xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

523f9700ed13c3bae87c8fbe0de30f129cc31497

45

py

Python

server/util/__init__.py

jjojala/results

bfcf6820ff4b2dd05d8974bc98b0a59bc6c3585f

[ "Apache-2.0" ]

null

server/util/__init__.py

jjojala/results

bfcf6820ff4b2dd05d8974bc98b0a59bc6c3585f

[ "Apache-2.0" ]

7

2015-11-25T22:26:25.000Z

2016-10-18T22:14:35.000Z

server/util/__init__.py

jjojala/results

bfcf6820ff4b2dd05d8974bc98b0a59bc6c3585f

[ "Apache-2.0" ]

null

from .patch import patch, diff, PatchConflict

45

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87797c782ed9e6a7b609e077090ffd33cb3f5483

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py

Python

Game/lib/interface/__init__.py

brunnossanttos/Game.JackPot

323cbdaedbf3032dee438a37342bfa18a2f98c82

[ "MIT" ]

null

Game/lib/interface/__init__.py

brunnossanttos/Game.JackPot

323cbdaedbf3032dee438a37342bfa18a2f98c82

[ "MIT" ]

null

Game/lib/interface/__init__.py

brunnossanttos/Game.JackPot

323cbdaedbf3032dee438a37342bfa18a2f98c82

[ "MIT" ]

null

def linha(tam=65): return '\033[36m-' * tam def cabecalho(txt): print(linha()) print(txt.center(65)) print(linha())

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5e910efd3c321feafa27ff83a1356fc991d2cda2

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py

Python

t2vretrieval/models/criterion.py

aranciokov/ranp

ac9213e4f33b808258acd9dcf5ab08e3902642ed

[ "MIT" ]

3

2022-03-18T08:09:41.000Z

2022-03-23T08:42:03.000Z

t2vretrieval/models/criterion.py

aranciokov/ranp

ac9213e4f33b808258acd9dcf5ab08e3902642ed

[ "MIT" ]

null

t2vretrieval/models/criterion.py

aranciokov/ranp

ac9213e4f33b808258acd9dcf5ab08e3902642ed

[ "MIT" ]

null

import torch import torch.nn as nn import framework.configbase import framework.ops def cosine_sim(im, s): '''cosine similarity between all the image and sentence pairs ''' inner_prod = im.mm(s.t()) im_norm = torch.sqrt((im**2).sum(1).view(-1, 1) + 1e-18) s_norm = torch.sqrt((s**2).sum(1).view(1, -1) + 1e-18) sim = inner_prod / (im_norm * s_norm) return sim class ContrastiveLoss(nn.Module): '''compute contrastive loss ''' def __init__(self, margin=0, max_violation=False, direction='bi', topk=1): '''Args: direction: i2t for negative sentence, t2i for negative image, bi for both ''' super(ContrastiveLoss, self).__init__() self.margin = margin self.max_violation = max_violation self.direction = direction self.topk = topk def forward(self, scores, margin=None, average_batch=True, batch_relevance=None, threshold_pos=1.): ''' Args: scores: image-sentence score matrix, (batch, batch) the same row of im and s are positive pairs, different rows are negative pairs batch_relevance: image-sentence relevancy matrix (batch, batch) ''' if margin is None: margin = self.margin batch_size = scores.size(0) diagonal = scores.diag().view(batch_size, 1) # positive pairs # mask to clear diagonals which are positive pairs pos_masks = torch.eye(batch_size).bool().to(scores.device) batch_topk = min(batch_size, self.topk) if self.direction == 'i2t' or self.direction == 'bi': d1 = diagonal.expand_as(scores) # same collumn for im2s (negative sentence) # compare every diagonal score to scores in its collumn # caption retrieval cost_s = (margin + scores - d1).clamp(min=0) cost_s = cost_s.masked_fill(pos_masks, 0) if batch_relevance is not None: cost_s[batch_relevance >= threshold_pos] = 0 if self.max_violation: cost_s, _ = torch.topk(cost_s, batch_topk, dim=1) cost_s = cost_s / batch_topk if average_batch: cost_s = cost_s / batch_size else: if average_batch: cost_s = cost_s / (batch_size * (batch_size - 1)) cost_s = torch.sum(cost_s) if self.direction == 't2i' or self.direction == 'bi': d2 = diagonal.t().expand_as(scores) # same row for s2im (negative image) # compare every diagonal score to scores in its row cost_im = (margin + scores - d2).clamp(min=0) cost_im = cost_im.masked_fill(pos_masks, 0) if batch_relevance is not None: cost_im[batch_relevance >= threshold_pos] = 0 if self.max_violation: cost_im, _ = torch.topk(cost_im, batch_topk, dim=0) cost_im = cost_im / batch_topk if average_batch: cost_im = cost_im / batch_size else: if average_batch: cost_im = cost_im / (batch_size * (batch_size - 1)) cost_im = torch.sum(cost_im) if self.direction == 'i2t': return cost_s elif self.direction == 't2i': return cost_im else: return cost_s + cost_im class ContrastiveLossHP(nn.Module): '''compute contrastive loss ''' def __init__(self, margin=0, margin_pos=0, max_violation=False, direction='bi', topk=1): '''Args: direction: i2t for negative sentence, t2i for negative image, bi for both ''' super(ContrastiveLossHP, self).__init__() self.margin = margin self.margin_pos = margin_pos self.max_violation = max_violation self.direction = direction self.topk = topk def forward(self, scores, margin=None, average_batch=True, batch_relevance=None, threshold_pos=1., margin_pos=None): ''' Args: scores: image-sentence score matrix, (batch, batch) the same row of im and s are positive pairs, different rows are negative pairs batch_relevance: image-sentence relevancy matrix (batch, batch) ''' if margin is None: margin = self.margin if margin_pos is None: margin_pos = self.margin_pos batch_size = scores.size(0) diagonal = scores.diag().view(batch_size, 1) # positive pairs # mask to clear diagonals which are positive pairs pos_masks = torch.eye(batch_size).bool().to(scores.device) batch_topk = min(batch_size, self.topk) if self.direction == 'i2t' or self.direction == 'bi': d1 = diagonal.expand_as(scores) # same collumn for im2s (negative sentence) # compare every diagonal score to scores in its collumn # caption retrieval cost_s = (margin + scores - d1).clamp(min=0) cost_s = cost_s.masked_fill(pos_masks, 0) if batch_relevance is not None: cost_s[batch_relevance >= threshold_pos] = 0 if self.max_violation: cost_s, _ = torch.topk(cost_s, batch_topk, dim=1) cost_s = cost_s / batch_topk if average_batch: cost_s = cost_s / batch_size else: if average_batch: cost_s = cost_s / (batch_size * (batch_size - 1)) cost_s = torch.sum(cost_s) # we want a copy of scores in order to compute the negative-masked version as well hp_scores = scores.clone() # scores are the (v_i, q_j) hp_scores[batch_relevance < threshold_pos] = 1 # mask the negatives; positives have now score <= 1 hp_scores = hp_scores.masked_fill(pos_masks, 1) # we want to pick the argmin (hardest positive) hp_scores, _ = torch.topk(hp_scores, batch_topk, dim=1, largest=False) # -> s(q, v+) hp_cost_s = (margin_pos + scores - hp_scores).clamp(min=0) # we need to mask hp_cost again because 'scores' is not pos-masked hp_cost_s = hp_cost_s.masked_fill(pos_masks, 0) if batch_relevance is not None: hp_cost_s[batch_relevance >= threshold_pos] = 0 hp_cost_s, _ = torch.topk(hp_cost_s, batch_topk, dim=1) hp_cost_s = hp_cost_s / batch_topk if average_batch: hp_cost_s = hp_cost_s / batch_size hp_cost_s = torch.sum(hp_cost_s) if self.direction == 't2i' or self.direction == 'bi': d2 = diagonal.t().expand_as(scores) # same row for s2im (negative image) # compare every diagonal score to scores in its row cost_im = (margin + scores - d2).clamp(min=0) cost_im = cost_im.masked_fill(pos_masks, 0) if batch_relevance is not None: cost_im[batch_relevance >= threshold_pos] = 0 if self.max_violation: cost_im, _ = torch.topk(cost_im, batch_topk, dim=0) cost_im = cost_im / batch_topk if average_batch: cost_im = cost_im / batch_size else: if average_batch: cost_im = cost_im / (batch_size * (batch_size - 1)) cost_im = torch.sum(cost_im) # we want a copy of scores in order to compute the negative-masked version as well hp_scores_im = scores.clone() # scores are the (v_i, q_j) hp_scores_im[batch_relevance < threshold_pos] = 1 # mask the negatives; positives have now score <= 1 hp_scores_im = hp_scores_im.masked_fill(pos_masks, 1) # we want to pick the argmin (hardest positive) hp_scores_im, _ = torch.topk(hp_scores_im, batch_topk, dim=0, largest=False) # -> s(q, v+) hp_cost_im = (margin_pos + scores - hp_scores_im).clamp(min=0) # we need to mask hp_cost again because 'scores' is not pos-masked hp_cost_im = hp_cost_im.masked_fill(pos_masks, 0) if batch_relevance is not None: hp_cost_im[batch_relevance >= threshold_pos] = 0 hp_cost_im, _ = torch.topk(hp_cost_im, batch_topk, dim=0) hp_cost_im = hp_cost_im / batch_topk if average_batch: hp_cost_im = hp_cost_im / batch_size hp_cost_im = torch.sum(hp_cost_im) if self.direction == 'i2t': return cost_s, hp_cost_s elif self.direction == 't2i': return cost_im, hp_cost_im else: return cost_s + cost_im, hp_cost_s + hp_cost_im

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0dc63506330fdc60018049e930eed55c9aea71d2

27

py

Python

entity/__init__.py

tuannguyendang/montypython

c0b8ff7a8130e811ba16bfab8d5e013eac37f432

[ "Apache-2.0" ]

null

entity/__init__.py

tuannguyendang/montypython

c0b8ff7a8130e811ba16bfab8d5e013eac37f432

[ "Apache-2.0" ]

null

entity/__init__.py

tuannguyendang/montypython

c0b8ff7a8130e811ba16bfab8d5e013eac37f432

[ "Apache-2.0" ]

null

from .user import User, db

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216d4eb41692418c9a789beb8f220409a6f64736

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py

Python

polynomials_on_simplices/polynomial/polynomials_unit_simplex_bases.py

FAndersson/polynomials_on_simplices

f015a4772c817bfa99b0d6b726667a38a174b064

[ "MIT" ]

1

2021-03-17T11:41:21.000Z

polynomials_on_simplices/polynomial/polynomials_unit_simplex_bases.py

FAndersson/polynomials_on_simplices

f015a4772c817bfa99b0d6b726667a38a174b064

[ "MIT" ]

null

polynomials_on_simplices/polynomial/polynomials_unit_simplex_bases.py

FAndersson/polynomials_on_simplices

f015a4772c817bfa99b0d6b726667a38a174b064

[ "MIT" ]

null

"""Functionality for working with different bases for polynomials on the unit simplex, and for converting between these bases. """ import copy import numpy as np from polynomials_on_simplices.polynomial.polynomials_monomial_basis import ( dual_monomial_basis, dual_monomial_basis_fn, dual_vector_valued_monomial_basis, dual_vector_valued_monomial_basis_fn, monomial_basis, monomial_basis_fn, monomial_basis_fn_latex, monomial_basis_fn_latex_compact, monomial_basis_latex, monomial_basis_latex_compact, unique_identifier_monomial_basis, vector_valued_monomial_basis, vector_valued_monomial_basis_fn) from polynomials_on_simplices.polynomial.polynomials_unit_simplex_bernstein_basis import ( PolynomialBernstein, bernstein_basis, bernstein_basis_fn, bernstein_basis_fn_latex, bernstein_basis_fn_latex_compact, bernstein_basis_latex, bernstein_basis_latex_compact, dual_bernstein_basis, dual_bernstein_basis_fn, dual_vector_valued_bernstein_basis, dual_vector_valued_bernstein_basis_fn, unique_identifier_bernstein_basis, vector_valued_bernstein_basis, vector_valued_bernstein_basis_fn) from polynomials_on_simplices.polynomial.polynomials_unit_simplex_lagrange_basis import ( PolynomialLagrange, dual_lagrange_basis, dual_lagrange_basis_fn, dual_vector_valued_lagrange_basis, dual_vector_valued_lagrange_basis_fn, lagrange_basis, lagrange_basis_fn, lagrange_basis_fn_latex, lagrange_basis_fn_latex_compact, lagrange_basis_latex, lagrange_basis_latex_compact, unique_identifier_lagrange_basis, vector_valued_lagrange_basis, vector_valued_lagrange_basis_fn) def convert_polynomial_to_basis(p, target_basis): r""" Convert a polynomial in :math:`\mathcal{P}_r (\Delta_c^m)` to the given basis. :param p: Polynomial expanded in some basis. :param str target_basis: Unique identifier for the basis we want to expand the polynomial in. :return: Polynomial expanded in the given basis. """ if p.basis() == target_basis: return copy.deepcopy(p) if target_basis == unique_identifier_monomial_basis(): return p.to_monomial_basis() m = p.domain_dimension() n = p.target_dimension() r = p.degree() coeff = np.empty(p.coeff.shape) dual_basis = dual_polynomial_basis(r, m, target_basis) if n == 1: for i in range(len(coeff)): coeff[i] = dual_basis[i](p) else: # Handle each component of p separately for i in range(len(coeff)): for j in range(n): coeff[i][j] = dual_basis[i](p[j]) if target_basis == unique_identifier_lagrange_basis(): return PolynomialLagrange(coeff, r, m) else: if target_basis == unique_identifier_bernstein_basis(): return PolynomialBernstein(coeff, r, m) else: raise ValueError("Unknown polynomial basis") def polynomial_basis_fn(nu, r, basis): r""" Generate a basis polynomial in the space :math:`\mathcal{P}_r(\Delta_c^n)` (where n is equal to the length of nu) in the given basis. :param nu: Multi-index indicating which basis polynomial should be generated. :type nu: int or :class:`~polynomials_on_simplices.algebra.multiindex.MultiIndex` or Tuple[int, ...] :param int r: Degree of polynomial. :param str basis: Unique identifier for the basis we should generate a base polynomial for. :return: The base polynomial as specified by nu, r and basis. :rtype: Implementation of :class:`~polynomials_on_simplices.polynomial.polynomials_base.PolynomialBase`. """ if basis == unique_identifier_monomial_basis(): return monomial_basis_fn(nu) if basis == unique_identifier_lagrange_basis(): return lagrange_basis_fn(nu, r) if basis == unique_identifier_bernstein_basis(): return bernstein_basis_fn(nu, r) raise ValueError("Unknown polynomial basis") def polynomial_basis(r, n, basis): r""" Generate all base polynomials for the space :math:`\mathcal{P}_r(\Delta_c^n)` in the given basis. :param int r: Degree of the polynomial space. :param int n: Dimension of the unit simplex. :param str basis: Unique identifier for the basis we should generate base polynomials for. :return: List of base polynomials in the specified basis. """ if basis == unique_identifier_monomial_basis(): return monomial_basis(r, n) if basis == unique_identifier_lagrange_basis(): return lagrange_basis(r, n) if basis == unique_identifier_bernstein_basis(): return bernstein_basis(r, n) raise ValueError("Unknown polynomial basis") def vector_valued_polynomial_basis_fn(nu, r, i, n, basis): r""" Generate a basis polynomial for the space :math:`\mathcal{P}_r(\Delta_c^m, \mathbb{R}^n)` (where m is equal to the length of nu) in the given basis. :param nu: Multi-index indicating which basis polynomial should be generated. :type nu: int or :class:`~polynomials_on_simplices.algebra.multiindex.MultiIndex` or Tuple[int, ...] :param int r: Degree of polynomial. :param int i: Index of the vector component that is non-zero. :param int n: Dimension of the target. :param str basis: Unique identifier for the basis we should generate a base polynomial for. :return: The base polynomial as specified by nu, r and basis. :rtype: Implementation of :class:`~polynomials_on_simplices.polynomial.polynomials_base.PolynomialBase`. """ if basis == unique_identifier_monomial_basis(): return vector_valued_monomial_basis_fn(nu, i, n) if basis == unique_identifier_lagrange_basis(): return vector_valued_lagrange_basis_fn(nu, r, i, n) if basis == unique_identifier_bernstein_basis(): return vector_valued_bernstein_basis_fn(nu, r, i, n) raise ValueError("Unknown polynomial basis") def vector_valued_polynomial_basis(r, m, n, basis, ordering="interleaved"): r""" Generate all base polynomials for the space :math:`\mathcal{P}_r(\Delta_c^m, \mathbb{R}^n)` in the given basis. :param int r: Degree of the polynomial space. :param int m: Dimension of the domain. :param int n: Dimension of the target. :param str basis: Unique identifier for the basis we should generate base polynomials for. :param str ordering: How the vector valued basis functions are ordered. Can be "sequential" or "interleaved". For sequential, sorting is first done on the index of the component that is non-zero, and then the non-zero component is sorted in the same way as the scalar valued basis functions. For "interleaved" basis functions are first sorted on their non-zero component in the same way as scalar valued basis functions, and then they are sorted on the index of the component that is non-zero. :return: List of base polynomials in the specified basis. """ if n == 1: return polynomial_basis(m, r, basis) if basis == unique_identifier_monomial_basis(): return vector_valued_monomial_basis(r, m, n, ordering) if basis == unique_identifier_lagrange_basis(): return vector_valued_lagrange_basis(r, m, n, ordering) if basis == unique_identifier_bernstein_basis(): return vector_valued_bernstein_basis(r, m, n, ordering) raise ValueError("Unknown polynomial basis") def dual_polynomial_basis_fn(mu, r, basis): r""" Generate a dual basis function to a polynomial basis, i.e. the linear map :math:`q_{\mu, r} : \mathcal{P}_r(\Delta_c^n) \to \mathbb{R}` such that .. math:: q_{\mu, r}(p_{\nu, r}) = \delta_{\mu, \nu}, where :math:`p_{\nu, r}` is the degree r basis polynomial indexed by the multi-index :math:`\nu` in the given basis and .. math:: \delta_{\mu, \nu} = \begin{cases} 1 & \mu = \nu \\ 0 & \text{else} \end{cases}. :param mu: Multi-index indicating which dual basis function should be generated. :type mu: int or :class:`~polynomials_on_simplices.algebra.multiindex.MultiIndex` or Tuple[int, ...] :param int r: Degree of polynomial space. :param str basis: Unique identifier for the basis we should generate a dual base function for. :return: The dual basis function as specified by mu, r and basis. :rtype: Callable :math:`q_{\mu, r}(p)`. """ if basis == unique_identifier_monomial_basis(): return dual_monomial_basis_fn(mu) if basis == unique_identifier_lagrange_basis(): return dual_lagrange_basis_fn(mu, r) if basis == unique_identifier_bernstein_basis(): return dual_bernstein_basis_fn(mu, r) raise ValueError("Unknown polynomial basis") def dual_polynomial_basis(r, n, basis): r""" Generate all dual base functions for the space :math:`\mathcal{P}_r(\Delta_c^n)` in the given basis (i.e. a basis for :math:`\mathcal{P}_r(\Delta_c^n)^*`). :param int r: Degree of the polynomial space. :param int n: Dimension of the domain. :param str basis: Unique identifier for the basis we should generate dual base functions for. :return: List of dual base functions. :rtype: List[callable `q(p)`]. """ if basis == unique_identifier_monomial_basis(): return dual_monomial_basis(r, n) if basis == unique_identifier_lagrange_basis(): return dual_lagrange_basis(r, n) if basis == unique_identifier_bernstein_basis(): return dual_bernstein_basis(r, n) raise ValueError("Unknown polynomial basis") def dual_vector_valued_polynomial_basis_fn(mu, r, i, n, basis): r""" Generate a dual basis function to a vector valued polynomial basis, i.e. the linear map :math:`q_{\mu, i} : \mathcal{P}_r(\mathbb{R}^m, \mathbb{R}^n) \to \mathbb{R}` that satisfies .. math:: q_{\mu, i}(p_{\nu, j}) = \delta_{\mu, \nu} \delta_{i, j}, where :math:`p_{\nu, j}` is the degree :math:`|\nu|` vector valued basis polynomial indexed by the multi-index :math:`\nu` with a non-zero i:th component in the given basis (see :func:`vector_valued_polynomial_basis_fn`) and .. math:: \delta_{\mu, \nu} = \begin{cases} 1 & \mu = \nu \\ 0 & \text{else} \end{cases}. :param mu: Multi-index indicating which dual basis function should be generated. :type mu: int or :class:`~polynomials_on_simplices.algebra.multiindex.MultiIndex` or Tuple[int, ...]. :param int r: Degree of polynomial space. :param int i: Integer indicating which dual basis function should be generated. :param int n: Dimension of the target. :param str basis: Unique identifier for the basis we should generate a dual base function for. :return: The dual basis function as specified by mu, r and i. :rtype: Callable :math:`q_{\mu, i}(p)`. """ if basis == unique_identifier_monomial_basis(): return dual_vector_valued_monomial_basis_fn(mu, i, n) if basis == unique_identifier_lagrange_basis(): return dual_vector_valued_lagrange_basis_fn(mu, r, i, n) if basis == unique_identifier_bernstein_basis(): return dual_vector_valued_bernstein_basis_fn(mu, r, i, n) raise ValueError("Unknown polynomial basis") def dual_vector_valued_polynomial_basis(r, m, n, basis, ordering="interleaved"): r""" Generate all dual base functions for the space :math:`\mathcal{P}_r(\mathbb{R}^m, \mathbb{R}^n)` in the given basis (i.e. the basis for :math:`\mathcal{P}_r(\mathbb{R}^m, \mathbb{R}^n)^*`). See :func:`dual_vector_valued_polynomial_basis_fn`. :param int r: Degree of the polynomial space. :param int m: Dimension of the domain. :param int n: Dimension of the target. :param str basis: Unique identifier for the basis we should generate dual base functions for. :param str ordering: How the vector valued basis functions are ordered. Can be "sequential" or "interleaved". For sequential, sorting is first done on the index of the component that is non-zero, and then the non-zero component is sorted in the same way as the scalar valued basis functions. For "interleaved" basis functions are first sorted on their non-zero component in the same way as scalar valued basis functions, and then they are sorted on the index of the component that is non-zero. :return: List of dual base functions. :rtype: List[callable `q(p)`]. """ if basis == unique_identifier_monomial_basis(): return dual_vector_valued_monomial_basis(r, m, n, ordering) if basis == unique_identifier_lagrange_basis(): return dual_vector_valued_lagrange_basis(r, m, n, ordering) if basis == unique_identifier_bernstein_basis(): return dual_vector_valued_bernstein_basis(r, m, n, ordering) raise ValueError("Unknown polynomial basis") def polynomial_basis_fn_latex(nu, r, basis): r""" Generate Latex string for a basis polynomial for the space :math:`\mathcal{P}_r(\mathbb{R}^n)` (where n is equal to the length of nu) in the given basis. :param nu: Multi-index indicating which basis polynomial should be generated. :type nu: int or :class:`~polynomials_on_simplices.algebra.multiindex.MultiIndex` or Tuple[int, ...] :param int r: Degree of polynomial. :param str basis: Unique identifier for the basis we should generate a basis polynomial Latex string for. :return: Latex string for the base polynomial as specified by nu, r and basis. :rtype: str .. rubric:: Examples >>> polynomial_basis_fn_latex(3, 3, unique_identifier_monomial_basis()) 'x^3' >>> polynomial_basis_fn_latex((1, 1, 1), 3, unique_identifier_bernstein_basis()) '6 x_1 x_2 x_3' """ if basis == unique_identifier_monomial_basis(): return monomial_basis_fn_latex(nu) if basis == unique_identifier_lagrange_basis(): return lagrange_basis_fn_latex(nu, r) if basis == unique_identifier_bernstein_basis(): return bernstein_basis_fn_latex(nu, r) raise ValueError("Unknown polynomial basis") def polynomial_basis_fn_latex_compact(nu, r, basis): r""" Generate compact Latex string for a basis polynomial for the space :math:`\mathcal{P}_r(\mathbb{R}^n)` (where n is equal to the length of nu) in the given basis, using the common shorthand notation for the given basis. :param nu: Multi-index indicating which basis polynomial should be generated. :type nu: int or :class:`~polynomials_on_simplices.algebra.multiindex.MultiIndex` or Tuple[int, ...] :param int r: Degree of polynomial. :param str basis: Unique identifier for the basis we should generate a basis polynomial Latex string for. :return: Latex string for the base polynomial as specified by nu, r and basis. :rtype: str .. rubric:: Examples >>> polynomial_basis_fn_latex_compact(3, 3, unique_identifier_monomial_basis()) 'x^3' >>> polynomial_basis_fn_latex_compact((1, 1), 3, unique_identifier_monomial_basis()) 'x^{(1, 1)}' >>> polynomial_basis_fn_latex_compact((1, 1, 1), 3, unique_identifier_bernstein_basis()) 'b_{(1, 1, 1), 3}(x)' """ if basis == unique_identifier_monomial_basis(): return monomial_basis_fn_latex_compact(nu) if basis == unique_identifier_lagrange_basis(): return lagrange_basis_fn_latex_compact(nu, r) if basis == unique_identifier_bernstein_basis(): return bernstein_basis_fn_latex_compact(nu, r) raise ValueError("Unknown polynomial basis") def polynomial_basis_latex(r, n, basis): r""" Generate Latex strings for all base polynomials for the space :math:`\mathcal{P}_r(\Delta_c^n)` in the given basis. :param int r: Degree of the polynomial space. :param int n: Dimension of the unit simplex. :param str basis: Unique identifier for the basis we should generate base polynomial Latex strings for. :return: List of Latex strings for each base polynomials in the specified basis. :rtype: List[str] .. rubric:: Examples >>> polynomial_basis_latex(2,1,unique_identifier_monomial_basis()) ['1', 'x', 'x^2'] >>> polynomial_basis_latex(2,2,unique_identifier_bernstein_basis()) ['(1 - x_1 - x_2)^2', '2 x_1 (1 - x_1 - x_2)', 'x_1^2', '2 x_2 (1 - x_1 - x_2)', '2 x_1 x_2', 'x_2^2'] """ if basis == unique_identifier_monomial_basis(): return monomial_basis_latex(r, n) if basis == unique_identifier_lagrange_basis(): return lagrange_basis_latex(r, n) if basis == unique_identifier_bernstein_basis(): return bernstein_basis_latex(r, n) raise ValueError("Unknown polynomial basis") def polynomial_basis_latex_compact(r, n, basis): r""" Generate compact Latex strings for all base polynomials for the space :math:`\mathcal{P}_r(\Delta_c^n)` in the given basis. :param int r: Degree of the polynomial space. :param int n: Dimension of the unit simplex. :param str basis: Unique identifier for the basis we should generate base polynomial Latex strings for. :return: List of Latex strings for each base polynomials in the specified basis. :rtype: List[str] .. rubric:: Examples >>> polynomial_basis_latex_compact(2,1,unique_identifier_monomial_basis()) ['1', 'x', 'x^2'] >>> polynomial_basis_latex_compact(1,2,unique_identifier_bernstein_basis()) ['b_{(0, 0), 1}(x)', 'b_{(1, 0), 1}(x)', 'b_{(0, 1), 1}(x)'] """ if basis == unique_identifier_monomial_basis(): return monomial_basis_latex_compact(r, n) if basis == unique_identifier_lagrange_basis(): return lagrange_basis_latex_compact(r, n) if basis == unique_identifier_bernstein_basis(): return bernstein_basis_latex_compact(r, n) raise ValueError("Unknown polynomial basis") if __name__ == "__main__": import doctest doctest.testmod()

46.419271

117

0.711585

2,587

17,825

4.68535

0.065327

0.084481

0.090092

0.068311

0.885158

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0.772131

0.757611

0.744906

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0

0.005209

0.192314

17,825

383

118

46.54047

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0.535091

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1

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false

0

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0

0.387097

0

null

0

1

0

null

0

6

21e379b33534f46cb9532ac14f5517f762186c3a

1,640

py

Python

S11/tensornet/data/downloader.py

abishek-raju/EVA4B2

189f4062c85d91f43c1381087a9c89ff794e5428

[ "Apache-2.0" ]

null

S11/tensornet/data/downloader.py

abishek-raju/EVA4B2

189f4062c85d91f43c1381087a9c89ff794e5428

[ "Apache-2.0" ]

null

S11/tensornet/data/downloader.py

abishek-raju/EVA4B2

189f4062c85d91f43c1381087a9c89ff794e5428

[ "Apache-2.0" ]

null

import os from torchvision import datasets def download_cifar10(path=None, train=True, transform=None): """Download CIFAR10 dataset Args: path (str, optional): Path where dataset will be downloaded. If no path provided, data will be downloaded in a pre-defined directory. (default: None) train (bool, optional): If True, download the training data else download the test data. (default: True) transform (tensornet.Transformations, optional): Data transformations to be applied on the data. (default: None) Returns: Downloaded dataset. """ if path is None: path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'cifar10') return datasets.CIFAR10( path, train=train, download=True, transform=transform ) def download_mnist(path=None, train=True, transform=None): """Download MNIST dataset Args: path (str, optional): Path where dataset will be downloaded. If no path provided, data will be downloaded in a pre-defined directory. (default: None) train (bool, optional): If True, download the training data else download the test data. (default: True) transform (tensornet.Transformations, optional): Data transformations to be applied on the data. (default: None) Returns: Downloaded dataset. """ if path is None: path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'mnist') return datasets.MNIST( path, train=train, download=True, transform=transform )

32.8

82

0.64939

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1,640

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0.073934

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0.872038

0.716588

0

0.006617

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1,640

49

83

33.469388

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0.285714

0

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false

0

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0

0.428571

0

null

0

1

0

null

0

6

df00c262f23c33cd0cc1256e7568ff28d540498f

68

py

Python

airbyte-integrations/bases/base-singer/base_singer/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

[ "MIT" ]

6,215

2020-09-21T13:45:56.000Z

2022-03-31T21:21:45.000Z

airbyte-integrations/bases/base-singer/base_singer/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

[ "MIT" ]

8,448

2020-09-21T00:43:50.000Z

2022-03-31T23:56:06.000Z

airbyte-integrations/bases/base-singer/base_singer/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

[ "MIT" ]

1,251

2020-09-20T05:48:47.000Z

2022-03-31T10:41:29.000Z

from .singer_helpers import * # noqa from .source import * # noqa

22.666667

37

0.705882

9

68

5.222222

0.666667

0.425532

0

0.205882

68

2

38

34

0.87037

0.132353

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

df108d7aff5982e6c8f958d3b6117429d5e34a02

84

py

Python

jeri/core/models/fields/value.py

fmorgner/jeri

5b33411c0e25375e3e5928fc044581a24c56f3ad

[ "BSD-3-Clause" ]

null

jeri/core/models/fields/value.py

fmorgner/jeri

5b33411c0e25375e3e5928fc044581a24c56f3ad

[ "BSD-3-Clause" ]

null

jeri/core/models/fields/value.py

fmorgner/jeri

5b33411c0e25375e3e5928fc044581a24c56f3ad

[ "BSD-3-Clause" ]

null

from jeri.core.models.fields.base import Field class StringField(Field): pass

14

46

0.761905

12

84

5.333333

0.916667

0

0.154762

84

5

47

16.8

0.901408

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

df2950ff51ae397a312852ed9352d7e50064ae54

1,698

py

Python

run_evm.py

DarionRichie/simple_evm

bdcfbf7da345ba170db92cdd5ad7f62df7ead4d2

[ "MIT" ]

null

run_evm.py

DarionRichie/simple_evm

bdcfbf7da345ba170db92cdd5ad7f62df7ead4d2

[ "MIT" ]

null

run_evm.py

DarionRichie/simple_evm

bdcfbf7da345ba170db92cdd5ad7f62df7ead4d2

[ "MIT" ]

null

import binascii from simple_evm import VM ADDRESS = b'\x85\x82\xa2\x89\x02\xb9\xae\x93\xfc\x03\xdd\xb4\xae\xae\xe1\x8e\x85\x93\x12\xc1' SENDER = b'\xae\x03\x02\x18\x87\xc2\x22\x37\x6f\x12\xca\xf0\x21\x44\xa1\x2f\x19\x19\x92\xcf' code_hex = '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' code_bytes = binascii.unhexlify(code_hex) msg = { 'data': b'\x12\x06\x5f\xe0', 'value': 0, 'origin': SENDER, 'sender': SENDER, 'address': ADDRESS } state = { ADDRESS: { "balance": 100000000000000000, "nonce": 0, 'code': code_bytes, "storage": {} }, SENDER: { "balance": 100000000000000000, "nonce": 0, "storage": {} }, } m = VM(state, msg) pc = None while pc != m.pc: pc = m.pc r = m.step() print("return value") print(r)

43.538462

933

0.804476

112

1,698

12.151786

0.589286

0.010287

0.044085

0.045555

0

0.599735

0.111307

1,698

38

934

44.684211

0.302187

0

0.181818

0

0.060606

0.694166

0.636417

0

1

0

1

0

false

0

0.060606

0

0.060606

0

1

null

0

1

0

1

0

1

null

1

0

6

10d01d525a3c1c0a70074c3623d6fbe9abfa11c0

583

py

Python

stars/_import.py

Shimenrock/shimenrock.weblogic_toolset

6b5e4637fae3c626f686248c6281e1856c802831

[ "MIT" ]

null

stars/_import.py

Shimenrock/shimenrock.weblogic_toolset

6b5e4637fae3c626f686248c6281e1856c802831

[ "MIT" ]

null

stars/_import.py

Shimenrock/shimenrock.weblogic_toolset

6b5e4637fae3c626f686248c6281e1856c802831

[ "MIT" ]

null

from . import console from . import cve_2014_4210 from . import cve_2016_0638 from . import cve_2016_3510 from . import cve_2017_3248 from . import cve_2017_3506 from . import cve_2017_10271 from . import cve_2018_2628 from . import cve_2018_2893 from . import cve_2018_2894 from . import cve_2018_3191 from . import cve_2018_3245 from . import cve_2018_3252 from . import cve_2019_2618 from . import cve_2019_2725 from . import cve_2019_2729 from . import cve_2019_2888 from . import cve_2019_2890 from . import cve_2020_2551 from . import cve_2020_2555 from . import cve_2020_2883

26.5

28

0.819897

103

583

4.252427

0.291262

0.479452

0.593607

0.232877

0

0.322645

0.144082

583

21

29

27.761905

0.55511

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

8006e1afe921e09e3546c1c8dd18b0e8545d5440

445

py

Python

pymysql/tests/__init__.py

mathieulongtin/PyMySQL

41d9e6bff7e0a6d74d8aa327b6cb64d6e17baf7e

[ "MIT" ]

1

2017-11-08T08:15:45.000Z

pymysql/tests/__init__.py

mathieulongtin/PyMySQL

41d9e6bff7e0a6d74d8aa327b6cb64d6e17baf7e

[ "MIT" ]

null

pymysql/tests/__init__.py

mathieulongtin/PyMySQL

41d9e6bff7e0a6d74d8aa327b6cb64d6e17baf7e

[ "MIT" ]

4

2016-10-12T23:54:55.000Z

2020-07-25T23:28:25.000Z

from pymysql.tests.test_issues import * from pymysql.tests.test_basic import * from pymysql.tests.test_nextset import * from pymysql.tests.test_DictCursor import * from pymysql.tests.test_connection import TestConnection from pymysql.tests.test_SSCursor import * from pymysql.tests.thirdparty import * if __name__ == "__main__": try: import unittest2 as unittest except ImportError: import unittest unittest.main()

27.8125

56

0.770787

56

445

5.875

0.392857

0.234043

0.340426

0.364742

0.316109

0

0.002681

0.161798

445

15

57

29.666667

0.879357

0

0.017978

0

1

0

true

0

0.769231

0

0.769231

0

null

1

0

null

0

1

0

1

0

1

0

6

8019a04cc26ef5faf42c53e7d9cb20a7a8376b1c

41

py

Python

deepchem/models/tensorgraph/optimizers.py

hl2500/deepchem

09ed9c04110eb822c2d6c9be61c27da4939896f6

[ "MIT" ]

1

2020-06-23T03:59:15.000Z

deepchem/models/tensorgraph/optimizers.py

evenhe/deepchem

9d0fc5554b286117ae08b21b3f15877b06a1009e

[ "MIT" ]

null

deepchem/models/tensorgraph/optimizers.py

evenhe/deepchem

9d0fc5554b286117ae08b21b3f15877b06a1009e

[ "MIT" ]

1

2021-02-24T04:58:32.000Z

from deepchem.models.optimizers import *

20.5

40

0.829268

5

41

6.8

1

0

0.097561

41

1

41

0.918919

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

801c19f8b7796e5536631041f8089db4e399acf8

102

py

Python

test/fixtures/use_function.py

python-rope/pylsp-rope

431415560779881b57048dc563802705f7556bca

[ "MIT" ]

16

2021-10-03T07:18:20.000Z

2022-03-28T00:11:53.000Z

test/fixtures/use_function.py

python-rope/pylsp-rope

431415560779881b57048dc563802705f7556bca

[ "MIT" ]

7

2021-10-03T06:37:42.000Z

2021-11-02T17:13:27.000Z

test/fixtures/use_function.py

python-rope/pylsp-rope

431415560779881b57048dc563802705f7556bca

[ "MIT" ]

null

def add(a, b): return a + b def main(): a, b = 10, 20 print(f"{a} + {b} = {add(a, b)}")

12.75

37

0.411765

20

102

2.1

0.5

0.238095

0

0.058824

0.333333

102

7

38

14.571429

0.558824

0

0.22549

0

1

0.4

false

0

0.2

0.6

0.2

1

0

null

1

0

1

0

null

0

1

0

1

0

6

802e91133d9af3bb060cf0a19a45a1e62b8451b5

11,090

py

Python

spreadflow_xslt/test/test_xslt_proc.py

znerol/spreadflow-xslt

adb6d97d703d561f17878291e4a67130e101536a

[ "MIT" ]

null

spreadflow_xslt/test/test_xslt_proc.py

znerol/spreadflow-xslt

adb6d97d703d561f17878291e4a67130e101536a

[ "MIT" ]

null

spreadflow_xslt/test/test_xslt_proc.py

znerol/spreadflow-xslt

adb6d97d703d561f17878291e4a67130e101536a

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import codecs import copy import os from twisted.internet import defer from mock import Mock from testtools import TestCase, run_test_with from testtools.twistedsupport import AsynchronousDeferredRunTest from spreadflow_core.scheduler import Scheduler from spreadflow_delta.test.matchers import MatchesSendDeltaItemInvocation from spreadflow_xslt.proc import XSLT FIXTURE_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'fixtures') class XSLTTransformUnitTest(TestCase): @run_test_with(AsynchronousDeferredRunTest) @defer.inlineCallbacks def test_spec_document_example(self): """ Operates on fixtures/01-spec-document-example.* see: https://www.w3.org/TR/xslt#section-Examples """ xsl_path = os.path.join(FIXTURE_DIRECTORY, '01-spec-document-example.xsl') pipe = XSLT(xsl_path) input_data = b'' input_path = os.path.join(FIXTURE_DIRECTORY, '01-spec-document-example.xml') with open(input_path, 'rb') as input_file: input_data = input_file.read() expected_data = b'' expected_path = os.path.join(FIXTURE_DIRECTORY, '01-spec-document-example.html') with open(expected_path, 'rb') as expected_file: expected_data = expected_file.read() item = { 'inserts': ['a'], 'deletes': [], 'data': { 'a': { 'content': input_data } } } expected = copy.deepcopy(item) expected['data']['a']['content'] = expected_data matches = MatchesSendDeltaItemInvocation(expected, pipe) send = Mock(spec=Scheduler.send) yield pipe(item, send) self.assertEquals(send.call_count, 1) self.assertThat(send.call_args, matches) @run_test_with(AsynchronousDeferredRunTest) @defer.inlineCallbacks def test_spec_data_example(self): """ Operates on fixtures/02-spec-data-example.* see: https://www.w3.org/TR/xslt#section-Examples """ xsl_path = os.path.join(FIXTURE_DIRECTORY, '02-spec-data-example.xsl') pipe = XSLT(xsl_path, key='custom_content', destkey='custom_result', coiterate=None) input_data = b'' input_path = os.path.join(FIXTURE_DIRECTORY, '02-spec-data-example.xml') with open(input_path, 'rb') as input_file: input_data = input_file.read() expected_data = b'' expected_path = os.path.join(FIXTURE_DIRECTORY, '02-spec-data-example.html') with open(expected_path, 'rb') as expected_file: expected_data = expected_file.read() item = { 'inserts': ['b'], 'deletes': [], 'data': { 'b': { 'custom_content': input_data } } } expected = copy.deepcopy(item) expected['data']['b']['custom_result'] = expected_data matches = MatchesSendDeltaItemInvocation(expected, pipe) send = Mock(spec=Scheduler.send) yield pipe(item, send) self.assertEquals(send.call_count, 1) self.assertThat(send.call_args, matches) @run_test_with(AsynchronousDeferredRunTest) @defer.inlineCallbacks def test_literal_strparam(self): """ Operates on fixtures/03-literal-strparam.* see: https://www.w3.org/TR/xslt#section-Examples """ xsl_path = os.path.join(FIXTURE_DIRECTORY, '03-literal-strparam.xsl') pipe = XSLT(xsl_path, strparams={'extract_id': 'South'}) input_data = b'' input_path = os.path.join(FIXTURE_DIRECTORY, '03-literal-strparam-data.xml') with open(input_path, 'rb') as input_file: input_data = input_file.read() expected_data = b'' expected_path = os.path.join(FIXTURE_DIRECTORY, '03-literal-strparam-expected.xml') with open(expected_path, 'rb') as expected_file: expected_data = expected_file.read() item = { 'inserts': ['b'], 'deletes': [], 'data': { 'b': { 'content': input_data } } } expected = copy.deepcopy(item) expected['data']['b']['content'] = expected_data matches = MatchesSendDeltaItemInvocation(expected, pipe) send = Mock(spec=Scheduler.send) yield pipe(item, send) self.assertEquals(send.call_count, 1) self.assertThat(send.call_args, matches) @run_test_with(AsynchronousDeferredRunTest) @defer.inlineCallbacks def test_literal_rawparam(self): """ Operates on fixtures/04-literal-param.* see: https://www.w3.org/TR/xslt#section-Examples """ xsl_path = os.path.join(FIXTURE_DIRECTORY, '04-literal-param.xsl') pipe = XSLT(xsl_path, params={'extract_pos': '2'}) input_data = b'' input_path = os.path.join(FIXTURE_DIRECTORY, '04-literal-param-data.xml') with open(input_path, 'rb') as input_file: input_data = input_file.read() expected_data = b'' expected_path = os.path.join(FIXTURE_DIRECTORY, '04-literal-param-expected.xml') with open(expected_path, 'rb') as expected_file: expected_data = expected_file.read() item = { 'inserts': ['b'], 'deletes': [], 'data': { 'b': { 'content': input_data } } } expected = copy.deepcopy(item) expected['data']['b']['content'] = expected_data matches = MatchesSendDeltaItemInvocation(expected, pipe) send = Mock(spec=Scheduler.send) yield pipe(item, send) self.assertEquals(send.call_count, 1) self.assertThat(send.call_args, matches) @run_test_with(AsynchronousDeferredRunTest) @defer.inlineCallbacks def test_dynamic_strparam(self): """ Operates on fixtures/05-dynamic-strparam.* see: https://www.w3.org/TR/xslt#section-Examples """ xsl_path = os.path.join(FIXTURE_DIRECTORY, '05-dynamic-strparam.xsl') pipe = XSLT(xsl_path, strparamskey='params') input_data = b'' input_path = os.path.join(FIXTURE_DIRECTORY, '05-dynamic-strparam-data.xml') with open(input_path, 'rb') as input_file: input_data = input_file.read() expected_data = b'' expected_path = os.path.join(FIXTURE_DIRECTORY, '05-dynamic-strparam-expected.xml') with open(expected_path, 'rb') as expected_file: expected_data = expected_file.read() item = { 'inserts': ['b'], 'deletes': [], 'data': { 'b': { 'params': { 'extract_id': 'West', }, 'content': input_data } } } expected = copy.deepcopy(item) expected['data']['b']['content'] = expected_data matches = MatchesSendDeltaItemInvocation(expected, pipe) send = Mock(spec=Scheduler.send) yield pipe(item, send) self.assertEquals(send.call_count, 1) self.assertThat(send.call_args, matches) @run_test_with(AsynchronousDeferredRunTest) @defer.inlineCallbacks def test_dynamic_rawparam(self): """ Operates on fixtures/06-dynamic-param.* see: https://www.w3.org/TR/xslt#section-Examples """ xsl_path = os.path.join(FIXTURE_DIRECTORY, '06-dynamic-param.xsl') pipe = XSLT(xsl_path, paramskey='params') input_data = b'' input_path = os.path.join(FIXTURE_DIRECTORY, '06-dynamic-param-data.xml') with open(input_path, 'rb') as input_file: input_data = input_file.read() expected_data = b'' expected_path = os.path.join(FIXTURE_DIRECTORY, '06-dynamic-param-expected.xml') with open(expected_path, 'rb') as expected_file: expected_data = expected_file.read() item = { 'inserts': ['b'], 'deletes': [], 'data': { 'b': { 'params': { 'extract_pos': '1', }, 'content': input_data } } } expected = copy.deepcopy(item) expected['data']['b']['content'] = expected_data matches = MatchesSendDeltaItemInvocation(expected, pipe) send = Mock(spec=Scheduler.send) yield pipe(item, send) self.assertEquals(send.call_count, 1) self.assertThat(send.call_args, matches) @run_test_with(AsynchronousDeferredRunTest) @defer.inlineCallbacks def test_no_input_doc(self): """ Operates on fixtures/07-no-input-doc.* """ xsl_path = os.path.join(FIXTURE_DIRECTORY, '07-no-input-doc.xsl') pipe = XSLT(xsl_path, strparams={'who': 'slartibartfast'}, key=None, destkey='content') expected_data = b'' expected_path = os.path.join(FIXTURE_DIRECTORY, '07-no-input-doc-expected.xml') with open(expected_path, 'rb') as expected_file: expected_data = expected_file.read() item = { 'inserts': ['b'], 'deletes': [], 'data': { 'b': { } } } expected = copy.deepcopy(item) expected['data']['b']['content'] = expected_data matches = MatchesSendDeltaItemInvocation(expected, pipe) send = Mock(spec=Scheduler.send) yield pipe(item, send) self.assertEquals(send.call_count, 1) self.assertThat(send.call_args, matches) @run_test_with(AsynchronousDeferredRunTest) @defer.inlineCallbacks def test_encoded_output(self): """ Operates on fixtures/08-encoded-output.* """ xsl_path = os.path.join(FIXTURE_DIRECTORY, '08-encoded-output.xsl') pipe = XSLT(xsl_path, strparams={'who': 'Birgitta Jónsdóttir'}, key=None, destkey='content', encoding='utf-8') expected_data = '' expected_path = os.path.join(FIXTURE_DIRECTORY, '08-encoded-output-expected.xml') with codecs.open(expected_path, encoding='utf-8') as expected_file: expected_data = expected_file.read() item = { 'inserts': ['b'], 'deletes': [], 'data': { 'b': { } } } expected = copy.deepcopy(item) expected['data']['b']['content'] = expected_data matches = MatchesSendDeltaItemInvocation(expected, pipe) send = Mock(spec=Scheduler.send) yield pipe(item, send) self.assertEquals(send.call_count, 1) self.assertThat(send.call_args, matches)

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8053ee9849cbada17bdf127f561a167ecdceeb4f

30,174

py

Python

src/pyensae/languages/PigLexer.py

mohamedelkansouli/Ensae_py2

e54a05f90c6aa6e2a5065eac9f9ec10aca64b46a

[ "MIT" ]

null

src/pyensae/languages/PigLexer.py

mohamedelkansouli/Ensae_py2

e54a05f90c6aa6e2a5065eac9f9ec10aca64b46a

[ "MIT" ]

null

src/pyensae/languages/PigLexer.py

mohamedelkansouli/Ensae_py2

e54a05f90c6aa6e2a5065eac9f9ec10aca64b46a

[ "MIT" ]

null

# Generated from \Pig.g4 by ANTLR 4.7 from antlr4 import * from io import StringIO from typing.io import TextIO import sys def serializedATN(): with StringIO() as buf: buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\\") buf.write("\u02e3\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7") buf.write("\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r\t\r") buf.write("\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t\21\4\22\t\22\4\23") buf.write("\t\23\4\24\t\24\4\25\t\25\4\26\t\26\4\27\t\27\4\30\t\30") buf.write("\4\31\t\31\4\32\t\32\4\33\t\33\4\34\t\34\4\35\t\35\4\36") buf.write("\t\36\4\37\t\37\4 \t \4!\t!\4\"\t\"\4#\t#\4$\t$\4%\t%") buf.write("\4&\t&\4\'\t\'\4(\t(\4)\t)\4*\t*\4+\t+\4,\t,\4-\t-\4.") buf.write("\t.\4/\t/\4\60\t\60\4\61\t\61\4\62\t\62\4\63\t\63\4\64") buf.write("\t\64\4\65\t\65\4\66\t\66\4\67\t\67\48\t8\49\t9\4:\t:") buf.write("\4;\t;\4<\t<\4=\t=\4>\t>\4?\t?\4@\t@\4A\tA\4B\tB\4C\t") buf.write("C\4D\tD\4E\tE\4F\tF\4G\tG\4H\tH\4I\tI\4J\tJ\4K\tK\4L\t") buf.write("L\4M\tM\4N\tN\4O\tO\4P\tP\4Q\tQ\4R\tR\4S\tS\4T\tT\4U\t") buf.write("U\4V\tV\4W\tW\4X\tX\4Y\tY\4Z\tZ\4[\t[\4\\\t\\\4]\t]\4") buf.write("^\t^\4_\t_\4`\t`\4a\ta\4b\tb\3\2\3\2\3\2\3\2\3\2\3\2\3") buf.write("\2\3\3\3\3\3\3\3\3\3\3\3\4\3\4\3\4\3\4\3\4\3\4\3\4\3\5") buf.write("\3\5\3\5\3\5\3\5\3\5\3\5\3\5\3\6\3\6\3\6\3\6\3\6\3\6\3") buf.write("\7\3\7\3\7\3\7\3\7\3\7\3\7\3\7\3\b\3\b\3\b\3\b\3\b\3\b") buf.write("\3\b\3\b\3\b\3\t\3\t\3\t\3\t\3\t\3\t\3\t\3\t\3\n\3\n\3") buf.write("\n\3\n\3\n\3\13\3\13\3\13\3\13\3\13\3\13\3\f\3\f\3\f\3") buf.write("\f\3\f\3\f\3\r\3\r\3\r\3\r\3\r\3\r\3\16\3\16\3\16\3\16") buf.write("\3\16\3\17\3\17\3\17\3\20\3\20\3\20\3\20\3\21\3\21\3\21") buf.write("\3\21\3\22\3\22\3\22\3\23\3\23\3\23\3\24\3\24\3\24\3\24") buf.write("\3\24\3\24\3\25\3\25\3\25\3\25\3\25\3\25\3\26\3\26\3\26") buf.write("\3\26\3\26\3\26\3\27\3\27\3\27\3\27\3\27\3\27\3\27\3\27") buf.write("\3\27\3\30\3\30\3\31\3\31\3\31\3\31\3\31\3\31\3\31\3\31") buf.write("\3\31\3\32\3\32\3\32\3\32\3\32\3\32\3\32\3\32\3\32\3\32") buf.write("\3\33\3\33\3\33\3\33\3\33\3\33\3\34\3\34\3\34\3\34\3\35") buf.write("\3\35\3\35\3\36\3\36\3\36\3\36\3\37\3\37\3\37\3\37\3\37") buf.write("\3\37\3\37\3\37\3\37\3 \3 \3 \3 \3 \3 \3 \3 \3!\3!\3!") buf.write("\3!\3!\3\"\3\"\3\"\3\"\3#\3#\3#\3#\3#\3$\3$\3$\3$\3%\3") buf.write("%\3%\3%\3%\3&\3&\3&\3&\3&\3&\3\'\3\'\3\'\3\'\3\'\3\'\3") buf.write("\'\3(\3(\3(\3(\3(\3(\3(\3(\3(\3(\3)\3)\3)\3)\3)\3)\3)") buf.write("\3)\3)\3)\3*\3*\3*\3*\3+\3+\3+\3+\3+\3+\3,\3,\3,\3,\3") buf.write("-\3-\3-\3.\3.\3.\3.\3.\3/\3/\3/\3/\3/\3/\3/\3\60\3\60") buf.write("\3\60\3\60\3\60\3\60\3\60\3\60\3\61\3\61\3\61\3\61\3\61") buf.write("\3\61\3\62\3\62\3\62\3\62\3\62\3\62\3\62\3\62\3\62\3\62") buf.write("\3\63\3\63\3\63\3\63\3\63\3\64\3\64\3\64\3\64\3\64\3\64") buf.write("\3\65\3\65\3\65\3\65\3\65\3\65\3\66\3\66\3\66\3\66\3\66") buf.write("\3\66\3\66\3\67\3\67\3\67\3\67\3\67\3\67\3\67\38\38\3") 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buf.write("\u02cc\7\177\2\2\u02cc\u00ae\3\2\2\2\u02cd\u02ce\7]\2") buf.write("\2\u02ce\u00b0\3\2\2\2\u02cf\u02d0\7_\2\2\u02d0\u00b2") buf.write("\3\2\2\2\u02d1\u02d2\7%\2\2\u02d2\u00b4\3\2\2\2\u02d3") buf.write("\u02d4\7?\2\2\u02d4\u00b6\3\2\2\2\u02d5\u02d6\7.\2\2\u02d6") buf.write("\u00b8\3\2\2\2\u02d7\u02d8\7\60\2\2\u02d8\u00ba\3\2\2") buf.write("\2\u02d9\u02da\7\61\2\2\u02da\u00bc\3\2\2\2\u02db\u02dc") buf.write("\7\'\2\2\u02dc\u00be\3\2\2\2\u02dd\u02de\7-\2\2\u02de") buf.write("\u00c0\3\2\2\2\u02df\u02e0\7/\2\2\u02e0\u00c2\3\2\2\2") buf.write("\u02e1\u02e2\7A\2\2\u02e2\u00c4\3\2\2\2\24\2\u0242\u0244") buf.write("\u024a\u024e\u0253\u0257\u025c\u025f\u0263\u0270\u0272") buf.write("\u027b\u028a\u0293\u02af\u02bb\u02bf\2") return buf.getvalue() class PigLexer(Lexer): atn = ATNDeserializer().deserialize(serializedATN()) decisionsToDFA = [DFA(ds, i) for i, ds in enumerate(atn.decisionToState)] DEFINE = 1 LOAD = 2 FILTER = 3 FOREACH = 4 ORDER = 5 ARRANGE = 6 DISTINCT = 7 COGROUP = 8 JOIN = 9 CROSS = 10 UNION = 11 SPLIT = 12 INTO = 13 IF = 14 ALL = 15 ANY = 16 AS = 17 BY = 18 USING = 19 INNER = 20 OUTER = 21 ONSCHEMA = 22 STAR = 23 PARALLEL = 24 PARTITION = 25 GROUP = 26 AND = 27 OR = 28 NOT = 29 GENERATE = 30 FLATTEN = 31 EVAL = 32 ASC = 33 DESC = 34 INT = 35 LONG = 36 FLOAT = 37 DOUBLE = 38 CHARARRAY = 39 BYTEARRAY = 40 BAG = 41 TUPLE = 42 MAP = 43 IS = 44 NULL = 45 STREAM = 46 THROUGH = 47 STORE = 48 MAPREDUCE = 49 SHIP = 50 CACHE = 51 INPUT = 52 OUTPUT = 53 ERROR = 54 STDIN = 55 STDOUT = 56 LIMIT = 57 SAMPLE = 58 LEFT = 59 RIGHT = 60 FULL = 61 IDENTIFIER = 62 INTEGER = 63 LONGINTEGER = 64 DOUBLENUMBER = 65 FLOATNUMBER = 66 QUOTEDSTRING = 67 EXECCOMMAND = 68 DOLLAR = 69 WS = 70 SL_COMMENT = 71 ML_COMMENT = 72 FILTEROP = 73 COLON = 74 SEMI_COLON = 75 LEFT_PAREN = 76 RIGHT_PAREN = 77 LEFT_CURLYP = 78 RIGHT_CURLYP = 79 LEFT_BRACKET = 80 RIGHT_BRACKET = 81 POUND = 82 EQUAL = 83 COMMA = 84 PERIOD = 85 DIV = 86 PERCENT = 87 PLUS = 88 MINUS = 89 QMARK = 90 channelNames = [u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN"] modeNames = ["DEFAULT_MODE"] literalNames = ["<INVALID>", "'define'", "'load'", "'filter'", "'foreach'", "'order'", "'arrange'", "'distinct'", "'cogroup'", "'join'", "'cross'", "'union'", "'split'", "'into'", "'if'", "'all'", "'any'", "'as'", "'by'", "'using'", "'inner'", "'outer'", "'ONSCHEMA'", "'*'", "'parallel'", "'partition'", "'group'", "'and'", "'or'", "'not'", "'generate'", "'flatten'", "'eval'", "'asc'", "'desc'", "'int'", "'long'", "'float'", "'double'", "'chararray'", "'bytearray'", "'bag'", "'tuple'", "'map'", "'is'", "'null'", "'stream'", "'through'", "'store'", "'mapreduce'", "'ship'", "'cache'", "'input'", "'output'", "'stderr'", "'stdin'", "'stdout'", "'limit'", "'sample'", "'left'", "'right'", "'full'", "'$'", "':'", "';'", "'('", "')'", "'{'", "'}'", "'['", "']'", "'#'", "'='", "','", "'.'", "'/'", "'%'", "'+'", "'-'", "'?'"] symbolicNames = ["<INVALID>", "DEFINE", "LOAD", "FILTER", "FOREACH", "ORDER", "ARRANGE", "DISTINCT", "COGROUP", "JOIN", "CROSS", "UNION", "SPLIT", "INTO", "IF", "ALL", "ANY", "AS", "BY", "USING", "INNER", "OUTER", "ONSCHEMA", "STAR", "PARALLEL", "PARTITION", "GROUP", "AND", "OR", "NOT", "GENERATE", "FLATTEN", "EVAL", "ASC", "DESC", "INT", "LONG", "FLOAT", "DOUBLE", "CHARARRAY", "BYTEARRAY", "BAG", "TUPLE", "MAP", "IS", "NULL", "STREAM", "THROUGH", "STORE", "MAPREDUCE", "SHIP", "CACHE", "INPUT", "OUTPUT", "ERROR", "STDIN", "STDOUT", "LIMIT", "SAMPLE", "LEFT", "RIGHT", "FULL", "IDENTIFIER", "INTEGER", "LONGINTEGER", "DOUBLENUMBER", "FLOATNUMBER", "QUOTEDSTRING", "EXECCOMMAND", "DOLLAR", "WS", "SL_COMMENT", "ML_COMMENT", "FILTEROP", "COLON", "SEMI_COLON", "LEFT_PAREN", "RIGHT_PAREN", "LEFT_CURLYP", "RIGHT_CURLYP", "LEFT_BRACKET", "RIGHT_BRACKET", "POUND", "EQUAL", "COMMA", "PERIOD", "DIV", "PERCENT", "PLUS", "MINUS", "QMARK"] ruleNames = ["DEFINE", "LOAD", "FILTER", "FOREACH", "ORDER", "ARRANGE", "DISTINCT", "COGROUP", "JOIN", "CROSS", "UNION", "SPLIT", "INTO", "IF", "ALL", "ANY", "AS", "BY", "USING", "INNER", "OUTER", "ONSCHEMA", "STAR", "PARALLEL", "PARTITION", "GROUP", "AND", "OR", "NOT", "GENERATE", "FLATTEN", "EVAL", "ASC", "DESC", "INT", "LONG", "FLOAT", "DOUBLE", "CHARARRAY", "BYTEARRAY", "BAG", "TUPLE", "MAP", "IS", "NULL", "STREAM", "THROUGH", "STORE", "MAPREDUCE", "SHIP", "CACHE", "INPUT", "OUTPUT", "ERROR", "STDIN", "STDOUT", "LIMIT", "SAMPLE", "LEFT", "RIGHT", "FULL", "DIGIT", "LETTER", "SPECIALCHAR", "FSSPECIALCHAR", "IDENTIFIER", "FLOATINGPOINT", "INTEGER", "LONGINTEGER", "DOUBLENUMBER", "FLOATNUMBER", "QUOTEDSTRING", "EXECCOMMAND", "DOLLAR", "WS", "SL_COMMENT", "ML_COMMENT", "STRFILTEROP", "NUMFILTEROP", "FILTEROP", "COLON", "SEMI_COLON", "LEFT_PAREN", "RIGHT_PAREN", "LEFT_CURLYP", "RIGHT_CURLYP", "LEFT_BRACKET", "RIGHT_BRACKET", "POUND", "EQUAL", "COMMA", "PERIOD", "DIV", "PERCENT", "PLUS", "MINUS", "QMARK"] grammarFileName = "Pig.g4" def __init__(self, input=None, output: TextIO = sys.stdout): super().__init__(input, output) self.checkVersion("4.7") self._interp = LexerATNSimulator( self, self.atn, self.decisionsToDFA, PredictionContextCache()) self._actions = None self._predicates = None

