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: uncertainty: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: uncertaintyLow: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: age: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: depth: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: age: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: temperature: Archaeological Nunalleq.Ledger.2018: uncertainty: Archaeological Nunalleq.Ledger.2018: uncertaintyHigh: Archaeological Nunalleq.Ledger.2018: precipitation: Archaeological Nunalleq.Ledger.2018: uncertainty: 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_compilationName(compilationName)Filters datasets to return a new LiPD object that only keeps datasets that have the specific compilation
filter_by_datasetName(datasetName)Filters datasets to return a new LiPD object that only keeps datasets that have the specified names
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
filter_by_time(timeBound[, timeBoundType, ...])Filter the records according to a specified time interval and the length of the record within that interval.
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
Return the names of the compilation 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.
Return datasets as instances of the Dataset class
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([dsnames, 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, standardize, ...])Load LiPD files.
load_datasets(datasets)Loads instances of Dataset class into the LiPD graph
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, standardize=True, add_labels=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..
{'studyName': 'Madang, Papua New Guinea oxygen isotope record 1880-1993', 'createdBy': 'matlab', 'googleMetadataWorksheet': 'oruuxfm', 'maxYear': 1993.042, 'inCompilation3_': 'PAGES2k_v2.1.0', 'minYear': 1880.792, 'googleSpreadSheetKey': '1wf30P-s54OTBdLw4dyeaIN53VDoN0u_hOqIwvAeLxtc', 'dataContributor': {'name': 'Wu KLD'}, 'paleoData': [{'measurementTable': [{'filename': 'Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.paleo1measurement1.csv', 'googleWorkSheetKey': 'ov9tjw6', 'tableName': 'Kuhnert', 'columns': [{'measurementTableName': 'measurementTable1', 'sensorGenus': 'Porites', 'resolution': {'hasMaxValue': 0.25, 'hasMedianValue': 0.25, 'hasMeanValue': 0.25, 'hasMinValue': 0.25, 'units': 'yr AD'}, 'iso2kUI': 'CO01TUNG01A', 'hasMinValue': -5.515, 'hasMedianValue': -4.942, 'wDSPaleoUrl': 'https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/data-version-2.0.0/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.txt', 'hasMeanValue': -4.9453, 'variableType': 'measured', 'hasMaxValue': -4.344, 'qCCertification': 'KLD, NJA', 'paleoDataTableName': 'measTable', 'useInGlobalTemperatureAnalysis': True, 'pages2kID': 'Ocn_097', 'notes': '; climateInterpretation_seasonality changed - was originally seasonal', 'TSid': 'Ocean2kHR_140', 'variableName': 'd18O', 'proxyObservationType': 'd18O', 'interpretation': [{'variableDetail': 'sea@surface', 'scope': 'climate', 'direction': 'negative', 'seasonality': 'subannual', 'variable': 'temperature'}], 'inCompilationBeta': {'compilationName': 'Pages2kTemperature', 'compilationVersion': '2_1_1'}, 'measurementTableMD5': '793853407e414221c486d2e63b32dd87', 'ocean2kID': 'PacificMadangTudhope2001', 'number': 1, 'units': 'permil', 'proxy': 'd18O', 'archiveType': 'Coral'}, {'measurementTableMD5': '793853407e414221c486d2e63b32dd87', 'measurementTableName': 'measurementTable1', 'dataType': 'float', 'description': 'Year AD', 'variableName': 'year', 'hasMinValue': 1880.792, 'hasMaxValue': 1993.042, 'TSid': 'PYTDAS7AM1Y', 'variableType': 'inferred', 'wDSPaleoUrl': 'https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/data-version-2.0.0/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.txt', 'hasMedianValue': 1936.917, 'resolution': {'hasMeanValue': 0.25, 'hasMinValue': 0.25, 'hasMaxValue': 0.25, 'hasMedianValue': 0.25, 'units': 'yr AD'}, 'inferredVariableType': 'Year', 'hasMeanValue': 1936.917, 'number': 2, 'paleoDataTableName': 'measTable', 'units': 'yr AD', 'archiveType': 'Coral'}], 'missingValue': 'NaN'}]}], 'inCompilation2_': 'PAGES2k_v2.0.0', 'lipdVersion': 1.3, 'geo': {'geometry': {'coordinates': [145.8167, -5.2167, -2.2], 'type': 'Point'}, 'properties': {'type': 'http://linked.earth/ontology#Location', 'longitude': 145.8167, 'siteName': 'Madang Lagoon, Papua New Guinea', 'elevation': -2.2, 'latitude': -5.2167, 'ocean': 'Pacific', 'pages2kRegion': 'Ocean'}}, 'pub': [{'journal': 'Paleoceanography', 'author': [{'name': 'K. Halimeda Kilbourne'}, {'name': 'Jens Zinke'}, {'name': 'Cyril Giry'}, {'name': 'Jessica E. Tierney'}, {'name': 'Michael N. Evans'}, {'name': 'Casey P. Saenger'}, {'name': 'Henry C. Wu'}, {'name': 'Nerilie J. Abram'}, {'name': 'Kevin J. Anchukaitis'}], 'doi': '10.1002/2014PA002717', 