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argo.ipynb
{
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {
"celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"papermill": {
"default_parameters": {},
"environment_variables": {},
"input_path": "sparql.ipynb",
"output_path": "q/argo.ipynb",
"parameters": {
"q": "argo"
},
"version": "2.3.3"
},
"colab": {
"name": "argo.ipynb",
"provenance": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/MBcode/1fe85b1a87677968ea7c8804d56933d2/argo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"papermill": {},
"tags": [
"parameters"
],
"id": "44b4c990"
},
"source": [
"#github.com/nteract/papermill'parameters'tag used to inject them into template then post a gist runable by colab\n",
"q=None\n",
"q=\"norway\""
],
"id": "44b4c990",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"tags": [
"injected-parameters"
],
"id": "76216bb2"
},
"source": [
"# Parameters\n",
"q = \"argo\"\n"
],
"id": "76216bb2",
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"papermill": {},
"tags": [],
"colab": {
"base_uri": "https://localhost:8080/",
"height": 2309
},
"id": "dbc9c248",
"outputId": "7e7d3381-9d81-4470-fa56-db9ff92a5fdc"
},
"source": [
"%load_ext rpy2.ipython\n",
"%load_ext google.colab.data_table\n",
"import httpimport\n",
"with httpimport.github_repo('MBcode', 'ec'):\n",
" import ec\n",
"#ec.get_ec() #work from scratch version for a bit\n",
"#import ec\n",
"df=ec.txt_query(q)\n",
"df"
],
"id": "dbc9c248",
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"https://www.bco-dmo.org/dataset/836648\",\n\"urn:gleaner:milled:bcodmo:1ab4836221e9b387a791c3bce2caaf3974581fbf\",\n\"data\",\n\"Remotely-generated submesoscale coherent vortices (SCV) within Argo profiles\",\n\"<p>Final results for submesoscale coherent vortices (SCVs) detected in McCoy et al. (2020).<\\/p>\\r\\n\",\n\"Biological and Chemical Data Management Office\",\n\"No spatialCoverage\",\n\"Argo floats, mesoscale eddies, water masses, mode water, oceans\",\n\"2021-01-15\",\n\"\",\n\"0.625\"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"http://n2t.net/ark:/23942/g2nh03\",\n\"http://www.bigdata.com/rdf#nullGraph\",\n\"tool\",\n\"Argovis\",\n\"Access Argo profiles via API, or visualize temperature, salinity, and BGC data by location. View float trajectory forecasts, compare gridded fields, co-locate Argo data with weather events. Stay tuned for upcoming modules to co-locate Argo profiles with satellite and other Earth science datasets. See the Argovis quickstart page and tutorial page for more information about its features.Argo is an international program that deploys free drifting profiling floats distributed over the global oceans, to measure temperature, salinity, pressure in the upper 2,000m of the ocean. Deep Argo is is an extension of the Argo program to measure deeper than 2000m. Biogeochemical (BGC) Argo is an extension of the Argo program to include biogeochemical observations. As of Thursday, August 6, 2020 12:16 AM, Total number of Profiles in Argovis: 2068427; Number of Deep Profiles: 11676; Number of biogeochemical (BGC) Profiles: 148991, Dacs in Argovis: aoml, bodc, coriolis, csio, csiro,incois, jma, kma, kordi, meds, nmdis\",\n\"No Publisher\",\n\"No spatialCoverage\",\n\"Argo,Float,Oceanography,Climate,Weather Events,Grid,Biogeochemistry,BGC,Ocean,Atmospheric Rivers,Tropical Cyclones,Southern Ocean State Estimate,Trajectory,Data,sea water temperature,salinity,sea water pressure,conductivity,particle back scattering,particle beam attenuation,dissolved oxygen,chlorophyll,chlorophyll-a,dissolved organic matter,sea water,nitrate,sea water turbidity,bisulfide,sea water ph,downwelling irradiance,upwelling irradiance,downwelling photosynthetic\",\n\"No datePublished\",\n\"http://argovis.colorado.edu/docs/Argovis_Guide.html, http://argovis.colorado.edu/docs/Argovis_About.html, http://argovis.colorado.edu\",\n\"0.4375\"],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"http://n2t.net/ark:/23942/g2x06p\",\n\"http://www.bigdata.com/rdf#nullGraph\",\n\"tool\",\n\"Argovis API\",\n\"Access Argo profiles via API, i.e. temperature, salinity, and BGC data by location. Argo metadata, float trajectory forecasts, gridded fields, weather events are also available through API. Stay tuned for upcoming satellite data and other Earth science datasets. Argovis API returns JSON representation for Argo profiles, platforms and selections, and their metadata. Argovis API returns JSON representation also for other datasets in Argovis. Argo is an international program that deploys free drifting profiling floats distributed over the global oceans, to measure temperature, salinity, pressure in the upper 2,000m of the ocean. Deep Argo is is an extension of the Argo program to measure deeper than 2000m. Biogeochemical (BGC) Argo is an extension of the Argo program to include biogeochemical observations. As of Thursday, August 6, 2020 12:16 AM, Total number of Profiles in Argovis: 2068427; Number of Deep Profiles: 11676; Number of biogeochemical (BGC) Profiles: 148991, Dacs in Argovis: aoml, bodc, coriolis, csio, csiro,incois, jma, kma, kordi, meds, nmdis. See API calls for Argovis at https://argovis.colorado.edu/api-docs/ and https://argovis.colorado.edu/docs/Argovis_Tutorials.html, along with examples.