): ### generate a mesh_Shps array for each eccentricity and add it to mesh_Shps_list
### reset mesh shapes array, and the particle area/radius arrays:
#pss.mesh_Shps = np.zeros((n,ns,ns))
# Here, you are re-defining mesh_Shps, it is already defined in pySALESetup and unnecessary. If you just wish to reset them, do this:
pss.mesh_Shps *= 0.
part_area *= 0.
part_radii *= 0.
#part_angle = np.zeros((n)) ### add in list of angles
for i in range(n):
theta = angles[i] ### use the already generated list of angles
Rad = np.sqrt(
axis_ratios[j]
) * cppr ### radius calculated so that area stays constant, where cppr is radius of perfect circle
pss.mesh_Shps[i, :, :] = pss.gen_ellipse(
Rad, theta, eccen_list[j]
) ### Generate an ellipse of radius: Rad, angled at theta, and with eccentricity taken from eccen_list
part_area[i] = np.sum(
pss.mesh_Shps[i, :, :]) # Record the shape's area
part_radii[
i] = cppr # Record the shape's radius (semi-major radius in this case) ###need to change this?
#part_angle[i] = theta
#mesh_Shps_list.append(pss.mesh_Shps) ### append new mesh_Shps to mesh_Shps_list
# try to avoid appending things as it's a very costly programming technique (computationally)
mesh_Shps_array[j, :, :, :] = pss.mesh_Shps
#part_area_list.append(part_area)
#part_radii_list.append(part_radii)
#part_angle_list.append(part_angle)
############################################################################################################################################################
#r = cppr
#r = np.zeros((n,6))+cppr #+ cppr*np.random.randn(6)/4.
#r = np.random.randn(n)*np.sqrt(cppr_range) + cppr
#print r
for i in range(
n
): # n+1 as range starts at 0; i.e. you'll never get to i = n unless range goes to n+1!
r = pss.cppr_min + (i**SFDslope) * cppr_range / (
(n - 1)**SFDslope
) # generate radii that are incrementally greater for each circle produced
angle = np.random.random() * np.pi * 2.
this_eccen = eccen + ER * (np.random.random() * 2. - 1.)
#pss.mesh_Shps[i,:,:] = pss.gen_polygon(6,r)
pss.mesh_Shps[i, :, :] = pss.gen_ellipse(r, angle, this_eccen)
#pss.mesh_Shps[i,:,:] = pss.gen_circle(r)
part_area[i] = np.sum(pss.mesh_Shps[i, :, :])
part_radii.append(r)
part_angle.append(angle)
part_eccen.append(this_eccen)
part_angle = np.array(part_angle)
part_eccen = np.array(part_eccen)
""" #################################################################################### """
""" ####### sort particles into order of Large -> Small ####### """
""" #################################################################################### """
sort_indices = np.argsort(part_area) # Sort areas into smallest to largest
part_area = part_area[sort_indices] # Arrange as appropriate
pss.mesh_Shps = pss.mesh_Shps[sort_indices, :, :]
pss.generate_mesh(X_cells, Y_cells, mat_no, cppr, PR,
vol_frac) # Generate & set up the mesh
n = pss.N # n is the number of particles to generated as a 'library' to be placed into the mesh
part_area = np.zeros(
(n)) # N.B. particles can (and likely will) be placed more than once
part_radii = np.zeros(
(n)
) # part_area and part_radii are arrays to contain the area and radii of the 'library' particles
for i in range(n):
eccen = np.sqrt(8. / 9.)
theta = random.random() * np.pi
Rad = cppr
pss.mesh_Shps[i, :, :] = pss.gen_ellipse(
Rad, theta, eccen
) # Generate an ellipse of radius: Rad, angled at theta, and with eccentricity: eccen.
part_area[i] = np.sum(pss.mesh_Shps[i, :, :]) # Record the shape's area
part_radii[
i] = cppr # Record the shape's radius (semi-major radius in this case)
vol_placed_frac = 0.
placed_part_area = []
nmin = 0 # Max and min elements of the shape 'library'
nmax = n - 1
xcoords = [
] # Lists to record the coords, radii, 'library' number and particle number of each particle
ycoords = [
] # Start empty and append to them as we do not know how many particles will be generated
y_length = 1.e-3
GRIDSPC = x_length/X_cells # Physical distance/cell
mat_no = 5
pss.generate_mesh(X_cells,Y_cells,mat_no,cppr,PR,vol_frac) # Generate & set up the mesh
n = pss.N # n is the number of particles to generated as a 'library' to be placed into the mesh
part_area = np.zeros((n)) # N.B. particles can (and likely will) be placed more than once
part_radii = np.zeros((n)) # part_area and part_radii are arrays to contain the area and radii of the 'library' particles
for i in range(n):
eccen = np.sqrt(8./9.)
theta = random.random()*np.pi
Rad = cppr
pss.mesh_Shps[i,:,:] = pss.gen_ellipse(Rad,theta,eccen) # Generate an ellipse of radius: Rad, angled at theta, and with eccentricity: eccen.
part_area[i] = np.sum(pss.mesh_Shps[i,:,:]) # Record the shape's area
part_radii[i] = cppr # Record the shape's radius (semi-major radius in this case)
vol_placed_frac = 0.
placed_part_area = []
nmin = 0 # Max and min elements of the shape 'library'
nmax = n-1
xcoords = [] # Lists to record the coords, radii, 'library' number and particle number of each particle
ycoords = [] # Start empty and append to them as we do not know how many particles will be generated
radii = []
I_shape = []
J_shape = []
part_radii = []
part_angle = []
part_eccen = []
cppr_range = pss.cppr_max - pss.cppr_min
#r = cppr
#r = np.zeros((n,6))+cppr #+ cppr*np.random.randn(6)/4.
#r = np.random.randn(n)*np.sqrt(cppr_range) + cppr
#print r
for i in range(n): # n+1 as range starts at 0; i.e. you'll never get to i = n unless range goes to n+1!
r = pss.cppr_min + (i**SFDslope)*cppr_range/((n-1)**SFDslope) # generate radii that are incrementally greater for each circle produced
angle = np.random.random() * np.pi * 2.
this_eccen = eccen + ER * (np.random.random() * 2. - 1.)
#pss.mesh_Shps[i,:,:] = pss.gen_polygon(6,r)
pss.mesh_Shps[i,:,:] = pss.gen_ellipse(r,angle,this_eccen)
#pss.mesh_Shps[i,:,:] = pss.gen_circle(r)
part_area[i] = np.sum(pss.mesh_Shps[i,:,:])
part_radii.append(r)
part_angle.append(angle)
part_eccen.append(this_eccen)
part_angle = np.array(part_angle)
part_eccen = np.array(part_eccen)
""" #################################################################################### """
""" ####### sort particles into order of Large -> Small ####### """
""" #################################################################################### """
sort_indices = np.argsort(part_area) # Sort areas into smallest to largest
part_area = part_area[sort_indices] # Arrange as appropriate
