def read_files(self):
"""
read in the files to get appropriate information
"""
# --> read in model file
if self.model_fn is not None:
if os.path.isfile(self.model_fn) == True:
md_model = Model()
md_model.read_model_file(self.model_fn)
self.res_model = md_model.res_model
self.grid_east = md_model.grid_east / self.dscale
self.grid_north = md_model.grid_north / self.dscale
self.grid_z = md_model.grid_z / self.dscale
self.nodes_east = md_model.nodes_east / self.dscale
self.nodes_north = md_model.nodes_north / self.dscale
self.nodes_z = md_model.nodes_z / self.dscale
else:
raise mtex.MTpyError_file_handling(
'{0} does not exist, check path'.format(self.model_fn))
# --> read in data file to get station locations
if self.data_fn is not None:
if os.path.isfile(self.data_fn) == True:
md_data = Data()
md_data.read_data_file(self.data_fn)
self.station_east = md_data.station_locations.rel_east / self.dscale
self.station_north = md_data.station_locations.rel_north / self.dscale
self.station_elev = md_data.station_locations.elev / self.dscale
self.station_names = md_data.station_locations.station
else:
print 'Could not find data file {0}'.format(self.data_fn)
def test_read_gocad_sgrid_file(self):
if not os.path.isdir(self._model_dir):
self._model_dir = None
output_fn = os.path.basename(self._model_fn)
# read data file to get centre position
dObj = Data()
dObj.read_data_file(data_fn=self._data_fn)
# create a model object using the data object and read in gocad sgrid file
mObj = Model(data_obj=dObj, save_path=self._output_dir)
mObj.read_gocad_sgrid_file(self._sgrid_fn)
mObj.write_model_file()
output_data_file = os.path.normpath(
os.path.join(self._output_dir, output_fn))
self.assertTrue(os.path.isfile(output_data_file),
"output data file not found")
expected_data_file = os.path.normpath(self._model_fn_old_z_mesh)
self.assertTrue(
os.path.isfile(expected_data_file),
"Ref output data file does not exist, nothing to compare with")
is_identical, msg = diff_files(output_data_file, expected_data_file)
print(msg)
self.assertTrue(
is_identical,
"The output file is not the same with the baseline file.")
def _read_model_data(self):
"""
read in the files to get appropriate information
"""
# --> read in model file
if self.model_fn is not None and os.path.isfile(self.model_fn):
md_model = Model()
md_model.read_model_file(self.model_fn)
self.res_model = md_model.res_model
self.grid_east = md_model.grid_east / self.dscale
self.grid_north = md_model.grid_north / self.dscale
self.grid_z = md_model.grid_z / self.dscale
self.nodes_east = md_model.nodes_east / self.dscale
self.nodes_north = md_model.nodes_north / self.dscale
self.nodes_z = md_model.nodes_z / self.dscale
else:
raise Exception('Error with the Model file: %s. Please check.' % (self.model_fn))
# --> Optionally: read in data file to get station locations
if self.data_fn is not None and os.path.isfile(self.data_fn):
md_data = Data()
md_data.read_data_file(self.data_fn)
self.station_east = md_data.station_locations[
'rel_east'] / self.dscale # convert meters
self.station_north = md_data.station_locations[
'rel_north'] / self.dscale
self.station_names = md_data.station_locations['station']
else:
print(('Problem with the optional Data file: %s. Please check.' % self.data_fn))
total_horizontal_slices = self.grid_z.shape[0]
print(("Total Number of H-slices=", total_horizontal_slices))
return total_horizontal_slices
def main(data_file, model_file, output_file, source_proj=None):
"""
Generate an output netcdf file from data_file and model_file
:param data_file: modem.dat
:param model_file: modem.rho
:param output_file: output.nc
:param source_proj: None by defult. The UTM zone infered from the input non-uniform grid parameters
:return:
"""
# Define Data and Model Paths
data = Data()
data.read_data_file(data_fn=data_file)
# create a model object using the data object and read in model data
model = Model(data_obj=data)
model.read_model_file(model_fn=model_file)
center = data.center_point
if source_proj is None:
zone_number, is_northern, utm_zone = gis_tools.get_utm_zone(
center.lat.item(), center.lon.item())
#source_proj = Proj('+proj=utm +zone=%d +%s +datum=%s' % (zone_number, 'north' if is_northern else 'south', 'WGS84'))
epsg_code = gis_tools.get_epsg(center.lat.item(), center.lon.item())
print("Input data epsg code is infered as ", epsg_code)
else:
epsg_code = source_proj # integer
source_proj = Proj(init='epsg:' + str(epsg_code))
resistivity_data = {
'x':
center.east.item() + (model.grid_east[1:] + model.grid_east[:-1]) / 2,
'y':
center.north.item() +
(model.grid_north[1:] + model.grid_north[:-1]) / 2,
'z': (model.grid_z[1:] + model.grid_z[:-1]) / 2,
'resistivity':
np.transpose(model.res_model, axes=(2, 0, 1))
}
grid_proj = Proj(
init='epsg:4326') # output grid Coordinate systems: 4326, 4283, 3112
grid_proj = Proj(
init='epsg:4283') # output grid Coordinate system 4326, 4283, 3112
grid_proj = Proj(
init='epsg:3112') # output grid Coordinate system 4326, 4283, 3112
result = interpolate(resistivity_data, source_proj, grid_proj, center,
median_spacing(model.grid_east),
median_spacing(model.grid_north))
nc.write_resistivity_grid(output_file,
grid_proj,
result['latitude'],
result['longitude'],
result['depth'],
result['resistivity'],
z_label='depth')
def test_write_gocad_sgrid_file(self):
if not os.path.exists(self._sgrid_fn):
self._sgrid_fn = None
output_fn = os.path.basename(self._sgrid_fn)
# read data file to get centre position
dObj = Data()
dObj.read_data_file(data_fn=self._data_fn)
# get centre coordinates
centre = np.array([0., 0., 0.])
centre[0] = dObj.center_point['east']
centre[1] = dObj.center_point['north']
# create a model object using the data object and read in gocad sgrid file
mObj = Model(data_obj=dObj)
mObj.read_model_file(model_fn=self._model_fn)
mObj.save_path = self._output_dir
mObj.write_gocad_sgrid_file(origin=centre,
fn=os.path.join(self._output_dir,
output_fn[:-3]))
output_data_file = os.path.normpath(
os.path.join(self._output_dir, output_fn))
self.assertTrue(os.path.isfile(output_data_file),
"output data file not found")
expected_data_file = os.path.normpath(self._sgrid_fn)
self.assertTrue(
os.path.isfile(expected_data_file),
"Ref output data file does not exist, nothing to compare with")
is_identical, msg = diff_files(output_data_file, expected_data_file)
print(msg)
self.assertTrue(
is_identical,
"The output file is not the same with the baseline file.")
class PlotPTMaps(MTEllipse):
"""
Plot phase tensor maps including residual pt if response file is input.
:Plot only data for one period: ::
>>> import mtpy.modeling.ws3dinv as ws
>>> dfn = r"/home/MT/ws3dinv/Inv1/WSDataFile.dat"
>>> ptm = ws.PlotPTMaps(data_fn=dfn, plot_period_list=[0])
:Plot data and model response: ::
>>> import mtpy.modeling.ws3dinv as ws
>>> dfn = r"/home/MT/ws3dinv/Inv1/WSDataFile.dat"
>>> rfn = r"/home/MT/ws3dinv/Inv1/Test_resp.00"
>>> mfn = r"/home/MT/ws3dinv/Inv1/Test_model.00"
>>> ptm = ws.PlotPTMaps(data_fn=dfn, resp_fn=rfn, model_fn=mfn,
>>> ... plot_period_list=[0])
>>> # adjust colorbar
>>> ptm.cb_res_pad = 1.25
>>> ptm.redraw_plot()
========================== ================================================
Attributes Description
========================== ================================================
cb_pt_pad percentage from top of axes to place pt
color bar. *default* is .90
cb_res_pad percentage from bottom of axes to place
resistivity color bar. *default* is 1.2
cb_residual_tick_step tick step for residual pt. *default* is 3
cb_tick_step tick step for phase tensor color bar,
*default* is 45
data np.ndarray(n_station, n_periods, 2, 2)
impedance tensors for station data
data_fn full path to data fle
dscale scaling parameter depending on map_scale
ellipse_cmap color map for pt ellipses. *default* is
mt_bl2gr2rd
ellipse_colorby [ 'skew' | 'skew_seg' | 'phimin' | 'phimax'|
'phidet' | 'ellipticity' ] parameter to color
ellipses by. *default* is 'phimin'
ellipse_range (min, max, step) min and max of colormap, need
to input step if plotting skew_seg
ellipse_size relative size of ellipses in map_scale
ew_limits limits of plot in e-w direction in map_scale
units. *default* is None, scales to station
area
fig_aspect aspect of figure. *default* is 1
fig_dpi resolution in dots-per-inch. *default* is 300
fig_list list of matplotlib.figure instances for each
figure plotted.
fig_size [width, height] in inches of figure window
*default* is [6, 6]
font_size font size of ticklabels, axes labels are
font_size+2. *default* is 7
grid_east relative location of grid nodes in e-w direction
in map_scale units
grid_north relative location of grid nodes in n-s direction
in map_scale units
grid_z relative location of grid nodes in z direction
in map_scale units
model_fn full path to initial file
map_scale [ 'km' | 'm' ] distance units of map.
*default* is km
mesh_east np.meshgrid(grid_east, grid_north, indexing='ij')
mesh_north np.meshgrid(grid_east, grid_north, indexing='ij')
model_fn full path to model file
nodes_east relative distance betwen nodes in e-w direction
in map_scale units
nodes_north relative distance betwen nodes in n-s direction
in map_scale units
nodes_z relative distance betwen nodes in z direction
in map_scale units
ns_limits (min, max) limits of plot in n-s direction
*default* is None, viewing area is station area
pad_east padding from extreme stations in east direction
pad_north padding from extreme stations in north direction
period_list list of periods from data
plot_grid [ 'y' | 'n' ] 'y' to plot grid lines
*default* is 'n'
plot_period_list list of period index values to plot
*default* is None
plot_yn ['y' | 'n' ] 'y' to plot on instantiation
*default* is 'y'
res_cmap colormap for resisitivity values.
*default* is 'jet_r'
res_limits (min, max) resistivity limits in log scale
*default* is (0, 4)
res_model np.ndarray(n_north, n_east, n_vertical) of
model resistivity values in linear scale
residual_cmap color map for pt residuals.
*default* is 'mt_wh2or'
resp np.ndarray(n_stations, n_periods, 2, 2)
impedance tensors for model response
resp_fn full path to response file
save_path directory to save figures to
save_plots [ 'y' | 'n' ] 'y' to save plots to save_path
station_east location of stations in east direction in
map_scale units
station_fn full path to station locations file
station_names station names
station_north location of station in north direction in
map_scale units
subplot_bottom distance between axes and bottom of figure window
subplot_left distance between axes and left of figure window
subplot_right distance between axes and right of figure window
subplot_top distance between axes and top of figure window
title titiel of plot *default* is depth of slice
xminorticks location of xminorticks
yminorticks location of yminorticks
========================== ================================================
"""
def __init__(self, data_fn=None, resp_fn=None, model_fn=None, **kwargs):
# MTEllipse.__init__(self, **kwargs)
super(PlotPTMaps, self).__init__(**kwargs)
self.model_fn = model_fn
self.data_fn = data_fn
self.resp_fn = resp_fn
self.save_path = kwargs.pop('save_path', None)
if self.model_fn is not None and self.save_path is None:
self.save_path = os.path.dirname(self.model_fn)
elif self.model_fn is not None and self.save_path is None:
self.save_path = os.path.dirname(self.model_fn)
if self.save_path is not None:
if not os.path.exists(self.save_path):
os.mkdir(self.save_path)
self.save_plots = kwargs.pop('save_plots', 'y')
self.plot_period_list = kwargs.pop('plot_period_list', None)
self.period_dict = None
self.map_scale = kwargs.pop('map_scale', 'km')
# make map scale
if self.map_scale == 'km':
self.dscale = 1000.
elif self.map_scale == 'm':
self.dscale = 1.
