chump.objects.dataobjects.mfbin

set the file path of the file

set the model object

Example:

>>> model = {mf='gwf'}

apply scaling value a_scale to all values.

apply offset value a_offset to all values.

drop values not in limits.

Example:

>>> limits = [1000, 5000]

drop the absolute values not in abslimits.

Example:

>>> abslimits = [1, 1e10]

Filter data with layers larger than or euqal to minlayer.

Filter data with layers smaller than or euqal to maxlayer.

Filter data with time larger than or euqal to mintime.

Filter data with time smaller than or euqal to maxtime.

apply a function over the layers.

Example:

>>> lfunc = 'max'
calculate the maximum values over the layers

apply a function over time.

Example:

>>> tfunc = 'mean'
calculate the mean values over time

resample data to other time frequency. see https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html.

Example:

>>> resample = {rule='7D', func='mean'}

add values of another object.

Example:

>>> add = {other={csv='obslevel'}, fill_value=0}

subtract values of another object.

Example:

>>> subtract = {other={csv='obslevel'}, fill_value=0}

multiply values of another object.

Example:

>>> mul = {other={csv='obslevel'}, fill_value=1}

divide values of another object.

Example:

>>> div = {other={csv='obslevel'}, fill_value=1}

set the object as a result of a mathmatic expression. The expression will be evaluated by the Python's eval function.

Examples:

>>> [mfbin.simdiff]
>>> calculate = '(simheadA - simHeadB) - (simheadC - simHeadD)'

where simheadA, simheadB, simheadC and simheadD are names of other mfbin objects.

call a function in an external Python script after reading the data. extfunc sets the file name of the script. This script needs to be placed in the working directory. The function name must be "extfunc" and the first argument is the object. The dataframe of the object can be accessed through obj.dat where obj is the argument name in your function.

Example:

>>> # code inside the external script
>>> def extfunc(obj):
>>>     # extract results for the final 12 month
>>>     obj.dat = obj.dat.iloc[-12:]

set a scaling factor, default is 1.0. final time values = original time values * t_scale + t_offset

Example:

>>> t_scale = 86400 # convert days to seconds

set a time offset, default is 0.0. final time values = original time values * t_scale + t_offset

Example:

>>> t_offset = -365 # move time ahead by one year

plotting type of the grid data, must be 'contour', 'grid', 'cgrid', 'colorgrid', 'colormesh' or 'colorflood'.

plotting argument for the object

the color scale used for color grid, either 'Normalize' or 'LogNorm', default is 'Normalize' which is linear scale color map.

whether to plot the legend of this object, default is false.

Example:

>>> legend = true

the color bar argument for color grid plot.

Example:

>>> colorbararg = {shrink=0.88, pad=0.01, label='Simulated groundwater elevation (feet)'}

the contour label argument for contour plot.

Example:

>>> clabel = {}

levels used for contour plots.

Example:

>>> levels = [0,1,2,5,10,20,50,100,200,300,500]

level intervel for contour plots.

Example:

>>> dlevel = 10

set data label/name used in legend.

set writedata to true to export the data. The data will saved as a CSV or SHP file.

if true, write shapefile of the object.

Example:

>>> writeshp = true

call a function in an external Python script to write data. writefunc sets the file name of the script. This script needs to be placed in the working directory. The function name must be "writefunc" and the first argument is the object. The dataframe of the object can be accessed through obj.dat where obj is the argument name in your function.

Example:

>>> # code inside the external script
>>> import numpy as np
>>> def writefunc(obj):
>>>     # write head results as arrray
>>>     nrow = 50
>>>     ncol = 100
>>>     for (time,layer), r in obj.dat.iterrows():
>>>         np.savetxt(f'time{time}_layer{layer}.dat', r.reshape([nrow, ncol]))