def plot_analogs(configfile='config.txt', simday='all', **kwargs):
"""
"""
from datetime import datetime as dt
from netCDF4 import Dataset, MFDataset
import uuid
from flyingpigeon.visualisation import pdfmerge
simoutpdf = 'Analogs.pdf'
if (os.path.isfile(configfile) == True):
curdir, confile = os.path.split(os.path.abspath(configfile))
lines = [line.rstrip('\n') for line in open(configfile)]
for i in lines:
if 'archivefile' in i: arcfile = i.split('"')[1]
if 'simulationfile' in i: simfile = i.split('"')[1]
if 'outputfile' in i: analogfile = i.split('"')[1]
if 'nanalog' in i: nanalog = int(i.split(' =')[1])
if 'varname' in i: varname = i.split('"')[1]
if 'predictordom' in i: domain = i.split('"')[1]
analogfile = curdir + '/' + analogfile
arcfile = curdir + '/' + arcfile
simfile = curdir + '/' + simfile
try:
arc_times = get_time(arcfile)
sim_times = get_time(simfile)
except:
arc_times = get_time_nc(arcfile)
sim_times = get_time_nc(simfile)
sim_dataset = Dataset(simfile)
simvar = sim_dataset.variables[varname][:]
# TODO: check other names for lat/lon
lon = sim_dataset.variables['lon'][:]
lat = sim_dataset.variables['lat'][:]
sim_dataset.close()
domain = domain.split(",")
try:
domain = [float(i) for i in domain]
except:
domain = [lon[0], lon[-1], lat[-1], lat[0]]
outlist = []
total_simmin = np.min(simvar)
total_simmax = np.max(simvar)
cont = [line.rstrip('\n') for line in open(analogfile)]
Nlin = 30
for idx, item in enumerate(cont[1:]):
ana = item.split()
sim_date = dt.strptime(ana[0], '%Y%m%d')
an_dates = []
for dat in ana[1:1 + nanalog]:
an_dates.append(dt.strptime(dat, '%Y%m%d'))
c_dists = ana[1 + nanalog:1 + 2 * nanalog]
c_cors = ana[1 + 2 * nanalog:]
dists = np.zeros((nanalog), dtype=float)
cors = np.zeros((nanalog), dtype=float)
for i in range(0, nanalog):
dists[i] = float(c_dists[i])
cors[i] = float(c_cors[i])
min_dist = np.min(dists)
max_corr = np.max(cors)
w_dist = min_dist / dists
w_corr = cors / max_corr
sim_index = idx # day by day
tmp_i = []
for i in arc_times:
tmp_z = '%s-%s-%s' % (i.year, i.month, i.day)
tmp_i.append(tmp_z)
arc_index = []
for arc in an_dates:
arc_date_temp = '%s-%s-%s' % (arc.year, arc.month, arc.day)
arc_index.append(tmp_i.index(arc_date_temp))
simmin = np.min(simvar[sim_index, :, :])
simmax = np.max(simvar[sim_index, :, :])
# PLOT SIM ====================================
sim_title = 'Simulation Day: ' + ana[0]
output_file_name = 'sim_' + ana[0] + '.pdf'
output_file = pdf_from_analog(lon=lon,
lat=lat,
data=simvar[sim_index, :, :],
vmin=simmin,
vmax=simmax,
Nlin=Nlin,
domain=domain,
output=output_file_name,
title=sim_title)
outlist.append(str(output_file))
# PLOT Mean analogs ====================================
arc_dataset = Dataset(arcfile)
arcvar = arc_dataset.variables[varname][:]
arc_dataset.close()
mean_ana = np.zeros((len(arcvar[0, :, 0]), len(arcvar[0, 0, :])),
dtype=float)
for ida, art in enumerate(arc_index):
mean_ana = mean_ana + arcvar[art, :, :]
mean_ana = mean_ana / nanalog
output_an_file_name = 'ana_' + ana[0] + '.pdf'
an_title = 'Mean analogs for sim Day: ' + ana[0]
an_output_file = pdf_from_analog(lon=lon,
lat=lat,
data=mean_ana,
vmin=simmin,
vmax=simmax,
Nlin=Nlin,
domain=domain,
output=output_an_file_name,
title=an_title)
outlist.append(str(an_output_file))
# PLOT BEST (first) analog
output_ban_file_name = 'bana_' + ana[0] + '.pdf' #PDF!!
ban_title = 'BEST analog for sim Day ' + ana[0] + ' is: ' + ana[1]
ban_output_file = pdf_from_analog(lon=lon,
lat=lat,
data=arcvar[arc_index[0]],
vmin=simmin,
vmax=simmax,
Nlin=Nlin,
domain=domain,
output=output_ban_file_name,
title=ban_title)
outlist.append(str(ban_output_file))
# PLOT WORST (last) analog
output_wan_file_name = 'wana_' + ana[0] + '.pdf' #PDF!!
