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 = {} for {}".format(indices, taxon_name))
LOGGER.info("bbox={}".format(bbox))
except Exception as ex:
msg = 'failed to read in the arguments: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
LOGGER.info('indices {}'.format(indices))
try:
response.update_status('Fetching GBIF Data', 10)
gbifdic = sdm.get_gbif(taxon_name, bbox=bbox)
LOGGER.info('Fetched GBIF data')
except Exception as ex:
msg = 'failed to search gbif: {}'.format(str(ex))
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 Exception as ex:
msg = 'failed to write csv file: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
try:
response.update_status('plot map', 80)
latlon = sdm.latlon_gbifdic(gbifdic)
occurence_map = map_gbifoccurrences(latlon)
except Exception as ex:
msg = 'failed to plot occurence map: {}'.format(str(ex))
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 {}'.format(indices), 30)
ncs_indices = sdm.get_indices(resource=resources, indices=indices)
LOGGER.info('indice calculation done')
except Exception as ex:
msg = 'failed to calculate indices: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
try:
# sort indices
indices_dic = sdm.sort_indices(ncs_indices)
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 successfully 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('Plotting 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 %s '
% ncs_references)
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)
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 occurrence: {}'.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('Favourability 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 calculate reference indices: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
try:
archive_indices = archive(ncs_indices, format=archive_format)
LOGGER.info('indices added to archive')
except Exception as ex:
msg = 'failed adding indices to archive: {}'.format(str(ex))
LOGGER.exception(msg)
raise 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 Exception as ex:
msg = 'failed adding reference 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)
try:
stat_infosconcat = pdfmerge(stat_infos)
LOGGER.debug('pngs {}'.format(PAmask_pngs))
PAmask_png = concat_images(PAmask_pngs, orientation='h')
LOGGER.info('stat info 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_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: %s" %
len(resources))
except:
LOGGER.exception('failed to read in the arguments')
try:
gbif_url = request.inputs['gbif'][0].data
csv_file = download(gbif_url)
LOGGER.info('CSV file fetched sucessfully: %s' % csv_file)
except:
LOGGER.exception('failed to fetch GBIF file')
try:
response.update_status('read in latlon coordinates', 10)
latlon = sdm.latlon_gbifcsv(csv_file)
LOGGER.info('read in the latlon coordinates')
except:
LOGGER.exception('failed to extract the latlon points')
try:
response.update_status('plot map', 20)
occurence_map = map_gbifoccurrences(latlon)
LOGGER.info('GBIF occourence ploted')
except:
LOGGER.exception('failed to plot occurence map')
try:
# sort indices
indices_dic = sdm.sort_indices(resources)
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)
response.update_status('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)
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 process SDM chain for %s ' % key
LOGGER.exception(msg)
# raise Exception(msg)
try:
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)
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)
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 %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_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):
tic = dt.now()
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
LOGGER.info('Start process')
response.update_status('Execution started at : {}'.format(tic), 1)
######################################
# Read inputs
######################################
try:
candidate = archiveextract(
resource=rename_complexinputs(request.inputs['candidate']))
target = archiveextract(
resource=rename_complexinputs(request.inputs['target']))
location = request.inputs['location'][0].data
indices = [el.data for el in request.inputs['indices']]
dist = request.inputs['dist'][0].data
dateStartCandidate = request.inputs['dateStartCandidate'][0].data
dateEndCandidate = request.inputs['dateEndCandidate'][0].data
dateStartTarget = request.inputs['dateStartTarget'][0].data
dateEndTarget = request.inputs['dateEndTarget'][0].data
except Exception as e:
msg = 'Failed to read input parameter {}'.format(e)
LOGGER.error(msg)
raise Exception(msg)
response.update_status('Input parameters ingested', 2)
######################################
# Process inputs
######################################
try:
point = Point(*map(float, location.split(',')))
dateStartCandidate = dt.strptime(dateStartCandidate, '%Y-%m-%d')
dateEndCandidate = dt.strptime(dateEndCandidate, '%Y-%m-%d')
dateStartTarget = dt.strptime(dateStartTarget, '%Y-%m-%d')
dateEndTarget = dt.strptime(dateEndTarget, '%Y-%m-%d')
except Exception as e:
msg = 'failed to process inputs {} '.format(e)
LOGGER.error(msg)
raise Exception(msg)
LOGGER.debug("init took {}".format(dt.now() - tic))
response.update_status('Processed input parameters', 3)
######################################
# Extract target time series
######################################
savetarget = False
try:
