gwgen.parameterization module

Module holding the parameterization scripts for the weather generator

Classes

CloudParameterizer(stations, config, ...[, ...])	Parameterizer to extract the months with complete clouds
CloudParameterizerBase(stations, config, ...)	Abstract base class for cloud parameterizers
CompleteDailyCloud(stations, config, ...[, ...])	The parameterizer that calculates the days in complete months of cloud
CompleteDailyGHCNData(stations, config, ...)	The parameterizer that calculates the days in complete months
CompleteMonthlyCloud(stations, config, ...)	Parameterizer to extract the months with complete clouds
CompleteMonthlyGHCNData(stations, config, ...)	The parameterizer that calculates the monthly summaries from the daily
CompleteMonthlyWind(stations, config, ...[, ...])	Parameterizer to extract the months with complete wind
CrossCorrelation(stations, config, ...[, ...])	Class to calculate the cross correlation between the variables
DailyCloud(stations, config, project_config, ...)	Parameterizer to calculate the daily cloud values from hourly cloud data
DailyGHCNData(stations, config, ...[, data, ...])	The parameterizer that reads in the daily data
HourlyCloud(stations, config, ...[, data, ...])	Parameterizer that loads the hourly cloud data from the EECRA database
MarkovChain(stations, config, ...[, data, ...])	The parameterizer to calculate the Markov Chain parameters
MonthlyCloud(stations, config, ...[, data, ...])	Parameterizer to calculate the monthly cloud values from daily cloud
MonthlyGHCNData(stations, config, ...[, ...])	The parameterizer that calculates the monthly summaries from the daily
Parameterizer(stations, config, ...[, data, ...])	Base class for parameterization tasks
PrcpDistParams(stations, config, ...[, ...])	The parameterizer to calculate the precipitation distribution parameters
TemperatureParameterizer(stations, config, ...)	Parameterizer to correlate the monthly mean and standard deviation on
WindParameterizer(stations, config, ...[, ...])	Parameterizer to extract the months with complete clouds
YearlyCompleteDailyCloud(stations, config, ...)	The parameterizer that calculates the days in complete months of cloud
YearlyCompleteDailyGHCNData(stations, ...[, ...])	The parameterizer that calculates the days in complete months
YearlyCompleteMonthlyCloud(stations, config, ...)	Parameterizer to extract the months with complete clouds
YearlyCompleteMonthlyGHCNData(stations, ...)	The parameterizer that calculates the monthly summaries from the daily
YearlyCompleteMonthlyWind(stations, config, ...)	Parameterizer to extract the months with complete wind

Functions

cloud_func(x, a)	Function for fitting the mean of wet and dry cloud to the mean of all
cloud_sd_func(x, a)	Function for fitting the standard deviation of wet and dry cloud to the
default_cloud_config([args_type])	The default configuration for CloudParameterizerBase instances.
default_daily_ghcn_config([download])	The default configuration for DailyGHCNData instances.
default_prcp_config([thresh, threshs2compute])	The default configuration for PrcpDistParams instances.
default_temp_config([cutoff, tmin_range1, ...])	The default configuration for TemperatureParameterizer instances.
wind_sd_func(x, c1, c2, c3)

class gwgen.parameterization.CloudParameterizer(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CompleteMonthlyCloud

Parameterizer to extract the months with complete clouds

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

allow_files	bool(x) -> bool
cmonthly_cloud	cmonthly_cloud parameterization instance
dbname	str(object=’‘) -> string
ds	The dataframe of this parameterization task converted to a dataset
error_keys	dict() -> new empty dictionary
fmt	dict() -> new empty dictionary
has_run	bool(x) -> bool
name	str(object=’‘) -> string
namelist_keys	dict() -> new empty dictionary
setup_requires	list() -> new empty list
sql_dtypes
summary	str(object=’‘) -> string

Methods

create_project(ds)	Plot the relationship wet/dry cloud - mean cloud
make_run_config(sp, info, full_nml)	Configure with the wet/dry cloud - mean cloud correlation
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

allow_files = False

cmonthly_cloud

cmonthly_cloud parameterization instance

create_project(ds)

Plot the relationship wet/dry cloud - mean cloud

Parameters:ds (xarray.Dataset) – The dataset to plot

dbname = 'cloud_correlation'

ds

The dataframe of this parameterization task converted to a dataset

error_keys = {'cldf_sd_w': 'sd_cloud_wet.a_err', 'cldf_sd_d': 'sd_cloud_dry.a_err', 'cldf_d': 'mean_cloud_dry.a_err', 'cldf_w': 'mean_cloud_wet.a_err'}

fmt = {'xlim': (0, 1), 'yrange': (0, 1), 'xrange': (0, 1), 'bounds': ['minmax', 11, 0, 99], 'ylim': (0, 1), 'cmap': 'w_Reds', 'xlabel': 'on %(state)s days', 'cbar': '', 'legend': False, 'bins': 10}

has_run = True

make_run_config(sp, info, full_nml)

Configure with the wet/dry cloud - mean cloud correlation

Parameters:

