{ "cells": [ { "cell_type": "markdown", "id": "0", "metadata": {}, "source": [ "# Demo: Using Google Earth engine\n", "\n", "\n", " This example serves as a demonstration on how to interact with Google Earth Engine (GEE) from within the MetObs-toolkit framework." ] }, { "cell_type": "code", "execution_count": 1, "id": "1", "metadata": {}, "outputs": [], "source": [ "import metobs_toolkit" ] }, { "cell_type": "markdown", "id": "2", "metadata": {}, "source": [ "## GEE authentication\n", "\n", "Before proceeding, make sure you have **set up a Google developers account and a GEE project**. See [Using Google Earth Engine](https://metobs-toolkit.readthedocs.io/en/latest/topics/gee_authentication.html#using-google-earth-engine) for a detailed description of this.\n", "\n", "To test the GEE authentication we can use the ``metobs_toolkit.connect_to_gee`` method. If you authenticate using new credentials, make sure **NOT** to check the *read-only-scopes*. (See Extracting ERA5 data)." ] }, { "cell_type": "code", "execution_count": 2, "id": "3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%script true \n", "\n", "metobs_toolkit.connect_to_gee() #Uses a credetial file that is stored on your computer" ] }, { "cell_type": "markdown", "id": "4", "metadata": {}, "source": [ "If something is wrong with your credential (or it is the first time/ expiered credetials) you can force the creation of a new credential (file). You only need to run this if the `metobs_toolkit.connect_to_gee()` returned an error." ] }, { "cell_type": "code", "execution_count": 3, "id": "5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%script true \n", "\n", "metobs_toolkit.connect_to_gee(\n", " force=True, #create new credentials\n", " auth_mode='notebook', # 'notebook', 'localhost', 'gcloud' (requires gcloud installed) or 'colab' (works only in colab)\n", ")" ] }, { "cell_type": "markdown", "id": "6", "metadata": {}, "source": [ "For more details on authentication we refer to the `Using Google Earth Engine <../Using_gee.html>`_ page and the [authentication info page](https://developers.google.com/earth-engine/guides/auth) of GEE." ] }, { "cell_type": "markdown", "id": "7", "metadata": {}, "source": [ "## GEE interaction from the MetObs-toolkit\n", "\n", "There are two classes that facilitate the interaction with GEE and the metobs_toolkit:\n", "\n", " * ``GeeStaticDatasetManager``: This class handles GEE Datasets that do not have a time dimension (static).\n", " * ``GeeDynamicDatasetManager``: This class handles GEE Datasets that have a time dimension.\n", "\n", " Both classes holds all the information to extract data, at the location of your stations, \n", " from the corresponding GEE dataset. For the ``GeeStaticDatasetManager`` the extracted data will update the metadata of the stations and the corresponding ``Station.site`` attribute will be update. Typical examples are the extraction of Local Climate Zones (LCZ), altitude and landcoverfractions. \n", "\n", " When data is extracted using a ``GeeDynamicDatasetManager``, timeseries is typically extracted. These timeseries are stored in ``ModelTimeSeries``. Typical examples are the extraction of ERA5 equivalent timeseries for a sensor.\n", "\n", "\n", "By default some a GEEDatasetManager is create for some popular datasets:" ] }, { "cell_type": "code", "execution_count": 4, "id": "8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'LCZ': GEEStaticDatasetManager(name=LCZ, location=RUB/RUBCLIM/LCZ/global_lcz_map/latest),\n", " 'altitude': GEEStaticDatasetManager(name=altitude, location=CGIAR/SRTM90_V4),\n", " 'worldcover': GEEStaticDatasetManager(name=worldcover, location=ESA/WorldCover/v200),\n", " 'ERA5-land': GEEDynamicDatasetManager(name=ERA5-land, location=ECMWF/ERA5_LAND/HOURLY)}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "default_managers=metobs_toolkit.default_GEE_datasets\n", "default_managers" ] }, { "cell_type": "markdown", "id": "9", "metadata": {}, "source": [ "We can see that by default there are 3 ``GeeStaticDatasetManagers`` (lcz, altitude and worldcover), and one ``GeeDynamicDatasetManager`` (ERA5-land).\n", "\n", "Let's look at the local climate zones (LCZ) dataset manager as example:" ] }, { "cell_type": "code", "execution_count": 5, "id": "10", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", " General info of GEEStaticDataset \n", "================================================================================\n", "\n", "\n", "--- GEE Dataset details ---\n", "\n", " -name: LCZ\n", " -location: RUB/RUBCLIM/LCZ/global_lcz_map/latest\n", " -value_type: categorical\n", " -scale: 100\n", " -is_static: True\n", " -is_image: False\n", " -is_mosaic: True\n", " -credentials: Demuzere M.; Kittner J.; Martilli A.; Mills, G.; Moede, C.; S...