def plot_map(self, solution: Solution = None, contour_lines=False, show_faults=True, show_data=True,
show_all_data=False, figsize=(12,12)):
if solution is None:
solution = self.model.solutions
assert solution.geological_map is not None, 'Geological map not computed. Activate the topography grid.'
try:
geomap = solution.geological_map.reshape(self.model.grid.topography.values_3D[:, :, 2].shape)
except AttributeError:
warnings.warn('Geological map not computed. Activate the topography grid.')
if show_data:
self.plot_data(direction='z', at='topography', show_all_data=show_all_data)
else:
fig, ax = plt.subplots(figsize=figsize)
im = plt.imshow(geomap, origin='lower', extent=self.model.grid.topography.extent, cmap=self._cmap,
norm=self._norm, zorder=-100)
if contour_lines == True and show_data == False:
CS = ax.contour(self.model.grid.topography.values_3D[:, :, 2], cmap='Greys', linestyles='solid',
extent=self.model.grid.topography.extent)
ax.clabel(CS, inline=1, fontsize=10, fmt='%d')
plothelp.add_colorbar(im=im, label='elevation [m]', cs=CS, aspect=35)
if show_faults == True:
self.extract_section_fault_lines('topography', faults_only=True)
plt.title("Geological map", fontsize=15)
plt.xlabel('X')
plt.ylabel('Y')
def plot_map(self,
solution: Solution = None,
contour_lines=True,
show_faults=True,
show_data=False):
if solution is not None:
assert solution.geological_map is not None, 'Geological map not computed. Activate the topography grid.'
else:
solution = self.model.solutions
geomap = solution.geological_map.reshape(
self.model.grid.topography.values_3D[:, :, 2].shape)
if show_data:
self.plot_data(direction='z')
else:
fig, ax = plt.subplots(figsize=(6, 6))
im = plt.imshow(geomap,
origin='lower',
extent=self.model.grid.topography.extent,
cmap=self._cmap,
norm=self._norm)
if contour_lines == True and show_data == False:
CS = ax.contour(self.model.grid.topography.values_3D[:, :, 2],
cmap='Greys',
linestyles='solid',
extent=self.model.grid.topography.extent)
ax.clabel(CS, inline=1, fontsize=10, fmt='%d')
plothelp.add_colorbar(im=im,
label='elevation [m]',
cs=CS,
aspect=35)
if show_faults == True:
self.extract_section_fault_lines('topography')
plt.title("Geological map", fontsize=15)
plt.xlabel('X')
plt.ylabel('Y')
def show(self):
from gempy.plot.helpers import add_colorbar
if self.type == 'artificial':
fig, ax = plt.subplots()
CS= ax.contour(self.values_3D[:, :, 2], extent=(self.extent[:4]), colors='k', linestyles='solid')
ax.clabel(CS, inline=1, fontsize=10, fmt='%d')
CS2 = ax.contourf(self.values_3D[:, :, 2], extent=(self.extent[:4]), cmap='terrain')
add_colorbar(axes=ax, label='elevation [m]', cs=CS2)
else:
im = plt.imshow(np.flipud(self.values_3D[:,:,2]), extent=(self.extent[:4]))
add_colorbar(im=im, label='elevation [m]')
plt.axis('scaled')
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Model topography')
def plot_probabilities(plot, block, shape, T=False, extent=None):
fig, ax = plt.subplots(int(np.ceil(block.shape[0] / 2)),
2,
figsize=(16, 8))
ax = ax.flatten()
print(int(np.ceil(block.shape[0] / 2)))
print(block.shape)
for i in range(block.shape[0]):
allcolors = list(plot._color_lot.values())[plot.model.faults.n_faults:]
formnames = list(plot._color_lot.keys())[plot.model.faults.n_faults:]
c2 = allcolors[i]
title = formnames[i]
cmap = mcolors.LinearSegmentedColormap.from_list('c', ['#FFFFFF', c2])
if T:
plotblock = block[i].reshape(shape).T
else:
plotblock = block[i].reshape(shape)
im = ax[i].imshow(plotblock, origin='lower', cmap=cmap, extent=extent)
ax[i].set_title(title)
helpers.add_colorbar(im, label='probability')
def plot_topography(ax, geo_model, extent_val, **kwargs):
"""
Args:
ax:
geo_model:
extent_val:
**kwargs:
Returns:
"""
hillshade = kwargs.pop('show_hillshade', True)
contour = kwargs.pop('show_contour', False)
fill_contour = kwargs.pop('show_fill_contour', False)
azdeg = kwargs.pop('azdeg', 0)
altdeg = kwargs.pop('altdeg', 0)
cmap = kwargs.pop('cmap', 'terrain')
super = kwargs.pop('super_res', False)
colorbar = kwargs.pop("show_colorbar", False)
topo = geo_model._grid.topography
if super:
import skimage
topo_super_res = skimage.transform.resize(topo.values_2d, (1600, 1600),
order=3,
mode='edge',
anti_aliasing=True,
preserve_range=False)
values = topo_super_res[..., 2]
else:
values = topo.values_2d[..., 2]
if contour is True:
CS = ax.contour(values,
extent=extent_val,
colors='k',
linestyles='solid',
origin='lower')
ax.clabel(CS, inline=1, fontsize=10, fmt='%d')
if fill_contour is True:
CS2 = ax.contourf(values, extent=extent_val, cmap=cmap)
if colorbar:
from gempy.plot.helpers import add_colorbar
add_colorbar(axes=ax, label='elevation [m]', cs=CS2)
if hillshade is True:
from matplotlib.colors import LightSource
# Note: 180 degrees are subtracted because visualization in Sandbox is upside-down
ls = LightSource(azdeg=azdeg - 180, altdeg=altdeg)