# Using `call` creates a netCDF file in the tmp directory.
#
# Here we keep this stuff in memory
if savetarget:
prefix = 'target_ts'
target_ts = call(resource=target,
geom=point,
variable=indices,
time_range=[dateStartTarget, dateEndTarget],
select_nearest=True,
prefix=prefix)
#target_ts = [get_values(prefix+'.nc', ind) for ind in indices]
else:
trd = RequestDataset(
target,
variable=indices,
time_range=[dateStartTarget, dateEndTarget])
op = OcgOperations(trd,
geom=point,
select_nearest=True,
search_radius_mult=1.75)
out = op.execute()
target_ts = out.get_element()
except Exception as e:
msg = 'Target extraction failed {}'.format(e)
LOGGER.debug(msg)
raise Exception(msg)
response.update_status('Extracted target series', 5)
######################################
# Compute dissimilarity metric
######################################
response.update_status('Computing spatial analog', 6)
try:
output = call(
resource=candidate,
calc=[{
'func': 'dissimilarity',
'name': 'spatial_analog',
'kwds': {
'dist': dist,
'target': target_ts,
'candidate': indices
}
}],
time_range=[dateStartCandidate, dateEndCandidate],
)
except Exception as e:
msg = 'Spatial analog failed: {}'.format(e)
LOGGER.exception(msg)
raise Exception(msg)
add_metadata(output,
dist=dist,
indices=",".join(indices),
target_location=location,
candidate_time_range="{},{}".format(
dateStartCandidate, dateEndCandidate),
target_time_range="{},{}".format(dateStartTarget,
dateEndTarget))
response.update_status('Computed spatial analog', 95)
response.outputs['output_netcdf'].file = output
response.update_status('Execution completed', 100)
LOGGER.debug("Total execution took {}".format(dt.now() - tic))
return response
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
response.update_status('execution started at : {}'.format(dt.now()), 5)
################################
# reading in the input arguments
################################
try:
LOGGER.info('read in the arguments')
# resources = self.getInputValues(identifier='resources')
season = request.inputs['season'][0].data
LOGGER.info('season %s', season)
period = request.inputs['period'][0].data
LOGGER.info('period %s', period)
anualcycle = request.inputs['anualcycle'][0].data
start = dt.strptime(period.split('-')[0], '%Y%m%d')
end = dt.strptime(period.split('-')[1], '%Y%m%d')
LOGGER.debug('start: %s , end: %s ' % (start, end))
resource = archiveextract(resource=rename_complexinputs(request.inputs['resource']))
# resource = archiveextract(resource=[res.file for res in request.inputs['resource']])
url_Rdat = request.inputs['Rdat'][0].data
url_dat = request.inputs['dat'][0].data
url_ref_file = request.inputs['netCDF'][0].data # can be None
# season = self.getInputValues(identifier='season')[0]
# period = self.getInputValues(identifier='period')[0]
# anualcycle = self.getInputValues(identifier='anualcycle')[0]
LOGGER.info('period %s' % str(period))
LOGGER.info('season %s' % str(season))
LOGGER.info('read in the arguments')
LOGGER.info('url_ref_file: %s' % url_ref_file)
LOGGER.info('url_Rdat: %s' % url_Rdat)
LOGGER.info('url_dat: %s' % url_dat)
except Exception as e:
LOGGER.debug('failed to convert arguments %s ' % e)
############################
# fetching trainging data
############################
try:
dat = abspath(download(url_dat))
Rdat = abspath(download(url_Rdat))
LOGGER.info('training data fetched')
except Exception as e:
LOGGER.error('failed to fetch training data %s' % e)
##########################################################
# get the required bbox and time region from resource data
##########################################################
# from flyingpigeon.weatherregimes import get_level
try:
from flyingpigeon.ocgis_module import call
from flyingpigeon.utils import get_variable