\n", " -target band: LCZ_Filter\n", " -classification: \n", " -1: Compact highrise\n", " -2: Compact midrise\n", " -3: Compact lowrise\n", " -4: Open highrise\n", " -5: Open midrise\n", " -6: Open lowrise\n", " -7: Lightweight lowrise\n", " -8: Large lowrise\n", " -9: Sparsely built\n", " -10: Heavy industry\n", " -11: Dense Trees (LCZ A)\n", " -12: Scattered Trees (LCZ B)\n", " -13: Bush, scrub (LCZ C)\n", " -14: Low plants (LCZ D)\n", " -15: Bare rock or paved (LCZ E)\n", " -16: Bare soil or sand (LCZ F)\n", " -17: Water (LCZ G)\n", " -aggregation: \n", " -colors: \n", " -1: #8c0000\n", " -2: #d10000\n", " -3: #ff0000\n", " -4: #bf4d00\n", " -5: #ff6600\n", " -6: #ff9955\n", " -7: #faee05\n", " -8: #bcbcbc\n", " -9: #ffccaa\n", " -10: #555555\n", " -11: #006a00\n", " -12: #00aa00\n", " -13: #648525\n", " -14: #b9db79\n", " -15: #000000\n", " -16: #fbf7ae\n", " -17: #6a6aff\n", "\n" ] } ], "source": [ "default_managers['LCZ'].get_info()" ] }, { "cell_type": "markdown", "id": "11", "metadata": {}, "source": [ "As you can see, the DatasetManager contains info on where this dataset is stored on GEE, some details on the dataset and some defenitions (color schemes, aggregation, label defenitions)." ] }, { "cell_type": "markdown", "id": "12", "metadata": {}, "source": [ "# Extracting data from GEE\n", "\n", "As a demonstration we are going to extract data from GEE. When we use the MetObs-toolkit for it, it will **extract data from GEE only at the locations of the stations**. This extracted data is thus linked to a station (by location).\n", "\n", "Some common pracktices are to get the LCZ, altitude and some landcover information of all the stations. We start by importin the demo data and extract these details from corresponding datasets on GEE." ] }, { "cell_type": "code", "execution_count": 6, "id": "13", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Luchtdruk is present in the datafile, but not found in the template! This column will be ignored.\n", "Neerslagintensiteit is present in the datafile, but not found in the template! This column will be ignored.\n", "Neerslagsom is present in the datafile, but not found in the template! This column will be ignored.\n", "Rukwind is present in the datafile, but not found in the template! This column will be ignored.\n", "Luchtdruk_Zeeniveau is present in the datafile, but not found in the template! This column will be ignored.\n", "Globe Temperatuur is present in the datafile, but not found in the template! This column will be ignored.\n", "The following columns are present in the data file, but not in the template! They are skipped!\n", " ['Luchtdruk', 'Neerslagsom', 'Rukwind', 'Neerslagintensiteit', 'Globe Temperatuur', 'Luchtdruk_Zeeniveau']\n", "The following columns are found in the metadata, but not in the template and are therefore ignored: \n", "['sponsor', 'Network', 'stad', 'benaming']\n" ] } ], "source": [ "#Import the demo dataset\n", "dataset = metobs_toolkit.Dataset()\n", "dataset.import_data_from_file(\n", " template_file=metobs_toolkit.demo_template,\n", " input_data_file=metobs_toolkit.demo_datafile,\n", " input_metadata_file=metobs_toolkit.demo_metadatafile,\n", ")" ] }, { "cell_type": "markdown", "id": "14", "metadata": {}, "source": [ "The current metdata is rather limited:" ] }, { "cell_type": "code", "execution_count": 7, "id": "15", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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latlonaltitudeLCZschoolgeometry
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vlinder0150.9804383.815763NaNNaNUGentPOINT (3.81576 50.98044)
vlinder0251.0223813.709695NaNNaNUGentPOINT (3.7097 51.02238)
vlinder0351.3245814.952109NaNNaNHeilig GrafPOINT (4.95211 51.32458)
vlinder0451.3355224.934732NaNNaNHeilig GrafPOINT (4.93473 51.33552)
vlinder0551.0526543.675183NaNNaNSint-BarbaraPOINT (3.67518 51.05265)
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" ], "text/plain": [ " lat lon altitude LCZ school \\\n", "name \n", "vlinder01 50.980438 3.815763 NaN NaN UGent \n", "vlinder02 51.022381 3.709695 NaN NaN UGent \n", "vlinder03 51.324581 4.952109 NaN NaN Heilig Graf \n", "vlinder04 51.335522 4.934732 NaN NaN Heilig Graf \n", "vlinder05 51.052654 3.675183 NaN NaN Sint-Barbara \n", "\n", " geometry \n", "name \n", "vlinder01 POINT (3.81576 50.98044) \n", "vlinder02 POINT (3.7097 51.02238) \n", "vlinder03 POINT (4.95211 51.32458) \n", "vlinder04 POINT (4.93473 51.33552) \n", "vlinder05 POINT (3.67518 51.05265) " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.metadf.head(5)" ] }, { "cell_type": "markdown", "id": "16", "metadata": {}, "source": [ "To extract geospatial information for your stations, the **lat** and **lon** (latitude and longitude)\n", "of your stations must be present in the metadf. If so, then geospatial\n", "information will be extracted from GEE at these locations." ] }, { "cell_type": "markdown", "id": "17", "metadata": {}, "source": [ "## Extracting LCZ from GEE\n", "To extract the Local Climate Zones (LCZs) of your stations:" ] }, { "cell_type": "code", "execution_count": 8, "id": "18", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " LCZ\n", "name \n", "vlinder01 Low plants (LCZ D)\n", "vlinder02 Large lowrise\n", "vlinder03 Open midrise\n", "vlinder04 Sparsely built\n", "vlinder05 Water (LCZ G)\n" ] } ], "source": [ "LCZ_values = dataset.get_LCZ()\n", "\n", "# The LCZs for all your stations are extracted\n", "print(LCZ_values.head(5))" ] }, { "cell_type": "markdown", "id": "19", "metadata": {}, "source": [ "The LCZ data is returned by the ``Dataset.get_lcz()`` method. In addition metadata (stored in the ``Station.site`` attribute) of your dataset is also updated." ] }, { "cell_type": "code", "execution_count": 9, "id": "20", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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latlonaltitudeLCZschoolgeometry
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vlinder0150.9804383.815763NaNLow plants (LCZ D)UGentPOINT (3.81576 50.98044)
vlinder0251.0223813.709695NaNLarge lowriseUGentPOINT (3.7097 51.02238)
vlinder0351.3245814.952109NaNOpen midriseHeilig GrafPOINT (4.95211 51.32458)
vlinder0451.3355224.934732NaNSparsely builtHeilig GrafPOINT (4.93473 51.33552)
vlinder0551.0526543.675183NaNWater (LCZ G)Sint-BarbaraPOINT (3.67518 51.05265)
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" ], "text/plain": [ " lat lon altitude LCZ school \\\n", "name \n", "vlinder01 50.980438 3.815763 NaN Low plants (LCZ D) UGent \n", "vlinder02 51.022381 3.709695 NaN Large lowrise UGent \n", "vlinder03 51.324581 4.952109 NaN Open midrise Heilig Graf \n", "vlinder04 51.335522 4.934732 NaN Sparsely built Heilig Graf \n", "vlinder05 51.052654 3.675183 NaN Water (LCZ G) Sint-Barbara \n", "\n", " geometry \n", "name \n", "vlinder01 POINT (3.81576 50.98044) \n", "vlinder02 POINT (3.7097 51.02238) \n", "vlinder03 POINT (4.95211 51.32458) \n", "vlinder04 POINT (4.93473 51.33552) \n", "vlinder05 POINT (3.67518 51.05265) " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.metadf.head(5)" ] }, { "cell_type": "markdown", "id": "21", "metadata": {}, "source": [ "*Note* All the methods in this demo that are applied on a ``Dataset`` can also be applied on a ``Station``" ] }, { "cell_type": "code", "execution_count": 10, "id": "22", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The LCZ of vlinder21 is: Sparsely built\n", "================================================================================\n", " General Info of Site \n", "================================================================================\n", "\n", "Site of vlinder21:\n", " -Coordinates (51.260389, 2.991917) (latitude, longitude)\n", " -Altitude is unknown\n", " -LCZ: Sparsely built\n", " -Land cover fractions are unknown\n", " -Extra metadata from the metadata file:\n", " -school: Zeelyceum\n", "\n" ] } ], "source": [ "#demonstration an Station-level\n", "your_station = dataset.get_station('vlinder21')\n", "the_LCZ_of_your_statation = your_station.get_LCZ()\n", "\n", "print(\"The LCZ of vlinder21 is: \", the_LCZ_of_your_statation)\n", "\n", "#or instpect the site attribute of the station\n", "your_station.site.get_info()" ] }, { "cell_type": "markdown", "id": "23", "metadata": {}, "source": [ "## Extracting alitutde from GEE\n", "\n", "Similar as LCZ extraction we can get the altitude of the stations. " ] }, { "cell_type": "code", "execution_count": 11, "id": "24", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", " General info of GEEStaticDataset \n", "================================================================================\n", "\n", "\n", "--- GEE Dataset details ---\n", "\n", " -name: altitude\n", " -location: CGIAR/SRTM90_V4\n", " -value_type: numeric\n", " -scale: 100\n", " -is_static: True\n", " -is_image: True\n", " -is_mosaic: False\n", " -credentials: SRTM Digital Elevation Data Version 4\n", " -target band: elevation\n", " -classification: \n", " -aggregation: \n", " -colors: \n", "\n" ] }, { "data": { "text/html": [ "
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latlonaltitudeLCZschoolgeometry
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vlinder0150.9804383.81576312.0Low plants (LCZ D)UGentPOINT (3.81576 50.98044)
vlinder0251.0223813.7096957.0Large lowriseUGentPOINT (3.7097 51.02238)
vlinder0351.3245814.95210930.0Open midriseHeilig GrafPOINT (4.95211 51.32458)
vlinder0451.3355224.93473225.0Sparsely builtHeilig GrafPOINT (4.93473 51.33552)
vlinder0551.0526543.6751830.0Water (LCZ G)Sint-BarbaraPOINT (3.67518 51.05265)
