def initialize_landcover_parameters(landcover_file,
landcover_initial_fields_file,
dst_dir):
"""generate initial landcover_init_param parameters"""
lc_data_items = read_data_items_from_txt(landcover_initial_fields_file)
# print lc_data_items
field_names = lc_data_items[0]
lu_id = -1
for i, v in enumerate(field_names):
if StringClass.string_match(v, 'LANDUSE_ID'):
lu_id = i
break
data_items = lc_data_items[1:]
replace_dicts = dict()
for item in data_items:
for i, v in enumerate(item):
if i != lu_id:
if field_names[i].upper() not in replace_dicts.keys():
replace_dicts[field_names[i].upper()] = {
float(item[lu_id]): float(v)
}
else:
replace_dicts[field_names[i].upper()][float(
item[lu_id])] = float(v)
# print replace_dicts
# Generate GTIFF
for item, v in replace_dicts.items():
filename = dst_dir + SEP + item + '.tif'
print(filename)
RasterUtilClass.raster_reclassify(landcover_file, v, filename)
return replace_dicts['LANDCOVER'].values()
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 variable_table(db, var_file):
"""Import variables table"""
var_data_items = read_data_items_from_txt(var_file)
var_flds = var_data_items[0]
for i in range(1, len(var_data_items)):
dic = {}
for j in range(len(var_data_items[i])):
if StringClass.string_match(var_flds[j], VariableDesc.type):
dic[VariableDesc.type] = var_data_items[i][j]
elif StringClass.string_match(var_flds[j], VariableDesc.unit):
dic[VariableDesc.unit] = var_data_items[i][j]
# If this item existed already, then update it, otherwise insert one.
curfilter = {VariableDesc.type: dic[VariableDesc.type]}
db[DBTableNames.var_desc].find_one_and_replace(curfilter, dic, upsert=True)
def read_crop_lookup_table(crop_lookup_file):
"""read crop lookup table"""
FileClass.check_file_exists(crop_lookup_file)
data_items = read_data_items_from_txt(crop_lookup_file)
attr_dic = dict()
fields = data_items[0]
n = len(fields)
for i in range(n):
attr_dic[fields[i]] = dict()
for items in data_items[1:]:
cur_id = int(items[0])
for i in range(n):
dic = attr_dic[fields[i]]
try:
dic[cur_id] = float(items[i])
except ValueError:
dic[cur_id] = items[i]
return attr_dic
def sites_table(hydro_clim_db, site_file, site_type):
"""Import HydroClimate sites table"""
sites_loc = dict()
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 = dict()
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])
elif StringClass.string_match(site_flds[j], StationFields.name):
# unicode(site_data_items[i][j], 'gb2312')
dic[StationFields.name] = 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.lon):
dic[StationFields.lon] = float(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.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])
dic[StationFields.type] = site_type
curfilter = {StationFields.id: dic[StationFields.id],
StationFields.type: dic[StationFields.type]}
hydro_clim_db[DBTableNames.sites].find_one_and_replace(curfilter, dic, upsert=True)
if dic[StationFields.id] not in sites_loc.keys():
sites_loc[dic[StationFields.id]] = SiteInfo(dic[StationFields.id],
dic[StationFields.name],
dic[StationFields.lat],
dic[StationFields.lon],
dic[StationFields.x],
dic[StationFields.y],
dic[StationFields.elev])
hydro_clim_db[DBTableNames.sites].create_index([(StationFields.id, ASCENDING),
(StationFields.type, ASCENDING)])
return sites_loc
def calibrated_params_from_txt(cfg, maindb):
"""Read and update calibrated parameters."""
# 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.modelcfgs.filecali)
# print (field_names)
for i, cur_data_item in enumerate(data_items):
data_import = dict()
cur_filter = dict()
if len(cur_data_item) < 2:
raise RuntimeError("param.cali at least contain NAME and IMPACT fields!")
data_import[ModelParamFields.name] = cur_data_item[0]
data_import[ModelParamFields.impact] = float(cur_data_item[1])
cur_filter[ModelParamFields.name] = cur_data_item[0]
if len(cur_data_item) >= 3:
if cur_data_item[2] in [ModelParamFields.change_vc, ModelParamFields.change_ac,
ModelParamFields.change_rc, ModelParamFields.change_nc]:
data_import[ModelParamFields.change] = cur_data_item[2]
bulk.find(cur_filter).update({'$set': data_import})
# execute import operators
bulk.execute()
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)
def interpolate_observed_data_to_regular_interval(in_file, time_interval, start_time, end_time,
eliminate_zero=False,
time_sys_output='UTCTIME', day_divided_hour=0):
"""
Interpolate not regular observed data to regular time interval data.
