def main():
print(sys.argv)
print(SENDGRID_KEY)
print(BUILD_ID)
print(USER_REPO)
print(USER_BRANCH)
print(USER_TARGET)
print(USER_LIVE)
print(ARTIFACT_DIR)
print(REQUESTED_FOR_EMAIL)
print(ACCOUNT_KEY)
# Upload results to storage account, container
container = ''
try:
print('Uploading test results to storage account...')
container = get_container_name()
upload_files(container)
except Exception:
print(traceback.format_exc())
# Collect statistics
testdata = test_data.TestData(ARTIFACT_DIR)
testdata.collect()
# Write database
write_db()
# Send email
send_email(container, testdata)
def main():
print('Enter main()')
print(sys.argv)
print(SENDGRID_KEY)
print(BUILD_ID)
print(USER_REPO)
print(USER_BRANCH)
print(USER_TARGET)
print(USER_LIVE)
print(ARTIFACT_DIR)
print(REQUESTED_FOR_EMAIL)
print(ACCOUNT_KEY)
print(COMMIT_ID)
print(DB_PWD)
# Upload results to storage account, container
container = ''
try:
print('Uploading test results to storage account...')
container = get_container_name()
upload_files(container)
except Exception:
print(traceback.format_exc())
# Collect statistics
testdata = test_data.TestData(ARTIFACT_DIR)
testdata.collect()
# Send email
try:
send_email(container, testdata)
except Exception:
print(traceback.format_exc())
# Generate index.html
try:
container_url = 'https://clitestresultstac.blob.core.windows.net/' + container
generate_index.generate(container, container_url, testdata, USER_REPO,
USER_BRANCH, COMMIT_ID, USER_LIVE)
except Exception:
print(traceback.format_exc())
# Write database
try:
write_db(container, testdata)
except Exception:
print(traceback.format_exc())
print('Exit main()')
def main():
logger.warning('Enter main()')
logger.warning(sys.argv)
logger.warning(SENDGRID_KEY)
logger.warning(BUILD_ID)
logger.warning(USER_REPO)
logger.warning(USER_BRANCH)
logger.warning(USER_TARGET)
logger.warning(USER_LIVE)
logger.warning(ARTIFACT_DIR)
logger.warning(REQUESTED_FOR_EMAIL)
logger.warning(ACCOUNT_KEY)
logger.warning(COMMIT_ID)
logger.warning(DB_PWD)
# Upload results to storage account, container
container = ''
try:
logger.warning('Uploading test results to storage account...')
container = get_container_name()
upload_files(container)
except Exception:
logger.exception(traceback.format_exc())
# Collect statistics
testdata = test_data.TestData(ARTIFACT_DIR)
testdata.collect()
# Generate index.html, send email
try:
# Generate index.html
container_url = 'https://clitestresultstac.blob.core.windows.net/' + container
html_content = generate_index.generate(container, container_url, testdata, USER_REPO, USER_BRANCH, COMMIT_ID, USER_LIVE, USER_TARGET)
# Send email
send_email(html_content)
except Exception:
logger.exception(traceback.format_exc())
# Write database
# try:
# write_db(container, testdata)
# except Exception:
# logger.exception(traceback.format_exc())
logger.warning('Exit main()')
def setUp(self): self.test_data = test_data.TestData()
return rtrs
if __name__ == "__main__":
class dummy_args():
skip_all_data = False
skip_detail = False
skip_msg_progress = False
split = False
time_start = None
time_end = None
print("Line-by-line split test")
try:
for line in td.TestData().data():
if "transfer" not in line:
print(proton_split(line))
else:
pass # splitter does not split transfers
pass
except:
traceback.print_exc(file=sys.stdout)
pass
print("Canned data parse test")
data = td.TestData().data()
log_index = 0 # from file for router A
instance = 0 # all from router instance 0
comn = common.Common()
comn.args = dummy_args()
import test_data as data
x = data.TestData(40, True)
x.generate_data()
print(f"Inputs: \n {x.inputs}")
print(f"Targets: \n {x.targets}")
a = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
res = [x[0] for x in a]
print(res)
def main():
log_new('_main')
config = Config()
parameters = get_parameters(config)
screens_diff_dir = prepare_dir_structure(parameters, config)
current_data = test_data.TestData(parameters.current_dir,
config.RESULTS_EXTENSIONS)
baseline_data = test_data.TestData(parameters.baseline_dir,
config.RESULTS_EXTENSIONS)
current_images = current_data.get_files('screens')
current_images_dir = current_data.get_dir('screens')
baseline_images = baseline_data.get_files('screens')
baseline_images_dir = baseline_data.get_dir('screens')
logg('current_images', current_images)
logg('baseline_images', baseline_images)
current_log = current_data.get_files('logs')[0]
baseline_log = baseline_data.get_files('logs')[0]
if parameters.test_name is None: # case of local run
correct_images = []
else:
current_scenario = current_data.get_files('scenario')[0]
baseline_scenario = baseline_data.get_files('scenario')[0]
correct_images = jsoner.create_correct_list(current_scenario,
baseline_scenario)
images_lists_diff = list_comparison.compare_lists(current_images,
baseline_images,
correct_images)
log_diffs = log_comparison.compare_logs(current_log, baseline_log,
correct_images, config)
images_process_list = list_comparison.create_process_list(
correct_images, images_lists_diff, current_images_dir,
baseline_images_dir)
images_diffs = image_comparison.compare_images(images_process_list,
screens_diff_dir,
parameters.rgb_threshold,
parameters.size_threshold,
config)
stat, status = statistics.create_image_statistics(images_lists_diff,
images_diffs)
webreport = create_webreport(images_diffs, log_diffs, stat, status,
parameters, config)
save_file(os.path.join(config.TEMP_ARTIFACTS_DIR, 'report.html'),
webreport)
copy_directory(config.WEB_REPORT_LIB_DIR,
config.TEMP_ARTIFACTS_WEBREPORT_LIB_DIR)
copy_directory(config.TEMP_DATA_DIR, config.TEMP_ARTIFACTS_DATA_DIR)
return 0
print(sys.argv)
for i, arg in enumerate(sys.argv):
if sys.argv[i] == '-kf':
kfun = sys.argv[i + 1]
opt = int(sys.argv[i + 2])
if sys.argv[i] == '-slack':
slack = float(sys.argv[i + 1])
if sys.argv[i] == '-pid':
plotId = int(sys.argv[i + 1])
# What kernel function to use
kernel_function = lambda x, y: kf.functions[kfun](x, y, opt)
# Number of training samples
N = 200
# Generate our test data
data = test_data.TestData(N, True)
data.generate_data()
# Initial guess of the alpha vector
start = numpy.zeros(N, dtype='float64')
upper_bound = slack
# Lower and upper bounds for each value in alpha vector
B = [(0, upper_bound) for b in range(N)]
# Global variable for alpha, targets and support vectors
nonZeroAlpha = []
target_values = []
support_vectors = []
bValue = 0
# Pre-compute the matrix P by multiplying the every combination of target values, t, and kernel K.
preComputedMatrix = numpy.empty([N, N])
for i in range(N):
