def save_plot(txt_file, info_file, output_file, plot_description, options):
"""
Make plot from arguments.
:param txt_file: Name of *.txt file
:type txt_file: str
:param info_file: Name of *.info file
:type info_file: str
:param output_file: Desired name of output file
:type output_file: str
:param plot_description: Type of plot
:type plot_description: str
:param options: plot_options style arguments
:type options: namedtuple
"""
assert plot_description in PLOT_CLASS.keys()
# Fetch data
labels, data = data_loader.load(info_file, txt_file)
# Make file name for plot
if output_file is None:
name = basename(txt_file)
prefix = splitext(name)[0]
all_indexes = [options.xindex, options.yindex, options.zindex]
indexes = [str(i) for i in all_indexes if i is not None]
output_file = prefix + '_' + '_'.join(indexes) + ".pdf"
# Fix labels with info file
if info_file:
if options.xlabel is None and options.xindex:
options = options._replace(xlabel=labels[options.xindex])
if options.ylabel is None and options.yindex:
options = options._replace(ylabel=labels[options.yindex])
if options.zlabel is None and options.zindex:
options = options._replace(zlabel=labels[options.zindex])
# Make and save plot
figure = PLOT_CLASS[plot_description](data, options).figure()
plt.savefig(output_file)
print 'Output file = {}'.format(output_file)
print 'Summary = {}'.format(figure.summary)
def save_plot(txt_file, info_file, output_file, plot_description, options):
"""
Make plot from arguments.
:param txt_file: Name of *.txt file
:type txt_file: str
:param info_file: Name of *.info file
:type info_file: str
:param output_file: Desired name of output file
:type output_file: str
:param plot_description: Type of plot
:type plot_description: str
:param options: plot_options style arguments
:type options: namedtuple
"""
assert plot_description in PLOT_CLASS.keys()
# Fetch data
labels, data = data_loader.load(info_file, txt_file)
# Make file name for plot
if output_file is None:
name = basename(txt_file)
prefix = splitext(name)[0]
all_indexes = [options.xindex, options.yindex, options.zindex]
indexes = [str(i) for i in all_indexes if i is not None]
output_file = prefix + '_' + '_'.join(indexes) + ".pdf"
# Fix labels with info file
if info_file:
if options.xlabel is None and options.xindex:
options = options._replace(xlabel=labels[options.xindex])
if options.ylabel is None and options.yindex:
options = options._replace(ylabel=labels[options.yindex])
if options.zlabel is None and options.zindex:
options = options._replace(zlabel=labels[options.zindex])
# Make and save plot
figure = PLOT_CLASS[plot_description](data, options).figure()
plt.savefig(output_file)
print 'Output file = {}'.format(output_file)
print 'Summary = {}'.format(figure.summary)
>>> kde = kde_posterior_pdf(data[2], data[3], data[0])
>>> bin_centers = posterior_mode(kde.pdf, kde.bin_centers_x, kde.bin_centers_y)[0]
>>> [round(x, DOCTEST_PRECISION) for x in bin_centers]
[-1919.3356566959, 101.1291971019]
"""
# Find the maximum weighted count
max_count = np.max(pdf)
# Find the indices of bins having the max count
max_indices = np.argwhere(pdf == max_count)
# Issue a warning if we found more than one such bin
if len(max_indices) > 1:
warnings.warn("posterior mode: max count shared by {} bins".format(
len(max_indices)))
# Return the (x,y) bin centers of the corresponding cells
return [[bin_centers_x[x], bin_centers_y[y]] for x, y in max_indices]
if __name__ == "__main__":
import doctest
import superplot.data_loader as data_loader
GAUSS = "../example/gaussian_.txt"
GAUSS_DATA = data_loader.load(None, GAUSS)[1]
doctest.testmod(extraglobs={'data': GAUSS_DATA})
-1984.1097853705
>>> kde = kde_posterior_pdf(data[3], data[0])
>>> round(posterior_mode(kde.pdf, kde.bin_centers)[0], DOCTEST_PRECISION)
140.3991747766
"""
# Find the maximum weighted count.
max_count = max(pdf)
# Find the indices of bins having the max count.
max_indices = [i for i, j in enumerate(pdf) if j == max_count]
assert pdf.argmax() in max_indices
if len(max_indices) > 1:
warnings.warn("posterior_mode: max count shared by {} bins".format(
len(max_indices)
))
return [bin_centers[i] for i in max_indices]
if __name__ == "__main__":
import doctest
import superplot.data_loader as data_loader
GAUSS = "../example/gaussian_.txt"
GAUSS_DATA = data_loader.load(None, GAUSS)[1]
doctest.testmod(extraglobs={'data': GAUSS_DATA})