Args:
in_file: input data file, the basic format is as follows:
line 1: #<time_system> [<time_zone>], e.g., #LOCALTIME 8, #UTCTIME
line 2: DATETIME,field1,field2,...
line 3: YYYY-mm-dd HH:MM:SS,field1_value,field2_value,...
line 4: ...
...
Field name can be PCP, FLOW, SED
the unit is mm/h, m3/s, g/L (i.e., kg/m3), respectively.
time_interval: time interval, unit is minute, e.g., daily output is 1440
start_time: start time, the format must be 'YYYY-mm-dd HH:MM:SS', and the time system
is based on time_sys.
end_time: end time, see also start_time.
eliminate_zero: Boolean flag. If true, the time interval without original records will
not be output.
time_sys_output: time system of output time_system, the format must be
'<time_system> [<time_zone>]', e.g.,
'LOCALTIME'
'LOCALTIME 8'
'UTCTIME' (default)
day_divided_hour: If the time_interval is equal to N*1440, this parameter should be
carefully specified. The value must range from 0 to 23. e.g.,
day_divided_hour ==> day ranges (all expressed as 2013-02-03)
0 ==> 2013-02-03 00:00:00 to 2013-02-03 23:59:59 (default)
8 ==> 2013-02-03 08:00:00 to 2013-02-04 07:59:59
20 ==> 2013-02-03 20:00:00 to 2013-02-04 19:59:59
Returns:
The output data files are located in the same directory with the input file.
The nomenclature is: <field name>_<time system>_<time interval>_<nonzero>, e.g.,
pcp_utctime_1440_nonzero.txt, flow_localtime_60.txt
"""
FileClass.check_file_exists(in_file)
time_sys_input, time_zone_input = HydroClimateUtilClass.get_time_system_from_data_file(in_file)
data_items = read_data_items_from_txt(in_file)
flds = data_items[0][:]
data_items.remove(flds)
if not 0 <= day_divided_hour <= 23:
raise ValueError("Day divided hour must range from 0 to 23!")
try:
date_idx = flds.index('DATETIME')
flds.remove('DATETIME')
except ValueError:
raise ValueError("DATETIME must be one of the fields!")
# available field
available_flds = ['FLOW', 'SED', 'PCP']
def check_avaiable_field(cur_fld):
"""Check if the given field name is supported."""
support_flag = False
for fff in available_flds:
if fff.lower() in cur_fld.lower():
support_flag = True
break
return support_flag
ord_data = OrderedDict()
time_zone_output = time.timezone / -3600
if time_sys_output.lower().find('local') >= 0:
tmpstrs = StringClass.split_string(time_sys_output, [' '])
if len(tmpstrs) == 2 and MathClass.isnumerical(tmpstrs[1]):
time_zone_output = int(tmpstrs[1])
time_sys_output = 'LOCALTIME'
else:
time_sys_output = 'UTCTIME'
time_zone_output = 0
for item in data_items:
org_datetime = HydroClimateUtilClass.get_datetime_from_string(item[date_idx])
if time_sys_input == 'LOCALTIME':
org_datetime -= timedelta(hours=time_zone_input)
# now, org_datetime is UTC time.
if time_sys_output == 'LOCALTIME':
org_datetime += timedelta(hours=time_zone_output)
# now, org_datetime is consistent with the output time system
ord_data[org_datetime] = []
for i, v in enumerate(item):
if i == date_idx:
continue
if MathClass.isnumerical(v):
ord_data[org_datetime].append(float(v))
else:
ord_data[org_datetime].append(v)
# print (ord_data)
itp_data = OrderedDict()
out_time_delta = timedelta(minutes=time_interval)
sdatetime = HydroClimateUtilClass.get_datetime_from_string(start_time)
edatetime = HydroClimateUtilClass.get_datetime_from_string(end_time)
item_dtime = sdatetime
if time_interval % 1440 == 0:
item_dtime = sdatetime.replace(hour=0, minute=0, second=0) + \
timedelta(minutes=day_divided_hour * 60)
while item_dtime <= edatetime:
# print (item_dtime)
# if item_dtime.month == 12 and item_dtime.day == 31:
# print ("debug")
sdt = item_dtime # start datetime of records
edt = item_dtime + out_time_delta # end datetime of records
# get original data items
org_items = []
pre_dt = list(ord_data.keys())[0]
pre_added = False
for i, v in ord_data.items():
if sdt <= i < edt:
if not pre_added and pre_dt < sdt < i and sdt - pre_dt < out_time_delta:
# only add one item that less than sdt.
org_items.append([pre_dt] + ord_data.get(pre_dt))
pre_added = True
org_items.append([i] + v)
if i > edt:
break
pre_dt = i
if len(org_items) > 0:
org_items.append([edt]) # Just add end time for compute convenient
if org_items[0][0] < sdt:
org_items[0][0] = sdt # set the begin datetime of current time interval
# if eliminate time interval without original records
# initial interpolated list
itp_data[item_dtime] = [0.] * len(flds)
if len(org_items) == 0:
if eliminate_zero:
itp_data.popitem()
item_dtime += out_time_delta
continue
# core interpolation code
flow_idx = -1
for v_idx, v_name in enumerate(flds):
if not check_avaiable_field(v_name):
continue
if 'SED' in v_name.upper(): # FLOW must be existed
for v_idx2, v_name2 in enumerate(flds):
if 'FLOW' in v_name2.upper():
flow_idx = v_idx2
break
if flow_idx < 0:
raise RuntimeError("To interpolate SED, FLOW must be provided!")
