label_thin = []
### cycle through all files, retrieve data and plot
print files_path
for ind, ncfile in enumerate(files_path):
print "Working on {activefile}".format(activefile=ncfile)
#open/read netcdf files
df = EcoFOCI_netCDF(ncfile)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
ncdata = df.ncreadfile_dic()
df.close()
if args.timeseries_overlay:
nctime = EPIC2Datetime(ncdata['time'], ncdata['time2'])
def set_year_even(x):
if x.year % 2 == 0:
return x.replace(year=2000)
elif x.year % 2 == 1:
return x.replace(year=2001)
def set_year_odd(x):
if x.year % 2 == 0:
return x.replace(year=2000)
elif x.year % 2 == 1:
return x.replace(year=1999)
if nctime[0].year % 2 == 0:
nctime = [set_year_even(x) for x in nctime]
'-timebounds',
'--timebounds',
nargs=3,
type=str,
help='fixed timebounds start: yyyy-mm-dd end: yyyy-mm-dd "month"')
args = parser.parse_args()
print "Working on file %s " % args.DataPath
nc = EcoFOCI_netCDF(args.DataPath)
ncdata = nc.ncreadfile_dic()
g_atts = nc.get_global_atts()
nc.close()
cast_time = EPIC2Datetime(ncdata['time'], ncdata['time2'])
doy = np.array([x.timetuple().tm_yday for x in cast_time])
p1 = TimeseriesPorpertyPropertyPlot()
try:
t1 = p1.add_title(mooringid=global_atts['MOORING'],
lat=ncdata['lat'][0],
lon=ncdata['lon'][0],
depth=ncdata['depth'][0],
instrument=args.instname)
except KeyError:
t1 = p1.add_title(mooringid=global_atts['MOORING'],
lat=ncdata['latitude'][0],
lon=ncdata['longitude'][0],
depth=ncdata['depth'][0],
instrument=args.instname)
parser.add_argument(
"-fp",
'--full_path',
action="store_true",
help='provides full path to program: used if run as script')
args = parser.parse_args()
#read in WPAK data file
ncfile = args.DataPath
df = EcoFOCI_netCDF(ncfile)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
ncdata = df.ncreadfile_dic()
df.close()
nctime = get_UDUNITS(EPIC2Datetime(ncdata['time'], ncdata['time2']),
'days since 0001-1-1') + 1.0
# filter data to convert 1e35 -> np.nan
for keynames in ncdata.keys():
if keynames not in [
'time', 'time2', 'lat', 'lon', 'latitude', 'longitude', 'depth',
'dep'
]:
ncdata[keynames][np.where(ncdata[keynames][:, 0, 0, 0] >= 1e30), 0, 0,
0] = np.nan
p1 = TimeseriesWPAK(stylesheet='seaborn-poster', labelsize=16)
try:
t1 = p1.add_title(mooringid=global_atts['MOORING'],
lat=ncdata['lat'][0],
parser.add_argument('instname', metavar='instname', type=str,
help='instrument name')
parser.add_argument("-fp",'--full_path', action="store_true", help='provides full path to program: used if run as script')
args = parser.parse_args()
#read in 1d data file
ncfile = args.DataPath
df = EcoFOCI_netCDF(args.DataPath)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
#check that variable is in data file and exit if not
is_in_dic(args.epickey,vars_dic)
ncdata = df.ncreadfile_dic()
df.close()
nctime = get_UDUNITS(EPIC2Datetime(ncdata['time'],ncdata['time2']),'days since 0001-1-1') + 1.0
# filter data to convert 1e35 -> np.nan
ncdata[args.epickey[0]][np.where(ncdata[args.epickey[0]][:,0,0,0] >= 1e30),0,0,0] = np.nan
ncdata[args.epickey[1]][np.where(ncdata[args.epickey[1]][:,0,0,0] >= 1e30),0,0,0] = np.nan
ncdata[args.epickey[2]][np.where(ncdata[args.epickey[2]][:,0,0,0] >= 1e30),0,0,0] = np.nan
p1 = Timeseries3varPlot(plotstyle='k.-',stylesheet='ggplot')
try:
t1 = p1.add_title(mooringid=global_atts['MOORING'],
lat=ncdata['lat'][0],
lon=ncdata['lon'][0],
depth=ncdata['depth'][0],
instrument=args.instname)
print("Reading file for {}".format(filename))
ncin = Dataset(str(filename), 'r')
try:
data = ncin.variables[args.varname][:, 0, 0, 0]
except KeyError:
data = ncin.variables[altvarname][:, 0, 0, 0]
ncdata = {
'time': ncin.variables['time'][:],
'time2': ncin.variables['time2'][:]
}
ncin.close()
#Convert two word EPIC time to python datetime.datetime
# representation and then format for CF standards
dt_from_epic = np.array(EPIC2Datetime(ncdata['time'], ncdata['time2']))
#Find relevant chuck of times to keep based on arguments
dt_index = np.where((dt_from_epic >= start_date)
& (dt_from_epic <= end_date))
if key == 0:
eof_data = data[dt_index]
else:
try:
eof_data = np.vstack((eof_data, data[dt_index]))
except ValueError:
sys.exit("Exiting: timeseries have different lengths")
if args.normalize:
eof_data_std = np.std(eof_data, axis=1)
if args.tave and args.isCF:
#use argument string to set up frequency
for ind_file in ifile:
ds = xr.open_dataset(ind_file)
dsr = ds.resample(args.tave,
dim='time',
how='mean',
closed='right',
label='right')
print dsr.to_dataframe().to_csv()
ds.close()
elif args.tave and not args.isCF:
for ind_file in ifile:
ds = xr.open_dataset(ind_file, decode_times=False)
ds['time'] = EPIC2Datetime(ds.time.data, ds.time2.data)
ds = ds.drop('time2')
dsr = ds.resample(args.tave,
dim='time',
how='mean',
closed='right',
label='right')
print dsr.to_dataframe().to_csv()
ds.close()
if args.tdecim and not args.isCF:
for ind_file in ifile:
if args.tdecim in ['10T']:
df = EcoFOCI_netCDF(ind_file)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
if args.featureType:
featureType=args.featureType
else:
featureType=''
if args.operation in ['CF','CF Convert','CF_Convert']:
#generates near file
if args.is2D:
df = EcoFOCI_netCDF( args.sourcefile )
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
ncdata = df.ncreadfile_dic()
#Convert two word EPIC time to python datetime.datetime representation and then format for CF standards
dt_from_epic = EPIC2Datetime(ncdata['time'], ncdata['time2'])
if args.time_since_str:
time_since_str = " ".join(args.time_since_str)
CF_time = get_UDUNITS(dt_from_epic,time_since_str)
else:
time_since_str = 'days since 1900-01-01'
CF_time = get_UDUNITS(dt_from_epic,time_since_str)
try:
History=global_atts['History']
except:
History=''
###build/copy attributes and fill if empty
try:
data_cmnt = global_atts['DATA_CMNT']