function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def test_sum(self):
self.assertEqualOnAllArrayTypes(sum_values, [1, 2, 3, 4.1], 10.1)
self.assertEqualOnAllArrayTypes(sum_values, [-1.2, -2, -3, -4], -10.2)
self.assertEqualOnAllArrayTypes(sum_values, [], 0) | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_agg_autocorrelation_returns_max_lag_does_not_affect_other_results(self):
param = [{"f_agg": "mean", "maxlag": 1}, {"f_agg": "mean", "maxlag": 10}]
x = range(10)
res1 = dict(agg_autocorrelation(x, param=param))['f_agg_"mean"__maxlag_1']
res10 = dict(agg_autocorrelation(x, param=... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_augmented_dickey_fuller(self):
# todo: add unit test for the values of the test statistic
# the adf hypothesis test checks for unit roots,
# so H_0 = {random drift} vs H_1 = {AR(1) model}
# H0 is true
np.random.seed(seed=42)
x = np.cumsum(np.random.uniform(size... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_cid_ce(self):
self.assertEqualOnAllArrayTypes(cid_ce, [1, 1, 1], 0, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [0, 4], 2, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [100, 104], 2, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [1, 1, 1], 0, no... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_fourier_entropy(self):
self.assertAlmostEqualOnAllArrayTypes(
fourier_entropy, [1, 2, 1], 0.693147180, bins=2
)
self.assertAlmostEqualOnAllArrayTypes(
fourier_entropy, [1, 2, 1], 0.693147180, bins=5
)
self.assertAlmostEqualOnAllArrayTypes(
... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_ratio_beyond_r_sigma(self):
x = [0, 1] * 10 + [10, 20, -30] # std of x is 7.21, mean 3.04
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 3.0 / len(x), r=1)
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 2.0 / len(x), r=2)
self.assertEqualOnAllArrayTypes(rat... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_mean_change(self):
self.assertEqualOnAllArrayTypes(mean_change, [-2, 2, 5], 3.5)
self.assertEqualOnAllArrayTypes(mean_change, [1, 2, -1], -1)
self.assertEqualOnAllArrayTypes(mean_change, [10, 20], 10)
self.assertIsNanOnAllArrayTypes(mean_change, [1])
self.assertIsNanOnAl... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_median(self):
self.assertEqualOnAllArrayTypes(median, [1, 1, 2, 2], 1.5)
self.assertEqualOnAllArrayTypes(median, [0.5, 0.5, 2, 3.5, 10], 2)
self.assertEqualOnAllArrayTypes(median, [0.5], 0.5)
self.assertIsNanOnAllArrayTypes(median, []) | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_length(self):
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, 4], 4)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3], 3)
self.assertEqualOnAllArrayTypes(length, [1, 2], 2)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, np.NaN], 4)
self.assertEqualOnAllArrayTypes(l... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_variation_coefficient(self):
self.assertIsNanOnAllArrayTypes(
variation_coefficient, [1, 1, -1, -1],
)
self.assertAlmostEqualOnAllArrayTypes(
variation_coefficient, [1, 2, -3, -1], -7.681145747868608
)
self.assertAlmostEqualOnAllArrayTypes(
... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_skewness(self):
self.assertEqualOnAllArrayTypes(skewness, [1, 1, 1, 2, 2, 2], 0)
self.assertAlmostEqualOnAllArrayTypes(
skewness, [1, 1, 1, 2, 2], 0.6085806194501855
)
self.assertEqualOnAllArrayTypes(skewness, [1, 1, 1], 0)
self.assertIsNanOnAllArrayTypes(ske... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_root_mean_square(self):
self.assertAlmostEqualOnAllArrayTypes(
root_mean_square, [1, 1, 1, 2, 2], 1.4832396974191
)
self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [0], 0)
self.assertIsNanOnAllArrayTypes(root_mean_square, [])
self.assertAlmostEqualOnA... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_absolute_sum_of_changes(self):
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, 1, 1, 1, 2, 1], 2)
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, -1, 1, -1], 6)
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1], 0)
