chump.objects.dataobjects.tseries

set the file path of this data.

sheet set the sheet name when reading data from an Excel workbook. Default is None.

a string or a list of string defining the value columns in the data source.

Example:

>>> valcol = 'Simulated'
>>> valcol = ['Observed', 'Simulated']

set the plotting type of the time series, must be 'bar' or 'line'. Default is 'line'.

Example:

>>> plottype = 'bar'

plotting argument for the object

a dictionary to set additonal plotting arguments for different data columns.

Example:

>>> plotargs = {Observed={color='b'}, Simulated={color='r'}}

a dictionary containing a geospatial data object shp or CSV and table to define the locations of the plotting site/well.

Example:

>>> location = {shp='huc12'}

minimum number of value entry. For sites/wells with observation counts less than this number will be excluded. Default is 0.

Example:

>>> nmin = 5

a list of idcol and valcol names. if set, it stacks the columns in the table . it can be used for MF6 OBS outputs

Example:

>>> stack = ['WellName', 'SimulatedHead']

apply aggregation function based on the site/well id. Default is None.

Example:

>>> aggfunc = 'sum'

apply scaling value a_scale to all values.

apply offset value a_offset to all values.

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

drop values not in limits.

Example:

>>> limits = [1000, 5000]

drop the absolute values not in abslimits.

Example:

>>> abslimits = [1, 1e10]

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

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

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]))