def run(parsed_args):
if parsed_args.test:
print('Running tests...')
run_tests()
else:
print('Processing inputs...')
process_inputs(parsed_args)
def main():
args = init_args()
# unpack variables from args. allow flexible changes without using args
user = args.user
serial_number = args.s
file_path = args.f
jpeg_quality = args.q
downsample = args.d
test_number = args.test
type = args.type
if test_number != -1:
test.run_tests(user, serial_number)
else:
if args.type == "r": # receiver
utils.print_msg("Receiver!", DEBUG)
receiver_main(user, serial_number, file_path)
if args.type == "t": # transmitter
utils.print_msg("Transmitter!", DEBUG)
transmitter_main(user, serial_number, file_path, jpeg_quality,
downsample)
def run(self):
""" Execute the command.
"""
if run_tests() != 0:
raise RuntimeError("test suite failed")
return
def run(self):
""" Execute the command.
"""
if run_tests() != 0:
raise RuntimeError("test suite failed")
return
def testing_menu(self):
print("Testy:")
print("1. Test pojedynczego uruchomienia algorytmu")
print("2. Test wielokrotnego uruchomienia algorytmu")
print("3. Wizualizacja map używanych w testach")
print("R - Powrót")
choice = input()
if choice == "1":
test.single_run_test()
elif choice == "2":
test.run_tests(100)
elif choice == "3":
test.maps_test()
elif choice == "R" or "r":
self.main_menu()
else:
self.testing_menu()
def parse_args():
'''
Parse the arguments. Not using the python argparse library? No,
it does not seem to support simple inner groups in an intuative
way, e.g. (-t | -r REPO_PATH [GENERATION_MAX]), gets turned into
(-t -r REPO_PATH [GENERATION_MAX]), or complex hackary needs to
done on subparsers.
'''
try:
#Run test suite
if sys.argv[1]=="-t":
sys.path.insert(0, "../tests/")
from test import run_tests
print("Running Tests")
print("=============")
run_tests()
elif sys.argv[1]=="-r":
repo_path = sys.argv[2]
try:
gen_max = int(sys.argv[3])
except:
gen_max = DEFAULT_GEN_SIZE
run_simulation(repo_path, gen_max)
except IndexError:
logger.error(traceback.format_exc())
print_usage()
except Exception as e:
logger.error(traceback.format_exc())
print(e)
def main():
args = init_args()
# unpack variables from args. allow flexible changes without using args
user = args.user
serial_number = args.s
file_path = args.f
jpeg_quality = args.q
downsample = args.d
test_number = args.test
type = args.type
if test_number != -1:
test.run_tests(user, serial_number)
else:
if args.type == "r": # receiver
utils.print_msg("Receiver!", DEBUG)
receiver_main(user, serial_number, file_path)
if args.type == "t": # transmitter
utils.print_msg("Transmitter!", DEBUG)
transmitter_main(user, serial_number, file_path, jpeg_quality, downsample)
def test_results(): return jsonify(run_tests())
#!/usr/bin/env python
# encoding: utf-8
"""
run_tests.py
Created by Scott on 2012-12-26.
Copyright (c) 2012 Scott Rice. All rights reserved.
"""
import sys, os, inspect
from test import run_tests
# The code below is to allow test cases to import the class they are testing by
# using the syntax 'import ice.******'.
#
# This code was taken from a StackOverflow answer by sorin. Thanks bud!
# http://stackoverflow.com/questions/279237/python-import-a-module-from-a-folder
#
# Get a reference to the current directory, without using __file__, which fails
# in certain situations based on how you call the script in Windows
cmd_folder = os.path.realpath(os.path.abspath(os.path.split(inspect.getfile(inspect.currentframe()))[0]))
ice_folder = os.path.join(cmd_folder,"ice")
if cmd_folder not in sys.path:
sys.path.insert(0,cmd_folder)
if ice_folder not in sys.path:
sys.path.insert(1,ice_folder)
run_tests()
def main():
test.run_tests()
import test test.run_tests()
import test import sys from getopt import getopt import models opts = getopt(sys.argv[1:], 'm:')[0] model_name = opts[0][1] if len(opts) else None test.run_tests(model_name)
'-s',
type=int,
default=False,
help=
'seed for the random number generator (default: new seed at every run)'
)
parser.add_argument('--verbose',
'-v',
action='store_true',
help='show coloured graphs during calculation')
parser.add_argument(
'--path',
'-p',
default='test_cases.yml',
help=
'path to file describing test cases in YAML format (default: ./test_cases.yml). '
'See test_cases.yml for an example file.')
args = parser.parse_args()
seed = args.seed
if not seed:
seed = None
return args.path, args.verbose, seed
if __name__ == '__main__':
path, verbose, seed = parse_args()
results = run_tests(path, verbose=verbose, seed=seed)
print(results)
results.to_csv(f'results/{path}.csv', index=False)
def run_module_tests():
test.run_tests([test_echo, test_add])
#!/usr/bin/env python
"""Runs all unit tests with coverage analysis."""
