def plot_test_timing(plot, stats, marathon_type, test_type, xticks):
""" Plots a specific test graph.
In addition, it sets the legend title, and flags the highest scale reached.
:param plot: The matplotlib subplot object is the object that will be plotted
:type plot: matplotlib subplot
:param stats: This map contains the data to be plotted
:type stats: map
:param marathon_type: The type of marathon is part of the map key. For scale tests it is `root` (vs. mom1)
:type marathon_type: str
:param test_type: Defines the test type, usually {instances, count, group}
:type test_type: str
:param xticks: An array of scale targets (1, 10, 100) for the x axis of the plot
:type xticks: array
"""
deploy_time = stats.get(get_key(marathon_type, test_type, 'deploy_time'))
if deploy_time is None or len(deploy_time) == 0 or deploy_time[0] <= 0.0:
return
timings = np.array(deploy_time)
title = '{} Scale Times'.format(test_type.title())
timings = pad(timings, len(xticks))
timings_handle, = plot.plot(xticks, timings, label=title)
fail_index = index_of_first_failure(stats, marathon_type, test_type)
if fail_index > 0:
scale_at_fail = stats.get(get_key(marathon_type, test_type,
'max'))[fail_index]
time_at_fail = stats.get(
get_key(marathon_type, test_type, 'human_deploy_time'))[fail_index]
text = '{} at {}'.format(scale_at_fail, time_at_fail)
plot.text(fail_index, timings[fail_index], text, wrap=True)
def plot_test_timing(plot, stats, marathon_type, test_type, xticks):
""" Plots a specific test graph.
In addition, it sets the legend title, and flags the highest scale reached.
:param plot: The matplotlib subplot object is the object that will be plotted
:type plot: matplotlib subplot
:param stats: This map contains the data to be plotted
:type stats: map
:param marathon_type: The type of marathon is part of the map key. For scale tests it is `root` (vs. mom1)
:type marathon_type: str
:param test_type: Defines the test type, usually {instances, count, group}
:type test_type: str
:param xticks: An array of scale targets (1, 10, 100) for the x axis of the plot
:type xticks: array
"""
deploy_time = stats.get(get_key(marathon_type, test_type, 'deploy_time'))
if deploy_time is None or len(deploy_time) == 0 or deploy_time[0] <= 0.0:
return
timings = np.array(deploy_time)
title = '{} Scale Times'.format(test_type.title())
timings = pad(timings, len(xticks))
timings_handle, = plot.plot(xticks, timings, label=title)
fail_index = index_of_first_failure(stats, marathon_type, test_type)
if fail_index > 0:
scale_at_fail = stats.get(get_key(marathon_type, test_type, 'max'))[fail_index]
time_at_fail = stats.get(get_key(marathon_type, test_type, 'human_deploy_time'))[fail_index]
text = '{} at {}'.format(scale_at_fail, time_at_fail)
plot.text(fail_index, timings[fail_index], text, wrap=True)
def plot_test_errors(plot, stats, marathon_type, test_type, xticks):
""" Plots the number of errors for a given test
:param plot: The matplotlib subplot object is the object that will be plotted
:type plot: matplotlib subplot
:param stats: This map contains the data to be plotted
:type stats: map
:param marathon_type: The type of marathon is part of the map key. For scale tests it is `root` (vs. mom1)
:type marathon_type: str
:param test_type: Defines the test type, usually {instances, count, group}
:type test_type: str
:param xticks: An array of scale targets (1, 10, 100) for the x axis of the plot
:type xticks: array
"""
test_errors = stats.get(get_key(marathon_type, test_type, 'errors'))
if test_errors is None or len(test_errors) == 0:
return 0
plot.set_title("Errors During Test")
errors = np.array(test_errors)
title = '{} Errors'.format(test_type.title())
errors = pad(errors, len(errors))
errors_handle, = plot.plot(xticks,
errors,
label=title,
marker='o',
linestyle='None')
return max(test_errors)
def load(csvfile):
""" This is suppose to be short-term. Teammates have better ideas on how to structure the data.
I would like to not break the ability to call it directly from the test (instead of shelling out).
