def plot_roc(roc: ROC,
title: str = "",
label: str = "",
show=False,
save=False,
fig: plt.Figure = None):
if fig is None:
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.85, 0.8])
else:
ax = fig.get_axes()
assert len(ax) == 1
ax = ax[0]
lw = 2
fpr, tpr, _ = roc
ax.plot(fpr, tpr, lw=lw, label=label)
ax.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
ax.set(xlabel='FPR', ylabel='TPR', ylim=[0.0, 1.05], xlim=[0.0, 1.0])
ax.set_title(title, fontsize=15)
ax.legend(loc="lower right")
# fig.tight_layout()
if show:
fig.show()
if save:
fig.savefig(f'figs/{title}_{label}.png')
return fig
def create_thumbnail(infile, thumbfile,
width=180, height=180,
xoffset=0, yoffset=0, zoom=1,
cx=0.5, cy=0.6, dpi=100):
from matplotlib import image
from matplotlib.pyplot import Figure
baseout, extout = os.path.splitext(thumbfile)
im = image.imread(infile)
thumb = padded_thumbnail(im, (height, width), (yoffset, xoffset), zoom)
extension = extout.lower()
if extension == '.png':
from matplotlib.backends.backend_agg \
import FigureCanvasAgg as FigureCanvas
elif extension == '.pdf':
from matplotlib.backends.backend_pdf \
import FigureCanvasPDF as FigureCanvas
elif extension == '.svg':
from matplotlib.backends.backend_svg \
import FigureCanvasSVG as FigureCanvas
else:
raise ValueError("Can only handle extensions 'png', 'svg' or 'pdf'")
fig = Figure(figsize=(float(width) / dpi, float(height) / dpi), dpi=dpi)
canvas = FigureCanvas(fig)
ax = fig.add_axes([0, 0, 1, 1], aspect='auto',
frameon=False, xticks=[], yticks=[])
ax.imshow(thumb, aspect='auto', resample=True,
interpolation='bilinear')
fig.savefig(thumbfile, dpi=dpi)
return fig
def add_relpath_to_top_corner(figure: plt.Figure):
big_axis = figure.add_axes([0, 0, 1, 1], facecolor=(1, 1, 1, 0))
big_axis.text(0.99, 0.99,
os.path.relpath(os.path.abspath(os.curdir), os.path.expanduser("~/bioinf/handling")),
color="#CCCCCC",
horizontalalignment='right',
verticalalignment='top')
def add_inset_spectrum(figure: plt.Figure) -> plt.axis:
rect_transform = [.7, .7, .25,
.25] # (0,0 is bottom left; 1,1 is top right)
_axis = figure.add_axes(rect_transform)
_axis.get_xaxis().set_visible(False) # Hide tick labels
_axis.get_yaxis().set_visible(False) # Hide tick labels
_axis.set_yscale('log') # Set log scale
return _axis
def add_track_spectrum(figure: plt.Figure) -> plt.axis:
rect_transform = [0.125, 0.05, .775,
.15] # (0,0 is bottom left; 1,1 is top right)
_axis = figure.add_axes(rect_transform)
_axis.get_xaxis().set_visible(False) # Hide tick labels
_axis.get_yaxis().set_visible(False) # Hide tick labels
_axis.set_yscale('log') # Set log scale
_axis.set_facecolor('k') # Face color
return _axis
def plot_test_sample(fig: plt.Figure,
image: torch.Tensor,
predictions: torch.Tensor,
class_names: List[str],
true_class: str,
k: int = 10):
"""Plot test sample image with predictions on given Figure.
Args:
fig (plt.Figure): Figure to draw on.
image (torch.Tensor): Image to draw.
predictions (torch.Tensor): Preditions of network.
class_names (List[str]): Class names
true_class (str): True class of given image
k (int, optional): How many best predictions to plot as bar plot. Defaults to 10.
"""
im_ax = fig.add_axes([0.0, 0.0, 2 / 3, 1.0])
pred_ax = fig.add_axes([2 / 3, 1 / 8, 1 / 3, 7 / 8])
plot_im(im_ax, image, true_class)
plot_prediction_bar(pred_ax, predictions, class_names, k)
def isochrones_subfig(
fig_: Figure, x_: np.ndarray, y_: np.ndarray, color_: str = gray_
) -> Tuple[Axes, Tuple[float, float]]:
r"""Generate an isochrones subfig for embedding."""
