{ "cells": [ { "cell_type": "markdown", "id": "suited-forty", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", " \n", "
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" ] }, { "cell_type": "markdown", "id": "defensive-penguin", "metadata": {}, "source": [ "# NO2 over Spain with CAMS European air quality analysis using RELIANCE services\n", "\n", "## Analysis over a particular country and a town in the country of interest" ] }, { "cell_type": "markdown", "id": "duplicate-coaching", "metadata": {}, "source": [ "
\n", "How to discover RELIANCE datacube resources (spatial & temporal search and subsetting), share resources using EGI datahub, and use RoHub to create FAIR digital Objects
" ] }, { "cell_type": "markdown", "id": "streaming-utilization", "metadata": {}, "source": [ "This notebook shows how to discover and access the [Copernicus Atmosphere Monitoring](https://ads.atmosphere.copernicus.eu/#!/home) products available in the **RELIANCE** datacube resources, by using the functionalities provided in the **Adam API** . The process is structured in 7 steps, including example of data analysis and visualization with the Python libraries installed in the Jupyter environment as well as the creation of a FAIR digital object on [RoHUB](https://reliance.rohub.org/) where all the resources used and generated in this notebook are aggregated.\n", "\n", "You can customize this Jupyter notebook, for instance by updating the content of [Data Management](#1.Data_Management) section.\n", "\n", "- [1. Data Management](#1_Data_Management)\n", "- [2. Authentication](#2_Authentication)\n", "- [3. Datasets Discovery](#3_Datasets_Discovery)\n", "- [4. Products Discovery](#4_Products_Discovery)\n", "- [5. Data Access](#5_Data_Access)\n", "- [6. Data Analysis and Visualizarion](#6_Data_Analysis_Visualization)\n", "- [7. Create Research Object and Share my work](#7_rohub)" ] }, { "cell_type": "markdown", "id": "46e47939-5420-4275-b820-09b0eca5d5d0", "metadata": {}, "source": [ "\n", "## **Step 1: Data Management** " ] }, { "cell_type": "markdown", "id": "c2775b64-10f3-4726-b648-f26cc5947e5c", "metadata": {}, "source": [ "### Authors \n", "- Make sure you first register to RoHub at [https://reliance.rohub.org/](https://reliance.rohub.org/). \n", "- We recommend you use your [ORCID](https://orcid.org/) identifier to login and register to EOSC services.\n", "- In the list of authors, add any co-authors using the email address they used when they registered in RoHub." ] }, { "cell_type": "code", "execution_count": 1, "id": "27ae98cd-b52c-4299-a559-22cc56f8e838", "metadata": {}, "outputs": [], "source": [ "author_emails = ['annefou@geo.uio.no']\n", "contributor_emails = ['jeani@uio.no', 'mantovani@meeo.it']" ] }, { "cell_type": "markdown", "id": "1fbddffc-cc3f-425a-ba76-61bdad445d33", "metadata": {}, "source": [ "### Add the University of Olso and the Nordic e-Infrastructure Collaboration as publishers " ] }, { "cell_type": "code", "execution_count": 2, "id": "e8e7a032-a231-45b5-a05d-493e12eb0260", "metadata": {}, "outputs": [], "source": [ "UiO_organization = {\"org_id\":\"http://www.uio.no/english/\", \n", " \"display_name\": \"University of Oslo\", \n", " \"agent_type\": \"organization\",\n", " \"ror_identifier\":\"01xtthb56\",\n", " \"organization_url\": \"http://www.uio.no/english/\"}" ] }, { "cell_type": "code", "execution_count": 3, "id": "62bf9523-3bf9-4348-806f-df641f29032f", "metadata": {}, "outputs": [], "source": [ "NeIC_organization = {\"org_id\":\"https://neic.no/\",\n", " \"display_name\": \"Nordic e-Infrastructure Collaboration\", \n", " \"agent_type\": \"organization\",\n", " \"ror_identifier\":\"04jcwf484\",\n", " \"organization_url\": \"https://neic.no/\"}" ] }, { "cell_type": "code", "execution_count": 4, "id": "ce719f3a-eadf-43f1-b5fb-ea6e6116a89f", "metadata": {}, "outputs": [], "source": [ "list_publishers = [UiO_organization, NeIC_organization]" ] }, { "cell_type": "code", "execution_count": 5, "id": "2b1f1057-e64f-4263-af61-05fc55b96d90", "metadata": {}, "outputs": [], "source": [ "list_copyright_holders = [UiO_organization]" ] }, { "cell_type": "markdown", "id": "522436d4-bd1c-44d1-a9a4-d76e4620d6bf", "metadata": {}, "source": [ "### Add the funding\n", "- if your work is not funded set \n", "```\n", "funded_by = {}\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "id": "2c1ce214-c7ef-4ca7-a6d8-ed7d13e94e81", "metadata": {}, "outputs": [], "source": [ "funded_by = {\n", "\"grant_id\": \"101017502\",\n", "\"grant_Name\": \"RELIANCE\",\n", "\"grant_title\": \"Research Lifecycle Management for Earth Science Communities and Copernicus Users\",\n", "\"funder_name\": \"European Commission\",\n", "\"funder_doi\": \"10.13039/501100000781\",\n", "}" ] }, { "cell_type": "markdown", "id": "d2c308f8-4677-4083-a7d0-e4d859668e62", "metadata": {}, "source": [ "### Choose a license for your FAIR digital object" ] }, { "cell_type": "code", "execution_count": 7, "id": "d52d2be0-321d-4979-802a-8c81921ccf06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: rohub in /opt/conda/envs/cams/lib/python3.9/site-packages (1.0.7)\n", "Requirement already satisfied: requests in /opt/conda/envs/cams/lib/python3.9/site-packages (from rohub) (2.27.1)\n", "Requirement already satisfied: pandas in /opt/conda/envs/cams/lib/python3.9/site-packages (from rohub) (1.4.2)\n", "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/envs/cams/lib/python3.9/site-packages (from pandas->rohub) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /opt/conda/envs/cams/lib/python3.9/site-packages (from pandas->rohub) (2022.1)\n", "Requirement already satisfied: numpy>=1.18.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from pandas->rohub) (1.21.6)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->rohub) (2.0.12)\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->rohub) (3.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->rohub) (2021.10.8)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->rohub) (1.26.9)\n", "Requirement already satisfied: six>=1.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas->rohub) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install rohub" ] }, { "cell_type": "code", "execution_count": 8, "id": "345038aa-f1e2-4897-bf89-1d1ab3f38462", "metadata": {}, "outputs": [], "source": [ "import rohub" ] }, { "cell_type": "code", "execution_count": 9, "id": "a3a20bb8-4d0f-4d64-b006-7fbfe2d1ff78", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['0BSD', 'AAL', 'ADSL', 'AFL-1.1', 'AFL-1.2']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "licenses = rohub.list_available_licenses()\n", "# Update line below to print more licenses\n", "licenses[0:5]" ] }, { "cell_type": "code", "execution_count": 10, "id": "b8d1dcf9-22ac-4b9c-875e-857afd01df5a", "metadata": {}, "outputs": [], "source": [ "license = 'MIT'" ] }, { "cell_type": "markdown", "id": "521673e0-e4b9-45e8-9802-197dcb6d9f66", "metadata": {}, "source": [ "### Organize my data using EGI datahub\n", "- Define a prefix for my project (you may need to adjust it for your own usage on your infrastructure). \n", " - `input` folder where all the data used as input to my Jupyter Notebook is stored (and eventually shared)\n", " - `output` folder where all the results to keep are stored\n", " - `tool` folder where all the tools, including this Jupyter Notebook will be copied for sharing\n", "- Create all corresponding folders" ] }, { "cell_type": "markdown", "id": "a5457c11-6238-43d2-a98a-ef0b8bfecd9a", "metadata": {}, "source": [ "### Import Python packages" ] }, { "cell_type": "code", "execution_count": 11, "id": "solar-timing", "metadata": {}, "outputs": [], "source": [ "import os\n", "import warnings\n", "import pathlib" ] }, { "cell_type": "code", "execution_count": 12, "id": "heard-terminal", "metadata": {}, "outputs": [], "source": [ "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "id": "47e3232f-8bb1-4f3e-a23d-9fd0e54f5fdb", "metadata": {}, "source": [ "## Initialization\n", "- Choose a country and add its name and country code\n", "- Choose the variable to analyze (PM10, PM25, NO2, O3, etc.)\n", "- Choose the area for your analysis" ] }, { "cell_type": "markdown", "id": "c6303ab9-7790-4411-9483-efc6bda387ef", "metadata": {}, "source": [ "### Choose the country of interest" ] }, { "cell_type": "code", "execution_count": 13, "id": "65ebd85f-819a-4208-8136-1054df5e6b9a", "metadata": {}, "outputs": [], "source": [ "country_code = 'ES' \n", "country_fullname = \"Spain\"\n", "town_fullname = 'Madrid' \n", "town_coordinates = {'latitude': 40.4168, 'longitude': 3.7038}\n", "variable_name = 'NO2'\n", "variable_unit = 'µg m-3'\n", "variable_long_name = 'Nitrogen Dioxide'\n", "month_name = 'March'\n", "month_number = '03'\n", "month_nb_days = '31'" ] }, { "cell_type": "markdown", "id": "58dbc448-7309-4d42-be5e-c1e9896aa24c", "metadata": {}, "source": [ "### Geojson for selecting data from ADAM\n", "- The geometry field is extracted from a GeoJSON file, retrieving the value of the \"feature\" element.\n", "- To create a geojson file for the area of interest, you can use https://geojson.io/\n", "- Then paste the result below in the geojson variable" ] }, { "cell_type": "code", "execution_count": 14, "id": "7f127d8f-2822-42df-9969-51ba87ec4c11", "metadata": {}, "outputs": [], "source": [ "geojson = \"\"\"{\"type\": \"FeatureCollection\",\"features\": [{\"type\": \"Feature\",\"properties\": {},\"geometry\": {\"type\": \"Polygon\",\"coordinates\": [[[3.05419921875,42.601619944327965],[-1.69189453125,43.46886761482925],[-8.10791015625,43.866218006556394],[-9.60205078125,43.03677585761058],[-9.11865234375,42.24478535602799],[-9.03076171875,40.245991504199026],[-9.580078125,39.07890809706475],[-9.73388671875,38.70265930723801],[-9.25048828125,38.30718056188316],[-8.942871093749998,38.25543637637947],[-9.052734375,37.142803443716836],[-9.29443359375,36.79169061907076],[-8.10791015625,36.89719446989036],[-7.778320312499999,36.79169061907076],[-7.27294921875,37.07271048132943],[-6.78955078125,36.86204269508728],[-6.17431640625,36.06686213257888],[-5.69091796875,35.90684930677121],[-5.09765625,36.08462129606931],[-4.74609375,36.33282808737917],[-4.10888671875,36.59788913307022],[-3.09814453125,36.54494944148322],[-2.43896484375,36.56260003738545],[-2.04345703125,36.63316209558658],[-1.69189453125,37.16031654673677],[-1.34033203125,37.43997405227057],[-0.439453125,37.49229399862877],[-0.59326171875,37.75334401310656],[-0.37353515625,38.272688535980976],[0.263671875,38.59970036588819],[0.3955078125,38.839707613545144],[0.06591796875,38.94232097947902],[-0.17578125,39.2832938689385],[-0.19775390625,39.58875727696545],[0.24169921874999997,39.977120098439634],[0.68115234375,40.463666324587685],[1.07666015625,40.83043687764923],[1.58203125,41.062786068733026],[2.2412109375,41.178653972331674],[2.83447265625,41.541477666790286],[3.33984375,41.73852846935917],[3.3618164062499996,42.13082130188811],[3.05419921875,42.601619944327965]]]}}]}\"\"\"" ] }, { "cell_type": "markdown", "id": "27155406-3f0b-4302-8122-79889725a110", "metadata": {}, "source": [ "### Create folders" ] }, { "cell_type": "code", "execution_count": 15, "id": "7e282cc7-748c-4785-af96-1aeed4d334de", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WORKDIR FOLDER: /home/jovyan/datahub/Reliance/Climate_ES_NO2_March\n" ] } ], "source": [ "WORKDIR_FOLDER = os.path.join(os.environ['HOME'], \"datahub/Reliance/Climate\" + '_' + country_code + '_' + variable_name + '_' + month_name)\n", "print(\"WORKDIR FOLDER: \", WORKDIR_FOLDER)" ] }, { "cell_type": "code", "execution_count": 16, "id": "f1a03583-8a6a-4c36-939b-f0ab479dd673", "metadata": {}, "outputs": [], "source": [ "INPUT_DATA_DIR = os.path.join(WORKDIR_FOLDER, 'input')\n", "OUTPUT_DATA_DIR = os.path.join(WORKDIR_FOLDER, 'output')\n", "TOOL_DATA_DIR = os.path.join(WORKDIR_FOLDER, 'tool')\n", "\n", "list_folders = [INPUT_DATA_DIR, OUTPUT_DATA_DIR, TOOL_DATA_DIR]\n", "\n", "for folder in list_folders:\n", " pathlib.Path(folder).mkdir(parents=True, exist_ok=True)" ] }, { "cell_type": "markdown", "id": "18f4f0e0-27f4-4b94-af4d-31c9cd579c12", "metadata": {}, "source": [ "### Geojson file for selecting data from ADAM\n", "- We dissolve geojson in case we have more than one polygon and then save the results into a geojson file" ] }, { "cell_type": "code", "execution_count": 17, "id": "c3fe35e9-8160-434d-8a51-2fc09f251756", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/envs/cams/share/proj failed\n" ] } ], "source": [ "import cartopy\n", "import geopandas as gpd" ] }, { "cell_type": "code", "execution_count": 18, "id": "a6b74f45-b70b-4a4c-b5d6-88ea7cfc7f24", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/es.geo.json'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "local_path_geom = os.path.join(INPUT_DATA_DIR, country_code.lower() + '.geo.json')\n", "local_path_geom" ] }, { "cell_type": "code", "execution_count": 19, "id": "f81c3ab7-cc6f-4b06-9594-b50b28371887", "metadata": {}, "outputs": [], "source": [ "if (pathlib.Path(local_path_geom).exists()):\n", " os.remove(local_path_geom)\n", "f = open(local_path_geom, \"w\")\n", "f.write(geojson)\n", "f.close()" ] }, { "cell_type": "code", "execution_count": 20, "id": "245ceacb-ab07-46a4-90f6-03e4c9a5a958", "metadata": { "tags": [] }, "outputs": [], "source": [ "data = gpd.read_file(local_path_geom)" ] }, { "cell_type": "code", "execution_count": 21, "id": "91d2b98d-6873-4b3a-8cd4-7a5ad7540813", "metadata": {}, "outputs": [], "source": [ "single_shape = data.dissolve()" ] }, { "cell_type": "markdown", "id": "6e13dc84-8fe6-4b36-a45f-eabe28dd2d90", "metadata": {}, "source": [ "### Show area of interest" ] }, { "cell_type": "code", "execution_count": 22, "id": "7f1ca9e4-99cf-4fa4-9f99-af28b7e68a91", "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": [ "single_shape.plot()" ] }, { "cell_type": "code", "execution_count": 23, "id": "4957dfeb-eac2-47ab-b821-bab70df6b54d", "metadata": {}, "outputs": [], "source": [ "if (pathlib.Path(local_path_geom).exists()):\n", " os.remove(local_path_geom)" ] }, { "cell_type": "code", "execution_count": 24, "id": "98fa511a-9253-450b-b608-784b7bdb341e", "metadata": {}, "outputs": [], "source": [ "single_shape.to_file(local_path_geom, driver='GeoJSON')" ] }, { "cell_type": "markdown", "id": "functional-trade", "metadata": {}, "source": [ "## **Step 2: Authentication** " ] }, { "cell_type": "markdown", "id": "interesting-modification", "metadata": {}, "source": [ "The following lines of code will show the personal **Adam API-Key** of the user and the endpoint currently in use, that provides access to the products in the related catalogue. At the end of the execution, if the authentication process is successfull the personal token and the expiration time should be returned as outputs." ] }, { "cell_type": "code", "execution_count": 25, "id": "split-nutrition", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: adamapi in /opt/conda/envs/cams/lib/python3.9/site-packages (2.0.11)\n", "Requirement already satisfied: requests>=2.22.0 in /opt/conda/envs/cams/lib/python3.9/site-packages (from adamapi) (2.27.1)\n", "Requirement already satisfied: imageio in /opt/conda/envs/cams/lib/python3.9/site-packages (from adamapi) (2.18.0)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests>=2.22.0->adamapi) (1.26.9)\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests>=2.22.0->adamapi) (3.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests>=2.22.0->adamapi) (2021.10.8)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests>=2.22.0->adamapi) (2.0.12)\n", "Requirement already satisfied: pillow>=8.3.2 in /opt/conda/envs/cams/lib/python3.9/site-packages (from imageio->adamapi) (9.1.0)\n", "Requirement already satisfied: numpy in /opt/conda/envs/cams/lib/python3.9/site-packages (from imageio->adamapi) (1.21.6)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install adamapi" ] }, { "cell_type": "code", "execution_count": 26, "id": "740cf688-4717-4bc5-ab4a-7287496d33ea", "metadata": {}, "outputs": [], "source": [ "adam_key = open(os.path.join(os.environ['HOME'],\"adam-key\")).read().rstrip()" ] }, { "cell_type": "code", "execution_count": 27, "id": "norwegian-evans", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'expires_at': '2022-05-01T21:13:33.370Z',\n", " 'access_token': '059132032d614608a8ab13916d25548e',\n", " 'refresh_token': '02ec2cfced264681a1465de6c7bfd59d',\n", " 'expires_in': 3600}" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import adamapi as adam\n", "a = adam.Auth()\n", "\n", "a.setKey(adam_key)\n", "a.setAdamCore('https://reliance.adamplatform.eu')\n", "a.authorize() " ] }, { "cell_type": "markdown", "id": "mathematical-writing", "metadata": { "jupyter": { "outputs_hidden": true }, "tags": [] }, "source": [ "## **Step 3: Datasets Discovery**" ] }, { "cell_type": "markdown", "id": "adult-warning", "metadata": {}, "source": [ "After authorization, the user can browse the whole catalogue, structured as a JSON object after a pagination process, displaying all the available datasets. This operation can be executed with the **getDatasets()** function without including any argument. Some lines of code should be added to parse the Json object and extract the names of the datasets.The Json object can be handled as a Python dictionary." ] }, { "cell_type": "markdown", "id": "premier-thomas", "metadata": {}, "source": [ "### Pre-filter datasets\n", "\n", "We will discover all the available datasets in the ADAM platform but will only print elements of interest **EU_CAMS** e.g. [European air quality datasets](https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-forecasts?tab=overview) from Copernicus Atmosphere Monitoring Service" ] }, { "cell_type": "code", "execution_count": 28, "id": "controlled-soccer", "metadata": {}, "outputs": [], "source": [ "def list_datasets(a, search=\"\", dataset_name=\"\"):\n", " datasets = adam.Datasets(a)\n", " catalogue = datasets.getDatasets()\n", " datasetID = None\n", "\n", "# Extracting the size of the catalogue\n", "\n", " total = catalogue['properties']['totalResults']\n", " items = catalogue['properties']['itemsPerPage']\n", " pages = total // items\n", " \n", " print('\\033[1;34m')\n", " print('----------------------------------------------------------------------')\n", " print( 'List of available datasets:')\n", " print ('\\033[0;0m')\n", "\n", "# Extracting the list of datasets across the whole catalogue\n", "\n", " for i in range(0, pages):\n", " page = datasets.getDatasets(page=i)\n", " for element in page['content']:\n", " if search == \"\" or search in element['title']:\n", " print(element['title'] + \" --> datasetId = \" + element['datasetId'])\n", " if element['datasetId'].split(':')[1] == dataset_name:\n", " datasetID = element['datasetId']\n", " return datasets, datasetID" ] }, { "cell_type": "code", "execution_count": 29, "id": "3ddaa981-bb77-4a8a-a610-fedd54d1e288", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34m\n", "----------------------------------------------------------------------\n", "List of available datasets:\n", "\u001b[0;0m\n", "CAMS European air quality forecasts: C2H3NO5 --> datasetId = 69619:EU_CAMS_SURFACE_C2H3NO5_G\n", "CAMS European air quality forecasts: CO --> datasetId = 69620:EU_CAMS_SURFACE_CO_G\n", "EU_CAMS_SURFACE_NH3_G --> datasetId = 69621:EU_CAMS_SURFACE_NH3_G\n", "CAMS European air quality forecasts: NMVOC --> datasetId = 69622:EU_CAMS_SURFACE_NMVOC_G\n", "CAMS European air quality forecasts: NO2 --> datasetId = 69623:EU_CAMS_SURFACE_NO2_G\n", "EU_CAMS_SURFACE_NO_G --> datasetId = 69624:EU_CAMS_SURFACE_NO_G\n", "CAMS European air quality forecasts: O3 --> datasetId = 69625:EU_CAMS_SURFACE_O3_G\n", "CAMS European air quality forecasts: PM10 --> datasetId = 69626:EU_CAMS_SURFACE_PM10_G\n", "CAMS European air quality forecasts: PM25 --> datasetId = 69627:EU_CAMS_SURFACE_PM25_G\n", "CAMS European air quality forecasts: REC --> datasetId = 69628:EU_CAMS_SURFACE_REC_G\n", "CAMS European air quality forecasts: SIA --> datasetId = 69629:EU_CAMS_SURFACE_SIA_G\n", "CAMS European air quality forecasts: SO2 --> datasetId = 69630:EU_CAMS_SURFACE_SO2_G\n", "CAMS European air quality forecasts: TEC --> datasetId = 69631:EU_CAMS_SURFACE_TEC_G\n" ] } ], "source": [ "datasets, datasetID = list_datasets(a, search=\"CAMS\", dataset_name = 'EU_CAMS_SURFACE_' + variable_name + '_G')" ] }, { "cell_type": "markdown", "id": "complimentary-daily", "metadata": {}, "source": [ "We are interested by one variable only so we will discover the corresponding dataset and print its metadata, showing the data provenance." ] }, { "cell_type": "code", "execution_count": 30, "id": "7239ace3-3409-4f76-a147-523aab9bd361", "metadata": {}, "outputs": [], "source": [ "def get_metadata(datasetID, datasets, verbose=False):\n", " print('\\033[1;34m' + 'Metadata of ' + datasetID + ':')\n", " print ('\\033[0;0m')\n", " \n", " paged = datasets.getDatasets(datasetID)\n", " for i in paged.items():\n", " print(\"\\033[1m\" + str(i[0]) + \"\\033[0m\" + ': ' + str(i[1]))\n", " return paged" ] }, { "cell_type": "code", "execution_count": 31, "id": "d2eca1f9-4f01-4056-9d59-5568e4023223", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mMetadata of 69623:EU_CAMS_SURFACE_NO2_G:\n", "\u001b[0;0m\n", "\u001b[1mdatasetId\u001b[0m: 69623:EU_CAMS_SURFACE_NO2_G\n", "\u001b[1mcreationDate\u001b[0m: 2021-07-12T00:00:00Z\n", "\u001b[1mdataType\u001b[0m: Float32\n", "\u001b[1mepsg\u001b[0m: 4326\n", "\u001b[1mkeywords\u001b[0m: []\n", "\u001b[1mlicense\u001b[0m: {'documentationUrl': '', 'dataProviderName': 'ADS', 'dataProviderUrl': '', 'licenseId': '', 'dataPolicy': '', 'doi': '', 'credits': ''}\n", "\u001b[1mmaxValue\u001b[0m: [1.354510459350422e-07]\n", "\u001b[1mminValue\u001b[0m: [0.0]\n", "\u001b[1mnumberOfRecords\u001b[0m: 132998\n", "\u001b[1mprofile\u001b[0m: {'profileSchema': 'eo_profile_schema.json', 'name': 'Earth Observation', 'mission': 'CAMS', 'sensor': 'CAMS', 'processingLevel': 'forecast', 'instrument': '', 'platform': ''}\n", "\u001b[1mresolutionUnit\u001b[0m: degree\n", "\u001b[1mtemporalResolution\u001b[0m: Hourly\n", "\u001b[1munit\u001b[0m: \n", "\u001b[1munitDescription\u001b[0m: \n", "\u001b[1mupdateDate\u001b[0m: 2022-05-01T12:03:32Z\n", "\u001b[1mgeometry\u001b[0m: {'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}\n", "\u001b[1mresolutions\u001b[0m: [0.1]\n", "\u001b[1manyText\u001b[0m: 4326,ESA,Daily,ESA\n", "\u001b[1mapplications\u001b[0m: ['Atmosphere']\n", "\u001b[1mdatasetManager\u001b[0m: govoni@meeo.it\n", "\u001b[1mdatasetManagerOrganisation\u001b[0m: meeo\n", "\u001b[1mdescription\u001b[0m: CAMS NITROGEN DIOXIDE\n", "\u001b[1mtitle\u001b[0m: CAMS European air quality forecasts: NO2\n", "\u001b[1mnoDataValue\u001b[0m: 0\n", "\u001b[1mtimeReferenceSystem\u001b[0m: UTC\n", "\u001b[1munits\u001b[0m: µg m-3\n", "\u001b[1munitsDescription\u001b[0m: nitrogen dioxide\n", "\u001b[1mservices\u001b[0m: ['MWCS', 'ADAM', 'AdamApi']\n", "\u001b[1mtechnicalManager\u001b[0m: govoni@meeo.it\n", "\u001b[1mfiltersEnabled\u001b[0m: {'type': 'object', 'title': 'Dataset filters', 'properties': {'startDate': {'title': 'Start Date', 'type': 'string', 'format': 'date', 'text_rule': 'false', 'ops_only': 'false', 'math_rule': 'false', 'order_rule': 'false', 'pattern': '^d{4}-[01]d-[0-3]d(T[0-2]d:[0-9]d:[0-9]dZ)?