def execute(self):
from os.path import basename
from flyingpigeon import sdm
from flyingpigeon.utils import archive
self.status.set('Start process', 0)
try:
logger.info('reading the arguments')
resources = self.getInputValues(identifier='resources')
gbif = self.getInputValues(identifier='gbif')
#period = self.period.getValue()
period = self.getInputValues(identifier='period')
period = period[0]
#indices = self.input_indices.getValue()
indices = self.getInputValues(identifier='input_indices')
logger.debug("indices = %s", indices)
archive_format = self.archive_format.getValue()
except Exception as e:
logger.error('failed to read in the arguments %s ' % e)
logger.info('indices %s ' % indices)
try:
self.status.set('extract csv file with tree observations', 5)
csv_file = sdm.get_csv(gbif[0])
except Exception as e:
logger.exception('failed to extract csv file from url.')
try:
self.status.set('read in latlon coordinates of tree observations', 10)
latlon = sdm.get_latlon(csv_file)
except Exception as e:
logger.exception('failed to extract the latlon points')
tree_presents = 'tree_presents.png'
try:
self.status.set('plotting Tree presents based on coordinates', 15)
import matplotlib.pyplot as plt
from cartopy import config
from cartopy.util import add_cyclic_point
import cartopy.crs as ccrs
fig = plt.figure(figsize=(20,10), dpi=600, facecolor='w', edgecolor='k')
ax = plt.axes(projection=ccrs.Robinson(central_longitude=0))
ax.coastlines()
ax.set_global()
cs = plt.scatter(latlon[:,1], latlon[:,0], transform=ccrs.PlateCarree())
fig.savefig(tree_presents)
plt.close()
except Exception as e:
msg = 'plotting points failed'
logger.exception(msg)
with open(tree_presents, 'w') as fp:
# TODO: needs to be a png file
fp.write(msg)
try:
self.status.set('generating the PA mask', 20)
PAmask = sdm.get_PAmask(coordinates=latlon)
logger.info('PA mask sucessfully generated')
except Exception as e:
logger.exception('failed to generate the PA mask')
png_PA_mask = 'PA_mask.png'
try:
self.status.set('Ploting PA mask', 25)
fig = plt.figure(figsize=(20,10), dpi=300, facecolor='w', edgecolor='k')
cs = plt.contourf(PAmask)
fig.savefig(png_PA_mask)
plt.close()
except Exception as e:
msg = 'failed to plot the PA mask'
logger.exception(msg)
with open(png_PA_mask, 'w') as fp:
# TODO: needs to be a png file
fp.write(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:
archive_indices = archive(ncs_indices , format=archive_format)
logger.info('indices 3D added to tarfile')
except:
msg = 'failed adding indices to tar'
logger.exception(msg)
raise Exception(msg)
indices_dic = None
try:
# sort indices
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)
# try:
# # open tar files
# tar_reference = tarfile.open('reference.tar', "w")
# tar_indices = tarfile.open('indices.tar', "w")
# tar_info = tarfile.open('info.tar', "w")
# tar_prediction = tarfile.open('prediction.tar', "w")
# logger.info('tar files prepared')
# except:
# msg = 'tar file preparation failed'
# logger.exception(msg)
# raise Exception(msg)
ncs_references = []
species_files = []
statistics_info = []
for count,key in enumerate(indices_dic.keys()):
try:
self.status.set('Start processing of %s ' % key, 40 + count * 10)
ncs = indices_dic[key]
logger.info('with %s files' % len(ncs))
try:
ncs_references.extend(sdm.get_reference(ncs_indices=ncs, period=period))
logger.info('reference indice calculated %s ' % ncs_references)
except:
msg = 'failed adding ref indices to tar'
logger.exception(msg)
raise Exception(msg)
# for nc_reference in ncs_references:
# tar_reference.add(nc_reference,
# arcname = basename(nc_reference))# nc_reference.replace(os.path.abspath(os.path.curdir), ""))
# logger.info('reference indices added to tarfile')
except:
msg = 'failed to calculate reference indices.'
