def remove_static_shift_spatial_filter(edi_fn, radius=1000, num_freq=20,
freq_skip=4, shift_tol=.15, plot=False):
"""
Remove static shift from a station using a spatial median filter. This
will look at all the edi files in the same directory as edi_fn and find
those station within the given radius (meters). Then it will find
the medain static shift for the x and y modes and remove it, given that
it is larger than the shift tolerance away from 1. A new edi file will
be written in a new folder called SS.
Arguments
-----------------
**edi_fn** : string
full path to edi file to have static shift removed
**radius** : float
radius to look for nearby stations, in meters.
*default* is 1000 m
**num_freq** : int
number of frequencies calculate the median static
shift. This is assuming the first frequency is the
highest frequency. Cause usually highest frequencies
are sampling a 1D earth. *default* is 20
**freq_skip** : int
number of frequencies to skip from the highest
frequency. Sometimes the highest frequencies are
not reliable due to noise or low signal in the AMT
deadband. This allows you to skip those frequencies.
*default* is 4
**shift_tol** : float
Tolerance on the median static shift correction. If
the data is noisy the correction factor can be biased
away from 1. Therefore the shift_tol is used to stop
that bias. If 1-tol < correction < 1+tol then the
correction factor is set to 1. *default* is 0.15
**plot** : [ True | False ]
Boolean to plot the corrected response against the
non-corrected response. *default* is False
Returns
----------------
**new_edi_fn_ss** : string
new path to the edi file with static shift removed
**shift_corrections** : (float, float)
static shift corrections for x and y modes
**plot_obj** : mtplot.plot_multiple_mt_responses object
If plot is True a plot_obj is returned
If plot is False None is returned
"""
ss_x, ss_y = estimate_static_spatial_median(edi_fn,
radius=radius,
num_freq=num_freq,
freq_skip=freq_skip,
shift_tol=.15)
mt_obj = mt.MT(edi_fn)
s, z_ss = mt_obj.Z.no_ss(reduce_res_factor_x=ss_x,
reduce_res_factor_y=ss_y)
edi_path = os.path.dirname(edi_fn)
mt_obj.Z.z = z_ss
new_edi_fn = os.path.join(edi_path, 'SS', '{0}_ss.edi'.format(mt_obj.station))
if not os.path.exists(os.path.dirname(new_edi_fn)):
os.mkdir(os.path.dirname(new_edi_fn))
mt_obj.write_edi_file(new_fn=new_edi_fn)
if plot == True:
rpm = mtplot.plot_multiple_mt_responses(fn_list=[edi_fn, new_edi_fn],
plot_style='compare')
return new_edi_fn, s[0], rpm
else:
return new_edi_fn, s[0], None
def remove_static_shift_spatial_filter(edi_fn, radius=1000, num_freq=20,
freq_skip=4, shift_tol=.15, plot=False):
"""
Remove static shift from a station using a spatial median filter. This
will look at all the edi files in the same directory as edi_fn and find
those station within the given radius (meters). Then it will find
the medain static shift for the x and y modes and remove it, given that
it is larger than the shift tolerance away from 1. A new edi file will
be written in a new folder called SS.
Arguments
-----------------
**edi_fn** : string
full path to edi file to have static shift removed
**radius** : float
radius to look for nearby stations, in meters.
*default* is 1000 m
**num_freq** : int
number of frequencies calculate the median static
shift. This is assuming the first frequency is the
highest frequency. Cause usually highest frequencies
are sampling a 1D earth. *default* is 20
**freq_skip** : int
number of frequencies to skip from the highest
frequency. Sometimes the highest frequencies are
not reliable due to noise or low signal in the AMT
deadband. This allows you to skip those frequencies.
