def calibration_objectives(cali_obj, ind): """Evaluate the objectives of given individual. """ cali_obj.ID = ind.id model_args = cali_obj.model.ConfigDict model_args.setdefault('calibration_id', -1) model_args['calibration_id'] = ind.id model_obj = MainSEIMS(args_dict=model_args) # Set observation data to model_obj, no need to query database model_obj.SetOutletObservations(ind.obs.vars, ind.obs.data) # Execute model model_obj.SetMongoClient() model_obj.run() time.sleep(0.1) # Wait a moment in case of unpredictable file system error # read simulation data of the entire simulation period (include calibration and validation) if model_obj.ReadTimeseriesSimulations(): ind.sim.vars = model_obj.sim_vars[:] ind.sim.data = deepcopy(model_obj.sim_value) else: model_obj.clean(calibration_id=ind.id) model_obj.UnsetMongoClient() return ind # Calculate NSE, R2, RMSE, PBIAS, and RSR, etc. of calibration period ind.cali.vars, ind.cali.data = model_obj.ExtractSimData( cali_obj.cfg.cali_stime, cali_obj.cfg.cali_etime) ind.cali.sim_obs_data = model_obj.ExtractSimObsData( cali_obj.cfg.cali_stime, cali_obj.cfg.cali_etime) ind.cali.objnames, \ ind.cali.objvalues = model_obj.CalcTimeseriesStatistics(ind.cali.sim_obs_data, cali_obj.cfg.cali_stime, cali_obj.cfg.cali_etime) if ind.cali.objnames and ind.cali.objvalues: ind.cali.valid = True # Calculate NSE, R2, RMSE, PBIAS, and RSR, etc. of validation period if cali_obj.cfg.calc_validation: ind.vali.vars, ind.vali.data = model_obj.ExtractSimData( cali_obj.cfg.vali_stime, cali_obj.cfg.vali_etime) ind.vali.sim_obs_data = model_obj.ExtractSimObsData( cali_obj.cfg.vali_stime, cali_obj.cfg.vali_etime) ind.vali.objnames, \ ind.vali.objvalues = model_obj.CalcTimeseriesStatistics(ind.vali.sim_obs_data, cali_obj.cfg.vali_stime, cali_obj.cfg.vali_etime) if ind.vali.objnames and ind.vali.objvalues: ind.vali.valid = True # Get timespan ind.io_time, ind.comp_time, ind.simu_time, ind.runtime = model_obj.GetTimespan( ) # delete model output directory for saving storage model_obj.clean(calibration_id=ind.id) model_obj.UnsetMongoClient() return ind
def main(): cur_path = UtilClass.current_path(lambda: 0) SEIMS_path = os.path.abspath(cur_path + '../../..') model_paths = ModelPaths(SEIMS_path, 'dianbu2', 'demo_dianbu2_model') scenario_id = 0 seims_obj = MainSEIMS(model_paths.bin_dir, model_paths.model_dir, scenario_id=scenario_id) seims_obj.run()
def main(): cur_path = UtilClass.current_path(lambda: 0) SEIMS_path = os.path.abspath(cur_path + '../../..') model_paths = ModelPaths(SEIMS_path, 'dianbu2', 'demo_dianbu2_model') scenario_id = 0 seims_obj = MainSEIMS(model_paths.bin_dir, model_paths.model_dir, scenario_id=scenario_id) seims_obj.run()
def calibration_objectives(cali_obj, ind): """Evaluate the objectives of given individual. """ cali_obj.ID = ind.id model_args = cali_obj.model.ConfigDict model_args.setdefault('calibration_id', -1) model_args['calibration_id'] = ind.id model_obj = MainSEIMS(args_dict=model_args) # Set observation data to model_obj, no need to query database model_obj.SetOutletObservations(ind.obs.vars, ind.obs.data) # Execute model model_obj.run() time.sleep(0.1) # Wait a moment in case of unpredictable file system error # read simulation data of the entire simulation period (include calibration and validation) if model_obj.ReadTimeseriesSimulations(): ind.sim.vars = model_obj.sim_vars[:] ind.sim.data = deepcopy(model_obj.sim_value) else: return ind # Calculate NSE, R2, RMSE, PBIAS, and RSR, etc. of calibration period ind.cali.vars, ind.cali.data = model_obj.ExtractSimData(cali_obj.cfg.cali_stime, cali_obj.cfg.cali_etime) ind.cali.sim_obs_data = model_obj.ExtractSimObsData(cali_obj.cfg.cali_stime, cali_obj.cfg.cali_etime) ind.cali.objnames, \ ind.cali.objvalues = model_obj.CalcTimeseriesStatistics(ind.cali.sim_obs_data, cali_obj.cfg.cali_stime, cali_obj.cfg.cali_etime) if ind.cali.objnames and ind.cali.objvalues: ind.cali.valid = True # Calculate NSE, R2, RMSE, PBIAS, and RSR, etc. of validation period if cali_obj.cfg.calc_validation: ind.vali.vars, ind.vali.data = model_obj.ExtractSimData(cali_obj.cfg.vali_stime, cali_obj.cfg.vali_etime) ind.vali.sim_obs_data = model_obj.ExtractSimObsData(cali_obj.cfg.vali_stime, cali_obj.cfg.vali_etime) ind.vali.objnames, \ ind.vali.objvalues = model_obj.CalcTimeseriesStatistics(ind.vali.sim_obs_data, cali_obj.cfg.vali_stime, cali_obj.cfg.vali_etime) if ind.vali.objnames and ind.vali.objvalues: ind.vali.valid = True # Get timespan ind.io_time, ind.comp_time, ind.simu_time, ind.runtime = model_obj.GetTimespan() # delete model output directory for saving storage shutil.rmtree(model_obj.output_dir) return ind