\",\n\"No Publisher\",\n\"No spatialCoverage\",\n\"web service,Argo,Float,Oceanography,Climate,Weather Events,Grid,Biogeochemistry,BGC,Ocean,Atmospheric Rivers,Tropical Cyclones,Southern Ocean State Estimate,Trajectory,Data,sea water temperature,salinity,sea water pressure,conductivity,particle back scattering,particle beam attenuation,dissolved oxygen,chlorophyll,chlorophyll-a,dissolved organic matter,sea water,nitrate,sea water turbidity,bisulfide,sea water ph,downwelling irradiance,upwelling irradiance,downwelling photosynthetic\",\n\"No datePublished\",\n\"http://www.itsonlyamodel.us/argovis-python-api.html, http://argovis.colorado.edu/docs/Argovis_Tutorials.html, http://www.itsonlyamodel.us/argovis-r-api.html\",\n\"0.4375\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"http://n2t.net/ark:/23942/g2x06p\",\n\"http://www.bigdata.com/rdf#nullGraph\",\n\"tool\",\n\"Argovis API\",\n\"Access Argo profiles via API, i.e. temperature, salinity, and BGC data by location. Argo metadata, float trajectory forecasts, gridded fields, weather events are also available through API. Stay tuned for upcoming satellite data and other Earth science datasets. Argovis API returns JSON representation for Argo profiles, platforms and selections, and their metadata. Argovis API returns JSON representation also for other datasets in Argovis. Argo is an international program that deploys free drifting profiling floats distributed over the global oceans, to measure temperature, salinity, pressure in the upper 2,000m of the ocean. Deep Argo is is an extension of the Argo program to measure deeper than 2000m. Biogeochemical (BGC) Argo is an extension of the Argo program to include biogeochemical observations. As of Thursday, August 6, 2020 12:16 AM, Total number of Profiles in Argovis: 2068427; Number of Deep Profiles: 11676; Number of biogeochemical (BGC) Profiles: 148991, Dacs in Argovis: aoml, bodc, coriolis, csio, csiro,incois, jma, kma, kordi, meds, nmdis. See API calls for Argovis at http://argovis.colorado.edu/api-docs/ and http://argovis.colorado.edu/docs/Argovis_Tutorials.html, along with examples.\",\n\"No Publisher\",\n\"No spatialCoverage\",\n\"web service,Argo,Float,Oceanography,Climate,Weather Events,Grid,Biogeochemistry,BGC,Ocean,Atmospheric Rivers,Tropical Cyclones,Southern Ocean State Estimate,Trajectory,Data,sea water temperature,salinity,sea water pressure,conductivity,particle back scattering,particle beam attenuation,dissolved oxygen,chlorophyll,chlorophyll-a,dissolved organic matter,sea water,nitrate,sea water turbidity,bisulfide,sea water ph,downwelling irradiance,upwelling irradiance,downwelling photosynthetic\",\n\"No datePublished\",\n\"http://www.itsonlyamodel.us/argovis-python-api.html, http://argovis.colorado.edu/docs/Argovis_Tutorials.html, http://www.itsonlyamodel.us/argovis-r-api.html\",\n\"0.4375\"],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"DOI:10.1594/IEDA/307964\",\n\"urn:gleaner:milled:ieda:93edc5ef199da4984a2ac03fc332756d016b8523\",\n\"data\",\n\"Argo-I Navigation Data from the East Pacific Rise at 9N acquired during the Thomas Washington expedition VNTR03WT (1989)\",\n\"Abstract: This data set was acquired with a the towed Argo-I system during Thomas Washington expedition VNTR03WT conducted in 1989 (Chief Scientist: Dr. Rachel Haymon). These data files are of Text File (ASCII) format.\",\n\"Interdisciplinary Earth Data Alliance (IEDA)\",\n\"No spatialCoverage\",\n\"Navigation\",\n\"2008\",\n\"http://dx.doi.org/10.1594/IEDA/307964, http://www.marine-geo.org/tools/search/Files.php?data_set_uid=7964\",\n\"0.25\"],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n\"DOI:10.1594/IEDA/321900\",\n\"urn:gleaner:milled:ieda:dcdc31f8601897a6bf1ffbed1f6e5ae557156628\",\n\"data\",\n\"Processed Argo-II Navigation Data from the Mid-Atlantic Ridge acquired during the Atlantis expedition AT03-60 (2000)\",\n\"Abstract: This data set was acquired with a Navigation system on the towed Argo-II system during Atlantis expedition AT03-60 conducted in 2000 (Chief Scientist: Dr. Donna Blackman; Investigator(s): John A. Greene, Dr. Masako Tominaga, and Dr. Donna Blackman). These data files are of Text File (ASCII) format and include Navigation data that were processed after acquisition. Data were acquired as part of the project(s): Near-Bottom Geological and Geophysical Investigation of an Ocean Core Complex: Eastern Atlantis Ridge-Transform Intersection Massif. This data was cited by Greene et al., 2015.\",\n\"Interdisciplinary Earth Data Alliance (IEDA)\",\n\"No spatialCoverage\",\n\"Navigation\",\n\"2015\",\n\"http://dx.doi.org/10.1594/IEDA/321900, http://www.marine-geo.org/tools/search/Files.php?data_set_uid=21900\",\n\"0.25\"],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n\"DOI:10.1594/IEDA/307965\",\n\"urn:gleaner:milled:ieda:f062cde410bb6400fc9b904dad5830ccf458eba6\",\n\"data\",\n\"CTD Data from the East Pacific Rise at 9N acquired with Argo-I during the Thomas Washington expedition VNTR03WT (1989)\",\n\"Abstract: This data set was acquired with a towed CTD on Argo-I during Thomas Washington expedition VNTR03WT conducted in 1989 (Chief Scientist: Dr. Rachel Haymon). These data files are of Text File (ASCII) format and include Temperature, Pressure, and Conductivity data.