def build_geomodel(var_type, interpolated_variables, codes, grid, tg_grid_name,
tg_prop_name, ids, acceptance, rt_prop, num_models):
print('Creating a geologic model...')
int_variables = np.array(interpolated_variables).T
geomodel = []
idx = 0
for i in int_variables:
if np.isnan(i).all():
geomodel.append(float('nan'))
idx = idx + 1
else:
index = i.argmin(axis=0)
geomodel.append(float(codes[index]))
idx = idx + 1
cm = confusion_matrix(rt_prop, np.array(geomodel)[ids], normalize='true')
diag = np.diagonal(cm)
print(diag)
m = np.mean(diag)
#if m < acceptance:
if np.any(diag < acceptance):
print('Model rejected!')
pass
else:
sgems.set_property(tg_grid_name, tg_prop_name, geomodel)
num_models.append('x')
return geomodel
def execute(self):
print self.params
# ESTABELECA OS PARAMETROS INICIAIS
propertie = self.params['prop']['property']
grid = self.params['prop']['grid']
measured = float(self.params['Measured']['value'])
Indicated = float(self.params['Indicated']['value'])
Inferred = float(self.params['Inferred']['value'])
dx = float(self.params['dx']['value'])
dy = float(self.params['dy']['value'])
dz = float(self.params['dz']['value'])
grid = self.params['prop']['grid']
x = sgems.get_property(grid, "_X_")
y = sgems.get_property(grid, "_Y_")
z = sgems.get_property(grid, "_Z_")
distance = []
numx = int(round((max(x) - min(x))/dx,0)) + 1
numy = int(round((max(y) - min(y))/dy,0)) + 1
numz = int(round((max(z) - min(z))/dz,0)) + 1
cmd="NewCartesianGrid rescat::"+str(numx)+"::"+str(numy)+"::"+str(numz)+"::"+str(dx)+"::"+str(dy)+"::"+str(dz)+"::"+str(min(x))+"::"+str(min(y))+"::"+str(min(z))+"::0"
print cmd
sgems.execute(cmd)
xh = sgems.get_property("rescat","_X_")
yh = sgems.get_property("rescat","_Y_")
zh = sgems.get_property("rescat","_Z_")
v = []
for i, j, k in zip(xh,yh,zh):
valores = 0
menor_valor = 1000
for xind, yind, zind in zip(x,y,z):
distance = (math.sqrt((xind-i)**2+(yind-j)**2+(zind-k)**2))
if (distance < measured):
valores = 0
elif( distance < Indicated and distance > measured):
valores = 1
elif(distance < Inferred and distance > Indicated):
valores = 2
else:
valores = 3
if (valores < menor_valor):
menor_valor = valores
v.append(menor_valor)
print (v)
sgems.set_property("rescat","R",v)
return True
def build_geomodel(var_type, interpolated_variables, codes, grid, tg_grid_name,
tg_prop_name):
print('Creating a geologic model...')
if var_type == 'Indicators':
if len(interpolated_variables) == 1:
nn_results = helpers.nn(x, y, z, variables[0], grid)
if keep_variables == '1':
prop_name = 'nn_' + str(codes[0])
sgems.set_property(tg_grid_name, prop_name,
nn_results.tolist())
proportion = sum(nn_results == 1) / len(nn_results)
print('Cutting-off interpolated indicator property in {}'.format(
proportion.round(2)))
q = np.quantile(interpolated_variables[0], (1 - proportion))
solid = np.where(interpolated_variables[0] > q, 1, 0)
sgems.set_property(tg_grid_name, 'rt_{}'.format(codes[0]),
solid.tolist())
else:
int_variables = np.array(interpolated_variables).T
geomodel = []
idx = 0
for i in int_variables:
if np.isnan(i).all():
geomodel.append(float('nan'))
idx = idx + 1
else:
index = i.argmax(axis=0)
geomodel.append(float(codes[index]))
idx = idx + 1
sgems.set_property(tg_grid_name, tg_prop_name, geomodel)
print('Geologic model created!')
else:
if len(interpolated_variables) == 1:
solid = np.where(interpolated_variables[0] < 0, 1, 0)
sgems.set_property(tg_grid_name, 'rt_{}'.format(codes[0]),
solid.tolist())
else:
int_variables = np.array(interpolated_variables).T
geomodel = []
idx = 0
for i in int_variables:
if np.isnan(i).all():
geomodel.append(float('nan'))
idx = idx + 1
else:
index = i.argmin(axis=0)
geomodel.append(float(codes[index]))
idx = idx + 1
sgems.set_property(tg_grid_name, tg_prop_name, geomodel)
print('Geologic model created!')
return geomodel
def execute(self):
#aqui vai o codigo
#getting variables
grid_name = self.params['rt_prop']['grid']
prop_name = self.params['rt_prop']['property']
prop_values = sgems.get_property(grid_name, prop_name)
prop_values = np.array(prop_values)
point_transform_type = self.params['comboBox']['value']
if point_transform_type == 'Indicators':
#calculating indicators
nan_filter = np.isfinite(prop_values)
unique_rts = np.unique(prop_values[nan_filter])
for rt in unique_rts:
print('calculating indicators for rock type {}'.format(int(rt)))
ind_prop = prop_values == rt
ind_prop = np.where(ind_prop == True, 1., 0.)