time_range = [start, end]
variable = get_variable(resource)
if len(url_ref_file) > 0:
ref_file = download(url_ref_file)
model_subset = call(
resource=resource, variable=variable,
time_range=time_range, # conform_units_to=conform_units_to, geom=bbox, spatial_wrapping='wrap',
regrid_destination=ref_file, regrid_options='bil')
LOGGER.info('Dataset subset with regridding done: %s ' % model_subset)
else:
model_subset = call(
resource=resource, variable=variable,
time_range=time_range, # conform_units_to=conform_units_to, geom=bbox, spatial_wrapping='wrap',
)
LOGGER.info('Dataset time period extracted: %s ' % model_subset)
except:
LOGGER.exception('failed to make a data subset ')
#######################
# computing anomalies
#######################
try:
cycst = anualcycle.split('-')[0]
cycen = anualcycle.split('-')[0]
reference = [dt.strptime(cycst, '%Y%m%d'), dt.strptime(cycen, '%Y%m%d')]
model_anomal = wr.get_anomalies(model_subset, reference=reference)
#####################
# extracting season
#####################
model_season = wr.get_season(model_anomal, season=season)
except:
LOGGER.exception('failed to compute anualcycle or seasons')
#######################
# call the R scripts
#######################
import shlex
import subprocess
from flyingpigeon import config
from os.path import curdir, exists, join
try:
rworkspace = curdir
Rsrc = config.Rsrc_dir()
Rfile = 'weatherregimes_projection.R'
yr1 = start.year
yr2 = end.year
time = get_time(model_season, format='%Y%m%d')
# ip, output_graphics = mkstemp(dir=curdir ,suffix='.pdf')
ip, file_pca = mkstemp(dir=curdir, suffix='.txt')
ip, file_class = mkstemp(dir=curdir, suffix='.Rdat')
ip, output_frec = mkstemp(dir=curdir, suffix='.txt')
args = ['Rscript', join(Rsrc, Rfile), '%s/' % curdir,
'%s/' % Rsrc,
'%s' % model_season,
'%s' % variable,
'%s' % str(time).strip("[]").replace("'", "").replace(" ", ""),
# '%s' % output_graphics,
'%s' % dat,
'%s' % Rdat,
'%s' % file_pca,
'%s' % file_class,
'%s' % output_frec,
'%s' % season,
'%s' % start.year,
'%s' % end.year,
'%s' % 'MODEL']
LOGGER.info('Rcall builded')
except Exception as e:
msg = 'failed to build the R command %s' % e
LOGGER.error(msg)
raise Exception(msg)
try:
output, error = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
# , shell=True
LOGGER.info('R outlog info:\n %s ' % output)
LOGGER.debug('R outlog errors:\n %s ' % error)
if len(output) > 0:
response.update_status('**** weatherregime in R suceeded', 90)
else:
LOGGER.error('NO! output returned from R call')
except Exception as e:
msg = 'weatherregime in R %s ' % e
LOGGER.error(msg)
raise Exception(msg)
#################
# set the outputs
#################
response.update_status('Set the process outputs ', 95)
response.outputs['output_pca'].file = file_pca
response.outputs['output_classification'].file = file_class
response.outputs['output_netcdf'].file = model_season
response.outputs['output_frequency'].file = output_frec
response.update_status('done', 100)
return response
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
process_start_time = time.time() # measure process execution time ...
response.update_status('execution started at : %s ' % dt.now(), 5)
LOGGER.debug('starting segetalflora process execution')
response.update_status('starting calcualtion segetalflora', 5)
############################
# read argments to variables
############################
try:
resource = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))
climate_type = request.inputs['climate_type'][0].data
culture_type = request.inputs['culture_type'][0].data
LOGGER.info('urls for %s ncs found' % (len(resource)))
LOGGER.info('culture type: %s ' % (culture_type))
except Exception:
msg = 'Failed to read in the arguments.'
