def update_metadata(url, filename, contenttype, context):
try:
set_progress('RUNNING', 'Download {0}'.format(url), None, context)
tmpdir = tempfile.mkdtemp()
tmpfile = '{}/{}'.format(tmpdir, filename)
userid = context.get('user', {}).get('id')
settings = app.conf.get('bccvl', {})
src = build_source(url, userid, settings)
dst = build_destination('file://{}'.format(tmpfile), settings)
movelib.move(src, dst)
item = {
'filemetadata': extract_metadata(tmpfile, contenttype)
}
# Check that there are lon and lat columns
# if upload is of type csv, we validate column names as well
if contenttype == 'text/csv':
if ('headers' not in item['filemetadata']
or 'lat' not in item['filemetadata']['headers']
or 'lon' not in item['filemetadata']['headers']):
raise Exception("Missing 'lat'/'lon' column")
set_progress('RUNNING',
'Import metadata for {0}'.format(url),
None, context)
import_job = import_file_metadata_job([item], url, context)
import_job.link_error(set_progress_job(
"FAILED", "Metadata update failed for {0}".format(url),
None, context))
finish_job = set_progress_job(
"COMPLETED", 'Metadata update for {} complete'.format(url),
None, context)
(import_job | finish_job).delay()
except Exception as e:
set_progress('FAILED',
'Metadata update for {} failed: {}'.format(url, e),
None, context)
LOG.error('Metadata update for %s failed: %s', url, e, exc_info=True)
finally:
if tmpdir and os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
def download_occurrence_from_ala(params, context):
results = []
species = [] # a list of species metadata
ds_names = []
for dataset in params:
src = None
dst = None
occurrence_url = dataset['url'].rstrip('/') + "/occurrences/index/download"
query = dataset['query'] # i.e. qid:<qid> or lsid:<lsid>
qfilter = "zeroCoordinates,badlyFormedBasisOfRecord,detectedOutlier,decimalLatLongCalculationFromEastingNorthingFailed,missingBasisOfRecord,decimalLatLongCalculationFromVerbatimFailed,coordinatesCentreOfCountry,geospatialIssue,coordinatesOutOfRange,speciesOutsideExpertRange,userVerified,processingError,decimalLatLongConverionFailed,coordinatesCentreOfStateProvince,habitatMismatch"
email = context.get('user', {}).get('email', '')
ds_names.append(dataset.get('name', ''))
# downlaod occurrence file
# TODO: ignore file if not successfully download (exception), but continue??
tmpdir = tempfile.mkdtemp(prefix='ala_download_')
results.append(tmpdir)
src = build_source('ala://ala?url={}&query={}&filter={}&email={}'.format(occurrence_url, query, qfilter, email))
dst = build_destination('file://{}'.format(tmpdir))
movelib.move(src, dst)
# extract metadata and do other stuff....
set_progress('RUNNING', 'Extract metadata for {0} from ala'.format(dataset['query']), None, context)
# open ala_dateset.json
ala_ds = json.load(open(os.path.join(tmpdir, 'ala_dataset.json'), 'r'))
# collect files inside ds per datatype
files = dict(((f['dataset_type'], f) for f in ala_ds['files']))
