uid stringlengths 24 24 | split stringclasses 1
value | category stringclasses 2
values | content stringlengths 5 482k | signature stringlengths 1 14k | suffix stringlengths 1 482k | prefix stringlengths 9 14k | prefix_token_count int64 3 5.01k | prefix_token_budget int64 64 256 | element_token_count int64 1 292k | signature_token_count int64 1 5.01k | prefix_context_token_count int64 0 255 | repo stringlengths 7 112 | path stringlengths 4 208 | language stringclasses 1
value | name stringlengths 1 218 | qualname stringlengths 1 218 | start_line int64 1 26.7k | end_line int64 1 26.7k | signature_start_line int64 1 26.7k | signature_end_line int64 1 26.7k | source_hash stringlengths 40 40 | source_dataset stringclasses 1
value | source_split stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9601d8cf4798611b48192bde | train | class | class Sweep_wave_familiarity(Sweep_wave):
"""
This modulated version of the sweep wave is as per the Sweep wave but the reverse in direction of the sweep wave
can be instigated based on the state of the visual processing function. Here torf is used to evaluate the view
familiarity and the duration of th... | class Sweep_wave_familiarity(Sweep_wave):
| """
This modulated version of the sweep wave is as per the Sweep wave but the reverse in direction of the sweep wave
can be instigated based on the state of the visual processing function. Here torf is used to evaluate the view
familiarity and the duration of the sweepback is calculated based on the loc... | 2.0)
self.preconditions_satisfied = True
def set_new_heading_timer(self, duration):
self.new_heading_timer = rospy.Timer(rospy.Duration(duration), self.new_heading_callback, oneshot=True)
def new_heading_callback(self, event):
"""
callback from new heading timer creates and eve... | 256 | 256 | 2,671 | 10 | 245 | jannsta1/torf | src/flight_states.py | Python | Sweep_wave_familiarity | Sweep_wave_familiarity | 110 | 364 | 110 | 110 | c2307ec86cb854d9a07eaa5d432bb5cbd985359c | bigcode/the-stack | train |
257aee170085ab817b8b061e | train | class | class MinimalPublisher(Node):
def __init__(self):
super().__init__('minimal_publisher')
self.publisher_ = self.create_publisher(String, 'topic')
timer_period = 0.5 # seconds
self.timer = self.create_timer(timer_period, self.timer_callback)
self.i = 0
def timer_callback... | class MinimalPublisher(Node):
| def __init__(self):
super().__init__('minimal_publisher')
self.publisher_ = self.create_publisher(String, 'topic')
timer_period = 0.5 # seconds
self.timer = self.create_timer(timer_period, self.timer_callback)
self.i = 0
def timer_callback(self):
msg = String()
... | an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
class MinimalPublisher(Node):
| 64 | 64 | 124 | 5 | 58 | anasarrak/examples | rclpy/topics/minimal_publisher/examples_rclpy_minimal_publisher/publisher_member_function.py | Python | MinimalPublisher | MinimalPublisher | 21 | 35 | 21 | 22 | ed612fe2cbad1caf4f3847fcacdcd7d1b733eabb | bigcode/the-stack | train |
50efe06a7c68503295527d6d | train | function | def main(args=None):
rclpy.init(args=args)
minimal_publisher = MinimalPublisher()
rclpy.spin(minimal_publisher)
# Destroy the node explicitly
# (optional - otherwise it will be done automatically
# when the garbage collector destroys the node object)
minimal_publisher.destroy_node()
r... | def main(args=None):
| rclpy.init(args=args)
minimal_publisher = MinimalPublisher()
rclpy.spin(minimal_publisher)
# Destroy the node explicitly
# (optional - otherwise it will be done automatically
# when the garbage collector destroys the node object)
minimal_publisher.destroy_node()
rclpy.shutdown()
| 0
def timer_callback(self):
msg = String()
msg.data = 'Hello World: %d' % self.i
self.publisher_.publish(msg)
self.get_logger().info('Publishing: "%s"' % msg.data)
self.i += 1
def main(args=None):
| 64 | 64 | 74 | 5 | 58 | anasarrak/examples | rclpy/topics/minimal_publisher/examples_rclpy_minimal_publisher/publisher_member_function.py | Python | main | main | 38 | 49 | 38 | 38 | 8a234ebc84142dcba55f0dfe312cf0a1d06954ae | bigcode/the-stack | train |
62923ca5a456afc6b1de6883 | train | function | @asyncio.coroutine
def func2():
while True:
print("func2")
yield from asyncio.sleep(1)
| @asyncio.coroutine
def func2():
| while True:
print("func2")
yield from asyncio.sleep(1)
| import asyncio
@asyncio.coroutine
def func1():
while True:
print("func1")
yield from asyncio.sleep(10)
@asyncio.coroutine
def func2():
| 41 | 64 | 28 | 10 | 31 | taijiji/python-memo | async.py | Python | func2 | func2 | 11 | 15 | 11 | 12 | e73fa37068d74a583735775aa3546da1e71f4c4a | bigcode/the-stack | train |
9b4c79044c2eb82d13a000b5 | train | function | @asyncio.coroutine
def func1():
while True:
print("func1")
yield from asyncio.sleep(10)
| @asyncio.coroutine
def func1():
| while True:
print("func1")
yield from asyncio.sleep(10)
| import asyncio
@asyncio.coroutine
def func1():
| 13 | 64 | 28 | 10 | 2 | taijiji/python-memo | async.py | Python | func1 | func1 | 4 | 8 | 4 | 5 | 930e7c45d3a9239a52bdf4d1f39786a9a68be925 | bigcode/the-stack | train |
3fbcfab78adc6c9446c9b529 | train | class | class Migration(migrations.Migration):
dependencies = [
('nslc', '0009_auto_20210429_2335'),
]
operations = [
migrations.AlterField(
model_name='channel',
name='name',
field=models.CharField(blank=True, max_length=255),
),
]
| class Migration(migrations.Migration):
| dependencies = [
('nslc', '0009_auto_20210429_2335'),
]
operations = [
migrations.AlterField(
model_name='channel',
name='name',
field=models.CharField(blank=True, max_length=255),
),
]
| # Generated by Django 3.1.13 on 2022-05-25 17:29
from django.db import migrations, models
class Migration(migrations.Migration):
| 38 | 64 | 68 | 7 | 30 | pnsn/squac_api | app/nslc/migrations/0010_auto_20220525_1729.py | Python | Migration | Migration | 6 | 18 | 6 | 7 | 8c3e579744588144de923a7ac8ddc9456fe322ee | bigcode/the-stack | train |
798a893fc2854be58fdd6c3d | train | class | class CtcDecoder(object):
"""
CTC decoder (to decode a sequence of labels to words).
Parameters:
----------
vocabulary : list of str
Vocabulary of the dataset.
"""
def __init__(self,
vocabulary):
super().__init__()
self.blank_id = len(vocabulary)
... | class CtcDecoder(object):
| """
CTC decoder (to decode a sequence of labels to words).
Parameters:
----------
vocabulary : list of str
Vocabulary of the dataset.
"""
def __init__(self,
vocabulary):
super().__init__()
self.blank_id = len(vocabulary)
self.labels_map = dic... | _pl', 'oth_quartznet15x5_ru', 'oth_jasperdr10x5_en', 'oth_jasperdr10x5_en_nr',
'oth_quartznet15x5_ru34']
import torch.nn as nn
# import torch.nn.functional as F
# import editdistance
class CtcDecoder(object):
| 70 | 70 | 235 | 6 | 63 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | CtcDecoder | CtcDecoder | 12 | 52 | 12 | 12 | 796b05a3acbba6e84d6a0d75a1632321063a9da5 | bigcode/the-stack | train |
4eaa1d43510d7443049a917e | train | function | def oth_quartznet15x5_de(pretrained=False, num_classes=32, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_de_quartznet15x5_6ae5d87d.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_... | def oth_quartznet15x5_de(pretrained=False, num_classes=32, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_de_quartznet15x5_6ae5d87d.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_de(pretrained=False, num_classes=32, **kwargs):
| 64 | 64 | 112 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_de | oth_quartznet15x5_de | 163 | 169 | 163 | 163 | d6226a7a7fa71dd78298e05aab87cfcb6bc03422 | bigcode/the-stack | train |
2f82de63696dafb64feab524 | train | function | def oth_quartznet15x5_es(pretrained=False, num_classes=36, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_es_quartznet15x5_f2083912.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_... | def oth_quartznet15x5_es(pretrained=False, num_classes=36, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_es_quartznet15x5_f2083912.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_es(pretrained=False, num_classes=36, **kwargs):
| 64 | 64 | 109 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_es | oth_quartznet15x5_es | 181 | 187 | 181 | 181 | 65e2578660f9a9c590442f07c7ad4634dfe942f8 | bigcode/the-stack | train |
158ca0830a1f62752c469e91 | train | function | def _test():
import numpy as np
import torch
pretrained = True
audio_features = 64
models = [
# oth_quartznet5x5_en_ls,
# oth_quartznet15x5_en,
# oth_quartznet15x5_en_nr,
# oth_quartznet15x5_fr,
# oth_quartznet15x5_de,
# oth_quartznet15x5_it,
... | def _test():
| import numpy as np
import torch
pretrained = True
audio_features = 64
models = [
# oth_quartznet5x5_en_ls,
# oth_quartznet15x5_en,
# oth_quartznet15x5_en_nr,
# oth_quartznet15x5_fr,
# oth_quartznet15x5_de,
# oth_quartznet15x5_it,
# oth_quartz... | artznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_ru34(pretrained=False, num_classes=34, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet15x5_golos_1a... | 206 | 206 | 688 | 4 | 201 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | _test | _test | 253 | 311 | 253 | 253 | 567b403108733b6e80c8f9f63093ea6e5b23c31e | bigcode/the-stack | train |
61abd654e10933864e1bc309 | train | function | def oth_quartznet15x5_ru34(pretrained=False, num_classes=34, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet15x5_golos_1a63a2d8.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num... | def oth_quartznet15x5_ru34(pretrained=False, num_classes=34, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet15x5_golos_1a63a2d8.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| DecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_ru34(pretrained=False, num_classes=34, **kwargs):
| 64 | 64 | 112 | 22 | 41 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_ru34 | oth_quartznet15x5_ru34 | 235 | 241 | 235 | 235 | 013b2b7d59d37ac9cc3644aac08d52aec2d95e4b | bigcode/the-stack | train |
2c0c28a4e93fe019bb672243 | train | function | def oth_quartznet15x5_fr(pretrained=False, num_classes=43, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_fr_quartznet15x5_a3fdb084.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_... | def oth_quartznet15x5_fr(pretrained=False, num_classes=43, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_fr_quartznet15x5_a3fdb084.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_fr(pretrained=False, num_classes=43, **kwargs):
| 64 | 64 | 110 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_fr | oth_quartznet15x5_fr | 154 | 160 | 154 | 154 | b61bec8c05cb3053a8cb78ff0231a5777baab391 | bigcode/the-stack | train |
88c84df99c5017505f62fa1f | train | function | def oth_quartznet15x5_en(pretrained=False, num_classes=29, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet15x5Base-En_3dbcc2ff.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_... | def oth_quartznet15x5_en(pretrained=False, num_classes=29, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet15x5Base-En_3dbcc2ff.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_en(pretrained=False, num_classes=29, **kwargs):
| 64 | 64 | 109 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_en | oth_quartznet15x5_en | 136 | 142 | 136 | 136 | 5fc9aae20ada579b4d3ae66c764448b34f630fc2 | bigcode/the-stack | train |
0fd500e93a5a60247ce27ebe | train | function | def oth_jasperdr10x5_en(pretrained=False, num_classes=29, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "Jasper10x5Dr-En_2b94c9d1.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classe... | def oth_jasperdr10x5_en(pretrained=False, num_classes=29, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "Jasper10x5Dr-En_2b94c9d1.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_jasperdr10x5_en(pretrained=False, num_classes=29, **kwargs):
| 64 | 64 | 111 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_jasperdr10x5_en | oth_jasperdr10x5_en | 217 | 223 | 217 | 217 | 8340d5090e4815d5645a4a7d19edaddc5ae5bd43 | bigcode/the-stack | train |
4e0c006a9c800174173b5f90 | train | function | def oth_quartznet15x5_it(pretrained=False, num_classes=39, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_it_quartznet15x5_0f6e4537.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_... | def oth_quartznet15x5_it(pretrained=False, num_classes=39, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_it_quartznet15x5_0f6e4537.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_it(pretrained=False, num_classes=39, **kwargs):
| 64 | 64 | 112 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_it | oth_quartznet15x5_it | 172 | 178 | 172 | 172 | 3532e90292cefa3786d50d03b7590812b753ec03 | bigcode/the-stack | train |