wan_title = 'LAST analog for sim Day ' + ana[0] + ' is: ' + ana[
nanalog]
wan_output_file = pdf_from_analog(lon=lon,
lat=lat,
data=arcvar[arc_index[-1]],
vmin=simmin,
vmax=simmax,
Nlin=Nlin,
domain=domain,
output=output_wan_file_name,
title=wan_title)
outlist.append(str(wan_output_file))
# PLOT Max and Min correl analog
min_c_index = np.argmin(cors)
max_c_index = np.argmax(cors)
output_bcan_file_name = 'bcana_' + ana[0] + '.pdf' #PDF!!
bcan_title = 'Analog with max corr for sim Day ' + ana[
0] + ' is: ' + ana[1 + max_c_index]
bcan_output_file = pdf_from_analog(
lon=lon,
lat=lat,
data=arcvar[arc_index[max_c_index]],
vmin=simmin,
vmax=simmax,
Nlin=Nlin,
domain=domain,
output=output_bcan_file_name,
title=bcan_title)
outlist.append(str(bcan_output_file))
output_wcan_file_name = 'wcana_' + ana[0] + '.pdf' #PDF!!
wcan_title = 'Analog with min corr for sim Day ' + ana[
0] + ' is: ' + ana[1 + min_c_index]
wcan_output_file = pdf_from_analog(
lon=lon,
lat=lat,
data=arcvar[arc_index[min_c_index]],
vmin=simmin,
vmax=simmax,
Nlin=Nlin,
domain=domain,
output=output_wcan_file_name,
title=wcan_title)
outlist.append(str(wcan_output_file))
"""
# PLOT analogs dist weighted ====================================
mean_ana = np.zeros((len(arcvar[0,:,0]),len(arcvar[0,0,:])),dtype=float)
for ida, art in enumerate(arc_index):
mean_ana=mean_ana+arcvar[art,:,:]*w_dist[ida]
mean_ana = mean_ana/sum(w_dist)
# PLOT analogs corr weighted ====================================
mean_ana = np.zeros((len(arcvar[0,:,0]),len(arcvar[0,0,:])),dtype=float)
for ida, art in enumerate(arc_index):
mean_ana=mean_ana+arcvar[art,:,:]*w_corr[ida]
mean_ana = mean_ana/sum(w_corr)
"""
simoutpdf = pdfmerge(outlist)
# get the information from config file:
# netcdf files, period, Nanalogs, ouput analogs
# get the information from config file:
# netcdf files, period, Nanalogs, ouput analogs
else:
simoutpdf = 'Analogs.pdf'
# TODO: call this func with analogfile = '..',
# arguments came from kwargs
# need to prescribe all input info - to use with external analogs results.
# check kwargs: ncfiles, N analogs (?), periods, etc
return simoutpdf
def execute(self):
from os.path import basename
from flyingpigeon import sdm
from flyingpigeon.utils import archive, archiveextract # , get_domain
from flyingpigeon.visualisation import map_PAmask
init_process_logger('log.txt')
self.output_log.setValue('log.txt')
self.status.set('Start process', 0)
try:
self.status.set('reading the arguments', 5)
resources = archiveextract(
self.getInputValues(identifier='input_indices'))
csv_file = self.getInputValues(identifier='gbif')[0]
period = self.getInputValues(identifier='period')
period = period[0]
archive_format = self.archive_format.getValue()
except:
logger.error('failed to read in the arguments')
try:
self.status.set('read in latlon coordinates', 10)
latlon = sdm.latlon_gbifcsv(csv_file)
except:
logger.exception('failed to extract the latlon points')
try:
self.status.set('plot map', 20)
from flyingpigeon.visualisation import map_gbifoccurrences
# latlon = sdm.latlon_gbifdic(gbifdic)
occurence_map = map_gbifoccurrences(latlon)
except:
logger.exception('failed to plot occurence map')
# try:
# self.status.set('get domain', 30)
# domains = set()
# for indice in resources:
# # get_domain works only if metadata are set in a correct way
# domains = domains.union([basename(indice).split('_')[1]])
# if len(domains) == 1:
# domain = list(domains)[0]
# logger.info('Domain %s found in indices files' % domain)
# else:
# logger.warn('NOT a single domain in indices files %s' % domains)
# except:
# logger.exception('failed to get domains')
try:
# sort indices
indices_dic = sdm.sort_indices(resources)
logger.info('indice files sorted for %s Datasets' %
len(indices_dic.keys()))
except:
msg = 'failed to sort indices'
logger.exception(msg)
raise Exception(msg)
ncs_references = []
species_files = []
stat_infos = []
PAmask_pngs = []
self.status.set('Start processing for %s Datasets' %
len(indices_dic.keys()))
for count, key in enumerate(indices_dic.keys()):
try:
staus_nr = 40 + count * 10
self.status.set('Start processing of %s' % key, staus_nr)