def main(): wtsd_name = get_watershed_name( 'Specify watershed name to run SEIMS-based model.') if wtsd_name not in list(DEMO_MODELS.keys()): print('%s is not one of the available demo watershed: %s' % (wtsd_name, ','.join(list(DEMO_MODELS.keys())))) exit(-1) cur_path = UtilClass.current_path(lambda: 0) SEIMS_path = os.path.abspath(cur_path + '../../..') model_paths = ModelPaths(SEIMS_path, wtsd_name, DEMO_MODELS[wtsd_name]) scenario_id = 0 seims_obj = MainSEIMS(model_paths.bin_dir, model_paths.model_dir, scenario_id=scenario_id) seims_obj.run()
def main(): wtsd_name = get_watershed_name( 'Specify watershed name to run SEIMS-based model.') if wtsd_name not in list(DEMO_MODELS.keys()): print('%s is not one of the available demo watershed: %s' % (wtsd_name, ','.join(list(DEMO_MODELS.keys())))) exit(-1) cur_path = UtilClass.current_path(lambda: 0) SEIMS_path = os.path.abspath(cur_path + '../../..') model_paths = ModelPaths(SEIMS_path, wtsd_name, DEMO_MODELS[wtsd_name]) runmodel_cfg = write_runmodel_config_file(model_paths, 'runmodel.ini') seims_obj = MainSEIMS(args_dict=runmodel_cfg.ConfigDict) seims_obj.run() for l in seims_obj.runlogs: print(l)
def execute_seims_model(self): """Run SEIMS for evaluating environmental effectiveness. If execution fails, the `self.economy` and `self.environment` will be set the worst values. """ print_message('Scenario ID: %d, running SEIMS model...' % self.ID) seims_obj = MainSEIMS(self.bin_dir, self.model_dir, self.nthread, self.lyrmethod, self.hostname, self.port, self.ID) self.modelrun = seims_obj.run() return self.modelrun
def execute_seims_model(self): """Run SEIMS for evaluating environmental effectiveness. If execution fails, the `self.economy` and `self.environment` will be set the worst values. """ print_message('Scenario ID: %d, running SEIMS model...' % self.ID) seims_obj = MainSEIMS(self.bin_dir, self.model_dir, self.nthread, self.lyrmethod, self.hostname, self.port, self.ID) self.modelrun = seims_obj.run() return self.modelrun
class Scenario(object): """Base class of Scenario Analysis. Attributes: ID(integer): Unique ID in BMPScenario database -> BMP_SCENARIOS collection eval_timerange(float): Simulation time range, read from MongoDB, the unit is year. economy(float): Economical effectiveness, e.g., income minus expenses environment(float): Environmental effectiveness, e.g., reduction rate of soil erosion gene_num(integer): The number of genes of one chromosome, i.e., an individual gene_values(list): BMP identifiers on each location of gene. The length is gen_num. bmp_items(dict): BMP configuration items that can be imported to MongoDB directly. The key is `bson.objectid.ObjectId`, the value is scenario item dict. rules(boolean): Config BMPs randomly or rule-based. modelrun(boolean): Has SEIMS model run successfully? """ def __init__(self, cfg): # type: (SAConfig) -> None """Initialize.""" self.ID = -1 self.eval_timerange = 1. # unit: year self.economy = 0. self.environment = 0. self.worst_econ = cfg.worst_econ self.worst_env = cfg.worst_env self.gene_num = 0 self.gene_values = list() # type: List[int] self.bmp_items = dict() self.rule_mtd = cfg.bmps_cfg_method self.bmps_info = cfg.bmps_info self.bmps_retain = cfg.bmps_retain self.eval_info = cfg.eval_info self.export_sce_txt = cfg.export_sce_txt self.export_sce_tif = cfg.export_sce_tif self.scenario_dir = cfg.scenario_dir # predefined directories to store scenarios related # SEIMS-based model related self.modelcfg = cfg.model self.modelcfg_dict = self.modelcfg.ConfigDict self.model = MainSEIMS(args_dict=self.modelcfg_dict) self.model.SetMongoClient() self.model.ReadMongoDBData() self.scenario_db = self.model.ScenarioDBName self.model.ResetSimulationPeriod() # Reset the simulation period # Reset the starttime and endtime of the desired outputs according to evaluation period if ModelCfgFields.output_id in self.eval_info: self.model.ResetOutputsPeriod( self.eval_info[ModelCfgFields.output_id], cfg.eval_stime, cfg.eval_etime) else: print( 'Warning: No OUTPUTID is defined in BMPs_info. Please make sure the ' 'STARTTIME and ENDTIME of ENVEVAL are consistent with Evaluation period!' ) self.model.UnsetMongoClient() # Unset in time! # (Re)Calculate timerange in the unit of year dlt = cfg.eval_etime - cfg.eval_stime + timedelta(seconds=1) self.eval_timerange = (dlt.days * 86400. + dlt.seconds) / 86400. / 