DSSATTools.filex

This module implement the sections of the DSSAT FileX as python objects. All sections are implemented, excepting the enviromental modifications section. Environmental modifications can be easily implemented by modifying the Weather component of each experiment. One section object represents a single factor level in the experiment.

Each section is defined using the same parameter names (lowercase) of the DSSAT FileX. For example, planting date is defined as follow:

>>> planting = Planting(
>>>     pdate=date(1980, 6, 17), ppop=18, ppoe=18,
>>>     plme='S', plds='R', plrs=45, plrd=0, pldp=5
>>> )

Sections that include a schedule or soil profile (i.e. Initial conditions), are defined as a list of individual events or soil layers. For example, a fertilizer section with two fertilizer events is defined as follows:

>>> fertilizer = Fertilizer(table=[
>>>     FertilizerEvent(
>>>         fdate=date(1980, 7, 4), fmcd='FE005', fdep=5,
>>>         famn=80, facd='AP002'
>>>     ),
>>>     FertilizerEvent(
>>>         fdate=date(1980, 8, 7), fmcd='FE005', fdep=5,
>>>         famn=80, facd='AP002'
>>>     )
>>> ])

Other sections based on events are irrigation, residue, chemical, and tillage.

Note that the Fertilizer object is initialized by passing a list of FertilizerEvent objects in the ‘table’ parameter. The table parameter also accepts DataFrames, only if the column names of that DataFrame match the parameters for the individual event or layer object. Next is an example of this for the initial conditions section:

>>> initial_conditions = InitialConditions(
>>>        pcr='SG', icdat=date(1980, 6, 1), icrt=500, icnd=0,
>>>        icrn=1, icre=1, icres=1300, icren=.5, icrep=0, icrip=100, icrid=10,
>>>        table=pd.DataFrame([
>>>            (10, .06, 2.5, 1.8),
>>>            (22, .06, 2.5, 1.8),
>>>            (52, .195, 3., 4.5),
>>>            (82, .21, 3.5, 5.0),
>>>            (112, 0.2, 2., 2.0),
>>>            (142, 0.2, 1., 0.7),
>>>            (172, 0.2, 1., 0.6),
>>>        ], columns=['icbl', 'sh2o', 'snh4', 'sno3'])
>>>    )

Note that InitialConditions have more parameters besides the table parameter. The column names of the passed DataFrame are the same as the parameters for the InitialConditionsLayer class. The soil analysis section is the other section based on soil layers.

The field and cultivar sections are the only sections that need other DSSATTools objects as input parameters. In this section, the ‘wsta’ must be a WeatherStation object, and the ‘id_soil’ must be a SoilProfile object.

>>> field = Field(
>>>        id_field='ITHY0001', wsta=weather_station, flob=0, fldt='DR000', 
>>>        fldd=0, flds=0, id_soil=soil
>>>     )

‘wsta’ and ‘id_soil’ also receives weather station and soil profile ids as strings. However, this wouldn’t make sense in the context of running DSSAT using this package.

The cultivar can be defined either using the Cultivar class, or by directly using the one of the classes defined in DSSATTools.crop.

>>> cultivar = Sorghum('IB0026')
>>> cultivar = Cultivar(cr='SG', ingeno='IB0026', cname='CSH-1')

These two definitions will yield the same result. However, when the cultivar is defined using the crop class it is posible to modify the cultivar and ecotype coefficients. More information is found at the DSSATTools.crop module documentation.

Finally, the simulations controls is created by using the SimulationsControls class. Each sub-section of the simulation controls sections is created using its own class. Next example shows how to create the simulations controls defining only the general options.

>>> simulation_controls = SimulationControls(
>>>     general=SCGeneral(sdate=date(1980, 6, 1))
>>> )

The sections can be created from an existing FileX using the read_filex function. That function will return a dictionary, mapping each treatment to its correspondent section definitions. The next example reads an existing FileX, and then assigns the first treatment to the treatment variable. In this case, treatment is a dictionary mapping each section name to its python object.

>>> treatments = read_filex("Maize/BRPI0202.MZX")
>>> treatment = treatments[1]

The create_filex function returns the string of the FileX for for the passed sections defined as their python objects.

Functions

create_filex(field, cultivar, planting, ...)	Returns the FileX as a string
get_header_range(l, h, pars_fmt)	Get variable start and index in the header line
read_filex(filexpath)	It reads a FILEX and returns a dictionary where the keys are the treatments, and the values are the level objects.

Classes

AMHarvest(hfrst, hlast[, hpcnp, hpcnr, ...])
AMIrrigation([imdep, ithrl, ithru, iroff, ...])
AMNitrogen([nmdep, nmthr, namnt, ncode, naoff])
AMPlanting(pfrst, plast[, ph2ol, ph2ou, ...])
AMResidues([ripcn, rtime, ridep])
Chemical([table])
ChemicalEvent(cdate, chcod, chamt, chme[, ...])
Cultivar(cr, ingeno[, cname])
Fertilizer([table])
FertilizerEvent(fdate, fmcd, facd, fdep, famn)
Field(id_field, wsta, id_soil[, flsa, flob, ...])	Class to define a single field
Harvest(hdate[, hstg, hcom, hsize, hpc, ...])
InitialConditions(pcr[, icdat, icrt, icnd, ...])
InitialConditionsLayer(icbl, sh2o[, snh4, sno3])
Irrigation([table, efir, idep, ithr, iept, ...])
IrrigationEvent(idate, irval[, irop])
Mow([table])
MowEvent(date, mow, rsplf, mvs, rsht)
Planting(pdate, ppop, plrs[, ppoe, plds, ...])	Class to define a single planting event
Residue([table])
ResidueEvent(rdate, rcod, ramt, resn, resp, ...)
SCGeneral(sdate[, nyers, nreps, start, ...])
SCManagement([plant, irrig, ferti, resid, harvs])
SCMethods([wther, incon, light, evapo, ...])
SCOptions([water, nitro, symbi, phosp, ...])
SCOutputs([fname, ovvew, sumry, fropt, ...])
SimulationControls(general[, options, ...])
SoilAnalysis(sadat, table[, smhb, smpx, ...])
SoilAnalysisLayer(sabl[, sadm, saoc, sani, ...])
Tillage([table])
TillageEvent(tdate[, timpl, tdep, tname])
Treatment(**kwargs)	This class is not suposed to be used by users.