def draw_LX_bounds(self, ax: plt.Axes, redshifts_on: bool = True):
self.hconv = 0.70 / self.h
ax.axhspan(self.bins[0][0] * 1e44 * self.hconv**2,
self.bins[-1][1] * 1e44 * self.hconv**2,
facecolor='lime',
linewidth=0,
alpha=0.2)
ax.axhline(self.bins[0][0] * 1e44 * self.hconv**2,
color='lime',
linewidth=1,
alpha=0.1)
for i, (luminosity_min, luminosity_max, redshift_min,
redshift_max) in enumerate(self.bins):
ax.axhline(luminosity_max * 1e44 * self.hconv**2,
color='lime',
linewidth=1,
alpha=0.1)
# Print redshift bounds once every 2 bins to avoid clutter.
if i % 2 == 0 and redshifts_on:
ax.text(10**ax.get_xlim()[0],
10**(0.5 * np.log10(luminosity_min * luminosity_max)) *
1e44 * self.hconv**2,
f"$z$ = {redshift_min:.3f} - {redshift_max:.3f}",
horizontalalignment='left',
verticalalignment='center',
color='k',
alpha=0.3)
def _add_shading_to_axes(self, ax: Axes) -> None:
"""
Adds any required shading.
"""
common_args = dict(zorder=-10, alpha=0.3, color="grey", linewidth=0)
# First do horizontal
if self.x_shade[0] is not None:
ax.axvspan(self.x_lim[0], self.x_shade[0], **common_args)
if self.x_shade[1] is not None:
ax.axvspan(self.x_shade[1], self.x_lim[1], **common_args)
# Vertical
if self.y_shade[0] is not None:
ax.axhspan(self.y_lim[0], self.y_shade[0], **common_args)
if self.y_shade[1] is not None:
ax.axhspan(self.y_shade[1], self.y_lim[1], **common_args)
return
def error_array(ax: plt.Axes,
err_array: ListOrArray,
x_array: typing.Optional[ListOrArray] = None,
statistics: typing.Optional[typing.Dict[str, float]] = None,
threshold: float = None,
cumulative: bool = False,
color: str = 'grey',
name: str = "error",
title: str = "",
xlabel: str = "index",
ylabel: typing.Optional[str] = None,
subplot_arg: int = 111,
linestyle: str = "-",
marker: typing.Optional[str] = None):
"""
high-level function for plotting raw error values of a metric
:param fig: matplotlib axes
:param err_array: an nx1 array of values
:param x_array: an nx1 array of x-axis values
:param statistics: optional dictionary of {metrics.StatisticsType.value: value}
:param threshold: optional value for horizontal threshold line
:param cumulative: set to True for cumulative plot
:param name: optional name of the value array
:param title: optional plot title
:param xlabel: optional x-axis label
:param ylabel: optional y-axis label
:param subplot_arg: optional matplotlib subplot ID if used as subplot
:param linestyle: matplotlib linestyle
:param marker: optional matplotlib marker style for points
"""
if cumulative:
if x_array is not None:
ax.plot(x_array,
np.cumsum(err_array),
linestyle=linestyle,
marker=marker,
color=color,
label=name)
else:
ax.plot(np.cumsum(err_array),
linestyle=linestyle,
marker=marker,
color=color,
label=name)
else:
if x_array is not None:
ax.plot(x_array,
err_array,
linestyle=linestyle,
marker=marker,
color=color,
label=name)
else:
ax.plot(err_array,
linestyle=linestyle,
marker=marker,
color=color,
label=name)
if statistics is not None:
for stat_name, value in statistics.items():
color = next(ax._get_lines.prop_cycler)['color']
if stat_name == "std" and "mean" in statistics:
mean, std = statistics["mean"], statistics["std"]
ax.axhspan(mean - std / 2,
mean + std / 2,
color=color,
alpha=0.5,
label=stat_name)
else:
ax.axhline(y=value,
color=color,
linewidth=2.0,
label=stat_name)
if threshold is not None:
ax.axhline(y=threshold,
color='red',
linestyle='dashed',
linewidth=2.0,
label="threshold")
plt.ylabel(ylabel if ylabel else name)
plt.xlabel(xlabel)
plt.title(title)
plt.legend(frameon=True)
