def __init__(self):
inputs = [
ComplexInput(
'resource',
'Resource',
abstract=
'NetCDF Files or archive (tar/zip) containing NetCDF files.',
metadata=[Metadata('Info')],
min_occurs=1,
max_occurs=1000,
supported_formats=[
Format('application/x-netcdf'),
Format('application/x-tar'),
Format('application/zip'),
]),
LiteralInput(
"method",
"Method of robustness calculation",
abstract=
"Detailed information about the methods can be found in the documentation",
data_type='string',
default='signal_noise_ratio',
min_occurs=0,
max_occurs=1,
allowed_values=['signal_noise_ratio', 'Method_B', 'Method_C']),
LiteralInput(
"start",
"Start Year",
abstract=
"Beginn of the analysed period (e.g 19710101; if not set, the first consistend \
year of the ensemble will be taken)",
data_type='integer',
min_occurs=0,
max_occurs=1,
# default='1971'
# allowedValues=range(1900,2200)
),
LiteralInput(
'end',
"End Year",
abstract="End of the analysed period (e.g. 20501231 if not set, \
the last consistend year of the ensemble will be taken)",
data_type='integer',
min_occurs=0,
max_occurs=1,
# default='2000',
),
LiteralInput(
"timeslice",
"Time slice",
abstract=
"Time slice (in days) for robustness reference e.g. 3650",
data_type='integer',
min_occurs=0,
max_occurs=1,
# default='3560'
# allowedValues=range(1,50)
),
# self.variableIn = self.addLiteralInput(
# identifier="variable",
# title="Variable",
# abstract="Variable to be expected in the input files (Variable will be detected if not set, )",
# # default=None,
# type=type(''),
# minOccurs=0,
# maxOccurs=1,
# )
]
outputs = [
ComplexOutput(
'output_high',
'Mask for areas with high agreement',
abstract="netCDF file containing calculated robustness mask",
supported_formats=[Format('application/x-netcdf')],
as_reference=True,
),
ComplexOutput(
'output_low',
'Mask for areas with low agreement',
abstract="netCDF file containing calculated robustness mask",
supported_formats=[Format('application/x-netcdf')],
as_reference=True,
),
ComplexOutput(
'output_signal',
'Signal',
abstract=
"netCDF file containing calculated change of mean over the timeperiod and ensemble",
supported_formats=[Format('application/x-netcdf')],
as_reference=True,
),
ComplexOutput(
"output_graphic",
"Graphic",
abstract=
"Graphic showing the signal difference with high and low ensemble agreement",
supported_formats=[Format("image/png")],
as_reference=True,
),
ComplexOutput(
"output_text",
"Sourcefiles",
abstract="text file with a list of the used input data sets",
supported_formats=[Format("text/plain")],
as_reference=True,
),
ComplexOutput(
'output_log',
'Logging information',
abstract="Collected logs during process run.",
supported_formats=[Format('text/plain')],
as_reference=True,
)
]
super(RobustnessProcess, self).__init__(
self._handler,
identifier="robustness",
title="Ensemble robustness",
version="0.5",
metadata=[Metadata("LSCE", "http://www.lsce.ipsl.fr/")],
abstract=
"Calculates the robustness as the ratio of noise to signal in an ensemle of timeseries",
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True)
par_x01=20.0,
par_x02=1.0,
par_x03=20.0,
par_x04=5.0,
par_x05=0.5,
par_x06=1.0,
par_x07=1.0,
par_x08=1.0,
par_x09=15.0,
par_x10=15.0,
)
upperBounds = LiteralInput(
"upperBounds",
"Comma separated list of model parameters Upper Bounds",
abstract="UParameters: " + ", ".join(Uparams_defaults._fields),
data_type="string",
default=", ".join(str(p) for p in list(Uparams_defaults)),
min_occurs=0,
)
lowerBounds = LiteralInput(
"lowerBounds",
"Comma separated list of model parameters Lower Bounds",
abstract="LParameters: " + ", ".join(Lparams_defaults._fields),
data_type="string",
default=", ".join(str(p) for p in list(Lparams_defaults)),
min_occurs=0,
)
class OstrichMOHYSEProcess(OstrichProcess):
def __init__(self):
inputs = [
ComplexInput(
'resource',
'Resource',
abstract=
'NetCDF Files or archive (tar/zip) containing netCDF files.',
metadata=[Metadata('Info')],
min_occurs=1,
max_occurs=1000,
supported_formats=[
Format('application/x-netcdf'),
Format('application/x-tar'),
Format('application/zip'),
]),
LiteralInput(
"gbif",
"GBIF csv file",
abstract="GBIF table (csv) with tree occurence \
(output of 'GBIF data fetch' process )",
data_type='string',
min_occurs=1,
max_occurs=1,
default=
'http://localhost:8090/wpsoutputs/flyingpigeon/output_csv-abe15f64-c30d-11e6-bf63-142d277ef1f3.csv'
