xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

6fa4f17ff71dd0ae5155b92fda82e4d7d5fff9e9

2,017

py

Python

src/config/experiments/test.py

DanielTrosten/mvc

b0a08fc6c75bdb1fae796f82a7cbfb001bf02047

[ "MIT" ]

16

2021-04-13T13:21:12.000Z

2022-03-30T03:40:46.000Z

src/config/experiments/test.py

DanielTrosten/mvc

b0a08fc6c75bdb1fae796f82a7cbfb001bf02047

[ "MIT" ]

2

2021-08-13T14:02:19.000Z

2022-01-19T12:52:29.000Z

src/config/experiments/test.py

DanielTrosten/mvc

b0a08fc6c75bdb1fae796f82a7cbfb001bf02047

[ "MIT" ]

7

2021-04-13T14:27:49.000Z

2022-03-09T21:23:17.000Z

from config.defaults import Experiment, Dataset, SiMVC, MLP, DDC, Fusion, Loss, CoMVC blobs_overlap = Experiment( dataset_config=Dataset(name="blobs_overlap"), model_config=SiMVC( backbone_configs=( MLP(layers=[32, 32, 32], input_size=(2,)), MLP(layers=[32, 32, 32], input_size=(2,)), ), fusion_config=Fusion(method="weighted_mean", n_views=2), cm_config=DDC(n_clusters=3), loss_config=Loss( funcs="ddc_1|ddc_2|ddc_3", ), ), n_runs=1, n_epochs=10, ) blobs_overlap_contrast = Experiment( dataset_config=Dataset(name="blobs_overlap"), model_config=CoMVC( backbone_configs=( MLP(layers=[32, 32, 32], input_size=(2,)), MLP(layers=[32, 32, 32], input_size=(2,)), ), fusion_config=Fusion(method="weighted_mean", n_views=2), projector_config=None, cm_config=DDC(n_clusters=3), loss_config=Loss( funcs="ddc_1|ddc_2|ddc_3|contrast", ) ), n_runs=1, ) blobs_overlap_5 = Experiment( dataset_config=Dataset(name="blobs_overlap_5"), model_config=SiMVC( backbone_configs=( MLP(layers=[32, 32, 32], input_size=(2,)), MLP(layers=[32, 32, 32], input_size=(2,)), ), fusion_config=Fusion(method="weighted_mean", n_views=2), cm_config=DDC(n_clusters=5), loss_config=Loss( funcs="ddc_1|ddc_2|ddc_3", ), ), n_runs=1, ) blobs_overlap_5_contrast = Experiment( dataset_config=Dataset(name="blobs_overlap_5"), model_config=CoMVC( backbone_configs=( MLP(layers=[32, 32, 32], input_size=(2,)), MLP(layers=[32, 32, 32], input_size=(2,)), ), fusion_config=Fusion(method="weighted_mean", n_views=2), projector_config=None, cm_config=DDC(n_clusters=5), loss_config=Loss( funcs="ddc_1|ddc_2|ddc_3|contrast", ) ), n_runs=1, )

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7

6ff38e5a87c08ddb61d54b1bdd0e0f86d2350777

106

py

Python

Python/Tests/TestData/Grammar/DecoratorsClassDef.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

Python/Tests/TestData/Grammar/DecoratorsClassDef.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

Python/Tests/TestData/Grammar/DecoratorsClassDef.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

@fob class C: pass @fob.oar class C: pass @fob(oar) class C: pass @fob @oar class C: pass

8.153846

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106

3.1

0.3

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null

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12

b50964ac7171e26405b480e793db10a184c005a6

201

py

Python

blueprints/pages/login.py

LeaveMyYard/Hillelgram

4ba5131d84477ce5fb1479b4de2c2b2a1f09f8fd

[ "MIT" ]

null

blueprints/pages/login.py

LeaveMyYard/Hillelgram

4ba5131d84477ce5fb1479b4de2c2b2a1f09f8fd

[ "MIT" ]

2

2021-11-27T10:54:55.000Z

2021-11-27T12:57:02.000Z

blueprints/pages/login.py

LeaveMyYard/Hillelgram

4ba5131d84477ce5fb1479b4de2c2b2a1f09f8fd

[ "MIT" ]

null

from flask import Blueprint, render_template login_blueprint = Blueprint("login_blueprint", __name__) @login_blueprint.route("/login") def get_login_page(): return render_template("login.html")

22.333333

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null

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7

d22cae4485a0d759513e75bf5d9c96f3ead384ce

35,931

py

Python

codes/py_countreg.py

statcompute/py_countreg

3f62b8f16b95be5be46cacb93f544bbca6b1ec55

[ "MIT" ]

null

codes/py_countreg.py

statcompute/py_countreg

3f62b8f16b95be5be46cacb93f544bbca6b1ec55

[ "MIT" ]

null

codes/py_countreg.py

statcompute/py_countreg

3f62b8f16b95be5be46cacb93f544bbca6b1ec55

[ "MIT" ]

null

# py_countreg/py_countreg.py # exec(open('py_countreg/py_countreg.py').read()) # 0.0.4 import numpy, scipy from statsmodels.base.model import GenericLikelihoodModel as gll from statsmodels.api import Logit as logit #################### 01. Standard Poisson Regression #################### def _ll_stdpoisson(y, x, beta): """ The function calculates the log likelihood function of a standard poisson regression. Parameters: y : the frequency outcome x : variables of the poisson regression beta : coefficients of the poisson regression """ mu = numpy.exp(numpy.dot(x, beta)) pr = numpy.exp(-mu) * numpy.float_power(mu, y) / scipy.special.factorial(y) ll = numpy.log(pr) return(ll) ################################################################################ def stdpoisson(Y, X): """ The function estimates a standard poisson regression. Parameters: Y : a pandas series for the frequency outcome with integer values. X : a pandas dataframe with model variables that are all numeric values. Example: stdpoisson(Y, X).fit().summary() """ class stdpoisson(gll): def __init__(self, endog, exog, **kwds): super(stdpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): beta = params ll = _ll_stdpoisson(self.endog, self.exog, beta) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, **kwds): if start_params == None: start_params = numpy.zeros(self.exog.shape[1]) return(super(stdpoisson, self).fit(start_params = start_params, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X = X.copy() _X.insert(loc = 0, column = "_CONST", value = 1) return(stdpoisson(_Y, _X)) #################### 02. Negative Binomial Regression #################### def _ll_negbinom2(y, x, beta, alpha): """ The function calculates the log likelihood function of the negative binomial (NB-2) regression. Parameters: y : the frequency outcome x : variables of the negative binomial regression beta : coefficients of the negative binomial regression alpha : the dispersion parameter of the negative binomial regression """ mu = numpy.exp(numpy.dot(x, beta)) a1 = 1 / alpha pr = scipy.special.gamma(y + a1) / (scipy.special.gamma(y + 1) * scipy.special.gamma(a1)) * \ numpy.float_power(a1 / (a1 + mu), a1) * numpy.float_power(mu / (a1 + mu), y) ll = numpy.log(pr) return(ll) ################################################################################ def negbinom2(Y, X): """ The function estimates a negative binomial (NB-2) regression. Parameters: Y : a pandas series for the frequency outcome with integer values. X : a pandas dataframe with model variables that are all numeric values. Example: negbinom2(Y, X).fit().summary() """ class negbinom2(gll): def __init__(self, endog, exog, **kwds): super(negbinom2, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): alpha = params[-1] beta = params[:-1] ll = _ll_negbinom2(self.endog, self.exog, beta, alpha) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_ALPHA') if start_params == None: start_params = numpy.append(p0, a0) return(super(negbinom2, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X = X.copy() _X.insert(loc = 0, column = "_CONST", value = 1) p0 = stdpoisson(Y, X).fit(disp = 0).params a0 = 1 return(negbinom2(_Y, _X)) #################### 03. Generalized Poisson Regression #################### def _ll_genpoisson(y, x, beta, s): """ The function calculates the log likelihood function of the generalized poisson regression. Parameters: y : the frequency outcome x : variables of the generalized poisson regression beta : coefficients of the negative binomial regression s : the scale parameter for the generalized poisson distribution """ mu = numpy.exp(numpy.dot(x, beta)) xi = numpy.exp(s) _a = mu * (1 - xi) pr = _a / scipy.special.factorial(y) * numpy.exp(-_a - xi * y) * \ numpy.float_power(_a + xi * y, y - 1) ll = numpy.log(pr) return(ll) ################################################################################ def genpoisson(Y, X): """ The function estimates a generalized poisson regression. In addition to regression coefficients, there is a scale parameter S such that Xi = Exp(S). In a generalized poisson distribution, the VAR(Y) = E(Y) / [(1 - Xi) ^ 2] such that [(1 - Xi) ^ 2] > 1 indicates the under-dispersion and [(1 - Xi) ^ 2] < 1 indicates the over-dispersion. Parameters: Y : a pandas series for the frequency outcome with integer values. X : a pandas dataframe with model variables that are all numeric Example: genpoisson(Y, X).fit().summary() """ class genpoisson(gll): def __init__(self, endog, exog, **kwds): super(genpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): _s = params[-1] beta = params[:-1] ll = _ll_genpoisson(self.endog, self.exog, beta, _s) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_S') if start_params == None: start_params = numpy.append(p0, s0) return(super(genpoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X = X.copy() _X.insert(loc = 0, column = "_CONST", value = 1) p0 = stdpoisson(Y, X).fit(disp = 0).params s0 = numpy.log(max(1e-4, 1 - numpy.float_power(numpy.mean(Y) / numpy.var(Y), 0.5))) return(genpoisson(_Y, _X)) #################### 04. Hurdle Poisson Regression #################### def _ll_hdlpoisson(y, x1, x2, beta1, beta2): """ The function calculates the log likelihood function of the hurdle poisson regression. Parameters: y : the frequency outcome x1 : variables for the probability model in the hurdle poisson regression x2 : variables for the count model in the hurdle poisson regression beta1 : coefficients for the probability model in the hurdle poisson regression beta2 : coefficients for the count model in the hurdle poisson regression """ xb1 = numpy.dot(x1, beta1) xb2 = numpy.dot(x2, beta2) p0 = numpy.exp(xb1) / (1 + numpy.exp(xb1)) mu = numpy.exp(xb2) i0 = numpy.where(y == 0, 1, 0) pr = p0 * i0 + \ (1 - p0) * numpy.exp(-mu) * numpy.float_power(mu, y) / \ ((1 - numpy.exp(-mu)) * scipy.special.factorial(y)) * (1 - i0) ll = numpy.log(pr) return(ll) ################################################################################ def hdlpoisson(Y, X1, X2): """ The function estimates a hurdle poisson regression, which is the composite between point mess at zero and a zero-trucated poisson distribution. In the model output, estimated coefficients starting with "P0:" are used to predict the probability of zero outcomes and estimated coefficients starting with "MU:" are used to predict frequency outcomes for a zero-trucated poisson. Parameters: Y : a pandas series for the frequency outcome with integer values, including zeros. X1 : a pandas dataframe with the probability model variables that are all numeric values. X2 : a pandas dataframe with the count model variables that are all numeric values. Example: hdlpoisson(Y, X1, X2).fit().summary() """ class hdlpoisson(gll): def __init__(self, endog, exog, **kwds): super(hdlpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): d1 = _X1.shape[1] beta1 = params[:d1] beta2 = params[d1:] ll = _ll_hdlpoisson(self.endog, self.exog[:, :d1], self.exog[:, d1:], beta1, beta2) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): if start_params == None: start_params = numpy.concatenate([p10, p20]) return(super(hdlpoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X1 = X1.copy() _X2 = X2.copy() _X1.insert(loc = 0, column = "_CONST", value = 1) _X1.columns = ["P0:" + _ for _ in _X1.columns] _X2.insert(loc = 0, column = "_CONST", value = 1) _X2.columns = ["MU:" + _ for _ in _X2.columns] _X = _X1.join(_X2) p10 = logit(numpy.where(_Y == 0, 1, 0), _X1).fit(disp = 0).params p20 = ztrpoisson(Y[Y > 0], X2[Y > 0]).fit(disp = 0).params return(hdlpoisson(_Y, _X)) #################### 05. Zero-Inflated Poisson Regression #################### def _ll_zifpoisson(y, x1, x2, beta1, beta2): """ The function calculates the log likelihood function of the zero-inflated poisson regression. Parameters: y : the frequency outcome x1 : variables for the probability model in the zero-inflated poisson regression x2 : variables for the count model in the zero-inflated poisson regression beta1 : coefficients for the probability model in the zero-inflated poisson regression beta2 : coefficients for the count model in the zero-inflated poisson regression """ xb1 = numpy.dot(x1, beta1) xb2 = numpy.dot(x2, beta2) p0 = numpy.exp(xb1) / (1 + numpy.exp(xb1)) mu = numpy.exp(xb2) i0 = numpy.where(y == 0, 1, 0) pr = (p0 + (1 - p0) * numpy.exp(-mu)) * i0 + \ (1 - p0) * numpy.exp(-mu) * numpy.float_power(mu, y) / scipy.special.factorial(y) * (1 - i0) ll = numpy.log(pr) return(ll) ################################################################################ def zifpoisson(Y, X1, X2): """ The function estimates a zero-inflated poisson regression, which is the composite between point mess at zero and a standard poisson distribution. In the model outcome, estimated coefficients starting with "P0:" are used to predict the probability of zero outcomes and estimated coefficients starting with "MU:" are used to predict frequency outcomes for a standard poisson. Parameters: Y : a pandas series for the frequency outcome with integer values, including zeros. X1 : a pandas dataframe with the probability model variables that are all numeric values. X2 : a pandas dataframe with the count model variables that are all numeric values. Example: zifpoisson(Y, X1, X2).fit().summary() """ class zifpoisson(gll): def __init__(self, endog, exog, **kwds): super(zifpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): d1 = _X1.shape[1] beta1 = params[:d1] beta2 = params[d1:] ll = _ll_zifpoisson(self.endog, self.exog[:, :d1], self.exog[:, d1:], beta1, beta2) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): if start_params == None: start_params = numpy.concatenate([p10, p20]) return(super(zifpoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X1 = X1.copy() _X2 = X2.copy() _X1.insert(loc = 0, column = "_CONST", value = 1) _X1.columns = ["P0:" + _ for _ in _X1.columns] _X2.insert(loc = 0, column = "_CONST", value = 1) _X2.columns = ["MU:" + _ for _ in _X2.columns] _X = _X1.join(_X2) p10 = logit(numpy.where(_Y == 0, 1, 0), _X1).fit(disp = 0).params p20 = ztrpoisson(Y[Y > 0], X2[Y > 0]).fit(disp = 0).params return(zifpoisson(_Y, _X)) #################### 06. Conway-Maxwell Poisson Regression #################### def _ll_compoisson(y, x, beta, s): """ The function calculates the log likelihood function of the Conway-Maxwell poisson regression. Parameters: y : the frequency outcome. x : variables in the conway-maxwell poisson regression beta : coefficients in the conway maxwell poisson regression s : the scale parameter in the Conway-Maxwell distribution and is equal to log(nv) """ mu = numpy.exp(numpy.dot(x, beta)) nv = numpy.exp(s) _z = 0 for _n in range(100): _z = _z + numpy.float_power(mu, _n) / numpy.float_power(scipy.special.factorial(_n), nv) pr = numpy.float_power(mu, y) / numpy.float_power(scipy.special.factorial(y), nv) * numpy.float_power(_z, -1) ll = numpy.log(pr) return(ll) ################################################################################ def compoisson(Y, X): """ The function estimates a Conway-Maxwell poisson regression. Given MU = exp(x * beta), E(Y) ~= MU + nv / 2 - 0.5. In addition to estimated coefficients beta, there is a scaled parameter S such that nv = Exp(S). In the COMpoisson, since VAR(Y) ~= E(Y) / nv, nv > 1 suggests the under-dispersion and nv < 1 suggests the over-dispersion. Parameters: Y : a pandas series for the frequency outcome with integer values. X : a pandas dataframe with the probability model variables that are all numeric values. Example: compoisson(Y, X).fit().summary() """ class compoisson(gll): def __init__(self, endog, exog, **kwds): super(compoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): _s = params[-1] beta = params[:-1] ll = _ll_compoisson(self.endog, self.exog, beta, _s) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_S') if start_params == None: start_params = numpy.append(p0, s0) return(super(compoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X = X.copy() _X.insert(loc = 0, column = "_CONST", value = 1) p0 = stdpoisson(Y, X).fit(disp = 0).params s0 = numpy.log(numpy.mean(Y) / numpy.var(Y)) return(compoisson(_Y, _X)) #################### 07. Hurdle Negative Binomial Regression #################### def _ll_hdlnegbin2(y, x1, x2, beta1, beta2, alpha): """ The function calculates the log likelihood function of the hurdle negative binomial regression. Parameters: y : the frequency outcome x1 : variables for the probability model in the hurdle negative binomial regression x2 : variables for the count model in the hurdle negative binomial regression beta1 : coefficients for the probability model in the hurdle negative binomial regression beta2 : coefficients for the count model in the hurdle negative binomial regression alpha : the dispersion parameter in the negative binomial distribution """ xb1 = numpy.dot(x1, beta1) xb2 = numpy.dot(x2, beta2) p0 = numpy.exp(xb1) / (1 + numpy.exp(xb1)) mu = numpy.exp(xb2) i0 = numpy.where(y == 0, 1, 0) a1 = 1 / alpha pr = p0 * i0 + \ (1 - p0) / (1 - numpy.float_power(a1 / (a1 + mu), a1)) * \ scipy.special.gamma(y + a1) / (scipy.special.gamma(y + 1) * scipy.special.gamma(a1)) * \ numpy.float_power(a1 / (a1 + mu), a1) * numpy.float_power(mu / (a1 + mu), y) * (1 - i0) ll = numpy.log(pr) return(ll) ################################################################################ def hdlnegbin2(Y, X1, X2): """ The function estimates a hurdle negative binomial regression, which is the composite between point mess at zero and a zero-truncated negative binomial distribution. In the model outcome, estimated coefficients starting with "P0:" are used to predict the probability of zero outcomes and estimated coefficients starting with "MU:" are used to predict frequency outcomes for a zero-trucated negative binomial. Parameters: Y : a pandas series for the frequency outcome with integer values, including zeros. X1 : a pandas dataframe with the probability model variables that are all numeric values. X2 : a pandas dataframe with the count model variables that are all numeric values. Example: hdlnegbin2(Y, X1, X2).fit().summary() """ class hdlnegbin2(gll): def __init__(self, endog, exog, **kwds): super(hdlnegbin2, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): d1 = _X1.shape[1] beta1 = params[:d1] beta2 = params[d1:-1] alpha = params[-1] ll = _ll_hdlnegbin2(self.endog, self.exog[:, :d1], self.exog[:, d1:], beta1, beta2, alpha) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_ALPHA') if start_params == None: start_params = numpy.concatenate([p10, p20]) return(super(hdlnegbin2, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X1 = X1.copy() _X2 = X2.copy() _X1.insert(loc = 0, column = "_CONST", value = 1) _X1.columns = ["P0:" + _ for _ in _X1.columns] _X2.insert(loc = 0, column = "_CONST", value = 1) _X2.columns = ["MU:" + _ for _ in _X2.columns] _X = _X1.join(_X2) p10 = logit(numpy.where(_Y == 0, 1, 0), _X1).fit(disp = 0).params p20 = ztrnegbin2(Y[Y > 0], X2[Y > 0]).fit(disp = 0).params return(hdlnegbin2(_Y, _X)) #################### 08. Zero-Inflated Negative Binomial Regression #################### def _ll_zifnegbin2(y, x1, x2, beta1, beta2, alpha): """ The function calculates the log likelihood function of the zero-inflated negative binomial regression. Parameters: y : the frequency outcome x1 : variables for the probability model in the zero-inflated negative binomial regression x2 : variables for the count model in the zero-inflated negative binomial regression beta1 : coefficients for the probability model in the zero-inflated negative binomial regression beta2 : coefficients for the count model in the zero-inflated negative binomial regression alpha : the dispersion parameter in the negative binomial distribution """ xb1 = numpy.dot(x1, beta1) xb2 = numpy.dot(x2, beta2) p0 = numpy.exp(xb1) / (1 + numpy.exp(xb1)) mu = numpy.exp(xb2) i0 = numpy.where(y == 0, 1, 0) a1 = 1 / alpha pr = (p0 + (1 - p0) * numpy.float_power(a1 / (a1 + mu), a1)) * i0 + \ (1 - p0) * scipy.special.gamma(y + a1) / (scipy.special.gamma(y + 1) * scipy.special.gamma(a1)) * \ numpy.float_power(a1 / (a1 + mu), a1) * numpy.float_power(mu / (a1 + mu), y) * (1 - i0) ll = numpy.log(pr) return(ll) ################################################################################ def zifnegbin2(Y, X1, X2): """ The function estimates a zero-inflated negative binomial regression, which is the composite between point mess at zero and a negative binomial distribution. In the model outcome, estimated coefficients starting with "P0:" are used to predict the probability of zero outcomes and estimated coefficients starting with "MU:" are used to predict frequency outcomes for a standard negative binomial. Parameters: Y : a pandas series for the frequency outcome with integer values, including zeros. X1 : a pandas dataframe with the probability model variables that are all numeric values. X2 : a pandas dataframe with the count model variables that are all numeric values. Example: zifnegbin2(Y, X1, X2).fit().summary() """ class zifnegbin2(gll): def __init__(self, endog, exog, **kwds): super(zifnegbin2, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): d1 = _X1.shape[1] beta1 = params[:d1] beta2 = params[d1:-1] alpha = params[-1] ll = _ll_zifnegbin2(self.endog, self.exog[:, :d1], self.exog[:, d1:], beta1, beta2, alpha) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_ALPHA') if start_params == None: start_params = numpy.concatenate([p10, p20]) return(super(zifnegbin2, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X1 = X1.copy() _X2 = X2.copy() _X1.insert(loc = 0, column = "_CONST", value = 1) _X1.columns = ["P0:" + _ for _ in _X1.columns] _X2.insert(loc = 0, column = "_CONST", value = 1) _X2.columns = ["MU:" + _ for _ in _X2.columns] _X = _X1.join(_X2) p10 = logit(numpy.where(_Y == 0, 1, 0), _X1).fit(disp = 0).params p20 = ztrnegbin2(Y[Y > 0], X2[Y > 0]).fit(disp = 0).params return(zifnegbin2(_Y, _X)) #################### 09. Zero-truncated Poisson Regression #################### def _ll_ztrpoisson(y, x, beta): """ The function calculates the log likelihood function of the zero-truncated Poisson regression. Parameters: y : the frequency outcome without zero x : variables of the negative binomial regression beta : coefficients of the negative binomial regression """ mu = numpy.exp(numpy.dot(x, beta)) p0 = numpy.exp(-mu) pr = numpy.exp(-mu) * numpy.float_power(mu, y) / scipy.special.factorial(y) / (1 - p0) ll = numpy.log(pr) return(ll) ################################################################################ def ztrpoisson(Y, X): """ The function estimates a zero-truncated Poisson regression. Parameters: Y : a pandas series for the frequency outcome wit non-zero integer values. X : a pandas dataframe with model variables that are all numeric values. """ class ztrpoisson(gll): def __init__(self, endog, exog, **kwds): super(ztrpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): beta = params ll = _ll_ztrpoisson(self.endog, self.exog, beta) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): if start_params == None: start_params = numpy.zeros(self.exog.shape[1]) return(super(ztrpoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X = X.copy() _X.insert(loc = 0, column = "_CONST", value = 1) return(ztrpoisson(_Y, _X)) #################### 10. Zero-truncated Negative Binomial Regression #################### def _ll_ztrnegbin2(y, x, beta, alpha): """ The function calculates the log likelihood function of the zero-truncated negative binomial (NB-2) regression. Parameters: y : the frequency outcome with non-zero integer values. x : variables of the negative binomial regression beta : coefficients of the negative binomial regression alpha : the dispersion parameter of the zero-truncated negative binomial regression """ mu = numpy.exp(numpy.dot(x, beta)) a1 = 1 / alpha p0 = numpy.float_power(a1 / (a1 + mu), a1) pr = scipy.special.gamma(y + a1) / (scipy.special.gamma(y + 1) * scipy.special.gamma(a1)) * \ numpy.float_power(a1 / (a1 + mu), a1) * numpy.float_power(mu / (a1 + mu), y) / (1 - p0) ll = numpy.log(pr) return(ll) ################################################################################ def ztrnegbin2(Y, X): """ The function estimates a zero-truncated negative binomial (NB-2) regression. Parameters: Y : a pandas series for the frequency outcome with non-zero integer values. X : a pandas dataframe with model variables that are all numeric values. """ class ztrnegbin2(gll): def __init__(self, endog, exog, **kwds): super(ztrnegbin2, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): alpha = params[-1] beta = params[:-1] ll = _ll_ztrnegbin2(self.endog, self.exog, beta, alpha) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_ALPHA') if start_params == None: start_params = numpy.append(p0, 1) return(super(ztrnegbin2, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X = X.copy() _X.insert(loc = 0, column = "_CONST", value = 1) p0 = ztrpoisson(Y, X).fit(disp = 0).params a0 = 1 return(ztrnegbin2(_Y, _X)) #################### 11. Zero-truncated Generalized Poisson Regression #################### def _ll_ztgpoisson(y, x, beta, s): """ The function calculates the log likelihood function of the zero-truncated generalized poisson regression. Parameters: y : the frequency outcome with non-zero integer values. x : variables of the negative binomial regression beta : coefficients of the negative binomial regression s : the scaled parameter of the zero-truncated generalized poisson regression """ mu = numpy.exp(numpy.dot(x, beta)) xi = numpy.exp(s) _a = mu * (1 - xi) p0 = numpy.exp(-_a) pr = _a / scipy.special.factorial(y) * numpy.exp(-_a - xi * y) * \ numpy.float_power(_a + xi * y, y - 1) / (1 - p0) ll = numpy.log(pr) return(ll) ################################################################################ def ztgpoisson(Y, X): """ The function estimates a zero-truncated Generalized Poisson regression. The scaled parameter S = Log(Xi). In the Generalized Poisson distribution, VAR(Y) = E(Y) / [(1 - Xi) ^ 2] such that [(1 - Xi) ^ 2] > 1 means the under-dispersion and [(1 - Xi) ^ 2] < 1 means the over-dispersion. Parameters: Y : a pandas series for the frequency outcome wit non-zero integer values. X : a pandas dataframe with model variables that are all numeric values. """ class ztgpoisson(gll): def __init__(self, endog, exog, **kwds): super(ztgpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): _s = params[-1] beta = params[:-1] ll = _ll_ztgpoisson(self.endog, self.exog, beta, _s) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_S') if start_params == None: start_params = numpy.append(p0, s0) return(super(ztgpoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X = X.copy() _X.insert(loc = 0, column = "_CONST", value = 1) p0 = ztrpoisson(Y, X).fit(disp = 0).params s0 = numpy.log(max(1e-4, 1 - numpy.float_power(numpy.mean(Y) / numpy.var(Y), 0.5))) return(ztgpoisson(_Y, _X)) #################### 12. Zero-truncated Conway-Maxwell Poisson Regression #################### def _ll_ztcpoisson(y, x, beta, s): """ The function calculates the log likelihood function of the zero-truncated conway-maxwell poisson regression. Parameters: y : the frequency outcome with non-zero integer values. x : variables of the negative binomial regression beta : coefficients of the negative binomial regression s : the scaled parameter of the zero-truncated conway-maxwell poisson regression """ mu = numpy.exp(numpy.dot(x, beta)) nv = numpy.exp(s) _z = 0 for _n in range(100): _z = _z + numpy.float_power(mu, _n) / numpy.float_power(scipy.special.factorial(_n), nv) pr = numpy.float_power(mu, y) / numpy.float_power(scipy.special.factorial(y), nv) * numpy.float_power(_z, -1) / \ (1 - numpy.float_power(_z, -1)) ll = numpy.log(pr) return(ll) ################################################################################ def ztcpoisson(Y, X): """ The function estimates a zero-truncated Conway-Maxwell Poisson regression. The scaled parameter S = Log(nv). In the Conway-Maxwell Poisson distribution, VAR(Y) ~= E(Y) / nv such that nv > 1 means the under-dispersion and nv < 1 means the over-dispersion. Parameters: Y : a pandas series for the frequency outcome wit non-zero integer values. X : a pandas dataframe with model variables that are all numeric values. """ class ztcpoisson(gll): def __init__(self, endog, exog, **kwds): super(ztcpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): _s = params[-1] beta = params[:-1] ll = _ll_ztcpoisson(self.endog, self.exog, beta, _s) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_S') if start_params == None: start_params = numpy.append(p0, s0) return(super(ztcpoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X = X.copy() _X.insert(loc = 0, column = "_CONST", value = 1) p0 = ztrpoisson(Y, X).fit(disp = 0).params s0 = numpy.log(numpy.mean(Y) / numpy.var(Y)) return(ztcpoisson(_Y, _X)) #################### 13. Hurdle Generalized Poisson Regression #################### def _ll_hdgpoisson(y, x1, x2, beta1, beta2, s): """ The function calculates the log likelihood function of the hurdle generalized poisson regression. Parameters: y : the frequency outcome x1 : variables for the probability model in the hurdle generalized poisson regression x2 : variables for the count model in the hurdle generalized poisson regression beta1 : coefficients for the probability model in the hurdle generalized poisson regression beta2 : coefficients for the count model in the hurdle generalized poisson regression s : the scale parameter for the generalized poisson distribution """ xb1 = numpy.dot(x1, beta1) xb2 = numpy.dot(x2, beta2) p0 = numpy.exp(xb1) / (1 + numpy.exp(xb1)) mu = numpy.exp(xb2) xi = numpy.exp(s) _a = mu * (1 - xi) i0 = numpy.where(y == 0, 1, 0) pr = p0 * i0 + \ (1 - p0) * _a / scipy.special.factorial(y) * numpy.exp(-_a - xi * y) * \ numpy.float_power(_a + xi * y, y - 1) / (1 - numpy.exp(-_a)) * (1 - i0) ll = numpy.log(pr) return(ll) ################################################################################ def hdgpoisson(Y, X1, X2): """ The function estimates a hurdle generalized poisson regression, which is the composite between point mess at zero and a zero-trucated generalized poisson distribution. In the model outcome, estimated coefficients starting with "P0:" are used to predict the probability of zero outcomes and estimated coefficients starting with "MU:" are used to predict frequency outcomes for a zero-trucated generalized poisson. Parameters: Y : a pandas series for the frequency outcome with integer values, including zeros. X1 : a pandas dataframe with the probability model variables that are all numeric values. X2 : a pandas dataframe with the count model variables that are all numeric values. Example: hdgpoisson(Y, X1, X2).fit().summary() """ class hdgpoisson(gll): def __init__(self, endog, exog, **kwds): super(hdgpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): _s = params[-1] d1 = _X1.shape[1] beta1 = params[:d1] beta2 = params[d1:-1] ll = _ll_hdgpoisson(self.endog, self.exog[:, :d1], self.exog[:, d1:], beta1, beta2, _s) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_S') if start_params == None: start_params = numpy.concatenate([p10, p20]) return(super(hdgpoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X1 = X1.copy() _X2 = X2.copy() _X1.insert(loc = 0, column = "_CONST", value = 1) _X1.columns = ["P0:" + _ for _ in _X1.columns] _X2.insert(loc = 0, column = "_CONST", value = 1) _X2.columns = ["MU:" + _ for _ in _X2.columns] _X = _X1.join(_X2) p10 = logit(numpy.where(_Y == 0, 1, 0), _X1).fit(disp = 0).params p20 = ztgpoisson(Y[Y > 0], X2[Y > 0]).fit(disp = 0).params return(hdgpoisson(_Y, _X)) #################### 14. Zero-Inflated Generalized Poisson Regression #################### def _ll_zigpoisson(y, x1, x2, beta1, beta2, s): """ The function calculates the log likelihood function of the zero-inflated generalized poisson regression. Parameters: y : the frequency outcome x1 : variables for the probability model in the zero-inflated generalized poisson regression x2 : variables for the count model in the zero-inflated generalized poisson regression beta1 : coefficients for the probability model in the zero-inflated generalized poisson regression beta2 : coefficients for the count model in the zero-inflated generalized poisson regression s : the scale parameter for the generalized poisson distribution """ xb1 = numpy.dot(x1, beta1) xb2 = numpy.dot(x2, beta2) p0 = numpy.exp(xb1) / (1 + numpy.exp(xb1)) mu = numpy.exp(xb2) xi = numpy.exp(s) _a = mu * (1 - xi) i0 = numpy.where(y == 0, 1, 0) pr = (p0 + (1 - p0) * numpy.exp(-_a)) * i0 + \ (1 - p0) * _a / scipy.special.factorial(y) * numpy.exp(-_a - xi * y) * \ numpy.float_power(_a + xi * y, y - 1) * (1 - i0) ll = numpy.log(pr) return(ll) ################################################################################ def zigpoisson(Y, X1, X2): """ The function estimates a zero-inflated generalized poisson regression, which is the composite between point mess at zero and a zero-trucated generalized poisson distribution. In the model outcome, estimated coefficients starting with "P0:" are used to predict the probability of zero outcomes and estimated coefficients starting with "MU:" are used to predict frequency outcomes for a zero-trucated generalized poisson. Parameters: Y : a pandas series for the frequency outcome with integer values, including zeros. X1 : a pandas dataframe with the probability model variables that are all numeric values. X2 : a pandas dataframe with the count model variables that are all numeric values. Example: zigpoisson(Y, X1, X2).fit().summary() """ class zigpoisson(gll): def __init__(self, endog, exog, **kwds): super(zigpoisson, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): _s = params[-1] d1 = _X1.shape[1] beta1 = params[:d1] beta2 = params[d1:-1] ll = _ll_zigpoisson(self.endog, self.exog[:, :d1], self.exog[:, d1:], beta1, beta2, _s) return(-ll) def fit(self, start_params = None, maxiter = 10000, maxfun = 5000, method = "ncg", **kwds): self.exog_names.append('_S') if start_params == None: start_params = numpy.concatenate([p10, p20]) return(super(zigpoisson, self).fit(start_params = start_params, method = method, maxiter = maxiter, maxfun = maxfun, **kwds)) _Y = Y.copy() _X1 = X1.copy() _X2 = X2.copy() _X1.insert(loc = 0, column = "_CONST", value = 1) _X1.columns = ["P0:" + _ for _ in _X1.columns] _X2.insert(loc = 0, column = "_CONST", value = 1) _X2.columns = ["MU:" + _ for _ in _X2.columns] _X = _X1.join(_X2) p10 = logit(numpy.where(_Y == 0, 1, 0), _X1).fit(disp = 0).params p20 = ztgpoisson(Y[Y > 0], X2[Y > 0]).fit(disp = 0).params return(zigpoisson(_Y, _X))

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35,931

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965beed92f9f8b39b9b18ba204c09ea7f66dd728

13,174

py

Python

pyActionRecog/action_caffe.py

Ewenwan/DTPP

0a10dd8c61596d5326fbbe70dcac0eae59088c27

[ "BSD-2-Clause" ]

1

2019-05-07T01:00:18.000Z

pyActionRecog/action_caffe.py

Ewenwan/DTPP

0a10dd8c61596d5326fbbe70dcac0eae59088c27

[ "BSD-2-Clause" ]

null

pyActionRecog/action_caffe.py

Ewenwan/DTPP

0a10dd8c61596d5326fbbe70dcac0eae59088c27

[ "BSD-2-Clause" ]

1

2019-09-18T05:27:50.000Z

import sys proto_root = "/home2/lin_li/anaconda2/pkgs/libprotobuf-3.2.0-0/lib/" sys.path.insert(0, proto_root) import caffe from caffe.io import oversample import numpy as np from utils.io import flow_stack_oversample, fast_list2arr, rgb_stack_oversample, oversample_for_rgb_stack, flow_stack_oversample_new, oversample_for_flow_stack_test import cv2 import matplotlib.pyplot as plt class CaffeNet(object): def __init__(self, net_proto, net_weights, device_id, input_size=None): caffe.set_mode_gpu() caffe.set_device(device_id) self._net = caffe.Net(net_proto, net_weights, caffe.TEST) input_shape = self._net.blobs['data'].data.shape if input_size is not None: input_shape = input_shape[:2] + input_size transformer = caffe.io.Transformer({'data': input_shape}) #if self._net.blobs['data'].data.shape[1] == 3: #printf # transformer.set_transpose('data', (2, 0, 1)) # move image channels to outermost dimension # transformer.set_mean('data', np.array([104, 117, 123])) # subtract the dataset-mean value in each channel #else: # pass # non RGB data need not use transformer self._transformer = transformer self._sample_shape = self._net.blobs['data'].data.shape def predict_single_frame(self, frame, score_name, over_sample=True, multiscale=None, frame_size=None): if frame_size is not None: frame1 = fast_list2arr([x for x in frame]) frame = [cv2.resize(x, frame_size) for x in frame] #print frame1.shape if over_sample: if multiscale is None: os_frame = oversample(frame, (self._sample_shape[2], self._sample_shape[3])) else: os_frame = [] for scale in multiscale: resized_frame = [cv2.resize(x, (0,0), fx=1.0/scale, fy=1.0/scale) for x in frame] os_frame.extend(oversample(resized_frame, (self._sample_shape[2], self._sample_shape[3]))) else: os_frame = fast_list2arr(frame) #print os_frame.shape #data = fast_list2arr([self._transformer.preprocess('data', x) for x in os_frame]) def preprocess_1(r): r = r.transpose(2,0,1) r[0,:,:] = r[0,:,:] - 104 r[1,:,:] = r[1,:,:] - 117 r[2,:,:] = r[2,:,:] - 123 return r data = fast_list2arr([preprocess_1(x) for x in os_frame]) #print data.shape self._net.blobs['data'].reshape(*data.shape) self._net.reshape() out = self._net.forward(blobs=[score_name,], data=data) return out[score_name].copy() def predict_single_rgb_stack(self, frame, score_name, over_sample=True, multiscale=None, frame_size=None, stack_len=25): if frame_size is not None: frame = [cv2.resize(x, frame_size) for x in frame] if over_sample: if multiscale is None: os_frame = oversample_for_rgb_stack(frame, (self._sample_shape[2], self._sample_shape[3]),stack_len) else: os_frame = [] for scale in multiscale: resized_frame = [cv2.resize(x, (0,0), fx=1.0/scale, fy=1.0/scale) for x in frame] os_frame.extend(oversample(resized_frame, (self._sample_shape[2], self._sample_shape[3]))) else: os_frame = fast_list2arr(frame) def preprocess_1(r): r = r.transpose(2,0,1) r[0,:,:] = r[0,:,:] - 104 r[1,:,:] = r[1,:,:] - 117 r[2,:,:] = r[2,:,:] - 123 return r data = fast_list2arr([preprocess_1(x) for x in os_frame]) self._net.blobs['data'].reshape(*data.shape) self._net.reshape() out = self._net.forward(blobs=[score_name,], data=data) return out[score_name].copy() def predict_single_rgb_stack_memory(self, frame, score_name, over_sample=True, multiscale=None, frame_size=None, stack_len=25): if frame_size is not None: frame = [cv2.resize(x, frame_size) for x in frame] if over_sample: if multiscale is None: os_frame = oversample_for_rgb_stack(frame, (self._sample_shape[2], self._sample_shape[3]),stack_len) else: os_frame = [] for scale in multiscale: resized_frame = [cv2.resize(x, (0,0), fx=1.0/scale, fy=1.0/scale) for x in frame] os_frame.extend(oversample(resized_frame, (self._sample_shape[2], self._sample_shape[3]))) else: os_frame = fast_list2arr(frame) def preprocess_1(r): r = r.transpose(2,0,1) r[0,:,:] = r[0,:,:] - 104 r[1,:,:] = r[1,:,:] - 117 r[2,:,:] = r[2,:,:] - 123 return r data = fast_list2arr([preprocess_1(x) for x in os_frame]) # self._net.blobs['data'].reshape(*data.shape) # self._net.reshape() # # out = self._net.forward(blobs=[score_name,], data=data) # return out[score_name].copy() data_new = data.reshape(-1,3*stack_len,224,224) scores_new = [] for i in range(10): data_ele = data_new[i] self._net.blobs['data'].reshape(*data_ele.shape) self._net.reshape() out = self._net.forward(blobs=[score_name,], data=data_ele) scores_new.append(out[score_name].copy()) scores_new = np.array(scores_new).reshape(10,-1) return scores_new def predict_single_flow_stack_test(self, frame, score_name, over_sample=True, multiscale=None, frame_size=None, stack_len=25): if over_sample: if multiscale is None: os_frame = oversample_for_flow_stack_test(frame, (self._sample_shape[2], self._sample_shape[3]),stack_len) else: os_frame = [] for scale in multiscale: resized_frame = [cv2.resize(x, (0,0), fx=1.0/scale, fy=1.0/scale) for x in frame] os_frame.extend(oversample(resized_frame, (self._sample_shape[2], self._sample_shape[3]))) else: os_frame = fast_list2arr(frame) os_frame = np.array(os_frame).transpose(0,3,1,2) data = os_frame - np.float32(128.0) self._net.blobs['data'].reshape(*data.shape) self._net.reshape() out = self._net.forward(blobs=[score_name,], data=data) return out[score_name].copy() def predict_single_flow_stack_test_memory(self, frame, score_name, over_sample=True, multiscale=None, frame_size=None, stack_len=25): if over_sample: if multiscale is None: os_frame = oversample_for_flow_stack_test(frame, (self._sample_shape[2], self._sample_shape[3]),stack_len) else: os_frame = [] for scale in multiscale: resized_frame = [cv2.resize(x, (0,0), fx=1.0/scale, fy=1.0/scale) for x in frame] os_frame.extend(oversample(resized_frame, (self._sample_shape[2], self._sample_shape[3]))) else: os_frame = fast_list2arr(frame) os_frame = np.array(os_frame).transpose(0,3,1,2) data = os_frame - np.float32(128.0) # self._net.blobs['data'].reshape(*data.shape) # self._net.reshape() # out = self._net.forward(blobs=[score_name,], data=data) # return out[score_name].copy() data_new = data.reshape(-1,10*stack_len,224,224) scores_new = [] for i in range(10): data_ele = data_new[i] self._net.blobs['data'].reshape(*data_ele.shape) self._net.reshape() out = self._net.forward(blobs=[score_name,], data=data_ele) scores_new.append(out[score_name].copy()) scores_new = np.array(scores_new).reshape(10, -1) return scores_new def predict_single_flow_stack(self, frame, score_name, over_sample=True, frame_size=None): if frame_size is not None: frame = fast_list2arr([cv2.resize(x, frame_size) for x in frame]) else: frame = fast_list2arr(frame) if over_sample: os_frame = flow_stack_oversample(frame, (self._sample_shape[2], self._sample_shape[3])) else: os_frame = fast_list2arr([frame]) data = os_frame - np.float32(128.0) self._net.blobs['data'].reshape(*data.shape) self._net.reshape() out = self._net.forward(blobs=[score_name,], data=data) return out[score_name].copy() def predict_single_flow_stack_feature_map(self, frame, score_name, over_sample=False, frame_size=None, blobname = 'conv1/7x7_s2', dim = 30): if frame_size is not None: frame = fast_list2arr([cv2.resize(x, frame_size) for x in frame]) else: frame = fast_list2arr(frame) print "frame", frame.shape if over_sample: os_frame = flow_stack_oversample(frame, (self._sample_shape[2], self._sample_shape[3])) else: os_frame = fast_list2arr([frame]) print "os_frame", os_frame.shape # (10, 256, 340) # (10, 10, 224, 224) data = os_frame - np.float32(128.0) print data.shape #self._net.blobs['data'].reshape(*data.shape) print self._net.blobs['data'].data[0].shape self._net.blobs['data'].data[...] = data #self._net.reshape() out = self._net.forward()#data=data feat = self._net.blobs[blobname].data[0,:dim] return feat.copy() def predict_single_flow_rgb_stack(self, flow_frame, rgb_frame, score_name, over_sample=True, frame_size=None, multiscale=None, score_name_1=None): flow_1 = fast_list2arr([cv2.resize(x, frame_size) for x in flow_frame]) flow_2 = flow_stack_oversample(flow_1, (self._sample_shape[2], self._sample_shape[3])) flow_data = flow_2 - np.float32(128.0) rgb_1 = [cv2.resize(x, frame_size) for x in rgb_frame] rgb_2 = oversample(rgb_1, (self._sample_shape[2], self._sample_shape[3])) #rgb_data1 = fast_list2arr(os_frame_rgb) # print rgb_data1.shape def preprocess_1(r): r = r.transpose(2,0,1) r[0,:,:] = r[0,:,:] - 104 r[1,:,:] = r[1,:,:] - 117 r[2,:,:] = r[2,:,:] - 123 return r rgb_data = fast_list2arr([preprocess_1(x) for x in rgb_2]) #print flow_data.shape #print rgb_data.shape #flow_data = np.reshape(flow_data, (10,-1,224,224)) rgb_data = np.reshape(rgb_data, (10,-1,224,224)) #print flow_data.shape #print rgb_data.shape #data = np.array([], dtype = rgb_data[0].dtype) data = np.concatenate((flow_data, rgb_data), axis=1) #print data.shape self._net.blobs['data'].reshape(*data.shape) self._net.reshape() out = self._net.forward(blobs=[score_name,], data=data) if score_name_1 is not None: out_1 = self._net.forward(blobs=[score_name_1,], data=data) return out[score_name].copy(), out_1[score_name_1].copy() return out[score_name].copy() def predict_single_flow_rgb_stack_3(self, flow_frame, rgb_frame, score_name, over_sample=True, frame_size=None, multiscale=None, score_name_1=None, score_name_2=None): flow_1 = fast_list2arr([cv2.resize(x, frame_size) for x in flow_frame]) flow_2 = flow_stack_oversample(flow_1, (self._sample_shape[2], self._sample_shape[3])) flow_data = flow_2 - np.float32(128.0) rgb_1 = [cv2.resize(x, frame_size) for x in rgb_frame] rgb_2 = oversample(rgb_1, (self._sample_shape[2], self._sample_shape[3])) # rgb_data1 = fast_list2arr(os_frame_rgb) # print rgb_data1.shape def preprocess_1(r): r = r.transpose(2, 0, 1) r[0, :, :] = r[0, :, :] - 104 r[1, :, :] = r[1, :, :] - 117 r[2, :, :] = r[2, :, :] - 123 return r rgb_data = fast_list2arr([preprocess_1(x) for x in rgb_2]) # print flow_data.shape # print rgb_data.shape # flow_data = np.reshape(flow_data, (10,-1,224,224)) rgb_data = np.reshape(rgb_data, (10, -1, 224, 224)) # print flow_data.shape # print rgb_data.shape # data = np.array([], dtype = rgb_data[0].dtype) data = np.concatenate((flow_data, rgb_data), axis=1) # print data.shape self._net.blobs['data'].reshape(*data.shape) self._net.reshape() out = self._net.forward(blobs=[score_name, ], data=data) if score_name_1 is not None and score_name_2 is not None: out_1 = self._net.forward(blobs=[score_name_1, ], data=data) out_2 = self._net.forward(blobs=[score_name_2, ], data=data) # print "here" return out[score_name].copy(), out_1[score_name_1].copy(), out_2[score_name_2].copy() return out[score_name].copy()

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966f5ad7fe638cc4eb0694c610665c4b553994c5

44,090

py

Python

tests/unit/pypyr/pipeline_test.py

pypyr/pypyr-cli

dc0f694ac0c0e3c2844c1a20788c9af586a8a16e

[ "Apache-2.0" ]

31

2017-03-24T11:27:34.000Z

2020-05-27T20:06:28.000Z

tests/unit/pypyr/pipeline_test.py

pypyr/pypyr-cli

dc0f694ac0c0e3c2844c1a20788c9af586a8a16e

[ "Apache-2.0" ]

89

2017-04-12T09:50:32.000Z

2020-08-13T13:18:36.000Z

tests/unit/pypyr/pipeline_test.py

pypyr/pypyr-cli

dc0f694ac0c0e3c2844c1a20788c9af586a8a16e

[ "Apache-2.0" ]

6

2017-06-04T14:19:59.000Z

2020-02-10T13:16:40.000Z

"""pipeline.py unit tests. A lot of the tests for the Pipeline.new_pipe_and_args factory constructor exist in ./pipelinerunner_test.py, which tests at a higher level that the inputs from a run request map as expected into the Pipeline instance. """ import logging from unittest.mock import call, patch, Mock import pytest from pypyr.cache.loadercache import loader_cache from pypyr.cache.parsercache import contextparser_cache from pypyr.context import Context from pypyr.errors import (ContextError, KeyNotInContextError, PyModuleNotFoundError, Stop, StopPipeline, StopStepGroup) from pypyr.pipeline import Pipeline from pypyr.pipedef import PipelineDefinition, PipelineInfo from tests.common.utils import DeepCopyMagicMock from tests.common.utils import patch_logger def get_pipe_def(dict_in, info=None): """Wrap input dict & info into a PipelineDefinition.""" return PipelineDefinition(pipeline=dict_in, info=info) # region context parser # region parser mocks def mock_parser_arb(args): """Arbitrary mock function to execute instead of get_parsed_context.""" return Context({'key1': 'created in mock parser', 'key2': args}) def mock_parser_none(args): """Return None, mocking get_parsed_context.""" return None # endregion parser mocks @patch('pypyr.cache.loadercache.Loader.get_pipeline') def test_get_parsed_context_no_parser(mock_get_pipeline): """On get_parsed_context return empty Context when no parser specified.""" mock_get_pipeline.return_value = get_pipe_def({}) context = Context() pipeline = Pipeline('arb') pipeline.run(context) assert context == {} mock_get_pipeline.assert_called_once_with(name='arb', parent=None) @patch('pypyr.cache.loadercache.Loader.get_pipeline') def test_get_parsed_context_parser_not_found(mock_get_pipeline): """On get_parsed_context raise if parser module specified but not found.""" mock_get_pipeline.return_value = get_pipe_def({ 'context_parser': 'unlikelyblahmodulenameherexxssz'}) context = Context() pipeline = Pipeline('arb') with pytest.raises(PyModuleNotFoundError): pipeline.run(context) assert context == {} mock_get_pipeline.assert_called_once_with(name='arb', parent=None) @patch('pypyr.moduleloader.get_module') @patch('pypyr.cache.loadercache.Loader.get_pipeline') def test_get_parsed_context_parser_returns_none(mock_get_pipeline, mock_moduleloader): """On get_parsed_context return empty Context when parser returns None.""" mock_moduleloader.return_value.get_parsed_context = mock_parser_none mock_get_pipeline.return_value = get_pipe_def( {'context_parser': 'specifiedparserhere'}) pipeline = Pipeline('arb', context_args=['in arg here']) context = Context() pipeline.run(context) mock_moduleloader.assert_called_once_with('specifiedparserhere') mock_get_pipeline.assert_called_once_with(name='arb', parent=None) assert context == {} @patch('pypyr.moduleloader.get_module') @patch('pypyr.cache.loadercache.Loader.get_pipeline') def test_get_parsed_context_parser_pass(mock_get_pipeline, mock_moduleloader): """On get_parsed_context pass arg param and returns context.""" contextparser_cache.clear() mock_moduleloader.return_value.get_parsed_context = mock_parser_arb mock_get_pipeline.return_value = get_pipe_def( {'context_parser': 'specifiedparserhere'}) pipeline = Pipeline('arb', context_args='in arg here') context = Context() pipeline.run(context) mock_moduleloader.assert_called_once_with('specifiedparserhere') mock_get_pipeline.assert_called_once_with(name='arb', parent=None) assert isinstance(context, Context) assert len(context) == 2 assert context['key1'] == 'created in mock parser' assert context['key2'] == 'in arg here' @patch('pypyr.moduleloader.get_module', return_value=3) @patch('pypyr.cache.loadercache.Loader.get_pipeline') def test_get_parser_context_signature_wrong(mock_get_pipeline, mock_moduleloader): """Raise when parser found but no get_parsed_context attr.""" contextparser_cache.clear() mock_get_pipeline.return_value = get_pipe_def( {'context_parser': 'specifiedparserhere'}) pipeline = Pipeline('arb', context_args='in arg here') context = Context() with pytest.raises(AttributeError) as err_info: pipeline.run(context) mock_moduleloader.assert_called_once_with('specifiedparserhere') mock_get_pipeline.assert_called_once_with(name='arb', parent=None) assert str(err_info.value) == ("'int' object has no attribute " "'get_parsed_context'") @patch('pypyr.moduleloader.get_module') @patch('pypyr.cache.loadercache.Loader.get_pipeline') def test_prepare_context_empty_parse(mock_get_pipeline, mock_moduleloader): """Empty parsed_context works.""" contextparser_cache.clear() parser = Mock() parser.return_value = {} mock_moduleloader.return_value.get_parsed_context = parser mock_get_pipeline.return_value = get_pipe_def( {'context_parser': 'specifiedparserhere'}) pipeline = Pipeline('arb', context_args='arb context input') context = Context({'c1': 'cv1', 'c2': 'cv2'}) pipeline.run(context) mock_moduleloader.assert_called_once_with('specifiedparserhere') parser.assert_called_once_with('arb context input') mock_get_pipeline.assert_called_once_with(name='arb', parent=None) assert context == {'c1': 'cv1', 'c2': 'cv2'} @patch('pypyr.moduleloader.get_module') @patch('pypyr.cache.loadercache.Loader.get_pipeline') def test_prepare_context_with_parse_merge(mock_get_pipeline, mock_moduleloader): """On parsed_context override context.""" contextparser_cache.clear() parser = Mock() parser.return_value = {'a': 'av1', 'c1': 'new value from parsed'} mock_moduleloader.return_value.get_parsed_context = parser mock_get_pipeline.return_value = get_pipe_def( {'context_parser': 'specifiedparserhere'}) pipeline = Pipeline('arb', context_args='arb context input') context = Context({'c1': 'cv1', 'c2': 'cv2'}) pipeline.run(context) mock_moduleloader.assert_called_once_with('specifiedparserhere') parser.assert_called_once_with('arb context input') mock_get_pipeline.assert_called_once_with(name='arb', parent=None) assert context == {'a': 'av1', 'c1': 'new value from parsed', 'c2': 'cv2'} # endregion context parser # region loader def test_arbitrary_loader_module_not_found(): """Raise when loader not found.""" loader_cache.clear() pipeline = Pipeline('arb pipe', context_args='arb context input', loader='not_found_loader') with pytest.raises(PyModuleNotFoundError): pipeline.run(Context()) def test_loader_no_get_pipeline_definition(): """Arbitrary loader module without `get_pipeline_definition` function.""" loader_cache.clear() import sys current_module = sys.modules[__name__] pipeline = Pipeline('arb pipe', context_args='arb context input', loader=__name__) with patch_logger( 'pypyr.cache.loadercache', logging.ERROR) as mock_logger_error: with pytest.raises(AttributeError) as err: pipeline.run(Context()) assert str(err.value) == f"module '{__name__}' " \ "has no attribute 'get_pipeline_definition'" mock_logger_error.assert_called_once_with( f"The pipeline loader {current_module} doesn't have a " "get_pipeline_definition(pipeline_name, parent) function." ) @patch('pypyr.loaders.file.get_pipeline_definition') def test_empty_loader_set_up_to_default(mock_get_pipeline_definition): """Default loader should be pypyr.loaders.file.""" loader_cache.clear() mock_get_pipeline_definition.return_value = get_pipe_def({'steps': None}) pipeline = Pipeline('arb pipe', context_args='arb context input') pipeline.run(Context()) mock_get_pipeline_definition.assert_called_once_with( pipeline_name='arb pipe', parent=None ) @patch('pypyr.loaders.file.get_pipeline_definition') def test_empty_loader_set_up_to_default_with_parent( mock_get_pipeline_definition): """Default loader should be pypyr.loaders.file with parent.""" loader_cache.clear() mock_get_pipeline_definition.return_value = get_pipe_def({'steps': None}) pipeline = Pipeline('arb pipe', context_args='arb context input') pipeline.load_and_run_pipeline(Context(), parent='/arb/dir') mock_get_pipeline_definition.assert_called_once_with( pipeline_name='arb pipe', parent='/arb/dir' ) def test_arb_loader(): """Test loader set up.""" loader_cache.clear() pipeline = Pipeline('arb pipe', context_args='arb context input', loader='arbpack.arbloader', py_dir='tests') pipeline.load_and_run_pipeline(Context(), parent='/arb/dir') loader = loader_cache.get_pype_loader('arbpack.arbloader') assert loader.name == 'arbpack.arbloader' assert loader.get_pipeline('arb pipe', '/arb/dir').pipeline == { 'pipeline_name': 'arb pipe', 'parent': '/arb/dir'} loader_cache.clear() def test_arb_loader_no_parent(): """Test loader set up with no parent.""" loader_cache.clear() pipeline = Pipeline('arb pipe', context_args='arb context input', loader='arbpack.arbloader', py_dir='tests') pipeline.load_and_run_pipeline(Context()) loader = loader_cache.get_pype_loader('arbpack.arbloader') assert loader.name == 'arbpack.arbloader' assert loader.get_pipeline('arb pipe', None).pipeline == { 'pipeline_name': 'arb pipe', 'parent': None} loader_cache.clear() # endregion loader # region run_pipeline @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_pass_minimal( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Create implicit context if doesn't exist & no context parser.""" pipe_def = get_pipe_def({'arb': 'pipe'}) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'a': 'b'} mock_parser.return_value = parser pipeline = Pipeline('arb pipe', context_args='arb context input') context_instance = Context() with patch('pypyr.pipeline.Context') as mock_context: mock_context.return_value = context_instance pipeline.load_and_run_pipeline(None) mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_not_called() parser.assert_not_called() mock_context.assert_called_once() # assure that stack empty when done assert not context_instance._stack mock_steps_runner.assert_called_once_with( pipeline_body={'arb': 'pipe'}, context=context_instance) # No called steps sr = mock_steps_runner.return_value sr.run_step_groups.assert_called_once_with(groups=['steps'], success_group='on_success', failure_group='on_failure') sr.run_failure_step_group.assert_not_called() @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_pass_skip_parse_context( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Explicit False parse_input doesn't run parser.""" pipe_def = get_pipe_def({'arb': 'pipe'}) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'a': 'b'} mock_parser.return_value = parser context = Context({'c': 'd'}) pipeline = Pipeline('arb pipe', parse_input=False) pipeline.load_and_run_pipeline(context) mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_not_called() parser.assert_not_called() mock_steps_runner.assert_called_once_with( pipeline_body={'arb': 'pipe'}, context=context) # No called steps, just on_failure since err on parse context already sr = mock_steps_runner.return_value sr.run_step_groups.assert_called_once_with(groups=['steps'], success_group='on_success', failure_group='on_failure') sr.run_failure_step_group.assert_not_called() @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_parse_context_error( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """run_pipeline on_failure with Context as is if parse fails.""" pipe_def = get_pipe_def({'context_parser': 'arb parser'}) mock_get_pipe.return_value = pipe_def parser = Mock() parser.side_effect = ContextError mock_parser.return_value = parser context = Context({'c': 'd'}) pipeline = Pipeline('arb pipe', context_args='arb context input', parse_input=True) with pytest.raises(ContextError): pipeline.load_and_run_pipeline(context) assert context == {'c': 'd'} mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.assert_called_once_with( pipeline_body=pipe_def.pipeline, context=context) # No called steps, just on_failure since err on parse context already sr = mock_steps_runner.return_value sr.run_step_groups.assert_not_called() sr.run_failure_step_group.assert_called_once_with('on_failure') @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_steps_error_raises( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Run on_failure and raise error if steps group fails.""" # First time it runs is steps - give a KeyNotInContextError. pipe_def = get_pipe_def({'context_parser': 'arb parser'}) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'a': 'b'} mock_parser.return_value = parser context = Context({'c': 'd'}) pipeline = Pipeline('arb pipe', context_args='arb context input', parse_input=True) mock_steps_runner.return_value.run_step_groups.side_effect = ( KeyNotInContextError) with pytest.raises(KeyNotInContextError): pipeline.run(context) assert context == {'a': 'b', 'c': 'd'} mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.return_value.run_step_groups.assert_called_once_with( groups=['steps'], success_group='on_success', failure_group='on_failure' ) mock_steps_runner.assert_called_once_with( pipeline_body=pipe_def.pipeline, context=context) @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_with_existing_context_pass( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Pipeline runs with existing context.""" pipe_def = get_pipe_def({'context_parser': 'arb parser'}) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'1': 'context 1', '2': 'context2'} mock_parser.return_value = parser context = Context({'2': 'original', '3': 'new'}) pipeline = Pipeline('arb pipe', context_args='arb context input') pipeline.load_and_run_pipeline(context) assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.return_value.run_step_groups.assert_called_once_with( groups=['steps'], success_group='on_success', failure_group='on_failure' ) mock_steps_runner.assert_called_once_with( pipeline_body={'context_parser': 'arb parser'}, context={'1': 'context 1', '2': 'context2', '3': 'new'}) @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_with_dir_specified( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Py dir passed to add_sys_path.""" pipe_yaml = {'context_parser': 'arb parser'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'1': 'context 1', '2': 'context2'} mock_parser.return_value = parser context = Context({'2': 'original', '3': 'new'}) pipeline = Pipeline('arb pipe', context_args='arb context input', py_dir='/arb/dir') pipeline.load_and_run_pipeline(context) assert not context.is_in_pipeline_scope mock_add_sys_path.assert_called_once_with('/arb/dir') mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.return_value.run_step_groups.assert_called_once_with( groups=['steps'], success_group='on_success', failure_group='on_failure' ) mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={'1': 'context 1', '2': 'context2', '3': 'new'}) @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_with_group_specified( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Run pipeline with specified groups.""" pipe_yaml = {'arb': 'pipe'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'1': 'context 1', '2': 'context2'} mock_parser.return_value = parser context = Context({'2': 'original', '3': 'new'}) pipeline = Pipeline('arb pipe', context_args='arb context input', groups=['arb1', 'arb2']) pipeline.load_and_run_pipeline(context) assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_not_called() parser.assert_not_called() mock_steps_runner.return_value.run_step_groups.assert_called_once_with( groups=['arb1', 'arb2'], success_group=None, failure_group=None ) mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={'2': 'original', '3': 'new'}) @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_with_parent_specified( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Run pipeline with specified parent.""" pipe_yaml = {'arb': 'pipe'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'1': 'context 1', '2': 'context2'} mock_parser.return_value = parser context = Context({'2': 'original', '3': 'new'}) pipeline = Pipeline('arb pipe') pipeline.load_and_run_pipeline(context, '/parent') assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent='/parent') mock_parser.assert_not_called() parser.assert_not_called() mock_steps_runner.return_value.run_step_groups.assert_called_once_with( groups=['steps'], success_group='on_success', failure_group='on_failure' ) mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={'2': 'original', '3': 'new'}) @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_with_success_group_specified( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Run pipeline with specified success group.""" pipe_yaml = {'context_parser': 'arb parser'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'1': 'context 1', '2': 'context2'} mock_parser.return_value = parser context = Context({'2': 'original', '3': 'new'}) pipeline = Pipeline('arb pipe', context_args='arb context input', success_group='arb1') pipeline.load_and_run_pipeline(context) assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.return_value.run_step_groups.assert_called_once_with( groups=['steps'], success_group='arb1', failure_group=None ) mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={'1': 'context 1', '2': 'context2', '3': 'new'}) @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_with_failure_group_specified( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Run pipeline with specified failure group.""" pipe_yaml = {'context_parser': 'arb parser'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'1': 'context 1', '2': 'context2'} mock_parser.return_value = parser context = Context({'2': 'original', '3': 'new'}) pipeline = Pipeline('arb pipe', context_args='arb context input', failure_group='arb1') pipeline.load_and_run_pipeline(context) assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.return_value.run_step_groups.assert_called_once_with( groups=['steps'], success_group=None, failure_group='arb1' ) mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={'1': 'context 1', '2': 'context2', '3': 'new'}) @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_load_and_run_pipeline_with_group_and_failure_group_specified( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Pass run_pipeline with specified group and failure group.""" pipe_yaml = {'context_parser': 'arb parser'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.return_value = {'1': 'context 1', '2': 'context2'} mock_parser.return_value = parser context = Context({'2': 'original', '3': 'new'}) pipeline = Pipeline('arb pipe', context_args='arb context input', groups=['arb1'], failure_group='arb2') pipeline.load_and_run_pipeline(context) assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_get_pipe.assert_called_once_with(name='arb pipe', parent=None) mock_steps_runner.return_value.run_step_groups.assert_called_once_with( groups=['arb1'], success_group=None, failure_group='arb2' ) mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={'1': 'context 1', '2': 'context2', '3': 'new'}) @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_run_pipeline_parse_context_error_failure( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Run on_failure on context parse exception.""" pipe_yaml = {'context_parser': 'arb parser'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.side_effect = ValueError('arb') mock_parser.return_value = parser context = Context({'2': 'original', '3': 'new'}) pipeline = Pipeline('arb pipe', context_args='arb context input', groups=['gr'], success_group='sg', failure_group='fg') with pytest.raises(ValueError) as err: pipeline.run(context) assert str(err.value) == 'arb' assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={'2': 'original', '3': 'new'}) # No called steps, just on_failure since err on parse context already sr = mock_steps_runner.return_value sr.run_step_groups.assert_not_called() sr.run_failure_step_group.assert_called_once_with('fg') @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_run_pipeline_parse_context_error_failure_stop( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Run on_failure on context parser exception with Stop.""" pipe_yaml = {'context_parser': 'arb parser'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.side_effect = ValueError('arb') mock_parser.return_value = parser sr = mock_steps_runner.return_value sr.run_failure_step_group.side_effect = Stop() context = Context() pipeline = Pipeline('arb pipe', context_args='arb context input') with pytest.raises(Stop): pipeline.load_and_run_pipeline(context) assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={}) # No called steps, just on_failure since err on parse context already sr.run_step_groups.assert_not_called() sr.run_failure_step_group.assert_called_once_with('on_failure') @patch('pypyr.pipeline.StepsRunner', autospec=True) @patch('pypyr.cache.parsercache.contextparser_cache.get_context_parser') @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.moduleloader.add_sys_path') def test_run_pipeline_parse_context_error_failure_stopstepgroup( mock_add_sys_path, mock_get_pipe, mock_parser, mock_steps_runner): """Context failure handler swallows StopStepGroup.""" pipe_yaml = {'context_parser': 'arb parser'} pipe_def = get_pipe_def(pipe_yaml) mock_get_pipe.return_value = pipe_def parser = Mock() parser.side_effect = ValueError('arb') mock_parser.return_value = parser sr = mock_steps_runner.return_value sr.run_failure_step_group.side_effect = StopStepGroup() context = Context() pipeline = Pipeline('arb pipe', context_args='arb context input') with pytest.raises(ValueError) as err: pipeline.load_and_run_pipeline(context) assert str(err.value) == 'arb' assert not context.is_in_pipeline_scope mock_add_sys_path.assert_not_called() mock_parser.assert_called_once_with('arb parser') parser.assert_called_once_with('arb context input') mock_steps_runner.assert_called_once_with(pipeline_body=pipe_yaml, context={}) # No called steps, just on_failure since err on parse context already sr.run_step_groups.assert_not_called() sr.run_failure_step_group.assert_called_once_with('on_failure') # endregion run_pipeline # region Stop & StopPipeline # region stop helpers def get_test_pipeline_definition(pipeline): """Wrap input pipeline (dict) into a PipelineDefinition. Args: pipeline (dict-like): pipeline payload. Returns: PipelineDefinition with pipeline payload and arb PipelineInfo. """ return PipelineDefinition( pipeline=pipeline, info=PipelineInfo(pipeline_name='arbpipe', loader='arbloader', parent='arbdir')) def get_step_pipeline(): """Test pipeline for jump wrapped in PipelineDefinition.""" return get_test_pipeline_definition(get_step_pipeline_payload()) def get_step_pipeline_payload(): """Bare dict pipeline payload.""" return { 'sg1': [ 'sg1.step1', 'sg1.step2' ], 'sg2': [ 'sg2.step1', 'sg2.step2' ], 'sg3': [ 'sg3.step1', 'sg3.step2' ], 'sg4': [ 'sg4.step1', 'sg4.step2' ], 'sg5': [ 'sg5.step1' ], 'sg6': [ 'sg6.step1', 'sg6.step2' ] } def nothing_step(context): """Mock step.""" pass def stop_pipe_step(context): """Mock stop pipeline step.""" raise StopPipeline() def stop_all_step(context): """Mock stop all step.""" raise Stop() # endregion stop helpers @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.cache.stepcache.step_cache.get_step') def test_stop_pipeline(mock_step_cache, mock_get_pipe): """When StopPipeline stop pipeline execution.""" # Sequence: sg2 - sg2.1, 2.2 # sg3 - sg3.1 (StopPipeline) mock_step_cache.side_effect = [ nothing_step, # 2.1 nothing_step, # 2.2 stop_pipe_step, # 3.1 ] mock_get_pipe.return_value = get_test_pipeline_definition( get_step_pipeline_payload()) context = Context() pipeline = Pipeline('arb pipe', groups=['sg2', 'sg3', 'sg4', 'sg1'], success_group='sg5', failure_group=None) pipeline.run(context) assert not context.is_in_pipeline_scope assert mock_step_cache.mock_calls == [call('sg2.step1'), call('sg2.step2'), call('sg3.step1') ] @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.cache.stepcache.step_cache.get_step') def test_stop_pipeline_for(mock_step_cache, mock_get_pipe): """When StopPipeline stop pipeline execution in for loop.""" # Sequence: sg2 - sg2.1, 2.2 # sg3 - sg3.1 x2 (StopPipeline) nothing_mock = DeepCopyMagicMock() mock312 = DeepCopyMagicMock() def step31(context): mock312(context) if context['i'] == 'two': raise StopPipeline() mock_step_cache.side_effect = [ nothing_mock, # 2.1 nothing_mock, # 2.2 step31, # 3.1 ] mock_get_pipe.return_value = get_for_step_pipeline() context = Context() pipeline = Pipeline('arb pipe', groups=['sg2', 'sg3', 'sg4', 'sg1'], success_group='sg5', failure_group=None) pipeline.run(context) assert not context.is_in_pipeline_scope assert nothing_mock.mock_calls == [call({}), call({}) ] assert mock312.mock_calls == [call({'i': 'one'}), call({'i': 'two'})] assert mock_step_cache.mock_calls == [call('sg2.step1'), call('sg2.step2'), call('sg3.step1') ] def get_retry_step_pipeline(): """Test pipeline for retry loop.""" return { 'sg1': [ 'sg1.step1', 'sg1.step2' ], 'sg2': [ 'sg2.step1', 'sg2.step2' ], 'sg3': [ {'name': 'sg3.step1', 'retry': {'max': 3} }, 'sg3.step2' ], 'sg4': [ 'sg4.step1', 'sg4.step2' ], 'sg5': [ 'sg5.step1' ], 'sg6': [ 'sg6.step1', 'sg6.step2' ] } @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.cache.stepcache.step_cache.get_step') def test_stop_pipeline_retry(mock_step_cache, mock_get_pipe): """When StopPipeline stop pipeline execution in retry loop.""" # Sequence: sg2 - sg2.1, 2.2 # sg3 - sg3.1 x2 (StopPipeline) nothing_mock = DeepCopyMagicMock() mock312 = DeepCopyMagicMock() def step31(context): mock312(context) if context['retryCounter'] == 2: raise StopPipeline() else: raise ValueError(context['retryCounter']) mock_step_cache.side_effect = [ nothing_mock, # 2.1 nothing_mock, # 2.2 step31, # 3.1 ] pipe_yaml = get_retry_step_pipeline() mock_get_pipe.return_value = get_test_pipeline_definition(pipe_yaml) context = Context() pipeline = Pipeline('arb pipe', groups=['sg2', 'sg3', 'sg4', 'sg1'], success_group='sg5', failure_group=None) pipeline.run(context) assert not context.is_in_pipeline_scope assert nothing_mock.mock_calls == [call({}), call({}) ] assert mock312.mock_calls == [call({'retryCounter': 1}), call({'retryCounter': 2})] assert mock_step_cache.mock_calls == [call('sg2.step1'), call('sg2.step2'), call('sg3.step1') ] @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.cache.stepcache.step_cache.get_step') def test_stop_all(mock_step_cache, mock_get_pipe): """Stop stops pipeline execution.""" # Sequence: sg2 - sg2.1, 2.2 # sg3 - sg3.1 (StopPipeline) mock_step_cache.side_effect = [ nothing_step, # 2.1 nothing_step, # 2.2 stop_all_step, # 3.1 ] mock_get_pipe.return_value = get_step_pipeline() context = Context() pipeline = Pipeline('arb pipe', groups=['sg2', 'sg3', 'sg4', 'sg1'], success_group='sg5', failure_group=None) pipeline.run(context) assert not context.is_in_pipeline_scope assert mock_step_cache.mock_calls == [call('sg2.step1'), call('sg2.step2'), call('sg3.step1') ] def get_while_step_pipeline(): """Test pipeline for while.""" return get_test_pipeline_definition({ 'sg1': [ 'sg1.step1', 'sg1.step2' ], 'sg2': [ 'sg2.step1', 'sg2.step2' ], 'sg3': [ {'name': 'sg3.step1', 'while': { 'max': 3}, }, 'sg3.step2' ], 'sg4': [ 'sg4.step1', 'sg4.step2' ], 'sg5': [ 'sg5.step1' ], 'sg6': [ 'sg6.step1', 'sg6.step2' ] }) @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.cache.stepcache.step_cache.get_step') def test_stop_all_while(mock_step_cache, mock_get_pipe): """Stop stops pipeline execution inside a while.""" # Sequence: sg2 - sg2.1, 2.2 # sg3 - sg3.1 loop 3, StopPipeline on 2 nothing_mock = DeepCopyMagicMock() mock312 = DeepCopyMagicMock() def step31(context): mock312(context) if context['whileCounter'] == 2: raise Stop() mock_step_cache.side_effect = [ nothing_mock, # 2.1 nothing_mock, # 2.2 step31 # 3.1.2 ] mock_get_pipe.return_value = get_while_step_pipeline() context = Context() pipeline = Pipeline('arb pipe', groups=['sg2', 'sg3', 'sg4', 'sg1'], success_group='sg5', failure_group=None) pipeline.run(context) assert not context.is_in_pipeline_scope assert mock_step_cache.mock_calls == [call('sg2.step1'), call('sg2.step2'), call('sg3.step1') ] assert nothing_mock.mock_calls == [call({}), call({}) ] assert mock312.mock_calls == [call({'whileCounter': 1}), call({'whileCounter': 2})] def get_for_step_pipeline(): """Test pipeline for for loop.""" return get_test_pipeline_definition(get_for_step_pipeline_payload()) def get_for_step_pipeline_payload(): """Bare dict for for pipeline.""" return { 'sg1': [ 'sg1.step1', 'sg1.step2' ], 'sg2': [ 'sg2.step1', 'sg2.step2' ], 'sg3': [ {'name': 'sg3.step1', 'foreach': ['one', 'two', 'three'] }, 'sg3.step2' ], 'sg4': [ 'sg4.step1', 'sg4.step2' ], 'sg5': [ 'sg5.step1' ], 'sg6': [ 'sg6.step1', 'sg6.step2' ] } @patch('pypyr.cache.loadercache.Loader.get_pipeline') @patch('pypyr.cache.stepcache.step_cache.get_step') def test_stop_all_for(mock_step_cache, mock_get_pipe): """Stop stops pipeline execution inside a for loop.""" # Sequence: sg2 - sg2.1, 2.2 # sg3 - sg3.1 loop 3, StopPipeline on 2 nothing_mock = DeepCopyMagicMock() mock312 = DeepCopyMagicMock() def step31(context): mock312(context) if context['i'] == 'two': raise Stop() mock_step_cache.side_effect = [ nothing_mock, # 2.1 nothing_mock, # 2.2 step31 # 3.1.2 ] mock_get_pipe.return_value = get_for_step_pipeline() context = Context() pipeline = Pipeline('arb pipe', groups=['sg2', 'sg3', 'sg4', 'sg1'], success_group='sg5', failure_group=None) pipeline.run(context) assert not context.is_in_pipeline_scope assert mock_step_cache.mock_calls == [call('sg2.step1'), call('sg2.step2'), call('sg3.step1') ] assert nothing_mock.mock_calls == [call({}), call({}) ] assert mock312.mock_calls == [call({'i': 'one'}), call({'i': 'two'})] # endregion Stop & StopPipeline

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Python

CellProfiler/tests/modules/test_trackobjects.py

aidotse/Team-rahma.ai

66857731e1ca2472e0783e37ba472b55a7ac9cd4

[ "MIT" ]

null

CellProfiler/tests/modules/test_trackobjects.py

aidotse/Team-rahma.ai

66857731e1ca2472e0783e37ba472b55a7ac9cd4

[ "MIT" ]

null

CellProfiler/tests/modules/test_trackobjects.py

aidotse/Team-rahma.ai

66857731e1ca2472e0783e37ba472b55a7ac9cd4

[ "MIT" ]

