def calculate_znb(z_object = None, z_array = None, periods = None): """ Determine an array of Z_nb (depth dependent Niblett-Bostick transformed Z) from the 1D and 2D parts of an impedance tensor array Z. input: - Z output: - Z_nb The calculation of the Z_nb needs 6 steps: 1) Determine the dimensionality of the Z(T), discard all 3D parts 2) Rotate all Z(T) to TE/TM setup (T_parallel/T_ortho) 3) Transform every component individually by Niblett-Bostick 4) collect the respective 2 components each for equal/similar depths 5) interprete them as TE_nb/TM_nb 6) set up Z_nb(depth) If 1D layers occur inbetween 2D layers, the strike angle is undefined therein. We take an - arbitrarily chosen - linear interpolation of strike angle for these layers, with the values varying between the angles of the bounding upper and lower 2D layers (linearly w.r.t. the periods). Use the output for instance for the determination of NB-transformed phase tensors. Note: No propagation of errors implemented yet! """ #deal with inputs #if zobject: z = z_object.z periods = 1./z_object.freq #else: z = z periods = periods dimensions = MTge.dimensionality() angles = MTge.strike_angle(z) #reduce actual Z by the 3D layers: z2 = z[np.where(dimensions != 3)[0]] angles2 = angles[np.where(dimensions != 3)[0]] periods2 = periods[np.where(dimensions != 3)[0]] return Z_nb
def test_fun():
"""
test function
:return: T/F
"""
# mtObj = MT(r'C:\Git\mtpy\examples\data\edi_files\pb42c.edi')
# mtObj = MT(r'E:/Githubz/mtpy/examples/data/edi_files/pb42c.edi')
edifile = os.path.join(MTPY_ROOT, 'examples/data/edi_files/pb42c.edi')
mtObj = MT(edifile)
strike_angle_pb42c = np.array([[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[38.45662316, 128.45662316],
[28.61883115, 118.61883115],
[14.45341494, 104.45341494],
[8.43320651, 98.43320651],
[4.94952784, 94.94952784],
[2.09090369, 92.09090369],
[1.39146887, 91.39146887],
[0.39905337, 90.39905337],
[-5.49553673, 84.50446327],
[-6.28846049, 83.71153951],
[-7.31641788, 82.68358212],
[-10.45341947, 79.54658053],
[-7.07075086, 82.92924914],
[-7.5429295, 82.4570705],
[-6.06405688, 83.93594312],
[-3.54915951, 86.45084049],
[-3.12596637, 86.87403363],
[-0.47404093, 89.52595907],
[2.74343665, 92.74343665],
[4.78078759, 94.78078759],
[7.71125988, 97.71125988],
[11.0123521, 101.0123521],
[13.81639678, 103.81639678],
[13.60497071, 103.60497071],
[15.87672806, 105.87672806]])
strike_angle = mtg.strike_angle(z_object=mtObj.Z)
differ = np.abs(strike_angle[np.isfinite(strike_angle)] -
strike_angle_pb42c[np.isfinite(strike_angle_pb42c)]) < 1e-8
print(differ)
assert np.all(differ)
def test_get_strike_from_edi_file(self):
edifile = os.path.normpath(
os.path.join(TEST_MTPY_ROOT, 'examples/data/edi_files/pb42c.edi'))
mt_obj = MT(edifile)
strike_angle_pb42c = np.array([[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[38.45662316, 128.45662316],
[28.61883115, 118.61883115],
[14.45341494, 104.45341494],
[8.43320651, 98.43320651],
[4.94952784, 94.94952784],
[2.09090369, 92.09090369],
[1.39146887, 91.39146887],
[0.39905337, 90.39905337],
[-5.49553673, 84.50446327],
[-6.28846049, 83.71153951],
[-7.31641788, 82.68358212],
[-10.45341947, 79.54658053],
[-7.07075086, 82.92924914],
[-7.5429295, 82.4570705],
[-6.06405688, 83.93594312],
[-3.54915951, 86.45084049],
[-3.12596637, 86.87403363],
[-0.47404093, 89.52595907],
[2.74343665, 92.74343665],
[4.78078759, 94.78078759],
[7.71125988, 97.71125988],
[11.0123521, 101.0123521],
[13.81639678, 103.81639678],
[13.60497071, 103.60497071],
[15.87672806, 105.87672806]])
strike_angle = mtg.strike_angle(z_object=mt_obj.Z)
self.assertTrue(
np.allclose(strike_angle[np.isfinite(strike_angle)],
strike_angle_pb42c[np.isfinite(strike_angle_pb42c)],
1e-8))
def test_get_strike_from_edi_file(self):
edifile = os.path.normpath(
os.path.join(TEST_MTPY_ROOT, 'examples/data/edi_files/pb42c.edi'))
mt_obj = MT(edifile)
strike_angle_pb42c = np.array([[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[38.45662316, -51.54337684],
[28.61883115, -61.38116885],
[14.45341494, -75.54658506],
[8.43320651, -81.56679349],
[4.94952784, -85.05047216],
[2.09090369, -87.90909631],
[1.39146887, -88.60853113],
[0.39905337, -89.60094663],
[-5.49553673, 84.50446327],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan], [np.nan, np.nan],
[np.nan, np.nan],
[-6.06405688, 83.93594312],
[-3.54915951, 86.45084049],
[-3.12596637, 86.87403363],
[-0.47404093, 89.52595907],
[2.74343665, -87.25656335],
[4.78078759, -85.21921241],
[7.71125988, -82.28874012],
[11.0123521, -78.9876479],
[13.81639678, -76.18360322],
[13.60497071, -76.39502929],
[np.nan, np.nan]])
strike_angle = mtg.strike_angle(z_object=mt_obj.Z)
self.assertTrue(
np.allclose(strike_angle[np.isfinite(strike_angle)],
strike_angle_pb42c[np.isfinite(strike_angle_pb42c)],
1e-8))
def find_distortion(z_object, lo_dims = None):
"""
find optimal distortion tensor from z object
automatically determine the dimensionality over all frequencies, then find
the appropriate distortion tensor D
"""
z_obj = z_object
if lo_dims is None :
lo_dims = MTge.dimensionality(z_object = z_obj)
try:
if len(lo_dims) != len(z_obj.z):
lo_dims = MTge.dimensionality(z_object = z_obj)
except:
pass
#dictionary of values that should be no distortion in case distortion
#cannot be calculated for that component
dis_dict = {(0,0):1, (0,1):0, (1,0):0, (1,1):1}
lo_dis = []
