def main_menu():
print("\n-----AUDITORY SHIFT CIPHER MAIN MENU-----")
print("\n\tOptions:")
print("\t1. Create Message")
print("\t2. Encoder Settings")
print("\t3. Generate Tones")
print("\t4. Create Audio File")
print("\t5. Decoder Settings")
print("\t6. Decode Message")
print("\t7. Exit")
try:
menu_choice = int(input("Please Select an Option: "))
except ValueError:
print("\tError: Value Must Be A Number.\n")
main_menu()
if menu_choice == 1:
message.main()
elif menu_choice == 2:
code = 0
settings.settings_menu(code)
elif menu_choice == 3:
encoder.main()
elif menu_choice == 4:
audio_file.main()
elif menu_choice == 5:
code = 1
settings.settings_menu(code)
elif menu_choice == 6:
decoder.main()
elif menu_choice == 7:
print("Goodbye...")
sys.exit(0)
else:
print("\nInvalid Option...")
main_menu()
def test_decodeFileTest(self, safe_input):
with open('sflist', 'r') as sf2_names:
for name in sf2_names:
out = StringIO()
safe_input.side_effect = [1, 1, 2, 1, 2]
with redirect_stdout(out):
argv = ['-i', '../Soundfonts/' + name.strip(), '-o' 'Test']
decoder.main(argv)
self.assertIn("success", out.getvalue())
copyfile('./Test_samples.h', './AutoTest/Test_samples.h')
copyfile('./Test_samples.cpp', './AutoTest/Test_samples.cpp')
print("\nNow uploading " + name + " to the Teensy...\n")
check_output(arduino_upload_str, shell=True)
print("\nCompiled and uploaded " + name +
" to Teensy successfully! Next test...\n")
def run_pipeline():
'''
pipeline for classification of EGFR and ALK test use, result, and method
- reported will further classify the reports that passed through
the rule based filter as "reported"
- positive will ascertain the test result (pos or neg) for the reports
that pass through the reported SVM as "reported"
- method will ascertain the test method (FISH, MutAnalysis, Other) for
reports that pass through the reported SVM as "reported"
- insufficient will ascertain insuff vs unknown for the reports that
were labeled "not reported" by rule based and svm reported classifiers
- final class labels will be output to the FINAL_OUTPUT_DIRECTORY
'''
# rule based keyword filter
with open(pos_inst, 'w') as pos_out:
with open(neg_inst, 'w') as neg_out:
for instances in open(VECTOR_FILE, 'r').readlines():
vec = instances.strip().split('\t')
if 'NO_KEYWORD_IN_TEXT' in vec:
neg_out.write(vec[0] + '\tNotReported\n')
else:
pos_out.write(vec[0] + '\tReported\n')
# loop through SVM classifiers; "positive" and "negative" instances
# will be written to algorithm specific directories s
# o they can be used by subsequent algorithms
for algorithm_tuple in ALGORITHM_ORDER:
algorithm = algorithm_tuple[0]
label = algorithm_tuple[1]
model_file = algorithm + os.sep + TRAIN_BATCH + '.pkl'
num_features = int(open(algorithm + os.sep + 'num_features.txt', \
'r').read().strip())
print 'classifying with model ', model_file, '-', num_features, 'features'
# turn text feature vector into integer array
vector_to_array.main(TEST_NAME, algorithm, TRAIN_BATCH, VECTOR_FILE)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
decoder.main(num_features, model_file, algorithm, TRAIN_BATCH,
TEST_NAME, label)
final_output.output_final_class_labels(TEST_NAME, FINAL_OUTPUT_DIRECTORY)
def translate(input):
sf = open("input.en", "w+", encoding='utf-8')
sf.write(input)
sf.close()
FLAGS.data_dir = 't2t/data'
FLAGS.problem = 'translate_enzh_wmt32k'
FLAGS.model = 'transformer'
FLAGS.hparams_set = 'transformer_big'
FLAGS.output_dir = 't2t/train'
FLAGS.decode_hparams = 'beam_size=1,alpha=0.1'
FLAGS.decode_from_file = 'input.en'
FLAGS.decode_to_file = 'output.zh'
#sys.argv=['test.py', '--data_dir=t2t/data', '--problem=translate_enzh_wmt32k', '--model=transformer', '--hparams_set=transformer_big', '--output_dir=t2t/train', '--decode_hparams=beam_size=1,alpha=0.1', '--decode_from_file=input.en', '--decode_to_file=output.zh']
# print(FLAGS.data_dir)
t2t_decoder.main(sys.argv)
f = open("output.zh", encoding='utf-8')
output = f.read()
sf.close()
return output
def click_encoder():
global r, time
if string == "":
res['text'] = "You must drop an image first"
else:
res['text'] = "Đang nén..."
r, time = encoder.main(string[0:])
r_label['text'] = "Hệ số nén: " + str(r)
e_time_label['text'] = "Thời gian nén: " + str(time)
res['text'] = "Đang giải nén..."
time = decoder.main()
res['text'] = "Kết quả nén và giải nén: "
d_time_label['text'] = "Thời gian giải nén: " + str(time)
import scorer
import gflags
import logger
FLAGS = gflags.FLAGS
if __name__ == '__main__':
try:
argv = FLAGS(sys.argv) # parse flags
except gflags.FlagsError as e:
print('%s\nUsage: %s ARGS\n%s' % (e, sys.argv[0], FLAGS))
sys.exit(1)
import __main__
mydir = os.path.dirname(__main__.__file__)
if os.path.exists(FLAGS.rule_dump):
logger.writeln('unfiltered rules exists: %s' % FLAGS.rule_dump)
else:
extractor.main()
FLAGS.filter_file = FLAGS.input
scorer.main()
glue_grammar = os.path.join(mydir, 'test-extractor/monotonic_glue.gr')
if FLAGS.persent is None:
FLAGS.grammars = [FLAGS.filtered, glue_grammar]
else:
FLAGS.grammars = [FLAGS.persent, glue_grammar]
decoder.main()