null

import centrosome.filter import numpy import six.moves from cellprofiler_core.constants.measurement import ( GROUP_NUMBER, GROUP_INDEX, R_FIRST_IMAGE_NUMBER, R_SECOND_IMAGE_NUMBER, R_FIRST_OBJECT_NUMBER, R_SECOND_OBJECT_NUMBER, C_COUNT, MCA_AVAILABLE_POST_GROUP, M_LOCATION_CENTER_X, M_LOCATION_CENTER_Y, ) from cellprofiler_core.image import ImageSetList import cellprofiler_core.measurement from cellprofiler_core.object import ObjectSet, Objects import cellprofiler.modules.trackobjects import tests.modules from cellprofiler_core.pipeline import Pipeline, LoadException, RunException from cellprofiler_core.workspace import Workspace OBJECT_NAME = "objects" def test_load_v3(): file = tests.modules.get_test_resources_directory("trackobjects/v3.pipeline") with open(file, "r") as fd: data = fd.read() pipeline = Pipeline() def callback(caller, event): assert not isinstance(event, LoadException) pipeline.add_listener(callback) pipeline.load(six.moves.StringIO(data)) module = pipeline.modules()[0] assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) assert module.tracking_method == "LAP" assert module.object_name.value == "Nuclei" assert module.pixel_radius.value == 80 assert module.display_type.value == "Color and Number" assert not module.wants_image assert module.measurement == "AreaShape_Area" assert module.image_name == "TrackedCells" assert module.wants_second_phase assert module.split_cost == 41 assert module.merge_cost == 42 assert module.max_gap_score == 53 assert module.max_split_score == 54 assert module.max_merge_score == 55 assert module.max_frame_distance == 6 def test_load_v4(): file = tests.modules.get_test_resources_directory("trackobjects/v4.pipeline") with open(file, "r") as fd: data = fd.read() pipeline = Pipeline() def callback(caller, event): assert not isinstance(event, LoadException) pipeline.add_listener(callback) pipeline.load(six.moves.StringIO(data)) assert len(pipeline.modules()) == 3 for module, tracking_method, model, save_img, phase2, meas, dop in zip( pipeline.modules(), ("Measurements", "Overlap", "Distance"), ( cellprofiler.modules.trackobjects.M_BOTH, cellprofiler.modules.trackobjects.M_RANDOM, cellprofiler.modules.trackobjects.M_VELOCITY, ), (True, False, True), (True, False, True), ("Slothfulness", "Prescience", "Trepidation"), ( cellprofiler.modules.trackobjects.DT_COLOR_AND_NUMBER, cellprofiler.modules.trackobjects.DT_COLOR_ONLY, cellprofiler.modules.trackobjects.DT_COLOR_AND_NUMBER, ), ): assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) assert module.tracking_method == tracking_method assert module.model == model assert module.wants_image.value == save_img assert module.wants_second_phase.value == phase2 assert module.measurement == meas assert module.pixel_radius == 50 assert module.display_type == dop assert module.image_name == "TrackByLAP" assert module.radius_std == 3 assert module.radius_limit.min == 3.0 assert module.radius_limit.max == 10.0 assert module.gap_cost == 40 assert module.split_cost == 1 assert module.merge_cost == 1 assert module.max_gap_score == 51 assert module.max_split_score == 52 assert module.max_merge_score == 53 assert module.max_frame_distance == 4 def test_load_v5(): file = tests.modules.get_test_resources_directory("trackobjects/v5.pipeline") with open(file, "r") as fd: data = fd.read() pipeline = Pipeline() def callback(caller, event): assert not isinstance(event, LoadException) pipeline.add_listener(callback) pipeline.load(six.moves.StringIO(data)) assert len(pipeline.modules()) == 1 m = pipeline.modules()[0] assert isinstance(m, cellprofiler.modules.trackobjects.TrackObjects) assert m.tracking_method == "LAP" assert m.object_name == "Turtles" assert m.measurement == "Steadiness" assert m.pixel_radius == 44 assert m.display_type == cellprofiler.modules.trackobjects.DT_COLOR_AND_NUMBER assert not m.wants_image assert m.image_name == "TrackedTurtles" assert m.model == cellprofiler.modules.trackobjects.M_BOTH assert m.radius_std == 3 assert m.radius_limit.min == 3 assert m.radius_limit.max == 11 assert m.wants_second_phase assert m.gap_cost == 39 assert m.split_cost == 41 assert m.merge_cost == 42 assert m.max_frame_distance == 8 assert m.wants_minimum_lifetime assert m.min_lifetime == 2 assert not m.wants_maximum_lifetime assert m.max_lifetime == 1000 def test_load_v6(): file = tests.modules.get_test_resources_directory("trackobjects/v6.pipeline") with open(file, "r") as fd: data = fd.read() pipeline = Pipeline() def callback(caller, event): assert not isinstance(event, LoadException) pipeline.add_listener(callback) pipeline.load(six.moves.StringIO(data)) assert len(pipeline.modules()) == 1 m = pipeline.modules()[0] assert isinstance(m, cellprofiler.modules.trackobjects.TrackObjects) assert m.tracking_method == "LAP" assert m.object_name == "Turtles" assert m.measurement == "Steadiness" assert m.pixel_radius == 44 assert m.display_type == cellprofiler.modules.trackobjects.DT_COLOR_AND_NUMBER assert not m.wants_image assert m.image_name == "TrackedTurtles" assert m.model == cellprofiler.modules.trackobjects.M_BOTH assert m.radius_std == 3 assert m.radius_limit.min == 3 assert m.radius_limit.max == 11 assert m.wants_second_phase assert m.gap_cost == 39 assert m.split_cost == 41 assert m.merge_cost == 42 assert m.max_frame_distance == 8 assert m.wants_minimum_lifetime assert m.min_lifetime == 2 assert not m.wants_maximum_lifetime assert m.max_lifetime == 1000 assert m.mitosis_cost == 79 assert m.mitosis_max_distance == 41 def runTrackObjects(labels_list, fn=None, measurement=None): """Run two cycles of TrackObjects labels1 - the labels matrix for the first cycle labels2 - the labels matrix for the second cycle fn - a callback function called with the module and workspace. It has the signature, fn(module, workspace, n) where n is 0 when called prior to prepare_run, 1 prior to first iteration and 2 prior to second iteration. returns the measurements """ module = cellprofiler.modules.trackobjects.TrackObjects() module.set_module_num(1) module.object_name.value = OBJECT_NAME module.pixel_radius.value = 50 module.measurement.value = "measurement" measurements = cellprofiler_core.measurement.Measurements() measurements.add_all_measurements( "Image", GROUP_NUMBER, [1] * len(labels_list), ) measurements.add_all_measurements( "Image", GROUP_INDEX, list(range(1, len(labels_list) + 1)), ) pipeline = Pipeline() pipeline.add_module(module) image_set_list = ImageSetList() if fn: fn(module, None, 0) module.prepare_run( Workspace(pipeline, module, None, None, measurements, image_set_list) ) first = True for labels, index in zip(labels_list, list(range(len(labels_list)))): object_set = ObjectSet() objects = Objects() objects.segmented = labels object_set.add_objects(objects, OBJECT_NAME) image_set = image_set_list.get_image_set(index) if first: first = False else: measurements.next_image_set() if measurement is not None: measurements.add_measurement( OBJECT_NAME, "measurement", numpy.array(measurement[index]) ) workspace = Workspace( pipeline, module, image_set, object_set, measurements, image_set_list ) if fn: fn(module, workspace, index + 1) module.run(workspace) return measurements def test_track_nothing(): """Run TrackObjects on an empty labels matrix""" columns = [] def fn(module, workspace, index, columns=columns): if workspace is not None and index == 0: columns += module.get_measurement_columns(workspace.pipeline) measurements = runTrackObjects( (numpy.zeros((10, 10), int), numpy.zeros((10, 10), int)), fn ) features = [ feature for feature in measurements.get_feature_names(OBJECT_NAME) if feature.startswith(cellprofiler.modules.trackobjects.F_PREFIX) ] assert all( [column[1] in features for column in columns if column[0] == OBJECT_NAME] ) for feature in cellprofiler.modules.trackobjects.F_ALL: name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "50")) assert name in features value = measurements.get_current_measurement(OBJECT_NAME, name) assert len(value) == 0 features = [ feature for feature in measurements.get_feature_names("Image") if feature.startswith(cellprofiler.modules.trackobjects.F_PREFIX) ] assert all([column[1] in features for column in columns if column[0] == "Image"]) for feature in cellprofiler.modules.trackobjects.F_IMAGE_ALL: name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "50") ) assert name in features value = measurements.get_current_image_measurement(name) assert value == 0 def test_00_track_one_then_nothing(): """Run track objects on an object that disappears Regression test of IMG-1090 """ labels = numpy.zeros((10, 10), int) labels[3:6, 2:7] = 1 measurements = runTrackObjects((labels, numpy.zeros((10, 10), int))) feature = "_".join( ( cellprofiler.modules.trackobjects.F_PREFIX, cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT, OBJECT_NAME, "50", ) ) value = measurements.get_current_image_measurement(feature) assert value == 1 def test_track_one_distance(): """Track an object that doesn't move using distance""" labels = numpy.zeros((10, 10), int) labels[3:6, 2:7] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 1 module.tracking_method.value = "Distance" measurements = runTrackObjects((labels, labels), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "1")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert round(abs(m(cellprofiler.modules.trackobjects.F_TRAJECTORY_X) - 0), 7) == 0 assert round(abs(m(cellprofiler.modules.trackobjects.F_TRAJECTORY_Y) - 0), 7) == 0 assert ( round(abs(m(cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED) - 0), 7) == 0 ) assert ( round(abs(m(cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE) - 0), 7) == 0 ) assert m(cellprofiler.modules.trackobjects.F_LABEL) == 1 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 1 assert m(cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER) == 1 assert m(cellprofiler.modules.trackobjects.F_LIFETIME) == 2 def m(feature): name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "1") ) return measurements.get_current_image_measurement(name) assert m(cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_SPLIT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_MERGE_COUNT) == 0 check_relationships(measurements, [1], [1], [2], [1]) def test_track_one_moving(): """Track an object that moves""" labels_list = [] distance = 0 last_i, last_j = (0, 0) for i_off, j_off in ((0, 0), (2, 0), (2, 1), (0, 1)): distance = i_off - last_i + j_off - last_j last_i, last_j = (i_off, j_off) labels = numpy.zeros((10, 10), int) labels[4 + i_off : 7 + i_off, 4 + j_off : 7 + j_off] = 1 labels_list.append(labels) def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 3 module.tracking_method.value = "Distance" measurements = runTrackObjects(labels_list, fn) def m(feature, expected): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "3")) value_set = measurements.get_all_measurements(OBJECT_NAME, name) assert len(expected) == len(value_set) for values, x in zip(value_set, expected): assert len(values) == 1 assert round(abs(values[0] - x), 7) == 0 m(cellprofiler.modules.trackobjects.F_TRAJECTORY_X, [0, 0, 1, 0]) m(cellprofiler.modules.trackobjects.F_TRAJECTORY_Y, [0, 2, 0, -2]) m(cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED, [0, 2, 1, 2]) m(cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE, [0, 2, 3, 5]) m(cellprofiler.modules.trackobjects.F_LABEL, [1, 1, 1, 1]) m(cellprofiler.modules.trackobjects.F_LIFETIME, [1, 2, 3, 4]) m( cellprofiler.modules.trackobjects.F_LINEARITY, [1, 1, numpy.sqrt(5) / 3, 1.0 / 5.0], ) def m(feature): name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "3") ) return measurements.get_current_image_measurement(name) assert m(cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_SPLIT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_MERGE_COUNT) == 0 image_numbers = numpy.arange(1, len(labels_list) + 1) object_numbers = numpy.ones(len(image_numbers)) check_relationships( measurements, image_numbers[:-1], object_numbers[:-1], image_numbers[1:], object_numbers[1:], ) def test_track_split(): """Track an object that splits""" labels1 = numpy.zeros((11, 9), int) labels1[1:10, 1:8] = 1 labels2 = numpy.zeros((10, 10), int) labels2[1:6, 1:8] = 1 labels2[6:10, 1:8] = 2 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 5 module.tracking_method.value = "Distance" measurements = runTrackObjects((labels1, labels2, labels2), fn) def m(feature, idx): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "5")) values = measurements.get_measurement(OBJECT_NAME, name, idx + 1) assert len(values) == 2 return values labels = m(cellprofiler.modules.trackobjects.F_LABEL, 2) assert len(labels) == 2 assert numpy.all(labels == 1) parents = m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER, 1) assert numpy.all(parents == 1) assert numpy.all(m(cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER, 1) == 1) parents = m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER, 2) assert numpy.all(parents == numpy.array([1, 2])) assert numpy.all(m(cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER, 2) == 2) def m(feature): name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "5") ) return measurements.get_all_measurements("Image", name)[1] assert m(cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_SPLIT_COUNT) == 1 assert m(cellprofiler.modules.trackobjects.F_MERGE_COUNT) == 0 check_relationships( measurements, [1, 1, 2, 2], [1, 1, 1, 2], [2, 2, 3, 3], [1, 2, 1, 2] ) def test_track_negative(): """Track unrelated objects""" labels1 = numpy.zeros((10, 10), int) labels1[1:5, 1:5] = 1 labels2 = numpy.zeros((10, 10), int) labels2[6:9, 6:9] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 1 module.tracking_method.value = "Distance" measurements = runTrackObjects((labels1, labels2), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "1")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 2 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 0 def m(feature): name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "1") ) return measurements.get_current_image_measurement(name) assert m(cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT) == 1 assert m(cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT) == 1 assert m(cellprofiler.modules.trackobjects.F_SPLIT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_MERGE_COUNT) == 0 def test_track_ambiguous(): """Track disambiguation from among two possible parents""" labels1 = numpy.zeros((20, 20), int) labels1[1:4, 1:4] = 1 labels1[16:19, 16:19] = 2 labels2 = numpy.zeros((20, 20), int) labels2[10:15, 10:15] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 20 module.tracking_method.value = "Distance" measurements = runTrackObjects((labels1, labels2), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "20")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 2 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 2 def test_overlap_positive(): """Track overlapping objects""" labels1 = numpy.zeros((10, 10), int) labels1[3:6, 4:7] = 1 labels2 = numpy.zeros((10, 10), int) labels2[4:7, 5:9] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 2 module.tracking_method.value = "Overlap" measurements = runTrackObjects((labels1, labels2), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "2")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 1 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 1 def test_overlap_negative(): """Track objects that don't overlap""" labels1 = numpy.zeros((20, 20), int) labels1[3:6, 4:7] = 1 labels2 = numpy.zeros((20, 20), int) labels2[14:17, 15:19] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 2 module.tracking_method.value = "Overlap" measurements = runTrackObjects((labels1, labels2), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "2")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 2 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 0 def test_overlap_ambiguous(): """Track an object that overlaps two parents""" labels1 = numpy.zeros((20, 20), int) labels1[1:5, 1:5] = 1 labels1[15:19, 15:19] = 2 labels2 = numpy.zeros((20, 20), int) labels2[4:18, 4:18] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 2 module.tracking_method.value = "Overlap" measurements = runTrackObjects((labels1, labels2), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "2")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 2 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 2 def test_measurement_positive(): """Test tracking an object by measurement""" labels1 = numpy.zeros((10, 10), int) labels1[3:6, 4:7] = 1 labels2 = numpy.zeros((10, 10), int) labels2[4:7, 5:9] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 2 module.tracking_method.value = "Measurements" measurements = runTrackObjects((labels1, labels2), fn, [[1], [1]]) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "2")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 1 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 1 def test_measurement_negative(): """Test tracking with too great a jump between successive images""" labels1 = numpy.zeros((20, 20), int) labels1[3:6, 4:7] = 1 labels2 = numpy.zeros((20, 20), int) labels2[14:17, 15:19] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 2 module.tracking_method.value = "Measurements" measurements = runTrackObjects((labels1, labels2), fn, [[1], [1]]) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "2")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 2 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 0 def test_ambiguous(): """Test measurement with ambiguous parent choice""" labels1 = numpy.zeros((20, 20), int) labels1[1:5, 1:5] = 1 labels1[15:19, 15:19] = 2 labels2 = numpy.zeros((20, 20), int) labels2[6:14, 6:14] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 4 module.tracking_method.value = "Measurements" measurements = runTrackObjects((labels1, labels2), fn, [[1, 10], [9]]) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "4")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 2 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 2 def test_cross_numbered_objects(): """Test labeling when object 1 in one image becomes object 2 in next""" i, j = numpy.mgrid[0:10, 0:20] labels = (i > 5) + (j > 10) * 2 pp = numpy.array(list(centrosome.filter.permutations([1, 2, 3, 4]))) def fn(module, workspace, idx): if idx == 0: module.tracking_method.value = "LAP" measurements = runTrackObjects([numpy.array(p)[labels] for p in pp], fn) def m(feature, i): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature)) values = measurements[OBJECT_NAME, name, i + 1] assert len(values) == 4 return values for i, p in enumerate(pp): l = m(cellprofiler.modules.trackobjects.F_LABEL, i) numpy.testing.assert_array_equal(numpy.arange(1, 5), p[l - 1]) if i > 0: p_prev = pp[i - 1] order = numpy.lexsort([p]) expected_po = p_prev[order] po = m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER, i) numpy.testing.assert_array_equal(po, expected_po) pi = m(cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER, i) numpy.testing.assert_array_equal(pi, i) image_numbers, _ = numpy.mgrid[1 : (len(pp) + 1), 0:4] check_relationships( measurements, image_numbers[:-1, :].flatten(), pp[:-1, :].flatten(), image_numbers[1:, :].flatten(), pp[1:, :].flatten(), ) def test_measurement_columns(): """Test get_measurement_columns function""" module = cellprofiler.modules.trackobjects.TrackObjects() module.object_name.value = OBJECT_NAME module.tracking_method.value = "Distance" module.pixel_radius.value = 10 columns = module.get_measurement_columns(None) assert len(columns) == len(cellprofiler.modules.trackobjects.F_ALL) + len( cellprofiler.modules.trackobjects.F_IMAGE_ALL ) for object_name, features in ( (OBJECT_NAME, cellprofiler.modules.trackobjects.F_ALL), ("Image", cellprofiler.modules.trackobjects.F_IMAGE_ALL,), ): for feature in features: if object_name == OBJECT_NAME: name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, "10") ) else: name = "_".join( ( cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "10", ) ) index = [column[1] for column in columns].index(name) assert index != -1 column = columns[index] assert column[0] == object_name def test_measurement_columns_lap(): """Test get_measurement_columns function for LAP""" module = cellprofiler.modules.trackobjects.TrackObjects() module.object_name.value = OBJECT_NAME module.tracking_method.value = "LAP" module.model.value = cellprofiler.modules.trackobjects.M_BOTH second_phase = [ cellprofiler.modules.trackobjects.F_LINKING_DISTANCE, cellprofiler.modules.trackobjects.F_MOVEMENT_MODEL, ] for wants in (True, False): module.wants_second_phase.value = wants columns = module.get_measurement_columns(None) # 2, 2, 4 for the static model # 4, 4, 16 for the velocity model other_features = [ cellprofiler.modules.trackobjects.F_AREA, cellprofiler.modules.trackobjects.F_LINKING_DISTANCE, cellprofiler.modules.trackobjects.F_LINK_TYPE, cellprofiler.modules.trackobjects.F_MOVEMENT_MODEL, cellprofiler.modules.trackobjects.F_STANDARD_DEVIATION, ] if wants: other_features += [ cellprofiler.modules.trackobjects.F_GAP_LENGTH, cellprofiler.modules.trackobjects.F_GAP_SCORE, cellprofiler.modules.trackobjects.F_MERGE_SCORE, cellprofiler.modules.trackobjects.F_SPLIT_SCORE, cellprofiler.modules.trackobjects.F_MITOSIS_SCORE, ] assert ( len(columns) == len(cellprofiler.modules.trackobjects.F_ALL) + len(cellprofiler.modules.trackobjects.F_IMAGE_ALL) + len(other_features) + 2 + 2 + 4 + 4 + 4 + 16 ) kalman_features = [ cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_STATIC_MODEL, cellprofiler.modules.trackobjects.F_STATE, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_STATIC_MODEL, cellprofiler.modules.trackobjects.F_STATE, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_STATE, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_STATE, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_STATE, cellprofiler.modules.trackobjects.F_VX, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_STATE, cellprofiler.modules.trackobjects.F_VY, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_STATIC_MODEL, cellprofiler.modules.trackobjects.F_NOISE, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_STATIC_MODEL, cellprofiler.modules.trackobjects.F_NOISE, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_NOISE, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_NOISE, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_NOISE, cellprofiler.modules.trackobjects.F_VX, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_NOISE, cellprofiler.modules.trackobjects.F_VY, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_STATIC_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_X, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_STATIC_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_X, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_STATIC_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_Y, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_STATIC_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_X, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_X, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_X, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_X, cellprofiler.modules.trackobjects.F_VX, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_X, cellprofiler.modules.trackobjects.F_VY, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_Y, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_Y, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_Y, cellprofiler.modules.trackobjects.F_VX, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_Y, cellprofiler.modules.trackobjects.F_VY, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_VX, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_VX, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_VX, cellprofiler.modules.trackobjects.F_VX, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_VX, cellprofiler.modules.trackobjects.F_VY, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_VY, cellprofiler.modules.trackobjects.F_X, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_VY, cellprofiler.modules.trackobjects.F_Y, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_VY, cellprofiler.modules.trackobjects.F_VX, ), cellprofiler.modules.trackobjects.kalman_feature( cellprofiler.modules.trackobjects.F_VELOCITY_MODEL, cellprofiler.modules.trackobjects.F_COV, cellprofiler.modules.trackobjects.F_VY, cellprofiler.modules.trackobjects.F_VY, ), ] for object_name, features in ( ( OBJECT_NAME, cellprofiler.modules.trackobjects.F_ALL + kalman_features + other_features, ), ("Image", cellprofiler.modules.trackobjects.F_IMAGE_ALL,), ): for feature in features: if object_name == OBJECT_NAME: name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature) ) else: name = "_".join( ( cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, ) ) index = [column[1] for column in columns].index(name) assert index != -1 column = columns[index] assert column[0] == object_name if wants or feature in second_phase: assert len(column) == 4 assert MCA_AVAILABLE_POST_GROUP in column[3] assert column[3][MCA_AVAILABLE_POST_GROUP] else: assert ( (len(column) == 3) or (MCA_AVAILABLE_POST_GROUP not in column[3]) or (not column[3][MCA_AVAILABLE_POST_GROUP]) ) def test_measurements(): """Test the different measurement pieces""" module = cellprofiler.modules.trackobjects.TrackObjects() module.object_name.value = OBJECT_NAME module.image_name.value = "image" module.pixel_radius.value = 10 categories = module.get_categories(None, "Foo") assert len(categories) == 0 categories = module.get_categories(None, OBJECT_NAME) assert len(categories) == 1 assert categories[0] == cellprofiler.modules.trackobjects.F_PREFIX features = module.get_measurements(None, OBJECT_NAME, "Foo") assert len(features) == 0 features = module.get_measurements( None, OBJECT_NAME, cellprofiler.modules.trackobjects.F_PREFIX ) assert len(features) == len(cellprofiler.modules.trackobjects.F_ALL) assert all( [feature in cellprofiler.modules.trackobjects.F_ALL for feature in features] ) scales = module.get_measurement_scales( None, OBJECT_NAME, cellprofiler.modules.trackobjects.F_PREFIX, "Foo", "image" ) assert len(scales) == 0 for feature in cellprofiler.modules.trackobjects.F_ALL: scales = module.get_measurement_scales( None, OBJECT_NAME, cellprofiler.modules.trackobjects.F_PREFIX, feature, "image", ) assert len(scales) == 1 assert int(scales[0]) == 10 def make_lap2_workspace(objs, nimages, group_numbers=None, group_indexes=None): """Make a workspace to test the second half of LAP objs - a N x 7 array of "objects" composed of the following pieces per object objs[0] - image set # for object objs[1] - label for object objs[2] - parent image # objs[3] - parent object # objs[4] - x coordinate for object objs[5] - y coordinate for object objs[6] - area for object nimages - # of image sets group_numbers - group numbers for each image set, defaults to all 1 group_indexes - group indexes for each image set, defaults to range """ module = cellprofiler.modules.trackobjects.TrackObjects() module.set_module_num(1) module.object_name.value = OBJECT_NAME module.tracking_method.value = "LAP" module.wants_second_phase.value = True module.wants_lifetime_filtering.value = False module.wants_minimum_lifetime.value = False module.min_lifetime.value = 1 module.wants_maximum_lifetime.value = False module.max_lifetime.value = 100 module.pixel_radius.value = 50 pipeline = Pipeline() def callback(caller, event): assert not isinstance(event, RunException) pipeline.add_listener(callback) pipeline.add_module(module) m = cellprofiler_core.measurement.Measurements() if objs.shape[0] > 0: nobjects = numpy.bincount(objs[:, 0].astype(int)) else: nobjects = numpy.zeros(nimages, int) for i in range(nimages): m.next_image_set(i + 1) for index, feature, dtype in ( ( 1, module.measurement_name(cellprofiler.modules.trackobjects.F_LABEL), int, ), ( 2, module.measurement_name( cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER ), int, ), ( 3, module.measurement_name( cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER ), int, ), (4, M_LOCATION_CENTER_X, float), (5, M_LOCATION_CENTER_Y, float), ( 6, module.measurement_name(cellprofiler.modules.trackobjects.F_AREA), float, ), ): values = objs[objs[:, 0] == i, index].astype(dtype) m.add_measurement(OBJECT_NAME, feature, values, i + 1) m.add_measurement("Image", "ImageNumber", i + 1) m.add_measurement( "Image", GROUP_NUMBER, 1 if group_numbers is None else group_numbers[i], image_set_number=i + 1, ) m.add_measurement( "Image", GROUP_INDEX, i if group_indexes is None else group_indexes[i], image_set_number=i + 1, ) # # Add blanks of the right sizes for measurements that are recalculated # m.add_measurement( "Image", "_".join((C_COUNT, OBJECT_NAME)), nobjects[i], image_set_number=i + 1, ) for feature in ( cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED, cellprofiler.modules.trackobjects.F_DISPLACEMENT, cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE, cellprofiler.modules.trackobjects.F_TRAJECTORY_X, cellprofiler.modules.trackobjects.F_TRAJECTORY_Y, cellprofiler.modules.trackobjects.F_LINEARITY, cellprofiler.modules.trackobjects.F_LIFETIME, cellprofiler.modules.trackobjects.F_FINAL_AGE, cellprofiler.modules.trackobjects.F_LINKING_DISTANCE, cellprofiler.modules.trackobjects.F_LINK_TYPE, cellprofiler.modules.trackobjects.F_MOVEMENT_MODEL, cellprofiler.modules.trackobjects.F_STANDARD_DEVIATION, ): dtype = ( int if feature in ( cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER, cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER, cellprofiler.modules.trackobjects.F_LIFETIME, cellprofiler.modules.trackobjects.F_LINK_TYPE, cellprofiler.modules.trackobjects.F_MOVEMENT_MODEL, ) else float ) m.add_measurement( OBJECT_NAME, module.measurement_name(feature), numpy.NaN * numpy.ones(nobjects[i], dtype) if feature == cellprofiler.modules.trackobjects.F_FINAL_AGE else numpy.zeros(nobjects[i], dtype), image_set_number=i + 1, ) for feature in ( cellprofiler.modules.trackobjects.F_SPLIT_COUNT, cellprofiler.modules.trackobjects.F_MERGE_COUNT, ): m.add_measurement( "Image", module.image_measurement_name(feature), 0, image_set_number=i + 1, ) # # Figure out how many new and lost objects per image set # label_sets = [set() for i in range(nimages)] for row in objs: label_sets[row[0]].add(row[1]) if group_numbers is None: group_numbers = numpy.ones(nimages, int) if group_indexes is None: group_indexes = numpy.arange(nimages) + 1 # # New objects are ones without matching labels in the previous set # for i in range(0, nimages): if group_indexes[i] == 1: new_objects = len(label_sets[i]) lost_objects = 0 else: new_objects = sum( [1 for label in label_sets[i] if label not in label_sets[i - 1]] ) lost_objects = sum( [1 for label in label_sets[i - 1] if label not in label_sets[i]] ) m.add_measurement( "Image", module.image_measurement_name( cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT ), new_objects, image_set_number=i + 1, ) m.add_measurement( "Image", module.image_measurement_name( cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT ), lost_objects, image_set_number=i + 1, ) m.image_set_number = nimages image_set_list = ImageSetList() for i in range(nimages): image_set = image_set_list.get_image_set(i) workspace = Workspace(pipeline, module, image_set, ObjectSet(), m, image_set_list,) return workspace, module def check_measurements(workspace, d): """Check measurements against expected values workspace - workspace that was run d - dictionary of feature name and list of expected measurement values """ m = workspace.measurements assert isinstance(m,cellprofiler_core.measurement.Measurements) module = workspace.module assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) for feature, expected in list(d.items()): if numpy.isscalar(expected[0]): mname = module.image_measurement_name(feature) values = m.get_all_measurements("Image", mname) assert len(expected) == len(values), ( "Expected # image sets (%d) != actual (%d) for %s" % (len(expected), len(values), feature) ) assert all([v == e for v, e in zip(values, expected)]), ( "Values don't match for " + feature ) else: mname = module.measurement_name(feature) values = m.get_all_measurements(OBJECT_NAME, mname) assert len(expected) == len(values), ( "Expected # image sets (%d) != actual (%d) for %s" % (len(expected), len(values), feature) ) for i, (e, v) in enumerate(zip(expected, values)): assert len(e) == len(v), ( "Expected # of objects (%d) != actual (%d) for %s:%d" % (len(e), len(v), feature, i) ) numpy.testing.assert_almost_equal(v, e) def check_relationships( m, expected_parent_image_numbers, expected_parent_object_numbers, expected_child_image_numbers, expected_child_object_numbers, ): """Check the relationship measurements against expected""" expected_parent_image_numbers = numpy.atleast_1d(expected_parent_image_numbers) expected_child_image_numbers = numpy.atleast_1d(expected_child_image_numbers) expected_parent_object_numbers = numpy.atleast_1d(expected_parent_object_numbers) expected_child_object_numbers = numpy.atleast_1d(expected_child_object_numbers) assert isinstance(m,cellprofiler_core.measurement.Measurements) r = m.get_relationships( 1, cellprofiler.modules.trackobjects.R_PARENT, OBJECT_NAME, OBJECT_NAME ) actual_parent_image_numbers = r[R_FIRST_IMAGE_NUMBER] actual_parent_object_numbers = r[R_FIRST_OBJECT_NUMBER] actual_child_image_numbers = r[R_SECOND_IMAGE_NUMBER] actual_child_object_numbers = r[R_SECOND_OBJECT_NUMBER] assert len(actual_parent_image_numbers) == len(expected_parent_image_numbers) # # Sort similarly # for i1, o1, i2, o2 in ( ( expected_parent_image_numbers, expected_parent_object_numbers, expected_child_image_numbers, expected_child_object_numbers, ), ( actual_parent_image_numbers, actual_parent_object_numbers, actual_child_image_numbers, actual_child_object_numbers, ), ): order = numpy.lexsort((i1, o1, i2, o2)) for x in (i1, o1, i2, o2): x[:] = x[order] for expected, actual in zip( ( expected_parent_image_numbers, expected_parent_object_numbers, expected_child_image_numbers, expected_child_object_numbers, ), ( actual_parent_image_numbers, actual_parent_object_numbers, actual_child_image_numbers, actual_child_object_numbers, ), ): numpy.testing.assert_array_equal(expected, actual) def test_lap_none(): """Run the second part of LAP on one image of nothing""" with MonkeyPatchedDelete(): workspace, module = make_lap2_workspace(numpy.zeros((0, 7)), 1) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [numpy.zeros(0, int)], cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED: [numpy.zeros(0)], cellprofiler.modules.trackobjects.F_DISPLACEMENT: [numpy.zeros(0)], cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE: [ numpy.zeros(0) ], cellprofiler.modules.trackobjects.F_TRAJECTORY_X: [numpy.zeros(0)], cellprofiler.modules.trackobjects.F_TRAJECTORY_Y: [numpy.zeros(0)], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0], }, ) def test_lap_one(): """Run the second part of LAP on one image of one object""" with MonkeyPatchedDelete(): workspace, module = make_lap2_workspace( numpy.array([[0, 1, 0, 0, 100, 100, 25]]), 1 ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [numpy.array([1])], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]) ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]) ], cellprofiler.modules.trackobjects.F_DISPLACEMENT: [numpy.zeros(1)], cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE: [ numpy.zeros(1) ], cellprofiler.modules.trackobjects.F_TRAJECTORY_X: [numpy.zeros(1)], cellprofiler.modules.trackobjects.F_TRAJECTORY_Y: [numpy.zeros(1)], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0], }, ) def test_bridge_gap(): """Bridge a gap of zero frames between two objects""" with MonkeyPatchedDelete(): workspace, module = make_lap2_workspace( numpy.array([[0, 1, 0, 0, 1, 2, 25], [2, 2, 0, 0, 101, 102, 25]]), 3 ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The cost of bridging the gap should be 141. We set the alternative # score to 142 so that bridging wins. # module.gap_cost.value = 142 module.max_gap_score.value = 142 module.run_as_data_tool(workspace) distance = numpy.array([numpy.sqrt(2 * 100 * 100)]) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.zeros(0), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.zeros(0, int), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.zeros(0, int), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED: [ numpy.zeros(1), numpy.zeros(0), distance, ], cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE: [ numpy.zeros(1), numpy.zeros(0), distance, ], cellprofiler.modules.trackobjects.F_TRAJECTORY_X: [ numpy.zeros(1), numpy.zeros(0), numpy.array([100]), ], cellprofiler.modules.trackobjects.F_TRAJECTORY_Y: [ numpy.zeros(1), numpy.zeros(0), numpy.array([100]), ], cellprofiler.modules.trackobjects.F_LINEARITY: [ numpy.array([numpy.nan]), numpy.zeros(0), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.ones(1), numpy.zeros(0), numpy.array([2]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([numpy.nan]), numpy.zeros(0), numpy.array([2]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 0, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 0, 0], }, ) check_relationships(workspace.measurements, [1], [1], [3], [1]) def test_maintain_gap(): """Maintain object identity across a large gap""" with MonkeyPatchedDelete(): workspace, module = make_lap2_workspace( numpy.array([[0, 1, 0, 0, 1, 2, 25], [2, 2, 0, 0, 101, 102, 25]]), 3 ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The cost of creating the gap should be 140 and the cost of # bridging the gap should be 141. # module.gap_cost.value = 140 module.max_gap_score.value = 142 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.zeros(0), numpy.array([2]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.zeros(0), numpy.array([0]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.zeros(0), numpy.array([0]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 0, 1], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 1, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 0, 0], }, ) def test_filter_gap(): """Filter a gap due to an unreasonable score""" with MonkeyPatchedDelete(): workspace, module = make_lap2_workspace( numpy.array([[0, 1, 0, 0, 1, 2, 25], [2, 2, 0, 0, 101, 102, 25]]), 3 ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The cost of creating the gap should be 142 and the cost of # bridging the gap should be 141. However, the gap should be filtered # by the max score # module.gap_cost.value = 142 module.max_gap_score.value = 140 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.zeros(0), numpy.array([2]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.zeros(0), numpy.array([0]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.zeros(0), numpy.array([0]), ], }, ) def test_split(): """Track an object splitting""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 100, 100, 50], [1, 1, 1, 1, 110, 110, 25], [1, 2, 0, 0, 90, 90, 25], [2, 1, 2, 1, 113, 114, 25], [2, 2, 2, 2, 86, 87, 25], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The split score should be 20*sqrt(2) more than the null so a split # alternative cost of 15 is too much and 14 too little. Values # doulbed to mat # module.split_cost.value = 30 module.max_split_score.value = 30 module.run_as_data_tool(workspace) d200 = numpy.sqrt(200) tot = numpy.sqrt(13 ** 2 + 14 ** 2) lin = tot / (d200 + 5) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.array([1, 1]), numpy.array([1, 1]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.array([1, 1]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.array([1, 1]), numpy.array([1, 2]), ], cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED: [ numpy.zeros(1), numpy.ones(2) * d200, numpy.array([5, 5]), ], cellprofiler.modules.trackobjects.F_DISPLACEMENT: [ numpy.zeros(1), numpy.ones(2) * d200, numpy.array([tot, tot]), ], cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE: [ numpy.zeros(1), numpy.ones(2) * d200, numpy.ones(2) * d200 + 5, ], cellprofiler.modules.trackobjects.F_TRAJECTORY_X: [ numpy.zeros(1), numpy.array([10, -10]), numpy.array([3, -4]), ], cellprofiler.modules.trackobjects.F_TRAJECTORY_Y: [ numpy.zeros(1), numpy.array([10, -10]), numpy.array([4, -3]), ], cellprofiler.modules.trackobjects.F_LINEARITY: [ numpy.array([numpy.nan]), numpy.array([1, 1]), numpy.array([lin, lin]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.ones(1), numpy.array([2, 2]), numpy.array([3, 3]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([numpy.nan]), numpy.array([numpy.nan, numpy.nan]), numpy.array([3, 3]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 0, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 1, 0], }, ) def test_dont_split(): """Track an object splitting""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 100, 100, 50], [1, 1, 1, 1, 110, 110, 25], [1, 2, 0, 0, 90, 90, 25], [2, 1, 2, 1, 110, 110, 25], [2, 2, 2, 2, 90, 90, 25], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) module.split_cost.value = 28 module.max_split_score.value = 30 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.array([1, 2]), numpy.array([1, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.array([1, 0]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.array([1, 0]), numpy.array([1, 2]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.ones(1), numpy.array([2, 1]), numpy.array([3, 2]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([numpy.nan]), numpy.array([numpy.nan, numpy.nan]), numpy.array([3, 2]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 1, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 0, 0], }, ) def test_split_filter(): """Prevent a split by setting the filter too low""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 100, 100, 50], [1, 1, 1, 1, 110, 110, 25], [1, 2, 0, 0, 90, 90, 25], [2, 1, 2, 1, 110, 110, 25], [2, 2, 2, 2, 90, 90, 25], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) module.split_cost.value = 30 module.max_split_score.value = 28 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.array([1, 2]), numpy.array([1, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.array([1, 0]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.array([1, 0]), numpy.array([1, 2]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.array([1]), numpy.array([2, 1]), numpy.array([3, 2]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([numpy.nan]), numpy.array([numpy.nan, numpy.nan]), numpy.array([3, 2]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 1, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 0, 0], }, ) def test_merge(): """Merge two objects into one""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 110, 110, 25], [0, 2, 0, 0, 90, 90, 25], [1, 1, 1, 1, 110, 110, 25], [1, 2, 1, 2, 90, 90, 25], [2, 1, 2, 1, 100, 100, 50], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) module.merge_cost.value = 30 module.max_merge_score.value = 30 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1, 1]), numpy.array([1, 1]), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0, 0]), numpy.array([1, 1]), numpy.array([2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0, 0]), numpy.array([1, 2]), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.array([1, 1]), numpy.array([2, 2]), numpy.array([3]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([numpy.nan, numpy.nan]), numpy.array([numpy.nan, numpy.nan]), numpy.array([3]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [2, 0, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 1], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 0, 0], }, ) def test_dont_merge(): """Don't merge because of low alternative merge cost""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 110, 110, 25], [0, 2, 0, 0, 90, 90, 25], [1, 1, 1, 1, 110, 110, 25], [1, 2, 1, 2, 90, 90, 25], [2, 1, 2, 1, 100, 100, 50], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The cost of the merge is 2x 10x sqrt(2) which is between 28 and 29 # module.merge_cost.value = 28 module.max_merge_score.value = 30 module.run_as_data_tool(workspace) labels = workspace.measurements.get_all_measurements( OBJECT_NAME, module.measurement_name(cellprofiler.modules.trackobjects.F_LABEL) ) assert len(labels) == 3 assert len(labels[0]) == 2 assert labels[0][0] == 1 assert labels[0][1] == 2 assert len(labels[1]) == 2 assert labels[1][0] == 1 assert labels[1][1] == 2 assert len(labels[2]) == 1 assert labels[2][0] == 1 def test_filter_merge(): """Don't merge because of low alternative merge cost""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 110, 110, 25], [0, 2, 0, 0, 90, 90, 25], [1, 1, 1, 1, 110, 110, 25], [1, 2, 1, 2, 90, 90, 25], [2, 1, 2, 1, 100, 100, 50], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The cost of the merge is 2x 10x sqrt(2) which is between 28 and 29 # module.merge_cost.value = 30 module.max_merge_score.value = 28 module.run_as_data_tool(workspace) labels = workspace.measurements.get_all_measurements( OBJECT_NAME, module.measurement_name(cellprofiler.modules.trackobjects.F_LABEL) ) assert len(labels) == 3 assert len(labels[0]) == 2 assert labels[0][0] == 1 assert labels[0][1] == 2 assert len(labels[1]) == 2 assert labels[1][0] == 1 assert labels[1][1] == 2 assert len(labels[2]) == 1 assert labels[2][0] == 1 def test_img_1111(): """Regression test of img-1111""" data = numpy.array( [ [9, 1, 0, 0, 225, 20, 50], [9, 2, 0, 0, 116, 223, 31], [25, 3, 0, 0, 43, 291, 26], [28, 4, 0, 0, 410, 436, 24], [29, 5, 0, 0, 293, 166, 23], [29, 4, 29, 1, 409, 436, 24], [30, 5, 30, 1, 293, 167, 30], [32, 6, 0, 0, 293, 164, 69], [33, 6, 33, 1, 292, 166, 37], [35, 7, 0, 0, 290, 165, 63], [36, 7, 36, 1, 290, 166, 38], [39, 8, 0, 0, 287, 163, 28], [40, 8, 40, 1, 287, 163, 21], [44, 9, 0, 0, 54, 288, 20], [77, 10, 0, 0, 514, 211, 49], [78, 10, 78, 1, 514, 210, 42], [79, 10, 79, 1, 514, 209, 73], [80, 10, 80, 1, 514, 208, 49], [81, 10, 81, 1, 515, 209, 38], [98, 11, 0, 0, 650, 54, 24], [102, 12, 0, 0, 586, 213, 46], [104, 13, 0, 0, 586, 213, 27], [106, 14, 0, 0, 587, 212, 54], [107, 14, 107, 1, 587, 212, 40], [113, 15, 0, 0, 17, 145, 51], [116, 16, 0, 0, 45, 153, 21], [117, 17, 0, 0, 53, 148, 44], [117, 18, 0, 0, 90, 278, 87], [119, 19, 0, 0, 295, 184, 75], [120, 19, 120, 1, 295, 184, 79], [121, 19, 121, 1, 295, 182, 75], [123, 20, 0, 0, 636, 7, 20], [124, 20, 124, 1, 635, 7, 45], [124, 21, 0, 0, 133, 171, 22], [124, 22, 0, 0, 417, 365, 65], [126, 23, 0, 0, 125, 182, 77], [126, 24, 0, 0, 358, 306, 48], [126, 25, 0, 0, 413, 366, 60], [127, 26, 0, 0, 141, 173, 71], [127, 25, 127, 3, 413, 366, 35], [128, 27, 0, 0, 131, 192, 76], [129, 28, 0, 0, 156, 182, 74], [130, 29, 0, 0, 147, 194, 56], [131, 30, 0, 0, 152, 185, 56], [132, 30, 132, 1, 154, 188, 78], [133, 31, 0, 0, 142, 186, 64], [133, 32, 0, 0, 91, 283, 23], [134, 33, 0, 0, 150, 195, 80], ] ) data = data[:8, :] workspace, module = make_lap2_workspace(data, numpy.max(data[:, 0]) + 1) module.run_as_data_tool(workspace) def test_multi_group(): """Run several tests in different groups""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 1, 2, 25], [2, 2, 0, 0, 101, 102, 25], [3, 1, 0, 0, 100, 100, 50], [4, 1, 4, 1, 110, 110, 25], [4, 2, 0, 0, 90, 90, 25], [5, 1, 5, 1, 113, 114, 25], [5, 2, 5, 2, 86, 87, 25], [6, 1, 0, 0, 110, 110, 25], [6, 2, 0, 0, 90, 90, 25], [7, 1, 7, 1, 110, 110, 25], [7, 2, 7, 2, 90, 90, 25], [8, 1, 8, 1, 104, 102, 50], ] ), 9, group_numbers=[1, 1, 1, 2, 2, 2, 3, 3, 3], group_indexes=[1, 2, 3, 1, 2, 3, 1, 2, 3], ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The cost of bridging the gap should be 141. We set the alternative # score to 142 so that bridging wins. # module.gap_cost.value = 142 module.max_gap_score.value = 142 module.split_cost.value = 30 module.max_split_score.value = 30 module.merge_cost.value = 30 module.max_merge_score.value = 30 module.run_as_data_tool(workspace) distance = numpy.array([numpy.sqrt(2 * 100 * 100)]) d200 = numpy.sqrt(200) tot = numpy.sqrt(13 ** 2 + 14 ** 2) lin = tot / (d200 + 5) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.zeros(0), numpy.array([1]), numpy.array([1]), numpy.array([1, 1]), numpy.array([1, 1]), numpy.array([1, 1]), numpy.array([1, 1]), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.zeros(0), numpy.array([1]), numpy.array([0]), numpy.array([4, 4]), numpy.array([5, 5]), numpy.array([0, 0]), numpy.array([7, 7]), numpy.array([8]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.zeros(0), numpy.array([1]), numpy.array([0]), numpy.array([1, 1]), numpy.array([1, 2]), numpy.array([0, 0]), numpy.array([1, 2]), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_DISPLACEMENT: [ numpy.zeros(1), numpy.zeros(0), distance, numpy.zeros(1), numpy.ones(2) * d200, numpy.array([tot, tot]), numpy.zeros(2), numpy.zeros(2), numpy.array([10]), ], cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE: [ numpy.zeros(1), numpy.zeros(0), distance, numpy.zeros(1), numpy.ones(2) * d200, numpy.ones(2) * d200 + 5, numpy.zeros(2), numpy.zeros(2), numpy.array([10]), ], cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED: [ numpy.zeros(1), numpy.zeros(0), distance, numpy.zeros(1), numpy.ones(2) * d200, numpy.array([5, 5]), numpy.zeros(2), numpy.zeros(2), numpy.array([10]), ], cellprofiler.modules.trackobjects.F_TRAJECTORY_X: [ numpy.zeros(1), numpy.zeros(0), numpy.array([100]), numpy.zeros(1), numpy.array([10, -10]), numpy.array([3, -4]), numpy.zeros(2), numpy.zeros(2), numpy.array([-6]), ], cellprofiler.modules.trackobjects.F_TRAJECTORY_Y: [ numpy.zeros(1), numpy.zeros(0), numpy.array([100]), numpy.zeros(1), numpy.array([10, -10]), numpy.array([4, -3]), numpy.zeros(2), numpy.zeros(2), numpy.array([-8]), ], cellprofiler.modules.trackobjects.F_LINEARITY: [ numpy.array([numpy.nan]), numpy.zeros(0), numpy.array([1]), numpy.array([numpy.nan]), numpy.array([1, 1]), numpy.array([lin, lin]), numpy.array([numpy.nan, numpy.nan]), numpy.array([numpy.nan, numpy.nan]), numpy.ones(1), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.ones(1), numpy.zeros(0), numpy.array([2]), numpy.ones(1), numpy.array([2, 2]), numpy.array([3, 3]), numpy.ones(2), numpy.array([2, 2]), numpy.array([3]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([numpy.nan]), numpy.zeros(0), numpy.array([2]), numpy.array([numpy.nan]), numpy.array([numpy.nan, numpy.nan]), numpy.array([3, 3]), numpy.array([numpy.nan, numpy.nan]), numpy.array([numpy.nan, numpy.nan]), numpy.array([3]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [ 1, 0, 0, 1, 0, 0, 2, 0, 0, ], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [ 0, 0, 0, 0, 0, 0, 0, 0, 0, ], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [ 0, 0, 0, 0, 0, 0, 0, 0, 1, ], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [ 0, 0, 0, 0, 1, 0, 0, 0, 0, ], }, ) def test_filter_by_final_age(): """Filter an object by the final age""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 100, 100, 50], [1, 1, 1, 1, 110, 110, 50], [1, 2, 0, 0, 90, 90, 25], [2, 1, 2, 1, 100, 100, 50], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The split score should be between 14 and 15. Set the split # alternative cost to 28 so that the split is inhibited. # module.split_cost.value = 28 module.max_split_score.value = 30 # # The cost of the merge is 2x 10x sqrt(2) which is between 28 and 29 # module.merge_cost.value = 28 module.max_merge_score.value = 30 module.wants_lifetime_filtering.value = True module.wants_minimum_lifetime.value = True module.min_lifetime.value = 1 module.wants_maximum_lifetime.value = False module.max_lifetime.value = 100 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.array([1, numpy.NaN]), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.array([1, 0]), numpy.array([2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.array([1, 0]), numpy.array([1]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.array([1]), numpy.array([2, 1]), numpy.array([3]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([numpy.nan]), numpy.array([numpy.nan, 1]), numpy.array([3]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 1, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 0, 1], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 0, 0], }, ) def test_mitosis(): """Track a mitosis""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 103, 104, 50], [1, 2, 0, 0, 110, 110, 25], [1, 3, 0, 0, 90, 90, 25], [2, 2, 2, 1, 113, 114, 25], [2, 3, 2, 2, 86, 87, 25], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The parent is off by np.sqrt(3*3+4*4) = 5, so an alternative of # 4 loses and 6 wins # module.merge_cost.value = 1 module.gap_cost.value = 1 module.mitosis_cost.value = 6 module.mitosis_max_distance.value = 20 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.array([1, 1]), numpy.array([1, 1]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.array([1, 1]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.array([1, 1]), numpy.array([1, 2]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.ones(1), numpy.array([2, 2]), numpy.array([3, 3]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([numpy.nan]), numpy.array([numpy.nan, numpy.nan]), numpy.array([3, 3]), ], cellprofiler.modules.trackobjects.F_LINK_TYPE: [ numpy.array([cellprofiler.modules.trackobjects.LT_NONE]), numpy.array( [ cellprofiler.modules.trackobjects.LT_MITOSIS, cellprofiler.modules.trackobjects.LT_MITOSIS, ] ), numpy.array( [ cellprofiler.modules.trackobjects.LT_NONE, cellprofiler.modules.trackobjects.LT_NONE, ] ), ], cellprofiler.modules.trackobjects.F_MITOSIS_SCORE: [ numpy.array([numpy.nan]), numpy.array([5, 5]), numpy.array([numpy.nan, numpy.nan]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 0, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 1, 0], }, ) def test_no_mitosis(): """Don't track a mitosis""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 103, 104, 50], [1, 2, 0, 0, 110, 110, 25], [1, 3, 0, 0, 90, 90, 25], [2, 2, 2, 1, 113, 114, 25], [2, 3, 2, 2, 86, 87, 25], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The parent is off by np.sqrt(3*3+4*4) = 5, so an alternative of # 4 loses and 6 wins # module.merge_cost.value = 1 module.mitosis_cost.value = 4 module.mitosis_max_distance.value = 20 module.gap_cost.value = 1 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.array([2, 3]), numpy.array([2, 3]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.array([0, 0]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.array([0, 0]), numpy.array([1, 2]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.ones(1), numpy.array([1, 1]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([1]), numpy.array([numpy.nan, numpy.nan]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 2, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 1, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 0, 0], }, ) def test_mitosis_distance_filter(): """Don't track a mitosis""" workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 103, 104, 50], [1, 2, 0, 0, 110, 110, 25], [1, 3, 0, 0, 90, 90, 25], [2, 2, 2, 1, 113, 114, 25], [2, 3, 2, 2, 86, 87, 25], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) # # The parent is off by np.sqrt(3*3+4*4) = 5, so an alternative of # 4 loses and 6 wins # module.merge_cost.value = 1 module.mitosis_cost.value = 6 module.mitosis_max_distance.value = 15 module.gap_cost.value = 1 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.array([2, 3]), numpy.array([2, 3]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.array([0, 0]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.array([0, 0]), numpy.array([1, 2]), ], cellprofiler.modules.trackobjects.F_LIFETIME: [ numpy.ones(1), numpy.array([1, 1]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_FINAL_AGE: [ numpy.array([1]), numpy.array([numpy.nan, numpy.nan]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT: [1, 2, 0], cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT: [0, 1, 0], cellprofiler.modules.trackobjects.F_MERGE_COUNT: [0, 0, 0], cellprofiler.modules.trackobjects.F_SPLIT_COUNT: [0, 0, 0], }, ) def test_alternate_child_mitoses(): # Test that LAP can pick the best of two possible child alternates workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 103, 104, 50], [1, 2, 0, 0, 110, 110, 25], [1, 3, 0, 0, 91, 91, 25], [1, 4, 0, 0, 90, 90, 25], [2, 2, 2, 1, 113, 114, 25], [2, 3, 2, 2, 86, 87, 25], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) module.merge_cost.value = 1 module.gap_cost.value = 1 module.mitosis_cost.value = 6 module.mitosis_max_distance.value = 20 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1]), numpy.array([1, 1, 2]), numpy.array([1, 1]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0]), numpy.array([1, 1, 0]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0]), numpy.array([1, 1, 0]), numpy.array([1, 2]), ], }, ) def test_alternate_parent_mitoses(): # Test that LAP can pick the best of two possible parent alternates workspace, module = make_lap2_workspace( numpy.array( [ [0, 1, 0, 0, 100, 100, 50], [0, 2, 0, 0, 103, 104, 50], [1, 3, 0, 0, 110, 110, 25], [1, 4, 0, 0, 90, 90, 25], [2, 3, 2, 1, 113, 114, 25], [2, 4, 2, 2, 86, 87, 25], ] ), 3, ) assert isinstance(module, cellprofiler.modules.trackobjects.TrackObjects) module.merge_cost.value = 1 module.gap_cost.value = 1 module.mitosis_cost.value = 6 module.mitosis_max_distance.value = 20 module.run_as_data_tool(workspace) check_measurements( workspace, { cellprofiler.modules.trackobjects.F_LABEL: [ numpy.array([1, 2]), numpy.array([1, 1]), numpy.array([1, 1]), ], cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER: [ numpy.array([0, 0]), numpy.array([1, 1]), numpy.array([2, 2]), ], cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER: [ numpy.array([0, 0]), numpy.array([1, 1]), numpy.array([1, 2]), ], }, ) class MonkeyPatchedDelete(object): """Monkey patch np.delete inside of a scope For regression test of issue #1571 - negative indices in calls to numpy.delete Usage: with MonkeyPatchedDelete(): ... do test ... """ def __init__(self, test=None): __test = test def __enter__(self): self.old_delete = numpy.delete numpy.delete = self.monkey_patched_delete def __exit__(self, type, value, traceback): numpy.delete = self.old_delete def monkey_patched_delete(self, array, indices, axis): # __test.assertTrue(numpy.all(indices >= 0)) return self.old_delete(array, indices, axis) def test_save_image(): module = cellprofiler.modules.trackobjects.TrackObjects() module.set_module_num(1) module.object_name.value = OBJECT_NAME module.pixel_radius.value = 50 module.wants_image.value = True module.image_name.value = "outimage" measurements = cellprofiler_core.measurement.Measurements() measurements.add_image_measurement(GROUP_NUMBER, 1) measurements.add_image_measurement(GROUP_INDEX, 1) pipeline = Pipeline() pipeline.add_module(module) image_set_list = ImageSetList() module.prepare_run( Workspace(pipeline, module, None, None, measurements, image_set_list) ) first = True object_set = ObjectSet() objects = Objects() objects.segmented = numpy.zeros((640, 480), int) object_set.add_objects(objects, OBJECT_NAME) image_set = image_set_list.get_image_set(0) workspace = Workspace( pipeline, module, image_set, object_set, measurements, image_set_list ) module.run(workspace) image = workspace.image_set.get_image(module.image_name.value) shape = image.pixel_data.shape assert shape[0] == 640 assert shape[1] == 480 def test_get_no_gap_pair_scores(): for F, L, max_gap in ( (numpy.zeros((0, 3)), numpy.zeros((0, 3)), 1), (numpy.ones((1, 3)), numpy.ones((1, 3)), 1), (numpy.ones((2, 3)), numpy.ones((2, 3)), 1), ): t = cellprofiler.modules.trackobjects.TrackObjects() a, d = t.get_gap_pair_scores(F, L, max_gap) assert tuple(a.shape) == (0, 2) assert len(d) == 0 def test_get_gap_pair_scores(): L = numpy.array( [ [0.0, 0.0, 1, 0, 0, 0, 1], [1.0, 1.0, 5, 0, 0, 0, 1], [3.0, 3.0, 8, 0, 0, 0, 1], [2.0, 2.0, 9, 0, 0, 0, 1], [0.0, 0.0, 9, 0, 0, 0, 1], [0.0, 0.0, 9, 0, 0, 0, 1], ] ) F = numpy.array( [ [0.0, 0.0, 0, 0, 0, 0, 1], [1.0, 0.0, 4, 0, 0, 0, 1], [3.0, 0.0, 6, 0, 0, 0, 1], [4.0, 0.0, 7, 0, 0, 0, 1], [1.0, 0.0, 2, 0, 0, 0, 2], [1.0, 0.0, 2, 0, 0, 0, 0.5], ] ) expected = numpy.array([[0, 1], [0, 4], [0, 5], [1, 2], [1, 3]]) expected_d = numpy.sqrt( numpy.sum((L[expected[:, 0], :2] - F[expected[:, 1], :2]) ** 2, 1) ) expected_rho = numpy.array([1, 2, 2, 1, 1]) t = cellprofiler.modules.trackobjects.TrackObjects() a, d = t.get_gap_pair_scores(F, L, 4) order = numpy.lexsort((a[:, 1], a[:, 0])) a, d = a[order], d[order] numpy.testing.assert_array_equal(a, expected) numpy.testing.assert_array_almost_equal(d, expected_d * expected_rho) def test_neighbour_track_nothing(): """Run TrackObjects on an empty labels matrix""" columns = [] def fn(module, workspace, index, columns=columns): if workspace is not None and index == 0: columns += module.get_measurement_columns(workspace.pipeline) module.tracking_method.value = "Follow Neighbors" measurements = runTrackObjects( (numpy.zeros((10, 10), int), numpy.zeros((10, 10), int)), fn ) features = [ feature for feature in measurements.get_feature_names(OBJECT_NAME) if feature.startswith(cellprofiler.modules.trackobjects.F_PREFIX) ] assert all( [column[1] in features for column in columns if column[0] == OBJECT_NAME] ) for feature in cellprofiler.modules.trackobjects.F_ALL: name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "50")) assert name in features value = measurements.get_current_measurement(OBJECT_NAME, name) assert len(value) == 0 features = [ feature for feature in measurements.get_feature_names("Image") if feature.startswith(cellprofiler.modules.trackobjects.F_PREFIX) ] assert all([column[1] in features for column in columns if column[0] == "Image"]) for feature in cellprofiler.modules.trackobjects.F_IMAGE_ALL: name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "50") ) assert name in features value = measurements.get_current_image_measurement(name) assert value == 0 def test_00_neighbour_track_one_then_nothing(): """Run track objects on an object that disappears Regression test of IMG-1090 """ labels = numpy.zeros((10, 10), int) labels[3:6, 2:7] = 1 def fn(module, workspace, index): if workspace is not None and index == 0: module.tracking_method.value = "Follow Neighbors" measurements = runTrackObjects((labels, numpy.zeros((10, 10), int)), fn) feature = "_".join( ( cellprofiler.modules.trackobjects.F_PREFIX, cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT, OBJECT_NAME, "50", ) ) value = measurements.get_current_image_measurement(feature) assert value == 1 def test_neighbour_track_one_by_distance(): """Track an object that doesn't move.""" labels = numpy.zeros((10, 10), int) labels[3:6, 2:7] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 1 module.tracking_method.value = "Follow Neighbors" measurements = runTrackObjects((labels, labels), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "1")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert round(abs(m(cellprofiler.modules.trackobjects.F_TRAJECTORY_X) - 0), 7) == 0 assert round(abs(m(cellprofiler.modules.trackobjects.F_TRAJECTORY_Y) - 0), 7) == 0 assert ( round(abs(m(cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED) - 0), 7) == 0 ) assert ( round(abs(m(cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE) - 0), 7) == 0 ) assert m(cellprofiler.modules.trackobjects.F_LABEL) == 1 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 1 assert m(cellprofiler.modules.trackobjects.F_PARENT_IMAGE_NUMBER) == 1 assert m(cellprofiler.modules.trackobjects.F_LIFETIME) == 2 def m(feature): name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "1") ) return measurements.get_current_image_measurement(name) assert m(cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_SPLIT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_MERGE_COUNT) == 0 check_relationships(measurements, [1], [1], [2], [1]) def test_neighbour_track_one_moving(): """Track an object that moves""" labels_list = [] distance = 0 last_i, last_j = (0, 0) for i_off, j_off in ((0, 0), (2, 0), (2, 1), (0, 1)): distance = i_off - last_i + j_off - last_j last_i, last_j = (i_off, j_off) labels = numpy.zeros((10, 10), int) labels[4 + i_off : 7 + i_off, 4 + j_off : 7 + j_off] = 1 labels_list.append(labels) def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 3 module.tracking_method.value = "Follow Neighbors" measurements = runTrackObjects(labels_list, fn) def m(feature, expected): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "3")) value_set = measurements.get_all_measurements(OBJECT_NAME, name) assert len(expected) == len(value_set) for values, x in zip(value_set, expected): assert len(values) == 1 assert round(abs(values[0] - x), 7) == 0 m(cellprofiler.modules.trackobjects.F_TRAJECTORY_X, [0, 0, 1, 0]) m(cellprofiler.modules.trackobjects.F_TRAJECTORY_Y, [0, 2, 0, -2]) m(cellprofiler.modules.trackobjects.F_DISTANCE_TRAVELED, [0, 2, 1, 2]) m(cellprofiler.modules.trackobjects.F_INTEGRATED_DISTANCE, [0, 2, 3, 5]) m(cellprofiler.modules.trackobjects.F_LABEL, [1, 1, 1, 1]) m(cellprofiler.modules.trackobjects.F_LIFETIME, [1, 2, 3, 4]) m( cellprofiler.modules.trackobjects.F_LINEARITY, [1, 1, numpy.sqrt(5) / 3, 1.0 / 5.0], ) def m(feature): name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "3") ) return measurements.get_current_image_measurement(name) assert m(cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_SPLIT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_MERGE_COUNT) == 0 image_numbers = numpy.arange(1, len(labels_list) + 1) object_numbers = numpy.ones(len(image_numbers)) check_relationships( measurements, image_numbers[:-1], object_numbers[:-1], image_numbers[1:], object_numbers[1:], ) def test_neighbour_track_negative(): """Track unrelated objects""" labels1 = numpy.zeros((10, 10), int) labels1[1:5, 1:5] = 1 labels2 = numpy.zeros((10, 10), int) labels2[6:9, 6:9] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 1 module.tracking_method.value = "Follow Neighbors" measurements = runTrackObjects((labels1, labels2), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "1")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 2 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 0 def m(feature): name = "_".join( (cellprofiler.modules.trackobjects.F_PREFIX, feature, OBJECT_NAME, "1") ) return measurements.get_current_image_measurement(name) assert m(cellprofiler.modules.trackobjects.F_NEW_OBJECT_COUNT) == 1 assert m(cellprofiler.modules.trackobjects.F_LOST_OBJECT_COUNT) == 1 assert m(cellprofiler.modules.trackobjects.F_SPLIT_COUNT) == 0 assert m(cellprofiler.modules.trackobjects.F_MERGE_COUNT) == 0 def test_neighbour_track_ambiguous(): """Track disambiguation from among two possible parents""" labels1 = numpy.zeros((20, 20), int) labels1[1:4, 1:4] = 1 labels1[16:19, 16:19] = 2 labels2 = numpy.zeros((20, 20), int) labels2[10:15, 10:15] = 1 def fn(module, workspace, idx): if idx == 0: module.pixel_radius.value = 20 module.tracking_method.value = "Follow Neighbors" measurements = runTrackObjects((labels1, labels2), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "20")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] assert m(cellprofiler.modules.trackobjects.F_LABEL) == 2 assert m(cellprofiler.modules.trackobjects.F_PARENT_OBJECT_NUMBER) == 2 def test_neighbour_track_group_with_drop(): """Track groups with one lost""" labels1 = numpy.zeros((20, 20), int) labels1[2, 2] = 1 labels1[4, 2] = 2 labels1[2, 4] = 3 labels1[4, 4] = 4 labels2 = numpy.zeros((20, 20), int) labels2[16, 16] = 1 labels2[18, 16] = 2 # labels2[16,18] = 3 is no longer present labels2[18, 18] = 4 def fn(module, workspace, idx): if idx == 0: module.drop_cost.value = 100 # make it always try to match module.pixel_radius.value = 200 module.average_cell_diameter.value = 5 module.tracking_method.value = "Follow Neighbors" measurements = runTrackObjects((labels1, labels2), fn) def m(feature): name = "_".join((cellprofiler.modules.trackobjects.F_PREFIX, feature, "20")) values = measurements.get_current_measurement(OBJECT_NAME, name) assert len(values) == 1 return values[0] check_relationships(measurements, [1, 1, 1], [1, 2, 4], [2, 2, 2], [1, 2, 4])

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105,331

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1

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null

0

9

73a846f8233adcfa1595907818c78244573b86d9

36,694

py

Python

huobitrade/core.py

hadrianl/huobi

7cfceba39189552489c1d9c88169f93109ee76ba

[ "MIT" ]

177

2018-06-06T11:33:58.000Z

2022-01-22T03:58:52.000Z

huobitrade/core.py

jfhk/huobi

7cfceba39189552489c1d9c88169f93109ee76ba

[ "MIT" ]

8

2018-05-31T07:32:52.000Z

2021-04-30T00:44:53.000Z

huobitrade/core.py

jfhk/huobi

7cfceba39189552489c1d9c88169f93109ee76ba

[ "MIT" ]

61

2018-05-31T07:32:08.000Z

2021-10-10T09:15:30.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2018/9/20 0020 9:23 # @Author : Hadrianl # @File : core.py # @Contact : 137150224@qq.com import websocket as ws import gzip as gz import json from . import utils as u from .utils import logger, zmq_ctx from threading import Thread import datetime as dt from dateutil import parser from functools import wraps import zmq import pickle import time from abc import abstractmethod import uuid from .handler import BaseHandler from concurrent.futures import ThreadPoolExecutor logger.debug(f'<TESTING>LOG_TESTING') class BaseWebsocket(object): ws_count = 0 def __new__(cls, *args, **kwargs): cls.ws_count += 1 if cls is _AuthWS: from .utils import ACCESS_KEY, SECRET_KEY if not (ACCESS_KEY and SECRET_KEY): raise Exception('ACCESS_KEY或SECRET_KEY未设置！') return object.__new__(cls) def send_message(self, msg): # 发送消息 msg_json = json.dumps(msg).encode() self.ws.send(msg_json) def on_message(self, _msg): # 接收ws的消息推送并处理，包括了pingpong，处理订阅列表，以及处理数据推送 json_data = gz.decompress(_msg).decode() msg = json.loads(json_data) logger.debug(f'{msg}') @abstractmethod def pub_msg(self, msg): """核心的处理函数，如果是handle_func直接处理，如果是handler，推送到handler的队列""" raise NotImplementedError def on_error(self, error): logger.error(f'<错误>on_error:{error}') def on_close(self): logger.info(f'<连接>已断开与{self.addr}的连接') if not self._active: return if self._reconn > 0: logger.info(f'<连接>尝试与{self.addr}进行重连') self.__start() self._reconn -= 1 time.sleep(self._interval) else: logger.info(f'<连接>尝试与{self.addr}进行重连') self.__start() time.sleep(self._interval) def on_open(self): self._active = True logger.info(f'<连接>建立与{self.addr}的连接') # ------------------- 注册回调处理函数 ------------------------------- def register_onRsp(self, req): """ 添加回调处理函数的装饰器 :param req: 具体的topic，如 :return: """ def wrapper(_callback): callbackList = self._req_callbacks.setdefault(req, []) callbackList.append(_callback) return _callback return wrapper def unregister_onRsp(self, req): return self._req_callbacks.pop(req) # ------------------------------------------------------------------ # ------------------------- 注册handler ----------------------------- def register_handler(self, handler): # 注册handler if handler not in self._handlers: self._handlers.append(handler) handler.start(self.name) def unregister_handler(self, handler): # 注销handler if handler in self._handlers: self._handlers.remove(handler) handler.stop(self.name) def __add__(self, handler): if isinstance(handler, BaseHandler): self.register_handler(handler) else: raise Exception('{handler} is not aHandler') return self def __sub__(self, handler): if isinstance(handler, BaseHandler): self.unregister_handler(handler) else: raise Exception('{handler} is not aHandler') return self # ----------------------------------------------------------------- # --------------------- 注册handle_func -------------------------- def register_handle_func(self, topic): # 注册handle_func def _wrapper(_handle_func): if topic not in self._handle_funcs: self._handle_funcs[topic] = [] self._handle_funcs[topic].append(_handle_func) return _handle_func return _wrapper def unregister_handle_func(self, _handle_func_name, topic): """ 注销handle_func """ handler_list = self._handle_funcs.get(topic, []) for i, h in enumerate(handler_list): if h is _handle_func_name or h.__name__ == _handle_func_name: handler_list.pop(i) if self._handle_funcs.get(topic) == []: self._handle_funcs.pop(topic) # ----------------------------------------------------------------- # --------------------- handle属性 -------------------------------- @property def handlers(self): return self._handlers @property def handle_funcs(self): return self._handle_funcs @property def OnRsp_callbacks(self): return self._req_callbacks # ----------------------------------------------------------------- # -------------------------开关ws----------------------------------------- def run(self): if not hasattr(self, 'ws_thread') or not self.ws_thread.is_alive(): self.__start() def __start(self): self.ws = ws.WebSocketApp( self.addr, on_open=self.on_open, on_message=self.on_message, on_error=self.on_error, on_close=self.on_close, # on_data=self.on_data ) self.ws_thread = Thread(target=self.ws.run_forever, name=self.name) self.ws_thread.setDaemon(True) self.ws_thread.start() def stop(self): if hasattr(self, 'ws_thread') and self.ws_thread.is_alive(): self._active = False self.ws.close() # self.ws_thread.join() # ------------------------------------------------------------------------ class _AuthWS(BaseWebsocket): def __init__(self, host='api.huobi.br.com', reconn=10, interval=3): self._protocol = 'wss://' self._host = host self._path = '/ws/v1' self.addr = self._protocol + self._host + self._path self._threadPool = ThreadPoolExecutor(max_workers=3) # self.name = f'HuoBiAuthWS{self.ws_count}' self.name = f'HuoBiAuthWS_{uuid.uuid1()}' self.sub_dict = {} # 订阅列表 self._handlers = [] # 对message做处理的处理函数或处理类 self._req_callbacks = {} self._handle_funcs = {} self._auth_callbacks = [] self.ctx = zmq_ctx self.pub_socket = self.ctx.socket(zmq.PUB) self.pub_socket.bind(f'inproc://{self.name}') self._active = False self._reconn = reconn self._interval = interval def on_open(self): self._active = True logger.info(f'<连接>建立与{self.addr}的连接') self.auth() logger.info(f'<鉴权>向{self.addr}发起鉴权请求') def on_message(self, _msg): # 鉴权ws的消息处理 json_data = gz.decompress(_msg).decode() msg = json.loads(json_data) logger.debug(f'{msg}') op = msg['op'] if op == 'ping': pong = {'op': 'pong', 'ts': msg['ts']} self.send_message(pong) if msg.setdefault('err-code', 0) == 0: if op == 'notify': self.pub_msg(msg) elif op == 'sub': logger.info( f'<订阅>Topic:{msg["topic"]}订阅成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["cid"]}#') elif op == 'unsub': logger.info( f'<订阅>Topic:{msg["topic"]}取消订阅成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["cid"]}#') elif op == 'req': logger.info(f'<请求>Topic:{msg["topic"]}请求数据成功 #{msg["cid"]}#') OnRsp = self._req_callbacks.get(msg['topic'], []) def callbackThread(_m): for cb in OnRsp: try: cb(_m) except Exception as e: logger.error(f'<请求回调>{msg["topic"]}的回调函数{cb.__name__}异常-{e}') task = self._threadPool.submit(callbackThread, msg) # _t = Thread(target=callbackThread, args=(msg,)) # _t.setDaemon(True) # _t.start() elif op == 'auth': logger.info( f'<鉴权>鉴权成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["cid"]}#') for cb in self._auth_callbacks: cb() else: logger.error( f'<错误>{msg.get("cid")}-OP:{op} ErrTime:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} ErrCode:{msg["err-code"]} ErrMsg:{msg["err-msg"]}' ) def pub_msg(self, msg): """核心的处理函数，如果是handle_func直接处理，如果是handler，推送到handler的队列""" topic = msg.get('topic') self.pub_socket.send_multipart( [pickle.dumps(topic), pickle.dumps(msg)]) for h in self._handle_funcs.get(topic, []): h(msg) def auth(self, cid:str =''): from .utils import ACCESS_KEY, SECRET_KEY, createSign timestamp = dt.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%S') params = { "AccessKeyId": ACCESS_KEY, "SignatureMethod": "HmacSHA256", "SignatureVersion": "2", "Timestamp": timestamp,} signature = createSign(params, 'GET', self._host, self._path, SECRET_KEY) params['Signature'] = signature params['op'] = 'auth' params['cid'] = cid self.send_message(params) return 'auth', cid def sub_accounts(self, cid:str=''): msg = {'op': 'sub', 'cid': cid, 'topic': 'accounts'} self.send_message(msg) logger.info(f'<订阅>accouts-发送订阅请求 #{cid}#') return msg['topic'], cid def unsub_accounts(self, cid:str=''): msg = {'op': 'unsub', 'cid': cid, 'topic': 'accounts'} self.send_message(msg) logger.info(f'<订阅>accouts-发送订阅取消请求 #{cid}#') return msg['topic'], cid def sub_orders(self, symbol='*', cid:str=''): """ :param symbol: '*'为订阅所有订单变化 :param cid: :return: """ msg = {'op': 'sub', 'cid': cid, 'topic': f'orders.{symbol}'} self.send_message(msg) logger.info(f'<订阅>orders-发送订阅请求*{symbol}* #{cid}#') return msg['topic'], cid def unsub_orders(self, symbol='*', cid:str=''): """ :param symbol: '*'为订阅所有订单变化 :param cid: :return: """ msg = {'op': 'unsub', 'cid': cid, 'topic': f'orders.{symbol}'} self.send_message(msg) logger.info(f'<订阅>orders-发送取消订阅请求*{symbol}* #{cid}#') return msg['topic'], cid # ------------------------------------------------------------------------ # ----------------------帐户请求函数-------------------------------------- def req_accounts(self, cid:str=''): msg = {'op': 'req', 'cid': cid, 'topic': 'accounts.list'} self.send_message(msg) logger.info(f'<请求>accounts-发送请求 #{cid}#') return msg['topic'], cid def req_orders(self, acc_id, symbol, states:list, types:list=None, start_date=None, end_date=None, _from=None, direct=None, size=None, cid:str=''): states = ','.join(states) msg = {'op': 'req', 'account-id': acc_id, 'symbol': symbol, 'states': states, 'cid': cid, 'topic': 'orders.list'} if types: types = ','.join(types) msg['types'] = types if start_date: start_date = parser.parse(start_date).strftime('%Y-%m-%d') msg['start-date'] = start_date if end_date: end_date = parser.parse(end_date).strftime('%Y-%m-%d') msg['end-date'] = end_date if _from: msg['_from'] = _from if direct: msg['direct'] = direct if size: msg['size'] = size self.send_message(msg) logger.info(f'<请求>orders-发送请求 #{cid}#') return msg['topic'], cid def req_orders_detail(self, order_id, cid:str=''): msg = {'op': 'req', 'order-id': order_id, 'cid': cid, 'topic': 'orders.detail'} self.send_message(msg) logger.info(f'<请求>accounts-发送请求 #{cid}#') return msg['topic'], cid def after_auth(self,_func): # ws开启之后需要完成的初始化处理 @wraps(_func) def _callback(): try: _func() except Exception as e: logger.exception(f'afer_open回调处理错误{e}') self._auth_callbacks.append(_callback) return _callback class _HBWS(BaseWebsocket): def __init__(self, host='api.huobi.br.com', reconn=10, interval=3): self._protocol = 'wss://' self._host = host self._path = '/ws' self.addr = self._protocol + self._host + self._path self._threadPool = ThreadPoolExecutor(max_workers=3) # self.name = f'HuoBiWS{self.ws_count}' self.name = f'HuoBiWS_{uuid.uuid1()}' self.sub_dict = {} # 订阅列表 self._handlers = [] # 对message做处理的处理函数或处理类 self._req_callbacks = {} self._handle_funcs = {} self._open_callbacks = [] self.ctx = zmq_ctx self.pub_socket = self.ctx.socket(zmq.PUB) self.pub_socket.bind(f'inproc://{self.name}') self._active = False self._reconn = reconn self._interval = interval def on_open(self): self._active = True logger.info(f'<连接>建立与{self.addr}的连接') for topic, subbed in self.sub_dict.items(): msg = {'sub': subbed['topic'], 'id': subbed['id']} self.send_message(msg) else: logger.info(f'<订阅>初始化订阅完成') for fun in self._open_callbacks: fun() def on_message(self, _msg): # 接收ws的消息推送并处理，包括了pingpong，处理订阅列表，以及处理数据推送 json_data = gz.decompress(_msg).decode() msg = json.loads(json_data) logger.debug(f'{msg}') if 'ping' in msg: pong = {'pong': msg['ping']} self.send_message(pong) elif 'status' in msg: if msg['status'] == 'ok': if 'subbed' in msg: self.sub_dict.update({ msg['subbed']: { 'topic': msg['subbed'], 'id': msg['id'] } }) logger.info( f'<订阅>Topic:{msg["subbed"]}订阅成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["id"]}#' ) elif 'unsubbed' in msg: self.sub_dict.pop(msg['unsubbed']) logger.info( f'<订阅>Topic:{msg["unsubbed"]}取消订阅成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["id"]}#' ) elif 'rep' in msg: logger.info(f'<请求>Topic:{msg["rep"]}请求数据成功 #{msg["id"]}#') OnRsp = self._req_callbacks.get(msg['rep'], []) def callbackThread(_m): for cb in OnRsp: try: cb(_m) except Exception as e: logger.error(f'<请求回调>{msg["rep"]}的回调函数{cb.__name__}异常-{e}') task = self._threadPool.submit(callbackThread, msg) elif 'data' in msg: self.pub_msg(msg) # _t = Thread(target=callbackThread, args=(msg, )) # _t.setDaemon(True) # _t.start() elif msg['status'] == 'error': logger.error( f'<错误>{msg.get("id")}-ErrTime:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} ErrCode:{msg["err-code"]} ErrMsg:{msg["err-msg"]}' ) else: self.pub_msg(msg) def pub_msg(self, msg): """核心的处理函数，如果是handle_func直接处理，如果是handler，推送到handler的队列""" if 'ch' in msg: topic = msg.get('ch') self.pub_socket.send_multipart( [pickle.dumps(topic), pickle.dumps(msg)]) for h in self._handle_funcs.get(topic, []): h(msg) @staticmethod def _check_info(**kwargs): log = [] if 'period' in kwargs and kwargs['period'] not in u.PERIOD: log.append(f'<验证>不存在Period:{kwargs["period"]}') if 'depth' in kwargs and kwargs['depth'] not in u.DEPTH: log.append(f'<验证>不存在Depth:{kwargs["depth"]}') if log: for l in log: logger.warning(l) return False else: return True # ----------------------行情订阅函数--------------------------------------- def sub_overview(self, _id=''): msg = {'sub': 'market.overview', 'id': _id} self.send_message(msg) logger.info(f'<订阅>overview-发送订阅请求 #{_id}#') return msg['sub'], _id def unsub_overview(self, _id=''): msg = {'unsub': 'market.overview', 'id': _id} self.send_message(msg) logger.info(f'<订阅>overview-发送取消订阅请求 #{_id}#') return msg['unsub'], _id def sub_kline(self, symbol, period, _id=''): if self._check_info(symbol=symbol, period=period): msg = {'sub': f'market.{symbol}.kline.{period}', 'id': _id} self.send_message(msg) logger.info(f'<订阅>kline-发送订阅请求*{symbol}*@{period} #{_id}#') return msg['sub'], _id def unsub_kline(self, symbol, period, _id=''): if self._check_info(symbol=symbol, period=period): msg = {'unsub': f'market.{symbol}.kline.{period}', 'id': _id} self.send_message(msg) logger.info(f'<订阅>kline-发送取消订阅请求*{symbol}*@{period} #{_id}#') return msg['unsub'], _id def sub_depth(self, symbol, depth=0, _id=''): if self._check_info(symbol=symbol, depth=depth): msg = {'sub': f'market.{symbol}.depth.{u.DEPTH[depth]}', 'id': _id} self.send_message(msg) logger.info(f'<订阅>depth-发送订阅请求*{symbol}*@{u.DEPTH[depth]} #{_id}#') return msg['sub'], _id def unsub_depth(self, symbol, depth=0, _id=''): if self._check_info(symbol=symbol, depth=depth): msg = { 'unsub': f'market.{symbol}.depth.{u.DEPTH[depth]}', 'id': _id } self.send_message(msg) logger.info( f'<订阅>depth-发送取消订阅请求*{symbol}*@{u.DEPTH[depth]} #{_id}#') return msg['unsub'], _id def sub_tick(self, symbol, _id=''): if self._check_info(symbol=symbol): msg = {'sub': f'market.{symbol}.trade.detail', 'id': _id} self.send_message(msg) logger.info(f'<订阅>tick-发送订阅请求*{symbol}* #{_id}#') return msg['sub'], _id def unsub_tick(self, symbol, _id=''): if self._check_info(symbol=symbol): msg = {'unsub': f'market.{symbol}.trade.detail', 'id': _id} self.send_message(msg) logger.info(f'<订阅>tick-发送取消订阅请求*{symbol}* #{_id}#') return msg['unsub'], _id def sub_all_lastest_24h_ohlc(self, _id=''): msg = {'sub': f'market.tickers', 'id': _id} self.send_message(msg) logger.info(f'<订阅>all_ticks-发送订阅请求 #{_id}#') return msg['sub'], _id def unsub_all_lastest_24h_ohlc(self, _id=''): msg = {'unsub': f'market.tickers', 'id': _id} self.send_message(msg) logger.info(f'<订阅>all_ticks-发送取消订阅请求 #{_id}#') return msg['unsub'], _id # ------------------------------------------------------------------------- # -------------------------行情请求函数---------------------------------------- def req_kline(self, symbol, period, _id='', **kwargs): if self._check_info(symbol=symbol, period=period): msg = {'req': f'market.{symbol}.kline.{period}', 'id': _id} if '_from' in kwargs: _from = parser.parse(kwargs['_from']).timestamp() if isinstance( kwargs['_from'], str) else kwargs['_from'] msg.update({'from': int(_from)}) if '_to' in kwargs: _to = parser.parse(kwargs['_to']).timestamp() if isinstance( kwargs['_to'], str) else kwargs['_to'] msg.update({'to': int(_to)}) self.send_message(msg) logger.info(f'<请求>kline-发送请求*{symbol}*@{period} #{_id}#') return msg['req'], _id def req_depth(self, symbol, depth=0, _id=''): if self._check_info(depth=depth): msg = {'req': f'market.{symbol}.depth.{u.DEPTH[depth]}', 'id': _id} self.send_message(msg) logger.info(f'<请求>depth-发送请求*{symbol}*@{u.DEPTH[depth]} #{_id}#') return msg['req'], _id def req_tick(self, symbol, _id=''): msg = {'req': f'market.{symbol}.trade.detail', 'id': _id} self.send_message(msg) logger.info(f'<请求>tick-发送请求*{symbol}* #{_id}#') return msg['req'], _id def req_symbol(self, symbol, _id=''): msg = {'req': f'market.{symbol}.detail', 'id': _id} self.send_message(msg) logger.info(f'<请求>symbol-发送请求*{symbol}* #{_id}#') return msg['req'], _id # ------------------------------------------------------------------------- def after_open(self,_func): # ws开启之后需要完成的初始化处理 @wraps(_func) def _callback(): try: _func() except Exception as e: logger.exception(f'afer_open回调处理错误{e}') self._open_callbacks.append(_callback) return _callback class _HBDerivativesWS(BaseWebsocket): def __init__(self, host='www.hbdm.com', reconn=10, interval=3): self._protocol = 'wss://' self._host = host self._path = '/ws' self.addr = self._protocol + self._host + self._path self._threadPool = ThreadPoolExecutor(max_workers=3) # self.name = f'HuoBiWS{self.ws_count}' self.name = f'HuoBiDerivativesWS_{uuid.uuid1()}' self.sub_dict = {} # 订阅列表 self._handlers = [] # 对message做处理的处理函数或处理类 self._req_callbacks = {} self._handle_funcs = {} self._open_callbacks = [] self.ctx = zmq_ctx self.pub_socket = self.ctx.socket(zmq.PUB) self.pub_socket.bind(f'inproc://{self.name}') self._active = False self._reconn = reconn self._interval = interval def on_open(self): self._active = True logger.info(f'<连接>建立与{self.addr}的连接') for topic, subbed in self.sub_dict.items(): msg = {'sub': subbed['topic'], 'id': subbed['id']} self.send_message(msg) else: logger.info(f'<订阅>初始化订阅完成') for fun in self._open_callbacks: fun() def on_message(self, _msg): # 接收ws的消息推送并处理，包括了pingpong，处理订阅列表，以及处理数据推送 json_data = gz.decompress(_msg).decode() msg = json.loads(json_data) logger.debug(f'{msg}') if 'ping' in msg: pong = {'pong': msg['ping']} self.send_message(pong) elif 'status' in msg: if msg['status'] == 'ok': if 'subbed' in msg: self.sub_dict.update({ msg['subbed']: { 'topic': msg['subbed'], 'id': msg['id'] } }) logger.info( f'<订阅>Topic:{msg["subbed"]}订阅成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["id"]}#' ) elif 'unsubbed' in msg: self.sub_dict.pop(msg['unsubbed']) logger.info( f'<订阅>Topic:{msg["unsubbed"]}取消订阅成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["id"]}#' ) elif 'rep' in msg: logger.info(f'<请求>Topic:{msg["rep"]}请求数据成功 #{msg["id"]}#') OnRsp = self._req_callbacks.get(msg['rep'], []) def callbackThread(_m): for cb in OnRsp: try: cb(_m) except Exception as e: logger.error(f'<请求回调>{msg["rep"]}的回调函数{cb.__name__}异常-{e}') task = self._threadPool.submit(callbackThread, msg) elif 'data' in msg: self.pub_msg(msg) # _t = Thread(target=callbackThread, args=(msg, )) # _t.setDaemon(True) # _t.start() elif msg['status'] == 'error': logger.error( f'<错误>{msg.get("id")}-ErrTime:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} ErrCode:{msg["err-code"]} ErrMsg:{msg["err-msg"]}' ) else: self.pub_msg(msg) def pub_msg(self, msg): """核心的处理函数，如果是handle_func直接处理，如果是handler，推送到handler的队列""" if 'ch' in msg: topic = msg.get('ch') self.pub_socket.send_multipart( [pickle.dumps(topic), pickle.dumps(msg)]) for h in self._handle_funcs.get(topic, []): h(msg) @staticmethod def _check_info(**kwargs): log = [] if 'period' in kwargs and kwargs['period'] not in u.PERIOD: log.append(f'<验证>不存在Period:{kwargs["period"]}') if 'depth' in kwargs and kwargs['depth'] not in u.DerivativesDEPTH: log.append(f'<验证>不存在Depth:{kwargs["depth"]}') if log: for l in log: logger.warning(l) return False else: return True def sub_kline(self, symbol, period, _id=''): if self._check_info(symbol=symbol, period=period): msg = {'sub': f'market.{symbol}.kline.{period}', 'id': _id} self.send_message(msg) logger.info(f'<订阅>kline-发送订阅请求*{symbol}*@{period} #{_id}#') return msg['sub'], _id def unsub_kline(self, symbol, period, _id=''): if self._check_info(symbol=symbol, period=period): msg = {'unsub': f'market.{symbol}.kline.{period}', 'id': _id} self.send_message(msg) logger.info(f'<订阅>kline-发送取消订阅请求*{symbol}*@{period} #{_id}#') return msg['unsub'], _id def sub_depth(self, symbol, depth=0, _id=''): if self._check_info(symbol=symbol, depth=depth): msg = {'sub': f'market.{symbol}.depth.{u.DEPTH[depth]}', 'id': _id} self.send_message(msg) logger.info(f'<订阅>depth-发送订阅请求*{symbol}*@{u.DEPTH[depth]} #{_id}#') return msg['sub'], _id def unsub_depth(self, symbol, depth=0, _id=''): if self._check_info(symbol=symbol, depth=depth): msg = { 'unsub': f'market.{symbol}.depth.{u.DEPTH[depth]}', 'id': _id } self.send_message(msg) logger.info( f'<订阅>depth-发送取消订阅请求*{symbol}*@{u.DEPTH[depth]} #{_id}#') return msg['unsub'], _id def sub_last_24h_kline(self, symbol, _id=''): msg = {'sub': f'market.{symbol}.detail', 'id': _id} self.send_message(msg) logger.info(f'<订阅>Last_24h_kline-发送订阅请求*{symbol}* #{_id}#') return msg['sub'], _id def unsub_last_24h_kline(self, symbol, _id=''): msg = { 'unsub': f'market.{symbol}.detail', 'id': _id } self.send_message(msg) logger.info( f'<订阅>Last_24h_kline-发送取消订阅请求*{symbol}* #{_id}#') return msg['unsub'], _id def sub_tick(self, symbol, _id=''): if self._check_info(symbol=symbol): msg = {'sub': f'market.{symbol}.trade.detail', 'id': _id} self.send_message(msg) logger.info(f'<订阅>tick-发送订阅请求*{symbol}* #{_id}#') return msg['sub'], _id def unsub_tick(self, symbol, _id=''): if self._check_info(symbol=symbol): msg = {'unsub': f'market.{symbol}.trade.detail', 'id': _id} self.send_message(msg) logger.info(f'<订阅>tick-发送取消订阅请求*{symbol}* #{_id}#') return msg['unsub'], _id # ------------------------------------------------------------------------- # -------------------------行情请求函数---------------------------------------- def req_kline(self, symbol, period, _id='', **kwargs): if self._check_info(symbol=symbol, period=period): msg = {'req': f'market.{symbol}.kline.{period}', 'id': _id} if '_from' in kwargs: _from = parser.parse(kwargs['_from']).timestamp() if isinstance( kwargs['_from'], str) else kwargs['_from'] msg.update({'from': int(_from)}) if '_to' in kwargs: _to = parser.parse(kwargs['_to']).timestamp() if isinstance( kwargs['_to'], str) else kwargs['_to'] msg.update({'to': int(_to)}) self.send_message(msg) logger.info(f'<请求>kline-发送请求*{symbol}*@{period} #{_id}#') return msg['req'], _id def req_tick(self, symbol, _id=''): msg = {'req': f'market.{symbol}.trade.detail', 'id': _id} self.send_message(msg) logger.info(f'<请求>tick-发送请求*{symbol}* #{_id}#') return msg['req'], _id # ------------------------------------------------------------------------- def after_open(self,_func): # ws开启之后需要完成的初始化处理 @wraps(_func) def _callback(): try: _func() except Exception as e: logger.exception(f'afer_open回调处理错误{e}') self._open_callbacks.append(_callback) return _callback class _DerivativesAuthWS(BaseWebsocket): def __init__(self, host='api.hbdm.com', reconn=10, interval=3): self._protocol = 'wss://' self._host = host self._path = '/notification' self.addr = self._protocol + self._host + self._path self._threadPool = ThreadPoolExecutor(max_workers=3) self.name = f'HuoBiDerivativesAuthWS_{uuid.uuid1()}' self.sub_dict = {} # 订阅列表 self._handlers = [] # 对message做处理的处理函数或处理类 self._req_callbacks = {} self._handle_funcs = {} self._auth_callbacks = [] self.ctx = zmq_ctx self.pub_socket = self.ctx.socket(zmq.PUB) self.pub_socket.bind(f'inproc://{self.name}') self._active = False self._reconn = reconn self._interval = interval def on_open(self): self._active = True logger.info(f'<连接>建立与{self.addr}的连接') self.auth() logger.info(f'<鉴权>向{self.addr}发起鉴权请求') def on_message(self, _msg): # 鉴权ws的消息处理 json_data = gz.decompress(_msg).decode() msg = json.loads(json_data) logger.debug(f'{msg}') op = msg['op'] if op == 'ping': pong = {'op': 'pong', 'ts': msg['ts']} self.send_message(pong) if msg.setdefault('err-code', 0) == 0: if op == 'notify': self.pub_msg(msg) elif op == 'sub': logger.info( f'<订阅>Topic:{msg["topic"]}订阅成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["cid"]}#') elif op == 'unsub': logger.info( f'<订阅>Topic:{msg["topic"]}取消订阅成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} #{msg["cid"]}#') elif op == 'req': logger.info(f'<请求>Topic:{msg["topic"]}请求数据成功 #{msg["cid"]}#') OnRsp = self._req_callbacks.get(msg['topic'], []) def callbackThread(_m): for cb in OnRsp: try: cb(_m) except Exception as e: logger.error(f'<请求回调>{msg["topic"]}的回调函数{cb.__name__}异常-{e}') task = self._threadPool.submit(callbackThread, msg) # _t = Thread(target=callbackThread, args=(msg,)) # _t.setDaemon(True) # _t.start() elif op == 'auth': logger.info( f'<鉴权>鉴权成功 Time:{dt.datetime.fromtimestamp(msg["ts"] / 1000)}') for cb in self._auth_callbacks: cb() else: logger.error( f'<错误>{msg.get("cid")}-OP:{op} ErrTime:{dt.datetime.fromtimestamp(msg["ts"] / 1000)} ErrCode:{msg["err-code"]} ErrMsg:{msg["err-msg"]}' ) def pub_msg(self, msg): """核心的处理函数，如果是handle_func直接处理，如果是handler，推送到handler的队列""" topic = msg.get('topic') self.pub_socket.send_multipart( [pickle.dumps(topic), pickle.dumps(msg)]) for h in self._handle_funcs.get(topic, []): h(msg) def auth(self, cid:str =''): from .utils import ACCESS_KEY, SECRET_KEY, createSign timestamp = dt.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%S') params = { "AccessKeyId": ACCESS_KEY, "SignatureMethod": "HmacSHA256", "SignatureVersion": "2", "Timestamp": timestamp,} signature = createSign(params, 'GET', self._host, self._path, SECRET_KEY) params['Signature'] = signature params['op'] = 'auth' params['cid'] = cid params['type'] = 'api' self.send_message(params) return 'auth', cid # def sub_accounts(self, cid:str=''): # msg = {'op': 'sub', 'cid': cid, 'topic': 'accounts'} # self.send_message(msg) # logger.info(f'<订阅>accouts-发送订阅请求 #{cid}#') # return msg['topic'], cid # # def unsub_accounts(self, cid:str=''): # msg = {'op': 'unsub', 'cid': cid, 'topic': 'accounts'} # self.send_message(msg) # logger.info(f'<订阅>accouts-发送订阅取消请求 #{cid}#') # return msg['topic'], cid def sub_orders(self, symbol='*', cid:str=''): """ :param symbol: '*'为订阅所有订单变化 :param cid: :return: """ msg = {'op': 'sub', 'cid': cid, 'topic': f'orders.{symbol}'} self.send_message(msg) logger.info(f'<订阅>orders-发送订阅请求*{symbol}* #{cid}#') return msg['topic'], cid def unsub_orders(self, symbol='*', cid:str=''): """ :param symbol: '*'为订阅所有订单变化 :param cid: :return: """ msg = {'op': 'unsub', 'cid': cid, 'topic': f'orders.{symbol}'} self.send_message(msg) logger.info(f'<订阅>orders-发送取消订阅请求*{symbol}* #{cid}#') return msg['topic'], cid # # ------------------------------------------------------------------------ # # ----------------------帐户请求函数-------------------------------------- # def req_accounts(self, cid:str=''): # msg = {'op': 'req', 'cid': cid, 'topic': 'accounts.list'} # self.send_message(msg) # logger.info(f'<请求>accounts-发送请求 #{cid}#') # return msg['topic'], cid # # def req_orders(self, acc_id, symbol, states:list, # types:list=None, # start_date=None, end_date=None, # _from=None, direct=None, # size=None, cid:str=''): # states = ','.join(states) # msg = {'op': 'req', 'account-id': acc_id, 'symbol': symbol, 'states': states, 'cid': cid, # 'topic': 'orders.list'} # if types: # types = ','.join(types) # msg['types'] = types # # if start_date: # start_date = parser.parse(start_date).strftime('%Y-%m-%d') # msg['start-date'] = start_date # # if end_date: # end_date = parser.parse(end_date).strftime('%Y-%m-%d') # msg['end-date'] = end_date # # if _from: # msg['_from'] = _from # # if direct: # msg['direct'] = direct # # if size: # msg['size'] = size # # self.send_message(msg) # logger.info(f'<请求>orders-发送请求 #{cid}#') # return msg['topic'], cid # # def req_orders_detail(self, order_id, cid:str=''): # msg = {'op': 'req', 'order-id': order_id, 'cid': cid, 'topic': 'orders.detail'} # self.send_message(msg) # logger.info(f'<请求>accounts-发送请求 #{cid}#') # return msg['topic'], cid def after_auth(self,_func): # ws开启之后需要完成的初始化处理 @wraps(_func) def _callback(): try: _func() except Exception as e: logger.exception(f'afer_open回调处理错误{e}') self._auth_callbacks.append(_callback) return _callback

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7

fb4951e3f6f85b70885f23093a8a47325e0b4bfe

118

py

Python

Py60/main.py

xhexe/Py8R

44238c5403e7f76988760a040bf5c292824c22e7

[ "WTFPL" ]

null

Py60/main.py

xhexe/Py8R

44238c5403e7f76988760a040bf5c292824c22e7

[ "WTFPL" ]

null

Py60/main.py

xhexe/Py8R

44238c5403e7f76988760a040bf5c292824c22e7

[ "WTFPL" ]

null

from shutil import copyfile copyfile("/home/xhexe/Py/Py8R/files/text.txt", "/home/xhexe/Py/Py8R/files/textcopy.txt")

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py

Python

1/day1-1.py

das-keyboard/adventofcode-2017

4aa9cc67f2a2be5db1caa808dce00b579bd4d788

[ "Unlicense" ]

null

1/day1-1.py

das-keyboard/adventofcode-2017

4aa9cc67f2a2be5db1caa808dce00b579bd4d788

[ "Unlicense" ]

null

1/day1-1.py

das-keyboard/adventofcode-2017

4aa9cc67f2a2be5db1caa808dce00b579bd4d788

[ "Unlicense" ]

null

def process(data: str): sum = 0 for i in range(0, len(data)): if i == len(data) - 1: if data[i] == data[0]: sum += int(data[i]) else: if data[i] == data[i + 1]: sum += int(data[i]) return sum print(process("1122")) print(process("1111")) print(process("91212129")) print(process("1234")) print("Let's get real!") print(process("9384274494683632359351641411374573466273164687337536769779487433749179185568461296233353611992672753778126935276769885424719553291616136172298883156626254151278852582397949697874462178536295341822137377563322815527592267791213115418635363174876132196234374887626324931371241841242873783493835919238421879116421481543826222278152238576762132577763214642569545298668935216911493462229629786978273548147171384321525952959196377728493632872618291183256888417779495124837828187298244786175872713299271766246696631257484453347125176233373232245382158656142179687576388951175953419286858673221138553912229576523123114871637487978775855777483921896568333282333137175739746234262744256254149233843517254613981476355147487975859685936527161737644929119345127273149762325158784595946931447738173246311763677997888425452294562823751136515271874725143582623717324394587398371298523368386595426714148717735345237657249712685895921433468949182235146698174393928288313985355769799485511749423552935992391624424575278333625476148888355716967628454862834463357834291788479677576561681171516128495737923155533438413156639155128831349894646317546536886319328573512622325789672115171618195548534941184939233914166432349321992879287349932819135919518955561456615989137221875483561599493342981595678961836562435436285673764213941758954489582656271121429555455368545289416981624961261963953364918377483776322142975937971552271642224933926326665557787586927667898255947116988278131974381388514274833852552695679713424836536348449273149415872522111522749448188993159814183411853994579147867385867619467777654943169814287928966652552129439822741856512265955664872454951159255617513136142717471774698224566543617595742753244142364438589729356939483387466363477224283477843889679221229344974441624448489853764111425798141258155246636844914711222931548722647298953744242682551562166463942694715631497895981643174194294826868561578586851326262619731272665397711381459745281218196515155917877694663186732599688912878149242688741584822831861748845817871681621697944472377688658368145698614861456518138376989688166921187224726942589996534179549171859786241718727295379"))

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fb91df10a4703ba9cfc19a232acbd4eb3b699fed

3,408

py

Python

biosys/apps/main/tests/test_download_templates.py

parksandwildlife/biosys

0682cf1b4055e7cae59fb53045fa441af6d48f5e

[ "Apache-2.0" ]

2

2018-04-09T04:02:30.000Z

2019-08-20T03:12:55.000Z

biosys/apps/main/tests/test_download_templates.py

parksandwildlife/biosys

0682cf1b4055e7cae59fb53045fa441af6d48f5e

[ "Apache-2.0" ]

29

2016-01-20T08:14:15.000Z

2017-07-13T07:17:32.000Z

biosys/apps/main/tests/test_download_templates.py

parksandwildlife/biosys

0682cf1b4055e7cae59fb53045fa441af6d48f5e

[ "Apache-2.0" ]

5

2016-01-14T23:02:36.000Z

2016-09-21T05:35:03.000Z

import re from os import path from openpyxl import load_workbook from django.test import TestCase from django.test.client import Client from django.shortcuts import reverse from django.utils import six from rest_framework import status class TestDownloadSiteTemplates(TestCase): def test_lat_long_no_logging(self): """ Test lat-long template download. Important: Logging should not be necessary """ client = Client() url = reverse('download:site-template-lat-long') resp = client.get(url) self.assertEqual(resp.status_code, status.HTTP_200_OK) self.assertEqual(resp.get('content-type'), 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') content_disposition = resp.get('content-disposition') # should be something like: # 'attachment; filename=Sites_template_lat_long.xlsx match = re.match('attachment; filename=(.+)', content_disposition) self.assertIsNotNone(match) filename, ext = path.splitext(match.group(1)) self.assertEqual(ext, '.xlsx') self.assertEqual(filename, 'Sites_template_lat_long') # read content wb = load_workbook(six.BytesIO(resp.content), read_only=True) # one datasheet named 'Sites' expected_sheet_name = 'Sites' sheet_names = wb.sheetnames self.assertEqual(1, len(sheet_names)) self.assertEqual(sheet_names[0], expected_sheet_name) ws = wb[expected_sheet_name] rows = list(ws.rows) # only one row self.assertEqual(len(rows), 1) got_headers = [c.value for c in rows[0]] expected_headers = ['Name', 'Code', 'Description', 'Latitude', 'Longitude', 'Datum'] self.assertEqual(got_headers, expected_headers) def test_easting_northing_no_logging(self): """ Test easting-northing template download. Important: Logging should not be necessary """ client = Client() url = reverse('download:site-template-easting-northing') resp = client.get(url) self.assertEqual(resp.status_code, status.HTTP_200_OK) self.assertEqual(resp.get('content-type'), 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') content_disposition = resp.get('content-disposition') # should be something like: # 'attachment; filename=Sites_template_lat_long.xlsx match = re.match('attachment; filename=(.+)', content_disposition) self.assertIsNotNone(match) filename, ext = path.splitext(match.group(1)) self.assertEqual(ext, '.xlsx') self.assertEqual(filename, 'Sites_template_easting_northing') # read content wb = load_workbook(six.BytesIO(resp.content), read_only=True) # one datasheet named 'Sites' expected_sheet_name = 'Sites' sheet_names = wb.sheetnames self.assertEqual(1, len(sheet_names)) self.assertEqual(sheet_names[0], expected_sheet_name) ws = wb[expected_sheet_name] rows = list(ws.rows) # only one row self.assertEqual(len(rows), 1) got_headers = [c.value for c in rows[0]] expected_headers = ['Name', 'Code', 'Description', 'Easting', 'Northing', 'Datum', 'Zone'] self.assertEqual(got_headers, expected_headers)

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7

fb9b3556074257de7a56a580cd7fe819a540d804

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py

Python

fermi/multiband_bubble.py

maryprimary/frg

e789439f599eb884a6220ae5b471cf610b0c2b2a

[ "MIT" ]

null

fermi/multiband_bubble.py

maryprimary/frg

e789439f599eb884a6220ae5b471cf610b0c2b2a

[ "MIT" ]

12

2021-02-04T06:46:36.000Z

2021-07-01T00:43:38.000Z

fermi/multiband_bubble.py

maryprimary/frg

e789439f599eb884a6220ae5b471cf610b0c2b2a

[ "MIT" ]

null

"""定义在10.112中的bubble integrals """ import warnings import numpy from basics import Point from basics.point import middle_point #pylint: disable=pointless-string-statement #warnings.simplefilter('once', RuntimeWarning) def pi_ab_plus_ec(posia, negaa, lamb, qval, dispb, ksft, area): '''使用能量cutoff作为flow parameter的bubble\n posi是dispa为+LAMBDA的边，nega是dispa为-LAMBDA的边, lamb是LAMBDA\n dispb是第二个色散关系，qval是需要平移的大小，应该用一个Point来包装，\n kshf是动量相加的函数, 这个函数应该能处理好到第一布里渊区的映射\n ```(10.112)本身已经处理好了动量守恒，k, k-q是需要满足动量守恒的关系的，而处理好``` ```k-q到第一布里渊区的映射就处理好了Umklapp``` ''' ''' 10.112中的 PI^+(n, q) = +LAMBDA (2pi)^-2 beta^-1 Int_{k in k_n} G'(k)G(k - Q) 其中有一个beta是频率积分带来的，2pi^2是动量积分带来的 G(k)=CITA(LAMBDA < abs(disp(k))) / i*omega - disp(k) G'(k)=-DELTA(abs(disp(k))-LAMBDA) / i*omege - disp(k) 在零温的情况下10.112中的频率部分可以积分出来，此后的k都是不包含频率的 = +LAMBDA (2pi)^-2 Int_{k in k_n} CITA() -DELTA() { beta^-1 sum_{omega} [(i*omega-disp(k))(i*omega-disp(k - q))]^-1 } 花括号中的内容求和完之后等于 - CITA(-disp(k)disp(k-q)) / (abs(disp(k)) + abs(disp(k-p))) 积分会变成 = +LAMBDA (2pi)^-2 Int_{k in k_n} DELTA(abs(disp(k))-LAMBDA) CITA(LAMBDA<abs(disp(k-q))) CITA(-disp(k)disp(k-q)) / (abs(disp(k)) + abs(disp(k-p))) 因为采用的能量cutoff中有一个 DELTA(abs(disp(k))-LAMBDA)，disp(k)等于正的或者负的LAMBDA 而CITA(-disp(k)disp(k-q))限制了disp(k)和disp(k-q)符号相反所以上式变成 (第一项disp(k)=LAMBDA>0，于是disp(k-q)<0，而且abs(disp(k))=-disp(k)>LAMBDA) (第二项类似，分子中的abs(disp(k))都可以直接换成LAMBDA，abs(disp(k-q))也都知道符号) = +LAMBDA (2pi)^-2 Int_{k in kn} { DELTA(disp(k)-LAMBDA)CITA(-disp(k-q)-LAMBDA) / (LAMBDA - disp(k - q)) DELTA(disp(k)+LAMBDA)CITA(disp(k-q)-LAMBDA) / (LAMBDA + disp(k - q)) } 还可以从积分里面把DELTA给积分掉，这样对于二维平面的积分也会变成对 disp(k) = LAMBDA 或者 -LAMBDA的线的积分 = +LAMBDA (2pi)^-2 * [Int_{disp(k) = +LAMBDA} CITA(-disp(k-q)-LAMBDA) / (LAMBDA - disp(k - q))] +[Int_{disp(k) = -LAMBDA} CITA(disp(k-q)-LAMBDA) / (LAMBDA + disp(k - q)) ] ''' nega_q = Point(-qval.coord[0], -qval.coord[1], 1) #积分正LAMBDA的线 intposi = 0. for edg in posia: kval = middle_point(edg.ends[0], edg.ends[1]) kprim = ksft(kval, nega_q) #CITA disp_kprim = dispb(kprim.coord[0], kprim.coord[1]) if -disp_kprim < lamb: continue #线积分，计算线元的长度 intposi += edg.length / (lamb - disp_kprim) #积分负LAMBDA的线 intnega = 0. for edg in negaa: kval = middle_point(edg.ends[0], edg.ends[1]) kprim = ksft(kval, nega_q) #CITA disp_kprim = dispb(kprim.coord[0], kprim.coord[1]) if disp_kprim < lamb: continue intnega += edg.length / (lamb + disp_kprim) #乘上系数 result = lamb * (intposi + intnega) / area#numpy.square(numpy.pi*2) return result def pi_ab_minus_ec(posia, negaa, lamb, qval, dispb, ksft, area): '''使用能量cutoff作为flow parameter的bubble\n posi是dispa为+LAMBDA的边，nega是dispa为-LAMBDA的边, lamb是LAMBDA\n 这两个边应该是限制在dispa这个带的第n个patch中的，这两个边也就暗含了n\n dispa和dispb是两个带的色散关系\n qval是需要平移的大小，应该用一个Point来包装，\n kshf是动量相加的函数, 这个函数应该能处理好到第一布里渊区的映射\n ```(10.112)本身已经处理好了动量守恒，k, k-q是需要满足动量守恒的关系的，而处理好``` ```k-q到第一布里渊区的映射就处理好了Umklapp``` ''' ''' 10.112中的 PI^-(n, q) = -LAMBDA (2pi)^-2 beta^-1 Int_{k in k_n} G'(k)G(- k + Q) = -LAMBDA (2pi)^-2 Int_{k in k_n} CITA() -DELTA() { beta^-1 sum_{omega} [(i*omega-disp(k))(-i*omega-disp(-k + q))]^-1 } 在零温下这个频率积分等于，注意-k那里把频率也给反过来了 +CITA(+disp(k)disp(-k+q)) / (abs(disp(k)) + abs(disp(-k+q))) 原式就等于 = LAMBDA (2pi)^-2 Int_{k in k_n} { DELTA(abs(disp(k))-LAMBDA) CITA(abs(disp(-k+q)-LAMBDA)) CITA(disp(k)disp(-k+q)) / (abs(disp(k)) + abs(disp(-k+q))) } 第二个CITA限制了disp(k)和disp(-k+q)同号，积分积掉DELTA，分类讨论正负 = LAMBDA (2pi)^-2 { Int_{disp(k) = +LAMBDA} CITA(disp(-k+q) - LAMBDA) / (LAMBDA + disp(-k+q)) + Int_{disp(k) = -LAMBDA} CITA(-disp(-k+q) -LAMBDA) / (LAMBDA - disp(-k+q)) } ''' #积分正LAMBDA的线 intposi = 0. for edg in posia: kval = middle_point(edg.ends[0], edg.ends[1]) nega_k = Point(-kval.coord[0], -kval.coord[1], 1) kprim = ksft(nega_k, qval) #CITA disp_kprim = dispb(kprim.coord[0], kprim.coord[1]) if disp_kprim < lamb: continue #要计算线元的长度 intposi += edg.length / (lamb + disp_kprim) #积分负LAMBDA的线 intnega = 0. for edg in negaa: kval = middle_point(edg.ends[0], edg.ends[1]) nega_k = Point(-kval.coord[0], -kval.coord[1], 1) kprim = ksft(nega_k, qval) #CITA disp_kprim = dispb(kprim.coord[0], kprim.coord[1]) if -disp_kprim < lamb: continue intnega += edg.length / (lamb - disp_kprim) #乘上系数 result = lamb * (intposi + intnega) / area#numpy.square(numpy.pi*2) return result def pi_ab_plus_tf(ltris, tarea, lamb, dispa, dispb, qval, ksft, area): '''温度流的+ 这里的lamb就是T，ltris中的所有三角都应该要在同一个patch中, tarea是每个小三角形的面积，dispa是和k相关的那个能带，dispb是k-q相关的 ''' nega_q = Point(-qval.coord[0], -qval.coord[1], 1) result = 0. for tri in ltris: #这个小三角形的k值 kval = tri.center #k-q kprim = ksft(kval, nega_q) #epsilon_k eps_k = dispa(kval.coord[0], kval.coord[1]) #epsilon_{k-q} eps_kp = dispb(kprim.coord[0], kprim.coord[1]) if numpy.abs(eps_k - eps_kp) < 1.e-10: #如果特别小，可以利用 # lim (eps_k -> eps_kp) Pi^{+} = # 1/T (e^{eps/T} (-eps/T*e^{eps/T} + eps/T + e^{eps/T} + 1)) / (e^{eps/T} + 1)^3 bval = eps_kp / lamb #如果本身就很大，分母会比较大导致接近0 if bval > 25: warnings.warn("数值不稳定", RuntimeWarning) return 0. expb = numpy.exp(bval) num = expb * (-bval * expb + bval + expb + 1) den = numpy.power((1+expb), 3) d_val = num / den / lamb else: if (eps_k / lamb) > 25: warnings.warn("数值不稳定", RuntimeWarning) num_left = 0. else: #exp^{epsilon_k / T} exp_k_t = numpy.exp(eps_k / lamb) num_left = eps_k / lamb * exp_k_t / numpy.square(1 + exp_k_t) if (eps_kp / lamb) > 25: warnings.warn("数值不稳定", RuntimeWarning) num_righ = 0. else: #e^{epsilon_{k-q} / T} exp_kp_t = numpy.exp(eps_kp / lamb) num_righ = eps_kp / lamb * exp_kp_t\ / numpy.square(1 + exp_kp_t) d_val = (num_left - num_righ) / (eps_k - eps_kp) result += d_val * tarea result = result / area return result def pi_ab_minus_tf(ltris, tarea, lamb, dispa, dispb, qval, ksft, area): '''温度流的- 这里的lamb就是T，ltris中的所有三角都应该要在同一个patch中, tarea是每个小三角形的面积，dispa是和k相关的那个能带，dispb是-k+q相关的 ''' result = 0. for tri in ltris: #这个小三角形的k值 kval = tri.center nega_k = Point(-kval.coord[0], -kval.coord[1], 1) #-k+q kprim = ksft(nega_k, qval) #epsilon_k eps_k = dispa(kval.coord[0], kval.coord[1]) #-epsilon_{-k+q} neps_kp = -dispb(kprim.coord[0], kprim.coord[1]) #这个时候，因为epsilon_{-k+q}前面已经有了负号，分母上还是负号 if numpy.abs(eps_k - neps_kp) < 1.e-10: #如果两个数值比较接近, Pi^{-}和Pi^{+}的公式完全一样，就是第二个能量要加个负号 # lim (eps_k -> -eps_kp) Pi^{-} = # 1/T (e^{eps/T} (-eps/T*e^{eps/T} + eps/T + e^{eps/T} + 1)) / (e^{eps/T} + 1)^3 bval = eps_k / lamb if bval > 25: warnings.warn("数值不稳定", RuntimeWarning) return 0. expb = numpy.exp(bval) num = expb * (-bval * expb + bval + expb + 1) den = numpy.power((1+expb), 3) d_val = num / den / lamb else: if (eps_k / lamb) > 25: warnings.warn("数值不稳定", RuntimeWarning) num_left = 0. else: #e^{epsilon_k / T} exp_k_t = numpy.exp(eps_k / lamb) num_left = eps_k / lamb * exp_k_t / numpy.square(1 + exp_k_t) if (neps_kp / lamb) > 25: warnings.warn("数值不稳定", RuntimeWarning) num_righ = 0. else: #e^{-epsilon_{-k+q} / T} exp_nkp_t = numpy.exp(neps_kp / lamb) num_righ = neps_kp / lamb * exp_nkp_t\ / numpy.square(1 + exp_nkp_t) d_val = (num_left - num_righ) / (eps_k - neps_kp) result += d_val * tarea result = result / area return result def val_test(eps_k, neps_kp, lamb): #如果两个数值比较接近, Pi^{-}和Pi^{+}的公式完全一样，就是第二个能量要加个负号 # lim (eps_k -> -eps_kp) Pi^{-} = # 1/T (e^{eps/T} (-eps/T*e^{eps/T} + eps/T + e^{eps/T} + 1)) / (e^{eps/T} + 1)^3 bval = eps_k / lamb expb = numpy.exp(bval) num = expb * (-bval * expb + bval + expb + 1) den = numpy.power((1+expb), 3) d_val1 = num / den / lamb # #e^{epsilon_k / T} exp_k_t = numpy.exp(eps_k / lamb) #e^{-epsilon_{-k+q} / T} exp_nkp_t = numpy.exp(neps_kp / lamb) # num_left = eps_k / lamb * exp_k_t / numpy.square(1 + exp_k_t) num_righ = neps_kp / lamb * exp_nkp_t\ / numpy.square(1 + exp_nkp_t) #e^{epsilon_k / T} exp_k_t = numpy.exp(eps_k / lamb) d_val2 = (num_left - num_righ) / (eps_k - neps_kp) return d_val1, d_val2

37.284585

92

0.554331

1,339

9,433

3.772218

0.135176

0.054445

0.028509

0.010691

0.819244

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0

0.023838

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9,433

252

93

37.43254

0.724015

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0

1

0

1

0

null

0

7

fbd7d19e4361b4677c90c5cfedc3747f39479464

11,114

py

Python

tests/integration/test_linked_objects_it.py

corylevine/okta-sdk-python

c86b8fdc4525e84199143c27213c0aebc6b2af8f

[ "Apache-2.0" ]

145

2017-06-13T21:54:04.000Z

2022-02-25T05:44:34.000Z

tests/integration/test_linked_objects_it.py

corylevine/okta-sdk-python

c86b8fdc4525e84199143c27213c0aebc6b2af8f

[ "Apache-2.0" ]

146

2017-06-02T17:46:12.000Z

2022-03-29T15:52:15.000Z

tests/integration/test_linked_objects_it.py

corylevine/okta-sdk-python

c86b8fdc4525e84199143c27213c0aebc6b2af8f

[ "Apache-2.0" ]

98

2017-06-27T03:44:51.000Z

2022-03-23T04:58:18.000Z

import pytest from tests.mocks import MockOktaClient import okta.models as models from http import HTTPStatus from okta.errors.okta_api_error import OktaAPIError class TestLinkedObjectsResource: """ Integration Tests for the Linked Objects Resource """ SDK_PREFIX = "python_sdk" @pytest.mark.vcr() @pytest.mark.asyncio async def test_add_get_linked_object(self, fs): # Instantiate Mock Client client = MockOktaClient(fs) # Add Linked Object definition linked_object_model = models.LinkedObject({ "primary": models.LinkedObjectDetails({ "name": f"{TestLinkedObjectsResource.SDK_PREFIX}_primary_test", "title": "Primary", "description": "Primary Link Property", "type": models.LinkedObjectDetailsType.USER }), "associated": models.LinkedObjectDetails({ "name": f"{TestLinkedObjectsResource.SDK_PREFIX}_assoc_test", "title": "Associated", "description": "Associated Link Property", "type": models.LinkedObjectDetailsType.USER }) }) try: created_linked_object_definition, _, err = await client.\ add_linked_object_definition(linked_object_model) assert err is None assert isinstance(created_linked_object_definition, models.LinkedObject) assert created_linked_object_definition.primary assert created_linked_object_definition.associated # Retrieve by Primary Name retrieved_linked_object_definition, _, err = await \ client.get_linked_object_definition( linked_object_model.primary.name) assert err is None assert isinstance(retrieved_linked_object_definition, models.LinkedObject) assert retrieved_linked_object_definition.primary.name ==\ created_linked_object_definition.primary.name assert retrieved_linked_object_definition.associated.name ==\ created_linked_object_definition.associated.name assert retrieved_linked_object_definition.primary.title ==\ created_linked_object_definition.primary.title assert retrieved_linked_object_definition.associated.title ==\ created_linked_object_definition.associated.title assert retrieved_linked_object_definition.primary.type ==\ created_linked_object_definition.primary.type assert retrieved_linked_object_definition.associated.type ==\ created_linked_object_definition.associated.type # Retrieve by Associated Name retrieved_linked_object_definition, _, err = await \ client.get_linked_object_definition( linked_object_model.associated.name) assert err is None assert isinstance(retrieved_linked_object_definition, models.LinkedObject) assert retrieved_linked_object_definition.primary.name ==\ created_linked_object_definition.primary.name assert retrieved_linked_object_definition.associated.name ==\ created_linked_object_definition.associated.name assert retrieved_linked_object_definition.primary.title ==\ created_linked_object_definition.primary.title assert retrieved_linked_object_definition.associated.title ==\ created_linked_object_definition.associated.title assert retrieved_linked_object_definition.primary.type ==\ created_linked_object_definition.primary.type assert retrieved_linked_object_definition.associated.type ==\ created_linked_object_definition.associated.type finally: # Delete Linked Object definition _, err = await \ client.delete_linked_object_definition( linked_object_model.primary.name) assert err is None @pytest.mark.vcr() @pytest.mark.asyncio async def test_get_all_linked_objects(self, fs): # Instantiate Mock Client client = MockOktaClient(fs) # Add Linked Object definition linked_object_model_1 = models.LinkedObject({ "primary": models.LinkedObjectDetails({ "name": f"{TestLinkedObjectsResource.SDK_PREFIX}_primary_t1", "title": "Primary", "description": "Primary Link Property", "type": models.LinkedObjectDetailsType.USER }), "associated": models.LinkedObjectDetails({ "name": f"{TestLinkedObjectsResource.SDK_PREFIX}_assoc_t1", "title": "Associated", "description": "Associated Link Property", "type": models.LinkedObjectDetailsType.USER }) }) linked_object_model_2 = models.LinkedObject({ "primary": models.LinkedObjectDetails({ "name": f"{TestLinkedObjectsResource.SDK_PREFIX}_primary_t2", "title": "Primary", "description": "Primary Link Property", "type": models.LinkedObjectDetailsType.USER }), "associated": models.LinkedObjectDetails({ "name": f"{TestLinkedObjectsResource.SDK_PREFIX}_assoc_t2", "title": "Associated", "description": "Associated Link Property", "type": models.LinkedObjectDetailsType.USER }) }) try: created_linked_object_definition, _, err = await client.\ add_linked_object_definition(linked_object_model_1) assert err is None assert isinstance(created_linked_object_definition, models.LinkedObject) assert created_linked_object_definition.primary assert created_linked_object_definition.associated created_linked_object_definition_2, _, err = await client.\ add_linked_object_definition(linked_object_model_2) assert err is None assert isinstance(created_linked_object_definition_2, models.LinkedObject) assert created_linked_object_definition_2.primary assert created_linked_object_definition_2.associated # List all_linked_obj_defs, _, err = await\ client.list_linked_object_definitions() assert err is None assert len(all_linked_obj_defs) > 0 assert next((lo for lo in all_linked_obj_defs if linked_object_model_1.primary.name == lo.primary.name)) assert next((lo for lo in all_linked_obj_defs if linked_object_model_2.primary.name == lo.primary.name)) finally: errors = [] # Delete Linked Object definition try: _, err = await \ client.delete_linked_object_definition( linked_object_model_1.primary.name) assert err is None except Exception as exc: errors.append(exc) try: _, err = await \ client.delete_linked_object_definition( linked_object_model_2.primary.name) assert err is None except Exception as exc: errors.append(exc) assert len(errors) == 0 @pytest.mark.vcr() @pytest.mark.asyncio async def test_delete_linked_object(self, fs): # Instantiate Mock Client client = MockOktaClient(fs) # Add Linked Object definition linked_object_model = models.LinkedObject({ "primary": models.LinkedObjectDetails({ "name": f"{TestLinkedObjectsResource.SDK_PREFIX}_primary_test", "title": "Primary", "description": "Primary Link Property", "type": models.LinkedObjectDetailsType.USER }), "associated": models.LinkedObjectDetails({ "name": f"{TestLinkedObjectsResource.SDK_PREFIX}_assoc_test", "title": "Associated", "description": "Associated Link Property", "type": models.LinkedObjectDetailsType.USER }) }) try: created_linked_object_definition, _, err = await client.\ add_linked_object_definition(linked_object_model) assert err is None assert isinstance(created_linked_object_definition, models.LinkedObject) assert created_linked_object_definition.primary assert created_linked_object_definition.associated # Retrieve by Primary Name retrieved_linked_object_definition, _, err = await \ client.get_linked_object_definition( linked_object_model.primary.name) assert err is None assert isinstance(retrieved_linked_object_definition, models.LinkedObject) assert retrieved_linked_object_definition.primary.name ==\ created_linked_object_definition.primary.name assert retrieved_linked_object_definition.associated.name ==\ created_linked_object_definition.associated.name assert retrieved_linked_object_definition.primary.title ==\ created_linked_object_definition.primary.title assert retrieved_linked_object_definition.associated.title ==\ created_linked_object_definition.associated.title assert retrieved_linked_object_definition.primary.type ==\ created_linked_object_definition.primary.type assert retrieved_linked_object_definition.associated.type ==\ created_linked_object_definition.associated.type # Delete Linked Object definition _, err = await \ client.delete_linked_object_definition( linked_object_model.primary.name) # Retrieve by Primary Name retrieved_linked_object_definition, resp, err = await \ client.get_linked_object_definition( linked_object_model.primary.name) assert err is not None assert isinstance(err, OktaAPIError) assert resp.get_status() == HTTPStatus.NOT_FOUND assert retrieved_linked_object_definition is None finally: # Delete Linked Object definition try: _, err = await \ client.delete_linked_object_definition( linked_object_model.primary.name) except Exception: pass

44.995951

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11,114

6.453915

0.09217

0.187961

0.27027

0.151413

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0.878225

0.869932

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0

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0.320317

11,114

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0.004831

0.024155

0

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1

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0

8

83b431dc38202ad69d6c8a435c77cec889045689

10,725

py

Python

nova/api/openstack/compute/schemas/servers.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

nova/api/openstack/compute/schemas/servers.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

nova/api/openstack/compute/schemas/servers.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

2

2017-07-20T17:31:34.000Z

2020-07-24T02:42:19.000Z

begin_unit comment|'# Copyright 2014 NEC Corporation. All rights reserved.' nl|'\n' comment|'#' nl|'\n' comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may' nl|'\n' comment|'# not use this file except in compliance with the License. You may obtain' nl|'\n' comment|'# a copy of the License at' nl|'\n' comment|'#' nl|'\n' comment|'# http://www.apache.org/licenses/LICENSE-2.0' nl|'\n' comment|'#' nl|'\n' comment|'# Unless required by applicable law or agreed to in writing, software' nl|'\n' comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT' nl|'\n' comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the' nl|'\n' comment|'# License for the specific language governing permissions and limitations' nl|'\n' comment|'# under the License.' nl|'\n' nl|'\n' name|'import' name|'copy' newline|'\n' nl|'\n' name|'from' name|'nova' op|'.' name|'api' op|'.' name|'validation' name|'import' name|'parameter_types' newline|'\n' nl|'\n' nl|'\n' DECL|variable|base_create name|'base_create' op|'=' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'server'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'name'" op|':' name|'parameter_types' op|'.' name|'name' op|',' nl|'\n' string|"'imageRef'" op|':' name|'parameter_types' op|'.' name|'image_ref' op|',' nl|'\n' string|"'flavorRef'" op|':' name|'parameter_types' op|'.' name|'flavor_ref' op|',' nl|'\n' string|"'adminPass'" op|':' name|'parameter_types' op|'.' name|'admin_password' op|',' nl|'\n' string|"'metadata'" op|':' name|'parameter_types' op|'.' name|'metadata' op|',' nl|'\n' string|"'networks'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'array'" op|',' nl|'\n' string|"'items'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'fixed_ip'" op|':' name|'parameter_types' op|'.' name|'ip_address' op|',' nl|'\n' string|"'port'" op|':' op|'{' nl|'\n' string|"'oneOf'" op|':' op|'[' op|'{' string|"'type'" op|':' string|"'string'" op|',' string|"'format'" op|':' string|"'uuid'" op|'}' op|',' nl|'\n' op|'{' string|"'type'" op|':' string|"'null'" op|'}' op|']' nl|'\n' op|'}' op|',' nl|'\n' string|"'uuid'" op|':' op|'{' string|"'type'" op|':' string|"'string'" op|'}' op|',' nl|'\n' op|'}' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' nl|'\n' op|'}' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'name'" op|',' string|"'flavorRef'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' op|',' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'server'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' newline|'\n' nl|'\n' nl|'\n' DECL|variable|base_create_v20 name|'base_create_v20' op|'=' name|'copy' op|'.' name|'deepcopy' op|'(' name|'base_create' op|')' newline|'\n' name|'base_create_v20' op|'[' string|"'properties'" op|']' op|'[' string|"'server'" op|']' op|'[' nl|'\n' string|"'properties'" op|']' op|'[' string|"'name'" op|']' op|'=' name|'parameter_types' op|'.' name|'name_with_leading_trailing_spaces' newline|'\n' nl|'\n' nl|'\n' DECL|variable|base_create_v219 name|'base_create_v219' op|'=' name|'copy' op|'.' name|'deepcopy' op|'(' name|'base_create' op|')' newline|'\n' name|'base_create_v219' op|'[' string|"'properties'" op|']' op|'[' string|"'server'" op|']' op|'[' nl|'\n' string|"'properties'" op|']' op|'[' string|"'description'" op|']' op|'=' name|'parameter_types' op|'.' name|'description' newline|'\n' nl|'\n' nl|'\n' DECL|variable|base_update name|'base_update' op|'=' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'server'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'name'" op|':' name|'parameter_types' op|'.' name|'name' op|',' nl|'\n' op|'}' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' op|',' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'server'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' newline|'\n' nl|'\n' nl|'\n' DECL|variable|base_update_v20 name|'base_update_v20' op|'=' name|'copy' op|'.' name|'deepcopy' op|'(' name|'base_update' op|')' newline|'\n' name|'base_update_v20' op|'[' string|"'properties'" op|']' op|'[' string|"'server'" op|']' op|'[' nl|'\n' string|"'properties'" op|']' op|'[' string|"'name'" op|']' op|'=' name|'parameter_types' op|'.' name|'name_with_leading_trailing_spaces' newline|'\n' nl|'\n' DECL|variable|base_update_v219 name|'base_update_v219' op|'=' name|'copy' op|'.' name|'deepcopy' op|'(' name|'base_update' op|')' newline|'\n' name|'base_update_v219' op|'[' string|"'properties'" op|']' op|'[' string|"'server'" op|']' op|'[' nl|'\n' string|"'properties'" op|']' op|'[' string|"'description'" op|']' op|'=' name|'parameter_types' op|'.' name|'description' newline|'\n' nl|'\n' DECL|variable|base_rebuild name|'base_rebuild' op|'=' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'rebuild'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'name'" op|':' name|'parameter_types' op|'.' name|'name' op|',' nl|'\n' string|"'imageRef'" op|':' name|'parameter_types' op|'.' name|'image_ref' op|',' nl|'\n' string|"'adminPass'" op|':' name|'parameter_types' op|'.' name|'admin_password' op|',' nl|'\n' string|"'metadata'" op|':' name|'parameter_types' op|'.' name|'metadata' op|',' nl|'\n' string|"'preserve_ephemeral'" op|':' name|'parameter_types' op|'.' name|'boolean' op|',' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'imageRef'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' op|',' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'rebuild'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' newline|'\n' nl|'\n' nl|'\n' DECL|variable|base_rebuild_v20 name|'base_rebuild_v20' op|'=' name|'copy' op|'.' name|'deepcopy' op|'(' name|'base_rebuild' op|')' newline|'\n' name|'base_rebuild_v20' op|'[' string|"'properties'" op|']' op|'[' string|"'rebuild'" op|']' op|'[' nl|'\n' string|"'properties'" op|']' op|'[' string|"'name'" op|']' op|'=' name|'parameter_types' op|'.' name|'name_with_leading_trailing_spaces' newline|'\n' nl|'\n' DECL|variable|base_rebuild_v219 name|'base_rebuild_v219' op|'=' name|'copy' op|'.' name|'deepcopy' op|'(' name|'base_rebuild' op|')' newline|'\n' name|'base_rebuild_v219' op|'[' string|"'properties'" op|']' op|'[' string|"'rebuild'" op|']' op|'[' nl|'\n' string|"'properties'" op|']' op|'[' string|"'description'" op|']' op|'=' name|'parameter_types' op|'.' name|'description' newline|'\n' nl|'\n' DECL|variable|base_resize name|'base_resize' op|'=' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'resize'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'flavorRef'" op|':' name|'parameter_types' op|'.' name|'flavor_ref' op|',' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'flavorRef'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' op|',' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'resize'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' op|',' nl|'\n' op|'}' newline|'\n' nl|'\n' DECL|variable|create_image name|'create_image' op|'=' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'createImage'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'name'" op|':' name|'parameter_types' op|'.' name|'name' op|',' nl|'\n' string|"'metadata'" op|':' name|'parameter_types' op|'.' name|'metadata' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'name'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' nl|'\n' op|'}' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'createImage'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' nl|'\n' op|'}' newline|'\n' nl|'\n' nl|'\n' DECL|variable|create_image_v20 name|'create_image_v20' op|'=' name|'copy' op|'.' name|'deepcopy' op|'(' name|'create_image' op|')' newline|'\n' name|'create_image_v20' op|'[' string|"'properties'" op|']' op|'[' string|"'createImage'" op|']' op|'[' nl|'\n' string|"'properties'" op|']' op|'[' string|"'name'" op|']' op|'=' name|'parameter_types' op|'.' name|'name_with_leading_trailing_spaces' newline|'\n' nl|'\n' nl|'\n' DECL|variable|reboot name|'reboot' op|'=' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'reboot'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'type'" op|':' op|'{' nl|'\n' string|"'enum'" op|':' op|'[' string|"'HARD'" op|',' string|"'Hard'" op|',' string|"'hard'" op|',' string|"'SOFT'" op|',' string|"'Soft'" op|',' string|"'soft'" op|']' nl|'\n' op|'}' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'type'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' nl|'\n' op|'}' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'reboot'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' nl|'\n' op|'}' newline|'\n' nl|'\n' DECL|variable|trigger_crash_dump name|'trigger_crash_dump' op|'=' op|'{' nl|'\n' string|"'type'" op|':' string|"'object'" op|',' nl|'\n' string|"'properties'" op|':' op|'{' nl|'\n' string|"'trigger_crash_dump'" op|':' op|'{' nl|'\n' string|"'type'" op|':' string|"'null'" nl|'\n' op|'}' nl|'\n' op|'}' op|',' nl|'\n' string|"'required'" op|':' op|'[' string|"'trigger_crash_dump'" op|']' op|',' nl|'\n' string|"'additionalProperties'" op|':' name|'False' nl|'\n' op|'}' newline|'\n' endmarker|'' end_unit

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83ce8a7e61c9f89cf1e2d6d0e4fc8971302ed6e6

25,859

py

Python

bot/Run.py

Dechrissen/Twitch-Speedrunning-Bot

7dd693d1d0c6b25211427f4c761fbc7148506261

[ "MIT" ]

