def clear_cache(scope=None):
"""
Clear all cached data.
Parameters
----------
scope : {None, 'step', 'iteration', 'forever'}, optional
Clear cached values with a given scope.
By default all cached values are removed.
"""
if not scope:
_TABLE_CACHE.clear()
_COLUMN_CACHE.clear()
_INJECTABLE_CACHE.clear()
for m in _MEMOIZED.values():
m.value.clear_cached()
logger.debug('simulation cache cleared')
else:
for d in (_TABLE_CACHE, _COLUMN_CACHE, _INJECTABLE_CACHE):
items = toolz.valfilter(lambda x: x.scope == scope, d)
for k in items:
del d[k]
for m in toolz.filter(lambda x: x.scope == scope, _MEMOIZED.values()):
m.value.clear_cached()
logger.debug('cleared cached values with scope {!r}'.format(scope))
def clear_cache(scope=None):
"""
Clear all cached data.
Parameters
----------
scope : {None, 'step', 'iteration', 'forever'}, optional
Clear cached values with a given scope.
By default all cached values are removed.
"""
if not scope:
_TABLE_CACHE.clear()
_COLUMN_CACHE.clear()
_INJECTABLE_CACHE.clear()
for m in _MEMOIZED.values():
m.value.clear_cached()
logger.debug('simulation cache cleared')
else:
for d in (_TABLE_CACHE, _COLUMN_CACHE, _INJECTABLE_CACHE):
items = toolz.valfilter(lambda x: x.scope == scope, d)
for k in items:
del d[k]
for m in toolz.filter(lambda x: x.scope == scope, _MEMOIZED.values()):
m.value.clear_cached()
logger.debug('cleared cached values with scope {!r}'.format(scope))
def delete(self, artifact_or_id):
artifact_id = _artifact_id(artifact_or_id)
cs.ensure_delete(self)
new_artifacts = list(
t.filter(lambda a: a.id != artifact_id, self.artifacts))
if len(new_artifacts) == len(self.artifacts):
raise KeyError(artifact_id, self)
else:
self.artifacts = new_artifacts
def entrypoint(dockerfile):
"Return the entrypoint, if declared"
f = dockerfile.split("\n")[::-1] # reverse the lines
try:
entry_line = first(filter(lambda x: "ENTRYPOINT" in x, f))
except StopIteration as e:
# No ENTRYPOINT line was found
return None
else:
res = last(entry_line.partition("ENTRYPOINT")).strip()
try:
return json.loads(res)
except:
return res.split()
return None
def entrypoint(dockerfile):
"Return the entrypoint, if declared"
f = dockerfile.split("\n")[::-1] # reverse the lines
try:
entry_line = first(filter(lambda x: "ENTRYPOINT" in x, f))
except StopIteration as e:
# No ENTRYPOINT line was found
return None
else:
res = last(entry_line.partition("ENTRYPOINT")).strip()
try:
return json.loads(res)
except:
return res.split()
return None
def compute_up(t, seq, **kwargs):
predicate = rrowfunc(t.predicate, t._child)
return filter(predicate, seq)
def all_block(self) -> List[Block]: return t.filter(lambda x: isinstance(x, Block), self.values())
def compact(iter):
return filter(None, iter)
def compute_up(expr, seq, predicate, **kwargs):
preds = iter(predicate)
return filter(lambda _: next(preds), seq)
def filter_shorter_than(n, tokenset):
"""
Filters out tokens that have less than 'n' characters.
"""
return tlz.filter(lambda tkn: len(tkn) >= n, tokenset)
def filter_stopwords(tokenset):
"""
Filters out stopwords.
"""
return tlz.filter(lambda tkn: tkn not in STOPWORDS, tokenset)
def compute_up(t, seq, **kwargs):
predicate = rrowfunc(t.predicate, t._child)
return filter(predicate, seq)
def compute_up(expr, seq, predicate, **kwargs):
preds = iter(predicate)
return filter(lambda _: next(preds), seq)
def compact(_iter):
return filter(None, _iter)
def sfilter(pred, fn, x):
""" Yield only those results which satisfy the predicate """
for x in filter(pred, fn(x)):
yield x
def sfilter(pred, fn, x):
""" Yield only those results which satisfy the predicate """
for x in filter(pred, fn(x)):
yield x
def connected_mechanisms(order, network):
"""Returns an iterable the connected mechanisms of the given order within the given network."""
return filter(valid_mechanism(order, network.cm), pyphi.utils.powerset(network.node_indices))
def filter_stopwords(tokenset):
"""
Filters out tokens that are stopwords.
"""
return tlz.filter(not_stopword, tokenset)
def filter_whitespace(tokenset):
"""
Filters out tokens that are only whitespace.
"""
return tlz.filter(tlz.compose(bool, lambda string: string.strip()), tokenset)
def filter_stopwords(tokenset):
"""
Filters out stopwords.
"""
return tlz.filter(lambda tkn: tkn not in STOPWORDS, tokenset)
def all_block(self) -> List[Block]:
return t.filter(lambda x: isinstance(x, Block), self.values())
deps.number_of_edges()
deps.number_of_nodes()
deps.node['skimage']
deps.in_edges('skimage')
nodes = nx.katz_centrality(deps)
central = sorted(deps.nodes(), key=nodes.__getitem__, reverse=True)
central[:10]
central[:20]
central[:40]
central[40:80]
central.index('skimage')
central.index('scipy')
import pickle
stdlib = pickle.load(open('/Users/jni/projects/depsy/data/python_standard_libs.pickle', 'rb'))
central_nonstd = list(tz.filter(lambda x: x not in stdlib, central))
len(central_nonstd)
central_nonstd.index('scipy')
len(central)
central[:5]
nx.is_connected(deps.to_undirected())
len(packages)
deps_sym = deps.to_undirected()
import numpy as np
conncomps = list(nx.connected_component_subgraphs(deps_sym))
giant = conncomps[0]
giant_d = deps.subgraph(giant.nodes())
gpackages = giant_d.nodes()
A = nx.to_scipy_sparse_matrix(giant_d)
A.shape
A.dtype
def filter_whitespace(tokenset):
"""
Filters out tokens that are only whitespace.
"""
return tlz.filter(tlz.compose(bool, str.strip), tokenset)
def filter_longer_than(n, tokenset):
"""
Filters out tokens that have 'n' characters or more.
"""
return tlz.filter(lambda tkn: len(tkn) < n, tokenset)
def filter_longer_than(n, tokenset):
"""
Filters out tokens that have 'n' characters or more.
"""
return tlz.filter(lambda tkn: len(tkn) < n, tokenset)
def compute_up(expr, seq, **kwargs):
predicate = like_regex_predicate(expr)
return filter(predicate, seq)
# TODO extract zip of takeout
TAKEOUT_DIRECTORY_PATH = '/Users/guy/Downloads/Takeout'
CSV_DIRECTORY = 'Fit/Daily Aggregations'
AVERAGE_WEIGHT_FIELD = 'Average weight (kg)'
csv_dir = os.path.join(TAKEOUT_DIRECTORY_PATH, CSV_DIRECTORY)
files = os.listdir(csv_dir)
def is_daily_aggregation(file_name):
return re.match(r'\d{4}-\d{2}-\d{2}.csv', file_name)
daily_aggregations = toolz.filter(is_daily_aggregation, files)
data = []
for csv_file in daily_aggregations:
date_part = csv_file.split(".")[0]
date = dateparser.parse(date_part)
csv = pandas.read_csv(os.path.join(csv_dir, csv_file))
weight = csv[
csv[AVERAGE_WEIGHT_FIELD].notna()][AVERAGE_WEIGHT_FIELD].mean()
if pandas.notna(weight):
data.append({"date": date, "weight": weight})
# print(csv_file, weight)
sorted_data = sorted(data, key=operator.itemgetter('date'))
df = pandas.DataFrame(sorted_data)
df.to_csv('output.csv')
print(sorted_data)
y_clf = y.values.ravel() > 0
y_log = np.log(y.values.ravel() + 1)
df_variable = pd.read_excel('data/tap4fun.xlsx', sheet_name='Sheet1')
df_variable = df_variable.iloc[:, range(3)].rename(columns={
'字段名': 'column',
'字段解释': 'desc',
'数据时间': 'cycle'
})
df_variable['cat'] = df_variable.column.str.split('_').apply(lambda x: x[0])
# 因子合成
# 查询消费类因子
from toolz import filter
pay_cols = list(filter(lambda x: 'pay' in x, X.columns))
X['avg_online_minutes_log10'] = np.log10(X['avg_online_minutes'] + 1)
active_cols = list(filter(lambda x: 'avg_online' in x, X.columns))
achievement_cols = ['army_add', 'resource_add', 'acceleration_add']
for col in achievement_cols:
X[col + '_log'] = np.log(X[col] + 1)
X['achievement'] = np.log(X['army_add'] + X['resource_add'] + 1)
X['achievement_per_hour'] = X['achievement'] / X['avg_online_minutes'] / 60
X['acceleration_per_hour'] = X['acceleration_add'] / X[
'avg_online_minutes'] / 60
X.loc[X.avg_online_minutes == 0, 'achievement_per_hour'] = 0
X.loc[X.avg_online_minutes == 0, 'acceleration_per_hour'] = 0
X['acceleration_per_hour_log'] = np.log(X['acceleration_per_hour'] + 1)
def compute_up(t, seq, **kwargs):
predicate = optimize(t.predicate, seq)
predicate = rrowfunc(predicate, child(t))
return filter(predicate, seq)
def compute_up_1d(t, seq, **kwargs):
return toolz.count(filter(None, seq))
def compute_up_1d(t, seq, **kwargs):
return toolz.count(filter(None, seq))
def filter_shorter_than(n, tokenset):
"""
Filters out tokens that have less than 'n' characters.
"""
return tlz.filter(lambda tkn: len(tkn) >= n, tokenset)
def all_inline(self) -> List[Inline]: return t.filter(lambda x: isinstance(x, Inline), self.values())
def all_inline(self) -> List[Inline]:
return t.filter(lambda x: isinstance(x, Inline), self.values())
def compute_up(t, seq, **kwargs):
predicate = optimize(t.predicate, seq)
predicate = rrowfunc(predicate, child(t))
return filter(predicate, seq)
def filter_stopwords(stopwords, tokens):
return tz.filter(lambda t: t not in stopwords, tokens)
def compute_up(expr, seq, **kwargs):
predicate = like_regex_predicate(expr)
return filter(predicate, seq)
def filter_whitespace(tokenset):
"""
Filters out tokens that are only whitespace.
"""
return tlz.filter(tlz.compose(bool, str.strip), tokenset)