# occurrence data file
ala_csv = files['occurrence']['url'] # this is actually a zip file now
# read ala metadata from attribution file.
# May not have metadata for user uploaded dataset into sandbox
if files.get('attribution'):
ala_md_list = json.load(open(files['attribution']['url'], 'r'))
for md in ala_md_list:
species.append({
'scientificName': md.get('scientificName'),
'vernacularName': md.get('commonNameSingle') or md.get('scientificName'),
'taxonID': md.get('guid'),
'rank': md.get('rank'),
'genus': md.get('genus'),
'family': md.get('family'),
'order': md.get('order'),
'clazz': md.get('classs'),
'phylum': md.get('phylum'),
'kingdom': md.get('kingdom')
})
# Shall not happen
if len(results) == 0:
raise Exception("No occurrence dataset is downloaded from ALA")
# Combine all the occurrence and citation files from each download into 1 dataset
imported_date = datetime.datetime.now().strftime('%d/%m/%Y')
if len(results) > 1:
destdir = tempfile.mkdtemp(prefix='ala_download_')
results.append(destdir)
os.mkdir(os.path.join(destdir, 'data'))
combine_csv(results[:-1], 'data/ala_occurrence.csv', destdir)
combine_csv(results[:-1], 'data/ala_citation.csv', destdir)
# Zip it out and point to the new zip file
ala_csv = os.path.join(destdir, 'ala_occurrence.zip')
zip_occurrence_data(ala_csv,
os.path.join(destdir, 'data'),
['ala_occurrence.csv', 'ala_citation.csv'])
# Make a title & description for multispecies dataset
ds_name = ', '.join([name for name in ds_names if name])
if ds_name:
title = ds_name
else:
ds_name = ','.join([sp['scientificName'] for sp in species])
title = "{} occurrences".format(ds_name)
description = "Observed occurrences for {0}, imported from ALA on {1}".format(ds_name, imported_date)
else:
ds_name = ', '.join([name for name in ds_names if name])
if ds_name:
title = ds_name
description = "Observed occurrences for {0}, imported from ALA on {1}".format(ds_name, imported_date)
else:
title = ala_ds['title']
description = ala_ds['description']
species = species[0]
# build bccvl metadata:
bccvlmd = {
'genre': 'DataGenreSpeciesOccurrence',
'categories': ['occurrence'],
'species': species
}
# build item to import
item = {
'title': title,
'description': description,
'file': {
'url': 'file://{}'.format(ala_csv), # local file url
'contenttype': 'application/zip',
'filename': os.path.basename(ala_csv)
},
'bccvlmetadata': bccvlmd,
'filemetadata': extract_metadata(ala_csv, 'application/zip'),
}
return (item, results)
def createItem(fname, info, params):
# fname: full path to file
# info: ... from outputmap
name = os.path.basename(fname)
# layermd ... metadata about raster layer
layermd = {}
# bccvlmd ... bccvl specific metadata
bccvlmd = {}
genre = info.get('genre', None)
if genre:
bccvlmd['genre'] = genre
if genre in ('DataGenreSDMModel', 'DataGenreCP', 'DataGenreCP_ENVLOP', 'DataGenreClampingMask'):
if genre == 'DataGenreClampingMask':
layermd = {
'files': {name: {'layer': 'clamping_mask', 'data_type': 'Discrete'}}}
elif genre in ('DataGenreCP', 'DataGenreCP_ENVLOP'):
if params['function'] in ('circles', 'convhull', 'voronoihull'):
layermd = {
'files': {name: {'layer': 'projection_binary', 'data_type': 'Continuous'}}}
elif params['function'] in ('maxent',):
layermd = {
'files': {name: {'layer': 'projection_suitablity', 'data_type': 'Continuous'}}}
else:
layermd = {'files': {
name: {'layer': 'projection_probability', 'data_type': 'Continuous'}}}
# FIXME: find a cleaner way to attach metadata
for key in ('year', 'month', 'emsc', 'gcm'):
if key in params:
bccvlmd[key] = params[key]
elif genre == 'DataGenreSDMEval' and info.get('mimetype') == 'text/csv':
# Only get threshold value as from the output of Sama's evaluation script
# FIXME: should not depend on file name (has already changed once
# and caused disappearance of threshold values in biodiverse)
if fname.endswith('Loss function intervals table.csv'):
thresholds = extractThresholdValues(fname)
# FIXME: merge thresholds?
bccvlmd['thresholds'] = thresholds
elif genre == 'DataGenreBiodiverseOutput':
# Add in the srs and cellsize for Biodiverse
bccvlmd['srs'] = 'epsg:3577'
bccvlmd['cellsize'] = params['cluster_size']
# make sure we have a mimetype
mimetype = info.get('mimetype', None)
if mimetype is None:
mimetype = guess_mimetype(fname)
# extract file metadata
filemd = extract_metadata(fname, mimetype)
# FIXME: check keys to make sense
# -> merge layermd and filemetadata?
# -> merge bccvlmd and filemetadata?
return {
'file': {
'url': 'file://{}'.format(fname), # local file url
'contenttype': mimetype,
'filename': name
},
'title': name,
'description': info.get('title', u''),
'bccvlmetadata': bccvlmd,
'filemetadata': filemd,
'layermd': layermd,
'order': info.get('order', 999999)
}
def pull_occurrences_from_gbif(lsid, dest_url, context):
# 1. set progress
set_progress('RUNNING', 'Download {0} from gbif'.format(lsid), None, context)
# 2. do move
src = None
dst = None
try:
tmpdir = tempfile.mkdtemp(prefix='gbif_download_')
src = build_source('gbif://gbif?lsid={}'.format(lsid))
dst = build_destination('file://{}'.format(tmpdir))
movelib.move(src, dst)