logger.exception(msg)
raise Exception(msg)
try:
gam_model, predict_gam, gam_info = sdm.get_gam(ncs_references,PAmask)
statistics_info.append(gam_info)
self.status.set('GAM sucessfully trained', 70)
except:
msg = 'failed to train GAM'
logger.exception(msg)
raise Exception(msg)
try:
prediction = sdm.get_prediction(gam_model, ncs_indices)
self.status.set('prediction done', 80)
except:
msg = 'failed to predict'
logger.exception(msg)
raise Exception(msg)
try:
from numpy import invert, isnan, nan, broadcast_arrays, array, zeros, linspace, meshgrid
mask = invert(isnan(PAmask))
mask = broadcast_arrays(prediction, mask)[1]
prediction[mask==False] = nan
self.status.set('land sea mask for predicted data', 90)
except:
logger.exception('failed to mask predicted data')
try:
species_files.append(sdm.write_to_file(ncs_indices[0], prediction))
logger.info('Favourabillity written to file')
#tar_prediction.add(species_file,
# arcname = basename(species_file))#.replace(os.path.abspath(os.path.curdir), ""))
except:
msg = 'failed to write species file'
logger.exception(msg)
raise Exception(msg)
from flyingpigeon.visualisation import concat_images
statistics_infos = None
try:
statistics_infos = concat_images(statistics_info, orientation='v')
except:
msg = 'failed to concat images'
logger.exception(msg)
raise Exception(msg)
archive_references = None
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)
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:
# #tar_indices.close()
# #tar_reference.close()
# tar_prediction.close()
# #tar_info.close()
# logger.info('tar files closed')
# except:
# logger.exception('tar file closing failed')
# raise Exception
self.output_csv.setValue( csv_file )
self.output_gbif.setValue( tree_presents )
self.output_PA.setValue( png_PA_mask )
self.output_indices.setValue( archive_indices )
self.output_reference.setValue (archive_references)
self.output_prediction.setValue (archive_predicion)
self.output_info.setValue(statistics_infos)
self.status.set('done', 100)
def execute(self):
from os.path import basename
from flyingpigeon import sdm
from flyingpigeon.utils import archive
self.status.set('Start process', 0)
try:
logger.info('reading the arguments')
resources = self.getInputValues(identifier='resources')
gbif = self.getInputValues(identifier='gbif')
#period = self.period.getValue()
period = self.getInputValues(identifier='period')
period = period[0]
#indices = self.input_indices.getValue()
indices = self.getInputValues(identifier='input_indices')
logger.debug("indices = %s", indices)
archive_format = self.archive_format.getValue()
except Exception as e:
logger.error('failed to read in the arguments %s ' % e)
logger.info('indices %s ' % indices)
try:
self.status.set('extract csv file with tree observations', 5)
csv_file = sdm.get_csv(gbif[0])
except Exception as e:
logger.exception('failed to extract csv file from url.')
