imod.mf6 - Create Modflow 6 model¶
Create a structured Modflow 6 model.
- class imod.mf6.ConstantHead(head, print_input=False, print_flows=False, save_flows=False, observations=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Constant-Head package. Any number of CHD Packages can be specified for a single groundwater flow model; however, an error will occur if a CHD Package attempts to make a GWF cell a constant-head cell when that cell has already been designated as a constant-head cell either within the present CHD Package or within another CHD Package. In previous MODFLOW versions, it was not possible to convert a constant-head cell to an active cell. Once a cell was designated as a constant-head cell, it remained a constant-head cell until the end of the end of the simulation. In MODFLOW 6 a constant-head cell will become active again if it is not included as a constant-head cell in subsequent stress periods. Previous MODFLOW versions allowed specification of SHEAD and EHEAD, which were the starting and ending prescribed heads for a stress period. Linear interpolation was used to calculate a value for each time step. In MODFLOW 6 only a single head value can be specified for any constant-head cell in any stress period. The time-series functionality must be used in order to interpolate values to individual time steps.
- Parameters
head (array of floats (xr.DataArray)) – Is the head at the boundary.
print_input (({True, False}, optional)) – keyword to indicate that the list of constant head information will be written to the listing file immediately after it is read. Default is False.
print_flows (({True, False}, optional)) – Indicates that the list of constant head flow rates will be printed to the listing file for every stress period time step in which “BUDGET PRINT”is specified in Output Control. If there is no Output Control option and PRINT FLOWS is specified, then flow rates are printed for the last time step of each stress period. Default is False.
save_flows (({True, False}, optional)) – Indicates that constant head flow terms will be written to the file specified with “BUDGET FILEOUT” in Output Control. Default is False.
observations ([Not yet supported.]) – Default is None.
- all(dim=None, **kwargs)¶
Reduce this ConstantHead’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New ConstantHead object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this ConstantHead’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New ConstantHead object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this ConstantHead’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New ConstantHead object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of ConstantHead.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New ConstantHead object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of ConstantHead.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New ConstantHead object with cumsum applied to its data along the indicated dimension.
- Return type
- head¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this ConstantHead’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New ConstantHead object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this ConstantHead’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New ConstantHead object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this ConstantHead’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New ConstantHead object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this ConstantHead’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New ConstantHead object with min applied to its data and the indicated dimension(s) removed.
- Return type
- observations¶
- print_flows¶
- print_input¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this ConstantHead’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New ConstantHead object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- save_flows¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this ConstantHead’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New ConstantHead object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this ConstantHead’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New ConstantHead object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this ConstantHead’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New ConstantHead object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.Drainage(elevation, conductance, print_input=False, print_flows=False, save_flows=False, observations=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
The Drain package is used to simulate head-dependent flux boundaries. https://water.usgs.gov/ogw/modflow/mf6io.pdf#page=67
- Parameters
elevation (array of floats (xr.DataArray)) – elevation of the drain. (elev)
conductance (array of floats (xr.DataArray)) – is the conductance of the drain. (cond)
print_input (({True, False}, optional)) – keyword to indicate that the list of drain information will be written to the listing file immediately after it is read. Default is False.
print_flows (({True, False}, optional)) – Indicates that the list of drain flow rates will be printed to the listing file for every stress period time step in which “BUDGET PRINT” is specified in Output Control. If there is no Output Control option and PRINT FLOWS is specified, then flow rates are printed for the last time step of each stress period. Default is False.
save_flows (({True, False}, optional)) – Indicates that drain flow terms will be written to the file specified with “BUDGET FILEOUT” in Output Control. Default is False.
observations ([Not yet supported.]) – Default is None.
- all(dim=None, **kwargs)¶
Reduce this Drainage’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New Drainage object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this Drainage’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New Drainage object with any applied to its data and the indicated dimension(s) removed.
- Return type
- conductance¶
- count(dim=None, **kwargs)¶
Reduce this Drainage’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New Drainage object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of Drainage.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New Drainage object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of Drainage.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New Drainage object with cumsum applied to its data along the indicated dimension.
- Return type
- elevation¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this Drainage’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New Drainage object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this Drainage’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New Drainage object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this Drainage’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New Drainage object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this Drainage’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New Drainage object with min applied to its data and the indicated dimension(s) removed.
- Return type
- observations¶
- print_flows¶
- print_input¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this Drainage’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New Drainage object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- save_flows¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this Drainage’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New Drainage object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this Drainage’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New Drainage object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this Drainage’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New Drainage object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.Evapotranspiration(surface, rate, depth, proportion_rate, proportion_depth, fixed_cell=False, print_input=False, print_flows=False, save_flows=False, observations=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Evapotranspiration (EVT) Package. Any number of EVT Packages can be specified for a single groundwater flow model. All single-valued variables are free format. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=86
- Parameters
surface (array of floats (xr.DataArray)) – is the elevation of the ET surface (L). A time-series name may be specified.
rate (array of floats (xr.DataArray)) – is the maximum ET flux rate (LT −1). A time-series name may be specified.
depth (array of floats (xr.DataArray)) – is the ET extinction depth (L). A time-series name may be specified.
proportion_rate (array of floats (xr.DataArray)) – is the proportion of the maximum ET flux rate at the bottom of a segment (dimensionless). A time-series name may be specified. (petm)
proportion_depth (array of floats (xr.DataArray)) – is the proportion of the ET extinction depth at the bottom of a segment (dimensionless). A timeseries name may be specified. (pxdp)
fixed_cell (array of floats (xr.DataArray)) – indicates that evapotranspiration will not be reassigned to a cell underlying the cell specified in the list if the specified cell is inactive.
print_input (({True, False}, optional)) – keyword to indicate that the list of evapotranspiration information will be written to the listing file immediately after it is read. Default is False.
print_flows (({True, False}, optional)) – Indicates that the list of evapotranspiration flow rates will be printed to the listing file for every stress period time step in which “BUDGET PRINT”is specified in Output Control. If there is no Output Control option and PRINT FLOWS is specified, then flow rates are printed for the last time step of each stress period. Default is False.
save_flows (({True, False}, optional)) – Indicates that evapotranspiration flow terms will be written to the file specified with “BUDGET FILEOUT” in Output Control. Default is False.
observations ([Not yet supported.]) – Default is None.
- all(dim=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New Evapotranspiration object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New Evapotranspiration object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New Evapotranspiration object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of Evapotranspiration.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New Evapotranspiration object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of Evapotranspiration.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New Evapotranspiration object with cumsum applied to its data along the indicated dimension.
- Return type
- depth¶
- fixed_cell¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New Evapotranspiration object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New Evapotranspiration object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New Evapotranspiration object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New Evapotranspiration object with min applied to its data and the indicated dimension(s) removed.
- Return type
- observations¶
- print_flows¶
- print_input¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New Evapotranspiration object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- proportion_depth¶
- proportion_rate¶
- rate¶
- save_flows¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New Evapotranspiration object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New Evapotranspiration object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- surface¶
- var(dim=None, skipna=None, **kwargs)¶
Reduce this Evapotranspiration’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New Evapotranspiration object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.GeneralHeadBoundary(head, conductance, print_input=False, print_flows=False, save_flows=False, observations=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
The General-Head Boundary package is used to simulate head-dependent flux boundaries. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=75
- Parameters
head (array of floats (xr.DataArray)) – is the boundary head. (bhead)
conductance (array of floats (xr.DataArray)) – is the hydraulic conductance of the interface between the aquifer cell and the boundary.(cond)
print_input (({True, False}, optional)) – keyword to indicate that the list of general head boundary information will be written to the listing file immediately after it is read. Default is False.
print_flows (({True, False}, optional)) – Indicates that the list of general head boundary flow rates will be printed to the listing file for every stress period time step in which “BUDGET PRINT”is specified in Output Control. If there is no Output Control option and PRINT FLOWS is specified, then flow rates are printed for the last time step of each stress period. Default is False.
save_flows (({True, False}, optional)) – Indicates that general head boundary flow terms will be written to the file specified with “BUDGET FILEOUT” in Output Control. Default is False.
observations ([Not yet supported.]) – Default is None.
- all(dim=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with any applied to its data and the indicated dimension(s) removed.
- Return type
- conductance¶
- count(dim=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of GeneralHeadBoundary.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New GeneralHeadBoundary object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of GeneralHeadBoundary.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New GeneralHeadBoundary object with cumsum applied to its data along the indicated dimension.