'issue': 3.0, 'pages': '226-252', 'dataUrl': ['doi.org'], 'year': 2015, 'title': 'Tropical sea surface temperatures for the past four centuries reconstructed from coral archives', 'publisher': 'Wiley-Blackwell', 'citeKey': 'tierney2015tropicalseasurfacetempera', 'volume': '30'}, {'year': 2001, 'doi': '10.1126/science.1057969', 'title': 'Variability in the El Nino-Southern Oscillation Through a Glacial-Interglacial Cycle', 'journal': 'Science', 'author': [{'name': 'A. W. Tudhope'}], 'pages': '1511-1517', 'dataUrl': ['doi.org'], 'publisher': 'American Association for the Advancement of Science (AAAS)', 'citeKey': 'tudhope2001variabilityintheelninosou', 'volume': '291', 'issue': 5508.0}, {'url': ['https://www.ncdc.noaa.gov/paleo/study/1866'], 'institution': 'World Data Center for Paleoclimatology', 'author': [{'name': 'H. Kuhnert'}], 'title': 'World Data Center for Paleoclimatology', 'citeKey': 'kuhnert2001httpswwwncdcnoaagovpaleostudy1866DataCitation', 'urldate': 2001.0}], 'originalDataURL': 'https://www.ncdc.noaa.gov/paleo/study/1866', 'hasUrl': 'https://data.mint.isi.edu/files/lipd/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd', 'googleDataURL': 'https://docs.google.com/spreadsheets/d/1wf30P-s54OTBdLw4dyeaIN53VDoN0u_hOqIwvAeLxtc', 'changelog': [{'notes': 'Starting the changelog', 'timestamp': '2022-08-23 23:41:56 UTC', 'version': '1.0.0', 'curator': 'nicholas'}], 'datasetId': 'm8yv2VgG97zJmSg3XhqQ', 'inCompilation1_': 'Ocean2k_v1.0.0', 'dataSetName': 'Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', '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_archiveTypes()
Loading 16 LiPD files
Loaded..
['Marine sediment']
If searching for multiple archiveTypes, you can construct the name as follows:
from pylipd.utils.dataset import load_dir lipd = load_dir('Pages2k') Lfiltered = lipd.filter_by_archive_type('marine|coral') Lfiltered.get_all_archiveTypes()
Loading 16 LiPD files
Loaded..
['Marine sediment', 'Coral']
- filter_by_compilationName(compilationName)[source]
Filters datasets to return a new LiPD object that only keeps datasets that have the specific compilation
- Parameters:
compilationName (str) – The name of the compilation 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 available_dataset_names, load_datasets dsList = available_dataset_names() D = load_datasets(dsList) Dfiltered = D.filter_by_compilationName('Temp12k') Dfiltered.get_all_dataset_names()
['/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/ODP846.Lawrence.2006.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Eur-CoastofPortugal.Abrantes.2011.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Eur-SpannagelCave.Mangini.2005.lpd', "/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Arc-Kongressvatnet.D'Andrea.2012.lpd", '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Eur-FinnishLakelands.Helama.2014.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Eur-LakeSilvaplana.Trachsel.2010.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Eur-Stockholm.Leijonhufvud.2009.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Eur-SpanishPyrenees.Dorado-Linan.2012.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Ocn-RedSea.Felis.2000.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Ocn-AlboranSea436B.Nieto-Moreno.2013.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Eur-NorthernSpain.Martin-Chivelet.2011.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Ant-WAIS-Divide.Severinghaus.2012.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Asi-SourthAndMiddleUrals.Demezhko.2007.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Ocn-FeniDrift.Richter.2009.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Eur-NorthernScandinavia.Esper.2012.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Ocn-SinaiPeninsula,RedSea.Moustafa.2000.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Pages2k/Ocn-PedradeLume-CapeVerdeIslands.Moses.2006.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Temp12k/ElGygytgynCrater.Schwamborn.2006.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Temp12k/MD03-2601.Kim.2012.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Temp12k/NevadoHuascaran.Thompson.1995.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Temp12k/SouthernCalifornia.Ohlwein.2012.lpd', '/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/Temp12k/RC24_08GC.Arbuszewski.2013.lpd'] Loading 22 LiPD files
Loaded..
['MD03-2601.Kim.2012', 'RC24_08GC.Arbuszewski.2013', 'ElGygytgynCrater.Schwamborn.2006', 'NevadoHuascaran.Thompson.1995']
- filter_by_datasetName(datasetName)[source]
Filters datasets to return a new LiPD object that only keeps datasets that have the specified names
- Parameters:
datasetName (str) – The datasetNames 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_datasetName('Ocn-RedSea.Felis.2000') Lfiltered.get_all_dataset_names()
Loading 16 LiPD files
Loaded..
