def workflow(cfg, db):
"""
This function mainly to import measurement data to MongoDB
data type may include Q (discharge, m3/s), tn, tp, etc.
the required parameters that defined in configuration file (*.ini)
"""
if not cfg.use_observed:
return False
c_list = db.collection_names()
if not StringClass.string_in_list(DBTableNames.observes, c_list):
db.create_collection(DBTableNames.observes)
else:
db.drop_collection(DBTableNames.observes)
if not StringClass.string_in_list(DBTableNames.sites, c_list):
db.create_collection(DBTableNames.sites)
if not StringClass.string_in_list(DBTableNames.var_desc, c_list):
db.create_collection(DBTableNames.var_desc)
file_list = FileClass.get_full_filename_by_suffixes(
cfg.observe_dir, ['.txt'])
meas_file_list = []
site_loc = []
for fl in file_list:
if StringClass.is_substring('observed_', fl):
meas_file_list.append(fl)
else:
site_loc.append(fl)
ImportObservedData.data_from_txt(db, meas_file_list, site_loc,
cfg.spatials.subbsn)
return True
def workflow(cfg, main_db, clim_db):
"""Workflow"""
# 1. Find meteorology and precipitation sites in study area
thiessen_file_list = [cfg.meteo_sites_thiessen, cfg.prec_sites_thiessen]
type_list = [DataType.m, DataType.p]
if not cfg.cluster: # the entire basin
ImportHydroClimateSites.find_sites(main_db, cfg.climate_db, cfg.vecs.bsn,
FieldNames.basin, thiessen_file_list,
cfg.thiessen_field, type_list, cfg.storm_mode)
ImportHydroClimateSites.find_sites(main_db, cfg.climate_db, cfg.vecs.subbsn,
FieldNames.subbasin_id, thiessen_file_list,
cfg.thiessen_field, type_list,
cfg.storm_mode, cfg.cluster)
# 2. Import geographic information of each sites to Hydro-Climate database
c_list = clim_db.collection_names()
tables = [DBTableNames.sites, DBTableNames.var_desc]
for tb in tables:
if not StringClass.string_in_list(tb, c_list):
clim_db.create_collection(tb)
ImportHydroClimateSites.variable_table(clim_db, cfg.hydro_climate_vars)
site_m_loc = ImportHydroClimateSites.sites_table(clim_db, cfg.Meteo_sites, DataType.m)
site_p_loc = ImportHydroClimateSites.sites_table(clim_db, cfg.prec_sites, DataType.p)
# print (site_m_loc, site_p_loc)
return site_m_loc, site_p_loc
def initial_params_from_txt(cfg, maindb):
"""
import initial calibration parameters from txt data file.
Args:
cfg: SEIMS config object
maindb: MongoDB database object
"""
# delete if existed, create if not existed
c_list = maindb.collection_names()
if not StringClass.string_in_list(DBTableNames.main_parameter, c_list):
maindb.create_collection(DBTableNames.main_parameter)
else:
maindb.drop_collection(DBTableNames.main_parameter)
# create bulk operator
bulk = maindb[DBTableNames.main_parameter].initialize_ordered_bulk_op()
# read initial parameters from txt file
data_items = read_data_items_from_txt(cfg.paramcfgs.init_params_file)
field_names = data_items[0][0:]
# print (field_names)
for i, cur_data_item in enumerate(data_items):
if i == 0:
continue
# print cur_data_item
# initial one default blank parameter dict.
data_import = {ModelParamFields.name: '', ModelParamFields.desc: '',
ModelParamFields.unit: '', ModelParamFields.module: '',
ModelParamFields.value: DEFAULT_NODATA,
ModelParamFields.impact: DEFAULT_NODATA,
ModelParamFields.change: 'NC',
ModelParamFields.max: DEFAULT_NODATA,
ModelParamFields.min: DEFAULT_NODATA,
ModelParamFields.type: ''}
for k, v in data_import.items():
idx = field_names.index(k)
if cur_data_item[idx] == '':
if StringClass.string_match(k, ModelParamFields.change_ac):
data_import[k] = 0
elif StringClass.string_match(k, ModelParamFields.change_rc):
data_import[k] = 1
elif StringClass.string_match(k, ModelParamFields.change_nc):
data_import[k] = 0
elif StringClass.string_match(k, ModelParamFields.change_vc):
data_import[k] = DEFAULT_NODATA # Be careful to check NODATA when use!