- Return type
- head¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with min applied to its data and the indicated dimension(s) removed.
- Return type
- observations¶
- print_flows¶
- print_input¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- save_flows¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this GeneralHeadBoundary’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New GeneralHeadBoundary object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.GroundwaterFlowModel(newton=False, under_relaxation=False)[source]¶
Bases:
imod.mf6.model.Model
Contains data and writes consistent model input files
- write_qgis_project(directory, crs, aggregate_layers=False)[source]¶
Write qgis projectfile and accompanying netcdf files that can be read in qgis.
- Parameters
directory (Path) – directory of qgis project
crs (str, int,) – anything that can be converted to a pyproj.crs.CRS
filename (Optional, str) – name of qgis projectfile.
aggregate_layers (Optional, bool) – If True, aggregate layers by taking the mean, i.e. ds.mean(dim=”layer”)
- class imod.mf6.InitialConditions(head)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Initial Conditions (IC) Package information is read from the file that is specified by “IC6” as the file type. Only one IC Package can be specified for a GWF model. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=46
- Parameters
head (array of floats (xr.DataArray)) – is the initial (starting) head—that is, head at the beginning of the GWF Model simulation. STRT must be specified for all simulations, including steady-state simulations. One value is read for every model cell. For simulations in which the first stress period is steady state, the values used for STRT generally do not affect the simulation (exceptions may occur if cells go dry and (or) rewet). The execution time, however, will be less if STRT includes hydraulic heads that are close to the steadystate solution. A head value lower than the cell bottom can be provided if a cell should start as dry. (strt)
- all(dim=None, **kwargs)¶
Reduce this InitialConditions’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New InitialConditions object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this InitialConditions’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New InitialConditions object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this InitialConditions’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New InitialConditions object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of InitialConditions.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New InitialConditions object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of InitialConditions.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New InitialConditions object with cumsum applied to its data along the indicated dimension.
- Return type
- head¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this InitialConditions’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New InitialConditions object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this InitialConditions’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New InitialConditions object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this InitialConditions’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New InitialConditions object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this InitialConditions’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New InitialConditions object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this InitialConditions’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New InitialConditions object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- std(dim=None, skipna=None, **kwargs)¶
Reduce this InitialConditions’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New InitialConditions object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this InitialConditions’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New InitialConditions object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this InitialConditions’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New InitialConditions object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.Modflow6Simulation(name)[source]¶
Bases:
collections.UserDict
- time_discretization(times)[source]¶
Collect all unique times from boundary conditions and insert times from argument as well.
Function creates TimeDiscretization object which is set to self[“time_discretization”]
- Parameters
times (xr.DataArray of datetime-likes) – times to be inserted into model time discretization.
Note
To set the other parameters of the TimeDiscretization object, you have to set these to the object after calling this function.
Example
>>> simulation = imod.mf6.Modflow6Simulation("example") >>> simulation.time_discretization(times=["2000-01-01", "2000-01-02"]) >>> # Set number of timesteps >>> simulation["time_discretization"]["n_timesteps"] = 5
- class imod.mf6.NodePropertyFlow(icelltype, k, rewet=False, rewet_layer=None, rewet_factor=None, rewet_iterations=None, rewet_method=None, k22=None, k33=None, angle1=None, angle2=None, angle3=None, cell_averaging='harmonic', save_flows=False, starting_head_as_confined_thickness=False, variable_vertical_conductance=False, dewatered=False, perched=False, save_specific_discharge=False)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Node Property Flow package. In this package the hydraulic conductivity and rewetting in the model is specified. A single NPF Package is required for each GWF model. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=51
- Parameters
icelltype (array of int (xr.DataArray)) – flag for each cell that specifies how saturated thickness is treated. 0 means saturated thickness is held constant; >0 means saturated thickness varies with computed head when head is below the cell top; <0 means saturated thickness varies with computed head unless the starting_head_as_confined_thickness option is in effect. When starting_head_as_confined_thickness is in effect, a negative value of icelltype indicates that saturated thickness will be computed as strt-bot and held constant.
k (array of floats (xr.DataArray)) – is the hydraulic conductivity. For the common case in which the user would like to specify the horizontal hydraulic conductivity and the vertical hydraulic conductivity, then K should be assigned as the horizontal hydraulic conductivity, K33 should be assigned as the vertical hydraulic conductivity, and K22 and the three rotation angles should not be specified. When more sophisticated anisotropy is required, then K corresponds to the K11 hydraulic conductivity axis. All included cells (idomain > 0) must have a K value greater than zero
rewet (({True, False}, optional)) – activates model rewetting. Default is False.
rewet_layer (float) – is a combination of the wetting threshold and a flag to indicate which neighboring cells can cause a cell to become wet. If rewet_layer < 0, only a cell below a dry cell can cause the cell to become wet. If rewet_layer > 0, the cell below a dry cell and horizontally adjacent cells can cause a cell to become wet. If rewet_layer is 0, the cell cannot be wetted. The absolute value of rewet_layer is the wetting threshold. When the sum of BOT and the absolute value of rewet_layer at a dry cell is equaled or exceeded by the head at an adjacent cell, the cell is wetted. rewet_layer must be specified if “rewet” is specified in the OPTIONS block. If “rewet” is not specified in the options block, then rewet_layer can be entered, and memory will be allocated for it, even though it is not used. (WETDRY) Default is None.
rewet_factor – is a keyword and factor that is included in the calculation of the head that is initially established at a cell when that cell is converted from dry to wet. (WETFCT) Default is None.
rewet_iterations – is a keyword and iteration interval for attempting to wet cells. Wetting is attempted every rewet_iterations iteration. This applies to outer iterations and not inner iterations. If rewet_iterations is specified as zero or less, then the value is changed to 1. (IWETIT) Default is None.
rewet_method – is a keyword and integer flag that determines which equation is used to define the initial head at cells that become wet. If rewet_method is 0, h = BOT + rewet_factor (hm - BOT). If rewet_method is not 0, h = BOT + rewet_factor (THRESH). (IHDWET) Default is None.
k22 (array of floats (xr.DataArray)) – is the hydraulic conductivity of the second ellipsoid axis; for an unrotated case this is the hydraulic conductivity in the y direction. If K22 is not included, then K22 is set equal to K. For a regular MODFLOW grid (DIS Package is used) in which no rotation angles are specified, K22 is the hydraulic conductivity along columns in the y direction. For an unstructured DISU grid, the user must assign principal x and y axes and provide the angle for each cell face relative to the assigned x direction. All included cells (idomain > 0) must have a K22 value greater than zero. Default is None.
k33 (array of floats (xr.DataArray)) – is the hydraulic conductivity of the third ellipsoid axis; for an unrotated case, this is the vertical hydraulic conductivity. When anisotropy is applied, K33 corresponds to the K33 tensor component. All included cells (idomain > 0) must have a K33 value greater than zero. Default is None.
angle1 (float) – is a rotation angle of the hydraulic conductivity tensor in degrees. The angle represents the first of three sequential rotations of the hydraulic conductivity ellipsoid. With the K11, K22, and K33 axes of the ellipsoid initially aligned with the x, y, and z coordinate axes, respectively, angle1 rotates the ellipsoid about its K33 axis (within the x - y plane). A positive value represents counter-clockwise rotation when viewed from any point on the positive K33 axis, looking toward the center of the ellipsoid. A value of zero indicates that the K11 axis lies within the x - z plane. If angle1 is not specified, default values of zero are assigned to angle1, angle2, and angle3, in which case the K11, K22, and K33 axes are aligned with the x, y, and z axes, respectively. Default is None.
angle2 (float) – is a rotation angle of the hydraulic conductivity tensor in degrees. The angle represents the second of three sequential rotations of the hydraulic conductivity ellipsoid. Following the rotation by angle1 described above, angle2 rotates the ellipsoid about its K22 axis (out of the x - y plane). An array can be specified for angle2 only if angle1 is also specified. A positive value of angle2 represents clockwise rotation when viewed from any point on the positive K22 axis, looking toward the center of the ellipsoid. A value of zero indicates that the K11 axis lies within the x - y plane. If angle2 is not specified, default values of zero are assigned to angle2 and angle3; connections that are not user-designated as vertical are assumed to be strictly horizontal (that is, to have no z component to their orientation); and connection lengths are based on horizontal distances. Default is None.
angle3 (float) – is a rotation angle of the hydraulic conductivity tensor in degrees. The angle represents the third of three sequential rotations of the hydraulic conductivity ellipsoid. Following the rotations by angle1 and angle2 described above, angle3 rotates the ellipsoid about its K11 axis. An array can be specified for angle3 only if angle1 and angle2 are also specified. An array must be specified for angle3 if angle2 is specified. A positive value of angle3 represents clockwise rotation when viewed from any point on the positive K11 axis, looking toward the center of the ellipsoid. A value of zero indicates that the K22 axis lies within the x - y plane. Default is None.
cell_averaging (str) – Method calculating horizontal cell connection conductance. Options: {“harmonic”, “logarithmic”, “mean-log_k”, “mean-mean_k”} Default: “harmonic”
save_flows (({True, False}, optional)) – keyword to indicate that cell-by-cell flow terms will be written to the file specified with “budget save file” in Output Control. Default is False.
starting_head_as_confined_thickness (({True, False}, optional)) – indicates that cells having a negative icelltype are confined, and their cell thickness for conductance calculations will be computed as strt-bot rather than top-bot. (THICKSTRT) Default is False.
variable_vertical_conductance (({True, False}, optional)) – keyword to indicate that the vertical conductance will be calculated using the saturated thickness and properties of the overlying cell and the thickness and properties of the underlying cell. if the dewatered keyword is also specified, then the vertical conductance is calculated using only the saturated thickness and properties of the overlying cell if the head in the underlying cell is below its top. if these keywords are not specified, then the default condition is to calculate the vertical conductance at the start of the simulation using the initial head and the cell properties. the vertical conductance remains constant for the entire simulation. (VARIABLECV) Default is False.
dewatered (({True, False}, optional)) – If the dewatered keyword is specified, then the vertical conductance is calculated using only the saturated thickness and properties of the overlying cell if the head in the underlying cell is below its top. Default is False.
perched (({True, False}, optional)) – keyword to indicate that when a cell is overlying a dewatered convertible cell, the head difference used in Darcy’s Law is equal to the head in the overlying cell minus the bottom elevation of the overlying cell. If not specified, then the default is to use the head difference between the two cells. Default is False.
save_specific_discharge (({True, False}, optional)) – keyword to indicate that x, y, and z components of specific discharge will be calculated at cell centers and written to the cell-by-cell flow file, which is specified with”budget save file” in Output Control. If this option is activated, then additional information may be required in the discretization packages and the GWF Exchange package (if GWF models are coupled). Specifically, angldegx must be specified in the connectiondata block of the disu package; angldegx must also be specified for the GWF Exchange as an auxiliary variable. disu package has not been implemented yet. Default is False.
- all(dim=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New NodePropertyFlow object with all applied to its data and the indicated dimension(s) removed.
- Return type
- angle1¶
- angle2¶
- angle3¶
- any(dim=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New NodePropertyFlow object with any applied to its data and the indicated dimension(s) removed.
- Return type
- cell_averaging¶
- count(dim=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New NodePropertyFlow object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of NodePropertyFlow.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New NodePropertyFlow object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of NodePropertyFlow.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New NodePropertyFlow object with cumsum applied to its data along the indicated dimension.
- Return type
- dewatered¶
- icelltype¶
- k¶
- k22¶
- k33¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New NodePropertyFlow object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New NodePropertyFlow object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New NodePropertyFlow object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New NodePropertyFlow object with min applied to its data and the indicated dimension(s) removed.
- Return type
- perched¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New NodePropertyFlow object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- rewet¶
- rewet_factor¶
- rewet_iterations¶
- rewet_layer¶
- rewet_method¶
- save_flows¶
- save_specific_discharge¶
- starting_head_as_confined_thickness¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New NodePropertyFlow object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New NodePropertyFlow object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this NodePropertyFlow’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New NodePropertyFlow object with var applied to its data and the indicated dimension(s) removed.
- Return type
- variable_vertical_conductance¶
- class imod.mf6.OutputControl(save_head=None, save_budget=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
The Output Control Option determines how and when heads are printed to the listing file and/or written to a separate binary output file. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=47
Currently the settings “first”, “last”, “all”, and “frequency” are supported, the “steps” setting is not supported, because of its ragged nature. Furthermore, only one setting per stress period can be specified in imod-python.
- Parameters
save_head ({string, integer}, or xr.DataArray of {string, integer}, optional) – String or integer indicating output control for head file (.hds) If string, should be one of [“first”, “last”, “all”]. If integer, interpreted as frequency.
save_budget ({string, integer}, or xr.DataArray of {string, integer}, optional) – String or integer indicating output control for cell budgets (.cbc) If string, should be one of [“first”, “last”, “all”]. If integer, interpreted as frequency.
Examples
To specify a mix of both ‘frequency’ and ‘first’ setting, we need to specify an array with both integers and strings. For this we need to create a numpy object array first, otherwise xarray converts all to strings automatically.
>>> time = [np.datetime64("2000-01-01"), np.datetime64("2000-01-02")] >>> data = np.array(["last", 5], dtype="object") >>> save_head = xr.DataArray(data, coords={"time": time}, dims=("time")) >>> oc = imod.mf6.OutputControl(save_head=save_head, save_budget=None)
- all(dim=None, **kwargs)¶
Reduce this OutputControl’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New OutputControl object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this OutputControl’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New OutputControl object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this OutputControl’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New OutputControl object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of OutputControl.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New OutputControl object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of OutputControl.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New OutputControl object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this OutputControl’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New OutputControl object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this OutputControl’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New OutputControl object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this OutputControl’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New OutputControl object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this OutputControl’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New OutputControl object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this OutputControl’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New OutputControl object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- save_budget¶
- save_head¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this OutputControl’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New OutputControl object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this OutputControl’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New OutputControl object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this OutputControl’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New OutputControl object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.Recharge(rate, print_input=False, print_flows=False, save_flows=False, observations=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Recharge Package. Any number of RCH Packages can be specified for a single groundwater flow model. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=79
- Parameters
rate (array of floats (xr.DataArray)) – is the recharge flux rate (LT −1). This rate is multiplied inside the program by the surface area of the cell to calculate the volumetric recharge rate. A time-series name may be specified.
print_input (({True, False}, optional)) – keyword to indicate that the list of recharge information will be written to the listing file immediately after it is read. Default is False.
print_flows (({True, False}, optional)) – Indicates that the list of recharge flow rates will be printed to the listing file for every stress period time step in which “BUDGET PRINT”is specified in Output Control. If there is no Output Control option and PRINT FLOWS is specified, then flow rates are printed for the last time step of each stress period. Default is False.
save_flows (({True, False}, optional)) – Indicates that recharge flow terms will be written to the file specified with “BUDGET FILEOUT” in Output Control. Default is False.
observations ([Not yet supported.]) – Default is None.
- all(dim=None, **kwargs)¶
Reduce this Recharge’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New Recharge object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this Recharge’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New Recharge object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this Recharge’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New Recharge object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of Recharge.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New Recharge object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of Recharge.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New Recharge object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this Recharge’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New Recharge object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this Recharge’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New Recharge object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this Recharge’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New Recharge object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this Recharge’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New Recharge object with min applied to its data and the indicated dimension(s) removed.
- Return type
- observations¶
- print_flows¶
- print_input¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this Recharge’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New Recharge object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- rate¶
- save_flows¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this Recharge’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New Recharge object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this Recharge’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New Recharge object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this Recharge’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New Recharge object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.River(stage, conductance, bottom_elevation, print_input=False, print_flows=False, save_flows=False, observations=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
River package. Any number of RIV Packages can be specified for a single groundwater flow model. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=71
- Parameters
stage (array of floats (xr.DataArray)) – is the head in the river.
conductance (array of floats (xr.DataArray)) – is the riverbed hydraulic conductance.
bottom_elevation (array of floats (xr.DataArray)) – is the elevation of the bottom of the riverbed.
print_input (({True, False}, optional)) – keyword to indicate that the list of drain information will be written to the listing file immediately after it is read. Default is False.
print_flows (({True, False}, optional)) – Indicates that the list of drain flow rates will be printed to the listing file for every stress period time step in which “BUDGET PRINT” is specified in Output Control. If there is no Output Control option and PRINT FLOWS is specified, then flow rates are printed for the last time step of each stress period. Default is False.
save_flows (({True, False}, optional)) – Indicates that drain flow terms will be written to the file specified with “BUDGET FILEOUT” in Output Control. Default is False.
observations ([Not yet supported.]) – Default is None.
