def get_nodes_edges(tree, root_node=None):
''' 返回树中所有节点和边
'''
Node = namedtuple('Node', ['id', 'label'])
Edge = namedtuple('Edge', ['start', 'end'])
nodes, edges = [], []
if type(tree) is not dict:
return nodes, edges
if root_node is None:
label = '{}: {}'.format(tree['feat_idx'], tree['feat_val'])
root_node = Node._make([uuid.uuid4(), label])
nodes.append(root_node)
for sub_tree in (tree['left'], tree['right']):
if type(sub_tree) is dict:
node_label = '{}: {}'.format(sub_tree['feat_idx'], sub_tree['feat_val'])
else:
node_label = '{:.2f}'.format(sub_tree)
sub_node = Node._make([uuid.uuid4(), node_label])
nodes.append(sub_node)
edge = Edge._make([root_node, sub_node])
edges.append(edge)
sub_nodes, sub_edges = get_nodes_edges(sub_tree, root_node=sub_node)
nodes.extend(sub_nodes)
edges.extend(sub_edges)
return nodes, edges
def get_nodes_edges(tree, root_node=None):
''' 返回树中所有节点和边
'''
Node = namedtuple('Node', ['id', 'label'])
Edge = namedtuple('Edge', ['start', 'end'])
nodes, edges = [], []
if type(tree) is not dict:
return nodes, edges
if root_node is None:
label = '{}: {}'.format(tree['feat_idx'], tree['feat_val'])
root_node = Node._make([uuid.uuid4(), label])
nodes.append(root_node)
for sub_tree in (tree['left'], tree['right']):
if type(sub_tree) is dict:
node_label = '{}: {}'.format(sub_tree['feat_idx'],
sub_tree['feat_val'])
else:
node_label = '{:.2f}'.format(sub_tree)
sub_node = Node._make([uuid.uuid4(), node_label])
nodes.append(sub_node)
edge = Edge._make([root_node, sub_node])
edges.append(edge)
sub_nodes, sub_edges = get_nodes_edges(sub_tree, root_node=sub_node)
nodes.extend(sub_nodes)
edges.extend(sub_edges)
return nodes, edges
def test_gross_net_calculator_answer_format(self):
# specificially test the output format of the answer.
Gn_shell_type = namedtuple('Gross_Net_Shell',
'input_was_gross input_was_net')
Gn_output_type = namedtuple(
'Gross_Net_Output', 'dollars federal state fed_penalty summary')
# input_value is what is passed to the function
# dollars, fed, state, fed_penalty
# gross is the
input_value = (5555.56, 24, 4, 0)
input_was_gross = Gn_output_type(
Decimal(4000).quantize(Decimal('.01'), rounding=ROUND_HALF_DOWN),
Decimal(1333.33).quantize(Decimal('.01'),
rounding=ROUND_HALF_DOWN),
Decimal(222.22).quantize(Decimal('.01'), rounding=ROUND_HALF_DOWN),
Decimal(0).quantize(Decimal('.01'), rounding=ROUND_HALF_DOWN),
'''Assuming the input dollar value (${dollars:.2f}) is the Gross amount,
the Net amount is ${net:.2f},
{fed_pct:.2f}% withheld for Federal taxes is ${fed_dollars:.2f},
{state_pct:.2f}% withheld for State taxes is ${state_dollars:.2f},
and a {penalty_pct:.2f}% Federal Penalty tax is ${penalty_dollars:.2f}.'''.
format(
dollars=5555.56,
net=4000,
fed_pct=24.00,
fed_dollars=1333.33,
state_pct=4.00,
state_dollars=222.22,
penalty_pct=0.00,
penalty_dollars=0.00,
),
)
input_was_net = Gn_output_type(
Decimal(7716.06).quantize(Decimal('.01'),
rounding=ROUND_HALF_DOWN),
Decimal(1851.8544).quantize(Decimal('.01'),
rounding=ROUND_HALF_DOWN),
Decimal(308.64).quantize(Decimal('.01'), rounding=ROUND_HALF_DOWN),
Decimal(0).quantize(Decimal('.01'), rounding=ROUND_HALF_DOWN),
'''Assuming the input dollar value (${dollars:.2f}) is the Net amount,
the Gross amount is ${gross:.2f},
{fed_pct:.2f}% withheld for Federal taxes is ${fed_dollars:.2f},
{state_pct:.2f}% withheld for State taxes is ${state_dollars:.2f},
and a {penalty_pct:.2f}% Federal Penalty tax is ${penalty_dollars:.2f}.'''.
format(
dollars=5555.56,
gross=7716.06,
fed_pct=24.00,
fed_dollars=1851.85,
state_pct=4.00,
state_dollars=308.64,
penalty_pct=0.00,
penalty_dollars=0.00,
),
)
shell = Gn_shell_type(input_was_gross, input_was_net)
self.assertEqual(gross_net_calculator(*input_value), shell)
def classroom_model(db):
# `db` should be a fixture provided by the current dal testbench
class Teacher(Document):
name = fields.StrField(required=True)
class Meta:
register_document = False
collection = db.teacher
class Course(Document):
name = fields.StrField(required=True)
teacher = fields.ReferenceField(Teacher, required=True)
class Meta:
register_document = False
collection = db.course
class Student(Document):
name = fields.StrField(required=True)
birthday = fields.DateTimeField()
courses = fields.ListField(fields.ReferenceField(Course))
class Meta:
register_document = False
collection = db.student
allow_inheritance = True
return namedtuple('Mapping', ('Teacher', 'Course', 'Student'))(Teacher, Course, Student)
def load_ppi_data(root):
DataType = namedtuple('Dataset', ['num_classes', 'g'])
adj_full = sp.load_npz(os.path.join(root, 'ppi', 'adj_full.npz'))
G = dgl.from_scipy(adj_full)
nodes_num = G.num_nodes()
role = json.load(open(os.path.join(root, 'ppi', 'role.json'), 'r'))
tr = list(role['tr'])
te = list(role['te'])
va = list(role['va'])
mask = np.zeros((nodes_num, ), dtype=bool)
train_mask = mask.copy()
train_mask[tr] = True
val_mask = mask.copy()
val_mask[va] = True
test_mask = mask.copy()
test_mask[te] = True
G.ndata['train_mask'] = torch.tensor(train_mask, dtype=torch.bool)
G.ndata['val_mask'] = torch.tensor(val_mask, dtype=torch.bool)
G.ndata['test_mask'] = torch.tensor(test_mask, dtype=torch.bool)
feats = np.load(os.path.join(root, 'ppi', 'feats.npy'))
G.ndata['feat'] = torch.tensor(feats, dtype=torch.float)
class_map = json.load(
open(os.path.join(root, 'ppi', 'class_map.json'), 'r'))
labels = np.array([class_map[str(i)] for i in range(nodes_num)])
G.ndata['label'] = torch.tensor(labels, dtype=torch.float)
data = DataType(g=G, num_classes=labels.shape[1])
return data
def main():
args = parse_args()
local = namedtuple('local', ('host', 'port'))
local.host = args.local_host
local.port = args.local_port
remote = namedtuple('remote', ('host', 'port'))
remote.host = args.remote_host
remote.port = args.remote_port
queue = Queue()
listen_thread = Thread(target=listen_for_packets, args=(local, queue))
listen_thread.start()
send_packets(target=remote, queue=queue)
def classroom_model(db):
# `db` should be a fixture provided by the current dal testbench
class Teacher(Document):
name = fields.StrField(required=True)
class Meta:
register_document = False
collection = db.teacher
class Course(Document):
name = fields.StrField(required=True)
teacher = fields.ReferenceField(Teacher, required=True)
class Meta:
register_document = False
collection = db.course
class Student(Document):
name = fields.StrField(required=True)
birthday = fields.DateTimeField()
courses = fields.ListField(fields.ReferenceField(Course))
class Meta:
register_document = False
collection = db.student
allow_inheritance = True
return namedtuple('Mapping',
('Teacher', 'Course', 'Student'))(Teacher, Course,
Student)
def get_info():
item_xpath = '//*[@id="J_ShopSearchResult"]/div/div[@class="J_TItems"]'
source = WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.XPATH, item_xpath)))
'''获取名称，价格，销量，评价'''
name = '//dd[@class="detail"]/a'
# 名称
price = '//dd[@class="detail"]/div/div[1]/span[2]'
# 价格
sale_area = '//dd[@class="detail"]/div/div[@class="sale-area"]/span'
# 销量
judge = '//dd[@class="rates"]/div/h4/a'
# 评价
source1 = source.find_elements(By.XPATH, name)
names = [name.text for name in source1]
source2 = source.find_elements(By.XPATH, price)
prices = [price.text for price in source2]
source3 = source.find_elements(By.XPATH, sale_area)
sale_areas = [sale_area.text for sale_area in source3]
source4 = source.find_elements(By.XPATH, judge)
judges = [judge.text for judge in source4]
info = namedtuple('info', row)
how_many = len(names) - 8
# print(len(names),len(prices),len(sale_areas),len(judges))
# 推荐的商品没有评价
for i in range(how_many):
oneinfo = info(names[i], prices[i], sale_areas[i], judges[i])
infos.append(oneinfo)
print('Download one page')
def test_get_grid_posititions_from_shape_exceptions(self):
grid_str = """08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48"""
grid = [s.split() for s in grid_str.splitlines()]
Input = namedtuple('Input', ['shape', 'row', 'col', 'n'])
known_exceptions = [
(Input(self.SHAPE.VERTICAL, 19, 0, 4), IndexError),
(Input(self.SHAPE.HORIZONTAL, 19, 19, 4), IndexError),
(Input(self.SHAPE.HORIZONTAL, 19, 19, 4), IndexError),
]
for input_, output in known_exceptions:
#self.assertRaises(IndexError, IndexError)
self.assertRaises(output, hf.get_grid_posititions_from_shape, grid,
input_.shape, input_.row, input_.col, input_.n)
def record(self, winner, chips, player_point, dealer_point):
self.update(winner, chips, player_point, dealer_point)
Row = namedtuple('Row', [
'rounds', 'player_point', 'dealer_point', 'winner', 'remain_chips'
])
row = Row(self.rounds, self.player_point, self.dealer_point,
self.winner, self.remain_chips)
self.data.append(row)
class Prompter:
''' -- read the queue and wait until the trigger fires
used by the follower task to subscribe its event handlers to line updates
Asynchronous Iterator; may be called as coroutine
there is no way to reach StopAsyncIteration at the moment
'''
### datatype to send event details back to handler
Event = namedtuple('Event', ('ln', 'line', 'match'))
##############
__slots__ = (
'queue',
'_trigger',
'_file',
)
def __init__(
self,
queue: curio.Queue,
trigger: Trigger,
file: Path,
):
self.queue = queue
self._trigger = trigger
self._file = file
@property
def trigger(self):
return self._trigger
@property
def file(self):
return self._file
##############
async def __anext__(self):
''' async block until the queue produces a line that activates the trigger.
never reaches stop iteration
'''
while True:
(line, ln) = await self.queue.get()
match = self.trigger.check(line)
if match is not None:
return self.Event(ln, line, match)
raise StopAsyncIteration
def __aiter__(self):
''' Prompter object is an async iterator '''
return self
async def __call__(self):
''' Prompter object is a coroutine'''
try:
return await self.__anext__()
except StopAsyncIteration:
raise # do what
class BoundedProperty(namedtuple("BoundedProperty", ["min", "max", "delta"])):
def __new__(cls, minimum, maximum, delta=None):
if delta is None:
delta = abs(maximum - minimum)
return super().__new__(cls, minimum, maximum, delta)
def interpolate(self, val):
result = (self.max - val) / self.delta
return 1.0 - bound(0, result, 1)
def test_hex_tile_generator_known_answer(self):
Ans = namedtuple('Ans', ['input', 'ans'])
known_answers = [
Ans('builtin1', [2, 1, 4, 3, 5]),
Ans('ordered5', [1, 2, 3, 4, 5]),
]
for ans in known_answers:
test_te = list(te.hex_tile_generator(ans.input))
self.assertListEqual(ans.ans, test_te)
def load_data(args):
'''Wraps the dgl's load_data utility to handle ppi special case'''
DataType = namedtuple('Dataset', ['num_classes', 'g'])
if args.dataset == 'amazon2m':
amazon_data = AmazonDataset()
DataType = namedtuple('Dataset', ['num_classes', 'g'])
data = DataType(g=amazon_data[0], num_classes=amazon_data.num_classes)
return data
elif args.dataset == 'ppi':
train_dataset = PPIDataset('train')
train_graph = dgl.batch([train_dataset[i] for i in range(len(train_dataset))], edge_attrs=None, node_attrs=None)
val_dataset = PPIDataset('valid')
val_graph = dgl.batch([val_dataset[i] for i in range(len(val_dataset))], edge_attrs=None, node_attrs=None)
test_dataset = PPIDataset('test')
test_graph = dgl.batch([test_dataset[i] for i in range(len(test_dataset))], edge_attrs=None, node_attrs=None)
G = dgl.batch(
[train_graph, val_graph, test_graph], edge_attrs=None, node_attrs=None)
train_nodes_num = train_graph.number_of_nodes()
test_nodes_num = test_graph.number_of_nodes()
val_nodes_num = val_graph.number_of_nodes()
nodes_num = G.number_of_nodes()
assert (nodes_num == (train_nodes_num + test_nodes_num + val_nodes_num))
# construct mask
mask = np.zeros((nodes_num,), dtype=bool)
train_mask = mask.copy()
train_mask[:train_nodes_num] = True
val_mask = mask.copy()
val_mask[train_nodes_num:-test_nodes_num] = True
test_mask = mask.copy()
test_mask[-test_nodes_num:] = True
G.ndata['train_mask'] = torch.tensor(train_mask, dtype=torch.bool)
G.ndata['val_mask'] = torch.tensor(val_mask, dtype=torch.bool)
G.ndata['test_mask'] = torch.tensor(test_mask, dtype=torch.bool)
data = DataType(g=G, num_classes=train_dataset.num_labels)
return data
else:
dataset = _load_data(args)
data = DataType(g=dataset[0], num_classes=dataset.num_classes)
return data
def load_data(args, multilabel):
if not os.path.exists('graphsaintdata') and not os.path.exists('data'):
raise ValueError("The directory graphsaintdata does not exist!")
elif os.path.exists('graphsaintdata') and not os.path.exists('data'):
os.rename('graphsaintdata', 'data')
prefix = "data/{}".format(args.dataset)
DataType = namedtuple('Dataset', ['num_classes', 'train_nid', 'g'])
adj_full = scipy.sparse.load_npz(
'./{}/adj_full.npz'.format(prefix)).astype(np.bool)
g = dgl.from_scipy(adj_full)
num_nodes = g.num_nodes()
adj_train = scipy.sparse.load_npz(
'./{}/adj_train.npz'.format(prefix)).astype(np.bool)
train_nid = np.array(list(set(adj_train.nonzero()[0])))
role = json.load(open('./{}/role.json'.format(prefix)))
mask = np.zeros((num_nodes, ), dtype=bool)
train_mask = mask.copy()
train_mask[role['tr']] = True
val_mask = mask.copy()
val_mask[role['va']] = True
test_mask = mask.copy()
test_mask[role['te']] = True
feats = np.load('./{}/feats.npy'.format(prefix))
scaler = StandardScaler()
scaler.fit(feats[train_nid])
feats = scaler.transform(feats)
class_map = json.load(open('./{}/class_map.json'.format(prefix)))
class_map = {int(k): v for k, v in class_map.items()}
if multilabel:
# Multi-label binary classification
num_classes = len(list(class_map.values())[0])
class_arr = np.zeros((num_nodes, num_classes))
for k, v in class_map.items():
class_arr[k] = v
else:
num_classes = max(class_map.values()) - min(class_map.values()) + 1
class_arr = np.zeros((num_nodes, ))
for k, v in class_map.items():
class_arr[k] = v
g.ndata['feat'] = torch.tensor(feats, dtype=torch.float)
g.ndata['label'] = torch.tensor(
class_arr, dtype=torch.float if multilabel else torch.long)
g.ndata['train_mask'] = torch.tensor(train_mask, dtype=torch.bool)
g.ndata['val_mask'] = torch.tensor(val_mask, dtype=torch.bool)
g.ndata['test_mask'] = torch.tensor(test_mask, dtype=torch.bool)
data = DataType(g=g, num_classes=num_classes, train_nid=train_nid)
return data
class Dep(
namedtuple('dep',
['inputs', 'outputs', 'rule', 'params', 'use_existing'])):
def __new__(cls, inputs, outputs, rule, params=None, use_existing=False):
if not isinstance(inputs, list):
inputs = [inputs]
if not isinstance(outputs, list):
outputs = [outputs]
if params is None:
params = {}
return super().__new__(cls, inputs, outputs, rule, params,
use_existing)
def record(self, winner, chips, player_point, dealer_point):
self.update(winner, chips, player_point, dealer_point)
Row = namedtuple("Row", [
"rounds", "player_point", "dealer_point", "winner", "remain_chips"
])
row = Row(
self.rounds,
self.player_point,
self.dealer_point,
self.winner,
self.remain_chips,
)
self.data.append(row)
def final_function(ones):
df = create_blocks(ones)
sinking, sink = create_sinking_df(df)
r_f_splits = [split_columns(df, sinking, col, sink) for col in sinking]
sinking = [x['sinking'] for x in r_f_splits if 'sinking' in x.keys()]
sinking = pd.DataFrame(list(chain.from_iterable(sinking)))
sinking.name = 'sinking'
rising = [x['rising'] for x in r_f_splits if 'rising' in x.keys()]
rising = pd.DataFrame(list(chain.from_iterable(rising)))
rising.name = 'rising'
result = namedtuple('result', 'rising, sinking')
return result(rising.T, sinking.T)
def load_data(args):
'''Wraps the dgl's load_data utility to handle ppi special case'''
if args.dataset != 'ppi':
return _load_data(args)
train_dataset = PPIDataset('train')
val_dataset = PPIDataset('valid')
test_dataset = PPIDataset('test')
PPIDataType = namedtuple('PPIDataset', [
'train_mask', 'test_mask', 'val_mask', 'features', 'labels',
'num_labels', 'graph'
])
G = dgl.BatchedDGLGraph(
[train_dataset.graph, val_dataset.graph, test_dataset.graph],
edge_attrs=None,
node_attrs=None)
G = G.to_networkx()
# hack to dodge the potential bugs of to_networkx
for (n1, n2, d) in G.edges(data=True):
d.clear()
train_nodes_num = train_dataset.graph.number_of_nodes()
test_nodes_num = test_dataset.graph.number_of_nodes()
val_nodes_num = val_dataset.graph.number_of_nodes()
nodes_num = G.number_of_nodes()
assert (nodes_num == (train_nodes_num + test_nodes_num + val_nodes_num))
# construct mask
mask = np.zeros((nodes_num, ), dtype=bool)
train_mask = mask.copy()
train_mask[:train_nodes_num] = True
val_mask = mask.copy()
val_mask[train_nodes_num:-test_nodes_num] = True
test_mask = mask.copy()
test_mask[-test_nodes_num:] = True
# construct features
features = np.concatenate(
[train_dataset.features, val_dataset.features, test_dataset.features],
axis=0)
labels = np.concatenate(
[train_dataset.labels, val_dataset.labels, test_dataset.labels],
axis=0)
data = PPIDataType(graph=G,
train_mask=train_mask,
test_mask=test_mask,
val_mask=val_mask,
features=features,
labels=labels,
num_labels=121)
return data
def get_nodes_edges(tree, root_node=None):
'''
返回决策树所有节点和边
'''
Node = namedtuple('Node', ['id', 'label']) # 使用名称索引,避免内存越界
Edge = namedtuple('Edge', ['start', 'end'])
nodes, edges = [], []
if type(tree) is not dict: # 递归终止条件,决策树不可继续划分
return nodes, edges
if root_node is None: # 根节点为空,则从空节点开始
# 难道Tree的ID即本节点的feature ID?
label = '{}: {}'.format(tree['feat_id'], tree['feat_val'])
root_node = Node._make([uuid.uuid4(), label]) # 从 iterable 对象中创建新的实例
nodes.append(root_node)
for sub_tree in [tree['left'], tree['right']]: # 遍历左右子树
if type(sub_tree) is dict: # 非叶子结点
node_label = '{}: {}'.format(
sub_tree['feat_id'], sub_tree['feat_val'])
else:
node_label = '{}'.format(sub_tree) # 叶子结点, 叶子节点的值
sub_node = Node._make([uuid.uuid4(), node_label]) # 创建子节点
nodes.append(sub_node) # 插入子节点
edge = Edge._make([root_node, sub_node]) # 创建边
edges.append(edge) # 插入边
# 递归子树, 获取子树的节点和边
sub_nodes, sub_edges = get_nodes_edges(sub_tree, root_node=sub_node)
nodes.extend(sub_nodes)
edges.extend(sub_edges)
return nodes, edges
def __init__(self, *children, **options):
self.content = None
"""Raw content of the node"""
self.children = []
"""List of children of this node"""
OptionsProxy = namedtuple("OptionsProxy", self.valid_options)
self.options = OptionsProxy(**options)
"""Named tuple with the options for this node"""
if self.is_terminal:
if children:
self.content = children[0]
else:
self.children = list(children)
def main():
global args
args = parse_args()
client_condition = not args.listen and args.port > 0
listen_condition = args.listen and args.port > 0
target = namedtuple('target', ('host', 'port'))
target.host = args.host
target.port = args.port
if client_condition:
print('Clienting')
run_client_loop(target)
elif listen_condition:
print('Listening')
run_server_loop(target)
def test_hex_tile_generator_random(self):
RandBuiltins = namedtuple('RandBuiltins',
['builtin', 'n', 'tile_stack'])
known_answers = [
RandBuiltins('random', 29, None),
RandBuiltins('random', 10, None),
RandBuiltins('random', 13, None),
]
for ans in known_answers:
test_te = list(
te.hex_tile_generator(builtin=ans.builtin,
n=ans.n,
tile_stack=ans.tile_stack))
print('n={}, output={}'.format(ans.n, test_te))
self.assertEqual(len(test_te), ans.n)
def gen_pythagorean_triplet(min_c=5, max_c=100):
""" Returns the next set of Pythagorean triplets as a tuple (a, b, c).
Up to max_c
for a**2 + b**2 == c**2 and a < b
Generator
"""
# Originally for problem 9
# TDD
Pythag = namedtuple('Pythag', 'a b c')
return_tuple = Pythag(3, 4, 5)
for c in range(min_c, max_c):
for a in range(1, c):
for b in range(a + 1, c):
if a**2 + b**2 == c**2 and a < b:
return_tuple = Pythag(a, b, c)
yield return_tuple
class Dictionary(object):
'''Build a trie dictionary out of words defined in vocab.'''
Match = namedtuple('Match', 'prefix isword prefix_words')
def __init__(self, vocab=None):
defaultdict_maker = lambda: defaultdict(defaultdict_maker)
self._prefix_tree = defaultdict(defaultdict_maker)
self.extend(vocab or [])
def add(self, word):
it = self._prefix_tree
for ch in word:
it = it[ch]
it[''] = True
def extend(self, words):
for word in words:
self.add(word)
def partial_match(self, text):
return Match()
def prefix_words(self, text):
it = self._prefix_tree
for i, ch in enumerate(text):
it = it.get(ch, None)
if it is None:
break
if '' in it:
yield text[:i+1]
def isword(self, word):
match = self.partial_match(word)
return match is not None and match.isword
def print(self):
def print_dictionary(it=self, i=0):
for k, v in it.items():
if v is True:
print('{}EOW'.format(' '*i*2))
else:
print('{}{}:'.format(' '*i*2, k))
print_dictionary(v, i+1)
print_dictionary()
def zipsource():
Fo = namedtuple("Fo", ("filename", "is_dir"))
zipsource = LocalTargetAndZipSource("", "")
zipsource.zip = Mock()
zipsource.zip.filelist = [
Fo("src/", lambda: True),
Fo("src/en/", lambda: True),
Fo("src/en/to", lambda: False),
Fo("src/en/tre", lambda: False),
]
def namelist():
for i in ("src/", "src/en/", "src/en/to", "src/en/tre"):
yield i
zipsource.zip.namelist = namelist
zipsource.zip.read.return_value = b"data"
return zipsource
def classroom_model(instance):
@instance.register
class Teacher(Document):
name = fields.StrField(required=True)
@instance.register
class Course(Document):
name = fields.StrField(required=True)
teacher = fields.ReferenceField(Teacher, required=True, allow_none=True)
@instance.register
class Student(Document):
name = fields.StrField(required=True)
birthday = fields.DateTimeField()
courses = fields.ListField(fields.ReferenceField(Course))
return namedtuple('Mapping', ('Teacher', 'Course', 'Student'))(Teacher, Course, Student)
def test_bad_lazy_collection(self, dal_moke):
# Bad `dal` attribute
with pytest.raises(exceptions.NoCollectionDefinedError) as exc:
class Doc7(Document):
class Meta:
lazy_collection = lambda: None
class dal:
pass
assert exc.value.args[0] == (
"`dal` attribute must be a subclass of <class 'umongo.abstract.AbstractDal'>")
# Invalid lazy_collection's dal
LazyCollection = namedtuple('LazyCollection', ('dal', 'load'))
class BadDal:
pass
def load_collection():
pass
with pytest.raises(exceptions.NoCollectionDefinedError) as exc:
class Doc8(Document):
class Meta:
lazy_collection = LazyCollection(BadDal, load_collection)
assert exc.value.args[0] == (
"`dal` attribute must be a subclass of <class 'umongo.abstract.AbstractDal'>")
# Invalid lazy_collection's load
class GoodDal(AbstractDal):
@staticmethod
def io_validate_patch_schema(schema):
pass
class Doc9(Document):
class Meta:
lazy_collection = LazyCollection(GoodDal, load_collection)
with pytest.raises(exceptions.NoCollectionDefinedError) as exc:
Doc9.collection
assert exc.value.args[0] == "lazy_collection didn't returned a collection"
def list_to_pagination(items, already_sliced=False, page=1, per_page=None,
total=None, **kwargs):
"""
Convert the given list to a :class:`Pagination` object
:param already_sliced: If true, don't slice the given items with
page/per_page arguments
"""
if page < 1:
raise ValueError('page must be > 0')
total = total or len(items)
per_page = per_page or total
fields = ['items', 'page', 'per_page', 'total']
if kwargs:
fields += list(kwargs.keys())
Pagination = namedtuple('Pagination', fields)
if not already_sliced:
items = items[(page - 1) * per_page: page * per_page]
return Pagination(items, page, per_page, total, **kwargs)
def classroom_model(instance):
@instance.register
class Teacher(Document):
name = fields.StrField(required=True)
@instance.register
class Course(Document):
name = fields.StrField(required=True)
teacher = fields.ReferenceField(Teacher, required=True)
@instance.register
class Student(Document):
name = fields.StrField(required=True)
birthday = fields.DateTimeField()
courses = fields.ListField(fields.ReferenceField(Course))
class Meta:
allow_inheritance = True
return namedtuple('Mapping', ('Teacher', 'Course', 'Student'))(Teacher, Course, Student)
def another_test_user(app):
TestUser = namedtuple(
'TestUser', ['email', 'password', 'id', 'auth_headers']
)
with app.app_context():
email = '*****@*****.**'
password = '******'
user = User(email=email,
password=guard.encrypt_password(password),
is_verified=True)
db.session.add(user)
db.session.commit()
headers = {'Authorization': 'Bearer %s' % guard.encode_jwt_token(user)}
return TestUser(email=email,
password=password,
id=user.id,
auth_headers=headers)
def _get_clusters_based_on_tree_level(lvl, link):
LVL = namedtuple("Level","members,lvl")
clusters = [LVL(members=[x], lvl=0) for x in range(link.shape[0]+1)]
for i in range(link.shape[0]):
ix_1,ix_2 = int(link[i][0]), int(link[i][1])
group = clusters[ix_1].members + clusters[ix_2].members
max_group_number = max(clusters[ix_1].lvl,clusters[ix_2].lvl)
clusters[ix_1] = LVL(members=clusters[ix_1].members, lvl=max_group_number)
clusters[ix_2] = LVL(members=clusters[ix_2].members, lvl=max_group_number)
new_lvl = max_group_number + 1
clusters.append(LVL(members=sorted(group), lvl=new_lvl))
maximum_lvl = clusters[-1].lvl
clusters = [LVL(x.members,maximum_lvl-x.lvl) for x in clusters]
selection = [x for x in clusters if x.lvl==lvl]
lvl -= 1
while lvl > 0:
candidates = [x for x in clusters if x.lvl==lvl]
winner = [x for x in candidates if not set(selection[0].members).issubset(set(x.members))]
selection.append(winner[0])
lvl -= 1
return [s.members for s in selection]
def from_string(cls, format_str):
"""
Create a buffer format from a string. Generated buffer format are
cached, so this function is not expensive to call.
A format string is composed of N format token.
A format token follow these rules: ({number}{format char})[{name}]
Whitespaces are ignored.
Available format char:
f: float
d: double
b: byte
B: unsigned Byte
s: short
S: unsigned short
i: int
I: unsigned int
Example:
"(3i)[vertex](4f)[color]"
"(4f)[foo] (4f)[bar] (4d)[yolo]"
"""
format_str = format_str.replace(' ', '')
format_str_2 = ""
if len(format_str) == 0:
raise BufferFormatError('Format must be present')
# Create the tokens
tokens, offset = [], 0
for match in BufferFormat.pattern.finditer(format_str):
groups = match.groups()
_type, gl_type = BUFFER_FORMAT_TYPES_MAP.get(groups[1])
size=int(groups[0])
name=groups[2]
name_match = pyvars.match(name)
if name_match is None or name_match.span() != (0, len(name)):
raise ValueError('"{}" is not a valid variable name'.format(name))
token = BufferFormat.token(size=size, type=_type*size, name=name, gl_type=gl_type, offset=offset)
tokens.append(token)
offset += sizeof(token.type)
format_str_2 += format_str[match.start():match.end()]
if format_str_2 != format_str:
raise BufferFormatError('Format string is not valid')
bformat = super().__new__(cls)
# Save the tokens
bformat.tokens = tokens
# Build the item
bformat.item = namedtuple('V', [t.name for t in tokens])
# Build the structure
struct_fields = [(t.name, t.type) for t in tokens]
bformat.struct = type('BufferStruct', (Structure,), {'_fields_': struct_fields})
return bformat
# pylint: disable=redefined-builtin,redefined-outer-name,missing-docstring
# pylint: disable=too-few-public-methods,no-self-use,arguments-differ
# pylint: disable=dangerous-default-value,redefined-builtin,too-many-arguments
# pylint: disable=abstract-method
import os
import pickle
from functools import namedtuple, update_wrapper, wraps
import psutil
import redis
from . import config, constants
_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "currsize"])
def memory_full():
"""Check if the memory is too full for further caching."""
current_process = psutil.Process(os.getpid())
return (current_process.memory_percent() >
config.MAXIMUM_CACHE_MEMORY_PERCENTAGE)
class _HashedSeq(list):
"""This class guarantees that ``hash()`` will be called no more than once
per element. This is important because the ``lru_cache()`` will hash the
key multiple times on a cache miss.
"""
from functools import namedtuple
from barracuda.components.ai_component import AIComponent
from barracuda.components.status_component import StatusComponent
# from barracuda.components.render_component import RenderComponent
from barracuda.tools import Vect2D
class PositionComponent(Vect2D):
pass
RenderComponent = namedtuple('RenderComponent', ())
# AIComponent = namedtuple('AIComponent', ())
# __all__ = ('AIComponent', 'BehaviorComponent', 'BehaviorActions',
# 'StatusComponent', 'RenderComponent')
HealerComponent = namedtuple('HealerComponent', ('power', ))
AttackerComponent = namedtuple('AttackerComponent', ('power', ))
# AIComponent = namedtuple('AIComponent', ())
def __init__(self, page_number, total_pages, count):
self.number = page_number
Paginator = namedtuple('Paginator', ['num_pages', 'count'])
self.paginator = Paginator(total_pages, count)
from functools import namedtuple
from rwlock import RWLock
Message = namedtuple("Message", ["text", "login", "time"])
class ChatMessageStore:
def __init__(self):
self.__id = 1
self.__storage = []
self.__rwlock = RWLock()
def add_message(self, message):
self.__rwlock.acquire_write()
self.__storage.append((self.__id, message))
self.__id += 1
self.__rwlock.release()
def get_messages(self, start_id=0):
self.__rwlock.acquire_read()
result = ([message for id_, message in self.__storage if id_ >= start_id], self.__id)
self.__rwlock.release()
return result
from functools import namedtuple
from ecs import Component
KeyBind = namedtuple('KeyBind', ('key', 'callback', 'type'))
class InputComponent(Component):
def __init__(self, keymap=None):
super().__init__()
self.keymap = []
def add_keybind(self, key, callback, type='default'):
self.keymap.append(KeyBind(key, callback, type))
import threading
from functools import namedtuple
from concurrent import futures
import time
import csv
import requests
header = ["aid", "view", "danmaku", "reply", "favorite", "coin", "share"]
Video = namedtuple('Video', header)
headers = {
'X-Requested-With':
'XMLHttpRequest',
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/56.0.2924.87 Safari/537.36'
}
total = 1
result = []
lock = threading.Lock()
def run(url):
""" 启动爬虫
"""
global total
req = requests.get(url, headers=headers, timeout=6).json()
time.sleep(0.5) # 延迟，避免太快 ip 被封
try:
data = req['data']
video = Video(
from functools import namedtuple
from blinker import signal
move_event = signal('move')
action_event = signal('action')
HealAction = namedtuple('HealAction', ('power', ))
AttackAction = namedtuple('AttackAction', ('power', ))
# MoveEvent = namedtuple('MoveEvent', ('entity', 'start_pos', 'end_pos'))
# ActionEvent = namedtuple('AttackEvent', ('entity_actor', 'entity_target', 'action'))
"""
# file : bilibili.py
# author : shao
# date: 2017/11/3 0003
"""
import csv
import threading
import time
from concurrent import futures
from functools import namedtuple
import MySQLdb
import requests
header = ["aid", "view", "danmaku", "reply", "favorite", "coin", "share"]
Video = namedtuple('Video', header)
headers = {
'X-Requested-With': 'XMLHttpRequest',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/56.0.2924.87 Safari/537.36'}
total = 1
result = set()
lock = threading.Lock()
def run(url):
""" 启动爬虫
"""
global total
req = requests.get(url, headers=headers, timeout=6).json()
time.sleep(0.6) # 延迟，避免太快 ip 被封
from ctypes import byref, Structure, cast, POINTER, sizeof, c_void_p
from functools import lru_cache, namedtuple
from collections.abc import Sequence
from sys import modules
#Loaded extensions name are added in here
LOADED_EXTENSIONS = []
BUFFER_FORMAT_TYPES_MAP = { 'f': (GLfloat, GL_FLOAT), 'd': (GLdouble, GL_DOUBLE),
'b': (GLbyte, GL_BYTE), 'B': (GLubyte, GL_UNSIGNED_BYTE),
'i': (GLint, GL_INT), 'I': (GLuint, GL_UNSIGNED_INT),
's': (GLshort, GL_SHORT), 'S': (GLushort, GL_UNSIGNED_SHORT)}
pyvars = re.compile('[_a-zA-Z][_\w]+')
map_info = namedtuple('MappingInformation', ['access', 'target', 'ptr', 'size'])
def ptr_array(arr):
" Cast an array in a pointer "
return cast(arr, POINTER(arr._type_))
def eval_index(index, length):
if index < 0 and index >= (-length) :
return length+index
elif index >= length:
raise IndexError('Index "{}" out of bound, buffer has a length of "{}"'.format(index, length))
elif index < (-length):
raise IndexError('Index "{}" out of bound, buffer has a length of "{}"'.format(index, length))
return index
def __init__(self, min, max, c_min=Color((1,0,0)), c_max=Color((0,1,0))):
super().__init__()
Thickness = namedtuple("Thickness", ["min", "max", "delta"])
Col = namedtuple("Color", ["min", "max"])
self.thickness = Thickness(min, max, max - min)
self.color = Col(c_min, c_max)
from functools import namedtuple
EConst = namedtuple('EConst', 'val')
EVar = namedtuple('EVar', 'name')
EAbs = namedtuple('EAbs', ['arg', 'body'])
EApp = namedtuple('EApp', ['func', 'arg'])
ELet = namedtuple('ELet', ['var', 'val', 'body'])
TBase = namedtuple('TBase', 'name')
TVar = namedtuple('TVar', 'name')
TFunc = namedtuple('TFunc', ['arg', 'result'])
TScheme = namedtuple('TScheme', ['bound_types', 'type'])
TInt = TBase('TInt')
TBool = TBase('TBool')
def substitute(substitution, type_):
if isinstance(type_, TBase):
return type_
if isinstance(type_, TVar):
if type_ in substitution:
return substitution[type_]
return type_
if isinstance(type_, TFunc):
return TFunc(substitute(substitution, type_.arg),
substitute(substitution, type_.result))
if isinstance(type_, TScheme):
new_subs = {find:replace for find,replace in substition.items()
if find not in type_.bound_types}
return TScheme(type_.bound_types, substitute(new_subs, type_.type))
__author__ = 'dm'
from functools import namedtuple
Point = namedtuple('Point', 'x y')
IJPoint = namedtuple('IJPoint', 'i j')
Defense+3 80 0 3
"""
rings = [Item(ring.split()) for ring in rings.strip().splitlines()]
def shopping():
from itertools import combinations as comb
for w in list(comb(weapons, 1)):
for a in list(comb(armors, 0)) + list(comb(armors, 1)):
for r in list(comb(rings, 0)) + list(comb(rings, 1)) + list(comb(rings, 2)):
yield list(w) + list(a) + list(r)
from functools import namedtuple
Person = namedtuple('Person', ['hp', 'attack', 'defense'])
def defeat(p1: Person, p2: Person):
while p1.hp > 0 and p2.hp > 0:
hit = max(1, p1.attack - p2.defense)
p2 = p2._replace(hp=p2.hp - hit)
p1 = p1._replace(hp=p1.hp - max(1, p2.attack - p1.defense))
return p2.hp <= 0
# Boss
# Hit Points: 103
# Damage: 9
# Armor: 2
Memory-aware LRU Cache function decorator
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A modification of the builtin ``functools.lru_cache`` decorator that takes an
additional keyword argument, ``use_memory_up_to``. The cache is considered full
if there are fewer than ``use_memory_up_to`` bytes of memory available.
If ``use_memory_up_to`` is set, then ``maxsize`` has no effect.
Uses the ``psutil`` module to get the available memory.
"""
import psutil
from functools import RLock, update_wrapper, namedtuple
_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
class _HashedSeq(list):
""" This class guarantees that hash() will be called no more than once
per element. This is important because the lru_cache() will hash
the key multiple times on a cache miss.
"""
__slots__ = 'hashvalue'
def __init__(self, tup, hash=hash):
self[:] = tup
self.hashvalue = hash(tup)
def __hash__(self):
import subprocess
import shutil
import json
import wave
import os
import io
import logging
from functools import namedtuple
from synthesizer.sample import Sample
__all__ = ["AudiofileToWavStream", "StreamMixer", "VolumeFilter", "EndlessFramesFilter", "SampleStream"]
log = logging.getLogger("synthesizer.streaming")
AudioFormatProbe = namedtuple("AudioFormatProbe", ["rate", "channels", "sampformat", "fileformat", "duration"])
class AudiofileToWavStream(io.RawIOBase):
"""
Streams WAV PCM audio data from the given sound source file.
If the file is not already a .wav, and/or you want to resample it,
ffmpeg/ffprobe are used to convert it in the background.
For HQ resampling, ffmpeg has to be built with libsoxr support.
Input: audio file of any supported format
Output: stream of audio data in WAV PCM format
"""
ffmpeg_executable = "ffmpeg"
ffprobe_executable = "ffprobe"
)
from _freestyle import (
blendRamp,
evaluateColorRamp,
evaluateCurveMappingF,
)
import time
from mathutils import Vector
from math import pi, sin, cos, acos, radians
from itertools import cycle, tee
from functools import namedtuple
# named tuple primitives used for storing data.
Thickness = namedtuple("Thickness", ["min", "max", "delta"])
Range = namedtuple("Range", ["min", "max", "delta"])
Value = namedtuple("Value", ["min", "max", "delta"])
class ColorRampModifier(StrokeShader):
"""Primitive for the color modifiers """
def __init__(self, blend, influence, ramp):
StrokeShader.__init__(self)
self.blend = blend
self.influence = influence
self.ramp = ramp
def evaluate(self, t):
col = evaluateColorRamp(self.ramp, t)
return col.xyz # omit alpha
@lru_cache(maxsize=32)
def phase_to_direction(length):
"""
Returns a list of tuples each containing:
- the phase
- a Vector with the values of the cosine and sine of 2pi * phase (the direction)
"""
results = list()
for i in range(length):
phase = i / (length - 1)
results.append((phase, Vector((cos(2 * pi * phase), sin(2 * pi * phase)))))
return results
# A named tuple primitive used for storing data that has an upper and
# lower bound (e.g., thickness, range and certain values)
BoundedProperty = namedtuple("BoundedProperty", ["min", "max", "delta"])
class BoundingBox:
"""Object representing a bounding box consisting out of 2 2D vectors"""
__slots__ = (
"minimum",
"maximum",
"size",
"corners",
)
def __init__(self, minimum: Vector, maximum: Vector):
self.minimum = minimum
self.maximum = maximum
