def main():
# If a model file is not specified, a uniformly spaced model should be generated based on the data.the
# i.e., use sites as bounds, and use smallest period as a starting point for cell sizes.
files = sys.argv[1:]
for file in files:
if not utils.check_file(file):
print('File {} not found.'.format(file))
return
files = utils.sort_files(files=files)
try:
data = WSDS.Data(datafile=files['dat'])
except KeyError:
print('No data file given. Site locations will not be available.')
data = None
try:
model = WSDS.Model(files['model'])
except KeyError:
if data is None:
print('One or more of <model_file> and <data_file> must be given!')
return
else:
print('Generating initial model...')
model = WSDS.Model(data=data)
print([model.vals.shape, model.nx, model.ny, model.nz])
app = QtWidgets.QApplication(sys.argv)
viewer = model_viewer_2d(model=model, data=data)
viewer.show()
ret = app.exec_()
sys.exit(ret)
viewer.disconnect_mpl_events()
def main():
files = sys.argv[1:]
for file in files:
if not check_file(file):
print('File {} not found.'.format(file))
return
files = sort_files(files=files)
try:
data = WSDS.Data(datafile=files['dat'])
except KeyError:
print('No data file given. Site locations will not be available.')
data = None
try:
model = WSDS.Model(files['model'])
except KeyError:
print('Model must be specified')
return
app = Qt.QApplication(sys.argv)
viewer = ModelWindow(files)
viewer.show()
ret = app.exec_()
sys.exit(ret)
import pyMT.data_structures as WSDS
import pandas as pd
import numpy as np
# Define your data and list locations
data_file = 'C:/Users/eroots/phd/ownCloud/data/Regions/MetalEarth/swayze/swz_cull1/finish/swz_cull1i_Z.dat'
list_file = 'C:/Users/eroots/phd/ownCloud/data/Regions/MetalEarth/swayze/j2/swz_cull1.lst'
# Read data and list
data = WSDS.Data(data_file)
raw = WSDS.RawData(list_file)
# Define what info you want in the CSV (headers)
columns = ('site', 'X', 'Y', 'period', 'beta', 'alpha', 'det_phi', 'skew_phi',
'phi_1', 'phi_2', 'phi_3', 'phi_max', 'phi_min', 'Lambda',
'azimuth', 'delta')
c = 0
site_list = []
# Initialize numpy array with zeros
arr = np.zeros((len(data.site_names) * len(data.periods), len(columns)))
for site in data.site_names:
for ii, period in enumerate(data.periods):
phase_tensor = data.sites[site].phase_tensors[ii]
for jj, column in enumerate(columns):
# Can't pass string to np array, so ignore for now
if column == 'site':
# arr[ii, jj] = site
pass
# If looking for lat/longs, grab these from the RawData object
elif column == 'X':
arr[c, jj] = raw.sites[site].locations['Long']
elif column == 'Y':
from scipy.interpolate import griddata
import naturalneighbor as nn
import matplotlib.pyplot as plt
import numpy as np
import e_colours.colourmaps as cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
cmap = cm.jet_plus(64)
# listfile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\dbr15\j2\allsites.lst'
# listfile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\MetalEarth\j2\allbb.lst')
# datafile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\MetalEarth\j2\allbb.data')
# datafile = 'C:/Users/eric/Documents/MATLAB/MATLAB/Inversion/Regions/MetalEarth/swayze/swz_cull1/swz_cull1f_Z.dat'
datafile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/j2/cull_allSuperior.data'
listfile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/j2/culled_allSuperior.lst'
data = WSDS.Data(datafile=datafile, listfile=listfile)
raw = WSDS.RawData(listfile=listfile)
raw.locations = raw.get_locs(mode='latlong')
for ii in range(len(raw.locations)):
lon, lat = utils.project((raw.locations[ii, 1], raw.locations[ii, 0]), zone=16, letter='U')[2:]
raw.locations[ii, 1], raw.locations[ii, 0] = lon, lat
data.locations = raw.locations
save_path = 'C:/Users/eric/phd/ownCloud/Documents/Seminars/Seminar 3/Figures/Pseudosections/culled/'
# rmsites = [site for site in data.site_names if site[0] == 'e' or site[0] == 'd']
# data.remove_sites(rmsites)
# data.sort_sites(order='west-east')
rho = {site.name: utils.compute_rho(site)[0] for site in data.sites.values()}
pha = {site.name: utils.compute_phase(site)[0] for site in data.sites.values()}
rho_lim = [0, 5]
n_interp = 250
period = 14
import pyMT.data_structures as WSDS
import pyMT.utils as utils
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
# raw = WSDS.RawData(r'C:\Users\eric\Documents\MATLAB' +
# r'\MATLAB\Inversion\Regions\abi-gren\New\j2\allsites.lst')
datafile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Test_Models\dimensionality\synthCond.data'
raw = WSDS.Data(datafile)
site_bost = {}
for site in raw.sites.values():
bostick, depth = utils.compute_bost1D(site)[:2]
if 'TZXR' in site.components:
tzxr = site.data['TZXR']
tzyr = site.data['TZYR']
else:
tzxr = site.data['ZXXR'] * 0
tzyr = site.data['ZXYR'] * 0
# site_bost.update({site.name: {'bostick': bostick, 'depth': depth,
# 'Long': site.locations['Long'],
# 'Lat': site.locations['Lat'],
# 'TZXR': tzxr, 'TZYR': tzyr}})
site_bost.update({
site.name: {
'bostick': bostick,
'depth': depth,
'X': site.locations['Y'],
'Y': site.locations['X'],
'TZXR': tzxr,
setattr(data.sites[site].phase_tensors[ii], comp + '_error',
max_error)
# data.sites[site].errmap[comp] = error_map
# data.sites[site].apply_error_floor()
return data, smoothed
# C:\Users\eric\phd\ownCloud\data\Regions\MetalEarth\swayze\R2south_4\pt
# datafile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/swayze/R2south_4/pt/R2south_4d_Z_reset.dat'
# respfile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/swayze/R2south_4/pt/R2South_fine_fwd_Z.dat'
# listfile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/swayze/j2/R2south_4c.lst'
datafile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/swayze/' + \
'swz_cull1/finish/pt/swz_cull1i_Z_extended_periods.dat'
respfile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/swayze/swz_cull1/finish/swz_finish.dat'
listfile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/swayze/j2/swz_cull1.lst'
data = WSDS.Data(datafile=datafile, listfile=listfile)
resp = WSDS.Data(datafile=respfile, listfile=listfile)
raw = WSDS.RawData(listfile=listfile)
data.inv_type = 6
error_in_degrees = 3
site = '18-swz080m'
data, smoothed = regulate_errors(data,
raw,
multiplier=1.5,
fwidth=0.75,
sites=None)
components = ('PTXY', 'PTYX', 'PTXX', 'PTYY')
for comp in components[:2]:
phi_data = np.rad2deg(
np.arctan(
np.array([
########################################
# WESTERN SUPERIOR - DRYDEN
# model_file = r'C:\Users\eric\phd\ownCloud\data\Regions\wst\wsSC1\wsSC_final.model'
# base_data = r'C:\Users\eric\phd\ownCloud\data\Regions\wst\wsSC1\wsSC_final.dat'
# data_file = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/dryden/dry5/dry53.data'
# list_file = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/dryden/j2/dry5_3.lst'
# base_list = r'C:\Users\eric\phd\ownCloud\data\Regions\wst\j2\southcentral.lst'
# model_out = r'C:\Users\eric\phd\ownCloud\data\Regions\MetalEarth\dryden\wst2dry\wst2dry.model'
# data_out = r'C:\Users\eric\phd\ownCloud\data\Regions\MetalEarth\dryden\wst2dry.dat'
########################################
plot_it = 0
write_it = 1
azi = -0 # Rotate the non-base data back
mod = WSDS.Model(model_file)
data = WSDS.Data(datafile=data_file, listfile=list_file)
data.locations = data.get_locs(azi=-azi)
data.set_locs()
base_data = WSDS.Data(datafile=base_data, listfile=base_list)
for site in base_data.site_names:
if site in data.site_names:
x_diff = data.sites[site].locations['X'] - base_data.sites[
site].locations['X']
y_diff = data.sites[site].locations['Y'] - base_data.sites[
site].locations['Y']
break
data.locations[:, 0] -= x_diff
data.locations[:, 1] -= y_diff
file_format = 'wsinv3dmt'
depths_per_decade = (8, 10, 12, 14, 10)
x, y, z = (utils.edge2center(arr) for arr in (mod.dx, mod.dy, mod.dz))
if __name__ == '__main__':
# filename = 'F:/GJH/TNG&MTR-EDI/all.lst'
# filename = 'C:/users/eroots/phd/ownCloud/data/ArcMap/LegacyMT/ag_edi/ag/all.lst'
# filename = 'C:/Users/eric/Documents/MATLAB/MATLAB/Inversion/GJH/ForEric/TNG&MTR-EDI/all.lst'
# filename = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/j2/cull_allSuperior.data'
# listfile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/j2/culled_allSuperior.lst'
# out_path = 'C:/Users/eric/phd/ownCloud/Documents/Seminars/Seminar 3/Figures/PTs/'
filename = 'C:/Users/eroots/phd/ownCloud/data/Regions/MetalEarth/dryden/dry5/dry5_3.dat'
