in London, England). \n",
- "* Longitudes west of 0° have a negative value. \n",
- "* Longitudes east of 0° have a positive value\n",
- "\n",
- "### Some datasets use a 0°E to 360°E format \n",
- "* The 0° longitude is at the prime meridian \n",
- "* All longitudes have a positive value, increasing in value eastward of 0°. \n",
- "* Longitudes have a positive value\n",
- "\n",
- "### The longitude formats used in this example are different\n",
- "* Our station location longitudes above have values > 180, so they are using a 0 to 360 longitude format. \n",
- "* When we examined the global attributes for the satellite data, the longitude range showed a -18- t0 +180 format. \n",
- "longitude range: -179.975 to 179.975\n",
- "\n",
- "Your station location longitudes need to be in the same format as the dataset. We can write a quick script to check if the dataset or the station location file have negative number, indicating a -180 to +180 longitude. \n",
- "* If both longitude lists have the same format, then the station location longitude are returned unchanged.\n",
- "* If the dataset longitudes are -180 to 180 and the station location longitudes are 0-360, then 360 is subtracted from any station location longitude > 180\n",
- "* If the dataset longitudes are 0 to 360 and the station location longitudes are -180 to 180, then 360 is is added to any station location longitude < 0 \n",
- "\n",
- "### Create a function to correct the longitude format\n",
- "Where: \n",
- "dataset_lon is the longitude column from the xarray data array ( sst['longitude'] ) \n",
- "location_lon is the longitude column from the Pandas data frame, ( track_DF['sample_lon'] ) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "metadata": {},
- "outputs": [],
- "source": [
- "def is_it_180_to_180(dataset_lon, location_lon):\n",
- " #long_array = False\n",
- " #long_array = np.any((da.longitude > 0))\n",
- " datasat_is = np.any((dataset_lon < 0))\n",
- " location_is = np.any((location_lon < 0))\n",
- " if datasat_is and location_is:\n",
- " return location_lon\n",
- " \n",
- " elif not datasat_is and location_is:\n",
- " return [ x if x>0 else x+360 for x in location_lon ]\n",
- " elif datasat_is and not location_is:\n",
- " return [ x if x<=180 else x-360 for x in location_lon ]\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Apply the longitude format correction function\n",
- "Apply the dataset longitudes and the station location longitudes to the function and add the results to the Pandas data frame with the station information, with this useage:"
- ]
- },
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- " sample_date_sample_time sample_date sample_time sample_lat sample_lon \\\n",
- "0 2020-06-03 18:00:00 6/3/20 18:00 37.3 236.8 \n",
- "1 2020-06-04 19:34:00 6/4/20 19:34 36.1 237.5 \n",
- "2 2020-06-05 20:15:00 6/5/20 20:15 34.4 238.2 \n",
- "3 2020-06-06 18:45:00 6/6/20 18:45 32.2 239.8 \n",
- "4 2020-06-07 12:52:00 6/7/20 12:52 31.9 240.9 \n",
- "5 2020-06-08 14:23:00 6/8/20 14:23 31.9 241.8 \n",
- "\n",
- " iso_date sample_lon_corrected \n",
- "0 2020-06-03 -123.2 \n",
- "1 2020-06-04 -122.5 \n",
- "2 2020-06-05 -121.8 \n",
- "3 2020-06-06 -120.2 \n",
- "4 2020-06-07 -119.1 \n",
- "5 2020-06-08 -118.2 "
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- "execution_count": 80,
- "metadata": {},
- "output_type": "execute_result"
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- ],
- "source": [
- "track_DF['sample_lon_corrected'] = is_it_180_to_180(\n",
- " sst['longitude'], \n",
- " track_DF['sample_lon']\n",
- " )\n",
- "\n",
- "track_DF"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Select an area around each box to get satellite data\n",
- "* If you just use a single latitude and longitude pair to get the data, then the data from a single point will be returned. \n",
- "* To increase to chance getting data and improve your statistics, you can request a box of data around the station location, e.g. at latitude range and a longitude range. \n",
- "\n",
- "The box can increase by multiples of the geographical resolution of the dataset. For this dataset the spatial resolution was 0.05 degrees for the latitude and longitude. That is approximately 5 km resolution. So, a 5 pixel X 5 pixel box around each station location would result in a 25 x 25 km box (625 km^2) of data. \n",
- "\n",
- "Let's build a function to generate a latitude and longitude range box for each station location. The function asks for the following: \n",
- "* the station latitude and longitude\n",
- "* The latitude and longitude spatial resolution of the satellite dataset. We obtained these values from the satellite dataset metadata.\n",
- " * da.geospatial_lat_resolution\n",
- " * da.geospatial_lon_resolution\n",
- "* how many pixels you want added to each side of the latitude and longitude center point \n",
- " * For example if you choose \"2\" for the latitude, then you will get a latitude range that covers five pixels: the station latitude for the center plus two pixel on each side. If you also choose \"2\" for the longitude, then you would get a 5x5 box around the station location. \n",
- " * The default values for the function is \"1\", which is a 3x3 box \n",
- "\n",
- "Apply the dataset longitudes and the station location longitudes to the function below and add the results to the Pandas data frame with the station information, with this useage:\n",
- "```\n",
- "new_Pandas_dataframe_column = data_box(st_lat, st_lon, lat_res, lon_res, lat_pixels = 1, lon_pixels = 1)\n",
- "```\n",
- "where: \n",
- "* new_Pandas_dataframe_columns = track_DF['lat_min'], track_DF['lat_min'], track_DF['lon_min'], track_DF['lon_min']\n",
- "* st_lat = the latitude column from the Pandas station location data frame, track_DF['sample_lat']\n",
- "* st_lon = the longitude column from the Pandas station location data frame, track_DF['sample_lon_corrected']\n",
- " * make sure to use the corrected longitude column\n",
- "* lat_res = the spatial resolution for satellite latitude, da.geospatial_lat_resolution\n",
- "* lon_res = the spatial resolution for satellite longitude, da.geospatial_lon_resolution \n",
- "* lat_pixels = the number of pixels to add to each side of the latitude center point (default = 1) \n",
- "* lon_pixels = the number of pixels to add to each side of the longitude center point (default = 1)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "metadata": {},
- "outputs": [],
- "source": [
- "def data_box(\n",
- " st_lat, st_lon, \n",
- " lat_res, lon_res, \n",
- " lat_pixels = 1, lon_pixels = 1\n",
- " ):\n",
- " \n",
- " lt_min = st_lat - lat_res * lat_pixels\n",
- " lt_max = st_lat + lat_res * lat_pixels\n",
- " ln_min = st_lon - lon_res * lon_pixels\n",
- " ln_max = st_lon + lon_res * lon_pixels\n",
- "\n",
- " return lt_min, lt_max, ln_min, ln_max\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Apply the dataset longitudes and the station location longitudes to the function \n",
- "\n",
- "Add the results to the Pandas data frame with the station information, with this useage:\n",
- "\n",
- "new_Pandas_dataframe_column = data_box(st_lat, st_lon, lat_res, lon_res, lat_pixels = 1, lon_pixels = 1)\n",
- "where:"
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- " sample_date_sample_time sample_date sample_time sample_lat sample_lon \\\n",
- "0 2020-06-03 18:00:00 6/3/20 18:00 37.3 236.8 \n",
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- "5 2020-06-08 14:23:00 6/8/20 14:23 31.9 241.8 \n",
- "\n",
- " iso_date sample_lon_corrected lat_min lat_max lon_min lon_max \n",
- "0 2020-06-03 -123.2 37.2 37.4 -123.3 -123.1 \n",
- "1 2020-06-04 -122.5 36.0 36.2 -122.6 -122.4 \n",
- "2 2020-06-05 -121.8 34.3 34.5 -121.9 -121.7 \n",
- "3 2020-06-06 -120.2 32.1 32.3 -120.3 -120.1 \n",
- "4 2020-06-07 -119.1 31.8 32.0 -119.2 -119.0 \n",
- "5 2020-06-08 -118.2 31.8 32.0 -118.3 -118.1 "
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- "execution_count": 82,
- "metadata": {},
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- "source": [
- "# use the data_box function\n",
- "[\n",
- " track_DF['lat_min'], track_DF['lat_max'],\n",
- " track_DF['lon_min'], track_DF['lon_max']\n",
- "] = data_box(\n",
- " track_DF['sample_lat'], track_DF['sample_lon_corrected'],\n",
- " da.geospatial_lat_resolution, da.geospatial_lon_resolution,\n",
- " 2, 2\n",
- " )\n",
- " \n",
- "track_DF "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Get the data for each station location and generate some basic statistic\n",
- "#### We now have have functions and the input parameters needed to obtain satellite data for the cruise track\n",
- "* Use the sample_date_sample_time, lat_min, lat_max, lon_min, and lon_max columns from the track_DF in the get_data() function to download satellite data for each station.\n",
- "* Calculate the mean, standard deviation, and n stats, then add them to the track_DF"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "means [14.251244, 14.836244, 16.224995, 16.861244, 17.026245, 17.41437]\n",
- "stdevs [0.3615224, 0.12873788, 0.09499985, 0.048589565, 0.09873407, 0.10271109]\n",
- "ns [16, 16, 16, 16, 16, 16]\n"
- ]
- }
- ],
- "source": [
- "means = []\n",
- "stdevs = []\n",
- "ns = []\n",
- "\n",
- "for index, row in track_DF.iterrows():\n",
- " \n",
- " my_data = get_data(da, my_var,\n",
- " row['lat_min'], row['lat_max'], \n",
- " row['lon_min'], row['lon_max'], \n",
- " row['iso_date'], row['iso_date']\n",
- " )\n",
- " \n",
- " means.append(np.nanmean(my_data, axis=(1,2))[0])\n",
- " stdevs.append(np.nanstd(my_data, axis=(1,2))[0])\n",
- " ns.append(np.count_nonzero(my_data, axis=(1,2))[0])\n",
- "\n",
- "print('means', means)\n",
- "print('stdevs', stdevs)\n",
- "print('ns', ns)\n",
- "\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Add the statistics to the track_DF dataframe\n",
- "Uncomment the last line to save the dataframe as a CSV file called 'mbnms_results.csv'"
- ]
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14.836244
\n",
- "
0.128738
\n",
- "
16
\n",
- "
\n",
- "
\n",
- "
2
\n",
- "
2020-06-05 20:15:00
\n",
- "
6/5/20
\n",
- "
20:15
\n",
- "
34.4
\n",
- "
238.2
\n",
- "
2020-06-05
\n",
- "
-121.8
\n",
- "
34.3
\n",
- "
34.5
\n",
- "
-121.9
\n",
- "
-121.7
\n",
- "
16.224995
\n",
- "
0.095000
\n",
- "
16
\n",
- "
\n",
- "
\n",
- "
3
\n",
- "
2020-06-06 18:45:00
\n",
- "
6/6/20
\n",
- "
18:45
\n",
- "
32.2
\n",
- "
239.8
\n",
- "
2020-06-06
\n",
- "
-120.2
\n",
- "
32.1
\n",
- "
32.3
\n",
- "
-120.3
\n",
- "
-120.1
\n",
- "
16.861244
\n",
- "
0.048590
\n",
- "
16
\n",
- "
\n",
- "
\n",
- "
4
\n",
- "
2020-06-07 12:52:00
\n",
- "
6/7/20
\n",
- "
12:52
\n",
- "
31.9
\n",
- "
240.9
\n",
- "
2020-06-07
\n",
- "
-119.1
\n",
- "
31.8
\n",
- "
32.0
\n",
- "
-119.2
\n",
- "
-119.0
\n",
- "
17.026245
\n",
- "
0.098734
\n",
- "
16
\n",
- "
\n",
- "
\n",
- "
5
\n",
- "
2020-06-08 14:23:00
\n",
- "
6/8/20
\n",
- "
14:23
\n",
- "
31.9
\n",
- "
241.8
\n",
- "
2020-06-08
\n",
- "
-118.2
\n",
- "
31.8
\n",
- "
32.0
\n",
- "
-118.3
\n",
- "
-118.1
\n",
- "
17.414370
\n",
- "
0.102711
\n",
- "
16
\n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " sample_date_sample_time sample_date sample_time sample_lat sample_lon \\\n",
- "0 2020-06-03 18:00:00 6/3/20 18:00 37.3 236.8 \n",
- "1 2020-06-04 19:34:00 6/4/20 19:34 36.1 237.5 \n",
- "2 2020-06-05 20:15:00 6/5/20 20:15 34.4 238.2 \n",
- "3 2020-06-06 18:45:00 6/6/20 18:45 32.2 239.8 \n",
- "4 2020-06-07 12:52:00 6/7/20 12:52 31.9 240.9 \n",
- "5 2020-06-08 14:23:00 6/8/20 14:23 31.9 241.8 \n",
- "\n",
- " iso_date sample_lon_corrected lat_min lat_max lon_min lon_max \\\n",
- "0 2020-06-03 -123.2 37.2 37.4 -123.3 -123.1 \n",
- "1 2020-06-04 -122.5 36.0 36.2 -122.6 -122.4 \n",
- "2 2020-06-05 -121.8 34.3 34.5 -121.9 -121.7 \n",
- "3 2020-06-06 -120.2 32.1 32.3 -120.3 -120.1 \n",
- "4 2020-06-07 -119.1 31.8 32.0 -119.2 -119.0 \n",
- "5 2020-06-08 -118.2 31.8 32.0 -118.3 -118.1 \n",
- "\n",
- " mean stdev n \n",
- "0 14.251244 0.361522 16 \n",
- "1 14.836244 0.128738 16 \n",
- "2 16.224995 0.095000 16 \n",
- "3 16.861244 0.048590 16 \n",
- "4 17.026245 0.098734 16 \n",
- "5 17.414370 0.102711 16 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "track_DF['mean'] = means\n",
- "track_DF['stdev'] = stdevs\n",
- "track_DF['n'] = ns\n",
- "display(track_DF)\n",
- "\n",
- "# uncomment to save the data\n",
- "# track_DF.to_csv('crusetrack_results.csv', index = False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Extract data from an irregular bounding box\n",
- "Extract a timeseries of environmental data from an ERDDAP server within an irregular geographical bounding box (polygon), e.g. the boundaries of marine protected areaSimilar to the R rerddap-Xtractogon function\n",
- "\n",
- "## The example uses a polygon that outlines the Monterey Bay Marine Protected Area. \n",
- "\n",
- "* **Load a csv file (shelf_boundary_gom.csv) that defines the polygon's latitude and longitude coordinate** \n",
- "The polygon is a series of longitude and latitude coordinates. If you connect the coordinate pairs, like a dot-to-dot puzzle, you create an outline of the Monterey Bay Marine Protected Area."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
Longitude
\n",
- "
Latitude
\n",
- "
\n",
- " \n",
- " \n",
- "
\n",
- "
0
\n",
- "
-122.6275
\n",
- "
37.88108
\n",
- "
\n",
- "
\n",
- "
1
\n",
- "
-122.6273
\n",
- "
37.88096
\n",
- "
\n",
- "
\n",
- "
2
\n",
- "
-122.6269
\n",
- "
37.88097
\n",
- "
\n",
- "
\n",
- "
3
\n",
- "
-122.6265
\n",
- "
37.88077
\n",
- "
\n",
- "
\n",
- "
4
\n",
- "
-122.6260
\n",
- "
37.88077
\n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " Longitude Latitude\n",
- "0 -122.6275 37.88108\n",
- "1 -122.6273 37.88096\n",
- "2 -122.6269 37.88097\n",
- "3 -122.6265 37.88077\n",
- "4 -122.6260 37.88077"
- ]
- },
- "execution_count": 94,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Load CSV into Pandas\n",
- "myPolygon = pd.read_csv('shapes/mbnms.csv')\n",
- "\n",
- "# list the first 5 values as an example\n",
- "myPolygon.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create a shape from the polygon using the \"shapely\" module"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 95,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 95,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import shapely.vectorized\n",
- "from shapely.geometry import Polygon\n",
- "\n",
- "# Reform the csv data into lon, lat pairs\n",
- "poly = list(zip(list(myPolygon.Longitude), list(myPolygon.Latitude)))\n",
- "\n",
- "# create shape\n",
- "polyshape = Polygon(poly)\n",
- "polyshape"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create a mask from the polygon object\n",
- "* Make a copy of the SST data array\n",
- "* Turn the array it a mask layer by finding all of pixels that are inside the polygon. Give these pixels a value of True. Cells outside the polygon have a value of False.\n",
- "* Plot the mask on a map to see its location."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Flatten and make a list of lomn/lat pairs. Transpose\n",
- "# Make 2D grids of the lat and lon vectors\n",
- "p_lons, p_lats = np.meshgrid(sst.longitude, sst.latitude)\n",
- "\n",
- "mask = shapely.vectorized.contains(polyshape, p_lons, p_lats)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create a masked version of the sst data area \n",
- "### Data outside of the Monterey Bay National Marine Sanctuary is masked out.\n",
- "Plot the results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "\n",
- "plt.plot_date(sst_masked.time, masked_sst_df[\"mean\"], 'o')\n",
- "plt.gcf().autofmt_xdate()\n",
- "\n",
- "# uncomment to save the data\n",
- "# track_DF.to_csv('mbnms_results.csv', index = False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Appendix"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Installation instruction are at these links\n",
- "pandas: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html \n",
- "numpy: https://numpy.org/install/ \n",
- "xarray: http://xarray.pydata.org/en/latest/getting-started-guide/installing.html "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "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": "3.7.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/setup_py.md b/setup_py.md
new file mode 100644
index 0000000..4a942ba
--- /dev/null
+++ b/setup_py.md
@@ -0,0 +1,24 @@
+# Setting up a Python environment
+
+## Setup and test your python environment for the CoastWatch Satellite Course
+
+1. Make sure you have __python 3__ and __conda__ installed on your machine
+2. Download this repository.
