gw_utils

MODFLOW support utilities

`GsfReader`

a helper class to read a standard modflow-usg gsf file

Parameters:

Name	Type	Description	Default
`gsffilename`	`str`	filename	required

`get_node_coordinates(zcoord=False, zero_based=False)`

Parameters:

Name	Type	Description	Default
`zcoord`	`bool`	flag to add z coord to coordinates. Default is False	`False`
`zero_based`	`bool`	flag to subtract one from the node numbers in the returned node_coords dict. This is needed to support PstFrom. Default is False	`False`

Returns:

Name	Type	Description
`node_coords`		Dictionary containing x and y coordinates for each node

`get_node_data()`

Returns:

Name	Type	Description
`nodedf`		a pd.DataFrame containing Node information; Node, X, Y, Z, layer, numverts, vertidx

`get_vertex_coordinates()`

Returns:

Type	Description
	Dictionary containing list of x, y and z coordinates for each vertex

`apply_gage_obs(return_obs_file=False)`

apply the modflow gage obs post-processor

Parameters:

Name	Type	Description	Default
`return_obs_file`	`bool`	flag to return the processed observation file. Default is `False`.	`False`

Note

This is the companion function of gw_utils.setup_gage_obs()

`apply_hds_obs(hds_file, inact_abs_val=1e+20, precision='single', text='head')`

process a modflow head save file. A companion function to gw_utils.setup_hds_obs() that is called during the forward run process

Parameters:

Name	Type	Description	Default
`hds_file`	`str`	a modflow head save filename. if hds_file ends with 'ucn', then the file is treated as a UcnFile type.	required
`inact_abs_val`	`float`	the value that marks the minimum and maximum active value. values in the headsave file greater than `inact_abs_val` or less than -`inact_abs_val` are reset to `inact_abs_val`	`1e+20`

Returns: pandas.DataFrame: a dataframe with extracted simulated values. Note: This is the companion function to gw_utils.setup_hds_obs().

`apply_hds_timeseries(config_file=None, postprocess_inact=None)`

process a modflow binary file using a previously written configuration file

Parameters:

Name	Type	Description	Default
`config_file`	`str`	configuration file written by `pyemu.gw_utils.setup_hds_timeseries`. If `None`, looks for `hds_timeseries.config`	`None`
`postprocess_inact`	`float`	Inactive value in heads/ucn file e.g. mt.btn.cinit. If `None`, no inactive value processing happens. Default is `None`.	`None`

Note

This is the companion function of gw_utils.setup_hds_timeseries().

`apply_hfb_pars(par_file='hfb6_pars.csv')`

a function to apply HFB multiplier parameters.

Parameters:

Name	Type	Description	Default
`par_file`	`str`	the HFB parameter info file. Default is `hfb_pars.csv`	`'hfb6_pars.csv'`

Note

This is the companion function to gw_utils.write_hfb_zone_multipliers_template()

This is to account for the horrible HFB6 format that differs from other BCs making this a special case

Requires "hfb_pars.csv"

Should be added to the forward_run.py script

`apply_mflist_budget_obs(list_filename, flx_filename='flux.dat', vol_filename='vol.dat', start_datetime='1-1-1970', times=None)`

process a MODFLOW list file to extract flux and volume water budget entries.

Parameters:

Name	Type	Description	Default
`list_filename`	`str`	path and name of the existing modflow list file	required
`flx_filename`	`str`	output filename that will contain the budget flux observations. Default is "flux.dat"	`'flux.dat'`
`vol_filename`	`str`	output filename that will contain the budget volume observations. Default is "vol.dat"	`'vol.dat'`
`start_datetime`	`str`	a string that can be parsed into a pandas.TimeStamp. This is used to give budget observations meaningful names. Default is "1-1-1970".	`'1-1-1970'`
`times`	`np.ndarray`-like or `str`	An array of times to extract from the budget dataframes returned by the flopy MfListBudget(list_filename).get_dataframe() method. This can be useful to ensure consistent observation times for PEST. If type `str`, will assume `times=filename` and attempt to read single vector (no header or index) from file, parsing datetime using pandas. Array needs to be alignable with index of dataframe return by flopy method, care should be take to ensure that this is the case. If setup with `setup_mflist_budget_obs()` specifying `specify_times` argument `times` should be set to "budget_times.config".	`None`

Note: This is the companion function of gw_utils.setup_mflist_budget_obs().

Returns: tuple containing

 - **pandas.DataFrame**: a dataframe with flux budget information
 - **pandas.DataFrame**: a dataframe with cumulative budget information

`apply_mtlist_budget_obs(list_filename, gw_filename='mtlist_gw.dat', sw_filename='mtlist_sw.dat', start_datetime='1-1-1970')`

process an MT3D-USGS list file to extract mass budget entries.

Parameters:

Name	Type	Description	Default
`list_filename`	`str`	the path and name of an existing MT3D-USGS list file	required
`gw_filename`	`str`	the name of the output file with gw mass budget information. Default is "mtlist_gw.dat"	`'mtlist_gw.dat'`
`sw_filename`	`str`	the name of the output file with sw mass budget information. Default is "mtlist_sw.dat"	`'mtlist_sw.dat'`
`start_datatime`	`str`	an str that can be cast to a pandas.TimeStamp. Used to give observations a meaningful name	required

Returns:

Type	Description
	2-element tuple containing
	pandas.DataFrame: the gw mass budget dataframe
	pandas.DataFrame: (optional) the sw mass budget dataframe. If the SFT process is not active, this returned value is `None`.

Note

This is the companion function of gw_utils.setup_mtlist_budget_obs().

`apply_sfr_obs()`

apply the sfr observation process

Returns:

Type	Description
	pandas.DataFrame: a dataframe of aggregated sfr segment aquifer and outflow

Note

This is the companion function of gw_utils.setup_sfr_obs().

Requires sfr_obs.config.

Writes sfr_out_file+".processed", where sfr_out_file is defined in "sfr_obs.config"

`apply_sfr_parameters(seg_pars=True, reach_pars=False)`

thin wrapper around gw_utils.apply_sfr_seg_parameters()

Parameters:

Name	Type	Description	Default
`seg_pars`	`bool`	flag to apply segment-based parameters. Default is True	`True`
`reach_pars`	`bool`	flag to apply reach-based parameters. Default is False	`False`

Returns:

Type	Description
	flopy.modflow.ModflowSfr: the modified SFR package instance

Note

Expects "sfr_seg_pars.config" to exist

Expects nam_file +"backup.sfr" to exist

`apply_sfr_reach_obs()`

apply the sfr reach observation process.

Returns:

Type	Description
	`pd.DataFrame`: a dataframe of sfr aquifer and outflow ad segment,reach locations

Note

This is the companion function of gw_utils.setup_sfr_reach_obs().

Requires sfr_reach_obs.config.

Writes .processed, where is defined in "sfr_reach_obs.config"

`apply_sfr_seg_parameters(seg_pars=True, reach_pars=False)`

apply the SFR segment multiplier parameters.

Parameters:

Name	Type	Description	Default
`seg_pars`	`bool`	flag to apply segment-based parameters. Default is True	`True`
`reach_pars`	`bool`	flag to apply reach-based parameters. Default is False	`False`

Returns:

Type	Description
	flopy.modflow.ModflowSfr: the modified SFR package instance

Note

Expects "sfr_seg_pars.config" to exist

Expects nam_file +"backup.sfr" to exist

`apply_sft_obs()`

process an mt3d-usgs sft ASCII output file using a previous-written config file

Returns:

Type	Description
	pandas.DataFrame: a dataframe of extracted simulated outputs

Note

This is the companion function to gw_utils.setup_sft_obs().

`last_kstp_from_kper(hds, kper)`

function to find the last time step (kstp) for a give stress period (kper) in a modflow head save file.

Parameters:

Name	Type	Description	Default
`hds`	`flopy.utils.HeadFile`	head save file	required
`kper`	`int`	the zero-index stress period number	required

Returns:

Type	Description
	int: the zero-based last time step during stress period
	kper in the head save file

`load_sfr_out(sfr_out_file, selection=None)`

load an ASCII SFR output file into a dictionary of kper: dataframes.

Parameters:

Name	Type	Description	Default
`sfr_out_file`	`str`	SFR ASCII output file	required
`selection`	`pandas.DataFrame`	a dataframe of `reach` and `segment` pairs to load. If `None`, all reach-segment pairs are loaded. Default is `None`.	`None`

Returns:

Type	Description
	dict: dictionary of {kper:`pandas.DataFrame`} of SFR output.

Note

Aggregates flow to aquifer for segments and returns and flow out at downstream end of segment.

`modflow_hob_to_instruction_file(hob_file, ins_file=None)`

write an instruction file for a modflow head observation file

Parameters:

Name	Type	Description	Default
`hob_file`	`str`	the path and name of the existing modflow hob file	required
`ins_file`	`str`	the name of the instruction file to write. If `None`, `hob_file` +".ins" is used. Default is `None`.	`None`

Returns:

Type	Description
	pandas.DataFrame: a dataFrame with control file observation information

`modflow_hydmod_to_instruction_file(hydmod_file, ins_file=None)`

write an instruction file for a modflow hydmod file

Parameters:

Name	Type	Description	Default
`hydmod_file`	`str`	the path and name of the existing modflow hob file	required
`ins_file`	`str`	the name of the instruction file to write. If `None`, `hydmod_file` +".ins" is used. Default is `None`.	`None`

Returns:

Type	Description
	pandas.DataFrame: a dataFrame with control file observation information

Note

calls pyemu.gw_utils.modflow_read_hydmod_file()

`modflow_pval_to_template_file(pval_file, tpl_file=None)`

write a template file for a modflow parameter value file.

Parameters:

Name	Type	Description	Default
`pval_file`	`str`	the path and name of the existing modflow pval file	required
`tpl_file`	`str`	template file to write. If None, use `pval_file` +".tpl". Default is None	`None`

Note: Uses names in the first column in the pval file as par names.

Returns:

Type	Description
	pandas.DataFrame: a dataFrame with control file parameter information

`modflow_read_hydmod_file(hydmod_file, hydmod_outfile=None)`

read a binary hydmod file and return a dataframe of the results

Parameters:

Name	Type	Description	Default
`hydmod_file`	`str`	The path and name of the existing modflow hydmod binary file	required
`hydmod_outfile`	`str`	output file to write. If `None`, use `hydmod_file` +".dat". Default is `None`.	`None`

Returns:

Type	Description
	pandas.DataFrame: a dataFrame with hymod_file values

`modflow_sfr_gag_to_instruction_file(gage_output_file, ins_file=None, parse_filename=False)`

writes an instruction file for an SFR gage output file to read Flow only at all times

Parameters:

Name	Type	Description	Default
`gage_output_file`	`str`	the gage output filename (ASCII).	required
`ins_file`	`str`	the name of the instruction file to create. If None, the name is `gage_output_file` +".ins". Default is None	`None`
`parse_filename`	`bool`	if True, get the gage_num parameter by parsing the gage output file filename if False, get the gage number from the file itself	`False`

Returns:

Type	Description
	tuple containing
	pandas.DataFrame: a dataframe with obsnme and obsval for the sfr simulated flows.
	str: file name of instructions file relating to gage output.
	str: file name of processed gage output for all times

Note

Sets up observations for gage outputs only for the Flow column.

If parse_namefile is true, only text up to first '.' is used as the gage_num

`setup_gage_obs(gage_file, ins_file=None, start_datetime=None, times=None)`

setup a forward run post processor routine for the modflow gage file

Parameters:

Name	Type	Description	Default
`gage_file`	`str`	the gage output file (ASCII)	required
`ins_file`	`str`	the name of the instruction file to create. If None, the name is `gage_file`+".processed.ins". Default is `None`	`None`
`start_datetime`	`str`	a `pandas.to_datetime()` compatible `str`. If not `None`, then the resulting observation names have the datetime suffix. If `None`, the suffix is the output totim. Default is `None`.	`None`
`times`	[`float`]	a container of times to make observations for. If None, all times are used. Default is None.	`None`

Returns:

Type	Description
	tuple containing
	pandas.DataFrame: a dataframe with observation name and simulated values for the values in the gage file.
	str: file name of instructions file that was created relating to gage output.
	str: file name of processed gage output (processed according to times passed above.)

Note

Setups up observations for gage outputs (all columns).

This is the companion function of gw_utils.apply_gage_obs()

`setup_hds_obs(hds_file, kperk_pairs=None, skip=None, prefix='hds', text='head', precision='single', include_path=False)`

a function to setup using all values from a layer-stress period pair for observations.

Parameters:

Name	Type	Description	Default
`hds_file`	`str`	path and name of an existing MODFLOW head-save file. If the hds_file endswith 'ucn', then the file is treated as a UcnFile type.	required
`kperk_pairs`	`[(int, int)]`	a list of len two tuples which are pairs of kper (zero-based stress period index) and k (zero-based layer index) to setup observations for. If None, then all layers and stress period records found in the file will be used. Caution: a shit-ton of observations may be produced!	`None`
`skip`	`variable`	a value or function used to determine which values to skip when setting up observations. If np.scalar(skip) is True, then values equal to skip will not be used. If skip can also be a np.ndarry with dimensions equal to the model. Observations are set up only for cells with Non-zero values in the array. If not np.ndarray or np.scalar(skip), then skip will be treated as a lambda function that returns np.nan if the value should be skipped.	`None`
`prefix`	`str`	the prefix to use for the observation names. default is "hds".	`'hds'`
`text`	`str`	the text tag the flopy HeadFile instance. Default is "head"	`'head'`
`precision`	`str`	the precision string for the flopy HeadFile instance. Default is "single"	`'single'`
`include_path`	`bool`	flag to setup the binary file processing in directory where the hds_file	`False`

Returns:

Type	Description
	tuple containing
	str: the forward run script line needed to execute the headsave file observation operation
	pandas.DataFrame: a dataframe of pest control file information

Note

Writes an instruction file and a setup csv used construct a control file.

This is the companion function to gw_utils.apply_hds_obs().

`setup_hds_timeseries(bin_file, kij_dict, prefix=None, include_path=False, model=None, postprocess_inact=None, text=None, fill=None, precision='single')`

a function to setup a forward process to extract time-series style values from a binary modflow binary file (or equivalent format - hds, ucn, sub, cbb, etc).

Parameters:

Name	Type	Description	Default
`bin_file`	`str`	path and name of existing modflow binary file - headsave, cell budget and MT3D UCN supported.	required
`kij_dict`	`dict`	dictionary of site_name: [k,i,j] pairs. For example: `{"wel1":[0,1,1]}`.	required
`prefix`	`str`	string to prepend to site_name when forming observation names. Default is None	`None`
`include_path`	`bool`	flag to setup the binary file processing in directory where the hds_file is located (if different from where python is running). This is useful for setting up the process in separate directory for where python is running.	`False`
`model`	`flopy.mbase`	a `flopy.basemodel` instance. If passed, the observation names will have the datetime of the observation appended to them (using the flopy `start_datetime` attribute. If None, the observation names will have the zero-based stress period appended to them. Default is None.	`None`
`postprocess_inact`	`float`	Inactive value in heads/ucn file e.g. mt.btn.cinit. If `None`, no inactive value processing happens. Default is `None`.	`None`
`text`	`str`	the text record entry in the binary file (e.g. "constant_head"). Used to indicate that the binary file is a MODFLOW cell-by-cell budget file. If None, headsave or MT3D unformatted concentration file is assumed. Default is None	`None`
`fill`	`float`	fill value for NaNs in the extracted timeseries dataframe. If `None`, no filling is done, which may yield model run failures as the resulting processed timeseries CSV file (produced at runtime) may have missing values and can't be processed with the corresponding instruction file. Default is `None`.	`None`
`precision`	`str`	the precision of the binary file. Can be "single" or "double". Default is "single".	`'single'`

Returns:

Type	Description
	tuple containing
	str: the forward run command to execute the binary file process during model runs.
	pandas.DataFrame: a dataframe of observation information for use in the pest control file

Note

This function writes hds_timeseries.config that must be in the same dir where apply_hds_timeseries() is called during the forward run

Assumes model time units are days

This is the companion function of gw_utils.apply_hds_timeseries().

`setup_mflist_budget_obs(list_filename, flx_filename='flux.dat', vol_filename='vol.dat', start_datetime="1-1'1970", prefix='', save_setup_file=False, specify_times=None)`

setup observations of budget volume and flux from modflow list file.

Parameters:

Name	Type	Description	Default
`list_filename`	`str`	path and name of the existing modflow list file	required
`flx_filename`	`str`	output filename that will contain the budget flux observations. Default is "flux.dat"	`'flux.dat'`
`vol_filename`	`str`	output filename that will contain the budget volume observations. Default is "vol.dat"	`'vol.dat'`
`start_datetime`	`str`	a string that can be parsed into a pandas.TimeStamp. This is used to give budget observations meaningful names. Default is "1-1-1970".	`"1-1'1970"`
`prefix`	`str`	a prefix to add to the water budget observations. Useful if processing more than one list file as part of the forward run process. Default is ''.	`''`
`save_setup_file`	`bool`	a flag to save "setup"+ `list_filename` +".csv" file that contains useful control file information	`False`
`specify_times`	`np.ndarray`-like	An array of times to extract from the budget dataframes returned by the flopy MfListBudget(list_filename).get_dataframe() method. This can be useful to ensure consistent observation times for PEST. Array needs to be alignable with index of dataframe return by flopy method, care should be take to ensure that this is the case. If passed will be written to "budget_times.config" file as strings to be read by the companion `apply_mflist_budget_obs()` method at run time.	`None`

Returns:

Type	Description
	pandas.DataFrame: a dataframe with information for constructing a control file.

Note

This method writes instruction files and also a setup.csv to use when constructing a pest control file. The instruction files are named .ins and .ins, respectively

It is recommended to use the default values for flux_file and vol_file.

This is the companion function of gw_utils.apply_mflist_budget_obs().

`setup_mtlist_budget_obs(list_filename, gw_filename='mtlist_gw.dat', sw_filename='mtlist_sw.dat', start_datetime='1-1-1970', gw_prefix='gw', sw_prefix='sw', save_setup_file=False)`

setup observations of gw (and optionally sw) mass budgets from mt3dusgs list file.

Parameters:

Name	Type	Description	Default
`list_filename`	`str`	path and name of existing modflow list file	required
`gw_filename`	`str`	output filename that will contain the gw budget observations. Default is "mtlist_gw.dat"	`'mtlist_gw.dat'`
`sw_filename`	`str`	output filename that will contain the sw budget observations. Default is "mtlist_sw.dat"	`'mtlist_sw.dat'`
`start_datetime`	`str`	an str that can be parsed into a `pandas.TimeStamp`. used to give budget observations meaningful names. Default is "1-1-1970".	`'1-1-1970'`
`gw_prefix`	`str`	a prefix to add to the GW budget observations. Useful if processing more than one list file as part of the forward run process. Default is 'gw'.	`'gw'`
`sw_prefix`	`str`	a prefix to add to the SW budget observations. Useful if processing more than one list file as part of the forward run process. Default is 'sw'.	`'sw'`
`save_setup_file`	`bool`	a flag to save "setup"+ `list_filename` +".csv" file that contains useful control file information. Default is `False`.	`False`

Returns:

Type	Description
	tuple containing
	str: the command to add to the forward run script
	str: the names of the instruction files that were created
	pandas.DataFrame: a dataframe with information for constructing a control file

Note

writes an instruction file and also a setup.csv to use when constructing a pest control file

The instruction files are named out_filename +".ins"

It is recommended to use the default value for gw_filename or sw_filename.

This is the companion function of gw_utils.apply_mtlist_budget_obs().

`setup_sfr_obs(sfr_out_file, seg_group_dict=None, ins_file=None, model=None, include_path=False)`

setup observations using the sfr ASCII output file. Setups the ability to aggregate flows for groups of segments. Applies only flow to aquier and flow out.

Parameters:

Name	Type	Description	Default
`sft_out_file`	`str`	the name and path to an existing SFR output file	required
`seg_group_dict`	`dict`	a dictionary of SFR segments to aggregate together for a single obs. the key value in the dict is the base observation name. If None, all segments are used as individual observations. Default is None	`None`
`model`	`flopy.mbase`	a flopy model. If passed, the observation names will have the datetime of the observation appended to them. If None, the observation names will have the stress period appended to them. Default is None.	`None`
`include_path`	`bool`	flag to prepend sfr_out_file path to sfr_obs.config. Useful for setting up process in separate directory for where python is running.	`False`

Returns:

Type	Description
	pandas.DataFrame: dataframe of observation name, simulated value and group.

Note

This is the companion function of gw_utils.apply_sfr_obs().

This function writes "sfr_obs.config" which must be kept in the dir where "gw_utils.apply_sfr_obs()" is being called during the forward run

`setup_sfr_reach_obs(sfr_out_file, seg_reach=None, ins_file=None, model=None, include_path=False)`

setup observations using the sfr ASCII output file. Setups sfr point observations using segment and reach numbers.

Parameters:

Name	Type	Description	Default
`sft_out_file`	`str`	the path and name of an existing SFR output file	required
`seg_reach`	`varies`	a dict, or list of SFR [segment,reach] pairs identifying locations of interest. If `dict`, the key value in the dict is the base observation name. If None, all reaches are used as individual observations. Default is None - THIS MAY SET UP A LOT OF OBS!	`None`
`model`	`flopy.mbase`	a flopy model. If passed, the observation names will have the datetime of the observation appended to them. If None, the observation names will have the stress period appended to them. Default is None.	`None`
`include_path`	`bool`	a flag to prepend sfr_out_file path to sfr_obs.config. Useful for setting up process in separate directory for where python is running.	`False`

Returns:

Type	Description
	`pd.DataFrame`: a dataframe of observation names, values, and groups

Note

This is the companion function of gw_utils.apply_sfr_reach_obs().