• %(TaskBase.make_run_config.parameters)s –
• full_nml (dict) – The dictionary with all the namelists

name = 'cloud'

namelist_keys = {'cldf_sd_w': 'sd_cloud_wet.a', 'cldf_sd_d': 'sd_cloud_dry.a', 'cldf_d': 'mean_cloud_dry.a', 'cldf_w': 'mean_cloud_wet.a'}

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

setup_requires = ['cmonthly_cloud']

sql_dtypes

summary = 'Parameterize the cloud data'

class gwgen.parameterization.CloudParameterizerBase(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.Parameterizer

Abstract base class for cloud parameterizers

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

allow_files	bool(x) -> bool
args_type
default_config
setup_from
sql_dtypes
stations

Methods

eecra_ghcn_map()	Get a dataframe mapping from GHCN id to EECRA station_id
filter_stations(stations)	Get the GHCN stations that are also in the EECRA dataset
from_task(task, *args, **kwargs)	Create a new instance from another task

allow_files = False

args_type

default_config

eecra_ghcn_map()

Get a dataframe mapping from GHCN id to EECRA station_id

classmethod filter_stations(stations)

Get the GHCN stations that are also in the EECRA dataset

Parameters:stations (np.ndarray) – A string array with stations to use Returns:The ids in stations that can be mapped to the eecra dataset Return type:np.ndarray

classmethod from_task(task, *args, **kwargs)

Create a new instance from another task

Parameters:

• task (TaskBase) – The organizer to use the configuration from. Note that it can also be of a different type than this class
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

setup_from

sql_dtypes

stations

class gwgen.parameterization.CompleteDailyCloud(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.DailyCloud

The parameterizer that calculates the days in complete months of cloud data

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

cols	list() -> new empty list
daily_cloud	daily_cloud parameterization instance
dbname	str(object=’‘) -> string
monthly_cloud	monthly_cloud parameterization instance
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

init_from_scratch()
setup_from_scratch()

cols = ['wet_day', 'tmin', 'tmax', 'mean_cloud', 'wind']

daily_cloud

daily_cloud parameterization instance

dbname = 'complete_daily_cloud'

init_from_scratch()

monthly_cloud

monthly_cloud parameterization instance

name = 'cdaily_cloud'

setup_from_scratch()

setup_requires = ['daily_cloud', 'monthly_cloud']

summary = 'Get the days of the complete daily cloud months'

class gwgen.parameterization.CompleteDailyGHCNData(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.DailyGHCNData

The parameterizer that calculates the days in complete months

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

day	day parameterization instance
dbname	str(object=’‘) -> string
default_config
month	month parameterization instance
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

init_from_scratch()
setup_from_scratch()

day

day parameterization instance

dbname = 'complete_ghcn_daily'

default_config

init_from_scratch()

month

month parameterization instance

name = 'cday'

setup_from_scratch()

setup_requires = ['day', 'month']

summary = 'Get the days of the complete months'

class gwgen.parameterization.CompleteMonthlyCloud(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.MonthlyCloud

Parameterizer to extract the months with complete clouds

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

cols	list() -> new empty list
dbname	str(object=’‘) -> string
monthly_cloud	monthly_cloud parameterization instance
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

setup_from_scratch()

cols = ['wet_day', 'mean_cloud']

dbname = 'complete_monthly_cloud'

monthly_cloud

monthly_cloud parameterization instance

name = 'cmonthly_cloud'

setup_from_scratch()

setup_requires = ['monthly_cloud']

summary = 'Extract the months with complete cloud data'

class gwgen.parameterization.CompleteMonthlyGHCNData(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.MonthlyGHCNData

The parameterizer that calculates the monthly summaries from the daily data

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

dbname	str(object=’‘) -> string
month	month parameterization instance
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

setup_from_scratch()

dbname = 'complete_ghcn_monthly'

month

month parameterization instance

name = 'cmonth'

setup_from_scratch()

setup_requires = ['month']

summary = 'Extract the complete months from the monthly data'

class gwgen.parameterization.CompleteMonthlyWind(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CompleteMonthlyCloud

Parameterizer to extract the months with complete wind

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

cols	list() -> new empty list
dbname	str(object=’‘) -> string
monthly_cloud	monthly_cloud parameterization instance
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

cols = ['wet_day', 'wind']

dbname = 'complete_monthly_wind'

monthly_cloud

monthly_cloud parameterization instance

name = 'cmonthly_wind'

setup_requires = ['monthly_cloud']

summary = 'Extract the months with complete wind data'

class gwgen.parameterization.CrossCorrelation(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.Parameterizer

Class to calculate the cross correlation between the variables

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

cols	list() -> new empty list
dbname	str(object=’‘) -> string
has_run	bool(x) -> bool
name	str(object=’‘) -> string
namelist_keys	dict() -> new empty dictionary
setup_parallel	bool(x) -> bool
setup_requires	list() -> new empty list
sql_dtypes
summary	str(object=’‘) -> string
yearly_cdaily_cloud	yearly_cdaily_cloud parameterization instance