\",\n\"Interdisciplinary Earth Data Alliance (IEDA)\",\n\"No spatialCoverage\",\n\"Temperature, Conductivity, Pressure\",\n\"2008\",\n\"http://dx.doi.org/10.1594/IEDA/307965, http://www.marine-geo.org/tools/search/Files.php?data_set_uid=7965\",\n\"0.25\"],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n\"DOI:10.1594/IEDA/316631\",\n\"urn:gleaner:milled:ieda:a66512d280bba77784905c128e8bedbb76216993\",\n\"data\",\n\"Seafloor Morphology Types derived from Photos acquired with Argo-I at the East Pacific Rise at 9N during Thomas Washington expedition VNTR03WT (1989)\",\n\"Abstract: This data set is derived from photos acquired with the towed platform Argo-I during Thomas Washington expedition VNTR03WT conducted in 1989 (Chief Scientist: Dr. Rachel Haymon; Investigator(s): Dr. Dawn Wright). These data files are of Text File (ASCII) format and were cited by Wright et al., 1998.\",\n\"Interdisciplinary Earth Data Alliance (IEDA)\",\n\"No spatialCoverage\",\n\"Interpretation:Geologic\",\n\"2010\",\n\"http://www.marine-geo.org/tools/search/Files.php?data_set_uid=16631, http://dx.doi.org/10.1594/IEDA/316631\",\n\"0.21875\"],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n\"DOI:10.1594/IEDA/321899\",\n\"urn:gleaner:milled:ieda:aaa659af29890a796f8e9318131056504ab274a9\",\n\"data\",\n\"Down-looking photomosaics of the Domal Core of Atlantis Massif acquired with the towed platform Argo-II during Atlantis cruise AT03-60\",\n\"Abstract: Images used to create this data set were acquired with a camera mounted on the towed Argo-II system during Atlantis expedition AT03-60 conducted in 2000 (Chief Scientist: Dr. Donna Blackman), as part of the project: Near-Bottom Geological and Geophysical Investigation of an Ocean Core Complex: Eastern Atlantis Ridge-Transform Intersection Massif. Photomosaics were created in 2015 (Investigator(s): John A. Greene, Dr. Masako Tominaga, and Dr. Donna Blackman) and are described in Greene et al., 2015.\",\n\"Interdisciplinary Earth Data Alliance (IEDA)\",\n\"No spatialCoverage\",\n\"Photograph:Mosaic\",\n\"2015\",\n\"http://dx.doi.org/10.1594/IEDA/321899, http://www.marine-geo.org/tools/search/Files.php?data_set_uid=21899\",\n\"0.21875\"]],\n columns: [[\"number\", \"index\"], [\"string\", \"subj\"], [\"string\", \"g\"], [\"string\", \"resourceType\"], [\"string\", \"name\"], [\"string\", \"description\"], [\"string\", \"pubname\"], [\"string\", \"placenames\"], [\"string\", \"kw\"], [\"string\", \"datep\"], [\"string\", \"disurl\"], [\"string\", \"score\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n ",
"text/html": [
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" <th></th>\n",
" <th>subj</th>\n",
" <th>g</th>\n",
" <th>resourceType</th>\n",
" <th>name</th>\n",
" <th>description</th>\n",
" <th>pubname</th>\n",
" <th>placenames</th>\n",
" <th>kw</th>\n",
" <th>datep</th>\n",
" <th>disurl</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>https://www.bco-dmo.org/dataset/836648</td>\n",
" <td>urn:gleaner:milled:bcodmo:1ab4836221e9b387a791...</td>\n",
" <td>data</td>\n",
" <td>Remotely-generated submesoscale coherent vorti...</td>\n",
" <td>&lt;p&gt;Final results for submesoscale coherent vor...</td>\n",
" <td>Biological and Chemical Data Management Office</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>Argo floats, mesoscale eddies, water masses, m...</td>\n",
" <td>2021-01-15</td>\n",
" <td></td>\n",
" <td>0.625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>http://n2t.net/ark:/23942/g2nh03</td>\n",
" <td>http://www.bigdata.com/rdf#nullGraph</td>\n",
" <td>tool</td>\n",
" <td>Argovis</td>\n",
" <td>Access Argo profiles via API, or visualize tem...</td>\n",
" <td>No Publisher</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>Argo,Float,Oceanography,Climate,Weather Events...</td>\n",
" <td>No datePublished</td>\n",
" <td>http://argovis.colorado.edu/docs/Argovis_Guide...</td>\n",
" <td>0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>http://n2t.net/ark:/23942/g2x06p</td>\n",
" <td>http://www.bigdata.com/rdf#nullGraph</td>\n",
" <td>tool</td>\n",
" <td>Argovis API</td>\n",
" <td>Access Argo profiles via API, i.e. temperature...</td>\n",
" <td>No Publisher</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>web service,Argo,Float,Oceanography,Climate,We...</td>\n",
" <td>No datePublished</td>\n",
" <td>http://www.itsonlyamodel.us/argovis-python-api...</td>\n",
" <td>0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>http://n2t.net/ark:/23942/g2x06p</td>\n",
" <td>http://www.bigdata.com/rdf#nullGraph</td>\n",
" <td>tool</td>\n",
" <td>Argovis API</td>\n",
" <td>Access Argo profiles via API, i.e. temperature...</td>\n",
" <td>No Publisher</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>web service,Argo,Float,Oceanography,Climate,We...</td>\n",
" <td>No datePublished</td>\n",
" <td>http://www.itsonlyamodel.us/argovis-python-api...</td>\n",
" <td>0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>DOI:10.1594/IEDA/307964</td>\n",
" <td>urn:gleaner:milled:ieda:93edc5ef199da4984a2ac0...</td>\n",
" <td>data</td>\n",