ncs = indices_dic[key]
logger.info('with %s files' % len(ncs))
try:
self.status.set('generating the PA mask', 20)
PAmask = sdm.get_PAmask(coordinates=latlon, nc=ncs[0])
logger.info('PA mask sucessfully generated')
except:
logger.exception('failed to generate the PA mask')
try:
self.status.set('Ploting PA mask', 25)
PAmask_pngs.extend([map_PAmask(PAmask)])
except:
logger.exception('failed to plot the PA mask')
try:
ncs_reference = sdm.get_reference(ncs_indices=ncs,
period=period)
ncs_references.extend(ncs_reference)
logger.info('reference indice calculated %s ' %
ncs_references)
self.status.set('reference indice calculated',
staus_nr + 2)
except:
msg = 'failed to calculate the reference'
logger.exception(msg)
# raise Exception(msg)
try:
gam_model, predict_gam, gam_info = sdm.get_gam(
ncs_reference, PAmask, modelname=key)
stat_infos.append(gam_info)
self.status.set('GAM sucessfully trained', staus_nr + 5)
except:
msg = 'failed to train GAM for %s' % (key)
logger.exception(msg)
try:
prediction = sdm.get_prediction(gam_model, ncs)
self.status.set('prediction done', staus_nr + 7)
except:
msg = 'failed to predict tree occurence'
logger.exception(msg)
# raise Exception(msg)
# try:
# self.status.set('land sea mask for predicted data', staus_nr + 8)
# from numpy import invert, isnan, nan, broadcast_arrays # , array, zeros, linspace, meshgrid
# mask = invert(isnan(PAmask))
# mask = broadcast_arrays(prediction, mask)[1]
# prediction[mask is False] = nan
# except:
# logger.exception('failed to mask predicted data')
try:
species_files.append(sdm.write_to_file(ncs[0], prediction))
logger.info('Favourabillity written to file')
except:
msg = 'failed to write species file'
logger.exception(msg)
# raise Exception(msg)
except:
msg = 'failed to process SDM chain for %s ' % key
logger.exception(msg)
# raise Exception(msg)
try:
archive_references = None
archive_references = archive(ncs_references, format=archive_format)
logger.info('indices 2D added to archive')
except:
msg = 'failed adding 2D indices to archive'
logger.exception(msg)
raise Exception(msg)
archive_predicion = None
try:
archive_predicion = archive(species_files, format=archive_format)
logger.info('species_files added to archive')
except:
msg = 'failed adding species_files indices to archive'
logger.exception(msg)
raise Exception(msg)
try:
from flyingpigeon.visualisation import pdfmerge, concat_images
stat_infosconcat = pdfmerge(stat_infos)
logger.debug('pngs %s' % PAmask_pngs)
PAmask_png = concat_images(PAmask_pngs, orientation='h')
logger.info('stat infos pdfs and mask pngs merged')
except:
logger.exception('failed to concat images')
_, stat_infosconcat = tempfile.mkstemp(suffix='.pdf',
prefix='foobar-',
dir='.')
_, PAmask_png = tempfile.mkstemp(suffix='.png',
prefix='foobar-',
dir='.')
self.output_gbif.setValue(occurence_map)
self.output_PA.setValue(PAmask_png)
self.output_reference.setValue(archive_references)
self.output_prediction.setValue(archive_predicion)
self.output_info.setValue(stat_infosconcat)
self.status.set('done', 100)
def execute(self):
init_process_logger('log.txt')
self.output_log.setValue('log.txt')
from os.path import basename
from flyingpigeon import sdm
from flyingpigeon.utils import archive, archiveextract
self.status.set('Start process', 0)
try:
logger.info('reading the arguments')
resources = archiveextract(
self.getInputValues(identifier='resources'))
taxon_name = self.getInputValues(identifier='taxon_name')[0]
bbox_obj = self.BBox.getValue()
bbox = [
bbox_obj.coords[0][0], bbox_obj.coords[0][1],
bbox_obj.coords[1][0], bbox_obj.coords[1][1]
]
period = self.getInputValues(identifier='period')
period = period[0]
indices = self.getInputValues(identifier='input_indices')
archive_format = self.archive_format.getValue()
logger.debug("indices = %s for %s ", indices, taxon_name)
logger.info("bbox={0}".format(bbox))
except Exception as e:
logger.error('failed to read in the arguments %s ' % e)
logger.info('indices %s ' % indices)
try:
self.status.set('Fetching GBIF Data', 10)
gbifdic = sdm.get_gbif(taxon_name, bbox=bbox)
except Exception as e:
msg = 'failed to search gbif.'
