def test_select_contain_sentences(self):
sys.stdout = codecs.getwriter('utf_8')(sys.stdout)
al.main()# later, move to setUp
s, q= al.select_contain_sentences("おはよう")
print s
ok_(s)
raise
def main(path):
global file_data
file = open_file(path, 'r')
read_file(file)
for lex in file_data:
analyzer.main(lex)
def main():
basedir = "slices/"
fileext = ".json"
slices = [
Slice("slice1", "SQL injection"),
Slice("slice2", "SQL injection"),
Slice("slice3", "SQL injection"),
Slice("slice4", "SQL injection"),
Slice("slice5", "SQL injection"),
Slice("slice6", "Cross site scripting"),
Slice("slice7", "SQL injection"),
Slice("slice9", "SQL injection"),
Slice("slice10", "SQL injection"),
Slice("slice11", "SQL injection"),
Slice("safe_literal", None),
Slice("dot_equals", "SQL injection"),
Slice("while_backward_untaint", "SQL injection"),
Slice("while_untainted_guard", None),
Slice("untainted_offset", None),
Slice("trycatch", None),
Slice("tainted_try", "Cross site scripting"),
Slice("dowhile", "SQL injection"),
Slice("for", "SQL injection"),
Slice("sql_escaped", "SQL injection", "mysql_escape_string"),
]
original_stdout = sys.stdout
failures = 0
for s in slices:
with io.StringIO() as sys.stdout:
filename = basedir + s.filename + fileext
analyzer.main([None, filename])
if check_output(s, sys.stdout.getvalue()):
print("[ OK ]", s.filename, file=original_stdout)
else:
failures += 1
print("[FAIL]", s.filename, file=original_stdout)
sys.stdout = original_stdout
return 0 if failures == 0 else 1
def launch():
index = textentries.get(DEFAULT_IDX_NAME).get()
segment = textentries.get(DEFAULT_SEG_NAME).get()
path = textentries.get(PATH_REMINDER).get()
dim = textentries.get(DEFAULT_DIMEN_NAME).get()
method = textentries.get(DEFAULT_METHOD_NAME).get()
ang = textentries.get(DEFAULT_ROT_ANG_NAME).get()
axes_syn = analyzer.main([
analyzer.__name__, '-p', path, '-i', index, '-s', segment, '-d', dim,
'-r', ang, '-m', method
])
# plot on a figure
return axes_syn
import analyzer
import datetime
import pygal
# list to hold dates, message counts, and word counts for each user
# dataList = [[], "", [], [], "", [], []]
dataList = analyzer.main("_chat.txt", "words.txt")
# bar graph of the message count between the two users
chart = pygal.Bar(x_label_rotation=20,
width=2000,
height=1000,
truncate_legend=50)
chart.title = 'WhatsApp Chat Analysis: Messages'
chart.x_title = "Months"
chart.y_title = "Messages"
chart.x_labels = map(lambda d: d.strftime('%Y/%m'), dataList[0])
chart.add("%s's Messages" % (dataList[1]), dataList[2])
chart.add("%s's Messages" % (dataList[4]), dataList[5])
chart.render_to_file('messagesChart.svg')
# bar graph of the word count between the two users
chart = pygal.Bar(x_label_rotation=20,
width=2000,
height=1000,
truncate_legend=50)
chart.title = 'WhatsApp Chat Analysis: Words'
chart.x_title = "Months"
chart.y_title = "Words"
chart.x_labels = map(lambda d: d.strftime('%Y/%m'), dataList[0])
chart.add("%s's Words" % (dataList[1]), dataList[3])
def test_pickup_reply(self):
sys.stdout = codecs.getwriter('utf_8')(sys.stdout)
al.main()
ok_(al.pickup_reply(u"おはようございます"))
import argparse import analyzer parser = argparse.ArgumentParser() analyzer.init(parser) analyzer.main(parser.parse_args())
def main():
# Get cli args
kargs = get_args()
print(kargs)
instance = kargs["instance"]
# Final dataframe containing data for each experiment of the simulation
final_df = pd.DataFrame()
# Create folder with timestamp as name for storing
# simulation data
dir_path = create_timestamp_folder(
config_manager.SIMULATION_CONTROLLER_ARCHIVE_PATH)
if instance == "":
# 1) Generate instance configuration
print("> STEP 1 - Generating instance configuration...")
