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'),
]),
# BoundingBoxInput('bbox', 'Bounding Box',
# abstract='Bounding box to define the region for weather classification.'
# ' Default: -80, 20, 50, 70.',
# crss=['epsg:4326'],
# min_occurs=0),
LiteralInput(
"season",
"Time region",
abstract=
"Select the months to define the time region (all == whole year will be analysed)",
default="DJF",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=_TIMEREGIONS_.keys()),
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='-80,50,20,70',
),
LiteralInput(
"period",
"Period for weatherregime calculation",
abstract="Period for analysing the dataset",
default="19700101-20051231",
data_type='string',
min_occurs=1,
max_occurs=1,
),
LiteralInput(
"anualcycle",
"Period for anualcycle calculation",
abstract="Period for anual cycle calculation",
default="19700101-19991231",
data_type='string',
min_occurs=1,
max_occurs=1,
),
LiteralInput("method",
"Method of annual cycle calculation",
abstract="Method of annual cycle calculation",
default="cdo",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=['ocgis', 'cdo']),
LiteralInput("sseas",
"Serial or multiprocessing for annual cycle",
abstract="Serial or multiprocessing for annual cycle",
default="multi",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=['serial', 'multi']),
LiteralInput("kappa",
"Nr of Weather regimes",
abstract="Set the number of clusters to be detected",
default='4',
data_type='integer',
min_occurs=1,
max_occurs=1,
allowed_values=range(2, 11)),
]
outputs = [
ComplexOutput(
"Routput_graphic",
"Weather Regime Pressure map",
abstract="Weather Classification",
supported_formats=[Format('image/pdf')],
as_reference=True,
),
ComplexOutput(
"output_pca",
"R - datafile",
abstract="Principal components (PCA)",
supported_formats=[Format('text/plain')],
as_reference=True,
),
ComplexOutput(
"output_classification",
"R - workspace",
abstract="Weather regime classification",
supported_formats=[Format("application/octet-stream")],
as_reference=True,
),
ComplexOutput(
'output_netcdf',
'Subsets for one dataset',
abstract=
"Prepared netCDF file as input for weatherregime calculation",
as_reference=True,
supported_formats=[Format('application/x-netcdf')]),
ComplexOutput('output_log',
'Logging information',
abstract="Collected logs during process run.",
as_reference=True,
supported_formats=[Format('text/plain')]),
]
super(WeatherregimesmodelProcess, self).__init__(
self._handler,
identifier="weatherregimes_model",
title="Weather Regimes (based on climate model data)",
abstract=
'k-mean cluster analyse of the pressure patterns. Clusters are equivalent to weather regimes',
version="0.10",
metadata=[
Metadata('LSCE', 'http://www.lsce.ipsl.fr/en/index.php'),
Metadata('Doc',
'http://flyingpigeon.readthedocs.io/en/latest/'),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True,
)
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(
"Rdat",
"R - workspace",
abstract=
"R workspace as output from weather regime reference process",
data_type='string',
min_occurs=1,
max_occurs=1,
),
LiteralInput(
"dat",
"R - datafile",
abstract=
"R datafile as output from weather regime reference process",
data_type='string',
min_occurs=1,
max_occurs=1,
),
LiteralInput(
"netCDF",
"netCDF reference",
abstract=
"netCDF file as output from weather regime reference process",
data_type='string',
min_occurs=0,
max_occurs=1,
),
LiteralInput(
"season",
"Time region",
abstract=
"Select the months to define the time region (all == whole year will be analysed)",
default="DJF",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=_TIMEREGIONS_.keys()),
LiteralInput(
"period",
"Period for weatherregime calculation",
abstract="Period for analysing the dataset",
default="19700101-20101231",
data_type='string',
min_occurs=1,
max_occurs=1,
),
LiteralInput(
