from fatiando import gridder, utils
from fatiando.vis import mpl
# Generate random points
x, y = gridder.scatter((-2, 2, -2, 2), n=300, seed=1)
# And calculate 2D Gaussians on these points as sample data
def data(x, y):
return (utils.gaussian2d(x, y, -0.6, -1)
- utils.gaussian2d(x, y, 1.5, 1.5))
d = data(x, y)
# Extract a profile along the diagonal
p1, p2 = [-1.5, 0], [1.5, 1.5]
xp, yp, distance, dp = gridder.profile(x, y, d, p1, p2, 100)
dp_true = data(xp, yp)
mpl.figure()
mpl.subplot(2, 1, 2)
mpl.title("Irregular grid")
mpl.plot(xp, yp, '-k', label='Profile', linewidth=2)
mpl.contourf(x, y, d, (100, 100), 50, interp=True)
mpl.colorbar(orientation='horizontal')
mpl.legend(loc='lower right')
mpl.subplot(2, 1, 1)
mpl.title('Profile')
mpl.plot(distance, dp, '.b', label='Extracted')
mpl.plot(distance, dp_true, '-k', label='True')
mpl.xlim(distance.min(), distance.max())
mpl.legend(loc='lower right')
def slice_mesh(x, y, mesh, label, p1, p2, ax=None, interp=True, **kwargs):
"""
Get vertical xy section of the mesh at max/2 and plot it
Parameters:
* mesh
Fatiando mesh type
* scaled
Txt here
* label
Txt here
Txt here
* markerMax
Txt here
"""
if ax == None:
fig = plt.subplots()
ax = plt.gca()
Xaxis = []
for key, value in kwargs.items():
if key == 'Xaxis':
Xaxis = value
image = mesh.props[label]
toslice = np.reshape(image, [mesh.shape[0], mesh.shape[1] * mesh.shape[2]])
depths = mesh.get_zs()[:-1]
x_resolution = 1000
Z = []
X = []
Y = []
DIST = []
IMG = []
z = 0 * np.ones_like(x)
interp = True
for i, depth in enumerate(depths - z[0]): # Loop for RII extremas
u_layer = toslice[
i, :] # analysing extrema layers by layers from top to bottom
if interp == True:
xx, yy, distance, u_layer_p1p2 = gridder.profile(
x, y, u_layer, p1, p2, x_resolution)
else:
xx, yy, distance, u_layer_p1p2, u_layer_p1p2_smooth = profile_noInter(
x, y, u_layer, p1, p2, x_resolution)
zz = depth * np.ones(len(u_layer_p1p2))
Z.append(zz)
X.append(xx)
Y.append(yy)
DIST.append(distance)
IMG.append(u_layer_p1p2)
if 'dist' in Xaxis:
xaxis = DIST
else:
xaxis = X
cmap = ax.pcolormesh(np.array(xaxis), np.array(Z), np.array(IMG))
# if 'upwc' in label:
# plt.gca().invert_yaxis()
# ax.set_ylim(np.max(Z), np.min(Z))
# ax.set_xlim(np.max(xaxis), np.min(xaxis))
ax.set_xlabel('x (m)')
ax.set_ylabel('z (m)')
ax.set_title(label)
ax.set_aspect('equal')
return plt, cmap
def plot_line(x, y, data, p1, p2, ax=None, interp=True, **kwargs):
Xaxis = []
Vminmax = []
smooth_type = False
showfig = False
vmin = min(data)
vmax = max(data)
limx = None
limy = None
x_resolution = None
for key, value in kwargs.items():
if key == 'Xaxis':
Xaxis = value
if key == 'smooth':
smooth_type = value
if key == 'showfig':
showfig = value
if key == 'Vminmax':
Vminmax = value
vmin = Vminmax[0]
vmax = Vminmax[1]
if key == 'limx':
limx = value
if key == 'limy':
limy = value
if key == 'x_resolution':
x_resolution = value
# Extract a profile between points 1 and 2
if interp == False:
xx, yy, distance, profile, vp_smooth_dict = dEXP.profile_noInter(
x, y, data, p1, p2, size=x_resolution, showfig=showfig)
# xx, yy, distance, profile, profile_smooth = dEXP.profile_extra(x, y, data, p1, p2, 1000)
else:
xx, yy, distance, profile = gridder.profile(x, y, data, p1, p2, 1000,
'nearest')
if Xaxis is 'dist':
xaxis = distance
elif Xaxis is 'y':
