def test_downloads(self):
url = "https://storage.googleapis.com/simpeg/Chile_GRAV_4_Miller/"
cloudfiles = [
"LdM_grav_obs.grv",
"LdM_mesh.mesh",
"LdM_topo.topo",
"LdM_input_file.inp",
]
url1 = url + cloudfiles[0]
url2 = url + cloudfiles[1]
file_names = download([url1, url2],
folder="./test_urls",
overwrite=True)
# or
file_name = download(url1, folder="./test_url", overwrite=True)
# where
assert isinstance(file_names, list)
assert len(file_names) == 2
assert isinstance(file_name, str)
# clean up
shutil.rmtree(os.path.expanduser("./test_urls"))
shutil.rmtree(os.path.expanduser("./test_url"))
def download_and_unzip_data(
url="https://storage.googleapis.com/simpeg/bookpurnong/bookpurnong_inversion.tar.gz",
):
"""
Download the data from the storage bucket, unzip the tar file, return
the directory where the data are
"""
# download the data
downloads = utils.download(url)
# directory where the downloaded files are
directory = downloads.split(".")[0]
# unzip the tarfile
tar = tarfile.open(downloads, "r")
tar.extractall()
tar.close()
return downloads, directory
def download_and_unzip_data(
url="https://storage.googleapis.com/simpeg/em_examples/tdem_groundedsource/tdem_groundedsource.tar",
):
"""
Download the data from the storage bucket, unzip the tar file, return
the directory where the data are
"""
# download the data
downloads = utils.download(url)
# directory where the downloaded files are
directory = downloads.split(".")[0]
# unzip the tarfile
tar = tarfile.open(downloads, "r")
tar.extractall()
tar.close()
return downloads, directory
def test_surface2ind_topo(self):
file_url = (
"https://storage.googleapis.com/simpeg/tests/utils/vancouver_topo.xyz"
)
file2load = download(file_url)
vancouver_topo = np.loadtxt(file2load)
mesh_topo = discretize.TensorMesh(
[[(500.0, 24)], [(500.0, 20)], [(10.0, 30)]], x0="CCC")
indtopoCC = surface2ind_topo(mesh_topo,
vancouver_topo,
gridLoc="CC",
method="nearest")
indtopoN = surface2ind_topo(mesh_topo,
vancouver_topo,
gridLoc="N",
method="nearest")
assert len(np.where(indtopoCC)[0]) == 8729
assert len(np.where(indtopoN)[0]) == 8212
def NMOstackSingle(data, tintercept, v, timeFile):
dx = 20.0
xorig = np.arange(38) * dx
timdat = download(timeFile, verbose=False)
time = np.load(timdat)
singletrace = NMOstack(data, xorig, time, v)
_, ax = plt.subplots(1, 1, figsize=(7, 8))
kwargs = {
"skipt": 1,
"scale": 2.0,
"lwidth": 1.0,
"sampr": 0.004,
"ax": ax,
"clip": 10,
}
extent = [singletrace.min(), singletrace.max(), time.max(), time.min()]
ax.invert_yaxis()
ax.axis(extent)
wiggle(singletrace.reshape([1, -1]), **kwargs)
ax.set_xlabel("Amplitude")
ax.set_ylabel("Time (s)")
# -----------------
#
# Here we provide the file paths to assets we need to run the inversion. The
# path to the true model conductivity and chargeability models are also
# provided for comparison with the inversion results. These files are stored as a
# tar-file on our google cloud bucket:
# "https://storage.googleapis.com/simpeg/doc-assets/dcip3d.tar.gz"
#
#
#
# storage bucket where we have the data
data_source = "https://storage.googleapis.com/simpeg/doc-assets/dcip3d.tar.gz"
# download the data
downloaded_data = utils.download(data_source, overwrite=True)
# unzip the tarfile
tar = tarfile.open(downloaded_data, "r")
tar.extractall()
tar.close()
# path to the directory containing our data
dir_path = downloaded_data.split(".")[0] + os.path.sep
# files to work with
topo_filename = dir_path + "topo_xyz.txt"
dc_data_filename = dir_path + "dc_data.xyz"
ip_data_filename = dir_path + "ip_data.xyz"
########################################################
def setUp(self):
url = "https://storage.googleapis.com/simpeg/tests/em_utils/currents.npy"
self.basePath = download(url)
def run(plotIt=True, cleanAfterRun=True):
# Start by downloading files from the remote repository
# directory where the downloaded files are
url = "https://storage.googleapis.com/simpeg/Chile_GRAV_4_Miller/Chile_GRAV_4_Miller.tar.gz"
downloads = download(url, overwrite=True)
basePath = downloads.split(".")[0]
# unzip the tarfile
tar = tarfile.open(downloads, "r")
tar.extractall()
tar.close()
input_file = basePath + os.path.sep + "LdM_input_file.inp"
# %% User input
# Plotting parameters, max and min densities in g/cc
vmin = -0.6
vmax = 0.6
# weight exponent for default weighting
wgtexp = 3.0
# %%
# Read in the input file which included all parameters at once
# (mesh, topo, model, survey, inv param, etc.)
driver = GravityDriver_Inv(input_file)