LOGGER.exception(msg)
raise Exception(msg)
try:
if type(climate_type) != list:
climate_type = list([climate_type])
if type(culture_type) != list:
culture_type = list([culture_type])
LOGGER.info('arguments are lists')
except Exception:
msg = 'Failed to transform arguments to lists.'
LOGGER.exception(msg)
raise Exception(msg)
#############################
# get yearly mean temperature
#############################
nc_tasmean = sf.get_yrmean(resource)
#######################################
# main call for segetalflora processing
#######################################
nc_sf = sf.get_segetalflora(resource=nc_tasmean,
culture_type=culture_type,
climate_type=climate_type)
####################
# tar file archiving
####################
try:
response.update_status('preparting output', 99)
LOGGER.debug('length of sf: %s', len(nc_sf))
if len(nc_sf) == 1:
# TODO: fix pywps output formats OR use seperate output params.
response.outputs['out_segetalflora'].file = nc_sf[0]
response.outputs['out_segetalflora'].format = FORMATS.NETCDF
else:
response.outputs['out_segetalflora'].file = archive(
nc_sf, format='tar', dir_output='.', mode='w')
response.outputs['out_segetalflora'].format = Format(
'application/x-tar')
if len(nc_tasmean) == 1:
response.outputs['out_tasmean'].file = nc_tasmean[0]
response.outputs['out_segetalflora'].format = FORMATS.NETCDF
else:
response.outputs['out_tasmean'].file = archive(nc_tasmean,
format='tar',
dir_output='.',
mode='w')
response.outputs['out_segetalflora'].format = Format(
'application/x-tar')
except Exception:
msg = 'Failed to prepare output files.'
LOGGER.exception(msg)
raise Exception(msg)
response.update_status('done', 100)
LOGGER.debug("total execution took %s seconds.",
time.time() - process_start_time)
return response
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
# input files
LOGGER.debug("url=%s, mime_type=%s", request.inputs['resource'][0].url,
request.inputs['resource'][0].data_format.mime_type)
ncs = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))
# mime_type=request.inputs['resource'][0].data_format.mime_type)
# mosaic option
# TODO: fix defaults in pywps 4.x
if 'mosaic' in request.inputs:
mosaic = request.inputs['mosaic'][0].data
else:
mosaic = False
# regions used for subsetting
regions = [inp.data for inp in request.inputs['region']]
LOGGER.info('ncs = %s', ncs)
LOGGER.info('regions = %s', regions)
LOGGER.info('mosaic = %s', mosaic)
response.update_status("Arguments set for subset process", 0)
LOGGER.debug('starting: regions=%s, num_files=%s', len(regions),
len(ncs))
try:
results = clipping(
resource=ncs,
polygons=regions, # self.region.getValue(),
mosaic=mosaic,
spatial_wrapping='wrap',
# variable=variable,
# dir_output=os.path.abspath(os.curdir),
# dimension_map=dimension_map,
)
LOGGER.info('results %s' % results)
except:
msg = 'clipping failed'
LOGGER.exception(msg)
raise Exception(msg)
if not results:
raise Exception('no results produced.')