for v_idx, v_name in enumerate(flds):
if not check_avaiable_field(v_name):
continue
itp_value = 0.
itp_auxiliary_value = 0.
for org_item_idx, org_item_dtv in enumerate(org_items):
if org_item_idx == 0:
continue
org_item_dt = org_item_dtv[0]
pre_item_dtv = org_items[org_item_idx - 1]
pre_item_dt = pre_item_dtv[0]
tmp_delta_dt = org_item_dt - pre_item_dt
tmp_delta_secs = tmp_delta_dt.days * 86400 + tmp_delta_dt.seconds
if 'SED' in v_name.upper():
itp_value += pre_item_dtv[v_idx + 1] * pre_item_dtv[flow_idx + 1] * \
tmp_delta_secs
itp_auxiliary_value += pre_item_dtv[flow_idx + 1] * tmp_delta_secs
else:
itp_value += pre_item_dtv[v_idx + 1] * tmp_delta_secs
if 'SED' in v_name.upper():
if MathClass.floatequal(itp_auxiliary_value, 0.):
itp_value = 0.
print ("WARNING: Flow is 0 for %s, please check!" %
item_dtime.strftime('%Y-%m-%d %H:%M:%S'))
itp_value /= itp_auxiliary_value
elif 'FLOW' in v_name.upper():
itp_value /= (out_time_delta.days * 86400 + out_time_delta.seconds)
elif 'PCP' in v_name.upper(): # the input is mm/h, and output is mm
itp_value /= 3600.
itp_data[item_dtime][v_idx] = round(itp_value, 4)
item_dtime += out_time_delta
# for i, v in itp_data.items():
# print (i, v)
# output to files
work_path = os.path.dirname(in_file)
header_str = '#' + time_sys_output
if time_sys_output == 'LOCALTIME':
header_str = header_str + ' ' + str(time_zone_output)
for idx, fld in enumerate(flds):
if not check_avaiable_field(fld):
continue
file_name = fld + '_' + time_sys_output + '_' + str(time_interval)
if eliminate_zero:
file_name += '_nonzero'
file_name += '.txt'
out_file = work_path + os.sep + file_name
f = open(out_file, 'w')
f.write(header_str + '\n')
f.write('DATETIME,' + fld + '\n')
for i, v in itp_data.items():
cur_line = i.strftime('%Y-%m-%d %H:%M:%S') + ',' + str(v[idx]) + '\n'
f.write(cur_line)
f.close()
def regular_data_from_txt(climdb, data_file):
"""Regular precipitation data from text file."""
# delete existed precipitation data
climdb[DBTableNames.data_values].remove(
{DataValueFields.type: DataType.p})
clim_data_items = read_data_items_from_txt(data_file)
clim_flds = clim_data_items[0]
station_id = []
bulk = climdb[DBTableNames.data_values].initialize_ordered_bulk_op()
count = 0
for i in range(3, len(clim_flds)):
station_id.append(clim_flds[i])
for i, clim_data_item in enumerate(clim_data_items):
if i == 0:
continue
dic = dict()
precipitation = []
cur_y = 0
cur_m = 0
cur_d = 0
for j, clim_data_v in enumerate(clim_data_item):
if StringClass.string_match(clim_flds[j], DataValueFields.y):
cur_y = int(clim_data_v)
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)
else:
for k, cur_id in enumerate(station_id):
if StringClass.string_match(clim_flds[j], cur_id):
precipitation.append(float(clim_data_v))
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])
for j, cur_id in enumerate(station_id):
cur_dic = dict()
cur_dic[DataValueFields.value] = precipitation[j]
cur_dic[DataValueFields.id] = int(cur_id)
cur_dic[DataValueFields.type] = DataType.p
cur_dic[DataValueFields.time_zone] = dic[
DataValueFields.time_zone]
cur_dic[DataValueFields.local_time] = dic[
DataValueFields.local_time]
cur_dic[DataValueFields.utc] = dic[DataValueFields.utc]
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 count % 500 != 0:
bulk.execute()
# Create index
climdb[DBTableNames.data_values].create_index([
(DataValueFields.id, ASCENDING), (DataValueFields.type, ASCENDING),
(DataValueFields.utc, ASCENDING)
])