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6

33bb494b59105a91812f3254fe3c358e3ea18fe8

13,780

py

Python

dfirtrack_config/tests/main/test_main_config_forms.py

thomas-kropeit/dfirtrack

b1e0e659af7bc8085cfe2d269ddc651f9f4ba585

[ "Apache-2.0" ]

null

dfirtrack_config/tests/main/test_main_config_forms.py

thomas-kropeit/dfirtrack

b1e0e659af7bc8085cfe2d269ddc651f9f4ba585

[ "Apache-2.0" ]

6

2022-03-16T12:30:51.000Z

2022-03-28T01:34:45.000Z

dfirtrack_config/tests/main/test_main_config_forms.py

thomas-kropeit/dfirtrack

b1e0e659af7bc8085cfe2d269ddc651f9f4ba585

[ "Apache-2.0" ]

null

from django.test import TestCase from dfirtrack_artifacts.models import Artifactstatus from dfirtrack_config.forms import MainConfigForm from dfirtrack_main.models import Casestatus class MainConfigFormTestCase(TestCase): """main config form tests""" @classmethod def setUpTestData(cls): pass def test_main_config_system_name_editable_form_label(self): """test form label""" # get object form = MainConfigForm() # compare self.assertEqual( form.fields['system_name_editable'].label, 'Make system name editable (may require service restart)', ) def test_main_config_capitalization_form_label(self): """test form label""" # get object form = MainConfigForm() # compare self.assertEqual( form.fields['capitalization'].label, 'Capitalization of system names' ) def test_main_config_main_overview_form_label(self): """test form label""" # get object form = MainConfigForm() # compare self.assertEqual(form.fields['main_overview'].label, 'Main overview page') def test_main_config_artifactstatus_form_label(self): """test form label""" # get object form = MainConfigForm() # compare self.assertEqual( form.fields['artifactstatus_open'].label, 'Artifactstatus to be considered open', ) self.assertEqual( form.fields['artifactstatus_requested'].label, 'Artifactstatus setting the artifact requested time', ) self.assertEqual( form.fields['artifactstatus_acquisition'].label, 'Artifactstatus setting the artifact acquisition time', ) def test_main_config_casestatus_form_label(self): """test form label""" # get object form = MainConfigForm() # compare self.assertEqual( form.fields['casestatus_open'].label, 'Casestatus to be considered open' ) self.assertEqual( form.fields['casestatus_start'].label, 'Casestatus setting the case start time', ) self.assertEqual( form.fields['casestatus_end'].label, 'Casestatus setting the case end time' ) def test_main_config_statushistory_entry_numbers_form_label(self): """test form label""" # get object form = MainConfigForm() # compare self.assertEqual( form.fields['statushistory_entry_numbers'].label, 'Show only this number of last statushistory entries', ) def test_main_config_cron_form_label(self): """test form label""" # get object form = MainConfigForm() # compare self.assertEqual( form.fields['cron_export_path'].label, 'Export files created by scheduled tasks to this path', ) self.assertEqual( form.fields['cron_username'].label, 'Use this username for scheduled tasks (just for logging, does not have to exist)', ) def test_main_config_form_empty(self): """test minimum form requirements / INVALID""" # get object form = MainConfigForm( data={ 'cron_export_path': '/tmp', } ) # compare self.assertFalse(form.is_valid()) def test_main_config_form_statushistory_entry_numbers_filled(self): """test minimum form requirements / INVALID""" # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 9, 'cron_export_path': '/tmp', } ) # compare self.assertFalse(form.is_valid()) def test_main_config_form_cron_export_path_filled(self): """test minimum form requirements / INVALID""" # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 8, 'cron_export_path': '/tmp', } ) # compare self.assertFalse(form.is_valid()) def test_main_config_form_cron_username_filled(self): """test minimum form requirements / INVALID""" # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 7, 'cron_export_path': '/tmp', 'cron_username': 'cron', } ) # compare self.assertFalse(form.is_valid()) def test_main_config_form_capitalization_filled(self): """test minimum form requirements / INVALID""" # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 7, 'cron_export_path': '/tmp', 'cron_username': 'cron', 'capitalization': 'capitalization_keep', } ) # compare self.assertFalse(form.is_valid()) def test_main_config_form_main_overview_filled(self): """test minimum form requirements / VALID""" # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 6, 'cron_export_path': '/tmp', 'cron_username': 'cron', 'capitalization': 'capitalization_keep', 'main_overview': 'main_overview_system', } ) # compare self.assertTrue(form.is_valid()) def test_main_config_form_different_artifactstatus(self): """test custom field validation""" # create obects artifactstatus_1 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_1' ).artifactstatus_id artifactstatus_2 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_2' ).artifactstatus_id artifactstatus_3 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_3' ).artifactstatus_id artifactstatus_4 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_4' ).artifactstatus_id artifactstatus_5 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_5' ).artifactstatus_id artifactstatus_6 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_6' ).artifactstatus_id # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 5, 'cron_export_path': '/tmp', 'cron_username': 'cron', 'capitalization': 'capitalization_keep', 'main_overview': 'main_overview_system', 'artifactstatus_requested': [ artifactstatus_1, artifactstatus_2, artifactstatus_3, ], 'artifactstatus_acquisition': [ artifactstatus_4, artifactstatus_5, artifactstatus_6, ], } ) # compare self.assertTrue(form.is_valid()) def test_main_config_form_same_artifactstatus(self): """test custom field validation""" # create obects artifactstatus_1 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_1' ).artifactstatus_id artifactstatus_2 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_2' ).artifactstatus_id artifactstatus_3 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_3' ).artifactstatus_id artifactstatus_4 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_4' ).artifactstatus_id artifactstatus_5 = Artifactstatus.objects.create( artifactstatus_name='artifactstatus_5' ).artifactstatus_id # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 4, 'cron_export_path': '/tmp', 'cron_username': 'cron', 'capitalization': 'capitalization_keep', 'main_overview': 'main_overview_system', 'artifactstatus_requested': [ artifactstatus_1, artifactstatus_2, artifactstatus_3, ], 'artifactstatus_acquisition': [ artifactstatus_3, artifactstatus_4, artifactstatus_5, ], } ) # compare self.assertFalse(form.is_valid()) self.assertEqual( form.errors['artifactstatus_requested'], ['Same artifactstatus were chosen for requested and acquisition time.'], ) self.assertEqual( form.errors['artifactstatus_acquisition'], ['Same artifactstatus were chosen for requested and acquisition time.'], ) def test_main_config_form_different_casestatus(self): """test custom field validation""" # create obects casestatus_1 = Casestatus.objects.create( casestatus_name='casestatus_1' ).casestatus_id casestatus_2 = Casestatus.objects.create( casestatus_name='casestatus_2' ).casestatus_id casestatus_3 = Casestatus.objects.create( casestatus_name='casestatus_3' ).casestatus_id casestatus_4 = Casestatus.objects.create( casestatus_name='casestatus_4' ).casestatus_id casestatus_5 = Casestatus.objects.create( casestatus_name='casestatus_5' ).casestatus_id casestatus_6 = Casestatus.objects.create( casestatus_name='casestatus_6' ).casestatus_id # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 3, 'cron_export_path': '/tmp', 'cron_username': 'cron', 'capitalization': 'capitalization_keep', 'main_overview': 'main_overview_system', 'casestatus_start': [ casestatus_1, casestatus_2, casestatus_3, ], 'casestatus_end': [ casestatus_4, casestatus_5, casestatus_6, ], } ) # compare self.assertTrue(form.is_valid()) def test_main_config_form_same_casestatus(self): """test custom field validation""" # create obects casestatus_1 = Casestatus.objects.create( casestatus_name='casestatus_1' ).casestatus_id casestatus_2 = Casestatus.objects.create( casestatus_name='casestatus_2' ).casestatus_id casestatus_3 = Casestatus.objects.create( casestatus_name='casestatus_3' ).casestatus_id casestatus_4 = Casestatus.objects.create( casestatus_name='casestatus_4' ).casestatus_id casestatus_5 = Casestatus.objects.create( casestatus_name='casestatus_5' ).casestatus_id # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 2, 'cron_export_path': '/tmp', 'cron_username': 'cron', 'capitalization': 'capitalization_keep', 'main_overview': 'main_overview_system', 'casestatus_start': [ casestatus_1, casestatus_2, casestatus_3, ], 'casestatus_end': [ casestatus_3, casestatus_4, casestatus_5, ], } ) # compare self.assertFalse(form.is_valid()) self.assertEqual( form.errors['casestatus_start'], ['Same casestatus were chosen for start and end time.'], ) self.assertEqual( form.errors['casestatus_end'], ['Same casestatus were chosen for start and end time.'], ) def test_main_config_form_path_not_existent(self): """test custom field validation""" # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 6, 'cron_export_path': '/this_path_does_not_exist', 'cron_username': 'cron', 'capitalization': 'capitalization_keep', 'main_overview': 'main_overview_system', } ) # compare self.assertFalse(form.is_valid()) self.assertEqual( form.errors['cron_export_path'], ['Export path does not exist.'] ) def test_main_config_form_path_no_write_permission(self): """test custom field validation""" # get object form = MainConfigForm( data={ 'statushistory_entry_numbers': 6, 'cron_export_path': '/root', 'cron_username': 'cron', 'capitalization': 'capitalization_keep', 'main_overview': 'main_overview_system', } ) # compare self.assertFalse(form.is_valid()) self.assertEqual( form.errors['cron_export_path'], ['No write permission for export path.'] )

33.045564

95

0.566981

1,189

13,780

6.27418

0.101766

0.038338

0.028016

0.043298

0.837802

0.785121

0.742359

0.730831

0.704021

0

0.008777

0.346807

13,780

416

96

33.125

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0.070392

0

0.652733

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1

0.064309

false

0.003215

0.012862

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0

null

0

1

0

null

0

6

1d704f3d6ea83aef785f34d2b7fbf6956a2d9c5a

188

py

Python

cakechat/dialog_model/inference/candidates/__init__.py

sketscripter/emotional-chatbot-cakechat

470df58a2206a0ea38b6bed53b20cbc63bd3de24

[ "Apache-2.0" ]

1,608

2018-01-31T15:22:29.000Z

2022-03-30T19:59:16.000Z

cakechat/dialog_model/inference/candidates/__init__.py

GaelicThunder/cakechat

844507281b30d81b3fe3674895fe27826dba8438

[ "Apache-2.0" ]

64

2019-07-05T06:06:43.000Z

2021-08-02T05:22:31.000Z

cakechat/dialog_model/inference/candidates/__init__.py

Spark3757/chatbot

4e8eae70af2d5b68564d86b7ea0dbec956ae676f

[ "Apache-2.0" ]

690

2018-01-31T17:57:19.000Z

2022-03-30T07:07:41.000Z

from cakechat.dialog_model.inference.candidates.beamsearch import BeamsearchCandidatesGenerator from cakechat.dialog_model.inference.candidates.sampling import SamplingCandidatesGenerator

62.666667

95

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18

188

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0.270588

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0.042553

188

2

96

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null

0

1

0

1

0

6

1d8754f9bfef700734d82d93d883405866656b3e

141

py

Python

backslash/archiveable.py

oren0e/backslash-python

37f0fe37e21c384baa27b4f5b7210e79d02a65dc

[ "BSD-3-Clause" ]

null

backslash/archiveable.py

oren0e/backslash-python

37f0fe37e21c384baa27b4f5b7210e79d02a65dc

[ "BSD-3-Clause" ]

null

backslash/archiveable.py

oren0e/backslash-python

37f0fe37e21c384baa27b4f5b7210e79d02a65dc

[ "BSD-3-Clause" ]

null

class Archiveable(): def toggle_archived(self): self.client.api.call_function('toggle_archived', {self._get_id_key(): self.id})

28.2

87

0.70922

19

141

4.947368

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0.141844

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1

0

null

0

1

0

1

0

6

d598f91d8b14083925d05d418d3a9a4e9cb2e784

96

py

Python

oscar/lib/python2.7/site-packages/faker/providers/user_agent/en_US/__init__.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/faker/providers/user_agent/en_US/__init__.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/faker/providers/user_agent/en_US/__init__.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

from .. import Provider as UserAgentProvider class Provider(UserAgentProvider): pass

16

45

0.739583

9

96

7.888889

0.777778

0

0.208333

96

5

46

19.2

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1

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true

0.333333

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null

0

1

0

null

0

1

0

1

0

6

634223756ae7874ff7388e8ebbf1a23d102d9d88

127,775

py

Python

models.py

Tjoha1994/ship_in_transit_simulator

b022c336a0150cfe723cc499974395edb5a1cfdf

[ "MIT" ]

2

2021-07-22T07:33:10.000Z

2021-09-01T08:30:53.000Z

models.py

Tjoha1994/ship_in_transit_simulator

b022c336a0150cfe723cc499974395edb5a1cfdf

[ "MIT" ]

null

models.py

Tjoha1994/ship_in_transit_simulator

b022c336a0150cfe723cc499974395edb5a1cfdf

[ "MIT" ]

