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 |
|---|---|---|---|---|---|---|---|---|---|
701e355f58511927e5dc1ef3fd3948ebabedc9f2d2d45c252e8578dfd751d8ab | def get_gender(text):
'\n Determine gender by the count of referring pronouns in the biography (he, she,\n they).\n '
he_count = len(re.findall(' he ', text.lower()))
she_count = len(re.findall(' she ', text.lower()))
they_count = len(re.findall(' they ', text.lower()))
if (he_count == max(... | Determine gender by the count of referring pronouns in the biography (he, she,
they). | parlai/tasks/md_gender/wikipedia.py | get_gender | justinbuzzni/ParlAI | 9,228 | python | def get_gender(text):
'\n Determine gender by the count of referring pronouns in the biography (he, she,\n they).\n '
he_count = len(re.findall(' he ', text.lower()))
she_count = len(re.findall(' she ', text.lower()))
they_count = len(re.findall(' they ', text.lower()))
if (he_count == max(... | def get_gender(text):
'\n Determine gender by the count of referring pronouns in the biography (he, she,\n they).\n '
he_count = len(re.findall(' he ', text.lower()))
she_count = len(re.findall(' she ', text.lower()))
they_count = len(re.findall(' they ', text.lower()))
if (he_count == max(... |
d9480908cfca9575d02be97011ece6639eeed60079ebdaac3bfda40db171c705 | def get_num_samples(self, opt) -> int:
'\n Return the number of samples given the datatype.\n '
datatype = opt['datatype']
if ('train' in datatype):
return (12774693, 12774693)
elif ('valid' in datatype):
return (7410, 7410)
else:
return (7441, 7441) | Return the number of samples given the datatype. | parlai/tasks/md_gender/wikipedia.py | get_num_samples | justinbuzzni/ParlAI | 9,228 | python | def get_num_samples(self, opt) -> int:
'\n \n '
datatype = opt['datatype']
if ('train' in datatype):
return (12774693, 12774693)
elif ('valid' in datatype):
return (7410, 7410)
else:
return (7441, 7441) | def get_num_samples(self, opt) -> int:
'\n \n '
datatype = opt['datatype']
if ('train' in datatype):
return (12774693, 12774693)
elif ('valid' in datatype):
return (7410, 7410)
else:
return (7441, 7441)<|docstring|>Return the number of samples given the datatype... |
4ee56bc06f664f11507c88d301290c972a0fa571c51000f3e974abc588105c4d | def get_fold_chunks(self, opt) -> List[int]:
'\n Return a list of chunk IDs (integer).\n\n Given the datatype (train/test/valid), return the list of chunk IDs that\n correspond to that split.\n '
datatype = opt['datatype']
all_chunk_idxs = list(self.chunk_idx_to_file.keys())
... | Return a list of chunk IDs (integer).
Given the datatype (train/test/valid), return the list of chunk IDs that
correspond to that split. | parlai/tasks/md_gender/wikipedia.py | get_fold_chunks | justinbuzzni/ParlAI | 9,228 | python | def get_fold_chunks(self, opt) -> List[int]:
'\n Return a list of chunk IDs (integer).\n\n Given the datatype (train/test/valid), return the list of chunk IDs that\n correspond to that split.\n '
datatype = opt['datatype']
all_chunk_idxs = list(self.chunk_idx_to_file.keys())
... | def get_fold_chunks(self, opt) -> List[int]:
'\n Return a list of chunk IDs (integer).\n\n Given the datatype (train/test/valid), return the list of chunk IDs that\n correspond to that split.\n '
datatype = opt['datatype']
all_chunk_idxs = list(self.chunk_idx_to_file.keys())
... |
63183ec47de33654fcdeb8f6d36fdf522be01085090bfdc3e28078eacc0ff407 | def load_from_chunk(self, chunk_idx: int):
'\n [Abstract] Given the chunk index, load examples from that chunk.\n\n Return a list of tuples. The function `_create_message` will take these tuples\n to form the Message object that is returned by the teacher.\n '
output = []
chunk_p... | [Abstract] Given the chunk index, load examples from that chunk.
Return a list of tuples. The function `_create_message` will take these tuples
to form the Message object that is returned by the teacher. | parlai/tasks/md_gender/wikipedia.py | load_from_chunk | justinbuzzni/ParlAI | 9,228 | python | def load_from_chunk(self, chunk_idx: int):
'\n [Abstract] Given the chunk index, load examples from that chunk.\n\n Return a list of tuples. The function `_create_message` will take these tuples\n to form the Message object that is returned by the teacher.\n '
output = []
chunk_p... | def load_from_chunk(self, chunk_idx: int):
'\n [Abstract] Given the chunk index, load examples from that chunk.\n\n Return a list of tuples. The function `_create_message` will take these tuples\n to form the Message object that is returned by the teacher.\n '
output = []
chunk_p... |
8f600444dec219adfb0944d852f3824c30ccec6d901b1e6584a99bfb0790216c | def create_message(self, queue_output, entry_idx=0) -> 'Message':
'\n [Abstract] Given the tuple output of the queue, return an act.\n '
(par, title, lbl, gender, class_type) = queue_output
if (self.class_task == 'all'):
if (class_type == 'self'):
labels = gend_utils.UNKNOW... | [Abstract] Given the tuple output of the queue, return an act. | parlai/tasks/md_gender/wikipedia.py | create_message | justinbuzzni/ParlAI | 9,228 | python | def create_message(self, queue_output, entry_idx=0) -> 'Message':
'\n \n '
(par, title, lbl, gender, class_type) = queue_output
if (self.class_task == 'all'):
if (class_type == 'self'):
labels = gend_utils.UNKNOWN_LABELS['self']
else:
labels = [lbl]
... | def create_message(self, queue_output, entry_idx=0) -> 'Message':
'\n \n '
(par, title, lbl, gender, class_type) = queue_output
if (self.class_task == 'all'):
if (class_type == 'self'):
labels = gend_utils.UNKNOWN_LABELS['self']
else:
labels = [lbl]
... |
c617395fbaded790b7446ff86b5cfb8f5f3e51a20508c1c2b9dcd9cd326782da | def next_redirect(request, default, default_view, **get_kwargs):
'\n Handle the "where should I go next?" part of comment views.\n\n The next value could be a kwarg to the function (``default``), or a\n ``?next=...`` GET arg, or the URL of a given view (``default_view``). See\n the view modules for exam... | Handle the "where should I go next?" part of comment views.
The next value could be a kwarg to the function (``default``), or a
``?next=...`` GET arg, or the URL of a given view (``default_view``). See
the view modules for examples.
Returns an ``HttpResponseRedirect``. | django/contrib/comments/views/utils.py | next_redirect | riklaunim/django-custom-multisite | 790 | python | def next_redirect(request, default, default_view, **get_kwargs):
'\n Handle the "where should I go next?" part of comment views.\n\n The next value could be a kwarg to the function (``default``), or a\n ``?next=...`` GET arg, or the URL of a given view (``default_view``). See\n the view modules for exam... | def next_redirect(request, default, default_view, **get_kwargs):
'\n Handle the "where should I go next?" part of comment views.\n\n The next value could be a kwarg to the function (``default``), or a\n ``?next=...`` GET arg, or the URL of a given view (``default_view``). See\n the view modules for exam... |
72051ad9c4e52cae5e3609ae52e1be5fbba4d9f0c2d2dcdb3f4474c4ecc94ad5 | def confirmation_view(template, doc='Display a confirmation view.'):
'\n Confirmation view generator for the "comment was\n posted/flagged/deleted/approved" views.\n '
def confirmed(request):
comment = None
if ('c' in request.GET):
try:
comment = comments.ge... | Confirmation view generator for the "comment was
posted/flagged/deleted/approved" views. | django/contrib/comments/views/utils.py | confirmation_view | riklaunim/django-custom-multisite | 790 | python | def confirmation_view(template, doc='Display a confirmation view.'):
'\n Confirmation view generator for the "comment was\n posted/flagged/deleted/approved" views.\n '
def confirmed(request):
comment = None
if ('c' in request.GET):
try:
comment = comments.ge... | def confirmation_view(template, doc='Display a confirmation view.'):
'\n Confirmation view generator for the "comment was\n posted/flagged/deleted/approved" views.\n '
def confirmed(request):
comment = None
if ('c' in request.GET):
try:
comment = comments.ge... |
f9a20607dd79ab87c610c28c2d625a459c4e9e18d4a741442dd48240ead16d7d | def _init_lazy_if_proper(results, lazy):
'Initialize lazy operation properly.\n\n Make sure that a lazy operation is properly initialized,\n and avoid a non-lazy operation accidentally getting mixed in.\n\n Required keys in results are "imgs" if "img_shape" not in results,\n otherwise, Required keys in ... | Initialize lazy operation properly.
Make sure that a lazy operation is properly initialized,
and avoid a non-lazy operation accidentally getting mixed in.
Required keys in results are "imgs" if "img_shape" not in results,
otherwise, Required keys in results are "img_shape", add or modified keys
are "img_shape", "lazy... | mmaction/datasets/pipelines/augmentations.py | _init_lazy_if_proper | dumbPy/Video-Swin-Transformer | 648 | python | def _init_lazy_if_proper(results, lazy):
'Initialize lazy operation properly.\n\n Make sure that a lazy operation is properly initialized,\n and avoid a non-lazy operation accidentally getting mixed in.\n\n Required keys in results are "imgs" if "img_shape" not in results,\n otherwise, Required keys in ... | def _init_lazy_if_proper(results, lazy):
'Initialize lazy operation properly.\n\n Make sure that a lazy operation is properly initialized,\n and avoid a non-lazy operation accidentally getting mixed in.\n\n Required keys in results are "imgs" if "img_shape" not in results,\n otherwise, Required keys in ... |
e3f962c90ab210b1b5a9d3a97a06aea49781237919ae6e565f1afaf519dc2748 | @staticmethod
def default_transforms():
"Default transforms for imgaug.\n\n Implement RandAugment by imgaug.\n Plase visit `https://arxiv.org/abs/1909.13719` for more information.\n\n Augmenters and hyper parameters are borrowed from the following repo:\n https://github.com/tensorflow/tp... | Default transforms for imgaug.
Implement RandAugment by imgaug.
Plase visit `https://arxiv.org/abs/1909.13719` for more information.
Augmenters and hyper parameters are borrowed from the following repo:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py # noqa
Miss one augmente... | mmaction/datasets/pipelines/augmentations.py | default_transforms | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def default_transforms():
"Default transforms for imgaug.\n\n Implement RandAugment by imgaug.\n Plase visit `https://arxiv.org/abs/1909.13719` for more information.\n\n Augmenters and hyper parameters are borrowed from the following repo:\n https://github.com/tensorflow/tp... | @staticmethod
def default_transforms():
"Default transforms for imgaug.\n\n Implement RandAugment by imgaug.\n Plase visit `https://arxiv.org/abs/1909.13719` for more information.\n\n Augmenters and hyper parameters are borrowed from the following repo:\n https://github.com/tensorflow/tp... |
7c17478f29be5128f2344361979af7c70be22e982249cd52851fbaeb979298b0 | def imgaug_builder(self, cfg):
'Import a module from imgaug.\n\n It follows the logic of :func:`build_from_cfg`. Use a dict object to\n create an iaa.Augmenter object.\n\n Args:\n cfg (dict): Config dict. It should at least contain the key "type".\n\n Returns:\n obj... | Import a module from imgaug.
It follows the logic of :func:`build_from_cfg`. Use a dict object to
create an iaa.Augmenter object.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
Returns:
obj:`iaa.Augmenter`: The constructed imgaug augmenter. | mmaction/datasets/pipelines/augmentations.py | imgaug_builder | dumbPy/Video-Swin-Transformer | 648 | python | def imgaug_builder(self, cfg):
'Import a module from imgaug.\n\n It follows the logic of :func:`build_from_cfg`. Use a dict object to\n create an iaa.Augmenter object.\n\n Args:\n cfg (dict): Config dict. It should at least contain the key "type".\n\n Returns:\n obj... | def imgaug_builder(self, cfg):
'Import a module from imgaug.\n\n It follows the logic of :func:`build_from_cfg`. Use a dict object to\n create an iaa.Augmenter object.\n\n Args:\n cfg (dict): Config dict. It should at least contain the key "type".\n\n Returns:\n obj... |
b51996381a96762e3525aed653ab66f183661f70e6c743da5336cf6d079e5bb9 | @staticmethod
def _box_crop(box, crop_bbox):
'Crop the bounding boxes according to the crop_bbox.\n\n Args:\n box (np.ndarray): The bounding boxes.\n crop_bbox(np.ndarray): The bbox used to crop the original image.\n '
(x1, y1, x2, y2) = crop_bbox
(img_w, img_h) = ((x2 - ... | Crop the bounding boxes according to the crop_bbox.
Args:
box (np.ndarray): The bounding boxes.
crop_bbox(np.ndarray): The bbox used to crop the original image. | mmaction/datasets/pipelines/augmentations.py | _box_crop | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def _box_crop(box, crop_bbox):
'Crop the bounding boxes according to the crop_bbox.\n\n Args:\n box (np.ndarray): The bounding boxes.\n crop_bbox(np.ndarray): The bbox used to crop the original image.\n '
(x1, y1, x2, y2) = crop_bbox
(img_w, img_h) = ((x2 - ... | @staticmethod
def _box_crop(box, crop_bbox):
'Crop the bounding boxes according to the crop_bbox.\n\n Args:\n box (np.ndarray): The bounding boxes.\n crop_bbox(np.ndarray): The bbox used to crop the original image.\n '
(x1, y1, x2, y2) = crop_bbox
(img_w, img_h) = ((x2 - ... |
cda00a0fe0eac2fc32292ca397379b0347e9be8d3b9d56ef834f77940ae4aec8 | def _all_box_crop(self, results, crop_bbox):
"Crop the gt_bboxes and proposals in results according to crop_bbox.\n\n Args:\n results (dict): All information about the sample, which contain\n 'gt_bboxes' and 'proposals' (optional).\n crop_bbox(np.ndarray): The bbox used t... | Crop the gt_bboxes and proposals in results according to crop_bbox.
Args:
results (dict): All information about the sample, which contain
'gt_bboxes' and 'proposals' (optional).
crop_bbox(np.ndarray): The bbox used to crop the original image. | mmaction/datasets/pipelines/augmentations.py | _all_box_crop | dumbPy/Video-Swin-Transformer | 648 | python | def _all_box_crop(self, results, crop_bbox):
"Crop the gt_bboxes and proposals in results according to crop_bbox.\n\n Args:\n results (dict): All information about the sample, which contain\n 'gt_bboxes' and 'proposals' (optional).\n crop_bbox(np.ndarray): The bbox used t... | def _all_box_crop(self, results, crop_bbox):
"Crop the gt_bboxes and proposals in results according to crop_bbox.\n\n Args:\n results (dict): All information about the sample, which contain\n 'gt_bboxes' and 'proposals' (optional).\n crop_bbox(np.ndarray): The bbox used t... |
a5cab4d76535908ec1cb821682ff99f62c926930934e7fb079525e675867ad40 | def __call__(self, results):
'Performs the RandomCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (n... | Performs the RandomCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the RandomCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (n... | def __call__(self, results):
'Performs the RandomCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (n... |
4441536059873dfcd94ad51eef3bfbde19fedab7d51c6b32ef159dc17b76816f | @staticmethod
def get_crop_bbox(img_shape, area_range, aspect_ratio_range, max_attempts=10):
"Get a crop bbox given the area range and aspect ratio range.\n\n Args:\n img_shape (Tuple[int]): Image shape\n area_range (Tuple[float]): The candidate area scales range of\n out... | Get a crop bbox given the area range and aspect ratio range.
Args:
img_shape (Tuple[int]): Image shape
area_range (Tuple[float]): The candidate area scales range of
output cropped images. Default: (0.08, 1.0).
aspect_ratio_range (Tuple[float]): The candidate aspect
ratio range of output cro... | mmaction/datasets/pipelines/augmentations.py | get_crop_bbox | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def get_crop_bbox(img_shape, area_range, aspect_ratio_range, max_attempts=10):
"Get a crop bbox given the area range and aspect ratio range.\n\n Args:\n img_shape (Tuple[int]): Image shape\n area_range (Tuple[float]): The candidate area scales range of\n out... | @staticmethod
def get_crop_bbox(img_shape, area_range, aspect_ratio_range, max_attempts=10):
"Get a crop bbox given the area range and aspect ratio range.\n\n Args:\n img_shape (Tuple[int]): Image shape\n area_range (Tuple[float]): The candidate area scales range of\n out... |
bccf0b767e1e093e91e3120d1b9206e0a91c9d7b585e06a16e834afd38fe3334 | def __call__(self, results):
'Performs the RandomResizeCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
ass... | Performs the RandomResizeCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the RandomResizeCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
ass... | def __call__(self, results):
'Performs the RandomResizeCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
ass... |
3befd14110dd6d2c8944198214f692e2c372cbb349da2b3968d3fc9c27e62afb | def __call__(self, results):
'Performs the MultiScaleCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
asser... | Performs the MultiScaleCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the MultiScaleCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
asser... | def __call__(self, results):
'Performs the MultiScaleCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
asser... |
a0e445595f6ee2794fc72e21b86cec6f7de80d4f704048a9035cb29b1ed2d969 | @staticmethod
def _box_resize(box, scale_factor):
'Rescale the bounding boxes according to the scale_factor.\n\n Args:\n box (np.ndarray): The bounding boxes.\n scale_factor (np.ndarray): The scale factor used for rescaling.\n '
assert (len(scale_factor) == 2)
scale_facto... | Rescale the bounding boxes according to the scale_factor.
Args:
box (np.ndarray): The bounding boxes.
scale_factor (np.ndarray): The scale factor used for rescaling. | mmaction/datasets/pipelines/augmentations.py | _box_resize | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def _box_resize(box, scale_factor):
'Rescale the bounding boxes according to the scale_factor.\n\n Args:\n box (np.ndarray): The bounding boxes.\n scale_factor (np.ndarray): The scale factor used for rescaling.\n '
assert (len(scale_factor) == 2)
scale_facto... | @staticmethod
def _box_resize(box, scale_factor):
'Rescale the bounding boxes according to the scale_factor.\n\n Args:\n box (np.ndarray): The bounding boxes.\n scale_factor (np.ndarray): The scale factor used for rescaling.\n '
assert (len(scale_factor) == 2)
scale_facto... |
7da6559803f6ea35176d302785396f11c9850bf4bddc8f03dd32223c51d4a1eb | def __call__(self, results):
'Performs the Resize augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (not s... | Performs the Resize augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the Resize augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (not s... | def __call__(self, results):
'Performs the Resize augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (not s... |
3c9602093388e84b9535dc6015bdff0bc2dc8c0fb1a4f0a7a23dc5659cebbb8e | def __call__(self, results):
'Performs the Resize augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
short_edge = np.random.randint(self.scale_range[0], (self.scale_range[1] + 1))
resize = Re... | Performs the Resize augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the Resize augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
short_edge = np.random.randint(self.scale_range[0], (self.scale_range[1] + 1))
resize = Re... | def __call__(self, results):
'Performs the Resize augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
short_edge = np.random.randint(self.scale_range[0], (self.scale_range[1] + 1))
resize = Re... |
22878b7220116cf5ff2492dd8f3099d0c36cf9abbdf6abdf1a1d76adff45413a | @staticmethod
def _box_flip(box, img_width):
'Flip the bounding boxes given the width of the image.\n\n Args:\n box (np.ndarray): The bounding boxes.\n img_width (int): The img width.\n '
box_ = box.copy()
box_[(..., 0::4)] = (img_width - box[(..., 2::4)])
box_[(..., ... | Flip the bounding boxes given the width of the image.
Args:
box (np.ndarray): The bounding boxes.
img_width (int): The img width. | mmaction/datasets/pipelines/augmentations.py | _box_flip | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def _box_flip(box, img_width):
'Flip the bounding boxes given the width of the image.\n\n Args:\n box (np.ndarray): The bounding boxes.\n img_width (int): The img width.\n '
box_ = box.copy()
box_[(..., 0::4)] = (img_width - box[(..., 2::4)])
box_[(..., ... | @staticmethod
def _box_flip(box, img_width):
'Flip the bounding boxes given the width of the image.\n\n Args:\n box (np.ndarray): The bounding boxes.\n img_width (int): The img width.\n '
box_ = box.copy()
box_[(..., 0::4)] = (img_width - box[(..., 2::4)])
box_[(..., ... |
3895faa881ff7eab673e4a74ac9284ac5f94a9bf921d6cda679e6cda3410ed93 | def __call__(self, results):
'Performs the Flip augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (not sel... | Performs the Flip augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the Flip augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (not sel... | def __call__(self, results):
'Performs the Flip augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (not sel... |
5b483a7c00126ab485845c249e7bad7f9f8940c80d4e23af13279e49494dfe34 | @staticmethod
def brightness(img, delta):
'Brightness distortion.\n\n Args:\n img (np.ndarray): An input image.\n delta (float): Delta value to distort brightness.\n It ranges from [-32, 32).\n\n Returns:\n np.ndarray: A brightness distorted image.\n ... | Brightness distortion.
Args:
img (np.ndarray): An input image.
delta (float): Delta value to distort brightness.
It ranges from [-32, 32).
Returns:
np.ndarray: A brightness distorted image. | mmaction/datasets/pipelines/augmentations.py | brightness | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def brightness(img, delta):
'Brightness distortion.\n\n Args:\n img (np.ndarray): An input image.\n delta (float): Delta value to distort brightness.\n It ranges from [-32, 32).\n\n Returns:\n np.ndarray: A brightness distorted image.\n ... | @staticmethod
def brightness(img, delta):
'Brightness distortion.\n\n Args:\n img (np.ndarray): An input image.\n delta (float): Delta value to distort brightness.\n It ranges from [-32, 32).\n\n Returns:\n np.ndarray: A brightness distorted image.\n ... |
7607bd1f6815d2a818ad3dcb4cd55856e00973f844bd96079c0d80969cc591ac | @staticmethod
def contrast(img, alpha):
'Contrast distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to distort contrast.\n It ranges from [0.6, 1.4).\n\n Returns:\n np.ndarray: A contrast distorted image.\n '
... | Contrast distortion.
Args:
img (np.ndarray): An input image.
alpha (float): Alpha value to distort contrast.
It ranges from [0.6, 1.4).
Returns:
np.ndarray: A contrast distorted image. | mmaction/datasets/pipelines/augmentations.py | contrast | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def contrast(img, alpha):
'Contrast distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to distort contrast.\n It ranges from [0.6, 1.4).\n\n Returns:\n np.ndarray: A contrast distorted image.\n '
... | @staticmethod
def contrast(img, alpha):
'Contrast distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to distort contrast.\n It ranges from [0.6, 1.4).\n\n Returns:\n np.ndarray: A contrast distorted image.\n '
... |
271ff80d1949442ca1787d439988c62e041dc950713946c8b0a35f4faa5b4926 | @staticmethod
def saturation(img, alpha):
'Saturation distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to distort the saturation.\n It ranges from [0.6, 1.4).\n\n Returns:\n np.ndarray: A saturation distorted image.\... | Saturation distortion.
Args:
img (np.ndarray): An input image.
alpha (float): Alpha value to distort the saturation.
It ranges from [0.6, 1.4).
Returns:
np.ndarray: A saturation distorted image. | mmaction/datasets/pipelines/augmentations.py | saturation | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def saturation(img, alpha):
'Saturation distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to distort the saturation.\n It ranges from [0.6, 1.4).\n\n Returns:\n np.ndarray: A saturation distorted image.\... | @staticmethod
def saturation(img, alpha):
'Saturation distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to distort the saturation.\n It ranges from [0.6, 1.4).\n\n Returns:\n np.ndarray: A saturation distorted image.\... |
99c092a01ed531f7eaa71f13847add76ba8e11e98f8a24d188996661a37d04f5 | @staticmethod
def hue(img, alpha):
'Hue distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to control the degree of rotation\n for hue. It ranges from [-18, 18).\n\n Returns:\n np.ndarray: A hue distorted image.\n ... | Hue distortion.
Args:
img (np.ndarray): An input image.
alpha (float): Alpha value to control the degree of rotation
for hue. It ranges from [-18, 18).
