def load_and_prepare_images():
"""Load images defined above and prepare them for the background analysis.
Returns
-------
- Img, plume image
- Img, plume image vignetting corrected
- Img, sky radiance image
"""
# get custom load method for ECII
fun = pyplis.custom_image_import.load_ecII_fits
# Load the image objects and peform dark correction
plume, bg = pyplis.Img(PLUME_FILE, fun), pyplis.Img(BG_FILE, fun)
dark, offset = pyplis.Img(DARK_FILE, fun), pyplis.Img(OFFSET_FILE, fun)
# Model dark image for tExp of plume image
dark_plume = pyplis.image.model_dark_image(plume.meta["texp"], dark,
offset)
# Model dark image for tExp of background image
dark_bg = pyplis.image.model_dark_image(bg.meta["texp"], dark, offset)
plume.subtract_dark_image(dark_plume)
bg.subtract_dark_image(dark_bg)
# Blur the images (sigma = 1)
plume.add_gaussian_blurring(1)
bg.add_gaussian_blurring(1)
# Create vignetting correction mask from background image
vign = bg.img / bg.img.max() # NOTE: potentially includes y & x gradients
plume_vigncorr = pyplis.Img(plume.img / vign)
return plume, plume_vigncorr, bg
def prepare_aa_image_list(bg_corr_mode=6):
"""Get and prepare onband list for aa image mode.
The relevant gas free areas for background image modelling are set
automatically (see also ex. 3 for details)
:return: - on list in AA mode
"""
dataset = create_dataset()
geom, _ = find_viewing_direction(dataset.meas_geometry, False)
# Set plume background images for on and off
# this is the same image which is also used for example script NO
# demonstrating the plume background routines
path_bg_on = join(IMG_DIR,
'EC2_1106307_1R02_2015091607022602_F01_Etna.fts')
path_bg_off = join(IMG_DIR,
'EC2_1106307_1R02_2015091607022820_F02_Etna.fts')
# Get on and off lists and activate dark correction
lst = dataset.get_list("on")
off_list = dataset.get_list("off")
# Deactivate automatic reload in list while changing some list
# attributes
lst.auto_reload = False
off_list.auto_reload = False
lst.darkcorr_mode = True
off_list.darkcorr_mode = True
# Prepare on and offband background images
bg_on = pyplis.Img(path_bg_on)
bg_on.subtract_dark_image(lst.get_dark_image())
bg_off = pyplis.Img(path_bg_off)
bg_off.subtract_dark_image(off_list.get_dark_image())
# set the background images within the lists
lst.set_bg_img(bg_on)
off_list.set_bg_img(bg_off)
# automatically set gas free areas
lst.bg_model.guess_missing_settings(lst.current_img())
# Now update some of the information from the automatically set sky ref
# areas
lst.bg_model.xgrad_line_startcol = 20
lst.bg_model.xgrad_line_rownum = 25
off_list.bg_model.xgrad_line_startcol = 20
off_list.bg_model.xgrad_line_rownum = 25
# set background modelling mode
lst.bg_model.mode = bg_corr_mode
off_list.bg_model.mode = bg_corr_mode
lst.aa_mode = True # activate AA mode
off_list.auto_reload = True
lst.auto_reload = True
print("INITIATED AA LIST")
lst.meas_geometry = geom
return lst
# object, meaning, that whenever something changes in "fl", it also does
# in "aa_list.optflow")
# Now activate optical flow calculation in list (this slows down the
# speed of the analysis, since the optical flow calculation is
# comparatively slow
s = aa_list.optflow.settings
s.hist_dir_gnum_max = 10
s.hist_dir_binres = 10
s.hist_sigma_tol = PEAK_SIGMA_TOL
s.roi_rad = ROI_CONTRAST
aa_list.optflow_mode = True
plume_mask = pyplis.Img(aa_list.get_thresh_mask(MIN_AA))
plume_mask.show(tit="AA threshold mask")
figs.append(analyse_and_plot(aa_list, LINES))
figs.append(fl.plot_flow_histograms(PCS1, plume_mask.img))
figs.append(fl.plot_flow_histograms(PCS2, plume_mask.img))
# Show an image containing plume speed magnitudes (ignoring direction)
velo_img = pyplis.Img(fl.to_plume_speed(dist_img))
velo_img.show(vmin=0,
vmax=10,
cmap="Greens",
tit="Optical flow plume velocities",
zlabel="Plume velo [m/s]")
label=r"Uncorr: $\Phi_{SO2}=$%.2f (+/- %.2f) kg/s"
% (phi_uncorr / 1000.0, phi_uncorr_err / 1000.0))
ax3.plot(so2_cds_corr, "-g", lw=3,
label=r"Corr: $\Phi_{SO2}=$%.2f (+/- %.2f) kg/s"
% (phi_corr / 1000.0, phi_corr_err / 1000.0))
ax3.set_title("Cross section profile", fontsize=12)
ax3.legend(loc="best", framealpha=0.5, fancybox=True, fontsize=12)
ax3.set_xlim([0, len(pix_dists_line)])
ax3.set_ylim([0, 5e18])
ax3.set_ylabel(r"$S_{SO2}$ [cm$^{-2}$]", fontsize=14)
ax3.set_xlabel("PCS", fontsize=14)
ax3.grid()
# also plot plume pixel mask
ax4 = pyplis.Img(plume_pix_mask).show(cmap="gray", tit="Plume pixel mask")
if SAVEFIGS:
ax = [ax0, ax1, ax2, ax3, ax4]
for k in range(len(ax)):
ax[k].set_title("") # remove titles for saving
ax[k].figure.savefig(join(SAVE_DIR, "ex11_out_%d.%s"
% (k, FORMAT)),
format=FORMAT, dpi=DPI)
basemap.ax.set_axis_off()
basemap.ax.view_init(15, 345)
basemap.ax.figure.savefig(join(SAVE_DIR, "ex11_out_5.%s" % FORMAT),
format=FORMAT, dpi=DPI)
# IMPORTANT STUFF FINISHED (Below follow tests and display options)
# add some blurring to the image
img.add_gaussian_blurring(sigma_final=3)
# crop the image edges
roi_crop = [100, 100, 1244, 924] # [x0, y0, x1, y1]
img.crop(roi_abs=roi_crop)
# apply down scaling (gauss pyramid)
img.to_pyrlevel(2)
# ## Show image
img.show()
img.save_as_fits(SAVE_DIR, "ex0_1_imgsave_test")
img_reload = pyplis.Img(join(SAVE_DIR, "ex0_1_imgsave_test.fts"))
# print image information
print(img)
# ## IMPORTANT STUFF FINISHED - everything below is of minor importance
# for educational purposes
(options, args) = OPTPARSE.parse_args()
# If applicable, do some tests. This is done only if TESTMODE is active:
# testmode can be activated globally (see SETTINGS.py) or can also be
# activated from the command line when executing the script using the
# option --test 1
if int(options.test):
import numpy.testing as npt
aa_list = prepare_aa_image_list()
aa_list.pyrlevel = PYRLEVEL
if DILCORR:
aa_list.import_ext_coeffs_csv(EXT_ON)
aa_list.get_off_list().import_ext_coeffs_csv(EXT_OFF)
# Load DOAS calbration data and FOV information (see example 6)
doascalib = pyplis.doascalib.DoasCalibData()
doascalib.load_from_fits(file_path=CALIB_FILE)
doascalib.fit_calib_data()
# Load AA corr mask and set in image list(is normalised to DOAS FOV see
# ex7)
aa_corr_mask = pyplis.Img(CORR_MASK_FILE)
aa_list.senscorr_mask = aa_corr_mask