" <td>Argo-I Navigation Data from the East Pacific R...</td>\n",
" <td>Abstract: This data set was acquired with a th...</td>\n",
" <td>Interdisciplinary Earth Data Alliance (IEDA)</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>Navigation</td>\n",
" <td>2008</td>\n",
" <td>http://dx.doi.org/10.1594/IEDA/307964, http://...</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>DOI:10.1594/IEDA/321900</td>\n",
" <td>urn:gleaner:milled:ieda:dcdc31f8601897a6bf1ffb...</td>\n",
" <td>data</td>\n",
" <td>Processed Argo-II Navigation Data from the Mid...</td>\n",
" <td>Abstract: This data set was acquired with a Na...</td>\n",
" <td>Interdisciplinary Earth Data Alliance (IEDA)</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>Navigation</td>\n",
" <td>2015</td>\n",
" <td>http://dx.doi.org/10.1594/IEDA/321900, http://...</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>DOI:10.1594/IEDA/307965</td>\n",
" <td>urn:gleaner:milled:ieda:f062cde410bb6400fc9b90...</td>\n",
" <td>data</td>\n",
" <td>CTD Data from the East Pacific Rise at 9N acqu...</td>\n",
" <td>Abstract: This data set was acquired with a to...</td>\n",
" <td>Interdisciplinary Earth Data Alliance (IEDA)</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>Temperature, Conductivity, Pressure</td>\n",
" <td>2008</td>\n",
" <td>http://dx.doi.org/10.1594/IEDA/307965, http://...</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>DOI:10.1594/IEDA/316631</td>\n",
" <td>urn:gleaner:milled:ieda:a66512d280bba77784905c...</td>\n",
" <td>data</td>\n",
" <td>Seafloor Morphology Types derived from Photos ...</td>\n",
" <td>Abstract: This data set is derived from photos...</td>\n",
" <td>Interdisciplinary Earth Data Alliance (IEDA)</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>Interpretation:Geologic</td>\n",
" <td>2010</td>\n",
" <td>http://www.marine-geo.org/tools/search/Files.p...</td>\n",
" <td>0.21875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>DOI:10.1594/IEDA/321899</td>\n",
" <td>urn:gleaner:milled:ieda:aaa659af29890a796f8e93...</td>\n",
" <td>data</td>\n",
" <td>Down-looking photomosaics of the Domal Core of...</td>\n",
" <td>Abstract: Images used to create this data set ...</td>\n",
" <td>Interdisciplinary Earth Data Alliance (IEDA)</td>\n",
" <td>No spatialCoverage</td>\n",
" <td>Photograph:Mosaic</td>\n",
" <td>2015</td>\n",
" <td>http://dx.doi.org/10.1594/IEDA/321899, http://...</td>\n",
" <td>0.21875</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" subj ... score\n",
"0 https://www.bco-dmo.org/dataset/836648 ... 0.625\n",
"1 http://n2t.net/ark:/23942/g2nh03 ... 0.4375\n",
"2 http://n2t.net/ark:/23942/g2x06p ... 0.4375\n",
"3 http://n2t.net/ark:/23942/g2x06p ... 0.4375\n",
"4 DOI:10.1594/IEDA/307964 ... 0.25\n",
"5 DOI:10.1594/IEDA/321900 ... 0.25\n",
"6 DOI:10.1594/IEDA/307965 ... 0.25\n",
"7 DOI:10.1594/IEDA/316631 ... 0.21875\n",
"8 DOI:10.1594/IEDA/321899 ... 0.21875\n",
"\n",
"[9 rows x 11 columns]"
]
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gU3tNxOZU7IT",
"outputId": "4cc5e8ec-3aff-481d-aa18-d4a429aa6eea"
},
"source": [
"!ls -l"
],
"id": "gU3tNxOZU7IT",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total 12\n",
"-rw-r--r-- 1 root root 3013 Oct 26 21:10 log\n",
"drwxr-xr-x 1 root root 4096 Oct 8 13:45 sample_data\n",
"-rw-r--r-- 1 root root 2244 Oct 26 21:10 sparql-query.txt\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 205
},
"id": "fag3BU1nU9iu",
"outputId": "c233ac3a-ccaf-4115-b35b-5a19beeb556e"
},
"source": [
"wsq=ec.get_webservice_txt()\n",
"wsq"
],
"id": "fag3BU1nU9iu",
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'prefix schema: <https://schema.org/>\\n PREFIX schemaold: <http://schema.org/>\\n select DISTINCT ?dataname ?appname ?durl ?turl ?rrs ?altName ?description\\n WHERE\\n {\\n graph <${g}> {\\n ?s schemaold:distribution|schema:distribution ?dist ;\\n schemaold:name|schema:name ?dataname .\\n ?dist schemaold:encodingFormat|schema:encodingFormat ?type .\\n OPTIONAL {?dist schema:contentUrl ?durl }.\\n }\\n BIND (str(?type) as ?label)\\n SERVICE <http://132.249.238.169:8080/fuseki/ecrr/query> {\\n GRAPH <http://earthcube.org/gleaner-summoned>\\n\\n {\\n\\n ?rrs a schema:SoftwareApplication ;\\n schema:name ?appname ;\\n schema:supportingData ?df;\\n schema:description ?description .\\n ?df schema:encodingFormat ?label ;\\n schema:position \"input\".\\n ?rrs schema:potentialAction ?act.\\n ?act schema:target ?tar.\\n ?tar a schema:EntryPoint ;\\n schema:urlTemplate ?turl.\\n filter contains(?turl,\"{contentURL}\")\\n OPTIONAL{ ?rrs schema:alternateName ?altName }\\n }\\n }\\n }\\n'"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"papermill": {},
"tags": [],
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"id": "0837a275",
"outputId": "e3ea0574-f854-42bf-aea4-6b8db2fd331a"
},
"source": [
"#1st-cut:will have to cut&paste into vars, from Row of interest:\n",
"urn=\"urn:gleaner:milled:ieda:aaa659af29890a796f8e9318131056504ab274a9\"\n",
"dfR=ec.wget_rdf(urn)\n",
"#!ls -l or FilePaneBrowser on left, to see it\n",
"url,ext=\"http://dx.doi.org/10.1594/IEDA/321899\",None\n",
"dfR=ec.read_file(url,ext)\n",
"dfR"
],
"id": "0837a275",