),
LiteralInput(
"input_indices",
"Indices",
abstract="Climate indices related to growth conditions \
of tree species",
default=['TG_JJA', 'TNn_Jan'],
data_type='string',
min_occurs=1,
max_occurs=10,
allowed_values=_SDMINDICES_),
LiteralInput("period",
"Reference period",
abstract="Reference period for climate conditions\
(all = entire timeseries)",
default="all",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=[
'all', '1951-1980', '1961-1990', '1971-2000',
'1981-2010'
]),
LiteralInput(
"archive_format",
"Archive format",
abstract="Result files will be compressed into archives.\
Choose an appropriate format",
default="tar",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=['zip', 'tar'])
]
outputs = [
# self.output_csv = self.addComplexOutput(
# identifier="output_csv",
# title="Tree species table",
# abstract="Extracted CSV file containing the tree species table",
# formats=[{"mimeType": "text/csv"}],
# asReference=True,
# )
ComplexOutput(
"output_gbif",
"Graphic of GBIF coordinates",
abstract="PNG graphic file showing the presence of tree species\
according to the CSV file",
supported_formats=[Format('image/png')],
as_reference=True,
),
ComplexOutput(
"output_PA",
"Graphic of PA mask",
abstract="PNG graphic file showing PA mask generated based on\
netCDF spatial increment",
supported_formats=[Format('image/png')],
as_reference=True,
),
ComplexOutput(
"output_indices",
"Climate indices for growth conditions over all timesteps",
abstract=
"Archive (tar/zip) containing calculated climate indices",
supported_formats=[
Format('application/x-tar'),
Format('application/zip')
],
as_reference=True,
),
ComplexOutput(
"output_reference",
"Climate indices for growth conditions of reference period",
abstract=
"Archive (tar/zip) containing calculated climate indices",
supported_formats=[
Format('application/x-tar'),
Format('application/zip')
],
as_reference=True,
),
ComplexOutput(
"output_prediction",
"predicted growth conditions",
abstract="Archive containing files of the predicted\
growth conditions",
supported_formats=[
Format('application/x-tar'),
Format('application/zip')
],
as_reference=True,
),
ComplexOutput(
"output_info",
"GAM statistics information",
abstract="Graphics and information of the learning statistics",
supported_formats=[Format("application/pdf")],
as_reference=True,
),
ComplexOutput('output_log',
'Logging information',
abstract="Collected logs during process run.",
as_reference=True,
supported_formats=[Format('text/plain')])
]
super(SDMcsvProcess, self).__init__(
self._handler,
identifier="sdm_csv",
title="Species distribution Model (GBIF-CSV table as input)",
version="0.10",
metadata=[
Metadata("LWF", "http://www.lwf.bayern.de/"),
Metadata(
"Doc",
"http://flyingpigeon.readthedocs.io/en/latest/descriptions/index.html#species-distribution-model"
),
Metadata("paper",
"http://www.hindawi.com/journals/jcli/2013/787250/"),
Metadata(
"Tutorial",
"http://flyingpigeon.readthedocs.io/en/latest/tutorials/sdm.html"
),
],
abstract="Indices preparation for SDM process",
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True,
)
def __init__(self):
inputs = [
ComplexInput(
"obs",
"Stream flow observation",
abstract="Stream flow observation time series.",
supported_formats=(FORMATS.NETCDF, ),
),
LiteralInput(
"obs_var",
"Observation variable name",
abstract="Name of the variable in the observation dataset.",
data_type="string",
default="q_obs",
min_occurs=0,
max_occurs=1,
),
ComplexInput(
"hcst",
"Stream flow hindcast",
abstract=
"Stream flow hindcast time series, deterministic or ensemble.",
supported_formats=(FORMATS.NETCDF, ),
),
LiteralInput(
"hcst_var",
"Hindcast variable name",
abstract="Name of the variable in the hindcast dataset.",
data_type="string",
default="q_sim",
min_occurs=0,
max_occurs=1,
),
LiteralInput(
"skip_nans",
"Skip NaNs in metric evaluation",
abstract="Skip NaNs in hindcast evaluation computations",
data_type="boolean",
default=True,
min_occurs=0,
max_occurs=1,
),
LiteralInput(
"BSS_threshold",
"Threshold for Brier Skill Score exceeding given threshold",
abstract=
"Threshold for Brier Skill Score exceeding given threshold",
data_type="float",
default=0.7,
min_occurs=0,
max_occurs=1,
),
LiteralInput(
"metric",
"Forecast evaluation metric name",
abstract=
"One or multiple hindcast evaluation metric names. If None, defaults to all.",
