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feature_extract_01C.py
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import numpy as np
import pandas as pd
import hotspot_file_01
import matplotlib.pyplot as plt
import pymp
import math
import multiprocessing
from scipy import optimize
from sklearn import preprocessing
def detect_surface_cnt(df, type_choose=22, **kwargs):
if ('cnt_bounds' in kwargs):
cnt_bounds = kwargs['cnt_bounds']
else:
cnt_bounds = hotspot_file_01.extract_cnt_coords(df)
df_other = df.loc[~(df['type'] == type_choose)]
df_cnt = df.loc[df['type'] == type_choose]
df_1 = df_cnt.loc[:, ['x', 'y', 'z']]
df_2 = df_other.loc[:, ['x', 'y', 'z']]
Z = compute_distance(df_1.as_matrix(), df_2.as_matrix())
print "Z: ", Z
z = np.amin(Z)
idx_val = (np.argmin(Z))
idx_val = np.unravel_index(idx_val, Z.shape)
#print 0.5*(df_1.as_matrix()[idx_val[1], :] + df_2.as_matrix()[idx_val[0], :])
return None
def compute_distance(cnt_array, cnt_other_array):
dist_list = []
sort_dist_list = []
idx_list = []
N = 5
points = cnt_array
for j in range(0, len(cnt_other_array)):
single_point = cnt_other_array[j, :]
dist = np.sum(((points - single_point)**2), axis=1)
dist = np.sqrt(dist)
sort_dist = np.sort(dist)
sort_dist = sort_dist[:N]
idx_sort = np.argsort(dist)
idx_sort = idx_sort[:N]
dist_list.append(dist)
sort_dist_list.append(sort_dist)
idx_list.append(idx_sort)
return np.asarray(dist_list), sort_dist_list, idx_list
#start reading the line
def track_fun_bond(df_t, fun_type_list):
bond_list = []
for t in range(0, len(df_t)):
df = df_t[t]
z_array = check_bond(df=df, fun_type_list=fun_type_list)
bond_list.append(z_array)
return np.asarray(bond_list)
def check_bond(df, fun_type_list, cnt_type=22):
#this function is to determine if the bonding between the CNT and the functional group is retained
#fun_type -> functional group type
df_cnt = df.loc[df['type'] == cnt_type]
df_1 = df_cnt.loc[:, ['x', 'y', 'z']]
z_list = []
#the Z-list constains the mean distance of functional group with its nearest neighbors
#we do a loop as fun_type is a list, there may be more than one atom i'm interested in
for i in range(0, len(fun_type_list)):
fun_type = fun_type_list[i]
df_fun = df.loc[df['type'] == fun_type]
df_fun_loc = df_fun.loc[:, ['x', 'y', 'z']]
#fun_loc -> multiple locations where the functional groups are located
_, Z2, _ = compute_distance(df_1.as_matrix(), df_fun_loc.as_matrix())
z_mean = np.mean(Z2)
z_list.append(z_mean)
return np.asarray(z_list)
def local_features(df_cnt, df_other):
#This function provides the local features for each convololution box
cnt_loc = df_cnt.loc[:, ['x', 'y', 'z']]
#other atom
other_loc = df_other.loc[:, 'mass', 'x', 'y', 'z']
cnt_mat = cnt_loc.as_matrix()
other_mat = other_loc.as_matrix()
FR = feature_equation_01(cnt_mat=cnt_mat, other_mat=other_mat)
return FR
def feature_equation_01(atom_bound, unique_type, bounds=np.asarray([2.0, 5.0, 10.0]), intervals=np.asarray([0.1, 0.1, 0.5])):
#Z is a function of r
#here insert the list of
type_choose = 22
#the first thing to do is detect the atoms