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" ], "text/plain": [ " lat lon altitude LCZ school \\\n", "name \n", "vlinder01 50.980438 3.815763 12.0 Low plants (LCZ D) UGent \n", "vlinder02 51.022381 3.709695 7.0 Large lowrise UGent \n", "vlinder03 51.324581 4.952109 30.0 Open midrise Heilig Graf \n", "vlinder04 51.335522 4.934732 25.0 Sparsely built Heilig Graf \n", "vlinder05 51.052654 3.675183 0.0 Water (LCZ G) Sint-Barbara \n", "\n", " geometry \n", "name \n", "vlinder01 POINT (3.81576 50.98044) \n", "vlinder02 POINT (3.7097 51.02238) \n", "vlinder03 POINT (4.95211 51.32458) \n", "vlinder04 POINT (4.93473 51.33552) \n", "vlinder05 POINT (3.67518 51.05265) " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Get altitude\n", "altitude_data = dataset.get_altitude()\n", "\n", "#FYI, the details of the DEM dataset:\n", "metobs_toolkit.default_GEE_datasets['altitude'].get_info()\n", "\n", "#The metadata is updated (altitude in meters)\n", "dataset.metadf.head(5)" ] }, { "cell_type": "markdown", "id": "25", "metadata": {}, "source": [ "## Extracting landcover buffer fractions\n", "\n", "A more detailed description of the landcover/land use in the microenvironment can be extracted in the form of landcover fractions in a circular buffer for each station.\n", "\n", "You can select to aggregate the landcover classes to water - pervious and impervious, or set aggregation to false to extract the landcover classes as present in the worldcover_10m dataset." ] }, { "cell_type": "code", "execution_count": 12, "id": "26", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " water pervious impervious\n", "name buffer_radius \n", "vlinder01 100 0.000000 0.981781 0.018219\n", "vlinder02 100 0.000000 0.428575 0.571425\n", "vlinder03 100 0.000000 0.244885 0.755115\n", "vlinder04 100 0.000000 0.979632 0.020368\n", "vlinder05 100 0.446909 0.224624 0.328467\n" ] } ], "source": [ "aggregated_landcover = dataset.get_landcover_fractions(\n", " buffers=[100, 250], # a list of buffer radii in meters\n", " aggregate=True #if True, aggregate landcover classes to the water, pervious and impervious.\n", " )\n", "\n", "print(aggregated_landcover.head(5))" ] }, { "cell_type": "markdown", "id": "27", "metadata": {}, "source": [ "We have extracted the landcover fractions for multiple buffers. Similar as with LCZ and altitude extraction, the metadata of the stations is updated." ] }, { "cell_type": "code", "execution_count": 13, "id": "28", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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" ], "text/plain": [ " lat lon altitude LCZ school \\\n", "name \n", "vlinder01 50.980438 3.815763 12.0 Low plants (LCZ D) UGent \n", "vlinder02 51.022381 3.709695 7.0 Large lowrise UGent \n", "vlinder03 51.324581 4.952109 30.0 Open midrise Heilig Graf \n", "vlinder04 51.335522 4.934732 25.0 Sparsely built Heilig Graf \n", "vlinder05 51.052654 3.675183 0.0 Water (LCZ G) Sint-Barbara \n", "\n", " water_frac_100m pervious_frac_100m impervious_frac_100m \\\n", "name \n", "vlinder01 0.000000 0.981781 0.018219 \n", "vlinder02 0.000000 0.428575 0.571425 \n", "vlinder03 0.000000 0.244885 0.755115 \n", "vlinder04 0.000000 0.979632 0.020368 \n", "vlinder05 0.446909 0.224624 0.328467 \n", "\n", " water_frac_250m pervious_frac_250m impervious_frac_250m \\\n", "name \n", "vlinder01 0.000000 0.963641 0.036359 \n", "vlinder02 0.000000 0.535976 0.464024 \n", "vlinder03 0.000000 0.160759 0.839241 \n", "vlinder04 0.000000 0.881949 0.118051 \n", "vlinder05 0.242442 0.526955 0.230604 \n", "\n", " geometry \n", "name \n", "vlinder01 POINT (3.81576 50.98044) \n", "vlinder02 POINT (3.7097 51.02238) \n", "vlinder03 POINT (4.95211 51.32458) \n", "vlinder04 POINT (4.93473 51.33552) \n", "vlinder05 POINT (3.67518 51.05265) " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.metadf.head(5)\n" ] }, { "cell_type": "markdown", "id": "29", "metadata": {}, "source": [ "## Plotting a GeeStaticDatasetManager\n", "\n", "You can make an interactive plot of a static GEE dataset, by using the ``GeeStaticDatasetManager.make_gee_plot()`` method. Equivalent is the the ``Dataset.make_gee_plot()`` method, that will also show the locations of the stations on the map.\n", "\n", "*Note: Not all Python environments can visualize the interactive map. If that is the case for you, you can save the map\n", "as a (html) file, and open it with your browser.*" ] }, { "cell_type": "code", "execution_count": 14, "id": "30", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Get a GeeStaticDatasetManager to plot\n", "geemanager_to_plot = metobs_toolkit.default_GEE_datasets['LCZ']\n", "\n", "dataset.make_gee_plot(gee_manager=geemanager_to_plot)" ] }, { "cell_type": "markdown", "id": "31", "metadata": {}, "source": [ "## Extracting ERA5 timeseries \n", "\n", "Now we demonstrate how to use the `GeeDynamicDatasetManager`, and use the ERA5-land (GEE) dataset for it." ] }, { "cell_type": "code", "execution_count": 15, "id": "32", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", " General info of GEEDynamicDataset \n", "================================================================================\n", "\n", "\n", "--- GEE Dataset details ---\n", "\n", " -name: ERA5-land\n", " -location: ECMWF/ERA5_LAND/HOURLY\n", " -value_type: numeric\n", " -scale: 2500\n", " -is_static: False\n", " -is_image: False\n", " -is_mosaic: False\n", " -credentials: \n", " -time res: 1h\n", "\n", "--- Known Modelobstypes ---\n", "\n", " -temp : ModelObstype instance of temp\n", " -conversion: kelvin --> degree_Celsius\n", " -pressure : ModelObstype instance of pressure\n", " -conversion: pascal --> hectopascal\n", " -wind : ModelObstype_Vectorfield instance of wind\n", " -vectorfield that will be converted to: \n", " -wind_speed\n", " -wind_direction\n", " -conversion: meter / second --> meter / second\n", "\n" ] } ], "source": [ "era5_manager = metobs_toolkit.default_GEE_datasets['ERA5-land']\n", "era5_manager.get_info()" ] }, { "cell_type": "markdown", "id": "33", "metadata": {}, "source": [ "As you can see, a ``GeeDynamicDatasetManager`` holds the information on the location of the dataset on GEE and a collection of ``ModelObstype``s. These ``ModelObstypes`` are used to interpret the bands of the dataset to obstypes (unit conversions etc.)" ] }, { "cell_type": "markdown", "id": "34", "metadata": {}, "source": [ "As an example we donwload 2m temperature timeseries for one day, for all the stations (locations) in the dataset." ] }, { "cell_type": "code", "execution_count": 16, "id": "36", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/thoverga/Documents/VLINDER_github/MetObs_toolkit/src/metobs_toolkit/backend_collection/decorators.py:11: UserWarning: The datetime argument has no timezone information. Assuming UTC timezone.\n", " return func(*args, **kwargs)\n" ] }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "('name', 'datetime')", "rawType": "object", "type": "unknown" }, { "name": "temp", "rawType": "float64", "type": "float" } ], "ref": "eb2a4518-7d84-482d-ad5b-fa4b2904df8b", "rows": [ [ "('vlinder01', Timestamp('2022-09-02 00:00:00+0000', tz='UTC'))", "290.8556365966797" ], [ "('vlinder01', Timestamp('2022-09-02 01:00:00+0000', tz='UTC'))", "290.38067626953125" ], [ "('vlinder01', Timestamp('2022-09-02 02:00:00+0000', tz='UTC'))", "289.8893127441406" ], [ "('vlinder01', Timestamp('2022-09-02 03:00:00+0000', tz='UTC'))", "289.6437683105469" ], [ "('vlinder01', Timestamp('2022-09-02 04:00:00+0000', tz='UTC'))", "289.4191436767578" ] ], "shape": { "columns": 1, "rows": 5 } }, "text/html": [ "
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" ], "text/plain": [ " temp\n", "name datetime \n", "vlinder01 2022-09-02 00:00:00+00:00 290.855637\n", " 2022-09-02 01:00:00+00:00 290.380676\n", " 2022-09-02 02:00:00+00:00 289.889313\n", " 2022-09-02 03:00:00+00:00 289.643768\n", " 2022-09-02 04:00:00+00:00 289.419144" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "#Specify a start and end timestamp\n", "tstart = pd.Timestamp('2022-09-02 00:00:00')\n", "tend = pd.Timestamp('2022-09-03 00:00:00')\n", "\n", "#Extract the timeseries \n", "era5_temp = dataset.get_gee_timeseries_data(\n", " gee_dynamic_manager=era5_manager, #The datasetmanager to use\n", " startdt_utc=tstart,\n", " enddt_utc=tend,\n", " obstypes=['temp'], #the observationtypes to extract, must be knonw modelobstypes\n", " get_all_bands=False, #If true, all bands are extracted (but not stored in the stations if the band is unknwown)\n", " )\n", "\n", "era5_temp.head()" ] }, { "cell_type": "markdown", "id": "37", "metadata": {}, "source": [ "As you can see, the temperature data is :\n", "* Extracted as timeseries at the location of the stations\n", "* The timeseries are formatted in a pandas Dataframe\n", "* If the requested bands have a corresponding ModelObstype: \n", " * The timeseries are stored as ``ModelTimeSeries`` for each ``Station``.\n", " \n", "*NOTE* The returned Dataframe contains the raw values, thus in the naitive units (Kelvin in this example). This is not true for the modeldata stored in the `Station`s. \n", "\n", "The temperature data is thus stored as timeseries in each station." ] }, { "cell_type": "code", "execution_count": 17, "id": "38", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "dataset.get_station('vlinder12').modeldata" ] }, { "cell_type": "markdown", "id": "39", "metadata": {}, "source": [ "More convenient is to use the ``.modeldatadf`` attribute to get all the stored modeldata in a dataframe." ] }, { "cell_type": "code", "execution_count": 18, "id": "40", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { 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"(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder04')", "19.090424", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder05')", "17.78183", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder06')", "19.166595", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder07')", "19.166595", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder08')", "19.166595", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder09')", "18.113861", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder10')", "17.908783", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder11')", "18.738861", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder12')", "18.738861", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder13')", "18.738861", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius", "ERA5-land", "temperature_2m" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder14')", "18.434174", 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2022-09-03 00:00:00+00:00tempvlinder2419.035004ERA5-land:temperature_2m converted from kelvin...ERA5-landtemperature_2m