1

2021-09-21T06:26:43.000Z

""" This module provides classes that that can be used to setup and run simulation models of a ship in transit. """ import numpy as np import math import matplotlib.pyplot as plt import pandas as pd from collections import defaultdict from typing import NamedTuple, List import random class ShipConfiguration(NamedTuple): dead_weight_tonnage: float coefficient_of_deadweight_to_displacement: float bunkers: float ballast: float length_of_ship: float width_of_ship: float added_mass_coefficient_in_surge: float added_mass_coefficient_in_sway: float added_mass_coefficient_in_yaw: float mass_over_linear_friction_coefficient_in_surge: float mass_over_linear_friction_coefficient_in_sway: float mass_over_linear_friction_coefficient_in_yaw: float nonlinear_friction_coefficient__in_surge: float nonlinear_friction_coefficient__in_sway: float nonlinear_friction_coefficient__in_yaw: float class EnvironmentConfiguration(NamedTuple): current_velocity_component_from_north: float current_velocity_component_from_east: float wind_speed: float wind_direction: float class SimulationConfiguration(NamedTuple): route_name: str initial_north_position_m: float initial_east_position_m: float initial_yaw_angle_rad: float initial_forward_speed_m_per_s: float initial_sideways_speed_m_per_s: float initial_yaw_rate_rad_per_s: float initial_propeller_shaft_speed_rad_per_s: float machinery_system_operating_mode: int integration_step: float simulation_time: float class SimplifiedPropulsionSimulationConfiguration(NamedTuple): route_name: str initial_north_position_m: float initial_east_position_m: float initial_yaw_angle_rad: float initial_forward_speed_m_per_s: float initial_sideways_speed_m_per_s: float initial_yaw_rate_rad_per_s: float initial_thrust_force: float machinery_system_operating_mode: int integration_step: float simulation_time: float class DriftSimulationConfiguration(NamedTuple): initial_north_position_m: float initial_east_position_m: float initial_yaw_angle_rad: float initial_forward_speed_m_per_s: float initial_sideways_speed_m_per_s: float initial_yaw_rate_rad_per_s: float integration_step: float simulation_time: float class LoadOnPowerSources(NamedTuple): load_on_main_engine: float load_on_electrical: float load_percentage_on_main_engine: float load_percentage_on_electrical: float class MachineryModeParams(NamedTuple): main_engine_capacity: float electrical_capacity: float shaft_generator_state: str class IcebergConfiguration(NamedTuple): mass_tonnage: float coefficient_of_deadweight_to_displacement: float waterlinelength_of_iceberg: float width_of_iceberg: float height_of_iceberg: float shape_of_iceberg: str size_of_iceberg: str added_mass_coefficient_in_surge: float added_mass_coefficient_in_sway: float added_mass_coefficient_in_yaw: float mass_over_linear_friction_coefficient_in_surge: float mass_over_linear_friction_coefficient_in_sway: float mass_over_linear_friction_coefficient_in_yaw: float nonlinear_friction_coefficient__in_surge: float nonlinear_friction_coefficient__in_sway: float nonlinear_friction_coefficient__in_yaw: float class ZonesConfiguration(NamedTuple): n_pos: float e_pos: float object_radius: float coll_radius: float excl_radius: float zone1_radius: float zone2_radius: float zone3_radius: float class IceCost(NamedTuple): disconnect_cost: float light_col_cost: float medium_col_cost: float severe_col_cost: float towing_cost: float disconnect_time_cost: float towing_time_cost: float Ki_lowerbound_severe: float Ki_lowerbound_medium: float Ki_lowerbound_light: float class MachineryMode: def __init__(self, params: MachineryModeParams, name: str = "main"): self.main_engine_capacity = params.main_engine_capacity self.electrical_capacity = params.electrical_capacity self.shaft_generator_state = params.shaft_generator_state self.available_propulsion_power = 0 self.available_propulsion_power_main_engine = 0 self.available_propulsion_power_electrical = 0 self.name = name def update_available_propulsion_power(self, hotel_load): if self.shaft_generator_state == 'MOTOR': self.available_propulsion_power = self.main_engine_capacity + self.electrical_capacity - hotel_load self.available_propulsion_power_main_engine = self.main_engine_capacity self.available_propulsion_power_electrical = self.electrical_capacity - hotel_load elif self.shaft_generator_state == 'GEN': self.available_propulsion_power = self.main_engine_capacity - hotel_load self.available_propulsion_power_main_engine = self.main_engine_capacity - hotel_load self.available_propulsion_power_electrical = 0 else: # shaft_generator_state == 'off' self.available_propulsion_power = self.main_engine_capacity self.available_propulsion_power_main_engine = self.main_engine_capacity self.available_propulsion_power_electrical = 0 def distribute_load(self, load_perc, hotel_load): total_load_propulsion = load_perc * self.available_propulsion_power if self.shaft_generator_state == 'MOTOR': load_main_engine = min(total_load_propulsion, self.main_engine_capacity) load_electrical = total_load_propulsion + hotel_load - load_main_engine load_percentage_electrical = load_electrical / self.electrical_capacity if self.main_engine_capacity == 0: load_percentage_main_engine = 0 else: load_percentage_main_engine = load_main_engine / self.main_engine_capacity elif self.shaft_generator_state == 'GEN': # Here the rule is that electrical handles hotel as far as possible load_electrical = min(hotel_load, self.electrical_capacity) load_main_engine = total_load_propulsion + hotel_load - load_electrical load_percentage_main_engine = load_main_engine / self.main_engine_capacity if self.electrical_capacity == 0: load_percentage_electrical = 0 else: load_percentage_electrical = load_electrical / self.electrical_capacity else: # shaft_generator_state == 'off' load_main_engine = total_load_propulsion load_electrical = hotel_load load_percentage_main_engine = load_main_engine / self.main_engine_capacity load_percentage_electrical = load_electrical / self.electrical_capacity return LoadOnPowerSources( load_on_main_engine=load_main_engine, load_on_electrical=load_electrical, load_percentage_on_main_engine=load_percentage_main_engine, load_percentage_on_electrical=load_percentage_electrical ) class MachineryModes: def __init__(self, list_of_modes: List[MachineryMode]): self.list_of_modes = list_of_modes class MachinerySystemConfiguration(NamedTuple): hotel_load: float machinery_modes: MachineryModes rated_speed_main_engine_rpm: float linear_friction_main_engine: float linear_friction_hybrid_shaft_generator: float gear_ratio_between_main_engine_and_propeller: float gear_ratio_between_hybrid_shaft_generator_and_propeller: float propeller_inertia: float propeller_speed_to_torque_coefficient: float propeller_diameter: float propeller_speed_to_thrust_force_coefficient: float rudder_angle_to_sway_force_coefficient: float rudder_angle_to_yaw_force_coefficient: float max_rudder_angle_degrees: float class SimplifiedPropulsionMachinerySystemConfiguration(NamedTuple): hotel_load: float machinery_modes: MachineryModes thrust_force_dynamic_time_constant: float rudder_angle_to_sway_force_coefficient: float rudder_angle_to_yaw_force_coefficient: float max_rudder_angle_degrees: float class ShipModel: ''' Creates a ship model object that can be used to simulate a ship in transit The ships model is propelled by a single propeller and steered by a rudder. The propeller is powered by either the main engine, an auxiliary motor referred to as the hybrid shaft generator, or both. The model contains the following states: - North position of ship - East position of ship - Yaw angle (relative to north axis) - Surge velocity (forward) - Sway velocity (sideways) - Yaw rate - Propeller shaft speed Simulation results are stored in the instance variable simulation_results ''' def __init__(self, ship_config: ShipConfiguration, machinery_config: MachinerySystemConfiguration, environment_config: EnvironmentConfiguration, simulation_config: SimulationConfiguration): route_name = simulation_config.route_name if route_name != 'none': # Route following self.navigate = NavigationSystem(route_name) self.next_wpt = 1 self.prev_wpt = 0 payload = 0.9 * (ship_config.dead_weight_tonnage - ship_config.bunkers) lsw = ship_config.dead_weight_tonnage / ship_config.coefficient_of_deadweight_to_displacement \ - ship_config.dead_weight_tonnage self.mass = lsw + payload + ship_config.bunkers + ship_config.ballast self.l_ship = ship_config.length_of_ship # 80 self.w_ship = ship_config.width_of_ship # 16.0 self.x_g = 0 self.i_z = self.mass * (self.l_ship ** 2 + self.w_ship ** 2) / 12 # zero-frequency added mass self.x_du, self.y_dv, self.n_dr = self.set_added_mass(ship_config.added_mass_coefficient_in_surge, ship_config.added_mass_coefficient_in_sway, ship_config.added_mass_coefficient_in_yaw) self.t_surge = ship_config.mass_over_linear_friction_coefficient_in_surge self.t_sway = ship_config.mass_over_linear_friction_coefficient_in_sway self.t_yaw = ship_config.mass_over_linear_friction_coefficient_in_yaw self.ku = ship_config.nonlinear_friction_coefficient__in_surge # 2400.0 # non-linear friction coeff in surge self.kv = ship_config.nonlinear_friction_coefficient__in_sway # 4000.0 # non-linear friction coeff in sway self.kr = ship_config.nonlinear_friction_coefficient__in_yaw # 400.0 # non-linear friction coeff in yaw # Machinery system params self.machinery_modes = machinery_config.machinery_modes self.hotel_load = machinery_config.hotel_load # 200000 # 0.2 MW self.update_available_propulsion_power() mode = simulation_config.machinery_system_operating_mode self.mode = self.machinery_modes.list_of_modes[mode] # self.p_rated_me = machinery_config.mcr_main_engine # 2160000 # 2.16 MW # self.p_rated_hsg = machinery_config.mcr_hybrid_shaft_generator # 590000 # 0.59 MW self.w_rated_me = machinery_config.rated_speed_main_engine_rpm * np.pi / 30 # 1000 * np.pi / 30 # rated speed self.d_me = machinery_config.linear_friction_main_engine # 68.0 # linear friction for main engine speed self.d_hsg = machinery_config.linear_friction_hybrid_shaft_generator # 57.0 # linear friction for HSG speed self.r_me = machinery_config.gear_ratio_between_main_engine_and_propeller # 0.6 # gear ratio between main engine and propeller self.r_hsg = machinery_config.gear_ratio_between_hybrid_shaft_generator_and_propeller # 0.6 # gear ratio between main engine and propeller self.jp = machinery_config.propeller_inertia # 6000 # propeller inertia self.kp = machinery_config.propeller_speed_to_torque_coefficient # 7.5 # constant relating omega to torque self.dp = machinery_config.propeller_diameter # 3.1 # propeller diameter self.kt = machinery_config.propeller_speed_to_thrust_force_coefficient # 1.7 # constant relating omega to thrust force self.shaft_speed_max = 1.1 * self.w_rated_me * self.r_me # Used for saturation of power sources self.c_rudder_v = machinery_config.rudder_angle_to_sway_force_coefficient # 50000.0 # tuning param for simplified rudder response model self.c_rudder_r = machinery_config.rudder_angle_to_yaw_force_coefficient # 500000.0 # tuning param for simplified rudder response model self.rudder_ang_max = machinery_config.max_rudder_angle_degrees * np.pi / 180 # 30 * np.pi / 180 # Maximal rudder angle deflection (both ways) # Environmental conditions self.vel_c = np.array([environment_config.current_velocity_component_from_north, environment_config.current_velocity_component_from_east, 0.0]) self.wind_dir = environment_config.wind_direction self.wind_speed = environment_config.wind_speed # Operational parameters used to calculate loading percent on each power source self.p_rel_rated_hsg = 0.0 self.p_rel_rated_me = 0.0 # Configure machinery system according to self.mso # self.mso_mode = simulation_config.machinery_system_operating_mode # self.mode_selector(machinery_config.mcr_main_engine, # machinery_config.mcr_hybrid_shaft_generator) # Initial states (can be altered using self.set_state_vector(x)) self.n = simulation_config.initial_north_position_m self.e = simulation_config.initial_east_position_m self.psi = simulation_config.initial_yaw_angle_rad self.u = simulation_config.initial_forward_speed_m_per_s self.v = simulation_config.initial_sideways_speed_m_per_s self.r = simulation_config.initial_yaw_rate_rad_per_s self.omega = simulation_config.initial_propeller_shaft_speed_rad_per_s self.x = self.update_state_vector() self.states = np.empty(7) # Differentials self.d_n = self.d_e = self.d_psi = 0 self.d_u = self.d_v = self.d_r = 0 self.d_omega = 0 # Set up ship control systems self.initialize_shaft_speed_controller(kp=0.05, ki=0.005) self.initialize_ship_speed_controller(kp=7, ki=0.13) self.initialize_ship_heading_controller(kp=4, kd=90, ki=0.005) self.initialize_heading_filter(kp=0.5, kd=10, t=5000) # Set up integration self.int = EulerInt() # Instantiate the Euler integrator self.int.set_dt(simulation_config.integration_step) self.int.set_sim_time(simulation_config.simulation_time) # Instantiate ship draw plotting self.drw = ShipDraw() # Instantiate the ship drawing class self.ship_drawings = [[], []] # Arrays for storing ship drawing data # Fuel self.fuel_cons_me = 0.0 # Initial fuel cons for ME self.fuel_cons_electrical = 0.0 # Initial fuel cons for HSG self.fuel_cons = 0.0 # Initial total fuel cons self.power_me = [] # Array for storing ME power cons. data self.power_hsg = [] # Array for storing HSG power cons. data self.me_rated = [] # Array for storing ME rated power data self.hsg_rated = [] # Array for storing HSG rated power data self.load_hist = [] # Array for storing load percentage history self.fuel_rate_me = [] # Array for storing ME fuel cons. rate self.fuel_rate_hsg = [] # Array for storing HSG fuel cons. rate self.fuel_me = [] # Array for storing ME fuel cons. self.fuel_hsg = [] # Array for storing HSG fuel cons. self.fuel = [] # Array for storing total fuel cons self.fuel_rate = [] self.load_perc_me = [] self.load_perc_hsg = [] self.power_total = [] self.power_prop = [] # Wind effect on ship self.rho_a = 1.2 self.h_f = 8.0 # mean height above water seen from the front self.h_s = 8.0 # mean height above water seen from the side self.proj_area_f = self.w_ship * self.h_f # Projected are from the front self.proj_area_l = self.l_ship * self.h_s # Projected area from the side self.cx = 0.5 self.cy = 0.7 self.cn = 0.08 # Fuel consumption function parameters self.a_me = 128.89 self.b_me = -168.93 self.c_me = 246.76 self.a_dg = 180.71 self.b_dg = -289.90 self.c_dg = 324.90 self.simulation_results = defaultdict(list) def update_available_propulsion_power(self): for mode in self.machinery_modes.list_of_modes: mode.update_available_propulsion_power(self.hotel_load) def set_added_mass(self, surge_coeff, sway_coeff, yaw_coeff): ''' Sets the added mass in surge due to surge motion, sway due to sway motion and yaw due to yaw motion according to given coeffs. args: surge_coeff (float): Added mass coefficient in surge direction due to surge motion sway_coeff (float): Added mass coefficient in sway direction due to sway motion yaw_coeff (float): Added mass coefficient in yaw direction due to yaw motion returns: x_du (float): Added mass in surge y_dv (float): Added mass in sway n_dr (float): Added mass in yaw ''' x_du = self.mass * surge_coeff y_dv = self.mass * sway_coeff n_dr = self.i_z * yaw_coeff return x_du, y_dv, n_dr def mode_selector(self, mode: int): self.mode = self.machinery_modes.list_of_modes[mode] def spec_fuel_cons_me(self, load_perc): """ Calculate fuel consumption rate for the main engine. Args: load_perc (float): The fraction of the mcr load on the ME Returns: Number of kilograms of fuel per second used by ME """ rate = self.a_me * load_perc ** 2 + self.b_me * load_perc + self.c_me return rate / 3.6e9 def spec_fuel_cons_dg(self, load_perc): """ Calculate fuel consumption rate for a diesel generator. Args: load_perc (float): The fraction of the mcr load on the DG Returns: Number of kilograms of fuel per second used by DG """ rate = self.a_dg * load_perc ** 2 + self.b_dg * load_perc + self.c_dg return rate / 3.6e9 def load_perc(self, load_perc): """ Calculates the load percentage on the main engine and the diesel_gens based on the operating mode of the machinery system (MSO-mode). Args: load_perc (float): Current load on the machinery system as a fraction of the total power that can be delivered by the machinery system in the current mode. Returns: load_perc_me (float): Current load on the ME as a fraction of ME MCR load_perc_hsg (float): Current load on the HSG as a fraction of HSG MCR """ load_data = self.mode.distribute_load(load_perc=load_perc, hotel_load=self.hotel_load) return load_data.load_percentage_on_main_engine, load_data.load_percentage_on_electrical def fuel_consumption(self, load_perc): ''' Args: load_perc (float): The fraction of produced power over the online power production capacity. Returns: rate_me (float): Fuel consumption rate for the main engine rate_hsg (float): Fuel consumption rate for the HSG fuel_cons_me (float): Accumulated fuel consumption for the ME fuel_cons_hsg (float): Accumulated fuel consumption for the HSG fuel_cons (float): Total accumulated fuel consumption for the ship ''' ''' if self.mso_mode == 1: load_me = load_perc * self.p_rated_me + self.hotel_load load_perc_me = load_me / self.p_rated_me rate_me = load_me * self.spec_fuel_cons_me(load_perc_me) rate_hsg = 0.0 elif self.mso_mode == 2: load_me = load_perc * self.p_rated_me load_perc_me = load_me / self.p_rated_me load_hsg = self.hotel_load load_perc_hsg = load_hsg / self.p_rated_hsg rate_me = load_me * self.spec_fuel_cons_me(load_perc_me) rate_hsg = load_hsg * self.spec_fuel_cons_dg(load_perc_hsg) elif self.mso_mode == 3: load_hsg = (load_perc * self.p_rated_hsg + self.hotel_load) load_perc_hsg = load_hsg / self.p_rated_hsg rate_me = 0.0 rate_hsg = load_hsg * self.spec_fuel_cons_dg(load_perc_hsg) ''' load_data = self.mode.distribute_load(load_perc=load_perc, hotel_load=self.hotel_load) if load_data.load_on_main_engine == 0: rate_me = 0 else: rate_me = load_data.load_on_main_engine \ * self.spec_fuel_cons_me(load_data.load_percentage_on_main_engine) if load_data.load_percentage_on_electrical == 0: rate_electrical = 0 else: rate_electrical = load_data.load_on_electrical \ * self.spec_fuel_cons_dg(load_data.load_percentage_on_electrical) self.fuel_cons_me = self.fuel_cons_me + rate_me * self.int.dt self.fuel_cons_electrical = self.fuel_cons_electrical + rate_electrical * self.int.dt self.fuel_cons = self.fuel_cons + (rate_me + rate_electrical) * self.int.dt return rate_me, rate_electrical, self.fuel_cons_me, self.fuel_cons_electrical, self.fuel_cons def get_wind_force(self): ''' This method calculates the forces due to the relative wind speed, acting on teh ship in surge, sway and yaw direction. :return: Wind force acting in surge, sway and yaw ''' uw = self.wind_speed * np.cos(self.wind_dir - self.psi) vw = self.wind_speed * np.sin(self.wind_dir - self.psi) u_rw = uw - self.u v_rw = vw - self.v gamma_rw = -np.arctan2(v_rw, u_rw) wind_rw2 = u_rw ** 2 + v_rw ** 2 c_x = -self.cx * np.cos(gamma_rw) c_y = self.cy * np.sin(gamma_rw) c_n = self.cn * np.sin(2 * gamma_rw) tau_coeff = 0.5 * self.rho_a * wind_rw2 tau_u = tau_coeff * c_x * self.proj_area_f tau_v = tau_coeff * c_y * self.proj_area_l tau_n = tau_coeff * c_n * self.proj_area_l * self.l_ship return np.array([tau_u, tau_v, tau_n]) def update_state_vector(self): ''' Update the state vector according to the individual state values ''' return np.array([self.n, self.e, self.psi, self.u, self.v, self.r, self.omega]) def set_north_pos(self, val): ''' Set the north position of the ship and update the state vector ''' self.n = val self.x = self.update_state_vector() def set_east_pos(self, val): ''' Set the east position of the ship and update the state vector ''' self.e = val self.x = self.update_state_vector() def set_yaw_angle(self, val): ''' Set the yaw angle of the ship and update the state vector ''' self.psi = val self.x = self.update_state_vector() def set_surge_speed(self, val): ''' Set the surge speed of the ship and update the state vector ''' self.u = val self.x = self.update_state_vector() def set_sway_speed(self, val): ''' Set the sway speed of the ship and update the state vector ''' self.v = val self.x = self.update_state_vector() def set_yaw_rate(self, val): ''' Set the yaw rate of the ship and update the state vector ''' self.r = val self.x = self.update_state_vector() def set_shaft_speed(self, val): ''' Set the propeller shaft speed and update the state vector ''' self.omega = val self.x = self.update_state_vector() def initialize_shaft_speed_controller(self, kp, ki): ''' This method sets up and configures the shaft speed controller of the ship ''' self.shaft_speed_controller = ControllerLib() self.shaft_speed_controller.set_kp(kp) self.shaft_speed_controller.set_ki(ki) def initialize_ship_speed_controller(self, kp, ki): ''' This method sets up and configures the ship speed controller. ''' self.ship_speed_controller = ControllerLib() self.ship_speed_controller.set_kp(kp) self.ship_speed_controller.set_ki(ki) def initialize_ship_heading_controller(self, kp, kd, ki): ''' This method sets up and configures the ship heading controller. ''' self.ship_heading_controller = ControllerLib() self.ship_heading_controller.set_kp(kp) self.ship_heading_controller.set_kd(-kd) self.ship_heading_controller.set_ki(ki) def initialize_heading_filter(self, kp, kd, t): ''' This method sets up and configures a low pass filter to smooth the hading setpoint signal for the ship heading controller. ''' self.ship_heading_filter = ControllerLib() self.ship_heading_filter.set_kp(kp) self.ship_heading_filter.set_kd(kd) self.ship_heading_filter.set_T(t) def loadperc_from_speedref(self, speed_ref): ''' Calculates suitable machinery load percentage for the ship to track the speed reference signal. The shaft speed controller is used to calculate suitable shaft speed to follow the desired ship speed and suitable load percentage to follow the calculated shaft speed. The load percentage is the fraction of the produced power over the total power capacity in the current configuration. ''' ref_shaft_speed = self.ship_speed_controller.pi_ctrl(speed_ref, self.u, self.int.dt, -550, 550) ref_shaft_speed = ControllerLib.sat(ref_shaft_speed, 0, self.shaft_speed_max) load_perc = self.shaft_speed_controller.pi_ctrl(ref_shaft_speed, self.omega, self.int.dt) load_perc = ControllerLib.sat(load_perc, 0, 1.1) return load_perc def rudderang_from_headingref(self, heading_ref): ''' This method finds a suitable rudder angle for the ship to sail with the heading specified by "heading_ref" by using PID-controller. The rudder angle is saturated according to |self.rudder_ang_max|. The mathod should be called from within simulation loop if the user want the ship to follow a specified heading reference signal. ''' rudder_ang = self.ship_heading_controller.pid_ctrl(heading_ref, self.psi, self.int.dt) rudder_ang = ControllerLib.sat(rudder_ang, -self.rudder_ang_max, self.rudder_ang_max) return rudder_ang def rudderang_from_route(self): ''' This method finds a suitable rudder angle for the ship to follow a predefined route specified in the "navigate"-instantiation of the "NavigationSystem"-class. ''' self.next_wpt, self.prev_wpt = self.navigate.next_wpt(self.next_wpt, self.n, self.e) psi_d = self.navigate.los_guidance(self.next_wpt, self.n, self.e) return self.rudderang_from_headingref(psi_d) def print_next_wpt(self, ship_id): ''' Prints a string with the ship identification (ship_id) and its next waypoint, if the next waypoint is specified ''' if self.next_wpt != self.navigate.next_wpt(self.next_wpt, self.n, self.e)[0]: print('Current target waypoint for ' + ship_id + ' is: ' + str(self.next_wpt)) def set_next_wpt(self, wpt): ''' Sets the next waypoint to "wpt", where "wpt" is the index of the waypoint refering to the list of waypoints making up the route specified in the instantiation "navigate" of the class "NavigationSystem" ''' self.next_wpt = wpt def three_dof_kinematics(self): ''' Updates the time differientials of the north position, east position and yaw angle. Should be called in the simulation loop before the integration step. ''' vel = np.array([self.u, self.v, self.r]) dx = np.dot(self.rotation(), vel) self.d_n = dx[0] self.d_e = dx[1] self.d_psi = dx[2] def rotation(self): ''' Specifies the rotation matrix for rotations about the z-axis, such that "body-fixed coordinates" = rotation x "North-east-down-fixed coordinates" . ''' return np.array([[np.cos(self.psi), -np.sin(self.psi), 0], [np.sin(self.psi), np.cos(self.psi), 0], [0, 0, 1]]) def three_dof_kinetics(self, f_thrust, rudder_angle): ''' Calculates accelerations of the ship, as a funciton of thrust-force, rudder angle, wind forces and the states in the previous time-step. ''' # System matrices (did not include added mass yet) M_rb = np.array([[self.mass + self.x_du, 0, 0], [0, self.mass + self.y_dv, self.mass * self.x_g], [0, self.mass * self.x_g, self.i_z + self.n_dr]]) C_rb = np.array([[0, 0, -self.mass * (self.x_g * self.r + self.v)], [0, 0, self.mass * self.u], [self.mass * (self.x_g * self.r + self.v), -self.mass * self.u, 0]]) D = np.array([[self.mass / self.t_surge, 0, 0], [0, self.mass / self.t_sway, 0], [0, 0, self.i_z / self.t_yaw]]) D2 = np.array([[self.ku * self.u, 0, 0], [0, self.kv * self.v, 0], [0, 0, self.kr * self.r]]) # Forces acting (replace zero vectors with suitable functions) f_rudder_v, f_rudder_r = self.rudder(rudder_angle) F_wind = self.get_wind_force() F_waves = np.array([0, 0, 0]) F_ctrl = np.array([f_thrust, f_rudder_v, f_rudder_r]) # assembling state vector vel = np.array([self.u, self.v, self.r]) # Transforming current velocity to ship frame v_c = np.dot(np.linalg.inv(self.rotation()), self.vel_c) u_r = self.u - v_c[0] v_r = self.v - v_c[1] C_a = np.array([[0, 0, self.y_dv * v_r], [0, 0, -self.x_du * u_r], [-self.y_dv * v_r, self.x_du * u_r, 0]]) # Kinetic equation M_inv = np.linalg.inv(M_rb) dx = np.dot(M_inv, -np.dot(C_rb, vel) - np.dot(C_a, vel - v_c) - np.dot(D + D2, vel - v_c) + F_wind + F_waves + F_ctrl) self.d_u = dx[0] self.d_v = dx[1] self.d_r = dx[2] def rudder(self, delta): ''' This method takes in the rudder angle and returns the force i sway and yaw generated by the rudder. args: delta (float): The rudder angle in radians returs: v_force (float): The force in sway-direction generated by the rudder r_force (float): The yaw-torque generated by the rudder ''' u_c = np.dot(np.linalg.inv(self.rotation()), self.vel_c)[0] v_force = -self.c_rudder_v * delta * (self.u - u_c) r_force = -self.c_rudder_r * delta * (self.u - u_c) return v_force, r_force def shaft_eq(self, torque_main_engine, torque_hsg): ''' Updates the time differential of the shaft speed equation. ''' eq_me = (torque_main_engine - self.d_me * self.omega) / self.r_me eq_hsg = (torque_hsg - self.d_hsg * self.omega) / self.r_hsg self.d_omega = (eq_me + eq_hsg - self.kp * self.omega ** 2) / self.jp def thrust(self): ''' Updates the thrust force based on the shaft speed (self.omega) ''' return self.dp ** 4 * self.kt * self.omega * abs(self.omega) def main_engine_torque(self, load_perc): ''' Returns the torque of the main engine as a function of the load percentage parameter ''' # if self.omega >= 1 * np.pi / 30: # return load_perc * self.p_rel_rated_me / self.omega # else: # return 0 # return min(load_perc * self.p_rel_rated_me / (self.omega + 0.1), self.p_rel_rated_me / 5 * np.pi / 30) return min(load_perc * self.mode.available_propulsion_power_main_engine / (self.omega + 0.1), self.mode.available_propulsion_power_main_engine / 5 * np.pi / 30) def hsg_torque(self, load_perc): ''' Returns the torque of the HSG as a function of the load percentage parameter ''' # if self.omega >= 100 * np.pi / 30: # return load_perc * self.p_rel_rated_hsg / self.omega # else: # return 0 # return min(load_perc * self.p_rel_rated_hsg / (self.omega + 0.1), self.p_rel_rated_hsg / 5 * np.pi / 30) return min(load_perc * self.mode.available_propulsion_power_electrical / (self.omega + 0.1), self.mode.available_propulsion_power_electrical / 5 * np.pi / 30) def update_differentials(self, load_perc, rudder_angle): ''' This method should be called in the simulation loop. It will update the full differential equation of the ship. ''' self.three_dof_kinematics() self.shaft_eq(self.main_engine_torque(load_perc), self.hsg_torque(load_perc)) self.three_dof_kinetics(self.thrust(), rudder_angle) def integrate_differentials(self): ''' Integrates the differential equation one time step ahead using the euler intgration method with parameters set in the int-instantiation of the "EulerInt"-class. ''' self.set_north_pos(self.int.integrate(self.n, self.d_n)) self.set_east_pos(self.int.integrate(self.e, self.d_e)) self.set_yaw_angle(self.int.integrate(self.psi, self.d_psi)) self.set_surge_speed(self.int.integrate(self.u, self.d_u)) self.set_sway_speed(self.int.integrate(self.v, self.d_v)) self.set_yaw_rate(self.int.integrate(self.r, self.d_r)) self.set_shaft_speed(self.int.integrate(self.omega, self.d_omega)) def store_states(self): ''' Appends the current value of each state to an array. This is convenient when plotting. The method should be called within the simulation loop each time step. Then afterwars, an array containing for ecample the north-position for each time step is obtained as ...states[0] ''' self.states[0].append(self.n) self.states[1].append(self.e) self.states[2].append(self.psi) self.states[3].append(self.u) self.states[4].append(self.v) self.states[5].append(self.r) self.states[6].append(self.omega) def ship_snap_shot(self): ''' This method is used to store a map-view snap shot of the ship at the given north-east position and heading. It uses the ShipDraw-class. To plot a map view of the n-th ship snap-shot, use: plot(ship_drawings[1][n], ship_drawings[0][n]) ''' x, y = self.drw.local_coords() x_ned, y_ned = self.drw.rotate_coords(x, y, self.psi) x_ned_trans, y_ned_trans = self.drw.translate_coords(x_ned, y_ned, self.n, self.e) self.ship_drawings[0].append(x_ned_trans) self.ship_drawings[1].append(y_ned_trans) def store_simulation_data(self, load_perc): load_perc_me, load_perc_hsg = self.load_perc(load_perc) self.simulation_results['time [s]'].append(self.int.time) self.simulation_results['north position [m]'].append(self.n) self.simulation_results['east position [m]'].append(self.e) self.simulation_results['yaw angle [deg]'].append(self.t_yaw * 180 / np.pi) self.simulation_results['forward speed[m/s]'].append(self.u) self.simulation_results['sideways speed [m/s]'].append(self.v) self.simulation_results['yaw rate [deg/sec]'].append(self.r * 180 / np.pi) self.simulation_results['propeller shaft speed [rpm]'].append(self.omega * 30 / np.pi) self.simulation_results['commanded load fraction [-]'].append(load_perc) self.simulation_results['commanded load fraction me [-]'].append(load_perc_me) self.simulation_results['commanded load fraction hsg [-]'].append(load_perc_hsg) load_data = self.mode.distribute_load(load_perc=load_perc, hotel_load=self.hotel_load) self.simulation_results['power me [kw]'].append(load_data.load_on_main_engine / 1000) self.simulation_results['available power me [kw]'].append(self.mode.main_engine_capacity / 1000) self.simulation_results['power electrical [kw]'].append(load_data.load_on_electrical / 1000) self.simulation_results['available power electrical [kw]'].append(self.mode.electrical_capacity / 1000) self.simulation_results['power [kw]'].append((load_data.load_on_electrical + load_data.load_on_main_engine) / 1000) self.simulation_results['propulsion power [kw]'].append((load_perc * self.mode.available_propulsion_power) / 1000) rate_me, rate_hsg, cons_me, cons_hsg, cons = self.fuel_consumption(load_perc) self.simulation_results['fuel rate me [kg/s]'].append(rate_me) self.simulation_results['fuel rate hsg [kg/s]'].append(rate_hsg) self.simulation_results['fuel rate [kg/s]'].append(rate_me + rate_hsg) self.simulation_results['fuel consumption me [kg]'].append(cons_me) self.simulation_results['fuel consumption hsg [kg]'].append(cons_hsg) self.simulation_results['fuel consumption [kg]'].append(cons) self.simulation_results['motor torque [Nm]'].append(self.main_engine_torque(load_perc)) self.simulation_results['thrust force [kN]'].append(self.thrust() / 1000) self.fuel_me.append(cons_me) self.fuel_hsg.append(cons_hsg) self.fuel.append(cons) class ShipModelSimplifiedPropulsion: ''' Creates a ship model object that can be used to simulate a ship in transit The ships model is propelled by a single propeller and steered by a rudder. The propeller is powered by either the main engine, an auxiliary motor referred to as the hybrid shaft generator, or both. The model contains the following states: - North position of ship - East position of ship - Yaw angle (relative to north axis) - Surge velocity (forward) - Sway velocity (sideways) - Yaw rate - Propeller shaft speed Simulation results are stored in the instance variable simulation_results ''' def __init__(self, ship_config: ShipConfiguration, machinery_config: SimplifiedPropulsionMachinerySystemConfiguration, environment_config: EnvironmentConfiguration, simulation_config: SimplifiedPropulsionSimulationConfiguration): route_name = simulation_config.route_name if route_name != 'none': # Route following self.navigate = NavigationSystem(route_name) self.next_wpt = 1 self.prev_wpt = 0 payload = 0.9 * (ship_config.dead_weight_tonnage - ship_config.bunkers) lsw = ship_config.dead_weight_tonnage / ship_config.coefficient_of_deadweight_to_displacement \ - ship_config.dead_weight_tonnage self.mass = lsw + payload + ship_config.bunkers + ship_config.ballast self.l_ship = ship_config.length_of_ship # 80 self.w_ship = ship_config.width_of_ship # 16.0 self.x_g = 0 self.i_z = self.mass * (self.l_ship ** 2 + self.w_ship ** 2) / 12 # zero-frequency added mass self.x_du, self.y_dv, self.n_dr = self.set_added_mass(ship_config.added_mass_coefficient_in_surge, ship_config.added_mass_coefficient_in_sway, ship_config.added_mass_coefficient_in_yaw) self.t_surge = ship_config.mass_over_linear_friction_coefficient_in_surge self.t_sway = ship_config.mass_over_linear_friction_coefficient_in_sway self.t_yaw = ship_config.mass_over_linear_friction_coefficient_in_yaw self.ku = ship_config.nonlinear_friction_coefficient__in_surge # 2400.0 # non-linear friction coeff in surge self.kv = ship_config.nonlinear_friction_coefficient__in_sway # 4000.0 # non-linear friction coeff in sway self.kr = ship_config.nonlinear_friction_coefficient__in_yaw # 400.0 # non-linear friction coeff in yaw # Machinery system params self.machinery_modes = machinery_config.machinery_modes self.hotel_load = machinery_config.hotel_load # 200000 # 0.2 MW self.update_available_propulsion_power() mode = simulation_config.machinery_system_operating_mode self.mode = self.machinery_modes.list_of_modes[mode] self.thrust = simulation_config.initial_thrust_force self.d_thrust = 0 self.k_thrust = 2160 / 790 self.thrust_time_constant = machinery_config.thrust_force_dynamic_time_constant self.c_rudder_v = machinery_config.rudder_angle_to_sway_force_coefficient self.c_rudder_r = machinery_config.rudder_angle_to_yaw_force_coefficient # 500000.0 # tuning param for simplified rudder response model self.rudder_ang_max = machinery_config.max_rudder_angle_degrees * np.pi / 180 # 30 * np.pi / 180 # Maximal rudder angle deflection (both ways) # Environmental conditions self.vel_c = np.array([environment_config.current_velocity_component_from_north, environment_config.current_velocity_component_from_east, 0.0]) self.wind_dir = environment_config.wind_direction self.wind_speed = environment_config.wind_speed # Operational parameters used to calculate loading percent on each power source self.p_rel_rated_hsg = 0.0 self.p_rel_rated_me = 0.0 # Configure machinery system according to self.mso # self.mso_mode = simulation_config.machinery_system_operating_mode # self.mode_selector(machinery_config.mcr_main_engine, # machinery_config.mcr_hybrid_shaft_generator) # Initial states (can be altered using self.set_state_vector(x)) self.n = simulation_config.initial_north_position_m self.e = simulation_config.initial_east_position_m self.psi = simulation_config.initial_yaw_angle_rad self.u = simulation_config.initial_forward_speed_m_per_s self.v = simulation_config.initial_sideways_speed_m_per_s self.r = simulation_config.initial_yaw_rate_rad_per_s self.x = self.update_state_vector() self.states = np.empty(7) # Differentials self.d_n = self.d_e = self.d_psi = 0 self.d_u = self.d_v = self.d_r = 0 # Set up ship control systems self.initialize_ship_speed_controller(kp=7, ki=0.13) self.initialize_ship_heading_controller(kp=4, kd=90, ki=0.005) self.initialize_heading_filter(kp=0.5, kd=10, t=5000) # Set up integration self.int = EulerInt() # Instantiate the Euler integrator self.int.set_dt(simulation_config.integration_step) self.int.set_sim_time(simulation_config.simulation_time) # Instantiate ship draw plotting self.drw = ShipDraw() # Instantiate the ship drawing class self.ship_drawings = [[], []] # Arrays for storing ship drawing data # Fuel self.fuel_cons_me = 0.0 # Initial fuel cons for ME self.fuel_cons_electrical = 0.0 # Initial fuel cons for HSG self.fuel_cons = 0.0 # Initial total fuel cons self.power_me = [] # Array for storing ME power cons. data self.power_hsg = [] # Array for storing HSG power cons. data self.me_rated = [] # Array for storing ME rated power data self.hsg_rated = [] # Array for storing HSG rated power data self.load_hist = [] # Array for storing load percentage history self.fuel_rate_me = [] # Array for storing ME fuel cons. rate self.fuel_rate_hsg = [] # Array for storing HSG fuel cons. rate self.fuel_me = [] # Array for storing ME fuel cons. self.fuel_hsg = [] # Array for storing HSG fuel cons. self.fuel = [] # Array for storing total fuel cons self.fuel_rate = [] self.load_perc_me = [] self.load_perc_hsg = [] self.power_total = [] self.power_prop = [] # Wind effect on ship self.rho_a = 1.2 self.h_f = 8.0 # mean height above water seen from the front self.h_s = 8.0 # mean height above water seen from the side self.proj_area_f = self.w_ship * self.h_f # Projected are from the front self.proj_area_l = self.l_ship * self.h_s # Projected area from the side self.cx = 0.5 self.cy = 0.7 self.cn = 0.08 # Fuel consumption function parameters self.a_me = 128.89 self.b_me = -168.93 self.c_me = 246.76 self.a_dg = 180.71 self.b_dg = -289.90 self.c_dg = 324.90 self.simulation_results = defaultdict(list) def update_available_propulsion_power(self): for mode in self.machinery_modes.list_of_modes: mode.update_available_propulsion_power(self.hotel_load) def set_added_mass(self, surge_coeff, sway_coeff, yaw_coeff): ''' Sets the added mass in surge due to surge motion, sway due to sway motion and yaw due to yaw motion according to given coeffs. args: surge_coeff (float): Added mass coefficient in surge direction due to surge motion sway_coeff (float): Added mass coefficient in sway direction due to sway motion yaw_coeff (float): Added mass coefficient in yaw direction due to yaw motion returns: x_du (float): Added mass in surge y_dv (float): Added mass in sway n_dr (float): Added mass in yaw ''' x_du = self.mass * surge_coeff y_dv = self.mass * sway_coeff n_dr = self.i_z * yaw_coeff return x_du, y_dv, n_dr def mode_selector(self, mode: int): self.mode = self.machinery_modes.list_of_modes[mode] def spec_fuel_cons_me(self, load_perc): """ Calculate fuel consumption rate for the main engine. Args: load_perc (float): The fraction of the mcr load on the ME Returns: Number of kilograms of fuel per second used by ME """ rate = self.a_me * load_perc ** 2 + self.b_me * load_perc + self.c_me return rate / 3.6e9 def spec_fuel_cons_dg(self, load_perc): """ Calculate fuel consumption rate for a diesel generator. Args: load_perc (float): The fraction of the mcr load on the DG Returns: Number of kilograms of fuel per second used by DG """ rate = self.a_dg * load_perc ** 2 + self.b_dg * load_perc + self.c_dg return rate / 3.6e9 def load_perc(self, load_perc): """ Calculates the load percentage on the main engine and the diesel_gens based on the operating mode of the machinery system (MSO-mode). Args: load_perc (float): Current load on the machinery system as a fraction of the total power that can be delivered by the machinery system in the current mode. Returns: load_perc_me (float): Current load on the ME as a fraction of ME MCR load_perc_hsg (float): Current load on the HSG as a fraction of HSG MCR """ load_data = self.mode.distribute_load(load_perc=load_perc, hotel_load=self.hotel_load) return load_data.load_percentage_on_main_engine, load_data.load_percentage_on_electrical def fuel_consumption(self, load_perc): ''' Args: load_perc (float): The fraction of produced power over the online power production capacity. Returns: rate_me (float): Fuel consumption rate for the main engine rate_hsg (float): Fuel consumption rate for the HSG fuel_cons_me (float): Accumulated fuel consumption for the ME fuel_cons_hsg (float): Accumulated fuel consumption for the HSG fuel_cons (float): Total accumulated fuel consumption for the ship ''' ''' if self.mso_mode == 1: load_me = load_perc * self.p_rated_me + self.hotel_load load_perc_me = load_me / self.p_rated_me rate_me = load_me * self.spec_fuel_cons_me(load_perc_me) rate_hsg = 0.0 elif self.mso_mode == 2: load_me = load_perc * self.p_rated_me load_perc_me = load_me / self.p_rated_me load_hsg = self.hotel_load load_perc_hsg = load_hsg / self.p_rated_hsg rate_me = load_me * self.spec_fuel_cons_me(load_perc_me) rate_hsg = load_hsg * self.spec_fuel_cons_dg(load_perc_hsg) elif self.mso_mode == 3: load_hsg = (load_perc * self.p_rated_hsg + self.hotel_load) load_perc_hsg = load_hsg / self.p_rated_hsg rate_me = 0.0 rate_hsg = load_hsg * self.spec_fuel_cons_dg(load_perc_hsg) ''' load_data = self.mode.distribute_load(load_perc=load_perc, hotel_load=self.hotel_load) if load_data.load_on_main_engine == 0: rate_me = 0 else: rate_me = load_data.load_on_main_engine \ * self.spec_fuel_cons_me(load_data.load_percentage_on_main_engine) if load_data.load_percentage_on_electrical == 0: rate_electrical = 0 else: rate_electrical = load_data.load_on_electrical \ * self.spec_fuel_cons_dg(load_data.load_percentage_on_electrical) self.fuel_cons_me = self.fuel_cons_me + rate_me * self.int.dt self.fuel_cons_electrical = self.fuel_cons_electrical + rate_electrical * self.int.dt self.fuel_cons = self.fuel_cons + (rate_me + rate_electrical) * self.int.dt return rate_me, rate_electrical, self.fuel_cons_me, self.fuel_cons_electrical, self.fuel_cons def get_wind_force(self): ''' This method calculates the forces due to the relative wind speed, acting on teh ship in surge, sway and yaw direction. :return: Wind force acting in surge, sway and yaw ''' uw = self.wind_speed * np.cos(self.wind_dir - self.psi) vw = self.wind_speed * np.sin(self.wind_dir - self.psi) u_rw = uw - self.u v_rw = vw - self.v gamma_rw = -np.arctan2(v_rw, u_rw) wind_rw2 = u_rw ** 2 + v_rw ** 2 c_x = -self.cx * np.cos(gamma_rw) c_y = self.cy * np.sin(gamma_rw) c_n = self.cn * np.sin(2 * gamma_rw) tau_coeff = 0.5 * self.rho_a * wind_rw2 tau_u = tau_coeff * c_x * self.proj_area_f tau_v = tau_coeff * c_y * self.proj_area_l tau_n = tau_coeff * c_n * self.proj_area_l * self.l_ship return np.array([tau_u, tau_v, tau_n]) def update_state_vector(self): ''' Update the state vector according to the individual state values ''' return np.array([self.n, self.e, self.psi, self.u, self.v, self.r]) def set_north_pos(self, val): ''' Set the north position of the ship and update the state vector ''' self.n = val self.x = self.update_state_vector() def set_east_pos(self, val): ''' Set the east position of the ship and update the state vector ''' self.e = val self.x = self.update_state_vector() def set_yaw_angle(self, val): ''' Set the yaw angle of the ship and update the state vector ''' self.psi = val self.x = self.update_state_vector() def set_surge_speed(self, val): ''' Set the surge speed of the ship and update the state vector ''' self.u = val self.x = self.update_state_vector() def set_sway_speed(self, val): ''' Set the sway speed of the ship and update the state vector ''' self.v = val self.x = self.update_state_vector() def set_yaw_rate(self, val): ''' Set the yaw rate of the ship and update the state vector ''' self.r = val self.x = self.update_state_vector() def initialize_shaft_speed_controller(self, kp, ki): ''' This method sets up and configures the shaft speed controller of the ship ''' self.shaft_speed_controller = ControllerLib() self.shaft_speed_controller.set_kp(kp) self.shaft_speed_controller.set_ki(ki) def initialize_ship_speed_controller(self, kp, ki): ''' This method sets up and configures the ship speed controller. ''' self.ship_speed_controller = ControllerLib() self.ship_speed_controller.set_kp(kp) self.ship_speed_controller.set_ki(ki) def initialize_ship_heading_controller(self, kp, kd, ki): ''' This method sets up and configures the ship heading controller. ''' self.ship_heading_controller = ControllerLib() self.ship_heading_controller.set_kp(kp) self.ship_heading_controller.set_kd(-kd) self.ship_heading_controller.set_ki(ki) def initialize_heading_filter(self, kp, kd, t): ''' This method sets up and configures a low pass filter to smooth the hading setpoint signal for the ship heading controller. ''' self.ship_heading_filter = ControllerLib() self.ship_heading_filter.set_kp(kp) self.ship_heading_filter.set_kd(kd) self.ship_heading_filter.set_T(t) def loadperc_from_speedref(self, speed_ref): ''' Calculates suitable machinery load percentage for the ship to track the speed reference signal. The shaft speed controller is used to calculate suitable shaft speed to follow the desired ship speed and suitable load percentage to follow the calculated shaft speed. The load percentage is the fraction of the produced power over the total power capacity in the current configuration. ''' ref_shaft_speed = self.ship_speed_controller.pi_ctrl(speed_ref, self.u, self.int.dt, -550, 550) ref_shaft_speed = ControllerLib.sat(ref_shaft_speed, 0, self.shaft_speed_max) load_perc = self.shaft_speed_controller.pi_ctrl(ref_shaft_speed, self.omega, self.int.dt) load_perc = ControllerLib.sat(load_perc, 0, 1.1) return load_perc def rudderang_from_headingref(self, heading_ref): ''' This method finds a suitable rudder angle for the ship to sail with the heading specified by "heading_ref" by using PID-controller. The rudder angle is saturated according to |self.rudder_ang_max|. The mathod should be called from within simulation loop if the user want the ship to follow a specified heading reference signal. ''' rudder_ang = self.ship_heading_controller.pid_ctrl(heading_ref, self.psi, self.int.dt) rudder_ang = ControllerLib.sat(rudder_ang, -self.rudder_ang_max, self.rudder_ang_max) return rudder_ang def rudderang_from_route(self): ''' This method finds a suitable rudder angle for the ship to follow a predefined route specified in the "navigate"-instantiation of the "NavigationSystem"-class. ''' self.next_wpt, self.prev_wpt = self.navigate.next_wpt(self.next_wpt, self.n, self.e) psi_d = self.navigate.los_guidance(self.next_wpt, self.n, self.e) return self.rudderang_from_headingref(psi_d) def print_next_wpt(self, ship_id): ''' Prints a string with the ship identification (ship_id) and its next waypoint, if the next waypoint is specified ''' if self.next_wpt != self.navigate.next_wpt(self.next_wpt, self.n, self.e)[0]: print('Current target waypoint for ' + ship_id + ' is: ' + str(self.next_wpt)) def set_next_wpt(self, wpt): ''' Sets the next waypoint to "wpt", where "wpt" is the index of the waypoint refering to the list of waypoints making up the route specified in the instantiation "navigate" of the class "NavigationSystem" ''' self.next_wpt = wpt def three_dof_kinematics(self): ''' Updates the time differientials of the north position, east position and yaw angle. Should be called in the simulation loop before the integration step. ''' vel = np.array([self.u, self.v, self.r]) dx = np.dot(self.rotation(), vel) self.d_n = dx[0] self.d_e = dx[1] self.d_psi = dx[2] def rotation(self): ''' Specifies the rotation matrix for rotations about the z-axis, such that "body-fixed coordinates" = rotation x "North-east-down-fixed coordinates" . ''' return np.array([[np.cos(self.psi), -np.sin(self.psi), 0], [np.sin(self.psi), np.cos(self.psi), 0], [0, 0, 1]]) def three_dof_kinetics(self, load_perc, rudder_angle): ''' Calculates accelerations of the ship, as a funciton of thrust-force, rudder angle, wind forces and the states in the previous time-step. ''' # System matrices (did not include added mass yet) M_rb = np.array([[self.mass + self.x_du, 0, 0], [0, self.mass + self.y_dv, self.mass * self.x_g], [0, self.mass * self.x_g, self.i_z + self.n_dr]]) C_rb = np.array([[0, 0, -self.mass * (self.x_g * self.r + self.v)], [0, 0, self.mass * self.u], [self.mass * (self.x_g * self.r + self.v), -self.mass * self.u, 0]]) D = np.array([[self.mass / self.t_surge, 0, 0], [0, self.mass / self.t_sway, 0], [0, 0, self.i_z / self.t_yaw]]) D2 = np.array([[self.ku * self.u, 0, 0], [0, self.kv * self.v, 0], [0, 0, self.kr * self.r]]) # Forces acting (replace zero vectors with suitable functions) f_rudder_v, f_rudder_r = self.rudder(rudder_angle) self.update_thrust(load_perc) F_wind = self.get_wind_force() F_waves = np.array([0, 0, 0]) F_ctrl = np.array([self.thrust, f_rudder_v, f_rudder_r]) # assembling state vector vel = np.array([self.u, self.v, self.r]) # Transforming current velocity to ship frame v_c = np.dot(np.linalg.inv(self.rotation()), self.vel_c) u_r = self.u - v_c[0] v_r = self.v - v_c[1] C_a = np.array([[0, 0, self.y_dv * v_r], [0, 0, -self.x_du * u_r], [-self.y_dv * v_r, self.x_du * u_r, 0]]) # Kinetic equation M_inv = np.linalg.inv(M_rb) dx = np.dot(M_inv, -np.dot(C_rb, vel) - np.dot(C_a, vel - v_c) - np.dot(D + D2, vel - v_c) + F_wind + F_waves + F_ctrl) self.d_u = dx[0] self.d_v = dx[1] self.d_r = dx[2] def rudder(self, delta): ''' This method takes in the rudder angle and returns the force i sway and yaw generated by the rudder. args: delta (float): The rudder angle in radians returs: v_force (float): The force in sway-direction generated by the rudder r_force (float): The yaw-torque generated by the rudder ''' u_c = np.dot(np.linalg.inv(self.rotation()), self.vel_c)[0] v_force = -self.c_rudder_v * delta * (self.u - u_c) r_force = -self.c_rudder_r * delta * (self.u - u_c) return v_force, r_force def update_thrust(self, load_perc): ''' Updates the thrust force based on engine power ''' power = load_perc * (self.mode.available_propulsion_power_main_engine + self.mode.available_propulsion_power_electrical) self.d_thrust = (-self.k_thrust * self.thrust + power) / self.thrust_time_constant self.thrust = self.thrust + self.int.dt * self.d_thrust def update_differentials(self, load_perc, rudder_angle): ''' This method should be called in the simulation loop. It will update the full differential equation of the ship. ''' self.three_dof_kinematics() self.three_dof_kinetics(load_perc=load_perc, rudder_angle=rudder_angle) def integrate_differentials(self): ''' Integrates the differential equation one time step ahead using the euler intgration method with parameters set in the int-instantiation of the "EulerInt"-class. ''' self.set_north_pos(self.int.integrate(self.n, self.d_n)) self.set_east_pos(self.int.integrate(self.e, self.d_e)) self.set_yaw_angle(self.int.integrate(self.psi, self.d_psi)) self.set_surge_speed(self.int.integrate(self.u, self.d_u)) self.set_sway_speed(self.int.integrate(self.v, self.d_v)) self.set_yaw_rate(self.int.integrate(self.r, self.d_r)) def store_states(self): ''' Appends the current value of each state to an array. This is convenient when plotting. The method should be called within the simulation loop each time step. Then afterwars, an array containing for ecample the north-position for each time step is obtained as ...states[0] ''' self.states[0].append(self.n) self.states[1].append(self.e) self.states[2].append(self.psi) self.states[3].append(self.u) self.states[4].append(self.v) self.states[5].append(self.r) self.states[6].append(self.omega) def ship_snap_shot(self): ''' This method is used to store a map-view snap shot of the ship at the given north-east position and heading. It uses the ShipDraw-class. To plot a map view of the n-th ship snap-shot, use: plot(ship_drawings[1][n], ship_drawings[0][n]) ''' x, y = self.drw.local_coords() x_ned, y_ned = self.drw.rotate_coords(x, y, self.psi) x_ned_trans, y_ned_trans = self.drw.translate_coords(x_ned, y_ned, self.n, self.e) self.ship_drawings[0].append(x_ned_trans) self.ship_drawings[1].append(y_ned_trans) def store_simulation_data(self, load_perc): load_perc_me, load_perc_hsg = self.load_perc(load_perc) self.simulation_results['time [s]'].append(self.int.time) self.simulation_results['north position [m]'].append(self.n) self.simulation_results['east position [m]'].append(self.e) self.simulation_results['yaw angle [deg]'].append(self.t_yaw * 180 / np.pi) self.simulation_results['forward speed[m/s]'].append(self.u) self.simulation_results['sideways speed [m/s]'].append(self.v) self.simulation_results['yaw rate [deg/sec]'].append(self.r * 180 / np.pi) self.simulation_results['commanded load fraction [-]'].append(load_perc) self.simulation_results['commanded load fraction me [-]'].append(load_perc_me) self.simulation_results['commanded load fraction hsg [-]'].append(load_perc_hsg) load_data = self.mode.distribute_load(load_perc=load_perc, hotel_load=self.hotel_load) self.simulation_results['power me [kw]'].append(load_data.load_on_main_engine / 1000) self.simulation_results['available power me [kw]'].append(self.mode.main_engine_capacity / 1000) self.simulation_results['power electrical [kw]'].append(load_data.load_on_electrical / 1000) self.simulation_results['available power electrical [kw]'].append(self.mode.electrical_capacity / 1000) self.simulation_results['power [kw]'].append((load_data.load_on_electrical + load_data.load_on_main_engine) / 1000) self.simulation_results['propulsion power [kw]'].append((load_perc * self.mode.available_propulsion_power) / 1000) rate_me, rate_hsg, cons_me, cons_hsg, cons = self.fuel_consumption(load_perc) self.simulation_results['fuel rate me [kg/s]'].append(rate_me) self.simulation_results['fuel rate hsg [kg/s]'].append(rate_hsg) self.simulation_results['fuel rate [kg/s]'].append(rate_me + rate_hsg) self.simulation_results['fuel consumption me [kg]'].append(cons_me) self.simulation_results['fuel consumption hsg [kg]'].append(cons_hsg) self.simulation_results['fuel consumption [kg]'].append(cons) self.fuel_me.append(cons_me) self.fuel_hsg.append(cons_hsg) self.fuel.append(cons) self.simulation_results['thrust force [kN]'].append(self.thrust / 1000) class ShipModelWithoutPropulsion: ''' Creates a ship model object that can be used to simulate a ship drifting freely The model contains the following states: - North position of ship - East position of ship - Yaw angle (relative to north axis) - Surge velocity (forward) - Sway velocity (sideways) - Yaw rate Simulation results are stored in the instance variable simulation_results ''' def __init__(self, ship_config: ShipConfiguration, environment_config: EnvironmentConfiguration, simulation_config: DriftSimulationConfiguration): payload = 0.9 * (ship_config.dead_weight_tonnage - ship_config.bunkers) lsw = ship_config.dead_weight_tonnage / ship_config.coefficient_of_deadweight_to_displacement \ - ship_config.dead_weight_tonnage self.mass = lsw + payload + ship_config.bunkers + ship_config.ballast self.l_ship = ship_config.length_of_ship # 80 self.w_ship = ship_config.width_of_ship # 16.0 self.x_g = 0 self.i_z = self.mass * (self.l_ship ** 2 + self.w_ship ** 2) / 12 # zero-frequency added mass self.x_du, self.y_dv, self.n_dr = self.set_added_mass(ship_config.added_mass_coefficient_in_surge, ship_config.added_mass_coefficient_in_sway, ship_config.added_mass_coefficient_in_yaw) self.t_surge = ship_config.mass_over_linear_friction_coefficient_in_surge self.t_sway = ship_config.mass_over_linear_friction_coefficient_in_sway self.t_yaw = ship_config.mass_over_linear_friction_coefficient_in_yaw self.ku = ship_config.nonlinear_friction_coefficient__in_surge # 2400.0 # non-linear friction coeff in surge self.kv = ship_config.nonlinear_friction_coefficient__in_sway # 4000.0 # non-linear friction coeff in sway self.kr = ship_config.nonlinear_friction_coefficient__in_yaw # 400.0 # non-linear friction coeff in yaw # Environmental conditions self.vel_c = np.array([environment_config.current_velocity_component_from_north, environment_config.current_velocity_component_from_east, 0.0]) self.wind_dir = environment_config.wind_direction self.wind_speed = environment_config.wind_speed # Initial states (can be altered using self.set_state_vector(x)) self.n = simulation_config.initial_north_position_m self.e = simulation_config.initial_east_position_m self.psi = simulation_config.initial_yaw_angle_rad self.u = simulation_config.initial_forward_speed_m_per_s self.v = simulation_config.initial_sideways_speed_m_per_s self.r = simulation_config.initial_yaw_rate_rad_per_s self.x = self.update_state_vector() self.states = np.empty(6) # Differentials self.d_n = self.d_e = self.d_psi = 0 self.d_u = self.d_v = self.d_r = 0 self.hello = 'Hello' # Set up integration self.int = EulerInt() # Instantiate the Euler integrator self.int.set_dt(simulation_config.integration_step) self.int.set_sim_time(simulation_config.simulation_time) # Instantiate ship draw plotting self.drw = ShipDraw() # Instantiate the ship drawing class self.ship_drawings = [[], []] # Arrays for storing ship drawing data # Wind effect on ship self.rho_a = 1.2 self.h_f = 8.0 # mean height above water seen from the front self.h_s = 8.0 # mean height above water seen from the side self.proj_area_f = self.w_ship * self.h_f # Projected are from the front self.proj_area_l = self.l_ship * self.h_s # Projected area from the side self.cx = 0.5 self.cy = 0.7 self.cn = 0.08 self.simulation_results = defaultdict(list) def set_added_mass(self, surge_coeff, sway_coeff, yaw_coeff): ''' Sets the added mass in surge due to surge motion, sway due to sway motion and yaw due to yaw motion according to given coeffs. args: surge_coeff (float): Added mass coefficient in surge direction due to surge motion sway_coeff (float): Added mass coefficient in sway direction due to sway motion yaw_coeff (float): Added mass coefficient in yaw direction due to yaw motion returns: x_du (float): Added mass in surge y_dv (float): Added mass in sway n_dr (float): Added mass in yaw ''' x_du = self.mass * surge_coeff y_dv = self.mass * sway_coeff n_dr = self.i_z * yaw_coeff return x_du, y_dv, n_dr def get_wind_force(self): ''' This method calculates the forces due to the relative wind speed, acting on teh ship in surge, sway and yaw direction. :return: Wind force acting in surge, sway and yaw ''' uw = self.wind_speed * np.cos(self.wind_dir - self.psi) vw = self.wind_speed * np.sin(self.wind_dir - self.psi) u_rw = uw - self.u v_rw = vw - self.v gamma_rw = -np.arctan2(v_rw, u_rw) wind_rw2 = u_rw ** 2 + v_rw ** 2 c_x = -self.cx * np.cos(gamma_rw) c_y = self.cy * np.sin(gamma_rw) c_n = self.cn * np.sin(2 * gamma_rw) tau_coeff = 0.5 * self.rho_a * wind_rw2 tau_u = tau_coeff * c_x * self.proj_area_f tau_v = tau_coeff * c_y * self.proj_area_l tau_n = tau_coeff * c_n * self.proj_area_l * self.l_ship return np.array([tau_u, tau_v, tau_n]) def update_state_vector(self): ''' Update the state vector according to the individual state values ''' return np.array([self.n, self.e, self.psi, self.u, self.v, self.r]) def set_north_pos(self, val): ''' Set the north position of the ship and update the state vector ''' self.n = val self.x = self.update_state_vector() def set_east_pos(self, val): ''' Set the east position of the ship and update the state vector ''' self.e = val self.x = self.update_state_vector() def set_yaw_angle(self, val): ''' Set the yaw angle of the ship and update the state vector ''' self.psi = val self.x = self.update_state_vector() def set_surge_speed(self, val): ''' Set the surge speed of the ship and update the state vector ''' self.u = val self.x = self.update_state_vector() def set_sway_speed(self, val): ''' Set the sway speed of the ship and update the state vector ''' self.v = val self.x = self.update_state_vector() def set_yaw_rate(self, val): ''' Set the yaw rate of the ship and update the state vector ''' self.r = val self.x = self.update_state_vector() def three_dof_kinematics(self): ''' Updates the time differientials of the north position, east position and yaw angle. Should be called in the simulation loop before the integration step. ''' vel = np.array([self.u, self.v, self.r]) dx = np.dot(self.rotation(), vel) self.d_n = dx[0] self.d_e = dx[1] self.d_psi = dx[2] def rotation(self): ''' Specifies the rotation matrix for rotations about the z-axis, such that "body-fixed coordinates" = rotation x "North-east-down-fixed coordinates" . ''' return np.array([[np.cos(self.psi), -np.sin(self.psi), 0], [np.sin(self.psi), np.cos(self.psi), 0], [0, 0, 1]]) def three_dof_kinetics(self): ''' Calculates accelerations of the ship, as a funciton of wind forces and the states in the previous time-step. ''' # System matrices (did not include added mass yet) M_rb = np.array([[self.mass + self.x_du, 0, 0], [0, self.mass + self.y_dv, self.mass * self.x_g], [0, self.mass * self.x_g, self.i_z + self.n_dr]]) C_rb = np.array([[0, 0, -self.mass * (self.x_g * self.r + self.v)], [0, 0, self.mass * self.u], [self.mass * (self.x_g * self.r + self.v), -self.mass * self.u, 0]]) D = np.array([[self.mass / self.t_surge, 0, 0], [0, self.mass / self.t_sway, 0], [0, 0, self.i_z / self.t_yaw]]) D2 = np.array([[self.ku * self.u, 0, 0], [0, self.kv * self.v, 0], [0, 0, self.kr * self.r]]) F_wind = self.get_wind_force() F_waves = np.array([0, 0, 0]) # assembling state vector vel = np.array([self.u, self.v, self.r]) # Transforming current velocity to ship frame v_c = np.dot(np.linalg.inv(self.rotation()), self.vel_c) u_r = self.u - v_c[0] v_r = self.v - v_c[1] C_a = np.array([[0, 0, self.y_dv * v_r], [0, 0, -self.x_du * u_r], [-self.y_dv * v_r, self.x_du * u_r, 0]]) # Kinetic equation M_inv = np.linalg.inv(M_rb) dx = np.dot(M_inv, -np.dot(C_rb, vel) - -np.dot(C_a, vel - v_c) - np.dot(D + D2, vel - v_c) + F_wind) self.d_u = dx[0] self.d_v = dx[1] self.d_r = dx[2] def update_differentials(self): ''' This method should be called in the simulation loop. It will update the full differential equation of the ship. ''' self.three_dof_kinematics() self.three_dof_kinetics() def integrate_differentials(self): ''' Integrates the differential equation one time step ahead using the euler intgration method with parameters set in the int-instantiation of the "EulerInt"-class. ''' self.set_north_pos(self.int.integrate(self.n, self.d_n)) self.set_east_pos(self.int.integrate(self.e, self.d_e)) self.set_yaw_angle(self.int.integrate(self.psi, self.d_psi)) self.set_surge_speed(self.int.integrate(self.u, self.d_u)) self.set_sway_speed(self.int.integrate(self.v, self.d_v)) self.set_yaw_rate(self.int.integrate(self.r, self.d_r)) def store_states(self): ''' Appends the current value of each state to an array. This is convenient when plotting. The method should be called within the simulation loop each time step. Then afterwars, an array containing for ecample the north-position for each time step is obtained as ...states[0] ''' self.states[0].append(self.n) self.states[1].append(self.e) self.states[2].append(self.psi) self.states[3].append(self.u) self.states[4].append(self.v) self.states[5].append(self.r) def ship_snap_shot(self): ''' This method is used to store a map-view snap shot of the ship at the given north-east position and heading. It uses the ShipDraw-class. To plot a map view of the n-th ship snap-shot, use: plot(ship_drawings[1][n], ship_drawings[0][n]) ''' x, y = self.drw.local_coords() x_ned, y_ned = self.drw.rotate_coords(x, y, self.psi) x_ned_trans, y_ned_trans = self.drw.translate_coords(x_ned, y_ned, self.n, self.e) self.ship_drawings[0].append(x_ned_trans) self.ship_drawings[1].append(y_ned_trans) def store_simulation_data(self): self.simulation_results['time [s]'].append(self.int.time) self.simulation_results['north position [m]'].append(self.n) self.simulation_results['east position [m]'].append(self.e) self.simulation_results['yaw angle [deg]'].append(self.t_yaw * 180 / np.pi) self.simulation_results['forward speed[m/s]'].append(self.u) self.simulation_results['sideways speed [m/s]'].append(self.v) self.simulation_results['yaw rate [deg/sec]'].append(self.r * 180 / np.pi) self.simulation_results['wind speed [m/sec]'].append(self.wind_speed) class ControllerLib: ''' This class offers the following set of controllers : - P-controller - PI-controller - PD-controller - PID-controller - A second order filter - Signal saturation ''' def __init__(self): self.kp = 1.0 self.ki = 1.0 self.kd = 1.0 self.t = 1.0 self.prev_error = 0.0 self.error_i = 0.0 def set_error_i(self, val): ''' Reset/set the value of the error-integral to "val". Useful for PI and PID.controllers ''' self.error_i = val def set_kp(self, val): ''' Set the proportional gain constant ''' self.kp = val def set_kd(self, val): ''' Set the gain constant for the derivative term ''' self.kd = val def set_ki(self, val): ''' Set the gain constant for the integral term ''' self.ki = val def set_T(self, val): ''' Set the time constant. Only relevant for the low pass filter ''' self.T = val def p_ctrl(self, ref, meas): ''' Uses a proportional control law to calculate a control output ''' error = ref - meas return self.kp * error def pi_ctrl(self, ref, meas, dt, *args): ''' Uses a proportional-integral control law to calculate a control output. The optional argument is an 2x1 array and will specify lower and upper limit for error integration [lower, upper] ''' error = ref - meas error_i = self.error_i + error * dt if args: error_i = self.sat(error_i, args[0], args[1]) self.error_i = error_i return error * self.kp + error_i * self.ki def pd_ctrl(self, ref, meas, dt): ''' Uses a proportional-derivative control law to calculate a control output ''' error = ref - meas d_error = (error - self.prev_error) / dt self.prev_error = error return error * self.kp - d_error * self.kd def pid_ctrl(self, ref, meas, dt, *args): ''' Uses a proportional-derivative-integral control law to calculate a control output. The optional argument is a 2x1 array and will specify lower and upper [lower, upper] limit for error integration ''' error = ref - meas d_error = (error - self.prev_error) / dt error_i = self.error_i + error * dt if args: error_i = self.sat(error_i, args[0], args[1]) self.prev_error = error self.error_i = error_i return error * self.kp - d_error * self.kd + error_i * self.ki def filter_2(self, ref, x, v): ''' Calculates the two time differentials dx and dv which may be integrated to "smooth out" the reference signal "ref" ''' dx = v dv = (self.kp * (ref - x) - self.kd * v) / self.t return dx, dv @staticmethod def sat(val, low, hi): ''' Saturate the input val such that it remains between "low" and "hi" ''' return max(low, min(val, hi)) class EulerInt: ''' Provides methods relevant for using the Euler method to integrate an ODE. Usage: int=EulerInt() while int.time <= int.sim_time: dx = f(x) int.integrate(x,dx) int.next_time ''' def __init__(self): self.dt = 0.01 self.sim_time = 10 self.time = 0.0 self.times = [] self.global_times = [] def set_dt(self, val): ''' Sets the integrator step length ''' self.dt = val def set_sim_time(self, val): ''' Sets the upper time integration limit ''' self.sim_time = val def set_time(self, val): ''' Sets the time variable to "val" ''' self.time = val def next_time(self, time_shift=0): ''' Increment the time variable to the next time instance and store in an array ''' self.time = self.time + self.dt self.times.append(self.time) self.global_times.append(self.time + time_shift) def integrate(self, x, dx): ''' Performs the Euler integration step ''' return x + dx * self.dt class ShipDraw: ''' This class is used to calculate the coordinates of each corner of 80 meter long and 20meter wide ship seen from above, and rotate and translate the coordinates according to the ship heading and position ''' def __init__(self): self.l = 80.0 self.b = 20.0 def local_coords(self): ''' Here the ship is pointing along the local x-axix with its center of origin (midship) at the origin 1 denotes the left back corner 2 denotes the left starting point of bow curvatiure 3 denotes the bow 4 the right starting point of the bow curve 5 the right back cornier ''' x1, y1 = -self.l / 2, -self.b / 2 x2, y2 = self.l / 4, -self.b / 2 x3, y3 = self.l / 2, 0.0 x4, y4 = self.l / 4, self.b / 2 x5, y5 = -self.l / 2, self.b / 2 x = np.array([x1, x2, x3, x4, x5, x1]) y = np.array([y1, y2, y3, y4, y5, y1]) return x, y def rotate_coords(self, x, y, psi): ''' Rotates the ship an angle psi ''' x_t = np.cos(psi) * x - np.sin(psi) * y y_t = np.sin(psi) * x + np.cos(psi) * y return x_t, y_t def translate_coords(self, x_ned, y_ned, north, east): ''' Takes in coordinates of the corners of the ship (in the ned-frame) and translates them in the north and east direction according to "north" and "east" ''' x_t = x_ned + north y_t = y_ned + east return x_t, y_t class NavigationSystem: ''' This class provides a way of following a predifined route using line-og-sight (LOS) guidance law. The path to the textfile where the route is specified is given as an argument when calling the class. The route text file is formated as follows: x1 y1 x2 y2 ... where (x1,y1) are the coordinates to the first waypoint, (x2,y2) to the second, etc. ''' def __init__(self, route): self.load_waypoints(route) self.ra = 600 # Radius of acceptance for waypoints self.r = 450 # Lookahead distance def load_waypoints(self, route): ''' Reads the file containing the route and stores it as an array of north positions and an array of east positions ''' self.data = np.loadtxt(route) self.north = [] self.east = [] for i in range(0, (int(np.size(self.data) / 2))): self.north.append(self.data[i][0]) self.east.append(self.data[i][1]) def next_wpt(self, k, N, E): ''' Returns the index of the next and current waypoint. The method, if called at each time step, will detect when the ship has arrived close enough to a waypoint, to proceed ot the next waypoint. Example of usage in the method "rudderang_from_route()" from the ShipDyn-class. ''' if (self.north[k] - N) ** 2 + ( self.east[k] - E) ** 2 <= self.ra ** 2: # Check that we are within circle of acceptance if len(self.north) > k + 1: # If number of waypoints are greater than current waypoint index return k + 1, k # Then move on to next waypoint and let current become previous else: return k, k # At the end of the route, let the next wpt also be the previous wpt else: return k, k - 1 def los_guidance(self, k, x, y): ''' Returns the desired heading (i.e. reference signal to a ship heading controller). The parameter "k" is the index of the next waypoint. ''' dx = self.north[k] - self.north[k - 1] dy = self.east[k] - self.east[k - 1] alpha_k = math.atan2(dy, dx) e_ct = -(x - self.north[k - 1]) * math.sin(alpha_k) + (y - self.east[k - 1]) * math.cos(alpha_k) if e_ct ** 2 >= self.r ** 2: e_ct = 0.99 * self.r delta = math.sqrt(self.r ** 2 - e_ct ** 2) chi_r = math.atan(-e_ct / delta) return alpha_k + chi_r class StaticObstacle: ''' This class is used to define a static obstacle. It can only make circular obstacles. The class is instantiated with the following input paramters: - n_pos: The north coordinate of the center of the obstacle. - e_pos: The east coordinate of the center of the obstacle. - radius: The radius of the obstacle. ''' def __init__(self, n_pos, e_pos, radius): self.n = n_pos self.e = e_pos self.r = radius def distance(self, n_ship, e_ship): ''' Returns the distance from a ship with coordinates (north, east)= (n_ship, e_ship), to the closest point on the perifery of the circular obstacle. ''' rad_2 = (n_ship - self.n) ** 2 + (e_ship - self.e) ** 2 rad = np.sqrt(abs(rad_2)) return rad - self.r def plot_obst(self, ax): ''' This method can be used to plot the obstacle in a map-view. ''' # ax = plt.gca() ax.add_patch(plt.Circle((self.e, self.n), radius=self.r, fill=True, color='grey')) class Zones: def __init__(self, z_config: ZonesConfiguration, iceberg_config: IcebergConfiguration): self.n = z_config.n_pos self.e = z_config.e_pos self.r = z_config.coll_radius self.r0 = z_config.excl_radius self.r1 = z_config.zone1_radius self.r2 = z_config.zone2_radius self.r3 = z_config.zone3_radius self.collimargin = 0.5 * (iceberg_config.waterlinelength_of_iceberg + z_config.object_radius) def distance(self, n_iceberg, e_iceberg): ''' Returns the distance from a ship with coordinates (north, east)= (n_ship, e_ship), to the closest point on the perifery of the circular obstacle. ''' rad_2 = (n_iceberg - self.n) ** 2 + (e_iceberg - self.e) ** 2 rad = np.sqrt(abs(rad_2)) return rad def d_to_north(self, n_iceberg): rad = abs(n_iceberg - self.n) return rad def d_to_east(self, e_iceberg): rad = abs(e_iceberg - self.e) return rad def cpa_zone(self, d_to_s): """to calculate which zone the cpa (closest point of approach). d_to_s is the distance between the iceberg center and zone center""" if d_to_s - self.collimargin - self.r <= 0: cpazone = -1 # "Collision Zone" elif d_to_s - self.collimargin - self.r0 <= 0: cpazone = 0 # "Exclusion Zone" elif d_to_s - self.collimargin - self.r1 <= 0: cpazone = 1 # "Zone 1" elif d_to_s - self.collimargin - self.r2 <= 0: cpazone = 2 # "Zone 2" elif d_to_s - self.collimargin - self.r3 <= 0: cpazone = 3 # "Zone 3" else: cpazone = 4 # "outside all zones" return cpazone def d_to_exclusion(self, n_iceberg, e_iceberg): ''' Returns the distance from a ship with coordinates (north, east)= (n_ship, e_ship), to the closest point on the perifery of the circular obstacle. ''' rad_2 = (n_iceberg - self.n) ** 2 + (e_iceberg - self.e) ** 2 rad = np.sqrt(abs(rad_2)) return rad - self.r0 - self.collimargin def colli_event(self, n_iceberg, e_iceberg): rad_2 = (n_iceberg - self.n) ** 2 + (e_iceberg - self.e) ** 2 rad = np.sqrt(abs(rad_2)) if rad - self.collimargin - self.r <= 0: return 1 else: return 0 def breach_exclusion(self, n_iceberg, e_iceberg): rad_2 = (n_iceberg - self.n) ** 2 + (e_iceberg - self.e) ** 2 rad = np.sqrt(abs(rad_2)) if rad - self.collimargin + self.r0 <= 0: return 1 else: return 0 def plot_coll(self): ''' This method can be used to plot the obstacle in a map-view. ''' # ax = plt.gca() return plt.Circle((self.e, self.n), radius=self.r + self.collimargin, fill=False, color='red') def plot_excl(self): ''' This method can be used to plot the obstacle in a map-view. ''' # ax = plt.gca() return plt.Circle((self.e, self.n), radius=self.r0 + self.collimargin, fill=False, color='red') def plot_zone1(self): ''' This method can be used to plot the obstacle in a map-view. ''' # ax = plt.gca() return plt.Circle((self.e, self.n), radius=self.r1 + self.collimargin, fill=False, color='orange') def plot_zone2(self): ''' This method can be used to plot the obstacle in a map-view. ''' # ax = plt.gca() return plt.Circle((self.e, self.n), radius=self.r2 + self.collimargin, fill=False, color='blue') def plot_zone3(self): ''' This method can be used to plot the obstacle in a map-view. ''' # ax = plt.gca() return plt.Circle((self.e, self.n), radius=self.r3 + self.collimargin, fill=False, color='green') class IcebergDraw: ''' This class is used to calculate the coordinates of each corner of 80 meter long and 20meter wide ship seen from above, and rotate and translate the coordinates according to the ship heading and position ''' def __init__(self, iceberg_config: IcebergConfiguration): self.l = iceberg_config.waterlinelength_of_iceberg self.b = iceberg_config.width_of_iceberg def local_coords(self): ''' Here the ship is pointing along the local x-axix with its center of origin (midship) at the origin 1 denotes the left back corner 2 denotes the left starting point of bow curvatiure 3 denotes the bow 4 the right starting point of the bow curve 5 the right back cornier ''' x1, y1 = -self.l / 2, -self.b / 2 x2, y2 = self.l / 4, -self.b / 2 x3, y3 = self.l / 2, 0.0 x4, y4 = self.l / 4, self.b / 2 x5, y5 = -self.l / 2, self.b / 2 x = np.array([x1, x2, x3, x4, x5, x1]) y = np.array([y1, y2, y3, y4, y5, y1]) return x, y def rotate_coords(self, x, y, psi): ''' Rotates the ship an angle psi ''' x_t = np.cos(psi) * x - np.sin(psi) * y y_t = np.sin(psi) * x + np.cos(psi) * y return x_t, y_t def translate_coords(self, x_ned, y_ned, north, east): ''' Takes in coordinates of the corners of the ship (in the ned-frame) and translates them in the north and east direction according to "north" and "east" ''' x_t = x_ned + north y_t = y_ned + east return x_t, y_t class IcebergDriftingModel1: ''' Creates a iceberg model object that can be used to simulate a iceberg drifting freely The model contains the following states: - North position of iceberg - East position of iceberg - Yaw angle (relative to north axis) - Surge velocity (forward) - Sway velocity (sideways) - Yaw rate Simulation results are stored in the instance variable simulation_results ''' def __init__(self, iceberg_config: IcebergConfiguration, environment_config: EnvironmentConfiguration, simulation_config: DriftSimulationConfiguration): payload = 0.9 * (iceberg_config.mass_tonnage) lsw = iceberg_config.mass_tonnage / iceberg_config.coefficient_of_deadweight_to_displacement \ - iceberg_config.mass_tonnage self.mass = lsw + payload self.l_iceberg = iceberg_config.waterlinelength_of_iceberg # 80 self.w_iceberg = iceberg_config.width_of_iceberg # 16.0 self.x_g = 0 self.i_z = self.mass * (self.l_iceberg ** 2 + self.w_iceberg ** 2) / 12 # zero-frequency added mass self.x_du, self.y_dv, self.n_dr = self.set_added_mass(iceberg_config.added_mass_coefficient_in_surge, iceberg_config.added_mass_coefficient_in_sway, iceberg_config.added_mass_coefficient_in_yaw) self.t_surge = iceberg_config.mass_over_linear_friction_coefficient_in_surge self.t_sway = iceberg_config.mass_over_linear_friction_coefficient_in_sway self.t_yaw = iceberg_config.mass_over_linear_friction_coefficient_in_yaw self.ku = iceberg_config.nonlinear_friction_coefficient__in_surge # 2400.0 # non-linear friction coeff in surge self.kv = iceberg_config.nonlinear_friction_coefficient__in_sway # 4000.0 # non-linear friction coeff in sway self.kr = iceberg_config.nonlinear_friction_coefficient__in_yaw # 400.0 # non-linear friction coeff in yaw # Environmental conditions self.vel_c = np.array([environment_config.current_velocity_component_from_north, environment_config.current_velocity_component_from_east, 0.0]) self.wind_dir = environment_config.wind_direction self.wind_speed = environment_config.wind_speed # Initial states (can be altered using self.set_state_vector(x)) self.n = simulation_config.initial_north_position_m self.e = simulation_config.initial_east_position_m self.psi = simulation_config.initial_yaw_angle_rad self.u = simulation_config.initial_forward_speed_m_per_s self.v = simulation_config.initial_sideways_speed_m_per_s self.r = simulation_config.initial_yaw_rate_rad_per_s self.x = self.update_state_vector() self.states = np.empty(6) # Initial states (save as local values) self.n_initial = simulation_config.initial_north_position_m self.e_initial = simulation_config.initial_east_position_m self.psi_initial = simulation_config.initial_yaw_angle_rad self.u_initial = simulation_config.initial_forward_speed_m_per_s self.v_initial = simulation_config.initial_sideways_speed_m_per_s self.r_initial = simulation_config.initial_yaw_rate_rad_per_s # Differentials self.d_n = self.d_e = self.d_psi = 0 self.d_u = self.d_v = self.d_r = 0 # Set up integration self.int = EulerInt() # Instantiate the Euler integrator self.int.set_dt(simulation_config.integration_step) self.int.set_sim_time(simulation_config.simulation_time) # Instantiate ship draw plotting self.drw = IcebergDraw(iceberg_config) # Instantiate the ship drawing class self.iceberg_drawings = [[], []] # Arrays for storing ship drawing data # Wind effect on ship self.rho_a = 1.2 self.h_f = 8.0 # mean height above water seen from the front self.h_s = 8.0 # mean height above water seen from the side self.proj_area_f = self.w_iceberg * self.h_f # Projected are from the front self.proj_area_l = self.l_iceberg * self.h_s # Projected area from the side self.cx = 0.5 self.cy = 0.7 self.cn = 0.08 self.simulation_results = defaultdict(list) def set_added_mass(self, surge_coeff, sway_coeff, yaw_coeff): ''' Sets the added mass in surge due to surge motion, sway due to sway motion and yaw due to yaw motion according to given coeffs. args: surge_coeff (float): Added mass coefficient in surge direction due to surge motion sway_coeff (float): Added mass coefficient in sway direction due to sway motion yaw_coeff (float): Added mass coefficient in yaw direction due to yaw motion returns: x_du (float): Added mass in surge y_dv (float): Added mass in sway n_dr (float): Added mass in yaw ''' x_du = self.mass * surge_coeff y_dv = self.mass * sway_coeff n_dr = self.i_z * yaw_coeff return x_du, y_dv, n_dr def get_wind_force(self): ''' This method calculates the forces due to the relative wind speed, acting on teh ship in surge, sway and yaw direction. :return: Wind force acting in surge, sway and yaw ''' uw = self.wind_speed * np.cos(self.wind_dir - self.psi) vw = self.wind_speed * np.sin(self.wind_dir - self.psi) u_rw = uw - self.u v_rw = vw - self.v gamma_rw = -np.arctan2(v_rw, u_rw) wind_rw2 = u_rw ** 2 + v_rw ** 2 c_x = -self.cx * np.cos(gamma_rw) c_y = self.cy * np.sin(gamma_rw) c_n = self.cn * np.sin(2 * gamma_rw) tau_coeff = 0.5 * self.rho_a * wind_rw2 tau_u = tau_coeff * c_x * self.proj_area_f tau_v = tau_coeff * c_y * self.proj_area_l tau_n = tau_coeff * c_n * self.proj_area_l * self.l_iceberg return np.array([tau_u, tau_v, tau_n]) def update_state_vector(self): ''' Update the state vector according to the individual state values ''' return np.array([self.n, self.e, self.psi, self.u, self.v, self.r]) def set_north_pos(self, val): ''' Set the north position of the iceberg and update the state vector ''' self.n = val self.x = self.update_state_vector() def set_east_pos(self, val): ''' Set the east position of the iceberg and update the state vector ''' self.e = val self.x = self.update_state_vector() def set_yaw_angle(self, val): ''' Set the yaw angle of the iceberg and update the state vector ''' self.psi = val self.x = self.update_state_vector() def set_surge_speed(self, val): ''' Set the surge speed of the iceberg and update the state vector ''' self.u = val self.x = self.update_state_vector() def set_sway_speed(self, val): ''' Set the sway speed of the iceberg and update the state vector ''' self.v = val self.x = self.update_state_vector() def set_yaw_rate(self, val): ''' Set the yaw rate of the iceberg and update the state vector ''' self.r = val self.x = self.update_state_vector() def three_dof_kinematics(self): ''' Updates the time differientials of the north position, east position and yaw angle. Should be called in the simulation loop before the integration step. ''' vel = np.array([self.u, self.v, self.r]) dx = np.dot(self.rotation(), vel) self.d_n = dx[0] self.d_e = dx[1] self.d_psi = dx[2] def rotation(self): ''' Specifies the rotation matrix for rotations about the z-axis, such that "body-fixed coordinates" = rotation x "North-east-down-fixed coordinates" . ''' return np.array([[np.cos(self.psi), -np.sin(self.psi), 0], [np.sin(self.psi), np.cos(self.psi), 0], [0, 0, 1]]) def three_dof_kinetics(self): ''' Calculates accelerations of the iceberg, as a funciton of wind forces and the states in the previous time-step. ''' # System matrices (did not include added mass yet) M_rb = np.array([[self.mass + self.x_du, 0, 0], [0, self.mass + self.y_dv, self.mass * self.x_g], [0, self.mass * self.x_g, self.i_z + self.n_dr]]) C_rb = np.array([[0, 0, -self.mass * (self.x_g * self.r + self.v)], [0, 0, self.mass * self.u], [self.mass * (self.x_g * self.r + self.v), -self.mass * self.u, 0]]) D = np.array([[self.mass / self.t_surge, 0, 0], [0, self.mass / self.t_sway, 0], [0, 0, self.i_z / self.t_yaw]]) D2 = np.array([[self.ku * self.u, 0, 0], [0, self.kv * self.v, 0], [0, 0, self.kr * self.r]]) F_wind = self.get_wind_force() F_waves = np.array([0, 0, 0]) # assembling state vector vel = np.array([self.u, self.v, self.r]) # Transforming current velocity to ship frame v_c = np.dot(np.linalg.inv(self.rotation()), self.vel_c) u_r = self.u - v_c[0] v_r = self.v - v_c[1] C_a = np.array([[0, 0, self.y_dv * v_r], [0, 0, -self.x_du * u_r], [-self.y_dv * v_r, self.x_du * u_r, 0]]) # Kinetic equation M_inv = np.linalg.inv(M_rb) dx = np.dot(M_inv, -np.dot(C_rb, vel) - -np.dot(C_a, vel - v_c) - np.dot(D + D2, vel - v_c) + F_wind) self.d_u = dx[0] self.d_v = dx[1] self.d_r = dx[2] def update_differentials(self): ''' This method should be called in the simulation loop. It will update the full differential equation of the ship. ''' self.three_dof_kinematics() self.three_dof_kinetics() def integrate_differentials(self): ''' Integrates the differential equation one time step ahead using the euler intgration method with parameters set in the int-instantiation of the "EulerInt"-class. ''' self.set_north_pos(self.int.integrate(self.n, self.d_n)) self.set_east_pos(self.int.integrate(self.e, self.d_e)) self.set_yaw_angle(self.int.integrate(self.psi, self.d_psi)) self.set_surge_speed(self.int.integrate(self.u, self.d_u)) self.set_sway_speed(self.int.integrate(self.v, self.d_v)) self.set_yaw_rate(self.int.integrate(self.r, self.d_r)) def store_states(self): ''' Appends the current value of each state to an array. This is convenient when plotting. The method should be called within the simulation loop each time step. Then afterwars, an array containing for ecample the north-position for each time step is obtained as ...states[0] ''' self.states[0].append(self.n) self.states[1].append(self.e) self.states[2].append(self.psi) self.states[3].append(self.u) self.states[4].append(self.v) self.states[5].append(self.r) def iceberg_snap_shot(self): ''' This method is used to store a map-view snap shot of the ship at the given north-east position and heading. It uses the ShipDraw-class. To plot a map view of the n-th ship snap-shot, use: plot(ship_drawings[1][n], ship_drawings[0][n]) ''' x, y = self.drw.local_coords() x_ned, y_ned = self.drw.rotate_coords(x, y, self.psi) x_ned_trans, y_ned_trans = self.drw.translate_coords(x_ned, y_ned, self.n, self.e) self.iceberg_drawings[0].append(x_ned_trans) self.iceberg_drawings[1].append(y_ned_trans) def store_simulation_data(self): self.simulation_results['time [s]'].append(self.int.time) self.simulation_results['north position [m]'].append(self.n) self.simulation_results['east position [m]'].append(self.e) self.simulation_results['yaw angle [deg]'].append(self.t_yaw * 180 / np.pi) self.simulation_results['forward speed [m/s]'].append(self.u) self.simulation_results['sideways speed [m/s]'].append(self.v) self.simulation_results['yaw rate [deg/sec]'].append(self.r * 180 / np.pi) self.simulation_results['wind speed [m/sec]'].append(self.wind_speed) self.simulation_results['wind direction [radius]'].append(self.wind_dir) def restore_to_intial(self): self.n = self.n_initial self.e = self.e_initial self.psi = self.psi_initial self.u = self.u_initial self.v = self.v_initial self.r = self.r_initial class DistanceSimulation: """his class is for simulate drift multiple times to get a distribution of collision event and time to collision, closest point of approach, impact point in case of collision, when and where iceberg breach the exclusion zone, when and where the iceberg breach zone 1 when and where the iceberg breach zone 2 when and where the iceberg beach zone 3""" def __init__(self, round, iceberg_config: IcebergConfiguration, simulation_config: DriftSimulationConfiguration, environment_config: EnvironmentConfiguration, z_config: ZonesConfiguration): self.distance_results = defaultdict(list) self.iceberg = IcebergDriftingModel1(iceberg_config, environment_config, simulation_config) self.zones_config = Zones(z_config, iceberg_config) self.cpa_point = np.empty(6).tolist() self.col_point = np.empty(3).tolist() self.exc_point = np.empty(3).tolist() self.zone1_point = np.empty(3).tolist() self.zone2_point = np.empty(3).tolist() self.zone3_point = np.empty(3).tolist() self.breach_event = np.empty(5).tolist() self.round_results = defaultdict(list) # specify the number of simulation self.n = round self.dis_lists = np.empty(self.n, dtype=object) self.t_lists = np.empty(self.n, dtype=object) self.d_n_lists = np.empty(self.n, dtype=object) self.d_e_lists = np.empty(self.n, dtype=object) self.d_exc_lists = np.empty(self.n, dtype=object) self.d_zone1_lists = np.empty(self.n, dtype=object) self.d_zone2_lists = np.empty(self.n, dtype=object) self.d_zone3_lists = np.empty(self.n, dtype=object) self.n_lists = np.empty(self.n, dtype=object) self.e_lists = np.empty(self.n, dtype=object) self.windS_lists = np.empty(self.n, dtype=object) self.windD_lists = np.empty(self.n, dtype=object) self.u_lists = np.empty(self.n, dtype=object) self.v_lists = np.empty(self.n, dtype=object) self.yaw_lists = np.empty(self.n, dtype=object) self.yaw_angle_lists = np.empty(self.n, dtype=object) def simulation(self): max_wind_speed = 25 self.distance_results.clear() self.iceberg.simulation_results.clear() self.iceberg.restore_to_intial() self.iceberg.int.time = 0 continue_simulation = True while self.iceberg.int.time <= self.iceberg.int.sim_time and continue_simulation: #self.iceberg.wind_speed = random.random() * max_wind_speed self.iceberg.update_differentials() self.iceberg.integrate_differentials() self.iceberg.store_simulation_data() col = self.zones_config.colli_event(self.iceberg.n, self.iceberg.e) d = self.zones_config.distance(self.iceberg.n, self.iceberg.e) d_to_exc = d - self.zones_config.r0 - self.zones_config.collimargin d_to_zone1 = d - self.zones_config.r1 - self.zones_config.collimargin d_to_zone2 = d - self.zones_config.r2 - self.zones_config.collimargin d_to_zone3 = d - self.zones_config.r3 - self.zones_config.collimargin dn = self.zones_config.d_to_north(self.iceberg.n) de = self.zones_config.d_to_east(self.iceberg.e) t = self.iceberg.int.time self.distance_results['Time [s]'].append(t) self.distance_results['Distance between iceberg and structure [m]'].append(d) self.distance_results['Distance between iceberg and structure in north direction [m]'].append(dn) self.distance_results['Distance between iceberg and structure in east direction [m]'].append(de) self.distance_results['Distance to exclusion zone'].append(d_to_exc) self.distance_results['Distance to zone 1'].append(d_to_zone1) self.distance_results['Distance to zone 2'].append(d_to_zone2) self.distance_results['Distance to zone 3'].append(d_to_zone3) if col == 1: continue_simulation = False print('Collision occur at: ', self.iceberg.int.time, 's') print("Closest point to Structure:", self.zones_config.distance(self.iceberg.n, self.iceberg.e), 'm', "CPA:", self.iceberg.n, self.iceberg.e) elif self.iceberg.n > self.zones_config.r3 + self.zones_config.n: continue_simulation = False self.iceberg.int.next_time() def cpa(self): """distance_list = self.distance_results['Distance between iceberg and structure [m]'] time_list = self.distance_results['Time [s]'] d_north_list = distance_results['Distance between iceberg and structure in north direction [m]'] d_east_list = distance_results['Distance between iceberg and structure in east direction [m]']""" cpa_d = min(self.distance_results['Distance between iceberg and structure [m]']) cpa_idx = self.distance_results['Distance between iceberg and structure [m]'].index(cpa_d) cpa_time = self.distance_results['Time [s]'][cpa_idx] cpa_loc = np.empty(2).tolist() cpa_loc[0] = self.iceberg.simulation_results['north position [m]'][cpa_idx] cpa_loc[1] = self.iceberg.simulation_results['east position [m]'][cpa_idx] cpazone = self.zones_config.cpa_zone(cpa_d) cpa_speed_u = self.iceberg.simulation_results['forward speed [m/s]'][cpa_idx] cpa_speed_v = self.iceberg.simulation_results['sideways speed [m/s]'][cpa_idx] self.cpa_point = [cpa_d, cpazone, cpa_time, cpa_loc, cpa_speed_u, cpa_speed_v] if cpazone == -1: col = 1 exc_breach = 1 zone1_breach = 1 zone2_breach = 1 zone3_breach = 1 self.col_point = [cpa_time, self.iceberg.simulation_results['north position [m]'][cpa_idx], self.iceberg.simulation_results['east position [m]'][cpa_idx]] d_to_exc = self.distance_results['Distance to exclusion zone'] exc_idx = list(map(lambda i: i <= 0, d_to_exc)).index(True) self.exc_point = [self.iceberg.simulation_results['time [s]'][exc_idx], self.iceberg.simulation_results['north position [m]'][exc_idx], self.iceberg.simulation_results['east position [m]'][exc_idx]] d_to_zone1 = self.distance_results['Distance to zone 1'] zone1_idx = list(map(lambda i: i <= 0, d_to_zone1)).index(True) self.zone1_point = [self.iceberg.simulation_results['time [s]'][zone1_idx], self.iceberg.simulation_results['north position [m]'][zone1_idx], self.iceberg.simulation_results['east position [m]'][zone1_idx]] d_to_zone2 = self.distance_results['Distance to zone 2'] zone2_idx = list(map(lambda i: i <= 0, d_to_zone2)).index(True) self.zone2_point = [self.iceberg.simulation_results['time [s]'][zone2_idx], self.iceberg.simulation_results['north position [m]'][zone2_idx], self.iceberg.simulation_results['east position [m]'][zone2_idx]] d_to_zone3 = self.distance_results['Distance to zone 3'] zone3_idx = list(map(lambda i: i <= 0, d_to_zone3)).index(True) self.zone3_point = [self.iceberg.simulation_results['time [s]'][zone3_idx], self.iceberg.simulation_results['north position [m]'][zone3_idx], self.iceberg.simulation_results['east position [m]'][zone3_idx]] elif cpazone == 0: col = 0 exc_breach = 1 zone1_breach = 1 zone2_breach = 1 zone3_breach = 1 d_to_exc = self.distance_results['Distance to exclusion zone'] exc_idx = list(map(lambda i: i <= 0, d_to_exc)).index(True) self.exc_point = [self.iceberg.simulation_results['time [s]'][exc_idx], self.iceberg.simulation_results['north position [m]'][exc_idx], self.iceberg.simulation_results['east position [m]'][exc_idx]] d_to_zone1 = self.distance_results['Distance to zone 1'] zone1_idx = list(map(lambda i: i <= 0, d_to_zone1)).index(True) self.zone1_point = [self.iceberg.simulation_results['time [s]'][zone1_idx], self.iceberg.simulation_results['north position [m]'][zone1_idx], self.iceberg.simulation_results['east position [m]'][zone1_idx]] d_to_zone2 = self.distance_results['Distance to zone 2'] zone2_idx = list(map(lambda i: i <= 0, d_to_zone2)).index(True) self.zone2_point = [self.iceberg.simulation_results['time [s]'][zone2_idx], self.iceberg.simulation_results['north position [m]'][zone2_idx], self.iceberg.simulation_results['east position [m]'][zone2_idx]] d_to_zone3 = self.distance_results['Distance to zone 3'] zone3_idx = list(map(lambda i: i <= 0, d_to_zone3)).index(True) self.zone3_point = [self.iceberg.simulation_results['time [s]'][zone3_idx], self.iceberg.simulation_results['north position [m]'][zone3_idx], self.iceberg.simulation_results['east position [m]'][zone3_idx]] elif cpazone == 1: col = 0 exc_breach = 0 zone1_breach = 1 zone2_breach = 1 zone3_breach = 1 d_to_zone1 = self.distance_results['Distance to zone 1'] zone1_idx = list(map(lambda i: i <= 0, d_to_zone1)).index(True) self.zone1_point = [self.iceberg.simulation_results['time [s]'][zone1_idx], self.iceberg.simulation_results['north position [m]'][zone1_idx], self.iceberg.simulation_results['east position [m]'][zone1_idx]] d_to_zone2 = self.distance_results['Distance to zone 2'] zone2_idx = list(map(lambda i: i <= 0, d_to_zone2)).index(True) self.zone2_point = [self.iceberg.simulation_results['time [s]'][zone2_idx], self.iceberg.simulation_results['north position [m]'][zone2_idx], self.iceberg.simulation_results['east position [m]'][zone2_idx]] d_to_zone3 = self.distance_results['Distance to zone 3'] zone3_idx = list(map(lambda i: i <= 0, d_to_zone3)).index(True) self.zone3_point = [self.iceberg.simulation_results['time [s]'][zone3_idx], self.iceberg.simulation_results['north position [m]'][zone3_idx], self.iceberg.simulation_results['east position [m]'][zone3_idx]] elif cpazone == 2: col = 0 exc_breach = 0 zone1_breach = 0 zone2_breach = 1 zone3_breach = 1 d_to_zone2 = self.distance_results['Distance to zone 2'] zone2_idx = list(map(lambda i: i <= 0, d_to_zone2)).index(True) self.zone2_point = [self.iceberg.simulation_results['time [s]'][zone2_idx], self.iceberg.simulation_results['north position [m]'][zone2_idx], self.iceberg.simulation_results['east position [m]'][zone2_idx]] d_to_zone3 = self.distance_results['Distance to zone 3'] zone3_idx = list(map(lambda i: i <= 0, d_to_zone3)).index(True) self.zone3_point = [self.iceberg.simulation_results['time [s]'][zone3_idx], self.iceberg.simulation_results['north position [m]'][zone3_idx], self.iceberg.simulation_results['east position [m]'][zone3_idx]] elif cpazone == 3: col = 0 exc_breach = 0 zone1_breach = 0 zone2_breach = 0 zone3_breach = 1 d_to_zone3 = self.distance_results['Distance to zone 3'] zone3_idx = list(map(lambda i: i <= 0, d_to_zone3)).index(True) self.zone3_point = [self.iceberg.simulation_results['time [s]'][zone3_idx], self.iceberg.simulation_results['north position [m]'][zone3_idx], self.iceberg.simulation_results['east position [m]'][zone3_idx]] else: col = 0 exc_breach = 0 zone1_breach = 0 zone2_breach = 0 zone3_breach = 0 self.breach_event = [col, exc_breach, zone1_breach, zone2_breach, zone3_breach] def multsim(self): """this function is to conduct multiple simulations and record data fro each simulation""" n = 1 self.round_results.clear() while n <= self.n: self.simulation() self.cpa() self.round_results['simulation round'].append(n) self.round_results['distance between the closest point of approach (cpa) and the structure'].append( self.cpa_point[0]) self.round_results['zone of closest point of approach (cpa)'].append(self.cpa_point[1]) self.round_results['time when iceberg reaches the closest point of approach (cpa)'].append( self.cpa_point[2]) self.round_results['location of the closest point of approach (cpa)'].append(self.cpa_point[3]) self.round_results['breach event'].append(self.breach_event) self.round_results['where the iceberg breach the collision zone'].append(self.col_point[1:3]) self.round_results['when the iceberg breach the collision zone'].append(self.col_point[0]) self.round_results['where the iceberg breach the exclusion zone'].append(self.exc_point[1:3]) self.round_results['when the iceberg breach the exclusion zone'].append(self.exc_point[0]) self.round_results['where the iceberg breach the zone 1'].append(self.zone1_point[1:3]) self.round_results['when the iceberg breach the zone 1'].append(self.zone1_point[0]) self.round_results['where the iceberg breach the zone 2'].append(self.zone2_point[1::3]) self.round_results['when the iceberg breach the zone 2'].append(self.zone2_point[0]) self.round_results['where the iceberg breach the zone 3'].append(self.zone3_point[1:3]) self.round_results['when the iceberg breach the zone 3'].append(self.zone3_point[0]) self.round_results['forward speed when the iceberg approach the structure'].append(self.cpa_point[4]) self.round_results['sideways speed when the iceberg approach the structure'].append(self.cpa_point[5]) self.t_lists[n - 1] = self.distance_results['Time [s]'] self.dis_lists[n - 1] = self.distance_results['Distance between iceberg and structure [m]'] self.d_n_lists[n - 1] = self.distance_results[ 'Distance between iceberg and structure in north direction [m]'] self.d_e_lists[n - 1] = self.distance_results[ 'Distance between iceberg and structure in east direction [m]'] self.d_exc_lists[n - 1] = self.distance_results['Distance to exclusion zone'] self.d_zone1_lists[n - 1] = self.distance_results['Distance to zone 1'] self.d_zone2_lists[n - 1] = self.distance_results['Distance to zone 2'] self.d_zone3_lists[n - 1] = self.distance_results['Distance to zone 3'] self.n_lists[n - 1] = self.iceberg.simulation_results['north position [m]'] self.e_lists[n - 1] = self.iceberg.simulation_results['east position [m]'] self.windS_lists[n - 1] = self.iceberg.simulation_results['wind speed [m/sec]'] self.windD_lists[n - 1] = self.iceberg.simulation_results['wind direction [radius]'] self.u_lists[n - 1] = self.iceberg.simulation_results['forward speed [m/s]'] self.v_lists[n - 1] = self.iceberg.simulation_results['sideways speed [m/s]'] self.yaw_lists[n - 1] = self.iceberg.simulation_results['yaw rate [deg/sec]'] self.yaw_angle_lists[n - 1] = self.iceberg.simulation_results['yaw angle [deg]'] n += 1 def col_pro(self): prob = self.round_results['location of the closest point of approach (cpa)'].count(-1) / self.n return prob def exc_pro(self): prob = self.round_results['location of the closest point of approach (cpa)'].count(0) / self.n return prob def zone1_pro(self): prob = self.round_results['location of the closest point of approach (cpa)'].count(1) / self.n return prob def zone2_pro(self): prob = self.round_results['location of the closest point of approach (cpa)'].count(2) / self.n return prob def zone3_pro(self): prob = self.round_results['location of the closest point of approach (cpa)'].count(3) / self.n return prob def outside_pro(self): prob = 1 - self.round_results['location of the closest point of approach (cpa)'].count(4) / self.n return prob class Cost: def __init__(self, multi_simulation: DistanceSimulation, ice_cost_config: IceCost, env_config: EnvironmentConfiguration): self.dsim = multi_simulation self.tow_s_prob = 0.7 self.disconnect_s_prob = 0.98 self.icecost = ice_cost_config self.env = env_config def col_kinetics(self, col_velocity_2): kinetics = 0.5 * self.dsim.iceberg.mass * col_velocity_2 return kinetics def col_type(self, col_velocity_2): if self.col_kinetics(col_velocity_2) >= self.icecost.Ki_lowerbound_severe: col_type = "Severe Collision" elif self.col_kinetics(col_velocity_2) >= self.icecost.Ki_lowerbound_medium: col_type = "Medium Collision" elif self.col_kinetics(col_velocity_2) >= self.icecost.Ki_lowerbound_light: col_type = "Light Collision" else: col_type = "Collision can be ignored" return col_type def update_tow_success(self, tcpa): if tcpa < self.icecost.towing_time_cost: self.tow_s_prob = 0 elif tcpa >= 1.5 * self.icecost.towing_time_cost: self.tow_s_prob = 0.8 def cost_cal(self, col_event, col_velocity_2): col_type = self.col_type(col_velocity_2) if col_type == "Light Collision": col_cost = self.icecost.light_col_cost elif col_type == "Medium Collision": col_cost = self.icecost.medium_col_cost elif col_type == "Severe Collision": col_cost = self.icecost.severe_col_cost else: col_cost = 0 col_pro = col_event * np.array([1 - self.tow_s_prob, 1 - self.disconnect_s_prob, 1]) operation_cost = np.array([self.icecost.towing_cost, self.icecost.disconnect_cost, 0]) mean_cost = col_pro * col_cost + operation_cost return mean_cost def cost_msim(self): n = self.dsim.n m = 1 total_cost = np.zeros(3) col_times = 0 while m <= n: idx = m - 1 col_event = self.dsim.round_results['zone of closest point of approach (cpa)'][idx] col_event = self.dsim.round_results['breach event'][idx][0] tcpa = self.dsim.round_results['time when iceberg reaches the closest point of approach (cpa)'][idx] col_velocity_2 = self.dsim.round_results['forward speed when the iceberg approach the structure'][ idx] ** 2 + \ self.dsim.round_results['sideways speed when the iceberg approach the structure'][idx] ** 2 self.update_tow_success(tcpa) cost_single = self.cost_cal(col_event, col_velocity_2) total_cost += cost_single col_times += col_event m += 1 average_cost = total_cost / n return col_times, average_cost