lo_diserr = []
if 1 in lo_dims:
idx_1 = np.where(np.array(lo_dims) == 1)[0]
for idx in idx_1:
realz = np.real(z_obj.z[idx])
imagz = np.imag(z_obj.z[idx])
mat1 = np.matrix([[0, -1],[1, 0]])
gr = np.sqrt(np.linalg.det(realz))
gi = np.sqrt(np.linalg.det(imagz))
lo_dis.append(1./gr*np.dot(realz,mat1))
lo_dis.append(1./gi*np.dot(imagz,mat1))
if z_obj.zerr is not None:
#find errors of entries for calculating weights
lo_diserr.append(1./gr*\
np.array([[np.abs(z_obj.zerr[idx][0,1]),
np.abs(z_obj.zerr[idx][0,0])],
[np.abs(z_obj.zerr[idx][1,1]),
np.abs(z_obj.zerr[idx][1,0])]]))
lo_diserr.append(1./gi*\
np.array([[np.abs(z_obj.zerr[idx][0,1]),
np.abs(z_obj.zerr[idx][0,0])],
[np.abs(z_obj.zerr[idx][1,1]),
np.abs(z_obj.zerr[idx][1,0])]]))
else:
#otherwise go for evenly weighted average
lo_diserr.append(np.ones((2, 2)))
lo_diserr.append(np.ones((2, 2)))
dis = np.identity(2)
diserr = np.identity(2)
for i in range(2):
for j in range(2):
try:
dis[i,j], dummy = np.average(np.array([k[i, j]
for k in lo_dis]),
weights=np.array([1./(k[i,j])**2
for k in lo_diserr]),
returned=True)
diserr[i,j] = np.sqrt(1./dummy)
#if the distortion came out as nan set it to an appropriate
#value
if np.nan_to_num(dis[i,j]) == 0:
dis[i, j] = dis_dict[i, j]
diserr[i, j] = dis_dict[i, j]
except ZeroDivisionError:
print ('Could not get distortion for dis[{0}, {1}]'.format(
i, j)+' setting value to {0}'.format(dis_dict[i,j]))
dis[i, j] = dis_dict[i, j]
diserr[i, j] = dis_dict[i, j]*1e-6
return dis, diserr
if 2 in lo_dims:
idx_2 = np.where(np.array(lo_dims) == 2)[0]
#follow bibby et al. 2005 first alternative: P = 1
P = 1
lo_strikes = MTge.strike_angle(z_object = z_obj)
lo_tetms = []
lo_t = []
lo_tetm_errs =[]
for idx in idx_2:
mat = z_obj.z[idx]
ang = -lo_strikes[idx][0]
if np.isnan(ang):
ang = 0.
errmat = None
if z_obj.zerr is not None:
errmat = z_obj.zerr[idx]
tetm_mat, tetm_err = MTcc.rotatematrix_incl_errors(mat,
ang,
inmatrix_err=errmat)
lo_tetms.append(tetm_mat)
lo_tetm_errs.append(tetm_err)
realz = np.real(tetm_mat)
imagz = np.imag(tetm_mat)
lo_t.append(-4*P*realz[0,1]*realz[1,0]/np.linalg.det(realz) )
lo_t.append(-4*P*imagz[0,1]*imagz[1,0]/np.linalg.det(imagz) )
#since there is no 'wrong' solution by a different value of T, no
#error is given/calculated for T !
try:
#just add 0.1% for avoiding numerical issues in the squareroots
#later on
T = np.sqrt(max(lo_t))+0.001
except:
T = 2
for idx in range(len(lo_tetms)):
realz = np.real(lo_tetms[idx])
imagz = np.imag(lo_tetms[idx])
errmat = lo_tetm_errs[idx]
sr = np.sqrt(T**2+4*P*realz[0, 1]*realz[1, 0]/np.linalg.det(realz))
si = np.sqrt(T**2+4*P*imagz[0, 1]*imagz[1, 0]/np.linalg.det(imagz))
par_r = 2*realz[0, 1]/(T-sr)
orth_r = 2*realz[1, 0]/(T+sr)
par_i = 2*imagz[0, 1]/(T-si)
orth_i = 2*imagz[1, 0]/(T+si)
mat2_r = np.matrix([[0, 1./orth_r], [1./par_r, 0]])
mat2_i = np.matrix([[0, 1./orth_i], [1./par_i ,0]])
lo_dis.append(np.dot(realz,mat2_r))
lo_dis.append(np.dot(imagz,mat2_i))
if z_obj.zerr is not None:
#find errors of entries for calculating weights
sigma_sr = np.sqrt((-(2*P*realz[0,1]*realz[1,0]*\
realz[1,1]*errmat[0,0])/\
(np.linalg.det(realz)**2*sr))**2+\
((2*P*realz[0,0]*realz[1,0]*\
realz[1,1]*errmat[0,1])/\
(np.linalg.det(realz)**2*sr))**2+\
((2*P*realz[0,0]* realz[0,1]*\
realz[1,1]*errmat[1,0])/\
(np.linalg.det(realz)**2*sr))**2 +\
(-(2*P*realz[0,1]* realz[1,0]*\
realz[0,0]*errmat[1,1])/\
(np.linalg.det(realz)**2*sr))**2)
sigma_dr_11 = 0.5*sigma_sr
sigma_dr_22 = 0.5*sigma_sr
sigma_dr_12 = np.sqrt((mat2_r[0,1]/realz[0,0]*errmat[0,0])**2+\
(mat2_r[0,1]/realz[1,0]*errmat[1,0])**2+\
(0.5*realz[0,0]/realz[1,0]*sigma_sr)**2)
sigma_dr_21 = np.sqrt((mat2_r[1,0]/realz[1,1]*errmat[1,1])**2+\
(mat2_r[1,0]/realz[0,1]*errmat[0,1])**2+\
(0.5*realz[1,1]/realz[0,1]*sigma_sr)**2)
lo_diserr.append(np.array([[sigma_dr_11, sigma_dr_12],
[sigma_dr_21, sigma_dr_22]]))
sigma_si = np.sqrt((-(2*P*imagz[0,1]*imagz[1,0]*\
imagz[1,1]*errmat[0,0])/\
(np.linalg.det(imagz)**2*sr))**2+\
((2*P*imagz[0,0]*imagz[1,0]*\
imagz[1,1]*errmat[0,1])/\
(np.linalg.det(imagz)**2*sr))**2+\
((2*P*imagz[0,0]*imagz[0,1]*\
imagz[1,1]*errmat[1,0])/\
(np.linalg.det(imagz)**2*sr))**2+\
(-(2*P*imagz[0,1]*imagz[1,0]*\
imagz[0,0]*errmat[1,1])/\
(np.linalg.det(imagz)**2*sr))**2)
sigma_di_11 = 0.5*sigma_si
sigma_di_22 = 0.5*sigma_si
sigma_di_12 = np.sqrt((mat2_i[0,1]/imagz[0,0]*errmat[0,0])**2+\
(mat2_i[0,1]/imagz[1,0]*errmat[1,0])**2+\
(0.5*imagz[0,0]/imagz[1,0]*sigma_si)**2)
sigma_di_21 = np.sqrt((mat2_i[1,0]/imagz[1,1]*errmat[1,1])**2+\
(mat2_i[1,0]/imagz[0,1]*errmat[0,1])**2+\
(0.5*imagz[1,1]/imagz[0,1]*sigma_si)**2)
lo_diserr.append(np.array([[sigma_di_11, sigma_di_12],
[sigma_di_21, sigma_di_22]]))
else:
#otherwise go for evenly weighted average
lo_diserr.append(np.ones((2, 2)))
lo_diserr.append(np.ones((2, 2)))
dis = np.zeros((2, 2))
diserr = np.zeros((2, 2))
for i in range(2):
for j in range(2):
dis[i, j], dummy = np.average(np.array([k[i, j]
for k in lo_dis]),
weights=np.array([1./(k[i,j])**2
for k in lo_diserr]),
returned=True )
diserr[i, j] = np.sqrt(1./dummy)
return dis, diserr
#if only 3D, use identity matrix - no distortion calculated
dis = np.identity(2)
diserr = diserr = np.zeros((2, 2))
return dis, diserr
def calculate_znb(z_object=None, z_array=None, periods=None):
"""
Determine an array of Z_nb (depth dependent Niblett-Bostick transformed Z)
from the 1D and 2D parts of an impedance tensor array Z.
