def test_gmm_plot(self):
'''
Test the GMM plotting
'''
try:
gmm_util = gs.GmmUtility(gmm_file=os.path.join(
os.path.normpath(os.getcwd() + os.sep + os.pardir),
r'pygeostat\pygeostat\data\example_data\gmm_fit.out'),
data=gs.ExampleData('point2d_mv').data,
variable_names=['Var1', 'Var2', 'Var3'])
except Exception as ex:
self.fail('Unable to test GmmUtility \n{}'.format(str(ex)))
try:
gmm_util.bivariate_plot(var_index=[1, 2],
cmap='viridis',
title='Bivariate Plot')
except Exception as ex:
self.fail('Unable to test bivariate_plot \n{}'.format(str(ex)))
try:
gmm_util.summary_plot(pad=0.1)
except Exception as ex:
self.fail('Unable to test summary_plot \n{}'.format(str(ex)))
try:
gmm_util.univariate_conditional_plot(
conditioning_data=[0, 0, None])
except Exception as ex:
self.fail('Unable to test univariate_conditional_plot \n{}'.format(
str(ex)))
def test_simple_plot(self):
'''
Test the capability of plotting a simple scatter plot to compare estimate vs truth
'''
from matplotlib import pyplot as plt
output_file = os.path.join(self.out_dir, 'validation_plot1.png')
_, ax = plt.subplots(1, 1)
try:
data = gs.ExampleData('3d_estimate')
_ = gs.validation_plot(data.data['Estimate'],
data.data['True'],
stat_blk='minimal',
output_file=output_file)
_ = gs.validation_plot(data.data['Estimate'],
data.data['True'],
stat_blk='minimal',
output_file=output_file,
ax=ax)
except Exception as ex:
self.fail(
'Unable to test the validation_plot with simple settings\n{}'.
format(str(ex)))
self.assertEqual(os.path.isfile(output_file), True)
def setUp(self):
super().setUp()
grid_str = '''120 5.00 10.00 -nx, xmn, xsiz
110 1205.00 10.00 -ny, ymn, ysiz
1 0.5 1.0 -nz, zmn, zsiz'''
griddef = gs.GridDef(grid_str=grid_str)
self.data = gs.ExampleData("grid2d_surf", griddef=griddef)
def test_simple_plot(self):
'''
Test the capability of plotting by passing only the data file
'''
data = gs.ExampleData('accuracy_plot')
reals = data[list(data.columns[1:])].values
truth = data[list(data.columns)[0]].values
output_file = os.path.join(self.out_dir, 'accuracy_plot1.png')
try:
gs.accuracy_plot(truth=truth, reals=reals, output_file=output_file)
except Exception as ex:
self.fail(
'Unable to test the accuracy plot with simple settings \n{}'.
format(str(ex)))
self.assertEqual(os.path.isfile(output_file), True)
def test_simple_plot(self):
'''
Test the capability of plotting a CDF with probability scale
'''
output_file = os.path.join(self.out_dir, 'probability_plot1.png')
try:
data = gs.ExampleData('oilsands')
_ = gs.probability_plot(data.data['Fines'],
logscale=False,
output_file=output_file)
except Exception as ex:
self.fail('Unable to test the probability_plot \n{}'.format(
str(ex)))
self.assertEqual(os.path.isfile(output_file), True)
def test_experimental_plot(self):
'''
Test the capability of plotting experimental variograms
'''
output_file = os.path.join(self.out_dir, 'variogram_plot1.png')
try:
dfl = gs.ExampleData('experimental_variogram')
_ = gs.variogram_plot(dfl,
experimental=True,
figsize=(8, 4),
output_file=output_file)
except Exception as ex:
self.fail(
'Unable to test the variogram_plot for experimental variogram\n{}'
.format(str(ex)))
self.assertEqual(os.path.isfile(output_file), True)
import pygeostat as gs
dat = gs.ExampleData('point3d_ind_mv')
data = dat.data[dat.data['HoleID'] == 3]
gs.drill_plot(data['Elevation'], data['Lithofacies'])
def test_load_gslib_attributes(self):
dat = gs.ExampleData('oilsands')
self.assertEqual(dat.x.lower(), 'east')
self.assertEqual(dat.y.lower(), 'north')
self.assertEqual(dat.z.lower(), 'elevation')
def setUp(self):
warnings.simplefilter('ignore', category=ImportWarning)
self.grid = gs.GridDef([40, 0.5, 1, 40, 0.5, 1, 40, 0.5, 1])
self.testfl = gs.ExampleData('3d_grid', griddef=self.grid)
def test_infergriddef(self):
df2d = gs.ExampleData("point2d_ind")
df3d = gs.ExampleData("point3d_ind_mv")
#where xsiz = ysiz = zsiz, a float can also be provided.
