code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def _solve_eigen(self, X, y, shrinkage):
"""Eigenvalue solver.
The eigenvalue solver computes the optimal solution of the Rayleigh
coefficient (basically the ratio of between class scatter to within
class scatter). This solver supports both classification and
dimensionality reduc... | Eigenvalue solver.
The eigenvalue solver computes the optimal solution of the Rayleigh
coefficient (basically the ratio of between class scatter to within
class scatter). This solver supports both classification and
dimensionality reduction (with optional shrinkage).
Parameters
... | _solve_eigen | python | Jingkang50/OpenOOD | openood/postprocessors/mds_ensemble_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/mds_ensemble_postprocessor.py | MIT |
def compute_channel_distances(mavs, features, eu_weight=0.5):
"""
Input:
mavs (channel, C)
features: (N, channel, C)
Output:
channel_distances: dict of distance distribution from MAV
for each channel.
"""
eucos_dists, eu_dists, cos_dists = [], [], []
for channel, ... |
Input:
mavs (channel, C)
features: (N, channel, C)
Output:
channel_distances: dict of distance distribution from MAV
for each channel.
| compute_channel_distances | python | Jingkang50/OpenOOD | openood/postprocessors/openmax_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/openmax_postprocessor.py | MIT |
def fit_weibull(means, dists, categories, tailsize=20, distance_type='eucos'):
"""
Input:
means (C, channel, C)
dists (N_c, channel, C) * C
Output:
weibull_model : Perform EVT based analysis using tails of distances
and save weibull model parameters for re-adj... |
Input:
means (C, channel, C)
dists (N_c, channel, C) * C
Output:
weibull_model : Perform EVT based analysis using tails of distances
and save weibull model parameters for re-adjusting
softmax scores
| fit_weibull | python | Jingkang50/OpenOOD | openood/postprocessors/openmax_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/openmax_postprocessor.py | MIT |
def openmax(weibull_model,
categories,
input_score,
eu_weight,
alpha=10,
distance_type='eucos'):
"""Re-calibrate scores via OpenMax layer
Output:
openmax probability and softmax probability
"""
nb_classes = len(categories)
ranked_l... | Re-calibrate scores via OpenMax layer
Output:
openmax probability and softmax probability
| openmax | python | Jingkang50/OpenOOD | openood/postprocessors/openmax_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/openmax_postprocessor.py | MIT |
def update_distances(self,
cluster_centers,
only_new=True,
reset_dist=False):
"""Update min distances given cluster centers.
Args:
cluster_centers: indices of cluster centers
only_new: only calculate distance... | Update min distances given cluster centers.
Args:
cluster_centers: indices of cluster centers
only_new: only calculate distance for newly selected points and
update min_distances.
rest_dist: whether to reset min_distances.
| update_distances | python | Jingkang50/OpenOOD | openood/postprocessors/patchcore_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/patchcore_postprocessor.py | MIT |
def select_batch_(self, model, already_selected, N, **kwargs):
"""Diversity promoting active learning method that greedily forms a
batch to minimize the maximum distance to a cluster center among all
unlabeled datapoints.
Args:
model: model with scikit-like API with decision_f... | Diversity promoting active learning method that greedily forms a
batch to minimize the maximum distance to a cluster center among all
unlabeled datapoints.
Args:
model: model with scikit-like API with decision_function implemented
already_selected: index of datapoints alread... | select_batch_ | python | Jingkang50/OpenOOD | openood/postprocessors/patchcore_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/patchcore_postprocessor.py | MIT |
def kernel(feat, feat_t, prob, prob_t, split=2):
"""Kernel function (assume feature is normalized)"""
size = ceil(len(feat_t) / split)
rel_full = []
for i in range(split):
feat_t_ = feat_t[i * size:(i + 1) * size]
prob_t_ = prob_t[i * size:(i + 1) * size]
with torch.no_grad():
... | Kernel function (assume feature is normalized) | kernel | python | Jingkang50/OpenOOD | openood/postprocessors/relation_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/relation_postprocessor.py | MIT |
def get_relation(feat, feat_t, prob, prob_t, pow=1, chunk=50, thres=0.03):
"""Get relation values (top-k and summation)
Args:
feat (torch.Tensor [N,D]): features of the source data
feat_t (torch.Tensor [N',D]): features of the target data
prob (torch.Tensor [N,C]): probabilty vectors of... | Get relation values (top-k and summation)
Args:
feat (torch.Tensor [N,D]): features of the source data
feat_t (torch.Tensor [N',D]): features of the target data
prob (torch.Tensor [N,C]): probabilty vectors of the source data
prob_t (torch.Tensor [N',C]): probabilty vectors of the t... | get_relation | python | Jingkang50/OpenOOD | openood/postprocessors/relation_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/relation_postprocessor.py | MIT |
def __call__(self, img):
"""
Args:
img (Tensor): Tensor image of size (C, H, W).
Returns:
Tensor: Image with n_holes of dimension length x length
cut out of it.
"""
h = img.size(1)
w = img.size(2)
mask = np.ones((h, w), np.floa... |
Args:
img (Tensor): Tensor image of size (C, H, W).
Returns:
Tensor: Image with n_holes of dimension length x length
cut out of it.
| __call__ | python | Jingkang50/OpenOOD | openood/preprocessors/cutout_preprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/preprocessors/cutout_preprocessor.py | MIT |
def get_similarity_matrix(outputs, chunk=2, multi_gpu=False):
"""Compute similarity matrix.
- outputs: (B', d) tensor for B' = B * chunk
- sim_matrix: (B', B') tensor
"""
if multi_gpu:
outputs_gathered = []
for out in outputs.chunk(chunk):
gather_t = [
t... | Compute similarity matrix.