xaxis = xx
else:
xaxis = yy
if smooth_type is not False:
if smooth_type == True:
profile = vp_smooth_dict['Lowpass']
else:
profile = vp_smooth_dict[smooth_type]
# Plot the profile and the original map data
plt.figure()
ax = ax or plt.gca()
plt.subplot(1, 2, 1)
# plt.title(strname + '_data' + str(ZZ), fontsize=15)
plt.plot(xaxis, profile, '.k')
# plt.xlim(xaxis.min(), xaxis.max())
plt.grid()
if Xaxis is 'dist':
plt.xlabel('distance (m)')
elif Xaxis is 'y':
plt.xlabel('x (m)')
else:
plt.xlabel('y (m)')
plt.ylabel('voltage (V)')
plt.subplot(1, 2, 2)
plt.title("Original data")
plt.plot(xx, yy, '-k', label='Profile', linewidth=2)
# scale = np.abs([data.min(), data.max()]).max()
# plt.tricontourf(x, y, data, 50, cmap='RdBu_r', vmin=min(data), vmax=max(data))
plt.scatter(x, y, c=data, cmap='RdBu_r', vmin=vmin, vmax=vmax)
plt.colorbar(orientation='vertical', aspect=50)
plt.legend(loc='lower right')
plt.xlabel('x (m)')
plt.ylabel('y (m)')
plt.axis('square')
if limx is not None:
plt.xlim(limx[0], limx[1])
if limx is not None:
plt.ylim(limy[0], limy[1])
# plt.tight_layout()
for key, value in kwargs.items():
# print("{0} = {1}".format(key, value))
if key == 'title':
plt.title(value, fontsize=15)
if key == 'suptitle':
plt.suptitle(value, fontsize=15)
if key == 'savefig':
if value == True:
# plt.savefig(pathFig+ strname + '_data' + str(ZZ) + '.png')
plt.savefig('fig2d.png', r=400)
plt.show()
return xx, yy, distance, profile, ax, plt
# remove B return electrode effects B = [-104.16666666666667, -104.16666666666667, 0] gz_cor = dEXP.cor_field_B(x, y, z, gz, B, rho=100) gz_cor = gz xp, yp, gz = gridder.interp(x, y, gz, shape) xp, yp, gz_cor = gridder.interp(x, y, gz_cor, shape) zp = 0 # # ------------------------------- Export for CWT analysis # # Extract a profile between points 1 and 2 p1, p2 = [min(xp), 0], [max(xp), 0] # p1, p2 = [0, min(xp)], [0, max(xp)] xx, yy, distance, profile = gridder.profile(xp, yp, gz, p1, p2, 1000) # xx, yy, distance, profile_cor = gridder.profile(xp, yp, gz_cor, p1, p2, 1000) # ------------------------------- Export for CWT analysis ## Extract a profile between points 1 and 2 #p1, p2 = [0.1, 0.1], [12000, 0.1] #xx, yy, distance, profile = gridder.profile(xp, yp, gz, p1, p2, 1000) # Plot the profile and the original map data plt.figure() plt.subplot(2, 1, 1) #plt.title(strname + '_ztop' + str(za) +'_zbot'+ str(zb), fontsize=15) plt.plot(distance, profile, '.k') # plt.plot(distance, profile_cor, '.r') plt.xlim(distance.min(), distance.max())
# remove B return electrode effects using :mod:`uEXP.cor_field_B` using a resistivity of 1 Ohm.m
U_cor = uEXP.cor_field_B(xyzs[:-3, 0],
xyzs[:-3, 1],
xyzs[:-3, 2],
uT_elecs,
Bpos,
rho=model_prop['SoilR'])
#%%
# Compare the voltage seen by the surface electrode using 2d plot over a line
p1_elecs = [min(xyzs[:-3, 0]), (max(xyzs[:-3, 0]) + min(xyzs[:-3, 0])) / 2]
p2_elecs = [max(xyzs[:-3, 0]), (max(xyzs[:-3, 0]) + min(xyzs[:-3, 0])) / 2]
xx, yy, distance, profile = gridder.profile(xyzs[:-3, 0], xyzs[:-3, 1],
uT_elecs, p1_elecs, p2_elecs,
1000)
plt.figure()
# plt.title(strname + '_data' + str(ZZ), fontsize=15)
plt.plot(xx, profile, '.k', label='Raw')
xx, yy, distance, profile = gridder.profile(xyzs[:-3, 0], xyzs[:-3,
1], U_cor,
p1_elecs, p2_elecs, 1000)
plt.plot(xx, profile, '.r', label='Corrected')