"anualcycle",
"Period for anualcycle calculation",
abstract="Period for anual cycle calculation",
default="19700101-19991231",
data_type='string',
min_occurs=1,
max_occurs=1,
),
]
outputs = [
ComplexOutput(
"output_pca",
"R - datafile",
abstract="Principal components (PCA)",
supported_formats=[Format('text/plain')],
as_reference=True,
),
ComplexOutput(
"output_classification",
"R - workspace",
abstract="Weather regime classification",
supported_formats=[Format("application/octet-stream")],
as_reference=True,
),
ComplexOutput(
"output_frequency",
"Frequency",
abstract="Weather regime frequency values per year",
supported_formats=[Format('text/plain')],
as_reference=True,
),
ComplexOutput(
'output_netcdf',
'Subsets for one dataset',
abstract=
"Prepared netCDF file as input for weatherregime calculation",
as_reference=True,
supported_formats=[Format('application/x-netcdf')]),
ComplexOutput('output_log',
'Logging information',
abstract="Collected logs during process run.",
as_reference=True,
supported_formats=[Format('text/plain')]),
]
super(WeatherregimesprojectionProcess, self).__init__(
self._handler,
identifier="weatherregimes_projection",
title=
"Weather Regimes (Projection based on precalculated statistics)",
abstract=
'k-mean cluster analyse of the pressure patterns. Clusters are equivalent to weather regimes',
version="0.10",
metadata=[
Metadata('LSCE', 'http://www.lsce.ipsl.fr/en/index.php'),
Metadata('Doc',
'http://flyingpigeon.readthedocs.io/en/latest/'),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True,
)
def __init__(self):
inputs = [
# self.BBox = self.addBBoxInput(
# identifier="BBox",
# title="Bounding Box",
# abstract="coordinates to define the region for weather classification ('EPSG:4326')",
# minOccurs=1,
# maxOccurs=1,
# crss=['EPSG:4326']
# )
# Literal Input Data
# ------------------
# self.BBox = self.addLiteralInput(
# identifier="BBox",
# title="Region",
# abstract="coordinates to define the region: (minlon,maxlon,minlat,maxlat)",
# default='-80,22.5,50,70', # cdo syntax: 'minlon,maxlon,minlat,maxlat' ;
# ocgis syntax (minlon,minlat,maxlon,maxlat)
# type=type(''),
# minOccurs=1,
# maxOccurs=1,
# )
LiteralInput(
"season",
"Time region",
abstract=
"Select the months to define the time region (all == whole year will be analysed)",
default="DJF",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=_TIMEREGIONS_.keys() + [
'January', 'February', 'March', 'April', 'May', 'June',
'July', 'August', 'September', 'October', 'November',
'December'
]),
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='-80,50,20,70',
),
LiteralInput(
"period",
"Period for weatherregime calculation",
abstract="Period for analysing the dataset",
default="19700101-20101231",
data_type='string',
min_occurs=1,
max_occurs=1,
),
LiteralInput(
"anualcycle",
"Period for anualcycle calculation",
abstract="Period for anual cycle calculation",
default="19700101-19991231",
data_type='string',
min_occurs=1,
max_occurs=1,
),
LiteralInput("reanalyses",
"Reanalyses Data",
abstract="Choose a reanalyses dataset for comparison",
default="NCEP_slp",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=_PRESSUREDATA_),
LiteralInput("method",
"Method of annual cycle calculation",
abstract="Method of annual cycle calculation",
default="ocgis",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=['ocgis', 'cdo']),
LiteralInput("sseas",
"Serial or multiprocessing for annual cycle",
abstract="Serial or multiprocessing for annual cycle",
default="multi",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=['serial', 'multi']),
LiteralInput("kappa",
"Nr of Weather regimes",
abstract="Set the number of clusters to be detected",
default='4',
data_type='integer',
min_occurs=1,
max_occurs=1,
allowed_values=range(2, 11)),
]
outputs = [
ComplexOutput(
"Routput_graphic",
"Weather Regime Pressure map",