# prepare tar file
try:
tarf = archive(results)
LOGGER.info('Tar file prepared')
except:
msg = 'Tar file preparation failed'
LOGGER.exception(msg)
raise Exception(msg)
response.outputs['output'].file = tarf
i = next((i for i, x in enumerate(results) if x), None)
response.outputs['ncout'].file = results[i]
response.update_status("done", 100)
return response
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
try:
resources = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))
# indices = request.inputs['indices'][0].data
grouping = request.inputs['grouping'][0].data
# grouping = [inpt.data for inpt in request.inputs['grouping']]
if 'region' in request.inputs:
region = request.inputs['region'][0].data
else:
region = None
if 'mosaic' in request.inputs:
mosaic = request.inputs['mosaic'][0].data
else:
mosaic = False
percentile = request.inputs['percentile'][0].data
# refperiod = request.inputs['refperiod'][0].data
from datetime import datetime as dt
#
# if refperiod is not None:
# start = dt.strptime(refperiod.split('-')[0], '%Y%m%d')
# end = dt.strptime(refperiod.split('-')[1], '%Y%m%d')
# refperiod = [start, end]
# response.update_status('starting: indices=%s, grouping=%s, num_files=%s'
# % (indices, grouping, len(resources)), 2)
LOGGER.debug("grouping %s " % grouping)
LOGGER.debug("mosaic %s " % mosaic)
# LOGGER.debug("refperiod set to %s, %s " % (start, end))
# LOGGER.debug('indices= %s ' % indices)
LOGGER.debug('percentile: %s' % percentile)
LOGGER.debug('region %s' % region)
LOGGER.debug('Nr of input files %s ' % len(resources))
except Exception as e:
LOGGER.exception("failed to read in the arguments: %s" % e)
from flyingpigeon.utils import sort_by_filename
from flyingpigeon.ocgis_module import call
datasets = sort_by_filename(resources, historical_concatination=True)
results = []
kwds = {'percentile': percentile, 'window_width': 5}
calc = [{'func': 'daily_perc', 'name': 'dp', 'kwds': kwds}]
#
# ops = OcgOperations(dataset=rd, calc=calc,
# output_format='nc',
# time_region={'year': [1980, 1990]}
# ).execute()
try:
for key in datasets.keys():
try:
result = calc(
resource=datasets[key],
calc=calc,
# calc_grouping='year'
)
LOGGER.debug('percentile based indice done for %s' %
result)
results.extend(result)
except Exception as e:
LOGGER.exception(
"failed to calculate percentile based indice for %s: %s"
% key, e)
except Exception as e:
LOGGER.exception("failed to calculate percentile indices: %s" % e)
output_archive = archive(results)
response.outputs['output_archive'].file = output_archive
i = next((i for i, x in enumerate(results) if x), None)
if i is None:
i = "dummy.nc"
response.outputs['ncout'].file = results[i]
response.update_status("done", 100)
return response
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
LOGGER.info('Start process')
response.update_status('execution started at : {}'.format(dt.now()), 5)
process_start_time = time.time() # measure process execution time ...
start_time = time.time() # measure init ...
################################
# reading in the input arguments
################################
response.update_status('execution started at : %s ' % dt.now(), 5)
start_time = time.time() # measure init ...
################################
# reading in the input arguments
################################
try:
response.update_status('read input parameter : %s ' % dt.now(), 5)
resource = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))
refSt = request.inputs['refSt'][0].data
refEn = request.inputs['refEn'][0].data
dateSt = request.inputs['dataSt'][0].data
dateEn = request.inputs['dataEn'][0].data
seasonwin = request.inputs['seasonwin'][0].data
nanalog = request.inputs['nanalog'][0].data
bbox = [-80, 20, 50, 70]