ind_prop[~nan_filter] = float('nan')
indprop_name = 'indicators_rt_{}'.format(int(rt))
sgems.set_property(grid_name, indprop_name, ind_prop.tolist())
else:
x, y, z = np.array(sgems.get_X(grid_name)), np.array(sgems.get_Y(grid_name)), np.array(sgems.get_Z(grid_name))
coords_matrix = np.vstack((x,y,z)).T
#calculating signed distances
nan_filter = np.isfinite(prop_values)
unique_rts = np.unique(prop_values[nan_filter])
for rt in unique_rts:
print('calculating signed distances for rock type {}'.format(int(rt)))
filter_0 = prop_values != rt
filter_1 = prop_values == rt
points_0 = coords_matrix[filter_0]
points_1 = coords_matrix[filter_1]
sd_prop = []
for idx, pt in enumerate(prop_values):
if np.isnan(pt):
sd_prop.append(float('nan'))
else:
point = coords_matrix[idx]
if pt == rt:
sd_prop.append(-min_dist(point, points_0))
else:
sd_prop.append(min_dist(point, points_1))
sgems.set_property(grid_name, 'signed_distances_rt_{}'.format(int(rt)), sd_prop)
return True
def create_variable(grid, name, list):
lst_props_grid = sgems.get_property_list(grid)
prop_final_data_name = name
if (prop_final_data_name in lst_props_grid):
flag = 0
i = 1
while (flag == 0):
test_name = prop_final_data_name + '-' + str(i)
if (test_name not in lst_props_grid):
flag = 1
prop_final_data_name = test_name
i = i + 1
sgems.set_property(grid, prop_final_data_name, list)
def w_o_(cut_off): #define waste as 0 and ore as grade
for i in grid_value:
if i <= cut_off:
l_c.append(i)
else:
l_c.append(0)
return sgems.set_property(head_grid, new_prop, l_c), l_c
def w_o(cut_off): #define waste ore in 0 and 1
for i in grid_value:
if i >= cut_off:
l_c.append(i)
else:
l_c.append(0)
return sgems.set_property(head_grid, new_prop, l_c), l_c
def build_geomodels(interpolated_variables, codes, tg_grid_name, tg_prop_name,
ids, acceptance, rt_prop):
#implicit binary modeling
if len(interpolated_variables) == 1:
for t in interpolated_variables[int(codes[0])]:
solid = np.where(interpolated_variables[int(codes[0])][t] < 0, 1,
0)
sgems.set_property(tg_grid_name, 'rt_{}_{}'.format(codes[0], t),
solid.tolist())
#multicategorical
else:
accepted_combs = []
num_models = 0
combinations = combinate(codes)
for sc, c in enumerate(combinations):
int_variables = []
for idx, r in enumerate(codes):
int_variables.append(interpolated_variables[int(r)][c[idx]])
geomodel = []
idx = 0
for i in np.array(int_variables).T:
if np.isnan(i).all():
geomodel.append(float('nan'))
idx = idx + 1
else:
index = i.argmin(axis=0)
geomodel.append(float(codes[index]))
idx = idx + 1
cm = confusion_matrix(rt_prop,
np.array(geomodel)[ids],
normalize='true')
diag = np.diagonal(cm)
print(diag)
m = np.mean(diag)
#if m < acceptance:
if np.any(diag < acceptance):
pass
else:
sgems.set_property(tg_grid_name, tg_prop_name + '_' + str(sc),
geomodel)
num_models = num_models + 1
accepted_combs.append(c)
print('{} geologic models accepted!'.format(num_models))
print(accepted_combs)
def execute(self):
#aqui vai o codigo
#getting variables
prob_grid_name = self.params['gridselectorbasic']['value']
prob_var_names = self.params['orderedpropertyselector']['value']
sim_grid_name = self.params['gridselectorbasic_2']['value']
sim_var_names = self.params['orderedpropertyselector_2']['value']
tg_grid_name = sim_grid_name
tg_prop_name = self.params['lineEdit']['value']
prob_var_names_lst = prob_var_names.split(';')
sim_var_names_lst = sim_var_names.split(';')
codes = [int(i.split('_')[-3]) for i in prob_var_names_lst]
probs_matrix = np.array([sgems.get_property(prob_grid_name, p) for p in prob_var_names_lst])
reals = np.array([sgems.get_property(sim_grid_name, p) for p in sim_var_names_lst])
norm_reals = [norm.cdf(lst) for lst in reals]
#sampling cats
print('Sampling categories using the p-fields and building realizations...')
t1 = time.time()
probs_matrix = probs_matrix.T
for real_idx, r in enumerate(norm_reals):
realization = []
for idx, b in enumerate(np.array(r).T):
if np.isnan(probs_matrix[idx]).any():
realization.append(float('nan'))
else:
position = cat_random_sample(probs_matrix[idx], b)
realization.append(int(codes[position]))
sgems.set_property(tg_grid_name, tg_prop_name+'_real_'+str(real_idx), realization)
t2 = time.time()
print('Took {} seconds'.format(round((t2-t1), 2)))
print('Finished!')
return True
def repeat_all(grid, K, painter, n_iterations, n_steps_list, n_runs):
for i, max_steps in zip(xrange(n_runs), n_steps_list):
print i, "run"
cost, painter = search_best_run(painter, K, n_iterations,
max_steps)
#plt_imshowN([painter.connected_map, painter.connected_map_ordem], [0, 0], [dict(cmap='jet', interpolation='nearest'), dict(cmap='jet')], dim=(1,2))
plt_imshowN([
painter.connected_map * (painter.grid != 0),
painter.connected_map_ordem, painter.connected_map *
(painter.grid != 0), painter.connected_map_ordem
], [0, 0, 1, 1],
[dict(cmap='jet', interpolation='nearest')] * 4,
dim=(2, 2))
#np.reshape(grid, (1,np.product(grid.shape)))
maps_seed = (painter.connected_map * painter.connected_map)
maps_seed_ = np.ndarray.tolist(
maps_seed.ravel()) #maps__ = np.array(maps_).T
sgems.set_property(head_grid, new_prop1 + '-' + str(i),
maps_seed_)
maps_ = painter.connected_map_ordem
maps__ = np.ndarray.tolist(maps_.ravel())
#
sgems.set_property(head_grid, new_prop2 + '-' + str(i), maps__)
ee = np.ma.masked_array(grid, mask=maps_ == 0)
ee_ = ee.filled(0)
ee__ = np.ndarray.tolist(ee_.ravel())
sgems.set_property(head_grid, "sect_maps" + '-' + str(i), ee__)
def execute(self):
#aqui vai o codigo
#getting variables
grid_name = self.params['gridselectorbasic']['value']
prop_names = self.params['orderedpropertyselector']['value'].split(';')
gamma = float(self.params['doubleSpinBox']['value'])
var_type = self.params['comboBox']['value']
props_values = []
for v in prop_names:
props_values.append(sgems.get_property(grid_name, v))
props_values = np.array(props_values)
#calculating probs
print('Calculating probabilities...')
if var_type == "Signed Distances":
print('Applying softmax transformation...')
probs_matrix = np.array([sofmax_transformation(sds, gamma, var_type) for sds in props_values.T])
probs_matrix = probs_matrix.T
for i, p in enumerate(probs_matrix):
sgems.set_property(grid_name, prop_names[i]+'_gamma_'+str(gamma), p.tolist())
else:
print('Correcting proportions...')
probs_matrix = prob_correction(props_values)
for i, p in enumerate(probs_matrix):
sgems.set_property(grid_name, prop_names[i]+'_corrected_prop', p.tolist())
#calculating entropy
print('Calculating entropy...')
entropies = [entropy(probs) for probs in probs_matrix.T]
entropies = np.array(entropies)
entropies = (entropies - np.nanmin(entropies))/(np.nanmax(entropies) - np.nanmin(entropies))
sgems.set_property(grid_name, 'entropy_gamma_'+str(gamma), entropies.tolist())
print('Done!')
