def _plot_pca_scatter(model, ax: mpl.axes.Axes, dist_col: bool):
# calculate the magnitude away from origin (0, 0)
mag = np.linalg.norm(model.components_[:, :2], axis=1)
_x, _y = model.components_[:, 0], model.components_[:, 1]
_xl, _yl = [
"PC%d (%.2f)" % (i + 1, model.explained_variance_ratio_[i] * 100)
for i in range(2)
]
# plot
if dist_col:
scatter(
_x,
_y,
c=mag,
cmap="RdBu_r",
x_label=_xl,
y_label=_yl,
colorbar=False,
ax=ax,
)
else:
scatter(_x, _y, x_label=_xl, y_label=_yl, colorbar=False, ax=ax)
# add pca lines
ax.hlines(0, xmin=_x.min(), xmax=_x.max(), linestyle="--")
ax.vlines(0, ymin=_y.min(), ymax=_y.max(), linestyle="--")
ax.grid()
def plot_crosshairs(axes: mpl.axes.Axes, x_popt, y_popt, color: str = 'gray'):
""" plot the cross-hairs on the hitmap
Plot the cross-hairs that are the results from the fit of the projections
"""
axes.vlines([x_popt[0]], 0, 1, color=color)
axes.hlines([y_popt[0]], 0, 1, color=color)
def _best_eigenvector_plot(
x, y, labels: pd.Index, ax: mpl.axes.Axes, nk: Tuple[int, int] = (6, 5)
):
n_samples, n_pcs = nk
ax.scatter(x, y)
ax.hlines(0, -0.5, n_pcs - 0.5, linestyle="--")
annotate(x, y, labels, ax=ax, word_shorten=15)
ax.set_ylabel("Eigenvector")
ax.grid()
def plot(
self,
xlabel: str = "Method difference",
ylabel: str = "Folded CDF (%)",
label: str = "$M_1$ - $M_2$",
unit: str = None,
title: str = None,
color: str = "blue",
legend: bool = True,
square: bool = False,
show_hline: bool = True,
show_vlines: bool = True,
show_markers: bool = True,
ax: matplotlib.axes.Axes = None,
) -> matplotlib.axes.Axes:
"""Plot mountain plot
Parameters
----------
xlabel : str, optional
The label which is added to the X-axis. (default: "Method difference")
ylabel : str, optional
The label which is added to the Y-axis. (default: "Folded CDF (%)")
label : str, optional
mountaint line legend label (default:"Method 1 - Method 2" )
unit : str, optional
unit to disply for x-axis
title : str, optional
figure title, if none there will be no title
(default: None)
legend : bool, optional
If True, will provide a legend containing the computed regression equation.
(default: True)
square : bool, optional
If True, set the Axes aspect to "equal" so each cell will be
square-shaped. (default: True)
color : str, optional
Color for mountain plot elements
show_hline: bool, optional
If set show horizontal lines for iqr
show_vlines: bool, optional
If set show vertical lines at iqr and median
show_markers: bool, optional
If set show markers at iqr and median
ax : matplotlib.axes.Axes, optional
matplotlib axis object, if not passed, uses gca()
Returns
-------
matplotlib.axes.Axes
axes object with the plot
"""
ax = ax or plt.gca()
ax.step(
y=self.result["mountain"],
x=self.result["quantile"],
where="mid",
label=f"{label} AUC={self.result['auc']:.2f}",
color=color,
)
if show_hline:
ax.hlines(
self.result["mountain_iqr"],
xmin=self.result["iqr"][0],
xmax=self.result["iqr"][1],
color=color,
)
if show_vlines:
ax.vlines(
self.result["median"],
ymin=0,
ymax=50,
label=f"median={self.result['median']:.2f} {unit or ''}",
linestyle="--",
color=color,
)
if self.iqr > 0:
ax.vlines(
self.result["iqr"],
ymin=0,
ymax=50 - self.iqr / 2,
label=(
f"{self.iqr:.2f}% IQR ="
f"{self.result['iqr'][1] - self.result['iqr'][0]:.2f}"
"{unit or ''}"),
linestyle=":",
color=color,
)
if show_markers:
ax.plot(
self.result["median"],
self.result["mountain_median"],
"o",
color=color,
)
if self.iqr > 0:
ax.plot(
self.result["iqr"],
self.result["mountain_iqr"],
"o",
color=color,
)
u = f"({unit})" if unit else ""
ax.set(xlabel=f"{xlabel} {u}",
ylabel=ylabel or None,
title=title or None)
ax.legend(loc="upper left", fontsize="medium")
if legend:
ax.legend(loc="upper left", frameon=False)
if square:
ax.set_aspect("equal")
return ax