+ * Use the green __Code__ dropdown to select __Download Zip__ and unzip to a location on your computer
+ * Or [use this zip file link](https://github.com/CoastWatch-WestCoast/python_code/archive/refs/heads/satellite_course_jan_2022.zip)
+3. Use a terminal window to navigate to the unzipped folder
+
+The following commands:
+* Update conda
+* Create a new conda environment named 'coastwatch' and load the required modules to it
+* Activate the environment
+* Runs a script that checks for any missing modules
+* Launches jupyter-lab for displaying the jupyter notebook tutorials
+
+```
+conda update conda
+conda env create -f environment.yml
+conda activate coastwatch
+python check_modules.py
+jupyter-lab
+```
diff --git a/virtual_buoy_example_geopolar.ipynb b/virtual_buoy_timeseries.ipynb
similarity index 67%
rename from virtual_buoy_example_geopolar.ipynb
rename to virtual_buoy_timeseries.ipynb
index bc3ccf5..e30cd28 100644
--- a/virtual_buoy_example_geopolar.ipynb
+++ b/virtual_buoy_timeseries.ipynb
@@ -4,75 +4,51 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Make a virtual buoy with satellite data \n",
+ "
CoastWatch Python Exercises
\n",
+ "\n",
+ "# Create a virtual buoy from satellite data\n",
+ "> history | updated August 2021 \n",
+ "> owner | NOAA CoastWatch\n",
+ "\n",
"## Background\n",
- "There are buoys in many locations around the world that provide data streams of oceanic and atmospheric parameters. The data are often available through data centers like the those National Data Buoy Center (NDBC https://www.ndbc.noaa.gov) and ARGO floats program (http://www.argo.ucsd.edu). Despite these impressive efforts to monitor environmental conditions, in situ buoy data may not be available for your area of interest. Some locations are hard to access, making deploying and maintaining a buoy impractical. In addition, buoys are expensive to purchase, deploy and maintain. Therefore, limited funding may prevent installation of a buoy in the first place or the continued operation of a buoy already in place. Using satellite data to create virual buoys can provide a solution to monitoring surface environmental conditions at locations where it is not feasible to install a buoy. For example, the University of South Florida has developed a virtual buoy system for locations off the Florida coast (https://optics.marine.usf.edu/projects/vbs.html). \n",
+ "There are buoys in many locations around the world that provide data streams of oceanic and atmospheric parameters. The data are often available through data centers like the National Data Buoy Center (NDBC https://www.ndbc.noaa.gov) and the ARGO floats program (http://www.argo.ucsd.edu). *In-situ* buoy data may not be available for your area of interest due to many factors that present challenges to maintaining long-term in-situ sensors. Using satellite data to create virtual buoys can offer a solution by providing surface environmental conditions at locations where it is not feasible to install a buoy. For example, the University of South Florida has developed a virtual buoy system for locations off the Florida coast (https://optics.marine.usf.edu/projects/vbs.html). \n",
"\n",
"## Overview \n",
- "The following exercise will demonstrate the use of the ERDDAP data server to create a virtual buoy. For the scenario, we will envision that a virtual buoy is need to continue the datastream for an *in situ* buoy that was discontinued at the end of 2019. For this exercise we will use the National Data Buoy Center (NDBC) buoy # 49259, which is located off the California coast at 34.767N latitude and -121.497E longitude, and pretend that it was discontinued at the end of 2019. The buoy measures several oceanic variables, but we will continue the sea surface temperature (SST) datastream using NOAA GeoPolar Blended SST satellite dataset. \n",
+ "The following exercise will demonstrate the use of the ERDDAP data server to create a virtual buoy from satellite sea surface temperature data. For this scenario, a virtual buoy is needed to continue the datastream for an *in situ* buoy that was discontinued at the end of 2019. For this exercise we will use the National Data Buoy Center (NDBC) buoy # 49259, which is located off the California coast at 34.767N latitude and -121.497E longitude, and pretend that it was discontinued at the end of 2019. The buoy measures several oceanic variables that are all discontinued, but we will demonstrate using satellite data to continue the sea surface temperature datastream forward. \n",
" \n",
"### Steps in the exercise \n",
- "* __Create the virtual buoy using the ERDDAP server__ \n",
+ "1. __Create the virtual buoy using the ERDDAP server__ \n",
"\n",
- "* __Bring the virtual buoy data into Python and visualize__ \n",
+ "2. __Bring the virtual buoy data into Python and visualize it__ \n",
"\n",
- "* __Groundtruth the satellite data__ \n",
- " * Download buoy data and virtual buoy (satellite) data for the period from Jan. 2017 through Dec. 2019\n",
+ "3. __Groundtruth the satellite data__\n",
+ " * Download *in situ* buoy data and virtual buoy (satellite) data for the period from Jan. 2017 through Dec. 2019\n",
" * Resample the buoy data to match the temporal resolution of the virtual buoy data\n",
- " * Run a linear regression to model the relationship between virtual buoy SST and buoy SST data\n",
+ " * Run a linear regression to model the relationship between virtual buoy SST and *in situ* buoy SST data\n",
"\n",
- "* __Plot the results__\n",
+ "4. __Plot the results__\n",
"\n",
"\n",
"### The exercise demonstrates the following skills:\n",
"\n",
- "* __The use of ERDDAP to create a virtual buoy__ \n",
- "* __The use of the Pandas module to import and manipulate data__ \n",
- "* __Resampling data to bin into a lower resolution timestep__ \n",
- "* __Generating a linear regression model and statistics__\n",
- "* __Several approaches to plotting timeseries data__ \n",
- "* __Cleaning data to remove outlying data points__"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Steps involed in this exercise \n",
- "### Create the virtual buoy with ERDDAP \n",
- "\n",
- "__Visualize the data with a plot__ \n",
- "\n",
- "__Load buoy data obtained from the National Data Buoy Center (NDBC) website__ \n",
- "We will load buoy data store in a CSV file (buoy_46259_2016_2019.csv) into a Pandas dataframe. \n",
- "* The data were obrtained from the NDBC buoy #46259 (https://www.ndbc.noaa.gov/station_history.php?station=46259). \n",
- "* The buoy is at 34.767N latitude and the -121.497E longitude \n",
- "The file contains sampling date/time, mean wind direction, and surface water temperature for the years 2016-2019.\n",
- "* The sampling resolution for the data is a sample every 30 minutes \n",
- "\n",
- "__Resample the buoy data to match the temporal resolution of the satellite data__ \n",
- "The sampling resolution for the buoy data is a sample every 30 minutes. However, the temporal resolution for the satellite dataset is daily. We will downsample the buoy data by generating daily means in order to match the temporal resolution of the satellite. \n",
- "\n",
- "\n",
- "__Use the ERDDAP data server to subsample and download the satellite data__ \n",
- "We will generate ERDDAP URLs to subset the satellite data at the latitude and longitude of the buoy and for each day in the downsampled buoy dataset and then download the data. \n",
- "\n",
- "* Generate the ERDDAP data request URLs \n",
- "* Download the data and load it into a Pandas dataframe.\n",
- "\n",
- "__Plot buoy data and overlay the satellite data__"
+ "* The use of ERDDAP to create a virtual buoy \n",
+ "* The use of the Pandas module to import and manipulate data \n",
+ "* Resampling data to bin into a lower resolution timestep \n",
+ "* Generating a linear regression model and statistics\n",
+ "* Several approaches to plotting timeseries data \n",
+ "* Cleaning data to remove outlying data points"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Import some require Python modules"
+ "## Import some required Python modules"
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -83,327 +59,98 @@
"import os\n",
"import pandas as pd\n",
"import io\n",
- "import requests"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a virtual buoy with ERDDAP\n",
- "#### Selecting a dataset\n",
- "A good dataset for sea surface temperature (SST) is the NOAA GeoPolar Blended SST dataset (ERDDAP ID: `nesdisGeoPolarSSTN5SQNRT`). It blends together many satellite sensors to obtain good daily coverage of the globe, and then uses an interpellation method to fill in data gaps. \n",
- "#### Query ERDDAP for information about the satellite dataset \n",
- "ERDDAP has a feature that allows you to select and download metadata about any and all datasets. Click the link below to bring up the user interface for this feature: https://coastwatch.pfeg.noaa.gov/erddap/tabledap/allDatasets.html. \n",
- "\n",
- "Like all data in ERDDAP, even metadata, the values can be downloaded via a URL-based data request. Details about the feature are presented in the ERDDAP tutorial. For the purposes of this exercise, it is enough to know that we are pulling the metadata from this page into a CSV file and using it in Python\n",
- "\n",
- "__Build a function to harvest metadata from ERDDAP__ \n",
- "\n",
- "* The function builds the URL-based data request, sends the request to ERDDAP for the metedata, and loads the results into a Pandas data frame.\n",
- "* The function also uses the xarray module in OpenDAP mode to obtain a list of data variables.\n",
- "* Inputs to the function are \n",
- " 1. The location of the ERDDAP on the Internet: The NOAA GeoPolar blended SST dataset can be found on the CoastWatch West Coast Node ERDDAP at this URL: `https://coastwatch.pfeg.noaa.gov/erddap`\n",
- " 2. The unique ERDDAP ID for the dataset: For the NOAA GeoPolar blended SST dataset the ID is `nesdisGeoPolarSSTN5SQNRT`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Variable containing the WCN ERDDAP URL\n",
- "my_erddap_location = 'https://coastwatch.pfeg.noaa.gov/erddap'\n",
- "\n",
- "# The dataset ID for NOAA GeoPolar blended SST \n",
- "my_ds_ID = 'nesdisGeoPolarSSTN5SQNRT'\n",
+ "import requests\n",
"\n",
- "# A function to form the metadata request URL and \n",
- "# download the metadata into a Pandas data frame\n",
- "def get_dataset_info(erddapURL, datasetID):\n",
- " queryURL = \"\".join(['?datasetID,minLongitude,maxLongitude,longitudeSpacing,minLatitude,maxLatitude,latitudeSpacing,', \n",
- " 'minAltitude,maxAltitude,', \n",
- " 'minTime,maxTime,timeSpacing,griddap&datasetID=\"'])\n",
- " \n",
- " # Form the base URL leading to the allDatasets page\n",
- " baseURL = '/'.join([erddapURL, \"tabledap\", \"allDatasets.csv\"])\n",
- " \n",
- " # Form the data query part of the URL. \n",
- " # For this exercise we will only request: \n",
- " # 1) dataset ID, \n",
- " # 2) the distance between values longitude values, \n",
- " # 3) the minimum and maximum latitude, and the distance between values\n",
- " # 4) the minimum and maximum altitude values\n",
- " # 5) the minimum and maximum and time between time values\n",
- " fullURL = ''.join([baseURL, queryURL, datasetID,'\"'])\n",
- " \n",
- " s=requests.get(fullURL).content\n",
- " c=pd.read_csv(io.StringIO(s.decode('utf-8')), skiprows=[1])\n",
- " \n",
- " #print(c.griddap[0])\n",
- " # get the data variables using xarray via OpenDAP \n",
- " c['variables'] = [list(xr.open_dataset(c.griddap[0]).data_vars)]\n",
- " return c\n"
+ "np.warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "__Run the function and view the results__ "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ],
- "text/plain": [
- " griddap \\\n",
- "0 https://coastwatch.pfeg.noaa.gov/erddap/gridda... \n",
- "\n",
- " variables \n",
- "0 [analysed_sst, analysis_error, sea_ice_fractio... "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Metadata helpful for this project are listed below\n",
- " \n",
- "Time Range: 2002-09-01 to 2021-04-22\n",
- "Data Variables: ['analysed_sst', 'analysis_error', 'sea_ice_fraction', 'mask']\n",
- "Direct dataset URL: https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT\n"
- ]
- }
- ],
- "source": [
- "dataset_info = get_dataset_info(my_erddap_location, my_ds_ID)\n",
- "\n",
- "display(dataset_info.loc[:, 'datasetID':'latitudeSpacing'])\n",
- "display(dataset_info.loc[:, 'minAltitude':'timeSpacing'])\n",
- "display(dataset_info.loc[:, 'griddap':'variables'])\n",
- "\n",
- "print(\"Metadata helpful for this project are listed below\")\n",
- "print(\" \")\n",
- "print('Time Range:', dataset_info.minTime[0][:10], 'to', dataset_info.maxTime[0][:10] )\n",
- "print('Data Variables:', dataset_info.variables[0] )\n",
- "print('Direct dataset URL:', dataset_info.griddap[0]) "
+ "## 1. CREATE THE VIRTUAL BUOY "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Create a virtual buoy with ERDDAP\n",
- "We can make a virtual buoy very quickly using the ERDDAP visualization (make a graph) interface. \n",
+ "We can make a virtual buoy from satellite data very quickly online using ERDDAP's web visualization (make a graph) interface. \n",
+ "### Selecting a satellite dataset \n",
"\n",
- "### Selecting a dataset and generating a timeseries\n",
+ "One satellite dataset for sea surface temperature (SST) is the NOAA GeoPolar Blended SST dataset (ERDDAP ID: nesdisGeoPolarSSTN5SQNRT). It blends together many satellite sensors to obtain good daily coverage of the globe, and then uses an interpolation method to fill in spatial data gaps. \n",
"\n",
- "A good dataset for sea surface temperature (SST) is the NOAA GeoPolar Blended SST dataset. It blends together many satellite sensors to obtain good daily coverage of the globe, and then uses an interpellation method to fill in data gaps. On a separate can view the dataset on the ERDDAP interface (ERDDAP ID: `nesdisGeoPolarSSTN5SQNRT`). Click on the following link to go to the ERDDAP visualization page for the dataset: \n",
+ "Use a web browser to go to ERDDAP's visualization page for the GeoPolar Blended SST dataset: \n",
"https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph. \n",
"\n",
- "The NBDC buoy is located at 34.767N latitude and the -121.497E longitude, so we will use those coordinates to view and download data from ERDDAP. Please review the ERDDAP basics tutorial for a refresher on using ERDDAP. \n",
