xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

0706a99ed381161a3c1ab460e11daee648ac9c0f

74

py

Python

djangocms_versioning/test_utils/polls/views.py

NarenderRajuB/djangocms-versioning

aa7a16fe275a6d8a41781ffb1e10427de917c2d5

[ "BSD-3-Clause" ]

12

2018-09-04T10:33:16.000Z

2021-09-07T14:30:12.000Z

djangocms_versioning/test_utils/polls/views.py

NarenderRajuB/djangocms-versioning

aa7a16fe275a6d8a41781ffb1e10427de917c2d5

[ "BSD-3-Clause" ]

46

2018-07-31T08:45:17.000Z

2021-09-08T15:45:05.000Z

djangocms_versioning/test_utils/polls/views.py

NarenderRajuB/djangocms-versioning

aa7a16fe275a6d8a41781ffb1e10427de917c2d5

[ "BSD-3-Clause" ]

16

2018-08-30T19:08:45.000Z

2021-07-13T11:31:43.000Z

from django.views.generic import View class PreviewView(View): pass

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py

Python

09-2021-06-18/src/_solutions/bank_account_v0.py

eotp/python-FU-class

f0a7518b3e3204a77e8855bef91afeaabb0d52ac

[ "MIT" ]

1

2020-01-17T14:51:40.000Z

08-2022-06-23/src/_solutions/bank_account_v0.py

eotp/python-FU-WiSe1920

4f225430ef8a70faca8c86c77cc888524c8e0546

[ "MIT" ]

null

08-2022-06-23/src/_solutions/bank_account_v0.py

eotp/python-FU-WiSe1920

4f225430ef8a70faca8c86c77cc888524c8e0546

[ "MIT" ]

1

2020-12-04T15:37:28.000Z

class BankAccount: bank_name = 'My Bank' def print_bank_name(self): print('My name is', self.bank_name)

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py

Python

imgaug/augmenters/pooling.py

fmder/imgaug

4c81c7a7503b64f54d76144385ea4330fd7c8a84

[ "MIT" ]

null

imgaug/augmenters/pooling.py

fmder/imgaug

4c81c7a7503b64f54d76144385ea4330fd7c8a84

[ "MIT" ]

null

imgaug/augmenters/pooling.py

fmder/imgaug

4c81c7a7503b64f54d76144385ea4330fd7c8a84

[ "MIT" ]

null

""" Augmenters that apply pooling operations to images. List of augmenters: * :class:`AveragePooling` * :class:`MaxPooling` * :class:`MinPooling` * :class:`MedianPooling` """ from __future__ import print_function, division, absolute_import from abc import ABCMeta, abstractmethod import functools import six import numpy as np import imgaug as ia from . import meta from .. import parameters as iap def _compute_shape_after_pooling(image_shape, ksize_h, ksize_w): if any([axis == 0 for axis in image_shape]): return image_shape height, width = image_shape[0:2] if height % ksize_h > 0: height += ksize_h - (height % ksize_h) if width % ksize_w > 0: width += ksize_w - (width % ksize_w) return tuple([ height//ksize_h, width//ksize_w, ] + list(image_shape[2:])) @six.add_metaclass(ABCMeta) class _AbstractPoolingBase(meta.Augmenter): # TODO add floats as ksize denoting fractions of image sizes # (note possible overlap with fractional kernel sizes here) def __init__(self, kernel_size, keep_size=True, name=None, deterministic=False, random_state=None): super(_AbstractPoolingBase, self).__init__( name=name, deterministic=deterministic, random_state=random_state) self.kernel_size = iap.handle_discrete_kernel_size_param( kernel_size, "kernel_size", value_range=(0, None), allow_floats=False) self.keep_size = keep_size self._resize_hm_and_sm_arrays = True @abstractmethod def _pool_image(self, image, kernel_size_h, kernel_size_w): """Apply pooling method with given kernel height/width to an image.""" def _draw_samples(self, nb_rows, random_state): rss = random_state.duplicate(2) mode = "single" if self.kernel_size[1] is None else "two" kernel_sizes_h = self.kernel_size[0].draw_samples( (nb_rows,), random_state=rss[0]) if mode == "single": kernel_sizes_w = kernel_sizes_h else: kernel_sizes_w = self.kernel_size[1].draw_samples( (nb_rows,), random_state=rss[1]) return ( np.clip(kernel_sizes_h, 1, None), np.clip(kernel_sizes_w, 1, None) ) def _augment_batch(self, batch, random_state, parents, hooks): if batch.images is None and self.keep_size: return batch samples = self._draw_samples(batch.nb_rows, random_state) for column in batch.columns: value_aug = getattr( self, "_augment_%s_by_samples" % (column.name,) )(column.value, samples) setattr(batch, column.attr_name, value_aug) return batch def _augment_images_by_samples(self, images, samples): if not self.keep_size: images = list(images) kernel_sizes_h, kernel_sizes_w = samples gen = enumerate(zip(images, kernel_sizes_h, kernel_sizes_w)) for i, (image, ksize_h, ksize_w) in gen: if ksize_h >= 2 or ksize_w >= 2: image_pooled = self._pool_image( image, ksize_h, ksize_w) if self.keep_size: image_pooled = ia.imresize_single_image( image_pooled, image.shape[0:2]) images[i] = image_pooled return images def _augment_heatmaps_by_samples(self, heatmaps, samples): return self._augment_hms_and_segmaps_by_samples(heatmaps, samples, "arr_0to1") def _augment_segmentation_maps_by_samples(self, segmaps, samples): return self._augment_hms_and_segmaps_by_samples(segmaps, samples, "arr") def _augment_hms_and_segmaps_by_samples(self, augmentables, samples, arr_attr_name): if self.keep_size: return augmentables kernel_sizes_h, kernel_sizes_w = samples gen = enumerate(zip(augmentables, kernel_sizes_h, kernel_sizes_w)) for i, (augmentable, ksize_h, ksize_w) in gen: if ksize_h >= 2 or ksize_w >= 2: # We could also keep the size of the HM/SM array unchanged # here as the library can handle HMs/SMs that are larger # than the image. This might be inintuitive however and # could lead to unnecessary performance degredation. if self._resize_hm_and_sm_arrays: new_shape_arr = _compute_shape_after_pooling( getattr(augmentable, arr_attr_name).shape, ksize_h, ksize_w) augmentable = augmentable.resize(new_shape_arr[0:2]) new_shape = _compute_shape_after_pooling( augmentable.shape, ksize_h, ksize_w) augmentable.shape = new_shape augmentables[i] = augmentable return augmentables def _augment_keypoints_by_samples(self, keypoints_on_images, samples): if self.keep_size: return keypoints_on_images kernel_sizes_h, kernel_sizes_w = samples gen = enumerate(zip(keypoints_on_images, kernel_sizes_h, kernel_sizes_w)) for i, (kpsoi, ksize_h, ksize_w) in gen: if ksize_h >= 2 or ksize_w >= 2: new_shape = _compute_shape_after_pooling( kpsoi.shape, ksize_h, ksize_w) keypoints_on_images[i] = kpsoi.on_(new_shape) return keypoints_on_images def _augment_polygons_by_samples(self, polygons_on_images, samples): func = functools.partial(self._augment_keypoints_by_samples, samples=samples) return self._apply_to_polygons_as_keypoints(polygons_on_images, func, recoverer=None) def _augment_line_strings_by_samples(self, line_strings_on_images, samples): func = functools.partial(self._augment_keypoints_by_samples, samples=samples) return self._apply_to_cbaois_as_keypoints(line_strings_on_images, func) def _augment_bounding_boxes_by_samples(self, bounding_boxes_on_images, samples): func = functools.partial(self._augment_keypoints_by_samples, samples=samples) return self._apply_to_cbaois_as_keypoints(bounding_boxes_on_images, func) def get_parameters(self): """See :func:`imgaug.augmenters.meta.Augmenter.get_parameters`.""" return [self.kernel_size, self.keep_size] # TODO rename kernel size parameters in all augmenters to kernel_size # TODO add per_channel # TODO add upscaling interpolation mode? class AveragePooling(_AbstractPoolingBase): """ Apply average pooling to images. This augmenter pools images with kernel sizes ``H x W`` by averaging the pixel values within these windows. For e.g. ``2 x 2`` this halves the image size. Optionally, the augmenter will automatically re-upscale the image to the input size (by default this is activated). Note that this augmenter is very similar to ``AverageBlur``. ``AverageBlur`` applies averaging within windows of given kernel size *without* striding, while ``AveragePooling`` applies striding corresponding to the kernel size, with optional upscaling afterwards. The upscaling is configured to create "pixelated"/"blocky" images by default. .. note:: During heatmap or segmentation map augmentation, the respective arrays are not changed, only the shapes of the underlying images are updated. This is because imgaug can handle maps/maks that are larger/smaller than their corresponding image. dtype support:: See :func:`imgaug.imgaug.avg_pool`. Attributes ---------- kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional The kernel size of the pooling operation. * If an int, then that value will be used for all images for both kernel height and width. * If a tuple ``(a, b)``, then a value from the discrete range ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image and used for both kernel height and width. * If a StochasticParameter, then a value will be sampled per image from that parameter per image and used for both kernel height and width. * If a tuple of tuple of int given as ``((a, b), (c, d))``, then two values will be sampled independently from the discrete ranges ``[a..b]`` and ``[c..d]`` per image and used as the kernel height and width. * If a tuple of lists of int, then two values will be sampled independently per image, one from the first list and one from the second, and used as the kernel height and width. * If a tuple of StochasticParameter, then two values will be sampled indepdently per image, one from the first parameter and one from the second, and used as the kernel height and width. keep_size : bool, optional After pooling, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the pooled image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.AveragePooling(2) Create an augmenter that always pools with a kernel size of ``2 x 2``. >>> aug = iaa.AveragePooling(2, keep_size=False) Create an augmenter that always pools with a kernel size of ``2 x 2`` and does *not* resize back to the input image size, i.e. the resulting images have half the resolution. >>> aug = iaa.AveragePooling([2, 8]) Create an augmenter that always pools either with a kernel size of ``2 x 2`` or ``8 x 8``. >>> aug = iaa.AveragePooling((1, 7)) Create an augmenter that always pools with a kernel size of ``1 x 1`` (does nothing) to ``7 x 7``. The kernel sizes are always symmetric. >>> aug = iaa.AveragePooling(((1, 7), (1, 7))) Create an augmenter that always pools with a kernel size of ``H x W`` where ``H`` and ``W`` are both sampled independently from the range ``[1..7]``. E.g. resulting kernel sizes could be ``3 x 7`` or ``5 x 1``. """ # TODO add floats as ksize denoting fractions of image sizes # (note possible overlap with fractional kernel sizes here) def __init__(self, kernel_size, keep_size=True, name=None, deterministic=False, random_state=None): super(AveragePooling, self).__init__( kernel_size=kernel_size, keep_size=keep_size, name=name, deterministic=deterministic, random_state=random_state) def _pool_image(self, image, kernel_size_h, kernel_size_w): return ia.avg_pool( image, (kernel_size_h, kernel_size_w) ) class MaxPooling(_AbstractPoolingBase): """ Apply max pooling to images. This augmenter pools images with kernel sizes ``H x W`` by taking the maximum pixel value over windows. For e.g. ``2 x 2`` this halves the image size. Optionally, the augmenter will automatically re-upscale the image to the input size (by default this is activated). The maximum within each pixel window is always taken channelwise.. .. note:: During heatmap or segmentation map augmentation, the respective arrays are not changed, only the shapes of the underlying images are updated. This is because imgaug can handle maps/maks that are larger/smaller than their corresponding image. dtype support:: See :func:`imgaug.imgaug.max_pool`. Attributes ---------- kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional The kernel size of the pooling operation. * If an int, then that value will be used for all images for both kernel height and width. * If a tuple ``(a, b)``, then a value from the discrete range ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image and used for both kernel height and width. * If a StochasticParameter, then a value will be sampled per image from that parameter per image and used for both kernel height and width. * If a tuple of tuple of int given as ``((a, b), (c, d))``, then two values will be sampled independently from the discrete ranges ``[a..b]`` and ``[c..d]`` per image and used as the kernel height and width. * If a tuple of lists of int, then two values will be sampled independently per image, one from the first list and one from the second, and used as the kernel height and width. * If a tuple of StochasticParameter, then two values will be sampled indepdently per image, one from the first parameter and one from the second, and used as the kernel height and width. keep_size : bool, optional After pooling, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the pooled image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.MaxPooling(2) Create an augmenter that always pools with a kernel size of ``2 x 2``. >>> aug = iaa.MaxPooling(2, keep_size=False) Create an augmenter that always pools with a kernel size of ``2 x 2`` and does *not* resize back to the input image size, i.e. the resulting images have half the resolution. >>> aug = iaa.MaxPooling([2, 8]) Create an augmenter that always pools either with a kernel size of ``2 x 2`` or ``8 x 8``. >>> aug = iaa.MaxPooling((1, 7)) Create an augmenter that always pools with a kernel size of ``1 x 1`` (does nothing) to ``7 x 7``. The kernel sizes are always symmetric. >>> aug = iaa.MaxPooling(((1, 7), (1, 7))) Create an augmenter that always pools with a kernel size of ``H x W`` where ``H`` and ``W`` are both sampled independently from the range ``[1..7]``. E.g. resulting kernel sizes could be ``3 x 7`` or ``5 x 1``. """ # TODO add floats as ksize denoting fractions of image sizes # (note possible overlap with fractional kernel sizes here) def __init__(self, kernel_size, keep_size=True, name=None, deterministic=False, random_state=None): super(MaxPooling, self).__init__( kernel_size=kernel_size, keep_size=keep_size, name=name, deterministic=deterministic, random_state=random_state) def _pool_image(self, image, kernel_size_h, kernel_size_w): # TODO extend max_pool to support pad_mode and set it here # to reflection padding return ia.max_pool( image, (kernel_size_h, kernel_size_w) ) class MinPooling(_AbstractPoolingBase): """ Apply minimum pooling to images. This augmenter pools images with kernel sizes ``H x W`` by taking the minimum pixel value over windows. For e.g. ``2 x 2`` this halves the image size. Optionally, the augmenter will automatically re-upscale the image to the input size (by default this is activated). The minimum within each pixel window is always taken channelwise. .. note:: During heatmap or segmentation map augmentation, the respective arrays are not changed, only the shapes of the underlying images are updated. This is because imgaug can handle maps/maks that are larger/smaller than their corresponding image. dtype support:: See :func:`imgaug.imgaug.pool`. Attributes ---------- kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional The kernel size of the pooling operation. * If an int, then that value will be used for all images for both kernel height and width. * If a tuple ``(a, b)``, then a value from the discrete range ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image and used for both kernel height and width. * If a StochasticParameter, then a value will be sampled per image from that parameter per image and used for both kernel height and width. * If a tuple of tuple of int given as ``((a, b), (c, d))``, then two values will be sampled independently from the discrete ranges ``[a..b]`` and ``[c..d]`` per image and used as the kernel height and width. * If a tuple of lists of int, then two values will be sampled independently per image, one from the first list and one from the second, and used as the kernel height and width. * If a tuple of StochasticParameter, then two values will be sampled indepdently per image, one from the first parameter and one from the second, and used as the kernel height and width. keep_size : bool, optional After pooling, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the pooled image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.MinPooling(2) Create an augmenter that always pools with a kernel size of ``2 x 2``. >>> aug = iaa.MinPooling(2, keep_size=False) Create an augmenter that always pools with a kernel size of ``2 x 2`` and does *not* resize back to the input image size, i.e. the resulting images have half the resolution. >>> aug = iaa.MinPooling([2, 8]) Create an augmenter that always pools either with a kernel size of ``2 x 2`` or ``8 x 8``. >>> aug = iaa.MinPooling((1, 7)) Create an augmenter that always pools with a kernel size of ``1 x 1`` (does nothing) to ``7 x 7``. The kernel sizes are always symmetric. >>> aug = iaa.MinPooling(((1, 7), (1, 7))) Create an augmenter that always pools with a kernel size of ``H x W`` where ``H`` and ``W`` are both sampled independently from the range ``[1..7]``. E.g. resulting kernel sizes could be ``3 x 7`` or ``5 x 1``. """ # TODO add floats as ksize denoting fractions of image sizes # (note possible overlap with fractional kernel sizes here) def __init__(self, kernel_size, keep_size=True, name=None, deterministic=False, random_state=None): super(MinPooling, self).__init__( kernel_size=kernel_size, keep_size=keep_size, name=name, deterministic=deterministic, random_state=random_state) def _pool_image(self, image, kernel_size_h, kernel_size_w): # TODO extend pool to support pad_mode and set it here # to reflection padding return ia.min_pool( image, (kernel_size_h, kernel_size_w) ) class MedianPooling(_AbstractPoolingBase): """ Apply median pooling to images. This augmenter pools images with kernel sizes ``H x W`` by taking the median pixel value over windows. For e.g. ``2 x 2`` this halves the image size. Optionally, the augmenter will automatically re-upscale the image to the input size (by default this is activated). The median within each pixel window is always taken channelwise. .. note:: During heatmap or segmentation map augmentation, the respective arrays are not changed, only the shapes of the underlying images are updated. This is because imgaug can handle maps/maks that are larger/smaller than their corresponding image. dtype support:: See :func:`imgaug.imgaug.pool`. Attributes ---------- kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional The kernel size of the pooling operation. * If an int, then that value will be used for all images for both kernel height and width. * If a tuple ``(a, b)``, then a value from the discrete range ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image and used for both kernel height and width. * If a StochasticParameter, then a value will be sampled per image from that parameter per image and used for both kernel height and width. * If a tuple of tuple of int given as ``((a, b), (c, d))``, then two values will be sampled independently from the discrete ranges ``[a..b]`` and ``[c..d]`` per image and used as the kernel height and width. * If a tuple of lists of int, then two values will be sampled independently per image, one from the first list and one from the second, and used as the kernel height and width. * If a tuple of StochasticParameter, then two values will be sampled indepdently per image, one from the first parameter and one from the second, and used as the kernel height and width. keep_size : bool, optional After pooling, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the pooled image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.MedianPooling(2) Create an augmenter that always pools with a kernel size of ``2 x 2``. >>> aug = iaa.MedianPooling(2, keep_size=False) Create an augmenter that always pools with a kernel size of ``2 x 2`` and does *not* resize back to the input image size, i.e. the resulting images have half the resolution. >>> aug = iaa.MedianPooling([2, 8]) Create an augmenter that always pools either with a kernel size of ``2 x 2`` or ``8 x 8``. >>> aug = iaa.MedianPooling((1, 7)) Create an augmenter that always pools with a kernel size of ``1 x 1`` (does nothing) to ``7 x 7``. The kernel sizes are always symmetric. >>> aug = iaa.MedianPooling(((1, 7), (1, 7))) Create an augmenter that always pools with a kernel size of ``H x W`` where ``H`` and ``W`` are both sampled independently from the range ``[1..7]``. E.g. resulting kernel sizes could be ``3 x 7`` or ``5 x 1``. """ # TODO add floats as ksize denoting fractions of image sizes # (note possible overlap with fractional kernel sizes here) def __init__(self, kernel_size, keep_size=True, name=None, deterministic=False, random_state=None): super(MedianPooling, self).__init__( kernel_size=kernel_size, keep_size=keep_size, name=name, deterministic=deterministic, random_state=random_state) def _pool_image(self, image, kernel_size_h, kernel_size_w): # TODO extend pool to support pad_mode and set it here # to reflection padding return ia.median_pool( image, (kernel_size_h, kernel_size_w) )

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6

0755f4b2c3f21a5bd6057ccad2beb943076e9ed6

4,128

py

Python

agents/customization.py

jun2tong/Continual-Learning-Benchmark

b6fdcc56ec2821b945105b1a54cdea03b421a6b2

[ "MIT" ]

null

agents/customization.py

jun2tong/Continual-Learning-Benchmark

b6fdcc56ec2821b945105b1a54cdea03b421a6b2

[ "MIT" ]

null

agents/customization.py

jun2tong/Continual-Learning-Benchmark

b6fdcc56ec2821b945105b1a54cdea03b421a6b2

[ "MIT" ]

null

import torch from .default import NormalNN from .regularization import SI, EWC, EWC_online from .exp_replay import Naive_Rehearsal, GEM from modules.criterions import BCEauto def init_zero_weights(m): with torch.no_grad(): if type(m) == torch.nn.Linear: m.weight.zero_() m.bias.zero_() elif type(m) == torch.nn.ModuleDict: for l in m.values(): init_zero_weights(l) else: assert False, 'Only support linear layer' def NormalNN_reset_optim(agent_config): agent = NormalNN(agent_config) agent.reset_optimizer = True return agent def NormalNN_BCE(agent_config): agent = NormalNN(agent_config) agent.criterion_fn = BCEauto() return agent def SI_BCE(agent_config): agent = SI(agent_config) agent.criterion_fn = BCEauto() return agent def SI_splitMNIST_zero_init(agent_config): agent = SI(agent_config) agent.damping_factor = 1e-3 agent.reset_optimizer = True agent.model.last.apply(init_zero_weights) return agent def SI_splitMNIST_rand_init(agent_config): agent = SI(agent_config) agent.damping_factor = 1e-3 agent.reset_optimizer = True return agent def EWC_BCE(agent_config): agent = EWC(agent_config) agent.criterion_fn = BCEauto() return agent def EWC_mnist(agent_config): agent = EWC(agent_config) agent.n_fisher_sample = 60000 return agent def EWC_online_mnist(agent_config): agent = EWC(agent_config) agent.n_fisher_sample = 60000 agent.online_reg = True return agent def EWC_online_empFI(agent_config): agent = EWC(agent_config) agent.empFI = True return agent def EWC_zero_init(agent_config): agent = EWC(agent_config) agent.reset_optimizer = True agent.model.last.apply(init_zero_weights) return agent def EWC_rand_init(agent_config): agent = EWC(agent_config) agent.reset_optimizer = True return agent def EWC_reset_optim(agent_config): agent = EWC(agent_config) agent.reset_optimizer = True return agent def EWC_online_reset_optim(agent_config): agent = EWC_online(agent_config) agent.reset_optimizer = True return agent def Naive_Rehearsal_100(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 100 return agent def Naive_Rehearsal_200(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 200 return agent def Naive_Rehearsal_400(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 400 return agent def Naive_Rehearsal_1100(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 1100 return agent def Naive_Rehearsal_1400(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 1400 return agent def Naive_Rehearsal_4000(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 4000 return agent def Naive_Rehearsal_4400(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 4400 return agent def Naive_Rehearsal_5600(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 5600 return agent def Naive_Rehearsal_16000(agent_config): agent = Naive_Rehearsal(agent_config) agent.memory_size = 16000 return agent def GEM_100(agent_config): agent = GEM(agent_config) agent.memory_size = 100 return agent def GEM_200(agent_config): agent = GEM(agent_config) agent.memory_size = 200 return agent def GEM_400(agent_config): agent = GEM(agent_config) agent.memory_size = 400 return agent def GEM_1100(agent_config): agent = GEM(agent_config) agent.memory_size = 1100 return agent def GEM_4000(agent_config): agent = GEM(agent_config) agent.memory_size = 4000 return agent def GEM_4400(agent_config): agent = GEM(agent_config) agent.memory_size = 4400 return agent def GEM_16000(agent_config): agent = GEM(agent_config) agent.memory_size = 16000 return agent

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src/python/__init__.py

wmeddie/veda

52849411106071ebfdb7d0c86e34684c0e0fa843

[ "BSD-3-Clause" ]

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2020-07-20T07:37:06.000Z

2022-03-11T19:59:21.000Z

src/python/__init__.py

wmeddie/veda

52849411106071ebfdb7d0c86e34684c0e0fa843

[ "BSD-3-Clause" ]

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2020-09-07T10:53:01.000Z

2022-03-26T02:50:43.000Z

src/python/__init__.py

wmeddie/veda

52849411106071ebfdb7d0c86e34684c0e0fa843

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sim/unitytrainers/ppo/__init__.py

PranayKr/Bonsai_CodeBase

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sim/unitytrainers/ppo/__init__.py

PranayKr/Bonsai_CodeBase

4e9a7281364bd71f7cd466634ef8379eacb1b716

[ "Apache-2.0" ]

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2019-12-16T22:13:37.000Z

2022-02-10T01:05:42.000Z

sim/unitytrainers/ppo/__init__.py

PranayKr/Bonsai_CodeBase

4e9a7281364bd71f7cd466634ef8379eacb1b716

[ "Apache-2.0" ]

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from .models import * from .trainer import *

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python/testData/completion/className/thirdPartyPackageBundledDependenciesNotSuggested/site-packages/requests/__init__.py

06needhamt/intellij-community

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[ "Apache-2.0" ]

null

python/testData/completion/className/thirdPartyPackageBundledDependenciesNotSuggested/site-packages/requests/__init__.py

06needhamt/intellij-community

63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b

[ "Apache-2.0" ]

null

python/testData/completion/className/thirdPartyPackageBundledDependenciesNotSuggested/site-packages/requests/__init__.py

06needhamt/intellij-community

63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b

[ "Apache-2.0" ]

null

def request(): pass

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Python

tests/unit/dbestclient/io/test_stratifiedreservoir.py

qingzma/DBEstClient

d2cdf51bc3c69e50bcf4d1d516673b7d20843c16

[ "BSD-2-Clause" ]

11

2019-12-24T02:39:35.000Z

2022-03-21T22:39:41.000Z

tests/unit/dbestclient/io/test_stratifiedreservoir.py

Forever-MrX/DBEstClient

d2cdf51bc3c69e50bcf4d1d516673b7d20843c16

[ "BSD-2-Clause" ]

4

2019-12-09T09:48:17.000Z

2021-07-07T02:58:26.000Z

tests/unit/dbestclient/io/test_stratifiedreservoir.py

qingzma/DBEstClient

d2cdf51bc3c69e50bcf4d1d516673b7d20843c16

[ "BSD-2-Clause" ]

8

2019-11-08T02:10:37.000Z

2022-03-21T22:42:46.000Z

# Created by Qingzhi Ma at 2020-11-23 # All right reserved # Department of Computer Science # the University of Warwick # Q.Ma.2@warwick.ac.uk import unittest from dbestclient.io.stratifiedreservoir import StratifiedReservoir class TestStratifiedReservoir(unittest.TestCase): """""" def test_tpcds_1job_no_equality(self): sr = StratifiedReservoir( "data/tpcds/40G/ss_1k.csv", file_header="ss_sold_date_sk|ss_sold_time_sk|ss_item_sk|ss_customer_sk|ss_cdemo_sk|ss_hdemo_sk|ss_addr_sk|ss_store_sk|ss_promo_sk|ss_ticket_number|ss_quantity|ss_wholesale_cost|ss_list_price|ss_sales_price|ss_ext_discount_amt|ss_ext_sales_price|ss_ext_wholesale_cost|ss_ext_list_price|ss_ext_tax|ss_coupon_amt|ss_net_paid|ss_net_paid_inc_tax|ss_net_profit|none", n_jobs=1, capacity=5, ) sr.make_sample_no_distinct( gb_cols=["ss_store_sk"], equality_cols=None, feature_cols=["ss_sold_date_sk", "ss_ext_wholesale_cost"], label_cols=["ss_sales_price", "real", None], split_char="|", ) cate, fea, lbl = sr.get_categorical_features_label() self.assertEqual(sr.size(), 948) def test_tpcds_2job_no_equality(self): sr = StratifiedReservoir( "data/tpcds/40G/ss_1k.csv", file_header="ss_sold_date_sk|ss_sold_time_sk|ss_item_sk|ss_customer_sk|ss_cdemo_sk|ss_hdemo_sk|ss_addr_sk|ss_store_sk|ss_promo_sk|ss_ticket_number|ss_quantity|ss_wholesale_cost|ss_list_price|ss_sales_price|ss_ext_discount_amt|ss_ext_sales_price|ss_ext_wholesale_cost|ss_ext_list_price|ss_ext_tax|ss_coupon_amt|ss_net_paid|ss_net_paid_inc_tax|ss_net_profit|none", n_jobs=2, capacity=5, ) sr.make_sample_no_distinct( gb_cols=["ss_store_sk"], equality_cols=None, feature_cols=["ss_sold_date_sk", "ss_ext_wholesale_cost"], label_cols=["ss_sales_price", "real", None], split_char="|", ) self.assertEqual(sr.size(), 948) def test_tpcds_1job(self): sr = StratifiedReservoir( "data/tpcds/40G/ss_1k.csv", file_header="ss_sold_date_sk|ss_sold_time_sk|ss_item_sk|ss_customer_sk|ss_cdemo_sk|ss_hdemo_sk|ss_addr_sk|ss_store_sk|ss_promo_sk|ss_ticket_number|ss_quantity|ss_wholesale_cost|ss_list_price|ss_sales_price|ss_ext_discount_amt|ss_ext_sales_price|ss_ext_wholesale_cost|ss_ext_list_price|ss_ext_tax|ss_coupon_amt|ss_net_paid|ss_net_paid_inc_tax|ss_net_profit|none", n_jobs=1, capacity=5, ) sr.make_sample_no_distinct( gb_cols=["ss_store_sk"], equality_cols=["ss_coupon_amt"], feature_cols=["ss_sold_date_sk", "ss_ext_wholesale_cost"], label_cols=["ss_sales_price", "real", None], split_char="|", ) self.assertEqual(sr.size(), 1000) def test_tpcds_2job(self): sr = StratifiedReservoir( "data/tpcds/40G/ss_1k.csv", file_header="ss_sold_date_sk|ss_sold_time_sk|ss_item_sk|ss_customer_sk|ss_cdemo_sk|ss_hdemo_sk|ss_addr_sk|ss_store_sk|ss_promo_sk|ss_ticket_number|ss_quantity|ss_wholesale_cost|ss_list_price|ss_sales_price|ss_ext_discount_amt|ss_ext_sales_price|ss_ext_wholesale_cost|ss_ext_list_price|ss_ext_tax|ss_coupon_amt|ss_net_paid|ss_net_paid_inc_tax|ss_net_profit|none", n_jobs=2, capacity=5, ) sr.make_sample_no_distinct( gb_cols=["ss_store_sk"], equality_cols=["ss_coupon_amt"], feature_cols=["ss_sold_date_sk", "ss_ext_wholesale_cost"], label_cols=["ss_sales_price", "real", None], split_char="|", ) self.assertEqual(sr.size(), 1000) def test_toy_no_header_1(self): sr = StratifiedReservoir( "data/toy/toy.txt", file_header="range1,range2,cate1,cate2,gb1,gb2,label", n_jobs=1, capacity=5, ) sr.make_sample_no_distinct( gb_cols=["gb1", "gb2"], equality_cols=["cate1", "cate2"], feature_cols=["range1", "range2"], label_cols=["label", "real", None], split_char=",", ) cate, features, labels = sr.get_categorical_features_label() cate_target = [ ["store_id1", "cust_id2", "paris", "male"], ["store_id1", "cust_id1", "london", "male"], ["store_id1", "cust_id1", "london", "male"], ] cate = sorted(cate.tolist(), key=lambda words: ",".join(words)) cate_target = sorted(cate_target, key=lambda words: ",".join(words)) features_target = [[1.0, 2.0], [1.1, 2.1], [1.1, 2.1]] features_target = sorted(features_target, key=lambda words: words[0]) features = sorted(features.tolist(), key=lambda words: words[0]) labels_target = [1000.0, 2000.0, 3000.0] labels_target.sort() labels = labels.tolist() labels.sort() self.assertEqual(cate, cate_target) self.assertEqual(features, features_target) self.assertEqual(labels, labels_target) def test_toy_no_header_2(self): sr = StratifiedReservoir( "data/toy/toy.txt", file_header="range1,range2,cate1,cate2,gb1,gb2,label", n_jobs=1, capacity=5, ) sr.make_sample_no_distinct( gb_cols=["gb1", "gb2"], equality_cols=["cate1", "cate2"], feature_cols=["range1", "range2"], label_cols=["label", "real", None], split_char=",", ) cate, features, labels = sr.get_categorical_features_label() cate_target = [ ["store_id1", "cust_id2", "paris", "male"], ["store_id1", "cust_id1", "london", "male"], ["store_id1", "cust_id1", "london", "male"], ] cate = sorted(cate.tolist(), key=lambda words: ",".join(words)) cate_target = sorted(cate_target, key=lambda words: ",".join(words)) features_target = [[1.0, 2.0], [1.1, 2.1], [1.1, 2.1]] features_target = sorted(features_target, key=lambda words: words[0]) features = sorted(features.tolist(), key=lambda words: words[0]) labels_target = [1000.0, 2000.0, 3000.0] labels_target.sort() labels = labels.tolist() labels.sort() self.assertEqual(cate, cate_target) self.assertEqual(features, features_target) self.assertEqual(labels, labels_target) def test_toy_with_header_1job(self): sr = StratifiedReservoir("data/toy/toy_with_header.txt", n_jobs=1, capacity=5) sr.make_sample_no_distinct( gb_cols=["gb1", "gb2"], equality_cols=["cate1", "cate2"], feature_cols=["range1", "range2"], label_cols=["label", "real", None], split_char=",", ) cate, features, labels = sr.get_categorical_features_label() cate_target = [ ["store_id1", "cust_id2", "paris", "male"], ["store_id1", "cust_id1", "london", "male"], ["store_id1", "cust_id1", "london", "male"], ] cate = sorted(cate.tolist(), key=lambda words: ",".join(words)) cate_target = sorted(cate_target, key=lambda words: ",".join(words)) features_target = [[1.0, 2.0], [1.1, 2.1], [1.1, 2.1]] features_target = sorted(features_target, key=lambda words: words[0]) features = sorted(features.tolist(), key=lambda words: words[0]) labels_target = [1000.0, 2000.0, 3000.0] labels_target.sort() labels = labels.tolist() labels.sort() self.assertEqual(cate, cate_target) self.assertEqual(features, features_target) self.assertEqual(labels, labels_target) def test_toy_with_header_2job(self): sr = StratifiedReservoir("data/toy/toy_with_header.txt", n_jobs=2, capacity=5) sr.make_sample_no_distinct( gb_cols=["gb1", "gb2"], equality_cols=["cate1", "cate2"], feature_cols=["range1", "range2"], label_cols=["label", "real", None], split_char=",", ) cate, features, labels = sr.get_categorical_features_label() cate_target = [ ["store_id1", "cust_id2", "paris", "male"], ["store_id1", "cust_id1", "london", "male"], ["store_id1", "cust_id1", "london", "male"], ] cate = sorted(cate.tolist(), key=lambda words: ",".join(words)) cate_target = sorted(cate_target, key=lambda words: ",".join(words)) features_target = [[1.0, 2.0], [1.1, 2.1], [1.1, 2.1]] features_target = sorted(features_target, key=lambda words: words[0]) features = sorted(features.tolist(), key=lambda words: words[0]) labels_target = [1000.0, 2000.0, 3000.0] labels_target.sort() labels = labels.tolist() labels.sort() self.assertEqual(cate, cate_target) self.assertEqual(features, features_target) self.assertEqual(labels, labels_target) # def test_hw(self): # sr = StratifiedReservoir( # "../data/huawei/merged", # file_header="ts,apmac,accType,radioid,band,ssid,usermac,downSpeed,rssi,upLinkSpeed,downLinkSpeed,txDiscardRatio,latency,downBytes,upBytes,kpiCount,authTimeoutTimes,assoFailTimes,authFailTimes,dhcpFailTimes,assoSuccTimes,authSuccTimes,dhcpSuccTimes,dot1XSuccTimes,dot1XFailTimes,onlineSuccTimes,txDiscardFrames,txFrames,tenantId,siteId,siteName,directRegion,regionLevelOne,regionLevelTwo,regionLevelThree,regionLevelFour,regionLevelFive,regionLevelSix,regionLevelSeven,regionLevelEight,parentResId,acName,resId,apname,publicArea,vendor,duration,badCount,badTime,lowRssiCount,lowRssiDur,highLatencyCount,highLatencyDur,highDiscardCount,highDiscardDur,nonFiveGCount,nonFiveGDur,exception_flag,last_acc_rst,linkQuality,portal_succ_times,portal_fail_times,roam_succ_times,roam_fail_times", # n_jobs=8, # capacity=100, # ) # sr.make_sample( # gb_cols=["ts"], # equality_cols=["regionLevelEight", "ssid"], # feature_cols=["downSpeed"], # label_cols=["latency"], # split_char=",", # ) # ft = sr.get_ft() # # for key in ft: # # print(key, ft[key]) # # print("predictions", predictions) # self.assertEqual(81526479, sr.size()) if __name__ == "__main__": unittest.main() # TestStratifiedReservoir().test_tpcds_1job_no_equality() # TestStratifiedReservoir().test_tpcds_2job_no_equality() # TestStratifiedReservoir().test_tpcds_1job() # TestStratifiedReservoir().test_tpcds_2job() # TestStratifiedReservoir().test_toy_no_header_2() # TestStratifiedReservoir().test_toy_with_header_1job() # TestStratifiedReservoir().test_toy_with_header_2job() # TestStratifiedReservoir().test_hw()

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pyisic/_standards/jsic13/__init__.py

sayari-analytics/pyisic

42ed46f5bc446a0bbc0edf30b64bc4ab939dd033

[ "MIT" ]

3

2021-11-18T15:32:38.000Z

2022-02-28T19:16:14.000Z

pyisic/_standards/jsic13/__init__.py

sayari-analytics/pyisic

42ed46f5bc446a0bbc0edf30b64bc4ab939dd033

[ "MIT" ]

18

2021-06-28T19:17:49.000Z

2022-03-23T20:20:18.000Z

pyisic/_standards/jsic13/__init__.py

sayari-analytics/pyisic

42ed46f5bc446a0bbc0edf30b64bc4ab939dd033

[ "MIT" ]

null

# -*- coding: utf-8 -*- from .jsic13 import JSIC13 from .jsic13_to_isic4 import JSIC13_to_ISIC4

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services/director-v2/src/simcore_service_director_v2/models/schemas/dynamic_services/__init__.py

colinRawlings/osparc-simcore

bf2f18d5bc1e574d5f4c238d08ad15156184c310

[ "MIT" ]

25

2018-04-13T12:44:12.000Z

2022-03-12T15:01:17.000Z

services/director-v2/src/simcore_service_director_v2/models/schemas/dynamic_services/__init__.py

colinRawlings/osparc-simcore

bf2f18d5bc1e574d5f4c238d08ad15156184c310

[ "MIT" ]

2,553

2018-01-18T17:11:55.000Z

2022-03-31T16:26:40.000Z

services/director-v2/src/simcore_service_director_v2/models/schemas/dynamic_services/__init__.py

mrnicegyu11/osparc-simcore

b6fa6c245dbfbc18cc74a387111a52de9b05d1f4

[ "MIT" ]

20

2018-01-18T19:45:33.000Z

2022-03-29T07:08:47.000Z

from .scheduler import * from .service import *

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Python

python/interpret/perf/curve.py

zzzace2000/interpret

8a4b7c61378e1299870da3a581ae626e55ecb6a0

[ "MIT" ]

1

2021-12-27T13:42:01.000Z

python/interpret-core/interpret/perf/curve.py

LiYaangY/interpret

ab21bba5e139bee0234abe7c24efb6ffbcf36f9d

[ "MIT" ]

null

python/interpret-core/interpret/perf/curve.py

LiYaangY/interpret

ab21bba5e139bee0234abe7c24efb6ffbcf36f9d

[ "MIT" ]

2

2021-08-10T19:40:47.000Z

2021-11-16T16:01:22.000Z

# Copyright (c) 2019 Microsoft Corporation # Distributed under the MIT software license from ..api.base import ExplainerMixin, ExplanationMixin from ..utils import unify_data, gen_name_from_class, unify_predict_fn from sklearn.metrics import roc_curve, auc from sklearn.metrics import precision_recall_curve, average_precision_score import numpy as np class PR(ExplainerMixin): available_explanations = ["perf"] explainer_type = "perf" def __init__(self, predict_fn, feature_names=None, feature_types=None, **kwargs): self.predict_fn = predict_fn self.kwargs = kwargs self.feature_names = feature_names self.feature_types = feature_types def explain_perf(self, X, y, name=None): if name is None: name = gen_name_from_class(self) X, y, self.feature_names, self.feature_types = unify_data( X, y, self.feature_names, self.feature_types ) predict_fn = unify_predict_fn(self.predict_fn, X) scores = predict_fn(X) precision, recall, thresh = precision_recall_curve(y, scores) ap = average_precision_score(y, scores) abs_residuals = np.abs(y - scores) counts, values = np.histogram(abs_residuals, bins="doane") overall_dict = { "type": "perf_curve", "density": {"names": values, "scores": counts}, "scores": scores, "x_values": recall, "y_values": precision, "threshold": thresh, "auc": ap, } internal_obj = {"overall": overall_dict, "specific": None} return PRExplanation( "perf", internal_obj, feature_names=self.feature_names, feature_types=self.feature_types, name=name, ) class ROC(ExplainerMixin): available_explanations = ["perf"] explainer_type = "perf" def __init__(self, predict_fn, feature_names=None, feature_types=None, **kwargs): self.predict_fn = predict_fn self.kwargs = kwargs self.feature_names = feature_names self.feature_types = feature_types def explain_perf(self, X, y, name=None): if name is None: name = gen_name_from_class(self) X, y, self.feature_names, self.feature_types = unify_data( X, y, self.feature_names, self.feature_types ) predict_fn = unify_predict_fn(self.predict_fn, X) scores = predict_fn(X) fpr, tpr, thresh = roc_curve(y, scores) roc_auc = auc(fpr, tpr) abs_residuals = np.abs(y - scores) counts, values = np.histogram(abs_residuals, bins="doane") overall_dict = { "type": "perf_curve", "density": {"names": values, "scores": counts}, "scores": scores, "x_values": fpr, "y_values": tpr, "threshold": thresh, "auc": roc_auc, } internal_obj = {"overall": overall_dict, "specific": None} return ROCExplanation( "perf", internal_obj, feature_names=self.feature_names, feature_types=self.feature_types, name=name, ) class ROCExplanation(ExplanationMixin): explanation_type = None def __init__( self, explanation_type, internal_obj, feature_names=None, feature_types=None, name=None, selector=None, ): self.explanation_type = explanation_type self._internal_obj = internal_obj self.feature_names = feature_names self.feature_types = feature_types self.name = name self.selector = selector def data(self, key=None): if key is None: return self._internal_obj["overall"] return None def visualize(self, key=None): from ..visual.plot import plot_performance_curve data_dict = self.data(key) if data_dict is None: return None return plot_performance_curve( data_dict, xtitle="FPR", ytitle="TPR", baseline=True, title="ROC Curve: " + self.name, auc_prefix="AUC", ) class PRExplanation(ExplanationMixin): explanation_type = None def __init__( self, explanation_type, internal_obj, feature_names=None, feature_types=None, name=None, selector=None, ): self.explanation_type = explanation_type self._internal_obj = internal_obj self.feature_names = feature_names self.feature_types = feature_types self.name = name self.selector = selector def data(self, key=None): if key is None: return self._internal_obj["overall"] return None def visualize(self, key=None): from ..visual.plot import plot_performance_curve data_dict = self.data(key) if data_dict is None: return None return plot_performance_curve( data_dict, xtitle="Recall", ytitle="Precision", baseline=False, title="PR Curve: " + self.name, auc_prefix="Average Precision", )

28.782609

85

0.598754

592

5,296

5.089527

0.155405

0.079655

0.053103

0.076336

0.785264

0.77066

0.739462

0

0.001097

0.311367

5,296

183

86

28.939891

0.825062

0.015672

0

0.734694

0

0.052207

0

1

0.068027

false

0

0.047619

0

0.251701

0

null

0

1

0

1

0

null

0

6

53010313d7b765d7109635137aca8cfab8ca9b59

36

py

Python

s3zilla/__init__.py

rootVIII/s3zilla

cc0e343883c8ba01ce2d37879258dcafea3123e7

[ "MIT" ]

10

2019-04-22T10:11:44.000Z

2021-12-16T15:52:55.000Z

s3zilla/__init__.py

rootVIII/s3zilla

cc0e343883c8ba01ce2d37879258dcafea3123e7

[ "MIT" ]

null

s3zilla/__init__.py

rootVIII/s3zilla

cc0e343883c8ba01ce2d37879258dcafea3123e7

[ "MIT" ]

2

2019-08-26T01:33:46.000Z

2019-11-03T21:10:45.000Z

from s3zilla.s3zilla import S3Zilla

18

35

0.861111

5

36

6.2

0.6

0

0.09375

0.111111

36

1

36

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

530fb83fc8dedb02d15abea1de23fb66baf394e7

1,098

py

Python

iceworm/engine/ops/__init__.py

wrmsr0/iceworm

09431bb3cdc4f6796aafca41e37d42ebe0ddfeef

[ "BSD-3-Clause" ]

null

iceworm/engine/ops/__init__.py

wrmsr0/iceworm

09431bb3cdc4f6796aafca41e37d42ebe0ddfeef

[ "BSD-3-Clause" ]

1

2021-01-19T14:29:19.000Z

2021-01-19T14:34:27.000Z

iceworm/engine/ops/__init__.py

wrmsr0/iceworm

09431bb3cdc4f6796aafca41e37d42ebe0ddfeef

[ "BSD-3-Clause" ]

1

2020-12-31T22:29:52.000Z

from . import inject # noqa from .base import Annotation # noqa from .base import List # noqa from .base import ListExecutor # noqa from .base import Op # noqa from .base import OpExecutor # noqa from .base import OpGen # noqa from .base import Set # noqa from .base import Set # noqa from .conns import ConnOp # noqa from .conns import ConnsOp # noqa from .conns import CopyTable # noqa from .conns import CreateTable # noqa from .conns import CreateTableAs # noqa from .conns import CreateTableAsExecutor # noqa from .conns import CreateTableExecutor # noqa from .conns import DropTable # noqa from .conns import DropTableExecutor # noqa from .conns import Exec # noqa from .conns import ExecExecutor # noqa from .conns import InsertIntoEval # noqa from .conns import InsertIntoEvalExecutor # noqa from .conns import InsertIntoSelect # noqa from .conns import InsertIntoSelectExecutor # noqa from .conns import Transaction # noqa from .conns import TransactionExecutor # noqa from .driving import OpExecutionDriver # noqa from .transforms import OpTransformer # noqa

37.862069

51

0.769581

139

1,098

6.079137

0.244604

0.255621

0.261538

0.382249

0.059172

0

0.179417

1,098

28

52

39.214286

0.937847

0.126594

0

0.071429

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

5353283ede0f8d5866a1baa0800b6cab3bef6ee2

3,593

py

Python

data_utils/test_modelnet_dataset.py

zhuyuanxiang/PointNet-Series

d494ff803e1f0fe1ac51591ce1a1469646a82ea0

[ "MIT" ]

null

data_utils/test_modelnet_dataset.py

zhuyuanxiang/PointNet-Series

d494ff803e1f0fe1ac51591ce1a1469646a82ea0

[ "MIT" ]

null

data_utils/test_modelnet_dataset.py

zhuyuanxiang/PointNet-Series

d494ff803e1f0fe1ac51591ce1a1469646a82ea0

[ "MIT" ]

null

""" ================================================= @path : PointNet-Series -> test_modelnet_dataset.py @IDE : PyCharm @Author : zYx.Tom, 526614962@qq.com @Date : 2022-01-19 15:13 @Version: v0.1 @License: (C)Copyright 2020-2022, zYx.Tom @Reference: @Desc : ================================================== """ from argparse import Namespace from datetime import datetime from data_utils.modelnet_dataset import ModelNetDataset class TestClass: def test_modelnet10_test_1024pts(self): args = Namespace() args.uniform_sample = False args.modelnet10 = True data_path = "../data/pickle/modelnet/" myDataset = ModelNetDataset(root=data_path, args=args, split='test') assert myDataset.get_pickle_file_name() == "../data/pickle/modelnet/modelnet10_test_1024pts.dat" def test_modelnet10_test_1024pts_fps(self): args = Namespace() args.uniform_sample = True args.modelnet10 = True data_path = "../data/pickle/modelnet/" myDataset = ModelNetDataset(root=data_path, args=args, split='test') assert myDataset.get_pickle_file_name() == "../data/pickle/modelnet/modelnet10_test_1024pts_fps.dat" def test_modelnet10_train_1024pts(self): args = Namespace() args.uniform_sample = False args.modelnet10 = True data_path = "../data/pickle/modelnet/" myDataset = ModelNetDataset(root=data_path, args=args, split='train') assert myDataset.get_pickle_file_name() == "../data/pickle/modelnet/modelnet10_train_1024pts.dat" def test_modelnet10_train_1024pts_fps(self): args = Namespace() args.uniform_sample = True args.modelnet10 = True data_path = "../data/pickle/modelnet/" myDataset = ModelNetDataset(root=data_path, args=args, split='train') assert myDataset.get_pickle_file_name() == "../data/pickle/modelnet/modelnet10_train_1024pts_fps.dat" def test_modelnet40_test_1024pts(self): args = Namespace() args.uniform_sample = False args.modelnet10 = False data_path = "../data/pickle/modelnet/" myDataset = ModelNetDataset(root=data_path, args=args, split='test') assert myDataset.get_pickle_file_name() == "../data/pickle/modelnet/modelnet40_test_1024pts.dat" def test_modelnet40_test_1024pts_fps(self): args = Namespace() args.uniform_sample = True args.modelnet10 = False data_path = "../data/pickle/modelnet/" myDataset = ModelNetDataset(root=data_path, args=args, split='test') assert myDataset.get_pickle_file_name() == "../data/pickle/modelnet/modelnet40_test_1024pts_fps.dat" def test_modelnet40_train_1024pts(self): args = Namespace() args.uniform_sample = False args.modelnet10 = False data_path = "../data/pickle/modelnet/" myDataset = ModelNetDataset(root=data_path, args=args, split='train') assert myDataset.get_pickle_file_name() == "../data/pickle/modelnet/modelnet40_train_1024pts.dat" def test_modelnet40_train_1024pts_fps(self): args = Namespace() args.uniform_sample = True args.modelnet10 = False data_path = "../data/pickle/modelnet/" myDataset = ModelNetDataset(root=data_path, args=args, split='train') assert myDataset.get_pickle_file_name() == "../data/pickle/modelnet/modelnet40_train_1024pts_fps.dat" def main(name): print(f'Hi, {name}', datetime.now()) pass if __name__ == "__main__": __author__ = 'zYx.Tom' main(__author__)

38.223404

109

0.664347

412

3,593

5.512136

0.165049

0.056363

0.126816

0.073976

0.85557

0.835315

0.778512

0

0.049378

0.193988

3,593

93

110

38.634409

0.734807

0.087392

0

0.606061

0

0.208193

0.189544

0

0.121212

1

0.136364

false

0.015152

0.045455

0

0.19697

0.015152

0

null

0

1

0

null

0

6

536b8c0c1a42706ed77a7a11150a46baba787ecc

28,137

py

Python

test/unit/test_sap_cli_aunit.py

jakub-vaclavik-sap/sapcli

a0f40c3b2363bba0d34f705d92dd420d9adf3987

[ "Apache-2.0" ]

null

test/unit/test_sap_cli_aunit.py

jakub-vaclavik-sap/sapcli

a0f40c3b2363bba0d34f705d92dd420d9adf3987

[ "Apache-2.0" ]

null

test/unit/test_sap_cli_aunit.py

jakub-vaclavik-sap/sapcli

a0f40c3b2363bba0d34f705d92dd420d9adf3987

[ "Apache-2.0" ]

1

2022-01-10T03:58:03.000Z

#!/usr/bin/env python3 import sys import unittest from io import StringIO from types import SimpleNamespace from unittest.mock import patch, call, Mock, mock_open import sap.adt.cts import sap.cli.aunit from fixtures_adt_aunit import AUNIT_NO_TEST_RESULTS_XML, AUNIT_RESULTS_XML, GLOBAL_TEST_CLASS_AUNIT_RESULTS_XML, AUNIT_RESULTS_NO_TEST_METHODS_XML from fixtures_adt_coverage import ACOVERAGE_RESULTS_XML, ACOVERAGE_STATEMENTS_RESULTS_XML from infra import generate_parse_args from mock import Connection, Response from sap.cli.aunit import ResultOptions from sap.errors import SAPCliError parse_args = generate_parse_args(sap.cli.aunit.CommandGroup()) class TestAUnitWrite(unittest.TestCase): def setUp(self): self.connection = Connection() def assert_print_no_test_classes(self, mock_print): self.assertEqual( mock_print.call_args_list[0], call('* [tolerable] [noTestClasses] - The task definition does not refer to any test', file=sys.stdout)) self.assertEqual( mock_print.call_args_list[1], call('Successful: 0', file=sys.stdout)) self.assertEqual( mock_print.call_args_list[2], call('Warnings: 0', file=sys.stdout)) self.assertEqual( mock_print.call_args_list[3], call('Errors: 0', file=sys.stdout)) def test_aunit_invalid(self): with self.assertRaises(SAPCliError) as cm: sap.cli.aunit.run('wrongconn', SimpleNamespace(type='foo', output='human')) self.assertEqual(str(cm.exception), 'Unknown type: foo') def test_print_aunit_output_raises(self): with self.assertRaises(SAPCliError) as cm: sap.cli.aunit.print_aunit_output(SimpleNamespace(output='foo'), Mock(), Mock()) self.assertEqual(str(cm.exception), 'Unsupported output type: foo') def execute_run(self, *args, **kwargs): cmd_args = parse_args('run', *args, **kwargs) return cmd_args.execute(self.connection, cmd_args) def test_aunit_program(self): self.connection.set_responses( Response(status_code=200, text=AUNIT_NO_TEST_RESULTS_XML, headers={}) ) with patch('sap.cli.aunit.print') as mock_print: self.execute_run('program', '--output', 'human', 'yprogram', '--result', ResultOptions.ONLY_UNIT.value) self.assertEqual(len(self.connection.execs), 1) self.assertIn('programs/programs/yprogram', self.connection.execs[0].body) self.assert_print_no_test_classes(mock_print) def test_aunit_class_human(self): self.connection.set_responses(Response(status_code=200, text=AUNIT_NO_TEST_RESULTS_XML, headers={})) with patch('sap.cli.aunit.print') as mock_print: self.execute_run('class', 'yclass', '--output', 'human', '--result', ResultOptions.ONLY_UNIT.value) self.assertEqual(len(self.connection.execs), 1) self.assertIn('oo/classes/yclass', self.connection.execs[0].body) self.assert_print_no_test_classes(mock_print) def test_aunit_package(self): self.connection.set_responses(Response(status_code=200, text=AUNIT_NO_TEST_RESULTS_XML, headers={})) with patch('sap.cli.aunit.print') as mock_print: self.execute_run('package', 'ypackage', '--output', 'human', '--result', ResultOptions.ONLY_UNIT.value) self.assertEqual(len(self.connection.execs), 1) self.assertIn('packages/ypackage', self.connection.execs[0].body) self.assert_print_no_test_classes(mock_print) def test_aunit_junit4_no_test_methods(self): self.connection.set_responses(Response(status_code=200, text=AUNIT_RESULTS_NO_TEST_METHODS_XML, headers={})) with patch('sys.stdout', new_callable=StringIO) as mock_stdout: self.execute_run('package', 'ypackage', '--output', 'junit4', '--result', ResultOptions.ONLY_UNIT.value) self.assertEqual(len(self.connection.execs), 1) self.assertEqual( """<?xml version="1.0" encoding="UTF-8" ?> <testsuites name="ypackage"> <testsuite name="LTCL_TEST" package="ZCL_THEKING_MANUAL_HARDCORE" tests="0"/> </testsuites> """, mock_stdout.getvalue() ) def test_aunit_package_with_results(self): self.connection.set_responses(Response(status_code=200, text=AUNIT_RESULTS_XML, headers={})) with patch('sap.cli.aunit.print') as mock_print: exit_code = self.execute_run('package', 'ypackage', '--output', 'human', '--result', ResultOptions.ONLY_UNIT.value) self.assertEqual(exit_code, 3) self.assertEqual(len(self.connection.execs), 1) self.assertIn('packages/ypackage', self.connection.execs[0].body) self.assertEqual(mock_print.call_args_list[0], call('ZCL_THEKING_MANUAL_HARDCORE', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[1], call(' LTCL_TEST', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[2], call(' DO_THE_FAIL [ERR]', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[3], call(' DO_THE_WARN [SKIP]', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[4], call(' DO_THE_TEST [OK]', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[5], call(' LTCL_TEST_HARDER', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[6], call(' DO_THE_FAIL [ERR]', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[7], call(' DO_THE_TEST [OK]', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[8], call('ZEXAMPLE_TESTS', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[9], call(' LTCL_TEST', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[10], call(' DO_THE_FAIL [ERR]', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[11], call(' DO_THE_TEST [OK]', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[12], call('', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[13], call('ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST=>DO_THE_FAIL', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[14], call('* [critical] [failedAssertion] - Critical Assertion Error: \'I am supposed to fail\'', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[15], call('ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST_HARDER=>DO_THE_FAIL', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[16], call('* [critical] [failedAssertion] - Critical Assertion Error: \'I am supposed to fail\'', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[17], call('ZEXAMPLE_TESTS=>LTCL_TEST=>DO_THE_FAIL', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[18], call('* [critical] [failedAssertion] - Critical Assertion Error: \'I am supposed to fail\'', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[19], call('* [critical] [failedAssertion] - Error<LOAD_PROGRAM_CLASS_MISMATCH>', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[20], call('', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[21], call('Successful: 3', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[22], call('Warnings: 1', file=sys.stdout)) self.assertEqual(mock_print.call_args_list[23], call('Errors: 3', file=sys.stdout)) def test_aunit_package_with_results_raw(self): self.connection.set_responses(Response(status_code=200, text=AUNIT_RESULTS_XML, headers={})) with patch('sap.cli.aunit.print') as mock_print: exit_code = self.execute_run('package', 'ypackage', '--output', 'raw', '--result', ResultOptions.ONLY_UNIT.value) self.assertEqual(exit_code, 3) self.assertEqual(len(self.connection.execs), 1) self.assertIn('packages/ypackage', self.connection.execs[0].body) self.assertEqual(mock_print.call_args_list[0][0], (AUNIT_RESULTS_XML,)) def test_aunit_package_with_results_junit4(self): self.connection.set_responses(Response(status_code=200, text=AUNIT_RESULTS_XML, headers={})) with patch('sys.stdout', new_callable=StringIO) as mock_stdout: exit_code = self.execute_run('package', 'ypackage', '--output', 'junit4', '--result', ResultOptions.ONLY_UNIT.value) self.assertEqual(exit_code, 3) self.assertEqual(len(self.connection.execs), 1) self.assertIn('packages/ypackage', self.connection.execs[0].body) self.maxDiff = None self.assertEqual(mock_stdout.getvalue(), '''<?xml version="1.0" encoding="UTF-8" ?> <testsuites name="ypackage"> <testsuite name="LTCL_TEST" package="ZCL_THEKING_MANUAL_HARDCORE" tests="3"> <testcase name="DO_THE_FAIL" classname="ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST" status="ERR"> <system-err>True expected Test 'LTCL_TEST->DO_THE_FAIL' in Main Program 'ZCL_THEKING_MANUAL_HARDCORE===CP'.</system-err> <error type="failedAssertion" message="Critical Assertion Error: 'I am supposed to fail'">Include: <ZCL_THEKING_MANUAL_HARDCORE===CCAU> Line: <19> (DO_THE_FAIL)</error> </testcase> <testcase name="DO_THE_WARN" classname="ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST" status="SKIP"> <system-err>True expected Test 'LTCL_TEST->DO_THE_WARN' in Main Program 'ZCL_THEKING_MANUAL_HARDCORE===CP'.</system-err> <error type="failedAssertion" message="Warning: 'I am supposed to warn'">Include: <ZCL_THEKING_MANUAL_HARDCORE===CCAU> Line: <19> (DO_THE_WARN)</error> </testcase> <testcase name="DO_THE_TEST" classname="ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST" status="OK"/> </testsuite> <testsuite name="LTCL_TEST_HARDER" package="ZCL_THEKING_MANUAL_HARDCORE" tests="2"> <testcase name="DO_THE_FAIL" classname="ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST_HARDER" status="ERR"> <system-err>True expected Test 'LTCL_TEST_HARDER->DO_THE_FAIL' in Main Program 'ZCL_THEKING_MANUAL_HARDCORE===CP'.</system-err> <error type="failedAssertion" message="Critical Assertion Error: 'I am supposed to fail'">Include: <ZCL_THEKING_MANUAL_HARDCORE===CCAU> Line: <19> (DO_THE_FAIL)</error> </testcase> <testcase name="DO_THE_TEST" classname="ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST_HARDER" status="OK"/> </testsuite> <testsuite name="LTCL_TEST" package="ZEXAMPLE_TESTS" tests="2"> <testcase name="DO_THE_FAIL" classname="ZEXAMPLE_TESTS=>LTCL_TEST" status="ERR"> <system-err>True expected Test 'LTCL_TEST->DO_THE_FAIL' in Main Program 'ZEXAMPLE_TESTS'.</system-err> <error type="failedAssertion" message="Critical Assertion Error: 'I am supposed to fail'">Include: <ZEXAMPLE_TESTS> Line: <24> (DO_THE_FAIL) Include: <ZEXAMPLE_TESTS> Line: <25> (PREPARE_THE_FAIL)</error> <error type="failedAssertion" message="Error<LOAD_PROGRAM_CLASS_MISMATCH>"/> </testcase> <testcase name="DO_THE_TEST" classname="ZEXAMPLE_TESTS=>LTCL_TEST" status="OK"/> </testsuite> </testsuites> ''') def test_aunit_package_with_results_sonar(self): self.connection.set_responses(Response(status_code=200, text=AUNIT_RESULTS_XML, headers={})) with patch('sys.stdout', new_callable=StringIO) as mock_stdout: exit_code = self.execute_run('package', 'ypackage', '--output', 'sonar', '--result', ResultOptions.ONLY_UNIT.value) self.assertEqual(exit_code, 3) self.assertEqual(len(self.connection.execs), 1) self.assertIn('packages/ypackage', self.connection.execs[0].body) self.maxDiff = None self.assertEqual(mock_stdout.getvalue(), '''<?xml version="1.0" encoding="UTF-8" ?> <testExecutions version="1"> <file path="ypackage/ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST"> <testCase name="DO_THE_FAIL" duration="33"> <error message="Critical Assertion Error: 'I am supposed to fail'"> True expected Test 'LTCL_TEST->DO_THE_FAIL' in Main Program 'ZCL_THEKING_MANUAL_HARDCORE===CP'. Include: <ZCL_THEKING_MANUAL_HARDCORE===CCAU> Line: <19> (DO_THE_FAIL) </error> </testCase> <testCase name="DO_THE_WARN" duration="33"> <skipped message="Warning: 'I am supposed to warn'"> True expected Test 'LTCL_TEST->DO_THE_WARN' in Main Program 'ZCL_THEKING_MANUAL_HARDCORE===CP'. Include: <ZCL_THEKING_MANUAL_HARDCORE===CCAU> Line: <19> (DO_THE_WARN) </skipped> </testCase> <testCase name="DO_THE_TEST" duration="0"/> </file> <file path="ypackage/ZCL_THEKING_MANUAL_HARDCORE=>LTCL_TEST_HARDER"> <testCase name="DO_THE_FAIL" duration="0"> <error message="Critical Assertion Error: 'I am supposed to fail'"> True expected Test 'LTCL_TEST_HARDER->DO_THE_FAIL' in Main Program 'ZCL_THEKING_MANUAL_HARDCORE===CP'. Include: <ZCL_THEKING_MANUAL_HARDCORE===CCAU> Line: <19> (DO_THE_FAIL) </error> </testCase> <testCase name="DO_THE_TEST" duration="0"/> </file> <file path="ypackage/ZEXAMPLE_TESTS=>LTCL_TEST"> <testCase name="DO_THE_FAIL" duration="0"> <error message="Critical Assertion Error: 'I am supposed to fail'"> True expected Test 'LTCL_TEST->DO_THE_FAIL' in Main Program 'ZEXAMPLE_TESTS'. Include: <ZEXAMPLE_TESTS> Line: <24> (DO_THE_FAIL) Include: <ZEXAMPLE_TESTS> Line: <25> (PREPARE_THE_FAIL) </error> <error message="Error<LOAD_PROGRAM_CLASS_MISMATCH>"> </error> </testCase> <testCase name="DO_THE_TEST" duration="0"/> </file> </testExecutions> ''') def test_aunit_parser_results_global_class_tests(self): results = sap.adt.aunit.parse_aunit_response(GLOBAL_TEST_CLASS_AUNIT_RESULTS_XML).run_results output = StringIO() sap.cli.aunit.print_aunit_junit4(results, SimpleNamespace(name=['$TMP']), output) self.maxDiff = None self.assertEqual(output.getvalue(), '''<?xml version="1.0" encoding="UTF-8" ?> <testsuites name="$TMP"> <testsuite name="ZCL_TEST_CLASS" package="ZCL_TEST_CLASS" tests="1"> <testcase name="DO_THE_TEST" classname="ZCL_TEST_CLASS" status="OK"/> </testsuite> </testsuites> ''') def test_aunit_parser_results_global_class_tests_multiple_targets(self): results = sap.adt.aunit.parse_aunit_response(GLOBAL_TEST_CLASS_AUNIT_RESULTS_XML) output = StringIO() sap.cli.aunit.print_aunit_junit4(results.run_results, SimpleNamespace(name=['$TMP', '$LOCAL', '$BAR']), output) self.maxDiff = None self.assertEqual(output.getvalue(), '''<?xml version="1.0" encoding="UTF-8" ?> <testsuites name="$TMP|$LOCAL|$BAR"> <testsuite name="ZCL_TEST_CLASS" package="ZCL_TEST_CLASS" tests="1"> <testcase name="DO_THE_TEST" classname="ZCL_TEST_CLASS" status="OK"/> </testsuite> </testsuites> ''') def test_aunit_parser_results_global_class_tests_sonar(self): results = sap.adt.aunit.parse_aunit_response(GLOBAL_TEST_CLASS_AUNIT_RESULTS_XML).run_results output = StringIO() sap.cli.aunit.print_aunit_sonar(results, SimpleNamespace(name=['$TMP']), output) self.maxDiff = None self.assertEqual(output.getvalue(), '''<?xml version="1.0" encoding="UTF-8" ?> <testExecutions version="1"> <file path="$TMP/ZCL_TEST_CLASS=>ZCL_TEST_CLASS"> <testCase name="DO_THE_TEST" duration="0"/> <testCase name="ZCL_TEST_CLASS" duration="0"> <skipped message="The global test class [ZCL_TEST_CLASS] is not abstract"> You can find further informations in document <CHAP> <SAUNIT_TEST_CL_POOL> </skipped> </testCase> </file> </testExecutions> ''') @patch('os.walk') def test_print_aunit_sonar_filename_is_not_none(self, walk): walk.return_value = [('.', None, ['zcl_theking_manual_hardcore.clas.testclasses.abap', 'bar'])] results = sap.adt.aunit.parse_aunit_response(AUNIT_RESULTS_NO_TEST_METHODS_XML).run_results output = StringIO() sap.cli.aunit.print_aunit_sonar(results, SimpleNamespace(name=['foo']), output) self.assertEqual( '''<?xml version="1.0" encoding="UTF-8" ?> <testExecutions version="1"> <file path="zcl_theking_manual_hardcore.clas.testclasses.abap"> </file> </testExecutions> ''', output.getvalue() ) def test_print_acoverage_output_raises(self): with self.assertRaises(SAPCliError) as cm: sap.cli.aunit.print_acoverage_output(SimpleNamespace(coverage_output='foo'), Mock(), Mock(), Mock()) self.assertEqual(str(cm.exception), 'Unsupported output type: foo') @patch('sap.cli.aunit.get_acoverage_statements') def test_acoverage_package_with_results_raw(self, get_acoverage_statements): get_acoverage_statements.return_value = [] self.connection.set_responses( Response(status_code=200, text=AUNIT_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_RESULTS_XML, headers={}) ) with patch('sap.cli.aunit.print') as mock_print: exit_code = self.execute_run( 'package', 'ypackage', '--coverage-output', 'raw', '--result', ResultOptions.ONLY_COVERAGE.value ) self.assertEqual(exit_code, None) self.assertEqual(len(self.connection.execs), 2) self.assertEqual(mock_print.call_args_list[0], call(ACOVERAGE_RESULTS_XML, file=sys.stdout)) @patch('sap.cli.aunit.get_acoverage_statements') def test_acoverage_package_with_results_human(self, get_acoverage_statements): get_acoverage_statements.return_value = [] self.connection.set_responses( Response(status_code=200, text=AUNIT_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_RESULTS_XML, headers={}) ) with patch('sys.stdout', new_callable=StringIO) as mock_stdout: exit_code = self.execute_run( 'package', 'ypackage', '--coverage-output', 'human', '--result', ResultOptions.ONLY_COVERAGE.value ) self.assertEqual(exit_code, None) self.assertEqual(len(self.connection.execs), 2) self.assertEqual(mock_stdout.getvalue(), '''TEST_CHECK_LIST : 29.00% FOO===========================CP : 95.24% FOO : 95.24% METHOD_A : 100.00% METHOD_B : 75.00% BAR===========================CP : 0.00% ''') def test_acoverage_package_with_results_jacoco(self): self.connection.set_responses( Response(status_code=200, text=AUNIT_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_STATEMENTS_RESULTS_XML, headers={}), ) with patch('sys.stdout', new_callable=StringIO) as mock_stdout: exit_code = self.execute_run( 'package', 'ypackage', '--coverage-output', 'jacoco', '--result', ResultOptions.ONLY_COVERAGE.value ) self.assertEqual(exit_code, None) self.assertEqual(len(self.connection.execs), 3) self.assertEqual(mock_stdout.getvalue(), '''<?xml version="1.0" encoding="UTF-8" standalone="yes"?> <!DOCTYPE report PUBLIC "-//JACOCO//DTD Report 1.1//EN" "report.dtd"> <report name="ypackage"> <package name="TEST_CHECK_LIST"> <class name="FOO" sourcefilename="FOO"> <method name="METHOD_A" line="52"> <counter type="BRANCH" missed="2" covered="3"/> <counter type="METHOD" missed="0" covered="1"/> <counter type="INSTRUCTION" missed="0" covered="5"/> </method> <method name="METHOD_B" line="199"> <counter type="BRANCH" missed="1" covered="1"/> <counter type="METHOD" missed="0" covered="1"/> <counter type="INSTRUCTION" missed="2" covered="6"/> </method> <counter type="BRANCH" missed="7" covered="22"/> <counter type="METHOD" missed="0" covered="8"/> <counter type="INSTRUCTION" missed="3" covered="60"/> </class> <sourcefile name="FOO"> <line nr="53" mi="0" ci="1"/> <line nr="54" mi="0" ci="1"/> <line nr="55" mi="0" ci="1"/> <line nr="56" mi="0" ci="1"/> <line nr="59" mi="0" ci="1"/> <line nr="209" mi="0" ci="1"/> <line nr="212" mi="0" ci="1"/> <line nr="215" mi="0" ci="1"/> <line nr="216" mi="0" ci="1"/> <line nr="219" mi="0" ci="1"/> <line nr="220" mi="0" ci="1"/> <line nr="224" mi="1" ci="0"/> <line nr="225" mi="1" ci="0"/> </sourcefile> <class name="BAR" sourcefilename="BAR"> <counter type="BRANCH" missed="0" covered="0"/> <counter type="METHOD" missed="0" covered="0"/> <counter type="INSTRUCTION" missed="0" covered="0"/> </class> <counter type="BRANCH" missed="105" covered="29"/> <counter type="METHOD" missed="42" covered="10"/> <counter type="INSTRUCTION" missed="235" covered="96"/> </package> </report> ''') def test_result_option_all(self): self.connection.set_responses( Response(status_code=200, text=AUNIT_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_STATEMENTS_RESULTS_XML, headers={}), ) with patch('sap.cli.aunit.print') as mock_print: exit_code = self.execute_run( 'package', 'ypackage', '--output', 'raw', '--coverage-output', 'raw', '--result', ResultOptions.ALL.value ) self.assertEqual(exit_code, 3) self.assertEqual(len(self.connection.execs), 3) self.assertEqual(len(mock_print.call_args_list), 2) self.assertEqual(mock_print.call_args_list[0], call(AUNIT_RESULTS_XML, file=sys.stdout)) self.assertEqual(mock_print.call_args_list[1], call(ACOVERAGE_RESULTS_XML, file=sys.stdout)) def test_result_option_unit(self): self.connection.set_responses( Response(status_code=200, text=AUNIT_RESULTS_XML, headers={}) ) with patch('sap.cli.aunit.print') as mock_print: exit_code = self.execute_run( 'package', 'ypackage', '--output', 'raw', '--coverage-output', 'raw', '--result', ResultOptions.ONLY_UNIT.value ) self.assertEqual(exit_code, 3) self.assertEqual(len(self.connection.execs), 1) self.assertEqual(len(mock_print.call_args_list), 1) self.assertEqual(mock_print.call_args_list[0], call(AUNIT_RESULTS_XML, file=sys.stdout)) def test_result_option_coverage(self): self.connection.set_responses( Response(status_code=200, text=AUNIT_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_STATEMENTS_RESULTS_XML, headers={}), ) with patch('sap.cli.aunit.print') as mock_print: exit_code = self.execute_run( 'package', 'ypackage', '--output', 'raw', '--coverage-output', 'raw', '--result', ResultOptions.ONLY_COVERAGE.value ) self.assertEqual(exit_code, None) self.assertEqual(len(self.connection.execs), 3) self.assertEqual(len(mock_print.call_args_list), 1) self.assertEqual(mock_print.call_args_list[0], call(ACOVERAGE_RESULTS_XML, file=sys.stdout)) def test_coverage_filepath(self): self.connection.set_responses( Response(status_code=200, text=AUNIT_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_RESULTS_XML, headers={}), Response(status_code=200, text=ACOVERAGE_STATEMENTS_RESULTS_XML, headers={}), ) coverage_filepath = 'path/to/coverage' with patch('sap.cli.aunit.open', mock_open()) as mock_file: exit_code = self.execute_run( 'package', 'ypackage', '--output', 'raw', '--coverage-output', 'raw', '--result', ResultOptions.ONLY_COVERAGE.value, '--coverage-filepath', coverage_filepath ) mock_file.assert_called_with(coverage_filepath, 'w+') def test_aunit_parser_results_global_class_tests_sonar_multiple_targets(self): results = sap.adt.aunit.parse_aunit_response(GLOBAL_TEST_CLASS_AUNIT_RESULTS_XML) output = StringIO() sap.cli.aunit.print_aunit_sonar(results.run_results, SimpleNamespace(name=['$LOCAL', '$TMP']), output) self.maxDiff = None self.assertEqual(output.getvalue(), '''<?xml version="1.0" encoding="UTF-8" ?> <testExecutions version="1"> <file path="UNKNOWN_PACKAGE/ZCL_TEST_CLASS=>ZCL_TEST_CLASS"> <testCase name="DO_THE_TEST" duration="0"/> <testCase name="ZCL_TEST_CLASS" duration="0"> <skipped message="The global test class [ZCL_TEST_CLASS] is not abstract"> You can find further informations in document <CHAP> <SAUNIT_TEST_CL_POOL> </skipped> </testCase> </file> </testExecutions> ''') class TestAUnitCommandRunTransport(unittest.TestCase): @patch('sap.adt.cts.Workbench.fetch_transport_request') def test_not_found_transport(self, fake_fetch_transports): fake_fetch_transports.return_value = None connection = Mock() args = parse_args('run', 'transport', 'NPLK123456') with self.assertRaises(sap.errors.SAPCliError) as caught: args.execute(connection, args) self.assertEqual(str(caught.exception), 'The transport was not found: NPLK123456') @patch('sap.cli.core.printerr') @patch('sap.adt.cts.Workbench.fetch_transport_request') def test_no_testable_objects(self, fake_fetch_transports, fake_printerr): connection = Mock() fake_fetch_transports.return_value = sap.adt.cts.WorkbenchTransport( [], connection, 'NPLK123456', 'FILAK', 'Description', 'D') args = parse_args('run', 'transport', 'NPLK123456') ret = args.execute(connection, args) fake_printerr.assert_called_once_with('No testable objects found') self.assertEqual(ret, 1) @patch('sap.adt.cts.Workbench.fetch_transport_request') @patch('sap.adt.AUnit.execute') def test_all_kinds_and_more(self, fake_execute, fake_fetch_transports): connection = Connection() fake_fetch_transports.return_value = sap.adt.cts.WorkbenchTransport( [sap.adt.cts.WorkbenchTask('NPLK123456', [sap.adt.cts.WorkbenchABAPObject('R3TR', 'PROG', 'program', 'T', 'descr', 'X', '000000'), sap.adt.cts.WorkbenchABAPObject('R3TR', 'CLAS', 'class', 'T', 'descr', 'X', '000001'), ], connection, 'NPLK123457', 'FILAK', 'Description', 'D'), sap.adt.cts.WorkbenchTask('NPLK123456', [sap.adt.cts.WorkbenchABAPObject('R3TR', 'FUGR', 'functions', 'T', 'descr', 'X', '000000'), sap.adt.cts.WorkbenchABAPObject('R3TR', 'TABU', 'table', 'T', 'descr', 'X', '000001'), ], connection, 'NPLK123458', 'FILAK', 'Description', 'D'), ], connection, 'NPLK123456', 'FILAK', 'Description', 'D') class SentinelError(Exception): pass def assert_objects(obj_sets, activate_coverage): inclusive = [(ref.uri, ref.name) for ref in obj_sets.inclusive.references.references] self.assertEqual(inclusive, [('/sap/bc/adt/programs/programs/program', 'PROGRAM'), ('/sap/bc/adt/oo/classes/class', 'CLASS'), ('/sap/bc/adt/functions/groups/functions', 'FUNCTIONS')]) raise SentinelError() fake_execute.side_effect = assert_objects args = parse_args('run', 'transport', 'NPLK123456') with self.assertRaises(SentinelError): args.execute(connection, args) if __name__ == '__main__': unittest.main()

47.130653

186

0.674166

3,608

28,137

5.017461

0.090078

0.063802

0.039883

0.034746

0.815279

0.791747

0.758825

0.735734

0.723968

0.688063

0

0.019786

0.179124

28,137

596

187

47.209732

0.763995

0.000746

0

0.442368

0

0.139463

0.03223

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0.302181

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0.093458

false

0.003115

0.040498

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0.146417

0.196262

0

null

0

1

0

null

0

6

7267905e0aa66fec5b4ab84cbb86db0a6e1955c0

82

py

Python

3]. Competitive Programming/09]. HackerRank/2]. Tutorials/1]. 30 Days of Code/Python/Day_03.py

MLinesCode/The-Complete-FAANG-Preparation

2d0c7e8940eb2a58caaf4e978e548c08dd1f9a52

[ "MIT" ]

6,969

2021-05-29T11:38:30.000Z

2022-03-31T19:31:49.000Z

3]. Competitive Programming/09]. HackerRank/2]. Tutorials/1]. 30 Days of Code/Python/Day_03.py

MLinesCode/The-Complete-FAANG-Preparation

2d0c7e8940eb2a58caaf4e978e548c08dd1f9a52

[ "MIT" ]

75

2021-06-15T07:59:43.000Z

2022-02-22T14:21:52.000Z

3]. Competitive Programming/09]. HackerRank/2]. Tutorials/1]. 30 Days of Code/Python/Day_03.py

MLinesCode/The-Complete-FAANG-Preparation

2d0c7e8940eb2a58caaf4e978e548c08dd1f9a52

[ "MIT" ]

1,524

2021-05-29T16:03:36.000Z

2022-03-31T17:46:13.000Z

# 3rd Solution print(round(float(input())*(1+int(input())*.01+int(input())*.01)))

27.333333

66

0.634146

13

82

4

0.692308

0.307692

0.384615

0

0.076923

0.04878

82

3

66

27.333333

0.589744

0.146341

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

72700cef183574dc58e7c2d12c2d04b5a102376d

27

py

Python

pg_analyse/inspections/contrib/index_health/__init__.py

idlesign/pg_analyse

ba0f6fd0e6aa8ca6978055e241c03dd47cf7ce16

[ "BSD-3-Clause" ]

19

2020-03-11T19:30:01.000Z

2022-03-18T11:51:41.000Z

pg_analyse/inspections/contrib/index_health/__init__.py

idlesign/pg_analyse

ba0f6fd0e6aa8ca6978055e241c03dd47cf7ce16

[ "BSD-3-Clause" ]

2

2020-03-23T09:31:08.000Z

2020-04-28T09:32:49.000Z

pg_analyse/inspections/contrib/index_health/__init__.py

idlesign/pg_analyse

ba0f6fd0e6aa8ca6978055e241c03dd47cf7ce16

[ "BSD-3-Clause" ]

null

from .inspections import *

13.5

26

0.777778

3

27

7

1

0

0.148148

27

1

27

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

728931c8fb03871335012bd1b7af214066bd0bd6

14

py

Python

globals.py

vision-and-sensing/Adaptive-LiDAR-Sampling

fa49901cd9662393ffc2d267633ebe0b65be0a30

[ "MIT" ]

4

2021-02-22T15:08:12.000Z

2021-09-17T03:33:24.000Z

globals.py

vision-and-sensing/Adaptive-LiDAR-Sampling

fa49901cd9662393ffc2d267633ebe0b65be0a30

[ "MIT" ]

null

globals.py

vision-and-sensing/Adaptive-LiDAR-Sampling

fa49901cd9662393ffc2d267633ebe0b65be0a30

[ "MIT" ]

null

B = 1024 K = 4

7

8

0.5

4

14

1.75

1

0

0.555556

0.357143

14

2

9

7

0.222222

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1

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false

0

1

null

0

1

0

1

0

1

0

null

0

6

7290919ec4cdcbc5f4fefb78f2ee7dc6fa66bf39

7,267

py

Python

pytype/tools/pyi_checker/errors_test.py

adamcataldo/pytype

7163e85880b52d53d58044e53157e2a21988308e

[ "Apache-2.0" ]

2

2019-07-25T12:53:02.000Z

2019-08-18T16:26:16.000Z

pytype/tools/pyi_checker/errors_test.py

adamcataldo/pytype

7163e85880b52d53d58044e53157e2a21988308e

[ "Apache-2.0" ]

null

pytype/tools/pyi_checker/errors_test.py

adamcataldo/pytype

7163e85880b52d53d58044e53157e2a21988308e

[ "Apache-2.0" ]

null

"""Tests for pyi_checker errors. These are sanity checks to make sure error messages are correct. """ import textwrap from pytype.tools.pyi_checker import definitions from pytype.tools.pyi_checker import errors from pytype.tools.pyi_checker import test_utils as utils from typed_ast import ast3 import unittest class ErrorTest(unittest.TestCase): def test_missing_type_hint(self): src = utils.func_from_source("def test(a, b): return a + b") err = errors.MissingTypeHint(src) self.assertRegex( err.message, "No type hint found for function test.") def test_extra_type_hint(self): src = utils.func_from_source("def test(a, b) -> int: ...") err = errors.ExtraTypeHint(src) self.assertRegex( err.message, "Type hint for function test has no corresponding source definition.") def test_wrong_type_hint(self): src = utils.func_from_source("def test(a, b) -> int: ...") hint = utils.class_from_source("class test: ...") err = errors.WrongTypeHint(src, hint) self.assertRegex( err.message, r"^Type hint kind does not match source definition.\n.*" r"function test.*\n.*class test$") def test_wrong_decorators(self): src = utils.func_from_source( """ @dec1 @dec2 @dec3 def test(): ... """) hint = utils.func_from_source( """ @dec1 @decZ def test(): ... """) err = errors.WrongDecorators(src, hint) self.assertRegex( err.message, r"^Type hint for function test has incorrect decorators.\n" r".*Missing.*: dec2, dec3\n.*Extras.*: decZ$") def test_wrong_arg_count(self): src = utils.func_from_source("def test(a, b, *c, d, e, f, **g): pass") hint = utils.func_from_source("def test(a, *c, d, e, f, **g): ...") err = errors.WrongArgCount(src, hint) self.assertRegex( err.message, r"^Type hint for function test has the wrong number of arguments.\n" r".*Source:\s*def test$a, b, \.\.\.$\n" r".*Type hint:\s*def test$a, \.\.\.$$") def test_no_source_arg_count(self): src = utils.func_from_source("def test(*c): pass") hint = utils.func_from_source("def test(a, b): ...") err = errors.WrongArgCount(src, hint) self.assertRegex( err.message, r"Type hint for function test has the wrong number of arguments.\n" r".*Source:\s*def test$\.\.\.$\n" r".*Type hint:\s*def test$a, b$$") def test_no_hint_arg_count(self): src = utils.func_from_source("def test(a, b): pass") hint = utils.func_from_source("def test(*c): ...") err = errors.WrongArgCount(src, hint) self.assertRegex( err.message, r"Type hint for function test has the wrong number of arguments.\n" r".*Source:\s*def test$a, b$\n" r".*Type hint:\s*def test$\.\.\.$$") def test_wrong_kwonly_count(self): src = utils.func_from_source("def test(a, b, *c, d, e, f, **g): pass") hint = utils.func_from_source("def test(a, b, *c, d, e, f, h, **g): ...") err = errors.WrongKwonlyCount(src, hint) self.assertRegex( err.message, r"Type hint for function test has the wrong number " r"of keyword-only arguments.\n" r".*Source:\s*def test$\.\.\., \*c, d, e, f, \.\.\.$\n", r".*Type hint:\s*def test$\.\.\., \*c, d, e, f, h, \.\.\.$$") def test_no_source_kwonly_count(self): src = utils.func_from_source("def test(): pass") hint = utils.func_from_source("def test(*, a, b,): ...") err = errors.WrongKwonlyCount(src, hint) self.assertRegex( err.message, r"Type hint for function test has the wrong number " r"of keyword-only arguments.\n" r".*Source:\s*def test\n" r".*Type hint:\s*def test$\*, a, b$$") def test_no_hint_kwonly_count(self): src = utils.func_from_source("def test(*c, d, e, f, **g): pass") hint = utils.func_from_source("def test(*c): ...") err = errors.WrongKwonlyCount(src, hint) self.assertRegex( err.message, r"Type hint for function test has the wrong number " r"of keyword-only arguments.\n" r".*Source:\s*def test$\*c, d, e, f, ...$\n" r".*Type hint:\s*def test$...$$") def test_wrong_arg_name(self): src = utils.func_from_source("def test(a, b, e): pass") hint = utils.func_from_source("def test(a, b, c): ...") err = errors.WrongArgName(src, hint, "c") self.assertRegex( err.message, r"Function test has no argument named 'c'.\n" r".*Source:\s*def test$a, b, e$\n", r".*Type hint:\s*def test$a, b, c$\n") def test_wrong_kwonly_name(self): src = utils.func_from_source("def test(*, d, e, f): pass") hint = utils.func_from_source("def test(*, d, c, f): ...") err = errors.WrongKwonlyName(src, hint, "c") self.assertRegex( err.message, r"Function test has no keyword-only argument named 'c'.\n" r".*Source:\s*def test$\*, d, e, f$\n" r".*Type hint:\s*def test$\*, d, c, f$$") def test_no_source_vararg(self): src = utils.func_from_source("def test(a, b, **g): pass") hint = utils.func_from_source("def test(*a, **g): ...") err = errors.WrongVararg(src, hint) self.assertRegex( err.message, r"Type hint for function test should not have vararg '\*a'.") def test_no_hint_vararg(self): src = utils.func_from_source("def test(*c): pass") hint = utils.func_from_source("def test(a, b): ...") err = errors.WrongVararg(src, hint) self.assertRegex( err.message, r"Type hint for function test is missing the vararg '\*c'.") def test_wrong_vararg_name(self): src = utils.func_from_source("def test(a, b, *c, d, e): pass") hint = utils.func_from_source("def test(a, b, *z, d, e): ...") err = errors.WrongVararg(src, hint) self.assertRegex( err.message, r"Type hint for function test has wrong vararg name.\n" r".*Source:\s*def test$\.\.\., \*c, \.\.\.$\n" r".*Type hint:\s*def test$\.\.\., \*z, \.\.\.$$") def test_no_source_kwarg(self): src = utils.func_from_source("def test(): pass") hint = utils.func_from_source("def test(**a): ...") err = errors.WrongKwarg(src, hint) self.assertRegex( err.message, r"Type hint for function test should not have " r"keyword argument '\*\*a'\.") def test_no_hint_kwarg(self): src = utils.func_from_source("def test(**a): pass") hint = utils.func_from_source("def test(): pass") err = errors.WrongKwarg(src, hint) self.assertRegex( err.message, r"Type hint for function test is missing keyword argument '\*\*a'\.") def test_wrong_kwarg_name(self): src = utils.func_from_source("def test(a, b, **e): pass") hint = utils.func_from_source("def test(a, b, **c): ...") err = errors.WrongKwarg(src, hint) self.assertRegex( err.message, r"Type hint for function test has wrong keyword argument name.\n" r".*Source:\s*def test$\.\.\., \*\*e$\n" r".*Type hint:\s*def test$\.\.\., \*\*c$$") if __name__ == "__main__": unittest.main()

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0.101635

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0.696281

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0

null

0

1

0

6

72bc7ca8b72af9d6cb78a5606b0064365978d64c

3,292

py

Python

chaosk8s/crd/probes.py

kpk-pl/chaostoolkit-kubernetes

01558b3273fc52f6e1bcf575c1e0ae10ad4b2ce0

[ "Apache-2.0" ]

176

2017-10-14T10:19:24.000Z

2022-03-16T07:31:07.000Z

chaosk8s/crd/probes.py

kpk-pl/chaostoolkit-kubernetes

01558b3273fc52f6e1bcf575c1e0ae10ad4b2ce0

[ "Apache-2.0" ]

112

2017-12-11T13:51:48.000Z

2022-03-30T14:10:50.000Z

chaosk8s/crd/probes.py

kpk-pl/chaostoolkit-kubernetes

01558b3273fc52f6e1bcf575c1e0ae10ad4b2ce0

[ "Apache-2.0" ]

70

2018-01-23T23:37:42.000Z

2022-01-07T17:34:22.000Z

import json from typing import Any, Dict, List from chaoslib.exceptions import ActivityFailed from chaoslib.types import Secrets from kubernetes import client from kubernetes.client.rest import ApiException from chaosk8s import create_k8s_api_client __all__ = [ "get_custom_object", "get_cluster_custom_object", "list_custom_objects", "list_cluster_custom_objects", ] def get_custom_object( group: str, version: str, plural: str, name: str, ns: str = "default", secrets: Secrets = None, ) -> Dict[str, Any]: """ Get a custom object in the given namespace. Read more about custom resources here: https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/ """ # noqa: E501 api = client.CustomObjectsApi(create_k8s_api_client(secrets)) try: r = api.get_namespaced_custom_object( group, version, ns, plural, name, _preload_content=False ) return json.loads(r.data) except ApiException as x: raise ActivityFailed( f"Failed to create custom resource object: '{x.reason}' {x.body}" ) def list_custom_objects( group: str, version: str, plural: str, ns: str = "default", secrets: Secrets = None ) -> List[Dict[str, Any]]: """ List custom objects in the given namespace. Read more about custom resources here: https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/ """ # noqa: E501 api = client.CustomObjectsApi(create_k8s_api_client(secrets)) try: r = api.list_namespaced_custom_object( group, version, ns, plural, _preload_content=False ) return json.loads(r.data) except ApiException as x: raise ActivityFailed( f"Failed to create custom resource object: '{x.reason}' {x.body}" ) def get_cluster_custom_object( group: str, version: str, plural: str, name: str, secrets: Secrets = None ) -> Dict[str, Any]: """ Get a custom object cluster-wide. Read more about custom resources here: https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/ """ # noqa: E501 api = client.CustomObjectsApi(create_k8s_api_client(secrets)) try: r = api.get_cluster_custom_object( group, version, plural, name, _preload_content=False ) return json.loads(r.data) except ApiException as x: raise ActivityFailed( f"Failed to create custom resource object: '{x.reason}' {x.body}" ) def list_cluster_custom_objects( group: str, version: str, plural: str, secrets: Secrets = None ) -> List[Dict[str, Any]]: """ List custom objects cluster-wide. Read more about custom resources here: https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/ """ # noqa: E501 api = client.CustomObjectsApi(create_k8s_api_client(secrets)) try: r = api.list_cluster_custom_object( group, version, plural, _preload_content=False ) return json.loads(r.data) except ApiException as x: raise ActivityFailed( f"Failed to create custom resource object: '{x.reason}' {x.body}" )

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0.182266

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0.822566

0.777622

0.740169

0

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3,292

109

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0.020867

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false

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0.1

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0.214286

0

null

0

1

0

null

0

6

72c61b4f648b128ff13cd73e1a076801b5109dff

25,490

py

Python

tests/test_subscription.py

trag-stripe/dj-stripe

31fac752ab6540a917a554907d799e41ac3eba9c

[ "MIT" ]

null

tests/test_subscription.py

trag-stripe/dj-stripe

31fac752ab6540a917a554907d799e41ac3eba9c

[ "MIT" ]

null

tests/test_subscription.py

trag-stripe/dj-stripe

31fac752ab6540a917a554907d799e41ac3eba9c

[ "MIT" ]

null

""" dj-stripe Subscription Model Tests. """ from copy import deepcopy from decimal import Decimal from unittest.mock import patch from django.contrib.auth import get_user_model from django.test import TestCase from django.utils import timezone from stripe.error import InvalidRequestError from djstripe.enums import SubscriptionStatus from djstripe.models import Plan, Subscription from . import ( FAKE_CUSTOMER, FAKE_CUSTOMER_II, FAKE_PLAN, FAKE_PLAN_II, FAKE_PLAN_METERED, FAKE_PRODUCT, FAKE_SUBSCRIPTION, FAKE_SUBSCRIPTION_CANCELED, FAKE_SUBSCRIPTION_METERED, FAKE_SUBSCRIPTION_MULTI_PLAN, FAKE_SUBSCRIPTION_NOT_PERIOD_CURRENT, AssertStripeFksMixin, datetime_to_unix, ) class SubscriptionTest(AssertStripeFksMixin, TestCase): def setUp(self): self.user = get_user_model().objects.create_user( username="pydanny", email="pydanny@gmail.com" ) self.customer = FAKE_CUSTOMER.create_for_user(self.user) self.default_expected_blank_fks = { "djstripe.Customer.coupon", "djstripe.Subscription.pending_setup_intent", } @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_str( self, customer_retrieve_mock, product_retrieve_mock, plan_retrieve_mock ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) self.assertEqual( str(subscription), "{email} on {plan}".format( email=self.user.email, plan=str(subscription.plan) ), ) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_is_status_temporarily_current( self, customer_retrieve_mock, product_retrieve_mock, plan_retrieve_mock ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) subscription.canceled_at = timezone.now() + timezone.timedelta(days=7) subscription.current_period_end = timezone.now() + timezone.timedelta(days=7) subscription.cancel_at_period_end = True subscription.save() self.assertTrue(subscription.is_status_current()) self.assertTrue(subscription.is_status_temporarily_current()) self.assertTrue(subscription.is_valid()) self.assertTrue(subscription in self.customer.active_subscriptions) self.assertTrue(self.customer.has_active_subscription()) self.assertTrue(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_is_status_temporarily_current_false( self, customer_retrieve_mock, product_retrieve_mock, plan_retrieve_mock ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) subscription.current_period_end = timezone.now() + timezone.timedelta(days=7) subscription.save() self.assertTrue(subscription.is_status_current()) self.assertFalse(subscription.is_status_temporarily_current()) self.assertTrue(subscription.is_valid()) self.assertTrue(subscription in self.customer.active_subscriptions) self.assertTrue(self.customer.has_active_subscription()) self.assertTrue(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_is_status_temporarily_current_false_and_cancelled( self, customer_retrieve_mock, product_retrieve_mock, plan_retrieve_mock ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) subscription.status = SubscriptionStatus.canceled subscription.current_period_end = timezone.now() + timezone.timedelta(days=7) subscription.save() self.assertFalse(subscription.is_status_current()) self.assertFalse(subscription.is_status_temporarily_current()) self.assertFalse(subscription.is_valid()) self.assertFalse(subscription in self.customer.active_subscriptions) self.assertFalse(self.customer.has_active_subscription()) self.assertFalse(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch("stripe.Subscription.retrieve", autospec=True) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_extend( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription_fake["current_period_end"] = datetime_to_unix( timezone.now() - timezone.timedelta(days=20) ) subscription_retrieve_mock.return_value = subscription_fake subscription = Subscription.sync_from_stripe_data(subscription_fake) self.assertFalse(subscription in self.customer.active_subscriptions) self.assertEqual(self.customer.active_subscriptions.count(), 0) delta = timezone.timedelta(days=30) extended_subscription = subscription.extend(delta) self.assertNotEqual(None, extended_subscription.trial_end) self.assertTrue(self.customer.has_active_subscription()) self.assertTrue(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Subscription.retrieve", return_value=deepcopy(FAKE_SUBSCRIPTION), autospec=True, ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_extend_negative_delta( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION_NOT_PERIOD_CURRENT) subscription = Subscription.sync_from_stripe_data(subscription_fake) with self.assertRaises(ValueError): subscription.extend(timezone.timedelta(days=-30)) self.assertFalse(self.customer.has_active_subscription()) self.assertFalse(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Subscription.retrieve", return_value=deepcopy(FAKE_SUBSCRIPTION), autospec=True, ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_extend_with_trial( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) subscription.trial_end = timezone.now() + timezone.timedelta(days=5) subscription.save() delta = timezone.timedelta(days=30) new_trial_end = subscription.trial_end + delta extended_subscription = subscription.extend(delta) self.assertEqual( new_trial_end.replace(microsecond=0), extended_subscription.trial_end ) self.assertTrue(self.customer.has_active_subscription()) self.assertTrue(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Subscription.retrieve", return_value=deepcopy(FAKE_SUBSCRIPTION), autospec=True, ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_update( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) self.assertEqual(1, subscription.quantity) new_subscription = subscription.update(quantity=4) self.assertEqual(4, new_subscription.quantity) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch("stripe.Subscription.retrieve", autospec=True) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_update_set_empty_value( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription_fake.update({"tax_percent": Decimal(20.0)}) subscription_retrieve_mock.return_value = subscription_fake subscription = Subscription.sync_from_stripe_data(subscription_fake) self.assertEqual(Decimal(20.0), subscription.tax_percent) new_subscription = subscription.update(tax_percent=Decimal(0.0)) self.assertEqual(Decimal(0.0), new_subscription.tax_percent) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Subscription.retrieve", return_value=deepcopy(FAKE_SUBSCRIPTION), autospec=True, ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_update_with_plan_model( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) new_plan = Plan.sync_from_stripe_data(deepcopy(FAKE_PLAN_II)) self.assertEqual(FAKE_PLAN["id"], subscription.plan.id) new_subscription = subscription.update(plan=new_plan) self.assertEqual(FAKE_PLAN_II["id"], new_subscription.plan.id) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) self.assert_fks(new_plan, expected_blank_fks={}) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch("stripe.Subscription.retrieve", autospec=True) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_cancel_now( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) subscription.current_period_end = timezone.now() + timezone.timedelta(days=7) subscription.save() cancel_timestamp = datetime_to_unix(timezone.now()) canceled_subscription_fake = deepcopy(FAKE_SUBSCRIPTION) canceled_subscription_fake["status"] = SubscriptionStatus.canceled canceled_subscription_fake["canceled_at"] = cancel_timestamp canceled_subscription_fake["ended_at"] = cancel_timestamp subscription_retrieve_mock.return_value = ( canceled_subscription_fake ) # retrieve().delete() self.assertTrue(self.customer.has_active_subscription()) self.assertEqual(self.customer.active_subscriptions.count(), 1) self.assertTrue(self.customer.has_any_active_subscription()) new_subscription = subscription.cancel(at_period_end=False) self.assertEqual(SubscriptionStatus.canceled, new_subscription.status) self.assertEqual(False, new_subscription.cancel_at_period_end) self.assertEqual(new_subscription.canceled_at, new_subscription.ended_at) self.assertFalse(new_subscription.is_valid()) self.assertFalse(new_subscription in self.customer.active_subscriptions) self.assertFalse(self.customer.has_active_subscription()) self.assertFalse(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch("stripe.Subscription.retrieve", autospec=True) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_cancel_at_period_end( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): current_period_end = timezone.now() + timezone.timedelta(days=7) subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) subscription.current_period_end = current_period_end subscription.save() canceled_subscription_fake = deepcopy(FAKE_SUBSCRIPTION) canceled_subscription_fake["current_period_end"] = datetime_to_unix( current_period_end ) canceled_subscription_fake["canceled_at"] = datetime_to_unix(timezone.now()) subscription_retrieve_mock.return_value = ( canceled_subscription_fake ) # retrieve().delete() self.assertTrue(self.customer.has_active_subscription()) self.assertTrue(self.customer.has_any_active_subscription()) self.assertEqual(self.customer.active_subscriptions.count(), 1) self.assertTrue(subscription in self.customer.active_subscriptions) new_subscription = subscription.cancel(at_period_end=True) self.assertEqual(self.customer.active_subscriptions.count(), 1) self.assertTrue(new_subscription in self.customer.active_subscriptions) self.assertEqual(SubscriptionStatus.active, new_subscription.status) self.assertEqual(True, new_subscription.cancel_at_period_end) self.assertNotEqual(new_subscription.canceled_at, new_subscription.ended_at) self.assertTrue(new_subscription.is_valid()) self.assertTrue(self.customer.has_active_subscription()) self.assertTrue(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch("stripe.Subscription.retrieve", autospec=True) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_cancel_during_trial_sets_at_period_end( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) subscription.trial_end = timezone.now() + timezone.timedelta(days=7) subscription.save() cancel_timestamp = datetime_to_unix(timezone.now()) canceled_subscription_fake = deepcopy(FAKE_SUBSCRIPTION) canceled_subscription_fake["status"] = SubscriptionStatus.canceled canceled_subscription_fake["canceled_at"] = cancel_timestamp canceled_subscription_fake["ended_at"] = cancel_timestamp subscription_retrieve_mock.return_value = ( canceled_subscription_fake ) # retrieve().delete() self.assertTrue(self.customer.has_active_subscription()) self.assertTrue(self.customer.has_any_active_subscription()) new_subscription = subscription.cancel(at_period_end=False) self.assertEqual(SubscriptionStatus.canceled, new_subscription.status) self.assertEqual(False, new_subscription.cancel_at_period_end) self.assertEqual(new_subscription.canceled_at, new_subscription.ended_at) self.assertFalse(new_subscription.is_valid()) self.assertFalse(self.customer.has_active_subscription()) self.assertFalse(self.customer.has_any_active_subscription()) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", return_value=deepcopy(FAKE_PLAN), autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch("stripe.Subscription.retrieve", autospec=True) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER), autospec=True ) def test_cancel_and_reactivate( self, customer_retrieve_mock, subscription_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): current_period_end = timezone.now() + timezone.timedelta(days=7) subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) subscription.current_period_end = current_period_end subscription.save() canceled_subscription_fake = deepcopy(FAKE_SUBSCRIPTION) canceled_subscription_fake["current_period_end"] = datetime_to_unix( current_period_end ) canceled_subscription_fake["canceled_at"] = datetime_to_unix(timezone.now()) subscription_retrieve_mock.return_value = canceled_subscription_fake self.assertTrue(self.customer.has_active_subscription()) self.assertTrue(self.customer.has_any_active_subscription()) new_subscription = subscription.cancel(at_period_end=True) self.assertEqual(new_subscription.cancel_at_period_end, True) new_subscription.reactivate() subscription_reactivate_fake = deepcopy(FAKE_SUBSCRIPTION) reactivated_subscription = Subscription.sync_from_stripe_data( subscription_reactivate_fake ) self.assertEqual(reactivated_subscription.cancel_at_period_end, False) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("djstripe.models.Subscription._api_delete", autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Subscription.retrieve", return_value=deepcopy(FAKE_SUBSCRIPTION_CANCELED), ) def test_cancel_already_canceled( self, subscription_retrieve_mock, product_retrieve_mock, subscription_delete_mock, ): subscription_delete_mock.side_effect = InvalidRequestError( "No such subscription: sub_xxxx", "blah" ) subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) self.assertEqual(Subscription.objects.filter(status="canceled").count(), 0) subscription.cancel(at_period_end=False) self.assertEqual(Subscription.objects.filter(status="canceled").count(), 1) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("djstripe.models.Subscription._api_delete", autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) def test_cancel_error_in_cancel( self, product_retrieve_mock, subscription_delete_mock ): subscription_delete_mock.side_effect = InvalidRequestError( "Unexpected error", "blah" ) subscription_fake = deepcopy(FAKE_SUBSCRIPTION) subscription = Subscription.sync_from_stripe_data(subscription_fake) with self.assertRaises(InvalidRequestError): subscription.cancel(at_period_end=False) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks ) @patch("stripe.Plan.retrieve", autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER_II), autospec=True, ) @patch( "stripe.Subscription.retrieve", return_value=deepcopy(FAKE_SUBSCRIPTION_MULTI_PLAN), ) def test_sync_multi_plan( self, subscription_retrieve_mock, customer_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION_MULTI_PLAN) subscription = Subscription.sync_from_stripe_data(subscription_fake) self.assertIsNone(subscription.plan) self.assertIsNone(subscription.quantity) items = subscription.items.all() self.assertEqual(2, len(items)) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks | {"djstripe.Customer.subscriber", "djstripe.Subscription.plan"}, ) @patch("stripe.Plan.retrieve", autospec=True) @patch( "stripe.Product.retrieve", return_value=deepcopy(FAKE_PRODUCT), autospec=True ) @patch( "stripe.Customer.retrieve", return_value=deepcopy(FAKE_CUSTOMER_II), autospec=True, ) @patch( "stripe.Subscription.retrieve", return_value=deepcopy(FAKE_SUBSCRIPTION_METERED) ) def test_sync_metered_plan( self, subscription_retrieve_mock, customer_retrieve_mock, product_retrieve_mock, plan_retrieve_mock, ): subscription_fake = deepcopy(FAKE_SUBSCRIPTION_METERED) self.assertNotIn( "quantity", subscription_fake["items"]["data"], "Expect Metered plan SubscriptionItem to have no quantity", ) subscription = Subscription.sync_from_stripe_data(subscription_fake) items = subscription.items.all() self.assertEqual(1, len(items)) item = items[0] self.assertEqual(subscription.quantity, 1) # Note that subscription.quantity is 1, # but item.quantity isn't set on metered plans self.assertIsNone(item.quantity) self.assertEqual(item.plan.id, FAKE_PLAN_METERED["id"]) self.assert_fks( subscription, expected_blank_fks=self.default_expected_blank_fks )

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88

0.70816

2,691

25,490

6.372724

0.059086

0.05528

0.060937

0.086594

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0

0.052448

0

null

0

1

0

null

0

6

72d9d81f71b49d8005ec6b4e27ced53173df725d

42

py

Python

ku/ebm/__init__.py

tonandr/keras_unsupervised

fd2a2494bca2eb745027178e220b42b5e5882f94

[ "BSD-3-Clause" ]

4

2019-07-28T11:56:01.000Z

2021-11-06T02:50:58.000Z

ku/ebm/__init__.py

tonandr/keras_unsupervised

fd2a2494bca2eb745027178e220b42b5e5882f94

[ "BSD-3-Clause" ]

2

2021-06-30T01:00:07.000Z

2021-07-21T08:04:40.000Z

ku/ebm/__init__.py

tonandr/keras_unsupervised

fd2a2494bca2eb745027178e220b42b5e5882f94

[ "BSD-3-Clause" ]

null

from .dbn import DBN from .rbm import RBM

21

0.761905

8

42

4

0.5

0

0.190476

42

2

22

21

0.941176

0

1

0

true

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0

1

0

null

0

1

0

1

0

1

0

6

f405887e05b5221d6596043b63f721c3de200150

179

py

Python

bin/iamonds/hexiamonds-stacked-chevrons-6x6.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/iamonds/hexiamonds-stacked-chevrons-6x6.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/iamonds/hexiamonds-stacked-chevrons-6x6.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

1

2022-01-02T16:54:14.000Z

#!/usr/bin/env python # $Id$ """933 solutions""" import puzzler from puzzler.puzzles.hexiamonds import HexiamondsStackedChevrons_6x6 puzzler.run(HexiamondsStackedChevrons_6x6)

17.9

68

0.804469

20

179

7.1

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179

9

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0.217877

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true

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0

null

0

1

0

1

0

1

0

6

f40785b5c99a921cf7fcdf3eb6931610cb831a13

42

py

Python

helloworld/__init__.py

jtap159/helloworld

b0cae97df867a23ad0c64c521b18bf8a37676319

[ "MIT" ]

null

helloworld/__init__.py

jtap159/helloworld

b0cae97df867a23ad0c64c521b18bf8a37676319

[ "MIT" ]

null

helloworld/__init__.py

jtap159/helloworld

b0cae97df867a23ad0c64c521b18bf8a37676319

[ "MIT" ]

null

from helloworld.helloworld import sayhello

42

0.904762

5

42

7.6

0.8

0

0.071429

42

1

42

0.974359

0

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true

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null

0

1

0

1

0

1

0

6

f474075e2b2a39b0f7d276296dea02ff91914863

7,983

py

Python

netapp_activeiq_api/apis/capacity_api.py

woutercoppens/netapp-activeiq-api

a8f86355ecdd769953b69e38824b4db07c11c89e

[ "Apache-2.0" ]

3

2021-09-28T23:22:59.000Z

2021-11-23T14:53:54.000Z

netapp_activeiq_api/apis/capacity_api.py

woutercoppens/netapp-activeiq-api

a8f86355ecdd769953b69e38824b4db07c11c89e

[ "Apache-2.0" ]

null

netapp_activeiq_api/apis/capacity_api.py

woutercoppens/netapp-activeiq-api

a8f86355ecdd769953b69e38824b4db07c11c89e

[ "Apache-2.0" ]

1

2021-04-01T11:22:23.000Z

# coding: utf-8 from .api_client import ApiClient class CapacityApi: def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def get_capacity_details_by_level(self, id, level, **kwargs): # noqa: E501 """Provides the details about the systems nearing allocated capacity limit for a customer, site, group, cluster, watchlist or a set of serial numbers. # noqa: E501 Lists information about systems for a customer, site, group, cluster, watchlist or serial numbers that have exceeded 90 percent system capacity or are predicted to do so soon. Systems are grouped into the following categories: currently above 90 percent, expected to exceed 90 percent within 1 month, expected to exceed 90 percent within 3 months, expected to exceed 90 percent within 6 months, not expected to exceed 90 percent within 6 months. # noqa: E501 :param str id: Unique identifier for the level. Valid values are customer ID, site ID, group name, cluster, serial numbers and watchList id. (required) :param str level: Identifies the level for which information will be provided. Valid values are customer, site, group, cluster, serial_numbers and watchlist. (required) """ all_params = ["id", "level"] # noqa: E501 params = locals() for key, val in params["kwargs"].items(): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_capacity_details_by_level" % key ) params[key] = val del params["kwargs"] # verify the required parameter 'id' is set if "id" not in params or params["id"] is None: raise ValueError( "Missing the required parameter `id` when calling `get_capacity_details_by_level`" ) # noqa: E501 # verify the required parameter 'level' is set if "level" not in params or params["level"] is None: raise ValueError( "Missing the required parameter `level` when calling `get_capacity_details_by_level`" ) # noqa: E501 path_params = {} if "id" in params: path_params["id"] = params["id"] # noqa: E501 if "level" in params: path_params["level"] = params["level"] # noqa: E501 query_params = [] header_params = {} body_params = None return self.api_client.call_api( "/v2/capacity/details/level/{level}/id/{id}", "GET", path_params, query_params, header_params, body=body_params, ) def get_capacity_summary_by_level(self, id, level, **kwargs): # noqa: E501 """Provides the number of systems nearing allocated capacity limit for a customer, site, group, cluster watchlist or a set of serial numbers. # noqa: E501 Lists the number of systems for a customer, site, group, cluster, watchlist or a set of serial numbers that have exceeded 90 percent system capacity or are predicted to do so soon. Counts are provided for the following categories: currently above 90 percent, expected to exceed 90 percent within 1 month, expected to exceed 90 percent within 3 months, expected to exceed 90 percent within 6 months, sum of systems which are above 90 percent and expected to exceed 90 percent in 6 months, not expected to exceed 90 percent within 6 months. # noqa: E501 :param str id: Unique identifier for the level. Valid values are customer ID, site ID, group name, cluster id, serial numbers and watchList id. (required) :param str level: Identifies the level for which information will be provided. Valid values are customer, site, group, cluster, serial_numbers and watchlist. (required) """ all_params = ["id", "level"] # noqa: E501 params = locals() for key, val in params["kwargs"].items(): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_capacity_summary_by_level" % key ) params[key] = val del params["kwargs"] # verify the required parameter 'id' is set if "id" not in params or params["id"] is None: raise ValueError( "Missing the required parameter `id` when calling `get_capacity_summary_by_level`" ) # noqa: E501 # verify the required parameter 'level' is set if "level" not in params or params["level"] is None: raise ValueError( "Missing the required parameter `level` when calling `get_capacity_summary_by_level`" ) # noqa: E501 path_params = {} if "id" in params: path_params["id"] = params["id"] # noqa: E501 if "level" in params: path_params["level"] = params["level"] # noqa: E501 query_params = [] header_params = {} body_params = None return self.api_client.call_api( "/v2/capacity/summary/level/{level}/id/{id}", "GET", path_params, query_params, header_params, body=body_params, ) def get_capacity_trend_details_by_level(self, level, id, **kwargs): # noqa: E501 """Provides the capacity trending details about the systems for a customer, site, group, or a set of serial numbers. # noqa: E501 Returns the used and allocated capacity for each of the last 6 months of available data for each system by pagination # noqa: E501 :param str level: Identifies the level for which information will be provided. Valid values are customer, site, group, and serial_numbers. (required) :param str id: Unique identifier for the level. Valid values are customer ID, site ID, group name, and serial numbers. (required) :param float start: The index of the first system to return. :param float limit: Specifies the number of systems to be displayed on a page. The default value is 1000. """ all_params = ["level", "id", "start", "limit"] # noqa: E501 params = locals() for key, val in params["kwargs"].items(): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_capacity_trend_details_by_level" % key ) params[key] = val del params["kwargs"] # verify the required parameter 'level' is set if "level" not in params or params["level"] is None: raise ValueError( "Missing the required parameter `level` when calling `get_capacity_trend_details_by_level`" ) # noqa: E501 # verify the required parameter 'id' is set if "id" not in params or params["id"] is None: raise ValueError( "Missing the required parameter `id` when calling `get_capacity_trend_details_by_level`" ) # noqa: E501 path_params = {} if "level" in params: path_params["level"] = params["level"] # noqa: E501 if "id" in params: path_params["id"] = params["id"] # noqa: E501 query_params = [] if "start" in params: query_params.append(("start", params["start"])) # noqa: E501 if "limit" in params: query_params.append(("limit", params["limit"])) # noqa: E501 header_params = {} body_params = None return self.api_client.call_api( "/v1/capacity/trend/level/{level}/id/{id}", "GET", path_params, query_params, header_params, body=body_params, )

46.958824

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1,016

7,983

4.731299

0.140748

0.04327

0.049927

0.033701

0.86062

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169

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0.841509

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0.078261

0

null

0

1

0

null

0

6

be46de9477b6d12d489bac9e3f55d4cc12a067cb

16,914

py

Python

upwork/routers/task.py

alexandru-grajdeanu/python-upwork

ffe7994c084c88c455a386791e4ec62a93bb7b6a

[ "Apache-2.0", "BSD-3-Clause" ]

1

2020-05-17T17:13:28.000Z

upwork/routers/task.py

frolenkov-nikita/python-upwork

d052f5caedc632c73ad770b1f822a8a494f6b34b

[ "Apache-2.0", "BSD-3-Clause" ]

null

upwork/routers/task.py

frolenkov-nikita/python-upwork

d052f5caedc632c73ad770b1f822a8a494f6b34b

[ "Apache-2.0", "BSD-3-Clause" ]

null

# Python bindings to Upwork API # python-upwork version 0.5 # (C) 2010-2015 Upwork from upwork.compatibility import quote from upwork.namespaces import Namespace class Task(Namespace): api_url = 'otask/' version = 1 def get_team_tasks(self, company_id, team_id, paging_offset=0, paging_count=1000): """ Retrieve a list of all activities in the given team. This call returns both archived and active activities. The user authenticated must have been granted the appropriate access to the team. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :team_id: Team ID. Use the 'id' value from ``hr.get_team()`` API call. """ if paging_offset or not paging_count == 1000: data = {'page': '{0};{1}'.format(paging_offset, paging_count)} else: data = {} url = 'tasks/companies/{0}/teams/{1}/tasks'.format(company_id, team_id) return self.get(url, data=data) def get_company_tasks(self, company_id, paging_offset=0, paging_count=1000): """ Retrieve a list of all activities within a company. It is equivalent to the ``get_team_tasks`` so that ``team_id`` is equal to ``company_id`` which is parent team ID. This call returns both archived and active activities. The user authenticated must have been granted the appropriate access to the company. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. """ team_id = company_id return self.get_team_tasks( company_id, team_id, paging_offset=paging_offset, paging_count=paging_count ) def _encode_task_codes(self, task_codes): if isinstance(task_codes, (list, tuple)): return ';'.join(str(c) for c in task_codes) else: return str(task_codes) def get_team_specific_tasks(self, company_id, team_id, task_codes): """ Return a specific activities within a team. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :team_id: Team ID. Use the 'id' value from ``hr.get_team()`` API call. :task_codes: Task codes (must be a list, even of 1 item) """ task_codes = self._encode_task_codes(task_codes) url = 'tasks/companies/{0}/teams/{1}/tasks/{2}'.format( company_id, team_id, quote(task_codes)) result = self.get(url) try: return result["tasks"] or [] except KeyError: return result def get_company_specific_tasks(self, company_id, task_codes): """ Return a specific activities within a company. This is identical to ``get_team_specific_tasks``, so that ``team_id`` is the same as ``company_id``. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :task_codes: Task codes (must be a list, even of 1 item) """ team_id = company_id return self.get_team_specific_tasks( company_id, team_id, task_codes) def post_team_task(self, company_id, team_id, code, description, url, engagements=None, all_in_company=None): """ Create an activity within a team. The authenticated user needs to have hiring manager privileges *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :team_id: Team ID. Use the 'id' value from ``hr.get_team()`` API call. :code: Task code :description: Task description :url: Task URL :engagements: (optional) A list of engagements that are to be assigned to the created activity. It can be a single engagement ID, or an iterable of IDs. :all_in_company: (optional) If ``True``, assign the created activity to all engagements that are exist in the company at the moment. If both ``engagements`` and ``all_in_company`` are provided, ``engagements`` list will override the ``all_in_company`` setting. """ post_url = 'tasks/companies/{0}/teams/{1}/tasks'.format( company_id, team_id) data = {'code': code, 'description': description, 'url': url} if engagements: engagements = self._encode_task_codes(engagements) data['engagements'] = engagements if all_in_company: data['all_in_company'] = 1 result = self.post(post_url, data) return result def post_company_task(self, company_id, code, description, url, engagements=None, all_in_company=None): """ Create an activity within a company. This call is identical to ``post_team_task`` so that ``team_id`` is equal to ``company_id``. The authenticated user needs to have hiring manager privileges. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :code: Activity ID :description: Activity description :url: Activity URL :engagements: (optional) A list of engagements that are to be assigned to the created activity. It can be a single engagement ID, or an iterable of IDs. :all_in_company: (optional) If ``True``, assign the created activity to all engagements that are exist in the company at the moment. If both ``engagements`` and ``all_in_company`` are provided, ``engagements`` list will override the ``all_in_company`` setting. """ team_id = company_id return self.post_team_task( company_id, team_id, code, description, url, engagements=engagements, all_in_company=all_in_company) def put_team_task(self, company_id, team_id, code, description, url, engagements=None, all_in_company=None): """ Update an activity within a team. The authenticated user needs to have hiring manager privileges. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :team_id: Team ID. Use the 'id' value from ``hr.get_team()`` API call. :code: Task code :description: Task description :url: Task URL :engagements: (optional) A list of engagements that are to be assigned to the created activity. It can be a single engagement ID, or an iterable of IDs. :all_in_company: (optional) If ``True``, assign the updated activity to all engagements that are exist in the company at the moment. If both ``engagements`` and ``all_in_company`` are provided, ``engagements`` list will override the ``all_in_company`` setting. """ put_url = 'tasks/companies/{0}/teams/{1}/tasks/{2}'.format( company_id, team_id, quote(str(code))) data = {'code': code, 'description': description, 'url': url} if engagements: engagements = self._encode_task_codes(engagements) data['engagements'] = engagements if all_in_company: data['all_in_company'] = 1 result = self.put(put_url, data) return result def put_company_task(self, company_id, code, description, url, engagements=None, all_in_company=None): """ Update an activity within a company. This call is identical to ``put_team_task`` so that ``team_id`` is equal to ``company_id``. The authenticated user needs to have hiring manager privileges. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :code: Task code :description: Task description :url: Task URL :engagements: (optional) A list of engagements that are to be assigned to the created activity. It can be a single engagement ID, or an iterable of IDs. :all_in_company: (optional) If ``True``, assign the created activity to all engagements that are exist in the company at the moment. If both ``engagements`` and ``all_in_company`` are provided, ``engagements`` list will override the ``all_in_company`` setting. """ team_id = company_id return self.put_team_task( company_id, team_id, code, description, url, engagements=engagements, all_in_company=all_in_company) def archive_team_task(self, company_id, team_id, task_code): """Archive single activity within a team. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :team_id: Team ID. Use the 'id' value from ``hr.get_team()`` API call. :task_code: A single Activity ID as a string or a list or tuple of IDs. """ task_code = self._encode_task_codes(task_code) url = 'tasks/companies/{0}/teams/{1}/archive/{2}'.format( company_id, team_id, quote(task_code)) return self.put(url, data={}) def archive_company_task(self, company_id, task_code): """Archive single activity within a company. This call is identical to ``archive_team_task``, so that ``team_id`` is the same as ``company_id``. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :task_code: A single Activity ID as a string or a list or tuple of IDs. """ team_id = company_id return self.archive_team_task(company_id, team_id, task_code) def unarchive_team_task(self, company_id, team_id, task_code): """Unarchive single activity within a team. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :team_id: Team ID. Use the 'id' value from ``hr.get_team()`` API call. :task_code: A single Activity ID as a string or a list or tuple of IDs. """ task_code = self._encode_task_codes(task_code) url = 'tasks/companies/{0}/teams/{1}/unarchive/{2}'.format( company_id, team_id, quote(task_code)) return self.put(url, data={}) def unarchive_company_task(self, company_id, task_code): """Unarchive single activity within a company. This call is identical to ``unarchive_team_task``, so that ``team_id`` is the same as ``company_id``. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :task_code: A single Activity ID as a string or a list or tuple of IDs. """ team_id = company_id return self.unarchive_team_task(company_id, team_id, task_code) def assign_engagement(self, company_id, team_id, engagement, task_codes=None): """Assign an existing engagement to the list of activities. Note that activity will appear in contractor's team client only if his engagement is assigned to the activity and activities are activated for the ongoing contract. This will override assigned engagements for the given activities. For example, if you pass empty ``task_codes`` or just omit this parameter, contractor engagement will be unassigned from all Activities. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :team_id: Team ID. Use the 'id' value from ``hr.get_team()`` API call. :engagement: Engagement ID that will be assigned/unassigned to the given list of Activities. :task_codes: Task codes (must be a list, even of 1 item) """ task_codes = self._encode_task_codes(task_codes) url = 'tasks/companies/{0}/teams/{1}/engagements/{2}/tasks'.format( company_id, team_id, engagement) data = {'tasks': task_codes} result = self.put(url, data) return result def update_batch_tasks(self, company_id, csv_data): """ Batch update Activities using csv file contents. This call is experimental, use it on your own risk. *Parameters:* :company_id: Company ID. Use the ``parent_team__id`` value from ``hr.get_team()`` API call. :csv_data: Task records in csv format but with "<br>" as line separator - "companyid","teamid","userid","taskid","description","url" Example: "acmeinc","","","T1","A Task","http://example.com"<br> "acmeinc","acmeinc:dev","b42","T2","Task 2","" """ data = {'data': csv_data} url = 'tasks/companies/{0}/tasks/batch'.format(company_id) return self.put(url, data) class Task_V2(Namespace): api_url = 'tasks/' version = 2 def _encode_task_codes(self, task_codes): if isinstance(task_codes, (list, tuple)): return ';'.join(str(c) for c in task_codes) else: return str(task_codes) def list_engagement_activities(self, engagement_ref): """ Retrieve list of all activities assigned to the specific engagement. The user authenticated must have been granted the appropriate hiring manager permissions. *Parameters:* :engagement_ref: Engagement reference ID. You can get it using 'List engagemnets' API call. Example: `1234`. """ url = 'tasks/contracts/{0}'.format(engagement_ref) result = self.get(url) return result def assign_to_engagement(self, engagement_ref, task_codes=None): """Assign a list of activities to the existing engagement. Note that activity will appear in contractor's team client only if his engagement is assigned to the activity and activities are activated for the ongoing contract. This will override assigned engagements for the given activities. For example, if you pass empty ``task_codes`` or just omit this parameter, contractor engagement will be unassigned from all Activities. *Parameters:* :engagement_ref: Engagement ID that will be assigned/unassigned to the given list of Activities. :task_codes: Task codes (must be a list, even of 1 item) """ task_codes = self._encode_task_codes(task_codes) url = 'tasks/contracts/{0}'.format(engagement_ref) data = {'tasks': task_codes} result = self.put(url, data) return result

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null

0

1

0

null

0

6

be4dec4674b78bf4e1a8327c3d028260689ad9ac

2,897

py

Python

linkedin/appco.py

zzh-python/all-project

915a47fb42d63ff3a36814992283c2f4ed8703a3

[ "Apache-2.0" ]

58

2019-03-01T08:15:19.000Z

2022-03-28T03:16:17.000Z

linkedin/appco.py

zzh-python/all-project

915a47fb42d63ff3a36814992283c2f4ed8703a3

[ "Apache-2.0" ]

2

2020-06-08T08:07:46.000Z

2020-11-02T11:48:05.000Z

linkedin/appco.py

zzh-python/all-project

915a47fb42d63ff3a36814992283c2f4ed8703a3

[ "Apache-2.0" ]

37

2019-02-26T23:30:08.000Z

2022-01-27T05:10:18.000Z

import requests cook='appbot_convert={%22referer%22:null%2C%22landing%22:%22https://appbot.co/%22}; _ga=GA1.2.30329182.1540298320; intercom-id-glvjson7=f3876d87-0fec-45f4-89f2-85f1209950c4; appbot_session_active=true; cookieconsent_status=allow; intercom-session-glvjson7=UlhJK3ZCTHF6dFlBLzNVV2IyZDhjaWNSWTc4cDRuN2dZZ0dRa2dyMjZBd21SUlpJT0xnVlJ6ZzkxQlFiZTdUby0tbjRtS0hjS3hUenNxT1o5RDZOanUzQT09--223ba2c3f3a550394ee3dd5ad077e76c32fc0be0; remember_user_token=BAhbCFsGaQJOkkkiIiQyYSQxMCQxN0FGZHNDZ050RmRwT2JSRElDalguBjoGRVRJIhcxNTQwNDM2NDA4Ljk2MjM3NDcGOwBG--578f0df79fd57a0bc670f3b164c91fb874fa529d; _gid=GA1.2.998133860.1540436421; _gat=1; _hp2_id.116503402=%7B%22userId%22%3A%227121901154332925%22%2C%22pageviewId%22%3A%228825843111957251%22%2C%22sessionId%22%3A%222353689844032949%22%2C%22identity%22%3A%221509662199%40qq.com%22%2C%22trackerVersion%22%3A%224.0%22%2C%22identityField%22%3Anull%2C%22isIdentified%22%3A1%7D; _hp2_ses_props.116503402=%7B%22ts%22%3A1540436420819%2C%22d%22%3A%22app.appbot.co%22%2C%22h%22%3A%22%2Fapps%2F1393448-arena-of-valor%2Freviews%22%7D; _appbot_session=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--875d723f0968d9abea12c03687e14f9c665ffae2; filterOpen=true' header={ 'Cookie':cook, 'referer':'https://app.appbot.co/apps/1393448-arena-of-valor/reviews', 's':'980fb71da83a2a5e09431e5023b2e5373f7dbc7f', # 's':'786e31f6befa521723b0afe90eca31f937d09190' # 's':'7f581ec28840d94a3e84b61431ef62617e568273' # 's':'df760c8aecd6c8a60ab5f5d1fd61dda9ad1b3ee0' # 's':'7cab2996fdc06853ea6c4e7db9511680d5a8408a' # 's':'b2576c0bd7fafb1fa3dad789846ce7f769055419' } url='https://app.appbot.co/apps/1393448-arena-of-valor/reviews#/?dlangs=zh&end=2018-10-25&start=2018-07-27' url='https://app.appbot.co/data/apps/1393448/reviews?start=2018-07-27&end=2018-10-25&dlangs=zh&count=10&page=1' url='https://app.appbot.co/data/apps/10242/reviews?start=2018-07-27&end=2018-10-25&dlangs=zh-Hant&count=10&page=1' url='https://app.appbot.co/data/apps/10242/reviews?start=2018-07-27&end=2018-10-25&dlangs=zh&count=10&page=1' # url='https://app.appbot.co/data/apps/1393448/reviews?start=2018-07-27&end=2018-10-25&dlangs=zh&count=10&page=2&____c=c7d8c9c8e91be712870923d832de2fdd43b5a4bf' # url='https://app.appbot.co/data/apps/1393448/reviews?start=2018-07-27&end=2018-10-25&dlangs=zh&count=10&page=3' req=requests.get(url,headers=header) print(req.text)

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2,897

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103.464286

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null

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1

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null

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6

be99eca575d2ea20bbda8ac7e29af8f2bb6ccc82

3,013

py

Python

academicstoday/shared_auth/views/email_views.py

MikaSoftware/academicstoday-paas-django

cf58cf216d377ea97a2676cd594f96fb9d602a46

[ "BSD-3-Clause" ]

null

academicstoday/shared_auth/views/email_views.py

MikaSoftware/academicstoday-paas-django

cf58cf216d377ea97a2676cd594f96fb9d602a46

[ "BSD-3-Clause" ]

6

2020-06-05T17:54:00.000Z

2022-03-11T23:18:41.000Z

academicstoday/shared_auth/views/email_views.py

MikaSoftware/academicstoday-paas-django

cf58cf216d377ea97a2676cd594f96fb9d602a46

[ "BSD-3-Clause" ]

2

2020-05-01T12:50:38.000Z

2021-07-17T09:51:12.000Z

# -*- coding: utf-8 -*- from django.contrib.auth.decorators import login_required from django.contrib.auth.models import Group from django.core.exceptions import PermissionDenied from django.shortcuts import render from django.utils.translation import ugettext_lazy as _ from django.views.decorators.http import condition from shared_foundation import constants from shared_foundation.models import SharedUser from shared_foundation.utils import reverse_with_full_domain def reset_password_email_page(request, pr_access_code=None): # Find the user or error. try: me = SharedUser.objects.get(pr_access_code=pr_access_code) if not me.has_pr_code_expired(): # Indicate that the account is active. me.was_activated = True me.save() else: # Erro message indicating code expired. raise PermissionDenied(_('Access code expired.')) except SharedUser.DoesNotExist: raise PermissionDenied(_('Wrong access code.')) # Generate the data. url = reverse_with_full_domain( reverse_url_id='at_reset_password_master', resolve_url_args=[pr_access_code] ) web_view_url = reverse_with_full_domain( reverse_url_id='at_reset_password_email', resolve_url_args=[pr_access_code] ) param = { 'constants': constants, 'url': url, 'web_view_url': web_view_url, 'me': me } # DEVELOPERS NOTE: # - When copying the "Sunday" open source email theme into our code, we will # need to use a formatter to inline the CSS. # - https://templates.mailchimp.com/resources/inline-css/ return render(request, 'shared_auth/email/reset_password_email.html', param) def user_activation_email_page(request, pr_access_code=None): # Find the user or error. try: me = SharedUser.objects.get(pr_access_code=pr_access_code) if not me.has_pr_code_expired(): # Indicate that the account is active. me.was_activated = True me.save() else: # Erro message indicating code expired. raise PermissionDenied(_('Access code expired.')) except SharedUser.DoesNotExist: raise PermissionDenied(_('Wrong access code.')) # Generate the data. url = reverse_with_full_domain( reverse_url_id='at_user_activation_detail', resolve_url_args=[pr_access_code] ) web_view_url = reverse_with_full_domain( reverse_url_id='at_activate_email', resolve_url_args=[pr_access_code] ) param = { 'constants': constants, 'url': url, 'web_view_url': web_view_url, 'me': me } # DEVELOPERS NOTE: # - When copying the "Sunday" open source email theme into our code, we will # need to use a formatter to inline the CSS. # - https://templates.mailchimp.com/resources/inline-css/ return render(request, 'shared_auth/email/user_activation_email_view.html', param)

34.632184

86

0.683372

384

3,013

5.088542

0.296875

0.071648

0.061412

0.053736

0.733879

0

0.000433

0.233322

3,013

86

87

35.034884

0.845455

0.213409

0

0.610169

0

0.131378

0.069728

0

1

0.033898

false

0.067797

0.152542

0

0.220339

0

null

0

1

0

null

0

1

0

6

bebd450ab3863f6d821c7db6ce97715c424a445a

177

py

Python

texpy/main.py

PapaCharlie/texpy

214f0a2adfc8c57c052c706638785809a024f940

[ "MIT" ]

null

texpy/main.py

PapaCharlie/texpy

214f0a2adfc8c57c052c706638785809a024f940

[ "MIT" ]

null

texpy/main.py

PapaCharlie/texpy

214f0a2adfc8c57c052c706638785809a024f940

[ "MIT" ]

null

import integrals import plain from utils import tex_to_plain def parse(string, **flags): print integrals.main(string, **flags) or "", print plain.main(string, **flags)

22.125

48

0.723164

25

177

5.04

0.56

0.261905

0.238095

0

0.158192

177

8

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null

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null

1

0

1

0

null

0

1

0

1

0

6

fe25735416fdb12da8e97ddaf32ffd30accbb7d0

193

py

Python

web/website/apps.py

mnahinkhan/rnpfind

5aa956ddd528ab9ebd9588be845f78c449915b78

[ "MIT" ]

3

2021-06-08T03:55:03.000Z

2021-06-15T07:33:08.000Z

web/website/apps.py

mnahinkhan/RNPFind

8b561e087f943421c847dcb708ee386ee6439fa5

[ "MIT" ]

1

2022-02-24T15:34:24.000Z

2022-03-04T09:59:10.000Z

web/website/apps.py

mnahinkhan/RNPFind

8b561e087f943421c847dcb708ee386ee6439fa5

[ "MIT" ]

1

2021-07-22T04:13:34.000Z

""" Autogenerated by Django - lists app configurations """ from django.apps import AppConfig class WebsiteConfig(AppConfig): """ Autogenerated by Django """ name = "website"

14.846154

50

0.678756

19

193

6.894737

0.736842

0.229008

0.320611

0

0.217617

193

12

51

16.083333

0.86755

0.38342

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null

0

1

0

1

0

6

fe99f4c464f48ab5660d9574cbe8bdc2d37857a2

139

py

Python

weather/GeoWeatherExceptions.py

Gabriel737/CadorsMap

2bca28b8bda79caad1149bcedd1dc4953c84e13b

[ "MIT" ]

1

2021-12-11T21:11:06.000Z

weather/GeoWeatherExceptions.py

Gabriel737/CadorsMap

2bca28b8bda79caad1149bcedd1dc4953c84e13b

[ "MIT" ]

null

weather/GeoWeatherExceptions.py

Gabriel737/CadorsMap

2bca28b8bda79caad1149bcedd1dc4953c84e13b

[ "MIT" ]

1

2021-12-11T21:01:57.000Z

class GeoWeatherServiceFailedToLocateException(Exception): pass class GeoWeatherServiceFailedToRetrieveException(Exception): pass

23.166667

60

0.848921

8

139

14.75

0.625

0.220339

0

0.107914

139

5

61

27.8

0.951613

0

0.5

0

1

0

true

0.5

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null

1

0

1

0

null

0

1

0

6

fe9c883d3eeb6663b042b3c802445f66f98731cb

3,960

py

Python

arus/tests/test_scheduler.py

qutang/arus

ee422bbadc72635037944359d00475f698e8fc61

[ "MIT" ]

null

arus/tests/test_scheduler.py

qutang/arus

ee422bbadc72635037944359d00475f698e8fc61

[ "MIT" ]

264

2019-09-25T14:15:39.000Z

2022-03-11T10:11:38.000Z

arus/tests/test_scheduler.py

qutang/arus

ee422bbadc72635037944359d00475f698e8fc61

[ "MIT" ]

null

import pytest from .. import scheduler import os import time def task1(): print('task1 start on {}'.format(os.getpid())) time.sleep(2) print('task1 stop on {}'.format(os.getpid())) return 'task1', time.time(), os.getpid() def task2(): print('task2 start on {}'.format(os.getpid())) time.sleep(0.5) print('task2 stop on {}'.format(os.getpid())) return 'task2', time.time(), os.getpid() def task3(): print('task3 start on {}'.format(os.getpid())) time.sleep(1) print('task3 stop on {}'.format(os.getpid())) return 'task3', time.time(), os.getpid() class TestScheduler: @pytest.mark.parametrize('scheme', [scheduler.Scheduler.Scheme.SUBMIT_ORDER, scheduler.Scheduler.Scheme.EXECUTION_ORDER, scheduler.Scheduler.Scheme.AFTER_PREVIOUS_DONE]) @pytest.mark.parametrize('mode', [scheduler.Scheduler.Mode.THREAD, scheduler.Scheduler.Mode.PROCESS]) def test_modes_and_schemes_with_get_all_remaining_results(self, scheme, mode): sch = scheduler.Scheduler(mode=mode, scheme=scheme, max_workers=5) sch.submit(task1) sch.submit(task2) sch.submit(task3) results = sch.get_all_remaining_results() if mode == scheduler.Scheduler.Mode.THREAD: assert len(set([r[2] for r in results])) == 1 else: assert len(set([r[2] for r in results])) == 3 if scheme == scheduler.Scheduler.Scheme.EXECUTION_ORDER: assert results[0][0] == 'task2' assert results[1][0] == 'task3' assert results[2][0] == 'task1' else: assert results[0][0] == 'task1' assert results[1][0] == 'task2' assert results[2][0] == 'task3' if scheme == scheduler.Scheduler.Scheme.AFTER_PREVIOUS_DONE: assert results[2][1] > results[1][1] assert results[1][1] > results[0][1] elif scheme == scheduler.Scheduler.Scheme.EXECUTION_ORDER: assert results[2][1] > results[1][1] assert results[1][1] > results[0][1] else: assert results[0][1] > results[2][1] assert results[2][1] > results[1][1] sch.shutdown() @pytest.mark.parametrize('scheme', [scheduler.Scheduler.Scheme.SUBMIT_ORDER, scheduler.Scheduler.Scheme.EXECUTION_ORDER, scheduler.Scheduler.Scheme.AFTER_PREVIOUS_DONE]) @pytest.mark.parametrize('mode', [scheduler.Scheduler.Mode.THREAD, scheduler.Scheduler.Mode.PROCESS]) def test_modes_and_schemes_with_get_result(self, scheme, mode): sch = scheduler.Scheduler(mode=mode, scheme=scheme, max_workers=5) sch.submit(task1) sch.submit(task2) sch.submit(task3) results = [] while True: try: result = sch.get_result() results.append(result) except scheduler.Scheduler.ResultNotAvailableError: continue if len(results) == 3: break if mode == scheduler.Scheduler.Mode.THREAD: assert len(set([r[2] for r in results])) == 1 else: assert len(set([r[2] for r in results])) == 3 if scheme == scheduler.Scheduler.Scheme.EXECUTION_ORDER: assert results[0][0] == 'task2' assert results[1][0] == 'task3' assert results[2][0] == 'task1' else: assert results[0][0] == 'task1' assert results[1][0] == 'task2' assert results[2][0] == 'task3' if scheme == scheduler.Scheduler.Scheme.AFTER_PREVIOUS_DONE: assert results[2][1] > results[1][1] assert results[1][1] > results[0][1] elif scheme == scheduler.Scheduler.Scheme.EXECUTION_ORDER: assert results[2][1] > results[1][1] assert results[1][1] > results[0][1] else: assert results[0][1] > results[2][1] assert results[2][1] > results[1][1] sch.shutdown()

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null

0

1

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null

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6

22ccede72a12021d1108c6ef77107c6e04d590bc

34

py

Python

alexnet_cifar10/__init__.py

zhangjunpeng9354/Learning-Tensorflow-by-Models

9e6ab4da4ec66fb6e7934d129c57110c85e3d7da

[ "MIT" ]

1

2017-10-05T00:23:20.000Z

alexnet_cifar10/__init__.py

zhangjunpeng9354/Learning-Tensorflow-by-Models

9e6ab4da4ec66fb6e7934d129c57110c85e3d7da

[ "MIT" ]

null

alexnet_cifar10/__init__.py

zhangjunpeng9354/Learning-Tensorflow-by-Models

9e6ab4da4ec66fb6e7934d129c57110c85e3d7da

[ "MIT" ]

null

import input from model import *

8.5

19

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1

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1

0

6

22ececfb2a5d9e03f484b8c96fa703a80e2afaeb

1,671

py

Python

server/tests/models/test_arg_limit.py

athenianco/athenian-api

dd5556101a8c49703d6b0516e4268b9e8d8eda5b

[ "RSA-MD" ]

9

2020-10-11T22:12:03.000Z

2022-02-26T02:16:45.000Z

server/tests/models/test_arg_limit.py

athenianco/athenian-api

dd5556101a8c49703d6b0516e4268b9e8d8eda5b

[ "RSA-MD" ]

246

2019-12-05T06:37:30.000Z

2022-03-29T10:00:07.000Z

server/tests/models/test_arg_limit.py

athenianco/athenian-api

dd5556101a8c49703d6b0516e4268b9e8d8eda5b

[ "RSA-MD" ]

5

2019-12-04T22:38:05.000Z

2021-02-26T00:50:04.000Z

from sqlalchemy import select from sqlalchemy.dialects import postgresql, sqlite from athenian.api.models.metadata.github import Repository async def test_query_argument_limit_in(mdb): rows = await mdb.fetch_all(select([Repository]).where(Repository.full_name.in_( ["r%d" % i for i in range(1 << 16)] + ["src-d/go-git"]))) assert rows async def test_in_inlining(): check_any_values = "= ANY (VALUES ('r0'), ('r1')," sql = select([Repository]).where(Repository.full_name.in_( ["r%d" % i for i in range(1 << 3)] + ["src-d/go-git"])) postgres_sql = str(sql.compile(dialect=postgresql.dialect())) assert check_any_values not in postgres_sql sql = select([Repository]).where(Repository.full_name.in_( ["r%d" % i for i in range(1 << 16)] + ["src-d/go-git"])) postgres_sql = str(sql.compile(dialect=postgresql.dialect())) assert check_any_values not in postgres_sql sql = select([Repository]).where(Repository.full_name.in_any_values( ["r%d" % i for i in range(1 << 3)] + ["src-d/go-git"])) postgres_sql = str(sql.compile(dialect=postgresql.dialect())) assert check_any_values not in postgres_sql sql = select([Repository]).where(Repository.full_name.in_any_values( ["r%d" % i for i in range(1 << 16)] + ["src-d/go-git"])) postgres_sql = str(sql.compile(dialect=postgresql.dialect())) assert check_any_values in postgres_sql sql = select([Repository]).where(Repository.full_name.in_any_values( ["r%d" % i for i in range(1 << 16)] + ["src-d/go-git"])) postgres_sql = str(sql.compile(dialect=sqlite.dialect())) assert check_any_values not in postgres_sql

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null

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1

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1

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null

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6

22f2db204ed7d8c464d421bf34a26190a15b5dbd

367

py

Python

python/pyprepbuddy/cluster/cluster.py

veera83372/prep-buddy

d2abbf376e91b841b28476bd45026800fcd7a33c

[ "Apache-2.0" ]

null

python/pyprepbuddy/cluster/cluster.py

veera83372/prep-buddy

d2abbf376e91b841b28476bd45026800fcd7a33c

[ "Apache-2.0" ]

null

python/pyprepbuddy/cluster/cluster.py

veera83372/prep-buddy

d2abbf376e91b841b28476bd45026800fcd7a33c

[ "Apache-2.0" ]

1

2018-05-29T16:21:33.000Z

class Cluster(object): """ Cluster contains groups of values by their specified key """ def __init__(self, cluster): self.__cluster = cluster def __contains__(self, item): return self.__cluster.containsValue(item) def size(self): return self.__cluster.size() def get_cluster(self): return self.__cluster

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null

1

0

null

0

1

0

1

0

6

fe0e7caf2438cb95c1fa1892354fa2a67e9acf42

2,425

py

Python

sympy/crypto/__init__.py

msgoff/sympy

1e7daef7514902f5e89718fa957b7b36c6669a10

[ "BSD-3-Clause" ]

null

sympy/crypto/__init__.py

msgoff/sympy

1e7daef7514902f5e89718fa957b7b36c6669a10

[ "BSD-3-Clause" ]

null

sympy/crypto/__init__.py

msgoff/sympy

1e7daef7514902f5e89718fa957b7b36c6669a10

[ "BSD-3-Clause" ]

null

from sympy.crypto.crypto import ( cycle_list, encipher_shift, encipher_affine, encipher_substitution, check_and_join, encipher_vigenere, decipher_vigenere, bifid5_square, bifid6_square, encipher_hill, decipher_hill, encipher_bifid5, encipher_bifid6, decipher_bifid5, decipher_bifid6, encipher_kid_rsa, decipher_kid_rsa, kid_rsa_private_key, kid_rsa_public_key, decipher_rsa, rsa_private_key, rsa_public_key, encipher_rsa, lfsr_connection_polynomial, lfsr_autocorrelation, lfsr_sequence, encode_morse, decode_morse, elgamal_private_key, elgamal_public_key, decipher_elgamal, encipher_elgamal, dh_private_key, dh_public_key, dh_shared_key, padded_key, encipher_bifid, decipher_bifid, bifid_square, bifid5, bifid6, bifid10, decipher_gm, encipher_gm, gm_public_key, gm_private_key, bg_private_key, bg_public_key, encipher_bg, decipher_bg, encipher_rot13, decipher_rot13, encipher_atbash, decipher_atbash, encipher_railfence, decipher_railfence, ) __all__ = [ "cycle_list", "encipher_shift", "encipher_affine", "encipher_substitution", "check_and_join", "encipher_vigenere", "decipher_vigenere", "bifid5_square", "bifid6_square", "encipher_hill", "decipher_hill", "encipher_bifid5", "encipher_bifid6", "decipher_bifid5", "decipher_bifid6", "encipher_kid_rsa", "decipher_kid_rsa", "kid_rsa_private_key", "kid_rsa_public_key", "decipher_rsa", "rsa_private_key", "rsa_public_key", "encipher_rsa", "lfsr_connection_polynomial", "lfsr_autocorrelation", "lfsr_sequence", "encode_morse", "decode_morse", "elgamal_private_key", "elgamal_public_key", "decipher_elgamal", "encipher_elgamal", "dh_private_key", "dh_public_key", "dh_shared_key", "padded_key", "encipher_bifid", "decipher_bifid", "bifid_square", "bifid5", "bifid6", "bifid10", "decipher_gm", "encipher_gm", "gm_public_key", "gm_private_key", "bg_private_key", "bg_public_key", "encipher_bg", "decipher_bg", "encipher_rot13", "decipher_rot13", "encipher_atbash", "decipher_atbash", "encipher_railfence", "decipher_railfence", ]

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5.633588

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0

null

0

1

0

null

0

6

fe165b97b4f738953deab5dc97f971d96d7ae3e4

132

py

Python

login/admin.py

bibapple/LoginApp

63bae464076ae033097c88db25ec03a7b409d95e

[ "Apache-2.0" ]

null

login/admin.py

bibapple/LoginApp

63bae464076ae033097c88db25ec03a7b409d95e

[ "Apache-2.0" ]

10

2020-02-12T00:40:59.000Z

2022-01-13T01:20:39.000Z

login/admin.py

bibapple/LoginApp

63bae464076ae033097c88db25ec03a7b409d95e

[ "Apache-2.0" ]

null

from django.contrib import admin from . import models admin.site.register(models.User) admin.site.register(models.ConfirmEmail)

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132

5.888889

0.555556

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1

0

null

0

1

0

1

0

6

a3ace3e1a4c614e6fe172bd222a117b9196491e4

1,978

py

Python

test/test_cli.py

goneri/ansible-builder

a57334b2090a38d54931129b8d0308a2d0b361bd

[ "Apache-2.0" ]

1

2021-12-06T16:55:55.000Z

test/test_cli.py

goneri/ansible-builder

a57334b2090a38d54931129b8d0308a2d0b361bd

[ "Apache-2.0" ]

null

test/test_cli.py

goneri/ansible-builder

a57334b2090a38d54931129b8d0308a2d0b361bd

[ "Apache-2.0" ]

null

from ansible_builder.main import AnsibleBuilder from ansible_builder.cli import parse_args def prepare(args): args = parse_args(args) return AnsibleBuilder(**vars(args)) def test_custom_image(exec_env_definition_file, tmpdir): content = {'version': 1} path = str(exec_env_definition_file(content=content)) # test with 'container' sub-command aee = prepare(['container', 'build', '-f', path, '--build-arg', 'EE_BASE_IMAGE=my-custom-image', '-c', str(tmpdir)]) assert aee.build_args == {'EE_BASE_IMAGE': 'my-custom-image'} # test without 'container' sub-command (defaulting to 'container') aee = prepare(['build', '-f', path, '--build-arg', 'EE_BASE_IMAGE=my-custom-image', '-c', str(tmpdir)]) assert aee.build_args == {'EE_BASE_IMAGE': 'my-custom-image'} def test_custom_ansible_galaxy_cli_collection_opts(exec_env_definition_file, tmpdir): content = {'version': 1} path = str(exec_env_definition_file(content=content)) # test with 'container' sub-command aee = prepare(['container', 'build', '-f', path, '--build-arg', 'ANSIBLE_GALAXY_CLI_COLLECTION_OPTS=--pre', '-c', str(tmpdir)]) assert aee.build_args == {'ANSIBLE_GALAXY_CLI_COLLECTION_OPTS': '--pre'} # test without 'container' sub-command (defaulting to 'container') aee = prepare(['build', '-f', path, '--build-arg', 'ANSIBLE_GALAXY_CLI_COLLECTION_OPTS=--pre', '-c', str(tmpdir)]) assert aee.build_args == {'ANSIBLE_GALAXY_CLI_COLLECTION_OPTS': '--pre'} def test_build_context(good_exec_env_definition_path, tmpdir): path = str(good_exec_env_definition_path) build_context = str(tmpdir) # test with 'container' sub-command aee = prepare(['container', 'build', '-f', path, '-c', build_context]) assert aee.build_context == build_context # test without 'container' sub-command (defaulting to 'container') aee = prepare(['build', '-f', path, '-c', build_context]) assert aee.build_context == build_context

42.085106

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1,978

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0

null

0

1

0

null

0

6

a3cf29d2f473960a4ceeac97356e80b2fb81a39a

407

py

Python

exercises/complex-numbers/complex_numbers.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,177

2017-06-21T20:24:06.000Z

2022-03-29T02:30:55.000Z

exercises/complex-numbers/complex_numbers.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,890

2017-06-18T20:06:10.000Z

2022-03-31T18:35:51.000Z

exercises/complex-numbers/complex_numbers.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,095

2017-06-26T23:06:19.000Z

2022-03-29T03:25:38.000Z

class ComplexNumber: def __init__(self, real, imaginary): pass def __eq__(self, other): pass def __add__(self, other): pass def __mul__(self, other): pass def __sub__(self, other): pass def __truediv__(self, other): pass def __abs__(self): pass def conjugate(self): pass def exp(self): pass

14.535714

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0.338542

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27

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false

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null

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1

0

1

0

1

0

6

a3d46ad92b0cfc7891fd0a7e0dae3ce327269dfe

108

py

Python

validators.py

setivolkylany/DjangoAppTemplate

d26a63116d9f01e321374e3560b84836a1bea4c7

[ "MIT" ]

null

validators.py

setivolkylany/DjangoAppTemplate

d26a63116d9f01e321374e3560b84836a1bea4c7

[ "MIT" ]

null

validators.py

setivolkylany/DjangoAppTemplate

d26a63116d9f01e321374e3560b84836a1bea4c7

[ "MIT" ]

null

from django.utils.translation import ugettext_lazy as _ from django.core.exceptions import ValidationError

27

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14

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1

0

1

0

6

4304452939d42e97c3de7239c0970fa4ca4199e8

533

py

Python

faktotum/research/__init__.py

severinsimmler/extract

c1e76a29929e2334976b18ba9218403d85331f51

[ "MIT" ]

2

2020-02-19T14:29:21.000Z

2020-02-22T14:33:08.000Z

faktotum/research/__init__.py

severinsimmler/faktotum

c1e76a29929e2334976b18ba9218403d85331f51

[ "MIT" ]

null

faktotum/research/__init__.py

severinsimmler/faktotum

c1e76a29929e2334976b18ba9218403d85331f51

[ "MIT" ]

null

import logging import transformers logging.getLogger("transformers").setLevel(logging.ERROR) from faktotum.research import evaluation from faktotum.research.corpus import load_corpus, sentencize_corpus, tokenize_corpus from faktotum.research.knowledge import KnowledgeBase from faktotum.research.ontologia import FastText, TfIdf, Word2Vec from faktotum.research.utils import sentencize, tokenize from faktotum.research import vendor from faktotum.research import clustering from faktotum.research import regression, classification

38.071429

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0

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533

13

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1

0

null

0

1

0

1

0

1

0

6

432d758dcf8a65a9d80e8fbb5b38627ae441184d

198

py

Python

peleffybenchmarktools/dihedrals/__init__.py

martimunicoy/offpele-benchmarks

20af939ce60252c05e0c1e44b85cf89a4f8a2245

[ "MIT" ]

null

peleffybenchmarktools/dihedrals/__init__.py

martimunicoy/offpele-benchmarks

20af939ce60252c05e0c1e44b85cf89a4f8a2245

[ "MIT" ]

7

2020-08-07T14:51:02.000Z

2020-10-30T20:18:38.000Z

peleffybenchmarktools/dihedrals/__init__.py

martimunicoy/offpele-benchmarks

20af939ce60252c05e0c1e44b85cf89a4f8a2245

[ "MIT" ]

null

from .dihedralhandler import DihedralBenchmark from .energyhandler import (OpenMMEnergeticProfile, OpenFFEnergeticProfile, PELEEnergeticProfile, OFFPELEEnergeticProfile)

49.5

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null

0

1

0

1

0

1

0

6

4a60a030584af45b15a48ecb28bd359eaf0d478b

33,212

py

Python

test_sc3.py

burja8x/StreamAi

09d600632299f436ccb6706ec4e53f6250ded93d

[ "MIT" ]

null

test_sc3.py

burja8x/StreamAi

09d600632299f436ccb6706ec4e53f6250ded93d

[ "MIT" ]

null

test_sc3.py

burja8x/StreamAi

09d600632299f436ccb6706ec4e53f6250ded93d

[ "MIT" ]

null

from unittest import TestCase from sc2 import * import random import time class Test(TestCase): def test_1_admin_c(self): a = send_tx(sc.functions.changeFeeAccount(accX0.address), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.changeWaitTime(5, 10), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.changeFeeMake(Web3.toWei(0.00003, 'ether')), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.changeFeeTake(11), accA) # = 1.1 % self.assertEqual(a[1], 1) a = send_tx(sc.functions.changeWaitTime(11, 100010), accA) self.assertEqual(a[1], 1) def test_2_if_exist_fail(self): b = sc.functions.hosts(accP0.address).call() c = sc.functions.hosts(accP1.address).call() a = send_tx(sc.functions.newCProvider("roža", 100001, 256, 10, 10), accP0) #self.assertEqual(a[1], 0 if c[10] == 0 else 1) a = send_tx(sc.functions.newCProvider("apple", 100000, 256, 10, 10), accP1) #self.assertEqual(a[1], 0 if b[10] == 0 else 1) d = sc.functions.hosts(accP1.address).call() a = send_tx(sc.functions.newCProvider("ibm", 33358, 256, 20, 10), accP1) #self.assertEqual(a[1], 0 if d[10] == 0 else 1) def test_3_change_provider_data(self): i = random.randint(1000, 1000000) j = random.randint(3, 99) k = random.randint(3, 99) g = random.randint(3, 99) a = send_tx(sc.functions.changeCProviderData(i, j, g, k), accP1) self.assertEqual(a[1], 1) b = sc.functions.hosts(accP1.address).call() self.assertEqual(b[2], i) self.assertEqual(b[3], j) self.assertEqual(b[4], g) self.assertEqual(b[5], k) a = send_tx(sc.functions.changeCProviderData(1100000, 2000, 30, 200), accP1) self.assertEqual(a[1], 1) a = send_tx(sc.functions.changeCProviderData(1100000, 2000, 30, 200), accP0) self.assertEqual(a[1], 1) a = send_tx(sc.functions.changeCProviderData(1100000, 2000, 20, 200), accP2) self.assertEqual(a[1], 0) def test_4_change_provider_data_1(self): with self.assertRaises(Exception): a = send_tx(sc.functions.changeCProviderData(10000000000, 10, 1, 10), accP1) with self.assertRaises(Exception): a = send_tx(sc.functions.changeCProviderData(10000, 100000000, 1, 10), accP1) with self.assertRaises(Exception): a = send_tx(sc.functions.changeCProviderData(10000, 100, 10, 100000000), accP1) def test_5_change_provider_data_2(self): a = send_tx(sc.functions.changeCProviderData(1000, 10, 1, 10), accX1) self.assertEqual(a[1], 0) def test_6_allowCProvider(self): a = send_tx(sc.functions.allowCProvider(accP0.address, True), accX1) self.assertEqual(a[1], 0) a = send_tx(sc.functions.allowCProvider(accP0.address, True), accA) self.assertEqual(a[1], 1) hc = sc.functions.hostCounter().call() + 1 # error ==== print(hc) p = sc.functions.getProviders(1, hc).call() id = 0 for x in p: if x[11] == accP0.address: id = x[10] b = sc.functions.hostsIndex(id).call() self.assertEqual(b, accP0.address) a = send_tx(sc.functions.allowCProvider(accP0.address, False), accA) self.assertEqual(a[1], 1) b = sc.functions.hostsIndex(id).call() self.assertEqual(b, accP0.address) a = send_tx(sc.functions.allowCProvider(accP0.address, True), accA) self.assertEqual(a[1], 1) b = sc.functions.hostsIndex(id).call() self.assertEqual(b, accP0.address) def test_7_add_method(self): feeMake = Web3.fromWei(sc.functions.feeMake().call(), 'ether') a = send_tx(sc.functions.sell("Detekcija mask", 512, 1, False, Web3.toWei(0.00000012, 'ether'), "bafybeifk6r6ugz62kdrkeitqukase2fojvt6gfasafvzv3rykczww7qawm", False, "ec dockerHubLink"), accM0, value=feeMake) self.assertEqual(a[1], 1) a = send_tx(sc.functions.sell("Detekcija mask v2", 1024, 1, True, Web3.toWei(0.00000022, 'ether'), "bafybeifk6r6ugz62kdrkeitqukase2fojvt6gfasafvzv3rykczww7qawm", False, "ec dockerHubLink"), accM0, value=feeMake) self.assertEqual(a[1], 1) # https://nft.storage/files/ a = send_tx(sc.functions.sell("vreme", 4096, 4, False, Web3.toWei(0.00000019, 'ether'), "bafybeifk6r6ugz62kdrkeitqukase2fojvt6gfasafvzv3rykczww7qawm", False, "ec dockerHubLink"), accM1, value=feeMake) self.assertEqual(a[1], 1) def test_8_allow_method(self): m = sc.functions.methodCounter().call() a = send_tx(sc.functions.allowAiMethod(m - 2, True), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.allowAiMethod(m - 1, True), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.allowAiMethod(m, True), accA) self.assertEqual(a[1], 1) def test_9_set_c_price(self): m = sc.functions.methodCounter().call() p1 = Web3.toWei(0.000000671, 'ether') p2 = Web3.toWei(0.000000682, 'ether') a = send_tx(sc.functions.setContainerCost(m - 2, p1), accP0) self.assertEqual(a[1], 1) a = send_tx(sc.functions.allowCProvider(accP1.address, True), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.setContainerCost(m - 2, p2), accP1) self.assertEqual(a[1], 1) array = [accP0.address, accP1.address] b = sc.functions.getPricesOfProviders(m - 2, array).call() self.assertEqual(p1, b[0]) self.assertEqual(p2, b[1]) a = send_tx(sc.functions.setContainerCost(m - 2, 0), accP0) self.assertEqual(a[1], 1) b = sc.functions.getPricesOfProviders(m - 2, array).call() self.assertEqual(0, b[0]) a = send_tx(sc.functions.setContainerCost(m - 1, p1), accP0) self.assertEqual(a[1], 1) a = send_tx(sc.functions.setContainerCost(m - 2, p1), accP0) self.assertEqual(a[1], 1) class Test_Buy(TestCase): def test_1_Buy(self): buy_time = 16 method_id = sc.functions.methodCounter().call() - 2 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx(sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC link video streem..."), accU1, value=end_price) self.assertEqual(a[1], 1) end_price = Web3.fromWei(m + n + 1000, 'ether') a = send_tx(sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC link video streem..."), accU0, value=end_price) self.assertEqual(a[1], 1) c = sc.functions.dealIdCounter().call() l0 = sc.functions.locked(c - 1).call() l1 = sc.functions.locked(c).call() self.assertEqual(l0[0], l1[0]) self.assertEqual(l0[2], l1[2]) self.assertNotEqual(l0[4], l1[4]) end_price = Web3.fromWei(m + n - 1, 'ether') a = send_tx(sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC link video streem..."), accU0, value=end_price) self.assertEqual(a[1], 0) def test_2_Buy(self): buy_time = 21 method_id = sc.functions.methodCounter().call() - 2 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC link video streem..."), accU1, value=end_price) self.assertEqual(a[1], 1) dealId = sc.functions.dealIdCounter().call() b = send_tx(sc.functions.complaint(dealId, "not working !!!"), accU0) self.assertEqual(b[1], 0) b = send_tx(sc.functions.complaint(dealId, "not workinggdf !!!"), accU1) self.assertEqual(b[1], 1) b = send_tx(sc.functions.complaint(dealId, "not workihrtng !!!"), accU1) self.assertEqual(b[1], 0) b = send_tx(sc.functions.complaint(dealId, "not workihrtng !!!"), accU2) self.assertEqual(b[1], 0) def test_3_Buy(self): buy_time = 2 method_id = sc.functions.methodCounter().call() - 2 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC link video streem..."), accU1, value=end_price) self.assertEqual(a[1], 0) # time to small.... def test_4_Buy_Start_Stop(self): buy_time = 20 method_id = sc.functions.methodCounter().call() - 2 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) dealId = sc.functions.dealIdCounter().call() a = send_tx(sc.functions.start(dealId, "THIS is EC(# mqtt link)"), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.delivered(dealId, False), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.delivered(dealId, False), accA) self.assertEqual(a[1], 0) a = send_tx(sc.functions.start(dealId, "THIS is EC(# mqtt link)"), accA) self.assertEqual(a[1], 0) def test_5_Buy_Start_Stop(self): buy_time = 20 method_id = 2 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) dealId = sc.functions.dealIdCounter().call() a = send_tx(sc.functions.start(dealId, "THIS is EC(# mqtt link)"), accU1) self.assertEqual(a[1], 0) a = send_tx(sc.functions.delivered(dealId, False), accU1) self.assertEqual(a[1], 0) a = send_tx(sc.functions.start(dealId, "THIS is EC(# mqtt link)"), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.delivered(dealId, False), accU0) self.assertEqual(a[1], 0) a = send_tx(sc.functions.delivered(dealId, False), accA) self.assertEqual(a[1], 1) a = send_tx(sc.functions.delivered(dealId, False), accA) self.assertEqual(a[1], 0) def test_6_Buy_Disable(self): buy_time = 19 method_id = sc.functions.methodCounter().call() - 2 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) a = send_tx(sc.functions.allowAiMethod(method_id, False), accA) self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 0) a = send_tx(sc.functions.allowAiMethod(method_id, True), accA) self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .h7uz789678967....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) def test_7_Buy_Disable_P(self): buy_time = 19 method_id = sc.functions.methodCounter().call() - 2 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) a = send_tx(sc.functions.allowCProvider(accP0.address, False), accA) self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 0) a = send_tx(sc.functions.allowCProvider(accP0.address, True), accA) self.assertEqual(a[1], 1) def test_8_Buy_Disable_Acctive(self): buy_time = 19 method_id = sc.functions.methodCounter().call() - 2 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) methodX = sc.functions.aiMethods(method_id).call() if methodX[0] == accM0.address: acc = accM0 elif methodX[0] == accM1.address: acc = accM1 elif methodX[0] == accM2.address: acc = accM2 else: self.assertTrue(False) a = send_tx(sc.functions.activateAiMethod(method_id, False), acc) self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU0, value=end_price) self.assertEqual(a[1], 0) a = send_tx(sc.functions.activateAiMethod(method_id, True), acc) self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU2, value=end_price) self.assertEqual(a[1], 1) def test_9_Buy_Ram_Cpus(self): buy_time = 19 method_id = 1 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) p0 = sc.functions.hosts(accP0.address).call() print("maxram", p0[2], "maxCpus", p0[3], "maxGpus", p0[4], "maxInst", p0[5]) print("usedRam", p0[6], "usedCpus", p0[7], "usedGpus", p0[8], "usedInst", p0[9]) a = send_tx(sc.functions.changeCProviderData(p0[6] + 50, p0[3], p0[4], p0[5]), accP0) # ram self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 0) a = send_tx(sc.functions.changeCProviderData(p0[6], p0[7], p0[4], p0[9]), accP0) # gpu self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 0) a = send_tx(sc.functions.changeCProviderData(p0[2], p0[7], p0[4], p0[5]), accP0) # cpus self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 0) a = send_tx(sc.functions.changeCProviderData(p0[2], p0[3], p0[4], p0[9]), accP0) # cpus self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 0) a = send_tx(sc.functions.changeCProviderData(p0[2], p0[3], p0[4], p0[9] + 1), accP0) # inst self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) a = send_tx(sc.functions.changeCProviderData(p0[2], p0[3], p0[4], p0[5]), accP0) self.assertEqual(a[1], 1) p0 = sc.functions.hosts(accP0.address).call() print("maxram", p0[2], "maxCpus", p0[3], "maxGpus", p0[4], "maxInst", p0[5]) print("usedRam", p0[6], "usedCpus", p0[7], "usedGpus", p0[8], "usedInst", p0[9]) class Test_Buy_X(TestCase): def test_1_delivered(self): k_state = 0 for dealId in range(1, sc.functions.dealIdCounter().call()): ll = sc.functions.locked(dealId).call() print("locked:", ll) if ll[0] != 0: eth_provider = sc.functions.eth(ll[1]).call() eth_m_creator = sc.functions.eth(ll[3]).call() eth_fee = sc.functions.eth(sc.functions.feeAccount().call()).call() eth_buyer = sc.functions.eth(ll[6]).call() if k_state == 0: print("delivered without error") print(eth_provider, eth_m_creator, eth_fee, eth_buyer) a = send_tx(sc.functions.delivered(dealId, False), accA) self.assertEqual(a[1], 1) ttt = sc.functions.locked(dealId).call() self.assertEqual(ttt[0], 0) self.assertEqual(ttt[2], 0) self.assertEqual(ttt[4], 0) self.assertEqual(eth_buyer, sc.functions.eth(ll[6]).call()) self.assertEqual(eth_provider + ll[0], sc.functions.eth(ll[1]).call()) self.assertEqual(eth_m_creator + ll[2], sc.functions.eth(ll[3]).call()) self.assertEqual(eth_fee + ll[4], sc.functions.eth(sc.functions.feeAccount().call()).call()) k_state = 1 elif k_state == 1: print("delivered with error") print(eth_provider, eth_m_creator, eth_fee, eth_buyer) a = send_tx(sc.functions.delivered(dealId, True), accA) self.assertEqual(a[1], 1) ttt = sc.functions.locked(dealId).call() self.assertEqual(ttt[0], 0) self.assertEqual(ttt[2], 0) self.assertEqual(ttt[4], 0) self.assertEqual(eth_buyer + ll[0] + ll[2] + ll[4], sc.functions.eth(ll[6]).call()) self.assertEqual(eth_provider, sc.functions.eth(ll[1]).call()) self.assertEqual(eth_m_creator, sc.functions.eth(ll[3]).call()) self.assertEqual(eth_fee, sc.functions.eth(sc.functions.feeAccount().call()).call()) k_state = 0 print("end") # pazi da ni user == drugim (admin , ....) # for i in allAcc.keys(): # print(i, allAcc[i].address, Web3.fromWei(sc.functions.eth(allAcc[i].address).call(), 'ether')) def test_4_SAFU_user(self): # get first working method.... method_id = -1 pprice = -1 provider_a = "" p = sc.functions.getProviders(1, sc.functions.hostCounter().call() + 1).call() arrayProviders = [] for ww in p: if ww[1]: arrayProviders.append(ww[11]) n = sc.functions.methodCounter().call() m = sc.functions.getMethods(1, n + 1).call() y = 0 for x in m: if not x[9] and x[6] and x[7]: print(x) gg = 0 for pp in sc.functions.getPricesOfProviders(1+y, arrayProviders).call(): if pp != 0: print(pp) method_id = y+1 pprice = pp provider_a = arrayProviders[gg] gg += 1 y += 1 self.assertNotEqual(method_id, -1) self.assertNotEqual(pprice, -1) self.assertNotEqual(provider_a, "") # buy_time = 19 mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') print(end_price) a = send_tx(sc.functions.changeWaitTime(500, 500), accA) self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, provider_a, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) dealId = sc.functions.dealIdCounter().call() ll = sc.functions.locked(dealId).call() print("release Block ", ll[5], " l ", w3.eth.block_number) # eth_provider = sc.functions.eth(ll[1]).call() # eth_m_creator = sc.functions.eth(ll[3]).call() # eth_fee = sc.functions.eth(sc.functions.feeAccount().call()).call() # eth_buyer = sc.functions.eth(ll[6]).call() time.sleep(30) a = send_tx(sc.functions.returnToBuyerSAFU(dealId), accU1) self.assertEqual(a[1], 0) # #---------------------------------------------------------------------------- a = send_tx(sc.functions.changeWaitTime(0, 0), accA) self.assertEqual(a[1], 1) a = send_tx( sc.functions.buy(method_id, provider_a, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 1) dealId = sc.functions.dealIdCounter().call() ll = sc.functions.locked(dealId).call() print("release Block ", ll[5], " l ", w3.eth.block_number) time.sleep(30) a = send_tx(sc.functions.returnToBuyerSAFU(dealId), accU1) self.assertEqual(a[1], 1) def test_2_Withdraw(self): pass def test_3_BlockNumber(self): pass def test_5_change_AskUrl(self): print(sc.functions.url0().call()) print(sc.functions.url1().call()) url0 = "debela mis .com ...//" url1 = "slon je lacen" a = send_tx(sc.functions.changeAskUrl(url0, url1), accA) self.assertEqual(a[1], 1) u0 = sc.functions.url0().call() u1 = sc.functions.url1().call() print(u0, u1) self.assertEqual(u0, url0) self.assertEqual(u1, url1) a = send_tx(sc.functions.changeAskUrl( "https://stream-ai-api.aleksvujic.fun/api/v1/account/isAiMethodAllowedForUser?aiMethodId=", "&userEthAddress="), accA) self.assertEqual(a[1], 1) def test_6_change_Oracle(self): # https://market.link/search/jobs?network=42&page=1&search=get%20bool print(sc.functions.oracleAddr().call()) print(sc.functions.jobId().call()) # print(Web3.toBytes(hexstr="1bc99b4b57034ae4bcc3a6b6f6daaede")) # print(Web3.toBytes(text="1bc99b4b57034ae4bcc3a6b6f6daaede")) # a = send_tx(sc.functions.changeOracle("0x1b666ad0d20bC4F35f218120d7ed1e2df60627cC", 100000000000000000, # Web3.toBytes(text="1bc99b4b57034ae4bcc3a6b6f6daaede")), accA) a = send_tx(sc.functions.changeOracle("0x56dd6586DB0D08c6Ce7B2f2805af28616E082455", 100000000000000000, Web3.toBytes(text="1b2658f2d679437cb2d8db115c646d02")), accA) self.assertEqual(a[1], 1) print(sc.functions.oracleAddr().call()) print(sc.functions.jobId().call()) class Test_Buy_Oracle(TestCase): mmm_id = 0 def test_1_not_allowed(self): feeMake = Web3.fromWei(sc.functions.feeMake().call(), 'ether') a = send_tx(sc.functions.sell("Mask detection", 512, 1, True, Web3.toWei(0.00000013, 'ether'), "bafybeifk6r6ugz62kdrkeitqukase2fojvt6gfasafvzv3rykczww7qawm", True, "ec dockerHubLink"), accM0, value=feeMake) self.assertEqual(a[1], 1) method_id = sc.functions.methodCounter().call() a = send_tx(sc.functions.allowAiMethod(method_id, True), accA) self.assertEqual(a[1], 1) buy_time = 19 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accU1, value=end_price) self.assertEqual(a[1], 0) def test_2_allowed(self): method_id = sc.functions.methodCounter().call() m = sc.functions.aiMethods(method_id).call() self.assertTrue(m[9]) # if onlyAllowedUsers b = sc.functions.isUserAllowed(accX0.address, method_id).call() print(b) a = send_tx(sc.functions.allowUserToUseMethod(method_id, accX0.address, True), accA) self.assertEqual(a[1], 1) b = sc.functions.isUserAllowed(accX0.address, method_id).call() print(accX0.address, b) self.assertEqual(b, True) b = sc.functions.isUserAllowed(accU1.address, method_id).call() self.assertEqual(b, False) a = send_tx(sc.functions.setContainerCost(method_id, 10000000000), accP0) buy_time = 18 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() print("provider Price:", pprice) print("method Price:", mprice[5]) print("feeTake:", feeTake) n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accX0, value=end_price) self.assertEqual(a[1], 1) def test_3_allowed_false(self): method_id = sc.functions.methodCounter().call() m = sc.functions.aiMethods(method_id).call() print(m) self.assertTrue(m[9]) # if onlyAllowedUsers a = send_tx(sc.functions.allowUserToUseMethod(method_id, accX0.address, False), accA) self.assertEqual(a[1], 1) b = sc.functions.isUserAllowed(accX0.address, method_id).call() print(b) self.assertEqual(b, False) buy_time = 23 pprice = sc.functions.getProviderPrice(method_id, accP0.address).call() mprice = sc.functions.aiMethods(method_id).call() feeTake = sc.functions.feeTake().call() n = (pprice * buy_time) + (mprice[5] * buy_time) m = (n * feeTake) / 1000 end_price = Web3.fromWei(m + n, 'ether') a = send_tx( sc.functions.buy(method_id, accP0.address, pprice, mprice[5], buy_time, "EC .....m..."), accX0, value=end_price) self.assertEqual(a[1], 0) def test_4_call_link(self): method_id = sc.functions.methodCounter().call() m = sc.functions.aiMethods(method_id).call() self.assertTrue(m[9]) # if onlyAllowedUsers b = sc.functions.isUserAllowed(accX1.address, method_id).call() if b: a = send_tx(sc.functions.allowUserToUseMethod(method_id, accX1.address, False), accA) self.assertEqual(a[1], 1) b = sc.functions.isUserAllowed(accX1.address, method_id).call() self.assertEqual(b, False) a = send_tx(sc.functions.changeUserChecker(True), accA) self.assertEqual(a[1], 1) print(accX1.address, b) a = send_tx(scLink.functions.transferAndCall(sc.address, 100000000000000000, Web3.toBytes( hexstr=accX1.address[2:]) + Web3.toBytes(method_id)), accX0) self.assertEqual(a[1], 1) c = 0 while True: b = sc.functions.isUserAllowed(accX1.address, method_id).call() print(b) if b: print(b) break time.sleep(7) if c >= 20: self.assertTrue(False) c += 1 b = sc.functions.isUserAllowed(accX1.address, method_id).call() print(b) def test_5_call_link(self): method_id = sc.functions.methodCounter().call() m = sc.functions.aiMethods(method_id).call() self.assertTrue(m[9]) # if onlyAllowedUsers b = sc.functions.isUserAllowed(accX0.address, method_id).call() if not b: a = send_tx(sc.functions.allowUserToUseMethod(method_id, accX0.address, True), accA) self.assertEqual(a[1], 1) b = sc.functions.isUserAllowed(accX0.address, method_id).call() print(accX0.address, b) self.assertEqual(b, True) print(accX0.address[2:]) print(Web3.toBytes(hexstr=accX0.address[2:]) + Web3.toBytes(method_id)) a = send_tx(scLink.functions.transferAndCall(sc.address, 100000000000000000, Web3.toBytes( hexstr=accX0.address[2:]) + Web3.toBytes(method_id)), accX0) self.assertEqual(a[1], 1) c = 0 while True: b = sc.functions.isUserAllowed(accX0.address, method_id).call() print(b) if not b: print(b) break time.sleep(7) if c >= 20: self.assertTrue(False) c += 1 b = sc.functions.isUserAllowed(accX0.address, method_id).call() print(b)

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null

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1

0

null

0

6

4a748dafe7e9673c63c15540d11f40bb5a121e06

181

py

Python

main.py

barnett617/python_analysis

cb8d9cdbdcdf4176853aff9eebc0c759c28e330b

[ "MIT" ]

2

2020-07-27T16:16:10.000Z

2021-06-04T10:01:11.000Z

main.py

barnett617/python_analysis

cb8d9cdbdcdf4176853aff9eebc0c759c28e330b

[ "MIT" ]

null

main.py

barnett617/python_analysis

cb8d9cdbdcdf4176853aff9eebc0c759c28e330b

[ "MIT" ]

2

2019-05-09T03:44:20.000Z

2020-02-08T12:25:25.000Z

# -*-coding:utf-8-*- import module_histogram import module_line_graph import module_pie_chart module_histogram.show_plt() module_line_graph.show_plt() module_pie_chart.show_plt()

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null

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1

0

1

0

6

4abaf8431eebab28704456f1a02a4e1ee7959bbc

195

py

Python

main/admin.py

vipinkhushu/xunbao2017

e5f225b50976d42b8a577170e4556b59ad4b13e0

[ "MIT" ]

null

main/admin.py

vipinkhushu/xunbao2017

e5f225b50976d42b8a577170e4556b59ad4b13e0

[ "MIT" ]

null

main/admin.py

vipinkhushu/xunbao2017

e5f225b50976d42b8a577170e4556b59ad4b13e0

[ "MIT" ]

null

from django.contrib import admin from .models import player,question,message,logs admin.site.register(player) admin.site.register(question) admin.site.register(message) admin.site.register(logs)

27.857143

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null

0

1

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1

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6

4355dd84162a531daa81062c375f3f0c7be09dc7

142

py

Python

pages/main_page.py

RezerF/course-project-selenium-stepik

2579c23dca637679eb43a898f299582becc475b8

[ "Unlicense" ]

null

pages/main_page.py

RezerF/course-project-selenium-stepik

2579c23dca637679eb43a898f299582becc475b8

[ "Unlicense" ]

null

pages/main_page.py

RezerF/course-project-selenium-stepik

2579c23dca637679eb43a898f299582becc475b8

[ "Unlicense" ]

null

from .base_page import BasePage from .locators import MainPageLocators from .login_page import LoginPage class MainPage(BasePage): pass

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6

43574fda0e286cd03624f2cece4b2f5a5eea573f

5,991

py

Python

cryptohack/crossed-wires/decrypt.py

onealmond/hacking-lab

631e615944add02db3c2afef47bf1de7171eb065

[ "MIT" ]

9

2021-04-20T15:28:36.000Z

2022-03-08T19:53:48.000Z

cryptohack/crossed-wires/decrypt.py

onealmond/hacking-lab

631e615944add02db3c2afef47bf1de7171eb065

[ "MIT" ]

null

cryptohack/crossed-wires/decrypt.py

onealmond/hacking-lab

631e615944add02db3c2afef47bf1de7171eb065

[ "MIT" ]

6

2021-06-24T03:25:21.000Z

2022-02-20T21:44:52.000Z

#!/usr/bin/env python3 from Cryptodome.Util import number # Since encryption was use friends' key, e and d are useless N, _ = (21711308225346315542706844618441565741046498277716979943478360598053144971379956916575370343448988601905854572029635846626259487297950305231661109855854947494209135205589258643517961521594924368498672064293208230802441077390193682958095111922082677813175804775628884377724377647428385841831277059274172982280545237765559969228707506857561215268491024097063920337721783673060530181637161577401589126558556182546896783307370517275046522704047385786111489447064794210010802761708615907245523492585896286374996088089317826162798278528296206977900274431829829206103227171839270887476436899494428371323874689055690729986771, 2734411677251148030723138005716109733838866545375527602018255159319631026653190783670493107936401603981429171880504360560494771017246468702902647370954220312452541342858747590576273775107870450853533717116684326976263006435733382045807971890762018747729574021057430331778033982359184838159747331236538501849965329264774927607570410347019418407451937875684373454982306923178403161216817237890962651214718831954215200637651103907209347900857824722653217179548148145687181377220544864521808230122730967452981435355334932104265488075777638608041325256776275200067541533022527964743478554948792578057708522350812154888097) # (N, e) pairs from friends friend_keys = [(21711308225346315542706844618441565741046498277716979943478360598053144971379956916575370343448988601905854572029635846626259487297950305231661109855854947494209135205589258643517961521594924368498672064293208230802441077390193682958095111922082677813175804775628884377724377647428385841831277059274172982280545237765559969228707506857561215268491024097063920337721783673060530181637161577401589126558556182546896783307370517275046522704047385786111489447064794210010802761708615907245523492585896286374996088089317826162798278528296206977900274431829829206103227171839270887476436899494428371323874689055690729986771, 106979), (21711308225346315542706844618441565741046498277716979943478360598053144971379956916575370343448988601905854572029635846626259487297950305231661109855854947494209135205589258643517961521594924368498672064293208230802441077390193682958095111922082677813175804775628884377724377647428385841831277059274172982280545237765559969228707506857561215268491024097063920337721783673060530181637161577401589126558556182546896783307370517275046522704047385786111489447064794210010802761708615907245523492585896286374996088089317826162798278528296206977900274431829829206103227171839270887476436899494428371323874689055690729986771, 108533), (21711308225346315542706844618441565741046498277716979943478360598053144971379956916575370343448988601905854572029635846626259487297950305231661109855854947494209135205589258643517961521594924368498672064293208230802441077390193682958095111922082677813175804775628884377724377647428385841831277059274172982280545237765559969228707506857561215268491024097063920337721783673060530181637161577401589126558556182546896783307370517275046522704047385786111489447064794210010802761708615907245523492585896286374996088089317826162798278528296206977900274431829829206103227171839270887476436899494428371323874689055690729986771, 69557), (21711308225346315542706844618441565741046498277716979943478360598053144971379956916575370343448988601905854572029635846626259487297950305231661109855854947494209135205589258643517961521594924368498672064293208230802441077390193682958095111922082677813175804775628884377724377647428385841831277059274172982280545237765559969228707506857561215268491024097063920337721783673060530181637161577401589126558556182546896783307370517275046522704047385786111489447064794210010802761708615907245523492585896286374996088089317826162798278528296206977900274431829829206103227171839270887476436899494428371323874689055690729986771, 97117), (21711308225346315542706844618441565741046498277716979943478360598053144971379956916575370343448988601905854572029635846626259487297950305231661109855854947494209135205589258643517961521594924368498672064293208230802441077390193682958095111922082677813175804775628884377724377647428385841831277059274172982280545237765559969228707506857561215268491024097063920337721783673060530181637161577401589126558556182546896783307370517275046522704047385786111489447064794210010802761708615907245523492585896286374996088089317826162798278528296206977900274431829829206103227171839270887476436899494428371323874689055690729986771, 103231)] c = 20304610279578186738172766224224793119885071262464464448863461184092225736054747976985179673905441502689126216282897704508745403799054734121583968853999791604281615154100736259131453424385364324630229671185343778172807262640709301838274824603101692485662726226902121105591137437331463201881264245562214012160875177167442010952439360623396658974413900469093836794752270399520074596329058725874834082188697377597949405779039139194196065364426213208345461407030771089787529200057105746584493554722790592530472869581310117300343461207750821737840042745530876391793484035024644475535353227851321505537398888106855012746117 # factorization of N p = 134460556242811604004061671529264401215233974442536870999694816691450423689575549530215841622090861571494882591368883283016107051686642467260643894947947473532769025695530343815260424314855023688439603651834585971233941772580950216838838690315383700689885536546289584980534945897919914730948196240662991266027 q = 161469718942256895682124261315253003309512855995894840701317251772156087404025170146631429756064534716206164807382734456438092732743677793224010769460318383691408352089793973150914149255603969984103815563896440419666191368964699279209687091969164697704779792586727943470780308857107052647197945528236341228473 phi = (q-1)*(p-1) # reverse encryption process for key in friend_keys[::-1]: d = number.inverse(key[1], phi) c = pow(c, d, N) print(number.long_to_bytes(c))

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6

438fb4992c15aa12585160724986297ff437d702

50

py

Python

wrappers/__init__.py

CN-UPB/python-mano-wrappers

8e3607feaa97bc3e2c906ee8e4b25b21853ea6cf

[ "Apache-2.0" ]

null

wrappers/__init__.py

CN-UPB/python-mano-wrappers

8e3607feaa97bc3e2c906ee8e4b25b21853ea6cf

[ "Apache-2.0" ]

null

wrappers/__init__.py

CN-UPB/python-mano-wrappers

8e3607feaa97bc3e2c906ee8e4b25b21853ea6cf

[ "Apache-2.0" ]

null

from . import OSMClient from . import SONATAClient

25

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1

0

1

0

6

439ee04280092da8e3e2fb476a94d0855a3a410c

38

py

Python

server/main/views/__init__.py

jphacks/TK_1905

f4af0a26bacedde415f9f873c917fbdb4910e386

[ "MIT" ]

7

2019-10-26T05:44:14.000Z

2019-11-10T13:06:11.000Z

server/main/views/__init__.py

jphacks/TK_1905

f4af0a26bacedde415f9f873c917fbdb4910e386

[ "MIT" ]

2

2019-11-07T16:28:36.000Z

2020-06-06T00:12:58.000Z

server/main/views/__init__.py

jphacks/TK_1905

f4af0a26bacedde415f9f873c917fbdb4910e386

[ "MIT" ]

null

from .api import * from .web import *

12.666667

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1

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6

43d48d4af9983d49019d9d399d1288de878f4952

136

py

Python

dataPipelines/gc_crawler_status_tracker/config.py

Wildertrek/gamechanger-data

d087044594c722bd373cce1a48293d1a6da5d24e

[ "MIT" ]

18

2021-04-20T20:34:01.000Z

2021-11-08T10:28:17.000Z

dataPipelines/gc_crawler_status_tracker/config.py

Wildertrek/gamechanger-data

d087044594c722bd373cce1a48293d1a6da5d24e

[ "MIT" ]

15

2021-04-20T20:31:33.000Z

2022-03-18T16:00:44.000Z

dataPipelines/gc_crawler_status_tracker/config.py

ekmixon/gamechanger-crawlers

60a0cf20338fb3dc134eec117bccd519cede9288

[ "MIT" ]

8

2021-04-23T11:38:26.000Z

2021-11-17T22:42:38.000Z

from configuration.utils import get_connection_helper_from_env class Config: connection_helper = get_connection_helper_from_env()

22.666667

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5.944444

0.555556

0.448598

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1

0

null

0

1

0

6

43d9bcaaa6296e2c48b39e0064e587018059e361

116

py

Python

app/frontend/__init__.py

rblack42/flask-inventory

48c0cfaf1ab10d0891c5af9d2b609e2b9e44ed74

[ "BSD-3-Clause" ]

null

app/frontend/__init__.py

rblack42/flask-inventory

48c0cfaf1ab10d0891c5af9d2b609e2b9e44ed74

[ "BSD-3-Clause" ]

null

app/frontend/__init__.py

rblack42/flask-inventory

48c0cfaf1ab10d0891c5af9d2b609e2b9e44ed74

[ "BSD-3-Clause" ]

null

from flask import Blueprint frontend_blueprint = Blueprint('frontend', __name__) from app.frontend import views

14.5

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6.285714

0.571429

0.386364

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null

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1

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6

78e52b7cc185e858cab47c76a99b88cd05eace6a

84

py

Python

tests/test_pydent/test_models/models/test_collection.py

aquariumbio/trident

d1712cae544103fb145e3171894e4b35141f6813

[ "MIT" ]

5

2019-01-21T11:12:05.000Z

2020-03-05T20:52:14.000Z

tests/test_pydent/test_models/models/test_collection.py

aquariumbio/pydent

d1712cae544103fb145e3171894e4b35141f6813

[ "MIT" ]

28

2020-11-18T02:07:09.000Z

2021-06-08T15:49:41.000Z

tests/test_pydent/test_models/models/test_collection.py

aquariumbio/trident

d1712cae544103fb145e3171894e4b35141f6813

[ "MIT" ]

2

2021-02-27T19:23:45.000Z

2021-09-14T10:29:07.000Z

from pydent.models import Collection # TODO: mock tests for Collections and Parts

16.8

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6

6029b208ecebe8ef4127ac5645b9e1ecd61c1934

40,153

py

Python

mff/kernels/manybodykernel.py

alvarovm/mff

cd1b22b606dfd64d91dc94fece72ad6a707212af

[ "Apache-2.0" ]

14

2019-03-22T18:57:34.000Z

2021-12-15T11:37:17.000Z

mff/kernels/manybodykernel.py

alvarovm/mff

cd1b22b606dfd64d91dc94fece72ad6a707212af

[ "Apache-2.0" ]

4

2019-06-18T14:55:46.000Z

2019-11-26T19:34:59.000Z

mff/kernels/manybodykernel.py

alvarovm/mff

cd1b22b606dfd64d91dc94fece72ad6a707212af

[ "Apache-2.0" ]

3

2019-08-05T14:42:20.000Z

2022-03-16T18:48:54.000Z

# -*- coding: utf-8 -*- import logging import os.path import pickle from abc import ABCMeta, abstractmethod import numpy as np from mff.kernels.base import Kernel, Mffpath logger = logging.getLogger(__name__) def dummy_calc_ff(data): """ Function used when multiprocessing. Args: data (list of objects): contains all the information required for the computation of the kernel values Returns: result (array): the computed kernel values """ array, theta0, theta1, theta2, kertype = data if kertype == "single": with open(Mffpath / "k3_ff_s.pickle", 'rb') as f: fun = pickle.load(f) elif kertype == "multi": with open(Mffpath / "k3_ff_m.pickle", 'rb') as f: fun = pickle.load(f) result = np.zeros((len(array), 3, 3)) for i in np.arange(len(array)): result[i] = fun(np.zeros(3), np.zeros(3), array[i][0], array[i][1], theta0, theta1, theta2) return result def dummy_calc_ee(data): """ Function used when multiprocessing. Args: data (list of objects): contains all the information required for the computation of the kernel valuesf Returns: result (array): the computed kernel values """ array, theta0, theta1, theta2, kertype = data if kertype == "single": with open(Mffpath / "k3_ee_s.pickle", 'rb') as f: fun = pickle.load(f) elif kertype == "multi": with open(Mffpath / "k3_ee_m.pickle", 'rb') as f: fun = pickle.load(f) result = np.zeros(len(array)) for i in np.arange(len(array)): for conf1 in array[i][0]: for conf2 in array[i][1]: result[i] += fun(np.zeros(3), np.zeros(3), conf1, conf2, theta0, theta1, theta2) return result def dummy_calc_ef(data): """ Function used when multiprocessing. Args: data (list of objects): contains all the information required for the computation of the kernel values Returns: result (array): the computed kernel values """ array, theta0, theta1, theta2, kertype = data if kertype == "single": with open(Mffpath / "k3_ef_s.pickle", 'rb') as f: fun = pickle.load(f) elif kertype == "multi": with open(Mffpath / "k3_ef_m.pickle", 'rb') as f: fun = pickle.load(f) result = np.zeros((len(array), 3)) for i in np.arange(len(array)): conf2 = np.array(array[i][1], dtype='float') for conf1 in array[i][0]: conf1 = np.array(conf1, dtype='float') result[i] += -fun(np.zeros(3), np.zeros(3), conf1, conf2, theta0, theta1, theta2) return result class BaseManyBody(Kernel, metaclass=ABCMeta): """ Many body kernel class Handles the functions common to the single-species and multi-species three-body kernels. Args: kernel_name (str): To choose between single- and two-species kernel theta[0] (float) : lengthscale of the kernel theta[1] (float) : decay rate of the cutoff function theta[2] (float) : cutoff radius bounds (list) : bounds of the kernel function. Attributes: km_ee (object): Energy-energy kernel function km_ef (object): Energy-force kernel function km_ff (object): Force-force kernel function """ @abstractmethod def __init__(self, kernel_name, theta, bounds): super().__init__(kernel_name) self.theta = theta self.bounds = bounds self.km_ee, self.km_ef, self.km_ff = self.compile_theano() def calc(self, X1, X2, ncores=1): """ Calculate the energy-force kernel between two sets of configurations. Args: X1 (list): list of N1 Mx5 arrays containing xyz coordinates and atomic species X2 (list): list of N2 Mx5 arrays containing xyz coordinates and atomic species Returns: K (matrix): N2*3 matrix of the vector-valued kernels """ ker = np.zeros((len(X1) * 3, len(X2) * 3)) if ncores > 1: confs = [] for x1 in X1: for x2 in X2: confs.append(np.asarray([x1, x2])) n = len(confs) import sys sys.setrecursionlimit(100000) logger.info( 'Using %i cores for the 3-body force-force kernel calculation' % (ncores)) # Way to split the kernels functions to compute evenly across the nodes splitind = np.zeros(ncores + 1) factor = (n + (ncores - 1)) / ncores splitind[1:-1] = [(i + 1) * factor for i in np.arange(ncores - 1)] splitind[-1] = n splitind = splitind.astype(int) clist = [[confs[splitind[i]:splitind[i + 1]], self.theta[0], self.theta[1], self.theta[2], self.type] for i in np.arange(ncores)] # Shape is ncores * (ntrain*(ntrain+1)/2)/ncores import multiprocessing as mp pool = mp.Pool(ncores) result = pool.map(dummy_calc_ff, clist) pool.close() pool.join() result = np.concatenate(result).reshape((n, 3, 3)) for i in range(len(X1)): for j in range(len(X2)): ker[i * 3: i * 3 + 3, 3 * j:3 * j + 3] = result[(j + i * len(X2))] else: for i, conf1 in enumerate(X1): for j, conf2 in enumerate(X2): ker[i * 3:i * 3 + 3, 3 * j:3 * j + 3] += self.km_ff( conf1, conf2, self.theta[0], self.theta[1], self.theta[2]) return ker def calc_ef(self, X_glob, X, ncores=1, mapping = False): """ Calculate the energy-force kernel between two sets of configurations. Args: X1 (list): list of N1 Mx5 arrays containing xyz coordinates and atomic species X2 (list): list of N2 Mx5 arrays containing xyz coordinates and atomic species Returns: K (matrix): N2*3 matrix of the vector-valued kernels """ ker = np.zeros((len(X_glob), len(X) * 3)) if ncores > 1: confs = [] for x1 in X_glob: for x2 in X: confs.append(np.asarray([x1, x2])) n = len(confs) import sys sys.setrecursionlimit(100000) logger.info( 'Using %i cores for the 3-body energy-force kernel calculation' % (ncores)) # Way to split the kernels functions to compute evenly across the nodes splitind = np.zeros(ncores + 1) factor = (n + (ncores - 1)) / ncores splitind[1:-1] = [(i + 1) * factor for i in np.arange(ncores - 1)] splitind[-1] = n splitind = splitind.astype(int) clist = [[confs[splitind[i]:splitind[i + 1]], self.theta[0], self.theta[1], self.theta[2], self.type] for i in np.arange(ncores)] # Shape is ncores * (ntrain*(ntrain+1)/2)/ncores import multiprocessing as mp pool = mp.Pool(ncores) result = pool.map(dummy_calc_ef, clist) pool.close() pool.join() result = np.vstack(np.asarray(result)) for i in range(len(X_glob)): for j in range(len(X)): ker[i, 3 * j:3 * j + 3] = result[(j + i * len(X))] else: for i, x1 in enumerate(X_glob): for j, conf2 in enumerate(X): for conf1 in x1: ker[i, 3 * j:3 * j + 3] += self.km_ef( conf1, conf2, self.theta[0], self.theta[1], self.theta[2]) return ker def calc_ee(self, X1, X2, ncores=1, mapping = False): """ Calculate the energy-energy kernel between two global environments. Args: X1 (list): list of N1 Mx5 arrays containing xyz coordinates and atomic species X2 (list): list of N2 Mx5 arrays containing xyz coordinates and atomic species Returns: K (matrix): N1 x N2 matrix of the scalar-valued kernels """ if ncores > 1: # Used for multiprocessing confs = [] # Build a list of all input pairs which matrix needs to be computed for x1 in X1: for x2 in X2: confs.append(np.asarray([x1, x2])) n = len(confs) import sys sys.setrecursionlimit(100000) logger.info( 'Using %i cores for the 3-body energy-energy kernel calculation' % (ncores)) # Way to split the kernels functions to compute evenly across the nodes splitind = np.zeros(ncores + 1) factor = (n + (ncores - 1)) / ncores splitind[1:-1] = [(i + 1) * factor for i in np.arange(ncores - 1)] splitind[-1] = n splitind = splitind.astype(int) clist = [[confs[splitind[i]:splitind[i + 1]], self.theta[0], self.theta[1], self.theta[2], self.type] for i in np.arange(ncores)] # Shape is ncores * (ntrain*(ntrain+1)/2)/ncores import multiprocessing as mp pool = mp.Pool(ncores) result = pool.map(dummy_calc_ee, clist) pool.close() pool.join() result = np.concatenate(result).ravel() ker = np.zeros((len(X1), len(X2))) for i in range(len(X1)): for j in range(len(X2)): ker[i, j] = result[j + i*len(X2)] else: ker = np.zeros((len(X1), len(X2))) for i, x1 in enumerate(X1): for j, x2 in enumerate(X2): for conf1 in x1: for conf2 in x2: ker[i, j] += self.km_ee(conf1, conf2, self.theta[0], self.theta[1], self.theta[2]) return ker def calc_gram(self, X, ncores=1, eval_gradient=False): """ Calculate the force-force gram matrix for a set of configurations X. Args: X (list): list of N Mx5 arrays containing xyz coordinates and atomic species ncores (int): Number of CPU nodes to use for multiprocessing (default is 1) eval_gradient (bool): if True, evaluate the gradient of the gram matrix Returns: gram (matrix): N*3 x N*3 gram matrix of the matrix-valued kernels """ if eval_gradient: raise NotImplementedError('ERROR: GRADIENT NOT IMPLEMENTED YET') else: if ncores > 1: confs = [] for i in np.arange(len(X)): for j in np.arange(i + 1): thislist = np.asarray([X[i], X[j]]) confs.append(thislist) n = len(confs) logger.info( 'Using %i cores for the many-body force-force gram matrix calculation' % (ncores)) import sys sys.setrecursionlimit(100000) # Way to split the kernels functions to compute evenly across the nodes splitind = np.zeros(ncores + 1) factor = (n + (ncores - 1)) / ncores splitind[1:-1] = [(i + 1) * factor for i in np.arange(ncores - 1)] splitind[-1] = n splitind = splitind.astype(int) clist = [[confs[splitind[i]:splitind[i + 1]], self.theta[0], self.theta[1], self.theta[2], self.type] for i in np.arange(ncores)] # Shape is ncores * (ntrain*(ntrain+1)/2)/ncores import multiprocessing as mp pool = mp.Pool(ncores) result = pool.map(dummy_calc_ff, clist) pool.close() pool.join() result = np.concatenate(result).reshape((n, 3, 3)) off_diag = np.zeros((len(X) * 3, len(X) * 3)) diag = np.zeros((len(X) * 3, len(X) * 3)) for i in np.arange(len(X)): diag[3 * i:3 * i + 3, 3 * i:3 * i + 3] = result[i + i * (i + 1) // 2] for j in np.arange(i): off_diag[3 * i:3 * i + 3, 3 * j:3 * j + 3] = result[j + i * (i + 1) // 2] else: diag = np.zeros((X.shape[0] * 3, X.shape[0] * 3)) off_diag = np.zeros((X.shape[0] * 3, X.shape[0] * 3)) for i in np.arange(X.shape[0]): diag[3 * i:3 * i + 3, 3 * i:3 * i + 3] = \ self.km_ff(X[i], X[i], self.theta[0], self.theta[1], self.theta[2]) for j in np.arange(i): off_diag[3 * i:3 * i + 3, 3 * j:3 * j + 3] = \ self.km_ff(X[i], X[j], self.theta[0], self.theta[1], self.theta[2]) gram = diag + off_diag + off_diag.T return gram def calc_gram_e(self, X, ncores=1, eval_gradient=False): # Untested """ Calculate the energy-energy gram matrix for a set of configurations X. Args: X (list): list of N Mx5 arrays containing xyz coordinates and atomic species ncores (int): Number of CPU nodes to use for multiprocessing (default is 1) eval_gradient (bool): if True, evaluate the gradient of the gram matrix Returns: gram (matrix): N x N gram matrix of the scalar-valued kernels """ if eval_gradient: raise NotImplementedError('ERROR: GRADIENT NOT IMPLEMENTED YET') else: if ncores > 1: confs = [] # Build a list of all input pairs which matrix needs to be computed for i in np.arange(len(X)): for j in np.arange(i + 1): thislist = np.array([list(X[i]), list(X[j])]) confs.append(thislist) n = len(confs) import sys sys.setrecursionlimit(100000) logger.info( 'Using %i cores for the many-body energy-energy gram matrix calculation' % (ncores)) # Way to split the kernels functions to compute evenly across the nodes splitind = np.zeros(ncores + 1) factor = (n + (ncores - 1)) / ncores splitind[1:-1] = [(i + 1) * factor for i in np.arange(ncores - 1)] splitind[-1] = n splitind = splitind.astype(int) clist = [[confs[splitind[i]:splitind[i + 1]], self.theta[0], self.theta[1], self.theta[2], self.type] for i in np.arange(ncores)] # Shape is ncores * (ntrain*(ntrain+1)/2)/ncores import multiprocessing as mp pool = mp.Pool(ncores) result = pool.map(dummy_calc_ee, clist) pool.close() pool.join() result = np.concatenate(result).ravel() off_diag = np.zeros((len(X), len(X))) diag = np.zeros((len(X), len(X))) for i in np.arange(len(X)): diag[i, i] = result[i + i * (i + 1) // 2] for j in np.arange(i): off_diag[i, j] = result[j + i * (i + 1) // 2] else: diag = np.zeros((X.shape[0], X.shape[0])) off_diag = np.zeros((X.shape[0], X.shape[0])) for i in np.arange(X.shape[0]): for k, conf1 in enumerate(X[i]): diag[i, i] += self.km_ee(conf1, conf1, self.theta[0], self.theta[1], self.theta[2]) for conf2 in X[i][:k]: # *2 here to speed up the loop diag[i, i] += 2.0*self.km_ee( conf1, conf2, self.theta[0], self.theta[1], self.theta[2]) for j in np.arange(i): for conf1 in X[i]: for conf2 in X[j]: off_diag[i, j] += self.km_ee( conf1, conf2, self.theta[0], self.theta[1], self.theta[2]) gram = diag + off_diag + off_diag.T # Gram matrix is symmetric return gram def calc_gram_ef(self, X, X_glob, ncores=1, eval_gradient=False): """ Calculate the energy-force gram matrix for a set of configurations X. This returns a non-symmetric matrix which is equal to the transpose of the force-energy gram matrix. Args: X (list): list of N1 M1x5 arrays containing xyz coordinates and atomic species X_glob (list): list of N2 M2x5 arrays containing xyz coordinates and atomic species ncores (int): Number of CPU nodes to use for multiprocessing (default is 1) eval_gradient (bool): if True, evaluate the gradient of the gram matrix Returns: gram (matrix): N2 x N1*3 gram matrix of the vector-valued kernels """ gram = np.zeros((X_glob.shape[0], X.shape[0] * 3)) if eval_gradient: raise NotImplementedError('ERROR: GRADIENT NOT IMPLEMENTED YET') else: if ncores > 1: # Multiprocessing confs = [] for i in np.arange(len(X_glob)): for j in np.arange(len(X)): thislist = np.asarray([X_glob[i], X[j]]) confs.append(thislist) n = len(confs) import sys sys.setrecursionlimit(100000) logger.info( 'Using %i cores for the many-body energy-force gram matrix calculation' % (ncores)) # Way to split the kernels functions to compute evenly across the nodes splitind = np.zeros(ncores + 1) factor = (n + (ncores - 1)) / ncores splitind[1:-1] = [(i + 1) * factor for i in np.arange(ncores - 1)] splitind[-1] = n splitind = splitind.astype(int) clist = [[confs[splitind[i]:splitind[i + 1]], self.theta[0], self.theta[1], self.theta[2], self.type] for i in np.arange(ncores)] # Shape is ncores * (ntrain*(ntrain+1)/2)/ncores import multiprocessing as mp pool = mp.Pool(ncores) result = pool.map(dummy_calc_ef, clist) pool.close() pool.join() result = np.concatenate(result).ravel() for i in np.arange(X_glob.shape[0]): for j in np.arange(X.shape[0]): gram[i, 3 * j:3 * j + 3] = result[3 * (j + i * X.shape[0]):3 + 3*(j + i * X.shape[0])] else: for i in np.arange(X_glob.shape[0]): for j in np.arange(X.shape[0]): for k in X_glob[i]: gram[i, 3 * j:3 * j + 3] += self.km_ef( k, X[j], self.theta[0], self.theta[1], self.theta[2]) self.gram_ef = gram return gram def calc_diag(self, X): diag = np.zeros((X.shape[0] * 3)) for i in np.arange(X.shape[0]): diag[i * 3:(i + 1) * 3] = np.diag(self.km_ff(X[i], X[i], self.theta[0], self.theta[1], self.theta[2])) return diag def calc_diag_e(self, X): diag = np.zeros((X.shape[0])) for i in np.arange(X.shape[0]): diag[i] = self.km_ee(X[i], X[i], self.theta[0], self.theta[1], self.theta[2]) return diag @staticmethod @abstractmethod def compile_theano(): return None, None, None class ManyBodySingleSpeciesKernel(BaseManyBody): """Many body two species kernel. Args: theta[0] (float): lengthscale of the kernel theta[1] (float): decay rate of the cutoff function theta[2] (float): cutoff radius """ def __init__(self, theta=(1., 1., 1.), bounds=((1e-2, 1e2), (1e-2, 1e2), (1e-2, 1e2))): super().__init__(kernel_name='ManyBodySingleSpecies', theta=theta, bounds=bounds) self.type = "single" @staticmethod def compile_theano(): """ This function generates theano compiled kernels for energy and force learning ker_jkmn_withcutoff = ker_jkmn #* cutoff_ikmn The position of the atoms relative to the centrla one, and their chemical species are defined by a matrix of dimension Mx5 Returns: km_ee (func): energy-energy kernel km_ef (func): energy-force kernel km_ff (func): force-force kernel """ if not (os.path.exists(Mffpath / 'k3_ee_s.pickle') and os.path.exists(Mffpath / 'k3_ef_s.pickle') and os.path.exists(Mffpath / 'k3_ff_s.pickle')): print("Building Kernels") import theano.tensor as T from theano import function, scan logger.info("Started compilation of theano three body kernels") # -------------------------------------------------- # INITIAL DEFINITIONS # -------------------------------------------------- # positions of central atoms r1, r2 = T.dvectors('r1d', 'r2d') # positions of neighbours rho1, rho2 = T.dmatrices('rho1', 'rho2') # hyperparameter sig = T.dscalar('sig') # cutoff hyperparameters theta = T.dscalar('theta') rc = T.dscalar('rc') # positions of neighbours without chemical species rho1s = rho1[:, 0:3] rho2s = rho2[:, 0:3] # -------------------------------------------------- # RELATIVE DISTANCES TO CENTRAL VECTOR AND BETWEEN NEIGHBOURS # -------------------------------------------------- # first and second configuration r1j = T.sqrt(T.sum((rho1s[:, :] - r1[None, :]) ** 2, axis=1)) r2m = T.sqrt(T.sum((rho2s[:, :] - r2[None, :]) ** 2, axis=1)) rjk = T.sqrt( T.sum((rho1s[None, :, :] - rho1s[:, None, :]) ** 2, axis=2)) rmn = T.sqrt( T.sum((rho2s[None, :, :] - rho2s[:, None, :]) ** 2, axis=2)) # -------------------------------------------------- # BUILD THE KERNEL # -------------------------------------------------- # Squared exp of differences se_1j2m = T.exp(-(r1j[:, None] - r2m[None, :]) ** 2 / (2 * sig ** 2)) se_jkmn = T.exp(-(rjk[:, :, None, None] - rmn[None, None, :, :]) ** 2 / (2 * sig ** 2)) se_jk2m = T.exp(-(rjk[:, :, None] - r2m[None, None, :]) ** 2 / (2 * sig ** 2)) se_1jmn = T.exp(-(r1j[:, None, None] - rmn[None, :, :]) ** 2 / (2 * sig ** 2)) # Kernel not summed (cyclic permutations) k1n = (se_1j2m[:, None, :, None] * se_1j2m[None, :, None, :] * se_jkmn) k2n = (se_1jmn[:, None, :, :] * se_jk2m[:, :, None, :] * se_1j2m[None, :, :, None]) k3n = (se_1j2m[:, None, None, :] * se_jk2m[:, :, :, None] * se_1jmn[None, :, :, :]) # final shape is M1 M1 M2 M2 ker = k1n + k2n + k3n cut_j = 0.5*(1+T.cos(np.pi*r1j/rc)) cut_m = 0.5*(1+T.cos(np.pi*r2m/rc)) cut_jk = cut_j[:,None]*cut_j[None,:]*0.5*(1+T.cos(np.pi*rjk/rc)) cut_mn = cut_m[:,None]*cut_m[None,:]*0.5*(1+T.cos(np.pi*rmn/rc)) # -------------------------------------------------- # REMOVE DIAGONAL ELEMENTS AND ADD CUTOFF # -------------------------------------------------- # remove diagonal elements AND lower triangular ones from first configuration mask_jk = T.triu(T.ones_like(rjk)) - T.identity_like(rjk) # remove diagonal elements from second configuration mask_mn = T.ones_like(rmn) - T.identity_like(rmn) # Combine masks mask_jkmn = mask_jk[:, :, None, None] * mask_mn[None, None, :, :] # Apply mask and then apply cutoff functions ker = ker * mask_jkmn ker = T.sum(ker * cut_jk[:, :, None, None] * cut_mn[None, None, :, :]) ker = T.exp(ker / 1000) # -------------------------------------------------- # FINAL FUNCTIONS # -------------------------------------------------- # global energy energy kernel k_ee_fun = function( [r1, r2, rho1, rho2, sig, theta, rc], ker, on_unused_input='ignore') # global energy force kernel k_ef = T.grad(ker, r2) k_ef_fun = function( [r1, r2, rho1, rho2, sig, theta, rc], k_ef, on_unused_input='ignore') # local force force kernel k_ff = T.grad(ker, r1) k_ff_der, updates = scan(lambda j, k_ff, r2: T.grad(k_ff[j], r2), sequences=T.arange(k_ff.shape[0]), non_sequences=[k_ff, r2]) k_ff_fun = function( [r1, r2, rho1, rho2, sig, theta, rc], k_ff_der, on_unused_input='ignore') # Save the function that we want to use for multiprocessing # This is necessary because theano is a crybaby and does not want to access the # Automaticallly stored compiled object from different processes with open(Mffpath / 'k3_ee_s.pickle', 'wb') as f: pickle.dump(k_ee_fun, f) with open(Mffpath / 'k3_ef_s.pickle', 'wb') as f: pickle.dump(k_ef_fun, f) with open(Mffpath / 'k3_ff_s.pickle', 'wb') as f: pickle.dump(k_ff_fun, f) else: print("Loading Kernels") with open(Mffpath / "k3_ee_s.pickle", 'rb') as f: k_ee_fun = pickle.load(f) with open(Mffpath / "k3_ef_s.pickle", 'rb') as f: k_ef_fun = pickle.load(f) with open(Mffpath / "k3_ff_s.pickle", 'rb') as f: k_ff_fun = pickle.load(f) # WRAPPERS (we don't want to plug the position of the central element every time) def km_ee(conf1, conf2, sig, theta, rc): """ Many body kernel for global energy-energy correlation Args: conf1 (array): first configuration. conf2 (array): second configuration. sig (float): lengthscale hyperparameter theta[0] theta (float): cutoff decay rate hyperparameter theta[1] rc (float): cutoff distance hyperparameter theta[2] Returns: kernel (float): scalar valued energy-energy many-body kernel """ return k_ee_fun(np.zeros(3), np.zeros(3), conf1, conf2, sig, theta, rc) def km_ef(conf1, conf2, sig, theta, rc): """ Many body kernel for global energy-force correlation Args: conf1 (array): first configuration. conf2 (array): second configuration. sig (float): lengthscale hyperparameter theta[0] theta (float): cutoff decay rate hyperparameter theta[1] rc (float): cutoff distance hyperparameter theta[2] Returns: kernel (array): 3x1 energy-force many-body kernel """ return -k_ef_fun(np.zeros(3), np.zeros(3), conf1, conf2, sig, theta, rc) def km_ff(conf1, conf2, sig, theta, rc): """ Many body kernel for local force-force correlation Args: conf1 (array): first configuration. conf2 (array): second configuration. sig (float): lengthscale hyperparameter theta[0] theta (float): cutoff decay rate hyperparameter theta[1] rc (float): cutoff distance hyperparameter theta[2] Returns: kernel (matrix): 3x3 force-force 3-body kernel """ return k_ff_fun(np.zeros(3), np.zeros(3), conf1, conf2, sig, theta, rc) logger.info("Ended compilation of theano three body kernels") return km_ee, km_ef, km_ff class ManyBodyManySpeciesKernel(BaseManyBody): """Many body many species kernel. Args: theta[0] (float): lengthscale of the kernel theta[1] (float): decay rate of the cutoff function theta[2] (float): cutoff radius """ def __init__(self, theta=(1., 1., 1.), bounds=((1e-2, 1e2), (1e-2, 1e2), (1e-2, 1e2))): super().__init__(kernel_name='ManyBodyManySpecies', theta=theta, bounds=bounds) self.type = "multi" @staticmethod def compile_theano(): """ This function generates theano compiled kernels for energy and force learning ker_jkmn_withcutoff = ker_jkmn #* cutoff_ikmn The position of the atoms relative to the centrla one, and their chemical species are defined by a matrix of dimension Mx5 Returns: km_ee (func): energy-energy kernel km_ef (func): energy-force kernel km_ff (func): force-force kernel """ if not (os.path.exists(Mffpath / 'k3_ee_m.pickle') and os.path.exists(Mffpath / 'k3_ef_m.pickle') and os.path.exists(Mffpath / 'k3_ff_m.pickle')): print("Building Kernels") import theano.tensor as T from theano import function, scan logger.info("Started compilation of theano three body kernels") # -------------------------------------------------- # INITIAL DEFINITIONS # -------------------------------------------------- # positions of central atoms r1, r2 = T.dvectors('r1d', 'r2d') # positions of neighbours rho1, rho2 = T.dmatrices('rho1', 'rho2') # hyperparameter sig = T.dscalar('sig') # cutoff hyperparameters theta = T.dscalar('theta') rc = T.dscalar('rc') # positions of neighbours without chemical species rho1s = rho1[:, 0:3] rho2s = rho2[:, 0:3] alpha_1 = rho1[:, 3].flatten() alpha_2 = rho2[:, 3].flatten() alpha_j = rho1[:, 4].flatten() alpha_m = rho2[:, 4].flatten() alpha_k = rho1[:, 4].flatten() alpha_n = rho2[:, 4].flatten() # -------------------------------------------------- # RELATIVE DISTANCES TO CENTRAL VECTOR AND BETWEEN NEIGHBOURS # -------------------------------------------------- # first and second configuration r1j = T.sqrt(T.sum((rho1s[:, :] - r1[None, :]) ** 2, axis=1)) r2m = T.sqrt(T.sum((rho2s[:, :] - r2[None, :]) ** 2, axis=1)) rjk = T.sqrt( T.sum((rho1s[None, :, :] - rho1s[:, None, :]) ** 2, axis=2)) rmn = T.sqrt( T.sum((rho2s[None, :, :] - rho2s[:, None, :]) ** 2, axis=2)) # -------------------------------------------------- # CHEMICAL SPECIES MASK # -------------------------------------------------- # numerical kronecker def delta_alpha2(a1j, a2m): d = np.exp(-(a1j - a2m) ** 2 / (2 * 0.00001 ** 2)) return d # permutation 1 delta_alphas12 = delta_alpha2(alpha_1[0], alpha_2[0]) delta_alphasjm = delta_alpha2(alpha_j[:, None], alpha_m[None, :]) delta_alphas_jmkn = delta_alphasjm[:, None, :, None] * delta_alphasjm[None, :, None, :] delta_perm1 = delta_alphas12 * delta_alphas_jmkn # permutation 3 delta_alphas1m = delta_alpha2( alpha_1[0, None], alpha_m[None, :]).flatten() delta_alphasjn = delta_alpha2(alpha_j[:, None], alpha_n[None, :]) delta_alphask2 = delta_alpha2( alpha_k[:, None], alpha_2[None, 0]).flatten() delta_perm3 = delta_alphas1m[None, None, :, None] * delta_alphasjn[:, None, None, :] * \ delta_alphask2[None, :, None, None] # permutation 5 delta_alphas1n = delta_alpha2( alpha_1[0, None], alpha_n[None, :]).flatten() delta_alphasj2 = delta_alpha2( alpha_j[:, None], alpha_2[None, 0]).flatten() delta_alphaskm = delta_alpha2(alpha_k[:, None], alpha_m[None, :]) delta_perm5 = delta_alphas1n[None, None, None, :] * delta_alphaskm[None, :, :, None] * \ delta_alphasj2[:, None, None, None] # -------------------------------------------------- # BUILD THE KERNEL # -------------------------------------------------- # Squared exp of differences se_1j2m = T.exp(-(r1j[:, None] - r2m[None, :]) ** 2 / (2 * sig ** 2)) se_jkmn = T.exp(-(rjk[:, :, None, None] - rmn[None, None, :, :]) ** 2 / (2 * sig ** 2)) se_jk2m = T.exp(-(rjk[:, :, None] - r2m[None, None, :]) ** 2 / (2 * sig ** 2)) se_1jmn = T.exp(-(r1j[:, None, None] - rmn[None, :, :]) ** 2 / (2 * sig ** 2)) # Kernel not summed (cyclic permutations) k1n = (se_1j2m[:, None, :, None] * se_1j2m[None, :, None, :] * se_jkmn) k2n = (se_1jmn[:, None, :, :] * se_jk2m[:, :, None, :] * se_1j2m[None, :, :, None]) k3n = (se_1j2m[:, None, None, :] * se_jk2m[:, :, :, None] * se_1jmn[None, :, :, :]) # final shape is M1 M1 M2 M2 ker_loc = k1n * delta_perm1 + k2n * delta_perm3 + k3n * delta_perm5 # Faster version of cutoff (less calculations) cut_j = 0.5*(1+T.cos(np.pi*r1j/rc)) cut_m = 0.5*(1+T.cos(np.pi*r2m/rc)) cut_jk = cut_j[:,None]*cut_j[None,:]*0.5*(1+T.cos(np.pi*rjk/rc)) cut_mn = cut_m[:,None]*cut_m[None,:]*0.5*(1+T.cos(np.pi*rmn/rc)) # -------------------------------------------------- # REMOVE DIAGONAL ELEMENTS # -------------------------------------------------- # remove diagonal elements AND lower triangular ones from first configuration mask_jk = T.triu(T.ones_like(rjk)) - T.identity_like(rjk) # remove diagonal elements from second configuration mask_mn = T.ones_like(rmn) - T.identity_like(rmn) # Combine masks mask_jkmn = mask_jk[:, :, None, None] * mask_mn[None, None, :, :] # Apply mask and then apply cutoff functions ker_loc = ker_loc * mask_jkmn ker_loc = T.sum( ker_loc * cut_jk[:, :, None, None] * cut_mn[None, None, :, :]) ker_loc = T.exp(ker_loc / 20) # -------------------------------------------------- # FINAL FUNCTIONS # -------------------------------------------------- # energy energy kernel k_ee_fun = function( [r1, r2, rho1, rho2, sig, theta, rc], ker_loc, on_unused_input='ignore') # energy force kernel k_ef_cut = T.grad(ker_loc, r2) k_ef_fun = function( [r1, r2, rho1, rho2, sig, theta, rc], k_ef_cut, on_unused_input='ignore') # force force kernel k_ff_cut = T.grad(ker_loc, r1) k_ff_cut_der, updates = scan(lambda j, k_ff_cut, r2: T.grad(k_ff_cut[j], r2), sequences=T.arange(k_ff_cut.shape[0]), non_sequences=[k_ff_cut, r2]) k_ff_fun = function( [r1, r2, rho1, rho2, sig, theta, rc], k_ff_cut_der, on_unused_input='ignore') # Save the function that we want to use for multiprocessing # This is necessary because theano is a crybaby and does not want to access the # Automaticallly stored compiled object from different processes with open(Mffpath / 'k3_ee_m.pickle', 'wb') as f: pickle.dump(k_ee_fun, f) with open(Mffpath / 'k3_ef_m.pickle', 'wb') as f: pickle.dump(k_ef_fun, f) with open(Mffpath / 'k3_ff_m.pickle', 'wb') as f: pickle.dump(k_ff_fun, f) else: print("Loading Kernels") with open(Mffpath / "k3_ee_m.pickle", 'rb') as f: k_ee_fun = pickle.load(f) with open(Mffpath / "k3_ef_m.pickle", 'rb') as f: k_ef_fun = pickle.load(f) with open(Mffpath / "k3_ff_m.pickle", 'rb') as f: k_ff_fun = pickle.load(f) # WRAPPERS (we don't want to plug the position of the central element every time) def km_ee(conf1, conf2, sig, theta, rc): """ Many body kernel for energy-energy correlation Args: conf1 (array): first configuration. conf2 (array): second configuration. sig (float): lengthscale hyperparameter theta[0] theta (float): cutoff decay rate hyperparameter theta[1] rc (float): cutoff distance hyperparameter theta[2] Returns: kernel (float): scalar valued energy-energy many-body kernel """ return k_ee_fun(np.zeros(3), np.zeros(3), conf1, conf2, sig, theta, rc) def km_ef(conf1, conf2, sig, theta, rc): """ Many body kernel for energy-force correlation Args: conf1 (array): first configuration. conf2 (array): second configuration. sig (float): lengthscale hyperparameter theta[0] theta (float): cutoff decay rate hyperparameter theta[1] rc (float): cutoff distance hyperparameter theta[2] Returns: kernel (array): 3x1 energy-force many-body kernel """ return -k_ef_fun(np.zeros(3), np.zeros(3), conf1, conf2, sig, theta, rc) def km_ff(conf1, conf2, sig, theta, rc): """ Many body kernel for force-force correlation Args: conf1 (array): first configuration. conf2 (array): second configuration. sig (float): lengthscale hyperparameter theta[0] theta (float): cutoff decay rate hyperparameter theta[1] rc (float): cutoff distance hyperparameter theta[2] Returns: kernel (matrix): 3x3 force-force many-body kernel """ return k_ff_fun(np.zeros(3), np.zeros(3), conf1, conf2, sig, theta, rc) logger.info("Ended compilation of theano many body kernels") return km_ee, km_ef, km_ff

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6049de05de393fd3e880f87515b8150f65e31371

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py

Python

home/pi/blissflixx/chls/bfch_r_shortfilms/__init__.py

erick-guerra/Royalbox

967dbbdddc94b9968e6eba873f0d20328fd86f66

[ "MIT" ]

1

2022-01-29T11:17:58.000Z

home/pi/blissflixx/chls/bfch_r_shortfilms/__init__.py

erick-guerra/Royalbox

967dbbdddc94b9968e6eba873f0d20328fd86f66

[ "MIT" ]

null

home/pi/blissflixx/chls/bfch_r_shortfilms/__init__.py

erick-guerra/Royalbox

967dbbdddc94b9968e6eba873f0d20328fd86f66

[ "MIT" ]

null

import chanutils.reddit _SUBREDDIT = 'Shortfilms' _FEEDLIST = [ {'title':'Hot', 'url':'http://www.reddit.com/r/Shortfilms.json'}, {'title':'New', 'url':'http://www.reddit.com/r/Shortfilms/new.json'}, {'title':'Action & Adventure', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f01%27+&sort=top&restrict_sr=on'}, {'title':'Animation', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f02%27+&sort=top&restrict_sr=on'}, {'title':'Art Films', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f17%27+&sort=top&restrict_sr=on'}, {'title':'Comedy', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f05%27+&sort=top&restrict_sr=on'}, {'title':'Crime', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f06%27+&sort=top&restrict_sr=on'}, {'title':'Documentary', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f07%27+&sort=top&restrict_sr=on'}, {'title':'Drama', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f08%27+&sort=top&restrict_sr=on'}, {'title':'Experimental', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f09%27+&sort=top&restrict_sr=on'}, {'title':'Film Noir', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f10%27+&sort=top&restrict_sr=on'}, {'title':'Gay & Lesbian', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f11%27+&sort=top&restrict_sr=on'}, {'title':'Horror', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f12%27+&sort=top&restrict_sr=on'}, {'title':'Musical', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f13%27+&sort=top&restrict_sr=on'}, {'title':'Mystery', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f14%27+&sort=top&restrict_sr=on'}, {'title':'Parody', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f26%27+&sort=top&restrict_sr=on'}, {'title':'Romance', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f16%27+&sort=top&restrict_sr=on'}, {'title':'Sci-Fi & Fantasy', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f03%27+&sort=top&restrict_sr=on'}, {'title':'Surreal', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f23%27+&sort=top&restrict_sr=on'}, {'title':'Thriller', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f18%27+&sort=top&restrict_sr=on'}, {'title':'War', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f19%27+&sort=top&restrict_sr=on'}, {'title':'Western', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f20%27+&sort=top&restrict_sr=on'}, {'title':'World Cinema', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f21%27+&sort=top&restrict_sr=on'}, {'title':'Amateur', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f22%27+&sort=top&restrict_sr=on'}, {'title':'Genre Defying', 'url':'http://www.reddit.com/r/Shortfilms/search.json?q=flair%3A%27f27%27+&sort=top&restrict_sr=on'}, ] def name(): return 'Short Films' def image(): return "icon.png" def description(): return "Short Films from /r/Shortfilms subreddit (<a target='_blank' href='http://www.reddit.com/r/Shortfilms'>http://www.reddit.com/r/Shortfilms</a>)." def feedlist(): return _FEEDLIST def feed(idx): return chanutils.reddit.get_feed(_FEEDLIST[idx]) def search(q): return chanutils.reddit.search(_SUBREDDIT, q)

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60f0e74ca920ba5c7a99479d84da7cd02e0b3e56

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py

Python

contrib/tools/python/src/Lib/plat-mac/Carbon/IBCarbon.py

HeyLey/catboost

f472aed90604ebe727537d9d4a37147985e10ec2

[ "Apache-2.0" ]

6,989

2017-07-18T06:23:18.000Z

2022-03-31T15:58:36.000Z

python/src/Lib/plat-mac/Carbon/IBCarbon.py

weiqiangzheng/sl4a

d3c17dca978cbeee545e12ea240a9dbf2a6999e9

[ "Apache-2.0" ]

1,978

2017-07-18T09:17:58.000Z

2022-03-31T14:28:43.000Z

python/src/Lib/plat-mac/Carbon/IBCarbon.py

weiqiangzheng/sl4a

d3c17dca978cbeee545e12ea240a9dbf2a6999e9

[ "Apache-2.0" ]

1,228

2017-07-18T09:03:13.000Z

2022-03-29T05:57:40.000Z

from _IBCarbon import *

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py

Python

csv_to_dictionary/__init__.py

EthanDayley/csv_to_dictionary

a49103da8667e542aca30f797394af3e7d695aa5

[ "MIT" ]

1

2018-03-02T18:55:33.000Z

csv_to_dictionary/__init__.py

EthanDayley/csv_to_dictionary

a49103da8667e542aca30f797394af3e7d695aa5

[ "MIT" ]

null

csv_to_dictionary/__init__.py

EthanDayley/csv_to_dictionary

a49103da8667e542aca30f797394af3e7d695aa5

[ "MIT" ]

null

from .csv_to_dictionary import convert_simple

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880ffc1a0208c4b2c907ab0a28203a51fce736b9

31,415

py

Python

sigpy/interp.py

kmjohnson3/sigpy

6d5f9c66f7446a13b3615c31446bbce8adc5dfaa

[ "BSD-3-Clause" ]

196

2018-07-07T00:42:42.000Z

2022-03-22T02:30:24.000Z

sigpy/interp.py

kmjohnson3/sigpy

6d5f9c66f7446a13b3615c31446bbce8adc5dfaa

[ "BSD-3-Clause" ]

79

2018-10-12T19:53:21.000Z

2022-03-30T13:44:41.000Z

sigpy/interp.py

kmjohnson3/sigpy

6d5f9c66f7446a13b3615c31446bbce8adc5dfaa

[ "BSD-3-Clause" ]

68

2018-09-26T03:46:42.000Z

2022-03-11T03:51:49.000Z

# -*- coding: utf-8 -*- """Interpolation functions. """ import numpy as np import numba as nb from sigpy import backend, config, util __all__ = ['interpolate', 'gridding'] KERNELS = ['spline', 'kaiser_bessel'] def interpolate(input, coord, kernel='spline', width=2, param=1): r"""Interpolation from array to points specified by coordinates. Let :math:`x` be the input, :math:`y` be the output, :math:`c` be the coordinates, :math:`W` be the kernel width, and :math:`K` be the interpolation kernel, then the function computes, .. math :: y[j] = \sum_{i : \| i - c[j] \|_\infty \leq W / 2} K\left(\frac{i - c[j]}{W / 2}\right) x[i] There are two types of kernels: 'spline' and 'kaiser_bessel'. 'spline' uses the cardinal B-spline functions as kernels. The order of the spline can be specified using param. For example, param=1 performs linear interpolation. Concretely, for param=0, :math:`K(x) = 1`, for param=1, :math:`K(x) = 1 - |x|`, and for param=2, :math:`K(x) = \frac{9}{8} (1 - |x|)^2` for :math:`|x| > \frac{1}{3}` and :math:`K(x) = \frac{3}{4} (1 - 3 x^2)` for :math:`|x| < \frac{1}{3}`. These function expressions are derived from the reference wikipedia page by shifting and scaling the range to -1 to 1. When the coordinates specifies a uniformly spaced grid, it is recommended to use the original scaling with width=param + 1 so that the interpolation weights add up to one. 'kaiser_bessel' uses the Kaiser-Bessel function as kernel. Concretely, :math:`K(x) = I_0(\beta \sqrt{1 - x^2})`, where :math:`I_0` is the modified Bessel function of the first kind. The beta parameter can be specified with param. The modified Bessel function of the first kind is approximated using the power series, following the reference. Args: input (array): Input array of shape. coord (array): Coordinate array of shape [..., ndim] width (float or tuple of floats): Interpolation kernel full-width. kernel (str): Interpolation kernel, {'spline', 'kaiser_bessel'}. param (float or tuple of floats): Kernel parameter. Returns: output (array): Output array. References: https://en.wikipedia.org/wiki/Spline_wavelet#Cardinal_B-splines_of_small_orders http://people.math.sfu.ca/~cbm/aands/page_378.htm """ ndim = coord.shape[-1] batch_shape = input.shape[:-ndim] batch_size = util.prod(batch_shape) pts_shape = coord.shape[:-1] npts = util.prod(pts_shape) xp = backend.get_array_module(input) input = input.reshape([batch_size] + list(input.shape[-ndim:])) coord = coord.reshape([npts, ndim]) output = xp.zeros([batch_size, npts], dtype=input.dtype) if np.isscalar(param): param = xp.array([param] * ndim, coord.dtype) else: param = xp.array(param, coord.dtype) if np.isscalar(width): width = xp.array([width] * ndim, coord.dtype) else: width = xp.array(width, coord.dtype) if xp == np: _interpolate[kernel][ndim - 1](output, input, coord, width, param) else: # pragma: no cover _interpolate_cuda[kernel][ndim - 1]( input, coord, width, param, output, size=npts) return output.reshape(batch_shape + pts_shape) def gridding(input, coord, shape, kernel="spline", width=2, param=1): r"""Gridding of points specified by coordinates to array. Let :math:`y` be the input, :math:`x` be the output, :math:`c` be the coordinates, :math:`W` be the kernel width, and :math:`K` be the interpolation kernel, then the function computes, .. math :: x[i] = \sum_{j : \| i - c[j] \|_\infty \leq W / 2} K\left(\frac{i - c[j]}{W / 2}\right) y[j] There are two types of kernels: 'spline' and 'kaiser_bessel'. 'spline' uses the cardinal B-spline functions as kernels. The order of the spline can be specified using param. For example, param=1 performs linear interpolation. Concretely, for param=0, :math:`K(x) = 1`, for param=1, :math:`K(x) = 1 - |x|`, and for param=2, :math:`K(x) = \frac{9}{8} (1 - |x|)^2` for :math:`|x| > \frac{1}{3}` and :math:`K(x) = \frac{3}{4} (1 - 3 x^2)` for :math:`|x| < \frac{1}{3}`. These function expressions are derived from the reference wikipedia page by shifting and scaling the range to -1 to 1. When the coordinates specifies a uniformly spaced grid, it is recommended to use the original scaling with width=param + 1 so that the interpolation weights add up to one. 'kaiser_bessel' uses the Kaiser-Bessel function as kernel. Concretely, :math:`K(x) = I_0(\beta \sqrt{1 - x^2})`, where :math:`I_0` is the modified Bessel function of the first kind. The beta parameter can be specified with param. The modified Bessel function of the first kind is approximated using the power series, following the reference. Args: input (array): Input array. coord (array): Coordinate array of shape [..., ndim] width (float or tuple of floats): Interpolation kernel full-width. kernel (str): Interpolation kernel, {"spline", "kaiser_bessel"}. param (float or tuple of floats): Kernel parameter. Returns: output (array): Output array. References: https://en.wikipedia.org/wiki/Spline_wavelet#Cardinal_B-splines_of_small_orders http://people.math.sfu.ca/~cbm/aands/page_378.htm """ ndim = coord.shape[-1] batch_shape = shape[:-ndim] batch_size = util.prod(batch_shape) pts_shape = coord.shape[:-1] npts = util.prod(pts_shape) xp = backend.get_array_module(input) isreal = np.issubdtype(input.dtype, np.floating) input = input.reshape([batch_size, npts]) coord = coord.reshape([npts, ndim]) output = xp.zeros([batch_size] + list(shape[-ndim:]), dtype=input.dtype) if np.isscalar(param): param = xp.array([param] * ndim, coord.dtype) else: param = xp.array(param, coord.dtype) if np.isscalar(width): width = xp.array([width] * ndim, coord.dtype) else: width = xp.array(width, coord.dtype) if xp == np: _gridding[kernel][ndim - 1](output, input, coord, width, param) else: # pragma: no cover if isreal: _gridding_cuda[kernel][ndim - 1]( input, coord, width, param, output, size=npts) else: _gridding_cuda_complex[kernel][ndim - 1]( input, coord, width, param, output, size=npts) return output.reshape(shape) @nb.jit(nopython=True, cache=True) # pragma: no cover def _spline_kernel(x, order): if abs(x) > 1: return 0 if order == 0: return 1 elif order == 1: return 1 - abs(x) elif order == 2: if abs(x) > 1 / 3: return 9 / 8 * (1 - abs(x))**2 else: return 3 / 4 * (1 - 3 * x**2) @nb.jit(nopython=True, cache=True) # pragma: no cover def _kaiser_bessel_kernel(x, beta): if abs(x) > 1: return 0 x = beta * (1 - x**2)**0.5 t = x / 3.75 if x < 3.75: return 1 + 3.5156229 * t**2 + 3.0899424 * t**4 +\ 1.2067492 * t**6 + 0.2659732 * t**8 +\ 0.0360768 * t**10 + 0.0045813 * t**12 else: return x**-0.5 * np.exp(x) * ( 0.39894228 + 0.01328592 * t**-1 + 0.00225319 * t**-2 - 0.00157565 * t**-3 + 0.00916281 * t**-4 - 0.02057706 * t**-5 + 0.02635537 * t**-6 - 0.01647633 * t**-7 + 0.00392377 * t**-8) def _get_interpolate(kernel): if kernel == 'spline': kernel = _spline_kernel elif kernel == 'kaiser_bessel': kernel = _kaiser_bessel_kernel @nb.jit(nopython=True) # pragma: no cover def _interpolate1(output, input, coord, width, param): batch_size, nx = input.shape npts = coord.shape[0] for i in range(npts): kx = coord[i, -1] x0 = np.ceil(kx - width[-1] / 2) x1 = np.floor(kx + width[-1] / 2) for x in range(x0, x1 + 1): w = kernel((x - kx) / (width[-1] / 2), param[-1]) for b in range(batch_size): output[b, i] += w * input[b, x % nx] return output @nb.jit(nopython=True) # pragma: no cover def _interpolate2(output, input, coord, width, param): batch_size, ny, nx = input.shape npts = coord.shape[0] for i in range(npts): kx, ky = coord[i, -1], coord[i, -2] x0, y0 = (np.ceil(kx - width[-1] / 2), np.ceil(ky - width[-2] / 2)) x1, y1 = (np.floor(kx + width[-1] / 2), np.floor(ky + width[-2] / 2)) for y in range(y0, y1 + 1): wy = kernel((y - ky) / (width[-2] / 2), param[-2]) for x in range(x0, x1 + 1): w = wy * kernel((x - kx) / (width[-1] / 2), param[-1]) for b in range(batch_size): output[b, i] += w * input[b, y % ny, x % nx] return output @nb.jit(nopython=True) # pragma: no cover def _interpolate3(output, input, coord, width, param): batch_size, nz, ny, nx = input.shape npts = coord.shape[0] for i in range(npts): kx, ky, kz = coord[i, -1], coord[i, -2], coord[i, -3] x0, y0, z0 = (np.ceil(kx - width[-1] / 2), np.ceil(ky - width[-2] / 2), np.ceil(kz - width[-3] / 2)) x1, y1, z1 = (np.floor(kx + width[-1] / 2), np.floor(ky + width[-2] / 2), np.floor(kz + width[-3] / 2)) for z in range(z0, z1 + 1): wz = kernel((z - kz) / (width[-3] / 2), param[-3]) for y in range(y0, y1 + 1): wy = wz * kernel((y - ky) / (width[-2] / 2), param[-2]) for x in range(x0, x1 + 1): w = wy * kernel((x - kx) / (width[-1] / 2), param[-1]) for b in range(batch_size): output[b, i] += w * input[ b, z % nz, y % ny, x % nx] return output return _interpolate1, _interpolate2, _interpolate3 def _get_gridding(kernel): if kernel == 'spline': kernel = _spline_kernel elif kernel == 'kaiser_bessel': kernel = _kaiser_bessel_kernel @nb.jit(nopython=True) # pragma: no cover def _gridding1(output, input, coord, width, param): batch_size, nx = output.shape npts = coord.shape[0] for i in range(npts): kx = coord[i, -1] x0 = np.ceil(kx - width[-1] / 2) x1 = np.floor(kx + width[-1] / 2) for x in range(x0, x1 + 1): w = kernel((x - kx) / (width[-1] / 2), param[-1]) for b in range(batch_size): output[b, x % nx] += w * input[b, i] return output @nb.jit(nopython=True) # pragma: no cover def _gridding2(output, input, coord, width, param): batch_size, ny, nx = output.shape npts = coord.shape[0] for i in range(npts): kx, ky = coord[i, -1], coord[i, -2] x0, y0 = (np.ceil(kx - width[-1] / 2), np.ceil(ky - width[-2] / 2)) x1, y1 = (np.floor(kx + width[-1] / 2), np.floor(ky + width[-2] / 2)) for y in range(y0, y1 + 1): wy = kernel((y - ky) / (width[-2] / 2), param[-2]) for x in range(x0, x1 + 1): w = wy * kernel((x - kx) / (width[-1] / 2), param[-1]) for b in range(batch_size): output[b, y % ny, x % nx] += w * input[b, i] return output @nb.jit(nopython=True) # pragma: no cover def _gridding3(output, input, coord, width, param): batch_size, nz, ny, nx = output.shape npts = coord.shape[0] for i in range(npts): kx, ky, kz = coord[i, -1], coord[i, -2], coord[i, -3] x0, y0, z0 = (np.ceil(kx - width[-1] / 2), np.ceil(ky - width[-2] / 2), np.ceil(kz - width[-3] / 2)) x1, y1, z1 = (np.floor(kx + width[-1] / 2), np.floor(ky + width[-2] / 2), np.floor(kz + width[-3] / 2)) for z in range(z0, z1 + 1): wz = kernel((z - kz) / (width[-3] / 2), param[-3]) for y in range(y0, y1 + 1): wy = wz * kernel((y - ky) / (width[-2] / 2), param[-2]) for x in range(x0, x1 + 1): w = wy * kernel( (x - kx) / (width[-1] / 2), param[-1]) for b in range(batch_size): output[b, z % nz, y % ny, x % nx] += w * input[ b, i] return output return _gridding1, _gridding2, _gridding3 _interpolate = {} _gridding = {} for kernel in KERNELS: _interpolate[kernel] = _get_interpolate(kernel) _gridding[kernel] = _get_gridding(kernel) if config.cupy_enabled: # pragma: no cover import cupy as cp _spline_kernel_cuda = """ __device__ inline S kernel(S x, S order) { if (fabsf(x) > 1) return 0; if (order == 0) return 1; else if (order == 1) return 1 - fabsf(x); else if (fabsf(x) > 1 / 3) return 9 / 8 * (1 - fabsf(x)) * (1 - fabsf(x)); else return 3 / 4 * (1 - 3 * x * x); } """ _kaiser_bessel_kernel_cuda = """ __device__ inline S kernel(S x, S beta) { if (fabsf(x) > 1) return 0; x = beta * sqrt(1 - x * x); S t = x / 3.75; S t2 = t * t; S t4 = t2 * t2; S t6 = t4 * t2; S t8 = t6 * t2; if (x < 3.75) { S t10 = t8 * t2; S t12 = t10 * t2; return 1 + 3.5156229 * t2 + 3.0899424 * t4 + 1.2067492 * t6 + 0.2659732 * t8 + 0.0360768 * t10 + 0.0045813 * t12; } else { S t3 = t * t2; S t5 = t3 * t2; S t7 = t5 * t2; return exp(x) / sqrt(x) * ( 0.39894228 + 0.01328592 / t + 0.00225319 / t2 - 0.00157565 / t3 + 0.00916281 / t4 - 0.02057706 / t5 + 0.02635537 / t6 - 0.01647633 / t7 + 0.00392377 / t8); } } """ mod_cuda = """ __device__ inline int mod(int x, int n) { return (x % n + n) % n; } """ def _get_interpolate_cuda(kernel): if kernel == 'spline': kernel = _spline_kernel_cuda elif kernel == 'kaiser_bessel': kernel = _kaiser_bessel_kernel_cuda _interpolate1_cuda = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 1; const int batch_size = input.shape()[0]; const int nx = input.shape()[1]; const int coord_idx[] = {i, 0}; const S kx = coord[coord_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); for (int x = x0; x < x1 + 1; x++) { const S w = kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, mod(x, nx)}; const T v = (T) w * input[input_idx]; const int output_idx[] = {b, i}; output[output_idx] += v; } } """, name='interpolate1', preamble=kernel + mod_cuda, reduce_dims=False) _interpolate2_cuda = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 2; const int batch_size = input.shape()[0]; const int ny = input.shape()[1]; const int nx = input.shape()[2]; const int coordx_idx[] = {i, 1}; const S kx = coord[coordx_idx]; const int coordy_idx[] = {i, 0}; const S ky = coord[coordy_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int y0 = ceil(ky - width[ndim - 2] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); const int y1 = floor(ky + width[ndim - 2] / 2.0); for (int y = y0; y < y1 + 1; y++) { const S wy = kernel( ((S) y - ky) / (width[ndim - 2] / 2.0), param[ndim - 2]); for (int x = x0; x < x1 + 1; x++) { const S w = wy * kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, mod(y, ny), mod(x, nx)}; const T v = (T) w * input[input_idx]; const int output_idx[] = {b, i}; output[output_idx] += v; } } } """, name='interpolate2', preamble=kernel + mod_cuda, reduce_dims=False) _interpolate3_cuda = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 3; const int batch_size = input.shape()[0]; const int nz = input.shape()[1]; const int ny = input.shape()[2]; const int nx = input.shape()[3]; const int coordz_idx[] = {i, 0}; const S kz = coord[coordz_idx]; const int coordy_idx[] = {i, 1}; const S ky = coord[coordy_idx]; const int coordx_idx[] = {i, 2}; const S kx = coord[coordx_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int y0 = ceil(ky - width[ndim - 2] / 2.0); const int z0 = ceil(kz - width[ndim - 3] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); const int y1 = floor(ky + width[ndim - 2] / 2.0); const int z1 = floor(kz + width[ndim - 3] / 2.0); for (int z = z0; z < z1 + 1; z++) { const S wz = kernel( ((S) z - kz) / (width[ndim - 3] / 2.0), param[ndim - 3]); for (int y = y0; y < y1 + 1; y++) { const S wy = wz * kernel( ((S) y - ky) / (width[ndim - 2] / 2.0), param[ndim - 2]); for (int x = x0; x < x1 + 1; x++) { const S w = wy * kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, mod(z, nz), mod(y, ny), mod(x, nx)}; const T v = (T) w * input[input_idx]; const int output_idx[] = {b, i}; output[output_idx] += v; } } } } """, name='interpolate3', preamble=kernel + mod_cuda, reduce_dims=False) return _interpolate1_cuda, _interpolate2_cuda, _interpolate3_cuda def _get_gridding_cuda(kernel): if kernel == 'spline': kernel = _spline_kernel_cuda elif kernel == 'kaiser_bessel': kernel = _kaiser_bessel_kernel_cuda _gridding1_cuda = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 1; const int batch_size = output.shape()[0]; const int nx = output.shape()[1]; const int coord_idx[] = {i, 0}; const S kx = coord[coord_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); for (int x = x0; x < x1 + 1; x++) { const S w = kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, i}; const T v = (T) w * input[input_idx]; const int output_idx[] = {b, mod(x, nx)}; atomicAdd(&output[output_idx], v); } } """, name='gridding1', preamble=kernel + mod_cuda, reduce_dims=False) _gridding2_cuda = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 2; const int batch_size = output.shape()[0]; const int ny = output.shape()[1]; const int nx = output.shape()[2]; const int coordx_idx[] = {i, 1}; const S kx = coord[coordx_idx]; const int coordy_idx[] = {i, 0}; const S ky = coord[coordy_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int y0 = ceil(ky - width[ndim - 2] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); const int y1 = floor(ky + width[ndim - 2] / 2.0); for (int y = y0; y < y1 + 1; y++) { const S wy = kernel( ((S) y - ky) / (width[ndim - 2] / 2.0), param[ndim - 2]); for (int x = x0; x < x1 + 1; x++) { const S w = wy * kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, i}; const T v = (T) w * input[input_idx]; const int output_idx[] = {b, mod(y, ny), mod(x, nx)}; atomicAdd(&output[output_idx], v); } } } """, name='gridding2', preamble=kernel + mod_cuda, reduce_dims=False) _gridding3_cuda = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 3; const int batch_size = output.shape()[0]; const int nz = output.shape()[1]; const int ny = output.shape()[2]; const int nx = output.shape()[3]; const int coordz_idx[] = {i, 0}; const S kz = coord[coordz_idx]; const int coordy_idx[] = {i, 1}; const S ky = coord[coordy_idx]; const int coordx_idx[] = {i, 2}; const S kx = coord[coordx_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int y0 = ceil(ky - width[ndim - 2] / 2.0); const int z0 = ceil(kz - width[ndim - 3] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); const int y1 = floor(ky + width[ndim - 2] / 2.0); const int z1 = floor(kz + width[ndim - 3] / 2.0); for (int z = z0; z < z1 + 1; z++) { const S wz = kernel( ((S) z - kz) / (width[ndim - 3] / 2.0), param[ndim - 3]); for (int y = y0; y < y1 + 1; y++) { const S wy = wz * kernel( ((S) y - ky) / (width[ndim - 2] / 2.0), param[ndim - 2]); for (int x = x0; x < x1 + 1; x++) { const S w = wy * kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, i}; const T v = (T) w * input[input_idx]; const int output_idx[] = { b, mod(z, nz), mod(y, ny), mod(x, nx)}; atomicAdd(&output[output_idx], v); } } } } """, name='gridding3', preamble=kernel + mod_cuda, reduce_dims=False) return _gridding1_cuda, _gridding2_cuda, _gridding3_cuda def _get_gridding_cuda_complex(kernel): if kernel == 'spline': kernel = _spline_kernel_cuda elif kernel == 'kaiser_bessel': kernel = _kaiser_bessel_kernel_cuda _gridding1_cuda_complex = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 1; const int batch_size = output.shape()[0]; const int nx = output.shape()[1]; const int coord_idx[] = {i, 0}; const S kx = coord[coord_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); for (int x = x0; x < x1 + 1; x++) { const S w = kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, i}; const T v = (T) w * input[input_idx]; const int output_idx[] = {b, mod(x, nx)}; atomicAdd( reinterpret_cast<T::value_type*>( &(output[output_idx])), v.real()); atomicAdd( reinterpret_cast<T::value_type*>( &(output[output_idx])) + 1, v.imag()); } } """, name='gridding1_complex', preamble=kernel + mod_cuda, reduce_dims=False) _gridding2_cuda_complex = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 2; const int batch_size = output.shape()[0]; const int ny = output.shape()[1]; const int nx = output.shape()[2]; const int coordx_idx[] = {i, 1}; const S kx = coord[coordx_idx]; const int coordy_idx[] = {i, 0}; const S ky = coord[coordy_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int y0 = ceil(ky - width[ndim - 2] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); const int y1 = floor(ky + width[ndim - 2] / 2.0); for (int y = y0; y < y1 + 1; y++) { const S wy = kernel( ((S) y - ky) / (width[ndim - 2] / 2.0), param[ndim - 2]); for (int x = x0; x < x1 + 1; x++) { const S w = wy * kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, i}; const T v = (T) w * input[input_idx]; const int output_idx[] = {b, mod(y, ny), mod(x, nx)}; atomicAdd(reinterpret_cast<T::value_type*>( &(output[output_idx])), v.real()); atomicAdd(reinterpret_cast<T::value_type*>( &(output[output_idx])) + 1, v.imag()); } } } """, name='gridding2_complex', preamble=kernel + mod_cuda, reduce_dims=False) _gridding3_cuda_complex = cp.ElementwiseKernel( 'raw T input, raw S coord, raw S width, raw S param', 'raw T output', """ const int ndim = 3; const int batch_size = output.shape()[0]; const int nz = output.shape()[1]; const int ny = output.shape()[2]; const int nx = output.shape()[3]; const int coordz_idx[] = {i, 0}; const S kz = coord[coordz_idx]; const int coordy_idx[] = {i, 1}; const S ky = coord[coordy_idx]; const int coordx_idx[] = {i, 2}; const S kx = coord[coordx_idx]; const int x0 = ceil(kx - width[ndim - 1] / 2.0); const int y0 = ceil(ky - width[ndim - 2] / 2.0); const int z0 = ceil(kz - width[ndim - 3] / 2.0); const int x1 = floor(kx + width[ndim - 1] / 2.0); const int y1 = floor(ky + width[ndim - 2] / 2.0); const int z1 = floor(kz + width[ndim - 3] / 2.0); for (int z = z0; z < z1 + 1; z++) { const S wz = kernel( ((S) z - kz) / (width[ndim - 3] / 2.0), param[ndim - 3]); for (int y = y0; y < y1 + 1; y++) { const S wy = wz * kernel( ((S) y - ky) / (width[ndim - 2] / 2.0), param[ndim - 2]); for (int x = x0; x < x1 + 1; x++) { const S w = wy * kernel( ((S) x - kx) / (width[ndim - 1] / 2.0), param[ndim - 1]); for (int b = 0; b < batch_size; b++) { const int input_idx[] = {b, i}; const T v = (T) w * input[input_idx]; const int output_idx[] = { b, mod(z, nz), mod(y, ny), mod(x, nx)}; atomicAdd(reinterpret_cast<T::value_type*>( &(output[output_idx])), v.real()); atomicAdd(reinterpret_cast<T::value_type*>( &(output[output_idx])) + 1, v.imag()); } } } } """, name='gridding3_complex', preamble=kernel + mod_cuda, reduce_dims=False) return _gridding1_cuda_complex, _gridding2_cuda_complex, \ _gridding3_cuda_complex _interpolate_cuda = {} _gridding_cuda = {} _gridding_cuda_complex = {} for kernel in KERNELS: _interpolate_cuda[kernel] = _get_interpolate_cuda(kernel) _gridding_cuda[kernel] = _get_gridding_cuda(kernel) _gridding_cuda_complex[kernel] = _get_gridding_cuda_complex(kernel)

37.13357

87

0.467006

4,063

31,415

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0.109375

0

null

0

1

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null

0

6

7149511b8120586fe9092e9dabbf04e2fd82934c

236

py

Python

mason/engines/metastore/models/credentials/__init__.py

kyprifog/mason

bf45672124ef841bc16216c293034f4ccc506621

[ "Apache-2.0" ]

4

2021-04-12T17:49:34.000Z

2022-01-23T19:54:29.000Z

mason/engines/metastore/models/credentials/__init__.py

kyprifog/mason

bf45672124ef841bc16216c293034f4ccc506621

[ "Apache-2.0" ]

24

2021-04-30T18:40:25.000Z

2021-05-12T20:52:06.000Z

mason/engines/metastore/models/credentials/__init__.py

kyprifog/mason

bf45672124ef841bc16216c293034f4ccc506621

[ "Apache-2.0" ]

3

2021-04-12T19:40:43.000Z

2021-09-07T21:56:36.000Z

class MetastoreCredentials: def __init__(self): pass def to_dict(self): return {} class InvalidCredentials: def __init__(self, reason: str): self.reason = reason def to_dict(self): return {}

16.857143

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718457e56167d7666921e966a93c04a280206e61

5,283

py

Python

tests/conversation_manager/test_create_statement.py

Jack2313/WeChatterBot

377899e8cab4ca5eca9b0136207e2afb97d9acb2

[ "BSD-3-Clause" ]

1

2020-04-03T02:54:18.000Z

tests/conversation_manager/test_create_statement.py

Jack2313/WeChatterBot

377899e8cab4ca5eca9b0136207e2afb97d9acb2

[ "BSD-3-Clause" ]

7

2020-04-11T13:22:50.000Z

2020-05-14T00:19:37.000Z

tests/conversation_manager/test_create_statement.py

Jack2313/WeChatterBot

377899e8cab4ca5eca9b0136207e2afb97d9acb2

[ "BSD-3-Clause" ]

3

2020-04-11T12:09:56.000Z

2020-12-16T13:26:20.000Z

from unittest import TestCase from app.view.conversation_manager import generate_token import json from app import create_app class CreateStatementTestCase(TestCase): """ Unit tests for the Create Statement method. LJF: all tests clear 2020-5-12 """ def setUp(self): self.app = create_app().test_client() self.myheaders = {'Content-Type': 'application/json'} self.token = generate_token(b'buaa', 3600) # super().setUp() def test_no_attribute(self): data = {} r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) self.assertEqual(result['code'], 10000001) self.assertEqual(r.status_code, 400) def test_no_text(self): data = { 'response': '对话回复', 'username': 'wechatterbot', 'token': self.token } r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) self.assertEqual(result['code'], 10000001) self.assertEqual(r.status_code, 400) def test_no_response(self): data = { 'text': '对话内容', 'username': 'wechatterbot', 'token': self.token } r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) self.assertEqual(result['code'], 10000001) self.assertEqual(r.status_code, 400) def test_no_username(self): data = { 'response': '对话回复', 'token': self.token, 'text': '对话内容' } r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) self.assertEqual(result['code'], 10000001) self.assertEqual(r.status_code, 400) def test_wrong_json(self): data = { 'response': '对话回复', 'text': '对话内容', 'username': 'wechatterbot', 'token': self.token } r = self.app.post( 'admin/create_statement', data=data, headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) self.assertEqual(result['code'], 10000041) self.assertEqual(r.status_code, 400) def test_token_check_fail(self): data = { 'response': '对话回复', 'text': '对话内容', 'username': 'wechatterwhat', 'token': self.token } r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) self.assertEqual(result['code'], 10000044) self.assertEqual(r.status_code, 401) def test_empty_text(self): data = { 'response': '对话回复', 'text': '', 'username': 'wechatterbot', 'token': self.token } r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) self.assertEqual(result['code'], 10000045) self.assertEqual(r.status_code, 400) def test_empty_response(self): data = { 'response': '', 'text': '对话内容', 'username': 'wechatterbot', 'token': self.token } r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) self.assertEqual(result['code'], 10000045) self.assertEqual(r.status_code, 400) def test_successful_creation(self): data = { 'response': '对话回复', 'text': '对话内容', 'username': 'wechatterbot', 'token': self.token } r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) statement = result['statement'] self.assertEqual(r.status_code, 200) self.assertEqual(result['code'], 1) self.assertEqual(statement['text'], "对话内容") def test_successful_with_tags(self): data = { 'response': '对话回复', 'text': '对话内容', 'username': 'wechatterbot', 'token': self.token, 'tags': 'test' } r = self.app.post( 'admin/create_statement', data=json.dumps(data), headers=self.myheaders ) result = json.loads(r.data.decode('utf-8')) statement = result['statement'] self.assertEqual(r.status_code, 200) self.assertEqual(result['code'], 1) self.assertEqual(statement['text'], "对话内容")

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null

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718c22405923f2b023b6cc58157a258263e3a382

42

py

Python

scraper/middlewares/selenium/__init__.py

otrenav/cfe-rates-scraping

9e3c7be2cc166e69b6db7fda3f6db841ff9579ea

[ "MIT" ]

null

scraper/middlewares/selenium/__init__.py

otrenav/cfe-rates-scraping

9e3c7be2cc166e69b6db7fda3f6db841ff9579ea

[ "MIT" ]

null

scraper/middlewares/selenium/__init__.py

otrenav/cfe-rates-scraping

9e3c7be2cc166e69b6db7fda3f6db841ff9579ea

[ "MIT" ]

null

from .selenium import SeleniumMiddleware

14

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0.857143

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null

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null

0

1

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1

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1

0

6

719f3b6dff37f4fbd1ccba3133a1424330041a82

195

py

Python

_test_projects/basics/file_blueprint.py

oren0e/cob

f2a5d74a15f5262d7980e4cf1f1a20af29194ffb

[ "BSD-3-Clause" ]

2

2019-04-07T20:19:55.000Z

2021-05-27T10:23:31.000Z

_test_projects/basics/file_blueprint.py

oren0e/cob

f2a5d74a15f5262d7980e4cf1f1a20af29194ffb

[ "BSD-3-Clause" ]

126

2016-08-10T19:59:45.000Z

2021-11-26T06:58:16.000Z

_test_projects/basics/file_blueprint.py

oren0e/cob

f2a5d74a15f5262d7980e4cf1f1a20af29194ffb

[ "BSD-3-Clause" ]

6

2017-11-16T12:05:47.000Z

2021-11-24T09:21:17.000Z

# cob: type=blueprint mountpoint=/blueprints/file from flask import Blueprint blueprint = Blueprint('file_blueprint', __name__) @blueprint.route('/test') def route(): return 'this is file'

21.666667

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null

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6

4619f63f39c7bb94b175da3d7d83adafc5147f72

39

py

Python

test/tests/__init__.py

HansBug/treevalue

6f2f5b2de00b04a06201a87ccee678ade9deff57

[ "Apache-2.0" ]

null

test/tests/__init__.py

HansBug/treevalue

6f2f5b2de00b04a06201a87ccee678ade9deff57

[ "Apache-2.0" ]

1

2021-07-24T13:30:14.000Z

test/tests/__init__.py

HansBug/treevalue

6f2f5b2de00b04a06201a87ccee678ade9deff57

[ "Apache-2.0" ]

null

from .utils import float_eq, eq_extend

19.5

38

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4.285714

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39

0.882353

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true

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1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

1cb5c302ae15b077421f72e8eda900bedb621fca

29

py

Python

agents/__init__.py

petros94/monte-carlo-gridworld

787a6fb42476e55d7411731fa17a6603333f65a9

[ "MIT" ]

null

agents/__init__.py

petros94/monte-carlo-gridworld

787a6fb42476e55d7411731fa17a6603333f65a9

[ "MIT" ]

null

agents/__init__.py

petros94/monte-carlo-gridworld

787a6fb42476e55d7411731fa17a6603333f65a9

[ "MIT" ]

null

from agents.mc_agent import *

29

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5

29

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null

0

1

0

1

0

1

0

6

1cbc98a5f22158d8e060999c58f279f0eeeb8f9e

19

py

Python

htmotor/__init__.py

5elenay/htmotor

6e39046e979f51670adb85a7f6e736c2d9f7b97f

[ "MIT" ]

5

2021-06-15T17:33:13.000Z

2021-08-14T21:43:24.000Z

htmotor/__init__.py

5elenay/htmotor

6e39046e979f51670adb85a7f6e736c2d9f7b97f

[ "MIT" ]

null

htmotor/__init__.py

5elenay/htmotor

6e39046e979f51670adb85a7f6e736c2d9f7b97f

[ "MIT" ]

1

2021-09-20T21:13:01.000Z

from .html import *

19

0.736842

3

19

4.666667

1

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0.157895

19

1

19

0.875

0

1

0

true

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1

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null

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null

0

1

0

1

0

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0

6

1cdaa50ebd69b10aa636d9cec39829c0c21d5ac2

3,401

py

Python

mainService/mainService/apps/core_sample/migrations/0008_carbon_layer_disruption_layer_oil_layer_rock_layer.py

Godis715/Core-Sample-Analysis

892b3d322e9ce86dab0da9754b902b504b2e0d8b

[ "Apache-2.0" ]

2

2019-09-18T10:59:21.000Z

2019-10-02T16:50:05.000Z

mainService/mainService/apps/core_sample/migrations/0008_carbon_layer_disruption_layer_oil_layer_rock_layer.py

Godis715/Core-Sample-Analysis

892b3d322e9ce86dab0da9754b902b504b2e0d8b

[ "Apache-2.0" ]

78

2019-09-20T16:56:18.000Z

2022-03-12T00:04:37.000Z

mainService/mainService/apps/core_sample/migrations/0008_carbon_layer_disruption_layer_oil_layer_rock_layer.py

Godis715/Core-Sample-Analysis

892b3d322e9ce86dab0da9754b902b504b2e0d8b

[ "Apache-2.0" ]

1

2019-10-03T20:49:34.000Z

# Generated by Django 2.2.6 on 2019-10-22 18:36 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('core_sample', '0007_remove_markup_version'), ] operations = [ migrations.CreateModel( name='Rock_layer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('top', models.FloatField(verbose_name='Вверх')), ('bottom', models.FloatField(verbose_name='Низ')), ('class_label', models.IntegerField(choices=[(1, 'siltstone'), (2, 'sandstone'), (3, 'clay')], verbose_name='Класс')), ('markup', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core_sample.Markup', verbose_name='Разметка')), ], options={ 'verbose_name': 'Слой породы', 'verbose_name_plural': 'Слои породы', }, ), migrations.CreateModel( name='Oil_layer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('top', models.FloatField(verbose_name='Вверх')), ('bottom', models.FloatField(verbose_name='Низ')), ('class_label', models.IntegerField(choices=[(1, 'notDefined'), (2, 'low'), (3, 'high')], verbose_name='Класс')), ('markup', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core_sample.Markup', verbose_name='Разметка')), ], options={ 'verbose_name': 'Слой нефтенасыщенности', 'verbose_name_plural': 'Слои нефтенасыщенности', }, ), migrations.CreateModel( name='Disruption_layer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('top', models.FloatField(verbose_name='Вверх')), ('bottom', models.FloatField(verbose_name='Низ')), ('class_label', models.IntegerField(choices=[(1, 'none'), (2, 'low'), (3, 'high')], verbose_name='Класс')), ('markup', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core_sample.Markup', verbose_name='Разметка')), ], options={ 'verbose_name': 'Слой разрушенности', 'verbose_name_plural': 'Слои разрушенности', }, ), migrations.CreateModel( name='Carbon_layer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('top', models.FloatField(verbose_name='Вверх')), ('bottom', models.FloatField(verbose_name='Низ')), ('class_label', models.IntegerField(choices=[(1, 'notDefined'), (2, 'low'), (3, 'high')], verbose_name='Класс')), ('markup', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core_sample.Markup', verbose_name='Разметка')), ], options={ 'verbose_name': 'Слой карбонатности', 'verbose_name_plural': 'Слои карбонатности', }, ), ]

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0

null

0

6

1cf44b5b5dd37838b7e092bb842aab84879b162f

3,267

py

Python

util/svg_dict.py

widyaageng/Sudoku_auto

94b612fd3266cdd42d20973e98a89f90d664d57c

[ "BSD-2-Clause" ]

null

util/svg_dict.py

widyaageng/Sudoku_auto

94b612fd3266cdd42d20973e98a89f90d664d57c

[ "BSD-2-Clause" ]

null

util/svg_dict.py

widyaageng/Sudoku_auto

94b612fd3266cdd42d20973e98a89f90d664d57c

[ "BSD-2-Clause" ]

null

# Map for SVG graphics from HTML num_class = ['M8.954 30V3.545h-.267c-.738.41-6.706 4.655-7.71 5.311V5.883c.635-.41 6.767-4.758 7.977-5.476h2.789V30h-2.79z', 'M.12 9.57C.16 4.462 4.057.791 9.41.791c5.209 0 9.187 3.568 9.187 8.224 0 3.076-1.415 5.475-6.275 10.664l-7.998 8.53v.247h15.012V31H.284v-1.969L10.62 17.854c4.122-4.43 5.168-6.193 5.168-8.736 0-3.302-2.81-5.865-6.44-5.865-3.814 0-6.46 2.584-6.5 6.316v.02H.12v-.02z', 'M6.698 16.932v-2.42h3.466c3.814 0 6.46-2.338 6.46-5.722 0-3.22-2.646-5.537-6.317-5.537-3.67 0-6.255 2.174-6.542 5.537H1.038C1.366 3.95 5.037.792 10.41.792c5.045 0 9.064 3.404 9.064 7.67 0 3.568-2.05 6.173-5.496 6.932v.266c4.225.472 6.85 3.281 6.85 7.342 0 4.86-4.491 8.613-10.295 8.613-5.722 0-9.926-3.404-10.11-8.182H3.13c.246 3.322 3.322 5.721 7.382 5.721 4.286 0 7.424-2.645 7.424-6.214 0-3.711-2.912-6.008-7.65-6.008H6.699z', 'M15.855 30v-6.686H.987v-2.563C3.633 16.281 7.283 10.6 14.563.366h4.02v20.426h4.43v2.522h-4.43V30h-2.728zM3.92 20.628v.184h11.935V3.052h-.184C10.03 10.744 7.099 15.338 3.92 20.629z', 'M10.553 30.615c-5.373 0-9.474-3.445-9.782-8.264H3.52c.308 3.322 3.322 5.783 7.055 5.783 4.327 0 7.424-3.097 7.424-7.445 0-4.347-3.097-7.444-7.363-7.444-2.912 0-5.496 1.415-6.747 3.692H1.222l1.6-16.53h16.14V2.93H5.037l-.985 10.787h.267c1.415-1.846 3.876-2.912 6.768-2.912 5.68 0 9.72 4.08 9.72 9.802 0 5.866-4.245 10.008-10.254 10.008z', 'M10.964 31.595c-4 0-7.158-1.99-9.003-5.64C.648 23.638-.01 20.582-.01 16.83-.008 6.76 4.135.792 11.17.792c4.901 0 8.613 2.953 9.454 7.567h-2.871c-.739-3.076-3.323-5.045-6.624-5.045-5.312 0-8.347 4.963-8.409 13.74h.246c1.292-3.322 4.553-5.454 8.43-5.454 5.618 0 9.76 4.183 9.76 9.843 0 5.886-4.285 10.152-10.191 10.152zm-.041-2.482c4.204 0 7.403-3.281 7.403-7.567 0-4.368-3.097-7.506-7.383-7.506-4.225 0-7.485 3.158-7.485 7.3 0 4.41 3.24 7.773 7.465 7.773z', 'M3.017 30L16.696 3.155V2.93H.29V.407h19.277v2.625L6.01 30z', 'M10.533 31.615c-6.193 0-10.48-3.527-10.48-8.593 0-3.834 2.584-6.87 6.46-7.567v-.246c-3.22-.759-5.311-3.343-5.311-6.583 0-4.573 3.876-7.834 9.33-7.834 5.456 0 9.332 3.24 9.332 7.834 0 3.22-2.071 5.804-5.291 6.583v.246c3.855.697 6.46 3.732 6.46 7.567 0 5.086-4.286 8.593-10.5 8.593zm0-2.42c4.532 0 7.67-2.604 7.67-6.357 0-3.671-3.117-6.173-7.67-6.173-4.532 0-7.65 2.523-7.65 6.173 0 3.753 3.118 6.357 7.65 6.357zm0-14.95c3.896 0 6.562-2.174 6.562-5.393 0-3.343-2.666-5.64-6.562-5.64-3.897 0-6.563 2.297-6.563 5.64 0 3.199 2.666 5.393 6.563 5.393z', 'M10.897 31.595c-4.983 0-8.613-2.974-9.454-7.547h2.871c.718 3.015 3.22 5.045 6.624 5.045 5.23 0 8.203-4.779 8.408-13.064.02-.205-.102-.471-.123-.676H19.1c-1.271 3.26-4.552 5.434-8.428 5.434-5.66 0-9.762-4.163-9.762-9.803C.91 5.1 5.175.792 11.102.792c4 0 7.157 2.01 9.003 5.68 1.313 2.298 1.969 5.333 1.969 9.106 0 10.028-4.102 16.017-11.177 16.017zm.226-13.248c4.245 0 7.485-3.2 7.485-7.28 0-4.39-3.22-7.794-7.465-7.794-4.224 0-7.403 3.302-7.403 7.63 0 4.285 3.035 7.444 7.383 7.444z'] idx_list = [i + 1 for i in range(9)] num_to_svg = dict(zip(idx_list, num_class)) svg_to_num = dict(zip(num_class, idx_list)) def get_num_to_svg(num): return num_to_svg[num] def get_svg_to_num(svg): return svg_to_num[svg]

155.571429

560

0.657484

951

3,267

2.237645

0.384858

0.010338

0.005639

0.006579

0.028195

0.012218

0

0.639539

0.123661

3,267

21

561

155.571429

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0.5625

0.880408

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0

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0.125

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null

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1

0

1

0

1

0

null

0

1

0

6

1cf6c94250898b4e09aa80a4bc438cfbc8755fd5

1,205

py

Python

CCV/scripts/img_resize.py

YCJGG/Partial-video-retrieval

65eec9c87cd18e70103c42918c49e0552ec6cc21

[ "MIT" ]

2

2018-09-08T11:54:10.000Z

2018-10-09T13:48:09.000Z

CCV/scripts/img_resize.py

YCJGG/Partial-video-retrieval

65eec9c87cd18e70103c42918c49e0552ec6cc21

[ "MIT" ]

null

CCV/scripts/img_resize.py

YCJGG/Partial-video-retrieval

65eec9c87cd18e70103c42918c49e0552ec6cc21

[ "MIT" ]

null

from scipy import misc import multiprocessing as mp import glob import os frame_root = '../test_frames' folder_list = glob.glob(frame_root+'/*') def fun(folder): print folder img_list = glob.glob(folder+'/*.jpg') for img_name in img_list: img = misc.imread(img_name) if img.shape[1]>img.shape[0]: if img.shape[1] == 112: continue scale = float(112/float(img.shape[0])) img = misc.imresize(img,(int(img.shape[0] * scale + 1), 112)) else: if img.shape[0] == 112: continue scale = float(112/float(img.shape[1])) img = misc.imresize(img,(112, int(img.shape[1] * scale + 1))) misc.imsave(img_name, img) """ for folder in folder_list: print folder img_list = glob.glob(folder+'/*.jpg') for img_name in img_list: img = misc.imread(img_name) if img.shape[1]>img.shape[0]: if img.shape[1] == 112: continue scale = float(112/float(img.shape[0])) img = misc.imresize(img,(int(img.shape[0] * scale + 1), 112)) else: if img.shape[0] == 112: continue scale = float(112/float(img.shape[1])) img = misc.imresize(img,(112, int(img.shape[1] * scale + 1))) misc.imsave(img_name, img) """ pool = mp.Pool(processes=15) pool.map(fun, folder_list)

26.195652

64

0.652282

200

1,205

3.85

0.2

0.166234

0.093506

0.057143

0.763636

0

0.058233

0.173444

1,205

45

65

26.777778

0.714859

0

0.083333

0

0.031519

0

null

0

0.166667

null

0.041667

0

null

0

1

0

null

0

1

0

6

1cf8123070639be0b35afd4e181333d79a9527a4

456

py

Python

Script/deprecated/FishEditor/AssetImporter.py

yushroom/FishEngine_-Experiment

81e4c06f20f6b94dc561b358f8a11a092678aeeb

[ "MIT" ]

1

2018-12-20T02:38:44.000Z

Script/deprecated/FishEditor/AssetImporter.py

yushroom/FishEngine_-Experiment

81e4c06f20f6b94dc561b358f8a11a092678aeeb

[ "MIT" ]

null

Script/deprecated/FishEditor/AssetImporter.py

yushroom/FishEngine_-Experiment

81e4c06f20f6b94dc561b358f8a11a092678aeeb

[ "MIT" ]

1

2018-10-25T19:40:22.000Z

class AssetImporter: def __init__(self): pass @staticmethod def Create(path:str)->'AssetImporter': raise NotImplementedError @property def assetPath(self)->str: raise NotImplementedError def SaveAndReimport(self): from . import AssetDataBase AssetDataBase.ImportAsset(self.assetPath) @staticmethod def GetAtPath(path:str)->'AssetImporter': raise NotImplementedError

24

49

0.664474

39

456

7.666667

0.512821

0.240803

0.133779

0.167224

0.294314

0

0.256579

456

19

50

24

0.882006

0

0.333333

0

0.056893

0

1

0.333333

false

0.066667

0.4

0

0.8

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

1c27922d6b049cac7c323d3fd180bf4d862bd474

307

py

Python

skfem/element/element_tri/__init__.py

carlosal1015/scikit-fem

1e73a417e9b43fe0a36e29807792c41fa289b77d

[ "BSD-3-Clause" ]

null

skfem/element/element_tri/__init__.py

carlosal1015/scikit-fem

1e73a417e9b43fe0a36e29807792c41fa289b77d

[ "BSD-3-Clause" ]

null

skfem/element/element_tri/__init__.py

carlosal1015/scikit-fem

1e73a417e9b43fe0a36e29807792c41fa289b77d

[ "BSD-3-Clause" ]

null

from .element_tri_p1 import ElementTriP1 from .element_tri_p2 import ElementTriP2 from .element_tri_dg import ElementTriDG from .element_tri_p0 import ElementTriP0 from .element_tri_rt0 import ElementTriRT0 from .element_tri_morley import ElementTriMorley from .element_tri_argyris import ElementTriArgyris

38.375

50

0.885993

42

307

6.142857

0.428571

0.29845

0.379845

0

0.028674

0.091205

307

7

51

43.857143

0.896057

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

1c28c1f144372b74db75af034c1a5c109cdc8cea

92,810

py

Python

pennylane/ops/qubit/parametric_ops.py

MoritzWillmann/pennylane

2b07d22cfcc6406ba28e5c647062340b240a4ee5

[ "Apache-2.0" ]

null

pennylane/ops/qubit/parametric_ops.py

MoritzWillmann/pennylane

2b07d22cfcc6406ba28e5c647062340b240a4ee5

[ "Apache-2.0" ]

null

pennylane/ops/qubit/parametric_ops.py

MoritzWillmann/pennylane

2b07d22cfcc6406ba28e5c647062340b240a4ee5

[ "Apache-2.0" ]

null

# Copyright 2018-2021 Xanadu Quantum Technologies Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=too-many-arguments """ This submodule contains the discrete-variable quantum operations that are the core parameterized gates. """ # pylint:disable=abstract-method,arguments-differ,protected-access,invalid-overridden-method import functools import math from operator import matmul import numpy as np import pennylane as qml from pennylane.operation import AnyWires, Operation from pennylane.ops.qubit.non_parametric_ops import PauliX, PauliY, PauliZ, Hadamard from pennylane.operation import expand_matrix from pennylane.utils import pauli_eigs from pennylane.wires import Wires INV_SQRT2 = 1 / math.sqrt(2) stack_last = functools.partial(qml.math.stack, axis=-1) class RX(Operation): r""" The single qubit X rotation .. math:: R_x(\phi) = e^{-i\phi\sigma_x/2} = \begin{bmatrix} \cos(\phi/2) & -i\sin(\phi/2) \\ -i\sin(\phi/2) & \cos(\phi/2) \end{bmatrix}. **Details:** * Number of wires: 1 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(R_x(\phi)) = \frac{1}{2}\left[f(R_x(\phi+\pi/2)) - f(R_x(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`R_x(\phi)`. Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int] or int): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 1 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" basis = "X" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return -0.5 * PauliX(wires=self.wires) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(theta): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.RX.matrix` Args: theta (tensor_like or float): rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.RX.compute_matrix(torch.tensor(0.5)) tensor([[0.9689+0.0000j, 0.0000-0.2474j], [0.0000-0.2474j, 0.9689+0.0000j]]) """ c = qml.math.cos(theta / 2) s = qml.math.sin(theta / 2) if qml.math.get_interface(theta) == "tensorflow": c = qml.math.cast_like(c, 1j) s = qml.math.cast_like(s, 1j) # The following avoids casting an imaginary quantity to reals when backpropagating c = (1 + 0j) * c js = -1j * s return qml.math.stack([stack_last([c, js]), stack_last([js, c])], axis=-2) def adjoint(self): return RX(-self.data[0], wires=self.wires) def pow(self, z): return [RX(self.data[0] * z, wires=self.wires)] def _controlled(self, wire): CRX(*self.parameters, wires=wire + self.wires) def single_qubit_rot_angles(self): # RX(\theta) = RZ(-\pi/2) RY(\theta) RZ(\pi/2) pi_half = qml.math.ones_like(self.data[0]) * (np.pi / 2) return [pi_half, self.data[0], -pi_half] class RY(Operation): r""" The single qubit Y rotation .. math:: R_y(\phi) = e^{-i\phi\sigma_y/2} = \begin{bmatrix} \cos(\phi/2) & -\sin(\phi/2) \\ \sin(\phi/2) & \cos(\phi/2) \end{bmatrix}. **Details:** * Number of wires: 1 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(R_y(\phi)) = \frac{1}{2}\left[f(R_y(\phi+\pi/2)) - f(R_y(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`R_y(\phi)`. Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int] or int): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 1 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" basis = "Y" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return -0.5 * PauliY(wires=self.wires) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(theta): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.RY.matrix` Args: theta (tensor_like or float): rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.RY.compute_matrix(torch.tensor(0.5)) tensor([[ 0.9689, -0.2474], [ 0.2474, 0.9689]]) """ c = qml.math.cos(theta / 2) s = qml.math.sin(theta / 2) if qml.math.get_interface(theta) == "tensorflow": c = qml.math.cast_like(c, 1j) s = qml.math.cast_like(s, 1j) # The following avoids casting an imaginary quantity to reals when backpropagating c = (1 + 0j) * c s = (1 + 0j) * s return qml.math.stack([stack_last([c, -s]), stack_last([s, c])], axis=-2) def adjoint(self): return RY(-self.data[0], wires=self.wires) def pow(self, z): return [RY(self.data[0] * z, wires=self.wires)] def _controlled(self, wire): CRY(*self.parameters, wires=wire + self.wires) def single_qubit_rot_angles(self): # RY(\theta) = RZ(0) RY(\theta) RZ(0) return [0.0, self.data[0], 0.0] class RZ(Operation): r""" The single qubit Z rotation .. math:: R_z(\phi) = e^{-i\phi\sigma_z/2} = \begin{bmatrix} e^{-i\phi/2} & 0 \\ 0 & e^{i\phi/2} \end{bmatrix}. **Details:** * Number of wires: 1 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(R_z(\phi)) = \frac{1}{2}\left[f(R_z(\phi+\pi/2)) - f(R_z(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`R_z(\phi)`. Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int] or int): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 1 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" basis = "Z" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return -0.5 * PauliZ(wires=self.wires) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(theta): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.RZ.matrix` Args: theta (tensor_like or float): rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.RZ.compute_matrix(torch.tensor(0.5)) tensor([[0.9689-0.2474j, 0.0000+0.0000j], [0.0000+0.0000j, 0.9689+0.2474j]]) """ if qml.math.get_interface(theta) == "tensorflow": theta = qml.math.cast_like(theta, 1j) p = qml.math.exp(-0.5j * theta) z = qml.math.zeros_like(p) return qml.math.stack([stack_last([p, z]), stack_last([z, qml.math.conj(p)])], axis=-2) @staticmethod def compute_eigvals(theta): # pylint: disable=arguments-differ r"""Eigenvalues of the operator in the computational basis (static method). If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. seealso:: :meth:`~.RZ.eigvals` Args: theta (tensor_like or float): rotation angle Returns: tensor_like: eigenvalues **Example** >>> qml.RZ.compute_eigvals(torch.tensor(0.5)) tensor([0.9689-0.2474j, 0.9689+0.2474j]) """ if qml.math.get_interface(theta) == "tensorflow": theta = qml.math.cast_like(theta, 1j) p = qml.math.exp(-0.5j * theta) return stack_last([p, qml.math.conj(p)]) def adjoint(self): return RZ(-self.data[0], wires=self.wires) def pow(self, z): return [RZ(self.data[0] * z, wires=self.wires)] def _controlled(self, wire): CRZ(*self.parameters, wires=wire + self.wires) def single_qubit_rot_angles(self): # RZ(\theta) = RZ(\theta) RY(0) RZ(0) return [self.data[0], 0.0, 0.0] class PhaseShift(Operation): r""" Arbitrary single qubit local phase shift .. math:: R_\phi(\phi) = e^{i\phi/2}R_z(\phi) = \begin{bmatrix} 1 & 0 \\ 0 & e^{i\phi} \end{bmatrix}. **Details:** * Number of wires: 1 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(R_\phi(\phi)) = \frac{1}{2}\left[f(R_\phi(\phi+\pi/2)) - f(R_\phi(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`R_{\phi}(\phi)`. Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int] or int): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 1 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" basis = "Z" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return qml.Projector(np.array([1]), wires=self.wires) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) def label(self, decimals=None, base_label=None, cache=None): return super().label(decimals=decimals, base_label=base_label or "Rϕ", cache=cache) @staticmethod def compute_matrix(phi): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.PhaseShift.matrix` Args: phi (tensor_like or float): phase shift Returns: tensor_like: canonical matrix **Example** >>> qml.PhaseShift.compute_matrix(torch.tensor(0.5)) tensor([[0.9689-0.2474j, 0.0000+0.0000j], [0.0000+0.0000j, 0.9689+0.2474j]]) """ if qml.math.get_interface(phi) == "tensorflow": phi = qml.math.cast_like(phi, 1j) p = qml.math.exp(1j * phi) z = qml.math.zeros_like(p) return qml.math.stack([stack_last([qml.math.ones_like(p), z]), stack_last([z, p])], axis=-2) @staticmethod def compute_eigvals(phi): # pylint: disable=arguments-differ r"""Eigenvalues of the operator in the computational basis (static method). If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. seealso:: :meth:`~.PhaseShift.eigvals` Args: phi (tensor_like or float): phase shift Returns: tensor_like: eigenvalues **Example** >>> qml.PhaseShift.compute_eigvals(torch.tensor(0.5)) tensor([1.0000+0.0000j, 0.8776+0.4794j]) """ if qml.math.get_interface(phi) == "tensorflow": phi = qml.math.cast_like(phi, 1j) p = qml.math.exp(1j * phi) return stack_last([qml.math.ones_like(p), p]) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.PhaseShift.decomposition`. Args: phi (float): rotation angle :math:`\phi` wires (Any, Wires): wires that the operator acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.PhaseShift.compute_decomposition(1.234, wires=0) [RZ(1.234, wires=[0])] """ return [RZ(phi, wires=wires)] def adjoint(self): return PhaseShift(-self.data[0], wires=self.wires) def pow(self, z): return [PhaseShift(self.data[0] * z, wires=self.wires)] def _controlled(self, wire): ControlledPhaseShift(*self.parameters, wires=wire + self.wires) def single_qubit_rot_angles(self): # PhaseShift(\theta) = RZ(\theta) RY(0) RZ(0) return [self.data[0], 0.0, 0.0] class ControlledPhaseShift(Operation): r""" A qubit controlled phase shift. .. math:: CR_\phi(\phi) = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & e^{i\phi} \end{bmatrix}. .. note:: The first wire provided corresponds to the **control qubit**. **Details:** * Number of wires: 2 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(CR_\phi(\phi)) = \frac{1}{2}\left[f(CR_\phi(\phi+\pi/2)) - f(CR_\phi(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`CR_{\phi}(\phi)`. Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int]): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 2 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" basis = "Z" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return qml.Projector(np.array([1, 1]), wires=self.wires) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) def label(self, decimals=None, base_label=None, cache=None): return super().label(decimals=decimals, base_label=base_label or "Rϕ", cache=cache) @staticmethod def compute_matrix(phi): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.PhaseShift.matrix` Args: phi (tensor_like or float): phase shift Returns: tensor_like: canonical matrix **Example** >>> qml.PhaseShift.compute_matrix(torch.tensor(0.5)) tensor([[1.0+0.0j, 0.0+0.0j, 0.0+0.0j, 0.0000+0.0000j], [0.0+0.0j, 1.0+0.0j, 0.0+0.0j, 0.0000+0.0000j], [0.0+0.0j, 0.0+0.0j, 1.0+0.0j, 0.0000+0.0000j], [0.0+0.0j, 0.0+0.0j, 0.0+0.0j, 0.8776+0.4794j]]) """ if qml.math.get_interface(phi) == "tensorflow": phi = qml.math.cast_like(phi, 1j) exp_part = qml.math.exp(1j * phi) if qml.math.ndim(phi) > 0: ones = qml.math.ones_like(exp_part) zeros = qml.math.zeros_like(exp_part) matrix = [ [ones, zeros, zeros, zeros], [zeros, ones, zeros, zeros], [zeros, zeros, ones, zeros], [zeros, zeros, zeros, exp_part], ] return qml.math.stack([stack_last(row) for row in matrix], axis=-2) return qml.math.diag([1, 1, 1, exp_part]) @staticmethod def compute_eigvals(phi): # pylint: disable=arguments-differ r"""Eigenvalues of the operator in the computational basis (static method). If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. seealso:: :meth:`~.ControlledPhaseShift.eigvals` Args: phi (tensor_like or float): phase shift Returns: tensor_like: eigenvalues **Example** >>> qml.ControlledPhaseShift.compute_eigvals(torch.tensor(0.5)) tensor([1.0000+0.0000j, 1.0000+0.0000j, 1.0000+0.0000j, 0.8776+0.4794j]) """ if qml.math.get_interface(phi) == "tensorflow": phi = qml.math.cast_like(phi, 1j) exp_part = qml.math.exp(1j * phi) ones = qml.math.ones_like(exp_part) return stack_last([ones, ones, ones, exp_part]) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.ControlledPhaseShift.decomposition`. Args: phi (float): rotation angle :math:`\phi` wires (Iterable, Wires): wires that the operator acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.ControlledPhaseShift.compute_decomposition(1.234, wires=(0,1)) [PhaseShift(0.617, wires=[0]), CNOT(wires=[0, 1]), PhaseShift(-0.617, wires=[1]), CNOT(wires=[0, 1]), PhaseShift(0.617, wires=[1])] """ decomp_ops = [ qml.PhaseShift(phi / 2, wires=wires[0]), qml.CNOT(wires=wires), qml.PhaseShift(-phi / 2, wires=wires[1]), qml.CNOT(wires=wires), qml.PhaseShift(phi / 2, wires=wires[1]), ] return decomp_ops def adjoint(self): return ControlledPhaseShift(-self.data[0], wires=self.wires) def pow(self, z): return [ControlledPhaseShift(self.data[0] * z, wires=self.wires)] @property def control_wires(self): return Wires(self.wires[0]) CPhase = ControlledPhaseShift class Rot(Operation): r""" Arbitrary single qubit rotation .. math:: R(\phi,\theta,\omega) = RZ(\omega)RY(\theta)RZ(\phi)= \begin{bmatrix} e^{-i(\phi+\omega)/2}\cos(\theta/2) & -e^{i(\phi-\omega)/2}\sin(\theta/2) \\ e^{-i(\phi-\omega)/2}\sin(\theta/2) & e^{i(\phi+\omega)/2}\cos(\theta/2) \end{bmatrix}. **Details:** * Number of wires: 1 * Number of parameters: 3 * Number of dimensions per parameter: (0, 0, 0) * Gradient recipe: :math:`\frac{d}{d\phi}f(R(\phi, \theta, \omega)) = \frac{1}{2}\left[f(R(\phi+\pi/2, \theta, \omega)) - f(R(\phi-\pi/2, \theta, \omega))\right]` where :math:`f` is an expectation value depending on :math:`R(\phi, \theta, \omega)`. This gradient recipe applies for each angle argument :math:`\{\phi, \theta, \omega\}`. .. note:: If the ``Rot`` gate is not supported on the targeted device, PennyLane will attempt to decompose the gate into :class:`~.RZ` and :class:`~.RY` gates. Args: phi (float): rotation angle :math:`\phi` theta (float): rotation angle :math:`\theta` omega (float): rotation angle :math:`\omega` wires (Any, Wires): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 1 num_params = 3 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0, 0, 0) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(1,), (1,), (1,)] def __init__(self, phi, theta, omega, wires, do_queue=True, id=None): super().__init__(phi, theta, omega, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(phi, theta, omega): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.Rot.matrix` Args: phi (tensor_like or float): first rotation angle theta (tensor_like or float): second rotation angle omega (tensor_like or float): third rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.Rot.compute_matrix(torch.tensor(0.1), torch.tensor(0.2), torch.tensor(0.3)) tensor([[ 0.9752-0.1977j, -0.0993+0.0100j], [ 0.0993+0.0100j, 0.9752+0.1977j]]) """ # It might be that they are in different interfaces, e.g., # Rot(0.2, 0.3, tf.Variable(0.5), wires=0) # So we need to make sure the matrix comes out having the right type interface = qml.math._multi_dispatch([phi, theta, omega]) c = qml.math.cos(theta / 2) s = qml.math.sin(theta / 2) # If anything is not tensorflow, it has to be casted and then if interface == "tensorflow": phi = qml.math.cast_like(qml.math.asarray(phi, like=interface), 1j) omega = qml.math.cast_like(qml.math.asarray(omega, like=interface), 1j) c = qml.math.cast_like(qml.math.asarray(c, like=interface), 1j) s = qml.math.cast_like(qml.math.asarray(s, like=interface), 1j) # The following variable is used to assert the all terms to be stacked have same shape one = qml.math.ones_like(phi) * qml.math.ones_like(omega) c = c * one s = s * one mat = [ [ qml.math.exp(-0.5j * (phi + omega)) * c, -qml.math.exp(0.5j * (phi - omega)) * s, ], [ qml.math.exp(-0.5j * (phi - omega)) * s, qml.math.exp(0.5j * (phi + omega)) * c, ], ] return qml.math.stack([stack_last(row) for row in mat], axis=-2) @staticmethod def compute_decomposition(phi, theta, omega, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.Rot.decomposition`. Args: phi (float): rotation angle :math:`\phi` theta (float): rotation angle :math:`\theta` omega (float): rotation angle :math:`\omega` wires (Any, Wires): the wire the operation acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.Rot.compute_decomposition(1.2, 2.3, 3.4, wires=0) [RZ(1.2, wires=[0]), RY(2.3, wires=[0]), RZ(3.4, wires=[0])] """ decomp_ops = [ RZ(phi, wires=wires), RY(theta, wires=wires), RZ(omega, wires=wires), ] return decomp_ops def adjoint(self): phi, theta, omega = self.parameters return Rot(-omega, -theta, -phi, wires=self.wires) def _controlled(self, wire): CRot(*self.parameters, wires=wire + self.wires) def single_qubit_rot_angles(self): return self.data class MultiRZ(Operation): r""" Arbitrary multi Z rotation. .. math:: MultiRZ(\theta) = \exp(-i \frac{\theta}{2} Z^{\otimes n}) **Details:** * Number of wires: Any * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\theta}f(MultiRZ(\theta)) = \frac{1}{2}\left[f(MultiRZ(\theta +\pi/2)) - f(MultiRZ(\theta-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`MultiRZ(\theta)`. .. note:: If the ``MultiRZ`` gate is not supported on the targeted device, PennyLane will decompose the gate using :class:`~.RZ` and :class:`~.CNOT` gates. Args: theta (tensor_like or float): rotation angle :math:`\theta` wires (Sequence[int] or int): the wires the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = AnyWires num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(1,)] def __init__(self, theta, wires=None, do_queue=True, id=None): wires = Wires(wires) self.hyperparameters["num_wires"] = len(wires) super().__init__(theta, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(theta, num_wires): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.MultiRZ.matrix` Args: theta (tensor_like or float): rotation angle num_wires (int): number of wires the rotation acts on Returns: tensor_like: canonical matrix **Example** >>> qml.MultiRZ.compute_matrix(torch.tensor(0.1), 2) tensor([[0.9988-0.0500j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j], [0.0000+0.0000j, 0.9988+0.0500j, 0.0000+0.0000j, 0.0000+0.0000j], [0.0000+0.0000j, 0.0000+0.0000j, 0.9988+0.0500j, 0.0000+0.0000j], [0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.9988-0.0500j]]) """ eigs = qml.math.convert_like(pauli_eigs(num_wires), theta) if qml.math.get_interface(theta) == "tensorflow": theta = qml.math.cast_like(theta, 1j) eigs = qml.math.cast_like(eigs, 1j) if qml.math.ndim(theta) > 0: eigvals = [qml.math.exp(-0.5j * t * eigs) for t in theta] return qml.math.stack([qml.math.diag(eig) for eig in eigvals]) eigvals = qml.math.exp(-0.5j * theta * eigs) return qml.math.diag(eigvals) def generator(self): return -0.5 * functools.reduce(matmul, [qml.PauliZ(w) for w in self.wires]) @staticmethod def compute_eigvals(theta, num_wires): # pylint: disable=arguments-differ r"""Eigenvalues of the operator in the computational basis (static method). If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. seealso:: :meth:`~.MultiRZ.eigvals` Args: theta (tensor_like or float): rotation angle num_wires (int): number of wires the rotation acts on Returns: tensor_like: eigenvalues **Example** >>> qml.MultiRZ.compute_eigvals(torch.tensor(0.5), 3) tensor([0.9689-0.2474j, 0.9689+0.2474j, 0.9689+0.2474j, 0.9689-0.2474j, 0.9689+0.2474j, 0.9689-0.2474j, 0.9689-0.2474j, 0.9689+0.2474j]) """ eigs = qml.math.convert_like(pauli_eigs(num_wires), theta) if qml.math.get_interface(theta) == "tensorflow": theta = qml.math.cast_like(theta, 1j) eigs = qml.math.cast_like(eigs, 1j) if qml.math.ndim(theta) > 0: return qml.math.exp(qml.math.tensordot(-0.5j * theta, eigs, axes=0)) return qml.math.exp(-0.5j * theta * eigs) @staticmethod def compute_decomposition( theta, wires, **kwargs ): # pylint: disable=arguments-differ,unused-argument r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.MultiRZ.decomposition`. Args: theta (float): rotation angle :math:`\theta` wires (Iterable, Wires): the wires the operation acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.MultiRZ.compute_decomposition(1.2, wires=(0,1)) [CNOT(wires=[1, 0]), RZ(1.2, wires=[0]), CNOT(wires=[1, 0])] """ ops = [qml.CNOT(wires=(w0, w1)) for w0, w1 in zip(wires[~0:0:-1], wires[~1::-1])] ops.append(RZ(theta, wires=wires[0])) ops += [qml.CNOT(wires=(w0, w1)) for w0, w1 in zip(wires[1:], wires[:~0])] return ops def adjoint(self): return MultiRZ(-self.parameters[0], wires=self.wires) def pow(self, z): return [MultiRZ(self.data[0] * z, wires=self.wires)] class PauliRot(Operation): r""" Arbitrary Pauli word rotation. .. math:: RP(\theta, P) = \exp(-i \frac{\theta}{2} P) **Details:** * Number of wires: Any * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\theta}f(RP(\theta)) = \frac{1}{2}\left[f(RP(\theta +\pi/2)) - f(RP(\theta-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`RP(\theta)`. .. note:: If the ``PauliRot`` gate is not supported on the targeted device, PennyLane will decompose the gate using :class:`~.RX`, :class:`~.Hadamard`, :class:`~.RZ` and :class:`~.CNOT` gates. Args: theta (float): rotation angle :math:`\theta` pauli_word (string): the Pauli word defining the rotation wires (Sequence[int] or int): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) **Example** >>> dev = qml.device('default.qubit', wires=1) >>> @qml.qnode(dev) ... def example_circuit(): ... qml.PauliRot(0.5, 'X', wires=0) ... return qml.expval(qml.PauliZ(0)) >>> print(example_circuit()) 0.8775825618903724 """ num_wires = AnyWires num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" do_check_domain = False grad_method = "A" parameter_frequencies = [(1,)] _ALLOWED_CHARACTERS = "IXYZ" _PAULI_CONJUGATION_MATRICES = { "X": Hadamard.compute_matrix(), "Y": RX.compute_matrix(np.pi / 2), "Z": np.array([[1, 0], [0, 1]]), } def __init__(self, theta, pauli_word, wires=None, do_queue=True, id=None): super().__init__(theta, wires=wires, do_queue=do_queue, id=id) self.hyperparameters["pauli_word"] = pauli_word if not PauliRot._check_pauli_word(pauli_word): raise ValueError( f'The given Pauli word "{pauli_word}" contains characters that are not allowed.' " Allowed characters are I, X, Y and Z" ) num_wires = 1 if isinstance(wires, int) else len(wires) if not len(pauli_word) == num_wires: raise ValueError( f"The given Pauli word has length {len(pauli_word)}, length " f"{num_wires} was expected for wires {wires}" ) def label(self, decimals=None, base_label=None, cache=None): r"""A customizable string representation of the operator. Args: decimals=None (int): If ``None``, no parameters are included. Else, specifies how to round the parameters. base_label=None (str): overwrite the non-parameter component of the label cache=None (dict): dictionary that caries information between label calls in the same drawing Returns: str: label to use in drawings **Example:** >>> op = qml.PauliRot(0.1, "XYY", wires=(0,1,2)) >>> op.label() 'RXYY' >>> op.label(decimals=2) 'RXYY\n(0.10)' >>> op.label(base_label="PauliRot") 'PauliRot\n(0.10)' """ pauli_word = self.hyperparameters["pauli_word"] op_label = base_label or ("R" + pauli_word) if self.inverse: op_label += "⁻¹" # TODO[dwierichs]: Implement a proper label for parameter-broadcasted operators if decimals is not None and self.batch_size is None: param_string = f"\n({qml.math.asarray(self.parameters[0]):.{decimals}f})" op_label += param_string return op_label @staticmethod def _check_pauli_word(pauli_word): """Check that the given Pauli word has correct structure. Args: pauli_word (str): Pauli word to be checked Returns: bool: Whether the Pauli word has correct structure. """ return all(pauli in PauliRot._ALLOWED_CHARACTERS for pauli in set(pauli_word)) @staticmethod def compute_matrix(theta, pauli_word): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.PauliRot.matrix` Args: theta (tensor_like or float): rotation angle pauli_word (str): string representation of Pauli word Returns: tensor_like: canonical matrix **Example** >>> qml.PauliRot.compute_matrix(0.5, 'X') [[9.6891e-01+4.9796e-18j 2.7357e-17-2.4740e-01j] [2.7357e-17-2.4740e-01j 9.6891e-01+4.9796e-18j]] """ if not PauliRot._check_pauli_word(pauli_word): raise ValueError( f'The given Pauli word "{pauli_word}" contains characters that are not allowed.' " Allowed characters are I, X, Y and Z" ) interface = qml.math.get_interface(theta) if interface == "tensorflow": theta = qml.math.cast_like(theta, 1j) # Simplest case is if the Pauli is the identity matrix if set(pauli_word) == {"I"}: exp = qml.math.exp(-0.5j * theta) iden = qml.math.eye(2 ** len(pauli_word), like=theta) if qml.math.get_interface(theta) == "tensorflow": iden = qml.math.cast_like(iden, 1j) if qml.math.ndim(theta) == 0: return exp * iden return qml.math.stack([e * iden for e in exp]) # We first generate the matrix excluding the identity parts and expand it afterwards. # To this end, we have to store on which wires the non-identity parts act non_identity_wires, non_identity_gates = zip( *[(wire, gate) for wire, gate in enumerate(pauli_word) if gate != "I"] ) multi_Z_rot_matrix = MultiRZ.compute_matrix(theta, len(non_identity_gates)) # now we conjugate with Hadamard and RX to create the Pauli string conjugation_matrix = functools.reduce( qml.math.kron, [PauliRot._PAULI_CONJUGATION_MATRICES[gate] for gate in non_identity_gates], ) if interface == "tensorflow": conjugation_matrix = qml.math.cast_like(conjugation_matrix, 1j) # Note: we use einsum with reverse arguments here because it is not multi-dispatched # and the tensordot containing multi_Z_rot_matrix should decide about the interface return expand_matrix( qml.math.einsum( "...jk,ij->...ik", qml.math.tensordot(multi_Z_rot_matrix, conjugation_matrix, axes=[[-1], [0]]), qml.math.conj(conjugation_matrix), ), non_identity_wires, list(range(len(pauli_word))), ) def generator(self): pauli_word = self.hyperparameters["pauli_word"] wire_map = {w: i for i, w in enumerate(self.wires)} return -0.5 * qml.grouping.string_to_pauli_word(pauli_word, wire_map=wire_map) @staticmethod def compute_eigvals(theta, pauli_word): # pylint: disable=arguments-differ r"""Eigenvalues of the operator in the computational basis (static method). If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. seealso:: :meth:`~.PauliRot.eigvals` Returns: tensor_like: eigenvalues **Example** >>> qml.PauliRot.compute_eigvals(torch.tensor(0.5), "X") tensor([0.9689-0.2474j, 0.9689+0.2474j]) """ if qml.math.get_interface(theta) == "tensorflow": theta = qml.math.cast_like(theta, 1j) # Identity must be treated specially because its eigenvalues are all the same if set(pauli_word) == {"I"}: exp = qml.math.exp(-0.5j * theta) ones = qml.math.ones(2 ** len(pauli_word), like=theta) if qml.math.get_interface(theta) == "tensorflow": ones = qml.math.cast_like(ones, 1j) if qml.math.ndim(theta) == 0: return exp * ones return qml.math.tensordot(exp, ones, axes=0) return MultiRZ.compute_eigvals(theta, len(pauli_word)) @staticmethod def compute_decomposition(theta, wires, pauli_word): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.PauliRot.decomposition`. Args: theta (float): rotation angle :math:`\theta` pauli_word (string): the Pauli word defining the rotation wires (Iterable, Wires): the wires the operation acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.PauliRot.compute_decomposition(1.2, "XY", wires=(0,1)) [Hadamard(wires=[0]), RX(1.5707963267948966, wires=[1]), MultiRZ(1.2, wires=[0, 1]), Hadamard(wires=[0]), RX(-1.5707963267948966, wires=[1])] """ if isinstance(wires, int): # Catch cases when the wire is passed as a single int. wires = [wires] # Check for identity and do nothing if set(pauli_word) == {"I"}: return [] active_wires, active_gates = zip( *[(wire, gate) for wire, gate in zip(wires, pauli_word) if gate != "I"] ) ops = [] for wire, gate in zip(active_wires, active_gates): if gate == "X": ops.append(Hadamard(wires=[wire])) elif gate == "Y": ops.append(RX(np.pi / 2, wires=[wire])) ops.append(MultiRZ(theta, wires=list(active_wires))) for wire, gate in zip(active_wires, active_gates): if gate == "X": ops.append(Hadamard(wires=[wire])) elif gate == "Y": ops.append(RX(-np.pi / 2, wires=[wire])) return ops def adjoint(self): return PauliRot(-self.parameters[0], self.hyperparameters["pauli_word"], wires=self.wires) def pow(self, z): return [PauliRot(self.data[0] * z, self.hyperparameters["pauli_word"], wires=self.wires)] class CRX(Operation): r""" The controlled-RX operator .. math:: \begin{align} CR_x(\phi) &= \begin{bmatrix} & 1 & 0 & 0 & 0 \\ & 0 & 1 & 0 & 0\\ & 0 & 0 & \cos(\phi/2) & -i\sin(\phi/2)\\ & 0 & 0 & -i\sin(\phi/2) & \cos(\phi/2) \end{bmatrix}. \end{align} **Details:** * Number of wires: 2 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: The controlled-RX operator satisfies a four-term parameter-shift rule (see Appendix F, https://doi.org/10.1088/1367-2630/ac2cb3): .. math:: \frac{d}{d\phi}f(CR_x(\phi)) = c_+ \left[f(CR_x(\phi+a)) - f(CR_x(\phi-a))\right] - c_- \left[f(CR_x(\phi+b)) - f(CR_x(\phi-b))\right] where :math:`f` is an expectation value depending on :math:`CR_x(\phi)`, and - :math:`a = \pi/2` - :math:`b = 3\pi/2` - :math:`c_{\pm} = (\sqrt{2} \pm 1)/{4\sqrt{2}}` Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int]): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 2 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" basis = "X" grad_method = "A" parameter_frequencies = [(0.5, 1.0)] def generator(self): return -0.5 * qml.Projector(np.array([1]), wires=self.wires[0]) @ qml.PauliX(self.wires[1]) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) def label(self, decimals=None, base_label=None, cache=None): return super().label(decimals=decimals, base_label=base_label or "RX", cache=cache) @staticmethod def compute_matrix(theta): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.CRX.matrix` Args: theta (tensor_like or float): rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.CRX.compute_matrix(torch.tensor(0.5)) tensor([[1.0+0.0j, 0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 1.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.9689+0.0j, 0.0-0.2474j], [0.0+0.0j, 0.0+0.0j, 0.0-0.2474j, 0.9689+0.0j]]) """ interface = qml.math.get_interface(theta) c = qml.math.cos(theta / 2) s = qml.math.sin(theta / 2) if interface == "tensorflow": c = qml.math.cast_like(c, 1j) s = qml.math.cast_like(s, 1j) # The following avoids casting an imaginary quantity to reals when backpropagating c = (1 + 0j) * c js = -1j * s ones = qml.math.ones_like(js) zeros = qml.math.zeros_like(js) matrix = [ [ones, zeros, zeros, zeros], [zeros, ones, zeros, zeros], [zeros, zeros, c, js], [zeros, zeros, js, c], ] return qml.math.stack([stack_last(row) for row in matrix], axis=-2) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.CRot.decomposition`. Args: phi (float): rotation angle :math:`\phi` wires (Iterable, Wires): the wires the operation acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.CRX.compute_decomposition(1.2, wires=(0,1)) [RZ(1.5707963267948966, wires=[1]), RY(0.6, wires=[1]), CNOT(wires=[0, 1]), RY(-0.6, wires=[1]), CNOT(wires=[0, 1]), RZ(-1.5707963267948966, wires=[1])] """ pi_half = qml.math.ones_like(phi) * (np.pi / 2) decomp_ops = [ RZ(pi_half, wires=wires[1]), RY(phi / 2, wires=wires[1]), qml.CNOT(wires=wires), RY(-phi / 2, wires=wires[1]), qml.CNOT(wires=wires), RZ(-pi_half, wires=wires[1]), ] return decomp_ops def adjoint(self): return CRX(-self.data[0], wires=self.wires) def pow(self, z): return [CRX(self.data[0] * z, wires=self.wires)] @property def control_wires(self): return Wires(self.wires[0]) class CRY(Operation): r""" The controlled-RY operator .. math:: \begin{align} CR_y(\phi) &= \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0\\ 0 & 0 & \cos(\phi/2) & -\sin(\phi/2)\\ 0 & 0 & \sin(\phi/2) & \cos(\phi/2) \end{bmatrix}. \end{align} **Details:** * Number of wires: 2 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: The controlled-RY operator satisfies a four-term parameter-shift rule (see Appendix F, https://doi.org/10.1088/1367-2630/ac2cb3): .. math:: \frac{d}{d\phi}f(CR_y(\phi)) = c_+ \left[f(CR_y(\phi+a)) - f(CR_y(\phi-a))\right] - c_- \left[f(CR_y(\phi+b)) - f(CR_y(\phi-b))\right] where :math:`f` is an expectation value depending on :math:`CR_y(\phi)`, and - :math:`a = \pi/2` - :math:`b = 3\pi/2` - :math:`c_{\pm} = (\sqrt{2} \pm 1)/{4\sqrt{2}}` Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int]): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 2 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" basis = "Y" grad_method = "A" parameter_frequencies = [(0.5, 1.0)] def generator(self): return -0.5 * qml.Projector(np.array([1]), wires=self.wires[0]) @ qml.PauliY(self.wires[1]) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) def label(self, decimals=None, base_label=None, cache=None): return super().label(decimals=decimals, base_label=base_label or "RY", cache=cache) @staticmethod def compute_matrix(theta): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.CRY.matrix` Args: theta (tensor_like or float): rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.CRY.compute_matrix(torch.tensor(0.5)) tensor([[ 1.0000, 0.0000, 0.0000, 0.0000], [ 0.0000, 1.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.9689, -0.2474], [ 0.0000, 0.0000, 0.2474, 0.9689]], dtype=torch.float64) """ interface = qml.math.get_interface(theta) c = qml.math.cos(theta / 2) s = qml.math.sin(theta / 2) if interface == "tensorflow": c = qml.math.cast_like(c, 1j) s = qml.math.cast_like(s, 1j) # The following avoids casting an imaginary quantity to reals when backpropagating c = (1 + 0j) * c s = (1 + 0j) * s ones = qml.math.ones_like(s) zeros = qml.math.zeros_like(s) matrix = [ [ones, zeros, zeros, zeros], [zeros, ones, zeros, zeros], [zeros, zeros, c, -s], [zeros, zeros, s, c], ] return qml.math.stack([stack_last(row) for row in matrix], axis=-2) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.CRY.decomposition`. Args: phi (float): rotation angle :math:`\phi` wires (Iterable, Wires): wires that the operator acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.CRY.compute_decomposition(1.2, wires=(0,1)) [RY(0.6, wires=[1]), CNOT(wires=[0, 1]), RY(-0.6, wires=[1]), CNOT(wires=[0, 1])] """ decomp_ops = [ RY(phi / 2, wires=wires[1]), qml.CNOT(wires=wires), RY(-phi / 2, wires=wires[1]), qml.CNOT(wires=wires), ] return decomp_ops def adjoint(self): return CRY(-self.data[0], wires=self.wires) def pow(self, z): return [CRY(self.data[0] * z, wires=self.wires)] @property def control_wires(self): return Wires(self.wires[0]) class CRZ(Operation): r""" The controlled-RZ operator .. math:: \begin{align} CR_z(\phi) &= \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0\\ 0 & 0 & e^{-i\phi/2} & 0\\ 0 & 0 & 0 & e^{i\phi/2} \end{bmatrix}. \end{align} .. note:: The subscripts of the operations in the formula refer to the wires they act on, e.g. 1 corresponds to the first element in ``wires`` that is the **control qubit**. **Details:** * Number of wires: 2 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: The controlled-RZ operator satisfies a four-term parameter-shift rule (see Appendix F, https://doi.org/10.1088/1367-2630/ac2cb3): .. math:: \frac{d}{d\phi}f(CR_z(\phi)) = c_+ \left[f(CR_z(\phi+a)) - f(CR_z(\phi-a))\right] - c_- \left[f(CR_z(\phi+b)) - f(CR_z(\phi-b))\right] where :math:`f` is an expectation value depending on :math:`CR_z(\phi)`, and - :math:`a = \pi/2` - :math:`b = 3\pi/2` - :math:`c_{\pm} = (\sqrt{2} \pm 1)/{4\sqrt{2}}` Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int]): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 2 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" basis = "Z" grad_method = "A" parameter_frequencies = [(0.5, 1.0)] def generator(self): return -0.5 * qml.Projector(np.array([1]), wires=self.wires[0]) @ qml.PauliZ(self.wires[1]) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) def label(self, decimals=None, base_label=None, cache=None): return super().label(decimals=decimals, base_label=base_label or "RZ", cache=cache) @staticmethod def compute_matrix(theta): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.CRZ.matrix` Args: theta (tensor_like or float): rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.CRZ.compute_matrix(torch.tensor(0.5)) tensor([[1.0+0.0j, 0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 1.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.9689-0.2474j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.0+0.0j, 0.9689+0.2474j]]) """ if qml.math.get_interface(theta) == "tensorflow": theta = qml.math.cast_like(theta, 1j) exp_part = qml.math.exp(-1j * theta / 2) ones = qml.math.ones_like(exp_part) zeros = qml.math.zeros_like(exp_part) matrix = [ [ones, zeros, zeros, zeros], [zeros, ones, zeros, zeros], [zeros, zeros, exp_part, zeros], [zeros, zeros, zeros, qml.math.conj(exp_part)], ] return qml.math.stack([stack_last(row) for row in matrix], axis=-2) @staticmethod def compute_eigvals(theta): # pylint: disable=arguments-differ r"""Eigenvalues of the operator in the computational basis (static method). If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. seealso:: :meth:`~.CRZ.eigvals` Args: theta (tensor_like or float): rotation angle Returns: tensor_like: eigenvalues **Example** >>> qml.CRZ.compute_eigvals(torch.tensor(0.5)) tensor([1.0000+0.0000j, 1.0000+0.0000j, 0.9689-0.2474j, 0.9689+0.2474j]) """ if qml.math.get_interface(theta) == "tensorflow": theta = qml.math.cast_like(theta, 1j) exp_part = qml.math.exp(-0.5j * theta) o = qml.math.ones_like(exp_part) return stack_last([o, o, exp_part, qml.math.conj(exp_part)]) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.CRZ.decomposition`. Args: phi (float): rotation angle :math:`\phi` wires (Iterable, Wires): wires that the operator acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.CRZ.compute_decomposition(1.2, wires=(0,1)) [PhaseShift(0.6, wires=[1]), CNOT(wires=[0, 1]), PhaseShift(-0.6, wires=[1]), CNOT(wires=[0, 1])] """ decomp_ops = [ PhaseShift(phi / 2, wires=wires[1]), qml.CNOT(wires=wires), PhaseShift(-phi / 2, wires=wires[1]), qml.CNOT(wires=wires), ] return decomp_ops def adjoint(self): return CRZ(-self.data[0], wires=self.wires) def pow(self, z): return [CRZ(self.data[0] * z, wires=self.wires)] @property def control_wires(self): return Wires(self.wires[0]) class CRot(Operation): r""" The controlled-Rot operator .. math:: CR(\phi, \theta, \omega) = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0\\ 0 & 0 & e^{-i(\phi+\omega)/2}\cos(\theta/2) & -e^{i(\phi-\omega)/2}\sin(\theta/2)\\ 0 & 0 & e^{-i(\phi-\omega)/2}\sin(\theta/2) & e^{i(\phi+\omega)/2}\cos(\theta/2) \end{bmatrix}. .. note:: The first wire provided corresponds to the **control qubit**. **Details:** * Number of wires: 2 * Number of parameters: 3 * Number of dimensions per parameter: (0, 0, 0) * Gradient recipe: The controlled-Rot operator satisfies a four-term parameter-shift rule (see Appendix F, https://doi.org/10.1088/1367-2630/ac2cb3): .. math:: \frac{d}{d\mathbf{x}_i}f(CR(\mathbf{x}_i)) = c_+ \left[f(CR(\mathbf{x}_i+a)) - f(CR(\mathbf{x}_i-a))\right] - c_- \left[f(CR(\mathbf{x}_i+b)) - f(CR(\mathbf{x}_i-b))\right] where :math:`f` is an expectation value depending on :math:`CR(\mathbf{x}_i)`, and - :math:`\mathbf{x} = (\phi, \theta, \omega)` and `i` is an index to :math:`\mathbf{x}` - :math:`a = \pi/2` - :math:`b = 3\pi/2` - :math:`c_{\pm} = (\sqrt{2} \pm 1)/{4\sqrt{2}}` Args: phi (float): rotation angle :math:`\phi` theta (float): rotation angle :math:`\theta` omega (float): rotation angle :math:`\omega` wires (Sequence[int]): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 2 num_params = 3 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0, 0, 0) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(0.5, 1.0), (0.5, 1.0), (0.5, 1.0)] def __init__(self, phi, theta, omega, wires, do_queue=True, id=None): super().__init__(phi, theta, omega, wires=wires, do_queue=do_queue, id=id) def label(self, decimals=None, base_label=None, cache=None): return super().label(decimals=decimals, base_label=base_label or "Rot", cache=cache) @staticmethod def compute_matrix(phi, theta, omega): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.CRot.matrix` Args: phi(tensor_like or float): first rotation angle theta (tensor_like or float): second rotation angle omega (tensor_like or float): third rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.CRot.compute_matrix(torch.tensor(0.1), torch.tensor(0.2), torch.tensor(0.3)) tensor([[ 1.0+0.0j, 0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [ 0.0+0.0j, 1.0+0.0j, 0.0+0.0j, 0.0+0.0j], [ 0.0+0.0j, 0.0+0.0j, 0.9752-0.1977j, -0.0993+0.0100j], [ 0.0+0.0j, 0.0+0.0j, 0.0993+0.0100j, 0.9752+0.1977j]]) """ # It might be that they are in different interfaces, e.g., # CRot(0.2, 0.3, tf.Variable(0.5), wires=[0, 1]) # So we need to make sure the matrix comes out having the right type interface = qml.math._multi_dispatch([phi, theta, omega]) c = qml.math.cos(theta / 2) s = qml.math.sin(theta / 2) # If anything is not tensorflow, it has to be casted if interface == "tensorflow": phi = qml.math.cast_like(qml.math.asarray(phi, like=interface), 1j) omega = qml.math.cast_like(qml.math.asarray(omega, like=interface), 1j) c = qml.math.cast_like(qml.math.asarray(c, like=interface), 1j) s = qml.math.cast_like(qml.math.asarray(s, like=interface), 1j) # The following variable is used to assert the all terms to be stacked have same shape one = qml.math.ones_like(phi) * qml.math.ones_like(omega) c = c * one s = s * one o = qml.math.ones_like(c) z = qml.math.zeros_like(c) mat = [ [o, z, z, z], [z, o, z, z], [ z, z, qml.math.exp(-0.5j * (phi + omega)) * c, -qml.math.exp(0.5j * (phi - omega)) * s, ], [ z, z, qml.math.exp(-0.5j * (phi - omega)) * s, qml.math.exp(0.5j * (phi + omega)) * c, ], ] return qml.math.stack([stack_last(row) for row in mat], axis=-2) @staticmethod def compute_decomposition(phi, theta, omega, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.CRot.decomposition`. Args: phi (float): rotation angle :math:`\phi` theta (float): rotation angle :math:`\theta` omega (float): rotation angle :math:`\omega` wires (Iterable, Wires): the wires the operation acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.PhaseShift.compute_decomposition(1.234, wires=0) [RZ(-1.1, wires=[1]), CNOT(wires=[0, 1]), RZ(-2.3, wires=[1]), RY(-1.15, wires=[1]), CNOT(wires=[0, 1]), RY(1.15, wires=[1]), RZ(3.4, wires=[1])] """ decomp_ops = [ RZ((phi - omega) / 2, wires=wires[1]), qml.CNOT(wires=wires), RZ(-(phi + omega) / 2, wires=wires[1]), RY(-theta / 2, wires=wires[1]), qml.CNOT(wires=wires), RY(theta / 2, wires=wires[1]), RZ(omega, wires=wires[1]), ] return decomp_ops def adjoint(self): phi, theta, omega = self.parameters return CRot(-omega, -theta, -phi, wires=self.wires) @property def control_wires(self): return Wires(self.wires[0]) class U1(Operation): r""" U1 gate. .. math:: U_1(\phi) = e^{i\phi/2}R_z(\phi) = \begin{bmatrix} 1 & 0 \\ 0 & e^{i\phi} \end{bmatrix}. .. note:: The ``U1`` gate is an alias for the phase shift operation :class:`~.PhaseShift`. **Details:** * Number of wires: 1 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(U_1(\phi)) = \frac{1}{2}\left[f(U_1(\phi+\pi/2)) - f(U_1(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`U_1(\phi)`. Args: phi (float): rotation angle :math:`\phi` wires (Sequence[int] or int): the wire the operation acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 1 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return qml.Projector(np.array([1]), wires=self.wires) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(phi): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.U1.matrix` Args: phi (tensor_like or float): rotation angle Returns: tensor_like: canonical matrix **Example** >>> qml.U1.compute_matrix(torch.tensor(0.5)) tensor([[1.0000+0.0000j, 0.0000+0.0000j], [0.0000+0.0000j, 0.8776+0.4794j]]) """ if qml.math.get_interface(phi) == "tensorflow": phi = qml.math.cast_like(phi, 1j) p = qml.math.exp(1j * phi) z = qml.math.zeros_like(p) return qml.math.stack([stack_last([qml.math.ones_like(p), z]), stack_last([z, p])], axis=-2) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.U1.decomposition`. Args: phi (float): rotation angle :math:`\phi` wires (Any, Wires): Wire that the operator acts on. Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.U1.compute_decomposition(1.234, wires=0) [PhaseShift(1.234, wires=[0])] """ return [PhaseShift(phi, wires=wires)] def adjoint(self): return U1(-self.data[0], wires=self.wires) def pow(self, z): return [U1(self.data[0] * z, wires=self.wires)] class U2(Operation): r""" U2 gate. .. math:: U_2(\phi, \delta) = \frac{1}{\sqrt{2}}\begin{bmatrix} 1 & -\exp(i \delta) \\ \exp(i \phi) & \exp(i (\phi + \delta)) \end{bmatrix} The :math:`U_2` gate is related to the single-qubit rotation :math:`R` (:class:`Rot`) and the :math:`R_\phi` (:class:`PhaseShift`) gates via the following relation: .. math:: U_2(\phi, \delta) = R_\phi(\phi+\delta) R(\delta,\pi/2,-\delta) .. note:: If the ``U2`` gate is not supported on the targeted device, PennyLane will attempt to decompose the gate into :class:`~.Rot` and :class:`~.PhaseShift` gates. **Details:** * Number of wires: 1 * Number of parameters: 2 * Number of dimensions per parameter: (0, 0) * Gradient recipe: :math:`\frac{d}{d\phi}f(U_2(\phi, \delta)) = \frac{1}{2}\left[f(U_2(\phi+\pi/2, \delta)) - f(U_2(\phi-\pi/2, \delta))\right]` where :math:`f` is an expectation value depending on :math:`U_2(\phi, \delta)`. This gradient recipe applies for each angle argument :math:`\{\phi, \delta\}`. Args: phi (float): azimuthal angle :math:`\phi` delta (float): quantum phase :math:`\delta` wires (Sequence[int] or int): the subsystem the gate acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 1 num_params = 2 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0, 0) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(1,), (1,)] def __init__(self, phi, delta, wires, do_queue=True, id=None): super().__init__(phi, delta, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(phi, delta): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.U2.matrix` Args: phi (tensor_like or float): azimuthal angle delta (tensor_like or float): quantum phase Returns: tensor_like: canonical matrix **Example** >>> qml.U2.compute_matrix(torch.tensor(0.1), torch.tensor(0.2)) tensor([[ 0.7071+0.0000j, -0.6930-0.1405j], [ 0.7036+0.0706j, 0.6755+0.2090j]]) """ interface = qml.math._multi_dispatch([phi, delta]) # If anything is not tensorflow, it has to be casted and then if interface == "tensorflow": phi = qml.math.cast_like(qml.math.asarray(phi, like=interface), 1j) delta = qml.math.cast_like(qml.math.asarray(delta, like=interface), 1j) one = qml.math.ones_like(phi) * qml.math.ones_like(delta) mat = [ [one, -qml.math.exp(1j * delta) * one], [qml.math.exp(1j * phi) * one, qml.math.exp(1j * (phi + delta))], ] return INV_SQRT2 * qml.math.stack([stack_last(row) for row in mat], axis=-2) @staticmethod def compute_decomposition(phi, delta, wires): r"""Representation of the operator as a product of other operators (static method). .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.U2.decomposition`. Args: phi (float): azimuthal angle :math:`\phi` delta (float): quantum phase :math:`\delta` wires (Iterable, Wires): the subsystem the gate acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.U2.compute_decomposition(1.23, 2.34, wires=0) [Rot(2.34, 1.5707963267948966, -2.34, wires=[0]), PhaseShift(2.34, wires=[0]), PhaseShift(1.23, wires=[0])] """ pi_half = qml.math.ones_like(delta) * (np.pi / 2) decomp_ops = [ Rot(delta, pi_half, -delta, wires=wires), PhaseShift(delta, wires=wires), PhaseShift(phi, wires=wires), ] return decomp_ops def adjoint(self): phi, delta = self.parameters new_delta = qml.math.mod((np.pi - phi), (2 * np.pi)) new_phi = qml.math.mod((np.pi - delta), (2 * np.pi)) return U2(new_phi, new_delta, wires=self.wires) class U3(Operation): r""" Arbitrary single qubit unitary. .. math:: U_3(\theta, \phi, \delta) = \begin{bmatrix} \cos(\theta/2) & -\exp(i \delta)\sin(\theta/2) \\ \exp(i \phi)\sin(\theta/2) & \exp(i (\phi + \delta))\cos(\theta/2) \end{bmatrix} The :math:`U_3` gate is related to the single-qubit rotation :math:`R` (:class:`Rot`) and the :math:`R_\phi` (:class:`PhaseShift`) gates via the following relation: .. math:: U_3(\theta, \phi, \delta) = R_\phi(\phi+\delta) R(\delta,\theta,-\delta) .. note:: If the ``U3`` gate is not supported on the targeted device, PennyLane will attempt to decompose the gate into :class:`~.PhaseShift` and :class:`~.Rot` gates. **Details:** * Number of wires: 1 * Number of parameters: 3 * Number of dimensions per parameter: (0, 0, 0) * Gradient recipe: :math:`\frac{d}{d\phi}f(U_3(\theta, \phi, \delta)) = \frac{1}{2}\left[f(U_3(\theta+\pi/2, \phi, \delta)) - f(U_3(\theta-\pi/2, \phi, \delta))\right]` where :math:`f` is an expectation value depending on :math:`U_3(\theta, \phi, \delta)`. This gradient recipe applies for each angle argument :math:`\{\theta, \phi, \delta\}`. Args: theta (float): polar angle :math:`\theta` phi (float): azimuthal angle :math:`\phi` delta (float): quantum phase :math:`\delta` wires (Sequence[int] or int): the subsystem the gate acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 1 num_params = 3 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0, 0, 0) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(1,), (1,), (1,)] def __init__(self, theta, phi, delta, wires, do_queue=True, id=None): super().__init__(theta, phi, delta, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(theta, phi, delta): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.U3.matrix` Args: theta (tensor_like or float): polar angle phi (tensor_like or float): azimuthal angle delta (tensor_like or float): quantum phase Returns: tensor_like: canonical matrix **Example** >>> qml.U3.compute_matrix(torch.tensor(0.1), torch.tensor(0.2), torch.tensor(0.3)) tensor([[ 0.9988+0.0000j, -0.0477-0.0148j], [ 0.0490+0.0099j, 0.8765+0.4788j]]) """ # It might be that they are in different interfaces, e.g., # U3(0.2, 0.3, tf.Variable(0.5), wires=0) # So we need to make sure the matrix comes out having the right type interface = qml.math._multi_dispatch([theta, phi, delta]) c = qml.math.cos(theta / 2) s = qml.math.sin(theta / 2) # If anything is not tensorflow, it has to be casted and then if interface == "tensorflow": phi = qml.math.cast_like(qml.math.asarray(phi, like=interface), 1j) delta = qml.math.cast_like(qml.math.asarray(delta, like=interface), 1j) c = qml.math.cast_like(qml.math.asarray(c, like=interface), 1j) s = qml.math.cast_like(qml.math.asarray(s, like=interface), 1j) # The following variable is used to assert the all terms to be stacked have same shape one = qml.math.ones_like(phi) * qml.math.ones_like(delta) c = c * one s = s * one mat = [ [c, -s * qml.math.exp(1j * delta)], [s * qml.math.exp(1j * phi), c * qml.math.exp(1j * (phi + delta))], ] return qml.math.stack([stack_last(row) for row in mat], axis=-2) @staticmethod def compute_decomposition(theta, phi, delta, wires): r"""Representation of the operator as a product of other operators (static method). .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.U3.decomposition`. Args: theta (float): polar angle :math:`\theta` phi (float): azimuthal angle :math:`\phi` delta (float): quantum phase :math:`\delta` wires (Iterable, Wires): the subsystem the gate acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.U3.compute_decomposition(1.23, 2.34, 3.45, wires=0) [Rot(3.45, 1.23, -3.45, wires=[0]), PhaseShift(3.45, wires=[0]), PhaseShift(2.34, wires=[0])] """ decomp_ops = [ Rot(delta, theta, -delta, wires=wires), PhaseShift(delta, wires=wires), PhaseShift(phi, wires=wires), ] return decomp_ops def adjoint(self): theta, phi, delta = self.parameters new_delta = qml.math.mod((np.pi - phi), (2 * np.pi)) new_phi = qml.math.mod((np.pi - delta), (2 * np.pi)) return U3(theta, new_phi, new_delta, wires=self.wires) class IsingXX(Operation): r""" Ising XX coupling gate .. math:: XX(\phi) = \begin{bmatrix} \cos(\phi / 2) & 0 & 0 & -i \sin(\phi / 2) \\ 0 & \cos(\phi / 2) & -i \sin(\phi / 2) & 0 \\ 0 & -i \sin(\phi / 2) & \cos(\phi / 2) & 0 \\ -i \sin(\phi / 2) & 0 & 0 & \cos(\phi / 2) \end{bmatrix}. **Details:** * Number of wires: 2 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(XX(\phi)) = \frac{1}{2}\left[f(XX(\phi +\pi/2)) - f(XX(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`XX(\phi)`. Args: phi (float): the phase angle wires (int): the subsystem the gate acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 2 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return -0.5 * PauliX(wires=self.wires[0]) @ PauliX(wires=self.wires[1]) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_matrix(phi): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. .. seealso:: :meth:`~.IsingXX.matrix` Args: phi (tensor_like or float): phase angle Returns: tensor_like: canonical matrix **Example** >>> qml.IsingXX.compute_matrix(torch.tensor(0.5)) tensor([[0.9689+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000-0.2474j], [0.0000+0.0000j, 0.9689+0.0000j, 0.0000-0.2474j, 0.0000+0.0000j], [0.0000+0.0000j, 0.0000-0.2474j, 0.9689+0.0000j, 0.0000+0.0000j], [0.0000-0.2474j, 0.0000+0.0000j, 0.0000+0.0000j, 0.9689+0.0000j]], dtype=torch.complex128) """ c = qml.math.cos(phi / 2) s = qml.math.sin(phi / 2) if qml.math.get_interface(phi) == "tensorflow": c = qml.math.cast_like(c, 1j) s = qml.math.cast_like(s, 1j) # The following avoids casting an imaginary quantity to reals when backpropagating c = (1 + 0j) * c js = -1j * s z = qml.math.zeros_like(js) matrix = [ [c, z, z, js], [z, c, js, z], [z, js, c, z], [js, z, z, c], ] return qml.math.stack([stack_last(row) for row in matrix], axis=-2) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.IsingXX.decomposition`. Args: phi (float): the phase angle wires (Iterable, Wires): the subsystem the gate acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.IsingXX.compute_decomposition(1.23, wires=(0,1)) [CNOT(wires=[0, 1]), RX(1.23, wires=[0]), CNOT(wires=[0, 1]] """ decomp_ops = [ qml.CNOT(wires=wires), RX(phi, wires=[wires[0]]), qml.CNOT(wires=wires), ] return decomp_ops def adjoint(self): (phi,) = self.parameters return IsingXX(-phi, wires=self.wires) def pow(self, z): return [IsingXX(self.data[0] * z, wires=self.wires)] class IsingYY(Operation): r""" Ising YY coupling gate .. math:: \mathtt{YY}(\phi) = \begin{bmatrix} \cos(\phi / 2) & 0 & 0 & i \sin(\phi / 2) \\ 0 & \cos(\phi / 2) & -i \sin(\phi / 2) & 0 \\ 0 & -i \sin(\phi / 2) & \cos(\phi / 2) & 0 \\ i \sin(\phi / 2) & 0 & 0 & \cos(\phi / 2) \end{bmatrix}. **Details:** * Number of wires: 2 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(YY(\phi)) = \frac{1}{2}\left[f(YY(\phi +\pi/2)) - f(YY(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`YY(\phi)`. Args: phi (float): the phase angle wires (int): the subsystem the gate acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 2 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return -0.5 * PauliY(wires=self.wires[0]) @ PauliY(wires=self.wires[1]) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.IsingYY.decomposition`. Args: phi (float): the phase angle wires (Iterable, Wires): the subsystem the gate acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.IsingYY.compute_decomposition(1.23, wires=(0,1)) [CY(wires=[0, 1]), RY(1.23, wires=[0]), CY(wires=[0, 1])] """ return [ qml.CY(wires=wires), qml.RY(phi, wires=[wires[0]]), qml.CY(wires=wires), ] @staticmethod def compute_matrix(phi): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.IsingYY.matrix` Args: phi (tensor_like or float): phase angle Returns: tensor_like: canonical matrix **Example** >>> qml.IsingYY.compute_matrix(torch.tensor(0.5)) tensor([[0.9689+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.2474j], [0.0000+0.0000j, 0.9689+0.0000j, 0.0000-0.2474j, 0.0000+0.0000j], [0.0000+0.0000j, 0.0000-0.2474j, 0.9689+0.0000j, 0.0000+0.0000j], [0.0000+0.2474j, 0.0000+0.0000j, 0.0000+0.0000j, 0.9689+0.0000j]]) """ c = qml.math.cos(phi / 2) s = qml.math.sin(phi / 2) if qml.math.get_interface(phi) == "tensorflow": c = qml.math.cast_like(c, 1j) s = qml.math.cast_like(s, 1j) # The following avoids casting an imaginary quantity to reals when backpropagating c = (1 + 0j) * c js = 1j * s z = qml.math.zeros_like(js) matrix = [ [c, z, z, js], [z, c, -js, z], [z, -js, c, z], [js, z, z, c], ] return qml.math.stack([stack_last(row) for row in matrix], axis=-2) def adjoint(self): (phi,) = self.parameters return IsingYY(-phi, wires=self.wires) def pow(self, z): return [IsingYY(self.data[0] * z, wires=self.wires)] class IsingZZ(Operation): r""" Ising ZZ coupling gate .. math:: ZZ(\phi) = \begin{bmatrix} e^{-i \phi / 2} & 0 & 0 & 0 \\ 0 & e^{i \phi / 2} & 0 & 0 \\ 0 & 0 & e^{i \phi / 2} & 0 \\ 0 & 0 & 0 & e^{-i \phi / 2} \end{bmatrix}. **Details:** * Number of wires: 2 * Number of parameters: 1 * Number of dimensions per parameter: (0,) * Gradient recipe: :math:`\frac{d}{d\phi}f(ZZ(\phi)) = \frac{1}{2}\left[f(ZZ(\phi +\pi/2)) - f(ZZ(\phi-\pi/2))\right]` where :math:`f` is an expectation value depending on :math:`ZZ(\theta)`. Args: phi (float): the phase angle wires (int): the subsystem the gate acts on do_queue (bool): Indicates whether the operator should be immediately pushed into the Operator queue (optional) id (str or None): String representing the operation (optional) """ num_wires = 2 num_params = 1 """int: Number of trainable parameters that the operator depends on.""" ndim_params = (0,) """tuple[int]: Number of dimensions per trainable parameter that the operator depends on.""" grad_method = "A" parameter_frequencies = [(1,)] def generator(self): return -0.5 * PauliZ(wires=self.wires[0]) @ PauliZ(wires=self.wires[1]) def __init__(self, phi, wires, do_queue=True, id=None): super().__init__(phi, wires=wires, do_queue=do_queue, id=id) @staticmethod def compute_decomposition(phi, wires): r"""Representation of the operator as a product of other operators (static method). : .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.IsingZZ.decomposition`. Args: phi (float): the phase angle wires (Iterable, Wires): the subsystem the gate acts on Returns: list[Operator]: decomposition into lower level operations **Example:** >>> qml.IsingZZ.compute_decomposition(1.23, wires=0) [CNOT(wires=[0, 1]), RZ(1.23, wires=[1]), CNOT(wires=[0, 1])] """ return [ qml.CNOT(wires=wires), qml.RZ(phi, wires=[wires[1]]), qml.CNOT(wires=wires), ] @staticmethod def compute_matrix(phi): # pylint: disable=arguments-differ r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.IsingZZ.matrix` Args: phi (tensor_like or float): phase angle Returns: tensor_like: canonical matrix **Example** >>> qml.IsingZZ.compute_matrix(torch.tensor(0.5)) tensor([[0.9689-0.2474j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j], [0.0000+0.0000j, 0.9689+0.2474j, 0.0000+0.0000j, 0.0000+0.0000j], [0.0000+0.0000j, 0.0000+0.0000j, 0.9689+0.2474j, 0.0000+0.0000j], [0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.9689-0.2474j]]) """ if qml.math.get_interface(phi) == "tensorflow": phi = qml.math.cast_like(phi, 1j) neg_phase = qml.math.exp(-0.5j * phi) pos_phase = qml.math.exp(0.5j * phi) zeros = qml.math.zeros_like(pos_phase) matrix = [ [neg_phase, zeros, zeros, zeros], [zeros, pos_phase, zeros, zeros], [zeros, zeros, pos_phase, zeros], [zeros, zeros, zeros, neg_phase], ] return qml.math.stack([stack_last(row) for row in matrix], axis=-2) @staticmethod def compute_eigvals(phi): # pylint: disable=arguments-differ r"""Eigenvalues of the operator in the computational basis (static method). If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. seealso:: :meth:`~.IsingZZ.eigvals` Args: phi (tensor_like or float): phase angle Returns: tensor_like: eigenvalues **Example** >>> qml.IsingZZ.compute_eigvals(torch.tensor(0.5)) tensor([0.9689-0.2474j, 0.9689+0.2474j, 0.9689+0.2474j, 0.9689-0.2474j]) """ if qml.math.get_interface(phi) == "tensorflow": phi = qml.math.cast_like(phi, 1j) pos_phase = qml.math.exp(1.0j * phi / 2) neg_phase = qml.math.exp(-1.0j * phi / 2) return stack_last([neg_phase, pos_phase, pos_phase, neg_phase]) def adjoint(self): (phi,) = self.parameters return IsingZZ(-phi, wires=self.wires) def pow(self, z): return [IsingZZ(self.data[0] * z, wires=self.wires)]

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0.413019

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null

0

1

0

null

0

6

1c418861fb6e75e48a46198115b66dd8dd3e8209

137

py

Python

app/main/errors.py

geoffrey45/Baseline-news

d211a84e087a222cf1720808f4abe31b9315c632

[ "MIT" ]

null

app/main/errors.py

geoffrey45/Baseline-news

d211a84e087a222cf1720808f4abe31b9315c632

[ "MIT" ]

null

app/main/errors.py

geoffrey45/Baseline-news

d211a84e087a222cf1720808f4abe31b9315c632

[ "MIT" ]

null

from flask import render_template from . import main @main.app_errorhandler(404) def fof(error): return render_template('fof.html'),404

22.833333

39

0.79562

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137

5.047619

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null

1

0

1

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null

0

1

0

6

1c8defa0b35e732ccf1fe1cd71b47161957311c6

28

py

Python

01-Python/10-Flask/hello.py

Jerrywx/Python_Down

361d6bb8a5f7768c7064e97c40e4f485ece14a27

[ "Apache-2.0" ]

null

01-Python/10-Flask/hello.py

Jerrywx/Python_Down

361d6bb8a5f7768c7064e97c40e4f485ece14a27

[ "Apache-2.0" ]

null

01-Python/10-Flask/hello.py

Jerrywx/Python_Down

361d6bb8a5f7768c7064e97c40e4f485ece14a27

[ "Apache-2.0" ]

null

import flask print("Hello")

9.333333

14

0.75

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5.25

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null

0

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0

1

0

1

0

6

1c9be4c94a0dac9e94755cf96cf9c396f91ce138

6,007

py

Python

openldap/tests/test_check.py

volksman/integrations-core

34405662b09bf4a8c32feaed16a4745c7e1f24c0

[ "BSD-3-Clause" ]

null

openldap/tests/test_check.py

volksman/integrations-core

34405662b09bf4a8c32feaed16a4745c7e1f24c0

[ "BSD-3-Clause" ]

null

openldap/tests/test_check.py

volksman/integrations-core

34405662b09bf4a8c32feaed16a4745c7e1f24c0

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2018 # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import ldap3 import pytest from datadog_checks.dev.docker import get_docker_hostname from datadog_checks.utils.platform import Platform pytestmark = pytest.mark.integration @pytest.fixture def instance(): return { "url": "ldap://{}:3890".format(get_docker_hostname()), "username": "cn=monitor,dc=example,dc=org", "password": "monitor", "custom_queries": [{ "name": "stats", "search_base": "cn=statistics,cn=monitor", "search_filter": "(!(cn=Statistics))", }], "tags": ["test:integration"] } @pytest.fixture def instance_ssl(instance): instance["url"] = "ldaps://{}:6360".format(get_docker_hostname()) return instance def test_check(aggregator, check, openldap_server, instance): tags = ["url:{}".format(instance["url"]), "test:integration"] check.check(instance) aggregator.assert_service_check("openldap.can_connect", check.OK, tags=tags) aggregator.assert_metric("openldap.bind_time", tags=tags) aggregator.assert_metric("openldap.connections.current", tags=tags) aggregator.assert_metric("openldap.connections.max_file_descriptors", tags=tags) aggregator.assert_metric("openldap.connections.total", tags=tags) aggregator.assert_metric("openldap.operations.completed.total", tags=tags) aggregator.assert_metric("openldap.operations.initiated.total", tags=tags) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:abandon"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:abandon"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:add"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:add"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:bind"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:bind"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:compare"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:compare"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:delete"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:delete"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:extended"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:extended"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:modify"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:modify"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:modrdn"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:modrdn"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:search"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:search"]) aggregator.assert_metric("openldap.operations.completed", tags=tags + ["operation:unbind"]) aggregator.assert_metric("openldap.operations.initiated", tags=tags + ["operation:unbind"]) aggregator.assert_metric("openldap.statistics.bytes", tags=tags) aggregator.assert_metric("openldap.statistics.entries", tags=tags) aggregator.assert_metric("openldap.statistics.pdu", tags=tags) aggregator.assert_metric("openldap.statistics.referrals", tags=tags) aggregator.assert_metric("openldap.threads", tags=tags + ["status:active"]) aggregator.assert_metric("openldap.threads", tags=tags + ["status:backload"]) aggregator.assert_metric("openldap.threads", tags=tags + ["status:open"]) aggregator.assert_metric("openldap.threads", tags=tags + ["status:pending"]) aggregator.assert_metric("openldap.threads", tags=tags + ["status:starting"]) aggregator.assert_metric("openldap.threads.max", tags=tags) aggregator.assert_metric("openldap.threads.max_pending", tags=tags) aggregator.assert_metric("openldap.uptime", tags=tags) aggregator.assert_metric("openldap.waiter.read", tags=tags) aggregator.assert_metric("openldap.waiter.write", tags=tags) aggregator.assert_metric("openldap.query.duration", tags=tags + ["query:stats"]) aggregator.assert_metric("openldap.query.entries", tags=tags + ["query:stats"]) aggregator.assert_all_metrics_covered() def test_check_ssl(aggregator, check, openldap_server, instance_ssl): tags = ["url:{}".format(instance_ssl["url"]), "test:integration"] # Should fail certificate verification with pytest.raises(ldap3.core.exceptions.LDAPExceptionError): check.check(instance_ssl) aggregator.assert_service_check("openldap.can_connect", check.CRITICAL, tags=tags) instance_ssl["ssl_verify"] = False # Should work now check.check(instance_ssl) aggregator.assert_service_check("openldap.can_connect", check.OK, tags=tags) def test_check_connection_failure(aggregator, check, openldap_server, instance): instance["url"] = "bad_url" tags = ["url:{}".format(instance["url"]), "test:integration"] # Should fail certificate verification with pytest.raises(ldap3.core.exceptions.LDAPExceptionError): check.check(instance) aggregator.assert_service_check("openldap.can_connect", check.CRITICAL, tags=tags) @pytest.mark.skipif(not Platform.is_linux(), reason='Windows sockets are not file handles') def test_check_socket(aggregator, check, openldap_server, instance): instance["url"] = "ldapi://{}".format(openldap_server) tags = ["url:{}".format(instance["url"]), "test:integration"] check.check(instance) aggregator.assert_service_check("openldap.can_connect", check.OK, tags=tags)

54.117117

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681

6,007

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0.198238

0.176918

0.212854

0.290256

0.806957

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0.713891

0.587422

0.506796

0.161023

0

0.003007

0.1142

6,007

110

98

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0.812817

0.03163

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0

0.338726

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0

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0.011236

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0.011236

0.134831

0

null

0

1

0

null

0

1

0

6

98d5854b34df60365d8aaa9dc66c8001fa64c498

28

py

Python

strips/domains/__init__.py

yijiangh/pyplanners

ef1ae33e233f20cd93ce03cba363b0f14fd078bc

[ "MIT" ]

23

2017-11-13T23:56:25.000Z

2022-02-12T08:56:28.000Z

strips/domains/__init__.py

yijiangh/pyplanners

ef1ae33e233f20cd93ce03cba363b0f14fd078bc

[ "MIT" ]

1

2022-01-04T17:07:47.000Z

strips/domains/__init__.py

yijiangh/pyplanners

ef1ae33e233f20cd93ce03cba363b0f14fd078bc

[ "MIT" ]

6

2017-07-13T07:21:13.000Z

2022-03-25T08:21:57.000Z

from .blocks_world import *

14

27

0.785714

4

28

5.25

1

0

0.142857

28

1

28

0.875

0

1

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0

1

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1

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1

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null

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1

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null

0

1

0

1

0

1

0

6

c7275672cbe2bedb25747aca98a264314a64c40a

123

py

Python

03 Operators and Operands/logicaloperators.py

Himanshu44626748/Learn-Python

f3a4d997f2d29b146e5f7434f4801ae94bc3483f

[ "MIT" ]

2

2020-03-16T14:57:44.000Z

2020-11-29T07:45:54.000Z

03 Operators and Operands/logicaloperators.py

Himanshu44626748/Learn-Python

f3a4d997f2d29b146e5f7434f4801ae94bc3483f

[ "MIT" ]

null

03 Operators and Operands/logicaloperators.py

Himanshu44626748/Learn-Python

f3a4d997f2d29b146e5f7434f4801ae94bc3483f

[ "MIT" ]

1

2020-08-13T07:59:02.000Z

x = 20 y = 30 print((x==25 and y==30)) print((x==25 or y==30)) print(not(x==25 or y==30)) print((not(x==25) and y==30))

12.3

29

0.536585

30

123

2.2

0.3

0.227273

0.484848

0.272727

0.954545

0.530303

0

0.196078

0.170732

123

10

29

12.3

0.45098

0

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false

0

0.666667

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null

1

0

1

0

null

0

1

0

6

c7326dfe4af8d62fbff416efd4d0c585ac4447cd

8,744

py

Python

tests/test_insert.py

lovette/mysqlstmt

ef7fa56ee45046018d6a6cd2c64abce19a8b33a8

[ "BSD-3-Clause" ]

null

tests/test_insert.py

lovette/mysqlstmt

ef7fa56ee45046018d6a6cd2c64abce19a8b33a8

[ "BSD-3-Clause" ]

null

tests/test_insert.py

lovette/mysqlstmt

ef7fa56ee45046018d6a6cd2c64abce19a8b33a8

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import unittest from nose.tools import assert_equals, raises from mysqlstmt import Insert, Select from collections import OrderedDict class TestInsert(unittest.TestCase): def test_constructor_table_name(self): q = Insert('t1') sql_t = q.set_value('t1c1', 1).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (1)', None)) def test_set_value_int(self): q = Insert() sql_t = q.into_table('t1').set_value('t1c1', 1).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (1)', None)) def test_set_value_int_callable(self): q = Insert() sql_t = q.into_table('t1').set_value('t1c1', 1)() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (1)', None)) def test_set_value_ints(self): q = Insert() sql_t = q.into_table('t1').set_value('t1c1', 1).set_value('t1c2', 2).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`, `t1c2`) VALUES (1, 2)', None)) def test_dict_int(self): q = Insert() values = {'t1c1': 1} sql_t = q.into_table('t1').set_value(values).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (1)', None)) def test_dict_ints(self): q = Insert() values = OrderedDict([('t1c1', 1), ('t1c2', 2)]) sql_t = q.into_table('t1').set_value(values).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`, `t1c2`) VALUES (1, 2)', None)) def test_dict_strings(self): q = Insert() values = OrderedDict([('t1c1', 'a'), ('t1c2', 'b')]) sql_t = q.into_table('t1').set_value(values).sql() assert_equals(sql_t, ("INSERT INTO t1 (`t1c1`, `t1c2`) VALUES (?, ?)", ['a', 'b'])) def test_null(self): q = Insert() values = OrderedDict([('t1c1', 'a'), ('t1c2', None)]) sql_t = q.into_table('t1').set_value(values).sql() assert_equals(sql_t, ("INSERT INTO t1 (`t1c1`, `t1c2`) VALUES (?, NULL)", ['a'])) def test_function_value(self): q = Insert() sql_t = q.into_table('t1').set_value('t1c1', 'NOW()').sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (?)', ['NOW()'])) def test_function_raw_value(self): q = Insert() sql_t = q.into_table('t1').set_raw_value('t1c1', 'NOW()').sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (NOW())', None)) def test_function_raw_value_dict(self): q = Insert() sql_t = q.into_table('t1').set_raw_value({'t1c1': 'NOW()'}).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (NOW())', None)) def test_function_raw_value_with_valparams(self): q = Insert() sql_t = q.into_table('t1').set_raw_value('t1c1', 'PASSWORD(?)', value_params=('mypw',)).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (PASSWORD(?))', ['mypw'])) def test_function_raw_value_dict_with_valparams(self): q = Insert() sql_t = q.into_table('t1').set_raw_value({'t1c1': ('PASSWORD(?)', ('mypw',))}).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (PASSWORD(?))', ['mypw'])) def test_select_string_col(self): q = Insert() sql_t = q.into_table('t1').columns('t1c1').select('SELECT t2c1 FROM t2').sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) SELECT t2c1 FROM t2', None)) def test_select_string_cols(self): q = Insert() sql_t = q.into_table('t1').columns(['t1c1', 't1c2']).select('SELECT `t2c1`, `t2c2` FROM t2').sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`, `t1c2`) SELECT `t2c1`, `t2c2` FROM t2', None)) def test_select_obj_cols(self): q = Insert() qselect = Select('t2').columns(['t2c1', 't2c2']) sql_t = q.into_table('t1').columns(['t1c1', 't1c2']).select(qselect).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`, `t1c2`) SELECT `t2c1`, `t2c2` FROM t2', None)) def test_ignore(self): q = Insert('t1', ignore_error=True) sql_t = q.set_value('t1c1', 1).sql() assert_equals(sql_t, ('INSERT IGNORE INTO t1 (`t1c1`) VALUES (1)', None)) def test_function_batch_1x1(self): q = Insert() data = [['v1']] sql_t = q.into_table('t1').columns('t1c1').set_batch_value(data).sql() assert_equals(sql_t, ("INSERT INTO t1 (`t1c1`) VALUES (?)", data)) def test_function_batch_3x1(self): q = Insert() data = [['v1'], ['v2'], ['NOW()']] sql_t = q.into_table('t1').columns('t1c1').set_batch_value(data).sql() assert_equals(sql_t, ("INSERT INTO t1 (`t1c1`) VALUES (?)", data)) def test_function_batch_3x3(self): q = Insert() data = [['v1', 'v2', 'NOW()'], ['v1', 'v2', 'NOW()'], ['v1', 'v2', 'NOW()']] sql_t = q.into_table('t1').columns(['t1c1', 't1c2', 't1c3']).set_batch_value(data).sql() assert_equals(sql_t, ("INSERT INTO t1 (`t1c1`, `t1c2`, `t1c3`) VALUES (?, ?, ?)", data)) def test_function_batch_1x1_noparam(self): q = Insert(placeholder=False) data = [["'v1'"]] sql = q.into_table('t1').columns('t1c1').set_batch_value(data).sql() assert_equals(sql, "INSERT INTO t1 (`t1c1`) VALUES ('v1')") def test_function_batch_3x1_noparam(self): q = Insert(placeholder=False) data = [["'v1'"], ["'v2'"], ['NOW()']] sql = q.into_table('t1').columns('t1c1').set_batch_value(data).sql() assert_equals(sql, "INSERT INTO t1 (`t1c1`) VALUES ('v1'), ('v2'), (NOW())") def test_function_batch_3x3_noparam(self): q = Insert(placeholder=False) data = [["'r1v1'", "'r1v2'", 'NOW()'], ["'r2v1'", "'r2v2'", 'NOW()'], ["'r3v1'", "'r3v2'", 'NOW()']] sql = q.into_table('t1').columns(['t1c1', 't1c2', 't1c3']).set_batch_value(data).sql() assert_equals(sql, "INSERT INTO t1 (`t1c1`, `t1c2`, `t1c3`) VALUES ('r1v1', 'r1v2', NOW()), ('r2v1', 'r2v2', NOW()), ('r3v1', 'r3v2', NOW())") def test_dict_strings_utf_param(self): q = Insert() values = OrderedDict([('t1c1', u'äöü')]) sql_t = q.into_table('t1').set_value(values).sql() assert_equals(sql_t, ("INSERT INTO t1 (`t1c1`) VALUES (?)", [u'äöü'])) def test_dict_strings_utf_raw(self): q = Insert() sql_t = q.into_table('t1').set_raw_value('t1c1', u'"äöü"').sql() assert_equals(sql_t, (u'INSERT INTO t1 (`t1c1`) VALUES ("äöü")', None)) def test_dict_strings_utf_batch(self): q = Insert() data = [[u'äöü']] sql_t = q.into_table('t1').columns('t1c1').set_batch_value(data).sql() assert_equals(sql_t, ('INSERT INTO t1 (`t1c1`) VALUES (?)', data)) def test_dict_strings_utf_noparam(self): q = Insert(placeholder=False) sql = q.into_table('t1').set_value('t1c1', u'"äöü"').sql() assert_equals(sql, u'INSERT INTO t1 (`t1c1`) VALUES ("äöü")') def test_set_value_int_option(self): q = Insert() sql_t = q.set_option('LOW_PRIORITY').into_table('t1').set_value('t1c1', 1).sql() assert_equals(sql_t, ('INSERT LOW_PRIORITY INTO t1 (`t1c1`) VALUES (1)', None)) @raises(ValueError) def test_fail_no_tables(self): q = Insert() q.set_value('t1c1', 1).sql() @raises(ValueError) def test_fail_multi_tables(self): Insert(['t1', 't2']) @raises(ValueError) def test_fail_no_values(self): q = Insert('t1') q.sql() @raises(ValueError) def test_fail_set_columns(self): q = Insert() q.into_table('t1').columns('t1c1').set_value('t1c1', 1).sql() @raises(ValueError) def test_fail_select_with_set_value(self): q = Insert() q.into_table('t1').set_value('t1c1', 1).select('SELECT * FROM t2').sql() @raises(ValueError) def test_fail_select_no_columns(self): q = Insert() q.into_table('t1').select('SELECT * FROM t2').sql() @raises(ValueError) def test_fail_batch_values(self): q = Insert() data = [['v1']] q.into_table('t1').set_value('t1c1', 1).set_batch_value(data).sql() @raises(ValueError) def test_fail_batch_no_columns(self): q = Insert() data = [['v1']] q.into_table('t1').set_batch_value(data).sql() @raises(ValueError) def test_fail_batch_select(self): q = Insert() data = [['v1']] q.into_table('t1').columns('t1c1').set_batch_value(data).select('SELECT * FROM t2').sql() @raises(ValueError) def test_fail_select_with_params(self): q = Insert() qselect = Select('t2').columns(['t2c1']).where_value('t2c1', 't2v1') q.into_table('t1').columns(['t1c1']).select(qselect).sql()

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c73ba4d5a3586ccb983492f53d2077824a4a9c23

48

py

Python

imagepy/tools/Standard/rectangle_tol.py

dada1437903138/imagepy

65d9ce088894eef587054e04018f9d34ff65084f

[ "BSD-4-Clause" ]

1,178

2017-05-25T06:59:01.000Z

2022-03-31T11:38:53.000Z

imagepy/tools/Standard/rectangle_tol.py

TomisTony/imagepy

3c378ebaf72762b94f0826a410897757ebafe689

[ "BSD-4-Clause" ]

76

2017-06-10T17:01:50.000Z

2021-12-23T08:13:29.000Z

imagepy/tools/Standard/rectangle_tol.py

TomisTony/imagepy

3c378ebaf72762b94f0826a410897757ebafe689

[ "BSD-4-Clause" ]

315

2017-05-25T12:59:53.000Z

2022-03-07T22:52:21.000Z

from sciapp.action import RectangleROI as Plugin

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6

c754699085940e2d4eab000c0e3eaaa279d138db

1,823

py

Python

weasyl/test/login/test_get_account_verification_token.py

sl1-1/weasyl

d4f6bf3e33b85a2289a451d95d5b90ff24f5d539

[ "Apache-2.0" ]

1

2019-02-15T04:21:48.000Z

weasyl/test/login/test_get_account_verification_token.py

sl1-1/weasyl

d4f6bf3e33b85a2289a451d95d5b90ff24f5d539

[ "Apache-2.0" ]

254

2017-12-23T19:36:43.000Z

2020-04-14T21:46:13.000Z

weasyl/test/login/test_get_account_verification_token.py

sl1-1/weasyl

d4f6bf3e33b85a2289a451d95d5b90ff24f5d539

[ "Apache-2.0" ]

1

2017-12-23T18:42:16.000Z

from __future__ import absolute_import import pytest import arrow from weasyl import login from weasyl import define as d from weasyl.test.utils import Bag user_name = "test" email_addr = "test@weasyl.com" token = "a" * 40 # Main test password raw_password = "0123456789" @pytest.mark.usefixtures('db') def test_acct_verif_token_returned_if_email_provided_to_function(): form = Bag(username=user_name, password='0123456789', passcheck='0123456789', email=email_addr, emailcheck=email_addr, day='12', month='12', year=arrow.now().year - 19) d.engine.execute(d.meta.tables["logincreate"].insert(), { "token": token, "username": form.username, "login_name": form.username, "hashpass": login.passhash(raw_password), "email": form.email, "birthday": arrow.Arrow(2000, 1, 1), "unixtime": arrow.now(), }) acct_verification_token = login.get_account_verification_token(email=form.email, username=None) assert token == acct_verification_token @pytest.mark.usefixtures('db') def test_acct_verif_token_returned_if_username_provided_to_function(): form = Bag(username=user_name, password='0123456789', passcheck='0123456789', email=email_addr, emailcheck=email_addr, day='12', month='12', year=arrow.now().year - 19) d.engine.execute(d.meta.tables["logincreate"].insert(), { "token": token, "username": form.username, "login_name": form.username, "hashpass": login.passhash(raw_password), "email": form.email, "birthday": arrow.Arrow(2000, 1, 1), "unixtime": arrow.now(), }) acct_verification_token = login.get_account_verification_token(email=None, username=form.username) assert token == acct_verification_token

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6

c759c9ab8ec317c12e9c50fae9314fdd2af17606

30

py

Python

server/src/police_lineups/context/__init__.py

vabalcar/police-lineups

9c4a17d58e973d6db6e442bd9d5f4313ad4d51b7

[ "MIT" ]

null

server/src/police_lineups/context/__init__.py

vabalcar/police-lineups

9c4a17d58e973d6db6e442bd9d5f4313ad4d51b7

[ "MIT" ]

2

2021-09-24T11:43:58.000Z

2021-09-24T12:00:21.000Z

server/src/police_lineups/context/__init__.py

vabalcar/police-lineups

9c4a17d58e973d6db6e442bd9d5f4313ad4d51b7

[ "MIT" ]

null

from .user import UserContext

15

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0.833333

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null

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1

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6

c78cdbe4c128c78b587f6b2d42af30efcc44aa11

29

py

Python

{{cookiecutter.project_name}}/{{cookiecutter.app_name}}/schemas/__init__.py

LeroyShirto/cookiecutter-flask-restful-docker

a71fe98480fb38c3e353ce9b64dad4d7a1b0ccac

[ "MIT" ]

5

2018-05-12T15:34:11.000Z

2020-07-09T09:16:02.000Z

{{cookiecutter.project_name}}/{{cookiecutter.app_name}}/schemas/__init__.py

LeroyShirto/cookiecutter-flask-restful-docker

a71fe98480fb38c3e353ce9b64dad4d7a1b0ccac

[ "MIT" ]

1

2021-11-30T11:06:38.000Z

{{cookiecutter.project_name}}/{{cookiecutter.app_name}}/schemas/__init__.py

LeroyShirto/cookiecutter-flask-restful-docker

a71fe98480fb38c3e353ce9b64dad4d7a1b0ccac

[ "MIT" ]

1

2019-02-13T09:57:55.000Z

from .user import UserSchema

14.5

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6

4003385f88006a77739fb43ae3590c2882365db3

168

py

Python

tardis/rest/token_generator/__main__.py

maxfischer2781/tardis

a83ba0a02d2f153a8ab95b84ec78bc6ababa57a5

[ "MIT" ]

4

2018-05-22T13:22:06.000Z

2019-03-26T15:32:57.000Z

tardis/rest/token_generator/__main__.py

maxfischer2781/tardis

a83ba0a02d2f153a8ab95b84ec78bc6ababa57a5

[ "MIT" ]

50

2018-05-18T11:46:39.000Z

2019-04-26T07:29:45.000Z

tardis/rest/token_generator/__main__.py

maxfischer2781/tardis

a83ba0a02d2f153a8ab95b84ec78bc6ababa57a5

[ "MIT" ]

2

2018-12-12T13:15:59.000Z

2018-12-17T08:18:15.000Z

from .generate_token import generate_token import typer def generate_token_cli(): typer.run(generate_token) if __name__ == "__main__": generate_token_cli()

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0

1

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1

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6

400bb7ebd6eca5cb86cb65e33abcb57eb189869c

5,254

py

Python

plugin/src/py/android_screenshot_tests/test_device_name_calculator.py

xiphirx/screenshot-tests-for-android

d6c0107239cea8675e76c1e868701f50b5e46be2

[ "Apache-2.0" ]

1

2021-01-13T13:13:55.000Z

plugin/src/py/android_screenshot_tests/test_device_name_calculator.py

xiphirx/screenshot-tests-for-android

d6c0107239cea8675e76c1e868701f50b5e46be2

[ "Apache-2.0" ]

null

plugin/src/py/android_screenshot_tests/test_device_name_calculator.py

xiphirx/screenshot-tests-for-android

d6c0107239cea8675e76c1e868701f50b5e46be2

[ "Apache-2.0" ]

null

import sys import unittest from .device_name_calculator import DeviceNameCalculator if sys.version_info >= (3,): from unittest.mock import * else: from mock import * class TestDeviceNameCalculator(unittest.TestCase): def test_API_19_GP_XXHDPI_1080x1920_arm64_v8a_esES(self): def mock_data(parameters): if 'ro.build.version.sdk' in parameters: return '19' elif 'com.google.android.gms' in parameters: return 'package:/data/app/com.google.android.gms-pHwJaHhvXiRvuTo2Qxdbww==/base.apk' elif 'density' in parameters: return 'Physical density: 420' elif 'size' in parameters: return 'Physical size: 1080x1920' elif 'ro.product.cpu.abi' in parameters: return 'arm64-v8a' elif 'persist.sys.locale' in parameters: return 'es-ES' return None adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator.name() assert result == "API_19_GP_XXHDPI_1080x1920_arm64-v8a_es-ES" def test_API_23_NO_GP_XXHDPI_1080x1920_arm64_v8a_esES(self): def mock_data(parameters): if 'ro.build.version.sdk' in parameters: return '23' elif 'com.google.android.gms' in parameters: return None elif 'density' in parameters: return 'Physical density: 420' elif 'size' in parameters: return 'Physical size: 1080x1920' elif 'ro.product.cpu.abi' in parameters: return 'arm64-v8a' elif 'persist.sys.locale' in parameters: return 'es-ES' return None adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator.name() assert result == "API_23_NO_GP_XXHDPI_1080x1920_arm64-v8a_es-ES" def test_API_25_NO_GP_XXHDPI_1080x1920_x86_esES(self): def mock_data(parameters): if 'ro.build.version.sdk' in parameters: return '25' elif 'com.google.android.gms' in parameters: return None elif 'density' in parameters: return 'Physical density: 420' elif 'size' in parameters: return 'Physical size: 1080x1920' elif 'ro.product.cpu.abi' in parameters: return 'x86' elif 'persist.sys.locale' in parameters: return None elif 'ro.product.locale' in parameters: return 'es-ES' return None adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator.name() assert result == "API_25_NO_GP_XXHDPI_1080x1920_x86_es-ES" def density_10_to_LDPI(self): def mock_data(parameters): return 'Physical density: 10' adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator._screen_density_text() assert result == "LDPI" def density_140_to_MDPI(self): def mock_data(parameters): return 'Physical density: 140' adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator._screen_density_text() assert result == "MDPI" def density_200_to_HDPI(self): def mock_data(parameters): return 'Physical density: 200' adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator._screen_density_text() assert result == "HDPI" def density_250_to_XHDPI(self): def mock_data(parameters): return 'Physical density: 250' adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator._screen_density_text() assert result == "XHDPI" def density_340_to_XXHDPI(self): def mock_data(parameters): return 'Physical density: 340' adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator._screen_density_text() assert result == "XXHDPI" def density_500_to_XXXHDPI(self): def mock_data(parameters): return 'Physical density: 500' adb_executor = MagicMock() adb_executor.execute.side_effect = mock_data device_calculator = DeviceNameCalculator(adb_executor) result = device_calculator._screen_density_text() assert result == "XXXHDPI"

31.27381

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167

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0.440678

0

null

0

1

0

null

0

6

40689724aed30d6294012decca56364ee88a477e

2,216

py

Python

plot_2/plt_box_r.py

Mrchengyuan/modling-dynamic-system

a6bfac864c27cc92161f1fc88af605dda3106feb

[ "Unlicense" ]

1

2021-06-23T02:11:33.000Z

plot_2/plt_box_r.py

Mrchengyuan/modling-dynamic-system

a6bfac864c27cc92161f1fc88af605dda3106feb

[ "Unlicense" ]

null

plot_2/plt_box_r.py

Mrchengyuan/modling-dynamic-system

a6bfac864c27cc92161f1fc88af605dda3106feb

[ "Unlicense" ]

null

import matplotlib.pyplot as plt from matplotlib.pyplot import MultipleLocator font_titles = {'family': 'Times New Roman', 'color': 'black', 'weight': 'normal', 'size': 8,} font_labels = {'family': 'Times New Roman', 'color': 'black', 'weight': 'normal', 'size': 8,} import numpy as np time_scale = np.arange(0.01,0.51,0.01) xticks=[0,10,20,30,40,50] lable=[0,0.1,0.2,0.3,0.4,0.5] ytricks=[0,0.25,0.5,0.75,0.1] mae_hybrid_model_close_training=np.load('mae_error_hybrid_model.npy') mae_seq2seq_model_close_training=np.load('mae_error_seq2seq_model.npy') uy_mae_hybrid_model_close_training=mae_hybrid_model_close_training[:,:,0:1].reshape(-1,50) uy_mae_seq2seq_model_close_training=mae_seq2seq_model_close_training[:,:,0:1].reshape(-1,50) r_mae_hybrid_model_close_training=mae_hybrid_model_close_training[:,:,1:2].reshape(-1,50) r_mae_seq2seq_model_close_training=mae_seq2seq_model_close_training[:,:,1:2].reshape(-1,50) plt.figure(dpi=120,figsize=(12,3.3)) plt.subplot(121) plt.xlabel('Predicted Horizens (s)',fontdict=font_labels) plt.ylabel('$||e_{r,k}||$ (rad/s)',fontdict=font_labels) plt.grid(True,alpha=0.4) plt.title('Distribution of $||e_{r,k}||$,Hybrid model',font_titles) plt.boxplot(r_mae_hybrid_model_close_training[:,0:50],showfliers=False,boxprops = {'color':'blue',},whiskerprops={'linestyle':'--','dashes':(5,3)}) y_major_locator=MultipleLocator(0.005) ax=plt.gca() ax.yaxis.set_major_locator(y_major_locator) plt.xticks(xticks,labels=lable) ax=plt.gca() ax.yaxis.set_major_locator(y_major_locator) plt.tick_params(labelsize=8) plt.ylim(0,0.015) plt.subplot(122) plt.xlabel('Predicted Horizens (s)',fontdict=font_labels) plt.ylabel('$||e_{r,k}||$ (rad/s)',fontdict=font_labels) plt.grid(True,alpha=0.4) plt.title('Distribution of $||e_{r,k}||$,GRU Encoder-Decoder',font_titles) plt.boxplot(r_mae_seq2seq_model_close_training[:,0:50],showfliers=False,boxprops = {'color':'blue',},whiskerprops={'linestyle':'--','dashes':(5,3)}) plt.xticks(xticks,labels=lable) y_major_locator=MultipleLocator(0.005) ax=plt.gca() ax.yaxis.set_major_locator(y_major_locator) plt.tick_params(labelsize=8) plt.ylim(0,0.015) plt.savefig('箱线图r.png',dpi=600) plt.show()

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1

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1

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null

0

6

904b5e9027b3145ce2aa3ac382d18ca4ed33db7e

74

py

Python

fileconversions/conversions/rtf_to_pdf_conversion.py

wilbertom/fileconversions

c48fda9b2804524fc57d1f6963d09645825b0da6

[ "MIT" ]

null

fileconversions/conversions/rtf_to_pdf_conversion.py

wilbertom/fileconversions

c48fda9b2804524fc57d1f6963d09645825b0da6

[ "MIT" ]

null

fileconversions/conversions/rtf_to_pdf_conversion.py

wilbertom/fileconversions

c48fda9b2804524fc57d1f6963d09645825b0da6

[ "MIT" ]

null

from .conversion import Conversion class RtfToPdf(Conversion): pass

12.333333

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true

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null

0

1

0

1

0

6

906e33c70489770cbea96f5dcff1cd11b561770b

40

py

Python

coggle_ecs/__init__.py

MrGVSV/coggle-ecs

e75a5081575e794f52779ecdb87e514ec197f029

[ "MIT" ]

null

coggle_ecs/__init__.py

MrGVSV/coggle-ecs

e75a5081575e794f52779ecdb87e514ec197f029

[ "MIT" ]

null

coggle_ecs/__init__.py

MrGVSV/coggle-ecs

e75a5081575e794f52779ecdb87e514ec197f029

[ "MIT" ]

null

from coggle_ecs.parser import CoggleECS

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6

90d08ba3d35dc3628fe8ce0221f76ab0d343ca58

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py

Python

classpath/__init__.py

kimi641/pyJVM

9e2b2392044a8ddd41ff8dda18a26e307776ae34

[ "MIT" ]

null

classpath/__init__.py

kimi641/pyJVM

9e2b2392044a8ddd41ff8dda18a26e307776ae34

[ "MIT" ]

1

2021-01-21T09:38:24.000Z

classpath/__init__.py

kimi641/pyJVM

9e2b2392044a8ddd41ff8dda18a26e307776ae34

[ "MIT" ]

null

from .class_path import *

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null

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1

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1

0

6

90e141db9ea3ab744ea3a8ce48fdafaff4bacef5

115

py

Python

office365/sharepoint/principal/appprincipal_identity_provider.py

rikeshtailor/Office365-REST-Python-Client

ca7bfa1b22212137bb4e984c0457632163e89a43

[ "MIT" ]

544

2016-08-04T17:10:16.000Z

2022-03-31T07:17:20.000Z

office365/sharepoint/principal/appprincipal_identity_provider.py

rikeshtailor/Office365-REST-Python-Client

ca7bfa1b22212137bb4e984c0457632163e89a43

[ "MIT" ]

438

2016-10-11T12:24:22.000Z

2022-03-31T19:30:35.000Z

office365/sharepoint/principal/appprincipal_identity_provider.py

rikeshtailor/Office365-REST-Python-Client

ca7bfa1b22212137bb4e984c0457632163e89a43

[ "MIT" ]

202

2016-08-22T19:29:40.000Z

2022-03-30T20:26:15.000Z

from office365.sharepoint.base_entity import BaseEntity class AppPrincipalIdentityProvider(BaseEntity): pass

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115

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0

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null

0

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0

1

0

6

2971b7539cf24ef40f2147451c8311bdeb111f18

1,648

py

Python

tests/unit/clients/test_back_refs_delete.py

atsgen/tf-vcenter-manager

2cfa07f038b86b3087842c34abb96b15da0b36fb

[ "Apache-2.0" ]

1

2022-03-13T06:31:40.000Z

tests/unit/clients/test_back_refs_delete.py

atsgen/tf-vcenter-manager

2cfa07f038b86b3087842c34abb96b15da0b36fb

[ "Apache-2.0" ]

null

tests/unit/clients/test_back_refs_delete.py

atsgen/tf-vcenter-manager

2cfa07f038b86b3087842c34abb96b15da0b36fb

[ "Apache-2.0" ]

1

2020-08-25T12:46:12.000Z

""" Deleting objects in VNC should also delete it's back-ref objects. """ def test_delete_vmi(vnc_api_client, vnc_lib, vnc_vmi_1): vnc_vmi_1.get_instance_ip_back_refs.return_value = [{'uuid': 'instance-ip-uuid'}] vnc_lib.virtual_machine_interface_read.return_value = vnc_vmi_1 vnc_api_client.delete_vmi('vmi-uuid-1') vnc_lib.instance_ip_delete.assert_called_once_with(id='instance-ip-uuid') vnc_lib.virtual_machine_interface_delete.assert_called_once_with(id='vmi-uuid-1') def test_vmi_no_back_refs(vnc_api_client, vnc_lib, vnc_vmi_1): vnc_vmi_1.get_instance_ip_back_refs.return_value = None vnc_lib.virtual_machine_interface_read.return_value = vnc_vmi_1 vnc_api_client.delete_vmi('vmi-uuid-1') vnc_lib.instance_ip_delete.assert_not_called() def test_delete_vm(vnc_api_client, vnc_lib, vnc_vm, vnc_vmi_1): vnc_vm.get_virtual_machine_interface_back_refs.return_value = [{'uuid': 'vmi-uuid-1'}] vnc_lib.virtual_machine_read.return_value = vnc_vm vnc_vmi_1.get_instance_ip_back_refs.return_value = [{'uuid': 'instance-ip-uuid'}] vnc_lib.virtual_machine_interface_read.return_value = vnc_vmi_1 vnc_api_client.delete_vm('vm-uuid') vnc_lib.virtual_machine_interface_delete.assert_called_once_with(id='vmi-uuid-1') vnc_lib.virtual_machine_delete.assert_called_once_with(id='vm-uuid') def test_vm_no_back_refs(vnc_api_client, vnc_lib, vnc_vm): vnc_vm.get_virtual_machine_interface_back_refs.return_value = None vnc_lib.virtual_machine_read.return_value = vnc_vm vnc_api_client.delete_vm('vm-uuid') vnc_lib.virtual_machine_interface_delete.assert_not_called()

39.238095

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1,648

4.182143

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0.0538

0.153715

0.891546

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0.823228

0.790777

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0.101942

1,648

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91

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0

0.166667

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null

0

1

0

null

0

6

46352139922766c390ca513f9137205da017e2e4

129

py

Python

bi_reports_illustrate_bot/admin.py

BIChatbotGenerator/BICGen

45a96e171219f4543b14869bf832633b634ecc15

[ "Apache-2.0" ]

null

bi_reports_illustrate_bot/admin.py

BIChatbotGenerator/BICGen

45a96e171219f4543b14869bf832633b634ecc15

[ "Apache-2.0" ]

null

bi_reports_illustrate_bot/admin.py

BIChatbotGenerator/BICGen

45a96e171219f4543b14869bf832633b634ecc15

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import * admin.site.register([TelegramState, TelegramUser, TelegramChat, Report])

25.8

72

0.806202

15

129

6.933333

0.8

0.211538

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129

5

72

25.8

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0

null

0

1

0

1

0

1

0

6

468c46c0ec6ee310609c5a38a0469cf65e12adde

102

py

Python

python/triangle2.py

jeremyprice/strengths_name_tents

4b4bbec9d5e6d8c24b7ff98c855c3f58bb7e6aac

[ "Unlicense" ]

null

python/triangle2.py

jeremyprice/strengths_name_tents

4b4bbec9d5e6d8c24b7ff98c855c3f58bb7e6aac

[ "Unlicense" ]

null

python/triangle2.py

jeremyprice/strengths_name_tents

4b4bbec9d5e6d8c24b7ff98c855c3f58bb7e6aac

[ "Unlicense" ]

null

n = 1 while n < 5: print(n * "*") n = n + 1 while n > 0: print(n * "*") n = n - 1

12.75

18

0.333333

18

102

1.888889

0.333333

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0

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0

1

null

1

0

1

0

null

0

6

d3b9d3733e24c354c111d3ab33ff99d37caec131

3,440

py

Python

testcode/cleanh5.py

lachmann12/prismexp

2e9c739b6c8c10c44f9f81e84e87e47dac2c73f0

[ "Apache-2.0" ]

1

2020-11-10T13:33:26.000Z

testcode/cleanh5.py

MaayanLab/prismexp

39e6cad055d7698e466f0d197a1563d08d3e5eab

[ "Apache-2.0" ]

null

testcode/cleanh5.py

MaayanLab/prismexp

39e6cad055d7698e466f0d197a1563d08d3e5eab

[ "Apache-2.0" ]

null

from sklearn.cluster import KMeans import h5py as h5 import numpy as np import pandas as pd import random from typing import List import sys import os import time import math os.remove("mouse_matrix_3.h5") f1 = h5.File("mouse_matrix.h5", "r") exp = f1["data/expression"] f = h5.File("mouse_matrix_3.h5", "w") dset = f.create_dataset("data/expression", exp.shape, chunks=(2, 3000), dtype=np.int32, compression='gzip', compression_opts=9) steps = 500 step_size = math.floor(exp.shape[0]/steps) for i in range(0, steps+1): print(i) fromStep = i*step_size toStep = min((i+1)*step_size, exp.shape[0]) ee = exp[fromStep:toStep, :] dset[fromStep:toStep, :] = exp[fromStep:toStep, :] f.close() f1.close() ## benchmark me f = h5.File("mouse_matrix_2.h5", "r") sa = random.sample(set(range(0, 28000)), 2000) sa.sort() start = time.time() exp = f["data/expression"][sa, :] print("Extract samples: "+str(time.time()- start)) f.close() f = h5.File("mouse_matrix_2.h5", "r") sa = random.sample(set(range(0, 32000)), 10) sa.sort() start = time.time() exp = f["data/expression"][:, 5] print("Extract gene: "+str(time.time() - start)) f.close() f = h5.File("mouse_matrix_2.h5", "r") sa = random.sample(set(range(0, 32000)), 10) sa.sort() start = time.time() exp = f["data/expression"][:, sa] print("Extract gene (10): "+str(time.time() - start)) f.close() f1 = h5.File("mouse_matrix.h5", "r") f = h5.File("mouse_matrix_3.h5", "a") keys = list(f1["meta"].keys()) for k in keys: print(k) f.create_dataset("meta/"+k, data=f1["meta/"+k], compression='gzip', compression_opts=9) f.close() f1.close() f1 = h5.File("mouse_matrix.h5", "r") exp = f1["data/expression"] f = h5.File("mouse_matrix_2.h5", "w") f.close() f1.close() ## benchmark me f = h5.File("mouse_matrix_t.h5", "r") sa = random.sample(set(range(0, 284907)), 2000) sa.sort() start = time.time() exp = f["data/expression"][sa, :] print("Extract samples: "+str(time.time()- start)) f.close() f = h5.File("mouse_matrix_t.h5", "r") sa = random.sample(set(range(0, 32000)), 10) sa.sort() start = time.time() exp = f["data/expression"][:, 5] print("Extract gene: "+str(time.time() - start)) f.close() f = h5.File("mouse_matrix_t.h5", "r") sa = random.sample(set(range(0, 32000)), 10) sa.sort() start = time.time() exp = f["data/expression"][:, sa] print("Extract gene (10): "+str(time.time() - start)) f.close() ## benchmark me f = h5.File("mouse_matrix.h5", "r") sa = random.sample(set(range(0, 284907)), 2000) sa.sort() start = time.time() exp = f["data/expression"][sa, :] print("Extract samples: "+str(time.time()- start)) f.close() f = h5.File("mouse_matrix.h5", "r") sa = random.sample(set(range(0, 32000)), 10) sa.sort() start = time.time() exp = f["data/expression"][:, 5] print("Extract gene: "+str(time.time() - start)) f.close() f = h5.File("mouse_matrix.h5", "r") sa = random.sample(set(range(0, 32000)), 10) sa.sort() start = time.time() exp = f["data/expression"][:, sa] print("Extract gene (10): "+str(time.time() - start)) f.close() ## benchmark me f = h5.File("mouse_matrix_t.h5", "r") sa = random.sample(set(range(0, 284907)), 500) sa.sort() start = time.time() exp = f["data/expression"][sa, :] print("Extract samples: "+str(time.time()-start)) f.close() f = h5.File("mouse_matrix.h5", "r") sa.sort() start = time.time() exp2 = f["data/expression"][sa, :] print("Extract samples: "+str(time.time()-start)) f.close()

21.234568

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0.645349

572

3,440

3.818182

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161

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21.36646

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null

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null

0

6

d3e0399acff27310b02155dca49b2425de7657bc

106

py

Python

module/__init__.py

sp-nitech/DNN-HSMM

3476c262eb2b57bad9b85ea1f2bd282b0bafe49c

[ "BSD-3-Clause" ]

38

2021-03-15T08:42:22.000Z

2022-03-14T10:32:15.000Z

module/__init__.py

sp-nitech/DNN-HSMM

3476c262eb2b57bad9b85ea1f2bd282b0bafe49c

[ "BSD-3-Clause" ]

3

2021-07-07T02:11:08.000Z

2021-11-10T10:23:16.000Z

module/__init__.py

sp-nitech/DNN-HSMM

3476c262eb2b57bad9b85ea1f2bd282b0bafe49c

[ "BSD-3-Clause" ]

9

2021-03-15T09:55:42.000Z

2022-03-14T10:32:18.000Z

from .data import DataSet, DataCollate from .embedding import Model as Embedding from .model import Model

26.5

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0.820755

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106

5.8

0.533333

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0

0.141509

106

3

42

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true

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null

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1

0

1

0

1

0

6

318994bbbbda506333a40fbd07cbfdd9f438873d

3,392

py

Python

tests/test_datasets.py

Lucas-Prates/ruptures

9685818d08ca024c0abb6ecf6121f2f86fb26dba

[ "BSD-2-Clause" ]

1

2021-12-10T18:12:42.000Z

tests/test_datasets.py

Lucas-Prates/ruptures

9685818d08ca024c0abb6ecf6121f2f86fb26dba

[ "BSD-2-Clause" ]

null

tests/test_datasets.py

Lucas-Prates/ruptures

9685818d08ca024c0abb6ecf6121f2f86fb26dba

[ "BSD-2-Clause" ]

null

from itertools import product import pytest import numpy as np from ruptures.datasets import pw_constant, pw_linear, pw_normal, pw_wavy @pytest.mark.parametrize("func", [pw_constant, pw_linear, pw_normal, pw_wavy]) def test_empty_arg(func): func() @pytest.mark.parametrize( "func, n_samples, n_features, n_bkps, noise_std", product([pw_constant], range(20, 1000, 200), range(1, 4), [2, 5, 3], [None, 1, 2]), ) def test_constant(func, n_samples, n_features, n_bkps, noise_std): signal, bkps = func( n_samples=n_samples, n_features=n_features, n_bkps=n_bkps, noise_std=noise_std ) assert signal.shape == (n_samples, n_features) assert len(bkps) == n_bkps + 1 assert bkps[-1] == n_samples def test_seed(n_samples=200, n_features=3, n_bkps=5, noise_std=1, seed=12345): # pw_constant signal1, bkps1 = pw_constant( n_samples=n_samples, n_features=n_features, n_bkps=n_bkps, noise_std=noise_std, seed=seed, ) signal2, bkps2 = pw_constant( n_samples=n_samples, n_features=n_features, n_bkps=n_bkps, noise_std=noise_std, seed=seed, ) assert np.allclose(signal1, signal2) assert bkps1 == bkps2 # pw_normal signal1, bkps1 = pw_normal(n_samples=n_samples, n_bkps=n_bkps, seed=seed) signal2, bkps2 = pw_normal(n_samples=n_samples, n_bkps=n_bkps, seed=seed) assert np.allclose(signal1, signal2) assert bkps1 == bkps2 # pw_linear signal1, bkps1 = pw_linear( n_samples=n_samples, n_features=n_features, n_bkps=n_bkps, noise_std=noise_std, seed=seed, ) signal2, bkps2 = pw_linear( n_samples=n_samples, n_features=n_features, n_bkps=n_bkps, noise_std=noise_std, seed=seed, ) assert np.allclose(signal1, signal2) assert bkps1 == bkps2 # pw_wavy signal1, bkps1 = pw_wavy( n_samples=n_samples, n_bkps=n_bkps, noise_std=noise_std, seed=seed ) signal2, bkps2 = pw_wavy( n_samples=n_samples, n_bkps=n_bkps, noise_std=noise_std, seed=seed ) assert np.allclose(signal1, signal2) assert bkps1 == bkps2 @pytest.mark.parametrize( "func, n_samples, n_features, n_bkps, noise_std", product([pw_linear], range(20, 1000, 200), range(1, 4), [2, 5, 3], [None, 1, 2]), ) def test_linear(func, n_samples, n_features, n_bkps, noise_std): signal, bkps = func( n_samples=n_samples, n_features=n_features, n_bkps=n_bkps, noise_std=noise_std ) assert signal.shape == (n_samples, n_features + 1) assert len(bkps) == n_bkps + 1 assert bkps[-1] == n_samples @pytest.mark.parametrize( "func, n_samples, n_bkps, noise_std", product([pw_wavy], range(20, 1000, 200), [2, 5, 3], [None, 1, 2]), ) def test_wavy(func, n_samples, n_bkps, noise_std): signal, bkps = func(n_samples=n_samples, n_bkps=n_bkps, noise_std=noise_std) assert signal.shape == (n_samples,) assert len(bkps) == n_bkps + 1 assert bkps[-1] == n_samples @pytest.mark.parametrize( "func, n_samples, n_bkps", product([pw_normal], range(20, 1000, 200), [2, 5, 3]) ) def test_normal(func, n_samples, n_bkps): signal, bkps = func(n_samples=n_samples, n_bkps=n_bkps) assert signal.shape == (n_samples, 2) assert len(bkps) == n_bkps + 1 assert bkps[-1] == n_samples

30.017699

87

0.662736

522

3,392

4.01341

0.09387

0.156563

0.146062

0.093079

0.845823

0.820048

0.815752

0.791408

0.753222

0

0.041714

0.215507

3,392

112

88

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0.745584

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0.21978

1

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false

0

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0

0.10989

0

null

0

1

0

null

0

6

31ee5678b7708ef1fe20be9e9870713b065aa6db

101

py

Python

extinct/components/models/dino/__init__.py

olliethomas/extinct-dino

7d47d8f7763d9791fa8d5027898b27fcee0901c4

[ "Apache-2.0" ]

null

extinct/components/models/dino/__init__.py

olliethomas/extinct-dino

7d47d8f7763d9791fa8d5027898b27fcee0901c4

[ "Apache-2.0" ]

1

2021-10-13T14:21:10.000Z

extinct/components/models/dino/__init__.py

olliethomas/extinct-dino

7d47d8f7763d9791fa8d5027898b27fcee0901c4

[ "Apache-2.0" ]

null

from .dino import * from .eval import * from .head import * from .models import * from .vit import *

16.833333

21

0.70297

15

101

4.733333

0.466667

0.56338

0

0.19802

101

5

22

20.2

0.876543

0

1

0

true

0

1

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

9ecb9f947090fe6d662b6e26fbc51eb77d5ea5ce

168

py

Python

Book_Ladder/web/page/__init__.py

Rdjroot/BookLadder

d4e1f90572f2dda2e7c25890b99c965ded0f02c8

[ "MIT" ]

null

Book_Ladder/web/page/__init__.py

Rdjroot/BookLadder

d4e1f90572f2dda2e7c25890b99c965ded0f02c8

[ "MIT" ]

null

Book_Ladder/web/page/__init__.py

Rdjroot/BookLadder

d4e1f90572f2dda2e7c25890b99c965ded0f02c8

[ "MIT" ]

null

# -*- coding = utf-8 -*- # @Time:2021/3/713:30 # @Author:Linyu # @Software:PyCharm from flask import Blueprint page = Blueprint("page",__name__) import web.page.views

18.666667

33

0.696429

24

168

4.708333

0.833333

0.230089

0

0.075342

0.130952

168

9

34

18.666667

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false

0

0.666667

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0.666667

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null

1

0

1

0

null

0

1

0

1

0

6

9ef7e30ded83f0191f5b37633021bb760f535121

69

py

Python

lib/parsers/atom.py

RafalBuchner/turbo-snippets

740b70e7c588190b970921cdbaf2f465b7f0e968

[ "MIT" ]

null

lib/parsers/atom.py

RafalBuchner/turbo-snippets

740b70e7c588190b970921cdbaf2f465b7f0e968

[ "MIT" ]

null

lib/parsers/atom.py

RafalBuchner/turbo-snippets

740b70e7c588190b970921cdbaf2f465b7f0e968

[ "MIT" ]

null

from .base import BaseParser class AtomParser(BaseParser): pass

13.8

29

0.768116

8

69

6.625

0.875

0

0.173913

69

4

30

17.25

0.929825

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

73162334da544af48fd81794cde68883da1a70dc

73

py

Python

pymt/utils/__init__.py

mwtoews/pymt

81a8469b0d0d115d21186ec1d1c9575690d51850

[ "MIT" ]

38

2017-06-30T17:10:53.000Z

2022-01-05T07:38:03.000Z

pymt/utils/__init__.py

mwtoews/pymt

81a8469b0d0d115d21186ec1d1c9575690d51850

[ "MIT" ]

96

2017-04-04T18:52:41.000Z

2021-11-01T21:30:48.000Z

pymt/utils/__init__.py

mwtoews/pymt

81a8469b0d0d115d21186ec1d1c9575690d51850

[ "MIT" ]

15

2017-05-23T15:40:16.000Z

2021-06-14T21:30:28.000Z

from .utils import as_cwd, err, out __all__ = ["as_cwd", "err", "out"]

14.6

35

0.630137

12

73

3.333333

0.666667

0.25

0.4

0.55

0

0.178082

73

4

36

18.25

0.666667

0

0.164384

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

6

732f4bfcb3d8edbbd3ded216e2887a8fb0466a02

47

py

Python

src/adapy/action/__init__.py

LitterBot2017/Babysitter

ba189bbec20737670c3382bd3cccaa3a0e65b16c

[ "BSD-3-Clause" ]

null

src/adapy/action/__init__.py

LitterBot2017/Babysitter

ba189bbec20737670c3382bd3cccaa3a0e65b16c

[ "BSD-3-Clause" ]

null

src/adapy/action/__init__.py

LitterBot2017/Babysitter

ba189bbec20737670c3382bd3cccaa3a0e65b16c

[ "BSD-3-Clause" ]

null

from grasping import Grasp from rogue import *

15.666667

26

0.808511

7

47

5.428571

0.714286

0

0.170213

47

2

27

23.5

0.974359

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

735927ed116d3f5cb39d8324c45f4bcbf335b81e

61

py

Python

robot_ws/src/robot/spot_ros/spot_driver/scripts/__init__.py

ironWolf1990/ros-workspace

351ac9b15ab328cb2f1c77356383f0baa1204761

[ "MIT" ]

1

2021-05-13T17:52:25.000Z

robot_ws/src/robot/spot_ros/spot_driver/scripts/__init__.py

ironWolf1990/ros-workspace

351ac9b15ab328cb2f1c77356383f0baa1204761

[ "MIT" ]

null

robot_ws/src/robot/spot_ros/spot_driver/scripts/__init__.py

ironWolf1990/ros-workspace

351ac9b15ab328cb2f1c77356383f0baa1204761

[ "MIT" ]

null

import spot_ros import spot_wrapper.py import ros_helpers.py

15.25

22

0.868852

11

61

4.545455

0.545455

0.4

0

0.098361

61

3

23

20.333333

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

7df8b91dad762b43f7d4074c20df58b78cc4113e

201

py

Python

src/round2/pradnya/admin.py

sourabhedake/inc-pradnya-event-online-judge

90704b6816429415a5b74d46d200a903cad2d0e2

[ "MIT" ]

null

src/round2/pradnya/admin.py

sourabhedake/inc-pradnya-event-online-judge

90704b6816429415a5b74d46d200a903cad2d0e2

[ "MIT" ]

null

src/round2/pradnya/admin.py

sourabhedake/inc-pradnya-event-online-judge

90704b6816429415a5b74d46d200a903cad2d0e2

[ "MIT" ]

null

from django.contrib import admin from pradnya import models # Register your models here. admin.site.register(models.Questions) admin.site.register(models.user) admin.site.register(models.submissions)

28.714286

39

0.825871

28

201

5.928571

0.5

0.162651

0.307229

0.415663

0

0.084577

201

6

40

33.5

0.902174

0.129353

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6