try:
self.status.set('read in latlon coordinates of tree observations',
10)
latlon = sdm.get_latlon(csv_file)
except Exception as e:
logger.exception('failed to extract the latlon points')
tree_presents = 'tree_presents.png'
try:
self.status.set('plotting Tree presents based on coordinates', 15)
import matplotlib.pyplot as plt
from cartopy import config
from cartopy.util import add_cyclic_point
import cartopy.crs as ccrs
fig = plt.figure(figsize=(20, 10),
dpi=600,
facecolor='w',
edgecolor='k')
ax = plt.axes(projection=ccrs.Robinson(central_longitude=0))
ax.coastlines()
ax.set_global()
cs = plt.scatter(latlon[:, 1],
latlon[:, 0],
transform=ccrs.PlateCarree())
fig.savefig(tree_presents)
plt.close()
except Exception as e:
msg = 'plotting points failed'
logger.exception(msg)
with open(tree_presents, 'w') as fp:
# TODO: needs to be a png file
fp.write(msg)
try:
self.status.set('generating the PA mask', 20)
PAmask = sdm.get_PAmask(coordinates=latlon)
logger.info('PA mask sucessfully generated')
except Exception as e:
logger.exception('failed to generate the PA mask')
png_PA_mask = 'PA_mask.png'
try:
self.status.set('Ploting PA mask', 25)
fig = plt.figure(figsize=(20, 10),
dpi=300,
facecolor='w',
edgecolor='k')
cs = plt.contourf(PAmask)
fig.savefig(png_PA_mask)
plt.close()
except Exception as e:
msg = 'failed to plot the PA mask'
logger.exception(msg)
with open(png_PA_mask, 'w') as fp:
# TODO: needs to be a png file
fp.write(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:
archive_indices = archive(ncs_indices, format=archive_format)
logger.info('indices 3D added to tarfile')
except:
msg = 'failed adding indices to tar'
logger.exception(msg)
raise Exception(msg)
indices_dic = None
try:
# sort indices
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)
# try:
# # open tar files
# tar_reference = tarfile.open('reference.tar', "w")
# tar_indices = tarfile.open('indices.tar', "w")
# tar_info = tarfile.open('info.tar', "w")
# tar_prediction = tarfile.open('prediction.tar', "w")
# logger.info('tar files prepared')
# except:
# msg = 'tar file preparation failed'
# logger.exception(msg)
# raise Exception(msg)
ncs_references = []
species_files = []
statistics_info = []
for count, key in enumerate(indices_dic.keys()):
try:
self.status.set('Start processing of %s ' % key,
40 + count * 10)
ncs = indices_dic[key]
logger.info('with %s files' % len(ncs))
try:
ncs_references.extend(
sdm.get_reference(ncs_indices=ncs, period=period))
logger.info('reference indice calculated %s ' %
ncs_references)
except:
msg = 'failed adding ref indices to tar'
logger.exception(msg)
raise Exception(msg)
# for nc_reference in ncs_references:
# tar_reference.add(nc_reference,
# arcname = basename(nc_reference))# nc_reference.replace(os.path.abspath(os.path.curdir), ""))
# logger.info('reference indices added to tarfile')
except:
msg = 'failed to calculate reference indices.'
logger.exception(msg)
raise Exception(msg)
try:
gam_model, predict_gam, gam_info = sdm.get_gam(
ncs_references, PAmask)
statistics_info.append(gam_info)
self.status.set('GAM sucessfully trained', 70)
except:
msg = 'failed to train GAM'
logger.exception(msg)
raise Exception(msg)
try:
prediction = sdm.get_prediction(gam_model, ncs_indices)
self.status.set('prediction done', 80)
except:
msg = 'failed to predict'
logger.exception(msg)
raise Exception(msg)
try:
from numpy import invert, isnan, nan, broadcast_arrays, array, zeros, linspace, meshgrid
mask = invert(isnan(PAmask))
mask = broadcast_arrays(prediction, mask)[1]
prediction[mask == False] = nan
self.status.set('land sea mask for predicted data', 90)
except:
logger.exception('failed to mask predicted data')
try:
species_files.append(
sdm.write_to_file(ncs_indices[0], prediction))
logger.info('Favourabillity written to file')
#tar_prediction.add(species_file,
# arcname = basename(species_file))#.replace(os.path.abspath(os.path.curdir), ""))
except:
msg = 'failed to write species file'
logger.exception(msg)
raise Exception(msg)
from flyingpigeon.visualisation import concat_images