['Ocn-RedSea.Felis.2000']
If searching for multiple dataset names, you can construct the name as follows:
from pylipd.utils.dataset import load_dir lipd = load_dir('Pages2k') dsnames = ['Ocn-RedSea.Felis.2000','Ant-WAIS-Divide.Severinghaus.2012'] dsquery = '|'.join(dsnames) Lfiltered = lipd.filter_by_datasetName(dsquery) Lfiltered.get_all_dataset_names()
Loading 16 LiPD files
Loaded..
['Ocn-RedSea.Felis.2000', 'Ant-WAIS-Divide.Severinghaus.2012']
- 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-RedSea.Felis.2000', 'Eur-SpanishPyrenees.Dorado-Linan.2012', 'Ocn-SinaiPeninsula_RedSea.Moustafa.2000', 'Eur-LakeSilvaplana.Trachsel.2010', 'Eur-SpannagelCave.Mangini.2005']
- filter_by_time(timeBound, timeBoundType='any', recordLength=None)[source]
Filter the records according to a specified time interval and the length of the record within that interval. Note that this function assumes that all records use the same time representation.
If you are unsure about the time representation, you may need to use .get_timeseries_essentials.
- Parameters:
timeBound (list) – Minimum and Maximum age value to search for.
timeBoundType (str, optional) – The type of querying to perform. Possible values include: “any”, “entire”, and “entirely”. - any: Overlap any portions of matching datasets (default) - entirely: are entirely overlapped by matching datasets - entire: overlap entire matching datasets but dataset can be shorter than the bounds The default is ‘any’.
recordLength (float, optional) – The minimum length the record needs to have while matching the ageBound criteria. The default is None.
- Raises:
ValueError – timeBoundType must take the values in [“any”, “entire”, and “entirely”]
- Returns:
A new LiPD object that only contains datasets that have the specified time interval
- 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_time(timeBound=[0,1800]) Lfiltered.get_all_dataset_names()
Loading 16 LiPD files
Loaded..
['Eur-CoastofPortugal.Abrantes.2011', 'Eur-NorthernSpain.Martin-Chivelet.2011', 'Ocn-RedSea.Felis.2000', 'Eur-FinnishLakelands.Helama.2014', 'Eur-Stockholm.Leijonhufvud.2009', 'Eur-SpanishPyrenees.Dorado-Linan.2012', 'Ocn-AlboranSea436B.Nieto-Moreno.2013', 'Ant-WAIS-Divide.Severinghaus.2012', 'Eur-NorthernScandinavia.Esper.2012', 'Asi-SourthAndMiddleUrals.Demezhko.2007', 'Eur-LakeSilvaplana.Trachsel.2010', 'Eur-SpannagelCave.Mangini.2005', 'Ocn-FeniDrift.Richter.2009']
- 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', 'Marine sediment']
- get_all_compilation_names()[source]
Return the names of the compilation present in the LiPD object
- Returns:
l – A list returning the names of the available compilations.
- Return type:
list
Examples
from pylipd.utils.dataset import load_dir lipd = load_dir('Temp12k') df = lipd.get_all_compilation_names() print(df)
Loading 5 LiPD files
Loaded.. ['Temp12k', 'HoloceneAbruptChange', 'wNAm']
- 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-CoastofPortugal.Abrantes.2011 41.10 -8.9000 1 Eur-CoastofPortugal.Abrantes.2011 41.10 -8.9000 2 Eur-CoastofPortugal.Abrantes.2011 41.10 -8.9000 3 Eur-CoastofPortugal.Abrantes.2011 41.10 -8.9000 4 Eur-CoastofPortugal.Abrantes.2011 41.10 -8.9000 .. ... ... ... 123 Ocn-PedradeLume-CapeVerdeIslands.Moses.2006 16.76 -22.8883 124 Ocn-PedradeLume-CapeVerdeIslands.Moses.2006 16.76 -22.8883 125 Ocn-PedradeLume-CapeVerdeIslands.Moses.2006 16.76 -22.8883 126 Ocn-PedradeLume-CapeVerdeIslands.Moses.2006 16.76 -22.8883 127 Ocn-PedradeLume-CapeVerdeIslands.Moses.2006 16.76 -22.8883 geo_meanElev 0 -80.0 1 -80.0 2 -80.0 3 -80.0 4 -80.0 .. ... 123 -5.0 124 -5.0 125 -5.0 126 -5.0 127 -5.0 [128 rows x 4 columns]
- 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.. ['temperature', 'year', 'd18O', 'Uk37', 'trsgi', 'uncertainty_temperature', 'Mg_Ca', 'depth_top', 'depth_bottom', 'notes', 'MXD']
- 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/ODP846.Lawrence.2006.... PYTM9N6HCQM 1 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTI487BQDZ 2 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTGO6NV72Y 3 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTPQ0FJO1S 4 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTDW6AIJPW 5 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT2ZB6MLZ9 6 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT3ZMI0BXW 7 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTTD7XCQGS 8 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTUHE3XLGQ 9 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTXJB98403 10 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTTUPVG4K3 11 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT19MC8WE2 12 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT9CFQ4GK0 13 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTKRFVW61B 14 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTS96EE0CB 15 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTGOFY4KZD 16 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTCHXB40SL 17 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTPWX0LH3I 18 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTE5EC1JBW 19 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTLEHYPAYV 20 