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API Docs#

galaxias.basisOfRecord_values()#

A pandas.Series of accepted (but not mandatory) values for basisOfRecord values.

Parameters:

None –

Return type:

A pandas.Series of accepted (but not mandatory) values for basisOfRecord values..

Examples

>>> galaxias.basisOfRecord_values()

  basisOfRecord values
0     humanObservation
1   machineObservation
2       livingSpecimen
3    preservedSpecimen
4       fossilSpecimen
5     materialCitation
galaxias.build_archive(occurrences=None, events=None, occurrences_filename='occurrences.csv', events_filename='events.csv', publishing_dir='./data-publish/', metadata='eml.xml', schema='meta.xml', archive_name='dwca.zip', print_report=False)#

Checks all your files for Darwin Core compliance, and then creates the Darwin Core archive in your working directory.

Parameters:

  • occurrences (pandas DataFrame) – OPTIONAL: This is the dataframe holding your occurrence data. Default is None.

  • events (pandas DataFrame) – OPTIONAL: This is the dataframe holding your occurrence data. Default is None.

  • occurrences_filename (str) – Name of your occurrences file. Default value is 'occurrences.csv'.

  • events_filename (str) – Name of your events file. Default value is 'events.csv'.

  • publishing_dir (str) – Name of the directory where all your processed data lives. Default value is './data-publish/'.

  • metadata (str) – Name of your metadata xml. Default value is 'eml.xml'.

  • schema (str) – Name of your schema xml. Default value is 'meta.xml'.

  • archive_name (str) – Name of the Darwin Core Archive file you will create. Default value is 'dwca.zip'.

  • print_report (str) – Print your data report to screen. Default value is 'False'.

Return type:

Raises a ValueError if something is wrong, or returns None if it passes.

galaxias.check_archive(archive='dwca.zip', publishing_dir='./data-publish', username=None, email=None, password=None)#

Checks whether or not your Darwin Core Archive is formatted correctly.

Parameters:

  • archive (str) – Name of your Darwin Core Archive. Default is dwca.zip.

  • publishing_dir (str) – Name of the directory where all your finalised data lives. Default value is './data-publish/'.

  • GBIF (logical) – Flag to check if you are using the GBIF Validation tool. Default is False.

  • username (str) – GBIF username. Default is None.

  • email (str) – GBIF registered email. Default is None.

  • password (str) – GBIF password. Default is None.

Return type:

Raises a ValueError if something is wrong, or returns True if it passes.

galaxias.check_dataset(occurrences=None, events=None, occurrences_filename='occurrences.csv', events_filename='events.csv', publishing_dir='./data-publish', print_report=True)#

Checks whether or not your data meets the predefined Darwin Core standard. Calls the corella package for this.

Parameters:

  • occurrences (pandas DataFrame) – This is the dataframe holding your occurrence data. Default is None.

  • events (pandas DataFrame) – This is the dataframe holding your occurrence data. Default is None.

  • publishing_dir (str) – Name of the directory where all your processed data lives. Default value is './data-publish/'.

  • print_report (str) – Print your data report to screen. Default value is 'True'.

Return type:

A printed report detailing presence or absence of required data.

galaxias.check_directory(archive_name='dwca.zip', occurrences_filename='occurrences.csv', events_filename='events.csv', metadata='eml.xml', schema='meta.xml', publishing_dir='./data-publish/', print_report=False)#

Checks whether or not your Darwin Core Archive is formatted correctly.

Parameters:

None –

Return type:

Raises a ValueError if something is wrong, or returns True if it passes.

galaxias.check_metadata(eml_xml='eml.xml', eml_dir='./data-publish')#

Checks whether or not your eml xml file is formatted correctly for GBIF.

Parameters:

  • eml_xml (str) – Name of the eml xml file you want to validate. Default value is 'eml.xml'.

  • eml_dir (str) – Name of the directory to write the eml.xml. Default value is './'.

