def get_table_string_and_scripts_from_logs(start_stop_pairs, log_paths,
nsamples):
"""
This is for analysis of remote execution.
"""
# build the array for the R table
data_arr = []
sequence_lengths = []
midpoints = []
for start_stop_pair, log_path in zip(start_stop_pairs, log_paths):
start_pos, stop_pos = start_stop_pair
sequence_length = stop_pos - start_pos + 1
means, variations, covs = read_log(log_path, nsamples)
midpoint = (start_pos + stop_pos) / 2.0
row = [sequence_length, midpoint]
for values in means, variations, covs:
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_low, hpd_high = mcmc.get_hpd_interval(0.95, values)
row.extend([hpd_low, corr_info.mean, hpd_high])
data_arr.append(row)
sequence_lengths.append(sequence_length)
midpoints.append(midpoint)
# build the table string
table_string = RUtil.get_table_string(data_arr, g_headers)
# get the scripts
scripts = get_ggplot2_scripts(nsamples, sequence_lengths, midpoints)
# return the table string and scripts
return table_string, scripts
def get_table_string_and_scripts(start_stop_pairs, nsamples):
"""
Command-line only.
"""
# build the array for the R table
data_arr = []
sequence_lengths = []
midpoints = []
for start_pos, stop_pos in start_stop_pairs:
sequence_length = stop_pos - start_pos + 1
means, variations, covs = get_value_lists(start_pos, stop_pos,
nsamples)
midpoint = (start_pos + stop_pos) / 2.0
row = [sequence_length, midpoint]
for values in means, variations, covs:
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_low, hpd_high = mcmc.get_hpd_interval(0.95, values)
row.extend([hpd_low, corr_info.mean, hpd_high])
data_arr.append(row)
sequence_lengths.append(sequence_length)
midpoints.append(midpoint)
# build the table string
table_string = RUtil.get_table_string(data_arr, g_headers)
# get the scripts
scripts = get_ggplot2_scripts(nsamples, sequence_lengths, midpoints)
# return the table string and scripts
return table_string, scripts
def get_table_string_and_scripts(stop_positions, nsamples):
"""
Command-line only.
"""
start_position = 1
# build the array for the R table
data_arr = []
for stop_position in stop_positions:
sequence_length = stop_position - start_position + 1
means, variations, covs = get_value_lists(start_position,
stop_position, nsamples)
row = [sequence_length]
for values in means, variations, covs:
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_low, hpd_high = mcmc.get_hpd_interval(0.95, values)
row.extend([hpd_low, corr_info.mean, hpd_high])
data_arr.append(row)
# build the table string
table_string = RUtil.get_table_string(data_arr, g_headers)
# get the scripts
sequence_lengths = [x - start_position + 1 for x in stop_positions]
scripts = get_ggplot2_scripts(sequence_lengths)
# return the table string and scripts
return table_string, scripts
def get_table_string_and_scripts(stop_positions, nsamples):
"""
Command-line only.
"""
start_position = 1
# build the array for the R table
data_arr = []
for stop_position in stop_positions:
sequence_length = stop_position - start_position + 1
means, variations, covs = get_value_lists(
start_position, stop_position, nsamples)
row = [sequence_length]
for values in means, variations, covs:
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_low, hpd_high = mcmc.get_hpd_interval(0.95, values)
row.extend([hpd_low, corr_info.mean, hpd_high])
data_arr.append(row)
# build the table string
table_string = RUtil.get_table_string(data_arr, g_headers)
# get the scripts
sequence_lengths = [x - start_position + 1 for x in stop_positions]
scripts = get_ggplot2_scripts(sequence_lengths)
# return the table string and scripts
return table_string, scripts
def get_table_string_and_scripts_from_logs(
start_stop_pairs, log_paths, nsamples):
"""
This is for analysis of remote execution.
