#double check to make sure the mask files match up with the data files
for d,m in zip(datafiles, maskfiles):
[d_stat,d_date,d_type] = d.split('_')
[m_stat,m_date,m_type] = m.split('_')
print 'Checking mask/data match for %s'%(d_date)
if d_date == m_date and d_stat == m_stat:
print 'Check!'
continue
else:
sys.exit("Error: Mask files don't match data files")
#open, altitude resample, and concatenate data and mask files
for d,m in zip(datafiles, maskfiles):
d_temp, data_prod = LNC.lnc_reader(d)
d_realt = LNC.alt_resample(d_temp,altrange)
try:
d_event = pan.concat([d_event,d_realt])
except NameError:
d_event = d_realt
m_temp, data_prod = LNC.lnc_reader(m)
m_realt = LNC.alt_resample(m_temp,altrange)
try:
m_event = pan.concat([m_event,m_realt])
except NameError:
m_event = m_realt
"""
To plot from lidar data files
"""
#set up plot
theFig = plt.figure(1)
theFig.clf()
theAx = theFig.add_subplot(111)
theAx.set_title('')
theAx.set_ylabel('height (meters)')
theAx.set_xlabel('hours from July 6 00:00:00')
df, product = LNC.lnc_reader('/home/datatmp/nchaparr/UBC_20120707_BR532.txt')
data, timestamps, altitudes = LNC.pandas_to_numpy(df)
print 'scattering limits', data.max(), data.min()
data = data[:,:1300]
xtick_ind = [int(i*1436.0/10) for i in range(10)]
ytick_ind = [int(i*1300.0/10) for i in range(10)]
xticks = [timestamps[ind] for ind in xtick_ind]
yticks = [altitudes[:1300][ind] for ind in ytick_ind]
yticks = list(reversed(yticks))
xticks1 = [int(1.0*(timestamps[ind] - np.datetime64('2012-07-06T00:00:00.000000000-0700'))/np.timedelta64(1, 'h')+7) for ind in xtick_ind]
print 'timestamps and durations from 0hrs', timestamps[1], timestamps[1430], xticks, xticks1
im = theAx.imshow(data.T[::-1], aspect = .5, vmax=abs(data).max(), vmin=abs(data).min())
if BR_type in f:
BRfiles.append(f)
#make sure the files are in a common order of ascending date (assuming they're all
#from the same station
BRfiles.sort()
counter = 0
header = {}
#open, altitude resample, and concatenate data and mask files
dataprods = []
counter = 0
for br in BRfiles:
br_temp, tempprod = LNC.lnc_reader(br)
br_realt = LNC.alt_resample(br_temp,altrange)
if counter == 0:
br_event = br_realt
dataprods.append(BR_type)
else:
br_event = pan.concat([br_event,br_realt])
counter +=1
altrange = br_realt.columns.values
#sort by index to make certain data is in order then set date ranges to match
br_event = br_event.sort_index()
