def test_plot_comparison(self, example_shp, tmpdir):
matplotlib.pyplot.clf()
x, y, _ = spatial_efd.ProcessGeometry(example_shp[0])
coeffs = spatial_efd.CalculateEFD(x, y, 10)
ax = spatial_efd.InitPlot()
spatial_efd.plotComparison(ax, coeffs, 10, x, y)
spatial_efd.SavePlot(ax, 10, tmpdir, 'png')
assert os.path.isfile('{0}_10.png'.format(tmpdir))
def test_plot_comparison_norm(self, example_shp, tmpdir):
x, y, _ = spatial_efd.ProcessGeometry(example_shp[1])
coeffs = spatial_efd.CalculateEFD(x, y, 10)
coeffs, rotation = spatial_efd.normalize_efd(coeffs,
size_invariant=False)
ax = spatial_efd.InitPlot()
spatial_efd.plotComparison(ax, coeffs, 7, x, y, rotation=rotation)
spatial_efd.SavePlot(ax, 7, tmpdir, 'png')
assert os.path.isfile('{0}_7.png'.format(tmpdir))
def reduce_cnts_based_on_coeffs(coeffs: list,
cnts: list,
percentile=75,
plot=True) -> list:
avgcoeffs = spatial_efd.AverageCoefficients(coeffs)
SDcoeffs = spatial_efd.AverageSD(coeffs, avgcoeffs)
if plot:
median = np.median(np.array(coeffs), axis=0)
x_med, y_med = spatial_efd.inverse_transform(median, harmonic=10)
x_avg, y_avg = spatial_efd.inverse_transform(avgcoeffs, harmonic=10)
x_sd, y_sd = spatial_efd.inverse_transform(SDcoeffs, harmonic=10)
ax = spatial_efd.InitPlot()
spatial_efd.PlotEllipse(ax, x_avg, y_avg, color="w", width=2.0)
spatial_efd.PlotEllipse(ax, x_med, y_med, color="b", width=2.0)
# Plot avg +/- 1 SD error ellipses
spatial_efd.PlotEllipse(ax,
x_avg + x_sd,
y_avg + y_sd,
color="r",
width=1.0)
spatial_efd.PlotEllipse(ax,
x_avg - x_sd,
y_avg - y_sd,
color="r",
width=1.0)
plt.close("all")
arr = np.array(coeffs)
reshaped = np.reshape(arr, [arr.shape[0], -1])
MCD = MinCovDet()
MCD.fit(reshaped)
a = MCD.mahalanobis(reshaped)
if plot:
plt.boxplot(a)
plt.show()
plt.close("all")
percentile = np.percentile(a, percentile)
reduced = list(np.array(coeffs)[a < percentile])
avgcoeffs = spatial_efd.AverageCoefficients(reduced)
SDcoeffs = spatial_efd.AverageSD(reduced, avgcoeffs)
median = np.median(np.array(reduced), axis=0)
x_med, y_med = spatial_efd.inverse_transform(median, harmonic=10)
x_avg, y_avg = spatial_efd.inverse_transform(avgcoeffs, harmonic=10)
x_sd, y_sd = spatial_efd.inverse_transform(0.1 * SDcoeffs, harmonic=10)
if plot:
ax = spatial_efd.InitPlot()
spatial_efd.PlotEllipse(ax, x_avg, y_avg, color="w", width=2.0)
spatial_efd.PlotEllipse(ax, x_med, y_med, color="b", width=2.0)
# Plot avg +/- 1 SD error ellipses
spatial_efd.PlotEllipse(ax,
x_avg + x_sd,
y_avg + y_sd,
color="r",
width=1.0)
spatial_efd.PlotEllipse(ax,
x_avg - x_sd,
y_avg - y_sd,
color="r",
width=1.0)
i = 10
plt.figure()
ax = plt.gca()
spatial_efd.plotComparison(
ax,
coeffs[i],
10,
cnts[i][:, 0],
cnts[i][:, 1],
color1="w",
rotation=rots[i],
)
plt.show()
plt.close("all")
reduced_cnts = np.array(cnts)[a < percentile]
return reduced_cnts, a < percentile