xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

5c81f2bfdbd2eaa7d298b615226cb1c401f3ca0b

187

py

Python

necromancer/cli/shell_command.py

SeedyROM/necromancer

3a5d669bf7f6efb97e904431fc2bcce0fd2bc45f

[ "MIT" ]

null

necromancer/cli/shell_command.py

SeedyROM/necromancer

3a5d669bf7f6efb97e904431fc2bcce0fd2bc45f

[ "MIT" ]

11

2018-09-29T05:45:19.000Z

2021-06-01T22:39:29.000Z

necromancer/cli/shell_command.py

SeedyROM/necromancer

3a5d669bf7f6efb97e904431fc2bcce0fd2bc45f

[ "MIT" ]

1

2018-09-29T05:40:45.000Z

''' Usage: necro shell|s USER_COMMAND Options USER_COMMAND a command in the .necro.toml ''' from docopt import docopt if __name__ == '__main__': print(docopt(__doc__))

14.384615

52

0.679144

25

187

4.52

0.76

0.19469

0

0.219251

187

12

53

15.583333

0.773973

0.508021

0

0.095238

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

null

0

1

0

1

0

3

5c86464a3d05f2b2a5448d49a0741fcc1ad75863

787

py

Python

lib/utils.py

ngshiheng/six-percent

4c99d8f5f51bebd528097a1e8c3ac59cd56c3774

[ "MIT" ]

16

2019-06-02T08:01:49.000Z

2022-02-28T00:28:21.000Z

src/utils/encryption.py

ksern94/six-percent

a3eb637d72d47f396945a4488222d63ae93df53d

[ "MIT" ]

17

2019-12-12T10:28:08.000Z

2022-03-02T01:12:55.000Z

src/utils/encryption.py

ksern94/six-percent

a3eb637d72d47f396945a4488222d63ae93df53d

[ "MIT" ]

12

2019-12-12T10:32:01.000Z

2021-12-31T15:31:14.000Z

from cryptography.fernet import Fernet def generate_key() -> None: """ Generates a key and save it into a file """ key = Fernet.generate_key() with open("secret.key", "wb") as key_file: key_file.write(key) def load_key() -> bytes: """ Loads the key named `secret.key` from the current directory """ return open("secret.key", "rb").read() def encrypt_password(password: str) -> str: """ Returns an encrypted password """ encoded_password = password.encode() f = Fernet(load_key()) return f.encrypt(encoded_password).decode() def decrypt_password(hashed_password: str) -> str: """ Returns a decrypted password """ f = Fernet(load_key()) return f.decrypt(hashed_password.encode()).decode()

20.179487

63

0.635324

100

787

4.87

0.44

0.055441

0.053388

0.086242

0

0.229987

787

38

64

20.710526

0.80363

0.200762

0

0.142857

1

0

0.042254

0

1

0.285714

false

0.357143

0.071429

0

0.571429

0

null

0

1

0

null

0

1

0

1

0

3

5c9ae0e7e2b1d18da7e081cb6e3269620f60cbc6

210

py

Python

app/main/errors.py

youhaowei/ifriends-api

4f4483d91ec7e726a5c301a0c3cc1db1b6592f8f

[ "MIT" ]

null

app/main/errors.py

youhaowei/ifriends-api

4f4483d91ec7e726a5c301a0c3cc1db1b6592f8f

[ "MIT" ]

null

app/main/errors.py

youhaowei/ifriends-api

4f4483d91ec7e726a5c301a0c3cc1db1b6592f8f

[ "MIT" ]

null

from flask import render_template from . import main @main.app_errorhandler(404) def page_not_found(e): return "404", 404 @main.app_errorhandler(500) def internal_server_error(e): return "500", 500

16.153846

33

0.747619

32

210

4.6875

0.59375

0.093333

0.253333

0

0.101124

0.152381

210

12

34

17.5

0.741573

0

0.028571

0

1

0.25

false

0

0.25

0.75

0

null

0

1

0

null

0

1

0

1

0

3

5ca0875014df8ac0785a6721681b3daab627d633

1,365

py

Python

electrum/coins.py

wagerrdeveloper/WagerrElectrum

06b082a1aa3e831f648c2d27ca35150c8aea97a4

[ "MIT" ]

null

electrum/coins.py

wagerrdeveloper/WagerrElectrum

06b082a1aa3e831f648c2d27ca35150c8aea97a4

[ "MIT" ]

2

2021-06-02T00:29:07.000Z

2021-11-15T17:49:27.000Z

electrum/coins.py

wagerrdeveloper/WagerrElectrum

06b082a1aa3e831f648c2d27ca35150c8aea97a4

[ "MIT" ]

null

import sys class Coin(object): @classmethod def static_header_offset(cls, height): raise Exception('Not implemented') class Wagerr(Coin): PRE_ZEROCOIN_BLOCKS = 1 PRE_ZEROCOIN_HEADER_SIZE = 80 ZEROCOIN_HEADER_SIZE = 112 @classmethod def static_header_offset(cls, height): if height >= cls.PRE_ZEROCOIN_BLOCKS: return cls.PRE_ZEROCOIN_HEADER_SIZE * cls.PRE_ZEROCOIN_BLOCKS + cls.ZEROCOIN_HEADER_SIZE * (height - cls.PRE_ZEROCOIN_BLOCKS) return cls.PRE_ZEROCOIN_HEADER_SIZE * height def get_header_size(self, header: bytes): hex_to_int = lambda s: int.from_bytes(s, byteorder='little') if hex_to_int(header[0:4]) > 3: return self.ZEROCOIN_HEADER_SIZE return self.PRE_ZEROCOIN_HEADER_SIZE @classmethod def get_header_size_height(cls, height: int): if height in [1052605]: return cls.PRE_ZEROCOIN_HEADER_SIZE return cls.ZEROCOIN_HEADER_SIZE if height >= cls.PRE_ZEROCOIN_BLOCKS else cls.PRE_ZEROCOIN_HEADER_SIZE def check_header_size(self, header: bytes): size = self.get_header_size(header) if len(header) == self.PRE_ZEROCOIN_HEADER_SIZE: return True if len(header) == size: return True return False class WagerrTestnet(Wagerr): PRE_ZEROCOIN_BLOCKS = 1

31.744186

137

0.69011

182

1,365

4.851648

0.263736

0.1812

0.224236

0.166478

0.441676

0.287656

0.219706

0.12684

0

0.016299

0.235897

1,365

42

138

32.5

0.830297

0

0.272727

0

0.015385

0

1

0.151515

false

0

0.030303

0

0.666667

0

null

0

1

0

null

0

1

0

3

5cae12109771869a6ab7219474335cee226eaba9

81,854

py

Python

python/mxnet/symbol/gen_sparse.py

sneaxiy/NVIDIA-MxNet

ce30b18212fbf23f68c006a02cc034e417bb5518

[ "Apache-2.0" ]

null

python/mxnet/symbol/gen_sparse.py

sneaxiy/NVIDIA-MxNet

ce30b18212fbf23f68c006a02cc034e417bb5518

[ "Apache-2.0" ]

null

python/mxnet/symbol/gen_sparse.py

sneaxiy/NVIDIA-MxNet

ce30b18212fbf23f68c006a02cc034e417bb5518

[ "Apache-2.0" ]

