S = G
else:
beta = gn / gnp
s = g + s * beta
S = G + S * beta
gnp = gn
alpha = -gn / S.dot2()
p = p + s * alpha
R = R + S * alpha
x = prec(adj=0)[p]
return x
if __name__ == "__main__":
# test matrix and data
matrix = m8r.File([[1, 1, 1, 0], [1, 2, 0, 0], [1, 3, 1, 0], [1, 4, 0, 1],
[1, 5, 1, 1]])
y = m8r.File([3, 3, 5, 7, 9])
x0 = m8r.File([0, 0, 0, 0])
# matrix multiplication operator
matmult = m8r.matmult(mat=matrix)
# Using sfconjgrad
x = m8r.conjgrad(mod=x0, niter=6)[y, matmult]
y1 = matmult[x]
print x[:]
print y1[:]
# Using function above
causint = m8r.causint
matmult = m8r.matmult(mat=matrix)
def seisresult():
form = InputForm(request.form)
script = None
div = None
if request.method == 'POST' and form.validate() == False:
NumSamp = np.load('static/QC.npz')['arr_2']
InlineKeyNumber = np.load('static/QC.npz')['arr_0']
XlineKeyNumber = np.load('static/QC.npz')['arr_1']
upload_file = request.files['file']
filename = secure_filename(upload_file.filename)
upload_file.save(os.path.join(UPLOAD_FOLDER, filename))
image_height = 156
image_width = 156
image_depth = 100
num_channels = 1
images = []
# Reading the seismic using Pickle
#with open(os.path.join(UPLOAD_FOLDER, filename), 'rb') as f:
#test = pickle.load(f, encoding='bytes')
#test=np.array(test)
try:
seis = segyio.open(os.path.join(UPLOAD_FOLDER, filename),
iline=int(InlineKeyNumber),
xline=int(XlineKeyNumber))
seismic = segyio.cube(seis)
except:
seismic = np.load(os.path.join(UPLOAD_FOLDER, filename))
#np.savez_compressed('static/seishape.npz',seismic.shape[0],seismic.shape[1],seismic.shape[2])
seismic.tofile('static/seismic.asc', sep=" ")
os.system(
'echo in=static/seismic.asc n1=%d n2=%d n3=%d data_format=ascii_float | sfdd form=native | sfpatch w=100,156,156>static/seismicaf.rsf'
% (seismic.shape[2], seismic.shape[1], seismic.shape[0]))
e = m8r.File('static/seismicaf.rsf')
c = e[:]
test = c.reshape(-1, 156, 156, 100)
#print('Size of seismic volume is: %s' % str(test.shape))
m = -6.40475426972431e-05
s = 0.006666915856214509
test = (test - m) / s
graph = app.graph
## NOW the complete graph with values has been restored
y_pred = tf.nn.softmax(graph.get_tensor_by_name("Classifier/logits:0"))
## Let's feed the images to the input placeholders
#using the model for prediction
x_tensor = graph.get_tensor_by_name("Input/Placeholder:0")
#keep_prob is not always necessary it depends on your model
keep_prob = graph.get_tensor_by_name("Input/Placeholder_2:0")
is_training = graph.get_tensor_by_name("Input/Placeholder_3:0")
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.compat.v1.Session(graph=graph, config=config)
### Creating the feed_dict that is required to be fed to calculate y_pred
feed_dict_testing = {x_tensor: test, keep_prob: 0.7, is_training: True}
prob_iter_tot = []
pred_iter_tot = []
prob_variance_tot = []
for i in range(NumSamp):
result = sess.run(y_pred, feed_dict=feed_dict_testing)
prob_iter_tot.append(result)
#pred_iter_tot.append(np.reshape(np.argmax(result,axis = -1),[result.shape[0],-1]))
#pred_iter_tot = np.array(pred_iter_tot)
#pred_iter_tot = np.reshape(pred_iter_tot,(NumSamp,result.shape[0],-1))
#pred_iter_tot = np.transpose(pred_iter_tot,(1,0,2))
prob_iter_tot = np.array(prob_iter_tot)
prob_iter_tot = np.reshape(prob_iter_tot,
(NumSamp, test.shape[0], 156, 156, 100, 2))
#prob_iter_tot_transp = np.transpose(prob_iter_tot,(1,0,2,3,4,5))
#for k in range(test.shape[0]):
#prob_variance,pred = MAX_VOTE(pred_iter_tot[k,:,:], prob_iter_tot_transp[k,:,:,:,:,:],2)
#prob_variance_tot.append(prob_variance)
prob = np.nanmean(prob_iter_tot, axis=0)
var = np.nanvar(prob_iter_tot, axis=0)
#prob_variance_tot = np.array(prob_variance_tot)
#prob_variance_tot = np.reshape(prob_variance_tot,(test.shape[0],156,156,100,2))
test = np.reshape(test,
(c.shape[0], c.shape[1], c.shape[2], 156, 156, 100))
result1 = np.reshape(
prob[:, :, :, :,
