#target_point_density = np.array([5., 10., 20., 30., 40.])
target_pulse_density = np.array([25])
keys_2 = ['25']
#target_points = area*target_point_density
target_shots = (area * target_pulse_density).astype('int')
#---------------------------------------------------------------------------------------------------------------
# Load the data etc.
mask = plot_coordinates['plot'] == plot
affine = lstsq.least_squares_affine_matrix(plot_coordinates['x'][mask],
plot_coordinates['y'][mask],
plot_coordinates['x_prime'][mask],
plot_coordinates['y_prime'][mask])
plot_bbox = np.array(
lstsq.apply_affine_transformation(np.array(
[-20., 120.,
120., -20.]), np.array([120., 120., -20., -20.]), affine)).transpose(
) # simple square bounding box applied for all sensitivity analyses
pts, starting_ids, trees = io.load_lidar_file_by_polygon(
las_file, plot_bbox, filter_by_first_return_location=True)
pts[pts[:, 2] < 0, 2] = 0
n_returns = pts.shape[0]
shots = np.unique(pts[:, 6])
#n_shots=shots.size
#target_shots = (np.ceil(target_points*n_shots/float(n_returns))).astype('int')
subplots = {}
PAD_profiles_MH = {}
PAD_profiles_MH_wt = {}
PAD_profiles_rad1 = {}
#plot = 'Belian'
#plot = 'E'
#plot = 'B North'
plot = sys.argv[1]
if plot == 'BNorth':
plot = 'B North'
print(plot)
plot_width = 100.
sample_res = 0.5
#---------------------------------------------------------------------------------------------------------------
# Load the data etc.
print(plot_coordinates['plot'])
mask = plot_coordinates['plot']==plot
affine=lstsq.least_squares_affine_matrix(plot_coordinates['x'][mask],plot_coordinates['y'][mask],plot_coordinates['x_prime'][mask],plot_coordinates['y_prime'][mask])
plot_bbox = np.array(lstsq.apply_affine_transformation(np.array([0.,100.,100.,0.]),np.array([100.,100.,0.,0.]),affine)).transpose() # simple square bounding box applied for all sensitivity analyses
pts, starting_ids, trees = io.load_lidar_file_by_polygon(las_file,plot_bbox,filter_by_first_return_location=True)
pts[pts[:,2]<0,2]=0
n_returns = pts.shape[0]
# Loop through all the spatial scales of interest
print("generating sample grid")
# Now create the subplot grids
rows = int(plot_width/sample_res)
cols = int(plot_width/sample_res)
x=np.arange(0,plot_width+sample_res,sample_res)
y=np.arange(0,plot_width+sample_res,sample_res)
xv,yv=np.asarray(np.meshgrid(x,y))
datatype = {'names': ('plot', 'x', 'y', 'x_prime', 'y_prime'), 'formats': ('<U16','f16','f16','f16','f16')}
plot_coordinates = np.genfromtxt(gps_pts_file, skip_header = 0, delimiter = ',',dtype=datatype)
heights = np.arange(0.,80)+1
plot_width = 100.
#---------------------------------------------------------------------------------------------------------------
# Generate North arrows
n_arrow={}
n_arrow_text={}
for plot in (plots):
if plot in plot_coordinates['plot']:
mask = plot_coordinates['plot']==plot
affine=lstsq.least_squares_affine_matrix(plot_coordinates['x_prime'][mask],plot_coordinates['y_prime'][mask],
plot_coordinates['x'][mask],plot_coordinates['y'][mask])
n_arrow[plot] = np.array(lstsq.apply_affine_transformation(np.array([0.,0.]),
np.array([0.,15.]),affine)).transpose() # simple square bounding box applied for all sensitivity analyses
n_arrow_text[plot] = np.array(lstsq.apply_affine_transformation(np.array([0.,0.]),
np.array([0.,18.]),affine)).transpose() # simple square bounding box applied for all sensitivity analyses
else:
n_arrow[plot] = np.array(([0.,0.],[0.,15.])) # simple square bounding box applied for all sensitivity analyses
n_arrow_text[plot] = np.array(([0.,0.],[0.,18]))
shift_x = np.max(n_arrow[plot][:,0])-90
shift_y = np.max(n_arrow[plot][:,1])-90
n_arrow[plot][:,0]-=shift_x
n_arrow[plot][:,1]-=shift_y
n_arrow_text[plot][:,0]-=shift_x
n_arrow_text[plot][:,1]-=shift_y
# Plot up the figure
labels = ['OGF (MLA-01)','MLF (SAF-03)','HLF (SAF-02)','OP']
area = 10.**4
target_point_density = np.array([5., 10., 15., 20., 25., 30., 40.])
keys_2 = ['5', '10', '15', '20', '25', '30', '40']
target_points = area * target_point_density
#---------------------------------------------------------------------------------------------------------------
# Load the data etc.
mask = plot_coordinates['plot'] == alt_plot
affine = lstsq.least_squares_affine_matrix(plot_coordinates['x'][mask],
plot_coordinates['y'][mask],
plot_coordinates['x_prime'][mask],
plot_coordinates['y_prime'][mask])
plot_bbox = np.array(
lstsq.apply_affine_transformation(plot_coordinates['x'][mask],
plot_coordinates['y'][mask],
affine)).transpose()
pts, starting_ids, trees = io.load_lidar_file_by_polygon(
las_file, plot_bbox, filter_by_first_return_location=True)
n_returns = pts.shape[0]
shots = np.unique(pts[:, -1])
n_shots = shots.size
target_shots = (np.ceil(target_points * n_shots /
float(n_returns))).astype('int')
shots = np.unique(pts[:, -1])
subplots = {}
PAD_profiles_MH = {}
PAD_profiles_rad1 = {}
PAD_profiles_rad2 = {}