*default* is 4
**shift_tol** : float
Tolerance on the median static shift correction. If
the data is noisy the correction factor can be biased
away from 1. Therefore the shift_tol is used to stop
that bias. If 1-tol < correction < 1+tol then the
correction factor is set to 1. *default* is 0.15
**plot** : [ True | False ]
Boolean to plot the corrected response against the
non-corrected response. *default* is False
Returns
----------------
**new_edi_fn_ss** : string
new path to the edi file with static shift removed
**shift_corrections** : (float, float)
static shift corrections for x and y modes
**plot_obj** : mtplot.plot_multiple_mt_responses object
If plot is True a plot_obj is returned
If plot is False None is returned
"""
ss_x, ss_y = estimate_static_spatial_median(edi_fn,
radius=radius,
num_freq=num_freq,
freq_skip=freq_skip,
shift_tol=.15)
mt_obj = mt.MT(edi_fn)
s, z_ss = mt_obj.Z.no_ss(reduce_res_factor_x=ss_x,
reduce_res_factor_y=ss_y)
edi_path = os.path.dirname(edi_fn)
mt_obj.Z.z = z_ss
new_edi_fn = os.path.join(
edi_path, 'SS', '{0}_ss.edi'.format(
mt_obj.station))
if not os.path.exists(os.path.dirname(new_edi_fn)):
os.mkdir(os.path.dirname(new_edi_fn))
mt_obj.write_edi_file(new_fn=new_edi_fn)
if plot == True:
rpm = mtplot.plot_multiple_mt_responses(fn_list=[edi_fn, new_edi_fn],
plot_style='compare')
return new_edi_fn, s[0], rpm
else:
return new_edi_fn, s[0], None
def remove_static_shift_spatial_filter(edi_fn,
radius=1000,
num_freq=20,
freq_skip=4,
shift_tol=.15,
plot=False):
"""
Remove static shift from a station using a spatial median filter. This
will look at all the edi files in the same directory as edi_fn and find
those station within the given radius (meters). Then it will find
the medain static shift for the x and y modes and remove it, given that
it is larger than the shift tolerance away from 1. A new edi file will
be written in a new folder called SS.
Arguments
-----------------
**edi_fn** : string
full path to edi file to have static shift removed
**radius** : float
radius to look for nearby stations, in meters.
*default* is 1000 m
**num_freq** : int
number of frequencies calculate the median static
shift. This is assuming the first frequency is the
highest frequency. Cause usually highest frequencies
are sampling a 1D earth. *default* is 20
**freq_skip** : int
number of frequencies to skip from the highest
frequency. Sometimes the highest frequencies are
not reliable due to noise or low signal in the AMT
deadband. This allows you to skip those frequencies.
*default* is 4
**shift_tol** : float
Tolerance on the median static shift correction. If
the data is noisy the correction factor can be biased
away from 1. Therefore the shift_tol is used to stop
that bias. If 1-tol < correction < 1+tol then the
correction factor is set to 1. *default* is 0.15
**plot** : [ True | False ]
Boolean to plot the corrected response against the
non-corrected response. *default* is False
Returns
----------------
**new_edi_fn_ss** : string
new path to the edi file with static shift removed
**shift_corrections** : (float, float)
static shift corrections for x and y modes
**plot_obj** : mtplot.plot_multiple_mt_responses object
If plot is True a plot_obj is returned
If plot is False None is returned
"""
# convert meters to decimal degrees so we don't have to deal with zone
# changes
meter_to_deg_factor = 8.994423457456377e-06
dm_deg = radius * meter_to_deg_factor
# make a list of edi files in the directory
edi_path = os.path.dirname(edi_fn)
edi_list = [
os.path.join(edi_path, edi) for edi in os.listdir(edi_path)
if edi.find('.edi') > 0
]
edi_list.remove(edi_fn)
# read the edi file
mt_obj = mt.MT(edi_fn)
mt_obj.Z._compute_res_phase()
# Find stations near by and store them in a list
mt_obj_list = []
for kk, kk_edi in enumerate(edi_list):
mt_obj_2 = mt.MT(kk_edi)
delta_d = np.sqrt((mt_obj.lat-mt_obj_2.lat)**2+\
(mt_obj.lon-mt_obj_2.lon)**2)
if delta_d <= dm_deg:
mt_obj_2.delta_d = float(delta_d) / meter_to_deg_factor
mt_obj_list.append(mt_obj_2)
# extract the resistivity values from the near by stations
res_array = np.zeros((len(mt_obj_list), num_freq, 2, 2))
print 'These stations are within the given {0} m radius:'.format(radius)
for kk, mt_obj_kk in enumerate(mt_obj_list):