logger.exception(msg)
raise Exception(msg)
try:
self.status.set('write csv file', 70)
gbifcsv = sdm.gbifdic2csv(gbifdic)
except Exception as e:
msg = 'failed to write csv file.'
logger.exception(msg)
raise Exception(msg)
try:
self.status.set('plot map', 80)
from flyingpigeon.visualisation import map_gbifoccurrences
latlon = sdm.latlon_gbifdic(gbifdic)
occurence_map = map_gbifoccurrences(latlon)
except Exception as e:
msg = 'failed to plot occurence map.'
logger.exception(msg)
raise Exception(msg)
#################################
# calculate the climate indices
#################################
# get the indices
ncs_indices = None
try:
self.status.set(
'start calculation of climate indices for %s' % indices, 30)
ncs_indices = sdm.get_indices(resources=resources, indices=indices)
logger.info('indice calculation done')
except:
msg = 'failed to calculate indices'
logger.exception(msg)
raise Exception(msg)
try:
self.status.set('get domain', 30)
domains = set()
for resource in ncs_indices:
# get_domain works only if metadata are set in a correct way
domains = domains.union([basename(resource).split('_')[1]])
if len(domains) == 1:
domain = list(domains)[0]
logger.debug('Domain %s found in indices files' % domain)
else:
logger.error('Not a single domain in indices files %s' %
domains)
except Exception as e:
logger.exception('failed to get domains %s' % e)
try:
self.status.set('generating the PA mask', 20)
PAmask = sdm.get_PAmask(coordinates=latlon, domain=domain)
logger.info('PA mask sucessfully generated')
except Exception as e:
logger.exception('failed to generate the PA mask: %s' % e)
try:
self.status.set('Ploting PA mask', 25)
from flyingpigeon.visualisation import map_PAmask
PAmask_png = map_PAmask(PAmask)
except Exception as e:
logger.exception('failed to plot the PA mask: %s' % e)
try:
# sort indices
indices_dic = None
indices_dic = sdm.sort_indices(ncs_indices)
logger.info('indice files sorted for %s Datasets' %
len(indices_dic.keys()))
except:
msg = 'failed to sort indices'
logger.exception(msg)
raise Exception(msg)
ncs_references = []
species_files = []
stat_infos = []
for count, key in enumerate(indices_dic.keys()):
try:
staus_nr = 40 + count * 10
self.status.set('Start processing of %s' % key, staus_nr)
ncs = indices_dic[key]
logger.info('with %s files' % len(ncs))
try:
ncs_reference = sdm.get_reference(ncs_indices=ncs,
period=period)
ncs_references.extend(ncs_reference)
logger.info('reference indice calculated %s ' %
ncs_references)
except:
msg = 'failed to calculate the reference'
logger.exception(msg)
raise Exception(msg)
try:
gam_model, predict_gam, gam_info = sdm.get_gam(
ncs_reference, PAmask)
stat_infos.append(gam_info)
self.status.set('GAM sucessfully trained', staus_nr + 5)
except Exception as e:
msg = 'failed to train GAM for %s : %s' % (key, e)
logger.debug(msg)
try:
prediction = sdm.get_prediction(gam_model, ncs)
self.status.set('prediction done', staus_nr + 7)
except Exception as e:
msg = 'failed to predict tree occurence %s' % e
logger.exception(msg)
# raise Exception(msg)
try:
self.status.set('land sea mask for predicted data',
staus_nr + 8)
from numpy import invert, isnan, nan, broadcast_arrays # , array, zeros, linspace, meshgrid
mask = invert(isnan(PAmask))
mask = broadcast_arrays(prediction, mask)[1]
prediction[mask is False] = nan
except Exception as e:
logger.debug('failed to mask predicted data: %s' % e)
try:
species_files.append(sdm.write_to_file(ncs[0], prediction))
logger.info('Favourabillity written to file')
except Exception as e:
msg = 'failed to write species file %s' % e
logger.debug(msg)
# raise Exception(msg)
except Exception as e:
msg = 'failed to calculate reference indices. %s ' % e
logger.exception(msg)
raise Exception(msg)
try:
archive_indices = None
archive_indices = archive(ncs_indices, format=archive_format)
logger.info('indices added to archive')
except:
msg = 'failed adding indices to archive'
logger.exception(msg)
raise Exception(msg)
archive_references = None
try:
archive_references = archive(ncs_references, format=archive_format)
logger.info('indices reference added to archive')
except:
msg = 'failed adding reference indices to archive'
logger.exception(msg)
raise Exception(msg)
archive_predicion = None
try:
archive_predicion = archive(species_files, format=archive_format)
logger.info('species_files added to archive')
except:
msg = 'failed adding species_files indices to archive'
logger.exception(msg)
raise Exception(msg)
try:
from flyingpigeon.visualisation import pdfmerge
stat_infosconcat = pdfmerge(stat_infos)
logger.info('stat infos pdfs merged')
except:
logger.exception('failed to concat images')
_, stat_infosconcat = tempfile.mkstemp(suffix='.pdf',
prefix='foobar-',
dir='.')