# Top left isochrones 0
size_zoom_0: Tuple[float, float] = (0.65, 0.55)
posn_0: Tuple[float, float] = (0.0, 0.75)
axes_0 = fig_.add_axes([*posn_0, *size_zoom_0])
plt.axis("off")
n_isochrones = 6
for i_, sf_ in enumerate(np.linspace(0.5, 1.2, n_isochrones)):
plt.plot(
*(x_, sf_ * y_),
"-",
color=self.gray_color(i_, n_gray),
lw=2.5,
)
plt.xlim(0, 1)
plt.ylim(0, 1)
sf1_ = 1.3
sf2_ = 1.3
arrow_xy_: np.ndarray = np.array([0.2, 0.8])
arrow_dxy_: np.ndarray = np.array([-0.025, 0.15])
motion_xy_: Tuple[float, float] = (0.1, 0.8)
motion_angle_ = 23
my_arrow_style = ArrowStyle.Simple(
head_length=1 * sf1_, head_width=1 * sf1_, tail_width=0.1 * sf1_
)
axes_0.annotate(
"",
xy=arrow_xy_,
xytext=arrow_xy_ + arrow_dxy_ * sf2_,
transform=axes_0.transAxes,
arrowprops=dict(arrowstyle=my_arrow_style, color=color_, lw=3),
)
plt.text(
*motion_xy_,
"motion",
color=color_,
fontsize=16,
rotation=motion_angle_,
transform=axes_0.transAxes,
horizontalalignment="center",
verticalalignment="center",
)
return (axes_0, posn_0)
def add_axes(fig: Figure, rct: Tuple[float, float, float, float],
num: int) -> List[Axes]:
"""
add axes to figure in a rectangular with several panels vertically.
:param fig: figure to add axes.
:param rct: lower left point x, lower left point y, width, height.
:param num: number of panels.
:return: a list of axes.
"""
axs = []
x0, y0, w, h = rct
width = w
height = h / num
for n in range(num):
xax = x0
yax = y0 + h - (n + 1) * height
ax = fig.add_axes([xax, yax, width, height])
axs.append(ax)
return axs
def split_horizontal(fig: Figure, rct: Tuple[float, float, float, float],
frac: float) -> Tuple[Axes, Axes]:
"""
create a axes in figure with two separate panels in horizontal.
:param fig: figure to create axes.
:param rct: lower left point x, lower left point y, width, height.
:param frac: the fraction of left panel in width.
:return: two axes.
"""
x, y, w, h = rct
x0, y0 = x, y
w0, h0 = w * frac, h
x1, y1 = x + w0, y
w1, h1 = w - w0, h
rct0 = (x0, y0, w0, h0)
rct1 = (x1, y1, w1, h1)
ax0 = fig.add_axes(rct0)
ax1 = fig.add_axes(rct1)
return ax0, ax1
def create_thumbnail(infile,
thumbfile,
width=180,
height=180,
xoffset=0,
yoffset=0,
zoom=1,
cx=0.5,
cy=0.6,
dpi=100):
from matplotlib import image
from matplotlib.pyplot import Figure
baseout, extout = os.path.splitext(thumbfile)
im = image.imread(infile)
thumb = padded_thumbnail(im, (height, width), (yoffset, xoffset), zoom)
extension = extout.lower()
if extension == '.png':
from matplotlib.backends.backend_agg \
import FigureCanvasAgg as FigureCanvas
elif extension == '.pdf':
from matplotlib.backends.backend_pdf \
import FigureCanvasPDF as FigureCanvas
elif extension == '.svg':
from matplotlib.backends.backend_svg \
import FigureCanvasSVG as FigureCanvas
else:
raise ValueError("Can only handle extensions 'png', 'svg' or 'pdf'")
fig = Figure(figsize=(float(width) / dpi, float(height) / dpi), dpi=dpi)
canvas = FigureCanvas(fig)
ax = fig.add_axes([0, 0, 1, 1],
aspect='auto',
frameon=False,
xticks=[],
yticks=[])
ax.imshow(thumb, aspect='auto', resample=True, interpolation='bilinear')
fig.savefig(thumbfile, dpi=dpi)
return fig
def zoom_boxes(
fig_: Figure, ta_color_: str = gray2_, tb_color_: str = gray1_
) -> Tuple[Axes, Axes, Axes, Axes, str]: # mpl.axes._axes.
r"""Generate 'zoom' boxes."""