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" ], "text/plain": [ " value \\\n", "datetime obstype name \n", "2022-09-02 00:00:00+00:00 temp vlinder01 17.705658 \n", " vlinder02 17.631439 \n", " vlinder03 19.090424 \n", " vlinder04 19.090424 \n", " vlinder05 17.781830 \n", "... ... \n", "2022-09-03 00:00:00+00:00 temp vlinder24 19.035004 \n", " vlinder25 18.999847 \n", " vlinder26 19.835785 \n", " vlinder27 18.843597 \n", " vlinder28 18.562347 \n", "\n", " details \\\n", "datetime obstype name \n", "2022-09-02 00:00:00+00:00 temp vlinder01 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder02 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder03 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder04 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder05 ERA5-land:temperature_2m converted from kelvin... \n", "... ... \n", "2022-09-03 00:00:00+00:00 temp vlinder24 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder25 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder26 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder27 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder28 ERA5-land:temperature_2m converted from kelvin... \n", "\n", " modelname modelvariable \n", "datetime obstype name \n", "2022-09-02 00:00:00+00:00 temp vlinder01 ERA5-land temperature_2m \n", " vlinder02 ERA5-land temperature_2m \n", " vlinder03 ERA5-land temperature_2m \n", " vlinder04 ERA5-land temperature_2m \n", " vlinder05 ERA5-land temperature_2m \n", "... ... ... \n", "2022-09-03 00:00:00+00:00 temp vlinder24 ERA5-land temperature_2m \n", " vlinder25 ERA5-land temperature_2m \n", " vlinder26 ERA5-land temperature_2m \n", " vlinder27 ERA5-land temperature_2m \n", " vlinder28 ERA5-land temperature_2m \n", "\n", "[700 rows x 4 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.modeldatadf" ] }, { "cell_type": "markdown", "id": "b90b7255-8d36-4a65-aad0-b621486dfcf0", "metadata": {}, "source": [ "You can confirm that in the MetObs classes, all data is stored in standard-units of a specif `Obstype`. The temperature values are thus stored in degrees Celsius." ] }, { "cell_type": "markdown", "id": "41", "metadata": {}, "source": [ "## Plotting with GeeDynamicDataset\n", "\n", "There are two implementation to plot a dynamic dataset.\n", "\n", "1. You can plot the extracted timeseries using the `.make_plot_of_modeldata()` or use the `.make_plot(..., show_modeldata=True,... )` method.\n", "\n", "2. You can make an interactive spatial plot of the GEE dataset using the `.make_gee_plot()` method." ] }, { "cell_type": "code", "execution_count": 19, "id": "42", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot the modeldata and observational temperatures of a single station\n", "dataset.get_station('vlinder04').make_plot(obstype='temp', show_modeldata=True)" ] }, { "cell_type": "code", "execution_count": 20, "id": "43", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/thoverga/Documents/VLINDER_github/MetObs_toolkit/src/metobs_toolkit/backend_collection/decorators.py:11: UserWarning: The datetime argument has no timezone information. Assuming UTC timezone.\n", " return func(*args, **kwargs)\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Interactive spatial plot of the GEE dataset (with the stations as markers)\n", "\n", "dataset.make_gee_plot(\n", " gee_manager = era5_manager,\n", " timeinstance=pd.Timestamp('2022-09-02 13:00:00'),\n", " modelobstype='temp',\n", " vmax=None, #if None, the colorscale is optimalized for the extend of all stations\n", " vmin=None, #if None, the colorscale is optimalized for the extend of all stations\n", " )" ] }, { "cell_type": "markdown", "id": "44", "metadata": {}, "source": [ "## Transfer of larger amounts of data \n", "\n", "There is a limit to the amount of data that can be transferred directly from GEE. When the data cannot be transferred directly, **it will be written to a file on your Google Drive**. The location of the file will be printed out. When the writing to the file is done, you must download the file and import it using the ``Dataset.import_gee_data_from_file()`` method.\n", "\n", "*Note*: The reason not to check the *read-only* option upon GEE authentication is to enable possibility to write files to your google drive.\n", "\n", "As a demonstration we download the temperature and pressure timeseries from ERA5-land, for all the stations and for the same period as the observations (15 days). This amount of data is to large to transfer directly. " ] }, { "cell_type": "code", "execution_count": 21, "id": "45", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING::THE DATA AMOUNT IS TOO LARGE FOR INTERACTIVE SESSION, THE DATA WILL BE EXPORTED TO YOUR GOOGLE DRIVE!