# set DOAS calibration data in image list
aa_list.calib_data = doascalib
ana = pyplis.EmissionRateAnalysis(
imglist=aa_list,
bg_roi=LOG_ROI_SKY,
pcs_lines=pcs,
velo_glob=PLUME_VELO_GLOB,
velo_glob_err=PLUME_VELO_GLOB_ERR,
ref_check_lower_lim=REF_CHECK_LOWER,
ref_check_upper_lim=REF_CHECK_UPPER,
velo_dir_multigauss=HISTO_ANALYSIS_MULTIGAUSS,
def bg_img_off():
return pyplis.Img(BG_FILE_OFF, FUN).to_pyrlevel(0)
# SCRIPT MAIN FUNCTION
if __name__ == "__main__":
close("all")
ds = create_dataset()
on = ds.get_list("on")
on.darkcorr_mode = True
on.gaussian_blurring = 2
# Find and plot sky reference areas
on.bg_model.set_missing_ref_areas(on.current_img())
ax = on.bg_model.plot_sky_reference_areas(on.current_img())
# this is a beta version
kernel = np.ones((90, 90), dtype=np.uint8)
mask = on.prepare_bg_fit_mask(dilation=True,
dilate_kernel=kernel,
optflow_blur=0,
optflow_median=10,
i_min=1500,
i_max=2600,
plot_masks=True)
mask = pyplis.Img(mask)
ax2 = mask.show(tit="Input mask for surface fit")
on.set_bg_img_from_polyfit(mask.img)
ax3 = on.bg_img.show(tit="Surface fit result")
on.bg_model.mode = 5
on.tau_mode = True
on.show_current()
def bg_img_on():
return pyplis.Img(BG_FILE_ON, FUN).to_pyrlevel(0)
def plume_img_next():
return pyplis.Img(PLUME_FILE_NEXT, FUN).pyr_up(1)
def plume_img():
return pyplis.Img(PLUME_FILE, FUN).pyr_up(1)
ax0.set_xlim([0, 0.5])
# Get current AA image from image list
aa_init = aa_list.current_img()
# now determine sensitivity correction masks from the different cells
masks = {}
aa_imgs_corr = {}
for cd in cell_aa_calib.cd_vec:
mask = cellcalib.get_sensitivity_corr_mask("aa",
pos_x_abs=fov_x,
pos_y_abs=fov_y,
radius_abs=fov_extend,
cell_cd_closest=cd)
masks[cd] = mask
aa_imgs_corr[cd] = pyplis.Img(aa_init.img / mask.img)
# get mask corresponding to minimum cell CD
mask = list(masks.values())[np.argmin(list(masks.keys()))]
# assing mask to aa_list
aa_list.senscorr_mask = mask
# activate AA sensitivity correction in list
aa_list.sensitivity_corr_mode = True
# set DOAS calibration data in list ...
aa_list.calib_data = doascalib
# ... and activate calibration mode
aa_list.calib_mode = True
# convert the retrieval line to the specified pyramid level (script option)
pcs = PCS.convert(to_pyrlevel=PYRLEVEL)
# Load AA list
# includes viewing direction corrected geometry
aa_list = prepare_aa_image_list()
aa_list.pyrlevel = PYRLEVEL
# Load DOAS calbration data and FOV information (see example 6)
doascalib = pyplis.doascalib.DoasCalibData()
doascalib.load_from_fits(file_path=CALIB_FILE)
doascalib.fit_calib_polynomial()
# Load AA corr mask and set in image list(is normalised to DOAS FOV see
# ex7)
aa_corr_mask = pyplis.Img(CORR_MASK_FILE)
aa_list.aa_corr_mask = aa_corr_mask
# set DOAS calibration data in image list
aa_list.calib_data = doascalib
# you can check the settings first
aa_list.gaussian_blurring = 1
aa_list.calib_mode = False
aa_list.aa_mode = False
raw_disp = aa_list.show_current()
raw = aa_list.this.duplicate()
raw_disp.set_title("")
figs.append(raw_disp.figure)
aa_list.calib_mode = True
from cv2 import erode, dilate