#The idea is that for each unique type we will create an array
b = len(unique_type)
r1 = np.linspace(0, bounds[0], bounds[0]/intervals[0], endpoint=False)
r2 = np.linspace(bounds[0], bounds[1], (bounds[1] - bounds[0])/intervals[1], endpoint=False)
r3 = np.linspace(bounds[1], (bounds[2]), (bounds[2]-bounds[1])/intervals[2] + 1)
r = np.concatenate((r1, r2, r3), axis=0)
x = np.zeros((len(r), b))
for k in range(0, b):
type = unique_type[k]
sigma_r = 1
#Seggregate by type
#df_cnt = df.loc[df['type'] == type_choose]
#df_atom = df.loc[df['type'] == type]
idx_cnt = np.where(atom_bound[:, 1] == type_choose)
idx_fun = np.where(atom_bound[:, 1] == type)
cnt_mat = atom_bound[idx_cnt[0]]
fun_mat = atom_bound[idx_fun[0]]
#indexing out co-ordinates only
cnt_mat = cnt_mat[:, 2:5]
fun_mat = fun_mat[:, 2:5]
#cnt_mat = df_cnt.loc[:, ['x', 'y', 'z']].as_matrix()
#atom_mat = df_atom.loc[:, ['x', 'y', 'z']].as_matrix()
dist_mat = compute_distance(cnt_mat, fun_mat)
d = dist_mat[0].flatten()
for count, r_val in enumerate(r):
x[count, k] = np.sum(np.divide((np.exp((-(r_val - d)**2)/(sigma_r**2))), d))
return r, x
#-----------------------------------------------------------------------------
def feature_equation_02(r, cnt_mat, other_mat, other_type, unique_type=np.asarray([2, 4, 9, 12, 15, 18, 16]), type_choose=22):
b = len(unique_type)
cnt_mat = np.transpose(cnt_mat)
x = np.zeros((len(r), b))
for k in range(0, b):
type = unique_type[k]
#sigma_r = .05
sigma_r = 0.05
#Seggregate by type
#df_cnt = df.loc[df['type'] == type_choose]
#df_atom = df.loc[df['type'] == type]
#fun mat
idx_fun = np.where((other_type.flatten()) == type)[0]
fun_mat = other_mat[idx_fun]
dist_mat = compute_distance(cnt_mat, fun_mat)
#the correct one is in fun mat
d = dist_mat[0].flatten()
d = d[d>0.001]
d = d[d < 10.0]
#print d[d<2.0]
#print d.shape
#print np.amax(d)
#print np.amin(d)
for count, r_val in enumerate(r):
x[count, k] = np.sum(np.divide((np.exp((-(r_val - d)**2)/(sigma_r**2))), d))
return x
####-------------------------------------------------------------------------------
def feature_troubleshoot(df, type_choose=22):
# this is the block to featurize
df_other = df.loc[~(df['type'] == type_choose)]
df_cnt = df.loc[df['type'] == type_choose]
FR = local_features(df_cnt=df_cnt, df_other=df_other)
return None
def detect_locations(df, threshold=4.0, type_choose=22, **kwargs):
if 'fun_type' in kwargs:
fun_type = kwargs['fun_type']
df_fun = df.loc[df['type'] == fun_type]
elif 'fun_id' in kwargs:
fun_id = kwargs['fun_id']
df_fun = df.loc[df['id'] == fun_id]
df_cnt = df.loc[df['type'] == type_choose]
index_fun = df_fun.loc[:, ['id']].as_matrix()
index_cnt = df_cnt.loc[:, ['id']].as_matrix()
df_1 = df_cnt.loc[:, ['x', 'y', 'z']]
df_2 = df_fun.loc[:, ['x', 'y', 'z']]
z1, _, _ = compute_distance(df_1.as_matrix(), df_2.as_matrix())
idx_choose = np.where((np.asarray(z1) < threshold))
id_fun = np.unique(index_fun[idx_choose[0]])
id_cnt = np.unique(index_cnt[idx_choose[1]])
df_fun_out = df_fun[df_fun.id.isin(id_fun)]
df_cnt_out = df_cnt[df_cnt.id.isin(id_cnt)]
z1 = np.asarray(z1)