# if bbox_obj is not None:
# LOGGER.info("bbox_obj={0}".format(bbox_obj.coords))
# bbox = [bbox_obj.coords[0][0],
# bbox_obj.coords[0][1],
# bbox_obj.coords[1][0],
# bbox_obj.coords[1][1]]
# LOGGER.info("bbox={0}".format(bbox))
# else:
# bbox = None
# region = self.getInputValues(identifier='region')[0]
# bbox = [float(b) for b in region.split(',')]
# bbox_obj = self.BBox.getValue()
normalize = request.inputs['normalize'][0].data
distance = request.inputs['dist'][0].data
outformat = request.inputs['outformat'][0].data
timewin = request.inputs['timewin'][0].data
model_var = request.inputs['reanalyses'][0].data
model, var = model_var.split('_')
# experiment = self.getInputValues(identifier='experiment')[0]
# dataset, var = experiment.split('_')
# LOGGER.info('environment set')
LOGGER.info('input parameters set')
response.update_status('Read in and convert the arguments', 5)
except Exception as e:
msg = 'failed to read input prameter %s ' % e
LOGGER.error(msg)
raise Exception(msg)
######################################
# convert types and set environment
######################################
try:
refSt = dt.strptime(refSt[0], '%Y-%m-%d')
refEn = dt.strptime(refEn[0], '%Y-%m-%d')
dateSt = dt.strptime(dateSt[0], '%Y-%m-%d')
dateEn = dt.strptime(dateEn[0], '%Y-%m-%d')
if normalize == 'None':
seacyc = False
else:
seacyc = True
if outformat == 'ascii':
outformat = '.txt'
elif outformat == 'netCDF':
outformat = '.nc'
else:
LOGGER.error('output format not valid')
start = min(refSt, dateSt)
end = max(refEn, dateEn)
if bbox_obj is not None:
LOGGER.info("bbox_obj={0}".format(bbox_obj.coords))
bbox = [
bbox_obj.coords[0][0], bbox_obj.coords[0][1],
bbox_obj.coords[1][0], bbox_obj.coords[1][1]
]
LOGGER.info("bbox={0}".format(bbox))
else:
bbox = None
LOGGER.info('environment set')
except Exception as e:
msg = 'failed to set environment %s ' % e
LOGGER.error(msg)
raise Exception(msg)
LOGGER.debug("init took %s seconds.", time.time() - start_time)
response.update_status('Read in and convert the arguments', 5)
########################
# input data preperation
########################
# TODO: Check if files containing more than one dataset
response.update_status('Start preparing input data', 12)
start_time = time.time() # mesure data preperation ...
try:
variable = get_variable(resource)
archive = call(resource=resource,
time_range=[refSt, refEn],
geom=bbox,
spatial_wrapping='wrap')
simulation = call(resource=resource,
time_range=[dateSt, dateEn],
geom=bbox,
spatial_wrapping='wrap')
if seacyc is True:
seasoncyc_base, seasoncyc_sim = analogs.seacyc(
archive, simulation, method=normalize)
else:
seasoncyc_base = None
seasoncyc_sim = None
except Exception as e:
msg = 'failed to prepare archive and simulation files %s ' % e
LOGGER.debug(msg)
raise Exception(msg)
ip, output = mkstemp(dir='.', suffix='.txt')
output_file = path.abspath(output)
files = [path.abspath(archive), path.abspath(simulation), output_file]
LOGGER.debug("data preperation took %s seconds.",
time.time() - start_time)
############################
# generating the config file
############################
response.update_status('writing config file', 15)
start_time = time.time() # measure write config ...
try:
config_file = analogs.get_configfile(
files=files,
seasoncyc_base=seasoncyc_base,
seasoncyc_sim=seasoncyc_sim,
timewin=timewin,
varname=variable,
seacyc=seacyc,
cycsmooth=91,
nanalog=nanalog,
seasonwin=seasonwin,
distfun=distance,
outformat=outformat,
calccor=True,
silent=False,
period=[
dt.strftime(refSt, '%Y-%m-%d'),
dt.strftime(refEn, '%Y-%m-%d')
],
bbox="%s,%s,%s,%s" % (bbox[0], bbox[2], bbox[1], bbox[3]))
except Exception as e:
msg = 'failed to generate config file %s ' % e
LOGGER.debug(msg)
raise Exception(msg)
LOGGER.debug("write_config took %s seconds.", time.time() - start_time)
##############
# CASTf90 call
##############
import subprocess
import shlex
start_time = time.time() # measure call castf90
response.update_status('Start CASTf90 call', 20)
try:
# response.update_status('execution of CASTf90', 50)
cmd = 'analogue.out %s' % path.relpath(config_file)