return True
import sgems
mask=sgems.get_property('mask', 'facies')
porosity=sgems.get_property('kriging grid', 'estimated porosity')
for i in range(1,len(mask)) :
if mask[i] == 0:
porosity[i]= -9966699
sgems.set_property('kriging grid', 'estimated porosity', porosity )
def execute(self):
#aqui vai o codigo
#getting variables
var_type = self.params['comboBox']['value']
tg_grid_name = self.params['gridselector']['value']
tg_region_name = self.params['gridselector']['region']
tg_prop_name = self.params['lineEdit']['value']
keep_variables = self.params['checkBox_2']['value']
props_grid_name = self.params['gridselectorbasic']['value']
var_names = self.params['orderedpropertyselector']['value'].split(';')
codes = [v.split('_')[-1] for v in var_names]
#getting variograms
print('Getting variogram models')
variables = []
nan_filters = []
for v in var_names:
values = np.array(sgems.get_property(props_grid_name, v))
nan_filter = np.isfinite(values)
filtered_values = values[nan_filter]
nan_filters.append(nan_filter)
variables.append(filtered_values)
nan_filter = np.product(nan_filters, axis=0)
nan_filter = nan_filter == 1
x, y, z = np.array(sgems.get_X(props_grid_name))[nan_filter], np.array(
sgems.get_Y(props_grid_name))[nan_filter], np.array(
sgems.get_Z(props_grid_name))[nan_filter]
use_model_file = self.params['checkBox']['value']
if use_model_file == '1':
path = self.params['filechooser']['value']
variograms = helpers.modelfile_to_ar2gasmodel(path)
if len(variograms) == 1:
values_covs = list(variograms.values())
varg_lst = values_covs * len(codes)
variograms = {}
variograms[0] = varg_lst[0]
else:
p = self.params
varg_lst = helpers.ar2gemsvarwidget_to_ar2gascovariance(p)
if len(varg_lst) == 0:
variograms = {}
if len(varg_lst) == 1:
varg_lst = varg_lst * len(codes)
variograms = {}
variograms[0] = varg_lst[0]
else:
variograms = dict(zip(codes, varg_lst))
#interpolating variables
a2g_grid = helpers.ar2gemsgrid_to_ar2gasgrid(tg_grid_name,
tg_region_name)
interpolated_variables = interpolate_variables(
x, y, z, variables, codes, a2g_grid, variograms, keep_variables,
var_type, tg_prop_name, tg_grid_name)
#creating a geologic model
geomodel = build_geomodel(var_type, interpolated_variables, codes,
a2g_grid, tg_grid_name, tg_prop_name)
#Refining model
iterations = int(self.params['iterations']['value'])
fx, fy, fz = int(self.params['fx']['value']), int(
self.params['fy']['value']), int(self.params['fz']['value'])
if iterations > 0:
if len(variables) == 1:
print(
'Sorry! Refinemnt works only for multicategorical models.')
return False
grid = a2g_grid
geomodel = geomodel
if tg_region_name != '':
region = sgems.get_region(tg_grid_name, tg_region_name)
for i in range(iterations):
print('Refinemnt iteration {}'.format(i + 1))
print('Defining refinment zone...')
ref_zone, indices = refinement_zone(grid, geomodel)
if tg_region_name != '':
downscaled_grid, downscaled_props = helpers.downscale_properties(
grid, [ref_zone, geomodel,
np.array(region)], fx, fy, fz)
region = downscaled_props[2]
m1 = np.array(downscaled_props[0] == -999)
m2 = np.array(downscaled_props[2] == 1)
mask = m1 * m2
mask = np.where(mask == 1, True, False).tolist()
nx, ny, nz = downscaled_grid.dim()[0], downscaled_grid.dim(
)[1], downscaled_grid.dim()[2]
sx, sy, sz = downscaled_grid.cell_size(
)[0], downscaled_grid.cell_size(
)[1], downscaled_grid.cell_size()[2]
ox, oy, oz = downscaled_grid.origin(
)[0], downscaled_grid.origin()[1], downscaled_grid.origin(
)[2]
downscaled_grid = ar2gas.data.CartesianGrid(
nx, ny, nz, sx, sy, sz, ox, oy, oz)
else:
downscaled_grid, downscaled_props = helpers.downscale_properties(
grid, [ref_zone, geomodel], fx, fy, fz)
mask = downscaled_props[0] == -999
grid = downscaled_grid
grid_name = tg_grid_name + '_' + tg_prop_name + '_iteration_{}'.format(
i + 1)
helpers.ar2gasgrid_to_ar2gems(grid_name, grid)
masked_grid = ar2gas.data.MaskedGrid(downscaled_grid, mask)
interpolated_variables = interpolate_variables(
x, y, z, variables, codes, masked_grid, variograms,
keep_variables, var_type, tg_prop_name, grid_name)
geomodel = build_refined_geomodel(interpolated_variables,
downscaled_props, codes,
var_type)
sgems.set_property(grid_name, tg_prop_name, geomodel)
print('Finished!')