Methods

run(info, full_nml)
setup_from_db(**kwargs)	Set up the parameterizer from datatables already created
setup_from_file(**kwargs)	Set up the parameterizer from already stored files
setup_from_scratch()

cols = ['tmin', 'tmax', 'mean_cloud', 'wind']

dbname = 'cross_correlation'

has_run = True

name = 'corr'

namelist_keys = {'a': None, 'b': None}

run(info, full_nml)

setup_from_db(**kwargs)

Set up the parameterizer from datatables already created

setup_from_file(**kwargs)

Set up the parameterizer from already stored files

setup_from_scratch()

setup_parallel = False

setup_requires = ['yearly_cdaily_cloud']

sql_dtypes

summary = 'Cross corellation between temperature and cloudiness'

yearly_cdaily_cloud

yearly_cdaily_cloud parameterization instance

class gwgen.parameterization.DailyCloud(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CloudParameterizerBase

Parameterizer to calculate the daily cloud values from hourly cloud data

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

allow_files	bool(x) -> bool
dbname	str(object=’‘) -> string
hourly_cloud	hourly_cloud parameterization instance
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

calculate_daily(df)
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

allow_files = True

static calculate_daily(df)

dbname = 'daily_cloud'

hourly_cloud

hourly_cloud parameterization instance

name = 'daily_cloud'

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

setup_requires = ['hourly_cloud']

summary = 'Calculate the daily cloud values from hourly cloud data'

class gwgen.parameterization.DailyGHCNData(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.Parameterizer

The parameterizer that reads in the daily data

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

data_dir
dbname	str(object=’‘) -> string
default_config
flags	list() -> new empty list
http_single	str(object=’‘) -> string
http_source	str(object=’‘) -> string
name	str(object=’‘) -> string
raw_src_files
sql_dtypes
summary	str(object=’‘) -> string

Methods

init_from_scratch()	Reimplemented to download the data if not existent
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

data_dir

dbname = 'ghcn_daily'

default_config

flags = ['tmax_m', 'prcp_s', 'tmax_q', 'prcp_m', 'tmin_m', 'tmax_s', 'tmin_s', 'prcp_q', 'tmin_q']

http_single = 'ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/all/{}'

http_source = 'ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz'

init_from_scratch()

Reimplemented to download the data if not existent

name = 'day'

raw_src_files

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

sql_dtypes

summary = 'Read in the daily GHCN data'

class gwgen.parameterization.HourlyCloud(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CloudParameterizerBase

Parameterizer that loads the hourly cloud data from the EECRA database

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

allow_files	bool(x) -> bool
data_dir
dbname	str(object=’‘) -> string
mon_map	dict() -> new empty dictionary
months	list() -> new empty list
name	str(object=’‘) -> string
raw_dir	The directory where we expect the raw files
raw_src_files
sql_dtypes
src_files
summary	str(object=’‘) -> string
urls	dict() -> new empty dictionary
years	list() -> new empty list

Methods

download_src(src_dir[, force, keep])	Download the source files from the EECRA ftp server
eecra_fname(year, mon[, ext])	The the name of the eecra file
get_data_from_files(files)
get_eecra_url(year, mon)	Get the download path for the file for a specific year and month
init_from_scratch()	Reimplemented to download the data if not existent
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()	Set up the data

allow_files = True

data_dir

dbname = 'hourly_cloud'

download_src(src_dir, force=False, keep=False)

Download the source files from the EECRA ftp server

classmethod eecra_fname(year, mon, ext='')

The the name of the eecra file

Parameters:

• year (int) – The year of the data
• month (int) – The integer of the month between 1 and 12

get_data_from_files(files)

classmethod get_eecra_url(year, mon)

Get the download path for the file for a specific year and month

init_from_scratch()

Reimplemented to download the data if not existent

mon_map = {1: 'JAN', 2: 'FEB', 3: 'MAR', 4: 'APR', 5: 'MAY', 6: 'JUN', 7: 'JUL', 8: 'AUG', 9: 'SEP', 10: 'OCT', 11: 'NOV', 12: 'DEC'}

months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

name = 'hourly_cloud'

raw_dir

The directory where we expect the raw files

raw_src_files

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

Set up the data

sql_dtypes

src_files

summary = 'Hourly cloud data'

urls = {((1992, 3), (1996, 12)): 'http://cdiac.ess-dive.lbl.gov/ftp/ndp026c/land_199203_199612/', ((1997, 1), (2009, 12)): 'http://cdiac.ess-dive.lbl.gov/ftp/ndp026c/land_199701_200912/', ((1971, 1), (1977, 4)): 'http://cdiac.ess-dive.lbl.gov/ftp/ndp026c/land_197101_197704/', ((1982, 11), (1987, 6)): 'http://cdiac.ess-dive.lbl.gov/ftp/ndp026c/land_198211_198706/', ((1987, 7), (1992, 2)): 'http://cdiac.ess-dive.lbl.gov/ftp/ndp026c/land_198707_199202/', ((1977, 5), (1982, 10)): 'http://cdiac.ess-dive.lbl.gov/ftp/ndp026c/land_197705_198210/'}

years = [1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]

class gwgen.parameterization.MarkovChain(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.Parameterizer

