blob: 158635438aefb4e51e18c64613246e43735c4ede [file] [log] [blame]
Selects all rows and columns that satisfy the condition specified
and prints the matrix.
import sys, os, re, optparse
import common
from autotest_lib.client.common_lib import kernel_versions
from autotest_lib.tko import display, frontend, db, query_lib
# First do all the options parsing
parser = optparse.OptionParser()
parser.add_option('-x', '--x_axis', action='store', dest='x_axis',
parser.add_option('-y', '--y_axis', action='store', dest='y_axis',
parser.add_option('-c', '--condition', action='store', dest='condition')
(options, args) = parser.parse_args()
if options.condition:
where = query_lib.parse_scrub_and_gen_condition(
options.condition, frontend.test_view_field_dict)
# print("where clause:" % where)
where = None
# Grab the data
db = db.db()
test_data = frontend.get_matrix_data(db, options.x_axis, options.y_axis, where)
# Print everything
widest_row_header = max([len(y) for y in test_data.y_values])
data_column_width = max([max(13,len(x)) for x in test_data.x_values])
column_widths = [widest_row_header] + [data_column_width] * len(test_data.x_values)
format = ' | '.join(['%%%ds' % i for i in column_widths])
# Print headers
print format % tuple([''] + test_data.x_values)
# print data
for y in test_data.y_values:
line = [y]
for x in test_data.x_values:
data_point =[x][y]
good_status = db.status_idx['GOOD']
good = data_point.status_count.get(good_status, 0)
total = sum(data_point.status_count.values())
line.append('%5d / %-5d' % (good, total))
print format % tuple(line)