def load_era(var, year=None, STANDARDISE_COORDS=True, NORMALISE_TO_DATE_ONLY=True, ROLL_LON=False):
"""
Get ERA-Interim data array (xr)
Var list = ['T2', 't2max', 't2min']
Name in array = ['T2', 'MX2T', 'MN2T'], just call by da[da.name]
# 0: surface tempterature, T2_1979.nc, 1979-2016
# 1: max temperature, t2max_1979.nc, 1979-2017
# 2: min temperature, t2min_1979.nc, 1979-2017
"""
tic() # Measure time elapsed to load
# path_parent = '/group_workspaces/jasmin4/bas_climate/data/ecmwf/era-interim/day/'
path_parent = '/gws/nopw/j04/bas_climate/data/ecmwf1/era-interim/day/'
# path_parent = '/group_workspaces/jasmin4/bas_climate/data/ecmwf/era5'
if year is None: # Get all years
path = path_parent + var + '/*.nc'
# Rounding coordinates for ERA-Interim as different years have slightly different coordinate values below 2 decimals
# https://github.com/pydata/xarray/blob/aabda43c54c6336dc62cc9ec3c2050c9c656cff6/xarray/backends/api.py
ds = xr.open_mfdataset(path, preprocess=round_coords)
else:
# Get specific year
fname = path_parent + var + '/' + var + '_' + str(year) + '.nc'
ds = xr.open_dataset(fname)
da = ds[ds.name] # Extract main data array from dataset
da = standardise_da_coords(da, STANDARDISE_COORDS=STANDARDISE_COORDS, NORMALISE_TO_DATE_ONLY=NORMALISE_TO_DATE_ONLY, ROLL_LON=ROLL_LON)
toc()
return da.squeeze()
def convert_to_datetime64(da, verbose=True):
"""
! ONLY WORKS FOR 1D DATA (i.e. ONE GRID LOCATION)
Convert datetime format to standard python datetime by simple interpolation
"""
if type(da.time.values[0]) == np.datetime64:
#print("Already in datetime64 format.")
return da
print("Converting to datetime64... (retrieving all values and interpolating if necessary, may take a few mins)")
start_year = da.time.values[0].year
end_year = da.time.values[-1].year
# Create new data array
times = np.arange(str(start_year), str(end_year + 1), dtype='datetime64[D]')
# new_da = xr.DataArray(np.zeros(len(times)), coords=[('time', times)])
new_da = xr.DataArray(np.zeros(len(times)), coords=[('time', times)], attrs=da.attrs)
new_da['lon'] = da.coords['lon'].values
new_da['lat'] = da.coords['lat'].values
for yr in np.arange(start_year, end_year + 1):
if len(da.sel(time=str(yr))) != len(new_da.sel(time=str(yr))):
# Do interpolation of original array to fit standard datetime
if verbose:
print(" Processing year " + str(yr) + "/" + str(end_year))
tic()
leapyear = calendar.isleap(yr)
size = 365 if not leapyear else 366
arr = da.sel(time=str(yr)).values
arr_new = helper.interp1d(arr, size)
if verbose:
toc()
else:
arr_new = da.sel(time=str(yr)).values
# Replace array values with new data
ind_start = (new_da.indexes['time'] == pd.Timestamp(str(yr) + '-01-01')).argmax()
ind_end = (new_da.indexes['time'] == pd.Timestamp(str(yr) + '-12-31')).argmax()
new_da[ind_start:ind_end + 1] = arr_new
#print("Done")
return new_da
def tag2(tagid, begin_time, end_time, timestep, mode, utc, show, plot):
"""Parse user friendly tag query input and assemble wincc tag query"""
if timestep and not end_time:
end_time = datetime_to_str_without_ms(datetime.now())
query = tag_query_builder(tagid, begin_time, end_time, timestep, mode, utc)
if show:
print(query)
return
toc = tic()
try:
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
records = w.create_tag_records()
print("Fetched data in {time}.".format(time=round(toc(), 3)))
# print(tags)
# tags.plot()
for record in records:
print(record)
if plot:
plot_tag_records(records)
