pylipd package
Subpackages
Submodules
pylipd.utils.legacy_utils module
Legacy code from the LiPD utils in Python
- class pylipd.utils.legacy_utils.LiPD_Legacy[source]
Bases:
objectMethods
extract(d[, whichtables, mode, time])LiPD Version 1.3 Main function to initiate LiPD to TSOs conversion.
- extract(d, whichtables='meas', mode='paleo', time='age')[source]
LiPD Version 1.3 Main function to initiate LiPD to TSOs conversion.
Each object has a “paleoNumber” or “chronNumber” “tableNumber” “modelNumber” “time_id” “mode” - chronData or paleoData “tableType” - “meas” “ens” “summ”
- Parameters:
d (dict) – Metadata for one LiPD file
whichtables (str) – all, meas, summ, or ens
mode (str) – paleo or chron mode
- Return list _ts:
Time series
pylipd.lipd module
The LiPD class describes a LiPD (Linked Paleo Data) object. It contains an RDF Graph which is serialization of the LiPD data into an RDF graph containing terms from the LiPD Ontology <http://linked.earth/Ontology/release/core/1.2.0/index-en.html> How to browse and query LiPD objects is described in a short example below, while this notebook demonstrates how to use PyLiPD to view and query LiPD datasets.
- class pylipd.lipd.LiPD(graph=None)[source]
Bases:
RDFGraphThe LiPD class describes a LiPD (Linked Paleo Data) object. It contains an RDF Graph which is serialization of the LiPD data into an RDF graph containing terms from the LiPD Ontology <http://linked.earth/Ontology/release/core/1.2.0/index-en.html> How to browse and query LiPD objects is described in a short example below.
Examples
In this example, we read an online LiPD file and convert it into a time series object dictionary.
from pylipd.lipd import LiPD lipd = LiPD() lipd.load(["https://lipdverse.org/data/LCf20b99dfe8d78840ca60dfb1f832b9ec/1_0_1//Nunalleq.Ledger.2018.lpd"]) ts_list = lipd.get_timeseries(lipd.get_all_dataset_names()) for dsname, tsos in ts_list.items(): for tso in tsos: if 'paleoData_variableName' in tso: print(dsname+': '+tso['paleoData_variableName']+': '+tso['archiveType'])
Loading 1 LiPD files
Loaded..
Extracting timeseries from dataset: Nunalleq.Ledger.2018 ... Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: precipitationUncertainty: Archaeological Nunalleq.Ledger.2018: ageMedian: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: temperatureUncertainty: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: temperatureUncertainty: Archaeological Nunalleq.Ledger.2018: temperatureUncertainty: Archaeological Nunalleq.Ledger.2018: precipitationUncertainty: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: depth: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: ageMin: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: precipitationUncertainty: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: ageMax: Archaeological Nunalleq.Ledger.2018: temperatureUncertainty: Archaeological Nunalleq.Ledger.2018: temperatureUncertainty: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: precipitationUncertainty: Archaeological Nunalleq.Ledger.2018: precipitationUncertainty: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: temperatureUncertainty: Archaeological Nunalleq.Ledger.2018: age: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: temperatureUncertainty: Archaeological Nunalleq.Ledger.2018: temperatureUncertainty: Archaeological
Methods
clear()Clears the graph
convert_lipd_dir_to_rdf(lipd_dir, rdf_file)Convert a directory containing LiPD files into a single RDF file (to be used for uploading to Knowledge Bases like GraphDB)
copy()Makes a copy of the object
create_lipd(dsname, lipdfile)Create LiPD file for a dataset
filter_by_archive_type(archiveType)Filters datasets to return a new LiPD object that only keeps datasets that have the specified archive type
filter_by_geo_bbox(lonMin, latMin, lonMax, ...)Filters datasets to return a new LiPD object that only keeps datasets that fall within the bounding box
get(dsnames)Gets dataset(s) from the graph and returns the popped LiPD object
Returns a list of all the unique archiveTypes present in the LiPD object
Get all Dataset ids
Get all Dataset Names
get_all_graph_ids()Get all Graph ids
get_all_locations([dsname])Return geographical coordinates for all the datasets.
Get a list of all possible distinct variableNames.
Returns a list of all variables in the graph
get_bibtex([remote, save, path, verbose])Get BibTeX for loaded datasets
Get a list of unique properties attached to a dataset.
get_ensemble_tables([dsname, ...])Gets ensemble tables from the LiPD graph
get_lipd(dsname)Get LiPD json for a dataset
Get all the properties associated with a model
get_timeseries(dsnames[, to_dataframe])Get Legacy LiPD like Time Series Object (tso)
get_timeseries_essentials([dsname, mode])Returns specific properties for timeseries: 'dataSetName', 'archiveType', 'geo_meanLat', 'geo_meanLon',
Get a list of variable properties that can be used for querying
load(lipdfiles[, parallel])Load LiPD files.
load_from_dir(dir_path[, parallel, cutoff])Load LiPD files from a directory
load_remote_datasets(dsnames)Loads remote datasets into cache if a remote endpoint is set
merge(rdf)Merges the current LiPD object with another LiPD object
pop(dsnames)Pops dataset(s) from the graph and returns the popped LiPD object
query(query[, remote, result])Once data is loaded into the graph (or remote endpoint set), one can make SparQL queries to the graph
remove(dsnames)Removes dataset(s) from the graph
serialize()Returns RDF quad serialization of the current combined Graph .
set_endpoint(endpoint)Sets a SparQL endpoint for a remote Knowledge Base (example: GraphDB)
to_lipd_series([parallel])Converts the LiPD object to a LiPDSeries object
update_remote_datasets(dsnames)Updates local LiPD Graph for datasets to remote endpoint
- convert_lipd_dir_to_rdf(lipd_dir, rdf_file, parallel=False)[source]
Convert a directory containing LiPD files into a single RDF file (to be used for uploading to Knowledge Bases like GraphDB)
- Parameters:
lipd_dir (str) – Path to the directory containing lipd files
rdf_file (str) – Path to the output rdf file
- create_lipd(dsname, lipdfile)[source]
Create LiPD file for a dataset
- Parameters:
dsname (str) – dataset id
lipdfile (str) – path to LiPD file
- Returns:
lipdjson – LiPD json
- Return type:
dict
Examples
from pylipd.lipd import LiPD # Load a local file lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", ]) dsname = lipd.get_all_dataset_names()[0] lipd.create_lipd(dsname, "test.lpd")
Loading 1 LiPD files
Loaded..
