welltestpy.estimate¶
welltestpy subpackage providing routines to estimate pump test parameters.
Estimators¶
The following estimators are provided
ExtTheis3D (name, campaign[, val_ranges, …]) |
Class for an estimation of stochastic subsurface parameters. |
ExtTheis2D (name, campaign[, val_ranges, …]) |
Class for an estimation of stochastic subsurface parameters. |
Neuman2004 (name, campaign[, val_ranges, …]) |
Class for an estimation of stochastic subsurface parameters. |
Theis (name, campaign[, val_ranges, val_fix, …]) |
Class for an estimation of homogeneous subsurface parameters. |
ExtThiem3D (name, campaign[, make_steady, …]) |
Class for an estimation of stochastic subsurface parameters. |
ExtThiem2D (name, campaign[, make_steady, …]) |
Class for an estimation of stochastic subsurface parameters. |
Neuman2004Steady (name, campaign[, …]) |
Class for an estimation of stochastic subsurface parameters. |
Thiem (name, campaign[, make_steady, …]) |
Class for an estimation of homogeneous subsurface parameters. |
Base Classes¶
Transient¶
All transient estimators are derived from the following class
TransientPumping (name, campaign, type_curve, …) |
Class to estimate transient Type-Curve parameters. |
Steady Pumping¶
All steady estimators are derived from the following class
SteadyPumping (name, campaign, type_curve, …) |
Class to estimate steady Type-Curve parameters. |
-
class
ExtTheis3D
(name, campaign, val_ranges=None, val_fix=None, testinclude=None, generate=False)[source]¶ Bases:
welltestpy.estimate.transient_lib.TransientPumping
Class for an estimation of stochastic subsurface parameters.
With this class you can run an estimation of statistical subsurface parameters. It utilizes the extended theis solution in 3D which assumes a log-normal distributed transmissivity field with a gaussian correlation function and an anisotropy ratio 0 < e <= 1.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown at given time points. gen_setup
([prate_kw, rad_kw, time_kw, dummy])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. settime
([time, tmin, tmax, typ, steps])Set uniform time points for the observations. - name (
-
class
ExtTheis2D
(name, campaign, val_ranges=None, val_fix=None, testinclude=None, generate=False)[source]¶ Bases:
welltestpy.estimate.transient_lib.TransientPumping
Class for an estimation of stochastic subsurface parameters.
With this class you can run an estimation of statistical subsurface parameters. It utilizes the extended theis solution in 2D which assumes a log-normal distributed transmissivity field with a gaussian correlation function.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown at given time points. gen_setup
([prate_kw, rad_kw, time_kw, dummy])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. settime
([time, tmin, tmax, typ, steps])Set uniform time points for the observations. - name (
-
class
Neuman2004
(name, campaign, val_ranges=None, val_fix=None, testinclude=None, generate=False)[source]¶ Bases:
welltestpy.estimate.transient_lib.TransientPumping
Class for an estimation of stochastic subsurface parameters.
With this class you can run an estimation of statistical subsurface parameters. It utilizes the apparent Transmissivity from Neuman 2004 which assumes a log-normal distributed transmissivity field with an exponential correlation function.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown at given time points. gen_setup
([prate_kw, rad_kw, time_kw, dummy])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. settime
([time, tmin, tmax, typ, steps])Set uniform time points for the observations. - name (
-
class
Theis
(name, campaign, val_ranges=None, val_fix=None, testinclude=None, generate=False)[source]¶ Bases:
welltestpy.estimate.transient_lib.TransientPumping
Class for an estimation of homogeneous subsurface parameters.
With this class you can run an estimation of homogeneous subsurface parameters. It utilizes the theis solution.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown at given time points. gen_setup
([prate_kw, rad_kw, time_kw, dummy])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. settime
([time, tmin, tmax, typ, steps])Set uniform time points for the observations. - name (
-
class
ExtThiem3D
(name, campaign, make_steady=True, val_ranges=None, val_fix=None, testinclude=None, generate=False)[source]¶ Bases:
welltestpy.estimate.steady_lib.SteadyPumping
Class for an estimation of stochastic subsurface parameters.