\n", "WARNING::The timeseries will be written to your Drive in gee_timeseries_data/ERA5-land_timeseries_data_of_full_dataset_28_stations \n", "WARNING::The data is transfered! Open the following link in your browser: \n", "\n", "\n", "WARNING::https://drive.google.com/#folders/1QE0DA-YqeHh5gSUG2L1EWh7ndyxa7xNj \n", "\n", "\n", "WARNING::To upload the data to the model, use the Dataset.import_gee_data_from_file() method.\n", "WARNING::No data is returned by the GEE api request.\n" ] } ], "source": [ "\n", "#Extract the timeseries \n", "_nothing_returned = dataset.get_gee_timeseries_data(\n", " gee_dynamic_manager=era5_manager, #The datasetmanager to use\n", " startdt_utc=None, #If None, the startdt of the dataset observations is used\n", " enddt_utc=None, #If None, the startdt of the dataset observations is used\n", " obstypes=['temp', 'pressure'], #the observationtypes to extract, must be knonw modelobstypes\n", " )\n" ] }, { "cell_type": "markdown", "id": "46", "metadata": {}, "source": [ "The data request was to big for direct transfer, a (CSV) file is written to your Google Drive (See the link in the printed warning).\n", "\n", "Download that file, and then import the modeldata from the file." ] }, { "cell_type": "code", "execution_count": 22, "id": "47", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%script true \n", "\n", "#Import the ERA5 data\n", "dataset.import_gee_data_from_file(\n", " filepath= '... path to the downloaded file ...',\n", " gee_dynamic_manager=era5_manager\n", " )" ] }, { "cell_type": "markdown", "id": "48", "metadata": {}, "source": [ "## Extending the defaults\n", "\n", "The default GeeDatasetManager are illustrative and they may not be sufficient. Here are some demonstrations on how to extend upon the defaults." ] }, { "cell_type": "markdown", "id": "49", "metadata": {}, "source": [ "### Using a new ModelObstype\n", "\n", "In this demo we add the surface sensible heat flux as a new modelobstype and download the timeseries at the location of the stations.\n", "\n", "See the [details of the ERA5-land](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY) dataset." ] }, { "cell_type": "code", "execution_count": 23, "id": "50", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "('datetime', 'obstype')", "rawType": "object", "type": "unknown" }, { "name": "value", "rawType": "float32", "type": "float" }, { "name": "details", "rawType": "object", "type": "string" }, { "name": "modelname", "rawType": "object", 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21:00:00+00:00 sensible_heat_flux surface_sensible_heat_flux \n", "2022-09-15 22:00:00+00:00 sensible_heat_flux surface_sensible_heat_flux \n", "2022-09-15 23:00:00+00:00 sensible_heat_flux surface_sensible_heat_flux \n", "2022-09-16 00:00:00+00:00 sensible_heat_flux surface_sensible_heat_flux \n", "\n", "[386 rows x 4 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#1. Create a observation type\n", "sensible_heat_flux = metobs_toolkit.Obstype(\n", " name='sensible_heat_flux',\n", " std_unit='kJ/m^2',\n", " description='The 2m sensible heat flux.')\n", "\n", "#2. Make a ModelObstype\n", "sensible_heat_flux_era5 = metobs_toolkit.ModelObstype(\n", " obstype=sensible_heat_flux,\n", " model_unit='J/m^2', #see details on GEE\n", " model_band='surface_sensible_heat_flux') #see details on GEE\n", "\n", "#3. Add the ModelObstype to the dataset manager\n", "era5_manager = metobs_toolkit.default_GEE_datasets['ERA5-land']\n", "era5_manager.add_modelobstype(sensible_heat_flux_era5)\n", "\n", "\n", "#4. Extract some sensible heat flux data (for a single station as demo)\n", "sens_heat_flx_data = dataset.get_station('vlinder04').get_gee_timeseries_data(\n", " gee_manager=era5_manager,\n", " startdt_utc=None,\n", " enddt_utc=None,\n", " obstypes=['sensible_heat_flux'] #refer by obsname\n", " )\n", "#The sensible heat flux data is stored as modeldata in the station (if the direct transfer is succescfull!)\n", "\n", "#5. make a plot\n", "dataset.get_station('vlinder04').make_plot_of_modeldata(obstype='sensible_heat_flux')\n", "\n", "#6. You can also get the data using the modeldatadf attribute\n", "dataset.get_station('vlinder04').modeldatadf\n" ] }, { "cell_type": "markdown", "id": "51", "metadata": {}, "source": [ "### Defining a new GEE dataset\n", "\n", "In this example we create a new ``GeeDyanamicDatasetManager`` that is a JAXA reanalysis product for global precipitation, see [JAXA_GPM_L3_GSMaP_v8_operational](https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational) details.