3

2021-07-20T07:40:15.000Z

2021-08-03T08:39:17.000Z

# coding: utf-8# File content is auto-generated. Do not modify. # pylint: skip-file from ._internal import SymbolBase from ..base import _Null def ElementWiseSum(*args, **kwargs): r"""Adds all input arguments element-wise. .. math:: add\_n(a_1, a_2, ..., a_n) = a_1 + a_2 + ... + a_n ``add_n`` is potentially more efficient than calling ``add`` by `n` times. The storage type of ``add_n`` output depends on storage types of inputs - add_n(row_sparse, row_sparse, ..) = row_sparse - add_n(default, csr, default) = default - add_n(any input combinations longer than 4 (>4) with at least one default type) = default - otherwise, ``add_n`` falls all inputs back to default storage and generates default storage Defined in ../src/operator/tensor/elemwise_sum.cc:L156 This function support variable length of positional input. Parameters ---------- args : Symbol[] Positional input arguments name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def Embedding(data=None, weight=None, input_dim=_Null, output_dim=_Null, dtype=_Null, sparse_grad=_Null, name=None, attr=None, out=None, **kwargs): r"""Maps integer indices to vector representations (embeddings). This operator maps words to real-valued vectors in a high-dimensional space, called word embeddings. These embeddings can capture semantic and syntactic properties of the words. For example, it has been noted that in the learned embedding spaces, similar words tend to be close to each other and dissimilar words far apart. For an input array of shape (d1, ..., dK), the shape of an output array is (d1, ..., dK, output_dim). All the input values should be integers in the range [0, input_dim). If the input_dim is ip0 and output_dim is op0, then shape of the embedding weight matrix must be (ip0, op0). When "sparse_grad" is False, if any index mentioned is too large, it is replaced by the index that addresses the last vector in an embedding matrix. When "sparse_grad" is True, an error will be raised if invalid indices are found. Examples:: input_dim = 4 output_dim = 5 // Each row in weight matrix y represents a word. So, y = (w0,w1,w2,w3) y = [[ 0., 1., 2., 3., 4.], [ 5., 6., 7., 8., 9.], [ 10., 11., 12., 13., 14.], [ 15., 16., 17., 18., 19.]] // Input array x represents n-grams(2-gram). So, x = [(w1,w3), (w0,w2)] x = [[ 1., 3.], [ 0., 2.]] // Mapped input x to its vector representation y. Embedding(x, y, 4, 5) = [[[ 5., 6., 7., 8., 9.], [ 15., 16., 17., 18., 19.]], [[ 0., 1., 2., 3., 4.], [ 10., 11., 12., 13., 14.]]] The storage type of weight can be either row_sparse or default. .. Note:: If "sparse_grad" is set to True, the storage type of gradient w.r.t weights will be "row_sparse". Only a subset of optimizers support sparse gradients, including SGD, AdaGrad and Adam. Note that by default lazy updates is turned on, which may perform differently from standard updates. For more details, please check the Optimization API at: https://mxnet.incubator.apache.org/api/python/optimization/optimization.html Defined in ../src/operator/tensor/indexing_op.cc:L598 Parameters ---------- data : Symbol The input array to the embedding operator. weight : Symbol The embedding weight matrix. input_dim : int, required Vocabulary size of the input indices. output_dim : int, required Dimension of the embedding vectors. dtype : {'bfloat16', 'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32' Data type of weight. sparse_grad : boolean, optional, default=0 Compute row sparse gradient in the backward calculation. If set to True, the grad's storage type is row_sparse. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def FullyConnected(data=None, weight=None, bias=None, num_hidden=_Null, no_bias=_Null, cublas_algo_verbose=_Null, cublas_off=_Null, cublas_tensor_core=_Null, cublas_algo_fwd=_Null, cublas_algo_bwd_data=_Null, cublas_algo_bwd_weights=_Null, cublas_algo_fwd_prec=_Null, cublas_algo_bwd_prec=_Null, flatten=_Null, name=None, attr=None, out=None, **kwargs): r"""Applies a linear transformation: :math:`Y = XW^T + b`. If ``flatten`` is set to be true, then the shapes are: - **data**: `(batch_size, x1, x2, ..., xn)` - **weight**: `(num_hidden, x1 * x2 * ... * xn)` - **bias**: `(num_hidden,)` - **out**: `(batch_size, num_hidden)` If ``flatten`` is set to be false, then the shapes are: - **data**: `(x1, x2, ..., xn, input_dim)` - **weight**: `(num_hidden, input_dim)` - **bias**: `(num_hidden,)` - **out**: `(x1, x2, ..., xn, num_hidden)` The learnable parameters include both ``weight`` and ``bias``. If ``no_bias`` is set to be true, then the ``bias`` term is ignored. .. Note:: The sparse support for FullyConnected is limited to forward evaluation with `row_sparse` weight and bias, where the length of `weight.indices` and `bias.indices` must be equal to `num_hidden`. This could be useful for model inference with `row_sparse` weights trained with importance sampling or noise contrastive estimation. To compute linear transformation with 'csr' sparse data, sparse.dot is recommended instead of sparse.FullyConnected. Defined in ../src/operator/nn/fully_connected.cc:L287 Parameters ---------- data : Symbol Input data. weight : Symbol Weight matrix. bias : Symbol Bias parameter. num_hidden : int, required Number of hidden nodes of the output. no_bias : boolean, optional, default=0 Whether to disable bias parameter. cublas_algo_verbose : boolean, optional, default=0 Verboseness of algo selection. true = output selection, false = no output cublas_off : boolean, optional, default=0 Turn off full-control cublas for this layer. cublas_tensor_core : boolean or None, optional, default=None Allow Tensor Core math for default-chosen algos. cublas_algo_fwd : int or None, optional, default='None' Specified Forward GEMM Algorithm. cublas_algo_bwd_data : int or None, optional, default='None' Specified Backprop-to-Data GEMM Algorithm. cublas_algo_bwd_weights : int or None, optional, default='None' Specified Backprop-to-Weights GEMM Algorithm. cublas_algo_fwd_prec : {'None', 'float16', 'float32', 'float64'},optional, default='None' Precision of the computation of the forward GEMM kernel. Default is the tensor data type, or float32 if the tensor data type is float16. cublas_algo_bwd_prec : {'None', 'float16', 'float32', 'float64'},optional, default='None' Precision of the computation of the back-prop kernels. Default is the tensor data type, or float32 if the tensor data type is float16. flatten : boolean, optional, default=1 Whether to collapse all but the first axis of the input data tensor. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def LinearRegressionOutput(data=None, label=None, grad_scale=_Null, name=None, attr=None, out=None, **kwargs): r"""Computes and optimizes for squared loss during backward propagation. Just outputs ``data`` during forward propagation. If :math:`\hat{y}_i` is the predicted value of the i-th sample, and :math:`y_i` is the corresponding target value, then the squared loss estimated over :math:`n` samples is defined as :math:`\text{SquaredLoss}(\textbf{Y}, \hat{\textbf{Y}} ) = \frac{1}{n} \sum_{i=0}^{n-1} \lVert \textbf{y}_i - \hat{\textbf{y}}_i \rVert_2` .. note:: Use the LinearRegressionOutput as the final output layer of a net. The storage type of ``label`` can be ``default`` or ``csr`` - LinearRegressionOutput(default, default) = default - LinearRegressionOutput(default, csr) = default By default, gradients of this loss function are scaled by factor `1/m`, where m is the number of regression outputs of a training example. The parameter `grad_scale` can be used to change this scale to `grad_scale/m`. Defined in ../src/operator/regression_output.cc:L92 Parameters ---------- data : Symbol Input data to the function. label : Symbol Input label to the function. grad_scale : float, optional, default=1 Scale the gradient by a float factor name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def LogisticRegressionOutput(data=None, label=None, grad_scale=_Null, name=None, attr=None, out=None, **kwargs): r"""Applies a logistic function to the input. The logistic function, also known as the sigmoid function, is computed as :math:`\frac{1}{1+exp(-\textbf{x})}`. Commonly, the sigmoid is used to squash the real-valued output of a linear model :math:`wTx+b` into the [0,1] range so that it can be interpreted as a probability. It is suitable for binary classification or probability prediction tasks. .. note:: Use the LogisticRegressionOutput as the final output layer of a net. The storage type of ``label`` can be ``default`` or ``csr`` - LogisticRegressionOutput(default, default) = default - LogisticRegressionOutput(default, csr) = default The loss function used is the Binary Cross Entropy Loss: :math:`-{(y\log(p) + (1 - y)\log(1 - p))}` Where `y` is the ground truth probability of positive outcome for a given example, and `p` the probability predicted by the model. By default, gradients of this loss function are scaled by factor `1/m`, where m is the number of regression outputs of a training example. The parameter `grad_scale` can be used to change this scale to `grad_scale/m`. Defined in ../src/operator/regression_output.cc:L152 Parameters ---------- data : Symbol Input data to the function. label : Symbol Input label to the function. grad_scale : float, optional, default=1 Scale the gradient by a float factor name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def MAERegressionOutput(data=None, label=None, grad_scale=_Null, name=None, attr=None, out=None, **kwargs): r"""Computes mean absolute error of the input. MAE is a risk metric corresponding to the expected value of the absolute error. If :math:`\hat{y}_i` is the predicted value of the i-th sample, and :math:`y_i` is the corresponding target value, then the mean absolute error (MAE) estimated over :math:`n` samples is defined as :math:`\text{MAE}(\textbf{Y}, \hat{\textbf{Y}} ) = \frac{1}{n} \sum_{i=0}^{n-1} \lVert \textbf{y}_i - \hat{\textbf{y}}_i \rVert_1` .. note:: Use the MAERegressionOutput as the final output layer of a net. The storage type of ``label`` can be ``default`` or ``csr`` - MAERegressionOutput(default, default) = default - MAERegressionOutput(default, csr) = default By default, gradients of this loss function are scaled by factor `1/m`, where m is the number of regression outputs of a training example. The parameter `grad_scale` can be used to change this scale to `grad_scale/m`. Defined in ../src/operator/regression_output.cc:L120 Parameters ---------- data : Symbol Input data to the function. label : Symbol Input label to the function. grad_scale : float, optional, default=1 Scale the gradient by a float factor name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def _contrib_round_ste(data=None, name=None, attr=None, out=None, **kwargs): r"""Straight-through-estimator of `round()`. In forward pass, returns element-wise rounded value to the nearest integer of the input (same as `round()`). In backward pass, returns gradients of ``1`` everywhere (instead of ``0`` everywhere as in `round()`): :math:`\frac{d}{dx}{round\_ste(x)} = 1` vs. :math:`\frac{d}{dx}{round(x)} = 0`. This is useful for quantized training. Reference: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. Example:: x = round_ste([-1.5, 1.5, -1.9, 1.9, 2.7]) x.backward() x = [-2., 2., -2., 2., 3.] x.grad() = [1., 1., 1., 1., 1.] The storage type of ``round_ste`` output depends upon the input storage type: - round_ste(default) = default - round_ste(row_sparse) = row_sparse - round_ste(csr) = csr Defined in ../src/operator/contrib/stes_op.cc:L55 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def _contrib_sign_ste(data=None, name=None, attr=None, out=None, **kwargs): r"""Straight-through-estimator of `sign()`. In forward pass, returns element-wise sign of the input (same as `sign()`). In backward pass, returns gradients of ``1`` everywhere (instead of ``0`` everywhere as in ``sign()``): :math:`\frac{d}{dx}{sign\_ste(x)} = 1` vs. :math:`\frac{d}{dx}{sign(x)} = 0`. This is useful for quantized training. Reference: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. Example:: x = sign_ste([-2, 0, 3]) x.backward() x = [-1., 0., 1.] x.grad() = [1., 1., 1.] The storage type of ``sign_ste`` output depends upon the input storage type: - round_ste(default) = default - round_ste(row_sparse) = row_sparse - round_ste(csr) = csr Defined in ../src/operator/contrib/stes_op.cc:L80 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def abs(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise absolute value of the input. Example:: abs([-2, 0, 3]) = [2, 0, 3] The storage type of ``abs`` output depends upon the input storage type: - abs(default) = default - abs(row_sparse) = row_sparse - abs(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L720 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def adagrad_update(weight=None, grad=None, history=None, lr=_Null, epsilon=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, name=None, attr=None, out=None, **kwargs): r"""Update function for AdaGrad optimizer. Referenced from *Adaptive Subgradient Methods for Online Learning and Stochastic Optimization*, and available at http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf. Updates are applied by:: rescaled_grad = clip(grad * rescale_grad, clip_gradient) history = history + square(rescaled_grad) w = w - learning_rate * rescaled_grad / sqrt(history + epsilon) Note that non-zero values for the weight decay option are not supported. Defined in ../src/operator/optimizer_op.cc:L978 Parameters ---------- weight : Symbol Weight grad : Symbol Gradient history : Symbol History lr : float, required Learning rate epsilon : float, optional, default=1.00000001e-07 epsilon wd : float, optional, default=0 weight decay rescale_grad : float, optional, default=1 Rescale gradient to grad = rescale_grad*grad. clip_gradient : float, optional, default=-1 Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient). name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def adam_update(weight=None, grad=None, mean=None, var=None, lr=_Null, beta1=_Null, beta2=_Null, epsilon=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, lazy_update=_Null, name=None, attr=None, out=None, **kwargs): r"""Update function for Adam optimizer. Adam is seen as a generalization of AdaGrad. Adam update consists of the following steps, where g represents gradient and m, v are 1st and 2nd order moment estimates (mean and variance). .. math:: g_t = \nabla J(W_{t-1})\\ m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t\\ v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\ W_t = W_{t-1} - \alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon } It updates the weights using:: m = beta1*m + (1-beta1)*grad v = beta2*v + (1-beta2)*(grad**2) w += - learning_rate * m / (sqrt(v) + epsilon) However, if grad's storage type is ``row_sparse``, ``lazy_update`` is True and the storage type of weight is the same as those of m and v, only the row slices whose indices appear in grad.indices are updated (for w, m and v):: for row in grad.indices: m[row] = beta1*m[row] + (1-beta1)*grad[row] v[row] = beta2*v[row] + (1-beta2)*(grad[row]**2) w[row] += - learning_rate * m[row] / (sqrt(v[row]) + epsilon) Defined in ../src/operator/optimizer_op.cc:L686 Parameters ---------- weight : Symbol Weight grad : Symbol Gradient mean : Symbol Moving mean var : Symbol Moving variance lr : float, required Learning rate beta1 : float, optional, default=0.899999976 The decay rate for the 1st moment estimates. beta2 : float, optional, default=0.999000013 The decay rate for the 2nd moment estimates. epsilon : float, optional, default=9.99999994e-09 A small constant for numerical stability. wd : float, optional, default=0 Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight. rescale_grad : float, optional, default=1 Rescale gradient to grad = rescale_grad*grad. clip_gradient : float, optional, default=-1 Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient). lazy_update : boolean, optional, default=1 If true, lazy updates are applied if gradient's stype is row_sparse and all of w, m and v have the same stype name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def add_n(*args, **kwargs): r"""Adds all input arguments element-wise. .. math:: add\_n(a_1, a_2, ..., a_n) = a_1 + a_2 + ... + a_n ``add_n`` is potentially more efficient than calling ``add`` by `n` times. The storage type of ``add_n`` output depends on storage types of inputs - add_n(row_sparse, row_sparse, ..) = row_sparse - add_n(default, csr, default) = default - add_n(any input combinations longer than 4 (>4) with at least one default type) = default - otherwise, ``add_n`` falls all inputs back to default storage and generates default storage Defined in ../src/operator/tensor/elemwise_sum.cc:L156 This function support variable length of positional input. Parameters ---------- args : Symbol[] Positional input arguments name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def arccos(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise inverse cosine of the input array. The input should be in range `[-1, 1]`. The output is in the closed interval :math:`[0, \pi]` .. math:: arccos([-1, -.707, 0, .707, 1]) = [\pi, 3\pi/4, \pi/2, \pi/4, 0] The storage type of ``arccos`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L233 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def arccosh(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns the element-wise inverse hyperbolic cosine of the input array, \ computed element-wise. The storage type of ``arccosh`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L535 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def arcsin(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise inverse sine of the input array. The input should be in the range `[-1, 1]`. The output is in the closed interval of [:math:`-\pi/2`, :math:`\pi/2`]. .. math:: arcsin([-1, -.707, 0, .707, 1]) = [-\pi/2, -\pi/4, 0, \pi/4, \pi/2] The storage type of ``arcsin`` output depends upon the input storage type: - arcsin(default) = default - arcsin(row_sparse) = row_sparse - arcsin(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L187 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def arcsinh(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns the element-wise inverse hyperbolic sine of the input array, \ computed element-wise. The storage type of ``arcsinh`` output depends upon the input storage type: - arcsinh(default) = default - arcsinh(row_sparse) = row_sparse - arcsinh(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L494 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def arctan(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise inverse tangent of the input array. The output is in the closed interval :math:`[-\pi/2, \pi/2]` .. math:: arctan([-1, 0, 1]) = [-\pi/4, 0, \pi/4] The storage type of ``arctan`` output depends upon the input storage type: - arctan(default) = default - arctan(row_sparse) = row_sparse - arctan(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L282 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def arctanh(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns the element-wise inverse hyperbolic tangent of the input array, \ computed element-wise. The storage type of ``arctanh`` output depends upon the input storage type: - arctanh(default) = default - arctanh(row_sparse) = row_sparse - arctanh(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L579 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def broadcast_add(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise sum of the input arrays with broadcasting. `broadcast_plus` is an alias to the function `broadcast_add`. Example:: x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_add(x, y) = [[ 1., 1., 1.], [ 2., 2., 2.]] broadcast_plus(x, y) = [[ 1., 1., 1.], [ 2., 2., 2.]] Supported sparse operations: broadcast_add(csr, dense(1D)) = dense broadcast_add(dense(1D), csr) = dense Defined in ../src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L58 Parameters ---------- lhs : Symbol First input to the function rhs : Symbol Second input to the function name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def broadcast_div(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise division of the input arrays with broadcasting. Example:: x = [[ 6., 6., 6.], [ 6., 6., 6.]] y = [[ 2.], [ 3.]] broadcast_div(x, y) = [[ 3., 3., 3.], [ 2., 2., 2.]] Supported sparse operations: broadcast_div(csr, dense(1D)) = csr Defined in ../src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L187 Parameters ---------- lhs : Symbol First input to the function rhs : Symbol Second input to the function name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def broadcast_minus(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise difference of the input arrays with broadcasting. `broadcast_minus` is an alias to the function `broadcast_sub`. Example:: x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_sub(x, y) = [[ 1., 1., 1.], [ 0., 0., 0.]] broadcast_minus(x, y) = [[ 1., 1., 1.], [ 0., 0., 0.]] Supported sparse operations: broadcast_sub/minus(csr, dense(1D)) = dense broadcast_sub/minus(dense(1D), csr) = dense Defined in ../src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L106 Parameters ---------- lhs : Symbol First input to the function rhs : Symbol Second input to the function name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def broadcast_mul(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise product of the input arrays with broadcasting. Example:: x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_mul(x, y) = [[ 0., 0., 0.], [ 1., 1., 1.]] Supported sparse operations: broadcast_mul(csr, dense(1D)) = csr Defined in ../src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L146 Parameters ---------- lhs : Symbol First input to the function rhs : Symbol Second input to the function name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def broadcast_plus(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise sum of the input arrays with broadcasting. `broadcast_plus` is an alias to the function `broadcast_add`. Example:: x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_add(x, y) = [[ 1., 1., 1.], [ 2., 2., 2.]] broadcast_plus(x, y) = [[ 1., 1., 1.], [ 2., 2., 2.]] Supported sparse operations: broadcast_add(csr, dense(1D)) = dense broadcast_add(dense(1D), csr) = dense Defined in ../src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L58 Parameters ---------- lhs : Symbol First input to the function rhs : Symbol Second input to the function name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def broadcast_sub(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise difference of the input arrays with broadcasting. `broadcast_minus` is an alias to the function `broadcast_sub`. Example:: x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_sub(x, y) = [[ 1., 1., 1.], [ 0., 0., 0.]] broadcast_minus(x, y) = [[ 1., 1., 1.], [ 0., 0., 0.]] Supported sparse operations: broadcast_sub/minus(csr, dense(1D)) = dense broadcast_sub/minus(dense(1D), csr) = dense Defined in ../src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L106 Parameters ---------- lhs : Symbol First input to the function rhs : Symbol Second input to the function name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def cast_storage(data=None, stype=_Null, name=None, attr=None, out=None, **kwargs): r"""Casts tensor storage type to the new type. When an NDArray with default storage type is cast to csr or row_sparse storage, the result is compact, which means: - for csr, zero values will not be retained - for row_sparse, row slices of all zeros will not be retained The storage type of ``cast_storage`` output depends on stype parameter: - cast_storage(csr, 'default') = default - cast_storage(row_sparse, 'default') = default - cast_storage(default, 'csr') = csr - cast_storage(default, 'row_sparse') = row_sparse - cast_storage(csr, 'csr') = csr - cast_storage(row_sparse, 'row_sparse') = row_sparse Example:: dense = [[ 0., 1., 0.], [ 2., 0., 3.], [ 0., 0., 0.], [ 0., 0., 0.]] # cast to row_sparse storage type rsp = cast_storage(dense, 'row_sparse') rsp.indices = [0, 1] rsp.values = [[ 0., 1., 0.], [ 2., 0., 3.]] # cast to csr storage type csr = cast_storage(dense, 'csr') csr.indices = [1, 0, 2] csr.values = [ 1., 2., 3.] csr.indptr = [0, 1, 3, 3, 3] Defined in ../src/operator/tensor/cast_storage.cc:L71 Parameters ---------- data : Symbol The input. stype : {'csr', 'default', 'row_sparse'}, required Output storage type. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def cbrt(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise cube-root value of the input. .. math:: cbrt(x) = \sqrt[3]{x} Example:: cbrt([1, 8, -125]) = [1, 2, -5] The storage type of ``cbrt`` output depends upon the input storage type: - cbrt(default) = default - cbrt(row_sparse) = row_sparse - cbrt(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_pow.cc:L270 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def ceil(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise ceiling of the input. The ceil of the scalar x is the smallest integer i, such that i >= x. Example:: ceil([-2.1, -1.9, 1.5, 1.9, 2.1]) = [-2., -1., 2., 2., 3.] The storage type of ``ceil`` output depends upon the input storage type: - ceil(default) = default - ceil(row_sparse) = row_sparse - ceil(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L815 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def clip(data=None, a_min=_Null, a_max=_Null, name=None, attr=None, out=None, **kwargs): r"""Clips (limits) the values in an array. Given an interval, values outside the interval are clipped to the interval edges. Clipping ``x`` between `a_min` and `a_max` would be:: .. math:: clip(x, a_min, a_max) = \max(\min(x, a_max), a_min)) Example:: x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] clip(x,1,8) = [ 1., 1., 2., 3., 4., 5., 6., 7., 8., 8.] The storage type of ``clip`` output depends on storage types of inputs and the a_min, a_max \ parameter values: - clip(default) = default - clip(row_sparse, a_min <= 0, a_max >= 0) = row_sparse - clip(csr, a_min <= 0, a_max >= 0) = csr - clip(row_sparse, a_min < 0, a_max < 0) = default - clip(row_sparse, a_min > 0, a_max > 0) = default - clip(csr, a_min < 0, a_max < 0) = csr - clip(csr, a_min > 0, a_max > 0) = csr Defined in ../src/operator/tensor/matrix_op.cc:L693 Parameters ---------- data : Symbol Input array. a_min : float, required Minimum value a_max : float, required Maximum value name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def concat(*data, **kwargs): r"""Joins input arrays along a given axis. .. note:: `Concat` is deprecated. Use `concat` instead. The dimensions of the input arrays should be the same except the axis along which they will be concatenated. The dimension of the output array along the concatenated axis will be equal to the sum of the corresponding dimensions of the input arrays. The storage type of ``concat`` output depends on storage types of inputs - concat(csr, csr, ..., csr, dim=0) = csr - otherwise, ``concat`` generates output with default storage Example:: x = [[1,1],[2,2]] y = [[3,3],[4,4],[5,5]] z = [[6,6], [7,7],[8,8]] concat(x,y,z,dim=0) = [[ 1., 1.], [ 2., 2.], [ 3., 3.], [ 4., 4.], [ 5., 5.], [ 6., 6.], [ 7., 7.], [ 8., 8.]] Note that you cannot concat x,y,z along dimension 1 since dimension 0 is not the same for all the input arrays. concat(y,z,dim=1) = [[ 3., 3., 6., 6.], [ 4., 4., 7., 7.], [ 5., 5., 8., 8.]] Defined in ../src/operator/nn/concat.cc:L385 This function support variable length of positional input. Parameters ---------- data : Symbol[] List of arrays to concatenate dim : int, optional, default='1' the dimension to be concated. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def cos(data=None, name=None, attr=None, out=None, **kwargs): r"""Computes the element-wise cosine of the input array. The input should be in radians (:math:`2\pi` rad equals 360 degrees). .. math:: cos([0, \pi/4, \pi/2]) = [1, 0.707, 0] The storage type of ``cos`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L90 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def cosh(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns the hyperbolic cosine of the input array, computed element-wise. .. math:: cosh(x) = 0.5\times(exp(x) + exp(-x)) The storage type of ``cosh`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L409 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def degrees(data=None, name=None, attr=None, out=None, **kwargs): r"""Converts each element of the input array from radians to degrees. .. math:: degrees([0, \pi/2, \pi, 3\pi/2, 2\pi]) = [0, 90, 180, 270, 360] The storage type of ``degrees`` output depends upon the input storage type: - degrees(default) = default - degrees(row_sparse) = row_sparse - degrees(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L332 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def dot(lhs=None, rhs=None, transpose_a=_Null, transpose_b=_Null, forward_stype=_Null, name=None, attr=None, out=None, **kwargs): r"""Dot product of two arrays. ``dot``'s behavior depends on the input array dimensions: - 1-D arrays: inner product of vectors - 2-D arrays: matrix multiplication - N-D arrays: a sum product over the last axis of the first input and the first axis of the second input For example, given 3-D ``x`` with shape `(n,m,k)` and ``y`` with shape `(k,r,s)`, the result array will have shape `(n,m,r,s)`. It is computed by:: dot(x,y)[i,j,a,b] = sum(x[i,j,:]*y[:,a,b]) Example:: x = reshape([0,1,2,3,4,5,6,7], shape=(2,2,2)) y = reshape([7,6,5,4,3,2,1,0], shape=(2,2,2)) dot(x,y)[0,0,1,1] = 0 sum(x[0,0,:]*y[:,1,1]) = 0 The storage type of ``dot`` output depends on storage types of inputs, transpose option and forward_stype option for output storage type. Implemented sparse operations include: - dot(default, default, transpose_a=True/False, transpose_b=True/False) = default - dot(csr, default, transpose_a=True) = default - dot(csr, default, transpose_a=True) = row_sparse - dot(csr, default) = default - dot(csr, row_sparse) = default - dot(default, csr) = csr (CPU only) - dot(default, csr, forward_stype='default') = default - dot(default, csr, transpose_b=True, forward_stype='default') = default If the combination of input storage types and forward_stype does not match any of the above patterns, ``dot`` will fallback and generate output with default storage. .. Note:: If the storage type of the lhs is "csr", the storage type of gradient w.r.t rhs will be "row_sparse". Only a subset of optimizers support sparse gradients, including SGD, AdaGrad and Adam. Note that by default lazy updates is turned on, which may perform differently from standard updates. For more details, please check the Optimization API at: https://mxnet.incubator.apache.org/api/python/optimization/optimization.html Defined in ../src/operator/tensor/dot.cc:L77 Parameters ---------- lhs : Symbol The first input rhs : Symbol The second input transpose_a : boolean, optional, default=0 If true then transpose the first input before dot. transpose_b : boolean, optional, default=0 If true then transpose the second input before dot. forward_stype : {None, 'csr', 'default', 'row_sparse'},optional, default='None' The desired storage type of the forward output given by user, if thecombination of input storage types and this hint does not matchany implemented ones, the dot operator will perform fallback operationand still produce an output of the desired storage type. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def elemwise_add(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Adds arguments element-wise. The storage type of ``elemwise_add`` output depends on storage types of inputs - elemwise_add(row_sparse, row_sparse) = row_sparse - elemwise_add(csr, csr) = csr - elemwise_add(default, csr) = default - elemwise_add(csr, default) = default - elemwise_add(default, rsp) = default - elemwise_add(rsp, default) = default - otherwise, ``elemwise_add`` generates output with default storage Parameters ---------- lhs : Symbol first input rhs : Symbol second input name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def elemwise_div(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Divides arguments element-wise. The storage type of ``elemwise_div`` output is always dense Parameters ---------- lhs : Symbol first input rhs : Symbol second input name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def elemwise_mul(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Multiplies arguments element-wise. The storage type of ``elemwise_mul`` output depends on storage types of inputs - elemwise_mul(default, default) = default - elemwise_mul(row_sparse, row_sparse) = row_sparse - elemwise_mul(default, row_sparse) = row_sparse - elemwise_mul(row_sparse, default) = row_sparse - elemwise_mul(csr, csr) = csr - otherwise, ``elemwise_mul`` generates output with default storage Parameters ---------- lhs : Symbol first input rhs : Symbol second input name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def elemwise_sub(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs): r"""Subtracts arguments element-wise. The storage type of ``elemwise_sub`` output depends on storage types of inputs - elemwise_sub(row_sparse, row_sparse) = row_sparse - elemwise_sub(csr, csr) = csr - elemwise_sub(default, csr) = default - elemwise_sub(csr, default) = default - elemwise_sub(default, rsp) = default - elemwise_sub(rsp, default) = default - otherwise, ``elemwise_sub`` generates output with default storage Parameters ---------- lhs : Symbol first input rhs : Symbol second input name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def exp(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise exponential value of the input. .. math:: exp(x) = e^x \approx 2.718^x Example:: exp([0, 1, 2]) = [1., 2.71828175, 7.38905621] The storage type of ``exp`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_logexp.cc:L64 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def expm1(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns ``exp(x) - 1`` computed element-wise on the input. This function provides greater precision than ``exp(x) - 1`` for small values of ``x``. The storage type of ``expm1`` output depends upon the input storage type: - expm1(default) = default - expm1(row_sparse) = row_sparse - expm1(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_logexp.cc:L244 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def fix(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise rounded value to the nearest \ integer towards zero of the input. Example:: fix([-2.1, -1.9, 1.9, 2.1]) = [-2., -1., 1., 2.] The storage type of ``fix`` output depends upon the input storage type: - fix(default) = default - fix(row_sparse) = row_sparse - fix(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L872 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def floor(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise floor of the input. The floor of the scalar x is the largest integer i, such that i <= x. Example:: floor([-2.1, -1.9, 1.5, 1.9, 2.1]) = [-3., -2., 1., 1., 2.] The storage type of ``floor`` output depends upon the input storage type: - floor(default) = default - floor(row_sparse) = row_sparse - floor(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L834 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def ftrl_update(weight=None, grad=None, z=None, n=None, lr=_Null, lamda1=_Null, beta=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, name=None, attr=None, out=None, **kwargs): r"""Update function for Ftrl optimizer. Referenced from *Ad Click Prediction: a View from the Trenches*, available at http://dl.acm.org/citation.cfm?id=2488200. It updates the weights using:: rescaled_grad = clip(grad * rescale_grad, clip_gradient) z += rescaled_grad - (sqrt(n + rescaled_grad**2) - sqrt(n)) * weight / learning_rate n += rescaled_grad**2 w = (sign(z) * lamda1 - z) / ((beta + sqrt(n)) / learning_rate + wd) * (abs(z) > lamda1) If w, z and n are all of ``row_sparse`` storage type, only the row slices whose indices appear in grad.indices are updated (for w, z and n):: for row in grad.indices: rescaled_grad[row] = clip(grad[row] * rescale_grad, clip_gradient) z[row] += rescaled_grad[row] - (sqrt(n[row] + rescaled_grad[row]**2) - sqrt(n[row])) * weight[row] / learning_rate n[row] += rescaled_grad[row]**2 w[row] = (sign(z[row]) * lamda1 - z[row]) / ((beta + sqrt(n[row])) / learning_rate + wd) * (abs(z[row]) > lamda1) Defined in ../src/operator/optimizer_op.cc:L945 Parameters ---------- weight : Symbol Weight grad : Symbol Gradient z : Symbol z n : Symbol Square of grad lr : float, required Learning rate lamda1 : float, optional, default=0.00999999978 The L1 regularization coefficient. beta : float, optional, default=1 Per-Coordinate Learning Rate beta. wd : float, optional, default=0 Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight. rescale_grad : float, optional, default=1 Rescale gradient to grad = rescale_grad*grad. clip_gradient : float, optional, default=-1 Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient). name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def gamma(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns the gamma function (extension of the factorial function \ to the reals), computed element-wise on the input array. The storage type of ``gamma`` output is always dense Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def gammaln(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise log of the absolute value of the gamma function \ of the input. The storage type of ``gammaln`` output is always dense Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def log(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise Natural logarithmic value of the input. The natural logarithm is logarithm in base *e*, so that ``log(exp(x)) = x`` The storage type of ``log`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_logexp.cc:L77 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def log10(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise Base-10 logarithmic value of the input. ``10**log10(x) = x`` The storage type of ``log10`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_logexp.cc:L94 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def log1p(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise ``log(1 + x)`` value of the input. This function is more accurate than ``log(1 + x)`` for small ``x`` so that :math:`1+x\approx 1` The storage type of ``log1p`` output depends upon the input storage type: - log1p(default) = default - log1p(row_sparse) = row_sparse - log1p(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_logexp.cc:L199 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def log2(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise Base-2 logarithmic value of the input. ``2**log2(x) = x`` The storage type of ``log2`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_logexp.cc:L106 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def make_loss(data=None, name=None, attr=None, out=None, **kwargs): r"""Make your own loss function in network construction. This operator accepts a customized loss function symbol as a terminal loss and the symbol should be an operator with no backward dependency. The output of this function is the gradient of loss with respect to the input data. For example, if you are a making a cross entropy loss function. Assume ``out`` is the predicted output and ``label`` is the true label, then the cross entropy can be defined as:: cross_entropy = label * log(out) + (1 - label) * log(1 - out) loss = make_loss(cross_entropy) We will need to use ``make_loss`` when we are creating our own loss function or we want to combine multiple loss functions. Also we may want to stop some variables' gradients from backpropagation. See more detail in ``BlockGrad`` or ``stop_gradient``. The storage type of ``make_loss`` output depends upon the input storage type: - make_loss(default) = default - make_loss(row_sparse) = row_sparse Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L358 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def mean(data=None, axis=_Null, keepdims=_Null, exclude=_Null, name=None, attr=None, out=None, **kwargs): r"""Computes the mean of array elements over given axes. Defined in ../src/operator/tensor/./broadcast_reduce_op.h:L84 Parameters ---------- data : Symbol The input axis : Shape or None, optional, default=None The axis or axes along which to perform the reduction. The default, `axis=()`, will compute over all elements into a scalar array with shape `(1,)`. If `axis` is int, a reduction is performed on a particular axis. If `axis` is a tuple of ints, a reduction is performed on all the axes specified in the tuple. If `exclude` is true, reduction will be performed on the axes that are NOT in axis instead. Negative values means indexing from right to left. keepdims : boolean, optional, default=0 If this is set to `True`, the reduced axes are left in the result as dimension with size one. exclude : boolean, optional, default=0 Whether to perform reduction on axis that are NOT in axis instead. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def negative(data=None, name=None, attr=None, out=None, **kwargs): r"""Numerical negative of the argument, element-wise. The storage type of ``negative`` output depends upon the input storage type: - negative(default) = default - negative(row_sparse) = row_sparse - negative(csr) = csr Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def norm(data=None, ord=_Null, axis=_Null, out_dtype=_Null, keepdims=_Null, name=None, attr=None, out=None, **kwargs): r"""Computes the norm on an NDArray. This operator computes the norm on an NDArray with the specified axis, depending on the value of the ord parameter. By default, it computes the L2 norm on the entire array. Currently only ord=2 supports sparse ndarrays. Examples:: x = [[[1, 2], [3, 4]], [[2, 2], [5, 6]]] norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ] [5.3851647 6.3245554]] norm(x, ord=1, axis=1) = [[4., 6.], [7., 8.]] rsp = x.cast_storage('row_sparse') norm(rsp) = [5.47722578] csr = x.cast_storage('csr') norm(csr) = [5.47722578] Defined in ../src/operator/tensor/broadcast_reduce_norm_value.cc:L89 Parameters ---------- data : Symbol The input ord : int, optional, default='2' Order of the norm. Currently ord=1 and ord=2 is supported. axis : Shape or None, optional, default=None The axis or axes along which to perform the reduction. The default, `axis=()`, will compute over all elements into a scalar array with shape `(1,)`. If `axis` is int, a reduction is performed on a particular axis. If `axis` is a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of these matrices are computed. out_dtype : {None, 'float16', 'float32', 'float64', 'int32', 'int64', 'int8'},optional, default='None' The data type of the output. keepdims : boolean, optional, default=0 If this is set to `True`, the reduced axis is left in the result as dimension with size one. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def radians(data=None, name=None, attr=None, out=None, **kwargs): r"""Converts each element of the input array from degrees to radians. .. math:: radians([0, 90, 180, 270, 360]) = [0, \pi/2, \pi, 3\pi/2, 2\pi] The storage type of ``radians`` output depends upon the input storage type: - radians(default) = default - radians(row_sparse) = row_sparse - radians(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L351 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def relu(data=None, name=None, attr=None, out=None, **kwargs): r"""Computes rectified linear activation. .. math:: max(features, 0) The storage type of ``relu`` output depends upon the input storage type: - relu(default) = default - relu(row_sparse) = row_sparse - relu(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L85 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def retain(data=None, indices=None, name=None, attr=None, out=None, **kwargs): r"""Pick rows specified by user input index array from a row sparse matrix and save them in the output sparse matrix. Example:: data = [[1, 2], [3, 4], [5, 6]] indices = [0, 1, 3] shape = (4, 2) rsp_in = row_sparse_array(data, indices) to_retain = [0, 3] rsp_out = retain(rsp_in, to_retain) rsp_out.data = [[1, 2], [5, 6]] rsp_out.indices = [0, 3] The storage type of ``retain`` output depends on storage types of inputs - retain(row_sparse, default) = row_sparse - otherwise, ``retain`` is not supported Defined in ../src/operator/tensor/sparse_retain.cc:L53 Parameters ---------- data : Symbol The input array for sparse_retain operator. indices : Symbol The index array of rows ids that will be retained. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def rint(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise rounded value to the nearest integer of the input. .. note:: - For input ``n.5`` ``rint`` returns ``n`` while ``round`` returns ``n+1``. - For input ``-n.5`` both ``rint`` and ``round`` returns ``-n-1``. Example:: rint([-1.5, 1.5, -1.9, 1.9, 2.1]) = [-2., 1., -2., 2., 2.] The storage type of ``rint`` output depends upon the input storage type: - rint(default) = default - rint(row_sparse) = row_sparse - rint(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L796 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def round(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise rounded value to the nearest integer of the input. Example:: round([-1.5, 1.5, -1.9, 1.9, 2.1]) = [-2., 2., -2., 2., 2.] The storage type of ``round`` output depends upon the input storage type: - round(default) = default - round(row_sparse) = row_sparse - round(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L775 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def rsqrt(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise inverse square-root value of the input. .. math:: rsqrt(x) = 1/\sqrt{x} Example:: rsqrt([4,9,16]) = [0.5, 0.33333334, 0.25] The storage type of ``rsqrt`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_pow.cc:L221 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def sgd_mom_update(weight=None, grad=None, mom=None, lr=_Null, momentum=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, lazy_update=_Null, name=None, attr=None, out=None, **kwargs): r"""Momentum update function for Stochastic Gradient Descent (SGD) optimizer. Momentum update has better convergence rates on neural networks. Mathematically it looks like below: .. math:: v_1 = \nabla J(W_0)\\ v_t = \gamma v_{t-1} - \nabla J(W_{t-1})\\ W_t = W_{t-1} + \alpha * v_t It updates the weights using:: v = momentum * v - gradient weight += learning_rate * v Where the parameter ``momentum`` is the decay rate of momentum estimates at each epoch. However, if grad's storage type is ``row_sparse``, ``lazy_update`` is True and weight's storage type is the same as momentum's storage type, only the row slices whose indices appear in grad.indices are updated (for both weight and momentum):: for row in gradient.indices: v[row] = momentum[row] * v[row] - gradient[row] weight[row] += learning_rate * v[row] Defined in ../src/operator/optimizer_op.cc:L563 Parameters ---------- weight : Symbol Weight grad : Symbol Gradient mom : Symbol Momentum lr : float, required Learning rate momentum : float, optional, default=0 The decay rate of momentum estimates at each epoch. wd : float, optional, default=0 Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight. rescale_grad : float, optional, default=1 Rescale gradient to grad = rescale_grad*grad. clip_gradient : float, optional, default=-1 Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient). lazy_update : boolean, optional, default=1 If true, lazy updates are applied if gradient's stype is row_sparse and both weight and momentum have the same stype name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def sgd_update(weight=None, grad=None, lr=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, lazy_update=_Null, name=None, attr=None, out=None, **kwargs): r"""Update function for Stochastic Gradient Descent (SGD) optimizer. It updates the weights using:: weight = weight - learning_rate * (gradient + wd * weight) However, if gradient is of ``row_sparse`` storage type and ``lazy_update`` is True, only the row slices whose indices appear in grad.indices are updated:: for row in gradient.indices: weight[row] = weight[row] - learning_rate * (gradient[row] + wd * weight[row]) Defined in ../src/operator/optimizer_op.cc:L522 Parameters ---------- weight : Symbol Weight grad : Symbol Gradient lr : float, required Learning rate wd : float, optional, default=0 Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight. rescale_grad : float, optional, default=1 Rescale gradient to grad = rescale_grad*grad. clip_gradient : float, optional, default=-1 Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient). lazy_update : boolean, optional, default=1 If true, lazy updates are applied if gradient's stype is row_sparse. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def sigmoid(data=None, name=None, attr=None, out=None, **kwargs): r"""Computes sigmoid of x element-wise. .. math:: y = 1 / (1 + exp(-x)) The storage type of ``sigmoid`` output is always dense Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L119 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def sign(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise sign of the input. Example:: sign([-2, 0, 3]) = [-1, 0, 1] The storage type of ``sign`` output depends upon the input storage type: - sign(default) = default - sign(row_sparse) = row_sparse - sign(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L758 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def sin(data=None, name=None, attr=None, out=None, **kwargs): r"""Computes the element-wise sine of the input array. The input should be in radians (:math:`2\pi` rad equals 360 degrees). .. math:: sin([0, \pi/4, \pi/2]) = [0, 0.707, 1] The storage type of ``sin`` output depends upon the input storage type: - sin(default) = default - sin(row_sparse) = row_sparse - sin(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L47 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def sinh(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns the hyperbolic sine of the input array, computed element-wise. .. math:: sinh(x) = 0.5\times(exp(x) - exp(-x)) The storage type of ``sinh`` output depends upon the input storage type: - sinh(default) = default - sinh(row_sparse) = row_sparse - sinh(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L371 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def slice(data=None, begin=_Null, end=_Null, step=_Null, name=None, attr=None, out=None, **kwargs): r"""Slices a region of the array. .. note:: ``crop`` is deprecated. Use ``slice`` instead. This function returns a sliced array between the indices given by `begin` and `end` with the corresponding `step`. For an input array of ``shape=(d_0, d_1, ..., d_n-1)``, slice operation with ``begin=(b_0, b_1...b_m-1)``, ``end=(e_0, e_1, ..., e_m-1)``, and ``step=(s_0, s_1, ..., s_m-1)``, where m <= n, results in an array with the shape ``(|e_0-b_0|/|s_0|, ..., |e_m-1-b_m-1|/|s_m-1|, d_m, ..., d_n-1)``. The resulting array's *k*-th dimension contains elements from the *k*-th dimension of the input array starting from index ``b_k`` (inclusive) with step ``s_k`` until reaching ``e_k`` (exclusive). If the *k*-th elements are `None` in the sequence of `begin`, `end`, and `step`, the following rule will be used to set default values. If `s_k` is `None`, set `s_k=1`. If `s_k > 0`, set `b_k=0`, `e_k=d_k`; else, set `b_k=d_k-1`, `e_k=-1`. The storage type of ``slice`` output depends on storage types of inputs - slice(csr) = csr - otherwise, ``slice`` generates output with default storage .. note:: When input data storage type is csr, it only supports step=(), or step=(None,), or step=(1,) to generate a csr output. For other step parameter values, it falls back to slicing a dense tensor. Example:: x = [[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 9., 10., 11., 12.]] slice(x, begin=(0,1), end=(2,4)) = [[ 2., 3., 4.], [ 6., 7., 8.]] slice(x, begin=(None, 0), end=(None, 3), step=(-1, 2)) = [[9., 11.], [5., 7.], [1., 3.]] Defined in ../src/operator/tensor/matrix_op.cc:L498 Parameters ---------- data : Symbol Source input begin : Shape(tuple), required starting indices for the slice operation, supports negative indices. end : Shape(tuple), required ending indices for the slice operation, supports negative indices. step : Shape(tuple), optional, default=[] step for the slice operation, supports negative values. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def sqrt(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise square-root value of the input. .. math:: \textrm{sqrt}(x) = \sqrt{x} Example:: sqrt([4, 9, 16]) = [2, 3, 4] The storage type of ``sqrt`` output depends upon the input storage type: - sqrt(default) = default - sqrt(row_sparse) = row_sparse - sqrt(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_pow.cc:L170 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def square(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns element-wise squared value of the input. .. math:: square(x) = x^2 Example:: square([2, 3, 4]) = [4, 9, 16] The storage type of ``square`` output depends upon the input storage type: - square(default) = default - square(row_sparse) = row_sparse - square(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_pow.cc:L119 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def stop_gradient(data=None, name=None, attr=None, out=None, **kwargs): r"""Stops gradient computation. Stops the accumulated gradient of the inputs from flowing through this operator in the backward direction. In other words, this operator prevents the contribution of its inputs to be taken into account for computing gradients. Example:: v1 = [1, 2] v2 = [0, 1] a = Variable('a') b = Variable('b') b_stop_grad = stop_gradient(3 * b) loss = MakeLoss(b_stop_grad + a) executor = loss.simple_bind(ctx=cpu(), a=(1,2), b=(1,2)) executor.forward(is_train=True, a=v1, b=v2) executor.outputs [ 1. 5.] executor.backward() executor.grad_arrays [ 0. 0.] [ 1. 1.] Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L325 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def sum(data=None, axis=_Null, keepdims=_Null, exclude=_Null, name=None, attr=None, out=None, **kwargs): r"""Computes the sum of array elements over given axes. .. Note:: `sum` and `sum_axis` are equivalent. For ndarray of csr storage type summation along axis 0 and axis 1 is supported. Setting keepdims or exclude to True will cause a fallback to dense operator. Example:: data = [[[1, 2], [2, 3], [1, 3]], [[1, 4], [4, 3], [5, 2]], [[7, 1], [7, 2], [7, 3]]] sum(data, axis=1) [[ 4. 8.] [ 10. 9.] [ 21. 6.]] sum(data, axis=[1,2]) [ 12. 19. 27.] data = [[1, 2, 0], [3, 0, 1], [4, 1, 0]] csr = cast_storage(data, 'csr') sum(csr, axis=0) [ 8. 3. 1.] sum(csr, axis=1) [ 3. 4. 5.] Defined in ../src/operator/tensor/broadcast_reduce_sum_value.cc:L67 Parameters ---------- data : Symbol The input axis : Shape or None, optional, default=None The axis or axes along which to perform the reduction. The default, `axis=()`, will compute over all elements into a scalar array with shape `(1,)`. If `axis` is int, a reduction is performed on a particular axis. If `axis` is a tuple of ints, a reduction is performed on all the axes specified in the tuple. If `exclude` is true, reduction will be performed on the axes that are NOT in axis instead. Negative values means indexing from right to left. keepdims : boolean, optional, default=0 If this is set to `True`, the reduced axes are left in the result as dimension with size one. exclude : boolean, optional, default=0 Whether to perform reduction on axis that are NOT in axis instead. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def tan(data=None, name=None, attr=None, out=None, **kwargs): r"""Computes the element-wise tangent of the input array. The input should be in radians (:math:`2\pi` rad equals 360 degrees). .. math:: tan([0, \pi/4, \pi/2]) = [0, 1, -inf] The storage type of ``tan`` output depends upon the input storage type: - tan(default) = default - tan(row_sparse) = row_sparse - tan(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L140 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def tanh(data=None, name=None, attr=None, out=None, **kwargs): r"""Returns the hyperbolic tangent of the input array, computed element-wise. .. math:: tanh(x) = sinh(x) / cosh(x) The storage type of ``tanh`` output depends upon the input storage type: - tanh(default) = default - tanh(row_sparse) = row_sparse - tanh(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_trig.cc:L451 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def trunc(data=None, name=None, attr=None, out=None, **kwargs): r"""Return the element-wise truncated value of the input. The truncated value of the scalar x is the nearest integer i which is closer to zero than x is. In short, the fractional part of the signed number x is discarded. Example:: trunc([-2.1, -1.9, 1.5, 1.9, 2.1]) = [-2., -1., 1., 1., 2.] The storage type of ``trunc`` output depends upon the input storage type: - trunc(default) = default - trunc(row_sparse) = row_sparse - trunc(csr) = csr Defined in ../src/operator/tensor/elemwise_unary_op_basic.cc:L854 Parameters ---------- data : Symbol The input array. name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def where(condition=None, x=None, y=None, name=None, attr=None, out=None, **kwargs): r"""Return the elements, either from x or y, depending on the condition. Given three ndarrays, condition, x, and y, return an ndarray with the elements from x or y, depending on the elements from condition are true or false. x and y must have the same shape. If condition has the same shape as x, each element in the output array is from x if the corresponding element in the condition is true, and from y if false. If condition does not have the same shape as x, it must be a 1D array whose size is the same as x's first dimension size. Each row of the output array is from x's row if the corresponding element from condition is true, and from y's row if false. Note that all non-zero values are interpreted as ``True`` in condition. Examples:: x = [[1, 2], [3, 4]] y = [[5, 6], [7, 8]] cond = [[0, 1], [-1, 0]] where(cond, x, y) = [[5, 2], [3, 8]] csr_cond = cast_storage(cond, 'csr') where(csr_cond, x, y) = [[5, 2], [3, 8]] Defined in ../src/operator/tensor/control_flow_op.cc:L57 Parameters ---------- condition : Symbol condition array x : Symbol y : Symbol name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) def zeros_like(data=None, name=None, attr=None, out=None, **kwargs): r"""Return an array of zeros with the same shape, type and storage type as the input array. The storage type of ``zeros_like`` output depends on the storage type of the input - zeros_like(row_sparse) = row_sparse - zeros_like(csr) = csr - zeros_like(default) = default Examples:: x = [[ 1., 1., 1.], [ 1., 1., 1.]] zeros_like(x) = [[ 0., 0., 0.], [ 0., 0., 0.]] Parameters ---------- data : Symbol The input name : string, optional. Name of the resulting symbol. Returns ------- Symbol The result symbol. """ return (0,) __all__ = ['ElementWiseSum', 'Embedding', 'FullyConnected', 'LinearRegressionOutput', 'LogisticRegressionOutput', 'MAERegressionOutput', '_contrib_round_ste', '_contrib_sign_ste', 'abs', 'adagrad_update', 'adam_update', 'add_n', 'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctanh', 'broadcast_add', 'broadcast_div', 'broadcast_minus', 'broadcast_mul', 'broadcast_plus', 'broadcast_sub', 'cast_storage', 'cbrt', 'ceil', 'clip', 'concat', 'cos', 'cosh', 'degrees', 'dot', 'elemwise_add', 'elemwise_div', 'elemwise_mul', 'elemwise_sub', 'exp', 'expm1', 'fix', 'floor', 'ftrl_update', 'gamma', 'gammaln', 'log', 'log10', 'log1p', 'log2', 'make_loss', 'mean', 'negative', 'norm', 'radians', 'relu', 'retain', 'rint', 'round', 'rsqrt', 'sgd_mom_update', 'sgd_update', 'sigmoid', 'sign', 'sin', 'sinh', 'slice', 'sqrt', 'square', 'stop_gradient', 'sum', 'tan', 'tanh', 'trunc', 'where', 'zeros_like']

