def get_table_string_and_scripts(start_stop_pairs, nsamples, header_seq_pairs):
"""
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
midpoint = (start_pos + stop_pos) / 2.0
arr = get_loganalysis_array(
start_pos, stop_pos, nsamples, header_seq_pairs)
mean_low = arr[1][4]
mean_mean = arr[1][1]
mean_high = arr[1][5]
var_low = arr[2][4]
var_mean = arr[2][1]
var_high = arr[2][5]
cov_low = arr[3][4]
cov_mean = arr[3][1]
cov_high = arr[3][5]
row = [
sequence_length, midpoint,
mean_low, mean_mean, mean_high,
var_low, var_mean, var_high,
cov_low, cov_mean, cov_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 = beasttut.get_ggplot2_scripts(
nsamples, sequence_lengths, midpoints)
# return the table string and scripts
return table_string, scripts
def get_table_strings_and_scripts(
xmldata, alignment_id, start_stop_pairs,
nsamples):
"""
Command-line only.
@param xmldata: xml data already adjusted for nsamples and log filename
@param alignment_id: xml element id
@param start_stop_pairs: alignment interval bounds
@param nsamples: an extra parameter for script generation
@return: short table string, long table string, scripts (for short table)
"""
# init the array for the full R table
full_data_arr = []
# 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
midpoint = (start_pos + stop_pos) / 2.0
interval_xml_data = beast.set_alignment_interval(
xmldata, alignment_id, start_pos, stop_pos)
row_labels, col_labels, arr = get_loganalysis_labeled_array(
interval_xml_data)
stat_name_to_row_index = dict(
(x, i) for i, x in enumerate(row_labels))
summary_name_to_col_index = dict(
(x, i) for i, x in enumerate(col_labels))
# define row indices of interest
mean_row_index = stat_name_to_row_index['meanRate']
var_row_index = stat_name_to_row_index['coefficientOfVariation']
cov_row_index = stat_name_to_row_index['covariance']
# define column indices of interest
mean_col_index = summary_name_to_col_index['mean']
low_col_index = summary_name_to_col_index['hpdLower']
high_col_index = summary_name_to_col_index['hpdUpper']
row = [
sequence_length,
midpoint,
arr[mean_row_index][low_col_index],
arr[mean_row_index][mean_col_index],
arr[mean_row_index][high_col_index],
arr[var_row_index][low_col_index],
arr[var_row_index][mean_col_index],
arr[var_row_index][high_col_index],
arr[cov_row_index][low_col_index],
arr[cov_row_index][mean_col_index],
arr[cov_row_index][high_col_index],
]
data_arr.append(row)
# add rows to the full data array
for row_index, row_label in enumerate(row_labels):
for col_index, col_label in enumerate(col_labels):
row = [
sequence_length,
midpoint,
'"' + row_label + '"',
'"' + col_label + '"',
arr[row_index][col_index],
]
full_data_arr.append(row)
# add entries to some utility arrays
sequence_lengths.append(sequence_length)
midpoints.append(midpoint)
# build the table strings
table_string = RUtil.get_table_string(data_arr, g_headers)
full_table_string = RUtil.get_table_string(
full_data_arr,
[
'sequence.length',
'midpoint',
'statistic.name',
'posterior.analysis',
'value'
],
force_float=False,
)
# get the scripts
scripts = beasttut.get_ggplot2_scripts(
nsamples, sequence_lengths, midpoints)
# return the table string and scripts
return table_string, full_table_string, scripts
def get_table_strings_and_scripts(xmldata, alignment_id, start_stop_pairs,
nsamples):
"""
Command-line only.
@param xmldata: xml data already adjusted for nsamples and log filename
@param alignment_id: xml element id
@param start_stop_pairs: alignment interval bounds
@param nsamples: an extra parameter for script generation
@return: short table string, long table string, scripts (for short table)
"""
# init the array for the full R table
full_data_arr = []
# 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
midpoint = (start_pos + stop_pos) / 2.0
interval_xml_data = beast.set_alignment_interval(
xmldata, alignment_id, start_pos, stop_pos)
row_labels, col_labels, arr = get_loganalysis_labeled_array(
interval_xml_data)
stat_name_to_row_index = dict((x, i) for i, x in enumerate(row_labels))
summary_name_to_col_index = dict(
(x, i) for i, x in enumerate(col_labels))
# define row indices of interest
mean_row_index = stat_name_to_row_index['meanRate']
var_row_index = stat_name_to_row_index['coefficientOfVariation']
cov_row_index = stat_name_to_row_index['covariance']
# define column indices of interest
mean_col_index = summary_name_to_col_index['mean']
low_col_index = summary_name_to_col_index['hpdLower']
high_col_index = summary_name_to_col_index['hpdUpper']
row = [
sequence_length,
midpoint,
arr[mean_row_index][low_col_index],
arr[mean_row_index][mean_col_index],
arr[mean_row_index][high_col_index],
arr[var_row_index][low_col_index],
arr[var_row_index][mean_col_index],
arr[var_row_index][high_col_index],
arr[cov_row_index][low_col_index],
arr[cov_row_index][mean_col_index],
arr[cov_row_index][high_col_index],
]
data_arr.append(row)
# add rows to the full data array
for row_index, row_label in enumerate(row_labels):
for col_index, col_label in enumerate(col_labels):
row = [
sequence_length,
midpoint,
'"' + row_label + '"',
'"' + col_label + '"',
arr[row_index][col_index],
]
full_data_arr.append(row)
# add entries to some utility arrays
sequence_lengths.append(sequence_length)
midpoints.append(midpoint)
# build the table strings
table_string = RUtil.get_table_string(data_arr, g_headers)
full_table_string = RUtil.get_table_string(
full_data_arr,
[
'sequence.length', 'midpoint', 'statistic.name',
'posterior.analysis', 'value'
],
force_float=False,
)
# get the scripts
scripts = beasttut.get_ggplot2_scripts(nsamples, sequence_lengths,
midpoints)
# return the table string and scripts
return table_string, full_table_string, scripts