listfile = 'C:/Users/eroots/phd/ownCloud/data/Regions/MetalEarth/dryden/j2/dry5_3.lst'
out_path = 'C:/Users/eroots/phd/ownCloud/Documents/Dryden_paper/RoughFigures/PTs/'
out_file = 'dryden_PT_'
ext = '.png'
dpi = 600
save_fig = 1
cutoff_distance = 1000
data = WSDS.Data(filename, listfile=listfile)
raw = WSDS.RawData(listfile)
# data.locations = rawdata.get_locs(mode='latlong')
all_sites = deepcopy(data.site_names)
# Remove redunantly close points
for ii, site1 in enumerate(data.site_names):
for jj, site2 in enumerate(data.site_names):
dist = euclidean((data.locations[ii, 1], data.locations[ii, 0]),
(data.locations[jj, 1], data.locations[jj, 0]))
if dist < cutoff_distance and site1 in all_sites and (site1 !=
site2):
if site2 in all_sites:
all_sites.remove(site2)
rm_sites = [site for site in data.site_names if site not in all_sites]
data.remove_sites(sites=rm_sites)
import pyMT.data_structures as WSDS
import pyMT.utils as utils
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
import numpy as np
import e_colours.colourmaps as cm
cmap = cm.jet_plus(64)
# listfile = r'C:\Users\eric\phd\Kilauea\ConvertedEDIs\2018-517\allsites.lst'
# listfile = r'C:\Users\eric\phd\Kilauea\ConvertedEDIs\all\515-520.lst')
# listfile = r'C:\Users\eric\phd\Kilauea\ConvertedEDIs\all\allsites.lst'
# datafile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Test_Models\dimensionality\synthLayer.data'
# data = WSDS.RawData(listfile=listfile)
data = WSDS.Data(
datafile='C:/Users/eric/phd/Kilauea/stitched/1_day/Z/Kilauea_may_daily.data'
)
days = (501, 530)
hour_or_day = 1 # sets the interval in labels, so choose appropriately
n_interp = 300
cax_rho = [0, 4]
# data = WSDS.Data(datafile=datafile)
# rmsites = [site for site in data.site_names if site[0] == 'e' or site[0] == 'd']
# data.remove_sites(rmsites)
# data.sort_sites(order='west-east')
rho = {site.name: utils.compute_rho(site)[0] for site in data.sites.values()}
pha = {site.name: utils.compute_phase(site)[0] for site in data.sites.values()}
bost = {
site.name: utils.compute_bost1D(site)[0]
for site in data.sites.values()
}
depths = {
import numpy as np
import matplotlib.pyplot as plt
import pyMT.data_structures as WSDS
def gen_tiling(num_plots):
if num_plots < 3:
tiling = [num_plots, 1]
else:
s1 = np.floor(np.sqrt(num_plots))
s2 = np.ceil(num_plots / s1)
tiling = [int(s1), int(s2)]
return tiling
data = WSDS.Data(datafile='C:/Users/eroots/phd/ownCloud/data/Regions/afton/afton1/afton_cull1.dat',
listfile='C:/Users/eroots/phd/ownCloud/data/Regions/afton/j2/afton_cull1.lst')
plots_per_fig = 6
N = 45
bottom = 0
max_height = 4
plot_range = (-np.pi / 2, np.pi / 2)
width = np.pi / N
theta = np.linspace(plot_range[0], plot_range[1], N, endpoint=False)
figures = {}
p_idx = 0
tiling = gen_tiling(plots_per_fig)
for ii in range(int(np.ceil(data.NP / plots_per_fig))):
figures.update({ii: plt.figure()})
axes = []
def do_the_bosticks(listfile=None,
datafile=None,
comp=None,
N_interp=None,
z_bounds=None,
filter_width=1,
sites_slice=None):
# if listfile is None:
# print('Listfile is None')
# listfile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\abi-gren\New\j2\allsites.lst'
if datafile is None:
print('datafile is None')
datafile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\abi-gren\New\j2\allsites_1.data'
if listfile:
raw = WSDS.RawData(listfile=listfile)
else:
raw = WSDS.Data(datafile=datafile)
if not sites_slice:
sites_slice = ('p91011', 'p91009', 'e90005', 'e90001', 'g90003',
'p91003', 'p91006', 'p91006', 'p91008', 'p91012',
'e89001', 'e90002', 'g90004', 'g90002', 'g90006',
'p91013', 'p91014', 'p91015', 'p91016', 'p91017',
'p91019')
data = WSDS.Data(listfile=listfile, datafile=datafile)
NS = len(raw.site_names)
if not N_interp:
NZ_interp = 200
NXY_interp = 200
elif len(N_interp) == 1:
NZ_interp = N_interp
NXY_interp = N_interp
else:
NZ_interp = N_interp[0]
NXY_interp = N_interp[1]