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'no reader, doing:[!wget $url ],to see:[ !ls -l ]size:None or FileExplorerPane on the left[Warn:No File]'"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fBug980CanWk",
"outputId": "5e8db400-d47d-4678-8eb9-951d75c7fb58"
},
"source": [
"!ls -l"
],
"id": "fBug980CanWk",
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total 44\n",
"-rw-r--r-- 1 root root 11741 Oct 26 21:10 321899\n",
"-rw-r--r-- 1 root root 10945 May 20 21:49 aaa659af29890a796f8e9318131056504ab274a9.nt\n",
"-rw-r--r-- 1 root root 6161 Oct 26 21:10 log\n",
"drwxr-xr-x 1 root root 4096 Oct 8 13:45 sample_data\n",
"-rw-r--r-- 1 root root 1187 Oct 26 21:10 sparql_gettools_webservice.txt\n",
"-rw-r--r-- 1 root root 2244 Oct 26 21:10 sparql-query.txt\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 205
},
"id": "azh-STU8d7an",
"outputId": "2ed73703-0468-4ba9-a3ce-ee51339c8663"
},
"source": [
"dq=ec.get_download_txt()\n",
"dq"
],
"id": "azh-STU8d7an",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'PREFIX schema: <https://schema.org/>\\nPREFIX schemaold: <http://schema.org/>\\nselect DISTINCT ?rrs ?name (GROUP_CONCAT(DISTINCT ?curl1; SEPARATOR=\", \") AS ?curl) (GROUP_CONCAT(DISTINCT ?landingPage1; SEPARATOR=\", \") AS ?landingPage)\\n(GROUP_CONCAT(DISTINCT ?altName1; SEPARATOR=\", \") AS ?altName) (GROUP_CONCAT(DISTINCT ?landingName1; SEPARATOR=\", \") AS ?landingName) ?swversion ?identifier ?description\\nWHERE {\\n graph <${g}>\\n {\\n {\\n ?s schemaold:distribution|schema:distribution ?dist .\\n ?dist schemaold:encodingFormat|schema:encodingFormat ?type .\\n ?dist schemaold:contentUrl|schema:contentUrl|schema:url|schemaold:url ?curl1\\n }\\n UNION {\\n VALUES (?dataset) { ( schema:Dataset ) ( schemaold:Dataset ) }\\n ?s a ?dataset .\\n ?s schemaold:encodingFormat|schema:encodingFormat ?type .\\n }\\n }\\n BIND (str(?type) as ?label)\\n SERVICE <http://132.249.238.169:8080/fuseki/ecrr/query> {\\n GRAPH <http://earthcube.org/gleaner-summoned>\\n {\\n { ?rrs a schema:SoftwareApplication .\\n ?rrs schema:supportingData ?df.\\n ?df schema:encodingFormat ?label ;\\n schema:position \"input\".\\n ?rrs schema:name ?name;\\n schema:description ?description .\\n\\n ?rrs schema:subjectOf/schema:url ?landingPage1\\n OPTIONAL{ ?rrs schema:alternateName ?altName1 }\\n # vue does not like version... observables property, so swversion\\n OPTIONAL{ ?rrs schema:version ?swversion }\\n OPTIONAL{ ?rrs schema:identifier ?identifier }\\n optional{?rrs schema:subjectOf/schema:name ?landingName1 }\\n }\\n\\n }\\n }\\n}\\n GROUP BY ?rrs ?name ?description ?swversion ?identifier\\n'"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1181
},
"id": "Yq949lDZcX32",
"outputId": "a8776a72-a9b2-4495-fbe5-55afd64d40bb"
},
"source": [
"match=ec.search_download(urn) #replicate the finding a data-(downloadable)tool match\n",
"match"
],
"id": "Yq949lDZcX32",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"http://n2t.net/ark:/23942/g2xs70\",\n\"Corelyzer\",\n\"http://dx.doi.org/10.1594/IEDA/321899, http://www.marine-geo.org/tools/search/Files.php?data_set_uid=21899\",\n\"https://csdco.umn.edu/resources/software/corelyzer\",\n\"CoreWall\",\n\"linked web page\",\n\"2.1.0\",\n{\n 'v': null,\n 'f': \"null\",\n },\n\"Corelyzer is scalable, extensible visualization software developed to enhance the study of geological cores. Corelyzer enables fast and reliable inspection of core lithologies and associated data at any location and scale, minimizing the need for repeated core handling, while improving and facilitating interpretation. Corelyzer handles thousands of full-resolution core images efficiently, and runs on nearly any Mac, Windows, or Linux machine. Corelyzer was originally developed by E. Ito, S. Higgins, C. Jenkins, J. Leigh, and A. Johnson. CSDCO/LacCore developments have yielded major improvements to Corelyzer in recent years through continual feedback from facility visitors and the community at large. These efforts have enabled full utilization of the software for every project team that visits the facility and by an increasing range of institutions around the globe.\"]],\n columns: [[\"number\", \"index\"], [\"string\", \"rrs\"], [\"string\", \"name\"], [\"string\", \"curl\"], [\"string\", \"landingPage\"], [\"string\", \"altName\"], [\"string\", \"landingName\"], [\"string\", \"swversion\"], [\"number\", \"identifier\"], [\"string\", \"description\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n ",
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rrs</th>\n",
" <th>name</th>\n",
" <th>curl</th>\n",
" <th>landingPage</th>\n",
" <th>altName</th>\n",
" <th>landingName</th>\n",
" <th>swversion</th>\n",
" <th>identifier</th>\n",
" <th>description</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>http://n2t.net/ark:/23942/g2xs70</td>\n",
" <td>Corelyzer</td>\n",
" <td>http://dx.doi.org/10.1594/IEDA/321899, http://...</td>\n",
" <td>https://csdco.umn.edu/resources/software/corel...</td>\n",
" <td>CoreWall</td>\n",
" <td>linked web page</td>\n",
" <td>2.1.0</td>\n",
" <td>None</td>\n",
" <td>Corelyzer is scalable, extensible visualizatio...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rrs ... description\n",
"0 http://n2t.net/ark:/23942/g2xs70 ... Corelyzer is scalable, extensible visualizatio...\n",