The parameterizer to calculate the Markov Chain parameters

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Methods

calc_ndays(df)
calculate_probabilities(df)	Calculate the transition probabilities for one month across multiple
create_project(ds)	Create the project of the plots of the transition probabilities
make_run_config(sp, info, full_nml)	Configure the experiment with the MarkovChain relationships
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

Attributes

cday	cday parameterization instance
dbname	str(object=’‘) -> string
ds	The dataset of the data DataFrame
fmt	dict() -> new empty dictionary
has_run	bool(x) -> bool
kwargs	dict() -> new empty dictionary
name	str(object=’‘) -> string
namelist_keys	list() -> new empty list
setup_requires	list() -> new empty list
sql_dtypes
summary	str(object=’‘) -> string

classmethod calc_ndays(df)

classmethod calculate_probabilities(df)

Calculate the transition probabilities for one month across multiple years

cday

cday parameterization instance

create_project(ds)

Create the project of the plots of the transition probabilities

Parameters:ds (xarray.Dataset) – The dataset to plot

dbname = 'markov'

ds

The dataset of the data DataFrame

fmt = {'ci': None, 'cbar': '', 'xlim': (0, 1), 'fix': 0, 'bounds': ['minmax', 11, 0, 99], 'ylim': (0, 1), 'cmap': 'w_Reds', 'legendlabels': ['$%(symbol)s$ = %(slope)1.4f * %(xname)s, $R^2$ = %(rsquared)1.3f'], 'ylabel': '%(long_name)s', 'legend': {'loc': 'upper left'}, 'bins': 10}

has_run = True

kwargs = {'ci': None, 'cbar': '', 'xlim': (0, 1), 'fix': 0, 'bounds': ['minmax', 11, 0, 99], 'ylim': (0, 1), 'cmap': 'w_Reds', 'legendlabels': ['$%(symbol)s$ = %(slope)1.4f * %(xname)s, $R^2$ = %(rsquared)1.3f'], 'ylabel': '%(long_name)s', 'legend': {'loc': 'upper left'}, 'bins': 10}

make_run_config(sp, info, full_nml)

Configure the experiment with the MarkovChain relationships

Parameters:

• %(TaskBase.make_run_config.parameters)s –
• full_nml (dict) – The dictionary with all the namelists

name = 'markov'

namelist_keys = ['p101_1', 'p001_1', 'p001_2', 'p11_1', 'p11_2', 'p101_2']

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

setup_requires = ['cday']

sql_dtypes

summary = 'Calculate the markov chain parameterization'

class gwgen.parameterization.MonthlyCloud(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CloudParameterizerBase

Parameterizer to calculate the monthly cloud values from daily cloud

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

allow_files	bool(x) -> bool
daily_cloud	daily_cloud parameterization instance
dbname	str(object=’‘) -> string
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
sql_dtypes
summary	str(object=’‘) -> string

Methods

calculate_monthly(df)
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

allow_files = True

static calculate_monthly(df)

daily_cloud

daily_cloud parameterization instance

dbname = 'monthly_cloud'

name = 'monthly_cloud'

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

setup_requires = ['daily_cloud']

sql_dtypes

summary = 'Calculate the monthly cloud values from daily cloud data'

class gwgen.parameterization.MonthlyGHCNData(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.Parameterizer

The parameterizer that calculates the monthly summaries from the daily data

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

day	day parameterization instance
dbname	str(object=’‘) -> string
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
sql_dtypes
summary	str(object=’‘) -> string

Methods

monthly_summary(df)
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

day

day parameterization instance

dbname = 'ghcn_monthly'

static monthly_summary(df)

name = 'month'

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

setup_requires = ['day']

sql_dtypes

summary = 'Calculate monthly means from the daily GHCN data'

class gwgen.parameterization.Parameterizer(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.utils.TaskBase

Base class for parameterization tasks

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

error_keys	A mapping from the keys in the namelist that are modified by this
namelist_keys	A mapping from the keys in the namelist that are modified by this
task_config_keys	A mapping from the keys in the namelist that are modified by this
task_data_dir	The directory where to store data

Methods

get_config_key(nml_key)
get_error(nml_key)
get_task_for_nml_key(nml_key)
make_run_config(sp, info, full_nml)	Configure the experiment

error_keys = {}

A mapping from the keys in the namelist that are modified by this parameterization task to the error as stored in the configuration

classmethod get_config_key(nml_key)

get_error(nml_key)

classmethod get_task_for_nml_key(nml_key)

make_run_config(sp, info, full_nml)

Configure the experiment

Parameters:

• %(TaskBase.make_run_config.parameters)s –
• full_nml (dict) – The dictionary with all the namelists

namelist_keys = {}

A mapping from the keys in the namelist that are modified by this parameterization task to the key as it is used in the task information

task_config_keys = {}

A mapping from the keys in the namelist that are modified by this parameterization task to the key as it is used in the task_config attribute

task_data_dir

The directory where to store data

class gwgen.parameterization.PrcpDistParams(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.Parameterizer

