chump.objects.mobject2d

set the column name for site/well ID

apply scaling value a_scale to all values.

apply offset value a_offset to all values.

Filter data with layers larger than or euqal to minlayer.

Filter data with layers smaller than or euqal to maxlayer.

apply a function over the layers.

Example:

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

Filter data with time larger than or euqal to mintime.

Filter data with time smaller than or euqal to maxtime.

apply a function over time.

Example:

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

if transpose is true, apply transpose.

set external table object(s) to join this object.

Example:

>>> exttable = {csv='wells'}
>>> exttable = [{csv='NEwells'},{csv='KSwells'},{csv='COwells'}]

drop values not in limits.

Example:

>>> limits = [1000, 5000]

drop the absolute values not in abslimits.

Example:

>>> abslimits = [1, 1e10]

drop the well/site whose value count is smaller than countlimit.

rename index or columns.

Example:

>>> rename = {columns={qaq='Simulated'}}

replace value.

Example:

>>> replace = {1e30 = nan}

filter the columns of a DataFrame with a boolean expression. see https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html.

Example:

>>> filter = 'obsval > 1000'

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

filter a subset of data that will be plotted differernt. also see filter. The plotarg for the data subset is defined by plotarg_exclude.

Examples:

>>> exclude         = 'obsweight == 0'
>>> plotarg_exclude = {color = 'grey', alpha = 0.5}

plot arguments for the excluded data.

drop specified labels from rows or columns, see https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html.

Example:

>>> drop = {columns=['colA','colB']}

Fill NA/NaN values using the specified method., see https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html.

Example:

>>> fillna = 0
>>> fillna = {value=0}

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

Example:

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

provide rolling window calculations. see https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html.

Example:

>>> rolling = {window=12, func='sum'}

subtract values of another object.

Example:

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

add values of another object.

Example:

>>> add = {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}

concatenate data from other objects with current object.

Examples:

>>> concat = 'head1'
>>> concat = ['head1', 'head2']

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.

perform dataframe operation.

Example:

>>> dfopts = [{diff={}},{sub={other={table='avglevel'}}}]

write the data to a CSV file.

Example:

>>> to_csv = 'head.csv'

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:]

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]))

if second_y is true, the data will be plotted on secondary (on the right) Y axis.

Example:

>>> second_y = true