def test_logscale_invert_transform():
fig, ax = plt.subplots()
ax.set_yscale('log')
# get transformation from data to axes
tform = (ax.transAxes + ax.transData.inverted()).inverted()
# direct test of log transform inversion
inverted_transform = LogTransform(base=2).inverted()
assert isinstance(inverted_transform, InvertedLogTransform)
assert inverted_transform.base == 2
def get_scatter_view_lims(counts_df: pd.DataFrame) -> Tuple[float, float]:
"""Calculates scatter view limits for the counts dataframe"""
x0 = counts_df.min(axis='columns').where(lambda x: x != 0).dropna().min()
x1 = np.max(counts_df).max()
minpos = 1e-300
if not np.isfinite([x0, x1]).all() or not isinstance(x0, np.float) or x1 <= 0:
print("The provided dataset contains invalid values.")
return (minpos, minpos)
x0, x1 = (minpos if x0 <= 0 else x0,
minpos if x1 <= 0 else x1)
transform = LogTransform(base=2)
inverse_trans = transform.inverted()
x0t, x1t = transform.transform([x0, x1])
delta = (x1t - x0t) * mpl.rcParams.get('axes.xmargin', 0)
if not np.isfinite(delta): delta = 0
return inverse_trans.transform([x0t - delta, x1t + delta])
def test_logscale_transform_repr():
fig, ax = plt.subplots()
ax.set_yscale('log')
repr(ax.transData)
repr(LogTransform(10, nonpositive='clip'))