1

2019-09-15T05:22:00.000Z

bot/Run.py

Dechrissen/Twitch-Speedrunning-Bot

7dd693d1d0c6b25211427f4c761fbc7148506261

[ "MIT" ]

null

bot/Run.py

Dechrissen/Twitch-Speedrunning-Bot

7dd693d1d0c6b25211427f4c761fbc7148506261

[ "MIT" ]

null

import string import time import math import urllib.request from urllib.request import urlopen from json import loads from Socket import openSocket, sendMessage from Initialize import joinRoom from Read import getUser, getMessage from Settings import CHANNEL, COOLDOWN, IDENT, CHANNELPASS, SRC_USERNAME, GAMES, CATEGORIES #Returns the world record for the category that's written in the stream title def worldRecord(input): if input == message.lower().split()[0].strip(): #Check to see if an argument is specified first argument = False try: message.lower().split()[1] except IndexError as err: pass else: argument = True #Get the stream title from the Twitch API try: response = urlopen('https://api.twitch.tv/kraken/channels/{}?oauth_token={}'.format(CHANNEL, CHANNELPASS.strip('oauth:'))) except urllib.error.HTTPError as err: sendMessage(s, "Error: Invalid CHANNEL/CHANNELPASS in settings file") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) title = lst['status'].lower() game = None for i in range(len(GAMES)): if GAMES[i][0].lower() in title: game = GAMES[i][1] platform = GAMES[i][3] break category = None category_title = None #Check again to see if an argument was specified if argument == False: for i in range(len(CATEGORIES)): if CATEGORIES[i][0].lower() in title: category = CATEGORIES[i][1] category_title = CATEGORIES[i][0] break elif argument == True: specified_category = message.lower().split(input, 1)[-1].strip() for i in range(len(CATEGORIES)): if specified_category == CATEGORIES[i][0].lower(): category_title = CATEGORIES[i][0] category = CATEGORIES[i][1] break if category == None: sendMessage(s, "Error: Invalid category specified") cooldown() return if game == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return if category != None: response = urlopen('https://www.speedrun.com/api/v1/leaderboards/{}/category/{}?top=1&embed=players&platform={}'.format(game, category, platform)) readable = response.read().decode('utf-8') lst = loads(readable) runner = lst['data']['players']['data'][0]['names']['international'] time_in_sec = int(lst['data']['runs'][0]['run']['times']['realtime_t']) hours = divmod(time_in_sec, 3600) minutes = divmod(hours[1], 60) seconds = minutes[1] wr = '' if hours[0] > 0: wr = str(hours[0]) + "h " + str(minutes[0]) + "m " + str(seconds) + "s " elif minutes[0] > 0: wr = str(minutes[0]) + "m " + str(seconds) + "s " else: wr = str(seconds) + "s " sendMessage(s, "The " + category_title + " world record is " + wr + "by " + runner + ".") cooldown() return elif category == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return def second(input): if input == message.lower().split()[0].strip(): #Check to see if an argument is specified first argument = False try: message.lower().split()[1] except IndexError as err: pass else: argument = True #Get the stream title from the Twitch API try: response = urlopen('https://api.twitch.tv/kraken/channels/{}?oauth_token={}'.format(CHANNEL, CHANNELPASS.strip('oauth:'))) except urllib.error.HTTPError as err: sendMessage(s, "Error: Invalid CHANNEL/CHANNELPASS in settings file") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) title = lst['status'].lower() game = None for i in range(len(GAMES)): if GAMES[i][0].lower() in title: game = GAMES[i][1] platform = GAMES[i][3] break category = None category_title = None #Check again to see if an argument was specified if argument == False: for i in range(len(CATEGORIES)): if CATEGORIES[i][0].lower() in title: category = CATEGORIES[i][1] category_title = CATEGORIES[i][0] break elif argument == True: specified_category = message.lower().split(input, 1)[-1].strip() for i in range(len(CATEGORIES)): if specified_category == CATEGORIES[i][0].lower(): category_title = CATEGORIES[i][0] category = CATEGORIES[i][1] break if category == None: sendMessage(s, "Error: Invalid category specified") cooldown() return if game == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return if category != None: response = urlopen('https://www.speedrun.com/api/v1/leaderboards/{}/category/{}?top=2&embed=players&platform={}'.format(game, category, platform)) readable = response.read().decode('utf-8') lst = loads(readable) runner = lst['data']['players']['data'][1]['names']['international'] time_in_sec = int(lst['data']['runs'][1]['run']['times']['realtime_t']) hours = divmod(time_in_sec, 3600) minutes = divmod(hours[1], 60) seconds = minutes[1] place2nd = '' if hours[0] > 0: place2nd = str(hours[0]) + "h " + str(minutes[0]) + "m " + str(seconds) + "s " elif minutes[0] > 0: place2nd = str(minutes[0]) + "m " + str(seconds) + "s " else: place2nd = str(seconds) + "s " sendMessage(s, "The 2nd place time for " + category_title + " is " + place2nd + "by " + runner + ".") cooldown() return elif category == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return def third(input): if input == message.lower().split()[0].strip(): #Check to see if an argument is specified first argument = False try: message.lower().split()[1] except IndexError as err: pass else: argument = True #Get the stream title from the Twitch API try: response = urlopen('https://api.twitch.tv/kraken/channels/{}?oauth_token={}'.format(CHANNEL, CHANNELPASS.strip('oauth:'))) except urllib.error.HTTPError as err: sendMessage(s, "Error: Invalid CHANNEL/CHANNELPASS in settings file") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) title = lst['status'].lower() game = None for i in range(len(GAMES)): if GAMES[i][0].lower() in title: game = GAMES[i][1] platform = GAMES[i][3] break category = None category_title = None #Check again to see if an argument was specified if argument == False: for i in range(len(CATEGORIES)): if CATEGORIES[i][0].lower() in title: category = CATEGORIES[i][1] category_title = CATEGORIES[i][0] break elif argument == True: specified_category = message.lower().split(input, 1)[-1].strip() for i in range(len(CATEGORIES)): if specified_category == CATEGORIES[i][0].lower(): category_title = CATEGORIES[i][0] category = CATEGORIES[i][1] break if category == None: sendMessage(s, "Error: Invalid category specified") cooldown() return if game == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return if category != None: response = urlopen('https://www.speedrun.com/api/v1/leaderboards/{}/category/{}?top=3&embed=players&platform={}'.format(game, category, platform)) readable = response.read().decode('utf-8') lst = loads(readable) runner = lst['data']['players']['data'][2]['names']['international'] time_in_sec = int(lst['data']['runs'][2]['run']['times']['realtime_t']) hours = divmod(time_in_sec, 3600) minutes = divmod(hours[1], 60) seconds = minutes[1] place3rd = '' if hours[0] > 0: place3rd = str(hours[0]) + "h " + str(minutes[0]) + "m " + str(seconds) + "s " elif minutes[0] > 0: place3rd = str(minutes[0]) + "m " + str(seconds) + "s " else: place3rd = str(seconds) + "s " sendMessage(s, "The 3rd place time for " + category_title + " is " + place3rd + "by " + runner + ".") cooldown() return elif category == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return def fourth(input): if input == message.lower().split()[0].strip(): #Check to see if an argument is specified first argument = False try: message.lower().split()[1] except IndexError as err: pass else: argument = True #Get the stream title from the Twitch API try: response = urlopen('https://api.twitch.tv/kraken/channels/{}?oauth_token={}'.format(CHANNEL, CHANNELPASS.strip('oauth:'))) except urllib.error.HTTPError as err: sendMessage(s, "Error: Invalid CHANNEL/CHANNELPASS in settings file") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) title = lst['status'].lower() game = None for i in range(len(GAMES)): if GAMES[i][0].lower() in title: game = GAMES[i][1] platform = GAMES[i][3] break category = None category_title = None #Check again to see if an argument was specified if argument == False: for i in range(len(CATEGORIES)): if CATEGORIES[i][0].lower() in title: category = CATEGORIES[i][1] category_title = CATEGORIES[i][0] break elif argument == True: specified_category = message.lower().split(input, 1)[-1].strip() for i in range(len(CATEGORIES)): if specified_category == CATEGORIES[i][0].lower(): category_title = CATEGORIES[i][0] category = CATEGORIES[i][1] break if category == None: sendMessage(s, "Error: Invalid category specified") cooldown() return if game == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return if category != None: response = urlopen('https://www.speedrun.com/api/v1/leaderboards/{}/category/{}?top=4&embed=players&platform={}'.format(game, category, platform)) readable = response.read().decode('utf-8') lst = loads(readable) runner = lst['data']['players']['data'][3]['names']['international'] time_in_sec = int(lst['data']['runs'][3]['run']['times']['realtime_t']) hours = divmod(time_in_sec, 3600) minutes = divmod(hours[1], 60) seconds = minutes[1] place4th = '' if hours[0] > 0: place4th = str(hours[0]) + "h " + str(minutes[0]) + "m " + str(seconds) + "s " elif minutes[0] > 0: place4th = str(minutes[0]) + "m " + str(seconds) + "s " else: place4th = str(seconds) + "s " sendMessage(s, "The 4th place time for " + category_title + " is " + place4th + "by " + runner + ".") cooldown() return elif category == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return #Returns the channel owner's personal best time for the category that's written in the stream title def personalBest(input): if input == message.lower().split()[0]: category_specified = False try: message.split()[2] except IndexError as err: pass else: category_specified = True #Get the stream title from the Twitch API try: response = urlopen('https://api.twitch.tv/kraken/channels/{}?oauth_token={}'.format(CHANNEL, CHANNELPASS.strip('oauth:'))) except urllib.error.HTTPError as err: sendMessage(s, "Error: Invalid CHANNEL/CHANNELPASS in settings file") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) title = lst['status'].lower() game = None for i in range(len(GAMES)): if GAMES[i][0].lower() in title: game = GAMES[i][1].lower() platform_title = GAMES[i][2] break category_title = None if category_specified == True: category_title = message.lower().strip('!pb ') first_word = category_title.lower().split()[0] category_title = category_title.split(first_word, 1)[-1].strip() check = False for i in range(len(CATEGORIES)): if CATEGORIES[i][0].lower() == category_title: check = True category_title = CATEGORIES[i][0] break if check == False: sendMessage(s, "Error: Invalid category specified") cooldown() return elif category_specified == False: for i in range(len(CATEGORIES)): if CATEGORIES[i][0].lower() in title: category_title = CATEGORIES[i][0] break if game == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return username = None try: message.split()[1] except IndexError as err: username = SRC_USERNAME else: username = message.split()[1] if category_title != None: try: response = urlopen('https://www.speedrun.com/api/v1/users/{}/personal-bests?embed=category,game,platform'.format(username)) except urllib.error.HTTPError as err: sendMessage(s, "Error: Speedrun.com user not found") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) place = None time_in_sec = None for cat in lst['data']: if cat['category']['data']['name'].lower() == category_title.lower() and cat['game']['data']['abbreviation'].lower() == game and cat['platform']['data']['name'] == platform_title: time_in_sec = int(cat['run']['times']['realtime_t']) place = cat['place'] break if place == None: sendMessage(s, username.title() + " currently does not have a PB for " + category_title + " on the leaderboard.") cooldown() return ordinal = lambda n: "%d%s" % (n,"tsnrhtdd"[(math.floor(n/10)%10!=1)*(n%10<4)*n%10::4]) hours = divmod(time_in_sec, 3600) minutes = divmod(hours[1], 60) seconds = minutes[1] pb = '' if hours[0] > 0: pb = str(hours[0]) + "h " + str(minutes[0]) + "m " + str(seconds) + "s" elif minutes[0] > 0: pb = str(minutes[0]) + "m " + str(seconds) + "s" else: pb = str(seconds) + "s" sendMessage(s, username.title() + "\'s " + category_title + " PB is " + pb + " (" + ordinal(place) + " place).") cooldown() elif category_title == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return #Tells user the leaderboard standing of the channel owner, or a specified user def place(input): if input == message.lower().split()[0]: username = None try: message.split()[1] except IndexError as err: username = SRC_USERNAME else: username = message.split()[1] #Get the stream title from the Twitch API try: response = urlopen('https://api.twitch.tv/kraken/channels/{}?oauth_token={}'.format(CHANNEL, CHANNELPASS.strip('oauth:'))) except urllib.error.HTTPError as err: sendMessage(s, "Error: Invalid CHANNEL/CHANNELPASS in settings file") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) title = lst['status'].lower() game = None for i in range(len(GAMES)): if GAMES[i][0].lower() in title: game = GAMES[i][1].lower() platform_title = GAMES[i][2] break if game == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return category_title = None for i in range(len(CATEGORIES)): if CATEGORIES[i][0].lower() in title: category_title = CATEGORIES[i][0] break if category_title != None: try: response = urlopen('https://www.speedrun.com/api/v1/users/{}/personal-bests?embed=category,game,platform'.format(username)) except urllib.error.HTTPError as err: sendMessage(s, "Error: Speedrun.com user not found") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) place = None time_in_sec = None for cat in lst['data']: if cat['category']['data']['name'].lower() == category_title.lower() and cat['game']['data']['abbreviation'].lower() == game and cat['platform']['data']['name'] == platform_title: time_in_sec = int(cat['run']['times']['realtime_t']) place = cat['place'] break if place == None: sendMessage(s, username.title() + " currently does not have a PB for " + category_title + " on the leaderboard.") cooldown() return ordinal = lambda n: "%d%s" % (n,"tsnrhtdd"[(math.floor(n/10)%10!=1)*(n%10<4)*n%10::4]) sendMessage(s, username.title() + " is in " + ordinal(place) + " place for " + category_title + ".") elif category_title == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return def leaderboard(input): if input == message.lower().strip(): try: response = urlopen('https://api.twitch.tv/kraken/channels/{}?oauth_token={}'.format(CHANNEL, CHANNELPASS.strip('oauth:'))) except urllib.error.HTTPError as err: sendMessage(s, "Error: Invalid CHANNEL/CHANNELPASS in settings file") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) title = lst['status'].lower() game = None game_title = None for i in range(len(GAMES)): if GAMES[i][0].lower() in title: game = GAMES[i][1] game_title = GAMES[i][0] break category = None category_title = None for i in range(len(CATEGORIES)): if CATEGORIES[i][0].lower() in title: category = CATEGORIES[i][1] category_title = CATEGORIES[i][0] break if game == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return if category != None: sendMessage(s, game_title + " " + category_title + " Leaderboard: https://www.speedrun.com/{}#{}".format(game, category)) cooldown() return elif category == None: sendMessage(s, "No game and/or category detected in stream title.") cooldown() return #Returns a kadgar.net link with the channel owner and the other racers if a race is happening def raceCommand(input): if input == message.lower().strip(): #Get the stream title from the Twitch API try: response = urlopen('https://api.twitch.tv/kraken/channels/{}?oauth_token={}'.format(CHANNEL, CHANNELPASS.strip('oauth:'))) except urllib.error.HTTPError as err: sendMessage(s, "Error: Invalid CHANNEL/CHANNELPASS in settings file") cooldown() return readable = response.read().decode('utf-8') lst = loads(readable) title = lst['status'].lower() if 'race with' in title: pass elif 'race with' not in title: sendMessage(s, CHANNEL.title() + " is not currently racing or no racers detected in stream title.") cooldown() return title_list = title.split() r = title_list.index('with') + 1 contenders = [] length = len(title_list) diff = length - r while True: contenders.append(title_list[r].strip(',')) diff = diff - 1 r = r + 1 if diff == 0: break sendMessage(s, "Race link: http://kadgar.net/live/" + CHANNEL + "/".join(contenders)) cooldown() #Displays commands def getCommands(input): if input == message.strip().lower(): sendMessage(s, 'Commands: !wr • !2nd • !3rd • !4th • !pb • !place • !leaderboard • !race') cooldown() #Global cooldown def cooldown(): if user == CHANNEL: pass elif user: abort_after = COOLDOWN start = time.time() while True: delta = time.time() - start if delta >= abort_after: break #Checks if a message is from Twitch or a user def Console(line): if "PRIVMSG" in line: return False else: return True #Quits the bot program def quitCommand(input): if input == message.strip().lower() and user == CHANNEL: sendMessage(s, "[Disconnected]") quit() elif input == message.strip(): sendMessage(s, "@" + user.title() + " Only the channel owner may use the !kill command.") cooldown() s = openSocket() joinRoom(s) readbuffer = "" while True: readbuffer = s.recv(1024) readbuffer = readbuffer.decode() temp = readbuffer.split("\n") readbuffer = readbuffer.encode() readbuffer = temp.pop() for line in temp: print(line) if "PING" in line and Console(line): msgg = "PONG tmi.twitch.tv\r\n".encode() s.send(msgg) print(msgg) break user = getUser(line) message = getMessage(line) print(user + " said: " + message) response = urlopen('https://tmi.twitch.tv/group/user/{}/chatters'.format(CHANNEL)) readable = response.read().decode('utf-8') chatlist = loads(readable) chatters = chatlist['chatters'] moderators = chatters['moderators'] vips = chatters['vips'] viewers = chatters['viewers'] getCommands('!commands') worldRecord('!wr') second('!2nd') third('!3rd') fourth('!4th') personalBest('!pb') place('!place') leaderboard('!leaderboard') raceCommand('!race') quitCommand('!kill') continue

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0.800366

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0.730977

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0.014761

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25,859

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0.785605

0.042229

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0.758319

0

0.012259

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0.042032

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null

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1

0

null

0

7

f7fd8c191bd9b665e91705fe3371b26bde803c75

23,120

py

Python

ddganAE/architectures/cae/D2/cae.py

Zeff020/Adversarial_ROM

8c9e7ff86250e9370e5fdd2018f9ad04ded5f122

[ "MIT" ]

1

2021-12-27T06:14:32.000Z

ddganAE/architectures/cae/D2/cae.py

Zeff020/Adversarial_ROM

8c9e7ff86250e9370e5fdd2018f9ad04ded5f122

[ "MIT" ]

null

ddganAE/architectures/cae/D2/cae.py

Zeff020/Adversarial_ROM

8c9e7ff86250e9370e5fdd2018f9ad04ded5f122

[ "MIT" ]

3

2021-08-05T11:17:37.000Z

2021-09-02T02:37:44.000Z

""" Collection of encoders and decoders that can readily be imported and used by the 2D adversarial and convolutional autoencoder and predictive models. Note that these models are currently adjusted to a 55 by 42 input shape. """ from keras.layers import Dense, Flatten, Reshape, Conv2D, UpSampling2D, \ Cropping2D, MaxPool2D from keras.models import Sequential __author__ = "Zef Wolffs" __credits__ = [] __license__ = "MIT" __version__ = "1.0.0" __maintainer__ = "Zef Wolffs" __email__ = "zefwolffs@gmail.com" __status__ = "Development" def build_custom_conv_encoder(input_shape, latent_dim, initializer, info=False): """ Builds a 2D convolutional encoder Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. Returns: tf.keras.Model: encoder """ encoder = Sequential() encoder.add(Conv2D(32, (5, 5), padding="same", activation="relu", input_shape=input_shape, kernel_initializer=initializer)) encoder.add(Conv2D(64, (5, 5), strides=(2, 2), activation="relu", padding="same", kernel_initializer=initializer)) encoder.add(Conv2D(128, (5, 5), strides=(2, 2), activation="relu", padding="same", kernel_initializer=initializer)) encoder.add(Flatten()) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) return encoder def build_custom_conv_decoder(latent_dim, initializer, info=False): """ Builds a 2D convolutional decoder Args: latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. Returns: tf.keras.Model: encoder """ decoder = Sequential() decoder.add(Dense(78848, input_dim=latent_dim, kernel_initializer=initializer)) decoder.add(Reshape((56, 11, 128))) decoder.add(Conv2D(64, (5, 5), activation="relu", padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(32, (5, 5), activation="relu", padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(2, (5, 5), activation="sigmoid", padding="same", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((2, 1), (1, 1)))) if info: print(decoder.summary()) return decoder def build_omata_encoder_decoder(input_shape, latent_dim, initializer, info=False, act="elu", dense_act=None): """ This encoder-decoder pair currently works for 55 by 42 grids Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. act (str, optional): Activation function to use. Defaults to "elu". dense_act (str, optional): Dense layer activation function to use. Defaults to None. Returns: tuple: encoder, decoder pair """ encoder = Sequential() encoder.add(Conv2D(16, (3, 3), padding="same", activation=act, input_shape=input_shape, kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(8, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(8, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Flatten()) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) decoder = Sequential() decoder.add(Dense(392, input_dim=latent_dim, kernel_initializer=initializer, activation=dense_act)) decoder.add(Reshape((encoder.layers[6].input_shape[1], encoder.layers[6].input_shape[1], 8))) decoder.add(Conv2D(8, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(8, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(16, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(2, (3, 3), activation="linear", padding="same", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((1, 0), (1, 1)))) if info: print(decoder.summary()) return encoder, decoder def build_wider_omata_encoder_decoder(input_shape, latent_dim, initializer, info=False, act="elu", dense_act=None): """ This encoder-decoder pair currently works for 55 by 42 grids Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. act (str, optional): Activation function to use. Defaults to "elu". dense_act (str, optional): Dense layer activation function to use. Defaults to None. Returns: tuple: encoder, decoder pair """ encoder = Sequential() encoder.add(Conv2D(16, (5, 5), padding="same", activation=act, input_shape=input_shape, kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(32, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Flatten()) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) decoder = Sequential() decoder.add(Dense(2688, input_dim=latent_dim, kernel_initializer=initializer, activation=dense_act)) decoder.add(Reshape((encoder.layers[6].input_shape[1], encoder.layers[6].input_shape[2], 64))) decoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(32, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(16, (5, 5), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(2, (3, 3), activation="linear", padding="same", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((1, 0), (3, 3)))) if info: print(decoder.summary()) return encoder, decoder def build_wide_omata_encoder_decoder(input_shape, latent_dim, initializer, info=False, act="elu", dense_act=None): """ This encoder-decoder pair currently works for 55 by 42 grids Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. act (str, optional): Activation function to use. Defaults to "elu". dense_act (str, optional): Dense layer activation function to use. Defaults to None. Returns: tuple: encoder, decoder pair """ encoder = Sequential() encoder.add(Conv2D(32, (5, 5), padding="same", activation=act, input_shape=input_shape, kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(128, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Flatten()) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) decoder = Sequential() decoder.add(Dense(5376, input_dim=latent_dim, kernel_initializer=initializer, activation=dense_act)) decoder.add(Reshape((encoder.layers[6].input_shape[1], encoder.layers[6].input_shape[2], 128))) decoder.add(Conv2D(128, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(32, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(2, (3, 3), activation="linear", padding="same", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((1, 0), (3, 3)))) if info: print(decoder.summary()) return encoder, decoder def build_deeper_omata_encoder_decoder(input_shape, latent_dim, initializer, info=False, act="elu", dense_act=None): """ This encoder-decoder pair currently works for 55 by 42 grids Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. act (str, optional): Activation function to use. Defaults to "elu". dense_act (str, optional): Dense layer activation function to use. Defaults to None. Returns: tuple: encoder, decoder pair """ encoder = Sequential() encoder.add(Conv2D(32, (5, 5), padding="same", activation=act, input_shape=input_shape, kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(128, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Flatten()) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) decoder = Sequential() decoder.add(Dense(1536, input_dim=latent_dim, kernel_initializer=initializer, activation=dense_act)) decoder.add(Reshape((encoder.layers[8].input_shape[1], encoder.layers[8].input_shape[2], 128))) decoder.add(Conv2D(128, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(32, (3, 3), activation=act, padding="valid", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(2, (3, 3), activation="linear", padding="valid", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((2, 1), (0, 0)))) if info: print(decoder.summary()) return encoder, decoder def build_denser_omata_encoder_decoder(input_shape, latent_dim, initializer, info=False, act="elu", dense_act=None): """ This encoder-decoder pair currently works for 55 by 42 grids Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. act (str, optional): Activation function to use. Defaults to "elu". dense_act (str, optional): Dense layer activation function to use. Defaults to None. Returns: tuple: encoder, decoder pair """ encoder = Sequential() encoder.add(Conv2D(32, (5, 5), padding="same", activation=act, input_shape=input_shape, kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(128, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Flatten()) encoder.add(Dense(int(5376/2), kernel_initializer=initializer, activation=dense_act)) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) decoder = Sequential() decoder.add(Dense(int(5376/2), kernel_initializer=initializer, activation=dense_act, input_shape=(latent_dim,))) decoder.add(Dense(5376, kernel_initializer=initializer, activation=dense_act, input_shape=(int(5376/2),))) decoder.add(Reshape((encoder.layers[6].input_shape[1], encoder.layers[6].input_shape[2], 128))) decoder.add(Conv2D(128, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(32, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(2, (3, 3), activation="linear", padding="same", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((1, 0), (3, 3)))) decoder.build(input_shape) if info: print(decoder.summary()) return encoder, decoder def build_densest_omata_encoder_decoder(input_shape, latent_dim, initializer, info=False, act="elu", dense_act=None): """ This encoder-decoder pair currently works for 55 by 42 grids Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. act (str, optional): Activation function to use. Defaults to "elu". dense_act (str, optional): Dense layer activation function to use. Defaults to None. Returns: tuple: encoder, decoder pair """ encoder = Sequential() encoder.add(Conv2D(32, (5, 5), padding="same", activation=act, input_shape=input_shape, kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Flatten()) encoder.add(Dense(int(9856), kernel_initializer=initializer, activation=dense_act)) encoder.add(Dense(int(9856/2), kernel_initializer=initializer, activation=dense_act)) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) decoder = Sequential() decoder.add(Dense(int(9856/2), kernel_initializer=initializer, activation=dense_act, input_shape=(latent_dim,))) decoder.add(Dense(9856, kernel_initializer=initializer, activation=dense_act, input_shape=(int(9856/2),))) decoder.add(Dense(9856, kernel_initializer=initializer, activation=dense_act, input_shape=(int(9856),))) decoder.add(Reshape((encoder.layers[4].input_shape[1], encoder.layers[4].input_shape[2], 64))) decoder.add(Conv2D(64, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(32, (3, 3), activation=act, padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(2, (3, 3), activation="linear", padding="same", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((1, 0), (1, 1)))) decoder.build(input_shape) if info: print(decoder.summary()) return encoder, decoder def build_agostini_encoder_decoder(input_shape, latent_dim, initializer, info=False): """ This encoder-decoder pair currently works for 221 by 42 grids Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. Returns: tuple: encoder, decoder pair """ encoder = Sequential() encoder.add(Conv2D(16, (5, 5), padding="same", activation="relu", input_shape=input_shape, kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(32, (3, 3), activation="relu", padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(64, (3, 3), activation="relu", padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Flatten()) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) decoder = Sequential() decoder.add(Dense(9856, input_dim=latent_dim, kernel_initializer=initializer)) decoder.add(Reshape((encoder.layers[5].input_shape[1], encoder.layers[5].input_shape[2], 16))) decoder.add(Conv2D(64, (3, 3), activation="relu", padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(32, (3, 3), activation="relu", padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(16, (5, 5), activation="sigmoid", padding="same", kernel_initializer=initializer)) decoder.add(Conv2D(2, (3, 3), activation="sigmoid", padding="same", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((1, 2), (1, 1)))) if info: print(decoder.summary()) return encoder, decoder def build_mnist_wide_omata_encoder_decoder(input_shape, latent_dim, initializer, info=False): """ This encoder-decoder pair currently works for 28 by 28 grids so can work on MNIST dataset as a test Args: input_shape (tuple): Shape tuple of input grids latent_dim (int): Number of latent variables initializer (tf.keras.initializers.Initializer): Weights initializer info (bool, optional): Whether to print info. Defaults to False. Returns: tuple: encoder, decoder pair """ encoder = Sequential() encoder.add(Conv2D(128, (3, 3), padding="same", activation="relu", input_shape=input_shape, kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(64, (3, 3), activation="relu", padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Conv2D(32, (3, 3), activation="relu", padding="same", kernel_initializer=initializer)) encoder.add(MaxPool2D(padding="same")) encoder.add(Flatten()) encoder.add(Dense(latent_dim, activation="linear")) if info: print(encoder.summary()) decoder = Sequential() decoder.add(Dense(784, input_dim=latent_dim, kernel_initializer=initializer)) decoder.add(Reshape((encoder.layers[5].input_shape[1], encoder.layers[5].input_shape[2], 16))) decoder.add(Conv2D(32, (3, 3), activation="relu", padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(64, (3, 3), activation="relu", padding="same", kernel_initializer=initializer)) decoder.add(UpSampling2D()) decoder.add(Conv2D(1, (3, 3), activation="sigmoid", padding="same", kernel_initializer=initializer)) decoder.add(Cropping2D(cropping=((0, 0), (0, 0)))) if info: print(decoder.summary()) return encoder, decoder

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79319a292b194538fe6fed9f5c5e48746b45f4f8

179

py

Python

foronoi/nodes/__init__.py

yagna2652/foronoi

e5ce3ae825857a326267ee18e90f525e83ae2003

[ "MIT" ]

24

2018-09-02T00:38:16.000Z

2021-03-09T02:46:21.000Z

foronoi/nodes/__init__.py

yagna2652/foronoi

e5ce3ae825857a326267ee18e90f525e83ae2003

[ "MIT" ]

8

2019-12-23T04:15:30.000Z

2021-03-25T01:08:05.000Z

foronoi/nodes/__init__.py

yagna2652/foronoi

e5ce3ae825857a326267ee18e90f525e83ae2003

[ "MIT" ]

6

2020-01-01T10:26:55.000Z

2021-04-02T09:14:23.000Z

from foronoi.nodes.arc import Arc from foronoi.nodes.breakpoint import Breakpoint from foronoi.nodes.internal_node import InternalNode from foronoi.nodes.leaf_node import LeafNode

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7932c84412312175b892b0c8c71a4e053d978b2d

162

py

Python

python/maya/startup/__init__.py

CountZer0/PipelineConstructionSet

0aa73a8a63c72989b2d1c677efd78dad4388d335

[ "BSD-3-Clause" ]

21

2015-04-27T05:01:36.000Z

2021-11-22T13:45:14.000Z

python/maya/startup/__init__.py

0xb1dd1e/PipelineConstructionSet

621349da1b6d1437e95d0c9e48ee9f36d59f19fd

[ "BSD-3-Clause" ]

null

python/maya/startup/__init__.py

0xb1dd1e/PipelineConstructionSet

621349da1b6d1437e95d0c9e48ee9f36d59f19fd

[ "BSD-3-Clause" ]

7

2015-04-11T11:37:19.000Z

2020-05-22T09:49:04.000Z

""" Author: jason Created: Jul 7, 2012 Module: maya.startup.__init__ Purpose: to import mayaMenuBoot """ print "maya.startup.__init__ imported"

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1

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8

f717e5abe192eeacd489fb3abdcfc529c914593b

8,031

py

Python

src/tests/test_markdown2man.py

dante-signal31/markdown2man

ce57b905b01a6fb8fe6d3d0989af3a15f42c78cf

[ "BSD-3-Clause" ]

null

src/tests/test_markdown2man.py

dante-signal31/markdown2man

ce57b905b01a6fb8fe6d3d0989af3a15f42c78cf

[ "BSD-3-Clause" ]

null

src/tests/test_markdown2man.py

dante-signal31/markdown2man

ce57b905b01a6fb8fe6d3d0989af3a15f42c78cf

[ "BSD-3-Clause" ]

null

""" Test for markdown2man launcher.""" import gzip import os import sys import tempfile import test_common.fs.ops as test_ops from test_common.fs.temp import temp_dir # TODO: Refactor project layout to leave tests folder out of src. sys.path.append("src") import src.markdown2man as markdown2man def test_launcher_all_options_given(temp_dir): # Setup test. temporal_markdown_file = os.path.join(temp_dir, "README.md") test_ops.copy_file("src/tests/resources/README.md", temporal_markdown_file) command_args = [f"{temporal_markdown_file}", "cifra", "-s", "1", "-t", "cifra usage documentation"] expected_output_file = os.path.join(temp_dir, "cifra.1.gz") recovered_content = "" expected_content = "" with open("src/tests/resources/cifra.1") as manpage: expected_content = manpage.read() # Perform test. assert not os.path.exists(expected_output_file) markdown2man.main(command_args) assert os.path.exists(expected_output_file) with gzip.open(expected_output_file) as output_file: recovered_content = "".join(line.decode() for line in output_file.readlines()) assert recovered_content == expected_content def test_launcher_all_long_options_given(temp_dir): # Setup test. temporal_markdown_file = os.path.join(temp_dir, "README.md") test_ops.copy_file("src/tests/resources/README.md", temporal_markdown_file) command_args = [f"{temporal_markdown_file}", "cifra", "--manpage_section", "1", "--manpage_title", "cifra usage documentation"] expected_output_file = os.path.join(temp_dir, "cifra.1.gz") recovered_content = "" expected_content = "" with open("src/tests/resources/cifra.1") as manpage: expected_content = manpage.read() # Perform test. assert not os.path.exists(expected_output_file) markdown2man.main(command_args) assert os.path.exists(expected_output_file) with gzip.open(expected_output_file) as output_file: recovered_content = "".join(line.decode() for line in output_file.readlines()) assert recovered_content == expected_content def test_launcher_section_changed(temp_dir): # Setup test. temporal_markdown_file = os.path.join(temp_dir, "README.md") test_ops.copy_file("src/tests/resources/README.md", temporal_markdown_file) command_args = [f"{temporal_markdown_file}", "cifra", "-s", "2", "-t", "cifra usage documentation"] expected_output_file = os.path.join(temp_dir, "cifra.2.gz") recovered_content = "" expected_content = "" with open("src/tests/resources/cifra.1") as manpage: expected_content = manpage.read() expected_content = expected_content.replace(".TH \"cifra\" \"1\"", ".TH \"cifra\" \"2\"") # Perform test. assert not os.path.exists(expected_output_file) markdown2man.main(command_args) assert os.path.exists(expected_output_file) with gzip.open(expected_output_file) as output_file: recovered_content = "".join(line.decode() for line in output_file.readlines()) assert recovered_content == expected_content def test_launcher_section_omitted(temp_dir): # Setup test. temporal_markdown_file = os.path.join(temp_dir, "README.md") test_ops.copy_file("src/tests/resources/README.md", temporal_markdown_file) command_args = [f"{temporal_markdown_file}", "cifra", "-t", "cifra usage documentation"] expected_output_file = os.path.join(temp_dir, "cifra.1.gz") recovered_content = "" expected_content = "" with open("src/tests/resources/cifra.1") as manpage: expected_content = manpage.read() # Perform test. assert not os.path.exists(expected_output_file) markdown2man.main(command_args) assert os.path.exists(expected_output_file) with gzip.open(expected_output_file) as output_file: recovered_content = "".join(line.decode() for line in output_file.readlines()) assert recovered_content == expected_content def test_launcher_title_omitted(temp_dir): # Setup test. temporal_markdown_file = os.path.join(temp_dir, "README.md") test_ops.copy_file("src/tests/resources/README.md", temporal_markdown_file) command_args = [f"{temporal_markdown_file}", "cifra"] expected_output_file = os.path.join(temp_dir, "cifra.1.gz") recovered_content = "" expected_line = ".TH \"cifra\" \"1\" \"\" \"\" \"cifra\"\n" with open("src/tests/resources/cifra.1") as manpage: expected_content = manpage.read() # Perform test. assert not os.path.exists(expected_output_file) markdown2man.main(command_args) assert os.path.exists(expected_output_file) with gzip.open(expected_output_file) as output_file: recovered_content = [line.decode() for line in output_file.readlines()] assert expected_line in recovered_content def test_launcher_uncompressed(temp_dir): # Setup test. temporal_markdown_file = os.path.join(temp_dir, "README.md") test_ops.copy_file("src/tests/resources/README.md", temporal_markdown_file) command_args = [f"{temporal_markdown_file}", "cifra", "-s", "1", "-t", "cifra usage documentation", "-u"] expected_output_file = os.path.join(temp_dir, "cifra.1") recovered_content = "" expected_content = "" with open("src/tests/resources/cifra.1") as manpage: expected_content = manpage.read() # Perform test. assert not os.path.exists(expected_output_file) markdown2man.main(command_args) assert os.path.exists(expected_output_file) with open(expected_output_file) as output_file: recovered_content = output_file.read() assert recovered_content == expected_content def test_launcher_different_output_folder(temp_dir): with tempfile.TemporaryDirectory() as temp_output_folder: # Setup test. temporal_markdown_file = os.path.join(temp_dir, "README.md") test_ops.copy_file("src/tests/resources/README.md", temporal_markdown_file) command_args = [f"{temporal_markdown_file}", "cifra", "-s", "1", "-t", "cifra usage documentation", "-f", f"{temp_output_folder}"] expected_output_file = os.path.join(temp_output_folder, "cifra.1.gz") recovered_content = "" expected_content = "" with open("src/tests/resources/cifra.1") as manpage: expected_content = manpage.read() # Perform test. assert not os.path.exists(expected_output_file) markdown2man.main(command_args) assert os.path.exists(expected_output_file) with gzip.open(expected_output_file) as output_file: recovered_content = "".join(line.decode() for line in output_file.readlines()) assert recovered_content == expected_content def test_launcher_different_non_existing_output_folder(temp_dir): with tempfile.TemporaryDirectory() as temp_output_folder: # Setup test. temporal_markdown_file = os.path.join(temp_dir, "README.md") temp_output_subfolder = os.path.join(temp_output_folder, "man/") test_ops.copy_file("src/tests/resources/README.md", temporal_markdown_file) command_args = [f"{temporal_markdown_file}", "cifra", "-s", "1", "-t", "cifra usage documentation", "-f", f"{temp_output_subfolder}"] expected_output_file = os.path.join(temp_output_subfolder, "cifra.1.gz") recovered_content = "" expected_content = "" with open("src/tests/resources/cifra.1") as manpage: expected_content = manpage.read() # Perform test. assert not os.path.exists(expected_output_file) markdown2man.main(command_args) assert os.path.exists(expected_output_file) with gzip.open(expected_output_file) as output_file: recovered_content = "".join(line.decode() for line in output_file.readlines()) assert recovered_content == expected_content

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7

f752845674c26214bf3d5d00aaba4581e39e6040

27,612

py

Python

source/deepsecurity/api/policy_log_inspection_rule_details_api.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-10-30T16:40:09.000Z

source/deepsecurity/api/policy_log_inspection_rule_details_api.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-07-28T20:19:03.000Z

source/deepsecurity/api/policy_log_inspection_rule_details_api.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-10-30T16:40:02.000Z

# coding: utf-8 """ Trend Micro Deep Security API Copyright 2018 - 2020 Trend Micro Incorporated.<br/>Get protected, stay secured, and keep informed with Trend Micro Deep Security's new RESTful API. Access system data and manage security configurations to automate your security workflows and integrate Deep Security into your CI/CD pipeline. # noqa: E501 OpenAPI spec version: 12.5.841 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from deepsecurity.api_client import ApiClient class PolicyLogInspectionRuleDetailsApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def describe_log_inspection_rule_on_policy(self, policy_id, log_inspection_rule_id, api_version, **kwargs): # noqa: E501 """Describe an log inspection rule # noqa: E501 Describe an log inspection rule including policy-level overrides. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.describe_log_inspection_rule_on_policy(policy_id, log_inspection_rule_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int policy_id: The ID number of the policy. (required) :param int log_inspection_rule_id: The ID number of the log inspection rule. (required) :param str api_version: The version of the api being called. (required) :param bool overrides: Show only overrides defined for the current policy. :return: LogInspectionRule If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.describe_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, api_version, **kwargs) # noqa: E501 else: (data) = self.describe_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, api_version, **kwargs) # noqa: E501 return data def describe_log_inspection_rule_on_policy_with_http_info(self, policy_id, log_inspection_rule_id, api_version, **kwargs): # noqa: E501 """Describe an log inspection rule # noqa: E501 Describe an log inspection rule including policy-level overrides. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.describe_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int policy_id: The ID number of the policy. (required) :param int log_inspection_rule_id: The ID number of the log inspection rule. (required) :param str api_version: The version of the api being called. (required) :param bool overrides: Show only overrides defined for the current policy. :return: LogInspectionRule If the method is called asynchronously, returns the request thread. """ all_params = ['policy_id', 'log_inspection_rule_id', 'api_version', 'overrides'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method describe_log_inspection_rule_on_policy" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'policy_id' is set if ('policy_id' not in params or params['policy_id'] is None): raise ValueError("Missing the required parameter `policy_id` when calling `describe_log_inspection_rule_on_policy`") # noqa: E501 # verify the required parameter 'log_inspection_rule_id' is set if ('log_inspection_rule_id' not in params or params['log_inspection_rule_id'] is None): raise ValueError("Missing the required parameter `log_inspection_rule_id` when calling `describe_log_inspection_rule_on_policy`") # noqa: E501 # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `describe_log_inspection_rule_on_policy`") # noqa: E501 if 'policy_id' in params and not re.search('\\d+', str(params['policy_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `policy_id` when calling `describe_log_inspection_rule_on_policy`, must conform to the pattern `/\\d+/`") # noqa: E501 if 'log_inspection_rule_id' in params and not re.search('\\d+', str(params['log_inspection_rule_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `log_inspection_rule_id` when calling `describe_log_inspection_rule_on_policy`, must conform to the pattern `/\\d+/`") # noqa: E501 collection_formats = {} path_params = {} if 'policy_id' in params: path_params['policyID'] = params['policy_id'] # noqa: E501 if 'log_inspection_rule_id' in params: path_params['logInspectionRuleID'] = params['log_inspection_rule_id'] # noqa: E501 query_params = [] if 'overrides' in params: query_params.append(('overrides', params['overrides'])) # noqa: E501 header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/policies/{policyID}/loginspection/rules/{logInspectionRuleID}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='LogInspectionRule', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def list_log_inspection_rules_on_policy(self, policy_id, api_version, **kwargs): # noqa: E501 """List log inspection rules # noqa: E501 Lists all log inspection rules assigned to a policy. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_log_inspection_rules_on_policy(policy_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int policy_id: The ID number of the policy. (required) :param str api_version: The version of the api being called. (required) :param bool overrides: Show only rules assigned to the current policy. :return: LogInspectionRules If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.list_log_inspection_rules_on_policy_with_http_info(policy_id, api_version, **kwargs) # noqa: E501 else: (data) = self.list_log_inspection_rules_on_policy_with_http_info(policy_id, api_version, **kwargs) # noqa: E501 return data def list_log_inspection_rules_on_policy_with_http_info(self, policy_id, api_version, **kwargs): # noqa: E501 """List log inspection rules # noqa: E501 Lists all log inspection rules assigned to a policy. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_log_inspection_rules_on_policy_with_http_info(policy_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int policy_id: The ID number of the policy. (required) :param str api_version: The version of the api being called. (required) :param bool overrides: Show only rules assigned to the current policy. :return: LogInspectionRules If the method is called asynchronously, returns the request thread. """ all_params = ['policy_id', 'api_version', 'overrides'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method list_log_inspection_rules_on_policy" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'policy_id' is set if ('policy_id' not in params or params['policy_id'] is None): raise ValueError("Missing the required parameter `policy_id` when calling `list_log_inspection_rules_on_policy`") # noqa: E501 # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `list_log_inspection_rules_on_policy`") # noqa: E501 if 'policy_id' in params and not re.search('\\d+', str(params['policy_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `policy_id` when calling `list_log_inspection_rules_on_policy`, must conform to the pattern `/\\d+/`") # noqa: E501 collection_formats = {} path_params = {} if 'policy_id' in params: path_params['policyID'] = params['policy_id'] # noqa: E501 query_params = [] if 'overrides' in params: query_params.append(('overrides', params['overrides'])) # noqa: E501 header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/policies/{policyID}/loginspection/rules', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='LogInspectionRules', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def modify_log_inspection_rule_on_policy(self, policy_id, log_inspection_rule_id, log_inspection_rule, api_version, **kwargs): # noqa: E501 """Modify an log inspection rule # noqa: E501 Modify an log inspection rule assigned to a policy. Any unset elements will be left unchanged. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.modify_log_inspection_rule_on_policy(policy_id, log_inspection_rule_id, log_inspection_rule, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int policy_id: The ID number of the policy. (required) :param int log_inspection_rule_id: The ID number of the log inspection rule to modify. (required) :param LogInspectionRule log_inspection_rule: The settings of the log inspection rule to modify. (required) :param str api_version: The version of the api being called. (required) :param bool overrides: Show only overrides defined for the current policy. :return: LogInspectionRule If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.modify_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, log_inspection_rule, api_version, **kwargs) # noqa: E501 else: (data) = self.modify_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, log_inspection_rule, api_version, **kwargs) # noqa: E501 return data def modify_log_inspection_rule_on_policy_with_http_info(self, policy_id, log_inspection_rule_id, log_inspection_rule, api_version, **kwargs): # noqa: E501 """Modify an log inspection rule # noqa: E501 Modify an log inspection rule assigned to a policy. Any unset elements will be left unchanged. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.modify_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, log_inspection_rule, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int policy_id: The ID number of the policy. (required) :param int log_inspection_rule_id: The ID number of the log inspection rule to modify. (required) :param LogInspectionRule log_inspection_rule: The settings of the log inspection rule to modify. (required) :param str api_version: The version of the api being called. (required) :param bool overrides: Show only overrides defined for the current policy. :return: LogInspectionRule If the method is called asynchronously, returns the request thread. """ all_params = ['policy_id', 'log_inspection_rule_id', 'log_inspection_rule', 'api_version', 'overrides'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method modify_log_inspection_rule_on_policy" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'policy_id' is set if ('policy_id' not in params or params['policy_id'] is None): raise ValueError("Missing the required parameter `policy_id` when calling `modify_log_inspection_rule_on_policy`") # noqa: E501 # verify the required parameter 'log_inspection_rule_id' is set if ('log_inspection_rule_id' not in params or params['log_inspection_rule_id'] is None): raise ValueError("Missing the required parameter `log_inspection_rule_id` when calling `modify_log_inspection_rule_on_policy`") # noqa: E501 # verify the required parameter 'log_inspection_rule' is set if ('log_inspection_rule' not in params or params['log_inspection_rule'] is None): raise ValueError("Missing the required parameter `log_inspection_rule` when calling `modify_log_inspection_rule_on_policy`") # noqa: E501 # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `modify_log_inspection_rule_on_policy`") # noqa: E501 if 'policy_id' in params and not re.search('\\d+', str(params['policy_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `policy_id` when calling `modify_log_inspection_rule_on_policy`, must conform to the pattern `/\\d+/`") # noqa: E501 if 'log_inspection_rule_id' in params and not re.search('\\d+', str(params['log_inspection_rule_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `log_inspection_rule_id` when calling `modify_log_inspection_rule_on_policy`, must conform to the pattern `/\\d+/`") # noqa: E501 collection_formats = {} path_params = {} if 'policy_id' in params: path_params['policyID'] = params['policy_id'] # noqa: E501 if 'log_inspection_rule_id' in params: path_params['logInspectionRuleID'] = params['log_inspection_rule_id'] # noqa: E501 query_params = [] if 'overrides' in params: query_params.append(('overrides', params['overrides'])) # noqa: E501 header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'log_inspection_rule' in params: body_params = params['log_inspection_rule'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/policies/{policyID}/loginspection/rules/{logInspectionRuleID}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='LogInspectionRule', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def reset_log_inspection_rule_on_policy(self, policy_id, log_inspection_rule_id, api_version, **kwargs): # noqa: E501 """Reset log inspection rule overrides # noqa: E501 Remove all overrides for an log inspection rule from a policy. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.reset_log_inspection_rule_on_policy(policy_id, log_inspection_rule_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int policy_id: The ID number of the policy. (required) :param int log_inspection_rule_id: The ID number of the log inspection rule to reset. (required) :param str api_version: The version of the api being called. (required) :param bool overrides: Show only overrides defined for the current policy. :return: LogInspectionRule If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.reset_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, api_version, **kwargs) # noqa: E501 else: (data) = self.reset_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, api_version, **kwargs) # noqa: E501 return data def reset_log_inspection_rule_on_policy_with_http_info(self, policy_id, log_inspection_rule_id, api_version, **kwargs): # noqa: E501 """Reset log inspection rule overrides # noqa: E501 Remove all overrides for an log inspection rule from a policy. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.reset_log_inspection_rule_on_policy_with_http_info(policy_id, log_inspection_rule_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int policy_id: The ID number of the policy. (required) :param int log_inspection_rule_id: The ID number of the log inspection rule to reset. (required) :param str api_version: The version of the api being called. (required) :param bool overrides: Show only overrides defined for the current policy. :return: LogInspectionRule If the method is called asynchronously, returns the request thread. """ all_params = ['policy_id', 'log_inspection_rule_id', 'api_version', 'overrides'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method reset_log_inspection_rule_on_policy" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'policy_id' is set if ('policy_id' not in params or params['policy_id'] is None): raise ValueError("Missing the required parameter `policy_id` when calling `reset_log_inspection_rule_on_policy`") # noqa: E501 # verify the required parameter 'log_inspection_rule_id' is set if ('log_inspection_rule_id' not in params or params['log_inspection_rule_id'] is None): raise ValueError("Missing the required parameter `log_inspection_rule_id` when calling `reset_log_inspection_rule_on_policy`") # noqa: E501 # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `reset_log_inspection_rule_on_policy`") # noqa: E501 if 'policy_id' in params and not re.search('\\d+', str(params['policy_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `policy_id` when calling `reset_log_inspection_rule_on_policy`, must conform to the pattern `/\\d+/`") # noqa: E501 if 'log_inspection_rule_id' in params and not re.search('\\d+', str(params['log_inspection_rule_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `log_inspection_rule_id` when calling `reset_log_inspection_rule_on_policy`, must conform to the pattern `/\\d+/`") # noqa: E501 collection_formats = {} path_params = {} if 'policy_id' in params: path_params['policyID'] = params['policy_id'] # noqa: E501 if 'log_inspection_rule_id' in params: path_params['logInspectionRuleID'] = params['log_inspection_rule_id'] # noqa: E501 query_params = [] if 'overrides' in params: query_params.append(('overrides', params['overrides'])) # noqa: E501 header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/policies/{policyID}/loginspection/rules/{logInspectionRuleID}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='LogInspectionRule', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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7

f7546364aa146d729cb8fd1cae565084e05c8ba3

26,533

py

Python

pirates/leveleditor/worldData/A_GyedoVegasIsland.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/A_GyedoVegasIsland.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/A_GyedoVegasIsland.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, Vec4, Vec3 objectStruct = {'Objects': {'1149705583.09Shochet': {'Type': 'Island','Name': 'VegasIsland','File': '','Environment': 'Interior','Minimap': False,'Objects': {'1149705605.5Shochet': {'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)},'1149705605.5Shochet0': {'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)},'1149705607.02Shochet': {'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)},'1149705607.63Shochet': {'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)},'1149705619.08Shochet': {'Type': 'Cell Portal Area','Name': 'cell_pier','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1149705619.08Shochet0': {'Type': 'Parlor Game','Category': 'Blackjack','BetMultiplier': '1','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(421.053, -131.608, 5.287),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/Cardtable_HalfCircle'}},'1149705632.05Shochet': {'Type': 'Parlor Game','Category': 'Holdem','BetMultiplier': '1','Hpr': VBase3(45.0, 0.0, 0.0),'Pos': Point3(443.184, -123.256, 5.295),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/Cardtable_Pill'}},'1169451658.54Shochet': {'Type': 'Searchable Container','Aggro Radius': 5.0,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(508.36, -134.581, 5.213),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/desk_gov'},'searchTime': '6.0','type': 'Desk'},'1169451790.01Shochet': {'Type': 'Searchable Container','Aggro Radius': 5.0,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(480.699, -161.909, 5.213),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/wellA'},'searchTime': '6.0','type': 'WellA'},'1171348677.72Shochet': {'Type': 'Interactive Prop','Hpr': VBase3(96.302, 0.0, 0.0),'Pos': Point3(305.078, -115.012, 4.769),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/dummy_zero'},'interactAble': 'player','interactType': 'hit'},'1186785500.34Shochet': {'Type': 'Player Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Index': 1,'Pos': Point3(485.258, -86.884, 5.213),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}}},'Pos': Point3(0.0, 0.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1149706548.67Shochet': {'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)},'1149706548.67Shochet0': {'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)},'1149706577.28Shochet': {'Type': 'Cell Portal Area','Name': 'cell_spanish_town','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1149706632.97Shochet': {'Type': 'Spawn Node','Aggro Radius': '12.0000','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '2','Patrol Radius': '12.0000','Pause Chance': '100','Pause Duration': '30','Pos': Point3(500.886, 153.538, 45.292),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Scorpion','Start State': 'Ambush','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','Visual': {'Model': 'models/misc/smiley'}},'1157596044.44jasyeung': {'Type': 'Townsperson','Category': 'MedicineMan','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','Hpr': VBase3(-173.418, 0.0, 0.0),'Instanced World': 'None','Level': '37','Notice Animation 1': '','Notice Animation 2': '','Patrol Radius': 12,'Pos': Point3(454.118, 103.165, 41.541),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Requires Quest Interest': False,'Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Start State': 'Idle','StartFrame': '0','Team': 'Villager','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','spawnTimeBegin': 0.0,'spawnTimeEnd': 0.0},'1169192829.53Shochet': {'Type': 'Townsperson','Category': 'Commoner','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','Hpr': VBase3(128.625, 0.0, 0.0),'Instanced World': 'None','Level': '37','Notice Animation 1': '','Notice Animation 2': '','Patrol Radius': 12,'Pos': Point3(572.566, 129.349, 42.792),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Requires Quest Interest': False,'Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Start State': 'Idle','StartFrame': '0','Team': 'Villager','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','spawnTimeBegin': 0.0,'spawnTimeEnd': 0.0},'1169616338.61Shochet': {'Type': 'Townsperson','Category': 'Shipwright','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','Hpr': VBase3(95.14, 0.0, 0.0),'Instanced World': 'None','Level': '37','Notice Animation 1': '','Notice Animation 2': '','Patrol Radius': 12,'Pos': Point3(594.082, 77.364, 42.876),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Requires Quest Interest': False,'Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Start State': 'Idle','StartFrame': '0','Team': 'Player','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','spawnTimeBegin': 0.0,'spawnTimeEnd': 0.0},'1169616428.63Shochet': {'Type': 'Object Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(470.936, 243.777, 49.657),'Priority': '1','Scale': VBase3(1.0, 1.0, 1.0),'SpawnDelay': '10','Spawnables': 'Buried Treasure','Visual': {'Color': (0.8, 0.2, 0.65, 1),'Model': 'models/misc/smiley'},'startingDepth': '5'},'1171691663.17Shochet': {'Type': 'Townsperson','Category': 'Gypsy','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','Hpr': VBase3(-161.55, 0.0, 0.0),'Instanced World': 'None','Level': '37','Notice Animation 1': '','Notice Animation 2': '','Patrol Radius': 12,'Pos': Point3(446.386, 327.483, 54.071),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Requires Quest Interest': False,'Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Start State': 'Idle','StartFrame': '0','Team': 'Villager','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','spawnTimeBegin': 0.0,'spawnTimeEnd': 0.0}},'Pos': Point3(0.0, 0.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1154059325.91Shochet': {'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)},'1154059325.91Shochet0': {'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)},'1154059341.09Shochet': {'Type': 'Animal','Hpr': Point3(0.0, 0.0, 0.0),'Patrol Radius': 12,'Pos': Point3(223.47, -25.232, 5.178),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Pig','Start State': 'Walk','StartFrame': '0'},'1154059344.67Shochet': {'Type': 'Animal','Hpr': Point3(0.0, 0.0, 0.0),'Patrol Radius': 12,'Pos': Point3(230.53, -15.957, 5.073),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Chicken','Start State': 'Walk','StartFrame': '0'},'1154059351.97Shochet': {'Type': 'Animal','Hpr': Point3(0.0, 0.0, 0.0),'Patrol Radius': 12,'Pos': Point3(239.433, -6.269, 4.768),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Rooster','Start State': 'Walk','StartFrame': '0'},'1154059362.19Shochet': {'Type': 'Creature','Boss': False,'Boss Name': 'Anonymous','Hpr': Point3(0.0, 0.0, 0.0),'Level': '37','Patrol Radius': 12,'Pos': Point3(-93.119, -296.023, 1.391),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Crab','Start State': 'Idle','StartFrame': '0'},'1154059366.69Shochet': {'Type': 'Creature','Boss': False,'Boss Name': 'Anonymous','Hpr': Point3(0.0, 0.0, 0.0),'Level': '37','Patrol Radius': 12,'Pos': Point3(267.997, 4.319, 7.507),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'FlyTrap','Start State': 'Idle','StartFrame': '0'},'1157596022.35jasyeung': {'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)},'1157596022.35jasyeung0': {'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)},'1165018948.05sdnaik': {'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)},'1165018950.47sdnaik': {'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)},'1169192695.17Shochet': {'Type': 'Port Collision Sphere','Name': 'VegasPort','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(147.216, -168.582, 0.0),'Scale': VBase3(470.212, 470.212, 470.212),'VisSize': '','Visual': {'Color': (0.5, 0.5, 1.0, 0.2),'Model': 'models/misc/smiley'}},'1169192874.58Shochet': {'Type': 'Cell Portal Area','Name': 'cell_green_area','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1165019061.13sdnaik': {'Type': 'Spawn Node','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '2','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(119.094, -74.897, 4.167),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Bat','Start State': 'Ambush','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}}},'Pos': Point3(0.0, 0.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1169192882.95Shochet': {'Type': 'Cell Portal Area','Name': 'cell_shanty_town','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1165019080.61sdnaik': {'Type': 'Spawn Node','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '2','Patrol Radius': 12,'Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(180.048, 221.152, 66.347),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Wasp','Start State': 'Patrol','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}}},'Pos': Point3(0.0, 0.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1169192926.38Shochet': {'Type': 'Spawn Node','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(640.128, -165.933, 6.871),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Alligator','Start State': 'Patrol','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1169193027.28Shochet': {'Type': 'Cell Portal Area','Name': 'cell_graveyard','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1169193004.98Shochet': {'Type': 'Spawn Node','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(-255.945, 98.577, 68.242),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Stump','Start State': 'Patrol','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}}},'Pos': Point3(0.0, 0.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1169449533.4Shochet': {'Type': 'Searchable Container','Aggro Radius': 5.0,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(494.568, -149.091, 5.213),'Scale': VBase3(3.885, 3.885, 3.885),'Visual': {'Color': (1.0, 0.0, 0.0, 1.0),'Model': 'models/props/barrel'},'searchTime': '6.0','type': 'Barrel'},'1169616489.03Shochet': {'Type': 'Creature','Boss': False,'Boss Name': 'Anonymous','Hpr': Point3(0.0, 0.0, 0.0),'Level': '37','Patrol Radius': 12,'Pos': Point3(327.376, 1.76, 10.049),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Stump','Start State': 'Idle','StartFrame': '0'},'1179593088.0Shochet1': {'Type': 'Creature','Boss': False,'Boss Name': 'Anonymous','Hpr': Point3(0.0, 0.0, 0.0),'Level': '37','Patrol Radius': 12,'Pos': Point3(271.381, -60.852, 2.396),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Scorpion','Start State': 'Idle','StartFrame': '0'},'1186784633.39Shochet': {'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)},'1186784634.77Shochet': {'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)},'1186784662.83Shochet': {'Type': 'Dinghy','Aggro Radius': '12.0000','Hpr': VBase3(90.585, 0.0, 0.0),'Location': 'Water','Pos': Point3(373.406, -138.477, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/shipparts/dingy-geometry_High'}},'1186785480.13Shochet': {'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)},'1186785480.14Shochet': {'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)},'1187140148.8gjeon': {'Type': 'Cannon','Hpr': Point3(0.0, 0.0, 0.0),'MaxPower': '1.0','MinPower': '0.2','Pos': Point3(376.873, -67.173, 4.441),'Scale': VBase3(2.833, 2.833, 2.833),'Visual': {'Model': 'models/shipparts/cannon_hi'}},'1187407776.73gjeon': {'Type': 'Building Exterior','File': 'A_GyedoInterior','ExtUid': '1187407776.73gjeon0','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1234924975.76caoconno': {'Type': 'Door Locator Node','Name': 'door_locator','Hpr': VBase3(-180.0, 0.0, 0.0),'Pos': Point3(-8.035, -1.826, 0.068),'Scale': VBase3(1.0, 1.0, 1.0)},'1234924977.02caoconno': {'Type': 'Door Locator Node','Name': 'door_locator_2','Hpr': VBase3(-180.0, 0.0, 0.0),'Pos': Point3(8.421, -1.87, 0.156),'Scale': VBase3(1.0, 1.0, 1.0)}},'Pos': Point3(378.723, -30.806, 8.78),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Door': 'models/buildings/shanty_guildhall_door','Interior': 'models/buildings/interior_shanty_guildhall','Model': 'models/buildings/spanish_npc_house_a_exterior','SignFrame': '','SignImage': 'models/buildings/sign1_eng_a_icon_blacksmith'}},'1187634515.47gjeon': {'Type': 'Barrel','DisableCollision': False,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(592.624, -103.901, 2.332),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/barrel_worn'}},'1187634559.22gjeon': {'Type': 'Barrel','DisableCollision': False,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(604.356, -93.711, 2.764),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/barrel_worn'}},'1191607138.69gjeon': {'Type': 'Animated Avatar','Category': 'cast','Animation Track': 'shovel','AuraFX': 'None','Effect Type': 'None','Hpr': Point3(74.754, 0.0, 0.0),'Pos': Point3(640.247, -90.735, 2.301),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'StartFrame': '0','SubCategory': 'models/char/jr_2000','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None'},'1191607940.19gjeon': {'Type': 'Animated Avatar - Townfolk','Animation Track': 'flute','Holiday': '','Hpr': Point3(85.387, 0.0, 0.0),'Pos': Point3(630.709, -86.24, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Beard': 6,'Belt': 2,'BeltColor': 3,'Coat': 2,'CoatColor': 14,'Gender': 'f','Hair': 4,'HairColor': 1,'Hat': 2,'Mustache': 4,'Pants': 1,'PantsColor': 2,'Shape': 2,'Shirt': 1,'ShirtColor': 1,'Shoe': 2,'Skin': 11,'Sock': 0}},'1234924978.07caoconno': {'Type': 'Door Locator Node','Name': 'door_locator','Hpr': VBase3(-180.0, 0.0, 0.0),'Pos': Point3(0.127, -4.354, 4.971),'Scale': VBase3(1.0, 1.0, 1.0)},'1234924978.08caoconno': {'Type': 'Door Locator Node','Name': 'door_locator_2','Hpr': VBase3(-180.0, 0.0, 0.0),'Pos': Point3(8.421, -1.87, 0.156),'Scale': VBase3(1.0, 1.0, 1.0)},'1235676532.77gjeon': {'Type': 'Building Exterior','File': '','ExtUid': '1235676532.77gjeon0','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1235676532.83gjeon': {'Type': 'Door Locator Node','Name': 'door_locator','GridPos': Point3(656.644, -209.671, 11.583),'Hpr': VBase3(-180.0, 0.0, 0.0),'Pos': Point3(0.127, -4.354, 4.971),'Scale': VBase3(1.0, 1.0, 1.0)}},'Pos': Point3(656.517, -205.317, 6.612),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Door': 'models/buildings/shanty_guildhall_door','Model': 'models/buildings/shanty_npc_house_a_exterior','SignFrame': '','SignImage': 'models/buildings/sign1_eng_a_icon_barber'}},'1240961664.0gjeon1': {'Type': 'Spawn Node','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(639.664, -118.133, 3.765),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Noob Skeleton','Start State': 'Ambush','StartFrame': '0','Team': 'default','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1275692669.07gjeon': {'Type': 'Townsperson','Category': 'Commoner','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','HelpID': 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'["Objects"]["1149705583.09Shochet"]["Objects"]["1275692669.07gjeon"]'}} extraInfo = {'camPos': Point3(652.314, -154.956, 60.7173),'camHpr': VBase3(-5.3369, -32.9357, 0),'focalLength': 1.39999997616,'skyState': 2,'fog': 0}

8,844.333333

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f77f5fdc4ee4b2f799ff62dcb0683554c965717f

15,011

py

Python

safe_transaction_service/contracts/decoder_abis/sight.py

byteflyfunny/safe-transaction-service

2a1a855d9881181a57692057aeb91c9fd8ae3de5

[ "MIT" ]

67

2019-08-16T16:26:42.000Z

2022-03-21T20:32:43.000Z

safe_transaction_service/contracts/decoder_abis/sight.py

byteflyfunny/safe-transaction-service

2a1a855d9881181a57692057aeb91c9fd8ae3de5

[ "MIT" ]

550

2019-07-11T12:09:06.000Z

2022-03-31T16:32:00.000Z

safe_transaction_service/contracts/decoder_abis/sight.py

byteflyfunny/safe-transaction-service

2a1a855d9881181a57692057aeb91c9fd8ae3de5

[ "MIT" ]

83

2019-12-06T11:22:32.000Z

2022-03-30T10:09:22.000Z

# flake8: noqa E501 import json conditional_token_abi = json.loads( '[{"constant":true,"inputs":[{"name":"owner","type":"address"},{"name":"id","type":"uint256"}],"name":"balanceOf","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"interfaceId","type":"bytes4"}],"name":"supportsInterface","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"bytes32"},{"name":"","type":"uint256"}],"name":"payoutNumerators","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"from","type":"address"},{"name":"to","type":"address"},{"name":"ids","type":"uint256[]"},{"name":"values","type":"uint256[]"},{"name":"data","type":"bytes"}],"name":"safeBatchTransferFrom","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"owners","type":"address[]"},{"name":"ids","type":"uint256[]"}],"name":"balanceOfBatch","outputs":[{"name":"","type":"uint256[]"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"operator","type":"address"},{"name":"approved","type":"bool"}],"name":"setApprovalForAll","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"","type":"bytes32"}],"name":"payoutDenominator","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"owner","type":"address"},{"name":"operator","type":"address"}],"name":"isApprovedForAll","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"from","type":"address"},{"name":"to","type":"address"},{"name":"id","type":"uint256"},{"name":"value","type":"uint256"},{"name":"data","type":"bytes"}],"name":"safeTransferFrom","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"anonymous":false,"inputs":[{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":true,"name":"oracle","type":"address"},{"indexed":true,"name":"questionId","type":"bytes32"},{"indexed":false,"name":"outcomeSlotCount","type":"uint256"}],"name":"ConditionPreparation","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":true,"name":"oracle","type":"address"},{"indexed":true,"name":"questionId","type":"bytes32"},{"indexed":false,"name":"outcomeSlotCount","type":"uint256"},{"indexed":false,"name":"payoutNumerators","type":"uint256[]"}],"name":"ConditionResolution","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"stakeholder","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"partition","type":"uint256[]"},{"indexed":false,"name":"amount","type":"uint256"}],"name":"PositionSplit","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"stakeholder","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"partition","type":"uint256[]"},{"indexed":false,"name":"amount","type":"uint256"}],"name":"PositionsMerge","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"redeemer","type":"address"},{"indexed":true,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":false,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"indexSets","type":"uint256[]"},{"indexed":false,"name":"payout","type":"uint256"}],"name":"PayoutRedemption","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"operator","type":"address"},{"indexed":true,"name":"from","type":"address"},{"indexed":true,"name":"to","type":"address"},{"indexed":false,"name":"id","type":"uint256"},{"indexed":false,"name":"value","type":"uint256"}],"name":"TransferSingle","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"operator","type":"address"},{"indexed":true,"name":"from","type":"address"},{"indexed":true,"name":"to","type":"address"},{"indexed":false,"name":"ids","type":"uint256[]"},{"indexed":false,"name":"values","type":"uint256[]"}],"name":"TransferBatch","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"owner","type":"address"},{"indexed":true,"name":"operator","type":"address"},{"indexed":false,"name":"approved","type":"bool"}],"name":"ApprovalForAll","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"value","type":"string"},{"indexed":true,"name":"id","type":"uint256"}],"name":"URI","type":"event"},{"constant":false,"inputs":[{"name":"oracle","type":"address"},{"name":"questionId","type":"bytes32"},{"name":"outcomeSlotCount","type":"uint256"}],"name":"prepareCondition","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"questionId","type":"bytes32"},{"name":"payouts","type":"uint256[]"}],"name":"reportPayouts","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"partition","type":"uint256[]"},{"name":"amount","type":"uint256"}],"name":"splitPosition","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"partition","type":"uint256[]"},{"name":"amount","type":"uint256"}],"name":"mergePositions","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"indexSets","type":"uint256[]"}],"name":"redeemPositions","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"conditionId","type":"bytes32"}],"name":"getOutcomeSlotCount","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"oracle","type":"address"},{"name":"questionId","type":"bytes32"},{"name":"outcomeSlotCount","type":"uint256"}],"name":"getConditionId","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"pure","type":"function"},{"constant":true,"inputs":[{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"indexSet","type":"uint256"}],"name":"getCollectionId","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"collateralToken","type":"address"},{"name":"collectionId","type":"bytes32"}],"name":"getPositionId","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"pure","type":"function"}]' ) market_maker_abi = json.loads( '[{"constant":true,"inputs":[{"name":"interfaceId","type":"bytes4"}],"name":"supportsInterface","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[],"name":"resume","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"pmSystem","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"outcomeTokenAmounts","type":"int256[]"},{"name":"collateralLimit","type":"int256"}],"name":"trade","outputs":[{"name":"netCost","type":"int256"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"close","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"withdrawFees","outputs":[{"name":"fees","type":"uint256"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"renounceOwnership","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"pause","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"fundingChange","type":"int256"}],"name":"changeFunding","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"owner","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"isOwner","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"whitelist","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"outcomeTokenCost","type":"uint256"}],"name":"calcMarketFee","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"collateralToken","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"_operator","type":"address"},{"name":"","type":"address"},{"name":"","type":"uint256[]"},{"name":"","type":"uint256[]"},{"name":"","type":"bytes"}],"name":"onERC1155BatchReceived","outputs":[{"name":"","type":"bytes4"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"stage","outputs":[{"name":"","type":"uint8"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"funding","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"uint256"}],"name":"conditionIds","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"atomicOutcomeSlotCount","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"fee","outputs":[{"name":"","type":"uint64"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"_fee","type":"uint64"}],"name":"changeFee","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"operator","type":"address"},{"name":"","type":"address"},{"name":"","type":"uint256"},{"name":"","type":"uint256"},{"name":"","type":"bytes"}],"name":"onERC1155Received","outputs":[{"name":"","type":"bytes4"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"newOwner","type":"address"}],"name":"transferOwnership","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"FEE_RANGE","outputs":[{"name":"","type":"uint64"}],"payable":false,"stateMutability":"view","type":"function"},{"anonymous":false,"inputs":[{"indexed":false,"name":"initialFunding","type":"uint256"}],"name":"AMMCreated","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMPaused","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMResumed","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMClosed","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"fundingChange","type":"int256"}],"name":"AMMFundingChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"newFee","type":"uint64"}],"name":"AMMFeeChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"fees","type":"uint256"}],"name":"AMMFeeWithdrawal","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"transactor","type":"address"},{"indexed":false,"name":"outcomeTokenAmounts","type":"int256[]"},{"indexed":false,"name":"outcomeTokenNetCost","type":"int256"},{"indexed":false,"name":"marketFees","type":"uint256"}],"name":"AMMOutcomeTokenTrade","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"previousOwner","type":"address"},{"indexed":true,"name":"newOwner","type":"address"}],"name":"OwnershipTransferred","type":"event"},{"constant":true,"inputs":[{"name":"outcomeTokenAmounts","type":"int256[]"}],"name":"calcNetCost","outputs":[{"name":"netCost","type":"int256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"outcomeTokenIndex","type":"uint8"}],"name":"calcMarginalPrice","outputs":[{"name":"price","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"}]' ) market_maker_factory_abi = json.loads( '[{"constant":true,"inputs":[],"name":"implementationMaster","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"inputs":[],"payable":false,"stateMutability":"nonpayable","type":"constructor"},{"anonymous":false,"inputs":[{"indexed":true,"name":"creator","type":"address"},{"indexed":false,"name":"lmsrMarketMaker","type":"address"},{"indexed":false,"name":"pmSystem","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":false,"name":"conditionIds","type":"bytes32[]"},{"indexed":false,"name":"fee","type":"uint64"},{"indexed":false,"name":"funding","type":"uint256"}],"name":"LMSRMarketMakerCreation","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"previousOwner","type":"address"},{"indexed":true,"name":"newOwner","type":"address"}],"name":"OwnershipTransferred","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"initialFunding","type":"uint256"}],"name":"AMMCreated","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"target","type":"address"},{"indexed":false,"name":"clone","type":"address"}],"name":"CloneCreated","type":"event"},{"constant":false,"inputs":[{"name":"consData","type":"bytes"}],"name":"cloneConstructor","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"pmSystem","type":"address"},{"name":"collateralToken","type":"address"},{"name":"conditionIds","type":"bytes32[]"},{"name":"fee","type":"uint64"},{"name":"whitelist","type":"address"},{"name":"funding","type":"uint256"}],"name":"createLMSRMarketMaker","outputs":[{"name":"lmsrMarketMaker","type":"address"}],"payable":false,"stateMutability":"nonpayable","type":"function"}]' )

1,154.692308

7,374

0.659183

1,525

15,011

6.481967

0.098361

0.062317

0.131108

0.057865

0.839049

0.757714

0.722408

0.676378

0.624987

0.609914

0

0.018428

0.002265

15,011

12

7,375

1,250.916667

0.641584

0.001133

0

0.3

0.990195

0

1

0

false

0

0.1

0

0.1

0

null

0

1

0

1

0

1

0

1

null

0

9

f79c82336527701b43ddb503f8ccda04b069c4e0

8,030

py

Python

benchmarks/import_cost/functions_100_with_1_contract.py

kklein/icontract

718ef1733cc2cce6d3c8f59a5a37de96f8be6664

[ "MIT" ]

244

2018-08-15T22:58:58.000Z

2022-03-12T16:10:39.000Z

benchmarks/import_cost/functions_100_with_1_contract.py

kklein/icontract

718ef1733cc2cce6d3c8f59a5a37de96f8be6664

[ "MIT" ]

157

2018-08-29T21:36:47.000Z

2022-02-14T19:30:24.000Z

benchmarks/import_cost/functions_100_with_1_contract.py

kklein/icontract

718ef1733cc2cce6d3c8f59a5a37de96f8be6664

[ "MIT" ]

23

2019-04-24T11:09:10.000Z

2022-02-14T15:56:26.000Z

#!/usr/bin/env python3 import icontract @icontract.require(lambda x: x > 0) def some_func0(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func1(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func2(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func3(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func4(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func5(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func6(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func7(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func8(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func9(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func10(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func11(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func12(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func13(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func14(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func15(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func16(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func17(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func18(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func19(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func20(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func21(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func22(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func23(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func24(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func25(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func26(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func27(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func28(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func29(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func30(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func31(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func32(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func33(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func34(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func35(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func36(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func37(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func38(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func39(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func40(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func41(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func42(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func43(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func44(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func45(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func46(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func47(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func48(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func49(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func50(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func51(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func52(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func53(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func54(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func55(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func56(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func57(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func58(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func59(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func60(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func61(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func62(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func63(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func64(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func65(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func66(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func67(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func68(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func69(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func70(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func71(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func72(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func73(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func74(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func75(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func76(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func77(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func78(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func79(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func80(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func81(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func82(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func83(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func84(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func85(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func86(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func87(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func88(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func89(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func90(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func91(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func92(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func93(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func94(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func95(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func96(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func97(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func98(x: int) -> None: pass @icontract.require(lambda x: x > 0) def some_func99(x: int) -> None: pass

15.964215

35

0.637733

1,306

8,030

3.844564

0.088055

0.318662

0.43816

0.458076

0.87393

0.867556

0

0.046001

0.212204

8,030

502

36

15.996016

0.747708

0.002615

0

0.664452

0

1

0.332226

false

0.332226

0.003322

0

0.335548

0

null

1

0

null

0

1

0

1

0

11

f7a1d6fbbfdca9f78dcd50cacb077aa663d3fa10

5,238

py

Python

hallo/test/modules/math/test_simplify_fraction.py

joshcoales/Hallo

17145d8f76552ecd4cbc5caef8924bd2cf0cbf24

[ "MIT" ]

1

2018-05-19T22:27:20.000Z

hallo/test/modules/math/test_simplify_fraction.py

joshcoales/Hallo

17145d8f76552ecd4cbc5caef8924bd2cf0cbf24

[ "MIT" ]

75

2015-09-26T18:07:18.000Z

2022-01-04T07:15:11.000Z

hallo/test/modules/math/test_simplify_fraction.py

SpangleLabs/Hallo

17145d8f76552ecd4cbc5caef8924bd2cf0cbf24

[ "MIT" ]

1

2021-04-10T12:02:47.000Z

from hallo.events import EventMessage def test_fraction_simple(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 6/4") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert "3/2." in data[0].text[-4:], "Simplify fraction fails for small fractions." def test_fraction_complex(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 360679/22") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( "32789/2." in data[0].text[-8:] ), "Simplify fraction fails for large fractions." def test_fraction_multi_slash(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 360679/22/2") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( "error" in data[0].text.lower() ), "Simplify fraction should return error when given more than 1 slash." def test_fraction_integer(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 22/2") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( " 11." == data[0].text[-4:] ), "Simplify fraction should return integer when result is integer." def test_fraction_one_arg(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 104779") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( "error" in data[0].text.lower() ), "Simplify fraction should return error when not given a fraction." def test_fraction_unsimplify(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 17/3") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert "17/3." == data[0].text[-5:] def test_factors_float(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 17.5/2") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( "error" in data[0].text.lower() ), "Simplify fraction should return error when given a float." test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 17/2.2") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( "error" in data[0].text.lower() ), "Simplify fraction should return error when given a float." test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 6.6/2.2") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( "error" in data[0].text.lower() ), "Simplify fraction should return error when given a float." def test_factors_negative(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 24/-10") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( " -12/5." in data[0].text[-7:] ), "Simplify fraction not working for negative denominators." test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction -24/10") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( " -12/5." in data[0].text[-7:] ), "Simplify fraction not working for negative numerators." test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "fraction 24/10") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( " 12/5." in data[0].text[-6:] ), "Simplify fraction not working for negative numerators & denominators." def test_factors_word(hallo_getter): test_hallo = hallo_getter({"math"}) test_hallo.function_dispatcher.dispatch( EventMessage(test_hallo.test_server, None, test_hallo.test_user, "factors hello/7") ) data = test_hallo.test_server.get_send_data(1, test_hallo.test_user, EventMessage) assert ( "error" in data[0].text.lower() ), "Simplify fraction should return error when invalid number used."

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5,238

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null

0

1

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1

0

null

0

7

e398fc2a4b73410264c949acde42f0ab49d17f84

1,450

py

Python

tests/test_cell.py

Hourann/game-of-life

3b979ef992ad28810231c105888d0e0e0e582bb8

[ "Apache-2.0" ]

null

tests/test_cell.py

Hourann/game-of-life

3b979ef992ad28810231c105888d0e0e0e582bb8

[ "Apache-2.0" ]

null

tests/test_cell.py

Hourann/game-of-life

3b979ef992ad28810231c105888d0e0e0e582bb8

[ "Apache-2.0" ]

null

from unittest import TestCase from src.cell import Cell class TestCell(TestCase): def test_should_turn_dead_when_a_live_cell_have_2_alive_neighbour(self): # given cell = Cell(True) number_of_alive_neighbour = 1 # when cell.next(number_of_alive_neighbour) # then self.assertFalse(cell.state) def test_should_turn_dead_when_a_cell_have_more_than_3_alive_neighbour(self): # given cell = Cell(True) number_of_alive_neighbour = 4 # when cell.next(number_of_alive_neighbour) # then self.assertFalse(cell.state) def test_should_turn_alive_when_a_cell_have_3_alive_neighbour(self): # given cell = Cell(False) number_of_alive_neighbour = 3 # when cell.next(number_of_alive_neighbour) # then self.assertTrue(cell.state) def test_should_keep_dead_when_a_dead_cell_have_2_alive_neighbour(self): # given cell = Cell(False) number_of_alive_neighbour = 2 # when cell.next(number_of_alive_neighbour) # then self.assertFalse(cell.state) def test_should_keep_alive_when_a_alive_cell_have_2_alive_neighbour(self): # given cell = Cell(True) number_of_alive_neighbour = 2 # when cell.next(number_of_alive_neighbour) # then self.assertTrue(cell.state)

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81

0.655862

187

1,450

4.631016

0.197861

0.242494

0.150115

0.254042

0.841801

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0.009615

0.282759

1,450

63

82

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0

null

1

0

1

0

null

0

8

e3bd6ec2674605c47c31fbfbe8907ffce13d874d

3,479

py

Python

Sender/plugins/__init__.py

Keys-007/AnonymousSender

d32368fc713e6dac45beba6089766aebaa708a8d

[ "MIT" ]

null

Sender/plugins/__init__.py

Keys-007/AnonymousSender

d32368fc713e6dac45beba6089766aebaa708a8d

[ "MIT" ]

null

Sender/plugins/__init__.py

Keys-007/AnonymousSender

d32368fc713e6dac45beba6089766aebaa708a8d

[ "MIT" ]

null

''' The Giant Penis License (GPL) Copyright (c) 2021 @InukaAisth ▄▄██▄██▄▄ ▄█ █ █▄ ▄█ █▄ █ █ █ █ █ █ █ █ █ █ █▄ █ ▄█ █ ▄▄▄ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ ▄████▄█ █▄████▄ ▄█ █▄ █ █ █ █ █ █ █ █ █ ▄▄█▄▄ █ █ █ █ █ █▄ ▄█ █▄ ▄█ █▄▄▄▄▄█ █▄▄▄▄▄█ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 😂 There is no such penis lisence do anything you like ''' from os.path import dirname, basename, isfile, join import glob # Loading Modules # Also can use plugins dir defined in client for this. This is good for modular way modules = glob.glob(join(dirname(__file__), "*.py")) __all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith("__init__.py") ]

42.950617

84

0.566542

469

3,479

4.4371

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0.043248

0.059106

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0.818837

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3,479

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1

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1

0

null

0

1

0

8

e3c384a6f6a62c272174f99dd5c829d195cb4262

227

py

Python

pybarycuda/api.py

postmalloc/barycuda

8b27cc1fd3839c6b6a089e3de816f3cf3e32367a

[ "MIT" ]

2

2020-10-21T03:20:05.000Z

2021-05-31T04:31:05.000Z

pybarycuda/api.py

postmalloc/barycuda

8b27cc1fd3839c6b6a089e3de816f3cf3e32367a

[ "MIT" ]

null

pybarycuda/api.py

postmalloc/barycuda

8b27cc1fd3839c6b6a089e3de816f3cf3e32367a

[ "MIT" ]

null

import pybarycuda.core as bary def point_in_simplex(pts, n, dim, verts): return list(map(bool, bary.point_in_simplex(pts, n, dim, verts))) def bary_simplex(pts, n, dim, verts): return bary.bary_simplex(pts, n, dim, verts)

32.428571

67

0.735683

40

227

4.025

0.425

0.248447

0.273292

0.347826

0.68323

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0

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227

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32.428571

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null

0

1

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1

0

7

58251b75f08dd2a6b3278b73cfbdb1e8358c73fc

12,535

py

Python

ixnetwork_restpy/testplatform/sessions/ixnetwork/availablehardware/chassis/card/card.py

rfrye-github/ixnetwork_restpy

23eeb24b21568a23d3f31bbd72814ff55eb1af44

[ "MIT" ]

null

ixnetwork_restpy/testplatform/sessions/ixnetwork/availablehardware/chassis/card/card.py

rfrye-github/ixnetwork_restpy

23eeb24b21568a23d3f31bbd72814ff55eb1af44

[ "MIT" ]

null

ixnetwork_restpy/testplatform/sessions/ixnetwork/availablehardware/chassis/card/card.py

rfrye-github/ixnetwork_restpy

23eeb24b21568a23d3f31bbd72814ff55eb1af44

[ "MIT" ]

null

# MIT LICENSE # # Copyright 1997 - 2020 by IXIA Keysight # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. from ixnetwork_restpy.base import Base from ixnetwork_restpy.files import Files class Card(Base): """This command allows the user to view version and type information for the card. The Card class encapsulates a list of card resources that are managed by the system. A list of resources can be retrieved from the server using the Card.find() method. """ __slots__ = () _SDM_NAME = 'card' _SDM_ATT_MAP = { 'AggregationMode': 'aggregationMode', 'AggregationSupported': 'aggregationSupported', 'AvailableModes': 'availableModes', 'CardId': 'cardId', 'Description': 'description', } def __init__(self, parent): super(Card, self).__init__(parent) @property def Aggregation(self): """ Returns ------- - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.availablehardware.chassis.card.aggregation.aggregation.Aggregation): An instance of the Aggregation class Raises ------ - ServerError: The server has encountered an uncategorized error condition """ from ixnetwork_restpy.testplatform.sessions.ixnetwork.availablehardware.chassis.card.aggregation.aggregation import Aggregation return Aggregation(self) @property def Port(self): """ Returns ------- - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.availablehardware.chassis.card.port.port.Port): An instance of the Port class Raises ------ - ServerError: The server has encountered an uncategorized error condition """ from ixnetwork_restpy.testplatform.sessions.ixnetwork.availablehardware.chassis.card.port.port import Port return Port(self) @property def AggregationMode(self): """ Returns ------- - str(notSupported | mixed | normal | tenGigAggregation | fortyGigAggregation | singleMode | dualMode | hundredGigNonFanOut | fortyGigFanOut | threeByTenGigFanOut | eightByTenGigFanOut | fourByTwentyFiveGigNonFanOut | twoByTwentyFiveGigNonFanOut | oneByFiftyGigNonFanOut | fortyGigNonFanOut | oneByTenGigFanOut | fourByTenGigFanOut | incompatibleMode | hundredGigCapturePlayback | fortyGigCapturePlayback | novusHundredGigNonFanOut | novusFourByTwentyFiveGigNonFanOut | novusTwoByFiftyGigNonFanOut | novusOneByFortyGigNonFanOut | novusFourByTenGigNonFanOut | krakenOneByFourHundredGigNonFanOut | krakenOneByTwoHundredGigNonFanOut | krakenTwoByOneHundredGigFanOut | krakenFourByFiftyGigFanOut | aresOneOneByFourHundredGigNonFanOut | aresOneTwoByTwoHundredGigFanOut | aresOneFourByOneHundredGigFanOut | aresOneFourByOneHundredGigMacSecFanOut | aresOneEightByFiftyGigFanOut | uhdOneHundredEightByHundredGigNonFanOut | uhdOneHundredEightByFortyGigNonFanOut | uhdOneHundredSixteenByFiftyGigFanOut | uhdOneHundredThirtyTwoByTwentyFiveGigFanOut | uhdOneHundredThirtyTwoByTenGigFanOut): Gets or sets the aggregation mode. """ return self._get_attribute(self._SDM_ATT_MAP['AggregationMode']) @AggregationMode.setter def AggregationMode(self, value): self._set_attribute(self._SDM_ATT_MAP['AggregationMode'], value) @property def AggregationSupported(self): """ Returns ------- - bool: (read only) If true, indicates that the card is operating in resource group mode and not in normal mode """ return self._get_attribute(self._SDM_ATT_MAP['AggregationSupported']) @property def AvailableModes(self): """ Returns ------- - list(str[notSupported | mixed | normal | tenGigAggregation | fortyGigAggregation | singleMode | dualMode | hundredGigNonFanOut | fortyGigFanOut | threeByTenGigFanOut | eightByTenGigFanOut | fourByTwentyFiveGigNonFanOut | twoByTwentyFiveGigNonFanOut | oneByFiftyGigNonFanOut | fortyGigNonFanOut | oneByTenGigFanOut | fourByTenGigFanOut | incompatibleMode | hundredGigCapturePlayback | fortyGigCapturePlayback | novusHundredGigNonFanOut | novusFourByTwentyFiveGigNonFanOut | novusTwoByFiftyGigNonFanOut | novusOneByFortyGigNonFanOut | novusFourByTenGigNonFanOut | krakenOneByFourHundredGigNonFanOut | krakenOneByTwoHundredGigNonFanOut | krakenTwoByOneHundredGigFanOut | krakenFourByFiftyGigFanOut | aresOneOneByFourHundredGigNonFanOut | aresOneTwoByTwoHundredGigFanOut | aresOneFourByOneHundredGigFanOut | aresOneFourByOneHundredGigMacSecFanOut | aresOneEightByFiftyGigFanOut | uhdOneHundredEightByHundredGigNonFanOut | uhdOneHundredEightByFortyGigNonFanOut | uhdOneHundredSixteenByFiftyGigFanOut | uhdOneHundredThirtyTwoByTwentyFiveGigFanOut | uhdOneHundredThirtyTwoByTenGigFanOut]): Gets the supported port resource group modes on the card. """ return self._get_attribute(self._SDM_ATT_MAP['AvailableModes']) @property def CardId(self): """ Returns ------- - number: Identifier for the card on the chassis. """ return self._get_attribute(self._SDM_ATT_MAP['CardId']) @property def Description(self): """ Returns ------- - str: Description of the card. """ return self._get_attribute(self._SDM_ATT_MAP['Description']) def update(self, AggregationMode=None): """Updates card resource on the server. Args ---- - AggregationMode (str(notSupported | mixed | normal | tenGigAggregation | fortyGigAggregation | singleMode | dualMode | hundredGigNonFanOut | fortyGigFanOut | threeByTenGigFanOut | eightByTenGigFanOut | fourByTwentyFiveGigNonFanOut | twoByTwentyFiveGigNonFanOut | oneByFiftyGigNonFanOut | fortyGigNonFanOut | oneByTenGigFanOut | fourByTenGigFanOut | incompatibleMode | hundredGigCapturePlayback | fortyGigCapturePlayback | novusHundredGigNonFanOut | novusFourByTwentyFiveGigNonFanOut | novusTwoByFiftyGigNonFanOut | novusOneByFortyGigNonFanOut | novusFourByTenGigNonFanOut | krakenOneByFourHundredGigNonFanOut | krakenOneByTwoHundredGigNonFanOut | krakenTwoByOneHundredGigFanOut | krakenFourByFiftyGigFanOut | aresOneOneByFourHundredGigNonFanOut | aresOneTwoByTwoHundredGigFanOut | aresOneFourByOneHundredGigFanOut | aresOneFourByOneHundredGigMacSecFanOut | aresOneEightByFiftyGigFanOut | uhdOneHundredEightByHundredGigNonFanOut | uhdOneHundredEightByFortyGigNonFanOut | uhdOneHundredSixteenByFiftyGigFanOut | uhdOneHundredThirtyTwoByTwentyFiveGigFanOut | uhdOneHundredThirtyTwoByTenGigFanOut)): Gets or sets the aggregation mode. Raises ------ - ServerError: The server has encountered an uncategorized error condition """ return self._update(self._map_locals(self._SDM_ATT_MAP, locals())) def find(self, AggregationMode=None, AggregationSupported=None, AvailableModes=None, CardId=None, Description=None): """Finds and retrieves card resources from the server. All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve card resources from the server. To retrieve an exact match ensure the parameter value starts with ^ and ends with $ By default the find method takes no parameters and will retrieve all card resources from the server. Args ---- - AggregationMode (str(notSupported | mixed | normal | tenGigAggregation | fortyGigAggregation | singleMode | dualMode | hundredGigNonFanOut | fortyGigFanOut | threeByTenGigFanOut | eightByTenGigFanOut | fourByTwentyFiveGigNonFanOut | twoByTwentyFiveGigNonFanOut | oneByFiftyGigNonFanOut | fortyGigNonFanOut | oneByTenGigFanOut | fourByTenGigFanOut | incompatibleMode | hundredGigCapturePlayback | fortyGigCapturePlayback | novusHundredGigNonFanOut | novusFourByTwentyFiveGigNonFanOut | novusTwoByFiftyGigNonFanOut | novusOneByFortyGigNonFanOut | novusFourByTenGigNonFanOut | krakenOneByFourHundredGigNonFanOut | krakenOneByTwoHundredGigNonFanOut | krakenTwoByOneHundredGigFanOut | krakenFourByFiftyGigFanOut | aresOneOneByFourHundredGigNonFanOut | aresOneTwoByTwoHundredGigFanOut | aresOneFourByOneHundredGigFanOut | aresOneFourByOneHundredGigMacSecFanOut | aresOneEightByFiftyGigFanOut | uhdOneHundredEightByHundredGigNonFanOut | uhdOneHundredEightByFortyGigNonFanOut | uhdOneHundredSixteenByFiftyGigFanOut | uhdOneHundredThirtyTwoByTwentyFiveGigFanOut | uhdOneHundredThirtyTwoByTenGigFanOut)): Gets or sets the aggregation mode. - AggregationSupported (bool): (read only) If true, indicates that the card is operating in resource group mode and not in normal mode - AvailableModes (list(str[notSupported | mixed | normal | tenGigAggregation | fortyGigAggregation | singleMode | dualMode | hundredGigNonFanOut | fortyGigFanOut | threeByTenGigFanOut | eightByTenGigFanOut | fourByTwentyFiveGigNonFanOut | twoByTwentyFiveGigNonFanOut | oneByFiftyGigNonFanOut | fortyGigNonFanOut | oneByTenGigFanOut | fourByTenGigFanOut | incompatibleMode | hundredGigCapturePlayback | fortyGigCapturePlayback | novusHundredGigNonFanOut | novusFourByTwentyFiveGigNonFanOut | novusTwoByFiftyGigNonFanOut | novusOneByFortyGigNonFanOut | novusFourByTenGigNonFanOut | krakenOneByFourHundredGigNonFanOut | krakenOneByTwoHundredGigNonFanOut | krakenTwoByOneHundredGigFanOut | krakenFourByFiftyGigFanOut | aresOneOneByFourHundredGigNonFanOut | aresOneTwoByTwoHundredGigFanOut | aresOneFourByOneHundredGigFanOut | aresOneFourByOneHundredGigMacSecFanOut | aresOneEightByFiftyGigFanOut | uhdOneHundredEightByHundredGigNonFanOut | uhdOneHundredEightByFortyGigNonFanOut | uhdOneHundredSixteenByFiftyGigFanOut | uhdOneHundredThirtyTwoByTwentyFiveGigFanOut | uhdOneHundredThirtyTwoByTenGigFanOut])): Gets the supported port resource group modes on the card. - CardId (number): Identifier for the card on the chassis. - Description (str): Description of the card. Returns ------- - self: This instance with matching card resources retrieved from the server available through an iterator or index Raises ------ - ServerError: The server has encountered an uncategorized error condition """ return self._select(self._map_locals(self._SDM_ATT_MAP, locals())) def read(self, href): """Retrieves a single instance of card data from the server. Args ---- - href (str): An href to the instance to be retrieved Returns ------- - self: This instance with the card resources from the server available through an iterator or index Raises ------ - NotFoundError: The requested resource does not exist on the server - ServerError: The server has encountered an uncategorized error condition """ return self._read(href) def RefreshInfo(self): """Executes the refreshInfo operation on the server. Refresh the hardware information. Raises ------ - NotFoundError: The requested resource does not exist on the server - ServerError: The server has encountered an uncategorized error condition """ payload = { "Arg1": self } return self._execute('refreshInfo', payload=payload, response_object=None)

66.322751

1,168

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0.011121

0.733533

0.709046

0.703486

0.691617

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0

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12,535

188

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66.675532

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false

0

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0

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1

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1

0

null

0

1

0

1

0

7

5853ad5db62e6387567f5260866468228cd5174d

134

py

Python

dash/lambda.py

tobias-pook/stocktinker

df328673c30fb606024529a01328c3cfb8558d0c

[ "MIT" ]

null

dash/lambda.py

tobias-pook/stocktinker

df328673c30fb606024529a01328c3cfb8558d0c

[ "MIT" ]

9

2017-12-08T18:33:24.000Z

2018-02-05T21:02:30.000Z

dash/lambda.py

tobias-pook/stocktinker

df328673c30fb606024529a01328c3cfb8558d0c

[ "MIT" ]

4

2018-08-07T02:47:09.000Z

2020-11-16T20:35:43.000Z

import awsgi from dash.index import app def lambda_handler(event, context): return awsgi.response(app.server, event, context)

26.8

53

0.761194

19

134

5.315789

0.736842

0.237624

0

0.156716

134

5

53

26.8

0.893805

0

1

0.25

false

0

0.5

0.25

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null

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1

0

null

0

1

0

1

0

7

5881f592a6ee3a1c4ea6811e904541e5e1c34f64

72

py

Python

py_pdf_term/analysis/__init__.py

kumachan-mis/pdf-slides-term

cf3319e4de723bd9424d23141803342d3c649103

[ "MIT" ]

1

2021-01-08T16:05:30.000Z

py_pdf_term/analysis/__init__.py

kumachan-mis/py-slides-term

1e9337b97ae8968950489e728fc7aeeeb7eb1f4b

[ "MIT" ]

21

2021-01-03T13:50:59.000Z

2021-06-17T00:27:49.000Z

py_pdf_term/analysis/__init__.py

kumachan-mis/pdf-slides-term

cf3319e4de723bd9424d23141803342d3c649103

[ "MIT" ]

null

from ._analysis import * # NoQA from ._analysis import __all__ # NoQA

24

38

0.736111

9

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0.555556

0.510638

0.765957

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0.194444

72

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0.125

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true

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null

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1

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1

0

7

588e684c46ee6ab5e5acd4c87742b6b701b0b817

530

py

Python

eval_covid20cases_timm-regnetx_002_Emboss.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

eval_covid20cases_timm-regnetx_002_Emboss.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

eval_covid20cases_timm-regnetx_002_Emboss.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

import os ls=["python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_0_Emboss.yml", "python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_1_Emboss.yml", "python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_2_Emboss.yml", "python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_3_Emboss.yml", "python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_4_Emboss.yml", ] for l in ls: os.system(l)

48.181818

100

0.843396

80

530

5.2125

0.3

0.119904

0.143885

0.227818

0.901679

0

0.06012

0.058491

530

11

101

48.181818

0.775551

0

0.875706

0.640301

0

1

0

false

0

0.111111

0

0.111111

0

null

0

1

0

1

null

0

9

5892180353da79459e8414e9e20d411c1ab7e2c9

31,140

py

Python

hanibal/ans_reporte/crear_informe_caja_excel.py

Christian-Castro/castro_odoo8

8247fdb20aa39e043b6fa0c4d0af509462ab3e00

[ "Unlicense" ]

null

hanibal/ans_reporte/crear_informe_caja_excel.py

Christian-Castro/castro_odoo8

8247fdb20aa39e043b6fa0c4d0af509462ab3e00

[ "Unlicense" ]

null

hanibal/ans_reporte/crear_informe_caja_excel.py

Christian-Castro/castro_odoo8

8247fdb20aa39e043b6fa0c4d0af509462ab3e00

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- import openpyxl from openpyxl import Workbook import openpyxl.worksheet import unicodedata from copy import deepcopy from openpyxl.chart import ( Reference, Series, BarChart ) from openpyxl.chart.marker import DataPoint from openpyxl.drawing.fill import PatternFillProperties, ColorChoice from openpyxl import Workbook from openpyxl.styles import PatternFill, Border, Side, Alignment, Protection, Font from openpyxl.styles.borders import Border, Side from openpyxl.drawing.image import Image from datetime import datetime, date, timedelta import time import locale global root def crear_wb_informe(): wb = openpyxl.Workbook() return wb def unicodeText(text): try: text = unicodedata.unicode(text, 'utf-8') except TypeError: return text def crea_hoja_info(wb, title, flag): sheet = wb.active if(flag == 0): #sheet.page_setup.paperSize = sheet.PAPERSIZE_A4_SMALL #sheet.print_options.scale = 100 sheet.page_margins.left = 0.1 sheet.page_margins.right = 0.1 sheet.page_margins.top = 0.5 sheet.page_margins.bottom = 0.5 #sheet.page_setup.orientation = sheet.ORIENTATION_PORTRAIT #sheet.sheet_properties.pageSetUpPr.fitToPage = True sheet.page_setup.fitToWidht = False #sheet.print_options.horizontalCentered = True if(flag == 1): #sheet.page_setup.paperSize = sheet.PAPERSIZE_A4_SMALL #sheet.print_options.scale = 100 #sheet.sheet_properties.pageSetUpPr.fitToPage = True sheet.page_setup.fitToWidth = False sheet.page_margins.left = 0.1 sheet.page_margins.right = 0.1 sheet.page_margins.top = 0.5 sheet.page_margins.bottom = 0.5 #sheet.page_setup.orientation = sheet.ORIENTATION_PORTRAIT #sheet.print_options.horizontalCentered = True sheet.title = title return sheet def crea_hoja_info_pdf(wb, title, flag): sheet = wb.active if(flag == 0): #sheet.page_setup.paperSize = sheet.PAPERSIZE_A4_SMALL #sheet.print_options.scale = 100 sheet.page_margins.left = 0.1 sheet.page_margins.right = 0.1 sheet.page_margins.top = 0.5 sheet.page_margins.bottom = 0.5 #sheet.page_setup.orientation = sheet.ORIENTATION_PORTRAIT #sheet.sheet_properties.pageSetUpPr.fitToPage = True sheet.page_setup.fitToWidht = False #sheet.print_options.horizontalCentered = True if(flag == 1): #sheet.page_setup.paperSize = sheet.PAPERSIZE_A4_SMALL #sheet.print_options.scale = 100 #sheet.sheet_properties.pageSetUpPr.fitToPage = True sheet.page_setup.fitToWidth = False sheet.page_margins.left = 0.1 sheet.page_margins.right = 0.1 sheet.page_margins.top = 0.5 sheet.page_margins.bottom = 0.5 #sheet.page_setup.orientation = sheet.ORIENTATION_PORTRAIT #sheet.print_options.horizontalCentered = True sheet.title = title return sheet def border_tabla(sheet, col, colfin, fil, filfin, styleleft, styletop, styleright, stylebottom): colfin=colfin+1 filfin=filfin+2 border_cell = Border(left=Side(style=styleleft), top=Side(style=styletop), right=Side(style=styleright), bottom=Side(style=stylebottom)) for i in range(fil, filfin-1): for j in range(col, colfin): sheet.cell(row=i, column=j).border = border_cell def columnas_filas(sheet, flag, celda, value): if (flag == 0): sheet.column_dimensions[celda].width = value if (flag == 1): sheet.row_dimensions[int(celda)].height = value def poner_border(sheet, fil, col, styleleft, styletop, styleright, stylebottom): border_cell = Border(left=Side(style=styleleft), top=Side(style=styletop), right=Side(style=styleright), bottom=Side(style=stylebottom)) sheet.cell(row=fil, column=col).border = border_cell def Informe(sheet, dic,lista_alumnos,cant_alumno,filtro): columnas_filas(sheet, 0, 'A', 10.00) columnas_filas(sheet, 0, 'B', 5.00) columnas_filas(sheet, 0, 'C', 10.00) columnas_filas(sheet, 0, 'D', 7.00) columnas_filas(sheet, 0, 'E', 12.00) columnas_filas(sheet, 0, 'F', 10.00) columnas_filas(sheet, 0, 'G', 10.00) columnas_filas(sheet, 0, 'H', 7.00) columnas_filas(sheet, 0, 'I', 10.00) alignment_title = Alignment(horizontal='center', vertical='center') fuente = Font(bold=False, size=6, name='arial') fuente3 = Font(bold=True, size=8, name='arial') fuente2 = Font(bold=True, size=6, name='arial') fila = 3 fila1 = 2 acum=1 cont=0 col=2 col1=4 fil=4 coli=2 colf=2 sheet.merge_cells('A2:I2') sheet['A2'].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top') sheet['A2'].font = fuente3 sheet['A2']= 'REPORTE DE CAJA' sheet['H1'].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['H1'].font = fuente2 sheet['H1']= 'Usuario' usuario_id=str(dic['usuario_id'].encode('utf-8')) sheet['I1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['I1'].font = fuente sheet['I1']= str(dic['usuario_id'].encode('utf-8')) sheet['A1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A1'].font = fuente2 sheet['A1']= 'Cia' sheet.merge_cells('B1:C1') sheet['B1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B1'].font = fuente sheet['B1']= str(dic['company_id'].encode('utf-8')) sheet['A3'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A3'].font = fuente2 sheet['A3']= 'Fecha Emision:' #fecha_actual = datetime.strftime(datetime.now(), '%d-%m-%Y %H:%M:%S') fecha_actual = dic['fecha_corte'] sheet.merge_cells('B3:C3') sheet['B3'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B3'].font = fuente sheet['B3']= fecha_actual poner_border(sheet,1,1,'medium','medium','none','none') poner_border(sheet,1,2,'none','medium','none','none') poner_border(sheet,1,3,'none','medium','none','none') poner_border(sheet,1,4,'none','medium','none','none') poner_border(sheet,1,5,'none','medium','none','none') poner_border(sheet,1,5,'none','medium','none','none') poner_border(sheet,1,6,'none','medium','none','none') poner_border(sheet,1,7,'none','medium','none','none') poner_border(sheet,1,8,'none','medium','none','none') poner_border(sheet,1,9,'none','medium','medium','none') poner_border(sheet,2,1,'medium','none','none','none') poner_border(sheet,2,9,'none','none','medium','none') poner_border(sheet,3,1,'medium','none','none','medium') poner_border(sheet,3,2,'none','none','none','medium') poner_border(sheet,3,3,'none','none','none','medium') poner_border(sheet,3,4,'none','none','none','medium') poner_border(sheet,3,5,'none','none','none','medium') poner_border(sheet,3,6,'none','none','none','medium') poner_border(sheet,3,7,'none','none','none','medium') poner_border(sheet,3,8,'none','none','none','medium') poner_border(sheet,3,9,'none','none','medium','medium') fecha_ini=dic['fecha_desde'] fecha_fin=dic['fecha_hasta'] fecha=str(" Desde: "+dic['fecha_desde']+" Hasta: "+dic['fecha_hasta']) sheet.merge_cells('D4:F4') sheet['D4'].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top') sheet['D4'].font = fuente2 sheet['D4']= str(" Desde: "+dic['fecha_desde']+" Hasta: "+dic['fecha_hasta']) sheet['A5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A5'].font = fuente2 sheet['A5']= 'Origen' sheet['B5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B5'].font = fuente2 sheet['B5']= 'Fecha' sheet['C5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['C5'].font = fuente2 sheet['C5']= 'FACTURA' sheet['D5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['D5'].font = fuente2 sheet['D5']= 'MONTO' sheet['E5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['E5'].font = fuente2 sheet['E5']= 'Alumno' sheet['F5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['F5'].font = fuente2 sheet['F5']= 'Banco' sheet['G5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['G5'].font = fuente2 sheet['G5']= 'DOCUMENTO' sheet['H5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['H5'].font = fuente2 sheet['H5']= 'Fecha Cheque' sheet['I5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['I5'].font = fuente2 sheet['I5']= 'Comentario' fila=6 total_general=0.0 saldo_general=0.0 dic={} lista_datos=[] for recorrer in lista_alumnos: sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A'+str(fila)].font = fuente2 dic={} if recorrer['tipo']=='efe': sheet['A'+str(fila)]= 'Efectivo' dic['tipo']='Efectivo' elif recorrer['tipo']=='ch': sheet['A'+str(fila)]= 'Cheque' dic['tipo']='Cheque' elif recorrer['tipo']=='tc': sheet['A'+str(fila)]= 'Tarjeta de Credito' dic['tipo']='Tarjeta de Credito' elif recorrer['tipo']=='dep': sheet['A'+str(fila)]= 'Deposito Bancario' dic['tipo']='Deposito Bancario' elif recorrer['tipo']=='trans': sheet['A'+str(fila)]= 'Transferencia Bancaria' dic['tipo']='Transferencia Bancaria' elif recorrer['tipo']=='nc': sheet['A'+str(fila)]= 'Nota de Credito' dic['tipo']='Nota de Credito' elif recorrer['tipo']=='rti': sheet['A'+str(fila)]= 'Retencion iva' dic['tipo']='Retencion iva' elif recorrer['tipo']=='rtf': sheet['A'+str(fila)]= 'Retencion fuente' dic['tipo']='Retencion fuente' elif recorrer['tipo']=='liq': sheet['A'+str(fila)]= 'Liquidacion' dic['tipo']='Liquidacion' fila=fila+1 saldo=0.0 total=0.0 dic['cantidad']=len(recorrer['detalle']) for det in recorrer['detalle']: sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A'+str(fila)].font = fuente sheet['A'+str(fila)]= det['numero'] sheet['B'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B'+str(fila)].font = fuente sheet['B'+str(fila)]= det['fecha_pago'] sheet['C'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['C'+str(fila)].font = fuente sheet['C'+str(fila)]= det['factura'] sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila)].font = fuente sheet['D'+str(fila)]= "{:,}".format(float(det['monto'])).replace(',','~').replace('.',',').replace('~','.') sheet['E'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['E'+str(fila)].font = fuente if det['cliente']==False: sheet['E'+str(fila)]= '' else: sheet['E'+str(fila)]= det['cliente'] sheet['F'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['F'+str(fila)].font = fuente if det['banco']==False: sheet['F'+str(fila)]= '' elif det['banco']==0: sheet['F'+str(fila)]= '' else: sheet['F'+str(fila)]= det['banco'] sheet['G'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['G'+str(fila)].font = fuente if det['documento']==False: sheet['G'+str(fila)]= '' else: sheet['G'+str(fila)]= det['documento'] sheet['H'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['H'+str(fila)].font = fuente if det['fecha_ch']==False: sheet['H'+str(fila)]= '' else: sheet['H'+str(fila)]= det['fecha_ch'] sheet['I'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['I'+str(fila)].font = fuente if det['comentario']==False: sheet['I'+str(fila)]= '' elif det['comentario']==0: sheet['I'+str(fila)]= '' else: sheet['I'+str(fila)]= det['comentario'] total=total+float(det['monto']) fila=fila+1 sheet['C'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['C'+str(fila)].font = fuente2 sheet['C'+str(fila)]= 'TOTAL' sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila)].font = fuente2 sheet['D'+str(fila)]= "{:,}".format(float(total)).replace(',','~').replace('.',',').replace('~','.') dic['total']=total total_general = total_general + total fila= fila + 1 lista_datos.append(dic) sheet['C'+str(fila+1)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['C'+str(fila+1)].font = fuente2 sheet['C'+str(fila+1)]= 'TOTAL GENERAL' sheet['D'+str(fila+1)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila+1)].font = fuente2 sheet['D'+str(fila+1)]= "{:,}".format(float(total_general)).replace(',','~').replace('.',',').replace('~','.') sheet.merge_cells('B'+str(fila+2)+':C'+str(fila+2)) sheet['B'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B'+str(fila+2)].font = fuente2 sheet['B'+str(fila+2)]= 'RESUMEN DE VALORES' fila=fila+3 for d in lista_datos: sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A'+str(fila)].font = fuente2 sheet['A'+str(fila)]= d['tipo'] sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila)].font = fuente2 sheet['D'+str(fila)]= "{:,}".format(float(d['total'])).replace(',','~').replace('.',',').replace('~','.') sheet['H'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['H'+str(fila)].font = fuente2 sheet['H'+str(fila)]= d['cantidad'] sheet['I'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['I'+str(fila)].font = fuente2 sheet['I'+str(fila)]= 'VECES' fila=fila+1 sheet['D'+str(fila+1)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila+1)].font = fuente2 sheet['D'+str(fila+1)]= "{:,}".format(float(total_general)).replace(',','~').replace('.',',').replace('~','.') columnas_filas(sheet, 1, str(fila+5), 10.00) sheet.merge_cells('D'+str(fila+5)+':F'+str(fila+5)) sheet['D'+str(fila+5)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='center') sheet['D'+str(fila+5)].font = fuente2 sheet['D'+str(fila+5)]= usuario_id poner_border(sheet,fila+5,4,'none','thin','none','none') poner_border(sheet,fila+5,5,'none','thin','none','none') poner_border(sheet,fila+5,6,'none','thin','none','none') columnas_filas(sheet, 1, str(fila+6), 8.00) sheet.merge_cells('D'+str(fila+6)+':F'+str(fila+6)) sheet['D'+str(fila+6)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='center') sheet['D'+str(fila+6)].font = fuente2 sheet['D'+str(fila+6)]= fecha columnas_filas(sheet, 1, str(fila+7), 8.00) sheet.merge_cells('D'+str(fila+7)+':F'+str(fila+7)) sheet['D'+str(fila+7)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='center') sheet['D'+str(fila+7)].font = fuente2 sheet['D'+str(fila+7)]= filtro def Informe_pdf(sheet, dic,lista_alumnos,cant_alumno,filtro): columnas_filas(sheet, 0, 'A', 10.00) columnas_filas(sheet, 0, 'B', 5.00) columnas_filas(sheet, 0, 'C', 10.00) columnas_filas(sheet, 0, 'D', 7.00) columnas_filas(sheet, 0, 'E', 1.00) columnas_filas(sheet, 0, 'F', 12.00) columnas_filas(sheet, 0, 'G', 10.00) columnas_filas(sheet, 0, 'H', 10.00) columnas_filas(sheet, 0, 'I', 7.00) columnas_filas(sheet, 0, 'J', 10.00) alignment_title = Alignment(horizontal='center', vertical='center') fuente = Font(bold=False, size=6, name='arial') fuente3 = Font(bold=True, size=8, name='arial') fuente2 = Font(bold=True, size=6, name='arial') fila = 3 fila1 = 2 acum=1 cont=0 col=2 col1=4 fil=4 coli=2 colf=2 sheet.merge_cells('A2:I2') sheet['A2'].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top') sheet['A2'].font = fuente3 sheet['A2']= 'REPORTE DE CAJA' sheet['H1'].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['H1'].font = fuente2 sheet['H1']= 'Usuario' usuario_id=str(dic['usuario_id'].encode('utf-8')) sheet['I1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['I1'].font = fuente sheet['I1']= str(dic['usuario_id'].encode('utf-8')) sheet['A1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A1'].font = fuente2 sheet['A1']= 'Cia' sheet.merge_cells('B1:C1') sheet['B1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B1'].font = fuente sheet['B1']= str(dic['company_id'].encode('utf-8')) sheet['A3'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A3'].font = fuente2 sheet['A3']= 'Fecha Emision:' #fecha_actual = datetime.strftime(datetime.now(), '%d-%m-%Y %H:%M:%S') fecha_actual = dic['fecha_corte'] sheet.merge_cells('B3:C3') sheet['B3'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B3'].font = fuente sheet['B3']= fecha_actual poner_border(sheet,1,1,'medium','medium','none','none') poner_border(sheet,1,2,'none','medium','none','none') poner_border(sheet,1,3,'none','medium','none','none') poner_border(sheet,1,4,'none','medium','none','none') poner_border(sheet,1,5,'none','medium','none','none') poner_border(sheet,1,5,'none','medium','none','none') poner_border(sheet,1,6,'none','medium','none','none') poner_border(sheet,1,7,'none','medium','none','none') poner_border(sheet,1,8,'none','medium','none','none') poner_border(sheet,1,9,'none','medium','none','none') poner_border(sheet,1,10,'none','medium','medium','none') poner_border(sheet,2,1,'medium','none','none','none') poner_border(sheet,2,10,'none','none','medium','none') poner_border(sheet,3,1,'medium','none','none','medium') poner_border(sheet,3,2,'none','none','none','medium') poner_border(sheet,3,3,'none','none','none','medium') poner_border(sheet,3,4,'none','none','none','medium') poner_border(sheet,3,5,'none','none','none','medium') poner_border(sheet,3,6,'none','none','none','medium') poner_border(sheet,3,7,'none','none','none','medium') poner_border(sheet,3,8,'none','none','none','medium') poner_border(sheet,3,9,'none','none','none','medium') poner_border(sheet,3,10,'none','none','medium','medium') fecha_ini=dic['fecha_desde'] fecha_fin=dic['fecha_hasta'] fecha=str(" Desde: "+dic['fecha_desde']+" Hasta: "+dic['fecha_hasta']) sheet.merge_cells('D4:F4') sheet['D4'].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top') sheet['D4'].font = fuente2 sheet['D4']= str(" Desde: "+dic['fecha_desde']+" Hasta: "+dic['fecha_hasta']) sheet['A5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A5'].font = fuente2 sheet['A5']= 'Origen' sheet['B5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B5'].font = fuente2 sheet['B5']= 'Fecha' sheet['C5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['C5'].font = fuente2 sheet['C5']= 'FACTURA' sheet['D5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['D5'].font = fuente2 sheet['D5']= 'MONTO' sheet['F5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['F5'].font = fuente2 sheet['F5']= 'Alumno' sheet['G5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['G5'].font = fuente2 sheet['G5']= 'Banco' sheet['H5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['H5'].font = fuente2 sheet['H5']= 'DOCUMENTO' sheet['I5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['I5'].font = fuente2 sheet['I5']= 'Fecha Cheque' sheet['J5'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['J5'].font = fuente2 sheet['J5']= 'Comentario' fila=6 total_general=0.0 saldo_general=0.0 dic={} lista_datos=[] for recorrer in lista_alumnos: sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A'+str(fila)].font = fuente2 dic={} if recorrer['tipo']=='efe': sheet['A'+str(fila)]= 'Efectivo' dic['tipo']='Efectivo' elif recorrer['tipo']=='ch': sheet['A'+str(fila)]= 'Cheque' dic['tipo']='Cheque' elif recorrer['tipo']=='tc': sheet['A'+str(fila)]= 'Tarjeta de Credito' dic['tipo']='Tarjeta de Credito' elif recorrer['tipo']=='dep': sheet['A'+str(fila)]= 'Deposito Bancario' dic['tipo']='Deposito Bancario' elif recorrer['tipo']=='trans': sheet['A'+str(fila)]= 'Transferencia Bancaria' dic['tipo']='Transferencia Bancaria' elif recorrer['tipo']=='nc': sheet['A'+str(fila)]= 'Nota de Credito' dic['tipo']='Nota de Credito' elif recorrer['tipo']=='rti': sheet['A'+str(fila)]= 'Retencion iva' dic['tipo']='Retencion iva' elif recorrer['tipo']=='rtf': sheet['A'+str(fila)]= 'Retencion fuente' dic['tipo']='Retencion fuente' elif recorrer['tipo']=='liq': sheet['A'+str(fila)]= 'Liquidacion' dic['tipo']='Liquidacion' fila=fila+1 saldo=0.0 total=0.0 dic['cantidad']=len(recorrer['detalle']) for det in recorrer['detalle']: columnas_filas(sheet, 1, str(fila), 15.00) sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A'+str(fila)].font = fuente sheet['A'+str(fila)]= det['numero'] sheet['B'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B'+str(fila)].font = fuente sheet['B'+str(fila)]= det['fecha_pago'] sheet['C'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['C'+str(fila)].font = fuente sheet['C'+str(fila)]= det['factura'] sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila)].font = fuente monto="{:.2f}".format(float(det['monto'])) sheet['D'+str(fila)].number_format = '"$"#,##0.00' sheet['D'+str(fila)]= "{:,}".format(float(monto)).replace(',','~').replace('.',',').replace('~','.') #sheet['D'+str(fila)]= monto sheet['F'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['F'+str(fila)].font = fuente if det['cliente']==False: sheet['F'+str(fila)]= '' elif det['cliente']==0: sheet['F'+str(fila)]= '' else: sheet['F'+str(fila)]= det['cliente'] sheet['G'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['G'+str(fila)].font = fuente if det['banco']==False: sheet['G'+str(fila)]= '' elif det['banco']==0: sheet['G'+str(fila)]= '' else: sheet['G'+str(fila)]= det['banco'] sheet['H'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['H'+str(fila)].font = fuente if det['documento']==False: sheet['H'+str(fila)]= '' else: sheet['H'+str(fila)]= det['documento'] sheet['I'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['I'+str(fila)].font = fuente if det['fecha_ch']==False: sheet['I'+str(fila)]= '' else: sheet['I'+str(fila)]= det['fecha_ch'] sheet['J'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top') sheet['J'+str(fila)].font = fuente if det['comentario']==False: sheet['J'+str(fila)]= '' elif det['comentario']==0: sheet['J'+str(fila)]= '' else: sheet['J'+str(fila)]= det['comentario'] total=total+float(det['monto']) fila=fila+1 sheet['C'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['C'+str(fila)].font = fuente2 sheet['C'+str(fila)]= 'TOTAL' sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila)].font = fuente2 total_1="{:.2f}".format(float(total)) sheet['D'+str(fila)].number_format = '"$"#,##0.00' sheet['D'+str(fila)]= "{:,}".format(float(total_1)).replace(',','~').replace('.',',').replace('~','.') dic['total']=total total_general = total_general + total fila= fila + 1 lista_datos.append(dic) sheet['C'+str(fila+1)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['C'+str(fila+1)].font = fuente2 sheet['C'+str(fila+1)]= 'TOTAL GENERAL' sheet['D'+str(fila+1)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila+1)].font = fuente2 total_2="{:.2f}".format(float(total_general)) sheet['D'+str(fila+1)].number_format = '"$"#,##0.00' sheet['D'+str(fila+1)]= "{:,}".format(float(total_2)).replace(',','~').replace('.',',').replace('~','.') sheet.merge_cells('B'+str(fila+2)+':C'+str(fila+2)) sheet['B'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['B'+str(fila+2)].font = fuente2 sheet['B'+str(fila+2)]= 'RESUMEN DE VALORES' fila=fila+3 for d in lista_datos: columnas_filas(sheet, 1, str(fila), 10.00) sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['A'+str(fila)].font = fuente2 sheet['A'+str(fila)]= d['tipo'] sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila)].font = fuente2 total_3="{:.2f}".format(float(d['total'])) sheet['D'+str(fila)].number_format = '"$"#,##0.00' sheet['D'+str(fila)]= "{:,}".format(float(total_3)).replace(',','~').replace('.',',').replace('~','.') sheet['H'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['H'+str(fila)].font = fuente2 sheet['H'+str(fila)]= d['cantidad'] sheet['I'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top') sheet['I'+str(fila)].font = fuente2 sheet['I'+str(fila)]= 'VECES' fila=fila+1 sheet['D'+str(fila+1)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top') sheet['D'+str(fila+1)].font = fuente2 total_4="{:.2f}".format(float(total_general)) sheet['D'+str(fila+1)].number_format = '"$"#,##0.00' sheet['D'+str(fila+1)]= "{:,}".format(float(total_4)).replace(',','~').replace('.',',').replace('~','.') columnas_filas(sheet, 1, str(fila+5), 10.00) sheet.merge_cells('D'+str(fila+5)+':F'+str(fila+5)) sheet['D'+str(fila+5)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='center') sheet['D'+str(fila+5)].font = fuente2 sheet['D'+str(fila+5)]= usuario_id poner_border(sheet,fila+5,4,'none','thin','none','none') poner_border(sheet,fila+5,5,'none','thin','none','none') poner_border(sheet,fila+5,6,'none','thin','none','none') columnas_filas(sheet, 1, str(fila+6), 8.00) sheet.merge_cells('D'+str(fila+6)+':F'+str(fila+6)) sheet['D'+str(fila+6)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='center') sheet['D'+str(fila+6)].font = fuente2 sheet['D'+str(fila+6)]= fecha columnas_filas(sheet, 1, str(fila+7), 20.00) sheet.merge_cells('D'+str(fila+7)+':F'+str(fila+7)) sheet['D'+str(fila+7)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top') sheet['D'+str(fila+7)].font = fuente2 sheet['D'+str(fila+7)]= filtro

