def test_count():
"occurrences.count - basic test"
res = occurrences.count(taxonKey=3329049)
assert int == res.__class__
def test_count():
"occurrences.count - basic test"
res = occurrences.count(taxonKey = 3329049)
assert int == res.__class__
from pygbif import species, occurrences
from numpy import nan, empty
TName = "Fagus sylvatica"
key = species.name_backbone(name=TName, rank="species")["usageKey"]
n = occurrences.count(taxonKey=key, isGeoreferenced=True)
if n > 200000:
max = 200000
else:
max = n
results = occurrences.search(taxonKey=key, limit=max)
print '(', key, ')', '-', format(n, ','), " ocurrence(s)"
# lonslats = []
latlon = empty([max, 2], dtype=float, order='C')
for i, x in enumerate(results["results"]):
#try:
#if x['continent'].find('_') != -1:
#Continent = ' '.join(x['continent'].split('_')).title()
#else:
#Continent = x['continent'].capitalize()
#except:
#Continent = ""
#try:
#Country = x['country']
#except:
#Country = ""
#try:
def search_species(entry):
# To be used when calling the method
name_input = entry
# User input for testing purposes
# name_input = input("Enter a common species name: ")
results = {}
# Uses the pygbif method to find results
suggest = species.name_suggest(q=name_input, rank='SPECIES', limit=25)
# Pulls the results from the dataset in json format
suggest_data = suggest['data']['results']
# print(suggest_data)
# Formats the data for pythonic purposes
data = suggest_data
# Reads data for each result
for o in data:
# Finds the gbif key
key = o['key']
# Uses pygbif to find number of occurrences of species key
occurs = occurrences.count(taxonKey=key)
# Searches occurrence data for the species key
occur_search = occurrences.search(taxonKey=key)
# print('occur search: ' + str(occur_search))
# for country in countries:
# print(country)
# print(occur_search)
# Runs if species has occurred more than zero times
if occurs > 0:
try:
# Tries to retrieve scientific name
canon_name = o['canonicalName']
except:
continue
# Vernacular name init
vern_name = ''
# Variable for list of vernacular names
names = o['vernacularNames']
# Summary init
summary = ''
try:
# If match found
print('Scientific name: ' + canon_name)
print('Vernacular names: ')
match_found = False
# Reads from results in matched name
for name in names:
# Variable for vernacular name
vern_name = name['vernacularName']
# Used if all languages want to be included
# print(name['vernacularName'])
language = (name['language'])
# Can be changed if user wants to select a specific language
if language == 'eng':
# Checks if vernacular name is matched with search input
if name_input in vern_name and match_found is False:
match_found = True
print(vern_name)
# If no exact match is found it reads the first vernacular name
if match_found is False:
name_store = []
for name in names:
vern_name = name['vernacularName']
language = (name['language'])
if vern_name not in name_store:
name_store.append(vern_name)
if language == 'eng':
print(vern_name)
# Adds scientific and vernacular name to results dictionary
results.setdefault(canon_name, []).append(vern_name)
# print(wikipedia.search(canon_name))
try:
# pulls the wiki page based on canonical name in url (usually works)
# desc = wikipedia.page(canon_name, auto_suggest=True)
# alternative wiki page including all sections
# page = wikipedia.WikipediaPage(canon_name)
# pulls the summary page for the species
summary = wikipedia.summary(canon_name, sentences=2)
# experimental for section pages
# sections = page.sections
# for section in sections:
# print(section)
# github push error, delete this
print(summary)
results.setdefault(canon_name, []).append(summary)
# print(desc.content)
except:
print("No description found")
print('GBIF Key: ' + str(key))
print('GBIF Species Page: ' + 'http://www.gbif.org/species/' + str(key))
print('Count: ' + str(occurs))
# This reads results for occurences and finds countries the species
# was observed in and how many occurrences there
occur_data = occur_search['results']
countries = {}
for occur in occur_data:
try:
country = occur['country']
if country not in countries:
countries[country] = 1
else:
countries[country] += 1
# print(occur['country'])
except:
continue
# for country in countries:
# print(country)
sorted_countries = sorted(countries.items(), key=lambda x: x[1], reverse=True)
if countries != {}:
print('Top 3 Countries Observed: ')
for country in sorted_countries[:3]:
print(str(country))
print('\n')
# return canon_name, vern_name, summary
except:
continue
print(results)
return results
from pygbif import species, occurrences
from numpy import nan, empty
TName = "Fagus sylvatica"
key = species.name_backbone(name=TName, rank="species")["usageKey"]
n = occurrences.count(taxonKey=key, isGeoreferenced=True)
if n > 200000:
max = 200000
else:
max = n
results = occurrences.search(taxonKey=key, limit=max)
print '(', key, ')', '-', format(n, ','), " ocurrence(s)"
# lonslats = []
latlon = empty([max,2], dtype=float, order='C')
for i, x in enumerate(results["results"]):
#try:
#if x['continent'].find('_') != -1:
#Continent = ' '.join(x['continent'].split('_')).title()
#else:
#Continent = x['continent'].capitalize()
#except:
#Continent = ""
#try:
#Country = x['country']
#except:
#Country = ""
#try:
