sequence stringlengths 1.19k 35k | code stringlengths 75 8.58k |
|---|---|
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'filter_sorted_apps'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'va... | def filter_sorted_apps(admin_apps, request):
sorted_apps = []
for orig_app_spec in appsettings.DASHBOARD_SORTED_APPS:
app_spec = orig_app_spec.copy()
app_spec['models'] = _build_app_models(
request, admin_apps, app_spec['models'], ensure_all_models=True
)
if app_spec[... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'render_stats'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def render_stats(stats, sort, format):
output = StdoutWrapper()
if hasattr(stats, "stream"):
stats.stream = output.stream
stats.sort_stats(*sort)
getattr(stats, format)()
return output.stream |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'render_queries'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def render_queries(queries, sort):
output = StringIO()
if sort == 'order':
print >>output, " time query"
for query in queries:
print >>output, " %8s %s" % (query["time"], query["sql"])
return output
if sort == 'time':
def sorter(x, y):
return cmp(x... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'process_request'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value... | def process_request(self, request):
def unpickle(params):
stats = unpickle_stats(b64decode(params.get('stats', '')))
queries = cPickle.loads(b64decode(params.get('queries', '')))
return stats, queries
if request.method != 'GET' and \
not (request.META.get(
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'dedupe_and_sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def dedupe_and_sort(sequence, first=None, last=None):
first = first or []
last = last or []
new_sequence = [i for i in first if i in sequence]
for item in sequence:
if item not in new_sequence and item not in last:
new_sequence.append(item)
new_sequence.extend([i for i in last if... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sortedSemver'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': ... | def sortedSemver(versions, sort="asc"):
if versions and isinstance(versions, (list, tuple)):
if PY2:
return sorted(versions, cmp=semver.compare, reverse=True if sort.upper() == "DESC" else False)
else:
from functools import cmp_to_key
return sorted(versions, key=c... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8', '12']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'unique'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': ... | def unique(arr, tolerance=1e-6) -> np.ndarray:
arr = sorted(arr.flatten())
unique = []
while len(arr) > 0:
current = arr[0]
lis = [xi for xi in arr if np.abs(current - xi) < tolerance]
arr = [xi for xi in arr if not np.abs(lis[0] - xi) < tolerance]
xi_lis_average = sum(lis) /... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_get_cache_key'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'se... | def _get_cache_key(self):
keys = list(self.params.keys())
keys.sort()
cache_key = str()
for key in keys:
if key != "api_sig" and key != "api_key" and key != "sk":
cache_key += key + self.params[key]
return hashlib.sha1(cache_key.encode("utf-8")).hexdig... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '25']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'fast_float'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '11', '14', '19', '22']}; {'id': '4', 'type': 'identifier', 'c... | def fast_float(
x,
key=lambda x: x,
nan=None,
_uni=unicodedata.numeric,
_nan_inf=NAN_INF,
_first_char=POTENTIAL_FIRST_CHAR,
):
if x[0] in _first_char or x.lstrip()[:3] in _nan_inf:
try:
x = float(x)
return nan if nan is not None and x != x else x
excep... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '19']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'fast_int'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '11', '16']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def fast_int(
x,
key=lambda x: x,
_uni=unicodedata.digit,
_first_char=POTENTIAL_FIRST_CHAR,
):
if x[0] in _first_char:
try:
return long(x)
except ValueError:
try:
return _uni(x, key(x)) if len(x) == 1 else key(x)
except TypeError:
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_and_print_entries'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [],... | def sort_and_print_entries(entries, args):
is_float = args.number_type in ("float", "real", "f", "r")
signed = args.signed or args.number_type in ("real", "r")
alg = (
natsort.ns.FLOAT * is_float
| natsort.ns.SIGNED * signed
| natsort.ns.NOEXP * (not args.exp)
| natsort.ns.PA... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'natsort_key'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8']}; {'id': '4', 'type': 'identifier', 'children':... | def natsort_key(val, key, string_func, bytes_func, num_func):
if key is not None:
val = key(val)
try:
return string_func(val)
except (TypeError, AttributeError):
if type(val) in (bytes,):
return bytes_func(val)
try:
return tuple(
natsor... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'parse_number_factory'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': ... | def parse_number_factory(alg, sep, pre_sep):
nan_replace = float("+inf") if alg & ns.NANLAST else float("-inf")
def func(val, _nan_replace=nan_replace, _sep=sep):
return _sep, _nan_replace if val != val else val
if alg & ns.PATH and alg & ns.UNGROUPLETTERS and alg & ns.LOCALEALPHA:
return la... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '12']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'natsort_keygen'}, {'id': '3', 'type': 'parameters', 'children': ['4', '7']}; {'id': '4', 'type': 'default_parameter', 'children': ['5... | def natsort_keygen(key=None, alg=ns.DEFAULT):
try:
ns.DEFAULT | alg
except TypeError:
msg = "natsort_keygen: 'alg' argument must be from the enum 'ns'"
raise ValueError(msg + ", got {}".format(py23_str(alg)))
if alg & ns.LOCALEALPHA and natsort.compat.locale.dumb_sort():
alg ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '16']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'natsorted'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def natsorted(seq, key=None, reverse=False, alg=ns.DEFAULT):
key = natsort_keygen(key, alg)
return sorted(seq, reverse=reverse, key=key) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '16']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'humansorted'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children': []... | def humansorted(seq, key=None, reverse=False, alg=ns.DEFAULT):
return natsorted(seq, key, reverse, alg | ns.LOCALE) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '16']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'realsorted'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children': [],... | def realsorted(seq, key=None, reverse=False, alg=ns.DEFAULT):
return natsorted(seq, key, reverse, alg | ns.REAL) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '16']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'index_natsorted'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children'... | def index_natsorted(seq, key=None, reverse=False, alg=ns.DEFAULT):
if key is None:
newkey = itemgetter(1)
else:
def newkey(x):
return key(itemgetter(1)(x))
index_seq_pair = [[x, y] for x, y in enumerate(seq)]
index_seq_pair.sort(reverse=reverse, key=natsort_keygen(newkey, alg... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'order_by_index'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'v... | def order_by_index(seq, index, iter=False):
return (seq[i] for i in index) if iter else [seq[i] for i in index] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '21']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'find_records'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12', '15', '18']}; {'id': '4', 'type': 'identifie... | def find_records(self, collection_name, query={}, sort_by=None,
sort_direction=None, start=0, limit=None):
cursor = self._get_collection(collection_name).find(query)
if sort_by is not None:
cursor = self._apply_sort(cursor, sort_by, sort_direction)
cursor = curso... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_apply_sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def _apply_sort(cursor, sort_by, sort_direction):
if sort_direction is not None and sort_direction.lower() == "desc":
sort = pymongo.DESCENDING
else:
sort = pymongo.ASCENDING
return cursor.sort(sort_by, sort) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '26']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_runs'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'chil... | def get_runs(self, sort_by=None, sort_direction=None, start=0, limit=None, query={"type": "and", "filters": []}):
all_run_ids = os.listdir(self.directory)
def run_iterator():
blacklist = set(["_sources"])
for id in all_run_ids:
if id in blacklist:
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '4']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_runs'}, {'id': '3', 'type': 'parameters', 'children': []}; {'id': '4', 'type': 'block', 'children': ['5', '13', '21', '29', '38', ... | def get_runs():
data = current_app.config["data"]
draw = parse_int_arg("draw", 1)
start = parse_int_arg("start", 0)
length = parse_int_arg("length", -1)
length = length if length >= 0 else None
order_column = request.args.get("order[0][column]")
order_dir = request.args.get("order[0][dir]")
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'generate_querystring'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value... | def generate_querystring(params):
if not params:
return None
parts = []
for param, value in sorted(params.items()):
if not isinstance(value, dict):
parts.append(urlencode({param: value}))
else:
for key, sub_value in sorted(value.items()):
compo... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'filter_catalog'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def filter_catalog(catalog, **kwargs):
bright_limit = kwargs.get('bright_limit', 1.00)
max_bright = kwargs.get('max_bright', None)
min_bright = kwargs.get('min_bright', 20)
colname = kwargs.get('colname', 'vegamag')
phot_column = catalog[colname]
num_sources = len(phot_column)
sort_indx = np... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_timeseries'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value... | def sort_timeseries(self, ascending=True):
if ascending and self._sorted:
return
sortorder = 1
if not ascending:
sortorder = -1
self._predefinedSorted = False
self._timeseriesData.sort(key=lambda i: sortorder * i[0])
self._sorted = ascending
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sorted_timeseries'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def sorted_timeseries(self, ascending=True):
sortorder = 1
if not ascending:
sortorder = -1
data = sorted(self._timeseriesData, key=lambda i: sortorder * i[0])
newTS = TimeSeries(self._normalized)
for entry in data:
newTS.add_entry(*entry)
newTS._s... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_calculate_values_to_forecast'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'childre... | def _calculate_values_to_forecast(self, timeSeries):
if self._forecastUntil is None:
return
if not timeSeries.is_sorted():
raise ValueError("timeSeries has to be sorted.")
if not timeSeries.is_normalized():
raise ValueError("timeSeries has to be normalized.")
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_bucket'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12']}; {'id': '4', 'type': 'identifier', 'children'... | def get_bucket(self, bucket, marker=None, max_keys=None, prefix=None):
args = []
if marker is not None:
args.append(("marker", marker))
if max_keys is not None:
args.append(("max-keys", "%d" % (max_keys,)))
if prefix is not None:
args.append(("prefix",... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'findunique'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'l... | def findunique(lst, key):
return sorted(set([item[key.lower()] for item in lst])) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'make_input_dataframe_by_entity'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'c... | def make_input_dataframe_by_entity(tax_benefit_system, nb_persons, nb_groups):
input_dataframe_by_entity = dict()
person_entity = [entity for entity in tax_benefit_system.entities if entity.is_person][0]
person_id = np.arange(nb_persons)
input_dataframe_by_entity = dict()
input_dataframe_by_entity[p... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'multi_sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'r... | def multi_sort(remotes, sort):
exploded_alpha = list()
exploded_semver = list()
if 'alpha' in sort:
alpha_max_len = max(len(r['name']) for r in remotes)
for name in (r['name'] for r in remotes):
exploded_alpha.append([ord(i) for i in name] + [0] * (alpha_max_len - len(name)))
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_params'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 's... | def get_params(self, ctx):
self.params.sort(key=self.custom_sort)
return super(ClickGroup, self).get_params(ctx) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'configure_switch_entries'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'childre... | def configure_switch_entries(self, switch_ip, port_bindings):
prev_vlan = -1
prev_vni = -1
prev_port = None
prev_native_vlan = 0
starttime = time.time()
port_bindings.sort(key=lambda x: (x.port_id, x.vlan_id, x.vni))
self.driver.capture_and_print_timeshot(
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_sort_resources_per_hosting_device'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'childre... | def _sort_resources_per_hosting_device(resources):
hosting_devices = {}
for key in resources.keys():
for r in resources.get(key) or []:
if r.get('hosting_device') is None:
continue
hd_id = r['hosting_device']['id']
hosting_d... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '21']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'compute'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12', '15', '18']}; {'id': '4', 'type': 'identifier', '... | def compute(self, t, yerr=1.123e-12, check_sorted=True,
A=None, U=None, V=None):
t = np.atleast_1d(t)
if check_sorted and np.any(np.diff(t) < 0.0):
raise ValueError("the input coordinates must be sorted")
if check_sorted and len(t.shape) > 1:
raise ValueEr... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'group_dict'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'i... | def group_dict(items, keyfunc):
result = collections.defaultdict(list)
for i in items:
key = keyfunc(i)
result[key].append(i)
return result |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_split_dict'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'dic'}... | def _split_dict(dic):
'''Split dict into sorted keys and values
>>> _split_dict({'b': 2, 'a': 1})
(['a', 'b'], [1, 2])
'''
keys = sorted(dic.keys())
return keys, [dic[k] for k in keys] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'monitor'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'args'}, {... | def monitor(args):
''' Retrieve status of jobs submitted from a given workspace, as a list
of TSV lines sorted by descending order of job submission date'''
r = fapi.list_submissions(args.project, args.workspace)
fapi._check_response_code(r, 200)
statuses = sorted(r.json(), key=lambda k: k['subm... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'tcsort'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'item'}, {'... | def tcsort(item):
return len(item[1]) + sum(tcsort(kv) for kv in item[1].items()) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sortProperties'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def sortProperties(self, properties):
for prop, objects in properties.items():
objects.sort(key=self._globalSortKey)
return sorted(properties, key=lambda p: self.predicate_rank[p]) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'getSubOrder'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'exist... | def getSubOrder(existing):
alpha = list(zip(*sorted(((k, v['rec']['label']) for k, v in existing.items()), key=lambda a: a[1])))[0]
depths = {}
def getDepth(id_):
if id_ in depths:
return depths[id_]
else:
if id_ in existing:
names_above = getDepth(exi... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'insert_sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': '... | def insert_sort(node, target):
sort = target.sort
lang = target.lang
collator = Collator.createInstance(Locale(lang) if lang else Locale())
for child in target.tree:
if collator.compare(sort(child) or '', sort(node) or '') > 0:
child.addprevious(node)
break
else:
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'do_sort_by'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def do_sort_by(self, element, decl, pseudo):
if ',' in decl.value:
css, flags = split(decl.value, ',')
else:
css = decl.value
flags = None
sort = css_to_func(serialize(css), serialize(flags or ''),
self.css_namespaces, self.state['la... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'keys'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, {'id... | def keys(self):
keys = list()
for n in range(len(self)):
key = self.get_value()
if not key in ['', None]: keys.append(key)
return keys |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'set_item'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'value':... | def set_item(self, key, value):
keys = list(self.keys())
if key in keys:
self.set_value(1,keys.index(key),str(value))
else:
self.set_value(0,len(self), str(key))
self.set_value(1,len(self)-1, str(value)) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'insert_ordered'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def insert_ordered(value, array):
index = 0
for n in range(0,len(array)):
if value >= array[n]: index = n+1
array.insert(index, value)
return index |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'remove_dataset'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def remove_dataset(self, dataset=None, **kwargs):
self._kwargs_checks(kwargs)
if dataset is None and not len(kwargs.items()):
raise ValueError("must provide some value to filter for datasets")
kind = kwargs.get('kind', None)
if kind is not None:
if isinstance(kind... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'calls_sorted'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self... | def calls_sorted(self):
def _z(call):
if isinstance(call.z.value, np.ndarray):
return np.mean(call.z.value.flatten())
elif isinstance(call.z.value, float) or isinstance(call.z.value, int):
return call.z.value
else:
return -np.in... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_filter_library_state'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def _filter_library_state(self, items):
if not items:
return items
top_most_item = items[0]
top_most_state_v = top_most_item if isinstance(top_most_item, StateView) else top_most_item.parent
state = top_most_state_v.model.state
global_gui_config = gui_helper_state_mac... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_filter_hovered_items'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children':... | def _filter_hovered_items(self, items, event):
items = self._filter_library_state(items)
if not items:
return items
top_most_item = items[0]
second_top_most_item = items[1] if len(items) > 1 else None
first_state_v = next(filter(lambda item: isinstance(item, (NameView... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'compare_variables'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children... | def compare_variables(tree_model, iter1, iter2, user_data=None):
path1 = tree_model.get_path(iter1)[0]
path2 = tree_model.get_path(iter2)[0]
name1 = tree_model[path1][0]
name2 = tree_model[path2][0]
name1_as_bits = ' '.join(format(ord(x), 'b') for x in name1)
name2_as_bit... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'load_hook_files'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'p... | def load_hook_files(pathname):
global hooks
if sys.version_info[0] > 2 and sys.version_info[1] > 4:
fsglob = sorted(glob.iglob(pathname, recursive=True))
else:
fsglob = sorted(glob.iglob(pathname))
for path in fsglob:
real_path = os.path.realpath(path)
if os.path.dirname(... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def sort(imports, separate=True, import_before_from=True, **classify_kwargs):
if separate:
def classify_func(obj):
return classify_import(
obj.import_statement.module, **classify_kwargs
)
types = ImportType.__all__
else:
def classify_func(obj):
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_recursive'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'da... | def sort_recursive(data):
newdict = {}
for i in data.items():
if type(i[1]) is dict:
newdict[i[0]] = sort_recursive(i[1])
else:
newdict[i[0]] = i[1]
return OrderedDict(sorted(newdict.items(), key=lambda item: (compare_type(type(item[1])), item[0]))) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'names_to_abbreviations'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def names_to_abbreviations(reporters):
names = {}
for reporter_key, data_list in reporters.items():
for data in data_list:
abbrevs = data['editions'].keys()
sort_func = lambda x: str(data['editions'][x]['start']) + x
abbrevs = sorted(abbrevs, key=sort_func)
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'flatten'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'weights'}... | def flatten(weights):
if isinstance(weights, pd.DataFrame):
wts = weights.stack().reset_index()
wts.columns = ["date", "contract", "generic", "weight"]
elif isinstance(weights, dict):
wts = []
for key in sorted(weights.keys()):
wt = weights[key].stack().reset_index()
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'calc_trades'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '9']}; {'id': '4', 'type': 'identifier', 'chil... | def calc_trades(current_contracts, desired_holdings, trade_weights, prices,
multipliers, **kwargs):
if not isinstance(trade_weights, dict):
trade_weights = {"": trade_weights}
generics = []
for key in trade_weights:
generics.extend(trade_weights[key].columns)
if not set(d... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_multiplier'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def get_multiplier(weights, root_generic_multiplier):
if len(root_generic_multiplier) > 1 and not isinstance(weights, dict):
raise ValueError("For multiple generic instruments weights must be a "
"dictionary")
mults = []
intrs = []
for ast, multiplier in root_generic_mul... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'roller'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def roller(timestamps, contract_dates, get_weights, **kwargs):
timestamps = sorted(timestamps)
contract_dates = contract_dates.sort_values()
_check_contract_dates(contract_dates)
weights = []
validate_inputs = True
ts = timestamps[0]
weights.extend(get_weights(ts, contract_dates,
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'strictly_monotonic'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value':... | def strictly_monotonic(bb):
'''
bb is an index array which may have numerous double or triple
occurrences of indices, such as for example the decay_index_pointer.
This method removes all entries <= -, then all dublicates and
finally returns a sorted list of indices.
'''
cc=bb[np.where(bb>=0)... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_sort_shared_logical_disks'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def _sort_shared_logical_disks(logical_disks):
is_shared = (lambda x: True if ('share_physical_disks' in x and
x['share_physical_disks']) else False)
num_of_disks = (lambda x: x['number_of_physical_disks']
if 'number_of_physical_disks' in x else
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_versions'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': []... | def get_versions(cls, bucket, key, desc=True):
filters = [
cls.bucket_id == as_bucket_id(bucket),
cls.key == key,
]
order = cls.created.desc() if desc else cls.created.asc()
return cls.query.filter(*filters).order_by(cls.key, order) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_by'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self... | def sort_by(self, *ids):
files = {str(f_.file_id): f_.key for f_ in self}
self.filesmap = OrderedDict([
(files.get(id_, id_), self[files.get(id_, id_)].dumps())
for id_ in ids
])
self.flush() |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sorted_files_from_bucket'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [... | def sorted_files_from_bucket(bucket, keys=None):
keys = keys or []
total = len(keys)
sortby = dict(zip(keys, range(total)))
values = ObjectVersion.get_by_bucket(bucket).all()
return sorted(values, key=lambda x: sortby.get(x.key, total)) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_data'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'x'... | def sort_data(x, y):
xy = sorted(zip(x, y))
x, y = zip(*xy)
return x, y |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'unsort_vector'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value':... | def unsort_vector(data, indices_of_increasing):
return numpy.array([data[indices_of_increasing.index(i)] for i in range(len(data))]) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_compute_sorted_indices'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'va... | def _compute_sorted_indices(self):
sorted_indices = []
for to_sort in [self.y] + self.x:
data_w_indices = [(val, i) for (i, val) in enumerate(to_sort)]
data_w_indices.sort()
sorted_indices.append([i for val, i in data_w_indices])
self._yi_sorted = sorted_indic... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'specify_data_set'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children'... | def specify_data_set(self, x_input, y_input, sort_data=False):
if sort_data:
xy = sorted(zip(x_input, y_input))
x, y = zip(*xy)
x_input_list = list(x_input)
self._original_index_of_xvalue = [x_input_list.index(xi) for xi in x]
if len(set(self._original... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_aggregate'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children': [],... | def _aggregate(data, norm=True, sort_by='value', keys=None):
'''
Counts the number of occurances of each item in 'data'.
Inputs
data: a list of values.
norm: normalize the resulting counts (as percent)
sort_by: how to sort the retured data. Options are 'value' and 'count'.
Output
a non-r... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'list_files'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'd... | def list_files(d, extension=None):
'''
Lists files in a given directory.
Args:
d (str): Path to a directory.
extension (str): If supplied, only files that contain the
specificied extension will be returned. Default is ``False``,
which returns all files in ``d``.
R... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_collections'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children'... | def get_collections(db, collection=None, prefix=None, suffix=None):
'''
Returns a sorted list of collection names found in ``db``.
Arguments:
db (Database): A pymongo Database object. Can be obtained
with ``get_db``.
collection (str): Name of a collection. If the collection is
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'leaf_nodes'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}... | def leaf_nodes(self):
deps = {item for sublist in self.edges.values() for item in sublist}
return self.nodes - deps |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, {'id... | def sort(self):
while self.nodes:
iterated = False
for node in self.leaf_nodes():
iterated = True
self.prune_node(node)
yield node
if not iterated:
raise CyclicGraphError("Sorting has found a cyclic graph.") |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cycles'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, {'... | def cycles(self):
def walk_node(node, seen):
if node in seen:
yield (node,)
return
seen.add(node)
for edge in self.edges[node]:
for cycle in walk_node(edge, set(seen)):
yield (node,) + cycle
cycles = ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_prepare_imports'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def _prepare_imports(self, dicts):
pseudo_ids = set()
pseudo_matches = {}
prepared = dict(super(OrganizationImporter, self)._prepare_imports(dicts))
for _, data in prepared.items():
parent_id = data.get('parent_id', None) or ''
if parent_id.startswith('~'):
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'initial'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '8']}; {'id': '4', 'type': 'identifier', 'children': [], 'va... | def initial(self, request, *args, **kwargs):
super(FlatMultipleModelMixin, self).initial(request, *args, **kwargs)
assert not (self.sorting_field and self.sorting_fields), \
'{} should either define ``sorting_field`` or ``sorting_fields`` property, not both.' \
.format(self.__cla... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'prepare_sorting_fields'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def prepare_sorting_fields(self):
if self.sorting_parameter_name in self.request.query_params:
self._sorting_fields = [
_.strip() for _ in self.request.query_params.get(self.sorting_parameter_name).split(',')
]
if self._sorting_fields:
self._sorting_fi... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'JSONList'}, {'id': '3', 'type': 'parameters', 'children': ['4', '6']}; {'id': '4', 'type': 'list_splat_pattern', 'children': ['5']}, {... | def JSONList(*args, **kwargs):
type_ = JSON
try:
if kwargs.pop("unique_sorted"):
type_ = JSONUniqueListType
except KeyError:
pass
return MutationList.as_mutable(type_(*args, **kwargs)) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'setup_coords'}, {'id': '3', 'type': 'parameters', 'children': ['4', '7', '10', '13']}; {'id': '4', 'type': 'default_parameter', 'chil... | def setup_coords(arr_names=None, sort=[], dims={}, **kwargs):
try:
return OrderedDict(arr_names)
except (ValueError, TypeError):
pass
if arr_names is None:
arr_names = repeat('arr{0}')
elif isstring(arr_names):
arr_names = repeat(arr_names)
dims = OrderedDict(dims)
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_tdata'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 't_... | def get_tdata(t_format, files):
def median(arr):
return arr.min() + (arr.max() - arr.min())/2
import re
from pandas import Index
t_pattern = t_format
for fmt, patt in t_patterns.items():
t_pattern = t_pattern.replace(fmt, patt)
t_pattern = re.compile(t_pattern)
time = list(ra... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_set_and_filter'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 's... | def _set_and_filter(self):
fmtos = []
seen = set()
for key in self._force:
self._registered_updates.setdefault(key, getattr(self, key).value)
for key, value in chain(
six.iteritems(self._registered_updates),
six.iteritems(
{... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_sorted_by_priority'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [... | def _sorted_by_priority(self, fmtos, changed=None):
def pop_fmto(key):
idx = fmtos_keys.index(key)
del fmtos_keys[idx]
return fmtos.pop(idx)
def get_children(fmto, parents_keys):
all_fmtos = fmtos_keys + parents_keys
for key in fmto.children + ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_kwargs'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': '... | def sort_kwargs(kwargs, *param_lists):
return chain(
({key: kwargs.pop(key) for key in params.intersection(kwargs)}
for params in map(set, param_lists)), [kwargs]) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'iterkeys'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, ... | def iterkeys(self):
patterns = self.patterns
replace = self.replace
seen = set()
for key in six.iterkeys(self.base):
for pattern in patterns:
m = pattern.match(key)
if m:
ret = m.group('key') if replace else m.group()
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'keys'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, {'id... | def keys(self):
k = list(dict.keys(self))
k.sort()
return k |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '23']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_reports_page'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17', '20']}; {'id': '4', 'type': 'iden... | def get_reports_page(self, is_enclave=None, enclave_ids=None, tag=None, excluded_tags=None,
from_time=None, to_time=None):
distribution_type = None
if is_enclave:
distribution_type = DistributionType.ENCLAVE
elif not is_enclave:
distribution_type ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'hashify_targets'}, {'id': '3', 'type': 'parameters', 'children': ['4', '8']}; {'id': '4', 'type': 'typed_parameter', 'children':... | def hashify_targets(targets: list, build_context) -> list:
return sorted(build_context.targets[target_name].hash(build_context)
for target_name in listify(targets)) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'stable_reverse_topological_sort'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children':... | def stable_reverse_topological_sort(graph):
if not graph.is_directed():
raise networkx.NetworkXError(
'Topological sort not defined on undirected graphs.')
seen = set()
explored = set()
for v in sorted(graph.nodes()):
if v in explored:
continue
fringe = [v... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'walk_target_deps_topological_order'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'ch... | def walk_target_deps_topological_order(self, target: Target):
all_deps = get_descendants(self.target_graph, target.name)
for dep_name in topological_sort(self.target_graph):
if dep_name in all_deps:
yield self.targets[dep_name] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_print_message'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': ... | def _print_message(self, prefix, message, verbose=True):
'Prints a message and takes care of all sorts of nasty code'
output = ['\n', prefix, message['message']]
if verbose:
verbose_output = []
if message['description']:
verbose_output.append(
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '23']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'finish'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17', '20']}; {'id': '4', 'type': 'identifier', '... | def finish(self, items=None, sort_methods=None, succeeded=True,
update_listing=False, cache_to_disc=True, view_mode=None):
'''Adds the provided items to the XBMC interface.
:param items: an iterable of items where each item is either a
dictionary with keys/values suitable for ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'followingPrefix'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'p... | def followingPrefix(prefix):
prefixBytes = array('B', prefix)
changeIndex = len(prefixBytes) - 1
while (changeIndex >= 0 and prefixBytes[changeIndex] == 0xff ):
changeIndex = changeIndex - 1;
if(changeIndex < 0):
return None
newBytes = array('B', prefix[0:... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sorted_maybe_numeric'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value... | def sorted_maybe_numeric(x):
all_numeric = all(map(str.isdigit, x))
if all_numeric:
return sorted(x, key=int)
else:
return sorted(x) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_by'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self... | def sort_by(self, sf):
params = join_params(self.parameters, {"sf": sf})
return self.__class__(**params) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_contributor_sort_value'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children':... | def get_contributor_sort_value(self, obj):
user = obj.contributor
if user.first_name or user.last_name:
contributor = user.get_full_name()
else:
contributor = user.username
return contributor.strip().lower() |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_gen_cache_key_for_slice'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'ch... | def _gen_cache_key_for_slice(url_dict, start_int, total_int, authn_subj_list):
key_url_dict = copy.deepcopy(url_dict)
key_url_dict['query'].pop('start', None)
key_url_dict['query'].pop('count', None)
key_json = d1_common.util.serialize_to_normalized_compact_json(
{
'url_dict': key_ur... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'normalize'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'rp_pyxb... | def normalize(rp_pyxb):
def sort(r, a):
d1_common.xml.sort_value_list_pyxb(_get_attr_or_list(r, a))
rp_pyxb.preferredMemberNode = set(_get_attr_or_list(rp_pyxb, 'pref')) - set(
_get_attr_or_list(rp_pyxb, 'block')
)
sort(rp_pyxb, 'block')
sort(rp_pyxb, 'pref') |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'normalize'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'body_pa... | def normalize(body_part_tup,):
return '\n\n'.join(
[
'{}\n\n{}'.format(
str(p.headers[b'Content-Disposition'], p.encoding), p.text
)
for p in sorted(
body_part_tup, key=lambda p: p.headers[b'Content-Disposition']
)
]
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'save_json'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'py... | def save_json(py_obj, json_path):
with open(json_path, 'w', encoding='utf-8') as f:
f.write(serialize_to_normalized_pretty_json(py_obj)) |
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