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py

Python

connectFourLab/game/__init__.py

yuriharrison/connect-four-lab

8c90535df91a45e8976b368f3cea4558478abe64

[ "MIT" ]

null

connectFourLab/game/__init__.py

yuriharrison/connect-four-lab

8c90535df91a45e8976b368f3cea4558478abe64

[ "MIT" ]

null

connectFourLab/game/__init__.py

yuriharrison/connect-four-lab

8c90535df91a45e8976b368f3cea4558478abe64

[ "MIT" ]

null

from .game import RunGame

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636c59041befa426bc7a8469477671983a887031

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py

Python

rest_fhir/mixins/vread.py

weynelucas/django-rest-fhir

560a0aadd0cfa43b6dc58f995c86015f6eefb768

[ "MIT" ]

2

2021-05-07T12:16:27.000Z

2021-12-16T20:45:36.000Z

rest_fhir/mixins/vread.py

weynelucas/django-rest-fhir

560a0aadd0cfa43b6dc58f995c86015f6eefb768

[ "MIT" ]

3

2021-05-10T19:40:33.000Z

2021-06-27T14:24:47.000Z

rest_fhir/mixins/vread.py

weynelucas/django-rest-fhir

560a0aadd0cfa43b6dc58f995c86015f6eefb768

[ "MIT" ]

1

2021-08-09T22:00:22.000Z

from .conditional_read import ConditionalReadMixin class VReadResourceMixin(ConditionalReadMixin): def vread(self, request, *args, **kwargs): return self.conditional_read(request, *args, **kwargs)

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63becbd4a16bc4710be0af8db94244112b0ef416

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py

Python

server/blueprints/store/controllers/__init__.py

Soopro/totoro

6be1af50496340ded9879a6450c8208ac9f97e72

[ "MIT" ]

null

server/blueprints/store/controllers/__init__.py

Soopro/totoro

6be1af50496340ded9879a6450c8208ac9f97e72

[ "MIT" ]

null

server/blueprints/store/controllers/__init__.py

Soopro/totoro

6be1af50496340ded9879a6450c8208ac9f97e72

[ "MIT" ]

1

2019-10-31T06:11:41.000Z

# coding=utf-8 from __future__ import absolute_import from .base import * from .book import * from .category import *

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py

Python

BAAlgorithmUtils/AVLTreeUtilTest.py

BenArvin/BAAlgorithmUtils

3af05cdaf70081e114ed1ca1713799cc0f6b861b

[ "MIT" ]

7

2018-12-29T09:04:48.000Z

2022-02-22T02:38:27.000Z

BAAlgorithmUtils/AVLTreeUtilTest.py

BenArvin/BAAlgorithmUtils

3af05cdaf70081e114ed1ca1713799cc0f6b861b

[ "MIT" ]

null

BAAlgorithmUtils/AVLTreeUtilTest.py

BenArvin/BAAlgorithmUtils

3af05cdaf70081e114ed1ca1713799cc0f6b861b

[ "MIT" ]

2

2019-01-07T08:03:38.000Z

2022-02-22T02:38:29.000Z

#!/usr/bin/python # -*- coding: UTF-8 -*- import sys, os sys.path.append('../') from BAAlgorithmUtils.AVLTreeUtil import AVLTree def start1(): print('\n********************************') trainSamples = [ {'key': 3, 'content': '3-1'}, {'key': 2, 'content': '2-1'}, {'key': 1, 'content': '1-1'}, {'key': 4, 'content': '4-1'}, {'key': 5, 'content': '5-1'}, {'key': 6, 'content': '6-1'}, {'key': 7, 'content': '7-1'}, {'key': 16, 'content': '16-1'}, {'key': 15, 'content': '15-1'}, {'key': 14, 'content': '14-1'}, {'key': 13, 'content': '13-1'}, {'key': 12, 'content': '12-1'}, {'key': 11, 'content': '11-1'}, {'key': 10, 'content': '10-1'}, {'key': 8, 'content': '8-1'}, {'key': 9, 'content': '9-1'}, ] trainsString = '' avlTree = AVLTree() for sample in trainSamples: trainsString = trainsString + ', ' + str(sample['key']) avlTree.set(sample['key'], sample['content']) print('Sample: ' + trainsString[2 : len(trainsString)]) print('\nAVL Tree:') avlTree.fullPrint() print('********************************\n') def start2(): print('\n********************************') trainSamples = [ {'key': 10, 'content': '10-1'}, {'key': 8, 'content': '8-1'}, {'key': 12, 'content': '12-1'}, {'key': 7, 'content': '7-1'}, {'key': 9, 'content': '9-1'}, {'key': 11, 'content': '11-1'}, {'key': 13, 'content': '13-1'}, {'key': 6, 'content': '6-1'}, ] trainsString = '' avlTree = AVLTree() for sample in trainSamples: trainsString = trainsString + ', ' + str(sample['key']) avlTree.set(sample['key'], sample['content']) print('Sample: ' + trainsString[2 : len(trainsString)]) print('\nAVL Tree:') avlTree.fullPrint() print('\nAfter delete 8:') avlTree.delete(8) avlTree.fullPrint() print('\nAfter delete 10:') avlTree.delete(10) avlTree.fullPrint() print('\nAfter add 14:') avlTree.set(14, '14-1') avlTree.fullPrint() print('********************************\n') def start3(): print('\n********************************') trainSamples = [ {'key': 10, 'content': '10-1'}, {'key': 8, 'content': '8-1'}, {'key': 12, 'content': '12-1'}, {'key': 7, 'content': '7-1'}, {'key': 9, 'content': '9-1'}, {'key': 11, 'content': '11-1'}, {'key': 13, 'content': '13-1'}, {'key': 6, 'content': '6-1'}, ] trainsString = '' avlTree = AVLTree() for sample in trainSamples: trainsString = trainsString + ', ' + str(sample['key']) avlTree.set(sample['key'], sample['content']) print('Sample: ' + trainsString[2 : len(trainsString)]) print('\nAVL Tree:') avlTree.fullPrint() print('\nAfter delete 10:') avlTree.delete(10) avlTree.fullPrint() print('********************************\n') if __name__ == '__main__': start1() start2() start3()

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895ef7b360faac4d33955e2c67b68485d3e0721c

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py

Python

src/astrildvisual/rays/__init__.py

Christovis/wys-ars

bb15f2d392842f9b32de12b5db5c86079bc97105

[ "MIT" ]

3

2021-07-27T14:45:58.000Z

2022-01-31T21:09:46.000Z

src/astrildvisual/rays/__init__.py

Christovis/wys-ars

bb15f2d392842f9b32de12b5db5c86079bc97105

[ "MIT" ]

1

2021-11-03T10:47:45.000Z

src/astrildvisual/rays/__init__.py

Christovis/wys-ars

bb15f2d392842f9b32de12b5db5c86079bc97105

[ "MIT" ]

1

2021-11-03T10:17:34.000Z

from .visuals import maps_with_vel_field

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py

Python

util/encode_util.py

lzpsgh/AscTrio

f969beece5dc93d29063da03793521bc54b814dd

[ "MIT" ]

5

2021-07-21T06:50:51.000Z

2022-03-31T04:18:28.000Z

util/encode_util.py

lzpsgh/AscTrio

f969beece5dc93d29063da03793521bc54b814dd

[ "MIT" ]

null

util/encode_util.py

lzpsgh/AscTrio

f969beece5dc93d29063da03793521bc54b814dd

[ "MIT" ]

1

2022-03-28T01:50:03.000Z

# coding : utf-8 # @Time : 2021/4/30 下午11:47

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896a39c5791384de17dfce2b48058275fad0de3f

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py

Python

2_convnets/1_mnist/model_zoo.py

rcassani/learning-deep

577447571936a7d6039195f216a90d5a9aed3fb7

[ "MIT" ]

1

2019-12-07T11:02:24.000Z

2_convnets/1_mnist/model_zoo.py

rcassani/learning-deep

577447571936a7d6039195f216a90d5a9aed3fb7

[ "MIT" ]

1

2020-04-24T01:21:41.000Z

2020-06-09T19:59:40.000Z

2_convnets/1_mnist/model_zoo.py

rcassani/learning-deep

577447571936a7d6039195f216a90d5a9aed3fb7

[ "MIT" ]

1

2019-05-25T21:47:12.000Z

import torch.nn as nn class small_cnn(nn.Module): # Model def __init__(self): super(small_cnn, self).__init__() self.features = nn.Sequential( nn.Conv2d(1, 10, kernel_size=3, padding=1), nn.ReLU(True), ) self.classifier = nn.Sequential( nn.Linear(10*28*28, 100), nn.ReLU(True), nn.Linear(100, 10), nn.Softmax() ) def forward(self, x): x = x.view([x.size(0),1,28,28]) x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x class medium_cnn(nn.Module): # Model def __init__(self): super(medium_cnn, self).__init__() self.features = nn.Sequential( nn.Conv2d(1, 10, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(10, 20, kernel_size=3, padding=1), nn.ReLU(True) ) self.classifier = nn.Sequential( nn.Linear(20*28*28, 100), nn.ReLU(True), nn.Linear(100, 10), nn.Softmax() ) def forward(self, x): x = x.view([x.size(0),1,28,28]) x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x

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6

982a5d868b8674d0f9ee5a397fa37ac39e444522

938

py

Python

ml_models/randomwalk.py

annakoretchko/algo_trading

9ca1b9307c4d477888e5f2e7f6d4f57a03ca3399

[ "MIT" ]

1

2022-01-12T14:49:52.000Z

ml_models/randomwalk.py

webclinic017/algo_trading-3

0ce3657dc7295ca6496f270f943f3e670ae199d2

[ "MIT" ]

null

ml_models/randomwalk.py

webclinic017/algo_trading-3

0ce3657dc7295ca6496f270f943f3e670ae199d2

[ "MIT" ]

1

2021-09-10T17:50:44.000Z

In [26]: symbol = '.SPX' In [27]: data = pd.DataFrame(raw[symbol]) In [28]: lags = 5 cols = [] for lag in range(1, lags + 1): col = 'lag_{}'.format(lag) 1 data[col] = data[symbol].shift(lag) 2 cols.append(col) 3 In [29]: data.head(7) Out[29]: .SPX lag_1 lag_2 lag_3 lag_4 lag_5 Date 2010-01-01 NaN NaN NaN NaN NaN NaN 2010-01-04 1132.99 NaN NaN NaN NaN NaN 2010-01-05 1136.52 1132.99 NaN NaN NaN NaN 2010-01-06 1137.14 1136.52 1132.99 NaN NaN NaN 2010-01-07 1141.69 1137.14 1136.52 1132.99 NaN NaN 2010-01-08 1144.98 1141.69 1137.14 1136.52 1132.99 NaN 2010-01-11 1146.98 1144.98 1141.69 1137.14 1136.52 1132.99 In [30]: data.dropna(inplace=True)

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0.202703

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0.414712

938

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40.782609

0.433515

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9831798c6a6fd25639f7f94cbdfb340bdefe8553

22,527

py

Python

tests/likelihoodtest/likelihoodtest.py

plewis/phycas

9f5a4d9b2342dab907d14a46eb91f92ad80a5605

[ "MIT" ]

3

2015-09-24T23:12:57.000Z

2021-04-12T07:07:01.000Z

tests/likelihoodtest/likelihoodtest.py

plewis/phycas

9f5a4d9b2342dab907d14a46eb91f92ad80a5605

[ "MIT" ]

null

tests/likelihoodtest/likelihoodtest.py

plewis/phycas

9f5a4d9b2342dab907d14a46eb91f92ad80a5605

[ "MIT" ]

1

2015-11-23T10:35:43.000Z

# This example is designed to check the likelihood calculation under most models # supported by Phycas. A data set is simulated under the most complex model, and # analyzed under a spectrum of simpler models. The data set is saved as a nexus file # complete with PAUP blocks that allow verification of Phycas's likelihood # calculations by PAUP. A second sweep of models is done for a real data set # (nyldna4.nex) and a paup command file is written to allow verification of Phycas' # likelihood calculations. from phycas import * def tryAllModels(fn): # Create string containing PAUP commands that will be added to the end of the # file fn to check results - we will add more commands to this string as we go paup_commands = [] #paup_commands.append('\n[!\n***** HKY+G+I (estimate everything) *****]') paup_commands.append('\n[!\n***** GTR+G+I (estimate everything) *****]') #paup_commands.append('lset nst=2 variant=hky basefreq=estimate tratio=estimate rates=gamma shape=estimate pinvar=estimate;') paup_commands.append('lset nst=6 basefreq=estimate rmatrix=estimate rates=gamma shape=estimate pinvar=estimate;') paup_commands.append('lscores 1 / userbrlen;') print print '************* Testing GTRModel *******************' # Compute likelihood using the GTR+G+I model print '\nGTR+G+I model' model.type = 'gtr' model.relrates = [1.8, 4.0, 1.5, 1.2, 5.0, 1.0] model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() ref_lnL = lnL print 'lnL = %.5f (this is the reference lnL)' % (lnL) paup_commands.append('\n[!\n***** GTR+G+I (using GTRModel) *****]') paup_commands.append('lset nst=6 basefreq=(0.1 0.2 0.3) rmatrix=(1.8 4.0 1.5 1.2 5.0) rates=gamma shape=1.2 pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas GTR+G+I lnL = %.5f]' % lnL) # Compute likelihood using the GTR+I model print '\nGTR+I model' model.type = 'gtr' model.relrates = [1.8, 4.0, 1.5, 1.2, 5.0, 1.0] model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 1 model.pinvar_model = True model.pinvar = 0.3 lnL = like() #ref_lnL = lnL print 'lnL = %.5f (%.5f worse than the reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('\n[!\n***** GTR+I (using GTRModel) *****]') paup_commands.append('lset nst=6 basefreq=(0.1 0.2 0.3) rmatrix=(1.8 4.0 1.5 1.2 5.0) rates=equal pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas GTR+I lnL = %.5f]' % lnL) # Compute likelihood using the GTR+G model print '\nGTR+G model' model.type = 'gtr' model.relrates = [1.8, 4.0, 1.5, 1.2, 5.0, 1.0] model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = False lnL = like() print 'lnL = %.5f (%.5f worse than the reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('\n[!\n***** GTR+G (using GTRModel) *****]') paup_commands.append('lset nst=6 basefreq=(0.1 0.2 0.3) rmatrix=(1.8 4.0 1.5 1.2 5.0) rates=gamma shape=1.2 pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas GTR+G lnL = %.5f]' % lnL) # ************* temporary below here ************** #print '\nGTR+psr model' #phycas.model = Likelihood.GTRModel() #phycas.model.setRelRates([1.8, 4.0, 1.5, 1.2, 5.0, 1.0]) #phycas.model.setNucleotideFreqs(0.1, 0.2, 0.3, 0.4) #phycas.model.setNGammaRates(4) #phycas.model.setShape(1.2) #phycas.model.setNotPinvarModel() #phycas.likelihood.usePatternSpecificRates() #phycas.likelihood.replaceModel(phycas.model) #phycas.likelihood.prepareForLikelihood(phycas.tree) #lnL = phycas.likelihood.calcLnL(phycas.tree) #print 'lnL = %.5f (%.5f worse than the reference lnL)' % (lnL, ref_lnL - lnL) #paup_commands.append('\n[!\n***** GTR+psr (actually, using GTR+G since no way to do psr in PAUP*) *****]') #paup_commands.append('lset nst=6 basefreq=(0.1 0.2 0.3) rmatrix=(1.8 4.0 1.5 1.2 5.0) rates=gamma shape=1.2 pinvar=0.0;') #paup_commands.append('lscores 1 / userbrlen;') #paup_commands.append('[!Phycas GTR+G lnL = %.5f]' % lnL) #phycas.likelihood.doNotUsePatternSpecificRates() # ************* temporary above here ************** # Compute likelihood using the GTR model print '\nGTR model' model.type = 'gtr' model.relrates = [1.8, 4.0, 1.5, 1.2, 5.0, 1.0] model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 1 model.pinvar_model = False model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('\n[!\n***** GTR (using GTRModel) *****]') paup_commands.append('lset nst=6 basefreq=(0.1 0.2 0.3) rmatrix=(1.8 4.0 1.5 1.2 5.0) rates=equal pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas GTR lnL = %.5f]' % lnL) print print '************* Testing HKYModel *******************' # Compute likelihood using the HKY+G+I model print '\nHKY+G+I model' model.type = 'hky' model.kappa = 4.0 model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() ref_lnL = lnL print 'lnL = %.5f (%.5f worse than the reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('\n[!\n***** HKY+G+I (using HKYModel) *****]') paup_commands.append('lset nst=2 variant=hky basefreq=(0.1 0.2 0.3) tratio=1.8333333 rates=gamma shape=1.2 pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas HKY+G+I lnL = %.5f]' % lnL) # Compute likelihood using the HKY+I model print '\nHKY+I model' model.type = 'hky' model.kappa = 4.0 model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() ref_lnL = lnL print 'lnL = %.5f (%.5f worse than the reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('\n[!\n***** HKY+I (using HKYModel) *****]') paup_commands.append('lset nst=2 variant=hky basefreq=(0.1 0.2 0.3) tratio=1.8333333 rates=equal pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas HKY+I lnL = %.5f]' % lnL) # Compute likelihood using the HKY+G model print '\nHKY+G model' model.type = 'hky' model.kappa = 4.0 model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than the reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('\n[!\n***** HKY+G (using HKYModel) *****]') paup_commands.append('lset nst=2 variant=hky basefreq=(0.1 0.2 0.3) tratio=1.8333333 rates=gamma shape=1.2 pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas HKY+G lnL = %.5f]' % lnL) # Compute likelihood using the HKY model print '\nHKY model' model.type = 'hky' model.kappa = 4.0 model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('\n[!\n***** HKY (using HKYModel) *****]') paup_commands.append('lset nst=2 variant=hky basefreq=(0.1 0.2 0.3) tratio=1.8333333 rates=equal pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas HKY lnL = %.5f]' % lnL) # Compute likelihood using the F81+G+I model print '\nF81+G+I model' model.type = 'hky' model.kappa = 1.0 model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** F81+G+I (using HKYModel) *****]') paup_commands.append('lset nst=1 basefreq=(0.1 0.2 0.3) rates=gamma shape=1.2 pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas F81+G+I lnL = %.5f]' % lnL) # Compute likelihood using the F81+I model print '\nF81+I model' model.type = 'hky' model.kappa = 1.0 model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** F81+I (using HKYModel) *****]') paup_commands.append('lset nst=1 basefreq=(0.1 0.2 0.3) rates=equal pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas F81+I lnL = %.5f]' % lnL) # Compute likelihood using the F81+G model print '\nF81+G model' model.type = 'hky' model.kappa = 1.0 model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** F81+G (using HKYModel) *****]') paup_commands.append('lset nst=1 basefreq=(0.1 0.2 0.3) rates=gamma shape=1.2 pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas F81+G lnL = %.5f]' % lnL) # Compute likelihood using the F81 model print '\nF81 model' model.type = 'hky' model.kappa = 1.0 model.state_freqs = [0.1, 0.2, 0.3, 0.4] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** F81 (using HKYModel) *****]') paup_commands.append('lset nst=1 basefreq=(0.1 0.2 0.3) rates=equal pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas F81 lnL = %.5f]' % lnL) # Compute likelihood using the K80+G+I model print '\nK80+G+I model' model.type = 'hky' model.kappa = 4.0 model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** K80+G+I (using HKYModel) *****]') paup_commands.append('lset nst=2 basefreq=equal tratio=2.0 rates=gamma shape=1.2 pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas K80+G+I lnL = %.5f]' % lnL) # Compute likelihood using the K80+I model print '\nK80+I model' model.type = 'hky' model.kappa = 4.0 model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** K80+I (using HKYModel) *****]') paup_commands.append('lset nst=2 basefreq=equal tratio=2.0 rates=equal pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas K80+I lnL = %.5f]' % lnL) # Compute likelihood using the K80+G model print '\nK80+G model' model.type = 'hky' model.kappa = 4.0 model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** K80+G (using HKYModel) *****]') paup_commands.append('lset nst=2 basefreq=equal tratio=2.0 rates=gamma shape=1.2 pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas K80+G lnL = %.5f]' % lnL) # Compute likelihood using the K80 model print '\nK80 model' model.type = 'hky' model.kappa = 4.0 model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** K80 (using HKYModel) *****]') paup_commands.append('lset nst=2 basefreq=equal tratio=2.0 rates=equal pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas K80 lnL = %.5f]' % lnL) # Compute likelihood using the JC+G+I model print '\nJC+G+I model' model.type = 'hky' model.kappa = 1.0 model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** JC+G+I (using HKYModel) *****]') paup_commands.append('lset nst=1 basefreq=equal rates=gamma shape=1.2 pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas JC+G+I lnL = %.5f]' % lnL) # Compute likelihood using the JC+I model print '\nJC+I model' model.type = 'hky' model.kappa = 1.0 model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** JC+I (using HKYModel) *****]') paup_commands.append('lset nst=1 basefreq=equal rates=equal pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas JC+I lnL = %.5f]' % lnL) # Compute likelihood using the JC+G model print '\nJC+G model' model.type = 'hky' model.kappa = 1.0 model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** JC+G (using HKYModel) *****]') paup_commands.append('lset nst=1 basefreq=equal rates=gamma shape=1.2 pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas JC+G lnL = %.5f]' % lnL) # Compute likelihood using the JC model print '\nJC model' model.type = 'hky' model.kappa = 1.0 model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** JC (using HKYModel) *****]') paup_commands.append('lset nst=1 basefreq=equal rates=equal pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas JC lnL = %.5f]' % lnL) print print '************** Testing JCModel *******************' # Compute likelihood using the JC+G+I model print '\nJC+G+I model' model.type = 'jc' #model.kappa = 1.0 #model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** JC+G+I (using JCModel) *****]') paup_commands.append('lset nst=1 basefreq=equal rates=gamma shape=1.2 pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas JC+G+I lnL = %.5f]' % lnL) # Compute likelihood using the JC+I model print '\nJC+I model' model.type = 'jc' #model.kappa = 1.0 #model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = True model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** JC+I (using JCModel) *****]') paup_commands.append('lset nst=1 basefreq=equal rates=equal pinvar=0.3;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas JC+I lnL = %.5f]' % lnL) # Compute likelihood using the JC+G model print '\nJC+G model' model.type = 'jc' #model.kappa = 1.0 #model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 4 model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** JC+G (using JCModel) *****]') paup_commands.append('lset nst=1 basefreq=equal rates=gamma shape=1.2 pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas JC+G lnL = %.5f]' % lnL) # Compute likelihood using the JC model print '\nJC model' model.type = 'jc' #model.kappa = 1.0 #model.state_freqs = [0.25, 0.25, 0.25, 0.25] model.num_rates = 1 #model.gamma_shape = 1.2 model.pinvar_model = False #model.pinvar = 0.3 lnL = like() print 'lnL = %.5f (%.5f worse than reference lnL)' % (lnL, ref_lnL - lnL) paup_commands.append('[!\n***** JC (using JCModel) *****]') paup_commands.append('lset nst=1 basefreq=equal rates=equal pinvar=0.0;') paup_commands.append('lscores 1 / userbrlen;') paup_commands.append('[!Phycas JC lnL = %.5f]' % lnL) # Add a PAUP block to the file named fn to make it easy to check the results f = file(fn, 'a') f.write('\n') f.write('\nbegin paup;') f.write('\n set criterion=likelihood storebrlen;') f.write('\nend;') f.write('\n') f.write('\nbegin trees;') f.write('\n translate') for i,nm in enumerate(blob.taxon_labels): if nm.count(' ') > 0: f.write("\n %d '%s'" % (i+1, nm)) else: f.write("\n %d %s" % (i+1, nm)) if i < len(blob.taxon_labels) - 1: f.write(',') else: f.write(';') f.write('\n utree t = %s' % model_tree_str) f.write('\nend;') f.write('\n') f.write('\nbegin paup;') f.write('\nlog file=paup.log start replace;\n') f.write('\n'.join(paup_commands)) f.write('\n\nlog stop;') f.write('\nend;') f.write('\n') f.close() def simulateData(fn): # NOT YET READY FOR PARTITIONED VERSION # Define the names of the taxa to use when the simulated data set is saved to a file phycas.taxon_names = ['P. parksii', 'P. articulata', 'P._gracilis', 'P. macrophylla'] # Create a simulation model #phycas.model = Likelihood.HKYModel() phycas.model = Likelihood.GTRModel() #phycas.model.setKappa(4.0) phycas.model.setRelRates([1.8, 4.0, 1.5, 1.2, 5.0, 1.0]) phycas.model.setNGammaRates(4) phycas.model.setShape(1.2) phycas.model.setNucleotideFreqs(0.1, 0.2, 0.3, 0.4) phycas.model.setPinvarModel() phycas.model.setPinvar(0.3) # Create a likelihood object to orchestrate both simulations and likelihood calculations phycas.likelihood = Likelihood.TreeLikelihood(phycas.model) # Prepare the tree for simulation (i.e. equip nodes with transition matrices) phycas.likelihood.prepareForSimulation(phycas.tree) # Simulation settings phycas.r.setSeed(13579) phycas.sim_nreps = 1 # ignored at present phycas.sim_outfile = 'simout.nex' #num_sites = 5000 num_sites = 100000 # Create a SimData object to hold the simulated data sim_data = Likelihood.SimData() # Simulate num_sites of data and store in sim_data # Use the function simulateFirst (rather than just simulate) in order # to force calculation of transition probabilities phycas.likelihood.simulateFirst(sim_data, phycas.tree, phycas.r, num_sites) # Save simulated data to a NEXUS file using taxon_names, datatype=dna and # using the symbols a, c, g, and t for state codes 0, 1, 2, and 3, respectively sim_data.saveToNexusFile('simulated.nex', phycas.taxon_names, 'dna', ('a','c','g','t')) # Copy the simulated data from sim_data to phycas.likelihood so that # we can compute the likelihood for the simulated data phycas.likelihood.copyDataFromSimData(sim_data) def createCommandFile(fn, dataf): outf = file(fn, 'w') outf.write('#nexus\n\n') outf.write('begin paup;\n') outf.write(" set nowarnroot;\n") outf.write(" exe '%s';\n" % dataf) outf.write('end;\n') outf.close() if __name__ == '__main__': print print '+------------------------------------------------+' print '| Analyzing nyldna4.nex |' print '+------------------------------------------------+' dataf = getPhycasTestData('nyldna4.nex') blob = readFile(dataf) nchar = blob.characters.getMatrix().getNChar() partition.validate(nchar) # Create a model tree model_tree_str = '(1:0.1,2:0.15,(3:0.025,4:0.15):0.05);' model_tree = TreeCollection(newick=model_tree_str) like.data_source = blob.characters like.tree_source = model_tree like.starting_edgelen_dist = None like.store_site_likes = False createCommandFile('check.nex', dataf) tryAllModels('check.nex') #doingSimTest = False #if doingSimTest: # print # print '+------------------------------------------------+' # print '| Analyzing Simulated Data |' # print '+------------------------------------------------+' # # simulateData('simulated.nex') # tryAllModels('simulated.nex') #else: # d = os.path.dirname(__file__) # o = open(os.path.join(d, 'reference_output','simulated.nex'), "rU") # t = open("simulated.nex", "w") # t.write(o.read()) # t.close() # o.close()

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null

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1

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6

984a3f7aca26815a33be01423d93ac7187208611

209

py

Python

scripts/pylint_custom_plugin/tests/test_files/copyright_header_acceptable.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

1

2022-02-01T18:50:12.000Z

scripts/pylint_custom_plugin/tests/test_files/copyright_header_acceptable.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

null

scripts/pylint_custom_plugin/tests/test_files/copyright_header_acceptable.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

null

# ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. # ------------------------------------ class Something(): def __init__(self): pass

20.9

38

0.425837

15

209

5.666667

1

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0.162679

209

9

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false

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1

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1

0

6

98b4e1643108d473a3f73c90f156756074f8bbb6

102

py

Python

tests/exog/random/random_exog_150_40.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

[ "BSD-3-Clause" ]

377

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/exog/random/random_exog_150_40.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

160

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/exog/random/random_exog_150_40.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

63

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import tests.exog.test_random_exogenous as testrandexog testrandexog.test_random_exogenous( 150,40);

25.5

55

0.862745

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0.714286

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null

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1

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6

7f3718b38514dd26f81177c48998fa881c34d511

4,751

py

Python

daiquiri/serve/tests/test_viewsets.py

agy-why/daiquiri

4d3e2ce51e202d5a8f1df404a0094a4e018dcb4d

[ "Apache-2.0" ]

14

2018-12-23T18:35:02.000Z

2021-12-15T04:55:12.000Z

daiquiri/serve/tests/test_viewsets.py

agy-why/daiquiri

4d3e2ce51e202d5a8f1df404a0094a4e018dcb4d

[ "Apache-2.0" ]

40

2018-12-20T12:44:05.000Z

2022-03-21T11:35:20.000Z

daiquiri/serve/tests/test_viewsets.py

agy-why/daiquiri

4d3e2ce51e202d5a8f1df404a0094a4e018dcb4d

[ "Apache-2.0" ]

5

2019-05-16T08:03:35.000Z

2021-08-23T20:03:11.000Z

from django.test import TestCase from test_generator.viewsets import TestViewsetMixin class ServeTestCase(TestCase): databases = ('default', 'data', 'tap', 'oai') fixtures = ( 'auth.json', 'metadata.json', 'jobs.json', 'queryjobs.json' ) users = ( ('admin', 'admin'), ('user', 'user'), ('test', 'test'), ('anonymous', None), ) class PublicRowTests(TestViewsetMixin, ServeTestCase): url_names = { 'viewset': 'serve:row' } status_map = { 'list_viewset': { 'admin': 200, 'user': 200, 'test': 200, 'anonymous': 200 } } def _test_list_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_data_obs', 'table': 'stars' }) class InternalRowTests(TestViewsetMixin, ServeTestCase): url_names = { 'viewset': 'serve:row' } status_map = { 'list_viewset': { 'admin': 200, 'user': 200, 'test': 200, 'anonymous': 404 } } def _test_list_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_data_sim', 'table': 'halos' }) class PrivateRowTests(TestViewsetMixin, ServeTestCase): url_names = { 'viewset': 'serve:row' } status_map = { 'list_viewset': { 'admin': 404, 'user': 404, 'test': 200, 'anonymous': 404 } } def _test_list_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_data_test', 'table': 'test' }) class NotFoundRowTests(TestViewsetMixin, ServeTestCase): url_names = { 'viewset': 'serve:row' } status_map = { 'list_viewset': { 'admin': 404, 'user': 404, 'test': 404, 'anonymous': 404 } } def _test_non_existing_schema_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'non_existing', 'table': 'stars' }) def _test_non_existing_table_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_data_obs', 'table': 'non_existing' }) def _test_non_existing_user_table_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_user_user', 'table': 'non_existing' }) class PublicColumnTests(TestViewsetMixin, ServeTestCase): url_names = { 'viewset': 'serve:column' } status_map = { 'list_viewset': { 'admin': 200, 'user': 200, 'test': 200, 'anonymous': 200 } } def _test_list_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_data_obs', 'table': 'stars' }) class InternalColumnTests(TestViewsetMixin, ServeTestCase): url_names = { 'viewset': 'serve:column' } status_map = { 'list_viewset': { 'admin': 200, 'user': 200, 'test': 200, 'anonymous': 404 } } def _test_list_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_data_sim', 'table': 'halos' }) class PrivateColumnTests(TestViewsetMixin, ServeTestCase): url_names = { 'viewset': 'serve:column' } status_map = { 'list_viewset': { 'admin': 404, 'user': 404, 'test': 200, 'anonymous': 404 } } def _test_list_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_data_test', 'table': 'test' }) class NotFoundColumnTests(TestViewsetMixin, ServeTestCase): url_names = { 'viewset': 'serve:column' } status_map = { 'list_viewset': { 'admin': 404, 'user': 404, 'test': 404, 'anonymous': 404 } } def _test_non_existing_schema_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'non_existing', 'table': 'stars' }) def _test_non_existing_table_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_data_obs', 'table': 'non_existing' }) def _test_non_existing_user_table_viewset(self, username): self.assert_list_viewset(username, query_params={ 'schema': 'daiquiri_user_user', 'table': 'non_existing' })

23.874372

68

0.568933

449

4,751

5.714922

0.13363

0.111458

0.088854

0.10756

0.858145

0

0.028872

0.300147

4,751

198

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0

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0

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0

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1

0.081081

false

0

0.013514

0

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0

null

0

1

0

null

0

6

7faefc43afce1ef5c3f56a6ac68d0c19e2ba7181

3,101

py

Python

epi/tests/test_chap_04_01_parity_of_a_word.py

totoro72/pt1

92cffb9b36ebe2023243446e560e54200b0bd6e9

[ "MIT" ]

null

epi/tests/test_chap_04_01_parity_of_a_word.py

totoro72/pt1

92cffb9b36ebe2023243446e560e54200b0bd6e9

[ "MIT" ]

17

2020-09-04T16:35:48.000Z

2022-03-02T03:21:39.000Z

epi/tests/test_chap_04_01_parity_of_a_word.py

totoro72/pt1

92cffb9b36ebe2023243446e560e54200b0bd6e9

[ "MIT" ]

null

import unittest from chap_04_01_parity_of_a_word import ( count_bits_naive, count_bits_better, compute_parity_w_cache, compute_parity_log, right_prop_rightmost_set_bit, compute_x_mod_power_of_2, is_x_power_of_2 ) class TestParity(unittest.TestCase): def test_count_bits_naive(self): self.assertEqual(count_bits_naive(0), 0) self.assertEqual(count_bits_naive(1), 1) self.assertEqual(count_bits_naive(2), 1) self.assertEqual(count_bits_naive(3), 2) self.assertEqual(count_bits_naive(7), 3) self.assertEqual(count_bits_naive(15), 4) with self.assertRaises(ValueError): count_bits_naive(-1) def test_count_bits_better(self): self.assertEqual(count_bits_better(0), 0) self.assertEqual(count_bits_better(1), 1) self.assertEqual(count_bits_better(2), 1) self.assertEqual(count_bits_better(3), 2) self.assertEqual(count_bits_better(7), 3) self.assertEqual(count_bits_better(15), 4) with self.assertRaises(ValueError): count_bits_naive(-1) def test_compute_parity_w_cache(self): self.assertEqual(compute_parity_w_cache(0), 0) self.assertEqual(compute_parity_w_cache(1), 1) self.assertEqual(compute_parity_w_cache(2), 1) self.assertEqual(compute_parity_w_cache(3), 0) self.assertEqual(compute_parity_w_cache(7), 1) self.assertEqual(compute_parity_w_cache(15), 0) # works with negatives! self.assertEqual(compute_parity_w_cache(-1), 0) self.assertEqual(compute_parity_w_cache(0x0001000100010001), 0) self.assertEqual(compute_parity_w_cache(0x00010011ffff000f), 1) def test_compute_parity_log(self): self.assertEqual(compute_parity_log(0), 0) self.assertEqual(compute_parity_log(1), 1) self.assertEqual(compute_parity_log(2), 1) self.assertEqual(compute_parity_log(3), 0) self.assertEqual(compute_parity_log(7), 1) self.assertEqual(compute_parity_log(15), 0) # works with negatives! self.assertEqual(compute_parity_log(-1), 0) self.assertEqual(compute_parity_log(0x0001000100010001), 0) self.assertEqual(compute_parity_log(0x00010011ffff000f), 1) def test_right_prop_rightmost_set_bit(self): # 0101 0000 = 2^4 + 2^6 = 16 + 64 = 80 # 0101 1111 = 80 + 15 = 95 self.assertEqual(right_prop_rightmost_set_bit(80), 95) # works for negative too # 1111....0000 = -1 - 15 = -16 # 1111....1111 = -1 self.assertEqual(right_prop_rightmost_set_bit(-16), -1) def test_compute_x_mod_power_of_2(self): self.assertEqual(compute_x_mod_power_of_2(77, 3), 5) def test_is_x_power_of_2(self): self.assertEqual(is_x_power_of_2(1), True) self.assertEqual(is_x_power_of_2(2), True) self.assertEqual(is_x_power_of_2(4), True) self.assertEqual(is_x_power_of_2(32), True) self.assertEqual(is_x_power_of_2(1024), True) self.assertEqual(is_x_power_of_2(1023), False)

38.283951

71

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0.211538

0.255061

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0.361842

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38.7625

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0.672131

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0.114754

false

0

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0

null

1

0

null

0

1

0

6

f687ed029da5b261be3f9a563f24ce9fb63a8a78

13,095

py

Python

src/ostorlab/agent/message/proto/v3/asset/ip/v6/whois/whois_pb2.py

bbhunter/ostorlab

968fe4e5b927c0cd159594c13b73f95b71150154

[ "Apache-2.0" ]

113

2022-02-21T09:30:14.000Z

2022-03-31T21:54:26.000Z

src/ostorlab/agent/message/proto/v3/asset/ip/v6/whois/whois_pb2.py

bbhunter/ostorlab

968fe4e5b927c0cd159594c13b73f95b71150154

[ "Apache-2.0" ]

2

2022-02-25T10:56:55.000Z

2022-03-24T13:08:06.000Z

src/ostorlab/agent/message/proto/v3/asset/ip/v6/whois/whois_pb2.py

bbhunter/ostorlab

968fe4e5b927c0cd159594c13b73f95b71150154

[ "Apache-2.0" ]

20

2022-02-28T14:25:04.000Z

2022-03-30T23:01:11.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: v3/asset/ip/v6/whois/whois.proto from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='v3/asset/ip/v6/whois/whois.proto', package='v3.asset.ip.v6.whois', syntax='proto2', serialized_options=None, create_key=_descriptor._internal_create_key, serialized_pb=b'\n v3/asset/ip/v6/whois/whois.proto\x12\x14v3.asset.ip.v6.whois\"L\n\x07Network\x12\x0c\n\x04\x63idr\x18\x01 \x01(\t\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x0e\n\x06handle\x18\x03 \x01(\t\x12\x15\n\rparent_handle\x18\x04 \x01(\t\"6\n\x07\x43ontact\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04kind\x18\x02 \x01(\t\x12\x0f\n\x07\x61\x64\x64ress\x18\x03 \x01(\t\"F\n\x06\x45ntity\x12\x0c\n\x04name\x18\x01 \x01(\t\x12.\n\x07\x63ontact\x18\x02 \x01(\x0b\x32\x1d.v3.asset.ip.v6.whois.Contact\"\x88\x02\n\x07Message\x12\x0c\n\x04host\x18\x01 \x02(\t\x12\x0c\n\x04mask\x18\x02 \x01(\t\x12\x12\n\x07version\x18\x03 \x02(\x05:\x01\x36\x12\x14\n\x0c\x61sn_registry\x18\x04 \x01(\t\x12\x12\n\nasn_number\x18\x05 \x01(\x05\x12\x18\n\x10\x61sn_country_code\x18\x06 \x01(\t\x12\x10\n\x08\x61sn_date\x18\x07 \x01(\t\x12\x17\n\x0f\x61sn_description\x18\x08 \x01(\t\x12.\n\x07network\x18\t \x01(\x0b\x32\x1d.v3.asset.ip.v6.whois.Network\x12.\n\x08\x65ntities\x18\n \x03(\x0b\x32\x1c.v3.asset.ip.v6.whois.Entity' ) _NETWORK = _descriptor.Descriptor( name='Network', full_name='v3.asset.ip.v6.whois.Network', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='cidr', full_name='v3.asset.ip.v6.whois.Network.cidr', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='v3.asset.ip.v6.whois.Network.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='handle', full_name='v3.asset.ip.v6.whois.Network.handle', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='parent_handle', full_name='v3.asset.ip.v6.whois.Network.parent_handle', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=58, serialized_end=134, ) _CONTACT = _descriptor.Descriptor( name='Contact', full_name='v3.asset.ip.v6.whois.Contact', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='name', full_name='v3.asset.ip.v6.whois.Contact.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='kind', full_name='v3.asset.ip.v6.whois.Contact.kind', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='address', full_name='v3.asset.ip.v6.whois.Contact.address', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=136, serialized_end=190, ) _ENTITY = _descriptor.Descriptor( name='Entity', full_name='v3.asset.ip.v6.whois.Entity', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='name', full_name='v3.asset.ip.v6.whois.Entity.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='contact', full_name='v3.asset.ip.v6.whois.Entity.contact', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=192, serialized_end=262, ) _MESSAGE = _descriptor.Descriptor( name='Message', full_name='v3.asset.ip.v6.whois.Message', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='host', full_name='v3.asset.ip.v6.whois.Message.host', index=0, number=1, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='mask', full_name='v3.asset.ip.v6.whois.Message.mask', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='version', full_name='v3.asset.ip.v6.whois.Message.version', index=2, number=3, type=5, cpp_type=1, label=2, has_default_value=True, default_value=6, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='asn_registry', full_name='v3.asset.ip.v6.whois.Message.asn_registry', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='asn_number', full_name='v3.asset.ip.v6.whois.Message.asn_number', index=4, number=5, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='asn_country_code', full_name='v3.asset.ip.v6.whois.Message.asn_country_code', index=5, number=6, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='asn_date', full_name='v3.asset.ip.v6.whois.Message.asn_date', index=6, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='asn_description', full_name='v3.asset.ip.v6.whois.Message.asn_description', index=7, number=8, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='network', full_name='v3.asset.ip.v6.whois.Message.network', index=8, number=9, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='entities', full_name='v3.asset.ip.v6.whois.Message.entities', index=9, number=10, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=265, serialized_end=529, ) _ENTITY.fields_by_name['contact'].message_type = _CONTACT _MESSAGE.fields_by_name['network'].message_type = _NETWORK _MESSAGE.fields_by_name['entities'].message_type = _ENTITY DESCRIPTOR.message_types_by_name['Network'] = _NETWORK DESCRIPTOR.message_types_by_name['Contact'] = _CONTACT DESCRIPTOR.message_types_by_name['Entity'] = _ENTITY DESCRIPTOR.message_types_by_name['Message'] = _MESSAGE _sym_db.RegisterFileDescriptor(DESCRIPTOR) Network = _reflection.GeneratedProtocolMessageType('Network', (_message.Message,), { 'DESCRIPTOR' : _NETWORK, '__module__' : 'v3.asset.ip.v6.whois.whois_pb2' # @@protoc_insertion_point(class_scope:v3.asset.ip.v6.whois.Network) }) _sym_db.RegisterMessage(Network) Contact = _reflection.GeneratedProtocolMessageType('Contact', (_message.Message,), { 'DESCRIPTOR' : _CONTACT, '__module__' : 'v3.asset.ip.v6.whois.whois_pb2' # @@protoc_insertion_point(class_scope:v3.asset.ip.v6.whois.Contact) }) _sym_db.RegisterMessage(Contact) Entity = _reflection.GeneratedProtocolMessageType('Entity', (_message.Message,), { 'DESCRIPTOR' : _ENTITY, '__module__' : 'v3.asset.ip.v6.whois.whois_pb2' # @@protoc_insertion_point(class_scope:v3.asset.ip.v6.whois.Entity) }) _sym_db.RegisterMessage(Entity) Message = _reflection.GeneratedProtocolMessageType('Message', (_message.Message,), { 'DESCRIPTOR' : _MESSAGE, '__module__' : 'v3.asset.ip.v6.whois.whois_pb2' # @@protoc_insertion_point(class_scope:v3.asset.ip.v6.whois.Message) }) _sym_db.RegisterMessage(Message) # @@protoc_insertion_point(module_scope)

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0

1

0

1

0

null

0

6

f68f15d3e56a51b1033c217916b97c3b8da1167c

82

py

Python

tests/test_cli.py

maggie-jiayizhang/wals3

fa8f9a9cf968920f16859a075a08502988c9ec4d

[ "Apache-2.0" ]

86

2015-03-18T07:47:54.000Z

2022-03-27T10:16:01.000Z

tests/test_cli.py

maggie-jiayizhang/wals3

fa8f9a9cf968920f16859a075a08502988c9ec4d

[ "Apache-2.0" ]

21

2015-01-15T14:13:06.000Z

2021-11-24T11:17:59.000Z

tests/test_cli.py

maggie-jiayizhang/wals3

fa8f9a9cf968920f16859a075a08502988c9ec4d

[ "Apache-2.0" ]

16

2015-02-13T05:31:11.000Z

2022-03-03T08:54:37.000Z

from wals3.scripts import initializedb def test_init(): assert initializedb

13.666667

38

0.780488

10

82

6.3

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0.014706

0.170732

82

5

39

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0.333333

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null

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null

0

1

0

1

0

1

0

6

f6db8d71dadd33c1f146c737b36b24ff658ba08a

21,068

py

Python

src/nodes.py

Agent-Hellboy/find-unused-import

81f555e5ae42a906a2db6e3049ffe23f7c2c2535

[ "MIT" ]

2

2022-01-07T14:50:17.000Z

2022-01-23T10:30:33.000Z

src/nodes.py

Agent-Hellboy/find-unused-import

81f555e5ae42a906a2db6e3049ffe23f7c2c2535

[ "MIT" ]

