"""Tools for electromagnetics."""
import numpy as np
import pygimli as pg
[docs]
def cmapDAERO():
"""Standardized colormap from A-AERO projects (purple=0.3 to red=500)."""
from matplotlib.colors import LinearSegmentedColormap
CMY = np.array([
[127, 255, 31], [111, 255, 47], [95, 255, 63], [79, 255, 79],
[63, 255, 95], [47, 255, 111], [31, 255, 127], [16, 255, 159],
[0, 223, 159], [0, 191, 159], [0, 159, 207], [0, 127, 175],
[0, 95, 175], [0, 63, 175], [0, 47, 175], [0, 31, 191], [0, 0, 255],
[0, 0, 159], [15, 0, 127], [47, 0, 143], [79, 0, 143], [111, 0, 143],
[143, 0, 127], [159, 31, 63], [175, 47, 31], [207, 63, 0],
[223, 111, 0], [231, 135, 0], [239, 159, 0], [255, 191, 47],
[239, 199, 63], [223, 207, 79], [207, 239, 111]], dtype=float)
RGB = 1.0 - CMY/255
return LinearSegmentedColormap.from_list('D-AERO', RGB)
def registerDAEROcmap():
"""Standardized colormap from A-AERO projects (purple=0.3 to red=500).
Example
-------
>>> import pygimli as pg
>>> cmap = pg.physics.em.tools.registerDAEROcmap()
>>> mesh = pg.createGrid(20,2)
>>> data = pg.x(mesh.cellCenters())
>>> _ = pg.show(mesh, data, cMap=cmap)
"""
from matplotlib.colors import LinearSegmentedColormap
import matplotlib as mpl
CMY = np.array([
[127, 255, 31], [111, 255, 47], [95, 255, 63], [79, 255, 79],
[63, 255, 95], [47, 255, 111], [31, 255, 127], [16, 255, 159],
[0, 223, 159], [0, 191, 159], [0, 159, 207], [0, 127, 175],
[0, 95, 175], [0, 63, 175], [0, 47, 175], [0, 31, 191], [0, 0, 255],
[0, 0, 159], [15, 0, 127], [47, 0, 143], [79, 0, 143], [111, 0, 143],
[143, 0, 127], [159, 31, 63], [175, 47, 31], [207, 63, 0],
[223, 111, 0], [231, 135, 0], [239, 159, 0], [255, 191, 47],
[239, 199, 63], [223, 207, 79], [207, 239, 111]], dtype=float)
RGB = 1.0 - CMY/255
daero = LinearSegmentedColormap.from_list('D-AERO', RGB)
mpl.colormaps.register(name='daero', cmap=daero)
return daero
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def xfplot(ax, DATA, x, freq, everyx=5, orientation='horizontal', aspect=30,
label=None, cMap="Spectral_r"):
"""Plot a matrix according to x and frequencies."""
from mpl_toolkits.axes_grid1 import make_axes_locatable
nt = list(range(0, len(x), everyx))
im = ax.matshow(DATA.T, interpolation='nearest', cmap=cMap)
ax.set_ylim(ax.get_ylim()[::-1])
ax.set_xticks(nt)
ax.set_xticklabels([f"{xi:g}" for xi in x[nt]])
ax.set_yticks(list(range(0, len(freq) + 1, 2)))
ax.set_yticklabels([f"{freq[i]:g}" for i in range(0, len(freq), 2)])
ax.set_xlabel('x [m]')
ax.set_ylabel('f [Hz]')
ax.xaxis.set_label_position('top')
ax.set_aspect("auto")
divider = make_axes_locatable(ax)
cax = divider.append_axes('bottom', size='5%', pad=0.3)
pg.plt.colorbar(im, ax=ax, cax=cax, orientation=orientation, aspect=aspect)
if label is not None:
cax.set_title(label)
# plt.colorbar(im, ax=ax, orientation=orientation, aspect=aspect)
return im
[docs]
class FDEMsystems:
"""FDEM system frequency/distance definitions."""
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def __init__(self):
"""Initialize FDEM system definitions."""
self.frequencies = {
'MaxMin10': 2**np.arange(10) * 110.,
'MaxMin8': 2**np.arange(8) * 110.,
'ResolveHCP': np.array([387., 1820., 8330., 41500., 133400.]),
'ResolveVCX': 5410.,
'ResolveHCPOld': np.array([380., 1770., 8300., 41000., 129500.]),
'BKS36a': np.array([386, 1817, 8360, 41420, 133200, 5390]),
'BKS60': np.array([380, 1773, 8300, 41000, 129500, 5410])
}
self.distances = {
'MaxMin10': np.array([3.66, 4.57, 5.49, 6.40, 7.32, 8.23, 9.14,
10.06, 10.97, 11.89]),
'MaxMin8': np.array([3.66, 5.49, 7.32, 9.14, 10.97,
12.80, 14.63, 16.46]),
'ResolveHCP': np.array([7.938, 7.931, 7.925, 7.912, 7.918]),
'ResolveVCX': 9.055,
'ResolveHCPOld': np.array([7.918, 7.918, 7.957, 8.033, 7.906]),
'BKS36a': np.array([7.938, 7.931, 7.925, 7.912, 7.918, 9.055]),
'BKS60': np.array([7.918, 7.918, 7.957, 8.033, 7.906, 9.042])
}
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def getFrequenciesDistances(self, system):
"""Get frequencies for a given system."""
return self.frequencies.get(system, None), \
self.distances.get(system, None)