self.assertEqualOnAllArrayT... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_longest_strike_above_mean(self):
self.assertEqualOnAllArrayTypes(
longest_strike_above_mean, [1, 2, 1, 2, 1, 2, 2, 1], 2
)
self.assertEqualOnAllArrayTypes(
longest_strike_above_mean, [1, 2, 3, 4, 5, 6], 3
)
self.assertEqualOnAllArrayTypes(longest_... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_count_below_mean(self):
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 2, 1, 2, 1, 2], 3)
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 1, 1, 1, 1, 2], 5)
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 1, 1, 1, 1], 0)
self.assertEqualOnAllArrayTypes(count_b... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_first_location_of_maximum(self):
self.assertAlmostEqualOnAllArrayTypes(
first_location_of_maximum, [1, 2, 1, 2, 1], 0.2
)
self.assertAlmostEqualOnAllArrayTypes(
first_location_of_maximum, [1, 2, 1, 1, 2], 0.2
)
self.assertAlmostEqualOnAllArrayType... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_first_location_of_minimum(self):
self.assertAlmostEqualOnAllArrayTypes(
first_location_of_minimum, [1, 2, 1, 2, 1], 0.0
)
self.assertAlmostEqualOnAllArrayTypes(
first_location_of_minimum, [2, 2, 1, 2, 2], 0.4
)
self.assertAlmostEqualOnAllArrayType... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_ratio_of_doubled_values(self):
self.assertAlmostEqualOnAllArrayTypes(
percentage_of_reoccurring_values_to_all_values, [1, 1, 2, 3, 4], 0.25
)
self.assertAlmostEqualOnAllArrayTypes(
percentage_of_reoccurring_values_to_all_values, [1, 1.5, 2, 3], 0
)
... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_sum_of_reoccurring_data_points(self):
self.assertAlmostEqualOnAllArrayTypes(
sum_of_reoccurring_data_points, [1, 1, 2, 3, 4, 4], 10
)
self.assertAlmostEqualOnAllArrayTypes(
sum_of_reoccurring_data_points, [1, 1.5, 2, 3], 0
)
self.assertAlmostEqual... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_fft_coefficient(self):
x = range(10)
param = [
{"coeff": 0, "attr": "real"},
{"coeff": 1, "attr": "real"},
{"coeff": 2, "attr": "real"},
{"coeff": 0, "attr": "imag"},
{"coeff": 1, "attr": "imag"},
{"coeff": 2, "attr": "imag... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def normal(y, mean_, sigma_):
return (
1
/ (2 * np.pi * sigma_ ** 2)
* np.exp(-((y - mean_) ** 2) / (2 * sigma_ ** 2))
) | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_number_peaks(self):
x = np.array([0, 1, 2, 1, 0, 1, 2, 3, 4, 5, 4, 3, 2, 1])
self.assertEqualOnAllArrayTypes(number_peaks, x, 2, 1)
self.assertEqualOnAllArrayTypes(number_peaks, x, 2, 2)
self.assertEqualOnAllArrayTypes(number_peaks, x, 1, 3)
self.assertEqualOnAllArrayTyp... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_number_cwt_peaks(self):
x = [1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1]
self.assertEqualOnAllArrayTypes(number_cwt_peaks, x, 2, 2) | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_cwt_coefficients(self):
x = [0.1, 0.2, 0.3]
param = [
{"widths": (1, 2, 3), "coeff": 2, "w": 1},
{"widths": (1, 3), "coeff": 2, "w": 3},
{"widths": (1, 3), "coeff": 5, "w": 3},
]
shuffle(param)
expected_index = [
"coeff_2_... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_time_reversal_asymmetry_statistic(self):
x = [1] * 10
self.assertAlmostEqualOnAllArrayTypes(
time_reversal_asymmetry_statistic, x, 0, 0
)
self.assertAlmostEqualOnAllArrayTypes(
time_reversal_asymmetry_statistic, x, 0, 1
)
self.assertAlmost... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_c3(self):
x = [1] * 10
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 0)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 1)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 2)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 3)
x = [1, 2, -3, 4]
# 1/2 *(1... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_sample_entropy(self):
# "random" list -> large entropy
ts = [
1,
4,
5,
1,
7,
3,
1,
2,
5,
8,
9,
7,
3,
7,
9,
... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_quantile(self):
self.assertAlmostEqualOnAllArrayTypes(
quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 1.0, 0.2
)
self.assertAlmostEqualOnAllArrayTypes(
quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 13, 0.9
)
self.assertAlmostEqualOnAllArrayTypes(
... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_value_count(self):
self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 10, value=1)
self.assertEqualPandasSeriesWrapper(value_count, list(range(10)), 1, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 0, value=0)
self.assertEqualPandasSeriesWrapper(val... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_approximate_entropy(self):
self.assertEqualOnAllArrayTypes(approximate_entropy, [1], 0, m=2, r=0.5)
self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2], 0, m=2, r=0.5)
self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2, 3], 0, m=2, r=0.5)
self.assertEqualOnAllArr... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_max_langevin_fixed_point(self):
"""
Estimating the intrinsic velocity of a dissipative soliton
"""
default_params = {"m": 3, "r": 30}
# active Brownian motion
ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(100000, v0=np.zeros(1))
... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test__aggregate_on_chunks(self):
self.assertListEqual(
_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg="max", chunk_len=2),
[1, 3],
)
self.assertListEqual(
_aggregate_on_chunks(x=pd.Series([1, 1, 3, 3]), f_agg="max", chunk_len=2),
[1, 3],... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_energy_ratio_by_chunks(self):
x = pd.Series(range(90), index=range(90))
param = [{"num_segments": 6, "segment_focus": i} for i in range(6)]
output = energy_ratio_by_chunks(x=x, param=param)
self.assertAlmostEqual(output[0][1], 0.0043, places=3)
self.assertAlmostEqual(ou... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_linear_trend_timewise_days(self):
"""Test linear_trend_timewise function with day intervals."""
# Try with different days
x = pd.Series(
[0, 24, 48, 72],
index=pd.DatetimeIndex(
[
"2018-01-01 04:00:00",
"201... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_linear_trend_timewise_years(self):
"""Test linear_trend_timewise function with year intervals."""
# Try with different days
x = pd.Series(
[
0,
365 * 24,
365 * 48,
365 * 72 + 24,
], # Add 24 to the ... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_count_above(self):
self.assertEqualPandasSeriesWrapper(count_above, [1] * 10, 1, t=1)
self.assertEqualPandasSeriesWrapper(count_above, list(range(10)), 1, t=0)
self.assertEqualPandasSeriesWrapper(count_above, list(range(10)), 0.5, t=5)
self.assertEqualPandasSeriesWrapper(
... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_benford_correlation(self):
# A test with list of random values
np.random.seed(42)
random_list = np.random.uniform(size=100)
# Fibonacci series is known to match the Newcomb-Benford's Distribution
fibonacci_list = [0, 1]
for i in range(2, 200):
fibona... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_matrix_profile_window(self):
# Test matrix profile output with specified window
np.random.seed(9999)
ts = np.random.uniform(size=2 ** 10)
w = 2 ** 5
subq = ts[0:w]
ts[0:w] = subq
ts[w + 100 : w + 100 + w] = subq
param = [
{"threshold":... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_matrix_profile_nan(self):
# Test matrix profile of NaNs (NaN output)
ts = np.random.uniform(size=2 ** 6)
ts[:] = np.nan
param = [
{"threshold": 0.98, "windows": None, "feature": "min"},
{"threshold": 0.98, "windows": None, "feature": "max"},
... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_estimate_friedrich_coefficients(self):
"""
Estimate friedrich coefficients
"""
default_params = {"m": 3, "r": 30}
# active Brownian motion
ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(10000, v0=np.zeros(1))
coeff = _estima... | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def test_friedrich_number_of_returned_features_is_equal_to_number_of_parameters(
self, | blue-yonder/tsfresh | [
7135,
1120,
7135,
61,
1477481357
] |
def run_L_D_simulation(self, L, D):