+ "We will create a virtual buoy at the location of an NDBC buoy to compare the buoy timeseries of the satellite data timeseries. The NBDC buoy is located at 34.767N latitude and the -121.497E longitude, so we will use those coordinates to view and download data from ERDDAP. \n",
+ "\n",
+ "*For an introduction to the ERDDAP data server, please see this online tutorial: \n",
+ "https://github.com/CoastWatch-WestCoast/ERDDAP-basics/blob/master/Readme.md*\n",
"\n",
- "__On the ERDDAP visualization interface, do the following:__ \n",
- "* Set the `Graph Type` to linsAndMarkers\n",
+ "### Generating a time-series of satellite data\n",
+ "__Using the ERDDAP visualization interface, do the following:__ \n",
+ "* Set the `Graph Type` to linesAndMarkers\n",
"* Set the `X Axis` to `time` and the `Y Axis` to `analysed_sst`\n",
- "* On the time (UTC) widget, set ` Start` to the Jan. 1, 2020 (`2020-01-01T12:00:00Z`) and the `Stop` to the most recent date (`2020-04-01T12:00:00Z` at the time time of this writing).\n",
+ "* On the time (UTC) widget, set ` Start` to Jan. 1, 2020 (`2020-01-01T12:00:00Z`) and `Stop` to the most recent date (`2020-04-01T12:00:00Z` at the time time of this writing).\n",
"* Set the `latitude (degrees_north)` to `34.767`, the latitude of our buoy, and the `longitude (degrees_east)` to `-121.497`, the longitude of our buoy. \n",
"* Click `Redraw the Graph` \n",
- "* For a review of how to use the ERDDAP data server, please see this online tutorial:\n",
- "https://coastwatch.pfeg.noaa.gov/projects/erddap/\n",
+ "\n",
"\n",
"The data for the virtual buoy will be displayed in the interface:\n",
"\n",
"\n",
"### You are finished making a virtual buoy! \n",
"\n",
- "This is your virtual buoy. If you wanted, you could get to produce an image for you to put on your virtual buoy website. However, you probably want to bring the data into Python to make better plots and to go through some validation steps. \n"
+ "* This is your virtual buoy. You have extracted satellite data at the location of your buoy and plotted a time-series of the satellite sst at this location. \n",
+ "* For this exercise we wil bring the data into Python to go through some validation steps and make better plots. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Load the virtual buoy data into Python \n",
- "Let's download the virtual buoy data directly from ERDDAP and into Python \n",
+ "## 2. BRING VIRTUAL BUOY DATA INTO PYTHON AND VISUALIZE IT\n",
+ "Next we will download the satellite SST virtual buoy data directly from ERDDAP and into Python. First we will get the url to the data from the using the ERDDAP web interface. Then we will use the url to request the data in csv format and load it into pandas. \n",
"\n",
- "### Generate the ERDDAP data request URL for the data \n",
- "* Go to the ERDDAP visualization page for where you created the virtual buoy. \n",
- "* Near the top of the ERDDAP interface, click the `Data Access Form` link. Clicking brings you to a new page where you can refine your data request. Our goal on this page is to obtain the data request URL to download the data as a CSV file. \n",
- "* Find the `File type:` dropdown and select `.csv`. Then push the \"Just `generate the URL button` and copy the link in the box to the right for th button. \n",
+ "### Generate the ERDDAP data request URL for the satellite data \n",
+ "* Go to the ERDDAP visualization page where you created the virtual buoy in Step 1. \n",
+ "(https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph?analysed_sst[(2020-01-01T12:00:00Z):(2020-04-01T12:00:00Z)][(34.775)][(-121.475)]&.draw=linesAndMarkers&.vars=time|analysed_sst|&.marker=5|5&.color=0x000000&.colorBar=|||||&.bgColor=0xffccccff) \n",
"\n",
- "__The data request URL should look like the following URL:__ \n",
+ "\n",
+ "* Near the top of the ERDDAP interface, click the `Data Access Form` link. \n",
+ "* The ERDDAP `Data Access Form` page allows you to refine your data request. Our goal on this page is to obtain the data request URL to download the data as a __.CSV__ file. \n",
+ "* Use the `File type:` dropdown and select `.csv`. Then click on the `Just generate the URL` button.\n",
+ "* Copy the url link that appears in the box to the right of the button. \n",
+ "\n",
+ "__The generated data request URL should look like this:__ \n",
">https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(2020-04-01T12:00:00Z)][(34.775):1:(34.775)][(-121.475):1:(-121.475)] \n",
"\n",
"__Getting the most up-to-date data__ \n",
- "At the time this tutorial was written, the most recent date was `2020-04-01T12:00:00Z` (April 1, 2020). However, you may be using the tutorial months later, so hardwiring `2020-04-01T12:00:00Z` into the URL will not give you the most recent data. We can use an ERDDAP trick to fix this problem. By changing the the most recent date (`2020-04-01T12:00:00Z`) to `last`, you will request to most recent data. The new URL looks like the following: \n",
+ "Using the `last` function to always request the latest data:\n",
+ "* We can use an ERDDAP feature to set the end date to be the most recent date available instead of hardcoding an actual date. \n",
+ "* At the time this tutorial was written, the most recent data available was from `2020-04-01T12:00:00Z` (April 1, 2020). \n",
+ "* By changing the end date (`2020-04-01T12:00:00Z`) to `last`, you will request to most recent data. \n",
+ "* The new URL looks like the following: \n",
">https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(last)][(34.775):1:(34.775)][(-121.475):1:(-121.475)] \n",
"\n",
"__Bring the data into a Pandas data frame__ \n",
- "You could put this URL directly into a browser and the CSV data file would download to your computer, but we will bring it directly into Python as a Pandas data frame.\n",
+ "* You could put this URL directly into a browser and the CSV data file would download to your computer.\n",
+ "* We will use the url to bring the csv data into Python as a Pandas data frame.\n",
"\n",
- "### Create a function to load the CSV data into Pandas \n",
- "We will be making several request to the ERDDAP server for CSV data that we will put into Pandas. Creating a function will save repeating the same code over again. Instead, just call the function. "
+ "### Create a python function to load the CSV data into Pandas \n",
+ "* We will be making several requests to the ERDDAP server for CSV data that we will put into Pandas data frames. \n",
+ "* Creating a function will save repeating the same code over again. \n",
+ "* Here we create a `get_csvdata` function. \n",
+ "* Pass this function the ERDDAP file url and it will return the data in a pandas data frame. "
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
- "# import the needed modules\n",
- "\n",
"def get_csvdata(erddapURL):\n",
" \n",
" '''\n",
@@ -438,13 +185,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Use the function to create the Pandas data frame with Virtual Buoy data\n",
- "The beginning of the resulting is printed below."
+ "### Get the satellite virtual buoy data\n",
+ "* Call the get_csvdata function with the ERDDAP URL\n",
+ "* It may take a while to retrieve the csv file, depending on your data request size and your network bandwidth.\n",
+ "* The data are reurned as a pandas data frame\n",
+ "* The first lines of the data frame will print once the data is retrieved"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -523,17 +273,16 @@
"4 2020-01-05T12:00:00Z 34.775 -121.475 13.869994"
]
},
- "execution_count": 5,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "erddap_data_request = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(last)][(34.775):1:(34.775)][(-121.475):1:(-121.475)]'\n",
- "# print(erddap_data_request, 'a')\n",
+ "erddap_data_request = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(2021-01-01T12:00:00Z)][(34.775):1:(34.775)][(-121.475):1:(-121.475)]'\n",
+ "\n",
"vir_buoy_df = get_csvdata(erddap_data_request)\n",
"\n",
- "# The head method prints out just the top 5 lines of the dataframe\n",
"vir_buoy_df.head()"
]
},
@@ -541,23 +290,20 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Clean up the data \n",
- "__The latitude and longitude columns are repetitious.__ \n",
- "\n",
- "1. Store the latitude and longitude values in a dictionary. \n",
- " \n",
+ "### Prepare the virtual buoy data for analysis and plotting \n",
+ "Clean up a bit. Put the lat/lon values into a dictionary and remove the latitude and longitude columns which are repetitive since we are looking at one single location. \n",
"\n",
- "2. Remove the latitude and longitude columns from the data frame with the Panda drop() function. The drop() function removes rows or columns by specifying label names and the axis containing the labels. \n",
- "\n",
- "\n",
- "* The first argument is the column to drop, by name. \n",
- "* The axis argument indicates if the name is a `row` (0) or a `column` (1). \n",
- "* The argument inplace=True argument applies the row or column deletion to the data frame in use (e.g. vir_buoy_df in this case). By default (inplace=False), a new dataframe would be created. \n"
+ "1. Create the dictionary with the location\n",
+ "2. Remove the latitude and longitude columns from the data frame with the Panda drop() function. \n",
+ " * The drop() function removes rows or columns by specifying label names and the axis containing the labels. \n",
+ " * The first argument is the column to drop, by name. \n",
+ " * The axis argument indicates if the name is a `row` (0) or a `column` (1). \n",
+ " * The argument inplace=True argument applies the row or column deletion to the data frame in use (e.g. vir_buoy_df in this case). By default (inplace=False), a new dataframe would be created. \n"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -624,7 +370,7 @@
"4 2020-01-05T12:00:00Z 13.869994"
]
},
- "execution_count": 6,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -645,19 +391,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Visualize the virtual buoy data from the satellite\n",
+ "## Visualize the virtual buoy data \n",
"Use the matplotlib python library. There are many ways to use matplotlib to plot data. Below is a simple approach. \n",
">Documentation for matplotlib can be found here: https://matplotlib.org"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"
"
]
@@ -673,10 +419,10 @@
"import matplotlib.pyplot as plt\n",
"import matplotlib.dates as mdates\n",
"\n",
- "# the %matplotlib is a magic function allow displaying results in notebooks\n",
+ "# the %matplotlib is a magic function that allows displaying results in notebooks\n",
"%matplotlib inline\n",
"\n",
- "# some tools for Pandas to work will with matplotlib\n",
+ "# some tools for Pandas to work well with matplotlib\n",
"from pandas.plotting import register_matplotlib_converters\n",
"register_matplotlib_converters()\n",
"\n",
@@ -711,32 +457,29 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Validate the Virtual Buoy measurements\n",
- "How confident are we that the virtual buoy measurement are similar to the buoy measurements? Validating or groundtruthing the virtual buoy data is an important step to be certain that your virtual buoy is accurate. Since both the buoy and the satellite data extend back in time before 2020, we can compare a few years of data (2017-2019) from both datastream to determine how well they correlate. \n",
+ "## 3. GROUNDTRUTH VIRTUAL BUOY MEASUREMENTS\n",
+ "How confident are we that the satellite SST measurements are similar to the *in situ* SST buoy measurements? Validating or groundtruthing the virtual buoy data is an important step to understand the accuracy of the data. Since both the *in situ* and the satellite data extend back in time before 2020, we can compare a few years of data (2017-2019) from both data streams to see how well they correlate. \n",
"\n",
- "To do that, we will do the following:\n",
- "* Download the data from NDBC buoy #49259 for the time period between Jan 1, 2017 and December 31, 2019. \n",
- "* Download the data from the satellite SST dataset for the time period same time period. \n",
- "* Evaluate, both visually and statistically, how close the two data stream are two one another. "
+ "We will do the following to compare the two data streams:\n",
+ "* Load new data from NDBC buoy #49259 for the time period between Jan 1, 2017 and December 31, 2019. \n",
+ "* Download data from the satellite SST dataset for the same time period. \n",
+ "* Evaluate, both visually and statistically, how the two data streams compare to one another. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Download the data from NDBC buoy\n",
- "\n",
- "#### Load the buoy data for 2017-2019 \n",
+ "### Load 2017-2019 NDBC buoy data\n",
+ " \n",
+ "For this portion of the training, we have created a .csv file of the buoy data from 2017-2019 (buoy_49259_2016_2019.csv). The buoy data was downloaded from the NDBC website. You can see the python code for that data download in Appendix A below. \n",
"\n",
- "* The data for 2017-2016 has been made available for you already in a CSV file (buoy_49259_2016_2019.csv). \n",
- "* If you prefer, you can get the data yourself via the NBDC website. \n",
- "https://www.ndbc.noaa.gov/download_data.php?filename=46259h2017.txt.gz&dir=data/historical/stdmet/ \n",
- "Steps on how to get the data with Python are included in Appendix A"
+ "* Load the buoy data file into a Pandas data frame and preview it. This may take a while - the csv file is 1.2 MB.\n"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -810,15 +553,14 @@
"4 2017-01-01 02:00:00 13.0"
]
},
- "execution_count": 8,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# load the buoy data file into a Pandas data frame and view\n",
- "buoy_data = pd.read_csv('data/buoy_46259_2017_2019.csv', parse_dates=['Time'])\n",
- "#buoy_data['Time'] = buoy_data['Time'].dt.date\n",
+ "buoy_data = pd.read_csv('./data/buoy_46259_2017_2019.csv', parse_dates=['Time'])\n",
+ "\n",
"print('# of timesteps =', buoy_data.shape[0] )\n",
"buoy_data.head()\n"
]
@@ -827,28 +569,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Plot the full resolution buoy data \n",
- "Use a slightly different method to plot the data than we used above. The plotting uses the plot() function, which is an integration of the `matplotlib` library into Pandas."