\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "52", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "('datetime', 'obstype')", "rawType": "object", "type": "unknown" }, { "name": "value", "rawType": "float32", "type": "float" }, { "name": "details", "rawType": "object", "type": "string" }, { "name": "modelname", "rawType": "object", "type": "string" }, { "name": "modelvariable", "rawType": "object", "type": "string" } ], "ref": "1716a1c3-bffa-4844-8984-1d390c956d33", "rows": [ [ "(Timestamp('2022-09-01 00:00:00+0000', tz='UTC'), 'precip_rate')", "0.0", "JAXA_GPM_reanalysis:hourlyPrecipRate converted from millimeter / hour -> millimeter / hour", "JAXA_GPM_reanalysis", "hourlyPrecipRate" ], [ "(Timestamp('2022-09-01 00:00:00+0000', tz='UTC'), 'sensible_heat_flux')", "-2671.6372", "ERA5-land:surface_sensible_heat_flux converted from joule / meter ** 2 -> kilojoule / meter ** 2", "ERA5-land", "surface_sensible_heat_flux" ], [ "(Timestamp('2022-09-01 01:00:00+0000', tz='UTC'), 'precip_rate')", "0.0", "JAXA_GPM_reanalysis:hourlyPrecipRate converted from millimeter / hour -> millimeter / hour", "JAXA_GPM_reanalysis", "hourlyPrecipRate" ], [ "(Timestamp('2022-09-01 01:00:00+0000', tz='UTC'), 'sensible_heat_flux')", "156.80426", "ERA5-land:surface_sensible_heat_flux converted from joule / 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JAXA_GPM_reanalysis:hourlyPrecipRate converted... \n", "... ... \n", "2022-09-15 22:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-15 23:00:00+00:00 precip_rate JAXA_GPM_reanalysis:hourlyPrecipRate converted... \n", " sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-16 00:00:00+00:00 precip_rate JAXA_GPM_reanalysis:hourlyPrecipRate converted... \n", " sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "\n", " modelname \\\n", "datetime obstype \n", "2022-09-01 00:00:00+00:00 precip_rate JAXA_GPM_reanalysis \n", " sensible_heat_flux ERA5-land \n", "2022-09-01 01:00:00+00:00 precip_rate JAXA_GPM_reanalysis \n", " sensible_heat_flux ERA5-land \n", "2022-09-01 02:00:00+00:00 precip_rate JAXA_GPM_reanalysis \n", "... ... \n", "2022-09-15 22:00:00+00:00 sensible_heat_flux ERA5-land \n", "2022-09-15 23:00:00+00:00 precip_rate JAXA_GPM_reanalysis \n", " sensible_heat_flux ERA5-land \n", "2022-09-16 00:00:00+00:00 precip_rate JAXA_GPM_reanalysis \n", " sensible_heat_flux ERA5-land \n", "\n", " modelvariable \n", "datetime obstype \n", "2022-09-01 00:00:00+00:00 precip_rate hourlyPrecipRate \n", " sensible_heat_flux surface_sensible_heat_flux \n", "2022-09-01 01:00:00+00:00 precip_rate hourlyPrecipRate \n", " sensible_heat_flux surface_sensible_heat_flux \n", "2022-09-01 02:00:00+00:00 precip_rate hourlyPrecipRate \n", "... ... \n", "2022-09-15 22:00:00+00:00 sensible_heat_flux surface_sensible_heat_flux \n", "2022-09-15 23:00:00+00:00 precip_rate hourlyPrecipRate \n", " sensible_heat_flux surface_sensible_heat_flux \n", "2022-09-16 00:00:00+00:00 precip_rate hourlyPrecipRate \n", " sensible_heat_flux surface_sensible_heat_flux \n", "\n", "[747 rows x 4 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#1. Create an pricipitation observationtype\n", "\n", "precip_rate = metobs_toolkit.Obstype(name='precip_rate',\n", " std_unit='mm/hour',\n", " description='Rain precipitation rate at surface.')\n", "#2. Create a ModelObstype \n", "precip_GPM = metobs_toolkit.ModelObstype(\n", " obstype=precip_rate,\n", " model_unit='mm/hour', #See GEE dataset details\n", " model_band='hourlyPrecipRate',#See GEE dataset details\n", " ) \n", "\n", "#3. Create a ModelDataManager\n", "GPM_v8_manager = metobs_toolkit.GEEDynamicDatasetManager(\n", " name='JAXA_GPM_reanalysis', \n", " location=\"JAXA/GPM_L3/GSMaP/v8/operational\", #See GEE dataset details\n", " value_type='numeric',\n", " scale=50,\n", " time_res='1h',\n", " modelobstypes=[precip_GPM],\n", " is_image=False,\n", " is_mosaic=False,\n", ")\n", "\n", "#4. Extract precipitation timeseries data\n", "precip_data = dataset.get_station('vlinder04').get_gee_timeseries_data(\n", " gee_manager=GPM_v8_manager,\n", " startdt_utc=None,\n", " enddt_utc=None,\n", " obstypes=['precip_rate'] #refer by obsname\n", " )\n", "\n", "# 5. make a plot\n", "dataset.get_station('vlinder04').make_plot_of_modeldata(obstype='precip_rate')\n", "\n", "# 6. You can also get the data using the modeldatadf attribute\n", "dataset.get_station('vlinder04').modeldatadf" ] }, { "cell_type": "code", "execution_count": 25, "id": "53", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/thoverga/Documents/VLINDER_github/MetObs_toolkit/src/metobs_toolkit/backend_collection/decorators.py:11: UserWarning: The datetime argument has no timezone information. Assuming UTC timezone.\n", " return func(*args, **kwargs)\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Or you can make an interactive spatial plot\n", "dataset.make_gee_plot(\n", " gee_manager=GPM_v8_manager,\n", " modelobstype='precip_rate',\n", " timeinstance=pd.Timestamp('2022-09-08 16:00:00'))\n" ] } ], "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": "3.11.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "3130c5a424d64841862d0b57e31cc9d1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": "500px" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }