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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

ConstantHead

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Drainage

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

Evapotranspiration

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

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

GeneralHeadBoundary

class imod.mf6.GroundwaterFlowModel(newton=False, under_relaxation=False)[source]

Bases: imod.mf6.model.Model

Contains data and writes consistent model input files

render(modeldirectory)[source]

Render model namefile

write(modelname, globaltimes)[source]

Write model namefile Write packages

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

InitialConditions

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

InitialConditions

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

InitialConditions

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

InitialConditions

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

InitialConditions

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

InitialConditions

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

InitialConditions

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

InitialConditions

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

InitialConditions

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

InitialConditions

render(directory, pkgname, *args, **kwargs)[source]
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

InitialConditions

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

InitialConditions

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

InitialConditions

class imod.mf6.Modflow6Simulation(name)[source]

Bases: collections.UserDict

render()[source]

Renders simulation namefile

time_discretization(times)[source]

Collect all unique times

update([E, ]**F)None.  Update D from mapping/iterable E and F.[source]

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

write(directory='.')[source]
write_qgis_project(crs, directory='.', aggregate_layers=False)[source]
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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

render(directory, pkgname, *args, **kwargs)[source]
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

NodePropertyFlow

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

NodePropertyFlow

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

NodePropertyFlow

variable_vertical_conductance
class imod.mf6.OutputControl(save_head, save_budget)[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

Parameters
  • save_head (bool, or xr.DataArray of bools) – Bool per stress period.

  • save_budget (bool, or xr.DataArray of bools) – Bool per stress period.

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

OutputControl

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

OutputControl

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

OutputControl

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

OutputControl

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

OutputControl

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

OutputControl

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

OutputControl

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

OutputControl

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

OutputControl

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

OutputControl

render(directory, pkgname, globaltimes)[source]
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

OutputControl

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

OutputControl

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

OutputControl

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

Recharge

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

River

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

River

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

River

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

River

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

River

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

River

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

River

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

River

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

River

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

River

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

River

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

River

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

River

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

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

Solution

imod.mf6.SolutionPresetComplex(print_option, csv_output, no_ptc)[source]
imod.mf6.SolutionPresetModerate(print_option, csv_output, no_ptc)[source]
imod.mf6.SolutionPresetSimple(print_option, csv_output, no_ptc)[source]
class imod.mf6.Storage(specific_storage, specific_yield, transient, convertible)[source]

Bases: xarray.core.common.DataWithCoords, xarray.core.arithmetic.DatasetArithmetic, Mapping

Storage Package. 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. The option to use a keyword to indicate that the SS array is read as storage coefficient rather than specific storage has not been implemented yet. https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=57

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 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

Storage

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

Storage

convertible
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

Storage

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

Storage

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

Storage

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

Storage

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

Storage

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

Storage

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

Storage

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

Storage

render(directory, pkgname, globaltimes)[source]
specific_storage
specific_yield
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

Storage

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

Storage

transient
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

Storage

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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

render(directory, pkgname, *args, **kwargs)[source]
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

StructuredDiscretization

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

StructuredDiscretization

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

StructuredDiscretization

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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

render()[source]
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

TimeDiscretization

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

TimeDiscretization

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

TimeDiscretization

write(directory, name)[source]
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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

UnsaturatedZoneFlow

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

Well

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

Well

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

Well

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

Well

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

Well

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

Well

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

Well

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

Well

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

Well

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

Well

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

Well

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

Well

to_sparse(arrlist, *args, **kwargs)[source]

Convert from dense arrays to list based input

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

Well

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.open_hds(hds_path, grb_path, dry_nan=False)[source]

Open head data

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.model.Model(dict=None, /, **kwargs)[source]

Bases: collections.UserDict

update([E, ]**F)None.  Update D from mapping/iterable E and F.[source]

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

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

AdvancedBoundaryCondition

write(directory, pkgname, globaltimes)[source]

writes the blockfile and binary data

directory is modelname

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

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

BoundaryCondition

write(directory, pkgname, globaltimes)[source]

writes the blockfile and binary data

directory is modelname

write_datafile(outpath, ds)[source]

Writes a modflow6 binary data file

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

Package

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

Package

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

Package

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

Package

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

Package

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

Package

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

Package

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

Package

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

Package

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

Package

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

Package

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

Package

to_sparse(arrlist, layer)[source]

Convert from dense arrays to list based input

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

Package

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