With this class you can run an estimation of statistical subsurface parameters. It utilizes the extended thiem solution in 3D which assumes a log-normal distributed transmissivity field with a gaussian correlation function and an anisotropy ratio 0 < e <= 1.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - make_steady (
bool
, optional) – State if the tests should be converted to steady observations. See:PumpingTest.make_steady
. Default: True - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown. gen_setup
([prate_kw, rad_kw, r_ref_kw, …])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. - name (
-
class
ExtThiem2D
(name, campaign, make_steady=True, val_ranges=None, val_fix=None, testinclude=None, generate=False)[source]¶ Bases:
welltestpy.estimate.steady_lib.SteadyPumping
Class for an estimation of stochastic subsurface parameters.
With this class you can run an estimation of statistical subsurface parameters. It utilizes the extended thiem solution in 2D which assumes a log-normal distributed transmissivity field with a gaussian correlation function.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - make_steady (
bool
, optional) – State if the tests should be converted to steady observations. See:PumpingTest.make_steady
. Default: True - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown. gen_setup
([prate_kw, rad_kw, r_ref_kw, …])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. - name (
-
class
Neuman2004Steady
(name, campaign, make_steady=True, val_ranges=None, val_fix=None, testinclude=None, generate=False)[source]¶ Bases:
welltestpy.estimate.steady_lib.SteadyPumping
Class for an estimation of stochastic subsurface parameters.
With this class you can run an estimation of statistical subsurface parameters from steady drawdown. It utilizes the apparent Transmissivity from Neuman 2004 which assumes a log-normal distributed transmissivity field with an exponential correlation function.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - make_steady (
bool
, optional) – State if the tests should be converted to steady observations. See:PumpingTest.make_steady
. Default: True - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown. gen_setup
([prate_kw, rad_kw, r_ref_kw, …])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. - name (
-
class
Thiem
(name, campaign, make_steady=True, val_ranges=None, val_fix=None, testinclude=None, generate=False)[source]¶ Bases:
welltestpy.estimate.steady_lib.SteadyPumping
Class for an estimation of homogeneous subsurface parameters.
With this class you can run an estimation of homogeneous subsurface parameters. It utilizes the thiem solution.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - make_steady (
bool
, optional) – State if the tests should be converted to steady observations. See:PumpingTest.make_steady
. Default: True - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown. gen_setup
([prate_kw, rad_kw, r_ref_kw, …])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. - name (
-
class
TransientPumping
(name, campaign, type_curve, val_ranges, val_fix=None, fit_type=None, val_kw_names=None, val_plot_names=None, testinclude=None, generate=False)[source]¶ Bases:
object
Class to estimate transient Type-Curve parameters.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - type_curve (
callable
) – The given type-curve. Output will be reshaped to flat array. - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - fit_type (
dict
orNone
) – Dictionary containing fitting type for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. fit_type can be “lin”, “log” (np.exp(val) will be used) or a callable function. By default, values will be fit linearly. Default: None - val_kw_names (
dict
orNone
) –Dictionary containing keyword names in the type-curve for each value.
{value-name: kwargs-name in type_curve}This is usefull if fitting is not done by linear values. By default, parameter names will be value names. Default: None
- val_plot_names (
dict
orNone
) –Dictionary containing keyword names in the type-curve for each value.
{value-name: string for plot legend}This is usefull to get better plots. By default, parameter names will be value names. Default: None
- testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown at given time points. gen_setup
([prate_kw, rad_kw, time_kw, dummy])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. settime
([time, tmin, tmax, typ, steps])Set uniform time points for the observations. -
gen_data
()[source]¶ Generate the observed drawdown at given time points.