# TODO: Is is better to use ls.hillshade or ls.shade??
hillshade_topography = ls.hillshade(values)
# vert_exag=0.3,
# blend_mode='overlay')
global hill
hill = ax.imshow(hillshade_topography,
cmap=plt.cm.gray,
origin='lower',
extent=extent_val,
alpha=0.4,
zorder=11,
aspect='auto')
def plot_mapview(
self,
show_lith: bool = True,
# show_boundary: bool = True,
show_hillshade: bool = True,
show_contour: bool = False,
show_only_faults: bool = False,
**kwargs):
cmap_type = kwargs.pop('cmap', 'YlOrRd')
if 'ax' in kwargs:
# append plot to existing axis
ax = kwargs.pop('ax')
else:
figsize = kwargs.pop("figsize", (10, 6))
fig, ax = plt.subplots(figsize=figsize)
if show_lith:
image = self.vertices_mapview[:,
2].reshape(self.model_resolution[:2])
self.lith = ax.imshow(
image,
origin='lower',
zorder=-10,
extent=self.model_extent[:4],
cmap=cmap_type,
#norm=norm,
aspect='auto')
fill_contour = kwargs.pop('show_fill_contour', False)
azdeg = kwargs.pop('azdeg', 0)
altdeg = kwargs.pop('altdeg', 0)
super = kwargs.pop('super_res', False)
colorbar = kwargs.pop("show_colorbar", False)
topo = self.grid.depth_grid[:, 2].reshape(self.model_resolution[:2])
#if super:
# import skimage
# topo_super_res = skimage.transform.resize(
# topo,
# (1600, 1600),
# order=3,
# mode='edge',
# anti_aliasing=True, preserve_range=False)
# values = topo_super_res[..., 2]
#else:
# values = topo.values_2d[..., 2]
if show_contour is True:
CS = ax.contour(topo,
extent=self.model_extent[:4],
colors='k',
linestyles='solid',
origin='lower')
ax.clabel(CS, inline=1, fontsize=10, fmt='%d')
if fill_contour is True:
CS2 = ax.contourf(topo, extent=self.model_extent[:4], cmap=cmap)
if colorbar:
from gempy.plot.helpers import add_colorbar
add_colorbar(axes=ax, label='elevation [m]', cs=CS2)
if show_hillshade:
from matplotlib.colors import LightSource
# Note: 180 degrees are subtracted because visualization in Sandbox is upside-down
ls = LightSource(azdeg=azdeg - 180, altdeg=altdeg)
# TODO: Is it better to use ls.hillshade or ls.shade??
hillshade_topography = ls.hillshade(topo)
# vert_exag=0.3,
# blend_mode='overlay')
self.hill = ax.imshow(hillshade_topography,
cmap=plt.cm.gray,
origin='lower',
extent=self.model_extent[:4],
alpha=0.4,
zorder=11,
aspect='auto')
def plot_results(self, geo_data, prop='val', direction='y', cell_number=0, contour=False,
cmap='viridis', alpha=0, legend=False, interpolation='nearest', show_data=True):
"""
TODO WRITE DOCSTRING
Args:
geo_data:
prop: property that should be plotted - "val", "var" or "both"
direction: x, y or z
cell_number:
contour:
cmap:
alpha:
legend:
Returns:
"""
a = np.full_like(self.mask, np.nan, dtype=np.double) #array like lith_block but with nan if outside domain
est_vals = self.results_df['est_value'].values
est_var = self.results_df['est_variance'].values
# set values
if prop == 'val':
a[np.where(self.mask == True)] = est_vals
elif prop == 'var':
a[np.where(self.mask == True)] = est_var
elif prop == 'both':
a[np.where(self.mask == True)] = est_vals
b = np.full_like(self.mask, np.nan, dtype=np.double)
b[np.where(self.mask == True)] = est_var
else:
print('prop must be val var or both')
#create plot object
p = visualization_2d.PlotSolution(geo_data)
_a, _b, _c, extent_val, x, y = p._slice(direction, cell_number)[:-2]
#colors
cmap = cm.get_cmap(cmap)
cmap.set_bad(color='w', alpha=alpha) #define color and alpha for nan values
# plot
if prop is not 'both':
if show_data:
plt.scatter(self.data_df[x].values, self.data_df[y].values, marker='*', s=9, c='k')
plot.plot_section(geo_data, direction=direction, cell_number=cell_number)
if contour == True:
im = plt.contourf(a.reshape(self.sol.grid.regular_grid.resolution)[_a, _b, _c].T, cmap=cmap,
