def sever_divergences(flow_table, vaa_table):
flow_table = read_dbf(flow_table)[["fromcomid", "tocomid"]]
vaa_table = read_dbf(vaa_table)[["comid", "streamcalc", "divergence"]]
for direction in ("to", "from"):
flow_table = pd.merge(flow_table,
vaa_table,
left_on="{}comid".format(direction),
right_on="comid")
flow_table = flow_table.rename(columns={
var: direction + var
for var in ("streamcalc", "divergence")
})
flow_table = flow_table[~((flow_table.fromstreamcalc > 0) &
(flow_table.tostreamcalc == 0))]
flow_table = flow_table[~(flow_table.todivergence == 2)]
new_table = flow_table[["fromcomid",
"tocomid"]].rename(columns={"fromcomid": "comid"})
return new_table
def get_residence_times(reservoir_table, volume_path):
# Read and reformat volume table
volume_table = read_dbf(volume_path)[["comid", "volumecorr"]]
volume_table = volume_table.rename(columns={
"comid": "wb_comid",
"volumecorr": "volume"
})
# Join reservoir table with volumes
joined_table = pd.merge(reservoir_table, volume_table, on="wb_comid")
joined_table['residence_time'] = joined_table.volume / (joined_table.flow *
0.0283168) / 86400.
del joined_table['volume']
return joined_table
def add_erom_data(erom_dir):
# Read all monthly EROM tables
master_table = None
months = list(map(str, range(1, 13))) + ['ma']
for month in months:
table_path = os.path.join(erom_dir,
"EROM_{}0001.dbf".format(month.zfill(2)))
fields = ["comid", "q0001e", "v0001e"
] if month == 'ma' else ["comid", "q0001e"]
erom_table = read_dbf(table_path)[fields]
erom_table.columns = [
c.replace("0001e", month) for c in erom_table.columns
]
if master_table is None:
master_table = erom_table
else:
master_table = pd.merge(master_table, erom_table, on='comid')
return master_table
def extract_tables(flow_table, flow_lines, vaa_table, gridcode_table):
extract_fields = [
["featureid", "gridcode"], # gridcode_table
["comid", "fcode", "wbareacomi"], # flow lines
["comid", "hydroseq", "lengthkm", "streamcalc"]
] # vaa_table
rename_fields = [("wbareacomi", "wb_comid"),
("terminalpa", "terminal_path")]
for i, (table, fields) in enumerate(
zip((gridcode_table, flow_lines, vaa_table, flow_table),
extract_fields)):
new_table = read_dbf(table)[fields]
if fields[0] != "comid":
new_table = new_table.rename(columns={fields[0]: 'comid'})
flow_table = pd.merge(flow_table, new_table, how='outer', on='comid')
# Rename fields and filter out COMID = 0
flow_table = flow_table.rename(
columns=dict(rename_fields))[flow_table.comid > 0]
return flow_table