abstract="Weather Classification",
supported_formats=[Format('image/pdf')],
as_reference=True,
),
ComplexOutput(
"output_pca",
"R - datafile",
abstract="Principal components (PCA)",
supported_formats=[Format('text/plain')],
as_reference=True,
),
ComplexOutput(
"output_classification",
"R - workspace",
abstract="Weather regime classification",
supported_formats=[Format("application/octet-stream")],
as_reference=True,
),
ComplexOutput(
'output_netcdf',
'Subsets for one dataset',
abstract=
"Prepared netCDF file as input for weatherregime calculation",
as_reference=True,
supported_formats=[Format('application/x-netcdf')]),
ComplexOutput('output_log',
'Logging information',
abstract="Collected logs during process run.",
as_reference=True,
supported_formats=[Format('text/plain')]),
]
super(WeatherregimesreanalyseProcess, self).__init__(
self._handler,
identifier="weatherregimes_reanalyse",
title="Weather Regimes (based on reanalyses data)",
abstract=
'k-mean cluster analyse of the pressure patterns. Clusters are equivalent to weather regimes',
version="0.10",
metadata=[
Metadata('LSCE', 'http://www.lsce.ipsl.fr/en/index.php'),
Metadata('Doc',
'http://flyingpigeon.readthedocs.io/en/latest/'),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True,
)
def __init__(self):
inputs = [
LiteralInput("reanalyses", "Reanalyses Data",
abstract="Choose a reanalyses dataset for comparison",
default="NCEP_slp",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=_PRESSUREDATA_
),
# LiteralInput("timeres", "Reanalyses temporal resolution",
# abstract="Temporal resolution of the reanalyses (only for 20CRV2)",
# default="day",
# data_type='string',
# min_occurs=0,
# max_occurs=1,
# allowed_values=['day', '6h']
# ),
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=0,
max_occurs=1,
default='-20,40,30,70',
),
LiteralInput("season", "Time region",
abstract="Select the months to define the time region (all == whole year will be analysed)",
default="DJF",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=['January','February','March','April','May','June','July','August','September','October','November','December'] + _TIMEREGIONS_.keys()
),
LiteralInput('dateSt', 'Start date of analysis period',
data_type='date',
abstract='First day of the period to be analysed',
default='1948-01-01',
min_occurs=0,
max_occurs=1,
),
LiteralInput('dateEn', 'End date of analysis period',
data_type='date',
abstract='Last day of the period to be analysed',
default='1950-12-31',
min_occurs=0,
max_occurs=1,
),
LiteralInput("dist", "Distance",
abstract="Distance function to define analogues",
default='euclidean',
data_type='string',
min_occurs=0,
max_occurs=1,
allowed_values=['euclidean', 'mahalanobis', 'cosine']
),
LiteralInput("method", "Method",
abstract="Method of calculation: Python(full dist matrix at once), R(full dist matrix at once), R_wrap(dist matrix row by row on multiCPUs)",
default='Python_wrap',
data_type='string',
min_occurs=0,
max_occurs=1,
allowed_values=['Python', 'Python_wrap', 'R', 'R_wrap']
),
]
outputs = [
ComplexOutput("ldist", "Distances File",
abstract="mulit-column text file",
supported_formats=[Format("text/plain")],
as_reference=True,
),
ComplexOutput("ldist_seas", "Distances File for selected season (selection from all results)",
abstract="mulit-column text file",
supported_formats=[Format("text/plain")],
as_reference=True,
),
ComplexOutput("ld_pdf", "Scatter plot dims/theta",
abstract="Scatter plot dims/theta",
supported_formats=[Format('image/pdf')],
as_reference=True,
),
ComplexOutput("ld2_pdf", "Scatter plot dims/theta",
abstract="Scatter plot dims/theta",
supported_formats=[Format('image/pdf')],
as_reference=True,
),
ComplexOutput("ld2_seas_pdf", "Scatter plot dims/theta for season",
abstract="Scatter plot dims/theta for season",
supported_formats=[Format('image/pdf')],
