def test__prepare_data(self, mock_compress):
mock_compress.side_effect = lambda i, **kv: i
rows_num = 100
load_duration = 1234.5
full_duration = 6789.1
sla = [{"foo": "bar"}]
data = []
for i in range(rows_num):
atomic_actions = {
"a1": i + 0.1,
"a2": i + 0.8,
}
row = {
"duration": i * 3.1,
"idle_duration": i * 0.2,
"error": [],
"atomic_actions": atomic_actions,
"scenario_output": {"errors": ["err"],
"data": {"out_key": "out_value"}}
}
data.append(row)
data[42]["error"] = ["foo", "bar", "spam"]
data[52]["error"] = ["spam", "bar", "foo"]
values_atomic_a1 = [i + 0.1 for i in range(rows_num)]
values_atomic_a2 = [i + 0.8 for i in range(rows_num)]
values_duration = [i * 3.1 for i in range(rows_num)]
values_duration[42] = 0
values_duration[52] = 0
values_idle = [i * 0.2 for i in range(rows_num)]
values_idle[42] = 0
values_idle[52] = 0
prepared_data = plot._prepare_data({"result": data,
"load_duration": load_duration,
"full_duration": full_duration,
"sla": sla,
"key": "foo_key"})
self.assertEqual(2, len(prepared_data["errors"]))
calls = [mock.call(values_atomic_a1),
mock.call(values_atomic_a2),
mock.call(values_duration),
mock.call(values_idle)]
mock_compress.assert_has_calls(calls)
expected_output = [{"key": "out_key",
"values": ["out_value"] * rows_num}]
expected_output_errors = [(i, [e])
for i, e in enumerate(["err"] * rows_num)]
self.assertEqual({
"total_durations": {"duration": values_duration,
"idle_duration": values_idle},
"atomic_durations": {"a1": values_atomic_a1,
"a2": values_atomic_a2},
"errors": [{"iteration": 42,
"message": "bar",
"traceback": "spam",
"type": "foo"},
{"iteration": 52,
"message": "bar",
"traceback": "foo",
"type": "spam"}],
"output": expected_output,
"output_errors": expected_output_errors,
"load_duration": load_duration,
"full_duration": full_duration,
"sla": sla,
}, prepared_data)
def test__prepare_data(self, mock_compress):
mock_compress.side_effect = lambda i, **kv: i
rows_num = 100
load_duration = 1234.5
full_duration = 6789.1
sla = [{"foo": "bar"}]
data = []
for i in range(rows_num):
atomic_actions = {
"a1": i + 0.1,
"a2": i + 0.8,
}
row = {
"duration": i * 3.1,
"idle_duration": i * 0.2,
"error": [],
"atomic_actions": atomic_actions,
"scenario_output": {
"errors": ["err"],
"data": {
"out_key": "out_value"
}
}
}
data.append(row)
data[42]["error"] = ["foo", "bar", "spam"]
data[52]["error"] = ["spam", "bar", "foo"]
values_atomic_a1 = [i + 0.1 for i in range(rows_num)]
values_atomic_a2 = [i + 0.8 for i in range(rows_num)]
values_duration = [i * 3.1 for i in range(rows_num)]
values_duration[42] = 0
values_duration[52] = 0
values_idle = [i * 0.2 for i in range(rows_num)]
values_idle[42] = 0
values_idle[52] = 0
prepared_data = plot._prepare_data({
"result": data,
"load_duration": load_duration,
"full_duration": full_duration,
"sla": sla,
"key": "foo_key"
})
self.assertEqual(2, len(prepared_data["errors"]))
calls = [
mock.call(values_atomic_a1),
mock.call(values_atomic_a2),
mock.call(values_duration),
mock.call(values_idle)
]
mock_compress.assert_has_calls(calls)
expected_output = [{
"key": "out_key",
"values": ["out_value"] * rows_num
}]
