Fix up yli.turnbull

Draw step function at configurable point on each Turnbull interval (previous documentation did not correctly describe the behaviour of the function)
Draw survival curve from time 0, survival 100%

yli/survival.py

@@ -118,13 +118,13 @@ def plot_survfunc_kaplanmeier(ax, time, status, ci, transform_x=None, transform_
 	
 	return handle
 
-def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_y=None, nan_policy='warn'):
+def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn'):
 	"""
 	Generate a Turnbull estimator plot, which extends the Kaplan–Meier estimator to interval-censored observations
 	
 	The intervals are assumed to be half-open intervals, (*left*, *right*]. *right* == *np.inf* implies the event was right-censored. Unlike :func:`yli.kaplanmeier`, times must be given as numeric dtypes and not as pandas timedelta.
 	
-	For ease of interpretation, the survival function is drawn as a step function at the midpoint of the estimate on each interval.
+	By default, the survival function is drawn as a step function at the midpoint of each Turnbull interval.
 	
 	Uses the Python *lifelines* and *matplotlib* libraries.
 	
@@ -136,6 +136,8 @@ def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_
 	:type time_right: str
 	:param by: Column in *df* to stratify by (categorical)
 	:type by: str
+	:param step_loc: Proportion along the length of each Turnbull interval to step down the survival function, e.g. 0 for left bound, 1 for right bound, 0.5 for interval midpoint (numeric)
+	:type step_loc: float
 	:param transform_x: Function to transform x axis by
 	:type transform_x: callable
 	:param transform_y: Function to transform y axis by
@@ -162,11 +164,11 @@ def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_
 		
 		for group in groups.groups:
 			subset = groups.get_group(group)
-			handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], transform_x, transform_y)
+			handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], step_loc, transform_x, transform_y)
 			handle.set_label('{} = {}'.format(by, group))
 	else:
 		# No grouping
-		plot_survfunc_turnbull(ax, df[time_left], df[time_right], transform_x, transform_y)
+		plot_survfunc_turnbull(ax, df[time_left], df[time_right], step_loc, transform_x, transform_y)
 	
 	ax.set_xlabel('Analysis time')
 	ax.set_ylabel('Survival probability')
@@ -176,8 +178,13 @@ def turnbull(df, time_left, time_right, by=None, *, transform_x=None, transform_
 	
 	return fig, ax
 
-def plot_survfunc_turnbull(ax, time_left, time_right, transform_x=None, transform_y=None):
-	import lifelines
+def plot_survfunc_turnbull(ax, time_left, time_right, step_loc=0.5, transform_x=None, transform_y=None):
+	xpoints, ypoints = calc_survfunc_turnbull(time_left, time_right, step_loc, transform_x, transform_y)
+	handle = ax.plot(xpoints, ypoints)[0]
+	return handle
+
+def calc_survfunc_turnbull(time_left, time_right, step_loc=0.5, transform_x=None, transform_y=None):
+	from lifelines.fitters.npmle import npmle
 	
 	EPSILON = 1e-10
 	
@@ -187,21 +194,25 @@ def plot_survfunc_turnbull(ax, time_left, time_right, transform_x=None, transfor
 	followup_right = time_right
 	
 	# Estimate the survival function
-	sf = lifelines.KaplanMeierFitter().fit_interval_censoring(followup_left, followup_right)
+	#sf = lifelines.KaplanMeierFitter().fit_interval_censoring(followup_left, followup_right)
+	
+	# Call lifelines.fitters.npmle.npmle directly so we can compute midpoints, etc.
+	sf_probs, turnbull_intervals = npmle(followup_left, followup_right)
+	
+	xpoints = [i.left*(1-step_loc) + i.right*step_loc for i in turnbull_intervals]
+	ypoints = 1 - np.cumsum(sf_probs)
 	
 	# Draw straight lines
-	xpoints = sf.survival_function_.index.to_numpy().repeat(2)[:-1]
-	med = (sf.survival_function_['NPMLE_estimate_upper'] + sf.survival_function_['NPMLE_estimate_lower']) / 2
-	ypoints = med.to_numpy().repeat(2)[1:]
+	# np.concatenate(...) to force starting drawing from time 0, survival 100%
+	xpoints = np.concatenate([[0], xpoints]).repeat(2)[1:]
+	ypoints = np.concatenate([[1], ypoints]).repeat(2)[:-1]
 	
 	if transform_x:
 		xpoints = transform_x(xpoints)
 	if transform_y:
 		ypoints = transform_y(ypoints)
 	
-	handle = ax.plot(xpoints, ypoints)[0]
-	
-	return handle
+	return xpoints, ypoints
 
 def survtime_to_numeric(df, time):
 	"""