def render_aerosol_subtype(filename, x_range, y_range, fig, pfig):
"""
Renders the Vertical Feature Mask on the current plot. Note that L2 data is organized
differently than L1. See comments below and the CALIPSO data product catalogue for more
information before editing
:param filename: L2 HDF file
:param x_range: Tuple of first and last profile index to load from ToolsWindow
:param y_range: Tuple of first and last altitude index to load from ToolsWindow
:param fig: Matplotlib backend object
:param pfig: Matplotlib backend object
"""
# 15 profiles are in 1 record of VFM data. At the highest altitudes 5 profiles are averaged
# together. In the mid altitudes 3 are averaged and at roughly 8 km or less, there are
# separate profiles.
prof_per_row = 15
# constant variables
alt_len = 545
first_alt = y_range[0]
last_alt = y_range[1]
first_lat = int(x_range[0]/prof_per_row)
last_lat = int(x_range[1]/prof_per_row)
colormap = 'dat/calipso-aerosol_subtype.cmap'
# naming products within the HDF file
with HDF(filename) as product:
time = product['Profile_UTC_Time'][first_lat:last_lat, 0]
minimum = min(product['Profile_UTC_Time'][::])[0]
maximum = max(product['Profile_UTC_Time'][::])[0]
# Determine how far the file can be viewed
if time[-1] >= maximum and len(time) < 950:
raise IndexError
if time[0] < minimum:
raise IndexError
height = product['metadata']['Lidar_Data_Altitudes'][33:-5:]
dataset = product['Feature_Classification_Flags'][first_lat:last_lat]
latitude = product['Latitude'][first_lat:last_lat, 0]
latitude = latitude[::prof_per_row]
time = np.array([ccplot.utils.calipso_time2dt(t) for t in time])
# Mask all unknown values
dataset = np.ma.masked_equal(dataset, -999)
# Give the number of rows in the dataset
num_rows = dataset.shape[0]
# Create an empty array the size of of L1 array so they match on the plot
unpacked_aerosol_subtype = np.zeros((alt_len, prof_per_row * num_rows), np.uint8)
# Assign the values from 0-7 to subtype
aerosol_subtype = extract_aerosol_subtype(dataset)
# Place 15-wide, alt_len-tall blocks of data into the
for i in range(num_rows):
unpacked_aerosol_subtype[:, prof_per_row * i:prof_per_row * (i + 1)] = \
vfm_row2block(aerosol_subtype[i, :])
aerosol_subtype = unpacked_aerosol_subtype
max_alt = 20
unif_alt = uniform_alt_2(max_alt, height)
regrid_aerosol_subtype = regrid_lidar(height, aerosol_subtype, unif_alt)
# Format color map
cmap = ccplot.utils.cmap(colormap)
cm = mpl.colors.ListedColormap(cmap['colors'] / 255.0)
cm.set_under(cmap['under'] / 255.0)
cm.set_over(cmap['over'] / 255.0)
cm.set_bad(cmap['bad'] / 255.0)
norm = mpl.colors.BoundaryNorm(cmap['bounds'], cm.N)
im = fig.imshow(
regrid_aerosol_subtype,
extent=(latitude[0], latitude[-1], first_alt, last_alt),
cmap=cm,
aspect='auto',
norm=norm,
interpolation='nearest',
)
fig.set_ylabel('Altitude (km)')
fig.set_xlabel('Latitude')
fig.set_title('Aerosol Subtype')
cbar_label = 'Aerosol Subtype Flags'
cbar = pfig.colorbar(im)
cbar.set_label(cbar_label)
cbar.ax.set_yticklabels(['N/a','Clean Marine','Dust','Polluted\nContinental','Clean\nContinental',
'Polluted\nDust','Smoke','Other'])
ax = fig.twiny()
ax.set_xlabel('Time')
ax.set_xlim(time[0], time[-1])
ax.get_xaxis().set_major_formatter(mpl.dates.DateFormatter('%H:%M:%S'))
fig.set_zorder(0)
ax.set_zorder(1)
title = fig.set_title('Aerosol Subtype')
title_xy = title.get_position()
title.set_position([title_xy[0], title_xy[1] * 1.07])
return ax
def render_depolarized(filename, x_range, y_range, fig, pfig):
x1 = x_range[0]
x2 = x_range[1]
h1 = y_range[0]
h2 = y_range[1]
colormap = 'dat/calipso-depolar.cmap'
averaging_width = 5
print('xrange: ' + str(x_range) + ', yrange: ' + str(y_range))
with HDF(filename) as product:
time = product['Profile_UTC_Time'][x1:x2, 0]
alt = product['metadata']['Lidar_Data_Altitudes']
minimum = min(product['Profile_UTC_Time'][::])[0]
maximum = max(product['Profile_UTC_Time'][::])[0]
latitude = product['Latitude'][x1:x2, 0]
# length of time determines how far the file can be viewed
if time[-1] >= maximum and len(time) < 950:
raise IndexError
if time[0] < minimum:
raise IndexError
time = np.array([ccplot.utils.calipso_time2dt(t) for t in time])
latitude = latitude[::averaging_width]
tot_532 = product['Total_Attenuated_Backscatter_532'][x1:x2].T
perp_532 = product['Perpendicular_Attenuated_Backscatter_532'][x1:x2].T
avg_tot_532 = avg_horz_data(tot_532, averaging_width)
avg_perp_532 = avg_horz_data(perp_532, averaging_width)
avg_parallel_AB = avg_tot_532 - avg_perp_532
depolar_ratio = avg_perp_532/avg_parallel_AB
# Put altitudes above 8.2 km on same spacing as lower ones
MAX_ALT = 20
unif_alt = uniform_alt_2(MAX_ALT, alt)
regrid_depolar_ratio = regrid_lidar(alt, depolar_ratio, unif_alt)
cmap = ccplot.utils.cmap(colormap)
cm = mpl.colors.ListedColormap(cmap['colors']/255.0)
cm.set_under(cmap['under']/255.0)
cm.set_over(cmap['over']/255.0)
cm.set_bad(cmap['bad']/255.0)
norm = mpl.colors.BoundaryNorm(cmap['bounds'], cm.N)
im = fig.imshow(
regrid_depolar_ratio,
extent=(latitude[0], latitude[-1], h1, h2),
cmap=cm,
norm=norm,
aspect='auto',
interpolation='nearest',
)
fig.set_ylabel('Altitude (km)')
fig.set_xlabel('Latitude')
fig.set_title("532 nm Depolarization Ratio")
cbar_label = 'Depolarization Ratio'
cbar = pfig.colorbar(im)
cbar.set_label(cbar_label)
ax = fig.twiny()
ax.set_xlabel('Time')
ax.set_xlim(time[0], time[-1])
ax.get_xaxis().set_major_formatter(mpl.dates.DateFormatter('%H:%M:%S'))
fig.set_zorder(0)
ax.set_zorder(1)
title = fig.set_title('532 nm Depolarized Ratio')
title_xy = title.get_position()
title.set_position([title_xy[0], title_xy[1]*1.07])
return ax
# Enable for debugging
# cgitb.enable()
#Set HDF File Name here
filename = str(sys.argv[1])
m = re.search('CAL_LID_L1-ValStage1-V3-30.(.+?)T([0-9\-]+)', filename)
if m:
date = m.group(1)
time = m.group(2)
name = 'Total_Attenuated_Backscatter_532'
label = 'Total Attenuated Backscatter 532nm (km$^{-1}$ sr$^{-1}$)'
colormap = '/usr/local/share/ccplot/cmap/calipso-backscatter.cmap'
output_dir = os.path.join(os.path.dirname(__file__), "images")
if __name__ == '__main__':
with HDF(filename) as product:
x1 = -1
h1 = 0 # km
h2 = 30 # km
nz = 600
x2 = 0
i = -1
if not os.path.exists(output_dir):
os.makedirs(output_dir)
granule_dir = os.path.join(output_dir, date, time)
if not os.path.exists(granule_dir):
os.makedirs(granule_dir)
def render_backscattered(filename, x_range, y_range, fig, pfig):
x1 = x_range[0]
x2 = x_range[1]
h1 = y_range[0]
h2 = y_range[1]
# averaging_width = 15
# Adjust the averaging with so its uniform per range
averaging_width = int((x2 - x1) / 1000)
if averaging_width < 5:
averaging_width = 5
if averaging_width > 15:
averaging_width = 15
colormap = 'dat/calipso-backscatter.cmap'
print('xrange: ' + str(x_range) + ', yrange: ' + str(y_range))
with HDF(filename) as product:
time = product['Profile_UTC_Time'][x1:x2, 0]
minimum = min(product['Profile_UTC_Time'][::])[0]
maximum = max(product['Profile_UTC_Time'][::])[0]
# length of time determines how far the file can be viewed
if time[-1] >= maximum and len(time) < 950:
raise IndexError
if time[0] < minimum:
raise IndexError
alt = product['metadata']['Lidar_Data_Altitudes']
dataset = product['Total_Attenuated_Backscatter_532'][x1:x2].T
latitude = product['Latitude'][x1:x2, 0]
latitude = latitude[::averaging_width]
print(np.shape(time))
time = np.array([ccplot.utils.calipso_time2dt(t) for t in time])
dataset = np.ma.masked_equal(dataset, -9999)
# The following method has been translated from MatLab code written by R. Kuehn 7/10/07
# Translated by Collin Pampalone 7/19/17
avg_dataset = avg_horz_data(dataset, averaging_width)
# Put altitudes above 8.2 km on same spacing as lower ones
MAX_ALT = 20
unif_alt = uniform_alt_2(MAX_ALT, alt)
regrid_dataset = regrid_lidar(alt, avg_dataset, unif_alt)
data = regrid_dataset
# End method
cmap = ccplot.utils.cmap(colormap)
cm = mpl.colors.ListedColormap(cmap['colors'] / 255.0)
cm.set_under(cmap['under'] / 255.0)
cm.set_over(cmap['over'] / 255.0)
cm.set_bad(cmap['bad'] / 255.0)
norm = mpl.colors.BoundaryNorm(cmap['bounds'], cm.N)
im = fig.imshow(
#data.T,
data,
extent=(latitude[0], latitude[-1], h1, h2),
cmap=cm,
aspect='auto',
norm=norm,
interpolation='nearest',
)
fig.set_ylabel('Altitude (km)')
fig.set_xlabel('Latitude')
fig.set_title("Averaged 532 nm Total Attenuated Backscatter")
cbar_label = 'Total Attenuated Backscatter 532nm (km$^{-1}$ sr$^{-1}$)'
cbar = pfig.colorbar(im)
cbar.set_label(cbar_label)
ax = fig.twiny()
ax.set_xlabel('Time')
ax.set_xlim(time[0], time[-1])
ax.get_xaxis().set_major_formatter(mpl.dates.DateFormatter('%H:%M:%S'))
fig.set_zorder(0)
ax.set_zorder(1)
title = fig.set_title('Averaged 532 nm Total Attenuated Backscatter')
title_xy = title.get_position()
title.set_position([title_xy[0], title_xy[1] * 1.07])
return ax