else:
if MathClass.isnumerical(cur_data_item[idx]):
data_import[k] = float(cur_data_item[idx])
else:
data_import[k] = cur_data_item[idx]
bulk.insert(data_import)
# execute import operators
bulk.execute()
# create index by parameter's type and name by ascending order.
maindb[DBTableNames.main_parameter].create_index([(ModelParamFields.type, ASCENDING),
(ModelParamFields.name, ASCENDING)])
def scenario_from_texts(cfg, main_db, scenario_db):
"""Import BMPs Scenario data to MongoDB
Args:
cfg: SEIMS configuration object
main_db: climate database
scenario_db: scenario database
Returns:
False if failed, otherwise True.
"""
if not cfg.use_scernario:
return False
print ("Import BMP Scenario Data... ")
bmp_files = FileClass.get_filename_by_suffixes(cfg.scenario_dir, ['.txt'])
bmp_tabs = []
bmp_tabs_path = []
for f in bmp_files:
bmp_tabs.append(f.split('.')[0])
bmp_tabs_path.append(cfg.scenario_dir + SEP + f)
# create if collection not existed
c_list = scenario_db.collection_names()
for item in bmp_tabs:
if not StringClass.string_in_list(item.upper(), c_list):
scenario_db.create_collection(item.upper())
else:
scenario_db.drop_collection(item.upper())
# Read subbasin.tif and dist2Stream.tif
subbasin_r = RasterUtilClass.read_raster(cfg.spatials.subbsn)
dist2stream_r = RasterUtilClass.read_raster(cfg.spatials.dist2stream_d8)
# End reading
for j, bmp_txt in enumerate(bmp_tabs_path):
bmp_tab_name = bmp_tabs[j]
data_array = read_data_items_from_txt(bmp_txt)
field_array = data_array[0]
data_array = data_array[1:]
for item in data_array:
dic = {}
for i, field_name in enumerate(field_array):
if MathClass.isnumerical(item[i]):
dic[field_name.upper()] = float(item[i])
else:
dic[field_name.upper()] = str(item[i]).upper()
if StringClass.string_in_list(ImportScenario2Mongo._LocalX, dic.keys()) and \
StringClass.string_in_list(ImportScenario2Mongo._LocalY, dic.keys()):
subbsn_id = subbasin_r.get_value_by_xy(
dic[ImportScenario2Mongo._LocalX.upper()],
dic[ImportScenario2Mongo._LocalY.upper()])
distance = dist2stream_r.get_value_by_xy(
dic[ImportScenario2Mongo._LocalX.upper()],
dic[ImportScenario2Mongo._LocalY.upper()])
if subbsn_id is not None and distance is not None:
dic[ImportScenario2Mongo._SUBBASINID] = float(subbsn_id)
dic[ImportScenario2Mongo._DISTDOWN] = float(distance)
scenario_db[bmp_tab_name.upper()].find_one_and_replace(dic, dic,
upsert=True)
else:
scenario_db[bmp_tab_name.upper()].find_one_and_replace(dic, dic,
upsert=True)