Return type:

Raises a ValueError if something is wrong, or returns None if it passes.

galaxias.check_schema(schema='meta.xml', publishing_dir='./data-publish/')#

Checks whether your schema (meta.xml) is formatted correctly.

Parameters:

  • schema (str) – File name of your schema file (default is meta.xml)

  • publishing_dir (str) – Folder where all your finalised data will be published

Return type:

A printed report detailing presence or absence of required data.

galaxias.countryCode_values()#

A pandas.Series of accepted (but not mandatory) values for countryCode values.

Parameters:

None –

Return type:

A pandas.Series of accepted (but not mandatory) values for countryCode values..

Examples

>>> galaxias.countryCode_values()

0      AD
1      AE
2      AF
3      AG
4      AI
       ..
244    YE
245    YT
246    ZA
247    ZM
248    ZW
Name: Code, Length: 249, dtype: object
galaxias.display_metadata_as_dataframe(metadata_md='metadata.md', working_dir='./')#

Writes the eml.xml file from the metadata markdown file into your current working directory. The eml.xml file is the metadata file containing things like authorship, licence, institution, etc.

Parameters:

  • metadata_md (str) – Name of the markdown file that you want to convert to EML. Default value is 'metadata.md'.

  • working_dir (str) – Name of your working directory. Default value is './'.

Return type:

pandas dataframe denoting all the information in the metadata file

galaxias.event_terms()#

A pandas.Series of accepted (but not mandatory) values for event data.

Parameters:

None –

Return type:

A pandas.Series of accepted (but not mandatory) values for event data.

Examples

>>> galaxias.event_terms()

0                     type
1                 modified
2                 language
3                  license
4             rightsHolder
              ...         
83         georeferencedBy
84       georeferencedDate
85    georeferenceProtocol
86     georeferenceSources
87     georeferenceRemarks
Name: term_localName, Length: 88, dtype: object
galaxias.occurrence_terms()#

A pandas.Series of accepted (but not mandatory) values for occurrence data.

Parameters:

None –

Return type:

A pandas.Series of accepted (but not mandatory) values for occurrence data.

Examples

>>> galaxias.occurrence_terms()

0                             type
1                         modified
2                         language
3                          license
4                     rightsHolder
                  ...             
212              relatedResourceID
213         relationshipOfResource
214        relationshipAccordingTo
215    relationshipEstablishedDate
216            relationshipRemarks
Name: term_localName, Length: 217, dtype: object
galaxias.set_abundance(dataframe=None, individualCount=None, organismQuantity=None, organismQuantityType=None)#

One of the functions you can use to check your data is set_abundance(). This function aims to check that you have the following:

  • individualCount: the number of individuals observed of a particular species

It can also (optionally) can check the following:

  • organismQuantity: a description of your individual counts

  • organismQuantityType: describes what your organismQuantity is

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • individualCount (str) – A column name that contains your individual counts (should be whole numbers).

  • organismQuantity (str) – A column name that contains a number or enumeration value for the quantity of organisms. Used together with organismQuantityType to provide context.

  • organismQuantityType (str) – A column name or phrase denoting the type of quantification system used for organismQuantity.

Return type:

pandas.DataFrame with the updated data.

Examples

>>> occ_abundance = galaxias.set_abundance(dataframe=occ,individualCount='count')
galaxias.set_collection(dataframe=None, datasetID=None, datasetName=None, catalogNumber=None)#

Checks for location information, as well as uncertainty and coordinate reference system. Also runs data checks on coordinate validity.

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • datasetID (str) – A column name or other string denoting the identifier for the set of data. May be a global unique identifier or an identifier specific to a collection or institution.

  • datasetName (str) – A column name or other string identifying the data set from which the record was derived.

  • catalogNumber (str) – A column name or other string denoting a unique identifier for the record within the data set or collection.