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT68HYMYHH 21 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT4Y96QMUU 22 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT10H23U2E 23 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT8BDSRW3H 24 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT7DLYN7X4 25 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTJZ4GLRYP 26 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTJ3PSH0LT 27 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYT95DVDUU3 28 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTYDOYFVYD 29 http://linked.earth/lipd/ODP846.Lawrence.2006.... PYTDIEKUM44 variableName 0 ukprime37 1 lower95 2 temp muller 3 depth comp 4 sst 5 interval 6 c. wuellerstorfi d18o 7 age 8 age 9 age 10 u. peregrina d13c 11 c. wuellerstorfi d13c 12 depth 13 depth 14 depth 15 depth 16 depth 17 depth 18 event 19 d18o 20 depth cr 21 d180 22 c37 total 23 section 24 median 25 sample label 26 site/hole 27 temp prahl 28 u. peregrina d18o 29 upper95
- 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 (None), creating the entry manually
([' @article{Tierney_2015, title={Tropical sea surface temperatures for the past four centuries reconstructed from coral archives}, volume={30}, ISSN={1944-9186}, url={http://dx.doi.org/10.1002/2014PA002717}, DOI={10.1002/2014pa002717}, number={3}, journal={Paleoceanography}, publisher={American Geophysical Union (AGU)}, author={Tierney, Jessica E. and Abram, Nerilie J. and Anchukaitis, Kevin J. and Evans, Michael N. and Giry, Cyril and Kilbourne, K. Halimeda and Saenger, Casey P. and Wu, Henry C. and Zinke, Jens}, year={2015}, month=mar, pages={226–252} }\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{Tudhope_2001, title={Variability in the El Niño-Southern Oscillation Through a Glacial-Interglacial Cycle}, volume={291}, ISSN={1095-9203}, url={http://dx.doi.org/10.1126/science.1057969}, DOI={10.1126/science.1057969}, number={5508}, journal={Science}, publisher={American Association for the Advancement of Science (AAAS)}, author={Tudhope, Alexander W. and Chilcott, Colin P. and McCulloch, Malcolm T. and Cook, Edward R. and Chappell, John and Ellam, Robert M. and Lea, David W. and Lough, Janice M. and Shimmield, Graham B.}, year={2001}, month=feb, pages={1511–1517} }\n', ' @article{Stott_2007, title={Southern Hemisphere and Deep-Sea Warming Led Deglacial Atmospheric CO\n 2\n Rise and Tropical Warming}, volume={318}, ISSN={1095-9203}, url={http://dx.doi.org/10.1126/science.1143791}, DOI={10.1126/science.1143791}, number={5849}, journal={Science}, publisher={American Association for the Advancement of Science (AAAS)}, author={Stott, Lowell and Timmermann, Axel and Thunell, Robert}, year={2007}, month=oct, pages={435–438} }\n', ' @article{Stott_2007, title={Comment on “Anomalous radiocarbon ages for foraminifera shells” by W. Broecker et al.: A correction to the western tropical Pacific MD9821‐81 record}, volume={22}, ISSN={1944-9186}, url={http://dx.doi.org/10.1029/2006PA001379}, DOI={10.1029/2006pa001379}, number={1}, journal={Paleoceanography}, publisher={American Geophysical Union (AGU)}, author={Stott, Lowell D.}, year={2007}, month=feb }\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 Tropical sea surface temperatures for the past... 1 World Data Center for Paleoclimatology 2 Variability in the El Nino-Southern Oscillatio... 3 Southern Hemisphere and deep-sea warming led d... 4 None authors doi \ 0 Jens Zinke and Cyril Giry and Jessica E. Tiern... 10.1002/2014PA002717 1 H. Kuhnert None 2 A. W. Tudhope 10.1126/science.1057969 3 L. Stott and R. Thunell and A. Timmermann 10.1126/science.1143791 4 10.1029/2006PA001379 pubyear year journal volume issue pages \ 0 None 2015.0 Paleoceanography 30 3.0 226-252 1 None NaN None None NaN None 2 None 2001.0 Science 291 5508.0 1511-1517 3 None 2007.0 Science 318 5849.0 435 438 4 None NaN None None NaN None type publisher report \ 0 journal-article Wiley-Blackwell None 1 dataCitation None None 2 journal-article American Association for the Advancement of Sc... None 3 None None None 4 None None None citeKey edition \ 0 tierney2015tropicalseasurfacetempera None 1 kuhnert2001httpswwwncdcnoaagovpaleostudy1866Da... None 2 tudhope2001variabilityintheelninosou None 3 WMGAVB7S None 4 None None institution url \ 0 None None 1 World Data Center for Paleoclimatology None 2 None None 3 None None 4 None None url2 0 None 1 https://www.ncdc.noaa.gov/paleo/study/1866 2 None 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.. ['createdBy', 'hasChangeLog', 'hasLocation', 'hasName', 'hasDatasetId', 'lipdVersion', 'hasPublication', 'hasOriginalDataUrl', 'hasPaleoData', 'hasArchiveType', 'minYear', 'googleMetadataWorksheet', 'maxYear', 'googleDataURL', 'hasSpreadsheetLink', 'type', 'hasUrl', 'inCompilation2_', 'inCompilation3_', 'inCompilation1_', 'hasContributor', 'studyName', 'hasNotes', 'hasInvestigator', 'hasFunding']
- get_datasets() list[Dataset][source]
Return datasets as instances of the Dataset class
- Returns:
A list of Dataset objects
- Return type:
list of pylipd.classes.Dataset
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') lipd.get_datasets()
Loading 16 LiPD files
Loaded..
