def translate_api():
start_time = datetime.datetime.now()
text = request.json['text']
languages = request.json['languages']
savetext = 'savetext' in request.json and request.json['savetext'] == True
if languages == 'eng-cat':
openNMT = openNMT_engcat
else:
openNMT = openNMT_cateng
# print("Input:" + text)
tokenizer = TextTokenizer()
sentences, translate = tokenizer.tokenize(text)
results = _launch_translate_threads(openNMT, text, sentences, translate)
translated = tokenizer.sentence_from_tokens(sentences, translate, results)
if savetext:
saved_filename = os.path.join(SAVED_TEXTS, "source.txt")
with open(saved_filename, "a") as text_file:
t = text.replace('\n', '')
text_file.write(f'{languages}\t{t}\n')
time_used = datetime.datetime.now() - start_time
words = len(text.split(' '))
usage = Usage()
usage.log(languages, words, time_used)
result = {}
result['text'] = text
result['translated'] = translated
result['time'] = str(time_used)
return json_answer(result)
def stats():
requested = request.args.get('date')
date_requested = datetime.datetime.strptime(requested, '%Y-%m-%d')
usage = Usage()
result = usage.get_stats(date_requested)
return json_answer(result)
def upload_file():
file = request.files['file']
email = request.values['email']
model_name = request.values['model_name']
if file.filename == '':
result = {}
result['error'] = "No s'ha especificat el fitxer"
return json_answer(result, 404)
if email == '':
result = {}
result['error'] = "No s'ha especificat el correu"
return json_answer(result, 404)
if file and _allowed_file(file.filename):
filename = uuid.uuid4().hex
fullname = os.path.join(UPLOAD_FOLDER, filename)
file.save(fullname)
save_file_to_process(fullname, email, model_name)
logging.debug("Saved file {0}".format(fullname))
result = []
usage = Usage()
usage.log(model_name, 0, datetime.timedelta(), 'file')
return json_answer(result)
result = {}
result['error'] = "Error desconegut"
return json_answer(result, 500)
def start(self):
""" All program flow is handled here """
# usage, init, checks
Usage.banner()
args = ArgsParser.parseArgs()
self.opts = vars(args)
Check.checkArgc()
Check.checkArgs(['-n', '-m'])
Check.checkActions(self.opts['action'])
Check.checkReportFormat(self.opts['format'])
# ini config
Wrapper.msg("Reading configuration file " + "'" + self.opts['config'] +
"'")
self.ini = IniConfig(self.opts['config'])
self.ini.readConfig()
Check.checkIniFile(self.ini.config)
# additional options from config file
self.getOptions()
### nessus actions ###
# nessus report
if self.opts['action'] in ACTIONS:
self.processReports(self.opts['action'])
# send reports
if self.opts['mail']:
if self.opts['action'] in ACTIONS:
self.sendReports(self.opts['action'])
return
def recognize_api():
if 'file' not in request.files:
return 'No file part'
file = request.files['file']
if file.filename == '':
return 'No filename'
time = datetime.datetime.now()
try:
with tempfile.NamedTemporaryFile() as temp_file:
recognizer = Recognizer()
filename = temp_file.name
file.save(filename)
text = recognizer.get_text(filename)
used = datetime.datetime.now() - time
usage = Usage()
usage.log()
return "Text reconegut: [{0}] - temps usat {1}".format(text, used)
except ValueError as e:
return (
"No és un fitxer d'àudio en format PCM WAV, AIFF/AIFF-C o FLAC ",
400)
def start(self):
""" All program flow is handled here """
# usage, init, checks
Usage.banner()
args = ArgsParser.parseArgs()
self.opts = vars(args)
Check.checkArgc()
Check.checkArgs(['-n', '-m'])
Check.checkActions(self.opts['action'])
Check.checkReportFormat(self.opts['format'])
# ini config
Wrapper.msg("Reading configuration file " + "'" +
self.opts['config'] + "'")
self.ini = IniConfig(self.opts['config'])
self.ini.readConfig()
Check.checkIniFile(self.ini.config)
# additional options from config file
self.getOptions()
### nessus actions ###
# nessus report
if self.opts['action'] in ACTIONS:
self.processReports(self.opts['action'])
# send reports
if self.opts['mail']:
if self.opts['action'] in ACTIONS:
self.sendReports(self.opts['action'])
return
def stats():
requested = request.args.get('date')
date_requested = datetime.datetime.strptime(requested, '%Y-%m-%d')
usage = Usage()
result = usage.get_stats(date_requested)
cnt = _get_processed_files(date_requested)
result["files"] = cnt
return json_answer(result)
def stats():
requested = request.args.get('date')
date_requested = datetime.datetime.strptime(requested, '%Y-%m-%d')
usage = Usage()
calls = usage.get_stats(date_requested)
result = {}
result['calls'] = calls
return json_answer(json.dumps(result, indent=4, separators=(',', ': ')))
def search_request():
source = request.args.get('source')
target = request.args.get('target')
project = ','.join(request.args.getlist('project'))
search = Search(source, target, project)
View = WebView()
result = View.do(search)
usage = Usage()
usage.log()
return result
def search_request():
source = request.args.get('source')
target = request.args.get('target')
project = ','.join(request.args.getlist('project'))
search = Search(source, target, project)
View = WebView()
result = View.do(search)
usage = Usage()
usage.log()
return result
def search_api():
source = request.args.get('source')
target = request.args.get('target')
page = request.args.get('page')
project = ','.join(request.args.getlist('project'))
search = Search(source, target, project)
results = SearchAPIResults()
json = results.do(search, page)
usage = Usage()
usage.log()
return json
def start():
argument = sys.argv[1:]
list_item = ToDoModel()
usage = Usage()
if not argument:
usage.usage_info()
elif argument[0] == "-l":
list_item.list_todo()
elif argument[0] == "-a":
list_item.add(sys.argv[2])
elif argument[0] == "-r":
list_item.remove(sys.argv[2])
elif argument[0] == "-c":
list_item.list_todo
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem2')
solution_dir = os.path.join("Problems", "Problem2Solution")
data_reader = DataReader(problem_dataset_dir)
training_data, testing_data = data_reader.get_data()
codons_table = data_reader.get_rna_codon_table()
for sample in training_data:
dna_string = sample[0][0]
amino_acid = sample[0][1]
output = sample[1]
dna = DNA(dna_string)
dna.set_codon_table(codons_table)
candidates = dna.get_dna_to_amino_acid_candidates(amino_acid)
if set(candidates) != set(output):
raise Exception("Output not matched!\nExpecting: " + str(output) +
"\nFound: " + str(candidates))
print("Passed training data..\n\n")
writer = DataWriter(solution_dir)
usage = Usage()
for sample in testing_data:
usage.start()
dna_string = sample[0][0]
amino_acid = sample[0][1]
dna = DNA(dna_string)
dna.set_codon_table(codons_table)
candidates = dna.get_dna_to_amino_acid_candidates(amino_acid)
usage.end()
writer.write_data((dna_string, amino_acid), candidates,
usage.get_execution_time(), usage.get_memory_usage())
print("DNA:\n" + dna_string)
print("Protein\n" + amino_acid)
print("\n\nOutput")
print("=====")
print(str(len(candidates)))
for substring in candidates:
print(substring)
print("\n\nExecution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Usage: " + str(usage.get_memory_usage()) + " MB")
def get_json(self, date_requested):
dao = ProjectMetaDataDao()
dao.open('statistics.db3')
total_words = 0
projects = dao.get_all()
for project in projects:
total_words += project.words
usage = Usage()
calls = usage.get_stats(date_requested)
results = {}
results['total_words'] = total_words
results['projects'] = len(projects)
results['searches'] = calls
return json.dumps(results, indent=4, separators=(',', ': '))
class BaseCommand(object):
def __init__(self):
self.api_url = None
self.http_header = {'Connection': 'close'}
self.cookies = {}
self.help_message = ""
self.longopts = []
self.vars = {}
self.command_name = "unknown"
def set_command_name(self, command_name):
self.command_name = command_name
def prepare(self, argv):
try:
longopts = ["help"] + ["%s=" % v for v in self.longopts]
opts, args = getopt.getopt(argv, "h", longopts=longopts)
except getopt.error, msg:
raise Usage(self.help_message)
longopts = ["--%s" % v for v in self.longopts]
# option processing
for option, value in opts:
if option in ("-h", "--help"):
raise Usage(self.help_message)
if option in longopts:
#Convert --option_name to option_name
self.vars[option[2:]] = value
def get_json(self, date_requested):
dao = ProjectMetaDataDao()
dao.open('statistics.db3')
total_words = 0
projects = dao.get_all()
for project in projects:
total_words += project.words
usage = Usage()
calls = usage.get_stats(date_requested)
results = {}
results['total_words'] = total_words
results['projects'] = len(projects)
results['searches'] = calls
return json.dumps(results, indent=4, separators=(',', ': '))
def prepare(self, argv):
self.help_message = self.help_message % self.command_name
BaseCommand.prepare(self, argv)
notebook_id = self.vars.get('notebook', None)
if notebook_id is None:
raise Usage(self.help_message)
self.notebook_id = self.build_available_notebook_id(notebook_id)
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem11')
solution_dir = os.path.join("Problems", "Problem11Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
scoring_matrix = data_reader.get_BLOSUM62_data()
for train_i in range(0, len(output)):
alpha_dna, beta_dna = test_cases[train_i]
case_output = output[train_i]
strings_algorithms = StringsAlgorithms(alpha_dna, beta_dna)
align = strings_algorithms.alignment(_type='global',
scoring_matrix=scoring_matrix)
if align != case_output:
raise Exception("Output not matched!\nExpecting: " +
str(case_output) + "\nFound: " + str(align))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
alpha_dna, beta_dna = test_cases[test_i]
strings_algorithms = StringsAlgorithms(alpha_dna, beta_dna)
align = strings_algorithms.alignment(_type='global',
scoring_matrix=scoring_matrix)
usage.end()
writer.write_data(test_i + 1, align, usage.get_execution_time(),
usage.get_memory_usage())
print("\n\nInput:\n" + alpha_dna + "\n" + beta_dna + "\n")
print("\n\nOutput")
print("=====")
print(align[0])
print(align[1][0])
print(align[1][1])
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def prepare(self, argv):
self.help_message = self.help_message % self.command_name
BaseCommand.prepare(self, argv)
interpreter_id = self.vars.get('interpreter', None)
if interpreter_id is None:
raise Usage(self.help_message)
self.interpreter_id = self.build_available_interpreter_id(
interpreter_id)
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem6')
solution_dir = os.path.join("Problems", "Problem6Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
case = test_cases[train_i]
case_output = output[train_i]
genome = case[0]
k = case[1][0]
l = case[1][1][0]
t = case[1][1][1]
dna = DNA(genome)
clumps_patterns = dna.get_clumps_patterns(int(k), int(t), int(l))
if clumps_patterns.sort() != case_output.sort():
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + clumps_patterns)
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
case = test_cases[test_i]
genome = case[0]
k = case[1][0]
l = case[1][1][0]
t = case[1][1][1]
dna = DNA(genome)
clumps_patterns = dna.get_clumps_patterns(int(k), int(t), int(l))
usage.end()
writer.write_data(test_i, clumps_patterns, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + genome + "\n" + str(k) + " " + str(l) + " " + str(t))
print("\n\nOutput")
print("=====")
for clump in clumps_patterns:
print(clump)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem10')
solution_dir = os.path.join("Problems", "Problem10Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
alpha_dna, beta_dna = test_cases[train_i]
case_output = output[train_i]
strings_algorithms = StringsAlgorithms(alpha_dna, beta_dna)
lcs = strings_algorithms.lcs('dp')
print(len(lcs))
if len(case_output) != len(lcs):
raise Exception("Output not matched!\nExpecting: " +
str(case_output) + "\nFound: " + str(lcs))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
alpha_dna, beta_dna = test_cases[test_i]
strings_algorithms = StringsAlgorithms(alpha_dna, beta_dna)
lcs = strings_algorithms.lcs('dp')
print(len(lcs))
usage.end()
writer.write_data(test_i + 1, lcs, usage.get_execution_time(),
usage.get_memory_usage())
print("\n\nInput:\n" + alpha_dna + "\n" + beta_dna + "\n")
print("\n\nOutput")
print("=====")
print(lcs)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
bot.add_cog(Input(bot))
bot.add_cog(Output(bot))
bot.add_cog(Administration(bot))
bot.add_cog(Usage(bot))
bot.run(TOKEN)
db_cursor.close()
db_connection.close()
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem16')
solution_dir = os.path.join("Problems", "Problem16Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
k_mers = test_cases[train_i]
graph = Graph()
adj_list = graph.get_debruijn_graph(_type='k_mers', k_mers=k_mers)
case_output = output[train_i]
if sorted(adj_list.items()) != sorted(case_output.items()):
raise Exception("Output not matched!\nExpecting: " +
str(sorted(case_output.items())) + "\nFound: " +
str(sorted(adj_list.items())))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
k_mers = test_cases[test_i]
adj_list = Graph().get_debruijn_graph(_type='k_mers', k_mers=k_mers)
usage.end()
writer.write_data(test_i + 1, adj_list, usage.get_execution_time(),
usage.get_memory_usage())
print("\n\nInput:\n" + str(k_mers) + "\n")
print("\n\nOutput")
print("=====")
print(adj_list)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main(argv=None):
if argv is None:
argv = sys.argv
# option processing
config_path = os.environ['HOME'] + "/.zdari.yml"
try:
opts, args = getopt.getopt(argv[1:], "h", ["help", "config="])
raise_usage = len(argv) < 2
next_argv_index = 1 if len(argv) >= 2 else 0
for option, value in opts:
if option in ("-h", "--help"):
raise_usage = True
if option in ("--config"):
config_path = value
remove_index = [
i for (i, v) in enumerate(argv) if v == '--config'
]
next_argv_index = 3 if len(argv) > 3 else 0
config = init(config_path)
command_type = argv[next_argv_index]
if raise_usage or command_type not in zeppelincmd.invoker.list_command_types(
):
raise Usage(help_message)
argv = argv[next_argv_index + 1:]
command_name = argv[0] if len(argv) > 0 else ""
if command_name not in zeppelincmd.invoker.list_commands(command_type):
raise Usage("""Command type: %s\nSupport Commands\n%s
""" % (command_type, '\n'.join([
" " + v
for v in zeppelincmd.invoker.list_commands(command_type)
])))
argv = argv[1:]
zeppelincmd.invoker.execute(command_type, command_name, config, argv)
except Usage, err:
print >> sys.stderr, str(err.msg)
return 2
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem13')
solution_dir = os.path.join("Problems", "Problem13Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
(k, genome) = test_cases[train_i]
case_output = output[train_i]
dna = DNA(genome)
k_mers = dna.get_k_mers(int(k))
if sorted(case_output) != sorted(k_mers):
raise Exception("Output not matched!\nExpecting: " +
str(case_output) + "\nFound: " + str(k_mers))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
(k, genome) = test_cases[test_i]
dna = DNA(genome)
k_mers = dna.get_k_mers(int(k))
usage.end()
writer.write_data(test_i + 1, k_mers, usage.get_execution_time(),
usage.get_memory_usage())
print("\n\nInput:\n" + k + "\n" + genome + "\n")
print("\n\nOutput")
print("=====")
print(k_mers)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem9')
solution_dir = os.path.join("Problems", "Problem9Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
case = test_cases[train_i]
case_output = output[train_i]
genome = case[0]
k = case[1][0]
d = case[1][1]
dna = DNA(genome)
k_mers = dna.most_frequent_missmatched_k_mer(int(k), int(d))
if set(case_output) != set(k_mers):
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + str(k_mers))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output) + 1, len(test_cases)):
usage.start()
case = test_cases[test_i]
genome = case[0]
k = case[1][0]
d = case[1][1]
dna = DNA(genome)
k_mers = dna.most_frequent_missmatched_k_mer(int(k), int(d))
usage.end()
writer.write_data(test_i, k_mers, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + genome + "\n" + str(k) + "\n" + str(d))
print("\n\nOutput")
print("=====")
print('\n'.join(map(lambda v: str(v), k_mers)))
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem5')
solution_dir = os.path.join("Problems", "Problem5Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
case = test_cases[train_i]
case_output = output[train_i]
pattern = case[0]
genome = case[1]
dna = DNA(genome)
pattern_indices = dna.get_pattern_indices(pattern)
if pattern_indices != case_output:
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + pattern_indices)
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
case = test_cases[test_i]
pattern = case[0]
genome = case[1]
dna = DNA(genome)
pattern_indices = dna.get_pattern_indices(pattern)
usage.end()
writer.write_data(test_i + 1, pattern_indices, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + pattern + "\n" + genome)
print("\n\nOutput")
print("=====")
print(pattern_indices)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def voice_api():
text = request.args.get('text')
token = request.args.get('token')
voice = request.args.get('voice') or "ona"
if voice not in ["ona", "pau"]:
return ("Bad Request", 400)
if token is None or getMD5(text) != token.lower():
return ("Forbidden", 403)
txt2wave = '/usr/bin/text2wave'
lame = '/usr/bin/lame'
with tempfile.NamedTemporaryFile() as encoded_file,\
tempfile.NamedTemporaryFile() as wave_file,\
tempfile.NamedTemporaryFile() as mp3_file:
text = _normalize(text)
f = open(encoded_file.name, 'wb')
f.write(text.encode('ISO-8859-15', 'ignore'))
f.close()
cmd = '{0} -o {1} {2} -eval "({3})"'.\
format(txt2wave, wave_file.name, encoded_file.name, festival_voices[voice])
p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
p.wait()
cmd = '{0} -f {1} {2}'.\
format(lame, wave_file.name, mp3_file.name)
p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
p.wait()
usage = Usage()
usage.log()
return send_file(mp3_file.name,
mimetype="audio/mp3",
as_attachment=False,
attachment_filename=mp3_file.name)
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem1')
solution_dir = os.path.join("Problems", "Problem1Solution")
data_reader = DataReader(problem_dataset_dir)
training_data, testing_data = data_reader.get_data()
codons_table = data_reader.get_rna_codon_table()
for sample in training_data:
rna_string = sample[0]
output = sample[1]
rna = RNA(rna_string)
rna.set_codons_table(codons_table)
amino_acid = rna.to_amino_acid()
if amino_acid != output:
raise Exception("Output not matched!\nExpecting: " + output +
"\nFound: " + amino_acid)
print("Passed training data..\n\n")
writer = DataWriter(solution_dir)
usage = Usage()
for sample in testing_data:
usage.start()
rna_string = sample[0]
rna = RNA(rna_string)
rna.set_codons_table(codons_table)
amino_acid = rna.to_amino_acid()
usage.end()
writer.write_data(rna_string, amino_acid, usage.get_execution_time(),
usage.get_memory_usage())
print("RNA:\n" + rna_string)
print("Protein:\n" + amino_acid)
print("\n\nExecution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Usage: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem11')
solution_dir = os.path.join("Problems", "Problem11Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
scoring_matrix = data_reader.get_BLOSUM62_data()
for train_i in range(0, len(output)):
alpha_dna, beta_dna = test_cases[train_i]
case_output = output[train_i]
strings_algorithms = StringsAlgorithms(alpha_dna, beta_dna)
align = strings_algorithms.alignment(_type='global', scoring_matrix=scoring_matrix)
if align != case_output:
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + str(align))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
alpha_dna, beta_dna = test_cases[test_i]
strings_algorithms = StringsAlgorithms(alpha_dna, beta_dna)
align = strings_algorithms.alignment(_type='global', scoring_matrix=scoring_matrix)
usage.end()
writer.write_data(test_i + 1, align, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + alpha_dna + "\n" + beta_dna + "\n")
print("\n\nOutput")
print("=====")
print(align[0])
print(align[1][0])
print(align[1][1])
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem17')
solution_dir = os.path.join("Problems", "Problem17Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
dna = test_cases[train_i][0]
k = test_cases[train_i][1][0]
score_matrix = test_cases[train_i][1][1]
case_output = output[train_i]
most_probable_k_mer = DNA(dna).get_most_probable_k_mer(int(k), score_matrix)
if most_probable_k_mer != case_output:
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + str(most_probable_k_mer))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output) - 1, len(test_cases)):
usage.start()
dna = test_cases[test_i][0]
k = test_cases[test_i][1][0]
score_matrix = test_cases[test_i][1][1]
most_probable_k_mer = DNA(dna).get_most_probable_k_mer(int(k), score_matrix)
usage.end()
writer.write_data(test_i + 1, most_probable_k_mer, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + str(dna) + "\n" + str(k) + "\n" + str(score_matrix))
print("\n\nOutput")
print("=====")
print(most_probable_k_mer)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem10')
solution_dir = os.path.join("Problems", "Problem10Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
alpha_dna, beta_dna = test_cases[train_i]
case_output = output[train_i]
strings_algorithms = StringsAlgorithms(alpha_dna, beta_dna)
lcs = strings_algorithms.lcs('dp')
print(len(lcs))
if len(case_output) != len(lcs):
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + str(lcs))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
alpha_dna, beta_dna = test_cases[test_i]
strings_algorithms = StringsAlgorithms(alpha_dna, beta_dna)
lcs = strings_algorithms.lcs('dp')
print(len(lcs))
usage.end()
writer.write_data(test_i + 1, lcs, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + alpha_dna + "\n" + beta_dna + "\n")
print("\n\nOutput")
print("=====")
print(lcs)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def apertium_translate_process(values):
global translate_calls, translate_chars
translate_calls += 1
start_time = datetime.datetime.now()
text = None
text = values['q']
translate_chars += len(text)
langpair = values['langpair']
savetext = 'savetext' in values and values['savetext'] == True
languages = _convert_apertium_languages_aliases_to_iso639_3(langpair)
if savetext:
saved_filename = os.path.join(SAVED_TEXTS, "source.txt")
with open(saved_filename, "a") as text_file:
t = text.replace('\n', '')
text_file.write(f'{languages}\t{t}\n')
openNMT = openNMTs[languages]
translated = openNMT.translate_parallel(text)
result = {}
result = _get_bias_message_if_needed(languages, text, result)
time_used = datetime.datetime.now() - start_time
words = len(text.split(' '))
usage = Usage()
usage.log(languages, words, time_used, 'form')
responseData = {}
responseData['translatedText'] = translated
result['responseStatus'] = 200
result['responseData'] = responseData
result['time'] = str(time_used)
return json_answer(result)
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem15')
solution_dir = os.path.join("Problems", "Problem15Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
k = test_cases[train_i][0]
dna = test_cases[train_i][1]
graph = Graph()
adj_list = graph.get_debruijn_graph(_type='string', k=int(k), dna_string=dna)
case_output = output[train_i]
if sorted(adj_list.items()) != sorted(case_output.items()):
raise Exception("Output not matched!\nExpecting: " + str(sorted(case_output.items())) + "\nFound: " + str(sorted(adj_list.items())))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
k = test_cases[test_i][0]
dna = test_cases[test_i][1]
adj_list = Graph().get_debruijn_graph(_type='string', k=int(k), dna_string=dna)
usage.end()
writer.write_data(test_i + 1, adj_list, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + str(k) + "\n" + dna + "\n")
print("\n\nOutput")
print("=====")
print(adj_list)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem2')
solution_dir = os.path.join("Problems", "Problem2Solution")
data_reader = DataReader(problem_dataset_dir)
training_data, testing_data = data_reader.get_data()
codons_table = data_reader.get_rna_codon_table()
for sample in training_data:
dna_string = sample[0][0]
amino_acid = sample[0][1]
output = sample[1]
dna = DNA(dna_string)
dna.set_codon_table(codons_table)
candidates = dna.get_dna_to_amino_acid_candidates(amino_acid)
if set(candidates) != set(output):
raise Exception("Output not matched!\nExpecting: " + str(output) + "\nFound: " + str(candidates))
print("Passed training data..\n\n")
writer = DataWriter(solution_dir)
usage = Usage()
for sample in testing_data:
usage.start()
dna_string = sample[0][0]
amino_acid = sample[0][1]
dna = DNA(dna_string)
dna.set_codon_table(codons_table)
candidates = dna.get_dna_to_amino_acid_candidates(amino_acid)
usage.end()
writer.write_data((dna_string, amino_acid), candidates, usage.get_execution_time(), usage.get_memory_usage())
print("DNA:\n" + dna_string)
print("Protein\n" + amino_acid)
print("\n\nOutput")
print("=====")
print(str(len(candidates)))
for substring in candidates:
print(substring)
print("\n\nExecution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Usage: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem14')
solution_dir = os.path.join("Problems", "Problem14Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
k_mers = test_cases[train_i]
case_output = output[train_i]
graph = Graph(k_mers)
adj_list = graph.get_overlap_graph()
if sorted(adj_list.items()) != sorted(case_output.items()):
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + str(adj_list))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
k_mers = test_cases[test_i]
graph = Graph(k_mers)
adj_list = graph.get_overlap_graph()
usage.end()
writer.write_data(test_i + 1, adj_list, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + str(k_mers) + "\n")
print("\n\nOutput")
print("=====")
print(adj_list)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem7')
solution_dir = os.path.join("Problems", "Problem7Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
case = test_cases[train_i]
case_output = np.array(output[train_i])
dna = DNA(case.strip())
min_skew_indices = dna.get_min_skew()
if not np.array_equal(case_output, min_skew_indices):
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + str(min_skew_indices))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
case = test_cases[test_i]
dna = DNA(case.strip())
min_skew_indices = dna.get_min_skew()
usage.end()
writer.write_data(test_i + 1, min_skew_indices, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + case + "\n")
print("\n\nOutput")
print("=====")
print(list(min_skew_indices))
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem13')
solution_dir = os.path.join("Problems", "Problem13Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
(k, genome) = test_cases[train_i]
case_output = output[train_i]
dna = DNA(genome)
k_mers = dna.get_k_mers(int(k))
if sorted(case_output) != sorted(k_mers):
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + str(k_mers))
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
(k, genome) = test_cases[test_i]
dna = DNA(genome)
k_mers = dna.get_k_mers(int(k))
usage.end()
writer.write_data(test_i + 1, k_mers, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + k + "\n" + genome + "\n")
print("\n\nOutput")
print("=====")
print(k_mers)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def __init__(self, path, repository, user):
self.path = path
self.gpu = GPU()
self.usage = Usage(15)
self.headers = [
self.usage.cpu_header, self.usage.disk_header,
self.usage.mem_ram_header, self.usage.mem_swap_header,
self.gpu.header
]
self.values = [
self.usage.cpu_csv, self.usage.disk_csv, self.usage.mem_ram_csv,
self.usage.mem_swap_csv, self.gpu.csv
]
self.logger = Logger(os.path.join(self.path, repository), delay=0)
self.logger.set_header(self.headers)
self.logger.set_csv(self.values)
self.saver = Saver()
self.saver.get_repository(path, repository, user)
def main():
problem_dataset_dir = os.path.join('Problems', 'Problem4')
solution_dir = os.path.join("Problems", "Problem4Solution")
data_reader = DataReader(problem_dataset_dir)
test_cases, output = data_reader.get_data()
for train_i in range(0, len(output)):
case = test_cases[train_i]
case_output = output[train_i]
dna = DNA(case)
reverse_complement = dna.reverse_complement()
if reverse_complement != case_output:
raise Exception("Output not matched!\nExpecting: " + str(case_output) + "\nFound: " + reverse_complement)
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
case = test_cases[test_i]
dna = DNA(case)
reverse_complement = dna.reverse_complement()
usage.end()
writer.write_data(test_i + 1, reverse_complement, usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + case)
print("\n\nOutput")
print("=====")
print(reverse_complement)
print("\n")
print("======")
print("Execution Time: " + str(usage.get_execution_time()) + " s")
print("Memory Used: " + str(usage.get_memory_usage()) + " MB")
def main(argv=None):
if argv is None:
argv = sys.argv
try:
try:
opts, args = getopt.getopt(argv[1:], 'h', ['help'])
except getopt.GetoptError as e:
raise Usage(e)
input_fc = "in_memory/temp"
output_file = os.path.join(config.config_dir,
"test_spagedi_export.txt")
for opt, arg in opts:
if opt in ('-h', '--help'):
print __doc__
return 0
# Prompt the user: what test are we doing?
defaults = Bunch({
'spagedi_file_path':
os.path.join(config.config_dir, "spagedi_data.txt"),
})
sequence = spagedi_user_inputs(spagedi_tree(), [], defaults)
sauce = prepare({
'standalone': True,
'sequence': sequence,
'input_fc': input_fc,
'spagedi_file_path': defaults.spagedi_file_path,
'output_file': output_file,
'analysis_type': '',
'order_by': 'Individual_ID',
'show_spagedi_output': True,
})
# Fire up Spagedi and crunch some numbers
spagedi = SpagediWrapper(**sauce)
parameters = spagedi.getParameterInfo()
return spagedi.execute(parameters, None)
except Usage as e:
print >> sys.stderr, e.msg
print >> sys.stderr, "for help use --help"
return 2
def get_data(self):
gpu = GPU()
usage = Usage(3)
data = []
data_list = [
usage.cpu_csv(),
usage.disk_csv(),
usage.mem_ram_csv(),
usage.mem_swap_csv(),
gpu.csv()
]
for i in data_list:
data += i.split(',')
return data
for train_i in range(0, len(output)):
case = test_cases[train_i]
case_output = output[train_i]
dna = DNA(case)
reverse_complement = dna.reverse_complement()
if reverse_complement != case_output:
raise Exception("Output not matched!\nExpecting: " +
str(case_output) + "\nFound: " +
reverse_complement)
print("Passed training data..")
writer = DataWriter(solution_dir)
usage = Usage()
for test_i in range(len(test_cases) - len(output), len(test_cases)):
usage.start()
case = test_cases[test_i]
dna = DNA(case)
reverse_complement = dna.reverse_complement()
usage.end()
writer.write_data(test_i + 1, reverse_complement,
usage.get_execution_time(), usage.get_memory_usage())
print("\n\nInput:\n" + case)
print("\n\nOutput")
print("=====")
def test_onlyfirst_list(self):
self.assertEqual(Usage.sum([a, b, b]), Usage(first=131, second=294))