The parameterizer to calculate the precipitation distribution parameters

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

cday	cday parameterization instance
dbname	str(object=’‘) -> string
default_config
ds	The dataset of the data dataframe
error_keys	dict() -> new empty dictionary
filtered_data	Return the data that only belongs to the specified threshold of the
fmt	default formatoptions for the
has_run	bool(x) -> bool
kwargs	default formatoptions for the
name	str(object=’‘) -> string
namelist_keys	dict() -> new empty dictionary
setup_requires	list() -> new empty list
sql_dtypes
summary	str(object=’‘) -> string
task_config_keys	dict() -> new empty dictionary

Methods

create_project(ds)	Make the gamma shape - number of wet days plot
make_run_config(sp, info, full_nml)	Configure the experiment with information on gamma scale and GP shape
plot_additionals([pdf])	Plot the histogram of GP shape
prcp_dist_params(df[, threshs])
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

cday

cday parameterization instance

create_project(ds)

Make the gamma shape - number of wet days plot

Parameters:%(TaskBase.create_project.parameters)s –

dbname = 'prcp_dist_params'

default_config

ds

The dataset of the data dataframe

error_keys = {'gp_shape': 'gpshape_std'}

filtered_data

Return the data that only belongs to the specified threshold of the task

fmt = {'legendlabels': ['$\\theta$ = %(slope)1.4f * $\\bar{{p}}_d$, $R^2$ = %(rsquared)1.3f'], 'yrange': (0, ['rounded', 95]), 'xrange': (0, ['rounded', 95]), 'fix': 0, 'bounds': ['minmax', 11, 0, 99], 'precision': 0.1, 'cmap': 'w_Reds', 'xlabel': '{desc}', 'cbar': '', 'ylabel': '{desc}', 'legend': {'loc': 'upper left'}, 'bins': 10}

default formatoptions for the psy_reg.plotters.DensityRegPlotter plotter

has_run = True

kwargs = {'legendlabels': ['$\\theta$ = %(slope)1.4f * $\\bar{{p}}_d$, $R^2$ = %(rsquared)1.3f'], 'yrange': (0, ['rounded', 95]), 'xrange': (0, ['rounded', 95]), 'fix': 0, 'bounds': ['minmax', 11, 0, 99], 'precision': 0.1, 'cmap': 'w_Reds', 'xlabel': '{desc}', 'cbar': '', 'ylabel': '{desc}', 'legend': {'loc': 'upper left'}, 'bins': 10}

default formatoptions for the psy_reg.plotters.DensityRegPlotter plotter

make_run_config(sp, info, full_nml)

Configure the experiment with information on gamma scale and GP shape

Parameters:%(Parameterizer.make_run_config.parameters)s –

name = 'prcp'

namelist_keys = {'gp_shape': 'gpshape', 'thresh': 'thresh', 'g_scale_coeff': 'slope'}

plot_additionals(pdf=None)

Plot the histogram of GP shape

Parameters:pdf (matplotlib.backends.backend_pdf.PdfPages) – The PdfPages instance which can be used to save the figure

prcp_dist_params(df, threshs=array([ 5., 7.5, 10., 12.5, 15., 17.5, 20. ]))

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

setup_requires = ['cday']

sql_dtypes

summary = 'Calculate the precipitation distribution parameters of the hybrid Gamma-GP'

task_config_keys = {'thresh': 'thresh'}

class gwgen.parameterization.TemperatureParameterizer(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.Parameterizer

Parameterizer to correlate the monthly mean and standard deviation on wet and dry days with the montly mean

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Methods

calc_monthly_props(df)	Calculate the statistics for one single month in one year
calculate_probabilities(df)	Calculate the statistics for one month across multiple years
create_project(ds)	Create the plots of the wet/dry - mean relationships
make_run_config(sp, info, full_nml)	Configure the experiment with the correlations of wet/dry temperature
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

Attributes

cday	cday parameterization instance
dbname	list() -> new empty list
default_config
ds	The dataframe of this parameterization task converted to a dataset
fmt	dict() -> new empty dictionary
has_run	bool(x) -> bool
name	str(object=’‘) -> string
namelist_keys	dict() -> new empty dictionary
sd_fit_fmt	dict() -> new empty dictionary
sd_hist_fmt	dict() -> new empty dictionary
setup_requires	list() -> new empty list
sql_dtypes
summary	str(object=’‘) -> string

static calc_monthly_props(df)

Calculate the statistics for one single month in one year

classmethod calculate_probabilities(df)

Calculate the statistics for one month across multiple years

cday

cday parameterization instance

create_project(ds)

Create the plots of the wet/dry - mean relationships

Parameters:%(TaskBase.create_project)s –

dbname = ['temperature_full', 'temperature']

default_config

ds

The dataframe of this parameterization task converted to a dataset

fmt = {'cbar': '', 'yrange': (['rounded', 5], ['rounded', 95]), 'xrange': (['rounded', 5], ['rounded', 95]), 'bounds': ['minmax', 11, 0, 99], 'precision': 0.1, 'cmap': 'w_Reds', 'xlabel': 'on %(state)s days', 'legendlabels': ['$%(symbol)s$ = %(intercept)1.4f + %(slope)1.4f * $%(xsymbol)s$'], 'legend': {'loc': 'upper left'}, 'bins': 10}

has_run = True

make_run_config(sp, info, full_nml)