statistics_infos = None
try:
statistics_infos = concat_images(statistics_info, orientation='v')
except:
msg = 'failed to concat images'
logger.exception(msg)
raise Exception(msg)
archive_references = None
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)
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:
# #tar_indices.close()
# #tar_reference.close()
# tar_prediction.close()
# #tar_info.close()
# logger.info('tar files closed')
# except:
# logger.exception('tar file closing failed')
# raise Exception
self.output_csv.setValue(csv_file)
self.output_gbif.setValue(tree_presents)
self.output_PA.setValue(png_PA_mask)
self.output_indices.setValue(archive_indices)
self.output_reference.setValue(archive_references)
self.output_prediction.setValue(archive_predicion)
self.output_info.setValue(statistics_infos)
self.status.set('done', 100)
ncs_indices = ['/home/nils/data/sdm/RX1day_EUR-11_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_19960101-20000101.nc', '/home/nils/data/sdm/TNn_EUR-11_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_Jan_19960101-20000101.nc','/home/nils/data/sdm/TG_EUR-11_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_19960101-20000101.nc'] ncs_references = ['/home/nils/data/sdm/RX1day_EUR-11_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_ref-1998-2000.nc','/home/nils/data/sdm/TG_EUR-11_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_ref-1998-2000.nc','/home/nils/data/sdm/TNn_EUR-11_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_Jan_ref-1998-2000.nc'] ncs_indices = ['/home/nils/data/sdm/TG_EUR-11_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_20010101-20050101.nc', '/home/nils/data/sdm/TG_EUR-11_ICHEC-EC-EARTH_historical_r12i1p1_CLMcom-CCLM4-8-17_v1_JJA_20010101-20050101.nc', '/home/nils/data/sdm/TG_EUR-11_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1_JJA_20010101-20050101.nc', '/home/nils/data/sdm/TG_EUR-11_MOHC-HadGEM2-ES_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_20010101-20050101.nc', '/home/nils/data/sdm/TG_EUR-11_MOHC-HadGEM2-ES_historical_r1i1p1_KNMI-RACMO22E_v1_JJA_20010101-20050101.nc'] csv = '/home/nils/Downloads/0013848-160118175350007.csv' latlon = sdm.get_latlon(csv) PApoints = sdm.get_PApoints(coordinates=latlon) gam_model, predict_ref, gam_info = sdm.get_gam(ncs_references,PApoints) prediction = sdm.get_prediction(gam_model, ncs_indices) from numpy import invert, isnan, nan, broadcast_arrays, array, zeros, linspace, meshgrid mask = invert(isnan(PApoints)) mask = broadcast_arrays(prediction, mask)[1] prediction[mask==False] = nan species_file = sdm.write_to_file(ncs_indices[0], prediction) tac = dt.now()
ncs_references = [
'/home/nils/.conda/envs/birdhouse/etc/pywps/TNn_EUR-11_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_Jan_ref-all.nc',
'/home/nils/.conda/envs/birdhouse/etc/pywps/TG_EUR-11_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_ref-all.nc',
'/home/nils/.conda/envs/birdhouse/etc/pywps/PRCPTOT_EUR-11_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_ref-all.nc'
]
ncs_indices = [
'/home/nils/data/sdm/TG_EUR-11_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_20010101-20050101.nc',
'/home/nils/data/sdm/TG_EUR-11_ICHEC-EC-EARTH_historical_r12i1p1_CLMcom-CCLM4-8-17_v1_JJA_20010101-20050101.nc',
'/home/nils/data/sdm/TG_EUR-11_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1_JJA_20010101-20050101.nc',
'/home/nils/data/sdm/TG_EUR-11_MOHC-HadGEM2-ES_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_JJA_20010101-20050101.nc',
'/home/nils/data/sdm/TG_EUR-11_MOHC-HadGEM2-ES_historical_r1i1p1_KNMI-RACMO22E_v1_JJA_20010101-20050101.nc'
]
csv = '/home/nils/Downloads/0013848-160118175350007.csv'
latlon = sdm.get_latlon(csv)
PApoints = sdm.get_PAmask(coordinates=latlon)
gam_model, predict_ref, gam_info = sdm.get_gam(ncs_references, PApoints)
print gam_model.names
import matplotlib.pyplot as plt
for i in range(len(gam_model)):
try:
plt.plot(gam_model[i])
plt.show()
except:
print 'failed for', i
