Plotting
- structure_factor.plotting.plot_poisson(x, axis, c='k', linestyle=(0, (5, 10)), label='Poisson')[source]
Plot the pair correlation function \(g_{poisson}\) and the structure factor \(S_{poisson}\) corresponding to the Poisson point process.
- Parameters
x (numpy.ndarray) – x coordinate.
axis (plt.Axes) – Axis on which to add the plot.
c (str, optional) – Color of the plot. see matplotlib . Defaults to “k”.
linestyle (tuple, optional) – Linstyle of the plot. see linestyle. Defaults to (0, (5, 10)).
label (regexp, optional) – Label of the plot. Defaults to “Poisson”.
- Returns
Plot of the pair correlation function and the structure factor of the Poisson point process over
x
.- Return type
plt.Axes
- structure_factor.plotting.plot_summary(x, y, axis, scale='log', label='mean $\\pm$ 3 $\\cdot$ std', fmt='b', ecolor='r', **binning_params)[source]
Loglog plot the summary results of
_bin_statistics()
i.e., means and errors bars (3 standard deviations).- Parameters
x (numpy.ndarray) – x coordinate.
y (numpy.ndarray) – y coordinate.
axis (plt.Axes) – Axis on which to add the plot.
label (regexp, optional) – Label of the plot. Defaults to r”mean $pm$ 3 $cdot$ std”.
- Returns
Plot of the results of
_bin_statistics()
applied onx
andy
.- Return type
plt.Axes
- structure_factor.plotting.plot_exact(x, y, axis, label)[source]
Loglog plot of a callable function
y
evaluated on the vectorx
.- Parameters
x (numpy.ndarray) – x coordinate.
y (numpy.ndarray) – y coordinate.
axis (plt.Axes) – Axis on which to add the plot.
label (regexp, optional) – Label of the plot.
- Returns
Plot of
y
with respect tox
.- Return type
plt.Axes
- structure_factor.plotting.plot_approximation(x, y, axis, rasterized, label, color, linestyle, marker, markersize, scale='log')[source]
Loglog plot of
y
w.r.t.x
.- Parameters
x (numpy.ndarray) – x coordinate.
y (numpy.ndarray) – y coordinate.
axis (plt.Axes) – Axis on which to add the plot.
rasterized (bool) – Rasterized option of matlplotlib.plot.
label (regexp, optional) – Label of the plot.
color (matplotlib.color) – Color of the plot. see color .
linestyle (tuple) –
Style of the plot. see linestyle.
marker (matplotlib.marker) – Marker of marker.
markersize (float) – Marker size.
scale (str, optional) – Trigger between plot scales of plt.Axes. Defaults to log.
- Returns
Loglog plot of
y
w.r.t.x
- Return type
plt.Axes
- structure_factor.plotting.plot_estimation_showcase(k_norm, estimation, axis=None, scale='log', exact_sf=None, error_bar=False, label='$\\widehat{S}$', rasterized=True, file_name='', **binning_params)[source]
Loglog plot of the results of the scattering intensity
scattering_intensity()
, with the means and error bars over specific number of bins found via_bin_statistics()
.- Parameters
k_norm (numpy.ndarray) – Wavenumbers.
estimation (numpy.ndarray) – Scattering intensity corresponding to
k_norm
.axis (plt.Axes, optional) – Axis on which to add the plot. Defaults to None.
scale (str, optional) – Trigger between plot scales of matplotlib.plot. Defaults to log.
exact_sf (callable, optional) – Structure factor of the point process. Defaults to None.
error_bar (bool, optional) – If
True
,k_norm
and correspondinglyestimation
are divided into sub-intervals (bins). Over each bin, the mean and the standard deviation ofestimation
are derived and visualized on the plot. Note that each error bar corresponds to the mean +/- 3 standard deviation. To specify the number of bins, add it to the kwargs argumentbinning_params
. For more details see_bin_statistics()
. Defaults to False.rasterized (bool, optional) –
Rasterized option of matlplotlib.plot. Defaults to True.
file_name (str, optional) – Name used to save the figure. The available output formats depend on the backend being used. Defaults to “”.
