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upload_discharge.py
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upload_discharge.py
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#!/usr/bin/python3
import csv
import datetime
import getopt
import json
import os
import sys
import traceback
import pandas as pd
from db_util import MySqlAdapter, get_type_by_date, get_event_id, create_event_id
def usage():
usageText = """
Usage: ./CSVTODAT.py [-d YYYY-MM-DD] [-h]
-h --help Show usage
-d --date Model State Date in YYYY-MM. Default is current date.
-t --time Model State Time in HH:MM:SS. Default is current time.
--start-date Start date of timeseries which need to run the forecast in YYYY-MM-DD format. Default is same as -d(date).
--start-time Start time of timeseries which need to run the forecast in HH:MM:SS format. Default is same as -t(date).
-T --tag Tag to differential simultaneous Forecast Runs E.g. wrf1, wrf2 ...
-f --forceInsert Force Insert into the database. May override existing values.
-n --name Name field value of the Run table in Database. Use time format such as 'Cloud-1-<%H:%M:%S>' to replace with time(t).
"""
print(usageText)
def extract_forecast_timeseries(timeseries, extract_date, extract_time, by_day=False):
"""
Extracted timeseries upward from given date and time
E.g. Consider timeseries 2017-09-01 to 2017-09-03
date: 2017-09-01 and time: 14:00:00 will extract a timeseries which contains
values that timestamp onwards
"""
print('LibForecastTimeseries:: extractForecastTimeseries')
if by_day:
extract_date_time = datetime.strptime(extract_date, '%Y-%m-%d')
else:
extract_date_time = datetime.strptime('%s %s' % (extract_date, extract_time), '%Y-%m-%d %H:%M:%S')
is_date_time = isinstance(timeseries[0][0], datetime)
new_timeseries = []
for i, tt in enumerate(timeseries):
tt_date_time = tt[0] if is_date_time else datetime.strptime(tt[0], '%Y-%m-%d %H:%M:%S')
if tt_date_time >= extract_date_time:
new_timeseries = timeseries[i:]
break
return new_timeseries
def extract_forecast_timeseries_in_days(timeseries):
"""
Devide into multiple timeseries for each day
E.g. Consider timeseries 2017-09-01 14:00:00 to 2017-09-03 23:00:00
will devide into 3 timeseries with
[
[2017-09-01 14:00:00-2017-09-01 23:00:00],
[2017-09-02 14:00:00-2017-09-02 23:00:00],
[2017-09-03 14:00:00-2017-09-03 23:00:00]
]
"""
new_timeseries = []
if len(timeseries) > 0:
group_timeseries = []
is_date_time_obs = isinstance(timeseries[0][0], datetime)
prev_date = timeseries[0][0] if is_date_time_obs else datetime.strptime(timeseries[0][0], '%Y-%m-%d %H:%M:%S')
prev_date = prev_date.replace(hour=0, minute=0, second=0, microsecond=0)
for tt in timeseries:
# Match Daily
tt_date_time = tt[0] if is_date_time_obs else datetime.strptime(tt[0], '%Y-%m-%d %H:%M:%S')
if prev_date == tt_date_time.replace(hour=0, minute=0, second=0, microsecond=0):
group_timeseries.append(tt)
else:
new_timeseries.append(group_timeseries[:])
group_timeseries = []
prev_date = tt_date_time.replace(hour=0, minute=0, second=0, microsecond=0)
group_timeseries.append(tt)
return new_timeseries
def save_forecast_timeseries(adapter, time_series_data, model_date_time, opts):
print('CSVTODAT:: save_forecast_timeseries:: len', len(time_series_data), model_date_time)
req_date = datetime.datetime.strptime(model_date_time.strftime("%Y-%m-%d"), '%Y-%m-%d')
df = pd.pivot_table(time_series_data, columns=time_series_data['time'].str[:10])
days_list = df.columns.values
for day in days_list:
type = get_type_by_date(req_date, day)
sub_df = time_series_data.loc[time_series_data['time'].str[:10] == day]
if type != 'Error':
meta_data = {
'station': 'Hanwella',
'variable': 'Discharge',
'unit': 'm3/s',
'type': type,
'source': 'HEC-HMS',
'name': opts.get('run_name', 'Cloud-1'),
}
event_id = get_event_id(adapter, meta_data)
if event_id is None:
event_id = create_event_id(adapter, meta_data)
size = sub_df.shape[0]
def get_event_column(event_id, size, event_list=[]):
for i in range(0, size):
event_list.append(event_id)
return event_list
if size > 0:
sub_df.insert(loc=0, column='id', value=get_event_column(event_id, size))
print(sub_df)
def save_forecast_timeseries_data(adapter, time_series_data, run_date, opts):
df = pd.pivot_table(time_series_data, columns=time_series_data['time'].str[:10])
days_list = df.columns.values
for day in days_list:
type = get_type_by_date(run_date, day)
sub_df = time_series_data.loc[time_series_data['time'].str[:10] == day]
if type != 'Error':
meta_data = {
'station': 'Hanwella',
'variable': 'Discharge',
'unit': 'm3/s',
'type': type,
'source': 'HEC-HMS',
'name': opts.get('run_name', 'Cloud-1'),
}
event_id = get_event_id(adapter, meta_data)
if event_id is None:
event_id = create_event_id(adapter, meta_data)
size = sub_df.shape[0]
def get_event_column(event_id, size, event_list=[]):
for i in range(0, size):
event_list.append(event_id)
return event_list
if size > 0:
sub_df.insert(loc=0, column='id', value=get_event_column(event_id, size))
print(sub_df)
def upload_data_to_db(run_datetime, discharge_file, run_name, force_insert=False):
try:
CSV_NUM_METADATA_LINES = 2
DAT_WIDTH = 12
DISCHARGE_CSV_FILE = 'DailyDischarge.csv'
forceInsert = force_insert
runName = run_name
print('Open Discharge CSV ::', discharge_file)
time_series_data = pd.read_csv(discharge_file, names=['time', 'value'])
# Validate Discharge Timeseries
if not time_series_data.shape[0] > 0:
print('ERROR: Discharge timeseries length is zero.')
sys.exit(1)
# Save Forecast values into Database
opts = {
'forceInsert': forceInsert,
'runName': runName
}
save_forecast_timeseries(MySqlAdapter(), time_series_data, run_datetime, opts)
except Exception as e:
print(e)
traceback.print_exc()
def upload_discharge_data(run_date, discharge_file, run_name, force_insert=False):
try:
CSV_NUM_METADATA_LINES = 2
DAT_WIDTH = 12
DISCHARGE_CSV_FILE = 'DailyDischarge.csv'
forceInsert = force_insert
runName = run_name
print('Open Discharge CSV ::', discharge_file)
time_series_data = pd.read_csv(discharge_file, names=['time', 'value'])
# Validate Discharge Timeseries
if not time_series_data.shape[0] > 0:
print('ERROR: Discharge timeseries length is zero.')
sys.exit(1)
# Save Forecast values into Database
opts = {
'forceInsert': forceInsert,
'runName': runName
}
save_forecast_timeseries(MySqlAdapter(), time_series_data, run_date, opts)
except Exception as e:
print(e)
traceback.print_exc()