It will also generate an array containing all radii of all well combinations.
-
gen_setup
(prate_kw='rate', rad_kw='rad', time_kw='time', dummy=False)[source]¶ Generate the Spotpy Setup.
Parameters: - prate_kw (
str
, optional) – Keyword name for the pumping rate in the used type curve. Default: “rate” - rad_kw (
str
, optional) – Keyword name for the radius in the used type curve. Default: “rad” - time_kw (
str
, optional) – Keyword name for the time in the used type curve. Default: “time” - dummy (
bool
, optional) – Add a dummy parameter to the model. This could be used to equalize sensitivity analysis. Default: False
- prate_kw (
-
run
(rep=5000, parallel='seq', run=True, folder=None, dbname=None, traceplotname=None, fittingplotname=None, interactplotname=None, estname=None, plot_style='WTP')[source]¶ Run the estimation.
Parameters: - rep (
int
, optional) – The number of repetitions within the SCEua algorithm in spotpy. Default:5000
- parallel (
str
, optional) –State if the estimation should be run in parallel or not. Options:
"seq"
: sequential on one CPU"mpi"
: use the mpi4py package
Default:
"seq"
- run (
bool
, optional) – State if the estimation should be executed. Otherwise all plots will be done with the previous results. Default:True
- folder (
str
, optional) – Path to the output folder. IfNone
the CWD is used. Default:None
- dbname (
str
, optional) – File-name of the database of the spotpy estimation. IfNone
, it will be the current time +"_db"
. Default:None
- traceplotname (
str
, optional) – File-name of the parameter trace plot of the spotpy estimation. IfNone
, it will be the current time +"_paratrace.pdf"
. Default:None
- fittingplotname (
str
, optional) – File-name of the fitting plot of the estimation. IfNone
, it will be the current time +"_fit.pdf"
. Default:None
- interactplotname (
str
, optional) – File-name of the parameter interaction plot of the spotpy estimation. IfNone
, it will be the current time +"_parainteract.pdf"
. Default:None
- estname (
str
, optional) – File-name of the results of the spotpy estimation. IfNone
, it will be the current time +"_estimate"
. Default:None
- plot_style (str, optional) – Plot stlye. The default is “WTP”.
- rep (
-
sensitivity
(rep=None, parallel='seq', folder=None, dbname=None, plotname=None, traceplotname=None, sensname=None, plot_style='WTP')[source]¶ Run the sensitivity analysis.
Parameters: - rep (
int
, optional) – The number of repetitions within the FAST algorithm in spotpy. Default: estimated - parallel (
str
, optional) –State if the estimation should be run in parallel or not. Options:
"seq"
: sequential on one CPU"mpi"
: use the mpi4py package
Default:
"seq"
- folder (
str
, optional) – Path to the output folder. IfNone
the CWD is used. Default:None
- dbname (
str
, optional) – File-name of the database of the spotpy estimation. IfNone
, it will be the current time +"_sensitivity_db"
. Default:None
- plotname (
str
, optional) – File-name of the result plot of the sensitivity analysis. IfNone
, it will be the current time +"_sensitivity.pdf"
. Default:None
- traceplotname (
str
, optional) – File-name of the parameter trace plot of the spotpy sensitivity analysis. IfNone
, it will be the current time +"_senstrace.pdf"
. Default:None
- sensname (
str
, optional) – File-name of the results of the FAST estimation. IfNone
, it will be the current time +"_estimate"
. Default:None
- plot_style (str, optional) – Plot stlye. The default is “WTP”.
- rep (
-
setpumprate
(prate=-1.0)[source]¶ Set a uniform pumping rate at all pumpingwells wells.
We assume linear scaling by the pumpingrate.
Parameters: prate ( float
, optional) – Pumping rate. Default:-1.0
-
settime
(time=None, tmin=10.0, tmax=inf, typ='quad', steps=10)[source]¶ Set uniform time points for the observations.