# extract metadata and do other stuff....
set_progress('RUNNING', 'Extract metadata {0} from gbif'.format(lsid), None, context)
# open gbif_dateset.json
gbif_ds = json.load(open(os.path.join(tmpdir, 'gbif_dataset.json'), 'r'))
# collect files inside ds per datatype
files = dict(((f['dataset_type'], f) for f in gbif_ds['files']))
# read gbif metadata from attribution file
gbif_md = json.load(open(files['attribution']['url'], 'r'))
gbif_csv = files['occurrence']['url']
# build bccvl metadata:
bccvlmd = {
'genre': 'DataGenreSpeciesOccurrence',
'categories': ['occurrence'],
'species': {
'scientificName': gbif_md.get('scientificName', None),
'vernacularName': gbif_md.get('vernacularName', None),
'taxonID': gbif_md.get('key', None),
'rank': gbif_md.get('rank', None),
'genus': gbif_md.get('genus', None),
'genusGuid': gbif_md.get('genusKey', None),
'family': gbif_md.get('family', None),
'familyGuid': gbif_md.get('familyKey', None),
'order': gbif_md.get('order', None),
'orderGuid': gbif_md.get('orderKey', None),
'clazz': gbif_md.get('class', None),
'clazzGuid': gbif_md.get('classKey', None),
'phylum': gbif_md.get('phylum', None),
'phylumGuid': gbif_md.get('phylumKey', None),
'kingdom': gbif_md.get('kingdom', None),
'kingdomGuid': gbif_md.get('kingdomKey', None)
},
}
# build item to import
item = {
'title': gbif_ds['title'],
'description': gbif_ds['description'],
'file': {
'url': 'file://{}'.format(gbif_csv), # local file url
'contenttype': 'application/zip',
'filename': os.path.basename(gbif_csv)
},
'bccvlmetadata': bccvlmd,
'filemetadata': extract_metadata(gbif_csv, 'application/zip'),
}
# Add the number of occurrence records to the metadata
# TODO: This is a hack. Any better solution.
occurrence_csv_filename = os.path.join('data', 'gbif_occurrence.csv')
if occurrence_csv_filename in item['filemetadata']:
# FIXME: copy all occurrence metadata to zip level, for backwards
# compatibility... this should go away after we fully support 'layered'
# occurrence zips.
for key in ('rows', 'headers', 'bounds'): # what about 'species' ?
if key in item['filemetadata'][occurrence_csv_filename]['metadata']:
item['filemetadata'][key] = item['filemetadata'][occurrence_csv_filename]['metadata'][key]
# move data file to destination and build data_url
src = build_source('file://{}'.format(gbif_csv))
dst = build_destination(os.path.join(dest_url, os.path.basename(gbif_csv)), app.conf.get('bccvl', {}))
item['file']['url'] = dst['url']
movelib.move(src, dst)
# tell importer about new dataset (import it)
set_progress('RUNNING', 'Import gbif data {0}'.format(lsid), None, context)
cleanup_job = import_cleanup_job(dest_url, context)
import_job = import_ala_job([item], dest_url, context)
import_job.link_error(set_progress_job("FAILED", "Import of gbif data failed {0}".format(lsid), None, context))
import_job.link_error(cleanup_job)
finish_job = set_progress_job("COMPLETED", 'GBIF import {} complete'.format(lsid), None, context)
(import_job | cleanup_job | finish_job).delay()
except Exception as e:
set_progress('FAILED', 'Download {0} from gbif: {1}'.format(lsid, e), None, context)
import_cleanup(dest_url, context)
LOG.error('Download from %s to %s failed: %s', src, dest_url, e, exc_info=True)
finally:
if tmpdir and os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
def import_multi_species_csv(url, results_dir, import_context, context):
# url .... source file
# results_dir ... folder to place split files into
# context ... the context with user and orig dataset
try:
set_progress('RUNNING', 'Split {0}'.format(url), None, context)
# step 1: update main dataset metadata
tmpdir = tempfile.mkdtemp()
userid = context.get('user', {}).get('id')
settings = app.conf.get('bccvl', {})
src = build_source(url, userid, settings)
dst = build_destination('file://{}'.format(tmpdir), settings)
movelib.move(src, dst)
# Get the downloaded filename
tmpfile = glob.glob(os.path.join(tmpdir, '*'))[0]
# Extract occurrence file from downloaded file
mimetype, enc = mimetypes.guess_type(tmpfile)
if mimetype == 'application/zip':
src_occ_data = os.path.join('data', 'ala_occurrence.csv')