origin='lower', levels=25,
extent=extent_val, interpolation=interpolation)
if legend:
ax = plt.gca()
helpers.add_colorbar(axes=ax, label='prop', cs=im)
else:
im = plt.imshow(a.reshape(self.sol.grid.regular_grid.resolution)[_a, _b, _c].T, cmap=cmap,
origin='lower',
extent=extent_val, interpolation=interpolation)
if legend:
helpers.add_colorbar(im, label='pups', location='right')
else:
f, ax = plt.subplots(1, 2, sharex=True, sharey=True)
ax[0].title.set_text('Estimated value')
im1 = ax[0].imshow(a.reshape(self.sol.grid.regular_grid.resolution)[_a, _b, _c].T, cmap=cmap,
origin='lower', interpolation=interpolation,
extent=self.sol.grid.regular_grid.extent[[0, 1, 4, 5]])
helpers.add_colorbar(im1, label='unit')
ax[1].title.set_text('Variance')
im2 = ax[1].imshow(b.reshape(self.sol.grid.regular_grid.resolution)[_a, _b, _c].T, cmap=cmap,
origin='lower', interpolation=interpolation,
extent=self.sol.grid.regular_grid.extent[[0, 1, 4, 5]])
helpers.add_colorbar(im2, label='unit')
plt.tight_layout()
def plot_topography(self, ax, fill_contour=False,
contour=True,
section_name=None,
cell_number=None, direction='y', block=None, **kwargs):
hillshade = kwargs.get('hillshade', True)
azdeg = kwargs.get('azdeg', 0)
altdeg = kwargs.get('altdeg', 0)
cmap = kwargs.get('cmap', 'terrain')
self.update_colot_lot()
section_name, cell_number, direction = self._check_default_section(ax, section_name, cell_number, direction)
if section_name is not None and section_name != 'topography':
p1 = self.model._grid.sections.df.loc[section_name, 'start']
p2 = self.model._grid.sections.df.loc[section_name, 'stop']
x, y, z = self._slice_topo_4_sections(p1, p2, self.model._grid.topography.resolution[0])
pseudo_x = np.linspace(0, self.model._grid.sections.df.loc[section_name, 'dist'], z.shape[0])
a = np.vstack((pseudo_x, z)).T
xy = np.append(a,
([self.model._grid.sections.df.loc[section_name, 'dist'], a[:, 1][-1]],
[self.model._grid.sections.df.loc[section_name, 'dist'],
self.model._grid.regular_grid.extent[5]],
[0, self.model._grid.regular_grid.extent[5]],
[0, a[:, 1][0]])).reshape(-1, 2)
ax.fill(xy[:, 0], xy[:, 1], 'k', zorder=10)
elif section_name == 'topography':
import skimage
from gempy.plot.helpers import add_colorbar
topo = self.model._grid.topography
topo_super_res = skimage.transform.resize(
topo.values_2d,
(1600, 1600),
order=3,
mode='edge',
anti_aliasing=True, preserve_range=False)
values = topo_super_res[:, :, 2].T
if contour is True:
CS = ax.contour(values, extent=(topo.extent[:4]),
colors='k', linestyles='solid', origin='lower')
ax.clabel(CS, inline=1, fontsize=10, fmt='%d')
if fill_contour is True:
CS2 = ax.contourf(values, extent=(topo.extent[:4]), cmap=cmap)
add_colorbar(axes=ax, label='elevation [m]', cs=CS2)
if hillshade is True:
from matplotlib.colors import LightSource
ls = LightSource(azdeg=azdeg, altdeg=altdeg)
hillshade_topography = ls.hillshade(values)
ax.imshow(hillshade_topography, origin='lower', extent=topo.extent[:4], alpha=0.5, zorder=11,
cmap='gray')
elif cell_number is not None or block is not None:
p1, p2 = self.calculate_p1p2(direction, cell_number)
resx = self.model._grid.regular_grid.resolution[0]
resy = self.model._grid.regular_grid.resolution[1]
x, y, z = self._slice_topo_4_sections(p1, p2, resx)
if direction == 'x':
a = np.vstack((y, z)).T
ext = self.model._grid.regular_grid.extent[[2, 3]]
elif direction == 'y':
a = np.vstack((x, z)).T
ext = self.model._grid.regular_grid.extent[[0, 1]]
else:
raise NotImplementedError
a = np.append(a,
([ext[1], a[:, 1][-1]],
[ext[1], self.model._grid.regular_grid.extent[5]],
[ext[0], self.model._grid.regular_grid.extent[5]],
[ext[0], a[:, 1][0]]))
line = a.reshape(-1, 2)
ax.fill(line[:, 0], line[:, 1], color='k')