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@@ -66,8 +66,8 @@ terms, E, H = fieldnlay(x, m, coord)
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Er = np.absolute(E)
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# |E|/|Eo|
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-#Eh = np.sqrt(Er[0, :, 0]**2 + Er[0, :, 1]**2 + Er[0, :, 2]**2)
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-Eh = Er[0, :, 0]**2 + Er[0, :, 1]**2 + Er[0, :, 2]**2
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+Eh = np.sqrt(Er[0, :, 0]**2 + Er[0, :, 1]**2 + Er[0, :, 2]**2)
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+#Eh = Er[0, :, 0]**2 + Er[0, :, 1]**2 + Er[0, :, 2]**2
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result = np.vstack((coordX, coordY, coordZ, Eh)).transpose()
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@@ -91,13 +91,13 @@ try:
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min_tick = max(0.1, min(min_tick, np.amin(edata)))
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max_tick = max(max_tick, np.amax(edata))
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#scale_ticks = np.power(10.0, np.linspace(np.log10(min_tick), np.log10(max_tick), 6))
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- scale_ticks = np.linspace(min_tick,min_tick*1.2, 11)
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+ scale_ticks = np.linspace(min_tick,max_tick, 11)
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#scale_ticks = np.linspace(0, 2, 11)
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# Interpolation can be 'nearest', 'bilinear' or 'bicubic'
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cax = ax.imshow(edata, interpolation = 'nearest', cmap = cm.afmhot,
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- #origin = 'lower', vmin = min_tick, vmax = max_tick,
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- origin = 'lower', vmin = 0.25, vmax = 1,
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+ origin = 'lower', vmin = min_tick, vmax = max_tick,
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+ #origin = 'lower', vmin = 0.25, vmax = 1,
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extent = (min(scale_x), max(scale_x), min(scale_y), max(scale_y))
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#,norm = LogNorm()
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)
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