def plot_results(summary_filenames, keys, **kwargs):
"""
Read the summary bootstrap files and create a plot using
plot_performance.
Params:
summary_filenames: list of str
The names of the summary bootstrap files, with the data
from all experiments.
keys: list of str
The keys to extract from the summary bootstrap
**kwargs is passed to plot_performance
"""
means = []
low_cis = []
high_cis = []
for fn in summary_filenames:
bs = Bootstrapper(write_raw_data=True)
bs.read_bootstrap_file(fn)
mean = []
low_ci = []
high_ci = []
for key in keys:
stats = bs.get_stats(key)
mean.append(stats[1] * 100)
low_ci.append(stats[2][0] * 100)
high_ci.append(stats[2][1] * 100)
means.append(mean)
low_cis.append(low_ci)
high_cis.append(high_ci)
print means, low_cis, high_cis
plot.plot_performance(means, low_cis, high_cis, **kwargs)
def consolidate_bootstraps(input_filenames, summary_filename):
bs = Bootstrapper(write_raw_data=True)
for fn in input_filenames:
bs.read_bootstrap_file(fn)
bs.print_summary(summary_filename)