# print 'BMP tables are imported.'
# Write BMP database name into Model workflow database
c_list = main_db.collection_names()
if not StringClass.string_in_list(DBTableNames.main_scenario, c_list):
main_db.create_collection(DBTableNames.main_scenario)
bmp_info_dic = dict()
bmp_info_dic[ImportScenario2Mongo._FLD_DB] = cfg.bmp_scenario_db
main_db[DBTableNames.main_scenario].find_one_and_replace(bmp_info_dic, bmp_info_dic,
upsert=True)
return True
def lookup_tables_as_collection_and_gridfs(cfg, maindb):
"""Import lookup tables (from txt file) as Collection and GridFS
Args:
cfg: SEIMS config object
maindb: workflow model database
"""
for tablename, txt_file in cfg.paramcfgs.lookup_tabs_dict.items():
# import each lookup table as a collection and GridFS file.
c_list = maindb.collection_names()
if not StringClass.string_in_list(tablename.upper(), c_list):
maindb.create_collection(tablename.upper())
else:
maindb.drop_collection(tablename.upper())
# initial bulk operator
bulk = maindb[tablename.upper()].initialize_ordered_bulk_op()
# delete if the tablename gridfs file existed
spatial = GridFS(maindb, DBTableNames.gridfs_spatial)
if spatial.exists(filename=tablename.upper()):
x = spatial.get_version(filename=tablename.upper())
spatial.delete(x._id)
# read data items
data_items = read_data_items_from_txt(txt_file)
field_names = data_items[0][0:]
item_values = [] # import as gridfs file
for i, cur_data_item in enumerate(data_items):
if i == 0:
continue
data_import = dict() # import as Collection
item_value = [] # import as gridfs file
for idx, fld in enumerate(field_names):
if MathClass.isnumerical(cur_data_item[idx]):
tmp_value = float(cur_data_item[idx])
data_import[fld] = tmp_value
item_value.append(tmp_value)
else:
data_import[fld] = cur_data_item[idx]
bulk.insert(data_import)
if len(item_value) > 0:
item_values.append(item_value)
bulk.execute()
# begin import gridfs file
n_row = len(item_values)
# print (item_values)
if n_row >= 1:
n_col = len(item_values[0])
for i in range(n_row):
if n_col != len(item_values[i]):
raise ValueError("Please check %s to make sure each item has "
"the same numeric dimension. The size of first "
"row is: %d, and the current data item is: %d" %
(tablename, n_col, len(item_values[i])))
else:
item_values[i].insert(0, n_col)
metadic = {ModelParamDataUtils.item_count: n_row,
ModelParamDataUtils.field_count: n_col}
cur_lookup_gridfs = spatial.new_file(filename=tablename.upper(), metadata=metadic)
header = [n_row]
fmt = '%df' % 1
s = pack(fmt, *header)
cur_lookup_gridfs.write(s)
fmt = '%df' % (n_col + 1)
for i in range(n_row):
s = pack(fmt, *item_values[i])
cur_lookup_gridfs.write(s)
cur_lookup_gridfs.close()
def model_io_configuration(cfg, maindb):
"""
Import Input and Output Configuration of SEIMS, i.e., file.in and file.out
Args:
cfg: SEIMS config object
maindb: MongoDB database object
"""
file_in_path = cfg.modelcfgs.filein
file_out_path = cfg.paramcfgs.init_outputs_file
# create if collection not existed
c_list = maindb.collection_names()
conf_tabs = [DBTableNames.main_filein, DBTableNames.main_fileout]
for item in conf_tabs:
if not StringClass.string_in_list(item, c_list):
maindb.create_collection(item)
else:
maindb.drop_collection(item)