- all(dim=None, **kwargs)¶
Reduce this River’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New River object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this River’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New River object with any applied to its data and the indicated dimension(s) removed.
- Return type
- bottom_elevation¶
- conductance¶
- count(dim=None, **kwargs)¶
Reduce this River’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New River object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of River.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New River object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of River.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New River object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this River’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New River object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this River’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New River object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this River’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New River object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this River’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New River object with min applied to its data and the indicated dimension(s) removed.
- Return type
- observations¶
- print_flows¶
- print_input¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this River’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New River object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- save_flows¶
- stage¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this River’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New River object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this River’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New River object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this River’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New River object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.Solution(outer_hclose, outer_maximum, inner_maximum, inner_hclose, inner_rclose, linear_acceleration, outer_rclosebnd=None, under_relaxation=None, under_relaxation_theta=None, under_relaxation_kappa=None, under_relaxation_gamma=None, under_relaxation_momentum=None, backtracking_number=None, backtracking_tolerance=None, backtracking_reduction_factor=None, backtracking_residual_limit=None, rclose_option=None, relaxation_factor=None, preconditioner_levels=None, preconditioner_drop_tolerance=None, number_orthogonalizations=None, scaling_method=None, reordering_method=None, print_option='summary', csv_output=False, no_ptc=False)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Iterative Model Solution. The model solution will solve all of the models that are added to it, as specified in the simulation name file, and will include Numerical Exchanges, if they are present. The iterative model solution requires specification of both nonlinear and linear settings. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=147
Three predifined solutions settings are available: SolutionPresetSimple, SolutionPresetModerate and SolutionPresetComplex. When using one of the predefined solutions only the print_option, csv_output, and no_ptc have to be defined. The default values for each are described below.
- Parameters
outer_hclose (float) – real value defining the head change criterion for convergence of the outer (nonlinear) iterations, in units of length. When the maximum absolute value of the head change at all nodes during an iteration is less than or equal to outer_hclose, iteration stops. Commonly, outer_hclose equals 0.01. SolutionPresetSimple: 0.001 SolutionPresetModerate: 0.01 SolutionPresetComplex: 0.1
outer_maximum (int) – integer value defining the maximum number of outer (nonlinear) iterations – that is, calls to the solution routine. For a linear problem outer_maximum should be 1. SolutionPresetSimple: 25 SolutionPresetModerate: 50 SolutionPresetComplex: 100
inner_maximum (int) – integer value defining the maximum number of inner (linear) iterations. The number typically depends on the characteristics of the matrix solution scheme being used. For nonlinear problems, inner_maximum usually ranges from 60 to 600; a value of 100 will be sufficient for most linear problems. SolutionPresetSimple: 50 SolutionPresetModerate: 100 SolutionPresetComplex: 500
inner_hclose (float) – real value defining the head change criterion for convergence of the inner (linear) iterations, in units of length. When the maximum absolute value of the head change at all nodes during an iteration is less than or equal to inner_hclose, the matrix solver assumes convergence. Commonly, inner_hclose is set an order of magnitude less than the outer_hclose value. SolutionPresetSimple: 0.001 SolutionPresetModerate: 0.01 SolutionPresetComplex: 0.1
inner_rclose (float) – real value that defines the flow residual tolerance for convergence of the IMS linear solver and specific flow residual criteria used. This value represents the maximum allowable residual at any single node. Value is in units of length cubed per time, and must be consistent with MODFLOW 6 length and time units. Usually a value of 1.0 × 10−1 is sufficient for the flow-residual criteria when meters and seconds are the defined MODFLOW 6 length and time. SolutionPresetSimple: 0.1 SolutionPresetModerate: 0.1 SolutionPresetComplex: 0.1
linear_acceleration (str) – options: {“cg”, “bicgstab”} a keyword that defines the linear acceleration method used by the default IMS linear solvers. CG - preconditioned conjugate gradient method. BICGSTAB - preconditioned bi-conjugate gradient stabilized method. SolutionPresetSimple: “cg” SolutionPresetModerate: “bicgstab” SolutionPresetComplex: “bicgstab”
outer_rclosebnd (float, optional) – real value defining the residual tolerance for convergence of model packages that solve a separate equation not solved by the IMS linear solver. This value represents the maximum allowable residual between successive outer iterations at any single model package element. An example of a model package that would use OUTER RCLOSEBND to evaluate convergence is the SFR package which solves a continuity equation for each reach. Default value: None SolutionPresetSimple: 0.1 SolutionPresetModerate: 0.1 SolutionPresetComplex: 0.1
under_relaxation (str, optional) – options: {“None”, “simple”, “cooley”, “bdb”} is an optional keyword that defines the nonlinear relative_rclose schemes used. By default under_relaxation is not used. None - relative_rclose is not used. simple - Simple relative_rclose scheme with a fixed relaxation factor is used. cooley - Cooley relative_rclose scheme is used. dbd - delta-bar-delta relative_rclose is used. Note that the relative_rclose schemes are used in conjunction with problems that use the Newton-Raphson formulation, however, experience has indicated that the Cooley relative_rclose and damping work well also for the Picard scheme with the wet/dry options of MODFLOW 6. Default value: None SolutionPresetSimple: None SolutionPresetModerate: “dbd” SolutionPresetComplex: “dbd”
under_relaxation_theta (float, optional) – real value defining the reduction factor for the learning rate (underrelaxation term) of the delta-bar-delta algorithm. The value of under relaxation theta is between zero and one. If the change in the variable (head) is of opposite sign to that of the previous iteration, the relative_rclose term is reduced by a factor of under relaxation theta. The value usually ranges from 0.3 to 0.9; a value of 0.7 works well for most problems. under relaxation theta only needs to be specified if under relaxation is dbd. Default value: None SolutionPresetSimple: 0.0 SolutionPresetModerate: 0.9 SolutionPresetComplex: 0.8
under_relaxation_kappa (float, optional) – real value defining the increment for the learning rate (relative_rclose term) of the delta-bar-delta algorithm. The value of under relaxation kappa is between zero and one. If the change in the variable (head) is of the same sign to that of the previous iteration, the relative_rclose term is increased by an increment of under_relaxation_kappa. The value usually ranges from 0.03 to 0.3; a value of 0.1 works well for most problems. under relaxation kappa only needs to be specified if under relaxation is dbd. Default value: None SolutionPresetSimple: 0.0 SolutionPresetModerate: 0.0001 SolutionPresetComplex: 0.0001
under_relaxation_gamma (float, optional) – real value defining the history or memory term factor of the delta-bardelta algorithm. under relaxation gamma is between zero and 1 but cannot be equal to one. When under relaxation gamma is zero, only the most recent history (previous iteration value) is maintained. As under relaxation gamma is increased, past history of iteration changes has greater influence on the memory term. The memory term is maintained as an exponential average of past changes. Retaining some past history can overcome granular behavior in the calculated function surface and therefore helps to overcome cyclic patterns of nonconvergence. The value usually ranges from 0.1 to 0.3; a value of 0.2 works well for most problems. under relaxation gamma only needs to be specified if under relaxation is not none. Default value: None SolutionPresetSimple: 0.0 SolutionPresetModerate: 0.0 SolutionPresetComplex: 0.0
under_relaxation_momentum (float, optional) – real value defining the fraction of past history changes that is added as a momentum term to the step change for a nonlinear iteration. The value of under relaxation momentum is between zero and one. A large momentum term should only be used when small learning rates are expected. Small amounts of the momentum term help convergence. The value usually ranges from 0.0001 to 0.1; a value of 0.001 works well for most problems. under relaxation momentum only needs to be specified if under relaxation is dbd. Default value: None SolutionPresetSimple: 0.0 SolutionPresetModerate: 0.0 SolutionPresetComplex: 0.0
backtracking_number (int, optional) – integer value defining the maximum number of backtracking iterations allowed for residual reduction computations. If backtracking number = 0 then the backtracking iterations are omitted. The value usually ranges from 2 to 20; a value of 10 works well for most problems. Default value: None SolutionPresetSimple: 0 SolutionPresetModerate: 0 SolutionPresetComplex: 20
backtracking_tolerance (float, optional) – real value defining the tolerance for residual change that is allowed for residual reduction computations. backtracking tolerance should not be less than one to avoid getting stuck in local minima. A large value serves to check for extreme residual increases, while a low value serves to control step size more severely. The value usually ranges from 1.0 to 106; a value of 104 works well for most problems but lower values like 1.1 may be required for harder problems. backtracking tolerance only needs to be specified if backtracking_number is greater than zero. Default value: None SolutionPresetSimple: 0.0 SolutionPresetModerate: 0.0 SolutionPresetComplex: 1.05
backtracking_reduction_factor (float, optional) – real value defining the reduction in step size used for residual reduction computations. The value of backtracking reduction factor is between 142 MODFLOW 6 – Description of Input and Output zero and one. The value usually ranges from 0.1 to 0.3; a value of 0.2 works well for most problems. backtracking_reduction_factor only needs to be specified if backtracking number is greater than zero. Default value: None SolutionPresetSimple: 0.0 SolutionPresetModerate: 0.0 SolutionPresetComplex: 0.1
backtracking_residual_limit (float, optional) – real value defining the limit to which the residual is reduced with backtracking. If the residual is smaller than backtracking_residual_limit, then further backtracking is not performed. A value of 100 is suitable for large problems and residual reduction to smaller values may only slow down computations. backtracking residual limit only needs to be specified if backtracking_number is greater than zero. Default value: None SolutionPresetSimple: 0.0 SolutionPresetModerate: 0.0 SolutionPresetComplex: 0.002
rclose_option (str, optional) – options: {“strict”, “l2norm_rclose”, “relative_rclose”} an optional keyword that defines the specific flow residual criterion used. strict– an optional keyword that is used to specify that inner rclose represents a infinity-norm (absolute convergence criteria) and that the head and flow convergence criteria must be met on the first inner iteration (this criteria is equivalent to the criteria used by the MODFLOW-2005 PCG package (Hill, 1990)). l2norm_rclose – an optionalkeyword that is used to specify that inner rclose represents a l-2 norm closure criteria instead of a infinity-norm (absolute convergence criteria). When l2norm_rclose is specified, a reasonable initial inner rclose value is 0.1 times the number of active cells when meters and seconds are the defined MODFLOW 6 length and time. relative_rclose – an optional keyword that is used to specify that inner_rclose represents a relative L-2 Norm reduction closure criteria instead of a infinity-Norm (absolute convergence criteria). When relative_rclose is specified, a reasonable initial inner_rclose value is 1.0 × 10−4 and convergence is achieved for a given inner (linear) iteration when ∆h ≤ inner_hclose and the current L-2 Norm is ≤ the product of the relativ_rclose and the initial L-2 Norm for the current inner (linear) iteration. If rclose_option is not specified, an absolute residual (infinity-norm) criterion is used. Default value: None SolutionPresetSimple: “strict” SolutionPresetModerate: “strict” SolutionPresetComplex: “strict”
relaxation_factor (float, optional) – optional real value that defines the relaxation factor used by the incomplete LU factorization preconditioners (MILU(0) and MILUT). relaxation_factor is unitless and should be greater than or equal to 0.0 and less than or equal to 1.0. relaxation_factor Iterative Model Solution 143 values of about 1.0 are commonly used, and experience suggests that convergence can be optimized in some cases with relax values of 0.97. A relaxation_factor value of 0.0 will result in either ILU(0) or ILUT preconditioning (depending on the value specified for preconditioner_levels and/or preconditioner_drop_tolerance). By default, relaxation_factor is zero. Default value: None SolutionPresetSimple: 0.0 SolutionPresetModerate: 0 SolutionPresetComplex: 0.0
preconditioner_levels (int, optional) – optional integer value defining the level of fill for ILU decomposition used in the ILUT and MILUT preconditioners. Higher levels of fill provide more robustness but also require more memory. For optimal performance, it is suggested that a large level of fill be applied (7 or 8) with use of a drop tolerance. Specification of a preconditioner_levels value greater than zero results in use of the ILUT preconditioner. By default, preconditioner_levels is zero and the zero-fill incomplete LU factorization preconditioners (ILU(0) and MILU(0)) are used. Default value: None SolutionPresetSimple: 0 SolutionPresetModerate: 0 SolutionPresetComplex: 5
preconditioner_drop_tolerance (float, optional) – optional real value that defines the drop tolerance used to drop preconditioner terms based on the magnitude of matrix entries in the ILUT and MILUT preconditioners. A value of 10−4 works well for most problems. By default, preconditioner_drop_tolerance is zero and the zero-fill incomplete LU factorization preconditioners (ILU(0) and MILU(0)) are used. Default value: None SolutionPresetSimple: 0 SolutionPresetModerate: 0.0 SolutionPresetComplex: 0.0001
number_orthogonalizations (int, optional) – optional integer value defining the interval used to explicitly recalculate the residual of the flow equation using the solver coefficient matrix, the latest head estimates, and the right hand side. For problems that benefit from explicit recalculation of the residual, a number between 4 and 10 is appropriate. By default, number_orthogonalizations is zero. Default value: None SolutionPresetSimple: 0 SolutionPresetModerate: 0 SolutionPresetComplex: 2
scaling_method (str) – options: {“None”, “diagonal”, “l2norm”} an optional keyword that defines the matrix scaling approach used. By default, matrix scaling is not applied. None - no matrix scaling applied. diagonal - symmetric matrix scaling using the POLCG preconditioner scaling method in Hill (1992). l2norm - symmetric matrix scaling using the L2 norm. Default value: None SolutionPresetSimple: None SolutionPresetModerate: None SolutionPresetComplex: None
reordering_method (str) – options: {“None”, “rcm”, “md”} an optional keyword that defines the matrix reordering approach used. By default, matrix reordering is not applied. None - original ordering. rcm - reverse Cuthill McKee ordering. md - minimum degree ordering Default value: None SolutionPresetSimple: None SolutionPresetModerate: None SolutionPresetComplex: None
print_option (str) – options: {“None”, “summary”, “all”} is a flag that controls printing of convergence information from the solver. None - means print nothing. summary - means print only the total number of iterations and nonlinear residual reduction summaries. all - means print linear matrix solver convergence information to the solution listing file and model specific linear matrix solver convergence information to each model listing file in addition to SUMMARY information. Default value: “summary” SolutionPresetSimple: No Default SolutionPresetModerate: No Default SolutionPresetComplex: No Default
csv_output (str, optional) – False if no csv is to be written for the output, enter str of filename if csv is to be written. Default value: False SolutionPresetSimple: No Default SolutionPresetModerate: No Default SolutionPresetComplex: No Default
no_ptc (({True, False}, optional)) – is a flag that is used to disable pseudo-transient continuation (PTC). Option only applies to steady-state stress periods for models using the Newton-Raphson formulation. For many problems, PTC can significantly improve convergence behavior for steady-state simulations, and for this reason it is active by default. In some cases, however, PTC can worsen the convergence behavior, especially when the initial conditions are similar to the solution. When the initial conditions are similar to, or exactly the same as, the solution and convergence is slow, then this NO PTC option should be used to deactivate PTC. This NO PTC option should also be used in order to compare convergence behavior with other MODFLOW versions, as PTC is only available in MODFLOW 6. Default value: False SolutionPresetSimple: No Default SolutionPresetModerate: No Default SolutionPresetComplex: No Default
- all(dim=None, **kwargs)¶
Reduce this Solution’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New Solution object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this Solution’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New Solution object with any applied to its data and the indicated dimension(s) removed.
- Return type
- backtracking_number¶
- backtracking_reduction_factor¶
- backtracking_residual_limit¶
- backtracking_tolerance¶
- count(dim=None, **kwargs)¶
Reduce this Solution’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New Solution object with count applied to its data and the indicated dimension(s) removed.
- Return type
- csv_output¶
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of Solution.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New Solution object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of Solution.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New Solution object with cumsum applied to its data along the indicated dimension.
- Return type
- inner_hclose¶
- inner_maximum¶
- inner_rclose¶
- linear_acceleration¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this Solution’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New Solution object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this Solution’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New Solution object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this Solution’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New Solution object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this Solution’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New Solution object with min applied to its data and the indicated dimension(s) removed.
- Return type
- no_ptc¶
- number_orthogonalizations¶
- outer_hclose¶
- outer_maximum¶
- outer_rclosebnd¶
- preconditioner_drop_tolerance¶
- preconditioner_levels¶
- print_option¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this Solution’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New Solution object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- rclose_option¶
- relaxation_factor¶
- reordering_method¶
- scaling_method¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this Solution’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New Solution object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this Solution’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New Solution object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- under_relaxation¶
- under_relaxation_gamma¶
- under_relaxation_kappa¶
- under_relaxation_momentum¶
- under_relaxation_theta¶
- var(dim=None, skipna=None, **kwargs)¶
Reduce this Solution’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New Solution object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.SpecificStorage(specific_storage, specific_yield, transient, convertible)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Storage Package with specific storage.
From wikipedia (https://en.wikipedia.org/wiki/Specific_storage):
“The specific storage is the amount of water that a portion of an aquifer releases from storage, per unit mass or volume of aquifer, per unit change in hydraulic head, while remaining fully saturated.”
If the STO Package is not included for a model, then storage changes will not be calculated, and thus, the model will be steady state. Only one STO Package can be specified for a GWF model.
- Parameters
specific_storage (array of floats (xr.DataArray)) – Is specific storage. Specific storage values must be greater than or equal to 0. (ss)
specific_yield (array of floats (xr.DataArray)) – Is specific yield. Specific yield values must be greater than or equal to 0. Specific yield does not have to be specified if there are no convertible cells (convertible=0 in every cell). (sy)
convertible (array of int (xr.DataArray)) – Is a flag for each cell that specifies whether or not a cell is convertible for the storage calculation. 0 indicates confined storage is used. >0 indicates confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top. (iconvert)
transient (({True, False})) – Boolean to indicate if the model is transient or steady-state.
- all(dim=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New SpecificStorage object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New SpecificStorage object with any applied to its data and the indicated dimension(s) removed.
- Return type
- convertible¶
- count(dim=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New SpecificStorage object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of SpecificStorage.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New SpecificStorage object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of SpecificStorage.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New SpecificStorage object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New SpecificStorage object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New SpecificStorage object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New SpecificStorage object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New SpecificStorage object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New SpecificStorage object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- specific_storage¶
- specific_yield¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New SpecificStorage object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New SpecificStorage object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- transient¶
- var(dim=None, skipna=None, **kwargs)¶
Reduce this SpecificStorage’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New SpecificStorage object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.Storage(**kwargs)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
- all(dim=None, **kwargs)¶
Reduce this Storage’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New Storage object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this Storage’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New Storage object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this Storage’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New Storage object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of Storage.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New Storage object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of Storage.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New Storage object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this Storage’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New Storage object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this Storage’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New Storage object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this Storage’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New Storage object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this Storage’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New Storage object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this Storage’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New Storage object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- std(dim=None, skipna=None, **kwargs)¶
Reduce this Storage’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New Storage object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this Storage’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New Storage object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this Storage’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New Storage object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.StorageCoefficient(storage_coefficient, specific_yield, transient, convertible)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Storage Package with a storage coefficient. Be careful, this is not the same as the specific storage.
From wikipedia (https://en.wikipedia.org/wiki/Specific_storage):
“Storativity or the storage coefficient is the volume of water released from storage per unit decline in hydraulic head in the aquifer, per unit area of the aquifer. Storativity is a dimensionless quantity, and is always greater than 0.
Under confined conditions:
S = Ss * b, where S is the storage coefficient, Ss the specific storage, and b the aquifer thickness.
Under unconfined conditions:
S = Sy, where Sy is the specific yield”
If the STO Package is not included for a model, then storage changes will not be calculated, and thus, the model will be steady state. Only one STO Package can be specified for a GWF model.
- Parameters
storage_coefficient (array of floats (xr.DataArray)) – Is specific storage. Storage coefficient values must be greater than or equal to 0. (ss)
specific_yield (array of floats (xr.DataArray)) – Is specific yield. Specific yield values must be greater than or equal to 0. Specific yield does not have to be specified if there are no convertible cells (convertible=0 in every cell). (sy)
convertible (array of int (xr.DataArray)) – Is a flag for each cell that specifies whether or not a cell is convertible for the storage calculation. 0 indicates confined storage is used. >0 indicates confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top. (iconvert)
transient (({True, False})) – Boolean to indicate if the model is transient or steady-state.
- all(dim=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New StorageCoefficient object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New StorageCoefficient object with any applied to its data and the indicated dimension(s) removed.
- Return type
- convertible¶
- count(dim=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New StorageCoefficient object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of StorageCoefficient.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New StorageCoefficient object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of StorageCoefficient.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New StorageCoefficient object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New StorageCoefficient object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New StorageCoefficient object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New StorageCoefficient object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New StorageCoefficient object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New StorageCoefficient object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- specific_yield¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New StorageCoefficient object with std applied to its data and the indicated dimension(s) removed.
- Return type
- storage_coefficient¶
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New StorageCoefficient object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- transient¶
- var(dim=None, skipna=None, **kwargs)¶
Reduce this StorageCoefficient’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New StorageCoefficient object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.StructuredDiscretization(top, bottom, idomain)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Discretization information for structered grids is specified using the file. (DIS6) Only one discretization input file (DISU6, DISV6 or DIS6) can be specified for a model. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=35
- Parameters
top (array of floats (xr.DataArray)) – is the top elevation for each cell in the top model layer.
bottom (array of floats (xr.DataArray)) – is the bottom elevation for each cell.
idomain (array of integers (xr.DataArray)) – Indicates the existence status of a cell. Horizontal discretization information will be derived from the x and y coordinates of the DataArray. If the idomain value for a cell is 0, the cell does not exist in the simulation. Input and output values will be read and written for the cell, but internal to the program, the cell is excluded from the solution. If the idomain value for a cell is 1, the cell exists in the simulation. if the idomain value for a cell is -1, the cell does not exist in the simulation. Furthermore, the first existing cell above will be connected to the first existing cell below. This type of cell is referred to as a “vertical pass through”cell.
- all(dim=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New StructuredDiscretization object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New StructuredDiscretization object with any applied to its data and the indicated dimension(s) removed.
- Return type
- bottom¶
- count(dim=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New StructuredDiscretization object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of StructuredDiscretization.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New StructuredDiscretization object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of StructuredDiscretization.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New StructuredDiscretization object with cumsum applied to its data along the indicated dimension.
- Return type
- idomain¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New StructuredDiscretization object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New StructuredDiscretization object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New StructuredDiscretization object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New StructuredDiscretization object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New StructuredDiscretization object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- std(dim=None, skipna=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New StructuredDiscretization object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New StructuredDiscretization object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- top¶
- var(dim=None, skipna=None, **kwargs)¶
Reduce this StructuredDiscretization’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New StructuredDiscretization object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.TimeDiscretization(timestep_duration, n_timesteps=1, timestep_multiplier=1.0)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Timing for all models of the simulation is controlled by the Temporal Discretization (TDIS) Package. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=17
- timestep_duration: float
is the length of a stress period. (PERLEN)
- n_timesteps: int, optional
is the number of time steps in a stress period (nstp). Default value: 1
- timestep_multiplier: float, optional
is the multiplier for the length of successive time steps. The length of a time step is calculated by multiplying the length of the previous time step by timestep_multiplier (TSMULT). Default value: 1.0
- all(dim=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New TimeDiscretization object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New TimeDiscretization object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New TimeDiscretization object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of TimeDiscretization.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New TimeDiscretization object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of TimeDiscretization.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New TimeDiscretization object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New TimeDiscretization object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New TimeDiscretization object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New TimeDiscretization object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New TimeDiscretization object with min applied to its data and the indicated dimension(s) removed.
- Return type
- n_timesteps¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New TimeDiscretization object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- std(dim=None, skipna=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New TimeDiscretization object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New TimeDiscretization object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- timestep_duration¶
- timestep_multiplier¶
- var(dim=None, skipna=None, **kwargs)¶
Reduce this TimeDiscretization’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New TimeDiscretization object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.UnsaturatedZoneFlow(surface_depression_depth, kv_sat, theta_res, theta_sat, theta_init, epsilon, infiltration_rate, et_pot=None, extinction_depth=None, extinction_theta=None, air_entry_potential=None, root_potential=None, root_activity=None, ntrailwaves=7, nwavesets=40, groundwater_ET_function=None, simulate_groundwater_seepage=False, print_input=False, print_flows=False, save_flows=False, observations=None, water_mover=None, timeseries=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Unsaturated Zone Flow (UZF) package.