- outputs: (B', d) tensor for B' = B * chunk
- sim_matrix: (B', B') tensor
| get_similarity_matrix | python | Jingkang50/OpenOOD | openood/trainers/csi_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/csi_trainer.py | MIT |
def Supervised_NT_xent(sim_matrix,
labels,
temperature=0.5,
chunk=2,
eps=1e-8,
multi_gpu=False):
"""Compute NT_xent loss.
- sim_matrix: (B', B') tensor for B' = B * chunk (first 2B are pos samples... | Compute NT_xent loss.
- sim_matrix: (B', B') tensor for B' = B * chunk (first 2B are pos samples)
| Supervised_NT_xent | python | Jingkang50/OpenOOD | openood/trainers/csi_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/csi_trainer.py | MIT |
def __init__(self, size=None, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.)):
"""Inception Crop size (tuple): size of forwarding image (C, W, H)
scale (tuple): range of size of the origin size cropped ratio (tuple):
range of aspect ratio of the origin aspect ratio cropped.
"""
sup... | Inception Crop size (tuple): size of forwarding image (C, W, H)
scale (tuple): range of size of the origin size cropped ratio (tuple):
range of aspect ratio of the origin aspect ratio cropped.
| __init__ | python | Jingkang50/OpenOOD | openood/trainers/csi_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/csi_trainer.py | MIT |
def __init__(self):
"""
img_size : (int, int, int)
Height and width must be powers of 2. E.g. (32, 32, 1) or
(64, 128, 3). Last number indicates number of channels, e.g. 1 for
grayscale or 3 for RGB
"""
super(HorizontalFlipLayer, self).__init__()
... |
img_size : (int, int, int)
Height and width must be powers of 2. E.g. (32, 32, 1) or
(64, 128, 3). Last number indicates number of channels, e.g. 1 for
grayscale or 3 for RGB
| __init__ | python | Jingkang50/OpenOOD | openood/trainers/csi_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/csi_trainer.py | MIT |
def init_center_c(train_loader, net, eps=0.1):
"""Initialize hypersphere center c as the mean from an initial forward pass
on the data."""
n_samples = 0
first_iter = True
train_dataiter = iter(train_loader)
net.eval()
with torch.no_grad():
for train_step in tqdm(range(1,
... | Initialize hypersphere center c as the mean from an initial forward pass
on the data. | init_center_c | python | Jingkang50/OpenOOD | openood/trainers/dsvdd_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/dsvdd_trainer.py | MIT |
def prepare_mixup(batch, alpha=1.0, use_cuda=True):
"""Returns mixed inputs, pairs of targets, and lambda."""
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = batch['data'].size()[0]
if use_cuda:
index = torch.randperm(batch_size).cuda()
else:
... | Returns mixed inputs, pairs of targets, and lambda. | prepare_mixup | python | Jingkang50/OpenOOD | openood/trainers/mixup_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/mixup_trainer.py | MIT |
def get_lr(step, dataset_size, base_lr=0.003):
"""Returns learning-rate for `step` or None at the end."""
supports = get_schedule(dataset_size)
# Linear warmup
if step < supports[0]:
return base_lr * step / supports[0]
# End of training
elif step >= supports[-1]:
return None
... | Returns learning-rate for `step` or None at the end. | get_lr | python | Jingkang50/OpenOOD | openood/trainers/mos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/mos_trainer.py | MIT |
def mixup_data(x, y, lam):
"""Returns mixed inputs, pairs of targets, and lambda."""
indices = torch.randperm(x.shape[0]).to(x.device)
mixed_x = lam * x + (1 - lam) * x[indices]
y_a, y_b = y, y[indices]
return mixed_x, y_a, y_b | Returns mixed inputs, pairs of targets, and lambda. | mixup_data | python | Jingkang50/OpenOOD | openood/trainers/mos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/mos_trainer.py | MIT |
def topk(output, target, ks=(1, )):
"""Returns one boolean vector for each k, whether the target is within the
output's top-k."""
_, pred = output.topk(max(ks), 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
return [correct[:k].max(0)[0] for k in ks] | Returns one boolean vector for each k, whether the target is within the
output's top-k. | topk | python | Jingkang50/OpenOOD | openood/trainers/mos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/mos_trainer.py | MIT |
def KNN_dis_search_distance(target,
index,
K=50,
num_points=10,
length=2000,
depth=342):
'''
data_point: Queue for searching k-th points
target: the target of the searc... |
data_point: Queue for searching k-th points
target: the target of the search
K
| KNN_dis_search_distance | python | Jingkang50/OpenOOD | openood/trainers/npos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/npos_trainer.py | MIT |
def KNN_dis_search_decrease(
target,
index,
K=50,
select=1,
):
'''
data_point: Queue for searching k-th points
target: the target of the search
K
'''
# Normalize the features
target_norm = torch.norm(target, p=2, dim=1, keepdim=True)
normed_target = target / target_norm
... |
data_point: Queue for searching k-th points
target: the target of the search
K
| KNN_dis_search_decrease | python | Jingkang50/OpenOOD | openood/trainers/npos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/npos_trainer.py | MIT |
def mixup_data(x, y, alpha=1.0):
"""Returns mixed inputs, pairs of targets, and lambda."""
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = x.size()[0]
index = torch.randperm(batch_size).cuda()
mixed_x = lam * x + (1 - lam) * x[index]
y_a, y_b = y... | Returns mixed inputs, pairs of targets, and lambda. | mixup_data | python | Jingkang50/OpenOOD | openood/trainers/regmixup_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/regmixup_trainer.py | MIT |
def preprocess_features(npdata, pca=256):
"""Preprocess an array of features.
Args:
npdata (np.array N * ndim): features to preprocess
pca (int): dim of output
Returns:
np.array of dim N * pca: data PCA-reduced, whitened and L2-normalized
"""
_, ndim = npdata.shape
npdata... | Preprocess an array of features.
Args:
npdata (np.array N * ndim): features to preprocess
pca (int): dim of output
Returns:
np.array of dim N * pca: data PCA-reduced, whitened and L2-normalized
| preprocess_features | python | Jingkang50/OpenOOD | openood/trainers/udg_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/udg_trainer.py | MIT |
def run_kmeans(x, nmb_clusters, verbose=False):
"""Runs kmeans on 1 GPU.
Args:
x: data
nmb_clusters (int): number of clusters
Returns:
list: ids of data in each cluster
"""
n_data, d = x.shape
# faiss implementation of k-means
clus = faiss.Clustering(d, nmb_clusters... | Runs kmeans on 1 GPU.
Args:
x: data
nmb_clusters (int): number of clusters
Returns:
list: ids of data in each cluster
| run_kmeans | python | Jingkang50/OpenOOD | openood/trainers/udg_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/udg_trainer.py | MIT |
def cluster(self, data, verbose=True):
"""Performs k-means clustering.
Args:
x_data (np.array N * dim): data to cluster
"""
# PCA-reducing, whitening and L2-normalization
xb = preprocess_features(data, pca=self.pca_dim)
if np.isnan(xb).any():
row_... | Performs k-means clustering.
Args:
x_data (np.array N * dim): data to cluster
| cluster | python | Jingkang50/OpenOOD | openood/trainers/udg_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/udg_trainer.py | MIT |
def log_sum_exp(value, num_classes=10, dim=None, keepdim=False):
"""Numerically stable implementation of the operation."""
value.exp().sum(dim, keepdim).log()
# TODO: torch.max(value, dim=None) threw an error at time of writing
weight_energy = torch.nn.Linear(num_classes, 1).cuda()
if dim is not No... | Numerically stable implementation of the operation. | log_sum_exp | python | Jingkang50/OpenOOD | openood/trainers/vos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/vos_trainer.py | MIT |
def get_local_rank() -> int:
"""
Returns:
The rank of the current process
within the local (per-machine) process group.
"""
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
assert (
_LOCAL_PROCESS_GROUP is not None
), 'Local ... |
Returns:
The rank of the current process
within the local (per-machine) process group.
| get_local_rank | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def get_local_size() -> int:
"""
Returns:
The size of the per-machine process group,
i.e. the number of processes per machine.
"""
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) |
Returns:
The size of the per-machine process group,
i.e. the number of processes per machine.
| get_local_size | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def synchronize():
"""Helper function to synchronize (barrier) among all processes when using
distributed training."""
if not dist.is_available():
return
if not dist.is_initialized():
return
world_size = dist.get_world_size()
if world_size == 1:
return
if dist.get_bac... | Helper function to synchronize (barrier) among all processes when using
distributed training. | synchronize | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def _get_global_gloo_group():
"""Return a process group based on gloo backend, containing all the ranks
The result is cached."""
if dist.get_backend() == 'nccl':
return dist.new_group(backend='gloo')
else:
return dist.group.WORLD | Return a process group based on gloo backend, containing all the ranks
The result is cached. | _get_global_gloo_group | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def all_gather(data, group=None):
"""Run all_gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
list[data]: list of ... | Run all_gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
list[data]: list of data gathered from each rank
| all_gather | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def gather(data, dst=0, group=None):
"""Run gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
dst (int): destination rank
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Re... | Run gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
dst (int): destination rank
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
list[data]: on dst, a list of... | gather | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def shared_random_seed():
"""
Returns:
int: a random number that is the same across all workers.
If workers need a shared RNG, they can use this shared seed to
create one.
All workers must call this function, otherwise it will deadlock.
"""
ints = np.random.randint(2**31)
... |
Returns:
int: a random number that is the same across all workers.
If workers need a shared RNG, they can use this shared seed to
create one.
All workers must call this function, otherwise it will deadlock.
| shared_random_seed | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def reduce_dict(input_dict, average=True):
"""Reduce the values in the dictionary from all processes so that process
with rank 0 has the reduced results.
Args:
input_dict (dict): inputs to be reduced.
All the values must be scalar CUDA Tensor.
average (bool): whether to do average o... | Reduce the values in the dictionary from all processes so that process
with rank 0 has the reduced results.
Args:
input_dict (dict): inputs to be reduced.
All the values must be scalar CUDA Tensor.
average (bool): whether to do average or sum
Returns:
a dict with the same k... | reduce_dict | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def setup_config(config_process_order=('merge', 'parse_args', 'parse_refs')):
"""Parsing configuration files and command line augments.
This method reads the command line to
1. extract and stack YAML config files,
2. collect modification in command line arguments,
so that the finalized conf... | Parsing configuration files and command line augments.
This method reads the command line to
1. extract and stack YAML config files,
2. collect modification in command line arguments,
so that the finalized configuration file is generated.
Note:
The default arguments allow the follo... | setup_config | python | Jingkang50/OpenOOD | openood/utils/config.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/config.py | MIT |
def launch(
main_func,
num_gpus_per_machine,
num_machines=1,
machine_rank=0,
dist_url=None,
args=(),
timeout=DEFAULT_TIMEOUT,
):
"""Launch multi-gpu or distributed training. This function must be called
on all machines involved in the training. It will spa... | Launch multi-gpu or distributed training. This function must be called
on all machines involved in the training. It will spawn child processes
(defined by ``num_gpus_per_machine``) on each machine.
