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text (string)
return "\n".join(new_body)
date_format = '%Y-%m-%d-%H-%M-%S' if has_time else '%Y-%m-%d'
help='Write additional debugging information '
epoch_one, epoch_two,
assert errback is not None
delattr(self, '_lazy_sftp')
raise ValueError("Error in config file.")
assert issubclass(initialState.dtype.type, np.integer) and initialState.ndim==1, "initialState %r is not a one-dimensional integer numpy array" % initialState
del ERROR_CLASS_MAP[klass]
nos1 = ib1[0] * ps1 + os1 # real offset for image 1
b = np.append(b, [self.get_single_score(p, centroids=c, sd=sd)])
opts['file_roots'] = {'base': [syspaths.BASE_FILE_ROOTS_DIR]}
display_name = '.'.join(components[1:])
bSizer1 = wx.StaticBoxSizer(wx.StaticBox(self.panel, wx.ID_ANY, "Import data to working directory"), wx.HORIZONTAL)
print("{:10} {}".format(*c))
self._ar_keyword = self._format_keyword(split_line[1])
options[constants.LABEL_CONFIG],
sentence = self._sentence(random.randint(5, 16))
items.append(_ETreeXmlToObject.fill_instance_element(item_element, item_type))
self.add(servicegroup)
return self.pin_mask(color, square) != BB_ALL
return (out,err.value)
cell.set_property('foreground', "white")
ax.text(xy[0],xy[1],txt,fontdict=fontdict,color=clr,zorder=zorder)
stamp, pos = load_le32(buf, pos)
digits = list(map(int, string))
response = requests.post(url, data=data, timeout=timeout)
subplot(2, 3, 6)
self.error(403)
vote_update["count"] = VOTE_COUNT
t = Token
indices = np.array([], dtype=np.uint64)
state_m = model if isinstance(model.core_element, State) else model.parent
newivmlist.append(file[1])
install(package, execute=not opts.dry_run)
labels = [tr('Global (%s)'), tr('Do not report'), tr('Custom')]
body.set('order', kwargs['order'])
uninstallable=uninstallable,
subscription=full_subscription, body={'ackIds': ack_ids}
aggregate = list()
result._bq_source_format = 'NEWLINE_DELIMITED_JSON'
time_step = 0
bk.write("mode: midWayWordInSentence(+SID,-WID).\n")
array([ 11.])
x = tf.layers.conv2d(x, output_filters, (3, 3), padding="SAME", name="conv2")
lb = pst.parameter_data.parlbnd.copy()
r = _apply(ramap[k], v, status=status, path=path+[k])
new_panel['genes'] = new_genes
url = self._url + "/lengths"
magic_method_codes=MagRec["magic_method_codes"].split(":")
path = try_
route_map : folium.folium.Map
r = _call_cache[key]
_return_freqs=False,
ttactor.SetCamera(followcam)
dW1 = np.dot(dh.T, epx)
regionfile : str
self.msg("Please answer y or n.")
xyz_x = 0.0
count_no_shape_fit = 0
time_array, [channel1, channel2]
return np.sum(counts > 1) / float(counts.shape[0])
started = super(Node, self).start(timeout=timeout)
type_ = type_.lower()
hobj.axon = [h.Section(name='axon[0]'), h.Section(name='axon[1]')]
['author', 'description', 'title'],
entity.put_annotation(key, value)
raise Exception("Unable to find child node %s" % current_name)
_logger.debug("Received command %s", message)
check_args = ['checktrans']
tf, note = tfi.create_db_entry(release.comment)
fmtstr = '[generate2] executing {} {} tasks using {} {} procs'
raise BinAsciiError(str(e))
priority = GLib.PRIORITY_LOW
self.modifiers[modifier] = False
counts = sorted(cell_barcode_counts.values(), reverse=True)
self._writer.write((self._username + '\n').encode('ascii'))
relative_jimage = np.subtract(to_jimage, jimage)
p = subprocess.Popen(scp_command)
key_columns_array = []
self._permutations = vars['permutations']
dc = ioloop.DelayedCallback(purge, self.registration_timeout, self.loop)
logger.debug("Not a valid JID: {0!r}".format(name))
receiver=transceiver.receiver,
analysis_id = "AN{}".format(source.zfill(6))
ad_path = os.path.join(neurommsig_excel_dir, 'alzheimers', 'alzheimers.xlsx')
raise ValueError("Can't write to file object %r" % fileobj)
return recipes
outfiles.append(Specout)
[ float(only_warning_count), 'warning', 'had warnings' ],
raise NameError('Unrecognised output request!')
self.mgrremove.execute(conn, migration_rqst)
self[key] = OrderedSet(self[key])
date_tokens = dictionary.split(date_string)
remove_headers = ('Content-Length',)
filename, fileext = os.path.splitext(os.path.basename(rawfilename))
errmsg = "'{}'; this pipeline can only deal with .bam, .fastq, " \
letter
chars.append(_escape_char(c, escape_char))
return rnPattern

Dataset of single lines of Python code taken from the CodeSearchNet dataset.

Context

This dataset allows checking the validity of Variational-Autoencoder latent spaces by testing what percentage of random/intermediate latent points can be greedily decoded into valid Python code.

Content

Each row has a parsable line of source code. {'text': '{python source code line}'}

Most lines are < 100 characters while all are under 125 characters.

Contains 2.6 million lines.

All code is in parsable into a python3 ast.

Models trained or fine-tuned on Fraser/python-lines