return df_fun_out, df_cnt_out
def plot_features(R, input_array, idx):
plt.plot(R, input_array[:, idx])
plt.xlabel('r (A)')
plt.ylabel('Z-value')
plt.savefig('fig_Zvalue.eps')
plt.show()
return None
def plot_features02(R, input_array, R2, input_array2, idx):
plt.plot(R, input_array[:, idx], R2, input_array2[:, idx], 'ro')
plt.xlabel('r (A)')
plt.ylabel('Z-value')
plt.savefig('fig_Zvalue.eps')
plt.show()
return None
#def coarsen_features
def feature_cleanup(rho_mat):
rho_out = rho_mat.copy()
rho_mat[rho_mat < 1.0e-3] = 0
mean_val = np.mean(rho_mat, axis=0)
std_val = np.std(rho_mat, axis=0)
for k in range(0, rho_mat.shape[0]):
rho_out[k, :, :] = rho_mat[k, :, :] - mean_val
rho_out[k, :, :] = np.divide(rho_out[k, :, :], std_val)
rho_out[np.isnan(rho_out)] = 0
return rho_out
def CNT_atoms(df, z_len, bound_default=np.asarray([1.0, 2.0, 5.0]), type_choose=22):
#the goal of this function is to flatten the CNT
#and for each atoms track surrounding atoms
df_cnt = df.loc[df['type']==type_choose]
df_other = df
#df_other = df.loc[df['type']!=type_choose]
y_min = np.amin(df_cnt.loc[:, 'y'].as_matrix())
y_max = np.amax(df_cnt.loc[:, 'y'].as_matrix())
y_mid = 0.5*(y_min + y_max)
df_cnt1 = df_cnt.loc[df['y'] > y_mid]
mat_xy = df_cnt.loc[:, ['x', 'y']].as_matrix()
#RBF_array, r_v = compute_RDF(df_cnt, df_other, z_len)
RBF_array, r_v = compute_RDF(df_cnt=df_cnt, df_other=df_other, z_len=z_len, bounds=np.asarray([1.0, 2.0, 5.0]), intervals=[0.01, 0.01, 0.01])
return RBF_array, r_v
def compute_RDF(df_cnt, df_other, z_len, bounds=np.asarray([1.0, 2.0, 5.0]), intervals=np.asarray([0.01, 0.01, 0.01])):
#parameters to compute the RDF
r1 = np.linspace(0, bounds[0], bounds[0] / intervals[0], endpoint=False)
r2 = np.linspace(bounds[0], bounds[1], (bounds[1] - bounds[0]) / intervals[1], endpoint=False)
r3 = np.linspace(bounds[1], (bounds[2]), (bounds[2] - bounds[1]) / intervals[2] + 1)
r = np.concatenate((r1, r2, r3), axis=0)
unique_type = np.asarray([2, 4, 9, 12, 15, 18, 23])
#unique_type = np.asarray([2, 4, 9, 12, 15, 18, 3]) #change this if things don't work
#for each CNT atom I want to compute an RDF
cnt_id = df_cnt.loc[:, ['id']].as_matrix()
#print "fun-CNT id: ", np.where(cnt_id == 3395)
other_id = df_other.loc[:, ['id']].as_matrix()
other_type = df_other.loc[:, ['type']].as_matrix()
#print "fun id: ", np.where(other_id == 3413)
cnt_mat = df_cnt.loc[:, ['x', 'y', 'z']].as_matrix()
other_mat = df_other.loc[:, ['x', 'y', 'z']].as_matrix()
#print "Troubleshoot: "
#print cnt_mat[254, :]
#print other_mat[3381, :]
#print other_type[3381]
#print cnt_mat.shape
# shared array
RBF_array = pymp.shared.array((len(cnt_mat), len(r), len(unique_type)), dtype='float64')
with pymp.Parallel(multiprocessing.cpu_count()) as p:
for i in p.range(len(cnt_mat)):
#for i in range(1):
cnt_choose = cnt_mat[i, :]
#print other_mat[2311, :]
cnt_choose = cnt_choose[:, None]
other_image = check_periodic(cnt_choose[2], other_mat, z_len)
##print other_image[2311, :]
#troubleshoot
RBF_array[i, :, :] = feature_equation_02(r=r, cnt_mat=cnt_choose, other_mat=other_image, other_type=other_type)