TODO: Support timeseries file? Observations? Water Mover?
- Parameters
surface_depression_depth (array of floats (xr.DataArray)) – is the surface depression depth of the UZF cell.
kv_sat (array of floats (xr.DataArray)) – is the vertical saturated hydraulic conductivity of the UZF cell. NOTE: the UZF package determines the location of inactive cells where kv_sat is np.nan
theta_res (array of floats (xr.DataArray)) – is the residual (irreducible) water content of the UZF cell.
theta_sat (array of floats (xr.DataArray)) – is the saturated water content of the UZF cell.
theta_init (array of floats (xr.DataArray)) – is the initial water content of the UZF cell.
epsilon (array of floats (xr.DataArray)) – is the epsilon exponent of the UZF cell.
infiltration_rate (array of floats (xr.DataArray)) – defines the applied infiltration rate of the UZF cell (LT -1).
ET_pot (array of floats (xr.DataArray, optional)) – defines the potential evapotranspiration rate of the UZF cell and specified GWF cell. Evapotranspiration is first removed from the unsaturated zone and any remaining potential evapotranspiration is applied to the saturated zone. If IVERTCON is greater than zero then residual potential evapotranspiration not satisfied in the UZF cell is applied to the underlying UZF and GWF cells.
extinction_depth (array of floats (xr.DataArray, optional)) – defines the evapotranspiration extinction depth of the UZF cell. If IVERTCON is greater than zero and EXTDP extends below the GWF cell bottom then remaining potential evapotranspiration is applied to the underlying UZF and GWF cells. EXTDP is always specified, but is only used if SIMULATE ET is specified in the OPTIONS block.
extinction_theta (array of floats (xr.DataArray, optional)) – defines the evapotranspiration extinction water content of the UZF cell. If specified, ET in the unsaturated zone will be simulated either as a function of the specified PET rate while the water content (THETA) is greater than the ET extinction water content
air_entry_potential (array of floats (xr.DataArray, optional)) – defines the air entry potential (head) of the UZF cell. If specified, ET will be simulated using a capillary pressure based formulation. Capillary pressure is calculated using the Brooks-Corey retention function (“air_entry”)
root_potential (array of floats (xr.DataArray, optional)) – defines the root potential (head) of the UZF cell. If specified, ET will be simulated using a capillary pressure based formulation. Capillary pressure is calculated using the Brooks-Corey retention function (“air_entry”
root_activity (array of floats (xr.DataArray, optional)) – defines the root activity function of the UZF cell. ROOTACT is the length of roots in a given volume of soil divided by that volume. Values range from 0 to about 3 cm-2, depending on the plant community and its stage of development. If specified, ET will be simulated using a capillary pressure based formulation. Capillary pressure is calculated using the Brooks-Corey retention function (“air_entry”
groundwater_ET_function (({"linear", "square"}, optional)) – keyword specifying that groundwater evapotranspiration will be simulated using either the original ET formulation of MODFLOW-2005 (“linear”). Or by assuming a constant ET rate for groundwater levels between land surface (TOP) and land surface minus the ET extinction depth (TOP-EXTDP) (“square”). In the latter case, groundwater ET is smoothly reduced from the PET rate to zero over a nominal interval at TOP-EXTDP.
simulate_seepage (({True, False}, optional)) – keyword specifying that groundwater discharge (GWSEEP) to land surface will be simulated. Groundwater discharge is nonzero when groundwater head is greater than land surface.
print_input (({True, False}, optional)) – keyword to indicate that the list of UZF information will be written to the listing file immediately after it is read. Default is False.
print_flows (({True, False}, optional)) – keyword to indicate that the list of UZF flow rates will be printed to the listing file for every stress period time step in which “BUDGET PRINT” is specified in Output Control. If there is no Output Control option and “PRINT FLOWS” is specified, then flow rates are printed for the last time step of each stress period. Default is False.
save_flows (({True, False}, optional)) – keyword to indicate that UZF flow terms will be written to the file specified with “BUDGET FILEOUT” in Output Control. Default is False.
observations ([Not yet supported.]) – Default is None.
water_mover ([Not yet supported.]) – Default is None.
timeseries ([Not yet supported.]) – Default is None. TODO: We could allow the user to either use xarray DataArrays to specify BCS or use a pd.DataFrame and use the MF6 timeseries files to read input. The latter could save memory for laterally large-scale models, through efficient use of the UZF cell identifiers.
- air_entry_potential¶
- all(dim=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of UnsaturatedZoneFlow.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New UnsaturatedZoneFlow object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of UnsaturatedZoneFlow.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New UnsaturatedZoneFlow object with cumsum applied to its data along the indicated dimension.
- Return type
- epsilon¶
- et_pot¶
- extinction_depth¶
- extinction_theta¶
- fill_stress_perioddata()[source]¶
Modflow6 requires something to be filled in the stress perioddata, even though the data is not used in the current configuration. Only an infiltration rate is required, the rest can be filled with dummy values if not provided.
- groundwater_ET_function¶
- infiltration_rate¶
- iuzno¶
- ivertcon¶
- kv_sat¶
- landflag¶
- linear_gwet¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with min applied to its data and the indicated dimension(s) removed.
- Return type
- ntrailwaves¶
- nwavesets¶
- observations¶
- print_flows¶
- print_input¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- render(directory, pkgname, globaltimes)[source]¶
Render fills in the template only, doesn’t write binary data
- root_activity¶
- root_potential¶
- save_flows¶
- simulate_et¶
- simulate_seepage¶
- square_gwet¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- surface_depression_depth¶
- theta_init¶
- theta_res¶
- theta_sat¶
- timeseries¶
- to_sparse(arrlist, layer)[source]¶
Convert from dense arrays to list based input, since the perioddata does not require cellids but iuzno, we hgave to override
- unsat_etae¶
- unsat_etwc¶
- var(dim=None, skipna=None, **kwargs)¶
Reduce this UnsaturatedZoneFlow’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New UnsaturatedZoneFlow object with var applied to its data and the indicated dimension(s) removed.
- Return type
- water_mover¶
- class imod.mf6.Well(layer, row, column, rate, print_input=False, print_flows=False, save_flows=False, observations=None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
WEL package. Any number of WEL Packages can be specified for a single groundwater flow model. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=63
- Parameters
layer (int or list of int) – Modellayer in which the well is located.
row (int or list of int) – Row in which the well is located.
column (int or list of int) – Column in which the well is located.
rate (float or list of floats) – is the volumetric well rate. A positive value indicates well (injection) and a negative value indicates discharge (extraction) (q).
print_input (({True, False}, optional)) – keyword to indicate that the list of well information will be written to the listing file immediately after it is read. Default is False.
print_flows (({True, False}, optional)) – Indicates that the list of well flow rates will be printed to the listing file for every stress period time step in which “BUDGET PRINT”is specified in Output Control. If there is no Output Control option and PRINT FLOWS is specified, then flow rates are printed for the last time step of each stress period. Default is False.
save_flows (({True, False}, optional)) – Indicates that well flow terms will be written to the file specified with “BUDGET FILEOUT” in Output Control. Default is False.
observations ([Not yet supported.]) – Default is None.
- all(dim=None, **kwargs)¶
Reduce this Well’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New Well object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this Well’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New Well object with any applied to its data and the indicated dimension(s) removed.
- Return type
- column¶
- count(dim=None, **kwargs)¶
Reduce this Well’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New Well object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of Well.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New Well object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of Well.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New Well object with cumsum applied to its data along the indicated dimension.
- Return type
- layer¶
- max(dim=None, skipna=None, **kwargs)¶
Reduce this Well’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New Well object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this Well’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New Well object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this Well’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New Well object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this Well’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New Well object with min applied to its data and the indicated dimension(s) removed.
- Return type
- observations¶
- print_flows¶
- print_input¶
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this Well’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New Well object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- rate¶
- row¶
- save_flows¶
- std(dim=None, skipna=None, **kwargs)¶
Reduce this Well’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New Well object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this Well’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New Well object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this Well’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New Well object with var applied to its data and the indicated dimension(s) removed.
