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 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'])
}
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
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 + ['']
parser = argparse.ArgumentParser(description='Trim NC files')
parser.add_argument('-i',
'--inputfiles',
nargs='+',
type=str,
help='full path to each file seperated by a space')
parser.add_argument('-o', '--outputfile', type=str, help='name of output file')
parser.add_argument('-ek', '--EPIC_KEY', nargs='+', help='EPIC Keys to keep')
args = parser.parse_args()
data = {}
for ncfile in args.inputfiles:
print "Reading {file}".format(file=ncfile)
###nc readin/out
df = EcoFOCI_netCDF(ncfile)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
tempdata = df.ncreadfile_dic()
for key in vars_dic.keys():
if not key in ['lat', 'lon', 'depth'
]: #non-increasing dimensions should be skipped
try:
data[key] = np.concatenate((data[key], tempdata[key]))
except:
data[key] = tempdata[key]
else: #only one value should be kept for non-increasing dimensions
data[key] = tempdata[key]
#df.close()
'--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:
plt.subplot2grid((3, 1), (1, 0), colspan=1, rowspan=3)
### set arbitrary max and min bounds to be changed later based on data bounds
databounds['max_t'] = 0
databounds['min_t'] = 100000000
databounds['max_v'] = -50
databounds['min_v'] = 50
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):
Options: point, timeSeries, trajectory, profile, timeSeriesProfile, trajectoryProfile'''
)
parser.add_argument("-o",
'--out_config',
action="store_true",
help='output to config file')
parser.add_argument("-in",
'--in_config',
action="store_true",
help='modify using current config file')
args = parser.parse_args()
###nc readin
ncfile = args.sourcefile
df = EcoFOCI_netCDF(ncfile)
global_atts = df.get_global_atts()
df.close()
if args.screen:
for k in global_atts.keys():
print "{0}: {1}".format(k, global_atts[k])
if args.out_config:
for k in global_atts.keys():
global_atts[k] = str(global_atts[k])
print "{0}: {1}".format(k, global_atts[k])
ConfigParserLocal.write_config("header_config.yaml", global_atts, 'yaml')
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
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'
action="store_true",
help='output to screen')
parser.add_argument("-o",
'--out_config',
action="store_true",
help='output to config file')
parser.add_argument("-in",
'--in_config',
action="store_true",
help='modify using current config file')
args = parser.parse_args()
###nc readin
ncfile = args.sourcefile
df = EcoFOCI_netCDF(ncfile)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
if args.screen:
for k in vars_dic.keys():
if k == args.varname:
atts = df.get_vars_attributes(var_name=k, var_type='long_name')
print "{0}: {1}".format(k, atts)
if args.out_config:
for k in vars_dic.keys():
if k == args.varname:
atts = df.get_vars_attributes(var_name=k, var_type='long_name')
print "{0}: {1}".format(k, atts)
help='instrument name')
parser.add_argument("-rot",
'--rotate',
type=float,
help='rotate vectors angle provided',
default=0.0)
parser.add_argument("-di",
'--depth_index',
type=int,
default=0,
help='0 indexed value for depth parameter to plot if 2d')
args = parser.parse_args()
#read in 1d data file
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]][:, args.depth_index, 0, 0] >= 1e30),
args.depth_index, 0, 0] = np.nan
ncdata[args.epickey[1]][np.where(
ncdata[args.epickey[1]][:, args.depth_index, 0, 0] >= 1e30),
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']),
def repl_var(nchandle, var_name, val=1e35):
nchandle.variables[var_name][:] = np.ones_like(nchandle.variables[var_name][:]) * float(val)
return
"""------------------------------- MAIN--------------------------------------------"""
parser = argparse.ArgumentParser(description='Replace EPIC Variable with 1e35 for all depths')
parser.add_argument('sourcefile', metavar='sourcefile', type=str,
help='complete path to netcdf file')
parser.add_argument('user_var', metavar='user_var', type=str,
help='EPIC Key Code or variable name')
parser.add_argument('Value', metavar='Value', type=str,
help='replacement value')
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()
print ncfile.split('/')[-1]
repl_var(df._getnchandle_(), args.user_var, val=args.Value)
df.close()
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'][:]
""" 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
help='Trim: start and end boundarys for trimming (inclusive)\
Format: yyyy-mm-ddThh:mm:ss start-date end-date')
args = parser.parse_args()
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']