###this is an extra block to troubleshoot where
other_test = df_other.loc[df_other['type']!=22]
test_mat = other_test.loc[:, ['x', 'y', 'z']].as_matrix()
D, _, _ = compute_distance(cnt_array=cnt_mat, cnt_other_array=test_mat)
#print D[D < 2.0]
#print np.where([D < 2.0])
return RBF_array, r
def check_periodic(cnt_z, other_mat, z_len):
other_z = other_mat[:, 2] #z-coordinate
z_min = np.amin(other_mat[:, 2])
z_max = np.amax(other_mat[:, 2])
z_mid = 0.5*(z_min + z_max)
if cnt_z < z_mid:
other_z[other_z > z_mid] = other_z[other_z> z_mid] - z_len
else:
other_z[other_z < z_mid] = other_z[other_z < z_mid] + z_len
other_mat[:, 2] = other_z
return other_mat
def discretize_features(RBF, new_intv=np.asarray([0.5, 0.25, 0.5]), old_intv=np.asarray([0.01, 0.01, 0.01]), bounds=np.asarray([1.0, 2.0, 5.0])):
factor_v = (np.divide(new_intv, old_intv)).astype(int)
#idx_new = (np.divide(bounds, new_intv)).astype(int)
#idx_old = (np.divide(bounds, old_intv)).astype(int)
idx_new = np.zeros((len(new_intv), ))
idx_old = np.zeros_like(idx_new)
temp_val = 0
bound_old = 0
temp2 = 0
for idx in range(0, len(new_intv)):
idx_new[idx] = temp_val + int((bounds[idx] - bound_old)/new_intv[idx])
idx_old[idx] = temp2 + int((bounds[idx] - bound_old) / old_intv[idx])
temp_val = int(idx_new[idx])
bound_old = int(bounds[idx])
temp2 = int(idx_old[idx])
idx_new = np.insert(idx_new, 0, 0)
idx_old = np.insert(idx_old, 0, 0)
idx_new = idx_new.astype(int)
idx_old = idx_old.astype(int)
# print idx_new
#print idx_old
#computing the size of the new array
r1 = np.linspace(0, bounds[0], bounds[0] / new_intv[0], endpoint=False) + 0.5*new_intv[0]
r2 = np.linspace(bounds[0], bounds[1], (bounds[1] - bounds[0]) / new_intv[1], endpoint=False) + 0.5*new_intv[1]
r3 = np.linspace(bounds[1], (bounds[2]), (bounds[2] - bounds[1]) / new_intv[2] + 1) + 0.5*new_intv[2]
r = np.concatenate((r1, r2, r3), axis=0)
RBF_out = np.zeros((RBF.shape[0], len(r), RBF.shape[2]))
#print "r: "
#print r
for k in range(0, RBF.shape[0]):
count = 0
#with pymp.Parallel(multiprocessing.cpu_count()) as p:
for i in range(len(new_intv)):
first_idx = idx_new[i]
last_idx = idx_new[i+1]
#print first_idx, last_idx
#print idx_new, idx_old
#print "i :", i
#print "first_idx: ", first_idx
#print "last_idx: ", last_idx
#print "factor_f: ", factor_v[i]
Z = RBF[k, :, :]
#print Z
for j in range(0, last_idx - first_idx):
#print j
#print count
#print idx_old[i] + j*factor_v[i], idx_old[i] + (j+1)*factor_v[i]
#print Z[idx_old[i] + j*factor_v[i]:idx_old[i] + (j+1)*factor_v[i], :]
try:
max_out = np.amax(Z[(idx_old[i] + j*factor_v[i]):(idx_old[i] + (j+1)*factor_v[i]), :], axis=0)
except:
max_out = np.zeros(RBF.shape[2], )
#replacing using a sum
#print "Max out"
#print max_out
#max_out = np.sum(Z[(idx_old[i] + j * factor_v[i]):(idx_old[i] + (j + 1) * factor_v[i]), :], axis=0)
RBF_out[k, count, :] = max_out
count += 1
#print "count: ", count
#print "r"
#print r
#cleaning up RBF
RBF_out[RBF_out < 0.00001] = 0
return r, RBF_out
###The following 3 functions are used to fit the CNT coordinates to a a circle.