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1

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null

0

7

54362bafc1f4971f35c228c9e3e57a3ae2201271

124

py

Python

web_google_maps/models/__init__.py

Yousif-Mobark/odoo11_cutom

35a09266a1d4d74569316886019c11ce41e9216b

[ "Apache-2.0" ]

null

web_google_maps/models/__init__.py

Yousif-Mobark/odoo11_cutom

35a09266a1d4d74569316886019c11ce41e9216b

[ "Apache-2.0" ]

null

web_google_maps/models/__init__.py

Yousif-Mobark/odoo11_cutom

35a09266a1d4d74569316886019c11ce41e9216b

[ "Apache-2.0" ]

1

2020-04-18T02:42:54.000Z

# -*- coding: utf-8 -*- # License AGPL-3 from . import ir_act_window_view from . import ir_ui_view from . import res_config

20.666667

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null

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1

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7

5453fd0caf52bc952d94eb655c02d79ae5dcbb15

1,872

py

Python

opentera-webrtc-native-client/OpenteraWebrtcNativeClient/python/test/configurations/signaling_server_configuration_test.py

introlab/opentera-webrtc

cf92ccd0b239646f6caf68e3638b8f28598ea609

[ "Apache-2.0" ]

12

2021-05-30T18:32:36.000Z

2022-03-25T12:31:57.000Z

opentera-webrtc-native-client/OpenteraWebrtcNativeClient/python/test/configurations/signaling_server_configuration_test.py

introlab/opentera-webrtc

cf92ccd0b239646f6caf68e3638b8f28598ea609

[ "Apache-2.0" ]

22

2021-03-17T12:18:42.000Z

2022-03-19T19:12:51.000Z

opentera-webrtc-native-client/OpenteraWebrtcNativeClient/python/test/configurations/signaling_server_configuration_test.py

introlab/opentera-webrtc-teleop

ecb671635832d6d66e0f2f0a7e90b0877ce7c338

[ "Apache-2.0" ]

1

2022-02-07T21:30:33.000Z

import unittest import opentera_webrtc_native_client as webrtc class SignalingServerConfigurationTestCase(unittest.TestCase): def test_create__url_client_name_room__should_set_the_attributes(self): testee = webrtc.SignalingServerConfiguration.create('url', 'name', 'room') self.assertEqual(testee.url, 'url') self.assertEqual(testee.client_name, 'name') self.assertEqual(testee.client_data, None) self.assertEqual(testee.room, 'room') self.assertEqual(testee.password, '') def test_create__url_client_name_client_data_room__should_set_the_attributes(self): testee = webrtc.SignalingServerConfiguration.create('url', 'name', {'data': 10}, 'room') self.assertEqual(testee.url, 'url') self.assertEqual(testee.client_name, 'name') self.assertEqual(testee.client_data, {'data': 10}) self.assertEqual(testee.room, 'room') self.assertEqual(testee.password, '') def test_create__url_client_name_room_password__should_set_the_attributes(self): testee = webrtc.SignalingServerConfiguration.create('url', 'name', room='room', password='password') self.assertEqual(testee.url, 'url') self.assertEqual(testee.client_name, 'name') self.assertEqual(testee.client_data, None) self.assertEqual(testee.room, 'room') self.assertEqual(testee.password, 'password') def test_create__url_client_name_client_data_room_password__should_set_the_attributes(self): testee = webrtc.SignalingServerConfiguration.create('url', 'name', {'data': 10}, 'room', 'password') self.assertEqual(testee.url, 'url') self.assertEqual(testee.client_name, 'name') self.assertEqual(testee.client_data, {'data': 10}) self.assertEqual(testee.room, 'room') self.assertEqual(testee.password, 'password')

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108

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1

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null

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1

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1

0

11

548f4928c3e8b8f6c47d9368d169e3c4d02a7f7b

254

py

Python

utility/__init__.py

LatvianPython/wind-experience

b634c020dff0a01152bb95b38e5f6f0e368d47f5

[ "MIT" ]

2

2018-12-20T20:31:21.000Z

2018-12-29T14:51:42.000Z

utility/__init__.py

LatvianPython/wind-experience

b634c020dff0a01152bb95b38e5f6f0e368d47f5

[ "MIT" ]

null

utility/__init__.py

LatvianPython/wind-experience

b634c020dff0a01152bb95b38e5f6f0e368d47f5

[ "MIT" ]

null

from utility.jupyter_utility import output_score from utility.jupyter_utility import output_feature_importance from utility.jupyter_utility import read_model_data from utility.data_download import download_all from utility.data_download import next_date

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254

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1

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1

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1

0

7

54c68b2fa2d41fc59fdfed232f3f11d7a780d3b0

1,428

py

Python

tests/core/shh-module/test_shh_filter.py

jsmeng324/web3.py

6f240dcf4f37f55f0ac09c90985674233f344c37

[ "MIT" ]

null

tests/core/shh-module/test_shh_filter.py

jsmeng324/web3.py

6f240dcf4f37f55f0ac09c90985674233f344c37

[ "MIT" ]

null

tests/core/shh-module/test_shh_filter.py

jsmeng324/web3.py

6f240dcf4f37f55f0ac09c90985674233f344c37

[ "MIT" ]

null

from web3.utils.compat import sleep def test_shh_sync_filter(web3, skip_if_testrpc): skip_if_testrpc(web3) topic = web3.toHex(text="test") shh_filter = web3.shh.filter({"topics": [topic]}) payloads = [] payloads.append(str.encode("payload1")) web3.shh.post({ "topics": [topic], "payload": web3.toHex(text=payloads[-1]), }) sleep(1) payloads.append(str.encode("payload2")) web3.shh.post({ "topics": [topic], "payload": web3.toHex(text=payloads[-1]), }) sleep(1) received_messages = shh_filter.get_new_entries() assert len(received_messages) > 1 for message in received_messages: assert message["payload"] in payloads def test_shh_async_filter(web3, skip_if_testrpc): skip_if_testrpc(web3) received_messages = [] topic = web3.toHex(text="test") shh_filter = web3.shh.filter({"topics": [topic]}) shh_filter.watch(received_messages.append) payloads = [] payloads.append(str.encode("payload1")) web3.shh.post({ "topics": [topic], "payload": web3.toHex(text=payloads[-1]), }) sleep(1) payloads.append(str.encode("payload2")) web3.shh.post({ "topics": [topic], "payload": web3.toHex(text=payloads[-1]), }) sleep(1) assert len(received_messages) > 1 for message in received_messages: assert message["payload"] in payloads

25.963636

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175

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5

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null

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0

null

0

7

49b5697d2dd18bfd436e0a4fe67791fd6c626d35

17,874

py

Python

tests/unit/states/test_nsxt_ip_blocks.py

kdsalvy/salt-ext-modules-vmware-1

9fdc941692e4c526f575f33b2ce23c1470582934

[ "Apache-2.0" ]

10

2021-11-02T20:24:44.000Z

2022-03-11T05:54:27.000Z

tests/unit/states/test_nsxt_ip_blocks.py

cmcmarrow/salt-ext-modules-vmware

c546a9f9ae121b7399dabae82f714117d0ab558d

[ "Apache-2.0" ]

83

2021-10-01T15:13:02.000Z

2022-03-31T16:22:40.000Z

tests/unit/states/test_nsxt_ip_blocks.py

cmcmarrow/salt-ext-modules-vmware

c546a9f9ae121b7399dabae82f714117d0ab558d

[ "Apache-2.0" ]

15

2021-09-30T23:17:27.000Z

2022-03-23T06:54:22.000Z

""" Unit Tests for nsxt_ip_blocks state """ from unittest.mock import MagicMock from unittest.mock import patch import pytest from saltext.vmware.states import nsxt_ip_blocks @pytest.fixture def configure_loader_modules(): return {nsxt_ip_blocks: {}} def _get_mocked_data(): mocked_ok_response = { "resource_type": "IpBlock", "id": "9b636d18-49a2-4e63-a1ec-10c0e50d554d", "cidr": "1.1.1.1/16", "display_name": "Create-from_salt", "description": "Check", "_create_user": "admin", "_create_time": 1615905790948, "_last_modified_user": "admin", "_last_modified_time": 1615905790948, "_system_owned": False, "_protection": "NOT_PROTECTED", "_revision": 0, } mocked_error_response = { "error": "The credentials were incorrect or the account specified has been locked." } mocked_hostname = "nsx-t.vmware.com" return mocked_hostname, mocked_ok_response, mocked_error_response def test_present_state_when_error_from_get_by_display_name(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name = MagicMock(return_value=mocked_error_response) with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name} ): result = nsxt_ip_blocks.present( name="test_present_using_basic_auth", hostname=mocked_hostname, username="username", password="password", cidr="1.1.1.1/24", display_name=mocked_ok_response["display_name"], ) assert result is not None assert result["changes"] == {} assert ( result["comment"] == "The credentials were incorrect or the account specified has been locked." ) assert not result["result"] def test_present_state_when_error_from_create(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name = MagicMock(return_value={"results": []}) mock_create = MagicMock(return_value=mocked_error_response) with patch.dict( nsxt_ip_blocks.__salt__, { "nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name, "nsxt_ip_blocks.create": mock_create, }, ): result = nsxt_ip_blocks.present( name="test_present_using_basic_auth", hostname=mocked_hostname, username="username", password="password", cidr="1.1.1.1/24", display_name=mocked_ok_response["display_name"], ) assert result is not None assert result["changes"] == {} assert ( result["comment"] == "The credentials were incorrect or the account specified has been locked." ) assert not result["result"] def test_present_state_when_error_from_update(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name = MagicMock(return_value={"results": [mocked_ok_response]}) mock_create = MagicMock(return_value=mocked_error_response) with patch.dict( nsxt_ip_blocks.__salt__, { "nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name, "nsxt_ip_blocks.update": mock_create, }, ): result = nsxt_ip_blocks.present( name="test_present_using_basic_auth", hostname=mocked_hostname, username="username", password="password", cidr="1.1.1.1/24", description="Sample description", display_name=mocked_ok_response["display_name"], ) assert result is not None assert result["changes"] == {} assert ( result["comment"] == "The credentials were incorrect or the account specified has been locked." ) assert not result["result"] def test_present_state_during_update_to_add_a_new_field(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mocked_updated_response = mocked_ok_response.copy() mocked_ok_response.pop("description") mock_get_using_display_name = MagicMock(return_value={"results": [mocked_ok_response]}) mocked_updated_response["description"] = "Sample description" mock_create = MagicMock(return_value=mocked_updated_response) with patch.dict( nsxt_ip_blocks.__salt__, { "nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name, "nsxt_ip_blocks.update": mock_create, }, ): result = nsxt_ip_blocks.present( name="test_present_using_basic_auth", hostname=mocked_hostname, username="username", password="password", cidr="1.1.1.1/16", description="Sample description", display_name=mocked_ok_response["display_name"], ) assert result is not None assert result["changes"]["old"] == mocked_ok_response assert result["changes"]["new"] == mocked_updated_response assert result["comment"] == "Updated IP Block Create-from_salt" assert result["result"] def test_present_to_create_when_module_returns_success_response(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock(return_value={"results": []}) mock_create_response = MagicMock(return_value=mocked_ok_response) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, { "nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response, "nsxt_ip_blocks.create": mock_create_response, }, ): result = nsxt_ip_blocks.present( name="test_present_using_basic_auth", hostname=mocked_hostname, username="username", password="password", cidr="1.1.1.1/16", display_name=display_name, ) assert result is not None assert result["changes"] == {"new": mocked_ok_response, "old": None} assert result["comment"] == "Created IP Block {}".format(display_name) assert result["result"] def test_present_to_update_when_module_returns_success_response(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mocked_updated_ip_block = mocked_ok_response.copy() mocked_updated_ip_block["description"] = "Updated Using Salt" mock_get_using_display_name_response = MagicMock(return_value={"results": [mocked_ok_response]}) mock_update_response = MagicMock(return_value=mocked_updated_ip_block) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, { "nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response, "nsxt_ip_blocks.update": mock_update_response, }, ): result = nsxt_ip_blocks.present( name="test_present_using_basic_auth", hostname=mocked_hostname, username="username", password="password", cidr="1.1.1.1/24", display_name=display_name, description="Updated Using Salt", ) assert result is not None assert result["changes"] == {"new": mocked_updated_ip_block, "old": mocked_ok_response} assert result["comment"] == "Updated IP Block {}".format(display_name) assert result["result"] def test_present_to_update_when_user_input_and_existing_ip_block_has_identical_fields(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock(return_value={"results": [mocked_ok_response]}) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response}, ): result = nsxt_ip_blocks.present( name="test_present_using_basic_auth", hostname=mocked_hostname, username="username", password="password", cidr="1.1.1.1/16", display_name=display_name, description="Check", ) assert result is not None assert len(result["changes"]) == 0 assert result["comment"] == "IP Address Block exists already, no action to perform" assert result["result"] def test_present_state_for_create_when_opts_test_is_true(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock(return_value={"results": []}) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response}, ): with patch.dict(nsxt_ip_blocks.__opts__, {"test": True}): result = nsxt_ip_blocks.present( name="test_absent_using_basic_auth", hostname=mocked_hostname, username="username", cidr="1.1.1.1/24", password="password", display_name=display_name, ) assert result is not None assert len(result["changes"]) == 0 assert result["comment"] == "State present will create IP Block with name {}".format( display_name ) assert result["result"] is None def test_present_state_for_update_when_opts_test_is_true(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock(return_value={"results": [mocked_ok_response]}) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response}, ): with patch.dict(nsxt_ip_blocks.__opts__, {"test": True}): result = nsxt_ip_blocks.present( name="test_absent_using_basic_auth", hostname=mocked_hostname, username="username", cidr="1.1.1.1/24", password="password", display_name=display_name, ) assert result is not None assert len(result["changes"]) == 0 assert result["comment"] == "State present will update IP Block with name {}".format( display_name ) assert result["result"] is None def test_present_state_when_get_by_display_name_returns_multiple_blocks_with_same_display_name(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock( return_value={"results": [mocked_ok_response, mocked_ok_response]} ) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response}, ): with patch.dict(nsxt_ip_blocks.__opts__, {"test": True}): result = nsxt_ip_blocks.present( name="test_absent_using_basic_auth", hostname=mocked_hostname, username="username", password="password", cidr="1.1.1.1/24", display_name=display_name, ) assert result is not None assert len(result["changes"]) == 0 assert result["comment"] == "Multiple IP Blocks found for the provided display name {}".format( display_name ) assert not result["result"] def test_absent_state_to_delete_when_module_returns_success_response(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock(return_value={"results": [mocked_ok_response]}) mock_delete_response = MagicMock(ok=True, return_value="IP Block deleted successfully") display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, { "nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response, "nsxt_ip_blocks.delete": mock_delete_response, }, ): result = nsxt_ip_blocks.absent( name="test_absent_using_basic_auth", hostname=mocked_hostname, username="username", password="password", display_name=display_name, ) assert result is not None assert result["changes"] == {"new": None, "old": mocked_ok_response} assert result["comment"] == "Deleted IP Block {}".format(display_name) assert result["result"] def test_absent_state_when_object_to_delete_does_not_exists(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock(return_value={"results": []}) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response}, ): result = nsxt_ip_blocks.absent( name="test_publish_using_basic_auth", hostname=mocked_hostname, username="username", password="password", display_name=display_name, ) assert result is not None assert result["changes"] == {} assert result["comment"] == "No IP Address Block found with name {}".format(display_name) assert result["result"] def test_absent_state_to_delete_when_opts_test_mode_is_true(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock(return_value={"results": [mocked_ok_response]}) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response}, ): with patch.dict(nsxt_ip_blocks.__opts__, {"test": True}): result = nsxt_ip_blocks.absent( name="test_absent_using_basic_auth", hostname=mocked_hostname, username="username", password="password", display_name=display_name, ) assert result is not None assert len(result["changes"]) == 0 assert result["comment"] == "State absent will delete IP Block with name {}".format( display_name ) assert result["result"] is None def test_absent_state_when_object_to_delete_doesn_not_exists_and_opts_test_mode_is_true(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock(return_value={"results": []}) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response}, ): with patch.dict(nsxt_ip_blocks.__opts__, {"test": True}): result = nsxt_ip_blocks.absent( name="test_absent_using_basic_auth", hostname=mocked_hostname, username="username", password="password", display_name=display_name, ) assert result is not None assert len(result["changes"]) == 0 assert result[ "comment" ] == "State absent will do nothing as no IP Block found with name {}".format(display_name) assert result["result"] is None def test_absent_state_when_get_by_display_name_returns_multiple_blocks_with_same_display_name(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name_response = MagicMock( return_value={"results": [mocked_ok_response, mocked_ok_response]} ) display_name = mocked_ok_response["display_name"] with patch.dict( nsxt_ip_blocks.__salt__, {"nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name_response}, ): with patch.dict(nsxt_ip_blocks.__opts__, {"test": True}): result = nsxt_ip_blocks.absent( name="test_absent_using_basic_auth", hostname=mocked_hostname, username="username", password="password", display_name=display_name, ) assert result is not None assert len(result["changes"]) == 0 assert result["comment"] == "Multiple IP Blocks found for the provided display name {}".format( display_name ) assert not result["result"] def test_absent_when_nsxt_ip_blocks_delete_returns_error(): mocked_hostname, mocked_ok_response, mocked_error_response = _get_mocked_data() mock_get_using_display_name = MagicMock(return_value={"results": [mocked_ok_response]}) mock_create = MagicMock(return_value=mocked_error_response) with patch.dict( nsxt_ip_blocks.__salt__, { "nsxt_ip_blocks.get_by_display_name": mock_get_using_display_name, "nsxt_ip_blocks.delete": mock_create, }, ): result = nsxt_ip_blocks.absent( name="test_present_using_basic_auth", hostname=mocked_hostname, username="username", password="password", display_name=mocked_ok_response["display_name"], ) assert result is not None assert result["changes"] == {} assert ( result["comment"] == "The credentials were incorrect or the account specified has been locked." ) assert not result["result"]

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7

49dfe9d3115d73c0ef904575f169d4b7a2e2350f

9,872

py

Python

MultiCSVFetchYahooDiv.py

adamrvfisher/TechnicalAnalysisLibrary

38a22b2b2b5052623f81edb11b3c5460fc254e45

[ "Apache-2.0" ]

3

2019-04-26T11:13:14.000Z

2020-01-10T05:58:16.000Z

MultiCSVFetchYahooDiv.py

adamrvfisher/TechnicalAnalysisLibrary

38a22b2b2b5052623f81edb11b3c5460fc254e45

[ "Apache-2.0" ]

null

MultiCSVFetchYahooDiv.py

adamrvfisher/TechnicalAnalysisLibrary

38a22b2b2b5052623f81edb11b3c5460fc254e45

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ @author: Adam Reinhold Von Fisher - https://www.linkedin.com/in/adamrvfisher/ """ #This is an HTML scraper and formatting tool for dividend time series database construction #Import modules from pandas import read_csv import requests import pandas as pd import os import time from io import StringIO from CrumbCatcher import CrumbCatcher from pandas.parser import CParserError #Read in data #df = read_csv('refdfser.csv', sep = ',') df = pd.read_pickle('C:\\Users\\AmatVictoriaCuramIII\\Desktop\\Python\\Universe2018') #symbol = df.Symbol.values #Iterable ranger = range(0,len(df)) #For number of tickers for i in ranger[:5]: try: #Assign ticker ticker = str(df[i][:-4]) #Generate crumb artificialcrumb = CrumbCatcher(ticker) #Generate download url downloadurl = ("https://query1.finance.yahoo.com/v7/finance/download/" + ticker + "?period1=-631123200&period2=1598374000&interval=1d&events=div&crumb=" + artificialcrumb) #Line optional mainurl = ("https://finance.yahoo.com/quote/" + ticker + "/history?p=" + ticker) #Get response response = requests.post(downloadurl)#, data=CookieDict) #Format text datastr = response.text formatter = StringIO(datastr) strdf = pd.read_csv(formatter, sep = ',') #If bad response if strdf.columns[0] == '{"chart":{"result":null': print('The URL failed for ' + ticker) continue #Format date index strdf = strdf.set_index('Date') strdf.index = pd.to_datetime(strdf.index, format = "%Y/%m/%d") if len(strdf) == 0: print("No dividend history for " + str(df[i][:-4]) ) continue #Save to CSV strdf.to_csv(("F:\\Users\\AmatVictoriaCuram\\TemporaryCSV\\"+ ticker + "div.csv")) #Iteration tracking print(ticker) continue #Bad response except CParserError: print('Parser failed for ' + ticker) continue except ConnectionError: try: #Sleep, then retry last ticker, continue loop. print('ConnectionError on ' + str(ticker) + '.') print('Sleeping for 5 min.') time.sleep(301) print('Parsing for ' + ticker + '.') #Retrying parse #Generate crumb artificialcrumb = CrumbCatcher(ticker) #Generate download url downloadurl = ("https://query1.finance.yahoo.com/v7/finance/download/" + ticker + "?period1=-631123200&period2=1598374000&interval=1d&events=div&crumb=" + artificialcrumb) #Line optional mainurl = ("https://finance.yahoo.com/quote/" + ticker + "/history?p=" + ticker) #Get response response = requests.post(downloadurl)#, data=CookieDict) #Format text datastr = response.text formatter = StringIO(datastr) strdf = pd.read_csv(formatter, sep = ',') #If bad response if strdf.columns[0] == '{"chart":{"result":null': print('The URL failed for ' + ticker) continue #Format date index strdf = strdf.set_index('Date') strdf.index = pd.to_datetime(strdf.index, format = "%Y/%m/%d") #Save to CSV strdf.to_csv(("F:\\Users\\AmatVictoriaCuram\\TemporaryCSV\\"+ ticker + "div.csv")) #Iteration tracking print(ticker) continue #Bad response except CParserError: print('Parser failed for ' + ticker + '.') continue except requests.exceptions.SSLError: try: print('SSLError after Connection Error for ' + ticker + '.') #Sleep, then retry last ticker, continue loop. print('Sleeping for 61 seconds.') time.sleep(61) print('Parsing for ' + ticker + '.') #Retrying parse #Generate crumb artificialcrumb = CrumbCatcher(ticker) #Generate download url downloadurl = ("https://query1.finance.yahoo.com/v7/finance/download/" + ticker + "?period1=-631123200&period2=1598374000&interval=1d&events=div&crumb=" + artificialcrumb) #Line optional mainurl = ("https://finance.yahoo.com/quote/" + ticker + "/history?p=" + ticker) #Get response response = requests.post(downloadurl)#, data=CookieDict) #Format text datastr = response.text formatter = StringIO(datastr) strdf = pd.read_csv(formatter, sep = ',') #If bad response if strdf.columns[0] == '{"chart":{"result":null': print('The URL failed for ' + ticker) continue #Format date index strdf = strdf.set_index('Date') strdf.index = pd.to_datetime(strdf.index, format = "%Y/%m/%d") #Save to CSV strdf.to_csv(("F:\\Users\\AmatVictoriaCuram\\TemporaryCSV\\"+ ticker + "div.csv")) #Iteration tracking print(ticker) continue #Bad response except CParserError: print('Parser failed for ' + ticker + '.') continue except requests.exceptions.SSLError: print('Double SSLError after ConnectionError for ' + ticker + '.') continue except ConnectionError: print('Double ConnectionError for ' + ticker + '.') continue except requests.exceptions.SSLError: try: #Sleep, then retry last ticker, continue loop. print('SSLError on ' + str(ticker) + '.') print('Sleeping for 61 seconds.') time.sleep(61) print('Parsing for ' + ticker + '.') #Retrying parse #Generate crumb artificialcrumb = CrumbCatcher(ticker) #Generate download url downloadurl = ("https://query1.finance.yahoo.com/v7/finance/download/" + ticker + "?period1=-631123200&period2=1598374000&interval=1d&events=div&crumb=" + artificialcrumb) #Line optional mainurl = ("https://finance.yahoo.com/quote/" + ticker + "/history?p=" + ticker) #Get response response = requests.post(downloadurl)#, data=CookieDict) #Format text datastr = response.text formatter = StringIO(datastr) strdf = pd.read_csv(formatter, sep = ',') #If bad response if strdf.columns[0] == '{"chart":{"result":null': print('The URL failed for ' + ticker) continue #Format date index strdf = strdf.set_index('Date') strdf.index = pd.to_datetime(strdf.index, format = "%Y/%m/%d") #Save to CSV strdf.to_csv(("F:\\Users\\AmatVictoriaCuram\\TemporaryCSV\\"+ ticker + "div.csv")) #Iteration tracking print(ticker) continue #Bad response except CParserError: print('Parser failed for ' + ticker + '.') continue except requests.exceptions.SSLError: print('Double SSLError for ' + ticker + '.') continue except ConnectionError: try: #Sleep, then retry last ticker, continue loop. print('ConnectionError after SSLError on ' + str(ticker) + '.') print('Sleeping for 61 seconds.') time.sleep(61) print('Parsing for ' + ticker + '.') #Retrying parse #Generate crumb artificialcrumb = CrumbCatcher(ticker) #Generate download url downloadurl = ("https://query1.finance.yahoo.com/v7/finance/download/" + ticker + "?period1=-631123200&period2=1598374000&interval=1d&events=div&crumb=" + artificialcrumb) #Line optional mainurl = ("https://finance.yahoo.com/quote/" + ticker + "/history?p=" + ticker) #Get response response = requests.post(downloadurl)#, data=CookieDict) #Format text datastr = response.text formatter = StringIO(datastr) strdf = pd.read_csv(formatter, sep = ',') #If bad response if strdf.columns[0] == '{"chart":{"result":null': print('The URL failed for ' + ticker) continue #Format date index strdf = strdf.set_index('Date') strdf.index = pd.to_datetime(strdf.index, format = "%Y/%m/%d") #Save to CSV strdf.to_csv(("F:\\Users\\AmatVictoriaCuram\\TemporaryCSV\\"+ ticker + "div.csv")) #Iteration tracking print(ticker) continue #Bad response except CParserError: print('Parser failed after SSLError and ConnectionError for ' + ticker + '.') continue except requests.exceptions.SSLError: print('SSLError after SSLError and ConnectionEror for ' + ticker + '.') continue

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0.045999

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9,872

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0

0.823129

0

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0

1

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0

null

0

1

0

1

0

null

0

7

49eed69cc274e045b62b0bca0086550ec563f26b

4,343

py

Python

utils/image_compression.py

CeZh/Camera_Perception_Quality

22a6e6140c21557be215fd94eff75a2ede1d7136

[ "MIT" ]

2

2022-03-09T15:46:29.000Z

2022-03-11T19:47:01.000Z

utils/image_compression.py

CeZh/Camera_Perception_Quality

22a6e6140c21557be215fd94eff75a2ede1d7136

[ "MIT" ]

null

utils/image_compression.py

CeZh/Camera_Perception_Quality

22a6e6140c21557be215fd94eff75a2ede1d7136

[ "MIT" ]

1

2022-03-11T19:47:46.000Z

from torchvision import transforms import torch from utils.superpixel_slic import superpixel import numpy as np from torch_scatter import scatter_mean, scatter_std def transform_train(img, img_dim, **kwargs): # With superpixel if kwargs: img_transform_init = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.ToTensor() ]) img_tensor = img_transform_init(img) super_pixel = kwargs['super_pixel'] data = superpixel(img_tensor, super_pixel) normalize_transform = transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225)) final_transform = transforms.Compose([transforms.Resize((img_dim, img_dim)), transforms.Normalize(mean = (0.485, 0.456, 0.406, 0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225, 0.229, 0.224, 0.225))]) img = normalize_transform(data.img) data.img_super = final_transform(data.img_super) data.x = scatter_mean(img.view(img.shape[1]*img.shape[2], img.shape[0]), data.seg.view(img.shape[1]*img.shape[2]), dim=0) data.x_std = scatter_std(img.view(img.shape[1]*img.shape[2], img.shape[0]), data.seg.view(img.shape[1]*img.shape[2]), dim=0) data.x = torch.cat([data.x, data.x_std], dim=1) super_index, super_counts = torch.unique(data.seg, return_counts=True) data.pos = torch.cat([data.pos.int(), super_counts.unsqueeze(1)], dim=1) if data.pos.shape[0] < super_pixel['segments']: pos_pad = torch.zeros(super_pixel['segments']-data.pos.shape[0], 3) x_pad = torch.zeros(super_pixel['segments']-data.x.shape[0], 6) data.pos = torch.cat((data.pos, pos_pad), dim=0) data.x = torch.cat((data.x, x_pad), dim=0) return data # No superpixel img_transform = transforms.Compose( [transforms.RandomHorizontalFlip(), transforms.Resize((img_dim, img_dim)), transforms.ToTensor(), transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))]) trans_img = img_transform(img) return trans_img def transform_val(img, img_dim, **kwargs): if kwargs: img_transform_init = transforms.Compose([ transforms.ToTensor() ]) img_tensor = img_transform_init(img) super_pixel = kwargs['super_pixel'] data = superpixel(img_tensor, super_pixel) normalize_transform = transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) final_transform = transforms.Compose([transforms.Resize((img_dim, img_dim)), transforms.Normalize(mean=(0.485, 0.456, 0.406, 0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225, 0.229, 0.224, 0.225))]) img = normalize_transform(data.img) data.img_super = final_transform(data.img_super) data.x = scatter_mean(img.view(img.shape[1] * img.shape[2], img.shape[0]), data.seg.view(img.shape[1] * img.shape[2]), dim=0) data.x_std = scatter_std(img.view(img.shape[1] * img.shape[2], img.shape[0]), data.seg.view(img.shape[1] * img.shape[2]), dim=0) data.x = torch.cat([data.x, data.x_std], dim=1) super_index, super_counts = torch.unique(data.seg, return_counts=True) data.pos = torch.cat([data.pos.int(), super_counts.unsqueeze(1)], dim=1) if data.pos.shape[0] < super_pixel['segments']: pos_pad = torch.zeros(super_pixel['segments'] - data.pos.shape[0], 3) x_pad = torch.zeros(super_pixel['segments'] - data.x.shape[0], 6) data.pos = torch.cat((data.pos, pos_pad), dim=0) data.x = torch.cat((data.x, x_pad), dim=0) return data img_transform = transforms.Compose( [transforms.Resize((img_dim, img_dim)), transforms.ToTensor(), transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))]) trans_img = img_transform(img) return trans_img

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1

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0

7

49fb89f12f62f836ea70442948dad8fdca878d48

200

py

Python

scopus/utils/__init__.py

crew102/scopus

d8791c162cef4c2f830d983b435333d9d8eaf472

[ "MIT" ]

null

scopus/utils/__init__.py

crew102/scopus

d8791c162cef4c2f830d983b435333d9d8eaf472

[ "MIT" ]

null

scopus/utils/__init__.py

crew102/scopus

d8791c162cef4c2f830d983b435333d9d8eaf472

[ "MIT" ]

null

from scopus.utils.create_config import * from scopus.utils.get_content import * from scopus.utils.get_encoded_text import * from scopus.utils.parse_content import * from scopus.utils.startup import *

33.333333

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7

b7163108c80b92e090b901f494eead8db2fe93d5

7,284

py

Python

src/tests/test_ChangelogUpdater.py

wirecard/extension-release-info-updater

286fc2be9f72735653c79683c004459f74195037

[ "MIT" ]

null

src/tests/test_ChangelogUpdater.py

wirecard/extension-release-info-updater

286fc2be9f72735653c79683c004459f74195037

[ "MIT" ]

null

src/tests/test_ChangelogUpdater.py

wirecard/extension-release-info-updater

286fc2be9f72735653c79683c004459f74195037

[ "MIT" ]

null

from unittest import TestCase from src.ChangelogUpdater import ChangelogFileUpdater class TestChangelogUpdater(TestCase): def setUp(self) -> None: self.changelog_updater = ChangelogFileUpdater('woocommerce', 'v3.2.2', 'v3.2.1', ['7.1', '7.2'], ['7.2'], ['3.3.4', '3.8.0'], ['3.8.0']) self.changelog_updater_with_platform = ChangelogFileUpdater('woocommerce', 'v3.2.2', 'v3.2.1', ['7.1', '7.2'], ['7.2'], ['3.3.4', '3.8.0'], ['3.8.0'], ['5.0.3', '5.3'], ['5.3']) def test_get_compatible_php_versions_table_header_string(self): new_table_row = self.changelog_updater.get_compatible_php_versions_table_header_string( "| Overview " "| Woocommerce and Wordpress version " "| PHP 5.6 | PHP 7.0 | PHP 7.1 | PHP 7.2 |") self.assertEqual(new_table_row, "| Overview " "| Woocommerce and Wordpress version " "| PHP 7.1 | PHP 7.2 |") def test_get_tested_php_versions_table_string(self): new_table_row = self. \ changelog_updater. \ get_tested_php_versions_table_string("| Woocommerce version 3.8.0, Wordpress version 5.3 " "| :x: " "| :x: " "| :x: " "| " + u"\u2705" + " |") self.assertEqual(new_table_row, "| Woocommerce version 3.8.0, Wordpress version 5.3 " "| :x: " "| " + u"\u2705" + " |") def test_get_compatible_php_versions_table_string(self): new_table_row = self. \ changelog_updater. \ get_compatible_php_versions_table_string("| Woocommerce version 3.3.4 - 3.8.0, Wordpress version 5.0.3 - " "5.3 " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " |") self.assertEqual("| Woocommerce version 3.3.4 - 3.8.0, Wordpress version 5.0.3 - 5.3 " "| " + u"\u2705" + " " "| " + u"\u2705" + " |", new_table_row) def test_get_tested_shopsystem_and_platform_versions_table_string_no_platform(self): new_table_row = self. \ changelog_updater. \ get_tested_shopsystem_and_platform_versions_table_string("| Woocommerce version 1.1.1 " "| :x: " "| :x: " "| :x: " "| " + u"\u2705" + " |") self.assertEqual("| Woocommerce version 3.8.0 " "| :x: " "| :x: " "| :x: " "| " + u"\u2705" + " |", new_table_row) def test_get_compatibility_shopsystem_and_platform_versions_table_string_no_platform(self): new_table_row = self. \ changelog_updater. \ get_compatibility_shopsystem_and_platform_versions_table_string("| Woocommerce version 1.1.1 - 2.2.2 " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " |") self.assertEqual("| Woocommerce version 3.3.4 - 3.8.0 " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " |", new_table_row) def test_get_tested_shopsystem_and_platform_versions_table_string_with_platform(self): new_table_row = self. \ changelog_updater_with_platform. \ get_tested_shopsystem_and_platform_versions_table_string("| Woocommerce version 1.1.1," " Wordpress version 2.3 " "| :x: " "| :x: " "| :x: " "| " + u"\u2705" + " |") self.assertEqual("| Woocommerce version 3.8.0, Wordpress version 5.3 " "| :x: " "| :x: " "| :x: " "| " + u"\u2705" + " |", new_table_row) def test_get_compatiblity_shopsystem_and_platform_versions_table_string_with_platform(self): new_table_row = self. \ changelog_updater_with_platform. \ get_compatibility_shopsystem_and_platform_versions_table_string("| Woocommerce version 1.1.1 - 2.2.2, " "Wordpress version 3.3.3 - 4.4.4 " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " |") self.assertEqual("| Woocommerce version 3.3.4 - 3.8.0," " Wordpress version 5.0.3 - 5.3 " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " " "| " + u"\u2705" + " |", new_table_row) def test_get_row_separator_table_row(self): new_table_row = self.changelog_updater.get_row_separator_table_row( "|-----------------------" "|------------------------------------------------------------------" "|:-------:" "|:-------:" "|:-------:" "|:-------:|") self.assertEqual(new_table_row, "|-----------------------" "|------------------------------------------------------------------" "|:-------:" "|:-------:|")

60.7

134

0.337589

536

7,284

4.289179

0.097015

0.073075

0.076555

0.083515

0.905611

0.863854

0.797738

0.753371

0.724228

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0

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7,284

119

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0

null

0

1

0

1

0

null

0

7

b74172faee86cee11772cbf669f9ac52a4d7d12d

97

py

Python

pltools/train/__init__.py

PhoenixDL/PytorchLightningTools

86185062d4792e6d5eae002a5594bb7b900106a1

[ "MIT" ]

3

2020-05-18T06:34:52.000Z

2020-07-17T07:11:57.000Z

pltools/train/__init__.py

PhoenixDL/PytorchLightningTools

86185062d4792e6d5eae002a5594bb7b900106a1

[ "MIT" ]

6

2021-06-25T18:21:06.000Z

2021-06-25T18:21:32.000Z

pltools/train/__init__.py

PhoenixDL/PytorchLightningTools

86185062d4792e6d5eae002a5594bb7b900106a1

[ "MIT" ]

1

2020-05-18T06:34:56.000Z

from pltools.train.module import Module from pltools.train.lr_find import lr_find, plot_lr_curve

32.333333

56

0.85567

17

97

4.647059

0.529412

0.278481

0.405063

0

0.092784

97

2

57

48.5

0.897727

0

1

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true

0

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null

0

1

0

1

0

1

0

7

3ff471f6caa3e8921fb15437227211c7b3c5897e

12,643

py

Python

users/arxiv/users/auth/sessions/tests/test_unit.py

cul-it/arxiv-accounts

9d237ffc7de4ac7f3c94ad615252681792f53fb5

[ "MIT" ]

11

2018-12-29T17:55:16.000Z

2021-11-05T12:26:29.000Z

users/arxiv/users/auth/sessions/tests/test_unit.py

Https-github-com-sulaeman51/arxiv-auth

754fa083b0c8b43932f7393f5a4ab67d9f9f0444

[ "MIT" ]

24

2019-01-25T18:19:21.000Z

2022-02-04T01:04:29.000Z

users/arxiv/users/auth/sessions/tests/test_unit.py

Https-github-com-sulaeman51/arxiv-auth

754fa083b0c8b43932f7393f5a4ab67d9f9f0444

[ "MIT" ]

13

2019-01-10T22:01:43.000Z

2021-12-30T11:39:48.000Z

"""Tests for :mod:`arxiv.users.auth.sessions.store`.""" from unittest import TestCase, mock import time import jwt import json from datetime import datetime, timedelta from pytz import timezone, UTC from redis.exceptions import ConnectionError from .... import domain from .. import store EASTERN = timezone('US/Eastern') class TestDistributedSessionService(TestCase): """The store session service puts sessions in a key-value store.""" @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster') def test_create(self, mock_redis, mock_get_config): """Accept a :class:`.User` and returns a :class:`.Session`.""" mock_get_config.return_value = {'JWT_SECRET': 'foosecret'} mock_redis.exceptions.ConnectionError = ConnectionError mock_redis_connection = mock.MagicMock() mock_redis.StrictRedisCluster.return_value = mock_redis_connection ip = '127.0.0.1' remote_host = 'foo-host.foo.com' user = domain.User( user_id='1', username='theuser', email='the@user.com' ) auths = domain.Authorizations( classic=2, scopes=['foo:write'], endorsements=[] ) r = store.SessionStore('localhost', 7000, 0, 'foosecret') session = r.create(auths, ip, remote_host, user=user) cookie = r.generate_cookie(session) self.assertIsInstance(session, domain.Session) self.assertTrue(bool(session.session_id)) self.assertIsNotNone(cookie) self.assertEqual(mock_redis_connection.set.call_count, 1) @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster') def test_delete(self, mock_redis, mock_get_config): """Delete a session from the datastore.""" mock_get_config.return_value = {'JWT_SECRET': 'foosecret'} mock_redis.exceptions.ConnectionError = ConnectionError mock_redis_connection = mock.MagicMock() mock_redis.StrictRedisCluster.return_value = mock_redis_connection r = store.SessionStore('localhost', 7000, 0, 'foosecret') r.delete_by_id('fookey') self.assertEqual(mock_redis_connection.delete.call_count, 1) @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster') def test_connection_failed(self, mock_redis, mock_get_config): """:class:`.SessionCreationFailed` is raised when creation fails.""" mock_get_config.return_value = {'JWT_SECRET': 'foosecret'} mock_redis.exceptions.ConnectionError = ConnectionError mock_redis_connection = mock.MagicMock() mock_redis_connection.set.side_effect = ConnectionError mock_redis.StrictRedisCluster.return_value = mock_redis_connection ip = '127.0.0.1' remote_host = 'foo-host.foo.com' user = domain.User( user_id='1', username='theuser', email='the@user.com' ) auths = domain.Authorizations( classic=2, scopes=['foo:write'], endorsements=[] ) r = store.SessionStore('localhost', 7000, 0, 'foosecret') with self.assertRaises(store.SessionCreationFailed): r.create(auths, ip, remote_host, user=user) class TestGetSession(TestCase): """Tests for :func:`store.SessionStore.current_session().load`.""" @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster.StrictRedisCluster') def test_not_a_token(self, mock_get_redis, mock_get_config): """Something other than a JWT is passed.""" mock_get_config.return_value = { 'JWT_SECRET': 'barsecret', 'REDIS_HOST': 'redis', 'REDIS_PORT': '1234', 'REDIS_DATABASE': 4 } mock_redis = mock.MagicMock() mock_get_redis.return_value = mock_redis with self.assertRaises(store.InvalidToken): store.SessionStore.current_session().load('notatoken') @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster.StrictRedisCluster') def test_malformed_token(self, mock_get_redis, mock_get_config): """A JWT with missing claims is passed.""" secret = 'barsecret' mock_get_config.return_value = { 'JWT_SECRET': secret, 'REDIS_HOST': 'redis', 'REDIS_PORT': '1234', 'REDIS_DATABASE': 4 } mock_redis = mock.MagicMock() mock_get_redis.return_value = mock_redis required_claims = ['session_id', 'nonce'] for exc in required_claims: claims = {claim: '' for claim in required_claims if claim != exc} malformed_token = jwt.encode(claims, secret).decode('ascii') with self.assertRaises(store.InvalidToken): store.SessionStore.current_session().load(malformed_token) @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster.StrictRedisCluster') def test_token_with_bad_encryption(self, mock_get_redis, mock_get_config): """A JWT produced with a different secret is passed.""" secret = 'barsecret' mock_get_config.return_value = { 'JWT_SECRET': secret, 'REDIS_HOST': 'redis', 'REDIS_PORT': '1234', 'REDIS_DATABASE': 4 } mock_redis = mock.MagicMock() mock_get_redis.return_value = mock_redis start_time = datetime.now(tz=UTC) end_time = start_time + timedelta(seconds=7200) claims = { 'user_id': '1234', 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290099', 'expires': end_time.isoformat() } bad_token = jwt.encode(claims, 'nottherightsecret').decode('ascii') with self.assertRaises(store.InvalidToken): store.SessionStore.current_session().load(bad_token) @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster.StrictRedisCluster') def test_expired_token(self, mock_get_redis, mock_get_config): """A JWT produced with a different secret is passed.""" secret = 'barsecret' mock_get_config.return_value = { 'JWT_SECRET': secret, 'REDIS_HOST': 'redis', 'REDIS_PORT': '1234', 'REDIS_DATABASE': 4 } mock_redis = mock.MagicMock() start_time = datetime.now(tz=UTC) mock_redis.get.return_value = json.dumps({ 'user_id': '1234', 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290099', 'expires': start_time.isoformat(), }) mock_get_redis.return_value = mock_redis claims = { 'user_id': '1234', 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290099', 'expires': start_time.isoformat(), } expired_token = jwt.encode(claims, secret).decode('ascii') with self.assertRaises(store.InvalidToken): store.SessionStore.current_session().load(expired_token) @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster.StrictRedisCluster') def test_forged_token(self, mock_get_redis, mock_get_config): """A JWT with the wrong nonce is passed.""" start_time = datetime.now(tz=UTC) end_time = start_time + timedelta(seconds=7200) secret = 'barsecret' mock_get_config.return_value = { 'JWT_SECRET': secret, 'REDIS_HOST': 'redis', 'REDIS_PORT': '1234', 'REDIS_DATABASE': 4 } mock_redis = mock.MagicMock() mock_redis.get.return_value = jwt.encode({ 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290098', 'start_time': start_time.isoformat(), 'end_time': end_time.isoformat(), 'user': { 'user_id': '1235', 'username': 'foouser', 'email': 'foo@foo.com' } }, secret) mock_get_redis.return_value = mock_redis claims = { 'user_id': '1234', 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290099', # <- Doesn't match! 'expires': end_time.isoformat(), } expired_token = jwt.encode(claims, secret).decode('ascii') with self.assertRaises(store.InvalidToken): store.SessionStore.current_session().load(expired_token) @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster.StrictRedisCluster') def test_other_forged_token(self, mock_get_redis, mock_get_config): """A JWT with the wrong user_id is passed.""" start_time = datetime.now(tz=UTC) end_time = start_time + timedelta(seconds=7200) secret = 'barsecret' mock_get_config.return_value = { 'JWT_SECRET': secret, 'REDIS_HOST': 'redis', 'REDIS_PORT': '1234', 'REDIS_DATABASE': 4 } mock_redis = mock.MagicMock() mock_redis.get.return_value = jwt.encode({ 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290099', 'start_time': start_time.isoformat(), 'user': { 'user_id': '1235', 'username': 'foouser', 'email': 'foo@foo.com' } }, secret) mock_get_redis.return_value = mock_redis claims = { 'user_id': '1234', # <- Doesn't match! 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290099', 'expires': end_time.isoformat(), } expired_token = jwt.encode(claims, secret).decode('ascii') with self.assertRaises(store.InvalidToken): store.SessionStore.current_session().load(expired_token) @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster.StrictRedisCluster') def test_empty_session(self, mock_get_redis, mock_get_config): """Session has been removed, or may never have existed.""" start_time = datetime.now(tz=UTC) end_time = start_time + timedelta(seconds=7200) secret = 'barsecret' mock_get_config.return_value = { 'JWT_SECRET': secret, 'REDIS_HOST': 'redis', 'REDIS_PORT': '1234', 'REDIS_DATABASE': 4 } mock_redis = mock.MagicMock() mock_redis.get.return_value = '' # <- Empty record! mock_get_redis.return_value = mock_redis claims = { 'user_id': '1234', 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290099', 'expires': end_time.isoformat(), } expired_token = jwt.encode(claims, secret).decode('ascii') with self.assertRaises(store.UnknownSession): store.SessionStore.current_session().load(expired_token) @mock.patch(f'{store.__name__}.get_application_config') @mock.patch(f'{store.__name__}.rediscluster.StrictRedisCluster') def test_valid_token(self, mock_get_redis, mock_get_config): """A valid token is passed.""" start_time = datetime.now(tz=UTC) end_time = start_time + timedelta(seconds=7200) secret = 'barsecret' mock_get_config.return_value = { 'JWT_SECRET': secret, 'REDIS_HOST': 'redis', 'REDIS_PORT': '1234', 'REDIS_DATABASE': 4 } mock_redis = mock.MagicMock() mock_redis.get.return_value = jwt.encode({ 'session_id': 'ajx9043jjx00s', 'start_time': datetime.now(tz=UTC).isoformat(), 'nonce': '0039299290098', 'user': { 'user_id': '1234', 'username': 'foouser', 'email': 'foo@foo.com' } }, secret) mock_get_redis.return_value = mock_redis claims = { 'user_id': '1234', 'session_id': 'ajx9043jjx00s', 'nonce': '0039299290098', 'expires': end_time.isoformat(), } valid_token = jwt.encode(claims, secret).decode('ascii') session = store.SessionStore.current_session().load(valid_token) self.assertIsInstance(session, domain.Session, "Returns a session")

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1

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null

0

7

b75ef27e7bb2ea63dbd0acf3db845f8736cb6c8e

65

py

Python

bloc_client/internal/gen_uuid.py

fBloc/bloc-client-python

68b61610db0d0a30ba21807a18b4c81db8327500

[ "MIT" ]

null

bloc_client/internal/gen_uuid.py

fBloc/bloc-client-python

68b61610db0d0a30ba21807a18b4c81db8327500

[ "MIT" ]

null

bloc_client/internal/gen_uuid.py

fBloc/bloc-client-python

68b61610db0d0a30ba21807a18b4c81db8327500

[ "MIT" ]

null

import uuid def new_uuid() -> str: return str(uuid.uuid1())

13

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0.646154

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65

4.1

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0.2

65

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null

0

1

0

null

0

1

0

1

0

7

b78433f3b076b48786be15618cccd2135a098dad

510

py

Python

qt5ui_to_pyui/pyuic_powershell.py

DvaMishkiLapa/SmallScripts

4be08c95a1341df5cd9014cd9359e206977dd407

[ "Apache-2.0" ]

null

qt5ui_to_pyui/pyuic_powershell.py

DvaMishkiLapa/SmallScripts

4be08c95a1341df5cd9014cd9359e206977dd407

[ "Apache-2.0" ]

null

qt5ui_to_pyui/pyuic_powershell.py

DvaMishkiLapa/SmallScripts

4be08c95a1341df5cd9014cd9359e206977dd407

[ "Apache-2.0" ]

null

import os os.system("python -m PyQt5.uic.pyuic -x .\\ui\\main_window.ui -o .\\ui\\main_window.py") os.system("python -m PyQt5.uic.pyuic -x .\\ui\\login_stack.ui -o .\\ui\\login_stack.py") os.system("python -m PyQt5.uic.pyuic -x .\\ui\\add_inproject_dialog.ui -o .\\ui\\add_inproject_dialog.py") os.system("python -m PyQt5.uic.pyuic -x .\\ui\\add_new_user_dialog.ui -o .\\ui\\add_new_user_dialog.py") os.system("python -m PyQt5.uic.pyuic -x .\\ui\\add_new_project_dialog.ui -o .\\ui\\add_new_project_dialog.py")

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1

null

0

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7

b7963e03bf9ded666c376f2a1c532c59515b86ce

4,468

py

Python

dingtalk/python/alibabacloud_dingtalk/flashmeeting_1_0/client.py

aliyun/dingtalk-sdk

ab4f856b8cfe94f6b69f10a0730a2e5a7d4901c5

[ "Apache-2.0" ]

15

2020-08-27T04:10:26.000Z

2022-03-07T06:25:42.000Z

dingtalk/python/alibabacloud_dingtalk/flashmeeting_1_0/client.py

aliyun/dingtalk-sdk

ab4f856b8cfe94f6b69f10a0730a2e5a7d4901c5

[ "Apache-2.0" ]

1

2020-09-27T01:30:46.000Z

2021-12-29T09:15:34.000Z

dingtalk/python/alibabacloud_dingtalk/flashmeeting_1_0/client.py

aliyun/dingtalk-sdk

ab4f856b8cfe94f6b69f10a0730a2e5a7d4901c5

[ "Apache-2.0" ]

5

2020-08-27T04:07:44.000Z

2021-12-03T02:55:20.000Z

# -*- coding: utf-8 -*- # This file is auto-generated, don't edit it. Thanks. from Tea.core import TeaCore from alibabacloud_tea_openapi.client import Client as OpenApiClient from alibabacloud_tea_openapi import models as open_api_models from alibabacloud_tea_util.client import Client as UtilClient from alibabacloud_dingtalk.flashmeeting_1_0 import models as dingtalkflashmeeting__1__0_models from alibabacloud_tea_util import models as util_models from alibabacloud_openapi_util.client import Client as OpenApiUtilClient class Client(OpenApiClient): """ *\ """ def __init__( self, config: open_api_models.Config, ): super().__init__(config) self._endpoint_rule = '' if UtilClient.empty(self._endpoint): self._endpoint = 'api.dingtalk.com' def create_flash_meeting( self, request: dingtalkflashmeeting__1__0_models.CreateFlashMeetingRequest, ) -> dingtalkflashmeeting__1__0_models.CreateFlashMeetingResponse: runtime = util_models.RuntimeOptions() headers = dingtalkflashmeeting__1__0_models.CreateFlashMeetingHeaders() return self.create_flash_meeting_with_options(request, headers, runtime) async def create_flash_meeting_async( self, request: dingtalkflashmeeting__1__0_models.CreateFlashMeetingRequest, ) -> dingtalkflashmeeting__1__0_models.CreateFlashMeetingResponse: runtime = util_models.RuntimeOptions() headers = dingtalkflashmeeting__1__0_models.CreateFlashMeetingHeaders() return await self.create_flash_meeting_with_options_async(request, headers, runtime) def create_flash_meeting_with_options( self, request: dingtalkflashmeeting__1__0_models.CreateFlashMeetingRequest, headers: dingtalkflashmeeting__1__0_models.CreateFlashMeetingHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkflashmeeting__1__0_models.CreateFlashMeetingResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.event_id): body['eventId'] = request.event_id if not UtilClient.is_unset(request.title): body['title'] = request.title if not UtilClient.is_unset(request.creator): body['creator'] = request.creator real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkflashmeeting__1__0_models.CreateFlashMeetingResponse(), self.do_roarequest('CreateFlashMeeting', 'flashmeeting_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/flashmeeting/meetings', 'json', req, runtime) ) async def create_flash_meeting_with_options_async( self, request: dingtalkflashmeeting__1__0_models.CreateFlashMeetingRequest, headers: dingtalkflashmeeting__1__0_models.CreateFlashMeetingHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkflashmeeting__1__0_models.CreateFlashMeetingResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.event_id): body['eventId'] = request.event_id if not UtilClient.is_unset(request.title): body['title'] = request.title if not UtilClient.is_unset(request.creator): body['creator'] = request.creator real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkflashmeeting__1__0_models.CreateFlashMeetingResponse(), await self.do_roarequest_async('CreateFlashMeeting', 'flashmeeting_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/flashmeeting/meetings', 'json', req, runtime) )

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null

0

1

0

1

0

null

0

7

4d56699cf4392b0a9040899c985562b77a640eb1

4,780

py

Python

saleor/dashboard/credit/hours_chart.py

glosoftgroup/glosoftgroup-django-pos

b489c402939b9ebabd164c449e7da38fe849d550

[ "BSD-3-Clause" ]

2

2017-07-11T12:40:59.000Z

2017-10-18T18:02:46.000Z

saleor/dashboard/credit/hours_chart.py

glosoftgroup/glosoftgroup-django-pos

b489c402939b9ebabd164c449e7da38fe849d550

[ "BSD-3-Clause" ]

12

2017-06-19T07:20:41.000Z

2022-03-15T19:03:33.000Z

saleor/dashboard/credit/hours_chart.py

glosoftgroup/glosoftgroup-django-pos

b489c402939b9ebabd164c449e7da38fe849d550

[ "BSD-3-Clause" ]

null

from django.db.models import Sum from django.core.exceptions import ObjectDoesNotExist from ...sale.models import Sales, SoldItem from structlog import get_logger logger = get_logger(__name__) def get_hours_results(date, l, h): logger.info('get_hours_results') try: sales_at_date = Sales.objects.filter(created__contains=date) sales_at_h = sales_at_date.filter(created__hour__range=[l, h]) try: amount = Sales.objects.filter(pk__in=sales_at_h).aggregate(Sum('total_net'))['total_net__sum'] if amount is not None: return amount else: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount def get_hours_results_range(date_from, date_to, l, h): logger.info('get_hours_results_range') try: sales_at_date = Sales.objects.filter(created__range=[date_from, date_to]) sales_at_h = sales_at_date.filter(created__hour__range=[l, h]) try: amount = Sales.objects.filter(pk__in=sales_at_h).aggregate(Sum('total_net'))['total_net__sum'] if amount is not None: return amount else: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount def get_date_results_range(date_from, date_to): logger.info('get_date_results_range') try: sales_at_date = Sales.objects.filter(created__range=[date_from, date_to]) try: amount = Sales.objects.filter(pk__in=sales_at_date).aggregate(Sum('total_net'))['total_net__sum'] if amount is not None: return amount else: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount def get_date_results(date): logger.info('get_date_results') try: sales_at_date = Sales.objects.filter(created__contains=date) try: amount = Sales.objects.filter(pk__in=sales_at_date).aggregate(Sum('total_net'))['total_net__sum'] if amount is not None: return amount else: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount def get_category_results(category, year, month): logger.info('get_category_results') try: amount = SoldItem.objects.filter(product_category__contains=category, sales__created__year=year, sales__created__month=month).aggregate(Sum('total_cost'))['total_cost__sum'] if amount is not None: return amount else: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount def get_item_results(item, year, month): logger.info('get_item_results') try: amount = SoldItem.objects.filter(product_name__contains=item, sales__created__year=year, sales__created__month=month).aggregate(Sum('total_cost'))['total_cost__sum'] if amount is not None: return amount else: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount def get_user_results(user, year, month): logger.info('get_user_results') try: amount = Sales.objects.filter(user__name__contains=user, created__year=year, created__month=month).aggregate( Sum('total_net'))['total_net__sum'] if amount is not None: return amount else: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount def get_terminal_results(terminal, year, month): logger.info('get_terminal_results') try: amount = Sales.objects.filter(terminal__terminal_name__contains=terminal, created__year=year, created__month=month).aggregate(Sum('total_net'))['total_net__sum'] if amount is not None: return amount else: amount = 0 return amount except ObjectDoesNotExist: amount = 0 return amount

32.517007

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4,780

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0.102273

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0.143667

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0.809452

0.76673

0.718336

0

0.006325

0.338494

4,780

146

119

32.739726

0.830171

0

0.787402

0

0.072939

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0

1

0.062992

false

0

0.031496

0

0.314961

0

null

0

1

0

1

0

null

0

7

4d5ef040f41b4f06d7b5dfbb45743a29ba2fbd8e

36

py

Python

FlaskRESTFULAPITest_JE/JEAPIs/__init__.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

FlaskRESTFULAPITest_JE/JEAPIs/__init__.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

FlaskRESTFULAPITest_JE/JEAPIs/__init__.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

from JEAPIs.APIBlueprints import *

18

35

0.805556

4

36

7.25

1

0

0.138889

36

1

36

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0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

7

4d8231726af6b05e036c94f87bcaf8d6a46829f3

86,379

py

Python

homework/austen.py

mm5110/PIC16A

e2dab91439c2627f6a47f4bf6d16de8ba5977fe8

[ "MIT" ]

10

2020-11-07T04:07:34.000Z

2021-12-31T10:19:12.000Z

homework/austen.py

mm5110/PIC16A

e2dab91439c2627f6a47f4bf6d16de8ba5977fe8

[ "MIT" ]

16

2021-02-03T22:35:01.000Z

2021-05-24T21:28:56.000Z

homework/austen.py

mm5110/PIC16A

e2dab91439c2627f6a47f4bf6d16de8ba5977fe8

[ "MIT" ]

19

2020-11-11T05:44:53.000Z

2022-02-01T14:10:15.000Z

s = 'It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife. However little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered the rightful property of some one or other of their daughters. “My dear Mr. Bennet,” said his lady to him one day, “have you heard that Netherfield Park is let at last?” Mr. Bennet replied that he had not. “But it is,” returned she; “for Mrs. Long has just been here, and she told me all about it.” Mr. Bennet made no answer. “Do you not want to know who has taken it?” cried his wife impatiently. “You want to tell me, and I have no objection to hearing it.” This was invitation enough. “Why, my dear, you must know, Mrs. Long says that Netherfield is taken by a young man of large fortune from the north of England; that he came down on Monday in a chaise and four to see the place, and was so much delighted with it, that he agreed with Mr. Morris immediately; that he is to take possession before Michaelmas, and some of his servants are to be in the house by the end of next week.” “What is his name?” “Bingley.” “Is he married or single?” “Oh! Single, my dear, to be sure! A single man of large fortune; four or five thousand a year. What a fine thing for our girls!” “How so? How can it affect them?” “My dear Mr. Bennet,” replied his wife, “how can you be so tiresome! You must know that I am thinking of his marrying one of them.” “Is that his design in settling here?” “Design! Nonsense, how can you talk so! But it is very likely that he may fall in love with one of them, and therefore you must visit him as soon as he comes.” “I see no occasion for that. You and the girls may go, or you may send them by themselves, which perhaps will be still better, for as you are as handsome as any of them, Mr. Bingley may like you the best of the party.” “My dear, you flatter me. I certainly have had my share of beauty, but I do not pretend to be anything extraordinary now. When a woman has five grown-up daughters, she ought to give over thinking of her own beauty.” “In such cases, a woman has not often much beauty to think of.” “But, my dear, you must indeed go and see Mr. Bingley when he comes into the neighbourhood.” “It is more than I engage for, I assure you.” “But consider your daughters. Only think what an establishment it would be for one of them. Sir William and Lady Lucas are determined to go, merely on that account, for in general, you know, they visit no newcomers. Indeed you must go, for it will be impossible for us to visit him if you do not.” “You are over-scrupulous, surely. I dare say Mr. Bingley will be very glad to see you; and I will send a few lines by you to assure him of my hearty consent to his marrying whichever he chooses of the girls; though I must throw in a good word for my little Lizzy.” “I desire you will do no such thing. Lizzy is not a bit better than the others; and I am sure she is not half so handsome as Jane, nor half so good-humoured as Lydia. But you are always giving her the preference.” “They have none of them much to recommend them,” replied he; “they are all silly and ignorant like other girls; but Lizzy has something more of quickness than her sisters.” “Mr. Bennet, how can you abuse your own children in such a way? You take delight in vexing me. You have no compassion for my poor nerves.” “You mistake me, my dear. I have a high respect for your nerves. They are my old friends. I have heard you mention them with consideration these last twenty years at least.” “Ah, you do not know what I suffer.” “But I hope you will get over it, and live to see many young men of four thousand a year come into the neighbourhood.” “It will be no use to us, if twenty such should come, since you will not visit them.” “Depend upon it, my dear, that when there are twenty, I will visit them all.” Mr. Bennet was so odd a mixture of quick parts, sarcastic humour, reserve, and caprice, that the experience of three-and-twenty years had been insufficient to make his wife understand his character. Her mind was less difficult to develop. She was a woman of mean understanding, little information, and uncertain temper. When she was discontented, she fancied herself nervous. The business of her life was to get her daughters married; its solace was visiting and news. Chapter 2Mr. Bennet was among the earliest of those who waited on Mr. Bingley. He had always intended to visit him, though to the last always assuring his wife that he should not go; and till the evening after the visit was paid she had no knowledge of it. It was then disclosed in the following manner. Observing his second daughter employed in trimming a hat, he suddenly addressed her with: “I hope Mr. Bingley will like it, Lizzy.” “We are not in a way to know what Mr. Bingley likes,” said her mother resentfully, “since we are not to visit.” “But you forget, mamma,” said Elizabeth, “that we shall meet him at the assemblies, and that Mrs. Long promised to introduce him.” “I do not believe Mrs. Long will do any such thing. She has two nieces of her own. She is a selfish, hypocritical woman, and I have no opinion of her.” “No more have I,” said Mr. Bennet; “and I am glad to find that you do not depend on her serving you.” Mrs. Bennet deigned not to make any reply, but, unable to contain herself, began scolding one of her daughters. “Don’t keep coughing so, Kitty, for Heaven’s sake! Have a little compassion on my nerves. You tear them to pieces.” “Kitty has no discretion in her coughs,” said her father; “she times them ill.” “I do not cough for my own amusement,” replied Kitty fretfully. “When is your next ball to be, Lizzy?” “To-morrow fortnight.” “Aye, so it is,” cried her mother, “and Mrs. Long does not come back till the day before; so it will be impossible for her to introduce him, for she will not know him herself.” “Then, my dear, you may have the advantage of your friend, and introduce Mr. Bingley to her.” “Impossible, Mr. Bennet, impossible, when I am not acquainted with him myself; how can you be so teasing?” “I honour your circumspection. A fortnight’s acquaintance is certainly very little. One cannot know what a man really is by the end of a fortnight. But if we do not venture somebody else will; and after all, Mrs. Long and her nieces must stand their chance; and, therefore, as she will think it an act of kindness, if you decline the office, I will take it on myself.” The girls stared at their father. Mrs. Bennet said only, “Nonsense, nonsense!” “What can be the meaning of that emphatic exclamation?” cried he. “Do you consider the forms of introduction, and the stress that is laid on them, as nonsense? I cannot quite agree with you there. What say you, Mary? For you are a young lady of deep reflection, I know, and read great books and make extracts.” Mary wished to say something sensible, but knew not how. “While Mary is adjusting her ideas,” he continued, “let us return to Mr. Bingley.” “I am sick of Mr. Bingley,” cried his wife. “I am sorry to hear that; but why did not you tell me that before? If I had known as much this morning I certainly would not have called on him. It is very unlucky; but as I have actually paid the visit, we cannot escape the acquaintance now.” The astonishment of the ladies was just what he wished; that of Mrs. Bennet perhaps surpassing the rest; though, when the first tumult of joy was over, she began to declare that it was what she had expected all the while. “How good it was in you, my dear Mr. Bennet! But I knew I should persuade you at last. I was sure you loved your girls too well to neglect such an acquaintance. Well, how pleased I am! and it is such a good joke, too, that you should have gone this morning and never said a word about it till now.” “Now, Kitty, you may cough as much as you choose,” said Mr. Bennet; and, as he spoke, he left the room, fatigued with the raptures of his wife. “What an excellent father you have, girls!” said she, when the door was shut. “I do not know how you will ever make him amends for his kindness; or me, either, for that matter. At our time of life it is not so pleasant, I can tell you, to be making new acquaintances every day; but for your sakes, we would do anything. Lydia, my love, though you are the youngest, I dare say Mr. Bingley will dance with you at the next ball.” “Oh!” said Lydia stoutly, “I am not afraid; for though I am the youngest, I’m the tallest.” The rest of the evening was spent in conjecturing how soon he would return Mr. Bennet’s visit, and determining when they should ask him to dinner. Chapter 3Not all that Mrs. Bennet, however, with the assistance of her five daughters, could ask on the subject, was sufficient to draw from her husband any satisfactory description of Mr. Bingley. They attacked him in various ways—with barefaced questions, ingenious suppositions, and distant surmises; but he eluded the skill of them all, and they were at last obliged to accept the second-hand intelligence of their neighbour, Lady Lucas. Her report was highly favourable. Sir William had been delighted with him. He was quite young, wonderfully handsome, extremely agreeable, and, to crown the whole, he meant to be at the next assembly with a large party. Nothing could be more delightful! To be fond of dancing was a certain step towards falling in love; and very lively hopes of Mr. Bingley’s heart were entertained. “If I can but see one of my daughters happily settled at Netherfield,” said Mrs. Bennet to her husband, “and all the others equally well married, I shall have nothing to wish for.” In a few days Mr. Bingley returned Mr. Bennet’s visit, and sat about ten minutes with him in his library. He had entertained hopes of being admitted to a sight of the young ladies, of whose beauty he had heard much; but he saw only the father. The ladies were somewhat more fortunate, for they had the advantage of ascertaining from an upper window that he wore a blue coat, and rode a black horse. An invitation to dinner was soon afterwards dispatched; and already had Mrs. Bennet planned the courses that were to do credit to her housekeeping, when an answer arrived which deferred it all. Mr. Bingley was obliged to be in town the following day, and, consequently, unable to accept the honour of their invitation, etc. Mrs. Bennet was quite disconcerted. She could not imagine what business he could have in town so soon after his arrival in Hertfordshire; and she began to fear that he might be always flying about from one place to another, and never settled at Netherfield as he ought to be. Lady Lucas quieted her fears a little by starting the idea of his being gone to London only to get a large party for the ball; and a report soon followed that Mr. Bingley was to bring twelve ladies and seven gentlemen with him to the assembly. The girls grieved over such a number of ladies, but were comforted the day before the ball by hearing, that instead of twelve he brought only six with him from London—his five sisters and a cousin. And when the party entered the assembly room it consisted of only five altogether—Mr. Bingley, his two sisters, the husband of the eldest, and another young man. Mr. Bingley was good-looking and gentlemanlike; he had a pleasant countenance, and easy, unaffected manners. His sisters were fine women, with an air of decided fashion. His brother-in-law, Mr. Hurst, merely looked the gentleman; but his friend Mr. Darcy soon drew the attention of the room by his fine, tall person, handsome features, noble mien, and the report which was in general circulation within five minutes after his entrance, of his having ten thousand a year. The gentlemen pronounced him to be a fine figure of a man, the ladies declared he was much handsomer than Mr. Bingley, and he was looked at with great admiration for about half the evening, till his manners gave a disgust which turned the tide of his popularity; for he was discovered to be proud; to be above his company, and above being pleased; and not all his large estate in Derbyshire could then save him from having a most forbidding, disagreeable countenance, and being unworthy to be compared with his friend. Mr. Bingley had soon made himself acquainted with all the principal people in the room; he was lively and unreserved, danced every dance, was angry that the ball closed so early, and talked of giving one himself at Netherfield. Such amiable qualities must speak for themselves. What a contrast between him and his friend! Mr. Darcy danced only once with Mrs. Hurst and once with Miss Bingley, declined being introduced to any other lady, and spent the rest of the evening in walking about the room, speaking occasionally to one of his own party. His character was decided. He was the proudest, most disagreeable man in the world, and everybody hoped that he would never come there again. Amongst the most violent against him was Mrs. Bennet, whose dislike of his general behaviour was sharpened into particular resentment by his having slighted one of her daughters. Elizabeth Bennet had been obliged, by the scarcity of gentlemen, to sit down for two dances; and during part of that time, Mr. Darcy had been standing near enough for her to hear a conversation between him and Mr. Bingley, who came from the dance for a few minutes, to press his friend to join it. “Come, Darcy,” said he, “I must have you dance. I hate to see you standing about by yourself in this stupid manner. You had much better dance.” “I certainly shall not. You know how I detest it, unless I am particularly acquainted with my partner. At such an assembly as this it would be insupportable. Your sisters are engaged, and there is not another woman in the room whom it would not be a punishment to me to stand up with.” “I would not be so fastidious as you are,” cried Mr. Bingley, “for a kingdom! Upon my honour, I never met with so many pleasant girls in my life as I have this evening; and there are several of them you see uncommonly pretty.” “You are dancing with the only handsome girl in the room,” said Mr. Darcy, looking at the eldest Miss Bennet. “Oh! She is the most beautiful creature I ever beheld! But there is one of her sisters sitting down just behind you, who is very pretty, and I dare say very agreeable. Do let me ask my partner to introduce you.” “Which do you mean?” and turning round he looked for a moment at Elizabeth, till catching her eye, he withdrew his own and coldly said: “She is tolerable, but not handsome enough to tempt me; I am in no humour at present to give consequence to young ladies who are slighted by other men. You had better return to your partner and enjoy her smiles, for you are wasting your time with me.” Mr. Bingley followed his advice. Mr. Darcy walked off; and Elizabeth remained with no very cordial feelings toward him. She told the story, however, with great spirit among her friends; for she had a lively, playful disposition, which delighted in anything ridiculous. The evening altogether passed off pleasantly to the whole family. Mrs. Bennet had seen her eldest daughter much admired by the Netherfield party. Mr. Bingley had danced with her twice, and she had been distinguished by his sisters. Jane was as much gratified by this as her mother could be, though in a quieter way. Elizabeth felt Jane’s pleasure. Mary had heard herself mentioned to Miss Bingley as the most accomplished girl in the neighbourhood; and Catherine and Lydia had been fortunate enough never to be without partners, which was all that they had yet learnt to care for at a ball. They returned, therefore, in good spirits to Longbourn, the village where they lived, and of which they were the principal inhabitants. They found Mr. Bennet still up. With a book he was regardless of time; and on the present occasion he had a good deal of curiosity as to the event of an evening which had raised such splendid expectations. He had rather hoped that his wife’s views on the stranger would be disappointed; but he soon found out that he had a different story to hear. “Oh, my dear Mr. Bennet,” as she entered the room, “we have had a most delightful evening, a most excellent ball. I wish you had been there. Jane was so admired, nothing could be like it. Everybody said how well she looked; and Mr. Bingley thought her quite beautiful, and danced with her twice! Only think of that, my dear; he actually danced with her twice! and she was the only creature in the room that he asked a second time. First of all, he asked Miss Lucas. I was so vexed to see him stand up with her! But, however, he did not admire her at all; indeed, nobody can, you know; and he seemed quite struck with Jane as she was going down the dance. So he inquired who she was, and got introduced, and asked her for the two next. Then the two third he danced with Miss King, and the two fourth with Maria Lucas, and the two fifth with Jane again, and the two sixth with Lizzy, and the Boulanger—” “If he had had any compassion for me,” cried her husband impatiently, “he would not have danced half so much! For God’s sake, say no more of his partners. Oh that he had sprained his ankle in the first dance!” “Oh! my dear, I am quite delighted with him. He is so excessively handsome! And his sisters are charming women. I never in my life saw anything more elegant than their dresses. I dare say the lace upon Mrs. Hurst’s gown—” Here she was interrupted again. Mr. Bennet protested against any description of finery. She was therefore obliged to seek another branch of the subject, and related, with much bitterness of spirit and some exaggeration, the shocking rudeness of Mr. Darcy. “But I can assure you,” she added, “that Lizzy does not lose much by not suiting his fancy; for he is a most disagreeable, horrid man, not at all worth pleasing. So high and so conceited that there was no enduring him! He walked here, and he walked there, fancying himself so very great! Not handsome enough to dance with! I wish you had been there, my dear, to have given him one of your set-downs. I quite detest the man.” Chapter 4When Jane and Elizabeth were alone, the former, who had been cautious in her praise of Mr. Bingley before, expressed to her sister just how very much she admired him. “He is just what a young man ought to be,” said she, “sensible, good-humoured, lively; and I never saw such happy manners!—so much ease, with such perfect good breeding!” “He is also handsome,” replied Elizabeth, “which a young man ought likewise to be, if he possibly can. His character is thereby complete.” “I was very much flattered by his asking me to dance a second time. I did not expect such a compliment.” “Did not you? I did for you. But that is one great difference between us. Compliments always take you by surprise, and me never. What could be more natural than his asking you again? He could not help seeing that you were about five times as pretty as every other woman in the room. No thanks to his gallantry for that. Well, he certainly is very agreeable, and I give you leave to like him. You have liked many a stupider person.” “Dear Lizzy!” “Oh! you are a great deal too apt, you know, to like people in general. You never see a fault in anybody. All the world are good and agreeable in your eyes. I never heard you speak ill of a human being in your life.” “I would not wish to be hasty in censuring anyone; but I always speak what I think.” “I know you do; and it is that which makes the wonder. With your good sense, to be so honestly blind to the follies and nonsense of others! Affectation of candour is common enough—one meets with it everywhere. But to be candid without ostentation or design—to take the good of everybody’s character and make it still better, and say nothing of the bad—belongs to you alone. And so you like this man’s sisters, too, do you? Their manners are not equal to his.” “Certainly not—at first. But they are very pleasing women when you converse with them. Miss Bingley is to live with her brother, and keep his house; and I am much mistaken if we shall not find a very charming neighbour in her.” Elizabeth listened in silence, but was not convinced; their behaviour at the assembly had not been calculated to please in general; and with more quickness of observation and less pliancy of temper than her sister, and with a judgement too unassailed by any attention to herself, she was very little disposed to approve them. They were in fact very fine ladies; not deficient in good humour when they were pleased, nor in the power of making themselves agreeable when they chose it, but proud and conceited. They were rather handsome, had been educated in one of the first private seminaries in town, had a fortune of twenty thousand pounds, were in the habit of spending more than they ought, and of associating with people of rank, and were therefore in every respect entitled to think well of themselves, and meanly of others. They were of a respectable family in the north of England; a circumstance more deeply impressed on their memories than that their brother’s fortune and their own had been acquired by trade. Mr. Bingley inherited property to the amount of nearly a hundred thousand pounds from his father, who had intended to purchase an estate, but did not live to do it. Mr. Bingley intended it likewise, and sometimes made choice of his county; but as he was now provided with a good house and the liberty of a manor, it was doubtful to many of those who best knew the easiness of his temper, whether he might not spend the remainder of his days at Netherfield, and leave the next generation to purchase. His sisters were anxious for his having an estate of his own; but, though he was now only established as a tenant, Miss Bingley was by no means unwilling to preside at his table—nor was Mrs. Hurst, who had married a man of more fashion than fortune, less disposed to consider his house as her home when it suited her. Mr. Bingley had not been of age two years, when he was tempted by an accidental recommendation to look at Netherfield House. He did look at it, and into it for half-an-hour—was pleased with the situation and the principal rooms, satisfied with what the owner said in its praise, and took it immediately. Between him and Darcy there was a very steady friendship, in spite of great opposition of character. Bingley was endeared to Darcy by the easiness, openness, and ductility of his temper, though no disposition could offer a greater contrast to his own, and though with his own he never appeared dissatisfied. On the strength of Darcy’s regard, Bingley had the firmest reliance, and of his judgement the highest opinion. In understanding, Darcy was the superior. Bingley was by no means deficient, but Darcy was clever. He was at the same time haughty, reserved, and fastidious, and his manners, though well-bred, were not inviting. In that respect his friend had greatly the advantage. Bingley was sure of being liked wherever he appeared, Darcy was continually giving offense. The manner in which they spoke of the Meryton assembly was sufficiently characteristic. Bingley had never met with more pleasant people or prettier girls in his life; everybody had been most kind and attentive to him; there had been no formality, no stiffness; he had soon felt acquainted with all the room; and, as to Miss Bennet, he could not conceive an angel more beautiful. Darcy, on the contrary, had seen a collection of people in whom there was little beauty and no fashion, for none of whom he had felt the smallest interest, and from none received either attention or pleasure. Miss Bennet he acknowledged to be pretty, but she smiled too much. Mrs. Hurst and her sister allowed it to be so—but still they admired her and liked her, and pronounced her to be a sweet girl, and one whom they would not object to know more of. Miss Bennet was therefore established as a sweet girl, and their brother felt authorized by such commendation to think of her as he chose. Chapter 5Within a short walk of Longbourn lived a family with whom the Bennets were particularly intimate. Sir William Lucas had been formerly in trade in Meryton, where he had made a tolerable fortune, and risen to the honour of knighthood by an address to the king during his mayoralty. The distinction had perhaps been felt too strongly. It had given him a disgust to his business, and to his residence in a small market town; and, in quitting them both, he had removed with his family to a house about a mile from Meryton, denominated from that period Lucas Lodge, where he could think with pleasure of his own importance, and, unshackled by business, occupy himself solely in being civil to all the world. For, though elated by his rank, it did not render him supercilious; on the contrary, he was all attention to everybody. By nature inoffensive, friendly, and obliging, his presentation at St. James’s had made him courteous. Lady Lucas was a very good kind of woman, not too clever to be a valuable neighbour to Mrs. Bennet. They had several children. The eldest of them, a sensible, intelligent young woman, about twenty-seven, was Elizabeth’s intimate friend. That the Miss Lucases and the Miss Bennets should meet to talk over a ball was absolutely necessary; and the morning after the assembly brought the former to Longbourn to hear and to communicate. “You began the evening well, Charlotte,” said Mrs. Bennet with civil self-command to Miss Lucas. “You were Mr. Bingley’s first choice.” “Yes; but he seemed to like his second better.” “Oh! you mean Jane, I suppose, because he danced with her twice. To be sure that did seem as if he admired her—indeed I rather believe he did—I heard something about it—but I hardly know what—something about Mr. Robinson.” “Perhaps you mean what I overheard between him and Mr. Robinson; did not I mention it to you? Mr. Robinson’s asking him how he liked our Meryton assemblies, and whether he did not think there were a great many pretty women in the room, and which he thought the prettiest? and his answering immediately to the last question: ‘Oh! the eldest Miss Bennet, beyond a doubt; there cannot be two opinions on that point.’” “Upon my word! Well, that is very decided indeed—that does seem as if—but, however, it may all come to nothing, you know.” “My overhearings were more to the purpose than yours, Eliza,” said Charlotte. “Mr. Darcy is not so well worth listening to as his friend, is he?—poor Eliza!—to be only just tolerable.” “I beg you would not put it into Lizzy’s head to be vexed by his ill-treatment, for he is such a disagreeable man, that it would be quite a misfortune to be liked by him. Mrs. Long told me last night that he sat close to her for half-an-hour without once opening his lips.” “Are you quite sure, ma’am?—is not there a little mistake?” said Jane. “I certainly saw Mr. Darcy speaking to her.” “Aye—because she asked him at last how he liked Netherfield, and he could not help answering her; but she said he seemed quite angry at being spoke to.” “Miss Bingley told me,” said Jane, “that he never speaks much, unless among his intimate acquaintances. With them he is remarkably agreeable.” “I do not believe a word of it, my dear. If he had been so very agreeable, he would have talked to Mrs. Long. But I can guess how it was; everybody says that he is eat up with pride, and I dare say he had heard somehow that Mrs. Long does not keep a carriage, and had come to the ball in a hack chaise.” “I do not mind his not talking to Mrs. Long,” said Miss Lucas, “but I wish he had danced with Eliza.” “Another time, Lizzy,” said her mother, “I would not dance with him, if I were you.” “I believe, ma’am, I may safely promise you never to dance with him.” “His pride,” said Miss Lucas, “does not offend me so much as pride often does, because there is an excuse for it. One cannot wonder that so very fine a young man, with family, fortune, everything in his favour, should think highly of himself. If I may so express it, he has a right to be proud.” “That is very true,” replied Elizabeth, “and I could easily forgive his pride, if he had not mortified mine.” “Pride,” observed Mary, who piqued herself upon the solidity of her reflections, “is a very common failing, I believe. By all that I have ever read, I am convinced that it is very common indeed; that human nature is particularly prone to it, and that there are very few of us who do not cherish a feeling of self-complacency on the score of some quality or other, real or imaginary. Vanity and pride are different things, though the words are often used synonymously. A person may be proud without being vain. Pride relates more to our opinion of ourselves, vanity to what we would have others think of us.” “If I were as rich as Mr. Darcy,” cried a young Lucas, who came with his sisters, “I should not care how proud I was. I would keep a pack of foxhounds, and drink a bottle of wine a day.” “Then you would drink a great deal more than you ought,” said Mrs. Bennet; “and if I were to see you at it, I should take away your bottle directly.” The boy protested that she should not; she continued to declare that she would, and the argument ended only with the visit. Chapter 6The ladies of Longbourn soon waited on those of Netherfield. The visit was soon returned in due form. Miss Bennet’s pleasing manners grew on the goodwill of Mrs. Hurst and Miss Bingley; and though the mother was found to be intolerable, and the younger sisters not worth speaking to, a wish of being better acquainted with them was expressed towards the two eldest. By Jane, this attention was received with the greatest pleasure, but Elizabeth still saw superciliousness in their treatment of everybody, hardly excepting even her sister, and could not like them; though their kindness to Jane, such as it was, had a value as arising in all probability from the influence of their brother’s admiration. It was generally evident whenever they met, that he did admire her and to her it was equally evident that Jane was yielding to the preference which she had begun to entertain for him from the first, and was in a way to be very much in love; but she considered with pleasure that it was not likely to be discovered by the world in general, since Jane united, with great strength of feeling, a composure of temper and a uniform cheerfulness of manner which would guard her from the suspicions of the impertinent. She mentioned this to her friend Miss Lucas. “It may perhaps be pleasant,” replied Charlotte, “to be able to impose on the public in such a case; but it is sometimes a disadvantage to be so very guarded. If a woman conceals her affection with the same skill from the object of it, she may lose the opportunity of fixing him; and it will then be but poor consolation to believe the world equally in the dark. There is so much of gratitude or vanity in almost every attachment, that it is not safe to leave any to itself. We can all begin freely—a slight preference is natural enough; but there are very few of us who have heart enough to be really in love without encouragement. In nine cases out of ten a woman had better show more affection than she feels. Bingley likes your sister undoubtedly; but he may never do more than like her, if she does not help him on.” “But she does help him on, as much as her nature will allow. If I can perceive her regard for him, he must be a simpleton, indeed, not to discover it too.” “Remember, Eliza, that he does not know Jane’s disposition as you do.” “But if a woman is partial to a man, and does not endeavour to conceal it, he must find it out.” “Perhaps he must, if he sees enough of her. But, though Bingley and Jane meet tolerably often, it is never for many hours together; and, as they always see each other in large mixed parties, it is impossible that every moment should be employed in conversing together. Jane should therefore make the most of every half-hour in which she can command his attention. When she is secure of him, there will be more leisure for falling in love as much as she chooses.” “Your plan is a good one,” replied Elizabeth, “where nothing is in question but the desire of being well married, and if I were determined to get a rich husband, or any husband, I dare say I should adopt it. But these are not Jane’s feelings; she is not acting by design. As yet, she cannot even be certain of the degree of her own regard nor of its reasonableness. She has known him only a fortnight. She danced four dances with him at Meryton; she saw him one morning at his own house, and has since dined with him in company four times. This is not quite enough to make her understand his character.” “Not as you represent it. Had she merely dined with him, she might only have discovered whether he had a good appetite; but you must remember that four evenings have also been spent together—and four evenings may do a great deal.” “Yes; these four evenings have enabled them to ascertain that they both like Vingt-un better than Commerce; but with respect to any other leading characteristic, I do not imagine that much has been unfolded.” “Well,” said Charlotte, “I wish Jane success with all my heart; and if she were married to him to-morrow, I should think she had as good a chance of happiness as if she were to be studying his character for a twelvemonth. Happiness in marriage is entirely a matter of chance. If the dispositions of the parties are ever so well known to each other or ever so similar beforehand, it does not advance their felicity in the least. They always continue to grow sufficiently unlike afterwards to have their share of vexation; and it is better to know as little as possible of the defects of the person with whom you are to pass your life.” “You make me laugh, Charlotte; but it is not sound. You know it is not sound, and that you would never act in this way yourself.” Occupied in observing Mr. Bingley’s attentions to her sister, Elizabeth was far from suspecting that she was herself becoming an object of some interest in the eyes of his friend. Mr. Darcy had at first scarcely allowed her to be pretty; he had looked at her without admiration at the ball; and when they next met, he looked at her only to criticise. But no sooner had he made it clear to himself and his friends that she hardly had a good feature in her face, than he began to find it was rendered uncommonly intelligent by the beautiful expression of her dark eyes. To this discovery succeeded some others equally mortifying. Though he had detected with a critical eye more than one failure of perfect symmetry in her form, he was forced to acknowledge her figure to be light and pleasing; and in spite of his asserting that her manners were not those of the fashionable world, he was caught by their easy playfulness. Of this she was perfectly unaware; to her he was only the man who made himself agreeable nowhere, and who had not thought her handsome enough to dance with. He began to wish to know more of her, and as a step towards conversing with her himself, attended to her conversation with others. His doing so drew her notice. It was at Sir William Lucas’s, where a large party were assembled. “What does Mr. Darcy mean,” said she to Charlotte, “by listening to my conversation with Colonel Forster?” “That is a question which Mr. Darcy only can answer.” “But if he does it any more I shall certainly let him know that I see what he is about. He has a very satirical eye, and if I do not begin by being impertinent myself, I shall soon grow afraid of him.” On his approaching them soon afterwards, though without seeming to have any intention of speaking, Miss Lucas defied her friend to mention such a subject to him; which immediately provoking Elizabeth to do it, she turned to him and said: “Did you not think, Mr. Darcy, that I expressed myself uncommonly well just now, when I was teasing Colonel Forster to give us a ball at Meryton?” “With great energy; but it is always a subject which makes a lady energetic.” “You are severe on us.” “It will be her turn soon to be teased,” said Miss Lucas. “I am going to open the instrument, Eliza, and you know what follows.” “You are a very strange creature by way of a friend!—always wanting me to play and sing before anybody and everybody! If my vanity had taken a musical turn, you would have been invaluable; but as it is, I would really rather not sit down before those who must be in the habit of hearing the very best performers.” On Miss Lucas’s persevering, however, she added, “Very well, if it must be so, it must.” And gravely glancing at Mr. Darcy, “There is a fine old saying, which everybody here is of course familiar with: ‘Keep your breath to cool your porridge’; and I shall keep mine to swell my song.” Her performance was pleasing, though by no means capital. After a song or two, and before she could reply to the entreaties of several that she would sing again, she was eagerly succeeded at the instrument by her sister Mary, who having, in consequence of being the only plain one in the family, worked hard for knowledge and accomplishments, was always impatient for display. Mary had neither genius nor taste; and though vanity had given her application, it had given her likewise a pedantic air and conceited manner, which would have injured a higher degree of excellence than she had reached. Elizabeth, easy and unaffected, had been listened to with much more pleasure, though not playing half so well; and Mary, at the end of a long concerto, was glad to purchase praise and gratitude by Scotch and Irish airs, at the request of her younger sisters, who, with some of the Lucases, and two or three officers, joined eagerly in dancing at one end of the room. Mr. Darcy stood near them in silent indignation at such a mode of passing the evening, to the exclusion of all conversation, and was too much engrossed by his thoughts to perceive that Sir William Lucas was his neighbour, till Sir William thus began: “What a charming amusement for young people this is, Mr. Darcy! There is nothing like dancing after all. I consider it as one of the first refinements of polished society.” “Certainly, sir; and it has the advantage also of being in vogue amongst the less polished societies of the world. Every savage can dance.” Sir William only smiled. “Your friend performs delightfully,” he continued after a pause, on seeing Bingley join the group; “and I doubt not that you are an adept in the science yourself, Mr. Darcy.” “You saw me dance at Meryton, I believe, sir.” “Yes, indeed, and received no inconsiderable pleasure from the sight. Do you often dance at St. James’s?” “Never, sir.” “Do you not think it would be a proper compliment to the place?” “It is a compliment which I never pay to any place if I can avoid it.” “You have a house in town, I conclude?” Mr. Darcy bowed. “I had once had some thought of fixing in town myself—for I am fond of superior society; but I did not feel quite certain that the air of London would agree with Lady Lucas.” He paused in hopes of an answer; but his companion was not disposed to make any; and Elizabeth at that instant moving towards them, he was struck with the action of doing a very gallant thing, and called out to her: “My dear Miss Eliza, why are you not dancing? Mr. Darcy, you must allow me to present this young lady to you as a very desirable partner. You cannot refuse to dance, I am sure when so much beauty is before you.” And, taking her hand, he would have given it to Mr. Darcy who, though extremely surprised, was not unwilling to receive it, when she instantly drew back, and said with some discomposure to Sir William: “Indeed, sir, I have not the least intention of dancing. I entreat you not to suppose that I moved this way in order to beg for a partner.” Mr. Darcy, with grave propriety, requested to be allowed the honour of her hand, but in vain. Elizabeth was determined; nor did Sir William at all shake her purpose by his attempt at persuasion. “You excel so much in the dance, Miss Eliza, that it is cruel to deny me the happiness of seeing you; and though this gentleman dislikes the amusement in general, he can have no objection, I am sure, to oblige us for one half-hour.” “Mr. Darcy is all politeness,” said Elizabeth, smiling. “He is, indeed; but, considering the inducement, my dear Miss Eliza, we cannot wonder at his complaisance—for who would object to such a partner?” Elizabeth looked archly, and turned away. Her resistance had not injured her with the gentleman, and he was thinking of her with some complacency, when thus accosted by Miss Bingley: “I can guess the subject of your reverie.” “I should imagine not.” “You are considering how insupportable it would be to pass many evenings in this manner—in such society; and indeed I am quite of your opinion. I was never more annoyed! The insipidity, and yet the noise—the nothingness, and yet the self-importance of all those people! What would I give to hear your strictures on them!” “Your conjecture is totally wrong, I assure you. My mind was more agreeably engaged. I have been meditating on the very great pleasure which a pair of fine eyes in the face of a pretty woman can bestow.” Miss Bingley immediately fixed her eyes on his face, and desired he would tell her what lady had the credit of inspiring such reflections. Mr. Darcy replied with great intrepidity: “Miss Elizabeth Bennet.” “Miss Elizabeth Bennet!” repeated Miss Bingley. “I am all astonishment. How long has she been such a favourite?—and pray, when am I to wish you joy?” “That is exactly the question which I expected you to ask. A lady’s imagination is very rapid; it jumps from admiration to love, from love to matrimony, in a moment. I knew you would be wishing me joy.” “Nay, if you are serious about it, I shall consider the matter is absolutely settled. You will be having a charming mother-in-law, indeed; and, of course, she will always be at Pemberley with you.” He listened to her with perfect indifference while she chose to entertain herself in this manner; and as his composure convinced her that all was safe, her wit flowed long. Chapter 7Mr. Bennet’s property consisted almost entirely in an estate of two thousand a year, which, unfortunately for his daughters, was entailed, in default of heirs male, on a distant relation; and their mother’s fortune, though ample for her situation in life, could but ill supply the deficiency of his. Her father had been an attorney in Meryton, and had left her four thousand pounds. She had a sister married to a Mr. Phillips, who had been a clerk to their father and succeeded him in the business, and a brother settled in London in a respectable line of trade. The village of Longbourn was only one mile from Meryton; a most convenient distance for the young ladies, who were usually tempted thither three or four times a week, to pay their duty to their aunt and to a milliner’s shop just over the way. The two youngest of the family, Catherine and Lydia, were particularly frequent in these attentions; their minds were more vacant than their sisters’, and when nothing better offered, a walk to Meryton was necessary to amuse their morning hours and furnish conversation for the evening; and however bare of news the country in general might be, they always contrived to learn some from their aunt. At present, indeed, they were well supplied both with news and happiness by the recent arrival of a militia regiment in the neighbourhood; it was to remain the whole winter, and Meryton was the headquarters. Their visits to Mrs. Phillips were now productive of the most interesting intelligence. Every day added something to their knowledge of the officers’ names and connections. Their lodgings were not long a secret, and at length they began to know the officers themselves. Mr. Phillips visited them all, and this opened to his nieces a store of felicity unknown before. They could talk of nothing but officers; and Mr. Bingley’s large fortune, the mention of which gave animation to their mother, was worthless in their eyes when opposed to the regimentals of an ensign. After listening one morning to their effusions on this subject, Mr. Bennet coolly observed: “From all that I can collect by your manner of talking, you must be two of the silliest girls in the country. I have suspected it some time, but I am now convinced.” Catherine was disconcerted, and made no answer; but Lydia, with perfect indifference, continued to express her admiration of Captain Carter, and her hope of seeing him in the course of the day, as he was going the next morning to London. “I am astonished, my dear,” said Mrs. Bennet, “that you should be so ready to think your own children silly. If I wished to think slightingly of anybody’s children, it should not be of my own, however.” “If my children are silly, I must hope to be always sensible of it.” “Yes—but as it happens, they are all of them very clever.” “This is the only point, I flatter myself, on which we do not agree. I had hoped that our sentiments coincided in every particular, but I must so far differ from you as to think our two youngest daughters uncommonly foolish.” “My dear Mr. Bennet, you must not expect such girls to have the sense of their father and mother. When they get to our age, I dare say they will not think about officers any more than we do. I remember the time when I liked a red coat myself very well—and, indeed, so I do still at my heart; and if a smart young colonel, with five or six thousand a year, should want one of my girls I shall not say nay to him; and I thought Colonel Forster looked very becoming the other night at Sir William’s in his regimentals.” “Mamma,” cried Lydia, “my aunt says that Colonel Forster and Captain Carter do not go so often to Miss Watson’s as they did when they first came; she sees them now very often standing in Clarke’s library.” Mrs. Bennet was prevented replying by the entrance of the footman with a note for Miss Bennet; it came from Netherfield, and the servant waited for an answer. Mrs. Bennet’s eyes sparkled with pleasure, and she was eagerly calling out, while her daughter read, “Well, Jane, who is it from? What is it about? What does he say? Well, Jane, make haste and tell us; make haste, my love.” “It is from Miss Bingley,” said Jane, and then read it aloud. “MY DEAR FRIEND,—“If you are not so compassionate as to dine to-day with Louisa and me, we shall be in danger of hating each other for the rest of our lives, for a whole day’s tête-à-tête between two women can never end without a quarrel. Come as soon as you can on receipt of this. My brother and the gentlemen are to dine with the officers.—Yours ever, “CAROLINE BINGLEY” “With the officers!” cried Lydia. “I wonder my aunt did not tell us of that.” “Dining out,” said Mrs. Bennet, “that is very unlucky.” “Can I have the carriage?” said Jane. “No, my dear, you had better go on horseback, because it seems likely to rain; and then you must stay all night.” “That would be a good scheme,” said Elizabeth, “if you were sure that they would not offer to send her home.” “Oh! but the gentlemen will have Mr. Bingley’s chaise to go to Meryton, and the Hursts have no horses to theirs.” “I had much rather go in the coach.” “But, my dear, your father cannot spare the horses, I am sure. They are wanted in the farm, Mr. Bennet, are they not?” “They are wanted in the farm much oftener than I can get them.” “But if you have got them to-day,” said Elizabeth, “my mother’s purpose will be answered.” She did at last extort from her father an acknowledgment that the horses were engaged. Jane was therefore obliged to go on horseback, and her mother attended her to the door with many cheerful prognostics of a bad day. Her hopes were answered; Jane had not been gone long before it rained hard. Her sisters were uneasy for her, but her mother was delighted. The rain continued the whole evening without intermission; Jane certainly could not come back. “This was a lucky idea of mine, indeed!” said Mrs. Bennet more than once, as if the credit of making it rain were all her own. Till the next morning, however, she was not aware of all the felicity of her contrivance. Breakfast was scarcely over when a servant from Netherfield brought the following note for Elizabeth: “MY DEAREST LIZZY,—“I find myself very unwell this morning, which, I suppose, is to be imputed to my getting wet through yesterday. My kind friends will not hear of my returning till I am better. They insist also on my seeing Mr. Jones—therefore do not be alarmed if you should hear of his having been to me—and, excepting a sore throat and headache, there is not much the matter with me.—Yours, etc.” “Well, my dear,” said Mr. Bennet, when Elizabeth had read the note aloud, “if your daughter should have a dangerous fit of illness—if she should die, it would be a comfort to know that it was all in pursuit of Mr. Bingley, and under your orders.” “Oh! I am not afraid of her dying. People do not die of little trifling colds. She will be taken good care of. As long as she stays there, it is all very well. I would go and see her if I could have the carriage.” Elizabeth, feeling really anxious, was determined to go to her, though the carriage was not to be had; and as she was no horsewoman, walking was her only alternative. She declared her resolution. “How can you be so silly,” cried her mother, “as to think of such a thing, in all this dirt! You will not be fit to be seen when you get there.” “I shall be very fit to see Jane—which is all I want.” “Is this a hint to me, Lizzy,” said her father, “to send for the horses?” “No, indeed, I do not wish to avoid the walk. The distance is nothing when one has a motive; only three miles. I shall be back by dinner.” “I admire the activity of your benevolence,” observed Mary, “but every impulse of feeling should be guided by reason; and, in my opinion, exertion should always be in proportion to what is required.” “We will go as far as Meryton with you,” said Catherine and Lydia. Elizabeth accepted their company, and the three young ladies set off together. “If we make haste,” said Lydia, as they walked along, “perhaps we may see something of Captain Carter before he goes.” In Meryton they parted; the two youngest repaired to the lodgings of one of the officers’ wives, and Elizabeth continued her walk alone, crossing field after field at a quick pace, jumping over stiles and springing over puddles with impatient activity, and finding herself at last within view of the house, with weary ankles, dirty stockings, and a face glowing with the warmth of exercise. She was shown into the breakfast-parlour, where all but Jane were assembled, and where her appearance created a great deal of surprise. That she should have walked three miles so early in the day, in such dirty weather, and by herself, was almost incredible to Mrs. Hurst and Miss Bingley; and Elizabeth was convinced that they held her in contempt for it. She was received, however, very politely by them; and in their brother’s manners there was something better than politeness; there was good humour and kindness. Mr. Darcy said very little, and Mr. Hurst nothing at all. The former was divided between admiration of the brilliancy which exercise had given to her complexion, and doubt as to the occasion’s justifying her coming so far alone. The latter was thinking only of his breakfast. Her inquiries after her sister were not very favourably answered. Miss Bennet had slept ill, and though up, was very feverish, and not well enough to leave her room. Elizabeth was glad to be taken to her immediately; and Jane, who had only been withheld by the fear of giving alarm or inconvenience from expressing in her note how much she longed for such a visit, was delighted at her entrance. She was not equal, however, to much conversation, and when Miss Bingley left them together, could attempt little besides expressions of gratitude for the extraordinary kindness she was treated with. Elizabeth silently attended her. When breakfast was over they were joined by the sisters; and Elizabeth began to like them herself, when she saw how much affection and solicitude they showed for Jane. The apothecary came, and having examined his patient, said, as might be supposed, that she had caught a violent cold, and that they must endeavour to get the better of it; advised her to return to bed, and promised her some draughts. The advice was followed readily, for the feverish symptoms increased, and her head ached acutely. Elizabeth did not quit her room for a moment; nor were the other ladies often absent; the gentlemen being out, they had, in fact, nothing to do elsewhere. When the clock struck three, Elizabeth felt that she must go, and very unwillingly said so. Miss Bingley offered her the carriage, and she only wanted a little pressing to accept it, when Jane testified such concern in parting with her, that Miss Bingley was obliged to convert the offer of the chaise to an invitation to remain at Netherfield for the present. Elizabeth most thankfully consented, and a servant was dispatched to Longbourn to acquaint the family with her stay and bring back a supply of clothes. Chapter 8At five o’clock the two ladies retired to dress, and at half-past six Elizabeth was summoned to dinner. To the civil inquiries which then poured in, and amongst which she had the pleasure of distinguishing the much superior solicitude of Mr. Bingley’s, she could not make a very favourable answer. Jane was by no means better. The sisters, on hearing this, repeated three or four times how much they were grieved, how shocking it was to have a bad cold, and how excessively they disliked being ill themselves; and then thought no more of the matter: and their indifference towards Jane when not immediately before them restored Elizabeth to the enjoyment of all her former dislike. Their brother, indeed, was the only one of the party whom she could regard with any complacency. His anxiety for Jane was evident, and his attentions to herself most pleasing, and they prevented her feeling herself so much an intruder as she believed she was considered by the others. She had very little notice from any but him. Miss Bingley was engrossed by Mr. Darcy, her sister scarcely less so; and as for Mr. Hurst, by whom Elizabeth sat, he was an indolent man, who lived only to eat, drink, and play at cards; who, when he found her to prefer a plain dish to a ragout, had nothing to say to her. When dinner was over, she returned directly to Jane, and Miss Bingley began abusing her as soon as she was out of the room. Her manners were pronounced to be very bad indeed, a mixture of pride and impertinence; she had no conversation, no style, no beauty. Mrs. Hurst thought the same, and added: “She has nothing, in short, to recommend her, but being an excellent walker. I shall never forget her appearance this morning. She really looked almost wild.” “She did, indeed, Louisa. I could hardly keep my countenance. Very nonsensical to come at all! Why must she be scampering about the country, because her sister had a cold? Her hair, so untidy, so blowsy!” “Yes, and her petticoat; I hope you saw her petticoat, six inches deep in mud, I am absolutely certain; and the gown which had been let down to hide it not doing its office.” “Your picture may be very exact, Louisa,” said Bingley; “but this was all lost upon me. I thought Miss Elizabeth Bennet looked remarkably well when she came into the room this morning. Her dirty petticoat quite escaped my notice.” “You observed it, Mr. Darcy, I am sure,” said Miss Bingley; “and I am inclined to think that you would not wish to see your sister make such an exhibition.” “Certainly not.” “To walk three miles, or four miles, or five miles, or whatever it is, above her ankles in dirt, and alone, quite alone! What could she mean by it? It seems to me to show an abominable sort of conceited independence, a most country-town indifference to decorum.” “It shows an affection for her sister that is very pleasing,” said Bingley. “I am afraid, Mr. Darcy,” observed Miss Bingley in a half whisper, “that this adventure has rather affected your admiration of her fine eyes.” “Not at all,” he replied; “they were brightened by the exercise.” A short pause followed this speech, and Mrs. Hurst began again: “I have an excessive regard for Miss Jane Bennet, she is really a very sweet girl, and I wish with all my heart she were well settled. But with such a father and mother, and such low connections, I am afraid there is no chance of it.” “I think I have heard you say that their uncle is an attorney in Meryton.” “Yes; and they have another, who lives somewhere near Cheapside.” “That is capital,” added her sister, and they both laughed heartily. “If they had uncles enough to fill all Cheapside,” cried Bingley, “it would not make them one jot less agreeable.” “But it must very materially lessen their chance of marrying men of any consideration in the world,” replied Darcy. To this speech Bingley made no answer; but his sisters gave it their hearty assent, and indulged their mirth for some time at the expense of their dear friend’s vulgar relations. With a renewal of tenderness, however, they returned to her room on leaving the dining-parlour, and sat with her till summoned to coffee. She was still very poorly, and Elizabeth would not quit her at all, till late in the evening, when she had the comfort of seeing her sleep, and when it seemed to her rather right than pleasant that she should go downstairs herself. On entering the drawing-room she found the whole party at loo, and was immediately invited to join them; but suspecting them to be playing high she declined it, and making her sister the excuse, said she would amuse herself for the short time she could stay below, with a book. Mr. Hurst looked at her with astonishment. “Do you prefer reading to cards?” said he; “that is rather singular.” “Miss Eliza Bennet,” said Miss Bingley, “despises cards. She is a great reader, and has no pleasure in anything else.” “I deserve neither such praise nor such censure,” cried Elizabeth; “I am not a great reader, and I have pleasure in many things.” “In nursing your sister I am sure you have pleasure,” said Bingley; “and I hope it will be soon increased by seeing her quite well.” Elizabeth thanked him from her heart, and then walked towards the table where a few books were lying. He immediately offered to fetch her others—all that his library afforded. “And I wish my collection were larger for your benefit and my own credit; but I am an idle fellow, and though I have not many, I have more than I ever looked into.” Elizabeth assured him that she could suit herself perfectly with those in the room. “I am astonished,” said Miss Bingley, “that my father should have left so small a collection of books. What a delightful library you have at Pemberley, Mr. Darcy!” “It ought to be good,” he replied, “it has been the work of many generations.” “And then you have added so much to it yourself, you are always buying books.” “I cannot comprehend the neglect of a family library in such days as these.” “Neglect! I am sure you neglect nothing that can add to the beauties of that noble place. Charles, when you build your house, I wish it may be half as delightful as Pemberley.” “I wish it may.” “But I would really advise you to make your purchase in that neighbourhood, and take Pemberley for a kind of model. There is not a finer county in England than Derbyshire.” “With all my heart; I will buy Pemberley itself if Darcy will sell it.” “I am talking of possibilities, Charles.” “Upon my word, Caroline, I should think it more possible to get Pemberley by purchase than by imitation.” Elizabeth was so much caught with what passed, as to leave her very little attention for her book; and soon laying it wholly aside, she drew near the card-table, and stationed herself between Mr. Bingley and his eldest sister, to observe the game. “Is Miss Darcy much grown since the spring?” said Miss Bingley; “will she be as tall as I am?” “I think she will. She is now about Miss Elizabeth Bennet’s height, or rather taller.” “How I long to see her again! I never met with anybody who delighted me so much. Such a countenance, such manners! And so extremely accomplished for her age! Her performance on the pianoforte is exquisite.” “It is amazing to me,” said Bingley, “how young ladies can have patience to be so very accomplished as they all are.” “All young ladies accomplished! My dear Charles, what do you mean?” “Yes, all of them, I think. They all paint tables, cover screens, and net purses. I scarcely know anyone who cannot do all this, and I am sure I never heard a young lady spoken of for the first time, without being informed that she was very accomplished.” “Your list of the common extent of accomplishments,” said Darcy, “has too much truth. The word is applied to many a woman who deserves it no otherwise than by netting a purse or covering a screen. But I am very far from agreeing with you in your estimation of ladies in general. I cannot boast of knowing more than half-a-dozen, in the whole range of my acquaintance, that are really accomplished.” “Nor I, I am sure,” said Miss Bingley. “Then,” observed Elizabeth, “you must comprehend a great deal in your idea of an accomplished woman.” “Yes, I do comprehend a great deal in it.” “Oh! certainly,” cried his faithful assistant, “no one can be really esteemed accomplished who does not greatly surpass what is usually met with. A woman must have a thorough knowledge of music, singing, drawing, dancing, and the modern languages, to deserve the word; and besides all this, she must possess a certain something in her air and manner of walking, the tone of her voice, her address and expressions, or the word will be but half-deserved.” “All this she must possess,” added Darcy, “and to all this she must yet add something more substantial, in the improvement of her mind by extensive reading.” “I am no longer surprised at your knowing only six accomplished women. I rather wonder now at your knowing any.” “Are you so severe upon your own sex as to doubt the possibility of all this?” “I never saw such a woman. I never saw such capacity, and taste, and application, and elegance, as you describe united.” Mrs. Hurst and Miss Bingley both cried out against the injustice of her implied doubt, and were both protesting that they knew many women who answered this description, when Mr. Hurst called them to order, with bitter complaints of their inattention to what was going forward. As all conversation was thereby at an end, Elizabeth soon afterwards left the room. “Elizabeth Bennet,” said Miss Bingley, when the door was closed on her, “is one of those young ladies who seek to recommend themselves to the other sex by undervaluing their own; and with many men, I dare say, it succeeds. But, in my opinion, it is a paltry device, a very mean art.” “Undoubtedly,” replied Darcy, to whom this remark was chiefly addressed, “there is a meanness in all the arts which ladies sometimes condescend to employ for captivation. Whatever bears affinity to cunning is despicable.” Miss Bingley was not so entirely satisfied with this reply as to continue the subject. Elizabeth joined them again only to say that her sister was worse, and that she could not leave her. Bingley urged Mr. Jones being sent for immediately; while his sisters, convinced that no country advice could be of any service, recommended an express to town for one of the most eminent physicians. This she would not hear of; but she was not so unwilling to comply with their brother’s proposal; and it was settled that Mr. Jones should be sent for early in the morning, if Miss Bennet were not decidedly better. Bingley was quite uncomfortable; his sisters declared that they were miserable. They solaced their wretchedness, however, by duets after supper, while he could find no better relief to his feelings than by giving his housekeeper directions that every attention might be paid to the sick lady and her sister. Chapter 9Elizabeth passed the chief of the night in her sister’s room, and in the morning had the pleasure of being able to send a tolerable answer to the inquiries which she very early received from Mr. Bingley by a housemaid, and some time afterwards from the two elegant ladies who waited on his sisters. In spite of this amendment, however, she requested to have a note sent to Longbourn, desiring her mother to visit Jane, and form her own judgement of her situation. The note was immediately dispatched, and its contents as quickly complied with. Mrs. Bennet, accompanied by her two youngest girls, reached Netherfield soon after the family breakfast. Had she found Jane in any apparent danger, Mrs. Bennet would have been very miserable; but being satisfied on seeing her that her illness was not alarming, she had no wish of her recovering immediately, as her restoration to health would probably remove her from Netherfield. She would not listen, therefore, to her daughter’s proposal of being carried home; neither did the apothecary, who arrived about the same time, think it at all advisable. After sitting a little while with Jane, on Miss Bingley’s appearance and invitation, the mother and three daughters all attended her into the breakfast parlour. Bingley met them with hopes that Mrs. Bennet had not found Miss Bennet worse than she expected. “Indeed I have, sir,” was her answer. “She is a great deal too ill to be moved. Mr. Jones says we must not think of moving her. We must trespass a little longer on your kindness.” “Removed!” cried Bingley. “It must not be thought of. My sister, I am sure, will not hear of her removal.” “You may depend upon it, Madam,” said Miss Bingley, with cold civility, “that Miss Bennet will receive every possible attention while she remains with us.” Mrs. Bennet was profuse in her acknowledgments. “I am sure,” she added, “if it was not for such good friends I do not know what would become of her, for she is very ill indeed, and suffers a vast deal, though with the greatest patience in the world, which is always the way with her, for she has, without exception, the sweetest temper I have ever met with. I often tell my other girls they are nothing to her. You have a sweet room here, Mr. Bingley, and a charming prospect over the gravel walk. I do not know a place in the country that is equal to Netherfield. You will not think of quitting it in a hurry, I hope, though you have but a short lease.” “Whatever I do is done in a hurry,” replied he; “and therefore if I should resolve to quit Netherfield, I should probably be off in five minutes. At present, however, I consider myself as quite fixed here.” “That is exactly what I should have supposed of you,” said Elizabeth. “You begin to comprehend me, do you?” cried he, turning towards her. “Oh! yes—I understand you perfectly.” “I wish I might take this for a compliment; but to be so easily seen through I am afraid is pitiful.” “That is as it happens. It does not follow that a deep, intricate character is more or less estimable than such a one as yours.” “Lizzy,” cried her mother, “remember where you are, and do not run on in the wild manner that you are suffered to do at home.” “I did not know before,” continued Bingley immediately, “that you were a studier of character. It must be an amusing study.” “Yes, but intricate characters are the most amusing. They have at least that advantage.” “The country,” said Darcy, “can in general supply but a few subjects for such a study. In a country neighbourhood you move in a very confined and unvarying society.” “But people themselves alter so much, that there is something new to be observed in them for ever.” “Yes, indeed,” cried Mrs. Bennet, offended by his manner of mentioning a country neighbourhood. “I assure you there is quite as much of that going on in the country as in town.” Everybody was surprised, and Darcy, after looking at her for a moment, turned silently away. Mrs. Bennet, who fancied she had gained a complete victory over him, continued her triumph. “I cannot see that London has any great advantage over the country, for my part, except the shops and public places. The country is a vast deal pleasanter, is it not, Mr. Bingley?” “When I am in the country,” he replied, “I never wish to leave it; and when I am in town it is pretty much the same. They have each their advantages, and I can be equally happy in either.” “Aye—that is because you have the right disposition. But that gentleman,” looking at Darcy, “seemed to think the country was nothing at all.” “Indeed, Mamma, you are mistaken,” said Elizabeth, blushing for her mother. “You quite mistook Mr. Darcy. He only meant that there was not such a variety of people to be met with in the country as in the town, which you must acknowledge to be true.” “Certainly, my dear, nobody said there were; but as to not meeting with many people in this neighbourhood, I believe there are few neighbourhoods larger. I know we dine with four-and-twenty families.” Nothing but concern for Elizabeth could enable Bingley to keep his countenance. His sister was less delicate, and directed her eyes towards Mr. Darcy with a very expressive smile. Elizabeth, for the sake of saying something that might turn her mother’s thoughts, now asked her if Charlotte Lucas had been at Longbourn since her coming away. “Yes, she called yesterday with her father. What an agreeable man Sir William is, Mr. Bingley, is not he? So much the man of fashion! So genteel and easy! He has always something to say to everybody. That is my idea of good breeding; and those persons who fancy themselves very important, and never open their mouths, quite mistake the matter.” “Did Charlotte dine with you?” “No, she would go home. I fancy she was wanted about the mince-pies. For my part, Mr. Bingley, I always keep servants that can do their own work; my daughters are brought up very differently. But everybody is to judge for themselves, and the Lucases are a very good sort of girls, I assure you. It is a pity they are not handsome! Not that I think Charlotte so very plain—but then she is our particular friend.” “She seems a very pleasant young woman.” “Oh! dear, yes; but you must own she is very plain. Lady Lucas herself has often said so, and envied me Jane’s beauty. I do not like to boast of my own child, but to be sure, Jane—one does not often see anybody better looking. It is what everybody says. I do not trust my own partiality. When she was only fifteen, there was a man at my brother Gardiner’s in town so much in love with her that my sister-in-law was sure he would make her an offer before we came away. But, however, he did not. Perhaps he thought her too young. However, he wrote some verses on her, and very pretty they were.” “And so ended his affection,” said Elizabeth impatiently. “There has been many a one, I fancy, overcome in the same way. I wonder who first discovered the efficacy of poetry in driving away love!” “I have been used to consider poetry as the food of love,” said Darcy. “Of a fine, stout, healthy love it may. Everything nourishes what is strong already. But if it be only a slight, thin sort of inclination, I am convinced that one good sonnet will starve it entirely away.” Darcy only smiled; and the general pause which ensued made Elizabeth tremble lest her mother should be exposing herself again. She longed to speak, but could think of nothing to say; and after a short silence Mrs. Bennet began repeating her thanks to Mr. Bingley for his kindness to Jane, with an apology for troubling him also with Lizzy. Mr. Bingley was unaffectedly civil in his answer, and forced his younger sister to be civil also, and say what the occasion required. She performed her part indeed without much graciousness, but Mrs. Bennet was satisfied, and soon afterwards ordered her carriage. Upon this signal, the youngest of her daughters put herself forward. The two girls had been whispering to each other during the whole visit, and the result of it was, that the youngest should tax Mr. Bingley with having promised on his first coming into the country to give a ball at Netherfield. Lydia was a stout, well-grown girl of fifteen, with a fine complexion and good-humoured countenance; a favourite with her mother, whose affection had brought her into public at an early age. She had high animal spirits, and a sort of natural self-consequence, which the attention of the officers, to whom her uncle’s good dinners, and her own easy manners recommended her, had increased into assurance. She was very equal, therefore, to address Mr. Bingley on the subject of the ball, and abruptly reminded him of his promise; adding, that it would be the most shameful thing in the world if he did not keep it. His answer to this sudden attack was delightful to their mother’s ear: “I am perfectly ready, I assure you, to keep my engagement; and when your sister is recovered, you shall, if you please, name the very day of the ball. But you would not wish to be dancing when she is ill.” Lydia declared herself satisfied. “Oh! yes—it would be much better to wait till Jane was well, and by that time most likely Captain Carter would be at Meryton again. And when you have given your ball,” she added, “I shall insist on their giving one also. I shall tell Colonel Forster it will be quite a shame if he does not.” Mrs. Bennet and her daughters then departed, and Elizabeth returned instantly to Jane, leaving her own and her relations’ behaviour to the remarks of the two ladies and Mr. Darcy; the latter of whom, however, could not be prevailed on to join in their censure of her, in spite of all Miss Bingley’s witticisms on fine eyes. Chapter 10The day passed much as the day before had done. Mrs. Hurst and Miss Bingley had spent some hours of the morning with the invalid, who continued, though slowly, to mend; and in the evening Elizabeth joined their party in the drawing-room. The loo-table, however, did not appear. Mr. Darcy was writing, and Miss Bingley, seated near him, was watching the progress of his letter and repeatedly calling off his attention by messages to his sister. Mr. Hurst and Mr. Bingley were at piquet, and Mrs. Hurst was observing their game. Elizabeth took up some needlework, and was sufficiently amused in attending to what passed between Darcy and his companion. The perpetual commendations of the lady, either on his handwriting, or on the evenness of his lines, or on the length of his letter, with the perfect unconcern with which her praises were received, formed a curious dialogue, and was exactly in union with her opinion of each. “How delighted Miss Darcy will be to receive such a letter!” He made no answer. “You write uncommonly fast.” “You are mistaken. I write rather slowly.” “How many letters you must have occasion to write in the course of a year! Letters of business, too! How odious I should think them!” “It is fortunate, then, that they fall to my lot instead of yours.” “Pray tell your sister that I long to see her.” “I have already told her so once, by your desire.” “I am afraid you do not like your pen. Let me mend it for you. I mend pens remarkably well.” “Thank you—but I always mend my own.” “How can you contrive to write so even?” He was silent. “Tell your sister I am delighted to hear of her improvement on the harp; and pray let her know that I am quite in raptures with her beautiful little design for a table, and I think it infinitely superior to Miss Grantley’s.” “Will you give me leave to defer your raptures till I write again? At present I have not room to do them justice.” “Oh! it is of no consequence. I shall see her in January. But do you always write such charming long letters to her, Mr. Darcy?” “They are generally long; but whether always charming it is not for me to determine.” “It is a rule with me, that a person who can write a long letter with ease, cannot write ill.” “That will not do for a compliment to Darcy, Caroline,” cried her brother, “because he does not write with ease. He studies too much for words of four syllables. Do not you, Darcy?” “My style of writing is very different from yours.” “Oh!” cried Miss Bingley, “Charles writes in the most careless way imaginable. He leaves out half his words, and blots the rest.” “My ideas flow so rapidly that I have not time to express them—by which means my letters sometimes convey no ideas at all to my correspondents.” “Your humility, Mr. Bingley,” said Elizabeth, “must disarm reproof.” “Nothing is more deceitful,” said Darcy, “than the appearance of humility. It is often only carelessness of opinion, and sometimes an indirect boast.” “And which of the two do you call my little recent piece of modesty?” “The indirect boast; for you are really proud of your defects in writing, because you consider them as proceeding from a rapidity of thought and carelessness of execution, which, if not estimable, you think at least highly interesting. The power of doing anything with quickness is always prized much by the possessor, and often without any attention to the imperfection of the performance. When you told Mrs. Bennet this morning that if you ever resolved upon quitting Netherfield you should be gone in five minutes, you meant it to be a sort of panegyric, of compliment to yourself—and yet what is there so very laudable in a precipitance which must leave very necessary business undone, and can be of no real advantage to yourself or anyone else?” “Nay,” cried Bingley, “this is too much, to remember at night all the foolish things that were said in the morning. And yet, upon my honour, I believe what I said of myself to be true, and I believe it at this moment. At least, therefore, I did not assume the character of needless precipitance merely to show off before the ladies.” “I dare say you believed it; but I am by no means convinced that you would be gone with such celerity. Your conduct would be quite as dependent on chance as that of any man I know; and if, as you were mounting your horse, a friend were to say, ‘Bingley, you had better stay till next week,’ you would probably do it, you would probably not go—and at another word, might stay a month.” “You have only proved by this,” cried Elizabeth, “that Mr. Bingley did not do justice to his own disposition. You have shown him off now much more than he did himself.” “I am exceedingly gratified,” said Bingley, “by your converting what my friend says into a compliment on the sweetness of my temper. But I am afraid you are giving it a turn which that gentleman did by no means intend; for he would certainly think better of me, if under such a circumstance I were to give a flat denial, and ride off as fast as I could.” “Would Mr. Darcy then consider the rashness of your original intentions as atoned for by your obstinacy in adhering to it?” “Upon my word, I cannot exactly explain the matter; Darcy must speak for himself.” “You expect me to account for opinions which you choose to call mine, but which I have never acknowledged. Allowing the case, however, to stand according to your representation, you must remember, Miss Bennet, that the friend who is supposed to desire his return to the house, and the delay of his plan, has merely desired it, asked it without offering one argument in favour of its propriety.” “To yield readily—easily—to the persuasion of a friend is no merit with you.” “To yield without conviction is no compliment to the understanding of either.” “You appear to me, Mr. Darcy, to allow nothing for the influence of friendship and affection. A regard for the requester would often make one readily yield to a request, without waiting for arguments to reason one into it. I am not particularly speaking of such a case as you have supposed about Mr. Bingley. We may as well wait, perhaps, till the circumstance occurs before we discuss the discretion of his behaviour thereupon. But in general and ordinary cases between friend and friend, where one of them is desired by the other to change a resolution of no very great moment, should you think ill of that person for complying with the desire, without waiting to be argued into it?” “Will it not be advisable, before we proceed on this subject, to arrange with rather more precision the degree of importance which is to appertain to this request, as well as the degree of intimacy subsisting between the parties?” “By all means,” cried Bingley; “let us hear all the particulars, not forgetting their comparative height and size; for that will have more weight in the argument, Miss Bennet, than you may be aware of. I assure you, that if Darcy were not such a great tall fellow, in comparison with myself, I should not pay him half so much deference. I declare I do not know a more awful object than Darcy, on particular occasions, and in particular places; at his own house especially, and of a Sunday evening, when he has nothing to do.” Mr. Darcy smiled; but Elizabeth thought she could perceive that he was rather offended, and therefore checked her laugh. Miss Bingley warmly resented the indignity he had received, in an expostulation with her brother for talking such nonsense. “I see your design, Bingley,” said his friend. “You dislike an argument, and want to silence this.” “Perhaps I do. Arguments are too much like disputes. If you and Miss Bennet will defer yours till I am out of the room, I shall be very thankful; and then you may say whatever you like of me.” “What you ask,” said Elizabeth, “is no sacrifice on my side; and Mr. Darcy had much better finish his letter.” Mr. Darcy took her advice, and did finish his letter. When that business was over, he applied to Miss Bingley and Elizabeth for an indulgence of some music. Miss Bingley moved with some alacrity to the pianoforte; and, after a polite request that Elizabeth would lead the way which the other as politely and more earnestly negatived, she seated herself. Mrs. Hurst sang with her sister, and while they were thus employed, Elizabeth could not help observing, as she turned over some music-books that lay on the instrument, how frequently Mr. Darcy’s eyes were fixed on her. She hardly knew how to suppose that she could be an object of admiration to so great a man; and yet that he should look at her because he disliked her, was still more strange. She could only imagine, however, at last that she drew his notice because there was something more wrong and reprehensible, according to his ideas of right, than in any other person present. The supposition did not pain her. She liked him too little to care for his approbation. After playing some Italian songs, Miss Bingley varied the charm by a lively Scotch air; and soon afterwards Mr. Darcy, drawing near Elizabeth, said to her: “Do not you feel a great inclination, Miss Bennet, to seize such an opportunity of dancing a reel?” She smiled, but made no answer. He repeated the question, with some surprise at her silence. “Oh!” said she, “I heard you before, but I could not immediately determine what to say in reply. You wanted me, I know, to say ‘Yes,’ that you might have the pleasure of despising my taste; but I always delight in overthrowing those kind of schemes, and cheating a person of their premeditated contempt. I have, therefore, made up my mind to tell you, that I do not want to dance a reel at all—and now despise me if you dare.” “Indeed I do not dare.” Elizabeth, having rather expected to affront him, was amazed at his gallantry; but there was a mixture of sweetness and archness in her manner which made it difficult for her to affront anybody; and Darcy had never been so bewitched by any woman as he was by her. He really believed, that were it not for the inferiority of her connections, he should be in some danger. Miss Bingley saw, or suspected enough to be jealous; and her great anxiety for the recovery of her dear friend Jane received some assistance from her desire of getting rid of Elizabeth. She often tried to provoke Darcy into disliking her guest, by talking of their supposed marriage, and planning his happiness in such an alliance. “I hope,” said she, as they were walking together in the shrubbery the next day, “you will give your mother-in-law a few hints, when this desirable event takes place, as to the advantage of holding her tongue; and if you can compass it, do cure the younger girls of running after officers. And, if I may mention so delicate a subject, endeavour to check that little something, bordering on conceit and impertinence, which your lady possesses.” “Have you anything else to propose for my domestic felicity?” “Oh! yes. Do let the portraits of your uncle and aunt Phillips be placed in the gallery at Pemberley. Put them next to your great-uncle the judge. They are in the same profession, you know, only in different lines. As for your Elizabeth’s picture, you must not have it taken, for what painter could do justice to those beautiful eyes?” “It would not be easy, indeed, to catch their expression, but their colour and shape, and the eyelashes, so remarkably fine, might be copied.” At that moment they were met from another walk by Mrs. Hurst and Elizabeth herself. “I did not know that you intended to walk,” said Miss Bingley, in some confusion, lest they had been overheard. “You used us abominably ill,” answered Mrs. Hurst, “running away without telling us that you were coming out.” Then taking the disengaged arm of Mr. Darcy, she left Elizabeth to walk by herself. The path just admitted three. Mr. Darcy felt their rudeness, and immediately said: “This walk is not wide enough for our party. We had better go into the avenue.” But Elizabeth, who had not the least inclination to remain with them, laughingly answered: “No, no; stay where you are. You are charmingly grouped, and appear to uncommon advantage. The picturesque would be spoilt by admitting a fourth. Good-bye.” She then ran gaily off, rejoicing as she rambled about, in the hope of being at home again in a day or two. Jane was already so much recovered as to intend leaving her room for a couple of hours that evening.'