+ "### Plot the full resolution *in-situ* buoy data \n",
+ "This plot uses the plot() function, which is an integration of the `matplotlib` library into Pandas."
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[]"
+ "[]"
]
},
- "execution_count": 9,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"
"
]
@@ -871,8 +613,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Resample the buoy data\n",
- "The sampling resolution for the buoy data is a sample every 30 minutes. However, the temporal resolution for the satellite dataset is daily. Pandas makes it easy the downsample the 30 minute buoy data to create daily bins of the data that match the temporal resolution of the satellite. \n",
+ "### Resample the *in situ* buoy data\n",
+ "The sampling resolution for the buoy data is 30 minutes. However, the temporal resolution for the satellite dataset is daily. Pandas makes it easy the downsample the 30 minute buoy data to create daily bins of the data that match the temporal resolution of the satellite. \n",
"\n",
"Use the Pandas `resample` method (below) to generate daily means. \n",
"* The \"D\" is the resampling rule that sets the resampling interval. Here, \"D\" indicates resampling into daily bins. Similarly, using \"3D\" would resample into three-day bin and \"M\" would resample into monthly bins. \n",
@@ -883,7 +625,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -957,7 +699,7 @@
"2017-01-05 12.968750"
]
},
- "execution_count": 10,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -977,10 +719,10 @@
"source": [
"*Note 1: The resampled data frame (above) contains mean daily values. The \"Time\" column is now the index (first) column and it is in the Timestamp (datetime64) format.* \n",
"\n",
- "*Note 2: The length the the data frame (# of time steps) is 1398.* \n",
+ "*Note 2: The length of the data frame (# of time steps) is 1095.* \n",
"\n",
- "### Clean up the data\n",
- "The data is likely to contain data points that are outliers and that contain no data. Let's remove those points from the data frame. \n",
+ "### Clean up the *in-situ* data\n",
+ "The data is likely to contain data points that are outliers or that contain no data. Let's remove those points from the data frame. \n",
"* Apply a conservative allowable data range. \n",
" * For the lower end of the range, the freezing point of seawater (ca. -2). \n",
" * For the high end of the range, value unlikely to be seen in the area of interest (e.g. 45 degrees C). "
@@ -988,7 +730,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -997,7 +739,7 @@
"text": [
"# of timesteps before cleaning = 1095\n",
"# of timesteps after cleaning = 1091\n",
- "4 times were removed\n"
+ "4 time steps were removed\n"
]
}
],
@@ -1019,7 +761,7 @@
"# Drop values outside of the acceptable range from the data frame \n",
"buoy_data_resampled.drop(index_names, inplace=True)\n",
"print('# of timesteps after cleaning =', buoy_data_resampled.shape[0] )\n",
- "print(b4-buoy_data_resampled.shape[0], 'times were removed')\n",
+ "print(b4-buoy_data_resampled.shape[0], 'time steps were removed')\n",
"# buoy_data_resampled.head()"
]
},
@@ -1027,30 +769,30 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "__Note that the length the the data frame (# of time steps) went from 1095 to 1091, i.e. four timesteps were outside of the acceptable time range, and they were removed__. \n",
+ "__Note that the length of the data frame (# of time steps) went from 1095 to 1091, i.e. four timesteps were outside of the acceptable time range, and they were removed__. \n",
"\n",
"\n",
- "### Plot the results for the downsampled buoy data. \n"
+ "### Plot the cleaned and downsampled *in situ* buoy data \n"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[]"
+ "[]"
]
},
- "execution_count": 12,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"
"
]
@@ -1106,23 +848,32 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Download the data from the NOAA GeoPolar Blended SST dataset\n",
- "#### Query ERDDAP for information about the satellite dataset \n",
- "We will continue to use the NOAA GeoPolar blended SST dataset to validate the virtual buoy. ERDDAP has a feature that allows you to select and download metadata about any and all datasets. The user interface for this feature is at the following URL: https://coastwatch.pfeg.noaa.gov/erddap/tabledap/allDatasets.html. \n",
- "Like all data in ERDDAP, even metadata, the values can be downloaded via a URL-based data request. Details about the feature are presented in the ERDDAP tutorial. For the purposes of this exercise, it is enough to know that we are pulling the metadata from this page into a CSV file and using it in Python\n",
- "\n",
- "__Build a function to harvest metadata from ERDDAP__ \n",
- "\n",
- "* The function builds the URL-based data request, sends the request to ERDDAP for the metadata, and loads the results into a Pandas data frame.\n",
- "* The function also uses the xarray module in OpenDAP mode to obtain a list of data variables.\n",
- "* Inputs to the function are \n",
- " 1. The location of the ERDDAP on the internet: The NOAA GeoPolar blended SST dataset can be found on the CoastWatch West Coast Node ERDDAP at this URL: `https://coastwatch.pfeg.noaa.gov/erddap`\n",
- " 2. The unique ERDDAP ID for the dataset: For the NOAA GeoPolar blended SST dataset the ID is `nesdisGeoPolarSSTN5SQNRT`"
+ "### Download satellite data from the NOAA GeoPolar Blended SST dataset\n",
+ "Download the satellite SST data for 2017-2019 using the same SST NOAA GeoPolar Blended SST product.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Look at the satellite product's metadata\n",
+ "\n",
+ "Query ERDDAP for information about the satellite dataset. ERDDAP has a feature that allows you to select and download metadata about any and all datasets in that ERDDAP. Click the link below to bring up the user interface for this feature: https://coastwatch.pfeg.noaa.gov/erddap/tabledap/allDatasets.html. The metadata values can be downloaded via a URL-based data request. Details about this feature are presented in tutorial #1. For the purposes of this exercise, it is enough to know that we are pulling the metadata from this page into a CSV file and using it in Python.\n",
+ "\n",
+ "Next we will build a function to harvest metadata from ERDDAP. It builds the URL data request, sends a request to ERDDAP for the metadata, and loads the results into a Pandas data frame. It uses the `xarray` module in OpenDAP mode to obtain a list of data variables.\n",
+ "\n",
+ "Inputs are: \n",
+ "\n",
+ "* The location of the ERDDAP server on the Internet: The NOAA GeoPolar blended SST dataset can be found on the CoastWatch West Coast Node ERDDAP at this URL: https://coastwatch.pfeg.noaa.gov/erddap \n",
+ "\n",
+ "\n",
+ "* The unique ERDDAP ID for the dataset: For the NOAA GeoPolar blended SST dataset the ID is nesdisGeoPolarSSTN5SQNRT"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -1140,15 +891,15 @@
" 'minTime,maxTime,timeSpacing,griddap&datasetID=\"'])\n",
" \n",
" # Form the base URL leading to the allDatasets page\n",
- " baseURL = os.path.join(erddapURL, \"tabledap\", \"allDatasets.csv\")\n",
+ " baseURL = '/'.join([erddapURL, \"tabledap\", \"allDatasets.csv\"])\n",
" \n",
" # Form the data query part of the URL. \n",
" # For this exercise we will only request: \n",
" # 1) dataset ID, \n",
- " # 2) the distance between values longitude values, \n",
+ " # 2) the minimum and maximum longitude, and the distance between values, \n",
" # 3) the minimum and maximum latitude, and the distance between values\n",
" # 4) the minimum and maximum altitude values\n",
- " # 5) the minimum and maximum and time between time values\n",
+ " # 5) the minimum and maximum dates, and the distance between time values\n",
" fullURL = ''.join([baseURL, queryURL, datasetID,'\"'])\n",
" \n",
" s=requests.get(fullURL).content\n",
@@ -1164,12 +915,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "__Now run the function the function to obtain information about the dataset__"
+ "__Run the get_dataset_info function and view the results__ "
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -1262,8 +1013,8 @@
"
NaN
\n",
"
NaN
\n",
"
2002-09-01T12:00:00Z
\n",
- "
2021-04-22T12:00:00Z
\n",
- "
86476.212879
\n",
+ "
2022-01-10T12:00:00Z
\n",
+ "
86485.617214
\n",
" \n",
" \n",
"\n",
@@ -1271,10 +1022,10 @@
],
"text/plain": [
" minAltitude maxAltitude minTime maxTime \\\n",
- "0 NaN NaN 2002-09-01T12:00:00Z 2021-04-22T12:00:00Z \n",
+ "0 NaN NaN 2002-09-01T12:00:00Z 2022-01-10T12:00:00Z \n",
"\n",
" timeSpacing \n",
- "0 86476.212879 "
+ "0 86485.617214 "
]
},
"metadata": {},
@@ -1332,7 +1083,7 @@
"text": [
"Metadata helpful for this project are listed below\n",
" \n",
- "Time Range: 2002-09-01 to 2021-04-22\n",
+ "Time Range: 2002-09-01 to 2022-01-10\n",
"Data Variables: ['analysed_sst', 'analysis_error', 'sea_ice_fraction', 'mask']\n",
"Direct dataset URL: https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT\n"
]
@@ -1349,26 +1100,19 @@
"print(\" \")\n",
"print('Time Range:', dataset_info.minTime[0][:10], 'to', dataset_info.maxTime[0][:10] )\n",
"print('Data Variables:', dataset_info.variables[0] )\n",
- "print('Direct dataset URL:', dataset_info.griddap[0]) \n"
+ "print('Direct dataset URL:', dataset_info.griddap[0]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Using ERDDAP, subsample the satellite dataset and download the data\n",
- "##### Get the ERDDAP data request URL\n",
- "Two options to get the ERDDAP data request URL for the data: \n",
+ "### Get the ERDDAP data request URL for the satellite data\n",
+ "There are two options to get the ERDDAP data request URL for the data: \n",
" \n",
- "__Option 1.__ Use the the ERDDAP interface to like we did in CSV format, like we did above in the `Make a virtual buoy` section under `Bring the timeseries into Python`: This is quick but is not usable for automating scripts to download data. \n",
+ "__Option 1.__ Use the the ERDDAP interface like we did above in section 2: This is quick but is not usable for automating scripts to download data. \n",
"\n",
- ">Go to the ERDDAP interface at https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph and do the following: \n",
- "* Set the `Graph Type` to linsAndMarkers\n",
- "* Set the `X Axis` to `time` and the `Y Axis` to `analysed_sst`\n",
- "* On the time (UTC) widget, set ` Start` to the Jan. 1, 2017 (`2017-01-01T12:00:00Z`) and the `Stop` to Dec. 31, 2019 (`2019-12-31T12:00:00Z`).\n",
- "* Set the `latitude (degrees_north)` to `34.767`, the latitude of our buoy, and the `longitude (degrees_east)` to `-121.497`, the longitude of our buoy. \n",
- "* Click `Redraw the Graph`\n",
- "* Near the top of the ERDDAP interface, click the `Data Access Form` link. Clicking brings you to a new page where you can refine your data request. Our goal on this page is to obtain the data request URL to download the data as a CSV file. \n",
+ "* Go back to the `Data Access Form` page (https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.html) where you can refine your data request. Our goal on this page is to obtain the data request URL to download the data for 2017-2019 as a CSV file. \n",
"* Find the `File type:` dropdown and select `.csv`. Then push the \"Just `generate the URL button` and copy the link in the box to the right for th button. \n",
">\n",
">The data request URL should look like the following URL: \n",
@@ -1392,7 +1136,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -1437,12 +1181,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Create the ERDDAP data request URL"
+ "### Create the ERDDAP data request URL for the satellite data"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
@@ -1451,7 +1195,7 @@
"'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2017-01-01T00:00:00Z):(2019-12-31T00:00:00Z)][(34.775)][(-121.475)]'"
]
},
- "execution_count": 17,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -1465,15 +1209,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Feed the URL into our get_csvdata() function\n",
+ "### Use the URL into our get_csvdata() function to get the satellite data\n",
"The function reaches out to ERDDAP to download the data. \n",
- "__Note: Downloading may take a while, depending on your internet speed. \n",
+ "__Note: Be patient. Downloading may take a while, depending on your internet speed. \n",
"Note: If you get an error, then run function again. The server probably timed out__"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -1497,62 +1241,50 @@
" \n",
"
\n",
"
\n",
- "
time
\n",
- "
latitude
\n",
- "
longitude
\n",
- "
analysed_sst
\n",
+ "
\n",
+ "
Error {
\n",
"
\n",
" \n",
" \n",
"
\n",
- "
0
\n",
- "
2017-01-01T12:00:00Z
\n",
- "
34.775
\n",
- "
-121.475
\n",
- "
12.859994
\n",
+ "
message=\"Internal Server Error: There was a (temporary?) problem. Wait a minute
\n",
+ "
then try again. (In a browser
\n",
+ "
click the Reload button.)