size_zoom_AB: Tuple[float, float] = (0.3, 0.7)
size_zoom_C: Tuple[float, float] = (0.3, 0.7)
n_pts: int = 300
def zoomed_isochrones(
axes_: Axes,
name_text: str,
i_pt1_: Union[int, List[int]],
i_pt2_: int, # Union[int, List[int]],
do_many: bool = False,
do_legend: bool = False,
do_pts_only: bool = False,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Generate isochrones for zoom box."""
x_array = np.linspace(-1, 3, n_pts)
y_array1 = x_array * 0.5
y_array2 = y_array1 + 0.7
# Ta
if not do_pts_only:
plt.plot(
x_array,
y_array2,
"-",
color=ta_color_,
lw=2.5,
label=r"$T_a$",
)
xy_pt2 = np.array([x_array[i_pt2_], y_array2[i_pt2_]])
marker_style2 = dict(
marker="o",
fillstyle="none",
markersize=8,
markeredgecolor=ta_color_,
markerfacecolor=ta_color_,
markeredgewidth=2,
)
plt.plot(*xy_pt2, **marker_style2)
if not do_pts_only:
plt.text(
*(xy_pt2 + np.array([-0.03, 0.08])),
r"$\mathbf{a}$",
color=ta_color_,
fontsize=18,
rotation=0,
transform=axes_.transAxes,
horizontalalignment="center",
verticalalignment="center",
)
# Tb
if not do_pts_only:
plt.plot(
x_array,
y_array1,
"-",
color=tb_color_,
lw=2.5,
label=r"$T_b = T_a+\Delta{T}$",
)
# if isinstance(i_pt1_, List) else [i_pt1_]
i_pts1: List = [i_pt1_]
xy_pts1_tmp = np.array(
[
np.array([x_array[i_pt1__], y_array1[i_pt1__]])
for i_pt1__ in i_pts1
]
)
xy_pts1 = (
xy_pts1_tmp.T.reshape((xy_pts1_tmp.shape[2], 2))
if do_many
else xy_pts1_tmp
)
marker_style1 = marker_style2.copy()
marker_style1.update(
{"markeredgecolor": tb_color_, "markerfacecolor": "w"}
)
for xy_pt1_ in xy_pts1:
plt.plot(*xy_pt1_, **marker_style1)
if not do_pts_only:
b_label_i = 4 if do_many else 0
b_label_xy = xy_pts1[b_label_i] + np.array([0.03, -0.08])
b_label_text = (
r"$\{\mathbf{b}\}$" if do_many else r"$\mathbf{b}$"
)
plt.text(
*b_label_xy,
b_label_text,
color=tb_color_,
fontsize=18,
rotation=0,
transform=axes_.transAxes,
horizontalalignment="center",
verticalalignment="center",
)
if do_legend:
plt.legend(loc=[0.05, -0.35], fontsize=16, framealpha=0)
name_xy = [0.97, 0.03]
plt.text(
*name_xy,
name_text,
color=brown_,
fontsize=14,
rotation=0,
transform=axes_.transAxes,
horizontalalignment="right",
verticalalignment="bottom",
)
dx = x_array[1] - x_array[0]
dy = y_array1[1] - y_array1[0]
return (
xy_pts1 if do_many else xy_pts1[0],
xy_pt2,
np.array([dx, dy]),
)
def v_arrow(
axes_: Axes,
xy_pt1: np.ndarray,
xy_pt2: np.ndarray,
dxy: Tuple[float, float],
a_f: float = 0.54,
v_f: float = 0.5,
v_label: str = r"$\mathbf{v}$",
color_: str = red_,
do_dashing: bool = False,
do_label: bool = False,
) -> None:
v_lw = 1.5
axes_.arrow(
*((xy_pt1 * a_f + xy_pt2 * (1 - a_f))),
*((xy_pt1 - xy_pt2) * 0.01),
lw=1,
facecolor=color_,
edgecolor=color_,
head_width=0.05,
overhang=0.3,
transform=axes_.transAxes,
length_includes_head=True,
)
axes_.plot(
[xy_pt1[0], xy_pt2[0]],