file_in_items = read_data_items_from_txt(file_in_path)
file_out_items = read_data_items_from_txt(file_out_path)
for item in file_in_items:
file_in_dict = dict()
values = StringClass.split_string(StringClass.strip_string(item[0]), ['|'])
if len(values) != 2:
raise ValueError("One item should only have one Tag and one value string,"
" split by '|'")
file_in_dict[ModelCfgFields.tag] = values[0]
file_in_dict[ModelCfgFields.value] = values[1]
maindb[DBTableNames.main_filein].insert(file_in_dict)
# begin to import initial outputs settings
bulk = maindb[DBTableNames.main_fileout].initialize_unordered_bulk_op()
out_field_array = file_out_items[0]
out_data_array = file_out_items[1:]
# print out_data_array
for item in out_data_array:
file_out_dict = dict()
for i, v in enumerate(out_field_array):
if StringClass.string_match(ModelCfgFields.mod_cls, v):
file_out_dict[ModelCfgFields.mod_cls] = item[i]
elif StringClass.string_match(ModelCfgFields.output_id, v):
file_out_dict[ModelCfgFields.output_id] = item[i]
elif StringClass.string_match(ModelCfgFields.desc, v):
file_out_dict[ModelCfgFields.desc] = item[i]
elif StringClass.string_match(ModelCfgFields.unit, v):
file_out_dict[ModelCfgFields.unit] = item[i]
elif StringClass.string_match(ModelCfgFields.type, v):
file_out_dict[ModelCfgFields.type] = item[i]
elif StringClass.string_match(ModelCfgFields.stime, v):
file_out_dict[ModelCfgFields.stime] = item[i]
elif StringClass.string_match(ModelCfgFields.etime, v):
file_out_dict[ModelCfgFields.etime] = item[i]
elif StringClass.string_match(ModelCfgFields.interval, v):
file_out_dict[ModelCfgFields.interval] = item[i]
elif StringClass.string_match(ModelCfgFields.interval_unit, v):
file_out_dict[ModelCfgFields.interval_unit] = item[i]
elif StringClass.string_match(ModelCfgFields.filename, v):
file_out_dict[ModelCfgFields.filename] = item[i]
elif StringClass.string_match(ModelCfgFields.use, v):
file_out_dict[ModelCfgFields.use] = item[i]
elif StringClass.string_match(ModelCfgFields.subbsn, v):
file_out_dict[ModelCfgFields.subbsn] = item[i]
if file_out_dict.keys() is []:
raise ValueError("There are not any valid output item stored in file.out!")
bulk.insert(file_out_dict)
bulk.execute()
# begin to import the desired outputs
# create bulk operator
bulk = maindb[DBTableNames.main_fileout].initialize_ordered_bulk_op()
# read initial parameters from txt file
data_items = read_data_items_from_txt(cfg.modelcfgs.fileout)
# print (field_names)
for i, cur_data_item in enumerate(data_items):
data_import = dict()
cur_filter = dict()
# print (cur_data_item)
if len(cur_data_item) == 7:
data_import[ModelCfgFields.output_id] = cur_data_item[0]
data_import[ModelCfgFields.type] = cur_data_item[1]
data_import[ModelCfgFields.stime] = cur_data_item[2]
data_import[ModelCfgFields.etime] = cur_data_item[3]
data_import[ModelCfgFields.interval] = cur_data_item[4]
data_import[ModelCfgFields.interval_unit] = cur_data_item[5]
data_import[ModelCfgFields.subbsn] = cur_data_item[6]
data_import[ModelCfgFields.use] = 1
cur_filter[ModelCfgFields.output_id] = cur_data_item[0]
else:
raise RuntimeError("Items in file.out must have 7 columns, i.e., OUTPUTID,"
"TYPE,STARTTIME,ENDTIME,INTERVAL,INTERVAL_UNIT,SUBBASIN.")