\n",
"
\n",
"
\n",
- "
1
\n",
- "
2017-01-02T12:00:00Z
\n",
- "
34.775
\n",
- "
-121.475
\n",
- "
12.949994
\n",
+ "
(ERROR from data source: java.lang.RuntimeException: ERROR in getPrimitiveArrays for query=?analysed_sst%5B5236:5960%5D%5B2495:2495%5D%5B1170:1170%5D
\n",
+ "
NaN
\n",
+ "
NaN
\n",
"
\n",
"
\n",
- "
2
\n",
- "
2017-01-03T12:00:00Z
\n",
- "
34.775
\n",
- "
-121.475
\n",
- "
12.979994
\n",
+ "
Connection cannot be read https://www.star.nesdis.noaa.gov/thredds/dodsC/BlendedSST5kmNightAggGHRSSTLoM.dods?analysed_sst%5B5236:5960%5D%5B2495:2495%5D%5B1170:1170%5D)
\n",
+ "
NaN
\n",
+ "
NaN
\n",
"
\n",
"
\n",
- "
3
\n",
- "
2017-01-04T12:00:00Z
\n",
- "
34.775
\n",
- "
-121.475
\n",
- "
12.819994
\n",
+ "
(Cause: java.lang.RuntimeException: ERROR in getPrimitiveArrays for query=?analysed_sst%5B5236:5960%5D%5B2495:2495%5D%5B1170:1170%5D
\n",
+ "
NaN
\n",
+ "
NaN
\n",
"
\n",
"
\n",
- "
4
\n",
- "
2017-01-05T12:00:00Z
\n",
- "
34.775
\n",
- "
-121.475
\n",
- "
12.779994
\n",
+ "
Connection cannot be read https://www.star.nesdis.noaa.gov/thredds/dodsC/BlendedSST5kmNightAggGHRSSTLoM.dods?analysed_sst%5B5236:5960%5D%5B2495:2495%5D%5B1170:1170%5D)\";
\n",
+ "
NaN
\n",
+ "
NaN
\n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " time latitude longitude analysed_sst\n",
- "0 2017-01-01T12:00:00Z 34.775 -121.475 12.859994\n",
- "1 2017-01-02T12:00:00Z 34.775 -121.475 12.949994\n",
- "2 2017-01-03T12:00:00Z 34.775 -121.475 12.979994\n",
- "3 2017-01-04T12:00:00Z 34.775 -121.475 12.819994\n",
- "4 2017-01-05T12:00:00Z 34.775 -121.475 12.779994"
+ " Error {\n",
+ " message=\"Internal Server Error: There was a... then try again. (In a browser click the Reload button.)\n",
+ "(ERROR from data source: java.lang.RuntimeExcep... NaN NaN\n",
+ "Connection cannot be read https://www.star.nesd... NaN NaN\n",
+ "(Cause: java.lang.RuntimeException: ERROR in ge... NaN NaN\n",
+ "Connection cannot be read https://www.star.nesd... NaN NaN"
]
},
- "execution_count": 18,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -1566,8 +1298,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Clean up the data\n",
- "The data may contain data points that are outliers and that contain no data. Let's look for outliers and remove those points from the data frame if they exist. \n",
+ "### Clean up the satellite data\n",
+ "The satellite data may contain data points that are outliers or that contain no data. Let's look for outliers and remove those points from the data frame if they exist. \n",
"* Apply a conservative allowable data range. \n",
" * For the lower end of the range, the freezing point of seawater (ca. -2). \n",
" * For the high end of the range, value unlikely to be seen in the area of interest (e.g. 45 degrees C). "
@@ -1575,101 +1307,40 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "# of timesteps before cleaning = 1089\n",
- "# of timesteps after cleaning = 1091\n",
- "0 times were removed\n"
+ "# of timesteps before cleaning = 6\n"
]
},
{
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
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- "
time
\n",
- "
latitude
\n",
- "
longitude
\n",
- "
analysed_sst
\n",
- "
\n",
- " \n",
- " \n",
- "
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- "
0
\n",
- "
2017-01-01T12:00:00Z
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- " time latitude longitude analysed_sst\n",
- "0 2017-01-01T12:00:00Z 34.775 -121.475 12.859994\n",
- "1 2017-01-02T12:00:00Z 34.775 -121.475 12.949994\n",
- "2 2017-01-03T12:00:00Z 34.775 -121.475 12.979994\n",
- "3 2017-01-04T12:00:00Z 34.775 -121.475 12.819994\n",
- "4 2017-01-05T12:00:00Z 34.775 -121.475 12.779994"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "KeyError",
+ "evalue": "'analysed_sst'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m~/opt/anaconda3/envs/coastwatch/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3360\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/opt/anaconda3/envs/coastwatch/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32m~/opt/anaconda3/envs/coastwatch/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'analysed_sst'",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/45/98hpd1j11gxbpzfrcmk643500000gn/T/ipykernel_70672/2861935642.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Extract the temperature data into a data frame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msat_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analysed_sst'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Find the data that is within our acceptable range\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/opt/anaconda3/envs/coastwatch/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3453\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3454\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3455\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3456\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3457\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/opt/anaconda3/envs/coastwatch/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3361\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3365\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'analysed_sst'"
+ ]
}
],
"source": [
- "# Get # of timesteps before cleaning\n",
+ "# Get the number of timesteps before cleaning\n",
"b4 = sat_data.shape[0] \n",
"print('# of timesteps before cleaning =', b4 )\n",
"\n",
@@ -1687,7 +1358,7 @@
"sat_data.drop(index_names, inplace=True)\n",
"sat_data.shape\n",
"\n",
- "print('# of timesteps after cleaning =', buoy_data_resampled.shape[0] )\n",
+ "print('# of timesteps after cleaning =', sat_data.shape[0] )\n",
"print(b4-sat_data.shape[0], 'times were removed')\n",
"sat_data.head()\n"
]
@@ -1696,12 +1367,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Change time to match buoy time format and move time to the index location "
+ "### Prepare the timestamps\n",
+ "Edit the 'time' column to match buoy time format and move 'time' to the index location "
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
@@ -1788,7 +1460,7 @@
"2017-01-05 2017-01-05 12:00:00+00:00 34.775 -121.475 12.779994"
]
},
- "execution_count": 20,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -1812,12 +1484,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Trim buoy and satellite data to time in both datasets"
+ "### Trim buoy and satellite data to the times present in both datasets"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
@@ -1847,61 +1519,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Plot both datasets"
+ "### Generate a linear regression of satellite vs. *in-situ* buoy data"
]
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(12, 4))\n",
- "\n",
- "# set the minimun and maxium for the y axis\n",
- "plt.ylim(12, 20)\n",
- "\n",
- "# set the the plot.\n",
- "# The \"ro\" indicates a red filled circular marker. The \"b\" incicates a blue line.\n",
- "plt.plot(sat_data_final.index, sat_data_final.analysed_sst, \"ro\", label=\"Satellite\")\n",
- "plt.plot(buoy_data_final.index, buoy_data_final.WTMP_degC, \"b\", label=\"NDBC Buoy\")\n",
- "plt.legend(loc=\"upper right\")\n",
- "#plt.plot(sat_data_final.index, sat_data_final.analysed_sst, \"ro\", buoy_data_final.index, buoy_data_final.WTMP_degC, \"b\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Generate a linear regression of satellite vs. buoy data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
@@ -1909,7 +1532,7 @@
"output_type": "stream",
"text": [
"Y-Intecept: 0.5298046099717714\n",
- "Slope: 0.9558409867765579\n",
+ "Slope: 0.9558409867765578\n",
"R2: 0.9216794169492041\n"
]
},
@@ -1928,10 +1551,10 @@
"
![](\"https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.png?analysed_sst%5B(2020-01-01T12:00:00Z):(2020-04-01T12:00:00Z)%5D%5B(34.775)%5D%5B(-121.475)%5D&.draw=linesAndMarkers&.vars=time%7Canalysed_sst%7C&.marker=5%7C5&.color=0x000000&.colorBar=%7C%7C%7C%7C%7C&.bgColor=0xffccccff\")
\n",
"\n",
"### You are finished making a virtual buoy! \n",
"\n",
- "This is your virtual buoy. If you wanted, you could get to produce an image for you to put on your virtual buoy website. However, you probably want to bring the data into Python to make better plots and to go through some validation steps. \n"
+ "* This is your virtual buoy. You have extracted satellite data at the location of your buoy and plotted a time-series of the satellite sst at this location. \n",
+ "* For this exercise we wil bring the data into Python to go through some validation steps and make better plots. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Load the virtual buoy data into Python \n",
- "Let's download the virtual buoy data directly from ERDDAP and into Python \n",
+ "## 2. BRING VIRTUAL BUOY DATA INTO PYTHON AND VISUALIZE IT\n",
+ "Next we will download the satellite SST virtual buoy data directly from ERDDAP and into Python. First we will get the url to the data from the using the ERDDAP web interface. Then we will use the url to request the data in csv format and load it into pandas. \n",
"\n",
- "### Generate the ERDDAP data request URL for the data \n",
- "* Go to the ERDDAP visualization page for where you created the virtual buoy. \n",
- "* Near the top of the ERDDAP interface, click the `Data Access Form` link. Clicking brings you to a new page where you can refine your data request. Our goal on this page is to obtain the data request URL to download the data as a CSV file. \n",
- "* Find the `File type:` dropdown and select `.csv`. Then push the \"Just `generate the URL button` and copy the link in the box to the right for th button. \n",
+ "### Generate the ERDDAP data request URL for the satellite data \n",
+ "* Go to the ERDDAP visualization page where you created the virtual buoy in Step 1. \n",
+ "(https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph?analysed_sst[(2020-01-01T12:00:00Z):(2020-04-01T12:00:00Z)][(34.775)][(-121.475)]&.draw=linesAndMarkers&.vars=time|analysed_sst|&.marker=5|5&.color=0x000000&.colorBar=|||||&.bgColor=0xffccccff) \n",
"\n",
- "__The data request URL should look like the following URL:__ \n",
+ "\n",
+ "* Near the top of the ERDDAP interface, click the `Data Access Form` link. \n",
+ "* The ERDDAP `Data Access Form` page allows you to refine your data request. Our goal on this page is to obtain the data request URL to download the data as a __.CSV__ file. \n",
+ "* Use the `File type:` dropdown and select `.csv`. Then click on the `Just generate the URL` button.\n",
+ "* Copy the url link that appears in the box to the right of the button. \n",
+ "\n",
+ "__The generated data request URL should look like this:__ \n",
">https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(2020-04-01T12:00:00Z)][(34.775):1:(34.775)][(-121.475):1:(-121.475)] \n",
"\n",
"__Getting the most up-to-date data__ \n",
- "At the time this tutorial was written, the most recent date was `2020-04-01T12:00:00Z` (April 1, 2020). However, you may be using the tutorial months later, so hardwiring `2020-04-01T12:00:00Z` into the URL will not give you the most recent data. We can use an ERDDAP trick to fix this problem. By changing the the most recent date (`2020-04-01T12:00:00Z`) to `last`, you will request to most recent data. The new URL looks like the following: \n",
+ "Using the `last` function to always request the latest data:\n",
+ "* We can use an ERDDAP feature to set the end date to be the most recent date available instead of hardcoding an actual date. \n",
+ "* At the time this tutorial was written, the most recent data available was from `2020-04-01T12:00:00Z` (April 1, 2020). \n",
+ "* By changing the end date (`2020-04-01T12:00:00Z`) to `last`, you will request to most recent data. \n",
+ "* The new URL looks like the following: \n",
">https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(last)][(34.775):1:(34.775)][(-121.475):1:(-121.475)] \n",
"\n",
"__Bring the data into a Pandas data frame__ \n",
- "You could put this URL directly into a browser and the CSV data file would download to your computer, but we will bring it directly into Python as a Pandas data frame.\n",
+ "* You could put this URL directly into a browser and the CSV data file would download to your computer.\n",
+ "* We will use the url to bring the csv data into Python as a Pandas data frame.\n",
"\n",
- "### Create a function to load the CSV data into Pandas \n",
- "We will be making several request to the ERDDAP server for CSV data that we will put into Pandas. Creating a function will save repeating the same code over again. Instead, just call the function. "
+ "### Create a python function to load the CSV data into Pandas \n",
+ "* We will be making several requests to the ERDDAP server for CSV data that we will put into Pandas data frames. \n",
+ "* Creating a function will save repeating the same code over again. \n",
+ "* Here we create a `get_csvdata` function. \n",
+ "* Pass this function the ERDDAP file url and it will return the data in a pandas data frame. "
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
- "# import the needed modules\n",
- "\n",
"def get_csvdata(erddapURL):\n",
" \n",
" '''\n",
@@ -438,13 +185,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Use the function to create the Pandas data frame with Virtual Buoy data\n",
- "The beginning of the resulting is printed below."