1

2022-01-11T13:55:29.000Z

src/nodes.py

Agent-Hellboy/find-unused-import

81f555e5ae42a906a2db6e3049ffe23f7c2c2535

[ "MIT" ]

null

import re OBJECTS = [] def get_class_str(node_repr): node_str = type(node_repr) ptrn = re.search(r"<(.*?)>", str(node_str)) return ptrn.group(1).split("'")[1] class AST: @classmethod def parse_node(self, obj): pass class Add: @classmethod def parse_node(self, obj): pass class And: @classmethod def parse_node(self, obj): pass class AnnAssign: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.target)].parse_node(obj.target) AST_NODES[get_class_str(obj.value)].parse_node(obj.value) AST_NODES[get_class_str(obj.annotation)].parse_node(obj.annotation) class Assert: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.test)].parse_node(obj.test) AST_NODES[get_class_str(obj.msg)].parse_node(obj.msg) class Assign: @classmethod def parse_node(self, obj): for node in obj.targets: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class AsyncFor: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.orelse: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.target)].parse_node(obj.target) AST_NODES[get_class_str(obj.iter)].parse_node(obj.iter) class AsyncFunctionDef: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.args)].parse_node(obj.args) for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.decorator_list: AST_NODES[get_class_str(node)].parse_node(node) OBJECTS.append(obj.name) class AsyncWith: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.items: AST_NODES[get_class_str(node)].parse_node(node) class Attribute: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) OBJECTS.append(obj.attr) class AugAssign: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.target)].parse_node(obj.target) AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class AugLoad: @classmethod def parse_node(self, obj): pass class AugStore: @classmethod def parse_node(self, obj): pass class Await: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class BinOp: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.left)].parse_node(obj.left) AST_NODES[get_class_str(obj.right)].parse_node(obj.right) class BitAnd: @classmethod def parse_node(self, obj): pass class BitOr: @classmethod def parse_node(self, obj): pass class BitXor: @classmethod def parse_node(self, obj): pass class BoolOp: @classmethod def parse_node(self, obj): for node in obj.values: AST_NODES[get_class_str(node)].parse_node(node) class Break: @classmethod def parse_node(self, obj): pass class Bytes: @classmethod def parse_node(self, obj): pass class Call: @classmethod def parse_node(self, obj): for node in obj.args: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.keywords: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.func)].parse_node(obj.func) class ClassDef: @classmethod def parse_node(self, obj): OBJECTS.append(obj.name) for node in obj.bases: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.keywords: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.decorator_list: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.starargs)].parse_node(obj.starargs) AST_NODES[get_class_str(obj.kwargs)].parse_node(obj.kwargs) class Compare: @classmethod def parse_node(self, obj): for node in obj.comparators: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(node)].parse_node(node) class Constant: @classmethod def parse_node(self, obj): OBJECTS.append(obj.value) class Continue: @classmethod def parse_node(self, obj): pass class Del: @classmethod def parse_node(self, obj): pass class Delete: @classmethod def parse_node(self, obj): for node in obj.targets: AST_NODES[get_class_str(node)].parse_node(node) class Dict: @classmethod def parse_node(self, obj): for node in obj.keys: AST_NODES[get_class_str(node)].parse_node(obj.node) for node in obj.values: AST_NODES[get_class_str(node)].parse_node(obj.node) class DictComp: @classmethod def parse_node(self, obj): for node in obj.generators: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.key)].parse_node(obj.key) AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class Div: @classmethod def parse_node(self, obj): pass class Ellipsis: @classmethod def parse_node(self, obj): pass class Eq: @classmethod def parse_node(self, obj): pass class ExceptHandler: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.type)].parse_node(obj.type) OBJECTS.append(obj.name) class Expr: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class Expression: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.body)].parse_node(obj.body) class ExtSlice: @classmethod def parse_node(self, obj): pass class FloorDiv: @classmethod def parse_node(self, obj): pass class For: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.orelse: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.target)].parse_node(obj.target) AST_NODES[get_class_str(obj.iter)].parse_node(obj.iter) class FormattedValue: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class FunctionDef: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.args)].parse_node(obj.args) for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.decorator_list: AST_NODES[get_class_str(node)].parse_node(node) OBJECTS.append(obj.name) class FunctionType: @classmethod def parse_node(self, obj): pass class GeneratorExp: @classmethod def parse_node(self, obj): for node in obj.generators: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.elt)].parse_node(obj.elt) class Global: @classmethod def parse_node(self, obj): for name in obj.names: OBJECTS.append(name) class Gt: @classmethod def parse_node(self, obj): pass class GtE: @classmethod def parse_node(self, obj): pass class If: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.orelse: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.test)].parse_node(obj.test) class IfExp: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.test)].parse_node(obj.test) AST_NODES[get_class_str(obj.body)].parse_node(obj.body) AST_NODES[get_class_str(obj.orelse)].parse_node(obj.orelse) class Import: @classmethod def parse_node(self, obj): pass class ImportFrom: @classmethod def parse_node(self, obj): pass class In: @classmethod def parse_node(self, obj): pass class Index: @classmethod def parse_node(self, obj): pass class Interactive: @classmethod def parse_node(self, obj): pass class Invert: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.operand)].parse_node(obj.operand) class Is: @classmethod def parse_node(self, obj): pass class IsNot: @classmethod def parse_node(self, obj): pass class JoinedStr: @classmethod def parse_node(self, obj): for node in obj.values: AST_NODES[get_class_str(node)].parse_node(node) class LShift: @classmethod def parse_node(self, obj): pass class Lambda: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.args)].parse_node(obj.args) class List: @classmethod def parse_node(self, obj): for node in obj.elts: AST_NODES[get_class_str(node)].parse_node(obj.node) class ListComp: @classmethod def parse_node(self, obj): for node in obj.generators: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.elt)].parse_node(obj.elt) class Load: @classmethod def parse_node(self, obj): pass class Lt: @classmethod def parse_node(self, obj): pass class LtE: @classmethod def parse_node(self, obj): pass class MatMult: @classmethod def parse_node(self, obj): pass class Mod: @classmethod def parse_node(self, obj): pass class Module: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) class Mult: @classmethod def parse_node(self, obj): pass class Name: @classmethod def parse_node(self, obj): OBJECTS.append(obj.id) class NameConstant: @classmethod def parse_node(self, obj): pass class NamedExpr: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) AST_NODES[get_class_str(obj.target)].parse_node(obj.target) class NodeTransformer: @classmethod def parse_node(self, obj): pass class NodeVisitor: @classmethod def parse_node(self, obj): pass class Nonlocal: @classmethod def parse_node(self, obj): for name in obj.names: OBJECTS.append(name) class Not: @classmethod def parse_node(self, obj): pass class NotEq: @classmethod def parse_node(self, obj): pass class NotIn: @classmethod def parse_node(self, obj): pass class Num: @classmethod def parse_node(self, obj): pass class Or: @classmethod def parse_node(self, obj): pass class Param: @classmethod def parse_node(self, obj): pass class Pass: @classmethod def parse_node(self, obj): pass class Pow: @classmethod def parse_node(self, obj): pass class RShift: @classmethod def parse_node(self, obj): pass class Raise: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.exec)].parse_node(obj.exec) AST_NODES[get_class_str(obj.cause)].parse_node(obj.cause) class Return: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class Set: @classmethod def parse_node(self, obj): for node in obj.elts: AST_NODES[get_class_str(node)].parse_node(obj.node) class SetComp: @classmethod def parse_node(self, obj): for node in obj.generators: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.elt)].parse_node(obj.elt) class Slice: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.lower)].parse_node(obj.lower) AST_NODES[get_class_str(obj.upper)].parse_node(obj.upper) AST_NODES[get_class_str(obj.step)].parse_node(obj.step) class Starred: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class Store: @classmethod def parse_node(self, obj): pass class Str: @classmethod def parse_node(self, obj): pass class Sub: @classmethod def parse_node(self, obj): pass class Subscript: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) AST_NODES[get_class_str(obj.slice)].parse_node(obj.slice) class Suite: @classmethod def parse_node(self, obj): pass class Try: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.orelse: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.handlers: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.finalbody: AST_NODES[get_class_str(node)].parse_node(node) class Tuple: @classmethod def parse_node(self, obj): for node in obj.elts: AST_NODES[get_class_str(node)].parse_node(obj.node) class TypeIgnore: @classmethod def parse_node(self, obj): pass class UAdd: @classmethod def parse_node(self, obj): pass class USub: @classmethod def parse_node(self, obj): pass class UnaryOp: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.operand)].parse_node(obj.operand) class While: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.orelse: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.test)].parse_node(obj.test) class With: @classmethod def parse_node(self, obj): for node in obj.body: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.items: AST_NODES[get_class_str(node)].parse_node(node) class Yield: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class YieldFrom: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class _ABC: @classmethod def parse_node(self, obj): pass class alias: @classmethod def parse_node(self, obj): OBJECTS.append(obj.name) OBJECTS.append(obj.asname) class arg: @classmethod def parse_node(self, obj): OBJECTS.append(obj.arg) AST_NODES[get_class_str(obj.annotation)].parse_node(obj.annotation) class arguments: @classmethod def parse_node(self, obj): for node in obj.posonlyargs: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.kwonlyargs: AST_NODES[get_class_str(node)].parse_node(node) for node in obj.args: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.vararg)].parse_node(obj.vararg) AST_NODES[get_class_str(node.kwarg)].parse_node(node.kwarg) class boolop: @classmethod def parse_node(self, obj): pass class cmpop: @classmethod def parse_node(self, obj): pass class comprehension: @classmethod def parse_node(self, obj): for node in obj.ifs: AST_NODES[get_class_str(node)].parse_node(node) AST_NODES[get_class_str(obj.target)].parse_node(obj.target) AST_NODES[get_class_str(obj.iter)].parse_node(obj.iter) class excepthandler: @classmethod def parse_node(self, obj): pass class expr: @classmethod def parse_node(self, obj): pass class expr_context: @classmethod def parse_node(self, obj): pass class keyword: @classmethod def parse_node(self, obj): OBJECTS.append(obj.arg) AST_NODES[get_class_str(obj.value)].parse_node(obj.value) class mod: @classmethod def parse_node(self, obj): pass class operator: @classmethod def parse_node(self, obj): pass class slice: @classmethod def parse_node(self, obj): pass class stmt: @classmethod def parse_node(self, obj): pass class type_ignore: @classmethod def parse_node(self, obj): pass class unaryop: @classmethod def parse_node(self, obj): pass class withitem: @classmethod def parse_node(self, obj): AST_NODES[get_class_str(obj.context_expr)].parse_node(obj.context_expr) AST_NODES[get_class_str(obj.optional_vars)].parse_node(obj.optional_vars) AST_NODES = { "_ast.AST": AST, "_ast.Add": Add, "_ast.And": And, "_ast.AnnAssign": AnnAssign, "_ast.Assert": Assert, "_ast.Assign": Assign, "_ast.AsyncFor": AsyncFor, "_ast.AsyncFunctionDef": AsyncFunctionDef, "_ast.AsyncWith": AsyncWith, "_ast.Attribute": Attribute, "_ast.AugAssign": AugAssign, "_ast.AugLoad": AugLoad, "_ast.AugStore": AugStore, "_ast.Await": Await, "_ast.BinOp": BinOp, "_ast.BitAnd": BitAnd, "_ast.BitOr": BitOr, "_ast.BitXor": BitXor, "_ast.BoolOp": BoolOp, "_ast.Break": Break, "ast.Bytes": Bytes, "_ast.Call": Call, "_ast.ClassDef": ClassDef, "_ast.Compare": Compare, "_ast.Constant": Constant, "_ast.Continue": Continue, "_ast.Del": Del, "_ast.Delete": Delete, "_ast.Dict": Dict, "_ast.DictComp": DictComp, "_ast.Div": Div, "ast.Ellipsis": Ellipsis, "_ast.Eq": Eq, "_ast.ExceptHandler": ExceptHandler, "_ast.Expr": Expr, "_ast.Expression": Expression, "_ast.ExtSlice": ExtSlice, "_ast.FloorDiv": FloorDiv, "_ast.For": For, "_ast.FormattedValue": FormattedValue, "_ast.FunctionDef": FunctionDef, "_ast.FunctionType": FunctionType, "_ast.GeneratorExp": GeneratorExp, "_ast.Global": Global, "_ast.Gt": Gt, "_ast.GtE": GtE, "_ast.If": If, "_ast.IfExp": IfExp, "_ast.Import": Import, "_ast.ImportFrom": ImportFrom, "_ast.In": In, "_ast.Index": Index, "_ast.Interactive": Interactive, "_ast.Invert": Invert, "_ast.Is": Is, "_ast.IsNot": IsNot, "_ast.JoinedStr": JoinedStr, "_ast.LShift": LShift, "_ast.Lambda": Lambda, "_ast.List": List, "_ast.ListComp": ListComp, "_ast.Load": Load, "_ast.Lt": Lt, "_ast.LtE": LtE, "_ast.MatMult": MatMult, "_ast.Mod": Mod, "_ast.Module": Module, "_ast.Mult": Mult, "_ast.Name": Name, "ast.NameConstant": NameConstant, "_ast.NamedExpr": NamedExpr, "ast.NodeTransformer": NodeTransformer, "ast.NodeVisitor": NodeVisitor, "_ast.Nonlocal": Nonlocal, "_ast.Not": Not, "_ast.NotEq": NotEq, "_ast.NotIn": NotIn, "ast.Num": Num, "_ast.Or": Or, "_ast.Param": Param, "_ast.Pass": Pass, "_ast.Pow": Pow, "_ast.RShift": RShift, "_ast.Raise": Raise, "_ast.Return": Return, "_ast.Set": Set, "_ast.SetComp": SetComp, "_ast.Slice": Slice, "_ast.Starred": Starred, "_ast.Store": Store, "ast.Str": Str, "_ast.Sub": Sub, "_ast.Subscript": Subscript, "_ast.Suite": Suite, "_ast.Try": Try, "_ast.Tuple": Tuple, "_ast.TypeIgnore": TypeIgnore, "_ast.UAdd": UAdd, "_ast.USub": USub, "_ast.UnaryOp": UnaryOp, "_ast.While": While, "_ast.With": With, "_ast.Yield": Yield, "_ast.YieldFrom": YieldFrom, "ast._ABC": _ABC, "_ast.alias": alias, "_ast.arg": arg, "_ast.arguments": arguments, "_ast.boolop": boolop, "_ast.cmpop": cmpop, "_ast.comprehension": comprehension, "_ast.excepthandler": excepthandler, "_ast.expr": expr, "_ast.expr_context": expr_context, "_ast.keyword": keyword, "_ast.mod": mod, "_ast.operator": operator, "_ast.slice": slice, "_ast.stmt": stmt, "_ast.type_ignore": type_ignore, "_ast.unaryop": unaryop, "_ast.withitem": withitem, }

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81

0.64064

2,830

21,068

4.526502

0.063958

0.16089

0.180952

0.219048

0.747151

0.745589

0.733255

0.725059

0.543326

0.52943

0

0.000126

0.245396

21,068

979

82

21.519918

0.805636

0

0.612329

0

0.065312

0.000997

0

0.00274

1

0.168493

false

0.091781

0.006849

0

0.343836

0

null

0

1

0

1

0

null

0

1

0

6

f6e20f724e1e44cadec0c3278458f9f4a941e3b3

36

py

Python

Instance/config.py

simonkairu/pitches

6e475297f3f9c91dd601bb9ae9f774c6bb849ca9

[ "MIT" ]

null

Instance/config.py

simonkairu/pitches

6e475297f3f9c91dd601bb9ae9f774c6bb849ca9

[ "MIT" ]

null

Instance/config.py

simonkairu/pitches

6e475297f3f9c91dd601bb9ae9f774c6bb849ca9

[ "MIT" ]

null

SECRET_KEY='wvkwcw6nflmYWLrdlLUVcg'

18

35

0.888889

3

36

10.333333

1

0

0.028571

0.027778

36

1

36

0.857143

0

0.611111

0

1

0

false

0

1

null

0

1

0

1

null

0

6

100086e7fb33cea9af3340db96c87496054077b3

121

py

Python

staff_management_models/staff_group_payments/admin.py

reimibeta/django-staff-management-models

20fd718d18d39f333fb9e1ac231154db86a9b91c

[ "Apache-2.0" ]

null

staff_management_models/staff_group_payments/admin.py

reimibeta/django-staff-management-models

20fd718d18d39f333fb9e1ac231154db86a9b91c

[ "Apache-2.0" ]

null

staff_management_models/staff_group_payments/admin.py

reimibeta/django-staff-management-models

20fd718d18d39f333fb9e1ac231154db86a9b91c

[ "Apache-2.0" ]

null

from staff_management_models.staff_group_payments.class_admins.staff_worker_payment_admin import StaffWorkerPaymentAdmin

60.5

120

0.942149

15

121

7.066667

0.866667

0

0.033058

121

1

121

0.905983

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

1018f41969b218b1df5e3d8cce148c92bee0d665

118

py

Python

EduSim/Envs/TMS/Agent.py

bigdata-ustc/EduSim

849eed229c24615e5f2c3045036311e83c22ea68

[ "MIT" ]

18

2019-11-11T03:45:35.000Z

2022-02-09T15:31:51.000Z

EduSim/Envs/TMS/Agent.py

ghzhao78506/EduSim

cb10e952eb212d8a9344143f889207b5cd48ba9d

[ "MIT" ]

3

2020-10-23T01:05:57.000Z

2021-03-16T12:12:24.000Z

EduSim/Envs/TMS/Agent.py

bigdata-ustc/EduSim

849eed229c24615e5f2c3045036311e83c22ea68

[ "MIT" ]

6

2020-06-09T21:32:00.000Z

2022-03-12T00:25:18.000Z

# coding: utf-8 # 2020/5/7 @ tongshiwei from EduSim.SimOS import RandomAgent class TMSAgent(RandomAgent): pass

13.111111

36

0.728814

16

118

5.375

0.9375

0

0.072165

0.177966

118

8

37

14.75

0.814433

0.29661

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

63e320bd742e48993b6094fca258ab13228711a6

3,247

py

Python

tests/test_reading.py

emilioschepis/cdesf2

4a98b8608abd9422d818b5feca720e6bacd19642

[ "MIT" ]

null

tests/test_reading.py

emilioschepis/cdesf2

4a98b8608abd9422d818b5feca720e6bacd19642

[ "MIT" ]

null

tests/test_reading.py

emilioschepis/cdesf2

4a98b8608abd9422d818b5feca720e6bacd19642

[ "MIT" ]

null

import pandas as pd import numpy as np from datetime import datetime, timezone, timedelta from pandas._libs.tslibs import Timestamp from pm4py.objects.log import obj from cdesf2.utils import read_csv, read_xes def test_read_csv(): # TODO: Is there a reason to replace " " in the activity name with "_"? event_stream_test = read_csv("demo/Detail_Supplier_IW-Frozen.csv") assert isinstance(event_stream_test, obj.EventStream) assert len(event_stream_test) == 5000 first_event = event_stream_test[0] assert first_event["case:concept:name"] == "Case 496" assert first_event["concept:name"] == "Process Creation" assert isinstance(first_event["time:timestamp"], datetime) assert first_event["time:timestamp"] == datetime(2010, 9, 21, 9, 0, 13) second_event = event_stream_test[1] assert second_event["case:concept:name"] == "Case 12186" assert second_event["concept:name"] == "Process Creation" assert isinstance(second_event["time:timestamp"], datetime) assert second_event["time:timestamp"] == datetime(2010, 9, 21, 9, 0, 21) second_to_last_event = event_stream_test[-2] assert second_to_last_event["case:concept:name"] == "Case 8848" assert second_to_last_event["concept:name"] == "ME Fabrication Checker" assert isinstance(second_to_last_event["time:timestamp"], datetime) assert second_to_last_event["time:timestamp"] == datetime(2011, 4, 27, 8, 0, 59) last_event = event_stream_test[-1] assert last_event["case:concept:name"] == "Case 10634" assert last_event["concept:name"] == "ME Fabrication Checker" assert isinstance(last_event["time:timestamp"], datetime) assert last_event["time:timestamp"] == datetime(2011, 4, 27, 9, 0, 0) def test_read_xes(): event_stream_test = read_xes("demo/running-example.xes") assert isinstance(event_stream_test, obj.EventStream) assert len(event_stream_test) == 42 tzinfo = timezone(timedelta(seconds=3600)) first_event = event_stream_test[0] assert first_event["case:concept:name"] == "1" assert first_event["concept:name"] == "register request" assert isinstance(first_event["time:timestamp"], datetime) assert first_event["time:timestamp"] == datetime(2010, 12, 30, 11, 2, tzinfo=tzinfo) second_event = event_stream_test[1] assert second_event["case:concept:name"] == "1" assert second_event["concept:name"] == "examine thoroughly" assert isinstance(second_event["time:timestamp"], datetime) assert second_event["time:timestamp"] == datetime( 2010, 12, 31, 10, 6, tzinfo=tzinfo ) second_to_last_event = event_stream_test[-2] assert second_to_last_event["case:concept:name"] == "4" assert second_to_last_event["concept:name"] == "decide" assert isinstance(second_to_last_event["time:timestamp"], datetime) assert second_to_last_event["time:timestamp"] == datetime( 2011, 1, 9, 12, 2, tzinfo=tzinfo ) last_event = event_stream_test[-1] assert last_event["case:concept:name"] == "4" assert last_event["concept:name"] == "reject request" assert isinstance(last_event["time:timestamp"], datetime) assert last_event["time:timestamp"] == datetime(2011, 1, 12, 15, 44, tzinfo=tzinfo)

42.168831

88

0.716046

446

3,247

4.988789

0.213004

0.080899

0.129438

0.186966

0.762247

0.717303

0.705618

0.651685

0.648989

0.608539

0

0.045918

0.154912

3,247

76

89

42.723684

0.764942

0.02125

0

0.305085

0

0.21568

0.018262

0

0.013158

0.610169

1

0.033898

false

0

0.101695

0

0.135593

0

null

0

1

0

1

0

1

0

null

0

1

0

6

89d69cafac7cb233d1ca2659e1e0221da3b1a53d

5,867

py

Python

pirates/leveleditor/worldData/JungleTestIslandA.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

81

2018-04-08T18:14:24.000Z

2022-01-11T07:22:15.000Z

pirates/leveleditor/worldData/JungleTestIslandA.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

4

2018-09-13T20:41:22.000Z

2022-01-08T06:57:00.000Z

pirates/leveleditor/worldData/JungleTestIslandA.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

26

2018-05-26T12:49:27.000Z

2021-09-11T09:11:59.000Z

from pandac.PandaModules import Point3, VBase3 objectStruct = {'Locator Links': [['1157574848.27sdnaik', '1157574884.56sdnaik', 'Bi-directional'], ['1157574928.53sdnaik', '1157574884.58sdnaik', 'Bi-directional'], ['1157574928.66sdnaik', '1157574992.98sdnaik', 'Bi-directional'], ['1157574992.97sdnaik', '1157574848.3sdnaik', 'Bi-directional']],'Objects': {'1157574780.95sdnaik': {'Type': 'Island','Name': 'JungleTestIslandA','File': '','Objects': {'1157485817.84sdnaik': {'Type': 'Island Game Area','File': 'jungle_area_a','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1157574928.53sdnaik': {'Type': 'Locator Node','Name': 'portal_interior_1','GridPos': Point3(-3.925, -10.962, 221.101),'Hpr': VBase3(-153.319, 0.0, 0.0),'Pos': Point3(406.169, 250.261, 8.467),'Scale': VBase3(1.0, 1.0, 1.0)},'1157574928.66sdnaik': {'Type': 'Locator Node','Name': 'portal_interior_2','GridPos': Point3(-542.421, -643.139, 218.437),'Hpr': VBase3(96.892, 0.711, -0.076),'Pos': Point3(-120.867, -365.049, -0.26),'Scale': VBase3(1.0, 1.0, 1.0)},'1164917329.8sdnaik': {'Type': 'Locator Node','Name': 'portal_interior_3','GridPos': Point3(-1156.107, 144.641, 521.905),'Hpr': VBase3(-61.504, -3.202, 1.38),'Pos': Point3(-352.561, 241.702, 3.28),'Scale': VBase3(1.0, 1.0, 1.0)}},'Pos': Point3(-803.546, -97.061, 518.625),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/jungles/jungle_a_zero'}},'1157574848.27sdnaik': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1157574848.3sdnaik': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1157574853.39sdnaik': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1157574861.95sdnaik': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1157574884.55sdnaik': {'Type': 'Connector Tunnel','File': '','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1157574884.56sdnaik': {'Type': 'Locator Node','Name': 'portal_connector_1','Hpr': VBase3(-90.0, 0.0, 0.0),'Pos': Point3(0.0, 3.262, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1157574884.58sdnaik': {'Type': 'Locator Node','Name': 'portal_connector_2','GridPos': Point3(-313.651, -175.767, 131.091),'Hpr': VBase3(90.0, 0.0, 0.0),'Pos': Point3(95.197, 150.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)}},'Pos': Point3(-252.603, -296.91, 259.504),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/tunnels/tunnel_cave_left'}},'1157574992.92sdnaik': {'Type': 'Connector Tunnel','File': '','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1157574992.97sdnaik': {'Type': 'Locator Node','Name': 'portal_connector_1','Hpr': VBase3(-90.0, 0.0, 0.0),'Pos': Point3(0.0, 3.262, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1157574992.98sdnaik': {'Type': 'Locator Node','Name': 'portal_connector_2','GridPos': Point3(-488.418, -323.438, 197.465),'Hpr': VBase3(90.0, 0.0, 0.0),'Pos': Point3(95.197, 150.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)}},'Pos': Point3(-514.185, 421.494, 276.169),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/tunnels/tunnel_cave_left'}},'1161902850.89sdnaik': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1161902852.94sdnaik': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1161986237.39sdnaik': {'Type': 'Player Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(-199.458, -320.558, 0.843),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All'}},'Visual': {'Model': 'models/islands/bilgewater_zero'}}},'Node Links': [],'Layers': {},'ObjectIds': {'1157485817.84sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157485817.84sdnaik"]','1157574780.95sdnaik': '["Objects"]["1157574780.95sdnaik"]','1157574848.27sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574848.27sdnaik"]','1157574848.3sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574848.3sdnaik"]','1157574853.39sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574853.39sdnaik"]','1157574861.95sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574861.95sdnaik"]','1157574884.55sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574884.55sdnaik"]','1157574884.56sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574884.55sdnaik"]["Objects"]["1157574884.56sdnaik"]','1157574884.58sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574884.55sdnaik"]["Objects"]["1157574884.58sdnaik"]','1157574928.53sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157485817.84sdnaik"]["Objects"]["1157574928.53sdnaik"]','1157574928.66sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157485817.84sdnaik"]["Objects"]["1157574928.66sdnaik"]','1157574992.92sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574992.92sdnaik"]','1157574992.97sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574992.92sdnaik"]["Objects"]["1157574992.97sdnaik"]','1157574992.98sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157574992.92sdnaik"]["Objects"]["1157574992.98sdnaik"]','1161902850.89sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1161902850.89sdnaik"]','1161902852.94sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1161902852.94sdnaik"]','1161986237.39sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1161986237.39sdnaik"]','1164917329.8sdnaik': '["Objects"]["1157574780.95sdnaik"]["Objects"]["1157485817.84sdnaik"]["Objects"]["1164917329.8sdnaik"]'}}

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null

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1

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6

89e7148b94fbb6ff67562cec6d68812dbd3e3bf6

48

py

Python

cvdd/models/__init__.py

altescy/cvdd

57e4fe0fd30a6d2b67651ce076b63a9a8a6e7c7a

[ "MIT" ]

5

2021-07-11T08:40:43.000Z

2021-07-19T05:08:11.000Z

cvdd/models/__init__.py

altescy/cvdd

57e4fe0fd30a6d2b67651ce076b63a9a8a6e7c7a

[ "MIT" ]

null

cvdd/models/__init__.py

altescy/cvdd

57e4fe0fd30a6d2b67651ce076b63a9a8a6e7c7a

[ "MIT" ]

null

from cvdd.models.cvdd import CVDD # noqa: F401

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1

0

6

89efdfa0780385a69fa5893afa10657f79b8c298

9,188

py

Python

market_place_proj/app_market/migrations/0002_initial.py

grand-roman/market_place

219e9c6f108fc4edd7508e0f00078c0c78583f47

[ "BSD-2-Clause" ]

null

market_place_proj/app_market/migrations/0002_initial.py

grand-roman/market_place

219e9c6f108fc4edd7508e0f00078c0c78583f47

[ "BSD-2-Clause" ]

null

market_place_proj/app_market/migrations/0002_initial.py

grand-roman/market_place

219e9c6f108fc4edd7508e0f00078c0c78583f47

[ "BSD-2-Clause" ]

null

# Generated by Django 3.2.8 on 2021-12-23 12:58 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import mptt.fields class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('app_market', '0001_initial'), ] operations = [ migrations.AddField( model_name='review', name='user', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='reviews', to=settings.AUTH_USER_MODEL, verbose_name='user'), ), migrations.AddField( model_name='relatedgoodgroup', name='discount', field=models.ForeignKey(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='related_discounts', to='app_market.discount', verbose_name='discount'), ), migrations.AddField( model_name='relatedgoodgroup', name='group1', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='group1', to='app_market.goodgroup', verbose_name='Product category 1'), ), migrations.AddField( model_name='relatedgoodgroup', name='group2', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='group2', to='app_market.goodgroup', verbose_name='Product category 2'), ), migrations.AddField( model_name='payment', name='order', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='pay', to='app_market.order', verbose_name='order'), ), migrations.AddField( model_name='orderdetail', name='cart', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='order_detail', to='app_market.cart', verbose_name='Cart'), ), migrations.AddField( model_name='orderdetail', name='discount', field=models.ForeignKey(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='detail', to='app_market.discount', verbose_name='discount'), ), migrations.AddField( model_name='orderdetail', name='order', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='detail', to='app_market.order', verbose_name='order'), ), migrations.AddField( model_name='orderdetail', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='order_detail', to=settings.AUTH_USER_MODEL, verbose_name='order'), ), migrations.AddField( model_name='order', name='cart_discount', field=models.ForeignKey(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='order', to='app_market.discount', verbose_name='discount'), ), migrations.AddField( model_name='order', name='user', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='order', to=settings.AUTH_USER_MODEL, verbose_name='user'), ), migrations.AddField( model_name='goodview', name='good', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='views', to='app_market.good', verbose_name='good'), ), migrations.AddField( model_name='goodview', name='user', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='views', to=settings.AUTH_USER_MODEL, verbose_name='user'), ), migrations.AddField( model_name='good', name='category', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='good', to='app_market.category', verbose_name='Product category'), ), migrations.AddField( model_name='good', name='discount', field=models.ForeignKey(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='good', to='app_market.discount', verbose_name='discount'), ), migrations.AddField( model_name='good', name='files', field=models.ManyToManyField(blank=True, related_name='catalog', to='app_market.MediaFiles', verbose_name='related files'), ), migrations.AddField( model_name='good', name='group', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='group', to='app_market.goodgroup', verbose_name='group of products for discount'), ), migrations.AddField( model_name='good', name='image', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, related_name='good', to='app_market.mediafiles', verbose_name='image for good'), ), migrations.AddField( model_name='good', name='tag', field=models.ManyToManyField(related_name='good', to='app_market.Tag', verbose_name='Tags'), ), migrations.AddField( model_name='discount', name='variants', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='discount', to='app_market.discountvariants', verbose_name='discount options'), ), migrations.AddField( model_name='category', name='discount', field=models.ForeignKey(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='category', to='app_market.discount', verbose_name='discount'), ), migrations.AddField( model_name='category', name='icon', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='icon', to='app_market.mediafiles', verbose_name='related files'), ), migrations.AddField( model_name='category', name='parent', field=mptt.fields.TreeForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='children', to='app_market.category'), ), migrations.AddField( model_name='catalog', name='delivery', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='catalog', to='app_market.delivery', verbose_name='delivery'), ), migrations.AddField( model_name='catalog', name='discount', field=models.ForeignKey(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='catalog', to='app_market.discount', verbose_name='discount'), ), migrations.AddField( model_name='catalog', name='good', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='catalog', to='app_market.good', verbose_name='product'), ), migrations.AddField( model_name='catalog', name='seller', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='catalog', to='app_market.seller', verbose_name='seller'), ), migrations.AddField( model_name='cartsale', name='discount', field=models.ForeignKey(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='cart_sale', to='app_market.discount', verbose_name='discount'), ), migrations.AddField( model_name='cart', name='cart_discount', field=models.ForeignKey(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='cart', to='app_market.discount', verbose_name='discount'), ), migrations.AddField( model_name='cart', name='catalog', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='cart', to='app_market.catalog', verbose_name='catalog_good'), ), migrations.AddField( model_name='cart', name='good', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='cart', to='app_market.good', verbose_name='good'), ), migrations.AddField( model_name='cart', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='cart', to=settings.AUTH_USER_MODEL, verbose_name='user'), ), ]

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null

0

1

0

1

0

1

0

null

0

6

d6089b133347f222fbcfd38934ebc034c6fbe312

232

py

Python

strategies.py

JMan-Zx/game_of_hog

9e75fe246905092fc37e46c314cfee04316b1371

[ "MIT" ]

null

strategies.py

JMan-Zx/game_of_hog

9e75fe246905092fc37e46c314cfee04316b1371

[ "MIT" ]

null

strategies.py

JMan-Zx/game_of_hog

9e75fe246905092fc37e46c314cfee04316b1371

[ "MIT" ]

null

from game_matrix import game_matrix def optimal(player_score, opponent_score): # take off the win_rate, not needed return game_matrix(player_score, opponent_score)[0] def roll_4(player_score, opponent_score): return 4

25.777778

55

0.780172

36

232

4.722222

0.555556

0.176471

0.335294

0.423529

0

0.015306

0.155172

232

8

56

29

0.852041

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0

1

0.4

false

0

0.2

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1

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null

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1

0

1

0

null

0

1

0

1

0

6

d640cdcd379049a0929051fb7f9350b9d263d736

32

py

Python

aiotg/__init__.py

domclick/aiotg

9228af2727ea88ee60b46678511dd701b965afe3

[ "MIT" ]

435

2015-06-30T17:50:42.000Z

2022-03-24T10:08:59.000Z

aiotg/__init__.py

domclick/aiotg

9228af2727ea88ee60b46678511dd701b965afe3

[ "MIT" ]

65

2015-08-14T10:25:49.000Z

2021-07-29T14:01:56.000Z

aiotg/__init__.py

domclick/aiotg

9228af2727ea88ee60b46678511dd701b965afe3

[ "MIT" ]

71

2015-07-20T22:14:47.000Z

2022-01-21T11:20:59.000Z

from aiotg.bot import * # noqa

16

31

0.6875

5

32

4.4

1

0

0.21875

32

1

32

0.88

0.125

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d65674ebd0afd658cb831b24a3c0347e6d767352

209

py

Python

modules/debug/sources/__init__.py

AnthonyEdvalson/Machina

fefb058591dd7b62817c75277d5ca0eb6dbd8c3a

[ "MIT" ]

null

modules/debug/sources/__init__.py

AnthonyEdvalson/Machina

fefb058591dd7b62817c75277d5ca0eb6dbd8c3a

[ "MIT" ]

null

modules/debug/sources/__init__.py

AnthonyEdvalson/Machina

fefb058591dd7b62817c75277d5ca0eb6dbd8c3a

[ "MIT" ]

null

from modules.debug.sources.brokensource import BrokenSource from modules.debug.sources.source1 import Source1 from modules.debug.sources.source2 import Source2 sources = [Source1, Source2, BrokenSource]

34.833333

60

0.822967

25

209

6.88

0.32

0.19186

0.27907

0.401163

0

0.032258

0.110048

209

5

61

41.8

0.892473

0

1

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false

0

0.75

0

0.75

0

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null

0

1

0

1

0

null

0

1

0

1

0

6

c3884df21f8506c3208765a75e80a5447431a310

66

py

Python

devito/passes/equations/__init__.py

speglich/devito

b636f7694eb6a1e19b0f2c48f44ff63613029a7b

[ "MIT" ]

1

2021-03-25T21:23:03.000Z

devito/passes/equations/__init__.py

speglich/devito

b636f7694eb6a1e19b0f2c48f44ff63613029a7b

[ "MIT" ]

52

2020-10-12T19:29:09.000Z

2022-03-10T14:05:22.000Z

devito/passes/equations/__init__.py

alisiahkoohi/devito

f535a44dff12de2837eb6e3217a65ffb2d371cb8

[ "MIT" ]

1

2020-06-02T03:31:11.000Z

from .linearity import * # noqa from .buffering import * # noqa

22

32

0.69697

8

66

5.75

0.625

0.434783

0

0.212121

66

2

33

0.884615

0.136364

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

c3bf84eddf3053d89dbd376832d9725192054646

131

py

Python

xvision/api/face.py

jimmysue/xvision

bf5aa567a197b3e4c9fdd285c80b4f7512d14d7a

[ "MIT" ]

3

2021-04-08T10:50:53.000Z

2021-11-15T07:26:16.000Z

xvision/api/face.py

jimmysue/xvision

bf5aa567a197b3e4c9fdd285c80b4f7512d14d7a

[ "MIT" ]

3

2021-08-05T07:40:52.000Z

2021-11-16T05:53:29.000Z

xvision/api/face.py

jimmysue/xvision

bf5aa567a197b3e4c9fdd285c80b4f7512d14d7a

[ "MIT" ]

1

2021-12-15T05:57:48.000Z

def load_face_detector(checkpoint, *args, **kwargs): pass def load_face_alignmentor(checkpoint, *args, **kwargs): pass

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Python

anygraph/unittests/test_linkers.py

gemerden/anygraph

c20cab82ad4a7f4117690a445e136c2b0e84f0f3

[ "MIT" ]

10

2020-06-11T14:11:58.000Z

2021-12-31T11:59:26.000Z

anygraph/unittests/test_linkers.py

gemerden/anygraph

c20cab82ad4a7f4117690a445e136c2b0e84f0f3

[ "MIT" ]

null

anygraph/unittests/test_linkers.py

gemerden/anygraph

c20cab82ad4a7f4117690a445e136c2b0e84f0f3

[ "MIT" ]

null

import unittest import uuid from random import choice from anygraph import One, Many class TestLinkers(unittest.TestCase): def test_one(self): class TestOne(object): next = One() def __init__(self, name): self.name = name bob = TestOne('bob') ann = TestOne('ann') bob.next = ann assert bob.next is ann assert list(TestOne.next.iterate(bob)) == [bob, ann] del bob.next assert bob.next is None bob.next = ann ann.next = bob assert bob.next is ann assert ann.next is bob bob.next = bob assert bob.next is bob def test_triangle(self): class TestOne(object): next = One() def __init__(self, name): self.name = name bob = TestOne('bob') ann = TestOne('ann') kik = TestOne('kik') bob.next = ann ann.next = kik kik.next = bob assert bob.next is ann assert ann.next is kik assert kik.next is bob assert list(TestOne.next.iterate(bob)) == [bob, ann, kik] del ann.next assert ann.next is None assert bob.next is ann assert kik.next is bob def test_many(self): class TestMany(object): nexts = Many() def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') pete = TestMany('pete') howy = TestMany('howy') bob.nexts.include(ann, pete) pete.nexts.include(howy) assert ann in bob.nexts assert howy in pete.nexts assert howy not in bob.nexts assert list(TestMany.nexts.iterate(bob)) == [bob, ann, pete, howy] def test_cyclic(self): class TestMany(object): nexts = Many(cyclic=False) def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') pete = TestMany('pete') howy = TestMany('howy') bob.nexts.include(ann, pete) pete.nexts.include(howy) with self.assertRaises(ValueError): howy.nexts.include(bob) def test_to_self(self): class TestMany(object): nexts = Many(to_self=False) def __init__(self, name): self.name = name bob = TestMany('bob') with self.assertRaises(ValueError): bob.nexts.include(bob) with self.assertRaises(ValueError): bob.nexts = [bob] def test_on_link(self): on_link_results = [] on_unlink_results = [] def on_link(one, two): on_link_results.append((one, two)) def on_unlink(one, two): on_unlink_results.append((one, two)) class TestMany(object): nexts = Many(on_link=on_link, on_unlink=on_unlink) def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') pete = TestMany('pete') howy = TestMany('howy') bob.nexts.include(ann, pete) pete.nexts.include(howy) assert on_link_results == [(bob, ann), (bob, pete), (pete, howy)] del bob.nexts assert on_unlink_results == [(bob, ann), (bob, pete)] def test_visitor(self): class TestMany(object): nexts = Many() def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') pete = TestMany('pete') howy = TestMany('howy') bob.nexts.include(ann, pete) ann.nexts.include(howy) pete.nexts.include(howy) people = [] def on_visit(obj): people.append(obj) TestMany.nexts.visit(bob, on_visit=on_visit, breadth_first=False) assert people == [bob, ann, howy, pete] del people[:] TestMany.nexts.visit(bob, on_visit=on_visit, breadth_first=True) assert people == [bob, ann, pete, howy] def test_builder_many_by_name(self): class TestBuilder(object): nexts = Many() def __init__(self, name, iterable=()): self.name = name self.items = list(iterable) def extend(self, *items): self.items.extend(items) def __iter__(self): for item in self.items: yield item bob = TestBuilder('bob') ann = TestBuilder('ann') pete = TestBuilder('pete') howy = TestBuilder('howy') bob.extend(ann, pete) pete.extend(howy, bob) ann.extend(pete) howy.extend(ann) assert bob.nexts == set() TestBuilder.nexts.build(bob) assert bob.nexts == {ann, pete} assert howy.nexts == {ann} def test_builder_many_by_func_cyclic(self): class TestBuilder(object): nexts = Many() def __init__(self, name, iterable=()): self.name = name self.items = list(iterable) def extend(self, *items): self.items.extend(items) bob = TestBuilder('bob') ann = TestBuilder('ann') pete = TestBuilder('pete') howy = TestBuilder('howy') bob.extend(ann, pete) pete.extend(howy, bob) ann.extend(pete) howy.extend(ann) TestBuilder.nexts.build(bob, key=lambda obj: obj.items) assert bob.nexts == {ann, pete} assert pete.nexts == {howy, bob} assert howy.nexts == {ann} assert TestBuilder.nexts.in_cycle(bob) assert TestBuilder.nexts.is_cyclic(bob) def test_builder_one(self): class TestBuilder(object): next = One() def __init__(self, name, other=None): self.name = name self.other = other def __str__(self): return self.name bob = TestBuilder('bob') ann = TestBuilder('ann') pip = TestBuilder('pip') bob.other = ann ann.other = pip assert bob.next is None TestBuilder.next.build(bob, 'other') assert bob.next == ann assert ann.next == pip class TestDoubleLinkers(unittest.TestCase): def test_one_one(self): class TestOneOne(object): left = One('right') right = One('left') def __init__(self, name): self.name = name bob = TestOneOne('bob') ann = TestOneOne('ann') bob.left = ann assert bob.left is ann assert ann.right is bob assert list(TestOneOne.left.iterate(bob)) == [bob, ann] del bob.left assert bob.left is None assert ann.right is None bob.left = ann ann.right = None assert bob.left is None assert ann.right is None bob.left = bob assert bob.left is bob assert bob.right is bob del bob.left assert bob.left is None assert bob.right is None def test_triangle(self): class TestOne(object): next = One('prev') prev = One('next') def __init__(self, name): self.name = name bob = TestOne('bob') ann = TestOne('ann') kik = TestOne('kik') bob.next = ann ann.next = kik kik.next = bob assert bob.next is ann assert ann.next is kik assert kik.next is bob assert bob.prev is kik assert kik.prev is ann assert ann.prev is bob assert list(TestOne.next.iterate(bob)) == [bob, ann, kik] del ann.prev assert ann.prev is None assert bob.next is None assert ann.next is kik assert kik.next is bob def test_many_many(self): class TestMany(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') pete = TestMany('pete') howy = TestMany('howy') bob.nexts.include(ann, pete) pete.nexts.include(howy) assert ann in bob.nexts assert bob in ann.prevs assert howy in pete.nexts assert howy not in bob.nexts assert list(TestMany.nexts.iterate(bob)) == [bob, ann, pete, howy] def test_one_many(self): class TestOneMany(object): parent = One('children') children = Many('parent') def __init__(self, name): self.name = name bob = TestOneMany('bob') ann = TestOneMany('ann') pete = TestOneMany('pete') howy = TestOneMany('howy') bob.children = [ann, pete] howy.parent = pete assert ann in bob.children assert bob is ann.parent assert howy in pete.children assert howy not in bob.children assert howy.parent.parent is bob assert list(TestOneMany.children.iterate(bob)) == [bob, ann, pete, howy] assert list(TestOneMany.parent.iterate(howy)) == [howy, pete, bob] del bob.children assert ann not in bob.children assert bob is not ann.parent assert howy.parent.parent is None def test_pairs(self): class Test(object): other = One('other') def __init__(self, name): self.name = name def __str__(self): return self.name bob = Test('bob') ann = Test('ann') kik = Test('kik') bob.other = ann assert bob.other is ann assert ann.other is bob assert list(Test.other.iterate(bob)) == [bob, ann] kik.other = ann assert bob.other is None assert kik.other is ann assert ann.other is kik def test_non_directed(self): class Test(object): others = Many('others') def __init__(self, name): self.name = name def __str__(self): return self.name bob = Test('bob') ann = Test('ann') kik = Test('kik') bob.others.include(ann) bob.others.include(kik) assert ann in bob.others assert kik in bob.others assert bob in ann.others assert bob in kik.others kik.others.include(ann) assert kik in ann.others assert ann in kik.others assert ann in bob.others assert kik in bob.others assert bob in ann.others assert bob in kik.others assert list(Test.others.iterate(bob)) == [bob, ann, kik] kik.others.exclude(ann) assert kik not in ann.others assert ann not in kik.others assert ann in bob.others assert kik in bob.others assert bob in ann.others assert bob in kik.others def test_cyclic(self): class TestMany(object): nexts = Many('prevs', cyclic=False) prevs = Many('nexts') def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') pete = TestMany('pete') howy = TestMany('howy') bob.nexts.include(ann, pete) pete.nexts.include(howy) with self.assertRaises(ValueError): howy.nexts.include(bob) with self.assertRaises(ValueError): bob.prevs.include(howy) def test_on_link(self): on_link_results = [] on_unlink_results = [] def on_link(one, two): on_link_results.append((one, two)) def on_unlink(one, two): on_unlink_results.append((one, two)) class TestMany(object): nexts = Many('prevs', on_link=on_link, on_unlink=on_unlink) prevs = Many('nexts') def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') pete = TestMany('pete') howy = TestMany('howy') bob.nexts.include(ann, pete) pete.nexts.include(howy) assert on_link_results == [(bob, ann), (bob, pete), (pete, howy)] del bob.nexts assert on_unlink_results == [(bob, ann), (bob, pete)] def test_custom_id(self): class TestMany(object): nexts = Many('prevs', get_id=lambda obj: hash(obj)) prevs = Many('nexts') def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') bob.nexts.include(ann) assert ann in bob.nexts assert bob in ann.prevs def test_visitor(self): class TestMany(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') pete = TestMany('pete') howy = TestMany('howy') bob.nexts.include(ann, pete) ann.nexts.include(howy) pete.nexts.include(howy) people = [] def on_visit(obj): people.append(obj) TestMany.nexts.visit(bob, on_visit=on_visit, breadth_first=False) assert people == [bob, ann, howy, pete] del people[:] TestMany.nexts.visit(bob, on_visit=on_visit, breadth_first=True) assert people == [bob, ann, pete, howy] def test_builder_many_by_name(self): class TestBuilder(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name, iterable=()): self.name = name self.items = list(iterable) def extend(self, *items): self.items.extend(items) def __iter__(self): for item in self.items: yield item def __str__(self): return self.name bob = TestBuilder('bob') ann = TestBuilder('ann') pete = TestBuilder('pete') howy = TestBuilder('howy') bob.extend(ann, pete) pete.extend(howy, bob) ann.extend(pete) howy.extend(ann) assert bob.nexts == set() assert ann.prevs == set() TestBuilder.nexts.build(bob) assert bob.nexts == {ann, pete} assert ann.prevs == {bob, howy} assert howy.nexts == {ann} assert howy.prevs == {pete} def test_builder_many_by_func(self): class TestBuilder(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name, iterable=()): self.name = name self.items = list(iterable) def extend(self, *items): self.items.extend(items) def __str__(self): return self.name bob = TestBuilder('bob') ann = TestBuilder('ann') pete = TestBuilder('pete') howy = TestBuilder('howy') bob.extend(ann, pete) pete.extend(howy, bob) ann.extend(pete) howy.extend(ann) assert bob.nexts == set() assert ann.prevs == set() TestBuilder.nexts.build(bob, key=lambda obj: obj.items) assert bob.nexts == {ann, pete} assert ann.prevs == {bob, howy} assert howy.nexts == {ann} assert howy.prevs == {pete} def test_builder_one(self): class TestBuilder(object): next = One('prev') prev = One('next') def __init__(self, name, other=None): self.name = name self.other = other def __str__(self): return self.name bob = TestBuilder('bob') ann = TestBuilder('ann') pip = TestBuilder('pip') bob.other = ann ann.other = pip assert bob.next is None assert ann.prev is None TestBuilder.next.build(bob, 'other') assert bob.next == ann assert ann.prev == bob assert ann.next == pip assert pip.prev == ann def test_gather(self): class Test(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name): self.name = name def __repr__(self): return self.name bob = Test('bob') ann = Test('ann') pete = Test('pete') howy = Test('howy') bob.nexts.include(ann) ann.prevs.include(pete) pete.nexts.include(howy, bob) assert Test.nexts.gather(bob) == [bob, ann, pete, howy] def test_gather_pairs_directed(self): class Test(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name): self.name = name def __repr__(self): return self.name nodes = [Test(str(i)) for i in range(5)] for node in nodes: # fully linked node.nexts.include(*nodes) assert len(nodes[0].nexts) == 5 assert len(nodes[0].prevs) == 5 nexts_pairs = Test.nexts.gather_pairs(nodes[0]) assert len(nexts_pairs) == 25 prevs_pairs = Test.prevs.gather_pairs(nodes[0]) assert len(prevs_pairs) == 25 def test_gather_pairs_non_directed(self): class Test(object): friends = Many('friends') def __init__(self, name): self.name = name def __repr__(self): return self.name nodes = [Test(str(i)) for i in range(5)] for node in nodes: # fully linked node.friends.include(*nodes) assert len(nodes[0].friends) == 5 nexts_pairs = Test.friends.gather_pairs(nodes[0]) assert len(nexts_pairs) == 25 def test_find_directed(self): class Test(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name): self.name = name def __repr__(self): return self.name nodes = [Test(i) for i in range(5)] for i, node1 in enumerate(nodes): for j, node2 in enumerate(nodes): if (i + j) % 2 == 1: node1.nexts.include(node2) found = Test.nexts.find(nodes[0], filter=lambda n: n.name in (1, 2)) assert len(found) == 2 def test_find_non_directed(self): class Test(object): friends = Many('friends') def __init__(self, name): self.name = name def __repr__(self): return self.name nodes = [Test(i) for i in range(5)] for i, node1 in enumerate(nodes): for j, node2 in enumerate(nodes): if (i + j) % 2 == 1: node1.friends.include(node2) found = Test.friends.find(nodes[0], filter=lambda n: n.name in (1, 2)) assert len(found) == 2 def test_reachable_directed(self): class Test(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name): self.name = name def __repr__(self): return self.name nodes = [Test(i) for i in range(5)] for i, node1 in enumerate(nodes): for j, node2 in enumerate(nodes): if j == i + 1: node1.nexts.include(node2) reachable = Test.nexts.reachable(nodes[0], nodes[4]) assert reachable reachable = Test.nexts.reachable(nodes[1], nodes[0]) assert not reachable def test_reachable_non_directed(self): class Test(object): friends = Many('friends') def __init__(self, name): self.name = name def __repr__(self): return self.name nodes = [Test(i) for i in range(5)] for i, node1 in enumerate(nodes): for j, node2 in enumerate(nodes): if j == i + 1: node1.friends.include(node2) reachable = Test.friends.reachable(nodes[0], nodes[4]) assert reachable reachable = Test.friends.reachable(nodes[1], nodes[0]) assert reachable def test_walk_directed(self): class Test(object): nexts = Many('prevs') prevs = Many('nexts') def __init__(self, name): self.name = name def __repr__(self): return self.name nodes = [Test(i) for i in range(5)] for i, node1 in enumerate(nodes): for j, node2 in enumerate(nodes): if j in (i + 1, i + 2): node1.nexts.include(node2) visited = list(Test.nexts.walk(nodes[0], key=lambda n: n.nexts[0] if len(n.nexts) else None)) assert visited == nodes def test_walk_non_directed(self): class Test(object): friends = Many('friends') def __init__(self, name): self.name = str(name) def __repr__(self): return self.name nodes = [Test(i) for i in range(5)] for i, node1 in enumerate(nodes): for j, node2 in enumerate(nodes): if j in (i + 1, i + 2): node1.friends.include(node2) visited = [] for node in Test.friends.walk(nodes[0], key=lambda n: n.friends[0]): visited.append(node) if len(visited) == 5: break assert len(set(visited)) == 2 def test_endpoints(self): class Test(object): nexts = Many(install=True) def __init__(self, name): self.name = str(name) def __repr__(self): return self.name nodes = [Test(i) for i in range(7)] for i, node1 in enumerate(nodes): for j, node2 in enumerate(nodes): if j in (i + 1, i + 2): node1.nexts.include(node2) assert nodes[0].endpoints() == [nodes[-1]] def test_install_one(self): class TestMany(object): one = One('one', install=True) def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') bob.one = ann assert ann.one is bob assert bob.one is ann assert bob.iterate.__name__ == 'iterate' assert list(bob.iterate()) == [bob, ann] assert bob.shortest_path.__name__ == 'shortest_path' assert bob.shortest_path(ann) == [bob, ann] assert bob.in_cycle() def test_install_many(self): class TestMany(object): nexts = Many(install=True) def __init__(self, name): self.name = name bob = TestMany('bob') ann = TestMany('ann') bob.nexts.include(ann) ann.nexts.include(bob) assert ann in bob.nexts assert bob in ann.nexts assert bob.iterate.__name__ == 'iterate' assert list(bob.iterate()) == [bob, ann] assert bob.shortest_path.__name__ == 'shortest_path' assert bob.shortest_path(ann) == [bob, ann] assert bob.in_cycle() def test_install_error(self): with self.assertRaises(RuntimeError): class TestMany(object): nexts = Many(install=True) prevs = Many(install=True) def test_install_some(self): class Test(object): nexts = Many(install=('iterate',)) t1 = Test() t2 = Test() t1.nexts.include(t2) assert list(t1.iterate()) == [t1, t2] with self.assertRaises(AttributeError): t1.in_cycle() def test_install_non_directed(self): class Friend(object): friends = Many('friends', install=True) def __init__(self, name): self.name = name bob = Friend('bob') ann = Friend('ann') bob.friends.include(ann) assert ann in bob.friends assert bob in ann.friends assert bob.iterate.__name__ == 'iterate' assert list(bob.iterate()) == [bob, ann] assert bob.shortest_path.__name__ == 'shortest_path' assert ann.shortest_path(bob) == [ann, bob] assert bob.in_cycle() def test_directed_image(self): names = ('ann', 'bob', 'fred', 'betty', 'eric', 'charley', 'claudia', 'lars', 'jan') class Object(object): nexts = Many(install=True) def __init__(self, key): self.key = key nodes = [Object(name) for name in names] for _ in range(20): choice(nodes).nexts.include(choice(nodes)) try: nodes[0].save_image('/data/nexts.png', label_getter=lambda obj: obj.key, view=False) except RuntimeError as error: # graphviz not installed print(error) def test_undirected_image(self): names = ('ann', 'bob', 'fred', 'betty', 'eric', 'charley', 'claudia', 'lars', 'jan', 'imogen') # len() == 10 class Person(object): friends = Many('friends', install=True) def __init__(self, name): self.name = name nodes = [Person(name) for name in names] for _ in range((len(names) * len(names)) // 3): person1 = choice(nodes) person2 = choice(nodes) if person1 is not person2: person1.friends.include(person2) try: nodes[0].save_image('/data/friends.png', label_getter=lambda obj: obj.name, view=False) except RuntimeError as error: # graphviz not installed print(error)

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6

c3e09c556896dd2fa796ac85763c805d61969d77

12,008

py

Python

ansible/library/openstack-ansible-modules-master_zip_old/tests/test_keystone_service.py

ggwena/BadCops

fe9e7438a74abb8383488c6aca6dcb42ae049dcc

[ "MIT" ]