$'}, 'endDate': {'title': 'End Date', 'type': 'string', 'format': 'date', 'ops_only': 'false', 'text_rule': 'false', 'math_rule': 'false', 'order_rule': 'false', 'pattern': '^d{4}-[01]d-[0-3]d(T[0-2]d:[0-9]d:[0-9]dZ)?$'}}}\n", "\u001b[1mpreviews\u001b[0m: \n", "\u001b[1mstarDate\u001b[0m: 2018-07-12T00:00:00Z\n", "\u001b[1mendDate\u001b[0m: 2022-04-30T23:00:00Z\n", "\u001b[1maccounting\u001b[0m: {'unit': 0.0032}\n" ] } ], "source": [ "metadata_variable = get_metadata(datasetID, datasets, verbose=True)" ] }, { "cell_type": "markdown", "id": "electronic-special", "metadata": {}, "source": [ "## **Step 4: Products Discovery**" ] }, { "cell_type": "markdown", "id": "ahead-terminal", "metadata": {}, "source": [ "The products discovery operation related to a specific dataset is implemented in the Adam API with the **getProducts()** function. A combined **spatial and temporal search** can be requested by specifying the **datasetId** for the selected dataset, the **geometry** argument that specifies the Area Of Interest, and a temporal range defined by `startDate` and `endDate` . The geometry must **always** be defined by a **GeoJson object** that describes the polygon in the **counterclockwise winding order**. The optional arguments `startIndex` and `maxRecords` can set the list of the results returned as an output. The results of the search are displayed with their metadata and they are sorted starting from the most recent product." ] }, { "cell_type": "markdown", "id": "twelve-president", "metadata": {}, "source": [ "### Search data" ] }, { "cell_type": "code", "execution_count": 32, "id": "hydraulic-opening", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: geojson_rewind in /opt/conda/envs/cams/lib/python3.9/site-packages (1.0.2)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install geojson_rewind" ] }, { "cell_type": "code", "execution_count": 33, "id": "capable-ministry", "metadata": {}, "outputs": [], "source": [ "from geojson_rewind import rewind\n", "import json" ] }, { "cell_type": "markdown", "id": "higher-burns", "metadata": {}, "source": [ "The GeoJson object needs to be rearranged according to the counterclockwise winding order. This operation is executed in the next few lines to obtain \n", "a geometry that meets the requirements of the method. **Geom_1** is the final result to be used in the discovery operation." ] }, { "cell_type": "code", "execution_count": 34, "id": "offensive-rochester", "metadata": {}, "outputs": [], "source": [ "with open(local_path_geom) as f:\n", " geom_dict = json.load(f)\n", "output = rewind(geom_dict) \n", "geom_1 = str(geom_dict['features'][0]['geometry'])" ] }, { "cell_type": "markdown", "id": "pretty-shark", "metadata": {}, "source": [ "Copernicus air quality analyses are hourly product but when we select a given date, we will only get the first 10 products. \n", "Below, we make a list of the first 10 available products for the 1st day of the studied month in 2020 e.g. we restrict our search to this date." ] }, { "cell_type": "code", "execution_count": 35, "id": "expensive-subsection", "metadata": {}, "outputs": [], "source": [ "start_date = '2019-' + month_number + '-01'\n", "end_date = start_date" ] }, { "cell_type": "code", "execution_count": 36, "id": "functioning-gibraltar", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mList of available products (maximum 10 products printed):\n", "\u001b[0;0m\n", "\u001b[1;31;1m#1\n", "\u001b[0m\n", "_id: {'$oid': '60efc38a6dfebc080600e73b'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T23:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301230000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:38Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e73b', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e73b'}\n", "------------------------------------\n", "\u001b[1;31;1m#2\n", "\u001b[0m\n", "_id: {'$oid': '60efc38a6dfebc080600e735'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T22:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301220000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:38Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e735', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e735'}\n", "------------------------------------\n", "\u001b[1;31;1m#3\n", "\u001b[0m\n", "_id: {'$oid': '60efc38a6dfebc080600e738'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T21:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301210000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:38Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e738', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e738'}\n", "------------------------------------\n", "\u001b[1;31;1m#4\n", "\u001b[0m\n", "_id: {'$oid': '60efc38a6dfebc080600e732'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T20:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301200000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:38Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e732', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e732'}\n", "------------------------------------\n", "\u001b[1;31;1m#5\n", "\u001b[0m\n", "_id: {'$oid': '60efc38a6dfebc080600e72a'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T19:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301190000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:38Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e72a', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e72a'}\n", "------------------------------------\n", "\u001b[1;31;1m#6\n", "\u001b[0m\n", "_id: {'$oid': '60efc38a6dfebc080600e72e'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T18:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301180000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:38Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e72e', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e72e'}\n", "------------------------------------\n", "\u001b[1;31;1m#7\n", "\u001b[0m\n", "_id: {'$oid': '60efc38a6dfebc080600e722'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T17:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301170000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:37Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e722', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e722'}\n", "------------------------------------\n", "\u001b[1;31;1m#8\n", "\u001b[0m\n", "_id: {'$oid': '60efc38a6dfebc080600e724'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T16:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301160000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:37Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e724', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc38a6dfebc080600e724'}\n", "------------------------------------\n", "\u001b[1;31;1m#9\n", "\u001b[0m\n", "_id: {'$oid': '60efc3896dfebc080600e71e'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T15:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301150000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:37Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc3896dfebc080600e71e', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc3896dfebc080600e71e'}\n", "------------------------------------\n", "\u001b[1;31;1m#10\n", "\u001b[0m\n", "_id: {'$oid': '60efc3896dfebc080600e71a'}\n", "datasetId: EU_CAMS_SURFACE_NO2_G\n", "productDate: 2019-03-01T14:00:00Z\n", "productId: EU_CAMS_SURFACE_NO2_G_20190301140000.tif\n", "geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 69.999997], [-25.000012, 69.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}\n", "insertDate: 2021-07-15T05:11:37Z\n", "status: Online\n", "productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc3896dfebc080600e71a', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60efc3896dfebc080600e71a'}\n", "------------------------------------\n" ] } ], "source": [ "search = adam.Search( a )\n", "results = search.getProducts(\n", " datasetID, \n", " geometry=geom_1,\n", " startDate=start_date,\n", " endDate=end_date\n", " )\n", "\n", "# Printing the results\n", "\n", "print('\\033[1;34m' + 'List of available products (maximum 10 products printed):')\n", "print ('\\033[0;0m')\n", "\n", "count = 1\n", "for i in results['content']:\n", " print(\"\\033[1;31;1m\" + \"#\" + str(count))\n", " print ('\\033[0m')\n", " for k in i.items():\n", " print(str(k[0]) + ': ' + str(k[1]))\n", " count = count+1\n", " print('------------------------------------')" ] }, { "cell_type": "markdown", "id": "alone-ordinance", "metadata": {}, "source": [ "## **Step 5: Data Access**" ] }, { "cell_type": "markdown", "id": "brave-occupation", "metadata": {}, "source": [ "After the data discovery operation that retrieves the availability of products in the catalogue, it is possible to access the data with the **getData** function. Each product in the output list intersects the selected geometry and the following example shows how to access a specific product from the list of results obtained in the previous step. While the **datasetId** is always a mandatory parameter, for each data access request the **getData** function needs only one of the following arguments: **geometry** or **productId** , that is the value of the **_id** field in each product metadata. In the case of a **spatial and temporal search** the geometry must be provided to the function, together with the time range of interest. \n", "The output of the **getData** function is always