+ "### Get the satellite virtual buoy data\n",
+ "* Call the get_csvdata function with the ERDDAP URL\n",
+ "* It may take a while to retrieve the csv file, depending on your data request size and your network bandwidth.\n",
+ "* The data are reurned as a pandas data frame\n",
+ "* The first lines of the data frame will print once the data is retrieved"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -523,17 +273,16 @@
"4 2020-01-05T12:00:00Z 34.775 -121.475 13.869994"
]
},
- "execution_count": 5,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "erddap_data_request = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(last)][(34.775):1:(34.775)][(-121.475):1:(-121.475)]'\n",
- "# print(erddap_data_request, 'a')\n",
+ "erddap_data_request = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(2021-01-01T12:00:00Z)][(34.775):1:(34.775)][(-121.475):1:(-121.475)]'\n",
+ "\n",
"vir_buoy_df = get_csvdata(erddap_data_request)\n",
"\n",
- "# The head method prints out just the top 5 lines of the dataframe\n",
"vir_buoy_df.head()"
]
},
@@ -541,23 +290,20 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Clean up the data \n",
- "__The latitude and longitude columns are repetitious.__ \n",
- "\n",
- "1. Store the latitude and longitude values in a dictionary. \n",
- " \n",
+ "### Prepare the virtual buoy data for analysis and plotting \n",
+ "Clean up a bit. Put the lat/lon values into a dictionary and remove the latitude and longitude columns which are repetitive since we are looking at one single location. \n",
"\n",
- "2. Remove the latitude and longitude columns from the data frame with the Panda drop() function. The drop() function removes rows or columns by specifying label names and the axis containing the labels. \n",
- "\n",
- "\n",
- "* The first argument is the column to drop, by name. \n",
- "* The axis argument indicates if the name is a `row` (0) or a `column` (1). \n",
- "* The argument inplace=True argument applies the row or column deletion to the data frame in use (e.g. vir_buoy_df in this case). By default (inplace=False), a new dataframe would be created. \n"
+ "1. Create the dictionary with the location\n",
+ "2. Remove the latitude and longitude columns from the data frame with the Panda drop() function. \n",
+ " * The drop() function removes rows or columns by specifying label names and the axis containing the labels. \n",
+ " * The first argument is the column to drop, by name. \n",
+ " * The axis argument indicates if the name is a `row` (0) or a `column` (1). \n",
+ " * The argument inplace=True argument applies the row or column deletion to the data frame in use (e.g. vir_buoy_df in this case). By default (inplace=False), a new dataframe would be created. \n"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -624,7 +370,7 @@
"4 2020-01-05T12:00:00Z 13.869994"
]
},
- "execution_count": 6,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -645,19 +391,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Visualize the virtual buoy data from the satellite\n",
+ "## Visualize the virtual buoy data \n",
"Use the matplotlib python library. There are many ways to use matplotlib to plot data. Below is a simple approach. \n",
">Documentation for matplotlib can be found here: https://matplotlib.org"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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eyeMIGmMuuI5rF2vBuIswgn9/Vb0M6AdQ1V7wKfodjmOB51V1fQ7HMIwRSS79cl2N/9OHfDqlOqVLomknyLGrKC+8+QLzl8/n6TeeZoihDGbhLbx7B3ZlFQeZmIKar2dCsZvGVxJhBH+fiDQSS+ISkf1xGq8HIiK3AI8CB4vIehE5N7bqs5hT1zBSyLVfrqvxN9c3s+iERWlLFqTTrLf3b2fxysWeJpl0eAn2RI0/yMTU09eTF63cCrX5E0bwXwjci2PbjwIrgPPT7aSqZ6jqXqpaq6r7quqvYss/p6qp3ZANY5STq4bqavzNDc3xuvTN9c2AI4iTu1qFKVjm1782HV41cXr7d2n8fiYmyJ+dv9hN4yuJMFE9f8SxyX8OR1NvU9U/FXZYhjH68NNE/UopJBLtitL5F0fYHnLVIUS7okRaI1z4kQsBeOrfnkoprRCU9JUrXq0aEzX+dLkD+dDKrVCbP76CX0SOcF9AC/AaTqXOqbFlhmHkkaB+sUGmD9dEtGXnFsARmq6JyG1Ikhg26eI+Fbg1/Kslt+5XiXiZqRJt/OnIh1buzs9N4tp3/L4FbRpfSQRp/D+OvX4B/A+wBKc2z/8A3imChmFkTWd7p2ez9HRdr4JMRG6v2W19qYIfHOG434T9OP3dp7P0tKWeGnJtVXAIZ1ON91NDspkqUeMPPF4etfJIa4RfnfIrAO787J0m9GP4Cn5VPUZVjwHWAkfEYuqPBA4HXirWAA1jtBBpjfja1LMJf1zXvY4xtTHB76Hxu7zV+xYTGiYQaY0wZ8ac+PIqqWLOjDn0D/UHjruh1r/UQuLYEm38frQ0t+RdK39mg1Mu7MglR1osf4wwzt13qmqX+0FVn8FpuG4YRhYEhWzuMWYPz32CTB9BTkxX4/ervqmqvLXDEfzJcf1DOsTSValPAcm82etZmSVlbEEavyDohcqahWvyKvSjXVEu/uvFAFlFSo1UwvRle05EfgncjBPSOQt4rqCjMowRSlBdevA2yaQzfXS2dw47ZuI+cY3fx9TTO9BL32AfExon+JqM/Ew56airrhs27iAbf7bRQ+noWNGRct4wjWlGOmEE/9nAPMCt8PQQcHXBRmQYIxg/4brgngX0DvSmrJvUOIlFJywKFFLuurN/fzb9Q/3DOmV1veE8rPuZety6PhMaJviajLYPOGOqraplYGggtJAeVzdu2LiDNP58OpYTsVh+b8KEc+5Q1Z+q6mmx10+t65ZhZIefwNncu9kzxHFM3ZhQmmmkNcLe4/bmrBlnDTOXBDl3o11R3nvtewH4zoPf8c2YdbOAZ+w5g6ELw2fwJpuAHln3iO+2c4+c67suFyyW35ugcM5lInKySGpVJhHZT0S+LyLnFHZ4hjGyyFTgrOteF9ohubVvK2Nrxw5b5pp6km38rsnpta2vAbBp+ya27NwSD+10aapt4ozWMwDilTn9ykEkkzzXe/9xr+d2Y2rHcNUnrgp1zEyxWH5vgjT+L+CUT35eRJ4QkbtF5AERWQ1cAzypqtcVZZSGMULwE0RBwjSsQ3Jr39Z43L5LXOPv3zbMqTzn9jkpTxj9Q/2MqxtHS3MLgsQjbI6dfizgVOsEWHTCopQbRDJeXb/czOJkwjSCyRY3lt8NSS1E1FAlEhTO+bqqnq+q+wOfAn4AfA04RFU/pqp3FGuQhjFScAWRG6+/99i9WXLyEs/aOomkK93QP9jPzsGdKYLfPeZjLz82rA6QX/2dzb2bWbNwDUMXDsVNRg+tfQiAFatXMO2KaQBcd+p1w24Q7dPbh+UgeHX9Glc3zvOchTa7RFojHLHXEXx8/4/nPWqoUgkTzomqrlHVR1X1KVUt3O3ZMEYBkdZIvL/svbPuJdIaid8QgpycQQ5J13k7rn64cK2SKppqm7h/9f2hNOvk80e7olz5xJXxz4lRSIk3iJfefCnF6Zt8s2rbqy3lfMUyuzTWNhb0yaLSCCX4DcPIL32DTrvqnYO7Ct2e+Z4zqa6qZnz9eM99gjRj14afrPFHu6LsGNhBT1/6OvpAypNAx4qO+FhdvJ4+0tUZinZFefzVx4FdN5diml2aapsyKhkx0jHBbxglYGBoAICdA7sE/9s73qZvsI+TDzw5Y4dkz05HsCcKfteBO6ThI3FamluGfQ4bDhlUZ2j+8vnMXTY3/lQyqIPx+RTL7NJYYxp/IkFRPTcUcRyGMSpJ1Phf3/o6AJ846BPDzD5hNGMvjT+o0YoXXjeXsOGQQXWGljy5pOQNUZpqm0zwJxCk8R9atFEYRoWSS8cs2GXyiXZFOWbpMQAsvG8h4MTNn3DACaEckl6CP12Skts/NzGCJ0zpZq8bRFCdIT9HcjGTqBprGkPVChotBAn+JhE5PLE8c1KpZsMY1eTaMQscU8/85fOZfdts3tj2BgAbtm1g7rK59A308Wbvm6FuLl6CP120TL86xdfOazvP9+biOp2TQzy9tk02E7lUibeYKWYSlWn8wxFV77u0iPQAT+DdX1dV9aOFHFgibW1tunLlymKdzjBCMe2KaZ5NUlqaW1izcE3gvnKRc1mNrR3L1n7vAmpNtU2MrRvL1r6tKXV4EoVvtCvKwnsWsql3E3uP3ZvLjruMSGskpS6Q71gQbpp5U8729mhXNF42IpFqqaa6qnqYkzh5DoXm2yu+zeWPXE7/d4MrjY40RORJVU0JpwrS+F9S1Y+65ZmTXkUT+oZRruSjDoyf0AfHDr5p26ZA+7gr3Df1bgLg1a2vxp86knMG/FCUBfcsCNwmDJHWiGdE0qAODnsSKUUSVVNtEwNDA/QPji7B70fBonpE5DoR2SAizyQt/7KI/J+IPCsilxXq/IZRaPxMFX41b1wSI3mCEIQhvCNy3JtLuj69Qbb3RDb3bs5LqWK/Es1uMbgfH/fjkiRRNdY4eRMW0ukQJPi/mfhBRGpjNv/dQx77BuD4pGMcA5wKHKqqhwA/ymCshlFWdLZ3enan6unrCRSifrXxkwkS2O5NJ8xTR7obkUshG5zvNW4vACY3Tc75HNngOqjNzu8QJPhnisghACLSDKwCbgT+JiJnpDuwqj4EJN/+5wGXqOrO2DYbshq1YZQBfqaNvsE+TyEa7Yoy+bLJTL48N+GXGFUTJtzyrBlnhTpuoRqcC8IH9vkAUDrB72ZKW2SPQ5DgP0pVn429Pxt4QVVbgSOB87M830HAUSLyPyLyZxF5r9+GIjJXRFaKyMqNGzdmeTrDKCx+po1kIeo6Pjf3bs7pfNVSPcw+fsDEA1K2SQ63nPnOmQDxp5NCRtkkt28E58ll+YvLAZjSNCXnc2SDafzDCRL8iXnaHwN+D07xthzOVwNMAD4AfAP4rYh4ep5UdUmsz2/blCml+bEYRjrCJjh1rOhI27s2DEM6FBf685fPZ8XqFSnbfHDfDw6zoT+6/lHAqb5ZLdX825H/VtBSxXe/eHfKMjdR7ZO/+WRJ2h6ajX84QYL/bRE5SUQOBz4M3AsgIjVAY5bnWw/cpg6PA0NAaZ79DCMPeAlLLyGar2SldzS/I/5+yZNLPLf505o/xd9Hu6J870/fi38e1EGWrlrKnBlzQsXmZ0PQXF/tebUkPW9N4x9OkOD/N+BLwPXAwgRNvx1YnuX5fg98FEBEDgLqgE1ZHsswSk6kNTLMwTu1eaqnEM3UjDKmdoxnGOZ3jvpO/L1fRmzicr+es3e/eHdK+eV8kW6uxS7XAPCXdX8B4Ogbjs4qw3qkEVSP/wVVPV5VD1PVGxKW36eqX093YBG5BXgUOFhE1ovIucB1wH6xEM9fA3PUL4PMMCqEIR1it/rdAHi5+2U6VnSkCJbO9s54iYR01FXXcc3J13DTzJvYa