##first to extract the x-y coordinates for each df in list
def fit_circle(df_list, type_choose=22):
center_list = []
R_list = []
#with pymp.Parallel(multiprocessing.cpu_count()) as p:
for i in range(len(df_list)):
df = df_list[i]
df_cnt = df.loc[df['type'] == type_choose]
xy_mat = df_cnt.loc[:, ['x', 'y']].as_matrix()
x_m = np.mean(xy_mat[:, 0])
y_m = np.mean(xy_mat[:, 1])
center_estimate = x_m, y_m
center, ier = optimize.leastsq(f, center_estimate, args=(xy_mat[:, 0], xy_mat[:, 1]))
xc, yc = center
Ri = calc_R(xy_mat[:, 0], xy_mat[:, 1], *center)
R = Ri.mean()
residu = np.sum((Ri - R) ** 2)
center_list.append(center)
R_list.append(R)
#plt.plot(xy_mat[:, 0], xy_mat[:, 1], 'ro')
#plt.show()
#plot_data_circle(x=xy_mat[:, 0], y=xy_mat[:, 1], xc=xc, yc=yc, R=R)
print center_list
print R_list
return center_list, R_list
def calc_R(x,y, xc, yc):
""" calculate the distance of each 2D points from the center (xc, yc) """
return np.sqrt((x-xc)**2 + (y-yc)**2)
def f(c, x, y):
""" calculate the algebraic distance between the data points and the mean circle centered at c=(xc, yc) """
Ri = calc_R(x, y, *c)
return Ri - Ri.mean()
def plot_data_circle(x,y, xc, yc, R):
f = plt.figure( facecolor='white') #figsize=(7, 5.4), dpi=72,
plt.axis('equal')
theta_fit = np.linspace(-math.pi, math.pi, 180)
x_fit = xc + R*np.cos(theta_fit)
y_fit = yc + R*np.sin(theta_fit)
plt.plot(x_fit, y_fit, 'b-' , label="fitted circle", lw=2)
plt.plot([xc], [yc], 'bD', mec='y', mew=1)
plt.xlabel('x')
plt.ylabel('y')
# plot data
plt.plot(x, y, 'ro', label='data', mew=1)
plt.legend(loc='best',labelspacing=0.1 )
plt.grid()
plt.show()
plt.title('Least Squares Circle')
return None
####with the above three functions, we found the best fit circle,
def transfor_rot(df_list, RBF_in, targets, z_cords, type_choose=22, N_alpha = 20):
del_alpha = np.linspace(0, 2*(math.pi), N_alpha, endpoint=False)
#This function tranforms based on the rotational symmetry
#for each df in df_list, extract xyz, rotate it by alpha
center_list, R_list = fit_circle(df_list)
center_out = []
CNT_out = []
RBF_out = []
for i in range(0, len(df_list)):
df = df_list[i]
df_cnt = df.loc[df['type']==type_choose]
#need to extract RBF
RBF_mat = RBF_in[i]
#extract box bounds
z_bounds = z_cords[i]
z_min = z_bounds[0]
z_max = z_bounds[1]
cnt_id = df_cnt.loc[:, ['id']].as_matrix()
xyz_mat = df_cnt.loc[:, ['x', 'y', 'z']].as_matrix()
xyz_trans = xyz_mat.copy()
xyz_trans[:, 0:2] = xyz_mat[:, 0:2] - center_list[i]
Z_val = xyz_mat[:, 2]
R_p = np.linalg.norm(xyz_trans[:, 0:2], axis=1)
theta_0 = np.arctan2(xyz_trans[:, 1], xyz_trans[:, 0])
#initialize a random array
cnt_aug = np.zeros((len(del_alpha), xyz_mat.shape[0], xyz_mat.shape[1]))
cnt_aug[:, :, 2] = Z_val
for j in range(len(del_alpha)):
theta = theta_0 + del_alpha[j]
cnt_aug[j, :, 0] = np.multiply(R_p, np.cos(theta))
cnt_aug[j, :, 1] = np.multiply(R_p, np.sin(theta))
cnt_aug[:, :, 0:2] = cnt_aug[:, :, 0:2] + center_list[i]
cnt_final = transform_trans(cnt_rot=cnt_aug, z_min=z_min, z_max=z_max)
#RBF_temp repeat
RBF_t = np.repeat(RBF_mat[None, :, :], len(cnt_final), axis=0)