[<pylipd.classes.dataset.Dataset at 0x7369bd4c1690>, <pylipd.classes.dataset.Dataset at 0x7369bd4c2490>, <pylipd.classes.dataset.Dataset at 0x7369bc123a10>, <pylipd.classes.dataset.Dataset at 0x7369b7481d90>, <pylipd.classes.dataset.Dataset at 0x7369bdd74250>, <pylipd.classes.dataset.Dataset at 0x7369be102050>, <pylipd.classes.dataset.Dataset at 0x7369b7aa1a10>, <pylipd.classes.dataset.Dataset at 0x7369b7a822d0>, <pylipd.classes.dataset.Dataset at 0x7369bd583610>, <pylipd.classes.dataset.Dataset at 0x7369b746ecd0>, <pylipd.classes.dataset.Dataset at 0x7369b7b1f290>, <pylipd.classes.dataset.Dataset at 0x7369b759cd10>, <pylipd.classes.dataset.Dataset at 0x7369b746f610>, <pylipd.classes.dataset.Dataset at 0x7369b6dd4a50>, <pylipd.classes.dataset.Dataset at 0x7369b7c4ddd0>, <pylipd.classes.dataset.Dataset at 0x7369b7b8dc90>]
- 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/ODP846.Lawrence.2006.... 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.. {'studyName': 'Madang, Papua New Guinea oxygen isotope record 1880-1993', 'pub': [{'pages': '226-252', 'author': [{'name': 'Casey P. Saenger'}, {'name': 'Henry C. Wu'}, {'name': 'Nerilie J. Abram'}, {'name': 'Kevin J. Anchukaitis'}, {'name': 'K. Halimeda Kilbourne'}, {'name': 'Jens Zinke'}, {'name': 'Cyril Giry'}, {'name': 'Jessica E. Tierney'}, {'name': 'Michael N. Evans'}], 'year': 2015, 'title': 'Tropical sea surface temperatures for the past four centuries reconstructed from coral archives', 'journal': 'Paleoceanography', 'issue': 3.0, 'publisher': 'Wiley-Blackwell', 'citeKey': 'tierney2015tropicalseasurfacetempera', 'doi': '10.1002/2014PA002717', 'dataUrl': ['doi.org'], 'volume': '30'}, {'publisher': 'American Association for the Advancement of Science (AAAS)', 'title': 'Variability in the El Nino-Southern Oscillation Through a Glacial-Interglacial Cycle', 'citeKey': 'tudhope2001variabilityintheelninosou', 'volume': '291', 'issue': 5508.0, 'dataUrl': ['doi.org'], 'author': [{'name': 'A. W. Tudhope'}], 'journal': 'Science', 'year': 2001, 'doi': '10.1126/science.1057969', 'pages': '1511-1517'}, {'title': 'World Data Center for Paleoclimatology', 'author': [{'name': 'H. Kuhnert'}], 'institution': 'World Data Center for Paleoclimatology', 'citeKey': 'kuhnert2001httpswwwncdcnoaagovpaleostudy1866DataCitation', 'url': ['https://www.ncdc.noaa.gov/paleo/study/1866'], 'urldate': 2001.0}], 'createdBy': 'matlab', 'googleMetadataWorksheet': 'oruuxfm', 'maxYear': 1993.042, 'inCompilation3_': 'PAGES2k_v2.1.0', 'minYear': 1880.792, 'googleSpreadSheetKey': '1wf30P-s54OTBdLw4dyeaIN53VDoN0u_hOqIwvAeLxtc', 'dataContributor': {'name': 'Wu KLD'}, 'paleoData': [{'measurementTable': [{'filename': 'Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.paleo1measurement1.csv', 'googleWorkSheetKey': 'ov9tjw6', 'tableName': 'Kuhnert', 'columns': [{'measurementTableMD5': '793853407e414221c486d2e63b32dd87', 'measurementTableName': 'measurementTable1', 'dataType': 'float', 'description': 'Year AD', 'variableName': 'year', 'hasMinValue': 1880.792, 'hasMaxValue': 1993.042, 'TSid': 'PYTDAS7AM1Y', 'variableType': 'inferred', 'wDSPaleoUrl': 'https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/data-version-2.0.0/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.txt', 'hasMedianValue': 1936.917, 'resolution': {'hasMeanValue': 0.25, 'hasMinValue': 0.25, 'hasMaxValue': 0.25, 'hasMedianValue': 0.25, 'units': 'yr AD'}, 'inferredVariableType': 'Year', 'hasMeanValue': 1936.917, 'number': 2, 'paleoDataTableName': 'measTable', 'units': 'yr AD', 'archiveType': 'Coral', '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, 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{'compilationName': 'Pages2kTemperature', 'compilationVersion': '2_1_1'}, 'measurementTableName': 'measurementTable1', 'sensorGenus': 'Porites', 'resolution': {'hasMaxValue': 0.25, 'hasMedianValue': 0.25, 'hasMeanValue': 0.25, 'hasMinValue': 0.25, 'units': 'yr AD'}, 'iso2kUI': 'CO01TUNG01A', 'hasMinValue': -5.515, 'hasMedianValue': -4.942, 'wDSPaleoUrl': 'https://www1.ncdc.noaa.gov/pub/data/paleo/pages2k/pages2k-temperature-v2-2017/data-version-2.0.0/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.txt', 'hasMeanValue': -4.9453, 'variableType': 'measured', 'hasMaxValue': -4.344, 'qCCertification': 'KLD, NJA', 'paleoDataTableName': 'measTable', 'useInGlobalTemperatureAnalysis': True, 'pages2kID': 'Ocn_097', 'notes': '; climateInterpretation_seasonality changed - was originally seasonal', 'TSid': 'Ocean2kHR_140', 'variableName': 'd18O', 'proxyObservationType': 'd18O', 'interpretation': [{'variableDetail': 'sea@surface', 'scope': 'climate', 'direction': 'negative', 'seasonality': 'subannual', 'variable': 'temperature'}], 'measurementTableMD5': '793853407e414221c486d2e63b32dd87', 'ocean2kID': 'PacificMadangTudhope2001', 'number': 1, 'units': 'permil', 'proxy': 'd18O', 'archiveType': 'Coral', '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, 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'http://linked.earth/ontology#Location', 