# system(cmd)
args = shlex.split(cmd)
output, error = subprocess.Popen(
args, stdout=subprocess.PIPE,
stderr=subprocess.PIPE).communicate()
LOGGER.info('analogue.out info:\n %s ' % output)
LOGGER.debug('analogue.out errors:\n %s ' % error)
response.update_status('**** CASTf90 suceeded', 90)
except Exception as e:
msg = 'CASTf90 failed %s ' % e
LOGGER.error(msg)
raise Exception(msg)
LOGGER.debug("castf90 took %s seconds.", time.time() - start_time)
response.update_status('preparting output', 99)
response.outputs['config'] = config_output_url # config_file )
response.outputs['analogs'] = output_file
response.outputs['output_netcdf'] = simulation
# response.outputs['output_html'] = output_av
response.update_status('execution ended', 100)
LOGGER.debug("total execution took %s seconds.",
time.time() - process_start_time)
return response
def _handler(self, request, response):
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
ncs = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))
var = get_variable(ncs[0])
LOGGER.info('variable to be plotted: %s' % var)
# mosaic = self.mosaic.getValue()
if 'region' in request.inputs:
regions = [inp.data for inp in request.inputs['region']]
try:
png_region = vs.plot_polygons(regions)
except Exception as e:
LOGGER.exception(
'failed to plot the polygon to world map: %s' % e)
o1, png_region = mkstemp(dir='.', suffix='.png')
# clip the demanded polygons
subsets = clipping(
resource=ncs,
variable=var,
polygons=regions,
mosaic=True,
spatial_wrapping='wrap',
)
else:
subsets = ncs
png_region = vs.plot_extend(ncs[0])
response.update_status('Arguments set for subset process', 0)
try:
tar_subsets = archive(subsets)
except Exception as e:
LOGGER.exception('failed to archive subsets: %s' % e)
_, tar_subsets = mkstemp(dir='.', suffix='.tar')
try:
png_uncertainty = vs.uncertainty(subsets, variable=var)
except Exception as e:
LOGGER.exception('failed to generate the uncertainty plot: %s' % e)
_, png_uncertainty = mkstemp(dir='.', suffix='.png')
try:
png_spaghetti = vs.spaghetti(
subsets,
variable=var,
)
except Exception as e:
LOGGER.exception('failed to generate the spaghetti plot: %s' % e)
_, png_spaghetti = mkstemp(dir='.', suffix='.png')
try:
from flyingpigeon import robustness as ro
signal, low_agreement_mask, high_agreement_mask, text_src = ro.signal_noise_ratio(
resource=subsets,
# start=None, end=None,
# timeslice=None,
# variable=var
)
# if title is None:
title = 'signal robustness of %s ' % (
var) # , end1, end2, start1, start2
png_robustness = vs.map_robustness(
signal,
high_agreement_mask,
low_agreement_mask,
# cmap=cmap,
# title=title
)
LOGGER.info('robustness graphic generated')
except Exception as e:
LOGGER.exception('failed to generate the robustness plot: %s' % e)
_, png_robustness = mkstemp(dir='.', suffix='.png')
factsheet = vs.factsheetbrewer(png_region=png_region,
png_uncertainty=png_uncertainty,
png_spaghetti=png_spaghetti,
png_robustness=png_robustness)
response.outputs['output_nc'].file = tar_subsets
response.outputs['output_factsheet'].file = factsheet
response.update_status("done", 100)
return response
def _handler(self, request, response):
response.update_status('starting uncertainty process', 0)
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
try:
ncfiles = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))
# TODO: See where 'method' should be called
method = request.inputs['method'][0].data
if 'start' in request.inputs:
start = request.inputs['start'][0].data
else:
start = None
if 'end' in request.inputs:
end = request.inputs['end'][0].data
else:
end = None
if 'timeslice' in request.inputs:
timeslice = request.inputs['timeslice'][0].data
else:
timeslice = None
response.update_status('arguments read', 5)
LOGGER.info('Successfully read in the arguments')
except Exception as ex:
msg = 'failed to read in the arguments: {}'.format(str(ex))