return True
def ar2gasprop_to_ar2gems(grid, grid_name, prop, prop_name):
sgems.execute('NewCartesianGrid {}::{}::{}::{}::{}::{}::{}::{}::{}::{}::0,00'.format(grid_name,
grid.dim()[0], grid.dim()[1], grid.dim()[2],
grid.cell_size()[0], grid.cell_size()[1], grid.cell_size()[2],
grid.origin()[0], grid.origin()[1], grid.origin()[2]))
sgems.set_property(grid_name, prop_name, prop)
def execute(self):
#aqui vai o codigo
#getting variables
tg_grid_name = self.params['gridselector']['value']
tg_region_name = self.params['gridselector']['region']
tg_prop_name = self.params['lineEdit']['value']
keep_variables = self.params['checkBox_2']['value']
props_grid_name = self.params['gridselectorbasic']['value']
var_names = self.params['orderedpropertyselector']['value'].split(';')
codes = [v.split('_')[-1] for v in var_names]
dcf_param = self.params['comboBox_2']['value']
f_min_o = float(self.params['doubleSpinBox']['value'])
f_min_p = float(self.params['doubleSpinBox_2']['value'])
acceptance = [
float(i) for i in self.params['lineEdit_9']['value'].split(',')
]
if len(acceptance) == 1 and len(codes) > 1:
acceptance = acceptance * len(codes)
acceptance = np.array(acceptance)
#kernels variables
function = self.params['comboBox']['value']
supports = [
float(i) for i in self.params['lineEdit_5']['value'].split(',')
]
azms = [
float(i) for i in self.params['lineEdit_2']['value'].split(',')
]
dips = [
float(i) for i in self.params['lineEdit_3']['value'].split(',')
]
rakes = [
float(i) for i in self.params['lineEdit_4']['value'].split(',')
]
nuggets = [
float(i) for i in self.params['lineEdit_6']['value'].split(',')
]
major_med = [
float(i) for i in self.params['lineEdit_7']['value'].split(',')
]
major_min = [
float(i) for i in self.params['lineEdit_8']['value'].split(',')
]
kernels_par = {
'supports': supports,
'azms': azms,
'dips': dips,
'rakes': rakes,
'nuggets': nuggets,
'major_meds': major_med,
'major_mins': major_min
}
for key in kernels_par:
if len(kernels_par[key]) == 1 and len(codes) > 1:
kernels_par[key] = kernels_par[key] * len(codes)
kernels_par['function'] = [function] * len(codes)
print(kernels_par)
#getting coordinates
variables = []
nan_filters = []
for v in var_names:
values = np.array(sgems.get_property(props_grid_name, v))
nan_filter = np.isfinite(values)
filtered_values = values[nan_filter]
nan_filters.append(nan_filter)
variables.append(filtered_values)
nan_filter = np.product(nan_filters, axis=0)
nan_filter = nan_filter == 1
x, y, z = np.array(sgems.get_X(props_grid_name))[nan_filter], np.array(
sgems.get_Y(props_grid_name))[nan_filter], np.array(
sgems.get_Z(props_grid_name))[nan_filter]
#Interpolating variables
a2g_grid = helpers.ar2gemsgrid_to_ar2gasgrid(tg_grid_name,
tg_region_name)
print('Computing results by RBF using a {} kernel...'.format(function))
interpolated_variables = {}
for idx, v in enumerate(variables):
rt = int(codes[idx])
interpolated_variables[rt] = {}
print('Interpolating RT {}'.format(rt))
if kernels_par['supports'][idx] == 0:
mask = v < 0
kernels_par['supports'][idx] = helpers.max_dist(
x, y, z, mask, a2g_grid)
print('Estimated support is {}'.format(
kernels_par['supports'][idx]))
rbf = RBF(x,
y,
z,
v,
function=kernels_par['function'][idx],
nugget=kernels_par['nuggets'][idx],
support=kernels_par['supports'][idx],
major_med=kernels_par['major_meds'][idx],
major_min=kernels_par['major_mins'][idx],
azimuth=kernels_par['azms'][idx],
dip=kernels_par['dips'][idx],
rake=kernels_par['rakes'][idx])
dc_param = dc_parametrization(v, dcf_param, f_min_o, f_min_p)
for t in dc_param:
print('Working on {}...'.format(t))
rbf.train(dc_param[t])
results = rbf.predict(a2g_grid)
interpolated_variables[rt][t] = results
if keep_variables == '1':
prop_name = 'interpolated_' + tg_prop_name + '_' + dcf_param + '_' + t + '_' + str(
rt)
sgems.set_property(tg_grid_name, prop_name,
results.tolist())
print('Done!')
#Generating geological models
print('Building geomodel...')
#getting closest node to each sample
ids = [
sgems.get_closest_nodeid(tg_grid_name, xi, yi, zi)
for xi, yi, zi in zip(x, y, z)
]
rt_prop = sd_to_cat(variables, codes)
build_geomodels(interpolated_variables, codes, tg_grid_name,
tg_prop_name, ids, acceptance, rt_prop)
print('Done!')
return True
def execute(self):
'''#Execute the funtion read_params
read_params(self.params)
print self.params'''
#Get the grid and rock type propery
grid = self.params['propertyselectornoregion']['grid']
prop = self.params['propertyselectornoregion']['property']
#Error message
if len(grid) == 0 or len(prop) == 0:
print 'Select the rocktype property'
return False
#Get the X, Y and Z coordinates and RT property
X = sgems.get_property(grid, '_X_')
Y = sgems.get_property(grid, '_Y_')
Z = sgems.get_property(grid, '_Z_')
RT = sgems.get_property(grid, prop)
elipsoide = self.params['ellipsoidinput']['value']
elipsoide_split = elipsoide.split()
range1 = float(elipsoide_split[0])
range2 = float(elipsoide_split[1])
range3 = float(elipsoide_split[2])
azimuth = float(elipsoide_split[3])
dip = float(elipsoide_split[4])
rake = float(elipsoide_split[5])
X, Y, Z = anis_search(X, Y, Z, range1, range2, range3, azimuth, dip, rake)
#Creates a list of all rock types
rt_list = []
for i in RT:
if i not in rt_list and not math.isnan(i):
rt_list.append(i)
#Sort the rock type list in crescent order
rt_list = [int(x) for x in rt_list]
rt_list.sort()
#Create a empty distance matrix
dist_matrix = np.zeros(shape = ((len(rt_list)), (len(RT))))
#Calculates the signed distances, and append it in the distance matrix
for i in range(len(rt_list)):
rock = rt_list[i]
for j in range(len(RT)):
if math.isnan(RT[j]):
dist_matrix[i][j] = float('nan')
elif RT[j] == rock:
dsmin = 1.0e21
for k in range(len(RT)):
if RT[j] != RT[k] and not math.isnan(RT[k]):
if (dist(X[j], Y[j], Z[j], X[k], Y[k], Z[k])) < dsmin:
dsmin = (dist(X[j], Y[j], Z[j], X[k], Y[k], Z[k]))
dist_matrix[i][j] = -dsmin
else:
dsmin = 1.0e21
for k in range(len(RT)):
if RT[k] == rock:
if (dist(X[j], Y[j], Z[j], X[k], Y[k], Z[k])) < dsmin:
dsmin = (dist(X[j], Y[j], Z[j], X[k], Y[k], Z[k]))
dist_matrix[i][j] = dsmin
#Creates the signed distances properties
lst_props_grid=sgems.get_property_list(grid)
for k in range(len(dist_matrix)):
prop_final_data_name = 'Signed_Distances_RT_' + str(rt_list[k])
if (prop_final_data_name in lst_props_grid):
flag=0
i=1
while (flag==0):
test_name=prop_final_data_name+'-'+str(i)
if (test_name not in lst_props_grid):
flag=1
prop_final_data_name=test_name
i=i+1
list = dist_matrix[k].tolist()
sgems.set_property(grid, prop_final_data_name, list)
return True
def execute(self):
'''# Execute the funtion read_params
read_params(self.params)
print self.params'''
#Get the grid and rock type propery
grid = self.params['propertyselectornoregion']['grid']
prop = self.params['propertyselectornoregion']['property']
#Get the X, Y and Z coordinates and RT property
X = sgems.get_property(grid, '_X_')
Y = sgems.get_property(grid, '_Y_')
Z = sgems.get_property(grid, '_Z_')
RT_data = sgems.get_property(grid, prop)
# Getting properties
grid_krig = self.params['gridselectorbasic_2']['value']
grid_var = self.params['gridselectorbasic']['value']
props = (self.params['orderedpropertyselector']['value']).split(';')
n_var = int(self.params['indicator_regionalization_input']
['number_of_indicator_group'])
n_prop = int(self.params['orderedpropertyselector']['count'])
min_cond = self.params['spinBox_2']['value']
max_cond = self.params['spinBox']['value']
# Error messages
if len(grid_var) == 0 or len(grid_krig) == 0:
print 'Select the variables'
return False
if n_var != n_prop:
print 'Number of variables and number of variograms models are diferent.'