self.compare(df3d.infergriddef(blksize=75),
df3d.infergriddef(blksize=[75, 75, 75]))
#where nx = ny = nz, an int can also be provided.
self.compare(df3d.infergriddef(nblk=60),
df3d.infergriddef(nblk=[60, 60, 60]))
#for 3D data, infergriddef() must return a 3D grid definition even if zsiz is given as None or 0 or 1.
self.compare(
df3d.infergriddef(blksize=[50, 60, 1]),
gs.GridDef('''20 135.0 50.0
19 1230.0 60.0
82 310.5 1.0'''))
self.compare(df3d.infergriddef(blksize=[50, 60, 1]),
df3d.infergriddef(blksize=[50, 60, None]))
self.compare(df3d.infergriddef(blksize=[50, 60, 1]),
df3d.infergriddef(blksize=[50, 60, 0]))
# nz given as None or 0 or 1 returns a 2D grid that covers the vertical extent of a 3D dataset.
self.compare(
df3d.infergriddef(nblk=[50, 60, 1]),
gs.GridDef('''50 119.8 19.6
60 1209.1 18.2
1 350.85 81.7'''))
self.compare(df3d.infergriddef(nblk=[50, 60, 1]),
df3d.infergriddef(nblk=[50, 60, None]))
self.compare(df3d.infergriddef(nblk=[50, 60, 1]),
df3d.infergriddef(nblk=[50, 60, 0]))
#if nblk is not integer,infergriddef(), must convert it to int and provide correct grid definition
_nblk = [12.3, 5.7, 10.11]
self.compare(df3d.infergriddef(nblk=_nblk),
df3d.infergriddef(nblk=[int(i) for i in _nblk]))
# If data is 2-D, zsiz must be provided as None. Otherwise it must raise exception.
with self.assertRaises(ValueError):
df2d.infergriddef(blksize=[50, 60, 4])
with self.assertRaises(ValueError):
df2d.infergriddef(blksize=75)
self.compare(
df2d.infergriddef(blksize=[50, 60, None]),
gs.GridDef('''24 25.0 50.0
19 1230.0 60.0
1 0.5 1.0'''))
#If data is 2-D, nz must be provided as None. Otherwise it must raise exception.
with self.assertRaises(ValueError):
df2d.infergriddef(nblk=[50, 60, 1])
with self.assertRaises(ValueError):
df2d.infergriddef(nblk=60)
self.compare(
df2d.infergriddef(nblk=[50, 60, None]),
gs.GridDef('''50 11.9 23.8
60 1209.1 18.2
1 0.5 1.0'''))
import pygeostat as gs
data_file = gs.ExampleData('3d_correlation')
loadmat = data_file.data.corr().iloc[3:6,6:9]
gs.loadings_plot(loadmat.values, figsize=(5,5), xticklabels=['PC1', 'PC2', 'PC3'], yticklabels=['InputVariable1', 'InputVariable2', 'InputVariable3'])
import pygeostat as gs
varcalcdat = gs.ExampleData('experimental_variogram')
varmodeldat = gs.ExampleData('variogram_model')
ax = gs.variogram_plot(varcalcdat.data, index=1, sill=False)
gs.variogram_plot(varmodeldat.data, index=1, experimental=False, ax=ax)
def setUp(self):
super().setUp()
self.data = gs.ExampleData("point3d_ind_mv")
def setUp(self):
super().setUp()
self.griddef = gs.GridDef([40, 0.5, 1, 40, 0.5, 1, 40, 0.5, 1])
self.data = gs.ExampleData('3d_grid', griddef=self.griddef)
import pygeostat as gs
#Load the data from output from varcal/varmodel file
varcalcdat = gs.ExampleData('experimental_variogram')
gs.variogram_plot(varcalcdat, index=1)
import pygeostat as gs
data = gs.ExampleData('oilsands')
gs.probability_plot(data.data['Fines'], logscale=False)
import pygeostat as gs
grid_str = '''120 5.00 10.00 -nx, xmn, xsiz
110 1205.00 10.00 -ny, ymn, ysiz
1 0.5 1.0 -nz, zmn, zsiz'''