Returns:
np.ndarray: A hue distorted image. | mmaction/datasets/pipelines/augmentations.py | hue | dumbPy/Video-Swin-Transformer | 648 | python | @staticmethod
def hue(img, alpha):
'Hue distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to control the degree of rotation\n for hue. It ranges from [-18, 18).\n\n Returns:\n np.ndarray: A hue distorted image.\n ... | @staticmethod
def hue(img, alpha):
'Hue distortion.\n\n Args:\n img (np.ndarray): An input image.\n alpha (float): Alpha value to control the degree of rotation\n for hue. It ranges from [-18, 18).\n\n Returns:\n np.ndarray: A hue distorted image.\n ... |
94492e707601201aa77fc3167c96467f842d362fddff3d7db7e9269c8a80521d | def __call__(self, results):
'Performs the CenterCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (n... | Performs the CenterCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the CenterCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (n... | def __call__(self, results):
'Performs the CenterCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, self.lazy)
if ('keypoint' in results):
assert (n... |
0a2752bcad604ea6cde549ad732271367875bf5179c8a1d8eece27b6e27c692b | def __call__(self, results):
'Performs the ThreeCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, False)
if (('gt_bboxes' in results) or ('proposals' in re... | Performs the ThreeCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the ThreeCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, False)
if (('gt_bboxes' in results) or ('proposals' in re... | def __call__(self, results):
'Performs the ThreeCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, False)
if (('gt_bboxes' in results) or ('proposals' in re... |
5a3f000b6cb764a0e20e00de15667e828d222a2cf68c9d7f9b227144917bd411 | def __call__(self, results):
'Performs the TenCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, False)
if (('gt_bboxes' in results) or ('proposals' in resu... | Performs the TenCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the TenCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, False)
if (('gt_bboxes' in results) or ('proposals' in resu... | def __call__(self, results):
'Performs the TenCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
_init_lazy_if_proper(results, False)
if (('gt_bboxes' in results) or ('proposals' in resu... |
1575652819b4543fe423259ccfe2532c1653084d2c003bec4e70b53391de74d6 | def __call__(self, results):
'Performs the MultiGroupCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
if (('gt_bboxes' in results) or ('proposals' in results)):
warnings.warn('Mult... | Performs the MultiGroupCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Performs the MultiGroupCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
if (('gt_bboxes' in results) or ('proposals' in results)):
warnings.warn('Mult... | def __call__(self, results):
'Performs the MultiGroupCrop augmentation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
if (('gt_bboxes' in results) or ('proposals' in results)):
warnings.warn('Mult... |
8d37214a5c19456be1b09d3617c86ff3838e4ffc763fda467e7081188d7e4a9f | def __call__(self, results):
'Perfrom the audio amplification.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
assert ('audios' in results)
results['audios'] *= self.ratio
results['amplify_ratio'] =... | Perfrom the audio amplification.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Perfrom the audio amplification.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
assert ('audios' in results)
results['audios'] *= self.ratio
results['amplify_ratio'] =... | def __call__(self, results):
'Perfrom the audio amplification.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
assert ('audios' in results)
results['audios'] *= self.ratio
results['amplify_ratio'] =... |
8a6b068e8ec3dd76aaaa9e10e75b546d1d6110fd24e73c8e01dcbef658b95cb0 | def __call__(self, results):
'Perform MelSpectrogram transformation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
try:
import librosa
except ImportError:
raise ImportError('Install li... | Perform MelSpectrogram transformation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline. | mmaction/datasets/pipelines/augmentations.py | __call__ | dumbPy/Video-Swin-Transformer | 648 | python | def __call__(self, results):
'Perform MelSpectrogram transformation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
try:
import librosa
except ImportError:
raise ImportError('Install li... | def __call__(self, results):
'Perform MelSpectrogram transformation.\n\n Args:\n results (dict): The resulting dict to be modified and passed\n to the next transform in pipeline.\n '
try:
import librosa
except ImportError:
raise ImportError('Install li... |
99855440a70829894b5215e14a1da58603eaf5c12d7e89666c67a741442821cf | def create(self, name, parent_id=1, order=None, id=None, name_en=None):
'\n 创建部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90205\n\n :param name: 部门名称。长度限制为1~32个字符，字符不能包括\\:?”<>｜\n :param parent_id: 父部门id，32位整型\n :param order: 在父部门中的次序值。order值大的排序靠前。有效的... | 创建部门
详情请参考
https://developer.work.weixin.qq.com/document/path/90205
:param name: 部门名称。长度限制为1~32个字符，字符不能包括\:?”<>｜
:param parent_id: 父部门id，32位整型
:param order: 在父部门中的次序值。order值大的排序靠前。有效的值范围是[0, 2^32)
:param id: 部门id，32位整型，指定时必须大于1。若不填该参数，将自动生成id
:param name_en: 英文名称。同一个层级的部门名称不能重复。需要在管理后台开启多语言支持才能生效。长度限制为1~32个字符，字符不能包括:... | wechatpy/work/client/api/department.py | create | vainl/wechatpy | 2,428 | python | def create(self, name, parent_id=1, order=None, id=None, name_en=None):
'\n 创建部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90205\n\n :param name: 部门名称。长度限制为1~32个字符，字符不能包括\\:?”<>｜\n :param parent_id: 父部门id，32位整型\n :param order: 在父部门中的次序值。order值大的排序靠前。有效的... | def create(self, name, parent_id=1, order=None, id=None, name_en=None):
'\n 创建部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90205\n\n :param name: 部门名称。长度限制为1~32个字符，字符不能包括\\:?”<>｜\n :param parent_id: 父部门id，32位整型\n :param order: 在父部门中的次序值。order值大的排序靠前。有效的... |
c05d77d3d2bd9b651b1bffab08bcb0eb463c6e58bc9fe6aa6b6377f499298863 | def update(self, id, name=None, parent_id=None, order=None, name_en=None):
'\n 更新部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90206\n\n :param id: 部门 id\n :param name: 部门名称。长度限制为1~32个字符，字符不能包括\\:?”<>｜\n :param parent_id: 父亲部门id\n :param order: 在父... | 更新部门
详情请参考
https://developer.work.weixin.qq.com/document/path/90206
:param id: 部门 id
:param name: 部门名称。长度限制为1~32个字符，字符不能包括\:?”<>｜
:param parent_id: 父亲部门id
:param order: 在父部门中的次序值。order值大的排序靠前。有效的值范围是[0, 2^32)
:param name_en: 英文名称。同一个层级的部门名称不能重复。需要在管理后台开启多语言支持才能生效。长度限制为1~32个字符，字符不能包括:*?"<>｜
:return: 返回的 JSON 数据包 | wechatpy/work/client/api/department.py | update | vainl/wechatpy | 2,428 | python | def update(self, id, name=None, parent_id=None, order=None, name_en=None):
'\n 更新部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90206\n\n :param id: 部门 id\n :param name: 部门名称。长度限制为1~32个字符，字符不能包括\\:?”<>｜\n :param parent_id: 父亲部门id\n :param order: 在父... | def update(self, id, name=None, parent_id=None, order=None, name_en=None):
'\n 更新部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90206\n\n :param id: 部门 id\n :param name: 部门名称。长度限制为1~32个字符，字符不能包括\\:?”<>｜\n :param parent_id: 父亲部门id\n :param order: 在父... |
3b4a4e2906464060b22fad37deb5037bf178e78e677327e23187684e3bf6ca19 | def delete(self, id):
'\n 删除部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90207\n\n :param id: 部门id。（注：不能删除根部门；不能删除含有子部门、成员的部门）\n :return: 返回的 JSON 数据包\n '
return self._get('department/delete', params={'id': id}) | 删除部门
详情请参考
https://developer.work.weixin.qq.com/document/path/90207
:param id: 部门id。（注：不能删除根部门；不能删除含有子部门、成员的部门）
:return: 返回的 JSON 数据包 | wechatpy/work/client/api/department.py | delete | vainl/wechatpy | 2,428 | python | def delete(self, id):
'\n 删除部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90207\n\n :param id: 部门id。（注：不能删除根部门；不能删除含有子部门、成员的部门）\n :return: 返回的 JSON 数据包\n '
return self._get('department/delete', params={'id': id}) | def delete(self, id):
'\n 删除部门\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90207\n\n :param id: 部门id。（注：不能删除根部门；不能删除含有子部门、成员的部门）\n :return: 返回的 JSON 数据包\n '
return self._get('department/delete', params={'id': id})<|docstring|>删除部门
详情请参考
https://devel... |
726f8ecbc0a876569047311a15d713088c996e752e842d9b9038b3220651dbc4 | def list(self, id=None):
'\n 获取指定部门列表\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90208\n\n 权限说明：\n 只能拉取token对应的应用的权限范围内的部门列表\n\n :param id: 部门id。获取指定部门及其下的子部门。 如果不填，默认获取全量组织架构\n :return: 部门列表\n '
if (id is None):
res = self._get... | 获取指定部门列表
详情请参考
https://developer.work.weixin.qq.com/document/path/90208
权限说明：
只能拉取token对应的应用的权限范围内的部门列表
:param id: 部门id。获取指定部门及其下的子部门。 如果不填，默认获取全量组织架构
:return: 部门列表 | wechatpy/work/client/api/department.py | list | vainl/wechatpy | 2,428 | python | def list(self, id=None):
'\n 获取指定部门列表\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90208\n\n 权限说明：\n 只能拉取token对应的应用的权限范围内的部门列表\n\n :param id: 部门id。获取指定部门及其下的子部门。 如果不填，默认获取全量组织架构\n :return: 部门列表\n '
if (id is None):
res = self._get... | def list(self, id=None):
'\n 获取指定部门列表\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/90208\n\n 权限说明：\n 只能拉取token对应的应用的权限范围内的部门列表\n\n :param id: 部门id。获取指定部门及其下的子部门。 如果不填，默认获取全量组织架构\n :return: 部门列表\n '
if (id is None):
res = self._get... |
695df7e7251ed8b0ece0efebe180e8c5f7364359cd121806a968efc98ec1539b | def simple_list(self, id=None):
'\n 获取子部门 ID 列表，和 list 接口相比，此接口只返回部门 ID，ORDER 和 PARENTID 字段\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/95350\n\n :param id: 部门id。获取指定部门及其下的子部门（以及子部门的子部门等等，递归）。 如果不填，默认获取全量组织架构\n :return: 部门列表\n '
if (id is None):
... | 获取子部门 ID 列表，和 list 接口相比，此接口只返回部门 ID，ORDER 和 PARENTID 字段
详情请参考
https://developer.work.weixin.qq.com/document/path/95350
:param id: 部门id。获取指定部门及其下的子部门（以及子部门的子部门等等，递归）。 如果不填，默认获取全量组织架构
:return: 部门列表 | wechatpy/work/client/api/department.py | simple_list | vainl/wechatpy | 2,428 | python | def simple_list(self, id=None):
'\n 获取子部门 ID 列表，和 list 接口相比，此接口只返回部门 ID，ORDER 和 PARENTID 字段\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/95350\n\n :param id: 部门id。获取指定部门及其下的子部门（以及子部门的子部门等等，递归）。 如果不填，默认获取全量组织架构\n :return: 部门列表\n '
if (id is None):
... | def simple_list(self, id=None):
'\n 获取子部门 ID 列表，和 list 接口相比，此接口只返回部门 ID，ORDER 和 PARENTID 字段\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/95350\n\n :param id: 部门id。获取指定部门及其下的子部门（以及子部门的子部门等等，递归）。 如果不填，默认获取全量组织架构\n :return: 部门列表\n '
if (id is None):
... |
f99b3c91a20407a81d21ac3baec80ea53e16ab9161cee82703d2b3b2195c085c | def get(self, id):
'\n 获取单个部门详情\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/95351\n\n :param id: 部门 ID\n :return: 部门信息\n '
res = self._get('department/get', params={'id': id})
return res['department'] | 获取单个部门详情
详情请参考
https://developer.work.weixin.qq.com/document/path/95351
:param id: 部门 ID
:return: 部门信息 | wechatpy/work/client/api/department.py | get | vainl/wechatpy | 2,428 | python | def get(self, id):
'\n 获取单个部门详情\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/95351\n\n :param id: 部门 ID\n :return: 部门信息\n '
res = self._get('department/get', params={'id': id})
return res['department'] | def get(self, id):
'\n 获取单个部门详情\n\n 详情请参考\n https://developer.work.weixin.qq.com/document/path/95351\n\n :param id: 部门 ID\n :return: 部门信息\n '
res = self._get('department/get', params={'id': id})
return res['department']<|docstring|>获取单个部门详情
详情请参考
https://developer.... |
f77501acbaf7797dce664a10e9eec45c9770e338ad9d84c1164751f245cabdd0 | def get_users(self, id, fetch_child=0, simple=True):
'\n 获取部门成员：https://developer.work.weixin.qq.com/document/path/90200\n\n 获取部门成员详情：https://developer.work.weixin.qq.com/document/path/90201\n\n :param id: 部门 id\n :param fetch_child: 1/0：是否递归获取子部门下面的成员\n :param simple: True 获取部门成员... | 获取部门成员：https://developer.work.weixin.qq.com/document/path/90200
获取部门成员详情：https://developer.work.weixin.qq.com/document/path/90201
:param id: 部门 id
:param fetch_child: 1/0：是否递归获取子部门下面的成员
:param simple: True 获取部门成员，False 获取部门成员详情
:return: 部门成员列表 | wechatpy/work/client/api/department.py | get_users | vainl/wechatpy | 2,428 | python | def get_users(self, id, fetch_child=0, simple=True):
'\n 获取部门成员：https://developer.work.weixin.qq.com/document/path/90200\n\n 获取部门成员详情：https://developer.work.weixin.qq.com/document/path/90201\n\n :param id: 部门 id\n :param fetch_child: 1/0：是否递归获取子部门下面的成员\n :param simple: True 获取部门成员... | def get_users(self, id, fetch_child=0, simple=True):
'\n 获取部门成员：https://developer.work.weixin.qq.com/document/path/90200\n\n 获取部门成员详情：https://developer.work.weixin.qq.com/document/path/90201\n\n :param id: 部门 id\n :param fetch_child: 1/0：是否递归获取子部门下面的成员\n :param simple: True 获取部门成员... |
69bda341c68ea10c176cae587756ecb76599dcc94896fa8a0e8816c0317b2ec3 | def get_map_users(self, id=None, key='name', fetch_child=0):
'\n 映射员工某详细字段到 ``user_id``\n\n 企业微信许多对员工操作依赖于 ``user_id`` ，但没有提供直接查询员工对应 ``user_id`` 的结构，\n\n 这里是一个变通的方法，常用于储存员工 ``user_id`` ，并用于后续查询或对单人操作（如发送指定消息）\n\n :param id: 部门 id， 如果不填，默认获取有权限的所有部门\n :param key: 员工详细信息字段 key，所指向的... | 映射员工某详细字段到 ``user_id``
企业微信许多对员工操作依赖于 ``user_id`` ，但没有提供直接查询员工对应 ``user_id`` 的结构，
这里是一个变通的方法，常用于储存员工 ``user_id`` ，并用于后续查询或对单人操作（如发送指定消息）
:param id: 部门 id， 如果不填，默认获取有权限的所有部门
:param key: 员工详细信息字段 key，所指向的值必须唯一
:param fetch_child: 1/0：是否递归获取子部门下面的成员
:return: dict - 部门成员指定字段到 user_id 的 map ``{ key: user_id }`` | wechatpy/work/client/api/department.py | get_map_users | vainl/wechatpy | 2,428 | python | def get_map_users(self, id=None, key='name', fetch_child=0):
'\n 映射员工某详细字段到 ``user_id``\n\n 企业微信许多对员工操作依赖于 ``user_id`` ，但没有提供直接查询员工对应 ``user_id`` 的结构，\n\n 这里是一个变通的方法，常用于储存员工 ``user_id`` ，并用于后续查询或对单人操作（如发送指定消息）\n\n :param id: 部门 id， 如果不填，默认获取有权限的所有部门\n :param key: 员工详细信息字段 key，所指向的... | def get_map_users(self, id=None, key='name', fetch_child=0):
'\n 映射员工某详细字段到 ``user_id``\n\n 企业微信许多对员工操作依赖于 ``user_id`` ，但没有提供直接查询员工对应 ``user_id`` 的结构，\n\n 这里是一个变通的方法，常用于储存员工 ``user_id`` ，并用于后续查询或对单人操作（如发送指定消息）\n\n :param id: 部门 id， 如果不填，默认获取有权限的所有部门\n :param key: 员工详细信息字段 key，所指向的... |
ff4916c67b60353a5d26e8ab7555270924e4a4e280b00824dc7efd324350fc6a | def __init__(self, graph, draw=False):
'\n Constructor\n :param graph: Graph of the network to analyze\n '
self.graph = graph
if draw:
nx.draw_planar(self.graph)
plt.show() | Constructor
:param graph: Graph of the network to analyze | general_methods/nodal_analysis.py | __init__ | bcornelusse/ELEC0053-circuits-electriques | 3 | python | def __init__(self, graph, draw=False):
'\n Constructor\n :param graph: Graph of the network to analyze\n '
self.graph = graph
if draw:
nx.draw_planar(self.graph)
plt.show() | def __init__(self, graph, draw=False):
'\n Constructor\n :param graph: Graph of the network to analyze\n '
self.graph = graph
if draw:
nx.draw_planar(self.graph)
plt.show()<|docstring|>Constructor
:param graph: Graph of the network to analyze<|endoftext|> |
f79303b134f82b4b10088de0b5b573ccac86cb003d107049de541ac90a74bf65 | def solve(self, reference_node):
'\n TODO Only handles the case of Resistors + independent current sources, should also handle VCT\n :param reference_node: reference node used for the solution\n :return: a map: node -> node potential\n '
passified_graph = nx.MultiDiGraph()
for (u... | TODO Only handles the case of Resistors + independent current sources, should also handle VCT
:param reference_node: reference node used for the solution
:return: a map: node -> node potential | general_methods/nodal_analysis.py | solve | bcornelusse/ELEC0053-circuits-electriques | 3 | python | def solve(self, reference_node):
'\n TODO Only handles the case of Resistors + independent current sources, should also handle VCT\n :param reference_node: reference node used for the solution\n :return: a map: node -> node potential\n '
passified_graph = nx.MultiDiGraph()
for (u... | def solve(self, reference_node):
'\n TODO Only handles the case of Resistors + independent current sources, should also handle VCT\n :param reference_node: reference node used for the solution\n :return: a map: node -> node potential\n '
passified_graph = nx.MultiDiGraph()
for (u... |
65e759e7c21cfcecf4b589781b4e559ae725ad4d47f14a6ffdb3491bd97c7ac7 | def plot_histogram(hist, x_label, y_label=None, is_num=True, is_ts=False, pdf_file_name='', top=20):
'Create and plot histogram of column values.\n\n :param hist: input numpy histogram = values, bin_edges\n :param str x_label: Label for histogram x-axis\n :param str y_label: Label for histogram y-axis\n ... | Create and plot histogram of column values.
:param hist: input numpy histogram = values, bin_edges
:param str x_label: Label for histogram x-axis
:param str y_label: Label for histogram y-axis
:param bool is_num: True if observable to plot is numeric
:param bool is_ts: True if observable to plot is a timestamp
:param ... | python/eskapade/visualization/vis_utils.py | plot_histogram | mbaak/Eskapade | 16 | python | def plot_histogram(hist, x_label, y_label=None, is_num=True, is_ts=False, pdf_file_name=, top=20):
'Create and plot histogram of column values.\n\n :param hist: input numpy histogram = values, bin_edges\n :param str x_label: Label for histogram x-axis\n :param str y_label: Label for histogram y-axis\n :... | def plot_histogram(hist, x_label, y_label=None, is_num=True, is_ts=False, pdf_file_name=, top=20):
'Create and plot histogram of column values.\n\n :param hist: input numpy histogram = values, bin_edges\n :param str x_label: Label for histogram x-axis\n :param str y_label: Label for histogram y-axis\n :... |
b4ec8ce4f7d60f6765bf4cf45f7b469fdb95b420e861d2d8299b497fe781d9cd | def plot_2d_histogram(hist, x_lim, y_lim, title, x_label, y_label, pdf_file_name):
'Plot 2d histogram with matplotlib.\n\n :param hist: input numpy histogram = x_bin_edges, y_bin_edges, bin_entries_2dgrid\n :param tuple x_lim: range tuple of x-axis (min,max)\n :param tuple y_lim: range tuple of y-axis (min... | Plot 2d histogram with matplotlib.
:param hist: input numpy histogram = x_bin_edges, y_bin_edges, bin_entries_2dgrid
:param tuple x_lim: range tuple of x-axis (min,max)
:param tuple y_lim: range tuple of y-axis (min,max)
:param str title: title of plot
:param str x_label: Label for histogram x-axis
:param str y_label:... | python/eskapade/visualization/vis_utils.py | plot_2d_histogram | mbaak/Eskapade | 16 | python | def plot_2d_histogram(hist, x_lim, y_lim, title, x_label, y_label, pdf_file_name):
'Plot 2d histogram with matplotlib.\n\n :param hist: input numpy histogram = x_bin_edges, y_bin_edges, bin_entries_2dgrid\n :param tuple x_lim: range tuple of x-axis (min,max)\n :param tuple y_lim: range tuple of y-axis (min... | def plot_2d_histogram(hist, x_lim, y_lim, title, x_label, y_label, pdf_file_name):
'Plot 2d histogram with matplotlib.\n\n :param hist: input numpy histogram = x_bin_edges, y_bin_edges, bin_entries_2dgrid\n :param tuple x_lim: range tuple of x-axis (min,max)\n :param tuple y_lim: range tuple of y-axis (min... |
256536e124796c5b3c4059bded77ed376f05efeb06382e9439b485a587fd66ec | def delete_smallstat(df, group_col, statlim=400):
'Remove low-statistics groups from dataframe.\n\n Function to make a new DataFrame that removes all groups of group_col that have less than statlim entries.\n\n :param df: pandas DataFrame\n :param str group_col: name of the column to group on\n :param i... | Remove low-statistics groups from dataframe.
Function to make a new DataFrame that removes all groups of group_col that have less than statlim entries.
:param df: pandas DataFrame
:param str group_col: name of the column to group on
:param int statlim: number of entries a group has to have to be statistically signifi... | python/eskapade/visualization/vis_utils.py | delete_smallstat | mbaak/Eskapade | 16 | python | def delete_smallstat(df, group_col, statlim=400):
'Remove low-statistics groups from dataframe.\n\n Function to make a new DataFrame that removes all groups of group_col that have less than statlim entries.\n\n :param df: pandas DataFrame\n :param str group_col: name of the column to group on\n :param i... | def delete_smallstat(df, group_col, statlim=400):
'Remove low-statistics groups from dataframe.\n\n Function to make a new DataFrame that removes all groups of group_col that have less than statlim entries.\n\n :param df: pandas DataFrame\n :param str group_col: name of the column to group on\n :param i... |
2c4940976f30c5874e843a65fd0f6e524f5c7e2e74c8c7176049ecfd469ada8a | def box_plot(df, cause_col, result_col='cost', pdf_file_name='', ylim_quant=0.95, ylim_high=None, ylim_low=0, rot=90, statlim=400, label_dict=None, title_add='', top=20):
"Make box plot.\n\n Function that plots the boxplot of the column df[result_col] in groups of cause_col. This means that\n the DataFrame is... | Make box plot.
Function that plots the boxplot of the column df[result_col] in groups of cause_col. This means that
the DataFrame is grouped-by on the cause column and then the distribution per group is plotted in a boxplot
using the standard pandas functionality.
Boxplots with less than statlim (default=400 ) entries... | python/eskapade/visualization/vis_utils.py | box_plot | mbaak/Eskapade | 16 | python | def box_plot(df, cause_col, result_col='cost', pdf_file_name=, ylim_quant=0.95, ylim_high=None, ylim_low=0, rot=90, statlim=400, label_dict=None, title_add=, top=20):
"Make box plot.\n\n Function that plots the boxplot of the column df[result_col] in groups of cause_col. This means that\n the DataFrame is gro... | def box_plot(df, cause_col, result_col='cost', pdf_file_name=, ylim_quant=0.95, ylim_high=None, ylim_low=0, rot=90, statlim=400, label_dict=None, title_add=, top=20):
"Make box plot.\n\n Function that plots the boxplot of the column df[result_col] in groups of cause_col. This means that\n the DataFrame is gro... |
2a36d462721c17071f32db141e6a12f0d36bdd14682a3b6ffef673fa06b941a0 | def plot_correlation_matrix(matrix_colors, x_labels, y_labels, pdf_file_name='', title='correlation', vmin=(- 1), vmax=1, color_map='RdYlGn', x_label='', y_label='', top=20, matrix_numbers=None, print_both_numbers=True):
"Create and plot correlation matrix.\n\n :param matrix_colors: input correlation matrix\n ... | Create and plot correlation matrix.
:param matrix_colors: input correlation matrix
:param list x_labels: Labels for histogram x-axis bins
:param list y_labels: Labels for histogram y-axis bins
:param str pdf_file_name: if set, will store the plot in a pdf file
:param str title: if set, title of the plot
:param float v... | python/eskapade/visualization/vis_utils.py | plot_correlation_matrix | mbaak/Eskapade | 16 | python | def plot_correlation_matrix(matrix_colors, x_labels, y_labels, pdf_file_name=, title='correlation', vmin=(- 1), vmax=1, color_map='RdYlGn', x_label=, y_label=, top=20, matrix_numbers=None, print_both_numbers=True):
"Create and plot correlation matrix.\n\n :param matrix_colors: input correlation matrix\n :para... | def plot_correlation_matrix(matrix_colors, x_labels, y_labels, pdf_file_name=, title='correlation', vmin=(- 1), vmax=1, color_map='RdYlGn', x_label=, y_label=, top=20, matrix_numbers=None, print_both_numbers=True):
"Create and plot correlation matrix.\n\n :param matrix_colors: input correlation matrix\n :para... |
013709e1831186416df2b26b9b56ba01caec17d90cf9b49be4c5cc697282cdf9 | def plot_overlay_histogram(hists, x_label, y_label=None, hist_names=[], is_num=True, is_ts=False, pdf_file_name='', top=20, width_in=None, xlim=None):
'Create and plot overlapping histograms of column values.\n\n :param hists: list of input numpy histogram = values, bin_edges\n :param str x_label: Label for h... | Create and plot overlapping histograms of column values.
:param hists: list of input numpy histogram = values, bin_edges
:param str x_label: Label for histogram x-axis
:param str y_label: Label for histogram y-axis
:param bool is_num: True if observable to plot is numeric
:param bool is_ts: True if observable to plot ... | python/eskapade/visualization/vis_utils.py | plot_overlay_histogram | mbaak/Eskapade | 16 | python | def plot_overlay_histogram(hists, x_label, y_label=None, hist_names=[], is_num=True, is_ts=False, pdf_file_name=, top=20, width_in=None, xlim=None):
'Create and plot overlapping histograms of column values.\n\n :param hists: list of input numpy histogram = values, bin_edges\n :param str x_label: Label for his... | def plot_overlay_histogram(hists, x_label, y_label=None, hist_names=[], is_num=True, is_ts=False, pdf_file_name=, top=20, width_in=None, xlim=None):
'Create and plot overlapping histograms of column values.\n\n :param hists: list of input numpy histogram = values, bin_edges\n :param str x_label: Label for his... |
11a30755253fffcf9a81af00ef4967ba29ca1ea7a90f630347b19a74d1c39743 | def plot_pair_grid(data, title, fpath, column_names=[], data2=None):
'Plot a pairgrid for one or two datasets\n :param array data: Input data to plot\n :param str title: Title of the plot\n :param str fpath: if set, will store the plot in a pdf file\n :param list column_names: list of column names to be... | Plot a pairgrid for one or two datasets
:param array data: Input data to plot
:param str title: Title of the plot
:param str fpath: if set, will store the plot in a pdf file
:param list column_names: list of column names to be give to the plot
:param array data2: second dataset to be plot in the pairgrid. | python/eskapade/visualization/vis_utils.py | plot_pair_grid | mbaak/Eskapade | 16 | python | def plot_pair_grid(data, title, fpath, column_names=[], data2=None):
'Plot a pairgrid for one or two datasets\n :param array data: Input data to plot\n :param str title: Title of the plot\n :param str fpath: if set, will store the plot in a pdf file\n :param list column_names: list of column names to be... | def plot_pair_grid(data, title, fpath, column_names=[], data2=None):
'Plot a pairgrid for one or two datasets\n :param array data: Input data to plot\n :param str title: Title of the plot\n :param str fpath: if set, will store the plot in a pdf file\n :param list column_names: list of column names to be... |
49d224934e9aeb86834b2f30a1e6baf0a9481018f033f39d13a5516843d5eb0d | def tick(lab):
'Get tick.'
if isinstance(lab, (float, int)):
lab = ('NaN' if np.isnan(lab) else '{0:.1f}'.format(lab))
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab | Get tick. | python/eskapade/visualization/vis_utils.py | tick | mbaak/Eskapade | 16 | python | def tick(lab):
if isinstance(lab, (float, int)):
lab = ('NaN' if np.isnan(lab) else '{0:.1f}'.format(lab))
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab | def tick(lab):
if isinstance(lab, (float, int)):
lab = ('NaN' if np.isnan(lab) else '{0:.1f}'.format(lab))
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab<|docstring|>Get tick.<|endoftext|> |
6cf9903a0838f70aeca3936a57689958c47744bc05bc098b842f117b22f76ad9 | def xtick(lab):
'Get x-tick.'
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab | Get x-tick. | python/eskapade/visualization/vis_utils.py | xtick | mbaak/Eskapade | 16 | python | def xtick(lab):
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab | def xtick(lab):
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab<|docstring|>Get x-tick.<|endoftext|> |
6cf9903a0838f70aeca3936a57689958c47744bc05bc098b842f117b22f76ad9 | def xtick(lab):
'Get x-tick.'
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab | Get x-tick. | python/eskapade/visualization/vis_utils.py | xtick | mbaak/Eskapade | 16 | python | def xtick(lab):
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab | def xtick(lab):
lab = str(lab)
if (len(lab) > top):
lab = (lab[:17] + '...')
return lab<|docstring|>Get x-tick.<|endoftext|> |
b0f5dcdb8dda91a5b177b8e184ae7ce05db2f5d27d7e83ea61148a22eea54f5e | def validate_persistent_hash(ht):
'NOT RPYTHON'
root = ht._root
assert (((root is None) and (ht._cnt == 0)) or ((root is not None) and (ht._cnt == root._size)))
if (root is not None):
validate_nodes(root) | NOT RPYTHON | pycket/hash/persistent_hash_map.py | validate_persistent_hash | namin/pycket | 129 | python | def validate_persistent_hash(ht):
root = ht._root
assert (((root is None) and (ht._cnt == 0)) or ((root is not None) and (ht._cnt == root._size)))
if (root is not None):
validate_nodes(root) | def validate_persistent_hash(ht):
root = ht._root
assert (((root is None) and (ht._cnt == 0)) or ((root is not None) and (ht._cnt == root._size)))
if (root is not None):
validate_nodes(root)<|docstring|>NOT RPYTHON<|endoftext|> |
4cf865fc2e1170a9ef7418368abe11cfdf4e1027d96264768e45e8d97e8a2ea1 | def validate_nodes(root):
'NOT RPYTHON'
subnodes = root._subnodes()
entries = root._entries()
subnode_count = sum((node._size for node in subnodes))
total = (subnode_count + len(entries))
assert (root._size == total)
for node in subnodes:
validate_nodes(node) | NOT RPYTHON | pycket/hash/persistent_hash_map.py | validate_nodes | namin/pycket | 129 | python | def validate_nodes(root):
subnodes = root._subnodes()
entries = root._entries()
subnode_count = sum((node._size for node in subnodes))
total = (subnode_count + len(entries))
assert (root._size == total)
for node in subnodes:
validate_nodes(node) | def validate_nodes(root):
subnodes = root._subnodes()
entries = root._entries()
subnode_count = sum((node._size for node in subnodes))
total = (subnode_count + len(entries))
assert (root._size == total)
for node in subnodes:
validate_nodes(node)<|docstring|>NOT RPYTHON<|endoftext|> |
db2ee5c560d9a9a0b633dfb72dcbdb6fc0af21f0eeaf59faa4aaf088760f5e8a | def _validate_node(self):
'NOT RPYTHON'
pass | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _validate_node | namin/pycket | 129 | python | def _validate_node(self):
pass | def _validate_node(self):
pass<|docstring|>NOT RPYTHON<|endoftext|> |
ba3a77cfb462846f193f7f83bf16633ca491b673543cff847ce5943135559e17 | def _entries(self):
'NOT RPYTHON'
pass | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _entries | namin/pycket | 129 | python | def _entries(self):
pass | def _entries(self):
pass<|docstring|>NOT RPYTHON<|endoftext|> |
4ffd3f0c67d735d19bec3bca6a11ab04bda6f29e2e26e590fbc4f788f9b24fee | def _subnodes(self):
'NOT RPYTHON'
pass | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _subnodes | namin/pycket | 129 | python | def _subnodes(self):
pass | def _subnodes(self):
pass<|docstring|>NOT RPYTHON<|endoftext|> |
21950ac7ccbd05909604a51b3b39a478c98d71bf30c3aba283cffa26d01f63dd | def _entries(self):
'NOT RPYTHON'
entries = []
for x in range((len(self._array) / 2)):
(key_or_none, val_or_node) = self.entry(x)
if ((key_or_none is not None) or (val_or_node is None)):
entries.append((key_or_none, val_or_node))
return entries | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _entries | namin/pycket | 129 | python | def _entries(self):
entries = []
for x in range((len(self._array) / 2)):
(key_or_none, val_or_node) = self.entry(x)
if ((key_or_none is not None) or (val_or_node is None)):
entries.append((key_or_none, val_or_node))
return entries | def _entries(self):
entries = []
for x in range((len(self._array) / 2)):
(key_or_none, val_or_node) = self.entry(x)
if ((key_or_none is not None) or (val_or_node is None)):
entries.append((key_or_none, val_or_node))
return entries<|docstring|>NOT RPYTHON<|endoftext|> |
0de899dc52f7169f164689c9c596794876e888579932d13c6982848100c72051 | def _subnodes(self):
'NOT RPYTHON'
subnodes = []
for x in range((len(self._array) / 2)):
(key_or_none, val_or_node) = self.entry(x)
if ((key_or_none is None) and (val_or_node is not None)):
assert isinstance(val_or_node, INode)
subnodes.append(val_or_node)
return ... | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _subnodes | namin/pycket | 129 | python | def _subnodes(self):
subnodes = []
for x in range((len(self._array) / 2)):
(key_or_none, val_or_node) = self.entry(x)
if ((key_or_none is None) and (val_or_node is not None)):
assert isinstance(val_or_node, INode)
subnodes.append(val_or_node)
return subnodes | def _subnodes(self):
subnodes = []
for x in range((len(self._array) / 2)):
(key_or_none, val_or_node) = self.entry(x)
if ((key_or_none is None) and (val_or_node is not None)):
assert isinstance(val_or_node, INode)
subnodes.append(val_or_node)
return subnodes<|doc... |
08b9258f25d0221e099b0b01b92a089608558e24d81bd68e548b9886a106a090 | @objectmodel.always_inline
def entry(self, index):
' Helper function to extract the ith key/value pair '
base = (index * 2)
key = self._array[base]
val = self._array[(base + 1)]
return (key, val) | Helper function to extract the ith key/value pair | pycket/hash/persistent_hash_map.py | entry | namin/pycket | 129 | python | @objectmodel.always_inline
def entry(self, index):
' '
base = (index * 2)
key = self._array[base]
val = self._array[(base + 1)]
return (key, val) | @objectmodel.always_inline
def entry(self, index):
' '
base = (index * 2)
key = self._array[base]
val = self._array[(base + 1)]
return (key, val)<|docstring|>Helper function to extract the ith key/value pair<|endoftext|> |
602778d126b62c4d5b491a42943a0fb1a1f0594b277765a63becf48333a2a2bd | def _entries(self):
'NOT RPYTHON'
return [] | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _entries | namin/pycket | 129 | python | def _entries(self):
return [] | def _entries(self):
return []<|docstring|>NOT RPYTHON<|endoftext|> |
9ba1aa61be9a724098738fee2cb94709e1ba04feb4c5a874e173fad3f9793fef | def _subnodes(self):
'NOT RPYTHON'
return [node for node in self._array if (node is not None)] | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _subnodes | namin/pycket | 129 | python | def _subnodes(self):
return [node for node in self._array if (node is not None)] | def _subnodes(self):
return [node for node in self._array if (node is not None)]<|docstring|>NOT RPYTHON<|endoftext|> |
08b9258f25d0221e099b0b01b92a089608558e24d81bd68e548b9886a106a090 | @objectmodel.always_inline
def entry(self, index):
' Helper function to extract the ith key/value pair '
base = (index * 2)
key = self._array[base]
val = self._array[(base + 1)]
return (key, val) | Helper function to extract the ith key/value pair | pycket/hash/persistent_hash_map.py | entry | namin/pycket | 129 | python | @objectmodel.always_inline
def entry(self, index):
' '
base = (index * 2)
key = self._array[base]
val = self._array[(base + 1)]
return (key, val) | @objectmodel.always_inline
def entry(self, index):
' '
base = (index * 2)
key = self._array[base]
val = self._array[(base + 1)]
return (key, val)<|docstring|>Helper function to extract the ith key/value pair<|endoftext|> |
2c0f896e0d03840f3ff8719a66ed0dc801f586656f0a6237caa628e3ec699d1d | def _entries(self):
'NOT RPYTHON'
entries = []
for x in range((len(self._array) / 2)):
key_or_none = self.keyat(x)
if (key_or_none is None):
continue
val = self.valat(x)
entries.append((key_or_none, val))
return entries | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _entries | namin/pycket | 129 | python | def _entries(self):
entries = []
for x in range((len(self._array) / 2)):
key_or_none = self.keyat(x)
if (key_or_none is None):
continue
val = self.valat(x)
entries.append((key_or_none, val))
return entries | def _entries(self):
entries = []
for x in range((len(self._array) / 2)):
key_or_none = self.keyat(x)
if (key_or_none is None):
continue
val = self.valat(x)
entries.append((key_or_none, val))
return entries<|docstring|>NOT RPYTHON<|endoftext|> |
205b7198ea47c88a0edb0cb48ae9323e98751048b94b1df3a17a4ad89612657f | def _subnodes(self):
'NOT RPYTHON'
return [] | NOT RPYTHON | pycket/hash/persistent_hash_map.py | _subnodes | namin/pycket | 129 | python | def _subnodes(self):
return [] | def _subnodes(self):
return []<|docstring|>NOT RPYTHON<|endoftext|> |
99c7d1a90cbe39c0ca76b8c554e01a8f04910310d3c328b2ad8d885d746e58db | def union(self, other):
'\n Performs a right biased union via iterated insertion. This could be\n made faster at the cost of me figuring out how to actually implement\n a proper union operation.\n This skews the asymptotics a little since the implementation of\n ... | Performs a right biased union via iterated insertion. This could be
made faster at the cost of me figuring out how to actually implement
a proper union operation.
This skews the asymptotics a little since the implementation of
iteration is O(n lg n) as is insertion. | pycket/hash/persistent_hash_map.py | union | namin/pycket | 129 | python | def union(self, other):
'\n Performs a right biased union via iterated insertion. This could be\n made faster at the cost of me figuring out how to actually implement\n a proper union operation.\n This skews the asymptotics a little since the implementation of\n ... | def union(self, other):
'\n Performs a right biased union via iterated insertion. This could be\n made faster at the cost of me figuring out how to actually implement\n a proper union operation.\n This skews the asymptotics a little since the implementation of\n ... |
04552f4e9506c339044eadd33af50c2f4b91a24fc3450e33014f27f192835c25 | def __change_students_names(self):
'Replaces `ё` with `е` in each name to sort names properly'
for student in self.students:
student.name = student.name.replace('ё', 'е') | Replaces `ё` with `е` in each name to sort names properly | src/export.py | __change_students_names | NChechulin/python-yandex-contest-tools | 3 | python | def __change_students_names(self):
for student in self.students:
student.name = student.name.replace('ё', 'е') | def __change_students_names(self):
for student in self.students:
student.name = student.name.replace('ё', 'е')<|docstring|>Replaces `ё` with `е` in each name to sort names properly<|endoftext|> |
242f893c45b12d1610b7ff87173ca16c702af13c0a340ad1d96e169ba7036c9a | def _task_result_to_symbol(self, task_result: TaskResult) -> str:
"Returns a symbol corresponding to student's result"
if (task_result == TaskResult.Solved):
return self.solved_symbol
elif (task_result == TaskResult.Banned):
return self.banned_symbol
return self.not_solved_symbol | Returns a symbol corresponding to student's result | src/export.py | _task_result_to_symbol | NChechulin/python-yandex-contest-tools | 3 | python | def _task_result_to_symbol(self, task_result: TaskResult) -> str:
if (task_result == TaskResult.Solved):
return self.solved_symbol
elif (task_result == TaskResult.Banned):
return self.banned_symbol
return self.not_solved_symbol | def _task_result_to_symbol(self, task_result: TaskResult) -> str:
if (task_result == TaskResult.Solved):
return self.solved_symbol
elif (task_result == TaskResult.Banned):
return self.banned_symbol
return self.not_solved_symbol<|docstring|>Returns a symbol corresponding to student's res... |
aff27ad196f698f88dffcc781666b4f962ea5f8906e5568a16ad7f7acac9add3 | def __set_filename(self, output_dir: Path) -> str:
'Returns a filename (stem) where the data will be saved'
name = datetime.now().strftime('%Y-%m-%d_%H_%M_%S')
self.filename = (output_dir / f'RESULTS_{name}.{self.extension}') | Returns a filename (stem) where the data will be saved | src/export.py | __set_filename | NChechulin/python-yandex-contest-tools | 3 | python | def __set_filename(self, output_dir: Path) -> str:
name = datetime.now().strftime('%Y-%m-%d_%H_%M_%S')
self.filename = (output_dir / f'RESULTS_{name}.{self.extension}') | def __set_filename(self, output_dir: Path) -> str:
name = datetime.now().strftime('%Y-%m-%d_%H_%M_%S')
self.filename = (output_dir / f'RESULTS_{name}.{self.extension}')<|docstring|>Returns a filename (stem) where the data will be saved<|endoftext|> |
4ea775981e43c2a0eb0bc19273c22667b3d0bfc88ea19de050cf9ffbad527898 | def __write_header_to_file(self):
'Writes column names'
raise NotImplementedError() | Writes column names | src/export.py | __write_header_to_file | NChechulin/python-yandex-contest-tools | 3 | python | def __write_header_to_file(self):
raise NotImplementedError() | def __write_header_to_file(self):
raise NotImplementedError()<|docstring|>Writes column names<|endoftext|> |
bd93d22e1c925fa3e8b9d2b64178326cfe325a084664e48d059e197fcd5e33c7 | def __write_students_data(self):
'Writes the results of students into the file'
raise NotImplementedError() | Writes the results of students into the file | src/export.py | __write_students_data | NChechulin/python-yandex-contest-tools | 3 | python | def __write_students_data(self):
raise NotImplementedError() | def __write_students_data(self):
raise NotImplementedError()<|docstring|>Writes the results of students into the file<|endoftext|> |
67d097e04aaad8e68b5c5bef5d7adeb59e4e25baaed05c645bbdc3587362f8d0 | def write(self):
'Writes the all of the data into a file'
raise NotImplementedError() | Writes the all of the data into a file | src/export.py | write | NChechulin/python-yandex-contest-tools | 3 | python | def write(self):
raise NotImplementedError() | def write(self):
raise NotImplementedError()<|docstring|>Writes the all of the data into a file<|endoftext|> |
38aabfd34df0f1576926492b32c7a08b9c9336ed63a998be0d69d70db5e78f18 | def __write_header_to_file(self):
'Writes column names'
columns = ['name']
for task in self.tasks:
columns.append(task.name)
with open(self.filename, 'x') as fh:
writer = csv.writer(fh)
writer.writerow(columns) | Writes column names | src/export.py | __write_header_to_file | NChechulin/python-yandex-contest-tools | 3 | python | def __write_header_to_file(self):
columns = ['name']
for task in self.tasks:
columns.append(task.name)
with open(self.filename, 'x') as fh:
writer = csv.writer(fh)
writer.writerow(columns) | def __write_header_to_file(self):
columns = ['name']
for task in self.tasks:
columns.append(task.name)
with open(self.filename, 'x') as fh:
writer = csv.writer(fh)
writer.writerow(columns)<|docstring|>Writes column names<|endoftext|> |
900ebbd0544279b6a0a8ae7c7659f6bd959263027bfeca99cc60263cecc3f41f | def __write_students_data(self):
'Writes the results of students into the file'
with open(self.filename, 'a') as fh:
writer = csv.writer(fh)
for student in self.students:
row = [student.name]
for task in self.tasks:
symbol = self._task_result_to_symbol(stu... | Writes the results of students into the file | src/export.py | __write_students_data | NChechulin/python-yandex-contest-tools | 3 | python | def __write_students_data(self):
with open(self.filename, 'a') as fh:
writer = csv.writer(fh)
for student in self.students:
row = [student.name]
for task in self.tasks:
symbol = self._task_result_to_symbol(student.results[task.name])
row.a... | def __write_students_data(self):
with open(self.filename, 'a') as fh:
writer = csv.writer(fh)
for student in self.students:
row = [student.name]
for task in self.tasks:
symbol = self._task_result_to_symbol(student.results[task.name])
row.a... |
0c2e363b85b8a4647f22f8241d7971e71fddb8cbbeff6c4763d77abb2d04b769 | def write(self):
'Writes the all of the data into a file'
self.__write_header_to_file()
self.__write_students_data()
print(f'File saved as {self.filename}') | Writes the all of the data into a file | src/export.py | write | NChechulin/python-yandex-contest-tools | 3 | python | def write(self):
self.__write_header_to_file()
self.__write_students_data()
print(f'File saved as {self.filename}') | def write(self):
self.__write_header_to_file()
self.__write_students_data()
print(f'File saved as {self.filename}')<|docstring|>Writes the all of the data into a file<|endoftext|> |
d1157ecef2e2a71ce409fdb92dcf6ef85b5eda8514cba84132620e4568aa842b | def __write_header_to_file(self):
'Writes column names'
columns = ['name']
for task in self.tasks:
columns.append(task.name)
ROW = 1
for col in range(len(columns)):
cell = self.worksheet.cell(row=ROW, column=(col + 1))
cell.value = columns[col] | Writes column names | src/export.py | __write_header_to_file | NChechulin/python-yandex-contest-tools | 3 | python | def __write_header_to_file(self):
columns = ['name']
for task in self.tasks:
columns.append(task.name)
ROW = 1
for col in range(len(columns)):
cell = self.worksheet.cell(row=ROW, column=(col + 1))
cell.value = columns[col] | def __write_header_to_file(self):
columns = ['name']
for task in self.tasks:
columns.append(task.name)
ROW = 1
for col in range(len(columns)):
cell = self.worksheet.cell(row=ROW, column=(col + 1))
cell.value = columns[col]<|docstring|>Writes column names<|endoftext|> |
cf13b2024e75b969015ef2cb2902acbf8d05968e31f7c69deb264c32bd33bd15 | def __write_students_data(self):
'Writes the results of students into the file'
for (row, student) in enumerate(self.students, start=2):
self.worksheet.cell(row=row, column=1).value = student.name
for (col, task) in enumerate(self.tasks, start=2):
symbol = self._task_result_to_symbol... | Writes the results of students into the file | src/export.py | __write_students_data | NChechulin/python-yandex-contest-tools | 3 | python | def __write_students_data(self):
for (row, student) in enumerate(self.students, start=2):
self.worksheet.cell(row=row, column=1).value = student.name
for (col, task) in enumerate(self.tasks, start=2):
symbol = self._task_result_to_symbol(student.results[task.name])
self.... | def __write_students_data(self):
for (row, student) in enumerate(self.students, start=2):
self.worksheet.cell(row=row, column=1).value = student.name
for (col, task) in enumerate(self.tasks, start=2):
symbol = self._task_result_to_symbol(student.results[task.name])
self.... |
c22f99dcc242cc1884fbd75151e8d05aec6997c3d67c000fcd7df9f67c3f3b67 | def __setup_workbook(self):
'Creates workbook and worksheet'
self.workbook = Workbook()
self.worksheet = self.workbook.active | Creates workbook and worksheet | src/export.py | __setup_workbook | NChechulin/python-yandex-contest-tools | 3 | python | def __setup_workbook(self):
self.workbook = Workbook()
self.worksheet = self.workbook.active | def __setup_workbook(self):
self.workbook = Workbook()
self.worksheet = self.workbook.active<|docstring|>Creates workbook and worksheet<|endoftext|> |
3adfd2f82adf7a56e8d39816877e7de52bfdbff4344368c2f095dabe4a7c52a4 | def write(self):
'Writes the all of the data into a file'
self.__setup_workbook()
self.__write_header_to_file()
self.__write_students_data()
self.workbook.save(self.filename)
print(f'File saved as {self.filename}') | Writes the all of the data into a file | src/export.py | write | NChechulin/python-yandex-contest-tools | 3 | python | def write(self):
self.__setup_workbook()
self.__write_header_to_file()
self.__write_students_data()
self.workbook.save(self.filename)
print(f'File saved as {self.filename}') | def write(self):
self.__setup_workbook()
self.__write_header_to_file()
self.__write_students_data()
self.workbook.save(self.filename)
print(f'File saved as {self.filename}')<|docstring|>Writes the all of the data into a file<|endoftext|> |
1a8ce7bc46cb88d0360cbad598ef8b8c7685fa801253dbf4adfe20174571b39d | def extract_initable(t: type, inst=None) -> Optional[Callable[(..., type)]]:
'Extract e.g `dict` from `Optional[dict]`.\n Returns None if non-extractable.\n\n >>> from typing import Optional, Dict, List\n >>> ei = extract_initable\n\n >>> ei(List[str]) is ei(List) is ei(list) is ei(Optional[List[str]]) ... | Extract e.g `dict` from `Optional[dict]`.
Returns None if non-extractable.
>>> from typing import Optional, Dict, List
>>> ei = extract_initable
>>> ei(List[str]) is ei(List) is ei(list) is ei(Optional[List[str]]) is list
True
>>> ei(Dict[str,int]) is ei(Dict) is ei(dict) is ei(Optional[Dict[str,int]]) is dict
True
... | timefred/dikt/dikt.py | extract_initable | giladbarnea/timefred | 0 | python | def extract_initable(t: type, inst=None) -> Optional[Callable[(..., type)]]:
'Extract e.g `dict` from `Optional[dict]`.\n Returns None if non-extractable.\n\n >>> from typing import Optional, Dict, List\n >>> ei = extract_initable\n\n >>> ei(List[str]) is ei(List) is ei(list) is ei(Optional[List[str]]) ... | def extract_initable(t: type, inst=None) -> Optional[Callable[(..., type)]]:
'Extract e.g `dict` from `Optional[dict]`.\n Returns None if non-extractable.\n\n >>> from typing import Optional, Dict, List\n >>> ei = extract_initable\n\n >>> ei(List[str]) is ei(List) is ei(list) is ei(Optional[List[str]]) ... |
b8c1006a4e65c0152ad6850749148a4e50abe3d2c9605d1a2d8b753e970aa4e4 | def __iter__(self):
'\n so `dict(model)` works.\n pydantic/main.py#L733\n '
(yield from self.__dict__.items()) | so `dict(model)` works.
pydantic/main.py#L733 | timefred/dikt/dikt.py | __iter__ | giladbarnea/timefred | 0 | python | def __iter__(self):
'\n so `dict(model)` works.\n pydantic/main.py#L733\n '
(yield from self.__dict__.items()) | def __iter__(self):
'\n so `dict(model)` works.\n pydantic/main.py#L733\n '
(yield from self.__dict__.items())<|docstring|>so `dict(model)` works.
pydantic/main.py#L733<|endoftext|> |
3693c8f3c9b2d1ab0b0bbcb9aa067bb27e1b213eeec23c423721b306b61f8c39 | @annotate(set_in_self=True)
def __getattribute__(self, name):
"Makes d.foo return d['foo']"
try:
item = super().__getitem__(name)
return item
except KeyError as e:
attr = super().__getattribute__(name)
return attr | Makes d.foo return d['foo'] | timefred/dikt/dikt.py | __getattribute__ | giladbarnea/timefred | 0 | python | @annotate(set_in_self=True)
def __getattribute__(self, name):
try:
item = super().__getitem__(name)
return item
except KeyError as e:
attr = super().__getattribute__(name)
return attr | @annotate(set_in_self=True)
def __getattribute__(self, name):
try:
item = super().__getitem__(name)
return item
except KeyError as e:
attr = super().__getattribute__(name)
return attr<|docstring|>Makes d.foo return d['foo']<|endoftext|> |
48688665b5017a950505a6684c868b23400a3af527f58eb631a0f9820bbc041b | def __setattr__(self, name: str, value) -> None:
"Makes d.foo = 'bar' also set d['foo']"
super().__setattr__(name, value)
self[name] = value | Makes d.foo = 'bar' also set d['foo'] | timefred/dikt/dikt.py | __setattr__ | giladbarnea/timefred | 0 | python | def __setattr__(self, name: str, value) -> None:
super().__setattr__(name, value)
self[name] = value | def __setattr__(self, name: str, value) -> None:
super().__setattr__(name, value)
self[name] = value<|docstring|>Makes d.foo = 'bar' also set d['foo']<|endoftext|> |
76a0edf22c2ace26c001f6f28295f63c39ad76fb529dcdf9b8e5986b7d28df67 | def load_wrf(filename):
'docstring for load_wrf'
data = []
datelist = []
qvdata = mygis.read_nc(filename, 'QVAPOR').data
qcdata = (((mygis.read_nc(filename, 'QCLOUD').data + mygis.read_nc(filename, 'QICE').data) + mygis.read_nc(filename, 'QSNOW').data) + mygis.read_nc(filename, 'QRAIN').data)
td... | docstring for load_wrf | helpers/wrf/compare_ideal.py | load_wrf | d-reynolds/HICAR | 61 | python | def load_wrf(filename):
data = []
datelist = []
qvdata = mygis.read_nc(filename, 'QVAPOR').data
qcdata = (((mygis.read_nc(filename, 'QCLOUD').data + mygis.read_nc(filename, 'QICE').data) + mygis.read_nc(filename, 'QSNOW').data) + mygis.read_nc(filename, 'QRAIN').data)
tdata = (mygis.read_nc(fil... | def load_wrf(filename):
data = []
datelist = []
qvdata = mygis.read_nc(filename, 'QVAPOR').data
qcdata = (((mygis.read_nc(filename, 'QCLOUD').data + mygis.read_nc(filename, 'QICE').data) + mygis.read_nc(filename, 'QSNOW').data) + mygis.read_nc(filename, 'QRAIN').data)
tdata = (mygis.read_nc(fil... |
ed275a0fba46c2e7164c636cac738b5d99092b8089e49c147acbf3560c937160 | def plot_panel(panel, d1, d0, title=''):
'docstring for plot_panel'
nrows = len(d1)
ncols = 3
currow = 0
for k in d1.keys():
plt.subplot(nrows, ncols, ((currow * ncols) + panel))
if (k != 'r'):
delta = (d1[k] - d0[k])
vrange = (max(abs(delta.min()), delta.max(... | docstring for plot_panel | helpers/wrf/compare_ideal.py | plot_panel | d-reynolds/HICAR | 61 | python | def plot_panel(panel, d1, d0, title=):
nrows = len(d1)
ncols = 3
currow = 0
for k in d1.keys():
plt.subplot(nrows, ncols, ((currow * ncols) + panel))
if (k != 'r'):
delta = (d1[k] - d0[k])
vrange = (max(abs(delta.min()), delta.max()) * 0.95)
if (v... | def plot_panel(panel, d1, d0, title=):
nrows = len(d1)
ncols = 3
currow = 0
for k in d1.keys():
plt.subplot(nrows, ncols, ((currow * ncols) + panel))
if (k != 'r'):
delta = (d1[k] - d0[k])
vrange = (max(abs(delta.min()), delta.max()) * 0.95)
if (v... |
3a5c1039766260ad8a82bd6c146707384aef0c16a4e384550c56e3fbb1e15251 | def main(wrffile, icarfiles):
'docstring for main'
if (len(sys.argv) > 1):
icar_dir = (sys.argv[1] + '/')
if (len(sys.argv) > 2):
title = sys.argv[2]
else:
title = ''
else:
icar_dir = 'output/'
print('Loading WRF data')
(wrf_data, dates) = load... | docstring for main | helpers/wrf/compare_ideal.py | main | d-reynolds/HICAR | 61 | python | def main(wrffile, icarfiles):
if (len(sys.argv) > 1):
icar_dir = (sys.argv[1] + '/')
if (len(sys.argv) > 2):
title = sys.argv[2]
else:
title =
else:
icar_dir = 'output/'
print('Loading WRF data')
(wrf_data, dates) = load_wrf(wrffile)
prin... | def main(wrffile, icarfiles):
if (len(sys.argv) > 1):
icar_dir = (sys.argv[1] + '/')
if (len(sys.argv) > 2):
title = sys.argv[2]
else:
title =
else:
icar_dir = 'output/'
print('Loading WRF data')
(wrf_data, dates) = load_wrf(wrffile)
prin... |
304b7abb3201ef1e8506747bcc7aa55859c6131a16ec7d20b41bdc27e3390fb6 | @registry.register_check('cloudsearch')
def cloudsearch_https_enforcement_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
'[CloudSearch.1] CloudSearch Domains should be configured to use enforce HTTPS-only communications'
iso8601Time = datetime.datetime.utcnow().replace(tzinfo=... | [CloudSearch.1] CloudSearch Domains should be configured to use enforce HTTPS-only communications | eeauditor/auditors/aws/Amazon_CloudSearch_Auditor.py | cloudsearch_https_enforcement_check | dreamz1974/ElectricEye | 442 | python | @registry.register_check('cloudsearch')
def cloudsearch_https_enforcement_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
iso8601Time = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
for domain in cloudsearch.describe_domains()['DomainStatusLi... | @registry.register_check('cloudsearch')
def cloudsearch_https_enforcement_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
iso8601Time = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
for domain in cloudsearch.describe_domains()['DomainStatusLi... |
386b86c1c7b7ab110c325022ba72e3aa4944af4bef05d44048a211a04f00296b | @registry.register_check('cloudsearch')
def cloudsearch_tls1dot2_policy_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
'[CloudSearch.2] CloudSearch Domains that enforce HTTPS-only communications should use TLS 1.2 cipher suites'
iso8601Time = datetime.datetime.utcnow().replace... | [CloudSearch.2] CloudSearch Domains that enforce HTTPS-only communications should use TLS 1.2 cipher suites | eeauditor/auditors/aws/Amazon_CloudSearch_Auditor.py | cloudsearch_tls1dot2_policy_check | dreamz1974/ElectricEye | 442 | python | @registry.register_check('cloudsearch')
def cloudsearch_tls1dot2_policy_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
iso8601Time = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
for domain in cloudsearch.describe_domains()['DomainStatusList... | @registry.register_check('cloudsearch')
def cloudsearch_tls1dot2_policy_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
iso8601Time = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
for domain in cloudsearch.describe_domains()['DomainStatusList... |
a38ae8f89114411ac091ffc9f528454dee2b7a4ad1dfc8a3bc400c8c403290cc | def runge_kutta_step(dsystem_dt, phi, z_mem, z_rnd, z_rnd2, tau):
'\n This function performs a single Runge-Kutta step from the current\n time to a time tau forward.\n\n PARAMETERS\n ----------\n 1. dsystem_dt : function\n a function that calculates the system derivatives\n 2. p... | This function performs a single Runge-Kutta step from the current
time to a time tau forward.
PARAMETERS
----------
1. dsystem_dt : function
a function that calculates the system derivatives
2. phi : array
the full hierarchy vector
3. z_mem : array
the memory terms for the bath
4. z_... | mesohops/dynamics/integrator_rk.py | runge_kutta_step | MesoscienceLab/mesohops | 7 | python | def runge_kutta_step(dsystem_dt, phi, z_mem, z_rnd, z_rnd2, tau):
'\n This function performs a single Runge-Kutta step from the current\n time to a time tau forward.\n\n PARAMETERS\n ----------\n 1. dsystem_dt : function\n a function that calculates the system derivatives\n 2. p... | def runge_kutta_step(dsystem_dt, phi, z_mem, z_rnd, z_rnd2, tau):
'\n This function performs a single Runge-Kutta step from the current\n time to a time tau forward.\n\n PARAMETERS\n ----------\n 1. dsystem_dt : function\n a function that calculates the system derivatives\n 2. p... |
dd22f9652defaf75f6effe8d17dc03ac2f48b6b2f440f8f66b901fafeda9d93b | def runge_kutta_variables(storage, noise, noise2, tau):
'\n This is a function that accepts a storage and noise objects and returns the\n pre-requisite variables for a runge-kutta integration step in a list\n that can be unraveled to correctly feed into runge_kutta_step.\n\n PARAMETERS\n ----------\n... | This is a function that accepts a storage and noise objects and returns the
pre-requisite variables for a runge-kutta integration step in a list
that can be unraveled to correctly feed into runge_kutta_step.
PARAMETERS
----------
1. storage : HopsStorage object
an instantiation of HopsStorage associated w... | mesohops/dynamics/integrator_rk.py | runge_kutta_variables | MesoscienceLab/mesohops | 7 | python | def runge_kutta_variables(storage, noise, noise2, tau):
'\n This is a function that accepts a storage and noise objects and returns the\n pre-requisite variables for a runge-kutta integration step in a list\n that can be unraveled to correctly feed into runge_kutta_step.\n\n PARAMETERS\n ----------\n... | def runge_kutta_variables(storage, noise, noise2, tau):
'\n This is a function that accepts a storage and noise objects and returns the\n pre-requisite variables for a runge-kutta integration step in a list\n that can be unraveled to correctly feed into runge_kutta_step.\n\n PARAMETERS\n ----------\n... |
60acd7f55d1da1e6287101a888ae17a44bab2aacb782d25b0c2a3f4872a7cb7a | def url_parser(file_name='urls.cfg') -> list:
'\n Function that parses urls file and returns currently selected urls\n '
urls = []
try:
with open(file_name, 'r') as url_file:
for line in url_file.readlines():
line = line[:(- 1)]
if ((len(line) != 0) ... | Function that parses urls file and returns currently selected urls | sneakerupdate.py | url_parser | TudorPescaru/SneakerUpdate | 1 | python | def url_parser(file_name='urls.cfg') -> list:
'\n \n '
urls = []
try:
with open(file_name, 'r') as url_file:
for line in url_file.readlines():
line = line[:(- 1)]
if ((len(line) != 0) and (line[0] != '#')):
urls.append(line)
... | def url_parser(file_name='urls.cfg') -> list:
'\n \n '
urls = []
try:
with open(file_name, 'r') as url_file:
for line in url_file.readlines():
line = line[:(- 1)]
if ((len(line) != 0) and (line[0] != '#')):
urls.append(line)
... |
6b5cf6cf1751f5d37a6318626770da56bfb98d6a7e696869bf3b06316c2822f9 | def html_parser(url: str) -> BeautifulSoup:
'\n Function that returns soup of page source from url\n '
req = requests.get(url)
response = req.text
link = BeautifulSoup(response, 'html.parser')
return link | Function that returns soup of page source from url | sneakerupdate.py | html_parser | TudorPescaru/SneakerUpdate | 1 | python | def html_parser(url: str) -> BeautifulSoup:
'\n \n '
req = requests.get(url)
response = req.text
link = BeautifulSoup(response, 'html.parser')
return link | def html_parser(url: str) -> BeautifulSoup:
'\n \n '
req = requests.get(url)
response = req.text
link = BeautifulSoup(response, 'html.parser')
return link<|docstring|>Function that returns soup of page source from url<|endoftext|> |
963ae0328d16d5b3aa3fdc9103c9476674bd07f0e768877cef45941c4e42ec45 | def pretty_print(sneakers: list, retailer: str) -> str:
'\n Function that prints raffles from retailer in formatted way\n '
print((((((color['BOLD'] + color['UNDERLINE']) + 'Raffles from ') + retailer) + ':') + color['END']))
for sneaker in sneakers:
raffle = ''
colors = ['CYAN', 'DARK... | Function that prints raffles from retailer in formatted way | sneakerupdate.py | pretty_print | TudorPescaru/SneakerUpdate | 1 | python | def pretty_print(sneakers: list, retailer: str) -> str:
'\n \n '
print((((((color['BOLD'] + color['UNDERLINE']) + 'Raffles from ') + retailer) + ':') + color['END']))
for sneaker in sneakers:
raffle =
colors = ['CYAN', 'DARKCYAN', 'BLUE', 'BOLD']
i = 0
for item in snea... | def pretty_print(sneakers: list, retailer: str) -> str:
'\n \n '
print((((((color['BOLD'] + color['UNDERLINE']) + 'Raffles from ') + retailer) + ':') + color['END']))
for sneaker in sneakers:
raffle =
colors = ['CYAN', 'DARKCYAN', 'BLUE', 'BOLD']
i = 0
for item in snea... |
91bc5014c6a9a2b7ea68aad8a7ab898c2e33be53a9234c632709b1f01d8ee21b | def footshop_raffle(link: BeautifulSoup) -> list:
'\n Function that gets the current raffles from FootShop\n '
sneakers = []
containers = link.find_all('div', class_=re.compile('container active-or-coming-soon'))
for container in containers:
cards = container.find_all('div', class_=re.comp... | Function that gets the current raffles from FootShop | sneakerupdate.py | footshop_raffle | TudorPescaru/SneakerUpdate | 1 | python | def footshop_raffle(link: BeautifulSoup) -> list:
'\n \n '
sneakers = []
containers = link.find_all('div', class_=re.compile('container active-or-coming-soon'))
for container in containers:
cards = container.find_all('div', class_=re.compile('card.*closing-soon'))
cards += containe... | def footshop_raffle(link: BeautifulSoup) -> list:
'\n \n '
sneakers = []
containers = link.find_all('div', class_=re.compile('container active-or-coming-soon'))
for container in containers:
cards = container.find_all('div', class_=re.compile('card.*closing-soon'))
cards += containe... |
f8b87cdab127340e2b2dfcaf1a468ac53ea8f9505d88843c54e17abdba2f9a0d | def svd_raffle(link: BeautifulSoup) -> list:
'\n Function that gets the current raffles from SVD\n '
sneakers = []
containers = link.find_all('li', attrs={'class': ['item', 'product']})
for container in containers:
state = container.find('span', class_='product-state__tag')
state =... | Function that gets the current raffles from SVD | sneakerupdate.py | svd_raffle | TudorPescaru/SneakerUpdate | 1 | python | def svd_raffle(link: BeautifulSoup) -> list:
'\n \n '
sneakers = []
containers = link.find_all('li', attrs={'class': ['item', 'product']})
for container in containers:
state = container.find('span', class_='product-state__tag')
state = state.get_text()
if (state == 'Raffle'... | def svd_raffle(link: BeautifulSoup) -> list:
'\n \n '
sneakers = []
containers = link.find_all('li', attrs={'class': ['item', 'product']})
for container in containers:
state = container.find('span', class_='product-state__tag')
state = state.get_text()
if (state == 'Raffle'... |
28e38c4fb3a4ebd26e3bf7915231632b2fc3932644f32a328f10be75abb87ac4 | def main():
'\n Main driver code\n '
urls = url_parser()
if urls:
for i in range(len(urls)):
link = html_parser(urls[i])
if (i == 0):
sneakers = footshop_raffle(link)
pretty_print(sneakers, 'FootShop')
elif (i == 1):
... | Main driver code | sneakerupdate.py | main | TudorPescaru/SneakerUpdate | 1 | python | def main():
'\n \n '
urls = url_parser()
if urls:
for i in range(len(urls)):
link = html_parser(urls[i])
if (i == 0):
sneakers = footshop_raffle(link)
pretty_print(sneakers, 'FootShop')
elif (i == 1):
sneakers ... | def main():
'\n \n '
urls = url_parser()
if urls:
for i in range(len(urls)):
link = html_parser(urls[i])
if (i == 0):
sneakers = footshop_raffle(link)
pretty_print(sneakers, 'FootShop')
elif (i == 1):
sneakers ... |
82e261e236f27f9bf7137f5a677fc71d0c3f74708af6364596c4a9e786f3a61a | @respond_to('^(gpoem|make a poem about) (?P<topic>.*)$')
def google_poem(self, message, topic):
'make a poem about __: show a google poem about __'
r = requests.get((('http://www.google.com/complete/search?output=toolbar&q=' + topic) + '%20'))
xmldoc = minidom.parseString(r.text)
item_list = xmldoc.getE... | make a poem about __: show a google poem about __ | will/plugins/fun/googlepoem.py | google_poem | dawn-minion/will | 349 | python | @respond_to('^(gpoem|make a poem about) (?P<topic>.*)$')
def google_poem(self, message, topic):
r = requests.get((('http://www.google.com/complete/search?output=toolbar&q=' + topic) + '%20'))
xmldoc = minidom.parseString(r.text)
item_list = xmldoc.getElementsByTagName('suggestion')
context = {'topi... | @respond_to('^(gpoem|make a poem about) (?P<topic>.*)$')
def google_poem(self, message, topic):
r = requests.get((('http://www.google.com/complete/search?output=toolbar&q=' + topic) + '%20'))
xmldoc = minidom.parseString(r.text)
item_list = xmldoc.getElementsByTagName('suggestion')
context = {'topi... |
0d6c1dbb44f4988827dd2462fa7a2033f3a3193193130675a9bd3ba68e6bf8cf | def __init__(self, df, utility, availability=None):
'"\n Initialize the class\n\n :param df: DataFrame\n '
self.df = df
self.utility = utility
self.params = []
self.params_choice = {}
for alt in self.utility.keys():
for pair in self.utility[alt]:
if (len(... | "
Initialize the class
:param df: DataFrame | code/classes/MNLogit.py | __init__ | glederrey/IEEE2018-SNM | 1 | python | def __init__(self, df, utility, availability=None):
'"\n Initialize the class\n\n :param df: DataFrame\n '
self.df = df
self.utility = utility
self.params = []
self.params_choice = {}
for alt in self.utility.keys():
for pair in self.utility[alt]:
if (len(... | def __init__(self, df, utility, availability=None):
'"\n Initialize the class\n\n :param df: DataFrame\n '
self.df = df
self.utility = utility
self.params = []
self.params_choice = {}
for alt in self.utility.keys():
for pair in self.utility[alt]:
if (len(... |
26af3bc6f5158cb67381d0ea2d3a7b4442123d710837a7cd41293fcca1278314 | def compute_utility(self, x, indices=None, alt=None):
'\n Compute the utility for a given alternative.\n\n If None are given, compute the utility for all alternatives\n\n :param x: Value for the parameters\n :param indices: Indices for which we compute the utility\n :param alt: St... | Compute the utility for a given alternative.
If None are given, compute the utility for all alternatives
:param x: Value for the parameters
:param indices: Indices for which we compute the utility
:param alt: String with an alternative
:return: Either a float or a dict | code/classes/MNLogit.py | compute_utility | glederrey/IEEE2018-SNM | 1 | python | def compute_utility(self, x, indices=None, alt=None):
'\n Compute the utility for a given alternative.\n\n If None are given, compute the utility for all alternatives\n\n :param x: Value for the parameters\n :param indices: Indices for which we compute the utility\n :param alt: St... | def compute_utility(self, x, indices=None, alt=None):
'\n Compute the utility for a given alternative.\n\n If None are given, compute the utility for all alternatives\n\n :param x: Value for the parameters\n :param indices: Indices for which we compute the utility\n :param alt: St... |
9f740d22f178cdc4e5a86196a6af9d852376aee60d14fb92c7d807a4400dab6b | def probabilities(self, x, indices=None):
'\n Compute probabilities for given parameters\n\n :param x: array with values of parameters\n :param indices: Array with indices\n :return:\n '
proba = {}
utilities = self.compute_utility(x, indices)
if (indices is None):
... | Compute probabilities for given parameters
:param x: array with values of parameters
:param indices: Array with indices
:return: | code/classes/MNLogit.py | probabilities | glederrey/IEEE2018-SNM | 1 | python | def probabilities(self, x, indices=None):
'\n Compute probabilities for given parameters\n\n :param x: array with values of parameters\n :param indices: Array with indices\n :return:\n '
proba = {}
utilities = self.compute_utility(x, indices)
if (indices is None):
... | def probabilities(self, x, indices=None):
'\n Compute probabilities for given parameters\n\n :param x: array with values of parameters\n :param indices: Array with indices\n :return:\n '
proba = {}
utilities = self.compute_utility(x, indices)
if (indices is None):
... |
fc1ca7e29befe7c79b97419a408e5d738fa8c714243e5d2bee1534dfae66f206 | def loglikelihood(self, x, indices=None):
'\n Log Likelihood for given parameters\n\n :param x: parameters\n :return:\n '
if (indices is None):
indices = np.array(range(len(self.df)))
if ((not isinstance(indices, list)) and (not isinstance(indices, np.ndarray))):
... | Log Likelihood for given parameters
:param x: parameters
:return: | code/classes/MNLogit.py | loglikelihood | glederrey/IEEE2018-SNM | 1 | python | def loglikelihood(self, x, indices=None):
'\n Log Likelihood for given parameters\n\n :param x: parameters\n :return:\n '
if (indices is None):
indices = np.array(range(len(self.df)))
if ((not isinstance(indices, list)) and (not isinstance(indices, np.ndarray))):
... | def loglikelihood(self, x, indices=None):
'\n Log Likelihood for given parameters\n\n :param x: parameters\n :return:\n '
if (indices is None):
indices = np.array(range(len(self.df)))
if ((not isinstance(indices, list)) and (not isinstance(indices, np.ndarray))):
... |
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