def test_RelativeArrowPatch(self):
fig, ax = new_fig(aspect=1, scale=3)
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
arrow1 = RelativeFancyArrow(0.5,
0.5,
0.0,
0.5,
width=1,
head_length=0.5,
head_width=2)
arrow2 = RelativeFancyArrow(0.5,
0.5,
0.5,
0.0,
width=1,
head_length=0.5,
head_width=2)
ax.add_patch(arrow1)
ax.add_patch(arrow2)
ax.plot(0.5,
0.5,
marker='o',
markersize=size_units2points(size=0.5, ax=ax),
alpha=0.5,
zorder=100)
self.assertTrue(True)
def hist_vlines(x,
name,
bins=100,
hl_idx=None,
hl_color=None,
hl_name=None,
lower_perc=None,
upper_perc=None):
if lower_perc is not None:
_range = (np.percentile(x, lower_perc), np.percentile(x, upper_perc))
else:
_range = None
fig, ax = new_fig(title=f'Histogram: {name}', scale=2)
hist = ax.hist(x, bins=bins, range=_range)
perc_i = []
if hl_idx is not None:
hl_idx, hl_color, hl_name = safe_scalar2array(hl_idx,
hl_color,
hl_name,
shape=np.size(hl_idx))
for i, c, n in zip(hl_idx, hl_color, hl_name):
perc_i.append(np.sum(x[i] > x))
label = None if n is None else f"{n} | {perc_i[-1]} / {len(x)}"
ax.vlines(x[i], ymin=0, ymax=len(x), lw=4, color=c, label=label)
ax.set_ylim(0, hist[0].max() * 1.02)
if lower_perc is not None:
ax.set_xlim(np.percentile(x, lower_perc), np.percentile(x, upper_perc))
if hl_name is not None:
ax.legend()
return ax, perc_i
def test_add_ticks(self):
fig, ax = new_fig()
set_ticks_position(ax=ax, position='all')
set_labels_position(ax=ax, position='all')
plt.pause(0.1)
add_ticks(ax=ax, ticks=0.55, labels='wow', axis='x')
add_ticks(ax=ax, ticks=(0.3, 0.5), labels=('wtf', 'ftw'), axis='y')
def test_AbsoluteFancyBboxPatch(self):
from wzk.mpl import new_fig, patches
fig, ax = new_fig(aspect=1)
ax.add_patch(
FancyBbox(xy=(0.1, 0.1),
boxstyle='Round4',
height=0.5,
width=0.5,
pad=0.1,
corner_size=0))
def test_set_ticks_position(self):
fig, ax = new_fig()
positions = [
'left', 'top', 'right', 'bottom', 'all', 'none', 'default',
'inverse', ('bottom', 'right'), ('top', 'left'), ('bottom', 'top'),
('left', 'right'), ('left', 'bottom', 'right'),
('top', 'bottom', 'right'), ('top', 'left', 'right'),
('top', 'left', 'bottom')
]
for p in positions:
set_ticks_position(ax=ax, position=p)
set_labels_position(ax=ax, position=p)
ax.cla()
ax.text(0.5, 0.5, ''.join(p))
plt.pause(0.1)
self.assertTrue(True)
def test_change_tick_appearance(self):
fig, ax = new_fig()
set_ticks_position(ax=ax, position='all')
set_labels_position(ax=ax, position='all')
plt.pause(0.05)
change_tick_appearance(ax,
position='bottom',
v=3,
size=40,
color='red')
change_tick_appearance(ax,
position='top',
v=0.4,
size=30,
color='blue')
plt.pause(0.05)
change_tick_appearance(ax,
position='left',
v=2,
size=40,
color='green')
change_tick_appearance(ax,
position='right',
v=0.6,
size=30,
color='magenta')
ax.text(0.5,
0.5,
'Each Axis should have one larger tick in different colors',
ha='center',
weight='bold')
ax.annotate('bottom', (0.6, 0), (0.6, 0.4),
arrowprops=dict(arrowstyle='->'))
ax.annotate('top', (0.4, 1), (0.4, 0.6),
arrowprops=dict(arrowstyle='->'))
ax.annotate('left', (0, 0.4), (0.4, 0.4),
arrowprops=dict(arrowstyle='->'))
ax.annotate('right', (1, 0.6), (0.6, 0.6),
arrowprops=dict(arrowstyle='->'))
self.assertTrue(True)
def correlation_plot(a,
b,
name_a,
name_b,
regression_line=True,
lower_perc=0,
upper_perc=100,
labels=None,
colors=None,
markers='o',
markersizes=None,
alphas=None,
zorders=None,
ax=None,
verbose=1,
**kwargs):
if ax is None:
fig, ax = new_fig(width=10, title=f"Correlation: {name_a} | {name_b}")
a, b = atleast_tuple(a, b, convert=False)
a_all = np.concatenate(a)
b_all = np.concatenate(b)
limits = ((np.percentile(a_all,
lower_perc), np.percentile(a_all, upper_perc)),
(np.percentile(b_all,
lower_perc), np.percentile(b_all, upper_perc)))
limits_1 = add_safety_limits(limits=limits, factor=0.01)
limits_2 = add_safety_limits(limits=limits, factor=0.02)
if regression_line:
s, i, r, p, _ = linregress(a_all, b_all)
x_reg = np.linspace(limits_1[0, 0], limits_1[0, 1], 3)
y_reg = x_reg * s + i
a += (x_reg, )
b += (y_reg, )
if verbose > 0:
print("slope: {:.4} | correlation: {:.4} | p {:.4}".format(
s, r, p))
labels, colors, markers, markersizes, alphas, zorders = \
safe_scalar2array(labels, colors, markers, markersizes, alphas, zorders, shape=len(a))
for i, (aa, bb, l, c, m, ms, al, z) in enumerate(
zip(a, b, labels, colors, markers, markersizes, alphas, zorders)):
if regression_line and i + 1 == len(a):
l = None if l is None else l.format(r)
ax.plot(aa, bb, color=c, label=l, ls=m, lw=ms, alpha=al, zorder=z)
else:
ax.plot(aa,
bb,
ls='',
marker=m,
color=c,
label=l,
markersize=ms,
alpha=al,
zorder=z,
**kwargs)
set_ax_limits(ax=ax, limits=limits_1)
if labels is not None:
ax.legend()
ax.set_xlabel(name_a)
ax.set_ylabel(name_b)
return ax
def test_remove_ticks(self):
fig, ax = new_fig()
plt.pause(0.1)
remove_ticks(ax=ax, v=[0, 0.4], axis='both')
self.assertTrue(True)