"\n",
"[1 rows x 9 columns]"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U_3HRwr0ZkHB",
"outputId": "da488d5d-e4cb-4e4d-80f6-cddf9a320285"
},
"source": [
"!ls -l"
],
"id": "U_3HRwr0ZkHB",
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total 52\n",
"-rw-r--r-- 1 root root 11741 Oct 26 21:10 321899\n",
"-rw-r--r-- 1 root root 10945 May 20 21:49 aaa659af29890a796f8e9318131056504ab274a9.nt\n",
"-rw-r--r-- 1 root root 10461 Oct 26 21:11 log\n",
"drwxr-xr-x 1 root root 4096 Oct 8 13:45 sample_data\n",
"-rw-r--r-- 1 root root 1814 Oct 26 21:11 sparql_gettools_download.txt\n",
"-rw-r--r-- 1 root root 1187 Oct 26 21:10 sparql_gettools_webservice.txt\n",
"-rw-r--r-- 1 root root 2244 Oct 26 21:11 sparql-query.txt\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 89
},
"id": "AXxiAMb2cHFg",
"outputId": "1de886e7-6093-4d85-b651-155842aee13b"
},
"source": [
"mws=ec.search_webservice(urn) #no ws for this urn\n",
"mws"
],
"id": "AXxiAMb2cHFg",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n window.createDataTable({\n data: [],\n columns: [[\"number\", \"index\"], [\"number\", \"dataname\"], [\"number\", \"appname\"], [\"number\", \"durl\"], [\"number\", \"turl\"], [\"number\", \"rrs\"], [\"number\", \"altName\"], [\"number\", \"description\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n ",
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataname</th>\n",
" <th>appname</th>\n",
" <th>durl</th>\n",
" <th>turl</th>\n",
" <th>rrs</th>\n",
" <th>altName</th>\n",
" <th>description</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [dataname, appname, durl, turl, rrs, altName, description]\n",
"Index: []"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dJ266grJeDUS"
},
"source": [
""
],
"id": "dJ266grJeDUS",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 188
},
"id": "vKkq-Z52fVKH",
"outputId": "f630e21a-e111-4278-faff-5b80d1973efe"
},
"source": [
"aj=ec.get_ec_txt(\"https://raw.githubusercontent.com/earthcube/GeoCODES-Metadata/main/metadata/argo.jsonld\")\n",
"aj"
],
"id": "vKkq-Z52fVKH",
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'{\\n \"@context\": {\\n \"@vocab\": \"https://schema.org/\",\\n \"prov\": \"http://www.w3.org/ns/prov#\",\\n \"provone\": \"http://purl.dataone.org/provone/2015/01/15/ontology#\"\\n },\\n \"@id\": \"metadata-doi:10.17882/42182\",\\n \"@type\":\"Dataset\",\\n \"identifier\": {\\n \"@type\": \"PropertyValue\",\\n \"propertyID\": \"https://registry.identifiers.org/registry/doi\",\\n \"value\": \"doi:10.17882/42182\",\\n \"url\": \"https://doi.org/10.17882/42182\"\\n },\\n \"isAccessibleForFree\": true,\\n \"url\": \"https://www.seanoe.org/data/00311/42182/\",\\n \"thumbnailUrl\": \"https://www.seanoe.org/data/00311/42182/thumbnail.gif\",\\n \"name\": \"Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)\",\\n \"description\": \"Argo is a global array of 3,000 free-drifting profiling floats that measures the temperature and salinity of the upper 2000 m of the ocean. This provides continuous monitoring of the temperature, salinity, and velocity of the upper ocean, with data being made publicly available within hours after collection. The array provides 100,000 temperature/salinity profiles and velocity measurements per year distributed over the global oceans at an average of 3-degree spacing. Some floats provide additional bio-geo parameters such as oxygen or chlorophyll. Data collected by Argo floats are publically available in near real-time via the Global Data Assembly Centers (GDACs) in Brest (France) and Monterey (California) after an automated quality control (QC), and in scientifically quality controlled form, delayed mode data, via the GDACs within six months of collection.\",\\n \"datePublished\": \"2021-09-10\",\\n \"license\": \"https://creativecommons.org/licenses/by/4.0/\",\\n \"keywords\": [\\n \"float\",\\n \"Argo\",\\n \"global ocean observing system\",\\n \"ocean circulation\",\\n \"in-situ\",\\n \"ocean pressure\",\\n \"sea water salinity\",\\n \"sea water temperature\",\\n \"multi-year\",\\n \"weather climate and seasonal observation\",\\n \"global-ocean\"\\n ],\\n \"publisher\": {\\n \"@type\": \"Organization\",\\n \"name\": \"SEANOE\",\\n \"url\": \"https://www.seanoe.org/\",\\n \"disambiguatingDescription\": \"Sea scientific open data publication\",\\n \"logo\": \"https://www.seanoe.org/html/images/seanoe-share.png\"\\n },\\n \"includedInDataCatalog\": {\\n \"@type\": \"DataCatalog\",\\n \"name\": \"SEANOE\",\\n \"url\": \"https://www.seanoe.org/\",\\n \"disambiguatingDescription\": \"Sea scientific open data publication\"\\n },\\n \"citation\": \"Argo (2021). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182\",\\n \"temporalCoverage\": \"2012-08-01/..\",\\n \"spatialCoverage\": {\\n \"@type\": \"Place\",\\n \"name\": \"Global Oceans\",\\n \"geo\": {\\n \"@type\": \"GeoShape\",\\n \"box\": \"-68 -179 85 179\"\\n }\\n },\\n \"distribution\": [\\n {\\n \"@type\": \"DataDownload\",\\n \"name\": \"Global Data Assembly Center (GDAC)\",\\n \"description\": \"For users interested in using the official Argo NetCDF files, the GDACs should be the route to access Argo data. Both GDACs offer access to the complete Argo data collection as managed by the Argo Data Management Team, including float metadata, detailed trajectory data, profile data and technical data all in NetCDF format. It is important to understand the naming system of the files as well as the variable names and quality control flags within each data file. See the ADMT Documentation page for the Argo User\\\\u2019s Manual and the Argo quality control manual for more information.