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4d8be5562e5514fc9f0a81982ca78b7e642f771b

20,240

py

Python

sci_analysis/test/test_determine_analysis_type.py

cmmorrow/sci-analysis

de65ba29fe210eb950daa3dbc2e956963a4770ef

[ "MIT" ]

17

2017-05-10T18:25:36.000Z

2021-12-23T14:43:49.000Z

sci_analysis/test/test_determine_analysis_type.py

cmmorrow/sci-analysis

de65ba29fe210eb950daa3dbc2e956963a4770ef

[ "MIT" ]

57

2016-08-22T23:58:05.000Z

2019-07-31T06:54:22.000Z

sci_analysis/test/test_determine_analysis_type.py

cmmorrow/sci-analysis

de65ba29fe210eb950daa3dbc2e956963a4770ef

[ "MIT" ]

null

import unittest import numpy as np import pandas as pd import scipy.stats as st from ..analysis import determine_analysis_type from ..analysis.exc import NoDataError from ..data import Vector, Categorical class MyTestCase(unittest.TestCase): def test_small_float_array(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_float_list(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30).tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_float_series(self): np.random.seed(123456789) input_array = pd.Series(st.norm.rvs(0, 1, 30)) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_large_float_array(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 10000) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_large_float_list(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 10000).tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_large_float_series(self): np.random.seed(123456789) input_array = pd.Series(st.norm.rvs(0, 1, 10000)) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_float32_array(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30).astype('float32') self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_float32_list(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30).astype('float32').tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_float32_series(self): np.random.seed(123456789) input_array = pd.Series(st.norm.rvs(0, 1, 30).astype('float32')) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_float16_array(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30).astype('float16') self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_float16_list(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30).astype('float16').tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_float16_series(self): np.random.seed(123456789) input_array = pd.Series(st.norm.rvs(0, 1, 30).astype('float16')) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_single_float_array(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 1) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_single_float_list(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 1).tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_single_float_series(self): np.random.seed(123456789) input_array = pd.Series(st.norm.rvs(0, 1, 1)) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_vector(self): np.random.seed(123456789) input_array = Vector(st.norm.rvs(0, 1, 30)) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_large_vector(self): np.random.seed(123456789) input_array = Vector(st.norm.rvs(0, 1, 10000)) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_array_with_nan(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30) input_array[4] = np.nan input_array[10] = np.nan input_array[17] = np.nan input_array[22] = np.nan input_array[24] = np.nan self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_list_with_nan(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30) input_array[4] = np.nan input_array[10] = np.nan input_array[17] = np.nan input_array[22] = np.nan input_array[24] = np.nan self.assertIsInstance(determine_analysis_type(input_array.tolist()), Vector) def test_small_series_with_nan(self): np.random.seed(123456789) input_array = st.norm.rvs(0, 1, 30) input_array[4] = np.nan input_array[10] = np.nan input_array[17] = np.nan input_array[22] = np.nan input_array[24] = np.nan self.assertIsInstance(determine_analysis_type(pd.Series(input_array)), Vector) def test_none(self): input_array = None self.assertRaises(ValueError, lambda: determine_analysis_type(input_array)) def test_empty_list(self): input_array = list() self.assertRaises(NoDataError, lambda: determine_analysis_type(input_array)) def test_empty_array(self): input_array = np.array([]) self.assertRaises(NoDataError, lambda: determine_analysis_type(input_array)) def test_empty_vector(self): input_array = Vector([]) self.assertRaises(NoDataError, lambda: determine_analysis_type(input_array)) def test_float_scalar(self): input_array = 3.14159256 self.assertRaises(ValueError, lambda: determine_analysis_type(input_array)) def test_small_int_array(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 30) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int_list(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 30).tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int_series(self): np.random.seed(123456789) input_array = pd.Series(np.random.randint(-10, 11, 30)) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_large_int_array(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 10000) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_large_int_list(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 10000).tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_large_int_series(self): np.random.seed(123456789) input_array = pd.Series(np.random.randint(-10, 11, 10000)) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int32_array(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 30).astype('int32') self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int32_list(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 30).astype('int32').tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int32_series(self): np.random.seed(123456789) input_array = pd.Series(np.random.randint(-10, 11, 30).astype('int32')) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int16_array(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 30).astype('int16') self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int16_list(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 30).astype('int16').tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int16_series(self): np.random.seed(123456789) input_array = pd.Series(np.random.randint(-10, 11, 30).astype('int16')) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int8_array(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 30).astype('int8') self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int8_list(self): np.random.seed(123456789) input_array = np.random.randint(-10, 11, 30).astype('int8').tolist() self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_small_int8_series(self): np.random.seed(123456789) input_array = pd.Series(np.random.randint(-10, 11, 30).astype('int8')) self.assertIsInstance(determine_analysis_type(input_array), Vector) def test_int_scalar(self): input_array = 3 self.assertRaises(ValueError, lambda: determine_analysis_type(input_array)) def test_small_cat_list(self): np.random.seed(123456789) input_array = ['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)] self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_small_cat_array(self): np.random.seed(123456789) input_array = np.array(['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)]) self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_small_cat_series(self): np.random.seed(123456789) input_array = pd.Series(['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)]) self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_large_cat_list(self): np.random.seed(123456789) input_array = ['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(10000)] self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_large_cat_array(self): np.random.seed(123456789) input_array = np.array(['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(10000)]) self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_large_cat_series(self): np.random.seed(123456789) input_array = pd.Series(['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(10000)]) self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_single_cat_list(self): input_array = ['a'] self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_single_cat_array(self): input_array = np.array(['a']) self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_single_cat_series(self): input_array = pd.Series(['a']) self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_small_categorical(self): np.random.seed(123456789) input_array = Categorical(['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)]) self.assertIsInstance(determine_analysis_type(input_array), Categorical) def test_large_categorical(self): np.random.seed(123456789) input_array = Categorical(['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(10000)]) self.assertIsInstance(determine_analysis_type(input_array), Categorical) def test_string_scalar(self): input_array = 'a' self.assertRaises(ValueError, lambda: determine_analysis_type(input_array)) def test_empty_categorical(self): input_array = Categorical([]) self.assertRaises(NoDataError, lambda: determine_analysis_type(input_array)) def test_small_cat_list_with_nan(self): np.random.seed(123456789) input_array = ['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)] input_array[4] = np.nan input_array[10] = np.nan input_array[17] = np.nan input_array[22] = np.nan input_array[24] = np.nan self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_small_cat_array_with_nan(self): np.random.seed(123456789) input_array = ['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)] input_array[4] = np.nan input_array[10] = np.nan input_array[17] = np.nan input_array[22] = np.nan input_array[24] = np.nan input_array = np.array(input_array) self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_small_cat_series_with_nan(self): np.random.seed(123456789) input_array = ['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)] input_array[4] = np.nan input_array[10] = np.nan input_array[17] = np.nan input_array[22] = np.nan input_array[24] = np.nan input_array = pd.Series(input_array) self.assertIsInstance(determine_analysis_type(input_array), Categorical) self.assertNotIsInstance(determine_analysis_type(input_array), Vector) def test_small_string_num_list(self): input_array = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'] self.assertIsInstance(determine_analysis_type(input_array), Categorical) def test_small_string_num_array(self): input_array = np.array(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']) self.assertIsInstance(determine_analysis_type(input_array), Categorical) def test_small_string_num_series(self): input_array = pd.Series(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']) self.assertIsInstance(determine_analysis_type(input_array), Categorical) def test_small_mixed_list(self): input_array = ['1', 'a', np.nan, 4, 5.0] self.assertIsInstance(determine_analysis_type(input_array), Categorical) def test_small_mixed_array(self): input_array = np.array(['1', 'a', np.nan, 4, 5.0]) self.assertIsInstance(determine_analysis_type(input_array), Categorical) def test_small_mixed_series(self): input_array = pd.Series(['1', 'a', np.nan, 4, 5.0]) self.assertIsInstance(determine_analysis_type(input_array), Categorical) def test_arrays_with_other(self): np.random.seed(123456789) input_1_array = st.norm.rvs(0, 1, 10000) input_2_array = st.norm.rvs(1, 1, 10000) self.assertIsInstance(determine_analysis_type(input_1_array, other=input_2_array), Vector) self.assertTrue(pd.Series(input_1_array) .equals(determine_analysis_type(input_1_array, other=input_2_array).data)) self.assertTrue(pd.Series(input_2_array) .equals(determine_analysis_type(input_1_array, other=input_2_array).other)) def test_series_with_other(self): np.random.seed(123456789) input_1_array = pd.Series(st.norm.rvs(0, 1, 10000)) input_2_array = pd.Series(st.norm.rvs(1, 1, 10000)) self.assertIsInstance(determine_analysis_type(input_1_array, other=input_2_array), Vector) self.assertTrue(input_1_array .equals(determine_analysis_type(input_1_array, other=input_2_array).data)) self.assertTrue(input_2_array .equals(determine_analysis_type(input_1_array, other=input_2_array).other)) def test_list_with_other(self): np.random.seed(123456789) input_1_array = pd.Series(st.norm.rvs(0, 1, 10000)).tolist() input_2_array = pd.Series(st.norm.rvs(1, 1, 10000)).tolist() self.assertIsInstance(determine_analysis_type(input_1_array, other=input_2_array), Vector) self.assertListEqual(input_1_array, determine_analysis_type(input_1_array, other=input_2_array).data.tolist()) self.assertListEqual(input_2_array, determine_analysis_type(input_1_array, other=input_2_array).other.tolist()) def test_vector_with_other(self): np.random.seed(123456789) input_1_array = st.norm.rvs(0, 1, 10000) input_2_array = st.norm.rvs(1, 1, 10000) vector = Vector(input_1_array, other=input_2_array) self.assertIsInstance(determine_analysis_type(vector), Vector) self.assertTrue(vector.data .equals(determine_analysis_type(input_1_array, other=input_2_array).data)) self.assertTrue(vector.other .equals(determine_analysis_type(input_1_array, other=input_2_array).other)) def test_vector_with_other_categorical(self): np.random.seed(123456789) input_1_array = st.norm.rvs(0, 1, 10000) input_2_array = ['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)] self.assertIsInstance(determine_analysis_type(input_1_array, other=input_2_array), Vector) self.assertTrue(pd.Series(input_1_array) .equals(determine_analysis_type(input_1_array, other=input_2_array).data)) self.assertTrue(all(determine_analysis_type(input_1_array, other=input_2_array).other.isnull())) def test_categorical_with_other_vector(self): np.random.seed(123456789) input_1_array = ['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)] input_2_array = st.norm.rvs(0, 1, 10000) self.assertIsInstance(determine_analysis_type(input_1_array, other=input_2_array), Categorical) def test_float_with_groups(self): np.random.seed(123456789) input_1_array = pd.DataFrame({'input': st.norm.rvs(size=2000), 'group': ['Group 1'] * 2000}) input_2_array = pd.DataFrame({'input': st.norm.rvs(1, size=2000), 'group': ['Group 2'] * 2000}) df = pd.concat([input_1_array, input_2_array]) self.assertIsInstance(determine_analysis_type(df['input'], groups=df['group']), Vector) self.assertEqual(len(determine_analysis_type(df['input'], groups=df['group']).groups), 2) def test_float_with_other_with_groups(self): np.random.seed(123456789) input_1_array = pd.DataFrame({'input1': st.norm.rvs(size=2000), 'input2': st.weibull_min.rvs(1.7, size=2000), 'group': ['Group 1'] * 2000}) input_2_array = pd.DataFrame({'input1': st.norm.rvs(1, size=2000), 'input2': st.weibull_min.rvs(1.7, size=2000), 'group': ['Group 2'] * 2000}) df = pd.concat([input_1_array, input_2_array]) self.assertIsInstance(determine_analysis_type(df['input1'], other=df['input2'], groups=df['group']), Vector) self.assertEqual(len(determine_analysis_type(df['input1'], other=df['input2'], groups=df['group']).groups), 2) def test_categorical_with_groups(self): np.random.seed(123456789) input_array = ['abcdefghijklmnopqrstuvwxyz'[:np.random.randint(1, 26)] for _ in range(30)] grp = ['Group 1' for _ in range(30)] self.assertIsInstance(determine_analysis_type(input_array, groups=grp), Categorical) if __name__ == '__main__': unittest.main()

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null

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7

4dbc1384a61c6f9ca30f3934c62fc1147d9adb40

4,605

py

Python

test.py

leonard-thong/dlwlrat

2b4b669a3b2f348d1b05125afd6b8a1907b6b212

[ "CC-BY-3.0" ]

2

2020-10-08T06:01:22.000Z

2020-12-15T16:28:27.000Z

test.py

dreamyang-liu/SciAnnotate

dfa41ec5c2c4b6665b5d9b059895be20e9e0cfe2

[ "CC-BY-3.0" ]

8

2020-12-09T02:55:20.000Z

2020-12-23T23:31:42.000Z

test.py

dreamyang-liu/SciAnnotate

dfa41ec5c2c4b6665b5d9b059895be20e9e0cfe2

[ "CC-BY-3.0" ]

4

2021-02-02T04:51:53.000Z

2021-07-18T17:00:04.000Z

def prehandle_data(**kwargs): collection = kwargs['collection'] document = kwargs['document'] directory = collection real_dir = real_directory(directory) document = path_join(real_dir, document) txt_file_path = document + '.txt' ann_file_path = txt_file_path[:-4] + '.ann' function_ann_file_path = txt_file_path[:-4] + '_func.ann' out = [] with open(txt_file_path, 'r') as txt_file: for line in txt_file.readlines(): sentence = dict() sentence['sentence'] = line sentence['annotation'] = [] out.append(sentence) return _prehandle_data(out, txt_file_path, ann_file_path,function_ann_file_path) def _prehandle_data(out, txt_file_path, ann_file_path, function_ann_file_path): res = dict() with open(ann_file_path, 'r') as ann_file: for line in ann_file.readlines(): line_num = -1 sentence = dict() sentence['sentence'] = '' sentence['annotation'] = [] data = [] line = line.replace('\t', ' ') info = line.split(' ') source_name = info[1].split('_')[0] temp = info[1].split('_')[1:] label = '' for i in range(len(temp)): label += temp[i] data.append(source_name) data.append(label) start = int(info[2]) end = int(info[3]) line_dict = judge_line(txt_file_path) line_start_index = [key for key in line_dict] line_start_index = sorted(line_start_index) for i in range(len(line_start_index)): if start > int(line_start_index[i]) and end < (int(line_start_index[i]) + len(line_dict[line_start_index[i]])): sentence['sentence']=line_dict[line_start_index[i]] start -= int(line_start_index[i]) end -= int(line_start_index[i]) line_num = i break ''' elif start > line_start_index[i] and end > (line_start_index[i] + len(line_dict[line_start_index[i]])): ''' data.append(start) data.append(end) out[line_num]['annotation'].append(data) with open(function_ann_file_path, 'r') as function_ann_file: for line in function_ann_file.readlines(): line_num = -1 sentence = dict() sentence['sentence'] = '' sentence['annotation'] = [] data = [] line = line.replace('\t', ' ') info = line.split(' ') source_name = info[1].split('_')[0] temp = info[1].split('_')[1:] label = '' for i in range(len(temp)): label += temp[i] data.append(source_name) data.append(label) start = int(info[2]) end = int(info[3]) line_dict = judge_line(txt_file_path) line_start_index = [key for key in line_dict] line_start_index = sorted(line_start_index) for i in range(len(line_start_index)): if start > int(line_start_index[i]) and end < (int(line_start_index[i]) + len(line_dict[line_start_index[i]])): sentence['sentence']=line_dict[line_start_index[i]] start -= int(line_start_index[i]) end -= int(line_start_index[i]) line_num = i break ''' elif start > line_start_index[i] and end > (line_start_index[i] + len(line_dict[line_start_index[i]])): ''' data.append(start) data.append(end) out[line_num]['annotation'].append(data) res['processedData'] = out return res def judge_line(txt_file_path): count = 0 line_dict = dict() with open(txt_file_path, 'r') as txt_file: for line in txt_file.readlines(): line_dict[count] = line count += len(line) return line_dict txt_file_path = '/Users/robin/research/brat/data/Local/test.txt' out = [] with open(txt_file_path, 'r') as txt_file: for line in txt_file.readlines(): sentence = dict() sentence['sentence'] = line sentence['annotation'] = [] out.append(sentence) res = _prehandle_data(out, '/Users/robin/research/brat/data/Local/test.txt', '/Users/robin/research/brat/data/Local/test.ann','/Users/robin/research/brat/data/Local/test_func.ann') print(res)

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7

15134cb122c5dfed422dc54d51cec34d57ff07b8

10,489

py

Python

unit_test/test_ecole_plot.py

pandat8/ML4LocalBranch_extend

001839ace3506c8410a30d1f4d3188a3cd95e2dd

[ "MIT" ]

4

2021-10-17T00:26:12.000Z

2021-12-06T08:41:02.000Z

unit_test/test_ecole_plot.py

pandat8/ML4LocalBranch

2fb38b12556ea5e62a0313f617e98cd163eaaf7f

[ "MIT" ]

null

unit_test/test_ecole_plot.py

pandat8/ML4LocalBranch

2fb38b12556ea5e62a0313f617e98cd163eaaf7f

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy instancetypes = ['setcovering', 'capacitedfacility', 'independentset', 'combinatorialauction'] modes = ['repair-slackvars', 'repair-supportbinvars', 'repair-binvars', 'improve-supportbinvars', 'improve-binvars'] instancetype = instancetypes[2] mode = modes[4] directory = './result/generated_instances/'+ instancetype +'/'+mode+'/' # modes = ['repair-nviolations','repair-nbinvars','improve'] # mode = modes[2] # # if mode == 'repair-nviolations': # directory = './result/generated_instances/setcovering_asym/repair/timesofviolations/' # elif mode == 'repair-nbinvars': # directory = './result/generated_instances/setcovering_asym/repair/timesofbinvars/' # elif mode == 'improve': # directory = './result/generated_instances/setcovering_asym/improve/' if mode == 'repair-nviolations': for i in range(100): if not i == 38: instance_name = instancetype + '-' + str(i) data = numpy.load(directory + instance_name + '.npz') neigh_sizes = data['neigh_sizes'] t = data['t'] objs = data['objs'] # objs = objs / objs[0] print(objs[0]) neigh_sizes = numpy.log10(neigh_sizes) if i==0: objs_all = objs t_all = t else: objs_all += objs t_all += t t_ave= t_all/99 objs_ave = objs_all/99 plt.clf() fig, ax = plt.subplots(2, 1, figsize=(6.4, 6.4)) fig.suptitle("LB to repair") fig.subplots_adjust(top=0.5) ax[0].plot(neigh_sizes, objs_ave) ax[0].set_title(instance_name, loc='right') ax[0].set_xlabel(r'$log(\alpha)$ '+'(Neighborhood size: '+ r'$K = \alpha \times N_{violations}$)') ax[0].set_ylabel(r'$N_{violations}$') ax[1].plot(neigh_sizes, t_ave) # ax[1].set_ylim([0, 31]) ax[1].set_ylabel("Solving time") plt.show() elif mode == 'repair-nbinvars': for i in range(100): if not i == 38: instance_name =instancetype + '-' + str(i) data = numpy.load(directory + instance_name + '.npz') neigh_sizes = data['neigh_sizes'] t = data['t'] objs = data['objs'] print(objs[0]) # objs = objs / objs[0] if i == 0: objs_all = objs t_all = t else: objs_all += objs t_all += t t_ave = t_all / 99 objs_ave = objs_all / 99 plt.clf() fig, ax = plt.subplots(2, 1, figsize=(6.4, 6.4)) fig.suptitle("LB to repair") fig.subplots_adjust(top=0.5) ax[0].plot(neigh_sizes, objs_ave) ax[0].set_title(instance_name, loc='right') ax[0].set_xlabel(r'$\alpha$ ' + '(Neighborhood size: ' + r'$K = \alpha \times N_{binvars}$)') ax[0].set_ylabel(r'$N_{violations}$') ax[1].plot(neigh_sizes, t_ave) # ax[1].set_ylim([0, 31]) ax[1].set_ylabel("Solving time") plt.show() elif mode == 'improve': for i in range(100): # if not i == 38: instance_name = instancetype + '-' + str(i) data = numpy.load(directory + instance_name + '.npz') neigh_sizes = data['neigh_sizes'] t = data['t'] objs = data['objs'] # objs = objs / objs[0] if i == 0: objs_all = objs t_all = t else: objs_all += objs t_all += t t_ave = t_all / 100 objs_ave = objs_all / 100 plt.clf() fig, ax = plt.subplots(2, 1, figsize=(6.4, 6.4)) fig.suptitle("LB to improve") fig.subplots_adjust(top=0.5) ax[0].plot(neigh_sizes, objs_ave) ax[0].set_title(instance_name, loc='right') ax[0].set_xlabel(r'$\alpha$ ' + '(Neighborhood size: ' + r'$K = \alpha \times N_{binvars}$)') ax[0].set_ylabel("Objective") ax[1].plot(neigh_sizes, t_ave) # ax[1].set_ylim([0,31]) ax[1].set_ylabel("Solving time") plt.show() elif mode == 'repair-slackvars': for i in range(100): if not i == 38: instance_name = instancetype + '-' + str(i) data = numpy.load(directory + instance_name + '.npz') neigh_sizes = data['neigh_sizes'] t = data['t'] objs = data['objs'] # objs = objs / objs[0] print(objs[0]) if i == 0: objs_all = objs t_all = t else: objs_all += objs t_all += t t_ave = t_all / 99 objs_ave = objs_all / 99 plt.clf() fig, ax = plt.subplots(2, 1, figsize=(6.4, 6.4)) fig.suptitle("LB to repair (over slack variables)") fig.subplots_adjust(top=0.5) ax[0].plot(neigh_sizes, objs_ave) ax[0].set_title(instance_name, loc='right') ax[0].set_xlabel(r'$\alpha$ ' + '(Neighborhood size: ' + r'$K = \alpha \times N_{violations}$)') ax[0].set_ylabel(r'$N_{violations}$') ax[1].plot(neigh_sizes, t_ave) # ax[1].set_ylim([0, 31]) ax[1].set_ylabel("Solving time") plt.show() elif mode == 'repair-supportbinvars': for i in range(100): if not i == 38: instance_name = instancetype + '-' + str(i) data = numpy.load(directory + instance_name + '.npz') neigh_sizes = data['neigh_sizes'] t = data['t'] objs = data['objs'] # objs = objs / objs[0] print(objs[0]) if i == 0: objs_all = objs t_all = t else: objs_all += objs t_all += t t_ave = t_all / 99 objs_ave = objs_all / 99 plt.clf() fig, ax = plt.subplots(2, 1, figsize=(6.4, 6.4)) fig.suptitle("LB to repair (over support of binary vars)") fig.subplots_adjust(top=0.5) ax[0].plot(neigh_sizes, objs_ave) ax[0].set_title(instance_name, loc='right') ax[0].set_xlabel(r'$\alpha$ ' + '(Neighborhood size: ' + r'$K = \alpha \times N_{supportofbins}$)') ax[0].set_ylabel(r'$N_{violations}$') ax[1].plot(neigh_sizes, t_ave) # ax[1].set_ylim([0, 31]) ax[1].set_ylabel("Solving time") plt.show() elif mode == 'repair-binvars': for i in range(100): if not i == 38: instance_name = instancetype + '-' + str(i) data = numpy.load(directory + instance_name + '.npz') neigh_sizes = data['neigh_sizes'] t = data['t'] objs = data['objs'] objs = objs / objs[0] if i == 0: objs_all = objs t_all = t else: objs_all += objs t_all += t t_ave = t_all / 99 objs_ave = objs_all / 99 plt.clf() fig, ax = plt.subplots(2, 1, figsize=(6.4, 6.4)) fig.suptitle("LB to repair (over binary variables)") fig.subplots_adjust(top=0.5) ax[0].plot(neigh_sizes, objs_ave) ax[0].set_title(instance_name, loc='right') ax[0].set_xlabel(r'$\alpha$ ' + '(Neighborhood size: ' + r'$K = \alpha \times N_{binvars}$)') ax[0].set_ylabel(r'$N_{violations}$') ax[1].plot(neigh_sizes, t_ave) # ax[1].set_ylim([0, 31]) ax[1].set_ylabel("Solving time") plt.show() elif mode =='improve-supportbinvars': for i in range(0, 100): # if not i == 38: instance_name = instancetype + '-' + str(i) print(instance_name) data = numpy.load(directory + instance_name + '.npz') neigh_sizes = data['neigh_sizes'] t = data['t'] objs = data['objs'] # objs = objs / objs[0] if i == 0: objs_all = objs t_all = t else: objs_all += objs t_all += t t_ave = t_all / 100 objs_ave = objs_all / 100 plt.clf() fig, ax = plt.subplots(2, 1, figsize=(6.4, 6.4)) fig.suptitle("LB to improve (over support of binary vars)") fig.subplots_adjust(top=0.5) ax[0].plot(neigh_sizes, objs_ave) ax[0].set_title(instance_name, loc='right') ax[0].set_xlabel(r'$\alpha$ ' + '(Neighborhood size: ' + r'$K = \alpha \times N_{supportofbins}$)') ax[0].set_ylabel("Objective") ax[1].plot(neigh_sizes, t_ave) # ax[1].set_ylim([0,31]) ax[1].set_ylabel("Solving time") plt.show() # elif mode =='improve-binvars': # for i in range(60, 70): # # if not i == 38: # instance_name = instancetype + '-' + str(i) # print(instance_name) # data = numpy.load(directory + instance_name + '.npz') # neigh_sizes = data['neigh_sizes'] # t = data['t'] # objs = data['objs'] # # objs = objs / objs[0] # # # plt.clf() # fig, ax = plt.subplots(2, 1, figsize=(6.4, 6.4)) # fig.suptitle("LB to improve (over all bins)") # fig.subplots_adjust(top=0.5) # ax[0].plot(neigh_sizes, objs) # ax[0].set_title(instance_name, loc='right') # ax[0].set_xlabel(r'$\alpha$ ' + '(Neighborhood size: ' + r'$K = \alpha \times N_{binvars}$)') # ax[0].set_ylabel("Objective") # ax[1].plot(neigh_sizes, t) # # ax[1].set_ylim([0,31]) # ax[1].set_ylabel("Solving time") # plt.show() elif mode =='improve-binvars': for i in range(0, 100): # if not i == 38: instance_name = instancetype + '-' + str(i) print(instance_name) data = numpy.load(directory + instance_name + '.npz') neigh_sizes = data['neigh_sizes'] t = data['t'] objs = data['objs'] t = t/30 objs = (objs - numpy.min(objs)) objs = objs / numpy.max(objs) if i == 0: objs_all = objs t_all = t else: objs_all += objs t_all += t t_ave = t_all / 100 objs_ave = objs_all / 100 alpha = 1/3 perf = alpha * t_ave + (1-alpha) * objs_ave print(neigh_sizes[numpy.where(perf == perf.min())]) plt.clf() fig, ax = plt.subplots(3, 1, figsize=(6.4, 6.4)) fig.suptitle("LB to improve (over all bins)") fig.subplots_adjust(top=0.5) ax[0].plot(neigh_sizes, objs_ave) ax[0].set_title(instance_name, loc='right') ax[0].set_xlabel(r'$\alpha$ ' + '(Neighborhood size: ' + r'$K = \alpha \times N_{binvars}$)') ax[0].set_ylabel("Objective") ax[1].plot(neigh_sizes, t_ave) # ax[1].set_ylim([0,31]) ax[1].set_ylabel("Solving time") ax[2].plot(neigh_sizes, perf) ax[2].set_ylabel("Performance score") plt.show()

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7

12a977a3f3bfad4e4d85d50fa271341bbd763dbb

94

py

Python

pytest_unittest_discovery_scenarios/under_subdir_of_root_plus_one_further_subdir/tests/test_base_stuff.py

d3r3kk/vscode-python-extras

e96da47ccf15dff8673c28bd3e8981b550a00a3d

[ "MIT" ]

null

pytest_unittest_discovery_scenarios/under_subdir_of_root_plus_one_further_subdir/tests/test_base_stuff.py

d3r3kk/vscode-python-extras

e96da47ccf15dff8673c28bd3e8981b550a00a3d

[ "MIT" ]

null

pytest_unittest_discovery_scenarios/under_subdir_of_root_plus_one_further_subdir/tests/test_base_stuff.py

d3r3kk/vscode-python-extras

e96da47ccf15dff8673c28bd3e8981b550a00a3d

[ "MIT" ]

null

def test_do_test(): assert 1 == 1 def test_do_other_test(): assert "blah" == "blah"

13.428571

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null

0

1

0

1

0

7

420057cf980bf87e242477bb6b78614c55464cd4

66,131

py

Python

cinder/tests/unit/volume/test_connection.py

lightsey/cinder

e03d68e42e57a63f8d0f3e177fb4287290612b24

[ "Apache-2.0" ]

3

2015-04-02T21:44:36.000Z

2016-04-29T21:19:04.000Z

cinder/tests/unit/volume/test_connection.py

lightsey/cinder

e03d68e42e57a63f8d0f3e177fb4287290612b24

[ "Apache-2.0" ]

3

2016-04-29T21:45:26.000Z

2016-05-04T19:41:23.000Z

cinder/tests/unit/volume/test_connection.py

lightsey/cinder

e03d68e42e57a63f8d0f3e177fb4287290612b24

[ "Apache-2.0" ]

4

2016-01-27T00:25:52.000Z

2021-03-25T19:54:08.000Z

# Copyright 2010 United States Government as represented by the # Administrator of the National Aeronautics and Space Administration. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Tests for Volume connection test cases.""" from unittest import mock import ddt from cinder import context from cinder import db from cinder import exception from cinder.message import message_field from cinder import objects from cinder.objects import fields from cinder.tests import fake_driver from cinder.tests.unit.api.v2 import fakes as v2_fakes from cinder.tests.unit import fake_constants as fake from cinder.tests.unit import fake_volume from cinder.tests.unit import utils as tests_utils from cinder.tests.unit import volume as base import cinder.volume import cinder.volume.targets import cinder.volume.targets.iscsi @ddt.ddt class DiscardFlagTestCase(base.BaseVolumeTestCase): def setUp(self): super(DiscardFlagTestCase, self).setUp() self.volume.driver = mock.MagicMock() db.volume_type_create(self.context, v2_fakes.fake_default_type_get( fake.VOLUME_TYPE2_ID)) self.vol_type = db.volume_type_get_by_name(self.context, 'vol_type_name') @ddt.data(dict(config_discard_flag=True, driver_discard_flag=None, expected_flag=True), dict(config_discard_flag=False, driver_discard_flag=None, expected_flag=None), dict(config_discard_flag=True, driver_discard_flag=True, expected_flag=True), dict(config_discard_flag=False, driver_discard_flag=True, expected_flag=True), dict(config_discard_flag=False, driver_discard_flag=False, expected_flag=False), dict(config_discard_flag=None, driver_discard_flag=True, expected_flag=True), dict(config_discard_flag=None, driver_discard_flag=False, expected_flag=False)) @ddt.unpack def test_initialize_connection_discard_flag(self, config_discard_flag, driver_discard_flag, expected_flag): self.volume.driver.create_export.return_value = None connector = {'ip': 'IP', 'initiator': 'INITIATOR'} conn_info = { 'driver_volume_type': 'iscsi', 'data': {'access_mode': 'rw', 'encrypted': False} } if driver_discard_flag is not None: conn_info['data']['discard'] = driver_discard_flag self.volume.driver.initialize_connection.return_value = conn_info def _safe_get(key): if key == 'report_discard_supported': return config_discard_flag else: return None self.volume.driver.configuration.safe_get.side_effect = _safe_get with mock.patch.object(objects, 'Volume') as mock_vol: volume = tests_utils.create_volume(self.context) volume.volume_type_id = None mock_vol.get_by_id.return_value = volume conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual(expected_flag, conn_info['data'].get('discard')) class VolumeConnectionTestCase(base.BaseVolumeTestCase): def setUp(self, *args, **kwargs): super(VolumeConnectionTestCase, self).setUp() db.volume_type_create(self.context, v2_fakes.fake_default_type_get( fake.VOLUME_TYPE2_ID)) self.vol_type = db.volume_type_get_by_name(self.context, 'vol_type_name') @mock.patch.object(cinder.volume.targets.iscsi.ISCSITarget, '_get_target_chap_auth') @mock.patch.object(db, 'volume_admin_metadata_get') @mock.patch.object(db.sqlalchemy.api, 'volume_get') @mock.patch.object(db, 'volume_update') def test_initialize_connection_fetchqos(self, _mock_volume_update, _mock_volume_get, _mock_volume_admin_metadata_get, mock_get_target): """Make sure initialize_connection returns correct information.""" _fake_admin_meta = [{'key': 'fake-key', 'value': 'fake-value'}] _fake_volume = {'volume_type_id': fake.VOLUME_TYPE_ID, 'name': 'fake_name', 'host': 'fake_host', 'id': fake.VOLUME_ID, 'volume_admin_metadata': _fake_admin_meta} fake_volume_obj = fake_volume.fake_volume_obj(self.context, **_fake_volume) _mock_volume_get.return_value = _fake_volume _mock_volume_update.return_value = _fake_volume _mock_volume_admin_metadata_get.return_value = { 'fake-key': 'fake-value'} connector = {'ip': 'IP', 'initiator': 'INITIATOR'} qos_values = {'consumer': 'front-end', 'specs': { 'key1': 'value1', 'key2': 'value2'} } with mock.patch.object(cinder.volume.volume_types, 'get_volume_type_qos_specs') as type_qos, \ mock.patch.object(cinder.tests.fake_driver.FakeLoggingVolumeDriver, 'initialize_connection') as driver_init: type_qos.return_value = dict(qos_specs=qos_values) driver_init.return_value = {'data': {}} mock_get_target.return_value = None qos_specs_expected = {'key1': 'value1', 'key2': 'value2'} # initialize_connection() passes qos_specs that is designated to # be consumed by front-end or both front-end and back-end conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector,) self.assertDictEqual(qos_specs_expected, conn_info['data']['qos_specs']) qos_values.update({'consumer': 'both'}) conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector) self.assertDictEqual(qos_specs_expected, conn_info['data']['qos_specs']) # initialize_connection() skips qos_specs that is designated to be # consumed by back-end only qos_values.update({'consumer': 'back-end'}) type_qos.return_value = dict(qos_specs=qos_values) conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector) self.assertIsNone(conn_info['data']['qos_specs']) @mock.patch.object(cinder.volume.targets.iscsi.ISCSITarget, '_get_target_chap_auth') @mock.patch.object(db, 'volume_admin_metadata_get') @mock.patch.object(db.sqlalchemy.api, 'volume_get') @mock.patch.object(db, 'volume_update') def test_initialize_connection_qos_per_gb(self, _mock_volume_update, _mock_volume_get, _mock_volume_admin_metadata_get, mock_get_target): """QoS test with no minimum value.""" _fake_admin_meta = [{'key': 'fake-key', 'value': 'fake-value'}] _fake_volume = {'size': 3, 'volume_type_id': fake.VOLUME_TYPE_ID, 'name': 'fake_name', 'host': 'fake_host', 'id': fake.VOLUME_ID, 'volume_admin_metadata': _fake_admin_meta} fake_volume_obj = fake_volume.fake_volume_obj(self.context, **_fake_volume) _mock_volume_get.return_value = _fake_volume _mock_volume_update.return_value = _fake_volume _mock_volume_admin_metadata_get.return_value = { 'fake-key': 'fake-value'} connector = {'ip': 'IP', 'initiator': 'INITIATOR'} qos_values = {'consumer': 'front-end', 'specs': { 'write_iops_sec_per_gb': 30, 'read_iops_sec_per_gb': 7700, 'total_iops_sec_per_gb': 300000, 'read_bytes_sec_per_gb': 10, 'write_bytes_sec_per_gb': 40, 'total_bytes_sec_per_gb': 1048576} } with mock.patch.object(cinder.volume.volume_types, 'get_volume_type_qos_specs') as type_qos, \ mock.patch.object(cinder.tests.fake_driver.FakeLoggingVolumeDriver, 'initialize_connection') as driver_init: type_qos.return_value = dict(qos_specs=qos_values) driver_init.return_value = {'data': {}} mock_get_target.return_value = None qos_specs_expected = {'write_iops_sec': 90, 'read_iops_sec': 23100, 'total_iops_sec': 900000, 'read_bytes_sec': 30, 'write_bytes_sec': 120, 'total_bytes_sec': 3145728} # initialize_connection() passes qos_specs that is designated to # be consumed by front-end or both front-end and back-end conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector,) self.assertDictEqual(qos_specs_expected, conn_info['data']['qos_specs']) qos_values.update({'consumer': 'both'}) conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector) self.assertDictEqual(qos_specs_expected, conn_info['data']['qos_specs']) @mock.patch.object(cinder.volume.targets.iscsi.ISCSITarget, '_get_target_chap_auth') @mock.patch.object(db, 'volume_admin_metadata_get') @mock.patch.object(db.sqlalchemy.api, 'volume_get') @mock.patch.object(db, 'volume_update') def test_initialize_connection_qos_per_gb_with_min_small( self, _mock_volume_update, _mock_volume_get, _mock_volume_admin_metadata_get, mock_get_target): """QoS test when volume size results in using minimum.""" _fake_admin_meta = [{'key': 'fake-key', 'value': 'fake-value'}] _fake_volume = {'size': 1, 'volume_type_id': fake.VOLUME_TYPE_ID, 'name': 'fake_name', 'host': 'fake_host', 'id': fake.VOLUME_ID, 'volume_admin_metadata': _fake_admin_meta} fake_volume_obj = fake_volume.fake_volume_obj(self.context, **_fake_volume) _mock_volume_get.return_value = _fake_volume _mock_volume_update.return_value = _fake_volume _mock_volume_admin_metadata_get.return_value = { 'fake-key': 'fake-value'} connector = {'ip': 'IP', 'initiator': 'INITIATOR'} qos_values = {'consumer': 'front-end', 'specs': { 'write_iops_sec_per_gb_min': 800, 'write_iops_sec_per_gb': 30, 'read_iops_sec_per_gb_min': 23100, 'read_iops_sec_per_gb': 7700, 'total_iops_sec_per_gb_min': 900000, 'total_iops_sec_per_gb': 300000, 'total_iops_sec_max': 15000000, 'read_bytes_sec_per_gb_min': 30, 'read_bytes_sec_per_gb': 10, 'write_bytes_sec_per_gb_min': 120, 'write_bytes_sec_per_gb': 40, 'total_bytes_sec_per_gb_min': 3145728, 'total_bytes_sec_per_gb': 1048576} } with mock.patch.object(cinder.volume.volume_types, 'get_volume_type_qos_specs') as type_qos, \ mock.patch.object(cinder.tests.fake_driver.FakeLoggingVolumeDriver, 'initialize_connection') as driver_init: type_qos.return_value = dict(qos_specs=qos_values) driver_init.return_value = {'data': {}} mock_get_target.return_value = None qos_specs_expected = {'write_iops_sec': 800, 'read_iops_sec': 23100, 'total_iops_sec': 900000, 'read_bytes_sec': 30, 'write_bytes_sec': 120, 'total_bytes_sec': 3145728} # initialize_connection() passes qos_specs that is designated to # be consumed by front-end or both front-end and back-end conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector,) self.assertDictEqual(qos_specs_expected, conn_info['data']['qos_specs']) qos_values.update({'consumer': 'both'}) conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector) self.assertDictEqual(qos_specs_expected, conn_info['data']['qos_specs']) @mock.patch.object(cinder.volume.targets.iscsi.ISCSITarget, '_get_target_chap_auth') @mock.patch.object(db, 'volume_admin_metadata_get') @mock.patch.object(db.sqlalchemy.api, 'volume_get') @mock.patch.object(db, 'volume_update') def test_initialize_connection_qos_per_gb_with_min_large( self, _mock_volume_update, _mock_volume_get, _mock_volume_admin_metadata_get, mock_get_target): """QoS test when volume size results in using per-gb values.""" _fake_admin_meta = [{'key': 'fake-key', 'value': 'fake-value'}] _fake_volume = {'size': 100, 'volume_type_id': fake.VOLUME_TYPE_ID, 'name': 'fake_name', 'host': 'fake_host', 'id': fake.VOLUME_ID, 'volume_admin_metadata': _fake_admin_meta} fake_volume_obj = fake_volume.fake_volume_obj(self.context, **_fake_volume) _mock_volume_get.return_value = _fake_volume _mock_volume_update.return_value = _fake_volume _mock_volume_admin_metadata_get.return_value = { 'fake-key': 'fake-value'} connector = {'ip': 'IP', 'initiator': 'INITIATOR'} qos_values = {'consumer': 'front-end', 'specs': { 'write_iops_sec_per_gb_min': 800, 'write_iops_sec_per_gb': 30, 'read_iops_sec_per_gb_min': 23100, 'read_iops_sec_per_gb': 7700, 'total_iops_sec_per_gb_min': 900000, 'total_iops_sec_per_gb': 300000, 'total_iops_sec_max': 15000000, 'read_bytes_sec_per_gb_min': 30, 'read_bytes_sec_per_gb': 10, 'write_bytes_sec_per_gb_min': 120, 'write_bytes_sec_per_gb': 40, 'total_bytes_sec_per_gb_min': 3145728, 'total_bytes_sec_per_gb': 1048576} } with mock.patch.object(cinder.volume.volume_types, 'get_volume_type_qos_specs') as type_qos, \ mock.patch.object(cinder.tests.fake_driver.FakeLoggingVolumeDriver, 'initialize_connection') as driver_init: type_qos.return_value = dict(qos_specs=qos_values) driver_init.return_value = {'data': {}} mock_get_target.return_value = None qos_specs_expected = {'write_iops_sec': 3000, 'read_iops_sec': 770000, 'total_iops_sec': 15000000, 'read_bytes_sec': 1000, 'write_bytes_sec': 4000, 'total_bytes_sec': 104857600} # initialize_connection() passes qos_specs that is designated to # be consumed by front-end or both front-end and back-end conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector,) self.assertDictEqual(qos_specs_expected, conn_info['data']['qos_specs']) qos_values.update({'consumer': 'both'}) conn_info = self.volume.initialize_connection( self.context, fake_volume_obj, connector) self.assertDictEqual(qos_specs_expected, conn_info['data']['qos_specs']) @mock.patch.object(fake_driver.FakeLoggingVolumeDriver, 'create_export') def test_initialize_connection_export_failure(self, _mock_create_export): """Test exception path for create_export failure.""" volume = tests_utils.create_volume( self.context, admin_metadata={'fake-key': 'fake-value'}, **self.volume_params) _mock_create_export.side_effect = exception.CinderException connector = {'ip': 'IP', 'initiator': 'INITIATOR'} self.assertRaises(exception.VolumeBackendAPIException, self.volume.initialize_connection, self.context, volume, connector) def test_initialize_connection_maintenance(self): """Test initialize connection in maintenance.""" test_meta1 = {'fake_key1': 'fake_value1', 'fake_key2': 'fake_value2'} volume = tests_utils.create_volume(self.context, metadata=test_meta1, **self.volume_params) volume['status'] = 'maintenance' volume_api = cinder.volume.api.API() self.assertRaises(exception.InvalidVolume, volume_api.initialize_connection, self.context, volume, None) @ddt.ddt class VolumeAttachDetachTestCase(base.BaseVolumeTestCase): def setUp(self): super(VolumeAttachDetachTestCase, self).setUp() self.patch('cinder.volume.volume_utils.clear_volume', autospec=True) self.user_context = context.RequestContext(user_id=fake.USER_ID, project_id=fake.PROJECT_ID) db.volume_type_create(self.context, v2_fakes.fake_default_type_get( fake.VOLUME_TYPE2_ID)) self.vol_type = db.volume_type_get_by_name(self.context, 'vol_type_name') @ddt.data(False, True) def test_run_attach_detach_volume_for_instance(self, volume_object): """Make sure volume can be attached and detached from instance.""" mountpoint = "/dev/sdf" # attach volume to the instance then to detach instance_uuid = '12345678-1234-5678-1234-567812345678' volume = tests_utils.create_volume(self.user_context, **self.volume_params) with volume.obj_as_admin(): volume.admin_metadata['readonly'] = True volume.save() volume_id = volume.id self.volume.create_volume(self.user_context, volume=volume) volume_passed = volume if volume_object else None attachment = self.volume.attach_volume(self.user_context, volume_id, instance_uuid, None, mountpoint, 'ro', volume=volume_passed) attachment2 = self.volume.attach_volume(self.user_context, volume_id, instance_uuid, None, mountpoint, 'ro', volume=volume_passed) self.assertEqual(attachment.id, attachment2.id) vol = objects.Volume.get_by_id(self.context, volume_id) self.assertEqual("in-use", vol.status) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment.attach_status) self.assertEqual(mountpoint, attachment.mountpoint) self.assertEqual(instance_uuid, attachment.instance_uuid) self.assertIsNone(attachment.attached_host) admin_metadata = vol.volume_admin_metadata self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='ro') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} volume = volume if volume_object else vol conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('ro', conn_info['data']['access_mode']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume=volume) self.volume.detach_volume(self.context, volume_id, attachment.id, volume=volume_passed) vol = objects.Volume.get_by_id(self.context, volume_id) self.assertEqual('available', vol.status) self.volume.delete_volume(self.context, volume) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume_id) @mock.patch('cinder.volume.manager.LOG', mock.Mock()) def test_initialize_connection(self): volume = mock.Mock(save=mock.Mock(side_effect=Exception)) with mock.patch.object(self.volume, 'driver') as driver_mock: self.assertRaises(exception.ExportFailure, self.volume.initialize_connection, self.context, volume, mock.Mock()) driver_mock.remove_export.assert_called_once_with(mock.ANY, volume) def test_run_attach_detach_2volumes_for_instance(self): """Make sure volume can be attached and detached from instance.""" # attach first volume to the instance mountpoint1 = "/dev/vdc" instance_uuid = '12345678-1234-5678-1234-567812345678' volume1 = tests_utils.create_volume( self.context, admin_metadata={'readonly': 'True'}, **self.volume_params) volume1_id = volume1['id'] self.volume.create_volume(self.context, volume1) attachment = self.volume.attach_volume(self.context, volume1_id, instance_uuid, None, mountpoint1, 'ro') vol1 = db.volume_get(context.get_admin_context(), volume1_id) self.assertEqual("in-use", vol1['status']) self.assertEqual('attached', attachment['attach_status']) self.assertEqual(mountpoint1, attachment['mountpoint']) self.assertEqual(instance_uuid, attachment['instance_uuid']) self.assertIsNone(attachment['attached_host']) admin_metadata = vol1['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='ro') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume1, connector) self.assertEqual('ro', conn_info['data']['access_mode']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume1) # attach 2nd volume to the instance mountpoint2 = "/dev/vdd" volume2 = tests_utils.create_volume( self.context, admin_metadata={'readonly': 'False'}, **self.volume_params) volume2_id = volume2['id'] self.volume.create_volume(self.context, volume2) attachment2 = self.volume.attach_volume(self.context, volume2_id, instance_uuid, None, mountpoint2, 'rw') vol2 = db.volume_get(context.get_admin_context(), volume2_id) self.assertEqual("in-use", vol2['status']) self.assertEqual('attached', attachment2['attach_status']) self.assertEqual(mountpoint2, attachment2['mountpoint']) self.assertEqual(instance_uuid, attachment2['instance_uuid']) self.assertIsNone(attachment2['attached_host']) admin_metadata = vol2['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='False', attached_mode='rw') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:02'} conn_info = self.volume.initialize_connection(self.context, volume2, connector) self.assertEqual('rw', conn_info['data']['access_mode']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume2) # detach first volume and then 2nd volume self.volume.detach_volume(self.context, volume1_id, attachment['id']) vol1 = db.volume_get(self.context, volume1_id) self.assertEqual('available', vol1['status']) self.volume.delete_volume(self.context, volume1) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume1_id) self.volume.detach_volume(self.context, volume2_id, attachment2['id']) vol2 = db.volume_get(self.context, volume2_id) self.assertEqual('available', vol2['status']) self.volume.delete_volume(self.context, volume2) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume2_id) def test_detach_invalid_attachment_id(self): """Make sure if the attachment id isn't found we raise.""" attachment_id = "notfoundid" volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, multiattach=False, **self.volume_params) self.volume.detach_volume(self.context, volume['id'], attachment_id) volume = db.volume_get(self.context, volume['id']) self.assertEqual('available', volume['status']) instance_uuid = '12345678-1234-5678-1234-567812345678' attached_host = 'fake_host' mountpoint = '/dev/fake' tests_utils.attach_volume(self.context, volume['id'], instance_uuid, attached_host, mountpoint) self.volume.detach_volume(self.context, volume['id'], attachment_id) volume = db.volume_get(self.context, volume['id']) self.assertEqual('in-use', volume['status']) def test_detach_no_attachments(self): self.volume_params['status'] = 'detaching' volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, multiattach=False, **self.volume_params) self.volume.detach_volume(self.context, volume['id']) volume = db.volume_get(self.context, volume['id']) self.assertEqual('available', volume['status']) def test_run_attach_detach_volume_for_instance_no_attachment_id(self): """Make sure volume can be attached and detached from instance.""" mountpoint = "/dev/sdf" # attach volume to the instance then to detach instance_uuid = '12345678-1234-5678-1234-567812345678' instance_uuid_2 = '12345678-4321-8765-4321-567812345678' volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, multiattach=True, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) attachment = self.volume.attach_volume(self.context, volume_id, instance_uuid, None, mountpoint, 'ro') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertEqual(instance_uuid, attachment['instance_uuid']) self.assertIsNone(attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='ro') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) attachment2 = self.volume.attach_volume(self.context, volume_id, instance_uuid_2, None, mountpoint, 'ro') connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('ro', conn_info['data']['access_mode']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) self.assertRaises(exception.InvalidVolume, self.volume.detach_volume, self.context, volume_id) self.volume.detach_volume(self.context, volume_id, attachment['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual('in-use', vol['status']) self.volume.detach_volume(self.context, volume_id, attachment2['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual('available', vol['status']) attachment = self.volume.attach_volume(self.context, volume_id, instance_uuid, None, mountpoint, 'ro') vol = db.volume_get(self.context, volume_id) self.assertEqual('in-use', vol['status']) self.volume.detach_volume(self.context, volume_id) vol = db.volume_get(self.context, volume_id) self.assertEqual('available', vol['status']) self.volume.delete_volume(self.context, volume) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume_id) def test_run_attach_detach_multiattach_volume_for_instances(self): """Make sure volume can be attached to multiple instances.""" mountpoint = "/dev/sdf" # attach volume to the instance then to detach instance_uuid = '12345678-1234-5678-1234-567812345678' volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, multiattach=True, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) attachment = self.volume.attach_volume(self.context, volume_id, instance_uuid, None, mountpoint, 'ro') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertTrue(vol['multiattach']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertEqual(instance_uuid, attachment['instance_uuid']) self.assertIsNone(attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='ro') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('ro', conn_info['data']['access_mode']) instance2_uuid = '12345678-1234-5678-1234-567812345000' mountpoint2 = "/dev/sdx" attachment2 = self.volume.attach_volume(self.context, volume_id, instance2_uuid, None, mountpoint2, 'ro') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertTrue(vol['multiattach']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment2['attach_status']) self.assertEqual(mountpoint2, attachment2['mountpoint']) self.assertEqual(instance2_uuid, attachment2['instance_uuid']) self.assertIsNone(attachment2['attached_host']) self.assertNotEqual(attachment, attachment2) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) self.volume.detach_volume(self.context, volume_id, attachment['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual('in-use', vol['status']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) self.volume.detach_volume(self.context, volume_id, attachment2['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual('available', vol['status']) self.volume.delete_volume(self.context, volume) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume_id) def test_run_attach_twice_multiattach_volume_for_instances(self): """Make sure volume can be attached to multiple instances.""" mountpoint = "/dev/sdf" # attach volume to the instance then to detach instance_uuid = '12345678-1234-5678-1234-567812345699' volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, multiattach=True, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) attachment = self.volume.attach_volume(self.context, volume_id, instance_uuid, None, mountpoint, 'ro') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertTrue(vol['multiattach']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertEqual(instance_uuid, attachment['instance_uuid']) self.assertIsNone(attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='ro') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('ro', conn_info['data']['access_mode']) mountpoint2 = "/dev/sdx" attachment2 = self.volume.attach_volume(self.context, volume_id, instance_uuid, None, mountpoint2, 'ro') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertTrue(vol['multiattach']) self.assertEqual('attached', attachment2['attach_status']) self.assertEqual(mountpoint, attachment2['mountpoint']) self.assertEqual(instance_uuid, attachment2['instance_uuid']) self.assertIsNone(attachment2['attached_host']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) def test_attach_detach_not_multiattach_volume_for_instances(self): """Make sure volume can't be attached to more than one instance.""" mountpoint = "/dev/sdf" # attach volume to the instance then to detach instance_uuid = '12345678-1234-5678-1234-567812345678' volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, multiattach=False, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) attachment = self.volume.attach_volume(self.context, volume_id, instance_uuid, None, mountpoint, 'ro') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertFalse(vol['multiattach']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertEqual(instance_uuid, attachment['instance_uuid']) self.assertIsNone(attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='ro') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('ro', conn_info['data']['access_mode']) instance2_uuid = '12345678-1234-5678-1234-567812345000' mountpoint2 = "/dev/sdx" self.assertRaises(exception.InvalidVolume, self.volume.attach_volume, self.context, volume_id, instance2_uuid, None, mountpoint2, 'ro') self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) self.volume.detach_volume(self.context, volume_id, attachment['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual('available', vol['status']) self.volume.delete_volume(self.context, volume) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume_id) def test_run_attach_detach_volume_for_host(self): """Make sure volume can be attached and detached from host.""" mountpoint = "/dev/sdf" volume = tests_utils.create_volume( self.context, admin_metadata={'readonly': 'False'}, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) attachment = self.volume.attach_volume(self.context, volume_id, None, 'fake_host', mountpoint, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertIsNone(attachment['instance_uuid']) # sanitized, conforms to RFC-952 and RFC-1123 specs. self.assertEqual('fake-host', attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='False', attached_mode='rw') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('rw', conn_info['data']['access_mode']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) self.volume.detach_volume(self.context, volume_id, attachment['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual("available", vol['status']) self.volume.delete_volume(self.context, volume) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume_id) def test_run_attach_detach_multiattach_volume_for_hosts(self): """Make sure volume can be attached and detached from hosts.""" mountpoint = "/dev/sdf" volume = tests_utils.create_volume( self.context, admin_metadata={'readonly': 'False'}, multiattach=True, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) attachment = self.volume.attach_volume(self.context, volume_id, None, 'fake_host', mountpoint, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertTrue(vol['multiattach']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertIsNone(attachment['instance_uuid']) # sanitized, conforms to RFC-952 and RFC-1123 specs. self.assertEqual('fake-host', attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='False', attached_mode='rw') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('rw', conn_info['data']['access_mode']) mountpoint2 = "/dev/sdx" attachment2 = self.volume.attach_volume(self.context, volume_id, None, 'fake_host2', mountpoint2, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment2['attach_status']) self.assertEqual(mountpoint2, attachment2['mountpoint']) self.assertIsNone(attachment2['instance_uuid']) # sanitized, conforms to RFC-952 and RFC-1123 specs. self.assertEqual('fake-host2', attachment2['attached_host']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) self.volume.detach_volume(self.context, volume_id, attachment['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual("in-use", vol['status']) self.volume.detach_volume(self.context, volume_id, attachment2['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual("available", vol['status']) self.volume.delete_volume(self.context, volume) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume_id) def test_run_attach_twice_multiattach_volume_for_hosts(self): """Make sure volume can be attached and detached from hosts.""" mountpoint = "/dev/sdf" volume = tests_utils.create_volume( self.context, admin_metadata={'readonly': 'False'}, multiattach=True, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) attachment = self.volume.attach_volume(self.context, volume_id, None, 'fake_host', mountpoint, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertTrue(vol['multiattach']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertIsNone(attachment['instance_uuid']) # sanitized, conforms to RFC-952 and RFC-1123 specs. self.assertEqual('fake-host', attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='False', attached_mode='rw') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('rw', conn_info['data']['access_mode']) mountpoint2 = "/dev/sdx" attachment2 = self.volume.attach_volume(self.context, volume_id, None, 'fake_host', mountpoint2, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertEqual('attached', attachment2['attach_status']) self.assertEqual(mountpoint, attachment2['mountpoint']) self.assertIsNone(attachment2['instance_uuid']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) def test_run_attach_detach_not_multiattach_volume_for_hosts(self): """Make sure volume can't be attached to more than one host.""" mountpoint = "/dev/sdf" volume = tests_utils.create_volume( self.context, admin_metadata={'readonly': 'False'}, multiattach=False, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) attachment = self.volume.attach_volume(self.context, volume_id, None, 'fake_host', mountpoint, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertFalse(vol['multiattach']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertIsNone(attachment['instance_uuid']) # sanitized, conforms to RFC-952 and RFC-1123 specs. self.assertEqual('fake-host', attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='False', attached_mode='rw') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('rw', conn_info['data']['access_mode']) mountpoint2 = "/dev/sdx" self.assertRaises(exception.InvalidVolume, self.volume.attach_volume, self.context, volume_id, None, 'fake_host2', mountpoint2, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual('in-use', vol['status']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, attachment['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertIsNone(attachment['instance_uuid']) # sanitized, conforms to RFC-952 and RFC-1123 specs. self.assertEqual('fake-host', attachment['attached_host']) self.assertRaises(exception.VolumeAttached, self.volume.delete_volume, self.context, volume) self.volume.detach_volume(self.context, volume_id, attachment['id']) vol = db.volume_get(self.context, volume_id) self.assertEqual('available', vol['status']) self.volume.delete_volume(self.context, volume) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume_id) def test_run_attach_detach_volume_with_attach_mode(self): instance_uuid = '12345678-1234-5678-1234-567812345678' mountpoint = "/dev/sdf" volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, **self.volume_params) volume_id = volume['id'] db.volume_update(self.context, volume_id, {'status': 'available', }) self.volume.attach_volume(self.context, volume_id, instance_uuid, None, mountpoint, 'ro') vol = db.volume_get(context.get_admin_context(), volume_id) attachment = vol['volume_attachment'][0] self.assertEqual('in-use', vol['status']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, vol['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertEqual(instance_uuid, attachment['instance_uuid']) self.assertIsNone(attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='ro') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('ro', conn_info['data']['access_mode']) self.volume.detach_volume(self.context, volume_id, attachment['id']) vol = db.volume_get(self.context, volume_id) attachment = vol['volume_attachment'] self.assertEqual('available', vol['status']) self.assertEqual(fields.VolumeAttachStatus.DETACHED, vol['attach_status']) self.assertEqual([], attachment) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(1, len(admin_metadata)) self.assertEqual('readonly', admin_metadata[0]['key']) self.assertEqual('True', admin_metadata[0]['value']) self.volume.attach_volume(self.context, volume_id, None, 'fake_host', mountpoint, 'ro') vol = db.volume_get(context.get_admin_context(), volume_id) attachment = vol['volume_attachment'][0] self.assertEqual('in-use', vol['status']) self.assertEqual(fields.VolumeAttachStatus.ATTACHED, vol['attach_status']) self.assertEqual(mountpoint, attachment['mountpoint']) self.assertIsNone(attachment['instance_uuid']) self.assertEqual('fake-host', attachment['attached_host']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='ro') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) connector = {'initiator': 'iqn.2012-07.org.fake:01'} conn_info = self.volume.initialize_connection(self.context, volume, connector) self.assertEqual('ro', conn_info['data']['access_mode']) self.volume.detach_volume(self.context, volume_id, attachment['id']) vol = db.volume_get(self.context, volume_id) attachment = vol['volume_attachment'] self.assertEqual('available', vol['status']) self.assertEqual(fields.VolumeAttachStatus.DETACHED, vol['attach_status']) self.assertEqual([], attachment) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(1, len(admin_metadata)) self.assertEqual('readonly', admin_metadata[0]['key']) self.assertEqual('True', admin_metadata[0]['value']) self.volume.delete_volume(self.context, volume) self.assertRaises(exception.VolumeNotFound, db.volume_get, self.context, volume_id) def test_run_manager_attach_detach_volume_with_wrong_attach_mode(self): # Not allow using 'read-write' mode attach readonly volume instance_uuid = '12345678-1234-5678-1234-567812345678' mountpoint = "/dev/sdf" volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) self.assertRaises(exception.InvalidVolumeAttachMode, self.volume.attach_volume, self.context, volume_id, instance_uuid, None, mountpoint, 'rw') # Assert a user message was created self.volume.message_api.create.assert_called_once_with( self.context, message_field.Action.ATTACH_VOLUME, resource_uuid=volume['id'], exception=mock.ANY) attachment = objects.VolumeAttachmentList.get_all_by_volume_id( context.get_admin_context(), volume_id)[0] self.assertEqual(fields.VolumeAttachStatus.ERROR_ATTACHING, attachment.attach_status) vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual(fields.VolumeAttachStatus.DETACHED, vol['attach_status']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='rw') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) db.volume_update(self.context, volume_id, {'status': 'available'}) self.assertRaises(exception.InvalidVolumeAttachMode, self.volume.attach_volume, self.context, volume_id, None, 'fake_host', mountpoint, 'rw') attachment = objects.VolumeAttachmentList.get_all_by_volume_id( context.get_admin_context(), volume_id)[0] self.assertEqual(fields.VolumeAttachStatus.ERROR_ATTACHING, attachment.attach_status) vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual(fields.VolumeAttachStatus.DETACHED, vol['attach_status']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(2, len(admin_metadata)) expected = dict(readonly='True', attached_mode='rw') ret = {} for item in admin_metadata: ret.update({item['key']: item['value']}) self.assertDictEqual(expected, ret) def test_run_api_attach_detach_volume_with_wrong_attach_mode(self): # Not allow using 'read-write' mode attach readonly volume instance_uuid = '12345678-1234-5678-1234-567812345678' mountpoint = "/dev/sdf" volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) volume_api = cinder.volume.api.API() self.assertRaises(exception.InvalidVolumeAttachMode, volume_api.attach, self.context, volume, instance_uuid, None, mountpoint, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual(fields.VolumeAttachStatus.DETACHED, vol['attach_status']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(1, len(admin_metadata)) self.assertEqual('readonly', admin_metadata[0]['key']) self.assertEqual('True', admin_metadata[0]['value']) db.volume_update(self.context, volume_id, {'status': 'available'}) self.assertRaises(exception.InvalidVolumeAttachMode, volume_api.attach, self.context, volume, None, 'fake_host', mountpoint, 'rw') vol = db.volume_get(context.get_admin_context(), volume_id) self.assertEqual(fields.VolumeAttachStatus.DETACHED, vol['attach_status']) admin_metadata = vol['volume_admin_metadata'] self.assertEqual(1, len(admin_metadata)) self.assertEqual('readonly', admin_metadata[0]['key']) self.assertEqual('True', admin_metadata[0]['value']) def test_detach_volume_while_uploading_to_image_is_in_progress(self): # If instance is booted from volume with 'Terminate on Delete' flag # set, and when we delete instance then it tries to delete volume # even it is in 'uploading' state. # It is happening because detach call is setting volume status to # 'available'. mountpoint = "/dev/sdf" # Attach volume to the instance instance_uuid = '12345678-1234-5678-1234-567812345678' volume = tests_utils.create_volume(self.context, admin_metadata={'readonly': 'True'}, **self.volume_params) volume_id = volume['id'] self.volume.create_volume(self.context, volume) self.volume.attach_volume(self.context, volume_id, instance_uuid, None, mountpoint, 'ro') # Change volume status to 'uploading' db.volume_update(self.context, volume_id, {'status': 'uploading'}) # Call detach api self.volume.detach_volume(self.context, volume_id) vol = db.volume_get(self.context, volume_id) # Check that volume status is 'uploading' self.assertEqual("uploading", vol['status']) self.assertEqual(fields.VolumeAttachStatus.DETACHED, vol['attach_status']) def test_volume_attach_in_maintenance(self): """Test attach the volume in maintenance.""" test_meta1 = {'fake_key1': 'fake_value1', 'fake_key2': 'fake_value2'} volume = tests_utils.create_volume(self.context, metadata=test_meta1, **self.volume_params) volume['status'] = 'maintenance' self.assertRaises(exception.InvalidVolume, self.volume_api.attach, self.context, volume, None, None, None, None) def test_volume_detach_in_maintenance(self): """Test detach the volume in maintenance.""" test_meta1 = {'fake_key1': 'fake_value1', 'fake_key2': 'fake_value2'} volume = tests_utils.create_volume(self.context, metadata=test_meta1, **self.volume_params) volume['status'] = 'maintenance' volume_api = cinder.volume.api.API() self.assertRaises(exception.InvalidVolume, volume_api.detach, self.context, volume, None)

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42145aadef6209503d3b265d7f9577475c6f6100

9,093

py

Python

Packs/DeHashed/Integrations/DeHashed/DeHashed_test.py

yyyogev/content

a4692b55873f900c4803f0cc020c1b1ad3e9e74c

[ "MIT" ]

1

2020-07-22T05:55:11.000Z

Packs/DeHashed/Integrations/DeHashed/DeHashed_test.py

nicoloereni/content

ddb88044c5b39a17894dd13e7ae260d9854afc30

[ "MIT" ]

null

Packs/DeHashed/Integrations/DeHashed/DeHashed_test.py

nicoloereni/content

ddb88044c5b39a17894dd13e7ae260d9854afc30

[ "MIT" ]

1

2020-07-22T23:24:05.000Z

import json import urllib DEHASHED_URL = "https://url.com/" # disable-secrets-detection INTEGRATION_CONTEXT_BRAND = "DeHashed" def load_test_data(json_path): with open(json_path) as f: return json.load(f) def test_module_command(requests_mock): """ Given: - Performs a basic GET request to check if the API is reachable and authentication is successful. When - Setting a new instance of the integration. Then - returns "ok". """ from DeHashed import Client, test_module test_data = load_test_data("test_data/search.json") url_params = {"query": 'vin:"test" "test1"'} encoded = urllib.parse.urlencode(url_params) requests_mock.get(f"{DEHASHED_URL}search?{encoded}", json=test_data["api_response"]) client = Client(base_url=f"{DEHASHED_URL}") client._headers = {} res = test_module(client) assert res == "ok" def test_search_command_using_is_operator_without_filter(requests_mock): """ Given: - "Is" operator, value to search, and not using any filters. When - Searching an object that matches the specified value. Then - returns Demisto outputs. """ from DeHashed import Client, dehashed_search_command test_data = load_test_data("test_data/search.json") expected_result = { "DeHashed.Search(val.Id==obj.Id)": test_data["expected_results"][ "full_results" ], "DeHashed.LastQuery(true)": { "ResultsFrom": 1, "ResultsTo": 2, "DisplayedResults": 2, "TotalResults": 2, "PageNumber": 1 }, } url_params = {"query": '"testgamil.co"'} encoded = urllib.parse.urlencode(url_params) requests_mock.get(f"{DEHASHED_URL}search?{encoded}", json=test_data["api_response"]) client = Client(base_url=f"{DEHASHED_URL}") client._headers = {} markdown, context, raw = dehashed_search_command(client, test_data["is_op_single"]) assert expected_result == context def test_search_command_using_contains_operator_without_filter(requests_mock): """ Given: - "Contains" operator, value to search. When - Searching an object that contains the specified value. Then - returns Demisto outputs. """ from DeHashed import Client, dehashed_search_command test_data = load_test_data("test_data/search.json") expected_result = { "DeHashed.Search(val.Id==obj.Id)": test_data["expected_results"][ "full_results" ], "DeHashed.LastQuery(true)": { "ResultsFrom": 1, "ResultsTo": 2, "DisplayedResults": 2, "TotalResults": 2, "PageNumber": 1 }, } url_params = {"query": "testgamil.co"} encoded = urllib.parse.urlencode(url_params) requests_mock.get(f"{DEHASHED_URL}search?{encoded}", json=test_data["api_response"]) client = Client(base_url=f"{DEHASHED_URL}") client._headers = {} markdown, context, raw = dehashed_search_command( client, test_data["contains_op_single"] ) assert expected_result == context def test_search_command_using_regex_operator_without_filter(requests_mock): """ Given: - "Regex" operator, value to search. When - Searching an object that contains the specified value. Then - returns Demisto outputs. """ from DeHashed import Client, dehashed_search_command test_data = load_test_data("test_data/search.json") expected_result = { "DeHashed.Search(val.Id==obj.Id)": test_data["expected_results"][ "full_results" ], "DeHashed.LastQuery(true)": { "ResultsFrom": 1, "ResultsTo": 2, "DisplayedResults": 2, "TotalResults": 2, "PageNumber": 1 }, } url_params = {"query": "/joh?n(ath[oa]n)/"} encoded = urllib.parse.urlencode(url_params) requests_mock.get(f"{DEHASHED_URL}search?{encoded}", json=test_data["api_response"]) client = Client(base_url=f"{DEHASHED_URL}") client._headers = {} markdown, context, raw = dehashed_search_command( client, test_data["regex_op_single"] ) assert expected_result == context def test_search_command_using_is_operator_with_filter_and_multi_values(requests_mock): """ Given: - "Is" operator, value to search and "email" as a filter. When - Searching an object that matches the specified value. Then - returns Demisto outputs. """ from DeHashed import Client, dehashed_search_command test_data = load_test_data("test_data/search.json") expected_result = { "DeHashed.Search(val.Id==obj.Id)": test_data["expected_results"][ "full_results" ], "DeHashed.LastQuery(true)": { "ResultsFrom": 1, "ResultsTo": 2, "DisplayedResults": 2, "TotalResults": 2, "PageNumber": 1 }, } url_params = {"query": 'email:"testgamil.co" "test1gmail.com"'} encoded = urllib.parse.urlencode(url_params) requests_mock.get(f"{DEHASHED_URL}search?{encoded}", json=test_data["api_response"]) client = Client(base_url=f"{DEHASHED_URL}") client._headers = {} markdown, context, raw = dehashed_search_command(client, test_data["is_op_multi"]) assert expected_result == context def test_search_command_using_contains_operator_with_filter_and_multi_values( requests_mock, ): """ Given: - "Contains" operator, value to search and "name" as a filter. When - Searching an object that contains the specified value. Then - returns Demisto outputs. """ from DeHashed import Client, dehashed_search_command test_data = load_test_data("test_data/search.json") expected_result = { "DeHashed.Search(val.Id==obj.Id)": test_data["expected_results"][ "full_results" ], "DeHashed.LastQuery(true)": { "ResultsFrom": 1, "ResultsTo": 2, "DisplayedResults": 2, "TotalResults": 2, "PageNumber": 1 }, } url_params = {"query": "name:(test1 OR test2)"} encoded = urllib.parse.urlencode(url_params) requests_mock.get(f"{DEHASHED_URL}search?{encoded}", json=test_data["api_response"]) client = Client(base_url=f"{DEHASHED_URL}") client._headers = {} markdown, context, raw = dehashed_search_command( client, test_data["contains_op_multi"] ) assert expected_result == context def test_search_command_using_regex_operator_with_filter_and_multi_values( requests_mock, ): """ Given: - "Regex" operator, value to search and "vin" as a filter. When - Searching an object that contains the specified value. Then - returns Demisto outputs. """ from DeHashed import Client, dehashed_search_command test_data = load_test_data("test_data/search.json") expected_result = { "DeHashed.Search(val.Id==obj.Id)": test_data["expected_results"][ "full_results" ], "DeHashed.LastQuery(true)": { "ResultsFrom": 1, "ResultsTo": 2, "DisplayedResults": 2, "TotalResults": 2, "PageNumber": 1 }, } url_params = {"query": "vin:/joh?n(ath[oa]n)/ /joh?n11(ath[oa]n)/"} encoded = urllib.parse.urlencode(url_params) requests_mock.get(f"{DEHASHED_URL}search?{encoded}", json=test_data["api_response"]) client = Client(base_url=f"{DEHASHED_URL}") client._headers = {} markdown, context, raw = dehashed_search_command( client, test_data["regex_op_multi"] ) assert expected_result == context def test_search_command_using_regex_operator_with_filter_and_change_result_range( requests_mock, ): """ Given: - "Regex" operator, value to search, "vin" as a filter and a range of results amount to return. When - Searching an object that contains the specified value. Then - returns Demisto outputs. """ from DeHashed import Client, dehashed_search_command test_data = load_test_data("test_data/search.json") expected_result = { "DeHashed.Search(val.Id==obj.Id)": test_data["expected_results_range"][ "full_results" ], "DeHashed.LastQuery(true)": { "ResultsFrom": 1, "ResultsTo": 1, "DisplayedResults": 1, "TotalResults": 2, "PageNumber": 1 }, } url_params = {"query": "vin:/joh?n(ath[oa]n)/ /joh?n11(ath[oa]n)/"} encoded = urllib.parse.urlencode(url_params) requests_mock.get(f"{DEHASHED_URL}search?{encoded}", json=test_data["api_response"]) client = Client(base_url=f"{DEHASHED_URL}") client._headers = {} markdown, context, raw = dehashed_search_command( client, test_data["regex_op_multi_range"] ) assert expected_result == context

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1

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null

0

7

421b068045048162f2a91f87c80e3dfa2ee98eed

6,202

py

Python

env/lib/python3.9/site-packages/sklearn_genetic/utils/cv_scores.py

wphoong/flappy_doge

c778f0e4820c1ed46e50a56f989d57df4f386736

[ "MIT" ]

null

env/lib/python3.9/site-packages/sklearn_genetic/utils/cv_scores.py

wphoong/flappy_doge

c778f0e4820c1ed46e50a56f989d57df4f386736

[ "MIT" ]

null

env/lib/python3.9/site-packages/sklearn_genetic/utils/cv_scores.py

wphoong/flappy_doge

c778f0e4820c1ed46e50a56f989d57df4f386736

[ "MIT" ]

null

import numpy as np from scipy.stats import rankdata def select_dict_keys(dictionary, keys): return {key: dictionary[key] for key in keys} def create_gasearch_cv_results_(logbook, space, return_train_score, metrics): cv_results = {} n_splits = len(logbook.chapters["parameters"].select("cv_scores")[0]) for parameter in space.parameters: cv_results[f"param_{parameter}"] = logbook.chapters["parameters"].select( parameter ) # Keys that are extended per metric in multi-metric for metric in metrics: for split in range(n_splits): cv_results[f"split{split}_test_{metric}"] = [ cv_scores[split] for cv_scores in logbook.chapters["parameters"].select(f"test_{metric}") ] cv_results[f"mean_test_{metric}"] = [ np.nanmean(cv_scores) for cv_scores in logbook.chapters["parameters"].select(f"test_{metric}") ] cv_results[f"std_test_{metric}"] = [ np.nanstd(cv_scores) for cv_scores in logbook.chapters["parameters"].select(f"test_{metric}") ] cv_results[f"rank_test_{metric}"] = rankdata( -np.array(cv_results[f"mean_test_{metric}"]), method="min" ).astype(int) if return_train_score: for split in range(n_splits): cv_results[f"split{split}_train_{metric}"] = [ cv_scores[split] for cv_scores in logbook.chapters["parameters"].select( f"train_{metric}" ) ] cv_results[f"mean_train_{metric}"] = [ np.nanmean(cv_scores) for cv_scores in logbook.chapters["parameters"].select( f"train_{metric}" ) ] cv_results[f"std_train_{metric}"] = [ np.nanstd(cv_scores) for cv_scores in logbook.chapters["parameters"].select( f"train_{metric}" ) ] cv_results[f"rank_train_{metric}"] = rankdata( -np.array(cv_results[f"mean_train_{metric}"]), method="min" ).astype(int) # These values are only one even with multi-metric cv_results["mean_fit_time"] = [ np.nanmean(fit_time) for fit_time in logbook.chapters["parameters"].select("fit_time") ] cv_results["std_fit_time"] = [ np.nanstd(fit_time) for fit_time in logbook.chapters["parameters"].select("fit_time") ] cv_results["mean_score_time"] = [ np.nanmean(score_time) for score_time in logbook.chapters["parameters"].select("score_time") ] cv_results["std_score_time"] = [ np.nanstd(score_time) for score_time in logbook.chapters["parameters"].select("score_time") ] cv_results["params"] = [ select_dict_keys(individual, space.parameters) for individual in logbook.chapters["parameters"] ] return cv_results def create_feature_selection_cv_results_(logbook, return_train_score, metrics): cv_results = {} n_splits = len(logbook.chapters["parameters"].select("cv_scores")[0]) # Keys that are extended per metric in multi-metric for metric in metrics: for split in range(n_splits): cv_results[f"split{split}_test_{metric}"] = [ cv_scores[split] for cv_scores in logbook.chapters["parameters"].select(f"test_{metric}") ] cv_results[f"mean_test_{metric}"] = [ np.nanmean(cv_scores) for cv_scores in logbook.chapters["parameters"].select(f"test_{metric}") ] cv_results[f"std_test_{metric}"] = [ np.nanstd(cv_scores) for cv_scores in logbook.chapters["parameters"].select(f"test_{metric}") ] cv_results[f"rank_test_{metric}"] = rankdata( -np.array(cv_results[f"mean_test_{metric}"]), method="min" ).astype(int) if return_train_score: for split in range(n_splits): cv_results[f"split{split}_train_{metric}"] = [ cv_scores[split] for cv_scores in logbook.chapters["parameters"].select( f"train_{metric}" ) ] cv_results[f"mean_train_{metric}"] = [ np.nanmean(cv_scores) for cv_scores in logbook.chapters["parameters"].select( f"train_{metric}" ) ] cv_results[f"std_train_{metric}"] = [ np.nanstd(cv_scores) for cv_scores in logbook.chapters["parameters"].select( f"train_{metric}" ) ] cv_results[f"rank_train_{metric}"] = rankdata( -np.array(cv_results[f"mean_train_{metric}"]), method="min" ).astype(int) # These values are only one even with multi-metric cv_results["mean_fit_time"] = [ np.nanmean(fit_time) for fit_time in logbook.chapters["parameters"].select("fit_time") ] cv_results["std_fit_time"] = [ np.nanstd(fit_time) for fit_time in logbook.chapters["parameters"].select("fit_time") ] cv_results["mean_score_time"] = [ np.nanmean(score_time) for score_time in logbook.chapters["parameters"].select("score_time") ] cv_results["std_score_time"] = [ np.nanstd(score_time) for score_time in logbook.chapters["parameters"].select("score_time") ] cv_results["n_features"] = [ np.sum(features) for features in logbook.chapters["parameters"].select("features") ] cv_results["rank_n_features"] = rankdata( np.array(cv_results["n_features"]), method="min" ).astype(int) cv_results["features"] = logbook.chapters["parameters"].select("features") return cv_results

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89

0.563367

700

6,202

4.722857

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0

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0

null

0

1

0

null

0

8

423da2bac92c2ab7035ad6c8d2716829a9fea096

6,511

py

Python

tests/test_targeted.py

rkeulemans/pymdown-extensions

e809f20f672cc350e2cb2c6c2d86e8438f329765

[ "MIT" ]

null

tests/test_targeted.py

rkeulemans/pymdown-extensions

e809f20f672cc350e2cb2c6c2d86e8438f329765

[ "MIT" ]