self.ew_limits = kwargs.pop('ew_limits', None)
self.ns_limits = kwargs.pop('ns_limits', None)
self.pad_east = kwargs.pop('pad_east', 2000)
self.pad_north = kwargs.pop('pad_north', 2000)
self.plot_grid = kwargs.pop('plot_grid', 'n')
self.fig_num = kwargs.pop('fig_num', 1)
self.fig_size = kwargs.pop('fig_size', [6, 6])
self.fig_dpi = kwargs.pop('dpi', 300)
self.fig_aspect = kwargs.pop('fig_aspect', 1)
self.title = kwargs.pop('title', 'on')
self.fig_list = []
self.xminorticks = kwargs.pop('xminorticks', 1000)
self.yminorticks = kwargs.pop('yminorticks', 1000)
self.residual_cmap = kwargs.pop('residual_cmap', 'mt_wh2or')
self.font_size = kwargs.pop('font_size', 7)
self.cb_tick_step = kwargs.pop('cb_tick_step', 45)
self.cb_residual_tick_step = kwargs.pop('cb_residual_tick_step', 3)
self.cb_pt_pad = kwargs.pop('cb_pt_pad', 1.2)
self.cb_res_pad = kwargs.pop('cb_res_pad', .5)
self.res_limits = kwargs.pop('res_limits', (0, 4))
self.res_cmap = kwargs.pop('res_cmap', 'jet_r')
# --> set the ellipse properties -------------------
self._ellipse_dict = kwargs.pop('ellipse_dict', {'size': 2})
self._read_ellipse_dict(self._ellipse_dict)
self.subplot_right = .99
self.subplot_left = .085
self.subplot_top = .92
self.subplot_bottom = .1
self.subplot_hspace = .2
self.subplot_wspace = .05
self.data_obj = None
self.resp_obj = None
self.model_obj = None
self.period_list = None
self.pt_data_arr = None
self.pt_resp_arr = None
self.pt_resid_arr = None
self.plot_yn = kwargs.pop('plot_yn', 'y')
if self.plot_yn == 'y':
self.plot()
def _read_files(self):
"""
get information from files
"""
# --> read in data file
self.data_obj = Data()
self.data_obj.read_data_file(self.data_fn)
# --> read response file
if self.resp_fn is not None:
self.resp_obj = Data()
self.resp_obj.read_data_file(self.resp_fn)
# --> read mode file
if self.model_fn is not None:
self.model_obj = Model()
self.model_obj.read_model_file(self.model_fn)
self._get_plot_period_list()
self._get_pt()
def _get_plot_period_list(self):
"""
get periods to plot from input or data file
"""
# --> get period list to plot
if self.plot_period_list is None:
self.plot_period_list = self.data_obj.period_list
else:
if type(self.plot_period_list) is list:
# check if entries are index values or actual periods
if type(self.plot_period_list[0]) is int:
self.plot_period_list = [self.data_obj.period_list[ii]
for ii in self.plot_period_list]
else:
pass
elif type(self.plot_period_list) is int:
self.plot_period_list = self.data_obj.period_list[self.plot_period_list]
elif type(self.plot_period_list) is float:
self.plot_period_list = [self.plot_period_list]
self.period_dict = dict([(key, value) for value, key in
enumerate(self.data_obj.period_list)])
def _get_pt(self):
"""
put pt parameters into something useful for plotting
"""
ns = len(self.data_obj.mt_dict.keys())
nf = len(self.data_obj.period_list)
data_pt_arr = np.zeros((nf, ns), dtype=[('phimin', np.float),
('phimax', np.float),
('skew', np.float),
('azimuth', np.float),
('east', np.float),
('north', np.float)])
if self.resp_fn is not None:
model_pt_arr = np.zeros((nf, ns), dtype=[('phimin', np.float),
('phimax', np.float),
('skew', np.float),
('azimuth', np.float),
('east', np.float),
('north', np.float)])
res_pt_arr = np.zeros((nf, ns), dtype=[('phimin', np.float),
('phimax', np.float),
('skew', np.float),
('azimuth', np.float),
('east', np.float),
('north', np.float),
('geometric_mean', np.float)])
for ii, key in enumerate(self.data_obj.mt_dict.keys()):
east = self.data_obj.mt_dict[key].grid_east / self.dscale
north = self.data_obj.mt_dict[key].grid_north / self.dscale
dpt = self.data_obj.mt_dict[key].pt
data_pt_arr[:, ii]['east'] = east
data_pt_arr[:, ii]['north'] = north
data_pt_arr[:, ii]['phimin'] = dpt.phimin[0]
data_pt_arr[:, ii]['phimax'] = dpt.phimax[0]
data_pt_arr[:, ii]['azimuth'] = dpt.azimuth[0]
data_pt_arr[:, ii]['skew'] = dpt.beta[0]
if self.resp_fn is not None:
mpt = self.resp_obj.mt_dict[key].pt
try:
rpt = mtpt.ResidualPhaseTensor(pt_object1=dpt,
pt_object2=mpt)
rpt = rpt.residual_pt
res_pt_arr[:, ii]['east'] = east
res_pt_arr[:, ii]['north'] = north
res_pt_arr[:, ii]['phimin'] = rpt.phimin[0]
res_pt_arr[:, ii]['phimax'] = rpt.phimax[0]
res_pt_arr[:, ii]['azimuth'] = rpt.azimuth[0]
res_pt_arr[:, ii]['skew'] = rpt.beta[0]
res_pt_arr[:, ii]['geometric_mean'] = np.sqrt(abs(rpt.phimin[0] * \
rpt.phimax[0]))
except mtex.MTpyError_PT:
print key, dpt.pt.shape, mpt.pt.shape
model_pt_arr[:, ii]['east'] = east
model_pt_arr[:, ii]['north'] = north
model_pt_arr[:, ii]['phimin'] = mpt.phimin[0]
model_pt_arr[:, ii]['phimax'] = mpt.phimax[0]
model_pt_arr[:, ii]['azimuth'] = mpt.azimuth[0]
model_pt_arr[:, ii]['skew'] = mpt.beta[0]
# make these attributes
self.pt_data_arr = data_pt_arr
if self.resp_fn is not None:
self.pt_resp_arr = model_pt_arr
self.pt_resid_arr = res_pt_arr
def plot(self):
"""
plot phase tensor maps for data and or response, each figure is of a
different period. If response is input a third column is added which is
the residual phase tensor showing where the model is not fitting the data
well. The data is plotted in km.
"""
# --> read in data first
if self.data_obj is None:
self._read_files()
# set plot properties
plt.rcParams['font.size'] = self.font_size
plt.rcParams['figure.subplot.left'] = self.subplot_left
plt.rcParams['figure.subplot.right'] = self.subplot_right
plt.rcParams['figure.subplot.bottom'] = self.subplot_bottom
plt.rcParams['figure.subplot.top'] = self.subplot_top
font_dict = {'size': self.font_size + 2, 'weight': 'bold'}
# make a grid of subplots
gs = gridspec.GridSpec(1, 3, hspace=self.subplot_hspace,
wspace=self.subplot_wspace)
# set some parameters for the colorbar
ckmin = float(self.ellipse_range[0])
ckmax = float(self.ellipse_range[1])
try:
ckstep = float(self.ellipse_range[2])
except IndexError:
if self.ellipse_cmap == 'mt_seg_bl2wh2rd':
raise ValueError('Need to input range as (min, max, step)')
else:
ckstep = 3
bounds = np.arange(ckmin, ckmax + ckstep, ckstep)
# set plot limits to be the station area
if self.ew_limits == None:
east_min = self.data_obj.data_array['rel_east'].min() - \
self.pad_east
east_max = self.data_obj.data_array['rel_east'].max() + \
self.pad_east
self.ew_limits = (east_min / self.dscale, east_max / self.dscale)
if self.ns_limits == None:
north_min = self.data_obj.data_array['rel_north'].min() - \
self.pad_north
north_max = self.data_obj.data_array['rel_north'].max() + \
self.pad_north
self.ns_limits = (north_min / self.dscale, north_max / self.dscale)
# -------------plot phase tensors------------------------------------
for ff, per in enumerate(self.plot_period_list):
data_ii = self.period_dict[per]
print 'Plotting Period: {0:.5g}'.format(per)
fig = plt.figure('{0:.5g}'.format(per), figsize=self.fig_size,
dpi=self.fig_dpi)
fig.clf()
if self.resp_fn is not None:
axd = fig.add_subplot(gs[0, 0], aspect='equal')
axm = fig.add_subplot(gs[0, 1], aspect='equal')
axr = fig.add_subplot(gs[0, 2], aspect='equal')
ax_list = [axd, axm, axr]
else:
axd = fig.add_subplot(gs[0, :], aspect='equal')
ax_list = [axd]
# plot model below the phase tensors
if self.model_fn is not None:
approx_depth, d_index = ws.estimate_skin_depth(self.model_obj.res_model.copy(),
self.model_obj.grid_z.copy() / self.dscale,
per,
dscale=self.dscale)
# need to add an extra row and column to east and north to make sure
# all is plotted see pcolor for details.
plot_east = np.append(self.model_obj.grid_east,
self.model_obj.grid_east[-1] * 1.25) / \
self.dscale
plot_north = np.append(self.model_obj.grid_north,
self.model_obj.grid_north[-1] * 1.25) / \
self.dscale
# make a mesh grid for plotting
# the 'ij' makes sure the resulting grid is in east, north
self.mesh_east, self.mesh_north = np.meshgrid(plot_east,
plot_north,
indexing='ij')
for ax in ax_list:
plot_res = np.log10(self.model_obj.res_model[:, :, d_index].T)
ax.pcolormesh(self.mesh_east,
self.mesh_north,
plot_res,
cmap=self.res_cmap,
vmin=self.res_limits[0],
vmax=self.res_limits[1])
# --> plot data phase tensors
for pt in self.pt_data_arr[data_ii]:
eheight = pt['phimin'] / \
self.pt_data_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ewidth = pt['phimax'] / \
self.pt_data_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ellipse = Ellipse((pt['east'],
pt['north']),
width=ewidth,
height=eheight,
angle=90 - pt['azimuth'])
# get ellipse color
if self.ellipse_cmap.find('seg') > 0:
ellipse.set_facecolor(mtcl.get_plot_color(pt[self.ellipse_colorby],
self.ellipse_colorby,
self.ellipse_cmap,
ckmin,
ckmax,
bounds=bounds))
else:
ellipse.set_facecolor(mtcl.get_plot_color(pt[self.ellipse_colorby],
self.ellipse_colorby,
self.ellipse_cmap,
ckmin,
ckmax))
axd.add_artist(ellipse)
# -----------plot response phase tensors---------------
if self.resp_fn is not None:
rcmin = np.floor(self.pt_resid_arr['geometric_mean'].min())
rcmax = np.floor(self.pt_resid_arr['geometric_mean'].max())
for mpt, rpt in zip(self.pt_resp_arr[data_ii],
self.pt_resid_arr[data_ii]):
eheight = mpt['phimin'] / \
self.pt_resp_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ewidth = mpt['phimax'] / \
self.pt_resp_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ellipsem = Ellipse((mpt['east'],
mpt['north']),
width=ewidth,
height=eheight,
angle=90 - mpt['azimuth'])
# get ellipse color
if self.ellipse_cmap.find('seg') > 0:
ellipsem.set_facecolor(mtcl.get_plot_color(mpt[self.ellipse_colorby],
self.ellipse_colorby,
self.ellipse_cmap,
ckmin,
ckmax,
bounds=bounds))
else:
ellipsem.set_facecolor(mtcl.get_plot_color(mpt[self.ellipse_colorby],
self.ellipse_colorby,
self.ellipse_cmap,
ckmin,
ckmax))
axm.add_artist(ellipsem)
# -----------plot residual phase tensors---------------
eheight = rpt['phimin'] / \
self.pt_resid_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ewidth = rpt['phimax'] / \
self.pt_resid_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ellipser = Ellipse((rpt['east'],
rpt['north']),
width=ewidth,
height=eheight,
angle=rpt['azimuth'])
# get ellipse color
rpt_color = np.sqrt(abs(rpt['phimin'] * rpt['phimax']))
if self.ellipse_cmap.find('seg') > 0:
ellipser.set_facecolor(mtcl.get_plot_color(rpt_color,
'geometric_mean',
self.residual_cmap,
ckmin,
ckmax,
bounds=bounds))
else:
ellipser.set_facecolor(mtcl.get_plot_color(rpt_color,
'geometric_mean',
self.residual_cmap,
ckmin,
ckmax))
axr.add_artist(ellipser)
# --> set axes properties
# data
axd.set_xlim(self.ew_limits)
axd.set_ylim(self.ns_limits)
axd.set_xlabel('Easting ({0})'.format(self.map_scale),
fontdict=font_dict)
axd.set_ylabel('Northing ({0})'.format(self.map_scale),
fontdict=font_dict)
# make a colorbar for phase tensors
# bb = axd.axes.get_position().bounds
bb = axd.get_position().bounds
y1 = .25 * (2 + (self.ns_limits[1] - self.ns_limits[0]) /
(self.ew_limits[1] - self.ew_limits[0]))
cb_location = (3.35 * bb[2] / 5 + bb[0],
y1 * self.cb_pt_pad, .295 * bb[2], .02)
cbaxd = fig.add_axes(cb_location)
cbd = mcb.ColorbarBase(cbaxd,
cmap=mtcl.cmapdict[self.ellipse_cmap],
norm=Normalize(vmin=ckmin,
vmax=ckmax),
orientation='horizontal')
cbd.ax.xaxis.set_label_position('top')
cbd.ax.xaxis.set_label_coords(.5, 1.75)
cbd.set_label(mtplottools.ckdict[self.ellipse_colorby])
cbd.set_ticks(np.arange(ckmin, ckmax + self.cb_tick_step,
self.cb_tick_step))
axd.text(self.ew_limits[0] * .95,
self.ns_limits[1] * .95,
'Data',
horizontalalignment='left',
verticalalignment='top',
bbox={'facecolor': 'white'},
fontdict={'size': self.font_size + 1})
# Model and residual
if self.resp_fn is not None:
for aa, ax in enumerate([axm, axr]):
ax.set_xlim(self.ew_limits)
ax.set_ylim(self.ns_limits)
ax.set_xlabel('Easting ({0})'.format(self.map_scale),
fontdict=font_dict)
plt.setp(ax.yaxis.get_ticklabels(), visible=False)
# make a colorbar ontop of axis
bb = ax.axes.get_position().bounds
y1 = .25 * (2 + (self.ns_limits[1] - self.ns_limits[0]) /
(self.ew_limits[1] - self.ew_limits[0]))
cb_location = (3.35 * bb[2] / 5 + bb[0],
y1 * self.cb_pt_pad, .295 * bb[2], .02)
cbax = fig.add_axes(cb_location)
if aa == 0:
cb = mcb.ColorbarBase(cbax,
cmap=mtcl.cmapdict[self.ellipse_cmap],
norm=Normalize(vmin=ckmin,
vmax=ckmax),
orientation='horizontal')
cb.ax.xaxis.set_label_position('top')
cb.ax.xaxis.set_label_coords(.5, 1.75)
cb.set_label(mtplottools.ckdict[self.ellipse_colorby])
cb.set_ticks(np.arange(ckmin, ckmax + self.cb_tick_step,
self.cb_tick_step))
ax.text(self.ew_limits[0] * .95,
self.ns_limits[1] * .95,
'Model',
horizontalalignment='left',
verticalalignment='top',
bbox={'facecolor': 'white'},
fontdict={'size': self.font_size + 1})
else:
cb = mcb.ColorbarBase(cbax,
cmap=mtcl.cmapdict[self.residual_cmap],
norm=Normalize(vmin=rcmin,
vmax=rcmax),
orientation='horizontal')
cb.ax.xaxis.set_label_position('top')
cb.ax.xaxis.set_label_coords(.5, 1.75)
cb.set_label(r"$\sqrt{\Phi_{min} \Phi_{max}}$")
cb_ticks = [rcmin, (rcmax - rcmin) / 2, rcmax]
cb.set_ticks(cb_ticks)
ax.text(self.ew_limits[0] * .95,
self.ns_limits[1] * .95,
'Residual',
horizontalalignment='left',
verticalalignment='top',
bbox={'facecolor': 'white'},
fontdict={'size': self.font_size + 1})
if self.model_fn is not None:
for ax in ax_list:
ax.tick_params(direction='out')
bb = ax.axes.get_position().bounds
y1 = .25 * (2 - (self.ns_limits[1] - self.ns_limits[0]) /
(self.ew_limits[1] - self.ew_limits[0]))
cb_position = (3.0 * bb[2] / 5 + bb[0],
y1 * self.cb_res_pad, .35 * bb[2], .02)
cbax = fig.add_axes(cb_position)
cb = mcb.ColorbarBase(cbax,
cmap=self.res_cmap,
norm=Normalize(vmin=self.res_limits[0],
vmax=self.res_limits[1]),
orientation='horizontal')
cb.ax.xaxis.set_label_position('top')
cb.ax.xaxis.set_label_coords(.5, 1.5)
cb.set_label('Resistivity ($\Omega \cdot$m)')
cb_ticks = np.arange(np.floor(self.res_limits[0]),
np.ceil(self.res_limits[1] + 1), 1)
cb.set_ticks(cb_ticks)
cb.set_ticklabels([mtplottools.labeldict[ctk] for ctk in cb_ticks])
plt.show()
self.fig_list.append(fig)
def redraw_plot(self):
"""
redraw plot if parameters were changed
use this function if you updated some attributes and want to re-plot.