\",\\n \"encodingFormat\": \"application/x-netcdf\",\\n \"url\": \"http://www.argodatamgt.org/Access-to-data/Argo-GDAC-ftp-and-https-servers\"\\n },\\n {\\n \"@type\": \"DataDownload\",\\n \"name\": \"GDAC synchronization service\",\\n \"description\": \"The rsync server vdmzrs.ifremer.fr server provides a synchronization service between the dac directory of the GDAC with a user mirror. See the ADMT website (http://www.argodatamgt.org/Access-to-data/Argo-GDAC-synchronization-service) for more details. From the user side, the rysnc service: 1.Downloads the new files; 2. Downloads the updated files; 3. Removes the files that have been removed from the GDAC; 4. Compresses/uncompresses the files during the transfer; 5. Preserves the files creation/update dates; 6. Lists all the files that have been transferred (easy to use for a user side post-processing)\",\\n \"url\": \"http://www.argodatamgt.org/Access-to-data/Argo-GDAC-synchronization-service\"\\n },\\n {\\n \"@type\": [\\n \"DataDownload\",\\n \"WebSite\"\\n ],\\n \"name\": \"Argovis Web Site\",\\n \"description\": \"Visualize ARGO temperature, salinity, and BioGeoChemical data by location. View float trajectory forecasts, compare gridded fields with Argovis\\' grid visualization module, or co-locate Argo data with weather events. Download profile or platform data in JSON format, or NetCDF time series data from platform. The Argovis database is a curated set of Argo profiles available from the Argo GDACs. Entire profiles are excluded if their position or time is bad. Profiles without a position are kept in the database and will not be visible on the home web interface, but will be displayed when looking at the platform plots and downloaded via the API. Profiles with an estimated position are in Argovis. Otherwise, all temperature, pressure and salinity data are included in Argovis if their QC flag is equal to \\'1\\'. BioGeoChemical data are included no matter the QC flag and Deep data under 2000 db is included if the QC flag is \\'2\\' for pressure and temperature and \\'3\\' for salinity. If adjusted data is available for a parameter, that is used. Otherwise, the raw data is used.\",\\n \"url\": \"https://argovis.colorado.edu/ng/home\"\\n },\\n {\\n \"@type\": [\\n \"DataDownload\",\\n \"WebAPI\"\\n ],\\n \"name\": \"Argovis WebAPI\",\\n \"serviceType\": \"Argovis API\",\\n \"documentation\": \"https://argovis.colorado.edu/api-docs/#/\",\\n \"description\": \"Access Argo profiles via API, i.e. temperature, salinity, and biogeochemical data by location. Argo metadata, float trajectory forecasts, gridded fields, weather events are also available through API\",\\n \"potentialAction\": {\\n \"@type\": \"SearchAction\",\\n \"target\": {\\n \"@type\": \"EntryPoint\",\\n \"urlTemplate\": \"https://argovis.colorado.edu/selection/profiles?startDate={start}&endDate={end}&shape={shape}&presRange={presRange}\",\\n \"description\": \"download profiles within a bounding box for specified start/end dates\",\\n \"httpMethod\": [\"GET\"]\\n },\\n \"query-input\": [\\n {\\n \"@type\": \"PropertyValueSpecification\",\\n \"valueName\": \"shape\",\\n \"description\": \"list of lists containing [lon, lat] coordinates that define a polygon; first and last coordinate pair should be the same poitn. example: shape = [[[-144.84375,36.031332],[-136.038755,36.210925],[-127.265625,35.746512],[-128.144531,22.755921],[-136.543795,24.835311],[-145.195313,26.431228],[-144.84375,36.031332]]]\",\\n \"valueRequired\": true\\n },\\n {\\n \"@type\": \"PropertyValueSpecification\",\\n \"valueName\": \"start\",\\n \"description\": \"string formatted as \\'YYYY-MM-DD\\'\",\\n \"valueRequired\": true\\n },\\n {\\n \"@type\": \"PropertyValueSpecification\",\\n \"valueName\": \"end\",\\n \"description\": \"string formatted as \\'YYYY-MM-DD\\'\",\\n \"valueRequired\": true\\n },\\n {\\n \"@type\": \"PropertyValueSpecification\",\\n \"valueName\": \"presRange\",\\n \"description\": \"a string of a list formatted as \\'[minimum pres,maximum pres]\\' (no spaces)\",\\n \"valueRequired\": false,\\n \"defaultValue\": \"None\"\\n }\\n ],\\n \"result\":{\\n \"@type\":\"Dataset\",\\n \"encodingFormat\":\"application/json\"\\n }\\n }\\n },\\n {\\n \"@type\": [\\n \"DataDownload\",\\n \"WebSite\"\\n ],\\n \"name\": \"Zip archive snapshots\",\\n \"description\": \"snapshots of all GDAC data generated monthly since 2014, before that at irregular intervals back to 2012\",\\n \"url\": \"https://www.seanoe.org/data/00311/42182/#\",\\n \"encodingFormat\": \"application/zip\"\\n },\\n {\\n \"@type\": [\\n \"DataDownload\",\\n \"WebSite\"\\n ],\\n \"name\": \"ERDDAP Server\",\\n \"description\": \"Web form to select ARGO data and download one of many offered formats. See https://www.ifremer.fr/erddap/tabledap/ArgoFloats.html#DAS for