def from_2netcdf(infile, parameter):
""" For moorings with a, and b deployments
Ingest and concatenate both files
"""
ncfile = infile[0]
###nc readin/out
nchandle = ncutil.ncopen(ncfile)
params = ncutil.get_vars(nchandle) #gets all of them
if not parameter in params:
### specified parameter doesn't exist
return ('no param')
ncdata1 = ncutil.ncreadfile_dic(nchandle, params)
ncutil.ncclose(nchandle)
ncfile = infile[1]
###nc readin/out
nchandle = ncutil.ncopen(ncfile)
params = ncutil.get_vars(nchandle) #gets all of them
if not parameter in params:
### specified parameter doesn't exist
return ('no param')
ncdata2 = ncutil.ncreadfile_dic(nchandle, params)
ncutil.ncclose(nchandle)
###data massaging
pytime1 = util.EPICdate2udunits(ncdata1['time'], ncdata1['time2'])
pytime2 = util.EPICdate2udunits(ncdata2['time'], ncdata2['time2'])
dt = 1. / pytime1['interval_min'] #data is 4 times daily
time_base = 'days'
### fill time gap
blanktime = np.array([])
for tg in np.arange(np.max(pytime1['timeint'])+dt,np.min(pytime2['timeint']),dt):
blanktime = np.hstack((blanktime,tg))
time = np.hstack((pytime1['timeint'], blanktime))
time = np.hstack((time,pytime2['timeint']))
# signal
data_interp = np.interp(blanktime,[np.max(pytime1['timeint']),np.min(pytime2['timeint'])],[ncdata1[parameter][-1,0,0,0], ncdata2[parameter][0,0,0,0]])
data = np.hstack((ncdata1[parameter][:,0,0,0], data_interp))
data = np.hstack((data, ncdata2[parameter][:,0,0,0]))
variance = np.var(data)
#normalize
print 'Variance = %s ' % (variance)
x = (data - np.mean(data)) / np.sqrt(variance)
variance = np.var(x)
return_data = {'data': data, 'anom': x, 'dt': dt, 'time':np.array(time), 'variance':variance, 'time_base':time_base}
return (return_data)
def from_2netcdf(infile, parameter):
""" Uses ncreadfile_dic which returns a dictionary of all data from netcdf
Assumes that there is an "a" and "b" mooring each year
"""
ncfile = infile[0]
###nc readin/out
nchandle = ncutil.ncopen(ncfile)
params = ncutil.get_vars(nchandle) #gets all of them
if not parameter in params:
### specified parameter doesn't exist
return ('no param')
ncdata1 = ncutil.ncreadfile_dic(nchandle, params)
ncutil.ncclose(nchandle)
ncfile = infile[1]
###nc readin/out
nchandle = ncutil.ncopen(ncfile)
params = ncutil.get_vars(nchandle) #gets all of them
if not parameter in params:
### specified parameter doesn't exist
return ('no param')
ncdata2 = ncutil.ncreadfile_dic(nchandle, params)
ncutil.ncclose(nchandle)
###data massaging
pytime1 = util.EPICdate2udunits(ncdata1['time'], ncdata1['time2'])
pytime2 = util.EPICdate2udunits(ncdata2['time'], ncdata2['time2'])
dt = 1. / pytime['interval_min'] #data is 4 times daily
time_base = 'days'
time = pytime['timeint']
#time = util.subsample(time, int(pytime.get('interval_min')) / 4)
# signal
data = ncdata1[parameter][:,0,0,0]
variance = np.var(data)
#normalize
print 'Variance = %s ' % (variance)
x = (data - np.mean(data)) / np.sqrt(variance)
variance = np.var(x)
return_data = {'data': data, 'anom': x, 'dt': dt, 'time':np.array(time), 'variance':variance, 'time_base':time_base}
return (return_data)
def from_netcdf(infile, parameter, detrend=False):
""" Uses ncreadfile_dic which returns a dictionary of all data from netcdf"""
ncfile = infile
###nc readin/out
nchandle = ncutil.ncopen(ncfile)
params = ncutil.get_vars(nchandle) #gets all of them
if not parameter in params:
### specified parameter doesn't exist
return ('no param')
ncdata = ncutil.ncreadfile_dic(nchandle, params)
ncutil.ncclose(nchandle)
###data massaging