except Exception as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
def tag(tagid, begin_time, end_time, timestep, mode, utc, show):
"""Parse user friendly tag query and assemble userunfriendly wincc query"""
query = tag_query_builder(tagid, begin_time, end_time, timestep, mode, utc)
if show:
print(query)
return
toc = tic()
try:
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
if w.rowcount():
print_tag_logging(w.fetchall())
# for rec in w.fetchall():
# print rec
print("Fetched data in {time}.".format(time=round(toc(), 3)))
except Exception as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
def tag2(tagid, begin_time, end_time, timestep, mode, utc, show, plot):
"""Parse user friendly tag query input and assemble wincc tag query"""
if timestep and not end_time:
end_time = datetime_to_str_without_ms(datetime.now())
query = tag_query_builder(tagid, begin_time, end_time, timestep, mode, utc)
if show:
print(query)
return
toc = tic()
try:
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
records = w.create_tag_records()
print("Fetched data in {time}.".format(time=round(toc(), 3)))
# print(tags)
# tags.plot()
for record in records:
print(record)
if plot:
plot_tag_records(records)
except Exception as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
def tag(tagid, begin_time, end_time, timestep, mode, utc, show):
"""Parse user friendly tag query and assemble userunfriendly wincc query"""
query = tag_query_builder(tagid, begin_time, end_time, timestep, mode, utc)
if show:
print(query)
return
toc = tic()
try:
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
if w.rowcount():
print_tag_logging(w.fetchall())
# for rec in w.fetchall():
# print rec
print("Fetched data in {time}.".format(time=round(toc(), 3)))
except Exception as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
def tagid_by_name(tagname):
"""Search hosts db for tag entries matching the given name.
Return tagid.
"""
try:
toc = tic()
mssql_conn = mssql(host_info.address,
strip_R_from_db_name(host_info.database))
mssql_conn.connect()
mssql_conn.execute("SELECT TLGTAGID, VARNAME FROM PDE#TAGs WHERE "
"VARNAME LIKE '%{name}%'".format(name=tagname))
if mssql_conn.rowcount():
for rec in mssql_conn.fetchall():
print rec
print("Fetched data in {time}.".format(time=round(toc(), 3)))
except Exception as e:
print(e)
finally:
mssql_conn.close()
def tagid_by_name(tagname):
"""Search hosts db for tag entries matching the given name.
Return tagid.
"""
try:
toc = tic()
mssql_conn = mssql(host_info.address,
strip_R_from_db_name(host_info.database))
mssql_conn.connect()
mssql_conn.execute("SELECT TLGTAGID, VARNAME FROM PDE#TAGs WHERE "
"VARNAME LIKE '%{name}%'".format(name=tagname))
if mssql_conn.rowcount():
for rec in mssql_conn.fetchall():
print rec
print("Fetched data in {time}.".format(time=round(toc(), 3)))
except Exception as e:
print(e)
finally:
mssql_conn.close()
def operator_messages(begin_time, end_time, text, utc, show):
"""Query db for operator messages."""
query = om_query_builder(eval_datetime(begin_time),
eval_datetime(end_time), text, utc)
if show:
print(query)
return
try:
toc = tic()
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
w.print_operator_messages()
print("Fetched data in {time}.".format(time=round(toc(), 3)))
except WinCCException as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
def operator_messages(begin_time, end_time, text, utc, show):
"""Query db for operator messages."""