28.128522

903

0.603367

11,113

81,854

4.371097

0.079996

0.016366

0.027421

0.033514

0.656353

0.614254

0.591403

0.54529

0.523757

0.509593

0

0.024746

0.27229

81,854

2,910

903

28.128522

0.790753

0.795441

0

0.328889

1

0

0.066555

0.005128

0

1

0.328889

false

0

0.008889

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

3

5cb850670717d98eb2c3c0d61bd2ce612b6a60ae

297

py

Python

demo/__init__.py

astropenguin/demo-flake8-black

dc2cc44114f30ef622d820c58dea4824f6dcbdf8

[ "MIT" ]

null

demo/__init__.py

astropenguin/demo-flake8-black

dc2cc44114f30ef622d820c58dea4824f6dcbdf8

[ "MIT" ]

6

2019-08-22T07:49:37.000Z

2021-02-02T22:50:13.000Z

demo/__init__.py

astropenguin/demo-flake8-black

dc2cc44114f30ef622d820c58dea4824f6dcbdf8

[ "MIT" ]

null

__version__ = "0.0.1" def func(a: int, b: int = 0, c: int = 0) -> int: """Test function. Args: a (int): Parameter A. b (int, optional): Parameter B. c (int, optional): Parameter C. Returns: sum: The sum of A, B, and C. """ return a + b + c

17.470588

48

0.488215

45

297

3.133333

0.444444

0.042553

0.283688

0

0.026178

0.356902

297

16

49

18.5625

0.712042

0.545455

0

0.050505

0

1

0.333333

false

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

3

5ccbd87770a98d41b63d8a21877f8c6267509406

274

py

Python

OpenGLCffi/EGL/EXT/MESA/drm_image.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/EGL/EXT/MESA/drm_image.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/EGL/EXT/MESA/drm_image.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

from OpenGLCffi.EGL import params @params(api='egl', prms=['dpy', 'attrib_list']) def eglCreateDRMImageMESA(dpy, attrib_list): pass @params(api='egl', prms=['dpy', 'image', 'name', 'handle', 'stride']) def eglExportDRMImageMESA(dpy, image, name, handle, stride): pass

22.833333

69

0.70438

35

274

5.457143

0.514286

0.094241

0.125654

0.167539

0.434555

0

0.109489

274

11

70

24.909091

0.782787

0

0.285714

0

0.161765

0

1

0.285714

false

0.285714

0.142857

0

0.428571

0

null

0

1

0

null

0

1

0

1

0

3

7a306d1792d25c0de90ce79847ff8e01d61ae0dd

285

py

Python

User/admin.py

orik3ll0/generate_Csv

49902b1ddbe341e8c65fc453bfb0b5d807de1563

[ "MIT" ]

null

User/admin.py

orik3ll0/generate_Csv

49902b1ddbe341e8c65fc453bfb0b5d807de1563

[ "MIT" ]

null

User/admin.py

orik3ll0/generate_Csv

49902b1ddbe341e8c65fc453bfb0b5d807de1563

[ "MIT" ]

null

from django.contrib import admin from User.models import * # Register your models here. admin.site.register(Separator) admin.site.register(StringCharacter) admin.site.register(SchemaColumn) admin.site.register(Schema) admin.site.register(InputType) admin.site.register(Generated_csv)

25.909091

36

0.82807

38

285

6.184211

0.473684

0.229787

0.434043

0

0.070175

285

11

37

25.909091

0.886792

0.091228

0

1

0

true

0

0.25

0

0.25

0

null

1

0

null

0

1

0

3

7a4f4b92bad79a1056bcae996e212d9032e90ba6

5,218

py

Python

autoviml/custom_scores.py

goparajug/AutoML_Install

3091148bb1a26b5fc9ccf67658c376adb1df9890

[ "Apache-2.0" ]

1

2019-12-02T18:24:27.000Z

autoviml/custom_scores.py

goparajug/AutoML_Install

3091148bb1a26b5fc9ccf67658c376adb1df9890

[ "Apache-2.0" ]

null

autoviml/custom_scores.py

goparajug/AutoML_Install

3091148bb1a26b5fc9ccf67658c376adb1df9890

[ "Apache-2.0" ]

null

import numpy as np from sklearn import model_selection from sklearn.metrics import confusion_matrix, mean_squared_error from sklearn import metrics from sklearn import model_selection, metrics #Additional sklearn functions from sklearn.metrics import accuracy_score,f1_score,roc_auc_score,log_loss from sklearn.metrics import mean_squared_error,median_absolute_error,mean_absolute_error from sklearn.metrics import classification_report, confusion_matrix,mean_squared_log_error from sklearn.metrics import precision_recall_curve from sklearn.model_selection import cross_val_score, StratifiedKFold, KFold from sklearn.metrics import make_scorer from sklearn.metrics import accuracy_score from sklearn.metrics import average_precision_score from sklearn.metrics import f1_score from sklearn.metrics import log_loss from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import roc_auc_score ##################################################################################### from sklearn.metrics import confusion_matrix def balanced_accuracy_score(y_true, y_pred, sample_weight=None, adjusted=False): C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight) with np.errstate(divide='ignore', invalid='ignore'): per_class = np.diag(C) / C.sum(axis=1) if np.any(np.isnan(per_class)): warnings.warn('y_pred contains classes not in y_true') per_class = per_class[~np.isnan(per_class)] score = np.mean(per_class) if adjusted: n_classes = len(per_class) chance = 1 / n_classes score -= chance score /= 1 - chance return score def accu(results, y_cv): return (results==y_cv).astype(int).sum(axis=0)/(y_cv.shape[0]) def rmse(results, y_cv): return np.sqrt(np.mean((results - y_cv)**2, axis=0)) ######## Defining objective functions for HyperOpt here ###################### def gini(truth, predictions): g = np.asarray(np.c_[truth, predictions, np.arange(len(truth)) ], dtype=np.float) g = g[np.lexsort((g[:,2], -1*g[:,1]))] gs = g[:,0].cumsum().sum() / g[:,0].sum() gs -= (len(truth) + 1) / 2. return gs / len(truth) def gini_sklearn(truth, predictions): return gini(truth, predictions) / gini(truth, truth) def gini_meae(truth, predictions): score = median_absolute_error(truth, predictions) return score def gini_msle(truth, predictions): score = mean_squared_log_error(truth, predictions) return score def gini_mae(truth, predictions): score = mean_absolute_error(truth, predictions) return score def gini_mse(truth, predictions): score = mean_squared_error(truth, predictions) return score def gini_rmse(truth, predictions): score = np.sqrt(mean_squared_error(truth, predictions)) return score def gini_accuracy(truth, predictions): return accuracy_score(truth, predictions) def gini_bal_accuracy(truth, predictions): try: return balanced_accuracy_score(truth, predictions) except: return accuracy_score(truth, predictions) def gini_roc(truth, predictions): return roc_auc_score(truth, predictions) def gini_precision(truth, predictions,pos_label=1): return precision_score(truth, predictions,average=None)[pos_label] def gini_average_precision(truth, predictions): return average_precision_score(truth, predictions.argmax(axis=1),average='weighted') def gini_weighted_precision(truth, predictions): return precision_score(truth, predictions.argmax(axis=1),average='weighted') def gini_macro_precision(truth, predictions): return precision_score(truth, predictions.argmax(axis=1),average='macro') def gini_micro_precision(truth, predictions): return precision_score(truth, predictions.argmax(axis=1),average='micro') def gini_samples_precision(truth, predictions): return precision_score(truth, predictions.argmax(axis=1),average='samples') def gini_f1(truth, predictions,pos_label=1): return f1_score(truth, predictions,average=None)[pos_label] def gini_weighted_f1(truth, predictions): return f1_score(truth, predictions.argmax(axis=1),average='weighted') def gini_macro_f1(truth, predictions): return f1_score(truth, predictions.argmax(axis=1),average='macro') def gini_micro_f1(truth, predictions): return f1_score(truth, predictions.argmax(axis=1),average='micro') def gini_samples_f1(truth, predictions): return f1_score(truth, predictions.argmax(axis=1),average='samples') def gini_log_loss(truth, predictions): return log_loss(truth, predictions,normalize=True) def gini_recall(truth, predictions,pos_label=1): return recall_score(truth, predictions,average=None)[pos_label] def gini_weighted_recall(truth, predictions): return recall_score(truth, predictions.argmax(axis=1),average='weighted') def gini_samples_recall(truth, predictions): return recall_score(truth, predictions.argmax(axis=1),average='samples') def gini_macro_recall(truth, predictions): return recall_score(truth, predictions.argmax(axis=1),average='macro') def gini_micro_recall(truth, predictions): return recall_score(truth, predictions.argmax(axis=1),average='micro')

38.940299

90

0.746838

707

5,218

5.305516

0.15983

0.234604

0.129032

0.089576

0.623034

0.539323

0.425487

0.392695

0.366302

0.327113

0

0.009253

0.130126

5,218

133

91

39.233083

0.81714

0.014373

0

0.076923

0

0.026269

0

1

0.288462

false

0

0.182692

0.211538

0.769231

0

null

1

0

null

0

1

0

1

0

3

7a81a416c589b0a26bf90dd8ef75fbd62821c188

854

py

Python

oracle-linux-image-tools/bin/mkpasswd.py

MarkkuPekkarinen/oracle-linux

72d45e8d4b6b0276a919040b293ef3acbcbe5349

[ "UPL-1.0" ]

null

oracle-linux-image-tools/bin/mkpasswd.py

MarkkuPekkarinen/oracle-linux

72d45e8d4b6b0276a919040b293ef3acbcbe5349

[ "UPL-1.0" ]

null

oracle-linux-image-tools/bin/mkpasswd.py

MarkkuPekkarinen/oracle-linux

72d45e8d4b6b0276a919040b293ef3acbcbe5349

[ "UPL-1.0" ]

null

#!/usr/bin/env python3 """ Simple wrapper to generate SHA512 hash for a password. Copyright (c) 2019-2022 Oracle and/or its affiliates. Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl. Description: Python < 3.3 does not have crypt.mksalt() we mimmic https://github.com/python/cpython/blob/master/Lib/crypt.py DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS HEADER. """ from crypt import crypt from random import SystemRandom from string import ascii_letters, digits from sys import argv, exit def mksalt(): """Generate SHA512 salt.""" sr = SystemRandom() return '$6$' + ''.join(sr.choice(ascii_letters + digits + './') for char in range(16)) if len(argv) != 2: print("Usage: " + argv[0] + " password") exit(1) print(crypt(argv[1], mksalt()))

25.117647

67

0.685012

126

854

4.626984

0.698413

0.048027

0.06175

0

0.037572

0.189696

854

33

68

25.878788

0.804913

0.514052

0

1

0

0.052239

0

1

0.083333

false

0.083333

0.333333

0

0.5

0.166667

0

null

0

1

0

null

0

1

0

3

7a9fbe968399595af1514397f7891e565af0ad26

736

py

Python

python/testData/refactoring/argumentList/addKeyArgument/addArgumentFile.after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2018-12-29T09:53:39.000Z

2018-12-29T09:53:42.000Z

python/testData/refactoring/argumentList/addKeyArgument/addArgumentFile.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/argumentList/addKeyArgument/addArgumentFile.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from stub import * import stub class MyOldClass(metaclass=ABCMeta): pass class MyNewClass(object,metaclass=ABCMeta): pass class MyNewClass_2(object, datetime,metaclass=ABCMeta): pass class NewClass_3(stub.object,metaclass=ABCMeta): pass class NewClass_4(stub.object, stub.datetime,metaclass=ABCMeta): pass class NewClass_5(stub.datetime, foo=stub.object,metaclass=ABCMeta): pass spam = "new_param" my_function(new_param=spam) my_function_1("some_param",new_param=spam) my_function_2(named_param="ham",new_param=spam) my_function_3("some_param", "some_param_2", named_param="ham",new_param=spam) my_function_4("some_param", "some_param_2", named_param=stub.object, named_param_2="eggs",new_param=spam)

23

105

0.779891

111

736

4.891892

0.243243

0.176796

0.220994

0.230203

0.725599

0.370166

0.219153

0.132597

0

0.016641

0.101902

736

32

105

23

0.804841

0

0.3

0

0.09905

0

1

0

false

0.3

0.1

0

0.4

0

null

0

1

0

null

0

1

0

3

7aaf3a39e8ad728f3ec63ff595de86dd72e5dafe

1,562

py

Python

application/core/models.py

vfabi/passreset

5f37c6efa0c1b01e2526c80a4c8d729831b7b235

[ "Apache-2.0" ]

null

application/core/models.py

vfabi/passreset

5f37c6efa0c1b01e2526c80a4c8d729831b7b235

[ "Apache-2.0" ]

null

application/core/models.py

vfabi/passreset

5f37c6efa0c1b01e2526c80a4c8d729831b7b235

[ "Apache-2.0" ]

1

2021-01-26T13:51:20.000Z

#!/usr/bin/env python3 # -*- coding:utf-8 -*- """ @project: passreset @component: core @copyright: © 2020 by vfabi @author: vfabi @support: vfabi @initial date: 2020-05-08 21:08:07 @license: this file is subject to the terms and conditions defined in file 'LICENSE', which is part of this source code package @description: @todo: """ import json import random from datetime import datetime, timedelta from jsondb import Database from .utils import variables class ResetLinkModel: """Database model to store generated reset links.""" def __init__(self): self.db = Database(variables['db']) def generate(self): random_string = ''.join(random.choice('abcdefghijklmnopqrstuvwxyz1234567890') for _ in range(30)) dt = datetime.now() + timedelta(days=+1) dt_string = dt.strftime("%Y%m%d%H%M%S") reset_string = f'{random_string}-{dt_string}' return reset_string def _check_expire(self, resetlink): now = datetime.now() dt_expire_string = resetlink.split('-')[1] dt_expire = datetime.strptime(dt_expire_string, "%Y%m%d%H%M%S") return dt_expire > now def add(self, resetlink, email): self.db.data(key=resetlink, value=email) def get(self, resetlink): return self.db.data(key=resetlink) def exists(self, resetlink): if resetlink in self.db: return self._check_expire(resetlink) return False def delete(self, resetlink): self.db.delete(resetlink)

27.892857

105

0.645967

202

1,562

4.89604

0.49505

0.030334

0.006067

0.008089

0.056623

0.012133

0

0.028571

0.238156

1,562

55

106

28.4

0.801681

0.238796

0

0.079295

0.055507

0

0.018182

0

1

0.241379

false

0

0.172414

0.034483

0.62069

0

null

0

null

0

1

0

1

0

1

0

3

8fa72bb1f2333c32b795f832c72fa33dbc5625ee

724

py

Python

chigre/models/pub.py

javiercp/holdmybeer

1b8f847c9a9d35880b8461c16c6ec7630bde86f5

[ "MIT" ]

null

chigre/models/pub.py

javiercp/holdmybeer

1b8f847c9a9d35880b8461c16c6ec7630bde86f5

[ "MIT" ]

53

2018-06-14T16:17:51.000Z

2021-07-21T04:31:55.000Z

chigre/models/pub.py

javiercp/holdmybeer

1b8f847c9a9d35880b8461c16c6ec7630bde86f5

[ "MIT" ]

null

from django.conf import settings from django.db import models from .singletonmodel import SingletonModel from .common import get_sentinel_user class Pub(SingletonModel): name = models.CharField(max_length=100, blank=True, default='') motto = models.TextField(blank=True, default='') description = models.TextField(blank=True, default='') address = models.TextField(blank=True, default='') lat = models.DecimalField(max_digits=20, decimal_places=16, null=True, blank=True) lng = models.DecimalField(max_digits=20, decimal_places=16, null=True, blank=True) telephone = models.CharField(max_length=50, blank=True, default='') logo = models.CharField(max_length=100, blank=True, default='')

48.266667

86

0.747238

94

724

5.659574

0.404255

0.135338

0.180451

0.135338

0.565789

0.390977

0.229323

0

0.025397

0.129834

724

15

87

48.266667

0.819048

0

1

0

false

0

0.307692

0

1

0

null

0

1

0

null

0

1

0

1

0

3

8fab62ee81519b42fa0d118cb8800e7a75d0c32e

198

py

Python

app/admin/forms.py

MikeLing/Simpleblog

457a73fc6558c75f19020627f3f85e18b0009aa7

[ "MIT" ]

81

2017-06-30T03:21:08.000Z

2021-02-24T09:02:04.000Z

app/admin/forms.py

Ayx03/Simpleblog

94c8e14729d837395de419043df7fbf6b44469c2

[ "MIT" ]

3

2020-03-24T17:27:46.000Z

2021-02-02T22:07:09.000Z

app/admin/forms.py

pauldevsodre412/quokka-master

7a508f8d7f1a9068c16ce0f1a308c7702b6f96cf

[ "MIT" ]

34

2017-06-28T13:16:42.000Z

2021-01-10T10:31:50.000Z

from flask_wtf import FlaskForm from wtforms.validators import Length from wtforms import TextAreaField class NoticeForm(FlaskForm): body = TextAreaField('notice', validators=[Length(0, 25)])

24.75

62

0.792929

24

198

6.5

0.625

0.141026

0

0.017341

0.126263

198

7

63

28.285714

0.884393

0

0.030303

0

1

0

false

0

0.6

0

1

0

1

0

null

0

1

0

null

0

1

0

3

8fb4af0bcd6165ba24860f7dc0cb3905e3da1677

157

py

Python

src/favorites/urls.py

PhaseDMS/phase

4f776d0b1b5e7916a3e26aee890b3c2b9454ef0e

[ "MIT" ]

2

2021-09-10T19:40:30.000Z

2022-01-31T07:15:51.000Z

src/favorites/urls.py

PhaseDMS/phase

4f776d0b1b5e7916a3e26aee890b3c2b9454ef0e

[ "MIT" ]

null

src/favorites/urls.py

PhaseDMS/phase

4f776d0b1b5e7916a3e26aee890b3c2b9454ef0e

[ "MIT" ]

1

2021-09-10T19:40:42.000Z

from django.urls import path from .views import FavoriteList urlpatterns = [ # Favorites path("", FavoriteList.as_view(), name="favorite_list"), ]

17.444444

59

0.707006

18

157

6.055556

0.777778

0

0.171975

157

8

60

19.625

0.838462

0.057325

0

0.089041

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

8fc6ea7f97fd4789158601fc6f18da89e6c3ad4b

385

py

Python

mne/channels/data/neighbors/__init__.py

dgwakeman/mne-python

3cc7a3f8456d78c828355f1860dd7e0297e59c73

[ "BSD-3-Clause" ]

4

2017-01-08T15:36:30.000Z

2022-03-08T13:07:27.000Z

mne/channels/data/neighbors/__init__.py

dgwakeman/mne-python

3cc7a3f8456d78c828355f1860dd7e0297e59c73

[ "BSD-3-Clause" ]

4

2015-04-20T16:10:47.000Z

2016-11-01T13:32:48.000Z

mne/channels/data/neighbors/__init__.py

dgwakeman/mne-python

3cc7a3f8456d78c828355f1860dd7e0297e59c73

[ "BSD-3-Clause" ]

2

2018-04-02T06:45:11.000Z

2018-07-16T23:39:02.000Z

# Neighbor definitions for clustering permutation analysis. # This is a selection of files from http://fieldtrip.fcdonders.nl/template # Additional definitions can be obtained through the FieldTrip software. # For additional information on how these definitions were computed, please # consider the related fieldtrip documentation: # http://fieldtrip.fcdonders.nl/template/neighbours.

55

75

0.820779

48

385

6.583333

0.75

0.082278

0.139241

0.151899

0.202532

0

0.116883

385

6

76

64.166667

0.929412

0.966234

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

null

0

1

0

3

8fc778aac183397dcb802d425bf802394a183781

63

py

Python

src/Exercises/__init__.py

juliendroletnoel/ExercisesGenerator

15e9156d17c853ea338a8cf3499e877a5bdf28b2

[ "MIT" ]

null

src/Exercises/__init__.py

juliendroletnoel/ExercisesGenerator

15e9156d17c853ea338a8cf3499e877a5bdf28b2

[ "MIT" ]

null

src/Exercises/__init__.py

juliendroletnoel/ExercisesGenerator

15e9156d17c853ea338a8cf3499e877a5bdf28b2

[ "MIT" ]

null

__all__ = ['ExerciseGenerator'] from . import ExerciseGenerator

31.5

31

0.809524

5

63

9.4

0.8

0

0.095238

63

2

32

31.5

0.824561

0

0.265625

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

8fcc362c64c779accc8f2b03b72b0dff442edbbc

202

py

Python

sc2clanman/apps.py

paskausks/sc2cm

9c80e581933531496333d4a54c40174d4fb583a5

[ "MIT" ]

null

sc2clanman/apps.py

paskausks/sc2cm

9c80e581933531496333d4a54c40174d4fb583a5

[ "MIT" ]

null

sc2clanman/apps.py

paskausks/sc2cm

9c80e581933531496333d4a54c40174d4fb583a5

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from django.apps import AppConfig class ClanManConfig(AppConfig): name = 'sc2clanman' verbose_name = 'Clan Manager' version_id = '0.1.7beta'

20.2

33

0.673267

26

202

5.153846

0.923077

0

0.036364

0.183168

202

9

34

22.444444

0.775758

0.212871

0

0.197452

0

1

0

false

0

0.2

0

1

0

1

0

null

0

1

0

null

0

1

0

3

8fcd257a922a7bab9431def39dd03e7cb93b5353

221

py

Python

features/steps/common_steps.py

kirbycope/altunity-headspin-python

a2c272fa561cad7b2ed9c463f3060420dea48d6f

[ "MIT" ]

null

features/steps/common_steps.py

kirbycope/altunity-headspin-python

a2c272fa561cad7b2ed9c463f3060420dea48d6f

[ "MIT" ]

null

features/steps/common_steps.py

kirbycope/altunity-headspin-python

a2c272fa561cad7b2ed9c463f3060420dea48d6f

[ "MIT" ]

null

from behave import * import test_data # I open the <name> scene @given('I open the {name} scene') def step_impl(context, name): # Loads the scene mentioned by its name. test_data.altUnityDriver.load_scene(name)

22.1

45

0.728507

35

221

4.485714

0.6

0.101911

0.152866

0.216561

0

0.176471

221

9

46

24.555556

0.862637

0.280543

0

0.147436

0

1

0.2

false

0

0.4

0

0.6

0

null

0

1

0

null

0

1

0

1

0

3

8fcf92cad2287bb49192176b798d2daa7d72abae

1,308

py

Python

examples/Oscillators/MallsOscillator/plotresults.py

doublefloyd/beluga

740bda376634945ef51bf1cf946fcbe002e9bc7f

[ "MIT" ]

20

2017-10-02T13:09:58.000Z

2022-03-28T20:50:35.000Z

examples/Oscillators/MallsOscillator/plotresults.py

doublefloyd/beluga

740bda376634945ef51bf1cf946fcbe002e9bc7f

[ "MIT" ]

187

2018-02-04T20:35:03.000Z

2021-01-27T15:04:18.000Z

examples/Oscillators/MallsOscillator/plotresults.py

doublefloyd/beluga

740bda376634945ef51bf1cf946fcbe002e9bc7f

[ "MIT" ]

12

2018-01-19T04:00:09.000Z

2022-03-28T16:44:17.000Z

from beluga.utils import load import matplotlib.pyplot as plt data = load('data.beluga') sol_set = data['solutions'] traj = sol_set[-1][-1] continuation = sol_set[-1] L = len(continuation) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.y[:, 0], sol.y[:, 1], linestyle='-', color=(1*(ind/L), 0, 1*(L-ind)/L)) plt.title('Phase Plot') plt.xlabel('$x_1$') plt.ylabel('$x_2$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.u[:, 0], linestyle='-', color=(1*(ind/L), 0, 1*(L-ind)/L)) plt.plot([traj.t[0], traj.t[-1]], [1, 1], color='k', linestyle='--') plt.plot([traj.t[0], traj.t[-1]], [-1, -1], color='k', linestyle='--') plt.title('Control History Plot') plt.xlabel('Time [s]') plt.ylabel('Control, $u$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.dual[:, 0], linestyle='-', color=(1*(ind/L), 0, 1*(L-ind)/L)) plt.title('$\\lambda_{x1}$ History Plot') plt.xlabel('Time [s]') plt.ylabel('$\\lambda_{x1}$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.dual[:, 1], linestyle='-', color=(1*(ind/L), 0, 1*(L-ind)/L)) plt.title('$\\lambda_{x2}$ History Plot') plt.xlabel('Time [s]') plt.ylabel('$\\lambda_{x2}$') plt.grid(True) plt.show()

26.693878

88

0.626147

224

1,308

3.616071

0.214286

0.039506

0.059259

0.074074

0.728395

0.68642

0.587654

0

0.02916

0.108563

1,308

48

89

27.25

0.665523

0

0.394737

0

0.146789

0

1

0

false

0

0.052632

0

0.052632

0

null

0

1

0

null

0

3

8fe0c57b4dacd83a912f33306a528fea61f29b2f

238

py

Python

stix_shifter/stix_transmission/src/modules/async_dummy/async_dummy_ping.py

kant/stix-shifter

164ea13c4fc34815df786897c8d882dcdc499680

[ "Apache-2.0" ]

null

stix_shifter/stix_transmission/src/modules/async_dummy/async_dummy_ping.py

kant/stix-shifter

164ea13c4fc34815df786897c8d882dcdc499680

[ "Apache-2.0" ]

null

stix_shifter/stix_transmission/src/modules/async_dummy/async_dummy_ping.py

kant/stix-shifter

164ea13c4fc34815df786897c8d882dcdc499680

[ "Apache-2.0" ]

null

from ..base.base_ping import BasePing class AsyncDummyPing(BasePing): def __init__(self, host, port, path): self.host = host self.port = port self.path = path def ping(self): return 'async ping'

19.833333

41

0.617647

30

238

4.733333

0.5

0.112676

0

0.285714

238

11

42

21.636364

0.835294

0

0.042017

0

1

0.25

false

0

0.125

0.625

0

null

0

null

0

1

0

1

0

3

8ff821ae1f01b4b8acfd674e95d5625f1b2bd735

150

py

Python

src/account/urls.py

Jlopezjlx/devhub

73e1a4ad89464e5c5462df3c9f1a0e611c40b85e

[ "Apache-2.0" ]

null

src/account/urls.py

Jlopezjlx/devhub

73e1a4ad89464e5c5462df3c9f1a0e611c40b85e

[ "Apache-2.0" ]

null

src/account/urls.py

Jlopezjlx/devhub

73e1a4ad89464e5c5462df3c9f1a0e611c40b85e

[ "Apache-2.0" ]

null

from django.urls import path from account import views urlpatterns = [ path('', views.index, name='index'), path(r'login/', views.login) ]