input:
- Z (object or array)
- periods (mandatory, if Z is just array)
output:
- Z_nb
The calculation of the Z_nb needs 6 steps:
1) Determine the dimensionality of the Z(T), discard all 3D parts
2) Rotate all Z(T) to TE/TM setup (T_parallel/T_ortho)
3) Transform every component individually by Niblett-Bostick
4) collect the respective 2 components each for equal/similar depths
5) interprete them as TE_nb/TM_nb
6) set up Z_nb(depth)
If 1D layers occur inbetween 2D layers, the strike angle is undefined therein.
We take an - arbitrarily chosen - linear interpolation of strike angle for
these layers, with the values varying between the angles of the bounding
upper and lower 2D layers (linearly w.r.t. the periods).
Use the output for instance for the determination of
NB-transformed phase tensors.
Note:
No propagation of errors implemented yet!
"""
#deal with inputs
#if zobject:
#z = z_object.z
#periods = 1./z_object.freq
#else:
z = z_array
periods = periods
dimensions = MTge.dimensionality(z)
angles = MTge.strike_angle(z)
#reduce actual Z by the 3D layers:
z2 = z[np.where(dimensions != 3)[0]]
angles2 = angles[np.where(dimensions != 3)[0]]
periods2 = periods[np.where(dimensions != 3)[0]]
angles_incl1D = interpolate_strike_angles(angles2[:, 0], periods2)
z3 = MTz.rotate_z(z2, -angles_incl1D)[0]
#at this point we assume that the two modes are the off-diagonal elements!!
#TE is element (1,2), TM at (2,1)
lo_nb_max = []
lo_nb_min = []
app_res = MTz.z2resphi(z3, periods2)[0]
phase = MTz.z2resphi(z3, periods2)[1]
for i, per in enumerate(periods):
te_rho, te_depth = rhophi2rhodepth(app_res[i][0, 1], phase[i][0, 1],
per)
tm_rho, tm_depth = rhophi2rhodepth(app_res[i][1, 0], phase[i][1, 0],
per)
if te_rho > tm_rho:
lo_nb_max.append([te_depth, te_rho])
lo_nb_min.append([tm_depth, tm_rho])
else:
lo_nb_min.append([te_depth, te_rho])
lo_nb_max.append([tm_depth, tm_rho])
return np.array(lo_nb_max), np.array(lo_nb_min)
def calculate_rho_minmax(z_object=None, z_array=None, periods=None):
"""
Determine 2 arrays of Niblett-Bostick transformed aparent resistivities:
minumum and maximum values for respective periods.
Values are calculated from the 1D and 2D parts of an impedance tensor array Z.
input:
- Z (object or array)
- periods (mandatory, if Z is just array)
output:
- n x 3 array, depth/rho_nb/angle for rho_nb max
- n x 3 array, depth/rho_nb/angle for rho_nb min
The calculation is carried out by :
1) Determine the dimensionality of the Z(T), discard all 3D parts
2) loop over periods
* rotate Z and calculate app_res_NB for off-diagonal elements
* find maximum and minimum values
* write out respective depths and rho values
Note:
No propagation of errors implemented yet!