complete list of variables in data structure\",\\n \"url\": \"https://www.ifremer.fr/erddap/tabledap/ArgoFloats.html\"\\n }\\n ],\\n \"variableMeasured\": [\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"water temperature\",\\n \"unitText\": \"Celsius\"\\n },\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"pressure\",\\n \"description\": \"water pressure, proxy for depth\",\\n \"unitText\": \"decibar\"\\n },\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"salinity\",\\n \"unitText\": \"psu\"\\n },\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"dissolved oxygen\",\\n \"unitText\": \"micromole/kg\"\\n },\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"Chlorophyll-A\",\\n \"unitText\": \"milligram/cubic meter\"\\n },\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"latitude\",\\n \"unitText\": \"decimal degree\"\\n },\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"longitude\",\\n \"unitText\": \"decimal degree\"\\n },\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"date\",\\n \"description\": \"clendar date of observation\"\\n },\\n {\\n \"@type\": \"PropertyValue\",\\n \"name\": \"time\",\\n \"description\": \"Time of obseration\"\\n }\\n ]\\n}'"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lsHWM9vDfmhU",
"outputId": "8a36ec3d-9b2c-4175-fa33-019aecf84861"
},
"source": [
"ec.xml2nt(\"argo.jsonld\",\"json-ld\")\n",
"!ls -l"
],
"id": "lsHWM9vDfmhU",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total 112\n",
"-rw-r--r-- 1 root root 11741 Oct 14 03:10 321899\n",
"-rw-r--r-- 1 root root 11741 Oct 14 03:11 321899.1\n",
"-rw-r--r-- 1 root root 10945 May 20 21:49 aaa659af29890a796f8e9318131056504ab274a9.nt\n",
"-rw-r--r-- 1 root root 10787 Oct 14 03:15 argo.jsonld\n",
"-rw-r--r-- 1 root root 19090 Oct 14 03:17 argo.nt\n",
"-rw-r--r-- 1 root root 13947 Oct 14 03:08 ec.py\n",
"-rw-r--r-- 1 root root 8390 Oct 14 03:17 log\n",
"drwxr-xr-x 1 root root 4096 Oct 8 13:45 sample_data\n",
"-rw-r--r-- 1 root root 1814 Oct 14 03:14 sparql_gettools_download.txt\n",
"-rw-r--r-- 1 root root 1187 Oct 14 03:12 sparql_gettools_webservice.txt\n",
"-rw-r--r-- 1 root root 2244 Oct 14 03:09 sparql-query.txt\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/rdflib/plugins/serializers/nt.py:28: UserWarning: NTSerializer does not use custom encoding.\n",
" warnings.warn(\"NTSerializer does not use custom encoding.\")\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 93
},
"id": "c0PqX4X3eRLc",
"outputId": "2bec4ac5-b163-4b17-bc8e-c5d0db405fb2"
},
"source": [
"def v4qry(var,qt):\n",
" \"var + query-type 2 df\"\n",
" sqs = eval(\"get_\" + qt + \"_txt()\")\n",
" iqt = v2iqt(var,sqs)\n",
" return iqt2df(iqt)\n",
" \n",
"def get_notebook_txt(url=\"https://raw.githubusercontent.com/MBcode/ec/master/NoteBook/sparql_gettools_notebook.txt\"):\n",
" return ec.get_ec_txt(url)\n",
"\n",
"#can try your own: sparql_gettools_notebook.txt, right now same as webservice, but will need to use servicetype\n",
"#in SR's argo NoteBook will have to use the urn MetaData to fill in rest of the blanks to run the api call for that dataset\n",
"#to run on present endpoint, will need to assert argo md there, but can also run queries right in the NB w/rdflib..\n",
"\n",
"def search_notebook(urn):\n",
" return v4qry(urn,\"notebook\")\n",
"\n",
"mnb=search_notebook(urn)\n",
"mnb"
],
"id": "c0PqX4X3eRLc",
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n window.createDataTable({\n data: [],\n columns: [[\"number\", \"index\"], [\"number\", \"dataname\"], [\"number\", \"appname\"], [\"number\", \"durl\"], [\"number\", \"turl\"], [\"number\", \"rrs\"], [\"number\", \"altName\"], [\"number\", \"description\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n ",
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataname</th>\n",
" <th>appname</th>\n",
" <th>durl</th>\n",
" <th>turl</th>\n",
" <th>rrs</th>\n",
" <th>altName</th>\n",
" <th>description</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [dataname, appname, durl, turl, rrs, altName, description]\n",
"Index: []"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"papermill": {},
"tags": [],
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "219d36af",
"outputId": "0b7dc4cf-c833-427b-d5ec-52fec0feead0"
},
"source": [
"!ls -l \n",
"ec.viz() #shows .nt metadata from urn, can use for (rdf)xml as well "
],
"id": "219d36af",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total 112\n",
"-rw-r--r-- 1 root root 11741 Oct 14 03:10 321899\n",
"-rw-r--r-- 1 root root 11741 Oct 14 03:11 321899.1\n",
"-rw-r--r-- 1 root root 10945 May 20 21:49 aaa659af29890a796f8e9318131056504ab274a9.nt\n",
"-rw-r--r-- 1 root root 10787 Oct 14 03:15 argo.jsonld\n",
"-rw-r--r-- 1 root root 19090 Oct 14 03:17 argo.nt\n",
"-rw-r--r-- 1 root root 13947 Oct 14 03:08 ec.py\n",
"-rw-r--r-- 1 root root 8390 Oct 14 03:17 log\n",
"drwxr-xr-x 1 root root 4096 Oct 8 13:45 sample_data\n",
"-rw-r--r-- 1 root root 1814 Oct 14 03:14 sparql_gettools_download.txt\n",
"-rw-r--r-- 1 root root 1187 Oct 14 03:12 sparql_gettools_webservice.txt\n",
"-rw-r--r-- 1 root root 2244 Oct 14 03:09 sparql-query.txt\n"
]
},
{
"output_type": "display_data",
"data": {
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