:Example: ::
>>> # change the color and marker of the xy components
>>> import mtpy.modeling.occam2d as occam2d
>>> ocd = occam2d.Occam2DData(r"/home/occam2d/Data.dat")
>>> p1 = ocd.plotAllResponses()
>>> #change line width
>>> p1.lw = 2
>>> p1.redraw_plot()
"""
for fig in self.fig_list:
plt.close(fig)
self.plot()
def save_figure(self, save_path=None, fig_dpi=None, file_format='pdf',
orientation='landscape', close_fig='y'):
"""
save_figure will save the figure to save_fn.
Arguments:
-----------
**save_fn** : string
full path to save figure to, can be input as
* directory path -> the directory path to save to
in which the file will be saved as
save_fn/station_name_PhaseTensor.file_format
* full path -> file will be save to the given
path. If you use this option then the format
will be assumed to be provided by the path
**file_format** : [ pdf | eps | jpg | png | svg ]
file type of saved figure pdf,svg,eps...
**orientation** : [ landscape | portrait ]
orientation in which the file will be saved
*default* is portrait
**fig_dpi** : int
The resolution in dots-per-inch the file will be
saved. If None then the dpi will be that at
which the figure was made. I don't think that
it can be larger than dpi of the figure.
**close_plot** : [ y | n ]
* 'y' will close the plot after saving.
* 'n' will leave plot open
:Example: ::
>>> # to save plot as jpg
>>> import mtpy.modeling.occam2d as occam2d
>>> dfn = r"/home/occam2d/Inv1/data.dat"
>>> ocd = occam2d.Occam2DData(dfn)
>>> ps1 = ocd.plotPseudoSection()
>>> ps1.save_plot(r'/home/MT/figures', file_format='jpg')
"""
if fig_dpi is None:
fig_dpi = self.fig_dpi
if not os.path.isdir(save_path):
try:
os.mkdir(save_path)
except:
raise IOError('Need to input a correct directory path')
for fig in self.fig_list:
per = fig.canvas.get_window_title()
save_fn = os.path.join(save_path, 'PT_DepthSlice_{0}s.{1}'.format(
per, file_format))
fig.savefig(save_fn, dpi=fig_dpi, format=file_format,
orientation=orientation, bbox_inches='tight')
if close_fig == 'y':
plt.close(fig)
else:
pass
self.fig_fn = save_fn
print ('Saved figure to: ' + self.fig_fn)
def modem2geotiff(data_file, model_file, output_file, source_proj=None):
"""
Generate an output geotiff file from a modems.dat file and related modems.rho model file
:param data_file: modem.dat
:param model_file: modem.rho
:param output_file: output.tif
:param source_proj: None by defult. The UTM zone infered from the input non-uniform grid parameters
:return:
"""
# Define Data and Model Paths
data = Data()
data.read_data_file(data_fn=data_file)
# create a model object using the data object and read in model data
model = Model(data_obj=data)
model.read_model_file(model_fn=model_file)
center = data.center_point
if source_proj is None:
zone_number, is_northern, utm_zone = gis_tools.get_utm_zone(
center.lat.item(), center.lon.item())
#source_proj = Proj('+proj=utm +zone=%d +%s +datum=%s' % (zone_number, 'north' if is_northern else 'south', 'WGS84'))
epsg_code = gis_tools.get_epsg(center.lat.item(), center.lon.item())
print("Input data epsg code is infered as ", epsg_code)
else:
epsg_code = source_proj # integer
source_proj = Proj(init='epsg:' + str(epsg_code))
resistivity_data = {
'x':
center.east.item() + (model.grid_east[1:] + model.grid_east[:-1]) / 2,
'y':
center.north.item() +
(model.grid_north[1:] + model.grid_north[:-1]) / 2,
'z': (model.grid_z[1:] + model.grid_z[:-1]) / 2,
'resistivity':
np.transpose(model.res_model, axes=(2, 0, 1))
}
grid_proj = Proj(
init='epsg:4326') # output grid Coordinate systems: 4326, 4283, 3112
# grid_proj = Proj(init='epsg:4283') # output grid Coordinate system 4326, 4283, 3112
# grid_proj = Proj(init='epsg:3112') # output grid Coordinate system 4326, 4283, 3112
result = modem2nc.interpolate(resistivity_data, source_proj, grid_proj,
center,
modem2nc.median_spacing(model.grid_east),
modem2nc.median_spacing(model.grid_north))
# nc.write_resistivity_grid(output_file, grid_proj,
# result['latitude'], result['longitude'], result['depth'],
# result['resistivity'], z_label='depth')
print("result['latitude'] ==", result['latitude'])
print("result['longitude'] ==", result['longitude'])
print("result['depth'] ==", result['depth'])
origin = (result['latitude'][0], result['longitude'][0])
pixel_width = result['longitude'][1] - result['longitude'][0]
pixel_height = result['latitude'][1] - result['latitude'][0]
# write the depth_index
depth_index = 1
resis_data = result['resistivity'][depth_index, :, :]
resis_data2 = resis_data[::
-1] # flipped upside down to get geotiff mapped correctly.
array2geotiff_writer(output_file, origin, pixel_width, pixel_height,
resis_data2)
return output_file
class PlotPTMaps(mtplottools.MTEllipse):
"""
Plot phase tensor maps including residual pt if response file is input.
:Plot only data for one period: ::
>>> import mtpy.modeling.ws3dinv as ws
>>> dfn = r"/home/MT/ws3dinv/Inv1/WSDataFile.dat"
>>> ptm = ws.PlotPTMaps(data_fn=dfn, plot_period_list=[0])
:Plot data and model response: ::
>>> import mtpy.modeling.ws3dinv as ws
>>> dfn = r"/home/MT/ws3dinv/Inv1/WSDataFile.dat"
>>> rfn = r"/home/MT/ws3dinv/Inv1/Test_resp.00"
>>> mfn = r"/home/MT/ws3dinv/Inv1/Test_model.00"
>>> ptm = ws.PlotPTMaps(data_fn=dfn, resp_fn=rfn, model_fn=mfn,
>>> ... plot_period_list=[0])
>>> # adjust colorbar
>>> ptm.cb_res_pad = 1.25
>>> ptm.redraw_plot()
========================== ================================================
Attributes Description
========================== ================================================
cb_pt_pad percentage from top of axes to place pt
color bar. *default* is .90
cb_res_pad percentage from bottom of axes to place
resistivity color bar. *default* is 1.2
cb_residual_tick_step tick step for residual pt. *default* is 3
cb_tick_step tick step for phase tensor color bar,
*default* is 45
data np.ndarray(n_station, n_periods, 2, 2)
impedance tensors for station data
data_fn full path to data fle
dscale scaling parameter depending on map_scale
ellipse_cmap color map for pt ellipses. *default* is
mt_bl2gr2rd
ellipse_colorby [ 'skew' | 'skew_seg' | 'phimin' | 'phimax'|
'phidet' | 'ellipticity' ] parameter to color
ellipses by. *default* is 'phimin'
ellipse_range (min, max, step) min and max of colormap, need
to input step if plotting skew_seg
ellipse_size relative size of ellipses in map_scale
ew_limits limits of plot in e-w direction in map_scale
units. *default* is None, scales to station
area
fig_aspect aspect of figure. *default* is 1
fig_dpi resolution in dots-per-inch. *default* is 300
fig_list list of matplotlib.figure instances for each
figure plotted.
fig_size [width, height] in inches of figure window
*default* is [6, 6]
font_size font size of ticklabels, axes labels are
font_size+2. *default* is 7
grid_east relative location of grid nodes in e-w direction
in map_scale units
grid_north relative location of grid nodes in n-s direction
in map_scale units
grid_z relative location of grid nodes in z direction
in map_scale units
model_fn full path to initial file
map_scale [ 'km' | 'm' ] distance units of map.
*default* is km
mesh_east np.meshgrid(grid_east, grid_north, indexing='ij')
mesh_north np.meshgrid(grid_east, grid_north, indexing='ij')
model_fn full path to model file
nodes_east relative distance betwen nodes in e-w direction
in map_scale units
nodes_north relative distance betwen nodes in n-s direction
in map_scale units
nodes_z relative distance betwen nodes in z direction
in map_scale units
ns_limits (min, max) limits of plot in n-s direction
*default* is None, viewing area is station area
pad_east padding from extreme stations in east direction
pad_north padding from extreme stations in north direction
period_list list of periods from data
plot_grid [ 'y' | 'n' ] 'y' to plot grid lines
*default* is 'n'
plot_period_list list of period index values to plot
*default* is None
plot_yn ['y' | 'n' ] 'y' to plot on instantiation
*default* is 'y'
res_cmap colormap for resisitivity values.
*default* is 'jet_r'
res_limits (min, max) resistivity limits in log scale
*default* is (0, 4)
res_model np.ndarray(n_north, n_east, n_vertical) of
model resistivity values in linear scale
residual_cmap color map for pt residuals.
*default* is 'mt_wh2or'
resp np.ndarray(n_stations, n_periods, 2, 2)
impedance tensors for model response
resp_fn full path to response file
save_path directory to save figures to
save_plots [ 'y' | 'n' ] 'y' to save plots to save_path
station_east location of stations in east direction in
map_scale units
station_fn full path to station locations file
station_names station names
station_north location of station in north direction in
map_scale units
subplot_bottom distance between axes and bottom of figure window
subplot_left distance between axes and left of figure window
subplot_right distance between axes and right of figure window
subplot_top distance between axes and top of figure window
title titiel of plot *default* is depth of slice
xminorticks location of xminorticks
yminorticks location of yminorticks
========================== ================================================
"""
def __init__(self, data_fn=None, resp_fn=None, model_fn=None, **kwargs):
# MTEllipse.__init__(self, **kwargs)
super(PlotPTMaps, self).__init__(**kwargs)
self.model_fn = model_fn
self.data_fn = data_fn
self.resp_fn = resp_fn
self.save_path = kwargs.pop('save_path', None)
if self.model_fn is not None and self.save_path is None:
self.save_path = os.path.dirname(self.model_fn)
elif self.model_fn is not None and self.save_path is None:
self.save_path = os.path.dirname(self.model_fn)
if self.save_path is not None:
if not os.path.exists(self.save_path):
os.mkdir(self.save_path)
self.save_plots = kwargs.pop('save_plots', 'y')
self.plot_period_list = kwargs.pop('plot_period_list', None)
self.period_dict = None
self.d_index = kwargs.pop('d_index', None)
self.map_scale = kwargs.pop('map_scale', 'km')
# make map scale
if self.map_scale == 'km':
self.dscale = 1000.
elif self.map_scale == 'm':
self.dscale = 1.