"""
#deal with inputs
#if zobject:
#z = z_object.z
#periods = 1./z_object.freq
#else:
z = z_array
periods = periods
dimensions = MTge.dimensionality(z)
angles = MTge.strike_angle(z)
#reduce actual Z by the 3D layers:
z2 = z[np.where(dimensions != 3)[0]]
angles2 = angles[np.where(dimensions != 3)[0]]
periods2 = periods[np.where(dimensions != 3)[0]]
lo_nb_max = []
lo_nb_min = []
rotsteps = 360
rotangles = np.arange(rotsteps) * 180. / rotsteps
for i, per in enumerate(periods2):
z_curr = z2[i]
temp_vals = np.zeros((rotsteps, 4))
for j, d in enumerate(rotangles):
new_z = MTcc.rotatematrix_incl_errors(z_curr, d)[0]
#print i,per,j,d
res = MTz.z2resphi(new_z, per)[0]
phs = MTz.z2resphi(new_z, per)[1]
te_rho, te_depth = rhophi2rhodepth(res[0, 1], phs[0, 1], per)
tm_rho, tm_depth = rhophi2rhodepth(res[1, 0], phs[1, 0], per)
temp_vals[j, 0] = te_depth
temp_vals[j, 1] = te_rho
temp_vals[j, 2] = tm_depth
temp_vals[j, 3] = tm_rho
column = (np.argmax([np.max(temp_vals[:, 1]),
np.max(temp_vals[:, 3])])) * 2 + 1
maxidx = np.argmax(temp_vals[:, column])
max_rho = temp_vals[maxidx, column]
max_depth = temp_vals[maxidx, column - 1]
max_ang = rotangles[maxidx]
#alternative 1
min_column = (np.argmin(
[np.max(temp_vals[:, 1]),
np.max(temp_vals[:, 3])])) * 2 + 1
if max_ang <= 90:
min_ang = max_ang + 90
else:
min_ang = max_ang - 90
minidx = np.argmin(np.abs(rotangles - min_ang))
min_rho = temp_vals[minidx, min_column]
min_depth = temp_vals[minidx, min_column - 1]
lo_nb_max.append([max_depth, max_rho, max_ang])
lo_nb_min.append([min_depth, min_rho])
return np.array(lo_nb_max), np.array(lo_nb_min)
def calculate_znb(z_object = None, z_array = None, periods = None): """ Determine an array of Z_nb (depth dependent Niblett-Bostick transformed Z) from the 1D and 2D parts of an impedance tensor array Z. input: - Z (object or array) - periods (mandatory, if Z is just array) output: - Z_nb The calculation of the Z_nb needs 6 steps: 1) Determine the dimensionality of the Z(T), discard all 3D parts 2) Rotate all Z(T) to TE/TM setup (T_parallel/T_ortho) 3) Transform every component individually by Niblett-Bostick 4) collect the respective 2 components each for equal/similar depths 5) interprete them as TE_nb/TM_nb 6) set up Z_nb(depth) If 1D layers occur inbetween 2D layers, the strike angle is undefined therein. We take an - arbitrarily chosen - linear interpolation of strike angle for these layers, with the values varying between the angles of the bounding upper and lower 2D layers (linearly w.r.t. the periods). Use the output for instance for the determination of NB-transformed phase tensors. Note: No propagation of errors implemented yet! """ #deal with inputs #if zobject: #z = z_object.z #periods = 1./z_object.freq #else: z = z_array periods = periods dimensions = MTge.dimensionality(z) angles = MTge.strike_angle(z) #reduce actual Z by the 3D layers: z2 = z[np.where(dimensions != 3)[0]] angles2 = angles[np.where(dimensions != 3)[0]] periods2 = periods[np.where(dimensions != 3)[0]] angles_incl1D = interpolate_strike_angles(angles2[:,0],periods2) z3 = MTz.rotate_z(z2,-angles_incl1D)[0] #at this point we assume that the two modes are the off-diagonal elements!! #TE is element (1,2), TM at (2,1) lo_nb_max = [] lo_nb_min = [] app_res = MTz.z2resphi(z3,periods2)[0] phase = MTz.z2resphi(z3,periods2)[1] for i,per in enumerate(periods): te_rho, te_depth = rhophi2rhodepth(app_res[i][0,1], phase[i][0,1], per) tm_rho, tm_depth = rhophi2rhodepth(app_res[i][1,0], phase[i][1,0], per) if te_rho > tm_rho: lo_nb_max.append([te_depth, te_rho]) lo_nb_min.append([tm_depth, tm_rho]) else: lo_nb_min.append([te_depth, te_rho]) lo_nb_max.append([tm_depth, tm_rho]) return np.array(lo_nb_max), np.array(lo_nb_min)
def calculate_rho_minmax(z_object = None, z_array = None, periods = None):
"""
Determine 2 arrays of Niblett-Bostick transformed aparent resistivities:
minumum and maximum values for respective periods.
Values are calculated from the 1D and 2D parts of an impedance tensor array Z.
input:
- Z (object or array)
- periods (mandatory, if Z is just array)
output:
- n x 3 array, depth/rho_nb/angle for rho_nb max
- n x 3 array, depth/rho_nb/angle for rho_nb min
The calculation is carried out by :
1) Determine the dimensionality of the Z(T), discard all 3D parts
2) loop over periods
* rotate Z and calculate app_res_NB for off-diagonal elements
* find maximum and minimum values
* write out respective depths and rho values
Note:
No propagation of errors implemented yet!
"""
#deal with inputs
#if zobject:
#z = z_object.z
#periods = 1./z_object.freq
#else:
z = z_array
periods = periods
dimensions = MTge.dimensionality(z)
angles = MTge.strike_angle(z)
#reduce actual Z by the 3D layers:
z2 = z[np.where(dimensions != 3)[0]]
angles2 = angles[np.where(dimensions != 3)[0]]
periods2 = periods[np.where(dimensions != 3)[0]]
lo_nb_max = []
lo_nb_min = []
rotsteps = 360
rotangles = np.arange(rotsteps)*180./rotsteps
for i,per in enumerate(periods2):
z_curr = z2[i]
temp_vals = np.zeros((rotsteps,4))
for jj,d in enumerate(rotangles):
new_z = MTcc.rotatematrix_incl_errors(z_curr, d)[0]
#print i,per,jj,d
res = MTz.z2resphi(new_z,per)[0]
phs = MTz.z2resphi(new_z,per)[1]
te_rho, te_depth = rhophi2rhodepth(res[0,1], phs[0,1], per)
tm_rho, tm_depth = rhophi2rhodepth(res[1,0], phs[1,0], per)
temp_vals[jj,0] = te_depth
temp_vals[jj,1] = te_rho
temp_vals[jj,2] = tm_depth
temp_vals[jj,3] = tm_rho
column = (np.argmax([ np.max(temp_vals[:,1]),
np.max(temp_vals[:,3])]))*2 + 1
maxidx = np.argmax(temp_vals[:,column])
max_rho = temp_vals[maxidx,column]
max_depth = temp_vals[maxidx,column-1]
max_ang = rotangles[maxidx]
#alternative 1
min_column = (np.argmin([ np.max(temp_vals[:,1]),
np.max(temp_vals[:,3])]))*2 + 1
if max_ang <= 90:
min_ang = max_ang + 90
else:
min_ang = max_ang - 90
minidx = np.argmin(np.abs(rotangles-min_ang))
min_rho = temp_vals[minidx,min_column]
min_depth = temp_vals[minidx,min_column-1]
lo_nb_max.append([max_depth, max_rho, max_ang])
lo_nb_min.append([min_depth, min_rho])
return np.array(lo_nb_max), np.array(lo_nb_min)
def calculate_depth_nb(z_object = None, z_array = None, periods = None):
"""
Determine an array of Z_nb (depth dependent Niblett-Bostick transformed Z)
from the 1D and 2D parts of an impedance tensor array Z.
The calculation of the Z_nb needs 6 steps:
1) Determine the dimensionality of the Z(T), discard all 3D parts
2) Rotate all Z(T) to TE/TM setup (T_parallel/T_ortho)
3) Transform every component individually by Niblett-Bostick
4) collect the respective 2 components each for equal/similar depths
5) interprete them as TE_nb/TM_nb
6) set up Z_nb(depth)
If 1D layers occur inbetween 2D layers, the strike angle is undefined therein.