locs = np.zeros((NS, 2))
all_depths = []
all_bosticks = []
for ii, site_name in enumerate(raw.site_names):
site = raw.sites[site_name]
locs[ii, 1] = data.sites[site.name].locations['X'] / 1000
locs[ii, 0] = data.sites[site.name].locations['Y'] / 1000
# locs[ii, 1] = raw.sites[site.name].locations['Long']
# locs[ii, 0] = raw.sites[site.name].locations['Lat']
bostick, depth = utils.compute_bost1D(site,
comp=comp,
filter_width=filter_width)[:2]
if any(np.isnan(depth)):
print(site_name)
sys.exit()
all_depths.append(depth)
all_bosticks.append(bostick)
all_depths_flat = np.array(utils.flatten_list(all_bosticks))
data_points = np.zeros((len(all_depths_flat), 3))
c = 0
for ii, ds in enumerate(all_depths):
for d in ds:
data_points[c, 0] = locs[ii, 0]
data_points[c, 1] = locs[ii, 1]
data_points[c, 2] = d
c += 1
try:
y = [data.sites[site].locations['X'] / 1000
for site in sites_slice] # if site in data.site_names]
x = [data.sites[site].locations['Y'] / 1000
for site in sites_slice] # if site in data.site_names]
# y = [raw.sites[site].locations['Long'] for site in sites_slice] # if site in data.site_names]
# x = [raw.sites[site].locations['Lat'] for site in sites_slice] # if site in data.site_names]
except KeyError as e:
if (type(sites_slice[0]) == str):
print('Site {} not found. Check your site list.'.format(e))
print(e)
sys.exit()
else:
x = sites_slice[:int(len(sites_slice) / 2)]
y = sites_slice[int(len(sites_slice) / 2):]
# This sorting is south-north. Might not make sense for west-east profiles.
x, y = ([y2 for (x2, y2) in sorted(zip(y, x))],
[x2 for (x2, y2) in sorted(zip(y, x))])
print(x, y)
print(raw.site_names)
print(data.periods)
print('Data file is {}'.format(datafile))
print('List file is {}'.format(listfile))
V = np.array(utils.flatten_list(all_bosticks))
if not z_bounds:
z_bounds = (0.1, 300)
zi = np.logspace(np.log10(z_bounds[0]), np.log10(z_bounds[1]), NZ_interp)
yi = np.linspace(min(y), max(y), NXY_interp)
xi = np.interp(yi, y, x)
X = np.ndarray.flatten(np.tile(xi, (NZ_interp, 1)), order='F')
Y = np.ndarray.flatten(np.tile(yi, (NZ_interp, 1)), order='F')
Z = np.ndarray.flatten(np.tile(zi, (1, NXY_interp)), order='F')
query_points = np.transpose(np.array((X, Y, Z)))
return data_points, query_points, V
import pyMT.data_structures as WSDS
import pyMT.utils as utils
import matplotlib.pyplot as plt
import numpy as np
listfile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\aften\j2\deci_4.lst'
raw = WSDS.RawData(listfile)
data = WSDS.Data(
r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\aften\deci0_4\deci_4_1.data',
listfile=listfile)
site = 'l6_351'
raw_site = raw.sites[site]
data_site = data.sites[site]
fig = plt.figure()
fig.add_subplot(121)
plt.errorbar(np.log10(data_site.periods),
np.sqrt(data_site.periods) * data_site.data['ZXYR'],
xerr=None,
yerr=np.sqrt(data_site.periods) * data_site.used_error['ZXYR'],
marker='o',
mec='k',
linestyle='')
plt.errorbar(np.log10(raw_site.periods),
np.sqrt(raw_site.periods) * raw_site.data['ZXYR'],
xerr=None,
yerr=None,
marker='o',
linestyle='')
error_map = np.zeros(data_site.data['ZXYR'].shape)
def main(in_file, out_file):
data = WSDS.Data(in_file)
# if data.inv_type == 5:
# print('Ignoring transfer function data...\n')
# data.inv_type = 1
data.write(outfile=out_file, file_format='modem')
def main_mesh(args, data=None):
if data is None:
datafile = WSIO.verify_input('Data file to use?', expected='read')
data = WSDS.Data(datafile=datafile)
avg_rho = np.mean([
utils.compute_rho(site, calc_comp='det', errtype='none')
for site in data.sites.values()
])
print('Average determinant apparent resistivity is {}'.format(avg_rho))
bg_resistivity = WSIO.verify_input('Background resistivity?',
expected=float,
default=avg_rho)
xmesh = utils.generate_lateral_mesh(site_locs=data.locations[:, 0])
ymesh = utils.generate_lateral_mesh(site_locs=data.locations[:, 1])
max_depth = min([500, utils.skin_depth(bg_resistivity, data.periods[-1])])
min_depth = min([1, utils.skin_depth(bg_resistivity, data.periods[0])])
print(
'Note: Default min and max depths are based on skin depth of lowest and highest periods.'
