mecoda-minka 1.5.0

Library to download information using Minka API.

Library to extract information collected in the Minka API. This library is part of MECODA (ModulE for Citizen Observatory Data Analysis), aimed to facilitate analysis and viewing of citizen science data.

This repository is part of the work made by the EMBIMOS Reseach Group, at Marine Sciences Institute (CSIC) of Barcelona.

Installation

pip install mecoda-minka

Use

Get observations

With get_obs you can extract data from the observations collected in the API. The function supports combinations of these arguments, which act as filters, getting the observations in descending order of id, with a maximum of 20,000 (API limitation):

Argument	Descrition	Example
query	Word or phrase found in the data of an observation	query="quercus quercus"
project_name	Name of a project	project_name="urbamar"
id_project	Identification number of a project	id_project=806
id_obs	Identification number of a specific observation	id_obs=425
user	Name of user who has uploaded the observations	user="zolople"
taxon	One of the main taxonomies	taxon="fungi"
place_id	Identification number of a place	place_id=1011
place_name	Name of a place	place_name="Barcelona"
year	Year of observations	year=2019

For the taxon argument the possible values are: chromista, protozoa, animalia, mollusca, arachnida, insecta, aves, mammalia, amphibia, reptilia, actinopterygii, fungi, plantae, cnidaria, annelida, platyhelminthes, echinodermata, bryozoa, porifera, elasmobranchii, crustacea y ctenophora.

Example of use:

from mecoda_minka import get_obs

observations = get_obs(year=2018, taxon='fungi')

observations is an object list Observation.

Get projects

With get_project you can get the information of the projects collected in the API. The function supports a single argument, which can be the project identification number or the name of the project. In case the name does not correspond exclusively to a project, it returns the information from the list of projects that include that word.

Example of use:

from mecoda_minka import get_project

projects = get_project("urbamar")

projects es siempre una lista de objetos Project.

Get count of observations by taxonomic family

With get_count_by_taxon we can know the number of observations that correspond to each of the taxonomic families. The function does not take any argument.

Example of use:

from mecoda_minka import get_count_by_taxon

count = get_count_by_taxon()

count is a dictionary with the structure {taxonomy: number of observations}

Models

The models are defined using objects from [Pydantic] (https://pydantic-docs.helpmanual.io/). Type validation of all attributes is done and data can be extracted with the dict or json method.

Observation

The object Observation contains the information of each of the observations registered in [Minka] (https://minka-sdg.org/observations) and has the following attributes:

Attribute	Type	Description	Default value
id	int	Observation number
captive	Optional[bool]	State of captivity	None
created_at	Optional[datetime]	Creation date	None
updated_at	Optional[datetime]	Update date	None
observed_on	Optional[date]	Observation date	None
description	Optional[str]	Observation description	None
iconic_taxon	Optional[IconicTaxon]	Taxonomic family	None
taxon_id	Optional[int]	Identification number of the specific taxonomy	None
taxon_name	Optional[str]	Name of the species observed	None
taxon_ancestry	Optional[str]	String of the taxonomic sequence to which the observation corresponds, with identifiers separated by /	None
latitude	Optional[float]	Latitude	None
longitude	Optional[float]	Longitude	None
place_name	Optional[str]	Observation site name	None
quality_grade	Optional[QualityGrade]	Quality grade: research, casual o needs_id	None
user_id	Optional[int]	User identification number	None
user_login	Optional[str]	User registration name	None
photos	List[Photo]	Object lists Photo, that include information about each photograph of the observation	[]
num_identification_agreements	Optional[int]	Number of votes in favor of identification	None
num_identification_disagreements	Optional[int]	Number of unfavorable votes to identification	None

Project

The Project object contains the information of each of the projects registered in [Minka] (https://minka-sdg.org/observations) and has the following attributes:

Attribute	Type	Description	Default value
id	int	Project identification number
title	str	Title of the project
description	Optional[str]	Project description	None
created_at	Optional[datetime]	Project creation date	None
updated_at	Optional[datetime]	Project update date	None
latitude	Optional[float]	Latitude	None
longitude	Optional[float]	Longitude	None
place_id	Optional[int]	Place identification number	None
parent_id	Optional[int]	Identification number of the project in which it is included	None
children_id	List[int]	Identification numbers of the projects it has inside	[]
user_id	Optional[int]	Identification number of the user who creates it	None
icon_url	Optional[str]	Link to project icon	None
observed_taxa_count	Optional[int]	Number of observations included in the project	None

Photo

The Photo object contains the information of each photography linked to an observation and has the following attributes.

Attribute	Type	Description	Default value
id	int	Photo identification number
large_url	str	Link to large format photo
medium_url	str	Link to the photograph in medium format
small_url	str	Link to the photo in small format

Contributions

To contribute to this library, follow the steps below.

• You need to have Python 3.7 or higher, virtualenv and git installed.

• Create a github fork of this project.

• Clone your fork and enter the directory

  git clone [e-mail]:<your_username>/mecoda_minka.git
  cd mecoda_minka
  

• Configure your virtualenv to run the tests:

  virtualenv -p `which python3.7` env
  source env/bin/activate
  

• Install mecoda_minka and its dependencies.

  pip3 install -e .
  pip3 install -r requirements-dev.txt
  

• Create a new branch and make your changes:

  git checkout -b mi-nueva-rama
  

• Run the tests with:

  python -m pytest --cov-report term-missing --cov src tests
  

  If you need to pass a specific test, you can use pytest -k <test-name>.

• Update the documentation.

• Make commit, push and create your pull request.

Upload a new version

• Switch to master and update:

  git checkout master
  git pull
  

• Create a new branch:

  git checkout -b <branch-name>
  git pull
  

• Make changes to the code

• Run the tests:

  python -m pytest --cov-report term-missing --cov src tests
  

• Edit the setup.py file to upload the version, which means changing the version argument in the setup function. The convention is 0.1.0 == major.minor.patch. major is to introduce changes that break the existing code. minor refers to changes that add functionality but do not break existing code. patch refers to changes that fix bugs but do not add functionality.

• Make commit and push:

  git add .
  git commit -m "<comment>"
  git push --set-upstream origin <branch-name>
  

• Follow the link to github returned by the push and merge.

• Update master:

  git checkout master
  git pull
  

• Create tag with the new version:

  git tag <new-version>
  git push --tags
  

• Build the package:

  rm dist/ build/ -r
  python setup.py -q bdist_wheel
  python setup.py -q sdist
  

• Upload package to pypi:

  twine upload -r pypi dist/*
  

Thanks for contributing to this project!