return False
#Creating an empty list to store the interpolated distances
SG_OK_list = []
# Loop in every variable
for i in xrange(0, n_var):
# Getting variables
prop_HD = props[i]
prop_name = "Interpolated_" + str(prop_HD)
prop_name_var = "Interpolated_" + str(prop_HD) + ' krig_var'
var_str = ''
indicator_group = "Indicator_group_" + str(i + 1)
elipsoide = self.params['ellipsoidinput']['value']
n_struct = int(
self.params['indicator_regionalization_input'][indicator_group]
['Covariance_input']['structures_count'])
# Error message
if n_struct == 0:
print 'Variogram have no structures'
return False
# Loop in every variogram structure
for j in xrange(0, n_struct):
# Getting variogram parameters
Structure = "Structure_" + str(j + 1)
cov_type = self.params['indicator_regionalization_input'][
indicator_group]['Covariance_input'][Structure][
'Two_point_model']['type']
cont = self.params['indicator_regionalization_input'][
indicator_group]['Covariance_input'][Structure][
'Two_point_model']['contribution']
if cov_type == 'Nugget Covariance':
#Writing variogram parameters on a variable in nugget effect case
var_str = var_str + '<{} type="{}"> <Two_point_model contribution="{}" type="{}" > </Two_point_model> </Structure_1> '.format(
Structure, 'Covariance', cont, cov_type, Structure)
else:
range1 = self.params['indicator_regionalization_input'][
indicator_group]['Covariance_input'][Structure][
'Two_point_model']['ranges']['range1']
range2 = self.params['indicator_regionalization_input'][
indicator_group]['Covariance_input'][Structure][
'Two_point_model']['ranges']['range2']
range3 = self.params['indicator_regionalization_input'][
indicator_group]['Covariance_input'][Structure][
'Two_point_model']['ranges']['range3']
rake = self.params['indicator_regionalization_input'][
indicator_group]['Covariance_input'][Structure][
'Two_point_model']['angles']['rake']
dip = self.params['indicator_regionalization_input'][
indicator_group]['Covariance_input'][Structure][
'Two_point_model']['angles']['dip']
azimuth = self.params['indicator_regionalization_input'][
indicator_group]['Covariance_input'][Structure][
'Two_point_model']['angles']['azimuth']
# Writing variogram parameters on a variable in other cases
var_str = var_str + '<{} type="{}"> <Two_point_model contribution="{}" type="{}" > <ranges range1="{}" range2="{}" range3="{}" /> <angles azimuth="{}" dip="{}" rake="{}" /> </Two_point_model> </{}> '.format(
Structure, 'Covariance', cont, cov_type, range1,
range2, range3, azimuth, dip, rake, Structure)
# Calling ordinary kriging for each variable, using the variograms parameters above
sgems.execute(
'RunGeostatAlgorithm kriging::/GeostatParamUtils/XML::<parameters> <algorithm name="kriging" /> <Variogram structures_count="{}" > {} </Variogram> <ouput_kriging_variance value="1" /> <output_n_samples_ value="0" /> <output_average_distance value="0" /> <output_sum_weights value="0" /> <output_sum_positive_weights value="0" /> <output_lagrangian value="0" /> <Nb_processors value="-2" /> <Grid_Name value="{}" region="" /> <Property_Name value="{}" /> <Hard_Data grid="{}" property="{}" region="" /> <Kriging_Type type="Ordinary Kriging (OK)" > <parameters /> </Kriging_Type> <do_block_kriging value="1" /> <npoints_x value="5" /> <npoints_y value="5" /> <npoints_z value="5" /> <Min_Conditioning_Data value="{}" /> <Max_Conditioning_Data value="{}" /> <Search_Ellipsoid value="{}" /> <AdvancedSearch use_advanced_search="0"></AdvancedSearch> </parameters>'
.format(n_struct, var_str, grid_krig, prop_name, grid_var,
prop_HD, min_cond, max_cond, elipsoide))
SG_OK_list.append(sgems.get_property(grid_krig, prop_name))
#Deleting kriged distances
sgems.execute('DeleteObjectProperties {}::{}'.format(
grid_krig, prop_name))
sgems.execute('DeleteObjectProperties {}::{}'.format(
grid_krig, prop_name_var))
RT = (self.params['orderedpropertyselector']['value']).split(';')
#Determinig geomodel based on minimum estimed signed distance function
GeoModel = SG_OK_list[0][:]
t = 0
for i in range(len(SG_OK_list[0])):
sgmin = 10e21
for j in range(len(SG_OK_list)):
if SG_OK_list[j][i] < sgmin:
sgmin = SG_OK_list[j][i]
t = j
if math.isnan(SG_OK_list[j][i]):
GeoModel[i] = float('nan')
else:
GeoModel[i] = (int(RT[t].split('RT_')[-1]))
#Creating GeoModel property
lst_props_grid = sgems.get_property_list(grid_krig)
prop_final_data_name = 'Geologic_Model'
if (prop_final_data_name in lst_props_grid):
flag = 0
i = 1
while (flag == 0):
test_name = prop_final_data_name + '-' + str(i)
if (test_name not in lst_props_grid):
flag = 1
prop_final_data_name = test_name
i = i + 1
#Assign conditioning data to grid node
for i in range(len(RT_data)):
if not math.isnan(RT_data[i]):
closest_node = sgems.get_closest_nodeid(
grid_krig, X[i], Y[i], Z[i])
GeoModel[closest_node] = RT_data[i]
sgems.set_property(grid_krig, prop_final_data_name, GeoModel)
#Operating softmax transformation
if self.params['softmax_check']['value'] == '1':
gamma = float(self.params['Gamma']['value'])
Prob_list = SG_OK_list[:]
for i in range(len(SG_OK_list[0])):
soma = 0
for j in range(len(SG_OK_list)):
soma = soma + math.exp(-SG_OK_list[j][i] / gamma)
for j in range(len(SG_OK_list)):
Prob_list[j][i] = math.exp(
-SG_OK_list[j][i] / gamma) / soma
#Creating probabilities propreties
for k in range(len(Prob_list)):
prop_final_data_name = 'Probability_RT' + str(
RT[k].split('RT_')[-1])
if (prop_final_data_name in lst_props_grid):
flag = 0
i = 1
while (flag == 0):
test_name = prop_final_data_name + '-' + str(i)
if (test_name not in lst_props_grid):
flag = 1
prop_final_data_name = test_name
i = i + 1
sgems.set_property(grid_krig, prop_final_data_name,