- Return type
- imod.mf6.open_cbc(cbc_path: Union[str, pathlib.Path], grb_path: Union[str, pathlib.Path]) Dict[str, xarray.core.dataarray.DataArray] [source]¶
Open modflow6 cell-by-cell (.cbc) file.
The data is lazily read per timestep and automatically converted into (dense) xr.DataArrays. The conversion is done via the information stored in the Binary Grid File (GRB).
Currently only structured discretization (DIS) is supported. The flow-ja-face data is automatically converted into “right-face-flow”, “front-face-flow” and “lower-face-flow”.
- Parameters
cbc_path (str, pathlib.Path) – Path to the cell-by-cell flows file
grb_path (str, pathlib.Path) – Path to the binary grid file
- Returns
cbc_content – DataArray contains float64 data of the budgets, with dimensions (“time”, “layer”, “y”, “x”).
- Return type
dict[str, xr.DataArray]
Examples
Open a cbc file:
>>> import imod >>> cbc_content = imod.mf6.open_cbc("budgets.cbc", "my-model.grb")
Check the contents:
>>> print(cbc_content.keys())
Get the drainage budget, compute a time mean for the first layer:
>>> drn_budget = cbc_content["drn] >>> mean = drn_budget.sel(layer=1).mean("time")
- imod.mf6.read_cbc_headers(cbc_path: Union[str, pathlib.Path]) Dict[str, List[Union[imod.mf6.out.Imeth1Header, imod.mf6.out.Imeth6Header]]] [source]¶
Read all the header data from a cell-by-cell (.cbc) budget file.
All budget data for a MODFLOW6 model is stored in a single file. This function collects all header data, as well as the starting byte position of the actual budget data.
This function groups the headers per TEXT record (e.g. “flow-ja-face”, “drn”, etc.). The headers are stored as a list of named tuples. flow-ja-face, storage-ss, and storage-sy are written using IMETH=1, all others with IMETH=6.
- Parameters
cbc_path (str, pathlib.Path) – Path to the budget file.
- Returns
headers – Dictionary containing a list of headers per TEXT record in the budget file.
- Return type
Dict[List[UnionImeth1Header, Imeth6Header]]
- class imod.mf6.pkgbase.AdvancedBoundaryCondition(data_vars: Optional[Mapping[Hashable, Any]] = None, coords: Optional[Mapping[Hashable, Any]] = None, attrs: Optional[Mapping[Hashable, Any]] = None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Class dedicated to advanced boundary conditions, since MF6 does not support binary files for Advanced Boundary conditions.
The advanced boundary condition packages are: “uzf”, “lak”, “maw”, “str”.
- all(dim=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of AdvancedBoundaryCondition.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New AdvancedBoundaryCondition object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of AdvancedBoundaryCondition.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New AdvancedBoundaryCondition object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- std(dim=None, skipna=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this AdvancedBoundaryCondition’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New AdvancedBoundaryCondition object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.pkgbase.BoundaryCondition(data_vars: Optional[Mapping[Hashable, Any]] = None, coords: Optional[Mapping[Hashable, Any]] = None, attrs: Optional[Mapping[Hashable, Any]] = None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
BoundaryCondition is used to share methods for specific stress packages with a time component.
It is not meant to be used directly, only to inherit from, to implement new packages.
This class only supports list input, not the array input which is used in
Package
.- all(dim=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New BoundaryCondition object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New BoundaryCondition object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New BoundaryCondition object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of BoundaryCondition.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New BoundaryCondition object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of BoundaryCondition.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New BoundaryCondition object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New BoundaryCondition object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New BoundaryCondition object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New BoundaryCondition object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New BoundaryCondition object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New BoundaryCondition object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- render(directory, pkgname, globaltimes)[source]¶
Render fills in the template only, doesn’t write binary data
- std(dim=None, skipna=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New BoundaryCondition object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New BoundaryCondition object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this BoundaryCondition’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New BoundaryCondition object with var applied to its data and the indicated dimension(s) removed.
- Return type
- class imod.mf6.pkgbase.Package(data_vars: Optional[Mapping[Hashable, Any]] = None, coords: Optional[Mapping[Hashable, Any]] = None, attrs: Optional[Mapping[Hashable, Any]] = None)[source]¶
Bases:
xarray.core.common.DataWithCoords
,xarray.core.arithmetic.DatasetArithmetic
,Mapping
Package is used to share methods for specific packages with no time component.
It is not meant to be used directly, only to inherit from, to implement new packages.
This class only supports array input, not the list input which is used in
BoundaryCondition
.- all(dim=None, **kwargs)¶
Reduce this Package’s data by applying all along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply all. By default all is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data.
- Returns
reduced – New Package object with all applied to its data and the indicated dimension(s) removed.
- Return type
- any(dim=None, **kwargs)¶
Reduce this Package’s data by applying any along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply any. By default any is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data.
- Returns
reduced – New Package object with any applied to its data and the indicated dimension(s) removed.
- Return type
- count(dim=None, **kwargs)¶
Reduce this Package’s data by applying count along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply count. By default count is applied over all dimensions.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data.
- Returns
reduced – New Package object with count applied to its data and the indicated dimension(s) removed.
- Return type
- cumprod(dim=None, skipna=None, **kwargs)¶
Apply cumprod along some dimension of Package.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumprod.
axis (int or sequence of int, optional) – Axis over which to apply cumprod. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumprod.
- Returns
cumvalue – New Package object with cumprod applied to its data along the indicated dimension.
- Return type
- cumsum(dim=None, skipna=None, **kwargs)¶
Apply cumsum along some dimension of Package.
- Parameters
dim (str or sequence of str, optional) – Dimension over which to apply cumsum.
axis (int or sequence of int, optional) – Axis over which to apply cumsum. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to cumsum.
- Returns
cumvalue – New Package object with cumsum applied to its data along the indicated dimension.
- Return type
- max(dim=None, skipna=None, **kwargs)¶
Reduce this Package’s data by applying max along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply max. By default max is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data.
- Returns
reduced – New Package object with max applied to its data and the indicated dimension(s) removed.
- Return type
- mean(dim=None, skipna=None, **kwargs)¶
Reduce this Package’s data by applying mean along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply mean. By default mean is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.
- Returns
reduced – New Package object with mean applied to its data and the indicated dimension(s) removed.
- Return type
- median(dim=None, skipna=None, **kwargs)¶
Reduce this Package’s data by applying median along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply median. By default median is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data.
- Returns
reduced – New Package object with median applied to its data and the indicated dimension(s) removed.
- Return type
- min(dim=None, skipna=None, **kwargs)¶
Reduce this Package’s data by applying min along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply min. By default min is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data.
- Returns
reduced – New Package object with min applied to its data and the indicated dimension(s) removed.
- Return type
- prod(dim=None, skipna=None, **kwargs)¶
Reduce this Package’s data by applying prod along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply prod. By default prod is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data.
- Returns
reduced – New Package object with prod applied to its data and the indicated dimension(s) removed.
- Return type
- std(dim=None, skipna=None, **kwargs)¶
Reduce this Package’s data by applying std along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply std. By default std is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data.
- Returns
reduced – New Package object with std applied to its data and the indicated dimension(s) removed.
- Return type
- sum(dim=None, skipna=None, **kwargs)¶
Reduce this Package’s data by applying sum along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply sum. By default sum is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
min_count (int, default: None) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. New in version 0.10.8: Added with the default being None. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data.
- Returns
reduced – New Package object with sum applied to its data and the indicated dimension(s) removed.
- Return type
- var(dim=None, skipna=None, **kwargs)¶
Reduce this Package’s data by applying var along some dimension(s).
- Parameters
dim (str or sequence of str, optional) – Dimension(s) over which to apply var. By default var is applied over all dimensions.
skipna (bool, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
keep_attrs (bool, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.
**kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data.
- Returns
reduced – New Package object with var applied to its data and the indicated dimension(s) removed.
- Return type
- write_netcdf(directory, pkgname, aggregate_layers=False)[source]¶
Write to netcdf. Useful for generating .qgs projectfiles to view model input. These files cannot be used to run a modflow model.
- Parameters
directory (Path) – directory of qgis project
pkgname (str) – package name
aggregate_layers (bool) – If True, aggregate layers by taking the mean, i.e. ds.mean(dim=”layer”)
- Returns
has_dims – list of variables that have an x and y dimension.
- Return type
list of str