index after table header:
0 - target - expected scale
1 - max - actual scale
2 - deploy_time
3 - human_deploy_time
4 - launch_status
5 - deployment_status
6 - errors
"""
row_keys = [
'target', 'max', 'deploy_time', 'human_deploy_time', 'launch_status',
'deployment_status', 'errors'
]
stats = empty_stats()
current_marathon = None
current_test_type = None
index_from_header = 0
with open(csvfile, 'r') as f:
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
for i, row in enumerate(reader):
if 'Marathon:' in row:
current_marathon = row[1]
current_test_type = row[2]
index_from_header = 0
elif len(row) > 0:
key = get_key(current_marathon, current_test_type,
row_keys[index_from_header])
stats[key] = row
index_from_header += 1
return stats
def plot_test_errors(plot, stats, marathon_type, test_type, xticks):
""" Plots the number of errors for a given test
:param plot: The matplotlib subplot object is the object that will be plotted
:type plot: matplotlib subplot
:param stats: This map contains the data to be plotted
:type stats: map
:param marathon_type: The type of marathon is part of the map key. For scale tests it is `root` (vs. mom1)
:type marathon_type: str
:param test_type: Defines the test type, usually {instances, count, group}
:type test_type: str
:param xticks: An array of scale targets (1, 10, 100) for the x axis of the plot
:type xticks: array
"""
test_errors = stats.get(get_key(marathon_type, test_type, 'errors'))
if test_errors is None or len(test_errors) == 0:
return 0
plot.set_title("Errors During Test")
errors = np.array(test_errors)
title = '{} Errors'.format(test_type.title())
errors = pad(errors, len(errors))
errors_handle, = plot.plot(xticks, errors, label=title, marker='o', linestyle='None')
return max(test_errors)
def load(csvfile):
""" This is suppose to be short-term. Teammates have better ideas on how to structure the data.
I would like to not break the ability to call it directly from the test (instead of shelling out).
index after table header:
0 - target - expected scale
1 - max - actual scale
2 - deploy_time
3 - human_deploy_time
4 - launch_status
5 - deployment_status
6 - errors
"""
row_keys = ['target', 'max', 'deploy_time', 'human_deploy_time', 'launch_status', 'deployment_status', 'errors']
stats = empty_stats()
current_marathon = None
current_test_type = None
index_from_header = 0
with open(csvfile, 'r') as f:
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
for i, row in enumerate(reader):
if 'Marathon:' in row:
current_marathon = row[1]
current_test_type = row[2]
index_from_header = 0
elif len(row) > 0:
key = get_key(current_marathon, current_test_type, row_keys[index_from_header])
stats[key] = row
index_from_header += 1
return stats
def index_of_first_failure(stats, marathon_type, test_type):
""" Finds the first occurance of an error during a deployment
"""
index = -1
deploy_status = stats.get(get_key(marathon_type, test_type, 'deployment_status'))
for status in deploy_status:
index += 1
if "f" == status:
return index
return -1
def get_scale_targets(stats, marathon_type, test_types):
""" Returns the scale targets 1, 10, 100, 1000
It is possible that some tests are ignored so we may have to
loop to grab the right list.
:param stats: This map contains the data to be plotted
:type stats: map
:param marathon_type: The type of marathon is part of the map key. For scale tests it is `root` (vs. mom1)
:type marathon_type: str
:param test_types: An array of test types to be graphed, usually {instances, count, group}
:type test_types: array
"""
targets = None
for test_type in test_types:
targets = stats.get(get_key(marathon_type, test_type, 'target'))
if targets and len(targets) > 0:
return targets
return targets
def get_scale_targets(stats, marathon_type, test_types):
""" Returns the scale targets 1, 10, 100, 1000
It is possible that some tests are ignored so we may have to
loop to grab the right list.
:param stats: This map contains the data to be plotted
:type stats: map
:param marathon_type: The type of marathon is part of the map key. For scale tests it is `root` (vs. mom1)
:type marathon_type: str
:param test_types: An array of test types to be graphed, usually {instances, count, group}
:type test_types: array
"""
targets = None
for test_type in test_types:
targets = stats.get(get_key(marathon_type, test_type, 'target'))
if targets and len(targets) > 0:
return targets
return targets
def error_graph_enabled(stats, marathon_type, test_types):
""" Returns true if there is any error data to graph
:param stats: This map contains the data to be plotted
:type stats: map
:param marathon_type: The type of marathon is part of the map key. For scale tests it is `root` (vs. mom1)
:type marathon_type: str
:param test_types: An array of test types to be graphed, usually {instances, count, group}
:type test_types: array
"""
enabled = False
for test_type in test_types:
test_errors_key = get_key(marathon_type, test_type, 'errors')
if test_errors_key is not None:
test_errors = stats.get(test_errors_key)
# if there are test errors... graph them
if test_errors is not None and len(test_errors) > 0:
return True
return False
def error_graph_enabled(stats, marathon_type, test_types):
""" Returns true if there is any error data to graph
:param stats: This map contains the data to be plotted
:type stats: map
:param marathon_type: The type of marathon is part of the map key. For scale tests it is `root` (vs. mom1)
:type marathon_type: str
:param test_types: An array of test types to be graphed, usually {instances, count, group}
:type test_types: array
"""
enabled = False
for test_type in test_types:
test_errors_key = get_key(marathon_type, test_type, 'errors')
if test_errors_key is not None:
test_errors = stats.get(test_errors_key)
# if there are test errors... graph them
if test_errors is not None and len(test_errors) > 0:
return True
return False