expected_output_errors = [(i, [e])
for i, e in enumerate(["err"] * rows_num)]
self.assertEqual(
{
"total_durations": {
"duration": values_duration,
"idle_duration": values_idle
},
"atomic_durations": {
"a1": values_atomic_a1,
"a2": values_atomic_a2
},
"errors": [{
"iteration": 42,
"message": "bar",
"traceback": "spam",
"type": "foo"
}, {
"iteration": 52,
"message": "bar",
"traceback": "foo",
"type": "spam"
}],
"output":
expected_output,
"output_errors":
expected_output_errors,
"load_duration":
load_duration,
"full_duration":
full_duration,
"sla":
sla,
}, prepared_data)
def test__process_main_time(self):
result = {
"result": [
{
"error": [],
"duration": 1,
"idle_duration": 2,
"atomic_actions": {},
"scenario_output": {"errors": [], "data": {}}
},
{
"error": ["some", "error", "occurred"],
"duration": 1,
"idle_duration": 1,
"atomic_actions": {},
"scenario_output": {"errors": [], "data": {}}
},
{
"error": [],
"duration": 2,
"idle_duration": 3,
"atomic_actions": {},
"scenario_output": {"errors": [], "data": {}}
}
],
"sla": "foo_sla",
"load_duration": 1234.5,
"full_duration": 6789.1
}
output = plot._process_main_duration(result,
plot._prepare_data(result))
self.assertEqual({
"pie": [
{"key": "success", "value": 2},
{"key": "errors", "value": 1}
],
"iter": [
{
"key": "duration",
"values": [(1, 1.0), (2, 0), (3, 2.0)]
},
{
"key": "idle_duration",
"values": [(1, 2.0), (2, 0), (3, 3.0)]
}
],
"histogram": [
{
"key": "task",
"method": "Square Root Choice",
"values": [{"x": 1.0, "y": 1.0}, {"x": 1.0, "y": 0.0}]
},
{
"key": "task",
"method": "Sturges Formula",
"values": [{"x": 1.0, "y": 1.0}, {"x": 1.0, "y": 0.0}]
},
{
"key": "task",
"method": "Rice Rule",
"values": [{"x": 1.0, "y": 1.0}, {"x": 1.0, "y": 0.0},
{"x": 1.0, "y": 0.0}]
},
{
"key": "task",
"method": "One Half",
"values": [{"x": 2.0, "y": 2.0}]
}
]
}, output)
def test__process_main_time(self):
result = {
"result": [{
"error": [],
"duration": 1,
"idle_duration": 2,
"atomic_actions": {},
"scenario_output": {
"errors": [],
"data": {}
}
}, {
"error": ["some", "error", "occurred"],
"duration": 1,
"idle_duration": 1,
"atomic_actions": {},
"scenario_output": {
"errors": [],
"data": {}
}
}, {
"error": [],
"duration": 2,
"idle_duration": 3,
"atomic_actions": {},
"scenario_output": {
"errors": [],
"data": {}
}
}],
"sla":
"foo_sla",
"load_duration":
1234.5,
"full_duration":
6789.1
}
output = plot._process_main_duration(result,
plot._prepare_data(result))
self.assertEqual(
{
"pie": [{
"key": "success",
"value": 2
}, {
"key": "errors",
"value": 1
}],
"iter": [{
"key": "duration",
"values": [(1, 1.0), (2, 0), (3, 2.0)]
}, {
"key": "idle_duration",
"values": [(1, 2.0), (2, 0), (3, 3.0)]
}],
"histogram": [
{
"key": "task",
"method": "Square Root Choice",
"values": [{
"x": 1.0,
"y": 1.0
}, {
"x": 1.0,
"y": 0.0
}]
}, {
"key": "task",
"method": "Sturges Formula",
"values": [{
"x": 1.0,
"y": 1.0
}, {
"x": 1.0,
"y": 0.0
}]
}, {
"key":
"task",
"method":
"Rice Rule",
"values": [{
"x": 1.0,
"y": 1.0
}, {
"x": 1.0,
"y": 0.0
}, {
"x": 1.0,
"y": 0.0
}]
}, {
"key": "task",
"method": "One Half",
"values": [{
"x": 2.0,
"y": 2.0
}]
}
]
}, output)