bulk.find(cur_filter).update({'$set': data_import})
# execute import operators
bulk.execute()
def data_from_txt(hydro_clim_db, obs_txts_list, sites_info_txts_list,
subbsn_file):
"""
Read observed data from txt file
Args:
hydro_clim_db: hydro-climate dababase
obs_txts_list: txt file paths of observed data
sites_info_txts_list: txt file paths of site information
subbsn_file: subbasin raster file
Returns:
True or False
"""
# 1. Read monitor station information, and store variables information and station IDs
variable_lists = []
site_ids = []
for site_file in sites_info_txts_list:
site_data_items = read_data_items_from_txt(site_file)
site_flds = site_data_items[0]
for i in range(1, len(site_data_items)):
dic = {}
for j in range(len(site_data_items[i])):
if StringClass.string_match(site_flds[j],
StationFields.id):
dic[StationFields.id] = int(site_data_items[i][j])
site_ids.append(dic[StationFields.id])
elif StringClass.string_match(site_flds[j],
StationFields.name):
dic[StationFields.name] = StringClass.strip_string(
site_data_items[i][j])
elif StringClass.string_match(site_flds[j],
StationFields.type):
types = StringClass.split_string(
StringClass.strip_string(site_data_items[i][j]),
',')
elif StringClass.string_match(site_flds[j],
StationFields.lat):
dic[StationFields.lat] = float(site_data_items[i][j])
elif StringClass.string_match(site_flds[j],
StationFields.lon):
dic[StationFields.lon] = float(site_data_items[i][j])
elif StringClass.string_match(site_flds[j],
StationFields.x):
dic[StationFields.x] = float(site_data_items[i][j])
elif StringClass.string_match(site_flds[j],
StationFields.y):
dic[StationFields.y] = float(site_data_items[i][j])
elif StringClass.string_match(site_flds[j],
StationFields.unit):
dic[StationFields.unit] = StringClass.strip_string(
site_data_items[i][j])
elif StringClass.string_match(site_flds[j],
StationFields.elev):
dic[StationFields.elev] = float(site_data_items[i][j])
elif StringClass.string_match(site_flds[j],
StationFields.outlet):
dic[StationFields.outlet] = float(
site_data_items[i][j])
for j, cur_type in enumerate(types):
site_dic = dict()
site_dic[StationFields.id] = dic[StationFields.id]
site_dic[StationFields.name] = dic[StationFields.name]
site_dic[StationFields.type] = cur_type
site_dic[StationFields.lat] = dic[StationFields.lat]
site_dic[StationFields.lon] = dic[StationFields.lon]
site_dic[StationFields.x] = dic[StationFields.x]
site_dic[StationFields.y] = dic[StationFields.y]
site_dic[StationFields.elev] = dic[StationFields.elev]
site_dic[StationFields.outlet] = dic[StationFields.outlet]
# Add SubbasinID field
matched, cur_subbsn_id = ImportObservedData.match_subbasin(
subbsn_file, site_dic)
if not matched:
break
cur_subbsn_id_str = ''
for tmp_id in cur_subbsn_id:
if tmp_id is not None:
cur_subbsn_id_str += str(tmp_id) + ','
cur_subbsn_id_str = cur_subbsn_id_str[:-1]
site_dic[StationFields.id] = cur_subbsn_id_str
curfilter = {
StationFields.id: site_dic[StationFields.id],
StationFields.type: site_dic[StationFields.type]
}
# print (curfilter)
hydro_clim_db[DBTableNames.sites].find_one_and_replace(
curfilter, site_dic, upsert=True)
var_dic = dict()
var_dic[StationFields.type] = types[j]
var_dic[StationFields.unit] = dic[StationFields.unit]
if var_dic not in variable_lists:
variable_lists.append(var_dic)
site_ids = list(set(site_ids))
# 2. Read measurement data and import to MongoDB
bulk = hydro_clim_db[
DBTableNames.observes].initialize_ordered_bulk_op()
count = 0
for measDataFile in obs_txts_list:
# print measDataFile
obs_data_items = read_data_items_from_txt(measDataFile)
# If the data items is EMPTY or only have one header row, then goto
# next data file.
if obs_data_items == [] or len(obs_data_items) == 1:
continue
obs_flds = obs_data_items[0]
required_flds = [
StationFields.id, DataValueFields.y, DataValueFields.m,
DataValueFields.d, DataValueFields.type, DataValueFields.value
]
for fld in required_flds:
if not StringClass.string_in_list(
fld, obs_flds): # data can not meet the request!