a **.zip** file containing the data retrieved with the data access request, providing the spatial **subset** of the product. The zip file will contain a geotiff file for each of the spatial subsets extracted in the selected time range." ] }, { "cell_type": "markdown", "id": "collective-estimate", "metadata": {}, "source": [ "#### Define a function to select a time range and get data" ] }, { "cell_type": "code", "execution_count": 37, "id": "indie-coaching", "metadata": {}, "outputs": [], "source": [ "def getZipData(auth, dataset_info):\n", " if not (pathlib.Path(pathlib.Path(dataset_info['outputFname']).stem).exists() or pathlib.Path(dataset_info['outputFname']).exists()):\n", " data=adam.GetData(auth)\n", " image = data.getData(\n", " datasetId = dataset_info['datasetID'],\n", " startDate = dataset_info['startDate'],\n", " endDate = dataset_info['endDate'],\n", " geometry = dataset_info['geometry'],\n", " outputFname = dataset_info['outputFname'])\n", " print(image)" ] }, { "cell_type": "markdown", "id": "younger-invalid", "metadata": {}, "source": [ "#### Get variable of interest for each day of the month we study for 2019, 2020 and 2021 (time 00:00:00)\n", "\n", "This process can take a bit of time so be patient!" ] }, { "cell_type": "code", "execution_count": 38, "id": "charitable-nightmare", "metadata": {}, "outputs": [], "source": [ "import time\n", "from IPython.display import clear_output" ] }, { "cell_type": "code", "execution_count": 39, "id": "sudden-insert", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1mProcessing time: 0.0\n" ] } ], "source": [ "start = time.time()\n", "\n", "for year in ['2019', '2020', '2021']:\n", " datasetInfo = {\n", " 'datasetID' : datasetID,\n", " 'startDate' : year + '-' + month_number + '-01',\n", " 'endDate' : year + '-' + month_number + '-' + month_nb_days,\n", " 'geometry' : geom_1,\n", " 'outputFname' : INPUT_DATA_DIR + '/' + variable_name + '_' + country_code + '_ADAMAPI_' + year + '.zip'\n", " }\n", " getZipData(a, datasetInfo)\n", " \n", "end = time.time()\n", "clear_output(wait=True)\n", "delta1 = end - start\n", "print('\\033[1m'+'Processing time: ' + str(round(delta1,2)))" ] }, { "cell_type": "markdown", "id": "current-commercial", "metadata": {}, "source": [ "## **Step 6: Data Analysis and Visualization**" ] }, { "cell_type": "markdown", "id": "collectible-tragedy", "metadata": {}, "source": [ "The data retrieved via the Adam API is now available as a zip file that must be unzipped to directly handle the data in a geotiff format. Then with the Python packages provided in the Jupyter environment it is possible to process and visualized the requested product." ] }, { "cell_type": "markdown", "id": "separated-temple", "metadata": {}, "source": [ "#### Unzip data" ] }, { "cell_type": "code", "execution_count": 40, "id": "eight-willow", "metadata": {}, "outputs": [], "source": [ "import zipfile" ] }, { "cell_type": "code", "execution_count": 41, "id": "extensive-checkout", "metadata": {}, "outputs": [], "source": [ "def unzipData(filename, out_prefix):\n", " with zipfile.ZipFile(filename, 'r') as zip_ref:\n", " zip_ref.extractall(path = os.path.join(out_prefix, pathlib.Path(filename).stem))" ] }, { "cell_type": "code", "execution_count": 42, "id": "354824b9-cdec-4b4c-a662-0027e4001106", "metadata": {}, "outputs": [], "source": [ "for year in ['2019', '2020', '2021']:\n", " filename = INPUT_DATA_DIR + '/' + variable_name + '_' + country_code + '_ADAMAPI_' + year + '.zip'\n", " target_file = pathlib.Path(os.path.join(INPUT_DATA_DIR, pathlib.Path(pathlib.Path(filename).stem)))\n", " if not target_file.exists():\n", " unzipData(filename, INPUT_DATA_DIR)" ] }, { "cell_type": "markdown", "id": "initial-gregory", "metadata": {}, "source": [ "#### Read data and make a monthly average" ] }, { "cell_type": "code", "execution_count": 43, "id": "looking-moderator", "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import xesmf as xe\n", "import glob" ] }, { "cell_type": "markdown", "id": "8f8040ce-ee94-4fbd-ab2c-72c7d72c4e48", "metadata": {}, "source": [ "We need to regrid data." ] }, { "cell_type": "code", "execution_count": 44, "id": "fb4571b6-23a5-4850-956c-fff6ec69b8e7", "metadata": {}, "outputs": [], "source": [ "def read_file(filename, variable, metadata, factor=1):\n", " tmp = xr.open_rasterio(filename, parse_coordinates=True)\n", " # Convert our xarray.DataArray into a xarray.Dataset\n", " tmp = tmp.to_dataset('band')*factor\n", " # Rename the dimensions to make it CF-convention compliant\n", " tmp = tmp.rename_dims({'y': 'latitude', 'x':'longitude'})\n", " # Rename the variable to a more useful name\n", " tmp = tmp.rename_vars({1: variable, 'y':'latitude', 'x':'longitude'})\n", " tmp[variable].attrs = {'units' : metadata['units'], 'long_name' : metadata['description']}\n", " return tmp" ] }, { "cell_type": "code", "execution_count": 45, "id": "275b77d2-ae4f-41ac-bd23-ef2443a132cd", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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       "Dimensions:    (latitude: 80, longitude: 132)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 43.85 43.75 43.65 43.55 ... 36.15 36.05 35.95\n",
       "  * longitude  (longitude) float64 -9.75 -9.65 -9.55 -9.45 ... 3.15 3.25 3.35\n",
       "Data variables:\n",
       "    NO2        (latitude, longitude) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    transform:      (0.1, 0.0, -9.800012009298998, 0.0, -0.1, 43.899996997613...\n",
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" ], "text/plain": [ "\n", "Dimensions: (latitude: 80, longitude: 132)\n", "Coordinates:\n", " * latitude (latitude) float64 43.85 43.75 43.65 43.55 ... 36.15 36.05 35.95\n", " * longitude (longitude) float64 -9.75 -9.65 -9.55 -9.45 ... 3.15 3.25 3.35\n", "Data variables:\n", " NO2 (latitude, longitude) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", "Attributes:\n", " transform: (0.1, 0.0, -9.800012009298998, 0.0, -0.1, 43.899996997613...\n", " crs: +init=epsg:4326\n", " res: (0.1, 0.1)\n", " is_tiled: 0\n", " nodatavals: (0.0,)\n", " scales: (1.0,)\n", " offsets: (0.0,)\n", " AREA_OR_POINT: Area" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "output_grid = read_file(INPUT_DATA_DIR + '/' + variable_name + '_' + country_code + '_ADAMAPI_2021/eu_cams_surface_no2_g_2021-' + month_number + '-' + month_nb_days + 't000000.tif', variable_name, metadata_variable)\n", "output_grid" ] }, { "cell_type": "markdown", "id": "twenty-venice", "metadata": {}, "source": [ "We now read these files using `xarray`. First, we make a list of all the geotiff files in a given folder. To ensure each raster is labelled correctly with its time, we can use a helper function `paths_to_datetimeindex()` to extract time information from the file paths we obtained above. We then load and concatenate each dataset along the time dimension using `xarray.open_rasterio()`, convert the resulting `xarray.DataArray` to a `xarray.Dataset`, and give the variable a more useful name (**PM10**)" ] }, { "cell_type": "code", "execution_count": 46, "id": "backed-memory", "metadata": {}, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 47, "id": "upset-finance", "metadata": {}, "outputs": [], "source": [ "def paths_to_datetimeindex(paths):\n", " return [datetime.strptime(date.split('_')[-1].split('.')[0], '%Y-%m-%dt%f') for date in paths]" ] }, { "cell_type": "code", "execution_count": 48, "id": "00ba1ed9-3de0-4a93-bdcd-36a78cbe781a", "metadata": {}, "outputs": [], "source": [ "def getData(dirtif, variable, metadata, factor=1, grid_out=None):\n", " geotiff_list = glob.glob(dirtif)\n", " # Create variable used for time axis\n", " time_var = xr.Variable('time', paths_to_datetimeindex(geotiff_list))\n", " # Load in and concatenate all individual GeoTIFFs\n", " xarray_list = []\n", " if grid_out is not None:\n", " nlats = len(grid_out.latitude.values)\n", " nlons = len(grid_out.longitude.values)\n", " for i in geotiff_list:\n", " tmp = read_file(i, variable, metadata, factor=factor)\n", " if grid_out is not None:\n", " print(\"regridding \", i)\n", " regridder = xe.Regridder(tmp, grid_out, 'conservative')\n", " tmp_regrid = regridder(tmp, keep_attrs=True)\n", " xarray_list.append(tmp_regrid)\n", " else:\n", " xarray_list.append(tmp)\n", " #print(xarray_list[0:2])\n", " geotiffs_da = xr.concat(xarray_list, dim=time_var)\n", " return geotiffs_da" ] }, { "cell_type": "code", "execution_count": 49, "id": "owned-replication", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "regridding /home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/NO2_ES_ADAMAPI_2021/eu_cams_surface_no2_g_2021-03-30t000000.tif\n", "regridding /home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/NO2_ES_ADAMAPI_2021/eu_cams_surface_no2_g_2021-03-29t000000.tif\n", "regridding /home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/NO2_ES_ADAMAPI_2021/eu_cams_surface_no2_g_2021-03-28t000000.tif\n", "regridding /home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/NO2_ES_ADAMAPI_2021/eu_cams_surface_no2_g_2021-03-27t000000.tif\n", "regridding /home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/NO2_ES_ADAMAPI_2021/eu_cams_surface_no2_g_2021-03-26t000000.tif\n", "regridding /home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/NO2_ES_ADAMAPI_2021/eu_cams_surface_no2_g_2021-03-25t000000.tif\n", "regridding /home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/NO2_ES_ADAMAPI_2021/eu_cams_surface_no2_g_2021-03-24t000000.tif\n", "regridding /home/jovyan/datahub/Reliance/Climate_ES_NO2_March/input/NO2_ES_ADAMAPI_2021/eu_cams_surface_no2_g_2021-03-23t000000.tif\n", "regridding 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"metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: cmaps in /opt/conda/envs/cams/lib/python3.9/site-packages (1.0.5)\n", "Requirement already satisfied: holoviews<1.14.8 in /opt/conda/envs/cams/lib/python3.9/site-packages (1.14.7)\n", "Requirement already satisfied: GeoViews in /opt/conda/envs/cams/lib/python3.9/site-packages (1.9.5)\n", "Requirement already satisfied: cartopy in /opt/conda/envs/cams/lib/python3.9/site-packages (0.20.2)\n", "Requirement already satisfied: matplotlib in /opt/conda/envs/cams/lib/python3.9/site-packages (from cmaps) (3.5.1)\n", "Requirement already satisfied: numpy in /opt/conda/envs/cams/lib/python3.9/site-packages (from cmaps) (1.21.6)\n", "Requirement already satisfied: pyviz-comms>=0.7.4 in /opt/conda/envs/cams/lib/python3.9/site-packages (from holoviews<1.14.8) (2.2.0)\n", "Requirement already satisfied: pandas>=0.20.0 in /opt/conda/envs/cams/lib/python3.9/site-packages (from 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"Requirement already satisfied: importlib-metadata>=4.4 in /opt/conda/envs/cams/lib/python3.9/site-packages (from markdown->panel>=0.8.0->holoviews<1.14.8) (4.11.3)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->panel>=0.8.0->holoviews<1.14.8) (2.0.12)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->panel>=0.8.0->holoviews<1.14.8) (1.26.9)\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->panel>=0.8.0->holoviews<1.14.8) (3.3)\n", "Requirement already satisfied: zipp>=0.