Skip to content

poisson plot confidence interval #131

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jul 22, 2021
Merged

poisson plot confidence interval #131

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 6 additions & 5 deletions csep/utils/plots.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1307,14 +1307,15 @@ def plot_poisson_consistency_test(eval_results, normalize=False, one_sided_lower
capsize = plot_args.get('capsize', 4)
hbars = plot_args.get('hbars', True)
tight_layout = plot_args.get('tight_layout', True)
percentile = plot_args.get('percentile', 95)

fig, ax = pyplot.subplots(figsize=figsize)
xlims = []
for index, res in enumerate(results):
# handle analytical distributions first, they are all in the form ['name', parameters].
if res.test_distribution[0] == 'poisson':
plow = scipy.stats.poisson.ppf(0.025, res.test_distribution[1])
phigh = scipy.stats.poisson.ppf(0.975, res.test_distribution[1])
plow = scipy.stats.poisson.ppf((1 - percentile/100.)/2., res.test_distribution[1])
phigh = scipy.stats.poisson.ppf(1 - (1 - percentile/100.)/2., res.test_distribution[1])
observed_statistic = res.observed_statistic
# empirical distributions
else:
Expand All @@ -1326,11 +1327,11 @@ def plot_poisson_consistency_test(eval_results, normalize=False, one_sided_lower
observed_statistic = res.observed_statistic
# compute distribution depending on type of test
if one_sided_lower:
plow = numpy.percentile(test_distribution, 5)
plow = numpy.percentile(test_distribution, 100 - percentile)
phigh = numpy.percentile(test_distribution, 100)
else:
plow = numpy.percentile(test_distribution, 2.5)
phigh = numpy.percentile(test_distribution, 97.5)
plow = numpy.percentile(test_distribution, (100 - percentile)/2.)
phigh = numpy.percentile(test_distribution, 100 - (100 - percentile)/2.)

if not numpy.isinf(observed_statistic): # Check if test result does not diverges
low = observed_statistic - plow
Expand Down