def main():
roloroot = '/home/sbraden/Dropbox/calibration/ROLO/datasetChips/'
feature = 'serenitatis'
# Read in ROLO data and process into I/F reflectance
lookup = caltools.get_filter_lookup(roloroot) # filter_id => band lookup table
chips = caltools.process_rolo_data(roloroot, feature, lookup)
# STEP 3: (optional) appy the Lommel-Seeliger correction
incidence = chips[feature+'_incidence']
emission = chips[feature+'_emission']
LS = caltools.lommel_seeliger(incidence, emission)
chips['LS'] = pd.Series(LS.values)
#chips[feature] = chips[feature]*LS # apply LS
# STEP 4: set data to only the appropriate phases by setting others to nan
# Negative phase angle values correspond to waxing lunar phases
chips.topo_phase[chips.topo_phase > 0] = np.nan
chips.topo_phase = abs(chips.topo_phase.values)
# Plotting
index = 24 #695 nm 17 => 353 nm
band = int(lookup.loc[index].values)
plt.figure()
chips[chips['filter_id'] == index].plot(x='topo_phase', y=feature, marker='o', lw=0)
title = feature+'-phase-curve-ROLO-band-'+str(band)
plt.title(title)
plt.ylabel('I/F')
plt.savefig(title+'.png', dpi=200)
plt.show()
plt.close
def main():
WAC_root = '/home/sbraden/data/rolo_chip_cal/'
ROLO_root = '/home/sbraden/Dropbox/calibration/ROLO/datasetChips/'
feature = 'serenitatis'
# Read in ROLO data and process into I/F reflectance
lookup = caltools.get_filter_lookup(roloroot) # filter_id => band lookup table
chips = caltools.process_rolo_data(roloroot, feature, lookup)
# STEP 3: (optional) appy the Lommel-Seeliger correction
incidence = chips[feature+'_incidence']
emission = chips[feature+'_emission']
LS = caltools.lommel_seeliger(incidence, emission)
chips['LS'] = pd.Series(LS.values)
#chips[feature] = chips[feature]*LS # apply LS
# STEP 4: set data to only the appropriate phases by setting others to nan
# Negative phase angle values correspond to waxing lunar phases
chips.topo_phase[chips.topo_phase > 0] = np.nan
chips.topo_phase = abs(chips.topo_phase.values)
###########################################
# groups for phase angles
phase_groups = [15, 20, 25, 30, 35, 40]
# STEP 3: all values for the chip of interest are in I/F,
# index chips by filter_id
# df=pd.DataFrame(chips, index=chips['filter_id'])
# sort by phase, bin by phase?, then group/aggregate, interp to WAC bands
# see if I can replace this loop by some other kind of selection
data = []
columns = ['band','phase','avg','stdev','num']
for phase in phase_groups:
group = subchip[(subchip['real_phase'] > phase-2) & (subchip['real_phase'] < phase+2)]
output = band, phase, chip_rad.mean(), chip_rad.std(), len(chip_rad.index)
data.append(output)
all_data = pd.DataFrame(data, columns=columns)
grouped = all_data.groupby(['phase', 'band']) # indexed by phase group then band
# print all_data.groupby(['band', 'phase']).mean().avg
# print all_data.groupby(['band', 'phase']).mean().std
# create a MultiIndex from the groups through Aggregation
# This averages multiple observations in the same filter together
avg_multi_index = grouped.aggregate(np.mean)
averages = avg_multi_index['avg']
stdevs = avg_multi_index['stdev']
# READ IN AND PROCESS WAC Data
wac_dataframe = read_WAC(WAC_root, feature)
# print wac_dataframe
# not the best way to define this... ok for now
#customized
rolo_bands = [353, 405, 413, 467, 476, 550, 545, 555, 667, 695, 706]
for phase in phase_groups:
# interpolate data and stdev to WAC bands
rolo_iof_interp = caltools.interp2wacbands(rolo_bands, iof_df.values)
rolo_iof_stdev_interp = caltools.interp2wacbands(rolo_bands,
iof_df_stdev.values)
cal_factor = wac_dataframe.loc[phase].avg/rolo_iof_interp
x = cal_factor.index
y = cal_factor.values
p = rolo_iof_interp
p_stdev = rolo_iof_stdev_interp
r = wac_dataframe.loc[phase].avg
r_stdev = wac_dataframe.loc[phase].stdev
u = np.power(p_stdev/p, 2)
w = np.power(r_stdev/r, 2)
yerr = y * np.power(u+w, 0.5)
plt.errorbar(x, y, yerr=yerr, marker='o', label=str(phase),
c=colorloop.next())
# print phase, cal_factor
plt.legend(loc='best')
plt.xlabel('WAC bands [nm]', fontsize=14)
plt.ylabel('ROLO-derived calibration factor', fontsize=14)
filename = feature+ '_cal_factor_abs'
plt.title(filename)
plt.savefig(filename+'.png', dpi=200)
plt.close
