def test_vectors_start_end_time_at_load():
trimmed = results.read_result_files(RESULT_FILES,
vector_start_time=40,
vector_end_time=60)
df = results.get_vectors(trimmed)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "vectors_start_end_time.csv"),
"content mismatch")
from omnetpp.scave import results, chart, vectorops as ops
import matplotlib.pyplot as plt
params = chart.get_properties()
# This expression selects the results (you might be able to logically simplify it)
filter_expression = params["filter"]
# The data is returned as a Pandas DataFrame
df = results.get_vectors(filter_expression,
include_attrs=True,
include_itervars=True)
# This is where the vector operations will be added:
# <|> vectorops marker <|>
# You can perform any transformations on the data here
print(df)
plt.xlabel('Simulation time (s)')
title, legend = chart.extract_label_columns(df)
for t in df.itertuples(index=False):
style = dict()
interp = t.interpolationmode if 'interpolationmode' in df else 'sample-hold' if 'enum' in df else None
if interp:
if interp == "none":
style['linestyle'] = ' '
import math
from omnetpp.scave import results, chart, utils, vectorops as ops
# get chart properties
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
start_time = float(props["vector_start_time"] or -math.inf)
end_time = float(props["vector_end_time"] or math.inf)
# query vector data into a data frame
df = results.get_vectors(filter_expression,
include_attrs=True,
include_itervars=True,
start_time=start_time,
end_time=end_time)
# apply vector operations
df = ops.perform_vector_ops(df, props["vector_operations"])
# plot
utils.plot_vectors(df, props)
utils.postconfigure_plot(props)
utils.export_image_if_needed(props)
utils.export_data_if_needed(df, props)
def test_vectors_end_time():
trimmed = results.read_result_files(RESULT_FILES)
df = results.get_vectors(trimmed, end_time=80)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "vectors_end_time.csv"),
"content mismatch")
def test_vectors_with_attrs():
df = results.get_vectors(r, include_attrs=True)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "vectors_with_attrs.csv"),
"content mismatch")
def test_vectors():
df = results.get_vectors(r)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "vectors.csv"), "content mismatch")