Return type:

pandas.DataFrame with the updated data

Examples

>>> occ_coll = galaxias.set_collection(dataframe=occ,datasetID='id')
galaxias.set_coordinates(dataframe=None, decimalLatitude=None, decimalLongitude=None, geodeticDatum=None, coordinateUncertaintyInMeters=None, coordinatePrecision=None)#

Checks for location information, as well as uncertainty and coordinate reference system. Also runs data checks on coordinate validity.

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • decimalLatitude (str) – A column name that contains your latitudes (units in degrees).

  • decimalLongitude (str) – A column name that contains your longitudes (units in degrees).

  • geodeticDatum (str) – A column name or a str with he datum or spatial reference system that coordinates are recorded against (usually “WGS84” or “EPSG:4326”). This is often known as the Coordinate Reference System (CRS). If your coordinates are from a GPS system, your data are already using WGS84.

  • coordinateUncertaintyInMeters (str, float or int) – A column name (str) or a float/int with the value of the coordinate uncertainty. coordinateUncertaintyInMeters will typically be around 30 (metres) if recorded with a GPS after 2000, or 100 before that year.

  • coordinatePrecision (str, float or int) – Either a column name (str) or a float/int with the value of the coordinate precision. coordinatePrecision should be no less than 0.00001 if data were collected using GPS.

Return type:

pandas.DataFrame with the updated data

Examples

Standardising Occurrences

galaxias.set_datetime(dataframe=None, eventDate=None, year=None, month=None, day=None, eventTime=None, string_to_datetime=False, yearfirst=True, dayfirst=False, time_format='mixed')#

Checks for time information, such as the date an occurrence occurred. Also runs checks on the validity of the format of the date.

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • eventDate (str) – A column name (str) denoting the column with the dates of the events, or a str or datetime.datetime object denoting the date of the event.

  • year (str or int) – A column name (str) denoting the column with the dates of the events, or an int denoting the year of the event.

  • month (str or int) – A column name (str) denoting the column with the dates of the events, or an int denoting the month of the event.

  • day (str or int) – A column name (str) denoting the column with the dates of the events, or an int denoting the day of the event.

  • eventTime (str) – A column name (str) denoting the column with the dates of the events, or a str denoting the time of the event.

  • string_to_datetime (logical) – An argument that tells corella to convert dates that are in a string format to a datetime format. Default is False.

  • yearfirst (logical) – An argument to specify whether or not the day is first when converting your string to datetime. Default is True.

  • dayfirst (logical) – An argument to specify whether or not the day is first when converting your string to datetime. Default is False.

  • time_format (str) – A str denoting the original format of the dates that are being converted from a str to a datetime object. Default is 'mixed'.

Return type:

pandas.DataFrame with the updated data

Examples

Standardising Occurrences Standardising Events

galaxias.set_events(dataframe=None, eventID=None, parentEventID=None, eventType=None, Event=None, samplingProtocol=None, event_hierarchy=None, sep='-')#

Identify or format columns that contain information about an Event. An “Event” in Darwin Core Standard refers to an action that occurs at a place and time. Examples include:

  • A specimen collecting event

  • A survey or sampling event

  • A camera trap image capture

  • A marine trawl

  • A camera trap deployment event

  • A camera trap burst image event (with many images for one observation)

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • eventID (str, logical) – A column name (str) that contains a unique identifier for your event. Can also be set to True to generate values. Parameters for these values can be specified with the arguments sequential_id, add_sequential_id, composite_id, sep and random_id

  • sep (char) – Separation character for composite IDs. Default is -.

  • parentEventID (str) – A column name (str) that contains a unique ID belonging to an event below it in the event hierarchy.

  • eventType (str) – A column name (str) or a str denoting what type of event you have.

  • Event (str) – A column name (str) or a str denoting the name of the event.

  • samplingProtocol (str or) – Either a column name (str) or a str denoting how you collected the data, i.e. “Human Observation”.