[xy_pt1[1], xy_pt2[1]],
ls="--" if do_dashing else "-",
lw=v_lw,
color=color_,
)
f = v_f
v_xy = (
xy_pt1[0] * f + xy_pt2[0] * (1 - f) + dxy[0],
xy_pt1[1] * f + xy_pt2[1] * (1 - f) + dxy[1],
)
if do_label:
axes_.text(
*v_xy,
v_label,
color=color_,
fontsize=18,
rotation=0,
transform=axes_.transAxes,
horizontalalignment="right",
verticalalignment="bottom",
)
def p_bones(
axes_: Axes,
xy_pt1: np.ndarray,
xy_pt2: np.ndarray,
dxy: np.ndarray,
p_f: float = 0.9,
color_: str = blue_,
n_bones: int = 5,
do_primary: bool = True,
) -> None:
alpha_ = 0.7
p_dashing = [1, 0] # [4, 4]
p_lw = 3
axes_.plot(
[xy_pt1[0], xy_pt2[0]],
[xy_pt1[1], xy_pt2[1]],
dashes=p_dashing,
lw=p_lw,
color=color_,
alpha=1 if do_primary else alpha_,
)
bones = np.linspace(1, 0, n_bones, endpoint=False)
for i_, f in enumerate(bones):
x = xy_pt1[0] * f + xy_pt2[0] * (1 - f)
y = xy_pt1[1] * f + xy_pt2[1] * (1 - f)
sf = 4 if do_primary else 3
dx = dxy[0] * sf
dy = dxy[1] * sf
x_pair = [x - dx, x + dx]
y_pair = [y - dy, y + dy]
axes_.plot(
x_pair,
y_pair,
lw=5 if i_ == 0 else 2.5,
color=color_,
alpha=1 if do_primary else alpha_,
)
f = p_f
p_xy = (
xy_pt1[0] * f + xy_pt2[0] * (1 - f) - 0.1,
xy_pt1[1] * f + xy_pt2[1] * (1 - f) - 0.1,
)
if do_primary:
axes_.text(
*p_xy,
r"$\mathbf{\widetilde{p}}$",
color=color_,
fontsize=18,
rotation=0,
transform=axes_.transAxes,
horizontalalignment="right",
verticalalignment="bottom",
)
def psi_label(
axes_: Axes,
xy_pt1_B: np.ndarray,
xy_pt2_B: np.ndarray,
xy_pt1_C: np.ndarray,
xy_pt2_C: np.ndarray,
color_: str = red_,
) -> None:
label_xy = [0.5, 0.53]
axes_.text(
*label_xy,
r"$\psi$",
color=color_,
fontsize=20,
rotation=0,
transform=axes_.transAxes,
horizontalalignment="center",
verticalalignment="center",
)
angle_B = np.rad2deg(
np.arctan(
(xy_pt2_B[1] - xy_pt1_B[1])
/ (xy_pt2_B[0] - xy_pt1_B[0])
)
)
angle_C = np.rad2deg(
np.arctan(
(xy_pt2_C[1] - xy_pt1_C[1])
/ (xy_pt2_C[0] - xy_pt1_C[0])
)
)
radius = 0.95
axes_.add_patch(
Arc(
xy_pt2_B,
radius,
radius,
color=color_,
linewidth=2,
fill=False,
zorder=2,
# transform=axes_.transAxes,
angle=angle_B,
theta1=0,
theta2=angle_C - angle_B,
)
)
def beta_label(
axes_: Axes,
xy_pt1_B: np.ndarray,
xy_pt2_B: np.ndarray,
color_: str = blue_,
) -> None:
label_xy = [0.28, 0.47]
axes_.text(
*label_xy,
r"$\beta$",
color=color_,
fontsize=20,
rotation=0,
transform=axes_.transAxes,
horizontalalignment="center",
verticalalignment="center",
)
angle_ref = -90
angle_B = np.rad2deg(
np.arctan(
(xy_pt2_B[1] - xy_pt1_B[1])
/ (xy_pt2_B[0] - xy_pt1_B[0])
)
)
radius = 0.88
axes_.add_patch(
Arc(
xy_pt2_B,
radius,
radius,
color=color_,
linestyle="--",
linewidth=2,
fill=False,
zorder=2,
# transform=axes_.transAxes,
angle=angle_ref,
theta1=0,
theta2=angle_B - angle_ref,
)
)
axes_.plot(
[xy_pt2_B[0], xy_pt2_B[0]],
[xy_pt2_B[1], xy_pt2_B[1] - 0.5],