1b184ab9d3228b62bf0b1a54 | train | function | def oth_quartznet15x5_en_nr(pretrained=False, num_classes=29, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet15x5NR-En_b05e34f3.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num... | def oth_quartznet15x5_en_nr(pretrained=False, num_classes=29, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet15x5NR-En_b05e34f3.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| DecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_en_nr(pretrained=False, num_classes=29, **kwargs):
| 64 | 64 | 110 | 22 | 41 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_en_nr | oth_quartznet15x5_en_nr | 145 | 151 | 145 | 145 | 2eee49d83ccf8d98307afa0095d1142471663035 | bigcode/the-stack | train |
399399a0d6e6e37d1fd9eef5 | train | function | def oth_jasperdr10x5_en_nr(pretrained=False, num_classes=29, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_en_jasper10x5dr_0d5ebc6c.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num... | def oth_jasperdr10x5_en_nr(pretrained=False, num_classes=29, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_en_jasper10x5dr_0d5ebc6c.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| DecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_jasperdr10x5_en_nr(pretrained=False, num_classes=29, **kwargs):
| 64 | 64 | 114 | 22 | 41 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_jasperdr10x5_en_nr | oth_jasperdr10x5_en_nr | 226 | 232 | 226 | 226 | b3dc18e8833816eaf7b9e0407fa697bb0b0ece6a | bigcode/the-stack | train |
9fbd2ae5d101f51e2e5ce8d8 | train | function | def _calc_width(net):
import numpy as np
net_params = filter(lambda p: p.requires_grad, net.parameters())
weight_count = 0
for param in net_params:
weight_count += np.prod(param.size())
return weight_count
| def _calc_width(net):
| import numpy as np
net_params = filter(lambda p: p.requires_grad, net.parameters())
weight_count = 0
for param in net_params:
weight_count += np.prod(param.size())
return weight_count
| _1a63a2d8.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def _calc_width(net):
| 64 | 64 | 55 | 6 | 57 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | _calc_width | _calc_width | 244 | 250 | 244 | 244 | 00211311e5dd960feef4da7007dfac59f049e45d | bigcode/the-stack | train |
49eccb0f7750c9a4354c4768 | train | function | def oth_quartznet15x5_ru(pretrained=False, num_classes=35, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_ru_quartznet15x5_88a3e5aa.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_... | def oth_quartznet15x5_ru(pretrained=False, num_classes=35, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_ru_quartznet15x5_88a3e5aa.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_ru(pretrained=False, num_classes=35, **kwargs):
| 64 | 64 | 112 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_ru | oth_quartznet15x5_ru | 208 | 214 | 208 | 208 | e428656dd1425086807a8180c03b44ca4f7f65d9 | bigcode/the-stack | train |
7d2cdbe2b4b74c3f5ed4d107 | train | function | def oth_quartznet15x5_pl(pretrained=False, num_classes=34, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_pl_quartznet15x5_9dd685f7.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_... | def oth_quartznet15x5_pl(pretrained=False, num_classes=34, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_pl_quartznet15x5_9dd685f7.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_pl(pretrained=False, num_classes=34, **kwargs):
| 64 | 64 | 111 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_pl | oth_quartznet15x5_pl | 199 | 205 | 199 | 199 | 8da1813a9164f22bf4f9685bb36d4708ae3a10cb | bigcode/the-stack | train |
5ec7846e359b97529f5e6b29 | train | function | def oth_quartznet15x5_ca(pretrained=False, num_classes=39, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_ca_quartznet15x5_b1a4fa3c.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_... | def oth_quartznet15x5_ca(pretrained=False, num_classes=39, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "stt_ca_quartznet15x5_b1a4fa3c.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
def oth_quartznet15x5_ca(pretrained=False, num_classes=39, **kwargs):
| 64 | 64 | 112 | 21 | 42 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet15x5_ca | oth_quartznet15x5_ca | 190 | 196 | 190 | 190 | b1f26819bf4876404c11f9e506f195c5c27d54f6 | bigcode/the-stack | train |
9e6d255f99ded3ce0810133f | train | class | class QuartzNet(nn.Module):
def __init__(self,
raw_net,
num_classes):
super(QuartzNet, self).__init__()
self.in_size = None
self.num_classes = num_classes
self.preprocessor = raw_net.preprocessor
self.encoder = raw_net.encoder
self.d... | class QuartzNet(nn.Module):
| def __init__(self,
raw_net,
num_classes):
super(QuartzNet, self).__init__()
self.in_size = None
self.num_classes = num_classes
self.preprocessor = raw_net.preprocessor
self.encoder = raw_net.encoder
self.decoder = raw_net.decoder
... | .split()
# words += len(r_list)
# scores += editdistance.eval(h_list, r_list)
#
# self.scores += scores
# self.words += words
#
# def compute(self):
# return float(self.scores) / self.words
class QuartzNet(nn.Module):
| 64 | 64 | 197 | 6 | 57 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | QuartzNet | QuartzNet | 92 | 120 | 92 | 92 | 24d809aa80034ae9650b19e039eb4281e65ef8e4 | bigcode/the-stack | train |
b6d7bd5852b7bab108dbe434 | train | function | def oth_quartznet5x5_en_ls(pretrained=False, num_classes=29, **kwargs):
from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet5x5LS-En_08ecf82a.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_c... | def oth_quartznet5x5_en_ls(pretrained=False, num_classes=29, **kwargs):
| from nemo.collections.asr.models import EncDecCTCModel
quartznet_nemo_path = path_pref + "QuartzNet5x5LS-En_08ecf82a.nemo"
raw_net = EncDecCTCModel.restore_from(quartznet_nemo_path)
net = QuartzNet(raw_net=raw_net, num_classes=num_classes)
net = net.cpu()
return net#, raw_net
| lens)
x = self.decoder(x)
return x, lens
# path_pref = "../../../../../imgclsmob_data/nemo/"
path_pref = "../imgclsmob_data/nemo/"
def oth_quartznet5x5_en_ls(pretrained=False, num_classes=29, **kwargs):
| 63 | 64 | 110 | 22 | 41 | naviocean/imgclsmob | pytorch/pytorchcv/models/others/oth_quartznet.py | Python | oth_quartznet5x5_en_ls | oth_quartznet5x5_en_ls | 127 | 133 | 127 | 127 | 9445bdc99077ff398296f4a8f854d1bf2cc612df | bigcode/the-stack | train |
eb4c2ad47153725290e74fe0 | train | function | def sorted_values(dictionary):
algo = list(dictionary.values())
algo.sort()
return algo
| def sorted_values(dictionary):
| algo = list(dictionary.values())
algo.sort()
return algo
| #Write a function that accepts a dictionary as input and returns a sorted list of all the values in the dictionary.
#Assume that the values of this dictionary are just integers.
dictionary = {"juan": 3, "pedro": 1, "carlos": 2}
def sorted_values(dictionary):
| 64 | 64 | 19 | 5 | 59 | chars32/edx_python | Weeks/Week7/Dictionaries/Excercise2.py | Python | sorted_values | sorted_values | 6 | 12 | 6 | 7 | 46cdde4205213947dc9f1b794b38378bbbb3f751 | bigcode/the-stack | train |
7a7e24da3bd7c664f13348c7 | train | function | def get_version():
sys.path.insert(0, ".")
vers = importlib.import_module('version')
return vers.VERSION
| def get_version():
| sys.path.insert(0, ".")
vers = importlib.import_module('version')
return vers.VERSION
| _NAME)
base_formatter = logging.Formatter(
"%(asctime)s %(name)s:%(levelname)s %(message)s")
hdlr.setFormatter(base_formatter)
logger.addHandler(hdlr)
logger.setLevel(logging.DEBUG)
def pick_grp(name):
return name.split('-')[1]
def get_version():
| 64 | 64 | 28 | 4 | 60 | auth0/oidctest | test_tool/cp/test_op/op_test_tool.py | Python | get_version | get_version | 49 | 52 | 49 | 49 | a8430ad1775d7612d96239f9aeb6df286df64155 | bigcode/the-stack | train |
465cfb7153cd9aa14f281b77 | train | function | def pick_grp(name):
return name.split('-')[1]
| def pick_grp(name):
| return name.split('-')[1]
| op_test.log'
hdlr = logging.FileHandler(LOGFILE_NAME)
base_formatter = logging.Formatter(
"%(asctime)s %(name)s:%(levelname)s %(message)s")
hdlr.setFormatter(base_formatter)
logger.addHandler(hdlr)
logger.setLevel(logging.DEBUG)
def pick_grp(name):
| 64 | 64 | 13 | 5 | 59 | auth0/oidctest | test_tool/cp/test_op/op_test_tool.py | Python | pick_grp | pick_grp | 45 | 46 | 45 | 45 | 7a388252368f933c5cb65f29358d0a90be31305b | bigcode/the-stack | train |
645d2979623b454eb7bbacd0 | train | function | def make_webenv(config, rest):
if args.tag:
qtag = quote_plus(args.tag)
else:
qtag = 'default'
ent_conf = None
try:
ent_conf = rest.construct_config(quote_plus(args.issuer), qtag)
except Exception as err:
print('iss:{}, tag:{}'.format(quote_plus(args.issuer), qtag))
... | def make_webenv(config, rest):
| if args.tag:
qtag = quote_plus(args.tag)
else:
qtag = 'default'
ent_conf = None
try:
ent_conf = rest.construct_config(quote_plus(args.issuer), qtag)
except Exception as err:
print('iss:{}, tag:{}'.format(quote_plus(args.issuer), qtag))
for m in traceback.form... | Formatter(base_formatter)
logger.addHandler(hdlr)
logger.setLevel(logging.DEBUG)
def pick_grp(name):
return name.split('-')[1]
def get_version():
sys.path.insert(0, ".")
vers = importlib.import_module('version')
return vers.VERSION
def make_webenv(config, rest):
| 67 | 67 | 226 | 9 | 57 | auth0/oidctest | test_tool/cp/test_op/op_test_tool.py | Python | make_webenv | make_webenv | 55 | 85 | 55 | 55 | 904c19372b66e6015e73ba37a29ac88b4e73f988 | bigcode/the-stack | train |
5625115607169a6462229799 | train | function | def inc_dict(key):
global dict_data
try:
dict_data[key] = dict_data[key]+1
except KeyError:
dict_data[key] = 1
| def inc_dict(key):
| global dict_data
try:
dict_data[key] = dict_data[key]+1
except KeyError:
dict_data[key] = 1
| glob
import numpy as np
# lists for values
repository = "dbGAP"
xmlpath = "./data/"
# functions
def parse(xmlpath):
phs = ET.parse(xmlpath)
root = phs.getroot()
return(root)
dict_data = dict()
def inc_dict(key):
| 64 | 64 | 39 | 5 | 59 | social-link-analytics-group-bsc/biohackathon-project-35 | dbpgap/archived/explore_keys.py | Python | inc_dict | inc_dict | 26 | 31 | 26 | 26 | cd4cd9b97220d4b419a1b0d744de2ff1c1f990d3 | bigcode/the-stack | train |
51efa0ed334e43412b1e946c | train | function | def parse(xmlpath):
phs = ET.parse(xmlpath)
root = phs.getroot()
return(root)
| def parse(xmlpath):
| phs = ET.parse(xmlpath)
root = phs.getroot()
return(root)
| xml.etree.ElementTree as ET
from xml.etree.ElementTree import ParseError
import pandas as pd
import xml.etree as etree
import glob
import numpy as np
# lists for values
repository = "dbGAP"
xmlpath = "./data/"
# functions
def parse(xmlpath):
| 64 | 64 | 26 | 5 | 58 | social-link-analytics-group-bsc/biohackathon-project-35 | dbpgap/archived/explore_keys.py | Python | parse | parse | 18 | 21 | 18 | 18 | 903ffd7cadd8b312b9325da22f93f7badb44c64a | bigcode/the-stack | train |
d963f5a06d6b344409f3e7b2 | train | class | class InvalidMarkerError(Exception):
"""Error raised with marker is invalid"""
pass
| class InvalidMarkerError(Exception):
| """Error raised with marker is invalid"""
pass
| ", "centos8", "awsbatch"),
("*", "*", "ubuntu1804", "awsbatch"),
("*", "*", "ubuntu1604", "awsbatch"),
("us-gov-east-1", "*", "c4.xlarge", "*"),
]
class InvalidMarkerError(Exception):
| 64 | 64 | 18 | 6 | 58 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | InvalidMarkerError | InvalidMarkerError | 29 | 32 | 29 | 29 | 912ec3c5451d998161f962c7421ff66fa5080f28 | bigcode/the-stack | train |
bcc97a87d635747c15615862 | train | function | def _validate_marker(marker_name, expected_args, args_count):
if args_count != len(expected_args):
logging.error(
"Marker {marker_name} requires the following args: {args}".format(
marker_name=marker_name, args=expected_args
)
)
raise InvalidMarkerErro... | def _validate_marker(marker_name, expected_args, args_count):
| if args_count != len(expected_args):
logging.error(
"Marker {marker_name} requires the following args: {args}".format(
marker_name=marker_name, args=expected_args
)
)
raise InvalidMarkerError
| " values: {allowed_values}".format(
test_name=item.name,
test_args_value=test_args_value,
marker=marker_name,
allowed_values=allowed_values,
)
)
logging.debug(skip_message)
items.remove(i... | 64 | 64 | 65 | 13 | 51 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | _validate_marker | _validate_marker | 234 | 241 | 234 | 234 | b1b5868065840f1573679b8120eeaf18709e7971 | bigcode/the-stack | train |
24bbdb1a933757a61a43056f | train | function | def check_marker_dimensions(items):
"""
Execute all tests that are annotated with @pytest.mark.dimensions and have the args
(region, instance, os, scheduler) match those specified in the marker.
"*" can be used to identify all values for a specific argument.
Example:
@pytest.mark.dimension... | def check_marker_dimensions(items):
| """
Execute all tests that are annotated with @pytest.mark.dimensions and have the args
(region, instance, os, scheduler) match those specified in the marker.
"*" can be used to identify all values for a specific argument.
