def line():
"""Create an example retrieval line."""
return pyplis.LineOnImage(630,
780,
1000,
350,
pyrlevel_def=0,
normal_orientation="left")
XS = np.sin(np.deg2rad(23)) * DS
# 400/2 pixels in the horizontal gives horizontal pixel size
DX = XS / 200. * 1000
print("DX, ", DX, DY, DZ, DS, XS)
# Clear sky ROI
BG_ROI = [0, 10, 0, 40]
# Setup optical flow class (you may play with the parameters)
### DEFINE PCS LINES
pcs1 = pyplis.LineOnImage(
COL_NUM1,
ROW_BOTTOM,
COL_NUM1,
ROW_TOP,
line_id="A",
normal_orientation="right", #"left",#
color="#e67300")
pcs2 = pyplis.LineOnImage(
COL_NUM2,
ROW_BOTTOM,
COL_NUM2,
ROW_TOP,
line_id="B",
normal_orientation="right", #"left",
color="#1a1aff")
### LOAD IMAGES
ImagesAA = load_MYSTIC_images(filebase,
wavelengthA,
ax.set_xlabel("PCS", fontsize=16)
ax.legend(loc="best", fancybox=True, framealpha=0.5, fontsize=12)
return fig
# SCRIPT MAIN FUNCTION
if __name__ == "__main__":
close("all")
# Create a background model with relevant sky reference areas
bg_model = init_background_model()
# Define exemplary plume cross section line
pcs_line = pyplis.LineOnImage(x0=530,
y0=730,
x1=890,
y1=300,
line_id="example PCS",
color="lime")
plume, plume_vigncorr, bg = load_and_prepare_images()
auto_params, fig0 = autosettings_vs_manual_settings(bg_model)
# Script option
if USE_AUTO_SETTINGS:
bg_model.update(**auto_params)
# Model 4 exemplary tau images
# list to store figures of tau plotted tau images
_tau_figs = []
ROW_TOP = 90+150
elif case_here=='CASEC':
COL_NUM1L = 250
COL_NUM1R = 250
COL_NUM2 = 290
COL_NUM3 = 300
ROW_BOTTOM = 0+150
ROW_TOP = 110+150
# Clear sky ROI
BG_ROI = [0, 10, 0, 40]
# Setup optical flow class (you may play with the parameters)
FLOW = pyplis.OptflowFarneback()
PCS1 = pyplis.LineOnImage(COL_NUM1L, ROW_BOTTOM, COL_NUM1R, ROW_TOP,
line_id="A", normal_orientation="right", #"left",#
color="#e67300")
PCS2 = pyplis.LineOnImage(COL_NUM2, ROW_BOTTOM, COL_NUM2, ROW_TOP,
line_id="B", normal_orientation="right", #"left",
color="#1a1aff")
PCS3 = pyplis.LineOnImage(COL_NUM3, ROW_BOTTOM, COL_NUM3, ROW_TOP,
line_id="C", normal_orientation="right", #"left",
color="#1a1a00")
pcs1 = PCS1
pcs2 = PCS2
pcs3 = PCS3
# in this object the results from the optical flow histogram analysis
# are stored (i.e. time series of local average displacement vector)
props1 = pyplis.LocalPlumeProperties(roi_id=pcs1.line_id,
color=pcs1.color)
# IMPORT GLOBAL SETTINGS
from SETTINGS import IMG_DIR, SAVEFIGS, SAVE_DIR, FORMAT, DPI, OPTPARSE
# IMPORTS FROM OTHER EXAMPLE SCRIPTS
from ex10_bg_imglists import get_bg_image_lists
# Check script version
check_version()
# SCRIPT OPTONS
# lower boundary for I0 value in dilution fit
I0_MIN = 0.0
AA_THRESH = 0.03
# exemplary plume cross section line for emission rate retrieval (is also used
# for full analysis in ex12)
PCS_LINE = pyplis.LineOnImage(x0=530, y0=586, x1=910, y1=200, line_id="pcs")
# Retrieval lines for dilution correction (along these lines, topographic
# distances and image radiances are determined for fitting the atmospheric
# extinction coefficients)
TOPO_LINE1 = pyplis.LineOnImage(1100, 650, 1000, 900, line_id="flank far",
color="lime",
linestyle="-")
TOPO_LINE2 = pyplis.LineOnImage(1000, 990, 1100, 990, line_id="flank close",
color="#ff33e3",
linestyle="-")
# all lines in this array are used for the analysis
USE_LINES = [TOPO_LINE1, TOPO_LINE2]
# specify pixel resolution of topographic distance retrieval (every nth pixel
# Load cell calibration (see example 5)
cellcalib = perform_auto_cell_calib()
# get cell calibration
cellcalib.prepare_calib_data(
pos_x_abs=fov_x, # change if you want it for a specific pix
pos_y_abs=fov_y, # change if you want it for a specific pix
radius_abs=fov_extend, # radius of retrieval disk
on_id="on", # ImgList ID of onband filter
off_id="off") # ImgList ID of offband filter
cell_aa_calib = cellcalib.calib_data["aa"]
# Define lines on image for plume profiles
pcs1 = pyplis.LineOnImage(620, 700, 940, 280,
line_id="center")
pcs2 = pyplis.LineOnImage(40, 40, 40, 600,
line_id="edge")
# Plot DOAS calibration polynomial
ax0 = doascalib.plot(add_label_str="DOAS")
ax0 = cellcalib.calib_data["aa"].plot(ax=ax0, c="r")
ax0.set_title("")
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 = {}