- structure_factor.plotting.plot_estimation_imshow(k_norm, si, axis, file_name)[source]
Color level 2D plot, centered on zero.
- Parameters
k_norm (numpy.ndarray) – Wavenumbers.
si (numpy.ndarray) – Scattering intensity corresponding to
k_norm
.axis (plt.Axes) – Axis on which to add the plot.
file_name (str, optional) – Name used to save the figure. The available output formats depend on the backend being used. Defaults to “”.
- structure_factor.plotting.plot_estimation_all(point_pattern, k_norm, estimation, exact_sf=None, error_bar=False, label='$\\widehat{S}$', rasterized=True, file_name='', window_res=None, scale='log', **binning_params)[source]
Construct 3 subplots: point pattern, associated scattering intensity plot, associated scattering intensity color level (only for 2D point processes).
- Parameters
point_pattern (
PointPattern
) – Object of type PointPattern containing a realizationpoint_pattern.points
of a point process, the window where the points were simulatedpoint_pattern.window
and (optionally) the intensity of the point processpoint_pattern.intensity
.k_norm (numpy.ndarray) – Wavenumbers.
estimation (numpy.ndarray) – Scattering intensity corresponding to
k_norm
.exact_sf (callable, optional) – Structure factor of the point process. Defaults to None.
error_bar (bool, optional) – If
True
,k_norm
and correspondinglyestimation
are divided into sub-intervals (bins). Over each bin, the mean and the standard deviation ofestimation
are derived and visualized on the plot. Note that each error bar corresponds to the mean +/- 3 standard deviation. To specify the number of bins, add it to the kwargs argumentbinning_params
. For more details see_bin_statistics()
. Defaults to False.rasterized (bool, optional) –
Rasterized option of matlplotlib.plot. Defaults to True.
file_name (str, optional) – Name used to save the figure. The available output formats depend on the backend being used. Defaults to “”.
window_res (
AbstractSpatialWindow
, optional) – New restriction window. It is useful when the sample of points is large, so for time and visualization purposes, it is better to restrict the plot of the point process to a smaller window. Defaults to None.scale (str, optional) –
Trigger between plot scales of matplotlib.plot. Defaults to log.
- structure_factor.plotting.plot_sf_hankel_quadrature(k_norm, estimation, axis, scale, k_norm_min, exact_sf, color, error_bar, label, file_name, **binning_params)[source]
Plot the approximations of the structure factor (results of
quadrature_estimator_isotropic()
) with means and error bars over bins, see_bin_statistics()
.- Parameters
k_norm (numpy.ndarray) – Vector of wavenumbers (i.e., norms of waves) on which the structure factor has been approximated.
estimation (numpy.ndarray) – Approximation of the structure factor corresponding to
k_norm
.axis (plt.Axes) – Support axis of the plots.
scale (str) –
Trigger between plot scales of matplotlib.plot.
k_norm_min (float) – Estimated lower bound of the wavenumbers (only when
estimation
was approximated using Ogata quadrature).exact_sf (callable) – Theoretical structure factor of the point process.
error_bar (bool) – If
True
,k_norm
and correspondinglysi
are divided into sub-intervals (bins). Over each bin, the mean and the standard deviation ofsi
are derived and visualized on the plot. Note that each error bar corresponds to the mean +/- 3 standard deviation. To specify the number of bins, add it to the kwargs argumentbinning_params
. For more details see_bin_statistics()
. Defaults to False.file_name (str) – Name used to save the figure. The available output formats depend on the backend being used.
label (regexp) – Label of the plot.
- Keyword Arguments
binning_params – (dict): Used when
error_bar=True
, by the methodutils_bin_statistics()
as keyword arguments (except"statistic"
) ofscipy.stats.binned_statistic
.