self.output_csv.setValue(gbifcsv)
self.output_gbif.setValue(occurence_map)
self.output_PA.setValue(PAmask_png)
self.output_indices.setValue(archive_indices)
self.output_reference.setValue(archive_references)
self.output_prediction.setValue(archive_predicion)
self.output_info.setValue(stat_infosconcat)
self.status.set('done', 100)
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
response.update_status('Start process', 0)
try:
LOGGER.info('reading the arguments')
resources = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))
taxon_name = request.inputs['taxon_name'][0].data
bbox = [-180, -90, 180, 90]
# bbox_obj = self.BBox.getValue()
# bbox = [bbox_obj.coords[0][0],
# bbox_obj.coords[0][1],
# bbox_obj.coords[1][0],
# bbox_obj.coords[1][1]]
period = request.inputs['period']
period = period[0].data
indices = [inpt.data for inpt in request.inputs['indices']]
archive_format = request.inputs['archive_format'][0].data
LOGGER.exception("indices = %s for %s ", indices, taxon_name)
LOGGER.info("bbox={0}".format(bbox))
except:
LOGGER.exception('failed to read in the arguments')
LOGGER.info('indices %s ' % indices)
try:
response.update_status('Fetching GBIF Data', 10)
gbifdic = sdm.get_gbif(taxon_name, bbox=bbox)
LOGGER.info('Fetched GBIF data')
except:
msg = 'failed to search gbif.'
LOGGER.exception(msg)
raise Exception(msg)
try:
response.update_status('write csv file', 70)
gbifcsv = sdm.gbifdic2csv(gbifdic)
LOGGER.info('GBIF data written to file')
except:
msg = 'failed to write csv file.'
LOGGER.exception(msg)
raise Exception(msg)
try:
response.update_status('plot map', 80)
latlon = sdm.latlon_gbifdic(gbifdic)
occurence_map = map_gbifoccurrences(latlon)
except:
msg = 'failed to plot occurence map.'
LOGGER.exception(msg)
raise Exception(msg)
#################################
# calculate the climate indices
#################################
# get the indices
ncs_indices = None
try:
response.update_status(
'start calculation of climate indices for %s' % indices, 30)
ncs_indices = sdm.get_indices(resource=resources, indices=indices)
LOGGER.info('indice calculation done')
except:
msg = 'failed to calculate indices'
LOGGER.exception(msg)
raise Exception(msg)
try:
# sort indices
indices_dic = sdm.sort_indices(ncs_indices)
LOGGER.info('indice files sorted in dictionary')
except:
msg = 'failed to sort indices'
LOGGER.exception(msg)
indices_dic = {'dummy': []}
ncs_references = []
species_files = []
stat_infos = []
PAmask_pngs = []
response.update_status('Start processing for %s Datasets' %
len(indices_dic.keys()))
for count, key in enumerate(indices_dic.keys()):
try:
staus_nr = 40 + count * 10
response.update_status('Start processing of %s' % key,
staus_nr)
ncs = indices_dic[key]
LOGGER.info('with %s files' % len(ncs))
try:
response.update_status('generating the PA mask', 20)
PAmask = sdm.get_PAmask(coordinates=latlon, nc=ncs[0])
LOGGER.info('PA mask sucessfully generated')
except:
LOGGER.exception('failed to generate the PA mask')
try:
response.update_status('Ploting PA mask', 25)
PAmask_pngs.extend([map_PAmask(PAmask)])
except:
LOGGER.exception('failed to plot the PA mask')
try:
ncs_reference = sdm.get_reference(ncs_indices=ncs,
period=period)
ncs_references.extend(ncs_reference)
LOGGER.info('reference indice calculated %s ' %
ncs_references)
except:
msg = 'failed to calculate the reference'
LOGGER.exception(msg)
try:
gam_model, predict_gam, gam_info = sdm.get_gam(
ncs_reference, PAmask)
stat_infos.append(gam_info)
response.update_status('GAM sucessfully trained',
staus_nr + 5)
except:
msg = 'failed to train GAM for %s' % (key)
LOGGER.exception(msg)
try:
prediction = sdm.get_prediction(gam_model, ncs)
response.update_status('prediction done', staus_nr + 7)
except:
msg = 'failed to predict tree occurence'
LOGGER.exception(msg)
# raise Exception(msg)
# try:
# response.update_status('land sea mask for predicted data', staus_nr + 8)
# from numpy import invert, isnan, nan, broadcast_arrays # , array, zeros, linspace, meshgrid
# mask = invert(isnan(PAmask))
# mask = broadcast_arrays(prediction, mask)[1]
# prediction[mask is False] = nan
# except:
# LOGGER.exception('failed to mask predicted data')
try:
species_files.append(sdm.write_to_file(ncs[0], prediction))
LOGGER.info('Favourabillity written to file')
except:
msg = 'failed to write species file'
LOGGER.exception(msg)
# raise Exception(msg)
except:
msg = 'failed to calculate reference indices'
LOGGER.exception(msg)
raise Exception(msg)
try:
archive_indices = archive(ncs_indices, format=archive_format)
LOGGER.info('indices added to archive')
except:
msg = 'failed adding indices to archive'
LOGGER.exception(msg)
archive_indices = tempfile.mkstemp(suffix='.tar',
prefix='foobar-',
dir='.')