Args:
main_func: a function that will be called by `main_func(*args)`
num_gpus_per_machine (i... | launch | python | Jingkang50/OpenOOD | openood/utils/launch.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/launch.py | MIT |
def mkdir_if_missing(dirname):
"""Create dirname if it is missing."""
if not osp.exists(dirname):
try:
os.makedirs(dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise | Create dirname if it is missing. | mkdir_if_missing | python | Jingkang50/OpenOOD | openood/utils/logger.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/logger.py | MIT |
def setup_logger(config):
"""generate exp directory to save configs, logger, checkpoints, etc.
Args:
config: all configs of the experiment
"""
print('------------------ Config --------------------------', flush=True)
print(config, flush=True)
print(u'\u2500' * 70, flush=True)
outpu... | generate exp directory to save configs, logger, checkpoints, etc.
Args:
config: all configs of the experiment
| setup_logger | python | Jingkang50/OpenOOD | openood/utils/logger.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/logger.py | MIT |
async def maigret(
username: str,
site_dict: Dict[str, MaigretSite],
logger,
query_notify=None,
proxy=None,
tor_proxy=None,
i2p_proxy=None,
timeout=3,
is_parsing_enabled=False,
id_type="username",
debug=False,
forced=False,
max_connections=100,
no_progressbar=Fals... | Main search func
Checks for existence of username on certain sites.
Keyword Arguments:
username -- Username string will be used for search.
site_dict -- Dictionary containing sites data in MaigretSite objects.
query_notify -- Object with base type of QueryNotif... | maigret | python | soxoj/maigret | maigret/checking.py | https://github.com/soxoj/maigret/blob/master/maigret/checking.py | MIT |
def timeout_check(value):
"""Check Timeout Argument.
Checks timeout for validity.
Keyword Arguments:
value -- Time in seconds to wait before timing out request.
Return Value:
Floating point number representing the time (in seconds) that should be
used for the timeout.
... | Check Timeout Argument.
Checks timeout for validity.
Keyword Arguments:
value -- Time in seconds to wait before timing out request.
Return Value:
Floating point number representing the time (in seconds) that should be
used for the timeout.
NOTE: Will raise an exception ... | timeout_check | python | soxoj/maigret | maigret/checking.py | https://github.com/soxoj/maigret/blob/master/maigret/checking.py | MIT |
def notify_about_errors(
search_results: QueryResultWrapper, query_notify, show_statistics=False
) -> List[Tuple]:
"""
Prepare error notifications in search results, text + symbol,
to be displayed by notify object.
Example:
[
("Too many errors of type "timeout" (50.0%)", "!")
("... |
Prepare error notifications in search results, text + symbol,
to be displayed by notify object.
Example:
[
("Too many errors of type "timeout" (50.0%)", "!")
("Verbose error statistics:", "-")
]
| notify_about_errors | python | soxoj/maigret | maigret/errors.py | https://github.com/soxoj/maigret/blob/master/maigret/errors.py | MIT |
async def increment_progress(self, count):
"""Update progress by calling the provided progress function."""
if self.progress:
if asyncio.iscoroutinefunction(self.progress):
await self.progress(count)
else:
self.progress(count)
await... | Update progress by calling the provided progress function. | increment_progress | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
async def worker(self):
"""Consume tasks from the queue and process them."""
while True:
try:
f, args, kwargs = self.queue.get_nowait()
except asyncio.QueueEmpty:
return
query_future = f(*args, **kwargs)
query_task = create... | Consume tasks from the queue and process them. | worker | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
async def _run(self, queries: Iterable[QueryDraft]):
"""Main runner function to execute tasks with progress tracking."""
self.results: List[Any] = []
queries_list = list(queries)
min_workers = min(len(queries_list), self.workers_count)
workers = [create_task_func()(self.worker())... | Main runner function to execute tasks with progress tracking. | _run | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
async def worker(self):
"""Process tasks from the queue and put results into the results queue."""
while True:
task = await self.queue.get()
if task is self._stop_signal:
self.queue.task_done()
break
try:
f, args, kwarg... | Process tasks from the queue and put results into the results queue. | worker | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
async def run(self, queries: Iterable[Callable[..., Any]]):
"""Run workers to process queries in parallel."""
start_time = time.time()
# Add tasks to the queue
for t in queries:
await self.queue.put(t)
# Create workers
workers = [
asyncio.create_... | Run workers to process queries in parallel. | run | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
def __init__(
self,
result=None,
verbose=False,
print_found_only=False,
skip_check_errors=False,
color=True,
):
"""Create Query Notify Print Object.
Contains information about a specific method of notifying the results
of a query.
Key... | Create Query Notify Print Object.
Contains information about a specific method of notifying the results
of a query.
Keyword Arguments:
self -- This object.
result -- Object of type QueryResult() containing
resu... | __init__ | python | soxoj/maigret | maigret/notify.py | https://github.com/soxoj/maigret/blob/master/maigret/notify.py | MIT |
def start(self, message, id_type):
"""Notify Start.
Will print the title to the standard output.
Keyword Arguments:
self -- This object.
message -- String containing username that the series
of queries are about... | Notify Start.
Will print the title to the standard output.
Keyword Arguments:
self -- This object.
message -- String containing username that the series
of queries are about.
Return Value:
Nothing.
... | start | python | soxoj/maigret | maigret/notify.py | https://github.com/soxoj/maigret/blob/master/maigret/notify.py | MIT |
def update(self, result, is_similar=False):
"""Notify Update.
Will print the query result to the standard output.
Keyword Arguments:
self -- This object.
result -- Object of type QueryResult() containing
result... | Notify Update.
Will print the query result to the standard output.
Keyword Arguments:
self -- This object.
result -- Object of type QueryResult() containing
results for this query.
Return Value:
Nothin... | update | python | soxoj/maigret | maigret/notify.py | https://github.com/soxoj/maigret/blob/master/maigret/notify.py | MIT |
def __init__(
self,
username,
site_name,
site_url_user,
status,
ids_data=None,
query_time=None,
context=None,
error=None,
tags=[],
):
"""
Keyword Arguments:
self -- This object.
username... |
Keyword Arguments:
self -- This object.
username -- String indicating username that query result
was about.
site_name -- String which identifies site.
site_url_user -- String containing URL f... | __init__ | python | soxoj/maigret | maigret/result.py | https://github.com/soxoj/maigret/blob/master/maigret/result.py | MIT |
def __str__(self):
"""Convert Object To String.
Keyword Arguments:
self -- This object.
Return Value:
Nicely formatted string to get information about this object.
"""
status = str(self.status)
if self.context is not None:
#... | Convert Object To String.
Keyword Arguments:
self -- This object.
Return Value:
Nicely formatted string to get information about this object.
| __str__ | python | soxoj/maigret | maigret/result.py | https://github.com/soxoj/maigret/blob/master/maigret/result.py | MIT |
def extract_id_from_url(self, url: str) -> Optional[Tuple[str, str]]:
"""
Extracts username from url.
It's outdated, detects only a format of https://example.com/{username}
"""
if not self.url_regexp:
return None
match_groups = self.url_regexp.match(url)
... |
Extracts username from url.
It's outdated, detects only a format of https://example.com/{username}
| extract_id_from_url | python | soxoj/maigret | maigret/sites.py | https://github.com/soxoj/maigret/blob/master/maigret/sites.py | MIT |
def ranked_sites_dict(
self,
reverse=False,
top=sys.maxsize,
tags=[],
names=[],
disabled=True,
id_type="username",
):
"""
Ranking and filtering of the sites list
Args:
reverse (bool, optional): Reverse the sorting order. De... |
Ranking and filtering of the sites list
Args:
reverse (bool, optional): Reverse the sorting order. Defaults to False.
top (int, optional): Maximum number of sites to return. Defaults to sys.maxsize.
tags (list, optional): List of tags to filter sites by. Defaults to... | ranked_sites_dict | python | soxoj/maigret | maigret/sites.py | https://github.com/soxoj/maigret/blob/master/maigret/sites.py | MIT |
def _format_top_items(
self, title, items_dict, limit, is_markdown, valid_items=None
):
"""Helper method to format top items lists"""
output = f"Top {limit} {title}:\n"
for item, count in sorted(items_dict.items(), key=lambda x: x[1], reverse=True)[
:limit
]:
... | Helper method to format top items lists | _format_top_items | python | soxoj/maigret | maigret/sites.py | https://github.com/soxoj/maigret/blob/master/maigret/sites.py | MIT |
def attribute(self, attribute_name, db=None, default=None): # type: (str, CanMatrix, typing.Any) -> typing.Any
"""Get Board unit attribute by its name.
:param str attribute_name: attribute name.
:param CanMatrix db: Optional database parameter to get global default attribute value.
:pa... | Get Board unit attribute by its name.
:param str attribute_name: attribute name.
:param CanMatrix db: Optional database parameter to get global default attribute value.
:param default: Default value if attribute doesn't exist.
:return: Return the attribute value if found, else `default`... | attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_attribute(self, attribute, value): # type (attribute: str, value: typing.Any) -> None
"""
Add the Attribute to current ECU. If the attribute already exists, update the value.
:param str attribute: Attribute name
:param any value: Attribute value
"""
try:
... |
Add the Attribute to current ECU. If the attribute already exists, update the value.
:param str attribute: Attribute name
:param any value: Attribute value
| add_attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def attribute(self, attributeName, db=None, default=None):
# type: (str, CanMatrix, typing.Any) -> typing.Any
"""Get any Signal attribute by its name.
:param str attributeName: attribute name, can be mandatory (ex: start_bit, size) or optional (customer) attribute.
:param CanMatrix db: ... | Get any Signal attribute by its name.
:param str attributeName: attribute name, can be mandatory (ex: start_bit, size) or optional (customer) attribute.
:param CanMatrix db: Optional database parameter to get global default attribute value.
:param default: Default value if attribute doesn't exi... | attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_attribute(self, attribute, value):
"""
Add user defined attribute to the Signal. Update the value if the attribute already exists.
:param str attribute: attribute name
:param value: attribute value
"""
try:
self.attributes[attribute] = str(value)
... |
Add user defined attribute to the Signal. Update the value if the attribute already exists.
:param str attribute: attribute name
:param value: attribute value
| add_attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_values(self, value, valueName):
"""
Add named Value Description to the Signal.
:param int or str value: signal value (0xFF)
:param str valueName: Human readable value description ("Init")
"""
if isinstance(value, defaultFloatFactory):
self.values[valu... |
Add named Value Description to the Signal.
:param int or str value: signal value (0xFF)
:param str valueName: Human readable value description ("Init")
| add_values | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def set_startbit(self, start_bit, bitNumbering=None, startLittle=None):
"""
Set start_bit.
bitNumbering is 1 for LSB0/LSBFirst, 0 for MSB0/MSBFirst.
If bit numbering is consistent with byte order (little=LSB0, big=MSB0)
(KCD, SYM), start bit unmodified.
Otherwise reverse... |
Set start_bit.
bitNumbering is 1 for LSB0/LSBFirst, 0 for MSB0/MSBFirst.
If bit numbering is consistent with byte order (little=LSB0, big=MSB0)
(KCD, SYM), start bit unmodified.
Otherwise reverse bit numbering. For DBC, ArXML (OSEK),
both little endian and big endian us... | set_startbit | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def get_startbit(self, bit_numbering=None, start_little=None):
"""Get signal start bit. Handle byte and bit order."""
startBitInternal = self.start_bit
# convert from big endian start bit at
# start bit(msbit) to end bit(lsbit)
if start_little is True and self.is_little_endian is... | Get signal start bit. Handle byte and bit order. | get_startbit | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def calculate_raw_range(self):
"""Compute raw signal range based on Signal bit width and whether the Signal is signed or not.
:return: Signal range, i.e. (0, 15) for unsigned 4 bit Signal or (-8, 7) for signed one.
:rtype: tuple
"""
factory = (
self.float_factory
... | Compute raw signal range based on Signal bit width and whether the Signal is signed or not.
:return: Signal range, i.e. (0, 15) for unsigned 4 bit Signal or (-8, 7) for signed one.
:rtype: tuple
| calculate_raw_range | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def set_min(self, min=None):
# type: (canmatrix.types.OptionalPhysicalValue) -> canmatrix.types.OptionalPhysicalValue
"""Set minimal physical Signal value.
:param min: minimal physical value. If None and enabled (`calc_min_for_none`), compute using `calc_min`
"""
self.min = min
... | Set minimal physical Signal value.
:param min: minimal physical value. If None and enabled (`calc_min_for_none`), compute using `calc_min`
| set_min | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def calc_min(self): # type: () -> canmatrix.types.PhysicalValue
"""Compute minimal physical Signal value based on offset and factor and `calculate_raw_range`."""
rawMin = self.calculate_raw_range()[0]
return self.offset + (self.float_factory(rawMin) * self.factor) | Compute minimal physical Signal value based on offset and factor and `calculate_raw_range`. | calc_min | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def set_max(self, max=None):
# type: (canmatrix.types.OptionalPhysicalValue) -> canmatrix.types.OptionalPhysicalValue
"""Set maximal signal value.
:param max: minimal physical value. If None and enabled (`calc_max_for_none`), compute using `calc_max`
"""
self.max = max
... | Set maximal signal value.
:param max: minimal physical value. If None and enabled (`calc_max_for_none`), compute using `calc_max`
| set_max | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def calc_max(self): # type: () -> canmatrix.types.PhysicalValue
"""Compute maximal physical Signal value based on offset, factor and `calculate_raw_range`."""
rawMax = self.calculate_raw_range()[1]
return self.offset + (self.float_factory(rawMax) * self.factor) | Compute maximal physical Signal value based on offset, factor and `calculate_raw_range`. | calc_max | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def phys2raw(self, value=None):
# type: (canmatrix.types.OptionalPhysicalValue) -> canmatrix.types.RawValue
"""Return the raw value (= as is on CAN).
:param value: (scaled) value compatible with `decimal` or value choice to encode
:return: raw unscaled value as it appears on the bus
... | Return the raw value (= as is on CAN).
:param value: (scaled) value compatible with `decimal` or value choice to encode
:return: raw unscaled value as it appears on the bus
:rtype: int or decimal.Decimal
| phys2raw | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def raw2phys(self, value, decode_to_str=False):
# type: (canmatrix.types.RawValue, bool) -> typing.Union[canmatrix.types.PhysicalValue, str]
"""Decode the given raw value (= as is on CAN).
:param value: raw value compatible with `decimal`.
:param bool decode_to_str: If True, try to get ... | Decode the given raw value (= as is on CAN).
:param value: raw value compatible with `decimal`.
:param bool decode_to_str: If True, try to get value representation as *string* ('Init' etc.)
:return: physical value (scaled)
| raw2phys | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def by_name(self, name): # type: (str) -> typing.Union[Signal, None]
"""
Find a Signal in the group by Signal name.
:param str name: Signal name to find
:return: signal contained in the group identified by name
:rtype: Signal
"""
for test in self.signals:
... |
Find a Signal in the group by Signal name.
:param str name: Signal name to find
:return: signal contained in the group identified by name
:rtype: Signal
| by_name | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def grouper(iterable, n, fillvalue=None):
"""Collect data into fixed-length chunks or blocks."""
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue) | Collect data into fixed-length chunks or blocks. | grouper | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def unpack_bitstring(length, is_float, is_signed, bits):
# type: (int, bool, bool, typing.Any) -> typing.Union[float, int]
"""
returns a value calculated from bits
:param length: length of signal in bits
:param is_float: value is float
:param bits: value as bits (array/iterable)
:param is_si... |
returns a value calculated from bits
:param length: length of signal in bits
:param is_float: value is float
:param bits: value as bits (array/iterable)
:param is_signed: value is signed
:return:
| unpack_bitstring | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def pack_bitstring(length, is_float, value, signed):
"""
returns a value in bits
:param length: length of signal in bits
:param is_float: value is float
:param value: value to encode
:param signed: value is signed
:return:
"""
if is_float:
types = {
32: '>f',
... |
returns a value in bits
:param length: length of signal in bits
:param is_float: value is float
:param value: value to encode
:param signed: value is signed
:return:
| pack_bitstring | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_signal(self, signal):
# type: (Signal) -> Signal
"""
Add Signal to Pdu.
:param Signal signal: Signal to be added.
:return: the signal added.
"""
self.signals.append(signal)
return self.signals[len(self.signals) - 1] |
Add Signal to Pdu.
:param Signal signal: Signal to be added.
:return: the signal added.
| add_signal | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_signal_group(self,
Name: str,
Id: int,
signalNames: typing.Sequence[str],
e2e_properties: typing.Optional[AutosarE2EProperties] = None) -> None:
"""Add new SignalGroup to the Frame. Add given signals ... | Add new SignalGroup to the Frame. Add given signals to the group.
:param str Name: Group name
:param int Id: Group id
:param list of str signalNames: list of Signal names to add. Non existing names are ignored.
| add_signal_group | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def signal_by_name(self, name):
# type: (str) -> typing.Union[Signal, None]
"""
Get signal by name.
:param str name: signal name to be found.
:return: signal with given name or None if not found
"""
for signal in self.signals:
if signal.name == name:
... |
Get signal by name.
:param str name: signal name to be found.
:return: signal with given name or None if not found
| signal_by_name | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def is_multiplexed(self): # type: () -> bool
"""Frame is multiplexed if at least one of its signals is a multiplexer."""
for sig in self.signals:
if sig.is_multiplexer:
return True
return False | Frame is multiplexed if at least one of its signals is a multiplexer. | is_multiplexed | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def get_multiplexer(self): # type: () -> typing.Union[Signal, None]
"""get multiplexer signal if any in frame."""
for sig in self.signals:
if sig.is_multiplexer:
return sig
return None | get multiplexer signal if any in frame. | get_multiplexer | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def get_signals_for_multiplexer_value(self, mux_value):
# type: (int) -> typing.Sequence[Signal]
"""Find Frame Signals by given muxer value.
:param int mux_value: muxer value
:return: list of signals relevant for given muxer value.
:rtype: list of signals
"""
muxe... | Find Frame Signals by given muxer value.
:param int mux_value: muxer value
:return: list of signals relevant for given muxer value.
:rtype: list of signals
| get_signals_for_multiplexer_value | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def effective_cycle_time(self):
"""Calculate effective cycle time for frame, depending on singal cycle times"""
min_cycle_time_list = [y for y in [x.cycle_time for x in self.signals] + [self.cycle_time] if y != 0]
if len(min_cycle_time_list) == 0:
return 0
elif len(min_cycle_... | Calculate effective cycle time for frame, depending on singal cycle times | effective_cycle_time | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def attribute(self, attribute_name, db=None, default=None):
# type: (str, typing.Optional[CanMatrix], typing.Any) -> typing.Any
"""Get any Frame attribute by its name.
:param str attribute_name: attribute name, can be mandatory (ex: id) or optional (customer) attribute.
:param CanMatrix... | Get any Frame attribute by its name.
:param str attribute_name: attribute name, can be mandatory (ex: id) or optional (customer) attribute.
:param CanMatrix db: Optional database parameter to get global default attribute value.
:param default: Default value if attribute doesn't exist.
:... | attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_signal_group(self,
Name: str,
Id: int,
signalNames: typing.Sequence[str],
e2e_properties: typing.Optional[AutosarE2EProperties] = None) -> None:
"""Add new SignalGroup to the Frame. Add given signals t... | Add new SignalGroup to the Frame. Add given signals to the group.
:param str Name: Group name
:param int Id: Group id
:param list of str signalNames: list of Signal names to add. Non existing names are ignored.
| add_signal_group | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def signal_group_by_name(self, name):
# type: (str) -> typing.Union[SignalGroup, None]
"""Get signal group.
:param str name: group name
:return: SignalGroup by name or None if not found.
:rtype: SignalGroup
"""
for signalGroup in self.signalGroups:
if... | Get signal group.
:param str name: group name
:return: SignalGroup by name or None if not found.
:rtype: SignalGroup
| signal_group_by_name | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_pdu(self, pdu):
# type: (Pdu) -> Pdu
"""
Add Pdu to Frame.
:param Pdu pdu: Pdu to be added.
:return: the pdu added.
"""
self.pdus.append(pdu)
return self.pdus[len(self.pdus) - 1] |
Add Pdu to Frame.
:param Pdu pdu: Pdu to be added.
:return: the pdu added.
| add_pdu | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def pdu_by_name(self, name):
# type: (str) -> typing.Union[Pdu, None]
"""Get PDU.
:param str name: PDU name
:return: PDU by name or None if not found.
:rtype: Pdu
"""
for pdu in self.pdus:
if pdu.name == name:
return pdu
return... | Get PDU.
:param str name: PDU name
:return: PDU by name or None if not found.
:rtype: Pdu
| pdu_by_name | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def pdu_by_id(self, pdu_id):
# type: (int) -> typing.Union[Pdu, None]
"""Get PDU.
:param int pdu_id: PDU id
:return: PDU by id or None if not found.
:rtype: Pdu
"""
for pdu in self.pdus:
if pdu.id == pdu_id:
return pdu
return N... | Get PDU.
:param int pdu_id: PDU id
:return: PDU by id or None if not found.
:rtype: Pdu
| pdu_by_id | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_signal(self, signal):
# type: (Signal) -> Signal
"""
Add Signal to Frame.
:param Signal signal: Signal to be added.
:return: the signal added.
"""
self.signals.append(signal)
return self.signals[len(self.signals) - 1] |
Add Signal to Frame.
:param Signal signal: Signal to be added.
:return: the signal added.
| add_signal | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_receiver(self, receiver):
# type: (str) -> None
"""Add receiver ECU Name to Frame.
:param str receiver: receiver name
"""
if receiver not in self.receivers:
self.receivers.append(receiver) | Add receiver ECU Name to Frame.
:param str receiver: receiver name
| add_receiver | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def signal_by_name(self, name):
# type: (str) -> typing.Union[Signal, None]
"""
Get signal by name.
:param str name: signal name to be found.
:return: signal with given name or None if not found
"""
for signal in self.signals:
if signal.name == name:
... |
Get signal by name.
:param str name: signal name to be found.
:return: signal with given name or None if not found
| signal_by_name | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def glob_signals(self, glob_str):
# type: (str) -> typing.Sequence[Signal]
"""Find Frame Signals by given glob pattern.
:param str glob_str: glob pattern for signal name. See `fnmatch.fnmatchcase`
:return: list of Signals by glob pattern.
:rtype: list of Signal
"""
... | Find Frame Signals by given glob pattern.
:param str glob_str: glob pattern for signal name. See `fnmatch.fnmatchcase`
:return: list of Signals by glob pattern.
:rtype: list of Signal
| glob_signals | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_attribute(self, attribute, value):
# type: (str, typing.Any) -> None
"""
Add the attribute with value to customer Frame attribute-list. If Attribute already exits, modify its value.
:param str attribute: Attribute name
:param any value: attribute value
"""
... |
Add the attribute with value to customer Frame attribute-list. If Attribute already exits, modify its value.
:param str attribute: Attribute name
:param any value: attribute value
| add_attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def del_attribute(self, attribute):
# type: (str) -> typing.Any
"""
Remove attribute from customer Frame attribute-list.
:param str attribute: Attribute name
"""
if attribute in self.attributes:
del self.attributes[attribute] |
Remove attribute from customer Frame attribute-list.
:param str attribute: Attribute name
| del_attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_comment(self, comment):
# type: (str) -> None
"""
Set Frame comment.
:param str comment: Frame comment
"""
self.comment = comment |
Set Frame comment.
:param str comment: Frame comment
| add_comment | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def calc_dlc(self):
# type: () -> None
"""
Compute minimal Frame DLC (length) based on its Signals
:return: Message DLC
"""
max_bit = 0
for sig in self.signals:
if sig.get_startbit() + int(sig.size) > max_bit:
max_bit = sig.get_startbi... |
Compute minimal Frame DLC (length) based on its Signals
:return: Message DLC
| calc_dlc | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def fit_dlc(self):
"""
Compute next allowed DLC (length) for current Frame
"""
max_byte = self.size
last_size = 8
for max_size in [12, 16, 20, 24, 32, 48, 64]:
if max_byte > last_size and max_byte < max_size:
self.size = max_size
... |
Compute next allowed DLC (length) for current Frame
| fit_dlc | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def get_frame_layout(self):
# type: () -> typing.Sequence[typing.Sequence[str]]
"""
get layout of frame.
Represents the bit usage in the frame by means of a list with n items (n bits of frame length).
Every item represents one bit and contains a list of signals (object refs) wit... |
get layout of frame.
Represents the bit usage in the frame by means of a list with n items (n bits of frame length).
Every item represents one bit and contains a list of signals (object refs) with each signal, occupying that bit.
Bits with empty list are unused.
Example: [[], ... | get_frame_layout | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def create_dummy_signals(self): # type: () -> None
"""Create big-endian dummy signals for unused bits.
Names of dummy signals are *_Dummy_<frame.name>_<index>*
"""
bitfield = self.get_frame_layout()
startBit = -1
sigCount = 0
for index, bit_signals in enumerate(... | Create big-endian dummy signals for unused bits.
Names of dummy signals are *_Dummy_<frame.name>_<index>*
| create_dummy_signals | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def update_receiver(self): # type: () -> None
"""
Collect Frame receivers out of receiver given in each signal. Add them to `self.receiver` list.
"""
self.receivers = []
for sig in self.signals:
for receiver in sig.receivers:
self.add_receiver(receive... |
Collect Frame receivers out of receiver given in each signal. Add them to `self.receiver` list.
| update_receiver | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def signals_to_bytes(self, data):
# type: (typing.Mapping[str, canmatrix.types.RawValue]) -> bytes
"""Return a byte string containing the values from data packed
according to the frame format.
:param data: data dictionary of signal : rawValue
:return: A byte string of the packed... | Return a byte string containing the values from data packed
according to the frame format.
:param data: data dictionary of signal : rawValue
:return: A byte string of the packed values.
| signals_to_bytes | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def encode(self, data=None):
# type: (typing.Optional[typing.Mapping[str, typing.Any]]) -> bytes
"""Return a byte string containing the values from data packed
according to the frame format.
:param dict data: data dictionary
:return: A byte string of the packed values.
"... | Return a byte string containing the values from data packed
according to the frame format.
:param dict data: data dictionary
:return: A byte string of the packed values.
| encode | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def bytes_to_bitstrings(data):
# type: (bytes) -> typing.Tuple[str, str]
"""Return two arrays big and little containing bits of given data (bytearray)
:param data: bytearray of bits (little endian).
i.e. bytearray([0xA1, 0xA2, 0xA3, 0xA4, 0xA5, 0xA6, 0xA7, 0xA8])
:return: bi... | Return two arrays big and little containing bits of given data (bytearray)
:param data: bytearray of bits (little endian).
i.e. bytearray([0xA1, 0xA2, 0xA3, 0xA4, 0xA5, 0xA6, 0xA7, 0xA8])
:return: bit arrays in big and little byteorder
| bytes_to_bitstrings | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def bitstring_to_signal_list(signals, big, little, size):
# type: (typing.Sequence[Signal], str, str, int) -> typing.Sequence[canmatrix.types.RawValue]
"""Return OrderedDictionary with Signal Name: object decodedSignal (flat / without support for multiplexed frames)
:param signals: Iterable of ... | Return OrderedDictionary with Signal Name: object decodedSignal (flat / without support for multiplexed frames)
:param signals: Iterable of signals (class signal) to decode from frame.
:param big: bytearray of bits (big endian).
:param little: bytearray of bits (little endian).
:param s... | bitstring_to_signal_list | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.