def load(self):
self.data = {}
for ind, ncfile in enumerate(self.files_path):
print("Working on {0}").format(ncfile)
df = EcoFOCI_netCDF(ncfile)
df.get_global_atts()
vars_dic = df.get_vars()
if self.plot_var in vars_dic:
ncdata = df.ncreadfile_dic()
df.close()
ncdata[self.plot_var][np.where(
ncdata[self.plot_var] > 1e34)] = np.nan
else:
continue
self.ncdata = ncdata #only save last file
self.data[self.pointer_dic['nominal_depth'][ind]] = {
'data': ncdata[self.plot_var][:, 0, 0, 0],
'time': EPIC2Datetime(ncdata['time'], ncdata['time2'])
}
metavar='sourcefile',
type=str,
help='complete path to netcdf file')
parser.add_argument('-m360',
'--m360',
action="store_true",
help='make range 0-360')
args = parser.parse_args()
###nc readin
ncfile = args.sourcefile
df = EcoFOCI_netCDF(ncfile)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
data = df.ncreadfile_dic()
if 'lon' in df.variables.keys():
if args.m360:
df.variables['lon'][:] = -1. * df.variables['lon'][:] + 360
else:
df.variables['lon'][:] = -1. * df.variables['lon'][:]
vars_dic['lon'].units = 'degree_east'
elif 'longitude' in df.variables.keys():
if args.m360:
df.variables['longitude'][:] = -1. * df.variables['longitude'][:] + 360
else:
df.variables['longitude'][:] = -1. * df.variables['longitude'][:]
vars_dic['longitude'].units = 'degree_east'
else:
print("Longitude variable not found")
if args.multi:
### cycle through all files, retrieve data and plot
print files_path
writer = pd.ExcelWriter('data/' + MooringID + '_' + plot_var + '.xlsx',
engine='xlsxwriter',
datetime_format='YYYY-MM-DD HH:MM:SS')
label_thin = []
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()
nctime = EPIC2Datetime(ncdata['time'], ncdata['time2'])
#find and replace missing values with nans so they don't plot
try:
ncdata[plot_var][np.where(ncdata[plot_var] > 1e30)] = np.nan
try:
label_thin = label_thin + [label[ind]]
except TypeError:
label_thin = label_thin + ['']
except KeyError:
pass
nargs=2,
type=float,
help='fixed temperature scale (min max)')
parser.add_argument(
'-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'],
import numpy as np import gsw from io_utils.EcoFOCI_netCDF_read import EcoFOCI_netCDF #godas is a function of time, depth, lat, lon (12, 40, 418, 360) godas_ptemp = '/Volumes/WDC_internal/Users/bell/Data_Local/Reanalysis_Files/GODAS/pottmp.1980.nc' godas_sal = '/Volumes/WDC_internal/Users/bell/Data_Local/Reanalysis_Files/GODAS/salt.1980.nc' #GODAS PTEMP df = EcoFOCI_netCDF(godas_ptemp) global_atts = df.get_global_atts() vars_dic = df.get_vars() gd_ptmp = df.ncreadfile_dic() df.close() #GODAS SAL df = EcoFOCI_netCDF(godas_sal) global_atts = df.get_global_atts() vars_dic = df.get_vars() gd_sal = df.ncreadfile_dic() df.close() #ABS Sal f(sal, pres, lat, lon) #pressure needs to be determined from depth ABS_SA = np.ones_like(gd_sal['salt']) CONS_T = np.ones_like(gd_sal['salt']) for time_ind, month in enumerate(gd_sal['time']):
help='output non-epic formatted netcdf as csv')
parser.add_argument("-is_whoi","--is_whoi", action="store_true",
help='flag if is directly from WHOI')
parser.add_argument("-plots","--plots", action="store_true",
help='generate plots')
args = parser.parse_args()
###nc readin/out
file1 = '/Volumes/WDC_internal/Users/bell/ecoraid/2016/Additional_FieldData/ArcticHeat/AlamoFloats/netcdf/arctic_heat_alamo_profiles_9058_9f75_d5e5_f5f9.nc'
df = EcoFOCI_netCDF(file1)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
dims = df.get_dims()
data0 = df.ncreadfile_dic()
df.close()
file2 = '/Volumes/WDC_internal/Users/bell/ecoraid/2016/Additional_FieldData/ArcticHeat/AlamoFloats/netcdf/arctic_heat_alamo_profiles_9115_bb97_cc7e_a9c0.nc'
df = EcoFOCI_netCDF(file2)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
dims = df.get_dims()
data1 = df.ncreadfile_dic()
if args.is_whoi:
timestr = 'days since 1950-01-01T00:00:00Z'
else:
timestr = 'seconds since 1970-01-01'
skipped_vars = ['STATION_PARAMETERS','FLOAT_SERIAL_NO',