+xew9Z9/ICPAwQKrsTSyqXoOevl4C3m+ZOJdkW59OFLAbLOsB5pBBVp+4KIHBh7LyJyvYhsEZGnY+afQFT1DFXdS1VrVXVfVf2Vqvap6ixVfY+qHqGqD+RzMoZRbAaGBhjUQbb0bQH8BUukNULnseFs6G6T8khrhFXnrRq2bsvOLfGkLT8S+9eWouesl4O3mOdPpmNFR0qj91I8dZQTQaaeBcCa2PszcIq2TcfpwvWzwg7LMCoDNxkrufSxl2D50L4fir+f0DDB95iJkUKTmoa3ZNyyc0tg1c15bfOG9a8tVc9ZLweviyBF7XlbiqeecidI8A+oqhuGcBJwo6puVtX7gTGFH5phlD/JmmQiyYIlSKAnkqgNJ4de9uzs8RVYgqQ0LXfLNjRUN8SXuREuhSRIqCpa1MzdUjz1lDtBgn9IRPYSkQYch+79CesK/8sxjAogsZ5+MsmCJTGG3y+RKVkbT7ZDL39xeVaCLNHhu7l3c8Ft3EFjCWosXwg62ztTbnbFavlYrgQJ/guAlTjmnjvdZC4R+Qjwz8IPzTDKH1fjr6uuG7bcS7AM0/gThN8eY/bwDKv0suX//PGfs2n7plDnc/HKISi0jbuchGqkNcLikxbHP5eiSFy5ERTVcxfQArxLVb+QsGol8JlCD8wwMiHXhijZ4tr45x4xNx5+6SdYNm/fpfG7mawAf5z9R8+wSj9b/rb+bQwN7fIpJGfzJlMKG3eQUPXLdi4ksw+dTZVU0XFUR0mKxJUbgdU5VXVAVd9KWrZNVcNVmTKMIpCPhijZ4mr87fu1M6lpEvPa5vkKlsfWP+Z5jPtX3++5PEgwD+hA/P3YurGBgqxUNm6/xiylsK2LCA01DYE+mdGENVs3Kh6/0sQL7llQ8KcA18bfUNPgtPfzKQkQ7Yry4JoHPdf95JGfeC4PKyC7d3YHzq9UkT2lOq8fjTWNJvhjmOA3Kh4/zXhz7+aCPwW4gqShpoHGWu++ru4TiV+Z5Vd6XvFcHiYRyiVofpm0T8wnpTqvHw01DVadM0ZNug1E5Hc4Gbf3qCYFKxtGGTCxcWKoqpfuU0A+BY9r46+vrvc1JQTF3YMTsul2zErE/bzgngWh59exosO3d24pBG6pzutFQ00DOwZN44dwGv/VwJnAiyJyiYi8s8BjMozQRLuibNm5JfT2+eo05TJM4/cx9aRzog7qYKC2vun8Tdw88+ZQ8fejOSkpHWbj30Vawa+q96tqBDgCJ7TzjyLyiIicLRKy+IhhFAi/csd+NefdffKFa+Ovr6n3NfWEsdWnC6+MtEaYdeiswHmFPddoxQT/LkLZ+EVkEvA54PPA34BFODeCPxZsZIYRgrXdaz2XJ5dQSCSfWnEYjb+zvZOmmvS2+nTj2mPMHgzpkK/df7QnJaWjsdacuy5pBb+I3Ab8BWgCTlbVU1T1N6r6ZWBsoQdoGH5Eu6KepYvBCSX0yxDNp1acaOP30/gjrREu/dil8c+J1TMzGdfuY5x21z8+7sfxUtDusUrtOK0EzLm7i7TOXeBKvyqaqtqW5/EYRmg6VnT4Rsp0tneCwqzbZw1bnm+tOIzGD3D0tKMB+M3pv6F/qJ+5y+YOc/iGGdcLm18AnM5b1VXVvH+f9/PY571zA4xUGmoahiXRjWbCmHr+IiJfEZH/jr2+bLZ9oxwIMo1EWiOc9q7Thi0rhFbsCv76mvpAjdIVOJMaJ2UV5hjtinLt/14LOEXOBoYGWPnqylFdUz5TzMa/izAa/9VALeCW/ZsdW/b5Qg3KMMIwtXmqp41/YuNEALp3dAOwW8NubO/fzuoFq/Fp8ZwV0a4onX9xtPRDrjqEgyce7CtY3DIF7tgyDXPsWNGRUhBuUAd9wzeNVEzw7yKMxv9eVZ2jqg/EXmcD7y30wAwjHX4JTm/2vsm0K6Zxc9fNAEzfbTp9g3157bfqJmV173RuLuu61/Hg2gfp2dnjue3nlzl60sm3nJyVlm415XPHMnd3EUbwD4rI/u4HEdkP8G72aRhFxDWZNNc3Awxz9K7tXssFD1wAwLTdpgH5LQ7mlZQ1MDTAgA6Q2E3UvUG4536l55WsMoitpnzuNNQ0ePpgSlXgr5SEEfzfAB4UkT+JyJ+BB4C0PXcNoxhEWiN87rDPIUiKo9fN0py+23Qgv4I/SNNO1Cr96ghlmkvg9XRTW1Vr4ZsZ4GXqmb98PrNvm12SAn+lJEwC1wrgQOArsdfBqupdbcowSkDPzh7f6B6A6RPyL/iDNO1ErTJfJhr36aa+un7YMrPvh8cV/O4TWbQryuKVi1N+O6OhH2+YOP6zcOrvHwbMAD4TW5Zuv+tEZIOIPJOw7Hsi8oqIPBV7nZjD2A0DgC19W6ip8o9T+Pb93wag/cb2vD3Ke2ngbnMUN7Jn/vL5vjekbEw0kdYIM981M27SOmb6MRkfYzTTUOO0n+wb7AOCw4FHuu8klHM34XUU8D3glBD73QAc77H8p6p6WOzl35HZMELSs7OHqeOn+ma09vQ7Dtd8P8on9rGd1DiJsw87G3A0/vnL53P1yqs998s2lyDaFWX5C8vjwurJV5/MYtSjF1fwu+aeIOE+0n0nYUw9X054fQE4HKgLsd9DQPFb7Rijjp6+HqZPmM6Sk5cwoWFC2u1zfZSPO2x37Pp59w70svqt1QAc9PODfIU+kFUugXvOLX27CtJd8+Q1I94WnU/cIneuKc5PuAsy4n0n2dTj345j88+WL4nI0zFTkO9VKiJzRWSliKzcuHFjDqczRjo9O3sYVz+OSGuEOTPmhNonl0f5Bfcs8HTY/uGffwAI9DdAcFtCP7ycxDsHd454W3S+iHZF+e6D3wWgbUkb0a4oJx54YkrJD0E4r+28Ee87CVOPfxnEf8lVwLuB32Z5vquBH8SO9wPgx8A5Xhuq6hJgCUBbW1vwlWSManr6ehhXN45oV5QrH78y1D7ZPspHu6KhauP74VenJx0Wx5897tOSe+N8pecVzv792YikRoLNOnQWV33iKq/DjCjCZO7+KOH9ALBWVddnczJVfcN9LyLXAndlcxzDSGTLzi2Mrx9Px4qOYb1o/cilXk+uGvbcI+dmtV+6LGXDH6+nJa9S3gD3/9O7//FII4yN/8/uC/g/wLtPXAhEZK+Ej6cBz/htaxhhiHZFebP3TX7xxC98SzQnkmu9nlw07MaaRj489cNZ7dvZ3hmvyJlIT1+P2fnTkMn/7PWtrxdwJOWDr+AXkQ/EkrZuE5HDY2GZzwBviIhXtE7y/rcAjwIHi8h6ETkXuExEukTkaeAY4Kt5mocxCol2RZl7ZzgNur66nqOmHsWahWtyst/6mYjG1I5J6ZBVlXR59Q70Zh1RFGmNML5+fMryvsE+s/OnIROz3pSmKQUcSfkQpPFfCfwncAtOtu7nVXVP4F+Ai9MdWFXPUNW9VLVWVfdV1V+p6mxVbVXVQ2N1/V/LyyyMUUnHig62D6Svv1NXXcchUw7JqEWjH16ad1NtE9ecfA0/O+Fn8WUtzS3U19Qn755TRJFfAprZ+YPJpGn9pw/5dNbnqaTSD0GCv0ZV/6CqtwKvq+pjAKr6fHGGZhjBhBF4jTWNXHfqdRyy+yHxgmq5ktj+sK66Lm46OudwJ07hgn+5gDUL1/jW5s9WUFu9nuxws56DHOv7jNsHgEP3ODSrc7gO5Eop/RAk+BN71yX/gi3Kxig5fgKvpbmF1t1bAbi4/WIirRGa65vjZZqzxb24E8sjJ7Z4rJIqxtWNY8vOLYHdwbIV1F6aq7VbDEekNRLYjvPvX/w7AOfff35WGnu+ajIViyDBP0NEtohID3Bo7L37ubVI4zMMXzrbO4fVroFdgtA1s7hRL80NzWzZuWVY5cxM8avIOef2OXEhMb5+PH977W/MXTbXM54/F0GdTQMXYxdBEVDTFzn1nN7e8XZWGnulhdv6hnOqanYBx4aRA9GuKB0rOljXvY6pzVPpbO/0FWyR1girXlvF5Y9ejiDx7QG63ugC4Ot/+DpVVVWMrx/PoA6yvX87Y+rGZDU2v4t4UAeZu8xxMo+vH88Trz7h6XuoluqcBbUVZsuOaFc00Mfj5T9xNfYw37dfuG25muGyydw1jIKQjZ20bR+n7XPXvC7WLFwDMMwcs3H7RuYum8vzmxzXVC52/qCL2BUS4+vH+zqch3TIhHaJ6FjR4Ru7H0Tyzd7PgXvigd71Jv2WlxoT/EbZkI2d1NXi3FBHv2Pc+uytAOz7k32zjrhIdxGv617H+Prx8SqdyZSr9jcayIdD3UsxmXXbLKq/X+1bm+m3z2Zb5KCwmOA3yoZs7KRuq8Nx9eMCt93avxXIvkJntCvK0lVLA7eZ2jyV8fXjmdw0OSWm35ywpSUXh7qLl1IBBDqNN/duLsvIHhP8RtmQTbhiT58j+MfWjU27bSKZRlz4XfQurmAfXz+eKqkaJjDMCVt6Monld0kM24XsnxrKMbInKHO3JyGSJ/HVIyK5Z8IYRhJ+F2eQ1tSzs4fGmsZ4I5ZMLnC/C9nLjht00buCHeC///7frN+ynssevgyAOz97Z87ZwkbuhInlT2ZIh4Y9GWZbF6kcI3t8Bb+qjlPV8R6vcaqamjtuGDniV1Z5a99WzrnjHE/h7xZoSzxGcsjjpMZJnufzejrwczD7XfQtzS3DnMruE4hb8+WGp24InLNRPCKtEZaetjRFMRCE9untnjcF98kwXVRQEOXo2wlt6hGR3UVkqvsq5KCM0cvyF5d7LverSdPT1xO377tEWiOsWbiGoQuHWLNwDYtOWBTa5u7nHPYqxZx4DD9T0O3P316WNt7RipdicNPMm7j/rPt9bfXrutdlHRVUrr6dMD13TxGRF4HVwJ+BNcA9BR6XUSDKtZ6IO66gx2KvdW4t/iAirRF++vGfxj8H2dwzeSyfM2NO/Bh++ylaljbe0UyyYuD+D4N8TGEqv7q4xfkmNU4qW99OGI3/B8AHgBdUdTrQDjxc0FEZBcHPjDF/+fyS3gwSxxVElVSljNHtvpWO2TNmA3DpsZcG2twzeSy/+8VdLaOD9itHG6+Ripd/SBAOmHiAb/kNL4Zi1W4+Ov2jZSn0IZzg71fVzUCViFSp6oPAYYUdllEI/MwYi1cuzrm4VC5PEukiZlwGdTBljGE0ftjVaLu337twmku2zuHO9k5f4WDNUioDLx+ToqxYvSJtO00vbv37rWX1VJ1IGMH/toiMBR4CoiKyCKcTl1HGJAvi+cvn+2rUyT/qTEMdc61MmI1GvL1/OwvuWcDTbzzN8heXp73AqqSKuuo634qZLq4N2I0SCiJRy4+0Rjiv7TzP7axZSuWQ+BSXD8q1SmcYwX8qTnXOrwL3Av8ATi7koIzc8BLEfpmFfmQijP2aj4e9eWQb9bC5dzMDQ44OEuYCa6xpTKvxw/CmJ0FPE8lOu6s+cZVnBJE1S6kcCmGWK8cqnWFaL25T1UFVHVDVpar6s5jpp6wpVydmMfASxJkSVhgHNR8PexFlk1zjRboLrLG2Ma3GD07FTbdolxue6YWX/daapVQ2hTLLre1eW1ZyKExUT2Ii1w4RGSz3BK5Ka4rgRbY3riBBHBZBQoegBQnasDcP17ySWOPGq79sGIIEbGNNOMG/efuu72+3+t08t2lpbvFcbs1SKpdMYvXbp7fHQ0InNU5iTG36iq/lJIfCaPyJiVwNwL/itGUsWyqtKUIymd64Em8Sc25PTYDKlEwcWUGCNpP45UhrZJhwvLj94vjnhuqG0McJErCNtY3sGNgRuH+0K8rh1xwe//z2zrdTtgmKzbZmKZVLJrH6L735UjwkdNP5m9j67a3ohcrNM28OjAAqFzmUca0eVf098NF024nIdSKyIdakPXndv4uIisjkTM8fBj9hVG6PW35kcuNKvkkM6mBexhBWM8mnJrth24b4+zNaz2DtwrVMaJgQ+mJMJ2AbahoCbfzud/naVv9W0Onq7lizlMolE3Oc37aR1khaxakczH5hTD0zE16ni8glhGu9eANwvMfx3gF8DCjY7IOEUakft8KYcMJWqYx2RZlz+5yc7flehNVMgsIYw2o20a4oLT9tGfaY7T46b+vbFupmFkbApjP1pAsrdcszpBPifglCRnmTiRITtK2fGTCb8xSK9DFrwyN4BnAyd09Nt5OqPiQi0zxW/RQ4H7gjxLmzorO9k7nL5vpexJl01sknrkbpjsu9CT287mHufvHueNepiY0TPe30XrXB86XhexFUxCyxS5afhhOk2bjH8AsxvfP/7qSqqoq+ob6049xz7J7xejlBNNY2sq1vW/zz/OXzWbxycWjT1trutUS7oibIRyjp5IZLuifLoOOUi9kvjOD/paoOy9QVkQ8DG3y290VETgFeUdVVIsGZcCIyF5gLMHVqZndI98L8+n1f541tb3huk0kKdr7wM+EkhloGjSuxEUg+InfSEVTELPHm5YdfhETyMbw46/dn+TY0SeaNrd7/42QaaxrZtH0T4Aj9TENcgXiLRRP+Iw/3fzrn9jm+ClVLc0tgO9DE48y7a96wqLAw+xaLMDb+n4dcFoiINAEdwAVhtlfVJarapqptU6ZMyfR0RFojdBwVbGqYv3x+xsfNhVxte25yST4id9KRSREzPzb3bmb+8vkp5q2wN62+wfTaPsA+4/cJtV1j7a44/iVPLgm1TzLl4pwzCkOkNRLYWCWT43zzw9+Mfz5m2jFlZfYLqsf/QRH5OjBFRL6W8PoekE0j9v2B6cAqEVkD7Av8r4jsmcWx0hLtivKtFd8K3GbxysVFtfXnattzbxz5EDyTGicFtgjMRxEzgKtXXs3nfv+5YRFK+b5pXdx+cajtEm38uZjIysE5ZxSOfPkIJzXtSuZzy3SXS35RkMZfB4zFMQeNS3htAU7P9ESq2qWqu6vqNFWdBqwHjlDV1zMedQjCaKbFrpyYa6KSazrJVfAIwqbzN3HdqdcNiz758Ds+DMDL3S/Ha5Ank83Ny82uLRTpzIYuDTUNvNX7FtOumBa4XRVVnHLwKew+ZnfP9eXgnDMKR7rrNOxTX2IW93ObnkMuEmbdNmuYEjT7ttlxy4N7U5CLhJrv1yAXScFuDkGNWP4M/BB4WFUvSnj9RFVfTHdgEbkFeBQ4WETWi8i5+Rt2esIKx1yFaCZ3cDfUL1vcmi+5Ch53/8Tok872Tla+uhII7kvb2d6ZdXJVoUi8eIJY+/Zaevp60vp3hhhi686ttE9vT1lXLs45o3AkhuT6EUZuPPX6U