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 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_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 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
def rotate(self, alpha):
"""
Rotate PT array. Change the rotation angles attribute respectively.
Rotation angle must be given in degrees. All angles are referenced to
geographic North, positive in clockwise direction.
(Mathematically negative!)
In non-rotated state, X refs to North and Y to East direction.
"""
if self._pt is None :
print 'pt-array is "None" - I cannot rotate that'
return
if np.iterable(self.rotation_angle) == 0:
self.rotation_angle = np.array([self.rotation_angle
for ii in self.pt])
#check for iterable list/set of angles - if so, it must have length 1
#or same as len(pt):
if np.iterable(alpha) == 0:
try:
degreeangle = float(alpha%360)
except:
print '"Angle" must be a valid number (in degrees)'
return
#make an n long list of identical angles
lo_angles = [degreeangle for i in self.pt]
else:
if len(alpha) == 1:
try:
degreeangle = float(alpha%360)
except:
print '"Angle" must be a valid number (in degrees)'
return
#make an n long list of identical angles
lo_angles = [degreeangle for i in self.pt]
else:
try:
lo_angles = [ float(i%360) for i in alpha]
except:
print '"Angles" must be valid numbers (in degrees)'
return
self.rotation_angle = list((np.array(lo_angles) + \
np.array(self.rotation_angle))%360)
if len(lo_angles) != len(self._pt):
print 'Wrong number Number of "angles" - need %i '%(len(self._pt))
self.rotation_angle = 0.
return
pt_rot = copy.copy(self._pt)
pt_err_rot = copy.copy(self._pt_err)
for idx_freq in range(len(self._pt)):
angle = lo_angles[idx_freq]
if np.isnan(angle):
angle = 0.
if self.pt_err is not None:
pt_rot[idx_freq], pt_err_rot[idx_freq] = MTcc.rotatematrix_incl_errors(self.pt[idx_freq,:,:], angle, self.pt_err[idx_freq,:,:])
else:
pt_rot[idx_freq], pt_err_rot = MTcc.rotatematrix_incl_errors(self.pt[idx_freq,:,:], angle)
#--> set the rotated tensors as the current attributes
self._pt = pt_rot
self._pt_err = pt_err_rot
def rotate(self,alpha):
"""
Rotate PT array. Change the rotation angles attribute respectively.
Rotation angle must be given in degrees. All angles are referenced to
geographic North, positive in clockwise direction.
(Mathematically negative!)
In non-rotated state, X refs to North and Y to East direction.
"""
if self._pt is None :
print 'pt-array is "None" - I cannot rotate that'
return
if np.iterable(self.rotation_angle) == 0:
self.rotation_angle = np.array([self.rotation_angle
for ii in self.pt])
#check for iterable list/set of angles - if so, it must have length 1
#or same as len(pt):
if np.iterable(alpha) == 0:
try:
degreeangle = float(alpha%360)
except:
print '"Angle" must be a valid number (in degrees)'
return
#make an n long list of identical angles
lo_angles = [degreeangle for i in self.pt]
else:
if len(alpha) == 1:
try:
degreeangle = float(alpha%360)
except:
print '"Angle" must be a valid number (in degrees)'
return
#make an n long list of identical angles
lo_angles = [degreeangle for i in self.pt]
else:
try:
lo_angles = [ float(i%360) for i in alpha]
except:
print '"Angles" must be valid numbers (in degrees)'
return
self.rotation_angle = list((np.array(lo_angles) + \
np.array(self.rotation_angle))%360)
if len(lo_angles) != len(self._pt):
print 'Wrong number Number of "angles" - need %i '%(len(self._pt))
self.rotation_angle = 0.
return
pt_rot = copy.copy(self._pt)
pterr_rot = copy.copy(self._pt_err)
for idx_freq in range(len(self._pt)):
angle = lo_angles[idx_freq]
if np.isnan(angle):
angle = 0.
if self.pt_err is not None:
pt_rot[idx_freq], pterr_rot[idx_freq] = MTcc.rotatematrix_incl_errors(self.pt[idx_freq,:,:], angle, self.pt_err[idx_freq,:,:])
else:
pt_rot[idx_freq], pterr_rot = MTcc.rotatematrix_incl_errors(self.pt[idx_freq,:,:], angle)
#--> set the rotated tensors as the current attributes
self._pt = pt_rot
self._pt_err = pterr_rot