15

40

0.666667

20

150

5

0.6

0

0.186667

150

9

41

16.666667

0.819672

0

0.073333

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

8ff96309128ff88e13401a9415e689150bad9e8e

1,238

py

Python

home/tests/login-logout.py

caggri/FOFviz

776ab387d832a86eea1a1b9064040d9b012494a7

[ "MIT" ]

2

2020-05-24T22:28:53.000Z

2020-05-25T21:58:24.000Z

home/tests/login-logout.py

caggri/FOFviz

776ab387d832a86eea1a1b9064040d9b012494a7

[ "MIT" ]

null

home/tests/login-logout.py

caggri/FOFviz

776ab387d832a86eea1a1b9064040d9b012494a7

[ "MIT" ]

1

2021-10-16T12:26:29.000Z

from selenium import webdriver import time chromedriver = "C:/Users/deniz/chromedriver/chromedriver" driver = webdriver.Chrome(chromedriver) driver.get('http://127.0.0.1:8000/') usr = "hasan" pwd = "123456" user_dropdown = '//*[@id="userDropdown"]' username_input = '//*[@id="id_username"]' password_input = '//*[@id="id_password"]' login_submit = '//*[@id="loginBtn"]' logout_btn = '//*[@id="content"]/nav/ul/li[4]/div/a[4]' logout = '//*[@id="logoutModal"]/div/div/div[3]/a' time.sleep(3) driver.find_element_by_xpath(user_dropdown).click() time.sleep(3) driver.find_element_by_xpath(username_input).send_keys("asafsdasdaf") time.sleep(1) driver.find_element_by_xpath(password_input).send_keys("afsdsafasdfa") time.sleep(1) driver.find_element_by_xpath(login_submit).click() time.sleep(3) driver.find_element_by_xpath(username_input).clear() driver.find_element_by_xpath(username_input).send_keys(usr) time.sleep(1) driver.find_element_by_xpath(password_input).send_keys(pwd) time.sleep(1) driver.find_element_by_xpath(login_submit).click() time.sleep(5) driver.find_element_by_xpath(user_dropdown).click() time.sleep(2) driver.find_element_by_xpath(logout_btn).click() time.sleep(2) driver.find_element_by_xpath(logout).click()

29.47619

70

0.771405

190

1,238

4.742105

0.289474

0.122087

0.207547

0.231964

0.602664

0.591565

0.532741

0

0.024723

0.052504

1,238

41

71

30.195122

0.743393

0

0.382353

0

0.210824

0.150242

0

1

0

false

0.088235

0.058824

0

0.058824

0

null

0

1

0

null

0

1

0

3

890064321f80e3774ced8084d0bb7197a0b5c92d

366

py

Python

tests/test_percent.py

dillonm197/pytest.percent

b41d1751a169fc56f4bda0af4af566e36b11914a

[ "MIT" ]

2

2020-08-16T17:14:27.000Z

2020-08-20T21:50:26.000Z

tests/test_percent.py

dillonm197/pytest.percent

b41d1751a169fc56f4bda0af4af566e36b11914a

[ "MIT" ]

1

2020-11-17T14:27:21.000Z

tests/test_percent.py

dillonm197/pytest.percent

b41d1751a169fc56f4bda0af4af566e36b11914a

[ "MIT" ]

1

2020-11-17T14:25:46.000Z

import pytest def test_1(required_percent): assert isinstance(required_percent, int) def test_2(): assert True def test_3(): assert True def test_4(): assert True def test_5(): assert False @pytest.mark.skip def test_6(): pass @pytest.mark.xfail def test_7(): assert False @pytest.mark.xfail def test_8(): assert True

9.891892

44

0.669399

55

366

4.272727

0.418182

0.238298

0.165957

0.217021

0.187234

0

0.02847

0.23224

366

36

45

10.166667

0.807829

0

0.4

0

0.35

1

0.4

false

0.05

0

0.45

0

null

1

0

1

0

null

0

1

0

3

8900e6f125f10a41f7d95e9ebdc91b298264598f

1,951

py

Python

async_gui/gevent_tasks.py

timeyyy/async_gui

805a3c016055885dc4449a8718fa397547bae2d8

[ "BSD-2-Clause" ]

38

2015-01-13T01:23:03.000Z

2022-01-20T11:04:23.000Z

async_gui/gevent_tasks.py

robertpro/async_gui

df0fcf1a44c693860f1ae57d9d3bcc1541aec1b8

[ "BSD-2-Clause" ]

5

2016-12-23T10:12:30.000Z

2019-04-16T17:17:42.000Z

async_gui/gevent_tasks.py

robertpro/async_gui

df0fcf1a44c693860f1ae57d9d3bcc1541aec1b8

[ "BSD-2-Clause" ]

14

2015-12-04T02:56:21.000Z

2020-11-17T05:10:50.000Z

""" gevent_tasks ~~~~~~~~~~~~ Tasks executing in ``gevent`` Pool .. note:: You need to apply gevent monkey-patch yourself, see `docs <http://www.gevent.org/gevent.monkey.html>`_ """ from concurrent import futures from gevent.pool import Pool import gevent from .tasks import Task, MultiTask # TODO docs about monkey_patch class GeventPoolExecutor(futures.Executor): """ Wrapper for `gevent.pool.Pool` """ def __init__(self, max_workers): self.max_workers = max_workers self._pool = Pool(max_workers) def submit(self, fn, *args, **kwargs): greenlet = self._pool.spawn(fn, *args, **kwargs) return GeventFuture(greenlet) def shutdown(self, wait=True): self._pool.kill(block=wait) # TODO more greenlet methods, also check not overridden Future methods class GeventFuture(futures.Future): """ Wrapper for `Greenlet` """ def __init__(self, greenlet): super(GeventFuture, self).__init__() #self._greenlet = gevent.Greenlet() self._greenlet = greenlet def result(self, timeout=None): try: return self._greenlet.get(timeout=timeout) except gevent.Timeout as e: raise futures.TimeoutError(e) def exception(self, timeout=None): # todo timeout return self._greenlet.exception def running(self): return not self.done() def done(self): return self._greenlet.ready() class GTask(Task): """ Task executed in `gevent` Pool """ executor_class = GeventPoolExecutor class MultiGTask(MultiTask): """ Multiple tasks executed in `gevent` Pool simultaneously """ executor_class = GeventPoolExecutor def wait(self, executor, spawned_futures, timeout=None): executor._pool.join(timeout) return all(f.done() for f in spawned_futures)

26.013333

71

0.630446

219

1,951

5.47032

0.369863

0.0601

0.03005

0.033389

0

0.262942

1,951

74

72

26.364865

0.833102

0.248078

0

0.057143

0

0.040541

0

1

0.257143

false

0

0.114286

0.085714

0.714286

0

null

0

null

0

1

0

1

0

1

0

3

891800f1fc8a6a00b4f678969ab0561ce763b1dd

2,601

py

Python

nba_data/data/players.py

jaebradley/nba_data

30d817bbc1c5474774f97f3800354492e382d206

[ "MIT" ]

8

2017-01-07T13:32:16.000Z

2019-08-08T17:36:26.000Z

nba_data/data/players.py

jaebradley/nba_data

30d817bbc1c5474774f97f3800354492e382d206

[ "MIT" ]

72

2016-09-01T01:21:07.000Z

2021-03-25T21:41:38.000Z

nba_data/data/players.py

jaebradley/nba_data

30d817bbc1c5474774f97f3800354492e382d206

[ "MIT" ]

4

2016-12-06T10:30:59.000Z

2021-09-08T21:23:43.000Z

from nba_data.data.base_query_parameter import BaseQueryParameter class Player(BaseQueryParameter): def __init__(self, name, id): self.name = name self.id = id def __unicode__(self): return '{0} | {1}'.format(self.get_additional_unicode(), self.get_base_unicode()) def get_base_unicode(self): return 'name: {name} | id: {id}'.format(name=self.name, id=self.id) def get_additional_unicode(self): raise NotImplementedError('Implement in concrete classes') @staticmethod def get_query_parameter_name(): return "PlayerId" class TeamPlayer(Player): def __init__(self, name, id, team): self.team = team Player.__init__(self, name, id) def get_base_unicode(self): return 'team: {team} | {base_unicode}'.format(team=self.team, base_unicode=Player.get_base_unicode(self)) def get_additional_unicode(self): raise NotImplementedError('Implement in concrete classes') class SeasonPlayer(Player): def __init__(self, id, name, jersey, team_seasons): self.jersey = jersey self.team_seasons = team_seasons Player.__init__(self, name=name, id=id) def get_additional_unicode(self): return 'jersey: {jersey} | team seasons: {team_seasons}'.format(jersey=self.jersey, team_seasons=self.team_seasons) class CommonAllPlayer(TeamPlayer): def __init__(self, name, id, team): TeamPlayer.__init__(self, name, id, team) def get_additional_unicode(self): return '' class DetailedPlayer(TeamPlayer): def __init__(self, name, team, id, birth_date, height, weight, jersey, position): self.birth_date = birth_date self.height = height self.weight = weight self.jersey = jersey self.position = position self.jersey = jersey TeamPlayer.__init__(self, name=name, id=id, team=team) def get_additional_unicode(self): return 'birth date: {birth_date} | height: {height} | weight: {weight} | ' 'jersey: {jersey} | ' \ 'position: {position}'.format(birth_data=self.birth_date, height=self.height, weight=self.weight, jersey=self.jersey, position=self.position) class BoxScorePlayer(TeamPlayer): def __init__(self, name, team, id, status): self.status = status TeamPlayer.__init__(self, name=name, id=id, team=team) def get_additional_unicode(self): return 'status: {status}'.format(status=self.status)

33.346154

113

0.646674

305

2,601

5.219672

0.144262

0.060302

0.075377

0.105528

0.368719

0.342965

0.248116

0.18593

0

0.001016

0.242983

2,601

77

114

33.779221

0.807517

0

0.314815

0

0.113033

0

1

0.296296

false

0

0.018519

0.148148

0.574074

0

null

0

null

0

1

0

1

0

3

64e90358930ed919c419c63d29d6e6958127dd1c

153

py

Python

app/api/data/__init__.py

nccr-itmo/FEDOT.Web

9b6f7b66de277ea34d6d5ed621b99a3f938db61b

[ "BSD-3-Clause" ]

23

2020-12-24T11:05:01.000Z

2022-03-31T20:29:12.000Z

app/api/data/__init__.py

nccr-itmo/FedotWeb

763fb1f39ad2b69104b6568e6f941c4c67762e34

[ "BSD-3-Clause" ]

42

2021-01-11T09:38:31.000Z

2022-03-25T17:19:05.000Z

app/api/data/__init__.py

nccr-itmo/FedotWeb

763fb1f39ad2b69104b6568e6f941c4c67762e34

[ "BSD-3-Clause" ]

5

2021-03-31T04:38:31.000Z

2022-03-31T20:29:26.000Z

BASE_ROUTE = 'data' def register_routes(api, app): from .controller import api as data_api api.add_namespace(data_api, path=f"/{BASE_ROUTE}")

19.125

54

0.718954

24

153

4.333333

0.666667

0.173077

0

0.163399

153

7

55

21.857143

0.8125

0

0.111111

0

1

0.25

false

0

0.25

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

64e9570b103bac8198b8f1abd0ee6f32b9bfc554

906

py

Python

src/backend/common/sitevars/tests/google_analytics_id_test.py

ofekashery/the-blue-alliance

df0e47d054161fe742ac6198a6684247d0713279

[ "MIT" ]

266

2015-01-04T00:10:48.000Z

2022-03-28T18:42:05.000Z

src/backend/common/sitevars/tests/google_analytics_id_test.py

ofekashery/the-blue-alliance

df0e47d054161fe742ac6198a6684247d0713279

[ "MIT" ]

2,673

2015-01-01T20:14:33.000Z

2022-03-31T18:17:16.000Z

src/backend/common/sitevars/tests/google_analytics_id_test.py

ofekashery/the-blue-alliance

df0e47d054161fe742ac6198a6684247d0713279

[ "MIT" ]

230

2015-01-04T00:10:48.000Z

2022-03-26T18:12:04.000Z

from backend.common.sitevars.google_analytics_id import ContentType, GoogleAnalyticsID def test_key(): assert GoogleAnalyticsID.key() == "google_analytics.id" def test_description(): assert ( GoogleAnalyticsID.description() == "Google Analytics ID for logging API requests" ) def test_default_sitevar(): default_sitevar = GoogleAnalyticsID._fetch_sitevar() assert default_sitevar is not None default_json = {"GOOGLE_ANALYTICS_ID": ""} assert default_sitevar.contents == default_json assert default_sitevar.description == "Google Analytics ID for logging API requests" def test_google_analytics_id_empty(): assert GoogleAnalyticsID.google_analytics_id() is None def test_google_analytics_id(): test_id = "abc" GoogleAnalyticsID.put(ContentType(GOOGLE_ANALYTICS_ID=test_id)) assert GoogleAnalyticsID.google_analytics_id() == test_id

28.3125

88

0.760486

105

906

6.247619

0.285714

0.228659

0.259146

0.096037

0.408537

0.170732

0

0.15894

906

31

89

29.225806

0.860892

0

0.142384

0

0.35

1

0.25

false

0

0.05

0

0.3

0

null

1

0

null

0

1

0

3

64f4c8372cbf8ee1403d1e103b769998af62d436

2,688

py

Python

lib/temporal.py

dpla/zen

4bd3c3621aa8fa82e22b2514b93a0978a3179771

[ "Apache-2.0" ]

3

2016-01-31T02:55:07.000Z

2022-03-17T17:49:49.000Z

lib/temporal.py

dpla/zen

4bd3c3621aa8fa82e22b2514b93a0978a3179771

[ "Apache-2.0" ]

null

lib/temporal.py

dpla/zen

4bd3c3621aa8fa82e22b2514b93a0978a3179771

[ "Apache-2.0" ]

null

''' ''' # Useful resources: # http://wiki.python.org/moin/WorkingWithTime # http://seehuhn.de/pages/pdate #Other tools we can use: # http://code.google.com/p/parsedatetime/ # import feedparser import datetime from zen import dateparser, iso8601 from amara.lib.date import timezone, UTC def mods_convention_date(d): #Feedparser extension to parse a date format idiosyncratic to MODS if len(d) == 14: try: #FIXME: converting via tuple to datetime, then back to tuple. Wasteful. return datetime.datetime(int(d[0:4]), int(d[4:6]), int(d[6:8]), int(d[8:10]), int(d[10:12]), int(d[12:]), 0, UTC).timetuple() #.utctimetuple() except ValueError: return None return None # def plain_year(d): if len(d) == 4: try: #FIXME: converting via tuple to datetime, then back to tuple. Wasteful. return datetime.datetime(int(d[0:4]), 1, 1).timetuple() except ValueError: return None return None feedparser.registerDateHandler(mods_convention_date) feedparser.registerDateHandler(plain_year) def smart_parse_date(date): ''' Accepts a string or unicode date to be parsed and returns a datetime.datetime result A very restrictive list of dates that can be parsed (i.e. some date formats not listed here should work): W3C dates, documented here: http://www.w3.org/TR/NOTE-datetime A subset of undelimited ISO-8601 dates work (as prevalent in LC MODS). YYYYDDMM YYYYDDMMhhmmss In general the dates have to be internationally unambiguous, Y2K-safe One exception is support for US convention, Y2K-safe year dates. MM/DD/YYYY ''' date = date.strip() #FIXME: Yes, layers on layers. Streamline it. try: dt = iso8601.parse_date(dateparser.to_iso8601(date)) return dt except (KeyboardInterrupt, SystemExit): raise except Exception, e: pass #try: # date = unicode(date, 'utf-8') # try: if len(date) == 4: try: return datetime.datetime(int(date), 1, 1) except ValueError: pass parts = date.split(u'/') if len(parts) == 3: return datetime.datetime(int(parts[2]), int(parts[0]), int(parts[1])) except (KeyboardInterrupt, SystemExit): raise except Exception, e: pass try: dt = datetime.datetime(*feedparser._parse_date(date)[:7]) except (KeyboardInterrupt, SystemExit): raise except Exception, e: dt = None return dt smart_parse_date.serviceid = u'http://purl.org/com/zepheira/zen/smartparsedate'

27.71134

154

0.635045

354

2,688

4.782486

0.426554

0.016539

0.051979

0.059067

0.249262

0.206734

0.174838

0.102776

0

0.026065

0.257813

2,688

96

155

28

0.822556

0.170759

0

0.54

0

0.029197

0

0.020833

0

null

0.06

0.08

null

0

null

0

null

0

1

0

1

0

1

0

3

8f09778495c98e4ab900c67c78630fbb8930e2f2

68

py

Python

youtube_dl/version.py

builder07/ytdl

2c0a5d50af7ecc7302c813d649ee72dcd457a50a

[ "Unlicense" ]

null

youtube_dl/version.py

builder07/ytdl

2c0a5d50af7ecc7302c813d649ee72dcd457a50a

[ "Unlicense" ]

null

youtube_dl/version.py

builder07/ytdl

2c0a5d50af7ecc7302c813d649ee72dcd457a50a

[ "Unlicense" ]

null

from __future__ import unicode_literals __version__ = '2015.09.28'

17

39

0.808824

9

68

5.111111

1

0

0.133333

0.117647

68

3

40

22.666667

0.633333

0

0.147059

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

8f1bc690c415488050f5924c443ab06d236df132

245

py

Python

tests/test_cards.py

tarasivashchuk/portfolio

74939e175c77e2e5b3e428eda6319fe016e0ddf4

[ "Unlicense", "MIT" ]

null

tests/test_cards.py

tarasivashchuk/portfolio

74939e175c77e2e5b3e428eda6319fe016e0ddf4

[ "Unlicense", "MIT" ]

null

tests/test_cards.py

tarasivashchuk/portfolio

74939e175c77e2e5b3e428eda6319fe016e0ddf4

[ "Unlicense", "MIT" ]

null

from portfolio import cards def test_get_projects_markdown(): projects = cards.get_projects_markdown() assert isinstance(projects, list) assert len(projects) > 0 for project in projects: assert isinstance(project, str)

24.5

44

0.730612

30

245

5.8

0.6

0.126437

0.218391

0

0.005102

0.2

245

9

45

27.222222

0.882653

0

0.428571

1

0.142857

false

0

0.142857

0

0.285714

0

null

0

1

0

1

0

null

0

1

0

3

8f3524b310a52302e44c0802fe16e68ded00e12e

215

py

Python

tests/fixtures/defxmlschema/chapter10/example1004.py

nimish/xsdata

7afe2781b66982428cc1731f53c065086acd35c1

[ "MIT" ]

null

tests/fixtures/defxmlschema/chapter10/example1004.py

nimish/xsdata

7afe2781b66982428cc1731f53c065086acd35c1

[ "MIT" ]

null

tests/fixtures/defxmlschema/chapter10/example1004.py

nimish/xsdata

7afe2781b66982428cc1731f53c065086acd35c1

[ "MIT" ]

null

from enum import Enum class SmallSizeType(Enum): """ :cvar VALUE_2: :cvar VALUE_4: :cvar VALUE_6: :cvar SMALL: """ VALUE_2 = "2" VALUE_4 = "4" VALUE_6 = "6" SMALL = "small"

14.333333

26

0.539535

29

215

3.793103

0.37931

0.245455

0

0.062069

0.325581

215

14

27

15.357143

0.696552

0.265116

0

0.061538

0

1

0

false

0

0.166667

0

1

0

1

0

null

1

0

null

0

1

0

3

8f35e1d43c53b4f4dab150b5f9efd9020027a54c

847

py

Python

source/upload_user_stats.py

CheyenneNS/metrics

cfeeac6d01d99679897a998b193d630ada169c61

[ "MIT" ]

null

source/upload_user_stats.py

CheyenneNS/metrics

cfeeac6d01d99679897a998b193d630ada169c61

[ "MIT" ]

null

source/upload_user_stats.py

CheyenneNS/metrics

cfeeac6d01d99679897a998b193d630ada169c61

[ "MIT" ]

null

import time import methods_upload_user_stats import datetime print("############################################") print("User Stats Upload (UTC): " + str(datetime.datetime.utcnow())) start_time = time.time() user_stats_dict = methods_upload_user_stats.get_user_info_from_auth2() user_stats_dict = methods_upload_user_stats.get_internal_users(user_stats_dict) user_stats_dict = methods_upload_user_stats.get_user_orgs_count(user_stats_dict) user_stats_dict = methods_upload_user_stats.get_user_narrative_stats(user_stats_dict) user_stats_dict = methods_upload_user_stats.get_institution_and_country(user_stats_dict) print("--- gather data %s seconds ---" % (time.time() - start_time)) methods_upload_user_stats.upload_user_data(user_stats_dict) print("--- including user info and user_stats upload %s seconds ---" % (time.time() - start_time))

47.055556

98

0.781582

123

847

4.878049

0.243902

0.285

0.216667

0.256667

0.485

0.401667

0.338333

0.268333

0

0.001271

0.070838

847

17

99

49.823529

0.761118

0

0.188389

0.052133

0

1

0

false

0

0.214286

0

0.214286

0.285714

0

null

1

0

null

0

3

8f3b9fa42cb03da28c10b740dee72b4fc3af278a

1,482

py

Python

app/stats/data.py

projectweekend/THPL-Data-API

28995ad93d5d16cb9da10f52f30ae16d34c4c5c3

[ "MIT" ]

null

app/stats/data.py

projectweekend/THPL-Data-API

28995ad93d5d16cb9da10f52f30ae16d34c4c5c3

[ "MIT" ]

null

app/stats/data.py

projectweekend/THPL-Data-API

28995ad93d5d16cb9da10f52f30ae16d34c4c5c3

[ "MIT" ]

null

from app import db def latest_reading(sensor): query = """ WITH result AS ( SELECT * FROM thpl_data WHERE sensor = %s ORDER BY logged_at DESC LIMIT 1 ) SELECT ROW_TO_JSON(result.*) FROM result; """ with db.cursor() as cursor: cursor.execute(query, (sensor, )) result = cursor.fetchone() return result[0] if result else None def hourly_stats(sensor, start_day, end_day): query = """ WITH result AS ( SELECT * FROM thpl_hourly_agg WHERE sensor = %s AND hour >= %s AND hour < %s ORDER BY hour DESC ) SELECT JSON_AGG(result.*) FROM result; """ with db.cursor() as cursor: cursor.execute(query, (sensor, start_day, end_day, )) result = cursor.fetchone() return result[0] if result else None def daily_stats(sensor, start_day, end_day): query = """ WITH result AS ( SELECT * FROM thpl_daily_agg WHERE sensor = %s AND day >= %s AND day < %s ORDER BY day DESC ) SELECT JSON_AGG(result.*) FROM result; """ with db.cursor() as cursor: cursor.execute(query, (sensor, start_day, end_day, )) result = cursor.fetchone() return result[0] if result else None

25.551724

61

0.509447

170

1,482

4.317647

0.258824

0.059946

0.076294

0.092643

0.782016

0.73297

0.690736

0

0.004525

0.403509

1,482

57

62

26

0.825792

0

0.588235

0

0.533738

0.01417

0

1

0.058824

false

0

0.019608

0

0.137255

0

null

0

1

0

1

0

null

0

3

8f45f2a335cb11b4d642185ee87044a60a1ba434

518

py

Python

apps/users/migrations/0017_auto_20200807_1727.py

lucasjaroszewski/incremental-game

bae8823f986be0fd046bd50195d43fbc548fad90

[ "MIT" ]

null

apps/users/migrations/0017_auto_20200807_1727.py

lucasjaroszewski/incremental-game

bae8823f986be0fd046bd50195d43fbc548fad90

[ "MIT" ]

5

2021-06-09T17:54:51.000Z

2022-03-12T00:46:49.000Z

apps/users/migrations/0017_auto_20200807_1727.py

lucasjaroszewski/incremental-game

bae8823f986be0fd046bd50195d43fbc548fad90

[ "MIT" ]

1

2020-09-27T18:26:15.000Z

# Generated by Django 3.0.6 on 2020-08-07 20:27 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('users', '0016_auto_20200807_1719'), ] operations = [ migrations.RenameField( model_name='fish', old_name='man_eating_swamp', new_name='man_eating_marsh', ), migrations.RenameField( model_name='fish', old_name='rinde_port', new_name='rinde', ), ]

21.583333

47

0.567568

55

518

5.090909

0.672727

0.15

0.185714

0.214286

0.292857

0

0.088319

0.322394

518

23

48

22.521739

0.709402

0.086873

0

0.352941

1

0

0.176221

0.048832

0

1

0

false

0

0.058824

0

0.235294

0

null

0

1

0

1

0

null

0

3

8f4ec9ee4cd0d2d6b3b82e7717f7eecb86c43dce

212

py

Python

Semester.py

griffincosgrove/frat-cash

0b658e99d606ddf208c3179493bb1b24b95a7f4f

[ "Apache-2.0" ]

null

Semester.py

griffincosgrove/frat-cash

0b658e99d606ddf208c3179493bb1b24b95a7f4f

[ "Apache-2.0" ]

null

Semester.py

griffincosgrove/frat-cash

0b658e99d606ddf208c3179493bb1b24b95a7f4f

[ "Apache-2.0" ]

null

class Semester: def __init__(self, season:str, year:int, brother_id:int, amount:float): self.season = season self.year = year self.brother_id = brother_id self.amount = amount

30.285714

75

0.641509

28

212

4.607143

0.464286

0.209302

0

0.264151

212

7

76

30.285714

0.826923

0

1

0.166667

false

0

0.333333

0

1

0

null

1

0

1

0

null

0

3

8f5fcdce435474efded83e8603d4446f32162c98

749

py

Python

twitoff/app.py

dmhliu/twitoff

4c880eedf0a0925fe9200b1cf2a09d555a372adc

[ "MIT" ]

null

twitoff/app.py

dmhliu/twitoff

4c880eedf0a0925fe9200b1cf2a09d555a372adc

[ "MIT" ]

4

2021-06-08T22:12:41.000Z

2022-03-12T00:48:37.000Z

twitoff/app.py

dmhliu/twitoff

4c880eedf0a0925fe9200b1cf2a09d555a372adc

[ "MIT" ]

null

import os from os import getenv from flask import Flask, render_template from flask_bootstrap import Bootstrap from .models import DB, User,Tweet, add_test_users from .twitter import add_or_update_user, add_users, update_all_users from .predict import predict_user from .admin.routes import admin from .main.routes import main def create_app(): # TODO add config from file app = Flask(__name__) # create Flask instance Bootstrap(app) # create bootstrap instance app.config['SQLALCHEMY_DATABASE_URI'] = getenv('DATABASE_URL') app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False app.register_blueprint(main) app.register_blueprint(admin) DB.init_app(app) # db instance setup return app

34.045455

68

0.748999

103

749

5.213592

0.417476

0.03352

0.070764

0

0.184246

749

21

69

35.666667

0.878887

0.124166

0

0.1

0.081538

0

0.047619

0

1

0.055556

false

0

0.5

0

0.611111

0.111111

0

null

0

null

0

1

0

1

0

1

0

3

8f6b2959d73d9d52381f20389378e8944866a3c4

5,179

py

Python

up_revendas/cars/migrations/0008_auto_20210126_2052.py

MikaelSantilio/uprevendas-api

f04312988ffe3231f68ae0ebeaed9eaf0a7914b0

[ "MIT" ]

