body_hash
stringlengths 64
64
| body
stringlengths 23
109k
| docstring
stringlengths 1
57k
| path
stringlengths 4
198
| name
stringlengths 1
115
| repository_name
stringlengths 7
111
| repository_stars
float64 0
191k
| lang
stringclasses 1
value | body_without_docstring
stringlengths 14
108k
| unified
stringlengths 45
133k
|
|---|---|---|---|---|---|---|---|---|---|
87414c955a0530474f58392fced7a41c0416a87516d3ac5f01ee84e24b7db3c3
|
def is_subnet_of(a, b):
'\n Check if network-b is subnet of network-a\n '
if (a.network_address != b.network_address):
return False
return (a.prefixlen >= b.prefixlen)
|
Check if network-b is subnet of network-a
|
openr/py/openr/utils/ipnetwork.py
|
is_subnet_of
|
arshanh/openr
| 1 |
python
|
def is_subnet_of(a, b):
'\n \n '
if (a.network_address != b.network_address):
return False
return (a.prefixlen >= b.prefixlen)
|
def is_subnet_of(a, b):
'\n \n '
if (a.network_address != b.network_address):
return False
return (a.prefixlen >= b.prefixlen)<|docstring|>Check if network-b is subnet of network-a<|endoftext|>
|
c35b496293c98963861033ece8d7d8657fdf568aaef6bae5f7528de8c33808a7
|
def contain_any_prefix(prefix, ip_networks):
'\n Utility function to check if prefix contain any of the prefixes/ips\n\n :returns: True if prefix contains any of the ip_networks else False\n '
if (ip_networks is None):
return True
prefix = ipaddress.ip_network(prefix)
return any((is_subnet_of(prefix, net) for net in ip_networks))
|
Utility function to check if prefix contain any of the prefixes/ips
:returns: True if prefix contains any of the ip_networks else False
|
openr/py/openr/utils/ipnetwork.py
|
contain_any_prefix
|
arshanh/openr
| 1 |
python
|
def contain_any_prefix(prefix, ip_networks):
'\n Utility function to check if prefix contain any of the prefixes/ips\n\n :returns: True if prefix contains any of the ip_networks else False\n '
if (ip_networks is None):
return True
prefix = ipaddress.ip_network(prefix)
return any((is_subnet_of(prefix, net) for net in ip_networks))
|
def contain_any_prefix(prefix, ip_networks):
'\n Utility function to check if prefix contain any of the prefixes/ips\n\n :returns: True if prefix contains any of the ip_networks else False\n '
if (ip_networks is None):
return True
prefix = ipaddress.ip_network(prefix)
return any((is_subnet_of(prefix, net) for net in ip_networks))<|docstring|>Utility function to check if prefix contain any of the prefixes/ips
:returns: True if prefix contains any of the ip_networks else False<|endoftext|>
|
e65db7e41adfbde8496c561802848424bb4fd30efda7691b5a4a2531ebd2629a
|
def _bbox2pymesh(element):
'\n Convert the bounding box of <element> into a pymesh Mesh in world coordinates.\n\n Inputs:\n element (ProjectObject)\n\n Return:\n pymesh.Mesh - Mesh representation of the oriented bounding box in\n world coordinates.\n '
vertices = (element.pose * element.bounds.corners())
(i1, i2, i3, i4, i5, i6, i7, i8) = range(8)
faces = [[i1, i2, i3], [i1, i3, i4], [i4, i3, i8], [i4, i8, i5], [i5, i8, i7], [i5, i7, i6], [i6, i7, i2], [i6, i2, i1], [i1, i4, i5], [i1, i5, i6], [i2, i7, i8], [i2, i8, i3]]
return pymesh.form_mesh(vertices.T, np.array(faces))
|
Convert the bounding box of <element> into a pymesh Mesh in world coordinates.
Inputs:
element (ProjectObject)
Return:
pymesh.Mesh - Mesh representation of the oriented bounding box in
world coordinates.
|
sumo/metrics/bb_evaluator.py
|
_bbox2pymesh
|
RishabhJain2018/sumo-challenge
| 70 |
python
|
def _bbox2pymesh(element):
'\n Convert the bounding box of <element> into a pymesh Mesh in world coordinates.\n\n Inputs:\n element (ProjectObject)\n\n Return:\n pymesh.Mesh - Mesh representation of the oriented bounding box in\n world coordinates.\n '
vertices = (element.pose * element.bounds.corners())
(i1, i2, i3, i4, i5, i6, i7, i8) = range(8)
faces = [[i1, i2, i3], [i1, i3, i4], [i4, i3, i8], [i4, i8, i5], [i5, i8, i7], [i5, i7, i6], [i6, i7, i2], [i6, i2, i1], [i1, i4, i5], [i1, i5, i6], [i2, i7, i8], [i2, i8, i3]]
return pymesh.form_mesh(vertices.T, np.array(faces))
|
def _bbox2pymesh(element):
'\n Convert the bounding box of <element> into a pymesh Mesh in world coordinates.\n\n Inputs:\n element (ProjectObject)\n\n Return:\n pymesh.Mesh - Mesh representation of the oriented bounding box in\n world coordinates.\n '
vertices = (element.pose * element.bounds.corners())
(i1, i2, i3, i4, i5, i6, i7, i8) = range(8)
faces = [[i1, i2, i3], [i1, i3, i4], [i4, i3, i8], [i4, i8, i5], [i5, i8, i7], [i5, i7, i6], [i6, i7, i2], [i6, i2, i1], [i1, i4, i5], [i1, i5, i6], [i2, i7, i8], [i2, i8, i3]]
return pymesh.form_mesh(vertices.T, np.array(faces))<|docstring|>Convert the bounding box of <element> into a pymesh Mesh in world coordinates.
Inputs:
element (ProjectObject)
Return:
pymesh.Mesh - Mesh representation of the oriented bounding box in
world coordinates.<|endoftext|>
|
c8cf6fcd29f08dfb9ed63408a22d9fbff3384dc0e094ad5fc2742665160b22fb
|
def __init__(self, submission, ground_truth, settings=None):
'\n Constructor. Computes similarity between all elements in the\n submission and ground_truth and also computes\n data association caches.\n\n Inputs:\n submission (ProjectScene) - Submitted scene to be evaluated\n ground_truth (ProjectScene) - The ground truth scene\n settings (dict) - configuration for the evaluator. See\n Evaluator.py for recognized keys and values.\n '
for e in submission.elements.values():
posed_corners = e.pose.transform_all_from(e.bounds.corners())
e.posed_bbox = ComputeBbox().from_point_cloud(posed_corners)
for e in ground_truth.elements.values():
posed_corners = e.pose.transform_all_from(e.bounds.corners())
e.posed_bbox = ComputeBbox().from_point_cloud(posed_corners)
super(BBEvaluator, self).__init__(submission, ground_truth, settings)
|
Constructor. Computes similarity between all elements in the
submission and ground_truth and also computes
data association caches.
Inputs:
submission (ProjectScene) - Submitted scene to be evaluated
ground_truth (ProjectScene) - The ground truth scene
settings (dict) - configuration for the evaluator. See
Evaluator.py for recognized keys and values.
|
sumo/metrics/bb_evaluator.py
|
__init__
|
RishabhJain2018/sumo-challenge
| 70 |
python
|
def __init__(self, submission, ground_truth, settings=None):
'\n Constructor. Computes similarity between all elements in the\n submission and ground_truth and also computes\n data association caches.\n\n Inputs:\n submission (ProjectScene) - Submitted scene to be evaluated\n ground_truth (ProjectScene) - The ground truth scene\n settings (dict) - configuration for the evaluator. See\n Evaluator.py for recognized keys and values.\n '
for e in submission.elements.values():
posed_corners = e.pose.transform_all_from(e.bounds.corners())
e.posed_bbox = ComputeBbox().from_point_cloud(posed_corners)
for e in ground_truth.elements.values():
posed_corners = e.pose.transform_all_from(e.bounds.corners())
e.posed_bbox = ComputeBbox().from_point_cloud(posed_corners)
super(BBEvaluator, self).__init__(submission, ground_truth, settings)
|
def __init__(self, submission, ground_truth, settings=None):
'\n Constructor. Computes similarity between all elements in the\n submission and ground_truth and also computes\n data association caches.\n\n Inputs:\n submission (ProjectScene) - Submitted scene to be evaluated\n ground_truth (ProjectScene) - The ground truth scene\n settings (dict) - configuration for the evaluator. See\n Evaluator.py for recognized keys and values.\n '
for e in submission.elements.values():
posed_corners = e.pose.transform_all_from(e.bounds.corners())
e.posed_bbox = ComputeBbox().from_point_cloud(posed_corners)
for e in ground_truth.elements.values():
posed_corners = e.pose.transform_all_from(e.bounds.corners())
e.posed_bbox = ComputeBbox().from_point_cloud(posed_corners)
super(BBEvaluator, self).__init__(submission, ground_truth, settings)<|docstring|>Constructor. Computes similarity between all elements in the
submission and ground_truth and also computes
data association caches.
Inputs:
submission (ProjectScene) - Submitted scene to be evaluated
ground_truth (ProjectScene) - The ground truth scene
settings (dict) - configuration for the evaluator. See
Evaluator.py for recognized keys and values.<|endoftext|>
|
aed25298d7adb0b05f70fcefef5ab2726de1b584ac487950401e45b24b0d9c51
|
def evaluate_all(self):
'\n Computes all metrics for the submission\n\n Return:\n metrics (dict) - Keys/values are:\n "shape_score" : float\n "rotation_error" : float\n "translation_error" : float\n "semantics_score" : float\n "perceptual_score" : float\n '
metrics = {}
metrics['shape_score'] = self.shape_score()
(rotation_error, translation_error) = self.pose_error()
metrics['rotation_error'] = rotation_error
metrics['translation_error'] = translation_error
metrics['semantics_score'] = self.semantics_score()
metrics['perceptual_score'] = self.perceptual_score()
return metrics
|
Computes all metrics for the submission
Return:
metrics (dict) - Keys/values are:
"shape_score" : float
"rotation_error" : float
"translation_error" : float
"semantics_score" : float
"perceptual_score" : float
|
sumo/metrics/bb_evaluator.py
|
evaluate_all
|
RishabhJain2018/sumo-challenge
| 70 |
python
|
def evaluate_all(self):
'\n Computes all metrics for the submission\n\n Return:\n metrics (dict) - Keys/values are:\n "shape_score" : float\n "rotation_error" : float\n "translation_error" : float\n "semantics_score" : float\n "perceptual_score" : float\n '
metrics = {}
metrics['shape_score'] = self.shape_score()
(rotation_error, translation_error) = self.pose_error()
metrics['rotation_error'] = rotation_error
metrics['translation_error'] = translation_error
metrics['semantics_score'] = self.semantics_score()
metrics['perceptual_score'] = self.perceptual_score()
return metrics
|
def evaluate_all(self):
'\n Computes all metrics for the submission\n\n Return:\n metrics (dict) - Keys/values are:\n "shape_score" : float\n "rotation_error" : float\n "translation_error" : float\n "semantics_score" : float\n "perceptual_score" : float\n '
metrics = {}
metrics['shape_score'] = self.shape_score()
(rotation_error, translation_error) = self.pose_error()
metrics['rotation_error'] = rotation_error
metrics['translation_error'] = translation_error
metrics['semantics_score'] = self.semantics_score()
metrics['perceptual_score'] = self.perceptual_score()
return metrics<|docstring|>Computes all metrics for the submission
Return:
metrics (dict) - Keys/values are:
"shape_score" : float
"rotation_error" : float
"translation_error" : float
"semantics_score" : float
"perceptual_score" : float<|endoftext|>
|
737caee2c3d1843c9606a141aa9f5d69c142aa85b74b120cf1748c0303e550b7
|
def _shape_similarity(self, element1, element2):
'\n Similarity function that compares the bounding boxes of\n <element1> and <element2>\n\n Inputs:\n element1 (ProjectObject)\n element2 (ProjectObject)\n\n Return:\n float - bounding box IoU (Equation 1 in SUMO white paper)\n '
bbox1 = element1.posed_bbox
bbox2 = element2.posed_bbox
for axis in range(3):
if ((bbox1.min_corner[axis] > bbox2.max_corner[axis]) or (bbox2.min_corner[axis] > bbox1.max_corner[axis])):
return 0
box1 = _bbox2pymesh(element1)
box2 = _bbox2pymesh(element2)
inter = pymesh.boolean(box1, box2, operation='intersection')
(ivert, ifaces, _) = remove_duplicated_vertices_raw(inter.vertices, inter.faces)
inter_mesh = pymesh.form_mesh(ivert, ifaces)
intersection = abs(inter_mesh.volume)
union = ((abs(box1.volume) + abs(box2.volume)) - intersection)
return (intersection / union)
|
Similarity function that compares the bounding boxes of
<element1> and <element2>
Inputs:
element1 (ProjectObject)
element2 (ProjectObject)
Return:
float - bounding box IoU (Equation 1 in SUMO white paper)
|
sumo/metrics/bb_evaluator.py
|
_shape_similarity
|
RishabhJain2018/sumo-challenge
| 70 |
python
|
def _shape_similarity(self, element1, element2):
'\n Similarity function that compares the bounding boxes of\n <element1> and <element2>\n\n Inputs:\n element1 (ProjectObject)\n element2 (ProjectObject)\n\n Return:\n float - bounding box IoU (Equation 1 in SUMO white paper)\n '
bbox1 = element1.posed_bbox
bbox2 = element2.posed_bbox
for axis in range(3):
if ((bbox1.min_corner[axis] > bbox2.max_corner[axis]) or (bbox2.min_corner[axis] > bbox1.max_corner[axis])):
return 0
box1 = _bbox2pymesh(element1)
box2 = _bbox2pymesh(element2)
inter = pymesh.boolean(box1, box2, operation='intersection')
(ivert, ifaces, _) = remove_duplicated_vertices_raw(inter.vertices, inter.faces)
inter_mesh = pymesh.form_mesh(ivert, ifaces)
intersection = abs(inter_mesh.volume)
union = ((abs(box1.volume) + abs(box2.volume)) - intersection)
return (intersection / union)
|
def _shape_similarity(self, element1, element2):
'\n Similarity function that compares the bounding boxes of\n <element1> and <element2>\n\n Inputs:\n element1 (ProjectObject)\n element2 (ProjectObject)\n\n Return:\n float - bounding box IoU (Equation 1 in SUMO white paper)\n '
bbox1 = element1.posed_bbox
bbox2 = element2.posed_bbox
for axis in range(3):
if ((bbox1.min_corner[axis] > bbox2.max_corner[axis]) or (bbox2.min_corner[axis] > bbox1.max_corner[axis])):
return 0
box1 = _bbox2pymesh(element1)
box2 = _bbox2pymesh(element2)
inter = pymesh.boolean(box1, box2, operation='intersection')
(ivert, ifaces, _) = remove_duplicated_vertices_raw(inter.vertices, inter.faces)
inter_mesh = pymesh.form_mesh(ivert, ifaces)
intersection = abs(inter_mesh.volume)
union = ((abs(box1.volume) + abs(box2.volume)) - intersection)
return (intersection / union)<|docstring|>Similarity function that compares the bounding boxes of
<element1> and <element2>
Inputs:
element1 (ProjectObject)
element2 (ProjectObject)
Return:
float - bounding box IoU (Equation 1 in SUMO white paper)<|endoftext|>
|
57768f56ee63c34723896cd80c44fd2f3109dcc1de401a23b112efb015229599
|
@apply_defaults
def __init__(self, redshift_conn_id='', operator_mode=LoadOperatorMode.Table, query='', table='', *args, **kwargs):
'LoadFactOperator constructor. Defines the parameters required for the operator.'
super(LoadFactOperator, self).__init__(*args, **kwargs)
self.redshift_conn_id = redshift_conn_id
self.operator_mode = operator_mode
self.query = query
self.table = table
|
LoadFactOperator constructor. Defines the parameters required for the operator.
|
05-data-pipelines/plugins/operators/load_fact.py
|
__init__
|
Ceridan/data-engineering-projects
| 0 |
python
|
@apply_defaults
def __init__(self, redshift_conn_id=, operator_mode=LoadOperatorMode.Table, query=, table=, *args, **kwargs):
super(LoadFactOperator, self).__init__(*args, **kwargs)
self.redshift_conn_id = redshift_conn_id
self.operator_mode = operator_mode
self.query = query
self.table = table
|
@apply_defaults
def __init__(self, redshift_conn_id=, operator_mode=LoadOperatorMode.Table, query=, table=, *args, **kwargs):
super(LoadFactOperator, self).__init__(*args, **kwargs)
self.redshift_conn_id = redshift_conn_id
self.operator_mode = operator_mode
self.query = query
self.table = table<|docstring|>LoadFactOperator constructor. Defines the parameters required for the operator.<|endoftext|>
|
62b51fde2a8752216a8379448df8a418a3c2f9a2ffb5add0f1da256193cf404b
|
def execute(self, context):
'Load fact table from staging.'
try:
self.log.info('Initialing Postgres hook (for Redshift)')
redshift = PostgresHook(postgres_conn_id=self.redshift_conn_id)
if (self.operator_mode == LoadOperatorMode.Table):
sql = table_to_query_map[self.table]
self.log.info(f'Loading data from staging to table "{self.table}"')
else:
sql = self.query
self.log.info(f'''Loading data using custom query:
{self.query}''')
redshift.run(sql)
self.log.info(f'Load operation successfully completed')
except psycopg2.Error as e:
self.log.error(f'Error occurred during during LOAD operation: {e}')
raise
|
Load fact table from staging.
