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feat(plotting): add colormap argument to plot_cohorts #137

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feat(plotting): add colormap argument to plot_cohorts #137

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14 changes: 13 additions & 1 deletion convoys/plotting.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import numpy
from matplotlib import colors as mcolors
from matplotlib import pyplot
import convoys.multi

Expand All @@ -17,7 +18,8 @@
def plot_cohorts(G, B, T, t_max=None, model='kaplan-meier',
ci=None, ax=None, plot_kwargs={}, plot_ci_kwargs={},
groups=None, specific_groups=None,
label_fmt='%(group)s (n=%(n).0f, k=%(k).0f)'):
label_fmt='%(group)s (n=%(n).0f, k=%(k).0f)',
colormap=None):
''' Helper function to fit data using a model and then plot the cohorts.

:param G: list with group assignment
Expand All @@ -37,6 +39,7 @@ def plot_cohorts(G, B, T, t_max=None, model='kaplan-meier',
:param groups: list of group labels
:param specific_groups: subset of groups to plot
:param label_fmt: custom format for the labels to use in the legend
:param colormap: a colormap to use for the lines that will be plotted

See :meth:`convoys.utils.get_arrays` which is handy for converting
a Pandas dataframe into arrays `G`, `B`, `T`.
Expand Down Expand Up @@ -73,6 +76,11 @@ def plot_cohorts(G, B, T, t_max=None, model='kaplan-meier',
t = numpy.linspace(0, t_max, 1000)
_, y_max = ax.get_ylim()
ax.set_prop_cycle(None) # Reset to first color
if isinstance(colormap, mcolors.ListedColormap):
num_groups = len(groups)
colors = colormap(numpy.linspace(0, 1, num_groups))
else:
colors = None
for i, group in enumerate(specific_groups):
j = groups.index(group) # matching index of group

Expand All @@ -84,6 +92,8 @@ def plot_cohorts(G, B, T, t_max=None, model='kaplan-meier',
p_y, p_y_lo, p_y_hi = m.predict_ci(j, t, ci=ci).T
merged_plot_ci_kwargs = {'alpha': 0.2}
merged_plot_ci_kwargs.update(plot_ci_kwargs)
if colors is not None:
merged_plot_ci_kwargs['color'] = colors[i]
p = ax.fill_between(t, 100. * p_y_lo, 100. * p_y_hi,
**merged_plot_ci_kwargs)
color = p.get_facecolor()[0] # reuse color for the line
Expand All @@ -94,6 +104,8 @@ def plot_cohorts(G, B, T, t_max=None, model='kaplan-meier',
merged_plot_kwargs = {'color': color, 'linewidth': 1.5,
'alpha': 0.7}
merged_plot_kwargs.update(plot_kwargs)
if colors is not None:
merged_plot_kwargs['color'] = colors[i]
ax.plot(t, 100. * p_y, label=label, **merged_plot_kwargs)
y_max = max(y_max, 110. * max(p_y))

Expand Down