griddef = gs.GridDef(grid_str=grid_str)
data_fl = gs.ExampleData("grid2d_surf", griddef=griddef)
ax = gs.slice_plot(data_fl, var="Thickness")
_ = gs.contour_plot(data_fl, var="Thickness", ax=ax, clabel=True)
import pygeostat as gs
data = gs.ExampleData('3d_estimate')
gs.validation_plot(data.data['Estimate'],
data.data['True'],
stat_blk='minimal')
def setUp(self):
super().setUp()
refdata = gs.ExampleData('3d_correlation')
self.loadmat = refdata.data.corr().iloc[3:6, 6:9]
import pygeostat as gs
# load some data
data = gs.ExampleData('3d_grid',
griddef=gs.GridDef([40, 1, 2, 40, 1, 2, 40, 0.5, 1]))
# plot the grid slices
_ = gs.grid_slice_plot(data, orient='xz', n_slice=5)
import pygeostat as gs
griddef = gs.GridDef([40, 0.5, 1, 40, 0.5, 1, 40, 0.5, 1])
data_file = gs.ExampleData('3d_grid', griddef)
gs.slice_plot(data_file, orient='xy', cmap='viridis', slice_thickness=20)
def setUp(self):
super().setUp()
dfl = gs.ExampleData("point3d_ind_mv")
data = dfl[dfl.variables]
self.data_cor = data.corr()
import pygeostat as gs
import numpy as np
# Load the data, which registers the variables attribute
data_file1 = gs.ExampleData('point3d_ind_mv')
data_file2 = gs.ExampleData('point3d_ind_mv')
mask = np.random.rand(len(data_file2))<0.3
data_file2.data = data_file2.data[mask]
# Plot with the standard orientation
fig = gs.scatter_plots_lu(data_file1, data_file2, titles=('Data', 'Realization'), s=10, nmax=1000,
stat_xy=(0.95, 0.95), pad=(-1, -1), figsize=(10, 10))
# Plot with aligned orientation to ease comparison
fig = gs.scatter_plots_lu(data_file1, data_file2, titles=('Data', 'Realization'), s=10, nmax=1000,
stat_xy=(0.95, 0.95), pad=(-1, -1), figsize=(10, 10), cmap='jet',
align_orient=True)
import pygeostat as gs
# Load the data, which registers the variables attribute
data_file = gs.ExampleData('point3d_ind_mv')
# Plot with the default KDE coloring
fig = gs.scatter_plots(data_file,
nmax=1000,
stat_xy=(0.95, 0.95),
pad=(-1, -1),
s=10,
figsize=(10, 10))
import pygeostat as gs
# load some data
dfl = gs.ExampleData("point3d_ind_mv")
cats = [1, 2, 3, 4, 5]
colors = gs.catcmapfromcontinuous("Spectral", 5).colors
# build the required cat dictionaries
dfl.catdict = {c: "RT {:02d}".format(c) for c in cats}
colordict = {c: colors[i] for i, c in enumerate(cats)}
# plot the histogram_plot
ax = gs.histogram_plot(dfl,
cat=True,
color=colordict,
figsize=(7, 4),
rotateticks=(45, 0),
label_count=True)
def setUp(self):
super().setUp()
dat = gs.ExampleData('point3d_ind_mv')
self.data = dat.data[dat.data['HoleID'] == 3]
import pygeostat as gs
data_file = gs.ExampleData('accuracy_plot')
reals = data_file[list(data_file.columns[1:])].values
truth = data_file[list(data_file.columns)[0]].values
gs.accuracy_plot(truth=truth, reals=reals)
import pygeostat as gs
data_file = gs.ExampleData("point3d_ind_mv")
data = data_file[data_file.variables]
data_cor = data.corr()
gs.correlation_matrix_plot(data_cor.values, cmap='bwr')
import pygeostat as gs
varmodeldat = gs.ExampleData('variogram_model')
gs.variogram_plot(varmodeldat, index=1, experimental=False)