1

2015-06-16T14:42:48.000Z

ansible/library/openstack-ansible-modules-master_zip_old/tests/test_keystone_service.py

ggwena/BadCops

fe9e7438a74abb8383488c6aca6dcb42ae049dcc

[ "MIT" ]

null

ansible/library/openstack-ansible-modules-master_zip_old/tests/test_keystone_service.py

ggwena/BadCops

fe9e7438a74abb8383488c6aca6dcb42ae049dcc

[ "MIT" ]

null

import keystone_service import mock from nose.tools import assert_equal, assert_list_equal, assert_is_none from nose import SkipTest def setup(): keystone = mock.MagicMock() service = mock.Mock(id="b6a7ff03f2574cd9b5c7c61186e0d781", type="identity", description="Keystone Identity Service") # Can't set <name> field in mock in initializer service.name = "keystone" keystone.services.list = mock.Mock(return_value=[service]) endpoint = mock.Mock(id="600759628a214eb7b3acde39b1e85180", service_id="b6a7ff03f2574cd9b5c7c61186e0d781", publicurl="http://192.168.206.130:5000/v2.0", internalurl="http://192.168.206.130:5000/v2.0", adminurl="http://192.168.206.130:35357/v2.0", region="RegionOne") keystone.endpoints.list = mock.Mock(return_value=[endpoint]) return keystone @mock.patch('keystone_service.ensure_endpoint_absent') @mock.patch('keystone_service.ensure_service_absent') @mock.patch('keystone_service.ensure_endpoint_present') @mock.patch('keystone_service.ensure_service_present') def test_dispatch_service_present(mock_ensure_service_present, mock_ensure_endpoint_present, mock_ensure_service_absent, mock_ensure_endpoint_absent): """ Dispatch: service present """ # Setup mock_ensure_service_present.return_value = (True, None) mock_ensure_endpoint_present.return_value = (True, None) manager = mock.MagicMock() manager.attach_mock(mock_ensure_service_present, 'ensure_service_present') manager.attach_mock(mock_ensure_service_absent, 'ensure_service_absent') manager.attach_mock(mock_ensure_endpoint_present, 'ensure_endpoint_present') manager.attach_mock(mock_ensure_endpoint_absent, 'ensure_endpoint_absent') keystone = setup() name = "keystone" service_type = "identity" description = "Keystone Identity Service" state = "present" public_url = "http://192.168.206.130:5000/v2.0" internal_url = "http://192.168.206.130:5000/v2.0" admin_url = "http://192.168.206.130:35357/v2.0" region = "RegionOne" check_mode = False # Code under test keystone_service.dispatch(keystone, name, service_type, description, public_url, internal_url, admin_url, region, state, check_mode) expected_calls = [mock.call.ensure_service_present(keystone, name, service_type, description, check_mode), mock.call.ensure_endpoint_present(keystone, name, public_url, internal_url, admin_url, region, check_mode)] assert_equal(manager.mock_calls, expected_calls) @mock.patch('keystone_service.ensure_endpoint_absent') @mock.patch('keystone_service.ensure_service_absent') @mock.patch('keystone_service.ensure_endpoint_present') @mock.patch('keystone_service.ensure_service_present') def test_dispatch_service_absent(mock_ensure_service_present, mock_ensure_endpoint_present, mock_ensure_service_absent, mock_ensure_endpoint_absent): """ Dispatch: service absent """ # Setup mock_ensure_service_absent.return_value = True mock_ensure_endpoint_absent.return_value = True manager = mock.MagicMock() manager.attach_mock(mock_ensure_service_present, 'ensure_service_present') manager.attach_mock(mock_ensure_service_absent, 'ensure_service_absent') manager.attach_mock(mock_ensure_endpoint_present, 'ensure_endpoint_present') manager.attach_mock(mock_ensure_endpoint_absent, 'ensure_endpoint_absent') keystone = setup() name = "keystone" service_type = "identity" description = "Keystone Identity Service" region = "RegionOne" state = "absent" public_url = "http://192.168.206.130:5000/v2.0" internal_url = "http://192.168.206.130:5000/v2.0" admin_url = "http://192.168.206.130:35357/v2.0" check_mode = False # Code under test keystone_service.dispatch(keystone, name, service_type, description, public_url, internal_url, admin_url, region, state, check_mode) expected_calls = [ mock.call.ensure_endpoint_absent(keystone, name, check_mode), mock.call.ensure_service_absent(keystone, name, check_mode) ] assert_list_equal(manager.mock_calls, expected_calls) def test_ensure_service_present_when_present(): """ ensure_services_present when the service is present""" # Setup keystone = setup() name = "keystone" service_type = "identity" description = "Keystone Identity Service" check_mode = False # Code under test (changed, id) = keystone_service.ensure_service_present(keystone, name, service_type, description, check_mode) # Assertions assert not changed assert_equal(id, "b6a7ff03f2574cd9b5c7c61186e0d781") def test_ensure_service_present_when_present_check(): """ ensure_services_present when the service is present, check mode""" # Setup keystone = setup() name = "keystone" service_type = "identity" description = "Keystone Identity Service" check_mode = True # Code under test (changed, id) = keystone_service.ensure_service_present(keystone, name, service_type, description, check_mode) # Assertions assert not changed assert_equal(id, "b6a7ff03f2574cd9b5c7c61186e0d781") def test_ensure_service_present_when_absent(): """ ensure_services_present when the service is absent""" # Setup keystone = setup() service = mock.Mock(id="a7ebed35051147d4abbe2ee049eeb346") keystone.services.create = mock.Mock(return_value=service) name = "nova" service_type = "compute" description = "Compute Service" check_mode = False # Code under test (changed, id) = keystone_service.ensure_service_present(keystone, name, service_type, description, check_mode) # Assertions assert changed assert_equal(id, "a7ebed35051147d4abbe2ee049eeb346") keystone.services.create.assert_called_with(name=name, service_type=service_type, description=description) def test_ensure_service_present_when_absent_check(): """ ensure_services_present when the service is absent, check mode""" # Setup keystone = setup() service = mock.Mock(id="a7ebed35051147d4abbe2ee049eeb346") keystone.services.create = mock.Mock(return_value=service) name = "nova" service_type = "compute" description = "Compute Service" check_mode = True # Code under test (changed, id) = keystone_service.ensure_service_present(keystone, name, service_type, description, check_mode) # Assertions assert changed assert_equal(id, None) assert not keystone.services.create.called def test_get_endpoint_present(): """ get_endpoint when endpoint is present """ keystone = setup() endpoint = keystone_service.get_endpoint(keystone, "keystone") assert_equal(endpoint.id, "600759628a214eb7b3acde39b1e85180") def test_ensure_endpoint_present_when_present(): """ ensure_endpoint_present when the endpoint is present """ # Setup keystone = setup() name = "keystone" public_url = "http://192.168.206.130:5000/v2.0" internal_url = "http://192.168.206.130:5000/v2.0" admin_url = "http://192.168.206.130:35357/v2.0" region = "RegionOne" check_mode = False # Code under test (changed, id) = keystone_service.ensure_endpoint_present(keystone, name, public_url, internal_url, admin_url, region, check_mode) # Assertions assert not changed assert_equal(id, "600759628a214eb7b3acde39b1e85180") def test_ensure_endpoint_present_when_present_check(): """ ensure_endpoint_present when the endpoint is present, check mode""" # Setup keystone = setup() name = "keystone" public_url = "http://192.168.206.130:5000/v2.0" internal_url = "http://192.168.206.130:5000/v2.0" admin_url = "http://192.168.206.130:35357/v2.0" region = "RegionOne" check_mode = True # Code under test (changed, id) = keystone_service.ensure_endpoint_present(keystone, name, public_url, internal_url, admin_url, region, check_mode) # Assertions assert not changed assert_equal(id, "600759628a214eb7b3acde39b1e85180") def test_ensure_endpoint_present_when_absent(): """ ensure_endpoint_present when the endpoint is absent """ # Setup keystone = setup() # Mock out the endpoints create endpoint = mock.Mock(id="622386d836b14fd986d9cec7504d208a", publicurl="http://192.168.206.130:8774/v2/%(tenant_id)s", internalurl="http://192.168.206.130:8774/v2/%(tenant_id)s", adminurl="http://192.168.206.130:8774/v2/%(tenant_id)s", region="RegionOne") keystone.endpoints.create = mock.Mock(return_value=endpoint) # We need to add a service, but not an endpoint service = mock.Mock(id="0ad62de6cfe044c7a77ad3a7f2851b5d", type="compute", description="Compute Service") service.name = "nova" keystone.services.list.return_value.append(service) name = "nova" public_url = "http://192.168.206.130:8774/v2/%(tenant_id)s" internal_url = "http://192.168.206.130:8774/v2/%(tenant_id)s" admin_url = "http://192.168.206.130:8774/v2/%(tenant_id)s" region = "RegionOne" check_mode = False # Code under test (changed, id) = keystone_service.ensure_endpoint_present(keystone, name, public_url, internal_url, admin_url, region, check_mode) # Assertions assert changed assert_equal(id, "622386d836b14fd986d9cec7504d208a") keystone.endpoints.create.assert_called_with( service_id="0ad62de6cfe044c7a77ad3a7f2851b5d", publicurl="http://192.168.206.130:8774/v2/%(tenant_id)s", internalurl="http://192.168.206.130:8774/v2/%(tenant_id)s", adminurl="http://192.168.206.130:8774/v2/%(tenant_id)s", region="RegionOne") def test_ensure_endpoint_present_when_absent_check(): """ ensure_endpoint_present when the endpoint is absent, check mode""" # Setup keystone = setup() # We need to add a service, but not an endpoint service = mock.Mock(id="0ad62de6cfe044c7a77ad3a7f2851b5d", type="compute", description="Compute Service") service.name = "nova" keystone.services.list.return_value.append(service) name = "nova" public_url = "http://192.168.206.130:8774/v2/%(tenant_id)s" internal_url = "http://192.168.206.130:8774/v2/%(tenant_id)s" admin_url = "http://192.168.206.130:8774/v2/%(tenant_id)s" region = "RegionOne" check_mode = True # Code under test (changed, id) = keystone_service.ensure_endpoint_present(keystone, name, public_url, internal_url, admin_url, region, check_mode) # Assertions assert changed assert_is_none(id) assert not keystone.endpoints.create.called

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6

617bb18c3a9869be98d3113fef109768a928d02e

43

py

Python

dataset/bdd100k/__init__.py

masszhou/lane_detector

e28fe4adbd4c804e45c9bd86743739196bc30105

[ "MIT" ]

4

2020-10-07T03:31:42.000Z

2022-03-23T04:10:56.000Z

dataset/bdd100k/__init__.py

masszhou/lane_detector

e28fe4adbd4c804e45c9bd86743739196bc30105

[ "MIT" ]

null

dataset/bdd100k/__init__.py

masszhou/lane_detector

e28fe4adbd4c804e45c9bd86743739196bc30105

[ "MIT" ]

1

2020-11-16T07:13:53.000Z

from .bdd100k_loader import DatasetBDD100K

21.5

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0

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6187d6f8a3a2fa1d86d960edf723250d59a4d237

27,696

py

Python

src/an_MakeFigs.py

mbonnema/SWAV

d5dd4dd1a88de008f27b0232c536491c7dc84623

[ "CNRI-Python" ]

null

src/an_MakeFigs.py

mbonnema/SWAV

d5dd4dd1a88de008f27b0232c536491c7dc84623

[ "CNRI-Python" ]

null

src/an_MakeFigs.py

mbonnema/SWAV

d5dd4dd1a88de008f27b0232c536491c7dc84623

[ "CNRI-Python" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Nov 12 09:16:31 2021 @author: mbonnema """ import matplotlib.pyplot as plt import matplotlib.dates as mdates import matplotlib.lines as mlines import matplotlib.colors as colors import matplotlib.ticker as ticker from matplotlib import cm import datetime import pandas as pd import geopandas as geo import numpy as np import contextily as ctx from InterpS1 import InterpS1 import scipy from shapely.geometry import shape from SumArea import SumArea from SumArea import SumAreaSq from Smooth import Smooth import ee ee.Initialize() def add_basemap(ax, zoom, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png'): xmin, xmax, ymin, ymax = ax.axis() basemap, extent = ctx.bounds2img(xmin, ymin, xmax, ymax, zoom=zoom) ax.imshow(basemap, extent=extent, interpolation='bilinear') # restore original x/y limits ax.axis((xmin, xmax, ymin, ymax)) #--Figure 1a------------------------------------------------------------------- def Fig1a(lat,lon,Ltype,A_d): area = A_d fig = plt.figure(figsize=(8,5), dpi=300) ax = fig.add_subplot() ax.set_aspect('equal') world = geo.read_file(geo.datasets.get_path('naturalearth_lowres')) worldPlot=world.plot(ax=ax, color='white', edgecolor='black', linewidth=0.5) Lakesdf = pd.DataFrame( {'LakeType': Ltype, 'Latitude': lat, 'Longitude': lon}) Lakesgdf = geo.GeoDataFrame( Lakesdf, geometry=geo.points_from_xy(Lakesdf.Longitude, Lakesdf.Latitude)) colorMapmax = [] for t in Ltype: if t == 2: colorMapmax.append('#ff0000') elif t == 1: colorMapmax.append('#0000ff') markerSize = [] for a in area: if a < 10: markerSize.append(0.1) elif a < 100 and a >= 10: markerSize.append(1) elif a < 1000 and a >= 100: markerSize.append(5) elif a < 10000 and a >=1000: markerSize.append(10) elif a >= 10000: markerSize.append(20) LakePlot = Lakesgdf.plot(ax=ax, c=colorMapmax, markersize=markerSize, linewidths=0, marker='o') BlueLake = mlines.Line2D([], [], color='blue', marker='o',markersize=3, label='Lakes', linewidth = 0) RedRes = mlines.Line2D([], [], color='Red', marker='o',markersize=3, label='Reservoirs', linewidth = 0) lakesize0 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(0.1), label='1 $\mathregular{km^{2}}$', linewidth = 0) lakesize1 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(1), label='10 $\mathregular{km^{2}}$', linewidth = 0) lakesize2 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(5), label='100 $\mathregular{km^{2}}$', linewidth = 0) lakesize3 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(10), label='1000 $\mathregular{km^{2}}$', linewidth = 0) lakesize4 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(20), label='>10000 $\mathregular{km^{2}}$', linewidth = 0) plt.legend(handles=[BlueLake,RedRes,lakesize0,lakesize1,lakesize2,lakesize3,lakesize4], loc="lower left", fontsize=8) plt.gca().axes.get_xaxis().set_visible(False) plt.gca().axes.get_yaxis().set_visible(False) plt.ylim([-60,90]) plt.xlim([-180,180]) plt.show() #--Figure 1b------------------------------------------------------------------- def Fig1b(lat, lon, Amin, Amax, A_d, Ltype): area = A_d markerScale = 1 fig = plt.figure(figsize=(8,5), dpi=300) ax = fig.add_subplot() ax.set_aspect('equal') world = geo.read_file(geo.datasets.get_path('naturalearth_lowres')) worldPlot=world.plot(ax=ax, color='white', edgecolor='black', linewidth=0.5) states = geo.read_file('MapData/usa-states-census-2014.shp') statePlot=states.plot(ax=ax, color='white', edgecolor='black', linewidth=0.5) Lakesdf = pd.DataFrame( {'LakeType': Ltype, 'Latitude': lat, 'Longitude': lon}) Lakesgdf = geo.GeoDataFrame( Lakesdf, geometry=geo.points_from_xy(Lakesdf.Longitude, Lakesdf.Latitude)) colorMapmin = [] for t in Ltype: if t == 2: colorMapmin.append('#ff8888') elif t == 1: colorMapmin.append('#8888ff') markerSizemin = [] for a in Amin: markerSizemin.append(a*markerScale) #markerSizemin.append((a/markerScale)**3) colorMapmax = [] for t in Ltype: if t == 2: colorMapmax.append('#ff0000') elif t == 1: colorMapmax.append('#0000ff') markerSizemax = [] for a in Amax: markerSizemax.append(a*markerScale) #markerSizemax.append((a/markerScale)**3) LakePlotmax = Lakesgdf.plot(ax=ax, c=colorMapmax, markersize=markerSizemax, marker='.', linewidth=1.5) LakePlotmin = Lakesgdf.plot(ax=ax, c=colorMapmin, markersize=markerSizemin, marker='.') BlueLake = mlines.Line2D([], [], color='blue', marker='o',markersize=3, label='Lakes', linewidth = 0) RedRes = mlines.Line2D([], [], color='Red', marker='o',markersize=3, label='Reservoirs', linewidth = 0) lakesize0 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(1), label='1 $\mathregular{km^{2}}$', linewidth = 0) lakesize1 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(5), label='5 $\mathregular{km^{2}}$', linewidth = 0) lakesize2 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(10), label='10 $\mathregular{km^{2}}$', linewidth = 0) lakesize3 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(20), label='20 $\mathregular{km^{2}}$', linewidth = 0) lakesize_blank = mlines.Line2D([], [], color='white', marker='o',markersize=0, label='', linewidth = 0) lakesize4 = mlines.Line2D([], [], color='grey', marker='o',markersize=np.sqrt(100), label='100 $\mathregular{km^{2}}$', linewidth = 0) plt.legend(handles=[BlueLake,RedRes,lakesize0,lakesize1,lakesize2,lakesize3,lakesize_blank,lakesize4, lakesize_blank], loc="lower left", fontsize=4) plt.gca().axes.get_xaxis().set_visible(False) plt.gca().axes.get_yaxis().set_visible(False) plt.ylim([32,50]) plt.xlim([-125,-115]) plt.show() #--Figure 3a------------------------------------------------------------------- def Fig3a(D_int,A_int,WE_int,LE_int,Type, D_int_jrc, A_int_jrc): Asum,Dates = SumArea(A_int,D_int) Asum_jrc_raw, Dates_jrc = SumArea(A_int_jrc,D_int_jrc) Asum_jrc = [] for a in Asum_jrc_raw: a = float(a) Asum_jrc.append(a) Asum_jrc = np.array(Asum_jrc) #WEsum,Dates = SumArea(WE_int,D_int) #LEsum,Dates = SumArea(LE_int,D_int) #AsumUp = np.array(Asum) + np.array(WEsum) #AsumDown = np.array(Asum) - np.array(LEsum) formatDates = [] for d in Dates: formatDates.append(datetime.datetime.fromtimestamp(d/1000)) lake_ids = [] res_ids = [] for key in Type: if Type[key] == 1: lake_ids.append(key) else: res_ids.append(key) D_int_lake = {k: D_int[k] for k in D_int.keys() & lake_ids } D_int_res = {k: D_int[k] for k in D_int.keys() & res_ids } A_int_lake = {k: A_int[k] for k in A_int.keys() & lake_ids } A_int_res = {k: A_int[k] for k in A_int.keys() & res_ids } #WE_int_lake = {k: WE_int[k] for k in WE_int.keys() & lake_ids } #WE_int_res = {k: WE_int[k] for k in WE_int.keys() & res_ids } #LE_int_lake = {k: LE_int[k] for k in LE_int.keys() & lake_ids } #LE_int_res = {k: LE_int[k] for k in LE_int.keys() & res_ids } Asum_lake, Dates_lake = SumArea(A_int_lake,D_int_lake) #WEsum_lake, Dates_lake = SumArea(WE_int_lake,D_int_lake) #LEsum_lake, Dates_lake = SumArea(LE_int_lake,D_int_lake) Asum_res, Dates_res = SumArea(A_int_res,D_int_res) #WEsum_res, Dates_res = SumArea(WE_int_res,D_int_res) #LEsum_res, Dates_res = SumArea(LE_int_res,D_int_res) print(np.mean(Asum_res)) print(np.mean(Asum_lake)) ''' AsumUp_lake = np.array(Asum_lake) + np.array(WEsum_lake) AsumDown_lake = np.array(Asum_lake) - np.array(LEsum_lake) AsumUp_res = np.array(Asum_res) + np.array(WEsum_res) AsumDown_res = np.array(Asum_res) - np.array(LEsum_res) ''' fig = plt.figure(figsize=(4,2), dpi=300) ax = fig.add_subplot() ''' ax.plot(formatDates,Asum,c='purple',label='All SWB',linewidth=1) #plt.fill_between(formatDates,AsumUp,AsumDown,color='purple',alpha=0.1) ax.plot(formatDates,Asum_lake,c='blue',label='Natural Lakes',linewidth=1) #plt.fill_between(formatDates,AsumUp_lake,AsumDown_lake,color='blue',alpha=0.1) ax.plot(formatDates,Asum_res,c='red',label='Artificial Reservoirs',linewidth=1) #plt.fill_between(formatDates,AsumUp_res,AsumDown_res,color='red',alpha=0.1) ax.plot(formatDates,Asum_jrc,c='black',label='Lakes > 10,000 km2',linewidth=1) ''' A1 = Asum_jrc A2 = Asum_jrc + Asum_lake A3 = A2 + Asum_res plt.fill_between(formatDates,A1,color='black',alpha=0.3) plt.fill_between(formatDates,A2, A1, color='blue',alpha=0.3) plt.fill_between(formatDates,A3, A2, color='red',alpha=0.3) plt.gca().set_ylabel('Total Water Surface Area $\mathregular{km^{2}}$', fontsize = 12) plt.title('World Lake and Reservoir Surface Area') plt.gca().set_ylim(0,2500000) years = mdates.YearLocator() months = mdates.MonthLocator() years_fmt = mdates.DateFormatter('%Y') plt.gca().xaxis.set_major_formatter(years_fmt) plt.gca().xaxis.set_major_locator(years) plt.gca().xaxis.set_minor_locator(months) plt.gca().set_xlim(datetime.date(2017,1,1),datetime.date(2020,1,1)) plt.gca().format_xdata = mdates.DateFormatter('%Y-%m-%d') #plt.gca().format_ydata = lambda x: '$%0.9f' % x # format the price. #plt.gca().y_labels = ax.get_yticks() #plt.gca().yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1e')) plt.ticklabel_format(axis="y", style="sci", scilimits=(0,0), useMathText=True) #plt.gca().grid(True) #plt.gca().legend(frameon=False, loc='upper right', ncol=1) #--Figure 3b------------------------------------------------------------------- def Fig3b(D_int,A_int,WE_int,LE_int,Type): Asum,Dates = SumArea(A_int,D_int) Avg = np.mean(Asum) Asum = np.array(Asum) - Avg WEsum,Dates = SumAreaSq(WE_int,D_int) LEsum,Dates = SumAreaSq(LE_int,D_int) AsumUp = Smooth(np.array(Asum) + np.array(WEsum)) AsumDown = Smooth(np.array(Asum) - np.array(LEsum)) Asum = Smooth(Asum) formatDates = [] for d in Dates: formatDates.append(datetime.datetime.fromtimestamp(d/1000)) lake_ids = [] res_ids = [] for key in Type: if Type[key] == 1: lake_ids.append(key) else: res_ids.append(key) D_int_lake = {k: D_int[k] for k in D_int.keys() & lake_ids } D_int_res = {k: D_int[k] for k in D_int.keys() & res_ids } A_int_lake = {k: A_int[k] for k in A_int.keys() & lake_ids } A_int_res = {k: A_int[k] for k in A_int.keys() & res_ids } WE_int_lake = {k: WE_int[k] for k in WE_int.keys() & lake_ids } WE_int_res = {k: WE_int[k] for k in WE_int.keys() & res_ids } LE_int_lake = {k: LE_int[k] for k in LE_int.keys() & lake_ids } LE_int_res = {k: LE_int[k] for k in LE_int.keys() & res_ids } Asum_lake, Dates_lake = SumArea(A_int_lake,D_int_lake) WEsum_lake, Dates_lake = SumAreaSq(WE_int_lake,D_int_lake) LEsum_lake, Dates_lake = SumAreaSq(LE_int_lake,D_int_lake) Asum_res, Dates_res = SumArea(A_int_res,D_int_res) WEsum_res, Dates_res = SumAreaSq(WE_int_res,D_int_res) LEsum_res, Dates_res = SumAreaSq(LE_int_res,D_int_res) Avg_lake = np.mean(Asum_lake) Asum_lake = np.array(Asum_lake) - Avg_lake Avg_res = np.mean(Asum_res) Asum_res = np.array(Asum_res) - Avg_res AsumUp_lake = Smooth(np.array(Asum_lake) + np.array(WEsum_lake)) AsumDown_lake = Smooth(np.array(Asum_lake) - np.array(LEsum_lake)) AsumUp_res = Smooth(np.array(Asum_res) + np.array(WEsum_res)) AsumDown_res = Smooth(np.array(Asum_res) - np.array(LEsum_res)) Asum_lake = Smooth(Asum_lake) Asum_res = Smooth(Asum_res) fig = plt.figure(figsize=(6,3), dpi=200) ax = fig.add_subplot() ax.plot(formatDates,Asum,c='purple',label='All SWB',linewidth=1) #plt.fill_between(formatDates,AsumUp,AsumDown,color='purple',alpha=0.1) ax.plot(formatDates,Asum_lake,c='blue',label='Natural Lakes',linewidth=1) #plt.fill_between(formatDates,AsumUp_lake,AsumDown_lake,color='blue',alpha=0.1) ax.plot(formatDates,Asum_res,c='red',label='Artificial Reservoirs',linewidth=1) #plt.fill_between(formatDates,AsumUp_res,AsumDown_res,color='red',alpha=0.1) plt.gca().set_ylabel('Water Surface Area Anomalies $\mathregular{km^{2}}$', fontsize = 12) plt.title('Global Lake and Reservoir Surface Area Anomalies') plt.gca().set_ylim(-15000,15000) years = mdates.YearLocator() months = mdates.MonthLocator() years_fmt = mdates.DateFormatter('%Y') plt.gca().xaxis.set_major_formatter(years_fmt) plt.gca().xaxis.set_major_locator(years) plt.gca().xaxis.set_minor_locator(months) plt.gca().set_xlim(datetime.date(2017,1,1),datetime.date(2020,1,1)) plt.gca().format_xdata = mdates.DateFormatter('%Y-%m-%d') plt.gca().format_ydata = lambda x: '$%1.2f' % x # format the price. plt.gca().grid(True) plt.gca().legend(frameon=False, loc='lower right', ncol=1,fontsize = 12) #--Figure 3c------------------------------------------------------------------- def Fig3c(D_int,A_int,WE_int,LE_int,Type): Asum,Dates = SumArea(A_int,D_int) Avg = np.mean(Asum) Asum = np.array(Asum) - Avg WEsum,Dates = SumAreaSq(WE_int,D_int) LEsum,Dates = SumAreaSq(LE_int,D_int) AsumUp = Smooth((np.array(Asum) + np.array(WEsum))/Avg*100) AsumDown = Smooth((np.array(Asum) - np.array(LEsum))/Avg*100) Asum = Smooth(Asum/Avg*100) formatDates = [] for d in Dates: formatDates.append(datetime.datetime.fromtimestamp(d/1000)) lake_ids = [] res_ids = [] for key in Type: if Type[key] == 1: lake_ids.append(key) else: res_ids.append(key) D_int_lake = {k: D_int[k] for k in D_int.keys() & lake_ids } D_int_res = {k: D_int[k] for k in D_int.keys() & res_ids } A_int_lake = {k: A_int[k] for k in A_int.keys() & lake_ids } A_int_res = {k: A_int[k] for k in A_int.keys() & res_ids } WE_int_lake = {k: WE_int[k] for k in WE_int.keys() & lake_ids } WE_int_res = {k: WE_int[k] for k in WE_int.keys() & res_ids } LE_int_lake = {k: LE_int[k] for k in LE_int.keys() & lake_ids } LE_int_res = {k: LE_int[k] for k in LE_int.keys() & res_ids } Asum_lake, Dates_lake = SumArea(A_int_lake,D_int_lake) WEsum_lake, Dates_lake = SumAreaSq(WE_int_lake,D_int_lake) LEsum_lake, Dates_lake = SumAreaSq(LE_int_lake,D_int_lake) Asum_res, Dates_res = SumArea(A_int_res,D_int_res) WEsum_res, Dates_res = SumAreaSq(WE_int_res,D_int_res) LEsum_res, Dates_res = SumAreaSq(LE_int_res,D_int_res) Avg_lake = np.mean(Asum_lake) Asum_lake = np.array(Asum_lake) - Avg_lake Avg_res = np.mean(Asum_res) Asum_res = np.array(Asum_res) - Avg_res AsumUp_lake = Smooth((np.array(Asum_lake) + np.array(WEsum_lake))/Avg_lake*100) AsumDown_lake = Smooth((np.array(Asum_lake) - np.array(LEsum_lake))/Avg_lake*100) AsumUp_res = Smooth((np.array(Asum_res) + np.array(WEsum_res))/Avg_res*100) AsumDown_res = Smooth((np.array(Asum_res) - np.array(LEsum_res))/Avg_res*100) Asum_lake = Smooth(Asum_lake/Avg_lake*100) Asum_res = Smooth(Asum_res/Avg_res*100) fig = plt.figure(figsize=(6,3), dpi=200) ax = fig.add_subplot() ax.plot(formatDates,Asum,c='purple',label='All SWB',linewidth=1) #plt.fill_between(formatDates,AsumUp,AsumDown,color='purple',alpha=0.1) ax.plot(formatDates,Asum_lake,c='blue',label='Natural Lakes',linewidth=1) #plt.fill_between(formatDates,AsumUp_lake,AsumDown_lake,color='blue',alpha=0.1) ax.plot(formatDates,Asum_res,c='red',label='Artificial Reservoirs',linewidth=1) #plt.fill_between(formatDates,AsumUp_res,AsumDown_res,color='red',alpha=0.1) plt.gca().set_ylabel('Relative Water Surface Area Anomalies [%]', fontsize = 12) plt.title('Global Lake and Reservoir Surface Area Anomalies') plt.gca().set_ylim(-4,4) years = mdates.YearLocator() months = mdates.MonthLocator() years_fmt = mdates.DateFormatter('%Y') plt.gca().xaxis.set_major_formatter(years_fmt) plt.gca().xaxis.set_major_locator(years) plt.gca().xaxis.set_minor_locator(months) plt.gca().set_xlim(datetime.date(2017,1,1),datetime.date(2020,1,1)) plt.gca().format_xdata = mdates.DateFormatter('%Y-%m-%d') plt.gca().format_ydata = lambda x: '$%1.2f' % x # format the price. plt.gca().grid(True) plt.gca().legend(frameon=False, loc='lower right', ncol=1,fontsize = 12) #--Figure 3b------------------------------------------------------------------- def Fig3b_alt(D_int,A_int,LakesByBasin): fig = plt.figure(figsize=(8,5), dpi=300) ax = fig.add_subplot() ax.set_aspect('equal') world = geo.read_file(geo.datasets.get_path('naturalearth_lowres')) worldPlot=world.plot(ax=ax, color='white', edgecolor='black', linewidth=0.5) cmap = cm.get_cmap('Blues', 256) basins = ee.FeatureCollection("WWF/HydroSHEDS/v1/Basins/hybas_2") basinNumbers = basins.aggregate_array('PFAF_ID').getInfo() lakes = ee.FeatureCollection('users/matthewbonnema/HydroLAKES') lakes = lakes.filter(ee.Filter.gte('Lake_area',1)) PolyList = [] AV = [] #basinNumbers = basinNumbers[0:2] #print(basinNumbers[-1]) for N in basinNumbers: print('Loading geometry for basin: '+str(N)) b = ee.Feature(basins.filter(ee.Filter.eq('PFAF_ID',N)).first()) Geo= b.geometry().simplify(100) Geometry = Geo.getInfo() geo_shape = shape(Geometry) PolyList.append(geo_shape) print('\tGeometry loaded') print('Aggregating lake area for basin: '+str(N)) lakeID = LakesByBasin[str(N)] #basinLakes = lakes.filterBounds(Geo) #lakeID = basinLakes.aggregate_array('Hylak_id').getInfo() A = {k: A_int[k] for k in A_int.keys() & lakeID } D = {k: D_int[k] for k in D_int.keys() & lakeID } Asum,Dates = SumArea(A,D) Asum = np.array(Asum) Amax = np.max(Asum) Amin = np.min(Asum) Avar = Amax-Amin AV.append(Avar) print('\tFinished aggregation') maxVar = np.max(AV) C = np.array(AV)/maxVar #print(C) n = len(PolyList) i = 0 for P,c in zip(PolyList,C): i = i+1 print('Drawing shape number '+str(i)+'/'+str(n)) c = float(c) try: if P.geom_type == 'Polygon': plt.fill(*P.exterior.xy,c=cmap(c)) elif P.geom_type == 'MultiPolygon': for geom in P.geoms: plt.fill(*geom.exterior.xy,c=cmap(c)) print('\tFinished drawing') except: print('\tFailed to draw polygon') norm = colors.Normalize(vmin=0, vmax=maxVar, clip=False) plt.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax) plt.gca().axes.get_xaxis().set_visible(False) plt.gca().axes.get_yaxis().set_visible(False) plt.ylim([-60,90]) plt.xlim([-180,180]) plt.show() #--Figure 3c------------------------------------------------------------------- def Fig3c_alt(D_int,A_int,LakesByBasin): fig = plt.figure(figsize=(8,5), dpi=300) ax = fig.add_subplot() ax.set_aspect('equal') world = geo.read_file(geo.datasets.get_path('naturalearth_lowres')) worldPlot=world.plot(ax=ax, color='white', edgecolor='black', linewidth=0.5) cmap = cm.get_cmap('Blues', 256) basins = ee.FeatureCollection("WWF/HydroSHEDS/v1/Basins/hybas_2") basinNumbers = basins.aggregate_array('PFAF_ID').getInfo() lakes = ee.FeatureCollection('users/matthewbonnema/HydroLAKES') lakes = lakes.filter(ee.Filter.gte('Lake_area',1)) PolyList = [] AV = [] #basinNumbers = basinNumbers[0:2] #print(basinNumbers[-1]) for N in basinNumbers: print('Loading geometry for basin: '+str(N)) b = ee.Feature(basins.filter(ee.Filter.eq('PFAF_ID',N)).first()) Geo= b.geometry().simplify(100) Geometry = Geo.getInfo() geo_shape = shape(Geometry) PolyList.append(geo_shape) print('\tGeometry loaded') print('Aggregating lake area for basin: '+str(N)) lakeID = LakesByBasin[str(N)] #basinLakes = lakes.filterBounds(Geo) #lakeID = basinLakes.aggregate_array('Hylak_id').getInfo() A = {k: A_int[k] for k in A_int.keys() & lakeID } D = {k: D_int[k] for k in D_int.keys() & lakeID } Asum,Dates = SumArea(A,D) Asum = np.array(Asum) Amax = np.max(Asum) Amin = np.min(Asum) Avar = Amax-Amin Amean = np.mean(Asum) AvarP = Avar/Amean*100 AV.append(AvarP) print('\tFinished aggregation') maxVar = np.max(AV) C = np.array(AV)/maxVar #print(C) n = len(PolyList) i = 0 for P,c in zip(PolyList,C): i = i+1 print('Drawing shape number '+str(i)+'/'+str(n)) c = float(c) try: if P.geom_type == 'Polygon': plt.fill(*P.exterior.xy,c=cmap(c),alpha=0.8) elif P.geom_type == 'MultiPolygon': for geom in P.geoms: plt.fill(*geom.exterior.xy,c=cmap(c),alpha=0.8) print('\tFinished drawing') except: print('\tFailed to draw polygon') norm = colors.Normalize(vmin=0, vmax=maxVar, clip=False) plt.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax) plt.gca().axes.get_xaxis().set_visible(False) plt.gca().axes.get_yaxis().set_visible(False) plt.ylim([-60,90]) plt.xlim([-180,180]) plt.show() #--Figure 4a------------------------------------------------------------------- def Fig4a(Avp, A_d): Avp_subdiv = [] labels = [] positions = [] sub = 2 n_sub = range(sub*4) for n in n_sub: Avp_sel = Avp[np.array([A_d>=10**(n/2),A_d<10**((n+1)/2)]).all(axis=0)] Avp_subdiv.append(Avp_sel) positions.append(np.mean([10**(n/2),10**((n+1)/2)])) #positions.append(10**(n/2)) if n % 2 == 0: labels.append('$10^{'+str(n//2)+'}$') else: labels.append('$10^{'+str(n)+'/2}$') Avp_subdiv.append(Avp[A_d>=10000]) labels.append('>$10^4$') positions.append(np.mean([10**(8/2),10**(9/2)])) w = 0.42 width = lambda p, w: 10**(np.log10(p)+w/2.)-10**(np.log10(p)-w/2.) widths = width(positions,w) #widths[-1] = width(positions[-1],w*1.4) fig = plt.figure(figsize=(8,5), dpi=300) ax = fig.add_subplot() ax.boxplot(Avp_subdiv,showfliers=False,positions=positions,widths=widths) ax.set_ylabel('Relative Water Surface Area Variability [%]', fontsize = 12) ax.set_xlabel('Nominal Surface Area $\mathregular{km^{2}}$', fontsize = 12) plt.title('Global SWB Area Variability') #ax.boxplot(Avp_subdiv,showfliers=False,labels=labels,widths=0.7) ax.set_xscale('log') ax.minorticks_off() #--Figure 4b------------------------------------------------------------------- def Fig4b(Avp, A_d, Ltype): Avp_lake = Avp[Ltype==1] Avp_res = Avp[Ltype==2] A_d_lake = A_d[Ltype==1] A_d_res = A_d[Ltype==2] Avp_subdiv_lake = [] Avp_subdiv_res= [] labels = [] positionsl = [] positionsr = [] sub = 2 n_sub = range(sub*4) for n in n_sub: Avp_sel_l = Avp_lake[np.array([A_d_lake>=10**(n/2),A_d_lake<10**((n+1)/2)]).all(axis=0)] Avp_sel_r = Avp_res[np.array([A_d_res>=10**(n/2),A_d_res<10**((n+1)/2)]).all(axis=0)] #t,p = scipy.stats.ttest_ind(Avp_sel_l ,Avp_sel_r, equal_var=0) #print(n/2,t,p) Avp_subdiv_lake.append(Avp_sel_l) Avp_subdiv_res.append(Avp_sel_r) ''' positionsl.append(np.mean([np.mean([10**(n/2),10**((n+1)/2)]),10**(n/2)])) positionsr.append(np.mean([np.mean([10**(n/2),10**((n+1)/2)]),10**((n+1)/2)])) ''' positionsl.append(np.mean([10**(n/2),10**((n+1)/2)])) positionsr.append(np.mean([10**(n/2),10**((n+1)/2)]) - 0.02*np.mean([10**(n/2),10**((n+1)/2)])) #positions.append(10**(n/2)) if n % 2 == 0: labels.append('>$10^{'+str(n//2)+'}$') else: labels.append('>$10^{'+str(n)+'/2}$') Avp_subdiv_lake.append(Avp[A_d>=10000]) #labels.append('>$10^4$') positionsl.append(np.mean([10**(8/2),10**(9/2)])) w = 0.38 width = lambda p, w: 10**(np.log10(p)+w/2.)-10**(np.log10(p)-w/2.) widthsl = width(positionsl,w) widthsr = width(positionsr,w)*0.92 #widths[-1] = width(positions[-1],w*1.4) fig = plt.figure(figsize=(8,5), dpi=300) ax = fig.add_subplot() bpl = ax.boxplot(Avp_subdiv_lake,showfliers=False,positions=positionsl,widths=widthsl,notch=True) bpr = ax.boxplot(Avp_subdiv_res,showfliers=False,positions=positionsr,widths=widthsr,notch=True) for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']: plt.setp(bpl[element], color='blue') for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']: plt.setp(bpr[element], color='red') ax.set_ylabel('Relative Water Surface Area Variability [%]', fontsize = 12) ax.set_xlabel('Nominal Surface Area $\mathregular{km^{2}}$', fontsize = 12) plt.title('Global Lake and Reservoir Area Variability') ax.set_xscale('log') ax.minorticks_off() #--Figure 4c------------------------------------------------------------------- def Fig4c(Av, A_d, Ltype): Av_lake = Av[Ltype==1] Av_res = Av[Ltype==2] A_d_lake = A_d[Ltype==1] A_d_res = A_d[Ltype==2] Av_total_lake = [] Av_total_res= [] Av_total = [] positions = [] sub = 2 n_sub = range(sub*4) for n in n_sub: Av_sel_l = Av_lake[np.array([A_d_lake>=10**(n/2),A_d_lake<10**((n+1)/2)]).all(axis=0)] Av_sel_r = Av_res[np.array([A_d_res>=10**(n/2),A_d_res<10**((n+1)/2)]).all(axis=0)] Av_total_lake.append(np.sum(Av_sel_l)) Av_total_res.append(np.sum(Av_sel_r)) Av_total.append(np.sum(Av_sel_l)+np.sum(Av_sel_r)) positions.append(np.mean([10**(n/2),10**((n+1)/2)])) Av_total_gt = np.sum(Av[A_d>=10000]) fig = plt.figure(figsize=(8,5), dpi=300) ax = fig.add_subplot() w = 0.42 width = lambda p, w: 10**(np.log10(p)+w/2.)-10**(np.log10(p)-w/2.) widths = width(positions,w) ax.bar(x=positions,height=Av_total_res, width=widths, bottom=Av_total_lake, color='red') print(np.sum(Av_total_res)) positions.append(np.mean([10**(8/2),10**(9/2)])) widths = width(positions,w) Av_total_lake.append(Av_total_gt) ax.bar(x=positions,height=Av_total_lake, width=widths, color='blue') print(np.sum(Av_total_lake)) ax.set_ylabel('Total Surface Area Amplitude $\mathregular{km^{2}}$', fontsize = 12) ax.set_xlabel('Nominal Surface Area $\mathregular{km^{2}}$', fontsize = 12) plt.title('Total Lake and Reservoir Area Amplitude') ax.set_xscale('log') ax.minorticks_off()

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false

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0.035363

0

null

0

1

0

null

0

6

61b1012aae2d2936f129d55b00373321422825fc

25

py

Python

app.py

galenguyer/vigilant

7fa21029b37ca047198eeee26fa36575c82d7bbc

[ "MIT" ]

null

app.py

galenguyer/vigilant

7fa21029b37ca047198eeee26fa36575c82d7bbc

[ "MIT" ]

null

app.py

galenguyer/vigilant

7fa21029b37ca047198eeee26fa36575c82d7bbc

[ "MIT" ]

null

from vigilant import app

12.5

24

0.84

4

25

5.25

1

0

0.16

25

1

25

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f606e56ba3d5c848c746af97bdbc52ea7e98df9c

29

py

Python

limix_ext/logreg/__init__.py

glimix/limix-ext

7cf7a3b2b02f6a73cbba90f1945a06b9295b7357

[ "MIT" ]

24

2020-11-22T21:02:37.000Z

2022-02-19T14:26:47.000Z

limix_ext/logreg/__init__.py

glimix/limix-ext

7cf7a3b2b02f6a73cbba90f1945a06b9295b7357

[ "MIT" ]

73

2020-10-23T07:40:45.000Z

2022-03-29T17:56:33.000Z

limix_ext/logreg/__init__.py

glimix/limix-ext

7cf7a3b2b02f6a73cbba90f1945a06b9295b7357

[ "MIT" ]

21

2020-12-28T17:00:10.000Z

2021-06-21T23:52:32.000Z

from .predict import predict

14.5

28

0.827586

4

29

6

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29

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0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

f652898fd2b443e0239ef1384dd5ff8d07382f67

6,191

py

Python

src/gestureInfo.py

DennisMelamed/crazyFrog

25ffa0e6616f7195d034189eef7348d16157215e

[ "MIT" ]

2

2018-01-19T13:16:36.000Z

2018-01-19T18:39:35.000Z

src/gestureInfo.py

DennisMelamed/crazyFrog

25ffa0e6616f7195d034189eef7348d16157215e

[ "MIT" ]

null

src/gestureInfo.py

DennisMelamed/crazyFrog

25ffa0e6616f7195d034189eef7348d16157215e

[ "MIT" ]

1

2018-09-28T14:30:59.000Z

# # Author: Owen Levin # from numpy import dot import rospkg rospack = rospkg.RosPack() macro_folder = rospack.get_path('crazy_frog') + "/macros/" file_ext = ".csv" # Index of Gestures gestures = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23] num_var = [22,23] Digit = 14 Negate = 15 numbers = [0,1,2,3,4,5,6,7,8,9,Digit,Negate] Run = 10 END = 11 RecordMacro = 12 CallMacro = 13 Repeat = 16 MoveX = 17 MoveY = 18 MoveZ = 19 Wait = 20 actions = [CallMacro, MoveX, MoveY, MoveZ, Wait] NOP = 21 SetNumberVar = 22 CallNumberVar = 15 def make_num_var_dict(): rec_num_dict = {} not_END = gestures[:END]+gestures[END+1:] for gesture in gestures: rec_num_dict[gesture] = gestures idle_legal_gestures = { 0: numbers+[END], # 0 is the digit 0 1: numbers+[END], # 1 is the digit 1 2: numbers+[END], # 2 is the digit 2 3: numbers+[END], # 3 is the digit 3 4: numbers+[END], # 4 is the digit 4 5: numbers+[END], # 5 is the digit 5 6: numbers+[END], # 6 is the digit 6 7: numbers+[END], # 7 is the digit 7 8: numbers+[END], # 8 is the digit 8 9: numbers+[END], # 9 is the digit 9 Digit: numbers[:10]+[Negate], # 14 is the digit/number command. The next value should be the next digit in the number being constructed. Negate: numbers[:10], # 15 is negation of a digit. (Can be used before any digit in a number to change the sign) Run: [END,Digit,CallNumberVar], # 10 is the run command which takes a number (which previously recorded macro to run) as a parameter END: [Run,RecordMacro,SetNumberVar], # 11 is the end scope/ cancel gesture (note: one frequently will have entered a scope from just ending a previous scope) } # 21 is No op which if recognized, will be do nothing, but is mentioned here for completeness number_var_name_legal_gestures = make_num_var_dict() recording_legal_gestures = { 0: numbers+[END], # 0 is the digit 0 1: numbers+[END], # 1 is the digit 1 2: numbers+[END], # 2 is the digit 2 3: numbers+[END], # 3 is the digit 3 4: numbers+[END], # 4 is the digit 4 5: numbers+[END], # 5 is the digit 5 6: numbers+[END], # 6 is the digit 6 7: numbers+[END], # 7 is the digit 7 8: numbers+[END], # 8 is the digit 8 9: numbers+[END], # 9 is the digit 9 Digit: numbers[:10]+[Negate], # 14 is the digit/number command. The next value should be the next digit in the number being constructed. Negate: numbers[:10], # 15 is negation of a digit. (Can be used before any digit in a number to change the sign) END: [END,RecordMacro,SetNumberVar,CallMacro,Repeat,MoveX,MoveY,MoveZ,Wait], # 11 is the end scope/ cancel gesture (note: one frequently will have entered a scope from just ending a previous scope) RecordMacro: [END,Digit,CallNumberVar], # 12 is the record macro command. The current scope is set to Recording and the next expected value should be a digit CallMacro: [END,Digit,CallNumberVar], # 13 is the call macro commmand. The next expected value(s) should be a digit(s) (which previously recorded macro to call) Repeat: [END,Digit,CallNumberVar], # 16 is the repeat command which starts a Repeating scope and takes first a number parameter n, then an arbitrary number of actions to repeat x times. # (If n is 0 or negative, nothing happens) MoveX: [END,Digit,CallNumberVar]+actions, # 17 is move in the x direction (left is negative, right is positive). Expects a number (of decimeters) to move next MoveY: [END,Digit,CallNumberVar]+actions, # 18 is move in the y direction (backward is negative, forward is positive)). Expects a number (of decimeters) to move next MoveZ: [END,Digit,CallNumberVar]+actions, # 19 is move in the z direction (down is negative, up is positive). Expects a number (of decimeters) to move next Wait: [END,Digit,CallNumberVar]+actions, # 20 is the wait command. (Expects a number (of seconds) to wait next } # 21 is No op which if recognized, will be do nothing, but is mentioned here for completeness repeating_legal_gestures = { 0: numbers+[END], # 0 is the digit 0 1: numbers+[END], # 1 is the digit 1 2: numbers+[END], # 2 is the digit 2 3: numbers+[END], # 3 is the digit 3 4: numbers+[END], # 4 is the digit 4 5: numbers+[END], # 5 is the digit 5 6: numbers+[END], # 6 is the digit 6 7: numbers+[END], # 7 is the digit 7 8: numbers+[END], # 8 is the digit 8 9: numbers+[END], # 9 is the digit 9 Digit: numbers[:10]+[Negate], # 14 is the digit/number command. The next value should be the next digit in the number being constructed. Negate: numbers[:10], # 15 is negation of a digit. (Can be used before any digit in a number to change the sign) END: [END,RecordMacro,SetNumberVar,CallMacro,Repeat,MoveX,MoveY,MoveZ,Wait], # 11 is the end scope/ cancel gesture (note: one frequently will have entered a scope from just ending a previous scope) RecordMacro: [END,Digit,CallNumberVar], # 12 is the record macro command. The current scope is set to Recording and the next expected value should be a digit CallMacro: [END,Digit,CallNumberVar], # 13 is the call macro commmand. The next expected value(s) should be a digit(s) (which previously recorded macro to call) Repeat: [END,Digit,CallNumberVar], # 16 is the repeat command which starts a Repeating scope and takes first a number parameter n, then an arbitrary number of actions to repeat x times. # (If n is 0 or negative, nothing happens) MoveX: [END,Digit,CallNumberVar]+actions, # 17 is move in the x direction (left is negative, right is positive). Expects a number (of decimeters) to move next MoveY: [END,Digit,CallNumberVar]+actions, # 18 is move in the y direction (backward is negative, forward is positive)). Expects a number (of decimeters) to move next MoveZ: [END,Digit,CallNumberVar]+actions, # 19 is move in the z direction (down is negative, up is positive). Expects a number (of decimeters) to move next Wait: [END,Digit,CallNumberVar]+actions, # 20 is the wait command. (Expects a number (of seconds) to wait next } legal_gestures_in_scope = { # Each scope is mapped to a dictionary containing gestures allowed after the previous input "Idle": idle_legal_gestures, RecordMacro: recording_legal_gestures, Repeat: repeating_legal_gestures, }

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py

Python

nBody-master/nbody/utils/__init__.py

craigboger/nbody-sim

1b93c5db395f49c54e78c9dfb7e5590fbfad23cc

[ "MIT" ]

null

nBody-master/nbody/utils/__init__.py

craigboger/nbody-sim

1b93c5db395f49c54e78c9dfb7e5590fbfad23cc

[ "MIT" ]

null

nBody-master/nbody/utils/__init__.py

craigboger/nbody-sim

1b93c5db395f49c54e78c9dfb7e5590fbfad23cc

[ "MIT" ]

null

from .Counter import Counter

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py

Python

apps/account/models.py

picsldev/pyerp

e998e3e99a4e45033d54a6b1df50697f7288f67f

[ "MIT" ]

null

apps/account/models.py

picsldev/pyerp

e998e3e99a4e45033d54a6b1df50697f7288f67f

[ "MIT" ]

11

2020-06-05T22:50:37.000Z

2022-02-10T09:05:56.000Z

apps/account/models.py

gvizquel/pyerp

c859f7293cabd1003f79112463cee93ac89fccba

[ "MIT" ]

null

# Librerias en carpetas locales from .submodels.accountmove import PyAccountMove from .submodels.accountplan import PyAccountPlan

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py

Python

saopy/qoi/__init__.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

2

2016-11-03T14:57:45.000Z

2019-05-13T13:21:08.000Z

saopy/qoi/__init__.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

null

saopy/qoi/__init__.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

1

2020-07-23T11:27:15.000Z

import saopy.model from saopy.model import qoi___Accuracy as Accuracy from saopy.model import qoi___Age as Age from saopy.model import qoi___Authority as Authority from saopy.model import qoi___Bandwidth as Bandwidth from saopy.model import qoi___Completeness as Completeness from saopy.model import qoi___Confidentiality as Confidentiality from saopy.model import qoi___Correctness as Correctness from saopy.model import qoi___Cost as Cost from saopy.model import qoi___Deviation as Deviation from saopy.model import qoi___Encryption as Encryption from saopy.model import qoi___EnergyConsumption as EnergyConsumption from saopy.model import qoi___Frequency as Frequency from saopy.model import qoi___Jitter as Jitter from saopy.model import qoi___Latency as Latency from saopy.model import qoi___LicenceDefinition as LicenceDefinition from saopy.model import qoi___MayBePublished as MayBePublished from saopy.model import qoi___MayBeUsed as MayBeUsed from saopy.model import qoi___MonetaryConsumption as MonetaryConsumption from saopy.model import qoi___NetworkConsumption as NetworkConsumption from saopy.model import qoi___NetworkPerformance as NetworkPerformance from saopy.model import qoi___Ordered as Ordered from saopy.model import qoi___PacketLoss as PacketLoss from saopy.model import qoi___Precision as Precision from saopy.model import qoi___PublicKey as PublicKey from saopy.model import qoi___Quality as Quality from saopy.model import qoi___Queuing as Queuing from saopy.model import qoi___QueuingType as QueuingType from saopy.model import qoi___Reputation as Reputation from saopy.model import qoi___Resolution as Resolution from saopy.model import qoi___Security as Security from saopy.model import qoi___Signing as Signing from saopy.model import qoi___Throughput as Throughput from saopy.model import qoi___Timeliness as Timeliness from saopy.model import qoi___Volatility as Volatility

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py

Python

Documents/RacimoAire/ADC/Libreria/ADS1115-master/Onion Omega Python/ADS1115_4Channel.py

JoseSalamancaCoy/RACIMO_AIRE

628d6ff184a30af0efd25bff675b0006500d4ba2

[ "MIT" ]

null

Documents/RacimoAire/ADC/Libreria/ADS1115-master/Onion Omega Python/ADS1115_4Channel.py

JoseSalamancaCoy/RACIMO_AIRE

628d6ff184a30af0efd25bff675b0006500d4ba2

[ "MIT" ]

null

Documents/RacimoAire/ADC/Libreria/ADS1115-master/Onion Omega Python/ADS1115_4Channel.py

JoseSalamancaCoy/RACIMO_AIRE

628d6ff184a30af0efd25bff675b0006500d4ba2

[ "MIT" ]

null

# Distributed with a free-will license. # Use it any way you want, profit or free, provided it fits in the licenses of its associated works. # ADS1115 # This code is designed to work with the ADS1115_I2CADC I2C Mini Module available from ControlEverything.com. # https://www.controleverything.com/content/Analog-Digital-Converters?sku=ADS1115_I2CADC#tabs-0-product_tabset-2 from OmegaExpansion import onionI2C import time # Get I2C bus i2c = onionI2C.OnionI2C() # ADS1115 address, 0x48(72) # Select configuration register, 0x01(01) # 0xC483(50307) AINP = AIN0 and AINN = GND, +/- 2.048V # Continuous conversion mode, 128SPS data = [0xC4,0x83] i2c.writeBytes(0x48, 0x01, data) time.sleep(0.5) # ADS1115 address, 0x48(72) # Read data back from 0x00(00), 2 bytes # raw_adc MSB, raw_adc LSB data = i2c.readBytes(0x48, 0x00, 2) # Convert the data raw_adc = data[0] * 256 + data[1] if raw_adc > 32767: raw_adc -= 65535 # Output data to screen print "Digital Value of Analog Input on Channel-0: %d" %raw_adc # ADS1115 address, 0x48(72) # Select configuration register, 0x01(01) # 0xD483(54403) AINP = AIN1 and AINN = GND, +/- 2.048V # Continuous conversion mode, 128SPS data = [0xD4,0x83] i2c.writeBytes(0x48, 0x01, data) time.sleep(0.5) # ADS1115 address, 0x48(72) # Read data back from 0x00(00), 2 bytes # raw_adc MSB, raw_adc LSB data = i2c.readBytes(0x48, 0x00, 2) # Convert the data raw_adc = data[0] * 256 + data[1] if raw_adc > 32767: raw_adc -= 65535 # Output data to screen print "Digital Value of Analog Input on Channel-1: %d" %raw_adc # ADS1115 address, 0x48(72) # Select configuration register, 0x01(01) # 0xE483(58499) AINP = AIN2 and AINN = GND, +/- 2.048V # Continuous conversion mode, 128SPS data = [0xE4,0x83] i2c.writeBytes(0x48, 0x01, data) time.sleep(0.5) # ADS1115 address, 0x48(72) # Read data back from 0x00(00), 2 bytes # raw_adc MSB, raw_adc LSB data = i2c.readBytes(0x48, 0x00, 2) # Convert the data raw_adc = data[0] * 256 + data[1] if raw_adc > 32767: raw_adc -= 65535 # Output data to screen print "Digital Value of Analog Input on Channel-2: %d" %raw_adc # ADS1115 address, 0x48(72) # Select configuration register, 0x01(01) # 0xF483(62595) AINP = AIN3 and AINN = GND, +/- 2.048V # Continuous conversion mode, 128SPS data = [0xF4,0x83] i2c.writeBytes(0x48, 0x01, data) time.sleep(0.5) # ADS1115 address, 0x48(72) # Read data back from 0x00(00), 2 bytes # raw_adc MSB, raw_adc LSB data = i2c.readBytes(0x48, 0x00, 2) # Convert the data raw_adc = data[0] * 256 + data[1] if raw_adc > 32767: raw_adc -= 65535 # Output data to screen print "Digital Value of Analog Input on Channel-3: %d" %raw_adc

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145388f977523f2be500192512a342122731d326