)
min_depth = WSIO.verify_input('Depth of first layer?',
expected=float,
default=min_depth)
max_depth = WSIO.verify_input('Depth of last layer?',
expected=float,
default=max_depth)
NZ = WSIO.verify_input('Total # of layers or # of layers per decade?',
expected='numtuple',
default=60)
zmesh = utils.generate_zmesh(min_depth=min_depth,
max_depth=max_depth,
NZ=NZ)
model = WSDS.Model()
model.dx = xmesh
model.dy = ymesh
model.dz = zmesh
model.vals = bg_resistivity * np.ones((model.nx, model.ny, model.nz))
if '-i' in args:
viewer = meshview(model, data)
viewer.show()
else:
nxpads = WSIO.verify_input('Number of pads in X direction',
expected=int,
default=5)
nypads = WSIO.verify_input('Number of pads in y direction',
expected=int,
default=5)
xpads = [model.xCS[0]]
ypads = [model.yCS[0]]
for ii in range(nxpads):
xpads.append(
WSIO.verify_input('Pad size',
expected=float,
default=xpads[ii] * 1.2))
xpads = xpads[1:]
for ii in range(nypads):
ypads.append(
WSIO.verify_input('Pad size',
expected=float,
default=ypads[ii] * 1.2))
ypads = ypads[:1]
model.dx = xpads[-1::-1] + model.dx + xpads
model.dy = ypads[-1::-1] + model.dy + ypads
modelname = WSIO.verify_input('Model name',
expected='write',
default='mod')
if '.model' not in modelname:
modelname = ''.join([modelname, '.model'])
model.write(modelname)
return
def gen_tiling(num_plots):
if num_plots < 3:
tiling = [num_plots, 1]
else:
s1 = np.floor(np.sqrt(num_plots))
s2 = np.ceil(num_plots / s1)
tiling = [int(s1), int(s2)]
return tiling
# data = WSDS.Data(datafile='C:/Users/eroots/phd/ownCloud/data/Regions/afton/afton1/afton_cull1.dat',
# listfile='C:/Users/eroots/phd/ownCloud/data/Regions/afton/j2/afton_cull1.lst')
# data = WSDS.Data(datafile='C:/Users/eroots/phd/ownCloud/data/Regions/MetalEarth/malartic/mal1/moresites/finish/mal5_all.dat',
# listfile='C:/Users/eroots/phd/ownCloud/data/Regions/MetalEarth/malartic/j2/mal5.lst')
# data = WSDS.RawData(listfile='C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/matheson/j2/mat_westLine.lst')
data = WSDS.Data('C:/Users/eroots/phd/ownCloud/data/Regions/afton/sorted_lines.dat')
# NP = len(data.master_period_list().keys())
NP = data.NP
plots_per_fig = 6
N = 45
bottom = 0
max_height = 4
plot_range = (-np.pi / 2, np.pi / 2)
width = np.pi / N
theta = np.linspace(plot_range[0], plot_range[1], N, endpoint=False)
figures = {}
figures_TF = {}
p_idx = 0
tiling = gen_tiling(plots_per_fig)
# for ii in range(int(np.ceil(data.NP / plots_per_fig))):