We take an - arbitrarily chosen - linear interpolation of strike angle for
these layers, with the values varying between the angles of the bounding
upper and lower 2D layers (linearly w.r.t. the periods).
Use the output for instance for the determination of
NB-transformed phase tensors.
Note:
No propagation of errors implemented yet!
Arguments
-------------
*z_object* : mtpy.core.z object
*z_array* : np.ndarray [num_periods, 2, 2]
*periods* : np.ndarray(num_periods)
only input if input z_array, otherwise periods are extracted
from z_object.freq
Returns
------------------
*depth_array* : np.ndarray(num_periods,
dtype=['period', 'depth_min', 'depth_max',
'rho_min', 'rho_max'])
numpy structured array with keywords.
- period --> period in s
- depth_min --> minimum depth estimated (m)
- depth_max --> maximum depth estimated (m)
- rho_min --> minimum resistivity estimated (Ohm-m)
- rho_max --> maximum resistivity estimated (Ohm-m)
Example
------------
>>> import mtpy.analysis.niblettbostick as nb
>>> depth_array = nb.calculate_znb(z_object=z1)
>>> # plot the results
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax = fig.add_subplot(1,1,1)
>>> ax.semilogy(depth_array['depth_min'], depth_array['period'])
>>> ax.semilogy(depth_array['depth_max'], depth_array['period'])
>>> plt.show()
"""
#deal with inputs
if z_object is not None:
z = z_object.z
periods = 1./z_object.freq
else:
z = z_array
periods = periods
dimensions = MTge.dimensionality(z_array=z)
angles = MTge.strike_angle(z_array=z)
#reduce actual Z by the 3D layers:
# z_2d = z[np.where(dimensions != 3)[0]]
angles_2d = np.nan_to_num(angles[np.where(dimensions != 3)][:, 0])
periods_2d = periods[np.where(dimensions != 3)]
# interperpolate strike angle onto all periods
# make a function for strike using only 2d angles
strike_interp = spi.interp1d(periods_2d, angles_2d,
bounds_error=False,
fill_value=0)
strike_angles = strike_interp(periods)
# rotate z to be along the interpolated strike angles
z_rot = MTz.rotate_z(z, strike_angles)[0]
# angles_incl1D = interpolate_strike_angles(angles_2d, periods_2d)
# z3 = MTz.rotate_z(z_2d, -angles_incl1D)[0]
#at this point we assume that the two modes are the off-diagonal elements!!
#TE is element (1,2), TM at (2,1)
# lo_nb_max = []
# lo_nb_min = []
depth_array = np.zeros(periods.shape[0],
dtype=[('period', np.float),
('depth_min', np.float),
('depth_max', np.float),
('rho_min', np.float),
('rho_max', np.float)])
# app_res, app_res_err, phase, phase_err = MTz.z2resphi(z3, periods_2d)
app_res, app_res_err, phase, phase_err = MTz.z2resphi(z_rot, periods)
for ii, per in enumerate(periods):
te_rho, te_depth = rhophi2rhodepth(app_res[ii, 0, 1],
phase[ii, 0, 1],
per)
tm_rho, tm_depth = rhophi2rhodepth(app_res[ii, 1, 0],
phase[ii, 1, 0],
per)
depth_array[ii]['period'] = per
depth_array[ii]['depth_min'] = min([te_depth, tm_depth])
depth_array[ii]['depth_max'] = max([te_depth, tm_depth])
depth_array[ii]['rho_min'] = min([te_rho, tm_rho])
depth_array[ii]['rho_max'] = max([te_rho, tm_rho])
return depth_array
def find_distortion(z_object, g='det', num_freq=None, lo_dims=None):
"""
find optimal distortion tensor from z object
automatically determine the dimensionality over all frequencies, then find
the appropriate distortion tensor D
Parameters
----------
**z_object** : mtpy.core.z object
**g** : [ 'det' | '01' | '10 ]
type of distortion correction
*default* is 'det'
**num_freq** : int
number of frequencies to look for distortion from
the index 0
*default* is None, meaning all frequencies are used
**lo_dims** : list
list of dimensions for each frequency
*default* is None, meaning calculated from data
Returns
-------
**distortion** : np.ndarray(2, 2)
distortion array all real values
**distortion_err** : np.ndarray(2, 2)
distortion error array
Examples
--------
:Estimate Distortion: ::
>>> import mtpy.analysis.distortion as distortion
>>> dis, dis_err = distortion.find_distortion(z_obj, num_freq=12)
"""
if num_freq is not None:
if num_freq > z_object.freq.size:
num_freq = z_object.freq.size
print('Number of frequencies to sweep over is too high for z')
print('setting num_freq to {0}'.format(num_freq))
else:
num_freq = z_object.freq.size
z_obj = MTz.Z(z_object.z[0:num_freq], z_object.z_err[0:num_freq],
z_object.freq[0:num_freq])
g = 'det'
dim_arr = MTge.dimensionality(z_object=z_obj)
st_arr = -1 * MTge.strike_angle(z_object=z_obj)[:, 0]
dis = np.zeros_like(z_obj.z, dtype=np.float)
dis_err = np.ones_like(z_obj.z, dtype=np.float)
#dictionary of values that should be no distortion in case distortion
#cannot be calculated for that component