This function writes "sfr_reach_obs.config" which must be kept in the dir where "apply_sfr_reach_obs()" is being called during the forward run

`setup_sfr_reach_parameters(nam_file, model_ws='.', par_cols=['strhc1'])`

Setup multiplier parameters for reach data, when reachinput option is specified in sfr.

Parameters:

Name	Type	Description	Default
`nam_file`	`str`	MODFLOw name file. DIS, BAS, and SFR must be available as pathed in the nam_file. Optionally, `nam_file` can be an existing `flopy.modflow.Modflow`.	required
`model_ws`	`str`	model workspace for flopy to load the MODFLOW model from	`'.'`
`par_cols`	[`str`]	a list of segment data entries to parameterize	`['strhc1']`
`tie_hcond`	`bool`	flag to use same mult par for hcond1 and hcond2 for a given segment. Default is `True`.	required
`include_temporal_pars`	[`str`]	list of spatially-global multipliers to set up for each stress period. Default is None	required

Returns:

Type	Description
	pandas.DataFrame: a dataframe with useful parameter setup information

Note

Similar to gw_utils.setup_sfr_seg_parameters(), method will apply params to sfr reachdata

Can load the dis, bas, and sfr files with flopy using model_ws. Or can pass a model object (SFR loading can be slow)

This is the companion function of gw_utils.apply_sfr_reach_parameters() Skips values = 0.0 since multipliers don't work for these

`setup_sfr_seg_parameters(nam_file, model_ws='.', par_cols=None, tie_hcond=True, include_temporal_pars=None)`

Setup multiplier parameters for SFR segment data.

Parameters:

Name	Type	Description	Default
`nam_file`	`str`	MODFLOw name file. DIS, BAS, and SFR must be available as pathed in the nam_file. Optionally, `nam_file` can be an existing `flopy.modflow.Modflow`.	required
`model_ws`	`str`	model workspace for flopy to load the MODFLOW model from	`'.'`
`par_cols`	[`str`]	a list of segment data entries to parameterize	`None`
`tie_hcond`	`bool`	flag to use same mult par for hcond1 and hcond2 for a given segment. Default is `True`.	`True`
`include_temporal_pars`	[`str`]	list of spatially-global multipliers to set up for each stress period. Default is None	`None`

Returns:

Type	Description
	pandas.DataFrame: a dataframe with useful parameter setup information

Note

This function handles the standard input case, not all the cryptic SFR options. Loads the dis, bas, and sfr files with flopy using model_ws.

This is the companion function to gw_utils.apply_sfr_seg_parameters() . The number (and numbering) of segment data entries must consistent across all stress periods.

Writes nam_file +"backup.sfr" as the backup of the original sfr file Skips values = 0.0 since multipliers don't work for these

`setup_sft_obs(sft_file, ins_file=None, start_datetime=None, times=None, ncomp=1)`

writes a post-processor and instruction file for a mt3d-usgs sft output file

Parameters:

Name	Type	Description	Default
`sft_file`	`str`	path and name of an existing sft output file (ASCII)	required
`ins_file`	`str`	the name of the instruction file to create. If None, the name is `sft_file`+".ins". Default is `None`.	`None`
`start_datetime`	`str`	a pandas.to_datetime() compatible str. If not None, then the resulting observation names have the datetime suffix. If None, the suffix is the output totim. Default is `None`.	`None`
`times`	[`float`]	a list of times to make observations for. If None, all times found in the file are used. Default is None.	`None`
`ncomp`	`int`	number of components in transport model. Default is 1.	`1`

Returns:

Type	Description
	pandas.DataFrame: a dataframe with observation names and values for the sft simulated
	concentrations.

Note

This is the companion function to gw_utils.apply_sft_obs().

`write_hfb_template(m)`

write a template file for an hfb (yuck!)

Parameters:

Name	Type	Description	Default
`m`	`flopy.modflow.Modflow`	a model instance with an HFB package	required

Returns: tuple containing

 - **str**: name of the template file that was created

 - **pandas.DataFrame**: a dataframe with use control file info for the
   HFB parameters

`write_hfb_zone_multipliers_template(m)`

write a template file for an hfb using multipliers per zone (double yuck!)

Parameters:

Name	Type	Description	Default
`m`	`flopy.modflow.Modflow`	a model instance with an HFB package	required

Returns:

Type	Description
	tuple containing
	dict: a dictionary with original unique HFB conductivity values and their corresponding parameter names
	str: the template filename that was created