LOGGER.exception(msg)
raise Exception(msg)
response.outputs['output_text'].file = write_fileinfo(ncfiles)
# LOGGER.debug('variable set to %s' % variable)
# if method == 'signal_noise_ratio':
signal, low_agreement_mask, high_agreement_mask, text_src = erob.signal_noise_ratio(
resource=ncfiles,
start=start,
end=end,
timeslice=timeslice,
# variable=variable
) # graphic,
LOGGER.debug('Robustness calculated')
try:
# LOGGER.info('variable to be plotted: %s' % variable)
from flyingpigeon.visualisation import map_robustness
# if title is None:
title = 'signal robustness' # , end1, end2, start1, start2
graphic = map_robustness(
signal,
high_agreement_mask,
low_agreement_mask,
# variable=variable,
# cmap=cmap,
title=title)
LOGGER.info('graphic generated')
except Exception as ex:
LOGGER.exception('graphic generation failed: {}'.format(str(ex)))
_, graphic = mkstemp(dir='.', suffix='.png')
response.update_status('process worker done', 95)
response.outputs['output_signal'].file = signal
response.outputs['output_high'].file = high_agreement_mask
response.outputs['output_low'].file = low_agreement_mask
response.outputs['output_graphic'].file = graphic
response.update_status('uncertainty process done', 100)
return response
def _handler(self, request, response):
from flyingpigeon.utils import archive, archiveextract
from tempfile import mkstemp
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
ncs = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))
# mosaic = self.mosaic.getValue()
regions = [inp.data for inp in request.inputs['region']]
response.update_status('Arguments set for subset process', 0)
LOGGER.debug('starting: regions=%s, num_files=%s' % (len(regions), len(ncs)))
try:
from flyingpigeon.visualisation import plot_polygons
png_country = plot_polygons(regions)
except:
LOGGER.exception('failed to plot the polygon to world map')
o1, png_country = mkstemp(dir='.', suffix='.png')
# clip the demanded polygons
from flyingpigeon.subset import clipping
subsets = clipping(resource=ncs, variable=None,
dimension_map=None,
calc=None,
output_format='nc',
calc_grouping=None,
time_range=None,
time_region=None,
historical_concatination=True,
prefix=None,
spatial_wrapping='wrap',
polygons=regions,
mosaic=True
)
try:
tar_subsets = archive(subsets)
except:
LOGGER.exception('failed to archive subsets')
_, tar_subsets = mkstemp(dir='.', suffix='.tar')
try:
from flyingpigeon.visualisation import uncertainty
png_uncertainty = uncertainty(subsets)
except:
LOGGER.exception('failed to generate the uncertainty plot')
_, png_uncertainty = mkstemp(dir='.', suffix='.png')
try:
from flyingpigeon.visualisation import spaghetti
png_spaghetti = spaghetti(subsets)
except:
LOGGER.exception('failed to generate the spaghetti plot')
_, png_spaghetti = mkstemp(dir='.', suffix='.png')
try:
from flyingpigeon import robustness as erob
from flyingpigeon.utils import get_variable
variable = get_variable(ncs[0])
signal, low_agreement_mask, high_agreement_mask, text_src = erob.method_A(resource=subsets,
# start=None, end=None,
# timeslice=None,
variable=variable
)
LOGGER.info('variable to be plotted: %s' % variable)
from flyingpigeon.visualisation import map_robustness
# if title is None:
title = 'signal robustness of %s ' % (variable) # , end1, end2, start1, start2
png_robustness = map_robustness(signal,
high_agreement_mask,
low_agreement_mask,
# cmap=cmap,
title=title)
LOGGER.info('graphic generated')
except:
LOGGER.exception('failed to generate the robustness plot')
_, png_robustness = mkstemp(dir='.', suffix='.png')
from flyingpigeon.visualisation import factsheetbrewer
factsheet = factsheetbrewer(png_country=png_country,
png_uncertainty=png_uncertainty,
png_spaghetti=png_spaghetti,
png_robustness=png_robustness)
response.outputs['output_nc'].file = tar_subsets
response.outputs['output_factsheet'].file = factsheet
response.update_status("done", 100)
return response