plt.grid()
plt.xlabel('x (m)')
plt.ylabel('u (V)')
plt.legend()
# Generate random points
x, y = gridder.scatter((-2, 2, -2, 2), n=300, seed=1)
# And calculate 2D Gaussians on these points as sample data
def data(x, y):
return (utils.gaussian2d(x, y, -0.6, -1) -
utils.gaussian2d(x, y, 1.5, 1.5))
d = data(x, y)
# Extract a profile along the diagonal
p1, p2 = [-1.5, 0], [1.5, 1.5]
xp, yp, distance, dp = gridder.profile(x, y, d, p1, p2, 100)
dp_true = data(xp, yp)
mpl.figure()
mpl.subplot(2, 1, 2)
mpl.title("Irregular grid")
mpl.plot(xp, yp, '-k', label='Profile', linewidth=2)
mpl.contourf(x, y, d, (100, 100), 50, interp=True)
mpl.colorbar(orientation='horizontal')
mpl.legend(loc='lower right')
mpl.subplot(2, 1, 1)
mpl.title('Profile')
mpl.plot(distance, dp, '.b', label='Extracted')
mpl.plot(distance, dp_true, '-k', label='True')
mpl.xlim(distance.min(), distance.max())
mpl.legend(loc='lower right')
The function :func:`fatiando.gridder.profile` can be used to extract a profile
of data from a map. It interpolates the data onto the profile points so you can
specify the profile in any direction and use irregular point data as input.
"""
import matplotlib.pyplot as plt
import numpy as np
from fatiando import gridder, utils
# Generate random points
x, y = gridder.scatter((-2, 2, -2, 2), n=1000, seed=1)
# And calculate 2D Gaussians on these points as sample data
data = 2*utils.gaussian2d(x, y, -0.6, -1) - utils.gaussian2d(x, y, 1.5, 1.5)
# Extract a profile between points 1 and 2
p1, p2 = [-1.5, -0.5], [1.5, 1.5]
xp, yp, distance, profile = gridder.profile(x, y, data, p1, p2, 100)
# Plot the profile and the original map data
plt.figure()
plt.subplot(2, 1, 1)
plt.title('Extracted profile points')
plt.plot(distance, profile, '.k')
plt.xlim(distance.min(), distance.max())
plt.grid()
plt.subplot(2, 1, 2)
plt.title("Original data")
plt.plot(xp, yp, '-k', label='Profile', linewidth=2)
scale = np.abs([data.min(), data.max()]).max()
plt.tricontourf(x, y, data, 50, cmap='RdBu_r', vmin=-scale, vmax=scale)
plt.colorbar(orientation='horizontal', aspect=50)
plt.legend(loc='lower right')
The function :func:`fatiando.gridder.profile` can be used to extract a profile
of data from a map. It interpolates the data onto the profile points so you can
specify the profile in any direction and use irregular point data as input.
"""
import matplotlib.pyplot as plt
import numpy as np
from fatiando import gridder, utils
# Generate random points
x, y = gridder.scatter((-2, 2, -2, 2), n=1000, seed=1)
# And calculate 2D Gaussians on these points as sample data
data = 2 * utils.gaussian2d(x, y, -0.6, -1) - utils.gaussian2d(x, y, 1.5, 1.5)
# Extract a profile between points 1 and 2
p1, p2 = [-1.5, -0.5], [1.5, 1.5]
xp, yp, distance, profile = gridder.profile(x, y, data, p1, p2, 100)
# Plot the profile and the original map data
plt.figure()
plt.subplot(2, 1, 1)
plt.title('Extracted profile points')
plt.plot(distance, profile, '.k')
plt.xlim(distance.min(), distance.max())
plt.grid()
plt.subplot(2, 1, 2)
plt.title("Original data")
plt.plot(xp, yp, '-k', label='Profile', linewidth=2)
scale = np.abs([data.min(), data.max()]).max()
plt.tricontourf(x, y, data, 50, cmap='RdBu_r', vmin=-scale, vmax=scale)
plt.colorbar(orientation='horizontal', aspect=50)
plt.legend(loc='lower right')