1], (c.shape[0], c.shape[1], c.shape[2], 156, 156, 100))
var1 = np.reshape(var[:, :, :, :, 1],
(c.shape[0], c.shape[1], c.shape[2], 156, 156, 100))
#var2 = np.reshape(prob_variance_tot[:,:,:,:,1],(c.shape[0],c.shape[2],156,156,100))
var1.tofile('static/var1.asc', sep=" ")
#var2.tofile('static/var2.asc', sep=" ")
result1.tofile('static/result1.asc', sep=" ")
test.tofile('static/test.asc', sep=" ")
os.system(
'echo in=static/test.asc n1=100 n2=156 n3=156 n4=%d n5=%d n6=%d data_format=ascii_float | sfdd form=native | sfpatch inv=y weight=y n0=%d,%d,%d>static/test.rsf'
% (c.shape[2], c.shape[1], c.shape[0], seismic.shape[2],
seismic.shape[1], seismic.shape[0]))
f = m8r.File('static/test.rsf')
a = f[:]
a = np.reshape(a,
(seismic.shape[0], seismic.shape[1], seismic.shape[2]))
os.system(
'echo in=static/result1.asc n1=100 n2=156 n3=156 n4=%d n5=%d n6=%d data_format=ascii_float | sfdd form=native | sfpatch inv=y weight=y n0=%d,%d,%d>static/result1.rsf'
% (c.shape[2], c.shape[1], c.shape[0], seismic.shape[2],
seismic.shape[1], seismic.shape[0]))
g = m8r.File('static/result1.rsf')
b = g[:]
b = np.reshape(b,
(seismic.shape[0], seismic.shape[1], seismic.shape[2]))
os.system(
'echo in=static/var1.asc n1=100 n2=156 n3=156 n4=%d n5=%d n6=%d data_format=ascii_float | sfdd form=native | sfpatch inv=y weight=y n0=%d,%d,%d>static/var1.rsf'
% (c.shape[2], c.shape[1], c.shape[0], seismic.shape[2],
seismic.shape[1], seismic.shape[0]))
h = m8r.File('static/var1.rsf')
k = h[:]
k = np.reshape(k,
(seismic.shape[0], seismic.shape[1], seismic.shape[2]))
#os.system('echo in=static/var2.asc n1=100 n2=156 n3=156 n4=%d n5=%d n6=%d data_format=ascii_float | sfdd form=native | sfpatch inv=y weight=y n0=%d,%d,%d>static/var2.rsf' % (c.shape[2],c.shape[1],c.shape[0],seismic.shape[2],seismic.shape[1],seismic.shape[0]))
#l = m8r.File('static/var2.rsf')
#l1 = l[:]
#l1 = np.reshape(l1,(seismic.shape[0],seismic.shape[1],seismic.shape[2]))
np.savez_compressed('static/result.npz', b)
np.savez_compressed('static/test.npz', seismic)
np.savez_compressed('static/var.npz', k)
#np.savez_compressed('static/var2.npz',l1)
#form = InputForm(request.form)
return render_template("channel_index6.html",
form=form,
script=script,
div=div)
if request.method == "POST" and form.validate():
# Determine the selected slice
current_slice = form.Position.data
# Determine the selected slice number
current_slice_number = form.Number.data
result = np.load('static/result.npz')['arr_0']
test = np.load('static/test.npz')['arr_0']
var = np.load('static/var.npz')['arr_0']
#var2 = np.load('static/var2.npz')['arr_0']
#rsfarray_1 = m8r.File(test)
#rsfarray_2 = m8r.put(d1=0.004, o1=0).patch(inv=True, weight=True, n0=[100,312,312])[rsfarray_1]
#test = rsfarray_2[:]
#rsfarray_1 = m8r.File(result1)
#rsfarray_2 = m8r.put(d1=0.004, o1=0).patch(inv=True, weight=True, n0=[100,312,312])[rsfarray_1]
#result = rsfarray_2[:]
#print(result.shape)
#print(test.shape)
# Create the plot
plot = create_figure(result, var, test, current_slice,
current_slice_number)
# Embed plot into HTML via Flask Render
script, div = components(plot)
return render_template("channel_index6.html",
form=form,
script=script,
div=div)
def save_numpy_array_as_rsf_file(numpy_array, target_file_name):
out = m8r.File(numpy_array)
out.sfin()
s = g
S = G
else:
beta = gn / gnp
s = g + s * beta
S = G + S * beta
gnp = gn
alpha = -gn / S.cdot2()
x = x + s * alpha
R = R + S * alpha
return x
if __name__ == "__main__":
# test matrix and data
matrix = m8r.File([[1, 1, 1, 0], [1, 2, 0, 0], [1, 3, 1, 0], [1, 4, 0, 1],
[1, 5, 1, 1]])
cmatrix = m8r.cmplx[matrix, matrix]
cmatmult = m8r.cmatmult(mat=cmatrix)
yr = m8r.File([3, 2, 5, 6, 9])
yi = m8r.File([3, 4, 5, 8, 9])
y = m8r.cmplx[yr, yi]
x0 = m8r.File([0, 0, 0, 0])
x1 = m8r.cmplx[x0, x0]
# Using sfcconjgrad
x = m8r.cconjgrad(mod=x1, niter=6)[y, cmatmult]
y1 = cmatmult[x]
print x[:]
print y1[:]
# Using function above