def run(**kwargs):
params = get_params(kwargs)
configure_logging(params)
if only_instance_running(params):
logging.info('Starting execution.')
write_pid_file(params) # create lock to avoid concurrent executions
current_exec_time = utcnow()
last_exec_time = replace_exec_time(current_exec_time, params['history_path'])
if 'config' in params:
config = params['config']
else:
config = load_config(params['config_path'])
config['current_exec_time'] = current_exec_time
config['last_exec_time'] = last_exec_time
config['query_folder'] = params['query_folder']
config['output_folder'] = params['output_folder']
config['wikis_path'] = params['wikis_path']
reader = Reader(config)
selector = Selector(reader, config)
executor = Executor(selector, config)
writer = Writer(executor, config)
writer.run()
delete_pid_file(params) # free lock for other instances to execute
logging.info('Execution complete.')
else:
logging.warning('Another instance is already running. Exiting.')
def run(**kwargs):
params = get_params(kwargs)
configure_logging(params)
if only_instance_running(params):
logging.info('Starting execution.')
write_pid_file(params) # create lock to avoid concurrent executions
current_exec_time = utcnow()
config = load_config(params['config_path'])
config['current_exec_time'] = current_exec_time
config['query_folder'] = params['query_folder']
config['output_folder'] = params['output_folder']
config['reruns'], rerun_files = read_reruns(params['query_folder'])
reader = Reader(config)
selector = Selector(reader, config)
executor = Executor(selector, config)
writer = Writer(executor, config, configure_graphite(config))
writer.run()
delete_reruns(rerun_files) # delete rerun files that have been processed
delete_pid_file(params) # free lock for other instances to execute
logging.info('Execution complete.')
else:
logging.warning('Another instance is already running. Exiting.')
def dual(use_spi=False, soft=True):
ssd0 = setup(False, soft) # I2C display
ssd1 = setup(True, False) # SPI instance
Writer.set_textpos(ssd0, 0, 0) # In case previous tests have altered it
wri0 = Writer(ssd0, small, verbose=False)
wri0.set_clip(False, False, False)
Writer.set_textpos(ssd1, 0, 0) # In case previous tests have altered it
wri1 = Writer(ssd1, small, verbose=False)
wri1.set_clip(False, False, False)
nfields = []
dy = small.height() + 6
col = 15
for n, wri in enumerate((wri0, wri1)):
nfields.append([])
y = 2
for txt in ('X:', 'Y:', 'Z:'):
Label(wri, y, 0, txt)
nfields[n].append(Label(wri, y, col, wri.stringlen('99.99'), True))
y += dy
for _ in range(10):
for n, wri in enumerate((wri0, wri1)):
for field in nfields[n]:
value = int.from_bytes(uos.urandom(3),'little')/167772
field.value('{:5.2f}'.format(value))
wri.device.show()
utime.sleep(1)
for wri in (wri0, wri1):
Label(wri, 0, 64, ' DONE ', True)
wri.device.show()
def test():
log = lambda message: sys.stdout.write(message)
writer = Writer('D:/exe/imod/IMOD_USER/pusair-output')
try:
log('Reading config... ')
config.parse()
if config.config['gradient'] > 1:
raise ValueError('Maximum gradient is 1')
log('Done\n')
p, adj = slurp.prep(
# fbore=str('D:/exe/imod/IMOD_USER/pusair-input/Boreholes_Dimas.ipf'),
fbore=str('D:/exe/imod/IMOD_USER/pusair-input/Boreholes_Jakarta.ipf'),
fscreen=str('data/well_M_z_all.ipf'),
config=config,
log=log)
interpolator = Interpolator(p, adj, writer, log)
interpolator.interpolate()
log('\n[DONE]')
except Exception as e:
log('\n\n[ERROR] {}'.format(e))
traceback.print_exc()
writer.reset()
def do_build(args):
#load config
config_file = os.path.join(args.src_dir,"_config.py")
try:
config.init(config_file)
except config.ConfigNotFoundException:
print >>sys.stderr, ("No configuration found: %s" % config_file)
parser.exit(1, "Want to make a new site? Try `blogofile init`\n")
logger.info("Running user's pre_build() function..")
writer = Writer(output_dir="_site")
if config.blog_enabled == True:
config.pre_build()
posts = post.parse_posts("_posts")
if args.include_drafts:
drafts = post.parse_posts("_drafts", config)
for p in drafts:
p.draft = True
else:
drafts = None
writer.write_blog(posts, drafts)
else:
#Build the site without a blog
writer.write_site()
logger.info("Running user's post_build() function..")
config.post_build()
def handle_standard_io(parser):
parser.read_file(sys.stdin)
#parser.print_file() # what is this for?
parser.build_structure() # build doc from actual file
writer = Writer()
writer.write(parser.node_file)
for line in writer.buffer:
print line.rstrip()
def do_build(args, load_config=True):
if load_config:
config_init(args)
writer = Writer(output_dir=util.path_join("_site",util.fs_site_path_helper()))
logger.debug("Running user's pre_build() function..")
config.pre_build()
writer.write_site()
logger.debug("Running user's post_build() function..")
config.post_build()
def render_html(name, source, translator_class=None):
writer = Writer()
if translator_class is not None:
writer.translator_class = translator_class
settings = {"stylesheet_path": "/static/html4css1.css,/static/main.css",
"embed_stylesheet": False,
"file_insertion_enabled": False,
"raw_enabled": False,
"xml_declaration": False}
return publish_string(source, writer_name="html", writer=writer,
settings_overrides=settings).decode()
def do_build(args):
#load config
try:
# Always load the _config.py from the current directory.
# We already changed to the directory specified with --src-dir
config.init("_config.py")
except config.ConfigNotFoundException:
print >>sys.stderr, ("No configuration found in source dir: %s" % args.src_dir)
parser.exit(1, "Want to make a new site? Try `blogofile init`\n")
writer = Writer(output_dir="_site")
logger.debug("Running user's pre_build() function..")
config.pre_build()
writer.write_site()
logger.debug("Running user's post_build() function..")
config.post_build()
def test(use_spi=False):
ssd = setup(use_spi) # Create a display instance
rhs = WIDTH -1
ssd.line(rhs - 20, 0, rhs, 20, 1)
square_side = 10
ssd.fill_rect(rhs - square_side, 0, square_side, square_side, 1)
wri = Writer(ssd, freesans20)
Writer.set_textpos(ssd, 0, 0) # verbose = False to suppress console output
wri.printstring('Sunday\n')
wri.printstring('12 Aug 2018\n')
wri.printstring('10.30am')
ssd.show()
def __init__(self, module):
self.module = module
self.buf = Writer() # modul-ebene
self.function_buf = Writer() # funktionen kommen seperat
self.label_count = 0
self.jump_table = {} # offsets der funktionen
def main():
print "Welcome to draugiem.lv message downloader "
sys.stdout.write("Email: ")
sys.stdout.flush()
email = raw_input()
password = getpass.getpass()
try:
downloader = MessageDownloader(email, password)
except DraugiemException:
print "invalid username/password"
sys.exit(1)
def progress_show(current, total):
sys.stdout.write("%3d of %d\r" % (current, total))
sys.stdout.flush()
print "[1/4] downloading inbox"
downloader.get_messages(type = 'in', progress_callback = progress_show)
print "[2/4] downloading outbox"
downloader.get_messages(type = 'out', progress_callback = progress_show)
print "[3/4] sorting messages"
msgs = downloader.get_all_messages()
sys.stdout.write("Enter path: ")
sys.stdout.flush()
path = raw_input()
if not os.path.exists(path):
os.mkdir(path)
print "[4/4] writing"
for user in msgs:
w = Writer(os.path.join(path, "%s.html" % (downloader.get_user_info(user).replace("/", ""))))
w.start(downloader.users[user])
for item in msgs[user]:
w.write(item)
w.end()
def inverse(use_spi=False, soft=True):
ssd = setup(use_spi, soft) # Create a display instance
rhs = WIDTH -1
ssd.line(rhs - 20, 0, rhs, 20, 1)
square_side = 10
ssd.fill_rect(rhs - square_side, 0, square_side, square_side, 1)
Writer.set_textpos(ssd, 0, 0) # In case previous tests have altered it
wri = Writer(ssd, freesans20, verbose=False)
wri.set_clip(False, False, False) # Char wrap
wri.printstring('Sunday\n')
wri.printstring('12 Aug 2018\n')
wri.printstring('10.30am', True) # Inverse text
ssd.show()
def wrap(use_spi=False, soft=True):
ssd = setup(use_spi, soft) # Create a display instance
Writer.set_textpos(ssd, 0, 0) # In case previous tests have altered it
wri = Writer(ssd, freesans20, verbose=False)
wri.set_clip(False, False, True) # Word wrap
wri.printstring('the quick brown fox jumps over')
ssd.show()
def main(argv):
try:
opts, args = getopt.getopt(argv, "i:o:", ["input", "output"])
except getopt.GetoptError:
usage()
sys.exit(2)
for o, a in opts:
if o in ("-i", "--input"):
inputFile = a
elif o in ("-o", "--output"):
outputFile = a
parser = Parser(inputFile)
writer = Writer()
writer.fromParser(parser)
# TODO implement dumping to stdout
writer.dumpToFile(outputFile)
def test_word_count(self):
file_name = 'data/01-train-input.txt'
reader = Reader(file_name)
reader.read_file()
unigram = TrainUnigram(reader.word_list, reader.total_word_count)
unigram.train_unigram()
file_name = 'data/unigram_model'
write_model = Writer(file_name, unigram.word_dict)
write_model.write_file()
model = Model(file_name)
model.read_model()
file_name = 'data/01-test-input.txt'
test = Evaluater(file_name, model.word_dict)
test.evaluate_model()
print('entropy is ' + str(test.H / test.total_word_count))
print('coverage is ' + str(1.0 * (test.total_word_count - test.unknown_word_count) / test.total_word_count))
def test_word_count(self):
file_name = 'data/02-train-input.txt'
reader = Reader(file_name)
reader.read_file()
ngram = TrainNgram(reader.word_list, 3)
ngram.train()
print({k: v for k, v in ngram.word_dict.items()})
print({k: v for k, v in ngram.lambda_word_dict.items()})
file_name = 'data/ngram_model'
writer_model = Writer(file_name, ngram.word_dict)
writer_model.write_file()
model = ReadNgram(file_name)
model.read_model()
file_name = 'data/02-train-input.txt'
test = EvaluateModel(file_name, model.word_dict, ngram.lambda_word_dict)
test.evaluate()
print('entropy is ' + str(test.H / test.total_word_count))
def main(args):
stats = Stats()
transactions = TransactionsList(args.infile)
if args.algorithm == 'apriori':
algorithm = Apriori(transactions, args.minsup)
else:
algorithm = Dic(transactions, args.minsup, args.m)
large_sets, counter = algorithm.get_large_sets_and_counter()
stats.record_post_large_sets()
rules = RulesGenerator.generate_rules(large_sets, args.minconf, counter, transactions)
stats.record_post_rules()
writer = Writer(args.outfile)
writer.add_args(args)
writer.add_stats(stats)
writer.add_rules(rules)
writer.write()
def test_word_count(self):
fileName = 'data/02-train-input.txt'
reader = Reader(fileName)
reader.file_Read()
bigram = TrainBigram(reader.word_list)
bigram.train()
print({k: v for k, v in bigram.word_dict.items()})
print({k: v for k, v in bigram.lambda_word_dict.items()})
fileName = 'data/bigram_model'
writemodel = Writer(fileName, bigram.word_dict)
writemodel.write_file()
model = ModelReader(fileName)
model.read_model()
fileName = 'data/02-train-input.txt'
test = EvaluateModel(fileName, model.word_dict, bigram.lambda_word_dict)
test.evaluate_model()
print('entropy is ' + str(test.H / test.total_word_count))
def handle_files(parser, files, dir_source, dir_dest):
for filename in files:
print '--------', filename, '--------'
# TODO: handle exceptions on file
file = open(filename, 'r')
node_file = parser.read_file(file)
file.close()
#parser.print_file() # what is this for?
parser.build_structure(node_file) # build doc from actual file
writer = Writer()
writer.write(node_file)
filepath = fs.transform_filepath(filename, dir_source, dir_dest)
print 'path', filename, dir_source, dir_dest, filepath
# TODO: handle exceptions on file
file = open(filepath, 'w')
file.write(''.join(writer.buffer))
file.close()
def test_word_count(self):
file_name = 'data/01-train-input.txt'
reader = Reader(file_name)
reader.read_file()
unigram = TrainUnigram(reader.word_list, reader.total_word_count)
unigram.train()
file_name = 'data/unigram_model'
writemodel = Writer(file_name, unigram.word_dict)
writemodel.write_file()
file_name = 'data/03-train-input.txt'
cfeature = FeatureReader(file_name)
cfeature.read_feature()
print({k: v for k, v in cfeature.feature_dict.items()})
online = OnlineLearning(cfeature.feature_dict, unigram.word_dict, 'UNI:')
online.online_learning()
print({k: v for k, v in online.phi.items()})
prediction = OnePrediction('data/03-train.txt', online.weight, online.phi, 'UNI:')
prediction.predict()
def handle_buffer(buffer):
markup_input = markup.find_markup('rst', 'python')
markup_output = markup.find_markup('rst', 'python')
#print buffer
if not markup_output:
markup_output = markup_input
# TODO pass markup directly instead of assigning to Node
Node.writer = markup_output.writer
Node.reader = markup_input.reader
Node.lang = markup_input.lang
parser = markup_input.parser
node = parser.read_file(buffer)
#parser.print_file() # what is this for?
parser.build_structure(node) # build doc from actual file
writer = Writer()
writer.write(node)
return writer.buffer
def __init__(self, utils):
"""
Constructor
@param utils: A module containing a lot of utility-functions as
well as data which is needed almost everywhere.
@type utils: L{SpecDebugUtils}
"""
#: A module containing a lot of utility-functions.
#: @type: L{SpecDebugUtils}
self.__utils = utils
#: A utility class that allows us to print the countertrace.
#: @type: L{Writer}
self.__writer = Writer(utils)
def parse(self, filepath):
""" 解析 """
writer = Writer(filepath, self)
self.writer = writer
while not(self.lookAhead() is None):
node = self.parseProgram()
if not(node is None):
node.run(writer)
# スクリプトの終端を書き込む
writer.writeString(Code.CODE_END)
writer.writeCrlf()
# 終了処理
writer.finalize()
print "convert done."
def __init__(self, utils, counterstrategy, z_array, countertrace):
"""
Constructor
@param utils: A module containing a lot of utility-functions as
well as data which is needed almost everywhere.
@type utils: L{SpecDebugUtils}
@param counterstrategy: A counterstrategy, i.e., a strategy for the
environment to find inputs so that the system is forced to
violate the specification.
@type counterstrategy: L{BDD}
@param z_array: z_array[a] contains the intermediate results of the
fixpoint computation in the variable Z of the computation of
the winning region for the environment. 'a' counts the
iterations of the fixpoint computation in Z. It is used to
figure out how often the value of jx might still change in the
future of the play. (If the play is in z_array[a], jx might
change at most a-1 times.)
@type z_array: list<L{BDD}>
@param countertrace: A sequence of inputs so that the system is forced
to violate its specification.
@type countertrace: L{InfiniteTraceOfBdds}
"""
#: A module containing a lot of utility-functions.
#: @type: L{SpecDebugUtils}
self.__utils = utils
#: A winning strategy for the environment.
#: @type: L{BDD}
self.__counterstrategy = counterstrategy
#: Intermediate results of the fixpoint computation in the variable Z.
#: @type: list<L{BDD}>
self.__z_array = z_array
#: A sequence of inputs so that the system is forced to violate its
#: specification.
#: @type: L{InfiniteTraceOfBdds}
self.__countertrace = countertrace
#: A utility class that allows us to print the summary of the play.
#: @type: L{Writer}
self.__writer = Writer(utils)
def process(self):
util.log('METHOD : %s' % self.name, 'debug')
if 0:
from androguard.core import bytecode
bytecode.method2png('/tmp/graphs/%s#%s.png' % \
(self.method.get_class_name().split('/')[-1][:-1], self.name),
self.metha)
graph = construct(self.basic_blocks, self.var_to_name, self.exceptions)
self.graph = graph
if graph is None:
return
if 0:
util.create_png(self.basic_blocks, graph, '/tmp/blocks')
#'dad_graphs/blocks')
defs, uses = build_def_use(graph, self.lparams)
dead_code_elimination(graph, uses, defs)
register_propagation(graph, uses, defs)
# After the DCE pass, some nodes may be empty, so we can simplify the
# graph to delete these nodes.
# We start by restructuring the the graph by spliting the conditional
# nodes into a pre-header and a header part.
# We then simplify the graph by merging multiple statement nodes into
# a single statement node when possible. This also delete empty nodes.
graph.split_if_nodes()
graph.simplify()
graph.reset_rpo()
idoms = immediate_dominator(graph)
identify_structures(graph, idoms)
if 0:
util.create_png(self.basic_blocks, graph, '/tmp/structured')
# 'dad_graphs/structured')
self.writer = Writer(graph, self)
self.writer.write_method()
def __init__(self, **kwargs):
"""Initialize the keycode bindings and all controllers, create output dir."""
settings = {
'experimentName' : 'someNameHere',
'outputRootDir' : os.path.normpath(os.path.expanduser("~/kauferdata")),
'keyBindings' : {
'quit' : 'q',
'toggleStimulator' : 't',
'triggerStimulator' : 's'
},
'audio' : {},
'gui' : {},
'stimulator' : { 'activeProtocolName' : 'nucleusAccumbensExample' },
'tracker' : {},
'videoIn' : {},
'videoOut' : {},
'writer' : {},
}
settings.update(kwargs)
trialDir = "{0}_{1}_{2}".format(time.strftime("%y%m%d%H%M%S"),
settings['experimentName'], settings['stimulator']['activeProtocolName'])
settings['outputDataDir'] = os.path.join(settings['outputRootDir'], trialDir)
self.keyBindings = settings['keyBindings']
self.keycodeBindings = {k: getattr(opencvgui.keycodes, v)
for k,v in self.keyBindings.items() }
os.makedirs(os.path.join(settings['outputDataDir'], 'audio'))
self.writeExperimentData(settings)
for x in ['audio', 'videoOut', 'writer']:
kwargs[x] = {} if not kwargs.has_key(x) else kwargs[x]
kwargs[x]['outputDataDir'] = settings['outputDataDir']
# gui and writer both need a reference to the app instance because they
# draw on the state of so many other controllers
self.audio = Audio(**settings['audio'])
self.gui = Gui(self, **settings['gui'])
self.stimulator = Stimulator(**settings['stimulator'])
self.tracker = Tracker(**settings['tracker'])
self.videoIn = VideoIn(**settings['videoIn'])
self.videoOut = VideoOut(**settings['videoOut'])
self.writer = Writer(self, **settings['writer'])
def main():
my_runner = Runner(Getter(), Writer(), Deleter(), Copier())
my_runner.run_it()
class Abstractor(object):
"""High level model that handles intializing the underlying network
architecture, saving, updating examples, and predicting examples.
"""
# --------------------------------------------------------------------------
# Initialization
# --------------------------------------------------------------------------
def __init__(self, args, src_dict, tgt_dict, state_dict=None):
# Book-keeping.
self.args = args
self.word_dict = src_dict
self.args.vocab_size = len(src_dict)
self.tgt_dict = tgt_dict
self.args.tgt_vocab_size = len(tgt_dict)
self.updates = 0
self.use_cuda = False
self.parallel = False
if args.model_type == 'rnn':
self.network = Writer(args, tgt_dict)
else:
raise RuntimeError('Unsupported model: %s' % args.model_type)
# Load saved state
if state_dict:
# Load buffer separately
if 'fixed_embedding' in state_dict:
fixed_embedding = state_dict.pop('fixed_embedding')
self.network.load_state_dict(state_dict)
self.network.register_buffer('fixed_embedding', fixed_embedding)
else:
self.network.load_state_dict(state_dict)
if self.args.ema:
self.ema = ExponentialMovingAverage(0.999)
for name, param in self.network.named_parameters():
if param.requires_grad:
self.ema.register(name, param.data)
def expand_dictionary(self, words):
# TODO: at present, only supported for source dictionary
"""Add words to the Reader dictionary if they do not exist. The
underlying embedding matrix is also expanded (with random embeddings).
Args:
words: iterable of tokens to add to the dictionary.
Output:
added: set of tokens that were added.
"""
to_add = {self.word_dict.normalize(w) for w in words
if w not in self.word_dict}
# Add words to dictionary and expand embedding layer
if len(to_add) > 0:
logger.info('Adding %d new words to dictionary...' % len(to_add))
for w in to_add:
self.word_dict.add(w)
self.args.vocab_size = len(self.word_dict)
logger.info('New vocab size: %d' % len(self.word_dict))
old_embedding = self.network.encoder.word_embeddings.embedding.weight.data
self.network.encoder.word_embeddings.embedding = torch.nn.Embedding(self.args.vocab_size,
self.args.emsize,
padding_idx=0)
new_embedding = self.network.encoder.word_embeddings.embedding.weight.data
new_embedding[:old_embedding.size(0)] = old_embedding
# Return added words
return to_add
@staticmethod
def load_embeddings(word_dict, words, embedding_file,
emb_layer, fix_embeddings):
"""Load pretrained embeddings for a given list of words, if they exist.
#TODO: update args
Args:
words: iterable of tokens. Only those that are indexed in the
dictionary are kept.
embedding_file: path to text file of embeddings, space separated.
"""
words = {w for w in words if w in word_dict}
logger.info('Loading pre-trained embeddings for %d words from %s' %
(len(words), embedding_file))
# When normalized, some words are duplicated. (Average the embeddings).
vec_counts, embedding = {}, {}
with open(embedding_file) as f:
for line in f:
parsed = line.rstrip().split(' ')
assert (len(parsed) == emb_layer.word_vec_size + 1)
w = word_dict.normalize(parsed[0])
if w in words:
vec = torch.Tensor([float(i) for i in parsed[1:]])
if w not in vec_counts:
vec_counts[w] = 1
embedding[w] = vec
else:
logging.warning(
'WARN: Duplicate embedding found for %s' % w
)
vec_counts[w] = vec_counts[w] + 1
embedding[w].add_(vec)
for w, c in vec_counts.items():
embedding[w].div_(c)
emb_layer.init_word_vectors(word_dict, embedding, fix_embeddings)
logger.info('Loaded %d embeddings (%.2f%%)' %
(len(vec_counts), 100 * len(vec_counts) / len(words)))
def load_src_embeddings(self, words, embedding_file):
"""Load pretrained embeddings for a given list of words, if they exist.
Args:
words: iterable of tokens. Only those that are indexed in the
dictionary are kept.
embedding_file: path to text file of embeddings, space separated.
"""
self.load_embeddings(self.word_dict, words, embedding_file,
self.network.encoder.word_embeddings.embedding,
self.args.fix_embeddings)
def load_tgt_embeddings(self, words, embedding_file):
"""Load pretrained embeddings for a given list of words, if they exist.
Args:
words: iterable of tokens. Only those that are indexed in the
dictionary are kept.
embedding_file: path to text file of embeddings, space separated.
"""
fix_embeddings = not self.args.share_decoder_embeddings and self.args.fix_embeddings
self.load_embeddings(self.tgt_dict, words, embedding_file,
self.network.word_embeddings.embedding,
fix_embeddings)
def init_optimizer(self, state_dict=None, use_gpu=True):
"""Initialize an optimizer for the free parameters of the network.
Args:
state_dict: optimizer's state dict
use_gpu: required to move state_dict to GPU
"""
if self.args.fix_embeddings:
for p in self.network.encoder.word_embeddings.embedding.parameters():
p.requires_grad = False
if not self.args.share_decoder_embeddings and self.args.fix_embeddings:
for p in self.network.word_embeddings.embedding.parameters():
p.requires_grad = False
parameters = [p for p in self.network.parameters() if p.requires_grad]
if self.args.optimizer == 'sgd':
self.optimizer = optim.SGD(parameters, self.args.learning_rate,
momentum=self.args.momentum,
weight_decay=self.args.weight_decay)
elif self.args.optimizer == 'adamax':
self.optimizer = optim.Adamax(parameters,
weight_decay=self.args.weight_decay)
else:
raise RuntimeError('Unsupported optimizer: %s' % self.args.optimizer)
if state_dict is not None:
self.optimizer.load_state_dict(state_dict)
# TODO: fix me [temporary: https://github.com/pytorch/pytorch/issues/2830]
if use_gpu:
for state in self.optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
# --------------------------------------------------------------------------
# Learning
# --------------------------------------------------------------------------
def _make_src_map(self, data):
""" ? """
src_size = max([t.size(0) for t in data])
src_vocab_size = max([t.max() for t in data]) + 1
alignment = torch.zeros(len(data), src_size, src_vocab_size)
for i, sent in enumerate(data):
for j, t in enumerate(sent):
alignment[i, j, t] = 1
return alignment
def _align(self, data):
""" ? """
tgt_size = max([t.size(0) for t in data])
alignment = torch.zeros(len(data), tgt_size).long()
for i, sent in enumerate(data):
alignment[i, :sent.size(0)] = sent
return alignment
def update(self, ex):
"""Forward a batch of examples; step the optimizer to update weights."""
if not self.optimizer:
raise RuntimeError('No optimizer set.')
# Train mode
self.network.train()
# To enable copy attn, collect source map and alignment info
if self.args.copy_attn:
assert 'src_map' in ex and 'alignment' in ex
source_map = self._make_src_map(ex['src_map'])
alignment = self._align(ex['alignment'])
source_map = Variable(source_map.cuda(async=True)) if self.use_cuda \
else Variable(source_map)
alignment = Variable(alignment.cuda(async=True)) if self.use_cuda \
else Variable(alignment)
else:
pcs = machine.Pin('X2', machine.Pin.OUT_PP)
prst = machine.Pin('X3', machine.Pin.OUT_PP)
spi = machine.SPI(1)
display = SSD1306_SPI(WIDTH, HEIGHT, spi, pdc, prst, pcs)
else: # I2C
# Pyb SSD
# 3v3 Vin
# Gnd Gnd
# Y9 CLK
# Y10 DATA
HEIGHT = 64
pscl = machine.Pin('Y9', machine.Pin.OUT_PP)
psda = machine.Pin('Y10', machine.Pin.OUT_PP)
i2c = machine.I2C(scl=pscl, sda=psda)
display = SSD1306_I2C(WIDTH, HEIGHT, i2c)
serif = Writer(display, freeserif)
sans = Writer(display, freesans20)
Writer.set_clip(True, True) # Disable auto scrolling and wrapping.
serif.printstring('Tuesday\n')
sans.printstring('8 Nov 2016\n')
sans.printstring('10.30am')
display.show()
def scroll_test(x, y):
t = utime.ticks_us()
display.scroll(x, y) # 125ms
print(utime.ticks_diff(utime.ticks_us(), t))
display.show()
def _make_writer(self):
template = '<html><head></head><body>{}</body></html>'
return Writer(template)
def __init__(self, states, symbols, acceptableStates, q0, transitions):
super(EpsilonNFA, self).__init__(states, symbols, acceptableStates, q0,
transitions)
self.simulationLog = ""
self.writer = Writer(self)
logger.info('PARAMETER ...')
logger.info(opt)
logger.info('Load Dataset ...')
dataset = DataLoader(opt, opt.train_data)
dataset_size = len(dataset)
print('#training meshes = %d' % dataset_size)
logger.info('#training meshes = %d', dataset_size)
model = ClassifierModel(opt)
num_total_params = sum(p.numel() for p in model.net.parameters())
num_trainable_params = sum(p.numel() for p in model.net.parameters() if p.requires_grad)
print('Number of total paramters: %d, number of trainable parameters: %d' % (num_total_params, num_trainable_params))
logger.info('Number of total paramters: %d, number of trainable parameters: %d', num_total_params, num_trainable_params)
writer = Writer(opt)
total_steps = 0
train_start_time = time.time()
best_tst_acc = 0.0
torch.manual_seed(1)
cudnn.benchmark = False
cudnn.deterministic = True
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
iter_data_time = time.time()
epoch_iter = 0
heappop_error_train = 0
logger.info('Epoch %d started ...', epoch)
writer.reset_counter()
return self.name
def _process(self, msg):
""" Send message to measure unit and process answer
:param msg: message to send
:returns: parsed answer (dictionary)
"""
ans = self.measureIface.Send(msg)
if self.measureIface.state != self.measureIface.IF_OK:
return {}
res = self.measureUnit.Result(msg, ans)
if self.writerUnit is not None and res is not None and len(res) > 0:
self.writerUnit.WriteData(res)
return res
if __name__ == "__main__":
from iface import Iface
from measureunit import MeasureUnit
from writer import Writer
mu = MeasureUnit('Test', 'Proba')
iface = Iface('iface')
wrt = Writer()
print(mu.GetName())
print(mu.GetType())
print(iface.GetName())
print(iface.GetState())
a = Instrument("test instrument", mu, iface, wrt)
def scroll(use_spi=False, soft=True):
ssd = setup(use_spi, soft) # Create a display instance
rhs = WIDTH -1
ssd.line(rhs - 20, 0, rhs, 20, 1)
square_side = 10
ssd.fill_rect(rhs - square_side, 0, square_side, square_side, 1)
Writer.set_textpos(ssd, 0, 0) # In case previous tests have altered it
wri = Writer(ssd, freesans20, verbose=False)
wri.set_clip(False, False, False) # Char wrap
wri.printstring('Sunday\n')
wri.printstring('12 Aug 2018\n')
wri.printstring('10.30am')
for x in range(5):
ssd.show()
utime.sleep(2)
wri.printstring('\nCount = {:2d}'.format(x))
ssd.show()
utime.sleep(2)
wri.printstring('\nDone.')
ssd.show()
def func_train(self):
self.writer = Writer(self.files)
tim.init(period=3 * 1000, mode=Timer.ONE_SHOT, callback=page6)
def page6(tim):
oled.poweroff()
def BatteryPage(tim):
global BattLevel
oled.fill(0)
icon = util.loadImage('icons/battery' + str(BattLevel) + '4.pbm')
oled.blit(icon, 100, 0)
oled.show()
if (BattLevel < 4):
BattLevel += 1
tim.init(period=1 * 1000, mode=Timer.ONE_SHOT, callback=BatteryPage)
else:
tim.init(period=1 * 1000, mode=Timer.ONE_SHOT, callback=page2)
i2c = machine.I2C(-1, PinScl, PinSda)
print('')
print(i2c.scan())
oled = ssd1306.SSD1306_I2C(OLED_WIDTH, OLED_HEIGHT, i2c)
wrtr = Writer(oled, font6)
Writer.set_textpos(oled, 0, 0)
wrtr.printstring("Hello World!")
oled.show()
tim = Timer(-1)
tim.init(period=3 * 1000, mode=Timer.ONE_SHOT, callback=BatteryPage)
def update_bottom_area(msg):
Writer.set_textpos(OLED, row=44, col=0)
WRI_BT.printstring(msg)
OLED.show()
from machine import I2C
from machine import Pin
from ssd1306 import SSD1306_I2C
import machine
from writer import Writer
import time
import dseg_18
import rsu_text_font_14 as dseg_12
days = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']
scl = Pin(22)
sda = Pin(21)
i2c = I2C(scl=scl, sda=sda, freq=450000)
oled = SSD1306_I2C(128, 64, i2c)
dseg18 = Writer(oled, dseg_18)
dseg12 = Writer(oled, dseg_12)
rtc = machine.RTC()
rtc.ntp_sync(server="pool.ntp.org")
time.sleep(2)
while not rtc.synced():
machine.idle()
while True:
_tm = time.localtime()
print(_tm)
oled.fill(0)
dseg12.set_textpos(5,40)
dseg12.printstring('{0:02d}/{1:02d}/{2} {3}'.format(_tm[2], _tm[1], _tm[0], days[_tm[6]]))
dseg18.set_textpos(30,20)
dseg18.printstring('{0:02d}:{1:02d}:{2:02d}'.format(_tm[3] + 6, _tm[4], _tm[5]))
oled.show()
time.sleep(1)
class DvMethod():
def __init__(self, methanalysis):
self.method = methanalysis.get_method()
self.metha = methanalysis
self.name = self.method.get_name()
self.lparams = []
self.basic_blocks = [bb for bb in methanalysis.basic_blocks.get()]
self.var_to_name = {}
self.writer = None
access = self.method.get_access_flags()
self.access = [flag for flag in util.ACCESS_FLAGS_METHODS
if flag & access]
desc = self.method.get_descriptor()
self.type = util.get_type(desc.split(')')[-1])
self.params_type = util.get_params_type(desc)
self.exceptions = methanalysis.exceptions.exceptions
code = self.method.get_code()
if code is None:
util.log('No code : %s %s' % (self.name,
self.method.get_class_name()),
'debug')
else:
start = code.registers_size - code.ins_size
if 0x8 not in self.access:
self.var_to_name[start] = ThisParam(start, self.name)
self.lparams.append(start)
start += 1
num_param = 0
for ptype in self.params_type:
param = start + num_param
self.lparams.append(param)
self.var_to_name.setdefault(param, Param(param, ptype))
num_param += util.get_type_size(ptype)
def process(self):
util.log('METHOD : %s' % self.name, 'debug')
if 0:
from androguard.core import bytecode
bytecode.method2png('/tmp/graphs/%s#%s.png' % \
(self.method.get_class_name().split('/')[-1][:-1], self.name),
self.metha)
graph = construct(self.basic_blocks, self.var_to_name, self.exceptions)
self.graph = graph
if graph is None:
return
if 0:
util.create_png(self.basic_blocks, graph, '/tmp/blocks')
#'dad_graphs/blocks')
defs, uses = build_def_use(graph, self.lparams)
dead_code_elimination(graph, uses, defs)
register_propagation(graph, uses, defs)
# After the DCE pass, some nodes may be empty, so we can simplify the
# graph to delete these nodes.
# We start by restructuring the the graph by spliting the conditional
# nodes into a pre-header and a header part.
# We then simplify the graph by merging multiple statement nodes into
# a single statement node when possible. This also delete empty nodes.
graph.split_if_nodes()
graph.simplify()
graph.reset_rpo()
idoms = immediate_dominator(graph)
identify_structures(graph, idoms)
if 0:
util.create_png(self.basic_blocks, graph, '/tmp/structured')
# 'dad_graphs/structured')
self.writer = Writer(graph, self)
self.writer.write_method()
def show_source(self):
if self.writer is not None:
print self.writer
def get_source(self):
if self.writer is not None:
return '%s' % self.writer
return ''
def __repr__(self):
return 'Method %s' % self.name
def __init__(self, vg):
self.vg = vg
wri_big = Writer(vg.oled, pacifico_35, verbose=False)
wri_small = Writer(vg.oled, font6, verbose=False)
wri_fa = Writer(vg.oled, fa_30, verbose=False)
self.lbl_status = Label(wri_small, 0, 0, 127)
self.cur_scr = -1
self.scr_objs = [
[
# km/h screen
# dynamic objects have a call back (cb) lambda
Label(wri_big,
16,
0,
'22.2',
right=True,
cb=lambda: '{:0.1f}'.format(self.vg.getSpeed())),
# static objects have None
Label(wri_small, 37, 90, 'km/h')
],
[
# voltage screen
Label(wri_big,
16,
0,
'2222',
right=True,
cb=lambda: str(self.vg.ina.getV())),
Label(wri_small, 37, 100, 'mV')
],
[
# current screen
Label(wri_big,
16,
0,
'-222',
right=True,
cb=lambda: '{:4d}'.format(self.vg.ina.getI())),
Label(wri_small, 37, 100, 'mA')
],
[
# power screen
Label(wri_big,
16,
0,
'-2.22',
right=True,
cb=lambda: '{:0.2f}'.format(self.vg.ina.getV() * self.vg.
ina.getI() / 1000000)),
Label(wri_small, 37, 110, 'W')
],
[
# Hotspot on off screen
Label(wri_fa,
22,
8,
'',
cb=lambda: '' if self.vg.isAP else ''),
Label(wri_small, 22, 65, 'AP mode'),
Label(wri_small, 38, 65, 'velogen')
]
]
refresh(self.vg.oled, True)
def dual(use_spi=False, soft=True):
ssd0 = setup(False, soft) # I2C display
ssd1 = setup(True, False) # SPI instance
Writer.set_textpos(ssd0, 0, 0) # In case previous tests have altered it
wri0 = Writer(ssd0, small, verbose=False)
wri0.set_clip(False, False, False)
Writer.set_textpos(ssd1, 0, 0) # In case previous tests have altered it
wri1 = Writer(ssd1, small, verbose=False)
wri1.set_clip(False, False, False)
nfields = []
dy = small.height() + 6
col = 15
for n, wri in enumerate((wri0, wri1)):
nfields.append([])
y = 2
for txt in ('X:', 'Y:', 'Z:'):
Label(wri, y, 0, txt)
nfields[n].append(Label(wri, y, col, wri.stringlen('99.99'), True))
y += dy
for _ in range(10):
for n, wri in enumerate((wri0, wri1)):
for field in nfields[n]:
value = int.from_bytes(uos.urandom(3),'little')/167772
field.value('{:5.2f}'.format(value))
wri.device.show()
utime.sleep(1)
for wri in (wri0, wri1):
Label(wri, 0, 64, ' DONE ', True)
wri.device.show()
import time
from tkinter import *
import tkinter.filedialog as fd
from tkinter import messagebox
import os
import sys
import argparse
from pathlib import Path
from random import uniform
from writer import Writer
FILE_NAME = 'text.txt'
DEFAULT_INTERVAL = 0
DEFAULT_TIMEOUT = 5
writer = Writer(DEFAULT_INTERVAL)
def createParser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument('--source', '-s', type=Path, help='Путь к файлу с текстом, из которого нужно переписать.')
parser.add_argument('--clipboard', '-clip', type=bool, help='Переписать ли из буффера обмена.')
parser.add_argument('--interval_start', '-int_s', type=float, help='Начало интервала печати между каждым символом.', default=DEFAULT_INTERVAL)
parser.add_argument('--interval_end', '-int_e', type=float, help='Конец интервала печати между каждым символом.', default=DEFAULT_INTERVAL)
parser.add_argument('--timeout', '-t', type=float, help='Задержка перед началом печати.', default=DEFAULT_TIMEOUT)
return parser
def choose_file():
global FILE_NAME
def tabs(use_spi=False, soft=True):
ssd = setup(use_spi, soft) # Create a display instance
Writer.set_textpos(ssd, 0, 0) # In case previous tests have altered it
wri = Writer(ssd, fixed, verbose=False)
wri.set_clip(False, False, False) # Char wrap
wri.printstring('1\t2\n')
wri.printstring('111\t22\n')
wri.printstring('1111\t1')
ssd.show()
def __init__(self, i2c):
self.ssd1306 = SSD1306_I2C(128, 64, i2c)
self.writer = Writer(self.ssd1306, freesans20, verbose=False)
self.writer.set_clip(True, True)
def fonts(use_spi=False, soft=True):
ssd = setup(use_spi, soft) # Create a display instance
Writer.set_textpos(ssd, 0, 0) # In case previous tests have altered it
wri = Writer(ssd, freesans20, verbose=False)
wri.set_clip(False, False, False) # Char wrap
wri_f = Writer(ssd, small, verbose=False)
wri_f.set_clip(False, False, False) # Char wrap
wri_f.printstring('Sunday\n')
wri.printstring('12 Aug 2018\n')
wri.printstring('10.30am')
ssd.show()
class PushdownAutomaton(Automaton):
"""
A deterministic pushdown automaton with epsilon transitions
behavior simulator.
"""
def __init__(self, states, symbols, stacksymbols, acceptableStates, q0, z0,
transitions):
"""
Stores the automaton definition.
states: States automaton can be in
symbols: Input symbols that can be feed to the automaton entry
stacksymbols: symbols that can be stored on the automatons stack
acceptableStates: States in which the automaton should be when the
simulation ends if the automaton accepts the
char sequence fed to it
q0: Start state
z0: Start stack symbols
tranistions: List of tranisitions the automaton can make
"""
super(PushdownAutomaton,
self).__init__(states, symbols, acceptableStates, q0,
transitions)
self.stacksymbols = stacksymbols
self.z0 = z0
self.stack = []
self.simulationLog = ""
self.writer = Writer(self)
@staticmethod
def parseAutomatonDefinition(definition):
"""
Parses the automaton definition (if the definition is correctly given)
and returns a class instance.
"""
if len(definition) < 8:
return None
pushdownAutomaton = \
PushdownAutomaton(definition[1], definition[2], definition[3]\
, definition[4], definition[5], definition[6], definition[7:])
return pushdownAutomaton
def simulate(self, input):
"""
Simulates the behavior of the automaton for a given input.
"""
#print and log the information about the start of simulation
self.writer.writeSimulationIntro()
#initialize the automaton according to the definition
currentState = self.q0
self.stack = []
self.stack.extend(["$", self.z0])
#print and log the state of the automaton on the start of simulation
self.writer.writeCurrentState(currentState)
currentState = self.makeEpsilonTransition(currentState)
#simulate behaviour for every character in the given input
for index, character in enumerate(input):
currentState = self.makeTransition(currentState, character)
if currentState is None:
#end the simulation with an unsuccesful annotation
self.writer.writeSimulationEnd(currentState, False)
return self.simulationLog
#don't make epsilon tranisions if the automaton is in an acceptable state
#and has digested all the characters in the given sequence
if index + 1 < len(
input) or currentState not in self.acceptableStates:
currentState = self.makeEpsilonTransition(currentState)
#if the automaton digests all characters without stopping end the
#simulation with successfull annotation
self.writer.writeSimulationEnd(currentState, True)
return self.simulationLog
def makeTransition(self, currentState, character, isEpsilon=False):
"""
Makes a tranisition given a current state and an input character.
Uses different behaviour if the transition is an epsilon tranisition.
Returns the next state of the automaton (None if the transition can't
be made).
"""
nextState = None
addToStack = None #character to be added to the stack
poppedCharacter = "$"
#get the character that is currently on top of stack
try:
poppedCharacter = self.stack.pop()
except Exception as e:
print("EXCEPTION:", currentState, character, isEpsilon)
#concatinate the parameters to get a key in the tranitions dictionary
currentConfiguration = currentState + "," + character + "," + poppedCharacter
#if the key is contained in the dictionary
if currentConfiguration in self.transitions:
#proccess the transition
nextState = self.transitions.get(currentConfiguration)[0]
addToStack = list(self.transitions.get(currentConfiguration)[1])
#add the characters specified by the transition to the top of stack
for i in range(len(addToStack) - 1, -1, -1):
if addToStack[i] == "$":
break
self.stack.append(addToStack[i])
#print and log the current state of the automaton
self.writer.writeCurrentState(nextState, currentState, character)
#if the transition is not possible and the tried transition was an
#epsilon transition, return the popped character to the stack
elif isEpsilon:
self.stack.append(poppedCharacter)
return nextState
def makeEpsilonTransition(self, currentState):
"""
Makes epsilon transition from a given current state until the
epsilon tranistion can't be made or the state transitioned to
is an acceptable state of the automaton.
"""
nextState = self.makeTransition(currentState, "$", True)
#if the epsilon transition cannot be made
if nextState is None:
#return the last state automaton was found in before the transition
return currentState
#return the current state if it is an acceptable state
if nextState in self.acceptableStates:
return nextState
#otherwise try to make a new epsilon transition recursively
return self.makeEpsilonTransition(nextState)
def getStackString(self):
"""
Returns a string representation of the stack
"""
return "".join(self.stack[:0:-1])
def __str__(self):
return ('Automaton definition (pushdown automaton):\n'
'States: {automaton.states}\n'
'symboles: {automaton.symbols}\n'
'Stack symboles: {automaton.stacksymbols}\n'
'Acceptable states: {automaton.acceptableStates}\n'
'Starting state: {automaton.q0}\n'
'Starting stack: {automaton.z0}\n')\
.format(automaton = self)
def rjust(use_spi=False, soft=True):
ssd = setup(use_spi, soft) # Create a display instance
Writer.set_textpos(ssd, 0, 0) # Previous tests may have altered it
wri = Writer(ssd, freesans20, verbose=False)
wri.set_clip(False, False, False) # Char wrap
my_str = 'Sunday\n'
l = wri.stringlen(my_str)
Writer.set_textpos(ssd, col = WIDTH - l)
wri.printstring(my_str)
my_str = '12 Aug 2018\n'
l = wri.stringlen(my_str)
Writer.set_textpos(ssd, col = WIDTH - l)
wri.printstring(my_str)
my_str = '10.30am'
l = wri.stringlen(my_str)
Writer.set_textpos(ssd, col = WIDTH - l)
wri.printstring(my_str)
ssd.show()
class EpsilonNFA(Automaton):
"""
An non-deterministic finite automaton with epsilon transitions
behavior simulator.
"""
def __init__(self, states, symbols, acceptableStates, q0, transitions):
super(EpsilonNFA, self).__init__(states, symbols, acceptableStates, q0,
transitions)
self.simulationLog = ""
self.writer = Writer(self)
@staticmethod
def parseAutomatonDefinition(definition):
"""
Parses the automaton definition (if the definition is correctly given)
and returns a class instance.
"""
if len(definition) < 6:
return None
epsilonNFA = \
EpsilonNFA(definition[1], definition[2], definition[3]\
, definition[4], definition[5:])
return epsilonNFA
def simulate(self, input):
"""
Simulates the behavior of the automaton for a given
character sequence (input).
"""
#initialize simulation with starting state and an epsilon transition
self.writer.writeSimulationIntro()
currentStates = {self.q0}
currentStates = currentStates.union(
self.makeEpsilonTransition(currentStates))
self.writer.writeCurrentStates(currentStates)
for character in input:
nextStates = self.makeTransition(currentStates, character)
nextStates = nextStates.union(
self.makeEpsilonTransition(nextStates))
currentStates.clear()
currentStates = currentStates.union(nextStates)
#if transitions did not result in any new states, add hashtag
if not currentStates:
currentStates.add('#')
self.writer.writeCurrentStates(currentStates)
self.writer.writeSimulationEnd(currentStates, success=True)
return self.simulationLog
def makeTransition(self, currentStates, character, isEpsilon=False):
"""
Makes a tranisition given a current state and an input character.
Uses different behavior if the transition is an epsilon tranisition.
Returns the next states of the automaton (None if the transition can't
be made).
"""
nextStates = set()
for state in currentStates:
currentConfiguration = state + "," + character
if currentConfiguration in self.transitions:
newStates = self.transitions.get(currentConfiguration)
nextStates = nextStates.union(newStates)
self.writer.writeTransition(state, character, newStates,
isEpsilon)
nextStates.discard('#') #hashtas will be added later if needed
return nextStates
def makeEpsilonTransition(self, currentStates):
"""
Makes a transition with an epsilon transition character recursively
until the transition cannot be made anymore.
Original call af the function will return all new states gathered
in upper described recursive search.
"""
nextStates = self.makeTransition(currentStates, '$', True)
#if epsilon transition did not occur or it started an infitine loop
if not nextStates or nextStates == currentStates:
return currentStates #end recursion
else:
return nextStates.union(self.makeEpsilonTransition(nextStates))
def __str__(self):
return ('Automaton definition '
'(non-finite automaton with epsilon transitions automaton):\n'
'States: {automaton.states}\n'
'symboles: {automaton.symbols}\n'
'Acceptable states: {automaton.acceptableStates}\n'
'Starting state: {automaton.q0}\n')\
.format(automaton = self)
class DvMethod():
def __init__(self, methanalysis):
self.method = methanalysis.get_method()
self.metha = methanalysis
self.name = self.method.get_name()
self.lparams = []
self.basic_blocks = [bb for bb in methanalysis.basic_blocks.get()]
self.var_to_name = {}
self.writer = None
access = self.method.get_access_flags()
self.access = [
flag for flag in util.ACCESS_FLAGS_METHODS if flag & access
]
desc = self.method.get_descriptor()
self.type = util.get_type(desc.split(')')[-1])
self.params_type = util.get_params_type(desc)
self.exceptions = methanalysis.exceptions.exceptions
code = self.method.get_code()
if code is None:
util.log(
'No code : %s %s' % (self.name, self.method.get_class_name()),
'debug')
else:
start = code.registers_size - code.ins_size
if 0x8 not in self.access:
self.var_to_name[start] = ThisParam(start, self.name)
self.lparams.append(start)
start += 1
num_param = 0
for ptype in self.params_type:
param = start + num_param
self.lparams.append(param)
self.var_to_name.setdefault(param, Param(param, ptype))
num_param += util.get_type_size(ptype)
def process(self):
util.log('METHOD : %s' % self.name, 'debug')
if 0:
from androguard.core import bytecode
bytecode.method2png('/tmp/graphs/%s#%s.png' % \
(self.method.get_class_name().split('/')[-1][:-1], self.name),
self.metha)
graph = construct(self.basic_blocks, self.var_to_name, self.exceptions)
self.graph = graph
if graph is None:
return
if 0:
util.create_png(self.basic_blocks, graph, '/tmp/blocks')
#'dad_graphs/blocks')
defs, uses = build_def_use(graph, self.lparams)
dead_code_elimination(graph, uses, defs)
register_propagation(graph, uses, defs)
# After the DCE pass, some nodes may be empty, so we can simplify the
# graph to delete these nodes.
# We start by restructuring the the graph by spliting the conditional
# nodes into a pre-header and a header part.
# We then simplify the graph by merging multiple statement nodes into
# a single statement node when possible. This also delete empty nodes.
graph.split_if_nodes()
graph.simplify()
graph.reset_rpo()
idoms = immediate_dominator(graph)
identify_structures(graph, idoms)
if 0:
util.create_png(self.basic_blocks, graph, '/tmp/structured')
# 'dad_graphs/structured')
self.writer = Writer(graph, self)
self.writer.write_method()
def show_source(self):
if self.writer is not None:
print self.writer
def get_source(self):
if self.writer is not None:
return '%s' % self.writer
return ''
def __repr__(self):
return 'Method %s' % self.name
def crawling(self, category_name):
# Multi Process PID
print(category_name + " PID: " + str(os.getpid()))
writer = Writer(category_name=category_name, date=self.date)
# 기사 URL 형식
url = "http://news.naver.com/main/list.nhn?mode=LSD&mid=sec&sid1=" + str(
self.categories.get(category_name)) + "&date="
# start_year년 start_month월 ~ end_year의 end_month 날짜까지 기사를 수집합니다.
day_urls = self.make_news_page_url(url, self.date['start_year'], self.date['end_year'],
self.date['start_month'], self.date['end_month'], self.date['start_day'],
self.date['end_day'])
print(category_name + " Urls are generated")
print("The crawler starts")
for URL in day_urls:
regex = re.compile("date=(\d+)")
news_date = regex.findall(URL)[0]
request = self.get_url_data(URL)
document = BeautifulSoup(request.content, 'html.parser')
# html - newsflash_body - type06_headline, type06
# 각 페이지에 있는 기사들 가져오기
post_temp = document.select('.newsflash_body .type06_headline li dl')
post_temp.extend(document.select('.newsflash_body .type06 li dl'))
# 각 페이지에 있는 기사들의 url 저장
post = []
for line in post_temp:
post.append(line.a.get('href')) # 해당되는 page에서 모든 기사들의 URL을 post 리스트에 넣음
del post_temp
for content_url in post: # 기사 URL
# 크롤링 대기 시간
sleep(0.01)
# 기사 HTML 가져옴
request_content = self.get_url_data(content_url)
try:
document_content = BeautifulSoup(request_content.content, 'html.parser')
except:
continue
try:
# 기사 제목 가져옴
tag_headline = document_content.find_all('h3', {'id': 'articleTitle'}, {'class': 'tts_head'})
text_headline = '' # 뉴스 기사 제목 초기화
text_headline = text_headline + ArticleParser.clear_headline(
str(tag_headline[0].find_all(text=True)))
if not text_headline: # 공백일 경우 기사 제외 처리
continue
# 기사 본문 가져옴
tag_content = document_content.find_all('div', {'id': 'articleBodyContents'})
text_sentence = '' # 뉴스 기사 본문 초기화
text_sentence = text_sentence + ArticleParser.clear_content(str(tag_content[0].find_all(text=True)))
if not text_sentence: # 공백일 경우 기사 제외 처리
continue
# # 기사 언론사 가져옴
# tag_company = document_content.find_all('meta', {'property': 'me2:category1'})
# text_company = '' # 언론사 초기화
# text_company = text_company + str(tag_company[0].get('content'))
# if not text_company: # 공백일 경우 기사 제외 처리
# continue
#
# # CSV 작성
# wcsv = writer.get_writer_csv()
# wcsv.writerow([news_date, category_name, text_company, text_headline, text_sentence, content_url])
#
# del text_company, text_sentence, text_headline
# del tag_company
# del tag_content, tag_headline
# del request_content, document_content
# CSV 작성
wcsv = writer.get_writer_csv()
wcsv.writerow([text_sentence])
del text_headline
del tag_headline
del request_content, document_content
except Exception as ex: # UnicodeEncodeError ..
# wcsv.writerow([ex, content_url])
del request_content, document_content
pass
writer.close()
from ssd1306 import SSD1306_SPI
WIDTH = const(128)
HEIGHT = const(64)
pdc = machine.Pin(27, machine.Pin.OUT)
pcs = machine.Pin(26, machine.Pin.OUT)
sck_pin = machine.Pin(19, machine.Pin.OUT)
mosi_pin = machine.Pin(23, machine.Pin.OUT)
miso_pin = machine.Pin(25, machine.Pin.IN)
prst = machine.Pin(18, machine.Pin.OUT)
spi = machine.SPI(1,
baudrate=1000000,
sck=sck_pin,
mosi=mosi_pin,
miso=miso_pin)
ssd = SSD1306_SPI(WIDTH, HEIGHT, spi, pdc, prst, pcs)
import freesans20
from writer import Writer
wri2 = Writer(ssd, freesans20, verbose=True)
Writer.set_clip(True, True)
Writer.set_textpos(0, 0)
wri2.printstring('MicroPython\n')
ssd.show()
def combo_write():
reader = Reader()
writer = Writer(reader)
writer.write_unshown()
def test():
pcs = machine.Pin('Y5', machine.Pin.OUT_PP, value=0) # Active high
spi = machine.SPI(2)
ssd = SHARP(spi, pcs)
rhs = ssd.width - 1
ssd.line(rhs - 80, 0, rhs, 80, 1)
square_side = 40
ssd.fill_rect(rhs - square_side, 0, square_side, square_side, 1)
wri = Writer(ssd, freesans20)
Writer.set_textpos(ssd, 0, 0) # verbose = False to suppress console output
wri.printstring('Sunday\n')
wri.printstring('12 Aug 2018\n')
wri.printstring('10.30am')
wri = Writer(ssd, arial_50)
Writer.set_textpos(ssd, 0, 120)
wri.printstring('10:30')
ssd.show()
def handle_command(command, details, channel, respond = True):
"""
Receives commands directed at the bot and determines if they
are valid commands. If so, then acts on the commands. If not,
returns back what it needs for clarification.
"""
response = False
if command == "learn":
learner = Learner()
response = learner.learn(details[0], " ".join(details[1:]))
elif command == "unlearn":
learner = Learner()
content = None
if len(details) > 1:
content = " ".join(details[1:])
response = learner.unlearn(details[0], content)
elif command == "commands":
learner = Learner()
response = learner.list_commands()
elif command == "list":
learner = Learner()
response = learner.list(details[0])
elif command == "cowsay":
out = subprocess.check_output(['cowsay', " ".join(details)])
response = "```" + out + "```"
elif command == "meme":
memer = Memer()
if not details or len(details) == 0:
response = memer.list_templates()
else:
template = details.pop(0).strip()
parts = [x.strip() for x in " ".join(details).split(",")]
top = parts[0] if len(parts) > 0 else None
bottom = parts[1] if len(parts) > 1 else None
response = memer.get_meme(template, top, bottom)
elif command == "hostname":
response = "slurms coming to you live from: `%s (%s)`" % (subprocess.check_output("hostname -A", shell=True).strip(), subprocess.check_output("hostname -i", shell=True).strip())
elif command == "write":
writer = Writer()
response = writer.get_writing(" ".join(details))
elif command == "imglearn":
learner = Learner()
imgur = Imgur()
image_url = imgur.save_from_url(" ".join(details[1:]))
response = learner.learn(details[0], image_url)
elif command == "++" or command == "endorse":
plusser = Plusser()
reason = ""
if len(details) > 1:
reason = " ".join(details[1:])
response = plusser.plus(details[0], reason)
elif command == "plusses":
plusser = Plusser()
response = plusser.get(details[0])
elif command == "leaders" or command == "leader_board":
plusser = Plusser()
response = plusser.leader_board()
elif command == "monthly_leaders" or command == "monthly_leader_board":
plusser = Plusser()
months_ago = 0
if details and len(details) > 0:
months_ago = details[0]
response = plusser.monthly_leader_board(months_ago)
elif command == "youtube":
query = " ".join(details)
videos = youtube.youtube_search(query)
if len(videos) > 0:
response = videos[-1]
else:
response = "sorry, couldnt find any videos for %s" % query
elif command == "echo":
response = " ".join(details)
elif command == "pipe":
pipe(command, details, channel)
elif command == "doom":
doom = Doom()
response = doom.doom(details)
else:
"""
see if a randomly entered command is something that was previously learned
"""
learner = Learner()
response = learner.get(command)
if response and respond:
slack_client.api_call("chat.postMessage", channel=channel,
text=response, as_user=True)
elif not respond:
return response
UNIX_TS_CONVERT = 946684800 # necessary to convert 'epoch since 01-01-2000' to 'epoch since 01-01-1970'
SCL_PIN = 4 # D2
SDA_PIN = 0 # D3
DHT_PIN = 12 # D6
LED1_PIN = 2 # D4
# LED2_PIN = 16 # D0
LED1 = machine.Pin(LED1_PIN, machine.Pin.OUT)
# LED2 = machine.Pin(LED2_PIN, machine.Pin.OUT)
RTC = machine.RTC()
DHT = dht.DHT22(machine.Pin(DHT_PIN))
WLAN = network.WLAN(network.STA_IF)
WLAN_AP = network.WLAN(network.AP_IF)
Token = ""
I2C = machine.I2C(scl=machine.Pin(SCL_PIN), sda=machine.Pin(SDA_PIN))
OLED = ssd1306.SSD1306_I2C(128, 64, I2C, 0x3c)
WRI = Writer(OLED, font_sensor_35)
WRI_BT = Writer(OLED, font_sensor_20)
with open('env.json') as fp:
SECRETS = ujson.loads(fp.read())
fp.close()
SENSOR_ID = SECRETS['sensor']['name'].upper()
client = MQTTClient(client_id=SENSOR_ID,
server=SECRETS['mqtt']['host'],
port=SECRETS['mqtt']['port'],
user=SECRETS['mqtt']['user'],
password=SECRETS['mqtt']['pass'])
WLAN_AP.active(False)
def main():
temp = float('NaN')
def update_main_area(msg):
Writer.set_textpos(OLED, row=0, col=0)
WRI.printstring(msg)
OLED.show()
)
# Create the plotter, this object will render all plots to the notebook.
plotter = Plotter(
datasets=datasets,
z_samples=z_samples,
test_s=0.9,
train_s=0.2,
zline_s=5,
zline_skip=datasets.x_test.shape[0] // 50,
)
# Create the writer, this object will write information that can be browsed using tensorboard.
writer = Writer(
datasets_target_function=datasets.target_function_desc,
trainer_params=trainer.params_desc,
datasets_params=datasets.params_desc,
)
# Create the tester, this object will run goal 1, goal 2 and emd tests.
tester = Tester(
experiment=experiment,
z_samples=z_samples,
datasets=datasets,
plotter=plotter,
writer=writer,
model=model,
device=device,
)
# %%