rot_mat = np.matrix([[0, -1], [1, 0]])
for idx, dim in enumerate(dim_arr):
if np.any(z_obj.z[idx] == 0.0 + 0.0j) == True:
dis[idx] = np.identity(2)
print('Found a zero in z at {0}, skipping'.format(idx))
continue
if dim == 1:
if g in ['01', '10']:
gr = np.abs(z_obj.z.real[idx, int(g[0]), int(g[1])])
gi = np.abs(z_obj.z.imag[idx, int(g[0]), int(g[1])])
else:
gr = np.sqrt(np.linalg.det(z_obj.z.real[idx]))
gi = np.sqrt(np.linalg.det(z_obj.z.imag[idx]))
dis[idx] = np.mean(np.array([
(1. / gr * np.dot(z_obj.z.real[idx], rot_mat)),
(1. / gi * np.dot(z_obj.z.imag[idx], rot_mat))
]),
axis=0)
if z_obj.z_err is not None:
# find errors of entries for calculating weights
gr_err = 1. / gr * np.abs(z_obj.z_err[idx])
gr_err[np.where(gr_err == 0.0)] = 1.0
gi_err = 1. / gi * np.abs(z_obj.z_err[idx])
gi_err[np.where(gi_err == 0.0)] = 1.0
dis_err[idx] = np.mean(np.array([gi_err, gr_err]), axis=0)
elif dim == 2:
P = 1
strike_ang = st_arr[idx]
if np.isnan(strike_ang):
strike_ang = 0.0
if z_obj.z_err is not None:
err_arr = z_obj.z_err[idx]
err_arr[np.where(err_arr == 0.0)] = 1.0
else:
err_arr = None
tetm_arr, tetm_err = MTcc.rotatematrix_incl_errors(
z_obj.z[idx], strike_ang, inmatrix_err=err_arr)
tetm_r = tetm_arr.real
tetm_i = tetm_arr.imag
t_arr_r = -4 * P * tetm_r[0, 1] * tetm_r[1,
0] / np.linalg.det(tetm_r)
t_arr_i = -4 * P * tetm_i[0, 1] * tetm_i[1,
0] / np.linalg.det(tetm_i)
try:
T = np.sqrt(max([t_arr_r, t_arr_i])) + .001
except ValueError:
T = 2
sr = np.sqrt(T**2 + 4 * P * tetm_r[0, 1] * tetm_r[1, 0] /
np.linalg.det(tetm_r))
si = np.sqrt(T**2 + 4 * P * tetm_i[0, 1] * tetm_i[1, 0] /
np.linalg.det(tetm_i))
par_r = 2 * tetm_r[0, 1] / (T - sr)
orth_r = 2 * tetm_r[1, 0] / (T + sr)
par_i = 2 * tetm_i[0, 1] / (T - si)
orth_i = 2 * tetm_i[1, 0] / (T + si)
mat2_r = np.matrix([[0, 1. / orth_r], [1. / par_r, 0]])
mat2_i = np.matrix([[0, 1. / orth_i], [1. / par_i, 0]])
avg_mat = np.mean(np.array(
[np.dot(tetm_r, mat2_r),
np.dot(tetm_i, mat2_i)]),
axis=0)
dis[idx] = avg_mat
if err_arr is not None:
# find errors of entries for calculating weights
sigma_sr = np.sqrt((-(2 * P * tetm_r[0, 1] * tetm_r[1, 0] * \
tetm_r[1, 1] * err_arr[0, 0]) / \
(np.linalg.det(tetm_r) ** 2 * sr)) ** 2 + \
((2 * P * tetm_r[0, 0] * tetm_r[1, 0] *
tetm_r[1, 1] * err_arr[0, 1]) /
(np.linalg.det(tetm_r) ** 2 * sr)) ** 2 + \
((2 * P * tetm_r[0, 0] * tetm_r[0, 1] *
tetm_r[1, 1] * err_arr[1, 0]) / \
(np.linalg.det(tetm_r) ** 2 * sr)) ** 2 + \
(-(2 * P * tetm_r[0, 1] * tetm_r[1, 0] * \
tetm_r[0, 0] * err_arr[1, 1]) / \
(np.linalg.det(tetm_r) ** 2 * sr)) ** 2)
sigma_dr_11 = 0.5 * sigma_sr
sigma_dr_22 = 0.5 * sigma_sr
sigma_dr_12 = np.sqrt((mat2_r[0, 1] / tetm_r[0, 0] * err_arr[0, 0]) ** 2 + \
(mat2_r[0, 1] / tetm_r[1, 0] * err_arr[1, 0]) ** 2 + \
(0.5 * tetm_r[0, 0] / tetm_r[1, 0] * sigma_sr) ** 2)
sigma_dr_21 = np.sqrt((mat2_r[1, 0] / tetm_r[1, 1] * err_arr[1, 1]) ** 2 + \
(mat2_r[1, 0] / tetm_r[0, 1] * err_arr[0, 1]) ** 2 + \
(0.5 * tetm_r[1, 1] / tetm_r[0, 1] * sigma_sr) ** 2)
dis_err_r = np.array([[sigma_dr_11, sigma_dr_12],
[sigma_dr_21, sigma_dr_22]])
sigma_si = np.sqrt((-(2 * P * tetm_i[0, 1] * tetm_i[1, 0] * \
tetm_i[1, 1] * err_arr[0, 0]) / \
(np.linalg.det(tetm_i) ** 2 * sr)) ** 2 + \
((2 * P * tetm_i[0, 0] * tetm_i[1, 0] * \
tetm_i[1, 1] * err_arr[0, 1]) / \
(np.linalg.det(tetm_i) ** 2 * sr)) ** 2 + \
((2 * P * tetm_i[0, 0] * tetm_i[0, 1] * \
tetm_i[1, 1] * err_arr[1, 0]) / \
(np.linalg.det(tetm_i) ** 2 * sr)) ** 2 + \
(-(2 * P * tetm_i[0, 1] * tetm_i[1, 0] * \
tetm_i[0, 0] * err_arr[1, 1]) / \
(np.linalg.det(tetm_i) ** 2 * sr)) ** 2)
sigma_di_11 = 0.5 * sigma_si
sigma_di_22 = 0.5 * sigma_si
sigma_di_12 = np.sqrt((mat2_i[0, 1] / tetm_i[0, 0] * err_arr[0, 0]) ** 2 + \
(mat2_i[0, 1] / tetm_i[1, 0] * err_arr[1, 0]) ** 2 + \
(0.5 * tetm_i[0, 0] / tetm_i[1, 0] * sigma_si) ** 2)
sigma_di_21 = np.sqrt((mat2_i[1, 0] / tetm_i[1, 1] * err_arr[1, 1]) ** 2 + \
(mat2_i[1, 0] / tetm_i[0, 1] * err_arr[0, 1]) ** 2 + \
(0.5 * tetm_i[1, 1] / tetm_i[0, 1] * sigma_si) ** 2)
dis_err_i = np.array([[sigma_di_11, sigma_di_12],
[sigma_di_21, sigma_di_22]])
dis_err[idx] = np.mean(np.array([dis_err_r, dis_err_i]))
else:
dis[idx] = np.identity(2)
nonzero_idx = np.array(list(set(np.nonzero(dis)[0])))
dis_avg, weights_sum = np.average(dis[nonzero_idx],
axis=0,
weights=(1. / dis_err[nonzero_idx])**2,
returned=True)
dis_avg_err = np.sqrt(1. / weights_sum)
return dis_avg, dis_avg_err
def find_distortion(z_object, g ='det', num_freq=None, lo_dims=None):
"""
find optimal distortion tensor from z object
automatically determine the dimensionality over all frequencies, then find