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py

Python

models.py

sunbingqi/CS224N_Final_Project

dcb967b90b2c9a780729dda2a780b0f0a715e0ad

[ "MIT" ]

null

models.py

sunbingqi/CS224N_Final_Project

dcb967b90b2c9a780729dda2a780b0f0a715e0ad

[ "MIT" ]

null

models.py

sunbingqi/CS224N_Final_Project

dcb967b90b2c9a780729dda2a780b0f0a715e0ad

[ "MIT" ]

null

"""Top-level model classes. Author: Chris Chute (chute@stanford.edu) """ import layers import torch import torch.nn as nn import torch.nn.functional as F from util import masked_softmax class BiDAFBASE(nn.Module): """Baseline BiDAF model for SQuAD. Based on the paper: "Bidirectional Attention Flow for Machine Comprehension" by Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi (https://arxiv.org/abs/1611.01603). Follows a high-level structure commonly found in SQuAD models: - Embedding layer: Embed word indices to get word vectors. - Encoder layer: Encode the embedded sequence. - Attention layer: Apply an attention mechanism to the encoded sequence. - Model encoder layer: Encode the sequence again. - Output layer: Simple layer (e.g., fc + softmax) to get final outputs. Args: word_vectors (torch.Tensor): Pre-trained word vectors. hidden_size (int): Number of features in the hidden state at each layer. drop_prob (float): Dropout probability. """ def __init__(self, word_vectors, hidden_size, drop_prob=0.): super(BiDAFBASE, self).__init__() self.emb = layers.EmbeddingBASE(word_vectors=word_vectors, hidden_size=hidden_size, drop_prob=drop_prob) self.enc = layers.RNNEncoder(input_size=hidden_size, hidden_size=hidden_size, num_layers=1, drop_prob=drop_prob) self.att = layers.BiDAFAttention(hidden_size=2 * hidden_size, drop_prob=drop_prob) self.mod = layers.RNNEncoder(input_size=8 * hidden_size, hidden_size=hidden_size, num_layers=2, drop_prob=drop_prob) self.out = layers.BiDAFOutput(hidden_size=hidden_size, drop_prob=drop_prob) def forward(self, cw_idxs, qw_idxs): c_mask = torch.zeros_like(cw_idxs) != cw_idxs q_mask = torch.zeros_like(qw_idxs) != qw_idxs c_len, q_len = c_mask.sum(-1), q_mask.sum(-1) c_emb = self.emb(cw_idxs) # (batch_size, c_len, hidden_size) q_emb = self.emb(qw_idxs) # (batch_size, q_len, hidden_size) c_enc = self.enc(c_emb, c_len) # (batch_size, c_len, 2 * hidden_size) q_enc = self.enc(q_emb, q_len) # (batch_size, q_len, 2 * hidden_size) att = self.att(c_enc, q_enc, c_mask, q_mask) # (batch_size, c_len, 8 * hidden_size) mod = self.mod(att, c_len) # (batch_size, c_len, 2 * hidden_size) out = self.out(att, mod, c_mask) # 2 tensors, each (batch_size, c_len) return out class BiDAF(nn.Module): """Baseline BiDAF model for SQuAD + Character Embedding. Based on the paper: "Bidirectional Attention Flow for Machine Comprehension" by Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi (https://arxiv.org/abs/1611.01603). Follows a high-level structure commonly found in SQuAD models: - Embedding layer: Embed word indices to get word vectors. - Encoder layer: Encode the embedded sequence. - Attention layer: Apply an attention mechanism to the encoded sequence. - Model encoder layer: Encode the sequence again. - Output layer: Simple layer (e.g., fc + softmax) to get final outputs. Args: word_vectors (torch.Tensor): Pre-trained word vectors. hidden_size (int): Number of features in the hidden state at each layer. drop_prob (float): Dropout probability. """ def __init__(self, word_vectors, hidden_size, character_vectors, char_channel_size, char_channel_width, drop_prob=0.): super(BiDAF, self).__init__() self.emb = layers.Embedding(word_vectors=word_vectors, hidden_size=hidden_size, character_vectors=character_vectors, char_channel_size=char_channel_size, char_channel_width=char_channel_width, drop_prob=drop_prob) self.enc = layers.RNNEncoder(input_size=hidden_size, hidden_size=hidden_size, num_layers=1, drop_prob=drop_prob) self.att = layers.BiDAFAttention(hidden_size=2 * hidden_size, drop_prob=drop_prob) self.mod = layers.RNNEncoder(input_size=8 * hidden_size, hidden_size=hidden_size, num_layers=2, drop_prob=drop_prob) self.out = layers.BiDAFOutput(hidden_size=hidden_size, drop_prob=drop_prob) def forward(self, cw_idxs, qw_idxs, cc_idxs, qc_idxs): c_mask = torch.zeros_like(cw_idxs) != cw_idxs q_mask = torch.zeros_like(qw_idxs) != qw_idxs c_len, q_len = c_mask.sum(-1), q_mask.sum(-1) c_emb = self.emb(cw_idxs, cc_idxs) # (batch_size, c_len, hidden_size) q_emb = self.emb(qw_idxs, qc_idxs) # (batch_size, q_len, hidden_size) c_enc = self.enc(c_emb, c_len) # (batch_size, c_len, 2 * hidden_size) q_enc = self.enc(q_emb, q_len) # (batch_size, q_len, 2 * hidden_size) print(c_enc.shape) print(q_enc.shape) # torch.Size([64, 273, 200]) # torch.Size([64, 21, 200]) att = self.att(c_enc, q_enc, c_mask, q_mask) # (batch_size, c_len, 8 * hidden_size) mod = self.mod(att, c_len) # (batch_size, c_len, 2 * hidden_size) out = self.out(att, mod, c_mask) # 2 tensors, each (batch_size, c_len) return out class QANet(nn.Module): def __init__(self, word_vectors, character_vectors, hidden_size, char_channel_size, char_channel_width, pad=0, drop_prob=0.1, num_head=1): super().__init__() self.emb = layers.Embedding(word_vectors=word_vectors, hidden_size=hidden_size, character_vectors=character_vectors, char_channel_size=char_channel_size, char_channel_width=char_channel_width, drop_prob=drop_prob) self.enc = layers.EncoderBlock(conv_num=4, hidden_size=hidden_size, num_head=num_head, k=7, drop_prob=drop_prob) self.att = layers.BiDAFAttention(hidden_size=hidden_size, drop_prob=drop_prob) self.att_conv = nn.Conv1d(hidden_size * 4, hidden_size, kernel_size=1, bias=False) nn.init.xavier_uniform_(self.att_conv.weight) self.mod = nn.ModuleList([layers.EncoderBlock(conv_num=2, hidden_size=hidden_size, num_head=num_head, k=5, drop_prob=drop_prob) for _ in range(7)]) self.out = layers.QANetOutput(hidden_size=hidden_size) def forward(self, cw_idxs, qw_idxs, cc_idxs, qc_idxs): c_mask = torch.zeros_like(cw_idxs) != cw_idxs q_mask = torch.zeros_like(qw_idxs) != qw_idxs c_emb = self.emb(cw_idxs, cc_idxs) # (batch_size, c_len, hidden_size) q_emb = self.emb(qw_idxs, qc_idxs) # (batch_size, q_len, hidden_size) c_enc = self.enc(c_emb.transpose(1, 2), c_mask, 1, 1) q_enc = self.enc(q_emb.transpose(1, 2), q_mask, 1, 1) c_enc = c_enc.permute((0, 2, 1)) q_enc = q_enc.permute((0, 2, 1)) att = self.att(c_enc, q_enc, c_mask, q_mask) # print(att.shape) torch.Size([64, 314, 400]) att = att.permute((0, 2, 1)) att = self.att_conv(att) for i, enc_block in enumerate(self.mod): att = enc_block(att, c_mask, i*(2+2)+1, 7) mod_1 = att for i, enc_block in enumerate(self.mod): att = enc_block(att, c_mask, i*(2+2)+1, 7) mod_2 = att for i, enc_block in enumerate(self.mod): att = enc_block(att, c_mask, i*(2+2)+1, 7) mod_3 = att out = self.out(mod_1, mod_2, mod_3, c_mask) return out

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145599ea0d8b8585ada70d693246b6d2c121413b

37

py

Python

folder2/testfile2.py

aviljoenn/pynetlab

14e2f12217b6d7137c5e047574cef36b475382dc

[ "Apache-2.0" ]

null

folder2/testfile2.py

aviljoenn/pynetlab

14e2f12217b6d7137c5e047574cef36b475382dc

[ "Apache-2.0" ]

null

folder2/testfile2.py

aviljoenn/pynetlab

14e2f12217b6d7137c5e047574cef36b475382dc

[ "Apache-2.0" ]

null

print("This is file 2 in folder 2")

12.333333

35

0.675676

8

37

3.125

0.875

0

0.068966

0.216216

37

2

36

18.5

0.793103

0

0.722222

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

145b774ac040ddf73e25781f110fbd00bf78a816

18

py

Python

Chapter1/R-1/9.py

GeorgeGkas/Data_Structures_and_Algorithms_in_Python

c4f8b590ab2dd008504e639607c62d5e5760009a

[ "MIT" ]

1

2017-05-18T09:43:38.000Z

Chapter1/R-1/9.py

GeorgeGkas/Data_Structures_and_Algorithms_in_Python

c4f8b590ab2dd008504e639607c62d5e5760009a

[ "MIT" ]

null

Chapter1/R-1/9.py

GeorgeGkas/Data_Structures_and_Algorithms_in_Python

c4f8b590ab2dd008504e639607c62d5e5760009a

[ "MIT" ]

null

range(50, 81, 10)

9

17

0.611111

4

18

2.75

1

0

0.4

0.166667

18

1

18

0.333333

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

147379d82fcdc1ad756e06bf99062f91ee672b0b

66

py

Python

examples/max/ex3.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

examples/max/ex3.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

examples/max/ex3.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

print(max([10, '1', '100', 90, '111', '2'], key=lambda x:int(x)))

33

65

0.515152

13

66

2.615385

0.923077

0

0.206897

0.121212

66

1

66

0.37931

0

0.121212

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

1ad3189b352da64045f42099eead2a05d277cf32

11,202

py

Python

test/unit/test_client.py

smarter-travel-media/artifacts

cdb29a17ede0924b122b3905a500442c62ae53b7

[ "MIT" ]

1

2017-02-10T20:55:17.000Z

test/unit/test_client.py

smarter-travel-media/artifacts

cdb29a17ede0924b122b3905a500442c62ae53b7

[ "MIT" ]

4

2015-12-21T19:26:05.000Z

2016-05-06T14:30:35.000Z

test/unit/test_client.py

smarter-travel-media/artifacts

cdb29a17ede0924b122b3905a500442c62ae53b7

[ "MIT" ]

1

2016-07-30T07:07:48.000Z

# -*- coding: utf-8 -*- """ """ import mock import pytest import requests @pytest.fixture def version_dao(): from stac.http import VersionApiDao return mock.Mock(spec=VersionApiDao) @pytest.fixture def url_generator(): from stac.client import MavenArtifactUrlGenerator return mock.Mock(spec=MavenArtifactUrlGenerator) class TestMavenArtifactoryClient(object): def test_get_version_url(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig url_generator.get_url.return_value = ('https://www.example.com/artifactory/libs-release/' 'com/example/services/login/3.9.1/login-3.9.1.jar') config = GenericArtifactoryClientConfig() config.is_integration = False config.http_dao = version_dao config.url_generator = url_generator client = GenericArtifactoryClient(config) url = client.get_version_url('com.example.services.login', 'jar', '3.9.1') assert ('https://www.example.com/artifactory/libs-release/' 'com/example/services/login/3.9.1/login-3.9.1.jar') == url def test_get_latest_version_snapshot(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig version_dao.get_most_recent_versions.return_value = ['1.3.0-SNAPSHOT'] config = GenericArtifactoryClientConfig() config.is_integration = True config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) version = maven_client.get_latest_version('com.example.users.service') assert '1.3.0-SNAPSHOT' == version def test_get_latest_version_snapshot_no_results(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig from stac.exceptions import NoMatchingVersionsError request = mock.Mock(spec=requests.Request) response = mock.Mock(spec=requests.Response) response.status_code = 404 error = requests.HTTPError("Something bad", request=request, response=response) version_dao.get_most_recent_versions.side_effect = error config = GenericArtifactoryClientConfig() config.is_integration = True config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) with pytest.raises(NoMatchingVersionsError): maven_client.get_latest_version('com.example.users.service') def test_get_latest_version_snapshot_only_release_results(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig from stac.exceptions import NoMatchingVersionsError version_dao.get_most_recent_versions.return_value = [] config = GenericArtifactoryClientConfig() config.is_integration = True config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) with pytest.raises(NoMatchingVersionsError): maven_client.get_latest_version('com.example.users.service') def test_get_latest_version_release(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig version_dao.get_most_recent_release.return_value = '4.13.4' config = GenericArtifactoryClientConfig() config.is_integration = False config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) version = maven_client.get_latest_version('com.example.users.service') assert '4.13.4' == version def test_get_latest_version_release_no_results(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig from stac.exceptions import NoMatchingVersionsError request = mock.Mock(spec=requests.Request) response = mock.Mock(spec=requests.Response) response.status_code = 404 error = requests.HTTPError("Something bad", request=request, response=response) version_dao.get_most_recent_release.side_effect = error config = GenericArtifactoryClientConfig() config.is_integration = False config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) with pytest.raises(NoMatchingVersionsError): maven_client.get_latest_version('com.example.users.service') def test_get_latest_versions_bad_limit(self): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig config = GenericArtifactoryClientConfig() maven_client = GenericArtifactoryClient(config) with pytest.raises(ValueError): maven_client.get_latest_versions('com.example.users.service', limit=0) def test_get_latest_versions_snapshot(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig version_dao.get_most_recent_versions.return_value = [ '1.3.0-SNAPSHOT', '1.2.1-SNAPSHOT', '1.1.0-SNAPSHOT'] config = GenericArtifactoryClientConfig() config.is_integration = True config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) versions = maven_client.get_latest_versions('com.example.users.service', limit=3) expected = [ '1.3.0-SNAPSHOT', '1.2.1-SNAPSHOT', '1.1.0-SNAPSHOT' ] assert expected == versions def test_get_latest_versions_snapshot_no_results(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig from stac.exceptions import NoMatchingVersionsError request = mock.Mock(spec=requests.Request) response = mock.Mock(spec=requests.Response) response.status_code = 404 error = requests.HTTPError("Something bad", request=request, response=response) version_dao.get_most_recent_versions.side_effect = error config = GenericArtifactoryClientConfig() config.is_integration = True config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) with pytest.raises(NoMatchingVersionsError): maven_client.get_latest_versions('com.example.users.service') def test_get_latest_versions_snapshot_only_release_results(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig from stac.exceptions import NoMatchingVersionsError version_dao.get_most_recent_versions.return_value = [] config = GenericArtifactoryClientConfig() config.is_integration = True config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) with pytest.raises(NoMatchingVersionsError): maven_client.get_latest_versions('com.example.users.service') def test_get_latest_versions_release(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig version_dao.get_most_recent_versions.return_value = ['1.2.1', '1.2.0', '1.1.1'] config = GenericArtifactoryClientConfig() config.is_integration = False config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) versions = maven_client.get_latest_versions('com.example.users.service', limit=3) expected = [ '1.2.1', '1.2.0', '1.1.1' ] assert expected == versions def test_get_latest_versions_release_no_results(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig from stac.exceptions import NoMatchingVersionsError request = mock.Mock(spec=requests.Request) response = mock.Mock(spec=requests.Response) response.status_code = 404 error = requests.HTTPError("Something bad", request=request, response=response) version_dao.get_most_recent_versions.side_effect = error config = GenericArtifactoryClientConfig() config.is_integration = False config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) with pytest.raises(NoMatchingVersionsError): maven_client.get_latest_versions('com.example.users.service') def test_get_latest_versions_release_only_snapshot_results(self, version_dao, url_generator): from stac.client import GenericArtifactoryClient, GenericArtifactoryClientConfig from stac.exceptions import NoMatchingVersionsError version_dao.get_most_recent_versions.return_value = [] config = GenericArtifactoryClientConfig() config.is_integration = False config.http_dao = version_dao config.url_generator = url_generator maven_client = GenericArtifactoryClient(config) with pytest.raises(NoMatchingVersionsError): maven_client.get_latest_versions('com.example.users.service') class TestMavenArtifactUrlGenerator(object): def test_get_version_url_with_descriptor(self): from stac.client import MavenArtifactUrlGenerator gen = MavenArtifactUrlGenerator('https://corp.example.com/artifactory', 'libs-release-local') url = gen.get_url('com.example.services', 'locations', 'jar', '4.5.1', 'sources') assert ('https://corp.example.com/artifactory/libs-release-local/' + 'com/example/services/locations/4.5.1/locations-4.5.1-sources.jar') == url def test_get_version_url_without_descriptor(self): from stac.client import MavenArtifactUrlGenerator gen = MavenArtifactUrlGenerator('https://corp.example.com/artifactory', 'libs-release-local') url = gen.get_url('com.example.services', 'locations', 'war', '4.5.1', None) assert ('https://corp.example.com/artifactory/libs-release-local/' + 'com/example/services/locations/4.5.1/locations-4.5.1.war') == url def test_parse_full_name_group_and_artifact(): from stac.client import _parse_full_name name = 'com.example.services.auth' group, artifact = _parse_full_name(name) assert 'com.example.services' == group assert 'auth' == artifact def test_parse_full_name_single_name(): from stac.client import _parse_full_name name = 'my-python-lib' group, artifact = _parse_full_name(name) assert '' == group assert 'my-python-lib' == artifact

39.864769

101

0.719247

1,210

11,202

6.409091

0.08595

0.058801

0.032495

0.046422

0.915667

0.90303

0.867054

0.850419

0.829014

0.825919

0

0.011023

0.198268

11,202

280

102

40.007143

0.852466

0.001875

0

0.693467

0

0.020101

0.110097

0.050752

0

0.055276

1

0.095477

false

0

0.145729

0

0.261307

0

null

0

1

0

null

0

6

210fcc7d2a18ce64a4348dbf5cceabde6b713f51

134

py

Python

tests/test_clean.py

jonparrott/readme_renderer

d356c29635bf06eb4b150e6ae2a224c952a15f1d

[ "Apache-2.0" ]

null

tests/test_clean.py

jonparrott/readme_renderer

d356c29635bf06eb4b150e6ae2a224c952a15f1d

[ "Apache-2.0" ]

null

tests/test_clean.py

jonparrott/readme_renderer

d356c29635bf06eb4b150e6ae2a224c952a15f1d

[ "Apache-2.0" ]

null

from readme_renderer.clean import clean def test_invalid_link(): assert clean('<a href="http://exam](ple.com">foo</a>') is None

22.333333

66

0.708955

22

134

4.181818

0.863636

0

0.126866

134

5

67

26.8

0.786325

0

0.283582

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2156b3977eed64fe4d6f067d04dbccc1bdd6dd75

17,213

py

Python

test/unit/transport/test_parser.py

doesitblend/ncclient

0730e23ce5de8f8615c9ff5e63d6fc9bba8b3cf1

[ "Apache-2.0" ]

null

test/unit/transport/test_parser.py

doesitblend/ncclient

0730e23ce5de8f8615c9ff5e63d6fc9bba8b3cf1

[ "Apache-2.0" ]

null

test/unit/transport/test_parser.py

doesitblend/ncclient

0730e23ce5de8f8615c9ff5e63d6fc9bba8b3cf1

[ "Apache-2.0" ]

null

import os import unittest from mock import patch import paramiko from ncclient import manager from ncclient.transport.ssh import SSHSession from ncclient.operations.third_party.juniper.rpc import * from ncclient.operations import RaiseMode from ncclient.transport.parser import DefaultXMLParser try: import selectors except ImportError: import selectors2 as selectors class TestSession(unittest.TestCase): @unittest.skipIf(sys.version_info.major == 2, "test not supported < Python3") @patch('ncclient.transport.SSHSession.connected') @patch('paramiko.channel.Channel.send_ready') @patch('paramiko.channel.Channel.send') @patch('ncclient.transport.ssh.SSHSession.close') @patch('paramiko.channel.Channel.recv') @patch('ncclient.transport.SSHSession') @patch('selectors.DefaultSelector.select') @patch('ncclient.operations.rpc.uuid4') def test_filter_xml_sax_on(self, mock_uuid4, mock_select, mock_session, mock_recv, mock_close, mock_send, mock_send_ready, mock_connected): mock_send.return_value = True mock_send_ready.return_value = -1 mock_uuid4.return_value = type('dummy', (), {'urn': "urn:uuid:e0a7abe3-fffa-11e5-b78e-b8e85604f858"}) device_handler = manager.make_device_handler({'name': 'junos', 'use_filter': True}) rpc = '<get-software-information/>' mock_recv.side_effect = self._read_file('get-software-information.xml') session = SSHSession(device_handler) session._connected = True session._channel = paramiko.Channel("c100") session.parser = session._device_handler.get_xml_parser(session) obj = ExecuteRpc(session, device_handler, raise_mode=RaiseMode.ALL) obj._filter_xml = '<multi-routing-engine-results><multi-routing-engine-item><re-name/></multi-routing-engine-item></multi-routing-engine-results>' session.run() resp = obj.request(rpc)._NCElement__doc[0] self.assertEqual(len(resp.xpath('multi-routing-engine-item/re-name')), 2) # as filter_xml is not having software-information, response wont contain it self.assertEqual(len(resp.xpath('multi-routing-engine-item/software-information')), 0) @unittest.skipIf(sys.version_info.major == 2, "test not supported < Python3") @patch('ncclient.transport.SSHSession.connected') @patch('paramiko.channel.Channel.send_ready') @patch('paramiko.channel.Channel.send') @patch('ncclient.transport.ssh.SSHSession.close') @patch('paramiko.channel.Channel.recv') @patch('ncclient.transport.SSHSession') @patch('selectors.DefaultSelector.select') @patch('ncclient.operations.rpc.uuid4') def test_filter_xml_delimiter_rpc_reply(self, mock_uuid4, mock_select, mock_session, mock_recv, mock_close, mock_send, mock_send_ready, mock_connected): mock_send.return_value = True mock_send_ready.return_value = -1 mock_uuid4.return_value = type('dummy', (), {'urn': "urn:uuid:e0a7abe3-fffa-11e5-b78e-b8e85604f858"}) device_handler = manager.make_device_handler({'name': 'junos', 'use_filter': True}) rpc = '<get-software-information/>' mock_recv.side_effect = self._read_file('get-software-information.xml')[:-1] + [b"</rpc-reply>]]>", b"]]>"] session = SSHSession(device_handler) session._connected = True session._channel = paramiko.Channel("c100") session.parser = session._device_handler.get_xml_parser(session) obj = ExecuteRpc(session, device_handler, raise_mode=RaiseMode.ALL) obj._filter_xml = '<multi-routing-engine-results><multi-routing-engine-item><re-name/></multi-routing-engine-item></multi-routing-engine-results>' session.run() resp = obj.request(rpc)._NCElement__doc[0] self.assertEqual(len(resp.xpath('multi-routing-engine-item/re-name')), 2) self.assertEqual(len(resp.xpath('multi-routing-engine-item/software-information')), 0) @unittest.skipIf(sys.version_info.major == 2, "test not supported < Python3") @patch('ncclient.transport.SSHSession.connected') @patch('paramiko.channel.Channel.send_ready') @patch('paramiko.channel.Channel.send') @patch('ncclient.transport.ssh.SSHSession.close') @patch('paramiko.channel.Channel.recv') @patch('ncclient.transport.SSHSession') @patch('selectors.DefaultSelector.select') @patch('ncclient.operations.rpc.uuid4') def test_filter_xml_delimiter_multiple_rpc_reply(self, mock_uuid4, mock_select, mock_session, mock_recv, mock_close, mock_send, mock_send_ready, mock_connected): mock_send.return_value = True mock_send_ready.return_value = -1 mock_uuid4.return_value = type('dummy', (), {'urn': "urn:uuid:e0a7abe3-fffa-11e5-b78e-b8e85604f858"}) device_handler = manager.make_device_handler({'name': 'junos', 'use_filter': True}) rpc = '<get-software-information/>' mock_recv.side_effect = self._read_file('get-software-information.xml')[:-1] + [b"</rpc-reply>]]>", b"]]><rpc-reply>"] + \ self._read_file('get-software-information.xml')[1:] session = SSHSession(device_handler) session._connected = True session._channel = paramiko.Channel("c100") session.parser = session._device_handler.get_xml_parser(session) obj = ExecuteRpc(session, device_handler, raise_mode=RaiseMode.ALL) obj._filter_xml = '<multi-routing-engine-results><multi-routing-engine-item><re-name/></multi-routing-engine-item></multi-routing-engine-results>' session.run() resp = obj.request(rpc)._NCElement__doc[0] self.assertEqual(len(resp.xpath('multi-routing-engine-item/re-name')), 2) self.assertEqual(len(resp.xpath('multi-routing-engine-item/software-information')), 0) @unittest.skipIf(sys.version_info.major == 2, "test not supported < Python3") @patch('ncclient.transport.SSHSession.connected') @patch('paramiko.channel.Channel.send_ready') @patch('paramiko.channel.Channel.send') @patch('ncclient.transport.ssh.SSHSession.close') @patch('paramiko.channel.Channel.recv') @patch('ncclient.transport.SSHSession') @patch('selectors.DefaultSelector.select') @patch('ncclient.operations.rpc.uuid4') def test_filter_xml_delimiter_multiple_rpc_in_parallel(self, mock_uuid4, mock_select, mock_session, mock_recv, mock_close, mock_send, mock_send_ready, mock_connected): mock_send.return_value = True mock_send_ready.return_value = -1 mock_uuid4.side_effect = [type('xyz', (), {'urn': "urn:uuid:ddef40cb-5745-481d-974d-7188f9f2bb33"}), type('pqr', (), {'urn': "urn:uuid:549ef9d1-024a-4fd0-88bf-047d25f0870d"})] device_handler = manager.make_device_handler({'name': 'junos', 'use_filter': True}) rpc = '<get-software-information/>' mock_recv.side_effect = [b""" <rpc-reply xmlns="urn:ietf:params:xml:ns:netconf:base:1.0" xmlns:junos="http://xml.juniper.net/junos/19.2I0/junos" xmlns:nc="urn:ietf:params:xml:ns:netconf:base:1.0" message-id="urn:uuid:ddef40cb-5745-481d-974d-7188f9f2bb33"> <ospf-neighbor-information xmlns="http://xml.juniper.net/junos/19.2I0/junos-routing"> <ospf-neighbor> <neighbor-address>13.1.1.2</neighbor-address> <interface-name>ge-0/0/0.1</interface-name> <ospf-neighbor-state>Exchange</ospf-neighbor-state> <neighbor-id>2.2.2.2</neighbor-id> <neighbor-priority>128</neighbor-priority> <activity-timer>36</activity-timer> <ospf-area>0.0.0.0</ospf-area> <options>0x52</options> <dr-address>13.1.1.1</dr-address> <bdr-address>13.1.1.2</bdr-address> <neighbor-up-time junos:seconds="17812"> 04:56:52 </neighbor-up-time> <neighbor-adjacency-time junos:seconds="17812"> 04:56:52 </neighbor-adjacency-time> <master-slave>slave</master-slave> <sequence-number>0x204b6fd</sequence-number> <dbd-retransmit-time>3</dbd-retransmit-time> <lsreq-retransmit-time>0</lsreq-retransmit-time> <lsa-list> Link state retransmission list: Type LSA ID Adv rtr Seq Router 1.1.1.1 1.1.1.1 0x80000019 OpaqArea 1.0.0.1 1.1.1.1 0x80000011 Router 3.3.3.3 3.3.3.3 0x80000004 Network 23.1.1.2 3.3.3.3 0x80000001 OpaqArea 1.0.0.1 2.2.2.2 0x80000002 OpaqArea 1.0.0.1 3.3.3.3 0x80000002 OpaqArea 1.0.0.3 1.1.1.1 0x80000002 OpaqArea 1.0.0.3 3.3.3.3 0x80000001 OpaqArea 1.0.0.4 2.2.2.2 0x80000001 </lsa-list> <ospf-neighbor-topology> <ospf-topology-name>default</ospf-topology-name> <ospf-topology-id>0</ospf-topology-id> <ospf-neighbor-topology-state>Forward Only</ospf-neighbor-topology-state> </ospf-neighbor-topology> </ospf-neighbor> </ospf-neighbor-information> </rpc-reply>]]>]]><rpc-reply xmlns="urn:ietf:params:xml:ns:netconf:base:1.0" xmlns:junos="http://xml.juniper.net/junos/19.2I0/junos" xmlns:nc="urn:ietf:params:xml:ns:netconf:base:1.0" message-id="urn:uuid:549ef9d1-024a-4fd0-88bf-047d25f0870d"> <pfe-statistics> <pfe-traffic-statistics> <pfe-input-packets>22450</pfe-input-packets> <input-pps>0</input-pps> <pfe-output-packets>31992</pfe-output-packets> <output-pps>0</output-pps> <pfe-fabric-input>0</pfe-fabric-input> <pfe-fabric-input-pps>0</pfe-fabric-input-pps> <pfe-fabric-output>0</pfe-fabric-output> <pfe-fabric-output-pps>0</pfe-fabric-output-pps> </pfe-traffic-statistics></pfe-statistics></rpc-reply>]]>]]>"""] session = SSHSession(device_handler) session._connected = True session._channel = paramiko.Channel("c100") session.parser = session._device_handler.get_xml_parser(session) obj = ExecuteRpc(session, device_handler, raise_mode=RaiseMode.ALL) obj = ExecuteRpc(session, device_handler, raise_mode=RaiseMode.ALL) obj._filter_xml = '<multi-routing-engine-results><multi-routing-engine-item><re-name/></multi-routing-engine-item></multi-routing-engine-results>' session.run() resp = obj.request(rpc)._NCElement__doc[0] self.assertEqual(len(resp.xpath('pfe-traffic-statistics')), 1) @unittest.skipIf(sys.version_info.major == 2, "test not supported < Python3") @patch('ncclient.transport.SSHSession.connected') @patch('paramiko.channel.Channel.send_ready') @patch('paramiko.channel.Channel.send') @patch('ncclient.transport.ssh.SSHSession.close') @patch('paramiko.channel.Channel.recv') @patch('ncclient.transport.SSHSession') @patch('selectors.DefaultSelector.select') @patch('ncclient.operations.rpc.uuid4') def test_filter_xml_delimiter_splited_rpc_reply(self, mock_uuid4, mock_select, mock_session, mock_recv, mock_close, mock_send, mock_send_ready, mock_connected): mock_send.return_value = True mock_send_ready.return_value = -1 mock_uuid4.return_value = type('dummy', (), {'urn': "urn:uuid:e0a7abe3-fffa-11e5-b78e-b8e85604f858"}) device_handler = manager.make_device_handler({'name': 'junos', 'use_filter': True}) rpc = '<get-software-information/>' mock_recv.side_effect = self._read_file('get-software-information.xml')[:-1] + [b"</rpc", b"-reply>]]>", b"]]><rpc-reply>"] + \ self._read_file('get-software-information.xml')[1:] session = SSHSession(device_handler) session._connected = True session._channel = paramiko.Channel("c100") session.parser = session._device_handler.get_xml_parser(session) obj = ExecuteRpc(session, device_handler, raise_mode=RaiseMode.ALL) obj._filter_xml = '<multi-routing-engine-results><multi-routing-engine-item><re-name/></multi-routing-engine-item></multi-routing-engine-results>' session.run() resp = obj.request(rpc)._NCElement__doc[0] self.assertEqual(len(resp.xpath('multi-routing-engine-item/re-name')), 2) self.assertEqual(len(resp.xpath('multi-routing-engine-item/software-information')), 0) @unittest.skipIf(sys.version_info.major == 2, "test not supported < Python3") @patch('ncclient.transport.SSHSession.connected') @patch('paramiko.channel.Channel.send_ready') @patch('paramiko.channel.Channel.send') @patch('ncclient.transport.ssh.SSHSession.close') @patch('paramiko.channel.Channel.recv') @patch('ncclient.transport.SSHSession') @patch('selectors.DefaultSelector.select') @patch('ncclient.operations.rpc.uuid4') def test_use_filter_xml_without_sax_input(self, mock_uuid4, mock_select, mock_session, mock_recv, mock_close, mock_send, mock_send_ready, mock_connected): mock_send.return_value = True mock_send_ready.return_value = -1 mock_uuid4.return_value = type('dummy', (), {'urn': "urn:uuid:e0a7abe3-fffa-11e5-b78e-b8e85604f858"}) device_handler = manager.make_device_handler({'name': 'junos', 'use_filter': True}) rpc = '<get-software-information/>' mock_recv.side_effect = self._read_file('get-software-information.xml') session = SSHSession(device_handler) session._connected = True session._channel = paramiko.Channel("c100") session.parser = session._device_handler.get_xml_parser(session) obj = ExecuteRpc(session, device_handler, raise_mode=RaiseMode.ALL) obj._filter_xml = None session.run() resp = obj.request(rpc)._NCElement__doc[0] self.assertEqual(len(resp.xpath('multi-routing-engine-item/re-name')), 2) self.assertEqual(len(resp.xpath('multi-routing-engine-item/software-information')), 2) @unittest.skipIf(sys.version_info.major == 2, "test not supported < Python3") @patch('ncclient.transport.SSHSession.connected') @patch('paramiko.channel.Channel.send_ready') @patch('paramiko.channel.Channel.send') @patch('ncclient.transport.ssh.SSHSession.close') @patch('paramiko.channel.Channel.recv') @patch('ncclient.transport.SSHSession') @patch('selectors.DefaultSelector.select') @patch('ncclient.operations.rpc.uuid4') def test_use_filter_False(self, mock_uuid4, mock_select, mock_session, mock_recv, mock_close, mock_send, mock_send_ready, mock_connected): mock_send.return_value = True mock_send_ready.return_value = -1 mock_uuid4.return_value = type('dummy', (), {'urn': "urn:uuid:e0a7abe3-fffa-11e5-b78e-b8e85604f858"}) device_handler = manager.make_device_handler({'name': 'junos', 'use_filter': False}) rpc = '<get-software-information/>' mock_recv.side_effect = self._read_file('get-software-information.xml') session = SSHSession(device_handler) session._connected = True session._channel = paramiko.Channel("c100") session.parser = session._device_handler.get_xml_parser(session) obj = ExecuteRpc(session, device_handler, raise_mode=RaiseMode.ALL) obj._filter_xml = '<multi-routing-engine-results><multi-routing-engine-item><re-name/></multi-routing-engine-item></multi-routing-engine-results>' session.run() resp = obj.request(rpc)._NCElement__doc[0] self.assertEqual(len(resp.xpath('multi-routing-engine-item/re-name')), 2) # as filter_xml is not having software-information, response wont contain it self.assertEqual(len(resp.xpath('multi-routing-engine-item/software-information')), 2) self.assertIsInstance(session.parser, DefaultXMLParser) def _read_file(self, fname): fpath = os.path.join(os.path.dirname(__file__), 'rpc-reply', fname) lines = [] with open(fpath, "rb") as fp: lines = fp.readlines() return lines

57.186047

247

0.638819

2,030

17,213

5.254187

0.108867

0.043878

0.060754

0.049503

0.833583

0.827677

0.823551

0.808738

0.799175

0.794487

0

0.039601

0.225411

17,213

300

248

57.376667

0.760369

0.008656

0

0.677193

0

0.031579

0.420726

0.321435

0

0.006037

0

0.049123

1

0.02807

false

0

0.042105

0

0.077193

0

null

0

1

0

null

0

6

0d10d8349157877d15f19a50159b633a70cf7138

25

py

Python

code/abc085_a_02.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/abc085_a_02.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/abc085_a_02.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

print("2018"+input()[4:])

25

0.6

4

25

3.75

1

0

0.2

0

25

1

25

0.4

0

0.153846

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

0d1a0685a787ba53bf67509347ace972cdd96b8f

11,476

py

Python

nz_snow_tools/util/write_fsca_to_netcdf.py

jonoconway/nz_snow_tools

7002fb401fb48225260fada6fd5b5b7ca5ad1184

[ "MIT" ]

3

2020-09-01T07:53:05.000Z

2021-02-02T20:28:37.000Z

nz_snow_tools/util/write_fsca_to_netcdf.py

jonoconway/nz_snow_tools

7002fb401fb48225260fada6fd5b5b7ca5ad1184

[ "MIT" ]

null

nz_snow_tools/util/write_fsca_to_netcdf.py

jonoconway/nz_snow_tools

7002fb401fb48225260fada6fd5b5b7ca5ad1184

[ "MIT" ]

null

""" writes arrays of fractional snow covered area to a net cdf file """ import netCDF4 as nc from time import strftime, gmtime import numpy as np def create_ncvar_temperaure(ds, no_time=False): if no_time == False: temp_var = ds.createVariable('air_temperature', 'f4', ('time', 'northing', 'easting',), zlib=True, complevel=4) else: temp_var = ds.createVariable('air_temperature', 'f4', ('northing', 'easting',), zlib=True, complevel=4) temp_var.setncatts({ 'long_name': 'surface air temperature', 'standard_name': 'air_temperature', 'units': 'K', 'description': "mean value over previous 1 hour", 'cell_methods': 'time: mean', 'missing': -9999., 'valid_min': 230., 'valid_max': 333. }) return temp_var def create_ncvar_shortwave(ds, no_time=False): if no_time == False: temp_var = ds.createVariable('surface_downwelling_shortwave_flux', 'f8', ('time', 'northing', 'easting',), zlib=True, complevel=4) else: temp_var = ds.createVariable('surface_downwelling_shortwave_flux', 'f8', ('northing', 'easting',), zlib=True, complevel=4) temp_var.setncatts({ 'long_name': 'downwelling shortwave radiation flux at surface', 'standard_name': 'surface_downwelling_shortwave_flux_in_air', 'units': 'W / m^2', 'description': "mean value over previous 1 hour", 'cell_methods': 'time: mean', 'missing': -9999., 'valid_min': 0., 'valid_max': 1500. }) return temp_var def create_ncvar_precipitation(ds, no_time=False): if no_time == False: precip_var = ds.createVariable('precipitation_amount', 'f8', ('time', 'northing', 'easting',), zlib=True, complevel=4) else: precip_var = ds.createVariable('precipitation_amount', 'f8', ('northing', 'easting',), zlib=True, complevel=4) precip_var.setncatts({ 'long_name': 'precipitation amount (mm)', 'standard_name': 'precipitation_amount', 'units': 'mm', 'description': "total value over previous 1 hour", 'cell_methods': 'time: sum', 'missing': -9999., 'valid_min': 0., 'valid_max': 2000. }) return precip_var def create_ncvar_fsca(ds): fsca_var = ds.createVariable('fsca', 'u8', ('time', 'northing', 'easting',), zlib=True, complevel=4) fsca_var.setncatts({ 'long_name': 'fractional snow covered area' # 'missing': -9999., # 'valid_min': 0, # 'valid_max': 100 }) return fsca_var def create_ncvar_swe(ds): swe_var = ds.createVariable('swe', 'f4', ('time', 'northing', 'easting',), zlib=True, complevel=4) swe_var.setncatts({ 'long_name': 'snow water equivalent', 'missing': -9999. # 'valid_min': 0, # 'valid_max': 100 }) return swe_var def create_ncvar_acc(ds): acc_var = ds.createVariable('acc', 'f4', ('time', 'northing', 'easting',), zlib=True, complevel=4) acc_var.setncatts({ 'long_name': 'snowfall in mm snow water equivalent', 'missing': -9999. # 'valid_min': 0, # 'valid_max': 100 }) return acc_var def create_ncvar_melt(ds): melt_var = ds.createVariable('melt', 'f4', ('time', 'northing', 'easting',), zlib=True, complevel=4) melt_var.setncatts({ 'long_name': 'melt in mm snow water equivalent', 'missing': -9999. # 'valid_min': 0, # 'valid_max': 100 }) return melt_var def create_lat_lons_for_NZTMgrid(extent_w=1.2e6, extent_e=1.4e6, extent_n=5.13e6, extent_s=4.82e6, resolution=250): """create grids of latitude and longitude corresponding to grid centres of data in nztm grid """ # create coordinates x_centres = np.arange(extent_w + resolution / 2, extent_e, resolution) y_centres = np.arange(extent_s + resolution / 2, extent_n, resolution) y_array, x_array = np.meshgrid(y_centres, x_centres, indexing='ij') lat_array, lon_array = nztm_to_wgs84(y_array, x_array) return lat_array, lon_array def write_nztm_grids_to_netcdf(fname, list_of_data_arrays, var_names, datetime_list, northings, eastings, lat_array, lon_array, elevation, no_time=False): """ Write a netCDF file containing fractional snow covered area data :param fname: string, full pathname of file to be created :param list_of_data_arrays: list, list containing data arrays to be saved [[time, northings, eastings],[time, northings, eastings]] :param var_names: list of strings corresponding to names of data arrays :param datetime_list: list of datetime objects corresponding to data :param northings: vector containing northings associated with data grid :param eastings: vector containing eastings associated with data grid :param lat_array: array containing longitudes of data grid :param lon_array: array containing latitudes of data grid :param elevation: array containing elevation of data grid :return: """ ds = nc.Dataset(fname, 'w') # add common attributes ds.institution = "Bodeker Scientific" ds.title = '' ds.source = '' ds.history = '' ds.references = '' ds.author = '' ds.email = '' ds.created = strftime("%Y-%m-%d %H:%M:%S", gmtime()) if no_time == False: ds.featureType = "timeSeries" else: ds.comment = 'timestamp {}'.format(datetime_list.strftime('%Y%m%d%H%M')) ds.Conventions = "CF-1.6" if no_time == False: ds.createDimension('time', ) t = ds.createVariable('time', 'f8', ('time',)) t.long_name = "time" t.units = 'days since 1900-01-01 00:00:00' t[:] = nc.date2num(datetime_list, units=t.units) ds.createDimension('northing', len(northings)) ds.createDimension('easting', len(eastings)) ds.createDimension('latitude', len(northings)) ds.createDimension('longitude', len(eastings)) # add northing and easting dimensions as well as lat/lon variables t = ds.createVariable('northing', 'f8', ('northing',)) t.axis = 'Y' t.long_name = "northing in NZTM" t.units = 'metres' t[:] = northings t = ds.createVariable('easting', 'f8', ('easting',)) t.axis = 'X' t.long_name = "easting in NZTM" t.units = 'metres' t[:] = eastings t = ds.createVariable('lat', 'f8', ('northing', 'easting',)) t.long_name = "latitude" t.standard_name = "latitude" t.units = "degrees north" t[:] = lat_array t = ds.createVariable('lon', 'f8', ('northing', 'easting',)) t.long_name = "longitude" t.standard_name = "longitude" t.units = "degrees east" t[:] = lon_array elevation_var = ds.createVariable('elevation', 'f8', ('northing', 'easting',), fill_value=-9999.) elevation_var.long_name = "elevation (meters)" elevation_var.standard_name = "surface_altitude" elevation_var.units = "meters" elevation_var[:] = elevation if 'precipitation_amount' in var_names: precip_var = create_ncvar_precipitation(ds, no_time=no_time) precip_var[:] = list_of_data_arrays[var_names.index('precipitation_amount')] if 'air_temperature' in var_names: temp_var = create_ncvar_temperaure(ds, no_time=no_time) temp_var[:] = list_of_data_arrays[var_names.index('air_temperature')] if 'surface_downwelling_shortwave_flux' in var_names: temp_var = create_ncvar_shortwave(ds, no_time=no_time) temp_var[:] = list_of_data_arrays[var_names.index('surface_downwelling_shortwave_flux')] if 'fsca' in var_names: fsca_var = create_ncvar_fsca(ds) fsca_var[:] = list_of_data_arrays[var_names.index('fsca')] if 'swe' in var_names: swe_var = create_ncvar_swe(ds) swe_var[:] = list_of_data_arrays[var_names.index('swe')] ds.close() def setup_nztm_grid_netcdf(fname, list_of_data_arrays, var_names, datetime_list, northings, eastings, lat_array, lon_array, elevation, no_time=False): """ Write a netCDF file containing fractional snow covered area data :param fname: string, full pathname of file to be created :param list_of_data_arrays: list, list containing data arrays to be saved [[time, northings, eastings],[time, northings, eastings]] :param var_names: list of strings corresponding to names of data arrays :param datetime_list: list of datetime objects corresponding to data :param northings: vector containing northings associated with data grid :param eastings: vector containing eastings associated with data grid :param lat_array: array containing longitudes of data grid :param lon_array: array containing latitudes of data grid :param elevation: array containing elevation of data grid :return: """ ds = nc.Dataset(fname, 'w') # add common attributes ds.institution = "Bodeker Scientific" ds.title = '' ds.source = '' ds.history = '' ds.references = '' ds.author = '' ds.email = '' ds.created = strftime("%Y-%m-%d %H:%M:%S", gmtime()) if no_time == False: ds.featureType = "timeSeries" else: ds.comment = 'timestamp {}'.format(datetime_list.strftime('%Y%m%d%H%M')) ds.Conventions = "CF-1.6" if no_time == False: ds.createDimension('time', ) t = ds.createVariable('time', 'f8', ('time',)) t.long_name = "time" t.units = 'days since 1900-01-01 00:00:00' t[:] = nc.date2num(datetime_list, units=t.units) ds.createDimension('northing', len(northings)) ds.createDimension('easting', len(eastings)) ds.createDimension('latitude', len(northings)) ds.createDimension('longitude', len(eastings)) # add northing and easting dimensions as well as lat/lon variables t = ds.createVariable('northing', 'f8', ('northing',)) t.axis = 'Y' t.long_name = "northing in NZTM" t.units = 'metres' t[:] = northings t = ds.createVariable('easting', 'f8', ('easting',)) t.axis = 'X' t.long_name = "easting in NZTM" t.units = 'metres' t[:] = eastings t = ds.createVariable('lat', 'f8', ('northing', 'easting',)) t.long_name = "latitude" t.standard_name = "latitude" t.units = "degrees north" t[:] = lat_array t = ds.createVariable('lon', 'f8', ('northing', 'easting',)) t.long_name = "longitude" t.standard_name = "longitude" t.units = "degrees east" t[:] = lon_array elevation_var = ds.createVariable('elevation', 'f8', ('northing', 'easting',), fill_value=-9999.) elevation_var.long_name = "elevation (meters)" elevation_var.standard_name = "surface_altitude" elevation_var.units = "meters" elevation_var[:] = elevation if 'precipitation_amount' in var_names: precip_var = create_ncvar_precipitation(ds, no_time=no_time) if 'air_temperature' in var_names: temp_var = create_ncvar_temperaure(ds, no_time=no_time) if 'surface_downwelling_shortwave_flux' in var_names: temp_var = create_ncvar_shortwave(ds, no_time=no_time) if 'fsca' in var_names: fsca_var = create_ncvar_fsca(ds) if 'swe' in var_names: swe_var = create_ncvar_swe(ds) if 'acc' in var_names: acc_var = create_ncvar_acc(ds) if 'melt' in var_names: melt_var = create_ncvar_melt(ds) return ds

35.639752

135

0.64526

1,476

11,476

4.817073

0.134824

0.020253

0.018565

0.032349

0.822504

0.817862

0.796765

0.764838

0.725738

0.705204

0

0.017923

0.222116

11,476

321

136

35.750779

0.778537

0.169833

0

0.680365

0

0.222128

0.022468

0

1

0.045662

false

0

0.013699

0

0.100457

0

null

0

1

0

null

0

6

0d2a57508331cc5221d9ba1f8f79249af10dd6d8

153

py

Python

models/backbone/__init__.py

trankha1655/pan_pp.origin

65775074ee06fef2c46aecd720821d0502ef3cd9

[ "Apache-2.0" ]

329

2020-09-02T11:11:27.000Z

2022-03-27T17:46:17.000Z

models/backbone/__init__.py

simplify23/pan_pp.pytorch

aa4774b1bf360d0a8e54d520483514d57521bf34

[ "Apache-2.0" ]

84

2020-09-04T00:33:05.000Z

2022-03-26T14:22:09.000Z

models/backbone/__init__.py

simplify23/pan_pp.pytorch

aa4774b1bf360d0a8e54d520483514d57521bf34

[ "Apache-2.0" ]

74

2020-09-03T01:12:25.000Z

2022-03-28T12:04:49.000Z

from .builder import build_backbone from .resnet import resnet18, resnet50, resnet101 __all__ = ['resnet18', 'resnet50', 'resnet101', 'build_backbone']

30.6

65

0.771242

17

153

6.588235

0.588235

0.232143

0.446429

0

0.102941

0.111111

153

4

66

38.25

0.720588

0

0.254902

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b4f803a29f61225b0633eade21b5cb7a018ea789

26,449

py

Python

optionvisualizer/option_formulas.py

GBERESEARCH/optionvisualizer

98830c478f7edbacb18f30d679ee4010c7ed7fe6

[ "MIT" ]

8

2020-12-08T15:22:02.000Z

2022-03-26T19:40:38.000Z

optionvisualizer/option_formulas.py

GBERESEARCH/optionvisualizer

98830c478f7edbacb18f30d679ee4010c7ed7fe6

[ "MIT" ]

null

optionvisualizer/option_formulas.py

GBERESEARCH/optionvisualizer

98830c478f7edbacb18f30d679ee4010c7ed7fe6

[ "MIT" ]

4

2020-11-19T23:08:05.000Z

2021-12-28T08:07:02.000Z

""" Option Pricing and Greeks formulas """ import numpy as np from optionvisualizer.utilities import Utils # pylint: disable=invalid-name class Option(): """ Calculate Black Scholes Option Price and Greeks """ @staticmethod def price(opt_params, params): """ Black Scholes Option Price Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Black Scholes Option Price. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) if opt_params['option'] == "call": opt_price = ( (opt_params['S'] * params['carry'] * params['Nd1']) - (opt_params['K'] * np.exp(-opt_params['r'] * opt_params['T']) * params['Nd2'])) elif opt_params['option'] == "put": opt_price = ( (opt_params['K'] * np.exp(-opt_params['r'] * opt_params['T']) * params['minusNd2']) - (opt_params['S'] * params['carry'] * params['minusNd1'])) else: print("Please supply an option type, 'put' or 'call'") np.nan_to_num(opt_price, copy=False) return opt_price @staticmethod def delta(opt_params, params): """ Sensitivity of the option price to changes in asset price Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Delta. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) if opt_params['option'] == 'call': opt_delta = params['carry'] * params['Nd1'] if opt_params['option'] == 'put': opt_delta = params['carry'] * (params['Nd1'] - 1) return opt_delta @staticmethod def theta(opt_params, params): """ Sensitivity of the option price to changes in time to maturity Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Theta. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) if opt_params['option'] == 'call': opt_theta = ( ((-opt_params['S'] * params['carry'] * params['nd1'] * opt_params['sigma']) / (2 * np.sqrt(opt_params['T'])) - (params['b'] - opt_params['r']) * (opt_params['S'] * params['carry'] * params['Nd1']) - (opt_params['r'] * opt_params['K']) * np.exp(-opt_params['r'] * opt_params['T']) * params['Nd2']) / 100) if opt_params['option'] == 'put': opt_theta = ( ((-opt_params['S'] * params['carry'] * params['nd1'] * opt_params['sigma'] ) / (2 * np.sqrt(opt_params['T'])) + (params['b'] - opt_params['r']) * (opt_params['S'] * params['carry'] * params['minusNd1']) + (opt_params['r'] * opt_params['K']) * np.exp(-opt_params['r'] * opt_params['T']) * params['minusNd2']) / 100) return opt_theta @staticmethod def gamma(opt_params, params): """ Sensitivity of delta to changes in the underlying asset price Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Gamma. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_gamma = ((params['nd1'] * params['carry']) / (opt_params['S'] * opt_params['sigma'] * np.sqrt(opt_params['T']))) return opt_gamma @staticmethod def vega(opt_params, params): """ Sensitivity of the option price to changes in volatility Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Vega. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_vega = ((opt_params['S'] * params['carry'] * params['nd1'] * np.sqrt(opt_params['T'])) / 100) return opt_vega @staticmethod def rho(opt_params, params): """ Sensitivity of the option price to changes in the risk free rate Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Rho. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) if opt_params['option'] == 'call': opt_rho = ( (opt_params['T'] * opt_params['K'] * np.exp(-opt_params['r'] * opt_params['T']) * params['Nd2']) / 10000) if opt_params['option'] == 'put': opt_rho = ( (-opt_params['T'] * opt_params['K'] * np.exp(-opt_params['r'] * opt_params['T']) * params['minusNd2']) / 10000) return opt_rho @staticmethod def vanna(opt_params, params): """ DdeltaDvol, DvegaDspot Sensitivity of delta to changes in volatility Sensitivity of vega to changes in the asset price Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Vanna. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_vanna = ( (((-params['carry'] * params['d2']) / opt_params['sigma']) * params['nd1']) / 100) return opt_vanna @classmethod def vomma(cls, opt_params, params): """ DvegaDvol, Vega Convexity, Volga, Vol Gamma Sensitivity of vega to changes in volatility Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Vomma. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_vomma = ( (cls.vega(opt_params, params) * ( (params['d1'] * params['d2']) / (opt_params['sigma']))) / 100) return opt_vomma @staticmethod def charm(opt_params, params): """ DdeltaDtime, Delta Bleed Sensitivity of delta to changes in time to maturity Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Charm. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) if opt_params['option'] == 'call': opt_charm = ( (-params['carry'] * ((params['nd1'] * ( (params['b'] / (opt_params['sigma'] * np.sqrt(opt_params['T']))) - (params['d2'] / (2 * opt_params['T'])))) + ((params['b'] - opt_params['r']) * params['Nd1']))) / 100) if opt_params['option'] == 'put': opt_charm = ( (-params['carry'] * ( (params['nd1'] * ( (params['b'] / (opt_params['sigma'] * np.sqrt(opt_params['T']))) - (params['d2'] / (2 * opt_params['T'])))) - ((params['b'] - opt_params['r']) * params['minusNd1']))) / 100) return opt_charm @classmethod def zomma(cls, opt_params, params): """ DgammaDvol Sensitivity of gamma to changes in volatility Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Zomma. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_zomma = ( (cls.gamma(opt_params, params) * ( (params['d1'] * params['d2'] - 1) / opt_params['sigma'])) / 100) return opt_zomma @classmethod def speed(cls, opt_params, params): """ DgammaDspot Sensitivity of gamma to changes in asset price 3rd derivative of option price with respect to spot Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Speed. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_speed = -( cls.gamma(opt_params, params) * (1 + ( params['d1'] / (opt_params['sigma'] * np.sqrt(opt_params['T'])))) / opt_params['S']) return opt_speed @classmethod def color(cls, opt_params, params): """ DgammaDtime, Gamma Bleed, Gamma Theta Sensitivity of gamma to changes in time to maturity Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Color. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_color = ( (cls.gamma(opt_params, params) * ( (opt_params['r'] - params['b']) + ( (params['b'] * params['d1']) / (opt_params['sigma'] * np.sqrt(opt_params['T']))) + ((1 - params['d1'] * params['d2']) / (2 * opt_params['T'])))) / 100) return opt_color @classmethod def ultima(cls, opt_params, params): """ DvommaDvol Sensitivity of vomma to changes in volatility 3rd derivative of option price wrt volatility Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Ultima. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_ultima = ( (cls.vomma(opt_params, params) * ( (1 / opt_params['sigma']) * (params['d1'] * params['d2'] - (params['d1'] / params['d2']) - (params['d2'] / params['d1']) - 1))) / 100) return opt_ultima @classmethod def vega_bleed(cls, opt_params, params): """ DvegaDtime Sensitivity of vega to changes in time to maturity. Parameters ---------- opt_params : Dict S : Float Underlying Stock Price. The default is 100. K : Float Strike Price. The default is 100. T : Float Time to Maturity. The default is 0.25 (3 months). r : Float Interest Rate. The default is 0.05 (5%). q : Float Dividend Yield. The default is 0. sigma : Float Implied Volatility. The default is 0.2 (20%). option : Str Option type, Put or Call. The default is 'call' params : Dict Dictionary of key parameters; used for refreshing distribution. Returns ------- Float Option Vega Bleed. """ # Update distribution parameters params = Utils.refresh_dist_params( opt_params=opt_params, params=params) opt_vega_bleed = ( (cls.vega(opt_params, params) * (opt_params['r'] - params['b'] + ((params['b'] * params['d1']) / (opt_params['sigma'] * np.sqrt(opt_params['T']))) - ((1 + (params['d1'] * params['d2']) ) / (2 * opt_params['T'])))) / 100) return opt_vega_bleed @classmethod def return_options(cls, opt_dict, params): """ Calculate option prices to be used in payoff diagrams. Parameters ---------- opt_dict : Dict Dictionary of option pricing parameters params : Dict Dictionary of key parameters Returns ------- From 1 to 4 sets of option values: Cx_0: Current option price; Float. Cx: Terminal Option payoff, varying by strike; Array Cx_G: Current option value, varying by strike; Array """ # Dictionary to store option legs option_legs = {} # create array of 1000 equally spaced points between 75% of # initial underlying price and 125% option_legs['SA'] = np.linspace( 0.75 * opt_dict['S'], 1.25 * opt_dict['S'], 1000) opt_params = { 'S':opt_dict['S'], 'K':opt_dict['K1'], 'T':opt_dict['T1'], 'r':opt_dict['r'], 'q':opt_dict['q'], 'sigma':opt_dict['sigma'], 'option':opt_dict['option1'], } # Calculate the current price of option 1 option_legs['C1_0'] = cls.price(opt_params=opt_params, params=params) # Calculate the prices at maturity for the range of strikes # in SA of option 1 change_params = {'S':option_legs['SA'], 'T':0} opt_params.update(change_params) option_legs['C1'] = cls.price(opt_params=opt_params, params=params) # Calculate the current prices for the range of strikes # in SA of option 1 change_params = {'T':opt_dict['T1']} opt_params.update(change_params) option_legs['C1_G'] = cls.price(opt_params=opt_params, params=params) if opt_dict['legs'] > 1: # Calculate the current price of option 2 change_params = {'S':opt_dict['S'], 'K':opt_dict['K2'], 'T':opt_dict['T2'], 'option':opt_dict['option2']} opt_params.update(change_params) option_legs['C2_0'] = cls.price( opt_params=opt_params, params=params) # Calculate the prices at maturity for the range of strikes # in SA of option 2 change_params = {'S':option_legs['SA'], 'T':0} opt_params.update(change_params) option_legs['C2'] = cls.price(opt_params=opt_params, params=params) # Calculate the current prices for the range of strikes # in SA of option 2 change_params = {'T':opt_dict['T2']} opt_params.update(change_params) option_legs['C2_G'] = cls.price( opt_params=opt_params, params=params) if opt_dict['legs'] > 2: # Calculate the current price of option 3 change_params = {'S':opt_dict['S'], 'K':opt_dict['K3'], 'T':opt_dict['T3'], 'option':opt_dict['option3']} opt_params.update(change_params) option_legs['C3_0'] = cls.price( opt_params=opt_params, params=params) # Calculate the prices at maturity for the range of strikes # in SA of option 3 change_params = {'S':option_legs['SA'], 'T':0} opt_params.update(change_params) option_legs['C3'] = cls.price(opt_params=opt_params, params=params) # Calculate the current prices for the range of strikes # in SA of option 3 change_params = {'T':opt_dict['T3']} opt_params.update(change_params) option_legs['C3_G'] = cls.price( opt_params=opt_params, params=params) if opt_dict['legs'] > 3: # Calculate the current price of option 4 change_params = {'S':opt_dict['S'], 'K':opt_dict['K4'], 'T':opt_dict['T4'], 'option':opt_dict['option4']} opt_params.update(change_params) option_legs['C4_0'] = cls.price( opt_params=opt_params, params=params) # Calculate the prices at maturity for the range of strikes # in SA of option 4 change_params = {'S':option_legs['SA'], 'T':0} opt_params.update(change_params) option_legs['C4'] = cls.price( opt_params=opt_params, params=params) # Calculate the current prices for the range of strikes # in SA of option 4 change_params = {'T':opt_dict['T4']} opt_params.update(change_params) option_legs['C4_G'] = cls.price( opt_params=opt_params, params=params) return option_legs