data_type="string",
allowed_values=all_metrics,
default=None,
min_occurs=0,
max_occurs=len(all_metrics),
),
]
outputs = [
ComplexOutput(
"metrics",
"Hindcast evaluation metrics values",
abstract=
"JSON dictionary of evaluation metrics averaged over the full period and "
"all members.",
supported_formats=(FORMATS.JSON, ),
),
]
super(HindcastEvaluationProcess, self).__init__(
self._handler,
identifier="hindcast-evaluation",
title=
"Hindcast evaluation based on the XSkillScore package for deterministic and ensemble hindcasts.",
version="1.0",
abstract=
"This process takes two NETCDF files (one containing the observed and the other the hindcast "
"data) "
"and computes hindcast evaluation metrics between them. Metrics are calculated according to if "
"there are multiple members in the dataset (probabilistic) or not (deterministic)",
metadata=[
Metadata("XSkillScore Documentation",
"https://pypi.org/project/xskillscore/")
],
inputs=inputs,
outputs=outputs,
keywords=["forecast evaluation", "ensemble", "deterministic"] +
list(all_metrics),
status_supported=True,
store_supported=True,
)
def __init__(self):
inputs = [
# *model_experiment_ensemble(
# models=['Defaults'],
# experiments=['historical'],
# ensembles=['r1i1p1'],
# start_end_year=(1850, 2005),
# start_end_defaults=(1850, 2005)),
*year_ranges((1850, 2005), (1990, 2005)),
LiteralInput('season',
'Season',
abstract='Choose a season like DJF.',
data_type='string',
allowed_values=['DJF', 'DJFM', 'NDJFM', 'JJA'],
default='JJA'),
LiteralInput('area',
'Area',
abstract='Area',
data_type='string',
allowed_values=['EU', 'EAT', 'PNA', 'NH'],
default='EU'),
LiteralInput('extreme',
'Extreme',
abstract='Extreme',
data_type='string',
allowed_values=[
'60th_percentile', '75th_percentile',
'90th_percentile', 'mean', 'maximum', 'std',
'trend'
],
default='75th_percentile'),
LiteralInput('numclus',
'Number of Clusters',
abstract='Numclus',
data_type='string',
allowed_values=['2', '3', '4'],
default='3'),
LiteralInput('perc',
'Percentage',
abstract='Percentage of total Variance',
data_type='string',
allowed_values=['70', '80', '90'],
default='80'),
]
outputs = [
ComplexOutput(
'plot',
'Output plot',
abstract='Generated output plot of ESMValTool processing.',
as_reference=True,
supported_formats=[Format('image/eps')]),
ComplexOutput(
'ens_extreme',
'ens_extreme',
abstract='Generated output data of ESMValTool processing.',
as_reference=True,
supported_formats=[FORMATS.NETCDF]),
ComplexOutput(
'ens_climatologies',
'ens_climatologies',
abstract='Generated output data of ESMValTool processing.',
as_reference=True,
supported_formats=[FORMATS.NETCDF]),
ComplexOutput(
'ens_anomalies',
'ens_anomalies',
abstract='Generated output data of ESMValTool processing.',
as_reference=True,
supported_formats=[FORMATS.NETCDF]),
ComplexOutput('statistics',
'Statistics',
abstract='Clustering Statictics',
as_reference=True,
supported_formats=[Format('text/plain')]),
ComplexOutput(
'archive',
'Archive',
abstract=
'The complete output of the ESMValTool processing as an zip archive.',
as_reference=True,
supported_formats=[Format('application/zip')]),
*default_outputs(),
]
super(EnsClus, self).__init__(
self._handler,
identifier="ensclus",
title="EnsClus - Ensemble Clustering",
version=runner.VERSION,
abstract="""Cluster analysis tool based on the k-means algorithm
for ensembles of climate model simulations. EnsClus group
ensemble members according to similar characteristics and
select the most representative member for each cluster.
Currently included are the models: ACCESS1-0, ACCESS1-3,
CanESM2, CCSM4, CESM1-BGC""",
metadata=[
Metadata('Estimated Calculation Time', '4 minutes'),
Metadata('ESMValTool', 'http://www.esmvaltool.org/'),
Metadata(
'Documentation',
'https://esmvaltool.readthedocs.io/en/latest/recipes/recipe_ensclus.html',
role=util.WPS_ROLE_DOC),
# Metadata(
# 'Media',
# util.diagdata_url() + '/ensclus/ensclus_thumbnail.png',
# role=util.WPS_ROLE_MEDIA),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True)
def __init__(self):
inputs = [
LiteralInput(
"indices",
"Earth Observation Product Indice",
abstract="Choose an indice based on Earth Observation Data",
default='NDVI',
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=['NDVI', 'BAI']),
LiteralInput(
'BBox',
'Bounding Box',
data_type='string',
abstract="Enter a bbox: min_lon, max_lon, min_lat, max_lat."