|
05-data-pipelines/plugins/operators/load_fact.py
|
execute
|
Ceridan/data-engineering-projects
| 0 |
python
|
def execute(self, context):
try:
self.log.info('Initialing Postgres hook (for Redshift)')
redshift = PostgresHook(postgres_conn_id=self.redshift_conn_id)
if (self.operator_mode == LoadOperatorMode.Table):
sql = table_to_query_map[self.table]
self.log.info(f'Loading data from staging to table "{self.table}"')
else:
sql = self.query
self.log.info(f'Loading data using custom query:
{self.query}')
redshift.run(sql)
self.log.info(f'Load operation successfully completed')
except psycopg2.Error as e:
self.log.error(f'Error occurred during during LOAD operation: {e}')
raise
|
def execute(self, context):
try:
self.log.info('Initialing Postgres hook (for Redshift)')
redshift = PostgresHook(postgres_conn_id=self.redshift_conn_id)
if (self.operator_mode == LoadOperatorMode.Table):
sql = table_to_query_map[self.table]
self.log.info(f'Loading data from staging to table "{self.table}"')
else:
sql = self.query
self.log.info(f'Loading data using custom query:
{self.query}')
redshift.run(sql)
self.log.info(f'Load operation successfully completed')
except psycopg2.Error as e:
self.log.error(f'Error occurred during during LOAD operation: {e}')
raise<|docstring|>Load fact table from staging.<|endoftext|>
|
af2f6e8779e96224534835b84646b32eee558ddbfdfc4fed0e0d8ed45923a2b5
|
def smooth_l1(x1, x2, sigma):
'Smooth L1 loss'
sigma2 = (sigma ** 2)
diff = (x1 - x2)
abs_diff = diff.abs()
mask = (abs_diff.detach() < (1.0 / sigma2)).float()
return (((mask * (sigma2 / 2.0)) * (diff ** 2)) + ((1 - mask) * (abs_diff - (0.5 / sigma2))))
|
Smooth L1 loss
|
seamseg/modules/losses.py
|
smooth_l1
|
urasakikeisuke/seamseg
| 282 |
python
|
def smooth_l1(x1, x2, sigma):
sigma2 = (sigma ** 2)
diff = (x1 - x2)
abs_diff = diff.abs()
mask = (abs_diff.detach() < (1.0 / sigma2)).float()
return (((mask * (sigma2 / 2.0)) * (diff ** 2)) + ((1 - mask) * (abs_diff - (0.5 / sigma2))))
|
def smooth_l1(x1, x2, sigma):
sigma2 = (sigma ** 2)
diff = (x1 - x2)
abs_diff = diff.abs()
mask = (abs_diff.detach() < (1.0 / sigma2)).float()
return (((mask * (sigma2 / 2.0)) * (diff ** 2)) + ((1 - mask) * (abs_diff - (0.5 / sigma2))))<|docstring|>Smooth L1 loss<|endoftext|>
|
3cafbea574ffa07ef690963164968898f6e470fff4e311a76647a7195431cb7b
|
@abc.abstractmethod
def write(self, filename: str, file: FileRep):
'\n Given\n filename of generated file\n file representation\n Write/Store generated file\n '
|
Given
filename of generated file
file representation
Write/Store generated file
|
tools/gen/dump.py
|
write
|
mingkaic/tenncor
| 1 |
python
|
@abc.abstractmethod
def write(self, filename: str, file: FileRep):
'\n Given\n filename of generated file\n file representation\n Write/Store generated file\n '
|
@abc.abstractmethod
def write(self, filename: str, file: FileRep):
'\n Given\n filename of generated file\n file representation\n Write/Store generated file\n '<|docstring|>Given
filename of generated file
file representation
Write/Store generated file<|endoftext|>
|
5a7aa1bc3dfb0a28b72cf026e7945cc81108814e5fae9d850f7f989ef8b963a6
|
def test_empty_keyword(self):
" Returns 4 individuals with the name 'label' "
authors = [a for a in scholarly.search_keyword('')]
self.assertEqual(len(authors), 4)
|
Returns 4 individuals with the name 'label'
|
scholarly/test.py
|
test_empty_keyword
|
nobrowning/scholarly
| 5 |
python
|
def test_empty_keyword(self):
" "
authors = [a for a in scholarly.search_keyword()]
self.assertEqual(len(authors), 4)
|
def test_empty_keyword(self):
" "
authors = [a for a in scholarly.search_keyword()]
self.assertEqual(len(authors), 4)<|docstring|>Returns 4 individuals with the name 'label'<|endoftext|>
|
92214e15a827927bf5bf5fc028410cedc6522c50dfcb9e032524d9b7d7a376e0
|
def test_multiple_authors(self):
" As of March 14, 2019 there are 34 'Zucker's "
authors = [a.name for a in scholarly.search_author('Zucker')]
self.assertEqual(len(authors), 58)
self.assertIn(u'Steven W Zucker', authors)
|
As of March 14, 2019 there are 34 'Zucker's
|
scholarly/test.py
|
test_multiple_authors
|
nobrowning/scholarly
| 5 |
python
|
def test_multiple_authors(self):
" "
authors = [a.name for a in scholarly.search_author('Zucker')]
self.assertEqual(len(authors), 58)
self.assertIn(u'Steven W Zucker', authors)
|
def test_multiple_authors(self):
" "
authors = [a.name for a in scholarly.search_author('Zucker')]
self.assertEqual(len(authors), 58)
self.assertIn(u'Steven W Zucker', authors)<|docstring|>As of March 14, 2019 there are 34 'Zucker's<|endoftext|>
|
40d0dc7eec7b4fdfd2c67ca0ce183a6a2bed9ce218e51a550d7ffc1e21884abf
|
def test_multiple_publications(self):
' As of March 14, 2019 there are 28 pubs that fit the search term'
pubs = [p.bib['title'] for p in scholarly.search_pubs_query('"naive physics" stability "3d shape"')]
self.assertEqual(len(pubs), 28)
self.assertIn(u'Visual perception of the physical stability of asymmetric three-dimensional objects', pubs)
|
As of March 14, 2019 there are 28 pubs that fit the search term
|
scholarly/test.py
|
test_multiple_publications
|
nobrowning/scholarly
| 5 |
python
|
def test_multiple_publications(self):
' '
pubs = [p.bib['title'] for p in scholarly.search_pubs_query('"naive physics" stability "3d shape"')]
self.assertEqual(len(pubs), 28)
self.assertIn(u'Visual perception of the physical stability of asymmetric three-dimensional objects', pubs)
|
def test_multiple_publications(self):
' '
pubs = [p.bib['title'] for p in scholarly.search_pubs_query('"naive physics" stability "3d shape"')]
self.assertEqual(len(pubs), 28)
self.assertIn(u'Visual perception of the physical stability of asymmetric three-dimensional objects', pubs)<|docstring|>As of March 14, 2019 there are 28 pubs that fit the search term<|endoftext|>
|
c5544f3cf9618f07e678e398df744977a495b8d49c590b9c7d7bbb136c9fa5fc
|
def np_wrap_to_pi(angles):
'Wrap angles between [-pi, pi]. Angles right at -pi or pi may flip.'
return (((angles + np.pi) % (2 * np.pi)) - np.pi)
|
Wrap angles between [-pi, pi]. Angles right at -pi or pi may flip.
|
src/monopsr/core/orientation_encoder.py
|
np_wrap_to_pi
|
minghanz/monopsr
| 104 |
python
|
def np_wrap_to_pi(angles):
return (((angles + np.pi) % (2 * np.pi)) - np.pi)
|
def np_wrap_to_pi(angles):
return (((angles + np.pi) % (2 * np.pi)) - np.pi)<|docstring|>Wrap angles between [-pi, pi]. Angles right at -pi or pi may flip.<|endoftext|>
|
3a7ede972682cb6d54304442d5f50de190c4207aa8f129546321f9f6c225ca99
|
def np_orientation_to_angle_bin(orientation, num_bins, overlap):
'Converts an orientation into an angle bin and residual.\n Example for 8 bins:\n 321\n 4 0\n 567\n Bin centres start at an angle of 0.0.\n\n Args:\n orientation: orientation angle in radians\n num_bins: number of angle bins\n overlap: amount of overlap for the bins in radians\n\n Returns:\n angle_bin: bin index\n residual: residual angle from the bin centre\n one_hot_valid_bins: one hot encoding of the valid bins\n '
two_pi = (2 * np.pi)
orientation_wrapped = (orientation % two_pi)
angle_per_bin = (two_pi / num_bins)
shifted_angle = ((orientation_wrapped + (angle_per_bin / 2)) % two_pi)
best_angle_bin = int((shifted_angle / angle_per_bin))
best_residual = (shifted_angle - ((best_angle_bin * angle_per_bin) + (angle_per_bin / 2)))
bin_centres = np.asarray([(angle_per_bin * bin_idx) for bin_idx in range(num_bins)])
residuals = np.arctan2(np.sin((orientation_wrapped - bin_centres)), np.cos((orientation_wrapped - bin_centres)))
valid_bins = [best_angle_bin]
if (overlap != 0.0):
bin_centre = (best_angle_bin * angle_per_bin)
upper_bound = (bin_centre + (0.5 * angle_per_bin))
lower_bound = (bin_centre - (0.5 * angle_per_bin))
actual_angle = ((best_angle_bin * angle_per_bin) + best_residual)
upper_bound_dist = np.abs((upper_bound - actual_angle))
lower_bound_dist = np.abs((lower_bound - actual_angle))
if (upper_bound_dist < overlap):
new_valid_bin = (best_angle_bin + 1)
if (new_valid_bin == num_bins):
new_valid_bin = 0
valid_bins.append(new_valid_bin)
elif (lower_bound_dist < overlap):
new_valid_bin = (best_angle_bin - 1)
if (new_valid_bin < 0):
new_valid_bin = (num_bins - 1)
valid_bins.append(new_valid_bin)
one_hot_valid_bins = np.zeros(num_bins)
one_hot_valid_bins[np.asarray(valid_bins)] = 1
return (best_angle_bin, residuals, one_hot_valid_bins)
|
Converts an orientation into an angle bin and residual.
Example for 8 bins:
321
4 0
567
Bin centres start at an angle of 0.0.
Args:
orientation: orientation angle in radians
num_bins: number of angle bins
overlap: amount of overlap for the bins in radians
Returns:
angle_bin: bin index
residual: residual angle from the bin centre
one_hot_valid_bins: one hot encoding of the valid bins
|
src/monopsr/core/orientation_encoder.py
|
np_orientation_to_angle_bin
|
minghanz/monopsr
| 104 |
python
|
def np_orientation_to_angle_bin(orientation, num_bins, overlap):
'Converts an orientation into an angle bin and residual.\n Example for 8 bins:\n 321\n 4 0\n 567\n Bin centres start at an angle of 0.0.\n\n Args:\n orientation: orientation angle in radians\n num_bins: number of angle bins\n overlap: amount of overlap for the bins in radians\n\n Returns:\n angle_bin: bin index\n residual: residual angle from the bin centre\n one_hot_valid_bins: one hot encoding of the valid bins\n '
two_pi = (2 * np.pi)
orientation_wrapped = (orientation % two_pi)
angle_per_bin = (two_pi / num_bins)
shifted_angle = ((orientation_wrapped + (angle_per_bin / 2)) % two_pi)
best_angle_bin = int((shifted_angle / angle_per_bin))
best_residual = (shifted_angle - ((best_angle_bin * angle_per_bin) + (angle_per_bin / 2)))
bin_centres = np.asarray([(angle_per_bin * bin_idx) for bin_idx in range(num_bins)])
residuals = np.arctan2(np.sin((orientation_wrapped - bin_centres)), np.cos((orientation_wrapped - bin_centres)))
valid_bins = [best_angle_bin]
if (overlap != 0.0):
bin_centre = (best_angle_bin * angle_per_bin)
upper_bound = (bin_centre + (0.5 * angle_per_bin))
lower_bound = (bin_centre - (0.5 * angle_per_bin))
actual_angle = ((best_angle_bin * angle_per_bin) + best_residual)
upper_bound_dist = np.abs((upper_bound - actual_angle))
lower_bound_dist = np.abs((lower_bound - actual_angle))
if (upper_bound_dist < overlap):
new_valid_bin = (best_angle_bin + 1)
if (new_valid_bin == num_bins):
new_valid_bin = 0
valid_bins.append(new_valid_bin)
elif (lower_bound_dist < overlap):
new_valid_bin = (best_angle_bin - 1)
if (new_valid_bin < 0):
new_valid_bin = (num_bins - 1)
valid_bins.append(new_valid_bin)
one_hot_valid_bins = np.zeros(num_bins)
one_hot_valid_bins[np.asarray(valid_bins)] = 1
return (best_angle_bin, residuals, one_hot_valid_bins)
|
def np_orientation_to_angle_bin(orientation, num_bins, overlap):
'Converts an orientation into an angle bin and residual.\n Example for 8 bins:\n 321\n 4 0\n 567\n Bin centres start at an angle of 0.0.\n\n Args:\n orientation: orientation angle in radians\n num_bins: number of angle bins\n overlap: amount of overlap for the bins in radians\n\n Returns:\n angle_bin: bin index\n residual: residual angle from the bin centre\n one_hot_valid_bins: one hot encoding of the valid bins\n '
two_pi = (2 * np.pi)
orientation_wrapped = (orientation % two_pi)
angle_per_bin = (two_pi / num_bins)
shifted_angle = ((orientation_wrapped + (angle_per_bin / 2)) % two_pi)
best_angle_bin = int((shifted_angle / angle_per_bin))
best_residual = (shifted_angle - ((best_angle_bin * angle_per_bin) + (angle_per_bin / 2)))
bin_centres = np.asarray([(angle_per_bin * bin_idx) for bin_idx in range(num_bins)])
residuals = np.arctan2(np.sin((orientation_wrapped - bin_centres)), np.cos((orientation_wrapped - bin_centres)))
valid_bins = [best_angle_bin]
if (overlap != 0.0):
bin_centre = (best_angle_bin * angle_per_bin)
upper_bound = (bin_centre + (0.5 * angle_per_bin))
lower_bound = (bin_centre - (0.5 * angle_per_bin))
actual_angle = ((best_angle_bin * angle_per_bin) + best_residual)
upper_bound_dist = np.abs((upper_bound - actual_angle))
lower_bound_dist = np.abs((lower_bound - actual_angle))
if (upper_bound_dist < overlap):
new_valid_bin = (best_angle_bin + 1)
if (new_valid_bin == num_bins):
new_valid_bin = 0
valid_bins.append(new_valid_bin)
elif (lower_bound_dist < overlap):
new_valid_bin = (best_angle_bin - 1)
if (new_valid_bin < 0):
new_valid_bin = (num_bins - 1)
valid_bins.append(new_valid_bin)
one_hot_valid_bins = np.zeros(num_bins)
one_hot_valid_bins[np.asarray(valid_bins)] = 1
return (best_angle_bin, residuals, one_hot_valid_bins)<|docstring|>Converts an orientation into an angle bin and residual.
Example for 8 bins:
321
4 0
567
Bin centres start at an angle of 0.0.
Args:
orientation: orientation angle in radians
num_bins: number of angle bins
overlap: amount of overlap for the bins in radians
Returns:
angle_bin: bin index
residual: residual angle from the bin centre
one_hot_valid_bins: one hot encoding of the valid bins<|endoftext|>
|
8a466a90004ad2a80004016c3c8930a7f4372c7d6fe45a84ad2872c2bf3efcfc
|
def np_angle_bin_to_orientation(angle_bin, residual, num_bins):
'Converts an angle bin and residual into an orientation between [-pi, pi]\n\n Args:\n angle_bin: bin index\n residual: residual angle from bin centre\n num_bins: number of angle bins\n\n Returns:\n angle: orientation angle in radians\n '
two_pi = (2 * np.pi)
angle_per_bin = (two_pi / num_bins)
angle_center = (angle_bin * angle_per_bin)
angle = (angle_center + residual)
if (angle < (- np.pi)):
angle = (angle + two_pi)
if (angle > np.pi):
angle = (angle - two_pi)
return angle
|
Converts an angle bin and residual into an orientation between [-pi, pi]
Args:
angle_bin: bin index
residual: residual angle from bin centre
num_bins: number of angle bins
Returns:
angle: orientation angle in radians
|
src/monopsr/core/orientation_encoder.py
|
np_angle_bin_to_orientation
|
minghanz/monopsr
| 104 |
python
|
def np_angle_bin_to_orientation(angle_bin, residual, num_bins):
'Converts an angle bin and residual into an orientation between [-pi, pi]\n\n Args:\n angle_bin: bin index\n residual: residual angle from bin centre\n num_bins: number of angle bins\n\n Returns:\n angle: orientation angle in radians\n '
two_pi = (2 * np.pi)
angle_per_bin = (two_pi / num_bins)
angle_center = (angle_bin * angle_per_bin)
angle = (angle_center + residual)
if (angle < (- np.pi)):
angle = (angle + two_pi)
if (angle > np.pi):
angle = (angle - two_pi)
return angle
|
def np_angle_bin_to_orientation(angle_bin, residual, num_bins):
'Converts an angle bin and residual into an orientation between [-pi, pi]\n\n Args:\n angle_bin: bin index\n residual: residual angle from bin centre\n num_bins: number of angle bins\n\n Returns:\n angle: orientation angle in radians\n '
two_pi = (2 * np.pi)
angle_per_bin = (two_pi / num_bins)
angle_center = (angle_bin * angle_per_bin)
angle = (angle_center + residual)
if (angle < (- np.pi)):
angle = (angle + two_pi)
if (angle > np.pi):
angle = (angle - two_pi)
return angle<|docstring|>Converts an angle bin and residual into an orientation between [-pi, pi]
Args:
angle_bin: bin index
residual: residual angle from bin centre
num_bins: number of angle bins
Returns:
angle: orientation angle in radians<|endoftext|>
|
2cc8dfaac921edb2fbcec22da756d2f6f31b8122a30e94fdb37af2fd41114d8f
|
def tf_orientation_to_angle_vector(orientations_tensor):
'Converts orientation angles into angle unit vector representation.\n e.g. 45 -> [0.717, 0.717], 90 -> [0, 1]\n\n Args:\n orientations_tensor: A tensor of shape (N,) of orientation angles\n\n Returns:\n A tensor of shape (N, 2) of angle unit vectors in the format [x, y]\n '
x = tf.cos(orientations_tensor)
y = tf.sin(orientations_tensor)
return tf.stack([x, y], axis=1)
|
Converts orientation angles into angle unit vector representation.
e.g. 45 -> [0.717, 0.717], 90 -> [0, 1]
Args:
orientations_tensor: A tensor of shape (N,) of orientation angles
Returns:
A tensor of shape (N, 2) of angle unit vectors in the format [x, y]
|
src/monopsr/core/orientation_encoder.py
|
tf_orientation_to_angle_vector
|
minghanz/monopsr
| 104 |
python
|
def tf_orientation_to_angle_vector(orientations_tensor):
'Converts orientation angles into angle unit vector representation.\n e.g. 45 -> [0.717, 0.717], 90 -> [0, 1]\n\n Args:\n orientations_tensor: A tensor of shape (N,) of orientation angles\n\n Returns:\n A tensor of shape (N, 2) of angle unit vectors in the format [x, y]\n '
x = tf.cos(orientations_tensor)
y = tf.sin(orientations_tensor)
return tf.stack([x, y], axis=1)
|
def tf_orientation_to_angle_vector(orientations_tensor):
'Converts orientation angles into angle unit vector representation.\n e.g. 45 -> [0.717, 0.717], 90 -> [0, 1]\n\n Args:\n orientations_tensor: A tensor of shape (N,) of orientation angles\n\n Returns:\n A tensor of shape (N, 2) of angle unit vectors in the format [x, y]\n '
x = tf.cos(orientations_tensor)
y = tf.sin(orientations_tensor)
return tf.stack([x, y], axis=1)<|docstring|>Converts orientation angles into angle unit vector representation.
e.g. 45 -> [0.717, 0.717], 90 -> [0, 1]
Args:
orientations_tensor: A tensor of shape (N,) of orientation angles
Returns:
A tensor of shape (N, 2) of angle unit vectors in the format [x, y]<|endoftext|>
|
5daeffe47893dfa7cbbacc59b9ca5fc71ed8442ac8d17963341564df7dfc9544
|
def tf_angle_vector_to_orientation(angle_vectors_tensor):
' Converts angle unit vectors into orientation angle representation.\n e.g. [0.717, 0.717] -> 45, [0, 1] -> 90\n\n Args:\n angle_vectors_tensor: a tensor of shape (N, 2) of angle unit vectors\n in the format [x, y]\n\n Returns:\n A tensor of shape (N,) of orientation angles\n '
x = angle_vectors_tensor[(:, 0)]
y = angle_vectors_tensor[(:, 1)]
return tf.atan2(y, x)
|
Converts angle unit vectors into orientation angle representation.
e.g. [0.717, 0.717] -> 45, [0, 1] -> 90
Args:
angle_vectors_tensor: a tensor of shape (N, 2) of angle unit vectors
in the format [x, y]
Returns:
A tensor of shape (N,) of orientation angles
|
src/monopsr/core/orientation_encoder.py
|
tf_angle_vector_to_orientation
|
minghanz/monopsr
| 104 |
python
|
def tf_angle_vector_to_orientation(angle_vectors_tensor):
' Converts angle unit vectors into orientation angle representation.\n e.g. [0.717, 0.717] -> 45, [0, 1] -> 90\n\n Args:\n angle_vectors_tensor: a tensor of shape (N, 2) of angle unit vectors\n in the format [x, y]\n\n Returns:\n A tensor of shape (N,) of orientation angles\n '
x = angle_vectors_tensor[(:, 0)]
y = angle_vectors_tensor[(:, 1)]
return tf.atan2(y, x)
|
def tf_angle_vector_to_orientation(angle_vectors_tensor):
' Converts angle unit vectors into orientation angle representation.\n e.g. [0.717, 0.717] -> 45, [0, 1] -> 90\n\n Args:\n angle_vectors_tensor: a tensor of shape (N, 2) of angle unit vectors\n in the format [x, y]\n\n Returns:\n A tensor of shape (N,) of orientation angles\n '
x = angle_vectors_tensor[(:, 0)]
y = angle_vectors_tensor[(:, 1)]
return tf.atan2(y, x)<|docstring|>Converts angle unit vectors into orientation angle representation.
e.g. [0.717, 0.717] -> 45, [0, 1] -> 90
Args:
angle_vectors_tensor: a tensor of shape (N, 2) of angle unit vectors
in the format [x, y]
Returns:
A tensor of shape (N,) of orientation angles<|endoftext|>
|
4154d8420b243df05c6a980a25a71a805808097f7bbb60527a2b4c50d1d333a8
|
def __init__(self, range=(0, 180), rgb=True):
'\n Args:\n range(list or tuple): range from which the applied hue offset is selected\n (maximum range can be [-90,90] for both uint8 and float32)\n rgb (bool): whether input is RGB or BGR.\n '
super(Hue, self).__init__()
rgb = bool(rgb)
self._init(locals())
|
Args:
range(list or tuple): range from which the applied hue offset is selected
(maximum range can be [-90,90] for both uint8 and float32)
rgb (bool): whether input is RGB or BGR.