try:
archive_references = archive(ncs_references, format=archive_format)
LOGGER.info('indices reference added to archive')
except:
msg = 'failed adding reference indices to archive'
LOGGER.exception(msg)
archive_references = tempfile.mkstemp(suffix='.tar',
prefix='foobar-',
dir='.')
try:
archive_prediction = archive(species_files, format=archive_format)
LOGGER.info('species_files added to archive')
except:
msg = 'failed adding species_files indices to archive'
LOGGER.exception(msg)
raise Exception(msg)
try:
stat_infosconcat = pdfmerge(stat_infos)
LOGGER.debug('pngs %s' % PAmask_pngs)
PAmask_png = concat_images(PAmask_pngs, orientation='h')
LOGGER.info('stat infos pdfs and mask pngs merged')
except:
LOGGER.exception('failed to concat images')
_, stat_infosconcat = tempfile.mkstemp(suffix='.pdf',
prefix='foobar-',
dir='.')
_, PAmask_png = tempfile.mkstemp(suffix='.png',
prefix='foobar-',
dir='.')
response.outputs['output_gbif'].file = occurence_map
response.outputs['output_PA'].file = PAmask_png
response.outputs['output_indices'].file = archive_indices
response.outputs['output_reference'].file = archive_references
response.outputs['output_prediction'].file = archive_prediction
response.outputs['output_info'].file = stat_infosconcat
response.outputs['output_csv'].file = gbifcsv
response.update_status('done', 100)
return response
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
response.update_status('Start process', 0)
try:
response.update_status('reading the arguments', 5)
resources = archiveextract(
resource=rename_complexinputs(request.inputs['resources']))
period = request.inputs['period']
period = period[0].data
archive_format = request.inputs['archive_format'][0].data
LOGGER.info("all arguments read in nr of files in resources: {}".format(len(resources)))
except Exception as ex:
LOGGER.exception('failed to read in the arguments: {}'.format(str(ex)))
try:
gbif_url = request.inputs['gbif'][0].data
csv_file = download(gbif_url)
LOGGER.info('CSV file fetched sucessfully: %s' % csv_file)
except Exception as ex:
LOGGER.exception('failed to fetch GBIF file: {}'.format(str(ex)))
try:
response.update_status('read in latlon coordinates', 10)
latlon = sdm.latlon_gbifcsv(csv_file)
LOGGER.info('read in the latlon coordinates')
except Exception as ex:
LOGGER.exception('failed to extract the latlon points: {}'.format(str(ex)))
try:
response.update_status('plot map', 20)
occurence_map = map_gbifoccurrences(latlon)
LOGGER.info('GBIF occourence ploted')
except Exception as ex:
LOGGER.exception('failed to plot occurrence map: {}'.format(str(ex)))
try:
# sort indices
indices_dic = sdm.sort_indices(resources)
LOGGER.info('indice files sorted in dictionary')
except Exception as ex:
msg = 'failed to sort indices: {}'.format(str(ex))
LOGGER.exception(msg)
indices_dic = {'dummy': []}
ncs_references = []
species_files = []
stat_infos = []
PAmask_pngs = []
response.update_status('Start processing for {} datasets'.format(len(indices_dic.keys())))
for count, key in enumerate(indices_dic.keys()):
try:
status_nr = 40 + count * 10
response.update_status('Start processing of {}'.format(key), status_nr)
ncs = indices_dic[key]
LOGGER.info('with {} files'.format(len(ncs)))
try:
response.update_status('generating the PA mask', 20)
PAmask = sdm.get_PAmask(coordinates=latlon, nc=ncs[0])
LOGGER.info('PA mask sucessfully generated')
except Exception as ex:
msg = 'failed to generate the PA mask: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
try:
response.update_status('Ploting PA mask', 25)
PAmask_pngs.extend([map_PAmask(PAmask)])
except Exception as ex:
msg = 'failed to plot the PA mask: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
try:
ncs_reference = sdm.get_reference(ncs_indices=ncs, period=period)
ncs_references.extend(ncs_reference)
LOGGER.info('reference indice calculated: {}'.format(ncs_references))
response.update_status('reference indice calculated', status_nr + 2)
except Exception as ex:
msg = 'failed to calculate the reference: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
try:
gam_model, predict_gam, gam_info = sdm.get_gam(ncs_reference, PAmask, modelname=key)
stat_infos.append(gam_info)
response.update_status('GAM sucessfully trained', status_nr + 5)