Parameters: - time (
numpy.ndarray
, optional) – Array of specified time points. IfNone
is given, they will be determind by the observation data. Default:None
- tmin (
float
, optional) – Minimal time value. It will set a minimal value of 10s. Default:10
- tmax (
float
, optional) – Maximal time value. Default:inf
- typ (
str
orfloat
, optional) –Typ of the time selection. You can select from:
"exp"
: for exponential behavior"log"
: for logarithmic behavior"geo"
: for geometric behavior"lin"
: for linear behavior"quad"
: for quadratic behavior"cub"
: for cubic behaviorfloat
: here you can specifi any exponent (“quad” would be equivalent to 2)
Default: “quad”
- steps (
int
, optional) – Number of generated time steps. Default: 10
- time (
-
campaign
= None¶ Copy of the input campaign to be modified
Type: welltestpy.data.Campaign
-
campaign_raw
= None¶ Copy of the original input campaign
Type: welltestpy.data.Campaign
-
data
= None¶ observation data
Type: numpy.ndarray
-
rad
= None¶ array of the radii from the wells
Type: numpy.ndarray
-
radnames
= None¶ names of the radii well combination
Type: numpy.ndarray
-
time
= None¶ time points of the observation
Type: numpy.ndarray
- name (
-
class
SteadyPumping
(name, campaign, type_curve, val_ranges, make_steady=True, val_fix=None, fit_type=None, val_kw_names=None, val_plot_names=None, testinclude=None, generate=False)[source]¶ Bases:
object
Class to estimate steady Type-Curve parameters.
Parameters: - name (
str
) – Name of the Estimation. - campaign (
welltestpy.data.Campaign
) – The pumping test campaign which should be used to estimate the paramters - type_curve (
callable
) – The given type-curve. Output will be reshaped to flat array. - val_ranges (
dict
) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Ranges should be a tuple containing min and max value. - make_steady (
bool
, optional) – State if the tests should be converted to steady observations. See:PumpingTest.make_steady
. Default: True - val_fix (
dict
orNone
) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. Default: None - fit_type (
dict
orNone
) – Dictionary containing fitting type for each value in the type-curve. Names should be as in the type-curve signiture or replaced in val_kw_names. fit_type can be “lin”, “log” (np.exp(val) will be used) or a callable function. By default, values will be fit linearly. Default: None - val_kw_names (
dict
orNone
) –Dictionary containing keyword names in the type-curve for each value.
{value-name: kwargs-name in type_curve}This is usefull if fitting is not done by linear values. By default, parameter names will be value names. Default: None
- val_plot_names (
dict
orNone
) –Dictionary containing keyword names in the type-curve for each value.
{value-name: string for plot legend}This is usefull to get better plots. By default, parameter names will be value names. Default: None
- testinclude (
dict
, optional) – dictonary of which tests should be included. IfNone
is given, all available tests are included. Default:None
- generate (
bool
, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default:False
Methods
gen_data
()Generate the observed drawdown. gen_setup
([prate_kw, rad_kw, r_ref_kw, …])Generate the Spotpy Setup. run
([rep, parallel, run, folder, dbname, …])Run the estimation. sensitivity
([rep, parallel, folder, dbname, …])Run the sensitivity analysis. setpumprate
([prate])Set a uniform pumping rate at all pumpingwells wells. -
gen_data
()[source]¶ Generate the observed drawdown.
It will also generate an array containing all radii of all well combinations.
-
gen_setup
(prate_kw='rate', rad_kw='rad', r_ref_kw='r_ref', h_ref_kw='h_ref', dummy=False)[source]¶ Generate the Spotpy Setup.