Configure the experiment with the correlations of wet/dry temperature to mean temperature

Parameters:

• %(TaskBase.make_run_config.parameters)s –
• full_nml (dict) – The dictionary with all the namelists

name = 'temp'

namelist_keys = {'tmin_sd_w': 'tminstddev_wet.coeffs', 'tmin_w1': 'tmin_wet.intercept', 'tmin_w2': 'tmin_wet.slope', 'tmin_sd_d': 'tminstddev_dry.coeffs', 'tmin_d1': 'tmin_dry.intercept', 'tmin_d2': 'tmin_dry.slope', 'tmax_sd_w': 'tmaxstddev_wet.coeffs', 'tmax_d2': 'tmax_dry.slope', 'tmax_d1': 'tmax_dry.intercept', 'tmax_w2': 'tmax_wet.slope', 'tmax_w1': 'tmax_wet.intercept', 'tmax_sd_d': 'tmaxstddev_dry.coeffs', 'tmax_sd_breaks': 'tmaxstddev.breaks', 'tmin_sd_breaks': 'tminstddev.breaks'}

sd_fit_fmt = {'xlabel': 'on %(state)s days', 'legend': {'loc': 'upper left'}}

sd_hist_fmt = {'yrange': (0, ['rounded', 95]), 'xrange': (['rounded', 5], ['rounded', 95]), 'bounds': ['minmax', 11, 0, 99], 'precision': 0.1, 'cmap': 'w_Reds', 'xlabel': 'on %(state)s days', 'cbar': '', 'bins': 10}

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

setup_requires = ['cday']

sql_dtypes

summary = 'Temperature mean correlations'

class gwgen.parameterization.WindParameterizer(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CompleteMonthlyWind

Parameterizer to extract the months with complete clouds

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

allow_files	bool(x) -> bool
cmonthly_wind	cmonthly_wind parameterization instance
dbname	str(object=’‘) -> string
ds	The dataframe of this parameterization task converted to a dataset
fmt	dict() -> new empty dictionary
has_run	bool(x) -> bool
name	str(object=’‘) -> string
namelist_keys	dict() -> new empty dictionary
setup_requires	list() -> new empty list
sql_dtypes
summary	str(object=’‘) -> string

Methods

create_project(ds)	Plot the relationship wet/dry cloud - mean cloud
make_run_config(sp, info, full_nml)	Configure with the wet/dry cloud - mean cloud correlation
setup_from_db(*args, **kwargs)
setup_from_file(*args, **kwargs)
setup_from_scratch()

allow_files = False

cmonthly_wind

cmonthly_wind parameterization instance

create_project(ds)

Plot the relationship wet/dry cloud - mean cloud

Parameters:ds (xarray.Dataset) – The dataset to plot

dbname = 'wind_correlation'

ds

The dataframe of this parameterization task converted to a dataset

fmt = {'legendlabels': ['$%(symbol)s = %(intercept)1.4f %(slope)+1.4f \\cdot %(xsymbol)s$'], 'yrange': (0, ['rounded', 95]), 'xrange': (0, ['rounded', 95]), 'fix': 0, 'bounds': ['minmax', 11, 0, 99], 'cmap': 'w_Reds', 'xlabel': 'on %(state)s days', 'cbar': '', 'legend': {'loc': 'upper left'}, 'bins': 100}

has_run = True

make_run_config(sp, info, full_nml)

Configure with the wet/dry cloud - mean cloud correlation

Parameters:

• %(TaskBase.make_run_config.parameters)s –
• full_nml (dict) – The dictionary with all the namelists

name = 'wind'

namelist_keys = {'wind_sd_w': 'sd_wind_wet', 'wind_d2': 'wind_dry.slope', 'wind_d1': 'wind_dry.intercept', 'wind_sd_d': 'sd_wind_dry', 'wind_w2': 'wind_wet.slope', 'wind_w1': 'wind_wet.intercept'}

setup_from_db(*args, **kwargs)

setup_from_file(*args, **kwargs)

setup_from_scratch()

setup_requires = ['cmonthly_wind']

sql_dtypes

summary = 'Parameterize the wind data'

class gwgen.parameterization.YearlyCompleteDailyCloud(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CompleteDailyCloud

The parameterizer that calculates the days in complete months of cloud data

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

allow_files	bool(x) -> bool
cdaily_cloud	cdaily_cloud parameterization instance
cmonthly_cloud	cmonthly_cloud parameterization instance
cols	list() -> new empty list
dbname	str(object=’‘) -> string
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

setup_from_scratch()

allow_files = False

cdaily_cloud

cdaily_cloud parameterization instance

cmonthly_cloud

cmonthly_cloud parameterization instance

cols = ['wet_day', 'mean_cloud', 'tmin', 'tmax', 'wind']

dbname = 'yearly_complete_daily_cloud'

name = 'yearly_cdaily_cloud'

setup_from_scratch()

setup_requires = ['cdaily_cloud', 'cmonthly_cloud']

summary = 'Get the days of the complete daily cloud months in complete years'

class gwgen.parameterization.YearlyCompleteDailyGHCNData(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CompleteDailyGHCNData

