diff --git a/bin/gwdetchar-lasso-correlation b/bin/gwdetchar-lasso-correlation
index 88d92a472..3812e6222 100755
--- a/bin/gwdetchar-lasso-correlation
+++ b/bin/gwdetchar-lasso-correlation
@@ -146,9 +146,9 @@ psig.add_argument('-b', '--band-pass', type=float, nargs=2, default=None,
psig.add_argument('-x', '--filter-padding', type=float, default=3.,
help='amount of time (seconds) to pad data for filtering')
-lsig = parser.add_argument_group('LASSO options')
+lsig = parser.add_argument_group('Lasso options')
lsig.add_argument('-a', '--alpha', default=None, type=float,
- help='alpha parameter for LASSO fit')
+ help='alpha parameter for lasso fit')
lsig.add_argument('-C', '--no-cluster', action='store_true', default=False,
help='do not generate clustered channel plots')
lsig.add_argument('-c', '--cluster-coefficient', default=.85, type=float,
@@ -283,7 +283,7 @@ except ValueError:
data = numpy.array([scale(ts.value) for ts in auxdata.values()]).T
-# -- perform LASSO regression -------------------------------------------------
+# -- perform lasso regression -------------------------------------------------
# create model
if args.alpha is None:
@@ -353,7 +353,7 @@ unsorted_results = sorted(unsorted_results, key=lambda x: abs(x[1]),
sorted_results = ([[x[0] for x in unsorted_results],
[y[1] for y in unsorted_results]])
resultstab = Table(data=(sorted_results[0], sorted_results[1]),
- names=('Channel', 'LASSO Coefficient'))
+ names=('Channel', 'Lasso coefficient'))
nonzerodata = dict()
nonzerocoef = dict()
@@ -368,14 +368,14 @@ while(i < len(sorted_results[1]) and abs(sorted_results[1][i]) > 0):
i += 1
usefultab = Table(data=(resultstab['Channel'][0:usefulcount],
- resultstab['LASSO Coefficient'][0:usefulcount]),
- names=('Channel', 'LASSO Coefficient'))
+ resultstab['Lasso coefficient'][0:usefulcount]),
+ names=('Channel', 'Lasso coefficient'))
-zeroed = resultstab['LASSO Coefficient'] == 0
+zeroed = resultstab['Lasso coefficient'] == 0
zeroedtab = Table(data=(resultstab[zeroed]['Channel'],), names=('Channels',))
# print results
-print('Found {} channels with |LASSO Coefficient| >= {}'.format(
+print('Found {} channels with |Lasso coefficient| >= {}'.format(
len(usefultab), args.threshold))
print(usefultab)
@@ -392,7 +392,7 @@ resultstab.write(resultsfile, format='ascii', overwrite=True)
zeroedtab.write(zerofile, format='ascii', overwrite=True)
flattab.write(flatfile, format='ascii', overwrite=True)
-# generate LASSO plots
+# generate lasso plots
modelFit = model.predict(data)
re_delim = re.compile('[:_-]')
@@ -403,14 +403,14 @@ fig = plt.figure(figsize=(12, 6))
fig.subplots_adjust(*p1)
ax = fig.add_subplot(1, 1, 1)
ax.plot(hour_axis, scale(primaryts.value), label=primary.replace('_', '\_'))
-ax.plot(hour_axis, modelFit, label='LASSO model')
+ax.plot(hour_axis, modelFit, label='Lasso model')
ax.margins(x=0)
ax.set_xlabel(auto_xlabel)
ax.set_ylabel('Scaled arbitrary units')
if range_is_primary:
- ax.set_title('LASSO Model of Range')
+ ax.set_title('Lasso Model of Range')
else:
- ax.set_title('LASSO Model of Primary Channel')
+ ax.set_title('Lasso Model of Primary Channel')
ax.legend(loc='best')
fig.canvas.draw_idle()
@@ -952,7 +952,7 @@ def style_table(html_table):
# write html
-title = '%s LASSO slow correlations: %d-%d' % (args.ifo, start, end)
+title = '%s Lasso slow correlations: %d-%d' % (args.ifo, start, end)
page = html.new_bootstrap_page(title=title)
page.div(class_='container')
@@ -982,7 +982,7 @@ write_param('Number of flat channels',
'%d (%s)' % (len(flatdata),
"flat channel list"
% (flatfile)))
-write_param('LASSO coefficient threshold', '%g' % args.threshold)
+write_param('Lasso coefficient threshold', '%g' % args.threshold)
write_param('Sigma for outlier removal', '%g' % args.remove_outliers)
write_param('Cluster coefficient threshold: ',
'%g' % args.cluster_coefficient)
@@ -993,7 +993,7 @@ page.div(class_='model')
page.div(class_='model-body')
page.div(class_='model-info')
-write_param('Model', 'LASSO')
+write_param('Model', 'Lasso')
write_param('Non-zero coefficients', '%d' % numpy.count_nonzero(model.coef_))
write_param('Alpha', '%g' % usedalpha)
write_param('Zero coefficients',
@@ -1004,7 +1004,7 @@ page.div(class_='results-table', align='center')
page.p('
%s' % style_table(df.to_html(
index=True,
formatters={
- 'LASSO Coefficient': format_coefficients,
+ 'Lasso coefficient': format_coefficients,
'Channel': format_channels,
'__index__': format_indices},
escape=False)))
@@ -1096,7 +1096,7 @@ for i, (ch, lassocoef, plot4, plot5, plot6, ts) in enumerate(results):
page.p('The amplitude data for this channel is flat (does not change)'
' for the chosen time period.')
elif abs(lassocoef) < args.threshold:
- page.p('LASSO coefficient below the threshold of %g.'
+ page.p('Lasso coefficient below the threshold of %g.'
% (args.threshold))
else:
for p in (plot4, plot5, plot6):