query = om_query_builder(eval_datetime(begin_time),
eval_datetime(end_time), text, utc)
if show:
print(query)
return
try:
toc = tic()
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
w.print_operator_messages()
print("Fetched data in {time}.".format(time=round(toc(), 3)))
except WinCCException as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
def tag2(tagid, begin_time, end_time, timestep, mode, utc, show, plot, outfile,
outfile_col_name, outfile_time_zone):
"""Parse user friendly tag query input and assemble wincc tag query"""
if timestep and not end_time:
end_time = datetime_to_str_without_ms(datetime.now())
query = tag_query_builder(tagid, begin_time, end_time, timestep, mode, utc)
if show:
print(query)
return
toc = tic()
try:
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
records = w.create_tag_records(utc)
print("Fetched data in {time}.".format(time=round(toc(), 3)))
if records:
if (outfile != ''):
with open(outfile, "w") as f:
# print(records.to_csv().encode("UTF-8"))
for rec in records:
f.write(
rec.to_csv(name=outfile_col_name.encode("UTF-8"),
tz=outfile_time_zone))
else:
for record in records:
print(record)
if plot:
plot_tag_records(records)
else:
logging.warning("No data returned.")
except Exception as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
def alarms(begin_time, end_time, text, utc, show, state, priority, priority2,
report, report_hostname):
"""Read alarms from given host in given time."""
query = alarm_query_builder(eval_datetime(begin_time),
eval_datetime(end_time),
text, utc, state, priority, priority2)
if show:
print(query)
return
try:
toc = tic()
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
if report:
alarms = w.create_alarm_record()
if report_hostname:
host_description = report_hostname
else:
host_description = host_info.description
if not end_time:
end_time = datetime_to_str_without_ms(datetime.now())
generate_alarms_report(alarms, begin_time, end_time,
host_description, text)
print(unicode(alarms))
else:
w.print_alarms()
print("Fetched data in {time}.".format(time=round(toc(), 3)))
except WinCCException as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
def alarms(begin_time, end_time, text, utc, show, state, priority, priority2,
report, report_hostname):
"""Read alarms from given host in given time."""
query = alarm_query_builder(eval_datetime(begin_time),
eval_datetime(end_time), text, utc, state,
priority, priority2)
if show:
print(query)
return
try:
toc = tic()
w = wincc(host_info.address, host_info.database)
w.connect()
w.execute(query)
if report:
alarms = w.create_alarm_record()
if report_hostname:
host_description = report_hostname
else:
host_description = host_info.description
if not end_time:
end_time = datetime_to_str_without_ms(datetime.now())
generate_alarms_report(alarms, begin_time, end_time,
host_description, text)
print(unicode(alarms))
else:
w.print_alarms()
print("Fetched data in {time}.".format(time=round(toc(), 3)))
except WinCCException as e:
print(e)
print(traceback.format_exc())
finally:
w.close()
##########
# 모델 저장
##########
if (not epoch == 0):
save_path = saver.save(sess, "./checkpoint/%s" % (TSET.name))
print("Model saved in file: %s" % save_path)
##########
# 레지듀얼 스케일 조절
##########
rs_factor = min(1.0, epoch / 100)
##########
# 트레이닝
##########
train_time = tic()
num_batch = TSET.num_batch_in_a_epoch
batch_trn = []
for i in range(num_batch):
# 배치 가져오기
batch_trn = dset_train.get_batch()
# 학습 - generator
[summary_train, _] = sess.run(
[merged, train_sr],
feed_dict={
in_lowv: batch_trn.arr4d_LR_v,
gt_highv: batch_trn.arr4d_HR_v,
op_rs: rs_factor
save_path = saver.save(sess,
"./checkpoint/model-%s" % (exp.train_name),
global_step=epoch)
print("Model saved in file: %s" % save_path)
##########
# 레지듀얼 스케일 조절
##########
rs_factor = 1.0
if (epoch < 100):
rs_factor = epoch / 100
##########
# 트레이닝
##########
train_time = tic()
gpu_time = 0
batch_time = 0
loss = 0.0
num_batch = exp.num_batch_in_a_epoch
batch_trn = []
for i in range(num_batch):
batch_st = time.time()
# 배치 가져오기
batch_trn = dset.get_batch()
st = tic()
# 학습 - 오토엔코더