raise ValueError(
"The %s can not meet the required format!" %
measDataFile)
for i in range(1, len(obs_data_items)):
dic = dict()
cur_y = 0
cur_m = 0
cur_d = 0
for j in range(len(obs_data_items[i])):
if StringClass.string_match(obs_flds[j], StationFields.id):
dic[StationFields.id] = int(obs_data_items[i][j])
# if current site ID is not included, goto next data item
if dic[StationFields.id] not in site_ids:
continue
elif StringClass.string_match(obs_flds[j],
DataValueFields.y):
cur_y = int(obs_data_items[i][j])
elif StringClass.string_match(obs_flds[j],
DataValueFields.m):
cur_m = int(obs_data_items[i][j])
elif StringClass.string_match(obs_flds[j],
DataValueFields.d):
cur_d = int(obs_data_items[i][j])
elif StringClass.string_match(obs_flds[j],
DataValueFields.type):
dic[DataValueFields.type] = obs_data_items[i][j]
elif StringClass.string_match(obs_flds[j],
DataValueFields.value):
dic[DataValueFields.value] = float(
obs_data_items[i][j])
dt = datetime(cur_y, cur_m, cur_d, 0, 0)
sec = time.mktime(dt.timetuple())
utc_time = time.gmtime(sec)
dic[DataValueFields.local_time] = dt
dic[DataValueFields.time_zone] = time.timezone / 3600
dic[DataValueFields.utc] = datetime(utc_time[0], utc_time[1],
utc_time[2], utc_time[3])
curfilter = {
StationFields.id: dic[StationFields.id],
DataValueFields.type: dic[DataValueFields.type],
DataValueFields.utc: dic[DataValueFields.utc]
}
bulk.find(curfilter).replace_one(dic)
count += 1
if count % 500 == 0:
bulk.execute()
bulk = hydro_clim_db[
DBTableNames.observes].initialize_ordered_bulk_op()
# db[DBTableNames.observes].find_one_and_replace(curfilter, dic, upsert=True)
if count % 500 != 0:
bulk.execute()
# 3. Add measurement data with unit converted
# loop variables list
added_dics = []
for curVar in variable_lists:
# print curVar
# if the unit is mg/L, then change the Type name with the suffix "Conc",
# and convert the corresponding data to kg if the discharge data is
# available.
cur_type = curVar[StationFields.type]
cur_unit = curVar[StationFields.unit]
# Find data by Type
for item in hydro_clim_db[DBTableNames.observes].find(
{StationFields.type: cur_type}):
# print item
dic = dict()
dic[StationFields.id] = item[StationFields.id]
dic[DataValueFields.value] = item[DataValueFields.value]
dic[StationFields.type] = item[StationFields.type]
dic[DataValueFields.local_time] = item[
DataValueFields.local_time]
dic[DataValueFields.time_zone] = item[
DataValueFields.time_zone]
dic[DataValueFields.utc] = item[DataValueFields.utc]
if cur_unit == "mg/L":
# update the Type name
dic[StationFields.type] = cur_type + "Conc"
curfilter = {
StationFields.id: dic[StationFields.id],
DataValueFields.type: cur_type,
DataValueFields.utc: dic[DataValueFields.utc]
}
hydro_clim_db[DBTableNames.observes].find_one_and_replace(
curfilter, dic, upsert=True)
dic[StationFields.type] = cur_type
# find discharge on current day
cur_filter = {
StationFields.type: "Q",
DataValueFields.utc: dic[DataValueFields.utc],
StationFields.id: dic[StationFields.id]
}
q_dic = hydro_clim_db[DBTableNames.observes].find_one(
filter=cur_filter)
q = -9999.