365. self.modelout_dir = None # determined in `execute_seims_model` based on unique scenario ID self.modelrun = False # indicate whether the model has been executed def set_unique_id(self, given_id=None): # type: (Optional[int]) -> int """Set unique ID.""" if given_id is None: self.ID = next(generate_uniqueid()) else: self.ID = given_id # Update scenario ID for self.modelcfg and self.model self.model.scenario_id = self.ID self.modelcfg.scenario_id = self.ID self.modelcfg_dict[ 'scenario_id'] = self.ID if self.modelcfg_dict else 0 return self.ID def rule_based_config(self, method, conf_rate): # type: (float, str) -> None """Config available BMPs to each gene of the chromosome by rule-based method. Virtual function that should be overridden in inherited Scenario class. """ pass def random_based_config(self, conf_rate): # type: (float) -> None """Config available BMPs to each gene of the chromosome by random-based method. Virtual function that should be overridden in inherited Scenario class. """ pass def decoding(self): """Decoding gene_values to bmp_items This function should be overridden. """ pass def export_to_mongodb(self): """Export current scenario to MongoDB. Delete the same ScenarioID if existed. """ # client = ConnectMongoDB(self.modelcfg.host, self.modelcfg.port) # conn = client.get_conn() conn = MongoDBObj.client db = conn[self.scenario_db] collection = db[DBTableNames.scenarios] try: # find ScenarioID, remove if existed. if collection.find({ 'ID': self.ID }, no_cursor_timeout=True).count(): collection.remove({'ID': self.ID}) except NetworkTimeout or Exception: # In case of unexpected raise pass for objid, bmp_item in viewitems(self.bmp_items): bmp_item['_id'] = ObjectId() collection.insert_one(bmp_item) # client.close() def export_scenario_to_txt(self): """Export current scenario information to text file. This function is better be called after `calculate_environment` and `calculate_environment` or in static method, e.g., `scenario_effectiveness`. """ if not self.export_sce_txt: return ofile = self.scenario_dir + os.path.sep + 'Scenario_%d.txt' % self.ID with open(ofile, 'w', encoding='utf-8') as outfile: outfile.write('Scenario ID: %d\n' % self.ID) outfile.write('Gene number: %d\n' % self.gene_num) outfile.write('Gene values: %s\n' % ', '.join( (repr(v) for v in self.gene_values))) outfile.write('Scenario items:\n') if len(self.bmp_items) > 0: header = list() for obj, item in viewitems(self.bmp_items): header = list(item.keys()) break outfile.write('\t'.join(header)) outfile.write('\n') for obj, item in viewitems(self.bmp_items): outfile.write('\t'.join( str(v) for v in list(item.values()))) outfile.write('\n') outfile.write( 'Effectiveness:\n\teconomy: %f\n\tenvironment: %f\n' % (self.economy, self.environment)) def export_scenario_to_gtiff(self): """Export the areal BMPs to gtiff for further analysis. This function should be overridden in inherited class. """ pass def import_from_mongodb(self, sid): """Import a specified Scenario (`sid`) from MongoDB. This function should be overridden in inherited class. Returns: True if succeed, otherwise False. """ pass def import_from_txt(self, sid): """Import a specified Scenario (`sid`) from text file. This function should be overridden in inherited class. Returns: True if succeed, otherwise False. """ pass def calculate_economy(self): """Calculate economical effectiveness, which is application specified.""" pass def calculate_environment(self): """Calculate environment effectiveness, which is application specified.""" pass def clean(self, scenario_id=None, calibration_id=None, delete_scenario=False, delete_spatial_gfs=False): """Clean the intermediate data.""" # model clean self.model.SetMongoClient() self.model.clean(scenario_id=scenario_id, calibration_id=calibration_id, delete_scenario=delete_scenario, delete_spatial_gfs=delete_spatial_gfs) self.model.UnsetMongoClient() def execute_seims_model(self): """Run SEIMS for evaluating environmental effectiveness. If execution fails, the `self.economy` and `self.environment` will be set the worst values. """ scoop_log('Scenario ID: %d, running SEIMS model...' % self.ID) self.model.scenario_id = self.ID self.modelout_dir = self.model.OutputDirectory self.model.SetMongoClient() self.model.run() self.model.UnsetMongoClient() self.modelrun = True return self.model.run_success def initialize(self, input_genes=None): # type: (Optional[List]) -> List """Initialize a scenario. Returns: A list contains BMPs identifier of each gene location. """ pass