except Exception as ex:
msg = 'failed to train GAM for {}: {}'.format(key, str(ex))
LOGGER.exception(msg)
raise Exception(msg)
try:
prediction = sdm.get_prediction(gam_model, ncs)
response.update_status('prediction done', status_nr + 7)
except Exception as ex:
msg = 'failed to predict tree occurence: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
# try:
# response.update_status('land sea mask for predicted data', status_nr + 8)
# from numpy import invert, isnan, nan, broadcast_arrays # , array, zeros, linspace, meshgrid
# mask = invert(isnan(PAmask))
# mask = broadcast_arrays(prediction, mask)[1]
# prediction[mask is False] = nan
# except:
# LOGGER.exception('failed to mask predicted data')
try:
species_files.append(sdm.write_to_file(ncs[0], prediction))
LOGGER.info('Favourabillity written to file')
except Exception as ex:
msg = 'failed to write species file: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
except Exception as ex:
msg = 'failed to process SDM chain for {} : {}'.format(key, str(ex))
LOGGER.exception(msg)
raise Exception(msg)
try:
archive_references = archive(ncs_references, format=archive_format)
LOGGER.info('indices 2D added to archive')
except Exception as ex:
msg = 'failed adding 2D indices to archive: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
archive_references = tempfile.mkstemp(suffix='.tar', prefix='foobar-', dir='.')
try:
archive_prediction = archive(species_files, format=archive_format)
LOGGER.info('species_files added to archive')
except Exception as ex:
msg = 'failed adding species_files indices to archive: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
archive_predicion = tempfile.mkstemp(suffix='.tar', prefix='foobar-', dir='.')
try:
from flyingpigeon.visualisation import pdfmerge, concat_images
stat_infosconcat = pdfmerge(stat_infos)
LOGGER.debug('pngs: {}'.format(PAmask_pngs))
PAmask_png = concat_images(PAmask_pngs, orientation='h')
LOGGER.info('stat infos pdfs and mask pngs merged')
except Exception as ex:
msg = 'failed to concat images: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
_, stat_infosconcat = tempfile.mkstemp(suffix='.pdf', prefix='foobar-', dir='.')
_, PAmask_png = tempfile.mkstemp(suffix='.png', prefix='foobar-', dir='.')
response.outputs['output_gbif'].file = occurence_map
response.outputs['output_PA'].file = PAmask_png
response.outputs['output_reference'].file = archive_references
response.outputs['output_prediction'].file = archive_prediction
response.outputs['output_info'].file = stat_infosconcat
response.update_status('done', 100)
return response
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
from os.path import basename
from flyingpigeon import sdm
from flyingpigeon.utils import archive, archiveextract, download
response.update_status('Start process', 0)
try:
LOGGER.info('reading the arguments')
resources_raw = archiveextract(
resource=rename_complexinputs(request.inputs['resources']))
csv_url = request.inputs['gbif'][0].data
period = request.inputs['period']
period = period[0].data
indices = request.inputs['input_indices']
archive_format = request.inputs['archive_format']
LOGGER.info('indices %s ' % indices)
LOGGER.debug('csv_url %s' % csv_url)
except Exception:
LOGGER.exception('failed to read in the arguments')
raise
try:
LOGGER.info('set up the environment')
csv_file = download(csv_url)
resources = archiveextract(resources_raw)
except:
LOGGER.exception('failed to set up the environment')
raise
try:
response.update_status('read in latlon coordinates', 10)
latlon = sdm.latlon_gbifcsv(csv_file)
LOGGER.info('got occurence coordinates %s ' % csv_file)
except:
LOGGER.exception(
'failed to extract the latlon points from file: %s' %
(csv_file))
try:
response.update_status('plot map', 20)
from flyingpigeon.visualisation import map_gbifoccurrences
# latlon = sdm.latlon_gbifdic(gbifdic)
occurence_map = map_gbifoccurrences(latlon)
except:
LOGGER.exception('failed to plot occurence map')
#################################
# calculate the climate indices
#################################
# get the indices
ncs_indices = None
try:
response.update_status(
'start calculation of climate indices for %s' % indices, 30)
ncs_indices = sdm.get_indices(resources=resources, indices=indices)