the appropriate distortion tensor D
Arguments
-------------
**z_object** : mtpy.core.z object
**g** : [ 'det' | '01' | '10 ]
type of distortion correction
*default* is 'det'
**num_freq** : int
number of frequencies to look for distortion from
the index 0
*default* is None, meaning all frequencies are used
**lo_dims** : list
list of dimensions for each frequency
*default* is None, meaning calculated from data
Returns
---------
**distortion** : np.ndarray(2, 2)
distortion array all real values
**distortion_err** : np.ndarray(2, 2)
distortion error array
Example:
---------
:Estimate Distortion: ::
>>> import mtpy.analysis.distortion as distortion
>>> dis, dis_err = distortion.find_distortion(z_obj, num_freq=12)
"""
z_obj = copy.deepcopy(z_object)
if num_freq is not None:
if num_freq > z_obj.freq.size:
num_freq = z_obj.freq.size
print 'Number of frequencies to sweep over is too high for z'
print 'setting num_freq to {0}'.format(num_freq)
else:
num_freq = z_obj.freq.size
z_obj.z = z_obj.z[0:num_freq]
z_obj.z_err = z_obj.z_err[0:num_freq]
z_obj.freq = z_obj.freq[0:num_freq]
g = 'det'
dim_arr = MTge.dimensionality(z_object=z_obj)
st_arr = -1*MTge.strike_angle(z_object=z_obj)[:, 0]
dis = np.zeros_like(z_obj.z, dtype=np.float)
dis_err = np.ones_like(z_obj.z, dtype=np.float)
#dictionary of values that should be no distortion in case distortion
#cannot be calculated for that component
rot_mat = np.matrix([[0, -1], [1, 0]])
for idx, dim in enumerate(dim_arr):
if np.any(z_obj.z[idx] == 0.0+0.0j) == True:
dis[idx] = np.identity(2)
print 'Found a zero in z at {0}, skipping'.format(idx)
continue
if dim == 1:
if g in ['01', '10']:
gr = np.abs(z_obj.z.real[idx, int(g[0]), int(g[1])])
gi = np.abs(z_obj.z.imag[idx, int(g[0]), int(g[1])])
else:
gr = np.sqrt(np.linalg.det(z_obj.z.real[idx]))
gi = np.sqrt(np.linalg.det(z_obj.z.imag[idx]))
dis[idx] = np.mean(np.array([(1./gr*np.dot(z_obj.z.real[idx],
rot_mat)),
(1./gi*np.dot(z_obj.z.imag[idx],
rot_mat))]),
axis=0)
if z_obj.z_err is not None:
#find errors of entries for calculating weights
gr_err = 1./gr*np.abs(z_obj.z_err[idx])
gr_err[np.where(gr_err == 0.0)] = 1.0
gi_err = 1./gi*np.abs(z_obj.z_err[idx])
gi_err[np.where(gi_err == 0.0)] = 1.0
dis_err[idx] = np.mean(np.array([gi_err, gr_err]),
axis=0)
elif dim == 2:
P = 1
strike_ang = st_arr[idx]
if np.isnan(strike_ang):
strike_ang = 0.0
if z_obj.z_err is not None:
err_arr = z_obj.z_err[idx]
err_arr[np.where(err_arr == 0.0)] = 1.0
else:
err_arr = None
tetm_arr, tetm_err = MTcc.rotatematrix_incl_errors(z_obj.z[idx],
strike_ang,
inmatrix_err=err_arr)
tetm_r = tetm_arr.real
tetm_i = tetm_arr.imag
t_arr_r = -4*P*tetm_r[0, 1]*tetm_r[1, 0]/np.linalg.det(tetm_r)
t_arr_i = -4*P*tetm_i[0, 1]*tetm_i[1, 0]/np.linalg.det(tetm_i)
try:
T = np.sqrt(max([t_arr_r, t_arr_i]))+.001
except ValueError:
T = 2
sr = np.sqrt(T**2+4*P*tetm_r[0, 1]*tetm_r[1, 0]/np.linalg.det(tetm_r))
si = np.sqrt(T**2+4*P*tetm_i[0, 1]*tetm_i[1, 0]/np.linalg.det(tetm_i))
par_r = 2*tetm_r[0, 1]/(T-sr)
orth_r = 2*tetm_r[1, 0]/(T+sr)
par_i = 2*tetm_i[0, 1]/(T-si)
orth_i = 2*tetm_i[1, 0]/(T+si)
mat2_r = np.matrix([[0, 1./orth_r], [1./par_r, 0]])
mat2_i = np.matrix([[0, 1./orth_i], [1./par_i ,0]])
avg_mat = np.mean(np.array([np.dot(tetm_r, mat2_r),
np.dot(tetm_i, mat2_i)]),
axis=0)
dis[idx] = avg_mat
if err_arr is not None:
#find errors of entries for calculating weights
sigma_sr = np.sqrt((-(2*P*tetm_r[0,1]*tetm_r[1,0]*\
tetm_r[1,1]*err_arr[0,0])/\
(np.linalg.det(tetm_r)**2*sr))**2+\
((2*P*tetm_r[0,0]*tetm_r[1,0]*\
tetm_r[1,1]*err_arr[0,1])/\
(np.linalg.det(tetm_r)**2*sr))**2+\
((2*P*tetm_r[0,0]* tetm_r[0,1]*\
tetm_r[1,1]*err_arr[1,0])/\
(np.linalg.det(tetm_r)**2*sr))**2 +\
(-(2*P*tetm_r[0,1]* tetm_r[1,0]*\
tetm_r[0,0]*err_arr[1,1])/\
(np.linalg.det(tetm_r)**2*sr))**2)
sigma_dr_11 = 0.5*sigma_sr
sigma_dr_22 = 0.5*sigma_sr
sigma_dr_12 = np.sqrt((mat2_r[0,1]/tetm_r[0,0]*err_arr[0,0])**2+\
(mat2_r[0,1]/tetm_r[1,0]*err_arr[1,0])**2+\
(0.5*tetm_r[0,0]/tetm_r[1,0]*sigma_sr)**2)
sigma_dr_21 = np.sqrt((mat2_r[1,0]/tetm_r[1,1]*err_arr[1,1])**2+\
(mat2_r[1,0]/tetm_r[0,1]*err_arr[0,1])**2+\
(0.5*tetm_r[1,1]/tetm_r[0,1]*sigma_sr)**2)
dis_err_r = np.array([[sigma_dr_11, sigma_dr_12],
[sigma_dr_21, sigma_dr_22]])
sigma_si = np.sqrt((-(2*P*tetm_i[0,1]*tetm_i[1,0]*\
tetm_i[1,1]*err_arr[0,0])/\
(np.linalg.det(tetm_i)**2*sr))**2+\
((2*P*tetm_i[0,0]*tetm_i[1,0]*\
tetm_i[1,1]*err_arr[0,1])/\
(np.linalg.det(tetm_i)**2*sr))**2+\
((2*P*tetm_i[0,0]*tetm_i[0,1]*\
tetm_i[1,1]*err_arr[1,0])/\
(np.linalg.det(tetm_i)**2*sr))**2+\
(-(2*P*tetm_i[0,1]*tetm_i[1,0]*\
tetm_i[0,0]*err_arr[1,1])/\
(np.linalg.det(tetm_i)**2*sr))**2)