The parameterizer that calculates the days in complete months

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

day	day parameterization instance
dbname	str(object=’‘) -> string
month	month parameterization instance
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

setup_from_scratch()

day

day parameterization instance

dbname = 'yearly_complete_ghcn_daily'

month

month parameterization instance

name = 'yearly_cday'

setup_from_scratch()

setup_requires = ['day', 'month']

summary = 'Get the days of the complete months in complete years'

class gwgen.parameterization.YearlyCompleteMonthlyCloud(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CompleteMonthlyCloud

Parameterizer to extract the months with complete clouds

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

allow_files	bool(x) -> bool
cmonthly_cloud	cmonthly_cloud parameterization instance
cols	list() -> new empty list
dbname	str(object=’‘) -> string
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

setup_from_scratch()

allow_files = False

cmonthly_cloud

cmonthly_cloud parameterization instance

cols = ['wet_day', 'mean_cloud']

dbname = 'yearly_complete_monthly_cloud'

name = 'yearly_cmonthly_cloud'

setup_from_scratch()

setup_requires = ['cmonthly_cloud']

summary = 'Extract the months with complete cloud data in complete years'

class gwgen.parameterization.YearlyCompleteMonthlyGHCNData(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.CompleteMonthlyGHCNData

The parameterizer that calculates the monthly summaries from the daily data

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

dbname	str(object=’‘) -> string
month	month parameterization instance
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

Methods

setup_from_scratch()

dbname = 'yearly_complete_ghcn_monthly'

month

month parameterization instance

name = 'yearly_cmonth'

setup_from_scratch()

setup_requires = ['month']

summary = 'Extract the complete months from the monthly data in complete years'

class gwgen.parameterization.YearlyCompleteMonthlyWind(stations, config, project_config, global_config, data=None, requirements=None, *args, **kwargs)

Bases: gwgen.parameterization.YearlyCompleteMonthlyCloud

Parameterizer to extract the months with complete wind

Parameters:

• stations (list) – The list of stations to process
• config (dict) – The configuration of the experiment
• project_config (dict) – The configuration of the underlying project
• global_config (dict) – The global configuration
• data (pandas.DataFrame) – The data to use. If None, use the setup() method
• requirements (list of TaskBase instances) – The required instances. If None, you must call the set_requirements() method later

Other Parameters:  

``*args, **kwargs`` – The configuration of the task. See the TaskConfig for arguments. Note that if you provide *args, you have to provide all possible arguments

Attributes

cmonthly_cloud
cmonthly_wind	cmonthly_wind parameterization instance
cols	list() -> new empty list
dbname	str(object=’‘) -> string
name	str(object=’‘) -> string
setup_requires	list() -> new empty list
summary	str(object=’‘) -> string

cmonthly_cloud

cmonthly_wind

cmonthly_wind parameterization instance

cols = ['wet_day', 'wind']

dbname = 'yearly_complete_monthly_wind'

name = 'yearly_cmonthly_wind'

setup_requires = ['cmonthly_wind']

summary = 'Extract the months with complete wind data in complete years'

gwgen.parameterization.cloud_func(x, a)

Function for fitting the mean of wet and dry cloud to the mean of all cloud

This function returns y with

\[y = ((-a - 1) / (a^2 x - a^2 - a)) - \frac{1}{a}\]

Parameters:

• x (np.ndarray) – The x input data
• a (float) – The parameter as mentioned in the equation above

gwgen.parameterization.cloud_sd_func(x, a)

Function for fitting the standard deviation of wet and dry cloud to the mean of wet or dry cloud

This function returns y with

\[y = a^2 x (1 - x)\]

Parameters:

• x (np.ndarray) – The x input data
• a (float) – The parameter as mentioned in the equation above

gwgen.parameterization.default_cloud_config(args_type='ghcn', *args, **kwargs)

The default configuration for CloudParameterizerBase instances. See also the CloudParameterizerBase.default_config attribute

Parameters:

• args_type (str) –

  The type of the stations. One of

  ghcn

  Stations are GHCN ids

  eecra

  Stations are EECRA station numbers

• setup_from ({ 'scratch' | 'file' | 'db' | None }) –

  The method how to setup the instance either from

  'scratch'

  To set up the task from the raw data

  'file'

  Set up the task from an existing file

  'db'

  Set up the task from a database

  None

  If the file name of this this task exists, use this one, otherwise a database is provided, use this one, otherwise go from scratch

• to_csv (bool) – If True, the data at setup will be written to a csv file
• to_db (bool) – If True, the data at setup will be written to into a database
• remove (bool) – If True and the old data file already exists, remove before writing to it
• skip_filtering (bool) – If True, skip the filtering for the correct stations in the datafile
• plot_output (str) – An alternative path to use for the PDF file of the plot
• nc_output (str) – An alternative path (or multiples depending on the task) to use for the netCDF file of the plot data
• project_output (str) – An alternative path to use for the psyplot project file of the plot
• new_project (bool) – If True, a new project will be created even if a file in project_output exists already
• project (str) – The path to a psyplot project file to use for this parameterization
• close (bool) – Close the project at the end

gwgen.parameterization.default_daily_ghcn_config(download=None, *args, **kwargs)

