solarwindpy.plotting.spiral

Spiral mesh plots and associated binning utilities.

Functions

calculate_bin_number_with_numba(mesh, x, y)
get_counts_per_bin(bins, x, y)

Classes

InitialSpiralEdges(x, y)
SpiralFilterThresholds(density, size)
SpiralMesh(x, y, initial_xedges, initial_yedges)
SpiralMeshBinID(id, fill, visited)
SpiralPlot2D(x, y[, z, logx, logy, ...])	2D spiral plotting with adaptive mesh refinement.

class InitialSpiralEdges(x, y)

Bases: tuple

count(value, /)

Return number of occurrences of value.

index(value, start=0, stop=sys.maxsize, /)

Return first index of value.

Raises ValueError if the value is not present.

x

Alias for field number 0

y

Alias for field number 1

class SpiralMeshBinID(id, fill, visited)

Bases: tuple

count(value, /)

Return number of occurrences of value.

fill

Alias for field number 1

id

Alias for field number 0

index(value, start=0, stop=sys.maxsize, /)

Return first index of value.

Raises ValueError if the value is not present.

visited

Alias for field number 2

class SpiralFilterThresholds(density, size)

Bases: tuple

count(value, /)

Return number of occurrences of value.

density

Alias for field number 0

index(value, start=0, stop=sys.maxsize, /)

Return first index of value.

Raises ValueError if the value is not present.

size

Alias for field number 1

get_counts_per_bin(bins, x, y)

calculate_bin_number_with_numba(mesh, x, y)

class SpiralMesh(x, y, initial_xedges, initial_yedges, min_per_bin=250)

Bases: object

__init__(x, y, initial_xedges, initial_yedges, min_per_bin=250)

property bin_id

property cat

pd.Categorical version of bin_id, with fill bin removed.

property data

property initial_edges

property mesh

property min_per_bin

property cell_filter_thresholds

property cell_filter

Boolean Series identifying properly filled mesh cells.

Series selects mesh cells that meet density and area criteria specified by mesh_cell_filter_thresholds().

Notes

Neither density nor size convert log-scale edges into linear scale. Doing so would overweight the area of mesh cells at larger values on a given axis.

set_cell_filter_thresholds(**kwargs)

Set or update the mesh_cell_filter_thresholds().

Parameters:

• density (scalar) – The density quantile above which we want to select bins, e.g. above the 0.01 quantile. This ensures that each bin meets some sufficient fill factor.

• size (scalar) – The size quantile below which we want to select bins, e.g. below the 0.99 quantile. This ensures that the bin isn’t so large that it will appear as an outlier.

set_initial_edges(xedges, yedges)

set_data(x, y)

set_min_per_bin(new)

initialize_bins()

static process_one_spiral_step(bins, x, y, min_per_bin)

generate_mesh()

calculate_bin_number()

place_spectra_in_mesh()

build_cat()

class SpiralPlot2D(x, y, z=None, logx=False, logy=False, initial_bins=5, clip_data=False)

Bases: PlotWithZdata, CbarMaker

2D spiral plotting with adaptive mesh refinement.

Examples

splot = SpiralPlot2D(…) splot.initialize_mesh()

__init__(x, y, z=None, logx=False, logy=False, initial_bins=5, clip_data=False)

property clim

property initial_bins

property grouped

property mesh

agg(fcn=None)

Aggregate the z-values into their bins.

build_grouped()

calc_initial_bins(nbins)

initialize_mesh(**kwargs)

set_clim(lower=None, upper=None)

Set the min (lower) and max (upper) counts per bin.

This limit is applied after the groupby.agg() is run.

set_data(x, y, z, clip)

make_plot(ax=None, cbar=True, limit_color_norm=False, cbar_kwargs=None, fcn=None, alpha_fcn=None, **kwargs)

plot_contours(ax=None, label_levels=True, cbar=True, limit_color_norm=False, cbar_kwargs=None, fcn=None, plot_edges=False, edges_kwargs=None, clabel_kwargs=None, skip_max_clbl=True, use_contourf=False, **kwargs)

Make a contour plot on ax using ax.contour.

Parameters:

• ax (mpl.axes.Axes, None) – If None, create an Axes instance from plt.subplots.

• label_levels (bool) – If True, add labels to contours with ax.clabel.

• cbar (bool) – If True, create color bar with labels.z.

• limit_color_norm (bool) – If True, limit the color range to 0.001 and 0.999 percentile range of the z-value, count or otherwise.

• cbar_kwargs (dict, None) – If not None, kwargs passed to self._make_cbar.

• fcn (FunctionType, None) – Aggregation function. If None, automatically select in agg().

• plot_edges (bool) – If True, plot the smoothed, extreme edges of the 2D histogram.

• clabel_kwargs (None, dict) – If not None, dictionary of kwargs passed to ax.clabel.

• skip_max_clbl (bool) – If True, don’t label the maximum contour. Primarily used when the maximum contour is, effectively, a point.

• maximum_color – The color for the maximum of the PDF.

• use_contourf (bool) – If True, use ax.contourf. Else use ax.contour.

• gaussian_filter_std (int) – If > 0, apply scipy.ndimage.gaussian_filter to the z-values using the standard deviation specified by gaussian_filter_std.

• gaussian_filter_kwargs (None, dict) – If not None and gaussian_filter_std > 0, passed to scipy.ndimage.gaussian_filter()

• kwargs – Passed to ax.pcolormesh(). If row or column normalized data, norm defaults to mpl.colors.Normalize(0, 1).

property clip

property data

property labels

property log

property logger

property path

Path for saving figure.

set_labels(**kwargs)

Set or update x, y, or z labels. Any label not specified in kwargs.

is propagated from self.labels.<x, y, or z>.

set_log(x=None, y=None)

set_path(new, add_scale=True)

Build the plot save path.

Parameters:

• new (str or Path) – If str and == “auto”, then build path from self.labels. Otherwise, assume parameter specifies the desired path and use Path(new).

• add_scale (bool) – If True, add information about the axis scales to the end of the path.