Parameters: - prate_kw (
str
, optional) – Keyword name for the pumping rate in the used type curve. Default: “rate” - rad_kw (
str
, optional) – Keyword name for the radius in the used type curve. Default: “rad” - r_ref_kw (
str
, optional) – Keyword name for the reference radius in the used type curve. Default: “r_ref” - h_ref_kw (
str
, optional) – Keyword name for the reference head in the used type curve. Default: “h_ref” - dummy (
bool
, optional) – Add a dummy parameter to the model. This could be used to equalize sensitivity analysis. Default: False
- prate_kw (
-
run
(rep=5000, parallel='seq', run=True, folder=None, dbname=None, traceplotname=None, fittingplotname=None, interactplotname=None, estname=None, plot_style='WTP')[source]¶ Run the estimation.
Parameters: - rep (
int
, optional) – The number of repetitions within the SCEua algorithm in spotpy. Default:5000
- parallel (
str
, optional) –State if the estimation should be run in parallel or not. Options:
"seq"
: sequential on one CPU"mpi"
: use the mpi4py package
Default:
"seq"
- run (
bool
, optional) – State if the estimation should be executed. Otherwise all plots will be done with the previous results. Default:True
- folder (
str
, optional) – Path to the output folder. IfNone
the CWD is used. Default:None
- dbname (
str
, optional) – File-name of the database of the spotpy estimation. IfNone
, it will be the current time +"_db"
. Default:None
- traceplotname (
str
, optional) – File-name of the parameter trace plot of the spotpy estimation. IfNone
, it will be the current time +"_paratrace.pdf"
. Default:None
- fittingplotname (
str
, optional) – File-name of the fitting plot of the estimation. IfNone
, it will be the current time +"_fit.pdf"
. Default:None
- interactplotname (
str
, optional) – File-name of the parameter interaction plot of the spotpy estimation. IfNone
, it will be the current time +"_parainteract.pdf"
. Default:None
- estname (
str
, optional) – File-name of the results of the spotpy estimation. IfNone
, it will be the current time +"_estimate"
. Default:None
- plot_style (str, optional) – Plot stlye. The default is “WTP”.
- rep (
-
sensitivity
(rep=None, parallel='seq', folder=None, dbname=None, plotname=None, traceplotname=None, sensname=None, plot_style='WTP')[source]¶ Run the sensitivity analysis.
Parameters: - rep (
int
, optional) – The number of repetitions within the FAST algorithm in spotpy. Default: estimated - parallel (
str
, optional) –State if the estimation should be run in parallel or not. Options:
"seq"
: sequential on one CPU"mpi"
: use the mpi4py package
Default:
"seq"
- folder (
str
, optional) – Path to the output folder. IfNone
the CWD is used. Default:None
- dbname (
str
, optional) – File-name of the database of the spotpy estimation. IfNone
, it will be the current time +"_sensitivity_db"
. Default:None
- plotname (
str
, optional) – File-name of the result plot of the sensitivity analysis. IfNone
, it will be the current time +"_sensitivity.pdf"
. Default:None
- traceplotname (
str
, optional) – File-name of the parameter trace plot of the spotpy sensitivity analysis. IfNone
, it will be the current time +"_senstrace.pdf"
. Default:None
- sensname (
str
, optional) – File-name of the results of the FAST estimation. IfNone
, it will be the current time +"_estimate"
. Default:None
- plot_style (str, optional) – Plot stlye. The default is “WTP”.
- rep (
-
setpumprate
(prate=-1.0)[source]¶ Set a uniform pumping rate at all pumpingwells wells.
We assume linear scaling by the pumpingrate.
Parameters: prate ( float
, optional) – Pumping rate. Default:-1.0
-
campaign
= None¶ Copy of the input campaign to be modified
Type: welltestpy.data.Campaign
-
campaign_raw
= None¶ Copy of the original input campaign
Type: welltestpy.data.Campaign
-
data
= None¶ observation data
Type: numpy.ndarray
-
rad
= None¶ array of the radii from the wells
Type: numpy.ndarray
-
radnames
= None¶ names of the radii well combination
Type: numpy.ndarray
- name (