remove plot_timeseries (for now)

convoys/__init__.py

@@ -173,66 +173,3 @@ def plot_cohorts(data, t_max=None, title=None, group_min_size=0, max_groups=100,
     pyplot.legend()
     pyplot.gca().grid(True)
     return result
-
-
-def plot_timeseries(data, window, model='kaplan-meier', group_min_size=0, max_groups=100, window_min_size=1, stride=None, title=None, time=False):
-    if stride is None:
-        stride = window
-
-    # Find limits
-    t_lo = min(created_at for _, created_at, _, _ in data)
-    t_hi = min(now for _, _, _, now in data)
-    t_unit, t_converter = get_timescale(t_hi - t_lo)
-
-    # Split data by group
-    groups, js = split_by_group(data, group_min_size, max_groups)
-
-    # PLOT
-    colors = seaborn.color_palette('hls', len(groups))
-    y_max = 0
-    for group, color in zip(sorted(groups), colors):
-        t1 = t_lo
-        ts, ys, y_los, y_his = [], [], [], []
-        js[group].sort(key=lambda x: x[0])
-        created_ats = [created_at for created_at, _, _ in js[group]]
-        while True:
-            t2 = t1 + window
-            i1 = bisect.bisect_left(created_ats, t1)
-            i2 = bisect.bisect_left(created_ats, t2)
-            if i2 >= len(js[group]):
-                break
-            data = js[group][i1:i2]
-            t1 += stride
-
-            X, B, T = get_arrays(data, t_converter)
-            if sum(B) < window_min_size:
-                continue
-
-            p = _models[model]()
-            p.fit(X, B, T)
-
-            if time:
-                y, y_lo, y_hi = p.predict_time([1], ci=0.95)
-            else:
-                y, y_lo, y_hi = p.predict_final([1], ci=0.95)
-            print('%30s %40s %.4f %.4f %.4f' % (group, t1, y, y_lo, y_hi))
-            ts.append(t2)
-            ys.append(y)
-            y_los.append(y_lo)
-            y_his.append(y_hi)
-
-        if not time:
-            ys, y_los, y_his = (100.*numpy.array(x) for x in (ys, y_los, y_his))
-        pyplot.plot(ts, ys, color=color, label='%s (%d)' % (group, len(js[group])))
-        pyplot.fill_between(ts, y_los, y_his, color=color, alpha=0.2)
-        y_max = max(y_max, 1.1 * max(ys))
-
-    if title:
-        pyplot.title(title)
-    if time:
-        pyplot.ylabel('Average time to conversion (%s)' % t_unit)
-    else:
-        pyplot.ylabel('Conversion rate %')
-    pyplot.ylim([0, y_max])
-    pyplot.legend()
-    pyplot.gca().grid(True)