instance_generator.main()
# Move instance generator output to final folder
copy_dir(config_manager.OUTPUT_INSTANCE_PATH, dir_path)
else:
print("> STEP 1 - Skip -- instance passed as param: {}...".format(
instance))
copy_file(instance, dir_path)
# Before simulations starts, remove all agent logs file from base foulder
remove_dir_content(config_manager.SIMULATION_AGENT_LOGGING_BASE_PATH)
# Execute each instance for a predefined number of times (ex. 5)
for i in range(0, config_manager.NUMBER_OF_SIMULATION_EXECUTION):
# Generate a random seed for the experiment
# Note: this is a very important point for experiment reproduction
# Is very important set seed for both library (random and np)
seed = np.random.randint(0, 4096)
random.seed(seed)
np.random.seed(seed)
# 2) Single simulation based on configuration file generated before
print("> STEP 2 - Simulation of instance...")
simulation.main(instance)
#time.sleep(2)
# 3) Analyze simulation output
print("> STEP 3 - Analyze output...")
analyzer.main()
# Create a dir for each iteration of the simulation
# Move analyzer output files to final foulder (separated for each iteration)
path = dir_path.joinpath("iteration_{}".format(i))
os.makedirs(path)
copy_dir(config_manager.ANALYZER_OUTPUT_PATH, path)
# Copy to this foulder also simulation results (weights for each agent)
# Also clean src dir of all content (avoiding file overwriting)
copy_dir(config_manager.SIMULATION_AGENT_LOGGING_BASE_PATH, path)
remove_dir_content(config_manager.SIMULATION_AGENT_LOGGING_BASE_PATH)
#time.sleep(2)
# 4) Load analyzer output file that contains index for comparison
print("> STEP 4 - Load Index df...")
df = pd.read_csv(config_manager.INDEX_COMPARISON_FILE,
delimiter='\t',
header=0,
index_col=0)
df.index.name = "strategy"
df["seed"] = [seed] * len(list(df.index.values))
df.set_index(["seed", df.index], inplace=True)
print(df)
if final_df.empty:
final_df = df
else:
final_df = pd.concat([final_df, df])
# print(final_df)
# time.sleep(5)
# 5) Export final results comparison table
print("> STEP 5 - Export final results table...")
print(final_df)
final_df.to_csv(config_manager.SIMULATION_CONTROLLER_OUTPUT_FILE,
sep='\t',
encoding='utf-8')
# Move instance generator output to final folder
copy_dir(config_manager.SIMULATION_CONTROLLER_OUTPUT_PATH, dir_path)
with open(
dir_path.joinpath(
config_manager.
SIMULATION_CONTROLLER_ARCHIVE_COMPARISON_FILE_NAME), "w") as f:
# Print mean result of comparison index after 5 executions
for s in config_manager.STRATEGIES:
print("---------------------------------------------")
f.write("---------------------------------------------\n")
print("> Strategy {}".format(s))
f.write("> Strategy {}\n".format(s))
res = final_df.query("strategy == @s")
for index in config_manager.INDEX_TO_MEAN_FOR_COMPARISON_FOR_TXT_FILE:
#print(res[index])
val = res[index].mean()
print(" > {}: {:0.2f}".format(index, val))
f.write(" > {}: {:0.2f}\n".format(index, val))
# Zip archive foulder with all previous gathered data
zip_foulder(dir_path, dir_path)
# Remove dir previously zipped
remove_dir_with_content(dir_path)