{'geo': {'geometry': {'coordinates': [145.8167, -5.2167, -2.2], 'type': 'Point'}, 'properties': {'type': 'http://linked.earth/ontology#Location', 'ocean': 'Pacific', 'pages2kRegion': 'Ocean', 'siteName': 'Madang Lagoon, Papua New Guinea'}}, 'originalDataURL': 'https://www.ncdc.noaa.gov/paleo/study/1866', 'inCompilation3_': 'PAGES2k_v2.1.0', 'pub': [{'issue': 5508.0, 'pages': '1511-1517', 'title': 'Variability in the El Nino-Southern Oscillation Through a Glacial-Interglacial Cycle', 'author': [{'name': 'A. W. Tudhope'}], 'year': 2001.0, 'citeKey': 'tudhope2001variabilityintheelninosou', 'dataUrl': 'doi.org', 'publisher': 'American Association for the Advancement of Science (AAAS)', 'journal': 'Science', 'volume': 291.0, 'doi': '10.1126/science.1057969', 'identifier': []}, {'urldate': 2001.0, 'institution': 'World Data Center for Paleoclimatology', 'citeKey': 'kuhnert2001httpswwwncdcnoaagovpaleostudy1866DataCitation', 'title': 'World Data Center for Paleoclimatology', 'url': 'https://www.ncdc.noaa.gov/paleo/study/1866', 'author': [{'name': 'H. Kuhnert'}], 'identifier': []}, {'citeKey': 'tierney2015tropicalseasurfacetempera', 'volume': 30.0, 'journal': 'Paleoceanography', 'author': [{'name': 'Jessica E. Tierney'}, {'name': 'Michael N. Evans'}, {'name': 'Henry C. Wu'}, {'name': 'Cyril Giry'}, {'name': 'Jens Zinke'}, {'name': 'K. Halimeda Kilbourne'}, {'name': 'Casey P. Saenger'}, {'name': 'Kevin J. Anchukaitis'}, {'name': 'Nerilie J. Abram'}], 'year': 2015.0, 'publisher': 'Wiley-Blackwell', 'pages': '226-252', 'issue': 3.0, 'doi': '10.1002/2014PA002717', 'dataUrl': 'doi.org', 'title': 'Tropical sea surface temperatures for the past four centuries reconstructed from coral archives', 'identifier': []}], 'paleoData': [{'measurementTable': [{'filename': 'Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.paleo1measurement1.csv', 'missingValue': 'NaN', 'googleWorkSheetKey': 'ov9tjw6', 'tableName': 'Kuhnert', 'columns': [{'interpretation': [{'interpDirection': 'negative', 'variable': 'T', 'variableDetail': 'sea@surface', 'scope': 'climate', 'seasonality': 'N/A (subannually resolved)'}], 'pages2kID': 'Ocn_097', 'hasMinValue': -5.515, 'proxy': 'd18O', 'notes': '; climateInterpretation_seasonality changed - was originally seasonal', 'paleoDataTableName': 'measTable', 'hasMeanValue': -4.9453, 'units': 'permil', 'sensorGenus': 'Porites', 'TSid': 'Ocean2kHR_140', 'number': 1, 'hasMedianValue': -4.942, 'ocean2kID': 'PacificMadangTudhope2001', 'proxyObservationType': 'd18O', 'iso2kUI': 'CO01TUNG01A', 'hasResolution': {'hasMeanValue': 0.25, 'hasMaxValue': 0.25, 'hasMinValue': 0.25, 'hasMedianValue': 0.25, 'units': 'AD'}, 'qCCertification': 'KLD, NJA', 'useInGlobalTemperatureAnalysis': True, 'variableName': 'd18O', 'measurementTableMD5': '793853407e414221c486d2e63b32dd87', 'inCompilationBeta': {'compilationVersion': '2_1_1', 'compilationName': 'Pages2kTemperature'}, 'measurementTableName': 'measurementTable1', 'wDSPaleoUrl': 'https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/data-version-2.0.0/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.txt', 'hasMaxValue': -4.344, 'variableType': 'measured'}, {'paleoDataTableName': 'measTable', 'measurementTableMD5': '793853407e414221c486d2e63b32dd87', 'hasMaxValue': 1993.042, 'TSid': 'PYTDAS7AM1Y', 'inferredVariableType': 'Year', 'hasMinValue': 1880.792, 'units': 'AD', 'hasResolution': {'units': 'AD', 'hasMaxValue': 0.25, 'hasMinValue': 0.25, 'hasMeanValue': 0.25, 'hasMedianValue': 0.25}, 'hasMeanValue': 1936.917, 'measurementTableName': 'measurementTable1', 'wDSPaleoUrl': 'https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/data-version-2.0.0/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.txt', 'dataType': 'float', 'variableName': 'year', 'number': 2, 'hasMedianValue': 1936.917, 'description': 'Year AD', 'variableType': 'inferred'}]}]}], 'datasetId': 'm8yv2VgG97zJmSg3XhqQ', 'maxYear': 1993.042, 'googleDataURL': 'https://docs.google.com/spreadsheets/d/1wf30P-s54OTBdLw4dyeaIN53VDoN0u_hOqIwvAeLxtc', 'createdBy': 'matlab', 'dataContributor': {'name': 'Wu KLD'}, 'hasUrl': 'https://data.mint.isi.edu/files/lipd/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd', 'inCompilation1_': 'Ocean2k_v1.0.0', 'minYear': 1880.792, 'studyName': 'Madang, Papua New Guinea oxygen isotope record 1880-1993', 'changelog': {'version': '1.0.0', 'curator': 'nicholas', 'timestamp': datetime.date(2022, 8, 23), 'notes': 'Starting the changelog'}, 'googleMetadataWorksheet': 'oruuxfm', 'googleSpreadSheetKey': '1wf30P-s54OTBdLw4dyeaIN53VDoN0u_hOqIwvAeLxtc', 'dataSetName': 'Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'inCompilation2_': 'PAGES2k_v2.0.0', 'lipdVersion': 1.3, 'archiveType': 'coral'}
- filter_by_archive_type(archiveType)[source]
Filters datasets to return a new LiPD object that only keeps datasets that have the specified archive type
- Parameters:
archiveType (str) – The archive type to filter by
- Returns:
A new LiPD object that only contains datasets that have the specified archive type (regex)
- Return type:
Examples
pyLipd ships with existing datasets that can be loaded directly through the package. Let’s load the Pages2k sample datasets using this method.
from pylipd.utils.dataset import load_dir lipd = load_dir('Pages2k') Lfiltered = lipd.filter_by_archive_type('marine') Lfiltered.get_all_dataset_names()
Loading 16 LiPD files
Loaded..
['Ocn-FeniDrift.Richter.2009', 'Eur-CoastofPortugal.Abrantes.2011', 'Ocn-AlboranSea436B.Nieto-Moreno.2013']
- filter_by_geo_bbox(lonMin, latMin, lonMax, latMax)[source]
Filters datasets to return a new LiPD object that only keeps datasets that fall within the bounding box
- Parameters:
lonMin (float) – Minimum longitude
latMin (float) – Minimum latitude
lonMax (float) – Maximum longitude
latMax (float) – Maximum latitude
- Returns:
A new LiPD object that only contains datasets that fall within the bounding box
- Return type:
Examples
pyLipd ships with existing datasets that can be loaded directly through the package. Let’s load the Pages2k sample datasets using this method.
from pylipd.utils.dataset import load_dir lipd = load_dir() Lfiltered = lipd.filter_by_geo_bbox(0,25,50,50) Lfiltered.get_all_dataset_names()
Loading 16 LiPD files
Loaded..
['Ocn-SinaiPeninsula_RedSea.Moustafa.2000', 'Ocn-RedSea.Felis.2000', 'Eur-LakeSilvaplana.Trachsel.2010', 'Eur-SpanishPyrenees.Dorado-Linan.2012', 'Eur-SpannagelCave.Mangini.2005']
- get(dsnames)[source]
Gets dataset(s) from the graph and returns the popped LiPD object
- Parameters:
dsnames (str or list of str) – dataset name(s) to get.
- Returns:
LiPD object with the retrieved dataset(s)
- Return type:
Examples
from pylipd.lipd import LiPD # Load LiPD files from a local directory lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", "../examples/data/MD98_2181.Stott.2007.lpd" ]) all_datasets = lipd.get_all_dataset_names() print("Loaded datasets: " + str(all_datasets)) ds = lipd.get(all_datasets[0]) print("Got dataset: " + str(ds.get_all_dataset_names()))
Loading 2 LiPD files
Loaded.. Loaded datasets: ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'MD98_2181.Stott.2007'] Got dataset: ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001']
- get_all_archiveTypes()[source]
Returns a list of all the unique archiveTypes present in the LiPD object
- Returns:
A list of archiveTypes
- Return type:
list
Examples
from pylipd.lipd import LiPD # Load Local files lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", "../examples/data/MD98_2181.Stott.2007.lpd" ]) print(lipd.get_all_archiveTypes())
Loading 2 LiPD files
Loaded.. ['coral', 'MarineSediment']
- get_all_dataset_ids()[source]
Get all Dataset ids
- Returns:
dsids (list)
A list of datasetnames
Examples
from pylipd.lipd import LiPD # Load local files lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", "../examples/data/MD98_2181.Stott.2007.lpd" ]) print(lipd.get_all_dataset_ids())
Loading 2 LiPD files
Loaded.. ['m8yv2VgG97zJmSg3XhqQ', 't0E8pOLYdyzmUspGZwbe']
- get_all_dataset_names()[source]
Get all Dataset Names
- Returns:
dsnames (list)
A list of datasetnames
Examples
from pylipd.lipd import LiPD # Load local files lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", "../examples/data/MD98_2181.Stott.2007.lpd" ]) print(lipd.get_all_dataset_names())
Loading 2 LiPD files
Loaded.. ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'MD98_2181.Stott.2007']
- get_all_locations(dsname=None)[source]
Return geographical coordinates for all the datasets.
- Parameters:
dsname (str, optional) – The name of the dataset for which to return the timeseries information. The default is None.
- Returns:
df – A pandas dataframe returning the latitude, longitude and elevation for each dataset
- Return type:
pandas.DataFrame
Examples
from pylipd.utils.dataset import load_dir lipd = load_dir('Pages2k') df = lipd.get_all_locations() print(df)
Loading 16 LiPD files
Loaded.. dataSetName geo_meanLat geo_meanLon \ 0 Eur-LakeSilvaplana.Trachsel.2010 46.5000 9.8000 1 Eur-SpanishPyrenees.Dorado-Linan.2012 42.5000 1.0000 2 Arc-Kongressvatnet.D'Andrea.2012 78.0217 13.9311 3 Eur-NorthernSpain.Martin-Chivelet.2011 42.9000 -3.5000 4 Ocn-SinaiPeninsula,RedSea.Moustafa.2000 27.8483 34.3100 5 Eur-Stockholm.Leijonhufvud.2009 59.3200 18.0600 6 Eur-NorthernScandinavia.Esper.2012 68.0000 25.0000 7 Ocn-RedSea.Felis.2000 27.8500 34.3200 8 Ocn-FeniDrift.Richter.2009 55.5000 -13.9000 9 Ant-WAIS-Divide.Severinghaus.2012 -79.4630 -112.1250 10 Eur-FinnishLakelands.Helama.2014 62.0000 28.3250 11 Ocn-AlboranSea436B.Nieto-Moreno.2013 36.2053 -4.3133 12 Eur-CoastofPortugal.Abrantes.2011 41.1000 -8.9000 13 Ocn-PedradeLume-CapeVerdeIslands.Moses.2006 16.7600 -22.8883 14 Eur-SpannagelCave.Mangini.2005 47.1000 11.6000 15 Asi-SourthAndMiddleUrals.Demezhko.2007 55.0000 59.5000 geo_meanElev 0 1791.0 1 1200.0 2 94.0 3 1250.0 4 -3.0 5 10.0 6 300.0 7 -6.0 8 -2543.0 9 1766.0 10 130.0 11 -1108.0 12 -80.0 13 -5.0 14 2347.0 15 1900.0
- get_all_variable_names()[source]
Get a list of all possible distinct variableNames. Useful for filtering and qeurying.