  • event_hierarchy (dict) – A dictionary containing a hierarchy of all events so they can be linked. For example, if you have a set of observations that were taken at a particular site, you can use the dict {1: “Site Visit”, 2: “Sample”, 3: “Observation”}.

Return type:

pandas.DataFrame with the updated data

Examples

Standardising Events

galaxias.set_individual_traits(dataframe=None, individualID=None, lifeStage=None, sex=None, vitality=None, reproductiveCondition=None)#

Checks for location information, as well as uncertainty and coordinate reference system. Also runs data checks on coordinate validity.

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • individualID (str) – A column name containing an identifier for an individual or named group of individual organisms represented in the Occurrence. Meant to accommodate resampling of the same individual or group for monitoring purposes. May be a global unique identifier or an identifier specific to a data set.

  • lifeStage (str) – A column name containing the age, class or life stage of an organism at the time of occurrence.

  • sex (str) – A column name or value denoting the sex of the biological individual.

  • vitality (str) – A column name or value denoting whether an organism was alive or dead at the time of collection or observation.

  • reproductiveCondition (str) – A column name or value denoting the reproductive condition of the biological individual.

Return type:

None - the occurrences dataframe is updated

Examples

>>> occ_traits = galaxias..set_individual_traits(dataframe=occ,individualID=['123456','123457'],
...                                              lifeStage='adult',sex=['male','female'],
...                                              vitality='alive',reproductiveCondition='not reproductive')
galaxias.set_license(dataframe=None, license=None, rightsHolder=None, accessRights=None)#

Checks for location information, as well as uncertainty and coordinate reference system. Also runs data checks on coordinate validity.

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • license (str) – A column name or value denoting a legal document giving official permission to do something with the resource. Must be provided as a url to a valid license.

  • rightsHolder (str) – A column name or value denoting the person or organisation owning or managing rights to resource.

  • accessRights (str) – A column name or value denoting any access or restrictions based on privacy or security.

Return type:

pandas.DataFrame with the updated data

Examples

>>> occ_lic = galaxias.set_license(dataframe=occ,license=['CC-BY 4.0 (Int)', 'CC-BY-NC 4.0 (Int)'],
...                                rightsHolder='The Regents of the University of California',
...                                accessRights=['','not-for-profit use only'])
galaxias.set_locality(dataframe=None, continent=None, country=None, countryCode=None, stateProvince=None, locality=None)#

Checks for additional location information, such as country and countryCode.

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • continent (str) – Either a column name (str) or a string denoting one of the seven continents.

  • country (str or pandas.Series) – Either a column name (str) or a string denoting the country.

  • countryCode (str or pandas.Series) – Either a column name (str) or a string denoting the countryCode.

  • stateProvince (str or pandas.Series) – Either a column name (str) or a string denoting the state or province.

  • locality (str or pandas.Series) – Either a column name (str) or a string denoting the locality.

Return type:

pandas.DataFrame with the updated data

Examples

>>> occ_loc = galaxias.set_locality(dataframe=occ,continent='Oceania',country='Australia')
galaxias.set_observer(dataframe=None, recordedBy=None, recordedByID=None)#

Checks for the name of the taxon you identified is present.

Parameters:

  • dataframe (pandas.DataFrame) – The pandas.DataFrame that contains your data to check

  • recordedBy (str) – A column name or name(s) of people, groups, or organizations responsible for recording the original occurrence. The primary collector or observer should be listed first.

  • recordedByID (str) – A column name or the globally unique identifier for the person, people, groups, or organizations responsible for recording the original occurrence.

Return type:

pandas.DataFrame with the updated data

Examples

>>> occ_obs = galaxias.set_observer(dataframe=occ,recordedBy='recorder',recordedByID='orcids')
galaxias.set_occurrences(occurrences=None, occurrenceID=None, catalogNumber=None, recordNumber=None, basisOfRecord=None, occurrenceStatus=None, sep='-', events=None, add_eventID=False, eventType=None)#

Checks for unique identifiers of each occurrence and how the occurrence was recorded.