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from importlib-metadata>=4.4->markdown->panel>=0.8.0->holoviews<1.14.8) (3.8.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install cmaps \"holoviews<1.14.8\" GeoViews cartopy" ] }, { "cell_type": "code", "execution_count": 53, "id": "known-thong", "metadata": {}, "outputs": [], "source": [ "import cartopy.crs as ccrs\n", "import matplotlib.pyplot as plt\n", "import cmaps" ] }, { "cell_type": "code", "execution_count": 54, "id": "c5505520-0c2a-498f-ad12-01c3b2f095ca", "metadata": {}, "outputs": [], "source": [ "# To plot over Norway, taking a central longitude of 60 is fine. You may want to change it when plotting over different geographical areas\n", "central_longitude = town_coordinates['latitude']" ] }, { "cell_type": "code", "execution_count": 55, "id": "mechanical-lottery", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# generate figure\n", "proj_plot = ccrs.Mercator(central_longitude=central_longitude)\n", "\n", "lcmap = cmaps.BlueYellowRed\n", "# Only plot values greater than 0\n", "p = geotiff_dm[variable_name].where(geotiff_dm[variable_name] > 0).plot(x='longitude', y='latitude',\n", " transform=ccrs.PlateCarree(),\n", " subplot_kws={\"projection\": proj_plot},\n", " size=8,\n", " col='year', col_wrap=3, robust=True,\n", " cmap=lcmap, add_colorbar=True)\n", "\n", "# We have to set the map's options on all four axes\n", "for ax,i in zip(p.axes.flat, geotiff_dm.year.values):\n", " ax.coastlines()\n", " ax.set_title('Surface ' + variable_name + '\\n' + month_name + ' ' + str(i), fontsize=10)\n", "\n", "plot_file = os.path.join(OUTPUT_DATA_DIR, variable_name + '_' + month_name + '_' + country_code + '_2019-2021.png')\n", "if os.path.exists(plot_file + '.bak'):\n", " os.remove(plot_file + '.bak')\n", "if os.path.exists(plot_file):\n", " os.rename(plot_file, plot_file + '.bak') \n", "plt.savefig(plot_file)" ] }, { "cell_type": "markdown", "id": "toxic-discretion", "metadata": {}, "source": [ "#### Plot one single date" ] }, { "cell_type": "code", "execution_count": 56, "id": "legislative-adoption", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig=plt.figure(figsize=(10,10))\n", "# Define the projection\n", "crs=ccrs.PlateCarree()\n", "\n", "# We're using cartopy and are plotting in Orthographic projection \n", "# (see documentation on cartopy)\n", "ax = plt.subplot(1, 1, 1, projection=ccrs.Mercator(central_longitude=central_longitude))\n", "ax.coastlines(resolution='10m')\n", "\n", "# custom colormap\n", "\n", "lcmap = cmaps.BlueYellowRed\n", "\n", "# We need to project our data to the new Mercator projection and for this we use `transform`.\n", "# we set the original data projection in transform (here PlateCarree)\n", "# we only plot values greather than 0\n", "img = geotiff_ds[variable_name].where(geotiff_ds[variable_name] > 0).sel(time='2021-' + month_number + '-15').plot(ax=ax,\n", " transform=ccrs.PlateCarree(),\n", " cmap=lcmap) \n", "\n", "# Title for plot\n", "plt.title('Surface ' + variable_name + '\\n 15th ' + month_name + ' 2021 over ' + country_fullname,\n", " fontsize = 16, fontweight = 'bold', pad=10)\n", "\n", "plot_file = os.path.join(OUTPUT_DATA_DIR, variable_name + '_' + month_name +' _' + country_code + '_2021-' + month_number + '-15.png')\n", "if os.path.exists(plot_file + '.bak'):\n", " os.remove(plot_file + '.bak')\n", "if os.path.exists(plot_file):\n", " os.rename(plot_file, plot_file + '.bak') \n", "plt.savefig(plot_file)" ] }, { "cell_type": "code", "execution_count": 57, "id": "3b55c774-cc88-4778-a843-d8746dd0e25b", "metadata": {}, "outputs": [], "source": [ "geotiff_ds = geotiff_ds.sortby('time')" ] }, { "cell_type": "markdown", "id": "00b430fb-cd20-4cae-a1c8-f7bab0dc05fd", "metadata": {}, "source": [ "#### Save Data Cube selection into netCDF" ] }, { "cell_type": "code", "execution_count": 58, "id": "017e6edb-5478-4471-8036-4afcca4dc3aa", "metadata": {}, "outputs": [], "source": [ "output_file = os.path.join(OUTPUT_DATA_DIR, variable_name + \"_\" + month_name + \"_\" + country_code + \"_2019-2021.nc\")\n", "if os.path.exists(output_file + '.bak'):\n", " os.remove(output_file + '.bak')\n", "if os.path.exists(output_file):\n", " os.rename(output_file, output_file + '.bak') \n", "geotiff_ds.to_netcdf(output_file)" ] }, { "cell_type": "markdown", "id": "04c204a4-3a31-4805-aaf5-1d88cf3f3748", "metadata": {}, "source": [ "## **Step 7: Create Research Object and Share my work**" ] }, { "cell_type": "markdown", "id": "ded3d204-e278-4d46-9d93-a04e5df8956d", "metadata": {}, "source": [ "## Create Research Object in ROHUB" ] }, { "cell_type": "code", "execution_count": 59, "id": "cfb8d48c-c378-4837-a7cd-99a759ef1c77", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: rohub in /opt/conda/envs/cams/lib/python3.9/site-packages (1.0.7)\n", "Requirement already satisfied: requests in /opt/conda/envs/cams/lib/python3.9/site-packages (from rohub) (2.27.1)\n", "Requirement already satisfied: pandas in /opt/conda/envs/cams/lib/python3.9/site-packages (from rohub) (1.4.2)\n", "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/envs/cams/lib/python3.9/site-packages (from pandas->rohub) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /opt/conda/envs/cams/lib/python3.9/site-packages (from pandas->rohub) (2022.1)\n", "Requirement already satisfied: numpy>=1.18.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from pandas->rohub) (1.21.6)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->rohub) (1.26.9)\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->rohub) (3.3)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->rohub) (2.0.12)\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/cams/lib/python3.9/site-packages (from requests->rohub) (2021.10.8)\n", "Requirement already satisfied: six>=1.5 in /opt/conda/envs/cams/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas->rohub) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install rohub" ] }, { "cell_type": "code", "execution_count": 60, "id": "24d4ac14-50e1-4b7f-b233-09f66ec892a1", "metadata": {}, "outputs": [], "source": [ "import os\n", "import pathlib\n", "from rohub import rohub, settings" ] }, { "cell_type": "markdown", "id": "95e3211d-783a-40c1-bedf-9b51794e44ca", "metadata": {}, "source": [ "#### Authenticating\n", "\n", "- If the code cell below fails, make sure you have created the two files:\n", " - `rohub-user`: contains your rohub username\n", " - `rohub-pwd`: contains your rohub password" ] }, { "cell_type": "code", "execution_count": 61, "id": "98639345-9ea8-457d-9532-3c4aa6031801", "metadata": {}, "outputs": [], "source": [ "rohub_user = open(os.path.join(os.environ['HOME'],\"rohub-user\")).read().rstrip()\n", "rohub_pwd = open(os.path.join(os.environ['HOME'],\"rohub-pwd\")).read().rstrip()" ] }, { "cell_type": "code", "execution_count": 62, "id": "5fcd2c68-0a16-4c3d-9087-64d3cb89fd7f", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logged successfully as annefou@geo.uio.no.\n" ] } ], "source": [ "rohub.login(username=rohub_user, password=rohub_pwd)" ] }, { "cell_type": "markdown", "id": "17fc23ec-5e0c-4879-b7de-410b74b90df0", "metadata": {}, "source": [ "## Create a new Exectuable RO" ] }, { "cell_type": "code", "execution_count": 63, "id": "b6160f17-24ed-43dd-9920-e1f72152472d", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Research Object was successfully created with id = 2f4c4963-ba0f-4069-a211-988fb62006ef\n" ] } ], "source": [ "ro_title = variable_name + ' (' + month_name + ' 2019, 2020, 2021) in ' + country_fullname + \" Jupyter notebook demonstrating the usage of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services\"\n", "ro_research_areas = [\"Earth sciences\"]\n", "ro_description = \"This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of \" + \\\n", " variable_name + \" over a given geographical area, here \" + country_fullname\n", "ro = rohub.ros_create(title=ro_title, research_areas=ro_research_areas, \n", " description=ro_description, \n", " use_template=True,\n", " ros_type=\"Executable Research Object\")" ] }, { "cell_type": "markdown", "id": "1b994bfa-3a31-4d73-a58f-6d7d8aa34d51", "metadata": {}, "source": [ "## Show metadata" ] }, { "cell_type": "code", "execution_count": 64, "id": "b29df6ed-e245-41d3-bdd9-5a2189d3679f", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'identifier': '2f4c4963-ba0f-4069-a211-988fb62006ef',\n", " 'shared_link': 'https://w3id.org/ro-id/2f4c4963-ba0f-4069-a211-988fb62006ef',\n", " 'title': 'NO2 (March 2019, 2020, 2021) in Spain Jupyter notebook demonstrating the usage of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services',\n", " 'description': 'This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of NO2 over a given geographical area, here Spain',\n", " 'status': 'LIVE',\n", " 'access_mode': 'PUBLIC',\n", " 'type': 'Executable Research Object',\n", " 'template': 'Executable Research Object folders structure',\n", " 'created': '2022-05-01T20:15:42.899723Z',\n", " 'creator': 'annefou@geo.uio.no',\n", " 'modificator': None,\n", " 'modified': '2022-05-01T20:15:43.260263Z',\n", " 'importer': None,\n", " 'research_areas': ['Earth sciences'],\n", " 'owner': 'annefou@geo.uio.no',\n", " 'editors': [],\n", " 'readers': [],\n", " 'rating': '0.00',\n", " 'number_of_ratings': 0,\n", " 'number_of_likes': 0,\n", " 'number_of_dislikes': 0,\n", " 'completeness': 0,\n", " 'size': 0,\n", " 'creation_mode': 'MANUAL',\n", " 'doi': None,\n", " 'api_link': 'https://api.rohub.org/api/ros/2f4c4963-ba0f-4069-a211-988fb62006ef/'}" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ro.show_metadata()" ] }, { "cell_type": "markdown", "id": "93ae4992-c839-4cc8-9fe1-0418a0a23363", "metadata": {}, "source": [ "## Add additional authors and/or contributors to our Research Object" ] }, { "cell_type": "code", "execution_count": 65, "id": "b6ac1e0d-0cb9-4084-852b-8b8f663dbde2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Agent: annefou@geo.uio.no recognized in the system.\n" ] }, { "data": { "text/plain": [ "[{'identifier': '62277187-0f2e-4320-a6b0-7fe467f1b1c1',\n", " 'username': 'annefou@geo.uio.no',\n", " 'name': 'annefou@geo.uio.no',\n", " 'type': 'RohubUser',\n", " 'order': 0,\n", " 'display_name': 'Anne Fouilloux',\n", " 'orcid_identifier': '0000-0002-1784-2920',\n", " 'affiliation': 'Nordic e-Infrastructure Collaboration (NeIC)'}]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ro.set_authors(agents=author_emails)" ] }, { "cell_type": "code", "execution_count": 66, "id": "baae2136-734d-472c-a56c-a962f44dc420", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Agent: jeani@uio.no recognized in the system.\n", "Agent: mantovani@meeo.it recognized in the system.