self.ew_limits = kwargs.pop('ew_limits', None)
self.ns_limits = kwargs.pop('ns_limits', None)
self.pad_east = kwargs.pop('pad_east', 2000)
self.pad_north = kwargs.pop('pad_north', 2000)
self.plot_grid = kwargs.pop('plot_grid', 'n')
self.fig_num = kwargs.pop('fig_num', 1)
self.fig_size = kwargs.pop('fig_size', [6, 6])
self.fig_dpi = kwargs.pop('dpi', 300)
self.fig_aspect = kwargs.pop('fig_aspect', 1)
self.title = kwargs.pop('title', 'on')
self.fig_list = []
self.xminorticks = kwargs.pop('xminorticks', 1000)
self.yminorticks = kwargs.pop('yminorticks', 1000)
self.residual_cmap = kwargs.pop('residual_cmap', 'mt_wh2or')
self.font_size = kwargs.pop('font_size', 7)
self.cb_tick_step = kwargs.pop('cb_tick_step', 45)
self.cb_residual_tick_step = kwargs.pop('cb_residual_tick_step', 3)
self.cb_pt_pad = kwargs.pop('cb_pt_pad', 1.2)
self.cb_res_pad = kwargs.pop('cb_res_pad', .5)
self.res_limits = kwargs.pop('res_limits', (0, 4))
self.res_cmap = kwargs.pop('res_cmap', 'jet_r')
# --> set the ellipse properties -------------------
self._ellipse_dict = kwargs.pop(
'ellipse_dict', {
'size': 2,
'ellipse_range': [0, 0],
'ellipse_colorby': 'skew',
'ellipse_cmap': 'mt_bl2gr2rd'
})
self._read_ellipse_dict(self._ellipse_dict)
self.ellipse_size = kwargs.pop('ellipse_size',
self._ellipse_dict['size'])
self.subplot_right = .99
self.subplot_left = .085
self.subplot_top = .92
self.subplot_bottom = .1
self.subplot_hspace = .2
self.subplot_wspace = .05
self.data_obj = None
self.resp_obj = None
self.model_obj = None
self.period_list = None
self.pt_data_arr = None
self.pt_resp_arr = None
self.pt_resid_arr = None
# FZ: do not call plot in the constructor! it's not pythonic
self.plot_yn = kwargs.pop('plot_yn', 'n')
if self.plot_yn == 'y':
self.plot()
def _read_files(self):
"""
get information from files
"""
# --> read in data file
self.data_obj = Data()
self.data_obj.read_data_file(self.data_fn)
# --> read response file
if self.resp_fn is not None:
self.resp_obj = Data()
self.resp_obj.read_data_file(self.resp_fn)
# --> read mode file
if self.model_fn is not None:
self.model_obj = Model()
self.model_obj.read_model_file(self.model_fn)
self._get_plot_period_list()
self._get_pt()
def _get_plot_period_list(self):
"""
get periods to plot from input or data file
"""
# --> get period list to plot
if self.plot_period_list is None:
self.plot_period_list = self.data_obj.period_list
else:
if isinstance(self.plot_period_list, list):
# check if entries are index values or actual periods
if isinstance(self.plot_period_list[0], int):
self.plot_period_list = [
self.period_list[ii] for ii in self.plot_period_list
]
else:
pass
elif isinstance(self.plot_period_list, int):
self.plot_period_list = self.period_list[self.plot_period_list]
elif isinstance(self.plot_period_list, float):
self.plot_period_list = [self.plot_period_list]
self.period_dict = dict([
(key, value) for value, key in enumerate(self.data_obj.period_list)
])
def _get_pt(self):
"""
put pt parameters into something useful for plotting
"""
ns = len(self.data_obj.mt_dict.keys())
nf = len(self.data_obj.period_list)
data_pt_arr = np.zeros(
(nf, ns),
dtype=[('phimin', np.float), ('phimax', np.float),
('skew', np.float), ('azimuth', np.float),
('east', np.float), ('north', np.float), ('lon', np.float),
('lat', np.float), ('station', 'S10')])
if self.resp_fn is not None:
model_pt_arr = np.zeros(
(nf, ns),
dtype=[('phimin', np.float), ('phimax', np.float),
('skew', np.float), ('azimuth', np.float),
('east', np.float), ('north', np.float),
('lon', np.float), ('lat', np.float),
('station', 'S10')])
res_pt_arr = np.zeros(
(nf, ns),
dtype=[('phimin', np.float), ('phimax', np.float),
('skew', np.float), ('azimuth', np.float),
('east', np.float), ('north', np.float),
('lon', np.float), ('lat', np.float),
('geometric_mean', np.float), ('station', 'S10')])
for ii, key in enumerate(self.data_obj.mt_dict.keys()):
east = self.data_obj.mt_dict[key].grid_east / self.dscale
north = self.data_obj.mt_dict[key].grid_north / self.dscale
lon = self.data_obj.mt_dict[key].lon
lat = self.data_obj.mt_dict[key].lat
dpt = self.data_obj.mt_dict[key].pt
data_pt_arr[:, ii]['east'] = east
data_pt_arr[:, ii]['north'] = north
data_pt_arr[:, ii]['lon'] = lon
data_pt_arr[:, ii]['lat'] = lat
data_pt_arr[:, ii]['phimin'] = dpt.phimin
data_pt_arr[:, ii]['phimax'] = dpt.phimax
data_pt_arr[:, ii]['azimuth'] = dpt.azimuth
data_pt_arr[:, ii]['skew'] = dpt.beta
data_pt_arr[:, ii]['station'] = self.data_obj.mt_dict[key].station
if self.resp_fn is not None:
mpt = self.resp_obj.mt_dict[key].pt
try:
rpt = mtpt.ResidualPhaseTensor(pt_object1=dpt,
pt_object2=mpt)
rpt = rpt.residual_pt
res_pt_arr[:, ii]['east'] = east
res_pt_arr[:, ii]['north'] = north
res_pt_arr[:, ii]['lon'] = lon
res_pt_arr[:, ii]['lat'] = lat
res_pt_arr[:, ii]['phimin'] = rpt.phimin
res_pt_arr[:, ii]['phimax'] = rpt.phimax
res_pt_arr[:, ii]['azimuth'] = rpt.azimuth
res_pt_arr[:, ii]['skew'] = rpt.beta
res_pt_arr[:, ii]['station'] = self.data_obj.mt_dict[
key].station
res_pt_arr[:, ii]['geometric_mean'] = np.sqrt(
abs(rpt.phimin[0] * rpt.phimax[0]))
except mtex.MTpyError_PT:
print key, dpt.pt.shape, mpt.pt.shape
model_pt_arr[:, ii]['east'] = east
model_pt_arr[:, ii]['north'] = north
model_pt_arr[:, ii]['lon'] = lon
model_pt_arr[:, ii]['lat'] = lat
model_pt_arr[:, ii]['phimin'] = mpt.phimin
model_pt_arr[:, ii]['phimax'] = mpt.phimax
model_pt_arr[:, ii]['azimuth'] = mpt.azimuth
model_pt_arr[:, ii]['skew'] = mpt.beta
model_pt_arr[:, ii]['station'] = self.data_obj.mt_dict[
key].station
# make these attributes
self.pt_data_arr = data_pt_arr
if self.resp_fn is not None:
self.pt_resp_arr = model_pt_arr
self.pt_resid_arr = res_pt_arr
def plot_on_axes(self,
ax,
m,
periodIdx,
ptarray='data',
ellipse_size_factor=10000,
cvals=None,
map_scale='m',
centre_shift=[0, 0],
**kwargs):
'''
Plots phase tensors for a given period index.
:param ax: plot axis
:param m: basemap instance
:param periodIdx: period index
:param ptarray: name of data-array to access for retrieving attributes;
can be either 'data', 'resp' or 'resid'
:param ellipse_size_factor: factor to control ellipse size
:param cvals: list of colour values for colouring each ellipse; must be of
the same length as the number of tuples for each period
:param map_scale: map length scale
:param kwargs: list of relevant matplotlib arguments (e.g. zorder, alpha, etc.)
'''
assert (periodIdx >= 0 and periodIdx < len(self.plot_period_list)), \
'Error: Index for plot-period out of bounds.'
k = periodIdx
pt_array = getattr(self, 'pt_' + ptarray + '_arr')
for i in range(len(pt_array[k])):
lon = pt_array[k]['lon'][i]
lat = pt_array[k]['lat'][i]
phimax = pt_array[k]['phimax'][i] / pt_array[k]['phimax'].max()
phimin = pt_array[k]['phimin'][i] / pt_array[k]['phimax'].max()
az = pt_array[k]['azimuth'][i]
if ptarray == 'resid':
phimin = np.abs(phimin)
nskew = pt_array[k]['skew'][i]
# print az
if (phimax > 0 and phimin > 0):
c = None
if (cvals is not None): c = cvals[i]
if (c is not None): kwargs['facecolor'] = c
if m is None:
x = pt_array[k]['east'][i]
y = pt_array[k]['north'][i]
if map_scale == 'km':
x /= 1e3
y /= 1e3
else:
x, y = m(lon, lat)
e = Ellipse([x, y], phimax * ellipse_size_factor,
phimin * ellipse_size_factor, az, **kwargs)
ax.add_artist(e)
# end if
# end for
# end func
def plot(self, period=0, save2file=None, **kwargs):
""" Plot phase tensor maps for data and or response, each figure is of a
different period. If response is input a third column is added which is
the residual phase tensor showing where the model is not fitting the data
well. The data is plotted in km.
Args:
period: the period index to plot, default=0
Returns:
"""
print("The input parameter period is", period)
# --> read in data first
if self.data_obj is None:
self._read_files()
# set plot properties
plt.rcParams['font.size'] = self.font_size
plt.rcParams['figure.subplot.left'] = self.subplot_left
plt.rcParams['figure.subplot.right'] = self.subplot_right
plt.rcParams['figure.subplot.bottom'] = self.subplot_bottom
plt.rcParams['figure.subplot.top'] = self.subplot_top
font_dict = {'size': self.font_size + 2, 'weight': 'bold'}
# make a grid of subplots
gs = gridspec.GridSpec(1,
3,
hspace=self.subplot_hspace,
wspace=self.subplot_wspace)
# set some parameters for the colorbar
ckmin = float(self.ellipse_range[0])
ckmax = float(self.ellipse_range[1])
try:
ckstep = float(self.ellipse_range[2])
except IndexError:
if self.ellipse_cmap == 'mt_seg_bl2wh2rd':
raise ValueError('Need to input range as (min, max, step)')
else:
ckstep = 3
bounds = np.arange(ckmin, ckmax + ckstep, ckstep)
# set plot limits to be the station area
if self.ew_limits is None:
east_min = self.data_obj.data_array['rel_east'].min() - \
self.pad_east
east_max = self.data_obj.data_array['rel_east'].max() + \
self.pad_east
self.ew_limits = (east_min / self.dscale, east_max / self.dscale)
if self.ns_limits is None:
north_min = self.data_obj.data_array['rel_north'].min() - \
self.pad_north
north_max = self.data_obj.data_array['rel_north'].max() + \
self.pad_north
self.ns_limits = (north_min / self.dscale, north_max / self.dscale)
# -------------plot phase tensors------------------------------------
if period > len(self.plot_period_list) - 1:
print("Error: the period exceeds the max value:",
len(self.plot_period_list) - 1)
# FZ: changed below to plot a given period index
# for ff, per in enumerate(self.plot_period_list):
for ff, per in enumerate(self.plot_period_list[period:period + 1]):
# FZ
print(ff, per)
print(self.plot_period_list)
data_ii = self.period_dict[per]
print 'Plotting Period: {0:.5g}'.format(per)
fig = plt.figure('{0:.5g}'.format(per),
figsize=self.fig_size,
dpi=self.fig_dpi)
fig.clf()
if self.resp_fn is not None:
axd = fig.add_subplot(gs[0, 0], aspect='equal')
axm = fig.add_subplot(gs[0, 1], aspect='equal')
axr = fig.add_subplot(gs[0, 2], aspect='equal')
ax_list = [axd, axm, axr]
else:
axd = fig.add_subplot(gs[0, :], aspect='equal')
ax_list = [axd]
# plot model below the phase tensors
if self.model_fn is not None:
gridzcentre = np.mean(
[self.model_obj.grid_z[1:], self.model_obj.grid_z[:-1]],
axis=0)
if self.d_index is not None:
approx_depth, d_index = ws.estimate_skin_depth(
self.model_obj.res_model.copy(),
gridzcentre / self.dscale,
per,
dscale=self.dscale)
else:
d_index = self.d_index
approx_depth = self.model_obj.grid_z[d_index]
