From fedfe5ca538bc99761c830aa08500e737ba85025 Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Wed, 12 Feb 2020 20:26:26 -0600
Subject: [PATCH] Add iterable confidence intervals to the PP plots
---
bilby/core/result.py | 29 ++++++++++++++++++++---------
1 file changed, 20 insertions(+), 9 deletions(-)
diff --git a/bilby/core/result.py b/bilby/core/result.py
index 630830dfc..e5e1b5544 100644
--- a/bilby/core/result.py
+++ b/bilby/core/result.py
@@ -1528,6 +1528,7 @@ def plot_multiple(results, filename=None, labels=None, colours=None,
def make_pp_plot(results, filename=None, save=True, confidence_interval=0.9,
lines=None, legend_fontsize='x-small', keys=None, title=True,
+ confidence_interval_alpha=0.1,
**kwargs):
"""
Make a P-P plot for a set of runs with injected signals.
@@ -1549,6 +1550,8 @@ def make_pp_plot(results, filename=None, save=True, confidence_interval=0.9,
The font size for the legend
keys: list
A list of keys to use, if None defaults to search_parameter_keys
+ confidence_interval_alpha: float, list, optional
+ The transparency for the background condifence interval
kwargs:
Additional kwargs to pass to matplotlib.pyplot.plot
@@ -1576,18 +1579,26 @@ def make_pp_plot(results, filename=None, save=True, confidence_interval=0.9,
x_values = np.linspace(0, 1, 1001)
- # Putting in the confidence bands
N = len(credible_levels)
- edge_of_bound = (1. - confidence_interval) / 2.
- lower = scipy.stats.binom.ppf(1 - edge_of_bound, N, x_values) / N
- upper = scipy.stats.binom.ppf(edge_of_bound, N, x_values) / N
- # The binomial point percent function doesn't always return 0 @ 0,
- # so set those bounds explicitly to be sure
- lower[0] = 0
- upper[0] = 0
fig, ax = plt.subplots()
- ax.fill_between(x_values, lower, upper, alpha=0.2, color='k')
+ if isinstance(confidence_interval, float):
+ confidence_interval = [confidence_interval]
+ if isinstance(confidence_interval_alpha, float):
+ confidence_interval_alpha = [confidence_interval_alpha] * len(confidence_interval)
+ elif len(confidence_interval_alpha) != len(confidence_interval):
+ raise ValueError(
+ "confidence_interval_alpha must have the same length as confidence_interval")
+
+ for ci, alpha in zip(confidence_interval, confidence_interval_alpha):
+ edge_of_bound = (1. - ci) / 2.
+ lower = scipy.stats.binom.ppf(1 - edge_of_bound, N, x_values) / N
+ upper = scipy.stats.binom.ppf(edge_of_bound, N, x_values) / N
+ # The binomial point percent function doesn't always return 0 @ 0,
+ # so set those bounds explicitly to be sure
+ lower[0] = 0
+ upper[0] = 0
+ ax.fill_between(x_values, lower, upper, alpha=alpha, color='k')
pvalues = []
logger.info("Key: KS-test p-value")
--
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