Prob_list[k])
#Operating servo-system
if self.params['servo_check']['value'] == '1':
var_rt_grid = self.params['targe_prop']['grid']
var_rt_st = self.params['targe_prop']['property']
var_rt_region = self.params['targe_prop']['region']
if len(var_rt_grid) == 0 or len(var_rt_st) == 0:
print 'Select the target proportion property'
return False
#Getting variables
var_rt = sgems.get_property(var_rt_grid, var_rt_st)
#Getting parameters
lambda1 = float(self.params['Lambda']['value'])
mi = lambda1 / (1 - lambda1)
#Checking if a region exist
if len(var_rt_region) == 0:
#Variable without a region
var_region = var_rt
else:
region_rt = sgems.get_region(var_rt_grid, var_rt_region)
#Geting the variable inside the region
var_region = []
for i in range(len(var_rt)):
if region_rt[i] == 1:
var_region.append(var_rt[i])
#Getting the target proportion
target_prop = proportion(var_region, RT)
#Getting the random path
ran_path = random_path(Prob_list[0])
#Removing the blocks outside the region from randon path
if len(var_rt_region) != 0:
for i in range(len(region_rt)):
if region_rt[i] == 0:
ran_path.remove(i)
#servo system
p = 0
GeoModel_corrected = GeoModel[:]
visited_rts = []
for j in ran_path:
visited_rts.append(GeoModel[j])
instant_proportions = proportion(visited_rts, RT)
sgmax = 10e-21
for i in range(len(Prob_list)):
Prob_list[i][j] = Prob_list[i][j] + (
mi * (target_prop[i] - instant_proportions[i]))
if Prob_list[i][j] > sgmax:
sgmax = Prob_list[i][j]
p = i
GeoModel_corrected[j] = int(RT[p][-1])
visited_rts[-1] = int(RT[p].split('RT_')[-1])
#Correcting servo servo-system by the biggest proportion on a neighborhood
GeoModel_corrected_servo_prop = GeoModel_corrected[:]
ran_path_servo_correction = random_path(
GeoModel_corrected_servo_prop)
for i in ran_path_servo_correction:
vizinhanca = neighb(grid_krig, i)
blk_geo_model_corrected_servo = []
for j in vizinhanca:
blk_geo_model_corrected_servo.append(
GeoModel_corrected_servo_prop[j])
proportions_servo = proportion(
blk_geo_model_corrected_servo, RT)
indice_max_prop = proportions_servo.index(
max(proportions_servo))
GeoModel_corrected_servo_prop[i] = int(
RT[indice_max_prop].split('RT_')[-1])
#Creating Geologic_Model_Servo_System property
prop_final_data_name = 'Geologic_Model_Servo_System'
if (prop_final_data_name in lst_props_grid):
flag = 0
i = 1
while (flag == 0):
test_name = prop_final_data_name + '-' + str(i)
if (test_name not in lst_props_grid):
flag = 1
prop_final_data_name = test_name
i = i + 1
#Creating Geologic_Model_Corrected property
prop_final_data_name1 = 'Geologic_Model_Corrected'
if (prop_final_data_name1 in lst_props_grid):
flag = 0
i = 1
while (flag == 0):
test_name1 = prop_final_data_name1 + '-' + str(i)
if (test_name1 not in lst_props_grid):
flag = 1
prop_final_data_name1 = test_name1
i = i + 1
#Assign conditioning data to grid node
for i in range(len(RT_data)):
if not math.isnan(RT_data[i]):
closest_node = sgems.get_closest_nodeid(
grid_krig, X[i], Y[i], Z[i])
GeoModel_corrected[closest_node] = RT_data[i]
GeoModel_corrected_servo_prop[closest_node] = RT_data[
i]
#Setting properties
sgems.set_property(grid_krig, prop_final_data_name,
GeoModel_corrected)
sgems.set_property(grid_krig, prop_final_data_name1,
GeoModel_corrected_servo_prop)
return True
def execute(self):
dif = []
Prop1 = sgems.get_property(self.params["GridName1"]["grid"], self.prop1)
Prop2 = sgems.get_property(self.params["GridName2"]["grid"], self.prop2)
dif2 = []
for i in range(len(Prop1)):
# if(Prop1[i] != sgems.nan() and Prop2[i] != sgems.nan()):
if math.isnan(Prop1[i]) == False and math.isnan(Prop2[i]) == False:
dif.append((Prop1[i] - Prop2[i]) / Prop1[i])
else:
dif.append(sgems.nan())
final_prop = "Deviation"
lst = sgems.get_property_list(self.params["GridName1"]["grid"])
if final_prop in lst:
flag = 0
i = 1
while flag == 0:
new_prop_name = final_prop + "_" + str(i)
if new_prop_name not in lst:
flag = 1
final_prop = new_prop_name
i = i + 1
print final_prop
# for i in range(len(Prop1)):
# dif2.append((Prop1[i]+dif[i]))
sgems.set_property(self.params["GridName1"]["grid"], final_prop, dif)
# sgems.set_property(self.params["GridName2"]["grid"],"sum",dif2)
PP = []
a = float(self.cte)
print a, self.cte
for i in range(len(Prop2)):
if self.check == "1":
if self.radio1 == "1":
print Prop1[i], sgems.nan()
# if(Prop1[i]==float(sgems.nan())):
if math.isnan(Prop1[i]) == True:
print "estou no laco i =", i,
PP.append(Prop2[i] + Prop2[i] * a)
else:
PP.append(Prop2[i])
if self.radio2 == "1":
if math.isnan(Prop1[i]) == True:
PP.append(Prop2[i] - Prop2[i] * a)
else:
PP.append(Prop2[i])
final_prop2 = "Post_process"
lst2 = sgems.get_property_list(self.params["GridName2"]["grid"])
if final_prop2 in lst2:
flag = 0
i = 1
while flag == 0:
new_prop_name2 = final_prop2 + "_" + str(i)
if new_prop_name2 not in lst2:
flag = 1
final_prop2 = new_prop_name2
i = i + 1
print final_prop2
sgems.set_property(self.params["GridName2"]["grid"], final_prop2, PP)
return True
def interpolate_variables(x, y, z, variables, codes, grid, vargs,
keep_variables, var_type, tg_prop_name,
tg_grid_name):
print('Computing results by ar2gas dual kriging...')
interpolated_variables = []
if len(vargs) == 0:
print(
'Interpolating using the same covariance model for all variables')
for idx, v in enumerate(variables):
rt = int(codes[idx])
print('Interpolating RT {}'.format(rt))
if var_type == 'Indicators':
mask = v == 1
else:
mask = v < 0
var_range = helpers.max_dist(x, y, z, mask, grid)
print('Estimated range is {}'.format(var_range))
nugget = 0.01
cov = [
ar2gas.compute.Covariance.nugget(nugget),
ar2gas.compute.Covariance.gaussian(1 - nugget, var_range,
var_range, var_range, 0.,
0., 0.)