import test
if __name__ == '__main__':
test.run_tests(with_coverage=True)
def train(model):
print("Model done")
# x_train = []
# y_train = []
# # preprocessing_function :
# function that will be implied on each input. The function will run after the image is
# resized and augmented. The function should take one argument: one image (Numpy tensor
# with rank 3), and should output a Numpy tensor with the same shape.
# we create two instances with the same arguments
data_gen_args = dict(
preprocessing_function=random_crop,
# rescale=1. / 255,
# featurewise_center=True,
# featurewise_std_normalization=True,
horizontal_flip=True,
vertical_flip=True,
validation_split=0.1)
x_image_gen = ImageDataGenerator(**data_gen_args)
y_image_gen = ImageDataGenerator(**data_gen_args)
print("Before Img Gen FIT")
# Provide the same seed and keyword arguments to the fit and flow methods
seed = 1
# compute quantities required for featurewise normalization (std, center)
# x_image_gen.fit(x_train, augment=True, seed=seed) # TODO: x_train NEED to be 4 dimensional
# y_image_gen.fit(y_train, augment=True, seed=seed)
x_gen = x_image_gen.flow_from_directory(
'pictures/keras_test',
target_size=(img_width // scale_fact, img_width // scale_fact),
batch_size=1,
class_mode=None, # TODO: could be "input"
save_to_dir="pictures/keras_test/training/training",
# save_prefix="t0_",
subset="training",
interpolation="lanczos",
seed=seed)
y_gen = y_image_gen.flow_from_directory(
'pictures/keras_test',
target_size=(img_width, img_width),
batch_size=1,
class_mode=None, # TODO: was None
save_to_dir="pictures/keras_test/training/validation",
# save_prefix="t0_",
subset="training",
interpolation="lanczos",
seed=seed)
print("Before Zip")
# combine generators into one which yields x and y together
train_generator = itertools.zip_longest(x_gen, y_gen)
optimizer = Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)
model.compile(optimizer=optimizer, loss='mean_squared_error')
print("Before fit_generator")
model.fit_generator(
train_generator,
verbose=2,
steps_per_epoch=
12, # equal to (nbr samples of your dataset) // (batch size)
epochs=6,
callbacks=get_callbacks())
run_tests(model)
def run():
"""
The main method starts the GUI after asking for an api key if necessary.
"""
if not check_internet_connection_and_obtain_api_key():
return 0
# Create the data folder if it doesn't exist.
if not os.path.exists(Config.data_folder):
os.makedirs(Config.data_folder)
if Config.online:
if len(sys.argv) > 1:
if sys.argv[1] in {'--test', '-t'}:
import test
test.run_tests()
return 0
elif sys.argv[1] in ('--download-molecule-images', '-dmi'):
import res_gen.image_downloader as id
id.download_images()
return 0
elif sys.argv[1] in ("-gba", "--generate-broadener-availability"):
import res_gen.generate_broadener_availability as gba
gba.generate_availability()
return 0
if Config.high_dpi:
# Enable High DPI display with PyQt5
QtWidgets.QApplication.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling, True)
# Fix for mac-based systems...
os.environ['no_proxy'] = '*'
##
# The following blocks of code verify the hapi API key is in place, and it
# is valid. If it is not valid or in place the user will we prompted for
# one. This code also checks for a working internet connection, as hapi
# needs one to download data. In the future, if there is no internet
# connection the GUI should simply disable the features that require it,
# and there could be a periodic check for internet connection that will
# re-enable them.
from metadata.molecule_meta import MoleculeMeta
WorkRequest.start_work_process()
# Hapi is now started automatically in the work process
# start = HapiWorker(WorkRequest.START_HAPI, {})
# start.start() # When a start_hapi request is sent, it starts automatically.
_ = MoleculeMeta(0)
from metadata.xsc_meta import CrossSectionMeta
# If the cache is expired, download a list of the cross section meta file.
# This also populates the CrossSectionMeta.molecule_metas field.
_ = CrossSectionMeta(0)
app = QtWidgets.QApplication(sys.argv)
app.setStyle(QStyleFactory.create("Fusion"))
window = MainWindow()
window.gui.adjustSize()
TextReceiver.init(window)
_qt_result = app.exec_()
TextReceiver.redirect_close()
close = HapiWorker(WorkRequest.END_WORK_PROCESS, {}, callback=None)
close.safe_exit()
WorkRequest.WORKER.process.join()
HapiThread.kill_all()
return 0
def test_results():
return jsonify(run_tests())
def run_module_tests():
test.run_tests([test_echo,
test_add])
from test import run_tests run_tests()
def run(self):
"""Run experiment"""
print(self.tmp_dir)
run_tests(self.args_tuple)