# need to add an extra row and column to east and north to make sure
# all is plotted see pcolor for details.
plot_east = np.append(self.model_obj.grid_east,
self.model_obj.grid_east[-1] * 1.25) / \
self.dscale
plot_north = np.append(self.model_obj.grid_north,
self.model_obj.grid_north[-1] * 1.25) / \
self.dscale
# make a mesh grid for plotting
# the 'ij' makes sure the resulting grid is in east, north
try:
self.mesh_east, self.mesh_north = np.meshgrid(
plot_east, plot_north, indexing='ij')
except TypeError:
self.mesh_east, self.mesh_north = [
arr.T for arr in np.meshgrid(plot_east, plot_north)
]
for ax in ax_list:
plot_res = np.log10(self.model_obj.res_model[:, :,
d_index].T)
ax.pcolormesh(self.mesh_east,
self.mesh_north,
plot_res,
cmap=self.res_cmap,
vmin=self.res_limits[0],
vmax=self.res_limits[1])
# --> plot data phase tensors
for pt in self.pt_data_arr[data_ii]:
eheight = pt['phimin'] / \
self.pt_data_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ewidth = pt['phimax'] / \
self.pt_data_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ellipse = Ellipse((pt['east'], pt['north']),
width=ewidth,
height=eheight,
angle=90 - pt['azimuth'],
**kwargs)
# get ellipse color
if self.ellipse_cmap.find('seg') > 0:
ellipse.set_facecolor(
mtcl.get_plot_color(pt[self.ellipse_colorby],
self.ellipse_colorby,
self.ellipse_cmap,
ckmin,
ckmax,
bounds=bounds))
else:
ellipse.set_facecolor(
mtcl.get_plot_color(pt[self.ellipse_colorby],
self.ellipse_colorby,
self.ellipse_cmap, ckmin, ckmax))
axd.add_artist(ellipse)
# -----------plot response phase tensors---------------
if self.resp_fn is not None:
rcmin = np.floor(self.pt_resid_arr['geometric_mean'].min())
rcmax = np.floor(self.pt_resid_arr['geometric_mean'].max())
for mpt, rpt in zip(self.pt_resp_arr[data_ii],
self.pt_resid_arr[data_ii]):
eheight = mpt['phimin'] / \
self.pt_resp_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ewidth = mpt['phimax'] / \
self.pt_resp_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ellipsem = Ellipse((mpt['east'], mpt['north']),
width=ewidth,
height=eheight,
angle=90 - mpt['azimuth'],
**kwargs)
# get ellipse color
if self.ellipse_cmap.find('seg') > 0:
ellipsem.set_facecolor(
mtcl.get_plot_color(mpt[self.ellipse_colorby],
self.ellipse_colorby,
self.ellipse_cmap,
ckmin,
ckmax,
bounds=bounds))
else:
ellipsem.set_facecolor(
mtcl.get_plot_color(mpt[self.ellipse_colorby],
self.ellipse_colorby,
self.ellipse_cmap, ckmin,
ckmax))
axm.add_artist(ellipsem)
# -----------plot residual phase tensors---------------
eheight = rpt['phimin'] / \
self.pt_resid_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ewidth = rpt['phimax'] / \
self.pt_resid_arr[data_ii]['phimax'].max() * \
self.ellipse_size
ellipser = Ellipse((rpt['east'], rpt['north']),
width=ewidth,
height=eheight,
angle=rpt['azimuth'],
**kwargs)
# get ellipse color
rpt_color = np.sqrt(abs(rpt['phimin'] * rpt['phimax']))
if self.ellipse_cmap.find('seg') > 0:
ellipser.set_facecolor(
mtcl.get_plot_color(rpt_color,
'geometric_mean',
self.residual_cmap,
ckmin,
ckmax,
bounds=bounds))
else:
ellipser.set_facecolor(
mtcl.get_plot_color(rpt_color, 'geometric_mean',
self.residual_cmap, ckmin,
ckmax))
axr.add_artist(ellipser)
# --> set axes properties
# data
axd.set_xlim(self.ew_limits)
axd.set_ylim(self.ns_limits)
axd.set_xlabel('Easting ({0})'.format(self.map_scale),
fontdict=font_dict)
axd.set_ylabel('Northing ({0})'.format(self.map_scale),
fontdict=font_dict)
# make a colorbar for phase tensors
# bb = axd.axes.get_position().bounds
bb = axd.get_position().bounds
y1 = .25 * (2 + (self.ns_limits[1] - self.ns_limits[0]) /
(self.ew_limits[1] - self.ew_limits[0]))
cb_location = (3.35 * bb[2] / 5 + bb[0], y1 * self.cb_pt_pad,
.295 * bb[2], .02)
cbaxd = fig.add_axes(cb_location)
cbd = mcb.ColorbarBase(cbaxd,
cmap=mtcl.cmapdict[self.ellipse_cmap],
norm=Normalize(vmin=ckmin, vmax=ckmax),
orientation='horizontal')
cbd.ax.xaxis.set_label_position('top')
cbd.ax.xaxis.set_label_coords(.5, 1.75)
cbd.set_label(mtplottools.ckdict[self.ellipse_colorby])
cbd.set_ticks(
np.arange(ckmin, ckmax + self.cb_tick_step, self.cb_tick_step))
axd.text(self.ew_limits[0] * .95,
self.ns_limits[1] * .95,
'Data',
horizontalalignment='left',
verticalalignment='top',
bbox={'facecolor': 'white'},
fontdict={'size': self.font_size + 1})
# Model and residual
if self.resp_fn is not None:
for aa, ax in enumerate([axm, axr]):
ax.set_xlim(self.ew_limits)
ax.set_ylim(self.ns_limits)
ax.set_xlabel('Easting ({0})'.format(self.map_scale),
fontdict=font_dict)
plt.setp(ax.yaxis.get_ticklabels(), visible=False)
# make a colorbar ontop of axis
bb = ax.axes.get_position().bounds
y1 = .25 * (2 + (self.ns_limits[1] - self.ns_limits[0]) /
(self.ew_limits[1] - self.ew_limits[0]))
cb_location = (3.35 * bb[2] / 5 + bb[0],
y1 * self.cb_pt_pad, .295 * bb[2], .02)
cbax = fig.add_axes(cb_location)
if aa == 0:
cb = mcb.ColorbarBase(
cbax,
cmap=mtcl.cmapdict[self.ellipse_cmap],
norm=Normalize(vmin=ckmin, vmax=ckmax),
orientation='horizontal')
cb.ax.xaxis.set_label_position('top')
cb.ax.xaxis.set_label_coords(.5, 1.75)
cb.set_label(mtplottools.ckdict[self.ellipse_colorby])
cb.set_ticks(
np.arange(ckmin, ckmax + self.cb_tick_step,
self.cb_tick_step))
ax.text(self.ew_limits[0] * .95,
self.ns_limits[1] * .95,
'Model',
horizontalalignment='left',
verticalalignment='top',
bbox={'facecolor': 'white'},
fontdict={'size': self.font_size + 1})
else:
cb = mcb.ColorbarBase(
cbax,
cmap=mtcl.cmapdict[self.residual_cmap],
norm=Normalize(vmin=rcmin, vmax=rcmax),
orientation='horizontal')
cb.ax.xaxis.set_label_position('top')
cb.ax.xaxis.set_label_coords(.5, 1.75)
cb.set_label(r"$\sqrt{\Phi_{min} \Phi_{max}}$")
cb_ticks = [rcmin, (rcmax - rcmin) / 2, rcmax]
cb.set_ticks(cb_ticks)
ax.text(self.ew_limits[0] * .95,
self.ns_limits[1] * .95,
'Residual',
horizontalalignment='left',
verticalalignment='top',
bbox={'facecolor': 'white'},
fontdict={'size': self.font_size + 1})
if self.model_fn is not None:
for ax in ax_list:
ax.tick_params(direction='out')
bb = ax.axes.get_position().bounds
y1 = .25 * (2 - (self.ns_limits[1] - self.ns_limits[0]) /
(self.ew_limits[1] - self.ew_limits[0]))
cb_position = (3.0 * bb[2] / 5 + bb[0],
y1 * self.cb_res_pad, .35 * bb[2], .02)
cbax = fig.add_axes(cb_position)
cb = mcb.ColorbarBase(cbax,
cmap=self.res_cmap,
norm=Normalize(
vmin=self.res_limits[0],
vmax=self.res_limits[1]),
orientation='horizontal')
cb.ax.xaxis.set_label_position('top')
cb.ax.xaxis.set_label_coords(.5, 1.5)
cb.set_label('Resistivity ($\Omega \cdot$m)')
cb_ticks = np.arange(np.floor(self.res_limits[0]),
np.ceil(self.res_limits[1] + 1), 1)
cb.set_ticks(cb_ticks)
cb.set_ticklabels(
[mtplottools.labeldict[ctk] for ctk in cb_ticks])
if save2file is not None:
fig.savefig(save2file, dpi=self.fig_dpi, bbox_inches='tight')
plt.show()
self.fig_list.append(fig)
return fig
def redraw_plot(self):
"""
redraw plot if parameters were changed
use this function if you updated some attributes and want to re-plot.
:Example: ::
>>> # change the color and marker of the xy components
>>> import mtpy.modeling.occam2d as occam2d
>>> ocd = occam2d.Occam2DData(r"/home/occam2d/Data.dat")
>>> p1 = ocd.plotAllResponses()
>>> #change line width
>>> p1.lw = 2
>>> p1.redraw_plot()
"""
for fig in self.fig_list:
plt.close(fig)
self.plot()
def _get_pt_data_list(self, attribute, xykeys=['east', 'north']):
headerlist = ['period', 'station'] + xykeys + \
['azimuth', 'phimin', 'phimax', 'skew']
data = getattr(self, attribute).T.copy()
indices = np.argsort(data['station'][:, 0])
data = data[indices].T
dtype = []
for val in headerlist:
if val == 'station':
dtype.append((val, 'S10'))
else:
dtype.append((val, np.float))
data_to_write = np.zeros(np.product(data.shape), dtype=dtype)
data_to_write['period'] = np.vstack([self.plot_period_list] *
data.shape[1]).T.flatten()
for val in headerlist[1:]:
if val in ['east', 'north']:
data[val] *= self.dscale
data_to_write[val] = data[val].flatten()
return data_to_write, headerlist
def get_period_attributes(self, periodIdx, key, ptarray='data'):
'''
Returns, for a given period, a list of attribute values for key
(e.g. skew, phimax, etc.).
:param periodIdx: index of period; print out _plot_period for periods available
:param key: attribute key
:param ptarray: name of data-array to access for retrieving attributes;
can be either 'data', 'resp' or 'resid'
:return: numpy array of attribute values
'''
# load data if necessary
if self.data_obj is None:
self._read_files()
assert (periodIdx >= 0 and periodIdx < len(self.plot_period_list)), \
'Error: Index for plot-period out of bounds.'
pk = periodIdx
try:
vals = getattr(self, 'pt_' + ptarray + '_arr')[pk][key]
return vals
except:
print 'Attribute %s not found' % ('pt_' + ptarray + '_arr')
logging.error(traceback.format_exc())
exit(-1)
return None
# end func
def write_pt_data_to_text(self, savepath='.'):
if self.pt_data_arr is None:
self._read_files()
for att in ['pt_data_arr', 'pt_resp_arr', 'pt_resid_arr']:
if hasattr(self, att):
data_to_write, headerlist = self._get_pt_data_list(att)
header = ' '.join(headerlist)
filename = op.join(savepath, att[:-4] + '.txt')
if att == 'pt_resid_arr':
data_to_write['azimuth'] = 90. - data_to_write['azimuth']
np.savetxt(filename,
data_to_write,
header=header,
fmt=[
'%.4e', '%s', '%.2f', '%.2f', '%.2f', '%.2f',
'%.2f', '%.3f'
])
def write_pt_data_to_gmt(self,
period=None,
epsg=None,
savepath='.',
center_utm=None,
colorby='phimin',
attribute='data',
clim=None):
"""
write data to plot phase tensor ellipses in gmt.
saves a gmt script and text file containing ellipse data
provide:
period to plot (seconds)
epsg for the projection the model was projected to
(google "epsg your_projection_name" and you will find it)
centre_utm - utm coordinates for centre position of model, if not
provided, script will try and extract it from data file
colorby - what to colour the ellipses by, 'phimin', 'phimax', or 'skew'
attribute - attribute to plot 'data', 'resp', or 'resid' for data,
response or residuals
"""
att = 'pt_{}_arr'.format(attribute)
# if centre utm not provided, get station locations from the data
# object
project = False
xykeys = ['lon', 'lat']
if epsg is not None:
if center_utm is not None:
project = True
else:
if hasattr(self.data_obj, 'center_position'):
if np.all(np.array(self.data_obj.center_position) > 0):
project = True
center_utm = self.data_obj.project_xy(
self.data_obj.center_position[0],
self.data_obj.center_position[1],
epsg_from=4326,
epsg_to=epsg)
if project:
xykeys = ['east', 'north']
# get text data list
data, headerlist = self._get_pt_data_list(att, xykeys=xykeys)
# extract relevant columns in correct order
periodlist = data['period']
columns = xykeys + [colorby, 'azimuth', 'phimax', 'phimin']
gmtdata = np.vstack([data[i] for i in columns]).T
# make a filename based on period
if period >= 1.:
suffix = '%1i' % round(period)
else:
nzeros = np.abs(np.int(np.floor(np.log10(period))))
fmt = '%0' + str(nzeros + 1) + 'i'
suffix = fmt % (period * 10**nzeros)
filename = 'ellipse_' + attribute + '.' + suffix
if period is not None:
# extract relevant period
unique_periods = np.unique(periodlist)
closest_period = unique_periods[
np.abs(unique_periods -
period) == np.amin(np.abs(unique_periods - period))]
# indices to select all occurrances of relevant period (to nearest
# 10^-8 s)
pind = np.where(np.abs(closest_period - periodlist) < 1e-8)[0]
else:
# take the first period
pind = 0
# select relevant periods
periodlist, gmtdata = periodlist[pind], gmtdata[pind]
if project:
gmtdata[:, 0] += center_utm[0]
gmtdata[:, 1] += center_utm[1]
# now that x y coordinates are in utm, project to lon/lat
self.data_obj.epsg = epsg
gmtdata[:, 0], gmtdata[:, 1] = self.data_obj.project_xy(
gmtdata[:, 0], gmtdata[:, 1])
# normalise by maximum value of phimax
norm = np.amax(gmtdata[:, 4])
gmtdata[:, 5] /= norm
gmtdata[:, 4] /= norm
if attribute != 'resid':
gmtdata[:, 3] = 90. - gmtdata[:, 3]
# write to text file in correct format
fmt = ['%+11.6f', '%+10.6f'] + ['%+9.4f'] * 2 + ['%8.4f'] * 2
np.savetxt(op.join(savepath, filename), gmtdata, fmt)
# write gmt script
xmin, xmax = gmtdata[:, 0].min(), gmtdata[:, 0].max()
ymin, ymax = gmtdata[:, 1].min(), gmtdata[:, 1].max()
pad = min(ymax - ymin, xmax - xmin) / 10.
tr = -int(np.log10(20. * (xmax - xmin)))
tickspacing = int(np.round(20. * (xmax - xmin), tr))
scalebarlat = int(round(ymax + ymin) / 2.)
if clim is None:
cr = int(np.ceil(-np.log10(np.amax(gmtdata[:, 2]))))
clim = np.round([gmtdata[:, 2].min(), gmtdata[:, 2].max()],
cr).astype(int)
gmtlines = [
line + '\n' for line in [
'w={}'.format(xmin - pad), 'e={}'.format(xmax + pad),
's={}'.format(ymin -
pad), 'n={}'.format(ymax +
pad), r"wesn=$w/$s/$e/$n'r'",
'', '# define output file and remove it if it exists',
'PS={}.ps'.format(filename.replace('.', '')), 'rm $PS', '',
'# set gmt parameters', 'gmtset FORMAT_GEO_MAP ddd:mm:ss',
'gmtset FONT_ANNOT_PRIMARY 9p,Helvetica,black',
'gmtset MAP_FRAME_TYPE fancy', '', '# make colour palette',
'makecpt -Cpolar -T{}/{} -Z > {}.cpt'.format(
clim[0], clim[1], colorby), '', '# draw coastline',
'pscoast -R$wesn -JM18c -W0.5p -Ba1f1/a1f1WSen -Gwhite -Slightgrey -Lfx14c/1c/{}/{}+u -Df -P -K >> $PS'
.format(scalebarlat, tickspacing), '', '# draw ellipses',
'psxy {} -R -J -P -Se -C{}.cpt -W0.01p -O >> $PS'.format(
filename,
colorby), '', '# save to png', 'ps2raster -Tg -A -E400 $PS'
]
]
with open(op.join(savepath, 'gmtscript_{}.gmt'.format(attribute)),
'wb') as scriptfile:
scriptfile.writelines(gmtlines)
def save_figure(self,
save_path=None,
fig_dpi=None,
file_format='pdf',
orientation='landscape',
close_fig='y'):
"""
save_figure will save the figure to save_fn.