if q_dic is not None: # and q_dic.has_key(DataValueFields.value):
q = q_dic[DataValueFields.value]
else:
continue
if cur_unit == "mg/L":
# convert mg/L to kg
dic[DataValueFields.value] = round(
dic[DataValueFields.value] * q * 86400. / 1000., 2)
elif cur_unit == "kg":
dic[StationFields.type] = cur_type + "Conc"
# convert kg to mg/L
dic[DataValueFields.value] = round(
dic[DataValueFields.value] / q * 1000. / 86400., 2)
# add new data item
added_dics.append(dic)
# import to MongoDB
for dic in added_dics:
curfilter = {
StationFields.id: dic[StationFields.id],
DataValueFields.type: dic[DataValueFields.type],
DataValueFields.utc: dic[DataValueFields.utc]
}
hydro_clim_db[DBTableNames.observes].find_one_and_replace(
curfilter, dic, upsert=True)
def daily_data_from_txt(climdb, data_txt_file, sites_info_dict):
"""Import climate data table"""
# delete existed precipitation data
climdb[DBTableNames.data_values].remove(
{DataValueFields.type: DataType.m})
clim_data_items = read_data_items_from_txt(data_txt_file)
clim_flds = clim_data_items[0]
# PHUCalDic is used for Calculating potential heat units (PHU)
# for each climate station and each year.
# format is {StationID:{Year1:[values],Year2:[Values]...}, ...}
# PHUCalDic = {}
# format: {StationID1: climateStats1, ...}
hydro_climate_stats = {}
required_flds = [
DataValueFields.y, DataValueFields.m, DataValueFields.d,
DataType.max_tmp, DataType.min_tmp, DataType.rm, DataType.ws
]
for fld in required_flds:
if not StringClass.string_in_list(fld, clim_flds):
raise ValueError(
"Meteorological Daily data is invalid, please Check!")
# Create bulk object
bulk = climdb[DBTableNames.data_values].initialize_ordered_bulk_op()
count = 0
for i, cur_clim_data_item in enumerate(clim_data_items):
if i == 0:
continue
dic = dict()
cur_ssd = DEFAULT_NODATA
cur_y = 0
cur_m = 0
cur_d = 0
for j, clim_data_v in enumerate(cur_clim_data_item):
if StringClass.string_match(clim_flds[j], DataValueFields.id):
dic[DataValueFields.id] = int(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataValueFields.y):
cur_y = int(clim_data_v)
dic[DataValueFields.y] = cur_y
elif StringClass.string_match(clim_flds[j], DataValueFields.m):
cur_m = int(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataValueFields.d):
cur_d = int(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataType.mean_tmp):
dic[DataType.mean_tmp] = float(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataType.min_tmp):
dic[DataType.min_tmp] = float(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataType.max_tmp):
dic[DataType.max_tmp] = float(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataType.pet):
dic[DataType.pet] = float(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataType.sr):
dic[DataType.sr] = float(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataType.ws):
dic[DataType.ws] = float(clim_data_v)
elif StringClass.string_match(clim_flds[j], DataType.rm):
dic[DataType.rm] = float(clim_data_v) * 0.01
elif StringClass.string_match(clim_flds[j], DataType.ssd):
cur_ssd = float(clim_data_v)
# Date transformation
dt = datetime(cur_y, cur_m, cur_d, 0, 0)
sec = time.mktime(dt.timetuple())
utc_time = time.gmtime(sec)
dic[DataValueFields.local_time] = dt
dic[DataValueFields.time_zone] = time.timezone / 3600
dic[DataValueFields.utc] = datetime(utc_time[0], utc_time[1],
utc_time[2], utc_time[3])
# Do if some of these data are not provided
if DataType.mean_tmp not in dic.keys():
dic[DataType.mean_tmp] = (dic[DataType.max_tmp] +
dic[DataType.min_tmp]) / 2.
if DataType.sr not in dic.keys():
if cur_ssd == DEFAULT_NODATA:
raise ValueError(DataType.sr + " or " + DataType.ssd +
" must be provided!")