]
vargs[rt] = cov
results = ar2gas_dual_krig(cov, x, y, z, v, grid)
interpolated_variables.append(results)
if keep_variables == '1':
prop_name = 'interpolated_' + var_type + '_' + tg_prop_name + '_' + str(
rt)
sgems.set_property(tg_grid_name, prop_name, results.tolist())
elif len(vargs) == 1:
print(
'Interpolating using the same covariance model for all variables')
cov = list(vargs.values())[0]
for idx, v in enumerate(variables):
rt = int(codes[idx])
print('Interpolating RT {}'.format(rt))
results = ar2gas_dual_krig(cov, x, y, z, v, grid)
interpolated_variables.append(results)
if keep_variables == '1':
prop_name = 'interpolated_' + var_type + '_' + tg_prop_name + '_' + str(
rt)
sgems.set_property(tg_grid_name, prop_name, results.tolist())
else:
for idx, v in enumerate(variables):
rt = int(codes[idx])
print('Interpolating using one covariance model per variables')
print('Interpolating RT {}'.format(rt))
results = ar2gas_dual_krig(vargs[rt], x, y, z, v, grid)
interpolated_variables.append(results)
if keep_variables == '1':
prop_name = 'interpolated_' + var_type + '_' + tg_prop_name + '_' + str(
rt)
sgems.set_property(tg_grid_name, prop_name, results.tolist())
print('Finished interpolating!')
return interpolated_variables
def execute(self):
'''#Execute the funtion read_params
read_params(self.params)
print self.params'''
#Get the grid and rock type propery
grid = self.params['propertyselectornoregion']['grid']
prop = self.params['propertyselectornoregion']['property']
#Error message
if len(grid) == 0 or len(prop) == 0:
print 'Select the rocktype property'
return False
#Get the X, Y and Z coordinates and RT property
X = sgems.get_property(grid, '_X_')
Y = sgems.get_property(grid, '_Y_')
Z = sgems.get_property(grid, '_Z_')
RT = sgems.get_property(grid, prop)
elipsoide = self.params['ellipsoidinput']['value']
elipsoide_split = elipsoide.split()
range1 = float(elipsoide_split[0])
range2 = float(elipsoide_split[1])
range3 = float(elipsoide_split[2])
azimuth = float(elipsoide_split[3])
dip = float(elipsoide_split[4])
rake = float(elipsoide_split[5])
X, Y, Z = anis_search(X, Y, Z, range1, range2, range3, azimuth, dip,
rake)
#Creates a list of all rock types
rt_list = []
for i in RT:
if i not in rt_list and not math.isnan(i):
rt_list.append(i)
#Sort the rock type list in crescent order
rt_list = [int(x) for x in rt_list]
rt_list.sort()
#Create a empty distance matrix
dist_matrix = np.zeros(shape=((len(rt_list)), (len(RT))))
#Calculates the signed distances, and append it in the distance matrix
for i in range(len(rt_list)):
rock = rt_list[i]
for j in range(len(RT)):
if math.isnan(RT[j]):
dist_matrix[i][j] = float('nan')
elif RT[j] == rock:
dsmin = 1.0e21
for k in range(len(RT)):
if RT[j] != RT[k] and not math.isnan(RT[k]):
if (dist(X[j], Y[j], Z[j], X[k], Y[k],
Z[k])) < dsmin:
dsmin = (dist(X[j], Y[j], Z[j], X[k], Y[k],
Z[k]))
dist_matrix[i][j] = -dsmin
else:
dsmin = 1.0e21
for k in range(len(RT)):
if RT[k] == rock:
if (dist(X[j], Y[j], Z[j], X[k], Y[k],
Z[k])) < dsmin:
dsmin = (dist(X[j], Y[j], Z[j], X[k], Y[k],
Z[k]))
dist_matrix[i][j] = dsmin
#Creates the signed distances properties
lst_props_grid = sgems.get_property_list(grid)
for k in range(len(dist_matrix)):
prop_final_data_name = 'Signed_Distances_RT_' + str(rt_list[k])
if (prop_final_data_name in lst_props_grid):
flag = 0
i = 1
while (flag == 0):
test_name = prop_final_data_name + '-' + str(i)
if (test_name not in lst_props_grid):
flag = 1
prop_final_data_name = test_name
i = i + 1
list = dist_matrix[k].tolist()
sgems.set_property(grid, prop_final_data_name, list)
return True
def execute(self):
#aqui vai o codigo
#getting variables
tg_grid_name = self.params['gridselector']['value']
tg_region_name = self.params['gridselector']['region']
tg_prop_name = self.params['lineEdit']['value']
prop_grid_name = self.params['propertyselectornoregion']['grid']
prop_name = self.params['propertyselectornoregion']['property']
cmin = str_to_float(self.params['lineEdit_3']['value'])
cmax = str_to_float(self.params['lineEdit_2']['value'])
gamma_min = str_to_float(self.params['lineEdit_5']['value'])
gamma_max = str_to_float(self.params['lineEdit_4']['value'])
n = int(self.params['spinBox']['value'])
n_folds = int(self.params['spinBox_2']['value'])
values = np.array(sgems.get_property(prop_grid_name, prop_name))
nan_filter = np.isfinite(values)
y_val = values[nan_filter]
x, y, z = np.array(sgems.get_X(prop_grid_name))[nan_filter], np.array(
sgems.get_Y(prop_grid_name))[nan_filter], np.array(
sgems.get_Z(prop_grid_name))[nan_filter]
X = np.array([x, y, z]).T
grid = helpers.ar2gemsgrid_to_ar2gasgrid(tg_grid_name, tg_region_name)
X_new = grid.locations()
#scaling
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X)
X_new = scaler.fit_transform(X_new)
#grid search parameters
C = np.linspace(cmin, cmax, n)
gamma = np.linspace(gamma_min, gamma_max, n)
param_grid = [
{
'estimator__C': C,
'estimator__gamma': gamma,
'estimator__kernel': ["rbf"] * n
},
]
#clf
print("Tunning parameters...")