Arguments:
-----------
**save_fn** : string
full path to save figure to, can be input as
* directory path -> the directory path to save to
in which the file will be saved as
save_fn/station_name_PhaseTensor.file_format
* full path -> file will be save to the given
path. If you use this option then the format
will be assumed to be provided by the path
**file_format** : [ pdf | eps | jpg | png | svg ]
file type of saved figure pdf,svg,eps...
**orientation** : [ landscape | portrait ]
orientation in which the file will be saved
*default* is portrait
**fig_dpi** : int
The resolution in dots-per-inch the file will be
saved. If None then the dpi will be that at
which the figure was made. I don't think that
it can be larger than dpi of the figure.
**close_plot** : [ y | n ]
* 'y' will close the plot after saving.
* 'n' will leave plot open
:Example: ::
>>> # to save plot as jpg
>>> import mtpy.modeling.occam2d as occam2d
>>> dfn = r"/home/occam2d/Inv1/data.dat"
>>> ocd = occam2d.Occam2DData(dfn)
>>> ps1 = ocd.plotPseudoSection()
>>> ps1.save_plot(r'/home/MT/figures', file_format='jpg')
"""
if fig_dpi is None:
fig_dpi = self.fig_dpi
if os.path.isdir(save_path) == False:
try:
os.mkdir(save_path)
except:
raise IOError('Need to input a correct directory path')
for fig in self.fig_list:
per = fig.canvas.get_window_title()
save_fn = os.path.join(
save_path, 'PT_DepthSlice_{0}s.{1}'.format(per, file_format))
fig.savefig(save_fn,
dpi=fig_dpi,
format=file_format,
orientation=orientation,
bbox_inches='tight')
if close_fig == 'y':
plt.close(fig)
else:
pass
self.fig_fn = save_fn
print 'Saved figure to: ' + self.fig_fn
def create_geogrid(data_file,
model_file,
out_dir,
x_pad=None,
y_pad=None,
z_pad=None,
x_res=None,
y_res=None,
center_lat=None,
center_lon=None,
epsg_code=None,
depths=None,
angle=None,
rotate_origin=False,
log_scale=False):
"""Generate an output geotiff file and ASCII grid file.
Args:
data_file (str): Path to the ModEM .dat file. Used to get the
grid center point.
model_file (str): Path to the ModEM .rho file.
out_dir (str): Path to directory for storing output data. Will
be created if it does not exist.
x_pad (int, optional): Number of east-west padding cells. This
number of cells will be cropped from the east and west
sides of the grid. If None, pad_east attribute of model
will be used.
y_pad (int, optional): Number of north-south padding cells. This
number of cells will be cropped from the north and south
sides of the grid. If None, pad_north attribute of model
will be used.
z_pad (int, optional): Number of depth padding cells. This
number of cells (i.e. slices) will be cropped from the
bottom of the grid. If None, pad_z attribute of model
will be used.
x_res (int, optional): East-west cell size in meters. If None,
cell_size_east attribute of model will be used.
y_res (int, optional): North-south cell size in meters. If
None, cell_size_north of model will be used.
epsg_code (int, optional): EPSG code of the model CRS. If None,
is inferred from the grid center point.
depths (list of int, optional): A list of integers,
eg, [0, 100, 500], of the depth in metres of the slice to
retrieve. Will find the closes slice to each depth
specified. If None, all slices are selected.
center_lat (float, optional): Grid center latitude in degrees.
If None, the model's center point will be used.
center_lon (float, optional): Grid center longitude in degrees.
If None, the model's center point will be used.
angle (float, optional): Angle in degrees to rotate image by.
If None, no rotation is performed.
rotate_origin (bool, optional): If True, image will be rotated
around the origin (upper left point). If False, the image
will be rotated around the center point.
log_scale (bool, optional): If True, the data will be scaled using log10.
"""
if not os.path.exists(out_dir):
os.mkdir(out_dir)
model = Model()
model.read_model_file(model_fn=model_file)
data = Data()
data.read_data_file(data_fn=data_file)
center = data.center_point
center_lat = center.lat.item() if center_lat is None else center_lat
center_lon = center.lon.item() if center_lon is None else center_lon
if epsg_code is None:
zone_number, is_northern, utm_zone = gis_tools.get_utm_zone(
center_lat, center_lon)
epsg_code = gis_tools.get_epsg(center_lat, center_lon)
_logger.info(
"Input data epsg code has been inferred as {}".format(epsg_code))
print("Loaded model")
# Get the center point of the model grid cells to use as points
# in a resistivity grid.
ce = _get_centers(model.grid_east)
cn = _get_centers(model.grid_north)
cz = _get_centers(model.grid_z)
print("Grid shape with padding: E = {}, N = {}, Z = {}".format(
ce.shape, cn.shape, cz.shape))
# Get X, Y, Z paddings
x_pad = model.pad_east if x_pad is None else x_pad
y_pad = model.pad_north if y_pad is None else y_pad
z_pad = model.pad_z if z_pad is None else z_pad
print("Stripping padding...")
# Remove padding cells from the grid
ce = _strip_padding(ce, x_pad)
cn = _strip_padding(cn, y_pad)
cz = _strip_padding(cz, z_pad, keep_start=True)
print("Grid shape without padding: E = {}, N = {}, Z = {}".format(
ce.shape, cn.shape, cz.shape))
x_res = model.cell_size_east if x_res is None else x_res
y_res = model.cell_size_north if y_res is None else y_res
# BM: The cells have been defined by their center point for making
# our grid and interpolating the resistivity model over it. For
# display purposes, GDAL expects the origin to be the upper-left
# corner of the image. So take the upper left-cell and shift it
# half a cell west and north so we get the upper-left corner of
# the grid as GDAL origin.
origin = _get_gdal_origin(ce, x_res, center.east, cn, y_res, center.north)
target_gridx, target_gridy = _build_target_grid(ce, x_res, cn, y_res)
resgrid_nopad = _strip_resgrid(model.res_model, y_pad, x_pad, z_pad)
indicies = _get_depth_indicies(cz, depths)
for di in indicies:
print("Writing out slice {:.0f}m...".format(cz[di]))
data = _interpolate_slice(ce, cn, resgrid_nopad, di, target_gridx,
target_gridy, log_scale)
if log_scale:
output_file = 'DepthSlice{:.0f}m_log10.tif'.format(cz[di])
else:
output_file = 'DepthSlice{:.0f}m.tif'.format(cz[di])
output_file = os.path.join(out_dir, output_file)
array2geotiff_writer(output_file,
origin,
x_res,
-y_res,
data[::-1],
epsg_code=epsg_code,
angle=angle,
center=center,
rotate_origin=rotate_origin)
print("Complete!")
print("Geotiffs are located in '{}'".format(os.path.dirname(output_file)))
return output_file
# =============================================================================
inv_path = Path(
r"c:\Users\jpeacock\OneDrive - DOI\Geothermal\GreatBasin\modem_inv\gb_01")
basename = "gb_z03_t02_c02_046"
metadata_path = inv_path.joinpath("netcdf_metadata.json")
pad = 12
model_epsg = 32611
# =============================================================================
m = Model()
m.read_model_file(inv_path.joinpath(f"{basename}.rho"))
d = Data()
d.read_data_file(inv_path.joinpath(f"{basename}.dat"))
center = d.center_point
with open(metadata_path, "r") as fid:
metadata = json.load(fid)
# need to project points onto a lat/lon grid
model_crs = pyproj.CRS(f"epsg:{model_epsg}")
x_crs = pyproj.CRS("epsg:4326")
translator = pyproj.Transformer.from_crs(model_crs, x_crs)
east, north = np.broadcast_arrays(
m.grid_north[pad:-(pad + 1), None] + center.north,
m.grid_east[None, pad:-(pad + 1)] + center.east,
)
import os
from scipy.interpolate import RegularGridInterpolator
# wd = r'M:\AusLAMP\AusLAMP_NSW\Release\Model_release\MT075_DepthSlice_ArcGIS_ascii_grids'
# wdmod = r'C:\Users\u64125\OneDrive - Geoscience Australia\AusLAMP_NSW\Modelling\ModEM\NSWinv141'
wd = r'C:\Data\Alison_201910\MyOutput'
wdmod = r'C:\Data\Alison_201910\Alison_ModEM_Grid\MT075_ModEM_files'
filestem = 'Modular_MPI_NLCG_004'
mObj = Model()
mObj.read_model_file(os.path.join(wdmod,filestem+'.rho'))
dObj = Data()
dObj.read_data_file(os.path.join(wdmod,'ModEM_Data.dat'))
gce,gcn,gcz = [np.mean([arr[:-1],arr[1:]],axis=0) for arr in [mObj.grid_east,mObj.grid_north,mObj.grid_z]]
gce,gcn = gce[6:-6],gcn[6:-6] # padding big-sized edge cells
# ge,gn = mObj.grid_east[6:-6],mObj.grid_north[6:-6]
print(gce)
print(gcn)
print(gcz)
print("Shapes E, N Z =", gce.shape, gcn.shape, gcz.shape)
fileext = '.asc'
ascfilelist = [ff for ff in os.listdir(wd) if ff.endswith(fileext)]
resgrid_nopad = mObj.res_model[::-1][6:-6,6:-6]
class DataModelAnalysis(object):
def __init__(self, filedat, filerho, plot_orient='ew', **kwargs):
"""Constructor
:param filedat: path2file.dat
:param filerho: path2file.rho
:param plot_orient: plot orientation ['ew','ns', 'z']
"""
self.datfile = filedat
self.rhofile = filerho
# plot orientation 'ns' (north-south),'ew' (east-west) or
# 'z' (horizontal slice))
self.plot_orientation = plot_orient
# slice location, in local grid coordinates (if it is a z slice, this
# is slice depth)
# self.slice_location = kwargs.pop('slice_location', 1000)
# maximum distance in metres from vertical slice location and station
self.station_dist = kwargs.pop('station_dist', 50000)
# z limits (positive down so order is reversed)
self.zlim = kwargs.pop('zlim', (200000, -2000))
# colour limits
self.clim = kwargs.pop('clim', [0.3, 3.7])
self.fig_size = kwargs.pop('fig_size', [12, 10])
self.font_size = kwargs.pop('font_size', 16)
self.border_linewidth = 2
self.map_scale = kwargs.pop('map_scale', 'm')
# make map scale
if self.map_scale == 'km':
self.dscale = 1000.
elif self.map_scale == 'm':
self.dscale = 1.