#convert numpy array to list:
cnt_list = array_to_list(cnt_final)
RBF_list = array_to_list(RBF_t)
if len(CNT_out) > 0:
CNT_out = CNT_out + cnt_list
RBF_out = RBF_out + RBF_list
else:
CNT_out = cnt_list
RBF_out = RBF_list
y_out = (np.repeat(targets[:, None], repeats=len(cnt_final), axis=1)).flatten()
##append array here
#plt.plot(cnt_aug[0, :, 0], cnt_aug[0, :, 1], 'ko')
#plt.plot(cnt_aug[5, :, 0], cnt_aug[5, :, 1], 'ro')
#plt.plot(cnt_aug[8, :, 0], cnt_aug[8, :, 1], 'bo')
#plt.show()
#print "z_lo, z_hi: ", z_min, z_max
#print "cnt_aug: "
# print cnt_aug[0,:10, :]
#print "cnt_final: "
#print cnt_final[0, :10, :]
#print cnt_final[2, :10, :]
#print cnt_final[10, :10, :]
#print cnt_final.shape
#plt.plot(cnt_final[0, :, 1], cnt_final[0, :, 2], 'go')
#plt.plot(cnt_final[11, :, 1], cnt_final[21, :, 2], 'ro')
#plt.show()
#print cnt_final.shape
#idx_1 = np.where(cnt_final < 30.0)
#bool_val = cnt_final < 30.0
#cnt_choose = cnt_final[bool_val[:, :, 2]]
#cnt_1 = cnt_final[0, :, :]
#bool2 = cnt_1 < 30.0
#cnt_c2 = cnt_1[bool2[:, 1]]
## we can compute the angle f
#cnt_aug = transform_trans(cnt_rot=cnt_aug, z_min=)
#CNT_out = np.concatenate(CNT_out, axis=0)
print "Troubleshoot: "
print CNT_out[0].shape
print (CNT_out[0])[:, 2]
print RBF_out[0].shape
print len(CNT_out)
print len(RBF_out)
#Troubleshoot
Z = CNT_out[0]
X = Z.reshape(CNT_out[0].shape[1], CNT_out[0].shape[2])
X2 = Z.reshape(CNT_out[1].shape[1], CNT_out[1].shape[2])
#plt.plot(X[:, 1], X[:, 2], 'go')
#plt.plot(X2[:, 1], X2[:, 2], 'go')
#plt.plot(cnt_final[21, :, 1], cnt_final[21, :, 2], 'ro')
#plt.show()
#RBF_out = np.asarray(RBF_out)
#CNT_f = CNT_out.reshape(-1, CNT_out.shape[2], CNT_out.shape[3])
#RBF_f = RBF_out.reshape(-1, RBF_out.shape[2], RBF_out.shape[3])
return CNT_out, RBF_out, y_out, center_list
def linearize_cicle(cnt_list, center_list):
theta_list = []
for k in range(0, len(cnt_list)): #k is th enumbner of models
cnt_xy = cnt_list[k][:, 0:2]
theta = np.arctan(np.divide((cnt_xy[:, 1] - center_list[k][1]), (cnt_xy[:, 0] - center_list[k][0])))
theta_list.append(theta)
return theta_list
def transform_trans(cnt_rot, z_min, z_max, N_t = 20):
#cnt_rot -> 3-D array [#rot copies, cnt_atoms, coords]
#N_t -> number of translational copies
delta = np.linspace(0, (z_max - z_min), N_t, endpoint=False)
Z_trans = np.zeros((cnt_rot.shape[0]*(N_t), cnt_rot.shape[1]))
cnt_trans = np.zeros((cnt_rot.shape[0]*(N_t), cnt_rot.shape[1], cnt_rot.shape[2]))
for k in range(0, cnt_rot.shape[0]):
Z_pick = cnt_rot[k, :, 2]
for i in range(0, cnt_rot.shape[1]):
Z_trans[k*(N_t):(k+1)*(N_t), i] = Z_pick[i] + delta
cnt_trans[k*N_t:(k+1)*N_t, i, 0:2] = cnt_rot[k, i, 0:2]
Z_trans[Z_trans > z_max] = Z_trans[Z_trans > z_max] - (z_max - z_min)
cnt_trans[:, :, 2] = Z_trans
#create a new array
#for each rotational copy, create a translational copy as well:
return cnt_trans
def discretize_2D(cnt_array, RBF_array, center_array= np.asarray([30.0, 30.0]), grid = np.asarray([4.0, 2.0]), x_min=25.5, x_max = 35.5, z_min = 8.0, z_max = 52.0):
#cnt_array -> (k x i x j) k-> data # i -> cnt atom # j -> coords array -> k lists of (i * j) data