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, range=(0, 180), rgb=True):
'\n Args:\n range(list or tuple): range from which the applied hue offset is selected\n (maximum range can be [-90,90] for both uint8 and float32)\n rgb (bool): whether input is RGB or BGR.\n '
super(Hue, self).__init__()
rgb = bool(rgb)
self._init(locals())
|
def __init__(self, range=(0, 180), rgb=True):
'\n Args:\n range(list or tuple): range from which the applied hue offset is selected\n (maximum range can be [-90,90] for both uint8 and float32)\n rgb (bool): whether input is RGB or BGR.\n '
super(Hue, self).__init__()
rgb = bool(rgb)
self._init(locals())<|docstring|>Args:
range(list or tuple): range from which the applied hue offset is selected
(maximum range can be [-90,90] for both uint8 and float32)
rgb (bool): whether input is RGB or BGR.<|endoftext|>
|
7fab59e0aaaf17514a873f887502e12f71d10322eedd49afc500d837e1f340d5
|
def __init__(self, delta, clip=True):
'\n Args:\n delta (float): Randomly add a value within [-delta,delta]\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(Brightness, self).__init__()
assert (delta > 0)
self._init(locals())
|
Args:
delta (float): Randomly add a value within [-delta,delta]
clip (bool): clip results to [0,255] even when data type is not uint8.
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, delta, clip=True):
'\n Args:\n delta (float): Randomly add a value within [-delta,delta]\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(Brightness, self).__init__()
assert (delta > 0)
self._init(locals())
|
def __init__(self, delta, clip=True):
'\n Args:\n delta (float): Randomly add a value within [-delta,delta]\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(Brightness, self).__init__()
assert (delta > 0)
self._init(locals())<|docstring|>Args:
delta (float): Randomly add a value within [-delta,delta]
clip (bool): clip results to [0,255] even when data type is not uint8.<|endoftext|>
|
8cc3e7345efffb65aa018c15d81652fdfa00fe07ba5528285fc6515030373c03
|
def __init__(self, range, clip=True):
'\n Args:\n range (tuple): Randomly scale the image by a factor in (range[0], range[1])\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(BrightnessScale, self).__init__()
self._init(locals())
|
Args:
range (tuple): Randomly scale the image by a factor in (range[0], range[1])
clip (bool): clip results to [0,255] even when data type is not uint8.
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, range, clip=True):
'\n Args:\n range (tuple): Randomly scale the image by a factor in (range[0], range[1])\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(BrightnessScale, self).__init__()
self._init(locals())
|
def __init__(self, range, clip=True):
'\n Args:\n range (tuple): Randomly scale the image by a factor in (range[0], range[1])\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(BrightnessScale, self).__init__()
self._init(locals())<|docstring|>Args:
range (tuple): Randomly scale the image by a factor in (range[0], range[1])
clip (bool): clip results to [0,255] even when data type is not uint8.<|endoftext|>
|
12c20ba092ebf5f418dd65509bb283321efc8fc579ebab567aeb5ab26928179e
|
def __init__(self, factor_range, rgb=None, clip=True):
'\n Args:\n factor_range (list or tuple): an interval to randomly sample the `contrast_factor`.\n rgb (bool or None): if None, use the mean per-channel.\n clip (bool): clip to [0, 255] even when data type is not uint8.\n '
super(Contrast, self).__init__()
self._init(locals())
|
Args:
factor_range (list or tuple): an interval to randomly sample the `contrast_factor`.
rgb (bool or None): if None, use the mean per-channel.
clip (bool): clip to [0, 255] even when data type is not uint8.
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, factor_range, rgb=None, clip=True):
'\n Args:\n factor_range (list or tuple): an interval to randomly sample the `contrast_factor`.\n rgb (bool or None): if None, use the mean per-channel.\n clip (bool): clip to [0, 255] even when data type is not uint8.\n '
super(Contrast, self).__init__()
self._init(locals())
|
def __init__(self, factor_range, rgb=None, clip=True):
'\n Args:\n factor_range (list or tuple): an interval to randomly sample the `contrast_factor`.\n rgb (bool or None): if None, use the mean per-channel.\n clip (bool): clip to [0, 255] even when data type is not uint8.\n '
super(Contrast, self).__init__()
self._init(locals())<|docstring|>Args:
factor_range (list or tuple): an interval to randomly sample the `contrast_factor`.
rgb (bool or None): if None, use the mean per-channel.
clip (bool): clip to [0, 255] even when data type is not uint8.<|endoftext|>
|
dfc7736cfc0acf2e59a734170977c65269083be59fa24a57c0a879de6167f63c
|
def __init__(self, all_channel=True):
'\n Args:\n all_channel (bool): if True, normalize all channels together. else separately.\n '
self._init(locals())
|
Args:
all_channel (bool): if True, normalize all channels together. else separately.
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, all_channel=True):
'\n Args:\n all_channel (bool): if True, normalize all channels together. else separately.\n '
self._init(locals())
|
def __init__(self, all_channel=True):
'\n Args:\n all_channel (bool): if True, normalize all channels together. else separately.\n '
self._init(locals())<|docstring|>Args:
all_channel (bool): if True, normalize all channels together. else separately.<|endoftext|>
|
825c0b7c2475b5959133d8cebc0dec3288cdd761cac6b0cb6ddf50e668434504
|
def __init__(self, size_range=(0, 3), sigma_range=(0, 0), symmetric=True, max_size=None):
"\n Args:\n size_range (tuple[int]): Gaussian window size would be 2 * size +\n 1, where size is randomly sampled from this [low, high) range.\n sigma_range (tuple[float]): min,max of the sigma value. 0 means\n opencv's default.\n symmetric (bool): whether to use the same size & sigma for x and y.\n max_size (int): deprecated\n "
super(GaussianBlur, self).__init__()
if (not isinstance(size_range, (list, tuple))):
size_range = (0, size_range)
assert isinstance(sigma_range, (list, tuple)), sigma_range
if (max_size is not None):
log_deprecated('GaussianBlur(max_size=)', 'Use size_range= instead!', '2020-09-01')
size_range = (0, max_size)
self._init(locals())
|
Args:
size_range (tuple[int]): Gaussian window size would be 2 * size +
1, where size is randomly sampled from this [low, high) range.
sigma_range (tuple[float]): min,max of the sigma value. 0 means
opencv's default.
symmetric (bool): whether to use the same size & sigma for x and y.
max_size (int): deprecated
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, size_range=(0, 3), sigma_range=(0, 0), symmetric=True, max_size=None):
"\n Args:\n size_range (tuple[int]): Gaussian window size would be 2 * size +\n 1, where size is randomly sampled from this [low, high) range.\n sigma_range (tuple[float]): min,max of the sigma value. 0 means\n opencv's default.\n symmetric (bool): whether to use the same size & sigma for x and y.\n max_size (int): deprecated\n "
super(GaussianBlur, self).__init__()
if (not isinstance(size_range, (list, tuple))):
size_range = (0, size_range)
assert isinstance(sigma_range, (list, tuple)), sigma_range
if (max_size is not None):
log_deprecated('GaussianBlur(max_size=)', 'Use size_range= instead!', '2020-09-01')
size_range = (0, max_size)
self._init(locals())
|
def __init__(self, size_range=(0, 3), sigma_range=(0, 0), symmetric=True, max_size=None):
"\n Args:\n size_range (tuple[int]): Gaussian window size would be 2 * size +\n 1, where size is randomly sampled from this [low, high) range.\n sigma_range (tuple[float]): min,max of the sigma value. 0 means\n opencv's default.\n symmetric (bool): whether to use the same size & sigma for x and y.\n max_size (int): deprecated\n "
super(GaussianBlur, self).__init__()
if (not isinstance(size_range, (list, tuple))):
size_range = (0, size_range)
assert isinstance(sigma_range, (list, tuple)), sigma_range
if (max_size is not None):
log_deprecated('GaussianBlur(max_size=)', 'Use size_range= instead!', '2020-09-01')
size_range = (0, max_size)
self._init(locals())<|docstring|>Args:
size_range (tuple[int]): Gaussian window size would be 2 * size +
1, where size is randomly sampled from this [low, high) range.
sigma_range (tuple[float]): min,max of the sigma value. 0 means
opencv's default.
symmetric (bool): whether to use the same size & sigma for x and y.
max_size (int): deprecated<|endoftext|>
|
2fb90bbac9c2ddb530f52ad6c53b15dd23d87dce20bf599c08ba8c7a0af3bc58
|
def __init__(self, range=((- 0.5), 0.5)):
'\n Args:\n range(list or tuple): gamma range\n '
super(Gamma, self).__init__()
self._init(locals())
|
Args:
range(list or tuple): gamma range
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, range=((- 0.5), 0.5)):
'\n Args:\n range(list or tuple): gamma range\n '
super(Gamma, self).__init__()
self._init(locals())
|
def __init__(self, range=((- 0.5), 0.5)):
'\n Args:\n range(list or tuple): gamma range\n '
super(Gamma, self).__init__()
self._init(locals())<|docstring|>Args:
range(list or tuple): gamma range<|endoftext|>
|
57292982346341d3118a5d85a3151d41f4292efb6e90f2d5a7abacfa3bdcdb05
|
def __init__(self, min=0, max=255):
'\n Args:\n min, max: the clip range\n '
self._init(locals())
|
Args:
min, max: the clip range
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, min=0, max=255):
'\n Args:\n min, max: the clip range\n '
self._init(locals())
|
def __init__(self, min=0, max=255):
'\n Args:\n min, max: the clip range\n '
self._init(locals())<|docstring|>Args:
min, max: the clip range<|endoftext|>
|
81759df06421e2bb311c91e588c8d05a93843dcafc213629245479b777537ec2
|
def __init__(self, alpha=0.4, rgb=True, clip=True):
'\n Args:\n alpha(float): maximum saturation change.\n rgb (bool): whether input is RGB or BGR.\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super().__init__()
rgb = bool(rgb)
assert (alpha < 1)
self._init(locals())
|
Args:
alpha(float): maximum saturation change.
rgb (bool): whether input is RGB or BGR.
clip (bool): clip results to [0,255] even when data type is not uint8.
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, alpha=0.4, rgb=True, clip=True):
'\n Args:\n alpha(float): maximum saturation change.\n rgb (bool): whether input is RGB or BGR.\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super().__init__()
rgb = bool(rgb)
assert (alpha < 1)
self._init(locals())
|
def __init__(self, alpha=0.4, rgb=True, clip=True):
'\n Args:\n alpha(float): maximum saturation change.\n rgb (bool): whether input is RGB or BGR.\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super().__init__()
rgb = bool(rgb)
assert (alpha < 1)
self._init(locals())<|docstring|>Args:
alpha(float): maximum saturation change.
rgb (bool): whether input is RGB or BGR.
clip (bool): clip results to [0,255] even when data type is not uint8.<|endoftext|>
|
c53e9464bbc86854e0da61e077e54b0e1d77ef2ee11ec437b5845abda0ee103a
|
def __init__(self, std, eigval, eigvec, clip=True):
'\n Args:\n std (float): maximum standard deviation\n eigval: a vector of (3,). The eigenvalues of 3 channels.\n eigvec: a 3x3 matrix. Each column is one eigen vector.\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(Lighting, self).__init__()
eigval = np.asarray(eigval, dtype='float32')
eigvec = np.asarray(eigvec, dtype='float32')
assert (eigval.shape == (3,))
assert (eigvec.shape == (3, 3))
self._init(locals())
|
Args:
std (float): maximum standard deviation
eigval: a vector of (3,). The eigenvalues of 3 channels.
eigvec: a 3x3 matrix. Each column is one eigen vector.
clip (bool): clip results to [0,255] even when data type is not uint8.
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, std, eigval, eigvec, clip=True):
'\n Args:\n std (float): maximum standard deviation\n eigval: a vector of (3,). The eigenvalues of 3 channels.\n eigvec: a 3x3 matrix. Each column is one eigen vector.\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(Lighting, self).__init__()
eigval = np.asarray(eigval, dtype='float32')
eigvec = np.asarray(eigvec, dtype='float32')
assert (eigval.shape == (3,))
assert (eigvec.shape == (3, 3))
self._init(locals())
|
def __init__(self, std, eigval, eigvec, clip=True):
'\n Args:\n std (float): maximum standard deviation\n eigval: a vector of (3,). The eigenvalues of 3 channels.\n eigvec: a 3x3 matrix. Each column is one eigen vector.\n clip (bool): clip results to [0,255] even when data type is not uint8.\n '
super(Lighting, self).__init__()
eigval = np.asarray(eigval, dtype='float32')
eigvec = np.asarray(eigvec, dtype='float32')
assert (eigval.shape == (3,))
assert (eigvec.shape == (3, 3))
self._init(locals())<|docstring|>Args:
std (float): maximum standard deviation
eigval: a vector of (3,). The eigenvalues of 3 channels.
eigvec: a 3x3 matrix. Each column is one eigen vector.
clip (bool): clip results to [0,255] even when data type is not uint8.<|endoftext|>
|
0df937370edd0ecbe4b56466297548812267193541360797129eedb11ff4f4f9
|
def __init__(self, min=0, max=255, all_channel=True):
'\n Args:\n max (float): The new maximum value\n min (float): The new minimum value\n all_channel (bool): if True, normalize all channels together. else separately.\n '
self._init(locals())
|
Args:
max (float): The new maximum value
min (float): The new minimum value
all_channel (bool): if True, normalize all channels together. else separately.
|
tensorpack/dataflow/imgaug/imgproc.py
|
__init__
|
gopalakrishna-r/tensorpack
| 4,404 |
python
|
def __init__(self, min=0, max=255, all_channel=True):
'\n Args:\n max (float): The new maximum value\n min (float): The new minimum value\n all_channel (bool): if True, normalize all channels together. else separately.\n '
self._init(locals())
|
def __init__(self, min=0, max=255, all_channel=True):
'\n Args:\n max (float): The new maximum value\n min (float): The new minimum value\n all_channel (bool): if True, normalize all channels together. else separately.\n '
self._init(locals())<|docstring|>Args:
max (float): The new maximum value
min (float): The new minimum value
all_channel (bool): if True, normalize all channels together. else separately.<|endoftext|>
|
bd54a39cd50ba56140002b4a23a12297be9f294193bc04417f9b98e717759e26
|
@click.group()
@click.option('-l', '--log-level', type=LogLevel(), default=logging.WARNING)
def cmd(log_level):
'Classification tools for Spack Errors\n\n Provides tools to get job log traces and classify them based on a taxonomy\n of errors. Logs are grepped for strings and matching error class columns are\n set to true in a resulting CSV. multiple classes may match a job log and so\n errors may be \'deconflicted\' based on a pirorty list.\n\n Error CSVs must be in the proper format. These can be exported from the\n following Metabase Analytic:\n\n https://metabase.spack.io/question/16-job-errors-api-ink\n\n Example Usage:\n\n \x08\n # Download the Error CSV from Metabase. You must specify\n # how far back you would like to look for errors (e.g. "7 DAYS")\n \x08\n # Assume you have downloaded the CSV to 20211227-7days.csv\n $> error-classification.py -l INFO get-logs -t [GITLAB_API_TOKEN] 20211227-7days.csv\n \x08\n # Note: logs are downloaded into error_logs/ directory by default\n $> error-classification.py -l INFO classify 20211227-7days.csv\n \x08\n # Note: Annotated CSV is saved to 20211227-7days_annotated.csv\n $> error-classification.py stats 20211227-7days_annotated.csv\n ...\n\n '
logging.basicConfig(level=log_level)
|
Classification tools for Spack Errors
Provides tools to get job log traces and classify them based on a taxonomy
of errors. Logs are grepped for strings and matching error class columns are
set to true in a resulting CSV. multiple classes may match a job log and so
errors may be 'deconflicted' based on a pirorty list.
Error CSVs must be in the proper format. These can be exported from the
following Metabase Analytic:
https://metabase.spack.io/question/16-job-errors-api-ink
Example Usage:
# Download the Error CSV from Metabase. You must specify
# how far back you would like to look for errors (e.g. "7 DAYS")
# Assume you have downloaded the CSV to 20211227-7days.csv
$> error-classification.py -l INFO get-logs -t [GITLAB_API_TOKEN] 20211227-7days.csv
# Note: logs are downloaded into error_logs/ directory by default
$> error-classification.py -l INFO classify 20211227-7days.csv
# Note: Annotated CSV is saved to 20211227-7days_annotated.csv
$> error-classification.py stats 20211227-7days_annotated.csv
...
|
scripts/error-classification.py
|
cmd
|
spack/testing-sandbox
| 0 |
python
|
@click.group()
@click.option('-l', '--log-level', type=LogLevel(), default=logging.WARNING)
def cmd(log_level):
'Classification tools for Spack Errors\n\n Provides tools to get job log traces and classify them based on a taxonomy\n of errors. Logs are grepped for strings and matching error class columns are\n set to true in a resulting CSV. multiple classes may match a job log and so\n errors may be \'deconflicted\' based on a pirorty list.\n\n Error CSVs must be in the proper format. These can be exported from the\n following Metabase Analytic:\n\n https://metabase.spack.io/question/16-job-errors-api-ink\n\n Example Usage:\n\n \x08\n # Download the Error CSV from Metabase. You must specify\n # how far back you would like to look for errors (e.g. "7 DAYS")\n \x08\n # Assume you have downloaded the CSV to 20211227-7days.csv\n $> error-classification.py -l INFO get-logs -t [GITLAB_API_TOKEN] 20211227-7days.csv\n \x08\n # Note: logs are downloaded into error_logs/ directory by default\n $> error-classification.py -l INFO classify 20211227-7days.csv\n \x08\n # Note: Annotated CSV is saved to 20211227-7days_annotated.csv\n $> error-classification.py stats 20211227-7days_annotated.csv\n ...\n\n '
logging.basicConfig(level=log_level)
|
@click.group()
@click.option('-l', '--log-level', type=LogLevel(), default=logging.WARNING)
def cmd(log_level):
'Classification tools for Spack Errors\n\n Provides tools to get job log traces and classify them based on a taxonomy\n of errors. Logs are grepped for strings and matching error class columns are\n set to true in a resulting CSV. multiple classes may match a job log and so\n errors may be \'deconflicted\' based on a pirorty list.\n\n Error CSVs must be in the proper format. These can be exported from the\n following Metabase Analytic:\n\n https://metabase.spack.io/question/16-job-errors-api-ink\n\n Example Usage:\n\n \x08\n # Download the Error CSV from Metabase. You must specify\n # how far back you would like to look for errors (e.g. "7 DAYS")\n \x08\n # Assume you have downloaded the CSV to 20211227-7days.csv\n $> error-classification.py -l INFO get-logs -t [GITLAB_API_TOKEN] 20211227-7days.csv\n \x08\n # Note: logs are downloaded into error_logs/ directory by default\n $> error-classification.py -l INFO classify 20211227-7days.csv\n \x08\n # Note: Annotated CSV is saved to 20211227-7days_annotated.csv\n $> error-classification.py stats 20211227-7days_annotated.csv\n ...\n\n '
logging.basicConfig(level=log_level)<|docstring|>Classification tools for Spack Errors
Provides tools to get job log traces and classify them based on a taxonomy
of errors. Logs are grepped for strings and matching error class columns are
set to true in a resulting CSV. multiple classes may match a job log and so
errors may be 'deconflicted' based on a pirorty list.