print '\t{0} --> {1:.1f} m'.format(mt_obj_kk.station,
mt_obj_kk.delta_d)
interp_freq = mt_obj.Z.freq[freq_skip:num_freq + freq_skip]
Z_interp, Tip_interp = mt_obj_kk.interpolate(interp_freq)
Z_interp._compute_res_phase()
res_array[kk, :, :, :] = Z_interp.resistivity[0:num_freq, :, :]
#compute the static shift of x-components
static_shift_x = mt_obj.Z.resistivity[freq_skip:num_freq+freq_skip, 0, 1]/\
np.median(res_array[:, :, 0, 1], axis=0)
static_shift_x = np.median(static_shift_x)
##check to see if the estimated static shift is within given tolerance
if 1 - shift_tol < static_shift_x and static_shift_x < 1 + shift_tol:
static_shift_x = 1.0
#compute the static shift of y-components
static_shift_y = mt_obj.Z.resistivity[freq_skip:num_freq+freq_skip, 1, 0]/\
np.median(res_array[:, :, 1, 0], axis=0)
static_shift_y = np.median(static_shift_y)
##check to see if the estimated static shift is within given tolerance
if 1 - shift_tol < static_shift_y and static_shift_y < 1 + shift_tol:
static_shift_y = 1.0
print 'x static shift is {0:.3f}'.format(static_shift_x)
print 'y static shift is {0:.3f}'.format(static_shift_y)
s, z_ss = mt_obj.Z.no_ss(reduce_res_factor_x=static_shift_x,
reduce_res_factor_y=static_shift_y)
mt_obj.Z.z = z_ss
new_edi_fn = os.path.join(edi_path, 'SS',
'{0}_ss.edi'.format(mt_obj.station))
mt_obj.write_edi_file(new_fn=new_edi_fn)
if plot == True:
rpm = mtplot.plot_multiple_mt_responses(fn_list=[edi_fn, new_edi_fn],
plot_style='compare')
return new_edi_fn, s[0], rpm
else:
return new_edi_fn, s[0], None
"SG1901": ["jmz017", "jmz010"],
"SG1902": ["jmz018"],
"SG1903": ["jmz005"],
"SG1904": ["jmz027", "jmz026"],
"SG1905": ["jmz020"],
"SG1906": ["BND009"],
}
edi_list_good = list(sorted(glob.glob("{0}/*.edi".format(edi_path_good))))
edi_list_old = list(sorted(glob.glob("{0}/*.edi".format(edi_path_old))))
for edi_good in edi_list_good:
station = os.path.basename(edi_good)[0:-4]
if station in list(match_dict.keys()):
edi_add = [
os.path.join(edi_path_sage, m_fn + ".edi")
for m_fn in match_dict[station]
]
edi_list = edi_add + [edi_good]
else:
edi_list = [edi_good]
ptm = mtplot.plot_multiple_mt_responses(
fn_list=edi_list,
plot_tipper="yr",
plot_style="compare",
tipper_limits=(-0.3, 0.3),
)
ptm.fig.savefig(os.path.join(edi_path_good, station + "compare.png"),
dpi=900)
data_arr = data_arr[np.nonzero(data_arr['freq'])]
sort_index = np.argsort(data_arr['freq'])
# check to see if the sorted indexes are descending or ascending,
# make sure that frequency is descending
if data_arr['freq'][0] > data_arr['freq'][1]:
sort_index = sort_index[::-1]
data_arr = data_arr[sort_index]
new_z = z.Z(data_arr['z'],
data_arr['z_err'],
data_arr['freq'])
# check for all zeros in tipper, meaning there is only
# one unique value
if np.unique(data_arr['tipper']).size > 1:
new_t = z.Tipper(data_arr['tipper'],
data_arr['tipper_err'],
data_arr['freq'])
else:
new_t = z.Tipper()
mt_obj = mt.MT(edi_01)
mt_obj.Z = new_z
mt_obj.Tipper = new_t
n_edi_fn = mt_obj.write_mt_file(fn_basename=c_edi_fn.name)
ptm = mtplot.plot_multiple_mt_responses(fn_list=[edi_01, edi_02, n_edi_fn],
plot_style='compare')
def remove_static_shift_spatial_filter(edi_fn, radius=1000, num_freq=20,
freq_skip=4, shift_tol=.15, plot=False):
"""
Remove static shift from a station using a spatial median filter. This
will look at all the edi files in the same directory as edi_fn and find
those station within the given radius (meters). Then it will find
the medain static shift for the x and y modes and remove it, given that
it is larger than the shift tolerance away from 1. A new edi file will
be written in a new folder called SS.
Arguments
-----------------
**edi_fn** : string
full path to edi file to have static shift removed
**radius** : float
radius to look for nearby stations, in meters.
*default* is 1000 m
**num_freq** : int
number of frequencies calculate the median static
shift. This is assuming the first frequency is the
highest frequency. Cause usually highest frequencies
are sampling a 1D earth. *default* is 20
**freq_skip** : int
number of frequencies to skip from the highest
frequency. Sometimes the highest frequencies are
not reliable due to noise or low signal in the AMT
deadband. This allows you to skip those frequencies.