LOGGER.info('indice calculation done')
except:
msg = 'failed to calculate indices'
LOGGER.exception(msg)
raise Exception(msg)
try:
response.update_status('get domain', 30)
domains = set()
for resource in ncs_indices:
# get_domain works only if metadata are set in a correct way
domains = domains.union([basename(resource).split('_')[1]])
if len(domains) == 1:
domain = list(domains)[0]
LOGGER.debug('Domain %s found in indices files' % domain)
else:
LOGGER.error('Not a single domain in indices files %s' %
domains)
except:
LOGGER.exception('failed to get domains')
try:
response.update_status('generating the PA mask', 20)
PAmask = sdm.get_PAmask(coordinates=latlon, domain=domain)
LOGGER.info('PA mask sucessfully generated')
except:
LOGGER.exception('failed to generate the PA mask')
try:
response.update_status('Ploting PA mask', 25)
from flyingpigeon.visualisation import map_PAmask
PAmask_png = map_PAmask(PAmask)
except:
LOGGER.exception('failed to plot the PA mask')
try:
# sort indices
indices_dic = None
indices_dic = sdm.sort_indices(ncs_indices)
LOGGER.info('indice files sorted for %s Datasets' %
len(indices_dic.keys()))
except:
msg = 'failed to sort indices'
LOGGER.exception(msg)
raise Exception(msg)
ncs_references = []
species_files = []
stat_infos = []
for count, key in enumerate(indices_dic.keys()):
try:
staus_nr = 40 + count * 10
response.update_status('Start processing of %s' % key,
staus_nr)
ncs = indices_dic[key]
LOGGER.info('with %s files' % len(ncs))
try:
ncs_reference = sdm.get_reference(ncs_indices=ncs,
period=period)
ncs_references.extend(ncs_reference)
LOGGER.info('reference indice calculated %s ' %
ncs_references)
except:
msg = 'failed to calculate the reference'
LOGGER.exception(msg)
raise Exception(msg)
try:
gam_model, predict_gam, gam_info = sdm.get_gam(
ncs_reference, PAmask)
stat_infos.append(gam_info)
response.update_status('GAM sucessfully trained',
staus_nr + 5)
except:
msg = 'failed to train GAM for %s' % (key)
LOGGER.debug(msg)
try:
prediction = sdm.get_prediction(gam_model, ncs)
response.update_status('prediction done', staus_nr + 7)
except:
msg = 'failed to predict tree occurence'
LOGGER.exception(msg)
# raise Exception(msg)
try:
response.update_status('land sea mask for predicted data',
staus_nr + 8)
from numpy import invert, isnan, nan, broadcast_arrays # , array, zeros, linspace, meshgrid
mask = invert(isnan(PAmask))
mask = broadcast_arrays(prediction, mask)[1]
prediction[mask is False] = nan
except:
LOGGER.debug('failed to mask predicted data')
try:
species_files.append(sdm.write_to_file(ncs[0], prediction))
LOGGER.info('Favourabillity written to file')
except:
msg = 'failed to write species file'
LOGGER.debug(msg)
# raise Exception(msg)
except:
msg = 'failed to calculate reference indices.'
LOGGER.exception(msg)
raise Exception(msg)
try:
archive_indices = None
archive_indices = archive(ncs_indices, format=archive_format)
LOGGER.info('indices added to archive')
except:
msg = 'failed adding indices to archive'
LOGGER.exception(msg)
raise Exception(msg)
archive_references = None
try:
archive_references = archive(ncs_references, format=archive_format)
LOGGER.info('indices reference added to archive')
except:
msg = 'failed adding reference indices to archive'
LOGGER.exception(msg)
raise Exception(msg)
archive_predicion = None
try:
archive_predicion = archive(species_files, format=archive_format)
LOGGER.info('species_files added to archive')
except:
msg = 'failed adding species_files indices to archive'
LOGGER.exception(msg)
raise Exception(msg)
try:
from flyingpigeon.visualisation import pdfmerge
stat_infosconcat = pdfmerge(stat_infos)
LOGGER.info('stat infos pdfs merged')
except:
LOGGER.exception('failed to concat images')
_, stat_infosconcat = tempfile.mkstemp(suffix='.pdf',
prefix='foobar-',
dir='.')
# self.output_csv.setValue(csv_file)
response.outputs['output_gbif'].file = occurence_map
response.outputs['output_PA'].file = PAmask_png
response.outputs['output_indices'].file = archive_indices
response.outputs['output_reference'].file = archive_references
response.outputs['archive_predicion'].file = archive_predicion
response.outputs['output_info'].file = stat_infosconcat
response.update_status('done', 100)
return response