Error CSVs must be in the proper format. These can be exported from the
following Metabase Analytic:
https://metabase.spack.io/question/16-job-errors-api-ink
Example Usage:
# Download the Error CSV from Metabase. You must specify
# how far back you would like to look for errors (e.g. "7 DAYS")
# Assume you have downloaded the CSV to 20211227-7days.csv
$> error-classification.py -l INFO get-logs -t [GITLAB_API_TOKEN] 20211227-7days.csv
# Note: logs are downloaded into error_logs/ directory by default
$> error-classification.py -l INFO classify 20211227-7days.csv
# Note: Annotated CSV is saved to 20211227-7days_annotated.csv
$> error-classification.py stats 20211227-7days_annotated.csv
...<|endoftext|>
|
fd9c6e5a69a4c2d3851aeaa520c56a5c9a5d374634dfb85ef0215baf89fe7c80
|
@cmd.command()
@click.option('-o', '--output', default='error_logs', type=click.Path(file_okay=False), help='Output directory for error logs.')
@click.option('-t', '--token', required=True, default=(lambda : os.environ.get('API_TOKEN')), help='Spack GitLab API Token (or API_TOKEN environment variable)')
@click.option('-c', '--cache', default='error_log', help='Requests cache file name')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def get_logs(error_csv, output, token, cache):
'Scrape Logs from Gitlab into a local directory.\n\n '
os.makedirs(output, exist_ok=True)
scraper = JobLogScraper(token, session_name=cache, out_dir=output)
scraper.process_csv(error_csv)
|
Scrape Logs from Gitlab into a local directory.
|
scripts/error-classification.py
|
get_logs
|
spack/testing-sandbox
| 0 |
python
|
@cmd.command()
@click.option('-o', '--output', default='error_logs', type=click.Path(file_okay=False), help='Output directory for error logs.')
@click.option('-t', '--token', required=True, default=(lambda : os.environ.get('API_TOKEN')), help='Spack GitLab API Token (or API_TOKEN environment variable)')
@click.option('-c', '--cache', default='error_log', help='Requests cache file name')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def get_logs(error_csv, output, token, cache):
'\n\n '
os.makedirs(output, exist_ok=True)
scraper = JobLogScraper(token, session_name=cache, out_dir=output)
scraper.process_csv(error_csv)
|
@cmd.command()
@click.option('-o', '--output', default='error_logs', type=click.Path(file_okay=False), help='Output directory for error logs.')
@click.option('-t', '--token', required=True, default=(lambda : os.environ.get('API_TOKEN')), help='Spack GitLab API Token (or API_TOKEN environment variable)')
@click.option('-c', '--cache', default='error_log', help='Requests cache file name')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def get_logs(error_csv, output, token, cache):
'\n\n '
os.makedirs(output, exist_ok=True)
scraper = JobLogScraper(token, session_name=cache, out_dir=output)
scraper.process_csv(error_csv)<|docstring|>Scrape Logs from Gitlab into a local directory.<|endoftext|>
|
9666558ee2f32cdc5bebd2b1212560cab0274341c9cba4355c761b6ef745b128
|
@cmd.command()
@click.option('-i', '--input-dir', default='error_logs', type=click.Path(exists=True, file_okay=False), help='Directory containing job logs')
@click.option('--deconflict/--no-deconflict', default=True, help='Boolean to deconflict the classified rrors')
@click.option('-o', '--output', default=None, help='Save annotated CSV to this file name (default [ERROR_CSV]_annotated.csv)')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def classify(error_csv, input_dir, deconflict, output):
'Classify errors in the CSV based on the taxonomy.\n\n '
if (output is None):
path = Path(error_csv.file_name)
output = os.path.join(path.parents[0], f'{path.stem}_annotated.csv')
classifier = ErrorClassifier(error_csv.file_name, log_dir=input_dir)
classifier.classify()
logging.info(f'Error overlap:{os.linesep}{classifier.correlations()}')
if deconflict:
classifier.deconflict()
logging.info(f'Post-deconflict error overlap:{os.linesep}{classifier.correlations()}')
logging.info(f'Saving to {output}')
classifier.df.to_csv(output)
|
Classify errors in the CSV based on the taxonomy.
|
scripts/error-classification.py
|
classify
|
spack/testing-sandbox
| 0 |
python
|
@cmd.command()
@click.option('-i', '--input-dir', default='error_logs', type=click.Path(exists=True, file_okay=False), help='Directory containing job logs')
@click.option('--deconflict/--no-deconflict', default=True, help='Boolean to deconflict the classified rrors')
@click.option('-o', '--output', default=None, help='Save annotated CSV to this file name (default [ERROR_CSV]_annotated.csv)')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def classify(error_csv, input_dir, deconflict, output):
'\n\n '
if (output is None):
path = Path(error_csv.file_name)
output = os.path.join(path.parents[0], f'{path.stem}_annotated.csv')
classifier = ErrorClassifier(error_csv.file_name, log_dir=input_dir)
classifier.classify()
logging.info(f'Error overlap:{os.linesep}{classifier.correlations()}')
if deconflict:
classifier.deconflict()
logging.info(f'Post-deconflict error overlap:{os.linesep}{classifier.correlations()}')
logging.info(f'Saving to {output}')
classifier.df.to_csv(output)
|
@cmd.command()
@click.option('-i', '--input-dir', default='error_logs', type=click.Path(exists=True, file_okay=False), help='Directory containing job logs')
@click.option('--deconflict/--no-deconflict', default=True, help='Boolean to deconflict the classified rrors')
@click.option('-o', '--output', default=None, help='Save annotated CSV to this file name (default [ERROR_CSV]_annotated.csv)')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def classify(error_csv, input_dir, deconflict, output):
'\n\n '
if (output is None):
path = Path(error_csv.file_name)
output = os.path.join(path.parents[0], f'{path.stem}_annotated.csv')
classifier = ErrorClassifier(error_csv.file_name, log_dir=input_dir)
classifier.classify()
logging.info(f'Error overlap:{os.linesep}{classifier.correlations()}')
if deconflict:
classifier.deconflict()
logging.info(f'Post-deconflict error overlap:{os.linesep}{classifier.correlations()}')
logging.info(f'Saving to {output}')
classifier.df.to_csv(output)<|docstring|>Classify errors in the CSV based on the taxonomy.<|endoftext|>
|
f74c358e8912a4b9dca0a760ee5269192667ec5e44d2e7ce12dd6c777f6f66dc
|
@cmd.command()
@click.option('-i', '--input-dir', default='error_logs', type=click.Path(exists=True, file_okay=False), help='Directory containing job logs')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
@click.argument('error_class')
def random_log(error_csv, error_class, input_dir):
'Print a random log from the given error_class.\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=input_dir)
try:
(idx, path) = classifier.random_log(error_class)
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
with open(path, 'r') as fh:
click.echo(fh.read())
logging.info(f'Finished printing {path}')
logging.info(f"See: {classifier.df.loc[idx]['job_link']}")
|
Print a random log from the given error_class.
|
scripts/error-classification.py
|
random_log
|
spack/testing-sandbox
| 0 |
python
|
@cmd.command()
@click.option('-i', '--input-dir', default='error_logs', type=click.Path(exists=True, file_okay=False), help='Directory containing job logs')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
@click.argument('error_class')
def random_log(error_csv, error_class, input_dir):
'\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=input_dir)
try:
(idx, path) = classifier.random_log(error_class)
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
with open(path, 'r') as fh:
click.echo(fh.read())
logging.info(f'Finished printing {path}')
logging.info(f"See: {classifier.df.loc[idx]['job_link']}")
|
@cmd.command()
@click.option('-i', '--input-dir', default='error_logs', type=click.Path(exists=True, file_okay=False), help='Directory containing job logs')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
@click.argument('error_class')
def random_log(error_csv, error_class, input_dir):
'\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=input_dir)
try:
(idx, path) = classifier.random_log(error_class)
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
with open(path, 'r') as fh:
click.echo(fh.read())
logging.info(f'Finished printing {path}')
logging.info(f"See: {classifier.df.loc[idx]['job_link']}")<|docstring|>Print a random log from the given error_class.<|endoftext|>
|
412fefc6019546fef5adcf08f553daf1de9c8dbf59ee1fb5721dfb63c8210609
|
@cmd.command()
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def overlap(error_csv):
'Print correlation statsitics from an annotated Error CSV.\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
click.echo(classifier.correlations())
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
|
Print correlation statsitics from an annotated Error CSV.
|
scripts/error-classification.py
|
overlap
|
spack/testing-sandbox
| 0 |
python
|
@cmd.command()
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def overlap(error_csv):
'\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
click.echo(classifier.correlations())
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
|
@cmd.command()
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def overlap(error_csv):
'\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
click.echo(classifier.correlations())
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)<|docstring|>Print correlation statsitics from an annotated Error CSV.<|endoftext|>
|
ddabf5421a427a798b016a41e166224cb009b0709e45a1d54e1db5accf642c38
|
@cmd.command()
@click.option('-o', '--output', default=None, help='Save annotated CSV to this file name (default [ERROR_CSV] - destructive!)')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def deconflict(error_csv, output):
'Deconflict an annotated error CSV.\n\n '
if (output is None):
output = error_csv.file_name
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
classifier.deconflict()
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
logging.info(f'Saving to {output}')
classifier.df.to_csv(output)
|
Deconflict an annotated error CSV.
|
scripts/error-classification.py
|
deconflict
|
spack/testing-sandbox
| 0 |
python
|
@cmd.command()
@click.option('-o', '--output', default=None, help='Save annotated CSV to this file name (default [ERROR_CSV] - destructive!)')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def deconflict(error_csv, output):
'\n\n '
if (output is None):
output = error_csv.file_name
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
classifier.deconflict()
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
logging.info(f'Saving to {output}')
classifier.df.to_csv(output)
|
@cmd.command()
@click.option('-o', '--output', default=None, help='Save annotated CSV to this file name (default [ERROR_CSV] - destructive!)')
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def deconflict(error_csv, output):
'\n\n '
if (output is None):
output = error_csv.file_name
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
classifier.deconflict()
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
logging.info(f'Saving to {output}')
classifier.df.to_csv(output)<|docstring|>Deconflict an annotated error CSV.<|endoftext|>
|
56201a288b157e77829c58a9f7adb9ed916fac30c7d37288f104080592fe7b9e
|
@cmd.command()
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def stats(error_csv):
'Print error counts and percentages.\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
click.echo(classifier.stats())
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
|
Print error counts and percentages.
|
scripts/error-classification.py
|
stats
|
spack/testing-sandbox
| 0 |
python
|
@cmd.command()
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def stats(error_csv):
'\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
click.echo(classifier.stats())
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)
|
@cmd.command()
@click.argument('error_csv', type=ErrorLogCSVType(mode='r'))
def stats(error_csv):
'\n\n '
classifier = ErrorClassifier(error_csv.file_name, log_dir=None)
try:
click.echo(classifier.stats())
except RuntimeError as e:
logging.error(str(e))
sys.exit(1)<|docstring|>Print error counts and percentages.<|endoftext|>
|
8729b7bf85d9d23399985e3c6e60ab40255b0fc6ebb2e78dbaaf314c4487540d
|
def _verify_df(self):
"Verify we have pulled logs for the Dataframe.\n\n Checks to make sure the files in self.log_dir are consistent with the\n job id's in the CSV file this Dataframe represents.\n\n "
if (self.log_dir is not None):
log_files = set([int(Path(s).stem) for s in glob.glob(f'{self.log_dir}/*.log')])
idx = set(self.df.index)
def _log_file(id):
return f' {self.log_dir}/{id}.log'
if (log_files - idx):
raise RuntimeError(f'Log files present which are not in CSV: {os.linesep}{os.linesep.join([_log_file(s) for s in (log_files - idx)])}')
if (idx - log_files):
raise RuntimeError(f'Errors in CSV without job logs (the following are missing): {os.linesep}{os.linesep.join([_log_file(s) for s in (idx - log_files)])}{os.linesep}Try running "get-logs" on {self.csv_path}')
|
Verify we have pulled logs for the Dataframe.
Checks to make sure the files in self.log_dir are consistent with the
job id's in the CSV file this Dataframe represents.
|
scripts/error-classification.py
|
_verify_df
|
spack/testing-sandbox
| 0 |
python
|
def _verify_df(self):
"Verify we have pulled logs for the Dataframe.\n\n Checks to make sure the files in self.log_dir are consistent with the\n job id's in the CSV file this Dataframe represents.\n\n "
if (self.log_dir is not None):
log_files = set([int(Path(s).stem) for s in glob.glob(f'{self.log_dir}/*.log')])
idx = set(self.df.index)
def _log_file(id):
return f' {self.log_dir}/{id}.log'
if (log_files - idx):
raise RuntimeError(f'Log files present which are not in CSV: {os.linesep}{os.linesep.join([_log_file(s) for s in (log_files - idx)])}')
if (idx - log_files):
raise RuntimeError(f'Errors in CSV without job logs (the following are missing): {os.linesep}{os.linesep.join([_log_file(s) for s in (idx - log_files)])}{os.linesep}Try running "get-logs" on {self.csv_path}')
|
def _verify_df(self):
"Verify we have pulled logs for the Dataframe.\n\n Checks to make sure the files in self.log_dir are consistent with the\n job id's in the CSV file this Dataframe represents.\n\n "
if (self.log_dir is not None):
log_files = set([int(Path(s).stem) for s in glob.glob(f'{self.log_dir}/*.log')])
idx = set(self.df.index)
def _log_file(id):
return f' {self.log_dir}/{id}.log'
if (log_files - idx):
raise RuntimeError(f'Log files present which are not in CSV: {os.linesep}{os.linesep.join([_log_file(s) for s in (log_files - idx)])}')
if (idx - log_files):
raise RuntimeError(f'Errors in CSV without job logs (the following are missing): {os.linesep}{os.linesep.join([_log_file(s) for s in (idx - log_files)])}{os.linesep}Try running "get-logs" on {self.csv_path}')<|docstring|>Verify we have pulled logs for the Dataframe.
Checks to make sure the files in self.log_dir are consistent with the
job id's in the CSV file this Dataframe represents.<|endoftext|>
|
f1af9ac444ec64905747643c16b7f9fbe8caf6ed4e7cb1f79eaadee8d13148d5
|
def _kind(self, r):
"Classfies the runner type.\n\n Used to generate the 'kind' column for the CSV.\n\n "
if pd.isnull(r):
return 'None'
elif r.startswith('uo'):
return 'UO'
else:
return 'AWS'
|
Classfies the runner type.
Used to generate the 'kind' column for the CSV.
|
scripts/error-classification.py
|
_kind
|
spack/testing-sandbox
| 0 |
python
|
def _kind(self, r):
"Classfies the runner type.\n\n Used to generate the 'kind' column for the CSV.\n\n "
if pd.isnull(r):
return 'None'
elif r.startswith('uo'):
return 'UO'
else:
return 'AWS'
|
def _kind(self, r):
"Classfies the runner type.\n\n Used to generate the 'kind' column for the CSV.\n\n "
if pd.isnull(r):
return 'None'
elif r.startswith('uo'):
return 'UO'
else:
return 'AWS'<|docstring|>Classfies the runner type.
Used to generate the 'kind' column for the CSV.<|endoftext|>
|
017e47b799ce26e2ddc53bbd64aa18b1915c8da2479d2b0a818638e72c76e63a
|
def _grep_for_ids(self, match_string):
'Subprocess out to grep. Return job ids that match match_string.'
_match_group = '1'
output = subprocess.getoutput(f'grep -l "{match_string}" {self.log_dir}/*.log | sed -e "s|^.*/\(.*\).log|\{_match_group}|"')
return ([int(s) for s in output.split('\n')] if output else [])
|
Subprocess out to grep. Return job ids that match match_string.
|
scripts/error-classification.py
|
_grep_for_ids
|
spack/testing-sandbox
| 0 |
python
|
def _grep_for_ids(self, match_string):
_match_group = '1'
output = subprocess.getoutput(f'grep -l "{match_string}" {self.log_dir}/*.log | sed -e "s|^.*/\(.*\).log|\{_match_group}|"')
return ([int(s) for s in output.split('\n')] if output else [])
|
def _grep_for_ids(self, match_string):
_match_group = '1'
output = subprocess.getoutput(f'grep -l "{match_string}" {self.log_dir}/*.log | sed -e "s|^.*/\(.*\).log|\{_match_group}|"')
return ([int(s) for s in output.split('\n')] if output else [])<|docstring|>Subprocess out to grep. Return job ids that match match_string.<|endoftext|>
|
78cb318d2461df8bea5cf1882e539c0a148e121d44bf07f196c30be9d600dc98
|
def _other_errors(self, df):
"Classify all ids that do not have at least one other error as\n 'other_erorrs'\n\n "
target_columns = list((set(self.error_columns) - set(['other_errors'])))
return df[target_columns].apply((lambda row: (not any(list(row)))), axis=1)
|
Classify all ids that do not have at least one other error as
'other_erorrs'
|
scripts/error-classification.py
|
_other_errors
|
spack/testing-sandbox
| 0 |
python
|
def _other_errors(self, df):
"Classify all ids that do not have at least one other error as\n 'other_erorrs'\n\n "
target_columns = list((set(self.error_columns) - set(['other_errors'])))
return df[target_columns].apply((lambda row: (not any(list(row)))), axis=1)
|
def _other_errors(self, df):
"Classify all ids that do not have at least one other error as\n 'other_erorrs'\n\n "
target_columns = list((set(self.error_columns) - set(['other_errors'])))
return df[target_columns].apply((lambda row: (not any(list(row)))), axis=1)<|docstring|>Classify all ids that do not have at least one other error as
'other_erorrs'<|endoftext|>
|
88d359c7d0ccc1786c0a5e12d97a4e3352e270a8612d77d56216a42ad59f8438
|
def is_annotated(self):
'Return True if Dataframe has columns from taxonomy.\n\n '
if (set(self.taxonomy.keys()) <= set(self.df.columns)):
return True
return False
|
Return True if Dataframe has columns from taxonomy.
|
scripts/error-classification.py
|
is_annotated
|
spack/testing-sandbox
| 0 |
python
|
def is_annotated(self):
'\n\n '
if (set(self.taxonomy.keys()) <= set(self.df.columns)):
return True
return False
|
def is_annotated(self):
'\n\n '
if (set(self.taxonomy.keys()) <= set(self.df.columns)):
return True
return False<|docstring|>Return True if Dataframe has columns from taxonomy.<|endoftext|>
|
2cb1e18a2b4743c894b474188c1e093b6b81cc88c6a38926ed237067019932b7
|
def is_deconflicted(self):
'Return True if error columns have been deconflicted.\n\n '
return (not (self.df[self.error_columns].apply((lambda r: len([_ for _ in r if (_ is True)])), axis=1) > 1).any())
|
Return True if error columns have been deconflicted.
|
scripts/error-classification.py
|
is_deconflicted
|
spack/testing-sandbox
| 0 |
python
|
def is_deconflicted(self):
'\n\n '
return (not (self.df[self.error_columns].apply((lambda r: len([_ for _ in r if (_ is True)])), axis=1) > 1).any())
|
def is_deconflicted(self):
'\n\n '
return (not (self.df[self.error_columns].apply((lambda r: len([_ for _ in r if (_ is True)])), axis=1) > 1).any())<|docstring|>Return True if error columns have been deconflicted.<|endoftext|>
|
9eeaf9161c741c08319d71c771da9767ce7ed6186fdd7546f11562861b1b36d3
|
def init_dataframe(self, csv_path, log_dir):
"Initialize the Dataframe.\n\n Verifies logs exist for each job id, converts created_at to datetime and\n set the 'kind' column for each type of runner.\n\n "
self.log_dir = log_dir
self.csv_path = csv_path
self.df = pd.read_csv(csv_path, index_col='id', infer_datetime_format=True)
self._verify_df()
self.df['created_at'] = pd.to_datetime(self.df['created_at'])
self.df['kind'] = self.df['runner'].apply(self._kind)
|
Initialize the Dataframe.
Verifies logs exist for each job id, converts created_at to datetime and
set the 'kind' column for each type of runner.
|
scripts/error-classification.py
|
init_dataframe
|
spack/testing-sandbox
| 0 |
python
|
def init_dataframe(self, csv_path, log_dir):
"Initialize the Dataframe.\n\n Verifies logs exist for each job id, converts created_at to datetime and\n set the 'kind' column for each type of runner.\n\n "
self.log_dir = log_dir
self.csv_path = csv_path
self.df = pd.read_csv(csv_path, index_col='id', infer_datetime_format=True)
self._verify_df()
self.df['created_at'] = pd.to_datetime(self.df['created_at'])
self.df['kind'] = self.df['runner'].apply(self._kind)
|
def init_dataframe(self, csv_path, log_dir):
"Initialize the Dataframe.\n\n Verifies logs exist for each job id, converts created_at to datetime and\n set the 'kind' column for each type of runner.\n\n "
self.log_dir = log_dir
self.csv_path = csv_path
self.df = pd.read_csv(csv_path, index_col='id', infer_datetime_format=True)
self._verify_df()
self.df['created_at'] = pd.to_datetime(self.df['created_at'])
self.df['kind'] = self.df['runner'].apply(self._kind)<|docstring|>Initialize the Dataframe.
Verifies logs exist for each job id, converts created_at to datetime and
set the 'kind' column for each type of runner.<|endoftext|>
|
5c4b4426cf6047c36fbc812cbfe2dad27d25a83a8ebe83f4d320fa0ea06a670d
|
def classify(self):
'Classify all the errors based on job logs.\n\n '
for (col, expr) in self.taxonomy.items():
if callable(expr):
self.df[col] = expr(self.df)
else:
if isinstance(expr, str):
expr = [expr]
self.df[col] = False
for s in expr:
ids = self._grep_for_ids(s)
if bool(ids):
self.df.at[(ids, col)] = True
try:
counts = self.df[col].value_counts().loc[True]
except KeyError:
counts = 0
logging.info(f'Processed {col} ({counts})')
|
Classify all the errors based on job logs.
|
scripts/error-classification.py
|
classify
|
spack/testing-sandbox
| 0 |
python
|
def classify(self):
'\n\n '
for (col, expr) in self.taxonomy.items():
if callable(expr):
self.df[col] = expr(self.df)
else:
if isinstance(expr, str):
expr = [expr]
self.df[col] = False
for s in expr:
ids = self._grep_for_ids(s)
if bool(ids):
self.df.at[(ids, col)] = True
try:
counts = self.df[col].value_counts().loc[True]
except KeyError:
counts = 0
logging.info(f'Processed {col} ({counts})')
|
def classify(self):
'\n\n '
for (col, expr) in self.taxonomy.items():
if callable(expr):
self.df[col] = expr(self.df)
else:
if isinstance(expr, str):
expr = [expr]
self.df[col] = False
for s in expr:
ids = self._grep_for_ids(s)
if bool(ids):
self.df.at[(ids, col)] = True
try:
counts = self.df[col].value_counts().loc[True]
except KeyError:
counts = 0
logging.info(f'Processed {col} ({counts})')<|docstring|>Classify all the errors based on job logs.<|endoftext|>
|
9f33e0ca84aec6d2b6e9170e87bde03b1782f4ef69033a1c16d5b8ea92cfdf67
|
def correlations(self):
'Return a dataframe with statistics on correlations between error classes.\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
def _overlap(columns):
for (a, b) in itertools.combinations(columns, 2):
numerator = len(self.df[((self.df[a] == True) & (self.df[b] == True))])
denominator = len(self.df[((self.df[a] == True) | (self.df[b] == True))])
if ((a != b) and (numerator > 0)):
(yield (a, b, numerator, denominator, round(((numerator / float(denominator)) * 100), 2)))
o = pd.DataFrame(list(_overlap(self.error_columns)), columns=['A', 'B', 'overlap', 'total', 'percent'])
o.set_index(['A', 'B'], inplace=True)
o.sort_values('percent', ascending=False, inplace=True)
return o
|
Return a dataframe with statistics on correlations between error classes.
|
scripts/error-classification.py
|
correlations
|
spack/testing-sandbox
| 0 |
python
|
def correlations(self):
'\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
def _overlap(columns):
for (a, b) in itertools.combinations(columns, 2):
numerator = len(self.df[((self.df[a] == True) & (self.df[b] == True))])
denominator = len(self.df[((self.df[a] == True) | (self.df[b] == True))])
if ((a != b) and (numerator > 0)):
(yield (a, b, numerator, denominator, round(((numerator / float(denominator)) * 100), 2)))
o = pd.DataFrame(list(_overlap(self.error_columns)), columns=['A', 'B', 'overlap', 'total', 'percent'])
o.set_index(['A', 'B'], inplace=True)
o.sort_values('percent', ascending=False, inplace=True)
return o
|
def correlations(self):
'\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
def _overlap(columns):
for (a, b) in itertools.combinations(columns, 2):
numerator = len(self.df[((self.df[a] == True) & (self.df[b] == True))])
denominator = len(self.df[((self.df[a] == True) | (self.df[b] == True))])
if ((a != b) and (numerator > 0)):
(yield (a, b, numerator, denominator, round(((numerator / float(denominator)) * 100), 2)))
o = pd.DataFrame(list(_overlap(self.error_columns)), columns=['A', 'B', 'overlap', 'total', 'percent'])
o.set_index(['A', 'B'], inplace=True)
o.sort_values('percent', ascending=False, inplace=True)
return o<|docstring|>Return a dataframe with statistics on correlations between error classes.<|endoftext|>
|
81125c30f5c2227a2bb81f169abc2a23b75370574d96b4cfbe0249fe6da6942a
|
def deconflict(self):
'Deconflicts error classes based on deconflict_order.\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
def _deconflict(A):
'Prefer errors in Column A'
target = list((set(self.error_columns) - set([A])))
if self.df[A].any():
self.df.loc[(self.df[A], target)] = False
for column in self.deconflict_order:
_deconflict(column)
|
Deconflicts error classes based on deconflict_order.
|
scripts/error-classification.py
|
deconflict
|
spack/testing-sandbox
| 0 |
python
|
def deconflict(self):
'\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
def _deconflict(A):
'Prefer errors in Column A'
target = list((set(self.error_columns) - set([A])))
if self.df[A].any():
self.df.loc[(self.df[A], target)] = False
for column in self.deconflict_order:
_deconflict(column)
|
def deconflict(self):
'\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
def _deconflict(A):
'Prefer errors in Column A'
target = list((set(self.error_columns) - set([A])))
if self.df[A].any():
self.df.loc[(self.df[A], target)] = False
for column in self.deconflict_order:
_deconflict(column)<|docstring|>Deconflicts error classes based on deconflict_order.<|endoftext|>
|
a0141944502e742698438fff0ad848fd2c5fdf88c9d64ddf222bfc8a787acfa0
|
def random_log(self, error_class):
'Return the path to a random log file in the given error_class.\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
if (error_class not in ErrorClassifier().error_columns):
raise RuntimeError(f""""{error_class}" not one of: {os.linesep}{os.linesep.join([(' ' + s) for s in ErrorClassifier().error_columns])}""")
idx = random.choice(self.df[self.df[error_class]].index)
return (idx, f'{self.log_dir}/{idx}.log')
|
Return the path to a random log file in the given error_class.
|
scripts/error-classification.py
|
random_log
|
spack/testing-sandbox
| 0 |
python
|
def random_log(self, error_class):
'\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
if (error_class not in ErrorClassifier().error_columns):
raise RuntimeError(f{error_class}" not one of: {os.linesep}{os.linesep.join([(' ' + s) for s in ErrorClassifier().error_columns])}")
idx = random.choice(self.df[self.df[error_class]].index)
return (idx, f'{self.log_dir}/{idx}.log')
|
def random_log(self, error_class):
'\n\n '
if (not self.is_annotated()):
raise RuntimeError('Dataframe does not contain error annotations!')
if (error_class not in ErrorClassifier().error_columns):
raise RuntimeError(f{error_class}" not one of: {os.linesep}{os.linesep.join([(' ' + s) for s in ErrorClassifier().error_columns])}")
idx = random.choice(self.df[self.df[error_class]].index)
return (idx, f'{self.log_dir}/{idx}.log')<|docstring|>Return the path to a random log file in the given error_class.<|endoftext|>
|
339580a32c956ec718ece611d7285e1541887aa01a98efdbea4929cb3c775c82
|
def _deconflict(A):
'Prefer errors in Column A'
target = list((set(self.error_columns) - set([A])))
if self.df[A].any():
self.df.loc[(self.df[A], target)] = False
|
Prefer errors in Column A
|
scripts/error-classification.py
|
_deconflict
|
spack/testing-sandbox
| 0 |
python
|
def _deconflict(A):
target = list((set(self.error_columns) - set([A])))
if self.df[A].any():
self.df.loc[(self.df[A], target)] = False
|
def _deconflict(A):
target = list((set(self.error_columns) - set([A])))
if self.df[A].any():
self.df.loc[(self.df[A], target)] = False<|docstring|>Prefer errors in Column A<|endoftext|>
|
b41a18839196280923a130e4bdd078d91de7560d2543d24d508436bf445394af
|
def get_blade_slot(self):
'\n Return blade slot\n dmidecode output is:\n ` Location In Chassis: Slot 03`\n '
if self.is_blade():
return self.baseboard[0].get('Location In Chassis').strip()
return None
|
Return blade slot
dmidecode output is:
` Location In Chassis: Slot 03`
|
netbox_agent/vendors/dell.py
|
get_blade_slot
|
markd69/netbox-agent
| 84 |
python
|
def get_blade_slot(self):
'\n Return blade slot\n dmidecode output is:\n ` Location In Chassis: Slot 03`\n '
if self.is_blade():
return self.baseboard[0].get('Location In Chassis').strip()
return None
|
def get_blade_slot(self):
'\n Return blade slot\n dmidecode output is:\n ` Location In Chassis: Slot 03`\n '
if self.is_blade():
return self.baseboard[0].get('Location In Chassis').strip()
return None<|docstring|>Return blade slot
dmidecode output is:
` Location In Chassis: Slot 03`<|endoftext|>
|
1c751cac5f982618a3f9fc18a941e868e9d9af04681159323a2e6be8f198d1e5
|
def get_power_consumption(self):
'\n Parse omreport output like this\n\n Amperage\n PS1 Current 1 : 1.8 A\n PS2 Current 2 : 1.4 A\n '
value = []
if (not is_tool('omreport')):
logging.error('omreport does not seem to be installed, please debug')
return value
data = subprocess.getoutput('omreport chassis pwrmonitoring')
amperage = False
for line in data.splitlines():
if line.startswith('Amperage'):
amperage = True
continue
if amperage:
if line.startswith('PS'):
amp_value = line.split(':')[1].split()[0]
value.append(amp_value)
else:
break
return value
|
Parse omreport output like this
Amperage
PS1 Current 1 : 1.8 A
PS2 Current 2 : 1.4 A
|
netbox_agent/vendors/dell.py
|
get_power_consumption
|
markd69/netbox-agent
| 84 |
python
|
def get_power_consumption(self):
'\n Parse omreport output like this\n\n Amperage\n PS1 Current 1 : 1.8 A\n PS2 Current 2 : 1.4 A\n '
value = []
if (not is_tool('omreport')):
logging.error('omreport does not seem to be installed, please debug')
return value
data = subprocess.getoutput('omreport chassis pwrmonitoring')
amperage = False
for line in data.splitlines():
if line.startswith('Amperage'):
amperage = True
continue
if amperage:
if line.startswith('PS'):
amp_value = line.split(':')[1].split()[0]
value.append(amp_value)
else:
break
return value
|
def get_power_consumption(self):
'\n Parse omreport output like this\n\n Amperage\n PS1 Current 1 : 1.8 A\n PS2 Current 2 : 1.4 A\n '
value = []
if (not is_tool('omreport')):
logging.error('omreport does not seem to be installed, please debug')
return value
data = subprocess.getoutput('omreport chassis pwrmonitoring')
amperage = False
for line in data.splitlines():
if line.startswith('Amperage'):
amperage = True
continue
if amperage:
if line.startswith('PS'):
amp_value = line.split(':')[1].split()[0]
value.append(amp_value)
else:
break
return value<|docstring|>Parse omreport output like this
Amperage
PS1 Current 1 : 1.8 A
PS2 Current 2 : 1.4 A<|endoftext|>
|
2d456bb03c7c00740ebed87e9f7e352577a7cb725d07753e6fbcdf14e57c9a67
|
def get_expansion_product(self):
'\n Get the extension slot that is on a pair slot number\n next to the compute slot that is on an odd slot number\n '
raise NotImplementedError
|
Get the extension slot that is on a pair slot number
next to the compute slot that is on an odd slot number
|
netbox_agent/vendors/dell.py
|
get_expansion_product
|
markd69/netbox-agent
| 84 |
python
|
def get_expansion_product(self):
'\n Get the extension slot that is on a pair slot number\n next to the compute slot that is on an odd slot number\n '
raise NotImplementedError
|
def get_expansion_product(self):
'\n Get the extension slot that is on a pair slot number\n next to the compute slot that is on an odd slot number\n '
raise NotImplementedError<|docstring|>Get the extension slot that is on a pair slot number
next to the compute slot that is on an odd slot number<|endoftext|>
|
ebddd6c1a89bc85b023799080920e64e85bc3260c6ca6239a4f960031c4b06d9
|
def is_expansion_slot(self, server):
'\n Return True if its an extension slot\n '
raise NotImplementedError
|
Return True if its an extension slot
|
netbox_agent/vendors/dell.py
|
is_expansion_slot
|
markd69/netbox-agent
| 84 |
python
|
def is_expansion_slot(self, server):
'\n \n '
raise NotImplementedError
|
def is_expansion_slot(self, server):
'\n \n '
raise NotImplementedError<|docstring|>Return True if its an extension slot<|endoftext|>
|
6a1344e8092e23f6396c80b35ac3e45500750dca455e64093b8034c2b9ab4eea
|
def get_blade_expansion_slot(self):
'\n Expansion slot are always the compute bay number + 1\n '
raise NotImplementedError
|
Expansion slot are always the compute bay number + 1
|
netbox_agent/vendors/dell.py
|
get_blade_expansion_slot
|
markd69/netbox-agent
| 84 |
python
|
def get_blade_expansion_slot(self):
'\n \n '
raise NotImplementedError
|
def get_blade_expansion_slot(self):
'\n \n '
raise NotImplementedError<|docstring|>Expansion slot are always the compute bay number + 1<|endoftext|>
|
7ba0bc8bd47a9b247abcb6a33abc86d3481ea43ff6b6c011e335b60ee39a81cb
|
def own_expansion_slot(self):
'\n Say if the device can host an extension card based\n on the product name\n '
pass
|
Say if the device can host an extension card based
on the product name
|
netbox_agent/vendors/dell.py
|
own_expansion_slot
|
markd69/netbox-agent
| 84 |
python
|
def own_expansion_slot(self):
'\n Say if the device can host an extension card based\n on the product name\n '
pass
|
def own_expansion_slot(self):
'\n Say if the device can host an extension card based\n on the product name\n '
pass<|docstring|>Say if the device can host an extension card based
on the product name<|endoftext|>
|
8977c3d33fa44afe2fc88d4f5f28ec830d05092caf3e7cdd69eb9db969a3e9ab
|
def _get_information(self):
'Returns information about the given league.'
return requests.get(API_URLS['league_classic'].format(self.league_id)).json()
|
Returns information about the given league.
|
fpl/models/classic_league.py
|
_get_information
|
emre/fpl
| 0 |
python
|
def _get_information(self):
return requests.get(API_URLS['league_classic'].format(self.league_id)).json()
|
def _get_information(self):
return requests.get(API_URLS['league_classic'].format(self.league_id)).json()<|docstring|>Returns information about the given league.<|endoftext|>
|
12619527e7e1b3477e405d5e0bd41457555a44e6bd716e351af752a38da5c5b0
|
def get_standings(self):
'Returns league standings for all teams in the league.'
standings = []
for page in itertools.count(start=1):
url = '{}?ls-page={}'.format(API_URLS['league_classic'].format(self.league_id), page)
page_results = requests.get(url).json()['standings']['results']
if page_results:
standings.extend(page_results)
else:
self.standings = standings
break
|
Returns league standings for all teams in the league.
|
fpl/models/classic_league.py
|
get_standings
|
emre/fpl
| 0 |
python
|
def get_standings(self):
standings = []
for page in itertools.count(start=1):
url = '{}?ls-page={}'.format(API_URLS['league_classic'].format(self.league_id), page)
page_results = requests.get(url).json()['standings']['results']
if page_results:
standings.extend(page_results)
else:
self.standings = standings
break
|
def get_standings(self):
standings = []
for page in itertools.count(start=1):
url = '{}?ls-page={}'.format(API_URLS['league_classic'].format(self.league_id), page)
page_results = requests.get(url).json()['standings']['results']
if page_results:
standings.extend(page_results)
else:
self.standings = standings
break<|docstring|>Returns league standings for all teams in the league.<|endoftext|>
|
d9ac5dedb2b3b53007f8f1a54320ba4239ff23e9a37e35c5e4b51fb348170264
|
def except_error_db_add(testcase, object, Error):
'\n\tFails the provided testcase if an error of type Error is NOT excepted when attempting to add the provided object to\n\tthe databse and then committing.\n\t'
try:
db.session.add(object)
db.session.commit()
except Error:
db.session.rollback()
except Exception:
testcase.fail('An unexpected exception was raised, rather than the expected {}.'.format(Error.__name__))
else:
testcase.fail('Expected an {} to be raised, however it was not.'.format(Error.__name__))
|
Fails the provided testcase if an error of type Error is NOT excepted when attempting to add the provided object to
the databse and then committing.
|
server/dbentitytests.py
|
except_error_db_add
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def except_error_db_add(testcase, object, Error):
'\n\tFails the provided testcase if an error of type Error is NOT excepted when attempting to add the provided object to\n\tthe databse and then committing.\n\t'
try:
db.session.add(object)
db.session.commit()
except Error:
db.session.rollback()
except Exception:
testcase.fail('An unexpected exception was raised, rather than the expected {}.'.format(Error.__name__))
else:
testcase.fail('Expected an {} to be raised, however it was not.'.format(Error.__name__))
|
def except_error_db_add(testcase, object, Error):
'\n\tFails the provided testcase if an error of type Error is NOT excepted when attempting to add the provided object to\n\tthe databse and then committing.\n\t'
try:
db.session.add(object)
db.session.commit()
except Error:
db.session.rollback()
except Exception:
testcase.fail('An unexpected exception was raised, rather than the expected {}.'.format(Error.__name__))
else:
testcase.fail('Expected an {} to be raised, however it was not.'.format(Error.__name__))<|docstring|>Fails the provided testcase if an error of type Error is NOT excepted when attempting to add the provided object to
the databse and then committing.<|endoftext|>
|
5983a3229e59f4abe710f070400a42c794c3184bde2de113971e9b7d0f98cf4d
|
def except_error_db_commit(testcase, Error):
'\n\tFails the provided testcase if an error of type Error is NOT excepted when attempting to make a commit to the\n\tdatabase.\n\t'
try:
db.session.commit()
except Error:
db.session.rollback()
except Exception:
testcase.fail('An unexpected exception was raised, rather than the expected {}.'.format(Error.__name__))
else:
testcase.fail('Expected an {} to be raised, however it was not.'.format(Error.__name__))
|
Fails the provided testcase if an error of type Error is NOT excepted when attempting to make a commit to the
database.
|
server/dbentitytests.py
|
except_error_db_commit
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def except_error_db_commit(testcase, Error):
'\n\tFails the provided testcase if an error of type Error is NOT excepted when attempting to make a commit to the\n\tdatabase.\n\t'
try:
db.session.commit()
except Error:
db.session.rollback()
except Exception:
testcase.fail('An unexpected exception was raised, rather than the expected {}.'.format(Error.__name__))
else:
testcase.fail('Expected an {} to be raised, however it was not.'.format(Error.__name__))
|
def except_error_db_commit(testcase, Error):
'\n\tFails the provided testcase if an error of type Error is NOT excepted when attempting to make a commit to the\n\tdatabase.\n\t'
try:
db.session.commit()
except Error:
db.session.rollback()
except Exception:
testcase.fail('An unexpected exception was raised, rather than the expected {}.'.format(Error.__name__))
else:
testcase.fail('Expected an {} to be raised, however it was not.'.format(Error.__name__))<|docstring|>Fails the provided testcase if an error of type Error is NOT excepted when attempting to make a commit to the
database.<|endoftext|>
|
c3ada9e94469e761ac1cb6811080d4ae36f2498db9fc7be74c40313f9ceeda8b
|
def setUp(self):
'Rebuild all tables before test.'
db.create_all()
|
Rebuild all tables before test.
|
server/dbentitytests.py
|
setUp
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def setUp(self):
db.create_all()
|
def setUp(self):
db.create_all()<|docstring|>Rebuild all tables before test.<|endoftext|>
|
3f4df6c7b67a8a667dce1ce8dc5fb766955351719974e62af8d03678c6268544
|
def tearDown(self):
'Drop all tables before test.'
db.session.rollback()
db.drop_all()
|
Drop all tables before test.
|
server/dbentitytests.py
|
tearDown
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def tearDown(self):
db.session.rollback()
db.drop_all()
|
def tearDown(self):
db.session.rollback()
db.drop_all()<|docstring|>Drop all tables before test.<|endoftext|>
|
8ee2f8279e8f83e8f9fb5d2305c39bcdb07b36375ec3bbb79f3477b89172dab5
|
def test_create_reader(self):
'Tests that a reader can be created as expected.'
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()
|
Tests that a reader can be created as expected.
|
server/dbentitytests.py
|
test_create_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_create_reader(self):
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()
|
def test_create_reader(self):
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()<|docstring|>Tests that a reader can be created as expected.<|endoftext|>
|
42edd52cfc41e228bd0cd507d4e332b369a89af759c409aa5ed5ab1dee3cef19
|
def test_datatypes_reader(self):
'Tests that non-string datatype constraints of attributes in Reader are enforced by the database.'
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader.id = MAGIC_STRING
except_error_db_add(self, reader, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
reader.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader.card_id = MAGIC_STRING
except_error_db_add(self, reader, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
reader.card_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
Tests that non-string datatype constraints of attributes in Reader are enforced by the database.
|
server/dbentitytests.py
|
test_datatypes_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_datatypes_reader(self):
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader.id = MAGIC_STRING
except_error_db_add(self, reader, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
reader.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader.card_id = MAGIC_STRING
except_error_db_add(self, reader, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
reader.card_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
def test_datatypes_reader(self):
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader.id = MAGIC_STRING
except_error_db_add(self, reader, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
reader.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader.card_id = MAGIC_STRING
except_error_db_add(self, reader, IntegrityError)
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
reader.card_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)<|docstring|>Tests that non-string datatype constraints of attributes in Reader are enforced by the database.<|endoftext|>
|
2c2fa1ffabc4852919e0d24b72b5a64d725a2255cc879d5b74e2067e4b74ecc1
|
def test_nullable_false_reader(self):
'Tests that attributes in Reader with nullable=False cannot be None.'
except_error_db_add(self, Reader(email=None, password='abc123', name='Peter', surname='Parker'), IntegrityError)
except_error_db_add(self, Reader(email='[email protected]', password='abc123', name=None, surname='Parker'), IntegrityError)
except_error_db_add(self, Reader(email='[email protected]', password='abc123', name='Peter', surname=None), IntegrityError)
try:
Reader(email='[email protected]', password=None, name='Peter', surname='Parker')
except ValueError:
pass
except Exception:
self.fail('Unexpected exception raised')
else:
self.fail('ValueError not raised')
|
Tests that attributes in Reader with nullable=False cannot be None.
|
server/dbentitytests.py
|
test_nullable_false_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_nullable_false_reader(self):
except_error_db_add(self, Reader(email=None, password='abc123', name='Peter', surname='Parker'), IntegrityError)
except_error_db_add(self, Reader(email='[email protected]', password='abc123', name=None, surname='Parker'), IntegrityError)
except_error_db_add(self, Reader(email='[email protected]', password='abc123', name='Peter', surname=None), IntegrityError)
try:
Reader(email='[email protected]', password=None, name='Peter', surname='Parker')
except ValueError:
pass
except Exception:
self.fail('Unexpected exception raised')
else:
self.fail('ValueError not raised')
|
def test_nullable_false_reader(self):
except_error_db_add(self, Reader(email=None, password='abc123', name='Peter', surname='Parker'), IntegrityError)
except_error_db_add(self, Reader(email='[email protected]', password='abc123', name=None, surname='Parker'), IntegrityError)
except_error_db_add(self, Reader(email='[email protected]', password='abc123', name='Peter', surname=None), IntegrityError)
try:
Reader(email='[email protected]', password=None, name='Peter', surname='Parker')
except ValueError:
pass
except Exception:
self.fail('Unexpected exception raised')
else:
self.fail('ValueError not raised')<|docstring|>Tests that attributes in Reader with nullable=False cannot be None.<|endoftext|>
|
9b212e831daaf5fb3a2cdb956176fdffa404e7d81bdef872e4a76bb45912a932
|
def test_nullable_reader(self):
'Tests that attributes in Reader with nullable=True actually can be None.'
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()
reader.card_id = MAGIC_INTEGER
db.session.commit()
reader.card_id = None
db.session.commit()
|
Tests that attributes in Reader with nullable=True actually can be None.
|
server/dbentitytests.py
|
test_nullable_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_nullable_reader(self):
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()
reader.card_id = MAGIC_INTEGER
db.session.commit()
reader.card_id = None
db.session.commit()
|
def test_nullable_reader(self):
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()
reader.card_id = MAGIC_INTEGER
db.session.commit()
reader.card_id = None
db.session.commit()<|docstring|>Tests that attributes in Reader with nullable=True actually can be None.<|endoftext|>
|
c63426d7d01a829924a1141302b21c4be9364bd3ef29ca75600d81e3cce951a6
|
def test_uniqueness_reader(self):
'Tests that uniqueness constraints of attributes in Reader are enforced by the database.'
reader1 = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader2 = Reader(email='[email protected]', password='def456', name='Mary Jane', surname='Watson')
db.session.add(reader1)
db.session.commit()
except_error_db_add(self, reader2, IntegrityError)
reader1.card_id = MAGIC_INTEGER
reader2.email = '[email protected]'
reader2.card_id = MAGIC_INTEGER
except_error_db_add(self, reader2, IntegrityError)
|
Tests that uniqueness constraints of attributes in Reader are enforced by the database.
|
server/dbentitytests.py
|
test_uniqueness_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_uniqueness_reader(self):
reader1 = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader2 = Reader(email='[email protected]', password='def456', name='Mary Jane', surname='Watson')
db.session.add(reader1)
db.session.commit()
except_error_db_add(self, reader2, IntegrityError)
reader1.card_id = MAGIC_INTEGER
reader2.email = '[email protected]'
reader2.card_id = MAGIC_INTEGER
except_error_db_add(self, reader2, IntegrityError)
|
def test_uniqueness_reader(self):
reader1 = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader2 = Reader(email='[email protected]', password='def456', name='Mary Jane', surname='Watson')
db.session.add(reader1)
db.session.commit()
except_error_db_add(self, reader2, IntegrityError)
reader1.card_id = MAGIC_INTEGER
reader2.email = '[email protected]'
reader2.card_id = MAGIC_INTEGER
except_error_db_add(self, reader2, IntegrityError)<|docstring|>Tests that uniqueness constraints of attributes in Reader are enforced by the database.<|endoftext|>
|
6db63f792f3a0a234da56c92a21289b35d2d8f3348d052b24df97914d9b01e55
|
def test_password_hashed_reader(self):
'Tests that the value of the password attribute in Reader is encrypted.'
plaintext_pw = 'abc123'
reader = Reader(email='[email protected]', password=plaintext_pw, name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()
queried_pw = Reader.query.filter_by(email='[email protected]', name='Peter', surname='Parker').first().password
self.assertNotEqual(plaintext_pw, queried_pw)
self.assertNotEqual(plaintext_pw, reader.password)
self.assertEqual(queried_pw, reader.password)
self.assertIsNotNone(queried_pw)
self.assertIsNotNone(reader.password)
|
Tests that the value of the password attribute in Reader is encrypted.
|
server/dbentitytests.py
|
test_password_hashed_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_password_hashed_reader(self):
plaintext_pw = 'abc123'
reader = Reader(email='[email protected]', password=plaintext_pw, name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()
queried_pw = Reader.query.filter_by(email='[email protected]', name='Peter', surname='Parker').first().password
self.assertNotEqual(plaintext_pw, queried_pw)
self.assertNotEqual(plaintext_pw, reader.password)
self.assertEqual(queried_pw, reader.password)
self.assertIsNotNone(queried_pw)
self.assertIsNotNone(reader.password)
|
def test_password_hashed_reader(self):
plaintext_pw = 'abc123'
reader = Reader(email='[email protected]', password=plaintext_pw, name='Peter', surname='Parker')
db.session.add(reader)
db.session.commit()
queried_pw = Reader.query.filter_by(email='[email protected]', name='Peter', surname='Parker').first().password
self.assertNotEqual(plaintext_pw, queried_pw)
self.assertNotEqual(plaintext_pw, reader.password)
self.assertEqual(queried_pw, reader.password)
self.assertIsNotNone(queried_pw)
self.assertIsNotNone(reader.password)<|docstring|>Tests that the value of the password attribute in Reader is encrypted.<|endoftext|>
|
8fa3c44db39290bb08e79f3150368a11adfa7370d8f3394848a55656a43ffccd
|
def test_password_functions_reader(self):
'Tests that password functions in Reader behaves as expected.'
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
pw_before = reader.password
reader.set_password('def456')
pw_after = reader.password
self.assertNotEqual(pw_before, pw_after)
self.assertTrue(reader.check_password('def456'))
|
Tests that password functions in Reader behaves as expected.
|
server/dbentitytests.py
|
test_password_functions_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_password_functions_reader(self):
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
pw_before = reader.password
reader.set_password('def456')
pw_after = reader.password
self.assertNotEqual(pw_before, pw_after)
self.assertTrue(reader.check_password('def456'))
|
def test_password_functions_reader(self):
reader = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
pw_before = reader.password
reader.set_password('def456')
pw_after = reader.password
self.assertNotEqual(pw_before, pw_after)
self.assertTrue(reader.check_password('def456'))<|docstring|>Tests that password functions in Reader behaves as expected.<|endoftext|>
|
c4cebb0ae17a1540f7115da2cd7fc26ac8eff827f9f43f382bcbeefb8edc2640
|
def test_id_increment_reader(self):
'Tests that the id attribute in Reader is consistent.'
reader1 = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader2 = Reader(email='[email protected]', password='def456', name='Mary Jane', surname='Watson')
db.session.add(reader1)
db.session.add(reader2)
db.session.commit()
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Peter', surname='Parker').first().id, 1)
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Mary Jane', surname='Watson').first().id, 2)
db.session.delete(reader1)
db.session.commit()
self.assertIsNone(Reader.query.filter_by(id=1).first())
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Mary Jane', surname='Watson').first().id, 2)
reader3 = Reader(email='[email protected]', password='ghi789', name='Harry', surname='Osborn')
db.session.add(reader3)
db.session.commit()
self.assertIsNone(Reader.query.filter_by(id=1).first())
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Harry', surname='Osborn').first().id, 3)
|
Tests that the id attribute in Reader is consistent.
|
server/dbentitytests.py
|
test_id_increment_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_id_increment_reader(self):
reader1 = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader2 = Reader(email='[email protected]', password='def456', name='Mary Jane', surname='Watson')
db.session.add(reader1)
db.session.add(reader2)
db.session.commit()
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Peter', surname='Parker').first().id, 1)
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Mary Jane', surname='Watson').first().id, 2)
db.session.delete(reader1)
db.session.commit()
self.assertIsNone(Reader.query.filter_by(id=1).first())
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Mary Jane', surname='Watson').first().id, 2)
reader3 = Reader(email='[email protected]', password='ghi789', name='Harry', surname='Osborn')
db.session.add(reader3)
db.session.commit()
self.assertIsNone(Reader.query.filter_by(id=1).first())
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Harry', surname='Osborn').first().id, 3)
|
def test_id_increment_reader(self):
reader1 = Reader(email='[email protected]', password='abc123', name='Peter', surname='Parker')
reader2 = Reader(email='[email protected]', password='def456', name='Mary Jane', surname='Watson')
db.session.add(reader1)
db.session.add(reader2)
db.session.commit()
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Peter', surname='Parker').first().id, 1)
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Mary Jane', surname='Watson').first().id, 2)
db.session.delete(reader1)
db.session.commit()
self.assertIsNone(Reader.query.filter_by(id=1).first())
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Mary Jane', surname='Watson').first().id, 2)
reader3 = Reader(email='[email protected]', password='ghi789', name='Harry', surname='Osborn')
db.session.add(reader3)
db.session.commit()
self.assertIsNone(Reader.query.filter_by(id=1).first())
self.assertEqual(Reader.query.filter_by(email='[email protected]', name='Harry', surname='Osborn').first().id, 3)<|docstring|>Tests that the id attribute in Reader is consistent.<|endoftext|>
|
93a0751f5ec0c1a174e701b186593e0843e8f048c7a2a170aaa970debfc48e22
|
def test_create_approver(self):
'Tests that an approver can be created as expected.'
approver = Approver(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(approver)
db.session.commit()
|
Tests that an approver can be created as expected.
|
server/dbentitytests.py
|
test_create_approver
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_create_approver(self):
approver = Approver(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(approver)
db.session.commit()
|
def test_create_approver(self):
approver = Approver(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(approver)
db.session.commit()<|docstring|>Tests that an approver can be created as expected.<|endoftext|>
|
59714295d607bbdfec6a601d00e135323bbaade51207ad40bb279d2ae3e4a741
|
def test_inheritance_approver(self):
'\n\t\tTests that when an approver is created, this also results in an entry in the Reader table thanks to the\n\t\tinheritance.\n\t\t'
approver = Approver(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(approver)
db.session.commit()
self.assertIsNotNone(Reader.query.filter_by(id=approver.id).first())
self.assertIsNone(Admin.query.filter_by(id=approver.id).first())
|
Tests that when an approver is created, this also results in an entry in the Reader table thanks to the
inheritance.
|
server/dbentitytests.py
|
test_inheritance_approver
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_inheritance_approver(self):
'\n\t\tTests that when an approver is created, this also results in an entry in the Reader table thanks to the\n\t\tinheritance.\n\t\t'
approver = Approver(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(approver)
db.session.commit()
self.assertIsNotNone(Reader.query.filter_by(id=approver.id).first())
self.assertIsNone(Admin.query.filter_by(id=approver.id).first())
|
def test_inheritance_approver(self):
'\n\t\tTests that when an approver is created, this also results in an entry in the Reader table thanks to the\n\t\tinheritance.\n\t\t'
approver = Approver(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(approver)
db.session.commit()
self.assertIsNotNone(Reader.query.filter_by(id=approver.id).first())
self.assertIsNone(Admin.query.filter_by(id=approver.id).first())<|docstring|>Tests that when an approver is created, this also results in an entry in the Reader table thanks to the
inheritance.<|endoftext|>
|
54507c84a7651bca15d8f4480849cfb47387181281f461925416de93c0236dbd
|
def test_create_admin(self):
'Tests that an admin can be created as expected.'
admin = Admin(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(admin)
db.session.commit()
|
Tests that an admin can be created as expected.
|
server/dbentitytests.py
|
test_create_admin
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_create_admin(self):
admin = Admin(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(admin)
db.session.commit()
|
def test_create_admin(self):
admin = Admin(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(admin)
db.session.commit()<|docstring|>Tests that an admin can be created as expected.<|endoftext|>
|
bb978e2ab86efb48c97ca62bacbaba0a3c8f75a198102a7cca67d2c5e2cc972e
|
def test_inheritance_admin(self):
'\n\t\tTests that when an admin is created, this also results in an entry in the Approver and Reader tables thanks to\n\t\tthe inheritance.\n\t\t'
admin = Admin(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(admin)
db.session.commit()
self.assertIsNotNone(Approver.query.filter_by(id=admin.id).first())
self.assertIsNotNone(Reader.query.filter_by(id=admin.id).first())
|
Tests that when an admin is created, this also results in an entry in the Approver and Reader tables thanks to
the inheritance.
|
server/dbentitytests.py
|
test_inheritance_admin
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_inheritance_admin(self):
'\n\t\tTests that when an admin is created, this also results in an entry in the Approver and Reader tables thanks to\n\t\tthe inheritance.\n\t\t'
admin = Admin(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(admin)
db.session.commit()
self.assertIsNotNone(Approver.query.filter_by(id=admin.id).first())
self.assertIsNotNone(Reader.query.filter_by(id=admin.id).first())
|
def test_inheritance_admin(self):
'\n\t\tTests that when an admin is created, this also results in an entry in the Approver and Reader tables thanks to\n\t\tthe inheritance.\n\t\t'
admin = Admin(email='[email protected]', password='abc123', name='Peter', surname='Parker')
db.session.add(admin)
db.session.commit()
self.assertIsNotNone(Approver.query.filter_by(id=admin.id).first())
self.assertIsNotNone(Reader.query.filter_by(id=admin.id).first())<|docstring|>Tests that when an admin is created, this also results in an entry in the Approver and Reader tables thanks to
the inheritance.<|endoftext|>
|
ff32a0e25894f4935ca006de8eb2803dd5b537495dd8a7dd73ec584b2f077c8c
|
def test_create_room(self):
'Tests that a room can be created as expected.'
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
db.session.commit()
|
Tests that a room can be created as expected.
|
server/dbentitytests.py
|
test_create_room
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_create_room(self):
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
db.session.commit()
|
def test_create_room(self):
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
db.session.commit()<|docstring|>Tests that a room can be created as expected.<|endoftext|>
|
2fefe03375a93ac9893ff0c362cca433d18c358ff66a7835c073abab3f53c969
|
def test_datatypes_room(self):
'Tests that non-string datatype constraints of attributes in Room are enforced by the database.'
room = Room(name='ISYtan1', text_id='ISY1')
room.id = MAGIC_STRING
except_error_db_add(self, room, IntegrityError)
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
room.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
Tests that non-string datatype constraints of attributes in Room are enforced by the database.
|
server/dbentitytests.py
|
test_datatypes_room
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_datatypes_room(self):
room = Room(name='ISYtan1', text_id='ISY1')
room.id = MAGIC_STRING
except_error_db_add(self, room, IntegrityError)
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
room.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
def test_datatypes_room(self):
room = Room(name='ISYtan1', text_id='ISY1')
room.id = MAGIC_STRING
except_error_db_add(self, room, IntegrityError)
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
room.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)<|docstring|>Tests that non-string datatype constraints of attributes in Room are enforced by the database.<|endoftext|>
|
250e0db0411295276742d044db7993a0054a2b62acd7cb57c25cb849c9018807
|
def test_nullable_false_room(self):
'Tests that attributes in Room with nullable=False cannot be None.'
except_error_db_add(self, Room(name=None, text_id='ISY1'), IntegrityError)
|
Tests that attributes in Room with nullable=False cannot be None.
|
server/dbentitytests.py
|
test_nullable_false_room
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_nullable_false_room(self):
except_error_db_add(self, Room(name=None, text_id='ISY1'), IntegrityError)
|
def test_nullable_false_room(self):
except_error_db_add(self, Room(name=None, text_id='ISY1'), IntegrityError)<|docstring|>Tests that attributes in Room with nullable=False cannot be None.<|endoftext|>
|
8fb253ef5e4031a7c3e74ae032e73557a8eca71ec1baa701450cb398388dbe44
|
def test_nullable_room(self):
'Tests that attributes in Room with nullable=True actually can be None.'
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
db.session.commit()
room.representation_json = MAGIC_STRING
db.session.commit()
room.representation_json = None
db.session.commit()
|
Tests that attributes in Room with nullable=True actually can be None.
|
server/dbentitytests.py
|
test_nullable_room
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_nullable_room(self):
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
db.session.commit()
room.representation_json = MAGIC_STRING
db.session.commit()
room.representation_json = None
db.session.commit()
|
def test_nullable_room(self):
room = Room(name='ISYtan1', text_id='ISY1')
db.session.add(room)
db.session.commit()
room.representation_json = MAGIC_STRING
db.session.commit()
room.representation_json = None
db.session.commit()<|docstring|>Tests that attributes in Room with nullable=True actually can be None.<|endoftext|>
|
cf3a4417c0a61766044371cbc0f73594596a31f4f8681d6be5f1f78a1955c05f
|
def test_create_card_reader(self):
'Tests that a card reader can be created as expected.'
card_reader1 = CardReader()
db.session.add(card_reader1)
db.session.commit()
room_a = Room(name='ISYtan1', text_id='ISY1')
room_b = Room(name='ISYtan2', text_id='ISY2')
db.session.add(room_a)
db.session.add(room_b)
db.session.commit()
card_reader2 = CardReader(room_a=room_a, room_b=room_b)
db.session.add(card_reader2)
db.session.commit()
|
Tests that a card reader can be created as expected.
|
server/dbentitytests.py
|
test_create_card_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_create_card_reader(self):
card_reader1 = CardReader()
db.session.add(card_reader1)
db.session.commit()
room_a = Room(name='ISYtan1', text_id='ISY1')
room_b = Room(name='ISYtan2', text_id='ISY2')
db.session.add(room_a)
db.session.add(room_b)
db.session.commit()
card_reader2 = CardReader(room_a=room_a, room_b=room_b)
db.session.add(card_reader2)
db.session.commit()
|
def test_create_card_reader(self):
card_reader1 = CardReader()
db.session.add(card_reader1)
db.session.commit()
room_a = Room(name='ISYtan1', text_id='ISY1')
room_b = Room(name='ISYtan2', text_id='ISY2')
db.session.add(room_a)
db.session.add(room_b)
db.session.commit()
card_reader2 = CardReader(room_a=room_a, room_b=room_b)
db.session.add(card_reader2)
db.session.commit()<|docstring|>Tests that a card reader can be created as expected.<|endoftext|>
|
4f13099363fd477dbd661fbf0f0b77eb5804a812598f994697bf6c40b4a17e93
|
def test_datatypes_card_reader(self):
'Tests that non-string datatype constraints of attributes in CardReader are enforced by the database.'
card_reader = CardReader()
card_reader.id = MAGIC_STRING
except_error_db_add(self, card_reader, IntegrityError)
card_reader = CardReader()
db.session.add(card_reader)
card_reader.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
card_reader1 = CardReader()
card_reader1.room_a_id = MAGIC_STRING
except_error_db_add(self, card_reader1, IntegrityError)
card_reader1 = CardReader()
db.session.add(card_reader1)
card_reader1.room_a_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
card_reader2 = CardReader()
card_reader2.room_b_id = MAGIC_STRING
except_error_db_add(self, card_reader2, IntegrityError)
card_reader2 = CardReader()
db.session.add(card_reader2)
card_reader2.room_b_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
Tests that non-string datatype constraints of attributes in CardReader are enforced by the database.
|
server/dbentitytests.py
|
test_datatypes_card_reader
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_datatypes_card_reader(self):
card_reader = CardReader()
card_reader.id = MAGIC_STRING
except_error_db_add(self, card_reader, IntegrityError)
card_reader = CardReader()
db.session.add(card_reader)
card_reader.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
card_reader1 = CardReader()
card_reader1.room_a_id = MAGIC_STRING
except_error_db_add(self, card_reader1, IntegrityError)
card_reader1 = CardReader()
db.session.add(card_reader1)
card_reader1.room_a_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
card_reader2 = CardReader()
card_reader2.room_b_id = MAGIC_STRING
except_error_db_add(self, card_reader2, IntegrityError)
card_reader2 = CardReader()
db.session.add(card_reader2)
card_reader2.room_b_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
def test_datatypes_card_reader(self):
card_reader = CardReader()
card_reader.id = MAGIC_STRING
except_error_db_add(self, card_reader, IntegrityError)
card_reader = CardReader()
db.session.add(card_reader)
card_reader.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
card_reader1 = CardReader()
card_reader1.room_a_id = MAGIC_STRING
except_error_db_add(self, card_reader1, IntegrityError)
card_reader1 = CardReader()
db.session.add(card_reader1)
card_reader1.room_a_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
card_reader2 = CardReader()
card_reader2.room_b_id = MAGIC_STRING
except_error_db_add(self, card_reader2, IntegrityError)
card_reader2 = CardReader()
db.session.add(card_reader2)
card_reader2.room_b_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)<|docstring|>Tests that non-string datatype constraints of attributes in CardReader are enforced by the database.<|endoftext|>
|
3ddb69f0090d26c6d3d6ba9810561ac0473892bf15e05de4a4bdf193d8e1a9e9
|
def test_create_access_group(self):
'Tests that an access group can be created as expected.'
ag = AccessGroup(name='Basic')
db.session.add(ag)
db.session.commit()
|
Tests that an access group can be created as expected.
|
server/dbentitytests.py
|
test_create_access_group
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_create_access_group(self):
ag = AccessGroup(name='Basic')
db.session.add(ag)
db.session.commit()
|
def test_create_access_group(self):
ag = AccessGroup(name='Basic')
db.session.add(ag)
db.session.commit()<|docstring|>Tests that an access group can be created as expected.<|endoftext|>
|
11dfb9fe7e692de6bd6ed07a2ef66064580780f9a2263a4ee5c5eec98d52d82b
|
def test_uniqueness_access_group(self):
'Tests that uniqueness constraints of attributes in AccessGroup are enforced by the database.'
ag1 = AccessGroup(name='Basic')
ag2 = AccessGroup(name='Basic')
db.session.add(ag1)
db.session.commit()
except_error_db_add(self, ag2, IntegrityError)
|
Tests that uniqueness constraints of attributes in AccessGroup are enforced by the database.
|
server/dbentitytests.py
|
test_uniqueness_access_group
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_uniqueness_access_group(self):
ag1 = AccessGroup(name='Basic')
ag2 = AccessGroup(name='Basic')
db.session.add(ag1)
db.session.commit()
except_error_db_add(self, ag2, IntegrityError)
|
def test_uniqueness_access_group(self):
ag1 = AccessGroup(name='Basic')
ag2 = AccessGroup(name='Basic')
db.session.add(ag1)
db.session.commit()
except_error_db_add(self, ag2, IntegrityError)<|docstring|>Tests that uniqueness constraints of attributes in AccessGroup are enforced by the database.<|endoftext|>
|
3b797e8a03d600d12db09acba7accfc84e9f45f5a60a3dfc76bd5b84e35a6e33
|
def test_create_access_group_request(self):
'Tests that an access group request can be created as expected.'
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
db.session.commit()
|
Tests that an access group request can be created as expected.
|
server/dbentitytests.py
|
test_create_access_group_request
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_create_access_group_request(self):
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
db.session.commit()
|
def test_create_access_group_request(self):
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
db.session.commit()<|docstring|>Tests that an access group request can be created as expected.<|endoftext|>
|
0f39f344f7fffb6b419f905b37b37fb5052ca815ce6a6ea9cfa0f219d82b8e68
|
def test_datatypes_access_group_request(self):
'\n\t\tTests that non-string datatype constraints of attributes in AccessGroupRequest are enforced by the database.\n\t\t'
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.reader_id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.reader_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.ag_id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.ag_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.datetime_requested = MAGIC_STRING
except_error_db_add(self, agr, StatementError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.datetime_requested = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
Tests that non-string datatype constraints of attributes in AccessGroupRequest are enforced by the database.
|
server/dbentitytests.py
|
test_datatypes_access_group_request
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_datatypes_access_group_request(self):
'\n\t\t\n\t\t'
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.reader_id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.reader_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.ag_id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.ag_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.datetime_requested = MAGIC_STRING
except_error_db_add(self, agr, StatementError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.datetime_requested = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
def test_datatypes_access_group_request(self):
'\n\t\t\n\t\t'
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.reader_id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.reader_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.ag_id = MAGIC_STRING
except_error_db_add(self, agr, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.ag_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.datetime_requested = MAGIC_STRING
except_error_db_add(self, agr, StatementError)
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
db.session.add(agr)
agr.datetime_requested = MAGIC_STRING
except_error_db_commit(self, IntegrityError)<|docstring|>Tests that non-string datatype constraints of attributes in AccessGroupRequest are enforced by the database.<|endoftext|>
|
a4287e932bfcfaf0268216831b710c1d4bca349a5e2ac173cadf62dfca4f3481
|
def test_nullable_false_access_group_request(self):
'Tests that attributes in AccessGroupRequest with nullable=False cannot be None.'
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.datetime_requested = None
db.session.add(agr)
db.session.commit()
self.assertIsNotNone(AccessGroupRequest.query.filter_by(id=agr.id).first().datetime_requested)
|
Tests that attributes in AccessGroupRequest with nullable=False cannot be None.
|
server/dbentitytests.py
|
test_nullable_false_access_group_request
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_nullable_false_access_group_request(self):
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.datetime_requested = None
db.session.add(agr)
db.session.commit()
self.assertIsNotNone(AccessGroupRequest.query.filter_by(id=agr.id).first().datetime_requested)
|
def test_nullable_false_access_group_request(self):
agr = AccessGroupRequest(reader=None, ag=None, justification=MAGIC_STRING)
agr.datetime_requested = None
db.session.add(agr)
db.session.commit()
self.assertIsNotNone(AccessGroupRequest.query.filter_by(id=agr.id).first().datetime_requested)<|docstring|>Tests that attributes in AccessGroupRequest with nullable=False cannot be None.<|endoftext|>
|
957e9e71fb974617ecff0c13b2f072390060db64ff0bc2d57dadacfa1d01e95a
|
def test_varying_length_access_group_request(self):
'Tests that the value of String attributes in AccessGroupRequest can have different lengths.'
agr_lorem1 = AccessGroupRequest(reader=None, ag=None, justification=LOREM1)
agr_lorem5 = AccessGroupRequest(reader=None, ag=None, justification=LOREM5)
db.session.add(agr_lorem1)
db.session.add(agr_lorem5)
db.session.commit()
|
Tests that the value of String attributes in AccessGroupRequest can have different lengths.
|
server/dbentitytests.py
|
test_varying_length_access_group_request
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_varying_length_access_group_request(self):
agr_lorem1 = AccessGroupRequest(reader=None, ag=None, justification=LOREM1)
agr_lorem5 = AccessGroupRequest(reader=None, ag=None, justification=LOREM5)
db.session.add(agr_lorem1)
db.session.add(agr_lorem5)
db.session.commit()
|
def test_varying_length_access_group_request(self):
agr_lorem1 = AccessGroupRequest(reader=None, ag=None, justification=LOREM1)
agr_lorem5 = AccessGroupRequest(reader=None, ag=None, justification=LOREM5)
db.session.add(agr_lorem1)
db.session.add(agr_lorem5)
db.session.commit()<|docstring|>Tests that the value of String attributes in AccessGroupRequest can have different lengths.<|endoftext|>
|
0e0b059d7a74b8128ee869b33c786df1127f9c12d8837745af5023e2b68f0a6c
|
def test_create_room_request(self):
'Tests that a room request can be created as expected.'
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
db.session.commit()
|
Tests that a room request can be created as expected.
|
server/dbentitytests.py
|
test_create_room_request
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_create_room_request(self):
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
db.session.commit()
|
def test_create_room_request(self):
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
db.session.commit()<|docstring|>Tests that a room request can be created as expected.<|endoftext|>
|
3e7b33fc03080205f52dd000bfeb4897338eac0c0bca6efd1db836e5c0f22666
|
def test_datatypes_room_request(self):
'Tests that non-string datatype constraints of attributes in RoomRequest are enforced by the database.'
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.reader_id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.reader_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.room_id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.room_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.datetime_requested = MAGIC_STRING
except_error_db_add(self, rr, StatementError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.datetime_requested = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
Tests that non-string datatype constraints of attributes in RoomRequest are enforced by the database.
|
server/dbentitytests.py
|
test_datatypes_room_request
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_datatypes_room_request(self):
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.reader_id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.reader_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.room_id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.room_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.datetime_requested = MAGIC_STRING
except_error_db_add(self, rr, StatementError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.datetime_requested = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
|
def test_datatypes_room_request(self):
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.reader_id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.reader_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.room_id = MAGIC_STRING
except_error_db_add(self, rr, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.room_id = MAGIC_STRING
except_error_db_commit(self, IntegrityError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.datetime_requested = MAGIC_STRING
except_error_db_add(self, rr, StatementError)
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
db.session.add(rr)
rr.datetime_requested = MAGIC_STRING
except_error_db_commit(self, IntegrityError)<|docstring|>Tests that non-string datatype constraints of attributes in RoomRequest are enforced by the database.<|endoftext|>
|
1a5ae728cd471193cb6cf468dc48269da05aa9e0fe08cd1da6a32b47eaf588ab
|
def test_nullable_false_room_request(self):
'Tests that attributes in RoomRequest with nullable=False cannot be None.'
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.datetime_requested = None
db.session.add(rr)
db.session.commit()
self.assertIsNotNone(RoomRequest.query.filter_by(id=rr.id).first().datetime_requested)
|
Tests that attributes in RoomRequest with nullable=False cannot be None.
|
server/dbentitytests.py
|
test_nullable_false_room_request
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_nullable_false_room_request(self):
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.datetime_requested = None
db.session.add(rr)
db.session.commit()
self.assertIsNotNone(RoomRequest.query.filter_by(id=rr.id).first().datetime_requested)
|
def test_nullable_false_room_request(self):
rr = RoomRequest(reader=None, room=None, justification=MAGIC_STRING)
rr.datetime_requested = None
db.session.add(rr)
db.session.commit()
self.assertIsNotNone(RoomRequest.query.filter_by(id=rr.id).first().datetime_requested)<|docstring|>Tests that attributes in RoomRequest with nullable=False cannot be None.<|endoftext|>
|
c9b7d0feba97f7eb2235f494f975f76963d8f251fe951ab40471e6b5bcc69178
|
def test_varying_length_room_request(self):
'Tests that the value of String attributes in AccessGroupRequest can have different lengths.'
rr_lorem1 = RoomRequest(reader=None, room=None, justification=LOREM1)
rr_lorem5 = RoomRequest(reader=None, room=None, justification=LOREM5)
db.session.add(rr_lorem1)
db.session.add(rr_lorem5)
db.session.commit()
|
Tests that the value of String attributes in AccessGroupRequest can have different lengths.
|
server/dbentitytests.py
|
test_varying_length_room_request
|
TDDD96-Kandidatgrupp-1-2020/Visual1ze
| 6 |
python
|
def test_varying_length_room_request(self):
rr_lorem1 = RoomRequest(reader=None, room=None, justification=LOREM1)
rr_lorem5 = RoomRequest(reader=None, room=None, justification=LOREM5)
db.session.add(rr_lorem1)
db.session.add(rr_lorem5)
db.session.commit()
|
def test_varying_length_room_request(self):
rr_lorem1 = RoomRequest(reader=None, room=None, justification=LOREM1)
rr_lorem5 = RoomRequest(reader=None, room=None, justification=LOREM5)
db.session.add(rr_lorem1)
db.session.add(rr_lorem5)
db.session.commit()<|docstring|>Tests that the value of String attributes in AccessGroupRequest can have different lengths.<|endoftext|>
|
bd853404b0b3aa578a6d3af561f3e781bcb5a26fc0f61cc55b6ca6f4df9e892a
|
def act(self, previous_observation: np.ndarray) -> Tuple[(Any, dict)]:
'Choose random action.'
return (self.action_space.sample(), {})
|
Choose random action.
|
pachinko/time_period_step_agent.py
|
act
|
datavaluepeople/pachinko
| 0 |
python
|
def act(self, previous_observation: np.ndarray) -> Tuple[(Any, dict)]:
return (self.action_space.sample(), {})
|
def act(self, previous_observation: np.ndarray) -> Tuple[(Any, dict)]:
return (self.action_space.sample(), {})<|docstring|>Choose random action.<|endoftext|>
|
c6af8949ec9251855652797d1bb6aa72d1c37846567fb619a9546c01f416d9f8
|
def act(self, previous_observation: np.ndarray) -> Tuple[(Any, dict)]:
'Choose highest conversion rate action (with some exploration).'
n_conversions = previous_observation[1]
if (self.previous_action is not None):
self.conversion_counts[self.previous_action][0] += 1
self.conversion_counts[self.previous_action][1] += n_conversions
conversion_rate = {action: ((n_conv / n_chosen) if (n_chosen != 0) else np.inf) for (action, (n_chosen, n_conv)) in self.conversion_counts.items()}
best_action = max(conversion_rate, key=(lambda k: conversion_rate[k]))
if (np.random.rand() < self.epsilon):
action = self.action_space.sample()
else:
action = best_action
self.previous_action = action
return (action, {})
|
Choose highest conversion rate action (with some exploration).
|
pachinko/time_period_step_agent.py
|
act
|
datavaluepeople/pachinko
| 0 |
python
|
def act(self, previous_observation: np.ndarray) -> Tuple[(Any, dict)]:
n_conversions = previous_observation[1]
if (self.previous_action is not None):
self.conversion_counts[self.previous_action][0] += 1
self.conversion_counts[self.previous_action][1] += n_conversions
conversion_rate = {action: ((n_conv / n_chosen) if (n_chosen != 0) else np.inf) for (action, (n_chosen, n_conv)) in self.conversion_counts.items()}
best_action = max(conversion_rate, key=(lambda k: conversion_rate[k]))
if (np.random.rand() < self.epsilon):
action = self.action_space.sample()
else:
action = best_action
self.previous_action = action
return (action, {})
|
def act(self, previous_observation: np.ndarray) -> Tuple[(Any, dict)]:
n_conversions = previous_observation[1]
if (self.previous_action is not None):
self.conversion_counts[self.previous_action][0] += 1
self.conversion_counts[self.previous_action][1] += n_conversions
conversion_rate = {action: ((n_conv / n_chosen) if (n_chosen != 0) else np.inf) for (action, (n_chosen, n_conv)) in self.conversion_counts.items()}
best_action = max(conversion_rate, key=(lambda k: conversion_rate[k]))
if (np.random.rand() < self.epsilon):
action = self.action_space.sample()
else:
action = best_action
self.previous_action = action
return (action, {})<|docstring|>Choose highest conversion rate action (with some exploration).<|endoftext|>
|
e31f59b79ba7328d3f68cfc47df47e8bafe441a7fc141a4d02fcab41de07e648
|
@staticmethod
def compute_UCB_gamma(total_trials: int, total_successes: int, prior_alpha: float=1.0, prior_beta: float=0.0001, ucb_percentile: float=0.95) -> float:
'Compute Bayesian update on Gamma dist with priors and compute upper percentile value.'
alpha = (prior_alpha + total_successes)
beta = (prior_beta + total_trials)
return gamma.ppf(ucb_percentile, alpha, scale=(1 / beta))
|
Compute Bayesian update on Gamma dist with priors and compute upper percentile value.
|
pachinko/time_period_step_agent.py
|
compute_UCB_gamma
|
datavaluepeople/pachinko
| 0 |
python
|
@staticmethod
def compute_UCB_gamma(total_trials: int, total_successes: int, prior_alpha: float=1.0, prior_beta: float=0.0001, ucb_percentile: float=0.95) -> float:
alpha = (prior_alpha + total_successes)
beta = (prior_beta + total_trials)
return gamma.ppf(ucb_percentile, alpha, scale=(1 / beta))
|
@staticmethod
def compute_UCB_gamma(total_trials: int, total_successes: int, prior_alpha: float=1.0, prior_beta: float=0.0001, ucb_percentile: float=0.95) -> float:
alpha = (prior_alpha + total_successes)
beta = (prior_beta + total_trials)
return gamma.ppf(ucb_percentile, alpha, scale=(1 / beta))<|docstring|>Compute Bayesian update on Gamma dist with priors and compute upper percentile value.<|endoftext|>
|
f6195283be933eaaeea9bd9b417bc4159a7068086802f0d3cccde7340f7321ae
|
def act(self, previous_observation: np.ndarray) -> Any:
'Choose action with highest upper confidence bound for step in period.\n\n Also use previous observation to update upper confidence bound for previous step in period.\n '
current_idx = (self.step_number % self.period_length)
prev_idx = ((self.step_number - 1) % self.period_length)
if (self.previous_action is not None):
self.conversion_counts[prev_idx][self.previous_action][0] += 1
self.conversion_counts[prev_idx][self.previous_action][1] += previous_observation[1]
self.upper_confidence_bounds[prev_idx][self.previous_action] = self.compute_UCB_gamma(self.conversion_counts[prev_idx][self.previous_action][0], self.conversion_counts[prev_idx][self.previous_action][1])
best_action = max(self.upper_confidence_bounds[current_idx], key=(lambda k: self.upper_confidence_bounds[current_idx][k]))
self.previous_action = best_action
self.step_number += 1
agent_info = {'ucb_selected_action': self.upper_confidence_bounds[current_idx][best_action]}
return (best_action, agent_info)
|
Choose action with highest upper confidence bound for step in period.
Also use previous observation to update upper confidence bound for previous step in period.
|
pachinko/time_period_step_agent.py
|
act
|
datavaluepeople/pachinko
| 0 |
python
|
def act(self, previous_observation: np.ndarray) -> Any:
'Choose action with highest upper confidence bound for step in period.\n\n Also use previous observation to update upper confidence bound for previous step in period.\n '
current_idx = (self.step_number % self.period_length)
prev_idx = ((self.step_number - 1) % self.period_length)
if (self.previous_action is not None):
self.conversion_counts[prev_idx][self.previous_action][0] += 1
self.conversion_counts[prev_idx][self.previous_action][1] += previous_observation[1]
self.upper_confidence_bounds[prev_idx][self.previous_action] = self.compute_UCB_gamma(self.conversion_counts[prev_idx][self.previous_action][0], self.conversion_counts[prev_idx][self.previous_action][1])
best_action = max(self.upper_confidence_bounds[current_idx], key=(lambda k: self.upper_confidence_bounds[current_idx][k]))
self.previous_action = best_action
self.step_number += 1
agent_info = {'ucb_selected_action': self.upper_confidence_bounds[current_idx][best_action]}
return (best_action, agent_info)
|
def act(self, previous_observation: np.ndarray) -> Any:
'Choose action with highest upper confidence bound for step in period.\n\n Also use previous observation to update upper confidence bound for previous step in period.\n '
current_idx = (self.step_number % self.period_length)
prev_idx = ((self.step_number - 1) % self.period_length)
if (self.previous_action is not None):
self.conversion_counts[prev_idx][self.previous_action][0] += 1
self.conversion_counts[prev_idx][self.previous_action][1] += previous_observation[1]
self.upper_confidence_bounds[prev_idx][self.previous_action] = self.compute_UCB_gamma(self.conversion_counts[prev_idx][self.previous_action][0], self.conversion_counts[prev_idx][self.previous_action][1])
best_action = max(self.upper_confidence_bounds[current_idx], key=(lambda k: self.upper_confidence_bounds[current_idx][k]))
self.previous_action = best_action
self.step_number += 1
agent_info = {'ucb_selected_action': self.upper_confidence_bounds[current_idx][best_action]}
return (best_action, agent_info)<|docstring|>Choose action with highest upper confidence bound for step in period.
Also use previous observation to update upper confidence bound for previous step in period.<|endoftext|>
|
9104b8302efa2a26074fc42ab8603967b2ca5c2bfa0cea1ad29c7f4d207d6e64
|
def set_disabled_input(self):
' Метод делающий поля ввода неактивными'
self.ui.label_new_message.setText('Для выбора получателя дважды кликните на нем в окне контактов.')
self.ui.text_message.clear()
if self.history_model:
self.history_model.clear()
self.ui.btn_clear.setDisabled(True)
self.ui.btn_send.setDisabled(True)
self.ui.text_message.setDisabled(True)
self.encryptor = None
self.current_chat = None
self.current_chat_key = None
|
Метод делающий поля ввода неактивными
|
Lib/site-packages/client/main_window.py
|
set_disabled_input
|
fochoao/cpython
| 0 |
python
|
def set_disabled_input(self):
' '
self.ui.label_new_message.setText('Для выбора получателя дважды кликните на нем в окне контактов.')
self.ui.text_message.clear()
if self.history_model:
self.history_model.clear()
self.ui.btn_clear.setDisabled(True)
self.ui.btn_send.setDisabled(True)
self.ui.text_message.setDisabled(True)
self.encryptor = None
self.current_chat = None
self.current_chat_key = None
|
def set_disabled_input(self):
' '
self.ui.label_new_message.setText('Для выбора получателя дважды кликните на нем в окне контактов.')
self.ui.text_message.clear()
if self.history_model:
self.history_model.clear()
self.ui.btn_clear.setDisabled(True)
self.ui.btn_send.setDisabled(True)
self.ui.text_message.setDisabled(True)
self.encryptor = None
self.current_chat = None
self.current_chat_key = None<|docstring|>Метод делающий поля ввода неактивными<|endoftext|>
|
cc248e647cad6d95084a14788c060e198e02a926abfd2c56a0491fa3a504c9bd
|
def history_list_update(self):
'\n Метод заполняющий соответствующий QListView\n историей переписки с текущим собеседником.\n '
list = sorted(self.database.get_history(self.current_chat), key=(lambda item: item[3]))
if (not self.history_model):
self.history_model = QStandardItemModel()
self.ui.list_messages.setModel(self.history_model)
self.history_model.clear()
length = len(list)
start_index = 0
if (length > 20):
start_index = (length - 20)
for i in range(start_index, length):
item = list[i]
if (item[1] == 'in'):
mess = QStandardItem(f'''Входящее от {item[3].replace(microsecond=0)}:
{item[2]}''')
mess.setEditable(False)
mess.setBackground(QBrush(QColor(255, 213, 213)))
mess.setTextAlignment(Qt.AlignLeft)
self.history_model.appendRow(mess)
else:
mess = QStandardItem(f'''Исходящее от {item[3].replace(microsecond=0)}:
{item[2]}''')
mess.setEditable(False)
mess.setTextAlignment(Qt.AlignRight)
mess.setBackground(QBrush(QColor(204, 255, 204)))
self.history_model.appendRow(mess)
self.ui.list_messages.scrollToBottom()
|
Метод заполняющий соответствующий QListView
историей переписки с текущим собеседником.
|
Lib/site-packages/client/main_window.py
|
history_list_update
|
fochoao/cpython
| 0 |
python
|
def history_list_update(self):
'\n Метод заполняющий соответствующий QListView\n историей переписки с текущим собеседником.\n '
list = sorted(self.database.get_history(self.current_chat), key=(lambda item: item[3]))
if (not self.history_model):
self.history_model = QStandardItemModel()
self.ui.list_messages.setModel(self.history_model)
self.history_model.clear()
length = len(list)
start_index = 0
if (length > 20):
start_index = (length - 20)
for i in range(start_index, length):
item = list[i]
if (item[1] == 'in'):
mess = QStandardItem(f'Входящее от {item[3].replace(microsecond=0)}:
{item[2]}')
mess.setEditable(False)
mess.setBackground(QBrush(QColor(255, 213, 213)))
mess.setTextAlignment(Qt.AlignLeft)
self.history_model.appendRow(mess)
else:
mess = QStandardItem(f'Исходящее от {item[3].replace(microsecond=0)}:
{item[2]}')
mess.setEditable(False)
mess.setTextAlignment(Qt.AlignRight)
mess.setBackground(QBrush(QColor(204, 255, 204)))
self.history_model.appendRow(mess)
self.ui.list_messages.scrollToBottom()
|
def history_list_update(self):
'\n Метод заполняющий соответствующий QListView\n историей переписки с текущим собеседником.\n '
list = sorted(self.database.get_history(self.current_chat), key=(lambda item: item[3]))
if (not self.history_model):
self.history_model = QStandardItemModel()
self.ui.list_messages.setModel(self.history_model)
self.history_model.clear()
length = len(list)
start_index = 0
if (length > 20):
start_index = (length - 20)
for i in range(start_index, length):
item = list[i]
if (item[1] == 'in'):
mess = QStandardItem(f'Входящее от {item[3].replace(microsecond=0)}:
{item[2]}')
mess.setEditable(False)
mess.setBackground(QBrush(QColor(255, 213, 213)))
mess.setTextAlignment(Qt.AlignLeft)
self.history_model.appendRow(mess)
else:
mess = QStandardItem(f'Исходящее от {item[3].replace(microsecond=0)}:
{item[2]}')
mess.setEditable(False)
mess.setTextAlignment(Qt.AlignRight)
mess.setBackground(QBrush(QColor(204, 255, 204)))
self.history_model.appendRow(mess)
self.ui.list_messages.scrollToBottom()<|docstring|>Метод заполняющий соответствующий QListView
историей переписки с текущим собеседником.<|endoftext|>
|
024fc5b51f091775082bf1b99d42397e15bf4f826cfcaa6137d12630541997cf
|
def select_active_user(self):
'Метод обработчик события двойного клика по списку контактов.'
self.current_chat = self.ui.list_contacts.currentIndex().data()
self.set_active_user()
|
Метод обработчик события двойного клика по списку контактов.
|
Lib/site-packages/client/main_window.py
|
select_active_user
|
fochoao/cpython
| 0 |
python
|
def select_active_user(self):
self.current_chat = self.ui.list_contacts.currentIndex().data()
self.set_active_user()
|
def select_active_user(self):
self.current_chat = self.ui.list_contacts.currentIndex().data()
self.set_active_user()<|docstring|>Метод обработчик события двойного клика по списку контактов.<|endoftext|>
|
e59f8bdf602682e56a9a25f037602eec132bcf1fa9fbe9e2ed58598c1e934e4b
|
def set_active_user(self):
'Метод активации чата с собеседником.'
try:
self.current_chat_key = self.transport.key_request(self.current_chat)
logger.debug(f'Загружен открытый ключ для {self.current_chat}')
if self.current_chat_key:
self.encryptor = PKCS1_OAEP.new(RSA.import_key(self.current_chat_key))
except (OSError, json.JSONDecodeError):
self.current_chat_key = None
self.encryptor = None
logger.debug(f'Не удалось получить ключ для {self.current_chat}')
if (not self.current_chat_key):
self.messages.warning(self, 'Ошибка', 'Для выбранного пользователя нет ключа шифрования.')
return
self.ui.label_new_message.setText(f'Введите сообщенние для {self.current_chat}:')
self.ui.btn_clear.setDisabled(False)
self.ui.btn_send.setDisabled(False)
self.ui.text_message.setDisabled(False)
self.history_list_update()
|
Метод активации чата с собеседником.
|
Lib/site-packages/client/main_window.py
|
set_active_user
|
fochoao/cpython
| 0 |
python
|
def set_active_user(self):
try:
self.current_chat_key = self.transport.key_request(self.current_chat)
logger.debug(f'Загружен открытый ключ для {self.current_chat}')
if self.current_chat_key:
self.encryptor = PKCS1_OAEP.new(RSA.import_key(self.current_chat_key))
except (OSError, json.JSONDecodeError):
self.current_chat_key = None
self.encryptor = None
logger.debug(f'Не удалось получить ключ для {self.current_chat}')
if (not self.current_chat_key):
self.messages.warning(self, 'Ошибка', 'Для выбранного пользователя нет ключа шифрования.')
return
self.ui.label_new_message.setText(f'Введите сообщенние для {self.current_chat}:')
self.ui.btn_clear.setDisabled(False)
self.ui.btn_send.setDisabled(False)
self.ui.text_message.setDisabled(False)
self.history_list_update()
|
def set_active_user(self):
try:
self.current_chat_key = self.transport.key_request(self.current_chat)
logger.debug(f'Загружен открытый ключ для {self.current_chat}')
if self.current_chat_key:
self.encryptor = PKCS1_OAEP.new(RSA.import_key(self.current_chat_key))
except (OSError, json.JSONDecodeError):
self.current_chat_key = None
self.encryptor = None
logger.debug(f'Не удалось получить ключ для {self.current_chat}')
if (not self.current_chat_key):
self.messages.warning(self, 'Ошибка', 'Для выбранного пользователя нет ключа шифрования.')
return
self.ui.label_new_message.setText(f'Введите сообщенние для {self.current_chat}:')
self.ui.btn_clear.setDisabled(False)
self.ui.btn_send.setDisabled(False)
self.ui.text_message.setDisabled(False)
self.history_list_update()<|docstring|>Метод активации чата с собеседником.<|endoftext|>
|
45d12f4a6287a48eee06c18b47df2d7f17fdd05ab5325ae80301aa188c4b6019
|
def clients_list_update(self):
'Метод обновляющий список контактов.'
contacts_list = self.database.get_contacts()
self.contacts_model = QStandardItemModel()
for i in sorted(contacts_list):
item = QStandardItem(i)
item.setEditable(False)
self.contacts_model.appendRow(item)
self.ui.list_contacts.setModel(self.contacts_model)
|
Метод обновляющий список контактов.
|
Lib/site-packages/client/main_window.py
|
clients_list_update
|
fochoao/cpython
| 0 |
python
|
def clients_list_update(self):
contacts_list = self.database.get_contacts()
self.contacts_model = QStandardItemModel()
for i in sorted(contacts_list):
item = QStandardItem(i)
item.setEditable(False)
self.contacts_model.appendRow(item)
self.ui.list_contacts.setModel(self.contacts_model)
|
def clients_list_update(self):
contacts_list = self.database.get_contacts()
self.contacts_model = QStandardItemModel()
for i in sorted(contacts_list):
item = QStandardItem(i)
item.setEditable(False)
self.contacts_model.appendRow(item)
self.ui.list_contacts.setModel(self.contacts_model)<|docstring|>Метод обновляющий список контактов.<|endoftext|>
|
25151433b1aaa9782beccab2a378dd7e790e15113ea9b31dda045614f8be2ba7
|
def add_contact_window(self):
'Метод создающий окно - диалог добавления контакта'
global select_dialog
select_dialog = AddContactDialog(self.transport, self.database)
select_dialog.btn_ok.clicked.connect((lambda : self.add_contact_action(select_dialog)))
select_dialog.show()
|
Метод создающий окно - диалог добавления контакта
|
Lib/site-packages/client/main_window.py
|
add_contact_window
|
fochoao/cpython
| 0 |
python
|
def add_contact_window(self):
global select_dialog
select_dialog = AddContactDialog(self.transport, self.database)
select_dialog.btn_ok.clicked.connect((lambda : self.add_contact_action(select_dialog)))
select_dialog.show()
|
def add_contact_window(self):
global select_dialog
select_dialog = AddContactDialog(self.transport, self.database)
select_dialog.btn_ok.clicked.connect((lambda : self.add_contact_action(select_dialog)))
select_dialog.show()<|docstring|>Метод создающий окно - диалог добавления контакта<|endoftext|>
|
659867b24b95064256dc12a4b3b8014478973e86ec98d44c088aed558705552a
|
def add_contact_action(self, item):
'Метод обработчк нажатия кнопки "Добавить"'
new_contact = item.selector.currentText()
self.add_contact(new_contact)
item.close()
|
Метод обработчк нажатия кнопки "Добавить"
|
Lib/site-packages/client/main_window.py
|
add_contact_action
|
fochoao/cpython
| 0 |
python
|
def add_contact_action(self, item):
new_contact = item.selector.currentText()
self.add_contact(new_contact)
item.close()
|
def add_contact_action(self, item):
new_contact = item.selector.currentText()
self.add_contact(new_contact)
item.close()<|docstring|>Метод обработчк нажатия кнопки "Добавить"<|endoftext|>
|
d6abbc4dfc5b0888b57036ae18bb1db1e9087bbe5de987be236b99fab6c4f493
|
def add_contact(self, new_contact):
'\n Метод добавляющий контакт в серверную и клиентсткую BD.\n После обновления баз данных обновляет и содержимое окна.\n '
try:
self.transport.add_contact(new_contact)
except ServerError as err:
self.messages.critical(self, 'Ошибка сервера', err.text)
except OSError as err:
if err.errno:
self.messages.critical(self, 'Ошибка', 'Потеряно соединение с сервером!')
self.close()
self.messages.critical(self, 'Ошибка', 'Таймаут соединения!')
else:
self.database.add_contact(new_contact)
new_contact = QStandardItem(new_contact)
new_contact.setEditable(False)
self.contacts_model.appendRow(new_contact)
logger.info(f'Успешно добавлен контакт {new_contact}')
self.messages.information(self, 'Успех', 'Контакт успешно добавлен.')
|
Метод добавляющий контакт в серверную и клиентсткую BD.
После обновления баз данных обновляет и содержимое окна.
|
Lib/site-packages/client/main_window.py
|
add_contact
|
fochoao/cpython
| 0 |
python
|
def add_contact(self, new_contact):
'\n Метод добавляющий контакт в серверную и клиентсткую BD.\n После обновления баз данных обновляет и содержимое окна.\n '
try:
self.transport.add_contact(new_contact)
except ServerError as err:
self.messages.critical(self, 'Ошибка сервера', err.text)
except OSError as err:
if err.errno:
self.messages.critical(self, 'Ошибка', 'Потеряно соединение с сервером!')
self.close()
self.messages.critical(self, 'Ошибка', 'Таймаут соединения!')
else:
self.database.add_contact(new_contact)
new_contact = QStandardItem(new_contact)
new_contact.setEditable(False)
self.contacts_model.appendRow(new_contact)
logger.info(f'Успешно добавлен контакт {new_contact}')
self.messages.information(self, 'Успех', 'Контакт успешно добавлен.')
|
def add_contact(self, new_contact):
'\n Метод добавляющий контакт в серверную и клиентсткую BD.\n После обновления баз данных обновляет и содержимое окна.\n '
try:
self.transport.add_contact(new_contact)
except ServerError as err:
self.messages.critical(self, 'Ошибка сервера', err.text)
except OSError as err:
if err.errno:
self.messages.critical(self, 'Ошибка', 'Потеряно соединение с сервером!')
self.close()
self.messages.critical(self, 'Ошибка', 'Таймаут соединения!')
else:
self.database.add_contact(new_contact)
new_contact = QStandardItem(new_contact)
new_contact.setEditable(False)
self.contacts_model.appendRow(new_contact)
logger.info(f'Успешно добавлен контакт {new_contact}')
self.messages.information(self, 'Успех', 'Контакт успешно добавлен.')<|docstring|>Метод добавляющий контакт в серверную и клиентсткую BD.
После обновления баз данных обновляет и содержимое окна.<|endoftext|>
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.