*default* is 4
**shift_tol** : float
Tolerance on the median static shift correction. If
the data is noisy the correction factor can be biased
away from 1. Therefore the shift_tol is used to stop
that bias. If 1-tol < correction < 1+tol then the
correction factor is set to 1. *default* is 0.15
**plot** : [ True | False ]
Boolean to plot the corrected response against the
non-corrected response. *default* is False
Returns
----------------
**new_edi_fn_ss** : string
new path to the edi file with static shift removed
**shift_corrections** : (float, float)
static shift corrections for x and y modes
**plot_obj** : mtplot.plot_multiple_mt_responses object
If plot is True a plot_obj is returned
If plot is False None is returned
"""
# convert meters to decimal degrees so we don't have to deal with zone
# changes
meter_to_deg_factor = 8.994423457456377e-06
dm_deg = radius*meter_to_deg_factor
# make a list of edi files in the directory
edi_path = os.path.dirname(edi_fn)
edi_list = [os.path.join(edi_path, edi) for edi in os.listdir(edi_path)
if edi.find('.edi') > 0]
edi_list.remove(edi_fn)
# read the edi file
mt_obj = mt.MT(edi_fn)
mt_obj.Z._compute_res_phase()
# Find stations near by and store them in a list
mt_obj_list = []
for kk, kk_edi in enumerate(edi_list):
mt_obj_2 = mt.MT(kk_edi)
delta_d = np.sqrt((mt_obj.lat-mt_obj_2.lat)**2+\
(mt_obj.lon-mt_obj_2.lon)**2)
if delta_d <= dm_deg:
mt_obj_2.delta_d = float(delta_d)/meter_to_deg_factor
mt_obj_list.append(mt_obj_2)
# extract the resistivity values from the near by stations
res_array = np.zeros((len(mt_obj_list), num_freq, 2, 2))
print 'These stations are within the given {0} m radius:'.format(radius)
for kk, mt_obj_kk in enumerate(mt_obj_list):
print '\t{0} --> {1:.1f} m'.format(mt_obj_kk.station, mt_obj_kk.delta_d)
interp_freq = mt_obj.Z.freq[freq_skip:num_freq+freq_skip]
Z_interp, Tip_interp = mt_obj_kk.interpolate(interp_freq)
Z_interp._compute_res_phase()
res_array[kk, :, :, :] = Z_interp.resistivity[0:num_freq, :, :]
#compute the static shift of x-components
static_shift_x = mt_obj.Z.resistivity[freq_skip:num_freq+freq_skip, 0, 1]/\
np.median(res_array[:, :, 0, 1], axis=0)
static_shift_x = np.median(static_shift_x)
##check to see if the estimated static shift is within given tolerance
if 1-shift_tol < static_shift_x and static_shift_x < 1+shift_tol:
static_shift_x = 1.0
#compute the static shift of y-components
static_shift_y = mt_obj.Z.resistivity[freq_skip:num_freq+freq_skip, 1, 0]/\
np.median(res_array[:, :, 1, 0], axis=0)
static_shift_y = np.median(static_shift_y)
##check to see if the estimated static shift is within given tolerance
if 1-shift_tol < static_shift_y and static_shift_y < 1+shift_tol:
static_shift_y = 1.0
print 'x static shift is {0:.3f}'.format(static_shift_x)
print 'y static shift is {0:.3f}'.format(static_shift_y)
s, z_ss = mt_obj.Z.no_ss(reduce_res_factor_x=static_shift_x,
reduce_res_factor_y=static_shift_y)
mt_obj.Z.z = z_ss
new_edi_fn = os.path.join(edi_path, 'SS', '{0}_ss.edi'.format(mt_obj.station))
if not os.path.exists(os.path.dirname(new_edi_fn)):
os.mkdir(os.path.dirname(new_edi_fn))
mt_obj.write_edi_file(new_fn=new_edi_fn)
if plot == True:
rpm = mtplot.plot_multiple_mt_responses(fn_list=[edi_fn, new_edi_fn],
plot_style='compare')
return new_edi_fn, s[0], rpm
else:
return new_edi_fn, s[0], None
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 26 17:14:04 2017
@author: jpeacock
"""
import os
import mtpy.imaging.mtplot as mtplot
ga_dir = r"d:\Peacock\MTData\sev\Folsom_edi_original"
birrp_dir = r"d:\Peacock\MTData\sev\EDI_Files_birrp\Edited"
for ga_fn in os.listdir(ga_dir):
for birrp_fn in os.listdir(birrp_dir):
if birrp_fn[0:-4] in ga_fn:
fn_list = [
os.path.join(ga_dir, ga_fn),
os.path.join(birrp_dir, birrp_fn)
]
pmr = mtplot.plot_multiple_mt_responses(fn_list=fn_list,
plot_style='compare',
plot_tipper='yr')
pmr.fig.savefig(os.path.join(
birrp_dir, '{0}_compare.png'.format(birrp_fn[0:-4])),
dpi=600)
mtplot.plotnresponses.plt.close('all')
break
