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 setUp(self):
'''
Method called to prepare the test fixture. This is called immediately before calling the test method;
other than AssertionError or SkipTest, any exception raised by this method will be considered
an error rather than a test failure. The default implementation does nothing.
'''
griddef_str = '''120 5.0 10.0
110 1205.0 10.0
1 0.5 1.0'''
self.griddef = gs.GridDef(griddef_str)
def setUp(self):
super().setUp()
self.griddef = gs.GridDef([10, 1, 0.5, 10, 1, 0.5, 1, 1, 0.5])
size = self.griddef.count()
mean = 2.5
scale = 3
self.nreal = 100
self.ref_data = pd.DataFrame(
{'value': np.random.normal(mean, scale, size=size)})
for i, offset in enumerate(np.random.rand(self.nreal) * 0.04 - 0.02):
data = pd.DataFrame(
{'value': np.random.normal(mean + offset, scale, size=size)})
gs.write_gslib(
data, os.path.join(self.out_dir, 'real{}.out'.format(i + 1)))
def _validate_griddef_or_None(s):
"""
A validation method to convert input s to pygeostat grid def and raise an error if not convertable.
"""
import pygeostat as gs
if s is None:
return None
elif isinstance(s, str):
try:
return gs.GridDef(s)
except AssertionError:
raise ValueError(
'{} should be a pygeostat.GridDef object or griddef string!'.
format(s))
elif isinstance(s, gs.GridDef):
return s
else:
raise ValueError(
'{} should be a pygeostat.GridDef object or griddef string!'.
format(s))
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)
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
# 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)
def gen_job_list(nreals_true, nreals, nphase, nstope, seed, dirlist,
fval_pars, min_width, max_width, rot, clip_bounds,
vert_bounds, true_data, de_pars, len_comp, grid1,
grid2, grid3, varg1, varg2):
'''Generate job list for parallel processing of VOI workflow'''
# generate directories
gen_directories(dirlist, nreals_true, nphase)
refdir, dhdir, simdir1, chksim, gsdir, simdir2, stopedir, postdir, outdir, plotdir, pardir, tmp = dirlist
# generate seed lists
rseed_lst0 = gs.rseed_list(nreals_true, seed+1000)
rseed_lst1 = gs.rseed_list(nreals_true*nreals*nphase, seed+2000)
rseed_lst2 = gs.rseed_list(nreals_true*nreals*nphase, seed+3000)
rseed_lst3 = gs.rseed_list(nreals_true*nphase, seed+4000)
# grid definitons
with open(grid1, 'r') as f:
grid = f.read()
griddef1 = gs.GridDef(grid)
with open(grid2, 'r') as f:
grid = f.read()
griddef2 = gs.GridDef(grid)
with open(grid3, 'r') as f:
grid = f.read()
griddef3 = gs.GridDef(grid)
# variogram models
with open(varg1, 'r') as f:
varlg = f.read()
with open(varg2, 'r') as f:
varhg = f.read()
jobs_ref_sim = []
jobs_ref_post = []
jobs = []
for t in range(nreals_true):
# reference model params
# usgsim (1)
with open(pardir + 'usgsim1.par', 'r') as f:
parstr_ref = f.read()
parstr_ref = parstr_ref.format(rseed=rseed_lst0[t],
griddef=griddef1,
true_data=true_data,
outfl=refdir+f'real{t+1}.gsb',
varhg=varhg)
parpath = pardir + f't{t+1}/usgsim_ref{t+1}.par'
with open(parpath, 'w', newline='', encoding='utf8') as pf:
pf.write(parstr_ref)
jobs_ref_sim.append((dict(parstr=parstr_ref, parfile=parpath)))
# reference model block avg params
with open(pardir + 'ublkavg.par', 'r') as f:
parstr_avg = f.read()
parstr_avg = parstr_avg.format(simfl=refdir + f'real{t+1}.gsb',
griddef=griddef1,
griddef_block=griddef3,
outfl=refdir + f'realblk{t+1}.gsb')
parpath = pardir + f't{t+1}/blkavg_ref{t+1}.par'
with open(parpath, 'w', newline='', encoding='utf8') as pf:
pf.write(parstr_avg)
jobs_ref_post.append((dict(parstr=parstr_avg, parfile=parpath)))
for j in range(nphase):
# resample params
grid = [griddef1.nx, griddef1.xmn, griddef1.xsiz,
griddef1.ny, griddef1.ymn, griddef1.ysiz,
griddef1.nz, griddef1.zmn, griddef1.zsiz]
sample_args = gen_sample_args(
t+1, j+1, grid, len_comp, refdir, dhdir)
# decluster params
with open(pardir + 'declus_nn2.par', 'r') as f:
parstr_nn = f.read()
parstr_nn = parstr_nn.format(datafl=dhdir+f't{t+1}/ddh_au_p{j+1}_all.out',
griddef=griddef1,
outfl=dhdir+f't{t+1}/ddh_au_p{j+1}_declus.out')
# simulate au
# usgsim (2) params
usgsim_pars2 = []