clf = OneVsOneClassifier(SVC())
model_tunning = GridSearchCV(clf, param_grid=param_grid, cv=n_folds)
model_tunning.fit(X, y_val)
print('accuracy ', model_tunning.best_score_)
print('parameters', model_tunning.best_params_)
clf = model_tunning.best_estimator_
print("Predicting...")
clf.fit(X, y_val)
results = clf.predict(X_new)
tp = np.ones(grid.size_of_mask()) * float('nan') if hasattr(
grid, 'mask') else np.ones(grid.size()) * float('nan')
if hasattr(grid, 'mask'):
mask = grid.mask()
r_idx = 0
for idx, val in enumerate(mask):
if val == True:
tp[idx] = results[r_idx]
r_idx = r_idx + 1
else:
tp = results
sgems.set_property(tg_grid_name, tg_prop_name, tp.tolist())
print('Done!')
return True
def execute(self):
#Execute the function read_params
read_params(self.params)
print self.params
# ----------------------------------------------------------------------
#
# Cut-offs domaining
#
# ----------------------------------------------------------------------
# checking if box is checked
if self.params['cutoff_check_box']['value'] == str(1):
# Getting variables
prop = self.params['prop_cutoff']['property']
grid_d = self.params['prop_cutoff']['grid']
cutoffs_user = (self.params['cutoffs_user']['value']).split()
prop_cutoff = sgems.get_property(grid_d, prop)
# substituting commas for points in users inputed cutoffs
cutoffs_user_no_comma = []
for i in cutoffs_user:
cutoffs_user_no_comma.append(float(i.replace(",", ".")))
coded_dataset = sample_class_cutoff(prop_cutoff, cutoffs_user_no_comma)
# setting the variable
prop_final_data_name = 'coded_cutoff_'+self.params['prop_cutoff']['property']
lst_props_grid = sgems.get_property_list(grid_d)
if (prop_final_data_name in lst_props_grid):
flag = 0
i = 1
while (flag == 0):
test_name = prop_final_data_name + '-' + str(i)
if (test_name not in lst_props_grid):
flag = 1
prop_final_data_name = test_name
i = i + 1
sgems.set_property(grid_d, prop_final_data_name, coded_dataset)
# ----------------------------------------------------------------------
#
# K-means clustering
#
# ----------------------------------------------------------------------
# checking if box is checked
if self.params['k_check_box']['value'] == str(1):
# Getting variables
grid_k = self.params['K_grid']['value']
nclus = int(self.params['k_number']['value'])
sec_props_k = (self.params['k_sec_var']['value']).split(';')
prim_var_k = self.params['K_prim_var']['value']
var_isotopic_kmeans, nan_indices = isotopic_dataset(grid_k, prim_var_k, sec_props_k)
#runing kmeans
k = KMeans(n_clusters=nclus).fit(var_isotopic_kmeans)
RT = k.labels_
RT_lst = []
m=0
for i in range(len(nan_indices)+len(RT)):
check = True
for j in nan_indices:
if i == j:
RT_lst.append(float('nan'))
check = False
if check == True:
RT_lst.append(RT[m])
m = m+1
create_variable(grid_k, 'KMeans', RT_lst)
# ----------------------------------------------------------------------
#
# Hierarchical clustering
#
# ----------------------------------------------------------------------
# checking if box is checked
if self.params['hier_check_box']['value'] == str(1):
# Getting variables
grid_h = self.params['hier_grid']['value']
sec_props_h = (self.params['hier_sec_var']['value']).split(';')
prim_var_h = self.params['hier_prim_var']['value']
criterion = self.params['criterion']['value']
treshold = float(self.params['treshold']['value'])
method_h = self.params['method']['value']
dist_metric = self.params['dist_met']['value']
var_isotopic_hier, nan_indices_h = isotopic_dataset(grid_h, prim_var_h, sec_props_h)
# generate the linkage matrix
Z = linkage(var_isotopic_hier, method = method_h, metric = dist_metric)
df = pd.DataFrame(Z)
df.to_csv('linkage_matrix.csv', index = False)
plt.figure(figsize=(20,20))
dn = dendrogram(Z)
plt.savefig('dendogram.png')
#printing cophenet corr.
c, coph_dists = cophenet(Z, pdist(var_isotopic_hier))
print "Cophenet correlation should be close to 1 : {}".format(c)
#runnig hierarchical clustering
try:
RT_lst_h = fcluster(Z, treshold, criterion= criterion)
except:
print 'erro'
RT_lst_h_final = []
m = 0
for i in range(len(nan_indices_h) + len(RT_lst_h)):
check = True
for j in nan_indices_h:
if i == j:
RT_lst_h_final.append(float('nan'))
check = False
if check == True:
RT_lst_h_final.append(RT_lst_h[m])
m = m + 1
create_variable(grid_h, 'Hierarchical', RT_lst_h_final)
# ----------------------------------------------------------------------
#
# GMM clustering
#
# ----------------------------------------------------------------------
# checking if box is checked
if self.params['gmm_checkbox']['value'] == str(1):
# getting variables
grid_gmm = self.params['gmm_grid']['value']
prim_var_gmm = self.params['gmm_prim']['value']
sec_gmm = (self.params['gmm_sec']['value']).split(';')
components = int(self.params['gmm_components']['value'])
cov_type = self.params['cov_type']['value']
var_isotopic_gmm, nan_indices_gmm = isotopic_dataset(grid_gmm, prim_var_gmm, sec_gmm)
gmm = GMM(n_components = components, covariance_type= cov_type).fit(var_isotopic_gmm)
RT_lst_gmm = gmm.predict(var_isotopic_gmm)
probs = gmm.predict_proba(var_isotopic_gmm)
probs_trans = probs.T
RT_lst_gmm_final = []
m = 0
for i in range(len(nan_indices_gmm) + len(RT_lst_gmm)):
check = True
for j in nan_indices_gmm:
if i == j:
RT_lst_gmm_final.append(float('nan'))
check = False
if check == True:
RT_lst_gmm_final.append(RT_lst_gmm[m])
m = m + 1
create_variable(grid_gmm, 'GMM', RT_lst_gmm_final)
for i,j in enumerate(probs_trans):
RT_lst_gmm_probs = []
m = 0
for k in range(len(nan_indices_gmm) + len(RT_lst_gmm)):
check = True
for l in nan_indices_gmm:
if k == l:
RT_lst_gmm_probs.append(float('nan'))
check = False
if check == True:
RT_lst_gmm_probs.append(probs_trans[i][m])
m = m + 1
create_variable(grid_gmm, 'GMM_prob_cluster_'+str(i), RT_lst_gmm_probs)
return True
