def get_geocoords(infile, lat='lat', lon='lon'):
df = EcoFOCI_netCDF(infile)
nchandle = df._getnchandle_()
data = {}
for j, v in enumerate([lat, lon]):
data[v] = nchandle.variables[v][:]
df.close()
return (data)
def from_netcdf_1dsplice(infile, height_ind, lat_ind, lon_ind):
""" Uses ncreadfile_dic which returns a dictionary of all data from netcdf"""
###nc readin/out
df = EcoFOCI_netCDF(infile)
nchandle = df._getnchandle_()
params = df.get_vars() #gets all of them
print "Parameters available: "
#print params
ncdata = ncreadfile_dic_slice(nchandle, params, height_ind=height_ind, lat_ind=lat_ind, lon_ind=lon_ind)
df.close()
return ncdata
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'])
}
parser = argparse.ArgumentParser(
description='Convert DegreesWest to DegreesEast inplace')
parser.add_argument('sourcefile',
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'][:]
parser = argparse.ArgumentParser(
description='convert netcdf file to erddap formatted file')
parser.add_argument('sourcefile',
metavar='sourcefile',
type=str,
help='path to .nc files')
parser.add_argument('add_dsg_idvar',
metavar='add_dsg_idvar',
type=str,
help='name of dsg style id variable')
args = parser.parse_args()
"---"
df = EcoFOCI_netCDF(args.sourcefile)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
nchandle = df._getnchandle_()
data = df.ncreadfile_dic()
try:
nchandle.createDimension('id_strlen', 5)
nchandle.createVariable('profileid',
'S1',
dimensions=('record_number', 'id_strlen'))
nchandle.variables['profileid'].cf_role = args.add_dsg_idvar
nchandle.variables['profileid'].long_name = 'profile_id'
except:
print "{0} - not added".format(args.add_dsg_idvar)
"""
databounds = {}
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 + ['']
'--temp_scale',
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:
""" import datetime 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
parser = argparse.ArgumentParser(description='Convert .nc to .csv screen output')
parser.add_argument('infile', metavar='infile', type=str, help='input file path')
parser.add_argument("-csv","--csv", action="store_true",
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'
metavar='inputpath',
type=str,
help='path to .nc file')
parser.add_argument('outputpath',
metavar='outputpath',
type=str,
help='path to .nc file')
parser.add_argument('-d',
'--depth',
action="store_true",
help='split file on depths')
args = parser.parse_args()
#read in netcdf data file
df = EcoFOCI_netCDF(args.inputpath)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
data = df.ncreadfile_dic()
print data
if args.depth:
if 'dep' in data.keys():
epic_depth_str = 'dep'
else:
epic_depth_str = 'depth'
for ind_depth, val_depth in enumerate(data[epic_depth_str]):
time_ind = np.ones_like(data['time'], dtype=bool)
output_file = args.outputpath + args.inputpath.split('/')[-1].split(
'.nc')[0] + '_' + str(int(val_depth)) + '.nc'
parser = argparse.ArgumentParser(description='add a variable to a .nc file')
parser.add_argument('sourcedir',
metavar='sourcedir',
type=str,
help='path to .nc files')
parser.add_argument('--add_epic_var',
type=str,
help='name of new epic variable')
args = parser.parse_args()
# If these variables are not defined, no data will be archived into the nc file for that parameter.
###nc readin
df = EcoFOCI_netCDF(args.sourcedir)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
nchandle = df._getnchandle_()
data = df.ncreadfile_dic()
if args.add_epic_var:
EPIC_VARS_dict = ConfigParserLocal.get_config(
'EcoFOCI_config/epickeys/epickey.json', 'json')
try:
epic_var_ind = (args.add_epic_var).split('_')[1]
print "Adding {0} by searching for {1}".format(args.add_epic_var,
epic_var_ind)
try:
newvar = nchandle.createVariable(
EPIC_VARS_dict[epic_var_ind]['EPIC_KEY'], 'f4',
parser.add_argument('plot_var',
metavar='plot_var',
type=str,
help='EPIC Key to plot')
parser.add_argument('depth_m',
metavar='depth_m',
type=int,
help='local max depth')
parser.add_argument('-fg',
'--FillGaps',
action="store_true",
help='Interpolate and Fill Gaps in bin averaged data')
args = parser.parse_args()
print "Working on {0}".format(args.gridded_file)
df = EcoFOCI_netCDF(args.gridded_file)
vars_dic = df.get_vars()
ncdata = df.ncreadfile_dic()
df.close()
depth_array = np.arange(0, args.depth_m + 1, 1)
###build empty array
gridarray = np.ones((len(ncdata['time']), len(depth_array))) * np.nan
for i, v in enumerate(ncdata['depth']):
if v in ncdata['depth']:
print "copying {0}".format(v)
gridarray[:, int(v)] = ncdata[args.plot_var][:, i, 0, 0]
extent = [
np.min(ncdata['time']),
this program using .yaml form
"""
if args.ifile.split('.')[-1] == 'nc':
ifile = [args.ifile]
elif args.ifile.split('.')[-1] == 'yaml':
pointer_file = ConfigParserLocal.get_config_yaml(args.ifile)
else:
print "Data file or pointer file format not recognized"
sys.exit()
##############
# process optional time average string flag
if args.filter:
for ind_file in ifile:
df = EcoFOCI_netCDF(ind_file)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
data = df.ncreadfile_dic()
df.close()
if args.filter == 'F35':
pass
elif args.filter == 'F29':
pass
else:
print "Choose a valid filter"
##############
# averaging/resampling
if args.tave and args.isCF:
#use argument string to set up frequency