with open(pardir + 'usgsim2.par', 'r') as f:
parstr_usg = f.read()
for i in range(nreals):
parstr_usg_fmt = parstr_usg.format(rseed=rseed_lst1[i+j*nreals+t*nphase*nreals],
griddef=griddef2,
outfl=simdir1 +
f't{t+1}/phase{j+1}/real{i+1}.gsb',
varhg=varhg,
data=dhdir + f't{t+1}/ddh_au_p{j+1}_declus.out')
usgsim_pars2.append(parstr_usg_fmt)
# add coords params
grid = [griddef2.nx, griddef2.xmn, griddef2.xsiz,
griddef2.ny, griddef2.ymn, griddef2.ysiz,
griddef2.nz, griddef2.zmn, griddef2.zsiz]
coord_args = gen_addcoord_args(t+1, j+1, nreals, grid, simdir1)
# mwa params
mwa_pars = []
with open(pardir + 'maketrend.par', 'r') as f:
parstr_mwa = f.read()
for i in range(nreals):
parstr_mwa_fmt = parstr_mwa.format(simfl=simdir1+f't{t+1}/phase{j+1}/real{i+1}_xyz.gsb',
outfl=gsdir +
f't{t+1}/phase{j+1}/real{i+1}_mwa.gsb',
griddef=griddef2)
mwa_pars.append(parstr_mwa_fmt)
# threshold params
thresh_args = gen_threshold_args(t+1, j+1, gsdir, nreals, 1.2, 3.3)
# tag ddh params
grid = [griddef2.nx, griddef2.xmn, griddef2.xsiz,
griddef2.ny, griddef2.ymn, griddef2.ysiz,
griddef2.nz, griddef2.zmn, griddef2.zsiz]
tag_args = gen_tag_args(t+1, j+1, nreals, grid, dhdir, gsdir)
# simulate au
# usgsim (3) params
usgsim_pars3 = []
with open(pardir + 'usgsim3.par', 'r') as f:
parstr_usg = f.read()
for i in range(nreals):
parstr_usg_fmt = parstr_usg.format(rseed=rseed_lst2[i+j*nreals+t*nphase*nreals],
griddef=griddef2,
outfl=simdir2 +
f't{t+1}/phase{j+1}/real{i+1}.gsb',
varlg=varlg,
varhg=varhg,
data=dhdir +
f't{t+1}/phase{j+1}/ddh_au_real{i+1}_tag.out',
rockfl=gsdir + f't{t+1}/phase{j+1}/real{i+1}_mwa.gsb')
usgsim_pars3.append(parstr_usg_fmt)
# block average params
blkavg_pars = []
with open(pardir + 'ublkavg.par', 'r') as f:
parstr_avg = f.read()
for i in range(nreals):
parstr_avg_fmt = parstr_avg.format(simfl=simdir2 + f't{t+1}/phase{j+1}/real{i+1}.gsb',
griddef=griddef2,
griddef_block=griddef3,
outfl=simdir2 + f't{t+1}/phase{j+1}/realblk{i+1}.gsb')
blkavg_pars.append(parstr_avg_fmt)
# merge reals params
with open(pardir + 'merge_reals_gsb.par', 'r') as f:
parstr_mrg = f.read()
parstr_mrg = parstr_mrg.format(simfl=simdir2 + f't{t+1}/phase{j+1}/realblk',
nreals=nreals,
outfl=postdir + f't{t+1}/phase{j+1}/real_merge.gsb')
# postsim params
with open(pardir + 'upostsim.par', 'r') as f:
parstr_post = f.read()
parstr_post = parstr_post.format(simfl=postdir + f't{t+1}/phase{j+1}/real_merge.gsb',
nreals=nreals,
outfl=postdir + f't{t+1}/phase{j+1}/postsim.gsb')
# clipping args
grid = [griddef3.nx, griddef3.xmn, griddef3.xsiz,
griddef3.ny, griddef3.ymn, griddef3.ysiz,
griddef3.nz, griddef3.zmn, griddef3.zsiz]
clip_args = gen_clip_args(
t+1, j+1, nreals, simdir2, simdir2, grid, clip_bounds)
# optimization args
de_args = gen_de_args(t+1, j+1, nreals, vert_bounds, 'verts.out', -3, [645, 670, 700],
20, nstope, 8, rseed_lst3[j +
t*nphase], simdir2, stopedir,
fval_pars, de_pars, min_width, max_width)
jobs.append((sample_args, parstr_nn, usgsim_pars2, coord_args, mwa_pars,
thresh_args, tag_args, usgsim_pars3, blkavg_pars, parstr_mrg,
parstr_post, clip_args, de_args, t+1, j+1, simdir1, simdir2,
dhdir, gsdir))
return [jobs_ref_sim, jobs_ref_post, jobs]
import pygeostat as gs
import pandas as pd
# Global setting using Parameters
gs.Parameters['data.griddef'] = gs.GridDef([10, 1, 0.5, 10, 1, 0.5, 2, 1, 0.5])
gs.Parameters['data.nreal'] = nreal = 100
size = gs.Parameters['data.griddef'].count()
reference_data = pd.DataFrame({'value': np.random.normal(0, 1, size=size)})
# Create example simulated data
simulated_data = pd.DataFrame(columns=['value'])
for i, offset in enumerate(np.random.rand(nreal) * 0.04 - 0.02):
simulated_data = simulated_data.append(
pd.DataFrame({'value': np.random.normal(offset, 1, size=size)}))
gs.histogram_plot_simulation(simulated_data,
reference_data,
reference_variable='value',
title='Histogram Reproduction',
grid=True)
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
# 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, nrow=2, ncol=5, start_slice=10, end_slice=25, n_slice=10, cmap='hot', vlim=(-3,3))