1

2021-08-24T00:32:12.000Z

up_revendas/cars/migrations/0008_auto_20210126_2052.py

MikaelSantilio/uprevendas-api

f04312988ffe3231f68ae0ebeaed9eaf0a7914b0

[ "MIT" ]

null

up_revendas/cars/migrations/0008_auto_20210126_2052.py

MikaelSantilio/uprevendas-api

f04312988ffe3231f68ae0ebeaed9eaf0a7914b0

[ "MIT" ]

null

# Generated by Django 3.0.11 on 2021-01-26 23:52 import django.core.validators from django.db import migrations, models import django.db.models.deletion import up_revendas.cars.validators class Migration(migrations.Migration): dependencies = [ ('cars', '0007_auto_20210126_1543'), ] operations = [ migrations.AlterField( model_name='brand', name='name', field=models.CharField(max_length=32, verbose_name='Nome'), ), migrations.AlterField( model_name='car', name='active', field=models.BooleanField(default=True, verbose_name='Ativo'), ), migrations.AlterField( model_name='car', name='brand', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='cars', to='cars.Brand', verbose_name='Marca'), ), migrations.AlterField( model_name='car', name='car_type', field=models.CharField(choices=[('hatch', 'Hatch'), ('sedan', 'Sedã'), ('suv', 'SUV'), ('van', 'Van/Utilitário'), ('pick-up', 'Pick-Up'), ('convertible', 'Conversível'), ('sport', 'Sport'), ('luxury', 'Luxo')], max_length=12, verbose_name='Tipo do carro'), ), migrations.AlterField( model_name='car', name='color', field=models.CharField(choices=[('black', 'Preto'), ('white', 'Branco'), ('silver', 'Prata'), ('red', 'Vermelho'), ('cinza', 'Cinza'), ('blue', 'Azul'), ('yellow', 'Amarelo'), ('green', 'Verde'), ('orange', 'Laranja'), ('other', 'Outra')], max_length=12, verbose_name='Cor'), ), migrations.AlterField( model_name='car', name='created_at', field=models.DateTimeField(auto_now_add=True, verbose_name='Criado em'), ), migrations.AlterField( model_name='car', name='license_plate', field=models.CharField(max_length=8, validators=[up_revendas.cars.validators.validateCarLicensePlate], verbose_name='Placa'), ), migrations.AlterField( model_name='car', name='mileage', field=models.IntegerField(validators=[django.core.validators.MinValueValidator(0)], verbose_name='Quilometragem'), ), migrations.AlterField( model_name='car', name='min_sale_value', field=models.FloatField(validators=[django.core.validators.MinValueValidator(0)], verbose_name='Valor min. de venda'), ), migrations.AlterField( model_name='car', name='model', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='cars', to='cars.Model', verbose_name='Modelo'), ), migrations.AlterField( model_name='car', name='sold', field=models.BooleanField(default=False, verbose_name='Vendido'), ), migrations.AlterField( model_name='car', name='transmission', field=models.CharField(choices=[('M', 'Manual'), ('AT', 'Automatic'), ('SA', 'Semi Automatic')], max_length=14, verbose_name='Transmissão'), ), migrations.AlterField( model_name='car', name='updated_at', field=models.DateTimeField(auto_now=True, verbose_name='Atualizado em'), ), migrations.AlterField( model_name='car', name='version', field=models.CharField(max_length=255, verbose_name='Versão'), ), migrations.AlterField( model_name='car', name='year', field=models.IntegerField(choices=[(1951, 1951), (1952, 1952), (1953, 1953), (1954, 1954), (1955, 1955), (1956, 1956), (1957, 1957), (1958, 1958), (1959, 1959), (1960, 1960), (1961, 1961), (1962, 1962), (1963, 1963), (1964, 1964), (1965, 1965), (1966, 1966), (1967, 1967), (1968, 1968), (1969, 1969), (1970, 1970), (1971, 1971), (1972, 1972), (1973, 1973), (1974, 1974), (1975, 1975), (1976, 1976), (1977, 1977), (1978, 1978), (1979, 1979), (1980, 1980), (1981, 1981), (1982, 1982), (1983, 1983), (1984, 1984), (1985, 1985), (1986, 1986), (1987, 1987), (1988, 1988), (1989, 1989), (1990, 1990), (1991, 1991), (1992, 1992), (1993, 1993), (1994, 1994), (1995, 1995), (1996, 1996), (1997, 1997), (1998, 1998), (1999, 1999), (2000, 2000), (2001, 2001), (2002, 2002), (2003, 2003), (2004, 2004), (2005, 2005), (2006, 2006), (2007, 2007), (2008, 2008), (2009, 2009), (2010, 2010), (2011, 2011), (2012, 2012), (2013, 2013), (2014, 2014), (2015, 2015), (2016, 2016), (2017, 2017), (2018, 2018), (2019, 2019), (2020, 2020), (2021, 2021)], verbose_name='Ano'), ), migrations.AlterField( model_name='model', name='brand', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='models', to='cars.Brand', verbose_name='Marca'), ), migrations.AlterField( model_name='model', name='name', field=models.CharField(max_length=32, verbose_name='Nome'), ), ]

50.77451

1,062

0.579649

558

5,179

5.270609

0.367384

0.058143

0.144509

0.16763

0.428086

0.393404

0.222033

0.196192

0.156069

0

0.156544

0.240201

5,179

101

1,063

51.277228

0.590851

0.008882

0

0.589474

1

0

0.121029

0.004483

0

1

0

false

0

0.042105

0

0.073684

0

null

0

1

0

1

0

1

0

null

0

3

56c5e20da2cd5885b05e167a228b34272f42dc84

667

py

Python

anormbookmarker/cli/visualization/sa_display.py

jakeogh/anormbookmarker

d8516cc47dd7e5a5484eb9c1e8f44155b7663897

[ "MIT" ]

2

2017-05-08T04:44:56.000Z

2017-08-21T06:41:05.000Z

anormbookmarker/cli/visualization/sa_display.py

jakeogh/anormbookmarker

d8516cc47dd7e5a5484eb9c1e8f44155b7663897

[ "MIT" ]

null

anormbookmarker/cli/visualization/sa_display.py

jakeogh/anormbookmarker

d8516cc47dd7e5a5484eb9c1e8f44155b7663897

[ "MIT" ]

null

#!/usr/bin/env python3 import click from anormbookmarker.model.__model__ import * from anormbookmarker.model.BookmarkClassConstructor import tagbookmarks_table from anormbookmarker.model.Word import WordMisSpelling from anormbookmarker.test.test_enviroment import Tag from anormbookmarker.test.test_enviroment import Bookmark from kcl.sqlalchemy.model.Filename import Filename #from kcl.sqlalchemy.model.FileRecord import Filename #from kcl.sqlalchemy.model.FileRecord import Path from kcl.sqlalchemy.visualization.sa_display import sa_display as kcl_sa_display @click.command() def sa_display(): #import IPython; IPython.embed() kcl_sa_display(globals())

35.105263

80

0.841079

85

667

6.435294

0.376471

0.173675

0.124314

0.120658

0.33638

0.179159

0

0.001647

0.089955

667

18

81

37.055556

0.899506

0.227886

0

1

0.090909

true

0

0.727273

0

0.818182

0

null

0

null

0

1

0

1

0

1

0

3

56d4ae2516a02d22b9e5eaeff2eea0ceaa9d5529

558

py

Python

dadvisor/datatypes/dataflow.py

dadvisor/core

31d59707eb9bf33f5bea4a8fb6fb1f0de9a37eba

[ "MIT" ]

null

dadvisor/datatypes/dataflow.py

dadvisor/core

31d59707eb9bf33f5bea4a8fb6fb1f0de9a37eba

[ "MIT" ]

null

dadvisor/datatypes/dataflow.py

dadvisor/core

31d59707eb9bf33f5bea4a8fb6fb1f0de9a37eba

[ "MIT" ]

null

from dadvisor.datatypes.address import Address class DataFlow(object): """ stores the transmission of a certain amount of data from the source to the destination """ def __init__(self, src: Address, dst: Address, size: int): self.src = src self.dst = dst self.size = size def __str__(self): return '{} > {}: {}'.format(self.src, self.dst, self.size) def __dict__(self): return {'src': self.src.__dict__(), 'dst': self.dst.__dict__(), 'size': self.size}

26.571429

94

0.578853

68

558

4.455882

0.441176

0.092409

0.066007

0

0.292115

558

20

95

27.9

0.767089

0.154122

0

0.04646

0

1

0.25

false

0

0.083333

0.166667

0.583333

0

null

0

null

0

1

0

1

0

3

56eb48b793e4e92c70b2a1ae53c67089c5aafd3a

2,090

py

Python

programs/sparse.py

thinkmoore/das

d9faabf3de987b890a5079b914f5aba597215b14

[ "CC0-1.0" ]

35

2019-04-16T19:37:01.000Z

2022-02-14T20:33:41.000Z

programs/sparse.py

thinkmoore/das

d9faabf3de987b890a5079b914f5aba597215b14

[ "CC0-1.0" ]

6

2019-06-05T19:41:15.000Z

2020-08-19T19:04:59.000Z

programs/sparse.py

thinkmoore/das

d9faabf3de987b890a5079b914f5aba597215b14

[ "CC0-1.0" ]

12

2019-05-02T19:38:06.000Z

2021-09-11T22:02:03.000Z

import scipy.sparse as ss import numpy as np class multiSparse: """ This class is used to store a multi-dimensional numpy array as a sparse array and transform it back to a dense array when needed. """ def __init__(self, array): if isinstance(array, np.ndarray): # a numpy ndarray is expected to initialize multiSparse self.sparse_array = ss.csr_matrix(array.flatten()) self.shape = array.shape else: raise TypeError("array must be of class numpy.ndarray") def toDense(self): temp = np.array(self.sparse_array.todense()) dense_array = temp.reshape(self.shape) return(dense_array) def __add__(self, b): assert self.shape == b.shape tmp = multiSparse(np.array([0])) tmp.shape = self.shape tmp.sparse_array = self.sparse_array + b.sparse_array return tmp def sum(self, dims = None): if dims: out = self.toDense().sum(dims) else: out = np.array(self.sparse_array.sum()) return out def __sub__(self, b): assert self.shape == b.shape tmp = multiSparse(np.array([0])) tmp.shape = self.shape tmp.sparse_array = self.sparse_array - b.sparse_array return tmp def __eq__(self, other): assert self.shape == other.shape if np.issubdtype(self.sparse_array.dtype, float): return np.isclose(np.array(self.sparse_array.todense()), np.array(other.sparse_array.todense()), equal_nan=True).all() else: return not (self.sparse_array != other.sparse_array).todense().any() def abs(self): self.sparse_array.data = np.abs(self.sparse_array.data) return self def sqrt(self): self.sparse_array = self.sparse_array.sqrt() return self def square(self): self.sparse_array.data = np.square(self.sparse_array.data) return self def max(self): self.sparse_array = self.sparse_array.max() return self

32.65625

133

0.609091

275

2,090

4.476364

0.269091

0.205524

0.194963

0.113729

0.450041

0.386678

0.298944

0.191714

0

0.001343

0.28756

2,090

63

134

33.174603

0.825386

0.088038

0

0.306122

0

0.019048

0

0.061224

1

0.204082

false

0

0.040816

0

0.44898

0

null

1

0

null

0

1

0

3

56f00acee7e799d52a86853be1de7f00199a53e7

79

py

Python

feelsbook/__init__.py

LionKingSimba/FeelsBook

c2c912a7d64f25de755c5fa7597779a3f74038f5

[ "MIT" ]

null

feelsbook/__init__.py

LionKingSimba/FeelsBook

c2c912a7d64f25de755c5fa7597779a3f74038f5

[ "MIT" ]

2

2019-01-19T21:58:16.000Z

2019-01-20T01:49:53.000Z

feelsbook/__init__.py

LionKingSimba/FeelsBook

c2c912a7d64f25de755c5fa7597779a3f74038f5

[ "MIT" ]

null

# -*- coding: utf-8 -*- # TODO: doc """feelsbook """ __version__ = (0, 0, 0)

9.875

23

0.506329

10

79

3.6

0.8

0.111111

0

0.064516

0.21519

79

7

24

11.285714

0.516129

0.531646

0

0.142857

0

1

0

false

0

1

0

null

0

1

0

null

0

1

0

3

710180788e46fb9672dd2581b521808730d01041

347

py

Python

src/onegov/ticket/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

src/onegov/ticket/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

src/onegov/ticket/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

from onegov.ticket.handler import Handler, HandlerRegistry handlers = HandlerRegistry() # noqa from onegov.ticket.model import Ticket from onegov.ticket.model import TicketPermission from onegov.ticket.collection import TicketCollection __all__ = [ 'Handler', 'handlers', 'Ticket', 'TicketCollection', 'TicketPermission' ]

21.6875

58

0.755043

34

347

7.588235

0.382353

0.155039

0.248062

0.162791

0.209302

0

0.158501

347

15

59

23.133333

0.883562

0.011527

0

0.155425

0

1

0

false

0

0.333333

0

0.333333

0

null

0

1

0

null

0

1

0

3

71052b040ab325664a81c80da34dc4d6f0a370d7

35

py

Python

src/RobotFrameworkCore/org.robotframework.ide.core-functions/src/test/python/scripts/res_test_robot_session_server/c/__init__.py

puraner/RED

d41b7c244ff2e449c541fd0691213fcf5a3b30c1

[ "Apache-2.0" ]

375

2015-11-02T19:15:30.000Z

2022-03-19T03:32:10.000Z

src/RobotFrameworkCore/org.robotframework.ide.core-functions/src/test/python/scripts/res_test_robot_session_server/c/__init__.py

puraner/RED

d41b7c244ff2e449c541fd0691213fcf5a3b30c1

[ "Apache-2.0" ]

433

2015-11-03T13:24:40.000Z

2022-03-30T11:20:14.000Z

src/RobotFrameworkCore/org.robotframework.ide.core-functions/src/test/python/scripts/res_test_robot_session_server/c/__init__.py

puraner/RED

d41b7c244ff2e449c541fd0691213fcf5a3b30c1

[ "Apache-2.0" ]

133

2016-05-02T02:20:06.000Z

2022-01-06T06:01:28.000Z

i = 1 while i > 0: print("true")

8.75

14

0.514286

7

35

2.571429

0.857143

0

0.08

0.285714

35

4

15

8.75

0.64

0

0.111111

0

1

0

false

0

0.333333

1

0

null

0

1

0

null

0

3

711baab41335aca75dd12ccc4d7f5df54ec9cd35

228

py

Python

spidermon/contrib/monitors/mixins/stats.py

zanachka/spidermon

d2840b6bbb6ba6d8a0ef633deac66588d243e615

[ "BSD-3-Clause" ]

405

2019-01-10T13:06:09.000Z

2022-03-30T20:14:58.000Z

spidermon/contrib/monitors/mixins/stats.py

zanachka/spidermon

d2840b6bbb6ba6d8a0ef633deac66588d243e615

[ "BSD-3-Clause" ]

226

2019-01-04T13:31:17.000Z

2022-03-28T21:06:10.000Z

spidermon/contrib/monitors/mixins/stats.py

zanachka/spidermon

d2840b6bbb6ba6d8a0ef633deac66588d243e615

[ "BSD-3-Clause" ]

87

2019-01-07T10:23:26.000Z

2022-02-22T04:38:04.000Z

from spidermon.exceptions import NotConfigured class StatsMonitorMixin: @property def stats(self): if not self.data.stats: raise NotConfigured("Stats not available!") return self.data.stats

22.8

55

0.684211

25

228

6.24

0.68

0.102564

0.166667

0

0.245614

228

9

56

25.333333

0.906977

0

0.087719

0

1

0.142857

false

0

0.142857

0

0.571429

0

1

0

null

0

1

0

null

0

1

0

3

713b63ae10a24f820ddf86ecff7bd7e2005a2760

313

py

Python

app/admin.py

beedev-services/dragonsEdgeCreations

13a80c96feb5c7eaf4823b0e039dec30e791c7b0

[ "MIT" ]

null

app/admin.py

beedev-services/dragonsEdgeCreations

13a80c96feb5c7eaf4823b0e039dec30e791c7b0

[ "MIT" ]

null

app/admin.py

beedev-services/dragonsEdgeCreations

13a80c96feb5c7eaf4823b0e039dec30e791c7b0

[ "MIT" ]

null

from django.contrib import admin from .models import * admin.site.register(User) admin.site.register(Profile) admin.site.register(Customer) admin.site.register(Account) admin.site.register(Event) admin.site.register(Format) admin.site.register(Language) admin.site.register(Product) admin.site.register(Picture)

24.076923

32

0.817891

44

313

5.818182

0.386364

0.316406

0.597656

0

0.057508

313

12

33

26.083333

0.867797

0

1

0

true

0

0.181818

0

0.181818

0

null

1

0

null

0

1

0

3

71415563b5394f16e97a62d06268a2caa9c29e62

274

py

Python

Leetcode/191. Number of 1 Bits/solution5.py

asanoviskhak/Outtalent

c500e8ad498f76d57eb87a9776a04af7bdda913d

[ "MIT" ]

51

2020-07-12T21:27:47.000Z

2022-02-11T19:25:36.000Z

Leetcode/191. Number of 1 Bits/solution5.py

CrazySquirrel/Outtalent

8a10b23335d8e9f080e5c39715b38bcc2916ff00

[ "MIT" ]

null

Leetcode/191. Number of 1 Bits/solution5.py

CrazySquirrel/Outtalent

8a10b23335d8e9f080e5c39715b38bcc2916ff00

[ "MIT" ]

32

2020-07-27T13:54:24.000Z

2021-12-25T18:12:50.000Z

TABLE8 = [0] * 2 ** 8 for index in range(len(TABLE8)): TABLE8[index] = (index & 1) + TABLE8[index >> 1] class Solution: def hammingWeight(self, n: int) -> int: return TABLE8[n & 0xff] + TABLE8[(n >> 8) & 0xff] + TABLE8[(n >> 16) & 0xff] + TABLE8[n >> 24]

27.4

102

0.558394

40

274

3.825

0.525

0.183007

0.215686

0

0.101449

0.244526

274

9

103

30.444444

0.637681

0

0.043796

0

1

0.166667

false

0

0.166667

0.5

0

null

0

1

0

1

0

null

0

1

0

3

856055b3f5bec174b12284852d80f1f727aef354

169

py

Python

config/settings/local.py

simongawlik/collect-and-share

7a2e3c549726b3321dcb914c7d184fa8442f683d

[ "MIT" ]

null

config/settings/local.py

simongawlik/collect-and-share

7a2e3c549726b3321dcb914c7d184fa8442f683d

[ "MIT" ]

null

config/settings/local.py

simongawlik/collect-and-share

7a2e3c549726b3321dcb914c7d184fa8442f683d

[ "MIT" ]

null

from .base import * SECRET_KEY = env('DJANGO_SECRET_KEY', default='b(sx)ez5cca37%)_&p(lv-6)r(8-d+x06_$t19500ij+hf$#tp') DEBUG = env.bool('DJANGO_DEBUG', default=True)

28.166667

99

0.715976

30

169

3.833333

0.8

0.156522

0

0.077419

0.08284

169

5

100

33.8

0.664516

0

0.333333

0.467456

0.295858

0

1

0

false

0

0.333333

0

0.333333

0

null

0

1

0

1

0

null

0

1

0

3

8565686454c13d3eef7860491b2b4992e21fe8c1

94

py

Python

Crudapp/apps.py

snragund/Student_System

5daffb3163ce9b1b96d5439ed38afce254b28d02

[ "MIT" ]

null

Crudapp/apps.py

snragund/Student_System

5daffb3163ce9b1b96d5439ed38afce254b28d02

[ "MIT" ]

null

Crudapp/apps.py

snragund/Student_System

5daffb3163ce9b1b96d5439ed38afce254b28d02

[ "MIT" ]

null

from django.apps import AppConfig class CrudappConfig(AppConfig): name = 'Crudapp'

15.666667

34

0.712766

10

94

6.7

0.9

0

0.212766

94

5

35

18.8

0.905405

0

0.078652

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

3

8583b3844de422ac6bf9937a4040bc2a1b10d443

144

py

Python

app/__init__.py

jwestgard/cost-calc

87482ea58735f8fa3109cdfe897785db96f7cef3

[ "MIT" ]

null

app/__init__.py

jwestgard/cost-calc

87482ea58735f8fa3109cdfe897785db96f7cef3

[ "MIT" ]

null

app/__init__.py

jwestgard/cost-calc

87482ea58735f8fa3109cdfe897785db96f7cef3

[ "MIT" ]

null

#!/usr/bin/env python3 from flask import Flask app = Flask(__name__) from app import views if __name__ == "__main__": app.run(debug=True)

16

26

0.715278

22

144

4.136364

0.681818

0

0.008333

0.166667

144

8

27

18

0.75

0.145833

0

0.065574

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

85b0c0d3fe274f3a277dcbab698996d7ef042a04

1,297

py

Python

aiotdlib/api/types/chat_event.py

mostafa-arshadi/aiotdlib

59f430a65dfb424fc69d471a0d7bcd77ad7acf08

[ "MIT" ]

37

2021-05-04T10:41:41.000Z

2022-03-30T13:48:05.000Z

aiotdlib/api/types/chat_event.py

mostafa-arshadi/aiotdlib

59f430a65dfb424fc69d471a0d7bcd77ad7acf08

[ "MIT" ]

13

2021-07-17T19:54:51.000Z

2022-02-26T06:50:00.000Z

aiotdlib/api/types/chat_event.py

mostafa-arshadi/aiotdlib

59f430a65dfb424fc69d471a0d7bcd77ad7acf08

[ "MIT" ]

7

2021-09-22T21:27:11.000Z

2022-02-20T02:33:19.000Z

# =============================================================================== # # # # This file has been generated automatically!! Do not change this manually! # # # # =============================================================================== # from __future__ import annotations from pydantic import Field from .chat_event_action import ChatEventAction from .message_sender import MessageSender from ..base_object import BaseObject class ChatEvent(BaseObject): """ Represents a chat event :param id: Chat event identifier :type id: :class:`int` :param date: Point in time (Unix timestamp) when the event happened :type date: :class:`int` :param member_id: Identifier of the user or chat who performed the action :type member_id: :class:`MessageSender` :param action: The action :type action: :class:`ChatEventAction` """ ID: str = Field("chatEvent", alias="@type") id: int date: int member_id: MessageSender action: ChatEventAction @staticmethod def read(q: dict) -> ChatEvent: return ChatEvent.construct(**q)

30.880952

83

0.508096

117

1,297

5.538462

0.504274

0.041667

0.040123

0

0.303007

1,297

41

84

31.634146

0.716814

0.585968

0

1

0

0.030568

0

1

0.071429

false

0

0.357143

0.071429

0.928571

0

null

0

1

0

null

0

1

0

1

0

3

a413705953f574eaf6e7f8f64d88bbde43219a1b

683

py

Python

ravendb/documents/operations/etl/sql/__init__.py

ravendb/RavenDB-Python-Client

6286b459b501e755fe8e8591a48acf8616605ccd

[ "MIT" ]

8

2016-10-08T17:45:44.000Z

2018-05-29T12:16:43.000Z

ravendb/documents/operations/etl/sql/__init__.py

ravendb/RavenDB-Python-Client

6286b459b501e755fe8e8591a48acf8616605ccd

[ "MIT" ]

5

2017-02-12T15:50:53.000Z

2017-09-18T12:25:01.000Z

ravendb/documents/operations/etl/sql/__init__.py

ravendb/RavenDB-Python-Client

6286b459b501e755fe8e8591a48acf8616605ccd

[ "MIT" ]

8

2016-07-03T07:59:12.000Z

2017-09-18T11:22:23.000Z

from typing import Optional from ravendb.documents.operations.connection_strings import ConnectionString import ravendb.serverwide from ravendb.documents.operations.etl.configuration import EtlConfiguration class SqlConnectionString(ConnectionString): def __init__(self, name: str, connection_string: Optional[str] = None, factory_name: Optional[str] = None): super().__init__(name) self.connection_string = connection_string self.factory_name = factory_name @property def get_type(self): return ravendb.serverwide.ConnectionStringType.SQL # todo: implement class SqlEtlConfiguration(EtlConfiguration[SqlConnectionString]): pass

31.045455

111

0.781845

71

683

7.295775

0.492958

0.092664

0.07722

0.11583

0

0.146413

683

21

112

32.52381

0.888508

0.021962

0

0.047619

0

1

0.142857

false

0.071429

0.285714

0.071429

0.642857

0

null

0

null

0

1

0

1

0

1

0

3

a4185610e31412a11ecc50c89da1ecb84b3a69e4

4,264

py

Python

SJM/m6A_peak_call_hg38.py

jhfoxliu/mod_RIP_peakcall_traditional

0dd141d7d830f6509c7c348483f0849b1462e9d4

[ "MIT" ]

null

SJM/m6A_peak_call_hg38.py

jhfoxliu/mod_RIP_peakcall_traditional

0dd141d7d830f6509c7c348483f0849b1462e9d4

[ "MIT" ]

null

SJM/m6A_peak_call_hg38.py

jhfoxliu/mod_RIP_peakcall_traditional

0dd141d7d830f6509c7c348483f0849b1462e9d4

[ "MIT" ]

null

import sys,os from sjm_tools import job,check_env if not sys.argv[1]: print "Usage: python %s [Sample Sheet]" % (sys.argv[0]) windows_file = check_env("/share/public1/data/liujh/database/human/ensembl_release104_GRCh38/sliding_window/Homo_sapiens.GRCh38.104.chr_patch_hapl_scaff.noheader.exons.w50s25.chr.txt") python = check_env('/share/public/apps/bin/python') bedtools = check_env('/share/public/apps/bedtools/2.29.2/bin/bedtools') perl = check_env('/share/public1/data/liujh/software/perlbrew/perls/perl-5.24.1/bin/perl') chr_size = "/share/public1/data/liujh/database/human/ensembl_release104_GRCh38/genome/Homo_sapiens.GRCh38.dna_sm.primary_assembly.chr.size" script_prefix = '/share/public1/data/liujh/script/m5C-seq/' PATH = "./" with open(sys.argv[1], "r") as input: for line in input.readlines(): if not line: continue name, input_prefix, input_winsize, ip_prefix, ip_winsize = line.strip().split("\t") SJM = name + "_call_peak.sjm" workpath = PATH + "/" + name + "_call_peak/" if os.path.isdir(workpath) == False: os.mkdir(workpath) JOB = job(workpath,SJM) JOB.step_start(step_name="CallPeak",memory="50G") JOB.add_process("{perl} {script_prefix}/rnaexp_rpkm_strand_ZH.pl {windows_file} {chr_size} ../{prefix}/{prefix}.minus.bedGraph ../{prefix}/{prefix}.plus.bedGraph {prefix}_window_rpkm.txt {winsize}".format(script_prefix=script_prefix,perl=perl, chr_size=chr_size, windows_file=windows_file, winsize=input_winsize, prefix=input_prefix)) JOB.add_process("{perl} {script_prefix}/rnaexp_rpkm_strand_ZH.pl {windows_file} {chr_size} ../{prefix}/{prefix}.minus.bedGraph ../{prefix}/{prefix}.plus.bedGraph {prefix}_window_rpkm.txt {winsize}".format(script_prefix=script_prefix,perl=perl, chr_size=chr_size, windows_file=windows_file, winsize=ip_winsize, prefix=ip_prefix)) JOB.add_process("{python} {script_prefix}/merge_input_eluate_win_RPKM.py {prefix_input}_window_rpkm.txt {prefix_ip}_window_rpkm.txt {name}_eluate_win_rpkm.txt".format(script_prefix=script_prefix,python=python, prefix_input=input_prefix, prefix_ip=ip_prefix, name=name)) JOB.add_process("{perl} {script_prefix}/calculate_winscore.pl {name}_eluate_win_rpkm.txt".format(script_prefix=script_prefix,perl=perl, name=name)) JOB.add_process("{perl} {script_prefix}/remove_data_RPKM1.pl {name}_eluate_win_rpkm_winscore.txt {name}_winscore_filtered.txt".format(script_prefix=script_prefix,perl=perl, name=name)) JOB.add_process("{perl} {script_prefix}/match_two_files.pl {name}_winscore_filtered.txt {windows_file} {name}_winscore_filtered_matched.txt".format(script_prefix=script_prefix,perl=perl, name=name, windows_file=windows_file)) JOB.add_process("cut -f 3,5,6,17,18 {name}_winscore_filtered_matched.txt > {name}_winscore_filtered_matched_formated.txt".format(script_prefix=script_prefix,name=name)) JOB.add_process("{python} {script_prefix}/m6A_add_strand.py {name}_winscore_filtered_matched_formated.txt > {name}_winscore_filtered_matched_formated.bed".format(script_prefix=script_prefix,python=python, name=name)) JOB.add_process("{perl} {script_prefix}/combine_peak.0.5.pl {name}_winscore_filtered_matched_formated.txt".format(script_prefix=script_prefix,perl=perl, name=name)) JOB.add_process("{perl} {script_prefix}/combine_peak2.pl {name}_winscore_filtered_matched_formated.txt".format(script_prefix=script_prefix,perl=perl, name=name)) JOB.add_process("grep -P 'Y$' {name}_winscore_filtered_matched_formated.txt.co.pk | cut -f 1-5 > {name}_winscore_filtered_matched_formated_greped.txt".format(script_prefix=script_prefix,name=name)) JOB.add_process("cat {name}_winscore_filtered_matched_formated_greped.txt {name}_winscore_filtered_matched_formated.txt.co.pk2 > {name}_peaklist.txt".format(script_prefix=script_prefix,name=name)) JOB.add_process("{python} {script_prefix}/add_strand_to_final_peaks.py {windows_file} {name}_peaklist.txt {name}_final_peaks_with_strand.bed".format(script_prefix=script_prefix,python=python, windows_file=windows_file, name=name)) JOB.step_end() JOB.job_finish() JOB.submit()

73.517241

342

0.75985

622

4,264

4.879421

0.231511

0.146293

0.055684

0.102801

0.627677

0.587809

0.539374

0.480066

0.438221

0.383855

0

0.014132

0.103893

4,264

57

343

74.807018

0.780162

0

0.342105

0.501876

0.415103

0

null

0

0.052632

null

0.026316

0

null

0

1

0

1

null

0

1

0

3

a4213442082b8b5dae80dbe7ae44c394b7a9c097

965

py

Python

band.py

Scuba-Chris/pythonic_garage_band

f09ae265e8069b4abacd3e8fcb4f12bd96149cb3

[ "MIT" ]

null

band.py

Scuba-Chris/pythonic_garage_band

f09ae265e8069b4abacd3e8fcb4f12bd96149cb3

[ "MIT" ]

null

band.py

Scuba-Chris/pythonic_garage_band

f09ae265e8069b4abacd3e8fcb4f12bd96149cb3

[ "MIT" ]

null

class Bands: all_bands = [] def __init__(self, band_name, members): self.band_name = 'band_name' self.members = members self.__class__.all_bands.append(self) def to_list(self): return self.all_bands def __str__(self): return f'the band name is {self.band_name}' def __repr__(self): return f'band: {self.band_name}' class Musician: def __init__(self, name, insturment): self.name = name self.insturment = insturment def __str__(self): return f'I am the {self.insturment}' def __repr__(self): return f'band member is: {self.insturment}' class Guitarist(Musician): def __init__(self, name): super().__init__(name, 'guitarist') class Singer(Musician): def __init__(self, name): super().__init__(name, 'singer') class Drummer(Musician): def __init__(self, name): super().__init__(name, 'drummer')

21.931818

51

0.620725

119

965

4.521008

0.226891

0.089219

0.10223

0.141264

0.388476

0.282528

0.200743

0

0.262176

965

43

52

22.44186

0.755618

0

0.241379

0

0.150571

0

1

0.344828

false

0

0.172414

0.724138

0

null

0

null

0

1

0

1

0

3

a42741f55fc1fc05cc1858ffac20225e8bf9f136

187

py

Python

tests/services/invalid_service.py

0x1EE7/tomodachi

8147b16d8be19b80b3bd7c5d8ed21c9863eaaa83

[ "MIT" ]

null

tests/services/invalid_service.py

0x1EE7/tomodachi

8147b16d8be19b80b3bd7c5d8ed21c9863eaaa83

[ "MIT" ]

null

tests/services/invalid_service.py

0x1EE7/tomodachi

8147b16d8be19b80b3bd7c5d8ed21c9863eaaa83

[ "MIT" ]

null

# flake8: noqa import tomodachi @tomodachi.service class InvalidService(tomodachi.Service): name = 'test_invalid' def syntax_error(self) -> error: # type: ignore pass

17

52

0.695187

21

187

6.095238

0.809524

0.25

0

0.006757

0.208556

187

10

53

18.7

0.858108

0.13369

0

0.075472

0

1

0.166667

false

0.166667

0

0.666667

0

1

0

null

1

0

null

0

1

0

3

a443894583d10265cfb128c8e3cf976cf4716477

154

py

Python

nonebot_plugin_7s_roll/__init__.py

7sDream/nonebot-plugin-7s-roll

113278f7d722a7c63d1b3a7f230be0d4c8db004c

[ "MIT" ]

null

nonebot_plugin_7s_roll/__init__.py

7sDream/nonebot-plugin-7s-roll

113278f7d722a7c63d1b3a7f230be0d4c8db004c

[ "MIT" ]

null

nonebot_plugin_7s_roll/__init__.py

7sDream/nonebot-plugin-7s-roll

113278f7d722a7c63d1b3a7f230be0d4c8db004c

[ "MIT" ]

null

from .command import cmd_roll, message_cmd_roll from .roll import roll as _roll __version__ = "0.1.2" from nonebot import export export().roll = _roll

17.111111

47

0.766234

25

154

4.36

0.52

0.12844

0

0.023077

0.155844

154

8

48

19.25

0.815385

0

0.032468

0

1

0

false

0

0.6

0

0.6

0

1

0

null

0

1

0

null

0

1

0

3

a4458d38625a2eefdacbfee10a17746570de51b4

217

py

Python

08_String/Step03/gamjapark.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

null

08_String/Step03/gamjapark.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

3

2020-11-04T05:38:53.000Z

2021-03-02T02:15:19.000Z

08_String/Step03/gamjapark.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

null

a = list(map(lambda i: ord(i) - 97, list(input()))) idx_array = [-1 for _ in range(26)] for i in range(len(a)): if idx_array[a[i]] == -1: idx_array[a[i]] = i for idx in idx_array: print(idx, end=' ')

24.111111

51

0.562212

42

217

2.785714

0.452381

0.273504

0.153846

0.17094

0

0.035928

0.230415

217

9

52

24.111111

0.664671

0

0.004587

0

1

0

false

0

0.142857

0

null

1

0

1

0

1

0

null

0

3

a494565751882f72557759dd933a7b257d6100a8

982

py

Python

raspy/io/raspi_gpio.py

cyrusbuilt/RasPy

1e34840cc90ea7f19317e881162209d3d819eb09

[ "MIT" ]

null

raspy/io/raspi_gpio.py

cyrusbuilt/RasPy

1e34840cc90ea7f19317e881162209d3d819eb09

[ "MIT" ]

null

raspy/io/raspi_gpio.py

cyrusbuilt/RasPy

1e34840cc90ea7f19317e881162209d3d819eb09

[ "MIT" ]

null

"""A Raspberry Pi GPIO Interface.""" from raspy import board_revision from raspy.io import gpio_pins from raspy.io.gpio import Gpio class RaspiGpio(Gpio): """A Raspberry Pi GPIO interface.""" def __init__(self): """Initialize a new instance of the raspy.io.raspi_gpio.RaspiGpio class.""" super(Gpio, self).__init__() @property def revision(self): """Get the board revision. :returns: The board revision. :rtype: int """ return board_revision.REV2 @property def inner_pin(self): """Get the inner pin being represented by this instance. :returns: The underlying physical pin. :rtype: RasPy.io.GpioPins """ return gpio_pins.GpioNone() def on_pin_state_change(self, psce): """Fire the pin state change event. :param raspy.io.pin_state_change_event.PinStateChangeEvent psce: The pin state change event. """ pass

23.95122

83

0.631365

122

982

4.918033

0.409836

0.058333

0.093333

0.095

0.156667

0

0.001401

0.272912

982

40

84

24.55

0.838936

0.453157

0

0.142857

0

1

0.285714

false

0.071429

0.214286

0

0.714286

0

null

0

null

0

1

0

1

0

1

0

3

a4957887385473489f2cb3339696ea87605c81e8

115

py

Python

test/secrets-example.py

dsalisky/EdgeAuth-Token-Python

f0d3ee42b61887ec6890bcc4b907c6ff0d687db7

[ "Apache-2.0" ]

19

2018-05-13T04:07:29.000Z

2022-01-29T18:48:30.000Z

test/secrets-example.py

dsalisky/EdgeAuth-Token-Python

f0d3ee42b61887ec6890bcc4b907c6ff0d687db7

[ "Apache-2.0" ]

4

2018-06-15T12:59:15.000Z

2021-01-11T07:47:58.000Z

test/secrets-example.py

dsalisky/EdgeAuth-Token-Python

f0d3ee42b61887ec6890bcc4b907c6ff0d687db7

[ "Apache-2.0" ]

12

2018-07-05T08:46:01.000Z

2022-02-14T16:37:20.000Z

AT_HOSTNAME = "hostname" AT_ENCRYPTION_KEY = "encryption key" AT_TRANSITION_KEY = "transition key" AT_SALT = "salt"

28.75

36

0.782609

16

115

5.25

0.375

0.309524

0

0.113043

115

4

37

28.75

0.823529

0

0.344828

0

1

0

false

0

1

0

null

1

0

1

0

null

0

3

f12313132092f47744037a168883927d77ca58af

2,708

py

Python

src/sima/simo/blueprints/liftlinecoupling.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

src/sima/simo/blueprints/liftlinecoupling.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

src/sima/simo/blueprints/liftlinecoupling.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

# # Generated with LiftLineCouplingBlueprint from dmt.blueprint import Blueprint from dmt.dimension import Dimension from dmt.attribute import Attribute from dmt.enum_attribute import EnumAttribute from dmt.blueprint_attribute import BlueprintAttribute from .simplecoupling import SimpleCouplingBlueprint class LiftLineCouplingBlueprint(SimpleCouplingBlueprint): """""" def __init__(self, name="LiftLineCoupling", package_path="sima/simo", description=""): super().__init__(name,package_path,description) self.attributes.append(Attribute("name","string","",default="")) self.attributes.append(Attribute("description","string","",default="")) self.attributes.append(Attribute("_id","string","",default="")) self.attributes.append(BlueprintAttribute("scriptableValues","sima/sima/ScriptableValue","",True,Dimension("*"))) self.attributes.append(BlueprintAttribute("endPoint1","sima/simo/SIMOBodyPoint","",False)) self.attributes.append(BlueprintAttribute("endPoint2","sima/simo/SIMOBodyPoint","",False)) self.attributes.append(EnumAttribute("failureMode","sima/simo/ActivationFailureMode","Failure mode of coupling element")) self.attributes.append(Attribute("failureTime","number","Earliest possible time of failure",default=0.0)) self.attributes.append(Attribute("breakingStrength","number","Breaking strength",default=0.0)) self.attributes.append(Attribute("numElements","integer","Number of elements",default=0)) self.attributes.append(Attribute("accIncluded","boolean","Flag for including acceleration of the line",default=True)) self.attributes.append(Attribute("diameter","number","Segment diameter",default=0.0)) self.attributes.append(Attribute("eMod","number","Modulus of elasticity",default=0.0)) self.attributes.append(Attribute("emFac","integer","Factor of elasticity - 2 for chains - 1 for other segment types",default=1)) self.attributes.append(Attribute("length","number","Initial, unstretched wire length",default=0.0)) self.attributes.append(Attribute("flexibility","number","Connection flexibility",default=0.0)) self.attributes.append(Attribute("damping","number","Material damping",default=0.0)) self.attributes.append(Attribute("uwia","number","Unit weight in air",default=0.0)) self.attributes.append(Attribute("watfac","number","The ratio of weight in water to weight in air",default=0.0)) self.attributes.append(Attribute("transverseDrag","number","Transverse drag coefficient",default=0.0)) self.attributes.append(Attribute("longitudinalDrag","number","Longitudinal drag coefficient",default=0.0))

77.371429

136

0.740399

299

2,708

6.662207

0.334448

0.14759

0.210843

0.24749

0.341365

0.290161

0.247992

0.049197

0

0.011618

0.110044

2,708

35

137

77.371429

0.814938

0.014771

0

1

0

0.322678

0.03836

0

1

0.033333

false

0

0.2

0

0.266667

0.233333

0

null

0

1

0

1

0

null

0

3

f123676bea07e4cffd2bdf3c30d3734c6726cbba

777

py

Python

src/domain/video.py

wlsouza/py-video-maker

7e39b884db8f6159489157e077705a68433a48c9

[ "MIT" ]

3

2020-02-16T18:28:29.000Z

2020-07-16T22:53:08.000Z

src/domain/video.py

wlsouza/py-video-maker

7e39b884db8f6159489157e077705a68433a48c9

[ "MIT" ]

10

2020-02-16T01:26:39.000Z

2022-03-12T00:58:39.000Z

src/domain/video.py

wlsouza/py-video-maker

7e39b884db8f6159489157e077705a68433a48c9

[ "MIT" ]

null

# !usr/bin/python # -*- coding: UTF-8 -*- import copy class Video: def __init__(self, search_prefix=None, search_term=None, language_prefix='en', state=0, resolution=(1920, 1080), sentences=[]): self.search_prefix = search_prefix self.search_term = search_term self.language_prefix = language_prefix self.state = state self.resolution = resolution self.sentences = sentences @property def title(self): return f'{self.search_prefix} {self.search_term}' @property def path(self): return f'../storage/{self.title} {self.language_prefix}' @property def video_width(self): return self.resolution[0] @property def video_height(self): return self.resolution[1]

25.064516

131

0.648649

94

777

5.170213

0.361702

0.102881

0.098765

0.090535

0.106996

0

0.020202

0.235521

777

30

132

25.9

0.79798

0.047619

0

0.190476

0

0.118046

0.061058

0

1

0.238095

false

0

0.047619

0.190476

0.52381

0

null

0

null

0

1

0

1

0

3

f12d920ea4520b7984bf41de9f0343691a28c9cf

160

py

Python

nexus/summa/schema/scimag.py

leoll2/hyperboria

30a0ae466b290208f690560160ef1f5c16e4a744

[ "Unlicense" ]

null

nexus/summa/schema/scimag.py

leoll2/hyperboria

30a0ae466b290208f690560160ef1f5c16e4a744

[ "Unlicense" ]

null

nexus/summa/schema/scimag.py

leoll2/hyperboria

30a0ae466b290208f690560160ef1f5c16e4a744

[ "Unlicense" ]

null

import yaml from tantipy import TantivyCoder with open('nexus/summa/schema/scimag.yaml') as file: scimag_coder = TantivyCoder(yaml.safe_load(file.read()))

26.666667

60

0.775

23

160

5.304348

0.73913

0

0.1125

160

5

61

32

0.859155

0

0.1875

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

f138f62266750737c77c82331e4369a082747f86

834

py

Python

agsadmin/sharing_admin/community/users/User.py

christopherblanchfield/agsadmin

989cb3795aacf285ccf74ee51b0de26bf2f48bc3

[ "BSD-3-Clause" ]

2

2015-12-07T05:53:29.000Z

2020-09-13T18:12:15.000Z

agsadmin/sharing_admin/community/users/User.py

christopherblanchfield/agsadmin

989cb3795aacf285ccf74ee51b0de26bf2f48bc3

[ "BSD-3-Clause" ]

4

2015-03-09T05:59:14.000Z

2018-01-09T00:12:56.000Z

agsadmin/sharing_admin/community/users/User.py

christopherblanchfield/agsadmin

989cb3795aacf285ccf74ee51b0de26bf2f48bc3

[ "BSD-3-Clause" ]

5

2015-03-09T01:05:24.000Z

2019-09-09T23:01:21.000Z

from __future__ import (absolute_import, division, print_function, unicode_literals) from builtins import (ascii, bytes, chr, dict, filter, hex, input, int, map, next, oct, open, pow, range, round, str, super, zip) from ...._utils import send_session_request from ..._PortalEndpointBase import PortalEndpointBase class User(PortalEndpointBase): @property def username(self): return self._pdata["username"] @property def _url_full(self): return "{0}/{1}".format(self._url_base, self.username) def __init__(self, requests_session, url_base, username): super().__init__(requests_session, url_base) self._pdata = {"username": username} def get_properties(self): """ Gets the properties of the item. """ return self._get()

30.888889

117

0.664269

97

834

5.391753

0.56701

0.040153

0.06501

0.08413

0

0.003096

0.22542

834

27

118

30.888889

0.806502

0.038369

0

0.117647

0

0.029525

0

1

0.235294

false

0

0.235294

0.117647

0.705882

0.058824

0

null

0

null

0

1

0

1

0

3

f13c7ea45ae78526a09b9934bfcd395214f7800f

48

py

Python

better_profanity/__init__.py

davidmcclure/better_profanity

b6f8ccbaffa9ef99321d81ed5290337c011b58fc

[ "MIT" ]

null

better_profanity/__init__.py

davidmcclure/better_profanity

b6f8ccbaffa9ef99321d81ed5290337c011b58fc

[ "MIT" ]

null

better_profanity/__init__.py

davidmcclure/better_profanity

b6f8ccbaffa9ef99321d81ed5290337c011b58fc

[ "MIT" ]

null

name = "better_profanity" __version__ = "0.4.0"

16

25

0.708333

7

48

4.142857

0.857143

0

0.071429

0.125

48

2

26

24

0.619048

0

0.4375

0

1

0

false

0

1

0

null

0

1

0

null

0

3

f14486be05daf31b9e2e4904d9c36ae798b8e8b8

164

py

Python

module/setup.py

mrdragonbear/covid-tracking

59db8f4268172b13e5e72f5edd3cc8b0019b6eb9

[ "MIT" ]

1

2021-04-29T07:45:51.000Z

module/setup.py

mrdragonbear/covid-tracking

59db8f4268172b13e5e72f5edd3cc8b0019b6eb9

[ "MIT" ]

3

2021-05-21T15:44:45.000Z

2021-05-21T15:45:27.000Z

module/setup.py

mrdragonbear/covid-tracking

59db8f4268172b13e5e72f5edd3cc8b0019b6eb9

[ "MIT" ]

1

2020-07-16T01:41:00.000Z

import setuptools as st st.setup(name='tracking', version='0.1', author='Benjamin Levy, Matthew Stewart', packages=st.find_packages())

20.5

49

0.628049

20

164

5.1

0.85

0

0.016129

0.243902

164

7

50

23.428571

0.806452

0

0.25

0

1

0

true

0

0.2

0

0.2

0

1

0

null

0

1

0

null

0

1

0

3

f153f9cedce4728dcd08bfc4f5587ef3d801b97d

110

py

Python

hidrocomp/eflow/__init__.py

clebsonpy/HydroComp

9d17fa533e8a15c760030df5246ff531ddb4cb22

[ "MIT" ]

4

2020-05-14T20:03:49.000Z

2020-05-22T19:56:43.000Z

hidrocomp/eflow/__init__.py

clebsonpy/HydroComp

9d17fa533e8a15c760030df5246ff531ddb4cb22

[ "MIT" ]

19

2019-06-27T18:12:27.000Z

2020-04-28T13:28:03.000Z

hidrocomp/eflow/__init__.py

clebsonpy/HydroComp

9d17fa533e8a15c760030df5246ff531ddb4cb22

[ "MIT" ]

null

from .era import Era from .rva import RVA from .iha import IHA __all__ = ['exceptions', 'IHA', 'Era', 'RVA']

18.333333

45

0.672727

17

110

4.117647

0.411765

0

0.172727

110

5

46

22

0.769231

0

0.172727

0

1

0

false

0

0.75

0

0.75

0

1

0

null

0

1

0

null

0

1

0

3

f168fc195692cd4d8b23d36b750b92e23db9091a

179

py

Python

ReportApi/login/urls.py

Antrony/ReportGen

a7f6e8e6c16e9c52fd00ec042dd1f904f11a91b1

[ "MIT" ]

null

ReportApi/login/urls.py

Antrony/ReportGen

a7f6e8e6c16e9c52fd00ec042dd1f904f11a91b1

[ "MIT" ]

null

ReportApi/login/urls.py

Antrony/ReportGen

a7f6e8e6c16e9c52fd00ec042dd1f904f11a91b1

[ "MIT" ]

null

from django.conf.urls import url from . import views urlpatterns = [ url('^$', views.Invalid, name='invalid'), url('^login/$', views.login_authenticate, name='login'), ]

22.375

60

0.664804

22

179

5.363636

0.545455

0

0.150838

179

7

61

25.571429

0.776316

0

0.122905

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

f17c980cd2c61c89f34b7832f3ee1d1eabb34ac4

362

py

Python

projects/crontab/model/mysql/sku_unit_cost/purchase_order.py

kingking888/crawler-pyspider

29ba13905c73081097df9ef646a5c8194eb024be

[ "Apache-2.0" ]

1

2021-12-07T15:48:51.000Z

projects/crontab/model/mysql/sku_unit_cost/purchase_order.py

GongSong/crawler-pyspider

b6dcec4afa4e1cd393f94627290a21ed95238676

[ "Apache-2.0" ]

null

projects/crontab/model/mysql/sku_unit_cost/purchase_order.py

GongSong/crawler-pyspider

b6dcec4afa4e1cd393f94627290a21ed95238676

[ "Apache-2.0" ]

1

2021-11-10T07:12:02.000Z

from pyspider.core.model.mysql_base import * """ 采购入库单 """ class PurchaseOrder(Model): warehouseOrderId = IntegerField(verbose_name='主键, 采购入库自增id', primary_key=True) purchaseId = IntegerField(verbose_name='采购单Id') submitTime = IntegerField(verbose_name='提交时间') class Meta: database = tg_common_db db_table = 'warehouse_order'

22.625

82

0.71547

40

362

6.25

0.775

0.228

0.276

0

0.176796

362

15

83

24.133333

0.838926

0

0.103152

0

1

0

false

0

0.125

0

0.75

0

null

1

0

null

0

1

0

3

f180e07d71498e4b2561c8dc6da263c0bf5eb661

10,747

py

Python

core/tests/test_helper.py

rodolfolotte/tec

b97c39db7c90b87b5fe61888cf63111b2a32df5a

[ "MIT" ]

4

2019-08-15T17:27:02.000Z

2022-03-15T13:36:26.000Z

core/tests/test_helper.py

rodolfolotte/tec

b97c39db7c90b87b5fe61888cf63111b2a32df5a

[ "MIT" ]

null

core/tests/test_helper.py

rodolfolotte/tec

b97c39db7c90b87b5fe61888cf63111b2a32df5a

[ "MIT" ]

2

2022-01-05T10:56:27.000Z

2022-03-03T14:20:11.000Z

import os import unittest import settings as settings import urllib.request from helper import InputFiles class FindOrbitReferenceHelperTest(unittest.TestCase): pass # def test_find_orbit_reference_default(self): # """ Passing all arguments as default, must return igr18471.sp3.Z """ # self.assertEqual(self._find_orbit_reference(), "igr18471.sp3.Z") # # def test_find_orbit_reference_R(self): # """ Passing the correspondent arguments as chpi1521.15o, 2015, R, must return igl18471.sp3.Z """ # self.assertEqual(self._find_orbit_reference(orbit_type="R"), "igl18471.sp3.Z") # # def test_find_year_orbit_reference_year(self): # """ Passing the correspondent arguments as chpi1521.15o, 2016, G, must return igr18992.sp3.Z """ # self.assertEqual(self._find_orbit_reference(year=2016), "igr18992.sp3.Z") # # def test_find_orbit_reference_R_year(self): # """ Passing the correspondent arguments as chpi1521.15o, 2016, R, must return igl18992.sp3.Z """ # self.assertEqual(self._find_orbit_reference(year=2016, orbit_type="R"), "igl18992.sp3.Z") # # def test_find_orbit_reference_wrong_year_2_digits(self): # """ Passing year 2 digits, must return None """ # self.assertEqual(self._find_orbit_reference(year=16), None) # # def test_find_orbit_reference_wrong_year_5_digits(self): # """ Passing year with 5 digits, must return None """ # self.assertEqual(self._find_orbit_reference(year=20164), None) # # def test_find_orbit_reference_rinex_no_station(self): # """ Passing rinex_name with wrong station iaga code, must return None """ # self.assertEqual(self._find_orbit_reference(rinex_name='chp1521.15o'), None) # # def test_find_orbit_reference_rinex_no_extension(self): # """ Passing rinex_name with no extension, must return None """ # self.assertEqual(self._find_orbit_reference(rinex_name='chpi1521'), None) # # def test_find_orbit_reference_rinex_empty(self): # """ Passing rinex_name empty, must return None """ # self.assertEqual(self._find_orbit_reference(rinex_name=''), None) # # def test_find_orbit_reference_rinex_with_incorrect_doy(self): # """ Passing rinex_name with wrong doy, must return None """ # self.assertEqual(self._find_orbit_reference(rinex_name='chpi3681.15o'), None) # # def test_find_orbit_reference_orbit_type_invalid(self): # """ Passing orbit_type invalid, must return None """ # self.assertEqual(self._find_orbit_reference(orbit_type='X'), None) # # def test_find_orbit_reference_orbit_type_empty(self): # """ Passing rinex_name with no extension, must return None """ # self.assertEqual(self._find_orbit_reference(orbit_type=''), None) # # def _find_orbit_reference(self, **kwargs): # input_files = InputFiles() # default = dict( # rinex_name='chpi1521.15o', # year=2015, # orbit_type='G' # ) # data = dict(default, **kwargs) # find = input_files._find_orbit_reference(data['rinex_name'], data['year'], data['orbit_type']) # return find class ChannelsURLHelperTest(unittest.TestCase): def test_download_channels_url_response(self): """HTML must contain text """ response = urllib.request.urlopen(settings.URL_GLONASS_CHANNELS).getcode() self.assertEqual(response, 200) class CheckURLExist(unittest.TestCase): pass # def test_url_download_exist(self): # url = urlopen(settings.URL_ORBIT + '1847/igr18471.sp3.Z') # if url != URLError: # return True # self.assertEqual(url, True) # class SetupFileAndDownloadHelperTest(unittest.TestCase): # def _setup_file_and_download(self, **kwargs): # input_files = InputFiles() # default = dict( # year=2015, # month=6, # file='chpi1521.15o', # file_type='DCB', # rinex_interval=30 # ) # data = dict(default, **kwargs) # find = input_files._setup_file_and_download(data['year'], data['month'], data['file'], data['file_type'], # data['rinex_interval']) # return find class DownloadFileHelperTest(unittest.TestCase): pass # def test_download_file_default(self): # """ Passing all arguments as default, must return complete path of igl18471.sp3.Z""" # self.assertEqual(self._download_file(), settings.PATH_ORBIT + '2015/igl18471.sp3.Z') # # def _download_file(self, **kwargs): # input_files = InputFiles() # default = dict( # year=2015, # filename='/home/lotte/embrace/tec/orbit/1847/igl18471.sp3.Z', # orbit_name='igl18471.sp3.Z', # gnss_week='1847', # what_to_download='Orbit', # ) # data = dict(default, **kwargs) # find = input_files._download_file(data) # return find # def test_download_orbit_default(self): # """ Passing uncompressed orbit_name, must return igl18471.sp3""" # self.assertEqual(self._download_orbit(), settings.PATH_ORBIT + '2015/igl18471.sp3') # # def test_download_orbit_with_orbit_name_empty(self): # """ Passing orbit_name empty must return None""" # self.assertEqual(self._download_orbit(orbit_name=''), None) # # def test_download_orbit_with_orbit_name_with_wrong_extension(self): # """ Passing orbit_name with wrong extension must return None """ # self.assertEqual(self._download_orbit(orbit_name='igl18471.sp5'), None) # # def test_download_orbit_with_orbit_name_with_uppercase(self): # """ Passing orbit_name with upper case, must return the same orbit_name in lower case: igl18471.sp3.Z""" # self.assertEqual(self._download_orbit(orbit_name='IGL18471.SP3.Z'), settings.PATH_ORBIT # + '2015/igl18471.sp3') # # def test_download_orbit_with_year_empty(self): # """ Passing year empty must return ...""" # self.assertEqual(self._download_orbit(year=''), settings.PATH_ORBIT + '...') # # def test_download_orbit_with_year_with_1_digit(self): # """ Passing year with one single digit, must return the empty orbit path""" # self.assertEqual(self._download_orbit(year=5), '') # # def test_download_orbit_with_year_with_3_digits(self): # """ Passing year with three digits, must return the empty orbit path""" # self.assertEqual(self._download_orbit(year=105), '') # # def test_download_orbit_with_year_with_2_digits(self): # """ Passing year with only two digits, must return the saved orbit path with correct 4-digits year folder""" # self.assertEqual(self._download_orbit(year=15), settings.PATH_ORBIT + '2015/igl18471.sp3.Z') # # def test_download_orbit_with_year_with_more_than_4_digits(self): # """ Passing year with more than 4 digits, must return the empty orbit path""" # self.assertEqual(self._download_orbit(year=20159), '') # # def test_download_orbit_with_year_as_string(self): # """ Passing year as string, must return the empty orbit path""" # self.assertEqual(self._download_orbit(year='2015a'), '') class CheckFilesExistHelperTest(unittest.TestCase): pass # def delete_all(self, folder): # for the_file in os.listdir(folder): # file_path = os.path.join(folder, the_file) # try: # if os.path.isfile(file_path): # os.unlink(file_path) # except Exception as e: # print(e) # # def create_file(self, folder, filename): # tmp_file = os.path.join(folder, filename) # with open(tmp_file, 'w+') as f: # f.write('Python mock testing. Delete me later!') # # def test_dcb_compressed_and_uncompressed_exists_return_uncompressed(self): # """ Passing default DCB, check if it exists, if so, returns absolute path of the uncompressed file """ # self.create_file(settings.PATH_DCB, 'P1C11506.DCB') # self.create_file(settings.PATH_DCB, 'P1C11506.DCB.Z') # self.assertTrue(os.path.isfile(settings.PATH_DCB + 'P1C11506.DCB')) # self.assertTrue(os.path.isfile(settings.PATH_DCB + 'P1C11506.DCB.Z')) # self.assertEqual(self._check_files_already_exist(), (False, settings.PATH_DCB + 'P1C11506.DCB')) # self.delete_all(settings.PATH_DCB) # # def test_dcb_only_compressed_exists_return_compressed(self): # """ Passing the DCB only with the P1C11506.DCB.Z file, make sure it exists, if it does, # returns the absolute path of the compressed file """ # self.create_file(settings.PATH_DCB, 'P1C11506.DCB.Z') # self.assertTrue(os.path.isfile(settings.PATH_DCB + 'P1C11506.DCB.Z')) # self.assertFalse(os.path.isfile(settings.PATH_DCB + 'P1C11506.DCB')) # self.assertEqual(self._check_files_already_exist(), (False, settings.PATH_DCB + 'P1C11506.DCB.Z')) # self.delete_all(settings.PATH_DCB) # # def test_dcb_no_file_exists(self): # """ Passing the DCB without any files, check if there is any file, if so, # returns the absolute path of the compressed file """ # self.assertFalse(os.path.isfile(settings.PATH_DCB + 'P1C11506.DCB.Z')) # self.assertFalse(os.path.isfile(settings.PATH_DCB + 'P1C11506.DCB')) # self.assertEqual(self._check_files_already_exist(), (True, settings.PATH_DCB + 'P1C11506.DCB.Z')) # self.delete_all(settings.PATH_DCB) # # def test_dcb_only_decompressed_exists_return_decompressed(self): # """ Passing the DCB only with the P1C11506.DCB file, make sure the file exists, # if so, returns the absolute path of the uncompressed file """ # self.create_file(settings.PATH_DCB, 'P1C11506.DCB') # self.assertTrue(os.path.isfile(settings.PATH_DCB + 'P1C11506.DCB')) # self.assertFalse(os.path.isfile(settings.PATH_DCB + 'P1C11506.DCB.Z')) # self.assertEqual(self._check_files_already_exist(), (False, settings.PATH_DCB + 'P1C11506.DCB')) # self.delete_all(settings.PATH_DCB) # # def _check_files_already_exist(self, **kwargs): # input_files = InputFiles() # default = dict( # path=settings.PATH_DCB, # filename='P1C11506.DCB.Z', # ) # data = dict(default, **kwargs) # find = input_files._check_files_already_exist(data['path'], data['filename']) # return find

48.192825

118

0.656741

1,327

10,747

5.051997

0.135644

0.03028

0.076521

0.054893

0.662291

0.605459

0.574732

0.490901

0.414379

0.349045

0

0.045259

0.222853

10,747

222

119

48.40991

0.757423

0.843956

0

0.235294

0

0.058824

1

0.058824

false

0.235294

0.294118

0

0.647059

0

null

0

1

0

null

0

1

0

1

0

3

f185314c22cdcd972b44d35430bcac14b0576997

2,693

py

Python

src/pycolocstats/tools/jobparamsdict.py

hyperbrowser/conglomerate

7996f222a38d11d0cc69fdc1ec05e8645fa44a30

[ "Apache-2.0" ]

2

2019-05-23T10:11:20.000Z

2020-02-04T11:36:06.000Z

src/pycolocstats/tools/jobparamsdict.py

hyperbrowser/conglomerate

7996f222a38d11d0cc69fdc1ec05e8645fa44a30

[ "Apache-2.0" ]

1

2020-02-03T10:41:18.000Z

2020-02-03T11:56:28.000Z

src/pycolocstats/tools/jobparamsdict.py

hyperbrowser/pycolocstats

7996f222a38d11d0cc69fdc1ec05e8645fa44a30

[ "Apache-2.0" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals from pycolocstats.core.types import PathStr, PathStrList from past.builtins import basestring from collections import MutableMapping __metaclass__ = type class JobParamsDict(MutableMapping): def __init__(self, paramDefDict): self._paramDefDict = paramDefDict self._params = dict() def __getitem__(self, key): return self._params[key] def __setitem__(self, key, val): assert key in self.getAllowedKeys(), \ '"{}" not in allowed keys: {}'.format(key, ', '.join(self.getAllowedKeys())) allowedType = self.getType(key) if allowedType == PathStr: assert isinstance(val, basestring), '"{}" not of correct type: {}'.format(val, str) #assert os.path.exists(val), 'File "{}" does not exist'.format(val) #TODO: Had to temporarily disable due to generic copying from dat to bed.. val = PathStr(val) elif allowedType == PathStrList: assert isinstance(val, list), '"{}" not of correct type: {}'.format(val, list) assert all(isinstance(f, basestring) for f in val), \ 'Some of the entries of "{}" are not of correct type: {}'.format(val, str) # assert all(os.path.exists(f) for f in val), \ # 'Some of the entries of "{}" do not exist'.format(val) #TODO: disabled due to dat to bed.. val = PathStrList(val) else: assert isinstance(val, allowedType), '"{}" (type:{}) not of correct type: {}'.format(val, type(val), allowedType) self._params[key] = val def __delitem__(self, key): del self._params[key] def __iter__(self): return iter(self._params) def __len__(self): return len(self._params) def getAllowedKeys(self): return self._paramDefDict.keys() def getType(self, key): return self._paramDefDict[key]['type'] def isMandatory(self, key): return self._paramDefDict[key]['mandatory'] def getAbsentMandatoryParameters(self): absentMandatoryParameters = [] for key in self.getAllowedKeys(): if self.isMandatory(key) and key not in self: absentMandatoryParameters.append(key) return absentMandatoryParameters def __repr__(self): retStr = repr(self._params) retStr += '\nAllowed params:\n' for key in self.getAllowedKeys(): retStr += '\t%s: %s %s\n' % (key, self.getType(key), '[x]' if self.isMandatory(key) else '[ ]') retStr += '[ ] for optional parameter, [x] for mandatory parameter' return retStr

37.929577

154

0.625325

310

2,693

5.270968

0.309677

0.04284

0.029376

0.039168

0.201958

0.144431

0.074663

0.033048

0

0.254735

2,693

70

155

38.471429

0.81415

0.103973

0

0.039216

0

0.118771

0

0.014286

0.098039

1

0.215686

false

0

0.078431

0.117647

0.470588

0.019608

0

null

0

null

0

1

0

1

0

1

0

3

f18849b52c2d1fd1bf363b3942dbbd3fc5cbbaf8

647

py

Python

dda_cert/tests/test_meta.py

emburse/dda-certification-test

6fc0ff93a07b5277d81de60ba45d3dc2e16eecb4

[ "BSD-2-Clause" ]

null

dda_cert/tests/test_meta.py

emburse/dda-certification-test

6fc0ff93a07b5277d81de60ba45d3dc2e16eecb4

[ "BSD-2-Clause" ]

null

dda_cert/tests/test_meta.py

emburse/dda-certification-test

6fc0ff93a07b5277d81de60ba45d3dc2e16eecb4

[ "BSD-2-Clause" ]

null

import json import unittest import requests from ofxtools.Parser import OFXTree from ..settings import DDA_ACCOUNT_TRANSACTIONS, OFX_FILE_PATH, ACCESS_TOKEN, DDA_ACCOUNTSDETAILS class TestMeta(unittest.TestCase): def setUp(self): pass def test_connection(self): pass def test_dda_interactionid(self): pass def test_authorization(self): self.auth_headers = {"Authorization": "Bearer {}".format(ACCESS_TOKEN)} request = requests.get(DDA_ACCOUNTSDETAILS, headers=self.auth_headers) self.assertEqual(request.status_code, 200) def test_content_negotiation(self): pass

23.962963

97

0.723338

76

647

5.934211

0.539474

0.070953

0.073171

0.099778

0

0.00578

0.197836

647

27

98

23.962963

0.863198

0

0.222222

0

0.033951

0

0.055556

1

0.277778

false

0.222222

0.277778

0

0.611111

0

null

0

null

0

1

0

1

0

1

0

3

f191253e22b12d294a990e1d3e6d7c156656a702

428

py

Python

test/python/PostingTest.py

sanel/ledger

81e1b4ff3c78cd18e5d8c3844eb8e406dc3c8836

[ "BSD-3-Clause" ]

3,509

2015-01-01T11:47:51.000Z

2022-03-30T09:22:43.000Z

test/python/PostingTest.py

sanel/ledger

81e1b4ff3c78cd18e5d8c3844eb8e406dc3c8836

[ "BSD-3-Clause" ]

651

2015-01-09T16:18:10.000Z

2022-03-26T23:52:00.000Z

test/python/PostingTest.py

sanel/ledger

81e1b4ff3c78cd18e5d8c3844eb8e406dc3c8836

[ "BSD-3-Clause" ]

440

2015-01-02T21:28:11.000Z

2022-03-25T05:38:08.000Z

# -*- coding: utf-8 -*- import unittest import exceptions import operator from ledger import * from StringIO import * from datetime import * class PostingTestCase(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_(self): pass def suite(): return unittest.TestLoader().loadTestsFromTestCase(PostingTestCase) if __name__ == '__main__': unittest.main()

16.461538

71

0.679907

46

428

6.130435

0.586957

0.085106

0.117021

0

0.003003

0.221963

428

25

72

17.12

0.843844

0.049065

0

0.176471

0

0.019753

0

1

0.235294

false

0.176471

0.352941

0.058824

0.705882

0

null

0

null

0

1

0

1

0

3

74c4676f24e22e0fbe2e97e1cd4c947ca214bbc2

153

py

Python

oarepo_oai_pmh_harvester/proxies.py

oarepo/invenio-oarepo-oai-pmh-harvester

399ef743ac9da23d36e655e072aa72ee1b332372

[ "MIT" ]

null

oarepo_oai_pmh_harvester/proxies.py

oarepo/invenio-oarepo-oai-pmh-harvester

399ef743ac9da23d36e655e072aa72ee1b332372

[ "MIT" ]

13

2020-11-04T13:47:55.000Z

2021-04-15T17:56:33.000Z

oarepo_oai_pmh_harvester/proxies.py

oarepo/oarepo-oai-pmh-harvester

399ef743ac9da23d36e655e072aa72ee1b332372

[ "MIT" ]

1

2020-05-14T07:59:12.000Z

from flask import current_app from werkzeug.local import LocalProxy current_oai_client = LocalProxy(lambda: current_app.extensions['oarepo-oai-client'])

38.25

84

0.843137

21

153

5.952381

0.619048

0.16

0

0.078431

153

4

84

38.25

0.886525

0

0.11039

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

3

74d547cfd391fb9d14089dda6d5f5fbef6a3f8a5

3,427

py

Python

img_favicon.py

folkengine/JetCreator

2003704c531ca9a6730dbd8d7f2c879312d944ef

[ "Apache-2.0" ]

null

img_favicon.py

folkengine/JetCreator

2003704c531ca9a6730dbd8d7f2c879312d944ef

[ "Apache-2.0" ]

null

img_favicon.py

folkengine/JetCreator

2003704c531ca9a6730dbd8d7f2c879312d944ef

[ "Apache-2.0" ]

null

#---------------------------------------------------------------------- # This file was generated by C:\Python25\Lib\site-packages\wx-2.8-msw-unicode\wx\tools\img2py.py # from wx import Image, Bitmap, Icon import cStringIO, zlib def getData(): return zlib.decompress( 'x\xda\x01\xd4\x03+\xfc\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\ \x00\x00 \x08\x06\x00\x00\x00szz\xf4\x00\x00\x00\x04sBIT\x08\x08\x08\x08|\ \x08d\x88\x00\x00\x03\x8bIDATX\x85\xc5\x97klSe\x18\xc7\x7f\xef9\xed\xda\xb5\ \xdd\xa8\xeb\xec\x9cd\xc9\xe6\xb0&\x18#\xc6(\xd1\xb0(\n\x0eG\xb8\x05GLX\xe2\ \x17\xa3\x04\xd4hP\x13t&\x98\r\xbf\x90h\xe2\x06Y\xf8`d\x18!r\t\xa0f\xb0\xa0s\ \xea\xbc\x8d\xc5!c\x85Ywi\xe9\xd8\xad[\xaf\xf3\xf4z\xfc\xe2t\x10l\xc7\xba\ \xd6\xff\xc7s\x9e\xff\xf3\xfe\xce\xfb\xbe\xcf\x93\xe7\x08!\xc9\xfc\x9f\xd2\ \x00$\xe21u\xbe\t\xa2\xfc\x8e\x8a\x96\x1cJ\xe7\xe5\x97\xe6\xbb0\x80\xcb\xe9b\ [\xfdS\xf4xN\xcf;GZ\x00\xa1\xa0\x82\xe3\xd28\xae\xc0\xb7\xc0\xfc61-\x00I\x96\ \xc83\xdc\xc6h\xf0\x02\xed\x83u\xd9\x07\x00PU\x15\x93\xc1\x8c\xdd\x7f\x90\ \xf6\xc1\xfa\xec\x03\xe8\x8d2mGGq\xfc,\xe8\x8f6\xf3\xd5\xc0.Tu\xee\xc7\x916\ \x80\x90\x12X,\x054\xbd:\xc2\xe9F\x0f\xfd\xcag\xb4\xbbj\xb3\x070\x1d\x88\xf3\ P\x95\x91\xc3g\xdf\xe3\xf2\x17e4l\xbf\x8a}\xeaSz=G\xb2\x03 !\x11\x0c\x06\x88\ \x96\xb4\xd0\xda\xd5\x88nb\x05Mo8\xe9\xf1\x1f@\x89y\xb3\x00 \x0b\xc6\x06TF\ \xc6\x878\x1f|\x87\x83gw\xe2\xe9\xb6\xd1r\xbc\x93\xf1\xf8\x8f\x99\x070\xe4\ \xcbt\x9d\xf3\xb1\xa7\xe6\n=\xdd\x7fp5\xaf\x81\x17k+\xe8\xfa2\x8e\'|)\xf3\ \x00\x01\x9fB\xf5\xf6{yv\xfd\xcb\xec\xd92\xc4\xe7\x87z(\xad\xec\xa6\xd0\x9a\ \xcf\xc4X\xea#\xd0\xa4\x0b@B\x837<\xc8\x0bu\x1bX\xf9h\x0b\xeb7\xadE\xda/\xb1\ \xe95\x0b"\x91\x93\xd2\xbe \x8d\xc8\xa03\xf1M\xdf\x07\xdc\xb7\xd6\xc3\xf1c\'\ i|\xc5I(\xe4\xe7N\xeb\x92\xcc\x03\x18\xf2\xb4\x1cmt`\xef\x08\xd36\xb4\x9b\ \x077O\xb2\xb5\xfay\x8e5\xb8\x91\xf5J\xa6\x01T\x0cfXv\xd7j\x9a\xdf\xf2\xd1\ \xbc\xdb\xcd\xd7}{Y\xb9-\x84\x7f8\x07\xf7x_\xa6\x01\xc0\xe7\xf3\xf2\xfa\x8e\ \x9dtv\x9e\'2\xf0\x00o>}\x11\xaf\xda\xcd\x86\x97,(\x81\xd4\xfe\xf4[\xb1\x10L\ (\x1e\xca\xcb\xcb\xf9\xbe\xe3;\xaa+w\xf0\xf6F;\xd6R\r\x8b\x8b\xeeN\xe9O\xbf\ \n\xfe\x86\x98\xd1\xbe\xfd\x8d\xa0\n>\xaam\xa2\xe2d?**\x02\xf1\x9f\xde\xb4w\ \xe0f\xfap\xdf\xfb\x14\xe9\x96\xd1\xf5k\'q\x82Ic3\x02 KZ\xaa\xd6l\xe4\xda`\ \x08A$\xfb\x00\x006\xdb\x124\x1a\x19\t}\xe6\x00\xb4\xc2D4|\xf3\xe1cl\xca\x89\ >W\x87\xc0\x984\xc7-_\xc2\xc9?\x1d8&\xcf\xe0\x8f\xb8\xf0\xfb\xe2\x94\xd8\x0c\ \xc4c\x80\xee\xfa8\x87\xfb\'V\xad{2e\xbe[\x02\xe8p\xee\xe5\xb7\xd1C\x84\xe3\ \x01$\xa1A\x96%\xee\x7f\xdcDTQ\x99\xfd\xa1\xc3\xc3nb\xdaQ*\x96>\xb70\x00*\tZ\ \xaf\xec\xa2w\xea0\xb9\x9a\x02\x0cZ\xcb?\xef\x94\x88\x97\x1bG\xf2~\xd7E\xb6\ \xae\xa9\'W*^\x18\x80\x8fO\xd43b\xfe\x84Bs1\xb3\xe7M\x95\x04\x92\xd0\xb0H_z]\ \xfc\x8a\xe5\x95\x90\xa4\xf6g+\xe5%\xbc\xe6\x1e\xe5\xc8\x89\x03\xdcQl%\x91\ \xf8\xf7\xb9\x10\x02%\xe6\xc1fY\xc7\xed\xc6\xa57\xb8\xe6\xb6\xf8\x9c\x00"\ \xb1i.\xfc0\xc2\xb47\x81\xb9PK\xaeIFgT\x99\xf4\r\xb38g5O\x94\xd5%\xedt\xa9$\ \x84$\'\xfd9U\xd58\xcfTo\xa6\xed\x97S\xac\xaa\xc9gQA\x0e\t\xc5\xc4\xf2{\xb6P\ S\xf5.\x06c\xf2:O\x1b`F\xad\xe7\xce`\xef\xbdLiY\t\x8f<\xfc\x18\xd6\xa2\xc2\ \xb4\x16\x9e\xd1_\xa4\x9cO6P\x7f\x0e\x9e\x00\x00\x00\x00IEND\xaeB`\x82\x80\ \xab\xe3<' ) def getBitmap(): return Bitmap(getImage()) def getImage(): stream = cStringIO.StringIO(getData()) return Image(stream) def getIcon(): icon = Icon() icon.CopyFromBitmap(getBitmap()) return icon

55.274194

96

0.702072

709

3,427

3.38787

0.499295

0.032473

0.026228

0.014988

0.007494

0

0.212039

0.035308

3,427

61

97

56.180328

0.514519

0.048147

0

1

0.673077

0

1

0.076923

false

0

0.038462

0.192308

0

null

0

1

0

1

0

null

0

3

74fe802d64e89eb6d40cc2d28b2a50ac8cdab9b5

134

py

Python

deploy/utils/__init__.py

Maozu/keybrl-tools

280d8c2eb36863ce33e4f57ac1829aa93daf3e0a

[ "MIT" ]

2

2020-11-25T06:14:33.000Z

2021-01-19T13:39:07.000Z

deploy/utils/__init__.py

Maozu/keybrl-tools

280d8c2eb36863ce33e4f57ac1829aa93daf3e0a

[ "MIT" ]

null

deploy/utils/__init__.py

Maozu/keybrl-tools

280d8c2eb36863ce33e4f57ac1829aa93daf3e0a

[ "MIT" ]

null

from .file_manager import FileManager from .oss_synchronizer import OSSSynchronizer __all__ = ['FileManager', 'OSSSynchronizer']

26.8

46

0.798507

13

134

7.769231

0.692308

0

0.126866

134

4

47

33.5

0.863248

0

0.2

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

3

2d0b8588b4436816bfeec6c0fb9ca00aaaf96775

118

py

Python

django/dashboard/views/logout.py

aistairc/voteclustering_aist

f1ee7409698a05a99ce40cdccbe4c2b1f8f81b4c

[ "MIT" ]

null

django/dashboard/views/logout.py

aistairc/voteclustering_aist

f1ee7409698a05a99ce40cdccbe4c2b1f8f81b4c

[ "MIT" ]

null

django/dashboard/views/logout.py

aistairc/voteclustering_aist

f1ee7409698a05a99ce40cdccbe4c2b1f8f81b4c

[ "MIT" ]

null

from django.shortcuts import redirect def logout(request): request.session.flush() return redirect("login")

16.857143

37

0.737288

14

118

6.214286

0.857143

0

0.161017

118

6

38

19.666667

0.878788

0

0.042373

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

3

2d1cd9cadeb744fd019b43771b85ff1de70127f4

208

py

Python

src/settings.py

juanmanuelcarrera/parking-butler

c856328660ef1ac5eab735c87d8d8a7e4cbc1a06

[ "MIT" ]

null

src/settings.py

juanmanuelcarrera/parking-butler

c856328660ef1ac5eab735c87d8d8a7e4cbc1a06

[ "MIT" ]

null

src/settings.py

juanmanuelcarrera/parking-butler

c856328660ef1ac5eab735c87d8d8a7e4cbc1a06

[ "MIT" ]

null

from mongoengine import connect, register_connection MONGO_URL = "mongodb://localhost:27017/parking_butler" #connect('chatemtbot', host=os.getenv('MONGO_URL')) register_connection('default', host=MONGO_URL)

34.666667

54

0.807692

26

208

6.230769

0.692308

0.148148

0

0.025641

0.0625

208

6

55

34.666667

0.805128

0.240385

0

0.297468

0.253165

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

2d34499a8939085f312a6f4702683ca378d10581

289

py

Python

src/cpp/setup.py

jxgu1016/BinActivateFunc_PyTorch

43d13d5ba1d62b9f8c249bfe31894883e50c0f6f

[ "MIT" ]

6

2018-06-08T16:06:24.000Z

2019-12-06T11:12:54.000Z

src/cpp/setup.py

jxgu1016/BinActivateFunc_PyTorch

43d13d5ba1d62b9f8c249bfe31894883e50c0f6f

[ "MIT" ]

null

src/cpp/setup.py

jxgu1016/BinActivateFunc_PyTorch

43d13d5ba1d62b9f8c249bfe31894883e50c0f6f

[ "MIT" ]

null

from setuptools import setup from torch.utils.cpp_extension import BuildExtension, CppExtension setup( name='BinActivateFunc_cpp', ext_modules=[ CppExtension('BinActivateFunc_cpp', ['BinActivateFunc.cpp']), ], cmdclass={ 'build_ext': BuildExtension })

24.083333

69

0.702422

27

289

7.333333

0.592593

0.272727

0

0.193772

289

11

70

26.272727

0.849785

0

0.228374

0

1

0

true

0

0.2

0

0.2

0

1

0

null

1

0

null

0

1

0

3

2d435a602818f1c711e3b322cc41bbbac154a1cd

3,611

py

Python

htm_rl/htm_rl/tests.py

pkuderov/htm_rl

00512efdbc052b48dfbe2f041b8713e2df90b735

[ "MIT" ]

null

htm_rl/htm_rl/tests.py

pkuderov/htm_rl

00512efdbc052b48dfbe2f041b8713e2df90b735

[ "MIT" ]

null

htm_rl/htm_rl/tests.py

pkuderov/htm_rl

00512efdbc052b48dfbe2f041b8713e2df90b735

[ "MIT" ]

null

from inspect import ismethod import numpy as np from htm.bindings.sdr import SDR # # from mdp_planner import DataEncoder, DataMultiEncoder, TemporalMemory, HtmAgent # # # class TestDataEncoder: # def __init__(self): # self.encoder = DataEncoder('-', n_vals=2, value_bits=3, activation_threshold=2) # # def test_encode_dense(self): # result = self.encoder.encode_dense(1) # expected = np.array([0, 0, 0, 1, 1, 1], dtype=np.int8) # assert np.array_equal(result, expected) # # def test_encode_sparse(self): # arr_sparse = self.encoder.encode_sparse(1) # assert arr_sparse == [3, 4, 5] # # def test_str_from_dense(self): # arr_dense = np.array([0, 0, 0, 1, 1, 1], dtype=np.int8) # res = self.encoder.str_from_dense(arr_dense) # assert res == '000 111' # # def test_decode_dense(self): # decoded = self.encoder.decode_dense(np.array([0, 1, 1, 0, 1, 0])) # assert decoded == [0] # # decoded = self.encoder.decode_dense(np.array([0, 1, 1, 1, 1, 1])) # assert decoded == [0, 1] # # def test_decode_sparse(self): # decoded = self.encoder.decode_sparse([1, 2, 4]) # assert decoded == [0] # # decoded = self.encoder.decode_sparse([1, 2, 3, 4, 5]) # assert decoded == [0, 1] # # def test_to_str(self): # assert str(self.encoder) == 'DataEncoder("-", v2 x b3)' # # # class TestDataMultiEncoder: # def __init__(self): # self.encoder = DataMultiEncoder(( # DataEncoder('1', 2, 4), # DataEncoder('2', 3, 2) # )) # # def test_encode_dense(self): # result = self.encoder.encode_dense((0, 2)) # expected = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], dtype=np.int8) # assert np.array_equal(result, expected) # # def test_encode_sparse(self): # result = self.encoder.encode_sparse((1, 1)) # expected = [4, 5, 6, 7, 10, 11] # assert result == expected # # def test_str_from_dense(self): # test_arr = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], dtype=np.int8) # result = self.encoder.str_from_dense(test_arr) # expected = '1111 0000 00 00 11' # assert result == expected # # # class TestHtmAgent: # def __init__(self): # self.encoder = DataMultiEncoder(( # DataEncoder('1', 2, 4), # DataEncoder('2', 3, 2) # )) # # total_bits == 14 # self.tm = TemporalMemory( # n_columns=self.encoder.total_bits, # cells_per_column=2, # activation_threshold=5, learning_threshold=3, # initial_permanence=.5, connected_permanence=.5 # ) # self.agent = HtmAgent(self.tm, self.encoder) # # def test_str_from_cells(self): # active_cells = SDR((self.tm.n_columns, self.tm.cells_per_column)) # active_cells.dense[5, 0] = 1 # active_cells.dense[9, 1] = 1 # # result = self.agent._str_from_cells(active_cells, 'test_name') # expected = '''0000 0100 00 00 00 test_name # 0000 0000 01 00 00''' # # assert result == expected # # # def _test_all(*objects): # def test_all_for_obj(obj): # for name in dir(obj): # attribute = getattr(obj, name) # if ismethod(attribute) and name.startswith('test_'): # attribute() # # for obj in objects: # test_all_for_obj(obj) # # # def test_all(): # _test_all( # TestDataEncoder(), # TestDataMultiEncoder(), # TestHtmAgent() # )

31.955752

89

0.572141

469

3,611

4.208955

0.198294

0.01925

0.021277

0.020263

0.468085

0.362209

0.316616

0.27001

0

0.063462

0.279978

3,611

112

90

32.241071

0.695769

0.916921

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

3

7413963657b14349c16034f26631cccd24607f46

723

py

Python

calculations.py

CharlesJ-ABu/MindMap

e067975fd90a842d20ca60b6a8deddae26cf4caa

[ "MIT" ]

null

calculations.py

CharlesJ-ABu/MindMap

e067975fd90a842d20ca60b6a8deddae26cf4caa

[ "MIT" ]

null

calculations.py

CharlesJ-ABu/MindMap

e067975fd90a842d20ca60b6a8deddae26cf4caa

[ "MIT" ]

1

2019-03-19T19:56:28.000Z

import math def dist(p1, p2): v = (p1[0]-p2[0], p1[1]-p2[1]) l = math.sqrt(sum([a*a for a in v])) return l def length(v): return dist((0,0,0), v) def normalize(v, thresold=0.0001): # normalize vector v l = math.sqrt(sum([a*a for a in v])) if l <= thresold: return (0,0) v = tuple([a/l for a in v]) return v def getDir(p1, p2): v = (p2[0]-p1[0], p2[1]-p1[1]) return v def luminance(c): luminance = 0.299*c[0] + 0.587*c[1] + 0.114*c[2] return luminance def clamp(x, bounds=[0,1]): return max(min(x, bounds[1]), bounds[0]) def dot(v1, v2): return sum([a*b for a, b in zip(v1, v2)]) def cosSim(v1, v2): N = dot(v1, v2) D = dist((0,0), v1)*dist((0,0), v2) if D <= 0.01: D = 0.01 return N/D

15.382979

49

0.571231

160

723

2.58125

0.275

0.029056

0.043584

0.050847

0.1477

0.101695

0

0.120209

0.206086

723

47

50

15.382979

0.599303

0.024896

0

0.142857

0

1

0.285714

false

0

0.035714

0.107143

0.642857

0

null

0

null

0

1

0

1

0

3

745677f726259ed680216f22d768bd3095775c46

96

py

Python

while_Schleife.py

ostseegloeckchen/basics

218b29c62f575d88c27104c05fee917b3466e8d0

[ "bzip2-1.0.6" ]

null

while_Schleife.py

ostseegloeckchen/basics

218b29c62f575d88c27104c05fee917b3466e8d0

[ "bzip2-1.0.6" ]

null

while_Schleife.py

ostseegloeckchen/basics

218b29c62f575d88c27104c05fee917b3466e8d0

[ "bzip2-1.0.6" ]

null

current_number=0 while current_number <=5: current_number +=1 print(current_number)