else:
print("Unknown map scale:", self.map_scale)
self.xminorticks = kwargs.pop('xminorticks', 10000)
self.yminorticks = kwargs.pop('yminorticks', 10000)
# read in the model data-file and rho-file
self._read_model_data()
return
def _read_model_data(self):
self.datObj = Data()
self.datObj.read_data_file(data_fn=self.datfile)
self.modObj = Model(model_fn=self.rhofile)
self.modObj.read_model_file()
self.ew_lim = (self.modObj.grid_east[self.modObj.pad_east],
self.modObj.grid_east[-self.modObj.pad_east - 1])
self.ns_lim = (self.modObj.grid_north[self.modObj.pad_north],
self.modObj.grid_north[-self.modObj.pad_north - 1])
# logger.debug("ns-limit %s", self.ns_lim)
# logger.debug("ew-limit %s", self.ew_lim)
# logger.info("station name list %s", self.datObj.station_locations['station'])
# logger.info("station Lat list %s", self.datObj.station_locations['lat'])
return
def find_stations_in_meshgrid(self):
"""
find the (station_Name, sX,sY) its associated index (sI,sJ) in the regular mesh grid (X[i],Y[j])
# print(len(sX), sX) # =number of stations
# print(len(sY), sY) # =number of stations
:return: station_dict
"""
station_dict = {}
sX, sY = self.datObj.station_locations.rel_east, self.datObj.station_locations.rel_north
station_names = self.datObj.station_locations.station
station_lats = self.datObj.station_locations.lat
station_lons = self.datObj.station_locations.lon
# get grid centres (finite element cells centres)
gceast, gcnorth = [
np.mean([arr[:-1], arr[1:]], axis=0)
for arr in [self.modObj.grid_east, self.modObj.grid_north]
]
n_stations = len(sX)
for n in xrange(n_stations):
xdist = np.abs(gceast - sX[n])
snos = np.where(xdist == np.amin(xdist))
ix = snos[0][0]
ydist = np.abs(gcnorth - sY[n])
snos = np.where(ydist == np.amin(ydist))
iy = snos[0][0]
logger.debug("Station Index: (%s, %s)", ix, iy)
station_dict[(ix, iy)] = [
station_names[n], sX[n], sY[n], station_lats[n],
station_lons[n]
] # Todo: get (station_name, lat, long)[n]
logger.debug(station_dict)
return station_dict
def set_plot_orientation(self, orient):
"""set a new plot orientation for plotting
:param orient: z, ew, ns
:return:
"""
if orient in ['z', 'ew', 'ns']:
self.plot_orientation = orient
else:
raise Exception("Error: unknown orientation value= %s" % orient)
def get_slice_data(self, slice_location):
"""
get the resistivity slices at the specified location
:param slice_location:
:return: slice data
"""
# get grid centres (finite element cells centres)
gcz = np.mean([self.modObj.grid_z[:-1], self.modObj.grid_z[1:]],
axis=0)
gceast, gcnorth = [
np.mean([arr[:-1], arr[1:]], axis=0)
for arr in [self.modObj.grid_east, self.modObj.grid_north]
]
# distance from slice to grid centre locations
if self.plot_orientation == 'ew':
sdist = np.abs(gcnorth - slice_location)
snos = np.where(sdist == np.amin(sdist))
sno = snos[0][0]
actual_location = gcnorth[sno]
elif self.plot_orientation == 'ns':
sdist = np.abs(gceast - slice_location)
snos = np.where(sdist == np.amin(sdist))
sno = snos[0][0]
actual_location = gceast[sno]
elif self.plot_orientation == 'z':
sdist = np.abs(gcz - slice_location)
# find the closest slice index to specified location
snos = np.where(sdist == np.amin(sdist))
sno = snos[0][0]
actual_location = gcz[sno]
print(type(snos), len(snos)) # ((index1), (index2), (index3))
# unpack the index tupple, and get the integer value as index number
# sno=snos[0][0]
logger.debug(
"the slice index number= %s and the actual location is %s", sno,
actual_location)
# get data for plotting
if self.plot_orientation == 'ew':
X, Y, res = self.modObj.grid_east, self.modObj.grid_z, np.log10(
self.modObj.res_model[sno, :, :].T)
ss = np.where(
np.abs(self.datObj.station_locations['rel_north'] -
np.median(gcnorth)) < self.station_dist)[0]
sX, sY = self.datObj.station_locations['rel_east'][
ss], self.datObj.station_locations['elev'][ss]
xlim = (self.modObj.grid_east[self.modObj.pad_east[1]],
self.modObj.grid_east[-self.modObj.pad_east[1] - 1])
ylim = self.zlim
title = 'East-west slice at {} meters north'.format(gcnorth[sno])
elif self.plot_orientation == 'ns':
X, Y, res = self.modObj.grid_north, self.modObj.grid_z, np.log10(
self.modObj.res_model[:, sno, :].T)
# indices for selecting stations close to profile
ss = np.where(
np.abs(self.datObj.station_locations['rel_east'] -
np.median(gceast)) < self.station_dist)[0]
sX, sY = self.datObj.station_locations['rel_north'][
ss], self.datObj.station_locations['elev'][ss]
xlim = (self.modObj.grid_north[self.modObj.pad_north[1]],
self.modObj.grid_north[-self.modObj.pad_north[1] - 1])
ylim = self.zlim
title = 'North-south slice at {} meters east'.format(gceast[sno])
elif self.plot_orientation == 'z': # for plotting X == EW Y == NS
Y, X, res = self.modObj.grid_north, self.modObj.grid_east, np.log10(
self.modObj.res_model[:, :, sno])
sY, sX = self.datObj.station_locations.rel_north, self.datObj.station_locations.rel_east
ylim = (self.modObj.grid_north[self.modObj.pad_north],
self.modObj.grid_north[-self.modObj.pad_north - 1])
xlim = (self.modObj.grid_east[self.modObj.pad_east],
self.modObj.grid_east[-self.modObj.pad_east - 1])
title = 'Horizontal Slice at Depth {} meters'.format(gcz[sno])
return (X, Y, res, sX, sY, xlim, ylim, title, actual_location)
def create_csv(self, csvfile='tests/temp/Resistivity.csv'):
"""
write ressitivity into the csvfile with the output columns:
StationName, Lat, Long, X, Y, Z, Log(Resistivity)
where (X,Y,Z) are relative distances in meters from the mesh's origin.
Projection/Coordinate system must be known in order to associate (Lat, Long) to (X, Y)
:return:
"""
self.set_plot_orientation('z')
z_cell_centres = np.mean(
[self.modObj.grid_z[:-1], self.modObj.grid_z[1:]], axis=0)
# csv_header = ['Station', 'Lat', 'Long', 'X', 'Y', 'Z', 'Log_Resisitivity']
csv_header = [
'X', 'Y', 'Z', 'Log_Resisitivity', 'StationName', 'StationX',
'StationY', 'Lat', 'Long'
]
stationd = self.find_stations_in_meshgrid()
csvrows = []
for zslice in z_cell_centres:
(X, Y, res, sX, sY, xlim, ylim, title,
Z_location) = self.get_slice_data(zslice)
# print (X,Y,res)
# print(sX,sY)
print(len(X), len(Y), Z_location, res.shape, len(sX), len(sY))
for i in xrange(len(X) - 1):
for j in xrange(len(Y) - 1):
st = stationd.get(
(i, j), None
) # filter and subset for station location meshgrids
if st is not None:
arow = [
X[i], Y[j], Z_location, res[j, i], st[0], st[1],
st[2], st[3], st[4], i, j
]
csvrows.append(arow)
with open(csvfile, "wb") as csvf:
writer = csv.writer(csvf)
writer.writerow(csv_header)
writer.writerows(csvrows)
logger.debug("Wrote data into CSV file %s", csvfile)
return csvfile
def plot_a_slice(self, slice_location=1000):
""" create a plot based on the input data and parameters
:return:
"""
(X, Y, res, sX, sY, xlim, ylim, title,
actual_location) = self.get_slice_data(slice_location)
# make the plot
fdict = {'size': self.font_size, 'weight': 'bold'}
plt.figure(figsize=self.fig_size)
plt.rcParams['font.size'] = self.font_size
# plot station locations
# print("station locations sX:", sX)
# print("station locations sY:", sY)
plt.plot(sX, sY, 'kv') # station marker:'kv'
mesh_plot = plt.pcolormesh(X, Y, res, cmap='bwr_r')
xlim2 = (xlim[0] / self.dscale, xlim[1] / self.dscale)
ylim2 = (ylim[0] / self.dscale, ylim[1] / self.dscale)
plt.xlim(*xlim)
plt.ylim(*ylim)
# set title
plt.title(title, fontdict=fdict)
# if self.plot_orientation == 'z':
# plt.gca().set_aspect('equal') # an axis may be too small to view
plt.gca().set_aspect('auto')
plt.clim(*self.clim)
# plt.colorbar()
# FZ: fix miss-placed colorbar
ax = plt.gca()
ax.xaxis.set_minor_locator(MultipleLocator(
self.xminorticks)) # /self.dscale
ax.yaxis.set_minor_locator(MultipleLocator(
self.yminorticks)) # /self.dscale
ax.tick_params(axis='both', which='minor', width=2, length=5)
ax.tick_params(axis='both',
which='major',
width=3,
length=15,
labelsize=20)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(self.border_linewidth)
# ax.tick_params(axis='both', which='major', labelsize=20)
# ax.tick_params(axis='both', which='minor', labelsize=20)
# http://stackoverflow.com/questions/10171618/changing-plot-scale-by-a-factor-in-matplotlib
xticks = ax.get_xticks() / self.dscale
ax.set_xticklabels(xticks)
yticks = ax.get_yticks() / self.dscale
ax.set_yticklabels(yticks)
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
# pad = separation from figure to colorbar
cax = divider.append_axes("right", size="5%", pad=0.2)
mycb = plt.colorbar(mesh_plot, cax=cax, use_gridspec=True)
mycb.outline.set_linewidth(self.border_linewidth)
mycb.set_label('Resistivity ($\Omega \cdot$m)', fontdict=fdict)
if self.plot_orientation == 'z':
ax.set_ylabel('Northing (' + self.map_scale + ')', fontdict=fdict)
ax.set_xlabel('Easting (' + self.map_scale + ')', fontdict=fdict)
ax.set_aspect(1)
if self.plot_orientation == 'ew':
ax.set_ylabel('Depth (' + self.map_scale + ')', fontdict=fdict)
ax.set_xlabel('Easting (' + self.map_scale + ')', fontdict=fdict)
if self.plot_orientation == 'ns':
ax.set_ylabel('Depth (' + self.map_scale + ')', fontdict=fdict)
ax.set_xlabel('Northing (' + self.map_scale + ')', fontdict=fdict)
plt.show()
return
def plot_multi_slices(self, slice_list=None):
"""
Visualize multiple slices specified by slice_list.
If it is None then will plot every slice at the cell-centres.
:param slice_list:
:return:
"""
if slice_list is None:
# slice_number = 100 # number of evenly spaced slices
if self.plot_orientation == 'ns':
# slice_locs = np.linspace(self.ns_lim[0], self.ns_lim[1], num=slice_number
# It's better to use cell centres
slice_locs = np.mean(
[self.modObj.grid_north[:-1], self.modObj.grid_north[1:]],
axis=0)
if self.plot_orientation == 'ew':
slice_locs = np.mean(
[self.modObj.grid_east[:-1], self.modObj.grid_east[1:]],
axis=0)
if self.plot_orientation == 'z':
slice_locs = np.mean(
[self.modObj.grid_z[:-1], self.modObj.grid_z[1:]], axis=0)
else:
slice_locs = slice_list
logger.debug("Slice locations= %s", slice_locs)
logger.debug("Number of slices to be visualised %s", len(slice_locs))
for dist in slice_locs:
sdist = int(dist)
print("**** The user-input slice location is: ****", sdist)
print(
"**** The actual location will be at the nearest cell centre ****"
)
# plot resistivity image at slices in three orientations at a given slice_location=sdist
self.plot_a_slice(slice_location=sdist
) # actual location will be nearest cell centre
plt.show()
:author: Jared Peacock
:license: MIT
"""
from mtpy.modeling.modem import Data
dfn = r"c:\Users\jpeacock\OneDrive - DOI\Geothermal\GreatBasin\modem_inv\inv_02\gb_modem_data_z03_t02_edits.dat"
new_fns = [
r"c:\Users\jpeacock\OneDrive - DOI\Geothermal\GreatBasin\modem_inv\inv_02\edi_files\AVG055.edi",
r"c:\Users\jpeacock\OneDrive - DOI\Geothermal\GreatBasin\modem_inv\inv_02\edi_files\AVG056.edi",
r"c:\Users\jpeacock\OneDrive - DOI\Geothermal\GreatBasin\modem_inv\inv_02\edi_files\SP05.edi",
r"c:\Users\jpeacock\OneDrive - DOI\Geothermal\GreatBasin\modem_inv\inv_02\edi_files\USArray.CAM02.2010.edi",
r"c:\Users\jpeacock\OneDrive - DOI\Geothermal\GreatBasin\modem_inv\inv_02\edi_files\USArray.CAM06.2010.edi",
]
d = Data()
d.read_data_file(dfn)
d.error_type_z = "eigen_floor"
d.error_value_z = 0.03
d.error_type_tipper = "abs_floor"
d.error_value_tipper = 0.02
d.model_epsg = 32611
d.data_array, d.mt_dict = d.add_station(fn=new_fns)
d.write_data_file(fn_basename="gb_modem_data_z03_t02_add.dat",
compute_error=False,
fill=False)
######## inputs ##############
wd = r'C:\mtpywin\mtpy\examples\model_files\ModEM_2'
savepath = r'C:\test'
model_fn = op.join(wd, 'Modular_MPI_NLCG_004.rho')
data_fn = op.join(wd, 'ModEM_Data.dat')
location_type = 'LL' # 'EN' to save eastings/northings, 'LL' to save longitude/latitude, need to provide model_epsg if using 'LL'
model_epsg = 28355 # epsg number model was projected in. common epsg numbers:
# 28351 (GDA94, mga zone 51), 28352 (mga zone 52), 28353 (mga zone 53),
# 28354 (mga zone 54), 28355 (mga zone 55), 28356 (mga zone 56)
# 3112 (Geoscience Australia Lambert)
# go to http://spatialreference.org/ref/epsg/?search=&srtext=Search for more info
model_utm_zone = '55S' # alternative to epsg, can provide utm zone
depth_indices = [44, 45, 46] # indices that define depth
##############################
# get the real-world origin from the data file
dataObj = Data(data_fn=data_fn, model_epsg=model_epsg)
dataObj.read_data_file()
origin = [dataObj.center_point['east'][0], dataObj.center_point['north'][0]]
modObj = Model()
modObj.read_model_file(model_fn=op.join(wd, model_fn))
modObj.write_xyres(origin=origin,
savepath=savepath,
location_type=location_type,
model_epsg=model_epsg,
model_utm_zone=None,
log_res=True,
outfile_basename='GMTtest',
depth_index=depth_indices)
stationdist = 50000
# z limits (positive down so order is reversed)
zlim = (1e5, -5e3)
# colour limits
clim = [0.3, 3.7]
iterfn = 'Modular_MPI_NLCG_019.rho'
datafn = 'ModEM_Data_noise10inv.dat'
# END INPUTS #
read_data = True
if read_data:
doo = Data()
doo.read_data_file(op.join(modeldir, datafn))
moo = Model(model_fn=op.join(modeldir, iterfn))
moo.read_model_file()
# get grid centres
gcz = np.mean([moo.grid_z[:-1], moo.grid_z[1:]], axis=0)
gceast, gcnorth = [np.mean([arr[:-1], arr[1:]], axis=0)
for arr in [moo.grid_east, moo.grid_north]]
# distance from slice to grid centre locations
if plotdir == 'ew':
sdist = np.abs(gcnorth - slice_location)
elif plotdir == 'ns':
sdist = np.abs(gceast - slice_location)
elif plotdir == 'z':
sdist = np.abs(gcz - slice_location)
wd = r'C:\mtpywin\mtpy\examples\model_files\ModEM_2'
savepath = r'C:/tmp'
# get epsg and centre position of model
epsg = 28353 # epsg code for projection the model was projected to when creating the grid
# define model and data files
model_fn = op.join(wd, 'Modular_MPI_NLCG_004.rho')
data_fn = op.join(wd, 'ModEM_Data.dat')
mObj = Model()
mObj.read_model_file(model_fn=model_fn)
dObj = Data()
dObj.read_data_file(data_fn=data_fn)
# get easting and northing of model grid
east = mObj.grid_east + dObj.center_point['east']
north = mObj.grid_north + dObj.center_point['north']
# grid centres
gcx, gcy = [[np.mean(arr[i:i + 2]) for i in range(len(arr) - 1)]
for arr in [east, north]]
# make a meshgrid, save the shape
east_grid, north_grid = np.meshgrid(east, north)
shape = east_grid.shape
# project grid to lat, lon
lonr, latr = gis_tools.epsg_project(east_grid, north_grid, epsg, 4326)
class PlotRMSMaps(object):
"""
plots the RMS as (data-model)/(error) in map view for all components
of the data file. Gets this infomration from the .res file output
by ModEM.