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2eca032916d2148a559be6a245373af27a7ed590

27,723

py

Python

PhdTester/phdTester/graph.py

Koldar/phdTester

b9eddc33409869fa327eef20dd0c05b336a71d97

[ "MIT" ]

1

2019-07-09T17:30:49.000Z

PhdTester/phdTester/graph.py

Koldar/phdTester

b9eddc33409869fa327eef20dd0c05b336a71d97

[ "MIT" ]

145

2019-05-22T17:22:50.000Z

2021-02-10T02:25:05.000Z

PhdTester/phdTester/graph.py

Koldar/phdTester

b9eddc33409869fa327eef20dd0c05b336a71d97

[ "MIT" ]

null

import abc import os from typing import Any, Iterable, Tuple, List, Dict class ISingleDirectedGraph(abc.ABC): @abc.abstractmethod def add_vertex(self, payload, aid: Any = None) -> Any: pass @abc.abstractmethod def get_vertex(self, aid) -> Any: pass @abc.abstractmethod def contains_vertex(self, aid) -> bool: pass @abc.abstractmethod def remove_vertex(self, aid) -> None: pass @abc.abstractmethod def vertices(self) -> Iterable[Tuple[Any, Any]]: pass def __getitem__(self, item) -> Any: return self.get_vertex(item) def __setitem__(self, key, value) -> None: self.add_vertex(aid=key, payload=value) def __contains__(self, item) -> bool: return self.contains_vertex(item) @abc.abstractmethod def add_edge(self, source, sink, payload) -> None: pass @abc.abstractmethod def get_edge(self, source, sink) -> Any: pass @abc.abstractmethod def contains_edge(self, source, sink) -> bool: pass @abc.abstractmethod def remove_edge(self, source, sink) -> None: pass @abc.abstractmethod def edges(self, source: Any = None, sink: Any = None) -> Iterable[Tuple[Any, Any, Any]]: pass @abc.abstractmethod def successors(self, source: Any) -> Iterable[Any]: pass @abc.abstractmethod def predecessors(self, sink: Any) -> Iterable[Any]: pass @abc.abstractmethod def out_edges(self, source: Any) -> Iterable[Tuple[Any, Any, Any]]: """ :param source: the key of the node whose out edges we want to compute :return: edges going out from source. it returns an iterable of 3 elements: the key of the source, the key of the sink and the payload attached to the edge """ pass @abc.abstractmethod def in_edges(self, source: Any) -> Iterable[Tuple[Any, Any, Any]]: """ :param source: the key of the node whose in edges we want to compute :return: edges going in source. it returns an iterable of 3 elements: the key of the source, the key of the sink and the payload attached to the edge """ pass def in_degree(self, n: Any) -> int: """ :param n: the key of the vertex :return: number of edges going in n """ return len(list(self.in_edges(n))) def out_degree(self, n: Any) -> int: """ :param n: the key of a vertex :return: number of edges going out from n """ return len(list(self.out_edges(n))) @property def roots(self) -> Iterable[Tuple[Any, Any]]: """ roots are vertices in the graph which have no predecessors :return: an iterable of tuples where the first element is the key of a root while the second one is the payload associated to the vertex """ for n, payload in self.vertices(): if self.in_degree(n) == 0: yield (n, payload) class IMultiDirectedGraph(abc.ABC): @abc.abstractmethod def add_vertex(self, payload, aid: Any = None) -> Any: pass @abc.abstractmethod def get_vertex(self, aid) -> Any: pass @abc.abstractmethod def contains_vertex(self, aid) -> bool: pass @abc.abstractmethod def remove_vertex(self, aid) -> None: pass @abc.abstractmethod def vertices(self) -> Iterable[Tuple[Any, Any]]: pass def __getitem__(self, item) -> Any: return self.get_vertex(item) def __setitem__(self, key, value) -> None: self.add_vertex(aid=key, payload=value) def __contains__(self, item) -> bool: return self.contains_vertex(item) @abc.abstractmethod def add_edge(self, source, sink, payload): """ add a new edge in the graph. between the same source and sink there can be multiple albeit different edges :param source: the source of the edge :param sink: the sink of the edge :param payload: the payload attached to the given edge :return: """ pass @abc.abstractmethod def get_edge(self, source, sink) -> Iterable[Tuple[Any, Any, Any]]: """ get the edges between a source and a sink :param source: :param sink: :return: """ pass @abc.abstractmethod def contains_edge(self, source, sink) -> bool: pass @abc.abstractmethod def remove_edge(self, source, sink, payload): pass @abc.abstractmethod def edges(self, source: Any = None, sink: Any = None) -> Iterable[Tuple[Any, Any, Any]]: pass @abc.abstractmethod def successors(self, source: Any) -> Iterable[Any]: """ nodes which are connected to a direct edge whose source is `source` :param source: :return: """ pass @abc.abstractmethod def predecessors(self, sink: Any) -> Iterable[Any]: """ nodes which are connected with a direct edge whose sink is `sink` :param sink: :return: """ pass @abc.abstractmethod def out_edges(self, source: Any) -> Iterable[Tuple[Any, Any, Any]]: """ :param source: the key of the node whose out edges we want to compute :return: edges going out from source. it returns an iterable of 3 elements: the key of the source, the key of the sink and the payload attached to the edge """ pass @abc.abstractmethod def in_edges(self, source: Any) -> Iterable[Tuple[Any, Any, Any]]: """ :param source: the key of the node whose in edges we want to compute :return: edges going in source. it returns an iterable of 3 elements: the key of the source, the key of the sink and the payload attached to the edge """ pass def in_degree(self, n: Any) -> int: """ :param n: the key of the vertex :return: number of edges going in n """ return len(list(self.in_edges(n))) def out_degree(self, n: Any) -> int: """ :param n: the key of a vertex :return: number of edges going out from n """ return len(list(self.out_edges(n))) @property def roots(self) -> Iterable[Tuple[Any, Any]]: """ roots are vertices in the graph which have no predecessors :return: an iterable of tuples where the first element is the key of a root while the second one is the payload associated to the vertex """ for n, payload in self.vertices(): if self.in_degree(n) == 0: yield (n, payload) class SimpleSingleDirectedGraph(ISingleDirectedGraph): next_id = 0 def __init__(self): self._vertices = {} # Dict[Any, Any] self._edges = {} # Dict[Any, Dict[Any, Any]] @staticmethod def generate_vertex_id() -> int: result = SimpleSingleDirectedGraph.next_id SimpleSingleDirectedGraph.next_id += 1 return result def check_vertex(self, vertex_name: str): # the node request may if vertex_name not in self._vertices: raise KeyError("node named '{}' not found. available names are {}".format(vertex_name, ', '.join(self._vertices.keys()))) def add_vertex(self, payload, aid: Any = None) -> Any: if aid is None: aid = SimpleSingleDirectedGraph.generate_vertex_id() if aid in self._vertices: raise KeyError(f"key {aid} is already indexing a vertex!") self._vertices[aid] = payload return aid def get_vertex(self, aid) -> Any: if aid not in self._vertices: raise KeyError(f"key {aid} is not indexing a vertex in the graph!") return self._vertices[aid] def contains_vertex(self, aid: Any) -> bool: return aid in self._vertices def remove_vertex(self, aid) -> None: if aid not in self: raise KeyError(f"vertex {aid} not found!") # remove all the edges involved in the vertex edges_to_remove = [] for s, t, payload in self.edges(source=aid, sink=None): edges_to_remove.append((s, t)) for s, t, payload in self.edges(source=None, sink=aid): edges_to_remove.append((s, t)) for s, t in edges_to_remove: self.remove_edge(s, t) del self._vertices[aid] def vertices(self) -> Iterable[Tuple[Any, Any]]: return map(lambda k: (k, self._vertices[k]), self._vertices) def add_edge(self, source, sink, payload) -> None: if source not in self._edges: self._edges[source] = {} if sink in self._edges[source]: raise KeyError(f"edge {source}->{sink} already exists") self._edges[source][sink] = payload def get_edge(self, source, sink) -> Any: return self._edges[source][sink] def contains_edge(self, source, sink) -> bool: if source not in self._edges: return False if sink not in self._edges[source]: return False return True def remove_edge(self, source, sink) -> None: del self._edges[source][sink] def edges(self, source: Any = None, sink: Any = None) -> Iterable[Tuple[Any, Any, Any]]: sources = self._vertices.keys() if source is None else [source] sinks = self._vertices.keys() if sink is None else [sink] for source in sources: if source not in self._edges: continue for sink in self._edges[source]: if sink not in sinks: continue yield (source, sink, self._edges[source][sink]) def successors(self, n: Any) -> Iterable[Any]: self.check_vertex(n) # the vertex may have no successors. In this case we generate the stop iteration immediately if n not in self._edges: raise StopIteration() for sink, payload in self._edges[n].items(): yield sink def predecessors(self, n: Any) -> Iterable[Any]: self.check_vertex(n) for source in self._edges: if n in self._edges[source]: yield source def out_edges(self, n: Any) -> Iterable[Tuple[Any, Any, Any]]: self.check_vertex(n) # the vertex may have no successors. In this case we generate the stop iteration immediately if n not in self._edges: raise StopIteration() for sink, payload in self._edges[n].items(): yield (n, sink, payload) def in_edges(self, n: Any) -> Iterable[Tuple[Any, Any, Any]]: self.check_vertex(n) for source in self._edges: if n in self._edges[source]: yield (source, n, self._edges[source][n]) class SimpleMultiDirectedGraph(IMultiDirectedGraph): next_id = 0 def __init__(self): self._vertices: Dict[Any, Any] = {} self._edges: Dict[Any, Dict[Any, List[Any]]] = {} @staticmethod def generate_vertex_id() -> int: result = SimpleMultiDirectedGraph.next_id SimpleMultiDirectedGraph.next_id += 1 return result def check_vertex(self, vertex_name: str): # the node request may if vertex_name not in self._vertices: raise KeyError("node named '{}' not found. available names are {}".format( vertex_name, ', '.join(self._vertices.keys()) )) def add_vertex(self, payload, aid: Any = None) -> Any: if aid is None: aid = SimpleMultiDirectedGraph.generate_vertex_id() if aid in self._vertices: raise KeyError(f"key {aid} is already indexing a vertex!") self._vertices[aid] = payload return aid def get_vertex(self, aid) -> Any: if aid not in self._vertices: raise KeyError(f"key {aid} is not indexing a vertex in the graph!") return self._vertices[aid] def contains_vertex(self, aid: Any) -> bool: return aid in self._vertices def remove_vertex(self, aid) -> None: if aid not in self: raise KeyError(f"vertex {aid} not found!") # remove all the edges involved in the vertex edges_to_remove = [] for s, t, payload in self.edges(source=aid, sink=None): edges_to_remove.append((s, t, payload)) for s, t, payload in self.edges(source=None, sink=aid): edges_to_remove.append((s, t, payload)) for s, t, payload in edges_to_remove: self.remove_edge(s, t, payload) del self._vertices[aid] def vertices(self) -> Iterable[Tuple[Any, Any]]: return map(lambda k: (k, self._vertices[k]), self._vertices) def add_edge(self, source, sink, payload): if source not in self._edges: self._edges[source] = {} if sink not in self._edges[source]: self._edges[source][sink] = [] self._edges[source][sink].append(payload) def get_edge(self, source, sink) -> Iterable[Tuple[Any, Any, Any]]: for payload in self._edges[source][sink]: yield (source, sink, payload) def contains_edge(self, source, sink) -> bool: if source not in self._edges: return False if sink not in self._edges[source]: return False return True def remove_edge(self, source, sink, payload): if source not in self._edges: raise KeyError(f"source {source} has no outgoing edges!") if sink not in self._edges[source]: raise KeyError(f"source {source} has no outgoing edges towards {sink}!") self._edges[source][sink].remove(payload) if len(self._edges[source][sink]) == 0: del self._edges[source][sink] def edges(self, source: Any = None, sink: Any = None) -> Iterable[Tuple[Any, Any, Any]]: sources = self._vertices.keys() if source is None else [source] sinks = self._vertices.keys() if sink is None else [sink] for source in sources: if source not in self._edges: continue for sink in self._edges[source]: if sink not in sinks: continue for payload in self._edges[source][sink]: yield (source, sink, payload) def successors(self, n: Any) -> Iterable[Any]: self.check_vertex(n) # the vertex may have no successors. In this case we generate the stop iteration immediately if n not in self._edges: raise StopIteration() visited = set() for sink, payload in self._edges[n].items(): if sink in visited: continue visited.add(sink) yield sink def predecessors(self, n: Any) -> Iterable[Any]: self.check_vertex(n) visited = set() for source in self._edges: if n in self._edges[source]: if source in visited: continue visited.add(source) yield source def out_edges(self, n: Any) -> Iterable[Tuple[Any, Any, Any]]: self.check_vertex(n) # the vertex may have no successors. In this case we generate the stop iteration immediately if n not in self._edges: raise StopIteration() for sink, edges in self._edges[n].items(): for payload in edges: yield (n, sink, payload) def in_edges(self, n: Any) -> Iterable[Tuple[Any, Any, Any]]: self.check_vertex(n) for source in self._edges: if n in self._edges[source]: for payload in self._edges[source][n]: yield (source, n, payload) class IMultiDirectedHyperGraph(abc.ABC): """ A hyper graph. Each edge is actually an hyper edge with one source and several sinks. Each hyper edge has attacched a single payload. Between the same source and sinks we have have several hyperedges """ class HyperEdge(object): """ Represents an hyper edge inside an hyperedge graph. For example A -> (B, C, D) """ def __init__(self, source: Any, sinks: Iterable[Any], payload: Any): """ Create a new hyper edge :param source: the vertex representing the sourc eof the hyperedge :param sinks: sorted list of the sinks of the hyperedge :param payload: object attached to the hyperedge """ self.source = source self.sinks = list(sinks) self.payload = payload def is_compliant(self, source: Any, sinks: Iterable[Any]) -> bool: """ :param source: :param sinks: :return: true if the source and the sinks of 2 hyper edges are the same """ return self.source == source and set(self.sinks) == set(sinks) def is_laid_on(self, vertices: Iterable[Any]) -> bool: """ :param vertices: the set of vertices id to handle :return: if all the endpoints belong to the given set, false otherwise """ return self.source in vertices and all(map(lambda sink: sink in vertices, self.sinks)) def __str__(self) -> str: return f"{self.source} -> {self.sinks}" @abc.abstractmethod def add_vertex(self, payload, aid: Any = None) -> Any: """ Insert a new vertex in the hypergraph :param payload: the paylaod attached to the vertex :param aid: the id of the vertex :raises KeyError: if aid is already an id of a vertex in this graph. If None we will set an id :return: the id of the newly generated vrtex """ pass @abc.abstractmethod def get_vertex(self, aid: Any) -> Any: """ :param aid: the id of the vertex we're looking for :raises KeyError: if aid is not an id of a vertex in the graph :return: the payload of the vertex we're looking for """ pass @abc.abstractmethod def contains_vertex(self, aid: Any) -> bool: """ :param aid: the id of the vertex we're looking for :return: true if aid is an id of a vertex inside th graph, false otehrwise """ pass @abc.abstractmethod def vertices(self) -> Iterable[Tuple[Any, Any]]: """ Iterable of all the vertices inside the graph :return: """ pass @abc.abstractmethod def size(self) -> int: """ :return: number of vertices in the graph """ pass def is_empty(self) -> bool: """ :return: true if the graph has no vertex, false otherwise """ return self.size() == 0 def get_vertices_number(self) -> int: return self.size() @abc.abstractmethod def add_edge(self, source: Any, sinks: Iterable[Any], payload) -> "IMultiDirectedHyperGraph.HyperEdge": """ add a new hyper edge in the hyper graph. between the same source and sink there can be multiple albeit different edges :param source: the source of the edge :param sinks: a sorted list of sinks of the hyper edge :param payload: the payload attached to the given hyper edge :return: the hyperedge representing the edge we want """ pass @abc.abstractmethod def get_edge(self, source: Any, sinks: Iterable[Any]) -> Iterable[HyperEdge]: """ get the edges between a source and several hyperedge sinks :param source: the source of the hyperedge we want :param sinks: the sinks of the hyper edge we want :return: an iterable of all the hyper edges having source `source` and exactly the sinks in `sinks` """ pass @abc.abstractmethod def contains_edge(self, source: Any, sinks: Iterable[Any]) -> bool: """ Check if an hyper edge with such source and sinks exists in the graph :param source: the id of the source of the hyper graph :param sinks: the ids of the sinks of the hyper graph :return: true if there is at least one hyper edge with exactly the source and the sinks given, false otherwise """ pass @abc.abstractmethod def successors(self, source: Any) -> Iterable[Any]: """ vertices which are connected to a direct hyperedge whose source is `source` :param source: id of the node handle :return: iterable of all the vertices which are successors to the node `source` """ pass @abc.abstractmethod def predecessors(self, sink: Any) -> Iterable[Any]: """ vertices which are connected with a direct hyper edge whose at **least** one sink is `sink` :param sink: id of a sink :return: iterable of vertices which has at least one sink identical to `sink` """ pass @abc.abstractmethod def edges(self) -> Iterable[HyperEdge]: """ :return: iterable of all the hyper edges in the graph """ pass @abc.abstractmethod def out_edges(self, source: Any) -> Iterable[HyperEdge]: """ iterable of hyper edges going out from a vertex :param source: the key of the vertex whose out edges we want to compute :return: hyper edges going out from source. """ pass @abc.abstractmethod def in_edges(self, source: Any) -> Iterable[HyperEdge]: """ iterable of hyper edges going in a vertex :param source: the key of the node whose in edges we want to compute :return: edges going in source. it returns an iterable of 3 elements: the key of the source, the key of the sink and the payload attached to the edge """ pass def in_degree(self, n: Any) -> int: """ :param n: the key of the vertex :return: number of hyper edges going inside the vertex n """ return len(list(self.in_edges(n))) def out_degree(self, n: Any) -> int: """ :param n: the key of a vertex :return: number of hyper edges going out from n """ return len(list(self.out_edges(n))) @property def roots(self) -> Iterable[Tuple[Any, Any]]: """ roots are vertices in the graph which have no predecessors :return: an iterable of tuples where the first element is the key of a root while the second one is the payload associated to the vertex """ for n, payload in self.vertices(): if self.in_degree(n) == 0: yield (n, payload) class DefaultMultiDirectedHyperGraph(IMultiDirectedHyperGraph): next_id = 0 def __init__(self): self._vertices: Dict[Any, Any] = {} self._edges: List["IMultiDirectedHyperGraph.HyperEdge"] = [] """ List of hyper edges. """ @staticmethod def _generate_vertex_id() -> int: """ generates a new vertex id :return: """ result = DefaultMultiDirectedHyperGraph.next_id DefaultMultiDirectedHyperGraph.next_id += 1 return result def add_vertex(self, payload, aid: Any = None) -> Any: if aid is None: aid = DefaultMultiDirectedHyperGraph._generate_vertex_id() if aid in self._vertices: raise KeyError(f"key {aid} is already an id of a vertex in this hypergraph!") self._vertices[aid] = payload return aid def get_vertex(self, aid: Any) -> Any: return self._vertices[aid] def contains_vertex(self, aid: Any) -> bool: return aid in self._vertices def vertices(self) -> Iterable[Tuple[Any, Any]]: yield from self._vertices.items() def size(self) -> int: return len(self._vertices) def add_edge(self, source: Any, sinks: Iterable[Any], payload) -> "IMultiDirectedHyperGraph.HyperEdge": result = IMultiDirectedHyperGraph.HyperEdge(source=source, sinks=list(sinks), payload=payload) self._edges.append(result) return result def get_edge(self, source: Any, sinks: Iterable[Any]) -> Iterable[IMultiDirectedHyperGraph.HyperEdge]: for edge in self._edges: if edge.is_compliant(source, sinks): yield edge def contains_edge(self, source: Any, sinks: Iterable[Any]) -> bool: for edge in self._edges: if edge.is_compliant(source, sinks): return True else: return False def successors(self, source: Any) -> Iterable[Any]: visited = set() for edge in self._edges: if edge.source == source: for sink in edge.sinks: if sink not in visited: visited.add(sink) yield sink def predecessors(self, sink: Any) -> Iterable[Any]: visited = set() for edge in self._edges: if sink in edge.sinks and edge.source not in visited: visited.add(sink) yield edge.source def edges(self) -> Iterable[IMultiDirectedHyperGraph.HyperEdge]: yield from self._edges def out_edges(self, source: Any) -> Iterable[IMultiDirectedHyperGraph.HyperEdge]: for edge in self._edges: if edge.source == source: yield edge def in_edges(self, source: Any) -> Iterable[IMultiDirectedHyperGraph.HyperEdge]: for edge in self._edges: if source in edge.sinks: yield edge def generate_image(self, output_file: str): with open(f"{output_file}.dot", "w") as dotfile: dotfile.write("digraph {\n") dotfile.write("\trankdir=\"TB\";\n") # add all edges for index, hyperedge in enumerate(self._edges): dotfile.write(f"\tN{hyperedge.source} -> HE{index:04d} [arrowhead=\"none\"];\n") for sink in hyperedge.sinks: dotfile.write(f"\tHE{index:04d} -> N{sink};\n") # add all vertices of graph for index, vertex in self._vertices.items(): dotfile.write(f"\tN{index} [label=\"{index}\"];\n") # add all vertices of hyper edges for index, hyperedge in enumerate(self._edges): dotfile.write(f"\tHE{index:04d} [shape=\"point\", label=\"\"];\n") dotfile.write("}\n") os.system(f"dot -Tsvg -o \"{output_file}.svg\" \"{output_file}.dot\"") os.remove(f"{output_file}.dot")

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2ef125506e13465d675d5c6f9775912164599a66

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py

Python

Config/__init__.py

Naopil/EldenBot

2b6f4e98dcfdf3720a6c4add4f694d0e15cd575a

[ "MIT" ]

null

Config/__init__.py

Naopil/EldenBot

2b6f4e98dcfdf3720a6c4add4f694d0e15cd575a

[ "MIT" ]

1

2019-11-16T19:01:01.000Z

Config/__init__.py

Naopil/EldenBot

2b6f4e98dcfdf3720a6c4add4f694d0e15cd575a

[ "MIT" ]

4

2018-07-22T23:13:26.000Z

2022-03-29T17:06:50.000Z

from .ConfigLoader import GlobalConfig

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2effd946f203625ed6a496f35c3a4a5cc5913b17

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py

Python

examples/NIPS/MNIST/noisy_sequence_detection/__init__.py

MarcRoigVilamala/DeepProbCEP

0d82f6c237b50122734afe758ef42bdb070ace7c

[ "MIT" ]

6

2020-09-10T03:40:53.000Z

2021-05-26T07:30:20.000Z

examples/NIPS/MNIST/noisy_sequence_detection/__init__.py

MarcRoigVilamala/DeepProbCEP

0d82f6c237b50122734afe758ef42bdb070ace7c

[ "MIT" ]

null

examples/NIPS/MNIST/noisy_sequence_detection/__init__.py

MarcRoigVilamala/DeepProbCEP

0d82f6c237b50122734afe758ef42bdb070ace7c

[ "MIT" ]

1

2020-11-23T15:55:57.000Z

from .scenario001 import *

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6

2c2b01f05edca46c62424fe33c6c7e0289a68bec

1,211

py

Python

delete2.py

Naxaes/TextEditor

b3ea7edce4ea92b7038db218aef89715286547e7

[ "MIT" ]

null

delete2.py

Naxaes/TextEditor

b3ea7edce4ea92b7038db218aef89715286547e7

[ "MIT" ]

null

delete2.py

Naxaes/TextEditor

b3ea7edce4ea92b7038db218aef89715286547e7

[ "MIT" ]

null

from tkinter import * root = Tk() text_area = Frame(root, bd=2, relief=SUNKEN) text_area.grid_rowconfigure(0, weight=1) text_area.grid_columnconfigure(0, weight=1) scrollbar_x = Scrollbar(text_area, orient=HORIZONTAL) scrollbar_x.grid(row=1, column=0, sticky=E + W) scrollbar_y = Scrollbar(text_area) scrollbar_y.grid(row=0, column=1, sticky=N + S) text = Text(text_area, wrap=NONE, bd=0, xscrollcommand=scrollbar_x.set, yscrollcommand=scrollbar_y.set) text.grid(row=0, column=0, sticky=N+S+E+W) scrollbar_x.config(command=text.xview) scrollbar_y.config(command=text.yview) text_area.pack(side=TOP) output_area = Frame(root, bd=2, relief=SUNKEN) output_area.grid_rowconfigure(0, weight=1) output_area.grid_columnconfigure(0, weight=1) scrollbar_x = Scrollbar(output_area, orient=HORIZONTAL) scrollbar_x.grid(row=1, column=0, sticky=E + W) scrollbar_y = Scrollbar(output_area) scrollbar_y.grid(row=0, column=1, sticky=N + S) text = Text(output_area, wrap=NONE, bd=0, xscrollcommand=scrollbar_x.set, yscrollcommand=scrollbar_y.set) text.grid(row=0, column=0, sticky=N+S+E+W) scrollbar_x.config(command=text.xview) scrollbar_y.config(command=text.yview) output_area.pack(side=BOTTOM) mainloop()

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null

0

6

25dff0016c69482af3215732d728bd4d52c10521

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py

Python

db/migrations/versions/8723fb0cc02e_create_tables.py

chriamue/flask-unchained-react-spa

610e099f3ece508f4c8a62d3704e4cc49f869194

[ "MIT" ]

5

2018-10-15T15:33:32.000Z

2021-01-13T23:03:48.000Z

db/migrations/versions/8723fb0cc02e_create_tables.py

chriamue/flask-unchained-react-spa

610e099f3ece508f4c8a62d3704e4cc49f869194

[ "MIT" ]

15

2018-10-15T20:14:21.000Z

2022-03-15T19:15:09.000Z

db/migrations/versions/8723fb0cc02e_create_tables.py

chriamue/flask-unchained-react-spa

610e099f3ece508f4c8a62d3704e4cc49f869194

[ "MIT" ]

4

2018-10-15T15:59:25.000Z

2020-04-11T17:48:35.000Z

"""create tables Revision ID: 8723fb0cc02e Revises: Create Date: 2018-04-23 10:21:06.996560 """ from alembic import op import sqlalchemy as sa import flask_unchained.bundles.sqlalchemy as flask_sqlalchemy_bundle # revision identifiers, used by Alembic. revision = '8723fb0cc02e' down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('category', sa.Column('name', sa.String(length=32), nullable=False), sa.Column('slug', sa.String(length=32), nullable=False), sa.Column('id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.PrimaryKeyConstraint('id', name=op.f('pk_category')) ) op.create_table('contact_submission', sa.Column('name', sa.String(length=64), nullable=False), sa.Column('email', sa.String(length=64), nullable=False), sa.Column('message', sa.Text(), nullable=False), sa.Column('id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.PrimaryKeyConstraint('id', name=op.f('pk_contact_submission')) ) op.create_table('role', sa.Column('name', sa.String(length=64), nullable=False), sa.Column('id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.PrimaryKeyConstraint('id', name=op.f('pk_role')) ) op.create_index(op.f('ix_role_name'), 'role', ['name'], unique=True) op.create_table('tag', sa.Column('name', sa.String(length=32), nullable=False), sa.Column('slug', sa.String(length=32), nullable=False), sa.Column('id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.PrimaryKeyConstraint('id', name=op.f('pk_tag')) ) op.create_table('user', sa.Column('email', sa.String(length=64), nullable=False), sa.Column('password', sa.String(), nullable=False), sa.Column('active', sa.Boolean(name='active'), nullable=False), sa.Column('confirmed_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), nullable=True), sa.Column('username', sa.String(length=64), nullable=False), sa.Column('first_name', sa.String(length=64), nullable=False), sa.Column('last_name', sa.String(length=64), nullable=False), sa.Column('id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.PrimaryKeyConstraint('id', name=op.f('pk_user')) ) op.create_index(op.f('ix_user_email'), 'user', ['email'], unique=True) op.create_index(op.f('ix_user_username'), 'user', ['username'], unique=True) op.create_table('series', sa.Column('title', sa.String(length=100), nullable=False), sa.Column('slug', sa.String(length=100), nullable=False), sa.Column('file_path', sa.String(length=255), nullable=True), sa.Column('header_image', sa.String(length=255), nullable=True), sa.Column('summary', sa.Text(), nullable=False), sa.Column('category_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=True), sa.Column('id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.ForeignKeyConstraint(['category_id'], ['category.id'], name=op.f('fk_series_category_id_category')), sa.PrimaryKeyConstraint('id', name=op.f('pk_series')) ) op.create_table('user_role', sa.Column('user_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('role_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.ForeignKeyConstraint(['role_id'], ['role.id'], name=op.f('fk_user_role_role_id_role')), sa.ForeignKeyConstraint(['user_id'], ['user.id'], name=op.f('fk_user_role_user_id_user')), sa.PrimaryKeyConstraint('user_id', 'role_id', name=op.f('pk_user_role')) ) op.create_table('series_article', sa.Column('part', sa.Integer(), nullable=False), sa.Column('series_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.ForeignKeyConstraint(['series_id'], ['series.id'], name=op.f('fk_series_article_series_id_series')), sa.PrimaryKeyConstraint('id', name=op.f('pk_series_article')) ) op.create_table('series_tag', sa.Column('series_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('tag_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.ForeignKeyConstraint(['series_id'], ['series.id'], name=op.f('fk_series_tag_series_id_series')), sa.ForeignKeyConstraint(['tag_id'], ['tag.id'], name=op.f('fk_series_tag_tag_id_tag')), sa.PrimaryKeyConstraint('series_id', 'tag_id', name=op.f('pk_series_tag')) ) op.create_table('article', sa.Column('title', sa.String(length=100), nullable=False), sa.Column('slug', sa.String(length=100), nullable=False), sa.Column('publish_date', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), nullable=False), sa.Column('last_updated', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), nullable=True), sa.Column('file_path', sa.String(length=255), nullable=True), sa.Column('header_image', sa.String(length=255), nullable=True), sa.Column('preview', sa.Text(), nullable=False), sa.Column('html', sa.Text(), nullable=False), sa.Column('author_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('article_series_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=True), sa.Column('category_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=True), sa.Column('id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.ForeignKeyConstraint(['article_series_id'], ['series_article.id'], name=op.f('fk_article_article_series_id_series_article')), sa.ForeignKeyConstraint(['author_id'], ['user.id'], name=op.f('fk_article_author_id_user')), sa.ForeignKeyConstraint(['category_id'], ['category.id'], name=op.f('fk_article_category_id_category')), sa.PrimaryKeyConstraint('id', name=op.f('pk_article')) ) op.create_table('article_tag', sa.Column('article_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('tag_id', flask_sqlalchemy_bundle.sqla.types.BigInteger(), nullable=False), sa.Column('created_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.Column('updated_at', flask_sqlalchemy_bundle.sqla.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), sa.ForeignKeyConstraint(['article_id'], ['article.id'], name=op.f('fk_article_tag_article_id_article')), sa.ForeignKeyConstraint(['tag_id'], ['tag.id'], name=op.f('fk_article_tag_tag_id_tag')), sa.PrimaryKeyConstraint('article_id', 'tag_id', name=op.f('pk_article_tag')) ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('article_tag') op.drop_table('article') op.drop_table('series_tag') op.drop_table('series_article') op.drop_table('user_role') op.drop_table('series') op.drop_index(op.f('ix_user_username'), table_name='user') op.drop_index(op.f('ix_user_email'), table_name='user') op.drop_table('user') op.drop_table('tag') op.drop_index(op.f('ix_role_name'), table_name='role') op.drop_table('role') op.drop_table('contact_submission') op.drop_table('category') # ### end Alembic commands ###

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0

1

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null

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6

d394ab8ef93d9fb13c17a4577026f96307df8d84

118

py

Python

kaggle_utils/__init__.py

Ynakatsuka/kaggle_utils

a23b62745bd7881e1a91c74e17612189d1f08784

[ "MIT" ]

122

2019-10-13T02:47:18.000Z

2022-02-21T02:01:06.000Z

kaggle_utils/__init__.py

Ynakatsuka/kaggle_utils

a23b62745bd7881e1a91c74e17612189d1f08784

[ "MIT" ]

null

kaggle_utils/__init__.py

Ynakatsuka/kaggle_utils

a23b62745bd7881e1a91c74e17612189d1f08784

[ "MIT" ]

19

2019-08-01T02:23:09.000Z

2022-01-15T11:22:01.000Z

from . import features from . import models from . import preprocess from . import utils from . import visualizations

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28

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15

118

6.2

0.466667

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6

d3a336a964acd8b72725345a3b5c1c93f17c03c0

79

py

Python

ContactHands/contact_hands_two_stream/engine/__init__.py

seoyoon130/Graduation_Project

9082cb93fb4f73c3a1577f63e906e6eb7f147dc4

[ "Apache-2.0" ]

26

2020-10-20T01:58:26.000Z

2022-02-24T11:48:10.000Z

ContactHands/contact_hands_two_stream/engine/__init__.py

seoyoon130/Graduation_Project

9082cb93fb4f73c3a1577f63e906e6eb7f147dc4

[ "Apache-2.0" ]

5

2020-10-21T05:39:08.000Z

2021-09-17T13:57:29.000Z

contact_hands_two_stream/engine/__init__.py

cvlab-stonybrook/ContactHands

6aba9a5f098b50529e589b7835264df9264844e9

[ "MIT" ]

1

2022-02-24T11:48:14.000Z

from .custom_arg_parser import * from .custom_predictor import CustomPredictor

26.333333

45

0.860759

10

79

6.5

0.7

0.307692

0

0.101266

79

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1

0

1

0

1

0

6

6c9ea02da1861cdcf3b0ad69f985e65134c63078

78

py

Python

models/__init__.py

mnanchev/restful_password_generator

31b58f4cccae35e8c9aacf3817425e73c20ca41c

[ "MIT" ]

null

models/__init__.py

mnanchev/restful_password_generator

31b58f4cccae35e8c9aacf3817425e73c20ca41c

[ "MIT" ]

1

2021-12-30T10:11:23.000Z

2022-01-05T16:56:16.000Z

models/__init__.py

mnanchev/restful_password_generator

31b58f4cccae35e8c9aacf3817425e73c20ca41c

[ "MIT" ]

null

from models.creator import CreatorModel from models.secret import SecretModel

26

39

0.871795

10

78

6.8

0.7

0.294118

0

0.102564

78

2

40

39

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true

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0

1

0

null

0

1

0

1

0

1

0

6

6cccf4ca8893430e484d7391559264b807dc4373

807

py

Python

hyaml/methods/units.py

latera/hyaml

4dc020434d25c182f8477ddd5582398e51501274

[ "Apache-2.0" ]

3

2020-04-12T15:55:11.000Z

2021-08-02T16:26:21.000Z

hyaml/methods/units.py

latera/hyaml

4dc020434d25c182f8477ddd5582398e51501274

[ "Apache-2.0" ]

null

hyaml/methods/units.py

latera/hyaml

4dc020434d25c182f8477ddd5582398e51501274

[ "Apache-2.0" ]

null

from datetime import timedelta def bytes(number): return int(number) _base = 1024 _kilo = _base ** 1 _mega = _base ** 2 _giga = _base ** 3 _tera = _base ** 4 def kilobytes(number): return bytes(number) * _kilo def megabytes(number): return bytes(number) * _mega def gigabytes(number): return bytes(number) * _giga def terabytes(number): return bytes(number) * _tera def seconds(number): return timedelta(seconds=number) def minutes(number): return timedelta(minutes=number) def hours(number): return timedelta(hours=number) def days(number): return timedelta(days=number) def weeks(number): return timedelta(weeks=number) def months(number): return timedelta(days=number * 30) def years(number): return timedelta(days=number * 365)

13.913793

39

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807

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0.168498

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0.200743

807

57

40

14.157895

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0

null

1

0

null

0

1

0

1

0

6

6ce18246c4105be740bfe93810eae477752832d9

100

py

Python

opfu/time.py

XavierDingRotman/OptionsFutures

bab0de0d66efe39f05e9ddf59460ec76547d9ada

[ "Apache-2.0" ]

1

2020-07-05T20:54:15.000Z

opfu/time.py

XavierDingRotman/OptionsFutures

bab0de0d66efe39f05e9ddf59460ec76547d9ada

[ "Apache-2.0" ]

null

opfu/time.py

XavierDingRotman/OptionsFutures

bab0de0d66efe39f05e9ddf59460ec76547d9ada

[ "Apache-2.0" ]

null

def get_T(trans_date, mature_date, base=365.2425): return (mature_date - trans_date).days/base

25

50

0.75

17

100

4.117647

0.647059

0.257143

0

0.08046

0.13

100

3

51

33.333333

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0

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0.5

false

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1

0

1

0

null

0

1

0

1

0

6

9f0f318a910353a60ce7b9fd45787385b356b1b0

31

py

Python

encoder_decoder/__init__.py

uyuutosa/naughty_parrot

5047530f190950d1dfbf2a52decea4b8769c20ae

[ "MIT" ]

null

encoder_decoder/__init__.py

uyuutosa/naughty_parrot

5047530f190950d1dfbf2a52decea4b8769c20ae

[ "MIT" ]

null

encoder_decoder/__init__.py

uyuutosa/naughty_parrot

5047530f190950d1dfbf2a52decea4b8769c20ae

[ "MIT" ]

null

from .encoder_decoder import *

15.5

30

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31

6

1

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0.129032

31

1

31

0.888889

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0

1

0

1

0

6

4cb443f8f3c8715058c667317142792cf7c9daeb

94

py

Python

src/datasets/__init__.py

chao1224/SGNN-EBM

bda4c486e8ecb9775b635757dbe1071878be7b8a

[ "MIT" ]

null

src/datasets/__init__.py

chao1224/SGNN-EBM

bda4c486e8ecb9775b635757dbe1071878be7b8a

[ "MIT" ]

1

2022-03-25T01:47:18.000Z

2022-03-25T01:50:12.000Z

src/datasets/__init__.py

chao1224/SGNN-EBM

bda4c486e8ecb9775b635757dbe1071878be7b8a

[ "MIT" ]

null

from .molecule_dataset import MoleculeDataset from .knowledge_graph_dataset import PPI_dataset

47

48

0.904255

12

94

6.75

0.666667

0.320988

0

0.074468

94

2

48

47

0.931034

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true

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1

0

null

0

1

0

1

0

1

0

6

4cd4d01ff5582fed967630dda76ec0f5c5e71936

319

py

Python

CrashCourse/makeing_pizzas.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

1

2019-01-04T05:47:50.000Z

CrashCourse/makeing_pizzas.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

null

CrashCourse/makeing_pizzas.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

null

#import pizza as p from pizza import make_pizza as mp #pizza.make_pizza(16, 'pepperoni') #pizza.make_pizza(12, "mushroom", "green peppers", 'extra cheese') #make_pizza(16, 'pepperoni') #make_pizza(12, "mushroom", "green peppers", 'extra cheese') mp(16, 'pepperoni') mp(12, "mushroom", "green peppers", 'extra cheese')

31.9

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319

4.765957

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0.375

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319

9

67

35.444444

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true

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0

1

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null

0

1

0

1

0

6

e223e4d9c816bdad72dcf3a58fe296841eb603c9

87

py

Python

app/post/__init__.py

ppolle/blog

9fcaccfb56e47d3b0b9850df77217d50da854d39

[ "Unlicense" ]

null

app/post/__init__.py

ppolle/blog

9fcaccfb56e47d3b0b9850df77217d50da854d39

[ "Unlicense" ]

null

app/post/__init__.py

ppolle/blog

9fcaccfb56e47d3b0b9850df77217d50da854d39

[ "Unlicense" ]

null

from flask import Blueprint post = Blueprint('post',__name__) from . import views,forms

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33

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87

5.416667

0.666667

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29

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false

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1

0

1

0

null

0

1

0

1

0

6

e28862a91351314af37b1163a06dc93ff56d22d1

148

py

Python

tests/test_maybe/test_nothing_singleton.py

ftruzzi/returns

42fbb70ffe68e01a848908021cf8ed1a6704b13d

[ "BSD-2-Clause" ]

2,233

2019-01-31T09:15:58.000Z

2022-03-31T22:13:19.000Z

tests/test_maybe/test_nothing_singleton.py

ftruzzi/returns

42fbb70ffe68e01a848908021cf8ed1a6704b13d

[ "BSD-2-Clause" ]

1,062

2019-01-30T22:44:03.000Z

2022-03-31T17:52:22.000Z

tests/test_maybe/test_nothing_singleton.py

ftruzzi/returns

42fbb70ffe68e01a848908021cf8ed1a6704b13d

[ "BSD-2-Clause" ]

105

2019-03-27T10:15:19.000Z

2022-02-28T21:34:41.000Z

from returns.maybe import _Nothing def test_nothing_singleton(): """Ensures `_Nothing` is a singleton.""" assert _Nothing() is _Nothing()

21.142857

44

0.716216

18

148

5.555556

0.666667

0.18

0

0.168919

148

6

45

24.666667

0.813008

0.22973

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e28ef86a319c7442f0171b76280b0851068fae9a

48

py

Python

babyai_sr/rl/utils/__init__.py

thomasaunger/babyai_sr

27fba5fb960640ebc405de83d5ab75b8c6d50ad7

[ "BSD-3-Clause" ]

null

babyai_sr/rl/utils/__init__.py

thomasaunger/babyai_sr

27fba5fb960640ebc405de83d5ab75b8c6d50ad7

[ "BSD-3-Clause" ]

null

babyai_sr/rl/utils/__init__.py

thomasaunger/babyai_sr

27fba5fb960640ebc405de83d5ab75b8c6d50ad7

[ "BSD-3-Clause" ]

null

from babyai_sr.rl.utils.penv import ParallelEnv

24

47

0.854167

8

48

5

1

0

0.083333

48

1

48

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2c3cd00cef5978d123932a805305d94813db80b5

60

py

Python

src/rpc/__init__.py

Ariam27/rpc

e6b5b1db9edb93805bcce0e81cae4c65f81fbbe6

[ "MIT" ]

null

src/rpc/__init__.py

Ariam27/rpc

e6b5b1db9edb93805bcce0e81cae4c65f81fbbe6

[ "MIT" ]

null

src/rpc/__init__.py

Ariam27/rpc

e6b5b1db9edb93805bcce0e81cae4c65f81fbbe6

[ "MIT" ]

null

from rpc.server import Server from rpc.client import Client

20

29

0.833333

10

60

5

0.5

0.28

0

0.133333

60

2

30

0.961538

0

1

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true

0

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1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

6

2c3d504a3435eacff6f659c0a10169c8437adfc8

66

py

Python

integration/ros2_workspace/src/test_py_package/test_py_package/test.py

rotu/colcon-bundle

b57dd328dca2750b31c6303e587d70913a9dfe9d

[ "Apache-2.0" ]

31

2018-10-19T18:16:37.000Z

2021-07-05T06:54:38.000Z

integration/ros2_workspace/src/test_py_package/test_py_package/test.py

rotu/colcon-bundle

b57dd328dca2750b31c6303e587d70913a9dfe9d

[ "Apache-2.0" ]

113

2018-10-24T17:33:50.000Z

2022-02-08T20:36:19.000Z

integration/ros2_workspace/src/test_py_package/test_py_package/test.py

rotu/colcon-bundle

b57dd328dca2750b31c6303e587d70913a9dfe9d

[ "Apache-2.0" ]

30

2018-10-19T18:16:08.000Z

2022-03-24T01:21:27.000Z

import rclpy def main(): print('Test py package succeeded!')

13.2

39

0.681818

9

66

5

1

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1

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1

0

6

2c5a397d59db4b044cb33df45aa49a13f9419944

29

py

Python

netflow/__init__.py

JackFram/Neural-Flow

83cea7aa933fa9650b42271ba4205208814d047b

[ "Apache-2.0" ]

1

2022-01-24T16:27:51.000Z

netflow/__init__.py

JackFram/Neural-Flow

83cea7aa933fa9650b42271ba4205208814d047b

[ "Apache-2.0" ]

null

netflow/__init__.py

JackFram/Neural-Flow

83cea7aa933fa9650b42271ba4205208814d047b

[ "Apache-2.0" ]

null

from .fx_interpreter import *

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null

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1

0

1

0

1

0

6

e2bf7ac4e9d738eb8638e8426b11ad3683099635

150

py

Python

modules/exception.py

alfie-max/Publish

0014cc45f2496c44c9171353dc42a58d73dd5490

[ "MIT" ]

1

2016-03-08T07:17:46.000Z

modules/exception.py

alfie-max/Publish

0014cc45f2496c44c9171353dc42a58d73dd5490

[ "MIT" ]

5

2021-03-18T19:55:25.000Z

2022-03-11T23:11:27.000Z

modules/exception.py

alfie-max/Publish

0014cc45f2496c44c9171353dc42a58d73dd5490

[ "MIT" ]

null

class UnhandledException(Exception): pass class AuthorizationError(Exception): pass class NetworkError(Exception): pass class Failed(Exception): pass

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1

0

null

0

1

0

6

e2c28465d71df4ca517906ac46851858992a534f

89

py

Python

tests/test_version.py

CanoaPBC/basesqlmodel

7425b75660bb22972cec940cf3b336f3021c7d21

[ "MIT" ]

29

2021-09-17T22:44:24.000Z

2022-03-10T13:43:24.000Z

tests/test_version.py

CanoaPBC/basesqlmodel

7425b75660bb22972cec940cf3b336f3021c7d21

[ "MIT" ]

6

2021-09-22T09:53:11.000Z

2022-01-17T12:09:18.000Z

tests/test_version.py

CanoaPBC/basesqlmodel

7425b75660bb22972cec940cf3b336f3021c7d21

[ "MIT" ]

4

2022-01-07T06:29:09.000Z

2022-03-12T11:20:07.000Z

import basesqlmodel def test_version(): assert basesqlmodel.__version__ == "0.1.0"

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1

0

1

0

6

e2dfd7cf127b419990c5db74e130062dc089bd0b

40

py

Python

pycoincap/__init__.py

natemago/pycoincap

45a4d6d3e467bd8ef6fb039a351bd6cf6c409a68

[ "MIT" ]

17

2017-10-03T18:45:12.000Z

2022-01-11T18:24:23.000Z

pycoincap/__init__.py

natemago/pycoincap

45a4d6d3e467bd8ef6fb039a351bd6cf6c409a68

[ "MIT" ]

8

2017-10-29T10:31:54.000Z

2019-11-07T12:36:17.000Z

pycoincap/__init__.py

natemago/pycoincap

45a4d6d3e467bd8ef6fb039a351bd6cf6c409a68

[ "MIT" ]

20

2017-10-29T20:19:21.000Z

2021-07-14T07:39:33.000Z

from pycoincap.core import CryptoMarket

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1

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0

1

0

true

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1

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1

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null

0

1

0

1

0

1

0

6

39398079e7a8f44c08538665a4d5fbd4898766cd

139

py

Python

models/__init__.py

Luodian/Learning-Invariant-Representations-and-Risks

f3058fe50e86660ca0c17ba0df41ece9af64c557

[ "MIT" ]

17

2021-04-22T03:24:38.000Z

2022-03-30T03:12:09.000Z

models/__init__.py

Luodian/Learning-Invariant-Representations-and-Risks

f3058fe50e86660ca0c17ba0df41ece9af64c557

[ "MIT" ]

5

2021-12-10T10:12:26.000Z

2022-03-31T00:01:58.000Z

models/__init__.py

Luodian/Learning-Invariant-Representations-and-Risks

f3058fe50e86660ca0c17ba0df41ece9af64c557

[ "MIT" ]

3

2021-05-19T06:12:14.000Z

2021-12-17T09:27:49.000Z

from .models import * from .LeNet import LeNet from .ResNet import * from .heads import * from .AlexNet import * from .CountingNet import *

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null

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1

0

1

0

1

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6

1a7f5da86975d5bc73d48bb7d4f5db50a117ec2e

26

py

Python

database/__init__.py

HazemMeqdad/anti-raid

9beef999a5e7ecb81e61343d430746e7963b966f

[ "MIT" ]

1

2021-10-17T11:39:20.000Z

database/__init__.py

HazemMeqdad/anti-raid

9beef999a5e7ecb81e61343d430746e7963b966f

[ "MIT" ]

null

database/__init__.py

HazemMeqdad/anti-raid

9beef999a5e7ecb81e61343d430746e7963b966f

[ "MIT" ]

null

from database.db import *

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25

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1

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1

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null

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null

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1

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1

0

1

0

6

1aaf88a975d35ee968ab0bb666f75f40d07c0f9a

118

py

Python

bitmovin_api_sdk/encoding/configurations/audio/dolby_digital_plus/customdata/__init__.py

bitmovin/bitmovin-api-sdk-python

5a85147669c84b8ca411cf2d4dbdddc92d85bbe7

[ "MIT" ]

11

2019-07-03T10:41:16.000Z

2022-02-25T21:48:06.000Z

bitmovin_api_sdk/encoding/configurations/audio/dolby_digital_plus/customdata/__init__.py

bitmovin/bitmovin-api-sdk-python

5a85147669c84b8ca411cf2d4dbdddc92d85bbe7

[ "MIT" ]

8

2019-11-23T00:01:25.000Z

2021-04-29T12:30:31.000Z

bitmovin_api_sdk/encoding/configurations/audio/dolby_digital_plus/customdata/__init__.py

bitmovin/bitmovin-api-sdk-python

5a85147669c84b8ca411cf2d4dbdddc92d85bbe7

[ "MIT" ]

13

2020-01-02T14:58:18.000Z

2022-03-26T12:10:30.000Z

from bitmovin_api_sdk.encoding.configurations.audio.dolby_digital_plus.customdata.customdata_api import CustomdataApi

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118

6.866667

0.866667

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null

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1

0

1

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1

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6

46fa53291b89c3606d59539411e61102b46d079b

398

py

Python

django/contrib/messages/tests/__init__.py

egenerat/gae-django

f12379483cf3917ed3cb46ca5ff0b94daf89fc50

[ "MIT" ]

3

2016-07-08T23:49:32.000Z

2018-04-15T22:55:01.000Z

django/contrib/messages/tests/__init__.py

egenerat/gae-django

f12379483cf3917ed3cb46ca5ff0b94daf89fc50

[ "MIT" ]

27

2017-02-05T15:57:04.000Z

2018-04-15T22:57:26.000Z

django/contrib/messages/tests/__init__.py

egenerat/gae-django

f12379483cf3917ed3cb46ca5ff0b94daf89fc50

[ "MIT" ]

null

from django.contrib.messages.tests.cookie import CookieTest from django.contrib.messages.tests.fallback import FallbackTest from django.contrib.messages.tests.middleware import MiddlewareTest from django.contrib.messages.tests.session import SessionTest from django.contrib.messages.tests.user_messages import \ UserMessagesTest, LegacyFallbackTest

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0.766332

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398

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null

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0

1

0

1

0

6

204e837f7c14943c6508decb3709405d6f4a7e25

148

py

Python

storyhub/sdk/service/output/OutputBoolean.py

wilzbach/hub-sdk-python

3dd2a3112276068080751198cb1fcc29975c0136

[ "Apache-2.0" ]

null

storyhub/sdk/service/output/OutputBoolean.py

wilzbach/hub-sdk-python

3dd2a3112276068080751198cb1fcc29975c0136

[ "Apache-2.0" ]

null

storyhub/sdk/service/output/OutputBoolean.py

wilzbach/hub-sdk-python

3dd2a3112276068080751198cb1fcc29975c0136

[ "Apache-2.0" ]

null

from storyhub.sdk.service.output.OutputBase import OutputBase class OutputBoolean(OutputBase): """ A service output boolean type. """

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148

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7

62

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0

1

0

true

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1

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1

0

null

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0

1

0

null

0

1

0

1

0

1

0

6

6461d2f38811009d551a78be09c74f412d603b57

168

py

Python

NintbotForDiscord/Utils.py

nint8835/DiscordUserBot

63e99c916680492cdfd4b00b7df5ef5c9640a204

[ "MIT" ]

3

2015-12-24T18:17:44.000Z

2016-05-20T07:19:29.000Z

NintbotForDiscord/Utils.py

nint8835/DiscordSelfBot

63e99c916680492cdfd4b00b7df5ef5c9640a204

[ "MIT" ]

null

NintbotForDiscord/Utils.py

nint8835/DiscordSelfBot

63e99c916680492cdfd4b00b7df5ef5c9640a204

[ "MIT" ]

1

2020-01-20T11:30:18.000Z

import discord from .Types import DiscordChannel def channel_is_private(channel: DiscordChannel) -> bool: return isinstance(channel, discord.abc.PrivateChannel)

21

58

0.803571

19

168

7

0.736842

0

0.125

168

7

59

24

0.904762

0

1

0.25

false

0

0.5

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

b37b02c311ced4b2372b0b554bd8f0bb95fcb2c9

971

py

Python

tests/test_depends.py

marvingabler/fastapi-limiter

fa272dd4d4a3fcf56ad98f5bb6fd81babce007dd

[ "Apache-2.0" ]

118

2020-11-09T07:21:41.000Z

2022-03-31T15:40:45.000Z

tests/test_depends.py

marvingabler/fastapi-limiter

fa272dd4d4a3fcf56ad98f5bb6fd81babce007dd

[ "Apache-2.0" ]

18

2020-12-30T10:52:25.000Z

2022-03-11T07:32:54.000Z

tests/test_depends.py

marvingabler/fastapi-limiter

fa272dd4d4a3fcf56ad98f5bb6fd81babce007dd

[ "Apache-2.0" ]

24

2021-01-07T04:08:57.000Z

2022-03-17T09:37:44.000Z

from time import sleep from starlette.testclient import TestClient from examples.main import app def test_limiter(): with TestClient(app) as client: response = client.get("/") assert response.status_code == 200 client.get("/") response = client.get("/") assert response.status_code == 429 sleep(5) response = client.get("/") assert response.status_code == 200 def test_limiter_multiple(): with TestClient(app) as client: response = client.get("/multiple") assert response.status_code == 200 response = client.get("/multiple") assert response.status_code == 429 sleep(5) response = client.get("/multiple") assert response.status_code == 200 response = client.get("/multiple") assert response.status_code == 429 sleep(10) response = client.get("/multiple") assert response.status_code == 200

23.682927

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0.61792

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0

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44

24.275

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0

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0

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0.185185

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null

0

1

0

1

0

1

0

null

0

6

37466b094c002e1c2619a912073292a3fc2e7a79

159

py

Python

workflow/ignite/decorators/__init__.py

Aiwizo/ml-workflow

88e104fce571dd3b76914626a52f9001342c07cc

[ "Apache-2.0" ]

4

2020-09-23T15:39:24.000Z

2021-09-12T22:11:00.000Z

workflow/ignite/decorators/__init__.py

Aiwizo/ml-workflow

88e104fce571dd3b76914626a52f9001342c07cc

[ "Apache-2.0" ]

4

2020-09-23T15:07:39.000Z

2020-10-30T10:26:24.000Z

workflow/ignite/decorators/__init__.py

Aiwizo/ml-workflow

88e104fce571dd3b76914626a52f9001342c07cc

[ "Apache-2.0" ]

null

from .evaluate import evaluate from .step import step from .to_device import to_device from .train import train from .update_cpu_model import update_cpu_model

26.5

46

0.842767

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0

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0

1

0

true

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