pytime = util.EPICdate2udunits(ncdata['time'], ncdata['time2'])
dt = 1. / pytime['interval_min'] #data is 4 times daily
time_base = 'days'
time = pytime['timeint']
#time = util.subsample(time, int(pytime.get('interval_min')) / 4)
# signal
data = ncdata[parameter][:, 0, 0, 0]
variance = np.var(data)
#test routine, set default to false
if detrend: #detrend if flag is true - default false
x = sp.signal.detrend(data, axis=0)
else:
x = data
#normalize
print 'Variance = %s ' % (variance)
x = (x - np.mean(x)) / np.sqrt(variance)
variance = np.var(x)
return_data = {
'data': data,
'anom': x,
'dt': dt,
'time': np.array(time),
'variance': variance,
'time_base': time_base
}
return (return_data)
def from_netcdf(infile, parameter, detrend=False):
""" Uses ncreadfile_dic which returns a dictionary of all data from netcdf"""
ncfile = infile
###nc readin/out
nchandle = ncutil.ncopen(ncfile)
params = ncutil.get_vars(nchandle) #gets all of them
if not parameter in params:
### specified parameter doesn't exist
return ('no param')
ncdata = ncutil.ncreadfile_dic(nchandle, params)
ncutil.ncclose(nchandle)
###data massaging
pytime = util.EPICdate2udunits(ncdata['time'], ncdata['time2'])
dt = 1. / pytime['interval_min'] #data is 4 times daily
time_base = 'days'
time = pytime['timeint']
#time = util.subsample(time, int(pytime.get('interval_min')) / 4)
# signal
data = ncdata[parameter][:,0,0,0]
variance = np.var(data)
#test routine, set default to false
if detrend: #detrend if flag is true - default false
x = sp.signal.detrend(data, axis=0)
else:
x = data
#normalize
print 'Variance = %s ' % (variance)
x = (x - np.mean(x)) / np.sqrt(variance)
variance = np.var(x)
return_data = {'data': data, 'anom': x, 'dt': dt, 'time':np.array(time), 'variance':variance, 'time_base':time_base}
return (return_data)
def from_2netcdf(infile, parameter):
""" For moorings with a, and b deployments
Ingest and concatenate both files
"""
ncfile = infile[0]
###nc readin/out
nchandle = ncutil.ncopen(ncfile)
params = ncutil.get_vars(nchandle) #gets all of them
if not parameter in params:
### specified parameter doesn't exist
return ('no param')
ncdata1 = ncutil.ncreadfile_dic(nchandle, params)
ncutil.ncclose(nchandle)
ncfile = infile[1]
###nc readin/out
nchandle = ncutil.ncopen(ncfile)
params = ncutil.get_vars(nchandle) #gets all of them
if not parameter in params:
### specified parameter doesn't exist
return ('no param')
ncdata2 = ncutil.ncreadfile_dic(nchandle, params)
ncutil.ncclose(nchandle)
###data massaging
pytime1 = util.EPICdate2udunits(ncdata1['time'], ncdata1['time2'])
pytime2 = util.EPICdate2udunits(ncdata2['time'], ncdata2['time2'])
dt = 1. / pytime1['interval_min'] #data is 4 times daily
time_base = 'days'
### fill time gap
blanktime = np.array([])
for tg in np.arange(
np.max(pytime1['timeint']) + dt, np.min(pytime2['timeint']), dt):
blanktime = np.hstack((blanktime, tg))
time = np.hstack((pytime1['timeint'], blanktime))
time = np.hstack((time, pytime2['timeint']))
# signal
data_interp = np.interp(
blanktime, [np.max(pytime1['timeint']),
np.min(pytime2['timeint'])],
[ncdata1[parameter][-1, 0, 0, 0], ncdata2[parameter][0, 0, 0, 0]])
data = np.hstack((ncdata1[parameter][:, 0, 0, 0], data_interp))
data = np.hstack((data, ncdata2[parameter][:, 0, 0, 0]))
variance = np.var(data)
#normalize
print 'Variance = %s ' % (variance)
x = (data - np.mean(data)) / np.sqrt(variance)
variance = np.var(x)
return_data = {
'data': data,
'anom': x,
'dt': dt,
'time': np.array(time),
'variance': variance,
'time_base': time_base
}
return (return_data)