The default configuration for DailyGHCNData instances. See also the DailyGHCNData.default_config attribute

Parameters:

• download ({ 'single' | 'all' | None }) – What to do if a stations file is missing. The default is None which raises an Error. Otherwise, if 'single', download the missing file from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/all/{}. If 'all' the entire tarball is downloaded from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz
• setup_from ({ 'scratch' | 'file' | 'db' | None }) –

  The method how to setup the instance either from

  'scratch'

  To set up the task from the raw data

  'file'

  Set up the task from an existing file

  'db'

  Set up the task from a database

  None

  If the file name of this this task exists, use this one, otherwise a database is provided, use this one, otherwise go from scratch

• to_csv (bool) – If True, the data at setup will be written to a csv file
• to_db (bool) – If True, the data at setup will be written to into a database
• remove (bool) – If True and the old data file already exists, remove before writing to it
• skip_filtering (bool) – If True, skip the filtering for the correct stations in the datafile
• plot_output (str) – An alternative path to use for the PDF file of the plot
• nc_output (str) – An alternative path (or multiples depending on the task) to use for the netCDF file of the plot data
• project_output (str) – An alternative path to use for the psyplot project file of the plot
• new_project (bool) – If True, a new project will be created even if a file in project_output exists already
• project (str) – The path to a psyplot project file to use for this parameterization
• close (bool) – Close the project at the end

gwgen.parameterization.default_prcp_config(thresh=5.0, threshs2compute=[5, 7.5, 10, 12.5, 15, 17.5, 20], *args, **kwargs)

The default configuration for PrcpDistParams instances. See also the PrcpDistParams.default_config attribute

Parameters:

• thresh (float) – The threshold to use for the configuration
• threshs2compute (list of floats) – The thresholds to compute during the setup of the data
• setup_from ({ 'scratch' | 'file' | 'db' | None }) –

  The method how to setup the instance either from

  'scratch'

  To set up the task from the raw data

  'file'

  Set up the task from an existing file

  'db'

  Set up the task from a database

  None

  If the file name of this this task exists, use this one, otherwise a database is provided, use this one, otherwise go from scratch

• to_csv (bool) – If True, the data at setup will be written to a csv file
• to_db (bool) – If True, the data at setup will be written to into a database
• remove (bool) – If True and the old data file already exists, remove before writing to it
• skip_filtering (bool) – If True, skip the filtering for the correct stations in the datafile
• plot_output (str) – An alternative path to use for the PDF file of the plot
• nc_output (str) – An alternative path (or multiples depending on the task) to use for the netCDF file of the plot data
• project_output (str) – An alternative path to use for the psyplot project file of the plot
• new_project (bool) – If True, a new project will be created even if a file in project_output exists already
• project (str) – The path to a psyplot project file to use for this parameterization
• close (bool) – Close the project at the end

gwgen.parameterization.default_temp_config(cutoff=10, tmin_range1=[-50, -40], tmin_range2=[25, 30], tmax_range1=[-40, -30], tmax_range2=[35, 45], *args, **kwargs)

The default configuration for TemperatureParameterizer instances. See also the PrcpDistParams.default_config attribute

Parameters:

• cutoff (int) – The minimum number of values that is required for fitting the standard deviation
• tmin_range1 (list of floats with length 2) – The ranges [vmin, vmax] to use for the extrapolation of minimum temperatures standard deviation below 0. The fit will be used for all points below the given vmax
• tmin_range2 (list of floats with length 2) – The ranges [vmin, vmax] to use for the extrapolation of minimum temperatures standard deviation above 0. The fit will be used for all points above the given vmin
• tmax_range1 (list of floats with length 2) – The ranges [vmin, vmax] to use for the extrapolation of maximum temperatures standard deviation below 0. The fit will be used for all points below the given vmax
• tmax_range2 (list of floats with length 2) – The ranges [vmin, vmax] to use for the extrapolation of maximum temperatures standard deviation above 0. The fit will be used for all points above the given vmin
• setup_from ({ 'scratch' | 'file' | 'db' | None }) –

  The method how to setup the instance either from

  'scratch'

  To set up the task from the raw data

  'file'

  Set up the task from an existing file

  'db'

  Set up the task from a database

  None

  If the file name of this this task exists, use this one, otherwise a database is provided, use this one, otherwise go from scratch

• to_csv (bool) – If True, the data at setup will be written to a csv file
• to_db (bool) – If True, the data at setup will be written to into a database
• remove (bool) – If True and the old data file already exists, remove before writing to it
• skip_filtering (bool) – If True, skip the filtering for the correct stations in the datafile
• plot_output (str) – An alternative path to use for the PDF file of the plot
• nc_output (str) – An alternative path (or multiples depending on the task) to use for the netCDF file of the plot data
• project_output (str) – An alternative path to use for the psyplot project file of the plot
• new_project (bool) – If True, a new project will be created even if a file in project_output exists already
• project (str) – The path to a psyplot project file to use for this parameterization
• close (bool) – Close the project at the end

gwgen.parameterization.wind_sd_func(x, c1, c2, c3)