sigma_di_11 = 0.5*sigma_si
sigma_di_22 = 0.5*sigma_si
sigma_di_12 = np.sqrt((mat2_i[0,1]/tetm_i[0,0]*err_arr[0,0])**2+\
(mat2_i[0,1]/tetm_i[1,0]*err_arr[1,0])**2+\
(0.5*tetm_i[0,0]/tetm_i[1,0]*sigma_si)**2)
sigma_di_21 = np.sqrt((mat2_i[1,0]/tetm_i[1,1]*err_arr[1,1])**2+\
(mat2_i[1,0]/tetm_i[0,1]*err_arr[0,1])**2+\
(0.5*tetm_i[1,1]/tetm_i[0,1]*sigma_si)**2)
dis_err_i = np.array([[sigma_di_11, sigma_di_12],
[sigma_di_21, sigma_di_22]])
dis_err[idx] = np.mean(np.array([dis_err_r, dis_err_i]))
else:
dis[idx] = np.identity(2)
nonzero_idx = np.array(list(set(np.nonzero(dis)[0])))
dis_avg, weights_sum = np.average(dis[nonzero_idx],
axis=0,
weights=(1./dis_err[nonzero_idx])**2,
returned=True)
dis_avg_err = np.sqrt(1./weights_sum)
return dis_avg, dis_avg_err
get dimensionality/strike angle from an edi file
"""
import os.path as op
import os
os.chdir(r'C:\mtpywin\mtpy') # change to path where mtpy is installed
import numpy as np
from mtpy.core.mt import MT
from mtpy.analysis.geometry import dimensionality, strike_angle
# directory containing edis
edi_path = r'C:\mtpywin\mtpy\examples\data\edi_files_2'
# edi file name
edi_file = os.path.join(edi_path, 'Synth00.edi')
# read edi file into an MT object
mtObj = MT(edi_file)
# determine strike angle for the 2d parts of impedance tensor
strike = strike_angle(
z_object=mtObj.Z,
skew_threshold=
5, # threshold in skew angle (degrees) to determine if data are 3d
eccentricity_threshold=
0.1 # threshold in phase ellipse eccentricity to determine if data are 2d (vs 1d)
)
print strike
[38.45662316, 128.45662316],
[28.61883115, 118.61883115],
[14.45341494, 104.45341494],
[8.43320651, 98.43320651],
[4.94952784, 94.94952784],
[2.09090369, 92.09090369],
[1.39146887, 91.39146887],
[0.39905337, 90.39905337],
[-5.49553673, 84.50446327],
[-6.28846049, 83.71153951],
[-7.31641788, 82.68358212],
[-10.45341947, 79.54658053],
[-7.07075086, 82.92924914],
[-7.5429295, 82.4570705],
[-6.06405688, 83.93594312],
[-3.54915951, 86.45084049],
[-3.12596637, 86.87403363],
[-0.47404093, 89.52595907],
[2.74343665, 92.74343665],
[4.78078759, 94.78078759],
[7.71125988, 97.71125988],
[11.0123521, 101.0123521],
[13.81639678, 103.81639678],
[13.60497071, 103.60497071],
[15.87672806, 105.87672806]])
strike_angle = mtg.strike_angle(z_object=mtObj.Z)
assert np.all(np.abs(strike_angle[np.isfinite(strike_angle)] - \
strike_angle_result[np.isfinite(strike_angle_result)] < 1e-8))
def analysis_edi(datadir):
""" analysis """
# Define the path to your edi file
edi_file = datadir + r"edifiles2\15125A.edi"
savepath = datadir
edi_path = datadir + 'edifiles2'
# Create an MT object
mt_obj = MT(edi_file)
# look at the skew values as a histogram
plt.hist(mt_obj.pt.beta, bins=50)
plt.xlabel('Skew angle (degree)')
plt.ylabel('Number of values')
plt.show()
# Have a look at the dimensionality
dim = dimensionality(z_object=mt_obj.Z,
skew_threshold=5,
eccentricity_threshold=0.1)
print(dim)
# calculate strike
strike = strike_angle(z_object=mt_obj.Z,
skew_threshold=5,
eccentricity_threshold=0.1)
# display the median strike angle for this station
# two values because of 90 degree ambiguity in strike
strikemedian = np.nanmedian(strike, axis=0)
print(strikemedian)
# Use dimensionality to mask a file
mask = dim < 3
# Apply masking. The new arrays z_array, z_err_array, and freq will
# exclude values where mask is False (i.e. the 3D parts)
new_Z_obj = Z(z_array=mt_obj.Z.z[mask],
z_err_array=mt_obj.Z.z_err[mask],
freq=mt_obj.Z.freq[mask])
new_Tipper_obj = Tipper(tipper_array=mt_obj.Tipper.tipper[mask],
tipper_err_array=mt_obj.Tipper.tipper_err[mask],
freq=mt_obj.Tipper.freq[mask])
# Write a new edi file
mt_obj.write_mt_file(save_dir=savepath,
fn_basename='Synth00_mask3d',
file_type='edi',
new_Z_obj=new_Z_obj,
new_Tipper_obj=new_Tipper_obj,
longitude_format='LONG',
latlon_format='dd')
# Plot strike
# Get full path to all files with the extension '.edi' in edi_path
edi_list = [
os.path.join(edi_path, ff) for ff in os.listdir(edi_path)
if ff.endswith('.edi')
]
# make a plot (try also plot_type = 1 to plot by decade)
strikeplot = PlotStrike(fn_list=edi_list, plot_type=2, plot_tipper='y')
# save to file
# strikeplot.save_plot(savepath,
# file_format='.png',
# fig_dpi=400)
strike = strikemedian[0] # 0 index chosen based on geological information
mt_obj.Z.rotate(strike)
mt_obj.Tipper.rotate(strike)
# check the rotation angle
print(mt_obj.Z.rotation_angle)
# Write a new edi file (as before)
mt_obj.write_mt_file(save_dir=savepath,
fn_basename='Synth00_rotate%1i' % strike,
file_type='edi',
longitude_format='LONG',
latlon_format='dd')