1

2021-02-07T05:27:54.000Z

tests/test_targeted.py

rkeulemans/pymdown-extensions

e809f20f672cc350e2cb2c6c2d86e8438f329765

[ "MIT" ]

null

"""Test `uniprops`.""" from pymdownx import util import unittest import pytest import markdown class TestUrlParse(unittest.TestCase): """Test UrlParse.""" def test_url(self): """Test URL.""" url = 'http://www.google.com' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'http') self.assertEqual(netloc, 'www.google.com') self.assertEqual(is_url, True) self.assertEqual(is_absolute, False) def test_fragment(self): """Test fragment.""" url = '#header' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, '') self.assertEqual(netloc, '') self.assertEqual(fragment, 'header') self.assertEqual(is_url, True) self.assertEqual(is_absolute, False) def test_file_windows(self): """Test file windows.""" url = 'file://c:/path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'file') self.assertEqual(path, '/c:/path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_file_windows_backslash(self): """Test file windows with backslash.""" url = r'file://c:\path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'file') self.assertEqual(path, '/c:/path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_file_windows_start_backslash(self): """Test file windows start with backslash.""" url = r'file://\c:\path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'file') self.assertEqual(path, '/c:/path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_file_windows_netpath(self): """Test file windows netpath.""" url = 'file://\\\\path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'file') self.assertEqual(path, '//path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_nix_path(self): """Test file Linux/Unix path.""" url = 'file:///path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'file') self.assertEqual(path, '/path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_windows_path_forward_slash(self): """Test windows path.""" url = 'c:/path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'file') self.assertEqual(path, '/c:/path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_windows_path_backslash(self): """Test file windows path with backslash.""" url = r'c:\path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'file') self.assertEqual(path, '/c:/path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_windows_netpath_forward_slash(self): """Test netpath with forward slash.""" url = '//file/path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, 'file') self.assertEqual(path, '//file/path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_windows_netpath_backslash(self): """Test windows netpath with backslash.""" url = '\\\\file\\path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, '') self.assertEqual(path, '\\\\file\\path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, True) def test_relative_path(self): """Test relative path.""" url = '../file/path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, '') self.assertEqual(path, '../file/path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, False) def test_windows_relative_path(self): """Test windows relative with backslash.""" url = '..\\file\\path' scheme, netloc, path, params, query, fragment, is_url, is_absolute = util.parse_url(url) self.assertEqual(scheme, '') self.assertEqual(path, '..\\file\\path') self.assertEqual(is_url, False) self.assertEqual(is_absolute, False) class TestSnippets(unittest.TestCase): """Targeted tests for Snippets.""" def test_bad_file_checked(self): """Test bad file when the check is enabled.""" with self.assertRaises(IOError): markdown.Markdown( extensions=['pymdownx.snippets'], extension_configs={'pymdownx.snippets': {'check_paths': True}} ).convert('--8<--- "bad.file"') def test_good_file_checked(self): """Test good file when the check is enabled.""" expected = "<p>Snippet</p>" rendered = markdown.Markdown( extensions=['pymdownx.snippets'], extension_configs={'pymdownx.snippets': { 'check_paths': True, 'base_path': 'tests/extensions/_snippets' }} ).convert('--8<--- "d.txt"') self.assertEqual(expected, rendered) def test_bad_file_unchecked(self): """Test bad file when the check is disabled.""" expected = "" rendered = markdown.Markdown( extensions=['pymdownx.snippets'], extension_configs={'pymdownx.snippets': {'check_paths': False}} ).convert('--8<--- "bad.file"') self.assertEqual(expected, rendered) def run(): """Run pytest.""" pytest.main( [ 'tests/test_targeted.py', '-p', 'no:pytest_cov' ] )

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null

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1

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7

424acb02ac1fde01ff9b5a4795a1c5f3f2126a9f

14,434

py

Python

tools/build/test/toolset-mock/src/clang-linux-3.9.0.py

anarthal/boost-unix-mirror

8c34eb2fe471d6c3113c680c1fbef29e7a8063a0

[ "BSL-1.0" ]

1

2021-08-15T13:07:07.000Z

tools/build/test/toolset-mock/src/clang-linux-3.9.0.py

anarthal/boost-unix-mirror

8c34eb2fe471d6c3113c680c1fbef29e7a8063a0

[ "BSL-1.0" ]

null

tools/build/test/toolset-mock/src/clang-linux-3.9.0.py

anarthal/boost-unix-mirror

8c34eb2fe471d6c3113c680c1fbef29e7a8063a0

[ "BSL-1.0" ]

1

2021-08-24T08:55:27.000Z

#!/usr/bin/python # coding: utf-8 # # Copyright 2017 Steven Watanabe # Copyright 2020 René Ferdinand Rivera Morell # # Distributed under the Boost Software License, Version 1.0. # (See accompanying file LICENSE_1_0.txt or copy at # http://www.boost.org/LICENSE_1_0.txt) from MockProgram import * command('clang++', '-print-prog-name=ar', stdout=script('ar.py')) command('clang++', '-print-prog-name=ranlib', stdout=script('ranlib.py')) # target-os=linux .. if allow_properties('target-os=linux', 'variant=debug', 'link=shared', 'threading=single', 'runtime-link=shared'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-fPIC', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/lib.o'), input_file(source='lib.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/libl1.so'), '-Wl,-soname', '-Wl,libl1.so', '-shared', '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/lib.o'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g', '-fPIC')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-fPIC', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/main.o'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/test'), '-Wl,-R', arg('-Wl,', target_path('bin/clang-linux-3.9.0/debug/libl1.so')), '-Wl,-rpath-link', arg('-Wl,', target_path('bin/clang-linux-3.9.0/debug/libl1.so')), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/main.o'), input_file('bin/clang-linux-3.9.0/debug/libl1.so'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g', '-fPIC')) if allow_properties('target-os=linux', 'variant=release', 'link=shared', 'threading=single', 'runtime-link=shared', 'strip=on'): command('clang++', unordered(ordered('-x', 'c++'), '-O3', '-Wno-inline', '-Wall', '-fPIC', '-DNDEBUG', '-c'), '-o', output_file('bin/clang-linux-3.9.0/release/lib.o'), input_file(source='lib.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/release/libl1.so'), '-Wl,-soname', '-Wl,libl1.so', '-shared', '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/release/lib.o'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-fPIC', '-Wl,--strip-all')) command('clang++', unordered(ordered('-x', 'c++'), '-O3', '-Wno-inline', '-Wall', '-fPIC', '-DNDEBUG', '-c'), '-o', output_file('bin/clang-linux-3.9.0/release/main.o'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/release/test'), '-Wl,-R', arg('-Wl,', target_path('bin/clang-linux-3.9.0/release/libl1.so')), '-Wl,-rpath-link', arg('-Wl,', target_path('bin/clang-linux-3.9.0/release/libl1.so')), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/release/main.o'), input_file('bin/clang-linux-3.9.0/release/libl1.so'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-fPIC', '-Wl,--strip-all')) if allow_properties('target-os=linux', 'variant=debug', 'link=shared', 'threading=multi', 'runtime-link=shared'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-pthread', '-fPIC', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/threading-multi/lib.o'), input_file(source='lib.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/threading-multi/libl1.so'), '-Wl,-soname', '-Wl,libl1.so', '-shared', '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/threading-multi/lib.o'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-lrt', '-Wl,--end-group', unordered('-g', '-pthread', '-fPIC')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-pthread', '-fPIC', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/threading-multi/main.o'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/threading-multi/test'), '-Wl,-R', arg('-Wl,', target_path('bin/clang-linux-3.9.0/debug/threading-multi/libl1.so')), '-Wl,-rpath-link', arg('-Wl,', target_path('bin/clang-linux-3.9.0/debug/threading-multi/libl1.so')), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/threading-multi/main.o'), input_file('bin/clang-linux-3.9.0/debug/threading-multi/libl1.so'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-lrt', '-Wl,--end-group', unordered('-g', '-pthread', '-fPIC')) if allow_properties('target-os=linux', 'variant=debug', 'link=static', 'threading=single', 'runtime-link=shared'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/lib.o'), input_file(source='lib.cpp')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/main.o'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/test'), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/link-static/main.o'), input_file('bin/clang-linux-3.9.0/debug/link-static/libl1.a'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', '-g') if allow_properties('target-os=linux', 'variant=debug', 'link=static', 'threading=single', 'runtime-link=static'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/lib.o'), input_file(source='lib.cpp')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/main.o'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/test'), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/main.o'), input_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/libl1.a'), '-Wl,--end-group', unordered('-g', '-static')) if allow_properties('target-os=linux', 'variant=debug', 'link=shared', 'threading=single', 'runtime-link=shared', 'architecture=x86', 'address-model=32'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-march=i686', '-m32', '-fPIC', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/lib.o'), input_file(source='lib.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/libl1.so'), '-Wl,-soname', '-Wl,libl1.so', '-shared', '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/lib.o'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g', '-march=i686', '-fPIC', '-m32')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-march=i686', '-m32', '-fPIC', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/main.o'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/test'), '-Wl,-R', arg('-Wl,', target_path('bin/clang-linux-3.9.0/debug/libl1.so')), '-Wl,-rpath-link', arg('-Wl,', target_path('bin/clang-linux-3.9.0/debug/libl1.so')), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/main.o'), input_file('bin/clang-linux-3.9.0/debug/libl1.so'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g', '-march=i686', '-fPIC', '-m32')) # target-os=windows .. if allow_properties('target-os=windows', 'variant=debug', 'link=shared', 'threading=single', 'runtime-link=shared'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/target-os-windows/lib.obj'), input_file(source='lib.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/target-os-windows/l1.dll'), '-shared', '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/target-os-windows/lib.obj'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/target-os-windows/main.obj'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/target-os-windows/test.exe'), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/target-os-windows/main.obj'), input_file('bin/clang-linux-3.9.0/debug/target-os-windows/l1.dll'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g')) if allow_properties('target-os=windows', 'variant=release', 'link=shared', 'threading=single', 'runtime-link=shared', 'strip=on'): command('clang++', unordered(ordered('-x', 'c++'), '-O3', '-Wno-inline', '-Wall', '-DNDEBUG', '-c'), '-o', output_file('bin/clang-linux-3.9.0/release/target-os-windows/lib.obj'), input_file(source='lib.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/release/strip-on/target-os-windows/l1.dll'), '-shared', '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/release/target-os-windows/lib.obj'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-Wl,--strip-all')) command('clang++', unordered(ordered('-x', 'c++'), '-O3', '-Wno-inline', '-Wall', '-DNDEBUG', '-c'), '-o', output_file('bin/clang-linux-3.9.0/release/strip-on/target-os-windows/main.obj'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/release/test'), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/release/strip-on/target-os-windows/main.obj'), input_file('bin/clang-linux-3.9.0/release/strip-on/target-os-windows/l1.dll'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-Wl,--strip-all')) if allow_properties('target-os=windows', 'variant=debug', 'link=shared', 'threading=multi', 'runtime-link=shared'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-pthread', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/target-os-windows/threading-multi/lib.obj'), input_file(source='lib.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/target-os-windows/threading-multi/l1.dll'), '-shared', '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/target-os-windows/threading-multi/lib.obj'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g', '-pthread')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-pthread', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/target-os-windows/threading-multi/main.obj'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/threading-multi/test'), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/target-os-windows/threading-multi/main.obj'), input_file('bin/clang-linux-3.9.0/debug/target-os-windows/threading-multi/l1.dll'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g', '-pthread')) if allow_properties('target-os=windows', 'variant=debug', 'link=static', 'threading=single', 'runtime-link=shared'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/target-os-windows/lib.obj'), input_file(source='lib.cpp')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/target-os-windows/main.obj'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/target-os-windows/test.exe'), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/link-static/target-os-windows/main.obj'), input_file('bin/clang-linux-3.9.0/debug/link-static/target-os-windows/libl1.lib'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', '-g') if allow_properties('target-os=windows', 'variant=debug', 'link=static', 'threading=single', 'runtime-link=static'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/target-os-windows/lib.obj'), input_file(source='lib.cpp')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/target-os-windows/main.obj'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/target-os-windows/test.exe'), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/link-static/target-os-windows/main.obj'), input_file('bin/clang-linux-3.9.0/debug/link-static/target-os-windows/libl1.lib'), '-Wl,--end-group', unordered('-g', '-static')) if allow_properties('target-os=windows', 'variant=debug', 'link=shared', 'threading=single', 'runtime-link=shared', 'architecture=x86', 'address-model=32'): command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-march=i686', '-m32', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/address-model-32/architecture-x86/target-os-windows/lib.obj'), input_file(source='lib.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/address-model-32/architecture-x86/target-os-windows/l1.dll'), '-shared', '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/address-model-32/architecture-x86/target-os-windows/lib.obj'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g', '-march=i686', '-m32')) command('clang++', unordered(ordered('-x', 'c++'), '-O0', '-fno-inline', '-Wall', '-g', '-march=i686', '-m32', '-c'), '-o', output_file('bin/clang-linux-3.9.0/debug/address-model-32/architecture-x86/target-os-windows/main.obj'), input_file(source='main.cpp')) command('clang++', '-o', output_file('bin/clang-linux-3.9.0/debug/address-model-32/architecture-x86/target-os-windows/test.exe'), '-Wl,--start-group', input_file('bin/clang-linux-3.9.0/debug/address-model-32/architecture-x86/target-os-windows/main.obj'), input_file('bin/clang-linux-3.9.0/debug/address-model-32/architecture-x86/target-os-windows/l1.dll'), '-Wl,-Bstatic', '-Wl,-Bdynamic', '-Wl,--end-group', unordered('-g', '-march=i686', '-m32')) main()

162.179775

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0.072578

0.117939

0.127012

0.967815

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0.954423

0

0.029397

0.059651

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164.022727

0.652767

0.02023

0

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0.607034

0.321633

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429dea3cfbfc5c6983ad4058292d2aa8db6357c6

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py

Python

test/unit/mongo_class/masterrep_connect.py

deepcoder42/mongo-lib

fa2b65587ab88ee90c9d85f12dd642c6295e0d94

[ "MIT" ]

null

test/unit/mongo_class/masterrep_connect.py

deepcoder42/mongo-lib

fa2b65587ab88ee90c9d85f12dd642c6295e0d94

[ "MIT" ]

null

test/unit/mongo_class/masterrep_connect.py

deepcoder42/mongo-lib

fa2b65587ab88ee90c9d85f12dd642c6295e0d94

[ "MIT" ]

null

#!/usr/bin/python # Classification (U) """Program: masterrep_connect.py Description: Unit testing of MasterRep.connect in mongo_class.py. Usage: test/unit/mongo_class/masterrep_connect.py Arguments: """ # Libraries and Global Variables # Standard import sys import os if sys.version_info < (2, 7): import unittest2 as unittest else: import unittest # Third-party import mock # Local sys.path.append(os.getcwd()) import mongo_class import version __version__ = version.__version__ class UnitTest(unittest.TestCase): """Class: UnitTest Description: Class which is a representation of a unit testing. Methods: setUp test_slaves_attr4 test_slaves_attr3 test_slaves_attr2 test_slaves_attr test_repset_attr2 test_repset_attr test_issecondary_attr2 test_issecondary_attr test_ismaster_attr2 test_ismaster_attr test_no_conn_list1 test_no_conn_list test_fail_connection2 test_fail_connection test_no_data2 test_no_data test_default2 test_default """ def setUp(self): """Function: setUp Description: Initialization for unit testing. Arguments: """ self.name = "Mongo_Server" self.user = "mongo_user" self.japd = "mongo_pd" self.host = "host_server" self.port = 27017 self.dbs = "test" self.coll = None self.db_auth = None self.repset = "mongo_repset" self.data = {"secondary": False, "ismaster": True, "issecondary": False, "setName": "mongo_repset", "hosts": ["slave1", "slave2"]} self.data2 = {"secondary": False, "ismaster": True, "issecondary": False, "setName": "mongo_repset"} self.msg = "Error: This is not a Master Replication server." @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_slaves_attr4(self, mock_fetch): """Function: test_slaves_attr4 Description: Test slaves attribute. Arguments: """ mock_fetch.return_value = self.data2 mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) mongo.connect() self.assertEqual(mongo.slaves, []) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_slaves_attr3(self, mock_fetch): """Function: test_slaves_attr3 Description: Test slaves attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual(mongo.connect(), (True, None)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_slaves_attr2(self, mock_fetch): """Function: test_slaves_attr2 Description: Test slaves attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) mongo.connect() self.assertEqual(mongo.slaves, ["slave1", "slave2"]) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_slaves_attr(self, mock_fetch): """Function: test_slaves_attr Description: Test slaves attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual(mongo.connect(), (True, None)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_repset_attr2(self, mock_fetch): """Function: test_repset_attr2 Description: Test repset attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) mongo.connect() self.assertEqual(mongo.repset, "mongo_repset") @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_repset_attr(self, mock_fetch): """Function: test_repset_attr Description: Test repset attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual(mongo.connect(), (True, None)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_issecondary_attr2(self, mock_fetch): """Function: test_issecondary_attr2 Description: Test issecondary attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) mongo.connect() self.assertFalse(mongo.issecondary) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_issecondary_attr(self, mock_fetch): """Function: test_issecondary_attr Description: Test issecondary attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual(mongo.connect(), (True, None)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_ismaster_attr2(self, mock_fetch): """Function: test_ismaster_attr2 Description: Test ismaster attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) mongo.connect() self.assertTrue(mongo.ismaster) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_ismaster_attr(self, mock_fetch): """Function: test_ismaster_attr Description: Test ismaster attribute. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual(mongo.connect(), (True, None)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_no_conn_list1(self, mock_fetch): """Function: test_no_conn_list1 Description: Test with no connections passed. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) mongo.conn = True mongo.connect() self.assertEqual( (mongo.name, mongo.user, mongo.japd, mongo.host, mongo.port, mongo.ismaster, mongo.issecondary), (self.name, self.user, self.japd, self.host, self.port, True, False)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_no_conn_list(self, mock_fetch): """Function: test_no_conn_list Description: Test with no connections passed. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) mongo.conn = True self.assertEqual(mongo.connect(), (True, None)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(False, "Error Message"))) @mock.patch("mongo_class.fetch_ismaster") def test_fail_connection2(self, mock_fetch): """Function: test_fail_connection2 Description: Test with failed connection. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual( (mongo.name, mongo.user, mongo.japd, mongo.host, mongo.port, mongo.ismaster, mongo.issecondary), (self.name, self.user, self.japd, self.host, self.port, None, None)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(False, "Error Message"))) @mock.patch("mongo_class.fetch_ismaster") def test_fail_connection(self, mock_fetch): """Function: test_fail_connection Description: Test with failed connection. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual(mongo.connect(), (False, "Error Message")) @mock.patch("mongo_class.Server.disconnect", mock.Mock(return_value=True)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster", mock.Mock(return_value={})) def test_no_data2(self): """Function: test_no_data2 Description: Test with no data returned. Arguments: """ mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual( (mongo.name, mongo.user, mongo.japd, mongo.host, mongo.port, mongo.ismaster, mongo.issecondary), (self.name, self.user, self.japd, self.host, self.port, None, None)) @mock.patch("mongo_class.Server.disconnect", mock.Mock(return_value=True)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster", mock.Mock(return_value={})) def test_no_data(self): """Function: test_no_data Description: Test with no data returned. Arguments: """ mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual(mongo.connect(), (False, self.msg)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_default2(self, mock_fetch): """Function: test_default2 Description: Test connect method with default arguments. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) mongo.connect() self.assertEqual( (mongo.name, mongo.user, mongo.japd, mongo.host, mongo.port, mongo.ismaster, mongo.issecondary), (self.name, self.user, self.japd, self.host, self.port, True, False)) @mock.patch("mongo_class.Server.connect", mock.Mock(return_value=(True, None))) @mock.patch("mongo_class.fetch_ismaster") def test_default(self, mock_fetch): """Function: test_default Description: Test connect method with default arguments. Arguments: """ mock_fetch.return_value = self.data mongo = mongo_class.MasterRep(self.name, self.user, self.japd, self.host, self.port) self.assertEqual(mongo.connect(), (True, None)) if __name__ == "__main__": unittest.main()

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null

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null

0

7

c45eaac8a40e0081ea0aa7ef65b1e693f3618fd1

419

py

Python

python/testData/formatter/fromImportTrailingCommaWithoutParentheses.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/formatter/fromImportTrailingCommaWithoutParentheses.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/formatter/fromImportTrailingCommaWithoutParentheses.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from module import foo from module import foo, bar from module import foo, bar, # | margin from module import foo, bar, baz from module import foo, \ bar from module import foo, \ bar, from module import foo, \ bar # comment from module import (foo, bar) from module import (foo, bar,) from module import ( foo, bar # comment )

18.217391

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1

0

1

0

11

c47deb999743e08e15b1c3504528bdf2d8c7910d

22,528

py

Python

AdventOfCode/Day11.py

JanStoltman/100DaysOfCode

1d18b76ed1e3e942e8392006a5d4bfb41484d047

[ "MIT" ]

null

AdventOfCode/Day11.py

JanStoltman/100DaysOfCode

1d18b76ed1e3e942e8392006a5d4bfb41484d047

[ "MIT" ]

null

AdventOfCode/Day11.py

JanStoltman/100DaysOfCode

1d18b76ed1e3e942e8392006a5d4bfb41484d047

[ "MIT" ]

null

steps 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cy = 0 cx = 0 mx = 0 for step in steps: if step == 'n': cy += 1 elif step == 's': cy -= 1 elif step == 'ne': cy += 1 cx += 1 elif step == 'nw': cx -= 1 elif step == 'se': cx += 1 elif step == 'sw': cy -= 1 cx -= 1 mx = max(abs(cy), abs(cx), abs(cy - cx), mx) print cy print cx print cy - cx print mx

776.827586

22,199

0.626953

8,288

22,528

1.704151

0.002534

0.226565

0.191164

0.161427

0.974936

0.911286

0.787737

0.628646

0.443005

0.321439

0

0.000491

0.004838

22,528

28

22,200

804.571429

0.629511

0

0.333333

0

0.041667

0.984996

0.984508

0

null

0

null

0.166667

0

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null

1

0

1

0

1

null

0

1

0

12

67b5577ec1585868af7a815b30970983d7da5309

4,543

py

Python

utility/generate_subprocess_graph.py

CamLeon/Challenge

893502ec1722932fc264ab412bcffa8d06095e44

[ "MIT" ]

2

2018-11-12T18:34:13.000Z

2018-11-12T18:36:21.000Z

utility/generate_subprocess_graph.py

CamLeon/Challenge

893502ec1722932fc264ab412bcffa8d06095e44

[ "MIT" ]

null

utility/generate_subprocess_graph.py

CamLeon/Challenge

893502ec1722932fc264ab412bcffa8d06095e44

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ############################ # Last modified by Muguette# ############################ import networkx as nx def generate_subprocess_graph(links): """ :param links: list of childhood tuples (parent -> child) :return: the tree of subprocesses """ G = nx.DiGraph(Type="Subprocesses") G.add_edges_from(links) return G def generate_subprocess_tre(links): """ :param links: list of childhood tuples (parent -> child) :return: the tree of subprocesses """ G = nx.DiGraph(Type="Subprocesses") G.add_edges_from(links) return G, links[0][0] # return parent with graph def generate_RIP_behavior_graph(records): """ :param records:list of tuples (process, RIP, API_called) :return: the graphs of API_calls of all subprocesses """ # Each subprocess is associated to one graph # LIC stands for Last I Checked forest_and_LIC = {} # forest_and_LIC = {subprocess : (graph, LIC)} # Equivalent to : #forest = {} # Forest = {subprocess : graph} #last_I_checked = {} # last_I_checked = {subprocess : last record (subprocess, _, _)} for i, record in enumerate(records): subprocess = record[0] RIP = record[1] API_call = record[2] if subprocess in forest_and_LIC.keys(): # This process had been seen before process_graph, process_LIC = forest_and_LIC[subprocess] # Equivalent to : #process_graph = forest[subprocess] #process_LIC = last_I_checked[subprocess] # This was the RIP of the last record (subprocess, _, _) lastRIP = records[process_LIC][1] if process_graph.has_edge(lastRIP, RIP): # The edge already exist, its weight is incremented process_graph[lastRIP][RIP]['weight'] += 1 else: # The edge is created process_graph.add_edge(lastRIP, RIP, weight=1) # We want to add API_call in the list of API_calls associated to this RIP iff it is not in already if API_call not in process_graph[RIP]['API_calls']: process_graph[RIP]['API_calls'].append(API_call) else: # This process had not been seen before process_graph = nx.DiGraph(process=subprocess) # We generate the node so that we can add the API_call associated to it process_graph.add_node(RIP, API_calls=[API_call]) # Generate the tuple (graph, LIC) associated to subprocess forest_and_LIC[subprocess] = (process_graph, i) return [forest_and_LIC[subprocess][0] for subprocess in forest_and_LIC.keys()] def generate_API_behavior_graph(records): """ :param records:list of tuples (process, RIP, API_called) :return: the graphs of API_calls of all subprocesses """ # Each subprocess is associated to one graph # LIC stands for Last I Checked forest_and_LIC = {} # forest_and_LIC = {subprocess : (graph, LIC)} # Equivalent to : #forest = {} # Forest = {subprocess : graph} #last_I_checked = {} # last_I_checked = {subprocess : last record (subprocess, _, _)} for i, record in enumerate(records): subprocess = record[0] RIP = record[1] API = record[2] if subprocess in forest_and_LIC.keys(): # This process had been seen before process_graph, process_LIC = forest_and_LIC[subprocess] # Equivalent to : #process_graph = forest[subprocess] #process_LIC = last_I_checked[subprocess] # This was the RIP of the last record (subprocess, _, _) lastRIP = records[process_LIC][1] lastAPI = records[process_LIC][2] if process_graph.has_edge((lastRIP, lastAPI) , (RIP, API)): # The edge already exist, its weight is incremented process_graph[(lastRIP, lastAPI)][(RIP, API)]['weight'] += 1 else: # The edge is created process_graph.add_edge((lastRIP, lastAPI), (RIP, API), weight=1) else: # This process had not been seen before process_graph = nx.DiGraph(process=subprocess) # Generate the tuple (graph, LIC) associated to subprocess forest_and_LIC[subprocess] = (process_graph, i) return [forest_and_LIC[subprocess][0] for subprocess in forest_and_LIC.keys()]

37.237705

110

0.609949

558

4,543

4.777778

0.175627

0.076519

0.063016

0.066017

0.846212

0.824081

0.803076

0.790698

0

0.005547

0.285714

4,543

121

111

37.545455

0.816025

0.412283

0

0.595745

1

0

0.021635

0

1

0.085106

false

0

0.021277

0

0.191489

0

null

0

1

0

1

0

null

0

7

67fae855d9bc494b92b6b66d608da6270812cb1a

21,942

py

Python

backend/billparser/tests/test_routes.py

Congress-Dev/congress-dev

a697b2590472e0e55f94cec35b3e57042649638d

[ "MIT" ]

9

2020-03-09T01:18:43.000Z

2022-01-28T10:07:05.000Z

backend/billparser/tests/test_routes.py

Congress-Dev/congress-dev

a697b2590472e0e55f94cec35b3e57042649638d

[ "MIT" ]

13

2020-03-24T12:57:38.000Z

2022-02-18T15:37:01.000Z

backend/billparser/tests/test_routes.py

Congress-Dev/congress-dev

a697b2590472e0e55f94cec35b3e57042649638d

[ "MIT" ]

1

2022-02-15T20:52:55.000Z

from unittest import TestCase, mock import json from billparser.__main__ import ( bills, app, bill_content, bill_content_tree, titles, versions, revisions, version, latest_sections, sections, contents, ) from billparser.db.models import ( USCChapter, USCSection, USCContent, USCContentDiff, Version, Legislation, LegislationVersion, LegislationContent, LegislationVersionEnum, LegislationChamber, LegislationType, ) # This is to ensure that the return values are the same # No matter what, these return values shouldn't change, or the frontend # Will need to change class TestRoutes(TestCase): @mock.patch("billparser.__main__.get_bills", return_value=[]) def test_bills_no_version(self, mock_get_bills): """ Should be returning a dict where the key is the bill, and the value is the bill metadata """ mock_get_bills.return_value = [ Legislation( legislation_id=1, legislation_type=LegislationType.Bill, chamber=LegislationChamber.House, title="Test House Bill", number=1 ), Legislation( legislation_id=2, legislation_type=LegislationType.Bill, chamber=LegislationChamber.Senate, title="Test Senate Bill", number=5 ), ] with app.app.test_request_context(): resp = bills() self.assertEqual( json.dumps( { "H-1": { "bill_id": "1", "chamber": "House", "bill_type": "BillTypes.Bill", "bill_number": "1", "bill_title": "Test House Bill", "versions": [], }, "S-5": { "bill_id": "2", "chamber": "Senate", "bill_type": "BillTypes.Bill", "bill_number": "5", "bill_title": "Test Senate Bill", "versions": [], }, } ), resp, ) @mock.patch("billparser.__main__.get_bills", return_value=[]) def test_bills_with_version(self, mock_get_bills): """ Should be returning a dict where the key is the bill, and the value is the bill metadata Should also include the given bill versions for the bill """ mock_get_bills.return_value = [ Legislation( legislation_id=1, legislation_type=LegislationType.Bill, chamber=LegislationChamber.House, title="Test House Bill", number=1, versions=[ LegislationVersion( legislation_version_id=1, legislation_id=1, legislation_version=LegislationVersionEnum.IH, ) ], ), Legislation( legislation_id=2, legislation_type=LegislationType.Bill, chamber=LegislationChamber.Senate, title="Test Senate Bill", number=5, versions=[ LegislationVersion( legislation_version_id=2, legislation_id=2, legislation_version=LegislationVersionEnum.IS, ) ], ), ] with app.app.test_request_context(): resp = bills() self.assertEqual( json.dumps( { "H-1": { "bill_id": "1", "chamber": "House", "bill_type": "BillTypes.Bill", "bill_number": "1", "bill_title": "Test House Bill", "versions": [ { "bill_version_id": "1", "bill_id": "1", "bill_version": "ih", # "base_version_id": "1", # This was removed in the translation } ], }, "S-5": { "bill_id": "2", "chamber": "Senate", "bill_type": "BillTypes.Bill", "bill_number": "5", "bill_title": "Test Senate Bill", "versions": [ { "bill_version_id": "2", "bill_id": "2", "bill_version": "is", # "base_version_id": "1", # This was removed in the translation } ], }, } ), resp, resp, ) @mock.patch("billparser.__main__.get_bill_contents", return_value=[]) def test_bill_content_1(self, mock_get_bill_contents): """ Should return the bill content objects """ mock_get_bill_contents.return_value = [ LegislationContent( legislation_content_id=1, parent_id=None, order_number=0, section_display="SS 1.)", heading="Test heading", content_str="Test content", legislation_version_id=1, content_type="section", action_parse=[], ), ] with app.app.test_request_context(): resp = bill_content("1") self.assertEqual( json.dumps( [ { "bill_content_id": 1, "content_type": "section", "order": 0, # "number": "1", # Removed "display": "SS 1.)", "heading": "Test heading", "content": "Test content", "version": "1", } ] ), resp, resp, ) @mock.patch("billparser.__main__.get_bill_contents", return_value=[]) def test_bill_content_2(self, mock_get_bill_contents): """ Should return the bill content objects, multiple contents """ self.maxDiff = None mock_get_bill_contents.return_value = [ LegislationContent( legislation_content_id=1, parent_id=None, order_number=0, section_display="SS 1.)", heading="Test heading", content_str="Test content", legislation_version_id=1, content_type="section", action_parse=[], ), LegislationContent( legislation_content_id=2, parent_id="1", order_number=0, section_display="a.)", heading="", content_str="Test subcontent", legislation_version_id=1, content_type="legis-body", action_parse=[], ), ] with app.app.test_request_context(): resp = bill_content("1") self.assertEqual( json.dumps( [ { "bill_content_id": 1, "content_type": "section", "order": 0, # "number": "1", # Removed "display": "SS 1.)", "heading": "Test heading", "content": "Test content", "version": "1", }, { "bill_content_id": 2, "content_type": "legis-body", "order": 0, "parent": "1", # "number": "a", # Removed "display": "a.)", "heading": "", "content": "Test subcontent", "version": "1", }, ] ), resp, resp, ) @mock.patch("billparser.__main__.get_bill_metadata", return_value=[]) @mock.patch("billparser.__main__.get_bill_contents", return_value=[]) def test_bill_content_tree_1(self, mock_get_bill_contents, mock_get_bill_metadata): """ Should return the bill content objects, and metadata """ mock_get_bill_metadata.return_value = { "chamber": "House", "number": "12", "version": "1", } mock_get_bill_contents.return_value = [ LegislationContent( legislation_content_id=1, parent_id=None, order_number=0, section_display="SS 1.)", heading="Test heading", content_str="Test content", legislation_version_id=1, content_type="section", action_parse=[], ), LegislationContent( legislation_content_id=2, parent_id=1, order_number=0, section_display="a.)", heading="", content_str="Test subcontent", legislation_version_id=1, content_type="legis-body", action_parse=[], ), ] with app.app.test_request_context(): resp = bill_content_tree("1") self.assertEqual( json.dumps( { "content": { "bill_content_id": 1, "content_type": "section", "order": 0, #"number": "1", # Removed "display": "SS 1.)", "heading": "Test heading", "content": "Test content", "version": "1", "child": [ { "bill_content_id": 2, "content_type": "legis-body", "order": 0, "parent": 1, # "number": "a", # Removed "display": "a.)", "heading": "", "content": "Test subcontent", "version": "1", "child": [], } ], }, "metadata": { "chamber": "House", "number": "12", "version": "1", }, } ), resp, resp, ) @mock.patch("billparser.__main__.get_chapters", return_value=[]) def test_chapters(self, mock_get_chapters): """ Should return the chapter objects """ mock_get_chapters.return_value = [ USCChapter( usc_chapter_id=1, usc_ident="/usc/1", short_title="01", document="usc", version_id=1, ) ] with app.app.test_request_context(): resp = titles() self.assertEqual( json.dumps( [{"chapter_id": 1, "ident": "/usc/1", "number": "01", "version": 1}] ), resp, resp, ) @mock.patch("billparser.__main__.get_versions", return_value=[]) def test_versions(self, mock_get_versions): """ Should return the version objects """ mock_get_versions.return_value = [ Version(version_id=1, base_id=1) ] with app.app.test_request_context(): resp = versions() self.assertEqual( json.dumps([{"version_id": 1, "title": "Legacy Title", "base_id": 1}]), resp, resp, ) @mock.patch("billparser.__main__.get_revisions", return_value=[]) def test_revisions(self, mock_get_versions): """ Should return the version objects without a base id """ mock_get_versions.return_value = [ Version( version_id=1, base_id=None ) ] with app.app.test_request_context(): resp = revisions() self.assertEqual( json.dumps([{"version_id": 1, "title": "Legacy Title"}]), resp, resp, ) @mock.patch("billparser.__main__.get_content_versions", return_value=[]) @mock.patch("billparser.__main__.get_diffs", return_value=[]) def test_get_version(self, mock_get_diffs, mock_get_content_versions): """ Should return the version objects without a base id """ mock_get_content_versions.return_value = [ USCContent( usc_content_id=1, usc_section_id=1, parent_id=None, usc_ident="/usc/s1/1", usc_guid="1-2-3", number="1", section_display="S 1.)", heading="Test - heading", content_str="content - str", version_id=1, ) ] mock_get_diffs.return_value = [ USCContentDiff( usc_content_diff_id=1, usc_chapter_id=1, usc_section_id=1, usc_content_id=1, order_number=0, number="1", section_display="test", heading="test - heading", content_str="test - content", version_id=1, ) ] with app.app.test_request_context(json={"version": 1}): resp = version() self.assertEqual( json.dumps( { "diffs": [ { "id": 1, "content_id": 1, "section_id": 1, "chapter_id": 1, "order": 0, "number": "1", "display": "test", "heading": "test - heading", "content": "test - content", "version": 1, } ], "contents": [ { "content_id": 1, "section_id": 1, "ident": "/usc/s1/1", "number": "1", "display": "S 1.)", "heading": "Test - heading", "content": "content - str", "version": 1, } ], } ), resp, resp, ) @mock.patch("billparser.__main__.get_latest_sections", return_value=[]) def test_latest_sections(self, mock_get_sections): """ Should return the section objects """ mock_get_sections.return_value = [ USCSection( usc_section_id=1, usc_ident="/usc/01/s1", number="1", section_display="S 1.)", heading="Test - Heading", usc_chapter_id=1, version_id=1, ) ] with app.app.test_request_context(): resp = latest_sections("1") self.assertEqual( json.dumps( [ { "section_id": 1, "ident": "/usc/01/s1", "number": "1", "display": "S 1.)", "heading": "Test - Heading", "chapter_id": 1, "version": 1, } ] ), resp, resp, ) @mock.patch("billparser.__main__.get_latest_sections", return_value=[]) def test_latest_sections(self, mock_get_sections): """ Should return the section objects """ mock_get_sections.return_value = [ USCSection( usc_section_id=1, usc_ident="/usc/01/s1", number="1", section_display="S 1.)", heading="Test - Heading", usc_chapter_id=1, version_id=1, ) ] with app.app.test_request_context(): resp = latest_sections("1") self.assertEqual( json.dumps( [ { "section_id": 1, "ident": "/usc/01/s1", "number": "1", "display": "S 1.)", "heading": "Test - Heading", "chapter_id": 1, "version": 1, } ] ), resp, resp, ) @mock.patch( "billparser.__main__.get_latest_base", return_value=Version(version_id=1) ) @mock.patch("billparser.__main__.get_sections", return_value=[]) def test_sections(self, mock_get_sections, mock_get_latest_base): """ Should return the section objects """ mock_get_sections.return_value = [ USCSection( usc_section_id=1, usc_ident="/usc/01/s1", number="1", section_display="S 1.)", heading="Test - Heading", usc_chapter_id=1, version_id=1, ) ] with app.app.test_request_context(): resp = sections("1") self.assertEqual( json.dumps( [ { "section_id": 1, "ident": "/usc/01/s1", "number": "1", "display": "S 1.)", "heading": "Test - Heading", "chapter_id": 1, "version": 1, } ] ), resp, resp, ) @mock.patch( "billparser.__main__.get_latest_base", return_value=Version(version_id=1) ) @mock.patch("billparser.__main__.get_content", return_value=[]) def test_contents(self, mock_get_content, mock_get_latest_base): """ Should return the content objects """ mock_get_content.return_value = [ USCContent( usc_content_id=1, usc_section_id=1, parent_id=None, order_number=0, usc_ident="/usc/01/s1", usc_guid="1-2-3", number="1", section_display="S 1.)", heading="Test - Heading", content_str="Content - Str", version_id=1, content_type="legis-body", ) ] with app.app.test_request_context(): resp = contents("1") self.assertEqual( json.dumps( [ { "content_id": 1, "content_type": "legis-body", "section_id": 1, "order": 0, "ident": "/usc/01/s1", "number": "1", "display": "S 1.)", "heading": "Test - Heading", "content": "Content - Str", "version": 1, } ] ), resp, resp, )

35.051118

100

0.383693

1,633

21,942

4.872015

0.080833

0.025641

0.027652

0.049145

0.823655

0.784565

0.74824

0.716315

0.704877

0.677979

0

0.019719

0.519278

21,942

625

101

35.1072

0.734547

0.04913

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0.638989

0

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0.028463

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0.023466

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0

0.00722

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0.032491

0

null

0

1

0

1

0

null

0

7

c00eff01bb8b846ec60b3ab90fc6e20b088eb651

115

py

Python

jaitool/annotation/COCO/__init__.py

Jitesh17/jaitool

bcbb014808045d65c0f5b2bd587b1a418271f61e

[ "MIT" ]

1

2021-01-22T00:38:41.000Z

jaitool/annotation/COCO/__init__.py

Jitesh17/jaitool

bcbb014808045d65c0f5b2bd587b1a418271f61e

[ "MIT" ]

null

jaitool/annotation/COCO/__init__.py

Jitesh17/jaitool

bcbb014808045d65c0f5b2bd587b1a418271f61e

[ "MIT" ]

1

2021-02-26T05:23:23.000Z

from .coco_dataset import * from .coco_result_dataset import * from .edit_coco_json import * from .vis_ann import *

28.75

34

0.8

18

115

4.777778

0.5

0.348837

0.395349

0

0.130435

115

4

35

28.75

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1

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true

0

1

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1

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

7

c06217b9ab0f7755adc860ea3d1999f131b06748

349

py

Python

tests/internal/instance_type/test_instance_type_m6_auto.py

frolovv/aws.ec2.compare

582805823492f833d65c0441c4a14dce697c12aa

[ "Apache-2.0" ]

null

tests/internal/instance_type/test_instance_type_m6_auto.py

frolovv/aws.ec2.compare

582805823492f833d65c0441c4a14dce697c12aa

[ "Apache-2.0" ]

null

tests/internal/instance_type/test_instance_type_m6_auto.py

frolovv/aws.ec2.compare

582805823492f833d65c0441c4a14dce697c12aa

[ "Apache-2.0" ]

1

2021-12-15T11:58:22.000Z

# Testing module instance_type.m6 import pytest import ec2_compare.internal.instance_type.m6 def test_get_internal_data_instance_type_m6_get_instances_list(): assert len(ec2_compare.internal.instance_type.m6.get_instances_list()) > 0 def test_get_internal_data_instance_type_m6_get(): assert len(ec2_compare.internal.instance_type.m6.get) > 0

34.9

76

0.848138

56

349

4.839286

0.339286

0.265683

0.309963

0.250923

0.826568

0.612546

0

0.034056

0.074499

349

9

77

38.777778

0.804954

0.088825

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

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null

1

0

1

0

null

0

1

0

1

0

1

0

10

22593b7aaef4673c3bd78d1a3f74495167f49dc0

151

py

Python

onepay_new/commands.py

shaoren0110/onepay_flask

c736971113763ab5e1a67c85d5599137f3a373fc

[ "MIT" ]

null

onepay_new/commands.py

shaoren0110/onepay_flask

c736971113763ab5e1a67c85d5599137f3a373fc

[ "MIT" ]

null

onepay_new/commands.py

shaoren0110/onepay_flask

c736971113763ab5e1a67c85d5599137f3a373fc

[ "MIT" ]

null

# -*- coding: utf-8 -*- from flask import request, redirect, url_for def redirect_back(html, **kwargs): return redirect(url_for(html, **kwargs))

21.571429

44

0.688742

21

151

4.809524

0.714286

0.217822

0.277228

0

0.007813

0.152318

151

7

45

21.571429

0.78125

0.139073

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

3f242f39616a288d30c663bd9ab5f87b6614d326

19,312

py

Python

src/Fig_2_supplement_5_Supralinear_network_with_initial_ISN.py

fmi-basel/gzenke-nonlinear-transient-amplification

f3b0c8c89b42c34f1aad740c7026865cf3164f1d

[ "MIT" ]

null

src/Fig_2_supplement_5_Supralinear_network_with_initial_ISN.py

fmi-basel/gzenke-nonlinear-transient-amplification

f3b0c8c89b42c34f1aad740c7026865cf3164f1d

[ "MIT" ]

3

2021-12-16T10:15:10.000Z

2021-12-16T12:54:24.000Z

src/Fig_2_supplement_5_Supralinear_network_with_initial_ISN.py

fmi-basel/gzenke-nonlinear-transient-amplification

f3b0c8c89b42c34f1aad740c7026865cf3164f1d

[ "MIT" ]

1

2021-12-16T10:02:43.000Z

import numpy as np import seaborn as sns import matplotlib.pyplot as plt from matplotlib import patches import matplotlib.patches as mpatches import scipy.io as sio # plotting configuration ratio = 1.5 figure_len, figure_width = 15*ratio, 12*ratio font_size_1, font_size_2 = 36*ratio, 36*ratio legend_size = 18*ratio line_width, tick_len = 3*ratio, 10*ratio marker_size = 15*ratio plot_line_width = 5*ratio hfont = {'fontname': 'Arial'} b_plotting_activity = True b_plotting_paradoxical_effect = False b_plotting_frozen_inhibition = False if b_plotting_activity: # simulation setup dt = 0.0001 T = int(9/dt) l_g_e_ini = [1.55, 1.8] for g_e_ini in l_g_e_ini: Jacobian_mat = np.zeros((2, 2)) * np.nan # neuronal parameters tau_e, tau_i = 0.020, 0.010 alpha_e, alpha_i = 2, 2 # short-term depression x, u_d = 1, 1 tau_x = 0.20 # network connectivity Jee = 1.8 Jie = 1.0 Jei = 1.0 Jii = 0.6 r_e, r_i = 0, 0 z_e, z_i = 0, 0 l_r_e, l_r_i, l_x = [], [], [] l_ISN_index = [] for i in range(T): if 50000 <= i < 70000: g_e, g_i = 3.0, 2 else: g_e, g_i = g_e_ini, 2 g_e = g_e * (g_e > 0) g_i = g_i * (g_i > 0) # SSN part z_e = Jee * x * r_e - Jei * r_i + g_e z_i = Jie * r_e - Jii * r_i + g_i z_e = z_e * (z_e > 0) z_i = z_i * (z_i > 0) r_e = r_e + (-r_e + np.power(z_e, alpha_e)) / tau_e * dt r_i = r_i + (-r_i + np.power(z_i, alpha_i)) / tau_i * dt r_e = r_e * (r_e > 0) r_i = r_i * (r_i > 0) x = x + ((1 - x) / tau_x - u_d * x * r_e) * dt x = np.clip(x, 0, 1) Jacobian_mat[0, 0] = 1.0 / tau_e * (x * Jee * alpha_e * np.power(r_e, (alpha_e - 1.0) / alpha_e) - 1) Jacobian_mat[0, 1] = 1.0 / tau_e * Jee * alpha_e * np.power(r_e, (2 * alpha_e - 1.0) / alpha_e) Jacobian_mat[1, 0] = - u_d * x Jacobian_mat[1, 1] = -1.0 / tau_x - u_d * r_e lambda_1 = np.linalg.eig(Jacobian_mat)[0][0] lambda_2 = np.linalg.eig(Jacobian_mat)[0][1] l_ISN_index.append(np.max([lambda_1.real, lambda_2.real])) l_r_e.append(r_e) l_r_i.append(r_i) l_x.append(x) l_r_e = np.asarray(l_r_e) l_r_i = np.asarray(l_r_i) l_x = np.asarray(l_x) l_ISN_index = np.asarray(l_ISN_index) # plotting plt.figure(figsize=(figure_len, figure_width)) ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(True) ax.spines['left'].set_visible(True) for axis in ['top', 'bottom', 'left', 'right']: ax.spines[axis].set_linewidth(line_width) plt.tick_params(width=line_width, length=tick_len) plt.yscale('symlog', linthreshy=0.1) plt.plot(l_r_e, color='blue', linewidth=plot_line_width) plt.plot(l_r_i, color='red', linewidth=plot_line_width) plt.xticks(np.arange(30000, 90000 + 5000, 20000), np.arange(0, 6 + 0.5, 2), fontsize=font_size_1, **hfont) plt.yticks([0, 1, 100, 10000], fontsize=font_size_1, **hfont) plt.xlabel('Time (s)', fontsize=font_size_1, **hfont) plt.ylabel('Firing rate (Hz)', fontsize=font_size_1, **hfont) plt.xlim([30000, 90000]) plt.ylim([0, 10000]) plt.legend(['Exc', 'Inh'], prop={"family": "Arial", 'size': font_size_1}, loc='upper right') if g_e_ini == 1.55: plt.savefig('paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_initial_non_ISN.png') plt.savefig('paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_initial_non_ISN.pdf') else: plt.savefig('paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_initial_ISN.png') plt.savefig('paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_initial_ISN.pdf') # plotting plt.figure(figsize=(figure_len, figure_width)) ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(True) ax.spines['left'].set_visible(True) for axis in ['top', 'bottom', 'left', 'right']: ax.spines[axis].set_linewidth(line_width) plt.tick_params(width=line_width, length=tick_len) plt.plot(l_x, linewidth=plot_line_width) plt.plot(np.sqrt(1.0/(Jee * alpha_e) * np.power(l_r_e, 1.0/alpha_e -1)), '--', linewidth=plot_line_width) plt.xticks(np.arange(30000, 90000 + 5000, 20000), np.arange(0, 6 + 0.5, 2), fontsize=font_size_1, **hfont) plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0], fontsize=font_size_1, **hfont) plt.xlabel('Time (s)', fontsize=font_size_1, **hfont) plt.ylabel('x', fontsize=font_size_1, **hfont) plt.xlim([30000, 90000]) plt.ylim([0, 1]) if g_e_ini == 1.55: plt.savefig('paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_x_initial_non_ISN.png') plt.savefig('paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_x_initial_non_ISN.pdf') else: plt.savefig('paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_x_initial_ISN.png') plt.savefig('paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_x_initial_ISN.pdf') # plotting plt.figure(figsize=(figure_len, figure_width)) ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(True) ax.spines['left'].set_visible(True) for axis in ['top', 'bottom', 'left', 'right']: ax.spines[axis].set_linewidth(line_width) plt.tick_params(width=line_width, length=tick_len) plt.yscale('symlog', linthreshy=10) plt.plot(l_ISN_index, linewidth=plot_line_width) plt.xticks(np.arange(30000, 90000 + 5000, 20000), np.arange(0, 6 + 0.5, 2), fontsize=font_size_1, **hfont) plt.yticks([-10000, -100, 0, 100, 10000], fontsize=font_size_1, **hfont) plt.xlabel('Time (s)', fontsize=font_size_1, **hfont) plt.ylabel('ISN index', fontsize=font_size_1, **hfont) plt.xlim([30000, 90000]) plt.ylim([-10000, 10000]) plt.hlines(y=0, xmin=30000, xmax=90000, colors='k', linestyles=[(0, (6, 6, 6, 6))], linewidth=line_width) if g_e_ini == 1.55: plt.savefig('paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_ISN_index_initial_non_ISN.png') plt.savefig('paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_ISN_index_initial_non_ISN.pdf') else: plt.savefig('paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_ISN_index_initial_ISN.png') plt.savefig('paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_ISN_index_initial_ISN.pdf') if b_plotting_paradoxical_effect: # simulation setup dt = 0.0001 T = int(9 / dt) # neuronal parameters tau_e, tau_i = 0.020, 0.010 alpha_e, alpha_i = 2, 2 # short-term depression x, u_d = 1, 1 tau_x = 0.20 # network connectivity Jee = 1.8 Jie = 1.0 Jei = 1.0 Jii = 0.6 l_b_before_stimulation = [True, False] for b_before_stimulation in l_b_before_stimulation: x = 1 r_e, r_i = 0, 0 z_e, z_i = 0, 0 l_r_e, l_r_i, l_x = [], [], [] for i in range(T): if 50000 <= i < 70000: g_e, g_i = 3.0, 2 else: g_e, g_i = 1.8, 2 if b_before_stimulation: if 42000 < i <= 49000: g_i = 2.1 else: pass else: if 62000 < i <= 69000: g_i = 2.1 else: pass g_e = g_e * (g_e > 0) g_i = g_i * (g_i > 0) # SSN part z_e = Jee * x * r_e - Jei * r_i + g_e z_i = Jie * r_e - Jii * r_i + g_i z_e = z_e * (z_e > 0) z_i = z_i * (z_i > 0) r_e = r_e + (-r_e + np.power(z_e, alpha_e)) / tau_e * dt r_i = r_i + (-r_i + np.power(z_i, alpha_i)) / tau_i * dt r_e = r_e * (r_e > 0) r_i = r_i * (r_i > 0) x = x + ((1 - x) / tau_x - u_d * x * r_e) * dt x = np.clip(x, 0, 1) l_r_e.append(r_e) l_r_i.append(r_i) l_x.append(x) l_r_e = np.asarray(l_r_e) l_r_i = np.asarray(l_r_i) l_x = np.asarray(l_x) if b_before_stimulation: plt.figure(figsize=(figure_len, figure_width)) ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(True) ax.spines['left'].set_visible(True) for axis in ['top', 'bottom', 'left', 'right']: ax.spines[axis].set_linewidth(line_width) plt.tick_params(width=line_width, length=tick_len) mean_e = l_r_e / np.mean(l_r_e[40000:42000]) mean_i = l_r_i / np.mean(l_r_i[40000:42000]) plt.plot(mean_e, color='blue', linewidth=plot_line_width) plt.plot(mean_i, color='red', linewidth=plot_line_width) plt.xticks([40000, 42000, 44000, 46000, 48000], [1.0, 1.2, 1.4, 1.6, 1.8], fontsize=font_size_1, **hfont) plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2], fontsize=font_size_1, **hfont) plt.xlabel('Time (s)', fontsize=font_size_1, **hfont) plt.ylabel('Normalized firing rate', fontsize=font_size_1, **hfont) plt.xlim([40000, 48000]) plt.ylim([0, 1.2]) plt.legend(['Exc', 'Inh'], prop={"family": "Arial", 'size': font_size_1}) plt.hlines(y=1, xmin=42000, xmax=50000, colors='k', linestyles=[(0, (6, 6, 6, 6))], linewidth=line_width) plt.savefig( 'paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_paradoxical_effect_before_stimulation_ISN.png') plt.savefig( 'paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_paradoxical_effect_before_stimulation_ISN.pdf') else: plt.figure(figsize=(figure_len, figure_width)) ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(True) ax.spines['left'].set_visible(True) for axis in ['top', 'bottom', 'left', 'right']: ax.spines[axis].set_linewidth(line_width) plt.tick_params(width=line_width, length=tick_len) mean_e = l_r_e / np.mean(l_r_e[60000:62000]) mean_i = l_r_i / np.mean(l_r_i[60000:62000]) plt.plot(mean_e, color='blue', linewidth=plot_line_width) plt.plot(mean_i, color='red', linewidth=plot_line_width) plt.xticks([60000, 62000, 64000, 66000, 68000], [3.0, 3.2, 3.4, 3.6, 3.8], fontsize=font_size_1, **hfont) plt.yticks([0.85, 0.9, 0.95, 1.0, 1.05], fontsize=font_size_1, **hfont) plt.xlabel('Time (s)', fontsize=font_size_1, **hfont) plt.ylabel('Normalized firing rate', fontsize=font_size_1, **hfont) plt.xlim([60000, 68000]) plt.ylim([0.85, 1.05]) plt.legend(['Exc', 'Inh'], prop={"family": "Arial", 'size': font_size_1}) plt.hlines(y=1, xmin=62000, xmax=70000, colors='k', linestyles=[(0, (6, 6, 6, 6))], linewidth=line_width) plt.savefig( 'paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_paradoxical_effect_during_stimulation_ISN.png') plt.savefig( 'paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_paradoxical_effect_during_stimulation_ISN.pdf') if b_plotting_frozen_inhibition: # simulation setup dt = 0.0001 T = int(9/dt) # neuronal parameters tau_e, tau_i = 0.020, 0.010 alpha_e, alpha_i = 2, 2 # adaptation x, u_d = 1, 1 tau_x = 0.20 # network connectivity Jee = 1.8 Jie = 1.0 Jei = 1.0 Jii = 0.6 l_b_before_stimulation = [True, False] l_g_e_ini = [1.55, 1.8] for g_e_ini in l_g_e_ini: for b_before_stimulation in l_b_before_stimulation: x = 1 r_e, r_i = 0, 0 z_e, z_i = 0, 0 l_r_e, l_r_i, l_x = [], [], [] for i in range(T): if b_before_stimulation: g_e, g_i = g_e_ini, 2 else: if 50000 <= i: g_e, g_i = 3.0, 2 else: g_e, g_i = g_e_ini, 2 if b_before_stimulation: if 42000 <= i < 42001: r_e = r_e + 0.01 else: pass else: if 62000 <= i < 62001: r_e = r_e + 0.01 else: pass g_e = g_e * (g_e > 0) g_i = g_i * (g_i > 0) # SSN part z_e = Jee * x * r_e - Jei * r_i + g_e z_i = Jie * r_e - Jii * r_i + g_i z_e = z_e * (z_e > 0) z_i = z_i * (z_i > 0) r_e = r_e + (-r_e + np.power(z_e, alpha_e)) / tau_e * dt if b_before_stimulation: if 42000 < i: pass else: r_i = r_i + (-r_i + np.power(z_i, alpha_i)) / tau_i * dt else: if 62000 < i: pass else: r_i = r_i + (-r_i + np.power(z_i, alpha_i)) / tau_i * dt r_e = r_e * (r_e > 0) r_i = r_i * (r_i > 0) x = x + ((1 - x) / tau_x - u_d * x * r_e) * dt x = np.clip(x, 0, 1) l_r_e.append(r_e) l_r_i.append(r_i) l_x.append(x) l_r_e = np.asarray(l_r_e) l_r_i = np.asarray(l_r_i) l_x = np.asarray(l_x) if b_before_stimulation: plt.figure(figsize=(figure_len, figure_width)) ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(True) ax.spines['left'].set_visible(True) for axis in ['top', 'bottom', 'left', 'right']: ax.spines[axis].set_linewidth(line_width) plt.tick_params(width=line_width, length=tick_len) plt.yscale('symlog', linthreshy=0.1) plt.plot(l_r_e, color='blue', linewidth=plot_line_width) plt.plot(l_r_i, color='red', linewidth=plot_line_width) plt.xticks(np.arange(30000, 90000 + 5000, 20000), np.arange(0, 6 + 0.5, 2), fontsize=font_size_1, **hfont) plt.yticks([0, 1, 100, 10000], fontsize=font_size_1, **hfont) plt.xlabel('Time (s)', fontsize=font_size_1, **hfont) plt.ylabel('Firing rate (Hz)', fontsize=font_size_1, **hfont) plt.xlim([30000, 90000]) plt.ylim([0, 10000]) plt.legend(['Exc', 'Inh'], prop={"family": "Arial", 'size': font_size_1}, loc='upper right') plt.vlines(x=42001, ymin=0, ymax=10000, colors='k', linestyles=[(0, (6, 6, 6, 6))], linewidth=line_width) if g_e_ini == 1.55: plt.savefig( 'paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_frozen_inhibition_before_stimulation_non_ISN.png') plt.savefig( 'paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_frozen_inhibition_before_stimulation_non_ISN.pdf') else: plt.savefig( 'paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_frozen_inhibition_before_stimulation_ISN.png') plt.savefig( 'paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_frozen_inhibition_before_stimulation_ISN.pdf') else: plt.figure(figsize=(figure_len, figure_width)) ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(True) ax.spines['left'].set_visible(True) for axis in ['top', 'bottom', 'left', 'right']: ax.spines[axis].set_linewidth(line_width) plt.tick_params(width=line_width, length=tick_len) plt.yscale('symlog', linthreshy=0.1) plt.plot(l_r_e, color='blue', linewidth=plot_line_width) plt.plot(l_r_i, color='red', linewidth=plot_line_width) plt.xticks(np.arange(30000, 90000 + 5000, 20000), np.arange(0, 6 + 0.5, 2), fontsize=font_size_1, **hfont) plt.yticks([0, 1, 100, 10000], fontsize=font_size_1, **hfont) plt.xlabel('Time (s)', fontsize=font_size_1, **hfont) plt.ylabel('Firing rate (Hz)', fontsize=font_size_1, **hfont) plt.xlim([30000, 90000]) plt.ylim([0, 10000]) plt.legend(['Exc', 'Inh'], prop={"family": "Arial", 'size': font_size_1}, loc='upper right') plt.vlines(x=62001, ymin=0, ymax=10000, colors='k', linestyles=[(0, (6, 6, 6, 6))], linewidth=line_width) if g_e_ini == 1.55: plt.savefig( 'paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_frozen_inhibition_during_stimulation_non_ISN.png') plt.savefig( 'paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_frozen_inhibition_during_stimulation_non_ISN.pdf') else: plt.savefig( 'paper_figures/png/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_frozen_inhibition_during_stimulation_ISN.png') plt.savefig( 'paper_figures/pdf/Revision_Fig_Point_1_1_Supralinear_network_2D_EE_STP_normalized_activity_frozen_inhibition_during_stimulation_ISN.pdf')

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py

Python

lasagne/tests/layers/test_dense.py

Poberun/Lasagne31ac7d2bbc

d93eeeaf671377977144b7c8b978114e1cfb779a

[ "MIT" ]

60

2015-01-29T21:54:04.000Z

2019-11-12T07:38:15.000Z

lasagne/tests/layers/test_dense.py

Poberun/Lasagne31ac7d2bbc

d93eeeaf671377977144b7c8b978114e1cfb779a

[ "MIT" ]

5

2015-06-15T00:21:47.000Z

2017-09-14T10:24:40.000Z

lasagne/tests/layers/test_dense.py

Poberun/Lasagne31ac7d2bbc

d93eeeaf671377977144b7c8b978114e1cfb779a

[ "MIT" ]

20

2015-04-28T00:21:41.000Z

2019-09-16T01:10:37.000Z

from mock import Mock import numpy as np import pytest import theano import lasagne class TestDenseLayer: @pytest.fixture def DenseLayer(self): from lasagne.layers.dense import DenseLayer return DenseLayer @pytest.fixture def layer_vars(self, dummy_input_layer): from lasagne.layers.dense import DenseLayer W = Mock() b = Mock() nonlinearity = Mock() W.return_value = np.ones((12, 3)) b.return_value = np.ones((3,)) * 3 layer = DenseLayer( dummy_input_layer, num_units=3, W=W, b=b, nonlinearity=nonlinearity, ) return { 'W': W, 'b': b, 'nonlinearity': nonlinearity, 'layer': layer, } @pytest.fixture def layer(self, layer_vars): return layer_vars['layer'] def test_init(self, layer_vars): layer = layer_vars['layer'] assert (layer.W.get_value() == layer_vars['W'].return_value).all() assert (layer.b.get_value() == layer_vars['b'].return_value).all() layer_vars['W'].assert_called_with((12, 3)) layer_vars['b'].assert_called_with((3,)) def test_init_none_nonlinearity_bias(self, DenseLayer, dummy_input_layer): layer = DenseLayer( dummy_input_layer, num_units=3, nonlinearity=None, b=None, ) assert layer.nonlinearity == lasagne.nonlinearities.identity assert layer.b is None def test_get_params(self, layer): assert layer.get_params() == [layer.W, layer.b] assert layer.get_params(regularizable=False) == [layer.b] assert layer.get_params(regularizable=True) == [layer.W] assert layer.get_params(trainable=True) == [layer.W, layer.b] assert layer.get_params(trainable=False) == [] assert layer.get_params(_nonexistent_tag=True) == [] assert layer.get_params(_nonexistent_tag=False) == [layer.W, layer.b] def test_get_output_shape_for(self, layer): assert layer.get_output_shape_for((5, 6, 7)) == (5, 3) def test_get_output_for(self, layer_vars): layer = layer_vars['layer'] nonlinearity = layer_vars['nonlinearity'] W = layer_vars['W']() b = layer_vars['b']() input = theano.shared(np.ones((2, 12))) result = layer.get_output_for(input) assert result is nonlinearity.return_value # Check that the input to the nonlinearity was what we expect # from dense layer, i.e. the dot product plus bias nonlinearity_arg = nonlinearity.call_args[0][0] assert (nonlinearity_arg.eval() == np.dot(input.get_value(), W) + b).all() def test_get_output_for_flattens_input(self, layer_vars): layer = layer_vars['layer'] nonlinearity = layer_vars['nonlinearity'] W = layer_vars['W']() b = layer_vars['b']() input = theano.shared(np.ones((2, 3, 4))) result = layer.get_output_for(input) assert result is nonlinearity.return_value # Check that the input to the nonlinearity was what we expect # from dense layer, i.e. the dot product plus bias nonlinearity_arg = nonlinearity.call_args[0][0] assert np.allclose(nonlinearity_arg.eval(), np.dot(input.get_value().reshape(2, -1), W) + b) def test_param_names(self, layer): assert layer.W.name == "W" assert layer.b.name == "b" def test_named_layer_param_names(self, DenseLayer, dummy_input_layer): layer = DenseLayer( dummy_input_layer, num_units=3, name="foo" ) assert layer.W.name == "foo.W" assert layer.b.name == "foo.b" class TestNINLayer: @pytest.fixture def dummy_input_layer(self): from lasagne.layers.input import InputLayer input_layer = InputLayer((2, 3, 4, 5)) mock = Mock(input_layer) mock.shape = input_layer.shape mock.input_var = input_layer.input_var mock.output_shape = input_layer.output_shape return mock @pytest.fixture def NINLayer(self): from lasagne.layers.dense import NINLayer return NINLayer @pytest.fixture def layer_vars(self, NINLayer, dummy_input_layer): W = Mock() b = Mock() nonlinearity = Mock() W.return_value = np.ones((3, 5)) b.return_value = np.ones((5,)) layer = NINLayer( dummy_input_layer, num_units=5, W=W, b=b, nonlinearity=nonlinearity, ) return { 'W': W, 'b': b, 'nonlinearity': nonlinearity, 'layer': layer, } @pytest.fixture def layer(self, layer_vars): return layer_vars['layer'] def test_init(self, layer_vars): layer = layer_vars['layer'] assert (layer.W.get_value() == layer_vars['W'].return_value).all() assert (layer.b.get_value() == layer_vars['b'].return_value).all() layer_vars['W'].assert_called_with((3, 5)) layer_vars['b'].assert_called_with((5,)) def test_init_none_nonlinearity_bias(self, NINLayer, dummy_input_layer): layer = NINLayer( dummy_input_layer, num_units=3, nonlinearity=None, b=None, ) assert layer.nonlinearity == lasagne.nonlinearities.identity assert layer.b is None def test_init_untie_biases(self, NINLayer, dummy_input_layer): layer = NINLayer( dummy_input_layer, num_units=5, untie_biases=True, ) assert (layer.b.shape.eval() == (5, 4, 5)).all() def test_get_params(self, layer): assert layer.get_params() == [layer.W, layer.b] assert layer.get_params(regularizable=False) == [layer.b] assert layer.get_params(regularizable=True) == [layer.W] assert layer.get_params(trainable=True) == [layer.W, layer.b] assert layer.get_params(trainable=False) == [] assert layer.get_params(_nonexistent_tag=True) == [] assert layer.get_params(_nonexistent_tag=False) == [layer.W, layer.b] def test_get_output_shape_for(self, layer): assert layer.get_output_shape_for((5, 6, 7, 8)) == (5, 5, 7, 8) @pytest.mark.parametrize("extra_kwargs", [ {}, {'untie_biases': True}, {'b': None}, ]) def test_get_output_for(self, dummy_input_layer, extra_kwargs): from lasagne.layers.dense import NINLayer nonlinearity = Mock() layer = NINLayer( dummy_input_layer, num_units=6, nonlinearity=nonlinearity, **extra_kwargs ) input = theano.shared(np.random.uniform(-1, 1, (2, 3, 4, 5))) result = layer.get_output_for(input) assert result is nonlinearity.return_value nonlinearity_arg = nonlinearity.call_args[0][0] X = input.get_value() X = np.rollaxis(X, 1).T X = np.dot(X, layer.W.get_value()) if layer.b is not None: if layer.untie_biases: X += layer.b.get_value()[:, np.newaxis].T else: X += layer.b.get_value() X = np.rollaxis(X.T, 0, 2) assert np.allclose(nonlinearity_arg.eval(), X) def test_param_names(self, layer): assert layer.W.name == "W" assert layer.b.name == "b" def test_named_layer_param_names(self, NINLayer, dummy_input_layer): layer = NINLayer( dummy_input_layer, num_units=3, name="foo" ) assert layer.W.name == "foo.W" assert layer.b.name == "foo.b" class TestNINLayer_c01b: @pytest.fixture def dummy_input_layer(self): from lasagne.layers.input import InputLayer input_layer = InputLayer((3, 4, 5, 2)) mock = Mock(input_layer) mock.shape = input_layer.shape mock.input_var = input_layer.input_var mock.output_shape = input_layer.output_shape return mock @pytest.fixture def NINLayer_c01b(self): try: from lasagne.layers.cuda_convnet import NINLayer_c01b except ImportError: pytest.skip("cuda_convnet not available") return NINLayer_c01b @pytest.fixture def layer_vars(self, NINLayer_c01b, dummy_input_layer): W = Mock() b = Mock() nonlinearity = Mock() W.return_value = np.ones((5, 3)) b.return_value = np.ones((5,)) layer = NINLayer_c01b( dummy_input_layer, num_units=5, W=W, b=b, nonlinearity=nonlinearity, ) return { 'W': W, 'b': b, 'nonlinearity': nonlinearity, 'layer': layer, } @pytest.fixture def layer(self, layer_vars): return layer_vars['layer'] def test_init(self, layer_vars): layer = layer_vars['layer'] assert (layer.W.get_value() == layer_vars['W'].return_value).all() assert (layer.b.get_value() == layer_vars['b'].return_value).all() layer_vars['W'].assert_called_with((5, 3)) layer_vars['b'].assert_called_with((5,)) def test_init_none_nonlinearity_bias(self, NINLayer_c01b, dummy_input_layer): layer = NINLayer_c01b( dummy_input_layer, num_units=3, nonlinearity=None, b=None, ) assert layer.nonlinearity == lasagne.nonlinearities.identity assert layer.b is None def test_init_untie_biases(self, NINLayer_c01b, dummy_input_layer): layer = NINLayer_c01b( dummy_input_layer, num_units=5, untie_biases=True, ) assert (layer.b.shape.eval() == (5, 4, 5)).all() def test_get_params(self, layer): assert layer.get_params() == [layer.W, layer.b] assert layer.get_params(regularizable=False) == [layer.b] assert layer.get_params(regularizable=True) == [layer.W] assert layer.get_params(trainable=True) == [layer.W, layer.b] assert layer.get_params(trainable=False) == [] assert layer.get_params(_nonexistent_tag=True) == [] assert layer.get_params(_nonexistent_tag=False) == [layer.W, layer.b] def test_get_output_shape_for(self, layer): assert layer.get_output_shape_for((6, 7, 8, 5)) == (5, 7, 8, 5) @pytest.mark.parametrize("extra_kwargs", [ {}, {'untie_biases': True}, {'b': None}, ]) def test_get_output_for(self, dummy_input_layer, NINLayer_c01b, extra_kwargs): nonlinearity = Mock() layer = NINLayer_c01b( dummy_input_layer, num_units=6, nonlinearity=nonlinearity, **extra_kwargs ) input = theano.shared(np.random.uniform(-1, 1, (3, 4, 5, 2))) result = layer.get_output_for(input) assert result is nonlinearity.return_value nonlinearity_arg = nonlinearity.call_args[0][0] X = input.get_value() W = layer.W.get_value() out = np.dot(W, X.reshape(X.shape[0], -1)) out = out.reshape(W.shape[0], X.shape[1], X.shape[2], X.shape[3]) if layer.b is not None: if layer.untie_biases: out += layer.b.get_value()[..., None] else: out += layer.b.get_value()[:, None, None, None] assert np.allclose(nonlinearity_arg.eval(), out)

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Python

config.py

AlexanderFengler/nn_likelihoods

2d0f63a63eb50f026b9492acba14708b23dfcaa4

[ "MIT" ]

2

2019-08-19T15:48:01.000Z

2020-03-13T12:47:23.000Z

config.py

AlexanderFengler/nn_likelihoods

2d0f63a63eb50f026b9492acba14708b23dfcaa4

[ "MIT" ]

null

config.py

AlexanderFengler/nn_likelihoods

2d0f63a63eb50f026b9492acba14708b23dfcaa4

[ "MIT" ]

6

2019-06-13T04:46:51.000Z

2021-01-27T18:26:59.000Z

import numpy as np import yaml import pickle import cddm_data_simulation as cd #import clba import boundary_functions as bf import os # "output_folder": "/users/afengler/data/kde/test/method_comparison/", # "model_folder": "/users/afengler/data/kde/test/keras_models/", # #"custom_objects": {"huber_loss": tf.losses.huber_loss}, # #"fcn_custom_objects": {"heteroscedastic_loss": tf.losses.huber_loss}, config = dict() config['model_paths'] = yaml.load(open("model_paths_simple.yaml")) # Add machine basic folders here config['base_data_folder'] = {'af_x7': '/media/data_cifs/projects/...', 'af_ccv': '/users/afengler/data/proj_nn_likelihoods/', 'af_home': '/Users/afengler/OneDrive/project_nn_likelihoods/data/nn_likelihoods/', 'af_home_test': '/Users/afengler/OneDrive/project_nn_likelihoods/data/tests/', 'kle_ccv': '', 'kle_home': '', 'default': ''} # Network params config['mlp_hyperparameters'] = {'hidden_layers': [100, 100, 120, 1], 'hidden_activations': ["tanh", "tanh", "tanh", "linear"], 'filters': [128, 128, 128, 128], 'batch_size': 100000, 'n_epochs': 100, # CHANGE AGAINs 'learning_rate': .002, # I think was originally 0.0002 'momentum': .7, 'model_type': "dnnregressor", 'optimizer': "adam", 'log': True, 'loss': "huber", 'gpu_x7': '2'} config['mlp_simulation_filters'] = {'mode': 20, # != (if mode is max_rt) 'choice_cnt': 10, # > (each choice receive at least 10 samples in simulator) 'mean_rt': 15, # < (mean_rt is smaller than specified value 'std': 0, # > (std is positive for each choice) 'mode_cnt_rel': 0.5 # < (mode does not receive more than a proportion of samples for each choice) } # Globally applied hyperparamters across all simulators config['dgp_hyperparameters_global'] = {'max_t': 20, 'binned_max_t': 10, 's': 1.0, 'delta_t': 0.001, 'n_samples': 100000} config['model_data'] = { "test":{ "dgp": cd.test, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix":'models/test/', "param_names": ['v', 'a', 'w', 'ndt'], "boundary_param_names": [], "param_bounds_network": [[-2.0, 2.0], [0.3, 2], [0.2, 0.8], [0.0, 2.0]], "param_bounds_sampler": [[-1.9, 1.9], [0.6, 1.4], [0.31, 0.69], [0.1, 0.9]], "param_bounds_cnn": [[-2.5, 2.5], [0.2, 2], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn":[], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]) }, "levy":{ "dgp": cd.levy_flexbound, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix":'models/levy', "param_names": ['v', 'a', 'w', 'alpha-diff', 'ndt'], "boundary_param_names": [], "param_bounds_network": [[-3.0, 3.0], [0.3, 2.0], [0.1, 0.9], [1.0, 2.0], [0.0, 2.0]], "param_bounds_sampler": [[-2.7, 2.7], [0.4, 1.7], [0.3, 0.7], [1.1, 1.9], [0.1, 1.9]], "param_bounds_cnn": [[-3, 3], [0.3, 2], [0.1, 0.9], [1.0, 2.0], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn":[], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]) }, "ddm":{ "dgp": cd.ddm_flexbound, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": 'models/ddm/', "param_names": ['v', 'a', 'w', 'ndt'], "boundary_param_names": [], "param_bounds_network": [[-3.0, 3.0], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0]], "param_bounds_sampler": [[-2.5, 2.5], [0.5, 2.2], [0.25, 0.75], [0.05, 1.95]], "param_bounds_cnn": [[-3.0, 3.0], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn":[], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "ddm_elife":{ "dgp": cd.ddm_flexbound, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": 'models/ddm_elife', "param_names": ['v', 'a', 'w', 'ndt'], "boundary_param_names": [], "param_bounds_network": [[-3.0, 3.0], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0]], "param_bounds_sampler": [[-2.5, 2.5], [0.5, 2.2], [0.25, 0.75], [0.05, 1.95]], "param_bounds_cnn": [[-3.0, 3.0], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn":[], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "ddm_analytic":{ "dgp": cd.ddm_flexbound, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": 'models/ddm_analytic/', "param_names": ['v', 'a', 'w', 'ndt'], "boundary_param_names": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_network": [], "boundary_param_bounds_cnn": [], "param_bounds_network": [[-3.0, 3.0], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0]], "param_bounds_sampler": [[-2.5, 2.5], [0.5, 2.2], [0.25, 0.75], [0.05, 1.95]], "param_bounds_cnn": [[-3.0, 3.0], [0.2, 2.2], [0.1, 0.9], [0.0, 2.0]], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.001], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "ddm_analytic_elife":{ "dgp": cd.ddm_flexbound, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": 'models/ddm_analytic_elife/', "param_names": ['v', 'a', 'w', 'ndt'], "boundary_param_names": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_network": [], "boundary_param_bounds_cnn": [], "param_bounds_network": [[-3.0, 3.0], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0]], "param_bounds_sampler": [[-2.5, 2.5], [0.5, 2.2], [0.25, 0.75], [0.05, 1.95]], "param_bounds_cnn": [[-3.0, 3.0], [0.2, 2.2], [0.1, 0.9], [0.0, 2.0]], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.001], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "ddm_sdv_analytic":{ "dgp": cd.ddm_sdv, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": 'models/ddm_sdv_analytic/', "param_names": ["v", "a", "w", "ndt", "sdv"], "boundary_param_names": [], "param_bounds_network": [[-3, 3], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0], [0.0, 2.5]], "param_bounds_sampler": [[-2.2, 2.2], [0.5, 2.2], [0.25, 0.75], [0.05, 1.95], [0.3, 2.2]], "param_bounds_cnn": [[-3, 3], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0], [0.0, 2.5]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn": [], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.001], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "ddm_sdv_analytic_elife":{ "dgp": cd.ddm_sdv, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": 'models/ddm_sdv_analytic_elife/', "param_names": ["v", "a", "w", "ndt", "sdv"], "boundary_param_names": [], "param_bounds_network": [[-3, 3], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0], [0.0, 2.5]], "param_bounds_sampler": [[-2.2, 2.2], [0.5, 2.2], [0.25, 0.75], [0.05, 1.95], [0.3, 2.2]], "param_bounds_cnn": [[-3, 3], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0], [0.0, 2.5]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn": [], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.001], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "ddm_sdv":{ "dgp": cd.ddm_sdv, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": "models/ddm_sdv/", "param_names": ["v", "a", "w", "ndt", "sdv"], "boundary_param_names": [], "param_bounds_network": [[- 2.5, 2.5], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0], [0.0, 2.5]], "param_bounds_sampler": [[- 2.2, 2.2], [0.5, 2.2], [0.25, 0.75], [0.05, 1.95], [0.3, 2.2]], "param_bounds_cnn": [[- 3, 3], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0], [0.0, 2.5]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn": [], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.001], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "ddm_sdv_elife":{ "dgp": cd.ddm_sdv, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": "models/ddm_sdv_elife/", "param_names": ["v", "a", "w", "ndt", "sdv"], "boundary_param_names": [], "param_bounds_network": [[-2.5, 2.5], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0], [0.0, 2.5]], "param_bounds_sampler": [[-2.2, 2.2], [0.5, 2.2], [0.25, 0.75], [0.05, 1.95], [0.3, 2.2]], "param_bounds_cnn": [[-3, 3], [0.3, 2.5], [0.1, 0.9], [0.0, 2.0], [0.0, 2.5]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn": [], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.001], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "angle":{ "dgp": cd.ddm_flexbound, "boundary": bf.angle, "boundary_multiplicative": False, "folder_suffix": "models/angle/", "param_names": ["v", "a", "w", "ndt"], "boundary_param_names": ["theta"], "param_bounds_network": [[-2.0, 2.0], [0.3, 2], [0.2, 0.8], [0.0, 2.0]], "param_bounds_sampler": [[-1.9, 1.9], [0.4, 1.9], [0.25, 0.75], [0.1, 1.9]], "param_bounds_cnn": [[-2.5, 2.5], [0.2, 2.0], [0.1, 0.9], [0.0, 2.0]], 'boundary_param_bounds_network':[[0, (np.pi / 2 - .1)]], "boundary_param_bounds_sampler": [[0.05, np.pi / 2 - .3]], "boundary_param_bounds_cnn": [[0, (np.pi / 2 - .2)]], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.angle], ['boundary_multiplicative', False], ['possible_choices', [-1, 1]]]), }, "angle2":{ "dgp": cd.ddm_flexbound, "boundary": bf.angle, "boundary_multiplicative": False, "folder_suffix": "models/angle2/", "param_names": ["v", "a", "w", "ndt"], "boundary_param_names": ["theta"], "param_bounds_network": [[-3.0, 3.0], [0.3, 2], [0.2, 0.8], [0.0, 2.0]], "param_bounds_sampler": [[-2.7, 2.7], [0.4, 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[0.0, 2.0]], "boundary_param_bounds_network": [[0.3, 5.0], [0.3, 7.0]], "boundary_param_bounds_sampler": [[0.5, 4.5], [0.5, 6.5]], "boundary_param_bounds_cnn": [[0.3, 5.0], [0.3, 7.0]], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.weibull_cdf], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "weibull_cdf_ext":{ "dgp": cd.ddm_flexbound, "boundary": bf.weibull_cdf, "boundary_multiplicative": True, "folder_suffix": "models/weibull_cdf_ext/", "param_names": ["v", "a", "w", "ndt"], "boundary_param_names": ["alpha", "beta"], "param_bounds_network": [[-2.5, 2.5], [0.3, 2.5], [0.2, 0.8], [0.0, 2.0]], "param_bounds_sampler": [[-2.2, 2.2], [0.5, 2.2], [0.3, 0.7], [0.1, 1.9]], "param_bounds_cnn": [[-2.5, 2.5], [0.3, 2.5], [0.2, 0.8], [0.0, 2.0]], 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"boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn": [], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [-1, 1]]]), }, "race_3":{ "dgp": cd.race_model, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": "models/race_3/", "param_names": ["v_0", "v_1", "v_2", "a", "w_0", "w_1", "w_2", "ndt"], "boundary_param_names": [], "param_bounds_network":[[0, 2.0], [0, 2.0], [0, 2.0], [1.0, 3.0], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.0, 1.0]], "param_bounds_sampler": [[0.1, 1.9], [0.1, 1.9], [0.1, 1.9], [1.1, 2.9], [0.21, 0.79], [0.21, 0.79], [0.21, 0.79], [0.1, 0.9]], "param_bounds_cnn": [[0.0, 2.5], [0.0, 2.5], [0.0, 2.5], [1.0, 3.0], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn": [], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [0, 1, 2]]]), }, "race_4":{ "dgp": cd.race_model, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": "models/race_4/", "param_names": ["v_0", "v_1", "v_2", "v_3", "a", "w_0", "w_1", "w_2", "w_3", "ndt"], "param_depends_on_n_choice": [1, 0, 1, 0], "boundary_param_names": [], "param_bounds_network":[[0, 2.0], [0, 2.0], [0, 2.0], [0, 2.0], [1.0, 3.0], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.0, 1.0]], "param_bounds_sampler": [[0.1, 1.9], [0.1, 1.9], [0.1, 1.9], [0.1, 1.9], [1.1, 2.9], [0.21, 0.79], [0.21, 0.79], [0.21, 0.79], [0.21, 0.79],[0.1, 0.9]], "param_bounds_cnn": [[0.0, 2.5], [0.0, 2.5], [0.0, 2.5], [0.0, 2.5], [1.0, 3.0], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn": [], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [0, 1, 2, 3]]]), }, "lca_3":{ "dgp": cd.lca, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": "models/lca_3/", "param_names": ['v_0', 'v_1', 'v_2', 'a', 'w_0', 'w_1', 'w_2', 'g', 'b', 'ndt'], "param_depends_on_n_choice": [1, 0, 1, 0, 0, 0], "boundary_param_names": [], "param_bounds_network": [[0, 2.0], [0, 2.0], [0, 2.0], [1.0, 3.0], [0.2, 0.8], [0.2, 0.8], [0.2, 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"param_depends_on_n_choice": [1, 0, 1, 0, 0, 0], "boundary_param_names": [], "param_bounds_network": [[0, 2.0], [0, 2.0], [0, 2.0], [0, 2.0], [1.0, 3.0], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [-1.0, 1.0], [-1.0, 1.0], [0.0, 1.0]], "param_bounds_sampler": [[0, 2.0], [0, 2.0], [0, 2.0], [0, 2.0], [1.0, 3.0], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [-0.9, 0.9], [-0.9, 0.9], [0.05, 0.95]], "param_bounds_cnn": [[0, 2.5], [0, 2.5], [0, 2.5], [0, 2.5], [1.0, 3.0], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [-1.0, 1.0], [-1.0, 1.0], [0.0, 2.0]], "boundary_param_bounds_cnn": [], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [0, 1, 2, 3]]]), }, "ddm_seq2":{ "dgp": cd.ddm_flexbound_seq2, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": "models/ddm_seq2/", "param_names": ['v_h', 'v_l_1', 'v_l_2', 'a', 'w_h', 'w_l_1', 'w_l_2', 'ndt'], "param_depends_on_n_choice": [0, 0, 0, 0, 0, 0, 0, 0], "boundary_param_names": [], "param_bounds_network": [[-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [0.3, 2], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8],[0.0, 2.0]], "param_bounds_sampler": [[-1.9, 1.9], [0.6, 1.4], [0.31, 0.69], [0.1, 0.9]], "param_bounds_cnn": [[-2.5, 2.5], [-2.5, 2.5], [-2.5, 2.5], [0.2, 2], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn":[], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [0, 1, 2, 3]]]), }, "ddm_seq2_angle":{ "dgp": cd.ddm_flexbound_seq2, "boundary": bf.angle, "boundary_multiplicative": False, "folder_suffix": "models/ddm_seq2_angle/", "param_names": ['v_h', 'v_l_1', 'v_l_2', 'a', 'w_h', 'w_l_1', 'w_l_2', 'ndt'], "param_depends_on_n_choice": [0, 0, 0, 0, 0, 0, 0, 0], "boundary_param_names": ['theta'], "param_bounds_network": [[-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [0.3, 2], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8],[0.0, 2.0]], "param_bounds_sampler": [[-1.9, 1.9], [0.6, 1.4], [0.31, 0.69], [0.1, 0.9]], "param_bounds_cnn": [[-2.5, 2.5], [-2.5, 2.5], [-2.5, 2.5], [0.2, 2], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network":[[0, (np.pi / 2 - .2)]], "boundary_param_bounds_sampler": [[0.05, np.pi / 2 - .3]], "boundary_param_bounds_cnn": [[0, (np.pi / 2 - .2)]], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.angle], ['boundary_multiplicative', False], ['possible_choices', [0, 1, 2, 3]]]), }, "ddm_par2":{ "dgp": cd.ddm_flexbound_par2, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": "models/ddm_par2/", "param_names": ['v_h', 'v_l_1', 'v_l_2', 'a', 'w_h', 'w_l_1', 'w_l_2', 'ndt'], "param_depends_on_n_choice": [0, 0, 0, 0, 0, 0, 0, 0], "boundary_param_names": [], "param_bounds_network": [[-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [0.3, 2], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8],[0.0, 2.0]], "param_bounds_sampler": [[-1.9, 1.9], [0.6, 1.4], [0.31, 0.69], [0.1, 0.9]], "param_bounds_cnn": [[-2.5, 2.5], [-2.5, 2.5], [-2.5, 2.5], [0.2, 2], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn":[], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [0, 1, 2, 3]]]), }, "ddm_par2_angle":{ "dgp": cd.ddm_flexbound_par2, "boundary": bf.angle, "boundary_multiplicative": False, "folder_suffix": "models/ddm_par2_angle/", "param_names": ['v_h', 'v_l_1', 'v_l_2', 'a', 'w_h', 'w_l_1', 'w_l_2', 'ndt'], "param_depends_on_n_choice": [0, 0, 0, 0, 0, 0, 0, 0], "boundary_param_names": ['theta'], "param_bounds_network": [[-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [0.3, 2], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8],[0.0, 2.0]], "param_bounds_sampler": [[-1.9, 1.9], [0.6, 1.4], [0.31, 0.69], [0.1, 0.9]], "param_bounds_cnn": [[-2.5, 2.5], [-2.5, 2.5], [-2.5, 2.5], [0.2, 2], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.0, 2.0]], "boundary_param_bounds_network":[[0, (np.pi / 2 - .2)]], "boundary_param_bounds_sampler": [[0.05, np.pi / 2 - .3]], "boundary_param_bounds_cnn": [[0, (np.pi / 2 - .2)]], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.angle], ['boundary_multiplicative', False], ['possible_choices', [0, 1, 2, 3]]]), }, "ddm_mic2":{ "dgp": cd.ddm_flexbound_mic2, "boundary": bf.constant, "boundary_multiplicative": True, "folder_suffix": "models/ddm_mic2/", "param_names": ['v_h', 'v_l_1', 'v_l_2', 'a', 'w_h', 'w_l_1', 'w_l_2', 'd' ,'ndt'], "param_depends_on_n_choice": [0, 0, 0, 0, 0, 0, 0, 0, 0], "boundary_param_names": [], "param_bounds_network": [[-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [0.3, 2], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.0, 1.0], [0.0, 2.0]], "param_bounds_sampler": [[-1.9, 1.9], [0.6, 1.4], [0.31, 0.69], [0.1, 0.9]], "param_bounds_cnn": [[-2.5, 2.5], [-2.5, 2.5], [-2.5, 2.5], [0.2, 2], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.0, 1.0], [0.0, 2.0]], "boundary_param_bounds_network": [], "boundary_param_bounds_sampler": [], "boundary_param_bounds_cnn":[], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.01], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.constant], ['boundary_multiplicative', True], ['possible_choices', [0, 1, 2, 3]]]), }, "ddm_mic2_angle":{ "dgp": cd.ddm_flexbound_mic2, "boundary": bf.angle, "boundary_multiplicative": False, "folder_suffix": "models/ddm_mic2_angle/", "param_names": ['v_h', 'v_l_1', 'v_l_2', 'a', 'w_h', 'w_l_1', 'w_l_2', 'd' ,'ndt'], "param_depends_on_n_choice": [0, 0, 0, 0, 0, 0, 0, 0, 0], "boundary_param_names": ['theta'], "param_bounds_network": [[-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [0.3, 2], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.0, 1.0], [0.0, 2.0]], "param_bounds_sampler": [[-1.9, 1.9], [0.6, 1.4], [0.31, 0.69], [0.1, 0.9]], "param_bounds_cnn": [[-2.5, 2.5], [-2.5, 2.5], [-2.5, 2.5], [0.2, 2], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.0, 1.0], [0.0, 2.0]], "boundary_param_bounds_network":[[0, (np.pi / 2 - .2)]], "boundary_param_bounds_sampler": [[0.05, np.pi / 2 - .3]], "boundary_param_bounds_cnn": [[0, (np.pi / 2 - .2)]], "dgp_hyperparameters": dict([['s', 1.0], ['delta_t', 0.001], ['max_t', config['dgp_hyperparameters_global']['max_t']], ['binned_max_t', config['dgp_hyperparameters_global']['binned_max_t']], ['n_samples', 20000], ['print_info', False], ['boundary', bf.angle], ['boundary_multiplicative', False], ['possible_choices', [0, 1, 2, 3]]]), }, }

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Python

rdoasis/algorithms/tasks/__init__.py

ResonantGeoData/RD-OASIS

6423aca34e5f4757279479b531241174e4cf98af

[ "Apache-2.0" ]

2

2022-01-28T02:45:55.000Z

2022-02-08T22:09:29.000Z

rdoasis/algorithms/tasks/__init__.py

ResonantGeoData/RD-OASIS

6423aca34e5f4757279479b531241174e4cf98af

[ "Apache-2.0" ]

31

2021-07-05T17:25:14.000Z

2022-03-29T14:36:07.000Z

rdoasis/algorithms/tasks/__init__.py

ResonantGeoData/RD-OASIS

6423aca34e5f4757279479b531241174e4cf98af

[ "Apache-2.0" ]

null

from .docker import run_algorithm_task_docker # noqa from .kubernetes import run_algorithm_task_k8s # noqa

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Python

pyfiction/agents/agent.py

FPreta/pyfiction

a8af76c6badb11aa442122b1f2c4fbda1cf2ac53

[ "MIT" ]

32

2016-05-28T06:12:38.000Z

2021-09-03T23:10:18.000Z

pyfiction/agents/agent.py

KailashDN/pyfiction

dc126d48578c53a3d2f95723c94da0afdd3282d0

[ "MIT" ]

4

2019-12-16T20:18:25.000Z

2022-03-01T11:23:10.000Z

pyfiction/agents/agent.py

KailashDN/pyfiction

dc126d48578c53a3d2f95723c94da0afdd3282d0

[ "MIT" ]

13

2017-08-15T13:14:00.000Z

2022-03-01T01:42:37.000Z

class Agent(object): def __init__(self): raise NotImplementedError("Agent is an abstract class.") def act(self, **kwargs): raise NotImplementedError("Agent is an abstract class.")

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Python

src/lib/plistlib.py

DTenore/skulpt

098d20acfb088d6db85535132c324b7ac2f2d212

[ "MIT" ]

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2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

src/lib/plistlib.py

wakeupmuyunhe/skulpt

a8fb11a80fb6d7c016bab5dfe3712517a350b347

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

src/lib/plistlib.py

wakeupmuyunhe/skulpt

a8fb11a80fb6d7c016bab5dfe3712517a350b347

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

import _sk_fail; _sk_fail._("plistlib")

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py

Python

model.py

Gonxolo/tarea3Sim

340e4b13e325b0b87fefd8fa323ed52ca5382c09

[ "MIT" ]

null

model.py

Gonxolo/tarea3Sim

340e4b13e325b0b87fefd8fa323ed52ca5382c09

[ "MIT" ]

null

model.py

Gonxolo/tarea3Sim

340e4b13e325b0b87fefd8fa323ed52ca5382c09

[ "MIT" ]

null

""" Model """ import numpy as np from numpy.random import rand import lib.basic_shapes as bs import lib.local_shapes as loc_s def generateSun(nTheta, nPhi): vertices = [] indices = [] theta_angs = np.linspace(0, np.pi, nTheta, endpoint=True) phi_angs = np.linspace(0, 2 * np.pi, nPhi, endpoint=True) start_index = 0 for theta_ind in range(len(theta_angs)-1): # vertical cos_theta = np.cos(theta_angs[theta_ind]) # z_top cos_theta_next = np.cos(theta_angs[theta_ind + 1]) # z_bottom sin_theta = np.sin(theta_angs[theta_ind]) sin_theta_next = np.sin(theta_angs[theta_ind + 1]) # d === c <---- z_top # | | # | | # a === b <--- z_bottom # ^ ^ # phi phi + dphi for phi_ind in range(len(phi_angs)-1): # horizontal cos_phi = np.cos(phi_angs[phi_ind]) cos_phi_next = np.cos(phi_angs[phi_ind + 1]) sin_phi = np.sin(phi_angs[phi_ind]) sin_phi_next = np.sin(phi_angs[phi_ind + 1]) # we will asume radius = 1, so scaling should be enough. # x = cosφ sinθ # y = sinφ sinθ # z = cosθ # X Y Z a = np.array([cos_phi * sin_theta_next, sin_phi * sin_theta_next , cos_theta_next]) b = np.array([cos_phi_next * sin_theta_next, sin_phi_next * sin_theta_next, cos_theta_next]) c = np.array([cos_phi_next * sin_theta , sin_phi_next * sin_theta , cos_theta]) d = np.array([cos_phi * sin_theta , sin_phi * sin_theta , cos_theta]) _vertex, _indices = loc_s.createColorQuadIndexation( start_index, a, b, c, d, color=[rand(), rand(), rand()] ) vertices += _vertex indices += _indices start_index += 4 return bs.Shape(vertices, indices) def generateSunNormals(nTheta, nPhi): vertices = [] indices = [] theta_angs = np.linspace(0, np.pi, nTheta, endpoint=True) phi_angs = np.linspace(0, 2 * np.pi, nPhi, endpoint=True) start_index = 0 for theta_ind in range(len(theta_angs)-1): # vertical cos_theta = np.cos(theta_angs[theta_ind]) # z_top cos_theta_next = np.cos(theta_angs[theta_ind + 1]) # z_bottom sin_theta = np.sin(theta_angs[theta_ind]) sin_theta_next = np.sin(theta_angs[theta_ind + 1]) # d === c <---- z_top # | | # | | # a === b <--- z_bottom # ^ ^ # phi phi + dphi for phi_ind in range(len(phi_angs)-1): # horizontal cos_phi = np.cos(phi_angs[phi_ind]) cos_phi_next = np.cos(phi_angs[phi_ind + 1]) sin_phi = np.sin(phi_angs[phi_ind]) sin_phi_next = np.sin(phi_angs[phi_ind + 1]) # we will asume radius = 1, so scaling should be enough. # x = cosφ sinθ # y = sinφ sinθ # z = cosθ # X Y Z a = np.array([cos_phi * sin_theta_next, sin_phi * sin_theta_next , cos_theta_next]) b = np.array([cos_phi_next * sin_theta_next, sin_phi_next * sin_theta_next, cos_theta_next]) c = np.array([cos_phi_next * sin_theta , sin_phi_next * sin_theta , cos_theta]) d = np.array([cos_phi * sin_theta , sin_phi * sin_theta , cos_theta]) _vertex, _indices = loc_s.createColorQuadIndexation( start_index, a, b, c, d, color=[rand(), rand(), rand()] ) vertices += _vertex indices += _indices start_index += 4 return bs.Shape(vertices, indices) def generateSphereShapeNormals(nTheta, nPhi): vertices = [] indices = [] theta_angs = np.linspace(0, np.pi, nTheta, endpoint=True) phi_angs = np.linspace(0, 2 * np.pi, nPhi, endpoint=True) start_index = 0 for theta_ind in range(len(theta_angs)-1): # vertical cos_theta = np.cos(theta_angs[theta_ind]) # z_top cos_theta_next = np.cos(theta_angs[theta_ind + 1]) # z_bottom sin_theta = np.sin(theta_angs[theta_ind]) sin_theta_next = np.sin(theta_angs[theta_ind + 1]) # d === c <---- z_top # | | # | | # a === b <--- z_bottom # ^ ^ # phi phi + dphi for phi_ind in range(len(phi_angs)-1): # horizontal cos_phi = np.cos(phi_angs[phi_ind]) cos_phi_next = np.cos(phi_angs[phi_ind + 1]) sin_phi = np.sin(phi_angs[phi_ind]) sin_phi_next = np.sin(phi_angs[phi_ind + 1]) # we will asume radius = 1, so scaling should be enough. # x = cosφ sinθ # y = sinφ sinθ # z = cosθ # X Y Z a = np.array([cos_phi * sin_theta_next, sin_phi * sin_theta_next , cos_theta_next]) b = np.array([cos_phi_next * sin_theta_next, sin_phi_next * sin_theta_next, cos_theta_next]) c = np.array([cos_phi_next * sin_theta , sin_phi_next * sin_theta , cos_theta]) d = np.array([cos_phi * sin_theta , sin_phi * sin_theta , cos_theta]) a_n = 2*np.array([cos_phi * sin_theta_next, sin_phi * sin_theta_next , cos_theta_next]) b_n = 2*np.array([cos_phi_next * sin_theta_next, sin_phi_next * sin_theta_next, cos_theta_next]) c_n = 2*np.array([cos_phi_next * sin_theta , sin_phi_next * sin_theta , cos_theta]) d_n = 2*np.array([cos_phi * sin_theta , sin_phi * sin_theta , cos_theta]) mu = 0.5 sigma = 0.1 color = np.random.normal(mu, sigma, 3) _vertex, _indices = loc_s.createColorSpecificNormals(start_index, a, b, c, d, a_n, b_n, c_n, d_n, color=color) vertices += _vertex indices += _indices start_index += 4 return bs.Shape(vertices, indices) def generateHumanNormals(nTheta, nPhi, color=(1.0,1.0,1.0)): vertices = [] indices = [] theta_angs = np.linspace(0, np.pi, nTheta, endpoint=True) phi_angs = np.linspace(0, 2 * np.pi, nPhi, endpoint=True) start_index = 0 for theta_ind in range(len(theta_angs)-1): # vertical cos_theta = np.cos(theta_angs[theta_ind]) # z_top cos_theta_next = np.cos(theta_angs[theta_ind + 1]) # z_bottom sin_theta = np.sin(theta_angs[theta_ind]) sin_theta_next = np.sin(theta_angs[theta_ind + 1]) # d === c <---- z_top # | | # | | # a === b <--- z_bottom # ^ ^ # phi phi + dphi for phi_ind in range(len(phi_angs)-1): # horizontal cos_phi = np.cos(phi_angs[phi_ind]) cos_phi_next = np.cos(phi_angs[phi_ind + 1]) sin_phi = np.sin(phi_angs[phi_ind]) sin_phi_next = np.sin(phi_angs[phi_ind + 1]) # we will asume radius = 1, so scaling should be enough. # x = cosφ sinθ # y = sinφ sinθ # z = cosθ # X Y Z a = np.array([cos_phi * sin_theta_next, sin_phi * sin_theta_next , cos_theta_next]) b = np.array([cos_phi_next * sin_theta_next, sin_phi_next * sin_theta_next, cos_theta_next]) c = np.array([cos_phi_next * sin_theta , sin_phi_next * sin_theta , cos_theta]) d = np.array([cos_phi * sin_theta , sin_phi * sin_theta , cos_theta]) a_n = 2*np.array([cos_phi * sin_theta_next, sin_phi * sin_theta_next , cos_theta_next]) b_n = 2*np.array([cos_phi_next * sin_theta_next, sin_phi_next * sin_theta_next, cos_theta_next]) c_n = 2*np.array([cos_phi_next * sin_theta , sin_phi_next * sin_theta , cos_theta]) d_n = 2*np.array([cos_phi * sin_theta , sin_phi * sin_theta , cos_theta]) mu = 0.5 sigma = 0.1 _vertex, _indices = loc_s.createColorSpecificNormals(start_index, a, b, c, d, a_n, b_n, c_n, d_n, color=color) vertices += _vertex indices += _indices start_index += 4 return bs.Shape(vertices, indices)

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py

Python

jupyterlabpymolpysnips/Tragjectories/loadAmberTrajs.py

MooersLab/pymolpysnips

50a89c85adf8006d85c1d6cd3f8aad7e440a0b92

[ "MIT" ]

null

jupyterlabpymolpysnips/Tragjectories/loadAmberTrajs.py

MooersLab/pymolpysnips

50a89c85adf8006d85c1d6cd3f8aad7e440a0b92

[ "MIT" ]

null

jupyterlabpymolpysnips/Tragjectories/loadAmberTrajs.py

MooersLab/pymolpysnips

50a89c85adf8006d85c1d6cd3f8aad7e440a0b92

[ "MIT" ]

null

cmd.do('load file.top, protein;') cmd.do('load file.rst, protein')

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Python

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_1/_pkg0_1_1_0/_pkg0_1_1_0_1/_mod0_1_1_0_1_2.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2018-12-29T09:53:39.000Z

2018-12-29T09:53:42.000Z

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_1/_pkg0_1_1_0/_pkg0_1_1_0_1/_mod0_1_1_0_1_2.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_1/_pkg0_1_1_0/_pkg0_1_1_0_1/_mod0_1_1_0_1_2.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

name0_1_1_0_1_2_0 = None name0_1_1_0_1_2_1 = None name0_1_1_0_1_2_2 = None name0_1_1_0_1_2_3 = None name0_1_1_0_1_2_4 = None

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py

Python

tests/utils/test_password_manager.py

python-pitfalls/poetry

008ba9fbfcc329ea3c86bc7e0cb6a71855b672cf

[ "MIT" ]

12,347

2019-12-12T07:07:32.000Z

2022-03-31T21:08:50.000Z

tests/utils/test_password_manager.py

python-pitfalls/poetry

008ba9fbfcc329ea3c86bc7e0cb6a71855b672cf

[ "MIT" ]

3,483

2019-12-11T20:20:20.000Z

2022-03-31T23:18:18.000Z

tests/utils/test_password_manager.py

python-pitfalls/poetry

008ba9fbfcc329ea3c86bc7e0cb6a71855b672cf

[ "MIT" ]

1,399

2019-12-12T12:27:46.000Z

2022-03-31T09:12:53.000Z

import os import pytest from poetry.utils.password_manager import KeyRing from poetry.utils.password_manager import KeyRingError from poetry.utils.password_manager import PasswordManager def test_set_http_password(config, with_simple_keyring, dummy_keyring): manager = PasswordManager(config) assert manager.keyring.is_available() manager.set_http_password("foo", "bar", "baz") assert "baz" == dummy_keyring.get_password("poetry-repository-foo", "bar") auth = config.get("http-basic.foo") assert "bar" == auth["username"] assert "password" not in auth def test_get_http_auth(config, with_simple_keyring, dummy_keyring): dummy_keyring.set_password("poetry-repository-foo", "bar", "baz") config.auth_config_source.add_property("http-basic.foo", {"username": "bar"}) manager = PasswordManager(config) assert manager.keyring.is_available() auth = manager.get_http_auth("foo") assert "bar" == auth["username"] assert "baz" == auth["password"] def test_delete_http_password(config, with_simple_keyring, dummy_keyring): dummy_keyring.set_password("poetry-repository-foo", "bar", "baz") config.auth_config_source.add_property("http-basic.foo", {"username": "bar"}) manager = PasswordManager(config) assert manager.keyring.is_available() manager.delete_http_password("foo") assert dummy_keyring.get_password("poetry-repository-foo", "bar") is None assert config.get("http-basic.foo") is None def test_set_pypi_token(config, with_simple_keyring, dummy_keyring): manager = PasswordManager(config) assert manager.keyring.is_available() manager.set_pypi_token("foo", "baz") assert config.get("pypi-token.foo") is None assert "baz" == dummy_keyring.get_password("poetry-repository-foo", "__token__") def test_get_pypi_token(config, with_simple_keyring, dummy_keyring): dummy_keyring.set_password("poetry-repository-foo", "__token__", "baz") manager = PasswordManager(config) assert manager.keyring.is_available() assert "baz" == manager.get_pypi_token("foo") def test_delete_pypi_token(config, with_simple_keyring, dummy_keyring): dummy_keyring.set_password("poetry-repository-foo", "__token__", "baz") manager = PasswordManager(config) assert manager.keyring.is_available() manager.delete_pypi_token("foo") assert dummy_keyring.get_password("poetry-repository-foo", "__token__") is None def test_set_http_password_with_unavailable_backend(config, with_fail_keyring): manager = PasswordManager(config) assert not manager.keyring.is_available() manager.set_http_password("foo", "bar", "baz") auth = config.get("http-basic.foo") assert "bar" == auth["username"] assert "baz" == auth["password"] def test_get_http_auth_with_unavailable_backend(config, with_fail_keyring): config.auth_config_source.add_property( "http-basic.foo", {"username": "bar", "password": "baz"} ) manager = PasswordManager(config) assert not manager.keyring.is_available() auth = manager.get_http_auth("foo") assert "bar" == auth["username"] assert "baz" == auth["password"] def test_delete_http_password_with_unavailable_backend(config, with_fail_keyring): config.auth_config_source.add_property( "http-basic.foo", {"username": "bar", "password": "baz"} ) manager = PasswordManager(config) assert not manager.keyring.is_available() manager.delete_http_password("foo") assert config.get("http-basic.foo") is None def test_set_pypi_token_with_unavailable_backend(config, with_fail_keyring): manager = PasswordManager(config) assert not manager.keyring.is_available() manager.set_pypi_token("foo", "baz") assert "baz" == config.get("pypi-token.foo") def test_get_pypi_token_with_unavailable_backend(config, with_fail_keyring): config.auth_config_source.add_property("pypi-token.foo", "baz") manager = PasswordManager(config) assert not manager.keyring.is_available() assert "baz" == manager.get_pypi_token("foo") def test_delete_pypi_token_with_unavailable_backend(config, with_fail_keyring): config.auth_config_source.add_property("pypi-token.foo", "baz") manager = PasswordManager(config) assert not manager.keyring.is_available() manager.delete_pypi_token("foo") assert config.get("pypi-token.foo") is None def test_keyring_raises_errors_on_keyring_errors(mocker, with_fail_keyring): mocker.patch("poetry.utils.password_manager.KeyRing._check") key_ring = KeyRing("poetry") with pytest.raises(KeyRingError): key_ring.set_password("foo", "bar", "baz") with pytest.raises(KeyRingError): key_ring.get_password("foo", "bar") with pytest.raises(KeyRingError): key_ring.delete_password("foo", "bar") def test_keyring_with_chainer_backend_and_not_compatible_only_should_be_unavailable( with_chained_keyring, ): key_ring = KeyRing("poetry") assert not key_ring.is_available() def test_get_http_auth_from_environment_variables(environ, config, with_simple_keyring): os.environ["POETRY_HTTP_BASIC_FOO_USERNAME"] = "bar" os.environ["POETRY_HTTP_BASIC_FOO_PASSWORD"] = "baz" manager = PasswordManager(config) auth = manager.get_http_auth("foo") assert "bar" == auth["username"] assert "baz" == auth["password"]

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88

0.738856

694

5,384

5.409222

0.086455

0.040757

0.096963

0.108684

0.879861

0.84017

0.763186

0.741343

0.688599

0

0.13893

5,384

170

89

31.670588

0.80975

0

0.654206

0

0.149331

0.05052

0

0.317757

1

0.140187

false

0.411215

0.046729

0

0.186916

0

null

0

1

0

null

0

1

0

7

e180d1f78e22e5b24e43d25b8c4599ccb8578cdf

4,359

py

Python

ababe/stru/tests/test_clarifier.py

shaobinqiu/pyabc

eefd322bdd0bb04ae6d42554d24140c6ffbd5c34

[ "MIT" ]

null

ababe/stru/tests/test_clarifier.py

shaobinqiu/pyabc

eefd322bdd0bb04ae6d42554d24140c6ffbd5c34

[ "MIT" ]

null

ababe/stru/tests/test_clarifier.py

shaobinqiu/pyabc

eefd322bdd0bb04ae6d42554d24140c6ffbd5c34

[ "MIT" ]

null

# coding: utf-8 # Distributed under the terms of the MIT License. import unittest import numpy as np from ababe.stru.scaffold import ModifiedCell from ababe.stru.clarifier import AtomRemoveClarifier, VerboseAtomRemoveClarifier from ababe.stru.element import Specie class TestAtomRemoveClarifier(unittest.TestCase): def setUp(self): self.latt = np.array([[4.898979, 0.000000, 0.000000], [2.449490, 4.242641, 0.000000], [1.632993, -0.000000, 4.618802]]) self.pos = np.array([[0.208333, 0.333333, 0.375000], [0.375000, 0.000000, 0.875000], [0.541667, 0.666667, 0.375000], [0.708333, 0.333333, 0.875000], [0.875000, 0.000000, 0.375000], [0.000000, 0.000000, 0.000000], [0.166667, 0.666667, 0.500000], [0.333333, 0.333333, 0.000000], [0.500000, 0.000000, 0.500000], [0.666667, 0.666667, 0.000000], [0.833333, 0.333333, 0.500000], [0.041667, 0.666667, 0.875000]]) self.numbers = np.array([16,16,16,16,16,30,30,30,30,30,30,55]) self.modcell = ModifiedCell(self.latt, self.pos, self.numbers) centers = np.array([[0.041667, 0.666667, 0.875000]]) ele = Specie('Zn') r = 2 self.nearZnClarifier = AtomRemoveClarifier(centers, r, Specie('Zn')) def test_clarify(self): expect_pos = np.array([[0.208333, 0.333333, 0.375000], [0.375000, 0.000000, 0.875000], [0.541667, 0.666667, 0.375000], [0.708333, 0.333333, 0.875000], [0.875000, 0.000000, 0.375000], [0.500000, 0.000000, 0.500000], [0.833333, 0.333333, 0.500000], [0.041667, 0.666667, 0.875000]]) expect_numbers = np.array([16,16,16,16,16,30,30,55]) expect_newcell = ModifiedCell(self.latt, expect_pos, expect_numbers) newcell = self.nearZnClarifier.clarify(self.modcell) self.assertEqual(newcell, expect_newcell) class TestVerboseAtomRemoveClarifier(unittest.TestCase): def setUp(self): self.latt = np.array([[4.898979, 0.000000, 0.000000], [2.449490, 4.242641, 0.000000], [1.632993, -0.000000, 4.618802]]) self.pos = np.array([[0.208333, 0.333333, 0.375000], [0.375000, 0.000000, 0.875000], [0.541667, 0.666667, 0.375000], [0.708333, 0.333333, 0.875000], [0.875000, 0.000000, 0.375000], [0.000000, 0.000000, 0.000000], [0.166667, 0.666667, 0.500000], [0.333333, 0.333333, 0.000000], [0.500000, 0.000000, 0.500000], [0.666667, 0.666667, 0.000000], [0.833333, 0.333333, 0.500000], [0.041667, 0.666667, 0.875000]]) self.numbers = np.array([16,16,16,16,55,30,30,30,30,30,30,55]) self.modcell = ModifiedCell(self.latt, self.pos, self.numbers) ele = Specie('Zn') r = 2 self.nearZnClarifier = VerboseAtomRemoveClarifier(Specie('Cs'), r, Specie('Zn')) def test_clarify(self): expect_pos = np.array([[0.208333, 0.333333, 0.375000], [0.375000, 0.000000, 0.875000], [0.541667, 0.666667, 0.375000], [0.708333, 0.333333, 0.875000], [0.875000, 0.000000, 0.375000], [0.041667, 0.666667, 0.875000]]) expect_numbers = np.array([16,16,16,16,55,55]) expect_newcell = ModifiedCell(self.latt, expect_pos, expect_numbers) newcell = self.nearZnClarifier.clarify(self.modcell) self.assertEqual(newcell, expect_newcell) if __name__ == "__main__": import nose2 nose2.main()

45.884211

88

0.490479

499

4,359

4.240481

0.152305

0.095936

0.086957

0.039698

0.816163

0.806238

0.766541

0.76465

0

0.376514

0.374857

4,359

94

89

46.37234

0.4

0.013994

0

0.779221

0

0.004191

0

0.025974

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0.051948

false

0

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1

0

1

0

1

0

null

0

9

e1ad0333c385ad536dd5441c979931a9da736795

5,382

py

Python

generate/generate_semeval_BERT_single.py

bubblemans/ABSA-BERT-pair

aced5582cefc6398e196da01773fbfee4cd9126b

[ "MIT" ]

462

2019-03-25T06:48:12.000Z

2022-03-31T08:34:06.000Z

generate/generate_semeval_BERT_single.py

bubblemans/ABSA-BERT-pair

aced5582cefc6398e196da01773fbfee4cd9126b

[ "MIT" ]

24

2019-04-27T16:35:19.000Z

2022-02-07T12:33:48.000Z

generate/generate_semeval_BERT_single.py

bubblemans/ABSA-BERT-pair

aced5582cefc6398e196da01773fbfee4cd9126b

[ "MIT" ]

139

2019-03-25T06:48:16.000Z

2022-03-19T14:00:12.000Z

import os data_dir='../data/semeval2014/' aspect_name = ['price', 'anecdotes', 'food', 'ambience', 'service'] dir_path = [data_dir + 'bert-single/' + i + '/' for i in aspect_name] for path in dir_path: if not os.path.exists(path): os.makedirs(path) with open(dir_path[0]+"test.csv", "w", encoding="utf-8") as g_price, \ open(dir_path[1]+"test.csv", "w", encoding="utf-8") as g_anecdotes,\ open(dir_path[2]+"test.csv", "w", encoding="utf-8") as g_food,\ open(dir_path[3]+"test.csv", "w", encoding="utf-8") as g_ambience,\ open(dir_path[4]+"test.csv", "w", encoding="utf-8") as g_service,\ open(data_dir+"Restaurants_Test_Gold.xml","r",encoding="utf-8") as f: s=f.readline().strip() while s: category=[] polarity=[] if "<sentence id" in s: left=s.find("id") right=s.find(">") id=s[left+4:right-1] while not "</sentence>" in s: if "<text>" in s: left=s.find("<text>") right=s.find("</text>") text=s[left+6:right] if "aspectCategory" in s: left=s.find("category=") right=s.find("polarity=") category.append(s[left+10:right-2]) left=s.find("polarity=") right=s.find("/>") polarity.append(s[left+10:right-2]) s=f.readline().strip() if "price" in category: g_price.write(id+"\t"+polarity[category.index("price")]+"\t"+"price"+"\t"+text+"\n") else: g_price.write(id + "\t" + "none" + "\t" + "price" + "\t" + text + "\n") if "anecdotes/miscellaneous" in category: g_anecdotes.write(id+"\t"+polarity[category.index("anecdotes/miscellaneous")]+"\t"+"anecdotes"+"\t"+text+"\n") else: g_anecdotes.write(id + "\t" + "none" + "\t" + "anecdotes" + "\t" + text + "\n") if "food" in category: g_food.write(id+"\t"+polarity[category.index("food")]+"\t"+"food"+"\t"+text+"\n") else: g_food.write(id + "\t" + "none" + "\t" + "food" + "\t" + text + "\n") if "ambience" in category: g_ambience.write(id+"\t"+polarity[category.index("ambience")]+"\t"+"ambience"+"\t"+text+"\n") else: g_ambience.write(id + "\t" + "none" + "\t" + "ambience" + "\t" + text + "\n") if "service" in category: g_service.write(id+"\t"+polarity[category.index("service")]+"\t"+"service"+"\t"+text+"\n") else: g_service.write(id + "\t" + "none" + "\t" + "service" + "\t" + text + "\n") else: s = f.readline().strip() with open(dir_path[0]+"train.csv", "w", encoding="utf-8") as g_price, \ open(dir_path[1]+"train.csv", "w", encoding="utf-8") as g_anecdotes,\ open(dir_path[2]+"train.csv", "w", encoding="utf-8") as g_food,\ open(dir_path[3]+"train.csv", "w", encoding="utf-8") as g_ambience,\ open(dir_path[4]+"train.csv", "w", encoding="utf-8") as g_service,\ open(data_dir+"Restaurants_Train.xml","r",encoding="utf-8") as f: s=f.readline().strip() while s: category=[] polarity=[] if "<sentence id" in s: left=s.find("id") right=s.find(">") id=s[left+4:right-1] while not "</sentence>" in s: if "<text>" in s: left=s.find("<text>") right=s.find("</text>") text=s[left+6:right] if "aspectCategory" in s: left=s.find("category=") right=s.find("polarity=") category.append(s[left+10:right-2]) left=s.find("polarity=") right=s.find("/>") polarity.append(s[left+10:right-1]) s=f.readline().strip() if "price" in category: g_price.write(id+"\t"+polarity[category.index("price")]+"\t"+"price"+"\t"+text+"\n") else: g_price.write(id + "\t" + "none" + "\t" + "price" + "\t" + text + "\n") if "anecdotes/miscellaneous" in category: g_anecdotes.write(id+"\t"+polarity[category.index("anecdotes/miscellaneous")]+"\t"+"anecdotes"+"\t"+text+"\n") else: g_anecdotes.write(id + "\t" + "none" + "\t" + "anecdotes" + "\t" + text + "\n") if "food" in category: g_food.write(id+"\t"+polarity[category.index("food")]+"\t"+"food"+"\t"+text+"\n") else: g_food.write(id + "\t" + "none" + "\t" + "food" + "\t" + text + "\n") if "ambience" in category: g_ambience.write(id+"\t"+polarity[category.index("ambience")]+"\t"+"ambience"+"\t"+text+"\n") else: g_ambience.write(id + "\t" + "none" + "\t" + "ambience" + "\t" + text + "\n") if "service" in category: g_service.write(id+"\t"+polarity[category.index("service")]+"\t"+"service"+"\t"+text+"\n") else: g_service.write(id + "\t" + "none" + "\t" + "service" + "\t" + text + "\n") else: s = f.readline().strip() print("Finished!")

