def summary(self):
"""Summary of test, containing statistic, p-value and critical values
"""
table_data = [('Test Statistic', '{0:0.3f}'.format(self.stat)),
('P-value', '{0:0.3f}'.format(self.pvalue)),
('Lags', '{0:d}'.format(self.lags))]
title = self._title
if not title:
title = self._test_name + " Results"
table = SimpleTable(table_data,
stubs=None,
title=title,
colwidths=18,
datatypes=[0, 1],
data_aligns=("l", "r"))
smry = Summary()
smry.tables.append(table)
cv_string = 'Critical Values: '
cv = self._critical_values.keys()
cv_numeric = array(lmap(lambda x: float(x.split('%')[0]), cv))
cv_numeric = sort(cv_numeric)
for val in cv_numeric:
p = str(int(val)) + '%'
cv_string += '{0:0.2f}'.format(self._critical_values[p])
cv_string += ' (' + p + ')'
if val != cv_numeric[-1]:
cv_string += ', '
extra_text = [
'Trend: ' + TREND_DESCRIPTION[self._trend], cv_string,
'Null Hypothesis: ' + self.null_hypothesis,
'Alternative Hypothesis: ' + self.alternative_hypothesis
]
smry.add_extra_txt(extra_text)
if self._summary_text:
smry.add_extra_txt(self._summary_text)
return smry
def summary(self):
"""Summary of test, containing statistic, p-value and critical values
"""
table_data = [('Test Statistic', '{0:0.3f}'.format(self.stat)),
('P-value', '{0:0.3f}'.format(self.pvalue)),
('Lags', '{0:d}'.format(self.lags))]
title = self._title
if not title:
title = self._test_name + " Results"
table = SimpleTable(table_data, stubs=None, title=title, colwidths=18,
datatypes=[0, 1], data_aligns=("l", "r"))
smry = Summary()
smry.tables.append(table)
cv_string = 'Critical Values: '
cv = self._critical_values.keys()
cv_numeric = array(lmap(lambda x: float(x.split('%')[0]), cv))
cv_numeric = sort(cv_numeric)
for val in cv_numeric:
p = str(int(val)) + '%'
cv_string += '{0:0.2f}'.format(self._critical_values[p])
cv_string += ' (' + p + ')'
if val != cv_numeric[-1]:
cv_string += ', '
extra_text = ['Trend: ' + TREND_DESCRIPTION[self._trend],
cv_string,
'Null Hypothesis: ' + self.null_hypothesis,
'Alternative Hypothesis: ' + self.alternative_hypothesis]
smry.add_extra_txt(extra_text)
if self._summary_text:
smry.add_extra_txt(self._summary_text)
return smry
for i, t in enumerate(T):
print("Time series length {0} for Trend {1}".format(t, tr))
now = datetime.datetime.now()
# Serial version
# out = lmap(wrapper, [t] * EX_NUM, [tr] * EX_NUM,
# [EX_SIZE] * EX_NUM, seeds)
# Parallel version
res = lview.map_async(wrapper, [t] * EX_NUM, [tr] * EX_NUM,
[EX_SIZE] * EX_NUM, seeds)
sleep_count = 0
while not res.ready():
sleep_count += 1
elapsed = datetime.datetime.now() - now
if sleep_count % 10:
print('Elapsed time {0}, waiting for results'.format(elapsed,
SLEEP))
time.sleep(SLEEP)
out = res.get()
# Prevent unnecessary results from accumulating
clear_cache(rc, lview)
elapsed = datetime.datetime.now() - now
print('Total time {0} for T={1}'.format(elapsed, t))
quantiles = lmap(lambda x: percentile(x, percentiles), out)
results[:, i, :] = array(quantiles).T
savez(filename, trend=tr, results=results,
percentiles=percentiles, T=T)
for i, t in enumerate(T):
print("Time series length {0} for Trend {1}".format(t, tr))
now = datetime.datetime.now()
# Serial version
# out = lmap(wrapper, [t] * EX_NUM, [tr] * EX_NUM,
# [EX_SIZE] * EX_NUM, seeds)
# Parallel version
res = lview.map_async(wrapper, [t] * EX_NUM, [tr] * EX_NUM,
[EX_SIZE] * EX_NUM, seeds)
sleep_count = 0
while not res.ready():
sleep_count += 1
elapsed = datetime.datetime.now() - now
if sleep_count % 10:
print('Elapsed time {0}, waiting for results'.format(elapsed,
SLEEP))
time.sleep(SLEEP)
out = res.get()
# Prevent unnecessary results from accumulating
clear_cache(rc, lview)
elapsed = datetime.datetime.now() - now
print('Total time {0} for T={1}'.format(elapsed, t))
q = lambda x: percentile(x, percentiles)
quantiles = lmap(q, out)
results[:, i, :] = array(quantiles).T
savez(filename, trend=tr, results=results, percentiles=percentiles, T=T)