\n" ] }, { "data": { "text/plain": [ "[{'identifier': '1ac03644-c78c-4ca2-b0b4-2d9e40f671af',\n", " 'username': 'jeani@uio.no',\n", " 'name': 'jeani@uio.no',\n", " 'type': 'ExternalUser',\n", " 'order': 0,\n", " 'display_name': 'Jean Iaquinta',\n", " 'orcid_identifier': '0000-0002-8763-1643',\n", " 'affiliation': 'UiO'},\n", " {'identifier': 'a498dafe-985b-4d0f-a7e4-ac8f0312f3fe',\n", " 'username': 'mantovani@meeo.it',\n", " 'name': 'mantovani@meeo.it',\n", " 'type': 'RohubUser',\n", " 'order': 1,\n", " 'display_name': 'Simone Mantovani'}]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ro.set_contributors(agents=contributor_emails)" ] }, { "cell_type": "markdown", "id": "d610d675-ce0a-4c28-8f70-cc898cb181ca", "metadata": {}, "source": [ "## Add publisher/copyright holder\n", "\n", "- Use [Research Organization Registry (ROR)](https://ror.org/) to find the identifier of your organization" ] }, { "cell_type": "markdown", "id": "90749caa-1db3-427a-a69e-07e5527aea88", "metadata": {}, "source": [ "### Add publishers " ] }, { "cell_type": "code", "execution_count": 67, "id": "b9775742-1c33-45f1-bd3f-b224ad07f616", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Agent: {'org_id': 'http://www.uio.no/english/', 'display_name': 'University of Oslo', 'agent_type': 'organization', 'ror_identifier': '01xtthb56', 'organization_url': 'http://www.uio.no/english/'} recognized in the system.\n", "Agent: {'org_id': 'https://neic.no/', 'display_name': 'Nordic e-Infrastructure Collaboration', 'agent_type': 'organization', 'ror_identifier': '04jcwf484', 'organization_url': 'https://neic.no/'} recognized in the system.\n" ] }, { "data": { "text/plain": [ "[{'identifier': '1d581e9d-25e2-4029-bdeb-6c055a0db8e4',\n", " 'username': 'https://ror.org/01xtthb56',\n", " 'name': 'https://ror.org/01xtthb56',\n", " 'type': 'Organization',\n", " 'order': 0,\n", " 'display_name': 'University of Oslo'},\n", " {'identifier': 'fbd9bdcf-4a1d-4ae8-9a90-6b9ec5a3b8b2',\n", " 'username': 'https://ror.org/04jcwf484',\n", " 'name': 'https://ror.org/04jcwf484',\n", " 'type': 'Organization',\n", " 'order': 1,\n", " 'display_name': 'Nordic e-Infrastructure Collaboration'}]" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ro.set_publishers(agents=list_publishers)" ] }, { "cell_type": "code", "execution_count": 68, "id": "0f8ecf9d-8390-4d7e-b01a-a07272c7ad9b", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Agent: {'org_id': 'http://www.uio.no/english/', 'display_name': 'University of Oslo', 'agent_type': 'organization', 'ror_identifier': '01xtthb56', 'organization_url': 'http://www.uio.no/english/'} recognized in the system.\n" ] }, { "data": { "text/plain": [ "[{'identifier': '1d581e9d-25e2-4029-bdeb-6c055a0db8e4',\n", " 'username': 'https://ror.org/01xtthb56',\n", " 'name': 'https://ror.org/01xtthb56',\n", " 'type': 'Organization',\n", " 'order': 0,\n", " 'display_name': 'University of Oslo'}]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ro.set_copyright_holders(agents=list_copyright_holders)" ] }, { "cell_type": "code", "execution_count": 69, "id": "a089ac8b-71c2-45ce-9c4f-9b2883bd2e24", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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identifierorganization_iddisplay_nameemailurlror_identifier
09196f718-5be7-40da-a64e-6c6c8928b826example_orgA@email.com(example)Organization Aexample_orgA@email.comNoneNone
1de98f5bb-083d-4822-9df7-b02f5d091c0benvironmental.ds.book@gmail.comEnvironmental Data Science Book Communityenvironmental.ds.book@gmail.comhttps://github.com/alan-turing-institute/envir...None
292459295-cc33-40a2-b2ca-e416ad73a95eneworg_1@example.orgExample Org 1neworg1@example.orghttp://neworg1.example.orgabcd123
3556f3671-87b4-4576-b39d-654942ef248eneworg_2@example.orgExample Org 2neworg2@example.orghttp://neworg2.example.orgabcd123
45835ec4b-1f51-4b3a-b4fc-924de28cb1d4https://ror.org/abcd56789Example Org 3neworg3@example.orghttp://neworg3.example.orgabcd56789
56c441255-cfb5-4a0c-868b-5b3b0ef0f6f9https://ror.org/01zv3gf04Finnish Geospatial Research InstituteNonehttp://www.fgi.fi/fgi/01zv3gf04
654dcdbe9-026a-4c43-854c-d7c4546f27a4https://ror.org/00qps9a02Istituto Nazionale di Geofisica e VulcanologiaNonehttp://www.ingv.it/en00qps9a02
7fbd9bdcf-4a1d-4ae8-9a90-6b9ec5a3b8b2https://ror.org/04jcwf484Nordic e-Infrastructure CollaborationNonehttps://neic.no/04jcwf484
864ec5402-1171-42f6-b9dd-6fa901ca9724https://ror.org/025cj6e44Poznan Supercomputing and Networking Centeroffice@man.poznan.plhttps://www.psnc.pl/025cj6e44
9ae2a953a-cb80-4bb3-b6a5-a1155ce15edchttps://ror.org/03srtnf24Universidade Federal do CearáNonehttp://www.ufc.br/03srtnf24
10a3773158-c078-4193-bc44-a1d39a2bfe01https://ror.org/03zga2b32University of BergenNonehttp://www.uib.no/03zga2b32
111d581e9d-25e2-4029-bdeb-6c055a0db8e4https://ror.org/01xtthb56University of OsloNonehttp://www.uio.no/english/01xtthb56
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" ], "text/plain": [ " identifier organization_id \\\n", "0 9196f718-5be7-40da-a64e-6c6c8928b826 example_orgA@email.com \n", "1 de98f5bb-083d-4822-9df7-b02f5d091c0b environmental.ds.book@gmail.com \n", "2 92459295-cc33-40a2-b2ca-e416ad73a95e neworg_1@example.org \n", "3 556f3671-87b4-4576-b39d-654942ef248e neworg_2@example.org \n", "4 5835ec4b-1f51-4b3a-b4fc-924de28cb1d4 https://ror.org/abcd56789 \n", "5 6c441255-cfb5-4a0c-868b-5b3b0ef0f6f9 https://ror.org/01zv3gf04 \n", "6 54dcdbe9-026a-4c43-854c-d7c4546f27a4 https://ror.org/00qps9a02 \n", "7 fbd9bdcf-4a1d-4ae8-9a90-6b9ec5a3b8b2 https://ror.org/04jcwf484 \n", "8 64ec5402-1171-42f6-b9dd-6fa901ca9724 https://ror.org/025cj6e44 \n", "9 ae2a953a-cb80-4bb3-b6a5-a1155ce15edc https://ror.org/03srtnf24 \n", "10 a3773158-c078-4193-bc44-a1d39a2bfe01 https://ror.org/03zga2b32 \n", "11 1d581e9d-25e2-4029-bdeb-6c055a0db8e4 https://ror.org/01xtthb56 \n", "\n", " display_name \\\n", "0 (example)Organization A \n", "1 Environmental Data Science Book Community \n", "2 Example Org 1 \n", "3 Example Org 2 \n", "4 Example Org 3 \n", "5 Finnish Geospatial Research Institute \n", "6 Istituto Nazionale di Geofisica e Vulcanologia \n", "7 Nordic e-Infrastructure Collaboration \n", "8 Poznan Supercomputing and Networking Center \n", "9 Universidade Federal do Ceará \n", "10 University of Bergen \n", "11 University of Oslo \n", "\n", " email \\\n", "0 example_orgA@email.com \n", "1 environmental.ds.book@gmail.com \n", "2 neworg1@example.org \n", "3 neworg2@example.org \n", "4 neworg3@example.org \n", "5 None \n", "6 None \n", "7 None \n", "8 office@man.poznan.pl \n", "9 None \n", "10 None \n", "11 None \n", "\n", " url ror_identifier \n", "0 None None \n", "1 https://github.com/alan-turing-institute/envir... None \n", "2 http://neworg1.example.org abcd123 \n", "3 http://neworg2.example.org abcd123 \n", "4 http://neworg3.example.org abcd56789 \n", "5 http://www.fgi.fi/fgi/ 01zv3gf04 \n", "6 http://www.ingv.it/en 00qps9a02 \n", "7 https://neic.no/ 04jcwf484 \n", "8 https://www.psnc.pl/ 025cj6e44 \n", "9 http://www.ufc.br/ 03srtnf24 \n", "10 http://www.uib.no/ 03zga2b32 \n", "11 http://www.uio.no/english/ 01xtthb56 " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "organizations = rohub.organizations_find()\n", "organizations" ] }, { "cell_type": "markdown", "id": "329a6446-3a61-4cb2-8596-3a647ea5d630", "metadata": {}, "source": [ "## Add RO Funding information" ] }, { "cell_type": "code", "execution_count": 70, "id": "c5749469-af20-4bb1-8105-d203daa4225d", "metadata": { "tags": [] }, "outputs": [], "source": [ "if funded_by:\n", " ro.add_funding(grant_identifier=funded_by[\"grant_id\"], grant_name=funded_by[\"grant_Name\"],\n", " funder_name=funded_by[\"funder_name\"], grant_title=funded_by[\"grant_title\"],\n", " funder_doi=funded_by[\"funder_doi\"])" ] }, { "cell_type": "markdown", "id": "9df5260b-45f3-443d-a267-de81347dd029", "metadata": {}, "source": [ "## Add RO license" ] }, { "cell_type": "code", "execution_count": 71, "id": "1720bd51-ee45-4092-a88a-f290dd2c7f1b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'identifier': 'MIT',\n", " 'title': 'MIT License',\n", " 'description': 'MIT License',\n", " 'created': '2016-09-12T02:51:35.895509+00:00',\n", " 'updated': '2021-10-07T08:21:23.483114+00:00',\n", " 'creator': None,\n", " 'status': 'active',\n", " 'url': 'https://opensource.org/licenses/MIT',\n", " 'custom': False}" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ro.set_license(license_id=license) " ] }, { "cell_type": "markdown", "id": "d5ff9bb6-e970-4a56-95bf-3eed86b737c4", "metadata": {}, "source": [ "## Aggregate Resources\n", "\n", "- We will be adding all the resources generated by our notebook (data and plots)\n", "- Our data and plots can also be shared in B2DROP so we will get the shared link from B2DROP and add it to our research object" ] }, { "cell_type": "markdown", "id": "e9321b46-de48-4f9e-a729-3cc757f99473", "metadata": {}, "source": [ "### List RO folders for this type of RO" ] }, { "cell_type": "code", "execution_count": 72, "id": "b8f82ad3-348f-424b-a239-3818ba3e4280", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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identifiernamedescriptioncreatorcreated_onmodified_onpath
088b7d9fa-3c6a-4232-950c-736c3e0b3768biblioNoneannefou@geo.uio.no2022-05-01T20:15:43.450956+00002022-05-01T20:15:43.451840+0000biblio
1b97cbc88-03ab-47cc-be9e-673245513627inputNoneannefou@geo.uio.no2022-05-01T20:15:43.451106+00002022-05-01T20:15:43.451943+0000input
299d35a77-ae94-488f-9188-3440d14f3f82outputNoneannefou@geo.uio.no2022-05-01T20:15:43.451189+00002022-05-01T20:15:43.452006+0000output
3af75adff-5015-4a9a-9317-432c577252fftoolNoneannefou@geo.uio.no2022-05-01T20:15:43.451263+00002022-05-01T20:15:43.452066+0000tool
\n", "