19.2

26

0.71875

13

96

5

0.538462

0.8

0

0.038961

0.197917

96

4

27

24

0.805195

0

1

0

false

0

0.25

1

0

null

1

0

1

0

null

0

3

745cb74dcec4628f09604c5ccd5d900c4f4989eb

3,263

py

Python

utils_assembly_operation.py

ikuroNoriiwa/shellcode_transform

fac7d04168d9f3888a63c7ce76cc93bc8bef0058

[ "Apache-2.0" ]

2

2021-09-13T09:40:12.000Z

2021-09-13T11:37:54.000Z

utils_assembly_operation.py

ikuroNoriiwa/shellcode_transform

fac7d04168d9f3888a63c7ce76cc93bc8bef0058

[ "Apache-2.0" ]

null

utils_assembly_operation.py

ikuroNoriiwa/shellcode_transform

fac7d04168d9f3888a63c7ce76cc93bc8bef0058

[ "Apache-2.0" ]

1

2021-07-09T17:06:46.000Z

#!/usr/bin/python3 def add_sub(shellcode: bytes, add_or_sub: bool=True, to_num: int=1, decode: bool=False) -> bytes: """Perform a add or sub encoding scheme on `shellcode`. :return: bytes object """ shellcode = bytearray(shellcode) encoded_payload = bytearray() calculated_num = (int(add_or_sub)*2-1) * to_num*(int(decode)*-2+1) for byte in shellcode: encoded_payload.append(byte + calculated_num) return bytes(encoded_payload) def right_left_rotation_bit(shellcode: bytes, right_or_left: bool=True, n: int=1) -> bytes: # print(bin(byte<<n>>8<<8), bin(byte<<n), bin((byte<<n)-(byte<<n>>8<<8)), bin(byte>>8-n), bin((byte<<n)-(byte<<n>>8<<8) + (byte>>(8-n)))) # print(bin(byte), bin(byte<<(8-n)), bin(byte>>n<<8), bin(byte>>n), bin((byte<<(8-n)) - (byte>>n<<8) + (byte>>n))) shellcode = bytearray(shellcode) encoded_payload = bytearray() for byte in shellcode: if right_or_left: encoded_payload.append((byte<<(8-n)) - (byte>>n<<8) + (byte>>n)) ## right else: ## left encoded_payload.append((byte<<n)-(byte<<n>>8<<8) + (byte>>(8-n))) return bytes(encoded_payload) def rolling_rotation(shellcode: bytes, right_or_left: bool=True, decode: bool=False) -> bytes: """ Perform a rolling xor encoding scheme on `shellcode`. :param shellcode: bytes object; data to be [en,de]coded :param decode: boolean, decrypt previously xor'd data :return: bytes object """ shellcode = bytearray(shellcode) if decode: shellcode.reverse() encoded_payload = bytearray() for i, byte in enumerate(shellcode): if i == len(shellcode) - 1: encoded_payload.append(shellcode[i]) # last byte doesn't need xor'd, common to system call Ox80 generally else: encoded_payload.append(shellcode[i] ^ shellcode[i + 1]) encoded_payload.reverse() else: encoded_payload = bytearray([shellcode.pop(0)]) # first byte left as is in the ciphertext for i, byte in enumerate(shellcode): encoded_payload+=bytearray(right_left_rotation_bit( bytes(byte), right_or_left, int(encoded_payload[i])%8)) return bytes(encoded_payload) def rolling_xor(shellcode: bytes, decode: bool=False) -> bytes: """ Perform a rolling xor encoding scheme on `shellcode`. :param shellcode: bytes object; data to be [en,de]coded :param decode: boolean, decrypt previously xor'd data :return: bytes object """ shellcode = bytearray(shellcode) if decode: shellcode.reverse() encoded_payload = bytearray() for i, byte in enumerate(shellcode): if i == len(shellcode) - 1: encoded_payload.append(shellcode[i]) # last byte doesn't need xor'd, common to system call Ox80 generally else: encoded_payload.append(shellcode[i] ^ shellcode[i + 1]) encoded_payload.reverse() else: encoded_payload = bytearray([shellcode.pop(0)]) # first byte left as is in the ciphertext for i, byte in enumerate(shellcode): encoded_payload.append(byte ^ encoded_payload[i]) return bytes(encoded_payload)

35.857143

141

0.630708

437

3,263

4.599542

0.169336

0.160199

0.079602

0.017413

0.812438

0.753234

0.633333

0.58806

0.561194

0.543284

0

0.014

0.233834

3,263

90

142

36.255556

0.79

0.288691

0

0.770833

0

1

0.083333

false

0

0.166667

0

null

0

1

0

1

0

null

0

3

745f3417083f56ba3a1170d3dedfc8b9679054f8

425

py

Python

rwa/__init__.py

DecBayComp/RWA-python

caf7a96a7edd0397887429f52800e72fdf29434d

[ "Apache-2.0" ]

1

2018-06-20T15:59:38.000Z

rwa/__init__.py

DecBayComp/RWA-python

caf7a96a7edd0397887429f52800e72fdf29434d

[ "Apache-2.0" ]

2

2018-06-11T16:15:01.000Z

2019-11-22T15:57:38.000Z

rwa/__init__.py

DecBayComp/RWA-python

caf7a96a7edd0397887429f52800e72fdf29434d

[ "Apache-2.0" ]

null

from . import storable from . import generic from . import lazy from . import sequence from .storable import Storable, StorableHandler from .generic import rwa_params, default_storable, namedtuple_storable, not_storable from .lazy import islazy, lazytype, lazyvalue try: from . import hdf5 from .hdf5 import hdf5_storable, hdf5_not_storable, hdf5_agnostic_modules, HDF5Store except ImportError: #pass raise

26.5625

88

0.790588

54

425

6.055556

0.444444

0.152905

0

0.016807

0.16

425

15

89

28.333333

0.89916

0.009412

0

1

0

true

0

0.833333

0

0.833333

0

null

0

null

0

1

0

1

0

1

0

3

746f42faf0f3abf28755341e97b870eb4ad0a5d1

2,069

py

Python

kombu/utils/functional.py

expa/kombu

528ac975bd3b815ebe7d1b5f92126d5abd01c6cc

[ "BSD-3-Clause" ]

39

2016-12-05T14:36:37.000Z

2021-07-29T18:22:34.000Z

kombu/utils/functional.py

alex/kombu

3cb79f9c8c4db3c47c66b5419fd7a40d09758b87

[ "BSD-3-Clause" ]

68

2016-12-12T20:38:47.000Z

2020-07-26T18:28:49.000Z

kombu/utils/functional.py

alex/kombu

3cb79f9c8c4db3c47c66b5419fd7a40d09758b87

[ "BSD-3-Clause" ]

120

2016-08-18T14:53:03.000Z

2020-06-16T13:27:20.000Z

from __future__ import absolute_import import sys from collections import Iterable, Mapping from kombu.five import string_t __all__ = ['lazy', 'maybe_evaluate', 'is_list', 'maybe_list'] class lazy(object): """Holds lazy evaluation. Evaluated when called or if the :meth:`evaluate` method is called. The function is re-evaluated on every call. Overloaded operations that will evaluate the promise: :meth:`__str__`, :meth:`__repr__`, :meth:`__cmp__`. """ def __init__(self, fun, *args, **kwargs): self._fun = fun self._args = args self._kwargs = kwargs def __call__(self): return self.evaluate() def evaluate(self): return self._fun(*self._args, **self._kwargs) def __str__(self): return str(self()) def __repr__(self): return repr(self()) def __eq__(self, rhs): return self() == rhs def __ne__(self, rhs): return self() != rhs def __deepcopy__(self, memo): memo[id(self)] = self return self def __reduce__(self): return (self.__class__, (self._fun, ), {'_args': self._args, '_kwargs': self._kwargs}) if sys.version_info[0] < 3: def __cmp__(self, rhs): if isinstance(rhs, self.__class__): return -cmp(rhs, self()) return cmp(self(), rhs) def maybe_evaluate(value): """Evaluates if the value is a :class:`lazy` instance.""" if isinstance(value, lazy): return value.evaluate() return value def is_list(l, scalars=(Mapping, string_t), iters=(Iterable, )): """Return true if the object is iterable (but not if object is a mapping or string).""" return isinstance(l, iters) and not isinstance(l, scalars or ()) def maybe_list(l, scalars=(Mapping, string_t)): """Return list of one element if ``l`` is a scalar.""" return l if l is None or is_list(l, scalars) else [l] # Compat names (before kombu 3.0) promise = lazy maybe_promise = maybe_evaluate

24.927711

73

0.615273

269

2,069

4.401487

0.304833

0.059122

0.047297

0.028716

0.08277

0

0.002626

0.263896

2,069

82

74

25.231707

0.774787

0.222813

0

0.030109

0

1

0.302326

false

0

0.093023

0.162791

0.744186

0

null

0

null

0

1

0

1

0

3

748ed28868581a95b707b4d1a4f4d1ceb756396d

401

py

Python

arcade_solutions/intro/alternating_sums.py

nickaigi/automatic-dollop

eb8222475c7871c1d5710242c5aed8c70ea0d2c8

[ "Unlicense" ]

null

arcade_solutions/intro/alternating_sums.py

nickaigi/automatic-dollop

eb8222475c7871c1d5710242c5aed8c70ea0d2c8

[ "Unlicense" ]

null

arcade_solutions/intro/alternating_sums.py

nickaigi/automatic-dollop

eb8222475c7871c1d5710242c5aed8c70ea0d2c8

[ "Unlicense" ]

null

def alternating_sums(a): even, odd = [], [] for i in range(len(a)): if i % 2 == 0: even.append(a[i]) else: odd.append(a[i]) return [sum(even), sum(odd)] def alternating_sums_short(a): return [ sum(a[::2]), sum(a[1::2]) ] if __name__ == '__main__': a = [50, 60, 60, 45, 70] alternating_sums(a) alternating_sums_short(a)

21.105263

33

0.511222

59

401

3.237288

0.440678

0.314136

0.188482

0.219895

0

0.054348

0.311721

401

18

34

22.277778

0.637681

0

0.01995

0

1

0.125

false

0

0.0625

0.25

0

null

1

0

null

0

3

74a635f2da3700c5b426525ef1a2e1bd84bb4147

420

py

Python

common_configs/base.py

nigma/django-common-configs

b4499415c71ffd317f395896f97b0fc96b1ad7bd

[ "BSD-3-Clause" ]

5

2015-01-07T22:27:47.000Z

2017-07-23T12:15:45.000Z

common_configs/base.py

nigma/django-common-configs

b4499415c71ffd317f395896f97b0fc96b1ad7bd

[ "BSD-3-Clause" ]

null

common_configs/base.py

nigma/django-common-configs

b4499415c71ffd317f395896f97b0fc96b1ad7bd

[ "BSD-3-Clause" ]

null

#-*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function, unicode_literals from configurations import Configuration as _Configuration, values class Configuration(_Configuration): @property def INSTALLED_APPS(self): return [] @property def MIDDLEWARE_CLASSES(self): return [] @property def TEMPLATE_CONTEXT_PROCESSORS(self): return []

20

82

0.704762

42

420

6.738095

0.666667

0.116608

0.127208

0.14841

0

0.00303

0.214286

420

20

83

21

0.854545

0.05

0

0.5

0

1

0.25

false

0

0.166667

0.25

0.75

0.083333

0

null

0

null

0

1

0

1

0

3

776bc32e2b2eee2412e69059442ef1f37aff8e70

146

py

Python

Scripts/all-fonts/close.py

miguelsousa/hTools2

eab400677c1b21bb2519a7354a142e167c2b39ba

[ "BSD-3-Clause" ]

null

Scripts/all-fonts/close.py

miguelsousa/hTools2

eab400677c1b21bb2519a7354a142e167c2b39ba

[ "BSD-3-Clause" ]

null

Scripts/all-fonts/close.py

miguelsousa/hTools2

eab400677c1b21bb2519a7354a142e167c2b39ba

[ "BSD-3-Clause" ]

null

# [h] close all fonts '''Close all open fonts.''' all_fonts = AllFonts() if len(all_fonts) > 0: for font in all_fonts: font.close()

16.222222

27

0.616438

23

146

3.782609

0.521739

0.367816

0

0.009009

0.239726

146

9

28

16.222222

0.774775

0.287671

0

1

0

false

0

1

0

null

1

0

1

0

null

0

3

777a6cf14d1f1795816ae487b69dc32e762df3e3

179

py

Python

log_filter.py

oldacre/Who-The-Hill

22b67424f95687aa62718337f5df333dd2b653f8

[ "Apache-2.0" ]

null

log_filter.py

oldacre/Who-The-Hill

22b67424f95687aa62718337f5df333dd2b653f8

[ "Apache-2.0" ]

4

2021-06-08T19:35:40.000Z

2022-03-11T23:18:30.000Z

log_filter.py

oldacre/Who-The-Hill

22b67424f95687aa62718337f5df333dd2b653f8

[ "Apache-2.0" ]

null

import logging class HealthcheckFilter(logging.Filter): def filter(self, log_record): if 'healthcheck' in log_record.msg: return False return True

25.571429

43

0.670391

21

179

5.619048

0.761905

0.152542

0

0.26257

179

7

44

25.571429

0.893939

0

0.061111

0

1

0.166667

false

0

0.166667

0

0.833333

0

1

0

null

0

1

0

null

0

1

0

3

777ed8b99f3018b91a2821a6fceffa31b3074a5c

125

py

Python

with_op/__init__.py

apiology/with_op

eb77469a296d2facf11a039fda3c25d0d2142a75

[ "MIT" ]

1

2019-07-24T19:35:19.000Z

with_op/__init__.py

apiology/with_op

eb77469a296d2facf11a039fda3c25d0d2142a75

[ "MIT" ]

231

2018-09-12T17:09:46.000Z

2021-07-11T20:07:05.000Z

with_op/__init__.py

apiology/with_op

eb77469a296d2facf11a039fda3c25d0d2142a75

[ "MIT" ]

null

"""Top-level package for with-op script.""" __author__ = """Vince Broz""" __email__ = 'vince@broz.cc' __version__ = '1.1.1'

20.833333

43

0.664

18

125

3.944444

0.777778

0.253521

0

0.027523

0.128

125

5

44

25

0.623853

0.296

0

0.341463

0

1

0

false

0

1

0

null

1

0

1

0

null

0

3

7797511096f6c510538ad668a695179836c38f99

239

py

Python

pysofe/quadrature/__init__.py

pysofe/pysofe

088d4061fcf194a85ff3332e7bdd3bde095e4f69

[ "BSD-3-Clause" ]

null

pysofe/quadrature/__init__.py

pysofe/pysofe

088d4061fcf194a85ff3332e7bdd3bde095e4f69

[ "BSD-3-Clause" ]

null

pysofe/quadrature/__init__.py

pysofe/pysofe

088d4061fcf194a85ff3332e7bdd3bde095e4f69

[ "BSD-3-Clause" ]

null

""" Provides the data structure for numerical integration. The purpose of these data structures is to give access to the quadrature points and weights for several spatial domains. """ import gaussian from .gaussian import GaussQuadSimp

21.727273

65

0.803347

33

239

5.818182

0.787879

0

0.158996

239

10

66

23.9

0.955224

0.736402

0

1

0

true

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

3

77af3a9dc1269362de9d3927b1bcf0c929023c1d

185

py

Python

pytest-verbose-parametrize/tests/integration/parametrize_ids/tests/unit/test_example.py

RaiVaibhav/pytest-plugins

b21eef7fb2d876b3910f4a476875f9f157275b49

[ "MIT" ]

282

2015-12-01T12:40:31.000Z

2019-10-30T23:30:54.000Z

pytest-verbose-parametrize/tests/integration/parametrize_ids/tests/unit/test_example.py

RaiVaibhav/pytest-plugins

b21eef7fb2d876b3910f4a476875f9f157275b49

[ "MIT" ]

126

2015-09-02T14:31:02.000Z

2019-10-21T20:32:18.000Z

pytest-verbose-parametrize/tests/integration/parametrize_ids/tests/unit/test_example.py

RaiVaibhav/pytest-plugins

b21eef7fb2d876b3910f4a476875f9f157275b49

[ "MIT" ]

55

2015-09-21T09:11:05.000Z

2019-10-27T00:44:32.000Z

import pytest @pytest.mark.parametrize(('f', 't'), [(sum, list), (len, int)]) def test_foo(f, t): assert isinstance(f([[1], [2]]), t) def test_bar(): # unparametrized pass

16.818182

63

0.594595

27

185

4

0.740741

0.037037

0

0.013158

0.178378

185

10

64

18.5

0.697368

0.075676

0

0.011834

0

0.166667

1

0.333333

false

0.166667

0

0.5

0

1

0

null

0

null

0

1

0

1

0

3

77dcf06a61fa8a09b95eb69913271f8cbf85fdb9

1,162

py

Python

tests/test_container.py

sturmianseq/pythondi

bfb538540f3119d79e68e07572bc6e2f9573d3bc

[ "Apache-2.0" ]

34

2019-11-12T08:45:16.000Z

2022-02-05T19:11:08.000Z

tests/test_container.py

sturmianseq/pythondi

bfb538540f3119d79e68e07572bc6e2f9573d3bc

[ "Apache-2.0" ]

2

2021-08-23T09:23:57.000Z

2021-12-13T04:41:24.000Z

tests/test_container.py

sturmianseq/pythondi

bfb538540f3119d79e68e07572bc6e2f9573d3bc

[ "Apache-2.0" ]

3

2020-09-22T16:10:35.000Z

2021-08-24T01:19:53.000Z

from pythondi import Container, Provider def test_has_shared_state(): c1 = Container c2 = Container assert c1.get() == c2.get() p1 = Provider(cls=int, new_cls=str) c1.set(provider=p1) assert c1.get() == c2.get() p2 = Provider(cls=str, new_cls=int) c2.set(provider=p2) assert c1.get() == c2.get() def test_has_shared_state_with_instance(): c1 = Container() c2 = Container() assert id(c1) != id(c2) assert c1.get() == c2.get() p1 = Provider(cls=int, new_cls=str) c1.set(provider=p1) assert c1.get() == c2.get() p2 = Provider(cls=str, new_cls=int) c2.set(provider=p2) assert c1.get() == c2.get() def test_clear(): Container.set(provider=Provider()) assert Container.get() is not None Container.clear() assert Container.get() is None def test_set(): Container.clear() assert Container.get() is None provider = Provider() Container.set(provider=provider) assert Container.get() == provider def test_get(): Container.clear() assert Container.get() is None Container.set(provider=Provider()) assert Container.get() is not None

21.924528

42

0.640275

164

1,162

4.445122

0.170732

0.105624

0.090535

0.106996

0.857339

0.740741

0.521262

0

0.032967

0.216867

1,162

52

43

22.346154

0.768132

0

0.631579

0

0.342105

1

0.131579

false

0

0.026316

0

0.157895

0

null

0

1

0

null

0

3

7adc57c668f27576fa0782daf119f72088b096fd

53

py

Python

persession/__init__.py

rishabhsingh971/login-session

b551798d4ca1f8e9f97f94cfc0c45ba2c52b07ba

[ "MIT" ]

3

2019-09-14T21:43:58.000Z

2019-10-13T08:43:40.000Z

persession/__init__.py

rishabhsingh971/login-session

b551798d4ca1f8e9f97f94cfc0c45ba2c52b07ba

[ "MIT" ]

1

2020-03-31T07:06:56.000Z

persession/__init__.py

rishabhsingh971/login-session

b551798d4ca1f8e9f97f94cfc0c45ba2c52b07ba

[ "MIT" ]

null

""" init """ from .main import * name = "persession"

13.25

19

0.603774

6

53

5.333333

1

0

0.188679

53

3

20

17.666667

0.744186

0.075472

0

0.243902

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

7afde0ad53fdb9ffc7ce39693d2278734e22b12f

303

py

Python

gaphor/RAAML/toolbox.py

mrmonkington/gaphor

f0fcd4deb90d24b14723840a689fac901f645a43

[ "Apache-2.0" ]

867

2018-01-09T00:19:09.000Z

2022-03-31T02:49:23.000Z

gaphor/RAAML/toolbox.py

mrmonkington/gaphor

f0fcd4deb90d24b14723840a689fac901f645a43

[ "Apache-2.0" ]

790

2018-01-13T23:47:07.000Z

2022-03-31T16:04:27.000Z

gaphor/RAAML/toolbox.py

sitedata/gaphor

c83eff0bd595d1a8e766a157f0268e5206eed22c

[ "Apache-2.0" ]

117

2018-01-09T02:24:49.000Z

2022-03-23T08:07:42.000Z

"""The action definition for the RAAML toolbox.""" from gaphor.diagram.diagramtoolbox import ToolboxDefinition, general_tools from gaphor.RAAML.fta.ftatoolbox import fta from gaphor.RAAML.stpa.stpatoolbox import stpa raaml_toolbox_actions: ToolboxDefinition = ( general_tools, fta, stpa, )

27.545455

74

0.788779

37

303

6.351351

0.513514

0.12766

0.246809

0

0.135314

303

10

75

30.3

0.896947

0.145215

0

1

0

true

0

0.375

0

0.375

0

null

0

1

0

null

0

1

0

1

0

3

bb0f5f3ad883aa6a144c404a7c47daf79faf456c

197

py

Python

setup.py

socc-io/Naver-VISION_AI_contest

e9961c8a42af952cc1367ce0b14a1b31ebe0d8f7

[ "Unlicense" ]

null

setup.py

socc-io/Naver-VISION_AI_contest

e9961c8a42af952cc1367ce0b14a1b31ebe0d8f7

[ "Unlicense" ]

null

setup.py

socc-io/Naver-VISION_AI_contest

e9961c8a42af952cc1367ce0b14a1b31ebe0d8f7

[ "Unlicense" ]

null

#nsml: soonmok/socc_image:v_4 from distutils.core import setup setup( name='nsml vision hackathon', version='1.0', description='nsml vision hackathon', install_requires=[ ] )

16.416667

40

0.685279

25

197

5.28

0.8

0.151515

0.287879

0

0.018987

0.19797

197

11

41

17.909091

0.816456

0.142132

0

0.267857

0

1

0

true

0

0.125

0

0.125

0

1

0

null

0

1

0

null

0

1

0

3

bb1361e77a08fcddc05d2b095409daa573b2c907

497

py

Python

bubble_sort/main.py

TJHomer/itp-w1-bubble-sort

c5959fe3df3e6344a963e6a92a50e16341d3f055

[ "MIT" ]

null

bubble_sort/main.py

TJHomer/itp-w1-bubble-sort

c5959fe3df3e6344a963e6a92a50e16341d3f055

[ "MIT" ]

null

bubble_sort/main.py

TJHomer/itp-w1-bubble-sort

c5959fe3df3e6344a963e6a92a50e16341d3f055

[ "MIT" ]

1

2020-10-19T04:19:53.000Z

"""This is the entry point of the program.""" def bubble_sort(list_of_numbers): for i in range(len(list_of_numbers)-1,0,-1): for j in range(i): if list_of_numbers[j] > list_of_numbers[j+1]: placeholder = list_of_numbers[j] list_of_numbers[j] = list_of_numbers[j+1] list_of_numbers[j+1] = placeholder return list_of_numbers if __name__ == '__main__': print(bubble_sort([9, 1, 3, 11, 7, 2, 42, 111]))

27.611111

57

0.591549

78

497

3.410256

0.423077

0.203008

0.43985

0.315789

0.409774

0.270677

0.218045

0

0.050847

0.287726

497

17

58

29.235294

0.700565

0.078471

0

0.017699

0

1

0.1

false

0

0.2

0.1

0

null

1

0

null

0

3

bb2071b583422ebbc3dcf7f992247801c566262b

288

py

Python

django/b3_propagation/middleware.py

ueni-ltd/istio-integration

34c1845858279ffc5354341d5c1088d1105e6dea

[ "MIT" ]

1

2018-11-19T10:18:47.000Z

django/b3_propagation/middleware.py

ueni-ltd/istio-integration

34c1845858279ffc5354341d5c1088d1105e6dea

[ "MIT" ]

null

django/b3_propagation/middleware.py

ueni-ltd/istio-integration

34c1845858279ffc5354341d5c1088d1105e6dea

[ "MIT" ]

1

2018-11-19T10:21:16.000Z

from b3_propagation import local_storage def request_store_middleware(get_response): def middleware(request): local_storage.request = request try: return get_response(request) finally: del local_storage.request return middleware

22.153846

43

0.684028

31

288

6.096774

0.516129

0.190476

0.201058

0

0.004762

0.270833

288

12

44

24

0.895238

0

1

0.222222

false

0

0.111111

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

3

bb301763790dd58890166561711890489371e8be

1,980

py

Python

downloads/managers.py

akondasif/Clone-test-repo

8a6fc17dc904015db76d4fe81965765466adfc55

[ "Apache-2.0" ]

null

downloads/managers.py

akondasif/Clone-test-repo

8a6fc17dc904015db76d4fe81965765466adfc55

[ "Apache-2.0" ]

1

2019-03-28T22:12:58.000Z

downloads/managers.py

akondasif/Clone-test-repo

8a6fc17dc904015db76d4fe81965765466adfc55

[ "Apache-2.0" ]

1

2019-04-03T20:26:54.000Z

from django.db.models import Manager from django.db.models.query import QuerySet class ReleaseQuerySet(QuerySet): def published(self): return self.filter(is_published=True) def draft(self): return self.filter(is_published=False) def downloads(self): """ For the main downloads landing page """ return self.select_related('release_page').filter( is_published=True, show_on_download_page=True, ).order_by('-release_date') def python2(self): return self.filter(version=2, is_published=True) def python3(self): return self.filter(version=3, is_published=True) def latest_python2(self): return self.python2().filter(is_latest=True) def latest_python3(self): return self.python3().filter(is_latest=True) def pre_release(self): return self.filter(pre_release=True) def released(self): return self.filter(is_published=True, pre_release=False) class ReleaseManager(Manager): def get_queryset(self): return ReleaseQuerySet(self.model, using=self._db) def published(self): return self.get_queryset().published() def draft(self): return self.get_queryset().draft() def downloads(self): """ For the main downloads landing page """ return self.get_queryset().downloads() def python2(self): return self.get_queryset().python2() def python3(self): return self.get_queryset().python3() def latest_python2(self): qs = self.get_queryset().latest_python2() if qs: return qs[0] else: return None def latest_python3(self): qs = self.get_queryset().latest_python3() if qs: return qs[0] else: return None def pre_release(self): return self.get_queryset().pre_release() def released(self): return self.get_queryset().released()

25.714286

64

0.638384

242

1,980

5.070248

0.210744

0.130399

0.159739

0.119804

0.577832

0.308068

0.197229

0.140179

0.09128

0

0.012195

0.254545

1,980

76

65

26.052632

0.819106

0.036364

0

0.490566

0

0.0132

0

1

0.358491

false

0

0.037736

0.283019

0.830189

0

null

0

null

0

1

0

1

0

3

bb3864161bf7cbcfde75ee32a663134f851edc59

189

py

Python

extract_links_from_html.py

unbiased-coder/python-extract-text

3f98a2c1f7a325691e0021d20fd4defedd969ab1

[ "Unlicense" ]

null

extract_links_from_html.py

unbiased-coder/python-extract-text

3f98a2c1f7a325691e0021d20fd4defedd969ab1

[ "Unlicense" ]

null

extract_links_from_html.py

unbiased-coder/python-extract-text

3f98a2c1f7a325691e0021d20fd4defedd969ab1

[ "Unlicense" ]

null

from bs4 import BeautifulSoup html = open('sample.html', 'r').read() soup = BeautifulSoup(html, features="lxml") for element in soup.find_all('a', href=True): print (element['href'])

23.625

45

0.693122

27

189

4.814815

0.777778

0.261538

0

0.006098

0.132275

189

7

46

27

0.786585

0

0.111111

0

1

0

false

0

0.2

0

0.2

1

0

null

1

0

1

0

null

0

3

bb59f22b4ccf6393306c8b7bdaab55f4c952a159

150

py

Python

templates/apps.py

DeeMATT/studious-waffle-website-builder

5077af0edae7a3fef74ceae0d7bad9ace28a3834

[ "MIT" ]

null

templates/apps.py

DeeMATT/studious-waffle-website-builder

5077af0edae7a3fef74ceae0d7bad9ace28a3834

[ "MIT" ]

1

2021-12-12T03:13:49.000Z

templates/apps.py

DeeMATT/studious-waffle-website-builder

5077af0edae7a3fef74ceae0d7bad9ace28a3834

[ "MIT" ]

null

from django.apps import AppConfig class TemplatesConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'templates'

21.428571

56

0.766667

17

150

6.647059

0.882353

0

0.146667

150

6

57

25

0.882813

0

0.253333

0.193333

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3