#RBF_array -> k lists
#specify 2D grid parameters
num_z = (np.ceil((z_max - z_min)/grid[0])).astype(int)
num_x = (np.ceil((x_max - x_min)/grid[1])).astype(int)
z_lin = np.linspace(z_min, z_max, num_z + 1)
x_lin = np.linspace(x_min, x_max, num_x + 1)
id_mat1 = np.zeros((num_z, num_x)) #stores corresponding id matrix
#read in the first entry in the list to get the shape
RBF_1 = np.zeros((len(RBF_array), num_z, num_x, (RBF_array[0]).shape[2]))
RBF_2 = np.zeros_like(RBF_1)
sum_val = 0
##---------------output---------------------------
for k in range(0, len(RBF_array)):
cnt_mat = np.squeeze(cnt_array[k], axis=0)
rbf_mat = np.squeeze(RBF_array[k], axis=0)
#print "cnt_mat troubleshoot"
#print len(cnt_mat)
#print cnt_mat.shape
#print rbf_mat.shape
yc = center_array[1]
bool_val = cnt_mat < yc
mat_1 = cnt_mat[bool_val[:, 1]]
mat_2 = cnt_mat[~bool_val[:, 1]]
mat_1 = np.delete(mat_1, 1, 1)
mat_2 = np.delete(mat_2, 1, 1)
count = 0
sum_val = 0
sum_val2 = 0
##let's process both arrays:
for i in range(0, num_z):
for j in range(0, num_x):
#see location
z_min = z_lin[i]
z_max = z_lin[i+1]
z_c = 0.5*(z_min + z_max)
x_min = z_lin[j]
x_max = z_lin[j + 1]
x_c = 0.5 * (x_min + x_max)
min_bound = np.asarray([x_lin[j], z_lin[i]])
max_bound = np.asarray([x_lin[j+1], z_lin[i+1]])
mat_11 = mat_1 - min_bound
mat_12 = max_bound - mat_1
bool_1 = np.logical_and(mat_11[:, 0] > 0, mat_11[:, 1] > 0)
bool_2 = np.logical_and(mat_12[:, 0] > 0, mat_12[:, 1] > 0)
bool_f = np.logical_and(bool_1, bool_2)
idx_1 = (np.where(bool_f==True)[0])
#select from RBF_mat
if len(idx_1) > 0:
RBF_1[k, i, j, :] = np.amax(rbf_mat[idx_1, :], axis=0)
else:
RBF_1[k, i, j, :] = 0
id_mat1[i, j] = count
####Do the same for the lower half
mat_21 = mat_2 - min_bound
mat_22 = max_bound - mat_2
bool_1 = np.logical_and(mat_21[:, 0] > 0, mat_21[:, 1] > 0)
bool_2 = np.logical_and(mat_22[:, 0] > 0, mat_22[:, 1] > 0)
bool_g = np.logical_and(bool_1, bool_2)
idx_2 = (np.where(bool_g == True)[0])
if len(idx_2) > 0:
RBF_2[k, i, j, :] = np.amax(rbf_mat[idx_2, :], axis=0)
else:
RBF_2[k, i, j, :] = 0
#count += 1
#center_array -> 3D array [k, i, j] k-> number of
#The idea is to divide the 2D box into two halves and then merge them together
#print RBF_1.shape
#print RBF_2.shape
return RBF_1, RBF_2, id_mat1
def RBF_reshape(RBF_array):
#RBF -> (i, type, r)
#i -> number of atoms
RBF_out = RBF_array.reshape(RBF_array.shape[0], (RBF_array.shape[1])*(RBF_array.shape[2]))
return RBF_out
###This function sets the RBF so that they can be processed using a CNN
def RBF_Setter(df_list, z_list):
RBF_list = []
r_list = []
for i in range(len(df_list)):
RBF, r_v = CNT_atoms(df_list[i], z_list[i])
r_d, RBF_d = discretize_features(RBF)
RBF_d = RBF_reshape(RBF_d)
RBF_list.append(RBF_d)
r_list.append(r_d)
#print total_files[i]
print RBF_d.shape
RBF_out = RBF_list
R_d = np.array(r_list)
return RBF_out, R_d
def RBF_Setter02(df_list, z_list, total_files):
RBF_list = []
r_list = []
for i in range(len(df_list)):
RBF, r_v = CNT_atoms(df_list[i], z_list[i])
r_d, RBF_d = discretize_features(RBF=RBF, new_intv=[0.5, 0.2, 0.5], bounds=np.asarray([1.0, 2.0, 5.0]))