else:
if dic[DataValueFields.id] in sites_info_dict.keys():
cur_lon, cur_lat = sites_info_dict[dic[
DataValueFields.id]].lon_lat()
dic[DataType.sr] = round(
HydroClimateUtilClass.rs(DateClass.day_of_year(dt),
float(cur_ssd),
cur_lat * PI / 180.), 1)
output_flds = [
DataType.mean_tmp, DataType.max_tmp, DataType.min_tmp,
DataType.rm, DataType.pet, DataType.ws, DataType.sr
]
for fld in output_flds:
cur_dic = dict()
if fld in dic.keys():
cur_dic[DataValueFields.value] = dic[fld]
cur_dic[DataValueFields.id] = dic[DataValueFields.id]
cur_dic[DataValueFields.utc] = dic[DataValueFields.utc]
cur_dic[DataValueFields.time_zone] = dic[
DataValueFields.time_zone]
cur_dic[DataValueFields.local_time] = dic[
DataValueFields.local_time]
cur_dic[DataValueFields.type] = fld
# Old code, insert or update one item a time, which is quite inefficiency
# Update by using bulk operation interface. lj
bulk.insert(cur_dic)
count += 1
if count % 500 == 0: # execute each 500 records
bulk.execute()
bulk = climdb[
DBTableNames.
data_values].initialize_ordered_bulk_op()
if dic[DataValueFields.id] not in hydro_climate_stats.keys():
hydro_climate_stats[dic[DataValueFields.id]] = ClimateStats()
hydro_climate_stats[dic[DataValueFields.id]].add_item(dic)
# execute the remained records
if count % 500 != 0:
bulk.execute()
for item, cur_climate_stats in hydro_climate_stats.items():
cur_climate_stats.annual_stats()
# Create index
climdb[DBTableNames.data_values].create_index([
(DataValueFields.id, ASCENDING), (DataValueFields.type, ASCENDING),
(DataValueFields.utc, ASCENDING)
])
# prepare dic for MongoDB
for s_id, stats_v in hydro_climate_stats.items():
for YYYY in stats_v.Count.keys():
cur_dic = dict()
cur_dic[DataValueFields.value] = stats_v.PHUTOT[YYYY]
cur_dic[DataValueFields.id] = s_id
cur_dic[DataValueFields.y] = YYYY
cur_dic[VariableDesc.unit] = "heat units"
cur_dic[VariableDesc.type] = DataType.phu_tot
curfilter = {
DataValueFields.id: s_id,
VariableDesc.type: DataType.phu_tot,
DataValueFields.y: YYYY
}
climdb[DBTableNames.annual_stats].find_one_and_replace(
curfilter, cur_dic, upsert=True)
# import annual mean temperature
cur_dic[VariableDesc.type] = DataType.mean_tmp
cur_dic[VariableDesc.unit] = "deg C"
cur_dic[DataValueFields.value] = stats_v.MeanTmp[YYYY]
curfilter = {
DataValueFields.id: s_id,
VariableDesc.type: DataType.mean_tmp,
DataValueFields.y: YYYY
}
climdb[DBTableNames.annual_stats].find_one_and_replace(
curfilter, cur_dic, upsert=True)
cur_dic[DataValueFields.value] = stats_v.PHU0
cur_dic[DataValueFields.id] = s_id
cur_dic[DataValueFields.y] = DEFAULT_NODATA
cur_dic[VariableDesc.unit] = "heat units"
cur_dic[VariableDesc.type] = DataType.phu0
curfilter = {
DataValueFields.id: s_id,
VariableDesc.type: DataType.phu0,
DataValueFields.y: DEFAULT_NODATA
}
climdb[DBTableNames.annual_stats].find_one_and_replace(curfilter,
cur_dic,
upsert=True)
# import annual mean temperature
cur_dic[VariableDesc.type] = DataType.mean_tmp0
cur_dic[VariableDesc.unit] = "deg C"
cur_dic[DataValueFields.value] = stats_v.MeanTmp0
curfilter = {
DataValueFields.id: s_id,
VariableDesc.type: DataType.mean_tmp0,
DataValueFields.y: DEFAULT_NODATA
}
climdb[DBTableNames.annual_stats].find_one_and_replace(curfilter,
cur_dic,
upsert=True)