# L = duplication length
# D = number of DCJs in each branch.
#
param = self.sim_parameters
# pre_dups (at root) and post_dups (at branches) to achieve 1.5 genes/family in average.
pre_duplications = int(0.43 * param.num_genes / L)
post_duplications = int(... | pedrofeijao/RINGO | [
4,
1,
4,
2,
1467712417
] |
def __init__(self, handler_):
assert callable(handler_)
super().__init__()
self._handler = handler_
self._event = threading.Event()
self._thread = None | uniflex/uniflex | [
3,
2,
3,
1,
1478094848
] |
def cancel(self):
if (not self._thread) or (not self._thread.is_alive()):
return
self._event.set()
# self._thread.join()
self._thread = None | uniflex/uniflex | [
3,
2,
3,
1,
1478094848
] |
def _timer(self, interval):
# Avoid cancellation during execution of self._callable()
cancel = self._event.wait(interval)
if cancel:
return
self._handler() | uniflex/uniflex | [
3,
2,
3,
1,
1478094848
] |
def __init__(self, app, ev_cls):
super(TimerEventSender, self).__init__(self._timeout)
self._app = app
self._ev_cls = ev_cls | uniflex/uniflex | [
3,
2,
3,
1,
1478094848
] |
def __init__(self, credentials):
output.startup_message(credentials)
self.credentials = credentials
self.reddit = self.connect()
self.NUM_POSTS = 20 | tylerbrockett/reddit-bot-buildapcsales | [
117,
10,
117,
4,
1447791069
] |
def disconnect(self):
self.reddit = None | tylerbrockett/reddit-bot-buildapcsales | [
117,
10,
117,
4,
1447791069
] |
def get_instance(self):
return self.reddit | tylerbrockett/reddit-bot-buildapcsales | [
117,
10,
117,
4,
1447791069
] |
def get_message(self, message_id):
return self.reddit.inbox.message(message_id) | tylerbrockett/reddit-bot-buildapcsales | [
117,
10,
117,
4,
1447791069
] |
def get_submissions(self, subreddit):
submissions = []
posts = 200 if (subreddit == 'all') else self.NUM_POSTS
try:
subs = self.reddit.subreddit(subreddit).new(limit=posts)
for submission in subs:
submissions.append(submission)
except Forbidden as ... | tylerbrockett/reddit-bot-buildapcsales | [
117,
10,
117,
4,
1447791069
] |
def check_invalid_subreddits(self, subreddits):
invalid = []
for subreddit in subreddits:
try:
for submission in self.reddit.subreddit(subreddit).new(limit=1):
print('subreddit is valid')
except Redirect: # was praw.errors.InvalidSubreddit wit... | tylerbrockett/reddit-bot-buildapcsales | [
117,
10,
117,
4,
1447791069
] |
def test_pack():
assert pwny.pack('I', 0x41424344) == b'DCBA' | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def test_pack_explicit_endian():
assert pwny.pack('I', 0x41424344, endian=pwny.Target.Endian.big) == b'ABCD' | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def test_pack_invalid_endian():
pwny.pack('I', 1, endian='invalid') | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def test_unpack_format_with_endian():
assert pwny.unpack('>I', b'ABCD') == (0x41424344,) | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def test_unpack_explicit_target():
assert pwny.unpack('I', b'ABCD', target=target_big_endian) == (0x41424344,) | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def test_unpack_invalid_endian():
pwny.unpack('I', 'AAAA', endian='invalid') | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def test_short_form_pack():
for width, num, bytestr in short_signed_data:
f = 'p%d' % width
yield check_short_form_pack, f, num, bytestr[::-1]
yield check_short_form_pack_endian, f, num, bytestr[::-1], pwny.Target.Endian.little
yield check_short_form_pack_endian, f, num, bytestr, pwn... | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def test_pointer_pack():
yield check_short_form_pack, 'p', -66052, b'\xfc\xfd\xfe\xff'
yield check_short_form_pack_endian, 'p', -66052, b'\xfc\xfd\xfe\xff', pwny.Target.Endian.little
yield check_short_form_pack_endian, 'p', -66052, b'\xff\xfe\xfd\xfc', pwny.Target.Endian.big
yield check_short_form_pack... | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def check_short_form_pack(f, num, bytestr):
assert getattr(pwny, f)(num) == bytestr | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def check_short_form_unpack(f, num, bytestr):
assert getattr(pwny, f)(bytestr) == num | edibledinos/pwnypack | [
120,
28,
120,
1,
1426724447
] |
def test_make_request_timeout():
"""
Remote calls should time out
"""
httpretty.register_uri(httpretty.GET, "www.example.com",
body=None,
)
# When I make an API request and receive no response
c = BaseClient()
# Then I should raise a New... | andrewgross/pyrelic | [
21,
12,
21,
2,
1333416631
] |
def test_make_request_non_200():
"""
Bad HTTP Responses should throw an error
"""
httpretty.register_uri(httpretty.GET, "http://foobar.com",
body="123", status=400)
# When I make an API request and receive a 400
c = BaseClient()
# Then I should raise the appropria... | andrewgross/pyrelic | [
21,
12,
21,
2,
1333416631
] |
def __init__(self, order, dot=True, **kwargs):
self.order = order
self._dot = dot
super(FixedPermutation, self).__init__(**kwargs) | mila-udem/blocks-extras | [
27,
40,
27,
9,
1430419450
] |
def input_dim(self):
return len(self.order) | mila-udem/blocks-extras | [
27,
40,
27,
9,
1430419450
] |
def apply(self, input_):
if self._dot:
return tensor.dot(input_, self._matrix)
else:
return tensor.take(input_, self._permutation, axis=1) | mila-udem/blocks-extras | [
27,
40,
27,
9,
1430419450
] |
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list_locations(
self,
subscription_id: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
if not next_link: | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list(
self,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
if not next_link: | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def build_list_by_subscription_request(
subscription_id: str,
*,
filter: Optional[str] = None,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def build_list_by_resource_group_request(
subscription_id: str,
resource_group_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def build_get_request(
subscription_id: str,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def build_create_or_update_request_initial(
subscription_id: str,
resource_group_name: str,
lab_name: str,
*,
json: JSONType = None,
content: Any = None,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def build_update_request_initial(
subscription_id: str,
resource_group_name: str,
lab_name: str,
*,
json: JSONType = None,
content: Any = None,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def build_delete_request_initial(
subscription_id: str,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def build_publish_request_initial(
subscription_id: str,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def build_sync_group_request_initial(
subscription_id: str,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list_by_subscription(
self,
filter: Optional[str] = None,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
if not next_link: | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def extract_data(pipeline_response):
deserialized = self._deserialize("PagedLabs", pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, iter(list_of_elem) | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list_by_resource_group(
self,
resource_group_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
if not next_link: | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def extract_data(pipeline_response):
deserialized = self._deserialize("PagedLabs", pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, iter(list_of_elem) | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get(
self,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def _create_or_update_initial(
self,
resource_group_name: str,
lab_name: str,
body: "_models.Lab",
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def begin_create_or_update(
self,
resource_group_name: str,
lab_name: str,
body: "_models.Lab",
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get_long_running_output(pipeline_response):
response = pipeline_response.http_response
deserialized = self._deserialize('Lab', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def _update_initial(
self,
resource_group_name: str,
lab_name: str,
body: "_models.LabUpdate",
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def begin_update(
self,
resource_group_name: str,
lab_name: str,
body: "_models.LabUpdate",
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get_long_running_output(pipeline_response):
response = pipeline_response.http_response
deserialized = self._deserialize('Lab', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def _delete_initial(
self,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def begin_delete(
self,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {}) | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def _publish_initial(
self,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def begin_publish(
self,
resource_group_name: str,
lab_name: str,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {}) | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.