def hist2d_scatter(x,
y,
bg_x,
bg_y,
axis: matplotlib.axes.Axes,
dataset_name: str,
normalize=True,
marker_color: str = 'k',
bins=300,
colormap_name: str = 'jet',
color_bar=False,
hist2dkw={},
scatterkw={}):
"""Plots 2D histogram with two parameters (e.g. BxGSM and ByGSM).
x, y (array_like): Values of the TPAs for the parameter that will be plotted on the x- or y-axis.
This data will correspond to the dots in the plot.
bg_x, bg_y (array_like): Values of the IMF over the period of the dataset.
These will form the background (colored tiles) of the plot
"""
colormap = cm.get_cmap(colormap_name)
axis.axhline(0, color='grey', zorder=1)
axis.axvline(0, color='grey', zorder=1)
omit_index = np.isnan(x) | np.isnan(y)
x = x[~omit_index]
y = y[~omit_index]
bg_omit_index = np.isnan(bg_x) | np.isnan(bg_y)
bg_x = bg_x[~bg_omit_index]
bg_y = bg_y[~bg_omit_index]
counts, xedges, yedges, im = axis.hist2d(bg_x,
bg_y,
bins=bins,
cmap=colormap,
density=normalize,
zorder=0,
**hist2dkw)
if color_bar:
cbar = plt.colorbar(im, ax=axis)
cbar.set_label('IMF probability distribution',
rotation=270,
labelpad=10)
scatter = axis.scatter(x,
y,
s=30,
marker='P',
edgecolors='w',
linewidth=0.5,
label=dataset_name,
c=marker_color[~omit_index] if isinstance(
marker_color, np.ndarray) else marker_color,
zorder=2,
**scatterkw)
axis.set_facecolor(colormap(0))
axis.legend(loc='upper left')
return scatter, counts, xedges, yedges, im
def plot(self,
ax: matplotlib.axes.Axes,
back_color: utils.Color = "white") -> None:
"""
Draw plot on ax
back_color: background color for plot
"""
ax.ticklabel_format(axis="y", scilimits=[0, 0])
ax.set_facecolor(back_color)
ax.set_title(f"{self.title}\n{self.group_name}")