- Returns:
A list of unique variableName
- Return type:
list
Examples
from pylipd.utils.dataset import load_dir lipd = load_dir('Pages2k') varName = lipd.get_all_variable_names() print(varName)
Loading 16 LiPD files
Loaded.. ['year', 'temperature', 'trsgi', 'Uk37', 'd18O', 'MXD', 'notes', 'depth_top', 'Mg_Ca.Mg/Ca', 'depth_bottom', 'uncertainty_temperature']
- get_all_variables()[source]
Returns a list of all variables in the graph
- Returns:
A dataframe of all variables in the graph with columns uri, varid, varname
- Return type:
pandas.DataFrame
Examples
from pylipd.lipd import LiPD lipd = LiPD() lipd.load([ "../examples/data/ODP846.Lawrence.2006.lpd" ]) df = lipd.get_all_variables() print(df)
Loading 1 LiPD files
Loaded.. uri TSID \ 0 http://linked.earth/lipd/paleo0measurement1.PY... PYTJZ4GLRYP 1 http://linked.earth/lipd/paleo0measurement1.PY... PYT19MC8WE2 2 http://linked.earth/lipd/chron0model0summary0.... PYT7DLYN7X4 3 http://linked.earth/lipd/chron0model0ensemble0... PYTUHE3XLGQ 4 http://linked.earth/lipd/paleo0measurement0.PY... PYT8BDSRW3H 5 http://linked.earth/lipd/chron0model0summary0.... PYTPWX0LH3I 6 http://linked.earth/lipd/chron0measurement0.PY... PYTTD7XCQGS 7 http://linked.earth/lipd/paleo0measurement0.PY... PYTKRFVW61B 8 http://linked.earth/lipd/chron0model0summary0.... PYTDIEKUM44 9 http://linked.earth/lipd/paleo0measurement0.PY... PYT95DVDUU3 10 http://linked.earth/lipd/paleo0measurement1.PY... PYT3ZMI0BXW 11 http://linked.earth/lipd/paleo0measurement0.PY... PYT2ZB6MLZ9 12 http://linked.earth/lipd/chron0model0ensemble0... PYTGOFY4KZD 13 http://linked.earth/lipd/chron0model0summary0.... PYT4Y96QMUU 14 http://linked.earth/lipd/paleo0measurement1.PY... PYTS96EE0CB 15 http://linked.earth/lipd/paleo0measurement0.PY... PYTM9N6HCQM 16 http://linked.earth/lipd/chron0measurement0.PY... PYT9CFQ4GK0 17 http://linked.earth/lipd/paleo0measurement1.PY... PYTTUPVG4K3 18 http://linked.earth/lipd/paleo0measurement0.PY... PYT10H23U2E 19 http://linked.earth/lipd/paleo0measurement1.PY... PYT68HYMYHH 20 http://linked.earth/lipd/paleo0measurement1.PY... PYTYDOYFVYD 21 http://linked.earth/lipd/chron0measurement0.PY... PYTLEHYPAYV 22 http://linked.earth/lipd/paleo0model0ensemble0... PYTDW6AIJPW 23 http://linked.earth/lipd/paleo0measurement0.PY... PYTXJB98403 24 http://linked.earth/lipd/paleo0measurement0.PY... PYTJ3PSH0LT 25 http://linked.earth/lipd/paleo0measurement0.PY... PYTGO6NV72Y 26 http://linked.earth/lipd/paleo0measurement1.PY... PYTE5EC1JBW 27 http://linked.earth/lipd/paleo0measurement1.PY... PYTPQ0FJO1S 28 http://linked.earth/lipd/paleo0model0ensemble0... PYTCHXB40SL 29 http://linked.earth/lipd/chron0model0summary0.... PYTI487BQDZ variableName 0 sample label 1 c. wuellerstorfi d13c.d13C 2 median 3 age 4 section 5 depth 6 age 7 depth 8 upper95 9 temp prahl 10 c. wuellerstorfi d18o.d18O 11 interval 12 depth 13 d180 14 depth 15 ukprime37.Uk37Prime 16 depth 17 u. peregrina d13c.d13C 18 c37 total.Alkenone 19 depth cr 20 u. peregrina d18o.d18O 21 d18o 22 sst 23 age 24 site/hole 25 temp muller 26 event 27 depth comp 28 depth 29 lower95
- get_bibtex(remote=True, save=True, path='mybiblio.bib', verbose=False)[source]
Get BibTeX for loaded datasets
- Parameters:
remote (bool) – (Optional) If set to True, will return the bibliography by checking against the DOI
save (bool) – (Optional) Whether to save the bibliography to a file
path (str) – (Optional) Path where to save the file
verbose (bool) – (Optional) Whether to print out on the console. Note that this option will turn on automatically if saving to a file fails.
- Returns:
bibs (list) – List of BiBTex entry
df (pandas.DataFrame) – Bibliography information in a Pandas DataFrame
Examples
from pylipd.lipd import LiPD # Fetch LiPD data from remote RDF Graph lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", "../examples/data/MD98_2181.Stott.2007.lpd" ]) print(lipd.get_bibtex(save=False))
Loading 2 LiPD files
Loaded..
Cannot find a matching record for the provided DOI, creating the entry manually
(['@article{Tudhope_2001,\n\tdoi = {10.1126/science.1057969},\n\turl = {https://doi.org/10.1126%2Fscience.1057969},\n\tyear = 2001,\n\tmonth = {feb},\n\tpublisher = {American Association for the Advancement of Science ({AAAS})},\n\tvolume = {291},\n\tnumber = {5508},\n\tpages = {1511--1517},\n\tauthor = {Alexander W. Tudhope and Colin P. Chilcott and Malcolm T. McCulloch and Edward R. Cook and John Chappell and Robert M. Ellam and David W. Lea and Janice M. Lough and Graham B. Shimmield},\n\ttitle = {Variability in the El Ni{\\~{n}}o-Southern Oscillation Through a Glacial-Interglacial Cycle},\n\tjournal = {Science}\n}', '@article{Tierney_2015,\n\tdoi = {10.1002/2014pa002717},\n\turl = {https://doi.org/10.1002%2F2014pa002717},\n\tyear = 2015,\n\tmonth = {mar},\n\tpublisher = {American Geophysical Union ({AGU})},\n\tvolume = {30},\n\tnumber = {3},\n\tpages = {226--252},\n\tauthor = {Jessica E. Tierney and Nerilie J. Abram and Kevin J. Anchukaitis and Michael N. Evans and Cyril Giry and K. Halimeda Kilbourne and Casey P. Saenger and Henry C. Wu and Jens Zinke},\n\ttitle = {Tropical sea surface temperatures for the past four centuries reconstructed from coral archives},\n\tjournal = {Paleoceanography}\n}', '@misc{kuhnert2001httpswwwncdcnoaagovpaleostudy1866DataCitation,\n author = "H. Kuhnert",\n title = "World Data Center for Paleoclimatology",\n institution = "World Data Center for Paleoclimatology",\n url = "https://www.ncdc.noaa.gov/paleo/study/1866"\n}\n', '@article{Stott_2007,\n\tdoi = {10.1029/2006pa001379},\n\turl = {https://doi.org/10.1029%2F2006pa001379},\n\tyear = 2007,\n\tmonth = {feb},\n\tpublisher = {American Geophysical Union ({AGU})},\n\tvolume = {22},\n\tnumber = {1},\n\tpages = {n/a--n/a},\n\tauthor = {Lowell D. Stott},\n\ttitle = {Comment on {\\textquotedblleft}Anomalous radiocarbon ages for foraminifera shells{\\textquotedblright} by W. Broecker et al.: A correction to the western tropical Pacific {MD}9821-81 record},\n\tjournal = {Paleoceanography}\n}', '@article{Stott_2007,\n\tdoi = {10.1126/science.1143791},\n\turl = {https://doi.org/10.1126%2Fscience.1143791},\n\tyear = 2007,\n\tmonth = {oct},\n\tpublisher = {American Association for the Advancement of Science ({AAAS})},\n\tvolume = {318},\n\tnumber = {5849},\n\tpages = {435--438},\n\tauthor = {Lowell Stott and Axel Timmermann and Robert Thunell},\n\ttitle = {Southern Hemisphere and Deep-Sea Warming Led Deglacial Atmospheric {CO}\n\t\t $\\less$sub$\\greater$2$\\less$/sub$\\greater$\n\t\t Rise and Tropical Warming},\n\tjournal = {Science}\n}'], dsname \ 0 Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001 1 Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001 2 Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001 3 MD98_2181.Stott.2007 4 MD98_2181.Stott.2007 title \ 0 Variability in the El Nino-Southern Oscillatio... 1 Tropical sea surface temperatures for the past... 2 World Data Center for Paleoclimatology 3 None 4 Southern Hemisphere and deep-sea warming led d... authors doi \ 0 A. W. Tudhope 10.1126/science.1057969 1 Jessica E. Tierney and Michael N. Evans and He... 10.1002/2014PA002717 2 H. Kuhnert None 3 10.1029/2006PA001379 4 L. Stott and A. Timmermann and R. Thunell 10.1126/science.1143791 pubyear year journal volume issue pages \ 0 None 2001.0 Science 291.0 5508.0 1511-1517 1 None 2015.0 Paleoceanography 30.0 3.0 226-252 2 None NaN None NaN NaN None 3 None NaN None NaN NaN None 4 None 2007.0 Science 318.0 5849.0 435 438 type publisher report \ 0 journal-article American Association for the Advancement of Sc... None 1 journal-article Wiley-Blackwell None 2 dataCitation None None 3 None None None 4 None None None citeKey edition \ 0 tudhope2001variabilityintheelninosou None 1 tierney2015tropicalseasurfacetempera None 2 kuhnert2001httpswwwncdcnoaagovpaleostudy1866Da... None 3 None None 4 WMGAVB7S None institution url \ 0 None None 1 None None 2 World Data Center for Paleoclimatology None 3 None None 4 None None url2 0 None 1 None 2 https://www.ncdc.noaa.gov/paleo/study/1866 3 None 4 None )
- get_dataset_properties()[source]
Get a list of unique properties attached to a dataset.