2m3UZTFKxczf/n8YRVq3SfSQkUhimpwRIOIPKCqaeP2C0lbW5uuXLkyo32mXTEttAM3W6eLl5OyqbYpbVhhww8b2Dm4M6NzJY/1C3d+IVQWajJ+4/P7vtwQxsRIHhHJix00nwjCTTNvCvzemy9pzrqLknuO89rO46pPXJX1MYzKIt11EcSESyfw9o63Q52nWqoDzY9hzueFiDypqm3Jy8M4d/8mIneKyOzEmP6MR1BkMtHKsr2rZpshPKFhQkbnSWRt91oirREu+EgoHzng/KjSJRMF5Q4kJ4kVQ+ini4VOJozZLheh754j39UbjfIm20zsaFc0tNCH9D6nfPuVwgj+icBmnGzdk2Ovk/I6igIQaY349lr1IptojbCJVolEu6K8vi17t4YgRLuivG+f94Xavq66jqWnLU2bTBRUYyaTSJ5MGlZ4MalxEnqhsmbhmoyFf9D3nq8ILnPsji6yzcTOVJZUSbAozrdfKUytnrM9XufkdRQFYtEJi1Lu1rXib5/O9KLOtCBXtCvK2b8/O2V52Hh1cLTOObfP4fbnbk+7bZVUcd2p14UyYQVpNpl8L4qGKljlR2J1S68xBfkXgr73xSsX++6Xyfdvjt3RRzaZ2JnKkhqp8VWaCuFXCgrnPD/29+ci8rPkV15HUSC87tb1tfW+22d6UWf6GOhXBKpvsI9qCR8hO6iDXPlE+jp5qhrab+F+V25T8kTNJtPvZXv/9oyethJJbm6S/P+7/pPXM69tXspFku57D8rOPffwc9NqXOnOYRiJZHrN9A31ef5GC1Xryde5KyInqepdIuJZ7lFVl+Z1JAFk49z1ItoVZdZts3zX3zzz5qwcvO4xp46fyn8e+5++x6i6qCpQADXVNuU1Gzcbh9AJN5/Aff+4D3B+vK6gm33b7NClDVqaW1jXvS7jdnVhHOMuyWUjgpzzYb73Q3c/lMdeecx3m3LKujTKnzDZ6WG47pTrOPvwVCtBWPycu0GC/0rgv1T1kazPmifyJfiDIn3G1I5h67e3ZnVcucjRPlcvWM203ab5bjf5ssm+yUuuYFlwz4K8JDhlIkRdXFNU4lNJbVUt4+vHhx6Te96gOjxeFFKwhonwEiTl5jC2biyLT1pswt7IinT1qMLQWNPItadcm/VvMJuonheBH4vIGhG5VEQOy+rMZUSQ3a1/qD9UvfvkWP3+wV1Ccr9F+/nG8gc1eairrosLvU3nb+LmmTcPa/IQxmE6qXFSzqWAvUxR/UP9aYW+O77E83a2d1IjYUpBha96mS1hEue8nggmNU4yoW9kjesbCApSqJIqxtaN9V3fO9DLnNvn5D2OPyiBa5GqfhD4CPAmcL2IPCciF4jIQXkdRZEIsrv59UVN1xTlhqduiG8blPjkZ9/3csAmOpM2nb+JT737U2nn9mbvmzmXAs42YkVRT+EdxtRTDLt5mIQcLyyCx8gHQUmP79/n/ewcCM7pGdTB0EmKYQkT1bNWVS9V1cOBM4HTgOfyNoIikk7z87rQ/WL1F9yzAICL/nxRyj7JoaHRrqjv4146B2y0K8qyF5b5rnfJR7RJLsdI/u46VnT4xia7juxiNilxb6Y3z7w5dOaxRfAY+SDSGuH6T14/LODBfZJ/6vWnQjUacjN886X5h2nEUisiJ4tIFLgHeAGn/WLF4Wp+fhE0Xhe6n9a3uXcz0a4or/a86rne3c99YvAjnXDpWNGRNkM3X1pzZ3snTTXZ1RJKnkeQtjyog/ExF9uUEmmNML5+fNrtLILHyCeuGVcvVPRCZdP5mxhbOzaj7Pt81hYLCuf8mIhch1NMbS5wN7C/qn4m1n6xIom0Rlh62tIUzV+QYfXuXYIE85zb59Bc3+y5zt0vKPkpjHBJZ24YWzc2b1pzpDXCklOWxG32YePxvZqzp7uhlbK8cWKuQDLWLtEoFr2DmZdcyZf5MUjj/zZOkbV3qerJqhpV1W0B21cMkdYIc2bMGeY0VZSlq5amPEp53QxcBnUwbTPuoH9UGOESJEDntc2j5z968iqgIq0R9p+4P599z2eZ3JS+JbJbvyZ5DGEcqqWyoft9py3NLdYu0Sga2VStzZf5Mci5e4yqXquq/upRBXP3i3enOB+9bPNLV4VLV3ATgJK1Rb8uVC3NLTll1N488+aCFQsThFufvTVtGFpLcws3zbzJcxxhHKqlsqFnW3/FMPJFtCvqG60nCO3T2zNKUsyUMLV6RiR+2qbbVxUy6zaFOsL/xS+/GBfofiGcbvhmGLKtFZIt0a4oL735UmDRqMR6Oula0LkO1XIStMX+Tg0jEdfv5xf1piiPrn+U89rOK9hvNG1Z5nIgXwlciQQl9bjmi0wacSfu62aS+iVvVEkVN552Y1kKmqAkM8guMQwyy7Q1jJFM2JLx2ZZiTiTjzN1yohCCP11KtSBMbJyYdRZtuvIL2QrQQpKupAVkV9bCMIxdpCsh4iIIQxfmVgI9l3r8IxLXweuHojmVTtjevz2w8Fo5Nu1ON55cSy4bhhHet1VIH9ioFfxAwZtquPHqfpRbZmi68RS7R7FhjETC+LYK7QMb1YK/0IK3WqoDe+yWW2ZomPGU283KMCqNdE2iihFsUDDBLyLXicgGEXkmYdkPRORpEXlKRP4gInsX6vxhKLTgHdRBznzPmdRV1VFTNbxgWTmGD4aJvS+3m5VhVCJeTaLcMO1i5JEUUuO/ATg+adnlqnqoqh4G3AWEbxxbAMIIumTG1o1FkFCNUyY1TqLlihb6hvqoraqN1+co1/DB5Nj7QsYRG8ZoptQhxQWN6hGRacBdqvoej3X/AUxV1XnpjlOIqB6XTGtmC8JNM2/KqDGJSzlG8gRhIZiGUdmUJJzTS/CLSCdwFtANHKOqG332nYtTI4ipU6ceuXZt9s0MQo31ovARK65GnE2DhXzE5hqGYYShbMI5VbVDVd8BRIEvBWy3RFXbVLVtypQpBR9XJrXa13Wvy8pM5O5rGIZRSkoZ1fNflFF550wE+dTmqSk2urD41e4xDMMoFkUV/CJyYMLHU4Dni3n+INLV6ndJdHAmdsrKtLuTYRhGqShkOOctOGWdDxaR9SJyLnCJiDwjIk8DxwELCnX+bAiq1Q/B8bVB7dUSCaoFbxiGUQzCdcPOAlU9w2Pxrwp1vnzhCvVMo1nc9elq3VgcvGEYpWbUFmkrFEGV9yotnNMwjMqmbKJ6Rjp+TuJJjZNM6BuGURYUzNQzWsnWVGQYhlEszNRjGIYxQjFTj2EYhgGY4DcMwxh1mOA3DMMYZZjgNwzDGGVUhHNXRDYC2ZbnnAxsyuNwSoXNo/wYSXOBkTOfkTIPyH0uLaqaUuWyIgR/LojISi+vdqVh8yg/RtJcYOTMZ6TMAwo3FzP1GIZhjDJM8BuGYYwyRoPgX1LqAeQJm0f5MZLmAiNnPiNlHlCguYx4G79hGIYxnNGg8RuGYRgJmOA3DMMYZZjgN4xRgoiEbw5tjGgqXvCPlB+ziExMeF/RcxKRo0UkJWmkEhGRr4vIcbH3Ff1/Aca5byp9LpU+/kRKMZeKFfwicqqILAVmlHosuSAix4vIQ8AVIvJjAK1Qj3vCXCLAzlKPJxdE5DgRuQ/4JnAWVPT/5WMi8lfgRyJyPlT0XEbEdQ+lnUtFNWIREVFVFZFjgB8A/cAHRWStqr5V4uGFJnaHrwLOBc4BLgb+BtwoIieo6j2lHF8mxOYiwGeAa4BzVfXW0o4qO2JzqQUuAD6C83+pA94rIrXAQKUJTBHZF/gecAnwJ+DXIjJJVb/pXk+lHF8mVPp1D+UjwypG40/6ka4GPg58A3g/cGjJBpYh7jxUdRD4K/D/VPUOYAewAXhWRKrcbUs41LQkzGUIeBW4EXgptu5TIrJvTGBW0lz6gDtU9ShVvRt4C/isqvZXipBM+q7fCXSp6jJV7QF+AXxVRA6MCaCy/r8ksRo4jgq87qG8ZFhFCH4R+RJwm4h8VUT2VNU1qvqaqj4AvAF8RET2KfEw05I0j71U9e+qOiAiRwC/B6bhmBZ+4u5SmpGmJ2EuXxORyTg3saeBq0XkeeDTwM+Bq9xdSjPS9Hj8X56ILa9V1T8D/xSRE0o7ynAkzWU88ALw/0Tkg7FNdgeeBb5TqjGGRUTmi8i/xt4L8LKqvl5p1z2UoQxT1bJ+AacBTwDHANcDVwKHJaw/FLgZmJm0n5R67GHnAUwHpsbejwHeBtpKPeYM5vIL4GBgbxzzyOGx7SYCG4EjSz3mDP8vM9zfUGwOvwSOK/VYs5jL1cAeOCbFG4CHgf+K/d5WAdNKPWafeYwDFgOvA1uBmtjyKve6rpTrPuA3dljC+qLPpRI0/vcDV6vqgzi2ytXAV9yVqvo0zpf6HhH5qIh8M7a83B7LveaxAEBVV6vqutj7bcBvgfElGmcYkueyBviGqr4KXKSqfwNQ1TdxnmTGlmaYoQj6v2hsDo04Fy2uGa5M8ZrLRar6K+ALwFdV9UxgHfA4sKVUAw1CHZPUn1V1T+AuHMUCHEGosW0q5bqHMpRhZfMjTrY1Jnz+J3AmgKquBZYDY0TklITNbwE+D/wGp351yWzKGc6jKWkeiMh3gEOAvxd+tMFkMJdlwDgROUVVdyRs/12cuTxfnBH7k+Pv62bgfSLSoI4/o6RkMJc7gQkicpo6PorHY9v9AOfJsqdIQ/YlYC53xv4uBM6I+SQGRaQmYZuyue69KGcZVjaCHyeaIk7C3e6/ge0icmrs82s40QnvFoexwCKgCzhUVb+RtH+xyXgeACJygjghdwcBp6vq68UZbiDZzuUoEXkQZy7/qqpvFGe4gWT1+4otawR+DQwWYZxhyHQuBwOIyIEicgfwHhztv784ww3Ecy6quk1EqmLXwVU45jZUdUBVVUTGAD+jfK57AESk2n1fzjKs5IJfRD4oIrcCl4vIu90vTkTcUNO3gNuBeTGveDeO6aAh9sXsABao6idU9bVSzAFymkdjbP1zwHmqelYp5wF5mcsa4IuqOruC51KfcOHdoarXllpQ5nKtxNa/jvN/OaXUN+OAuVQnm9NU9VvA9Ng+e4jIe2Mm0a+U+rqH+Fy+HxvrYMJyV3EoOxlWUsEvIrvjODruBjbj2FbPAefOHtusEbgP5y65RET2Bg7HiX91NYANRR76MHKcR19suzWq+kyRh55CnubysqqWg6kql7m464ddzKUiT9dKj6quL/LQU0gzl0FVHYppwc0Ju12K45z+C9AU27ak1z2AiMwBlgLfEZFPx5bVwDCNvfxkmJbW2/0x4JbY+zE4ca13Ae+MLfth7As7HCe64oc4j0hXAdWlHPtInIfNxeZSJnP5AXAvcFTs8wk4fqIfAbWlHn/SXI4F9sXJL1iXsLw69vd75fh/KfaX9Eng28AnYp+nAC8C+8c+TwQuxLm7N+GEnu2fdIymMvhnj4h52FxsLpUwFxzf0TtKPY+kuZwU+1xN7GaEk8vyg4Rtdy/X/0uxvqwpOGF9DwHn4WSonh5bdwlwRex9FfD/gGuBiQn7V5X6ixpJ87C52FwqZC5l86TiM5fTYuvqYn8PAbqBPTz2L5v/i2rxBP8HcOK83c+zgUdi72fgPNYdG/v8LpxQrjHl9oWNlHnYXGwuNpf8zSX22TXt/BK4Pvb+hFKP2+9VsCJtInIWuxJFnsRJWnDDnf6OkzYOTgjTr3GqU34SaMfJmKwF0BLHTY+UeYDNxeZSeEbZXLpinwVQAFX9vIgMxeZ0qYjcVw5zSSavgj/2BeyJY9caAv6BkzG4QFXfEJFqdZIw3kXMYx/7Um6Iefq/hVNU6guq+nY+x5YJI2UeYHOxuRSeUTyXCRCP3lERaQF+ihN59EUtgyg9X/L4KOQ+6hwE3Bx7X4NTqOu2pG1uBD4de79nwjHqSv0INFLmYXOxudhcijaXKbG/uwHvK/U8wrxy1vhjMavfB6pF5G6cGjOD4MSnishXgFdF5CPqVDoEp/DS6ljSw0wROV5V16tTErckjJR5gM0Fm0vBsbmkzOVEdeptPe5xirIjpwQuEfkIju1rAk4ddrexwDEi8j6IPwZ9Hyee1bWPnYOTxjweOEZLnFQyUuYBNhebS+GxuXjOZV3RB58LOT4aHQXMTvh8FTAP+BzwZGxZFY7N7LdAC7A/cAVwRKkfd0baPGwuNheby+idS0bzzvFLawLq2WX3igAXx94/BXw59r4N+HWpJzvS52FzKf2YbS42l0p45WTqUdXtqrpTd9Uy+RhO4w2As4F3ichdOCVHn4TyKpvqMlLmATYXm0vhsbmU51wyIS/hnDGbl+J0+3HraPfgpDa/B1itqq9A6cumBjFS5gE2l3LF5lKejKS5hCFf1TmHcBIvNgGHxu6Q3wWGVPWv7hdWAYyUeYDNpVyxuZQnI2ku6cmXzQgnpXkIp1DRuaW2YY32edhcyvdlcynP10iaS7qX27g4Z0RkX5z6FT9R1Z15OWgJGCnzAJtLuWJzKU9G0lzSkTfBbxiGYVQGJW+9aBiGYRQXE/yGYRijDBP8hmEYowwT/IZhGKMME/yGYRijDBP8hpGEiAyKyFMi8qyIrBKRr4lI4LUiItNE5MxijdEwcsEEv2Gk0quqh6nqITi1W04ELkyzzzTABL9REVgcv2EkISJbVXVswuf9gCeAyThleW8CxsRWf0lVHxGRx3Aahq8GlgI/Ay4Bjsap/vgLVb2maJMwjABM8BtGEsmCP7bsLZy+sD049Vt2iMiBwC2q2iYiRwP/rqonxbafC+yuqj8UkXrgYeBTqrq6mHMxDC/y2mzdMEYwbineWuBKETkMpz3fQT7bH4dT7Ov02Odm4ECcJwLDKCkm+A0jDTFTzyCwAcfW/wYwA8dHtsNvN5wmHvcVZZCGkQHm3DWMAERkCrAYuFIdu2gz8JqqDuEU9KqObdoDjEvY9T5gnojUxo5zkIiMwTDKANP4DSOVRhF5CsesM4DjzP1JbN1VwO9E5FPAg8C22PKngQERWQXcACzCifT531jHpo3AJ4szfMMIxpy7hmEYowwz9RiGYYwyTPAbhmGMMkzwG4ZhjDJM8BuGYYwyTPAbhmGMMkzwG4ZhjDJM8BuGYYwy/j8VdUGe4G3J/QAAAABJRU5ErkJggg==\n",
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"![](\"images/cw_logo_80.png\"){kind=logo}
CoastWatch Satellite Training Course