"""
# build the array for the R table
data_arr = []
sequence_lengths = []
midpoints = []
for start_stop_pair, log_path in zip(
start_stop_pairs, log_paths):
start_pos, stop_pos = start_stop_pair
sequence_length = stop_pos - start_pos + 1
means, variations, covs = read_log(log_path, nsamples)
midpoint = (start_pos + stop_pos) / 2.0
row = [sequence_length, midpoint]
for values in means, variations, covs:
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_low, hpd_high = mcmc.get_hpd_interval(0.95, values)
row.extend([hpd_low, corr_info.mean, hpd_high])
data_arr.append(row)
sequence_lengths.append(sequence_length)
midpoints.append(midpoint)
# build the table string
table_string = RUtil.get_table_string(data_arr, g_headers)
# get the scripts
scripts = get_ggplot2_scripts(nsamples, sequence_lengths, midpoints)
# return the table string and scripts
return table_string, scripts
def get_table_string_and_scripts(start_stop_pairs, nsamples):
"""
Command-line only.
"""
# build the array for the R table
data_arr = []
sequence_lengths = []
midpoints = []
for start_pos, stop_pos in start_stop_pairs:
sequence_length = stop_pos - start_pos + 1
means, variations, covs = get_value_lists(
start_pos, stop_pos, nsamples)
midpoint = (start_pos + stop_pos) / 2.0
row = [sequence_length, midpoint]
for values in means, variations, covs:
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_low, hpd_high = mcmc.get_hpd_interval(0.95, values)
row.extend([hpd_low, corr_info.mean, hpd_high])
data_arr.append(row)
sequence_lengths.append(sequence_length)
midpoints.append(midpoint)
# build the table string
table_string = RUtil.get_table_string(data_arr, g_headers)
# get the scripts
scripts = get_ggplot2_scripts(nsamples, sequence_lengths, midpoints)
# return the table string and scripts
return table_string, scripts
def get_html(values_name_pairs):
"""
Web based only.
"""
out = StringIO()
#
#print >> out, 'statistic:', name
#print >> out, 'mean:', corr_info.mean
#print >> out, 'standard error of mean:', corr_info.stdErrorOfMean
#print >> out, 'auto correlation time (ACT):', corr_info.ACT
#print >> out, 'standard deviation of ACT:', corr_info.stdErrOfACT
#print >> out, 'effective sample size (ESS):', corr_info.ESS
#print >> out, 'posterior density interval (0.95): [%f, %f]' % hpd
#print >> out
print >> out, '<html>'
# write the html head
print >> out, '<head>'
print >> out, '<script'
print >> out, ' type="text/javascript"'
print >> out, ' src="https://www.google.com/jsapi">'
print >> out, '</script>'
print >> out, '<script type="text/javascript">'
print >> out, " google.load('visualization', '1', {packages:['table']});"
print >> out, " google.setOnLoadCallback(drawTable);"
print >> out, " function drawTable() {"
print >> out, " var data = new google.visualization.DataTable();"
# add columns
print >> out, " data.addColumn('string', 'description');"
print >> out, " data.addColumn('number', '95% HPD low');"
print >> out, " data.addColumn('number', 'mean');"
print >> out, " data.addColumn('number', '95% HPD high');"
print >> out, " data.addColumn('number', 'ACT');"
print >> out, " data.addColumn('number', 'ESS');"
# add rows
print >> out, " data.addRows(3);"
# add entries
for i, (values, name) in enumerate(values_name_pairs):
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_low, hpd_high = mcmc.get_hpd_interval(0.95, values)
print >> out, " data.setCell(%d, 0, '%s');" % (i, name)
print >> out, " data.setCell(%d, 1, %f);" % (i, hpd_low)
print >> out, " data.setCell(%d, 2, %f);" % (i, corr_info.mean)
print >> out, " data.setCell(%d, 3, %f);" % (i, hpd_high)
print >> out, " data.setCell(%d, 4, %f);" % (i, corr_info.ACT)
print >> out, " data.setCell(%d, 5, %f);" % (i, corr_info.ESS)
print >> out, " var table = new google.visualization.Table("
print >> out, " document.getElementById('table_div'));"
print >> out, " table.draw(data, {showRowNumber: false});"
print >> out, " }"
print >> out, "</script>"
print >> out, '</head>'
# write the html body
print >> out, '<body><div id="table_div"></div></body>'
# end the html
print >> out, '</html>'
# return the html string
return out.getvalue().rstrip()
def get_html(values_name_pairs):
"""
Web based only.
"""
out = StringIO()
#
#print >> out, 'statistic:', name
#print >> out, 'mean:', corr_info.mean
#print >> out, 'standard error of mean:', corr_info.stdErrorOfMean
#print >> out, 'auto correlation time (ACT):', corr_info.ACT
#print >> out, 'standard deviation of ACT:', corr_info.stdErrOfACT
#print >> out, 'effective sample size (ESS):', corr_info.ESS
#print >> out, 'posterior density interval (0.95): [%f, %f]' % hpd
#print >> out
print >> out, '<html>'
# write the html head
print >> out, '<head>'
print >> out, '<script'
print >> out, ' type="text/javascript"'
print >> out, ' src="https://www.google.com/jsapi">'
print >> out, '</script>'
print >> out, '<script type="text/javascript">'
print >> out, " google.load('visualization', '1', {packages:['table']});"
print >> out, " google.setOnLoadCallback(drawTable);"
print >> out, " function drawTable() {"
print >> out, " var data = new google.visualization.DataTable();"
# add columns
print >> out, " data.addColumn('string', 'description');"
print >> out, " data.addColumn('number', '95% HPD low');"
print >> out, " data.addColumn('number', 'mean');"
print >> out, " data.addColumn('number', '95% HPD high');"
print >> out, " data.addColumn('number', 'ACT');"
print >> out, " data.addColumn('number', 'ESS');"
# add rows
print >> out, " data.addRows(3);"
# add entries
for i, (values, name) in enumerate(values_name_pairs):
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_low, hpd_high = mcmc.get_hpd_interval(0.95, values)
print >> out, " data.setCell(%d, 0, '%s');" % (i, name)
print >> out, " data.setCell(%d, 1, %f);" % (i, hpd_low)
print >> out, " data.setCell(%d, 2, %f);" % (i, corr_info.mean)
print >> out, " data.setCell(%d, 3, %f);" % (i, hpd_high)
print >> out, " data.setCell(%d, 4, %f);" % (i, corr_info.ACT)
print >> out, " data.setCell(%d, 5, %f);" % (i, corr_info.ESS)
print >> out, " var table = new google.visualization.Table("
print >> out, " document.getElementById('table_div'));"
print >> out, " table.draw(data, {showRowNumber: false});"
print >> out, " }"
print >> out, "</script>"
print >> out, '</head>'
# write the html body
print >> out, '<body><div id="table_div"></div></body>'
# end the html
print >> out, '</html>'
# return the html string
return out.getvalue().rstrip()
def forked_function(start_stop_n):
"""
This function should accept and return as little data as possible.
In particular do not return a huge multimegabyte nested list.
@param start_stop_n: start_pos, stop_pos, nsamples
@return: (corr_info, hpd_interval) for mean, variation, covariance
"""
start_pos, stop_pos, nsamples = start_stop_n
means, variations, covs = get_value_lists(start_pos, stop_pos, nsamples)
post_pairs = []
for values in means, variations, covs:
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_interval = mcmc.get_hpd_interval(0.95, values)
post_pairs.append((corr_info, hpd_interval))
return post_pairs
def forked_function(start_stop_n):
"""
This function should accept and return as little data as possible.
In particular do not return a huge multimegabyte nested list.
@param start_stop_n: start_pos, stop_pos, nsamples
@return: (corr_info, hpd_interval) for mean, variation, covariance
"""
start_pos, stop_pos, nsamples = start_stop_n
means, variations, covs = get_value_lists(start_pos, stop_pos, nsamples)
post_pairs = []
for values in means, variations, covs:
corr_info = mcmc.Correlation()
corr_info.analyze(values)
hpd_interval = mcmc.get_hpd_interval(0.95, values)
post_pairs.append((corr_info, hpd_interval))
return post_pairs