as_reference=True,
),
ComplexOutput('output_log', 'Logging information',
abstract="Collected logs during process run.",
as_reference=True,
supported_formats=[Format('text/plain')]
),
]
super(LocaldimsReaProcess, self).__init__(
self._handler,
identifier="localdims_rea",
title="Calculation of local dimentions and persistence (based on reanalyses data)",
abstract='Local dimentions and persistence',
version="0.10",
metadata=[
Metadata('LSCE', 'http://www.lsce.ipsl.fr/en/index.php'),
Metadata('Doc', 'http://flyingpigeon.readthedocs.io/en/latest/'),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True,
)
def __init__(self):
inputs = [
ComplexInput(
'resource',
'Resource',
abstract=
'NetCDF Files or archive (tar/zip) containing daily 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(
'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=0,
max_occurs=1,
default='-20,40,30,70',
),
LiteralInput(
"level",
"Vertical level",
abstract=
"Vertical level for geopotential (hPa), only if zg variable used",
default='500',
data_type='integer',
min_occurs=0,
max_occurs=1,
allowed_values=[1000, 850, 700, 500, 250, 100, 50, 10]),
LiteralInput(
"season",
"Time region",
abstract=
"Select the months to define the time region (all == whole year will be analysed)",
default="DJF",
data_type='string',
min_occurs=1,
max_occurs=1,
allowed_values=[
'January', 'February', 'March', 'April', 'May', 'June',
'July', 'August', 'September', 'October', 'November',
'December'
] + _TIMEREGIONS_.keys()),
LiteralInput(
'dateSt',
'Start date of analysis period',
data_type='date',
abstract='First day of the period to be analysed',
default='2000-01-01',
min_occurs=0,
max_occurs=1,
),
LiteralInput(
'dateEn',
'End date of analysis period',
data_type='date',
abstract='Last day of the period to be analysed',
default='2002-12-31',
min_occurs=0,
max_occurs=1,
),
LiteralInput("dist",
"Distance",
abstract="Distance function to define analogues",
default='euclidean',
data_type='string',
min_occurs=0,
max_occurs=1,
allowed_values=['euclidean', 'mahalanobis',
'cosine']),
LiteralInput(
"method",
"Method",
abstract=
"Method of calculation: Python(full dist matrix at once), R(full dist matrix at once), R_wrap(dist matrix row by row on multiCPUs)",
default='Python',
data_type='string',
min_occurs=0,
max_occurs=1,
allowed_values=['Python', 'Python_wrap', 'R', 'R_wrap']),
]
outputs = [
ComplexOutput(
"ldist",
"Distances File",
abstract="mulit-column text file",
supported_formats=[Format("text/plain")],
as_reference=True,
),
ComplexOutput(
"ldist_seas",
"Distances File for selected season (selection from all results)",
abstract="mulit-column text file",
supported_formats=[Format("text/plain")],
as_reference=True,
),
ComplexOutput(
"ld_pdf",
"Scatter plot dims/theta",
abstract="Scatter plot dims/theta",
supported_formats=[Format('image/pdf')],
as_reference=True,
),
ComplexOutput(
"ld2_pdf",
"Scatter plot dims/theta",
abstract="Scatter plot dims/theta",
supported_formats=[Format('image/pdf')],
as_reference=True,
),
ComplexOutput(
"ld2_seas_pdf",
"Scatter plot dims/theta for season",
abstract="Scatter plot dims/theta for season",
supported_formats=[Format('image/pdf')],
as_reference=True,
),
ComplexOutput('output_log',
'Logging information',
abstract="Collected logs during process run.",
as_reference=True,
supported_formats=[Format('text/plain')]),
]
super(LocaldimsModProcess, self).__init__(
self._handler,
identifier="localdims_mod",
title=
"Calculation of local dimentions and persistence (based on climate model data)",
abstract=
'Calculation of local dimentions and persistence (based on climate model data)',
version="0.10",
metadata=[
Metadata('LSCE', 'http://www.lsce.ipsl.fr/en/index.php'),
Metadata('Doc',
'http://flyingpigeon.readthedocs.io/en/latest/'),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True,
)