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • occurrenceID (str or bool) – Either a column name (str) or True (bool). If a column name is provided, the column will be renamed. If True is provided, unique identifiers will be generated in the dataset.

  • catalogNumber (str or bool) – Either a column name (str) or True (bool). If a column name is provided, the column will be renamed. If True is provided, unique identifiers will be generated in the dataset.

  • recordNumber (str or bool) – Either a column name (str) or True (bool). If a column name is provided, the column will be renamed. If True is provided, unique identifiers will be generated in the dataset.

  • sep (char) – Separation character for composite IDs. Default is -.

  • basisOfRecord (str) – Either a column name (str) or a valid value for basisOfRecord to add to the dataset.

  • occurrenceStatus (str) – Either a column name (str) or a valid value for occurrenceStatus to add to the dataset.

  • add_eventID (logic) – Either a column name (str) or a valid value for occurrenceStatus to add to the dataset.

  • events (pd.DataFrame) – Dataframe containing your events.

  • eventType (str) – Either a column name (str) or a valid value for eventType to add to the dataset.

Return type:

pandas.DataFrame with the updated data

Examples

Standardising Occurrences

galaxias.set_scientific_name(dataframe=None, scientificName=None, taxonRank=None, scientificNameAuthorship=None)#

Checks for the name of the taxon you identified is present.

Parameters:

  • dataframe (pandas.DataFrame) – pandas.DataFrame with your data

  • scientificName (str) – A column name (str) denoting all your scientific names.

  • taxonRank (str) – A column name (str) denoting the rank of your scientific names (species, genus etc.)

  • scientificNameAuthorship (str) – A column name (str) denoting who originated the scientific name.

Return type:

None - the occurrences dataframe is updated

Examples

Standardising Occurrences

galaxias.set_taxonomy(dataframe=None, kingdom=None, phylum=None, taxon_class=None, order=None, family=None, genus=None, specificEpithet=None, vernacularName=None)#

Adds extra taxonomic information. Also runs checks on whether or not the names are the correct data type.

Parameters:

  • dataframe (pandas.DataFrame) – The pandas.DataFrame that contains your data to check

  • kingdom (str,``list``) – A column name, kingdom name (str) or list of kingdom names (list).

  • phylum (str,``list``) – A column name, phylum name (str) or list of phylum names (list).

  • taxon_class (str,``list``) – A column name, class name (str) or list of class names (list).

  • order (str,``list``) – A column name, order name (str) or list of order names (list).

  • family (str,``list``) – A column name, family name (str) or list of family names (list).

  • genus (str,``list``) – A column name, genus name (str) or list of genus names (list).

  • specificEpithet (str,``list``) – A column name, specificEpithet name (str) or list of specificEpithet names (list). Note: If scientificName is Abies concolor, the specificEpithet is concolor.

  • vernacularName (str,``list``) – A column name, vernacularName name (str) or list of vernacularName names (list).

Return type:

None - the occurrences dataframe is updated

Examples

>>> occ_tax = galaxias.set_taxonomy(dataframe=occ,kingdom='Animalia',phylum='Chordata',taxon_class='Aves',
...                                 order='Psittaciformes',family='Cacatuidae',genus='Eolophus',
...                                 specificEpithet='roseicapilla',vernacularName='Galah')
galaxias.submit_archive(self)#

Currently opens a Github issue on the ALA to place your data.

Parameters:

None –

Return type:

Raises a ValueError if something is wrong, or returns True if it passes.

galaxias.suggest_workflow(occurrences=None, events=None)#

Suggests a workflow to ensure your data conforms with the pre-defined Darwin Core standard.

Parameters:

None –

Return type:

A printed report detailing presence or absence of required data.

Examples

Suggest a workflow for a small dataset

import pandas as pd
import galaxias
df = pd.DataFrame({'species': ['Callocephalon fimbriatum', 'Eolophus roseicapilla'], 'latitude': [-35.310, '-35.273'], 'longitude': [149.125, 149.133], 'eventDate': ['14-01-2023', '15-01-2023'], 'status': ['present', 'present']})
galaxias.suggest_workflow(occurrences=df)

── Darwin Core terms ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

── All DwC terms ──

Matched 1 of 5 column names to DwC terms:

✓ Matched: eventDate
✗ Unmatched: species, latitude, longitude, status

── Minimum required DwC terms occurrences ──

Type                       Matched term(s)    Missing term(s)
-------------------------  -----------------  -------------------------------------------------------------------------------
Identifier (at least one)  -                  occurrenceID OR catalogNumber OR recordNumber
Record type                -                  basisOfRecord
Scientific name            -                  scientificName
Location                   -                  decimalLatitude, decimalLongitude, geodeticDatum, coordinateUncertaintyInMeters
Date/Time                  eventDate          -

── Suggested workflow ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

── Occurrences ──

To make your occurrences Darwin Core compliant, use the following workflow:

corella.set_occurrences()
corella.set_scientific_name()
corella.set_coordinates()

Additional functions: set_abundance(), set_collection(), set_individual_traits(), set_license(), set_locality(), set_taxonomy()
None
galaxias.use_data(occurrences=None, events=None, occurrences_filename='occurrences.csv', events_filename='events.csv', publishing_dir='./data-publish')#

Writes occurrence and event files to your publishing directory.

Parameters:

  • occurrences (pandas.DataFrame) – The pandas.DataFrame that contains your occurrence data

  • events (pandas.DataFrame) – The pandas.DataFrame that contains your events data

  • occurrences_filename (str) – str containing the desired name for your occurrences file

  • events_filename (str) – str containing the desired name for your events file

  • publishing_dir (str) – str containing the name of your publishing directory

Return type:

None - files are written to disk

Examples

>>> galaxias.use_data(occurrences=occ,events=events)
galaxias.use_metadata(metadata_md='metadata.md', working_dir='./', publishing_dir='./data-publish', eml_xml='eml.xml')#

Writes the metadata file into an xml format in your publishing directory

Parameters:

  • metadata_md (str) – Name of the markdown file that you want to convert to EML. Default value is 'metadata.md'.

  • working_dir (str) – Name of your working directory. Default value is './'.

  • publishing_dir (str) – Name of the directory containing your data for publication. Default value is './'.

  • eml_xml (str) – Name of your eml xml file. Default value is 'eml.xml'.

Return type:

None

galaxias.use_metadata_template(metadata_md='metadata.md', working_dir='./', xml_url=None, print_notices=False)#

This function is for creating a metadata statement, either from a bulk

Parameters:

  • metadata_md (str) – Name of the metadata file you will edit. Default is 'metadata.md'.

  • working_dir (str) – Name of your working directory. Default value is './'.

  • xml_url (str) – URL of the eml xml file you want to emulate. Default is None.

Return type:

None

galaxias.use_schema(occurrences=None, events=None, occurrences_filename='occurrences.csv', events_filename='events.csv', publishing_dir='./data-publish/', metadata='eml.xml', schema='meta.xml')#

Makes the schema (metadata.xml) file from your metadata (eml.xml) file and information from your occurrences / events.

Parameters:

  • occurrences (pandas DataFrame) – OPTIONAL: This is the dataframe holding your occurrence data. Default is None.

  • events (pandas DataFrame) – OPTIONAL: This is the dataframe holding your occurrence data. Default is None.

  • occurrences_filename (str) – Name of your occurrences file. Default value is 'occurrences.csv'.

  • events_filename (str) – Name of your events file. Default value is 'events.csv'.

  • publishing_dir (str) – Name of the directory where all your processed data lives. Default value is './data-publish/'.

  • metadata (str) – Name of your metadata xml. Default value is 'eml.xml'.

  • schema (str) – Name of your schema xml. Default value is 'meta.xml'.

Return type:

None

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