with zipfile.ZipFile(tmpfile, 'r') as zipf:
occfile = os.path.join(tmpdir, src_occ_data)
zipf.extract(src_occ_data, tmpdir)
item = {
'filemetadata': extract_metadata(tmpfile, 'application/zip')
}
occmd = item['filemetadata'].get(src_occ_data, {}).get('metadata', {})
else:
# csv file
item = {
'filemetadata': extract_metadata(tmpfile, "text/csv")
}
occfile = tmpfile
occmd = item['filemetadata']
# Check that there are lon and lat columns
# if upload is of type csv, we validate column names as well
if ('headers' not in occmd
or 'lat' not in occmd['headers']
or 'lon' not in occmd['headers']):
raise Exception("Missing 'lat'/'lon' column")
set_progress('RUNNING',
'Import metadata for {0}'.format(url),
None, context)
import_md_job = import_file_metadata_job([item], url, context)
import_md_job.link_error(set_progress_job(
"FAILED", "Metadata update failed for {0}".format(url),
None, context))
# step 2: split csv file and create sub datasets
# start reading csv file and create new datasets which will be
# linked up with dataset collection item
# FIXME: large csv files should be streamed to seperate files (not read
# into ram like here)
f = io.open(occfile, 'r', encoding='utf-8', errors='ignore')
csvreader = UnicodeCSVReader(f)
headers = csvreader.next()
if 'species' not in headers:
raise Exception('missing species column')
speciesidx = headers.index('species')
# create dict with all data .... species column used as key, and rest
# is just added
data = {}
for row in csvreader:
if not row:
continue
species = row[speciesidx]
if species not in data:
# create new entry for species
fname = u'{0}.csv'.format(species).replace(
u'/', u'_').encode('idna')
# TODO: make sure fname contains only legal filename characters
fpath = os.path.join(tmpdir, fname)
file = io.open(fpath, 'wb')
fwriter = UnicodeCSVWriter(file)
fwriter.writerow(headers)
data[species] = {
'file': file,
'writer': fwriter,
'path': fpath,
'name': fname
}
data[species]['writer'].writerow(row)
# ok we have got all data and everything in separate files
# close all files
for species in data:
data[species]['file'].close()
del data[species]['file']
del data[species]['writer']
# extract metadata
for species in data:
data[species]['filemetadata'] = extract_metadata(
data[species]['path'],
'text/csv'
)
# send files to destination
for species in data:
src = build_source('file://{}'.format(data[species]['path']))
dst = build_destination(os.path.join(results_dir,
data[species]['name']),
app.conf.get('bccvl', {}))
data[species]['url'] = dst['url']
movelib.move(src, dst)
# all files uploaded .... send import jobs
set_progress('RUNNING', 'Create datasets for {0}'.format(
url), None, context)
items = []
for species in data:
# build item
item = {
'title': u'{0} occurrences'.format(species),
'description': '',
'file': {
'url': data[species]['url'],
'filename': data[species]['name'],
'contenttype': 'text/csv',
},
'bccvlmetadata': {
'genre': 'DataGenreSpeciesOccurrence',
'categories': ['occurrence'],
'species': {
'scientificName': species,
}
},
'filemetadata': data[species]['filemetadata'],
'_partof': {
# add back reference to orig dataset
# TODO: shouldn't use absolute path here
'path': context['context']
}
}
items.append(item)
# start import process
start_import = set_progress_job(
'RUNNING', 'Import results', None, context)
# What is results_dir being used for?
import_job = import_result_job(items, results_dir, import_context)
cleanup_job = import_cleanup_job(results_dir, context)
import_job.link_error(set_progress_job(
'FAILED', 'Multi species import failed', None, context))
import_job.link_error(cleanup_job)
finish_job = set_progress_job(
'COMPLETED', 'Task succeeded', None, context)
(start_import | import_md_job | import_job |
cleanup_job | finish_job).delay()
# FIXME: missing stuff...
# need to set multi species collection to finished at some stage
except Exception as e:
set_progress('FAILED',
'Error while splitting Multi Species CSV {}: {}'.format(
url, e),
None, context)
LOG.error('Multi species split for %s faild: %s', url, e, exc_info=True)
finally:
if tmpdir and os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
