The Utilities Module
- sfrmaker.utils.arbolate_sum(segment, lengths, routing, starting_asums=None)[source]
Compute the total length of all tributaries upstream from segment, including that segment, using the supplied lengths and routing connections.
- Parameters:
- segmentint or list of ints
Segment or node number that is also a key in the lengths and routing dictionaries.
- lengthsdict
Dictionary of lengths keyed by segment (node) numbers, including those in segment.
- routingdict
Dictionary describing routing connections between segments (nodes); values represent downstream connections.
- starting_asumsdict
Option to supply starting arbolate sum values for any of the segments. By default, None.
- Returns:
- asumfloat or dict
Arbolate sums for each segment.
- sfrmaker.utils.assign_layers(reach_data, botm_array, strtop_col='strtop', strthick_col='strthick', idomain=None, pad=1.0, inplace=False)[source]
- Assigns the appropriate layer for each SFR reach,
based on cell bottoms at location of reach.
- Parameters:
- reach_dataDataFrame
Table of reach information, similar to SFRData.reach_data
- botm_arrayndarary
3D numpy array of layer bottom elevations
- strtop_colstr
Column name of streambed top elevations in reach_data. by default, ‘strtop’.
- strthick_colstr
Column name of streambed bottom thickness in reach_data. by default, ‘strthick’
- idomainndarray (optional)
3D integer array of MODFLOW ibound or idomain values. Values > 0 are considered active. Reaches in cells with values < 1 will be moved to the highest active cell if possible. by default, None (all cells are assumed to be active)
- padscalar
Minimum distance that streambed bottom must be above layer bottom. When determining the layer or whether the streambed bottom is below the model bottom, streambed bottom - pad is used. Similarly, when lowering the model bottom to accomodate the streambed bottom, a value of streambed bottom - pad is used.
- inplacebool
If True, operate on reach_data and botm_array, otherwise, return 1D array of layer numbers and 2D array of new model botm elevations
- Returns:
- (if inplace=True)
- layers1D array of layer numbers
- new_model_botmsndarray or None
New model bottom elevations. If no cell bottoms require adjustment, None is returned. If one or more cells require adjustment and
idomain=None(not supplied), a 2D array is returned, for backward compatibility with previous versions of SFRmaker. Otherwise, a 3D array of new bottom elevations is returned. In the future, a 3D array will always be returned.
Notes
Streambed bottom = strtop - strthick When multiple reaches occur in a cell, the lowest streambed bottom is used in determining the layer and any corrections to the model bottom.
- sfrmaker.utils.get_input_arguments(kwargs, function, warn=False)[source]
Return subset of keyword arguments in kwargs dict that are valid parameters to a function or method.
- Parameters:
- kwargsdict (parameter names, values)
- functionfunction of class method
- Returns:
- input_kwargsdict
- sfrmaker.utils.get_layer(botm_array, i, j, elev)[source]
Return the layers for elevations at i, j locations.
- Parameters:
- botm_array3D numpy array of layer bottom elevations
- iscaler or sequence
row index (zero-based)
- jscaler or sequence
column index
- elevscaler or sequence
elevation (in same units as model)
- Returns:
- knp.ndarray (1-D) or scalar
zero-based layer index
- sfrmaker.utils.get_method_args_values(method, exclude=None)[source]
Get arguments for a function or method and their default values.
- Parameters:
- methodfunction or method
- Returns:
- argsdict
- sfrmaker.utils.update(d, u)[source]
Recursively update a dictionary of varying depth d with items from u. from: https://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth
- sfrmaker.utils.which(program)[source]
Check for existance of executable. https://stackoverflow.com/questions/377017/test-if-executable-exists-in-python
- sfrmaker.utils.width_from_arbolate_sum(asum, a=0.1193, b=0.5032, minimum_width=1.0, input_units='meters', output_units='meters')[source]
Estimate stream width from arbolate sum, using a power law regression comparing measured channel widths to arbolate sum. (after Leaf, 2020 and Feinstein et al. 2010, Appendix 2, p 266.)
\[width = unit\_conversion * a * {asum_{(meters)}}^b\]- Parameters:
- asum: float or 1D array
Arbolate sum in the input units.
- afloat
Multiplier parameter. Literature values: Feinstein et al (2010; Lake MI Basin): 0.1193 Leaf (2020; Mississippi Embayment): 0.0592
- bfloat
Exponent in power law relationship. Literature values: Feinstein et al (2010; Lake MI Basin): 0.5032 Leaf (2020; Mississippi Embayment): 0.5127
- minimum_widthfloat
Minimum width to be returned. By default, 1.
- input_unitsstr, any length unit; e.g. {‘m’, ‘meters’, ‘km’, etc.}
Length unit of asum
- output_unitsstr, any length unit; e.g. {‘m’, ‘meters’, ‘ft’, etc.}
Length unit of output width
- Returns:
- width: float
Estimated width in feet
Notes
The original relationship described by Feinstein et al (2010) was for arbolate sum in meters and output widths in feet. The \(u\) values above reflect this unit conversion. Therefore, the \(unit\_conversion\) parameter above includes conversion of the input to meters, and output from feet to the specified output units.
NaN arbolate sums are filled with the specified
minimum_width.References
see References Cited
Examples
Original equation from Feinstein et al (2010), for arbolate sum of 1,000 km: >>> width = width_from_arbolate_sum(1000, 0.1193, 0.5032, input_units=’kilometers’, output_units=’feet’) >>> round(width, 2) 124.69