{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spatial and temporal subsetting \n", "\n", "A common task in climate data analysis is subsetting files over a region of interest. Global model simulations and observations cover the entire globe, while impact analyses are often concerned with a region. Instead of downloading the entire file on a local disk, it is often more practical to subset it on the server and only download the relevant part. \n", "\n", "This can be done through two ways: interactive analysis using OPeNDAP, or a WPS request for a subsetter. Let's start with the most direct approach with OPeNDAP. The PAVICS THREDDS server provides two links for each file, a link to the file itself which will download the file locally when accessed, and a *dodsC* link which supports the OPeNDAP protocol. We'll use this link and simply pass it to our netCDF library, here `xarray`. \n", "\n", "## Subsetting with OPeNDAP" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import xarray as xr\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "# The dodsC link for the test file\n", "dap = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/'\n", "ncfile = 'birdhouse/testdata/flyingpigeon/cmip5/tasmax_Amon_MPI-ESM-MR_rcp45_r1i1p1_200601-200612.nc'\n", "\n", "# Here we open the file and subset it using xarray fonctionality, which communicates directly with \n", "# the OPeNDAP server to retrieve only the data needed. \n", "ds = xr.open_dataset(dap+ncfile)\n", "tas = ds.tasmax\n", "subtas = tas.sel(time=slice('2006-01-01', '2006-03-01'), lon=slice(188,330), lat=slice(6, 70))\n", "subtas.isel(time=0).plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Subset processes with WPS and FlyingPigeon\n", "\n", "PAVICS offers a number of subsetting processes through the FlyingPigeon WPS server:\n", " - subset_continents\n", " - subset_countries\n", " - subset_bbox\n", " - subset_wfs\n", " - subset\n", " \n", "The `subset_continents` and `subset_countries` use a predefined list of polygons for the subsetting. The `subset_bbox` takes the geographical coordinates of the two opposite corner of a rectangle to define the subset region, while both `subset_wfs` and `subset` use a polygon defined on a remote geoserver, identified by a typename and a feature id. The only difference between those two is that `subset` also does temporal subsetting. \n", "\n", "The first step to launch those services is to create a connexion to the WPS server using Birdy's `WPSClient`. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from birdy import WPSClient\n", "url = 'https://pavics.ouranos.ca/twitcher/ows/proxy/flyingpigeon/wps'\n", "fp = WPSClient(url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll use `fp.subset_continents`, so let's first check what arguments it expects and pass those to the function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method subset_continents in module birdy.client.base:\n", "\n", "subset_continents(region='Africa', mosaic=None, resource=None) method of birdy.client.base.WPSClient instance\n", " Return the data whose grid cells intersect the selected continents for each input dataset.\n", " \n", " Parameters\n", " ----------\n", " region : {'Africa', 'Asia', 'Australia', 'North America', 'Oceania', 'South America', 'Antarctica', 'Europe'}string\n", " Continent name.\n", " mosaic : boolean\n", " If True, selected regions will be merged into a single geometry.\n", " resource : ComplexData:mimetype:`application/x-netcdf`, :mimetype:`application/x-tar`, :mimetype:`application/zip`\n", " NetCDF Files or archive (tar/zip) containing netCDF files.\n", " \n", " Returns\n", " -------\n", " output : ComplexData:mimetype:`application/x-tar`\n", " Tar archive of the subsetted netCDF files.\n", " ncout : ComplexData:mimetype:`application/x-netcdf`\n", " NetCDF file with subset for one dataset.\n", " output_log : ComplexData:mimetype:`text/plain`\n", " Collected logs during process run.\n", "\n" ] } ], "source": [ "help(fp.subset_continents)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "thredds = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/fileServer/'\n", "ncfile = 'birdhouse/testdata/flyingpigeon/cmip5/tasmax_Amon_MPI-ESM-MR_rcp45_r1i1p1_200601-200612.nc'\n", "resp = fp.subset_continents(resource=thredds+ncfile, region='Africa')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The response we're getting can either include the data itself or a reference to the data. Using the `get` method of the response object, we'll get what was included in the response. If the response holds only a reference (link) to the output, we can retrieve it using the `get(as_obj=True)` method. Birdy will then inspect the file format of each output and try to find the appropriate way to open the file and return a Python object. A warning is issued if no converter is found, in which case the original reference is returned." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/david/src/birdy/birdy/client/outputs.py:65: UserWarning: No converter was found for mime type: application/x-tar\n", " warnings.warn(UserWarning(\"No converter was found for mime type: {}\".format(output.mimeType)))\n" ] } ], "source": [ "resp.get()\n", "tar_out, nc_out, log = resp.get(asobj=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll open the netCDF dataset using xarray and plot the result. Note that since nc_out is an already opened netcdf4.Dataset, we're using the `xr.backends.NetCDF4DataStore` function to open the dataset. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import xarray as xr\n", "ds = xr.open_dataset(xr.backends.NetCDF4DataStore(nc_out))\n", "ds.tasmax.isel(time=0).plot()" ] }, { "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }