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
   humanObservation
 machineObservation
     livingSpecimen
  preservedSpecimen
     fossilSpecimen
   materialCitation

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()

    AD
    AE
    AF
    AG
    AI
       ..
  YE
  YT
  ZA
  ZM
  ZW
Name: Code, Length: 249, dtype: object

class galaxias.dwca(working_dir='dwca_data', data_raw_dir='data_raw', data_proc_dir='data_processed', dwca_name='dwca.zip', occurrences=None, occurrences_archive_filename='occurrences.txt', multimedia=None, multimedia_archive_filename='multimedia.txt', events=None, events_archive_filename='events.txt', emof=None, emof_archive_filename='extendedMeasurementOrFact.txt', metadata_md='metadata.md', eml_xml='eml.xml', meta_xml='meta.xml', print_notices=True)#

Bases: object

check_dataset()#

Checks whether or not your data (only occurrences for now) meets the predefined Darwin Core standard. Calls the corella package for this.

Parameters:: None –
Return type:: A printed report detailing presence or absence of required data.

check_dwca()#

Checks whether or not your Darwin Core Archive meets the pre-defined standard.

Parameters:: None –
Return type:: Raises a ValueError if something is wrong, or returns True if it passes.

check_eml_xml()#

Checks whether or not your data (only occurrences for now) meets the predefined Darwin Core standard. Calls the corella package for this.

Parameters:: None –
Return type:: A printed report detailing presence or absence of required data.

check_meta_xml()#

Checks whether or not your data (only occurrences for now) meets the predefined Darwin Core standard. Calls the corella package for this.

Parameters:: None –
Return type:: A printed report detailing presence or absence of required data.

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()

    AD
    AE
    AF
    AG
    AI
       ..
  YE
  YT
  ZA
  ZM
  ZW
Name: Code, Length: 249, dtype: object

create_dwca()#

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

Parameters:: None –
Return type:: Raises a ValueError if something is wrong, or returns None if it passes.

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()

                   type
               modified
               language
                license
           rightsHolder
              ...         
       georeferencedBy
     georeferencedDate
  georeferenceProtocol
   georeferenceSources
   georeferenceRemarks
Name: term_localName, Length: 82, dtype: object

make_meta_xml()#

Makes the metadata.xml file from your eml.xml file and information from your occurrences / other included extensions. The metadata.xml file is your descriptor file, in that it describes what is in the DwCA.

Parameters:: None –
Return type:: None

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()

                           type
                       modified
                       language
                        license
                   rightsHolder
                  ...             
            relatedResourceID
       relationshipOfResource
      relationshipAccordingTo
  relationshipEstablishedDate
          relationshipRemarks
Name: term_localName, Length: 206, dtype: object

set_abundance(individualCount=None, organismQuantity=None, organismQuantityType=None)#

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

Parameters:

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

set_abundance vignette

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

set_collection vignette

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

set_coordinates vignette

set_datetime(check_events=False, eventDate=None, year=None, month=None, day=None, eventTime=None, string_to_datetime=False, yearfirst=True, dayfirst=False, time_format='%H:%m:%S')#

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

Parameters:

check_events (logical) – If True, will check the events file. If False, will check occurrences file. Default is False.
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 '%H:%m:%S'.

Return type:

None - the occurrences dataframe is updated

Examples

set_datetime vignette

set_events(eventID=None, parentEventID=None, eventType=None, Event=None, samplingProtocol=None, event_hierarchy=None, sequential_id=False, add_sequential_id='first', add_random_id='first', composite_id=None, sep='-', random_id=False)#

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) – The pandas.DataFrame that contains your data to check
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
sequential_id (logical) – Create sequential IDs and/or add sequential ids to composite ID. Default is False.
add_sequential_id (str) – Determine where to add sequential id in composite id. Values are first and last. Default is first.
composite_id (str, list) – str or list containing columns to create composite IDs. Can be combined with sequential ID.
sep (char) – Separation character for composite IDs. Default is -.
random_id (logical) – Create a random ID using the uuid package. Default is False.
add_random_id (str) – Determine where to add sequential id in random id. Values are first and last. Default is first.
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”}.
Returns –
------- – None - the occurrences dataframe is updated
Examples –
---------- – set_events vignette

set_individual_traits(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) – The pandas.DataFrame that contains your data to check
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:

pandas.DataFrame with the updated data.

Examples

set_individual_traits vignette

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

set_license vignette

set_locality(check_events=False, continent=None, country=None, countryCode=None, stateProvince=None, locality=None)#

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

Parameters:

check_events (logical) – Check to see if user wants to edit events dataframe. Default is False.
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:

None - the occurrences dataframe is updated

Examples

set_locality vignette

set_observer(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

set_observer vignette

set_occurrences(occurrenceID=None, catalogNumber=None, recordNumber=None, basisOfRecord=None, occurrenceStatus=None, sequential_id=False, add_sequential_id='first', composite_id=None, sep='-', random_id=False, add_random_id='first', add_eventID=False, eventType=None)#

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

Parameters:

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.
sequential_id (logical) – Create sequential IDs and/or add sequential ids to composite ID. Default is False.
add_sequential_id (str) – Determine where to add sequential id in composite id. Values are first and last. Default is first.
composite_id (str, list) – str or list containing columns to create composite IDs. Can be combined with sequential ID.
sep (char) – Separation character for composite IDs. Default is -.
random_id (logical) – Create a random ID using the uuid package. Default is False.
add_random_id (str) – Determine where to add sequential id in random id. Values are first and last. Default is first.
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.
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

set_occurrences vignette

set_scientific_name(scientificName=None, taxonRank=None, scientificNameAuthorship=None)#

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

Parameters:

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:

pandas.DataFrame with the updated data.

Examples

set_scientific_name vignette

set_taxonomy(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:

pandas.DataFrame with the updated data.

Examples

set_taxonomy vignette

suggest_workflow()#

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']})
my_dwca = galaxias.dwca(occurrences=df)
my_dwca.suggest_workflow()

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

── All DwC terms ──

Matched 1 of 5 column names to DwC terms:

✓ Matched: eventDate
✗ Unmatched: latitude, species, 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
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

validate_dwca()#

write_eml_xml()#

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()

                   type
               modified
               language
                license
           rightsHolder
              ...         
       georeferencedBy
     georeferencedDate
  georeferenceProtocol
   georeferenceSources
   georeferenceRemarks
Name: term_localName, Length: 82, 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()

                           type
                       modified
                       language
                        license
                   rightsHolder
                  ...             
            relatedResourceID
       relationshipOfResource
      relationshipAccordingTo
  relationshipEstablishedDate
          relationshipRemarks
Name: term_localName, Length: 206, dtype: object