Example:
@pytest.mark.dimensions("a", "b", "*", "d")
def te... | raise ValueError
dimensions_match = _compare_dimension_lists(args_values, marker.args)
if dimensions_match:
skip_message = (
"Skipping test {test_name} because dimensions {args_values} match {marker}: "
"{skip_values}".format(
... | 100 | 100 | 336 | 6 | 93 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | check_marker_dimensions | check_marker_dimensions | 191 | 231 | 191 | 191 | c473283348457cccd0e9a9fac673ba4f9f255144 | bigcode/the-stack | train |
8304446074306716e4ccbcce | train | function | def _compare_dimension_lists(list1, list2):
if len(list1) != len(list2):
return False
for d1, d2 in zip(list1, list2):
if d1 != "*" and d2 != "*" and d1 != d2:
return False
return True
| def _compare_dimension_lists(list1, list2):
| if len(list1) != len(list2):
return False
for d1, d2 in zip(list1, list2):
if d1 != "*" and d2 != "*" and d1 != d2:
return False
return True
| ):
if args_count != len(expected_args):
logging.error(
"Marker {marker_name} requires the following args: {args}".format(
marker_name=marker_name, args=expected_args
)
)
raise InvalidMarkerError
def _compare_dimension_lists(list1, list2):
| 64 | 64 | 67 | 11 | 52 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | _compare_dimension_lists | _compare_dimension_lists | 244 | 250 | 244 | 244 | 49a3a50c8e3e3c46c4580b7740de2e71e61ef4f6 | bigcode/the-stack | train |
474dd3a5ab67bef9273f63c5 | train | function | def check_marker_list(items, marker_name, arg_name):
"""
Skip all tests that are annotated with marker marker_name and have the arg value corresponding to arg_name
not listed in the list passed as first argument to the marker.
Example:
@pytest.mark.marker_name(["value1", "value2"])
def ... | def check_marker_list(items, marker_name, arg_name):
| """
Skip all tests that are annotated with marker marker_name and have the arg value corresponding to arg_name
not listed in the list passed as first argument to the marker.
Example:
@pytest.mark.marker_name(["value1", "value2"])
def test(arg_name)
The test is executed only if ... |
def add_default_markers(items):
"""
Add default markers for dimensions that need to be skipped by default for all tests.
:param items: pytest Item object markers are applied to.
"""
_add_unsupported_arm_dimensions()
for item in items:
for dimensions in UNSUPPORTED_DIMENSIONS:
... | 90 | 90 | 302 | 12 | 78 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | check_marker_list | check_marker_list | 75 | 110 | 75 | 75 | f52ca427c7dcdba931e19228ccbea1f7f41ba50b | bigcode/the-stack | train |
225ca348a310b9b3faa0ee60 | train | function | def add_default_markers(items):
"""
Add default markers for dimensions that need to be skipped by default for all tests.
:param items: pytest Item object markers are applied to.
"""
_add_unsupported_arm_dimensions()
for item in items:
for dimensions in UNSUPPORTED_DIMENSIONS:
... | def add_default_markers(items):
| """
Add default markers for dimensions that need to be skipped by default for all tests.
:param items: pytest Item object markers are applied to.
"""
_add_unsupported_arm_dimensions()
for item in items:
for dimensions in UNSUPPORTED_DIMENSIONS:
item.add_marker(pytest.mark.sk... | _os in oses_unsupported_by_arm:
UNSUPPORTED_DIMENSIONS.append(("*", instance_type, unsupported_os, "*"))
for unsupported_region in regions_unsupported_by_arm:
UNSUPPORTED_DIMENSIONS.append((unsupported_region, instance_type, "*", "*"))
def add_default_markers(items):
| 63 | 64 | 77 | 7 | 56 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | add_default_markers | add_default_markers | 63 | 72 | 63 | 63 | 2c88f884c5326bdc4d03e23021e8640db9c8e7f2 | bigcode/the-stack | train |
90519d8aaff485803623a863 | train | function | def _add_unsupported_arm_dimensions():
"""Add invalid dimensions due to lack of ARM instance types in some regions and ARM AMIs for certain OSes."""
arm_instance_types = ["m6g.xlarge"]
oses_unsupported_by_arm = ["centos7", "alinux", "ubuntu1604"]
regions_unsupported_by_arm = [
"us-west-1",
... | def _add_unsupported_arm_dimensions():
| """Add invalid dimensions due to lack of ARM instance types in some regions and ARM AMIs for certain OSes."""
arm_instance_types = ["m6g.xlarge"]
oses_unsupported_by_arm = ["centos7", "alinux", "ubuntu1604"]
regions_unsupported_by_arm = [
"us-west-1",
"ca-central-1",
"eu-west-2",... | ", "awsbatch"),
("*", "*", "ubuntu1804", "awsbatch"),
("*", "*", "ubuntu1604", "awsbatch"),
("us-gov-east-1", "*", "c4.xlarge", "*"),
]
class InvalidMarkerError(Exception):
"""Error raised with marker is invalid"""
pass
def _add_unsupported_arm_dimensions():
| 79 | 79 | 264 | 8 | 70 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | _add_unsupported_arm_dimensions | _add_unsupported_arm_dimensions | 35 | 60 | 35 | 35 | 5bff2ac71e72e893cb6d02934eb4a2e85112413e | bigcode/the-stack | train |
041f3afb37de347a5d68ceb4 | train | function | def check_marker_skip_list(items, marker_name, arg_name):
"""
Skip all tests that are annotated with marker marker_name and have the arg value corresponding to arg_name
listed in the list passed as first argument to the marker.
Example:
@pytest.mark.marker_name(["value1", "value2"])
def... | def check_marker_skip_list(items, marker_name, arg_name):
| """
Skip all tests that are annotated with marker marker_name and have the arg value corresponding to arg_name
listed in the list passed as first argument to the marker.
Example:
@pytest.mark.marker_name(["value1", "value2"])
def test(arg_name)
The test is executed only if arg_... | because {arg_name} {arg_value} is not in {marker} allowed values: "
"{allowed_values}".format(
test_name=item.name,
arg_name=arg_name,
arg_value=arg_value,
marker=marker_name,
allowed_values=allowed_values,
)
... | 87 | 87 | 290 | 13 | 74 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | check_marker_skip_list | check_marker_skip_list | 113 | 145 | 113 | 113 | 39334b8178c616d7ef5c178f16ce4f420c805634 | bigcode/the-stack | train |
0880fbf28eebb7bc61113a86 | train | function | def check_marker_skip_dimensions(items):
"""
Skip all tests that are annotated with @pytest.mark.skip_dimensions and have the args
(region, instance, os, scheduler) match those specified in the marker.
"*" can be used to identify all values for a specific argument.
Example:
@pytest.mark.sk... | def check_marker_skip_dimensions(items):
| """
Skip all tests that are annotated with @pytest.mark.skip_dimensions and have the args
(region, instance, os, scheduler) match those specified in the marker.
"*" can be used to identify all values for a specific argument.
Example:
@pytest.mark.skip_dimensions("a", "b", "*", "d")
... | skip_values = marker.args[0]
if arg_value in skip_values:
skip_message = (
"Skipping test {test_name} because {arg_name} {arg_value} is in {marker} allowed values:"
"{skip_values}".format(
test_name=item.name,
... | 108 | 108 | 360 | 7 | 101 | Takuya-Miyazaki/aws-parallelcluster | tests/integration-tests/conftest_markers.py | Python | check_marker_skip_dimensions | check_marker_skip_dimensions | 148 | 188 | 148 | 148 | a6cb5b21826714dcea402f4888d9491d4f41b05a | bigcode/the-stack | train |
62dd1f6c8023ac09e13ee2cd | train | class | class Server:
"""Server class for auto-complete system
This class defines application server for performing auto-complete functionality.
The main API search(str) searches a term in server and returns top results to the user.
New servers can be established from Neo4j database which stores search history... | class Server:
| """Server class for auto-complete system
This class defines application server for performing auto-complete functionality.
The main API search(str) searches a term in server and returns top results to the user.
New servers can be established from Neo4j database which stores search history.
Attribu... | """
Main module for auto-complete server
"""
from collections import deque, Counter
import logging
import logging.config
import yaml
import csv
from typing import List
# from nltk.corpus import words as en_corpus
from py2neo import Node
from src.Trienode import TrieNode
from src.Spell import Spell
from . import D... | 108 | 256 | 3,647 | 3 | 105 | weihesdlegend/Auto_complete_system | src/Server.py | Python | Server | Server | 24 | 521 | 24 | 24 | b977e8e03aff34c709db2c18519c9b96532ee59c | bigcode/the-stack | train |
24198c2ef9588a99b49d1a04 | train | function | def is_fare_code_valid(fare_code):
return fare_code in AVAILABLE_FARE_CODES
| def is_fare_code_valid(fare_code):
| return fare_code in AVAILABLE_FARE_CODES
| doubles
for group_code, group_name in FARE_CODE_GROUPS._doubles
}
fare_codes[SOCIAL_EVENT_FARE_CODE] = "Social Event"
return fare_codes
AVAILABLE_FARE_CODES = available_fare_codes()
def is_fare_code_valid(fare_code):
| 64 | 64 | 20 | 10 | 54 | judy2k/epcon | conference/fares.py | Python | is_fare_code_valid | is_fare_code_valid | 69 | 70 | 69 | 69 | 7ddcc4449944d3cafaae7668c44ad1d855c91559 | bigcode/the-stack | train |
6c6b2d24b27aeba51015a403 | train | function | def create_fare_for_conference(code, conference, price,
start_validity, end_validity,
vat_rate):
assert is_fare_code_valid(code)
assert isinstance(conference, str), "conference should be a string"
assert isinstance(vat_rate, Vat)
assert star... | def create_fare_for_conference(code, conference, price,
start_validity, end_validity,
vat_rate):
| assert is_fare_code_valid(code)
assert isinstance(conference, str), "conference should be a string"
assert isinstance(vat_rate, Vat)
assert start_validity <= end_validity
if code == SOCIAL_EVENT_FARE_CODE:
ticket_type = FARE_TICKET_TYPES.event
else:
ticket_type = FARE_TICKET_TYP... | in FARE_CODE_GROUPS._doubles
}
fare_codes[SOCIAL_EVENT_FARE_CODE] = "Social Event"
return fare_codes
AVAILABLE_FARE_CODES = available_fare_codes()
def is_fare_code_valid(fare_code):
return fare_code in AVAILABLE_FARE_CODES
def create_fare_for_conference(code, conference, price,
... | 89 | 89 | 297 | 26 | 62 | judy2k/epcon | conference/fares.py | Python | create_fare_for_conference | create_fare_for_conference | 73 | 112 | 73 | 76 | eef1cea8bd7067cd15d0f9ca63f11f35c4d36139 | bigcode/the-stack | train |
c7cdcf9ab58dd50f069eb3a1 | train | function | def available_fare_codes():
fare_codes = {
"T" + type_code + variant_code + group_code:
"%s %s %s" % (type_name, variant_name, group_name)
for type_code, type_name in FARE_CODE_TYPES._doubles
for variant_code, variant_name in FARE_CODE_VARIANTS._doubles
for group_code,... | def available_fare_codes():
| fare_codes = {
"T" + type_code + variant_code + group_code:
"%s %s %s" % (type_name, variant_name, group_name)
for type_code, type_name in FARE_CODE_TYPES._doubles
for variant_code, variant_name in FARE_CODE_VARIANTS._doubles
for group_code, group_name in FARE_CODE... | "groups": {
FARE_CODE_GROUPS.STUDENT: "^T..S$",
FARE_CODE_GROUPS.PERSONAL: "^T..P$",
FARE_CODE_GROUPS.COMPANY: "^T..C$",
}
}
def available_fare_codes():
| 64 | 64 | 119 | 6 | 58 | judy2k/epcon | conference/fares.py | Python | available_fare_codes | available_fare_codes | 52 | 63 | 52 | 52 | 38f11e06a8d6e6d36cffcc6c605042c7c8f6d85f | bigcode/the-stack | train |
01c08fa3f430b948c934c097 | train | function | def set_regular_fare_dates(conference, start_date, end_date):
fares = Fare.objects.filter(
conference=conference,
code__regex=FARE_CODE_REGEXES['types'][FARE_CODE_TYPES.REGULAR]
)
assert fares.count() == 9 # 3**2
fares.update(start_validity=start_date, end_validity=end_date)
| def set_regular_fare_dates(conference, start_date, end_date):
| fares = Fare.objects.filter(
conference=conference,
code__regex=FARE_CODE_REGEXES['types'][FARE_CODE_TYPES.REGULAR]
)
assert fares.count() == 9 # 3**2
fares.update(start_validity=start_date, end_validity=end_date)
| ['types'][FARE_CODE_TYPES.EARLY_BIRD]
)
assert early_birds.count() == 9 # 3**2
early_birds.update(start_validity=start_date, end_validity=end_date)
def set_regular_fare_dates(conference, start_date, end_date):
| 64 | 64 | 79 | 15 | 49 | judy2k/epcon | conference/fares.py | Python | set_regular_fare_dates | set_regular_fare_dates | 143 | 149 | 143 | 143 | 9f16e57eaeed8fd520d3d8bcd3cd149ad3376f6d | bigcode/the-stack | train |
1552e119439f4361a1b2fad5 | train | function | def set_early_bird_fare_dates(conference, start_date, end_date):
early_birds = Fare.objects.filter(
conference=conference,
code__regex=FARE_CODE_REGEXES['types'][FARE_CODE_TYPES.EARLY_BIRD]
)
assert early_birds.count() == 9 # 3**2
early_birds.update(start_validity=start_date, end_validi... | def set_early_bird_fare_dates(conference, start_date, end_date):
| early_birds = Fare.objects.filter(
conference=conference,
code__regex=FARE_CODE_REGEXES['types'][FARE_CODE_TYPES.EARLY_BIRD]
)
assert early_birds.count() == 9 # 3**2
early_birds.update(start_validity=start_date, end_validity=end_date)
| start_validity=None, end_validity=None,
vat_rate=vat_rate,
)
if print_output:
print("Created fare %s" % fare)
fares.append(fare)
return fares
def set_early_bird_fare_dates(conference, start_date, end_date):
| 64 | 64 | 91 | 18 | 45 | judy2k/epcon | conference/fares.py | Python | set_early_bird_fare_dates | set_early_bird_fare_dates | 134 | 140 | 134 | 134 | 3c68d732c58db6a78ae04b99968840b23e796b83 | bigcode/the-stack | train |