47.210526

126

0.479933

682

5,382

3.71261

0.099707

0.055292

0.063191

0.066351

0.924171

0.911532

0.911137

0.893365

0

0.011812

0.307878

5,382

113

127

47.628319

0.667919

0

0.792453

0

0.181007

0.025646

0

1

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false

0

0.009434

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0

null

0

1

0

1

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null

0

7

8362e150712455f45e6bcd8d4e1c073ce1f17558

21,005

py

Python

src/genie/libs/parser/iosxe/tests/ShowNvePeers/cli/equal/golden_output2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

204

2018-06-27T00:55:27.000Z

2022-03-06T21:12:18.000Z

src/genie/libs/parser/iosxe/tests/ShowNvePeers/cli/equal/golden_output2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

468

2018-06-19T00:33:18.000Z

2022-03-31T23:23:35.000Z

src/genie/libs/parser/iosxe/tests/ShowNvePeers/cli/equal/golden_output2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

309

2019-01-16T20:21:07.000Z

2022-03-30T12:56:41.000Z

expected_output = { "interface": { "nve1": { "vni": { "3000101": { "peer_ip": { "20.0.101.2": { "type": "L3CP", "rmac_num_rt": "5c71.0dfe.fb60", "evni": "3000101", "state": "UP", "flags": "A/M/4", "uptime": "1w0d" }, "30.0.107.78": { "type": "L3CP", "rmac_num_rt": "ac3a.6767.049f", "evni": "3000101", "state": "UP", "flags": "A/M/4", "uptime": "1w0d" } } }, "200051": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200051", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200051", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200051", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200052": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200052", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200052", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200052", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200053": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200053", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200053", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200053", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200054": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200054", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200054", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200054", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200055": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200055", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200055", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200055", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200056": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200056", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200056", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200056", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200057": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200057", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200057", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200057", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200058": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200058", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200058", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200058", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200059": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200059", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200059", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200059", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200060": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200060", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200060", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200060", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200061": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200061", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200061", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200061", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200062": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200062", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200062", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200062", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200063": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200063", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200063", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200063", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200064": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200064", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200064", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200064", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200065": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200065", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200065", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200065", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200066": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200066", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200066", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200066", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200067": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200067", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200067", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200067", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } }, "200068": { "peer_ip": { "20.0.101.2": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200068", "state": "UP", "flags": "N/A", "uptime": "2d02h" }, "20.0.101.3": { "type": "L2CP", "rmac_num_rt": "1", "evni": "200068", "state": "UP", "flags": "N/A", "uptime": "6d20h" }, "30.0.107.78": { "type": "L2CP", "rmac_num_rt": "4", "evni": "200068", "state": "UP", "flags": "N/A", "uptime": "6d20h" } } } } } } }

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3621959049b8b650e416e676f29d842d5d43b801

25,207

py

Python

tests/test_var.py

michelp/cxxheaderparser

83bb2903790cf448bf838cdb8a93ca96e758bd1a

[ "BSD-3-Clause" ]

12

2020-12-28T09:40:53.000Z

2022-03-13T15:36:21.000Z

tests/test_var.py

michelp/cxxheaderparser

83bb2903790cf448bf838cdb8a93ca96e758bd1a

[ "BSD-3-Clause" ]

28

2021-01-04T14:58:59.000Z

2022-01-03T03:00:16.000Z

tests/test_var.py

michelp/cxxheaderparser

83bb2903790cf448bf838cdb8a93ca96e758bd1a

[ "BSD-3-Clause" ]

1

2021-11-06T03:44:53.000Z

# Note: testcases generated via `python -m cxxheaderparser.gentest` from cxxheaderparser.types import ( Array, ClassDecl, EnumDecl, Enumerator, Field, FunctionType, FundamentalSpecifier, NameSpecifier, PQName, Parameter, Pointer, Reference, Token, Type, Value, Variable, ) from cxxheaderparser.simple import ClassScope, NamespaceScope, ParsedData, parse_string def test_var_unixwiz_ridiculous(): # http://unixwiz.net/techtips/reading-cdecl.html # # .. "we have no idea how this variable is useful, but at least we can # describe the type correctly" content = """ char *(*(**foo[][8])())[]; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="foo")]), type=Array( array_of=Array( array_of=Pointer( ptr_to=Pointer( ptr_to=FunctionType( return_type=Pointer( ptr_to=Array( array_of=Pointer( ptr_to=Type( typename=PQName( segments=[ FundamentalSpecifier( name="char" ) ] ) ) ), size=None, ) ), parameters=[], ) ) ), size=Value(tokens=[Token(value="8")]), ), size=None, ), ) ] ) ) def test_var_ptr_to_array15_of_ptr_to_int(): content = """ int *(*crocodile)[15]; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="crocodile")]), type=Pointer( ptr_to=Array( array_of=Pointer( ptr_to=Type( typename=PQName( segments=[FundamentalSpecifier(name="int")] ) ) ), size=Value(tokens=[Token(value="15")]), ) ), ) ] ) ) def test_var_ref_to_array(): content = """ int abase[3]; int (&aname)[3] = abase; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="abase")]), type=Array( array_of=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), size=Value(tokens=[Token(value="3")]), ), ), Variable( name=PQName(segments=[NameSpecifier(name="aname")]), type=Reference( ref_to=Array( array_of=Type( typename=PQName( segments=[FundamentalSpecifier(name="int")] ) ), size=Value(tokens=[Token(value="3")]), ) ), value=Value(tokens=[Token(value="abase")]), ), ] ) ) def test_var_ptr_to_array(): content = """ int zz, (*aname)[3] = &abase; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="zz")]), type=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), ), Variable( name=PQName(segments=[NameSpecifier(name="aname")]), type=Pointer( ptr_to=Array( array_of=Type( typename=PQName( segments=[FundamentalSpecifier(name="int")] ) ), size=Value(tokens=[Token(value="3")]), ) ), value=Value(tokens=[Token(value="&"), Token(value="abase")]), ), ] ) ) def test_var_multi_1(): content = """ int zz, (&aname)[3] = abase; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="zz")]), type=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), ), Variable( name=PQName(segments=[NameSpecifier(name="aname")]), type=Reference( ref_to=Array( array_of=Type( typename=PQName( segments=[FundamentalSpecifier(name="int")] ) ), size=Value(tokens=[Token(value="3")]), ) ), value=Value(tokens=[Token(value="abase")]), ), ] ) ) def test_var_array_of_fnptr_varargs(): content = """ void (*a3[3])(int, ...); """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="a3")]), type=Array( array_of=Pointer( ptr_to=FunctionType( return_type=Type( typename=PQName( segments=[FundamentalSpecifier(name="void")] ) ), parameters=[ Parameter( type=Type( typename=PQName( segments=[ FundamentalSpecifier(name="int") ] ) ) ) ], vararg=True, ) ), size=Value(tokens=[Token(value="3")]), ), ) ] ) ) def test_var_double_fnptr_varargs(): content = """ void (*(*a4))(int, ...); """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="a4")]), type=Pointer( ptr_to=Pointer( ptr_to=FunctionType( return_type=Type( typename=PQName( segments=[FundamentalSpecifier(name="void")] ) ), parameters=[ Parameter( type=Type( typename=PQName( segments=[ FundamentalSpecifier(name="int") ] ) ) ) ], vararg=True, ) ) ), ) ] ) ) def test_var_fnptr_voidstar(): content = """ void(*(*a5)(int)); """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="a5")]), type=Pointer( ptr_to=FunctionType( return_type=Pointer( ptr_to=Type( typename=PQName( segments=[FundamentalSpecifier(name="void")] ) ) ), parameters=[ Parameter( type=Type( typename=PQName( segments=[FundamentalSpecifier(name="int")] ) ) ) ], ) ), ) ] ) ) def test_var_fnptr_moreparens(): content = """ void (*x)(int(p1), int); """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="x")]), type=Pointer( ptr_to=FunctionType( return_type=Type( typename=PQName( segments=[FundamentalSpecifier(name="void")] ) ), parameters=[ Parameter( type=Type( typename=PQName( segments=[FundamentalSpecifier(name="int")] ) ), name="p1", ), Parameter( type=Type( typename=PQName( segments=[FundamentalSpecifier(name="int")] ) ) ), ], ) ), ) ] ) ) # From pycparser: # Pointer decls nest from inside out. This is important when different # levels have different qualifiers. For example: # # char * const * p; # # Means "pointer to const pointer to char" # # While: # # char ** const p; # # Means "const pointer to pointer to char" def test_var_ptr_to_const_ptr_to_char(): content = """ char *const *p; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="p")]), type=Pointer( ptr_to=Pointer( ptr_to=Type( typename=PQName( segments=[FundamentalSpecifier(name="char")] ) ), const=True, ) ), ) ] ) ) def test_var_const_ptr_to_ptr_to_char(): content = """ char **const p; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="p")]), type=Pointer( ptr_to=Pointer( ptr_to=Type( typename=PQName( segments=[FundamentalSpecifier(name="char")] ) ) ), const=True, ), ) ] ) ) def test_var_array_initializer1(): content = """ int x[3]{1, 2, 3}; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="x")]), type=Array( array_of=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), size=Value(tokens=[Token(value="3")]), ), value=Value( tokens=[ Token(value="{"), Token(value="1"), Token(value=","), Token(value="2"), Token(value=","), Token(value="3"), Token(value="}"), ] ), ) ] ) ) def test_var_array_initializer2(): content = """ int x[3] = {1, 2, 3}; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="x")]), type=Array( array_of=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), size=Value(tokens=[Token(value="3")]), ), value=Value( tokens=[ Token(value="{"), Token(value="1"), Token(value=","), Token(value="2"), Token(value=","), Token(value="3"), Token(value="}"), ] ), ) ] ) ) def test_var_extern_c(): content = """ extern "C" int x; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="x")]), type=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), # TODO: store linkage extern=True, ) ] ) ) def test_var_ns_1(): content = """ int N::x; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName( segments=[NameSpecifier(name="N"), NameSpecifier(name="x")] ), type=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), ) ] ) ) def test_var_ns_2(): content = """ int N::x = 4; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName( segments=[NameSpecifier(name="N"), NameSpecifier(name="x")] ), type=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), value=Value(tokens=[Token(value="4")]), ) ] ) ) def test_var_ns_3(): content = """ int N::x{4}; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName( segments=[NameSpecifier(name="N"), NameSpecifier(name="x")] ), type=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), value=Value( tokens=[Token(value="{"), Token(value="4"), Token(value="}")] ), ) ] ) ) def test_var_static_struct(): content = """ constexpr static struct SS {} s; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( classes=[ ClassScope( class_decl=ClassDecl( typename=PQName( segments=[NameSpecifier(name="SS")], classkey="struct" ) ) ) ], variables=[ Variable( name=PQName(segments=[NameSpecifier(name="s")]), type=Type( typename=PQName( segments=[NameSpecifier(name="SS")], classkey="struct" ) ), constexpr=True, static=True, ) ], ) ) def test_var_constexpr_enum(): content = """ constexpr enum E { EE } e = EE; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( enums=[ EnumDecl( typename=PQName( segments=[NameSpecifier(name="E")], classkey="enum" ), values=[Enumerator(name="EE")], ) ], variables=[ Variable( name=PQName(segments=[NameSpecifier(name="e")]), type=Type( typename=PQName( segments=[NameSpecifier(name="E")], classkey="enum" ) ), value=Value(tokens=[Token(value="EE")]), constexpr=True, ) ], ) ) def test_var_fnptr_in_class(): content = """ struct DriverFuncs { void *(*init)(); void (*write)(void *buf, int buflen); }; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( classes=[ ClassScope( class_decl=ClassDecl( typename=PQName( segments=[NameSpecifier(name="DriverFuncs")], classkey="struct", ) ), fields=[ Field( access="public", type=Pointer( ptr_to=FunctionType( return_type=Pointer( ptr_to=Type( typename=PQName( segments=[ FundamentalSpecifier(name="void") ] ) ) ), parameters=[], ) ), name="init", ), Field( access="public", type=Pointer( ptr_to=FunctionType( return_type=Type( typename=PQName( segments=[FundamentalSpecifier(name="void")] ) ), parameters=[ Parameter( type=Pointer( ptr_to=Type( typename=PQName( segments=[ FundamentalSpecifier( name="void" ) ] ) ) ), name="buf", ), Parameter( type=Type( typename=PQName( segments=[ FundamentalSpecifier(name="int") ] ) ), name="buflen", ), ], ) ), name="write", ), ], ) ] ) ) def test_var_extern(): content = """ extern int externVar; """ data = parse_string(content, cleandoc=True) assert data == ParsedData( namespace=NamespaceScope( variables=[ Variable( name=PQName(segments=[NameSpecifier(name="externVar")]), type=Type( typename=PQName(segments=[FundamentalSpecifier(name="int")]) ), extern=True, ) ] ) )

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7fc1908479e4b178881cf7e2038b85e65633179f

8,948

py

Python

tests/slack/slack_payloads.py

danpalmer/response

7699f14f9248636875dbf06a74e6ebdde018d0f2

[ "MIT" ]

null

tests/slack/slack_payloads.py

danpalmer/response

7699f14f9248636875dbf06a74e6ebdde018d0f2

[ "MIT" ]

null

tests/slack/slack_payloads.py

danpalmer/response

7699f14f9248636875dbf06a74e6ebdde018d0f2

[ "MIT" ]

null

users_list_response = { "ok": "True", "members": [ { "id": "W012A3CDE", "team_id": "T012AB3C4", "name": "spengler", "deleted": "False", "color": "9f69e7", "real_name": "spengler", "tz": "America/Los_Angeles", "tz_label": "Pacific Daylight Time", "tz_offset": -25200, "profile": { "avatar_hash": "ge3b51ca72de", "status_text": "Print is dead", "status_emoji": ":books:", "real_name": "Egon Spengler", "display_name": "spengler", "real_name_normalized": "Egon Spengler", "display_name_normalized": "spengler", "email": "spengler@ghostbusters.example.com", "image_24": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_32": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_48": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_72": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_192": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_512": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "team": "T012AB3C4", }, "is_admin": "True", "is_owner": "False", "is_primary_owner": "False", "is_restricted": "False", "is_ultra_restricted": "False", "is_bot": "False", "updated": 1502138686, "is_app_user": "False", "has_2fa": "False", }, { "id": "U12345678", "team_id": "T0G9PQBBK", "name": "glinda", "deleted": "False", "color": "9f69e7", "real_name": "Glinda Southgood", "tz": "America/Los_Angeles", "tz_label": "Pacific Daylight Time", "tz_offset": -25200, "profile": { "avatar_hash": "8fbdd10b41c6", "image_24": "https://a.slack-edge.com...png", "image_32": "https://a.slack-edge.com...png", "image_48": "https://a.slack-edge.com...png", "image_72": "https://a.slack-edge.com...png", "image_192": "https://a.slack-edge.com...png", "image_512": "https://a.slack-edge.com...png", "image_1024": "https://a.slack-edge.com...png", "image_original": "https://a.slack-edge.com...png", "first_name": "Glinda", "last_name": "Southgood", "title": "Glinda the Good", "phone": "", "skype": "", "real_name": "Glinda Southgood", "real_name_normalized": "Glinda Southgood", "display_name": "Glinda the Fairly Good", "display_name_normalized": "Glinda the Fairly Good", "email": "glenda@south.oz.coven", }, "is_admin": "True", "is_owner": "False", "is_primary_owner": "False", "is_restricted": "False", "is_ultra_restricted": "False", "is_bot": "False", "updated": 1480527098, "has_2fa": "False", }, ], "cache_ts": 1498777272, "response_metadata": {"next_cursor": ""}, } users_list_new = { "ok": "True", "members": [ { "id": "U10293847", "team_id": "T012AB3C4", "name": "venkman", "deleted": "False", "color": "9f69e7", "real_name": "venkman", "tz": "America/Los_Angeles", "tz_label": "Pacific Daylight Time", "tz_offset": -25200, "profile": { "avatar_hash": "ge3b51ca72de", "status_text": "Back off man, I'm a scientist!", "status_emoji": ":male-scientist:", "real_name": "Peter Venkman", "display_name": "venkman", "real_name_normalized": "Peter Venkman", "display_name_normalized": "venkman", "email": "venkman@ghostbusters.example.com", "image_24": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_32": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_48": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_72": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_192": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_512": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "team": "T012AB3C4", }, "is_admin": "True", "is_owner": "False", "is_primary_owner": "False", "is_restricted": "False", "is_ultra_restricted": "False", "is_bot": "False", "updated": 1502138686, "is_app_user": "False", "has_2fa": "False", } ], "cache_ts": 1498777272, "response_metadata": {"next_cursor": ""}, } users_list_page_1 = { "ok": True, "members": [ { "id": "W012A3CDE", "team_id": "T012AB3C4", "name": "spengler", "deleted": False, "color": "9f69e7", "real_name": "spengler", "tz": "America/Los_Angeles", "tz_label": "Pacific Daylight Time", "tz_offset": -25200, "profile": { "avatar_hash": "ge3b51ca72de", "status_text": "Print is dead", "status_emoji": ":books:", "real_name": "Egon Spengler", "display_name": "spengler", "real_name_normalized": "Egon Spengler", "display_name_normalized": "spengler", "email": "spengler@ghostbusters.example.com", "image_24": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_32": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_48": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_72": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_192": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "image_512": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", "team": "T012AB3C4", }, "is_admin": True, "is_owner": False, "is_primary_owner": False, "is_restricted": False, "is_ultra_restricted": False, "is_bot": False, "updated": 1502138686, "is_app_user": False, "has_2fa": False, } ], "cache_ts": 1498777272, "response_metadata": {"next_cursor": "page2"}, } users_list_page_2 = { "ok": True, "members": [ { "id": "W07QCRPA4", "team_id": "T0G9PQBBK", "name": "glinda", "deleted": False, "color": "9f69e7", "real_name": "Glinda Southgood", "tz": "America/Los_Angeles", "tz_label": "Pacific Daylight Time", "tz_offset": -25200, "profile": { "avatar_hash": "8fbdd10b41c6", "image_24": "https://a.slack-edge.com...png", "image_32": "https://a.slack-edge.com...png", "image_48": "https://a.slack-edge.com...png", "image_72": "https://a.slack-edge.com...png", "image_192": "https://a.slack-edge.com...png", "image_512": "https://a.slack-edge.com...png", "image_1024": "https://a.slack-edge.com...png", "image_original": "https://a.slack-edge.com...png", "first_name": "Glinda", "last_name": "Southgood", "title": "Glinda the Good", "phone": "", "skype": "", "real_name": "Glinda Southgood", "real_name_normalized": "Glinda Southgood", "display_name": "Glinda the Fairly Good", "display_name_normalized": "Glinda the Fairly Good", "email": "glenda@south.oz.coven", }, "is_admin": True, "is_owner": False, "is_primary_owner": False, "is_restricted": False, "is_ultra_restricted": False, "is_bot": False, "updated": 1480527098, "has_2fa": False, } ], "cache_ts": 1498777272, "response_metadata": {"next_cursor": ""}, }

39.946429

87

0.484913

756

8,948

5.505291

0.149471

0.033638

0.185968

0.198943

0.930322

0.922874

0

0.10895

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8,948

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40.125561

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0.497206

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0

null

0

1

0

1

0

null

0

9

7fcb5c51e2c8e522aa7af511ce357f004566f3eb

2,847

py

Python

benchmark/floating.py

tushushu/uvec

ab57251136d375a5e47a61af9a3262394795c0db

[ "BSD-3-Clause" ]

7

2021-11-29T02:43:15.000Z

2022-01-03T13:59:11.000Z

benchmark/floating.py

tushushu/uvec

ab57251136d375a5e47a61af9a3262394795c0db

[ "BSD-3-Clause" ]

87

2022-01-10T13:15:23.000Z

2022-03-31T12:10:15.000Z

benchmark/floating.py

tushushu/ulist

987d3a1bbcf2caab7ed2253d94921b1588e5175f

[ "BSD-3-Clause" ]

null

from random import random, seed import numpy as np from ulist.utils import Benchmarker seed(100) class AddOne(Benchmarker): def cases(self) -> list: return [ ([float(x) for x in range(100)],), ([float(x) for x in range(1000)],), ([float(x) for x in range(10000)],), ([float(x) for x in range(100000)],), ([float(x) for x in range(1000000)],), ] def ulist_fn(self, args) -> None: args[0] + 1.0 def other_fn(self, args) -> None: args[0] + 1.0 class ArraySum(Benchmarker): def cases(self) -> list: return [ ([random() for _ in range(100)],), ([random() for _ in range(1000)],), ([random() for _ in range(10000)],), ([random() for _ in range(100000)],), ([random() for _ in range(1000000)],), ] def ulist_fn(self, args) -> None: args[0].sum() def other_fn(self, args) -> None: args[0].sum() class LessThanOne(Benchmarker): def cases(self) -> list: return [ ([random() * 2 for _ in range(100)],), ([random() * 2 for _ in range(1000)],), ([random() * 2 for _ in range(10000)],), ([random() * 2 for _ in range(100000)],), ([random() * 2 for _ in range(1000000)],), ] def ulist_fn(self, args) -> None: args[0] < 1 def other_fn(self, args) -> None: args[0] < 1 class Max(Benchmarker): def cases(self) -> list: return [ ([float(x) for x in range(100)],), ([float(x) for x in range(1000)],), ([float(x) for x in range(10000)],), ([float(x) for x in range(100000)],), ([float(x) for x in range(1000000)],), ] def ulist_fn(self, args) -> None: args[0].max() def other_fn(self, args) -> None: args[0].max() class MulTwo(Benchmarker): def cases(self) -> list: return [ ([float(x) for x in range(100)],), ([float(x) for x in range(1000)],), ([float(x) for x in range(10000)],), ([float(x) for x in range(100000)],), ([float(x) for x in range(1000000)],), ] def ulist_fn(self, args) -> None: args[0] * 2.0 def other_fn(self, args) -> None: args[0] * 2.0 class Sort(Benchmarker): def cases(self) -> list: return [ ([random() for _ in range(100)],), ([random() for _ in range(1000)],), ([random() for _ in range(10000)],), ([random() for _ in range(100000)],), ([random() for _ in range(1000000)],), ] def ulist_fn(self, args) -> None: args[0].sort(ascending=True) def other_fn(self, args) -> None: np.sort(args[0])

26.119266

54

0.488584

364

2,847

3.747253

0.115385

0.153959

0.098974

0.109971

0.871701

0.807918

0.791789

0.754399

0.711877

0.64956

0

0.095491

0.3379

2,847

108

55

26.361111

0.628117

0

0.719512

0

1

0.219512

false

0

0.036585

0.073171

0.402439

0

null

0

1

0

1

0

null

0

1

0

8

7fcbc76e00826f0d7381f3a5bde7e260b3266753

2,027

py

Python

test/test_edit_group.py

Droriel/python_training

e0fbbf3df4289e5af606d9c752e99cab82c653a6

[ "Apache-2.0" ]

null

test/test_edit_group.py

Droriel/python_training

e0fbbf3df4289e5af606d9c752e99cab82c653a6

[ "Apache-2.0" ]

null

test/test_edit_group.py

Droriel/python_training

e0fbbf3df4289e5af606d9c752e99cab82c653a6

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from random import randrange from model.group import Group import random from test_addons import adjustments def test_edit_first_group_top_edit(app, db, json_groups, check_ui): if app.group.count() == 0: app.group.create(Group(name="test")) old_groups = db.get_group_list() # randrange - generuje losową wartość od 0 do podanego parametru group_to_edit = random.choice(old_groups) group = json_groups group.id = group_to_edit.id app.group.edit_group_by_id(group.id, group, edit_button='top') new_groups = db.get_group_list() for i in range(len(old_groups)): if old_groups[i] == group_to_edit: old_groups[i] = group assert sorted(new_groups, key=Group.id_or_max) == sorted(old_groups, key=Group.id_or_max) if check_ui: def clean(group): return Group(id=group.id, name=adjustments.clear_multiple_spaces(group.name).strip()) clear_new_groups = map(clean, new_groups) assert sorted(clear_new_groups, key=Group.id_or_max) == sorted(app.group.get_group_list(), key=Group.id_or_max) def test_edit_first_group_bottom_edit(app, db, json_groups, check_ui): if app.group.count() == 0: app.group.create(Group(name="test")) old_groups = db.get_group_list() group_to_edit = random.choice(old_groups) group = json_groups group.id = group_to_edit.id app.group.edit_group_by_id(group.id, group, edit_button='bottom') new_groups = db.get_group_list() for i in range(len(old_groups)): if old_groups[i] == group_to_edit: old_groups[i] = group assert sorted(new_groups, key=Group.id_or_max) == sorted(old_groups, key=Group.id_or_max) if check_ui: def clean(group): return Group(id=group.id, name=adjustments.clear_multiple_spaces(group.name).strip()) clear_new_groups = map(clean, new_groups) assert sorted(clear_new_groups, key=Group.id_or_max) == sorted(app.group.get_group_list(), key=Group.id_or_max)

40.54

119

0.701529

320

2,027

4.140625

0.190625

0.084528

0.060377

0.072453

0.869434

0.837736

0

0.002418

0.184016

2,027

49

120

41.367347

0.79867

0.041441

0

0.8

0

0.008767

0

0.1

1

0.1

false

0

0.1

0.05

0.25

0

null

0

1

0

1

0

null

0

7

7fdbe0aa2cc47d8ada644d20f9644573dc6e7203

33,305

py

Python

sdk/python/pulumi_azure/search/service.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

[ "ECL-2.0", "Apache-2.0" ]

109

2018-06-18T00:19:44.000Z

2022-02-20T05:32:57.000Z

sdk/python/pulumi_azure/search/service.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

[ "ECL-2.0", "Apache-2.0" ]

663

2018-06-18T21:08:46.000Z

2022-03-31T20:10:11.000Z

sdk/python/pulumi_azure/search/service.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

[ "ECL-2.0", "Apache-2.0" ]

41

2018-07-19T22:37:38.000Z

2022-03-14T10:56:26.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs from ._inputs import * __all__ = ['ServiceArgs', 'Service'] @pulumi.input_type class ServiceArgs: def __init__(__self__, *, resource_group_name: pulumi.Input[str], sku: pulumi.Input[str], allowed_ips: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, identity: Optional[pulumi.Input['ServiceIdentityArgs']] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, partition_count: Optional[pulumi.Input[int]] = None, public_network_access_enabled: Optional[pulumi.Input[bool]] = None, replica_count: Optional[pulumi.Input[int]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None): """ The set of arguments for constructing a Service resource. :param pulumi.Input[str] resource_group_name: The name of the Resource Group where the Search Service should exist. Changing this forces a new Search Service to be created. :param pulumi.Input[str] sku: The SKU which should be used for this Search Service. Possible values are `basic`, `free`, `standard`, `standard2`, `standard3`, `storage_optimized_l1` and `storage_optimized_l2`. Changing this forces a new Search Service to be created. :param pulumi.Input[Sequence[pulumi.Input[str]]] allowed_ips: A list of IPv4 addresses or CIDRs that are allowed access to the search service endpoint. :param pulumi.Input['ServiceIdentityArgs'] identity: An `identity` block as defined below. :param pulumi.Input[str] location: The Azure Region where the Search Service should exist. Changing this forces a new Search Service to be created. :param pulumi.Input[str] name: The Name which should be used for this Search Service. Changing this forces a new Search Service to be created. :param pulumi.Input[int] partition_count: The number of partitions which should be created. :param pulumi.Input[bool] public_network_access_enabled: Whether or not public network access is allowed for this resource. Defaults to `true`. :param pulumi.Input[int] replica_count: The number of replica's which should be created. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A mapping of tags which should be assigned to the Search Service. """ pulumi.set(__self__, "resource_group_name", resource_group_name) pulumi.set(__self__, "sku", sku) if allowed_ips is not None: pulumi.set(__self__, "allowed_ips", allowed_ips) if identity is not None: pulumi.set(__self__, "identity", identity) if location is not None: pulumi.set(__self__, "location", location) if name is not None: pulumi.set(__self__, "name", name) if partition_count is not None: pulumi.set(__self__, "partition_count", partition_count) if public_network_access_enabled is not None: pulumi.set(__self__, "public_network_access_enabled", public_network_access_enabled) if replica_count is not None: pulumi.set(__self__, "replica_count", replica_count) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ The name of the Resource Group where the Search Service should exist. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter def sku(self) -> pulumi.Input[str]: """ The SKU which should be used for this Search Service. Possible values are `basic`, `free`, `standard`, `standard2`, `standard3`, `storage_optimized_l1` and `storage_optimized_l2`. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "sku") @sku.setter def sku(self, value: pulumi.Input[str]): pulumi.set(self, "sku", value) @property @pulumi.getter(name="allowedIps") def allowed_ips(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ A list of IPv4 addresses or CIDRs that are allowed access to the search service endpoint. """ return pulumi.get(self, "allowed_ips") @allowed_ips.setter def allowed_ips(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "allowed_ips", value) @property @pulumi.getter def identity(self) -> Optional[pulumi.Input['ServiceIdentityArgs']]: """ An `identity` block as defined below. """ return pulumi.get(self, "identity") @identity.setter def identity(self, value: Optional[pulumi.Input['ServiceIdentityArgs']]): pulumi.set(self, "identity", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: """ The Azure Region where the Search Service should exist. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "location") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "location", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The Name which should be used for this Search Service. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="partitionCount") def partition_count(self) -> Optional[pulumi.Input[int]]: """ The number of partitions which should be created. """ return pulumi.get(self, "partition_count") @partition_count.setter def partition_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "partition_count", value) @property @pulumi.getter(name="publicNetworkAccessEnabled") def public_network_access_enabled(self) -> Optional[pulumi.Input[bool]]: """ Whether or not public network access is allowed for this resource. Defaults to `true`. """ return pulumi.get(self, "public_network_access_enabled") @public_network_access_enabled.setter def public_network_access_enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "public_network_access_enabled", value) @property @pulumi.getter(name="replicaCount") def replica_count(self) -> Optional[pulumi.Input[int]]: """ The number of replica's which should be created. """ return pulumi.get(self, "replica_count") @replica_count.setter def replica_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "replica_count", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ A mapping of tags which should be assigned to the Search Service. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @pulumi.input_type class _ServiceState: def __init__(__self__, *, allowed_ips: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, identity: Optional[pulumi.Input['ServiceIdentityArgs']] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, partition_count: Optional[pulumi.Input[int]] = None, primary_key: Optional[pulumi.Input[str]] = None, public_network_access_enabled: Optional[pulumi.Input[bool]] = None, query_keys: Optional[pulumi.Input[Sequence[pulumi.Input['ServiceQueryKeyArgs']]]] = None, replica_count: Optional[pulumi.Input[int]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, secondary_key: Optional[pulumi.Input[str]] = None, sku: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None): """ Input properties used for looking up and filtering Service resources. :param pulumi.Input[Sequence[pulumi.Input[str]]] allowed_ips: A list of IPv4 addresses or CIDRs that are allowed access to the search service endpoint. :param pulumi.Input['ServiceIdentityArgs'] identity: An `identity` block as defined below. :param pulumi.Input[str] location: The Azure Region where the Search Service should exist. Changing this forces a new Search Service to be created. :param pulumi.Input[str] name: The Name which should be used for this Search Service. Changing this forces a new Search Service to be created. :param pulumi.Input[int] partition_count: The number of partitions which should be created. :param pulumi.Input[str] primary_key: The Primary Key used for Search Service Administration. :param pulumi.Input[bool] public_network_access_enabled: Whether or not public network access is allowed for this resource. Defaults to `true`. :param pulumi.Input[Sequence[pulumi.Input['ServiceQueryKeyArgs']]] query_keys: A `query_keys` block as defined below. :param pulumi.Input[int] replica_count: The number of replica's which should be created. :param pulumi.Input[str] resource_group_name: The name of the Resource Group where the Search Service should exist. Changing this forces a new Search Service to be created. :param pulumi.Input[str] secondary_key: The Secondary Key used for Search Service Administration. :param pulumi.Input[str] sku: The SKU which should be used for this Search Service. Possible values are `basic`, `free`, `standard`, `standard2`, `standard3`, `storage_optimized_l1` and `storage_optimized_l2`. Changing this forces a new Search Service to be created. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A mapping of tags which should be assigned to the Search Service. """ if allowed_ips is not None: pulumi.set(__self__, "allowed_ips", allowed_ips) if identity is not None: pulumi.set(__self__, "identity", identity) if location is not None: pulumi.set(__self__, "location", location) if name is not None: pulumi.set(__self__, "name", name) if partition_count is not None: pulumi.set(__self__, "partition_count", partition_count) if primary_key is not None: pulumi.set(__self__, "primary_key", primary_key) if public_network_access_enabled is not None: pulumi.set(__self__, "public_network_access_enabled", public_network_access_enabled) if query_keys is not None: pulumi.set(__self__, "query_keys", query_keys) if replica_count is not None: pulumi.set(__self__, "replica_count", replica_count) if resource_group_name is not None: pulumi.set(__self__, "resource_group_name", resource_group_name) if secondary_key is not None: pulumi.set(__self__, "secondary_key", secondary_key) if sku is not None: pulumi.set(__self__, "sku", sku) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter(name="allowedIps") def allowed_ips(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ A list of IPv4 addresses or CIDRs that are allowed access to the search service endpoint. """ return pulumi.get(self, "allowed_ips") @allowed_ips.setter def allowed_ips(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "allowed_ips", value) @property @pulumi.getter def identity(self) -> Optional[pulumi.Input['ServiceIdentityArgs']]: """ An `identity` block as defined below. """ return pulumi.get(self, "identity") @identity.setter def identity(self, value: Optional[pulumi.Input['ServiceIdentityArgs']]): pulumi.set(self, "identity", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: """ The Azure Region where the Search Service should exist. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "location") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "location", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The Name which should be used for this Search Service. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="partitionCount") def partition_count(self) -> Optional[pulumi.Input[int]]: """ The number of partitions which should be created. """ return pulumi.get(self, "partition_count") @partition_count.setter def partition_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "partition_count", value) @property @pulumi.getter(name="primaryKey") def primary_key(self) -> Optional[pulumi.Input[str]]: """ The Primary Key used for Search Service Administration. """ return pulumi.get(self, "primary_key") @primary_key.setter def primary_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "primary_key", value) @property @pulumi.getter(name="publicNetworkAccessEnabled") def public_network_access_enabled(self) -> Optional[pulumi.Input[bool]]: """ Whether or not public network access is allowed for this resource. Defaults to `true`. """ return pulumi.get(self, "public_network_access_enabled") @public_network_access_enabled.setter def public_network_access_enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "public_network_access_enabled", value) @property @pulumi.getter(name="queryKeys") def query_keys(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ServiceQueryKeyArgs']]]]: """ A `query_keys` block as defined below. """ return pulumi.get(self, "query_keys") @query_keys.setter def query_keys(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ServiceQueryKeyArgs']]]]): pulumi.set(self, "query_keys", value) @property @pulumi.getter(name="replicaCount") def replica_count(self) -> Optional[pulumi.Input[int]]: """ The number of replica's which should be created. """ return pulumi.get(self, "replica_count") @replica_count.setter def replica_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "replica_count", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> Optional[pulumi.Input[str]]: """ The name of the Resource Group where the Search Service should exist. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="secondaryKey") def secondary_key(self) -> Optional[pulumi.Input[str]]: """ The Secondary Key used for Search Service Administration. """ return pulumi.get(self, "secondary_key") @secondary_key.setter def secondary_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "secondary_key", value) @property @pulumi.getter def sku(self) -> Optional[pulumi.Input[str]]: """ The SKU which should be used for this Search Service. Possible values are `basic`, `free`, `standard`, `standard2`, `standard3`, `storage_optimized_l1` and `storage_optimized_l2`. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "sku") @sku.setter def sku(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "sku", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ A mapping of tags which should be assigned to the Search Service. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags", value) class Service(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, allowed_ips: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, identity: Optional[pulumi.Input[pulumi.InputType['ServiceIdentityArgs']]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, partition_count: Optional[pulumi.Input[int]] = None, public_network_access_enabled: Optional[pulumi.Input[bool]] = None, replica_count: Optional[pulumi.Input[int]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, sku: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, __props__=None): """ Manages a Search Service. ## Example Usage ```python import pulumi import pulumi_azure as azure example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe") example_service = azure.search.Service("exampleService", resource_group_name=example_resource_group.name, location=example_resource_group.location, sku="standard") ``` ## Import Search Services can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:search/service:Service example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Search/searchServices/service1 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] allowed_ips: A list of IPv4 addresses or CIDRs that are allowed access to the search service endpoint. :param pulumi.Input[pulumi.InputType['ServiceIdentityArgs']] identity: An `identity` block as defined below. :param pulumi.Input[str] location: The Azure Region where the Search Service should exist. Changing this forces a new Search Service to be created. :param pulumi.Input[str] name: The Name which should be used for this Search Service. Changing this forces a new Search Service to be created. :param pulumi.Input[int] partition_count: The number of partitions which should be created. :param pulumi.Input[bool] public_network_access_enabled: Whether or not public network access is allowed for this resource. Defaults to `true`. :param pulumi.Input[int] replica_count: The number of replica's which should be created. :param pulumi.Input[str] resource_group_name: The name of the Resource Group where the Search Service should exist. Changing this forces a new Search Service to be created. :param pulumi.Input[str] sku: The SKU which should be used for this Search Service. Possible values are `basic`, `free`, `standard`, `standard2`, `standard3`, `storage_optimized_l1` and `storage_optimized_l2`. Changing this forces a new Search Service to be created. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A mapping of tags which should be assigned to the Search Service. """ ... @overload def __init__(__self__, resource_name: str, args: ServiceArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Manages a Search Service. ## Example Usage ```python import pulumi import pulumi_azure as azure example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe") example_service = azure.search.Service("exampleService", resource_group_name=example_resource_group.name, location=example_resource_group.location, sku="standard") ``` ## Import Search Services can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:search/service:Service example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Search/searchServices/service1 ``` :param str resource_name: The name of the resource. :param ServiceArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(ServiceArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, allowed_ips: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, identity: Optional[pulumi.Input[pulumi.InputType['ServiceIdentityArgs']]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, partition_count: Optional[pulumi.Input[int]] = None, public_network_access_enabled: Optional[pulumi.Input[bool]] = None, replica_count: Optional[pulumi.Input[int]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, sku: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = ServiceArgs.__new__(ServiceArgs) __props__.__dict__["allowed_ips"] = allowed_ips __props__.__dict__["identity"] = identity __props__.__dict__["location"] = location __props__.__dict__["name"] = name __props__.__dict__["partition_count"] = partition_count __props__.__dict__["public_network_access_enabled"] = public_network_access_enabled __props__.__dict__["replica_count"] = replica_count if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__.__dict__["resource_group_name"] = resource_group_name if sku is None and not opts.urn: raise TypeError("Missing required property 'sku'") __props__.__dict__["sku"] = sku __props__.__dict__["tags"] = tags __props__.__dict__["primary_key"] = None __props__.__dict__["query_keys"] = None __props__.__dict__["secondary_key"] = None super(Service, __self__).__init__( 'azure:search/service:Service', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, allowed_ips: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, identity: Optional[pulumi.Input[pulumi.InputType['ServiceIdentityArgs']]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, partition_count: Optional[pulumi.Input[int]] = None, primary_key: Optional[pulumi.Input[str]] = None, public_network_access_enabled: Optional[pulumi.Input[bool]] = None, query_keys: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceQueryKeyArgs']]]]] = None, replica_count: Optional[pulumi.Input[int]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, secondary_key: Optional[pulumi.Input[str]] = None, sku: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None) -> 'Service': """ Get an existing Service resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] allowed_ips: A list of IPv4 addresses or CIDRs that are allowed access to the search service endpoint. :param pulumi.Input[pulumi.InputType['ServiceIdentityArgs']] identity: An `identity` block as defined below. :param pulumi.Input[str] location: The Azure Region where the Search Service should exist. Changing this forces a new Search Service to be created. :param pulumi.Input[str] name: The Name which should be used for this Search Service. Changing this forces a new Search Service to be created. :param pulumi.Input[int] partition_count: The number of partitions which should be created. :param pulumi.Input[str] primary_key: The Primary Key used for Search Service Administration. :param pulumi.Input[bool] public_network_access_enabled: Whether or not public network access is allowed for this resource. Defaults to `true`. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceQueryKeyArgs']]]] query_keys: A `query_keys` block as defined below. :param pulumi.Input[int] replica_count: The number of replica's which should be created. :param pulumi.Input[str] resource_group_name: The name of the Resource Group where the Search Service should exist. Changing this forces a new Search Service to be created. :param pulumi.Input[str] secondary_key: The Secondary Key used for Search Service Administration. :param pulumi.Input[str] sku: The SKU which should be used for this Search Service. Possible values are `basic`, `free`, `standard`, `standard2`, `standard3`, `storage_optimized_l1` and `storage_optimized_l2`. Changing this forces a new Search Service to be created. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A mapping of tags which should be assigned to the Search Service. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _ServiceState.__new__(_ServiceState) __props__.__dict__["allowed_ips"] = allowed_ips __props__.__dict__["identity"] = identity __props__.__dict__["location"] = location __props__.__dict__["name"] = name __props__.__dict__["partition_count"] = partition_count __props__.__dict__["primary_key"] = primary_key __props__.__dict__["public_network_access_enabled"] = public_network_access_enabled __props__.__dict__["query_keys"] = query_keys __props__.__dict__["replica_count"] = replica_count __props__.__dict__["resource_group_name"] = resource_group_name __props__.__dict__["secondary_key"] = secondary_key __props__.__dict__["sku"] = sku __props__.__dict__["tags"] = tags return Service(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="allowedIps") def allowed_ips(self) -> pulumi.Output[Optional[Sequence[str]]]: """ A list of IPv4 addresses or CIDRs that are allowed access to the search service endpoint. """ return pulumi.get(self, "allowed_ips") @property @pulumi.getter def identity(self) -> pulumi.Output[Optional['outputs.ServiceIdentity']]: """ An `identity` block as defined below. """ return pulumi.get(self, "identity") @property @pulumi.getter def location(self) -> pulumi.Output[str]: """ The Azure Region where the Search Service should exist. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The Name which should be used for this Search Service. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "name") @property @pulumi.getter(name="partitionCount") def partition_count(self) -> pulumi.Output[int]: """ The number of partitions which should be created. """ return pulumi.get(self, "partition_count") @property @pulumi.getter(name="primaryKey") def primary_key(self) -> pulumi.Output[str]: """ The Primary Key used for Search Service Administration. """ return pulumi.get(self, "primary_key") @property @pulumi.getter(name="publicNetworkAccessEnabled") def public_network_access_enabled(self) -> pulumi.Output[Optional[bool]]: """ Whether or not public network access is allowed for this resource. Defaults to `true`. """ return pulumi.get(self, "public_network_access_enabled") @property @pulumi.getter(name="queryKeys") def query_keys(self) -> pulumi.Output[Sequence['outputs.ServiceQueryKey']]: """ A `query_keys` block as defined below. """ return pulumi.get(self, "query_keys") @property @pulumi.getter(name="replicaCount") def replica_count(self) -> pulumi.Output[int]: """ The number of replica's which should be created. """ return pulumi.get(self, "replica_count") @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Output[str]: """ The name of the Resource Group where the Search Service should exist. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "resource_group_name") @property @pulumi.getter(name="secondaryKey") def secondary_key(self) -> pulumi.Output[str]: """ The Secondary Key used for Search Service Administration. """ return pulumi.get(self, "secondary_key") @property @pulumi.getter def sku(self) -> pulumi.Output[str]: """ The SKU which should be used for this Search Service. Possible values are `basic`, `free`, `standard`, `standard2`, `standard3`, `storage_optimized_l1` and `storage_optimized_l2`. Changing this forces a new Search Service to be created. """ return pulumi.get(self, "sku") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: """ A mapping of tags which should be assigned to the Search Service. """ return pulumi.get(self, "tags")

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185c4b89d4fcd4fce137d5602176b546affa0461

30,789

py

Python

authentication_service/api/stubs.py

hedleyroos/core-authentication-service

4a59430cddf23c58322230dd1fe70998fcc46736

[ "BSD-3-Clause" ]

1

2018-03-15T12:49:05.000Z

authentication_service/api/stubs.py

hedleyroos/core-authentication-service

4a59430cddf23c58322230dd1fe70998fcc46736

[ "BSD-3-Clause" ]

215

2017-12-07T09:11:52.000Z

2022-03-11T23:19:59.000Z

authentication_service/api/stubs.py

hedleyroos/core-authentication-service

4a59430cddf23c58322230dd1fe70998fcc46736

[ "BSD-3-Clause" ]

1

2021-08-17T12:05:32.000Z

""" Do not modify this file. It is generated from the Swagger specification. """ import json from apitools.datagenerator import DataGenerator import authentication_service.api.schemas as schemas class AbstractStubClass(object): """ Implementations need to be derived from this class. """ # client_list -- Synchronisation point for meld @staticmethod def client_list(request, offset=None, limit=None, client_ids=None, client_token_id=None, *args, **kwargs): """ :param request: An HttpRequest :param offset: (optional) An optional query parameter specifying the offset in the result set to start from. :type offset: integer :param limit: (optional) An optional query parameter to limit the number of results returned. :type limit: integer :param client_ids: (optional) An optional list of client ids :type client_ids: array :param client_token_id: (optional) An optional client id to filter on. This is not the primary key. :type client_token_id: string """ raise NotImplementedError() # client_read -- Synchronisation point for meld @staticmethod def client_read(request, client_id, *args, **kwargs): """ :param request: An HttpRequest :param client_id: A string value identifying the client :type client_id: string """ raise NotImplementedError() # country_list -- Synchronisation point for meld @staticmethod def country_list(request, offset=None, limit=None, country_codes=None, *args, **kwargs): """ :param request: An HttpRequest :param offset: (optional) An optional query parameter specifying the offset in the result set to start from. :type offset: integer :param limit: (optional) An optional query parameter to limit the number of results returned. :type limit: integer :param country_codes: (optional) An optional list of country codes :type country_codes: array """ raise NotImplementedError() # country_read -- Synchronisation point for meld @staticmethod def country_read(request, country_code, *args, **kwargs): """ :param request: An HttpRequest :param country_code: A string value identifying the country :type country_code: string """ raise NotImplementedError() # invitation_send -- Synchronisation point for meld @staticmethod def invitation_send(request, invitation_id, language=None, *args, **kwargs): """ :param request: An HttpRequest :param invitation_id: :type invitation_id: string :param language: (optional) :type language: string """ raise NotImplementedError() # purge_expired_invitations -- Synchronisation point for meld @staticmethod def purge_expired_invitations(request, cutoff_date=None, *args, **kwargs): """ :param request: An HttpRequest :param cutoff_date: (optional) An optional cutoff date to purge invites before this date :type cutoff_date: string """ raise NotImplementedError() # organisation_list -- Synchronisation point for meld @staticmethod def organisation_list(request, offset=None, limit=None, organisation_ids=None, *args, **kwargs): """ :param request: An HttpRequest :param offset: (optional) An optional query parameter specifying the offset in the result set to start from. :type offset: integer :param limit: (optional) An optional query parameter to limit the number of results returned. :type limit: integer :param organisation_ids: (optional) An optional list of organisation ids :type organisation_ids: array """ raise NotImplementedError() # organisation_create -- Synchronisation point for meld @staticmethod def organisation_create(request, body, *args, **kwargs): """ :param request: An HttpRequest :param body: A dictionary containing the parsed and validated body :type body: dict """ raise NotImplementedError() # organisation_delete -- Synchronisation point for meld @staticmethod def organisation_delete(request, organisation_id, *args, **kwargs): """ :param request: An HttpRequest :param organisation_id: An integer identifying an organisation a user belongs to :type organisation_id: integer """ raise NotImplementedError() # organisation_read -- Synchronisation point for meld @staticmethod def organisation_read(request, organisation_id, *args, **kwargs): """ :param request: An HttpRequest :param organisation_id: An integer identifying an organisation a user belongs to :type organisation_id: integer """ raise NotImplementedError() # organisation_update -- Synchronisation point for meld @staticmethod def organisation_update(request, body, organisation_id, *args, **kwargs): """ :param request: An HttpRequest :param body: A dictionary containing the parsed and validated body :type body: dict :param organisation_id: An integer identifying an organisation a user belongs to :type organisation_id: integer """ raise NotImplementedError() # request_user_deletion -- Synchronisation point for meld @staticmethod def request_user_deletion(request, body, *args, **kwargs): """ :param request: An HttpRequest :param body: A dictionary containing the parsed and validated body :type body: dict """ raise NotImplementedError() # user_list -- Synchronisation point for meld @staticmethod def user_list(request, offset=None, limit=None, birth_date=None, country=None, date_joined=None, email=None, email_verified=None, first_name=None, gender=None, is_active=None, last_login=None, last_name=None, msisdn=None, msisdn_verified=None, nickname=None, organisation_id=None, updated_at=None, username=None, q=None, tfa_enabled=None, has_organisation=None, order_by=None, user_ids=None, site_ids=None, *args, **kwargs): """ :param request: An HttpRequest :param offset: (optional) An optional query parameter specifying the offset in the result set to start from. :type offset: integer :param limit: (optional) An optional query parameter to limit the number of results returned. :type limit: integer :param birth_date: (optional) An optional birth_date range filter :type birth_date: string :param country: (optional) An optional country filter :type country: string :param date_joined: (optional) An optional date joined range filter :type date_joined: string :param email: (optional) An optional case insensitive email inner match filter :type email: string :param email_verified: (optional) An optional email verified filter :type email_verified: boolean :param first_name: (optional) An optional case insensitive first name inner match filter :type first_name: string :param gender: (optional) An optional gender filter :type gender: string :param is_active: (optional) An optional is_active filter :type is_active: boolean :param last_login: (optional) An optional last login range filter :type last_login: string :param last_name: (optional) An optional case insensitive last name inner match filter :type last_name: string :param msisdn: (optional) An optional case insensitive MSISDN inner match filter :type msisdn: string :param msisdn_verified: (optional) An optional MSISDN verified filter :type msisdn_verified: boolean :param nickname: (optional) An optional case insensitive nickname inner match filter :type nickname: string :param organisation_id: (optional) An optional filter on the organisation id :type organisation_id: integer :param updated_at: (optional) An optional updated_at range filter :type updated_at: string :param username: (optional) An optional case insensitive username inner match filter :type username: string :param q: (optional) An optional case insensitive inner match filter across all searchable text fields :type q: string :param tfa_enabled: (optional) An optional filter based on whether a user has 2FA enabled or not :type tfa_enabled: boolean :param has_organisation: (optional) An optional filter based on whether a user belongs to an organisation or not :type has_organisation: boolean :param order_by: (optional) Fields and directions to order by, e.g. "-created_at,username". Add "-" in front of a field name to indicate descending order. :type order_by: array :param user_ids: (optional) An optional list of user ids :type user_ids: array :param site_ids: (optional) An optional list of site ids :type site_ids: array """ raise NotImplementedError() # user_delete -- Synchronisation point for meld @staticmethod def user_delete(request, user_id, *args, **kwargs): """ :param request: An HttpRequest :param user_id: A UUID value identifying the user. :type user_id: string """ raise NotImplementedError() # user_read -- Synchronisation point for meld @staticmethod def user_read(request, user_id, *args, **kwargs): """ :param request: An HttpRequest :param user_id: A UUID value identifying the user. :type user_id: string """ raise NotImplementedError() # user_update -- Synchronisation point for meld @staticmethod def user_update(request, body, user_id, *args, **kwargs): """ :param request: An HttpRequest :param body: A dictionary containing the parsed and validated body :type body: dict :param user_id: A UUID value identifying the user. :type user_id: string """ raise NotImplementedError() class MockedStubClass(AbstractStubClass): """ Provides a mocked implementation of the AbstractStubClass. """ GENERATOR = DataGenerator() @staticmethod def client_list(request, offset=None, limit=None, client_ids=None, client_token_id=None, *args, **kwargs): """ :param request: An HttpRequest :param offset: (optional) An optional query parameter specifying the offset in the result set to start from. :type offset: integer :param limit: (optional) An optional query parameter to limit the number of results returned. :type limit: integer :param client_ids: (optional) An optional list of client ids :type client_ids: array :param client_token_id: (optional) An optional client id to filter on. This is not the primary key. :type client_token_id: string """ response_schema = json.loads("""{ "items": { "properties": { "_post_logout_redirect_uris": { "description": "New-line delimited list of post-logout redirect URIs", "type": "string" }, "_redirect_uris": { "description": "New-line delimited list of redirect URIs", "type": "string" }, "client_id": { "description": "", "type": "string" }, "contact_email": { "description": "", "type": "string" }, "id": { "description": "", "type": "integer" }, "logo": { "description": "", "format": "uri", "type": "string" }, "name": { "description": "", "type": "string" }, "require_consent": { "description": "If disabled, the Server will NEVER ask the user for consent.", "type": "boolean" }, "response_type": { "description": "", "type": "string" }, "reuse_consent": { "description": "If enabled, the Server will save the user consent given to a specific client, so that user won't be prompted for the same authorization multiple times.", "type": "boolean" }, "terms_url": { "description": "External reference to the privacy policy of the client.", "type": "string" }, "website_url": { "description": "", "type": "string" } }, "required": [ "id", "client_id", "response_type" ], "type": "object", "x-scope": [ "" ] }, "type": "array" }""") if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def client_read(request, client_id, *args, **kwargs): """ :param request: An HttpRequest :param client_id: A string value identifying the client :type client_id: string """ response_schema = schemas.client if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def country_list(request, offset=None, limit=None, country_codes=None, *args, **kwargs): """ :param request: An HttpRequest :param offset: (optional) An optional query parameter specifying the offset in the result set to start from. :type offset: integer :param limit: (optional) An optional query parameter to limit the number of results returned. :type limit: integer :param country_codes: (optional) An optional list of country codes :type country_codes: array """ response_schema = json.loads("""{ "items": { "properties": { "code": { "maxLength": 2, "minLength": 2, "type": "string" }, "name": { "maxLength": 100, "type": "string" } }, "required": [ "code", "name" ], "type": "object", "x-scope": [ "" ] }, "type": "array" }""") if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def country_read(request, country_code, *args, **kwargs): """ :param request: An HttpRequest :param country_code: A string value identifying the country :type country_code: string """ response_schema = schemas.country if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def invitation_send(request, invitation_id, language=None, *args, **kwargs): """ :param request: An HttpRequest :param invitation_id: :type invitation_id: string :param language: (optional) :type language: string """ response_schema = schemas.__UNSPECIFIED__ if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def purge_expired_invitations(request, cutoff_date=None, *args, **kwargs): """ :param request: An HttpRequest :param cutoff_date: (optional) An optional cutoff date to purge invites before this date :type cutoff_date: string """ response_schema = schemas.__UNSPECIFIED__ if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def organisation_list(request, offset=None, limit=None, organisation_ids=None, *args, **kwargs): """ :param request: An HttpRequest :param offset: (optional) An optional query parameter specifying the offset in the result set to start from. :type offset: integer :param limit: (optional) An optional query parameter to limit the number of results returned. :type limit: integer :param organisation_ids: (optional) An optional list of organisation ids :type organisation_ids: array """ response_schema = json.loads("""{ "items": { "properties": { "created_at": { "format": "date-time", "readOnly": true, "type": "string" }, "description": { "type": "string" }, "id": { "type": "integer" }, "name": { "type": "string" }, "updated_at": { "format": "date-time", "readOnly": true, "type": "string" } }, "required": [ "id", "name", "description", "created_at", "updated_at" ], "type": "object", "x-scope": [ "" ] }, "type": "array" }""") if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def organisation_create(request, body, *args, **kwargs): """ :param request: An HttpRequest :param body: A dictionary containing the parsed and validated body :type body: dict """ response_schema = schemas.organisation if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def organisation_delete(request, organisation_id, *args, **kwargs): """ :param request: An HttpRequest :param organisation_id: An integer identifying an organisation a user belongs to :type organisation_id: integer """ response_schema = schemas.__UNSPECIFIED__ if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def organisation_read(request, organisation_id, *args, **kwargs): """ :param request: An HttpRequest :param organisation_id: An integer identifying an organisation a user belongs to :type organisation_id: integer """ response_schema = schemas.organisation if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def organisation_update(request, body, organisation_id, *args, **kwargs): """ :param request: An HttpRequest :param body: A dictionary containing the parsed and validated body :type body: dict :param organisation_id: An integer identifying an organisation a user belongs to :type organisation_id: integer """ response_schema = schemas.organisation if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def request_user_deletion(request, body, *args, **kwargs): """ :param request: An HttpRequest :param body: A dictionary containing the parsed and validated body :type body: dict """ response_schema = schemas.__UNSPECIFIED__ if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def user_list(request, offset=None, limit=None, birth_date=None, country=None, date_joined=None, email=None, email_verified=None, first_name=None, gender=None, is_active=None, last_login=None, last_name=None, msisdn=None, msisdn_verified=None, nickname=None, organisation_id=None, updated_at=None, username=None, q=None, tfa_enabled=None, has_organisation=None, order_by=None, user_ids=None, site_ids=None, *args, **kwargs): """ :param request: An HttpRequest :param offset: (optional) An optional query parameter specifying the offset in the result set to start from. :type offset: integer :param limit: (optional) An optional query parameter to limit the number of results returned. :type limit: integer :param birth_date: (optional) An optional birth_date range filter :type birth_date: string :param country: (optional) An optional country filter :type country: string :param date_joined: (optional) An optional date joined range filter :type date_joined: string :param email: (optional) An optional case insensitive email inner match filter :type email: string :param email_verified: (optional) An optional email verified filter :type email_verified: boolean :param first_name: (optional) An optional case insensitive first name inner match filter :type first_name: string :param gender: (optional) An optional gender filter :type gender: string :param is_active: (optional) An optional is_active filter :type is_active: boolean :param last_login: (optional) An optional last login range filter :type last_login: string :param last_name: (optional) An optional case insensitive last name inner match filter :type last_name: string :param msisdn: (optional) An optional case insensitive MSISDN inner match filter :type msisdn: string :param msisdn_verified: (optional) An optional MSISDN verified filter :type msisdn_verified: boolean :param nickname: (optional) An optional case insensitive nickname inner match filter :type nickname: string :param organisation_id: (optional) An optional filter on the organisation id :type organisation_id: integer :param updated_at: (optional) An optional updated_at range filter :type updated_at: string :param username: (optional) An optional case insensitive username inner match filter :type username: string :param q: (optional) An optional case insensitive inner match filter across all searchable text fields :type q: string :param tfa_enabled: (optional) An optional filter based on whether a user has 2FA enabled or not :type tfa_enabled: boolean :param has_organisation: (optional) An optional filter based on whether a user belongs to an organisation or not :type has_organisation: boolean :param order_by: (optional) Fields and directions to order by, e.g. "-created_at,username". Add "-" in front of a field name to indicate descending order. :type order_by: array :param user_ids: (optional) An optional list of user ids :type user_ids: array :param site_ids: (optional) An optional list of site ids :type site_ids: array """ response_schema = json.loads("""{ "items": { "properties": { "avatar": { "format": "uri", "type": "string" }, "birth_date": { "format": "date", "type": "string" }, "country_code": { "maxLength": 2, "minLength": 2, "type": "string" }, "created_at": { "format": "date-time", "readOnly": true, "type": "string" }, "date_joined": { "description": "", "format": "date-time", "readOnly": true, "type": "string" }, "email": { "description": "", "format": "email", "type": "string" }, "email_verified": { "type": "boolean" }, "first_name": { "description": "", "type": "string" }, "gender": { "type": "string" }, "id": { "description": "A UUID identifying the user", "format": "uuid", "readOnly": true, "type": "string" }, "is_active": { "description": "Designates whether this user should be treated as active. Deselect this instead of deleting accounts.", "type": "boolean" }, "last_login": { "description": "", "format": "date-time", "readOnly": true, "type": "string" }, "last_name": { "description": "", "type": "string" }, "msisdn": { "maxLength": 15, "type": "string" }, "msisdn_verified": { "type": "boolean" }, "organisation_id": { "readOnly": true, "type": "integer" }, "updated_at": { "format": "date-time", "readOnly": true, "type": "string" }, "username": { "description": "Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.", "readOnly": true, "type": "string" } }, "required": [ "id", "username", "is_active", "date_joined", "created_at", "updated_at" ], "type": "object", "x-scope": [ "" ] }, "type": "array" }""") if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def user_delete(request, user_id, *args, **kwargs): """ :param request: An HttpRequest :param user_id: A UUID value identifying the user. :type user_id: string """ response_schema = schemas.__UNSPECIFIED__ if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def user_read(request, user_id, *args, **kwargs): """ :param request: An HttpRequest :param user_id: A UUID value identifying the user. :type user_id: string """ response_schema = schemas.user if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema) @staticmethod def user_update(request, body, user_id, *args, **kwargs): """ :param request: An HttpRequest :param body: A dictionary containing the parsed and validated body :type body: dict :param user_id: A UUID value identifying the user. :type user_id: string """ response_schema = schemas.user if "type" not in response_schema: response_schema["type"] = "object" if response_schema["type"] == "array" and "type" not in response_schema["items"]: response_schema["items"]["type"] = "object" return MockedStubClass.GENERATOR.random_value(response_schema)

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a12d5cf97d06149a69f27cac653005b17e85655a

15,149

py

Python

python/cuxfilter/charts/bokeh/plots.py

kkraus14/cuxfilter

99d7cf67802270d24db0051162df4feb798f2e15

[ "Apache-2.0" ]

null

python/cuxfilter/charts/bokeh/plots.py

kkraus14/cuxfilter

99d7cf67802270d24db0051162df4feb798f2e15

[ "Apache-2.0" ]

null

python/cuxfilter/charts/bokeh/plots.py

kkraus14/cuxfilter

99d7cf67802270d24db0051162df4feb798f2e15

[ "Apache-2.0" ]

null

from ..core.aggregate import BaseAggregateChart import numpy as np from bokeh import events from bokeh.plotting import figure from bokeh.models import ColumnDataSource class Bar(BaseAggregateChart): """ Description: """ reset_event = events.Reset data_y_axis = "top" data_x_axis = "x" def format_source_data(self, source_dict, patch_update=False): """ format source Parameters: ----------- source_dict: {'X': [], 'Y': []} """ if patch_update is False: self.source = ColumnDataSource( { self.data_x_axis: np.array(source_dict["X"]), self.data_y_axis: np.array(source_dict["Y"]), } ) self.source_backup = self.source.to_df() else: patch_dict = { self.data_y_axis: [ (slice(len(source_dict["Y"])), np.array(source_dict["Y"])) ] } self.source.patch(patch_dict) def get_source_y_axis(self): """ get y axis column value """ if self.source is not None: return self.source.data[self.data_y_axis] return self.source def generate_chart(self): """ generate chart """ self.chart = figure( title=self.title, x_range=( self.source.data[self.data_x_axis] if self.x_dtype == "object" else None ), tools="pan, wheel_zoom, reset", active_scroll="wheel_zoom", active_drag="pan", ) if self.color is None: self.sub_chart = self.chart.vbar( x=self.data_x_axis, top=self.data_y_axis, width=0.9, source=self.source, **self.library_specific_params, ) else: self.sub_chart = self.chart.vbar( x=self.data_x_axis, top=self.data_y_axis, width=0.9, source=self.source, color=self.color, **self.library_specific_params, ) self.chart.xaxis.axis_label = self.x if self.x_axis_tick_formatter: self.chart.xaxis.formatter = self.x_axis_tick_formatter if self.y_axis_tick_formatter: self.chart.yaxis.formatter = self.y_axis_tick_formatter if self.autoscaling is False: self.chart.y_range.end = self.source.data[self.data_y_axis].max() if self.y != self.x: self.chart.yaxis.axis_label = self.y else: self.chart.yaxis.axis_label = self.aggregate_fn def update_dimensions(self, width=None, height=None): """ update dimensions """ if width is not None: self.chart.plot_width = width if height is not None: self.chart.plot_height = height def apply_mappers(self): """ apply dict mappers to x and y axes if provided """ if self.x_label_map is not None: self.chart.xaxis.major_label_overrides = self.x_label_map if self.y_label_map is not None: self.chart.yaxis.major_label_overrides = self.y_label_map def reload_chart(self, data, patch_update=True): """ reload chart """ self.calculate_source(data, patch_update=patch_update) def reset_chart(self, data: np.array = np.array([])): """ if len(data) is 0, reset the chart using self.source_backup Parmeters: ---------- data = list() --> update self.data_y_axis in self.source """ if data.size == 0: data = self.source_backup[self.data_y_axis] # verifying length is same as x axis x_axis_len = self.source.data[self.data_x_axis].size data = data[:x_axis_len] patch_dict = {self.data_y_axis: [(slice(data.size), data)]} self.source.patch(patch_dict) def apply_theme(self, properties_dict): """ apply thematic changes to the chart based on the input properties dictionary. """ if self.color is None: self.sub_chart.glyph.fill_color = properties_dict["chart_color"][ "color" ] self.sub_chart.glyph.line_color = properties_dict["chart_color"][ "color" ] self.chart.xgrid.grid_line_color = properties_dict["agg_charts_grids"][ "xgrid" ] self.chart.ygrid.grid_line_color = properties_dict["agg_charts_grids"][ "ygrid" ] # title self.chart.title.text_color = properties_dict["title"]["text_color"] self.chart.title.text_font = properties_dict["title"]["text_font"] self.chart.title.text_font_style = properties_dict["title"][ "text_font_style" ] self.chart.title.text_font_size = properties_dict["title"][ "text_font_size" ] # background, border, padding self.chart.background_fill_color = properties_dict[ "background_fill_color" ] self.chart.border_fill_color = properties_dict["border_fill_color"] self.chart.min_border = properties_dict["min_border"] self.chart.outline_line_width = properties_dict["outline_line_width"] self.chart.outline_line_alpha = properties_dict["outline_line_alpha"] self.chart.outline_line_color = properties_dict["outline_line_color"] # x axis title self.chart.xaxis.axis_label_text_font_style = properties_dict["xaxis"][ "axis_label_text_font_style" ] self.chart.xaxis.axis_label_text_color = properties_dict["xaxis"][ "axis_label_text_color" ] self.chart.xaxis.axis_label_standoff = properties_dict["xaxis"][ "axis_label_standoff" ] self.chart.xaxis.major_label_text_color = properties_dict["xaxis"][ "major_label_text_color" ] self.chart.xaxis.axis_line_width = properties_dict["xaxis"][ "axis_line_width" ] self.chart.xaxis.axis_line_color = properties_dict["xaxis"][ "axis_line_color" ] # y axis title self.chart.yaxis.axis_label_text_font_style = properties_dict["yaxis"][ "axis_label_text_font_style" ] self.chart.yaxis.axis_label_text_color = properties_dict["yaxis"][ "axis_label_text_color" ] self.chart.yaxis.axis_label_standoff = properties_dict["yaxis"][ "axis_label_standoff" ] self.chart.yaxis.major_label_text_color = properties_dict["yaxis"][ "major_label_text_color" ] self.chart.yaxis.axis_line_width = properties_dict["yaxis"][ "axis_line_width" ] self.chart.yaxis.axis_line_color = properties_dict["yaxis"][ "axis_line_color" ] # axis ticks self.chart.axis.major_tick_line_color = properties_dict["axis"][ "major_tick_line_color" ] self.chart.axis.minor_tick_line_color = properties_dict["axis"][ "minor_tick_line_color" ] self.chart.axis.minor_tick_out = properties_dict["axis"][ "minor_tick_out" ] self.chart.axis.major_tick_out = properties_dict["axis"][ "major_tick_out" ] self.chart.axis.major_tick_in = properties_dict["axis"][ "major_tick_in" ] # interactive slider self.datatile_active_color = properties_dict["widgets"][ "datatile_active_color" ] class Line(BaseAggregateChart): """ Description: """ reset_event = events.Reset data_y_axis = "y" data_x_axis = "x" def format_source_data(self, source_dict, patch_update=False): """ format source Parameters: ----------- source_dict: {'X': [], 'Y': []} """ if patch_update is False: self.source = ColumnDataSource( { self.data_x_axis: np.array(source_dict["X"]), self.data_y_axis: np.array(source_dict["Y"]), } ) self.source_backup = self.source.to_df() else: patch_dict = { self.data_y_axis: [ (slice(len(source_dict["Y"])), np.array(source_dict["Y"])) ] } self.source.patch(patch_dict) def get_source_y_axis(self): """ get y axis column value """ if self.source is not None: return self.source.data[self.data_y_axis] return self.source def generate_chart(self): """ generate chart """ self.chart = figure( title=self.title, x_range=( self.source.data[self.data_x_axis] if self.x_dtype == "object" else None ), tools="pan, wheel_zoom, reset", active_scroll="wheel_zoom", active_drag="pan", ) if self.x_axis_tick_formatter: self.chart.xaxis.formatter = self.x_axis_tick_formatter if self.y_axis_tick_formatter: self.chart.yaxis.formatter = self.y_axis_tick_formatter if self.autoscaling is False: self.chart.y_range.end = self.source.data[self.data_y_axis].max() if self.color is None: self.sub_chart = self.chart.line( x=self.data_x_axis, y=self.data_y_axis, source=self.source, **self.library_specific_params, ) else: self.sub_chart = self.chart.line( x=self.data_x_axis, y=self.data_y_axis, source=self.source, color=self.color, **self.library_specific_params, ) def update_dimensions(self, width=None, height=None): """ update dimensions """ if width is not None: self.chart.plot_width = width if height is not None: self.chart.plot_height = height def apply_mappers(self): """ apply dict mappers to x and y axes if provided """ if self.x_label_map is not None: self.chart.xaxis.major_label_overrides = self.x_label_map if self.y_label_map is not None: self.chart.yaxis.major_label_overrides = self.y_label_map def reload_chart(self, data, patch_update=True): """ reload chart """ self.calculate_source(data, patch_update=patch_update) def reset_chart(self, data: np.array = np.array([])): """ if len(data) is 0, reset the chart using self.source_backup. Parameters: ----------- data = list() --> update self.data_y_axis in self.source """ if data.size == 0: data = self.source_backup[self.data_y_axis] # verifying length is same as x axis x_axis_len = self.source.data[self.data_x_axis].size data = data[:x_axis_len] patch_dict = {self.data_y_axis: [(slice(data.size), data)]} self.source.patch(patch_dict) def apply_theme(self, properties_dict): """ apply thematic changes to the chart based on the input properties dictionary. """ if self.color is None: self.sub_chart.glyph.line_color = properties_dict["chart_color"][ "color" ] self.chart.xgrid.grid_line_color = properties_dict["agg_charts_grids"][ "xgrid" ] self.chart.ygrid.grid_line_color = properties_dict["agg_charts_grids"][ "ygrid" ] # title self.chart.title.text_color = properties_dict["title"]["text_color"] self.chart.title.text_font = properties_dict["title"]["text_font"] self.chart.title.text_font_style = properties_dict["title"][ "text_font_style" ] self.chart.title.text_font_size = properties_dict["title"][ "text_font_size" ] # background, border, padding self.chart.background_fill_color = properties_dict[ "background_fill_color" ] self.chart.border_fill_color = properties_dict["border_fill_color"] self.chart.min_border = properties_dict["min_border"] self.chart.outline_line_width = properties_dict["outline_line_width"] self.chart.outline_line_alpha = properties_dict["outline_line_alpha"] self.chart.outline_line_color = properties_dict["outline_line_color"] # x axis title self.chart.xaxis.axis_label_text_font_style = properties_dict["xaxis"][ "axis_label_text_font_style" ] self.chart.xaxis.axis_label_text_color = properties_dict["xaxis"][ "axis_label_text_color" ] self.chart.xaxis.axis_label_standoff = properties_dict["xaxis"][ "axis_label_standoff" ] self.chart.xaxis.major_label_text_color = properties_dict["xaxis"][ "major_label_text_color" ] self.chart.xaxis.axis_line_width = properties_dict["xaxis"][ "axis_line_width" ] self.chart.xaxis.axis_line_color = properties_dict["xaxis"][ "axis_line_color" ] # y axis title self.chart.yaxis.axis_label_text_font_style = properties_dict["yaxis"][ "axis_label_text_font_style" ] self.chart.yaxis.axis_label_text_color = properties_dict["yaxis"][ "axis_label_text_color" ] self.chart.yaxis.axis_label_standoff = properties_dict["yaxis"][ "axis_label_standoff" ] self.chart.yaxis.major_label_text_color = properties_dict["yaxis"][ "major_label_text_color" ] self.chart.yaxis.axis_line_width = properties_dict["yaxis"][ "axis_line_width" ] self.chart.yaxis.axis_line_color = properties_dict["yaxis"][ "axis_line_color" ] # axis ticks self.chart.axis.major_tick_line_color = properties_dict["axis"][ "major_tick_line_color" ] self.chart.axis.minor_tick_line_color = properties_dict["axis"][ "minor_tick_line_color" ] self.chart.axis.minor_tick_out = properties_dict["axis"][ "minor_tick_out" ] self.chart.axis.major_tick_out = properties_dict["axis"][ "major_tick_out" ] self.chart.axis.major_tick_in = properties_dict["axis"][ "major_tick_in" ] # interactive slider self.datatile_active_color = properties_dict["widgets"][ "datatile_active_color" ]

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a176cef7bd4adfdd213efda5255576dc20fe8f1c

406

py

Python

optalg/opt_solver/_cbc/__init__.py

ttinoco/OPTALG

9103b99e6bc3517c7052ab075cbc5bdad310a25a

[ "BSD-2-Clause" ]

10

2015-11-13T22:34:47.000Z

2020-01-31T17:54:02.000Z

optalg/opt_solver/_cbc/__init__.py

ttinoco/OPTALG

9103b99e6bc3517c7052ab075cbc5bdad310a25a

[ "BSD-2-Clause" ]

40

2016-05-08T12:22:01.000Z

2019-04-01T01:39:28.000Z

optalg/opt_solver/_cbc/__init__.py

romcon/OPTALG

5ebe18c7a98e3a0feaa7be2658a2fc4f97eeeef3

[ "BSD-2-Clause" ]

12

2016-06-30T19:30:12.000Z

2019-09-26T16:29:37.000Z

#****************************************************# # This file is part of OPTALG. # # # # Copyright (c) 2015-2017, Tomas Tinoco De Rubira. # # # # OPTALG is released under the BSD 2-clause license. # #****************************************************# from .ccbc import *

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7

a18c1d7f6285839b59fe2045da9d42f8808cb003

128

py

Python

proauth2/__init__.py

charlesthomas/proauth2

f88c8df966a1802414047ed304d02df1dd520097

[ "MIT" ]

null

proauth2/__init__.py

charlesthomas/proauth2

f88c8df966a1802414047ed304d02df1dd520097

[ "MIT" ]

null

proauth2/__init__.py

charlesthomas/proauth2

f88c8df966a1802414047ed304d02df1dd520097

[ "MIT" ]

null

#!/usr/bin/env python from proauth2 import Proauth2 from async_proauth2 import AsyncProauth2 from proauth2 import Proauth2Error

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7

a1c28fbeb10eaa55176878a6a6f934de7ce0186f

227

py

Python

lib/galaxy/selenium/toolbox/filters.py

rikeshi/galaxy

c536a877e4a9b3d12aa0d00fd4d5e705109a0d0a

[ "CC-BY-3.0" ]

1,085

2015-02-18T16:14:38.000Z

2022-03-30T23:52:07.000Z

lib/galaxy/selenium/toolbox/filters.py

rikeshi/galaxy

c536a877e4a9b3d12aa0d00fd4d5e705109a0d0a

[ "CC-BY-3.0" ]

11,253

2015-02-18T17:47:32.000Z

2022-03-31T21:47:03.000Z

lib/galaxy/selenium/toolbox/filters.py

rikeshi/galaxy

c536a877e4a9b3d12aa0d00fd4d5e705109a0d0a

[ "CC-BY-3.0" ]

1,000

2015-02-18T16:18:10.000Z

2022-03-29T08:22:56.000Z

def restrict_test(context, section): """ Disable the Test Section section This tool filter will disable the Test Section section. """ if section.name == 'Test Section': return False return True

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7

b813c1117b48cb3a8bf006f2f5039432a88b4813

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py

Python

tests/test_localtree.py

byteskeptical/sftpretty

0b242f7d32086aa50a308d0df9ad4578b05f2701

[ "BSD-3-Clause" ]

11

2021-06-04T21:27:35.000Z

2021-12-05T09:58:26.000Z

tests/test_localtree.py

byteskeptical/sftpretty

0b242f7d32086aa50a308d0df9ad4578b05f2701

[ "BSD-3-Clause" ]

null

tests/test_localtree.py

byteskeptical/sftpretty

0b242f7d32086aa50a308d0df9ad4578b05f2701

[ "BSD-3-Clause" ]

3

2021-08-30T09:17:27.000Z

2021-12-26T20:51:50.000Z

'''test sftpretty.localtree''' from common import conn, rmdir, VFS from sftpretty import Connection, localtree from tempfile import mkdtemp def test_localtree(sftpserver): '''test the localtree function, with recurse''' with sftpserver.serve_content(VFS): with Connection(**conn(sftpserver)) as sftp: localpath = mkdtemp() sftp.get_r('.', localpath) cwd = sftp.pwd directories = {} localtree(directories, localpath + cwd, '/') dkeys = [f'{localpath}/home/test', f'{localpath}/home/test/pub', f'{localpath}/home/test/pub/foo2'] dvalues = [[(f'{localpath}/home/test/pub', f'{localpath}/home/test/pub')], [(f'{localpath}/home/test/pub/foo1', f'{localpath}/home/test/pub/foo1'), (f'{localpath}/home/test/pub/foo2', f'{localpath}/home/test/pub/foo2')], [(f'{localpath}/home/test/pub/foo2/bar1', f'{localpath}/home/test/pub/foo2/bar1')]] assert sorted(directories.keys()) == dkeys assert sorted(directories.values()) == dvalues # cleanup local rmdir(localpath) def test_localtree_no_recurse(sftpserver): '''test the localtree function, without recursing''' with sftpserver.serve_content(VFS): with Connection(**conn(sftpserver)) as sftp: localpath = mkdtemp() sftp.chdir('pub/foo2') sftp.get_r('.', localpath) cwd = sftp.pwd directories = {} localtree(directories, localpath + cwd, '/', recurse=False) dkeys = [f'{localpath}/home/test/pub/foo2'] dvalues = [[(f'{localpath}/home/test/pub/foo2/bar1', f'{localpath}/home/test/pub/foo2/bar1')]] assert sorted(directories.keys()) == dkeys assert sorted(directories.values()) == dvalues # cleanup local rmdir(localpath)

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7

62960466f78b57982fde14d87dff2decc2ec00ee

121,028

py

Python

tools/models.py

UnIcOrn7618/MonthlyRunoffForecastByAutoReg

2d66c628141f001e4ffb3dc3b7520a0f0f0ff239

[ "MIT" ]

2

2020-09-24T13:31:06.000Z

2020-11-11T09:08:16.000Z

tools/models.py

UnIcOrn7618/MonthlyRunoffForecastByAutoReg

2d66c628141f001e4ffb3dc3b7520a0f0f0ff239

[ "MIT" ]

null

tools/models.py

UnIcOrn7618/MonthlyRunoffForecastByAutoReg

2d66c628141f001e4ffb3dc3b7520a0f0f0ff239

[ "MIT" ]

