def test_no_fill():
p = (ggplot(df, aes('x', group='factor(z)'))
+ geom_polygon(aes(y='y'), fill=None, color='red', size=2)
+ geom_polygon(aes(y='y+2'), fill='None', color='green', size=2)
+ geom_polygon(aes(y='y+4'), fill='none', color='blue', size=2)
)
assert p + _theme == 'no_fill'
def test_aesthetics():
p = (ggplot(df, aes('x', group='factor(z)')) + geom_polygon(aes(y='y')) +
geom_polygon(aes(y='y+3', alpha='z')) +
geom_polygon(aes(y='y+6', linetype='factor(z)'),
color='brown',
fill=None,
size=2) +
geom_polygon(aes(y='y+9', color='z'), fill=None, size=2) +
geom_polygon(aes(y='y+12', fill='factor(z)')) + geom_polygon(
aes(y='y+15', size='z'), color='yellow', show_legend=False))
assert p + _theme == 'aesthetics'
def expression_plot(
inFile,
cp1=1,
cp2=2,
model=None,
draw_distribution=True,
draw_points=True,
max_kernel_alpha=0.5,
color="expression",
):
par = get_params(inFile)
pl = (pn.ggplot(pn.aes(f"CP {cp1}", f"CP {cp2}", color=color)) +
pn.theme_minimal())
df = None
kdf = None
if draw_points:
if model is None:
index = [
f"sample {i+1}" for i in range(par["means"]["x_t"].shape[0])
]
if color != "expression":
raise Exception(
"A model must be passed to color other that by expression."
)
else:
index = model.counts.columns
columns = [f"CP {i+1}" for i in range(par["means"]["x_t"].shape[1])]
df_t = pd.DataFrame(par["means"]["x_t"], index=index, columns=columns)
df_t["expression"] = "tumor"
df_tf = pd.DataFrame(par["means"]["x_f"], index=index, columns=columns)
df_tf["expression"] = "non-tumor"
df = pd.concat([df_t, df_tf])
if model is not None:
df = df.merge(model.pheno,
"left",
left_index=True,
right_index=True)
pl += pn.geom_point(data=df, alpha=0.3)
if draw_distribution:
n_kernel = 0
for var in sorted(par["means"]):
n_kernel += "mus_f" in var
if "altStick" in par["note"] and not par["note"]["altStick"]:
tf = StickBreaking_legacy()
else:
tf = StickBreaking2()
elipses = list()
elipse_t = np.linspace(0, 2 * np.pi, 100)
for tissue_type in ["t", "f"]:
weights = tf.backward(
par["means"][f"w_{tissue_type}_stickbreaking__"]).eval()
n_dim = par["means"][f"x_{tissue_type}"].shape[1]
for kernel in range(n_kernel):
# get covariance elipse parameters
packed_cov = par["means"][
f"packed_L_{tissue_type}_{kernel}_cholesky-cov-packed__"]
lower = pm.expand_packed_triangular(n_dim,
packed_cov,
lower=True).eval()
cov = np.dot(lower, lower.T)[[cp1 - 1, cp2 -
1], :][:, [cp1 - 1, cp2 - 1]]
var, U = np.linalg.eig(cov)
theta = np.arccos(np.abs(U[0, 0]))
# parametrize elipse
width = 2 * np.sqrt(5.991 * var[0])
hight = 2 * np.sqrt(5.991 * var[1])
density = weights[kernel] / width * hight
x = width * np.cos(elipse_t)
y = hight * np.sin(elipse_t)
# rotation
c, s = np.cos(theta), np.sin(theta)
R = np.array(((c, -s), (s, c)))
path = np.dot(R, np.array([x, y]))
# position
pos = par["means"][f"mus_{tissue_type}_{kernel}"]
path += pos[[cp1 - 1, cp2 - 1]][:, None]
# make data frame
path_df = pd.DataFrame({
f"CP {cp1}": path[0, :],
f"CP {cp2}": path[1, :]
})
path_df["kernel"] = kernel
path_df["density"] = density
path_df["expression"] = ("tumor" if tissue_type == "t" else
"non-tumor")
path_df["expression-kernel"] = (f"tumor {kernel}"
if tissue_type == "t" else
f"non-tumor {kernel}")
elipses.append(path_df)
kdf = pd.concat(elipses)
density_scale = max_kernel_alpha / kdf["density"].max()
kdf["density"] *= density_scale
pl += pn.geom_polygon(
pn.aes(fill="expression",
group="expression-kernel",
alpha="density"),
data=kdf,
)
pl += pn.scale_alpha_continuous(range=(0, max_kernel_alpha))
return pl, df, kdf