#!/usr/bin/python """ mapstack.py - process mapstacks -------------------------------- Usage: mapstack -c inifile -C casename --clone clonemap -c name of the config file (in the case directory) """ import numpy import os import os.path import shutil, glob import getopt from wflow.wf_DynamicFramework import * from wflow.wflow_adapt import * # import scipy def usage(*args): sys.stdout = sys.stderr for msg in args: print msg print __doc__ sys.exit(0) class WflowModel(DynamicModel): """ The user defined model class. This is your work! """ def __init__(self, cloneMap, Dir, RunDir, configfile): """ *Required* The init function **must** contain what is shown below. Other functionality may be added by you if needed. """ DynamicModel.__init__(self) setclone(os.path.join(Dir, cloneMap)) self.runId = RunDir self.caseName = Dir self.Dir = Dir self.configfile = configfile def parameters(self): """ List all the parameters (both static and forcing here). Use the wf_updateparameters() function to update them in the initial section (static) and the dynamic section for dynamic parameters and forcing date. Possible parameter types are: + staticmap: Read at startup from map + statictbl: Read at startup from tbl, fallback to map (need Landuse, Soil and TopoId (subcatch) maps! + timeseries: read map for each timestep + monthlyclim: read a map corresponding to the current month (12 maps in total) + dailyclim: read a map corresponding to the current day of the year + hourlyclim: read a map corresponding to the current hour of the day (24 in total) :return: List of modelparameters """ modelparameters = [] # modelparameters.append(self.ParamType(name="locMap",stack='inLoc.map',type="staticmap",default=0.0,verbose=True,lookupmaps=[])) return modelparameters def stateVariables(self): """ *Required* Returns a list of state variables that are essential to the model. This list is essential for the resume and suspend functions to work. This function is specific for each model and **must** be present. This is where you specify the state variables of you model. If your model is stateless this function must return and empty array (states = []) In the simple example here the TSoil variable is a state for the model. """ states = [] return states def supplyCurrentTime(self): """ *Optional* Supplies the current time in seconds after the start of the run This function is optional. If it is not set the framework assumes the model runs with daily timesteps. Output: - time in seconds since the start of the model run """ return self.currentTimeStep() * int( configget(self.config, "model", "timestepsecs", "86400") ) def initial(self): """ *Required* Initial part of the model, executed only once. It reads all static model information (parameters) and sets-up the variables used in modelling. This function is required. The contents is free. However, in order to easily connect to other models it is advised to adhere to the directory structure used in the other models. """ #: pcraster option to calculate with units or cells. Not really an issue #: in this model but always good to keep in mind. setglobaloption("unittrue") self.timestepsecs = int( configget(self.config, "model", "timestepsecs", "86400") ) self.basetimestep = 86400 self.inTSS = configget(self.config, "model", "intss", "intss.tss") self.interpolmethod = configget(self.config, "model", "interpolmethod", "pol") # Reads all parameter from disk self.wf_updateparameters() def resume(self): """ *Required* This function is required. Read initial state maps (they are output of a previous call to suspend()). The implementation shown here is the most basic setup needed. """ pass def suspend(self): """ *Required* Suspends the model to disk. All variables needed to restart the model are saved to disk as pcraster maps. Use resume() to re-read them This function is required. """ self.wf_suspend(self.Dir) def dynamic(self): """ *Required* This is where all the time dependent functions are executed. Time dependent output should also be saved here. """ self.logger.debug("Processing step: " + str(self.currentTimeStep())) self.wf_updateparameters() # read the temperature map for each step (see parameters()) if hasattr(self, "locMap"): self.MapStack = timeinputscalar( os.path.join(self.caseName, self.inTSS), self.locMap ) self.MapStack = pcrut.interpolategauges(self.MapStack, self.interpolmethod) # self.MapStack = ifthen(self.locMap >= 1,self.MapStack) # The main function is used to run the program from the command line def main(argv=None): """ *Optional but needed it you want to run the model from the command line* Perform command line execution of the model. This example uses the getopt module to parse the command line options. The user can set the caseName, the runDir, the timestep and the configfile. """ global multpars caseName = "default" runId = "run_default" configfile = "mapstack.ini" _lastTimeStep = 10 _firstTimeStep = 1 timestepsecs = 86400 wflow_cloneMap = "clone.map" # This allows us to use the model both on the command line and to call # the model usinge main function from another python script. if argv is None: argv = sys.argv[1:] if len(argv) == 0: usage() return opts, args = getopt.getopt(argv, "C:S:T:c:s:R:", ["clone="]) for o, a in opts: if o == "-C": caseName = a if o == "-R": runId = a if o == "-c": configfile = a if o == "-s": timestepsecs = int(a) if o == "-T": _lastTimeStep = int(a) if o == "-S": _firstTimeStep = int(a) if o == "--clone": wflow_cloneMap = a if len(opts) <= 1: usage() myModel = WflowModel(wflow_cloneMap, caseName, runId, configfile) dynModelFw = wf_DynamicFramework( myModel, _lastTimeStep, firstTimestep=_firstTimeStep ) dynModelFw.createRunId(NoOverWrite=False, level=logging.DEBUG) dynModelFw._runInitial() dynModelFw._runResume() dynModelFw._runDynamic(_firstTimeStep, _lastTimeStep) dynModelFw._runSuspend() dynModelFw._wf_shutdown() if __name__ == "__main__": main()