1

2020-12-16T07:29:32.000Z

#### import basic external libs import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.set_cmap("viridis") import datetime import time #### import libs for optimize SVR or GBRT from sklearn.svm import SVR,NuSVR from sklearn.ensemble import GradientBoostingRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, mean_squared_log_error from sklearn.externals.joblib import Parallel, delayed from skopt.space import Real, Integer from skopt.utils import use_named_args from skopt import gp_minimize,forest_minimize, dummy_minimize from skopt.plots import plot_convergence,plot_objective,plot_evaluations from skopt import dump, load from skopt import Optimizer from skopt.benchmarks import branin from functools import partial from statsmodels.tsa.arima_model import ARIMA from random import seed from random import random seed(1) # from skopt.callbacks import CheckpointSaver import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers print(tf.__version__) from tensorflow.keras.callbacks import ReduceLROnPlateau,EarlyStopping import os root_path = os.path.dirname(os.path.abspath('_file_')) import sys sys.path.append(root_path) from config.globalLog import logger # import own coding libs from tools.plot_utils import plot_convergence_ from tools.plot_utils import plot_evaluations_ from tools.plot_utils import plot_objective_ from tools.plot_utils import plot_rela_pred from tools.plot_utils import plot_history from tools.plot_utils import plot_error_distribution from tools.dump_data import dum_pred_results ESVR_SPACE = [ # Penalty parameter `C` of the error term Real(0.1, 200, name='C'), # `epsilon` in epsilon-SVR model. It specifies the epsilon-tube # within which no penalty is associated in the training loss # function with points predicted within a distance epsilon from the actual value. Real(10**-6, 10**0, name='epsilon'), # kernel coefficient for 'rbf','poly' and 'sigmoid' Real(10**-6, 10**0, name='gamma'), ] DIMENSION_ESVR = ['C','epsilon','gamma'] DIMENSION_GBRT = ['max depth','learning rate','max features','min samples split','min samples leaf'] EPS_DPI = 2000 TIFF_DPI=1200 def multi_optimizer_esvr(root_path,station,predict_pattern,n_calls=100,cv=10): # Set the time series and model parameters predictor = 'esvr' data_path = root_path + '/'+station+'/data/'+predict_pattern+'/' model_path = root_path+'/'+station+'/projects/'+predictor+'/'+predict_pattern+'/multi_optimizer/history/' if not os.path.exists(model_path): os.makedirs(model_path) model_name = 'nc'+str(n_calls)+'_cv'+str(cv) logger.info("Build multiple optimizer epsilon SVR...") logger.info("Root path:{}".format(root_path)) logger.info("Station:{}".format(station)) logger.info("Predict pattern:{}".format(predict_pattern)) logger.info("Number of calls:{}".format(n_calls)) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) if os.path.exists(model_path +model_name+'_optimized_params.csv') : optimal_params = pd.read_csv(model_path +model_name+'_optimized_params.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") else: logger.info('Load learning samples...') # Load the training, development and testing samples train = pd.read_csv(data_path+'minmax_unsample_train.csv',index_col=False) dev = pd.read_csv(data_path+'minmax_unsample_dev.csv',index_col=False) test = pd.read_csv(data_path+'minmax_unsample_test.csv',index_col=False) train_dev = pd.concat([train,dev],axis=0) # shuffle the training samples train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) logger.info('Build SVR model and set the evaluation space of Bayesian optimization.') reg = SVR(tol=1e-4) # Set the space of hyper-parameters for tuning them space = ESVR_SPACE # Define an objective function of hyper-parameters tuning @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) def run(minimizer, n_iter=5): return [minimizer(objective, space, n_calls=n_calls, random_state=n) for n in range(n_iter)] #checkpoint_saver = CheckpointSaver(model_path+model_name+'/checkpoint.pkl',compress=9) # Random search dummy_res = run(dummy_minimize) # Gaussian processes gp_res = run(gp_minimize) # Random forest rf_res = run(partial(forest_minimize, base_estimator="RF")) # Extra trees et_res = run(partial(forest_minimize, base_estimator="ET")) plot = plot_convergence(("dummy_minimize", dummy_res), ("gp_minimize", gp_res), ("forest_minimize('rf')", rf_res), ("forest_minimize('et)", et_res), true_minimum=0.397887, yscale="log") plot.legend(loc="best", prop={'size': 6}, numpoints=1); plt.close('all') def esvr(root_path,station,predict_pattern,optimizer='gp',n_calls=100,cv=10): logger.info("Build monoscale epsilon SVR model ...") logger.info("Root path:{}".format(root_path)) logger.info("Station:{}".format(station)) logger.info("Predict pattern:{}".format(predict_pattern)) logger.info("Optimizer:{}".format(optimizer)) logger.info("Number of calls:{}".format(n_calls)) predictor = 'esvr' data_path = root_path + '/'+station+'/data/'+predict_pattern+'/' model_path = root_path+'/'+station+'/projects/'+predictor+'/'+predict_pattern+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) model_name = optimizer+'_nc'+str(n_calls)+'_cv'+str(cv) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) logger.info("Model name:{}".format(model_name)) # Load the training, development and testing samples logger.info('Load learning samples...') train = pd.read_csv(data_path+'minmax_unsample_train.csv',index_col=False) dev = pd.read_csv(data_path+'minmax_unsample_dev.csv',index_col=False) test = pd.read_csv(data_path+'minmax_unsample_test.csv',index_col=False) train_dev = pd.concat([train,dev],axis=0) # shuffle the training samples train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) if os.path.exists(model_path +model_name+'_optimized_params.csv'): optimal_params = pd.read_csv(model_path +model_name+'_optimized_params.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") esvr = SVR(C=optimal_params['C'][0], epsilon=optimal_params['epsilon'][0], gamma=optimal_params['gamma'][0]) train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = optimal_params['time_cost'][0], ) else: reg = SVR(tol=1e-4) # Set the space of hyper-parameters for tuning them space = ESVR_SPACE # Define an objective function of hyper-parameters tuning @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) # Tuning the hyper-parameters using Bayesian Optimization based on Gaussion Process start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_et': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end = time.process_time() time_cost = end-start dump(res,model_path+model_name+'_result.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result.pkl') # Visualizing the results of hyper-parameaters tuning plot_objective_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') # Plot the optimal hyperparameters logger.info('Best score=%.4f'%res.fun) logger.info(""" Best parameters: -C = %.8f -epsilon = %.8f -gamma = %.8f """%(res.x[0],res.x[1],res.x[2])) logger.info('Time cost:{} seconds'.format(time_cost)) # Construct the optimal hyperparameters to restore them params_dict={ 'C':res.x[0], 'epsilon':res.x[1], 'gamma':res.x[2], 'time_cost':time_cost, 'n_calls':n_calls, } # Transform the optimal hyperparameters dict to pandas DataFrame and restore it params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path +model_name+'_optimized_params.csv') # Initialize a SVR with the optimal hyperparameters esvr = SVR(C=res.x[0], epsilon=res.x[1], gamma=res.x[2]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions and cap the negative predictions to 0 train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = time_cost, ) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path +model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path +model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path +model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path+model_name+"_test_error.png") plt.close('all') def esvr_multi_seed(root_path,station,predict_pattern,optimizer='gp',n_calls=100,cv=10,iterations=10): logger.info("Build epsilon SVR with multiple seed...") logger.info("Root path:{}".format(root_path)) logger.info("Station:{}".format(station)) logger.info("Predict pattern:{}".format(predict_pattern)) logger.info("Optimizer:{}".format(optimizer)) logger.info("Number of calls:{}".format(n_calls)) # Set the time series and model parameters predictor = 'esvr' data_path = root_path + '/'+station+'/data/'+predict_pattern+'/' model_path = root_path+'/'+station+'/projects/'+predictor+'/'+predict_pattern+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) for random_state in range(1,iterations+1): model_name = optimizer+'_nc'+str(n_calls)+'_cv'+str(cv)+'_seed'+str(random_state) logger.info('Model Name:{}'.format(model_name)) # Load the training, development and testing samples train = pd.read_csv(data_path+'minmax_unsample_train.csv',index_col=False) dev = pd.read_csv(data_path+'minmax_unsample_dev.csv',index_col=False) test = pd.read_csv(data_path+'minmax_unsample_test.csv',index_col=False) train_dev = pd.concat([train,dev],axis=0) # shuffle the training samples train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) logger.info("Optimized params:{}".format(model_path +model_name+'_optimized_params.csv')) if os.path.exists(model_path +model_name+'_optimized_params.csv'): optimal_params = pd.read_csv(model_path +model_name+'_optimized_params.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") esvr = SVR(C=optimal_params['C'][0], epsilon=optimal_params['epsilon'][0], gamma=optimal_params['gamma'][0]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = optimal_params['time_cost'][0], ) else: reg = SVR(tol=1e-4) # Set the space of hyper-parameters for tuning them space = ESVR_SPACE # Define an objective function of hyper-parameters tuning @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) # Tuning the hyper-parameters using Bayesian Optimization based on Gaussion Process start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=random_state,verbose=True,n_jobs=-1) elif optimizer=='fr_bt': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=random_state,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=random_state,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end = time.process_time() time_cost = end-start dump(res,model_path+model_name+'_result_seed'+str(random_state)+'.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result_seed'+str(random_state)+'.pkl') # Visualizing the results of hyper-parameaters tuning plot_objective_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') # Plot the optimal hyperparameters logger.info('Best score=%.4f'%res.fun) logger.info(""" Best parameters: -C = %.8f -epsilon = %.8f -gamma = %.8f """%(res.x[0],res.x[1],res.x[2])) logger.info('Time cost:{} seconds'.format(time_cost)) # Construct the optimal hyperparameters to restore them params_dict={ 'C':res.x[0], 'epsilon':res.x[1], 'gamma':res.x[2], 'time_cost':time_cost, 'n_calls':n_calls, } # Transform the optimal hyperparameters dict to pandas DataFrame and restore it params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path +model_name+'_optimized_params.csv') # Initialize a SVR with the optimal hyperparameters esvr = SVR(C=res.x[0], epsilon=res.x[1], gamma=res.x[2]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions and cap the negative predictions to 0 train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = time_cost, ) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path +model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path +model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path +model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path+model_name+"_test_error.png") plt.close('all') def one_step_esvr(root_path,station,decomposer,predict_pattern,optimizer='gp',wavelet_level='db10-2',n_calls=100,cv=10): # Set project parameters logger.info('Build one-step epsilon SVR model...') logger.info('Root path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Optimizer:{}'.format(optimizer)) logger.info('Monther wavelet and decomposition level of WA:{}'.format(wavelet_level)) logger.info('Number of calls:{}'.format(n_calls)) predictor = 'esvr' signals = station+'_'+decomposer if decomposer == 'dwt' or decomposer=='modwt': data_path = root_path + '/'+signals+'/data/'+wavelet_level+'/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/history/' else: data_path = root_path + '/'+signals+'/data/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+predict_pattern+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) model_name = optimizer+'_nc'+str(n_calls)+'_cv'+str(cv) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) # load data train = pd.read_csv(data_path+'minmax_unsample_train.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev.csv') test = pd.read_csv(data_path+'minmax_unsample_test.csv') train_dev = pd.concat([train,dev],axis=0) # shuffle train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) if os.path.exists(model_path + model_name+'_optimized_params.csv'): optimal_params = pd.read_csv(model_path + model_name+'_optimized_params.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") esvr = SVR(C=optimal_params['C'][0], epsilon=optimal_params['epsilon'][0], gamma=optimal_params['gamma'][0]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = optimal_params['time_cost'][0], ) else: reg = SVR(tol=1e-4) space = ESVR_SPACE @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) #checkpoint_saver = CheckpointSaver(model_path+model_name+'/checkpoint.pkl',compress=9) start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_bt': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end = time.process_time() time_cost = end -start dump(res,model_path+model_name+'_result.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result.pkl') plot_objective_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') logger.info('Best score=%.4f'%res.fun) logger.info(""" Best parameters: -C = %.8f -epsilon = %.8f -gamma = %.8f """%(res.x[0],res.x[1],res.x[2])) logger.info('Time cost:{}'.format(time_cost)) params_dict={ 'C':res.x[0], 'epsilon':res.x[1], 'gamma':res.x[2], 'time_cost':(time_cost), 'n_calls':n_calls, } params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path + model_name+'_optimized_params.csv') esvr = SVR(C=res.x[0], epsilon=res.x[1], gamma=res.x[2]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path +model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path +model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path +model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path +model_name + "_test_error.png") plt.close('all') def one_step_esvr_multi_seed(root_path,station,decomposer,predict_pattern,optimizer='gp',wavelet_level='db10-2',n_calls=100,cv=10,iterations=10): logger.info('Build one-step epsilon SVR model with multiple seed...') logger.info('Root path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Optimizer:{}'.format(optimizer)) logger.info('Monther wavelet and decomposition level of WA:{}'.format(wavelet_level)) logger.info('Number of calls:{}'.format(n_calls)) logger.info('Seed iterations:{}'.format(iterations)) predictor = 'esvr' signals = station+'_'+decomposer if decomposer == 'dwt' or decomposer=='modwt': data_path = root_path + '/'+signals+'/data/'+wavelet_level+'/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/history/' else: data_path = root_path + '/'+signals+'/data/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+predict_pattern+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) for random_state in range(1,iterations+1): model_name = optimizer+'_nc'+str(n_calls)+'_cv'+str(cv)+'_seed'+str(random_state) logger.info('Model Name:{}'.format(model_name)) # load data train = pd.read_csv(data_path+'minmax_unsample_train.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev.csv') test = pd.read_csv(data_path+'minmax_unsample_test.csv') train_dev = pd.concat([train,dev],axis=0) # shuffle train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) logger.info("Optimized params:{}".format(model_path +model_name+'_optimized_params.csv')) if os.path.exists(model_path + model_name+'_optimized_params.csv'): optimal_params = pd.read_csv(model_path + model_name+'_optimized_params.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") esvr = SVR(C=optimal_params['C'][0], epsilon=optimal_params['epsilon'][0], gamma=optimal_params['gamma'][0]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = optimal_params['time_cost'][0], ) else: reg = SVR(tol=1e-4) space = ESVR_SPACE @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) #checkpoint_saver = CheckpointSaver(model_path+model_name+'/checkpoint.pkl',compress=9) start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=random_state,verbose=True,n_jobs=-1) elif optimizer=='fr_bt': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=random_state,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=random_state,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end = time.process_time() time_cost = end -start dump(res,model_path+model_name+'_result_seed'+str(random_state)+'.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result_seed'+str(random_state)+'.pkl') plot_objective_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') logger.info('Best score=%.4f'%res.fun) logger.info(""" Best parameters: -C = %.8f -epsilon = %.8f -gamma = %.8f """%(res.x[0],res.x[1],res.x[2])) logger.info('Time cost:{}'.format(time_cost)) params_dict={ 'C':res.x[0], 'epsilon':res.x[1], 'gamma':res.x[2], 'time_cost':(time_cost), 'n_calls':n_calls, } params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path + model_name+'_optimized_params.csv') esvr = SVR(C=res.x[0], epsilon=res.x[1], gamma=res.x[2]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path +model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path +model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path +model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path +model_name + "_test_error.png") plt.close('all') def multi_step_esvr(root_path,station,decomposer,predict_pattern,lags,model_id,optimizer='gp',wavelet_level='db10-2',n_calls=100,cv=10): logger.info('Build multi-step epsilon SVR model...') logger.info('Root path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Lags:{}'.format(lags)) logger.info('Model index:{}'.format(model_id)) logger.info('Optimizer:{}'.format(optimizer)) logger.info('Mother wavelet and decomposition level of WA:{}'.format(wavelet_level)) logger.info('Number of calls:{}'.format(n_calls)) if model_id>len(lags): raise Exception("The model id exceed the number of sub-signals") predictor = 'esvr' signals = station+'_'+decomposer if decomposer=='dwt' or decomposer=='modwt': data_path = root_path + '/'+signals+'/data/'+wavelet_level+'/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/s'+str(model_id)+'/history/' else: data_path = root_path + '/'+signals+'/data/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+predict_pattern+'/s'+str(model_id)+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) model_name = optimizer+'_nc'+str(n_calls)+'_cv'+str(cv)+'_s'+str(model_id) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) train = pd.read_csv(data_path+'minmax_unsample_train_s'+str(model_id)+'.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev_s'+str(model_id)+'.csv') test = pd.read_csv(data_path+'minmax_unsample_test_s'+str(model_id)+'.csv') train_dev = pd.concat([train,dev],axis=0) # shuffle train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) logger.info("Optimized params:{}".format(model_path + model_name +'_optimized_params_s' + str(model_id) +'.csv')) if os.path.exists(model_path + model_name +'_optimized_params_s' + str(model_id) +'.csv'): optimal_params = pd.read_csv(model_path + model_name +'_optimized_params_s' + str(model_id) +'.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") esvr = SVR(C=optimal_params['C'][0], epsilon=optimal_params['epsilon'][0], gamma=optimal_params['gamma'][0]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id_s'+str(model_id)+'.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = optimal_params['time_cost'][0], ) else: reg = SVR(tol=1e-4) space = ESVR_SPACE @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) #checkpoint_saver = CheckpointSaver(model_path+model_name+'/checkpoint.pkl',compress=9) start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_bt': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end=time.process_time() time_cost = end -start dump(res,model_path+model_name+'_result.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result.pkl') plot_objective_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') logger.info('Best score=%.4f'%res.fun) logger.info(""" Best parameters: -C = %.8f -epsilon = %.8f -gamma = %.8f """%(res.x[0],res.x[1],res.x[2])) logger.info('Time cost:{}'.format(time_cost)) params_dict={ 'C':res.x[0], 'epsilon':res.x[1], 'gamma':res.x[2], 'time_cost':(time_cost), 'n_calls':n_calls, } params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path + model_name +'_optimized_params_s' + str(model_id) +'.csv') esvr = SVR(C=res.x[0], epsilon=res.x[1], gamma=res.x[2]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id_s' + str(model_id) + '.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path + model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path + model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path + model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path + model_name + "_test_error.png",) plt.close('all') def multi_step_esvr_multi_seed(root_path,station,decomposer,predict_pattern,lags,model_id,optimizer='gp',wavelet_level='db10-2',n_calls=100,cv=10,iterations=10): logger.info('Roo path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Lags:{}'.format(lags)) logger.info('Model index:{}'.format(model_id)) logger.info('Optimizer:{}'.format(optimizer)) if model_id>len(lags): raise Exception("The model id exceed the number of sub-signals") predictor = 'esvr' signals = station+'_'+decomposer if decomposer=='dwt' or decomposer=='modwt': data_path = root_path + '/'+signals+'/data/'+wavelet_level+'/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/s'+str(model_id)+'/history/' else: data_path = root_path + '/'+signals+'/data/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+predict_pattern+'/s'+str(model_id)+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) for random_state in range(1,iterations+1): model_name = optimizer+'_nc'+str(n_calls)+'_cv'+str(cv)+'_s'+str(model_id)+'_seed'+str(random_state) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) train = pd.read_csv(data_path+'minmax_unsample_train_s'+str(model_id)+'.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev_s'+str(model_id)+'.csv') test = pd.read_csv(data_path+'minmax_unsample_test_s'+str(model_id)+'.csv') train_dev = pd.concat([train,dev],axis=0) # shuffle train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) logger.info("Optimized params:{}".format(model_path + model_name +'_optimized_params_s' + str(model_id) +'.csv')) if os.path.exists(model_path + model_name +'_optimized_params_s' + str(model_id) +'.csv'): optimal_params = pd.read_csv(model_path + model_name +'_optimized_params_s' + str(model_id) +'.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") esvr = SVR(C=optimal_params['C'][0], epsilon=optimal_params['epsilon'][0], gamma=optimal_params['gamma'][0]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id_s'+str(model_id)+'.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = optimal_params['time_cost'][0], ) else: reg = SVR(tol=1e-4) space = ESVR_SPACE @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) #checkpoint_saver = CheckpointSaver(model_path+model_name+'/checkpoint.pkl',compress=9) start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_bt': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end=time.process_time() time_cost = end -start dump(res,model_path+model_name+'_result_seed'+str(random_state)+'.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result_seed'+str(random_state)+'.pkl') plot_objective_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_ESVR,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') logger.info('Best score=%.4f'%res.fun) logger.info(""" Best parameters: -C = %.8f -epsilon = %.8f -gamma = %.8f """%(res.x[0],res.x[1],res.x[2])) logger.info('Time cost:{}'.format(time_cost)) params_dict={ 'C':res.x[0], 'epsilon':res.x[1], 'gamma':res.x[2], 'time_cost':(time_cost), 'n_calls':n_calls, } params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path + model_name +'_optimized_params_s' + str(model_id) +'.csv') esvr = SVR(C=res.x[0], epsilon=res.x[1], gamma=res.x[2]) # Do prediction with the opyimal model train_predictions = esvr.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = esvr.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = esvr.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id_s' + str(model_id) + '.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path + model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path + model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path + model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path + model_name + "_test_error.png",) plt.close('all') def gbrt(root_path,station,predict_pattern,optimizer='gp',n_calls=100,cv=10): logger.info('Root path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Optimizer:{}'.format(optimizer)) logger.info('Number of calls:{}'.format(n_calls)) predictor = 'gbrt' data_path = root_path + '/'+station+'/data/' model_path = root_path+'/'+station+'/projects/'+predictor+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) model_name = optimizer+'_nc'+str(n_calls)+'_cv'+str(cv) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) # load data train = pd.read_csv(data_path+'minmax_unsample_train.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev.csv') test = pd.read_csv(data_path+'minmax_unsample_test.csv') train_dev = pd.concat([train,dev],axis=0) # shuffle train_dev = train_dev.sample(frac=1) assert train.shape[1]==dev.shape[1]==test.shape[1]==train_dev.shape[1] train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) if os.path.exists(model_path +model_name+'_optimized_params.csv'): optimal_params = pd.read_csv(model_path +model_name+'_optimized_params.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") else: # Get the feature num n_features = train_dev_x.shape[1] reg = GradientBoostingRegressor(n_estimators=100,random_state=0) # The list hyper-parameters we want space = [ Integer(1,25,name='max_depth'), Real(10**-5,10**0,'log-uniform',name='learning_rate'), Integer(1,n_features,name='max_features'), Integer(2,100,name='min_samples_split'), Integer(1,100,name='min_samples_leaf'), ] @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) #checkpoint_saver = CheckpointSaver(model_path+model_name+'/checkpoint.pkl',compress=9) start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_bt': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end=time.process_time() time_cost = end-start dump(res,model_path+model_name+'_result.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result.pkl') plot_objective_(res,dimensions=DIMENSION_GBRT,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_GBRT,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') logger.info('Best score=%.4f'%res.fun) logger.info("""Best parameters: - max_depth=%d - learning_rate=%.6f - max_features=%d - min_samples_split=%d - min_samples_leaf=%d""" % (res.x[0], res.x[1], res.x[2], res.x[3], res.x[4])) # end=datetime.datetime.now() logger.info('Time cost:{}'.format(time_cost)) params_dict={ 'max_depth':res.x[0], 'learning_rate':res.x[1], 'max_features':res.x[2], 'min_samples_split':res.x[3], 'min_samples_leaf':res.x[4], 'time_cost':time_cost, 'n_calls':n_calls, } params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path +model_name+'_optimized_params.csv') GBR = GradientBoostingRegressor( max_depth=res.x[0], learning_rate=res.x[1], max_features=res.x[2], min_samples_split=res.x[3], min_samples_leaf=res.x[4]) # Do prediction with the opyimal model train_predictions = GBR.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = GBR.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = GBR.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path +model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path +model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path +model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path +model_name + "_test_error.png") plt.close('all') def one_step_gbrt(root_path,station,decomposer,predict_pattern,optimizer='gp',wavelet_level='db10-2',n_calls=100,cv=10): logger.info('Roo path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Optimizer:{}'.format(optimizer)) predictor = 'gbrt' signals = station+'_'+decomposer if decomposer=='dwt' or decomposer=='modwt': data_path = root_path + '/'+signals+'/data/'+wavelet_level+'/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/history/' else: data_path = root_path + '/'+signals+'/data/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+predict_pattern+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) model_name = optimizer+'_nc'+str(n_calls)+'_cv'+str(cv) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) # load data train = pd.read_csv(data_path+'minmax_unsample_train.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev.csv') test = pd.read_csv(data_path+'minmax_unsample_test.csv') train_dev = pd.concat([train,dev],axis=0) # shuffle train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) if os.path.exists(model_path + model_name+ '_optimized_params.csv'): optimal_params = pd.read_csv(model_path + model_name+ '_optimized_params.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") else: n_features = train_dev_x.shape[1] reg = GradientBoostingRegressor(n_estimators=100,random_state=0) # The list hyper-parameters we want space = [ Integer(1,25,name='max_depth'), Real(10**-5,10**0,'log-uniform',name='learning_rate'), Integer(1,n_features,name='max_features'), Integer(2,100,name='min_samples_split'), Integer(1,100,name='min_samples_leaf'), ] @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) #checkpoint_saver = CheckpointSaver(model_path+model_name+'/checkpoint.pkl',compress=9) start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_bt': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end=time.process_time() time_cost = end - start dump(res,model_path+model_name+'_result.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result.pkl') plot_objective_(res,dimensions=DIMENSION_GBRT,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_GBRT,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') logger.info('Best score=%.4f'%res.fun) logger.info("""Best parameters: - max_depth=%d - learning_rate=%.6f - max_features=%d - min_samples_split=%d - min_samples_leaf=%d""" % (res.x[0], res.x[1], res.x[2], res.x[3], res.x[4])) # end=datetime.datetime.now() logger.info('Time cost:{}'.format(time_cost)) params_dict={ 'max_depth':res.x[0], 'learning_rate':res.x[1], 'max_features':res.x[2], 'min_samples_split':res.x[3], 'min_samples_leaf':res.x[4], 'time_cost':(time_cost), 'n_calls':n_calls, } params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path + model_name+ '_optimized_params.csv') GBR = GradientBoostingRegressor( max_depth=res.x[0], learning_rate=res.x[1], max_features=res.x[2], min_samples_split=res.x[3], min_samples_leaf=res.x[4]) # Do prediction with the opyimal model train_predictions = GBR.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = GBR.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = GBR.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path + model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path + model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path + model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path + model_name + "_test_error.png",) plt.close('all') def multi_step_gbrt(root_path,station,decomposer,predict_pattern,lags,model_id,optimizer='gp',wavelet_level='db10-2',n_calls=100,cv=10): logger.info('Roo path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Lags:{}'.format(lags)) logger.info('Model index:{}'.format(model_id)) logger.info('Optimizer:{}'.format(optimizer)) logger.info('Monther wavelet and decomposition level of WA:{}'.format(wavelet_level)) logger.info('Number of calls:{}'.format(n_calls)) if model_id>len(lags): raise Exception("The model id exceed the number of sub-signals") # Set project parameters predictor = 'gbrt' signals = station+'_'+decomposer # Set the mode id: if decomposer=='dwt' or decomposer=='modwt': data_path = root_path + '/'+signals+'/data/'+wavelet_level+'/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/s'+str(model_id)+'/history/' else: data_path = root_path + '/'+signals+'/data/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+predict_pattern+'/s'+str(model_id)+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) model_name = station+'_'+decomposer+'_'+predictor+'_'+predict_pattern+'_s'+str(model_id) logger.info("Data Path:{}".format(data_path)) logger.info("Model Path:{}".format(model_path)) # load data train = pd.read_csv(data_path+'minmax_unsample_train_s'+str(model_id)+'.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev_s'+str(model_id)+'.csv') test = pd.read_csv(data_path+'minmax_unsample_test_s'+str(model_id)+'.csv') train_dev = pd.concat([train,dev],axis=0) # shuffle train_dev = train_dev.sample(frac=1) train_y = train['Y'] train_x = train.drop('Y', axis=1) dev_y = dev['Y'] dev_x = dev.drop('Y', axis=1) test_y = test['Y'] test_x = test.drop('Y', axis=1) train_dev_y = train_dev['Y'] train_dev_x = train_dev.drop('Y', axis=1) if os.path.exists(model_path + model_name+'_optimized_params_s' + str(model_id) +'.csv'): optimal_params = pd.read_csv(model_path + model_name+'_optimized_params_s' + str(model_id) +'.csv') pre_n_calls = optimal_params['n_calls'][0] if pre_n_calls==n_calls: logger.info("The n_calls="+str(n_calls)+" was already tuned") else: n_features = train_dev_x.shape[1] reg = GradientBoostingRegressor(n_estimators=100,random_state=0) # The list hyper-parameters we want space = [ Integer(1,25,name='max_depth'), Real(10**-5,10**0,'log-uniform',name='learning_rate'), Integer(1,n_features,name='max_features'), Integer(2,100,name='min_samples_split'), Integer(1,100,name='min_samples_leaf'), ] @use_named_args(space) def objective(**params): reg.set_params(**params) return -np.mean(cross_val_score(reg,train_dev_x,train_dev_y,cv=cv,n_jobs=-1,scoring='neg_mean_squared_error')) #checkpoint_saver = CheckpointSaver(model_path+model_name+'/checkpoint.pkl',compress=9) start = time.process_time() if optimizer=='gp': res = gp_minimize(objective,space,n_calls=n_calls ,random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_bt': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='ET',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='fr_rf': res = forest_minimize(objective,space,n_calls=n_calls,base_estimator='RF',random_state=0,verbose=True,n_jobs=-1) elif optimizer=='dm': res = dummy_minimize(objective,space,n_calls=n_calls) end=time.process_time() time_cost = end -start dump(res,model_path+model_name+'_result.pkl',store_objective=False) returned_results = load(model_path+model_name+'_result.pkl') plot_objective_(res,dimensions=DIMENSION_GBRT,fig_savepath=model_path+model_name+'_objective.png') plot_evaluations_(res,dimensions=DIMENSION_GBRT,fig_savepath=model_path+model_name+'_evaluation.png') plot_convergence_(res,fig_savepath=model_path+model_name+'_convergence.png') logger.info('Best score=%.4f'%res.fun) logger.info("""Best parameters: - max_depth=%d - learning_rate=%.6f - max_features=%d - min_samples_split=%d - min_samples_leaf=%d""" % (res.x[0], res.x[1], res.x[2], res.x[3], res.x[4])) # end=datetime.datetime.now() logger.info('Time cost:{}'.format(time_cost)) params_dict={ 'max_depth':res.x[0], 'learning_rate':res.x[1], 'max_features':res.x[2], 'min_samples_split':res.x[3], 'min_samples_leaf':res.x[4], 'time_cost':(time_cost), 'n_calls':n_calls, } params_df = pd.DataFrame(params_dict,index=[0]) params_df.to_csv(model_path + model_name+'_optimized_params_s' + str(model_id) +'.csv') GBR = GradientBoostingRegressor( max_depth=res.x[0], learning_rate=res.x[1], max_features=res.x[2], min_samples_split=res.x[3], min_samples_leaf=res.x[4]) # Do prediction with the opyimal model train_predictions = GBR.fit(train_dev_x,train_dev_y).predict(train_x) dev_predictions = GBR.fit(train_dev_x,train_dev_y).predict(dev_x) test_predictions = GBR.fit(train_dev_x,train_dev_y).predict(test_x) train_y=(train_y.values).flatten() dev_y=(dev_y.values).flatten() test_y=(test_y.values).flatten() norm_id = pd.read_csv(data_path + 'norm_unsample_id_s' + str(model_id) + '.csv') sMin = norm_id['series_min'][norm_id.shape[0]-1] sMax = norm_id['series_max'][norm_id.shape[0]-1] logger.debug('Series Min:\n {}'.format(sMin)) logger.debug('Series Max:\n {}'.format(sMax)) # Renormalized the records and predictions train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost) plot_rela_pred(train_y,train_predictions,fig_savepath=model_path + model_name + '_train_pred.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path + model_name + "_dev_pred.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path + model_name + "_test_pred.png") plot_error_distribution(test_y,test_predictions,fig_savepath=model_path + model_name + "_test_error.png") plt.close('all') def lstm(root_path,station,predict_pattern,seed, n_epochs=1000, batch_size=128, learn_rate=0.007, decay_rate=0.0, n_hidden_layers=1, hidden_units=[8], dropout_rates=[0.0], early_stop=True, retrain=False, warm_up=False, initial_epoch=None, ): logger.info('Build monoscale LSTM model...') logger.info('Model informattion:') logger.info('Root path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Seed:{}'.format(seed)) logger.info('Number of epochs:{}'.format(n_epochs)) logger.info('Batch size:{}'.format(batch_size)) logger.info('Learning rate:{}'.format(learn_rate)) logger.info('Decay rate of learning rate:{}'.format(decay_rate)) logger.info('Number of hidden layers:{}'.format(n_hidden_layers)) logger.info('Number of hidden units:{}'.format(hidden_units)) logger.info('Dropout rates:{}'.format(dropout_rates)) logger.info('Early stoping:{}'.format(early_stop)) logger.info('Retrain model:{}'.format(retrain)) logger.info('Warm up:{}'.format(warm_up)) logger.info('Initial epoch of warm up:{}'.format(initial_epoch)) predictor = 'lstm' data_path = root_path + '/'+station+'/data/'+predict_pattern+'/' model_path = root_path+'/'+station+'/projects/'+predictor+'/'+predict_pattern+'/' if not os.path.exists(model_path): os.makedirs(model_path) logger.info('Data path:{}'.format(data_path)) logger.info('Model path:{}'.format(model_path)) # 1.Import the sampled normalized data set from disk logger.info('Load learning samples...') train = pd.read_csv(data_path+'minmax_unsample_train.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev.csv') test = pd.read_csv(data_path+'minmax_unsample_test.csv') train_x = train train_y = train.pop('Y') train_y = train_y.as_matrix() dev_x = dev dev_y = dev.pop('Y') dev_y = dev_y.as_matrix() test_x = test test_y = test.pop('Y') test_y = test_y.as_matrix() # reshape the input features for LSTM train_x = (train_x.values).reshape(train_x.shape[0],1,train_x.shape[1]) dev_x = (dev_x.values).reshape(dev_x.shape[0],1,dev_x.shape[1]) test_x = (test_x.values).reshape(test_x.shape[0],1,test_x.shape[1]) model_name = 'LSTM-LR['+str(learn_rate)+\ ']-HU'+str(hidden_units)+\ '-EPS['+str(n_epochs)+\ ']-BS['+str(batch_size)+\ ']-DR'+str(dropout_rates)+\ '-DC['+str(decay_rate)+\ ']-SEED['+str(seed)+']' def build_model(): logger.info('Define LSTM model...') if n_hidden_layers==2: model = keras.Sequential( [ layers.LSTM(hidden_units[0],activation=tf.nn.relu,return_sequences=True,input_shape=(train_x.shape[1],train_x.shape[2])), layers.Dropout(dropout_rates[0], noise_shape=None, seed=seed), layers.LSTM(hidden_units[1],activation=tf.nn.relu,return_sequences=False), # first hidden layer if hasnext hidden layer layers.Dropout(dropout_rates[1], noise_shape=None, seed=seed), layers.Dense(1) ] ) else: model = keras.Sequential( [ layers.LSTM(hidden_units[0],activation=tf.nn.relu,input_shape=(train_x.shape[1],train_x.shape[2])), layers.Dropout(dropout_rates[0], noise_shape=None, seed=seed), layers.Dense(1) ] ) optimizer = keras.optimizers.Adam(learn_rate,decay=decay_rate) model.compile(loss='mean_squared_error',optimizer=optimizer,metrics=['mean_absolute_error','mean_squared_error']) return model logger.info('Set model parameters restore path...') cp_path = model_path+model_name+'\\' if not os.path.exists(cp_path): os.makedirs(cp_path) checkpoint_path = model_path+model_name+'\\cp.ckpt' #restore only the latest checkpoint after every update # checkpoint_path = model_path+'cp-{epoch:04d}.ckpt' #restore the checkpoint every period=x epoch checkpoint_dir = os.path.dirname(checkpoint_path) logger.info('checkpoint dir:{}'.format(checkpoint_dir)) cp_callback = keras.callbacks.ModelCheckpoint(checkpoint_path,save_best_only=True,mode='min',save_weights_only=True,verbose=1) # cp_callback = keras.callbacks.ModelCheckpoint(checkpoint_path,save_weights_only=True,period=5,verbose=1) # if not RESUME_TRAINING: # print("Removing previous artifacts...") # shutil.rmtree(checkpoint_dir, ignore_errors=True) # else: # print("Resuming training...") # initialize a new model model = build_model() model.summary() #print a simple description for the model """ # Evaluate before training or load trained weights and biases loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) # Try the model with initial weights and biases example_batch = train_x[:10] example_result = model.predict(example_batch) print(example_result) """ # 3.Train the model # Display training progress by printing a single dot for each completed epoch class PrintDot(keras.callbacks.Callback): def on_epoch_end(self, epoch, logs): if epoch % 100 == 0: print('') print('.', end='') files = os.listdir(checkpoint_dir) # reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto') reduce_lr = ReduceLROnPlateau(monitor='val_loss',min_lr=0.00001,factor=0.2, verbose=1,patience=10, mode='min') early_stopping = EarlyStopping(monitor='val_loss', mode='min',verbose=1,patience=100,restore_best_weights=True) warm_dir = 'LSTM-LR['+str(learn_rate)+\ ']-HU'+str(hidden_units)+\ '-EPS['+str(initial_epoch)+\ ']-BS['+str(batch_size)+\ ']-DR'+str(dropout_rates)+\ '-DC['+str(decay_rate)+\ ']-SEED['+str(seed)+']' logger.info("WARM UP PATH:{}".format(os.path.exists(model_path+warm_dir))) logger.info('Train the LSTM model ...') if retrain: # Retraining the LSTM model logger.info('retrain the model') if early_stop: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs,batch_size=batch_size ,validation_data=(dev_x,dev_y),verbose=1, callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end-start else: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs,batch_size=batch_size ,validation_data=(dev_x,dev_y),verbose=1,callbacks=[cp_callback]) end =time.process_time() time_cost = end-start # # Visualize the model's training progress using the stats stored in the history object hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch # print(hist.tail()) plot_history(history,model_path+model_name+'-MAE-ERRORS-TRAINTEST.png',model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') elif len(files)==0: # The current model has not been trained if os.path.exists(model_path+warm_dir) and warm_up: # Training the model using the trained weights and biases as initialized parameters logger.info('WARM UP FROM EPOCH '+str(initial_epoch)) # Warm up from the last epoch of the target model prev_time_cost = (pd.read_csv(model_path+warm_dir+'.csv')['time_cost'])[0] warm_path=model_path+warm_dir+'\\cp.ckpt' model.load_weights(warm_path) if early_stop: start=time.process_time() history = model.fit(train_x,train_y,initial_epoch=initial_epoch,epochs=n_epochs,batch_size=batch_size ,validation_data=(dev_x,dev_y),verbose=1, callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end - start + prev_time_cost else: start = time.process_time() history = model.fit(train_x,train_y,initial_epoch=initial_epoch,epochs=n_epochs,batch_size=batch_size ,validation_data=(dev_x,dev_y),verbose=1, callbacks=[ cp_callback, ]) end = time.process_time() time_cost = end - start + prev_time_cost hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch # print(hist.tail()) plot_history(history,model_path+model_name+'-MAE-ERRORS-TRAINTEST.png',model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') else: # Training entirely new model logger.info('new train') if early_stop: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs,batch_size=batch_size ,validation_data=(dev_x,dev_y),verbose=1,callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end -start else: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs,batch_size=batch_size ,validation_data=(dev_x,dev_y),verbose=1, callbacks=[ cp_callback, ]) end = time.process_time() time_cost = end - start hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch # print(hist.tail()) plot_history(history,model_path+model_name+'-MAE-ERRORS-TRAINTEST.png',model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') else: logger.info('#'*10+'Already Trained') time_cost = (pd.read_csv(model_path+model_name+'.csv')['time_cost'])[0] model.load_weights(checkpoint_path) # loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) """ # Evaluate after training or load trained weights and biases loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) print("Testing set Mean Abs Error: {:5.2f} ".format(mae)) """ logger.info('Predict the training, development and testing samples...') train_predictions = model.predict(train_x).flatten() dev_predictions = model.predict(dev_x).flatten() test_predictions = model.predict(test_x).flatten() # renormized the predictions and labels # load the normalized traindev indicators norm = pd.read_csv(data_path+'norm_unsample_id.csv') sMax = norm['series_max'][norm.shape[0]-1] sMin = norm['series_min'][norm.shape[0]-1] logger.debug('Series min:{}'.format(sMin)) logger.debug('Series max:{}'.format(sMax)) train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1,sMax - sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin dev_predictions = np.multiply(dev_predictions + 1,sMax - sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin test_predictions = np.multiply(test_predictions + 1,sMax - sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 logger.info('Dump the prediction results...') dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost, ) logger.info('Plot the prediction results...') plot_rela_pred(train_y,train_predictions,fig_savepath=model_path + model_name + '-TRAIN-PRED.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path + model_name + "-DEV-PRED.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path + model_name + "-TEST-PRED.png") plot_error_distribution(test_predictions,test_y,model_path+model_name+'-TEST-ERROR-DSTRI.png') plt.close('all') def one_step_lstm( root_path,station,decomposer,predict_pattern,seed, wavelet_level='db10-2', n_epochs=1000, batch_size=128, learn_rate=0.007, decay_rate=0.0, n_hidden_layers=1, hidden_units=[8], dropout_rates=[0.0], early_stop=True, retrain=False, warm_up=False, initial_epoch=None, ): logger.info('Build one-step LSTM model...') logger.info('Model informattion:') logger.info('Root path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Seed:{}'.format(seed)) logger.info('Monther wavelet and decomposition level of WA:{}'.format(wavelet_level)) logger.info('Number of epochs:{}'.format(n_epochs)) logger.info('Batch size:{}'.format(batch_size)) logger.info('Learning rate:{}'.format(learn_rate)) logger.info('Decay rate of learning rate:{}'.format(decay_rate)) logger.info('Number of hidden layers:{}'.format(n_hidden_layers)) logger.info('Number of hidden units:{}'.format(hidden_units)) logger.info('Dropout rates:{}'.format(dropout_rates)) logger.info('Early stoping:{}'.format(early_stop)) logger.info('Retrain model:{}'.format(retrain)) logger.info('Warm up:{}'.format(warm_up)) logger.info('Initial epoch of warm up:{}'.format(initial_epoch)) # Set project parameters predictor = 'lstm' predict_pattern = predict_pattern # hindcast or forecast signals = station+'_'+decomposer if decomposer=='dwt' or decomposer=='modwt': data_path = root_path + '/'+signals+'/data/'+wavelet_level+'/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/history/' else: data_path = root_path + '/'+signals+'/data/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+predict_pattern+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) logger.info('Data path:{}'.format(data_path)) logger.info('Model path:{}'.format(model_path)) ###################################################### logger.info('Load learning samples...') # 1.Import the sampled normalized data set from disk train = pd.read_csv(data_path+'minmax_unsample_train.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev.csv') test = pd.read_csv(data_path+'minmax_unsample_test.csv') # Split features from labels train_x = train train_y = train.pop('Y') train_y = train_y.as_matrix() dev_x = dev dev_y = dev.pop('Y') dev_y = dev_y.as_matrix() test_x = test test_y = test.pop('Y') test_y = test_y.as_matrix() # reshape the input features for LSTM train_x = (train_x.values).reshape(train_x.shape[0],1,train_x.shape[1]) dev_x = (dev_x.values).reshape(dev_x.shape[0],1,dev_x.shape[1]) test_x = (test_x.values).reshape(test_x.shape[0],1,test_x.shape[1]) # 2.Build LSTM model with keras model_name = 'LSTM-LR['+str(learn_rate)+\ ']-HU'+str(hidden_units)+\ '-EPS['+str(n_epochs)+\ ']-BS['+str(batch_size)+\ ']-DR'+str(dropout_rates)+\ '-DC['+str(decay_rate)+\ ']-SEED['+str(seed)+']' # RESUME_TRAINING = True def build_model(): logger.info('Build LSTM model...') if n_hidden_layers==2: model = keras.Sequential( [ layers.LSTM(hidden_units[0],activation=tf.nn.relu,return_sequences=True,input_shape=(train_x.shape[1],train_x.shape[2])), layers.Dropout(dropout_rates[0], noise_shape=None, seed=seed), layers.LSTM(hidden_units[1],activation=tf.nn.relu,return_sequences=False), # first hidden layer if hasnext hidden layer layers.Dropout(dropout_rates[1], noise_shape=None, seed=seed), layers.Dense(1) ] ) else: model = keras.Sequential( [ layers.LSTM(hidden_units[0],activation=tf.nn.relu,input_shape=(train_x.shape[1],train_x.shape[2])), layers.Dropout(dropout_rates[0], noise_shape=None, seed=seed), layers.Dense(1) ] ) optimizer = keras.optimizers.Adam(learn_rate,decay=decay_rate) model.compile(loss='mean_squared_error',optimizer=optimizer, metrics=['mean_absolute_error','mean_squared_error']) return model logger.info('Set model parameters restore path...') # set model's parameters restore path cp_path = model_path+model_name+'\\' if not os.path.exists(cp_path): os.makedirs(cp_path) checkpoint_path = model_path+model_name+'\\cp.ckpt' #restore only the latest checkpoint after every update # checkpoint_path = model_path+'cp-{epoch:04d}.ckpt' #restore the checkpoint every period=x epoch checkpoint_dir = os.path.dirname(checkpoint_path) logger.info('checkpoint dir:{}'.format(checkpoint_dir)) cp_callback = keras.callbacks.ModelCheckpoint(checkpoint_path,save_best_only=True,mode='min',save_weights_only=True,verbose=1) # cp_callback = keras.callbacks.ModelCheckpoint(checkpoint_path,save_weights_only=True,period=5,verbose=1) # if not RESUME_TRAINING: # print("Removing previous artifacts...") # shutil.rmtree(checkpoint_dir, ignore_errors=True) # else: # print("Resuming training...") # initialize a new model model = build_model() model.summary() #print a simple description for the model """ # Evaluate before training or load trained weights and biases loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) # Try the model with initial weights and biases example_batch = train_x[:10] example_result = model.predict(example_batch) print(example_result) """ # 3.Train the model # Display training progress by printing a single dot for each completed epoch class PrintDot(keras.callbacks.Callback): def on_epoch_end(self, epoch, logs): if epoch % 100 == 0: print('') print('.', end='') files = os.listdir(checkpoint_dir) from tensorflow.keras.callbacks import ReduceLROnPlateau,EarlyStopping # reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto') reduce_lr = ReduceLROnPlateau(monitor='val_loss',min_lr=0.00001,factor=0.2, verbose=1,patience=10, mode='min') early_stopping = EarlyStopping(monitor='val_loss', mode='min',verbose=1,patience=200,restore_best_weights=True) warm_dir = 'LSTM-LR['+str(learn_rate)+\ ']-HU'+str(hidden_units)+\ '-EPS['+str(initial_epoch)+\ ']-BS['+str(batch_size)+\ ']-DR'+str(dropout_rates)+\ '-DC['+str(decay_rate)+\ ']-SEED['+str(seed)+']' logger.info("WARM UP PATH:{}".format(os.path.exists(model_path+warm_dir))) # Training models logger.info('Train the LSTM model...') if retrain: # Retraining the LSTM model print('retrain the model') if early_stop: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end-start else: start=time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, ]) end = time.process_time() time_cost = end - start hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch # print(hist.tail()) plot_history(history, model_path+model_name+'-MAE-ERRORS-TRAINTEST.png', model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') elif len(files)==0:# The current model has not been trained # Training the model using the trained weights and biases as initialized parameters if os.path.exists(model_path+warm_dir) and warm_up: # Warm up from the last epoch of the target model logger.info('WARM UP FROM EPOCH '+str(initial_epoch)) prev_time_cost = (pd.read_csv(model_path+warm_dir+'.csv')['time_cost'])[0] warm_path=model_path+warm_dir+'\\cp.ckpt' model.load_weights(warm_path) if early_stop: start = time.process_time() history = model.fit(train_x,train_y, initial_epoch=initial_epoch, epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end-start+prev_time_cost else: start = time.process_time() history = model.fit(train_x,train_y, initial_epoch=initial_epoch, epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, ]) end = time.process_time() time_cost = end - start + prev_time_cost hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch # print(hist.tail()) plot_history(history, model_path+model_name+'-MAE-ERRORS-TRAINTEST.png', model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') else: print('new train') if early_stop: start = time.process_time() history = model.fit(train_x,train_y, epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end - start else: start = time.process_time() history = model.fit(train_x,train_y, epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[cp_callback,]) end = time.process_time() time_cost = end - start hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch # print(hist.tail()) plot_history(history, model_path+model_name+'-MAE-ERRORS-TRAINTEST.png', model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') else: logger.info('#'*10+'Already Trained') time_cost = (pd.read_csv(model_path+model_name+'.csv')['time_cost'])[0] model.load_weights(checkpoint_path) # loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) """ # Evaluate after training or load trained weights and biases loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) print("Testing set Mean Abs Error: {:5.2f} ".format(mae)) """ # 4. Predict the model # load the unsample data logger.info('Predict the training, development and testing samples...') train_predictions = model.predict(train_x).flatten() dev_predictions = model.predict(dev_x).flatten() test_predictions = model.predict(test_x).flatten() # renormized the predictions and labels # load the normalized traindev indicators norm = pd.read_csv(data_path+'norm_unsample_id.csv') sMax = norm['series_max'][norm.shape[0]-1] sMin = norm['series_min'][norm.shape[0]-1] logger.debug('Series min:{}'.format(sMin)) logger.debug('Series max:{}'.format(sMax)) train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1, sMax -sMin) / 2 + sMin train_predictions[train_predictions<0.0]=0.0 dev_predictions = np.multiply(dev_predictions + 1, sMax -sMin) / 2 + sMin dev_predictions[dev_predictions<0.0]=0.0 test_predictions = np.multiply(test_predictions + 1, sMax -sMin) / 2 + sMin test_predictions[test_predictions<0.0]=0.0 logger.info('Dump prediction results...') dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost = time_cost) logger.info('Plot the prediction results...') plot_rela_pred(train_y,train_predictions,fig_savepath=model_path + model_name + '-TRAIN-PRED.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path + model_name + "-DEV-PRED.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path + model_name + "-TEST-PRED.png") plot_error_distribution(test_predictions,test_y,model_path+model_name+'-TEST-ERROR-DSTRI.png') plt.close('all') def multi_step_lstm( root_path,station,decomposer,predict_pattern,lags,model_id,seed, wavelet_level='db10-2', n_epochs=1000, batch_size=128, learn_rate=0.007, decay_rate=0.0, n_hidden_layers=1, hidden_units=[8], dropout_rates=[0.0], early_stop=True, retrain=False, warm_up=False, initial_epoch=None, ): logger.info('Build multi-step LSTM model...') logger.info('Model informattion:') logger.info('Root path:{}'.format(root_path)) logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Predict pattern:{}'.format(predict_pattern)) logger.info('Lags:{}'.format(lags)) logger.info('Model index:{}'.format(model_id)) logger.info('Seed:{}'.format(seed)) logger.info('Monther wavelet and decomposition level of WA:{}'.format(wavelet_level)) logger.info('Number of epochs:{}'.format(n_epochs)) logger.info('Batch size:{}'.format(batch_size)) logger.info('Learning rate:{}'.format(learn_rate)) logger.info('Decay rate of learning rate:{}'.format(decay_rate)) logger.info('Number of hidden layers:{}'.format(n_hidden_layers)) logger.info('Number of hidden units:{}'.format(hidden_units)) logger.info('Dropout rates:{}'.format(dropout_rates)) logger.info('Early stoping:{}'.format(early_stop)) logger.info('Retrain model:{}'.format(retrain)) logger.info('Warm up:{}'.format(warm_up)) logger.info('Initial epoch of warm up:{}'.format(initial_epoch)) if model_id>len(lags): raise Exception("The model id exceed the number of sub-signals") # Set project parameters predictor = 'lstm' predict_pattern = predict_pattern # hindcast or forecast signals = station+'_'+decomposer # Set the model id model_id=model_id if decomposer=='dwt' or decomposer=='modwt': data_path = root_path + '/'+signals+'/data/'+wavelet_level+'/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/s'+str(model_id)+'/history/' else: data_path = root_path + '/'+signals+'/data/'+predict_pattern+'/' model_path = root_path+'/'+signals+'/projects/'+predictor+'/'+predict_pattern+'/s'+str(model_id)+'/history/' if not os.path.exists(model_path): os.makedirs(model_path) logger.info('Data path:{}'.format(data_path)) logger.info('Model path:{}'.format(model_path)) ###################################################### logger.info('Load learning samples...') # 1.Import the sampled normalized data set from disk train = pd.read_csv(data_path+'minmax_unsample_train_s'+str(model_id)+'.csv') dev = pd.read_csv(data_path+'minmax_unsample_dev_s'+str(model_id)+'.csv') test = pd.read_csv(data_path+'minmax_unsample_test_s'+str(model_id)+'.csv') # Split features from labels train_x = train train_y = train.pop('Y') train_y = train_y.as_matrix() dev_x = dev dev_y = dev.pop('Y') dev_y = dev_y.as_matrix() test_x = test test_y = test.pop('Y') test_y = test_y.as_matrix() # reshape the input features for LSTM train_x = (train_x.values).reshape(train_x.shape[0],1,train_x.shape[1]) dev_x = (dev_x.values).reshape(dev_x.shape[0],1,dev_x.shape[1]) test_x = (test_x.values).reshape(test_x.shape[0],1,test_x.shape[1]) # 2.Build LSTM model with keras model_name = 'LSTM-S'+str(model_id)+\ '-LR['+str(learn_rate)+\ ']-HU'+str(hidden_units)+\ '-EPS['+str(n_epochs)+\ ']-BS['+str(batch_size)+\ ']-DR'+str(dropout_rates)+\ '-DC['+str(decay_rate)+\ ']-SEED['+str(seed)+']' # RESUME_TRAINING = True def build_model(): logger.info('Build LSTM model...') if n_hidden_layers==2: model = keras.Sequential( [ layers.LSTM(hidden_units[0],activation=tf.nn.relu,return_sequences=True,input_shape=(train_x.shape[1],train_x.shape[2])), layers.Dropout(dropout_rates[0], noise_shape=None, seed=seed), layers.LSTM(hidden_units[1],activation=tf.nn.relu,return_sequences=False), # first hidden layer if hasnext hidden layer layers.Dropout(dropout_rates[1], noise_shape=None, seed=seed), layers.Dense(1) ] ) else: model = keras.Sequential( [ layers.LSTM(hidden_units[0],activation=tf.nn.relu,input_shape=(train_x.shape[1],train_x.shape[2])), layers.Dropout(dropout_rates[0], noise_shape=None, seed=seed), layers.Dense(1) ] ) optimizer = keras.optimizers.Adam(learn_rate,decay=decay_rate) model.compile(loss='mean_squared_error',optimizer=optimizer, metrics=['mean_absolute_error','mean_squared_error']) return model logger.info('Set model parameters restore path...') # set model's parameters restore path cp_path = model_path+model_name+'\\' if not os.path.exists(cp_path): os.makedirs(cp_path) checkpoint_path = model_path+model_name+'\\cp.ckpt' #restore only the latest checkpoint after every update # checkpoint_path = model_path+'cp-{epoch:04d}.ckpt' #restore the checkpoint every period=x epoch checkpoint_dir = os.path.dirname(checkpoint_path) logger.info('checkpoint dir:{}'.format(checkpoint_dir)) cp_callback = keras.callbacks.ModelCheckpoint(checkpoint_path,save_best_only=True,mode='min',save_weights_only=True,verbose=1) # cp_callback = keras.callbacks.ModelCheckpoint(checkpoint_path,save_weights_only=True,period=5,verbose=1) # if not RESUME_TRAINING: # print("Removing previous artifacts...") # shutil.rmtree(checkpoint_dir, ignore_errors=True) # else: # print("Resuming training...") # initialize a new model model = build_model() model.summary() #print a simple description for the model """ # Evaluate before training or load trained weights and biases loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) # Try the model with initial weights and biases example_batch = train_x[:10] example_result = model.predict(example_batch) print(example_result) """ # 3.Train the model # Display training progress by printing a single dot for each completed epoch class PrintDot(keras.callbacks.Callback): def on_epoch_end(self, epoch, logs): if epoch % 100 == 0: print('') print('.', end='') files = os.listdir(checkpoint_dir) # reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto') reduce_lr = ReduceLROnPlateau(monitor='val_loss',min_lr=0.00001,factor=0.2, verbose=1,patience=10, mode='min') early_stopping = EarlyStopping(monitor='val_loss', mode='min',verbose=1,patience=200,restore_best_weights=True) warm_dir = 'LSTM-S'+str(model_id)+\ '-LR['+str(learn_rate)+\ ']-HU'+str(hidden_units)+\ '-EPS['+str(initial_epoch)+\ ']-BS['+str(batch_size)+\ ']-DR'+str(dropout_rates)+\ '-DC['+str(decay_rate)+\ ']-SEED['+str(seed)+']' logger.info("WARM UP PATH:{}".format(os.path.exists(model_path+warm_dir))) # Training models logger.info('Train the LSTM model...') if retrain: # Retraining the LSTM model print('retrain the model') if early_stop: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end -start else: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, ]) end = time.process_time() time_cost = end - start hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch logger.debug(hist.tail()) plot_history(history, model_path+model_name+'-MAE-ERRORS-TRAINTEST.png', model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') elif len(files)==0: # The current model has not been trained # Training the model using the trained weights and biases as initialized parameters if os.path.exists(model_path+warm_dir) and warm_up: # Warm up from the last epoch of the target model print('WARM UP FROM EPOCH '+str(initial_epoch)) prev_time_cost = (pd.read_csv(model_path+warm_dir+'.csv')['time_cost'])[0] warm_path=model_path+warm_dir+'\\cp.ckpt' model.load_weights(warm_path) if early_stop: start = time.process_time() history = model.fit(train_x,train_y,initial_epoch=initial_epoch,epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end -start + prev_time_cost else: start = time.process_time() history = model.fit(train_x,train_y,initial_epoch=initial_epoch,epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, ]) end = time.process_time() time_cost = end - start + prev_time_cost hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch logger.debug(hist.tail()) plot_history(history, model_path+model_name+'-MAE-ERRORS-TRAINTEST.png', model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') else: logger.info('new train') if early_stop: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, early_stopping, ]) end = time.process_time() time_cost = end - start else: start = time.process_time() history = model.fit(train_x,train_y,epochs=n_epochs, batch_size=batch_size , validation_data=(dev_x,dev_y), verbose=1, callbacks=[ cp_callback, ]) end = time.process_time() time_cost = end-start hist = pd.DataFrame(history.history) hist.to_csv(model_path+model_name+'-HISTORY-TRAIN-TEST.csv') hist['epoch']=history.epoch logger.debug(hist.tail()) plot_history(history, model_path+model_name+'-MAE-ERRORS-TRAINTEST.png', model_path+model_name+'-MSE-ERRORS-TRAINTEST.png') else: logger.info('#'*10+'Already Trained') time_cost = (pd.read_csv(model_path+model_name+'.csv')['time_cost'])[0] model.load_weights(checkpoint_path) # loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) """ # Evaluate after training or load trained weights and biases loss, mae, mse = model.evaluate(test_x, test_y, verbose=1) print("Testing set Mean Abs Error: {:5.2f} ".format(mae)) """ # 4. Predict the model # load the unsample data logger.info('Predict the training, development and testing samples...') train_predictions = model.predict(train_x).flatten() dev_predictions = model.predict(dev_x).flatten() test_predictions = model.predict(test_x).flatten() # renormized the predictions and labels # load the normalized traindev indicators norm = pd.read_csv(data_path+'norm_unsample_id_s'+str(model_id)+'.csv') sMax = norm['series_max'][norm.shape[0]-1] sMin = norm['series_min'][norm.shape[0]-1] print('Series min:{}'.format(sMin)) print('Series max:{}'.format(sMax)) train_y = np.multiply(train_y + 1,sMax - sMin) / 2 + sMin train_predictions = np.multiply(train_predictions + 1,sMax - sMin) / 2 + sMin dev_y = np.multiply(dev_y + 1,sMax - sMin) / 2 + sMin dev_predictions = np.multiply(dev_predictions + 1,sMax - sMin) / 2 + sMin test_y = np.multiply(test_y + 1,sMax - sMin) / 2 + sMin test_predictions = np.multiply(test_predictions + 1,sMax - sMin) / 2 + sMin logger.info('Dump prediction results...') dum_pred_results( path = model_path+model_name+'.csv', train_y = train_y, train_predictions=train_predictions, dev_y = dev_y, dev_predictions = dev_predictions, test_y = test_y, test_predictions = test_predictions, time_cost=time_cost) logger.info('Plot the prediction results...') plot_rela_pred(train_y,train_predictions,fig_savepath=model_path + model_name + '-TRAIN-PRED.png') plot_rela_pred(dev_y,dev_predictions,fig_savepath=model_path + model_name + "-DEV-PRED.png") plot_rela_pred(test_y,test_predictions,fig_savepath=model_path + model_name + "-TEST-PRED.png") plot_error_distribution(test_predictions,test_y,model_path+model_name+'-TEST-ERROR-DSTRI.png') plt.close('all')

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161

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121,028

4.429274

0.029815

0.034476

0.03789

0.048715

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0.955282

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0.012982

0.23559

121,028

2,501

162

48.391843

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0.926175

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0.123782

0.021218

0

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1

0.014382

false

0

0.017258

0.000479

0.039789

0.006232

0

null

0

1

0

1

0

null

0

7

62a5a116c2a40c47892a3c35a37dcb4a40b8f3ae

113

py

Python

boa3_test/test_sc/interop_test/stdlib/Base58EncodeMismatchedType.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3_test/test_sc/interop_test/stdlib/Base58EncodeMismatchedType.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3_test/test_sc/interop_test/stdlib/Base58EncodeMismatchedType.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

from boa3.builtin.interop.stdlib import base58_encode def Main(key: int) -> str: return base58_encode(key)

18.833333

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false

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1

0

1

0

null

0

1

0

1

0

7

62b1e45cff35d277a481146c6bc53a2c086638fe

2,320

py

Python

test/nn/conv/test_general_conv.py

rusty1s/pytorch-geometric

ded9a7b10ad8ebc19c97e567c7bb1ae6605253db

[ "MIT" ]

2,350

2021-09-12T08:32:50.000Z

2022-03-31T18:09:36.000Z

test/nn/conv/test_general_conv.py

rusty1s/pytorch-geometric

ded9a7b10ad8ebc19c97e567c7bb1ae6605253db

[ "MIT" ]

588

2021-09-12T08:49:08.000Z

2022-03-31T21:02:13.000Z

test/nn/conv/test_general_conv.py

rusty1s/pytorch-geometric

ded9a7b10ad8ebc19c97e567c7bb1ae6605253db

[ "MIT" ]

505

2021-09-13T13:13:32.000Z

2022-03-31T15:54:00.000Z

import torch from torch_geometric.nn import GeneralConv def test_general_conv(): x1 = torch.randn(4, 8) e1 = torch.randn(4, 16) edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]]) conv = GeneralConv(8, 32, 16) assert conv.__repr__() == 'GeneralConv(8, 32)' out = conv(x1, edge_index, edge_attr=e1) assert out.size() == (4, 32) assert torch.allclose(conv(x1, edge_index, edge_attr=e1), out, atol=1e-7) conv = GeneralConv(8, 32, 16, skip_linear=True) assert conv.__repr__() == 'GeneralConv(8, 32)' out = conv(x1, edge_index, edge_attr=e1) assert out.size() == (4, 32) assert torch.allclose(conv(x1, edge_index, edge_attr=e1), out, atol=1e-7) conv = GeneralConv(8, 32, 16, directed_msg=False) assert conv.__repr__() == 'GeneralConv(8, 32)' out = conv(x1, edge_index, edge_attr=e1) assert out.size() == (4, 32) assert torch.allclose(conv(x1, edge_index, edge_attr=e1), out, atol=1e-7) conv = GeneralConv(8, 32, 16, heads=3) assert conv.__repr__() == 'GeneralConv(8, 32)' out = conv(x1, edge_index, edge_attr=e1) assert out.size() == (4, 32) assert torch.allclose(conv(x1, edge_index, edge_attr=e1), out, atol=1e-7) conv = GeneralConv(8, 32, 16, attention=True) assert conv.__repr__() == 'GeneralConv(8, 32)' out = conv(x1, edge_index, edge_attr=e1) assert out.size() == (4, 32) assert torch.allclose(conv(x1, edge_index, edge_attr=e1), out, atol=1e-7) conv = GeneralConv(8, 32, 16, heads=3, attention=True) assert conv.__repr__() == 'GeneralConv(8, 32)' out = conv(x1, edge_index, edge_attr=e1) assert out.size() == (4, 32) assert torch.allclose(conv(x1, edge_index, edge_attr=e1), out, atol=1e-7) conv = GeneralConv(8, 32, 16, heads=3, attention=True, attention_type='dot_product') assert conv.__repr__() == 'GeneralConv(8, 32)' out = conv(x1, edge_index, edge_attr=e1) assert out.size() == (4, 32) assert torch.allclose(conv(x1, edge_index, edge_attr=e1), out, atol=1e-7) conv = GeneralConv(8, 32, 16, l2_normalize=True) assert conv.__repr__() == 'GeneralConv(8, 32)' out = conv(x1, edge_index, edge_attr=e1) assert out.size() == (4, 32) assert torch.allclose(conv(x1, edge_index, edge_attr=e1), out, atol=1e-7)

39.322034

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3.900826

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0.158192

0.169492

0.870763

0.856638

0

0.082888

0.193966

2,320

58

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0.674332

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null

0

1

0

null

0

1

0

8

62b49304e7ee39f19375c5546a88a27a88f5e32c

15,936

py

Python

struntho/inference/maxmin_spmp_sequence.py

alexnowakvila/maxminloss

15c45da5b8c4c214ba2aa596931aff998e3f1c92

[ "Apache-2.0" ]

6

2020-07-28T12:13:50.000Z

2022-01-06T10:35:10.000Z

struntho/inference/maxmin_spmp_sequence.py

alexnowakvila/maxminloss

15c45da5b8c4c214ba2aa596931aff998e3f1c92

[ "Apache-2.0" ]

1

2021-07-12T15:10:19.000Z

struntho/inference/maxmin_spmp_sequence.py

alexnowakvila/maxminloss

15c45da5b8c4c214ba2aa596931aff998e3f1c92

[ "Apache-2.0" ]

4

2020-10-05T16:48:13.000Z

2021-05-04T13:59:24.000Z

import sys # sys.path.append("..") import cvxopt as cvx from cvxopt import matrix, solvers import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from scipy.linalg import toeplitz import scipy.special as sp from struntho.inference.sum_product_chain import sum_product_p from struntho.inference._sum_product_chain import viterbi, sum_product_c def softmax(a, b): c = b * np.exp(a) return c / c.sum(1, keepdims=True) def maxmin_spmp_sequence_p(nu_nodes, nu_edges, p, unary_potentials, pairwise_potentials, Loss, max_iter, eta, sum_product_cython=False): """ INPUT unary_potentials: length * n_states pairwise_potentials: n_states * n_states (pwpot same at all edges) edges: (length - 1) * 2 L: n_states * n_states OUPTUT node_marginals: length * n_states pairwise_marginals: (length - 1) * n_states * n_states """ # choose sum product functionality sum_product = sum_product_c if sum_product_cython else sum_product_p def grad_entropy(MU, edges): marginal_nodes, marginal_edges = MU grad_nodes = np.log(marginal_nodes + 1e-5) grad_edges = -np.log(marginal_edges + 1e-5) return grad_nodes, grad_edges n_states = pairwise_potentials.shape[0] length = unary_potentials.shape[0] # initialize optimization variables nu_nodes = np.log(nu_nodes + 1e-16) nu_edges = np.log(nu_edges + 1e-16) # initialize auxiliar variables q = np.zeros((length, n_states)) mu_nodes = np.zeros((length, n_states)) mu_edges = np.zeros((length - 1, n_states, n_states)) # initialize averages q_avg = np.zeros((length, n_states)) mu_avg_nodes = np.zeros((length, n_states)) mu_avg_edges = np.zeros((length - 1, n_states, n_states)) p_avg = np.zeros((length, n_states)) nu_avg_nodes = np.zeros((length, n_states)) nu_avg_edges = np.zeros((length - 1, n_states, n_states)) # repeated_potentials = np.tile(pairwise_potentials, length - 1) repeated_potentials = np.repeat(pairwise_potentials[np.newaxis, :, :], length - 1, axis=0) dual_gaps = [] max_iter = length * max_iter for k in range(max_iter): # FIRST PROXIMAL MAPPING q = softmax(-eta * np.dot(np.exp(nu_nodes), Loss.T), p) # prepare uscores uscores = eta * np.dot(p, Loss) + eta * unary_potentials - nu_nodes uscores[0] = uscores[0] + nu_nodes[0] uscores[-1] = uscores[-1] + nu_nodes[-1] bscores = eta * repeated_potentials + nu_edges sum_product(uscores, bscores, mu_nodes, mu_edges) # SECOND PROXIMAL MAPPING p = softmax(-eta * np.dot(np.exp(mu_nodes), Loss.T), p) # prepare uscores uscores = eta * np.dot(q, Loss) + eta * unary_potentials - nu_nodes uscores[0] = uscores[0] + nu_nodes[0] uscores[-1] = uscores[-1] + nu_nodes[-1] bscores = eta * repeated_potentials + nu_edges sum_product(uscores, bscores, nu_nodes, nu_edges) # UPDATE AVERAGES q_avg = k * q_avg / (k+1) + q / (k+1) mu_avg_nodes = k * mu_avg_nodes / (k+1) + np.exp(mu_nodes) / (k+1) mu_avg_edges = k * mu_avg_edges / (k+1) + np.exp(mu_edges) / (k+1) p_avg = k * p_avg / (k+1) + p / (k+1) nu_avg_nodes = k * nu_avg_nodes / (k+1) + np.exp(nu_nodes) / (k+1) nu_avg_edges = k * nu_avg_edges / (k+1) + np.exp(nu_edges) / (k+1) # COMPUTE DUAL GAP ymax = np.zeros(length, dtype=np.int32) viterbi(np.dot(q_avg, Loss) + unary_potentials, pairwise_potentials, ymax) # print("ymax", ymax) #make one hot encoding node_embeddings = np.zeros((length, n_states), dtype=np.int) gx = np.ogrid[:length] node_embeddings[gx, ymax] = 1 ##accumulated pairwise edges = np.stack((np.arange(0, length - 1), np.arange(1, length)), 1) sum_edge_embeddings = np.dot(node_embeddings[edges[:, 0]].T, node_embeddings[edges[:, 1]]) # compute value of y_max m1 = (np.dot(q_avg, Loss) + unary_potentials)[np.arange(length), ymax].sum() m2 = (pairwise_potentials * sum_edge_embeddings).sum() maxval = m1 + m2 en1 = (unary_potentials * mu_avg_nodes).sum() en2 = (repeated_potentials * mu_avg_edges).sum() minval = np.min(np.dot(mu_avg_nodes, Loss), axis=1).sum() + en1 + en2 dual_gap = maxval - minval # print("Iteration: {}. Dual gap: {}".format(k, dual_gap)) dual_gaps.append(dual_gap) # check for positive values # if mu_nodes.max() > 0.1: pdb.set_trace() out1 = [[mu_avg_nodes, mu_avg_edges], q_avg] out2 = [[nu_avg_nodes, nu_avg_edges], p_avg] return out1, out2, dual_gaps def maxmin_spmp_sequence_p2(nu_nodes, nu_edges, p, unary_potentials, pairwise_potentials, Loss, max_iter, eta, sum_product_cython=False): """ INPUT unary_potentials: length * n_states pairwise_potentials: n_states * n_states (pwpot same at all edges) edges: (length - 1) * 2 L: n_states * n_states OUPTUT node_marginals: length * n_states pairwise_marginals: (length - 1) * n_states * n_states """ # choose sum product functionality sum_product = sum_product_c if sum_product_cython else sum_product_p def grad_entropy(MU, edges): marginal_nodes, marginal_edges = MU grad_nodes = np.log(marginal_nodes + 1e-10) grad_edges = -np.log(marginal_edges + 1e-10) return grad_nodes, grad_edges n_states = pairwise_potentials.shape[0] length = unary_potentials.shape[0] # initialize optimization variables nu_nodes = np.log(nu_nodes + 1e-16) nu_edges = np.log(nu_edges + 1e-16) # initialize auxiliar variables q = np.zeros((length, n_states)) mu_nodes = np.zeros((length, n_states)) mu_edges = np.zeros((length - 1, n_states, n_states)) # initialize averages q_avg = np.zeros((length, n_states)) mu_avg_nodes = np.zeros((length, n_states)) mu_avg_edges = np.zeros((length - 1, n_states, n_states)) p_avg = np.zeros((length, n_states)) nu_avg_nodes = np.zeros((length, n_states)) nu_avg_edges = np.zeros((length - 1, n_states, n_states)) # repeated_potentials = np.tile(pairwise_potentials, length - 1) repeated_potentials = np.repeat(pairwise_potentials[np.newaxis, :, :], length - 1, axis=0) dual_gaps = [] max_iter = length * max_iter for k in range(max_iter): # FIRST PROXIMAL MAPPING q = softmax(-eta * np.dot(np.exp(nu_nodes), Loss.T), p) # prepare uscores uscores = eta * np.dot(p, Loss) + eta * unary_potentials - nu_nodes uscores[0] = uscores[0] + nu_nodes[0] uscores[-1] = uscores[-1] + nu_nodes[-1] bscores = eta * repeated_potentials + nu_edges sum_product(uscores, bscores, mu_nodes, mu_edges) # SECOND PROXIMAL MAPPING # if np.exp(mu_nodes).max() == np.inf or mu_nodes.min() == -np.inf: # import pdb; pdb.set_trace() p = softmax(-eta * np.dot(np.exp(mu_nodes), Loss.T), q) # prepare uscores uscores = eta * np.dot(q, Loss) + eta * unary_potentials - mu_nodes uscores[0] = uscores[0] + mu_nodes[0] uscores[-1] = uscores[-1] + mu_nodes[-1] bscores = eta * repeated_potentials + mu_edges # if np.isinf(uscores.max()): # import pdb; pdb.set_trace() sum_product(uscores, bscores, nu_nodes, nu_edges) # UPDATE AVERAGES q_avg = k * q_avg / (k+1) + q / (k+1) mu_avg_nodes = k * mu_avg_nodes / (k+1) + np.exp(mu_nodes) / (k+1) mu_avg_edges = k * mu_avg_edges / (k+1) + np.exp(mu_edges) / (k+1) p_avg = k * p_avg / (k+1) + p / (k+1) nu_avg_nodes = k * nu_avg_nodes / (k+1) + np.exp(nu_nodes) / (k+1) nu_avg_edges = k * nu_avg_edges / (k+1) + np.exp(nu_edges) / (k+1) # COMPUTE DUAL GAP ymax = np.zeros(length, dtype=np.int32) viterbi(np.dot(q_avg, Loss) + unary_potentials, pairwise_potentials, ymax) # print("ymax", ymax) #make one hot encoding node_embeddings = np.zeros((length, n_states), dtype=np.int) gx = np.ogrid[:length] node_embeddings[gx, ymax] = 1 ##accumulated pairwise edges = np.stack((np.arange(0, length - 1), np.arange(1, length)), 1) sum_edge_embeddings = np.dot(node_embeddings[edges[:, 0]].T, node_embeddings[edges[:, 1]]) # compute value of y_max m1 = (np.dot(q_avg, Loss) + unary_potentials)[np.arange(length), ymax].sum() m2 = (pairwise_potentials * sum_edge_embeddings).sum() maxval = m1 + m2 en1 = (unary_potentials * mu_avg_nodes).sum() en2 = (repeated_potentials * mu_avg_edges).sum() minval = np.min(np.dot(mu_avg_nodes, Loss), axis=1).sum() + en1 + en2 dual_gap = maxval - minval # print("Iteration: {}. Dual gap: {}".format(k, dual_gap)) dual_gaps.append(dual_gap) # check for positive values # if mu_nodes.max() > 0.1: pdb.set_trace() out1 = [[mu_avg_nodes, mu_avg_edges], q_avg] out2 = [[nu_avg_nodes, nu_avg_edges], p_avg] return out1, out2, dual_gaps # def CVXOPT(unary_potentials, pairwise_potentials, Loss): # Loss = matrix(Loss) # n_states = pairwise_potentials.shape[0] # length = unary_potentials.shape[0] # # COMPUTE MATRIX A # A1 = np.zeros((n_states, n_states ** 2)) # A2 = np.tile(np.arange(n_states), (n_states, n_states)) # for j in range(n_states): # A1[j, j * n_states: (j+1) * n_states] = 1. # A2[j] = -1 * (A2[j] % n_states == j).astype(float) # A3 = np.concatenate((A1, A2), axis=1) # A = np.zeros((n_states * (length - 2), (length - 1) * n_states ** 2)) # for l in range(length - 2): # A[l * n_states: (l+1) * n_states, l * n_states ** 2: (l+2) * n_states ** 2] = A3 # # A has shape n_states * (length - 1) X length * n_states ** 2 # # A4 = np.zeros((length - 1, (length - 1) * n_states ** 2)) # # for l in range(length - 1): # # A4[l, l * n_states ** 2 : (l+1) * n_states ** 2] = 1. # # A = np.concatenate((A, A4), axis=0) # # # insert part associated to z # # A = np.concatenate((A, np.zeros((A.shape[0], length))), axis=1) # # assert A.shape[0] == (length - 2) * n_states + length - 1 # A4 = np.zeros((1, (length - 1) * n_states ** 2)) # A4[0,:n_states ** 2] = 1. # A = np.concatenate((A, A4), axis=0) # # insert part associated to z # A = np.concatenate((A, np.zeros((A.shape[0], length))), axis=1) # assert A.shape[0] == (length - 2) * n_states + 1 # # COMPUTE VECTOR b # b = np.zeros(A.shape[0]) # b[(length - 2) * n_states:] = 1. # # COMPUTE MATRIX G # # we separate the computation between G1, G2, G3, G4 # G0 = np.zeros((n_states, n_states ** 2)) # for j in range(n_states): # g = np.ones((n_states, 1)).dot(Loss[[j], :]) # G0[j] = g.flatten() # G1 = np.zeros((length * n_states, (length - 1) * n_states ** 2)) # for l in range(length - 1): # G1[l * n_states: (l+1) * n_states, l * n_states ** 2: (l+1) * n_states ** 2] = G0 # G1[(length - 1) * n_states : length * n_states, (length - 2) * n_states ** 2 : (length - 1) * n_states ** 2] = G0 # G1 = -1 * G1 # G2 = np.zeros((length * n_states, length)) # for l in range(length): # G2[l * n_states: (l+1)* n_states, l] = 1. # G3 = -1 * np.eye((length - 1) * n_states ** 2) # G4 = np.zeros(((length - 1) * n_states ** 2, length)) # G = np.concatenate((G1, G2), axis=1) # G = np.concatenate((G, np.concatenate((G3, G4), axis=1)), axis=0) # # COMPUTE VECTOR h # h = np.zeros(G.shape[0]) # # COMPUTE COST VECTOR c # C1 = np.tile(pairwise_potentials.flatten(), (length - 1, 1)) # for l in range(length - 1): # C1[l] += unary_potentials[[l], :].transpose().dot(np.ones((1, n_states))).flatten() # C1[length - 2] += unary_potentials[[length - 1], :].transpose().dot(np.ones((1, n_states))).flatten() # # C1 has shape length - 1 X n_states ** 2 # C2 = np.ones(length) # c = -1 * np.concatenate((C1.flatten(), C2), axis=0) # # PRINT SHAPES # print("c has shape (length - 1) * n_states * n_states + length = {}".format(c.shape[0])) # print("G has shape (length * n_states + (length - 1) * n_states * n_states) X ((length - 1) * n_states * n_states + length) = {}".format(G.shape)) # print("h has shape length * n_states + (length - 1) * n_states * n_states = {}".format(h.shape[0])) # print("A has shape ((length - 2) * n_states + (length - 1)) X ((length - 1) * n_states * n_states + length) = {}".format(A.shape)) # print("b has shape (length - 2) * n_states + (length - 1) = {}".format(b.shape[0])) # assert c.shape[0] == (length - 1) * n_states * n_states + length # assert G.shape[0] == (length * n_states + (length - 1) * n_states * n_states) # assert G.shape[1] == ((length - 1) * n_states * n_states + length) # assert h.shape[0] == length * n_states + (length - 1) * n_states * n_states # # assert A.shape[0] == ((length - 2) * n_states + (length - 1)) # assert A.shape[0] == ((length - 2) * n_states + 1) # assert A.shape[1] == ((length - 1) * n_states * n_states + length) # # assert b.shape[0] == (length - 2) * n_states + (length - 1) # assert b.shape[0] == (length - 2) * n_states + 1 # # check rank # # print(A.shape[0] - np.linalg.matrix_rank(A)) # # pass to matrix format # c, G, h, A, b = matrix(c), matrix(G), matrix(h), matrix(A), matrix(b) # sol=solvers.lp(c, G, h, A, b) # en = -1 * sol['primal objective'] # dual_gap = sol['gap'] # mu = np.array(sol['x'][: (length - 1) * n_states ** 2]).flatten() # mu_edges = np.reshape(mu, (length - 1, n_states, n_states)) # mu_nodes = np.zeros((length, n_states)) # for l in range(length - 1): # mu_nodes[l] = mu_edges[l].sum(0) # mu_nodes[length - 1] = mu_edges[-1].sum(1) # out = [[mu_nodes, mu_edges], None] # return out, en, dual_gap if __name__ == "__main__": np.random.seed(1) eps = 1e-3 n_states = 5 Loss = np.ones((n_states, n_states)) np.fill_diagonal(Loss, 0.0) Loss = toeplitz(np.arange(n_states)) length = 10 unary_potentials = np.random.random_sample((length, n_states)) pairwise_potentials = np.random.random_sample((n_states, n_states)) edges = np.stack((np.arange(0, length - 1), np.arange(1, length)), 1) p = np.ones((length, n_states)) / n_states nu_nodes = np.ones((length, n_states)) / n_states nu_edges = np.ones((length - 1, n_states, n_states)) / (n_states ** 2) max_iter = 50 eta = 1 / (2 * np.max(Loss)) out, dual_gaps = maxmin_spmp_sequence_p(nu_nodes, nu_edges, p, unary_potentials, pairwise_potentials, Loss, max_iter, eta, sum_product_cython=True)

40.446701

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null

0

1

0

1

0

null

0

7

62b789628fb8711ec050ed22195efc1a42b1376c

1,625

py

Python

test/docstrings/codetag2.py

Setonas/MagicSetonas

ef76da5f27a0506b194c58072b81424e3ce985d7

[ "MIT" ]

5

2017-02-22T10:17:39.000Z

2021-04-06T16:36:13.000Z

test/docstrings/codetag2.py

Setonas/MagicSetonas

ef76da5f27a0506b194c58072b81424e3ce985d7

[ "MIT" ]

null

test/docstrings/codetag2.py

Setonas/MagicSetonas

ef76da5f27a0506b194c58072b81424e3ce985d7

[ "MIT" ]

1

2020-08-29T02:30:52.000Z

' foo bar XXX baz ' apibrėžti foo(): ' foo FIXME baz ' ' : punctuation.definition.string.begin.python, source.python, string.quoted.docstring.single.python foo bar : source.python, string.quoted.docstring.single.python XXX : keyword.codetag.notation.python, source.python, string.quoted.docstring.single.python baz : source.python, string.quoted.docstring.single.python ' : punctuation.definition.string.end.python, source.python, string.quoted.docstring.single.python : source.python apibrėžti : meta.function.python, source.python, storage.type.function.python : meta.function.python, source.python foo : entity.name.function.python, meta.function.python, source.python ( : meta.function.parameters.python, meta.function.python, punctuation.definition.parameters.begin.python, source.python ) : meta.function.parameters.python, meta.function.python, punctuation.definition.parameters.end.python, source.python : : meta.function.python, punctuation.section.function.begin.python, source.python : source.python ' : punctuation.definition.string.begin.python, source.python, string.quoted.docstring.single.python foo : source.python, string.quoted.docstring.single.python FIXME : keyword.codetag.notation.python, source.python, string.quoted.docstring.single.python baz : source.python, string.quoted.docstring.single.python ' : punctuation.definition.string.end.python, source.python, string.quoted.docstring.single.python

62.5

132

0.708923

181

1,625

6.364641

0.149171

0.1875

0.21875

0.208333

0.87066

0.803819

0.664931

0

0.182154

1,625

25

133

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0

1

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null

0

1

0

7

62c1b8d0e89dfbac955c76e93d4d715344e87b5d

17,179

py

Python

unit/protoss_unit.py

jixiaozhong/mind-SC2

eece7f165a74c7e448361e19b20327e38309ce81

[ "MIT" ]

30

2019-03-05T09:50:27.000Z

2019-08-28T11:33:43.000Z

unit/protoss_unit.py

jixiaozhong/mind-SC2

eece7f165a74c7e448361e19b20327e38309ce81

[ "MIT" ]

10

2020-01-05T15:22:37.000Z

2021-08-25T15:29:40.000Z

unit/protoss_unit.py

mindgameSC2/Thought-SC2

9c138416a65fd3c4093b2841b6163e81c60b9be5

[ "MIT" ]

6

2019-09-19T07:51:04.000Z

2022-01-23T11:02:51.000Z

from unit.units import Building, Creature, Queue class ProtossBuilding(Building): def __init__(self): super().__init__() self.shield = 0 self.shield_armor = 0 def getEquivalentHP(self, attack): if attack == 0: return self.hp + self.shield else: return self.hp * attack / max(attack - self.armor, 1) + \ self.shield * attack / max(attack - self.shield_armor, 1) class ProtossCreature(Creature): def __init__(self): super().__init__() self.shield = 0 self.shield_armor = 0 def getEquivalentHP(self, attack): if attack == 0: return self.hp + self.shield else: return self.hp * attack / max(attack - self.armor, 1) + \ self.shield * attack / max(attack - self.shield_armor, 1) # Basic Buildings class Nexus(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 400 self.gas_price = 0 self.build_time = 71 self.food_supply = 15 self.hp = 1000 self.shield = 1000 self.armor = 1 self.shield_armor = 0 self.queue = Queue() class Pylon(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 100 self.gas_price = 0 self.build_time = 18 self.food_supply = 8 self.hp = 200 self.shield = 200 self.armor = 1 self.shield_armor = 0 class Assimilator(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 75 self.gas_price = 0 self.build_time = 21 self.hp = 300 self.shield = 300 self.armor = 1 self.shield_armor = 0 class PhotonCannon(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 0 self.build_time = 29 self.hp = 150 self.shield = 150 self.armor = 1 self.shield_armor = 0 self.attack = 20 self.range = 7 self.dps = 22.4 class ShieldBattery(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 100 self.gas_price = 0 self.build_time = 29 self.hp = 150 self.shield = 150 self.armor = 1 self.shield_armor = 0 # Producing Buildings class Gateway(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 0 self.build_time = 46 self.hp = 500 self.shield = 500 self.armor = 1 self.shield_armor = 0 self.max_size = 5 self.queue = Queue() class Warpgate(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 0 self.gas_price = 0 self.build_time = 7 self.hp = 500 self.shield = 500 self.armor = 1 self.shield_armor = 0 class RoboticsFacility(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 200 self.gas_price = 100 self.build_time = 46 self.hp = 450 self.shield = 450 self.armor = 1 self.shield_armor = 0 class Stargate(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 150 self.build_time = 43 self.hp = 600 self.shield = 600 self.armor = 1 self.shield_armor = 0 # Technologic Buildings class Forge(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 0 self.build_time = 32 self.hp = 400 self.shield = 400 self.armor = 1 self.shield_armor = 0 class CyberneticsCore(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 0 self.build_time = 36 self.hp = 550 self.shield = 550 self.armor = 1 self.shield_armor = 0 class TwilightCouncil(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 0 self.build_time = 36 self.hp = 550 self.shield = 550 self.armor = 1 self.shield_armor = 0 class TemplarArchives(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 200 self.build_time = 36 self.hp = 500 self.shield = 500 self.armor = 1 self.shield_armor = 0 class DarkShrine(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 150 self.build_time = 71 self.hp = 500 self.shield = 500 self.armor = 1 self.shield_armor = 0 class RoboticsBBay(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 150 self.build_time = 46 self.hp = 500 self.shield = 500 self.armor = 1 self.shield_armor = 0 class FleetBeacon(ProtossBuilding): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 300 self.gas_price = 200 self.build_time = 43 self.hp = 500 self.shield = 500 self.armor = 1 self.shield_armor = 0 # Worker class Probe(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 50 self.gas_price = 0 self.build_time = 12 self.food_used = 1 self.hp = 20 self.shield = 20 self.armor = 0 self.shield_armor = 0 self.attribute = ['L', 'M'] self.attack = 5 self.range = 0 self.dps = 4.7 self.movement = 3.94 # Gateway Units class Zealot(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 100 self.gas_price = 0 self.build_time = 27 self.food_used = 2 self.hp = 100 self.shield = 50 self.armor = 1 self.shield_armor = 0 self.attribute = ['L', 'B'] self.attack = 16 self.range = 0 self.dps = 18.6 self.bonus_attack = {} self.movement = 3.15 class Stalker(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 125 self.gas_price = 50 self.build_time = 30 self.food_used = 2 self.hp = 80 self.shield = 80 self.armor = 1 self.shield_armor = 0 self.attribute = ['A', 'M'] self.attack = 13 self.range = 6 self.dps = 9.7 self.bonus_attack = {'A': 5} self.movement = 4.13 class Sentry(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 50 self.gas_price = 100 self.build_time = 26 self.food_used = 2 self.hp = 40 self.shield = 40 self.armor = 1 self.shield_armor = 0 self.attribute = ['L', 'M', 'P'] self.attack = 6 self.range = 5 self.dps = 8.5 self.movement = 3.15 class Adept(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 100 self.gas_price = 25 self.build_time = 27 self.food_used = 2 self.hp = 70 self.shield = 70 self.armor = 1 self.shield_armor = 0 self.attribute = ['L', 'B'] self.attack = 10 self.range = 4 self.dps = 6.2 self.bonus_attack = {'L': 12} self.movement = 3.5 class HighTemplar(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 50 self.gas_price = 150 self.build_time = 39 self.food_used = 2 self.hp = 40 self.shield = 40 self.armor = 0 self.shield_armor = 0 self.attribute = ['L', 'B', 'P'] self.attack = 4 self.range = 6 self.dps = 3.2 self.movement = 2.62 class DarkTemplar(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 125 self.gas_price = 125 self.build_time = 39 self.food_used = 2 self.hp = 40 self.shield = 80 self.armor = 1 self.shield_armor = 0 self.attribute = ['L', 'B', 'P'] self.attack = 45 self.range = 0 self.dps = 37.2 self.movement = 3.94 class Archon(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 0 self.gas_price = 0 self.build_time = 9 self.food_used = 4 self.hp = 10 self.shield = 350 self.armor = 0 self.shield_armor = 0 self.attribute = ['P', 'Ma'] self.attack = 25 self.range = 3 self.dps = 20 self.bonus_attack = {'B': 10} self.movement = 3.94 # Robotics Facility Units class Observer(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 25 self.gas_price = 75 self.build_time = 21 self.food_used = 1 self.hp = 40 self.shield = 20 self.armor = 0 self.shield_armor = 0 self.attack = 0 self.range = 0 self.dps = 0 self.movement = 2.62 class WarpPrism(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 200 self.gas_price = 0 self.build_time = 36 self.food_used = 2 self.hp = 80 self.shield = 100 self.armor = 0 self.shield_armor = 0 self.attribute = ['A', 'M', 'P'] self.attack = 0 self.range = 0 self.dps = 0 self.movement = 4.13 class Immortal(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 250 self.gas_price = 100 self.build_time = 39 self.food_used = 4 self.hp = 250 self.shield = 100 self.armor = 1 self.shield_armor = 0 self.attribute = ['A', 'M'] self.attack = 20 self.range = 6 self.dps = 19.2 self.bonus_attack = {'A': 30} self.movement = 3.15 class Colossus(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 300 self.gas_price = 200 self.build_time = 54 self.food_used = 6 self.hp = 200 self.shield = 150 self.armor = 1 self.shield_armor = 0 self.attribute = ['A', 'M', 'Ma'] self.attack = 20 self.range = 7 self.dps = 18.7 self.bonus_attack = {'L': 5} self.movement = 3.15 class Disruptor(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 150 self.build_time = 36 self.food_used = 3 self.hp = 100 self.shield = 100 self.armor = 1 self.shield_armor = 0 self.attribute = ['A', 'M'] self.attack = 0 self.range = 0 self.dps = 0 self.movement = 3.15 # Stargate Units class Phoenix(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 100 self.build_time = 25 self.food_used = 2 self.hp = 120 self.shield = 60 self.armor = 0 self.shield_armor = 0 self.attribute = ['L', 'M'] self.attack = 10 self.range = 5 self.dps = 12.6 self.bonus_attack = {'L': 5} self.movement = 5.95 class VoidRay(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 250 self.gas_price = 150 self.build_time = 43 self.food_used = 4 self.hp = 150 self.shield = 100 self.armor = 0 self.shield_armor = 0 self.attribute = ['A', 'M'] self.attack = 6 self.range = 6 self.dps = 16.8 self.bonus_attack = {'A': 4} self.movement = 3.5 class Oracle(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 150 self.gas_price = 150 self.build_time = 36 self.food_used = 3 self.hp = 100 self.shield = 60 self.armor = 0 self.shield_armor = 0 self.attribute = ['L', 'M', 'P'] self.attack = 15 self.range = 4 self.dps = 24.4 self.bonus_attack = {'L': 7} self.movement = 5.6 class Tempest(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 250 self.gas_price = 175 self.build_time = 43 self.food_used = 5 self.hp = 150 self.shield = 125 self.armor = 2 self.shield_armor = 0 self.attribute = ['A', 'M', 'Ma'] self.attack = 40 self.range = 15 self.dps = 16.97 self.bonus_attack = {'Ma': 22} self.movement = 3.5 class Carrier(ProtossCreature): def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 350 self.gas_price = 250 self.build_time = 64 self.food_used = 6 self.hp = 300 self.shield = 150 self.armor = 2 self.shield_armor = 0 self.attribute = ['A', 'M', 'Ma'] self.attack = 10 self.range = 8 self.dps = 37.8 self.movement = 2.62 class Interceptor(ProtossCreature): # created by Carrier def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 15 self.gas_price = 0 self.build_time = 11 self.food_used = 0 self.hp = 40 self.shield = 40 self.armor = 0 self.shield_armor = 0 self.attribute = ['L', 'M'] self.attack = 10 self.range = 2 self.dps = 4.7 self.movement = 10.5 class Mothership(ProtossCreature): # producted by Nexus def __init__(self): super().__init__() self.specialization() def specialization(self): self.mineral_price = 300 self.gas_price = 300 self.build_time = 71 self.food_used = 8 self.hp = 350 self.shield = 350 self.armor = 2 self.shield_armor = 0 self.attribute = ['A', 'M', 'P', 'Ma'] self.attack = 36 self.range = 7 self.dps = 22.8 self.movement = 2.62

20.772672

73

0.549566

2,005

17,179

4.466833

0.07581

0.091559

0.066994

0.067887

0.858531

0.800804

0.770992

0.736601

0.714046

0.699866

0

0.064657

0.349089

17,179

826

74

20.797821

0.736273

0.009023

0

0.780405

0

0.003703

0

1

0.128378

false

0

0.001689

0

0.201014

0

null

0

1

0

1

0

null

0

7

62e80a7c75d4538a8532bc9bd03d65c579aacd18

32

py

Python

python/quine.py

ahuglajbclajep/quine

5d8d41461f7bdec1dafba2b1939c0c2b2022c9a8

[ "MIT" ]

null

python/quine.py

ahuglajbclajep/quine

5d8d41461f7bdec1dafba2b1939c0c2b2022c9a8

[ "MIT" ]

null

python/quine.py

ahuglajbclajep/quine

5d8d41461f7bdec1dafba2b1939c0c2b2022c9a8

[ "MIT" ]

null

q='q=%r;print(q%%q)';print(q%q)

16

31

0.53125

9

32

1.888889

0.333333

0.352941

0.823529

0

0.03125

32

1

32

0.548387

0

0.5

0

1

0

false

0

1

null

1

0

1

0

null

0

1

0

7

1a28c56859c561eb00b0d006dddb2ed83b0b5924

217

py

Python

trochilidae/tests/test_interoperable_filter.py

MATTHEWFRAZER/trochilidae

35e907ba9dcb1f283f79f4f32d61db6b53a1ca97

[ "MIT" ]

null

trochilidae/tests/test_interoperable_filter.py

MATTHEWFRAZER/trochilidae

35e907ba9dcb1f283f79f4f32d61db6b53a1ca97

[ "MIT" ]

null

trochilidae/tests/test_interoperable_filter.py

MATTHEWFRAZER/trochilidae

35e907ba9dcb1f283f79f4f32d61db6b53a1ca97

[ "MIT" ]

1

2021-11-12T18:49:15.000Z

import pytest def test_interoperable_filter_import(): try: from trochilidae.interoperable_filter import interoperable_filter except Exception as ex: pytest.fail("import failed:{0}".format(ex))

31

73

0.741935

26

217

6

0.653846

0.365385

0.320513

0

0.005618

0.179724

217

7

74

31

0.870787

0

0.077982

0

1

0.166667

true

0

0.666667

0

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

a7ef51e0c4cf71bcc5f9f53ff2982019fd8d6d8a

98

py

Python

brainlit/algorithms/connect_fragments/__init__.py

NeuroDataDesign/brainl

fc99f59a9d835039dac713a028ac2521ac217e95

[ "Apache-2.0" ]

null

brainlit/algorithms/connect_fragments/__init__.py

NeuroDataDesign/brainl

fc99f59a9d835039dac713a028ac2521ac217e95

[ "Apache-2.0" ]

null

brainlit/algorithms/connect_fragments/__init__.py

NeuroDataDesign/brainl

fc99f59a9d835039dac713a028ac2521ac217e95

[ "Apache-2.0" ]

null

import brainlit.algorithms.connect_fragments from brainlit.algorithms.connect_fragments import *

24.5

51

0.877551

11

98

7.636364

0.545455

0.428571

0.595238

0.809524

0

0.071429

98

3

52

32.666667

0.923077

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

c50088fd368b2171cc613d837e2b6ffd9c5216e5

76,206

py

Python

try/data_processing/resume_pb2.py

searobbersduck/ResumeAnalyze

984484dd1c6af090ae1b7854bc931e06a9294586

[ "MIT" ]

null

try/data_processing/resume_pb2.py

searobbersduck/ResumeAnalyze

984484dd1c6af090ae1b7854bc931e06a9294586

[ "MIT" ]

null

try/data_processing/resume_pb2.py

searobbersduck/ResumeAnalyze

984484dd1c6af090ae1b7854bc931e06a9294586

[ "MIT" ]

null

# Generated by the protocol buffer compiler. 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full_name='com.inmind.idmg.search.common.Resume', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='chineseName', full_name='com.inmind.idmg.search.common.Resume.chineseName', 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, options=None), _descriptor.FieldDescriptor( name='englishName', full_name='com.inmind.idmg.search.common.Resume.englishName', 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, options=None), _descriptor.FieldDescriptor( name='birthday', full_name='com.inmind.idmg.search.common.Resume.birthday', 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, options=None), _descriptor.FieldDescriptor( name='gender', full_name='com.inmind.idmg.search.common.Resume.gender', index=3, number=4, 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, options=None), _descriptor.FieldDescriptor( name='currentJobTitle', full_name='com.inmind.idmg.search.common.Resume.currentJobTitle', index=4, number=5, 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, options=None), _descriptor.FieldDescriptor( name='expectFunctions', full_name='com.inmind.idmg.search.common.Resume.expectFunctions', index=5, number=6, 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, options=None), _descriptor.FieldDescriptor( name='qqNo', full_name='com.inmind.idmg.search.common.Resume.qqNo', 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, options=None), _descriptor.FieldDescriptor( name='wechatNo', full_name='com.inmind.idmg.search.common.Resume.wechatNo', 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, options=None), _descriptor.FieldDescriptor( name='industries', full_name='com.inmind.idmg.search.common.Resume.industries', index=8, number=9, 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, options=None), _descriptor.FieldDescriptor( name='expectIndustries', full_name='com.inmind.idmg.search.common.Resume.expectIndustries', 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, options=None), _descriptor.FieldDescriptor( name='expectLocations', full_name='com.inmind.idmg.search.common.Resume.expectLocations', index=10, number=11, 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, options=None), _descriptor.FieldDescriptor( name='expectSalaryDetail', full_name='com.inmind.idmg.search.common.Resume.expectSalaryDetail', index=11, number=12, 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, options=None), _descriptor.FieldDescriptor( name='expectSalaryInMonth', full_name='com.inmind.idmg.search.common.Resume.expectSalaryInMonth', index=12, number=13, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='salaryDetail', full_name='com.inmind.idmg.search.common.Resume.salaryDetail', index=13, number=14, 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, options=None), _descriptor.FieldDescriptor( name='salary', full_name='com.inmind.idmg.search.common.Resume.salary', index=14, number=15, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='jobSearchStatus', full_name='com.inmind.idmg.search.common.Resume.jobSearchStatus', index=15, number=16, 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, options=None), _descriptor.FieldDescriptor( name='address', full_name='com.inmind.idmg.search.common.Resume.address', index=16, number=17, 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, options=None), _descriptor.FieldDescriptor( name='seniority', full_name='com.inmind.idmg.search.common.Resume.seniority', index=17, number=18, 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, options=None), _descriptor.FieldDescriptor( name='maritalStatus', full_name='com.inmind.idmg.search.common.Resume.maritalStatus', index=18, number=19, 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, options=None), _descriptor.FieldDescriptor( name='educationDegree', full_name='com.inmind.idmg.search.common.Resume.educationDegree', index=19, number=20, 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, options=None), _descriptor.FieldDescriptor( name='siteUrls', full_name='com.inmind.idmg.search.common.Resume.siteUrls', index=20, number=21, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='registerLocationId', full_name='com.inmind.idmg.search.common.Resume.registerLocationId', index=21, number=22, 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, options=None), _descriptor.FieldDescriptor( name='registerLocation', full_name='com.inmind.idmg.search.common.Resume.registerLocation', index=22, number=23, 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, options=None), _descriptor.FieldDescriptor( name='partyInfo', full_name='com.inmind.idmg.search.common.Resume.partyInfo', index=23, number=24, 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, options=None), _descriptor.FieldDescriptor( name='typeCode', full_name='com.inmind.idmg.search.common.Resume.typeCode', index=24, number=25, 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, options=None), _descriptor.FieldDescriptor( name='source', full_name='com.inmind.idmg.search.common.Resume.source', index=25, number=26, 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, options=None), _descriptor.FieldDescriptor( name='introduce', full_name='com.inmind.idmg.search.common.Resume.introduce', index=26, number=27, 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, options=None), _descriptor.FieldDescriptor( name='languageCodes', full_name='com.inmind.idmg.search.common.Resume.languageCodes', index=27, number=28, type=5, cpp_type=1, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='languageSkills', full_name='com.inmind.idmg.search.common.Resume.languageSkills', index=28, number=29, 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, options=None), _descriptor.FieldDescriptor( name='skills', full_name='com.inmind.idmg.search.common.Resume.skills', index=29, number=30, 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, options=None), _descriptor.FieldDescriptor( name='certifications', full_name='com.inmind.idmg.search.common.Resume.certifications', index=30, number=31, 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, options=None), _descriptor.FieldDescriptor( name='privateEmail', full_name='com.inmind.idmg.search.common.Resume.privateEmail', index=31, number=32, 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, options=None), _descriptor.FieldDescriptor( name='workTel', full_name='com.inmind.idmg.search.common.Resume.workTel', index=32, number=33, 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, options=None), _descriptor.FieldDescriptor( name='mobile', full_name='com.inmind.idmg.search.common.Resume.mobile', index=33, number=34, 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, options=None), _descriptor.FieldDescriptor( name='otherTel', full_name='com.inmind.idmg.search.common.Resume.otherTel', index=34, number=35, 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, options=None), _descriptor.FieldDescriptor( name='jobGradeCodes', full_name='com.inmind.idmg.search.common.Resume.jobGradeCodes', index=35, number=36, type=5, cpp_type=1, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='currentWorkExperience', full_name='com.inmind.idmg.search.common.Resume.currentWorkExperience', index=36, number=37, 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, options=None), _descriptor.FieldDescriptor( name='pastWorkExperiences', full_name='com.inmind.idmg.search.common.Resume.pastWorkExperiences', index=37, number=38, 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, options=None), _descriptor.FieldDescriptor( name='projectExperiences', full_name='com.inmind.idmg.search.common.Resume.projectExperiences', index=38, number=39, 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, options=None), _descriptor.FieldDescriptor( name='educationExperiences', full_name='com.inmind.idmg.search.common.Resume.educationExperiences', index=39, number=40, 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, options=None), _descriptor.FieldDescriptor( name='idNumber', full_name='com.inmind.idmg.search.common.Resume.idNumber', index=40, number=41, 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, options=None), _descriptor.FieldDescriptor( name='startWorkDate', full_name='com.inmind.idmg.search.common.Resume.startWorkDate', index=41, number=42, 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, options=None), _descriptor.FieldDescriptor( name='updateTime', full_name='com.inmind.idmg.search.common.Resume.updateTime', index=42, number=43, 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, options=None), _descriptor.FieldDescriptor( name='avatarUrl', full_name='com.inmind.idmg.search.common.Resume.avatarUrl', index=43, number=44, 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, options=None), _descriptor.FieldDescriptor( name='workExperiences', full_name='com.inmind.idmg.search.common.Resume.workExperiences', index=44, number=45, 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, options=None), _descriptor.FieldDescriptor( name='expectPosition', full_name='com.inmind.idmg.search.common.Resume.expectPosition', index=45, number=46, 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, options=None), _descriptor.FieldDescriptor( name='score', full_name='com.inmind.idmg.search.common.Resume.score', index=46, number=47, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='tags', full_name='com.inmind.idmg.search.common.Resume.tags', index=47, number=48, 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, options=None), _descriptor.FieldDescriptor( name='extralInfos', full_name='com.inmind.idmg.search.common.Resume.extralInfos', index=48, number=49, 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, options=None), _descriptor.FieldDescriptor( name='nativeLocation', full_name='com.inmind.idmg.search.common.Resume.nativeLocation', index=49, number=50, 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, options=None), _descriptor.FieldDescriptor( name='nativeLocationId', full_name='com.inmind.idmg.search.common.Resume.nativeLocationId', index=50, number=51, 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, options=None), _descriptor.FieldDescriptor( name='resumeLang', full_name='com.inmind.idmg.search.common.Resume.resumeLang', index=51, number=52, 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, options=None), _descriptor.FieldDescriptor( name='country', full_name='com.inmind.idmg.search.common.Resume.country', index=52, number=53, 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, options=None), _descriptor.FieldDescriptor( name='countryId', full_name='com.inmind.idmg.search.common.Resume.countryId', index=53, number=54, 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, options=None), _descriptor.FieldDescriptor( name='negotiable', full_name='com.inmind.idmg.search.common.Resume.negotiable', index=54, number=55, 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, options=None), _descriptor.FieldDescriptor( name='age', full_name='com.inmind.idmg.search.common.Resume.age', index=55, number=56, 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, options=None), _descriptor.FieldDescriptor( name='skillDetail', full_name='com.inmind.idmg.search.common.Resume.skillDetail', index=56, number=57, 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, options=None), _descriptor.FieldDescriptor( name='expectIndustriesNorm', full_name='com.inmind.idmg.search.common.Resume.expectIndustriesNorm', index=57, number=58, 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, options=None), _descriptor.FieldDescriptor( name='expectPositionNorm', full_name='com.inmind.idmg.search.common.Resume.expectPositionNorm', index=58, number=59, 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, options=None), _descriptor.FieldDescriptor( name='industriesNorm', full_name='com.inmind.idmg.search.common.Resume.industriesNorm', index=59, number=60, 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, options=None), _descriptor.FieldDescriptor( name='currentJobTitleNorm', full_name='com.inmind.idmg.search.common.Resume.currentJobTitleNorm', index=60, number=61, 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, options=None), _descriptor.FieldDescriptor( name='expectFunctionsNorm', full_name='com.inmind.idmg.search.common.Resume.expectFunctionsNorm', index=61, number=62, 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, options=None), _descriptor.FieldDescriptor( name='languageSkillsNorm', full_name='com.inmind.idmg.search.common.Resume.languageSkillsNorm', index=62, number=63, 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, options=None), _descriptor.FieldDescriptor( name='certificationsNorm', full_name='com.inmind.idmg.search.common.Resume.certificationsNorm', index=63, number=64, 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=48, serialized_end=2445, ) _TAG = _descriptor.Descriptor( name='Tag', full_name='com.inmind.idmg.search.common.Tag', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='topSchool', full_name='com.inmind.idmg.search.common.Tag.topSchool', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='topMajor', full_name='com.inmind.idmg.search.common.Tag.topMajor', index=1, number=2, 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, options=None), _descriptor.FieldDescriptor( name='age', full_name='com.inmind.idmg.search.common.Tag.age', 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, options=None), _descriptor.FieldDescriptor( name='topCompany', full_name='com.inmind.idmg.search.common.Tag.topCompany', index=3, number=4, 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, options=None), _descriptor.FieldDescriptor( name='stable', full_name='com.inmind.idmg.search.common.Tag.stable', 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2447, serialized_end=2538, ) _DICT = _descriptor.Descriptor( name='Dict', full_name='com.inmind.idmg.search.common.Dict', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='code', full_name='com.inmind.idmg.search.common.Dict.code', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='title', full_name='com.inmind.idmg.search.common.Dict.title', 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2540, serialized_end=2575, ) _MAJORDICT = _descriptor.Descriptor( name='MajorDict', full_name='com.inmind.idmg.search.common.MajorDict', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='code', full_name='com.inmind.idmg.search.common.MajorDict.code', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='title', full_name='com.inmind.idmg.search.common.MajorDict.title', 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, options=None), _descriptor.FieldDescriptor( name='suggestMajor', full_name='com.inmind.idmg.search.common.MajorDict.suggestMajor', 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2577, serialized_end=2639, ) _LOCATION = _descriptor.Descriptor( name='Location', full_name='com.inmind.idmg.search.common.Location', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='id', full_name='com.inmind.idmg.search.common.Location.id', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='code', full_name='com.inmind.idmg.search.common.Location.code', index=1, number=2, 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, options=None), _descriptor.FieldDescriptor( name='name', full_name='com.inmind.idmg.search.common.Location.name', 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, options=None), _descriptor.FieldDescriptor( name='fullname', full_name='com.inmind.idmg.search.common.Location.fullname', index=3, number=4, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='ancestors', full_name='com.inmind.idmg.search.common.Location.ancestors', index=4, number=5, 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, options=None), _descriptor.FieldDescriptor( name='type', full_name='com.inmind.idmg.search.common.Location.type', 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, options=None), _descriptor.FieldDescriptor( name='level', full_name='com.inmind.idmg.search.common.Location.level', index=6, number=7, 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, options=None), _descriptor.FieldDescriptor( name='parentId', full_name='com.inmind.idmg.search.common.Location.parentId', index=7, number=8, 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2642, serialized_end=2817, ) _SALARY = _descriptor.Descriptor( name='Salary', full_name='com.inmind.idmg.search.common.Salary', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='salaryType', full_name='com.inmind.idmg.search.common.Salary.salaryType', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='salary', full_name='com.inmind.idmg.search.common.Salary.salary', index=1, number=2, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='month', full_name='com.inmind.idmg.search.common.Salary.month', index=2, number=3, 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2819, serialized_end=2878, ) _ADDRESS = _descriptor.Descriptor( name='Address', full_name='com.inmind.idmg.search.common.Address', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='location', full_name='com.inmind.idmg.search.common.Address.location', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='detail', full_name='com.inmind.idmg.search.common.Address.detail', 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2880, serialized_end=2964, ) _LANGUAGE = _descriptor.Descriptor( name='Language', full_name='com.inmind.idmg.search.common.Language', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='language', full_name='com.inmind.idmg.search.common.Language.language', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='grade', full_name='com.inmind.idmg.search.common.Language.grade', index=1, number=2, 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2966, serialized_end=3046, ) _WORKEXPERIENCE = _descriptor.Descriptor( name='WorkExperience', full_name='com.inmind.idmg.search.common.WorkExperience', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='org', full_name='com.inmind.idmg.search.common.WorkExperience.org', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='jobTitle', full_name='com.inmind.idmg.search.common.WorkExperience.jobTitle', 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, options=None), _descriptor.FieldDescriptor( name='jobgrade', full_name='com.inmind.idmg.search.common.WorkExperience.jobgrade', index=2, number=3, 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, options=None), _descriptor.FieldDescriptor( name='startedAt', full_name='com.inmind.idmg.search.common.WorkExperience.startedAt', 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, options=None), _descriptor.FieldDescriptor( name='endedAt', full_name='com.inmind.idmg.search.common.WorkExperience.endedAt', index=4, number=5, 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, options=None), _descriptor.FieldDescriptor( name='isOnJob', full_name='com.inmind.idmg.search.common.WorkExperience.isOnJob', index=5, number=6, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='department', full_name='com.inmind.idmg.search.common.WorkExperience.department', index=6, number=7, 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, options=None), _descriptor.FieldDescriptor( name='salaryDetail', full_name='com.inmind.idmg.search.common.WorkExperience.salaryDetail', index=7, number=8, 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, options=None), _descriptor.FieldDescriptor( name='salary', full_name='com.inmind.idmg.search.common.WorkExperience.salary', index=8, number=9, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='leaderJobTitle', full_name='com.inmind.idmg.search.common.WorkExperience.leaderJobTitle', index=9, number=10, 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, options=None), _descriptor.FieldDescriptor( name='nSubordinate', full_name='com.inmind.idmg.search.common.WorkExperience.nSubordinate', index=10, number=11, 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, options=None), _descriptor.FieldDescriptor( name='description', full_name='com.inmind.idmg.search.common.WorkExperience.description', index=11, number=12, 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, options=None), _descriptor.FieldDescriptor( name='functions', full_name='com.inmind.idmg.search.common.WorkExperience.functions', index=12, number=13, 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, options=None), _descriptor.FieldDescriptor( name='industryDict', full_name='com.inmind.idmg.search.common.WorkExperience.industryDict', index=13, number=14, 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, options=None), _descriptor.FieldDescriptor( name='suggestJob', full_name='com.inmind.idmg.search.common.WorkExperience.suggestJob', index=14, number=15, 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, options=None), _descriptor.FieldDescriptor( name='isBigCompany', full_name='com.inmind.idmg.search.common.WorkExperience.isBigCompany', index=15, number=16, 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, options=None), _descriptor.FieldDescriptor( name='jobId', full_name='com.inmind.idmg.search.common.WorkExperience.jobId', index=16, number=17, 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, options=None), _descriptor.FieldDescriptor( name='skillKeyWords', full_name='com.inmind.idmg.search.common.WorkExperience.skillKeyWords', index=17, number=18, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='corpDesc', full_name='com.inmind.idmg.search.common.WorkExperience.corpDesc', index=18, number=19, 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, options=None), _descriptor.FieldDescriptor( name='workPerformance', full_name='com.inmind.idmg.search.common.WorkExperience.workPerformance', index=19, number=20, 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, options=None), _descriptor.FieldDescriptor( name='leaveReason', full_name='com.inmind.idmg.search.common.WorkExperience.leaveReason', index=20, number=21, 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, options=None), _descriptor.FieldDescriptor( name='isIntern', full_name='com.inmind.idmg.search.common.WorkExperience.isIntern', index=21, number=22, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='orgNameNorm', full_name='com.inmind.idmg.search.common.WorkExperience.orgNameNorm', index=22, number=23, 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, options=None), _descriptor.FieldDescriptor( name='industryDictNorm', full_name='com.inmind.idmg.search.common.WorkExperience.industryDictNorm', index=23, number=24, 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, options=None), _descriptor.FieldDescriptor( name='jobTitleNorm', full_name='com.inmind.idmg.search.common.WorkExperience.jobTitleNorm', index=24, number=25, 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, options=None), _descriptor.FieldDescriptor( name='functionsNorm', full_name='com.inmind.idmg.search.common.WorkExperience.functionsNorm', index=25, number=26, 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3049, serialized_end=3847, ) _ORG = _descriptor.Descriptor( name='Org', full_name='com.inmind.idmg.search.common.Org', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='com.inmind.idmg.search.common.Org.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, options=None), _descriptor.FieldDescriptor( name='industryText', full_name='com.inmind.idmg.search.common.Org.industryText', 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, options=None), _descriptor.FieldDescriptor( name='coreName', full_name='com.inmind.idmg.search.common.Org.coreName', 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, options=None), _descriptor.FieldDescriptor( name='industry', full_name='com.inmind.idmg.search.common.Org.industry', 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, options=None), _descriptor.FieldDescriptor( name='location', full_name='com.inmind.idmg.search.common.Org.location', index=4, number=5, 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, options=None), _descriptor.FieldDescriptor( name='suggest', full_name='com.inmind.idmg.search.common.Org.suggest', 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, options=None), _descriptor.FieldDescriptor( name='nameAndInd', full_name='com.inmind.idmg.search.common.Org.nameAndInd', 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, options=None), _descriptor.FieldDescriptor( name='locAndNameAndInd', full_name='com.inmind.idmg.search.common.Org.locAndNameAndInd', 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, options=None), _descriptor.FieldDescriptor( name='locAndNameAndIndID', full_name='com.inmind.idmg.search.common.Org.locAndNameAndIndID', index=8, number=9, 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3850, serialized_end=4036, ) _DEPT = _descriptor.Descriptor( name='Dept', full_name='com.inmind.idmg.search.common.Dept', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='title', full_name='com.inmind.idmg.search.common.Dept.title', 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4038, serialized_end=4059, ) _PROJECTEXPERIENCE = _descriptor.Descriptor( name='ProjectExperience', full_name='com.inmind.idmg.search.common.ProjectExperience', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='startedAt', full_name='com.inmind.idmg.search.common.ProjectExperience.startedAt', 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, options=None), _descriptor.FieldDescriptor( name='endedAt', full_name='com.inmind.idmg.search.common.ProjectExperience.endedAt', 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, options=None), _descriptor.FieldDescriptor( name='isOnProject', full_name='com.inmind.idmg.search.common.ProjectExperience.isOnProject', index=2, number=3, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='org', full_name='com.inmind.idmg.search.common.ProjectExperience.org', index=3, number=4, 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, options=None), _descriptor.FieldDescriptor( name='jobTitle', full_name='com.inmind.idmg.search.common.ProjectExperience.jobTitle', index=4, number=5, 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, options=None), _descriptor.FieldDescriptor( name='title', full_name='com.inmind.idmg.search.common.ProjectExperience.title', 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, options=None), _descriptor.FieldDescriptor( name='description', full_name='com.inmind.idmg.search.common.ProjectExperience.description', 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, options=None), _descriptor.FieldDescriptor( name='technique', full_name='com.inmind.idmg.search.common.ProjectExperience.technique', 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, options=None), _descriptor.FieldDescriptor( name='performance', full_name='com.inmind.idmg.search.common.ProjectExperience.performance', index=8, number=9, 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, options=None), _descriptor.FieldDescriptor( name='orgNameNorm', full_name='com.inmind.idmg.search.common.ProjectExperience.orgNameNorm', index=9, number=10, 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4062, serialized_end=4302, ) _EDUCATIONEXPERIENCE = _descriptor.Descriptor( name='EducationExperience', full_name='com.inmind.idmg.search.common.EducationExperience', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='startedAt', full_name='com.inmind.idmg.search.common.EducationExperience.startedAt', 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, options=None), _descriptor.FieldDescriptor( name='endedAt', full_name='com.inmind.idmg.search.common.EducationExperience.endedAt', 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, options=None), _descriptor.FieldDescriptor( name='isOnSchool', full_name='com.inmind.idmg.search.common.EducationExperience.isOnSchool', index=2, number=3, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='type', full_name='com.inmind.idmg.search.common.EducationExperience.type', index=3, number=4, 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, options=None), _descriptor.FieldDescriptor( name='major', full_name='com.inmind.idmg.search.common.EducationExperience.major', index=4, number=5, 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, options=None), _descriptor.FieldDescriptor( name='school', full_name='com.inmind.idmg.search.common.EducationExperience.school', index=5, number=6, 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, options=None), _descriptor.FieldDescriptor( name='byEntranceExamination', full_name='com.inmind.idmg.search.common.EducationExperience.byEntranceExamination', index=6, number=7, 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, options=None), _descriptor.FieldDescriptor( name='schoolNorm', full_name='com.inmind.idmg.search.common.EducationExperience.schoolNorm', index=7, number=8, 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, options=None), _descriptor.FieldDescriptor( name='majorNorm', full_name='com.inmind.idmg.search.common.EducationExperience.majorNorm', 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4305, serialized_end=4649, ) _SCHOOL = _descriptor.Descriptor( name='School', full_name='com.inmind.idmg.search.common.School', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='id', full_name='com.inmind.idmg.search.common.School.id', index=0, number=1, 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, options=None), _descriptor.FieldDescriptor( name='code', full_name='com.inmind.idmg.search.common.School.code', index=1, number=2, 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, options=None), _descriptor.FieldDescriptor( name='title', full_name='com.inmind.idmg.search.common.School.title', 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, options=None), _descriptor.FieldDescriptor( name='schoolType', full_name='com.inmind.idmg.search.common.School.schoolType', index=3, number=4, 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, options=None), _descriptor.FieldDescriptor( name='suggest', full_name='com.inmind.idmg.search.common.School.suggest', index=4, number=5, 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, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4651, serialized_end=4737, ) _RESUME.fields_by_name['expectFunctions'].message_type = _DICT _RESUME.fields_by_name['industries'].message_type = _DICT _RESUME.fields_by_name['expectIndustries'].message_type = _DICT _RESUME.fields_by_name['expectLocations'].message_type = _LOCATION _RESUME.fields_by_name['expectSalaryDetail'].message_type = _SALARY _RESUME.fields_by_name['salaryDetail'].message_type = _SALARY _RESUME.fields_by_name['address'].message_type = _ADDRESS _RESUME.fields_by_name['registerLocation'].message_type = _LOCATION _RESUME.fields_by_name['languageSkills'].message_type = _LANGUAGE _RESUME.fields_by_name['skills'].message_type = _DICT _RESUME.fields_by_name['certifications'].message_type = _DICT _RESUME.fields_by_name['currentWorkExperience'].message_type = _WORKEXPERIENCE _RESUME.fields_by_name['pastWorkExperiences'].message_type = _WORKEXPERIENCE _RESUME.fields_by_name['projectExperiences'].message_type = _PROJECTEXPERIENCE _RESUME.fields_by_name['educationExperiences'].message_type = _EDUCATIONEXPERIENCE _RESUME.fields_by_name['workExperiences'].message_type = _WORKEXPERIENCE _RESUME.fields_by_name['tags'].message_type = _TAG _RESUME.fields_by_name['nativeLocation'].message_type = _LOCATION _RESUME.fields_by_name['country'].message_type = _LOCATION _RESUME.fields_by_name['expectIndustriesNorm'].message_type = _DICT _RESUME.fields_by_name['industriesNorm'].message_type = _DICT _RESUME.fields_by_name['expectFunctionsNorm'].message_type = _DICT _RESUME.fields_by_name['languageSkillsNorm'].message_type = _LANGUAGE _RESUME.fields_by_name['certificationsNorm'].message_type = _DICT _LOCATION.fields_by_name['ancestors'].message_type = _LOCATION _ADDRESS.fields_by_name['location'].message_type = _LOCATION _LANGUAGE.fields_by_name['language'].message_type = _DICT _WORKEXPERIENCE.fields_by_name['org'].message_type = _ORG _WORKEXPERIENCE.fields_by_name['department'].message_type = _DEPT _WORKEXPERIENCE.fields_by_name['salaryDetail'].message_type = _SALARY _WORKEXPERIENCE.fields_by_name['functions'].message_type = _DICT _WORKEXPERIENCE.fields_by_name['industryDict'].message_type = _DICT _WORKEXPERIENCE.fields_by_name['industryDictNorm'].message_type = _DICT _WORKEXPERIENCE.fields_by_name['functionsNorm'].message_type = _DICT _PROJECTEXPERIENCE.fields_by_name['org'].message_type = _ORG _EDUCATIONEXPERIENCE.fields_by_name['major'].message_type = _DICT _EDUCATIONEXPERIENCE.fields_by_name['school'].message_type = _SCHOOL _EDUCATIONEXPERIENCE.fields_by_name['schoolNorm'].message_type = _SCHOOL _EDUCATIONEXPERIENCE.fields_by_name['majorNorm'].message_type = _DICT DESCRIPTOR.message_types_by_name['Resume'] = _RESUME DESCRIPTOR.message_types_by_name['Tag'] = _TAG DESCRIPTOR.message_types_by_name['Dict'] = _DICT DESCRIPTOR.message_types_by_name['MajorDict'] = _MAJORDICT DESCRIPTOR.message_types_by_name['Location'] = _LOCATION DESCRIPTOR.message_types_by_name['Salary'] = _SALARY DESCRIPTOR.message_types_by_name['Address'] = _ADDRESS DESCRIPTOR.message_types_by_name['Language'] = _LANGUAGE DESCRIPTOR.message_types_by_name['WorkExperience'] = _WORKEXPERIENCE DESCRIPTOR.message_types_by_name['Org'] = _ORG DESCRIPTOR.message_types_by_name['Dept'] = _DEPT DESCRIPTOR.message_types_by_name['ProjectExperience'] = _PROJECTEXPERIENCE DESCRIPTOR.message_types_by_name['EducationExperience'] = _EDUCATIONEXPERIENCE DESCRIPTOR.message_types_by_name['School'] = _SCHOOL _sym_db.RegisterFileDescriptor(DESCRIPTOR) Resume = _reflection.GeneratedProtocolMessageType('Resume', (_message.Message,), dict( DESCRIPTOR = _RESUME, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Resume) )) _sym_db.RegisterMessage(Resume) Tag = _reflection.GeneratedProtocolMessageType('Tag', (_message.Message,), dict( DESCRIPTOR = _TAG, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Tag) )) _sym_db.RegisterMessage(Tag) Dict = _reflection.GeneratedProtocolMessageType('Dict', (_message.Message,), dict( DESCRIPTOR = _DICT, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Dict) )) _sym_db.RegisterMessage(Dict) MajorDict = _reflection.GeneratedProtocolMessageType('MajorDict', (_message.Message,), dict( DESCRIPTOR = _MAJORDICT, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.MajorDict) )) _sym_db.RegisterMessage(MajorDict) Location = _reflection.GeneratedProtocolMessageType('Location', (_message.Message,), dict( DESCRIPTOR = _LOCATION, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Location) )) _sym_db.RegisterMessage(Location) Salary = _reflection.GeneratedProtocolMessageType('Salary', (_message.Message,), dict( DESCRIPTOR = _SALARY, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Salary) )) _sym_db.RegisterMessage(Salary) Address = _reflection.GeneratedProtocolMessageType('Address', (_message.Message,), dict( DESCRIPTOR = _ADDRESS, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Address) )) _sym_db.RegisterMessage(Address) Language = _reflection.GeneratedProtocolMessageType('Language', (_message.Message,), dict( DESCRIPTOR = _LANGUAGE, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Language) )) _sym_db.RegisterMessage(Language) WorkExperience = _reflection.GeneratedProtocolMessageType('WorkExperience', (_message.Message,), dict( DESCRIPTOR = _WORKEXPERIENCE, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.WorkExperience) )) _sym_db.RegisterMessage(WorkExperience) Org = _reflection.GeneratedProtocolMessageType('Org', (_message.Message,), dict( DESCRIPTOR = _ORG, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Org) )) _sym_db.RegisterMessage(Org) Dept = _reflection.GeneratedProtocolMessageType('Dept', (_message.Message,), dict( DESCRIPTOR = _DEPT, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.Dept) )) _sym_db.RegisterMessage(Dept) ProjectExperience = _reflection.GeneratedProtocolMessageType('ProjectExperience', (_message.Message,), dict( DESCRIPTOR = _PROJECTEXPERIENCE, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.ProjectExperience) )) _sym_db.RegisterMessage(ProjectExperience) EducationExperience = _reflection.GeneratedProtocolMessageType('EducationExperience', (_message.Message,), dict( DESCRIPTOR = _EDUCATIONEXPERIENCE, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.EducationExperience) )) _sym_db.RegisterMessage(EducationExperience) School = _reflection.GeneratedProtocolMessageType('School', (_message.Message,), dict( DESCRIPTOR = _SCHOOL, __module__ = 'resume_pb2' # @@protoc_insertion_point(class_scope:com.inmind.idmg.search.common.School) )) _sym_db.RegisterMessage(School) DESCRIPTOR.has_options = True DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('P\001')) # @@protoc_insertion_point(module_scope)

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