'longitude': 145.8167, 'siteName': 'Madang Lagoon, Papua New Guinea', 'elevation': -2.2, 'latitude': -5.2167, 'ocean': 'Pacific', 'pages2kRegion': 'Ocean'}}, 'originalDataURL': 'https://www.ncdc.noaa.gov/paleo/study/1866', 'hasUrl': 'https://data.mint.isi.edu/files/lipd/Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001.lpd', 'googleDataURL': 'https://docs.google.com/spreadsheets/d/1wf30P-s54OTBdLw4dyeaIN53VDoN0u_hOqIwvAeLxtc', 'changelog': [{'notes': 'Starting the changelog', 'timestamp': '2022-08-23 23:41:56 UTC', 'version': '1.0.0', 'curator': 'nicholas'}], 'datasetId': 'm8yv2VgG97zJmSg3XhqQ', 'inCompilation1_': 'Ocean2k_v1.0.0', 'dataSetName': 'Ocn-MadangLagoonPapuaNewGuinea.Kuhnert.2001', '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)
['/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/ODP846.Lawrence.2006.lpd'] Loading 1 LiPD files
Loaded.. ['hasEnsembleTable', 'type', 'hasCode', 'hasSummaryTable']
- get_timeseries(dsnames, to_dataframe=False, mode='paleo', time='age')[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/LiPDVerse-dynamic") 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(dsnames=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:
dsnames (list) – array of dataset id or name strings
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)
['/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/ODP846.Lawrence.2006.lpd'] 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 .. ... ... ... ... 195 ODP846.Lawrence.2006 Marine sediment -3.1 -90.8 196 ODP846.Lawrence.2006 Marine sediment -3.1 -90.8 197 ODP846.Lawrence.2006 Marine sediment -3.1 -90.8 198 ODP846.Lawrence.2006 Marine sediment -3.1 -90.8 199 ODP846.Lawrence.2006 Marine sediment -3.1 -90.8 geo_meanElev paleoData_variableName \ 0 -3296.0 interval 1 -3296.0 c37 total 2 -3296.0 c. wuellerstorfi d18o 3 -3296.0 c. wuellerstorfi d18o 4 -3296.0 c. wuellerstorfi d18o .. ... ... 195 -3296.0 u. peregrina d18o 196 -3296.0 temp prahl 197 -3296.0 sample label 198 -3296.0 sample label 199 -3296.0 sample label paleoData_values paleoData_units \ 0 [15-16, 25-26, 35-36, 45-46, 55-56, 65-66, 75-... cm 1 [2.37, 2.1, 1.87, 2.74, 3.75, 7.62, 7.86, 7.73... nmol/kg 2 [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... per mil PDB 3 [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... per mil PDB 4 [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... per mil PDB .. ... ... 195 [0.14, 0.01, -0.1, -0.06, -0.17, -0.21, -0.41,... per mil PDB 196 [23.0, 23.1, 23.2, 22.0, 21.7, 21.4, 21.7, 21.... deg C 197 [138-846B-1H-1, 138-846B-1H-1, 138-846B-1H-1, ... unitless 198 [138-846B-1H-1, 138-846B-1H-1, 138-846B-1H-1, ... unitless 199 [138-846B-1H-1, 138-846B-1H-1, 138-846B-1H-1, ... unitless paleoData_proxy paleoData_proxyGeneral time_variableName \ 0 None None age 1 None None age 2 None None None 3 None None None 4 None None None .. ... ... ... 195 None None None 196 None None age 197 None None None 198 None None None 199 None None None 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 None None 3 None None 4 None None .. ... ... 195 None None 196 [5.228, 8.947, 11.966, 14.427, 16.502, 18.41, ... kyr BP 197 None None 198 None None 199 None None 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 comp [12.0, 23.0, 33.0, 33.0, 43.0, 53.0, 63.0, 73.... 3 depth cr [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 4 depth [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... .. ... ... 195 depth [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 196 depth [0.16, 0.26, 0.36, 0.46, 0.56, 0.66, 0.76, 0.8... 197 depth comp [12.0, 23.0, 33.0, 33.0, 43.0, 53.0, 63.0, 73.... 198 depth cr [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... 199 depth [0.12, 0.23, 0.33, 0.33, 0.43, 0.53, 0.63, 0.7... depth_units 0 m 1 m 2 mcd 3 rmcd 4 m .. ... 195 m 196 m 197 mcd 198 rmcd 199 m [200 rows x 16 columns]To 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)
['/home/docs/checkouts/readthedocs.org/user_builds/pylipd/conda/v1.4.1/lib/python3.11/site-packages/pylipd/data/ODP846.Lawrence.2006.lpd'] 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 geo_meanElev chronData_variableName \ 0 -3296.0 depth 1 -3296.0 d18o 2 -3296.0 age 3 -3296.0 depth 4 -3296.0 d18o 5 -3296.0 age 6 -3296.0 depth 7 -3296.0 d18o 8 -3296.0 age 9 -3296.0 depth 10 -3296.0 d18o 11 -3296.0 age 12 -3296.0 depth 13 -3296.0 d18o 14 -3296.0 age 15 -3296.0 depth 16 -3296.0 d18o 17 -3296.0 age 18 -3296.0 depth 19 -3296.0 d18o 20 -3296.0 age 21 -3296.0 depth 22 -3296.0 d18o 23 -3296.0 age chronData_values chronData_units \ 0 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 1 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... permil 2 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 3 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 4 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... permil 5 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 6 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 7 