Arguments:
------------------
**residual_fn** : string
full path to .res file
=================== =======================================================
Attributes Description
=================== =======================================================
fig matplotlib.figure instance for a single plot
fig_dpi dots-per-inch resolution of figure *default* is 200
fig_num number of fig instance *default* is 1
fig_size size of figure in inches [width, height]
*default* is [7,6]
font_size font size of tick labels, axis labels are +2
*default* is 8
marker marker style for station rms,
see matplotlib.line for options,
*default* is 's' --> square
marker_size size of marker in points. *default* is 10
pad_x padding in map units from edge of the axis to stations
at the extremeties in longitude.
*default* is 1/2 tick_locator
pad_y padding in map units from edge of the axis to stations
at the extremeties in latitude.
*default* is 1/2 tick_locator
period_index index of the period you want to plot according to
self.residual.period_list. *default* is 1
plot_yn [ 'y' | 'n' ] default is 'y' to plot on instantiation
plot_z_list internal variable for plotting
residual modem.Data instance that holds all the information
from the residual_fn given
residual_fn full path to .res file
rms_cmap matplotlib.cm object for coloring the markers
rms_cmap_dict dictionary of color values for rms_cmap
rms_max maximum rms to plot. *default* is 5.0
rms_min minimum rms to plot. *default* is 1.0
save_path path to save figures to. *default* is directory of
residual_fn
subplot_bottom spacing from axis to bottom of figure canvas.
*default* is .1
subplot_hspace horizontal spacing between subplots.
*default* is .1
subplot_left spacing from axis to left of figure canvas.
*default* is .1
subplot_right spacing from axis to right of figure canvas.
*default* is .9
subplot_top spacing from axis to top of figure canvas.
*default* is .95
subplot_vspace vertical spacing between subplots.
*default* is .01
tick_locator increment for x and y major ticks. *default* is
limits/5
=================== =======================================================
=================== =======================================================
Methods Description
=================== =======================================================
plot plot rms maps for a single period
plot_loop loop over all frequencies and save figures to save_path
read_residual_fn read in residual_fn
redraw_plot after updating attributes call redraw_plot to
well redraw the plot
save_figure save the figure to a file
=================== =======================================================
:Example: ::
>>> rms_plot = Plot_RMS_Maps(r"/home/ModEM/Inv1/mb_NLCG_030.res")
>>> # change some attributes
>>> rms_plot.fig_size = [6, 4]
>>> rms_plot.rms_max = 3
>>> rms_plot.redraw_plot()
>>> # happy with the look now loop over all periods
>>> rms_plot.plot_loop()
"""
def __init__(self, residual_fn, **kwargs):
self.residual_fn = residual_fn
self.residual = None
self.save_path = kwargs.pop(
'save_path', os.path.dirname(
self.residual_fn))
self.period_index = kwargs.pop(
'period_index', 0) # where is depth_index?
self.subplot_left = kwargs.pop('subplot_left', .1)
self.subplot_right = kwargs.pop('subplot_right', .9)
self.subplot_top = kwargs.pop('subplot_top', .95)
self.subplot_bottom = kwargs.pop('subplot_bottom', .1)
self.subplot_hspace = kwargs.pop('subplot_hspace', .1)
self.subplot_vspace = kwargs.pop('subplot_vspace', .01)
self.font_size = kwargs.pop('font_size', 8)
self.fig_size = kwargs.pop('fig_size', [7.75, 6.75])
self.fig_dpi = kwargs.pop('fig_dpi', 200)
self.fig_num = kwargs.pop('fig_num', 1)
self.fig = None
self.marker = kwargs.pop('marker', 's')
self.marker_size = kwargs.pop('marker_size', 10)
self.rms_max = kwargs.pop('rms_max', 5)
self.rms_min = kwargs.pop('rms_min', 0)
self.tick_locator = kwargs.pop('tick_locator', None)
self.pad_x = kwargs.pop('pad_x', None)
self.pad_y = kwargs.pop('pad_y', None)
self.plot_yn = kwargs.pop('plot_yn', 'y')
# colormap for rms, goes white to black from 0 to rms max and
# red below 1 to show where the data is being over fit
self.rms_cmap_dict = {'red': ((0.0, 1.0, 1.0),
(0.2, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'green': ((0.0, 0.0, 0.0),
(0.2, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.0),
(0.2, 1.0, 1.0),
(1.0, 0.0, 0.0))}
self.rms_cmap = colors.LinearSegmentedColormap('rms_cmap',
self.rms_cmap_dict,
256)
self.plot_z_list = [{'label': r'$Z_{xx}$', 'index': (0, 0), 'plot_num': 1},
{'label': r'$Z_{xy}$', 'index': (
0, 1), 'plot_num': 2},
{'label': r'$Z_{yx}$', 'index': (
1, 0), 'plot_num': 3},
{'label': r'$Z_{yy}$', 'index': (
1, 1), 'plot_num': 4},
{'label': r'$T_{x}$', 'index': (
0, 0), 'plot_num': 5},
{'label': r'$T_{y}$', 'index': (0, 1), 'plot_num': 6}]
if self.plot_yn == 'y':
self.plot()
def read_residual_fn(self):
if self.residual is None:
self.residual = Data()
self.residual.read_data_file(self.residual_fn)
else:
pass
def plot(self):
"""
plot rms in map view
"""
self.read_residual_fn()
font_dict = {'size': self.font_size + 2, 'weight': 'bold'}
rms_1 = 1. / self.rms_max
if self.tick_locator is None:
x_locator = np.round((self.residual.data_array['lon'].max() -
self.residual.data_array['lon'].min()) / 5, 2)
y_locator = np.round((self.residual.data_array['lat'].max() -
self.residual.data_array['lat'].min()) / 5, 2)
if x_locator > y_locator:
self.tick_locator = x_locator
elif x_locator < y_locator:
self.tick_locator = y_locator
if self.pad_x is None:
self.pad_x = self.tick_locator / 2
if self.pad_y is None:
self.pad_y = self.tick_locator / 2
plt.rcParams['font.size'] = self.font_size
plt.rcParams['figure.subplot.left'] = self.subplot_left
plt.rcParams['figure.subplot.right'] = self.subplot_right
plt.rcParams['figure.subplot.bottom'] = self.subplot_bottom
plt.rcParams['figure.subplot.top'] = self.subplot_top
plt.rcParams['figure.subplot.wspace'] = self.subplot_hspace
plt.rcParams['figure.subplot.hspace'] = self.subplot_vspace
self.fig = plt.figure(self.fig_num, self.fig_size, dpi=self.fig_dpi)
for p_dict in self.plot_z_list:
ax = self.fig.add_subplot(3, 2, p_dict['plot_num'], aspect='equal')
ii = p_dict['index'][0]
jj = p_dict['index'][0]
for r_arr in self.residual.data_array:
# calulate the rms self.residual/error
if p_dict['plot_num'] < 5:
rms = r_arr['z'][self.period_index, ii, jj].__abs__() / \
(r_arr['z_err'][self.period_index, ii, jj].real)
else:
rms = r_arr['tip'][self.period_index, ii, jj].__abs__() / \
(r_arr['tip_err'][self.period_index, ii, jj].real)
# color appropriately
if np.nan_to_num(rms) == 0.0:
marker_color = (1, 1, 1)
marker = '.'
marker_size = .1
marker_edge_color = (1, 1, 1)
if rms > self.rms_max:
marker_color = (0, 0, 0)
marker = self.marker
marker_size = self.marker_size
marker_edge_color = (0, 0, 0)
elif rms >= 1 and rms <= self.rms_max:
r_color = 1 - rms / self.rms_max + rms_1
marker_color = (r_color, r_color, r_color)
marker = self.marker
marker_size = self.marker_size
marker_edge_color = (0, 0, 0)
elif rms < 1:
r_color = 1 - rms / self.rms_max
marker_color = (1, r_color, r_color)
marker = self.marker
marker_size = self.marker_size
marker_edge_color = (0, 0, 0)
ax.plot(r_arr['lon'], r_arr['lat'],
marker=marker,
ms=marker_size,
mec=marker_edge_color,
mfc=marker_color,
zorder=3)
if p_dict['plot_num'] == 1 or p_dict['plot_num'] == 3:
ax.set_ylabel('Latitude (deg)', fontdict=font_dict)
plt.setp(ax.get_xticklabels(), visible=False)
elif p_dict['plot_num'] == 2 or p_dict['plot_num'] == 4:
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
elif p_dict['plot_num'] == 6:
plt.setp(ax.get_yticklabels(), visible=False)
ax.set_xlabel('Longitude (deg)', fontdict=font_dict)
else:
ax.set_xlabel('Longitude (deg)', fontdict=font_dict)
ax.set_ylabel('Latitude (deg)', fontdict=font_dict)
ax.text(self.residual.data_array['lon'].min() + .005 - self.pad_x,
self.residual.data_array['lat'].max() - .005 + self.pad_y,
p_dict['label'],
verticalalignment='top',
horizontalalignment='left',
bbox={'facecolor': 'white'},
zorder=3)
ax.tick_params(direction='out')
ax.grid(zorder=0, color=(.75, .75, .75))
# [line.set_zorder(3) for line in ax.lines]
ax.set_xlim(self.residual.data_array['lon'].min() - self.pad_x,
self.residual.data_array['lon'].max() + self.pad_x)
ax.set_ylim(self.residual.data_array['lat'].min() - self.pad_y,
self.residual.data_array['lat'].max() + self.pad_y)
ax.xaxis.set_major_locator(MultipleLocator(self.tick_locator))
ax.yaxis.set_major_locator(MultipleLocator(self.tick_locator))
ax.xaxis.set_major_formatter(FormatStrFormatter('%2.2f'))
ax.yaxis.set_major_formatter(FormatStrFormatter('%2.2f'))
# cb_ax = mcb.make_axes(ax, orientation='vertical', fraction=.1)
cb_ax = self.fig.add_axes([self.subplot_right + .02, .225, .02, .45])
color_bar = mcb.ColorbarBase(cb_ax,
cmap=self.rms_cmap,
norm=colors.Normalize(vmin=self.rms_min,
vmax=self.rms_max),
orientation='vertical')
color_bar.set_label('RMS', fontdict=font_dict)
self.fig.suptitle('period = {0:.5g} (s)'.format(self.residual.period_list[self.period_index]),
fontdict={'size': self.font_size + 3, 'weight': 'bold'})
plt.show()
def redraw_plot(self):
plt.close('all')
self.plot()
def save_figure(self, save_path=None, save_fn_basename=None,
save_fig_dpi=None, fig_format='.png', fig_close=True):
"""
save figure in the desired format
"""
if save_path is not None:
self.save_path = save_path
if save_fn_basename is not None:
pass
else:
save_fn_basename = '{0:02}_RMS_{1:.5g}_s.{2}'.format(self.period_index,
self.residual.period_list[
self.period_index],
fig_format)
save_fn = os.path.join(self.save_path, save_fn_basename)
if save_fig_dpi is not None:
self.fig_dpi = save_fig_dpi
self.fig.savefig(save_fn, dpi=self.fig_dpi)
print 'saved file to {0}'.format(save_fn)
if fig_close == True:
plt.close('all')
def plot_loop(self, fig_format='png'):
"""
loop over all periods and save figures accordingly
"""
self.read_residual_fn()
for f_index in range(self.residual.period_list.shape[0]):
# FZ:
print(self.residual.period_list)
print(f_index)
self.period_index = f_index
self.plot()
self.save_figure(fig_format=fig_format)
import numpy as np
from mtpy.modeling.modem import Data, Model
if __name__ == "__main__":
#workdir = r'C:\Git\mtpy\examples\data'
workdir = r'E:\Githubz\mtpy\examples\data'
modeldir = op.join(workdir,
'ModEM_files') # folder where *.rho files exist
read_data = True
iterfn = max([ff for ff in os.listdir(modeldir) if ff.endswith('.rho')])
if read_data:
doo = Data()
doo.read_data_file(op.join(modeldir, 'ModEM_Data.dat'))
moo = Model(model_fn=op.join(modeldir, iterfn))
moo.read_model_file()
snoew = 10
snons = 10
snoz = np.where(moo.grid_z > 80000)[0][0]
gcz = np.mean([moo.grid_z[:-1], moo.grid_z[1:]], axis=0)
plotdir = 'ew'
if plotdir == 'ew':
X, Y, res = moo.grid_east, moo.grid_z, np.log10(
moo.res_model[snoew, :, :].T)
xlim = (-25000, 25000)
ylim = (1e4, 0)
sliceinfo = ''