# %%
# Now we need to create the survey and model information.
# Access the mesh and survey information
mesh = driver.mesh #
survey = driver.survey
data_object = driver.data
# [survey, data_object] = driver.survey
# define gravity survey locations
rxLoc = survey.source_field.receiver_list[0].locations
# define gravity data and errors
d = data_object.dobs
# Get the active cells
active = driver.activeCells
nC = len(active) # Number of active cells
# Create active map to go from reduce set to full
activeMap = maps.InjectActiveCells(mesh, active, -100)
# Create static map
static = driver.staticCells
dynamic = driver.dynamicCells
staticCells = maps.InjectActiveCells(None,
dynamic,
driver.m0[static],
nC=nC)
mstart = driver.m0[dynamic]
# Get index of the center
midx = int(mesh.nCx / 2)
# %%
# Now that we have a model and a survey we can build the linear system ...
# Create the forward model operator
simulation = gravity.simulation.Simulation3DIntegral(survey=survey,
mesh=mesh,
rhoMap=staticCells,
actInd=active)
# %% Create inversion objects
reg = regularization.Sparse(mesh,
indActive=active,
mapping=staticCells,
gradientType="total")
reg.mref = driver.mref[dynamic]
reg.norms = np.c_[0.0, 1.0, 1.0, 1.0]
# reg.norms = driver.lpnorms
# Specify how the optimization will proceed
opt = optimization.ProjectedGNCG(
maxIter=20,
lower=driver.bounds[0],
upper=driver.bounds[1],
maxIterLS=10,
maxIterCG=20,
tolCG=1e-4,
)
# Define misfit function (obs-calc)
dmis = data_misfit.L2DataMisfit(data=data_object, simulation=simulation)
# create the default L2 inverse problem from the above objects
invProb = inverse_problem.BaseInvProblem(dmis, reg, opt)
# Specify how the initial beta is found
betaest = directives.BetaEstimate_ByEig(beta0_ratio=1e-2)
# IRLS sets up the Lp inversion problem
# Set the eps parameter parameter in Line 11 of the
# input file based on the distribution of model (DEFAULT = 95th %ile)
IRLS = directives.Update_IRLS(f_min_change=1e-4,
max_irls_iterations=40,
coolEpsFact=1.5,
beta_tol=5e-1)
# Preconditioning refreshing for each IRLS iteration
update_Jacobi = directives.UpdatePreconditioner()
sensitivity_weights = directives.UpdateSensitivityWeights()
# Create combined the L2 and Lp problem
inv = inversion.BaseInversion(
invProb,
directiveList=[sensitivity_weights, IRLS, update_Jacobi, betaest])
# %%
# Run L2 and Lp inversion
mrec = inv.run(mstart)
if cleanAfterRun:
os.remove(downloads)
shutil.rmtree(basePath)
# %%
if plotIt:
# Plot observed data
# The sign of the data is flipped here for the change of convention
# between Cartesian coordinate system (internal SimPEG format that
# expects "positive up" gravity signal) and traditional gravity data
# conventions (positive down). For example a traditional negative
# gravity anomaly is described as "positive up" in Cartesian coordinates
# and hence the sign needs to be flipped for use in SimPEG.
plot2Ddata(rxLoc, -d)