b43951fdca4ea38723c8823a | train | function | def pre_create_typical_fares_for_conference(conference, vat_rate,
print_output=False):
fares = []
for fare_code in AVAILABLE_FARE_CODES.keys():
fare = create_fare_for_conference(
code=fare_code,
conference=conference,
price... | def pre_create_typical_fares_for_conference(conference, vat_rate,
print_output=False):
| fares = []
for fare_code in AVAILABLE_FARE_CODES.keys():
fare = create_fare_for_conference(
code=fare_code,
conference=conference,
price=210, # random price, we'll change it later (div. by 3)
start_validity=None, end_validity=None,
vat_rate=v... | _type,
start_validity=start_validity,
end_validity=end_validity,
)
)
VatFare.objects.get_or_create(fare=fare, vat=vat_rate)
return fare
def pre_create_typical_fares_for_conference(conference, vat_rate,
print_output=False):
| 64 | 64 | 124 | 21 | 42 | judy2k/epcon | conference/fares.py | Python | pre_create_typical_fares_for_conference | pre_create_typical_fares_for_conference | 115 | 131 | 115 | 116 | 88d96afe058a8768fc909231ed1ea51f363acf43 | bigcode/the-stack | train |
53ca7b3a05357962fcc617c1 | train | class | class FIDInceptionA(models.inception.InceptionA):
"""InceptionA block patched for FID computation"""
def __init__(self, in_channels, pool_features):
super(FIDInceptionA, self).__init__(in_channels, pool_features)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self... | class FIDInceptionA(models.inception.InceptionA):
| """InceptionA block patched for FID computation"""
def __init__(self, in_channels, pool_features):
super(FIDInceptionA, self).__init__(in_channels, pool_features)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5... | )
if os.path.exists(LOCAL_FID_WEIGHTS):
state_dict = torch.load(LOCAL_FID_WEIGHTS, map_location=lambda storage, loc: storage)
else:
state_dict = load_url(FID_WEIGHTS_URL, progress=True)
inception.load_state_dict(state_dict)
return inception
class FIDInceptionA(models.inception.Inceptio... | 78 | 78 | 263 | 13 | 64 | Cospel/BasicSR | basicsr/archs/inception.py | Python | FIDInceptionA | FIDInceptionA | 189 | 211 | 189 | 189 | 66cb87176c8579d2c8240a1b5f54aa1b3baba5f8 | bigcode/the-stack | train |
895afbec27f3cff3e2e7fe13 | train | class | class FIDInceptionE_1(models.inception.InceptionE):
"""First InceptionE block patched for FID computation"""
def __init__(self, in_channels):
super(FIDInceptionE_1, self).__init__(in_channels)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
... | class FIDInceptionE_1(models.inception.InceptionE):
| """First InceptionE block patched for FID computation"""
def __init__(self, in_channels):
super(FIDInceptionE_1, self).__init__(in_channels)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(bran... | not use the padded zero's in
# its average calculation
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return torch.cat(outputs, 1)... | 100 | 100 | 336 | 15 | 85 | Cospel/BasicSR | basicsr/archs/inception.py | Python | FIDInceptionE_1 | FIDInceptionE_1 | 242 | 272 | 242 | 242 | 22b6a94eaa4b3b4019f62bfad58df967f2fcf5f6 | bigcode/the-stack | train |
a1f6a8c24713eeeea4ad23e9 | train | class | class FIDInceptionE_2(models.inception.InceptionE):
"""Second InceptionE block patched for FID computation"""
def __init__(self, in_channels):
super(FIDInceptionE_2, self).__init__(in_channels)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
... | class FIDInceptionE_2(models.inception.InceptionE):
| """Second InceptionE block patched for FID computation"""
def __init__(self, in_channels):
super(FIDInceptionE_2, self).__init__(in_channels)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(bra... | # Patch: Tensorflow's average pool does not use the padded zero's in
# its average calculation
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_po... | 109 | 109 | 364 | 15 | 94 | Cospel/BasicSR | basicsr/archs/inception.py | Python | FIDInceptionE_2 | FIDInceptionE_2 | 275 | 307 | 275 | 275 | aebf4b2788f99c4f289b48eec0f1e5dbecd1f49d | bigcode/the-stack | train |
806980c5b74524c1fe2314dd | train | class | class FIDInceptionC(models.inception.InceptionC):
"""InceptionC block patched for FID computation"""
def __init__(self, in_channels, channels_7x7):
super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.b... | class FIDInceptionC(models.inception.InceptionC):
| """InceptionC block patched for FID computation"""
def __init__(self, in_channels, channels_7x7):
super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2... | not use the padded zero's in
# its average calculation
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)... | 98 | 98 | 329 | 13 | 85 | Cospel/BasicSR | basicsr/archs/inception.py | Python | FIDInceptionC | FIDInceptionC | 214 | 239 | 214 | 214 | 684a6aa4e84d529b30b8ee417cc00a96fce7ee38 | bigcode/the-stack | train |
bcaef7d9a20c3d178c3e6a35 | train | function | def fid_inception_v3():
"""Build pretrained Inception model for FID computation.
The Inception model for FID computation uses a different set of weights
and has a slightly different structure than torchvision's Inception.
This method first constructs torchvision's Inception and then patches the
ne... | def fid_inception_v3():
| """Build pretrained Inception model for FID computation.
The Inception model for FID computation uses a different set of weights
and has a slightly different structure than torchvision's Inception.
This method first constructs torchvision's Inception and then patches the
necessary parts that are d... | output = []
if self.resize_input:
x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False)
if self.normalize_input:
x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1)
for idx, block in enumerate(self.blocks):
x = block(x)
... | 120 | 120 | 403 | 7 | 112 | Cospel/BasicSR | basicsr/archs/inception.py | Python | fid_inception_v3 | fid_inception_v3 | 155 | 186 | 155 | 155 | 2ca7ea60e959146c74d446e6007470e588c66dd9 | bigcode/the-stack | train |
a052c27b1eb51cacb627024c | train | class | class InceptionV3(nn.Module):
"""Pretrained InceptionV3 network returning feature maps"""
# Index of default block of inception to return,
# corresponds to output of final average pooling
DEFAULT_BLOCK_INDEX = 3
# Maps feature dimensionality to their output blocks indices
BLOCK_INDEX_BY_DIM = ... | class InceptionV3(nn.Module):
| """Pretrained InceptionV3 network returning feature maps"""
# Index of default block of inception to return,
# corresponds to output of final average pooling
DEFAULT_BLOCK_INDEX = 3
# Maps feature dimensionality to their output blocks indices
BLOCK_INDEX_BY_DIM = {
64: 0, # First max ... | # Modified from https://github.com/mseitzer/pytorch-fid/blob/master/pytorch_fid/inception.py # noqa: E501
# For FID metric
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.model_zoo import load_url
from torchvision import models
# Inception weights ported to Pytorch from
#... | 198 | 256 | 1,205 | 8 | 189 | Cospel/BasicSR | basicsr/archs/inception.py | Python | InceptionV3 | InceptionV3 | 17 | 152 | 17 | 17 | 4d577b9279dc88fc6c5c35effdb1e17e62954771 | bigcode/the-stack | train |
0251d323c0074f4756d32715 | train | class | class SentryAppInstallationToken(Model):
__core__ = False
api_token = FlexibleForeignKey('sentry.ApiToken')
sentry_app_installation = FlexibleForeignKey('sentry.SentryAppInstallation')
class Meta:
app_label = 'sentry'
db_table = 'sentry_sentryappinstallationtoken'
unique_togeth... | class SentryAppInstallationToken(Model):
| __core__ = False
api_token = FlexibleForeignKey('sentry.ApiToken')
sentry_app_installation = FlexibleForeignKey('sentry.SentryAppInstallation')
class Meta:
app_label = 'sentry'
db_table = 'sentry_sentryappinstallationtoken'
unique_together = (('sentry_app_installation', 'api_to... | .utils import timezone
from sentry.constants import SentryAppInstallationStatus
from sentry.db.models import (
BoundedPositiveIntegerField,
FlexibleForeignKey,
ParanoidModel,
Model,
)
def default_uuid():
return six.binary_type(uuid.uuid4())
class SentryAppInstallationToken(Model):
| 64 | 64 | 89 | 8 | 56 | lauryndbrown/sentry | src/sentry/models/sentryappinstallation.py | Python | SentryAppInstallationToken | SentryAppInstallationToken | 22 | 31 | 22 | 22 | b9b44a72f4a7a3b5c6b1a317f60e22ca7056815c | bigcode/the-stack | train |
64a1073035dad3d2ce1ba2ee | train | class | class SentryAppInstallation(ParanoidModel):
__core__ = True
sentry_app = FlexibleForeignKey('sentry.SentryApp',
related_name='installations')
# SentryApp's are installed and scoped to an Organization. They will have
# access, defined by their scopes, to Teams, Proje... | class SentryAppInstallation(ParanoidModel):
| __core__ = True
sentry_app = FlexibleForeignKey('sentry.SentryApp',
related_name='installations')
# SentryApp's are installed and scoped to an Organization. They will have
# access, defined by their scopes, to Teams, Projects, etc. under that
# Organization, imp... | oundedPositiveIntegerField,
FlexibleForeignKey,
ParanoidModel,
Model,
)
def default_uuid():
return six.binary_type(uuid.uuid4())
class SentryAppInstallationToken(Model):
__core__ = False
api_token = FlexibleForeignKey('sentry.ApiToken')
sentry_app_installation = FlexibleForeignKey('sent... | 131 | 131 | 438 | 10 | 121 | lauryndbrown/sentry | src/sentry/models/sentryappinstallation.py | Python | SentryAppInstallation | SentryAppInstallation | 34 | 86 | 34 | 34 | 3c0c8d2c7905eccfd7b7a69f2902ec7ceac3a379 | bigcode/the-stack | train |
9e000bf7fcb8ea1f9c916c09 | train | function | def default_uuid():
return six.binary_type(uuid.uuid4())
| def default_uuid():
| return six.binary_type(uuid.uuid4())
| absolute_import
import six
import uuid
from django.db import models
from django.utils import timezone
from sentry.constants import SentryAppInstallationStatus
from sentry.db.models import (
BoundedPositiveIntegerField,
FlexibleForeignKey,
ParanoidModel,
Model,
)
def default_uuid():
| 64 | 64 | 13 | 4 | 60 | lauryndbrown/sentry | src/sentry/models/sentryappinstallation.py | Python | default_uuid | default_uuid | 18 | 19 | 18 | 18 | 6af4eef7d95d0c5a9646d5c19e5a1ec199783cc9 | bigcode/the-stack | train |
dc6fb55d1fce2d144519f438 | train | class | class PartialDateTimeField(models.Field):
"""
A django model field for storing partial datetimes.
Accepts None, a partial_date.PartialDate object,
or a formatted string such as YYYY, YYYY-MM, YYYY-MM-DD, YYYY-MM-DDTHH, YYYY-MM-DDTHH:mm.
In the database it saves the date in a column of type DateTimeF... | class PartialDateTimeField(models.Field):
| """
A django model field for storing partial datetimes.
Accepts None, a partial_date.PartialDate object,
or a formatted string such as YYYY, YYYY-MM, YYYY-MM-DD, YYYY-MM-DDTHH, YYYY-MM-DDTHH:mm.
In the database it saves the date in a column of type DateTimeField
and uses the seconds to save the ... | ision == other.precision
else:
return NotImplemented
def __gt__(self, other):
if isinstance(other, PartialDate):
return self.__ge__(other) and not self.__eq__(other)
else:
return NotImplemented
def __ge__(self, other):
if isinstance(other, Pa... | 104 | 104 | 348 | 8 | 95 | JamesBisese/django_partial_datetime | partial_date/fields.py | Python | PartialDateTimeField | PartialDateTimeField | 144 | 188 | 144 | 144 | 312675ad9a84e8d119d7f82d4e0429f9120c6b72 | bigcode/the-stack | train |
e3e7f8bd48e8a80f958596a6 | train | class | class PartialDateTime(object):
YEAR = 0
MONTH = 1
DAY = 2
HOUR = 3
MINUTE = 4
_date = None
_precision = None
DATE_FORMATS = {YEAR: "%Y", MONTH: "%Y-%m", DAY: "%Y-%m-%d", HOUR: "%Y-%m-%d %H:00", MINUTE: "%Y-%m-%d %H:%M"}
def __init__(self, date, precision=DAY):
if isinstanc... | class PartialDateTime(object):
| YEAR = 0
MONTH = 1
DAY = 2
HOUR = 3
MINUTE = 4
_date = None
_precision = None
DATE_FORMATS = {YEAR: "%Y", MONTH: "%Y-%m", DAY: "%Y-%m-%d", HOUR: "%Y-%m-%d %H:00", MINUTE: "%Y-%m-%d %H:%M"}
def __init__(self, date, precision=DAY):
if isinstance(date, six.text_type):
... | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import datetime
import re
import six
from django.core import exceptions
from django.db import models
from django.utils.translation import gettext_lazy as _
# I have to figure out the regex for this. It's already kind of scary. Use 'T' to start any timep... | 149 | 256 | 869 | 6 | 143 | JamesBisese/django_partial_datetime | partial_date/fields.py | Python | PartialDateTime | PartialDateTime | 18 | 141 | 18 | 18 | 4809db547be1243580a2dc1044fef8a293650e20 | bigcode/the-stack | train |
e84c099fed361d9ecf25cf11 | train | function | def minimum_spanning_tree(adjacency_list):
"""
Get minimum spanning tree for graph represented as adjacency list
"""
remain_vertices = adjacency_list.copy()
edges_by_vertex = {}
mst = UndirectedGraph()
for vertex in remain_vertices.keys():
edges_by_vertex[vertex] = None
while re... | def minimum_spanning_tree(adjacency_list):
| """
Get minimum spanning tree for graph represented as adjacency list
"""
remain_vertices = adjacency_list.copy()
edges_by_vertex = {}
mst = UndirectedGraph()
for vertex in remain_vertices.keys():
edges_by_vertex[vertex] = None
while remain_vertices:
(vertex, adjacent_ed... | graph is of minimum overall weight (sum of all edges) among all such subgraphs.
- It is also required that there is exactly one,
exclusive path between any two nodes of the subgraph.
"""
import math
from src.graph import UndirectedGraph
def minimum_spanning_tree(adjacency_list):
| 64 | 64 | 207 | 10 | 53 | CarnaViire/training | python/src/minimum_spanning_tree.py | Python | minimum_spanning_tree | minimum_spanning_tree | 15 | 39 | 15 | 15 | bb8c117f6a8d48a95b0d25a4141aa542ad864763 | bigcode/the-stack | train |
9f63c1f19bf0ad48557c44ca | train | function | def __get_lighter_vertex(edges_by_vertex, v1, v2):
v1_weight = __get_weight_or_inf(edges_by_vertex[v1])
v2_weight = __get_weight_or_inf(edges_by_vertex[v2])
return v1 if v1_weight < v2_weight else v2
| def __get_lighter_vertex(edges_by_vertex, v1, v2):
| v1_weight = __get_weight_or_inf(edges_by_vertex[v1])
v2_weight = __get_weight_or_inf(edges_by_vertex[v2])
return v1 if v1_weight < v2_weight else v2
| for vertex in vertices:
if min_weight_vertex is None:
min_weight_vertex = vertex
min_weight_vertex = __get_lighter_vertex(
edges_by_vertex, vertex, min_weight_vertex)
return min_weight_vertex
def __get_lighter_vertex(edges_by_vertex, v1, v2):
| 64 | 64 | 64 | 16 | 47 | CarnaViire/training | python/src/minimum_spanning_tree.py | Python | __get_lighter_vertex | __get_lighter_vertex | 58 | 61 | 58 | 58 | faec85561e29dabc29464f80b7ea775d1f342599 | bigcode/the-stack | train |
815bdef46ea9acc29dd849e6 | train | function | def __get_weight_or_inf(edge):
return math.inf if not edge else edge.weight
| def __get_weight_or_inf(edge):
| return math.inf if not edge else edge.weight
| _vertex, v1, v2):
v1_weight = __get_weight_or_inf(edges_by_vertex[v1])
v2_weight = __get_weight_or_inf(edges_by_vertex[v2])
return v1 if v1_weight < v2_weight else v2
def __get_weight_or_inf(edge):
| 64 | 64 | 19 | 8 | 55 | CarnaViire/training | python/src/minimum_spanning_tree.py | Python | __get_weight_or_inf | __get_weight_or_inf | 64 | 65 | 64 | 64 | 14862e6a6458ae778f741c56c03e8c01490c4760 | bigcode/the-stack | train |
c733f8d9cd3d705d413bf1b6 | train | function | def __extract_next_vertex_with_edges(remain_vertices, edges_by_vertex):
vertex_with_min_weight = __find_vertex_with_min_weight(remain_vertices.keys(),
edges_by_vertex)
return vertex_with_min_weight, remain_vertices.pop(vertex_with_min_weight)
| def __extract_next_vertex_with_edges(remain_vertices, edges_by_vertex):
| vertex_with_min_weight = __find_vertex_with_min_weight(remain_vertices.keys(),
edges_by_vertex)
return vertex_with_min_weight, remain_vertices.pop(vertex_with_min_weight)
| is_lighter = edge.weight < __get_weight_or_inf(
edges_by_vertex[adjacent_vertex])
if is_not_visited and is_lighter:
edges_by_vertex[adjacent_vertex] = edge
return mst
def __extract_next_vertex_with_edges(remain_vertices, edges_by_vertex):
| 64 | 64 | 52 | 15 | 48 | CarnaViire/training | python/src/minimum_spanning_tree.py | Python | __extract_next_vertex_with_edges | __extract_next_vertex_with_edges | 42 | 45 | 42 | 42 | 397e5b865672eb6497ef5e23b34942d439aab71d | bigcode/the-stack | train |
08181b4803b75a517d838231 | train | function | def __find_vertex_with_min_weight(vertices, edges_by_vertex):
min_weight_vertex = None
for vertex in vertices:
if min_weight_vertex is None:
min_weight_vertex = vertex
min_weight_vertex = __get_lighter_vertex(
edges_by_vertex, vertex, min_weight_vertex)
return min_wei... | def __find_vertex_with_min_weight(vertices, edges_by_vertex):
| min_weight_vertex = None
for vertex in vertices:
if min_weight_vertex is None:
min_weight_vertex = vertex
min_weight_vertex = __get_lighter_vertex(
edges_by_vertex, vertex, min_weight_vertex)
return min_weight_vertex
| __extract_next_vertex_with_edges(remain_vertices, edges_by_vertex):
vertex_with_min_weight = __find_vertex_with_min_weight(remain_vertices.keys(),
edges_by_vertex)
return vertex_with_min_weight, remain_vertices.pop(vertex_with_min_weight)
def __find_ve... | 64 | 64 | 69 | 13 | 51 | CarnaViire/training | python/src/minimum_spanning_tree.py | Python | __find_vertex_with_min_weight | __find_vertex_with_min_weight | 48 | 55 | 48 | 48 | 0e478a1a3b51d5fd9cfd923d2e8c601e3ed13488 | bigcode/the-stack | train |
6911a3f329e6a6cd5690e526 | train | function | def check_syntax_warning(testcase, statement, errtext='',
*, lineno=1, offset=None):
# Test also that a warning is emitted only once.
from test.support import check_syntax_error
with warnings.catch_warnings(record=True) as warns:
warnings.simplefilter('always', SyntaxWarning... | def check_syntax_warning(testcase, statement, errtext='',
*, lineno=1, offset=None):
# Test also that a warning is emitted only once.
| from test.support import check_syntax_error
with warnings.catch_warnings(record=True) as warns:
warnings.simplefilter('always', SyntaxWarning)
compile(statement, '<testcase>', 'exec')
testcase.assertEqual(len(warns), 1, warns)
warn, = warns
testcase.assertTrue(issubclass(warn.catego... | import contextlib
import functools
import re
import sys
import warnings
def check_syntax_warning(testcase, statement, errtext='',
*, lineno=1, offset=None):
# Test also that a warning is emitted only once.
| 52 | 82 | 274 | 36 | 15 | shawwn/cpython | Lib/test/support/warnings_helper.py | Python | check_syntax_warning | check_syntax_warning | 8 | 35 | 8 | 10 | 4d0385fe8a32f083b4678e945c9b7fa094716da2 | bigcode/the-stack | train |
21022dc4bb6f42467f0a3aa9 | train | class | class WarningsRecorder(object):
"""Convenience wrapper for the warnings list returned on
entry to the warnings.catch_warnings() context manager.
"""
def __init__(self, warnings_list):
self._warnings = warnings_list
self._last = 0
def __getattr__(self, attr):
if len(self._... | class WarningsRecorder(object):
| """Convenience wrapper for the warnings list returned on
entry to the warnings.catch_warnings() context manager.
"""
def __init__(self, warnings_list):
self._warnings = warnings_list
self._last = 0
def __getattr__(self, attr):
if len(self._warnings) > self._last:
... | ):
@functools.wraps(test)
def wrapper(self, *args, **kwargs):
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=category)
return test(self, *args, **kwargs)
return wrapper
return decorator
class WarningsRecorder(object):
| 64 | 64 | 157 | 6 | 57 | shawwn/cpython | Lib/test/support/warnings_helper.py | Python | WarningsRecorder | WarningsRecorder | 54 | 74 | 54 | 54 | a18fd04c63e822fad3156db2a8d0f66a2294dc07 | bigcode/the-stack | train |
15bb8ecb6be7d5228a7e3fbe | train | function | @contextlib.contextmanager
def save_restore_warnings_filters():
old_filters = warnings.filters[:]
try:
yield
finally:
warnings.filters[:] = old_filters
| @contextlib.contextmanager
def save_restore_warnings_filters():
| old_filters = warnings.filters[:]
try:
yield
finally:
warnings.filters[:] = old_filters
| if reraise:
raise AssertionError("unhandled warning %s" % reraise[0])
if missing:
raise AssertionError("filter (%r, %s) did not catch any warning" %
missing[0])
@contextlib.contextmanager
def save_restore_warnings_filters():
| 64 | 64 | 38 | 13 | 51 | shawwn/cpython | Lib/test/support/warnings_helper.py | Python | save_restore_warnings_filters | save_restore_warnings_filters | 183 | 189 | 183 | 184 | 2f6995b74bcd220dbbea194540c58b6b45f0db20 | bigcode/the-stack | train |
6f3f2132be36f15b82082046 | train | function | @contextlib.contextmanager
def check_warnings(*filters, **kwargs):
"""Context manager to silence warnings.
Accept 2-tuples as positional arguments:
("message regexp", WarningCategory)
Optional argument:
- if 'quiet' is True, it does not fail if a filter catches nothing
(default True w... | @contextlib.contextmanager
def check_warnings(*filters, **kwargs):
| """Context manager to silence warnings.
Accept 2-tuples as positional arguments:
("message regexp", WarningCategory)
Optional argument:
- if 'quiet' is True, it does not fail if a filter catches nothing
(default True without argument,
default False if some filters are defined... | Error("%r has no attribute %r" % (self, attr))
@property
def warnings(self):
return self._warnings[self._last:]
def reset(self):
self._last = len(self._warnings)
@contextlib.contextmanager
def check_warnings(*filters, **kwargs):
| 64 | 64 | 149 | 16 | 48 | shawwn/cpython | Lib/test/support/warnings_helper.py | Python | check_warnings | check_warnings | 77 | 98 | 77 | 78 | 3518a6e204fa399c930f9eba02f0fa945b81487e | bigcode/the-stack | train |
2c8450785f2932c4bf7e5617 | train | function | def _filterwarnings(filters, quiet=False):
"""Catch the warnings, then check if all the expected
warnings have been raised and re-raise unexpected warnings.
If 'quiet' is True, only re-raise the unexpected warnings.
"""
# Clear the warning registry of the calling module
# in order to re-raise th... | def _filterwarnings(filters, quiet=False):
| """Catch the warnings, then check if all the expected
warnings have been raised and re-raise unexpected warnings.
If 'quiet' is True, only re-raise the unexpected warnings.
"""
# Clear the warning registry of the calling module
# in order to re-raise the warnings.
frame = sys._getframe(2)
... | @contextlib.contextmanager
def check_no_resource_warning(testcase):
"""Context manager to check that no ResourceWarning is emitted.
Usage:
with check_no_resource_warning(self):
f = open(...)
...
del f
You must remove the object which may emit ResourceWarning be... | 102 | 102 | 343 | 9 | 92 | shawwn/cpython | Lib/test/support/warnings_helper.py | Python | _filterwarnings | _filterwarnings | 144 | 180 | 144 | 144 | a9f8f97e0cf9d691c6b44553427e700a2b9c3c24 | bigcode/the-stack | train |
812f3878c434b1489c3a8a0f | train | function | @contextlib.contextmanager
def check_no_warnings(testcase, message='', category=Warning, force_gc=False):
"""Context manager to check that no warnings are emitted.
This context manager enables a given warning within its scope
and checks that no warnings are emitted even with that warning
enabled.
... | @contextlib.contextmanager
def check_no_warnings(testcase, message='', category=Warning, force_gc=False):
| """Context manager to check that no warnings are emitted.
This context manager enables a given warning within its scope
and checks that no warnings are emitted even with that warning
enabled.
If force_gc is True, a garbage collection is attempted before checking
for warnings. This may help to ... | ')
if not filters:
filters = (("", Warning),)
# Preserve backward compatibility
if quiet is None:
quiet = True
return _filterwarnings(filters, quiet)
@contextlib.contextmanager
def check_no_warnings(testcase, message='', category=Warning, force_gc=False):
| 64 | 64 | 172 | 24 | 40 | shawwn/cpython | Lib/test/support/warnings_helper.py | Python | check_no_warnings | check_no_warnings | 101 | 123 | 101 | 102 | 6b468b8123cc20b27e533e8adbb919d04040b17e | bigcode/the-stack | train |
96bd788910b0585e7dabb96c | train | function | @contextlib.contextmanager
def check_no_resource_warning(testcase):
"""Context manager to check that no ResourceWarning is emitted.
Usage:
with check_no_resource_warning(self):
f = open(...)
...
del f
You must remove the object which may emit ResourceWarning be... | @contextlib.contextmanager
def check_no_resource_warning(testcase):
| """Context manager to check that no ResourceWarning is emitted.
Usage:
with check_no_resource_warning(self):
f = open(...)
...
del f
You must remove the object which may emit ResourceWarning before
the end of the context manager.
"""
with check_no_w... | with warnings.catch_warnings(record=True) as warns:
warnings.filterwarnings('always',
message=message,
category=category)
yield
if force_gc:
gc_collect()
testcase.assertEqual(warns, [])
@contextlib.contextmanager... | 63 | 64 | 93 | 14 | 49 | shawwn/cpython | Lib/test/support/warnings_helper.py | Python | check_no_resource_warning | check_no_resource_warning | 126 | 141 | 126 | 127 | 18bb88b8904eb5adc5bb95fab14c7bdb84661f31 | bigcode/the-stack | train |
61095453cd3261cfae21f788 | train | function | def ignore_warnings(*, category):
"""Decorator to suppress deprecation warnings.
Use of context managers to hide warnings make diffs
more noisy and tools like 'git blame' less useful.
"""
def decorator(test):
@functools.wraps(test)
def wrapper(self, *args, **kwargs):
wit... | def ignore_warnings(*, category):
| """Decorator to suppress deprecation warnings.
Use of context managers to hide warnings make diffs
more noisy and tools like 'git blame' less useful.
"""
def decorator(test):
@functools.wraps(test)
def wrapper(self, *args, **kwargs):
with warnings.catch_warnings():
... | warnings.simplefilter('error', SyntaxWarning)
check_syntax_error(testcase, statement, errtext,
lineno=lineno, offset=offset)
# No warnings are leaked when a SyntaxError is raised.
testcase.assertEqual(warns, [])
def ignore_warnings(*, category):
| 63 | 64 | 106 | 8 | 55 | shawwn/cpython | Lib/test/support/warnings_helper.py | Python | ignore_warnings | ignore_warnings | 38 | 51 | 38 | 38 | c2e2a032a0cc65a6851a0b978a74d8b33474b60c | bigcode/the-stack | train |
75b2dfdac7b3fb0ec09606b8 | train | function | @pytest.mark.parametrize("version", [114, 117, 118, 119, None])
def test_chunked_categorical(version):
df = DataFrame({"cats": Series(["a", "b", "a", "b", "c"], dtype="category")})
df.index.name = "index"
with tm.ensure_clean() as path:
df.to_stata(path, version=version)
reader = StataReader... | @pytest.mark.parametrize("version", [114, 117, 118, 119, None])
def test_chunked_categorical(version):
| df = DataFrame({"cats": Series(["a", "b", "a", "b", "c"], dtype="category")})
df.index.name = "index"
with tm.ensure_clean() as path:
df.to_stata(path, version=version)
reader = StataReader(path, chunksize=2, order_categoricals=False)
for i, block in enumerate(reader):
bl... | [0]))
else:
fp = path
reread = read_stata(fp, index_col="index")
tm.assert_frame_equal(reread, df)
@pytest.mark.parametrize("version", [114, 117, 118, 119, None])
def test_chunked_categorical(version):
| 64 | 64 | 157 | 28 | 36 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_chunked_categorical | test_chunked_categorical | 1,938 | 1,948 | 1,938 | 1,939 | 410482e3542a77df937080512976254847c81314 | bigcode/the-stack | train |
974dea91fb882d679700bed6 | train | function | @pytest.mark.parametrize("version", [114, 117, 118, 119, None])
@pytest.mark.parametrize("use_dict", [True, False])
@pytest.mark.parametrize("infer", [True, False])
def test_compression(compression, version, use_dict, infer):
file_name = "dta_inferred_compression.dta"
if compression:
file_ext = "gz" if ... | @pytest.mark.parametrize("version", [114, 117, 118, 119, None])
@pytest.mark.parametrize("use_dict", [True, False])
@pytest.mark.parametrize("infer", [True, False])
def test_compression(compression, version, use_dict, infer):
| file_name = "dta_inferred_compression.dta"
if compression:
file_ext = "gz" if compression == "gzip" and not use_dict else compression
file_name += f".{file_ext}"
compression_arg = compression
if infer:
compression_arg = "infer"
if use_dict:
compression_arg = {"method"... | , f"stata-compat-{version}.dta")
expected = pd.read_stata(ref)
old_dta = pd.read_stata(old)
tm.assert_frame_equal(old_dta, expected, check_dtype=False)
@pytest.mark.parametrize("version", [114, 117, 118, 119, None])
@pytest.mark.parametrize("use_dict", [True, False])
@pytest.mark.parametrize("infer", [True,... | 104 | 104 | 349 | 57 | 47 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_compression | test_compression | 1,881 | 1,914 | 1,881 | 1,884 | 2b977c00403e690b5adf71e4aba3b71bfc4769db | bigcode/the-stack | train |
fea918e882b1f922cf76347d | train | function | def test_iterator_value_labels():
# GH 31544
values = ["c_label", "b_label"] + ["a_label"] * 500
df = DataFrame({f"col{k}": pd.Categorical(values, ordered=True) for k in range(2)})
with tm.ensure_clean() as path:
df.to_stata(path, write_index=False)
expected = pd.Index(["a_label", "b_lab... | def test_iterator_value_labels():
# GH 31544
| values = ["c_label", "b_label"] + ["a_label"] * 500
df = DataFrame({f"col{k}": pd.Categorical(values, ordered=True) for k in range(2)})
with tm.ensure_clean() as path:
df.to_stata(path, write_index=False)
expected = pd.Index(["a_label", "b_label", "c_label"], dtype="object")
with pd.... | match="chunksize must be a positive"):
StataReader(dta_file, chunksize=0)
with pytest.raises(ValueError, match="chunksize must be a positive"):
StataReader(dta_file, chunksize="apple")
def test_iterator_value_labels():
# GH 31544
| 64 | 64 | 172 | 13 | 51 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_iterator_value_labels | test_iterator_value_labels | 1,980 | 1,991 | 1,980 | 1,981 | 956b639b3469b59e1350b2ca15c94633839851b9 | bigcode/the-stack | train |
38b67ee183bc382892101f99 | train | function | def test_precision_loss():
df = DataFrame(
[[sum(2 ** i for i in range(60)), sum(2 ** i for i in range(52))]],
columns=["big", "little"],
)
with tm.ensure_clean() as path:
with tm.assert_produces_warning(
PossiblePrecisionLoss, match="Column converted from int64 to float6... | def test_precision_loss():
| df = DataFrame(
[[sum(2 ** i for i in range(60)), sum(2 ** i for i in range(52))]],
columns=["big", "little"],
)
with tm.ensure_clean() as path:
with tm.assert_produces_warning(
PossiblePrecisionLoss, match="Column converted from int64 to float64"
):
d... | ) as reader:
for j, chunk in enumerate(reader):
for i in range(2):
tm.assert_index_equal(chunk.dtypes[i].categories, expected)
tm.assert_frame_equal(chunk, df.iloc[j * 100 : (j + 1) * 100])
def test_precision_loss():
| 64 | 64 | 180 | 5 | 59 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_precision_loss | test_precision_loss | 1,994 | 2,008 | 1,994 | 1,994 | fddf0d576b3fdec62850bac2e22ee4510d97be29 | bigcode/the-stack | train |
e0e98f74037ca200c3d739db | train | function | @pytest.fixture
def parsed_114(dirpath):
dta14_114 = os.path.join(dirpath, "stata5_114.dta")
parsed_114 = read_stata(dta14_114, convert_dates=True)
parsed_114.index.name = "index"
return parsed_114
| @pytest.fixture
def parsed_114(dirpath):
| dta14_114 = os.path.join(dirpath, "stata5_114.dta")
parsed_114 = read_stata(dta14_114, convert_dates=True)
parsed_114.index.name = "index"
return parsed_114
| 27.0, 81.0],
"c": ["Atlanta", "Birmingham", "Cincinnati", "Detroit"],
}
)
@pytest.fixture
def dirpath(datapath):
return datapath("io", "data", "stata")
@pytest.fixture
def parsed_114(dirpath):
| 64 | 64 | 65 | 10 | 54 | oricou/pandas | pandas/tests/io/test_stata.py | Python | parsed_114 | parsed_114 | 57 | 62 | 57 | 58 | 2a536a53ddafed94b6a04ec909ea9c09a767207d | bigcode/the-stack | train |
619a4056c02809f138d0d86f | train | function | @pytest.fixture
def dirpath(datapath):
return datapath("io", "data", "stata")
| @pytest.fixture
def dirpath(datapath):
| return datapath("io", "data", "stata")
| 2, 3, 4],
"b": [1.0, 3.0, 27.0, 81.0],
"c": ["Atlanta", "Birmingham", "Cincinnati", "Detroit"],
}
)
@pytest.fixture
def dirpath(datapath):
| 64 | 64 | 23 | 10 | 54 | oricou/pandas | pandas/tests/io/test_stata.py | Python | dirpath | dirpath | 52 | 54 | 52 | 53 | 72175eb3b0fbd685938a898587e120a46df59907 | bigcode/the-stack | train |
189f0a9bdf76d00d82894fd5 | train | function | def test_compression_roundtrip(compression):
df = DataFrame(
[[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
index=["A", "B"],
columns=["X", "Y", "Z"],
)
df.index.name = "index"
with tm.ensure_clean() as path:
df.to_stata(path, compression=compression)... | def test_compression_roundtrip(compression):
| df = DataFrame(
[[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
index=["A", "B"],
columns=["X", "Y", "Z"],
)
df.index.name = "index"
with tm.ensure_clean() as path:
df.to_stata(path, compression=compression)
reread = read_stata(path, compressio... | tm.assert_series_equal(reread.dtypes, expected_dt)
assert reread.loc[0, "little"] == df.loc[0, "little"]
assert reread.loc[0, "big"] == float(df.loc[0, "big"])
def test_compression_roundtrip(compression):
| 63 | 64 | 185 | 9 | 54 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_compression_roundtrip | test_compression_roundtrip | 2,011 | 2,029 | 2,011 | 2,011 | ad7074cad681a0eaa5ad257ca0cb7229c21d261f | bigcode/the-stack | train |
9bb2da62b5426f48eeab99b7 | train | function | @pytest.mark.parametrize("version", [105, 108, 111, 113, 114])
def test_backward_compat(version, datapath):
data_base = datapath("io", "data", "stata")
ref = os.path.join(data_base, "stata-compat-118.dta")
old = os.path.join(data_base, f"stata-compat-{version}.dta")
expected = pd.read_stata(ref)
old... | @pytest.mark.parametrize("version", [105, 108, 111, 113, 114])
def test_backward_compat(version, datapath):
| data_base = datapath("io", "data", "stata")
ref = os.path.join(data_base, "stata-compat-118.dta")
old = os.path.join(data_base, f"stata-compat-{version}.dta")
expected = pd.read_stata(ref)
old_dta = pd.read_stata(old)
tm.assert_frame_equal(old_dta, expected, check_dtype=False)
| as path:
with pytest.raises(ValueError, match="You must use version 119"):
StataWriterUTF8(path, df, version=118)
@pytest.mark.parametrize("version", [105, 108, 111, 113, 114])
def test_backward_compat(version, datapath):
| 64 | 64 | 119 | 31 | 33 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_backward_compat | test_backward_compat | 1,871 | 1,878 | 1,871 | 1,872 | 194b656453e01d87bbd9d30a5ecdf21af5d690e9 | bigcode/the-stack | train |
cb03ae9575e8ea467fc13adb | train | function | @pytest.mark.parametrize("method", ["zip", "infer"])
@pytest.mark.parametrize("file_ext", [None, "dta", "zip"])
def test_compression_dict(method, file_ext):
file_name = f"test.{file_ext}"
archive_name = "test.dta"
df = DataFrame(np.random.randn(10, 2), columns=list("AB"))
df.index.name = "index"
wit... | @pytest.mark.parametrize("method", ["zip", "infer"])
@pytest.mark.parametrize("file_ext", [None, "dta", "zip"])
def test_compression_dict(method, file_ext):
| file_name = f"test.{file_ext}"
archive_name = "test.dta"
df = DataFrame(np.random.randn(10, 2), columns=list("AB"))
df.index.name = "index"
with tm.ensure_clean(file_name) as path:
compression = {"method": method, "archive_name": archive_name}
df.to_stata(path, compression=compressio... | = path
reread = read_stata(fp, index_col="index")
tm.assert_frame_equal(reread, df)
@pytest.mark.parametrize("method", ["zip", "infer"])
@pytest.mark.parametrize("file_ext", [None, "dta", "zip"])
def test_compression_dict(method, file_ext):
| 67 | 67 | 225 | 39 | 28 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_compression_dict | test_compression_dict | 1,917 | 1,935 | 1,917 | 1,919 | 101948726e5c2403142337a6bd3bee343cbe99e9 | bigcode/the-stack | train |
f49849c186e762426602b37a | train | function | def test_chunked_categorical_partial(dirpath):
dta_file = os.path.join(dirpath, "stata-dta-partially-labeled.dta")
values = ["a", "b", "a", "b", 3.0]
with StataReader(dta_file, chunksize=2) as reader:
with tm.assert_produces_warning(CategoricalConversionWarning):
for i, block in enumerat... | def test_chunked_categorical_partial(dirpath):
| dta_file = os.path.join(dirpath, "stata-dta-partially-labeled.dta")
values = ["a", "b", "a", "b", 3.0]
with StataReader(dta_file, chunksize=2) as reader:
with tm.assert_produces_warning(CategoricalConversionWarning):
for i, block in enumerate(reader):
assert list(block.ca... | )
for i, block in enumerate(reader):
block = block.set_index("index")
assert "cats" in block
tm.assert_series_equal(block.cats, df.cats.iloc[2 * i : 2 * (i + 1)])
def test_chunked_categorical_partial(dirpath):
| 64 | 65 | 219 | 10 | 54 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_chunked_categorical_partial | test_chunked_categorical_partial | 1,951 | 1,967 | 1,951 | 1,951 | 4ea7e135eeae906e2668934219b6978db59e1375 | bigcode/the-stack | train |
b8961891422d96b1683bb63e | train | function | def test_iterator_errors(dirpath):
dta_file = os.path.join(dirpath, "stata-dta-partially-labeled.dta")
with pytest.raises(ValueError, match="chunksize must be a positive"):
StataReader(dta_file, chunksize=-1)
with pytest.raises(ValueError, match="chunksize must be a positive"):
StataReader(d... | def test_iterator_errors(dirpath):
| dta_file = os.path.join(dirpath, "stata-dta-partially-labeled.dta")
with pytest.raises(ValueError, match="chunksize must be a positive"):
StataReader(dta_file, chunksize=-1)
with pytest.raises(ValueError, match="chunksize must be a positive"):
StataReader(dta_file, chunksize=0)
with pyte... | tm.assert_produces_warning(CategoricalConversionWarning):
with StataReader(dta_file, chunksize=5) as reader:
large_chunk = reader.__next__()
direct = read_stata(dta_file)
tm.assert_frame_equal(direct, large_chunk)
def test_iterator_errors(dirpath):
| 64 | 64 | 116 | 7 | 57 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_iterator_errors | test_iterator_errors | 1,970 | 1,977 | 1,970 | 1,970 | c1fc82f011616bd1a7433dba93692fb4ba4bfc70 | bigcode/the-stack | train |
2e753e5c808889bbdb5a6a00 | train | function | @pytest.mark.parametrize("to_infer", [True, False])
@pytest.mark.parametrize("read_infer", [True, False])
def test_stata_compression(compression_only, read_infer, to_infer):
compression = compression_only
ext = "gz" if compression == "gzip" else compression
filename = f"test.{ext}"
df = DataFrame(
... | @pytest.mark.parametrize("to_infer", [True, False])
@pytest.mark.parametrize("read_infer", [True, False])
def test_stata_compression(compression_only, read_infer, to_infer):
| compression = compression_only
ext = "gz" if compression == "gzip" else compression
filename = f"test.{ext}"
df = DataFrame(
[[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
index=["A", "B"],
columns=["X", "Y", "Z"],
)
df.index.name = "index"
to_co... | reread = pd.read_stata(contents, index_col="index")
tm.assert_frame_equal(df, reread)
@pytest.mark.parametrize("to_infer", [True, False])
@pytest.mark.parametrize("read_infer", [True, False])
def test_stata_compression(compression_only, read_infer, to_infer):
| 68 | 68 | 228 | 44 | 24 | oricou/pandas | pandas/tests/io/test_stata.py | Python | test_stata_compression | test_stata_compression | 2,032 | 2,053 | 2,032 | 2,034 | 13161dbb45cf7fa3886fa23e539576bd3de64637 | bigcode/the-stack | train |
d7c52e28bb084f81becd07f0 | train | class | class TestStata:
@pytest.fixture(autouse=True)
def setup_method(self, datapath):
self.dirpath = datapath("io", "data", "stata")
self.dta1_114 = os.path.join(self.dirpath, "stata1_114.dta")
self.dta1_117 = os.path.join(self.dirpath, "stata1_117.dta")
self.dta2_113 = os.path.join(... | class TestStata:
@pytest.fixture(autouse=True)
| def setup_method(self, datapath):
self.dirpath = datapath("io", "data", "stata")
self.dta1_114 = os.path.join(self.dirpath, "stata1_114.dta")
self.dta1_117 = os.path.join(self.dirpath, "stata1_117.dta")
self.dta2_113 = os.path.join(self.dirpath, "stata2_113.dta")
self.dta2_1... | .io.stata import (
CategoricalConversionWarning,
InvalidColumnName,
PossiblePrecisionLoss,
StataMissingValue,
StataReader,
StataWriterUTF8,
read_stata,
)
# TODO(ArrayManager) the stata code relies on BlockManager internals (eg blknos)
pytestmark = td.skip_array_manager_not_yet_implemented
... | 256 | 256 | 18,056 | 14 | 241 | oricou/pandas | pandas/tests/io/test_stata.py | Python | TestStata | TestStata | 65 | 1,868 | 65 | 66 | 4fe2b2586492298877e876fdd25a4393e600dcd5 | bigcode/the-stack | train |
9e744e355e8845e73087c76b | train | function | @pytest.fixture()
def mixed_frame():
return DataFrame(
{
"a": [1, 2, 3, 4],
"b": [1.0, 3.0, 27.0, 81.0],
"c": ["Atlanta", "Birmingham", "Cincinnati", "Detroit"],
}
)
| @pytest.fixture()
def mixed_frame():
| return DataFrame(
{
"a": [1, 2, 3, 4],
"b": [1.0, 3.0, 27.0, 81.0],
"c": ["Atlanta", "Birmingham", "Cincinnati", "Detroit"],
}
)
| StataMissingValue,
StataReader,
StataWriterUTF8,
read_stata,
)
# TODO(ArrayManager) the stata code relies on BlockManager internals (eg blknos)
pytestmark = td.skip_array_manager_not_yet_implemented
@pytest.fixture()
def mixed_frame():
| 64 | 64 | 76 | 7 | 56 | oricou/pandas | pandas/tests/io/test_stata.py | Python | mixed_frame | mixed_frame | 41 | 49 | 41 | 42 | 5056d9b5d2f00f791e1f88ce3e786a2ee3c0758d | bigcode/the-stack | train |
e0e17fa5d25f414d092dcbcf | train | class | class TestGMean(TestCase):
def test_1D_list(self):
a = (1,2,3,4)
actual = stats.gmean(a)
desired = power(1*2*3*4,1./4.)
assert_almost_equal(actual, desired,decimal=14)
desired1 = stats.gmean(a,axis=-1)
assert_almost_equal(actual, desired1, decimal=14)
def test_... | class TestGMean(TestCase):
| def test_1D_list(self):
a = (1,2,3,4)
actual = stats.gmean(a)
desired = power(1*2*3*4,1./4.)
assert_almost_equal(actual, desired,decimal=14)
desired1 = stats.gmean(a,axis=-1)
assert_almost_equal(actual, desired1, decimal=14)
def test_1D_array(self):
a = ... | 0.16666667]))
# test for namedtuple attribute results
attributes = ('frequency', 'lowerlimit', 'binsize', 'extrapoints')
res = stats.relfreq(a, numbins=4)
check_named_results(res, attributes)
# check array_like input is accepted
relfreqs2, lowlim, binsize, extrapoints = stats.relfreq([1, 4, 2,... | 130 | 130 | 435 | 7 | 123 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestGMean | TestGMean | 957 | 1,000 | 957 | 958 | 6b76e22f04b5521ac1393eb4a3f1574acf7f3de6 | bigcode/the-stack | train |
9b66e7333dc08bd1ad26376d | train | function | def test_normalitytests():
yield (assert_raises, ValueError, stats.skewtest, 4.)
yield (assert_raises, ValueError, stats.kurtosistest, 4.)
yield (assert_raises, ValueError, stats.normaltest, 4.)
# numbers verified with R: dagoTest in package fBasics
st_normal, st_skew, st_kurt = (3.92371918, 1.9807... | def test_normalitytests():
| yield (assert_raises, ValueError, stats.skewtest, 4.)
yield (assert_raises, ValueError, stats.kurtosistest, 4.)
yield (assert_raises, ValueError, stats.normaltest, 4.)
# numbers verified with R: dagoTest in package fBasics
st_normal, st_skew, st_kurt = (3.92371918, 1.98078826, -0.01403734)
pv_n... | ))))
# expected values
e_nobs, e_minmax = (20, (1.0, 2.0))
e_mean = 1.3999999999999999
e_var = 0.25263157894736848
e_skew = 0.4082482904638634
e_kurt = -1.8333333333333333
# actual values
a = stats.describe(x, axis=None)
assert_equal(a.nobs, e_n... | 211 | 212 | 709 | 6 | 205 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | test_normalitytests | test_normalitytests | 2,614 | 2,667 | 2,614 | 2,614 | c0cd61ca2e4d42160bf77f845330eed97fb04f3b | bigcode/the-stack | train |
26a5854a8ad7650b467b5679 | train | class | class TestMoments(TestCase):
"""
Comparison numbers are found using R v.1.5.1
note that length(testcase) = 4
testmathworks comes from documentation for the
Statistics Toolbox for Matlab and can be found at both
http://www.mathworks.com/access/helpdesk/help/toolbox/stats/kurto... | class TestMoments(TestCase):
| """
Comparison numbers are found using R v.1.5.1
note that length(testcase) = 4
testmathworks comes from documentation for the
Statistics Toolbox for Matlab and can be found at both
http://www.mathworks.com/access/helpdesk/help/toolbox/stats/kurtosis.shtml
http://www.... | 2, -t2, -t2, np.sqrt(3.)],
[1.0, -1.0, 1.0, -1.0]]
assert_array_almost_equal(z0, z0_expected)
assert_array_almost_equal(z1, z1_expected)
def test_zscore_ddof(self):
# Test use of 'ddof' keyword in zscore.
x = np.array([[0.0, 0.0, 1.0, 1.0],
... | 256 | 256 | 1,616 | 7 | 249 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestMoments | TestMoments | 1,427 | 1,555 | 1,427 | 1,427 | 830ff223a0c3ad755b52fdd5636fa4b98a75d175 | bigcode/the-stack | train |
035b5bced39895cd7aceb677 | train | class | class TestVariability(TestCase):
testcase = [1,2,3,4]
scalar_testcase = 4.
def test_signaltonoise(self):
# This is not in R, so used:
# mean(testcase, axis=0) / (sqrt(var(testcase) * 3/4))
# y = stats.signaltonoise(self.shoes[0])
# assert_approx_equal(y,4.5709967)
... | class TestVariability(TestCase):
| testcase = [1,2,3,4]
scalar_testcase = 4.
def test_signaltonoise(self):
# This is not in R, so used:
# mean(testcase, axis=0) / (sqrt(var(testcase) * 3/4))
# y = stats.signaltonoise(self.shoes[0])
# assert_approx_equal(y,4.5709967)
with warnings.catch_warnings()... | _warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
vals = stats.mode(arr)
assert_equal(vals[0][0], Point(2))
assert_equal(vals[1][0], 4)
def test_mode_result_attributes(self):
data1 = [3, 5, 1, 10, 23, 3, 2, 6, 8, 6, 10, 6]
actual = stat... | 256 | 256 | 1,607 | 7 | 249 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestVariability | TestVariability | 1,302 | 1,424 | 1,302 | 1,303 | 6454036a21313ae7ef85ae9e3a0368d4464c2dda | bigcode/the-stack | train |
6f8ce02c08e323fb7a39ae51 | train | class | class TestDescribe(TestCase):
def test_describe_scalar(self):
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
n, mm, m, v, sk, kurt = stats.describe(4.)
assert_equal(n, 1)
assert_equal(mm, (4.0, 4.0))
assert_equal(m, ... | class TestDescribe(TestCase):
| def test_describe_scalar(self):
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
n, mm, m, v, sk, kurt = stats.describe(4.)
assert_equal(n, 1)
assert_equal(mm, (4.0, 4.0))
assert_equal(m, 4.0)
assert_(np.isnan(... | 3,p3 = stats.ttest_1samp(rvn1[0,0,:], 1)
assert_array_almost_equal(t1,t2, decimal=14)
assert_almost_equal(t1[0,0],t3, decimal=14)
assert_equal(t1.shape, (n1,n2))
# test zero division problem
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
t... | 256 | 256 | 1,201 | 6 | 250 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestDescribe | TestDescribe | 2,514 | 2,611 | 2,514 | 2,514 | 0a6932811bcecfea55d926ad85f4c79f82e6919b | bigcode/the-stack | train |
9402026da50781ba9dab54b1 | train | function | def test_ttest_ind_with_uneq_var():
# check vs. R
a = (1, 2, 3)
b = (1.1, 2.9, 4.2)
pr = 0.53619490753126731
tr = -0.68649512735572582
t, p = stats.ttest_ind(a, b, equal_var=False)
assert_array_almost_equal([t,p], [tr, pr])
# test from desc stats API
assert_array_almost_equal(stats.t... | def test_ttest_ind_with_uneq_var():
# check vs. R
| a = (1, 2, 3)
b = (1.1, 2.9, 4.2)
pr = 0.53619490753126731
tr = -0.68649512735572582
t, p = stats.ttest_ind(a, b, equal_var=False)
assert_array_almost_equal([t,p], [tr, pr])
# test from desc stats API
assert_array_almost_equal(stats.ttest_ind_from_stats(*_desc_stats(a, b),
... | , nan_policy='omit'),
(0.24779670949091914, 0.80434267337517906))
assert_raises(ValueError, stats.ttest_ind, x, y, nan_policy='raise')
assert_raises(ValueError, stats.ttest_ind, x, y, nan_policy='foobar')
# test zero division problem
t, p = stats.ttest_... | 256 | 256 | 1,388 | 17 | 239 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | test_ttest_ind_with_uneq_var | test_ttest_ind_with_uneq_var | 2,352 | 2,460 | 2,352 | 2,353 | afb0628e59d9c6d7767160163224f0895143b27d | bigcode/the-stack | train |
80e0279f0af75205c0671243 | train | function | def test_binomtest3():
# test added for issue #2384
# test when x == n*p and neighbors
res3 = [stats.binom_test(v, v*k, 1./k) for v in range(1, 11)
for k in range(2, 11)]
assert_equal(res3, np.ones(len(res3), int))
#> bt=c()
#> for(i in as.single(1:10)... | def test_binomtest3():
# test added for issue #2384
# test when x == n*p and neighbors
| res3 = [stats.binom_test(v, v*k, 1./k) for v in range(1, 11)
for k in range(2, 11)]
assert_equal(res3, np.ones(len(res3), int))
#> bt=c()
#> for(i in as.single(1:10)){for(k in as.single(2:10)){bt = c(bt, binom.test(i-1, k*i,(1/k))$p.value); print(c(i+1, k*i,(1... | 7265625,1.0000000,0.7265625,0.2890625,
0.0703125,0.0078125],
[0.00390625,0.03906250,0.17968750,0.50781250,1.00000000,1.00000000,
0.50781250,0.17968750,0.03906250,0.00390625],
[0.001953125,0.021484375,0.109375000,0.343750000,0.753906250,1.000000000,
0.753906250,0.343750000,0.109375000,0.021484375,... | 256 | 256 | 2,073 | 28 | 228 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | test_binomtest3 | test_binomtest3 | 3,140 | 3,221 | 3,140 | 3,142 | e9269694037612c34d31559fe13c1e57d00a8e32 | bigcode/the-stack | train |
d0776727756d2300657cd006 | train | function | def test_friedmanchisquare():
# see ticket:113
# verified with matlab and R
# From Demsar "Statistical Comparisons of Classifiers over Multiple Data Sets"
# 2006, Xf=9.28 (no tie handling, tie corrected Xf >=9.28)
x1 = [array([0.763, 0.599, 0.954, 0.628, 0.882, 0.936, 0.661, 0.583,
... | def test_friedmanchisquare():
# see ticket:113
# verified with matlab and R
# From Demsar "Statistical Comparisons of Classifiers over Multiple Data Sets"
# 2006, Xf=9.28 (no tie handling, tie corrected Xf >=9.28)
| x1 = [array([0.763, 0.599, 0.954, 0.628, 0.882, 0.936, 0.661, 0.583,
0.775, 1.0, 0.94, 0.619, 0.972, 0.957]),
array([0.768, 0.591, 0.971, 0.661, 0.888, 0.931, 0.668, 0.583,
0.838, 1.0, 0.962, 0.666, 0.981, 0.978]),
array([0.771, 0.590, 0.968, 0.654, 0.886, 0.916... | 34.0,
-1.0, 29.5,
-0.5, 26.5,
0.0, 24.6,
0.5, 23.4,
0.67, 23.1,
1.0, 22.7,
1.5, 22.6,
2.0, 22.9,
3.0, 24.8,
5.0, 35.5,
10.0, 21.4e1,
]).reshape(-1, 2)
for lambda_, expected_stat in table5:
stat, p = stats.power_... | 256 | 256 | 1,081 | 67 | 189 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | test_friedmanchisquare | test_friedmanchisquare | 2,064 | 2,114 | 2,064 | 2,068 | 20087e13bb253a48b70c3420cb170e4ff6491d2d | bigcode/the-stack | train |
f1ee4bd70c5bf2ff23ab8448 | train | class | class TestThreshold(TestCase):
def test_basic(self):
a = [-1, 2, 3, 4, 5, -1, -2]
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
assert_array_equal(stats.threshold(a), a)
assert_array_equal(stats.threshold(a, 3, None... | class TestThreshold(TestCase):
| def test_basic(self):
a = [-1, 2, 3, 4, 5, -1, -2]
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
assert_array_equal(stats.threshold(a), a)
assert_array_equal(stats.threshold(a, 3, None, 0),
... | accurate numpy.power() implementation.
tc_no_mean = self.testcase_moment_accuracy - \
np.mean(self.testcase_moment_accuracy)
assert_allclose(np.power(tc_no_mean, 42).mean(),
stats.moment(self.testcase_moment_accuracy, 42))
class TestThreshold(TestCase):
| 64 | 64 | 182 | 6 | 58 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestThreshold | TestThreshold | 1,558 | 1,569 | 1,558 | 1,558 | 07dcfc1de618ed4d93e7e7f272a6d3f39c1f10de | bigcode/the-stack | train |
15027672d714af891ff5f5ee | train | function | def test_kendalltau():
# with some ties
x1 = [12, 2, 1, 12, 2]
x2 = [1, 4, 7, 1, 0]
expected = (-0.47140452079103173, 0.24821309157521476)
res = stats.kendalltau(x1, x2)
assert_approx_equal(res[0], expected[0])
assert_approx_equal(res[1], expected[1])
# test for namedtuple attribute res... | def test_kendalltau():
# with some ties
| x1 = [12, 2, 1, 12, 2]
x2 = [1, 4, 7, 1, 0]
expected = (-0.47140452079103173, 0.24821309157521476)
res = stats.kendalltau(x1, x2)
assert_approx_equal(res[0], expected[0])
assert_approx_equal(res[1], expected[1])
# test for namedtuple attribute results
attributes = ('correlation', 'pvalu... | , 4.0]
yr = [1.0, 2.5, 2.5, 4.0]
# Result of spearmanr should be the same as applying
# pearsonr to the ranks.
sr = stats.spearmanr(x, y)
pr = stats.pearsonr(xr, yr)
assert_almost_equal(sr, pr)
# W.II.E. Tabulate X against X, using BIG as a case weight. The values
... | 212 | 212 | 707 | 13 | 199 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | test_kendalltau | test_kendalltau | 567 | 620 | 567 | 568 | dcebd7e9903de0a62b90e1cfb35e04d0f7bdebda | bigcode/the-stack | train |
55f187f5862de0153c29ff73 | train | class | class TestFOneWay(TestCase):
def test_trivial(self):
# A trivial test of stats.f_oneway, with F=0.
F, p = stats.f_oneway([0,2], [0,2])
assert_equal(F, 0.0)
def test_basic(self):
# Despite being a floating point calculation, this data should
# result in F being exactly 2.... | class TestFOneWay(TestCase):
| def test_trivial(self):
# A trivial test of stats.f_oneway, with F=0.
F, p = stats.f_oneway([0,2], [0,2])
assert_equal(F, 0.0)
def test_basic(self):
# Despite being a floating point calculation, this data should
# result in F being exactly 2.0.
F, p = stats.f_one... | ,-50,3)))
fact = 1.8
c, low, upp = stats.sigmaclip(a, fact, fact)
assert_(c.min() > low)
assert_(c.max() < upp)
assert_equal(low, c.mean() - fact*c.std())
assert_equal(upp, c.mean() + fact*c.std())
assert_equal(c, np.linspace(9.5,10.5,11))
def test_sigmaclip_... | 186 | 186 | 623 | 8 | 178 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestFOneWay | TestFOneWay | 3,339 | 3,394 | 3,339 | 3,339 | b922b50fab1c9d1aea5c8ef6f92a39729ecbc2e9 | bigcode/the-stack | train |
091c91f0981722b62bc7e3cb | train | function | def test_binomtest():
# precision tests compared to R for ticket:986
pp = np.concatenate((np.linspace(0.1,0.2,5), np.linspace(0.45,0.65,5),
np.linspace(0.85,0.95,5)))
n = 501
x = 450
results = [0.0, 0.0, 1.0159969301994141e-304,
2.9752418572150531e-275, 7.76683829225352... | def test_binomtest():
# precision tests compared to R for ticket:986
| pp = np.concatenate((np.linspace(0.1,0.2,5), np.linspace(0.45,0.65,5),
np.linspace(0.85,0.95,5)))
n = 501
x = 450
results = [0.0, 0.0, 1.0159969301994141e-304,
2.9752418572150531e-275, 7.7668382922535275e-250,
2.3381250925167094e-099, 7.8284591587323951e-081,
9.9155... | )
finally:
np.seterr(**olderr)
class TestGeoMean(GeoMeanTestCase, TestCase):
def do(self, a, b, axis=None, dtype=None):
# Note this doesn't test when axis is not specified
x = stats.gmean(a, axis=axis, dtype=dtype)
assert_almost_equal(b, x)
assert_equal(x.dtype,... | 103 | 103 | 346 | 18 | 85 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | test_binomtest | test_binomtest | 3,095 | 3,114 | 3,095 | 3,096 | b30277bf9cccfd8346817d44d242ccadb5a46c41 | bigcode/the-stack | train |
3356f0eae6ebc017ed82b191 | train | class | class TestCorrSpearmanrTies(TestCase):
"""Some tests of tie-handling by the spearmanr function."""
def test_tie1(self):
# Data
x = [1.0, 2.0, 3.0, 4.0]
y = [1.0, 2.0, 2.0, 3.0]
# Ranks of the data, with tie-handling.
xr = [1.0, 2.0, 3.0, 4.0]
yr = [1.0, 2.5, 2.5,... | class TestCorrSpearmanrTies(TestCase):
| """Some tests of tie-handling by the spearmanr function."""
def test_tie1(self):
# Data
x = [1.0, 2.0, 3.0, 4.0]
y = [1.0, 2.0, 2.0, 3.0]
# Ranks of the data, with tie-handling.
xr = [1.0, 2.0, 3.0, 4.0]
yr = [1.0, 2.5, 2.5, 4.0]
# Result of spearmanr sho... | assert_approx_equal(r,1.0)
def test_spearmanr_result_attributes(self):
res = stats.spearmanr(X, X)
attributes = ('correlation', 'pvalue')
check_named_results(res, attributes)
class TestCorrSpearmanrTies(TestCase):
| 64 | 64 | 200 | 11 | 53 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestCorrSpearmanrTies | TestCorrSpearmanrTies | 540 | 554 | 540 | 540 | 920def69d8d522722d66e7e3aa1f0eff12bcaba7 | bigcode/the-stack | train |
35d3e28161d34c1d16b5d8e1 | train | class | class HarMeanTestCase:
def test_1dlist(self):
# Test a 1d list
a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
b = 34.1417152147
self.do(a, b)
def test_1darray(self):
# Test a 1d array
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
b = 34.1417152... | class HarMeanTestCase:
| def test_1dlist(self):
# Test a 1d list
a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
b = 34.1417152147
self.do(a, b)
def test_1darray(self):
# Test a 1d array
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
b = 34.1417152147
self.do(a, ... | result = np.array(
[[5.41666667, 1.04166667, -0.41666667, 1.04166667, 5.41666667],
[21.66666667, 4.16666667, -1.66666667, 4.16666667, 21.66666667]])
assert_array_almost_equal(stats.obrientransform(x1, 2*x1), result, decimal=8)
# Example from "O'Brien Test for Homogeneity of Variance"
# by ... | 256 | 256 | 993 | 6 | 250 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | HarMeanTestCase | HarMeanTestCase | 2,901 | 2,968 | 2,901 | 2,901 | aee4abff90553a0a1d2beea75e229b90868976bc | bigcode/the-stack | train |
6f2bbba4b2ff8739375b47f3 | train | class | class TestMannWhitneyU(TestCase):
X = [19.8958398126694, 19.5452691647182, 19.0577309166425, 21.716543054589,
20.3269502208702, 20.0009273294025, 19.3440043632957, 20.4216806548105,
19.0649894736528, 18.7808043120398, 19.3680942943298, 19.4848044069953,
20.7514611265663, 19.0894948874598,... | class TestMannWhitneyU(TestCase):
| X = [19.8958398126694, 19.5452691647182, 19.0577309166425, 21.716543054589,
20.3269502208702, 20.0009273294025, 19.3440043632957, 20.4216806548105,
19.0649894736528, 18.7808043120398, 19.3680942943298, 19.4848044069953,
20.7514611265663, 19.0894948874598, 19.4975522356628, 18.997117073427... | .normal(0, 1, 100000)
JB1, p1 = stats.jarque_bera(list(x))
JB2, p2 = stats.jarque_bera(tuple(x))
JB3, p3 = stats.jarque_bera(x.reshape(2, 50000))
assert_(JB1 == JB2 == JB3)
assert_(p1 == p2 == p3)
def test_jarque_bera_size(self):
assert_raises(ValueError, stats.jar... | 256 | 256 | 2,559 | 10 | 246 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestMannWhitneyU | TestMannWhitneyU | 2,717 | 2,839 | 2,717 | 2,717 | 286a4424fe89b81e8ebd2f6fc779ed91d35aa309 | bigcode/the-stack | train |
734f2c095f13f2d3e680ffc4 | train | function | def test_skewtest_too_few_samples():
# Regression test for ticket #1492.
# skewtest requires at least 8 samples; 7 should raise a ValueError.
x = np.arange(7.0)
assert_raises(ValueError, stats.skewtest, x)
| def test_skewtest_too_few_samples():
# Regression test for ticket #1492.
# skewtest requires at least 8 samples; 7 should raise a ValueError.
| x = np.arange(7.0)
assert_raises(ValueError, stats.skewtest, x)
| test_jarque_bera_size(self):
assert_raises(ValueError, stats.jarque_bera, [])
def test_skewtest_too_few_samples():
# Regression test for ticket #1492.
# skewtest requires at least 8 samples; 7 should raise a ValueError.
| 63 | 64 | 64 | 40 | 23 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | test_skewtest_too_few_samples | test_skewtest_too_few_samples | 2,703 | 2,707 | 2,703 | 2,705 | a208ddd81f8c964716864a747b29d8076d135cfa | bigcode/the-stack | train |
7902aedf2ea07c4a0904abcd | train | class | class TestCorrPearsonr(TestCase):
""" W.II.D. Compute a correlation matrix on all the variables.
All the correlations, except for ZERO and MISS, shoud be exactly 1.
ZERO and MISS should have undefined or missing correlations with the
other variables. The same should go for SPEARMAN corelat... | class TestCorrPearsonr(TestCase):
| """ W.II.D. Compute a correlation matrix on all the variables.
All the correlations, except for ZERO and MISS, shoud be exactly 1.
ZERO and MISS should have undefined or missing correlations with the
other variables. The same should go for SPEARMAN corelations, if
your program has ... | (x, upperlimit=9, inclusive=False), [8, 7])
assert_equal(stats.tmax(x, axis=1), [1, 3, 5, 7, 9])
assert_equal(stats.tmax(x, axis=None), 9)
x = np.arange(10.)
x[6] = np.nan
assert_equal(stats.tmax(x), np.nan)
assert_equal(stats.tmax(x, nan_policy='omit'), 9.)
asse... | 256 | 256 | 1,127 | 9 | 247 | zeehio/scipy | scipy/stats/tests/test_stats.py | Python | TestCorrPearsonr | TestCorrPearsonr | 135 | 269 | 135 | 135 | 25428f51148ca3a791d70cec85bb7b8ca26a7613 | bigcode/the-stack | train |
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