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... permil 8 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 9 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 10 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... permil 11 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 12 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 13 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... permil 14 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 15 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 16 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... permil 17 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 18 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 19 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... permil 20 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 21 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 22 [3.38, 3.46, 3.765, 4.14, 4.47, 4.99, 4.99, 4.... permil 23 [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... ky BP 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... 3 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 4 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 5 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 6 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 7 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 8 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 9 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 10 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 11 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 12 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 13 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 14 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 15 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 16 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 17 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 18 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 19 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 20 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 21 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 22 age [3.645, 7.99, 11.18, 13.803, 15.886, 17.93, 19... 23 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 3 ky BP depth 4 ky BP depth 5 ky BP depth 6 ky BP depth 7 ky BP depth 8 ky BP depth 9 ky BP depth 10 ky BP depth 11 ky BP depth 12 ky BP depth 13 ky BP depth 14 ky BP depth 15 ky BP depth 16 ky BP depth 17 ky BP depth 18 ky BP depth 19 ky BP depth 20 ky BP depth 21 ky BP depth 22 ky BP depth 23 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 3 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 4 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 5 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 6 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 7 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 8 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 9 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 10 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 11 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 12 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 13 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 14 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 15 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 16 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 17 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 18 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 19 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 20 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 21 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 22 [0.12, 0.23, 0.33, 0.43, 0.53, 0.63, 0.73, 0.8... m 23 [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.. ['hasType', 'foundInDataset', 'hasUnits', 'hasDescription', 'hasMaxValue', 'paleoDataTableName', 'hasColumnNumber', 'foundInTable', 'hasMedianValue', 'hasVariableId', 'hasArchiveType', 'hasResolution', 'hasMeanValue', 'hasStandardVariable', 'measurementTableName', 'wDSPaleoUrl', 'dataType', 'hasName', 'inferredVariableType', 'hasValues', 'hasMinValue', 'type', 'measurementTableMD5', 'precededBy', 'pages2kID', 'hasNotes', 'partOfCompilation', 'useInGlobalTemperatureAnalysis', 'hasInterpretation', 'calibratedVia', 'hasProxy', 'qCCertification', 'proxyObservationType', 'detail', 'ocean2kID', 'sensorSpecies', 'sensorGenus', 'iso2kUI', 'hasUncertainty', 'measurementMethod', 'measurementMaterial']
- load(lipdfiles, parallel=False, standardize=True, add_labels=True)[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_datasets(datasets: list[Dataset])[source]
Loads instances of Dataset class into the LiPD graph
- Parameters:
pylipd.classes.Dataset (list of) – A list of Dataset objects
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') dses = lipd.get_datasets() # Modify the datasets if needed, then write them to the same, or another LiPD object lipd2 = LiPD() lipd2.load_datasets(dses)
Loading 16 LiPD files
Loaded..
