def __init__(self, IN_pixc_main_file):
# 1 - Retrieve needed information from pixel cloud main file
sensor_main = myCdf.myNcReader(IN_pixc_main_file)
# 1.1 - Number of pixels in range dimension
self.altitude = sensor_main.getVarValue("altitude")
# 1.1 - Number of pixels in range dimension
self.azimuth_spacing = sensor_main.getAttValue("azimuth_spacing")
def __init__(self, IN_gdem_file, list_landtype_flags):
gdem_main = myCdf.myNcReader(IN_gdem_file, dim=2)
df, nb_lon, nb_lat = gdem_main.getVarValue2d('longitude', 'latitude')
self.dataframe = df
self.nb_lon = nb_lon
self.nb_lat = nb_lat
print("Calcul des longitudes et latitudes du gdem")
self.elevation = self.dataframe.elevation.values.reshape(self.nb_lat, self.nb_lon)
self.landtype = self.dataframe.landtype.values.reshape(self.nb_lat, self.nb_lon)
self.longitude = self.dataframe.longitude.values
self.latitude = self.dataframe.latitude.values
def __init__(self, IN_pixc_main_file, list_classif_flags):
# 1 - Retrieve needed information from pixel cloud main file
pixc_main = myCdf.myNcReader(IN_pixc_main_file)
# 1.1 - Number of pixels in range dimension
self.nr_pixels = pixc_main.getAttValue("nr_pixels")
# 1.2 - Number of pixels in azimuth dimension
self.nr_lines = pixc_main.getAttValue("nr_lines")
#~ # 1.3 - Cycle number
#~ self.cycle_num = pixc_main.getAttValue("cycle_number")
#~ # 1.4 - Pass number
#~ self.pass_num = pixc_main.getAttValue("pass_number")
#~ # 1.5 - Tile reference
#~ self.tile_ref = pixc_main.getAttValue("tile_ref")
# 1.6 - Classification flag
self.classif = pixc_main.getVarValue("classification")
# Get indices corresponding to input classification flags
TMP_classif = self.classif # Init temporary classif vector
self.selected_idx = None # Init wanted indices vector
self.nb_selected = 0
for classif_flag in list_classif_flags:
vInd = np.where(TMP_classif == int(classif_flag))[0]
print("[PixelCloud] %d pixels with classification flag = %d" %
(vInd.size, int(classif_flag)))
if (vInd.size != 0):
if (self.nb_selected == 0):
self.selected_idx = vInd
self.nb_selected += vInd.size
else:
self.selected_idx = np.array(
list(itertools.chain(self.selected_idx, vInd)))
self.nb_selected += vInd.size
self.nb_selected = self.selected_idx.size
print("[PixelCloud] => %d pixels to keep" % (self.nb_selected))
# Keep PixC data only for selected pixels
if (self.nb_selected != 0):
# 1.7 - Range indices of water pixels
self.range_idx = pixc_main.getVarValue("range_index")[
self.selected_idx]
# Number of water pixels
self.nb_water_pix = self.range_idx.size
# 1.8 - Azimuth indices of water pixels
self.azimuth_idx = pixc_main.getVarValue("azimuth_index")[
self.selected_idx]
# 1.12 - Pixel area
self.pixel_area = pixc_main.getVarValue("pixel_area")[
self.selected_idx]
# 1.14 - Longitude
self.longitude = pixc_main.getVarValue("longitude")[
self.selected_idx]
# 1.15 - Latitude
self.latitude = pixc_main.getVarValue("latitude")[
self.selected_idx]
# 1.16 - Height
self.height = pixc_main.getVarValue("height")[self.selected_idx]
pixc_main.close()
import profile
def normalize(vect):
norm = np.sqrt(np.dot(vect, vect))
return vect / norm #don t use me with np.zeroes(#)
## last JPL version
input_intf_path = "/work/ALT/swot/swotdev/SIMU_JPL/simu_US_land_-5dB/simu1/output/0001/cycle_0001_pass_0107_RightSwath_nlcd50_darkWater_25m_CBE/intf_truth.RightSwath.nc"
input_sensor_path = "/work/ALT/swot/swotdev/SIMU_JPL/simu_US_land_-5dB/simu1/output/0001/cycle_0001_pass_0107_RightSwath_nlcd50_darkWater_25m_CBE/sensor.nc"
## november binaries, SAM simulator
#~ input_intf_path = "/work/ALT/swot/swothr/users/desrochesd/runs/old/test_liv12_06.ref/makeInterferogramJPL_1.01/outputs/intf_test.LeftSwath.nc"
#~ input_sensor_path = "/work/ALT/swot/swothr/users/desrochesd/runs/old/test_liv12_06.ref/makeSensor_2.00/outputs/sensorfile.nc"
intf_main = myCdf.myNcReader(input_intf_path, dim=2)
df, nb_az, nb_rg = intf_main.getVarValue2d('record', 'nr_pixels')
dataframe = df
near_range = intf_main.content.attrs['near_range']
range_spacing = intf_main.content.attrs['range_spacing']
no_layover_x = dataframe.no_layover_x.values.reshape(nb_rg, nb_az)
no_layover_y = dataframe.no_layover_y.values.reshape(nb_rg, nb_az)
no_layover_z = dataframe.no_layover_z.values.reshape(nb_rg, nb_az)
height = dataframe.height.values.reshape(nb_rg, nb_az)
sensor_main = myCdf.myNcReader(input_sensor_path)
x_sensor = sensor_main.getVarValue("x")
y_sensor = sensor_main.getVarValue("y")
z_sensor = sensor_main.getVarValue("z")