Note: Some properties will return another object (e.g., ‘publishedIn’ will give you a Publication object with its own properties) Note: Not all datasets will have the same available properties (i.e., not filled in by a user)
- Returns:
clean_list – A list of avialable properties that can queried
- Return type:
list
Examples
from pylipd.utils.dataset import load_dir lipd = load_dir(name='Pages2k') dataset_properties = lipd.get_dataset_properties() print(dataset_properties)
Loading 16 LiPD files
Loaded.. ['hasSpreadsheetLink', 'name', 'hasLink', 'lipdVersion', 'includesPaleoData', 'minYear', 'publishedIn', 'type', 'hasChangeLog', 'createdBy', 'googleMetadataWorksheet', 'googleDataURL', 'proxyArchiveType', 'collectedFrom', 'datasetId', 'maxYear', 'hasUrl', 'inCompilation3_', 'inCompilation2_', 'inCompilation1_', 'studyName', 'author', 'contributor', 'fundedBy', 'notes']
- get_ensemble_tables(dsname=None, ensembleVarName=None, ensembleDepthVarName='depth')[source]
Gets ensemble tables from the LiPD graph
- Parameters:
dsname (str) – The name of the dataset if you wish to analyse one at a time (Set to “.*” to match all datasets with a common root)
ensembleVarName (None or str) – ensemble variable name. Default is None, which searches for names that contain “year” or “age” (Set to “.*” to match all ensemble variable names)
ensembleDepthVarName (str) – ensemble depth variable name. Default is ‘depth’ (Set to “.*” to match all ensemble depth variable names)
- Returns:
ensemble_tables – A dataframe containing the ensemble tables
- Return type:
dataframe
Examples
from pylipd.lipd import LiPD lipd = LiPD() lipd.load([ "../examples/data/ODP846.Lawrence.2006.lpd" ]) all_datasets = lipd.get_all_dataset_names() print("Loaded datasets: " + str(all_datasets)) ens_df = lipd.get_ensemble_tables( ensembleVarName="age", ensembleDepthVarName="depth" ) print(ens_df)
Loading 1 LiPD files
Loaded.. Loaded datasets: ['ODP846.Lawrence.2006']
datasetName ensembleTable \ 0 ODP846.Lawrence.2006 http://linked.earth/lipd/chron0model0ensemble0 ensembleVariableName ensembleVariableValues \ 0 age [[4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0,... ensembleVariableUnits ensembleDepthName \ 0 kyr BP depth ensembleDepthValues ensembleDepthUnits notes \ 0 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m None methodobj methods 0 None None
- get_lipd(dsname)[source]
Get LiPD json for a dataset
- Parameters:
dsname (str) – dataset id
- Returns:
lipdjson – LiPD json
- Return type:
dict
Examples
from pylipd.lipd import LiPD # Load a local LiPD file lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", ]) lipd_json = lipd.get_lipd(lipd.get_all_dataset_names()[0]) print(lipd_json)
Loading 1 LiPD files
Loaded.. {'geo': {'geometry': {'coordinates': [145.8167, -5.2167, -2.2], 'type': 'Point'}, 'properties': {'type': 'http://linked.earth/ontology#Location', 'ocean': 'Pacific', 'pages2kRegion': 'Ocean', 'siteName': 'Madang Lagoon, Papua New Guinea'}}, 'pub': [{'urldate': 2001.0, 'url': 'https://www.ncdc.noaa.gov/paleo/study/1866', 'title': 'World Data Center for Paleoclimatology', 'institution': 'World Data Center for Paleoclimatology', 'author': [{'name': 'H. Kuhnert'}], 'citeKey': 'kuhnert2001httpswwwncdcnoaagovpaleostudy1866DataCitation', 'identifier': []}, {'pages': '226-252', 'title': 'Tropical sea surface temperatures for the past four centuries reconstructed from coral archives', 'author': [{'name': 'Jens Zinke'}, {'name': 'K. Halimeda Kilbourne'}, {'name': 'Casey P. Saenger'}, {'name': 'Kevin J. Anchukaitis'}, {'name': 'Nerilie J. Abram'}, {'name': 'Jessica E. Tierney'}, {'name': 'Michael N. Evans'}, {'name': 'Henry C. Wu'}, {'name': 'Cyril Giry'}], 'doi': '10.1002/2014PA002717', 'citeKey': 'tierney2015tropicalseasurfacetempera', 'dataUrl': 'doi.org', 'year': 2015.0, 'volume': 30.0, 'issue': 3.0, 'journal': 'Paleoceanography', 'publisher': 'Wiley-Blackwell', 'identifier': []}, {'volume': 291.0, 'dataUrl': 'doi.org', 'doi': '10.1126/science.1057969', 'title': 'Variability in the El Nino-Southern Oscillation Through a Glacial-Interglacial Cycle', 'issue': 5508.0, 'pages': '1511-1517', 'publisher': 'American Association for the Advancement of Science (AAAS)', 'author': [{'name': 'A. W. Tudhope'}], 'year': 2001.0, 'journal': 'Science', 'citeKey': 'tudhope2001variabilityintheelninosou', 'identifier': []}], 'originalDataURL': 'https://www.ncdc.noaa.gov/paleo/study/1866', 'inCompilation3_': 'PAGES2k_v2.1.0', 'paleoData': [{'measurementTable': [{'filename': 'Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.paleo1measurement1.csv', 'missingValue': 'NaN', 'googleWorkSheetKey': 'ov9tjw6', 'tableName': 'Kuhnert', 'columns': [{'paleoDataTableName': 'measTable', 'measurementTableMD5': '793853407e414221c486d2e63b32dd87', 'hasMaxValue': 1993.042, 'TSid': 'PYTDAS7AM1Y', 'inferredVariableType': 'Year', 'hasMinValue': 1880.792, 'units': 'AD', 'hasResolution': {'units': 'AD', 'hasMaxValue': 0.25, 'hasMinValue': 0.25, 'hasMeanValue': 0.25, 'hasMedianValue': 0.25}, 'hasMeanValue': 1936.917, 'measurementTableName': 'measurementTable1', 'wDSPaleoUrl': 'https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/data-version-2.0.0/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.txt', 'dataType': 'float', 'variableName': 'year', 'number': 2, 'hasMedianValue': 1936.917, 'description': 'Year AD', 'variableType': 'inferred', 'values': [1993.042, 1992.792, 1992.542, 1992.292, 1992.042, 1991.792, 1991.542, 1991.292, 1991.042, 1990.792, 1990.542, 1990.292, 1990.042, 1989.792, 1989.542, 1989.292, 1989.042, 1988.792, 1988.542, 1988.292, 1988.042, 1987.792, 1987.542, 1987.292, 1987.042, 1986.792, 1986.542, 1986.292, 1986.042, 1985.792, 1985.542, 1985.292, 1985.042, 1984.792, 1984.542, 1984.292, 1984.042, 1983.792, 1983.542, 1983.292, 1983.042, 1982.792, 1982.542, 1982.292, 1982.042, 1981.792, 1981.542, 1981.292, 1981.042, 1980.792, 1980.542, 1980.292, 1980.042, 1979.792, 1979.542, 1979.292, 1979.042, 1978.792, 1978.542, 1978.292, 1978.042, 1977.792, 1977.542, 1977.292, 1977.042, 1976.792, 1976.542, 1976.292, 1976.042, 1975.792, 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1925.542, 1925.292, 1925.042, 1924.792, 1924.542, 1924.292, 1924.042, 1923.792, 1923.542, 1923.292, 1923.042, 1922.792, 1922.542, 1922.292, 1922.042, 1921.792, 1921.542, 1921.292, 1921.042, 1920.792, 1920.542, 1920.292, 1920.042, 1919.792, 1919.542, 1919.292, 1919.042, 1918.792, 1918.542, 1918.292, 1918.042, 1917.792, 1917.542, 1917.292, 1917.042, 1916.792, 1916.542, 1916.292, 1916.042, 1915.792, 1915.542, 1915.292, 1915.042, 1914.792, 1914.542, 1914.292, 1914.042, 1913.792, 1913.542, 1913.292, 1913.042, 1912.792, 1912.542, 1912.292, 1912.042, 1911.792, 1911.542, 1911.292, 1911.042, 1910.792, 1910.542, 1910.292, 1910.042, 1909.792, 1909.542, 1909.292, 1909.042, 1908.792, 1908.542, 1908.292, 1908.042, 1907.792, 1907.542, 1907.292, 1907.042, 1906.792, 1906.542, 1906.292, 1906.042, 1905.792, 1905.542, 1905.292, 1905.042, 1904.792, 1904.542, 1904.292, 1904.042, 1903.792, 1903.542, 1903.292, 1903.042, 1902.792, 1902.542, 1902.292, 1902.042, 1901.792, 1901.542, 1901.292, 1901.042, 1900.792, 1900.542, 1900.292, 1900.042, 1899.792, 1899.542, 1899.292, 1899.042, 1898.792, 1898.542, 1898.292, 1898.042, 1897.792, 1897.542, 1897.292, 1897.042, 1896.792, 1896.542, 1896.292, 1896.042, 1895.792, 1895.542, 1895.292, 1895.042, 1894.792, 1894.542, 1894.292, 1894.042, 1893.792, 1893.542, 1893.292, 1893.042, 1892.792, 1892.542, 1892.292, 1892.042, 1891.792, 1891.542, 1891.292, 1891.042, 1890.792, 1890.542, 1890.292, 1890.042, 1889.792, 1889.542, 1889.292, 1889.042, 1888.792, 1888.542, 1888.292, 1888.042, 1887.792, 1887.542, 1887.292, 1887.042, 1886.792, 1886.542, 1886.292, 1886.042, 1885.792, 1885.542, 1885.292, 1885.042, 1884.792, 1884.542, 1884.292, 1884.042, 1883.792, 1883.542, 1883.292, 1883.042, 1882.792, 1882.542, 1882.292, 1882.042, 1881.792, 1881.542, 1881.292, 1881.042, 1880.792]}, {'inCompilationBeta': {'compilationName': 'Pages2kTemperature', 'compilationVersion': '2_1_1'}, 'interpretation': [{'interpDirection': 'negative', 'variable': 'T', 'variableDetail': 'sea@surface', 'scope': 'climate', 'seasonality': 'N/A (subannually resolved)'}], 'pages2kID': 'Ocn_097', 'hasMinValue': -5.515, 'proxy': 'd18O', 'notes': '; climateInterpretation_seasonality changed - was originally seasonal', 'paleoDataTableName': 'measTable', 'hasMeanValue': -4.9453, 'units': 'permil', 'sensorGenus': 'Porites', 'TSid': 'Ocean2kHR_140', 'number': 1, 'hasMedianValue': -4.942, 'ocean2kID': 'PacificMadangTudhope2001', 'proxyObservationType': 'd18O', 'iso2kUI': 'CO01TUNG01A', 'hasResolution': {'hasMeanValue': 0.25, 'hasMaxValue': 0.25, 'hasMinValue': 0.25, 'hasMedianValue': 0.25, 'units': 'AD'}, 'qCCertification': 'KLD, NJA', 'useInGlobalTemperatureAnalysis': True, 'variableName': 'd18O', 'measurementTableMD5': '793853407e414221c486d2e63b32dd87', 'measurementTableName': 'measurementTable1', 'wDSPaleoUrl': 'https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/data-version-2.0.0/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.txt', 'hasMaxValue': -4.344, 'variableType': 'measured', 'values': [-4.827, -4.786, -4.693, -4.852, -4.991, -4.904, -4.855, -4.862, -4.856, -4.947, -5.005, -5.298, -5.196, -5.298, -5.106, -5.375, -5.169, -5.083, -4.996, -5.027, -4.846, -4.646, -4.589, -4.972, -4.917, -4.795, -4.759, -5.301, -5.12, -5.086, -5.103, -5.244, -5.186, -5.059, -4.971, -5.356, -5.206, -4.885, -4.756, -4.959, -4.812, -4.667, -4.494, -5.117, -5.189, -5.133, -5.081, -5.165, -5.049, -4.883, -4.839, -5.103, -5.083, -4.96, -4.921, -5.204, -5.082, -5.133, -5.026, -5.334, -5.129, -4.889, -4.855, -5.214, -5.003, -4.842, -4.864, -5.038, -4.878, -5.027, -5.181, -5.515, -5.334, -5.06, -4.958, -5.268, -5.228, -5.136, -5.123, -5.304, -5.072, -4.748, -4.785, -5.234, -5.164, -5.053, -5.03, -5.188, -4.931, -4.99, -5.142, -5.216, -5.09, -4.865, -4.894, -5.041, -5.037, -5.064, -5.11, -5.291, -5.198, -5.242, -5.297, -5.492, -5.382, -5.087, -5.009, -5.282, -4.831, -4.524, -4.556, -5.071, -4.995, -4.958, -4.991, -5.08, -4.942, -4.908, -4.848, -4.993, -4.964, 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-4.866, -5.211, -5.128, -5.037, -4.995, -5.131, -5.041, -4.969, -4.949, -5.052, -4.941, -4.671, -4.616, -5.11, -5.048, -4.751, -4.634, -5.052, -4.846, -4.742, -4.741, -4.903, -4.947, -4.848, -4.877, -4.886, -4.878, -4.956, -4.681, -4.941, -4.83, -5.185, -5.012, -4.967, -4.924, -4.77, -4.612, -4.957, -5.018, -4.97, -4.792, -4.788, -4.696, -4.525, -4.527, -4.721, -4.691, -4.853, -4.788, -4.931, -4.912, -4.954, -5.027, -4.937, -4.718, -4.512, -4.494, -4.675, -4.651, -4.666, -4.64, -4.849, -4.888, -4.833, -4.803, -4.863, -4.915, -4.733, -4.792, -4.872, -5.023, -4.923, -4.792, -4.906, -4.94, -4.801]}]}]}], 'datasetId': 'm8yv2VgG97zJmSg3XhqQ', 'maxYear': 1993.042, 'googleDataURL': 'https://docs.google.com/spreadsheets/d/1wf30P-s54OTBdLw4dyeaIN53VDoN0u_hOqIwvAeLxtc', 'createdBy': 'matlab', 'dataContributor': {'name': 'Wu KLD'}, 'hasUrl': 'https://data.mint.isi.edu/files/lipd/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd', 'inCompilation1_': 'Ocean2k_v1.0.0', 'minYear': 1880.792, 'studyName': 'Madang, Papua New Guinea oxygen isotope record 1880-1993', 'changelog': {'version': '1.0.0', 'curator': 'nicholas', 'timestamp': datetime.date(2022, 8, 23), 'notes': 'Starting the changelog'}, 'googleMetadataWorksheet': 'oruuxfm', 'googleSpreadSheetKey': '1wf30P-s54OTBdLw4dyeaIN53VDoN0u_hOqIwvAeLxtc', 'dataSetName': 'Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'inCompilation2_': 'PAGES2k_v2.0.0', 'lipdVersion': 1.3, 'archiveType': 'coral'}
- get_model_properties()[source]
Get all the properties associated with a model
- Returns:
A list of unique properties attached to models
- Return type:
List
Examples
from pylipd.utils.dataset import load_datasets lipd = load_datasets(names='ODP846') model_properties = lipd.get_model_properties() print(model_properties)
Loading 1 LiPD files
Loaded.. ['method', 'type', 'foundInEnsembleTable']
- get_timeseries(dsnames, to_dataframe=False)[source]
Get Legacy LiPD like Time Series Object (tso)
- Parameters:
dsnames (list) – array of dataset id or name strings
to_dataframe (bool {True; False}) – Whether to return a dataframe along the dictionary. Default is False
- Returns:
ts (dict) – A dictionary containing Time Series Object
df (Pandas.DataFrame) – If to_dataframe is set to True, returns a queriable Pandas DataFrame
Examples
from pylipd.lipd import LiPD # Fetch LiPD data from remote RDF Graph lipd_remote = LiPD() lipd_remote.set_endpoint("https://linkedearth.graphdb.mint.isi.edu/repositories/LiPDVerse2") ts_list = lipd_remote.get_timeseries(["Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001", "MD98_2181.Stott.2007", "Ant-WAIS-Divide.Severinghaus.2012"]) for dsname, tsos in ts_list.items(): for tso in tsos: if 'paleoData_variableName' in tso: print(dsname+': '+tso['paleoData_variableName']+': '+tso['archiveType'])
Extracting timeseries from dataset: Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001 ... Extracting timeseries from dataset: MD98_2181.Stott.2007 ... Extracting timeseries from dataset: Ant-WAIS-Divide.Severinghaus.2012 ...
- get_timeseries_essentials(dsname=None, mode='paleo')[source]
- Returns specific properties for timeseries: ‘dataSetName’, ‘archiveType’, ‘geo_meanLat’, ‘geo_meanLon’,
‘geo_meanElev’, ‘paleoData_variableName’, ‘paleoData_values’, ‘paleoData_units’, ‘paleoData_proxy’ (paleo only), ‘paleoData_proxyGeneral’ (paleo only), ‘time_variableName’, ‘time_values’, ‘time_units’, ‘depth_variableName’, ‘depth_values’, ‘depth_units’
- Parameters:
dsname (str, optional) – The name of the dataset for which to return the timeseries information. The default is None.
mode (paleo, chron) – Whether to retrun the information stored in the PaleoMeasurementTable or the ChronMeasurementTable. The default is ‘paleo’.
- Raises:
ValueError – Need to select either ‘chron’ or ‘paleo’
- Returns:
qres_df – A pandas dataframe returning the properties in columns for each series stored in a row of the dataframe
- Return type:
pandas.DataFrame
Example
from pylipd.utils.dataset import load_datasets lipd = load_datasets('ODP846.Lawrence.2006.lpd') df_paleo = lipd.get_timeseries_essentials(mode='paleo') print(df_paleo)
Loading 1 LiPD files
Loaded.. dataSetName archiveType geo_meanLat geo_meanLon \ 0 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 1 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 2 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 3 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 4 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 5 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 6 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 7 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 8 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 9 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 10 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 11 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 12 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 13 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 14 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 15 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 16 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 17 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 18 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 19 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 20 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 21 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 22 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 23 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 24 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 geo_meanElev paleoData_variableName \ 0 -3296.0 c37 total.Alkenone 1 -3296.0 interval 2 -3296.0 temp muller 3 -3296.0 event 4 -3296.0 event 5 -3296.0 event 6 -3296.0 u. peregrina d18o.d18O 7 -3296.0 u. peregrina d18o.d18O 8 -3296.0 u. peregrina d18o.d18O 9 -3296.0 ukprime37.Uk37Prime 10 -3296.0 sample label 11 -3296.0 sample label 12 -3296.0 sample label 13 -3296.0 u. peregrina d13c.d13C 14 -3296.0 u. peregrina d13c.d13C 15 -3296.0 u. peregrina d13c.d13C 16 -3296.0 temp prahl 17 -3296.0 section 18 -3296.0 c. wuellerstorfi d13c.d13C 19 -3296.0 c. wuellerstorfi d13c.d13C 20 -3296.0 c. wuellerstorfi d13c.d13C 21 -3296.0 c. wuellerstorfi d18o.d18O 22 -3296.0 c. wuellerstorfi d18o.d18O 23 -3296.0 c. wuellerstorfi d18o.d18O 24 -3296.0 site/hole paleoData_values paleoData_units \ 0 [2.37, 2.1, 1.87, 2.74, 3.75, 7.62, 7.86, 7.73... nmol/kg 1 [] cm 2 [23.545, 23.648, 23.752, 22.515, 22.206, 21.89... deg C 3 [] unitless 4 [] unitless 5 [] unitless 6 [0.14, 0.01, -0.1, -0.06, -0.17, -0.21, -0.41,... per mil PDB 7 [0.14, 0.01, -0.1, -0.06, -0.17, -0.21, -0.41,... per mil PDB 8 [0.14, 0.01, -0.1, -0.06, -0.17, -0.21, -0.41,... per mil PDB 9 [0.821, 0.824, 0.828, 0.787, 0.777, 0.767, 0.7... unitless 10 [] unitless 11 [] unitless 12 [] unitless 13 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... per mil PDB 14 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... per mil PDB 15 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... per mil PDB 16 [23.0, 23.1, 23.2, 22.0, 21.7, 21.4, 21.7, 21.... deg C 17 [] unitless 18 [3.38, 3.46, 3.65, 3.88, 4.14, 4.47, 4.99, 4.9... per mil PDB 19 [3.38, 3.46, 3.65, 3.88, 4.14, 4.47, 4.99, 4.9... per mil PDB 20 [3.38, 3.46, 3.65, 3.88, 4.14, 4.47, 4.99, 4.9... per mil PDB 21 [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... per mil PDB 22 [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... per mil PDB 23 [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... per mil PDB 24 [] unitless paleoData_proxy paleoData_proxyGeneral time_variableName \ 0 None None age 1 None None age 2 None None age 3 None None None 4 None None None 5 None None None 6 None None None 7 None None None 8 None None None 9 None None age 10 None None None 11 None None None 12 None None None 13 None None None 14 None None None 15 None None None 16 None None age 17 None None age 18 None None None 19 None None None 20 None None None 21 None None None 22 None None None 23 None None None 24 None None age time_values time_units \ 0 [5.228, 8.947, 11.966, 14.427, 16.502, 18.41, ... kyr BP 1 [5.228, 8.947, 11.966, 14.427, 16.502, 18.41, ... kyr BP 2 [5.228, 8.947, 11.966, 14.427, 16.502, 18.41, ... kyr BP 3 None None 4 None None 5 None None 6 None None 7 None None 8 None None 9 [5.228, 8.947, 11.966, 14.427, 16.502, 18.41, ... kyr BP 10 None None 11 None None 12 None None 13 None None 14 None None 15 None None 16 [5.228, 8.947, 11.966, 14.427, 16.502, 18.41, ... kyr BP 17 [5.228, 8.947, 11.966, 14.427, 16.502, 18.41, ... kyr BP 18 None None 19 None None 20 None None 21 None None 22 None None 23 None None 24 [5.228, 8.947, 11.966, 14.427, 16.502, 18.41, ... kyr BP depth_variableName depth_values \ 0 depth [0.16, 0.26, 0.36, 0.46, 0.56, 0.66, 0.76, 0.8... 1 depth [0.16, 0.26, 0.36, 0.46, 0.56, 0.66, 0.76, 0.8... 2 depth [0.16, 0.26, 0.36, 0.46, 0.56, 0.66, 0.76, 0.8... 3 depth comp [12.0, 23.0, 33.0, 33.0, 43.0, 53.0, 63.0, 73.... 4 depth cr [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 5 depth [] 6 depth comp [12.0, 23.0, 33.0, 33.0, 43.0, 53.0, 63.0, 73.... 7 depth cr [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 8 depth [] 9 depth [0.16, 0.26, 0.36, 0.46, 0.56, 0.66, 0.76, 0.8... 10 depth comp [12.0, 23.0, 33.0, 33.0, 43.0, 53.0, 63.0, 73.... 11 depth cr [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 12 depth [] 13 depth comp [12.0, 23.0, 33.0, 33.0, 43.0, 53.0, 63.0, 73.... 14 depth cr [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 15 depth [] 16 depth [0.16, 0.26, 0.36, 0.46, 0.56, 0.66, 0.76, 0.8... 17 depth [0.16, 0.26, 0.36, 0.46, 0.56, 0.66, 0.76, 0.8... 18 depth comp [12.0, 23.0, 33.0, 33.0, 43.0, 53.0, 63.0, 73.... 19 depth cr [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 20 depth [] 21 depth comp [12.0, 23.0, 33.0, 33.0, 43.0, 53.0, 63.0, 73.... 22 depth cr [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 23 depth [] 24 depth [0.16, 0.26, 0.36, 0.46, 0.56, 0.66, 0.76, 0.8... depth_units 0 m 1 m 2 m 3 mcd 4 rmcd 5 m 6 mcd 7 rmcd 8 m 9 m 10 mcd 11 rmcd 12 m 13 mcd 14 rmcd 15 m 16 m 17 m 18 mcd 19 rmcd 20 m 21 mcd 22 rmcd 23 m 24 mTo return the information stored in the ChronTable:
from pylipd.utils.dataset import load_datasets lipd = load_datasets('ODP846.Lawrence.2006.lpd') df_chron = lipd.get_timeseries_essentials(mode='chron') print(df_chron)
Loading 1 LiPD files
Loaded.. dataSetName archiveType geo_meanLat geo_meanLon \ 0 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 1 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 2 ODP846.Lawrence.2006 marine sediment -3.1 -90.8 geo_meanElev chronData_variableName \ 0 -3296.0 depth 1 -3296.0 age 2 -3296.0 d18o chronData_values chronData_units \ 0 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 1 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 2 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... per mil time_variableName time_values \ 0 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 1 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 2 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... time_units depth_variableName \ 0 ky BP depth 1 ky BP depth 2 ky BP depth depth_values depth_units 0 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 1 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 2 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m
- get_variable_properties()[source]
Get a list of variable properties that can be used for querying
- Returns:
A list of unique variable properties
- Return type:
list
Examples
from pylipd.utils.dataset import load_dir lipd = load_dir(name='Pages2k') variable_properties = lipd.get_variable_properties() print(variable_properties)
Loading 16 LiPD files
Loaded.. ['hasColumnNumber', 'hasMedianValue', 'hasResolution', 'hasVariableID', 'hasValues', 'foundInDataset', 'proxy', 'hasUnits', 'paleoDataTableName', 'interpretedAs', 'qCCertification', 'archiveType', 'wDSPaleoUrl', 'calibratedVia', 'useInGlobalTemperatureAnalysis', 'partOfCompilation', 'measurementTableName', 'inferredVariableType', 'type', 'pages2kID', 'hasMaxValue', 'foundInTable', 'hasMinValue', 'qCnotes', 'hasMeanValue', 'name', 'measurementTableMD5', 'dataType', 'description', 'hasProxySystem', 'proxyObservationType', 'notes', 'detail', 'ocean2kID', 'sensorGenus', 'iso2kUI', 'sensorSpecies', 'measurementMaterial', 'hasUncertainty', 'method', 'precededBy']
- load(lipdfiles, parallel=False)[source]
Load LiPD files.
- Parameters:
lipdfiles (list of str) – array of paths to lipd files (the paths could also be urls)
parallel (bool) – (Optional) set to True to process lipd files in parallel. You must run this function under the “__main__” process for this to work
Examples
In this example, we load LiPD files for an array of paths.
from pylipd.lipd import LiPD lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", "../examples/data/MD98_2181.Stott.2007.lpd", "../examples/data/Ant-WAIS-Divide.Severinghaus.2012.lpd", "https://lipdverse.org/data/LCf20b99dfe8d78840ca60dfb1f832b9ec/1_0_1/Nunalleq.Ledger.2018.lpd" ]) print(lipd.get_all_dataset_names())
Loading 4 LiPD files
Loaded.. ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'MD98_2181.Stott.2007', 'Ant-WAIS-Divide.Severinghaus.2012', 'Nunalleq.Ledger.2018']
- load_from_dir(dir_path, parallel=False, cutoff=None)[source]
Load LiPD files from a directory
- Parameters:
dir_path (str) – path to the directory containing lipd files
parallel (bool) – (Optional) set to True to process lipd files in parallel. You must run this function under the “__main__” process for this to work
cutoff (int) – (Optional) the maximum number of files to load at once.
Examples
In this example, we load LiPD files from a directory.
from pylipd.lipd import LiPD lipd = LiPD() lipd.load_from_dir("../examples/data") print(lipd.get_all_dataset_names())
Loading 4 LiPD files
Loaded.. ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'MD98_2181.Stott.2007', 'Ant-WAIS-Divide.Severinghaus.2012', 'ODP846.Lawrence.2006']
- load_remote_datasets(dsnames)[source]
Loads remote datasets into cache if a remote endpoint is set
- Parameters:
dsnames (array) – array of dataset names
Examples
from pylipd.lipd import LiPD # Fetch LiPD data from remote RDF Graph lipd_remote = LiPD() lipd_remote.set_endpoint("https://linkedearth.graphdb.mint.isi.edu/repositories/LiPDVerse2") lipd_remote.load_remote_datasets(["Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001", "MD98_2181.Stott.2007", "Ant-WAIS-Divide.Severinghaus.2012"]) print(lipd_remote.get_all_dataset_names())
Caching datasets from remote endpoint.. Making remote query to endpoint: https://linkedearth.graphdb.mint.isi.edu/repositories/LiPDVerse2
Done.. ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'MD98_2181.Stott.2007', 'Ant-WAIS-Divide.Severinghaus.2012']
- pop(dsnames)[source]
Pops dataset(s) from the graph and returns the popped LiPD object
- Parameters:
dsnames (str or list of str) – dataset name(s) to be popped.
- Returns:
LiPD object with the popped dataset(s)
- Return type:
Examples
from pylipd.lipd import LiPD # Load local files lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", "../examples/data/MD98_2181.Stott.2007.lpd" ]) all_datasets = lipd.get_all_dataset_names() print("Loaded datasets: " + str(all_datasets)) popped = lipd.pop(all_datasets[0]) print("Loaded datasets after pop: " + str(lipd.get_all_dataset_names())) print("Popped dataset: " + str(popped.get_all_dataset_names()))
Loading 2 LiPD files
Loaded.. Loaded datasets: ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'MD98_2181.Stott.2007'] Loaded datasets after pop: ['MD98_2181.Stott.2007'] Popped dataset: ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001']
- remove(dsnames)[source]
Removes dataset(s) from the graph
- Parameters:
dsnames (str or list of str) – dataset name(s) to be removed
Examples
from pylipd.lipd import LiPD # Load local files lipd = LiPD() lipd.load([ "../examples/data/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd", "../examples/data/MD98_2181.Stott.2007.lpd" ]) all_datasets = lipd.get_all_dataset_names() print("Loaded datasets: " + str(all_datasets)) lipd.remove(all_datasets[0]) print("Loaded datasets after remove: " + str(lipd.get_all_dataset_names()))
Loading 2 LiPD files
Loaded.. Loaded datasets: ['Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', 'MD98_2181.Stott.2007'] Loaded datasets after remove: ['MD98_2181.Stott.2007']
- to_lipd_series(parallel=False)[source]
Converts the LiPD object to a LiPDSeries object
- Parameters:
parallel (bool) – Whether to use parallel processing to load the data. Default is False
- Returns:
A LiPDSeries object
- Return type:
pylipd.lipd.LiPDSeries
Examples
from pylipd.lipd import LiPD lipd = LiPD() lipd.load([ "../examples/data/ODP846.Lawrence.2006.lpd" ]) S = lipd.to_lipd_series()
Loading 1 LiPD files
Loaded.. Creating LiPD Series... - Extracting dataset subgraphs
- Extracting variable subgraphs
Done..
pylipd.utils.lipd_to_rdf module
The LipdToRDF class helps in converting a LiPD file to an RDF Graph. It uses the SCHEMA dictionary (from globals/schema.py) to do the conversion
- class pylipd.utils.lipd_to_rdf.LipdToRDF[source]
Bases:
objectThe LipdToRDF class helps in converting a LiPD file to an RDF Graph. It uses the SCHEMA dictionary (from globals/schema.py) to do the conversion
Methods
convert(lipdpath)Convert LiPD file to RDF Graph
serialize(topath[, type])Write LiPD RDF Graph to RDF file (or Pickle file)
pylipd.utils.multi_processing module
- pylipd.utils.multi_processing.convert_lipd_to_graph(lipdfile)[source]
Worker that converts one lipdfile to an RDF graph
pylipd.utils.rdf_to_lipd module
The RDFToLiPD class helps in converting an RDF Graph to a LiPD file. It uses the SCHEMA dictionary (from globals/schema.py) to do the conversion
- class pylipd.utils.rdf_to_lipd.RDFToLiPD(graph)[source]
Bases:
objectThe RDFToLiPD class helps in converting an RDF Graph to a LiPD file. It uses the SCHEMA dictionary (from globals/schema.py) to do the conversion
Methods
convert(dsname, lipdfile)Convert RDF graph to a LiPD file
convert_to_json(dsname)Convert RDF graph to a LiPD file
pylipd.utils.rdfrdf_graph module
The RDF Graph class contains an RDF Graph using the RDFLib library, and allows querying over it using SPARQL. It also allows querying over a remote endpoint.
- class pylipd.utils.rdf_graph.RDFGraph(graph=None)[source]
Bases:
objectThe RDF Graph class contains an RDF Graph using the RDFLib library, and allows querying over it
Examples
from pylipd.utils.rdf_graph import RDFGraph # Load RDF file into graph rdf = RDFGraph() rdf.load(["../examples/rdf/graph.ttl"]) (result, result_df) = rdf.query("SELECT ?s ?p ?o WHERE {?s ?p ?o} LIMIT 10") print(result_df)
s \ 0 http://www.wikidata.org/entity/Q12418 1 http://example.org/bob#me 2 http://example.org/bob#me 3 http://data.europeana.eu/item/04802/243FA86189... 4 http://www.wikidata.org/entity/Q12418 5 http://example.org/bob#me 6 http://example.org/bob#me p \ 0 http://purl.org/dc/terms/creator 1 http://xmlns.com/foaf/0.1/topic_interest 2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 3 http://purl.org/dc/terms/subject 4 http://purl.org/dc/terms/title 5 http://xmlns.com/foaf/0.1/knows 6 http://schema.org/birthDate o 0 http://dbpedia.org/resource/Leonardo_da_Vinci 1 http://www.wikidata.org/entity/Q12418 2 http://xmlns.com/foaf/0.1/Person 3 http://www.wikidata.org/entity/Q12418 4 Mona Lisa 5 http://example.org/alice#me 6 1990-07-04Methods
clear()Clears the graph
copy()Makes a copy of the object
get(ids)Get id(s) from the graph and returns the LiPD object
Get all Graph ids
load(files[, graphid])Loads a RDF file into the graph
merge(rdf)Merges the current LiPD object with another LiPD object
pop(ids)Pops graph(s) from the combined graph and returns the popped RDF Graph
query(query[, remote, result])Once data is loaded into the graph (or remote endpoint set), one can make SparQL queries to the graph
remove(ids)Removes ids(s) from the graph
Returns RDF quad serialization of the current combined Graph .
set_endpoint(endpoint)Sets a SparQL endpoint for a remote Knowledge Base (example: GraphDB)
- get(ids)[source]
Get id(s) from the graph and returns the LiPD object
- Parameters:
ids (str or list of str) – graph id(s) to get.
- Returns:
RDFGraph object with the retrieved graph(s)
- Return type:
pylipd.utils.utils.rdf_graph.RDFGraph
Examples
from pylipd.utils.rdf_graph import RDFGraph # Fetch RDF graph data for given id(s) rdf = RDFGraph() rdf.load(["../examples/rdf/graph.ttl"], graphid="http://example.org/graph") rdf.get("http://example.org/graph")
<pylipd.utils.rdf_graph.RDFGraph at 0x7f9025eec6d0>
- get_all_graph_ids()[source]
Get all Graph ids
- Returns:
ids (list)
A list of graph ids
Examples
from pylipd.utils.rdf_graph import RDFGraph # Fetch RDF Graph Data rdf = RDFGraph() rdf.load(["../examples/rdf/graph.ttl"], graphid="http://example.org/graph") print(rdf.get_all_graph_ids())
['http://example.org/graph']
- load(files, graphid=None)[source]
Loads a RDF file into the graph
- Parameters:
rdf_file (str) – Path to the RDF file
Examples
from pylipd.utils.rdf_graph import RDFGraph # Load RDF file into graph rdf = RDFGraph() rdf.load(["../examples/rdf/graph.ttl"]) (result, result_df) = rdf.query("SELECT ?s ?p ?o WHERE {?s ?p ?o} LIMIT 10") print(result_df)
s \ 0 http://www.wikidata.org/entity/Q12418 1 http://example.org/bob#me 2 http://example.org/bob#me 3 http://data.europeana.eu/item/04802/243FA86189... 4 http://www.wikidata.org/entity/Q12418 5 http://example.org/bob#me 6 http://example.org/bob#me p \ 0 http://purl.org/dc/terms/creator 1 http://xmlns.com/foaf/0.1/topic_interest 2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 3 http://purl.org/dc/terms/subject 4 http://purl.org/dc/terms/title 5 http://xmlns.com/foaf/0.1/knows 6 http://schema.org/birthDate o 0 http://dbpedia.org/resource/Leonardo_da_Vinci 1 http://www.wikidata.org/entity/Q12418 2 http://xmlns.com/foaf/0.1/Person 3 http://www.wikidata.org/entity/Q12418 4 Mona Lisa 5 http://example.org/alice#me 6 1990-07-04
- merge(rdf)[source]
Merges the current LiPD object with another LiPD object
- Parameters:
rdf (pylipd.rdf_graph.RDFGraph) – RDFGraph object to merge with
- Returns:
merged RDFGraph object
- Return type:
- pop(ids)[source]
Pops graph(s) from the combined graph and returns the popped RDF Graph
- Parameters:
ids (str or list of str) – rdf id(s) to be popped.
- Returns:
RDFGraph object with the popped graph(s)
- Return type:
Examples
from pylipd.utils.rdf_graph import RDFGraph # Pop RDF graph data for given id(s) rdf = RDFGraph() rdf.load(["../examples/rdf/graph.ttl"], graphid="http://example.org/graph") popped = rdf.pop("http://example.org/graph")
- query(query, remote=False, result='sparql')[source]
Once data is loaded into the graph (or remote endpoint set), one can make SparQL queries to the graph
- Parameters:
query (str) – SparQL query
remote (bool) – (Optional) If set to True, the query will be made to the remote endpoint (if set)
result (str) – (Optional) Result return type
- Returns:
result (dict) – Dictionary of sparql variable and binding values
result_df (pandas.Dataframe) – Return the dictionary above as a pandas.Dataframe
Examples
from pylipd.utils.rdf_graph import RDFGraph rdf = RDFGraph() rdf.load(["../examples/rdf/graph.ttl"]) query = """PREFIX le: <http://linked.earth/ontology#> select ?s ?p ?o where { ?s ?p ?o } LIMIT 10 """ result, result_df = rdf.query(query) print(result_df)
s \ 0 http://www.wikidata.org/entity/Q12418 1 http://example.org/bob#me 2 http://example.org/bob#me 3 http://data.europeana.eu/item/04802/243FA86189... 4 http://www.wikidata.org/entity/Q12418 5 http://example.org/bob#me 6 http://example.org/bob#me p \ 0 http://purl.org/dc/terms/creator 1 http://xmlns.com/foaf/0.1/topic_interest 2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 3 http://purl.org/dc/terms/subject 4 http://purl.org/dc/terms/title 5 http://xmlns.com/foaf/0.1/knows 6 http://schema.org/birthDate o 0 http://dbpedia.org/resource/Leonardo_da_Vinci 1 http://www.wikidata.org/entity/Q12418 2 http://xmlns.com/foaf/0.1/Person 3 http://www.wikidata.org/entity/Q12418 4 Mona Lisa 5 http://example.org/alice#me 6 1990-07-04
- remove(ids)[source]
Removes ids(s) from the graph
- Parameters:
ids (str or list of str) – graph id(s) to be removed
Examples
from pylipd.utils.rdf_graph import RDFGraph # Remove RDF graph data for given id(s) rdf = RDFGraph() rdf.load(["../examples/rdf/graph.ttl"], graphid="http://example.org/graph") rdf.remove("http://example.org/graph")
- serialize()[source]
Returns RDF quad serialization of the current combined Graph .. rubric:: Examples
from pylipd.utils.rdf_graph import RDFGraph # Fetch RDF data rdf = RDFGraph() rdf.load(["../examples/rdf/graph.ttl"], graphid="http://example.org/graph") nquads = rdf.serialize() print(nquads[:10000]) print("...")
<http://www.wikidata.org/entity/Q12418> <http://purl.org/dc/terms/creator> <http://dbpedia.org/resource/Leonardo_da_Vinci> <http://example.org/graph> . <http://example.org/bob#me> <http://xmlns.com/foaf/0.1/topic_interest> <http://www.wikidata.org/entity/Q12418> <http://example.org/graph> . <http://example.org/bob#me> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://xmlns.com/foaf/0.1/Person> <http://example.org/graph> . <http://data.europeana.eu/item/04802/243FA8618938F4117025F17A8B813C5F9AA4D619> <http://purl.org/dc/terms/subject> <http://www.wikidata.org/entity/Q12418> <http://example.org/graph> . <http://www.wikidata.org/entity/Q12418> <http://purl.org/dc/terms/title> "Mona Lisa" <http://example.org/graph> . <http://example.org/bob#me> <http://xmlns.com/foaf/0.1/knows> <http://example.org/alice#me> <http://example.org/graph> . <http://example.org/bob#me> <http://schema.org/birthDate> "1990-07-04"^^<http://www.w3.org/2001/XMLSchema#date> <http://example.org/graph> . ...
- set_endpoint(endpoint)[source]
Sets a SparQL endpoint for a remote Knowledge Base (example: GraphDB)
- Parameters:
endpoint (str) – URL for the SparQL endpoint
Examples
from pylipd.utils.rdf_graph import RDFGraph # Fetch LiPD data from remote RDF Graph rdf = RDFGraph() rdf.set_endpoint("https://linkedearth.graphdb.mint.isi.edu/repositories/LiPDVerse2") (result, result_df) = rdf.query("SELECT ?s ?p ?o WHERE {?s ?p ?o} LIMIT 10")
pylipd.utils.utils module
- pylipd.utils.utils.sparql_results_to_df(results: SPARQLResult) DataFrame[source]
Export results from an rdflib SPARQL query into a pandas.DataFrame, using Python types. See https://github.com/RDFLib/rdflib/issues/1179.