- load_from_dir(dir_path, parallel=False, cutoff=None, standardize=True, add_labels=True)[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', 'ODP846.Lawrence.2006', 'MD98_2181.Stott.2007', 'Ant-WAIS-Divide.Severinghaus.2012']
- load_remote_datasets(dsnames, load_default_graph=True)[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/LiPDVerse-dynamic") 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/LiPDVerse-dynamic
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(standardize=True, add_labels=True)[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.multi_convert_to_rdf(filemap, parallel=True, standardize=True, add_labels=True)[source]
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://data.europeana.eu/item/04802/243FA86189... 2 http://example.org/bob#me 3 http://www.wikidata.org/entity/Q12418 4 http://example.org/bob#me 5 http://example.org/bob#me 6 http://example.org/bob#me p \ 0 http://purl.org/dc/terms/creator 1 http://purl.org/dc/terms/subject 2 http://xmlns.com/foaf/0.1/topic_interest 3 http://purl.org/dc/terms/title 4 http://schema.org/birthDate 5 http://xmlns.com/foaf/0.1/knows 6 http://www.w3.org/1999/02/22-rdf-syntax-ns#type o 0 http://dbpedia.org/resource/Leonardo_da_Vinci 1 http://www.wikidata.org/entity/Q12418 2 http://www.wikidata.org/entity/Q12418 3 Mona Lisa 4 1990-07-04 5 http://example.org/alice#me 6 http://xmlns.com/foaf/0.1/PersonMethods
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 0x7369b4aea4d0>
- 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())
['N4dd700e5db7a4db498661f1123072893']
- 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://data.europeana.eu/item/04802/243FA86189... 2 http://example.org/bob#me 3 http://www.wikidata.org/entity/Q12418 4 http://example.org/bob#me 5 http://example.org/bob#me 6 http://example.org/bob#me p \ 0 http://purl.org/dc/terms/creator 1 http://purl.org/dc/terms/subject 2 http://xmlns.com/foaf/0.1/topic_interest 3 http://purl.org/dc/terms/title 4 http://schema.org/birthDate 5 http://xmlns.com/foaf/0.1/knows 6 http://www.w3.org/1999/02/22-rdf-syntax-ns#type o 0 http://dbpedia.org/resource/Leonardo_da_Vinci 1 http://www.wikidata.org/entity/Q12418 2 http://www.wikidata.org/entity/Q12418 3 Mona Lisa 4 1990-07-04 5 http://example.org/alice#me 6 http://xmlns.com/foaf/0.1/Person
- 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://data.europeana.eu/item/04802/243FA86189... 2 http://example.org/bob#me 3 http://www.wikidata.org/entity/Q12418 4 http://example.org/bob#me 5 http://example.org/bob#me 6 http://example.org/bob#me p \ 0 http://purl.org/dc/terms/creator 1 http://purl.org/dc/terms/subject 2 http://xmlns.com/foaf/0.1/topic_interest 3 http://purl.org/dc/terms/title 4 http://schema.org/birthDate 5 http://xmlns.com/foaf/0.1/knows 6 http://www.w3.org/1999/02/22-rdf-syntax-ns#type o 0 http://dbpedia.org/resource/Leonardo_da_Vinci 1 http://www.wikidata.org/entity/Q12418 2 http://www.wikidata.org/entity/Q12418 3 Mona Lisa 4 1990-07-04 5 http://example.org/alice#me 6 http://xmlns.com/foaf/0.1/Person
- 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> _:Nb6c088355fd745ceb454fe8ec6a9f907 . <http://data.europeana.eu/item/04802/243FA8618938F4117025F17A8B813C5F9AA4D619> <http://purl.org/dc/terms/subject> <http://www.wikidata.org/entity/Q12418> _:Nb6c088355fd745ceb454fe8ec6a9f907 . <http://example.org/bob#me> <http://xmlns.com/foaf/0.1/topic_interest> <http://www.wikidata.org/entity/Q12418> _:Nb6c088355fd745ceb454fe8ec6a9f907 . <http://www.wikidata.org/entity/Q12418> <http://purl.org/dc/terms/title> "Mona Lisa" _:Nb6c088355fd745ceb454fe8ec6a9f907 . <http://example.org/bob#me> <http://schema.org/birthDate> "1990-07-04"^^<http://www.w3.org/2001/XMLSchema#date> _:Nb6c088355fd745ceb454fe8ec6a9f907 . <http://example.org/bob#me> <http://xmlns.com/foaf/0.1/knows> <http://example.org/alice#me> _:Nb6c088355fd745ceb454fe8ec6a9f907 . <http://example.org/bob#me> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://xmlns.com/foaf/0.1/Person> _:Nb6c088355fd745ceb454fe8ec6a9f907 . ...
- 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.