" ], "text/plain": [ " identifier name description \\\n", "0 88b7d9fa-3c6a-4232-950c-736c3e0b3768 biblio None \n", "1 b97cbc88-03ab-47cc-be9e-673245513627 input None \n", "2 99d35a77-ae94-488f-9188-3440d14f3f82 output None \n", "3 af75adff-5015-4a9a-9317-432c577252ff tool None \n", "\n", " creator created_on \\\n", "0 annefou@geo.uio.no 2022-05-01T20:15:43.450956+0000 \n", "1 annefou@geo.uio.no 2022-05-01T20:15:43.451106+0000 \n", "2 annefou@geo.uio.no 2022-05-01T20:15:43.451189+0000 \n", "3 annefou@geo.uio.no 2022-05-01T20:15:43.451263+0000 \n", "\n", " modified_on path \n", "0 2022-05-01T20:15:43.451840+0000 biblio \n", "1 2022-05-01T20:15:43.451943+0000 input \n", "2 2022-05-01T20:15:43.452006+0000 output \n", "3 2022-05-01T20:15:43.452066+0000 tool " ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myfolders = ro.list_folders()\n", "myfolders" ] }, { "cell_type": "markdown", "id": "0f4c1732-7431-4c44-b61e-e57b535367d3", "metadata": {}, "source": [ "## Aggregate internal resources" ] }, { "cell_type": "markdown", "id": "6200c842-c217-44a5-8ecf-7240c2ebb20b", "metadata": {}, "source": [ "### Add sketch to my RO" ] }, { "cell_type": "code", "execution_count": 73, "id": "0908d97c-7db0-4527-9cac-96a135779e1a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Resource was successfully created with id = faf2af90-0919-4f58-8bb3-412e21cc07c6\n" ] }, { "data": { "text/plain": [ "Resource(identifier=faf2af90-0919-4f58-8bb3-412e21cc07c6, post_request=False)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_file_path = os.path.join(OUTPUT_DATA_DIR, variable_name + '_' + month_name + '_' + country_code + '_2019-2021.png')\n", "res_res_type = \"Sketch\"\n", "res_title = variable_long_name + \" [\" + variable_unit + \"] over \" + country_fullname + \" for \" + month_name + \" 2019, 2020 and 2021\"\n", "res_description = \"Monthly average maps of CAMS \" + variable_long_name + \" [\" + variable_unit + \"] over \" + country_fullname + \" in 2019, 2020 and 2021\"\n", "res_folder = 'output'\n", "\n", "ro.add_internal_resource(res_type=res_res_type, file_path=res_file_path, title=res_title, description=res_description, folder=res_folder)" ] }, { "cell_type": "markdown", "id": "f3794372-e0df-48c0-863d-3c20d8bdbcd8", "metadata": {}, "source": [ "### Add conda environment to my RO" ] }, { "cell_type": "code", "execution_count": 74, "id": "0fd00c09-8090-4072-ba6d-a29b2517851f", "metadata": {}, "outputs": [], "source": [ "def copy_conda_env(local_conda_path, shared_conda_path):\n", " bkfile = shared_conda_path + '.bak'\n", " if os.path.exists(bkfile):\n", " os.remove(bkfile)\n", " if os.path.exists(shared_conda_path):\n", " os.rename(shared_conda_path, bkfile)\n", " shutil.copy2(local_conda_path, shared_conda_path)" ] }, { "cell_type": "code", "execution_count": 75, "id": "0fbe78ff-394f-4a92-93d9-fe75611a4ef0", "metadata": {}, "outputs": [], "source": [ "from datetime import date" ] }, { "cell_type": "code", "execution_count": 76, "id": "5d13ae20-855f-469e-a046-403b3327429e", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'shutil' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [76]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m local_conda_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./\u001b[39m\u001b[38;5;124m'\u001b[39m, conda_filename)\n\u001b[1;32m 3\u001b[0m shared_conda_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(TOOL_DATA_DIR, conda_filename)\n\u001b[0;32m----> 4\u001b[0m \u001b[43mcopy_conda_env\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_conda_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshared_conda_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m res_file_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(INPUT_DATA_DIR, shared_conda_path)\n\u001b[1;32m 7\u001b[0m res_res_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mScript\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", "Input \u001b[0;32mIn [74]\u001b[0m, in \u001b[0;36mcopy_conda_env\u001b[0;34m(local_conda_path, shared_conda_path)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(shared_conda_path):\n\u001b[1;32m 6\u001b[0m os\u001b[38;5;241m.\u001b[39mrename(shared_conda_path, bkfile)\n\u001b[0;32m----> 7\u001b[0m \u001b[43mshutil\u001b[49m\u001b[38;5;241m.\u001b[39mcopy2(local_conda_path, shared_conda_path)\n", "\u001b[0;31mNameError\u001b[0m: name 'shutil' is not defined" ] } ], "source": [ "for os_name, conda_filename in zip(['', 'linux-64', 'osx-64'], ['cams-conda.yml', 'conda-lock-linux-64.yml', 'conda-lock-osx-64.yml']):\n", " local_conda_path = os.path.join('./', conda_filename)\n", " shared_conda_path = os.path.join(TOOL_DATA_DIR, conda_filename)\n", " copy_conda_env(local_conda_path, shared_conda_path)\n", " \n", " res_file_path = os.path.join(INPUT_DATA_DIR, shared_conda_path)\n", " res_res_type = \"Script\"\n", " res_title = 'Conda environment ' + os_name\n", " if os_name == \"\":\n", " res_description = \"Conda environment used on EGI notebook on \" + date.today().strftime(\"%d/%m/%Y\")\n", " else:\n", " res_description = \"Conda environment generated with conda-lock for \" + os_name\n", " res_folder = 'input'\n", "\n", " ro.add_internal_resource(res_type=res_res_type, file_path=res_file_path, title=res_title, description=res_description, folder=res_folder)" ] }, { "cell_type": "markdown", "id": "e8b80472-19c3-4e21-a3ae-2b606d918ff2", "metadata": {}, "source": [ "## Aggregate external resources" ] }, { "cell_type": "markdown", "id": "e855b117-90d5-468a-92f7-2a24c6573f90", "metadata": {}, "source": [ "### Get shared link from datahub\n", "1. Retrieve your client identifier, client secret and refresh token from https://aai.egi.eu/fedcloud/\n", "2. Create a new file in your HOME area for instance using nano (keep the `$` character in front of HOME; this is meant to be an environment variable):\n", "```\n", "nano $HOME/egi_fedcloud.cfg\n", "```\n", "3. Paste your client identifier, client secret and refresh token using the following syntax (do not forget `{` and `}` as well as comma `,` and columns `:`\n", "```\n", "{\n", "\"id\": \"XXXXXXXXXXXXXXXXX\",\n", "\"secret\": \"YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY\",\n", "\"token\": \"ffffffffffffffffffffffff\"\n", "}\n", "```\n", "4. You are ready to go. If you have issues, check first that the syntax is correct in your egi_fedcloud.cfg, that this file is located in the correct folder and then finally that your refresh token is still valid (check on https://aai.egi.eu/fedcloud/)" ] }, { "cell_type": "code", "execution_count": null, "id": "34371d8a-5192-4356-a3c3-040b0de3e469", "metadata": {}, "outputs": [], "source": [ "import os\n", "import json\n", "import requests\n", "import datetime\n", "import shutil" ] }, { "cell_type": "markdown", "id": "3c6ab5eb-e1cb-41b1-b899-4891504bb7fa", "metadata": {}, "source": [ "#### EGI Datahub functions to initialize EGI datahub and get shared link" ] }, { "cell_type": "code", "execution_count": null, "id": "dfadd3d8-2d04-4ca5-aa43-dec5b5ebf418", "metadata": {}, "outputs": [], "source": [ "def egi_datahub_init():\n", " egi_fedcloud_filename = os.path.join(os.environ['HOME'], 'egi_fedcloud.cfg')\n", " with open(egi_fedcloud_filename, 'r') as convert_file:\n", " tmp = convert_file.read()\n", " egi_fedcloud_auth = json.loads(tmp)\n", " try:\n", " # Retrieving an OIDC token from Check-in\n", " data = {\n", " 'client_id': egi_fedcloud_auth['id'],\n", " 'client_secret': egi_fedcloud_auth['secret'],\n", " 'refresh_token': egi_fedcloud_auth['token'],\n", " 'scope': 'openid email profile',\n", " 'grant_type': 'refresh_token'\n", " }\n", " response = requests.post('https://aai.egi.eu/oidc/token', data=data, auth=(egi_fedcloud_auth['id'], \n", " egi_fedcloud_auth['secret']))\n", " #print(json.dumps(response.json(), indent=2))\n", " EGItoken = response.json()['access_token']\n", " headers = {\n", " 'X-Auth-Token': f\"egi:\" + EGItoken,\n", " 'Content-type': 'application/json',\n", " }\n", " # get current timestamp\n", " ts = datetime.datetime.now().timestamp()\n", " data = json.dumps({ \n", " 'name': 'REST and CDMI access token ' + str(ts), \n", " 'type': { \n", " 'accessToken': {} \n", " }, \n", " 'caveats': [ { \n", " 'type': 'interface', \n", " 'interface': 'rest' \n", " }] \n", " })\n", "\n", " response = requests.post('https://datahub.egi.eu/api/v3/onezone/user/tokens/named', headers=headers, data=data)\n", " DATAHUB_TOKEN = response.json()['token']\n", " return DATAHUB_TOKEN\n", " except:\n", " print(\"EGI Datahub Authentication problem: check your credentials\")" ] }, { "cell_type": "code", "execution_count": null, "id": "e348c819-cb8d-4457-ae3c-6e5f5c40f85d", "metadata": {}, "outputs": [], "source": [ "def egi_datahub_getlink(datahub_token, filename):\n", " bname = os.path.basename(filename)\n", " datahub_remote_prefix = 'https://cesnet-oneprovider-01.datahub.egi.eu/api/v3/oneprovider/files/'\n", " hname = filename.split('datahub/')[1]\n", " datahub_location = os.path.join(datahub_remote_prefix, hname)\n", " print(datahub_location)\n", " headers = { 'X-Auth-Token': datahub_token }\n", " response = requests.get(datahub_location, headers=headers)\n", " dh_fileid = response.json()[0]['id']\n", " \n", " headers = { 'X-Auth-Token': datahub_token, 'Content-Type': 'application/json',}\n", " data = json.dumps({ 'name': bname,\n", " 'fileId': dh_fileid\n", " })\n", " response = requests.post('https://cesnet-oneprovider-01.datahub.egi.eu/api/v3/oneprovider/shares', headers=headers, data=data)\n", " # print(json.dumps(response.json(), indent=2))\n", " shareIdGenerated=response.json()['shareId']\n", " \n", " headers = {'X-Auth-Token': datahub_token}\n", " response = requests.get('https://cesnet-oneprovider-01.datahub.egi.eu/api/v3/oneprovider/shares/'+shareIdGenerated, headers=headers) \n", " # print(json.dumps(response.json(), indent=2))\n", " publicURL = response.json()['publicUrl']\n", " return publicURL" ] }, { "cell_type": "markdown", "id": "e751890a-e99b-4afd-94c0-5d390bfce105", "metadata": {}, "source": [ "### EGI DataHub initialization " ] }, { "cell_type": "code", "execution_count": null, "id": "837cbef8-1f87-435f-89d4-598df91c22d9", "metadata": {}, "outputs": [], "source": [ "DATAHUB_TOKEN = egi_datahub_init()" ] }, { "cell_type": "markdown", "id": "2d6e6e90-4f5a-4bb0-a4b6-caf96debaf88", "metadata": {}, "source": [ "## Add inputs to my RO\n", "- I used ADAM to retrieve relevant data but I will be sharing what I retrieved from the data cube so that my collaborators do not have to re-download the same input data again." ] }, { "cell_type": "markdown", "id": "80b71bd0-b1ca-40e1-b389-5158a2505454", "metadata": {}, "source": [ "#### Geojson file used for rertieving data from ADAM data-cube" ] }, { "cell_type": "code", "execution_count": null, "id": "7009d65a-a7e9-4165-91b5-22fa3e22854c", "metadata": {}, "outputs": [], "source": [ "shared_input_path = os.path.join(INPUT_DATA_DIR, country_code.lower() + '.geo.json')\n", "print(shared_input_path)\n", "res_file_url = egi_datahub_getlink(DATAHUB_TOKEN, shared_input_path)\n", "res_type = \"Dataset\"\n", "res_title = \"Geojson for \" + country_fullname\n", "res_description = \"Geojson file used for retrieving data from the ADAM platform over \" + country_fullname\n", "res_folder = 'input'\n", "ro.add_external_resource(res_type=res_type, input_url=res_file_url, title=res_title, description=res_description, folder=res_folder)" ] }, { "cell_type": "markdown", "id": "3321bbac-3d1a-48b4-91a0-5ba0cd3e3435", "metadata": {}, "source": [ "#### Input data retrieved from ADAM data-cube" ] }, { "cell_type": "code", "execution_count": null, "id": "e48c303a-f07e-47c3-aa9c-9f6b89e4a2f1", "metadata": {}, "outputs": [], "source": [ "for year in ['2019', '2020', '2021']:\n", " shared_input_path = os.path.join(INPUT_DATA_DIR, variable_name + '_' + country_code + '_ADAMAPI_' + year + '.zip')\n", " print(shared_input_path)\n", " res_file_url = egi_datahub_getlink(DATAHUB_TOKEN, shared_input_path)\n", " res_type = \"Data Cube Product\"\n", " res_title = \"Data-Cube from ADAM platform over \" + country_fullname + \" in \" + month_name + \" \" + year\n", " res_description = \"This dataset is a data-Cube retrieved from the ADAM platform over \" + country_fullname + \" in \" + month_name + \" \" + year\n", " res_folder ='input'\n", " ro.add_external_resource(res_type=res_type, input_url=res_file_url, title=res_title, description=res_description, folder=res_folder)" ] }, { "cell_type": "markdown", "id": "30a76383-0dd3-4be5-832e-23ddfc39618e", "metadata": {}, "source": [ "### Add our Jupyter Notebook to our RO\n", "- Make a copy of the current notebook to the tool folder for sharing as an external resource" ] }, { "cell_type": "code", "execution_count": null, "id": "2ea55433-5a43-4146-8f2f-ad6a3b6203fa", "metadata": {}, "outputs": [], "source": [ "notebook_filename = 'RELIANCE_' + country_fullname + '_' + variable_name + '_month.ipynb'\n", "local_notebook_path = os.path.join('./', notebook_filename)\n", "shared_notebook_path = os.path.join(TOOL_DATA_DIR, notebook_filename)" ] }, { "cell_type": "code", "execution_count": null, "id": "fe99f43a-af32-48f3-907c-951e6e08af07", "metadata": {}, "outputs": [], "source": [ "shared_notebook_path" ] }, { "cell_type": "markdown", "id": "f2de8314-561a-4100-b0d4-b6e6e6b34351", "metadata": {}, "source": [ "#### Copy current notebook to shared datahub " ] }, { "cell_type": "code", "execution_count": null, "id": "08ba4a2c-76d7-470c-b85e-3965a7ff3de9", "metadata": {}, "outputs": [], "source": [ "bkfile = shared_notebook_path + '.bak'\n", "if os.path.exists(bkfile):\n", " os.remove(bkfile)\n", "if os.path.exists(shared_notebook_path):\n", " os.rename(shared_notebook_path, bkfile)\n", "shutil.copy2(local_notebook_path, shared_notebook_path)" ] }, { "cell_type": "markdown", "id": "d0f4098b-e8b1-49e4-851c-8d164380f3d4", "metadata": {}, "source": [ "### Create a shared link for my Jupyter Notebook" ] }, { "cell_type": "markdown", "id": "792f515b-988b-44d2-a555-57f961c166cc", "metadata": {}, "source": [ "#### Get public URL from EGI Datahub to share link in RO " ] }, { "cell_type": "code", "execution_count": null, "id": "ae96b55c-f027-410a-bd31-17590b2baf6c", "metadata": { "tags": [] }, "outputs": [], "source": [ "res_file_url = egi_datahub_getlink(DATAHUB_TOKEN, shared_notebook_path)\n", "res_type = \"Jupyter Notebook\"\n", "res_title = \"Jupyter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - Applied over \" + country_fullname + \" and variable \" + variable_long_name\n", "res_description = \"Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo\"\n", "res_folder = 'tool'\n", "ro.add_external_resource(res_type=res_type, input_url=res_file_url, title=res_title, description=res_description, folder=res_folder)" ] }, { "cell_type": "markdown", "id": "174a59a9-528d-477d-95c2-d6a61a00bca9", "metadata": {}, "source": [ "#### Add first plot as Image to my Research Object (external resource from EGI Datahub)" ] }, { "cell_type": "code", "execution_count": null, "id": "bb7f9de9-cb74-4ddf-a525-113145d1a2c7", "metadata": { "tags": [] }, "outputs": [], "source": [ "shared_plot_path = os.path.join(OUTPUT_DATA_DIR, variable_name + '_' + month_name + '_' + country_code + '_2019-2021.png')\n", "print(shared_plot_path)\n", "res_file_url = egi_datahub_getlink(DATAHUB_TOKEN, shared_plot_path)\n", "res_type = \"Image\"\n", "res_title = variable_long_name + \" [\" + variable_unit + \"] over \" + country_fullname + \" for \" + month_name + \" 2019, 2020 and 2021\"\n", "res_description = \"Monthly average maps of CAMS \" + variable_long_name + \" [\" + variable_unit + \"] over \" + country_fullname + \" in 2019, 2020 and 2021\"\n", "res_folder = 'output'\n", "ro.add_external_resource(res_type=res_type, input_url=res_file_url, title=res_title, description=res_description, folder=res_folder)" ] }, { "cell_type": "markdown", "id": "4233eaa8-a6bd-4d86-b6fd-e5ed675ac944", "metadata": {}, "source": [ "#### Add second plot as Image to my Research Object (external resource from EGI Datahub)" ] }, { "cell_type": "code", "execution_count": null, "id": "c44512c5-a989-45a8-9b47-a66c8e0bc3c1", "metadata": {}, "outputs": [], "source": [ "shared_plot_path = os.path.join(OUTPUT_DATA_DIR, variable_name + '_' + month_name + ' _' + country_code + '_2021-' + month_number + '-15.png')\n", "res_file_url = egi_datahub_getlink(DATAHUB_TOKEN, shared_plot_path)\n", "res_type = \"Image\"\n", "res_title = variable_long_name + \" [\" + variable_unit + \"] over \" + country_fullname + \" on \" + month_name + \" 15, 2021\"\n", "res_description=\"Daily average maps of CAMS \" + variable_long_name + variable_unit + \"] over \" + country_fullname + \" on \" + month_name + \" 15, 2021\"\n", "res_folder = 'output'\n", "ro.add_external_resource(res_type=res_type, input_url=res_file_url, title=res_title, description=res_description, folder=res_folder)" ] }, { "cell_type": "markdown", "id": "92b3e6eb-14a9-4f65-936f-2eb061688b4f", "metadata": {}, "source": [ "#### Add netCDF file corresponding to Data cube selection (external resource from EGI Datahub)" ] }, { "cell_type": "code", "execution_count": null, "id": "dd008fe4-c185-4567-a717-1ba03dee4bc9", "metadata": {}, "outputs": [], "source": [ "shared_plot_path = os.path.join(OUTPUT_DATA_DIR, variable_name + '_' + month_name + '_' + country_code + '_2019-2021.nc')\n", "res_file_url = egi_datahub_getlink(DATAHUB_TOKEN, shared_plot_path)\n", "res_type=\"Result\"\n", "res_title=\"netCDF data for daily \" + variable_name + \"over \" + country_fullname + \" in \" + month_name + \" 2019, 2020 and 2021\"\n", "res_description=\"netCDF data corresponding to daily average of CAMS \" + variable_long_name + \" [\" + variable_unit + \"] over \" + country_fullname + \" for \" + month_name + \" 2019, \" + month_name + \" 2020 and \" + month_name + \" 2021\"\n", "res_folder = 'output'\n", "ro.add_external_resource(res_type=res_type, input_url=res_file_url, title=res_title, description=res_description, folder=res_folder)" ] }, { "cell_type": "markdown", "id": "9fd455a2-6217-45c7-a69f-1249e82780be", "metadata": {}, "source": [ "## Additional metadata for the RO" ] }, { "cell_type": "markdown", "id": "8503028a-155b-4f37-a357-81cd08a23b79", "metadata": {}, "source": [ "### Add geolocation to my Research Object\n", "- We need to transform our geojson file into geojson-ld" ] }, { "cell_type": "code", "execution_count": null, "id": "bf9eb7fe-c60c-46e7-9683-208dfc656dc4", "metadata": {}, "outputs": [], "source": [ "from geojson_rewind import rewind\n", "import json" ] }, { "cell_type": "code", "execution_count": null, "id": "fa9ea80e-df4c-4e01-97a3-c1d665e3a540", "metadata": {}, "outputs": [], "source": [ "geojson_ld_file = os.path.join(INPUT_DATA_DIR, country_code.lower() + '.geo-ld.json')\n", "bkfile = geojson_ld_file + '.bak'\n", "if os.path.exists(bkfile):\n", " os.remove(bkfile)\n", "if os.path.exists(geojson_ld_file):\n", " os.rename(geojson_ld_file, bkfile)\n", "shutil.copy2(os.path.join(INPUT_DATA_DIR, country_code.lower() + '.geo.json'), geojson_ld_file)\n", "\n", "with open(geojson_ld_file , 'r+') as f:\n", " data = json.load(f)\n", " output = rewind(data)\n", " output['@context'] = { \"geojson\": \"https://purl.org/geojson/vocab#\" } \n", " f.seek(0) \n", " json.dump(output, f, indent=None)\n", " f.truncate()" ] }, { "cell_type": "code", "execution_count": null, "id": "96583bed-485e-44b7-9f28-a427af5f73cf", "metadata": {}, "outputs": [], "source": [ "geolocation_file_path = os.path.join(INPUT_DATA_DIR, country_code.lower() + '.geo.json')\n", "ro.add_geolocation(body_specification_json=geolocation_file_path)" ] }, { "cell_type": "markdown", "id": "cead124a-adc1-43f0-b7fa-d846df3c9d8b", "metadata": {}, "source": [ "### Add tags" ] }, { "cell_type": "code", "execution_count": null, "id": "9553b056-37c4-4366-a6dd-ba30360b39df", "metadata": {}, "outputs": [], "source": [ "ro.add_keywords(keywords=[country_fullname, \"CAMS\", \"air quality\", \"copernicus\", variable_name, \"jupyter-notebook\"])" ] }, { "cell_type": "markdown", "id": "5a3a8e18-c457-4577-b30f-4dfad3448997", "metadata": {}, "source": [ "### Export to RO-crate" ] }, { "cell_type": "code", "execution_count": null, "id": "7e35fe3b-c7fd-48f4-9549-e1d438eabf01", "metadata": {}, "outputs": [], "source": [ "ro.export_to_rocrate(filename=\"climate_EU-CAMS_\" + country_code + \"_\" + variable_name + \"_ro-crate\", use_format=\"zip\")" ] }, { "cell_type": "markdown", "id": "5899f543-fc33-4bfc-aba8-fe2351efbf8a", "metadata": { "tags": [ "EOSC", "Jupyter", "Notebook", "Pangeo" ] }, "source": [ "### Take a snapshot of my RO" ] }, { "cell_type": "code", "execution_count": null, "id": "57ac1fb9-fdbf-4325-8095-a8fcc1bedb9d", "metadata": {}, "outputs": [], "source": [ "#snapshot_id=ro.snapshot()" ] }, { "cell_type": "markdown", "id": "23626464-5bc4-458f-9826-f106b22c159a", "metadata": {}, "source": [ "### Archive and publish to Zenodo, optionally assign DOI " ] }, { "cell_type": "code", "execution_count": null, "id": "f55b77a8-dcef-4544-8681-cb894951b156", "metadata": {}, "outputs": [], "source": [ "snapshot_title=\"Jupyter Notebook Analysing the Air quality during Covid-19 pandemic using Copernicus Atmosphere Monitoring Service - Applied over \" + country_fullname + \" with \" + variable_long_name\n", "#snapshot_id_pub=ro.snapshot(title=snapshot_title, create_doi=True, publication_services=[\"Zenodo\"])\n", "#snapshot_id_pub" ] }, { "cell_type": "markdown", "id": "33b6ef6e-d811-40e2-9852-e92925324939", "metadata": {}, "source": [ "### Load the published Research Object" ] }, { "cell_type": "code", "execution_count": null, "id": "0c96bf47-7bd6-49db-8107-0267210beaec", "metadata": { "tags": [] }, "outputs": [], "source": [ "#published_ro = rohub.ros_load(identifier=snapshot_id)" ] }, { "cell_type": "markdown", "id": "5f36bdf4-06a0-4511-a376-3a5d5fe99c37", "metadata": {}, "source": [ "### Show the DOI and get the link" ] }, { "cell_type": "code", "execution_count": null, "id": "0cb0e238-72cb-4817-a531-7a035e503046", "metadata": {}, "outputs": [], "source": [ "#published_ro.show_publication()" ] }, { "cell_type": "markdown", "id": "a6ccc5b0-23bb-4159-833a-57fb1b45f4f4", "metadata": {}, "source": [ "### Fork and reuse existing RO to create derivative work" ] }, { "cell_type": "code", "execution_count": null, "id": "9d4482db-b29b-417a-9654-3cb9197566e6", "metadata": {}, "outputs": [], "source": [ "#fork_id=ro.fork(title=\"Forked Jupyter Notebook to analyze the Air quality during Covid-19 pandemic using Copernicus Atmosphere Monitoring Service\")\n", "#forked_ro = rohub.ros_load(identifier=fork_id)\n", "#forked_ro.show_metadata()" ] }, { "cell_type": "code", "execution_count": null, "id": "29a825b8-63df-4d91-9ffc-aa3cc295d977", "metadata": {}, "outputs": [], "source": [ "#ro.delete()" ] }, { "cell_type": "code", "execution_count": null, "id": "428dea99-ba3b-46c2-b756-2d53745ceacb", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "cams", "language": "python", "name": "cams" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }