def multi_tab():
import sys
input = sys.stdin.readline
count = 0
n, m = map(int, input().split())
orders = [x for x in map(int, input().split())]
plugs = []
for i, order in enumerate(orders):
if len(plugs) < n:
if order not in plugs:
plugs.append(order)
else:
if order not in plugs:
flag = False
next_orders = orders[i:]
changeable = 0
for j, plug in enumerate(plugs):
if plug not in next_orders:
plugs[j] = order
flag = True
count += 1
break
if next_orders.index(plugs[changeable]) < next_orders.index(plugs[j]):
changeable = j
if not flag:
plugs[changeable] = order
count += 1
return count
def _enumerate(reversible, reverse_index):
if reverse_index is False:
return builtins.enumerate(reversible)
else:
my_list = list(builtins.enumerate(reversed(reversible)))
my_list.reverse()
return my_list
def get_central_node(self, node_list):
sum_weight = sum(n.weight for n in node_list)
min_priority = min((n.priority for n in node_list))
weight_dir = 0
for index_dir, central_node_dir in enumerate(node_list):
weight_dir += central_node_dir.weight
if weight_dir >= sum_weight / 2 and central_node_dir.priority == min_priority and central_node_dir.type == BalancerNode.OPERATOR:
break
weight_rev = 0
for i, central_node_rev in enumerate(node_list[::-1]):
index_rev = len(node_list) - 1 - i
weight_rev += central_node_rev.weight
if weight_rev >= sum_weight / 2 and central_node_rev.priority == min_priority and central_node_rev.type == BalancerNode.OPERATOR:
break
dir_criteria = (weight_dir - (sum_weight / 2)) * (
1000 * int(central_node_dir.type != BalancerNode.OPERATOR))
rev_criteria = (weight_rev - (sum_weight / 2)) * (
1000 * int(central_node_rev.type != BalancerNode.OPERATOR))
index = index_rev if rev_criteria < dir_criteria else index_dir
return index
def _enumerate(reversible, reverse_index):
if reverse_index is False:
return builtins.enumerate(reversible)
else:
my_list = list(builtins.enumerate(reversed(reversible)))
my_list.reverse()
return my_list
async def _zip_inner_strict(aiters):
tried = 0
try:
while True:
items = []
for tried, _aiter in _sync_builtins.enumerate(
aiters): # noqa: B007
items.append(await anext(_aiter))
yield (*items, )
except StopAsyncIteration:
# after the first iterable provided an item, some later iterable was empty
if tried > 0:
plural = " " if tried == 1 else "s 1-"
raise ValueError(
f"zip() argument {tried+1} is shorter than argument{plural}{tried}"
)
# after the first iterable was empty, some later iterable may be not
sentinel = object()
for tried, _aiter in _sync_builtins.enumerate(aiters):
if await anext(_aiter, sentinel) is not sentinel:
plural = " " if tried == 1 else "s 1-"
raise ValueError(
f"zip() argument {tried+1} is longer than argument{plural}{tried}"
)
return
def plot_power_spectrum_two_layers(powers_negative,
powers_positive,
times,
title='',
figure_fname='',
only_power_spectrum=True,
high_gamma_max=120):
if not only_power_spectrum:
fig, (ax1, ax2, ax3) = plt.subplots(3) #, sharex=True)
else:
fig, ax1 = plt.subplots()
F, T = powers_negative.shape
freqs = np.concatenate([
np.arange(1, 30),
np.arange(31, 60, 3),
np.arange(60, high_gamma_max + 5, 5)
])
bands = dict(delta=[1, 4],
theta=[4, 8],
alpha=[8, 15],
beta=[15, 30],
gamma=[30, 55],
high_gamma=[65, high_gamma_max])
im1 = _plot_powers(powers_negative, ax1, times, freqs)
# cba = plt.colorbar(im1, shrink=0.25)
im2 = _plot_powers(powers_positive, ax1, times, freqs)
cbb = plt.colorbar(im2, shrink=0.25)
plt.ylabel('frequency (Hz)')
plt.xlabel('Time (s)')
plt.title(title)
if not only_power_spectrum:
for band_name, band_freqs in bands.items():
idx = [
k for k, f in enumerate(freqs)
if band_freqs[0] <= f <= band_freqs[1]
]
band_power = np.mean(powers_negative[idx, :], axis=0)
ax2.plot(band_power.T, label=band_name)
ax2.set_xlim([0, T])
# ax2.legend()
for band_name, band_freqs in bands.items():
idx = [
k for k, f in enumerate(freqs)
if band_freqs[0] <= f <= band_freqs[1]
]
band_power = np.mean(powers_positive[idx, :], axis=0)
ax3.plot(band_power.T, label=band_name)
ax3.set_xlim([0, T])
# ax3.legend()
if figure_fname != '':
print('Saving figure to {}'.format(figure_fname))
plt.savefig(figure_fname, dpi=300)
plt.close()
else:
plt.show()
def FRvec(obs,ehtim_convention=True):
""" Construct vectors of field rotation corrections for each station and hand
Args:
obs (obsdata): eht-imaging obsdata object containing VLBI data
ehtim_convention (bool): if true, assume the field rotation uses the
eht-imaging pre-rotation convention
Returns:
FR1: field rotation corrections for the first station
FR2: field rotation corrections for the second station
"""
# read the elevation angles for each station
el1 = obs.unpack(['el1'],ang_unit='rad')['el1']
el2 = obs.unpack(['el2'],ang_unit='rad')['el2']
# read the parallactic angles for each station
par1 = obs.unpack(['par_ang1'],ang_unit='rad')['par_ang1']
par2 = obs.unpack(['par_ang2'],ang_unit='rad')['par_ang2']
# get the observation array info
tarr = obs.tarr
# get arrays of station names
ant1 = obs.data['t1']
ant2 = obs.data['t2']
# get multiplicative prefactors for station 1
f_el1 = np.zeros_like(el1)
f_par1 = np.zeros_like(par1)
f_off1 = np.zeros_like(el1)
for ia, a1 in enumerate(ant1):
ind1 = (tarr['site'] == a1)
f_el1[ia] = tarr[ind1]['fr_elev']
f_par1[ia] = tarr[ind1]['fr_par']
f_off1[ia] = tarr[ind1]['fr_off']*eh.DEGREE
# get multiplicative prefactors for station 2
f_el2 = np.zeros_like(el2)
f_par2 = np.zeros_like(par2)
f_off2 = np.zeros_like(el2)
for ia, a2 in enumerate(ant2):
ind2 = (tarr['site'] == a2)
f_el2[ia] = tarr[ind2]['fr_elev']
f_par2[ia] = tarr[ind2]['fr_par']
f_off2[ia] = tarr[ind2]['fr_off']*eh.DEGREE
# combine to get field rotations for each station
FR1 = (f_el1*el1) + (f_par1*par1) + f_off1
FR2 = (f_el2*el2) + (f_par2*par2) + f_off2
# if pre-rotation has been applied, multiply by 2.0
if ehtim_convention:
FR1 *= 2.0
FR2 *= 2.0
return FR1, FR2
def enumerate(iterable=None, start=None):
if iterable is None and start is None:
return bpipe(enumerate, enumerate_start=0)
if iterable is None and start is not None:
return bpipe(enumerate, enumerate_start=start)
if iterable is not None:
if start is None:
return builtins.enumerate(iterable, start=0)
return builtins.enumerate(iterable, start=start)
def using_dic1(nums: List[int], target: int) -> List[int]:
nums_map = {}
#키와 값을 바꿔서 딕셔너리로 저장
for i, num in enumerate(nums):
nums_map[num] = i
#타겟에서 첫 번째 수를 뺀 결과를 키로 조회
for i, num in enumerate(nums):
if target - num in nums_map and i != nums_map[target - num]:
return nums.index(num), nums_map[target - num]
def from_wgs84(self, latitude: float, longitude: float) -> tuple:
"""
Converts WGS84 coordinates into RD coordinates
"""
dlat = 0.36 * (latitude - self.phi0)
dlon = 0.36 * (longitude - self.lam0)
x = self.x0 + sum([v * dlat ** self.Rp[i] * dlon ** self.Rq[i] for i, v in enumerate(self.Rpq)])
y = self.y0 + sum([v * dlat ** self.Sp[i] * dlon ** self.Sq[i] for i, v in enumerate(self.Spq)])
return x, y
def from_rd2(self, x: int, y: int) -> list:
dx = 1E-5 * (x - self.x0)
dy = 1E-5 * (y - self.y0)
latitude = self.phi0 + sum([
v * dx**self.Kp[i] * dy**self.Kq[i] for i, v in enumerate(self.Kpq)
]) / 3600
longitude = self.lam0 + sum([
v * dx**self.Lp[i] * dy**self.Lq[i] for i, v in enumerate(self.Lpq)
]) / 3600
return longitude
def gain_phase_prior(obs,ref_station='AA'):
""" Construct vector of prior means and inverse standard deviations
for gain phases
Args:
obs (obsdata): eht-imaging obsdata object containing VLBI data
ref_station (str): name of reference station
Returns:
gainphase_mu: prior means for gain phases
gainphase_kappa: prior inverse standard deviations for gain phases
"""
# get arrays of station names
ant1 = obs.data['t1']
ant2 = obs.data['t2']
# get array of timestamps
time = obs.data['time']
timestamps = np.unique(time)
# Determine the total number of gains that need to be solved for
N_gains = 0
T_gains = list()
A_gains = list()
for it, t in enumerate(timestamps):
ind_here = (time == t)
N_gains += len(np.unique(np.concatenate((ant1[ind_here],ant2[ind_here]))))
stations_here = np.unique(np.concatenate((ant1[ind_here],ant2[ind_here])))
for ii in range(len(stations_here)):
A_gains.append(stations_here[ii])
T_gains.append(t)
T_gains = np.array(T_gains)
A_gains = np.array(A_gains)
# initialize vectors of gain phase means and inverse standard deviations
gainphase_mu = np.zeros(N_gains)
gainphase_kappa = 0.0001*np.ones(N_gains)
# set reference station standard devation to be tiny
for it, t in enumerate(timestamps):
index = (T_gains == t)
ants_here = A_gains[index]
for ant in ants_here:
if ant == ref_station:
ind = ((T_gains == t) & (A_gains == ant))
gainphase_kappa[ind] = 10000.0
break
return gainphase_mu, gainphase_kappa
def from_rd(self, x: int, y: int) -> tuple:
"""
Converts RD coordinates into WGS84 coordinates
"""
dx = 1E-5 * (x - self.x0)
dy = 1E-5 * (y - self.y0)
latitude = self.phi0 + sum([
v * dx**self.Kp[i] * dy**self.Kq[i] for i, v in enumerate(self.Kpq)
]) / 3600
longitude = self.lam0 + sum([
v * dx**self.Lp[i] * dy**self.Lq[i] for i, v in enumerate(self.Lpq)
]) / 3600
return latitude, longitude
def initialize(self):
for i, ob in enumerate(self.objects):
print("Initializing object %d of %d..." %
(i + 1, len(self.objects)))
ob.initialize()
print("Initializing %d satellites..." % len(self.sat_group))
self.sat_group.initialize()
for i, an in enumerate(self.analyses):
print("Initializing analysis %d of %d..." %
(i + 1, len(self.analyses)))
an.initialize()
def parse(input, boost):
_, immune, infection = [
x.strip() for x in re.split(r'Immune System:|Infection:', input)
]
good, bad = set(), set()
for i, line in enumerate(immune.split('\n')):
sq = parse_squad(line, Type.Immune, i + 1, boost)
good.add(sq)
for i, line in enumerate(infection.split('\n')):
sq = parse_squad(line, Type.Infection, i + 1, 0)
bad.add(sq)
return good, bad
def print_tree(self, node):
rows = self.str_node(node)
max_len = max([len(r) for r in rows])
rows = [f'{r}{" "*(max_len - len(r))}' for r in rows]
transposed = [[] for _ in range(max_len)]
for j, r in enumerate(rows):
for i, s in enumerate(r):
transposed[i].append(s)
rows = [' '.join(r) for r in transposed]
for r in rows:
print(r)
print('----------------------------------------------')
def Clean_Wav_File(timeList):
Vaw_obj_list = [
"CF003 - Active - Day - (214).wav", "CF003 - Active - Day - (215).wav",
"CF003 - Active - Day - (216).wav"
]
#Vaw_obj_list = list_of_sound_file_name
for counter, i in enumerate(Vaw_obj_list):
newAudio = AudioSegment.from_wav("Data\\" + i)
for num, a in enumerate(timeList[counter]):
newAu = newAudio[a[0] * 1000:a[1] * 1000]
newAu.export("Cleaned Data\\Cleaned_Voice\\" + i + "--" +
(a[2])[:-1] + "--" + str(num) + ".wav",
format="wav")
def enumerate(reversible, reverse_index=False):
'''
Iterate over `(i, item)` pairs, where `i` is the index number of `item`.
This is an extension of the builtin `enumerate`. What it allows is to get a
reverse index, by specifying `reverse_index=True`. This causes `i` to count
down to zero instead of up from zero, so the `i` of the last member will be
zero.
'''
if reverse_index is False:
return builtins.enumerate(reversible)
else:
my_list = list(builtins.enumerate(reversed(reversible)))
my_list.reverse()
return my_list
def predict():
test_contents, test_labels = load_corpus('./dataset/test.txt',
word2id,
max_sen_len=50)
# 加载测试集
test_dataset = TensorDataset(
torch.from_numpy(test_contents).type(torch.float),
torch.from_numpy(test_labels).type(torch.long))
test_dataloader = DataLoader(dataset=test_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=2)
# 读取模型
model = TextCNN(config)
model.load_state_dict(torch.load(config.model_path))
model.eval()
model.to(device)
# 测试过程
count, correct = 0, 0
for _, (batch_x, batch_y) in enumerate(test_dataloader):
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
output = model(batch_x)
# correct += (output.argmax(1) == batch_y).float().sum().item()
correct += (output.argmax(1) == batch_y).sum().item()
count += len(batch_x)
# 打印准确率
print('test accuracy is {:.2f}%.'.format(100 * correct / count))
def MOD_check(data, O):
check_MOD = True
check_ED = False
char_check = ""
checked = False
for i, line in enumerate(data):
if "`show module`" in line:
char_check = ''.join(line.split()[0][0])
for l in data[i + 1:i + 10]:
if "Mod" in l.split():
for j in data[i:i + 1000]:
if "`show" not in j.split():
if "DS-X9316-SSNK9" in j:
#O.write("Disruptive Model Exists => Module " + j + "\n")
notes_arr.append(
"Disruptive Model Exists => Module " + j +
"\n")
check_MOD = False
if "Supervisor" in j:
if "active *" or "ha-standby" in j and not check_ED:
#O.write("Director Model" + "\n")
check_ED = True
if "faulty" in j or "shutdown" in j or "powered-dn" in j or "down" in j or "DOWN" in j \
or "UNKNOWN" in j or "fail" in j or "FAULTY" in j or "Faulty" in j or \
"Powered-DN" in j or "Powered-Down" in j:
#O.write("Faulty Module => " + j + "\n")
notes_arr.append("Faulty Module => " + j +
"\n")
check_MOD = False
break
else:
break
else:
continue
return check_MOD
def set_predicate(self):
"""
Sets a new predicate category in a predicate node
Used in the relationship change mode
"""
if self.selected_node is not None:
# extract user input
new_label = self.comboBox.currentText()
idx_t = self.selected_node.split(".")[1]
mapping = {self.selected_node: new_label + "." + idx_t}
# update list of relationship triples with the change
self.triples[int(idx_t)][1] = vocab["pred_name_to_idx"][new_label]
s = self.triples[int(idx_t)][0]
self.keep_box_idx[s] = 0
self.keep_image_idx[s] = 0
self.new_triples = self.triples
# objects remain the same
self.new_objs = self.objs
# update the networkx graph and the list of triples for visualization
for idx, [s, p, o] in enumerate(self.curr_triples):
if p == self.selected_node:
self.curr_triples[idx][1] = new_label + "." + idx_t
self.pos[new_label + "." + idx_t] = self.pos[self.selected_node]
del self.pos[self.selected_node]
self.graph = nx.relabel_nodes(self.graph, mapping, copy=False)
self.selected_node = self.comboBox.currentText()
self.mode = "reposition"
self.set_graph()
def simulate(self,
t_start=None,
t_stop=None,
t_step=None,
animate=False,
follow=None):
if t_start is not None: self.t_start = t_start
if t_stop is not None: self.t_stop = t_stop
if t_step is not None: self.t_step = t_step
self._generate_time_vector(self.t_start, self.t_stop, self.t_step)
t_sim_start = timer() # simulation start time
for i, tof in enumerate(self._tof_vector):
t_sim_step = timer()
self.tof_current = tof
self.step(animate)
print("t=%2.1f s (%5.3f ms)" % (self.t[i],
(timer() - t_sim_step) * 1000))
if not follow is None:
mlab.view(azimuth=np.mod(
-20 +
np.arctan2(follow._xyz[1].to(u.km).value, follow._xyz[0].
to(u.km).value) * 180 / np.pi, 360))
yield
# if animate:
# yield
print("Total simulation time: %5.5f s" % (timer() - t_sim_start))
def HW_check(data, O):
check_HW = True
v_check = False
for i, line in enumerate(data):
if "`show hardware`" in line and check_HW:
char_check = ''.join(line.split()[0][0])
for l in data[i + 1:i + 100]:
if "Switch is booted up" in l:
for j in data[i + 1:i + 10000]:
if char_check not in j.split() and not v_check:
if "kickstart:" in j:
kickstart_version = j.split(": ",
1)[1].split()[1]
#O.write("\n" + "Current Version = " + kickstart_version + "\n")
v_check = True
#O.write("version --- Checked" + "\n")
if "Module" in j or "PS" in j or "Fan" in j or "Xbar" in j:
if "faulty" in j or "shutdown" in j or "powered-dn" in j or "down" in j or "DOWN" in j \
or "UNKNOWN" in j or "fail" in j or "FAULTY" in j or "Faulty" in j or \
"Powered-DN" in j or "Powered-Down" in j:
#O.write("Faulty => " + j + "\n")
notes_arr.append("Faulty => " + j + "\n")
check_HW = False
break
else:
break
return kickstart_version
async def refine(self, input, output):
if input:
if self.place_input == 'last':
sources = (*self.sources, input)
else:
sources = (input, *self.sources)
else:
sources = self.sources
async with trio.open_nursery() as weld_nursery:
sources = [weld(weld_nursery, source) for source in sources]
async def pull_task(source, index, results: list):
try:
results.append((index, await source.__anext__()))
except StopAsyncIteration:
weld_nursery.cancel_scope.cancel()
while True:
results = []
async with trio.open_nursery() as pull_nursery:
for i, source in builtins.enumerate(sources):
pull_nursery.start_soon(pull_task, source, i, results)
await output(tuple(result for i, result in sorted(results, key=lambda packet: packet[0])))
for source in sources:
await source.aclose()
def model_vs_code(data):
flag_6_2 = False
flag_7_3 = False
flag_8_4 = False
upto_6_2 = ["9222i"]
upto_7_3 = ["9506", "9509", "9513", "9148"]
upto_8_4 = [
"9710", "9250i", "9706", "9148S", "9148T", "9396S", "9178", "9132T",
"9396T"
]
for i, line in enumerate(data):
if "`show sprom backplane 1`" in line:
for l in data[i:i + 500]:
if 'Product Number :' in l:
if "C" in l.split("-")[1]:
model = l.split("-")[1].split("C")[1]
for x in range(len(upto_6_2)):
if upto_6_2[x] in model:
flag_6_2 = True
break
for x in range(len(upto_7_3)):
if upto_7_3[x] in model:
flag_7_3 = True
break
for x in range(len(upto_8_4)):
if upto_8_4[x] in model:
flag_8_4 = True
break
return flag_6_2, flag_7_3, flag_8_4
async def zip_longest(*itrs: AnyIterable[Any],
fillvalue: Any = None) -> AsyncIterator[Tuple[Any, ...]]:
"""
Yield a tuple of items from mixed iterables until all are consumed.
If shorter iterables are exhausted, the default value will be used
until all iterables are exhausted.
Example:
a = range(3)
b = range(5)
async for a, b in zip_longest(a, b, fillvalue=-1):
a # 0, 1, 2, -1, -1
b # 0, 1, 2, 3, 4
"""
its: List[AsyncIterator[Any]] = [iter(itr) for itr in itrs]
itr_count = len(its)
finished = 0
while True:
values = await asyncio.gather(*[it.__anext__() for it in its],
return_exceptions=True)
for idx, value in builtins.enumerate(values):
if isinstance(value, AnyStop):
finished += 1
values[idx] = fillvalue
its[idx] = repeat(fillvalue)
elif isinstance(value, BaseException):
raise value
if finished >= itr_count:
break
yield tuple(values)
def test_extractall(self):
# Check numeric groups
res = evaluate(extractall('Step: (?P<no>\d+)', self.tempfile, 1))
for expected, v in builtins.enumerate(res, start=1):
self.assertEqual(str(expected), v)
# Check named groups
res = evaluate(extractall('Step: (?P<no>\d+)', self.tempfile, 'no'))
for expected, v in builtins.enumerate(res, start=1):
self.assertEqual(str(expected), v)
# Check convert function
res = evaluate(
extractall('Step: (?P<no>\d+)', self.tempfile, 'no', builtins.int))
for expected, v in builtins.enumerate(res, start=1):
self.assertEqual(expected, v)
def fn_bracket():
brackets = input()
stack = []
depths = []
for bracket in brackets:
flag = False
if stack:
if stack[-1] == "(" and bracket == ")":
flag = True
depths += [[len(stack), 2]]
stack.pop()
elif stack[-1] == "[" and bracket == "]":
flag = True
depths += [[len(stack), 3]]
stack.pop()
if bracket == '(' or bracket == '[':
flag = True
stack.append(bracket)
if not flag:
return 0
while depths:
max_index = max(enumerate(depths), key=lambda x:x[1][0])[0]
def using_dic2(nums: List[int], target: int) -> List[int]:
nums_map = {}
#하나의 for 문으로 통합
for i, num in enumerate(nums):
if target - num in nums_map:
return [nums_map[target - num], i]
nums_map[num] = i
def define_playlist_values(playlist_json):
""" Dictionary of only the values we need to transmit to slave players
'playlistInfo': dict{
total_duration: int*
total_items: int*
}
'mainPlaylist': list of dicts [{
sequenceName: str*
}]
'name': str
"""
values_to_send = [
{
"total_duration":
playlist_json["playlistInfo"]["total_duration"]
},
{
"total_items": playlist_json["playlistInfo"]["total_items"]
},
]
for i, value in enumerate(playlist_json["mainPlaylist"]):
values_to_send.append(
{"sequenceName" + str(i): value["sequenceName"]})
return values_to_send
def managerOrdersView(request, *args, **kwargs):
context = {}
if request.user.is_authenticated:
try:
orders = list(Order.objects.all())
except:
orders = []
# is_not_saved = True
for id, order in enumerate(orders):
if request.POST:
form = OrderStatusForm(request.POST, instance=order)
if form.is_valid():
form.save()
print(request.POST)
print("ZASEJWOWANE")
else:
print("Przypisany form1")
else:
form = OrderStatusForm(instance=order)
print("Przypisany form2")
orders[id] = (order, form, id)
print(orders)
print("Context:")
context = {"user": request.user, "userType": request.user.type,
"orders": orders}
print(context)
return render(request, "manager-orders.html", context)
else:
context = {}
return render(request, "manager-orders.html", context)
def test():
from operator import add
a = range(0, 100, 2)
b = range(100)
mab = map(add, a, b)
assert list(mab) == list(range(0, 150, 3))
iab = islice(mab, -10, None, 2)
assert list(iab) == list(range(120, 150, 6))
ieab = islice(enumerate(mab), -10, None, 2)
assert list(ieab) == list(builtins.enumerate(mab))[-10::2]
eiab = enumerate(iab)
assert list(eiab) == list(builtins.enumerate(range(120, 150, 6)))
fb = filter(lambda x: x % 2, b)
assert list(fb) == list(range(100)[1::2])
def range( title, *args ):
'''Progress logger identical to built in range'''
items = builtins.range(*args)
for index, item in builtins.enumerate(items):
with _current_log.context('{} {} ({:.0f}%)'.format(title, item, index*100/len(items))):
yield item
def test():
from operator import add
a = range(0, 100, 2)
b = range(100)
mab = map(add, a, b)
assert list(mab) == list(range(0, 150, 3))
iab = islice(mab, -10, None, 2)
assert list(iab) == list(range(120, 150, 6))
ieab = islice(enumerate(mab), -10, None, 2)
assert list(ieab) == list(builtins.enumerate(mab))[-10::2]
eiab = enumerate(iab)
assert list(eiab) == list(builtins.enumerate(range(120, 150, 6)))
fb = filter(lambda x: x%2, b)
assert list(fb) == list(range(100)[1::2])
def get_hamming_distance(motif1, motif2):
count = 0
for i, (char1, char2) in enumerate(zip(motif1, motif2)):
if char1 != char2:
count += 1
return count
def _enumerate(iterable, reverse_index):
if reverse_index is False:
return builtins.enumerate(iterable)
else:
from python_toolbox import sequence_tools
try:
length = sequence_tools.get_length(iterable)
except AttributeError:
iterable = nifty_collections.LazyTuple(iterable)
length = len(iterable)
return zip(range(length - 1, -1, -1), iterable)
def lineup_dict(aw_lineup, hm_lineup):
"""Returns a dictionary of lineups to be converted to columns.
Specifically, the columns are 'aw_player1' through 'aw_player5' and
'hm_player1' through 'hm_player5'.
:param aw_lineup: The away team's current lineup.
:param hm_lineup: The home team's current lineup.
:returns: A dictionary of lineups.
"""
return {
'{}_player{}'.format(tm, i+1): player
for tm, lineup in zip(['aw', 'hm'], [aw_lineup, hm_lineup])
for i, player in enumerate(lineup)
}
def enumerate(title, iterable): return iter(title, builtins.enumerate(iterable), length=_len(iterable))
def __reversed__(self):
l = len(self._iterable)
for i, value in builtins.enumerate(reversed(self._iterable)):
yield l-i-1+self._start, value
def enumerate( title, iterable ): '''Progress logger identical to built in enumerate''' return iter(title, builtins.enumerate(iterable), length=_len(iterable))
def __iter__(self):
yield from builtins.enumerate(self._iterable, self._start)