":",
color=color_,
)
def alpha_label(
axes_: Axes,
xy_pt1_B: np.ndarray,
xy_pt2_B: np.ndarray,
color_: str = red_,
) -> None:
label_xy = [0.55, 0.75]
axes_.text(
*label_xy,
r"$-\alpha$",
color=color_,
fontsize=20,
rotation=0,
transform=axes_.transAxes,
horizontalalignment="center",
verticalalignment="center",
)
# angle_ref = 0
angle_B = np.rad2deg(
np.arctan(
(xy_pt2_B[1] - xy_pt1_B[1])
/ (xy_pt2_B[0] - xy_pt1_B[0])
)
)
radius = 0.88
axes_.add_patch(
Arc(
xy_pt2_B,
radius,
radius,
color=color_,
linestyle="--",
linewidth=2,
fill=False,
zorder=2,
# transform=axes_.transAxes,
angle=angle_B,
theta1=0,
theta2=-angle_B,
)
)
axes_.plot(
[xy_pt2_B[0], xy_pt2_B[0] + 0.5],
[xy_pt2_B[1], xy_pt2_B[1]],
":",
color=color_,
)
# From zoom box D
posn_D = np.array(posn_0) + np.array([0.19, 0.25])
size_zoom_D = [0.042, 0.1]
axes_D = fig_.add_axes([*posn_D, *size_zoom_D])
remove_ticks_etc(axes_D)
axes_D.patch.set_alpha(0.5)
# Zoom any point pairing A
posn_A = [0, 0.15]
axes_A = fig_.add_axes([*posn_A, *size_zoom_AB])
remove_ticks_etc(axes_A)
i_pt2_A = 92
i_pts1_A = [i_pt2_A + i_ for i_ in np.arange(-43, 100, 15)]
# logging.info(type(i_pts1_A))
(xy_pts1_A, xy_pt2_A, _) = zoomed_isochrones(
axes_A, "free", i_pts1_A, i_pt2_A, do_many=True
)
for i_, xy_pt1_A in enumerate(xy_pts1_A):
v_arrow(
axes_A,
xy_pt1_A,
xy_pt2_A,
dxy=(0.12, 0.05),
do_dashing=True,
v_f=0.35,
v_label=r"$\{\mathbf{v}\}$",
do_label=bool(i_ == len(xy_pts1_A) - 1),
)
_ = zoomed_isochrones(axes_A, "", i_pts1_A, i_pt2_A, do_many=True)
# Zoom intrinsic pairing B
posn_B = [0.33, 0.28]
axes_B = fig_.add_axes([*posn_B, *size_zoom_AB])
remove_ticks_etc(axes_B)
i_pt2_B = i_pt2_A
i_pt1_B = i_pt2_A + 19
(xy_pt1_B, xy_pt2_B, dxy_B) = zoomed_isochrones(
axes_B, "isotropic", i_pt1_B, i_pt2_B
)
p_bones(axes_B, xy_pt1_B, xy_pt2_B, dxy_B)
v_arrow(
axes_B,
xy_pt1_B,
xy_pt2_B,
v_f=0.5,
dxy=(0.13, 0.02),
do_label=True,
)
_ = zoomed_isochrones(
axes_B, "", i_pt1_B, i_pt2_B, do_pts_only=True
)
# Zoom erosion-fn pairing C
posn_C = [0.66, 0.6]
axes_C = fig_.add_axes([*posn_C, *size_zoom_C])
remove_ticks_etc(axes_C)
i_pt1_C = i_pt1_B + 30
i_pt2_C = i_pt2_B
(xy_pt1_C, xy_pt2_C, dxy_C) = zoomed_isochrones(
axes_C, "anisotropic", i_pt1_C, i_pt2_C, do_legend=True
)
p_bones(axes_C, xy_pt1_C, xy_pt2_C, dxy_C, do_primary=False)
p_bones(axes_C, xy_pt1_B, xy_pt2_B, dxy_B)
v_arrow(
axes_C,
xy_pt1_C,
xy_pt2_C,
a_f=0.8,
v_f=0.72,
dxy=(0.1, 0.05),
do_label=True,
)
_ = zoomed_isochrones(
axes_C, "", i_pt1_C, i_pt2_C, do_legend=False, do_pts_only=True
)
psi_label(
axes_C, xy_pt1_B, xy_pt2_B, xy_pt1_C, xy_pt2_C, color_=purple_
)
beta_label(axes_C, xy_pt1_B, xy_pt2_B)
alpha_label(axes_C, xy_pt1_C, xy_pt2_C)
# Brown zoom boxes and tie lines
set_colors(Spine, [axes_A, axes_B, axes_C, axes_D], brown_)
return axes_A, axes_B, axes_C, axes_D, brown_