" min_lon=Western longitude,"
" max_lon=Eastern longitude,"
" min_lat=Southern or northern latitude,"
" max_lat=Northern or southern latitude."
" For example: -80,50,20,70",
min_occurs=1,
max_occurs=1,
default='14,15,8,9',
),
LiteralInput(
'start',
'Start Date',
data_type='date',
abstract='First day of the period to be searched for EO data.'
'(if not set, 30 days befor end of period will be selected',
default=(dt.now() - timedelta(days=30)).strftime('%Y-%m-%d'),
min_occurs=0,
max_occurs=1,
),
LiteralInput(
'end',
'End Date',
data_type='date',
abstract='Last day of the period to be searched for EO data.'
'(if not set, current day is set.)',
default=dt.now().strftime('%Y-%m-%d'),
min_occurs=0,
max_occurs=1,
),
LiteralInput(
'cloud_cover',
'Cloud Cover',
data_type='integer',
abstract='Max tollerated percentage of cloud cover',
default="30",
allowed_values=[0, 10, 20, 30, 40, 50, 60, 70, 80, 100]),
LiteralInput(
'username',
'User Name',
data_type='string',
abstract=
'Authentification user name for the COPERNICUS Sci-hub ',
# default='2013-12-31',
min_occurs=1,
max_occurs=1,
),
LiteralInput(
'password',
'Password',
data_type='string',
abstract=
'Authentification password for the COPERNICUS Sci-hub ',
min_occurs=1,
max_occurs=1,
),
]
outputs = [
# ComplexOutput("output_txt", "Files search result",
# abstract="Files found according to the search querry",
# supported_formats=[Format('text/plain')],
# as_reference=True,
# ),
ComplexOutput(
"output_plot",
"NDVI example file",
abstract="Plots in RGB colors",
supported_formats=[Format('image/png')],
as_reference=True,
),
ComplexOutput(
"output_archive",
"Tar archive",
abstract="Tar archive of the iamge files",
supported_formats=[Format("application/x-tar")],
as_reference=True,
),
ComplexOutput(
"output_log",
"Logging information",
abstract="Collected logs during process run.",
supported_formats=[Format("text/plain")],
as_reference=True,
)
]
super(EO_COP_indicesProcess, self).__init__(
self._handler,
identifier="EO_COPERNICUS_indices",
title="EO indices",
version="0.1",
abstract="Derivateing indices like NDVI based on",
metadata=[
Metadata('Documentation',
'http://flyingpigeon.readthedocs.io/en/latest/'),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True,
)
__author__ = 'Jachym Cepicky'
from pywps import Process, LiteralInput, ComplexOutput, ComplexInput, Format
from pywps.app.Common import Metadata
from pywps.validator.mode import MODE
from pywps.inout.formats import FORMATS
inpt_vector = ComplexInput(
'vector',
'Vector map',
supported_formats=[Format('application/gml+xml')],
mode=MODE.STRICT
)
inpt_size = LiteralInput('size', 'Buffer size', data_type='float')
out_output = ComplexOutput(
'output',
'HelloWorld Output',
supported_formats=[Format('application/gml+xml')]
)
inputs = [inpt_vector, inpt_size]
outputs = [out_output]
class DemoBuffer(Process):
def __init__(self):
super(DemoBuffer, self).__init__(
_handler,
SNOW_SWI_MAX=0.3,
SWI_REDUCT_COEFF=0.1,
DD_REFREEZE_TEMP=2.0,
REFREEZE_FACTOR=5.0,
REFREEZE_EXP=1.0,
PET_CORRECTION=3.0,
HMETS_RUNOFF_COEFF=1.0,
PERC_COEFF=0.02,
BASEFLOW_COEFF_1=0.1,
BASEFLOW_COEFF_2=0.01,
TOPSOIL=0.5,
PHREATIC=2.0)
upperBounds = LiteralInput('upperBounds', 'Comma separated list of model parameters Upper Bounds',
abstract='UParameters: ' + ', '.join(Uparams_defaults._fields),
data_type='string',
default=', '.join(str(p) for p in list(Uparams_defaults)),
min_occurs=0)
lowerBounds = LiteralInput('lowerBounds', 'Comma separated list of model parameters Lower Bounds',
abstract='LParameters: ' + ', '.join(Lparams_defaults._fields),
data_type='string',
default=', '.join(str(p) for p in list(Lparams_defaults)),
min_occurs=0)
class OstrichHMETSProcess(OstrichProcess):
"""
OSTRICH emulator for the HMETS model.
This process calibrates the HMETS model using a OSTRICH emulator. Users need to provide netCDF input
