def checkForceDistr(self, fileIn): with open(fileIn, "r") as fin: if self.ndims == 1: # use xmgrace instead pass if self.ndims == 2: force_x = np.zeros((self.binNum, self.binNum), dtype=np.float64) force_y = np.zeros((self.binNum, self.binNum), dtype=np.float64) i = 0 j = 0 for line in fin: line = line.split() force_x[i][j] = float(line[2]) force_y[i][j] = float(line[4]) j += 1 if j == self.binNum: j = 0 i += 1 force_x = np.delete(force_x, -1, 1) # rendering; prevent boundary error force_x = np.delete(force_x, -1, 0) force_y = np.delete(force_y, -1, 1) force_y = np.delete(force_y, -1, 0) r = rendering(self.ndims, self.half_boxboundary, self.binNum) r.render(force_x, name=str(self.abfCheckFlag + "_" + self.nnCheckFlag + "_" + "forcex2D")) r.render(force_y, name=str(self.abfCheckFlag + "_" + self.nnCheckFlag + "_" + "forcey2D"))
def integrator(ndims, cv, force, half_boxboundary, outputfile):
cv = np.array(cv)
force = np.array(force)
if ndims == 1: # OK
intg_interval = abs(cv[0] - cv[1])
accIntg = 0
FE = np.zeros(force.shape[0])
factor = intg_interval * 0.5
for i in range(len(force) - 1):
accIntg -= (force[i] + force[i+1]) # freeE = -Sf(x)dx
FE[i] = accIntg * factor
FE = paddingRightMostBin(FE)
with open(outputfile, "w") as fout:
for i, j in zip(cv, FE):
fout.write(str(i) + " " + str(j) + "\n")
#---------------------------------------------------------------------#
if ndims == 2: # probably OK?
intg_interval = abs(cv[1][0][0] - cv[1][0][1])
factor = intg_interval * 0.5
FE_X = 0
FE_Y = 0
acc_at_x = np.zeros((force.shape[1]))
acc_at_y = np.zeros((force.shape[1], force.shape[2]))
FE = np.zeros((force.shape[1], force.shape[2]))
for i in range(force.shape[1] - 1):
FE_X = (force[0][i][0] + force[0][i+1][0])
acc_at_x[i] -= FE_X
for j in range(force.shape[2]- 1):
FE_Y -= (force[1][i][j] + force[1][i][j+1])
acc_at_y[i][j] = FE_Y
FE_Y = 0
acc_at_x = np.append(acc_at_x, acc_at_x[-1])
for i in range(force.shape[1]):
for j in range(force.shape[2]):
FE[i][j] = (acc_at_x[i] + acc_at_y[i][j]) * factor
FE += 29.5
FE = paddingRightMostBin(FE)
with open(outputfile, "w") as fout:
for i in range(force.shape[1]):
for j in range(force.shape[2]):
fout.write(str(cv[0][i][j]) + " " + str(cv[1][i][j]) + " " + str(FE[i][j]) + "\n")
s = rendering(ndims, half_boxboundary, force.shape[1] - 1)
s.render(FE, name="FreeE_2D")
def checkBoltzDistr(self, fileIn, fileOut): with open(fileIn, "r") as fin: if self.ndims == 1: # use xmgrace instead prob_x = np.zeros((self.binNum), dtype = np.int32) nsamples = 0 for line in fin: line = line.split() if line[0] != "#": nsamples += 1 prob_x[getIndices(truncateFloat(float(line[2])), self.x_axis)] += 1 prob_x = np.array(prob_x) prob_x = (prob_x / nsamples) # final probability distribution with open(fileOut, "w") as fout: for i in range(len(prob_x)): # in xmgrace, one can discard the point on pi (boundary error) fout.write(str(self.x_axis[i]) + " " + str(prob_x[i]) + "\n") if self.ndims == 2: prob_xy = np.zeros((self.binNum, self.binNum), dtype = np.float64) nsamples = 0 for line in fin: line = line.split() if line[0] != "#": nsamples += 1 prob_xy[getIndices(truncateFloat(float(line[2])), self.x_axis)][getIndices(truncateFloat(float(line[4])), self.y_axis)] += 1 # 2 for cartcoord_1D, 2 4 for cartcoord_2D prob_xy = (prob_xy / nsamples) # final probability distribution with open(fileOut, "w") as fout: for i in range(self.binNum): # discard the point on the positive boundary (PBC issues) for j in range(self.binNum): # discard the point on the positive boundary (PBC issues) fout.write(str(self.x_axis[i]) + " ") fout.write(str(self.y_axis[j]) + " " + str(prob_xy[i][j]) + "\n") prob_xy = np.delete(prob_xy, -1, 0) # rendering; prevent boundary error prob_xy = np.delete(prob_xy, -1, 1) r = rendering(self.ndims, self.half_boxboundary, self.binNum, self.temperature) r.render(prob_xy, name=str(self.abfCheckFlag + "_" + self.nnCheckFlag + "_" + "boltz2D"))
variation = 4
betas = np.random.rand(10) * variation * 2 - variation
# betas[0] = np.random.rand() * 8 * 2 -8
variation = 2
betas[1] = np.random.rand() * variation * 2 - variation
# betas[3] = np.random.rand() * 8 * 2 -8
# betas[6] = np.random.rand() * 8 * 2 -8
# print(betas)
parameters = np.append(pose[:], betas[:])
# save frontal view image
img_name = '%d_0' % i
parameters = np.append(pose[:], betas[:])
angle = np.pi
axis = np.array([0, 1, 0])
rendering(m, parameters, batch_size, path, img_name,
args.width, args.height, angle, axis)
image = Image.open(join(path,img_name+'.png'))
output = scale_fixed_height(image,args.output_height,args.output_width)
output_name = join(path, img_name+'.png')
output.save(output_name)
# save side view image
img_name = '%d_1' % i
angle = np.pi/2
axis = np.array([0, 1, 0])
rendering(m, parameters, batch_size, path, img_name,
args.width, args.height, angle, axis)
image = Image.open(join(path,img_name+'.png'))
output = scale_fixed_height(image,args.output_height,args.output_width)
output_name = join(path, img_name+'.png')
#parameters = np.append(np.zeros(3),parameters)
#parameters[0] = np.pi
# generate mesh
img_name = '%d'%i
generate_mesh(parameters,batch_size,path,img_name)
# render silhouettes
'''
img_name = '%d'%(n) # set_0_predict
rendering(parameters,batch_size,path,img_name,400,400)
'''
img_name = '%d_0_p' % i
angle = np.pi
axis = np.array([0, 1, 0])
rendering(m, parameters, batch_size, path, img_name,
400, 400, angle, axis)
'''
img_name = '%d_0_p.png'%i
img_name = os.path.join(path, img_name)
image = Image.open(img_name)
output = scale_fixed_height(image,264,192)
img_name = '%d_0_p_s.png'%i
img_name = os.path.join(path, img_name)
output.save(img_name)
'''
# save side view image
img_name = '%d_1_p' % i
angle = np.pi/2
axis = np.array([0, 1, 0])