# %%
# Write output model and data files and print misfit stats.
# reconstructing l2 model mesh with air cells and active dynamic cells
L2out = activeMap * invProb.l2model
# reconstructing lp model mesh with air cells and active dynamic cells
Lpout = activeMap * mrec
# %%
# Plot out sections and histograms of the smooth l2 model.
# The ind= parameter is the slice of the model from top down.
yslice = midx + 1
L2out[L2out == -100] = np.nan # set "air" to nan
plt.figure(figsize=(10, 7))
plt.suptitle("Smooth Inversion: Depth weight = " + str(wgtexp))
ax = plt.subplot(221)
dat1 = mesh.plotSlice(
L2out,
ax=ax,
normal="Z",
ind=-16,
clim=(vmin, vmax),
pcolorOpts={"cmap": "bwr"},
)
plt.plot(
np.array([mesh.vectorCCx[0], mesh.vectorCCx[-1]]),
np.array([mesh.vectorCCy[yslice], mesh.vectorCCy[yslice]]),
c="gray",
linestyle="--",
)
plt.scatter(rxLoc[0:, 0], rxLoc[0:, 1], color="k", s=1)
plt.title("Z: " + str(mesh.vectorCCz[-16]) + " m")
plt.xlabel("Easting (m)")
plt.ylabel("Northing (m)")
plt.gca().set_aspect("equal", adjustable="box")
cb = plt.colorbar(dat1[0],
orientation="vertical",
ticks=np.linspace(vmin, vmax, 4))
cb.set_label("Density (g/cc$^3$)")
ax = plt.subplot(222)
dat = mesh.plotSlice(
L2out,
ax=ax,
normal="Z",
ind=-27,
clim=(vmin, vmax),
pcolorOpts={"cmap": "bwr"},
)
plt.plot(
np.array([mesh.vectorCCx[0], mesh.vectorCCx[-1]]),
np.array([mesh.vectorCCy[yslice], mesh.vectorCCy[yslice]]),
c="gray",
linestyle="--",
)
plt.scatter(rxLoc[0:, 0], rxLoc[0:, 1], color="k", s=1)
plt.title("Z: " + str(mesh.vectorCCz[-27]) + " m")
plt.xlabel("Easting (m)")
plt.ylabel("Northing (m)")
plt.gca().set_aspect("equal", adjustable="box")
cb = plt.colorbar(dat1[0],
orientation="vertical",
ticks=np.linspace(vmin, vmax, 4))
cb.set_label("Density (g/cc$^3$)")
ax = plt.subplot(212)
mesh.plotSlice(
L2out,
ax=ax,
normal="Y",
ind=yslice,
clim=(vmin, vmax),
pcolorOpts={"cmap": "bwr"},
)
plt.title("Cross Section")
plt.xlabel("Easting(m)")
plt.ylabel("Elevation")
plt.gca().set_aspect("equal", adjustable="box")
cb = plt.colorbar(
dat1[0],
orientation="vertical",
ticks=np.linspace(vmin, vmax, 4),
cmap="bwr",
)
cb.set_label("Density (g/cc$^3$)")
# %%
# Make plots of Lp model
yslice = midx + 1
Lpout[Lpout == -100] = np.nan # set "air" to nan
plt.figure(figsize=(10, 7))
plt.suptitle("Compact Inversion: Depth weight = " + str(wgtexp) +
": $\epsilon_p$ = " + str(round(reg.eps_p, 1)) +
": $\epsilon_q$ = " + str(round(reg.eps_q, 2)))
ax = plt.subplot(221)
dat = mesh.plotSlice(
Lpout,
ax=ax,
normal="Z",
ind=-16,
clim=(vmin, vmax),
pcolorOpts={"cmap": "bwr"},
)
plt.plot(
np.array([mesh.vectorCCx[0], mesh.vectorCCx[-1]]),
np.array([mesh.vectorCCy[yslice], mesh.vectorCCy[yslice]]),
c="gray",
linestyle="--",
)
plt.scatter(rxLoc[0:, 0], rxLoc[0:, 1], color="k", s=1)
plt.title("Z: " + str(mesh.vectorCCz[-16]) + " m")
plt.xlabel("Easting (m)")
plt.ylabel("Northing (m)")
plt.gca().set_aspect("equal", adjustable="box")
cb = plt.colorbar(dat[0],
orientation="vertical",
ticks=np.linspace(vmin, vmax, 4))
cb.set_label("Density (g/cc$^3$)")
ax = plt.subplot(222)
dat = mesh.plotSlice(
Lpout,
ax=ax,
normal="Z",
ind=-27,
clim=(vmin, vmax),
pcolorOpts={"cmap": "bwr"},
)
plt.plot(
np.array([mesh.vectorCCx[0], mesh.vectorCCx[-1]]),
np.array([mesh.vectorCCy[yslice], mesh.vectorCCy[yslice]]),
c="gray",
linestyle="--",
)
plt.scatter(rxLoc[0:, 0], rxLoc[0:, 1], color="k", s=1)
plt.title("Z: " + str(mesh.vectorCCz[-27]) + " m")
plt.xlabel("Easting (m)")
plt.ylabel("Northing (m)")
plt.gca().set_aspect("equal", adjustable="box")
cb = plt.colorbar(dat[0],
orientation="vertical",
ticks=np.linspace(vmin, vmax, 4))
cb.set_label("Density (g/cc$^3$)")
ax = plt.subplot(212)
dat = mesh.plotSlice(
Lpout,
ax=ax,
normal="Y",
ind=yslice,
clim=(vmin, vmax),
pcolorOpts={"cmap": "bwr"},
)
plt.title("Cross Section")
plt.xlabel("Easting (m)")
plt.ylabel("Elevation (m)")
plt.gca().set_aspect("equal", adjustable="box")
cb = plt.colorbar(dat[0],
orientation="vertical",
ticks=np.linspace(vmin, vmax, 4))
cb.set_label("Density (g/cc$^3$)")
